Keywords: Air pollution, Boundary layer, Cold surge, Haze, Lidar, PM2.5

The aim of this PhD project was to better understand and explain key factors for severe pollution events occurring under Northeast monsoon's impact during winter period in Hanoi.

The first part of the thesis presents the results deriving from a campaign carried out from January to March 2019 in Hanoi. The local meteorology and the origin of the air masses were investigated under the impact of the Northeast monsoon (also known as cold surges). The wind profile and vertical mixing were investigated using a Doppler lidar, and the structure of boundary layer was thoroughly studied by using an elastic Lidar for the first time in Hanoi. The severe polluted events in Hanoi were mainly related to transboundary Long-Range Transport (LRT) from China, besides local emission and regional transport from other polluted regions of Vietnam. Boundary-layer height (BLH) proved to be one of the key factors controlling air pollution in Hanoi by trapping pollution near the ground: the elastic Lidar measurements showed that the daily maximum BLH varied from 700 m to 1200 m during cloudy days, while it remained below 1000 m during hazy days.

The second part of the thesis is a broader analysis bearing on the full winter period (October to March) and on 14 years. Firstly, the semi-climatic trends in particle emissions in East Asia and mainland Southeast Asia were studied using the NAAPS model, that assimilates space-borne Aerosol Optical Depth (AOD) observations. From winter 2006/07 to winter 2010/11, the pollution plumes were most frequently located over the Indochina Peninsula, and the smoke partial AOD showed biomass burning activities to be responsible. From winter 2011/12 to winter 2019/20 however, the pollution plumes were mostly observed over eastern China and, to a lesser extent, Northern Vietnam, and the sulfate partial AOD indicated they came from coal-based industrial activities. Secondly, the impact of the northeast monsoon on the air masses origin was studied using back-trajectories (HYSPLIT model) with clustering over the 14 winter periods. During regular winters, northeastern air masses predominated, and could bring pollution from eastern China and the industrial region surrounding Hanoi (Red River Delta). However, the El Niño phenomenon perturbed the air masses trajectories during three winters (2014/15, 2015/16 and 2018/19), bringing more western air masses and thus, causing a temporary decrease of the sulfate AOD and simultaneous increase of the smoke AOD over Hanoi. Thirdly, a method to classify the winter days and highlight the influence of cold surges on PM2.5 variability was built, relying only on Hanoi local wind and NAAPS AOD value. This classification allowed to estimate that long-range transport from China during the onset of cold surges caused an average increase of around 30 % of the PM2.5 level in Hanoi. Additionally, the contribution of local pollution to the most severe pollution episodes, occurring during the persistence of cold surges, was estimated to be around 40 % of the average PM2.5.

Résumé

Cette thèse avait pour but d'améliorer notre compréhension des événements de pollution sévères en période de mousson du Nord-Est, pendant la période hivernale à Hanoi (Vietnam), et d'en identifier les facteurs clés.

La première partie présente les résultats d'une campagne menée de janvier à mars 2019 à Hanoi. La météorologie locale et l'origine des masses d'air ont été étudiées pour observer l'impact de la mousson de Nord-Est (aussi appelée intrusions froides). Le profil de vent et le mélange vertical ont été observés avec un Lidar Doppler, et la structure de la couche limite a été étudiée pour la première fois à Hanoi avec un Lidar élastique. Les résultats montrent que les événements de forte pollution à Hanoi sont principalement liés au transport à longue distance de polluants en provenance de Chine, auquel s'ajoutent des émissions locales et du transport régional provenant d'autres régions polluées du Vietnam. La hauteur de la couche limite s'est avérée être l'un des principaux facteurs contrôlant la pollution atmosphérique à Hanoi. Les mesures du Lidar élastique ont montré que le maximum journalier de cette hauteur varie entre 700 m et 1200 m pendant les jours nuageux peu pollués, mais reste inférieur à 1000 m pendant les jours de forte pollution.

La deuxième partie de la thèse a porté sur la période hivernale complète (octobre à mars) pendant la période 2006-2020. Dans un premier temps, les tendances semi-climatiques des émissions de particules en Asie de l'Est et en Asie du Sud-Est continentale ont été étudiées à l'aide du modèle NAAPS qui assimile les observations satellitaires d'épaisseur optique des aérosols (AOD). Cette étude a montré que de l'hiver 2006/07 à l'hiver 2010/11, les panaches de pollution ont été le plus souvent localisés au-dessus de la péninsule indochinoise, et l'AOD associée aux fumées a permis d'identifier la contribution dominante de la combustion de la biomasse. Par contre, de l'hiver 2011/12 à l'hiver 2019/20, les panaches de pollution ont été principalement observés au-dessus de l'est de la Chine et, dans une moindre mesure, au nord du Vietnam, et l'AOD associée aux sulfates indique que ces panaches provenaient essentiellement d'activités industrielles utilisant la combustion du charbon. Dans un second temps, l'impact de la mousson du nord-est sur l'origine des masses d'air a été étudié à l'aide de rétro-trajectoires (modèle HYSPLIT), avec un groupement en clusters sur les 14 hivers. En temps normal, les masses d'air du nord-est prédominent, ce qui peut apporter de la pollution de l'est de la Chine et de la région industrielle entourant Hanoi (delta du fleuve Rouge). Les épisodes El Niño ont perturbé les trajectoires des masses d'air pendant trois hivers (2014/15, 2015/16 et 2018/19), apportant davantage de masses d'air de l'ouest et provoquant ainsi une diminution temporaire de l'AOD associée aux sulfates et une augmentation simultanée de l'AOD associée aux fumées sur Hanoi. Enfin, dans un troisième temps, une méthode de classification des jours d'hiver et de mise en évidence de l'influence des fronts froid sur la variabilité des PM2,5 a été construite, en s'appuyant sur le vent local enregistré à Hanoi et l'AOD du modèle NAAPS. Cette classification a permis d'estimer que le transport de pollution sur de longues distances depuis la Chine au début des intrusions froides a provoqué une augmentation moyenne d'environ 30 % du niveau de PM2.5 à Hanoi. Par ailleurs, la contribution de la pollution locale aux épisodes de pollution les plus graves, qui se produisent lors de la persistance des vagues de froid, a été estimée à environ 40 % de la moyenne des PM2.5.

Mots clés:

Pollution atmosphérique; Couche limite ; Vagues de froid ; Brume; Lidar; PM2.5

Acknowledgement

I would like to begin by expressing my gratitude and admiration to my supervisors, Professor Hervé Delbare, Doctor Elsa Dieudonné, Associate Professor Thanh-Son Le, and Doctor Song-Tung Nguyen. I am indebted to Professor Hervé Delbare and Doctor Thanh-Son Le for providing me with my first opportunity to work in research and for helping me realize my potential. I would want to thank Elsa for teaching me proper problem-solving techniques and assisting me with my numerous technical troubles. Their expertise, tolerance, critical remarks, and helpful recommendations made the arduous process of writing a thesis enjoyable and gratifying. Without Professor Karine Deboudt's guidance, countless suggestions, and crucial contributions to this study, it would not have been possible to complete this research. Mr. Francois Ravetta and Mr. Hubert Loisel are also thanked for sitting on my committee. Without their help and commitment, my dissertation would not be feasible.

I would also want to thank the jury members for analyzing my effort and carefully reviewing this material. Thank you for the insightful conversation and comments; they have enabled me to have a better understanding of my own work.

France's Laboratoire de Physico-Chimie de l'Atmosphère (LPCA) and Université du Littoral Côte d'Opale (ULCO) are thanked for their contributions. Additionally, I'd like to thank my Vietnamese colleagues from the Institute of Environmental Technology and the Institute of Human Geography. It would be difficult to complete this journey without the friendship and assistance of Soulemane Halif Ngagine, Minh Nhut Ngo and Quynh-Hoa Nguyen. Therefore, thank you very much for listening to my complaints and engaging in intriguing conversations when we had the time, and honestly... thank you for everything! Last but not least, I would want to thank my family, my parents for bringing me to where I am with their unwavering trust in me, and my brother for his constant drive and unwavering support. Without the love of my family, none of this would be possible. They have been the driving force behind this attempt. Thank you for their support, love, and patience.

Extensive Summary in French Chapitre 1. Dynamique atmosphérique et aérosols à Hanoi et ses environs Description générale de la région du delta du Fleuve Rouge

Le Vietnam est situé dans la péninsule d'Indochine, qui correspond à la partie continentale de l'Asie du Sud-Est. La plaine du delta du Fleuve Rouge est une région du nord du Vietnam, qui est aussi la plus peuplée du pays. Hanoi est la plus grande ville du Vietnam et du delta du Fleuve Rouge, mais aussi la zone la plus densément peuplée, avec une population estimée à plus de 8 millions de personnes. Au cours de la dernière décennie, l'économie du delta du Fleuve Rouge a connu des changements structurels dus au développement industriel et à la modernisation des infrastructures. Le nombre de zones industrielles dans le delta du Fleuve Rouge représente 26 % du total du pays, faisant de cette région la plus industrialisée, mais aussi la plus polluée du Vietnam. En outre, la majorité des centrales électrique thermiques du nord du Vietnam sont également situées dans le delta du Fleuve Rouge.

Le climat dans le delta du Fleuve Rouge est de type tropical avec quatre saisons : printemps, été, automne et hiver. En hiver, de la mi-octobre à la fin mars, la mousson du nordest qui existe en Asie de l'Est apporte de l'air continental froid et sec au nord du Vietnam. Cependant, de janvier à mars, la météo régionale est parfois capricieuse avec des intrusions d'air humide en provenance du golfe du Tonkin, entraînant des épisodes réguliers de bruine et de brouillard. La mousson du nord-est d'Asie de l'Est (plus simplement appelée mousson du nord-est dans le reste de ce document) est un phénomène synoptique typique dans toute l'Asie de l'Est, qui a un impact substantiel sur la circulation atmosphérique dans sa partie continentale. Au Vietnam, la mousson du nord-est est également connue sous le nom de vagues de froid ou intrusions froides, qui entraînent des changements abrupts dans la météo locale. Dans le nord du Vietnam, les vagues de froid hivernales peuvent produire des baisses de température importantes d'octobre à mars. En ce qui concerne la pollution de l'air, une détérioration importante de la qualité de l'air pendant la mousson du nord-est a été observé dans de nombreuses régions telles que la Chine continentale, Taïwan, la Malaisie et le Vietnam. À Hanoi et dans tout le nord du Vietnam, les cycles répétés de mousson du nord-est provoquent une douzaine d'épidémies de pollution de plusieurs jours pendant l'hiver, ainsi, les concentrations de particules fines (PM en anglais) les plus élevées sont généralement enregistrées entre octobre et mars.

Dynamique de l'atmosphère

La plupart des activités humaines se déroulent dans la partie la plus basse de la troposphère, appelée couche limite atmosphérique (ou plus simplement couche limite). La profondeur typique de la couche limite varie de quelques dizaines de mètres à quelques kilomètres au-dessus du sol. Le développement de la couche limite dépend fortement du rayonnement solaire, du vent, de l'orographie et de la couverture de surface. La compréhension de la dynamique de la couche limite est essentielle pour expliquer le transport et la dispersion des polluants atmosphériques près du sol. La plupart des polluants sont émis dans l'atmosphère à partir de sources proches du sol et restent généralement confinés dans la couche limite. Les polluants peuvent être dispersés et mélangés par convection et turbulence au sein de la couche limite, mais l'inversion de température située au sommet de la couche limite constitue une barrière que peu de polluants parviennent à franchir pour atteindre l'atmosphère libre. L'altitude du sommet de la couche limite, ou hauteur de la couche limite est généralement liée à l'intensité de la turbulence ; plus la turbulence est forte, plus les polluants atmosphériques sont soulevés de la surface vers des altitudes plus élevées, ce qui atténue la pollution de l'air près du sol, et vice versa. Ainsi, de jour, les instabilités intenses existant dans la couche limite convective peuvent disperser les polluants loin de la source et nettoyer l'atmosphère près de la surface. En revanche, de nuit, lorsque la couche limite présente des conditions stables, les polluants sont piégés près de la surface et finissent par s'accumuler en grandes concentrations. Outre le transport, la hauteur de couche limite peut modifier la concentration et la distribution des précurseurs chimiques, affectant potentiellement la transformation chimique des polluants et la formation de particules secondaires.

Qualité de l'air à Hanoi

Les aérosols ont un ensemble complexe de conséquences sur le climat de la Terre, affectant celui-ci de deux manières fondamentales : en modifiant la quantité de chaleur qui entre et sort de l'atmosphère, et en modifiant le processus de formation des nuages. Selon la composition des aérosols, ils peuvent avoir un effet refroidissant ou un effet réchauffant. En revanche, les effets négatifs des aérosols sur la santé humaine sont plus clairs et plus directs. Ils consistent à la fois en des symptômes aigus à court terme (par exemple, des crises d'asthme ou des problèmes respiratoires), mais l'exposition à long terme à la pollution aux particules fines est clairement liée à un risque accru de décès. La pollution de l'air affecte les populations dans toutes les régions du globe, mais touche particulièrement les habitants des zones fortement industrialisées comme le delta du Fleuve Rouge.

Sur le RRD et Hanoi, il n'y a pas beaucoup de preuves de sources naturelles qui peuvent dégrader de manière significative la qualité de l'air (par exemple, les éruptions volcaniques ou les incendies de forêt). Cependant, les aérosols et autres types de polluants émis par les activités anthropiques dans l'atmosphère ont augmenté à Hanoi. La combustion de combustibles fossiles est l'une des principales sources anthropiques de pollution de l'air dans le RRD et à Hanoi, provenant de quatre grandes catégories d'activités : le transport, l'agriculture, l'industrie et la construction. Dans cette étude, les sources anthropiques sont également classées en fonction de l'emplacement de la source, y compris les émissions locales, les émissions de transport régional et à longue distance, qui correspondent respectivement aux sources situées à Hanoi ou dans un rayon inférieur ou supérieur à 200 km de Hanoi.

État de l'art de la recherche sur les aérosols dans la région

Dans cette région, plusieurs études sur les aérosols atmosphériques ont déjà été réalisées, dans le but de caractériser l'état de la pollution aux particules à Hanoi. La plupart de ces travaux se sont concentrés sur la pollution de l'air dans l'environnement urbain du centre de Hanoi, tandis que peu d'études ont été menées dans les régions fortement industrialisées du delta du Fleuve Rouge. La majorité des recherches susmentionnées se sont concentrées sur l'identification des sources de pollution et la caractérisation de la composition chimique des aérosols. Cependant, la plupart de ces travaux se sont limitées à une période d'étude de courte durée (de un mois à quelques mois) et il n'y a eu aucune analyse portant sur l'ensemble de la période hivernale (octobre à mars), ou sur les variations à long terme des concentrations de polluants à Hanoi. Bien que certains travaux portent sur une période d'étude plus longue, ceuxci sont basées sur des données plus anciennes datant d'une période où la pollution de l'air à Hanoi n'était pas aussi dramatique qu'elle l'est aujourd'hui.

Chapitre 2. Matériel et méthodes Présentation de la campagne VASP

La campagne vietnamienne d'analyse et de profilage des aérosols (VASP en anglais) a été organisée par le Laboratoire de Physico-Chimie de l'Atmosphère (LPCA) de l'Université du Littoral Côte d'Opale en collaboration avec l'Institut des Technologies de l'Environnement de l'Académie des Sciences et Technologies du Vietnam. Cette campagne s'est déroulée du 11 janvier au 22 mars 2019 à Hanoi. Les instruments suivants ont été installés sur le site de l'Institut des Technologies de l'Environnement, dans le centre de Hanoi :

• Un Lidar Doppler aussi appelé Lidar vent (modèle Windcube V2 de la société Leosphere/Vaisala). Celui-ci a fourni les profils verticaux du vent en 3D : la composante horizontale du vent (vitesse et direction), mais aussi sa composante verticale. La dispersion de la composante verticale du vent a été utilisée comme un indicateur du mélange vertical dans la couche limite. La zone d'altitude couverte par ce lidar était de 70 à 300 m au-dessus du niveau du sol environ. • Un Lidar élastique à micro-impulsions, aussi appelé Lidar aérosol (prototype SLIM prêté par le Laboratoire Atmosphère Milieux Observations Spatiales de Paris Sorbonne Université). En raison de la forte couverture nuageuse prévalant au-dessus du delta du Fleuve Rouge en hiver, les observations de ce lidar n'ont pas pu être traitées pour déterminer les profils des propriétés optiques des aérosols au-dessus de Hanoi. En revanche, ce lidar a été utilisé pour déterminer la hauteur de la couche limite. • Un système de prélèvement d'aérosols sur filtres en vue d'une analyse ultérieure de leur composition chimique, avec un filtre changés une fois par semaine, ce qui a donné 10 échantillons. Une fraction de chaque échantillon a été dédiée à l'analyse des ions NO 3 -et SO 4 2-hydrosolubles par chromatographie ionique, à l'aide d'un chromatographe ionique ICS 5000 + de Thermo Scientific Dionex équipé d'une colonne anionique CS 12A. Une autre fraction de chaque filtre a été dédiée à l'analyse par microscopie électronique à balayage/spectroscopie à rayons X à dispersion d'énergie (SEM/EDX). Les rapports élémentaires des éléments détectés (Na, Mg, Al, Si, S, Cl, K, Ca, Ti, Fe, Zn) ont été calculés. • Une station météo (Davis Instruments, Hayward, CA ; modèle Vantage Pro 2) mesurant la pression atmosphérique, l'humidité relative, la pluviométrie et le vent à basse altitude (environ 50 m au-dessus du sol).

Les trois premiers instruments étaient situés sur le toit d'un bâtiment de 15 m de haut environ, en bordure d'une avenue présentant un fort trafic automobile. La station météo était installée sur le toit d'un autre bâtiment, plus haut et plus isolé des constructions environnantes, afin de minimiser l'impact des bâtiments élevés autour du site.

Autres observations

Pour caractériser la concentration en particules fines à Hanoi, les concentrations de PM2.5 (concentration massique de particules d'un diamètre aérodynamique inférieur à 2.5 µm) mesurées sur le site de l'ambassade des États-Unis ont été utilisées. Ces mesures sont réalisées au moyen d'une jauge à absorption bêta calibrée selon les standards de l'agence de protection de l'environnement américaine (US EPA) et sont disponibles en ligne depuis décembre 2015. L'ambassade des États-Unis est également située dans le centre de Hanoi, à environ 3 km du site utilisé pendant la campagne VASP. Pour compléter les profils de vent mesurés par le Lidar Doppler jusqu'à 300 m d'altitude et valider les mesures de hauteur de couche limite dérivées des observations du Lidar aérosols, les profils de température, pression, humidité relative, vitesse et direction du vent mesurés par radiosondage ont été utilisés. Les radiosondes sont lancées deux fois par jour, à 07h00 et 19h00 heure locale (c'est-à-dire 00h00 et 12h00 en temps universel) depuis un troisième site, toujours situé au centre de Hanoi, à environ 3 km de celui utilisé pendant la campagne VASP et 1 km de l'ambassade des États-Unis.

L'Aerosol Robotic Network est un réseau mondial de capteurs d'aérosols de télédétection passifs basés au sol. Il s'agit d'un réseau bien établi d'environ 700 stations dotées de photomètres solaires fournissant des observations des propriétés optiques des aérosols telles que l'épaisseur optique des aérosols pour plusieurs longueurs d'onde du proche ultraviolet au proche infrarouge, l'albédo de diffusion simple et la distribution en taille (granulométrie) des particules. Dans ce travail, ce sont les moyennes journalières de l'épaisseur optique pour une longueur d'onde de 500 nm qui ont été utilisées (données de niveau 2.0, filtrées manuellement pour retirer les observations contaminées par des nuages). Ces observations ont servi de référence pour valider les épaisseurs optiques fournies par le modèle NAAPS. Toutes les données utilisées proviennent de la station L'Aerosol Robotic Network de Nghia-Do, située dans le centre de Hanoi ; elles couvrent les périodes hivernales (octobre à mars) de 2010 à 2019.

Modèles numériques

Le modèle HYSPLIT a été développé par le Air Ressources Laboratory de la National Oceanic and Atmospheric Administration et c'est l'un des modèles les plus largement utilisés pour calculer la trajectoire d'une parcelle d'air unique, ainsi que pour des simulations plus complexes de transport, de dispersion, de transformation chimique et de dépôt. La méthode sous-jacente au modèle HYSPLIT est une combinaison de la méthodologie eulérienne, qui utilise une grille tridimensionnelle fixe comme référentiel pour calculer les concentrations atmosphériques de polluants, et de la méthodologie lagrangienne, qui utilise un référentiel mobile suivant la parcelle d'air pour les calculs d'advection et de diffusion. Dans cette étude, le modèle HYSPLIT a été utilisé pour générer les rétro-trajectoires des masses d'air arrivant à Hanoi, dans le but de mettre en évidence les sources de pollution dont les émissions peuvent effectivement être transportées jusqu'à Hanoi. Afin de mettre en évidence la direction des masses d'air dominantes, le très grand nombre de trajectoires généré sur la période d'étude a été regroupé en classes (clusters) en utilisant la méthode des k-moyennes qui consiste à minimiser la variance spatiale totale à l'intérieur de chaque classe de trajectoires. Ainsi, les trajectoires avec un degré de similarité élevé seront regroupées dans la même classe, tandis que les trajectoires très divergentes seront séparées dans différentes classes.

Le système d'analyse et de prévision des aérosols de la marine américaine (NAAPS) est un modèle opérationnel de transport des aérosols produit par le Naval Research Laboratory. Ce modèle fournit des prévisions des concentrations d'aérosols à l'échelle mondiale, en combinant plusieurs jeux d'observations par satellite avec d'autres simulations mondiales d'aérosols. Ainsi, le modèle NAAPS fournit également une prévision de l'épaisseur optique toutes les 6 heures et pour une longueur d'onde de 550 nm. L'épaisseur optique est également décomposée en trois épaisseurs optiques partielles : « fumée » correspond à la contribution des aérosols carbonés issus de la combustion de biomasse, « poussière » à la contribution des aérosols minéraux émis par les déserts, et « sulfate » à la contribution des aérosols secondaires formés par réactions chimiques à partir des produits soufrés émis par la combustion du charbon. Dans ce travail, les épaisseurs optiques partielles du modèle NAAPS ont été utilisées pour étudier les tendances des grandes régions sources de particules dans une large région couvrant la péninsule d'Indochine ainsi que l'Asie du Sud-Est et de l'Est, et sur une période de 14 hivers (2006/07 à 2019/20). Afin de pallier le manque de mesures de concentrations en particules fines à Hanoi avant 2016, les épaisseurs optiques partielles de NAAPS ont ensuite été extraites pour le pixel couvrant Hanoi, et combinées à l'analyse en classes de trajectoires du modèle HYSPLIT afin de déterminer lesquelles des grandes régions source ont effectivement contribué à la variation interannuelle de la pollution particulaire à Hanoi. Enfin, sur la période récente, l'épaisseur optique partielle « sulfate » a été utilisée comme traceur du transport à grande distance des masses d'air polluées par les émissions des industries, notamment les centrales électriques à charbon.

Chapitre 3. Facteurs clés expliquant les épisodes sévères de pollution de l'air à Hanoi pendant la saison hivernale 2018/19

Déroulement de la campagne VASP La disponibilité des données de tous les appareils déployés pendant la campagne VASP était bonne, à l'exception du Lidar aérosols. De l'eau condensait à l'intérieur de la tête optique du Lidar aérosols, entraînant une perte très importante de puissance lumineuse, puis de niveau de signal. Malgré le remplacement des cristaux desséchants placé dans la tête optique, ce problème revenait au bout de quelques jours et rendait les observations inexploitables. De plus, le Lidar aérosols rencontrait également un problème récurrent de connexion entre l'ordinateur et le rack optoélectronique ; la prise USB était instable et se déconnectait tous les quelques jours.

En termes de conditions météorologiques, janvier a été ~3°C plus froid par rapport à février et mars et toute la campagne s'est déroulée pendant la période humide de l'hiver. La visibilité était à son plus bas en janvier en raison des fréquents épisodes de brouillard/brume, mais à son plus haut en février, où le temps a au contraire été inhabituellement clair. Globalement, janvier a été le mois le plus pollué et février, le mois le moins pollué. Pendant la campagne, les niveaux les plus élevés de concentration en particules fines PM2.5 étaient principalement liés à des périodes de vent faible, donc probablement dus à des émissions locales mal dispersées. De plus, des conditions d'humidité élevée sont susceptible de renforcer le niveau de PM2.5 par croissance hygroscopique des aérosols et par la formation d'aérosols secondaires via des réactions chimiques hétérogènes. Ceci est notamment démontré par une augmentation du rapport entre sulfate et nitrate en fonction de l'humidité relative dans les échantillons extrait des prélèvements hebdomadaires de particules réalisés pendant la campagne. Les moyennes journalières de la concentration en particules fines PM2.5 varient de 9 à 129 μg.m -3 , avec une moyenne et un écart type de 45 ± 25 μg.m -3 . Au cours de la campagne, quatre événements de pollution sévère se sont produits, caractérisés par des concentrations en PM2.5 supérieures à 50 μg.m -3 pendant plus de 48 heures selon la réglementation du Vietnam sur la qualité de l'air ambiant.

Les conditions météorologiques observés à Hanoi de janvier à mars ont été classées en quatre types distincts : temps clair (caractérisé par une visibilité élevée), temps brumeux (caractérisé par une visibilité faible, mais avec une humidité insuffisante pour former du brouillard), brouillard (caractérisé par une humidité très élevée, proche de la saturation) et temps nuageux (tous les autres jours). Pendant la campagne, les jours nuageux représentaient ~59 % du nombre total de jours, ils constituaient donc le type de conditions météorologiques le plus fréquent. Les jours brumeux étaient le deuxième type de temps le plus courant au cours de la campagne, soit environ 29 % au total. Ensuite, les jours brumeux et clairs représentaient respectivement ~11 % et ~1 %. Les jours brumeux sont caractérisés par les concentrations quotidiennes moyennes de PM2.5 les plus élevées des quatre types (moyenne ± écart-type de 75,8 ± 23,1 μg.m -3 ). De plus, les 4 événements sévères de pollution sont tous associés à des jours brumeux. Lors de ces événements, Hanoi était dominée par un système anticyclonique et se trouvait en dessous d'une région de subsidence de l'air, conditions qui sont favorables à l'accumulation de polluants en raison respectivement de vent faibles et d'une limitation de la hauteur de couche limite, conduisant à une brume persistant pendant plusieurs jours.

Impact de l'origine des masses d'air sur la composition des PM2.5

Les rétro-trajectoires HYSPLIT obtenues pour l'ensemble de la campagne (une parcelle d'air arrivant toute les heures à Hanoi à 100 m au-dessus du sol) ont été regroupées en six classes (clusters). Les trois classes pour lesquelles les masses d'air arrivent à Hanoi en provenance du Nord-Est, c'est-à-dire de Chine continentale, sont majoritaires. Les niveaux moyens de concentration en particules fines associés à chacune des six classes de trajectoires sont très variables. Les concentrations les plus élevées en PM2.5 ont été observées pour les trois classes de trajectoires provenant de Chine continentale, car ces masses d'air ont probablement accumulé la pollution émise dans l'est de la Chine et le nord-est du Vietnam. Des concentrations plus faibles de PM2.5 ont été observées pour les deux classes de trajectoires provenant de l'Est, lorsque la masse d'air a survolé la mer avant d'atteindre Hanoi. Cependant, ces masses d'air ne sont pas totalement propres pour autant, car les émissions locales et régionales, provenant de la métropole de Hanoi et du delta du Fleuve Rouge, contribuent à la concentration de PM2.5.

La composition chimique des échantillons hebdomadaires de particules a été comparée avec la prévalence des différentes classes de trajectoires afin de mettre en évidence une éventuelle relation entre la composition chimique des aérosols et l'origine des masses d'air, donc les zones sources potentielles. De façon cohérente avec les concentrations de PM2.5 mesurées à l'ambassade des États-Unis, les filtres les plus chargés en particules ont été prélevés pendant les semaines où les classes de trajectoires provenant du nord-est (Chine continentale) étaient dominantes, tandis que les filtres les moins chargés en particules ont été prélevés pendant les semaines où les classes de trajectoires provenant de l'Est (masses d'air marines) étaient dominantes. De plus, l'analyse des échantillons indique une composition chimique similaire, bien qu'il y ait eu une énorme différence dans les concentrations totales et dans les origines dominantes des masses d'air. Cela démontre une similitude dans la composition chimique des émissions de particules locales et de celles provenant du transport régional ou à longue distance, depuis d'autres régions polluées du Vietnam et de la Chine. Cela s'explique par la présence des mêmes types de sources industrielles et urbaines dans ces zones géographiques.

Impact de la dynamique atmosphérique sur les PM2.5

Grâce aux observations du Lidar aérosols, l'évolution de la couche limite au-dessus de Hanoi a pu être mesurée pour la première fois. Les moyennes horaires de la hauteur de couche limite ont été déterminées, puis le maximum journalier, et ils ont été associés aux différents types de conditions météorologiques. La hauteur maximum journalière a varié entre 700 et 1200 m au cours de la campagne, mais est toujours restée inférieure à 1000 m par temps brumeux ou en cas de brouillard. En effet, brume et brouillard limitent le développement de couche limite en réduisant la quantité de rayonnement solaire qui atteint le sol. De plus, avoir des mesures simultanées du profil du vent et de la hauteur de couche limite a permis de déterminer le coefficient de ventilation, un paramètre synthétique caractérisant la dispersion des polluants qui est d'un grand intérêt pour la surveillance et l'évaluation de la pollution de l'air. Une analyse fine de la relation entre les variations de la hauteur de couche limite, du coefficient de ventilation et de la concentration en particules fines PM2.5 a été effectuée pour les quatre événements de pollution sévère survenus pendant la campagne. Les niveaux élevés de concentration en PM2.5 observés pendant les jours brumeux sont cohérents avec la croissance plus lente de la couche limite et les faibles valeurs du coefficient de ventilation observés dans ce type de conditions météorologiques, entraînant de graves dégradations de la qualité de l'air à Hanoi. Cette analyse montre ainsi que la hauteur de couche limite, et plus largement la dynamique atmosphérique, sont des paramètres essentiels pour comprendre les mécanismes de la qualité de l'air à Hanoi pendant la période hivernale.

Chapitre 4. Comportement complexe de la pollution hivernale à Hanoi sur la période semi-climatique 2006 -2020

Validation de l'épaisseur optique du modèle NAAPS à Hanoi

Pour l'ensemble des hivers (octobre à mars) 2010/11 à 2018/19, les moyennes journalières de l'épaisseur optique en mesurées par le photomètre L'Aerosol Robotic Network de Hanoi ont été extraites et comparées aux valeurs fournies par le modèle NAAPS pour les mêmes jours et le pixel le plus proche de la station L'Aerosol Robotic Network. Une moyenne sur 6 mois a ensuite été calculée pour chaque hiver, afin d'évaluer la capacité du modèle à reproduire la variabilité interannuelle. Les résultats montrent que l'épaisseur optique simulée est largement sous-estimée par rapport à la valeur mesurée (d'un facteur 2 environ), mais que malgré cela, le modèle reproduit bien la tendance et les variations interannuelles de l'épaisseur optique. La capacité du modèle à reproduire la variabilité au jour le jour est plus limitée, puisque le coefficient de corrélation point à point n'est que de 0,66. Cependant, pour réaliser la classification des jours présentée en fin de chapitre, une prédiction précise de l'épaisseur optique n'est pas nécessaire, il suffit de pouvoir trier les jours en deux catégories « faible épaisseur optique » et « forte épaisseur optique » selon que la valeur d'épaisseur optique est inférieure ou supérieure à la valeur moyenne. Comme 73 % des jours sont correctement attribués à la catégorie « faible / forte épaisseur optique », le modèle NAAPS a été considéré comme pertinent pour discriminer les jours impactés ou non par un panache de polluants à grande échelle, issus du transport à longue distance ou régional.

Identification et tendances des zones sources an Asie de l'Est

L'épaisseur optique fournie par le modèle NAAPS a été extraite sur une large zone couvrant l'Asie de l'Est et la péninsule d'Indochine, pour les 14 hivers (octobre à mars) de 2006/07 à 2019/20. Les composantes « fumée », « poussière » et « sulfate », ainsi que la valeur totale de l'épaisseur optique ont été récupérées. Afin d'identifier les principales sources et leur évolution au fil des années, ces cartes représentant la moyenne sur 6 mois ont été tracées pour chaque hiver. La composante « poussière » de l'épaisseur optique est restée toujours très faible et n'a montré aucune caractéristique intéressante sur la zone étudiée. En revanche, les composantes « fumée » et « sulfate » de l'épaisseur optique présentaient des zones de fortes valeurs bien distinctes et des tendances interannuelles opposées. De l'hiver 2006/07 à l'hiver 2010/11, les valeurs les plus élevées de l'épaisseur optique totale (> 0,7) sont dues à de fortes valeurs de la composante « fumée » tandis que la composante « sulfate » ne représente qu'une fraction faible du total. Ces fortes valeurs ont été observées au-dessus de la péninsule d'Indochine et correspondent à des activités de combustion de la biomasse provenant à la fois des source naturelles (feux de forêts) et de sources anthropiques (brûlis des résidus de cultures) dans cette région. À l'opposé, en Chine continentale, les valeurs d'épaisseur optique sont encore modérées (de 0,4 à 0,6) pendant cette période. Cependant, de l'hiver 2011/12 à l'hiver 2019/20, des valeurs élevées (> 0,7) et même très élevées (> 1,0) de l'épaisseur optique, ont été observées en Chine continentale, dues à la composante « sulfate » exclusivement. Ces régions de Chine continentale sont des régions industrielles denses avec de nombreuses raffineries, usines et centrales électriques au charbon, indiquant que ces aérosols se sont formés dans un environnement riche en dioxyde de soufre, un polluant principalement émis par les activités industrielles utilisant le charbon.

Variabilité interannuelle de l'origine des masses d'air

Une analyse similaire à celle présentée au chapitre 3 a été appliquée : un ensemble de rétro-trajectoires arrivant au-dessus de Hanoi a été calculé en utilisant le modèle HYSPLIT, mais pour l'ensemble des 14 hivers de 2006/07 à 2019/20, puis ces trajectoires ont été groupées en six classes. Les classes n°1 à 3 correspondent à des masses d'air arrivant à Hanoi depuis le nord-est et représentent environ 70% du nombre total de rétro-trajectoires. Ces trajectoires se produisent lorsque le centre de l'anticyclone sibérien se déplace vers l'Est et elles passent audessus des zones densément peuplées et densément industrialisées de la Chine continentale et du nord du Vietnam. Par conséquent, ces classes de trajectoires sont susceptibles d'être associés à un transport à longue distance des polluants émis en Chine continentale et à un transport régional des polluants émis dans la plaine du delta du Fleuve Rouge vers Hanoi. La classe de trajectoires n°4 correspond aux masses d'air océaniques provenant de la mer de Chine méridionale et arrivant à Hanoi par le sud-est ; elle représentait ~15 % du nombre total de rétrotrajectoires. Quant aux classes n°5 et 6, elles correspondent à des masses d'air provenant du nord de l'Inde et du golfe du Bengale et arrivant à Hanoi par l'ouest ; elles représentaient ~14 % du nombre total de rétro-trajectoires. Ces masses d'air sont susceptibles d'être associées à un transport à longue distance des émissions issues des activités de combustion de biomasse dans le péninsule d'Indochine et le Nord de l'Inde. La fraction des trois familles de classes de rétro-trajectoires (nord-est, sud-est et ouest) est très variable d'un hiver à l'autre. Bien que les classes de nord-est soient toujours majoritaires, la part des classes d'ouest augmente fortement pendant les hivers où l'oscillation australe est dans une phase chaude (El Niño). En effet, il a été prouvé que le phénomène El Niño affaiblit la mousson de nord-est. En conséquence, la composante « fumée » de l'épaisseur optique augmente temporairement pendant les hivers El Niño, tandis que la composante « sulfate » diminue temporairement.

Classification des jours d'hiver

L'analyse suivante a porté sur la variabilité d'un jour à l'autre des concentrations en particules fines PM2.5 à Hanoi, en relation avec les cycles de la mousson de nord-est). Ce cycle est séparé en trois phases : Pendant cette phase, il y a peu de transport (régional ou à grande distance) et les polluants émis localement seront a priori dominants. • Le phase 3 correspond à l'intervalle entre deux vagues de froid, pendant lequel une intrusion d'air océanique depuis le sud-est peut se produire. Pendant cette phase, il n'y a pas de transport à longue distance depuis la Chine car la direction du vent ne le permet pas. Par ailleurs, comme pour la phase 1, les polluants émis localement sont pour une large part transportés hors de Hanoi par les vents plus forts prévalant pendant cette phase. Les émissions régionales seront donc dominantes.

À ce stade, les clusters HYSPLIT n'étaient plus assez précis pour identifier, parmi les périodes de phase 1, les cas de transport à longue distance ou régional. En conséquence, l'épaisseur optique fournies par le modèle NAAPS a été utilisée pour identifier les jours avec où un panache d'aérosols à grande échelle est présent au-dessus de Hanoi. Ainsi, une classification des jours d'hiver a été proposée. La méthode de classification est basée sur la vitesse du vent, la direction du vent et la composante sulfate de l'épaisseur optique (moyennes journalières). Lorsque la vitesse du vent est inférieure à sa valeur moyenne (3,1 m/s), la journée est classée comme « conditions stagnantes », ce qui correspond à la phase 2 du cycle de mousson. Lorsque la vitesse du vent est supérieure à sa valeur moyenne et que la direction du vent provient des quadrants sud-est, sud-ouest ou nord-ouest, la journée est classée comme « autres conditions », ce qui correspond à la phase 3 du cycle de mousson. Enfin, lorsque la vitesse du vent est supérieure à sa valeur moyenne et que la direction du vent provient du quadrant nord-est, la journée est classée en « nord-est épaisseur optique élevée » ou « nordest épaisseur optique faible » selon que la composante « sulfate » de l'épaisseur optique du modèle NAAPS est inférieure ou supérieure à sa valeur moyenne (0,63). Ces deux classes de jours correspondent à la phase 1 du cycle de mousson, dans les cas où l'arrivée de la vague de froid est associée ou pas au transport de polluants depuis la Chine et le delta du Fleuve Rouge. La différence entre les concentrations moyennes en particules fines pour les jours de début de vague de froid avec fortes ou faibles valeurs d'épaisseur optique, permet d'estimer la contribution du transport transfrontalier à longue distance depuis la Chine. Celle-ci augmente les niveaux de PM2.5 à Hanoi d'environ 30 % (plus 9 à 17 µg.m -3 ). La différence entre les concentrations moyennes en PM2.5 entre les jours de vague de froid avec transport transfrontalier à longue distance et les jours de conditions stagnantes permet d'estimer la contribution des émissions locales. Celle-ci correspond à environ 40 % (plus 24 à 29 µg.m -3 ). Le transport transfrontalier à longue distance depuis la Chine continentale n'est donc pas un contributeur majeur à la pollution de l'air à Hanoi face aux émissions locales et régionales.

Conclusion générale

Le manuscrit résumé ici présente trois années de travail mené afin d'identifier les facteurs cruciaux qui contribuent aux événements sévères de pollution aux particules pendant l'hiver à Hanoi. L'objectif principal de cette thèse était d'étudier l'impact des processus de dynamique atmosphérique et la contribution des sources éloignées aux événements de pollution de l'air à Hanoi. Dans ce travail, les données recueillies une campagne de terrain de courte durée ont été utilisés en combinaison avec des ensembles de données à long terme provenant de modèles numériques pour les propriétés des aérosols (NAAPS) et pour l'origine de la masse d'air (HYSPLIT). Les résultats obtenus ont mis en lumière la manière dont les processus de dynamique atmosphérique affectent les événements de pollution de l'air et le la localisation des sources qui contribuent aux événements de pollution à Hanoi pendant l'hiver. Cependant, un certain nombre de questions nécessitent davantage de recherches, notamment pour mieux discriminer les émissions locales du transport régional et du transport à longue distance. ............................................................................................................................ 45 Figure 1-10. The geo-potential height anomalies between Dec. 2015 andDec. 2014 in eastern Asia (i.e., the 2-14. Regional plots of AOD forecasts (6-hour average) at a wavelength of 550 nm for the three components (sulfate, dust, and smoke) over East Asia and Southeast Asia at 00:00 UTC on 25 th January, 2019...................................................................................................... 82 (h),(i),(j),(k

List of figures

(a)

The daily average of temperature (Temp), pressure, relative humidity (RH), wind speed (WS), wind direction (WD), visibility, PM2.5 concentration and Sulfate AOD over the winter period 2016/17. Black triangles on the horizontal axis mark the cold surges. The winter days include "Stagnant conditions" -SC, "Northeast high AOD" -NEHA, "Northeast low AOD" -NELA and "Other conditions" -OC. Phase 1, phase 2 and phase 3 were marked as red, grey and blue areas, The daily average of temperature (Temp), pressure, relative humidity (RH), wind speed (WS), wind direction (WD), visibility, PM2.5 concentration and Sulfate AOD over the winter period 2017/18. Black triangles on the horizontal axis mark the cold surges. The winter days include "Stagnant conditions" -SC, "Northeast high AOD" -NEHA, "Northeast low AOD" -NELA and "Other conditions" -OC. Phase 1, phase 2 and phase 3 were marked as red, grey and blue areas, The daily average of temperature (Temp), pressure, relative humidity (RH), wind speed (WS), wind direction (WD), visibility, PM2.5 concentration and Sulfate AOD over the winter period 2018/19. Black triangles on the horizontal axis mark the cold surges. The winter days include "Stagnant conditions" -SC, "Northeast high AOD" -NEHA, "Northeast low AOD" -NELA and "Other conditions" -OC. Phase 1, phase 2 and phase 3 were marked as red, grey and blue areas, The daily average of temperature (Temp), pressure, relative humidity (RH), wind speed (WS), wind direction (WD), visibility, PM2.5 concentration and Sulfate AOD over the winter period 2019/20. Black triangles on the horizontal axis mark the cold surges. The winter days include "Stagnant conditions" -SC, "Northeast high AOD" -NEHA, "Northeast low AOD" -NELA and "Other conditions" -OC. Phase 1, phase 2 and phase 3 were marked as red, grey and blue areas, respectively..................................................................................... 134

List of tables

List of abbreviations ABL:

Atmospheric boundary-layer AQI:

Air Quality Index AOD:

Aerosol Optical Depth AERONET:

Aerosol Robotic Network BB:

Biomass Burning BLH:

Boundary-layer height CBL:

Convective boundary-layer CRGM:

Cubic Root Gradient Method EU:

European Union EA:

East Asia EPA:

US Environmental Protection Agency ENSO:

El Niño-Southern Oscillation GM:

Gradient Method GDP:

Gross domestic product GRDP:

Gross Regional domestic product GDAS:

Global Data Assimilation System HYSPLIT:

Hybrid Single-Particle Lagrangian Integrated Trajectory IP:

Indochina Peninsula LRT:

Long-range transport LLJs:

Low-level jets LGM:

Logarithmic Gradient Method MODIS:

Moderate Resolution Imaging Spectroradiometer NAAPS:

U.S. Navy Aerosol Analysis and Prediction System PMF:

Positive Matrix Factorisation RH:

Relative Humidity RRD:

Red River Delta RCS:

Range Corrected Signal SBL:

Stable Boundary-layer SEA:

Southeast Asia STI:

Subsidence Temperature Inversions SEM/EDX: Scanning Electron Microscopy/Energy Dispersive X-ray Spectroscopy TSP:

Total Suspended Particulates TSV:

Total Spatial Variance TTPs:

Thermal Power Plants VC:

Ventilation Coefficient VOCs:

Volatile Organic Compounds VAST:

Vietnam Academy Science and Technology VASP:

Vietnamese Aerosol Sampling and Profiling WS:

Wind Speed WD:

Wind Direction WHO:

World Health Organization

General Introduction

General Introduction

Air pollution, especially in an urban framework, is gaining more and more attention and is remaining, after decades of studies, one of the most challenging environmental problem in modern society, together with climate issues. Sources of air pollution may indeed result in a complex mixture of anthropogenic and natural emissions, with potential detrimental impacts on human health, welfare, climate, etc. Understanding air pollution in order to limit its consequences, also needs to consider all the factors involved in atmospheric dynamical processes depending on geographical position, such as monsoon circulation, chemical features of gaseous and aerosol compounds such as sources, chemical transformation and deposition.

Asia regions, particularly in East Asia (i.e., China, Japan, South Korea, Taiwan, etc.) and Southeast Asia (i.e., Vietnam, Thailand, Laos, Malaysia, etc.) regions, are among the major hotspots of air pollution. Over East Asia region, mainland China is the most typical example for air pollution crisis. Many nations among Southeast Asia are considered to have a fast growth rate in air pollution. Specifically, Hanoi, Vietnam's capital, one of the Vietnamese cities with largest area and highest population has suffered bouts of repeated and continuous air pollution with high ambient levels of particulate matter in the last decades. The causes of air pollution can be clearly seen from the strong development of industries, growth of traffic as well as from the urbanization process that increases the large amounts of emissions causing pollution. Severe pollution events over Hanoi can be frequently seen during the winter (from October to March). This is due to its geographical location leading to typical atmospheric circulation in this season with unfavorable phenomena, such as haze, smog, subsidence, which allows the accumulation of primary and secondary pollutants in the air near the surface.

In order to better control air pollution in Hanoi, research on factors involved in these complex air pollution phenomena is particularly necessary. A complete knowledge of both the impact of atmospheric dynamical processes and the characterization of pollutants is essential to propose effective pollution reduction strategies. In fact, many studies on the air environment have been carried out with the aim of giving a picture of the current status of the air environment in Hanoi. However, what exactly causes the most severe air pollution episodes in Hanoi remains an open issue. Regarding the emission sources, the contribution of potential sources which affects air quality in Hanoi is still a heated debate due to the lack of long-term and continuous monitoring through online and offline techniques. Similarly, knowledge related to the impact of atmospheric dynamics, which can affect directly the dispersion, transport and dilution of emissions over Hanoi, still has limitations.

In this context, the focus of this thesis consisted in identifying the crucial factors that contributed to the severe pollution events in Hanoi during winter. For such a purpose, a field campaign, including remote-sensing observations, was conducted for the first time to study the atmospheric boundary-layer height as well as PM2.5 vertical mixing together with aerosol composition. These data will constitute the experimental core of the work. In addition, the second work will be the modelling part where AOD analyses from the global Navy Aerosol Analysis and Prediction System (NAAPS) is used. Through incorporating long-term AOD data into the HYSPLIT trajectories during 14 winters, this work confirms the large year-to-year variability of the Aerosol Optical Depth (AOD) time-series over Hanoi, and to relate it with the annual variations of the Northeast monsoon activity. Furthermore, the sulfate AOD from NAAPS model was combined with the wind parameters (speed and direction) in order to identify the days with or without transboundary long-range transport or regional transport, leading to quantify the aerosol transported due to the Northeast monsoon. This PhD project and the field campaign were financed by Institute Environmental Technology funded by Vietnam National Independent (Science & Technology) Program for Space Technology (CNVT/16-20) under the grant number VT-UD.11/17-20 and Laboratoire de Physico-Chimie de l'Atmosphère (LPCA, Dunkerque, Hauts-de-France region) from "Littoral Côte d'Opale" university, which was also supported by the IRenE and CLIMIBIO CPER ("Contrat de Plan Etat-Région" financed by French "Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation", the Hauts-de-France region and the European Regional Development Fund) and the CaPPA project (financed by French "Ministère de l'Enseignement Supérieur"). We appreciate LPCA for providing the Doppler Lidar and also LATMOS (CNRS & Paris Sorbonne University) for providing an aerosol micro-pulse Lidar.

The manuscript is divided into 4 chapters. After a brief introduction of the scientific background in chapter 1, the second chapter presents the Vietnamese Aerosol Sampling and Profiling campaign and the analytical methodology. The scientific study itself is then divided into two chapters and addresses the following questions:

(i) Which atmospheric factors directly affect the variation of ground-based PM2.5 concentrations during the winter over Hanoi? (ii) How does the atmospheric boundary-layer height and the vertical structure of the atmosphere affect the pollutant stagnation or ventilation over Hanoi? (iii) How many and what kind of potential sources are actually involved in the winter pollution events in Hanoi in relation with the Northeast monsoon circulation from East Asia over northern Vietnam? (iv) What is the contribution rate of the various sources to the air pollution issue in Hanoi? Chapter 3 presents the results of the Vietnamese Aerosol Sampling and Profiling campaign, in which the evolution of ABL as well as atmospheric dynamics were investigated for the first time in Hanoi, allowing questions (i) and (ii) to be addressed. In Chapter 4, the campaign results are replaced into a larger perspective and objectives (iii) and (iv) are addressed by a systematic technique for determining the influence of cold incursions on Hanoi's air quality. This approach completely integrates backward trajectories, satellite Aerosol Optical Depth data, ground-based PM2.5 data and weather monitoring data.

Chapter 1.

Atmospheric dynamics and aerosols in Hanoi and its surrounding area Chapter 1. Atmospheric dynamics and aerosols in Hanoi and its surrounding area

This chapter describes the geography and climate of the study area, with a special attention to the monsoon's activity and its connection to the El-Niño phenomenon during the winter period. In addition, the main sources of air pollution and a state of the air quality in these studied areas are presented. Chapter 1 is structured as follows:

• An overview of the geographical, economic and meteorological features of the study area (Section 1.1). • A presentation of the atmospheric dynamics phenomena that impact air pollution in the study area (Section 1.2). • A presentation of the different aspects of particle air pollution in Hanoi (Section 1.3).

• A state of the art of the previous studies in air pollution over Hanoi and its surrounding area (Section 1.4).

General description of the Red River Delta area 1.Natural Geography

Vietnam is located in the Indochina Peninsula (IP; i.e., Cambodia, Laos, Myanmar, Thailand and Vietnam), which is known as the continental portion of Southeast Asia. In addition, Southeast Asia also includes another portion which is called maritime continent (i.e., Brunei, Indonesia, Malaysia, Singapore and Timor). Figure 1-1 (a) shows the studied area including IP and a part of East Asia (EA) that is composed mainly of mainland China. The Red River Delta (RRD) plain is an area in northern Vietnam; with a surface of over 20,973 km², it occupies approximately 6,41 % of the total territory of Vietnam (General Statistics Office, 2019). The RRD extends from the latitude of 21° 3' North to about 19°5' North, and the longitude of 105° 17' East to 107° 7' East, as shown in . This area is bordered by the Northern midland and is thus mountainous on the north and west sides. The plain extends towards the South (North Central Coast), while the East is bordered by the Gulf of Tonkin with a coastline of 400 km. In general, the RRD's terrain is relatively low and flat compared to other areas in northern Vietnam.

The RRD, as shown in Figure 1-1 (c), consists of 9 provinces and 2 cities (Hanoi and Hai-Phong). Hanoi, the capital city, is located from 20° 53' to 21° 23' North latitude and 105° 44' to 106° 02' East longitude. Hanoi is the biggest city in Vietnam with a land area of 3.358,6 km 2 (General Statistics Office, 2019). This city is located about 100 km west of the Gulf of Tonkin (Figure 1-1.c). In addition, Hanoi's topography is heterogeneous including hills, mountains (around height of 500 m up to 1300 m) and plains, in which plains account for about three-quarters of the whole city.

Human Geography

The RRD is the most heavily inhabited region in Vietnam with a population of about 23,000,000 people (General Statistics Office, 2019), accounting for approximately 23.4 % of the country's population. The population density of the RRD (1,087 people/km 2 ) is the highest compared to other regions in the country, almost 4 times higher than the average density of the whole country. Within the RRD, Hanoi is the most dense area with an estimated population of over 8,000,000 people (General Statistics Office, 2019). However, the population distribution is unequal as most of the population is found in the urban areas. Hanoi is composed of urban areas and suburban areas with 30 administrative units, including 12 urban districts, 17 sub-urban districts and 1 town [START_REF] Nhung | Mortality Burden due to Exposure to Outdoor Fine Particulate Matter in Hanoi, Vietnam: Health Impact Assessment[END_REF].

Economic features

Natural resources

Several natural resources in the RRD contribute significantly to the growth of Vietnam's industry, including quarries of limestone, kaolin clay and coal, and also natural gas. Some mineral resources represent huge reserves, such as coal accounting for 98 percent of the country's entire reserves, kaolin (40 %), and limestone (25 %). In addition, the RRD has a long coastline, leading to favorable conditions for aquaculture, development of sea transport and tourism (General Statistics Office, 2019).

Infrastructure

The RRD is a region having a great economical potential with many outstanding advantages over other regions in Vietnam. Including the capital Hanoi, the RRD holds the position of the economic, scientific, technical and cultural center of the country. This region serves as both a transport interchange of northern Vietnam and a trading hub of the nation. With this important role, the best transport and infrastructure system of the country (i.e., road, railway, river, sea and air systems) has been developed there. For example, the total length of railway in the RRD plain is 1000 km, accounting for one third of the total length of railway lines across the country (General Statistics Office, 2019). In terms of roads, this area has many modern international standard expressways that have formed a convenient network linking provinces together. Regarding water transport, the presence of the Red River system has resulted in the development of river transport (accounting for 40 % of the total volume of the whole nation). Besides, the big ports in the Gulf of Tonkin brought huge advantages for sea transport and tourism. Additionally, two international airports (Hai-Phong and Noi-Bai airports) connect northern Vietnam and the RRD to the rest of the world.

Economic restructuring in the Red River Delta

As shown in Figure 12, in the recent years, the RRD plain ranks second in terms of economic scale among the socio-economic areas of the nation considering Gross domestic product (GDP). The share of the RRD in Vietnam's GDP even increased over the last 5 years, climbing from 27.7 % in 2015 to 29.5 % in 2020. Table 1-1 provides similar numbers, but for two 5-year averaging periods (2011 -2015 and 2016 -2020). Table 1-1 also indicates that the contribution of the RRD to national exports rose from 31 % to 35.6 % between these two periods. This result highlights the RRD's significance as an economic engine of Vietnam. In the region, Hanoi is still at the top, accounting for 43.4 % of the GDP of the RRD region in 2020 (Vietnam Economic Institute, 2022).

Over the past decade, the RRD's economy has experienced structural shifts due to the industrial development and its modernization, in parallel with a gradually decrease of the agricultural sector (Table 1-1). More precisely, the agriculture's share within the regional GDP declined from 7.94 % in 2011-2015 to 6.40 % in 2016-2020, while share of the industry and construction sector climbed from 39.27 % in 2011 to 45.12 % in 2020 (the industry alone increased from 28.9 % to 37.4 %).

.3.4 A development of the industry and power production sector

The number of industrial zones in the RRD accounts for 26 % of the country's total, making this region the most industrialized, and also the most polluted area in Vietnam. In addition, the majority of thermal power plants (TTPs) in northern Vietnam is also located within the RRD area, taking advantage of its closeness to the coal mines in northern Vietnam. Indeed, coal, and oil to a lesser extent, are the primary fuels for TTPs in Vietnam. The number of coal-based TPPs across Vietnam increased from 6 to 29 between 2009 and 2019 in order to meet the national electricity's growing demand [START_REF] Le | Assessing the Impact of Traffic Emissions on Fine Particulate Matter and Carbon Monoxide Levels in Hanoi through COVID-19 Social Distancing Periods[END_REF]. In more detail, the coal-based electricity production in Vietnam grew up by nearly tenfold in a decade, from 11.9 billion kWh in 2008 to 119.1 billion kWh in 2019.

Figure 1-1 (c) displays the location of the main industrial zones and the TTPs over the RRD plain. The key industries in the RRD include: metallurgy, mechanical engineering, chemicals, building materials, food processing, consumer goods production, mining industries (e.g., oil and gas extraction, limestone and kaolin mining). In general, most of the industrial zones and TTPs in the RRD are not located far away from Hanoi (within a radius of 100 km to 200 km), and all of them may contribute to the degradation of air quality in the capital. Indeed, most TTPs in northern Vietnam are not equipped with the most recent pollution mitigation system, thus emitting large amounts of pollutants. In particular, the nearest coal-based TTPs, which is located only 50 km away from Hanoi, has been recorded as the highest SO2 emitter among all TTPs in northern Vietnam.

Regional weather and seasons

The RRD and Hanoi are under the influence of the Southwest monsoon (summer) or alternatively the Northeast monsoon (winter), resulting in distinct weather patterns [START_REF] Phan | Observed climate variations and change in Vietnam[END_REF]. In general, weather in the RRD is characterized by a tropical climate with four seasons: spring, summer, fall and winter. During the summer, tropical air from the Bay of Bengal influences Vietnam when a continental low-pressure system from South Asia moves Easterly towards Vietnam, covering most of the country. This results in hot, humid weather with heavy rain over the RRD during the summer, which lasts usually from the middle of May to August. In winter, from the middle of October to the end of March, the Northeast monsoon brings continental cold and dry air to northern Vietnam. During the second part of the winter, from January to March, the regional weather is sometimes erratic with intrusions of humid air from the Gulf of Tonkin, leading to regular drizzle and fog episodes. Spring and autumn are two transitional seasons, usually shorter and lasting about two months; the exact dates may vary from year to year and covers April to May and September to October, respectively.

The average monthly temperature in Hanoi varies from 17°C to 31°C throughout the year (Figure 1-3) while the average monthly rainfall varies from around 20 mm to approximately 230 mm (Figure 1234). The relative humidity remains pretty constant throughout the year, with average monthly values remaining within a range of 70 % to 80 % (Figure 1 -4). Another important meteorological parameter, as shown in Figure 123, is the cloud coverage. Among the countries in the IP region, the percentage of cloud cover in northern Vietnam is the highest [START_REF] Lasko | Analysis of air pollution over Hanoi, Vietnam using multi-satellite and MERRA reanalysis datasets[END_REF]. Hanoi experiences a dense cloud cover (more than 60 % of the sky) for more than 75 % of the time in summer, from May to September. By contrast, in winter, from November to March, the rate of dense cloud covers is lower than 35 %. However, during the winter, there is a typical phenomenon where clouds at low altitude fill the sky over Hanoi, especially from late January to the end of March. Those clouds appear when the rising vaporsaturated air is blocked by an elevated temperature inversion (see Section 1.2.1.2), forming a layer of grey clouds at an altitude of about 1.5 km above the ground (below 850 hPa), which creates drizzle.

Regarding the winter period, October is the transition month from autumn to winter in Hanoi; therefore, there is a clear change with a decrease of temperature (Figure 1-3), humidity and rainfall (Figure 1234) is recorded between September and October. The monthly average temperature drops from approximately 25°C in October to 22°C in November; at the same time, rainfall and humidity also decrease. During the coldest months (December, January and February), nocturnal temperatures in the rural, more elevated, areas surrounding Hanoi often drop below freezing and morning frost is common. Meanwhile, the average monthly rainfall values in these months reach the bottom. In March, the weather becomes a bit warmer and more humid compared to the previous months. The average monthly temperature, rainfall and humidity increase simultaneously and during certain days, the daily humidity can be higher than 90 % and the air is even saturated.

Atmospheric dynamics 1.2.1 East Asian Northeast monsoon

Mechanism of the Northeast monsoon

The East Asia Northeast monsoon (more simply called Northeast monsoon in the rest of this manuscript) is a typical synoptic phenomenon across the EA region and continental part of Southeast Asia (SEA), that has a substantial impact on atmospheric circulation. An initial mechanism of the Northeast monsoon can be described briefly as below:

Firstly, the anticyclonic circulation dominates the entire troposphere in Siberia during the winter. A ridging high-pressure system from Siberia propagates southeastward around the edge of the Tibetan Plateau (Figure 12345) [START_REF] Kang | Early Holocene weakening and mid-to late Holocene strengthening of the East Asian winter monsoon[END_REF]. Then, the high-pressure center moves eastward towards the southeastern coastal parts of China and weakens over the oceans. Meanwhile, the high-pressure system proceeds southward to southern China, or even towards the IP region. In Vietnam, the Northeast monsoon is also known as cold surges or cold intrusions, because the Siberian anticyclone brings cold air to the IP. The high-pressure system reaches its maximum intensity in December and January, whereas the frequency of cold surges peaks in November and March [START_REF] Zhang | Climatology and Interannual Variation of the East Asian Winter Monsoon: Results from the 1979-95 NCEP/NCAR Reanalysis[END_REF]. Notably, the monsoon's activity varies from year to year depending on climate oscillation such as El Niño-Southern Oscillation [START_REF] Chang | Impact of the 2015 El Nino event on winter air quality in China[END_REF] (see Section 1.2.3).

Chapter 1. Atmospheric dynamics and aerosols in Hanoi and its surrounding area 38

The first half of the winter, between October and December, is called dry phase because a northerly to northeasterly flow from Siberia brings dry and cold air to mainland China and then, directly to northern Vietnam (red arrow) (Figure 1 -6.a). During the second half of the winter (from January to March), the Siberia High-pressure system sometimes moves to the East, so that air masses have to make a detour over the South China sea, then over the Gulf of Tonkin to reach northern Vietnam (purple arrow), as displayed in Figure 1-6 (b). As a result, these air masses bring humid air that generates smog, low stratus cloudiness, and drizzle over northern Vietnam, which is why the second half of the winter is called humid phase.

Impact on the weather, pollution and atmospheric stratification in Hanoi

Cold surges result in changes in the local weather, including a decrease of surface air temperature, an increase of sea-level air pressure, and a strengthening of the wind. In the winter, cold surges in northern Vietnam can produce significant temperature decreases from October to March. In the mountainous areas of northern Vietnam, the temperature may dip below 0°C causing snow and frost during the onset of cold surges. In the plain, a lowest temperature of around 5°C was recorded [START_REF] Minh | An outline of climatology and meteorology[END_REF]. When the Siberian high-pressure system weakens and disperses into several high-pressure centers, the cold surge ends and a new monsoon cycle may begin. At the end of the winter (February -April), an intrusion of the trade winds from the Gulf of Tonkin can occur between cold surges, bringing warm, humid air into the region.

Regarding air quality, an outstanding change in air pollution levels during the Northeast monsoon was observed in many regions such as mainland China, Taiwan, Malaysia and Vietnam [START_REF] Ashfold | Influence of Northeast Monsoon cold surges on air quality in Southeast Asia[END_REF][START_REF] Chuang | Quantifying PM2.5 from long-range transport and local pollution in Taiwan during winter monsoon: An efficient estimation method[END_REF]Cohen et al., 2010a;[START_REF] Junker | Evaluation of the effect of long-range transport of air pollutants on coastal atmospheric monitoring sites in and around Taiwan[END_REF]. For example, an increased PM10 level was recorded at numerous monitoring sites in Beijing, where cold surge arrives in four-day cycles on average [START_REF] Song | PM10 modeling of Beijing in the winter[END_REF]. Additionally, pollution episodes with an enormous increase of particulate matter (PM) were observed over Hong Kong with the existence of the cold surges [START_REF] Louie | Seasonal characteristics and regional transport of PM2.5 in Hong Kong[END_REF]. [START_REF] Chuang | Quantifying PM2.5 from long-range transport and local pollution in Taiwan during winter monsoon: An efficient estimation method[END_REF] found a relation between the change of PM10 concentration over Taiwan and the transported pollutants from mainland China over prolonged periods. [START_REF] Ashfold | Influence of Northeast Monsoon cold surges on air quality in Southeast Asia[END_REF] indicated that O3 was also transported southward to tropical SEA during the Northeast Monsoon from the EA region, and that this transport pathway is especially strong during cold surges. Especially over northern Vietnam, repeated Northeast monsoon cycles in Hanoi cause a dozen multi-day pollution outbreaks during the winter, thus, the highest concentrations of PM are usually recorded between October and March [START_REF] Hien | Air pollution episodes associated with East Asian winter monsoons[END_REF][START_REF] Ly | The effects of meteorological conditions and long-range transport on PM2.5 levels in Hanoi revealed from multi-site measurement using compact sensors and machine learning approach[END_REF].

Chapter 1. Atmospheric dynamics and aerosols in Hanoi and its surrounding area 40

Besides the change of local weather, the Northeast monsoon occurrence has been proven to be related with subsidence temperature inversions (STI) over Hanoi [START_REF] Hien | Air pollution episodes associated with East Asian winter monsoons[END_REF][START_REF] Trinh | Temperature inversion and air pollution relationship, and its effects on human health in Hanoi City, Vietnam[END_REF]. A STI occurs when a high-pressure system causes a layer of air to sink: the air compresses as it descends, creating a warmer layer in altitude. Over Hanoi, radiosonde observations have shown that STI may persist at both daytime and nighttime, at a variable altitude from 300 m to 1800 m above ground [START_REF] Trinh | Temperature inversion and air pollution relationship, and its effects on human health in Hanoi City, Vietnam[END_REF]. The STI layer acts as a cap, preventing the air buoyancy from rising further. As a result, a STI traps fog and mist, at the same time as it enhances the accumulation of dust and air particles near the ground.

Atmospheric boundary-layer (ABL)

Within the troposphere, most human activities take place in the lowest part of troposphere, called boundary-layer, which is defined as the layer directly responding to the variation of surface radiative forcing within an hour or less [START_REF] Stull | An Introduction to Boundary Layer Meteorology[END_REF]. The typical depth of the ABL varies from a few hundred meters to a few kilometers above ground. The boundary-layer development strongly depends on solar radiation, wind, orography and surface cover. Understanding ABL dynamics is critical for a variety of scientific domains such as the development of low clouds or severe wind shear (i.e., low-level jets) that affect take-offs and landings in the aviation sector. In this work, the features of ABL (e.g., depth, turbulent flux) are of course crucial for understanding the transport and dispersion of atmospheric pollutants near the ground.

Atmospheric boundary-layer structure

As shown in Figure 1-7, under fair-weather and cloud-free conditions, the ABL's evolution follows a predictable daily pattern according to solar heating of the surface. After sunrise, the surface is warmed by the sun and the convection mixes warm air which rapidly penetrates upward throughout the stable former nocturnal boundary-layer and then into the residual layer, starting the growth of the mixed layer or convective boundary-layer (CBL). The surface layer (SL) is the lowest part of the ABL, close to the ground, which is characterized by a relative homogeneity of momentum and heat turbulent fluxes. Meanwhile, the free atmosphere above is entrained downward, and thermals at the top of CBL overshoot upward. Entrainment is the name of this process in the so-called entrainment zone. The entrainment zone is thicker when turbulence and thermals are more intense, and it is narrower when a larger temperature inversion caps the ABL.

During the daytime, the CBL continues to grow and reaches its peak in the early afternoon. Winds, temperature, and humidity are supposed to be homogeneous with altitude in this layer, and the surface influences turbulence patterns at the bottom. The CBL continues to grow until about a half hour before the sunset, then eddies have less energy due to the reduction of surface fluxes, which results to the decay of the turbulence in the previous CBL. This process leads to the transformation of the CBL into a residual layer which exist just below the free atmosphere. When solar heating ceases completely, a positive vertical temperature gradient is created, resulting in the formation of a nocturnal stable boundary-layer (SBL). This layer maintains until the sun's radiative energy begins to heat the Earth's surface in the next morning, causing a new CBL to form and evolve repeatedly. To summarize, the daily heat exchanges in the surface determine the evolution of ABL height [START_REF] Garratt | The Atmospheric Boundary Layer[END_REF]. In addition to solar radiation, the diurnal fluctuation of the ABL over a continental region can also be influenced by meteorological conditions, soil moisture and surface roughness [START_REF] Davis | Effect of Clouds on the Diurnal Evolution of the Atmospheric Boundary-Layer Height Over a Tropical Coastal Station[END_REF] and also by the subsidence temperature inversion.

Figure 1-7. Schematic diagram of the diurnal cycle of the atmospheric boundary-layer under fair-weather and cloud-free conditions

Source: [START_REF] Stull | An Introduction to Boundary Layer Meteorology[END_REF].

However, the above-mentioned case only occurs under fair-weather and cloud-free conditions or above high-pressure regions. In fact, clouds usually appear in the troposphere, especially in low-pressure regions, and it is more difficult to define the ABL in this situation [START_REF] Stull | An Introduction to Boundary Layer Meteorology[END_REF], not to mention that the ABL over an urban region is one of the most complex and least understood circumstances, as shown in Figure 1-8 (a). As urbanization proceeds more quickly, cities become larger, hotter and more polluted, leading to the urban heat island phenomenon in the lower atmosphere (1 -2 km) [START_REF] Hidalgo | Advances in Urban Climate Modeling[END_REF]. The urban heat island occurs as long as (a) the sensible heat flux is higher in the city due to the man-made materials, (b) the latent heat flux is lower due to the lack of vegetative cover and (c) the heat fluxes are not balanced due to anthropogenic heat sources.

As shown in Figure 1-8 (c), the surface layer has two parts: an inertial sublayer and a roughness sublayer, which are defined according to Monin-Obukhov similarity theory. The inertial sublayer is the upper part of the surface layer, where Monin-Obukhov similarity theory is valid and vertical variation of the turbulent fluxes is negligible. The roughness sublayer is Chapter 1. Atmospheric dynamics and aerosols in Hanoi and its surrounding area one compartment of the surface layer where turbulence deviates from Monin-Obukhov similarity theory. Over urban regions, there is a deeper roughness sublayer due to the high buildings and street canyons which occupy the first tens of meters above the ground [START_REF] Piringer | No title found][END_REF]. Compared to the inertial sublayer above, where the turbulence is homogenous with altitude, the atmospheric flow within the roughness sublayer exhibits strong spatial variations in both the vertical and the horizontal directions, depending on the roughness element properties (Figure 1 -8.b). All these contributions drive the temporal evolution and vertical structure of the urban ABL. Therefore, the structure of the urban ABL is now considered to be heterogeneous, non-equilibrium and non-stationary, compared to the theoretical definition of the ABL. Source: [START_REF] Piringer | No title found][END_REF] In some cases when the residential area is located near the coast, the urban ABL is also affected by other weather factors such as sea breeze circulations [START_REF] Barlow | Progress in observing and modelling the urban boundary layer[END_REF]. In addition, the urban ABL structure is also affected by low-level jets (LLJs), i.e. low-altitude wind maxima [START_REF] Pichugina | Stable Boundary Layer Depth from High-Resolution Measurements of the Mean Wind Profile[END_REF]. For example, the structure of urban SBL is altered and the nighttime urban heat island intensity is significantly moderated by LLJs [START_REF] Tsiringakis | Interactions Between the Nocturnal Low-Level Jets and the Urban Boundary Layer: A Case Study over London[END_REF]. During nights with no or weak LLJs, vertical mixing is weakened, leading to the development of stronger temperature inversions. As a result, the depth of the nocturnal boundary-layer inside the city is reduced, leading to changes in the transport and dispersion of near-surface pollutants. Conversely, during nights with strong LLJs, vertical mixing below the Chapter 1. Atmospheric dynamics and aerosols in Hanoi and its surrounding area jet is increased by wind shear. As a result, atmospheric stability is much weaker, and the urban heat island has a much smaller impact on the SBL structure [START_REF] Hu | Impact of Low-Level Jets on the Nocturnal Urban Heat Island Intensity in Oklahoma City[END_REF].

Contrary to the mainland, the ABL's growth over oceans is slower and there is much less spatial variability compared to the ABL's growth over land. Indeed, water has a larger heat capacity, meaning that it absorbs larger amounts of heat from the sun with relatively little temperature change, leading to a slowly varying convection into the bottom of the ABL [START_REF] Garratt | The Atmospheric Boundary Layer[END_REF][START_REF] Stull | An Introduction to Boundary Layer Meteorology[END_REF]. A daily continuous boundary-layer with a capping inversion is frequently observed over the ocean. When the sea surface temperature is greater than the air temperature, such as during cold air outbreaks, this layer is a convective boundary-layer, but it may also be a stable marine boundary-layer over cold seas or during periods of warm air advection. In the absence of obstacles, the spatial variability is also greatly reduced compared to the ABL over land.

Impact of ABL dynamics on pollutants dispersion

Most of the pollutants are emitted into the atmosphere from near ground-level sources and are usually confined within the ABL. Pollutants can be dispersed and mixed by convection and turbulence within the convective boundary-layer, while very little of them can pass through this layer and reach the free atmosphere. The altitude of the ABL top, or boundary-layer height (BLH) is usually linked with the intensity of turbulence; the stronger turbulence is, the more air pollutants are lifted from the surface to higher altitudes, which alleviate air pollution near the ground, and vice versa [START_REF] Petäjä | Enhanced air pollution via aerosol-boundary layer feedback in China[END_REF][START_REF] Wei | Intermittent turbulence contributes to vertical dispersion of PM2.5 in the North China Plain: cases from Tianjin[END_REF]. Thus, the intense instabilities in the CBL can disperse the pollutants away from the source and clear the atmosphere near the surface. In contrast, under stable conditions within the SBL, the pollutants are trapped near the surface and eventually accumulate into large concentrations [START_REF] Sportisse | Fundamentals in Air Pollution[END_REF]. In other words, an increase of air pollutants is often associated with a shallow BLH, while a decrease of pollutants is accompanied by an uplift of the BLH.

If large amounts of pollutants emitted during the previous day are stored in the residual layer during the night, they are unlikely to be mixed down to the ground due to the limitation of turbulence. However, on the next morning, when the CBL starts to grow, the previous day's emissions contained in the residual layer can be entrained into the CBL by turbulence, then mixed down towards the ground. This process can cause an increase in pollutants' concentration in the morning and it is called "fumigation" [START_REF] Stull | An Introduction to Boundary Layer Meteorology[END_REF].

Apart from transport, BLH can alter the concentration and distribution of precursor particles, potentially affecting the chemical transformation of tiny particulate matter [START_REF] Ansari | Response of Inorganic PM to Precursor Concentrations[END_REF]. BLH is also an important parameter in air pollution models; it specifies the volume available for pollutant dispersion and is used in a variety of prediction and diagnostic approaches and/or models that evaluate pollutant concentrations [START_REF] Seibert | Review and intercomparison of operational methods for the determination of the mixing height[END_REF]. As a result, obtaining the BLH properly, especially during polluted periods, is critical to investigate air pollution concerns.

El Niño Southern Oscillation (ENSO)

Characteristics of ENSO

The El Niño Southern-Oscillation (ENSO) is a large-scale anomaly of the oceanatmospheric system with large disturbances in the sea currents and sea surface temperatures causing atmospheric anomalies in the equatorial region, primarily in the eastern and central Pacific Ocean [START_REF] Ropelewski | North American Precipitation and Temperature Patterns Associated with the El Niño/Southern Oscillation (ENSO)[END_REF]. The ENSO phenomenon is closely related to the east-west atmospheric circulation in the equatorial Pacific region. This circulation was discovered by J. Walker in 1924, so it is also known as the Walker circulation. The ENSO signature is represented by an anomaly of the sea surface temperature distribution and the eastwest barometric fluctuation called the south oscillation, to distinguish it from the barometric fluctuations in the northeast Atlantic and northern Pacific. Therefore, people often take these two parameters as quantitative indicators in studying the ENSO phenomenon.

During periods of weak trade wind, the sea surface temperature in the central and eastern Pacific Ocean rises abnormally, forming an anomalous low-pressure system. Over the South American coast, the weather is characterized by cloudy weather with heavy rain. That abnormal phenomenon is called El Niño and displayed in Figure 1-9 (a). Meanwhile, in the central and western Pacific Ocean, the sea surface temperature is abnormally low, forming an anomalous high-pressure system, limiting the development of convection and rain clouds so that Australia experiences severe drought. The El Niño phenomenon also affects the storm's trajectory due to the strong subtropical swift-flow, thus, the storm's orbit tends to move towards the poles. Every two to seven years, El Niño episodes can be observed. These occasions typically start in the middle of the year and persist for the next six to eighteen months. However, the amplitude, chronological history, and regional pattern of El Niño occurrences vary.

In contrast, when the trade winds blow stronger, cold flows descend from the poles to both sides of the equator, making the eastern Pacific Ocean surface anomalously cold and creating a high-pressure system offshore South America. This phenomenon opposite to the El Niño, is called La Niña (Figure 1-9.b). Due to the high-pressure system, convection is hindered over the Eastern Pacific Ocean, limiting the formation of cumulus clouds and causing severe droughts in South America. In the western Pacific Ocean, the opposite phenomenon occurs: the low-pressure system and warmer waters lead to the formation of clouds and increase precipitations. This causes great flooding over the west of the Pacific region during the La Niña phenomenon.

Impact of ENSO on the Northeast monsoon over East Asia

As mentioned above, ENSO occurrences are widely known for their ability to cause large anomalies in atmospheric general circulation and weather patterns. Many studies demonstrated the fact that the monsoon system and moisture fields in eastern Asia are affected by these shifts in atmospheric circulation. Weaker winter monsoons result from the unusual atmospheric circulation brought on by the El Niño event (i.e., stronger westerlies and a displacement of the polar front towards the north in EA) [START_REF] Chongyin | Interaction between anomalous winter monsoon in East Asia and El Nino events[END_REF]. According to the research of [START_REF] Chang | Impact of the 2015 El Nino event on winter air quality in China[END_REF], the Ural-Siberian blocking high-pressure system and the Asia trough were weakened during the 2015-ENSO, leading to a weaker winter monsoon, as less air masses were transported from Siberia to the south. Source: [START_REF] Chang | Impact of the 2015 El Nino event on winter air quality in China[END_REF] Besides, during El Niño years, a positive anomaly of the sea surface temperature was also recorded over the South China Sea reaching its peak during the month of February following the El Niño onset. This anomaly was proven to weaken the Northeast monsoon winds that flows along the eastern coast of China, resulting in more trade wind intrusions in between the monsoon cycles during the second phase of the winter [START_REF] Wang | Interannual variability of the South China Sea associated with El Niño[END_REF].

Background air quality in Hanoi

Atmospheric aerosols

Atmospheric aerosol, also known as particulate matter, is a term that describes solid or liquid particles suspended in air with dimension diameters ranging from 0.002 µm to about 100 µm. Aerosol particles vary widely in size, origin, chemical composition, number and distribution in space and time, and their duration in the atmosphere.

Size and life cycle of atmospheric particulate matter

The aerosols may be categorized according to their formation processes: primary (directly emitted into the atmosphere) and secondary (transformation and condensation process of chemical compounds in the atmosphere) aerosols. Atmospheric aerosols may include inorganic or organic compounds. Secondary organic aerosols can be produced by photochemistry, through the complex interaction of sunlight with VOCs or other gaseous species from natural or anthropic sources. Their formation processes are various, e.g. coagulation and condensation or photochemical reactions, chemical reactions of primary pollutants, among them or by reactions with pre-existing particles in the atmosphere. The most essential factor in defining the type of particles is their size. They may be categorized as coarse, fine, or ultrafine according to their aerodynamic diameter. Ultrafine particles usually have a diameter of less than 1 µm (PM1), fine particles have an aerodynamic diameter of less than 2.5 µm (PM2.5), whereas coarse particles have an aerodynamic diameter between 2.5 µm and 10 µm (PM10). The smaller the aerodynamic diameter is, the more likely particles will directly enter the human respiratory system and cause more severe impacts.

Particles experience a variety of modes throughout their lives that affect their size, shape, chemical and physical characteristics. The separation of particles into typical modes brings a thorough understanding of their life cycle. According to [START_REF] Bakshi | Natural Nanoparticles: Implications for Environment and Human Health[END_REF], the global evolution of particles is classified into four primary modes as following (Figure 1-11): i) Nucleation mode: refers to the smallest group of aerosols with a diameter of less than 0.02 µm, formed by steam condensation during combustion processes or by nucleation of gas molecules. They are mostly formed via chemical or photochemical reactions of gaseous compounds such as SO2, NOx, NH3 and VOCs.

ii) Aitken mode: aerosols with a diameter of 0.02 to 0.1 µm, often grouped with the nucleation mode, mainly formed by the coagulation of ultrafine particles within the nucleation mode. They are formed by condensation of additional inorganic or organic compounds. Nucleation and Aitken modes account for the bulk of particles, but due to their tiny size, they are rarely indicative of total particle mass.

iii) Accumulation mode: aerosols with a diameter of 0.1 to 2.5 µm, including primary particles (directly emitted in the atmosphere) and particles resulting from the condensation of vapor on the particles, as well as coagulation of particles from the nucleation mode. iv) Coarse mode: aerosols with a diameter greater than 2.5 µm emitted from anthropogenic and natural sources. The anthropogenic sources of coarse particles often refer to mechanical operations including agriculture, industrial process, mining, transport. In contrast, desert dust, sea spray, rock debris, biomass burning or volcanic ashes can be considered as typical natural sources. Coarse particles are characterized by their large settling velocities, making their residence time in the atmosphere relatively short compared to other modes.

Figure 1-11. Representation of aerosol size distribution and the main formation and removal processes that influence aerosols composition and concentration in the atmosphere

Source: [START_REF] Bakshi | Natural Nanoparticles: Implications for Environment and Human Health[END_REF] In general, aerosol lifetimes range from a few minutes to years, depending on the chemical components and the portion of the atmosphere where it stays. In reality, the residence period of aerosols in the atmosphere is fundamentally controlled by two regimes: dry and wet deposition. Wet deposition relates to the removal of both vapor and particulate matter during precipitation, which is one of the most effective ways to remove particulate matter from the environment. However, the effectiveness of wet deposition is influenced by particle size. As an example, a study of wet deposition in a lake environment [START_REF] Tai | Atmospheric deposition of particles at a sensitive alpine lake: Size-segregated daily and annual fluxes from passive sampling techniques[END_REF] assessed the fraction of particulate matter removed from the atmosphere to be between 54 and 71 % for diameters of 0.5 to 20 µm, and 76 to 86 % for diameters of 1 to 20 µm. Meanwhile, dry deposition is a removal process during which particles directly sink to the Earth's surface from the atmosphere under the effect of gravity. It is a permanent process, which depends on the state of the land surface (i.e., grass, plat plain and others) and on meteorological conditions.

Impacts of atmospheric aerosols

Aerosols have a complex set of consequences on the Earth, but their presence, quantity, and dispersion have been influenced significantly by human's activities. Aerosols usually affect climate in two basic ways: by altering the amount of heat that enters and exits the atmosphere, and by altering the formation process of clouds. Depending on the composition of aerosols, they have a cooling effect and others a warming effect. For example, black carbon particles absorb heat from the sun as it beats down. This warms the atmosphere while also cooling the Earth's surface by preventing heat from escaping. In contrast, sulfate or nitrate particles reflect nearly all radiation they encounter, resulting to help to cool the atmosphere.

By contrast, the aerosol impacts on human health are clearer and more direct, linked to a variety of negative health effects [START_REF] Pope | Fine particulate air pollution and human mortality: 25+ years of cohort studies[END_REF][START_REF] Pope | Health Effects of Fine Particulate Air Pollution: Lines that Connect[END_REF]. The negative impacts of aerosols consist of both short-term acute symptoms (i.e., asthma and bronchitis) and long-term chronic irritation and inflammation of the respiratory tract, which can potentially lead to cancer. Air pollution affects people in all areas of the globe, especially long-term air pollution exposure which is clearly linked to an increased risk of death. According to the World Health Organization report, particulate matter air pollution contributes to a considerable amount of that statistic, with an estimated 800,000 premature deaths each year, making it the world's 13 th biggest cause of death [START_REF] Anderson | Clearing the air: a review of the effects of particulate matter air pollution on human health[END_REF]. According to the Global Burden of Disease Report 2019, air pollution ranks 5 th among the risk factors for death and disability in Vietnam (MONRE, 2021). Air pollution, in Vietnam, might cause more than 52,000 premature deaths per year, resulting in yearly losses of up to US$ 13.2 billion, or nearly 5 % of the country's gross domestic product [START_REF] Ha | Air quality in Hanoi during the pandemic[END_REF]. The most vulnerable are the elderly and children, as both short and long-term exposure can impair lung function, cause respiratory infections, and trigger asthma. The most vulnerable people are the elderly and children, as both short and long-term exposure can impair lung function, cause respiratory infections, and trigger asthma.

Main pollution sources in Hanoi, EA and IP region

Over the RRD and Hanoi, there is not much evidence for natural sources which can significantly degrade air quality (e.g., volcanic eruptions or forest fires). However, aerosols and other kinds of pollutants emitted from anthropogenic activities into the atmosphere have been increasing in Hanoi. The combustion of fossil fuels is one of the main anthropogenic sources of air pollution in the RRD and Hanoi, originating from four main categories of activities: transport, agriculture, industry and construction. In this study, anthropogenic sources are also categorized based on the source location including local emissions, regional and long-range transport emissions, that correspond respectively to sources located within Hanoi or within a radius below or above 200 km from Hanoi.

Local emissions

Air quality over Hanoi can be directly impacted by the intense local emissions, e.g. from transportation, industrial production facilities, craft villages, construction operations and civil activities, as illustrated in 2016). The emission of air pollutants is increasing in tandem with the expansion of the transportation infrastructure system, the rise of the personal vehicles' fleet and change in the type of vehicles. Vehicle exhaust emissions, particularly from those with unqualified engines, continue to be one of the leading contributors to this large increase in air pollutant concentrations. For example, motorcycles account for a significant amount of CO, VOCs and total suspended particulates (TSP) emissions, whereas cars account for a significant portion of SO2 and NO2 emissions (MONRE, 2016). Moreover, public buses, which do not meet emission standards, are still being used widely in order to meet the increasing demand for public transportation in the city. Despite the fact that the better emission characteristics of modern engines may partially solve the pollution problem, it is likely to worsen as the number of vehicles and traffic congestion in the city continue to expand.

Figure 1-12. Local pollution sources within Hanoi including transportation, industrial production facilities, craft villages, construction operations and civil activities

Industrial production facilities or craft villages were originally built on the outskirts of Hanoi; however, due to the urban expansion many of these places are now situated in the inner city. They are often small and medium-size industrial production facilities with antiquated manufacturing technologies and many of them lack the technology necessary to handle dangerous emissions and fail to fulfill emission guidelines that are supposed to reduce air pollution. Notably, the use of coal as a fuel (usually low-quality coal), the usage of raw materials and chemicals in manufacturing technology lines, and other factors all contribute to air pollution in craft villages. Dust, CO2, CO, SO2, NOx, and VOCs are common waste components. Metal recycling, in particular, is the industrial business that produces the most Chapter 1. Atmospheric dynamics and aerosols in Hanoi and its surrounding area 51 pollution; the recycling and processing process also produce harmful gasses such as acid vapors, alkalis, and metal oxides (PbO, ZnO, Al2O3).

Civil activities such as domestic cooking and heating should also be taken into account as main contributors to the air pollution issue in Hanoi, although their contribution is not comparable with emissions from the industrial sectors. Over Hanoi, cooking operations utilizing low-quality coal as fuel continue to persist in residential areas, generating local pollution. However, in the most urbanized part of Hanoi region, this activity has diminished dramatically, with just a small amount persisting in suburban and rural areas. Additionally, incineration is currently the most widely used waste treatment method in Vietnam although the process of managing and using waste incinerators reveals a number of technological, administrative, and operational limits. Existing technologies have problems with emissions control, and as a result, in terms of the environment, this is still a source that has some negative effects on the air environment.

Construction operations are another source causing air pollution. Construction of apartment buildings, new urban areas, bridges and highways, house repairs, transportation of construction materials and trash, and other construction-related activities have occurred everywhere in the RRD over the recent years, particularly in Hanoi city. Dust protection during operations such as excavating and backfilling, dismantling existing works, and moving building materials, in particular, is still limited. Particularly, the density of vehicles carrying construction materials is large in Hanoi, most of these vehicles are not covered or washed before moving on the street or if so, the implementation is limited. As a result, a tremendous amount of dust is emitted into the air.

Regional transport (RT) of pollution

The contaminants in the atmosphere may travel a few to hundreds of kilometers depending on their characteristics. As a result, pollution sources from Hanoi's suburbs, or from other areas in northern Vietnam, can directly contribute to the air pollution issue in Hanoi. Significant sources of RT include industrial and agricultural activities (Figure 1-13).

Figure 1-13. Regional sources emit emission (industrial production, thermal power plant and biomass burning) into the atmosphere from suburbs and countryside over RRD plain.

Chapter 1. Atmospheric dynamics and aerosols in Hanoi and its surrounding area 52 Industrial productions from large-size facilities situated in the suburbs of Hanoi or farther within the RRD are considered to be one of the most significant source of air pollution in northern Vietnam. Pollutants arise from all stages of the processes: exploiting and supplying input materials, burning fossil fuels, manipulating volatile chemicals, etc. Sources of air pollution from industrial activities often have high concentrations of toxic substances, concentrated in one area. Toxic substances from industrial exhausts are classified into dust, inorganic and organic substances, chemicals and metals. The most common pollutants include NO2, SO2, VOCs, TSP, among which, SO2, NO2 and TSP emissions account for the majority of the pollutant load (Table 12). However, due to the diversity of industrial sources, the composition of emissions from the industrial sector varies greatly, and it is very difficult to attribute a peculiar source to particles found in the atmosphere.

Manufacturing industry group Emissions

Industries In addition, a large number of coal-based thermal power plants are also concentrated in the RRD (see Sec. 1.1.3.2). The majority of coal-based thermal power plants rely on backward technology and use low-quality coal, and thus do not fulfill environmental standards. The emissions from TTPs are very large, with emission fluxes up to thousands of m 3 /min and capable of dispersing over hundreds of kilometers towards the surrounding areas, especially urban areas located in the main wind direction from chimneys. Coal-based TTPs release a lot of SO2, NOx, and CO2; oil-based thermal power plants emit mostly CO2 and dust; and gasbased thermal power plants emit mostly CO2 and NOx. The unbalanced distribution of TPPs, along with antiquated technology, has put a lot of strain on the air quality in northern Vietnam. The SO2 concentrations detected in industrial zones or near coal-based TTPs in the north of Vietnam is greater than in the middle and the south of Vietnam (14 plants compared to 03 and 06 plants, respectively), according to monitoring data [START_REF] Monre | Ministry of Natural Resources and Environment[END_REF].

Biomass burning (BB) is another source with immediate impacts on air quality at the burning site and surrounding areas. In recent years, over the RRD plain, crop production has risen steadily, accompanied by an increase in the burning of post-harvest products such as rice straw, around the end of May or the end of October. Currently, rice straw is no longer the primary fuel in rural regions due to the availability of alternative energy sources such as electricity or gas. The primary way for disposing of the aforementioned rubbish is to burn it in the field. Outdoor straw burning is an unregulated combustion process that produces, for example, CO and VOCs with varying chemical reactivity and levels of harmfulness, and carbonaceous particles such as elemental carbon and primary organic aerosol. As a result, the burning of straw after each harvest has generated harmful haze events, not only near fire spots, but also in the neighboring areas. In fact, Hanoi experienced regularly episodes during which smoke from burning rice straw is transported from the suburbs, degrading the air quality of the city.

Transboundary long-range transport (LRT) of pollution

Air pollution has not just a localized and regional influence, but it may also cause harmful consequences across borders. The RRD area has been stated as a potential receptor for transboundary LRT from EA region under the impact of the Northeast monsoon during the winter period (Bao [START_REF] Anh | Key factors explaining severe air pollution episodes in Hanoi during 2019 winter season[END_REF][START_REF] Chifflet | Origins and discrimination between local and regional atmospheric pollution in Haiphong (Vietnam), based on metal (loid) concentrations and lead isotopic ratios in PM10[END_REF][START_REF] Hai | Effects of local, regional meteorology and emission sources on mass and compositions of particulate matter in Hanoi[END_REF][START_REF] Hien | Air pollution episodes associated with East Asian winter monsoons[END_REF]. Figure 1-14 indicates that coal-based TTPs with various sizes and capacities are abundant in mainland China. In fact, this is a sensitive issue and Hanoi does not have a set of direct measurements to quantify the influence of transboundary LRT from mainland China. However, in the winter, given the relative position of mainland China and northern Vietnam, and given the direction of the northeast monsoon winds (Sec. 1.2.1.1), it is inevitable that smoke and dust from China are transported towards northern Vietnam.

The IP region is notorious for frequent and large wildfires, as well as the burning of postharvest agricultural residue. The contribution of BB from the above-mentioned processes to the regional aerosol loadings is significant [START_REF] Gautam | Characterization of aerosols over the Indochina peninsula from satellitesurface observations during biomass burning pre-monsoon season[END_REF][START_REF] Reid | Observing and understanding the Southeast Asian aerosol system by remote sensing: An initial review and analysis for the Seven Southeast Asian Studies (7SEAS) program[END_REF]. In particular, BB activities reach their peak from the middle to the late winter monsoon period: emissions increase in January -February, reach their peak in March -April, and then quickly decline in May as the summer monsoon arrives. Some evidence clearly indicates that smoke originating from BB is transported aloft and over the mountain ranges of Myanmar and Laos, towards northern Vietnam and into the Pacific Ocean [START_REF] Nguyen | Carbonaceous aerosol composition in air masses influenced by large-scale biomass burning: a case study in northwestern Vietnam[END_REF][START_REF] Reid | Observing and understanding the Southeast Asian aerosol system by remote sensing: An initial review and analysis for the Seven Southeast Asian Studies (7SEAS) program[END_REF][START_REF] Tsay | Satellite-Surface Perspectives of Air Quality and Aerosol-Cloud Effects on the Environment: An Overview of 7-SEAS/BASELInE[END_REF]. Moreover, these large smoke plumes have been reported to reach occasionally as far as the southern Tibetan Plateau, Hong Kong, and central Taiwan [START_REF] Amnuaylojaroen | Long Range Transport of Southeast Asian PM2.5 Pollution to Northern Thailand during High Biomass Burning Episodes[END_REF][START_REF] Nguyen | Carbonaceous aerosol composition in air masses influenced by large-scale biomass burning: a case study in northwestern Vietnam[END_REF].

Long-term monitoring and chemical characterization of aerosols are still scarce, not only in northern Vietnam, but also in neighboring countries. Because of this, the assessment of detailed sources and of the impacts of transboundary LRT emissions on air quality over the RRD and Hanoi is still limited, compared to regional and local emissions.

Ambient air quality guidelines

In order to establish standards and targets for air quality management, various guidelines for ambient air pollution levels have been established depending on the geography and the development of the economy of different regions. Table 1-3 provides a comparison between the guidelines for ambient air established by the World Health Organization (WHO), by the European Union (EU) as well as the Vietnamese national guidelines on ambient air quality (QCVN 05:2013/BTNMT). The most hazardous pollutants are presented including sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), ground-level ozone (O3) and particulate matter (PM10 and PM2.5). In terms of public health, air pollution is qualified according to the concentration of atmospheric pollutants in comparison with the limit threshold1 or the alert threshold2 , with different time integration periods (1-hour, 8-hour, 24hour or annual average). Specifically, greater thresholds are set up in the Vietnam's guideline for many pollutants (e.g., SO2, NO2, PM10, PM2.5) compared to those in the WHO air quality guidelines. In addition, except for PM10 and PM2.5, the thresholds are relatively similar between Vietnam and EU air quality guidelines. In Vietnam, the air quality index (AQI) is another standard index used to determine the quality and quantity of effects on human health caused by air pollution. The concentration of a set of fundamental contaminants in the ambient air is represented by this composite index. The AQI value is generated using data from monitoring factors such as SO2, CO, NOx, O3, PM2.5 and PM10. Nowadays, a 5-level scale for AQI defined by the U.S. Environmental Protection Agency standard is used, although Vietnam has its own formula prescribed by the Vietnam Environment Administration for calculating AQI values (see Annex A).

Air quality and factors affecting air quality in Hanoi

Status of air quality in Hanoi

Among polluted cities in the SEA region, Hanoi's air pollution is ranked as worse than that of Bangkok (Thailand) but better than those of Manila (Malaysia) [START_REF] Kim Oanh | Particulate air pollution in six Asian cities: Spatial and temporal distributions, and associated sources[END_REF]. Over the RRD region, Hanoi is one of the cities which suffers of continuous air pollution episodes causing severe haze pollution events within or between regions. In the last decade, the air pollution issue in Hanoi has been escalating, resulting in rising expenses and harm to public health and the environment. The concentration of a few pollutants such as CO, PM2.5 and PM10 in Hanoi during the day, particularly in locations near active and dense roads, Chapter 1. Atmospheric dynamics and aerosols in Hanoi and its surrounding area 56 typically rises during rush hours and falls at midday or at night [START_REF] Monre | Ministry of Natural Resources and Environment[END_REF]. From 2010 to 2013, considering the Air Quality Index (AQI, Annex A), the number of days with unhealthy levels for sensitive groups (AQI = 101 -200) accounted for 40 % to 60 % of the monitored days at Hanoi, while 102 days and 20 days were associated with very unhealthy (AQI = 201 -300) and hazardous (AQI ≥ 300) levels, respectively [START_REF] Monre | Ministry of Natural Resources and Environment[END_REF].

Concerning particulate matter, the diurnal particulate concentrations fluctuate substantially throughout the year. During the years 2003 -2004, the number of days with levels of PM10 exceeding specified Vietnam's national standard for ambient air quality in 24-hour (≥ 150 μg m -3 ) was approximately 50 days per year [START_REF] Hien | Air pollution episodes associated with East Asian winter monsoons[END_REF] while PM2.5 concentrations exceeding standard (≥ 50 μg m -3 ) were also recorded regularly. Furthermore, the daily concentrations of PM2.5 and PM10 in Hanoi varied from 26 to 143 μg m -3 and from 37 to 165 μg m -3 respectively during the years 2006 -2007 [START_REF] Hai | Effects of local, regional meteorology and emission sources on mass and compositions of particulate matter in Hanoi[END_REF]. In the years 2018 -2020, the annual average values of PM2.5 and PM10 dust parameters at all air environment monitoring sites in Hanoi surpassed the national threshold for ambient air (QCVN 05:2013/BTNMT) by 1.1 to 2.2 times (MONRE, 2021). Alarmingly, when air pollution in Hanoi reached a record high in the last four months of 2019, the Ministry of Natural Resources and Environment had to issue a warning to the general public, particularly vulnerable groups such as children, pregnant women, the elderly, and people with respiratory diseases, to limit outdoor activities and reduce transportation to mitigate the risk of polluted air exposure.

Factors affecting air quality in Hanoi

Severe pollution events with a high level of particulate matter are regularly experienced in winter (October -March), when Hanoi is impacted by the Northeast monsoon [START_REF] Hien | Air pollution episodes associated with East Asian winter monsoons[END_REF][START_REF] Ly | The effects of meteorological conditions and long-range transport on PM2.5 levels in Hanoi revealed from multi-site measurement using compact sensors and machine learning approach[END_REF]. They are related to stagnant conditions that prevent mixing and increase air stagnation. However, the ultimate causes for explaining the most severe air pollution episodes in Hanoi have remained unanswered. There are many factors can involve in the air pollution in Hanoi as follow: (1) topography, such as mountains and valleys; (2) weather, such as wind, temperature, air turbulence, air pressure, rainfall and cloud cover and (3) the physical and chemical properties of pollutants: Firstly, as mentioned in Section 1.1.1, Hanoi is located in the RRD, which is low-lying areas in northern Vietnam surrounded by hills and mountains. The distribution of hills and mountains in the Northeast of Vietnam are often bow-shaped, so instead of blocking the northeast monsoon during the winter, it enhances the pervasion of northeast monsoon to penetrate deep into northern Vietnam. In addition, due to surrounded by hills and mountains, Hanoi can also experience a temperature inversion (upside down) during the winter, where cold air is trapped in a valley below a warmer layer of air. As a result, pollutants can be trapped in the cool layer of surface area for days causing harmful levels of air pollution. However, there is currently no research on topography's impacts on air pollution in Hanoi.

Secondly, the dispersion of pollutants depends on atmospheric stability. Wind speed is an important cause that determines how quickly pollutants are brought away from their sources. During the winter, the atmospheric stability over Hanoi is changed by the Northeast monsoon, which can also bring pollutants over Hanoi. In addition, during the winter period, the occurrence of several unfavorable phenomena, including haze, smog and subsidence can favor the accumulation of primary and secondary pollutants in the air near the surface, leading to severe pollution events over Hanoi and the RRD regions.

At last, due to the rapid urbanization, Hanoi's high level of particulate matter is attributed to an increase in local emissions from motor vehicles, construction, and other civil activities inside the city. Besides, the air quality in Hanoi is affected significantly by emissions from industrial zones and thermal power plants, which are located in the suburbs or neighboring provinces. Regarding the emission sources, evaluation and quantification of the contribution of potential sources that possibly affect air quality in Hanoi are still being debated due to a lack of long-term continuous monitoring through online and offline techniques. Therefore, the inadequate number of automated monitoring stations within Hanoi for many years also greatly affects the monitoring data in order to forecast air pollution. Only two stations are installed, which continuously track all parameters (e.g., CO, SO2, NO2, PM2.5, PM10) in order to assess the general air quality. In general, the adoption of a continuous monitoring of air quality in Hanoi is still hampered by a lack of suitable technical and financial.

Previous studies in air pollution over Hanoi and RRD

In the RRD region, several investigations on atmospheric aerosols have previously been carried out. Most of them focused on air pollution in the urban environment of Hanoi (Cohen et al., 2010b;[START_REF] Hai | Effects of local, regional meteorology and emission sources on mass and compositions of particulate matter in Hanoi[END_REF]Hien et al., 2002;[START_REF] Ly | The effects of meteorological conditions and long-range transport on PM2.5 levels in Hanoi revealed from multi-site measurement using compact sensors and machine learning approach[END_REF]. Otherwise, few investigations were conducted in the heavily industrialized regions [START_REF] Chifflet | Origins and discrimination between local and regional atmospheric pollution in Haiphong (Vietnam), based on metal (loid) concentrations and lead isotopic ratios in PM10[END_REF][START_REF] Hang | Chemical characterization and sources apportionment of fine particulate pollution in a mining town of Vietnam[END_REF]. Generally, the majority of the aforementioned research concentrated on identifying the sources of pollution and the chemical components of aerosols. One of the longestrunning campaigns was carried out by Cohen et al. (2010b) for which samples of particulate matter were collected over nearly eight years to determine the composition and sources of PM10 and PM2.5 using the positive Matrix Factorization (PMF) model. With this 8-year dataset from the period 2001-2008, Cohen et al. (2010b) ) found that PM2.5 compositions over Hanoi were composed of (29 ± 8) % ammonium sulfate, (8.9 ± 3.3) % soil, (28 ± 11) % organic matter, (0.6 ± 1.4) % salt and (9.2 ± 2.8) % black carbon. The authors estimated that the major source contributions to the fine particles were from automobiles and transports (40 ± 10) %, windblown soil (3.4 ± 2) %, secondary sulfates (7.8 ± 10) %, smoke from biomass burning (13 ± 6) %, ferrous and cement industries (19 ± 8) %, and coal combustion (17 ± 7) %. By using the same method, Hai and Kim Oanh, (2013) identified 7-source factors of PM2.5 in the period 2006 -2007: secondary mixed particulate matter (40 %), diesel traffic (10 %), residential/commercial cooking (16 %), secondary sulfate-rich (16 %), aged sea-salt mixed (11%), industry/incinerator Chapter 1. Atmospheric dynamics and aerosols in Hanoi and its surrounding area 58 (6 %), and construction/soil (1 %). Another attempt with PMF revealed the contributions of five major sources to the ultrafine particle mass including traffic (gasoline and diesel emissions, 46.28 %), secondary emissions (31.18 %), resident/commerce (12.23 %), industry (6.05 %), and road/construction (2.92 %) [START_REF] Nghiem | Chemical characterization and source apportionment of ambient nanoparticles: a case study in Hanoi, Vietnam[END_REF]. There are large differences between these studies, especially in the contribution of traffic emissions (diesel), which can be partly explained by 2 reasons (1) the different seasons during which the observations were recorded and ( 2) the rapid urbanization in Hanoi region, making data quickly outdated.

Additionally, based on shorter campaigns in the winter (from a month to few months), a few studies investigated the effects of climatic conditions on the composition and concentration of PM2.5 and PM10 [START_REF] Hien | Air pollution episodes associated with East Asian winter monsoons[END_REF][START_REF] Ly | The effects of meteorological conditions and long-range transport on PM2.5 levels in Hanoi revealed from multi-site measurement using compact sensors and machine learning approach[END_REF][START_REF] Ly | Characterizing PM2.5 in Hanoi with New High Temporal Resolution Sensor[END_REF][START_REF] Trinh | Temperature inversion and air pollution relationship, and its effects on human health in Hanoi City, Vietnam[END_REF]. From the early 2000s, first-ever attempts were undertaken to determine how PM2.5-10 pollution concentrations varied throughout the year, characterized by meteorological parameters (Hien et al., 2002). The author proved that daily changes in particle concentrations might be significantly accounted (up to 74 % of the daily changes of particulate matter) by meteorological factors. Particularly during the winter period, the study of [START_REF] Hien | Air pollution episodes associated with East Asian winter monsoons[END_REF] emphasized the relation between variation in composition and concentration of local pollutants such as PM10, PM2.5 and prolonged anticyclonic conditions established after the Northeast monsoon, resulting in a dozen multi-day pollution episodes that occurred from October to February in Hanoi in the years 2003 -2004. In addition to local emissions, the impact of transboundary LRT was studied using backward trajectories combined with chemical analysis. For example, there was clear evidence that soil from the Taklamakan and Gobi desert regions, as well as emissions from both mainland China and northern Vietnam's thermal power stations contributed to highest fine particle pollution days in Hanoi (Cohen et al., 2010a;[START_REF] Huy | Assessment of national emissions of air pollutants and climate forcers from thermal power plants and industrial activities in Vietnam[END_REF][START_REF] Ly | The effects of meteorological conditions and long-range transport on PM2.5 levels in Hanoi revealed from multi-site measurement using compact sensors and machine learning approach[END_REF]. The contributions from transboundary LRT aerosols driven by the Northeast monsoon were considered greater in winter than those coming from the monsoon in summer [START_REF] Hien | PMF receptor modelling of fine and coarse PM10 in air masses governing monsoon conditions in Hanoi, northern Vietnam[END_REF]. Similarly, [START_REF] Ly | The effects of meteorological conditions and long-range transport on PM2.5 levels in Hanoi revealed from multi-site measurement using compact sensors and machine learning approach[END_REF] emphasized that the contribution of transboundary LRT under the impact of the Northeast monsoon from the north and northeast trajectories from mainland China to local PM2.5 levels was significant. Along with studies focused on monsoon activities, investigations of the effects of temperature inversions on the concentration of some pollutants in the atmosphere in Hanoi were also conducted [START_REF] Trinh | Temperature inversion and air pollution relationship, and its effects on human health in Hanoi City, Vietnam[END_REF]. The author demonstrated that the concentrations of local pollutants such as NO2, SO2, PM10, and PM2.5 levels tended to rise as the inversion (subsidence) got stronger over Hanoi during the winter.

With the aim of quantify the contribution of the different pollution sources, many attempts have been made to assess the contribution of local pollution. The study of [START_REF] Hai | Effects of local, regional meteorology and emission sources on mass and compositions of particulate matter in Hanoi[END_REF] stated that the transboundary LRT was likely to represent a smaller part of the total PM2.5 (approximately 27 %) during the winter period. Recently, chemistry-transport models were also used for getting new and informative insights about air pollution in Hanoi. For instance, [START_REF] Nguyen | Air Quality Modeling Study on the Controlling Factors of Fine Particulate Matter (PM2.5) in Hanoi: A Case Study in December 2010[END_REF] With the aim of filling the gaps of the current air-quality monitoring networks, low-cost sensors, which are well correlated with a beta attenuation monitor (R 2 = 0.73) are considered to be an alternative approach when not many trustworthy monitoring station are built over Hanoi. [START_REF] Ly | Characterizing PM2.5 in Hanoi with New High Temporal Resolution Sensor[END_REF]. Besides, satellite observations have shown the potential to address the spatial limitation of ground-based monitoring stations and provide useful information on aerosol properties near the surface on a larger scale. For example, the long-term analysis of the spatio-temporal variations of aerosol optical depth (AOD) over northern Vietnam using satellite data (MODIS-AOD) or ground-based measurement (AERONET-AOD) was performed [START_REF] Bui | Study of aerosol optical properties at two urban areas in the north of Vietnam with the implication for biomass burning impacts[END_REF][START_REF] Luong | Contrasting seasonal pattern between ground-based PM2.5 and MODIS satellite-based aerosol optical depth (AOD) at an urban site in Hanoi, Vietnam[END_REF]. Both research analyzed the relationship between the vertically integrated AOD and the ground-based PM2.5 concentration, indicating the approach for missing information of air pollution's factors.

In addition, the effects of ambient air pollution on human health in Vietnam have also been thoroughly investigated [START_REF] Luong | The association between particulate air pollution and respiratory admissions among young children in Hanoi, Vietnam[END_REF][START_REF] Nhung | Mortality Burden due to Exposure to Outdoor Fine Particulate Matter in Hanoi, Vietnam: Health Impact Assessment[END_REF]. According to conclusion of [START_REF] Luong | The association between particulate air pollution and respiratory admissions among young children in Hanoi, Vietnam[END_REF], when PM10 and PM2.5 concentrations reached 10 µg/m 3 , the number of children hospitalized for respiratory disorders in Hanoi rose by 1.4 % and 2.2 %, respectively, between 2010 and 2011. In terms of adult health consequences, Nhung et al. (2020) demonstrated that a 34.4 µg/m 3 rise in the 2-day average level of PM2.5 corresponded to a 6.3 % increase in daily hospital admissions for ischemic heart disease in Hanoi.

All of the above studies provided a preliminary picture of the status of air pollution in the RRD region as well as the primary sources potentially responsible for this pollution. Additionally, research on atmospheric parameters such as boundary-layer height and vertical wind profiles, which are crucial in the evolution of aerosol composition and concentration, is still scarce in Vietnam. As a consequence, the significance of this work is obvious, and the comparison of its findings with earlier research will be possible.

Chapter 2. Material and methods

This chapter covers the details of the observations and numerical models used for the entire study. The instrumentation deployed during the Vietnamese Aerosol Sampling and Profiling (VASP) campaign is presented in detail, along with all the other data from external sources. Chapter 2 is structured as follows:

• An overview of the VASP campaign (Section 2.1) including the study sites, the instrumentation deployed and the data processing. • The presentation of external weather and air quality observations (Section 2.2).

• The presentation of the numerical models used to identify the air mass origin and to characterize the AOD variability (Section 2.3).

Overview of the VASP campaign

Description of the study sites

The The OS1 site was chosen as it appeared to be a promising location for the research and the characterization of atmospheric particulate pollution, particularly the effects of transportation and urban areas sources, with the ultimate goal of identifying the most effective methods to prevent and reduce pollution and improve the life quality of local residents. Table 2-1 provides an overview of the instruments installed during the field campaigns at the OS1 site and what can be observed with them. At this site, two different building were used: a location close to the ground was preferable for the aerosol sampling, the aerosol micro-pulsed Lidar and the Doppler Lidar (due to the blind zone), so they were installed on top of a 10 m high building (20 m above sea level). On the contrary, it was preferable to install the weather station at a higher elevation, above the building canopy, so a 30 m high building (40 m above sea level) was used. Both buildings were located within the campus of the Vietnam Academy Science and Technology (VAST) (21° 2' N, 105° 47' E). This site is part of a residential and mixed traffic area, including a ring road and several busy roads.

The OS2 and OS3 sites are located respectively at the US Embassy (21° 1' N, 105° 49' E), where PM2.5 measurements are carried out by the US Environmental Protection Agency, and at the Vietnam Meteorological and Hydrological Administration (VMHA) (21° 1' N, 105° 48' E), where meteorological observations are recorded. The areas surrounding the OS2 and the OS3 sites also consist of densely populated zones with busy traffic.

Instrument

Observable phenomena Time resolution

Altitude above ground-level

Altitude above sea-level

Doppler Lidar (Wind-cube 2)

• Transport and vertical exchanges in lower boundary-layer The instrumentation deployed at the OS1 site are presented in the following sections, along with the associated data processing. In general, these instruments demonstrated good operating rates, with the exception of the aerosol micro-pulse Lidar, which had missing data during the campaign owing to a weather effect (Section 3.1).

Weather observation

Doppler Lidar (Wind-cube V2)

The Doppler Lidar used during the VASP campaign is a device manufactured by Leosphere-Vaisala (www.vaisala.com). It is an eye-safe pulsed Doppler Lidar operating in the infrared region (1.543 µm) at low power (7 mW laser) with a high repetition rate of 10 kHz (Figure 2-2). It measures the Doppler shift due to the particle's motion along the laser beam direction and thus the radial wind speed, which is determined by heterodyne detection followed by fast Fourier transform analysis. The profiles of the three components of the wind are reconstructed using the Doppler Beam Swinging technique originally proposed for Doppler radars [START_REF] Koscielny | An Evaluation of the Accuracy of Some Radar Wind Profiling Techniques[END_REF], here combining four slanted beams (28° from zenith) pointing in the cardinal directions plus a vertical beam. The accumulation time is 1 second per beam so that a Doppler Beam Swinging cycle is repeated every 5s; then wind observations are averaged over 10-min periods. The laser pulse duration and shape allow for a 20-m vertical resolution, while the optics allow for a minimal/maximal range of 40/280 m above ground level (a.g.l). (the altitude at 40 m was not used due to the interference of nearby higher buildings). The Doppler Lidar provided the vertical profiles of the horizontal wind speed and direction, vertical wind dispersion and vertical wind speed with an accuracy of about 0.1 m s -1 in speed and 2° in direction. The average wind speed and the standard deviation were computed on 10-minute periods from the raw time series with a 5-seconds time step. The vertical wind dispersion, i.e. the standard deviation of the vertical wind speed (σw), characterizes the vertical mixing efficiency. Compared to traditional meteorological masts, Doppler Lidars can be deployed in locations such as high mountains or offshore platforms. In addition, the Doppler Lidars have been validated against traditional weather stations so they can replace them for operational use. For example, the Wind-cube V2 was utilized in an observation campaign at the Danish National Test Station for Large Wind Turbines, located at Høvsøre, Denmark in order to quantify wind gusts and make the Lidar observations comparable with those of traditional weather stations [START_REF] Suomi | Methodology for obtaining wind gusts using Doppler lidar[END_REF]). As a second example, [START_REF] Liao | Field measurement study on turbulence field by wind tower and Windcube Lidar in mountain valley[END_REF] performed a comprehensive investigation of the turbulence in a mountainous valley using wind observations from a Wind-cube V2.

Davis Vantage Pro 2 Automatic Weather Station

Davis Vantage Pro 2 automated meteorological station is a wireless data transmission system that links a group of reconfigurable meteorological sensors to an autonomous storage and display module. This equipment has several advantages, including an inexpensive price, automatic operations, and most noticeably, it can function, collect, log and store data without manual maintenance, an external energy source, or regular access to sensors and data. The station includes a pressure sensor, a temperature sensor, a humidity sensor, a wind anemometer, a rain gauge, and a radiation meter coupled with an autonomous storage and display module (Figure 23). As for the pressure sensor, the station saves the appropriate offset value to reliably convert atmospheric pressure into barometric pressure when the location altitude is entered. This station was placed on the rooftop of a 30 m high building to minimize the impact of highrise buildings around the site. Massen, (2013) came to the conclusion that temperature sensors behaved well with the accuracy of temperature, relative humidity, solar radiation, pressure and precipitation being ± 0.1°C, ± 1%, ± 1 W m -2 , ± 0.1 hPa and ± 0.2 mm, respectively.

Detection of the monsoon circulation

The monsoon episodes, also named cold surges or cold intrusions, have been variously defined depending on the research scope. For example, Hien et al. ( 2011) defined a cold surge in Hanoi during the winter period according to its temperature effect, namely a drop by 2°C or more in daily mean temperature within 2 days, or a temperature fall of at least 2°C within an hour. [START_REF] Chen | An East Asian Cold Surge: Case Study[END_REF] identified a cold surge event by the following changes of meteorology within 24 ∼ 48 hours: (a) the surface pressure increased by more than 5 mb, (b) the surface temperature dropped by more than 4°C, (c) the surface wind was stronger than 3 m s -1 . On the other hand, in Taiwan, the northeasterly monsoon was defined by ( 1) the daily maximum of surface temperature dropped by more than 8°C between two consecutive days; or (2) the daily minimum of surface temperature happened to be lower than 10°C [START_REF] Lin | Climate variability of cold surge and its impact on the air quality of Taiwan[END_REF]. In the present study, cold surges were selected when the daily mean temperature dropped by more than 4 o C and the prevailing wind speed (WS) was stronger than 3 m s -1 . The surface pressure was not used, but a condition was added on the northeasterly origin of the air mass, i.e. the wind direction (WD) had to be in the 0° to 90° range.

Figure 234shows an example of detection of a Northeast monsoon episode recorded on December 26 th , 2019. The onset of the cold surge is marked with a black triangle on the horizontal axes. On this day, the temperature dropped sharply with the appearance of the cold surge and this downward trend continued in the following days. Simultaneously, the wind speed increased strongly and was directed in the northeast direction. 66

Boundary-layer height observation

Aerosol micro-pulse Lidar (SLIM)

The aerosol micro-pulse Lidar (SLIM) is a custom-made Lidar developed at LATMOS in collaboration with CIMEL group. Due to the optoelectronic rack's sensitivity to temperature changes, it was chosen to install it in the VAST server room to take advantage of the air conditioning. As a result, the optical head was mounted on the roof, which is directly above the room housing the servers, next to the Doppler Lidar at the OS1 site (Figure 2345).

The SLIM Lidar operates at the edge of the visible spectrum (808 nm) at low power (12 mW laser) with a high repetition rate of 5 kHz and a low energy/pulse (~2 μJ), as shown in Table 23. The device was pointed at the zenith and the profiles of the elastic signal backscattered by the atmosphere were recorded with a vertical resolution of 15 m, averaged over 10-min. Then, the BLH was determined using profiles of the overlap and range-corrected signal, re-averaged over an hour. The Lidar measurement range extends from ~220 m (90 % overlap range) to 4 km; the signal is actually recorded up to 30 km, but the high altitudes are only used for accurate background noise determination. A correction of the overlap was made in order to rectify the backscattered signal within the blind zone, below 220 m. More details on the instrument can be found in [START_REF] Mariage | IAOOS microlidar-on-buoy development and first atmospheric observations obtained during 2014 and 2015 arctic drifts[END_REF]. The signal 𝑆 of an elastic Lidar as a function of the distance 𝑟 to the instrument (or the altitude 𝑧 when shooting at the zenith) follows the so-called Lidar equation, noted as Equation (2-1):

𝑆(𝑧) = 𝐵 + 𝐶 ⋅ 𝒪(z) z 2 ⋅ 𝛽(𝑧) ⋅ 𝑇 2 (𝑧) (2-1)
where:

𝐵 is the background noise due to the solar light entering the instrument. 𝐶 is the instrumental constant, including the emission power and the gain of the reception channel. 𝒪(𝑧) is the overlap function: as the telescope is tuned for observing the far range, when observing the close range, only part of the backscattered light will enter into the head of the optical fiber. The overlap is null at ground level and is full at the altitude of complete overlap 𝑧0, i.e. 𝒪(0) = 0 and 𝒪(𝑧 > 𝑧0) = 1. Next, 𝛽(𝑧) is the atmospheric backscatter coefficient (usually expressed in Mm.sr -1 ). It includes a contribution 𝛽𝑚 from the air molecules and a contribution 𝛽𝑝 from the particles in suspension in the air (aerosols): 𝛽(𝑧) = 𝛽𝑚(𝑧) + 𝛽𝑝(𝑧). At last, 𝑇(𝑧) is the atmospheric transmission between the ground and altitude 𝑧, which is calculated following the Equation (2-2):

𝑇(𝑧) = exp[-𝜏(𝑧)] = exp [-∫ α(r)d𝑟 𝑧 0 ] (2-2)
where:

𝜏(𝑧) is the atmospheric optical depth between the ground and altitude 𝑧 and 𝛼(𝑧) is the atmospheric extinction coefficient (usually expressed in km -1 ). This coefficient gathers the contribution of two different physical phenomena: the absorption and the scattering of light in directions that are not parallel to the laser beam direction. Similar to the backscatter coefficient, the extinction includes contributions from both the molecules and particles: 𝛼(𝑧) = 𝛼𝑚(𝑧) + 𝛼 𝑝 (𝑧). The transmission must be squared in the Lidar equation because both the going and return of the light through the atmosphere must be taken into account.

The first step of Lidar processing is to determine the background noise (noted 𝐵). This is done by averaging the signal over an altitude range for which there is no backscattered light (because the laser beam has already been extinguished by diffusion). For the SLIM Lidar, no atmospheric signal, even from elevated cirrus clouds, can be recorded over 13 km, so the average of the signal between 13 and 16 km is used to determine the background noise 𝐵. Then, the range corrected signal (RCS) noted 𝑃 is computed following the Equation (2-3):

𝑃(𝑧) = [𝑆(𝑧) -𝐵] ⋅ 𝑧 2 (2-3)
From Equations (2-1) and (2-3), it comes:

𝑃(𝑧) = 𝐶 ⋅ 𝒪(𝑧) ⋅ 𝛽(𝑧) ⋅ 𝑇 2 (𝑧) (2-4)
Besides, the RCS is usually plotted for quick visualization of the data, using a log scale as the dynamics of the signal can cover several orders of magnitude (Figure 23456). The blind zone due to the incomplete overlap in the lowest atmospheric layers is clearly visible below 220 m. The red areas correspond to the strong backscatter by the cloud's water droplets. Inside a cloud, the laser beam is quickly extinguished and no signal can pierce the cloud; therefore, the atmosphere above the cloud cannot be observed using a Lidar. The green areas correspond to the backscatter by the aerosols trapped in the atmospheric boundary-layer. The signal from the air molecules in the free troposphere is not visible in this figure; it would be necessary to average over several hours to make it stand out from the noise. The overlap function 𝒪(𝑧) is determined using the slope method [START_REF] Kunz | Inversion of lidar signals with the slope method[END_REF]. The atmosphere is supposed to be perfectly mixed, i.e. the backscatter and extinction coefficients are supposed to be constant with altitude. The integral in the transmission coefficient can then be computed easily and the expression of the RCS becomes:

{ α(z) = α0 β(z) = β0 ⇒ 𝑃(𝑧)=𝐶 ⋅ 𝒪(𝑧) ⋅ 𝛽0 ⋅ exp(-2𝛼0z) (2-5)
When taking the natural logarithm of this equation, one gets:

log 𝑃(𝑧) = log 𝐶 + log 𝒪(𝑧) + log 𝛽0 -2 𝛼0𝑧 (2-6)
In the complete overlap zone (𝑧 > 𝑧0), we have 𝒪(𝑧) = 1 ⇒ log 𝒪(𝑧) = 0. Therefore, log 𝑃(𝑧) will depend linearly upon altitude, with a slope -2 𝛼0 and an intercept log 𝐶 + log 𝛽0.

In reality, homogeneous conditions can be observed in the afternoon ABL, when the turbulent motions (updrafts & downdrafts) ensure a vigorous mixing of the air between the ground and the top of the ABL. In order to determine the overlap function with a good signalto-noise ratio, an average of the RCS over several hours is used (Figure 234567). Then, the RCS is fitted by a linear function ℓ(𝑧) between the complete overlap altitude (𝑧0 = 550 m) and the bottom of the transition zone between the ABL and free troposphere (700 m on Figure 23456). (2-7)

The overlap function is stable in time provided the optics of the Lidar are not touched and the mounting do not move due to thermal dilatation. During the VASP campaign, the overlap function was determined once on January 22 nd 2019 and used to correct the RCS for the whole campaign. This correction simply consists in dividing the RCS by the overlap function previously obtained, which produces the non-normalized attenuated backscatter noted ROCS [𝑃′(z)]:

𝑃′(𝑧) = P(z) 𝒪(r) = 𝐶 ⋅ 𝛽(𝑧) ⋅ 𝑇 2 (𝑧) (2-8)
Note that a blind zone of about 50 m subsist as the correction is considered to introduce too much uncertainty when 𝒪(𝑧) < 0.1.

Figure 2-8. Overlap function computed from the RCS and fit function of Figure 2-7 (red line). The uncertainty on the overlap (shaded area) is computed from the standard deviation of the RCS during the averaging period using a Monte-Carlo method 2.1.3.3 Boundary-layer height retrieval

Many techniques have been developed to determine the BLH, for example, through radiosonde measurements [START_REF] Seibert | Review and intercomparison of operational methods for the determination of the mixing height[END_REF], remote sensing methods [START_REF] Emeis | Multiple atmospheric layering and mixing-layer height in the Inn valley observed by remote sensing[END_REF] and model simulations [START_REF] Anderson | A Large-Eddy Simulation Model for Boundary-Layer Flow Over Surfaces with Horizontally Resolved but Vertically Unresolved Roughness Elements[END_REF]. Overall, radiosonde observations serve as the reference technique for the BLH determination, using the profiles of temperature and specific humidity [START_REF] Hennemuth | Determination of the Atmospheric Boundary Layer Height from Radiosonde and Lidar Backscatter[END_REF]. However, radiosondes are expensive so they are practically limited to fixed hours, usually 00:00 and 12:00 UTC. In Vietnam, this corresponds to 07:00 and 19:00 local time (LT) so that the radiosonde data have severe limitations for capturing the daytime ABL development.

Nowadays, for monitoring the structure of the boundary-layer, Lidar systems are commonly employed due to their high spatiotemporal resolution (e.g., [START_REF] Altstädter | Airborne observations of newly formed boundary layer aerosol particles under cloudy conditions[END_REF][START_REF] Boyouk | Impact of the mixing boundary layer on the relationship between PM2.5 and aerosol optical thickness[END_REF][START_REF] Quan | Evolution of planetary boundary layer under different weather conditions, and its impact on aerosol concentrations[END_REF][START_REF] Yang | Technical note: Boundary layer height determination from lidar for improving air pollution episode modeling: development of new algorithm and evaluation[END_REF]. Numerous methods have been proposed to accurately retrieve the BLH from the Lidar signal, such as the maximum variance method [START_REF] Hennemuth | Determination of the Atmospheric Boundary Layer Height from Radiosonde and Lidar Backscatter[END_REF], wavelet transform method [START_REF] Baars | Continuous monitoring of the boundary-layer top with lidar[END_REF], derivative method including first gradient method [START_REF] Flamant | Lidar determination of the entrainment zone thickness at the top of the unstable marine atmospheric boundary layer[END_REF], logarithm gradient method [START_REF] Senff | Remote sensing of turbulent ozone fluxes and the ozone budget in the convective boundary layer with DIAL and Radar-RASS: A case study[END_REF], and cubic root gradient method [START_REF] Yang | Technical note: Boundary layer height determination from lidar for improving air pollution episode modeling: development of new algorithm and evaluation[END_REF]. However, so far, there is no optimal reference algorithm for determining the BLH from the Lidar signal. In this study, the gradient method was applied: by clear sky, it places the ABL top at the altitude where the first derivative of the ROCS exhibits a strong negative peak. To detect this peak, three variants of the gradient method were tested: the first gradient method [START_REF] Flamant | Lidar determination of the entrainment zone thickness at the top of the unstable marine atmospheric boundary layer[END_REF], the first logarithmic gradient method and the cubic root gradient method [START_REF] Yang | Technical note: Boundary layer height determination from lidar for improving air pollution episode modeling: development of new algorithm and evaluation[END_REF].

A typical example of a 1-hour average vertical profile, along with the estimated BLH, is shown in Figure 23456789. The Lidar signal is strong within the ABL due to the substantial backscattering by pollution particles, while above the ABL, the ROCS diminishes via a transition zone and is much weaker in the clean free troposphere (Figure 2-9.a). This drop of ROCS at the ABL top results in a marked negative minimum of the ROCS first derivative (Figure 2-9.b) The altitude ℎ GM of this minimum of the ROCS first derivative represents the BLH in the first gradient method (GM):

ℎ GM = min { d(𝑅𝑂𝐶𝑆) d𝑧 } (2-9)
In the first logarithmic gradient method (LGM), the BLH is defined as the altitude ℎ LGM where the first derivative of the natural logarithm of the ROCS exhibits a strong negative minimum (Figure 2-9.c).

ℎ LGM = min { d ln(𝑅𝑂𝐶𝑆) d𝑧 }
(2-10)

Generally, the LGM technique shows good accuracy even in the presence of turbulent oscillations and has the advantage of being independent of any arbitrary threshold values [START_REF] Menut | Urban boundary-layer height determination from lidar measurements over the Paris area[END_REF]. However, it is very challenging to compute with noisy data, especially at longer ranges. As a result, some sort of spatial or temporal averaging is necessary [START_REF] Kovalev | Elastic lidar: theory, practice, and analysis methods[END_REF].

At last, the cubic root gradient method (CRGM) is a more recent algorithm for BLH determination. The BLH is defined as the altitude ℎ CRGM where the first derivative of the cubic root of the ROCS experiences a strong negative minimum (Figure 2 In general, the BLH retrieved using the three gradient methods were relatively similar during the VASP campaign so that the simplest first gradient method was preferred. The gradient methods, however, are applicable only by clear sky or when the ABL is topped by a single-layer optically-thin cloud through which the laser beam can pierce. In this regard, Figure 2-9 presents an ideal situation with a single well-defined minimum of the first derivatives. Unfortunately, during the VASP campaign, the most common conditions included heavy aerosol loading (hazy conditions), optically-thick low-altitude clouds or several sub-cloud layers (cloudy conditions). In such conditions, the ROCS profile is much more complex and the ROCS first derivative profile may exhibit several strong negative peaks (Figure 2-10). In these cases, the lowest-altitude minimum of the ROCS first derivative actually corresponds to the point where the laser beam is extinguished within the cloud, so this definition will cause an overestimation of the BLH, because thick clouds are not included in the ABL. For cloudy cases, the ABL top is defined as the base of the first layer of cloud, which is detected as the lowestaltitude maximum of the ROCS first derivative (Figure 2-10). However, this is not a problem when the main point is to follow the overall BLH variability and its influence over pollution.

Aerosol sampling and elemental analysis

TSP were collected by total filtration with a sampler composed of a filter holder, a sampling pump and a volumetric counter set up next to the Lidar devices, as shown in Figure 2-11. TSP were collected at the OS1 site by total filtration with 1-week period on cellulose filter (Whatman® grade 41) with a flow rate of 1.2 m 3 h -1 . The filter holders were previously cleaned and loaded with filters in a clean environment under a class 100 (ISO5) laminar flow hood and individually packed in sealed plastic bags. The filter holders were changed every Wednesday at 16:00 LT from 11 th January to 22 nd March, resulting in 10 TSP samples referenced from S1 to S10.

One fraction of each filter was dedicated to the analysis of the water soluble NO3 -and SO4 2-by ionic chromatography, using an ICS 5000 + ion chromatograph from Thermo Scientific Dionex equipped with an anionic CS 12A column (2x250 mm). After leaching of the sample in ultrapure water (18.2 MΩ cm) under ultrasonic shaking for 1-hour, the obtained aqueous solution was filtered (IC Millex®, Millipore) and analyzed using a KOH solution as eluent. Atmospheric concentration of ammonium is then deduced from nitrate and sulfate concentrations considering atmospheric nitric and sulfuric acids as completely neutralized by ammoniac.

Another fraction of each filter was dedicated to Scanning Electron Microscopy/Energy Dispersive X-ray Spectroscopy (SEM/EDX) analysis using a JEOL TM JSM7100F electron microscope, equipped with three ultrathin-window (30 mm 2 ) energy-dispersive X-ray detectors (Bruker TM XFlash 6/30). EDX spectra were acquired at an acceleration voltage of 15 kV, a probe current of 200 pA with a time acquisition of 40 seconds. 10 spectra per sample were acquired with large fields (1600 x 1100 µm 2 ) and summed to be considered as one. The elemental ratios of detected elements (Na, Mg, Al, Si, S, Cl, K, Ca, Ti, Fe, Zn) were calculated using PhiRoZ quantification procedure from the Esprit Software (Bruker Nano, Germany). The most important error in the quantitative analyses is mainly due to the heterogeneity of the particle deposition on the surface of the collection substrate with the large fields. To assess this, analyses were systematically performed at 10 different points on the substrate and averaged for each element. The associated relative errors depend on the element considered, they are less than 10 % for major elements and up to 20 % for minor elements. Atmospheric concentrations were finally computed based on the atmospheric SO4 2- concentrations measured by ionic chromatography and S relative content in particulate matter measured by EDX, considering Al, Si, Ti, Fe and Zn as oxides and Ca as calcite. Although the particulate carbon fraction has not been determined, the sum of the measured compounds represents a large part of the TSP since this sum is highly correlated with the online PM2.5 measured with Beta-Attenuation Mass Monitor at the OS2 site (y = 1.22x, R 2 = 0.83).

External observation

Ground-based PM2.5 data

During the VASP campaign, the OS2 site is located at the US Embassy, hosted PM2.5 Beta-Attenuation Mass Monitor system. This is a reliable instrument for continuous PM2.5 monitoring, which is applied widely over the world. The maintenance and calibration procedures of instruments in this network follow the US Environmental Protection Agency (EPA) standards, which ensure data quality and long-term consistency of the datasets. Using the well-accepted beta ray attenuation theory, the Beta-Attenuation Mass Monitor system automatically measures and records airborne particle concentration levels (in milligrams or micrograms per cubic meter). Regarding PM2.5 monitoring, the measuring principle of the Beta-Attenuation Mass Monitor is to use a pump to collect particulate matter on a fiberglass filter tape (flow rate of 16.7 liters/min). A small 14C (carbon-14) source produces a steady stream of high-energy electrons (sometimes referred to as beta rays) that pass through the particle collection area of the filter tape. The presence of PM2.5 impedes the movement of these beta rays and attenuates the signal recorded by the detector in relation with the amount of PM2.5 present in the sample. This is divided by the total volume of the pump air to give the PM2.5 concentration as shown in the published AQI website. Those large PM2.5 measuring devices suffer from errors due to humidity effects, but inside the EPA stations, there are separate devices that maintain a stable humidity inside the BAM instrument. Hourly PM2.5 data in Hanoi can then be retrieved from the EPA website (https://www.airnow.gov/). The ground-based PM2.5 data has only been available since December 2015. On September 6 th , 2019, the air quality monitor was moved and re-installed in the new location (OS2 new) in the center of Hanoi, as shown in Figure 2-1.

Radiosonde data and meteorological data

Radiosondes are launched from the OS3 site twice per day, at 07:00 and 19:00 LT (i.e., 00:00 and 12:00 UTC). This radiosonde generally offers profiles of temperature, pressure, relative humidity, wind speed and direction with a vertical resolution of 5 meters. All sounding profiles were extracted from the upper-air data center in the department of atmospheric science, the University of Wyoming (http://weather.uwyo.edu/upperair/sounding.html).

Among the BLH estimation methods, using radiosonde data is considered the most accurate method. BLH is defined as the altitude with the strongest negative gradient of relative humidity (RH) and the strongest positive gradient of potential temperature (θ). In this study, the radiosonde profiles were used as a reference for evaluating the BLH retrieved from the SLIM Lidar. In order to validate the meteorological data from the Davis Vantage Pro 2 automatic weather station, data from the meteorological station deployed in the OS3 site were retrieved for the period of VASP campaign, which will be presented in Chapter 3. This station is installed at the ground around 6 meters above the sea level. The information provided includes temperature, relative humidity, visibility, surface pressure and near surface wind speed and direction. In addition, the meteorological data from this site were also extracted for 4 winter periods from 2016/17 to 2019/20 in order to investigate the impact of the Northeast monsoon and variation of meteorological factors in different winter periods. This work is a part of the long-term analysis presented in Chapter 4. b, c andd) and radiosonde method (e) recorded in Hanoi at 07:00 LT on 24 th January, 2019.

Ground-based Aerosol Optical Depth (AOD) data

The Aerosol Robotic Network (AERONET) is a worldwide network of ground-based passive remote sensing aerosol sensors established by NASA and PHOTONS (PHOtométrie pour le Traitement Opérationnel de Normalisation Satellitaire; University of Lille, CNES, and CNRS-INSU) and is greatly expanded by other networks (e.g., RIMA, AeroSpan, AEROCAN, NEON, and CARSNET) and collaborators from national agencies, institutes, universities, or collaborators as individual scientists throughout the world. It is a well-established network of approximately 700 stations with ground-based solar photometer instruments providing observations of the aerosol optical characteristics such as Aerosol Optical Depth (AOD), single scattering albedo, and particle size distribution. All the AERONET stations impose CIMEL sun/sky spectral radiometers, which measure direct solar radiation. During daytime hours, the measurements are taken automatically every 15 minutes. Operational cloud screening is part of standard data processing. For more than 25 years, the data from the AERONET network has often been utilized in a range of aerosol-related projects, including satellite retrieval validation, because of its long-term operational history, good coverage of diverse parts of the world, and high data quality.

Within the AERONET network, the AOD is retrieved for wavelengths of 340, 380, 440, 500, 675, 870, 1020 and 1640 nm, respectively. The AOD data are available at three processing stages: level 1.0 (unscreened), level 1.5 (automatically cloud screened), and level 2.0 (manually cloud-screened, quality assured). This research used the daily average AOD values at 500-nm and Level 2.0 as reference data to validate the accuracy of AOD values from the NAAPS model (Section 4.1.1). The total uncertainty in AOD values from AERONET instruments is very low, approximately 0.01-0.02 (Eck et al., 1999). All data were extracted from the AERONET web download tool (http://aeronet.gsfc.nasa.gov) for the winter period (October to March) from 2010 to 2019 at the Nghia-Do station which is also located in the OS1 site.

Model

Air mass origin: HYSPLIT model

Presentation of the model

To pinpoint the location of air pollutants' emission sources and establish source-receptor relationships, back-trajectory modeling has been developed and applied along with air quality measurements in various researches. Tracking and predicting the release of radioactive material, wildfire smoke, wind-blown dust, pollutants from different stationary and mobile emission sources, allergens, and volcanic ash are some examples of the applications of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model throughout the world [START_REF] Castro | Decision support tool to improve the spatial distribution of air quality monitoring sites[END_REF][START_REF] Karion | Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study[END_REF][START_REF] Kosin | The investigation of possible stratospheric origin of ozone presence at the surface at night, using HYSPLIT trajectory analysis[END_REF][START_REF] Ngan | The evaluation of mixing methods in HYSPLIT using measurements from controlled tracer experiments[END_REF][START_REF] Wang | Identification of regional atmospheric PM10 transport pathways using HYSPLIT, MM5-CMAQ and synoptic pressure pattern analysis[END_REF]. The HYSPLIT model was developed by the National Oceanic and Atmospheric Administration Air Resources Laboratory, and is one of the most extensively used models for computing single air parcel trajectories, as well as complex transport, dispersion, chemical transformation, and deposition simulations [START_REF] Stein | NOAA's HYSPLIT Atmospheric Transport and Dispersion Modeling System[END_REF]. The method underlying the HYSPLIT model is a combination of the Eulerian methodology, which uses a fixed threedimensional grid as a frame of reference to calculate pollutant air concentrations, and of the Lagrangian methodology, which uses a moving frame of reference following the air parcel trajectory for advection and diffusion calculations [START_REF] Draxler | An Overview of the HYSPLIT_4 Modelling System for Trajectories, Dispersion, and Deposition[END_REF].

Over 30 years, HYSPLIT has evolved from predicting single, simplistic trajectories based on radiosonde measurements into a system that takes into account numerous, interdependent pollutants that are carried, diffused, and deposited on scales ranging from local to global. The HYSPLIT model can run through (1) an online version via the Real-time Environmental Applications and Display system interface or ( 2) an offline version using archived meteorological data of various spatial resolutions for regional or global air mass trajectory. The offline version was used in this work as it allows for a greater computational process than the online version. In this study, the fifth version of HYSPLIT, released in 2020, was used, which is publicly available from the Air Resources Laboratory website (http://ready.arl.noaa.gov/HYSPLIT.php). Besides this, the HYSPLIT modeling system can be piloted for mass trajectory generation using in R and Python software, respectively.

Set-up of the model and clustering process

In air pollution hotspots like EA and SEA, many scientific programs have previously employed the HYSPLIT model to investigate the potential and actual hazardous materials released into the atmosphere (Bao [START_REF] Anh | Key factors explaining severe air pollution episodes in Hanoi during 2019 winter season[END_REF]Cohen et al., 2010a;[START_REF] Hien | Air pollution episodes associated with East Asian winter monsoons[END_REF][START_REF] Sun | The vertical distribution of PM2.5 and boundarylayer structure during summer haze in Beijing[END_REF][START_REF] Wang | Identification of regional atmospheric PM10 transport pathways using HYSPLIT, MM5-CMAQ and synoptic pressure pattern analysis[END_REF]. For example, backward trajectory analysis with HYSPLIT was used to study the contributory sources of heavy pollution events in mainland China such as the Shenyang region [START_REF] Ma | Air pollutant emission characteristics and HYSPLIT model analysis during heating period in Shenyang, China[END_REF]. In northern Vietnam, Cohen et al. (2010a) identified long-range transport of both wind-blown soils from desert regions in northwest China and fine particles from coal-fired power stations from mainland China to northern Vietnam. In addition, the HYSPLIT model was used to track the pathway of radioactive dust transported from the damaged reactor at the Fukushima's Nuclear Power Plant into Vietnam through the Pacific Ocean [START_REF] Long | Atmospheric radionuclides from the Fukushima Dai-ichi nuclear reactor accident observed in Vietnam[END_REF].

This study employed the R software with Openair package [START_REF] Carslaw | openair -An R package for air quality data analysis[END_REF] to generate air mass backward trajectories and cluster them in order to highlight potential sources of transported emissions into Hanoi. The calculation of the backward trajectories was carried out in two parts corresponding to the work presented in Chapter 3 and Chapter 4, respectively. For Chapter 3, 72-h backward trajectories were computed, ending above the OS1 location every hour from 11 th January to 22 nd March, 2019. Based on the observation of the radiosonde profiles, four ending altitudes were used: 100 m a.g.l. (daytime surface layer or stable boundary-layer), 600 m a.g.l. (convective boundary-layer or residual boundary-layer), 1000 m a.g.l. (transition layer) and 1500 m a.g.l. (free troposphere). Concerning Chapter 4, 4days backward trajectories ending over the OS1 site at a 100 m a.g.l. altitude every one hour during the whole winter period (October to March) and for 14 years from winter 2006/07 to winter 2019/20. In both cases, the time-step within each trajectory was kept short (one hour) in spite of the number of trajectories to compute, in order to improve the model accuracy.

Weather reanalysis data from the Global Data Assimilation System (GDAS1) (ftp://gdasserver.iarc.uaf.edu/gdas1), which run 4 times a day, (i.e., at 00:00, 06:00, 12:00, and 18:00 UTC) with a 1° × 1° resolution, was selected as meteorological input for both parts. This reanalysis is derived from the National Center for Environmental Prediction (NCEP) weather forecast model. In addition, the GDAS data also have GDAS0.5 with 0.5° × 0.5° resolution and GDAS0.25 with 0.25° × 0.25° resolution. Although GDAS0.5 and GDAS0.25 have better horizontal and vertical resolutions, they should be utilized with caution since the produced backward trajectories are more uncertain than GDAS1 because the vertical velocity is lacking and must be estimated from the horizontal divergence. Therefore, GDAS1 is still advised for driving HYSPLIT models in locations with complex terrain, which is the case in northern Vietnam. Moreover, there is a lack of data for the study period, in particular, GDAS0.5 only has data from 2011 to present and GDAS0.25 only has data since 2015.

The HYSPLIT model has some computational uncertainties which were assess to result in a horizontal uncertainty of the trajectory location of 10 to 20% [START_REF] Draxler | An Overview of the HYSPLIT_4 Modelling System for Trajectories, Dispersion, and Deposition[END_REF]. Moreover, the uncertainty in the trajectory coordinates coming from the input wind fields was assessed to be of the order of 20 % of the travel distance [START_REF] Stohl | Computation, accuracy and applications of trajectories -A review and bibliography[END_REF]. In order to enhance the accuracy of this model, it is possible to combine a large number of trajectories in a statistical analysis [START_REF] Cabello | Influence of meteorological input data on backtrajectory cluster analysis -a seven year study for southeastern Spain[END_REF]. However, this can cause overlapping issues (Figure 2-13.a) when the analysis is conducted for a long period (3 to 6 months), leading to the difficulty in determining the exact direction of the dominant air masses. In order to avoid the overlapping problem, a cluster analysis was applied, using k-means clustering to create clusters of trajectories by calculating the total spatial variance (TSV) among trajectories. Trajectories with a greater degree of similarity will be grouped into the same cluster, while trajectories with a higher degree of heterogeneity will be separated into various clusters.

The Euclidean distance was used to compute the TSV even though this type of distance can lead to clustering results in which shorter trajectories are more likely to cluster together even if they do not originate from the same direction. This phenomenon occurs in all algorithms and has no bearing on the study's findings. Indeed, short-distance trajectories correspond to stagnant wind conditions and can then be grouped together regardless of their directions. The number of clusters is chosen based on the TSV plot as a function of the number of clusters. The clustering process begin by classifying each trajectory as a cluster; then, at each consecutive pass, two of the clusters are grouped together, selecting the combination with the lowest TSV. This interaction is repeated until all trajectories are grouped into a single cluster. In the first few iterations, the TSV slowly rises, however, it lifts significantly by the end of the grouping process. This increase occurs when disparate clusters are combined, suggesting where to stop the clustering process. The ideal final number of clusters is chosen just before the large increase in TSV [START_REF] Draxler | HYSPLIT User's Guide[END_REF]. An example is presented for the winter period including the VASP campaign (Figure 2-13.b), a jump in TSV appears when moving down from 6 to 5 clusters, suggesting that 6 clusters would be an appropriate selection though, the ultimate number of clusters is always selected with some subjectivity. In this study, the cluster analysis was performed on almost 1700 backward trajectories for the VASP campaign (Chapter 3) and 30,926 backward trajectories for the semi-climatic analysis (Chapter 4). 2-4. The NAAPS model is proven to be particularly useful for forecasts of dust transport from the Saharan desert to East Asia [START_REF] Park | Long-Range Transport of Saharan Dust to East Asia Observed with Lidars[END_REF], Mediterranean region [START_REF] Castagna | Concurrent Influence of Different Natural Sources on the Particulate Matter in the Central Mediterranean Region during a Wildfire Season[END_REF]. Large-scale smoke plumes coming from savannahs, tropical and boreal forests are also reliably predicted by the NAAPS model [START_REF] Hyer | Patterns of fire activity over Indonesia and Malaysia from polar and geostationary satellite observations[END_REF][START_REF] Jaffe | Long-range transport of Siberian biomass burning emissions and impact on surface ozone in western North America[END_REF]. The NAAPS version used in this study was NAAPS reanalysis with AOD assimilation on a 1° × 1° horizontal grid and 25 vertical sigma-pressure levels with a 6-hour temporal resolution (at 00:00, 06:00, 12:00 and 18:00 UTC) [START_REF] Lynch | An 11-year global gridded aerosol optical thickness reanalysis (v1.0) for atmospheric and climate sciences[END_REF]. This reanalysis is a modified version of NAAPS incorporating the quality-controlled AOD retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and from the Multi-angle Imaging Spectro Radiometer [START_REF] Hyer | An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals[END_REF][START_REF] Shi | An analysis of the collection 5 MODIS over-ocean aerosol optical depth product for its implication in aerosol assimilation[END_REF]. It currently provides a 120-hour forecast of four components including: dust, sea salt, biomass burning smoke, and a combined anthropogenic and biogenic fine species (ABF, including sulfate, primary and secondary organic aerosols) species. In addition, the NAAPS model also provides a forecast of the total AOD and its components (smoke partial AOD, dust partial AOD and sulfate partial AOD) every 6-hour at a wavelength of 550 nm. Figure 2-14 shows an example of the 6-hour averaged AOD in India, mainland China and Southeast Asia; such maps and the underlying data are available from the Naval Research Laboratory website (http://www.nrlmry.navy.mil/aerosol). Meteorological fields used in NAAPS were initially derived from the US Navy Operational Global Analysis and Prediction System and then replaced by the Navy Global Environment Model since 2013 [START_REF] Rubin | Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS) and its application of the Data Assimilation Research Testbed (DART) in support of aerosol forecasting[END_REF].

Modeled AOD: NAAPS

Each of the aerosol component's emission sources is specified independently in the NAAPS model. Firstly, dust emissions in NAAPS models are taken from the erodibility map of [START_REF] Ginoux | Sources and distributions of dust aerosols simulated with the GOCART model[END_REF] with some regional adjustments using space-based and ground-based AOD observations [START_REF] Lynch | An 11-year global gridded aerosol optical thickness reanalysis (v1.0) for atmospheric and climate sciences[END_REF]. Next, the physical processes linked to sea salt particles for the NAAPS model were established by [START_REF] Witek | Coupling an ocean wave model with a global aerosol transport model: A sea salt aerosol parameterization perspective[END_REF]. Smoke emissions in NAAPS have been available since 2001, based on geostationary satellite and MODIS fire products generated via the Fire Locating and Modeling of Burning Emissions project (FLAMBE) [START_REF] Hyer | Patterns of fire activity over Indonesia and Malaysia from polar and geostationary satellite observations[END_REF][START_REF] Reid | Global Monitoring and Forecasting of Biomass-Burning Smoke: Description of and Lessons from the Fire Locating and Modeling of Burning Emissions (FLAMBE) Program[END_REF]. As discussed above, the ABF includes sulfate, primary organic aerosols and secondary organic aerosols. In the atmosphere, sulfate aerosols are created by chemical reactions from two main precursors: (1) sulfur dioxide (SO2) from anthropogenic sources and (2) dimethyl sulfide (DMS) from biogenic sources. SO2 emissions were originally based on the Global Emissions InitiAtive inventory (1°×1° resolution), version 1A, for the year 1985 with a seasonal variation and a two-level vertical distribution [START_REF] Benkovitz | Global gridded inventories of anthropogenic emissions of sulfur and nitrogen[END_REF]. In the reanalysis version of NAAPS version, the SO2 emissions were updated to the Monitoring Atmospheric Composition & Climate/CityZen EU projects inventory 2005 -2010 average [START_REF] Diehl | Anthropogenic, biomass burning, and volcanic emissions of black carbon, organic carbon, and SO2 from 1980 to 2010 for hindcast model experiments[END_REF][START_REF] Granier | Evolution of anthropogenic and biomass burning emissions of air pollutants at global and regional scales during the 1980-2010 period[END_REF], which reflects the increased anthropogenic emission in India and mainland China over the past decade and also includes monthly variations. The DMS emissions are computed using the parameterization from Saltzman et al. (1993), with the monthly DMS seawater concentrations from Lana et al. (2011). In this model, the DMS emissions are converted into 95 % sulfur dioxide and 5 % sulfate. The primary organic aerosols and secondary organic aerosols emissions based on the monthly mean database for anthropogenic (industrial and transport) emissions from the Monitoring Atmospheric Composition & Climate/CityZen EU projects [START_REF] Granier | Evolution of anthropogenic and biomass burning emissions of air pollutants at global and regional scales during the 1980-2010 period[END_REF].

Extraction and validation of the data

In several previous studies, the results of the NAAPS-AOD were compared against the AERONET-AOD observations. The correlation coefficient (R) varies from 0.57 to 0.62 for the various datasets analyzed in several study areas located in Belsk and Leipzig (Central Europe) [START_REF] Maciszewska | A multiyear analysis of aerosol optical thickness over Europe and Central Poland using NAAPS model simulation[END_REF]. The systematic discrepancy between ground observations and NAAPS results suggests that NAAPS model tends to underestimate the optical depth of aerosols and does not resolve some aerosol events accurately. In Southeast Asia, NAAPS model was shown to overestimate the hygroscopicity of particles from biomass burning in the Southeast Asian Maritime Continent (Indonesia, Singapore, Malaysia, Brunei and Timor Leste) as well as anthropogenic particles transported from East Asia, which led to inaccurate extinction outputs [START_REF] Edwards | Assessment of NAAPS-RA performance in Maritime Southeast Asia during CAMP2EX (preprint)[END_REF]. The correlation coefficient obtained when comparing 24-h NAAPS-AOD with AERONET-AOD measurements was 0.46 over land and 0.73 over a coastal site in the Southeast Asian Maritime Continent [START_REF] Campbell | Characterizing the vertical profile of aerosol particle extinction and linear depolarization over Southeast Asia and the Maritime Continent: The 2007-2009 view from CALIOP[END_REF].

The bias of NAAPS model can arise from different steps in the modelling process. For example, NAAPS provides AOD values during the night, while there are no satellite AOD observations to assimilate, so there is still some interrogation about the accuracy of the algorithm for the night-time [START_REF] Maciszewska | A multiyear analysis of aerosol optical thickness over Europe and Central Poland using NAAPS model simulation[END_REF]. In addition, this study also showed that NAAPS model was more reliable for the higher aerosol events, while it may not represent accurately the lower continental aerosol concentrations, as denoted by small correlation values for AOD's below 0.3. Moreover, the bias of the model also comes from the uncertainty in the emission sources. For example, [START_REF] Chew | Relationship between aerosol optical depth and particulate matter over Singapore: Effects of aerosol vertical distributions[END_REF] showed that, due to the deficiencies in the FLAMBE database, NAAPS could not capture well the variability among different smokerelated pollution events and strongly underestimated the magnitude of the associated smoke aerosol events. This led to correlation coefficients3 ranging from 0.46 to 0.52 between NAAPS-AOD and PM2.5 concentrations in Singapore [START_REF] Chew | Relationship between aerosol optical depth and particulate matter over Singapore: Effects of aerosol vertical distributions[END_REF].

Despite the above limitations, the model results provide valuable information about the aerosol distribution over a large spatial area, especially over oceans where ground-based aerosol measurements are not easily obtained. Therefore, NAAPS model can be a useful tool to analyze and anticipate severe air pollution events in Vietnam. In Chapter 3, the NAAPS smoke and sulfate partial AOD will be used in combination with the HYSPLIT cluster analysis to determine the occurrences of transboundary LRT and identify the potential transboundary sources contributing the pollution episodes that happened in Hanoi during the VASP campaign. In Chapter 4, the NAAPS-AOD will be used to study the trends in the sources of particles in the IP, SEA and EA regions, by computing an average map for each winter of the study period (from winter 2006/07 to 2019/20). These maps provide an overview of the AOD variation over potential source regions, which possibly affect northern Vietnam through LRT. Finally, NAAPS AOD is used to fill in the lack of PM2.5 data in Hanoi: AOD extracted from the model for the pixel covering Hanoi is used as a proxy for investigating the long-term variation of pollutants during the 14-winter periods in Hanoi.

As mentioned above, the NAAPS-AOD reanalysis was found to be globally and regionally consistent with the AERONET-AOD data [START_REF] Holben | An emerging ground-based aerosol climatology: Aerosol optical depth from AERONET[END_REF][START_REF] Lynch | An 11-year global gridded aerosol optical thickness reanalysis (v1.0) for atmospheric and climate sciences[END_REF][START_REF] Maciszewska | A multiyear analysis of aerosol optical thickness over Europe and Central Poland using NAAPS model simulation[END_REF]. However, there was no specific validation for northern Vietnam, particularly in Hanoi. In order to validate the AOD data retrieved from NAAPS, Chapter 4 includes a comparison between the AOD reanalysis at 550 nm from NAAPS and the AOD observations at 500 nm from the ground-based AERONET station in Hanoi. This comparison was performed on a winter-average basis and on a daily basis.

Chapter 3.

Key factors explaining severe air pollution episodes in Hanoi during 2019 winter season Chapter 3. Key factors explaining severe air pollution episodes in Hanoi during 2018/19 winter season

The key factors explaining severe air pollution episodes in Hanoi were firstly studied based on a field campaign performed during 2018/19 winter, the VASP campaign. In this chapter, the results of operations from the VASP campaign are presented. Chapter 3 is structured as follows:

• A short preamble about the aerosol Lidar data availability (Section 3.1)

• An overview of the meteorological and air quality variability (Section 3.2 • The investigation of the impact of air mass origin on the air quality using aerosol filter sampling and back-trajectories from the HYSPLIT model (Section 3.3) • The presentation of the structure of the ABL and the vertical mixing and their impact on the hourly PM2.5 concentrations during four pollution episodes (Section 3.4)

The results of Sections 3.2 to 3.4 of this chapter were published in the journal of Atmospheric Pollution Research [START_REF] Anh | Key factors explaining severe air pollution episodes in Hanoi during 2019 winter season[END_REF].

Aerosol Lidar data availability

Data availability from all operational devices during the VASP campaign (described in Chapter 2) were all good, with the exception of the data availability from SLIM Lidar. In spite of the presence of a seal at the bottom of the Lidar optical head, plus several desiccant bags inside the head, water condensation occurred some days inside the optical head, as shown in Figure 3-1.

Figure 3-1. Condensation affects operation rate of SLIM Lidar during the VASP campaign and an example for the time-height cross-section of the range-corrected signal affected by condensation problems.

This resulted in a dramatic loss of light power, and then signal level, making the observations useless. The only solution was to monitor the signal level and, in case of a drop, climb up the roof to check the presence of condensed water. If visual examination confirmed the presence of water (Figure 3-1, left), it was necessary to open the optical head, dry the various surfaces without untuning the optics, change the desiccant bags, and reseal the head to the best possible. shows an example of data affected by condensation problems, which resulted in the values for BLH being impossible to estimate.

Unfortunately, the SLIM Lidar also encountered regularly a connection problem between the computer and the optoelectronic rack; the USB plug was unstable and tripped every few days. Each time, the connection had to be manually reset by unplugging/re-plugging the socket (which could not be done remotely), then rebooting the acquisition software.

Overview of basic meteorological parameters and pollution

Monthly variability

A summary of the meteorological conditions and their temporal variability for each month is shown in Table 3-2. In general, January was ~3°C colder compared to February and March and the whole campaign was carried out in the humid period (high RH). The visibility was at its lowest in January due to frequent fog/haze episodes, but at its highest in February. Moreover, monthly-mean PM2.5 concentrations were the highest in January, while the lowest concentrations were recorded in February, probably due to the decrease of industrial activity and transport during the Lunar New Year (from 1 st to 10 th February). .5 levels as a function of the WS and WD, using wind data from the Doppler Lidar at 100 m a.g.l. as input. This height was chosen to be just above the highest buildings in the area (which have 30 to 35 floors). The WS from the northeast and southeast were predominant during the three months, which is the typical kind of wind observed during the winter in Hanoi [START_REF] Lasko | Analysis of air pollution over Hanoi, Vietnam using multi-satellite and MERRA reanalysis datasets[END_REF]. This figure also explicitly demonstrates that the highest levels of PM2.5 were mainly related to weak WS, so probably due to local emissions that are not well dispersed. These local sources seem to be mainly located to the West of the OS1 site in January, to the Northeast in February and to the East and Southeast in March. Nevertheless, a finer analysis of the atmospheric dynamics is necessary to draw reliable conclusions on the origin of this pollution. 3-3 (a) shows the daily average temperature (T) in a range of 12°C -26°C. The weather in this period was under humid conditions with daily relative humidity always higher than 60 %. These humid conditions might facilitate a reinforcement in the level of PM2.5 through the formation of secondary aerosols by heterogeneous reactions and their hygroscopic growth in ambient air [START_REF] Marris | Fast changes in chemical composition and size distribution of fine particles during the near-field transport of industrial plumes[END_REF][START_REF] Tang | Aerosol Phase Transformation and Growth in the Atmosphere[END_REF]. This is notably demonstrated by an increase in the sulfate to nitrate ratio retrieved from our weekly particulate matter samples as a function of RH (Figure 34). Indeed, the sulfate can be formed in aqueous phase reaction at high RH, while the nitrate is predominantly formed by gas-phase or heterogeneous photochemical reaction [START_REF] Seinfeld | Atmospheric Chemistry and Physics: From Air Pollution to Climate Change[END_REF]. In addition, shows the result of dailyaverage value of solar radiation (Q) and the daily maximum of the hourly BLH values (max BLH) for each day during the study period. We can observe a relation between these two parameters, but the correlation is very weak (R 2 = 0.44) because the weather is often cloudy during the campaign. In conclusion, solar radiation cannot be a good indicator of the maximum BLH at this period of the year and the BLH needs to be measured with a reliable method. [START_REF] Hien | Air pollution episodes associated with East Asian winter monsoons[END_REF][START_REF] Huy | Assessment of national emissions of air pollutants and climate forcers from thermal power plants and industrial activities in Vietnam[END_REF]. The highest daily level of PM2.5 was recorded on 25 th January (129 μg m -3 ), while the lowest daily PM2.5 concentrations (~25 μg m -3 ) were mainly recorded in February. During the campaign, four heavily polluted events characterized by PM2.5 higher than 50 μg m -3 for more than 48 hours were observed (P1 -P4 marked by grey areas), based on the specified Vietnam's guideline of ambient air quality. Although this is not always the case, the daily variation of PM2.5 concentration is generally associated with local WS: high WS related to low concentrations of PM2.5, while lower WS corresponded to the high levels of PM2.5 (Figure 3

Parameters

Day-to-day variability

Types of weather and pollution episodes

In order to study the relation between PM concentration and meteorology, weather conditions were classified into four distinct types typically observed in Hanoi from January to March: "clear", "hazy", "foggy" and "cloudy" conditions, as indicated in . In this study, hazy days must meet two criteria: a daily visibility ≤ 5 km and a daily RH ≤ 95 %. Foggy days were distinguished against only by RH ≥ 90 %. The clear days were selected by a daily visibility ≥ 10 km. The rest of the days were considered as cloudy days. During the campaign, these cloudy days were observed in 41 days, which accounted for ~59 % of total days, so they were the most frequent type of weather condition. Afterward, the hazy days were the second most common weather type during the campaign which were 20 days and made up for ~29 % in total. Then foggy and clear days accounted for ~11 % and ~1 %, respectively.

As indicated in and (f), the daily RH values were very high (~90 %) on both hazy and foggy days. In fact, one of the parameters distinguishing these two types of day is the PM2.5 concentration: The hazy days are characterized by higher daily-average PM2.5 concentrations (75.8 ± 23.1 μg m -3 ) compared to foggy days, for which PM2.5 concentrations were 33.4 ± 9.5 μg m -3 . This is explained by the fog's ability to wash out the atmosphere and thus reduce air pollution. Indeed, the 4 pollution events (P1 -P4) are all associated with hazy days (Figure 3 -3.f). During these events, it is clear that Hanoi was dominated by a high-pressure system and below a subsidence region on 19 th January (P1), 24 th January (P2) and 28 th February (P3), as shown in Figure 3-5. This subsidence generated a very strong temperature inversion above the boundary-layer [START_REF] Myers | Observational Evidence That Enhanced Subsidence Reduces Subtropical Marine Boundary Layer Cloudiness[END_REF], and the calm conditions at the surface were favorable to the accumulation of pollutants, leading to widespread haze. For example, the levels of PM2.5 started to increase on 18 th January, when the WD shifted from strong northeast winds to weaker southwest and northwest winds on 19 th January (Figure 3-3.d) and remained at high level On the clear day, which was only observed on 21 st January, daily PM2.5 concentrations were 19.1 μg m -3 , while PM2.5 concentrations in cloudy days were 32.6 ± 12.6 μg m -3 . The daily-average PM2.5 concentrations can be also controlled by WS measured at 100 m a.g.l.: clear days are characterized by high daily WS (5.6 m s -1 ), while hazy days were related to lower WS (2.5 ± 0.5 m s -1 ). The WS on cloudy and foggy days were 4.1 ± 0.8 m s -1 and 3.3 ± 0.6 m s - 1 , respectively. In conclusion, a strong wind, which is conductive to the dispersion of pollutants, generally induces lower concentrations of pollutants than a weak wind does. indicates that there was a significant difference between WS recorded at altitude 50 m a.g.l. by the weather station and the Doppler Lidar observations at 280 m a.g.l. indicates that there was a significant difference between WS recorded at altitude 50 m a.g.l. by the weather station and the Doppler Lidar observations at 280 m a.g.l. This difference could be explained by the complex of vertical structure of wind and the atmospheric dynamics which are not easy to be fully monitored by meteorological ground-based devices. Thus, in order to clearly understand the role of the atmospheric dynamics on the dispersion of PM2.5, the vertical wind speeds need to be studied further by using the Doppler Lidar (see Section 3.4).

The occurrence of cold surges in the winter can also be a factor affecting PM2.5 concentrations in Hanoi. During our campaign, there were 5 cold surges marked with black triangles on the time axis in Figure 3-3. The onset of these cold surges defined in section 2.1.2.3 was detected by examining the temperature, WS and WD. For example, a first cold surge occurred in Hanoi from 14 th to 17 th January: the WD shifted from southeast to northeast direction (Figure 3 -3.d), with a dramatic increase of the WS and the temperature significantly dropped. After that, the next surges were recorded on 21 st January, 21 st February, 05 th March and 14 th March, correspondingly. shows that the arrival of a cold surge generally coincides with a sudden drop in PM2.5 concentrations, followed by a gradual increase on the following days when the temperature rises again. However, this decrease is not always of the same magnitude, the levels of PM2.5 clearly decrease at the arrival of the first two and the last cold surges of the campaign, but not really at the 3 rd and the 4 th cold surges. Thus, meteorological parameters associated with cold surges cannot completely predict the variability in PM2.5 levels. In more general terms, conventional meteorological parameters recorded on the ground (e.g., temperature, wind speed, etc.) influence pollution levels, but they do not seem to be the only determining parameters. In fact, it is necessary to take other factors into account to better understand the pollution events, such as the origin of air masses notably described by backward trajectory data or the structure of the boundary-layer.

Local against Long-Range Transport

Main air mass origins and associated average pollution level

The results of the HYSPLIT cluster analysis on air mass back-trajectories over the whole campaign are presented in Figure 3-6 to further analyze the impact of transboundary LRT. The back-trajectories of air masses arriving at 100 m a.g.l. in Hanoi are classified among six clusters, as shown in . Except for cluster #6a, the remaining 5 clusters all reach Hanoi at low altitude (below 1000 m) (i.e., inside the ABL). During these 5 clusters, the clusters arrived in Hanoi by the Northeast (i.e., clusters #2a, #4a and #5a) accounted for a dominant frequency with a total of 57 % of the time.

The mean PM2.5 levels associated with each of these six air mass clusters are highly variable as shown by the PM2.5 concentration and standard deviation (Figure 3-6.a). The highest PM2.5 concentrations were observed when the back-trajectories ending at 100 m belonged to clusters #2a (59.6 ± 34.3 μg m -3 in average) and #4a (48.5 ± 29.1 μg m -3 in average), as these air masses likely accumulated pollution from eastern China and the RRD. This situation represented a total of 36 % of time during the campaign. Lower PM2.5 concentrations were observed when the air mass back-trajectories belonged to clusters #1a (38.1 ± 23.1 μg m -3 in average) and #3a (32.9 ± 12.4 μg m -3 in average) which flew over the sea before reaching Hanoi, with local emissions and RRD's emissions predominantly contributing to these levels. This situation represented a total of 40 % of time during the campaign. Cluster #5a (21 % of time) corresponded to air masses coming at low altitude from the North (i.e., from central to south China, an area that is less populated and industrialized than the eastern coast) but still has large sources of pollution (the trajectory points towards the Chongqing and Chengdu large agglomerations), thus leading to an intermediate PM2.5 concentrations (41.3 ± 34.8 μg m -3 in average). Besides the dominated northeastern air masses during this time, the westerly trajectories of cluster #6a rarely occurred (3 % of time) and were associated with higher levels of PM2.5 (51.2 ± 27.6 μg m -3 in average). They flew from the IP region toward northern Vietnam, a forested area in which many burning spots were visible on the satellite observations of MODIS during the period of the campaign, especially in February and March (Figure 34567). It reveals that the majority of open fires were spotted in northern Thailand and northern Laos. The altitude of the cluster #6a is high, but still compatible with catching emissions from the ground, especially smoke from large forest fires that rises in altitude due to the heat. For the HYSPLIT back-trajectories arriving in Hanoi at 600 m a.g.l., 1000 m a.g.l. and 1500 m a.g.l. (Figure 3-6.b, c and d respectively), cluster analysis returned five clusters. The fraction of the westerly back-trajectories increased with altitude, representing 13 % of time in total at 600 m a.g.l. (clusters #3b and #4b) against only 3 % at 100 m a.g.l. (cluster #6a). This point is even more marked at higher altitudes, with 50 % and 87 % of westerly back-trajectories arriving at 1000 m a.g.l. (clusters #2c, #3c and #5c) and 1500 m a.g.l. (clusters #1d, #2d, #4d and #5d), respectively. Simultaneously, the fraction of the northeasterly back-trajectories decreased with altitude. Thereby, the Northeast monsoons (the Northeast clusters) was observed frequently at the heights of 100 m a.g.l. and 600 m a.g.l. compared to the higher altitudes. In the next sections, the relation of these air masses ending at the heights of 100 m a.g.l. and 600 m a.g.l. and the pollution will be further studied.

Impact of air mass origin on particle composition and concentration

To highlight a possible relation between the chemical composition of aerosols and potential source areas, the relative chemical composition of the weekly TSP samples and the origin of associated air masses ending at 100 m a.g.l. are presented in Figure 345678. The relative contribution of sea-salt composed of Na and Cl was low (below 5 % of total of TSP), although it was slightly higher for samples associated with a strong contribution from marine air masses (clusters #1a and #3a) (i.e., for samples S5, S6 and S8). In the meantime, a high contribution of minerals (Al, Si, Ca and Mg), metals (Fe, Zn) and secondary inorganic aerosols (sulfate, nitrate and ammonium) was observed during all weeks, even for sample S6 in which clean oceanic air masses were predominant. As a result, these non-sea-salt aerosols have a local origin in this case. The highest PM2.5 concentrations were observed in weekly samples S2, S3 and S9 (red rectangles) associated with air masses dominated by continental clusters #2a, #4a and #5a, while the lowest level of PM2.5 were observed in samples S6 and S8 (blue rectangles) associated with the domination of marine air masses from clusters #1a and #3a. Furthermore, a comparison of the TSP composition between these samples indicates a similar chemical composition although there was a huge difference in TSP concentrations and origins between these weeks. This demonstrates a similarity in the major chemical composition of local emissions and those coming from both RT and transboundary LRT including from polluted region in the RRD and from mainland China. This can be explained by the presence of the same types of industrial and urban sources in these geographical areas, especially coal-based thermal power plants.

During the first three pollution events (P1 -P3), the air masses originating from mainland China overwhelmed at lower heights, (i.e., clusters #2a, #4a and #5a at 100 m a.g.l and clusters #1b and #2b at 600 m a.g.l.), as shown in Figure 3-9 (a) and (b), respectively. However, the results shown in Figure 3-9 does not fit with the results in Figure 3-2, according to which steady winds from the northeast were associated with very low PM2.5 levels. This means that the local WD measured by the Doppler Lidar was not always in agreement with the air mass origin ending at 100 m a.g.l. and 600 m a.g.l. (i.e., to the large-scale WD data used by HYSPLIT to compute the back-trajectories). For instance, the WD on day 19 th and 20 th January (P1 event) indicate southwest and northwest direction respectively (Figure 3 -3.d), while the direction of the dominant air mass trajectories on these days was northeast direction (clusters #2a, #4a and cluster #5a). This difference could be explained by external local factors such as lake-breeze or topography which lead to a sudden change in the local wind. As a result, models like HYSPLIT are more likely to be effective than local wind data in studying the impact of transboundary LRT. According to air mass trajectories during the period P1 (18 th -20 th January), it was established that the aerosol might be carried from mainland China under impact of the northeast monsoon, then it continued to move southerly in the direction of Vietnam and towards Hanoi. This fact is also confirmed by the data of the global aerosol model NAAPS. As shown in Figure 3-10 (a), mainland China and the RRD were exposed to the highest sulfate AOD loading. It is clear that there was a transport of sulfate aerosols from mainland China (associated with high sulfate AOD in red contour) toward northern Vietnam during the period corresponding to the pollution event P1. Similar to the event P1, a transport of sulfate was observed during the P2 period from 23 rd to 28 th January (Figure 3-10.b) and the P3 period from 26 th to 28 th February (Figure 3-10.c). Especially during the P3, the IP region was also engulfed by smoke (blue contour). As for the last pollution event P4, the appearance of cluster #6a from the West suggests an impact of transported BB emission at elevated altitude. Moreover, the AOD data obtained from simulated-model NAAPS clearly shows the smoke (blue contour) arriving in northern Vietnam from open fires in the IP region from the 11 th to 14 th (Figure 3 -10.d).

On a large geographic scale, our results indicate a significant impact of transboundary LRT on the air quality during the last 3 months of the winter 2018/19, especially during pollution events. These results are consistent with the scenarios in which the Northeast monsoon air masses pass through eastern or central China toward the RRD plain and then to Hanoi, possibly carrying a large number of aerosols and gaseous pollutants. Consequently, the increase in PM2.5 during the pollution event includes a combination of essentially two main polluted sources, from the RRD and from mainland China in this case, which is in agreement with the conclusion of previous studies [START_REF] Hai | Effects of local, regional meteorology and emission sources on mass and compositions of particulate matter in Hanoi[END_REF][START_REF] Hien | PMF receptor modelling of fine and coarse PM10 in air masses governing monsoon conditions in Hanoi, northern Vietnam[END_REF][START_REF] Ly | The effects of meteorological conditions and long-range transport on PM2.5 levels in Hanoi revealed from multi-site measurement using compact sensors and machine learning approach[END_REF]. In addition, the pollution can sometimes come from high altitude transboundary LRT of BB smoke from the West, as observed in the event P4. The impact of BB from forest burning and burning activities in IP regions such as Laos and Thailand in the late winter (February -March) is also observed as mentioned in other studies [START_REF] Huang | Impact assessment of biomass burning on air quality in Southeast and East Asia during BASE-ASIA[END_REF][START_REF] Reid | Observing and understanding the Southeast Asian aerosol system by remote sensing: An initial review and analysis for the Seven Southeast Asian Studies (7SEAS) program[END_REF]. Nevertheless, the contribution of transboundary LRT alone does not fully explain the temporal variability of PM2.5 levels in Hanoi during the winter period. Thereby, a complementary analysis of the local atmospheric dynamic is necessary.

Role of the boundary-layer dynamics on PM2.5 levels during pollution events

As mentioned in the section 2.1.3, the evolution of the BL during a day follows the diurnal solar heating over the ground. However, the BLH development is strongly impeded under haze conditions because a large part of the incoming solar radiation is either absorbed or scattered by the fog, by clouds or by the pollution layer, thus reducing the solar flux reaching the surface and leading to increased stability [START_REF] Leng | Insights into a historic severe haze event in Shanghai: synoptic situation,boundary layer and pollutants[END_REF][START_REF] Quan | Evolution of planetary boundary layer under different weather conditions, and its impact on aerosol concentrations[END_REF]. Lower BLH on hazy days weakens the vertical mixing and directly results in the accumulation of aerosol at the surface, leading to an increase in aerosol concentration.

The daily maximum of the hourly BLH ranged from 700 m to 1200 m during cloudy days, depending on cloud properties (type, thickness and altitude), while it was typically below 1000 m during hazy days or foggy days (Figure 3 -3.f). As mentioned in Section 3.1, there are less days with retrieved BLH data than actual operational days of the campaign; this is because it was not always possible to retrieve the BLH due to the impact of low-altitude clouds and precipitation. Furthermore, it is clear that the daily concentration of PM2.5 is higher throughout a day when the maximum BLH is lower. For example, during the event P2, a sudden drop in the maximum BLH occurred from day 24 th to 25 th January, leading to a dramatic increase in daily PM2.5 level on day 25 th . Besides BLH, the impact of meteorological factors (e.g., humidity, temperature) is known to influence the variation of pollutant concentrations at the surface in the winter [START_REF] Hien | Air pollution episodes associated with East Asian winter monsoons[END_REF][START_REF] Nguyen | Air Quality Modeling Study on the Controlling Factors of Fine Particulate Matter (PM2.5) in Hanoi: A Case Study in December 2010[END_REF]. However, while previous studies only considered WS and WD data from ground observations, this study can precisely measure the hourly vertical structure of turbulence and vertical mixing profiles, and they seem to be determinant parameters governing the hourly variability of PM2.5 during pollution events.

In order to thoroughly examine the daily evolution of ABL and vertical mixing evolutions, four days corresponding to the most polluted days of each pollution event (P1 -P4), are selected: on 19 th January, 24 th January, 28 th February and 14 th March. shows the time series of BLH measured by the SLIM Lidar in Hanoi on these days. This study of BLH pioneer a complementary analysis on PM2.5 variations during pollution events, despite limited information about the BLH evolution in SEA region, particularly in Vietnam. High back-scattering coefficient values were recorded at altitudes lower than the BLH suggesting that the haze pollution was formed and evolved within the ABL.

Pollution event P1: 19 th January

The analysis of the most polluted day of event P1 (19 th January) indicates that the CBL grew constantly after sunrise (around 07:00 LT) when the incoming solar radiation increased. The growth rate of the boundary-layer was weak (≤ 50 m h -1 ) in the morning (Figure 3-12.a), but increased gradually in midday (~80 m h -1 ) and then remained moderate as medium altitude clouds were presented above the BL until 16:00 LT (Figure 3 -11.a). The maximum BLH on this day was 660m, observed at 16:00 LT before falling gradually. After the sunset, the SBL maintained at the heights of around 400 m to 450 m until the next morning and it remained capped by low-level clouds.

Regarding PM2.5, the concentration was already high (~90 μg m -3 ) at the beginning of the day which could be a consequence of the cumulative process from the day before. Besides this, the vertical mixing was weak (Figure 3-13.a) due to the influence of a high-pressure system associated with low SBL height, thus enhancing the accumulation of pollutants near the surface. During the first 2 hours of the day, level of PM2.5 increased to reach 106 μg m -3 at 02:00 LT. Afterward, the concentration of PM2.5 fell gradually to 68 μg m -3 between 02:00 and 07:00 LT although there was no significant variation of BLH at that moment. This decrease could be explained by the existence of weak LLJs in very stable conditions that were recorded by the Doppler Lidar between 02:00 and 05:0 LT around 180 m. The radiosonde profile recorded at 07:00 LT, as shown in , indicates the presence of others weak LLJs (2 -3 m s -1 ) located beyond the Doppler Lidar range and just below the SBL top. These jets were able to cause a sudden increase in vertical mixing at lower levels, leading to the dispersion of pollutants near the surface [START_REF] Mahrt | Stably Stratified Atmospheric Boundary Layers[END_REF][START_REF] Wei | Intermittent turbulence contributes to vertical dispersion of PM2.5 in the North China Plain: cases from Tianjin[END_REF].

After the sunrise, the concentration of PM2.5 started to rise following the increase of emitting activities, such as vehicles or cooking, during the rush hours, which occurred in a still slowly growing ABL. Around 16:00 LT, the PM2.5 levels decreased slightly when the BLH reached its maximum height, before rising once again from 18:00 LT. The increase in PM2.5 at this time probably might be due to the combination of the lower BLH due to the ABL's depletion and the second rush hours which started from 18:00 LT. Interestingly, the PM2.5 concentration continued to rise rapidly despite a considerable reduction of vehicle activities and others after the end of rush hour at around 20:00 LT. In addition, this phenomenon was associated with very weak vertical mixing as indicated by the low VC (< 500 m 2 s -1 ) observed at this time (Figure 3-13.a), leading to a poor dispersion of particles and an accumulation of PM2.5 near the surface. As mentioned above, the low VC values (< 1000 m 2 s -1 ) were observed during the whole day, leading to the high air quality potential. Indeed, the concentration of PM2.5 during day 19 th January were shown to be high during this whole day.

Pollution event P2: 24 th January

The analysis of 24 th January shows that the CBL also grew constantly after sunrise, along with the increase of incoming solar radiation in the morning (Figure 3 -12.b). However, the growth rate of the CBL during daytime on 24 th was faster (~100 m h -1 ) than that on day 19 th in the P1 event. The maximum height was 780 m, observed at 15:00 LT. Then, the BLH collapsed suddenly with a higher decay rate compared to day 19 th . At night, this layer maintained at a stable height of approximately 300 m from 22:00 LT until the next early morning.

At midnight, the PM2.5 concentration was high (~90 μg m -3 ) then decreased gradually until sunrise (at 07:00 LT), as indicated in . After that, the concentration of PM2.5 increased rapidly and reached a peak of 10:00 LT. This could be explained by enhanced transport activities in the first rush hour, associated with the low BLH in the morning. Furthermore, the wind speeds during this period were weak, with values ≤ 2 m s -1 , leading to low values of VC (< 1000 m 2 s) and mixing ). After 10:00 LT, the PM2.5 levels decreased, along with a rapid increase in BLH, which reached its maximum height at 15:00 LT. The increase in VC in this period may be attributed to the fact that both BLH and WS show an ascending trend, thus enhancing the air quality. From 16:00 LT, the PM2.5 concentrations tended to increase slightly once again and reached a peak at 20:00 LT. This trend was again the result of a combination of the decrease of BLH and the second rush hour after the sunset. Afterward, the PM2.5 concentration fell gradually but still remained at a high level (~70 μg m - 3 ) at night due to the low height of the SBL. Contrary to day 19 th , no LLJ was visible on this day within the SBL, either on the Doppler Lidar observations or on the radiosonde profiles . In general, this result is consistent with low BLH and weak WS measured at night on day 24 th January. Similar to day 19 th January, the low values of VC (< 2000 m 2 s -1 ) were recorded during the whole day.

Pollution event P3: 28 th February

Day 28 th was one of the rare hazy days in February, and this day was also associated with the third pollution event (P3). After sunrise, the height of the ABL started to increase with a weak growth rate (≤ 50 m h -1 ) from 07:00 to 11:00 LT (Figure 3 -12.c). From 11:00 to 14:00 LT, the BLH increased rapidly and reached its maximum height of 850 m at 14:00 LT (higher than itself on day 19 th and 24 th January). Then, the BLH decayed due to the decrease of the solar radiation in the late afternoon.

Concerning PM2.5 concentration, it was high at the beginning of the day (~80 μg m -3 ) and remained stable at this level until 12:00 LT (Figure 3 -12.c). This phenomenon might be due to the low height of the SBL at night and the slow growth of CBL in the morning, which weakened the vertical mixing (Figure 3-13.c), leading to the accumulation of PM2.5 near the ground. During the campaign, the BLH retrieved from 00:00 to 11:00 LT was generally lower than what was observed in previous cases (19 th and 24 th January), as it mainly varied within the range of 300 m up to 350 m. Afterward, the concentration of PM2.5 started to fall gradually with the simultaneous increase in the BLH, which reached its maximum altitude at 15:00 LT, resulting in the increase of the vertical mixing near the surface, as indicated in . In addition, an increase of VC (from 2000 to 4000 m 2 s -1 ) was also recorded at the moment. More generally, higher values of VC were observed on day 28 th due to the higher BLH values. Then, the concentration of PM2.5 declined more rapidly after 15:00 LT (from ~77 μg m -3 to less than 35 μg m -3 ) due to the increase in WS. shows that LLJs located just below the SBL top (~400 m a.g.l) were recorded at 19:00 LT. Then, these LLJs combined with the wind shear to form intermittent turbulence at lower levels were recorded by the Doppler Lidar during at night, from around 200 m a.g.l to 280 m a.g.l, enhancing the vertical mixing and increasing the dispersion of PM2.5 near the surface. Therefore, a downward trend in the level of PM2.5 was observed after the sunset (19:00 LT) to midnight. Although the values of VC were higher compared to day 19 th and 24 th January, the VC values of 28 th February were still recorded at low level (< 4000 m 2 s -1 ) during the whole day, explaining to the high potential air pollution on this day.

Pollution event P4: 14 th March

14 th March was the last day and the most polluted day during the pollution event noted P4. Concerning the boundary-layer, the heights of SBL on this day were commonly higher than those on other cases (with heights ≥ 500 m from 04:00 to 07:00 LT). Furthermore, CBL evolved slowly with a rate ≤ 40 m h -1 after the sunrise (Figure 3 -12.d). BLH reached the highest altitude of 890 m at 14:00 LT before decreasing over time as a normal scenario for the depletion of the CBL in the late afternoon. After 23:00 LT, the BLH could not be retrieved because of the precipitation.

At the beginning of the day, the PM2.5 concentration was lower than the above cases (~60 μg m -3 ) but it was still considered as a high concentration. This result is expected considering that this day had the highest value of VC (from 2000 to 6000 m 2 s -1 ) among the four case studies (Figure 3 -13.d). However, these VC values were still lower than 6000 m 2 s - 1 , leading to high potential air pollution on this day. Later on, the PM2.5 concentration suddenly increased from 03:00 LT and after two hours, it reached approximately 130 μg m -3 which was twice as much as its value at the beginning of the data. The results from Doppler Lidar shows that a low-level jet probably existed from 05:00 to 09:00 LT, though the top of the jet was visible only on the radiosonde profile at 07:00 LT (Figure 3 -14.d). This jet may have contributed to the recirculation of pollutants [START_REF] Prtenjak | Atmospheric dynamics and elevated ozone concentrations in the northern Adriatic[END_REF] above Hanoi, increasing the concentration of PM2.5. Also, the increased wind shear below the jet strengthened the vertical mixing, resulting in a decrease in the concentration of pollutants aloft and thereby increasing the concentration of those pollutants at ground [START_REF] Banta | Stable-boundary-layer regimes from the perspective of the low-level jet[END_REF]. Afterward, the PM2.5 concentration went down slightly but maintained at a high polluted level (≥ 100 μg m -3 ) until 14:00 LT, as the boundary-layer only grew moderately after sunrise. After the BLH reached its maximum height of 910 m at 14:00 LT, it tended to decrease gradually because of the depletion of the ABL. Simultaneously, the concentration of PM2.5 began to increase until 20:00 LT. Then, the concentration of PM2.5 fell suddenly by the arrival of a new cold surge, which resulted in a significant increase in WS, as indicated in the vertical wind profile in Figure 3-13. (d).

Conclusion

To identify the factors affecting air pollution, this study analyzed the relationship between multi-scale meteorological conditions and pollution events during the winter 2018/19. The mean PM2.5 concentration during the campaign that took place in Hanoi from 11 th January to 22 nd March, 2019 was 45 ± 25 μg.m -3 . The highest daily level of PM2.5 was recorded on 25 th January (129 µg m -3 ), while lower levels of PM2.5 were mainly observed in February.

Four severe pollution events were recorded during the campaign, associated with stagnant conditions. The results of HYSPLIT backward trajectories showed that the three first pollution events were mainly related to the occurrence of RT and transboundary LRT from the RRD, Vietnam and from mainland China, respectively. In addition, a mix of two transboundary LRT contributions was recorded during the fourth pollution event, that included industrial emissions from the Northeast and biomass burning from the West. However, the chemical composition of TSP was pretty constant over the 10-week of sampling, which included both periods associated with transboundary LRT aerosols and other periods with only local emissions. This indicates that locally emitted particles had a major chemical composition such as minerals (Al, Si, Ca and Mg), metals (Fe, Zn) and secondary inorganic aerosols (sulfate, nitrate and ammonium) which was relatively similar to those coming from the RT and transboundary LRT under the impact of Northeast monsoon.

In addition, the SLIM Lidar observations showed a large variability in the daytime BLH according to the different weather conditions: the daily maximum of the hourly BLH varied from 700 m up to 1200 m during cloudy days, while it was typically below 1000 m during hazy days. On hazy days, the height of the boundary-layer was compressed and grew slowly within just a few hundred meters due to the strong effect of haze. Because of the lower BLH, the accumulation of pollutants at the surface was reinforced, leading to severe degradations of the air quality in Hanoi. To sum up, the analysis of the typical days of pollution at the hourly scale highlights the importance of BLH on level of PM2.5 variation during the day, however, the BLH sometimes cannot explain alone the variation of PM2.5 concentrations and that complementary information on the atmospheric dynamics are often necessary.

The results from this study provides a new understanding of the mechanisms linking meteorological parameters, BLH and air quality during the winter period in Hanoi. However, this raise a question about whether the results presented here can be representative for whole winter period or whether they can explain air pollution events in the previous winter period. In addition, the same method needs to be testified for longer period to further assess the contents, types and controlling factors of atmospheric dynamics in polluted events in Hanoi under different weather conditions and winter seasons.

Chapter 4. Intricate behavior of the winter pollution in Hanoi over the 2006 -2020 semi-climatic period

In Chapter 3, leads were provided for explaining the occurrence of severe pollution episodes in Hanoi. However, the representativeness of those results for the whole winter or for other winters arises legitimately. Besides, information is lacking about the inter-annual variations of air pollutants in Northern Vietnam, in relation with changes in the Northeast monsoon. This hinders the possibility to detect trends in specific emission sources, quantify the local and remote sources' contributions, and fully understand the mechanisms of pollution events in Hanoi. To address the above-mentioned shortcomings, the second part of this study includes four steps:

• The validation of AOD data from the NAAPS model for Vietnam, by comparison with observations from the AERONET sun photometer in Hanoi (Section 4.1). • The exploitation of NAAPS AOD data to identify the pollution source areas in EA and SEA and their evolution over the last 14 years (Section 4.2). • A long-term analysis of the AOD trends in Hanoi in relation with the inter-annual variability of the Northeast monsoon under the influence of El Niño events (Section 4.3).

• The presentation of a systematic procedure for identifying the impact of the cold intrusions on the air quality in Hanoi at a daily scale, by fully combining backward trajectories, NAAPS AOD data, in-situ PM2.5 and weather observations (Section 4.4).

The results from Sections 4.2 to 4.4 of this chapter are under reviewed in the journal Atmospheric Environment.

Validation of NAAPS AOD data over Hanoi

As mentioned in section 2.3.2, NAAPS model is used to build up a 14-years database of the total AOD and its components (smoke, dust and sulfate AOD). With the aim of validating the NAAPS-AOD over northern Vietnam, the daily average of ground-based Level 2.0 AOD from AERONET was extracted for the winter period (October to March) from the winter 2010/11 to 2018/19 which is also the period when AOD data is available from this station. The 6-month winter mean AOD in Hanoi was computed from both NAAPS model and AERONET data, from winter 2010/11 to winter 2018/19. 4-1 (a) shows that the simulated AOD is largely underestimated compared to ground-based observations, as was already found by previous studies [START_REF] Chew | Relationship between aerosol optical depth and particulate matter over Singapore: Effects of aerosol vertical distributions[END_REF][START_REF] Maciszewska | A multiyear analysis of aerosol optical thickness over Europe and Central Poland using NAAPS model simulation[END_REF]. Part of the discrepancy between the observations and the model could be explained by the difference of wavelength between AERONET (AOD at 500 nm) and NAAPS (AOD at 550 nm). Assuming an Angstrom exponent of 1, the AERONET AOD values would decrease by 10% if the wavelength correction was applied. However, it was chosen not to apply the correction in this study in order not to add an extra cause of uncertainty. The AERONET AOD values could also be higher because they are measured in the center of Hanoi, while the spatial scale of NAAPS model (1°×1°) is not fine enough to resolve such a pollution hot-spot. A third reason for discrepancies could be the difference in satellite and ground-based observations data availability. AERONET delivers a daily AOD value provided there was at least one observation, i.e. at least one few-minute clear-sky period at any time during the day. On the contrary, the satellite AOD observations used to constrain NAAPS are recorded at precise moments of the day (around 11 a.m. and 1 p.m. for the MODIS instruments) so that NAAPS will not be constrained on a given day if the sky is cloudy at the satellites overpass times. It is therefore possible to have points in the comparison corresponding to unconstrained, i.e. lesser quality model data. In spite the large underestimation of the AOD by NAAPS, the model accurately reproduces the AOD trend and inter-annual variability over Hanoi. Therefore, NAAPS is considered reliable to study the evolution of particle sources in SEA and EA over 14 years (Section 4.2) and the impact of the inter-annual variations of air mass origin on air pollution in Hanoi (Section 4.3).

In order to assess the ability of NAAPS model to reproduce the day-to-day variability of the AOD, presents the scatter plot of the NAAPS-AOD model results versus the AERONET AOD from Hanoi station. The correlation coefficient between the model and the ground-based observations was moderate (0.66); the slope of the linear regression was far below 1 (0.52), in agreement with the large underestimation of the AOD by the model that was already pointed from , but the intercept was very small (0.08), meaning the model has no systematic bias compared to the observations. In this study, single-day NAAPS AOD values were used in a binary way (i.e. the AOD higher or lower than the average value) in order to discriminate between days impacted or not by long-range or regional transport. Therefore, to qualify NAAPS AOD for this purpose, the scatter plot was split into four quadrants using the mean of values of NAAPS-AOD and AERONET-AOD computed from all the coinciding data from the winters 2010/11 to 2018/19. 73 % of the points fall in the first quadrant (N1) and the third quadrant (N3), meaning they are correctly attributed to the low/high AOD category, which is a pretty good score for such a global-scale model. Therefore, NAAPS model was considered to be good enough to detect LRT or RT pollution in this study (Section 4.4).

Trends in the particle sources in East and Southeast Asia

Figure 4-2 shows the spatial distribution of the 6-month averaged-total AOD retrieved from the NAAPS model during the winter period in the EA and IP region from winter period 2006/07 to period 2019/20. With the aim of investigating the characteristics of AOD sources for the studied area, not only the total AOD but also its components (sulfate, smoke and dust partial AOD) were further analyzed. The dust partial AOD distribution did not show any interesting feature over the studied area, but the smoke and sulfate partial AOD maps are presented in  respectively, did display some intriguing attributes. By looking at the maps, the 14-year study period can be divided into 2 stages.

During the first stage, from the winter period 2006/07 to period 2010/11 (Figure 4-2.a -f, green square), high values of the total AOD (> 0.7) were observed frequently over the IP regions. According to previous studies, a dominant influence of BB activities originating from both natural (forest fires) and anthropogenic sources (agriculture) in this region was demonstrated [START_REF] Nguyen | Carbonaceous aerosol composition in air masses influenced by large-scale biomass burning: a case study in northwestern Vietnam[END_REF][START_REF] Reid | Observing and understanding the Southeast Asian aerosol system by remote sensing: An initial review and analysis for the Seven Southeast Asian Studies (7SEAS) program[END_REF]. This point is confirmed by the fact that high smoke partial AOD values (> 0.7) were retrieved by the NAAPS model over the same regions of the IP region (Figure 4-3.a -f).

Over the northern part of mainland China, moderate AOD values (from 0.4 to 0.6) were retrieved during the first stage (Figure 4-2.a -e, purple square). However, during the second stage of the study period, from the winter 2011/12 to the winter 2019/20, the AOD values observed in north China plain, eastern China areas started to rise steeply . High (> 0.7) and even very high (> 1.0) AOD values were observed in mainland China, especially during the last four winter periods (Figure 4-2.k -n, purple square). The high AOD values observed in these areas are consistent with the results from previous studies about the spatial distribution of AOD in mainland China [START_REF] Kuerban | Spatio-temporal patterns of air pollution in China from 2015 to 2018 and implications for health risks[END_REF][START_REF] Li | Pollution Trends in China from 2000 to 2017: A Multi-Sensor View from Space[END_REF]. These regions of mainland China are known as dense industrial regions with numerous refineries, factories and thermal power plants (Cohen et al., 2010a;[START_REF] Pu | Impact of long-range transport on aerosol properties at a regional background station in Northern China[END_REF]. Indeed, shows that the high values of total AOD observed in mainland China mainly consisted of sulfate AOD, indicating that these aerosols were formed in an environment rich in SO2, a pollutant mainly emitted from coal-based industrial activities.

Besides mainland China, high values of the total AOD started to be observed in the RRD since the winter 2016/17 (Figure 4-2.k -n, blue square), which were also mainly composed of sulfate AOD following the rapid development of industrial activities in the RRD. Our research is not the first to use AOD data to track air pollution changes over the RRD plain. The high values of AOD across the RRD plain were also confirmed in this investigation, in keeping with earlier studies [START_REF] Bui | Study of aerosol optical properties at two urban areas in the north of Vietnam with the implication for biomass burning impacts[END_REF][START_REF] Luong | Contrasting seasonal pattern between ground-based PM2.5 and MODIS satellite-based aerosol optical depth (AOD) at an urban site in Hanoi, Vietnam[END_REF].Therefore, there were three main aerosol sources in the EA that may have impacted air quality in Hanoi during the studied period: first, BB sources in the IP region from the winter period 2006/07 to the winter period 2011/12, then industrial sources in mainland China since the period 2011/12 and finally, closer industrial sources in the RRD plain from the period 2016/17. contrast, the air masses from cluster C2 also originated from Siberia but traveled a longer pathway over the South China Sea before reaching northern Vietnam via the Gulf of Tonkin. These air masses carry humid and cold air and sometimes bring heavy rain to northern Vietnam [START_REF] Minh | An outline of climatology and meteorology[END_REF]. Cluster C2 occurs when the Siberian high-pressure system shifts towards the East. In general, the altitude of all northeast clusters was lower than 2000 m a.g.l. ( Figure 4-5.b). Previous studies about the depth of boundary-layer in China and Vietnam found heights between 900 m a.g.l. and 1500 m a.g.l. during the winter (Bao [START_REF] Anh | Key factors explaining severe air pollution episodes in Hanoi during 2019 winter season[END_REF][START_REF] Leng | Insights into a historic severe haze event in Shanghai: synoptic situation,boundary layer and pollutants[END_REF][START_REF] Miao | Unraveling the relationships between boundary layer height and PM2.5 pollution in China based on four-year radiosonde measurements[END_REF], so these northeast air masses may travel within the boundary-layer and catch the pollutants emitted along the trajectory. At last, C1 clusters also originated from south China but over a much shorter distance, due to lower wind speed. These trajectories probably occur during prolonged anticyclonic episodes, which will be further explained in the next section. Furthermore, shows that these northeast air masses passed over the densely populated and densely industrialized areas in mainland China and northern Vietnam. Therefore, these clusters are likely to be associated with a transfer of the pollutants accumulated above mainland China and the RRD plain toward Hanoi.

Meanwhile, the cluster C4 corresponds to southeast air masses originating from South China Sea (Figure 4-5.a), which were considered clean flow in this study and represented ~15 % of the total number of back-trajectories. At last, there were also some westerly air masses coming from northern India and the Bay of Bengal (clusters C5 and C6) that accounted for ~14 % of the total. The latter air masses moved from a higher altitude than the northeast air masses did, especially cluster C6 that came from an approximate altitude of 5000 m a.g.l. . This can be explained by the geographical conditions, with mountains being on these cluster trajectories. Usually, westerly flows are recorded frequently only after the southwest monsoon starts, at the end of March or beginning of April [START_REF] Co | Levels and Composition of Ambient Particulate Matter at a Mountainous Rural Site in Northern Vietnam[END_REF]. The appearance of clusters C5 and C6 during the winter period might therefore correspond to anomalous activities of the air masses in northern Vietnam. As a matter of fact, BB aerosols from Indochina have been proven to occasionally impact the air quality in northern Vietnam and south China during the studied period [START_REF] Huang | Impact assessment of biomass burning on air quality in Southeast and East Asia during BASE-ASIA[END_REF]. However, the western air masses were not dominant during the studied period since they only account for around oneseventh of the total (Figure 4-5.a).

Impact of the air mass origin on the AOD

With the aim of highlighting the relationship between the semi-climatic oscillations and the air quality in Hanoi, the frequency of occurrence of clusters was computed for each of the 14 winters, along with the winter-averaged AOD values from the NAAPS model, for the pixel covering Hanoi. The time series of the cluster's frequency and AOD values (total AOD along with the smoke, sulfate and dust partial AODs) are presented in Figure 456.

The air masses originating from the northeast direction (clusters C1, C2 and C3) always outweighed their southeast and western counterparts, but their frequency varied widely from year to year, between 44 and 88 %. The western air masses (cluster C5 and C6) experienced even wider year-to-year variation, oscillating between 3 and 39 %, while the contribution of the southeast air masses (cluster C4) was more regular (10 to 18 %). In detail, a clearly larger contribution of western clusters was observed during two winters 2014/15 and 2015/16 (Figure 4-5.a). Some studies indicate that the high-pressure system in Siberia was weakened during these winters along with the existence of a strong El Niño episode, which led to less cold air being transported from Siberia toward the south during these years [START_REF] Chang | Impact of the 2015 El Nino event on winter air quality in China[END_REF][START_REF] Zhao | The effects of El Niño-Southern Oscillation on the winter haze pollution of China[END_REF]. Moreover, the same studies indicate that cyclonic anomalies near the Bay of Bengal were produced, then transported warm and moist air from the Bay of Bengal to the continental regions of south China and the IP region. Therefore, during El Niño phases of the southern oscillation, the Northeast monsoon impact was found to reduce and western air masses became more frequent, leading to an increase in the smoke AOD from fires in the IP region and northern India, and a relative decrease in sulfate AOD provided by the transboundary LRT from mainland China and RT from the RRD. It should be noticed, however, that the above-mentioned trends in sulfate and smoke partial AODs were interrupted during the winters 2014/15 and 2015/16; during those winters, the sulfate AOD suddenly fell down and the smoke AOD increased simultaneously. This interruption could be explained by the perturbation of air masses trajectories induced by El Niño: with the increased frequency of western air masses, Hanoi received more smoke originating from BB activities in the IP region. In contrast, the frequency of the northeast clusters reduced, corresponding to a decrease in the transboundary LRT and RT of sulfate particles from mainland China and the RRD plain, respectively. The very high sulfate AOD values and very low smoke AOD values observed during the last three winters (2017/18 and onwards), can be explained by the particularly high contribution of northeast air masses prevailing during these winters (78 % to 87 %) and by the particularly low contribution of western air masses (only 3 % to 4 %). This indicates that the activity of the Northeast monsoon dramatically affects the aerosols load in Hanoi during the winter period by the effect of RT and transboundary LRT.

However, meteorology, along with emissions, also explains the rapid increase in sulfate partial AOD from winter 2013/14 onwards. The growth in sulfur compounds emissions in the RRD plain and the mainland China likely played a role too. As mentioned in Section 1.1.3.3, the number of coal-fired TTPs in Vietnam increased significantly from 2009 to 2019. Specifically, ten TPPs were located in the RRD, with the nearest one and largest SO2 emitter being located only 50 km away from Hanoi. Thereby, the rise of sulfate AOD may also result from this increase in LP and RT following the rapid industrialization and coal consumption growth in Hanoi and its surrounding areas. Our findings highlight that the AOD increase may be associated with the growing number of coal-based TPPs over the RRD plain between 2011 and 2020. Regarding China's coal consumption, from 2004 to 2017, it experienced significant variability, from a rapid increase (2004 -2013) with an annual growth rate of 8.3 % to a slowly falling stage after 2014 [START_REF] Yuan | The future of coal in China[END_REF]. Furthermore, coal-based electricity production still accounts for 60 % of the total energy consumption in mainland China in 2015 [START_REF] Cui | A plant-by-plant strategy for high-ambition coal power phaseout in China[END_REF]. Previous studies indicated that secondary sulfate-rich compounds (products of the oxidation of SO2) from coal-fired power stations or industrial emissions could be transported to the downwind regions, including Vietnam, by transboundary LRT process, and subsequently transformed into an aerosol. Furthermore, sulfate was one of the most abundant species among all water-soluble ions in aerosols found in mainland China during winter [START_REF] Chuang | Quantifying PM2.5 from long-range transport and local pollution in Taiwan during winter monsoon: An efficient estimation method[END_REF][START_REF] Ding | Significant reduction of PM 2.5 in eastern China due to regional-scale emission control: evidence from SORPES in 2011-2018[END_REF]. In the end, the growth of sulfate AOD since the winter 2013/14 results from a combination between more sulfur compounds emissions in the RRD and in mainland China and more favorable wind paths for RT and transboundary LRT of these pollutants towards Hanoi. As a result, the sulfate partial AOD, which was shown to associate with the share of Chapter 3 shows the time series of some meteorological factors in the last 3 months of winter 2018/19, providing some initial evidences for the impact of weather on the variation of PM2.5 concentration. However, it could not be representative for the whole winter period. To extend this work, the time series of daily averages of some basic meteorological factors together with PM2.5 concentrations and Sulfate AOD over four winter periods from 2016/17 to 2019/20 were analyzed in this section.

Table 4-1 displays the mean of meteorological parameters and of PM2.5 during the last four winter periods in Hanoi, for which the PM2.5 data were available. There was no significant change of temperature, humidity, wind speed and number of cold surges over these winter periods. Regarding wind direction, the predominance was southeast, which accounted for half of the total frequency of WD. In addition, the contribution of northeast winds varied from 27.4 % to 34.6 %. In the case of sulfate AOD, there was still an upward trend in the winter 2016/17 during which a lower value (0.46 ± 0.27) was observed. For the last three years, the mean sulfate AOD value was pretty constant.

Classification of winter days in Hanoi

Instead of classifying days depending on the weather condition (i.e., clear days, foggy days, hazy days and cloudy days) in Chapter 3, the classification of winter days is proposed to identify the contribution of LP, RT and transboundary LRT on the air quality in Hanoi during the winter period. The classification method is illustrated in Figure 4-7 and presented as below:

(i) Wind speed (WS): This first parameter is used to distinguish between days that correspond to stagnant conditions and strong wind conditions. WS higher than the average value in Hanoi (3.1 m s -1 ) is used as a threshold to detect strong wind episodes. The remaining cases are assigned to "Stagnant conditions" days or SC.

(ii) Wind direction (WD): During strong wind days, WD is a key parameter for determining the days under monsoon's impact. As a result, the WD within 0° and 90° is used as the indicator of Northeast monsoon's days. The remaining cases are assigned to "Other conditions" or OC.

Ability of the classification to discriminate LP, RT and LRT

A winter monsoon circulation could be defined into 3 phases (Figure 4-8) as follows: The onset of cold surges was ascribed to phases 1 (red areas) which was noted by (1) a sudden decrease in daily temperature (decrease more than 4°C per day) combined with (2) a sharp increase in daily WS (≥ 3 m s -1 ). Phases 2 (grey area) referred to the periods occurring right after the phase 1, during which the high-pressure system persisted and engulfed northern Vietnam, which lasted commonly from 5 to 8 days. At the end of phase 2, there were two possible scenarios: (i) a new cold surge was observed, resulting in a new circulation of phases 1 and 2 repeating, or (ii) southeast trade winds from the ocean arrived, bringing warmer and more humid air. The second (ii) scenario, noted as phases 3 (blue areas), associated with an occurrence of trade wind which brings hot and humid wind from the ocean to northern Vietnam. As shown in Figure 4-8, phase 3 occurred more frequently and intensively during the last three months during the winters (January to March). Table 4-2 displays the key aspects of meteorology, sulfate AOD, and ground-based PM2.5 during each phase, computed over the whole 4 winters. Due to the onset of the cold intrusions, phases 1 were the coldest with an average temperature that was generally below the monthly average temperature (-2.16 ± 3.03°C). WS values above the monthly average were observed during both phase 1 and phase 3 while they were clearly lower during the stagnant phase 2. PM2.5 concentration in phase 2 (persistence of the anticyclone) was beyond the monthly mean; and phases 1 or phases 3 (trade winds episodes) showed an opposite result. Phases 3 had the lowest sulfate AOD (-0.09 ± 0.19 against ~ 0.03 for phases 1 and 2), as expected for marine air masses. Phase 1 might seem shorter but it still represented 21 % of the winter days and occupied 34 days (in average) during a winter. Phase 2 was dominant and represented 55 % of the winter days, it lasted 87 stagnant days (in average). Phase 3 accounted for 24 % of the days and occupied 38 days (in average) during the winter period. Regarding short-term variability, the results in this study seem to be incompatible to the previous study of [START_REF] Hien | Air pollution episodes associated with East Asian winter monsoons[END_REF]. However, the difference comes from the definition of cold surges: [START_REF] Hien | Air pollution episodes associated with East Asian winter monsoons[END_REF] defined cold surges only as the onset of cold intrusions (i.e., phase 1 only) while in this study, the term "cold surge" gathers the onset and prolongation of the cold intrusions (phase 1 and phase 2). Generally, the results from both studies indicate a similar behavior in which the ground-based PM2.5 concentration drops significantly with the arrival of a cold intrusion, then reaches its maximum level in the middle of the Northeast monsoon circulation. This drop of surface PM2.5 is mainly attributed to the dispersion of LP dominated by the higher wind, accompanying the cold surge onset, though it is difficult to precisely quantify the rate of dilution. As mentioned above, the onset of cold surges was ascribed to phase 1 (i.e., 19 th and 26 th December; 11 th , 16 th and 26 th January; 04 th February), as noted by black triangles. This phase lasted generally between 2 and 4 days where a rapid decrease in daily temperature was recorded (Figure 4-9.a) and WS simultaneously increased significantly . Notably, these changes occurred mainly during the first two days of phase 1. Moreover, the dispersion and transport of local pollution were greatly enhanced due to the strong WS, resulting in a daily low-level of PM2.5 (< 50 μg m - 3 ) throughout phases 1 (Figure 4-9.g). The sulfate AOD witnessed a general increase upon the cold air arrival during the four winter periods, possibly due to RT from the RRD plain as well as transboundary LRT from mainland China in elevated aerosol layers that did not impact ground-level concentrations. Therefore, there was no major correlation between sulfate AOD and ground-based PM2.5 during phases 1. The surface air pressure tended to rise in phase 1 due to the effect of the anticyclone, then it started to decrease in phase 2, as shown in As shown in both Figure 4-8 and Figure 4-9, the NEHA (red bars) and NELA (blue bars) days were mostly seen in phase 1 as they correspond to strong wind conditions with NE wind direction, which is typical for the cold surge onset. On these days, LP was substantially diluted while RT and transboundary LRT played a direct role in the formation of PM2.5. Figure 4-10 presents the average PM2.5 concentration for the different types of days and the four winters. In spite of the inter-annual variability in the overall level of PM2.5, the relative variations of concentrations between the different types of days exhibited the same behavior during the whole 4 winters. The lower concentrations observed in all types of day in the winter 2018/19 can be explained by the El Niño phenomenon that occurred during this winter. According to a study of [START_REF] Wang | Interannual variability of the South China Sea associated with El Niño[END_REF], the peak in the inter-annual sea surface temperature anomalies across the South China Sea occurs during the month of February following the El Niño onset, leading to the anomalous decent motion and weakening of the Northeast monsoon over the South China Sea. As shown in Figure 4-8.c, a long period of phase 3 with the prevailing southeast wind was observed in February 2019 as a consequence of the anomaly of El Niño onset that started in August 2018. Furthermore, only one weak cold surge was reported for this month. Moreover, not a single day in February during the winter 2018/19 had PM2.5 concentrations surpassed the national regulatory threshold (Figure 4-8.c); as a result, the PM2.5 levels were generally lower during this winter period than during the other three winter periods (Figure 4-10).

In addition, for the four winter periods, the average PM2.5 concentration was higher during the high-AOD days (NEHA) by about 9 to 17 μg m -3 (30 % to 35 %) compared to the low-AOD days (NELA) . This indicates that the transported pollutants can remain at high altitude due to the mixing down of these pollutants to the ground is limited, at least for some time, leading to higher AOD value on NEHA days. In contrast, the ground-based PM2.5 concentration was attenuated due to higher wind conditions. Therefore, even though AOD is difficult to relate to ground-level concentrations, it can be used as a proxy to distinguish between air masses impacted or not by RT as well as transboundary LRT during the cold surges. In addition, the difference of PM2.5 concentration between NEHA and NELA days can be considered to represent the contribution of transboundary LRT from mainland China. This result is very close to the 27 % of transboundary LRT contribution that Hai and Kim Oanh, (2013) found using elemental analysis on aerosol filter sampling. Indeed, when the monsoon air mass did not actually catch pollution from China, which resulted in a lower sulfate AOD value, the observed PM2.5 concentration represented only the LP and RT contributions. Conversely, when the monsoon air mass did travel from the mainland China to Hanoi, the PM2.5 concentration cumulated the LP and RT, plus the transboundary LRT contribution, which also resulted in a higher sulfate AOD value. Following this reasoning, transboundary LRT from mainland China representing a significant, but minor part of the PM2.5 level, was observed in Hanoi under the Northeast monsoon impact. Our analysis is that, as the higher winds associated with the cold surge onset disperse the local emissions, the RT is possibly the dominant cause of air pollution in Hanoi under the impact of the Northeast monsoon. Besides that, the SC days, which correspond to stagnant conditions, occurred mainly during phase 2 (Figure 4-9.g), after the onset of cold surges. During these days, the impact of the Northeast monsoon decreased and, as less pollutants were imported from mainland China, the formation of PM2.5 was a complex mix between local emissions and transported precursors from closer areas like the RRD. Indeed, pollutants from south China and the RRD could still be brought to Hanoi via trajectories belonging to cluster C2. The winter means of PM2.5 concentration (55.9 to 74.6 μg m -3 ) were significantly higher during SC days compared to all the other types of days . In particular, the difference of average PM2.5 concentrations between SC and NEHA days was approximately 24 -29 μg m -3 or about 33 % to 46 %, which can be interpreted as the contribution of LP. This result is lower than the range of values obtained by [START_REF] Nguyen | Air Quality Modeling Study on the Controlling Factors of Fine Particulate Matter (PM2.5) in Hanoi: A Case Study in December 2010[END_REF] using the Weather Research and Forecasting/Community Multiscale Air Quality Modeling (WRF/CMAQ) model (42 % to 57 % of LP contribution) but their study concerned only December 2010, during which a very strong El Niño episode was ongoing. Consequently, there was likely a lesser contribution of transboundary LRT from mainland China due to the weakening of the Northeast monsoon during this month, which in return increased the share of LP.

Conclusion

With the aim of better understanding the inter-annual and day-to-day variability of the winter particulate pollution in Hanoi, in relationship with the Northeast monsoon's influence, this study proposed a three-step approach. Firstly, the evolution of the aerosol sources in East Asia and mainland Southeast Asia was studied using the outputs from the NAAPS model, that assimilates satellite AOD observations and offers a better coverage than the actual measurements limited by the dense cloud cover in the region. The model also delivers the sulfate and smoke partial AOD, which attribute the particle plumes respectively to coal-burning industrial activities or biomass burning, the latter being either natural (forest fires) or anthropogenic (agriculture). The NAAPS -AOD map series were clearly split into two phases: from the winter 2006/07 to the winter 2010/11, the area with the highest AOD was the IP region, and this was mainly due to BB activities. However, from the winter period 2011/12 to the winter 2019/20 period, the AOD experienced a very sharp increase in eastern China and northern Vietnam (the RRD plain) following the rapid economic development of the area, though the AOD over Hanoi has stabilized during the last three winters of the study (from 2017/18 to 2019/20). Therefore, there was a switch in the dominant sources of particles in Hanoi, from BB until 2011 to industrial activities afterwards.

In the second step, this study aims to explain the large year-to-year variability of the AOD time-series over Hanoi, and to relate it with the annual variations of the Northeast monsoon activity. HYSPLIT back-trajectories were then computed and clustered over the 14 winter periods. During El Niño events (winters 2014/15, 2015/16 and 2018/19), the share of northeastern clusters, that correspond to the monsoon air masses, decreased while western clusters happened more frequently. This was associated with a temporary decrease in the sulfate partial AOD, and to a lesser extent, with an increase of the smoke partial AOD. Following the sources identified in the first part of the study, this result was expected as northeastern air masses transiting over mainland China are more susceptible to carry industrial pollution over Hanoi; conversely, western air masses from the IP region and northern India are more likely to bring BB aerosols over Hanoi.

The third step of the study was to relate the day-to-day variability of the PM2.5 time-series in Hanoi with the monsoon cycles and the possible import of the pollution from mainland China (transboundary long-range transport/LRT) or from the RRD plain (regional transport/RT). The monsoon cycle is divided into three phases: (1) the cold surge onset (the arrival of an anticyclonic northeastern air mass), ( 2) the cold surge persistence with stagnant conditions and (3) the inter-surge period (trade winds from the South China Sea). Using the wind speed and direction, all days from the last four winters (2016/17 to 2019/20) were ascribed to one of the three phases. At this stage, the HYSPLIT clusters were not relevant anymore to identify cases of transboundary LRT or RT. Indeed, an individual back-trajectory belonging to a northeastern cluster may or may not exactly overpass upwind pollution sources. The AOD from NAAPS model was therefore preferred to identify the days with or without transboundary LRT or RT and a classification of the winter days combining the wind speed and direction with the model AOD was developed and applied to the last four winters. By comparing the cold surge onset days with high and low AOD values, without or with transboundary LRT from mainland China, transboundary LRT was estimated to cause an average to increase by around 30 % in PM2.5 level in Hanoi (plus 9 -17 µg.m -3 ). The difference in the average PM2.5 concentrations between cold surge days with transboundary LRT and stagnant days can be attributed to the local pollution contribution and was estimated to be around 40 % (plus 24 -29 µg m -3 ). Transboundary LRT from mainland China is therefore not a major contributor to Hanoi's air pollution.

In the future, the source apportionment could be improved by using observations from an Aerosol Chemical Speciation Monitor bringing information on PM1 chemical composition. As the monsoon cycle phases are associated with different boundary-layer depth values, it would also be interesting to distinguish the effects of pollution advection and vertical dilution, which could be done using Lidar or ceilometer boundary-layer depth observations.

General Conclusions and Perspectives

The current manuscript presents three years of work in order to identify the crucial factors that contributed to the severe pollution events in Hanoi during the winter. The main objective of this dissertation is to investigate the impact of atmospheric dynamics processes and the contribution of remote sources to air pollution events. In this work, the short-term datasets of a field campaign have been used in combination with long-term datasets from numerical models for aerosol properties (NAAPS) and for air mass origin (HYSPLIT).

The first part of the study presents the results from the VASP campaign (from January to March, 2019), including the combination of Lidar observations to study (1) the structure of the ABL and ( 2) the vertical mixing, as well as (3) the analysis of aerosol composition, together with (4) HYSPLIT backward trajectories aiming at determining the actual sources that contributed to the severe pollution events observed during the campaign. Specifically, the analysis of dynamic processes focused on the structure of the ABL and vertical mixing, that was analyzed for the first time in Hanoi using Lidar measurement (aerosol Lidar and Doppler Lidar). During the whole campaign, these instruments proved to be able to monitor correctly in the real-time the ABL development and the vertical mixing. Concerning the ABL structure, we observed that the estimated values of daytime BLH changed significantly according to the different weather conditions (cloudy, foggy, hazy and clear-sky). During cloudy days, which prevailed throughout the campaign, the daily maximum of the hourly BLH varied from 700 m to 1200 m, whereas it was frequently lower than 1000 m during hazy days. Under the influence of haze, the BLH was compressed and expanded slowly within just a few hundred meters, reinforcing the accumulation of pollutants at the surface, leading to severe degradations of the air quality in Hanoi during hazy days.

Poor air quality in Hanoi possibly derived from local emission (LP), regional transport (RT) from the RRD plain and transboundary long-range transport (LRT) from mainland China. During the VASP campaign, there were four significant pollution episodes that were mostly related to stagnant weather conditions. According to the HYSPLIT back-trajectories' clusters, the first three pollution episodes were associated with transboundary LRT from mainland China and RT from the RRD region. The fourth pollution event resulted from a combination of transboundary LRT sources, including industrial emissions from mainland China and biomass burning from the IP region. In addition, the chemical composition of the aerosols remained pretty constant over the 10-week of sampling, though HYSPLIT clusters indicated that some periods were associated with RT and transboundary LRT while other periods had only local emissions. This simply demonstrates that the chemical composition of local pollution was mostly comparable to the one of pollution coming from RT and transboundary LRT.

In order to assess the representativeness of the VASP campaign results, the second part of this work consisted in a long-term analysis covering the whole winter period (October to March) over 14 years, from winter 2006/07 to winter 2019/20. This study combined a long-term dataset of AOD from the NAAPS model with back-trajectory clustering using the HYSPLIT model. The AOD and its components (sulfate, smoke and dust partial AODs) retrieved from the NAAPS model were used to study the evolution of the aerosol sources in the East Asia and Southeast Asia regions. During the first 5 winters (2006/07 to 2010/11), the highest AOD values were observed over the Indochina Peninsula region, and this was mainly due to anthropogenic and natural biomass burning activities. For the rest of the studied period, the AOD map series indicated a very sharp increase in eastern China and northern Vietnam (the RRD plain) following the rapid economic development of the area. Although this increasing trend broke off during the last three winters of the study (2017/18 to 2019/20), the AOD in Hanoi remained high. As a result, the major source of particles in Hanoi shifted from biomass burning until 2011, to industrial activities for the rest of the studied period.

In addition, a cluster analysis using the HYSPLIT model was carried out for the same period in order to assess the inter-annual variations of the Northeast monsoon activity, and their impact on the large year-to-year variability of the AOD time-series over Hanoi. During El Niño events (i.e., winters 2014/15, 2015/16 and 2018/19), the dominant back-trajectory clusters shifted from the northeasterly to the westerly direction. Simultaneously, the change in air mass origin resulted in a sudden drop of the sulfate partial AOD and a simultaneous increase of the smoke partial AOD, though to a lesser extent. Usually, the NE monsoon brings air masses which pass over polluted area in mainland China and are more likely to carry industrial pollution to Hanoi. But during El-Niño years, the northeast monsoon weakens, letting in more western air masses from the IP region and northern India that are more likely to bring BB aerosols from large wildfires as well as from the burning of post-harvest agricultural residue into Hanoi.

In order to determine the contributions of the local, regional and transboundary sources of particles, an analysis of the monsoon cycles characteristics was carried out over the four last winters (2016/17 to 2019/20) for which PM2.5 concentration observations were available. The monsoon cycle was divided into three phases as follow: (1) the cold surge onset (arrival of an anticyclonic northeastern air mass), ( 2) the cold surge persistence with stagnant conditions and (3) the inter-surge period (trade winds from the South China Sea). Besides this, the sulfate AOD from the NAAPS model was used as a tracer to identify the days with or without impact of transboundary LRT or RT. A classification of the winter days was developed, combining meteorological features (wind speed and direction) with NAAPS sulfate AOD, and this classification was applied to the above-mentioned four winters. The difference in the average level of PM2.5 between the different types of days allowed to estimate the contribution of transboundary LRT from mainland China to be around 30 %. Transboundary LRT from mainland China is thereby not a major contributor to Hanoi's air pollution; instead, the RT is possibly the dominant culprit of the air pollution in Hanoi under the impact of the Northeast monsoon. During the day observed with stagnant conditions, the estimated fraction of LP can be around 40 % while the contribution of transboundary LRT is considered to a lesser extent throughout these days. RT originating mainly from the RRD, therefore, is also found to contribute a large part to the fine particle pollution in Hanoi in this case.

As we have seen in the whole dissertation, the numerous findings from this dissertation shed light on how atmospheric dynamics processes affect air pollution events and how many sources contribute to pollution events in Hanoi during the winter. However, a number of issues need more research in subsequent studies.

First, there is the question of whether the BLH values observed during the VASP campaign are representative, especially as winter 2018/19 was influenced by an El Niño event. Subsequently, this raises the question of how the changes in the dominant air masses possibly affects the BLH and vertical mixing. It would also be interesting to investigate the other seasons in Hanoi, as the BLH is expected to be higher during the summer. In order to bring answers to these questions, it has been agreed to organize a new field campaign with lidar observations over a long period. However, the limitations encountered with the SLIM Lidar device during the VASP campaign will need to be addressed beforehand.

Furthermore, we have shown that the chemical composition of aerosols in Hanoi was pretty constant over the 10-week of sampling, which included both periods associated with transboundary LRT aerosols and other periods with only local emissions. At this stage, it was not possible to identify specific tracers for the different aerosol sources. In order to answer this question, the time resolution of the aerosol sampling should be reduced to daily instead of weekly during a future campaign. In addition, the source apportionment might be enhanced by utilizing observations from an Aerosol Chemical Speciation Monitor (ACSM), which provides additional information on the chemical composition of PM2.5.

Finally, the NAAPS Model underestimated the AOD over Southeast Asia region, however, it can display the trend of air pollutants over Northern Vietnam, especially the shifts of component of pollutants for the period 2006 -2020. In the future research, the scope of regional research will be prioritized in order to focus on assessing sources coming from within Northern Vietnam. The limited resolution of NAAPS model will be a concern to do the above objective. Regarding modelling, the limited resolution it would be possible to perform finer scale back-trajectories to better identify the regional particle sources. This could be done using weather data from high-resolution simulations by the Weather Research and Forecasting (WRF) model. In addition, although the classification of the winter days needs to be improved, it can continue play a role in the identification of the actual pollution sources for the pollution events over Hanoi in the future study. The classification of the winter days might be improved by incorporating more elaborated indicators than the local wind, such as Lamb weather types determined from weather reanalyzes.

Annex A: Supplementary material for AQI calculation in Vietnam

In Vietnam, a same level AQI scale defined by the U.S. Environmental Protection Agency standard is specified. On a 5-level scale for health implications, the composite AQI scale was evaluated on Table A

Source: AirNow.gov

The definition of the single-pollutant AQIs specified by the Vietnamese General Department of Environment (noted VN_AQIx with x the pollutant considered) is different from the U.S. EPA definition. For the gaseous pollutants, the hourly VN_AQIx values (x = SO2, CO,

Figure 1 - 1 .Figure 1 - 2 .Figure 1 - 3 .Figure 1 - 4 .

 11121314 Figure 1-1. The geographical map of (a) Vietnam and neighboring nations showing (b) the locations of northern Vietnam and Red River Delta and (c) the industrial zones and thermal power plants over Hanoi and its surrounding areas in the Red River Delta. ......................... 32 Figure 1-2. National GDP of Vietnam, regional GDP Vietnam for the different regions of Vietnam (bars, left axis) and share of the RRD in the national GDP (curve, right axis) during the period of 2015 -2020 ......................................................................................................... 34 Figure 1-3. Monthly average temperature and percentage of the sky covered by clouds over Hanoi throughout a year. Meteorological data are averaged for 10-year data from 2010 -2020. Cloudier part is defined as the percentage of sky covered by clouds is higher 60 % and the clearer part is defined as the percentage of sky covered by clouds is below 60 %. .......... 36 Figure 1-4. Monthly average relative humidity and rainfall over Hanoi throughout the year. Meteorological data are averaged for 10-year data from 2010 -2020. ................................. 37 Figure 1-5. The circulation of the East Asian winter monsoon derives from a high-pressure system over Siberia during the winter period. Black dashed arrows indicate the pathway of cold-dry polar air toward the Siberia region; black solid arrows indicate wind direction of the winter monsoon. Red and purple arrow indicate the main direction of the East Asian winter monsoon pathway. Global topographic map (lower left) shows interaction between pressure cells. ......................................................................................................................................... 38 Figure 1-6. Map of average 925hPa wind speed at (a) dry phase (October -December), (b) humid phase (January -March), during the winter period 1979 -2004 ................................. 39 Figure 1-7. Schematic diagram of the diurnal cycle of the atmospheric boundary-layer under fair-weather and cloud-free conditions .................................................................................. 41 Figure 1-8. Schematic of the urban boundary-layer including its vertical layers and different scales composed of (a) mesoscale, (b) local scale and (c) microscale. ................................... 42 Figure 1-9. Sea surface temperature, surface pressure and wind during (a) El Niño and (b) La Niña episode............................................................................................................................. 45 Figure 1-10. The geo-potential height anomalies between Dec. 2015 and Dec. 2014 in eastern Asia (i.e., the monthly mean values in 2015 minus the monthly mean values in 2014) at 500 hPa. Geo-potential height approximates the actual height of a pressure surface above mean sea-level. Anomalies of geopotential height consist of deviations in the geo-potential height compared to the reference height. ........................................................................................... 46 Figure 1-11. Representation of aerosol size distribution and the main formation and removal processes that influence aerosols composition and concentration in the atmosphere ............ 48 Figure 1-12. Local pollution sources within Hanoi including transportation, industrial production facilities, craft villages, construction operations and civil activities .................... 50 Figure 1-13. Regional sources emit emission (industrial production, thermal power plant and biomass burning) into the atmosphere from suburbs and countryside over RRD plain. ......... 51 Figure 1-14. Location of major coal fired power plants (dark circle) over East Asia (China) and Indochina regions. The circle size represents the capacity of thermal power plants. ...... 54

Figure 2 - 1 .Figure 2 - 2 .Figure 2 - 3 .

 212223 Figure 1-1. The geographical map of (a) Vietnam and neighboring nations showing (b) the locations of northern Vietnam and Red River Delta and (c) the industrial zones and thermal power plants over Hanoi and its surrounding areas in the Red River Delta. ......................... 32 Figure 1-2. National GDP of Vietnam, regional GDP Vietnam for the different regions of Vietnam (bars, left axis) and share of the RRD in the national GDP (curve, right axis) during the period of 2015 -2020 ......................................................................................................... 34 Figure 1-3. Monthly average temperature and percentage of the sky covered by clouds over Hanoi throughout a year. Meteorological data are averaged for 10-year data from 2010 -2020. Cloudier part is defined as the percentage of sky covered by clouds is higher 60 % and the clearer part is defined as the percentage of sky covered by clouds is below 60 %. .......... 36 Figure 1-4. Monthly average relative humidity and rainfall over Hanoi throughout the year. Meteorological data are averaged for 10-year data from 2010 -2020. ................................. 37 Figure 1-5. The circulation of the East Asian winter monsoon derives from a high-pressure system over Siberia during the winter period. Black dashed arrows indicate the pathway of cold-dry polar air toward the Siberia region; black solid arrows indicate wind direction of the winter monsoon. Red and purple arrow indicate the main direction of the East Asian winter monsoon pathway. Global topographic map (lower left) shows interaction between pressure cells. ......................................................................................................................................... 38 Figure 1-6. Map of average 925hPa wind speed at (a) dry phase (October -December), (b) humid phase (January -March), during the winter period 1979 -2004 ................................. 39 Figure 1-7. Schematic diagram of the diurnal cycle of the atmospheric boundary-layer under fair-weather and cloud-free conditions .................................................................................. 41 Figure 1-8. Schematic of the urban boundary-layer including its vertical layers and different scales composed of (a) mesoscale, (b) local scale and (c) microscale. ................................... 42 Figure 1-9. Sea surface temperature, surface pressure and wind during (a) El Niño and (b) La Niña episode............................................................................................................................. 45 Figure 1-10. The geo-potential height anomalies between Dec. 2015 and Dec. 2014 in eastern Asia (i.e., the monthly mean values in 2015 minus the monthly mean values in 2014) at 500 hPa. Geo-potential height approximates the actual height of a pressure surface above mean sea-level. Anomalies of geopotential height consist of deviations in the geo-potential height compared to the reference height. ........................................................................................... 46 Figure 1-11. Representation of aerosol size distribution and the main formation and removal processes that influence aerosols composition and concentration in the atmosphere ............ 48 Figure 1-12. Local pollution sources within Hanoi including transportation, industrial production facilities, craft villages, construction operations and civil activities .................... 50 Figure 1-13. Regional sources emit emission (industrial production, thermal power plant and biomass burning) into the atmosphere from suburbs and countryside over RRD plain. ......... 51 Figure 1-14. Location of major coal fired power plants (dark circle) over East Asia (China) and Indochina regions. The circle size represents the capacity of thermal power plants. ...... 54

Figure 2 - 4 .Figure 2 - 6 .

 2426 Figure 2-4. Example of determination of a Northeast monsoon occurrence on 26 th December 2018. The cold surges are marked with black triangles on the horizontal axes. Dashed circles represent the characteristic change of meteorological parameters (e.g., temperature, wind speed and wind direction). ....................................................................................................... 66 Figure 2-5. Photos of aerosol micro-pulse Lidar system installed at the OS1 site during the VASP campaign. ...................................................................................................................... 67 Figure 2-6. Time-height cross-section of the range-corrected signal (RCS), here noted Pr 2 , recorded by the SLIM Lidar over Hanoi on February 5 th 2019. Here and in the following figures, the RCS is noted Pr 2 , which is another usual notation. The units are still arbitrary as the instrumental constant has not yet been removed. .............................................................. 69 Figure 2-7. RCS of the SLIM Lidar averaged over 7.5 hours on January 22 nd 2019 during daytime (07:00 -14:30 LT). The black line is the original RCS and the red line is the RCS after smoothing using a Gaussian filter. The blue line is the linear fit of filtered RCS in the altitude zone limited by the dotted vertical lines. .................................................................................. 70 Figure 2-8. Overlap function computed from the RCS and fit function of Figure 2-7 (red line). The uncertainty on the overlap (shaded area) is computed from the standard deviation of the RCS during the averaging period using a Monte-Carlo method ............................................. 71 Figure 2-9. (a) The non-normalized ROCS profile and (b) the first gradient method, (c) logarithmic gradient method and (d) cubic root gradient method for a 1-hour average profile recorded in Hanoi on February 08 th 2019 (13:00 LT). The horizontal lines present the BLH retrieved by the different gradient methods. ............................................................................ 72 Figure 2-10. The ROCS profile and its first derivative during the complex cases with thick clouds (at 19:00 LT in February 05 th 2019). Grey line represents the estimation of BLH. .... 73 Figure 2-11. Photos of aerosol sampler at the OS1 site during the VASP campaign. The system includes a filter holder (2 m above the roof), a sampling pump and a volumetric counter. ... 74 Figure 2-12. The ROCS profile (a) and the retrieval BLH from different gradient methods (b, c and d) and radiosonde method (e) recorded in Hanoi at 07:00 LT on 24 th January, 2019. . 76 Figure 2-13. (a) An example of backward trajectories during the winter period 2018 -2019 and (b) the change in total spatial variance (TSV) for different cluster numbers. The red dashed circle presents a jump in TSV from 6 to 5 clusters (6 clusters would be an appropriate selection in this case). ............................................................................................................................. 80 Figure2-14. Regional plots of AOD forecasts (6-hour average) at a wavelength of 550 nm for the three components (sulfate, dust, and smoke) over East Asia and Southeast Asia at 00:00 UTC on 25th January, 2019...................................................................................................... 82

Figure 3 - 1 .Figure 3 - 2 .Figure 3 - 3 .Figure 3 - 4 .

 31323334 Figure 3-1. Condensation affects operation rate of SLIM Lidar during the VASP campaign and an example for the time-height cross-section of the range-corrected signal affected by condensation problems. ........................................................................................................... 85 Figure 3-2. Bivariate polar plots of PM2.5 concentrations at the OS1 site for each month during the VASP campaign. The radial and tangential axes represent the wind direction and speed in m s -1 at 100 m a.g.l. .................................................................................................................. 87 Figure 3-3. The daily average of (a) air temperature (T) and humidity (RH), (b) Solar radiation (Q) and the daily maximum of the hourly BLH values (max BLH), (c) wind speeds (WS) at 100 m and 280 m a.g.l. and ground-based, (d) wind direction (WD) at 100 m and 280 m a.g.l., (e) precipitation and visibility, (f) PM2.5 concentrations. Black triangles on the horizontal axis mark the cold surges. P1 -P4 represent polluted events as grey areas. ................................... 89 Figure 3-4. Relationship between relative humidity and sulfate to nitrate ratio for the 10 weekly TSP samples (From S1 to S10) collected during the VASP campaign. ......................... 90

Figure 3 - 5 .Figure 4 - 1 .

 3541 Figure 3-5. Synoptic weather charts at 850-hPa level generated by the National Center for Hydro-Meteorological Forecasting at 00:00 UTC on a) 19 th January, b) 24 th January and c) 28 th February. .......................................................................................................................... 92 Figure 3-6. Cluster of HYSPLIT 72-hour back-trajectories arriving in Hanoi at heights of (a) 100 m a.g.l., (b) 600 m a.g.l., (c) 1000 m a.g.l. and (d) 1500 m a.g.l. from 11 th January to 21 st March, 2019. The statistical summary of frequency (in brackets) and PM2.5 concentration (mean ± one standard deviation, in red writing) as a function of clusters of air masses arriving in Hanoi ending at 100 m a.g.l is presented in 3-6a. ............................................... 94 Figure 3-7. Show burned areas which were detected by MODIS Terra and Aqua from January to March, 2019 over the Indochina Peninsula region. ............................................................ 95 Figure 3-8. a) Relative chemical composition of the Total Suspended Particles (TSP) associated to the weekly samples collected at the OS1 site, along with the corresponding PM2.5 concentration; b) relative contribution of the HYSPLIT clusters for back-trajectories ending at 100 m a.g.l. in the ten weekly samples. Red rectangles represent the sample week S2, S3 and S9. Blue rectangles represent the sample week S6 and S8. ..................................................... 97 Figure 3-9 (a) and (b). Hourly PM2.5 concentrations colored as a function of the HYSPLIT cluster attribution for back-trajectories arriving at height of 100m and 600m a.g.l, respectively. Threshold (orange line) based on the specified Vietnam's standard for ambient air quality in 24-h (≥ 50 μg m -3 ). S1 -S10 represent the periods of time of the 10 weekly filter samples ..................................................................................................................................... 98 Figure 3-10 (a) and (b). Regional plots of AOD results (6-hour average) at a wavelength of 550 nm for three components: sulfate (red), dust (green), and smoke (blue) over East Asia (mainland China) and Southeast Asia (Indochina Peninsula region) from 18 th to 20 th January, 2019 and 24 th to 27 th January, 2019. ..................................................................................... 100 Figure 3-10 (c) and (d). Regional plots of AOD results (6-hour average) at a wavelength of 550 nm for three components: sulfate (red), dust (green), and smoke (blue) over East Asia (mainland China) and Southeast Asia (Indochina Peninsula region) from 26 th to 28 th February, 2019 and 11 th to 14 th March, 2019. .......................................................................................... 90 Figure 3-11. Time-height cross-sections of SLIM Lidar range-corrected signal (noted Pr²) and time series of boundary-layer height (BLH) on (a) 19 th January, (b) 24 th January, (c) 28 th February and (d) 14 th March. The BLH retrieved from SLIM (respectively radiosondes) profiles is represented as red circles (respectively black triangle). ...................................... 103 Figure 3-12. Hourly time series of BLH from the SLIM Lidar and PM2.5 concentrations on (a) 19 th January, (b) 24 th January, (c), 28 th February and (d) 14 th March. ................................. 104 Figure 3-13 (a) and (b). Time series of PM2.5 concentration and ventilation coefficient (upper) and time-height cross-sections of horizontal wind speeds (middle) and vertical mixing (below) measured by the Doppler Lidar on 19 th January and 24 th January, respectively. ................. 105 Figure 3-13 (a) and (b). Time series of PM2.5 concentration and ventilation coefficient (upper) and time-height cross-sections of horizontal wind speeds (middle) and vertical mixing (below) measured by the Doppler Lidar on 28 th February and 14 th March, respectively. .................. 105 Figure 3-14. Wind profiles measured by radiosondes on (a) 19 th January, (b) 24 th January, (c) 28 th February and (d) 14 th March at 07:00 and 19:00 LT. Grey line represents the BLH. ... 108

Figure 4

 4 Figure 4-2 (a), (b), (c), (d), (e) and (f). Time-averaged total AOD distribution during the winter periods from 2006/07 to 2011/12 over Red River Delta, northern Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina Peninsula region -green square and Cambodia -cyan square). ................................................................................... 117 Figure 4-2 (g), (h), (i), (j), (k) and (l). Time-averaged total AOD distribution during the winter periods from 2012/13 to 2017/18 over Red River Delta, northern Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina Peninsula region -green square and Cambodia -cyan square). ................................................................................... 118 Figure 4-2 (m) and (n). Time-averaged total AOD distribution from October to March during the winter periods from 2018/19 to 2019/20 over Red River Delta, Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina region -green square and Cambodia -cyan square) ....................................................................................................... 119 Figure 4-3 (a), (b), (c), (d), (e) and (f). Time-averaged smoke AOD distribution during the winter periods from 2006/07 to 2011/12 over Red River Delta, northern Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina Peninsula regiongreen square and Cambodia -cyan square). ......................................................................... 120 Figure 4-3 (g), (h), (i), (j), (k) and (l). Time-averaged smoke AOD distribution during the winter periods from 2012/13 to 2017/18 over Red River Delta, northern Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina Peninsula regiongreen square and Cambodia -cyan square). ......................................................................... 121 Figure 4-3 (m) and (n). Time-averaged smoke AOD distribution from October to March during the winter periods from 2018/19 to 2019/20 over Red River Delta, Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina region -green square and Cambodia -cyan square) ....................................................................................................... 122 Figure 4-4 (a), (b), (c), (d), (e) and (f). Time-averaged sulfate AOD distribution during the winter periods from 2006/07 to 2011/12 over Red River Delta, northern Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina Peninsula regiongreen square and Cambodia -cyan square). ......................................................................... 123 Figure 4-4 (g), (h), (i), (j), (k) and (l). Time-averaged sulfate AOD distribution during the winter periods from 2012/13 to 2017/18 over Red River Delta, northern Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina Peninsula regiongreen square and Cambodiacyan square). ........................................................................ 124 Figure 4-4 (m) and (n). Time-averaged sulfate AOD distribution from October to March during the winter periods from 2018/19 to 2019/20 over Red River Delta, Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina region -green square and Cambodia -cyan square) .................................................................................... 125 Figure 4-5 (a). Mean 96-h backward air mass trajectories ending at Hanoi resulting from the clustering from October to March during the winter period of 2006 -2020 and (b). mean altitudes for each of these mean trajectories ......................................................................... 126 Figure 4-6. Frequency of occurrences for each cluster and variation of AOD over Hanoi during 14 winter seasons from period 2006/07 to period 2019/20. ...................................... 128 Figure 4-7. The schematic diagram of the classification of winter days. The winter days include "Stagnant conditions" -SC, "Northeast high AOD" -NEHA, "Northeast low AOD" -NELA and "Other conditions" -OC. ................................................................................................ 131 Figure 4-8.(a)The daily average of temperature (Temp), pressure, relative humidity (RH), wind speed (WS), wind direction (WD), visibility, PM2.5 concentration and Sulfate AOD over the winter period 2016/17. Black triangles on the horizontal axis mark the cold surges. The winter days include "Stagnant conditions" -SC, "Northeast high AOD" -NEHA, "Northeast low AOD" -NELA and "Other conditions" -OC. Phase 1, phase 2 and phase 3 were marked as red, grey and blueareas, respectively..................................................................................... 134

Figure 4 -

 4 Figure 4-2 (a), (b), (c), (d), (e) and (f). Time-averaged total AOD distribution during the winter periods from 2006/07 to 2011/12 over Red River Delta, northern Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina Peninsula region -green square and Cambodia -cyan square). ................................................................................... 117 Figure 4-2 (g), (h), (i), (j), (k) and (l). Time-averaged total AOD distribution during the winter periods from 2012/13 to 2017/18 over Red River Delta, northern Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina Peninsula region -green square and Cambodia -cyan square). ................................................................................... 118 Figure 4-2 (m) and (n). Time-averaged total AOD distribution from October to March during the winter periods from 2018/19 to 2019/20 over Red River Delta, Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina region -green square and Cambodia -cyan square) ....................................................................................................... 119 Figure 4-3 (a), (b), (c), (d), (e) and (f). Time-averaged smoke AOD distribution during the winter periods from 2006/07 to 2011/12 over Red River Delta, northern Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina Peninsula regiongreen square and Cambodia -cyan square). ......................................................................... 120 Figure 4-3 (g), (h), (i), (j), (k) and (l). Time-averaged smoke AOD distribution during the winter periods from 2012/13 to 2017/18 over Red River Delta, northern Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina Peninsula regiongreen square and Cambodia -cyan square). ......................................................................... 121 Figure 4-3 (m) and (n). Time-averaged smoke AOD distribution from October to March during the winter periods from 2018/19 to 2019/20 over Red River Delta, Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina region -green square and Cambodia -cyan square) ....................................................................................................... 122 Figure 4-4 (a), (b), (c), (d), (e) and (f). Time-averaged sulfate AOD distribution during the winter periods from 2006/07 to 2011/12 over Red River Delta, northern Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina Peninsula regiongreen square and Cambodia -cyan square). ......................................................................... 123 Figure 4-4 (g), (h), (i), (j), (k) and (l). Time-averaged sulfate AOD distribution during the winter periods from 2012/13 to 2017/18 over Red River Delta, northern Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina Peninsula regiongreen square and Cambodiacyan square). ........................................................................ 124 Figure 4-4 (m) and (n). Time-averaged sulfate AOD distribution from October to March during the winter periods from 2018/19 to 2019/20 over Red River Delta, Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina region -green square and Cambodia -cyan square) .................................................................................... 125 Figure 4-5 (a). Mean 96-h backward air mass trajectories ending at Hanoi resulting from the clustering from October to March during the winter period of 2006 -2020 and (b). mean altitudes for each of these mean trajectories ......................................................................... 126 Figure 4-6. Frequency of occurrences for each cluster and variation of AOD over Hanoi during 14 winter seasons from period 2006/07 to period 2019/20. ...................................... 128 Figure 4-7. The schematic diagram of the classification of winter days. The winter days include "Stagnant conditions" -SC, "Northeast high AOD" -NEHA, "Northeast low AOD" -NELA and "Other conditions" -OC. ................................................................................................ 131 Figure 4-8.(a)The daily average of temperature (Temp), pressure, relative humidity (RH), wind speed (WS), wind direction (WD), visibility, PM2.5 concentration and Sulfate AOD over the winter period 2016/17. Black triangles on the horizontal axis mark the cold surges. The winter days include "Stagnant conditions" -SC, "Northeast high AOD" -NEHA, "Northeast low AOD" -NELA and "Other conditions" -OC. Phase 1, phase 2 and phase 3 were marked as red, grey and blueareas, respectively..................................................................................... 134

Figure 4 - 9 .

 49 Figure 4-8. (b)The daily average of temperature (Temp), pressure, relative humidity (RH), wind speed (WS), wind direction (WD), visibility, PM2.5 concentration and Sulfate AOD over the winter period 2017/18. Black triangles on the horizontal axis mark the cold surges. The winter days include "Stagnant conditions" -SC, "Northeast high AOD" -NEHA, "Northeast low AOD" -NELA and "Other conditions" -OC. Phase 1, phase 2 and phase 3 were marked as red, grey and blueareas, The daily average of temperature (Temp), pressure, relative humidity (RH), wind speed (WS), wind direction (WD), visibility, PM2.5 concentration and Sulfate AOD over the winter period 2018/19. Black triangles on the horizontal axis mark the cold surges. The winter days include "Stagnant conditions" -SC, "Northeast high AOD" -NEHA, "Northeast low AOD" -NELA and "Other conditions" -OC.Phase 1, phase 2 and phase 3 were marked as red, grey and blue areas, respectively..................................................................................... 134 Figure 4-8. (d)The daily average of temperature (Temp), pressure, relative humidity (RH), wind speed (WS), wind direction (WD), visibility, PM2.5 concentration and Sulfate AOD over the winter period 2019/20. Black triangles on the horizontal axis mark the cold surges. The winter days include "Stagnant conditions" -SC, "Northeast high AOD" -NEHA, "Northeast low AOD" -NELA and "Other conditions" -OC. Phase 1, phase 2 and phase 3 were marked as red, grey and blueareas, respectively..................................................................................... 134 Figure 4-9. The daily average (a) temperature (Temp, o C), (b) pressure (Pa), (c) relative humidity (RH, %), (d) wind speed (WS, m s -1 ), (e) wind direction (WD), (f) visibility (km), (g) PM2.5 concentration (μg m -3 ) and Sulfate AOD from 19 th December 2019 to 05 th February 2020. Black triangles on the horizontal axis were marked as the cold surges. Phase 1, phase 2 and phase 3 were marked as red, grey and blue areas, respectively. ...................................... 138 Figure 4-10. The averaged PM2.5 concentration for the different types of winter days during the four winter periods (period 2016 -2017 to period 2019 -2020). Error bars represent +/-1 standard deviation of uncertainty. ...................................................................................... 140

Figure 1 - 1 .

 11 Figure 1-1. The geographical map of (a) Vietnam and neighboring nations showing (b) the locations of northern Vietnam and Red River Delta and (c) the industrial zones and thermal power plants over Hanoi and its surrounding areas in the Red River Delta.

Figure 1 - 2 .

 12 Figure 1-2. National GDP of Vietnam, regional GDP Vietnam for the different regions of Vietnam (bars, left axis) and share of the RRD in the national GDP (curve, right axis) during the period of 2015 -2020 Source: Vietnam Economic Institute, (2022)

Figure 1 - 3 .

 13 Figure 1-3. Monthly average temperature and percentage of the sky covered by clouds over Hanoi throughout a year. Meteorological data are averaged for 10-year data from 2010 -2020. Cloudier part is defined as the percentage of sky covered by clouds is higher 60 % and the clearer part is defined as the percentage of sky covered by clouds is below 60 %.

Figure 1 - 4 .

 14 Figure 1-4. Monthly average relative humidity and rainfall over Hanoi throughout the year. Meteorological data are averaged for 10-year data from 2010 -2020.

Figure 1 - 5 .Figure 1 - 6 .

 1516 Figure 1-5. The circulation of the East Asian winter monsoon derives from a highpressure system over Siberia during the winter period. Black dashed arrows indicate the pathway of cold-dry polar air toward the Siberia region; black solid arrows indicate wind direction of the winter monsoon. Red and purple arrow indicate the main direction of the East Asian winter monsoon pathway. Global topographic map (lower left) shows interaction between pressure cells.Source: modified from[START_REF] Kang | Early Holocene weakening and mid-to late Holocene strengthening of the East Asian winter monsoon[END_REF]

Figure 1 - 8 .

 18 Figure 1-8. Schematic of the urban boundary-layer including its vertical layers and different scales composed of (a) mesoscale, (b) local scale and (c) microscale.

Figure 1 - 9 .

 19 Figure 1-9. Sea surface temperature, surface pressure and wind during (a) El Niño and (b) La Niña episode. Source: https://weatherworksinc.com/news/el-nino-la-nina-summer-2020

Figure 1 -

 1 Figure1-10 shows the anomalies of geopotential height at 500 hPa in December 2015; the negative anomalies observed from northern India to the IP region and south China (green square) indicate the presence of a low-pressure system, resulting in a stronger cyclonic circulation near the Bay of Bengal. As a result, more warm and moist air was transported to

Figure 1 -

 1 Figure 1-10. The geo-potential height anomalies between Dec. 2015 and Dec. 2014 in eastern Asia (i.e., the monthly mean values in 2015 minus the monthly mean values in 2014) at 500 hPa. Geo-potential height approximates the actual height of a pressure surface above mean sea-level. Anomalies of geopotential height consist of deviations in the geo-potential height compared to the reference height.

  Figure 1-12. Transportation is one of the most significant contributors to air pollution, particularly in metropolitan and densely populated regions such as Hanoi. According to a report of the Ministry of Natural Resources and Environment of Vietnam, traffic activities account for around 70 % of contribution sources causing air pollution (e.g., CO, NOx, SO2, VOCs, PM10, Chapter 1. Atmospheric dynamics and aerosols in Hanoi and its surrounding area 50 PM2.5 and dust caused by soil and sand from the surface street) in Hanoi in Hanoi (MONRE,

Figure 1 -

 1 Figure 1-14. Location of major coal fired power plants (dark circle) over East Asia (China) and Indochina regions. The circle size represents the capacity of thermal power plants. Source: https://bit.ly/3lS7Ev4

  VASP campaign was organized by the Laboratory for Physico-Chemistry of the Atmosphere (LPCA), Université du Littoral Côte d'Opale in collaboration with the Institute of Environmental Technology, Vietnam Academy Science and Technology. This campaign was designed to study the impact of the boundary-layer evolution, as well as the atmospheric dynamics on PM2.5 concentrations. It was carried out from 11 th January to 22 nd March, 2019 in Hanoi. Three observation sites (i.e., OS1, OS2 and OS3) located in the urban area were used and are displayed in Figure 2-1.

Figure 2 - 1 .

 21 Figure 2-1. Map showing the three observation sites (VAST -OS1), (USE -OS2) and (VMHA -OS3) selected for the VASP campaign. Since the winter 2019/20, PM2.5 monitoring station was transferred from USE -OS2 to OS2 new location. Red dash line represents the central districts of Hanoi.

Figure 2 - 2 .

 22 Figure 2-2. Photo of the Doppler Lidar operating during the VASP campaign. Table 2-2 briefly shows the main properties of the Doppler Lidar during the VASP campaign. The Doppler Lidar was installed on the roof of a 10 m high building within the OS1 site and set to retrieve the wind every 20 m from 60 m to 280 m a.g.l.(the altitude at 40 m was not used due to the interference of nearby higher buildings). The Doppler Lidar provided the vertical profiles of the horizontal wind speed and direction, vertical wind dispersion and vertical wind speed with an accuracy of about 0.1 m s -1 in speed and 2° in direction. The average wind speed and the standard deviation were computed on 10-minute periods from the raw time series with a 5-seconds time step. The vertical wind dispersion, i.e. the standard deviation of the vertical wind speed (σw), characterizes the vertical mixing efficiency.

Figure 2 - 3 .

 23 Figure 2-3. Photo of the automated weather station operating during the VASP campaign.

Figure 2 - 4 .

 24 Figure 2-4. Example of determination of a Northeast monsoon occurrence on 26 th December 2018. The cold surges are marked with black triangles on the horizontal axes. Dashed circles represent the characteristic change of meteorological parameters (e.g., temperature, wind speed and wind direction).

Figure 2 - 5 .

 25 Figure 2-5. Photos of aerosol micro-pulse Lidar system installed at the OS1 site during the VASP campaign.

Figure 2 -

 2 Figure 2-6. Time-height cross-section of the range-corrected signal (RCS), here noted Pr 2 , recorded by the SLIM Lidar over Hanoi on February 5 th 2019. Here and in the following figures, the RCS is noted Pr 2 , which is another usual notation. The units are still arbitrary as the instrumental constant has not yet been removed.

Figure 2 - 7 .

 27 Figure 2-7. RCS of the SLIM Lidar averaged over 7.5 hours on January 22 nd 2019 during daytime (07:00 -14:30 LT). The black line is the original RCS and the red line is the RCS after smoothing using a Gaussian filter. The blue line is the linear fit of filtered RCS in the altitude zone limited by the dotted vertical lines. In the incomplete overlap zone, the difference between the fit function and the actual RCS gives the overlap function (Figure 2-8): log 𝑃(𝑧) = ℓ(𝑧) + log 𝒪(𝑧) ⇒ 𝒪(𝑧) = exp [log 𝑃(𝑧) -ℓ(𝑧)](2-7)

Figure 2

 2 Figure 2-9. (a) The non-normalized ROCS profile and (b) the first gradient method, (c) logarithmic gradient method and (d) cubic root gradient method for a 1-hour average profile recorded in Hanoi on February 08 th 2019 (13:00 LT). The horizontal lines present the BLH retrieved by the different gradient methods.

Figure 2 -

 2 Figure 2-10. The ROCS profile and its first derivative during the complex cases with thick clouds (at 19:00 LT in February 05 th 2019). Grey line represents the estimation of BLH.

Figure 2 -

 2 Figure 2-11. Photos of aerosol sampler at the OS1 site during the VASP campaign. The system includes a filter holder (2 m above the roof), a sampling pump and a volumetric counter.

Figure 2 -

 2 12 presents an example of BLH estimation from radiosonde data in comparison with the values obtained from the three gradient methods presented in Section 2.1.3.3. The first gradient method (Figure 2-12.b) generally gave the results closest to the radiosonde analysis (Figure 2-12.e), which is also the reason it was chosen.

Figure 2 -

 2 Figure 2-12. The ROCS profile (a) and the retrieval BLH from different gradient methods (b, c and d) and radiosonde method (e) recorded in Hanoi at 07:00 LT on 24 th January, 2019.

Figure 2 -

 2 Figure 2-13. (a) An example of backward trajectories during the winter period 2018 -2019 and (b) the change in total spatial variance (TSV) for different cluster numbers. The red dashed circle presents a jump in TSV from 6 to 5 clusters (6 clusters would be an appropriate selection in this case).

2. 3 . 2 . 1

 321 Presentation of the model U.S. Navy Aerosol Analysis and Prediction System (NAAPS) -the world's first operational global aerosol forecast model -is an operational aerosol transport model produced by the U.S Navy at the Naval Research Laboratory in Monterey, California. This model provides forecasts of the three-dimensional aerosol concentrations on a global scale, combining the current satellite data streams with other available global aerosol simulations. Specifically, all satellite-based aerosol retrievals are assimilated to the NAAPS model, as shown in Table

Figure 2 -

 2 Figure 2-14. Regional plots of AOD forecasts (6-hour average) at a wavelength of 550 nm for the three components (sulfate, dust, and smoke) over East Asia and Southeast Asia at 00:00 UTC on 25 th January, 2019. Source: http://www.nrlmry.navy.mil/aerosol

Chapter 3 .Figure 3 - 2

 332 Figure3-2 shows the mean PM2.5 levels as a function of the WS and WD, using wind data from the Doppler Lidar at 100 m a.g.l. as input. This height was chosen to be just above the highest buildings in the area (which have 30 to 35 floors). The WS from the northeast and southeast were predominant during the three months, which is the typical kind of wind observed during the winter in Hanoi[START_REF] Lasko | Analysis of air pollution over Hanoi, Vietnam using multi-satellite and MERRA reanalysis datasets[END_REF]. This figure also explicitly demonstrates that the highest levels of PM2.5 were mainly related to weak WS, so probably due to local emissions that are not well dispersed. These local sources seem to be mainly located to the West of the OS1 site in January, to the Northeast in February and to the East and Southeast in March. Nevertheless, a finer analysis of the atmospheric dynamics is necessary to draw reliable conclusions on the origin of this pollution.

Figure 3 - 2 .

 32 Figure 3-2. Bivariate polar plots of PM2.5 concentrations at the OS1 site for each month during the VASP campaign. The radial and tangential axes represent the wind direction and speed in m s -1 at 100 m a.g.l.

Figure 3 - 3

 33 Figure3-3 displays a time-series of the daily averages of some basic meteorological factors along with the PM2.5 concentrations during the campaign. shows the daily average temperature (T) in a range of 12°C -26°C. The weather in this period was under humid conditions with daily relative humidity always higher than 60 %. These humid conditions might facilitate a reinforcement in the level of PM2.5 through the formation of secondary aerosols by heterogeneous reactions and their hygroscopic growth in ambient air[START_REF] Marris | Fast changes in chemical composition and size distribution of fine particles during the near-field transport of industrial plumes[END_REF][START_REF] Tang | Aerosol Phase Transformation and Growth in the Atmosphere[END_REF]. This is notably demonstrated by an increase in the sulfate to nitrate ratio retrieved from our weekly particulate matter samples as a function of RH (Figure3-4). Indeed, the sulfate can be formed in aqueous phase reaction at high RH, while the nitrate is predominantly formed by gas-phase or heterogeneous photochemical reaction[START_REF] Seinfeld | Atmospheric Chemistry and Physics: From Air Pollution to Climate Change[END_REF]. In addition, shows the result of dailyaverage value of solar radiation (Q) and the daily maximum of the hourly BLH values (max BLH) for each day during the study period. We can observe a relation between these two parameters, but the correlation is very weak (R 2 = 0.44) because the weather is often cloudy during the campaign. In conclusion, solar radiation cannot be a good indicator of the maximum BLH at this period of the year and the BLH needs to be measured with a reliable method.

Figure

  Figure3-3 displays a time-series of the daily averages of some basic meteorological factors along with the PM2.5 concentrations during the campaign. shows the daily average temperature (T) in a range of 12°C -26°C. The weather in this period was under humid conditions with daily relative humidity always higher than 60 %. These humid conditions might facilitate a reinforcement in the level of PM2.5 through the formation of secondary aerosols by heterogeneous reactions and their hygroscopic growth in ambient air[START_REF] Marris | Fast changes in chemical composition and size distribution of fine particles during the near-field transport of industrial plumes[END_REF][START_REF] Tang | Aerosol Phase Transformation and Growth in the Atmosphere[END_REF]. This is notably demonstrated by an increase in the sulfate to nitrate ratio retrieved from our weekly particulate matter samples as a function of RH (Figure3-4). Indeed, the sulfate can be formed in aqueous phase reaction at high RH, while the nitrate is predominantly formed by gas-phase or heterogeneous photochemical reaction[START_REF] Seinfeld | Atmospheric Chemistry and Physics: From Air Pollution to Climate Change[END_REF]. In addition, shows the result of dailyaverage value of solar radiation (Q) and the daily maximum of the hourly BLH values (max BLH) for each day during the study period. We can observe a relation between these two parameters, but the correlation is very weak (R 2 = 0.44) because the weather is often cloudy during the campaign. In conclusion, solar radiation cannot be a good indicator of the maximum BLH at this period of the year and the BLH needs to be measured with a reliable method.

Chapter 3 .

 3 Key factors explaining severe air pollution episodes in Hanoi during 2019 winter season 88 Concerning PM2.5, the daily values varied from 9 to 129 μg m -3 (Figure 3-3.f) with an average and standard deviation of 45 ± 25 μg m -3 , which are typical values recorded in Hanoi within this period

  .

Figure 3 - 3 .

 33 Figure 3-3. The daily average of (a) air temperature (T) and humidity (RH), (b) Solar radiation (Q) and the daily maximum of the hourly BLH values (max BLH), (c) wind speeds (WS) at 100 m and 280 m a.g.l. and ground-based, (d) wind direction (WD) at 100 m and 280 m a.g.l., (e) precipitation and visibility, (f) PM2.5 concentrations. Black triangles on the horizontal axis mark the cold surges. P1 -P4 represent polluted events as grey areas.

Figure 3 - 4 .

 34 Figure 3-4. Relationship between relative humidity and sulfate to nitrate ratio for the 10 weekly TSP samples (From S1 to S10) collected during the VASP campaign.

Figure 3 - 5 .

 35 Figure 3-5. Synoptic weather charts at 850-hPa level generated by the National Center for Hydro-Meteorological Forecasting at 00:00 UTC on a) 19 th January, b) 24 th January and c) 28 th February.

Figure 3 - 6 .

 36 Figure 3-6. Cluster of HYSPLIT 72-hour back-trajectories arriving in Hanoi at heights of (a) 100 m a.g.l., (b) 600 m a.g.l., (c) 1000 m a.g.l. and (d) 1500 m a.g.l. from 11 th January to 21 st March, 2019. The statistical summary of frequency (in brackets) and PM2.5 concentration (mean ± one standard deviation, in red writing) as a function of clusters of air masses arriving in Hanoi ending at 100 m a.g.l is presented in 3-6a.

Figure 3 - 7 .

 37 Figure 3-7. Show burned areas which were detected by MODIS Terra and Aqua from January to March, 2019 over the Indochina Peninsula region.

Chapter 3 .Figure 3 Figure 3

 333 Figure 3-8. a) Relative chemical composition of the Total Suspended Particles (TSP) associated to the weekly samples collected at the OS1 site, along with the corresponding PM2.5 concentration; b) relative contribution of the HYSPLIT clusters for back-trajectories ending at 100 m a.g.l. in the ten weekly samples. Red rectangles represent the sample week S2, S3 and S9. Blue rectangles represent the sample week S6 and S8.

Chapter 3 .Figure 3 -Figure 3 -

 333 Figure 3-10 (a) and (b). Regional plots of AOD results (6-hour average) at a wavelength of 550 nm for three components: sulfate (red), dust (green), and smoke (blue) over East Asia (mainland China) and Southeast Asia (Indochina Peninsula region) from 18 th to 20 th January, 2019 and 24 th to 27 th January, 2019.

Chapter 3 .Figure 3 -

 33 Figure 3-11. Time-height cross-sections of SLIM Lidar range-corrected signal (noted Pr²) and time series of boundary-layer height (BLH) on (a) 19 th January, (b) 24 th January, (c) 28 th February and (d) 14 th March. The BLH retrieved from SLIM (respectively radiosondes) profiles is represented as red circles (respectively black triangle).

Figure 3 -

 3 Figure 3-13 (a) and (b). Time series of PM2.5 concentration and ventilation coefficient (upper) and time-height cross-sections of horizontal wind speeds (middle) and vertical mixing (below) measured by the Doppler Lidar on 19 th January and 24 th January, respectively.

Figure 3 -

 3 Figure 3-13 (c) and (d). Time series of PM2.5 concentration and ventilation coefficient (upper) and time-height cross-sections of horizontal wind speeds (middle) and vertical mixing (below) measured by the Doppler Lidar on 28 th February and 14 th March, respectively.

Figure 3 -

 3 Figure 3-14. Wind profiles measured by radiosondes on (a) 19 th January, (b) 24 th January, (c) 28 th February and (d) 14 th March at 07:00 and 19:00 LT. Grey line represents the BLH.

Figure

   shows that the simulated AOD is largely underestimated compared to ground-based observations, as was already found by previous studies[START_REF] Chew | Relationship between aerosol optical depth and particulate matter over Singapore: Effects of aerosol vertical distributions[END_REF][START_REF] Maciszewska | A multiyear analysis of aerosol optical thickness over Europe and Central Poland using NAAPS model simulation[END_REF]. Part of the discrepancy between the observations and the model could be explained by the difference of wavelength between AERONET (AOD at 500 nm) and NAAPS (AOD at 550 nm). Assuming an Angstrom exponent of 1, the AERONET AOD values would decrease by 10% if the wavelength correction was applied. However, it was chosen not

Figure 4 - 1 .

 41 Figure 4-1. Comparison of the total AOD daily average values at 550 nm from the NAAPS model and at 500 nm from the Nghia-Do AERONET station: (a) 6-month winter averages from 2010/11 to 2018/2019 (the error bars represent the ±1σ standard deviation) and (b) point-topoint comparison. On panel (b), the plain red line represents the linear regression, while the blue dashed lines represent the all-point average AOD values. N is the total number of points included in the comparison, while N1 to N4 are the number of points falling in each quadrant.

Figure 4 -Figure 4 Figure 4 Figure 4 Figure 4 Figure 4 Figure 4 Figure 4 Figure 4 1

 4444444441 Figure 4-2 (a), (b), (c), (d), (e) and (f). Time-averaged total AOD distribution during the winter periods from 2006/07 to 2011/12 over Red River Delta, northern Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina Peninsula region -green square and Cambodia -cyan square).

   presents the mean backtrajectory for each of the 6 clusters (noted C1, C2, C3, C4, C5 and C6) that were computed from all the 96-hour back-trajectories (compared to 72-hour back-trajectories in Chapter 3) ending at 100 m a.g.l in Hanoi every hour. In agreement with past studies (Bao[START_REF] Anh | Key factors explaining severe air pollution episodes in Hanoi during 2019 winter season[END_REF][START_REF] Ly | The effects of meteorological conditions and long-range transport on PM2.5 levels in Hanoi revealed from multi-site measurement using compact sensors and machine learning approach[END_REF], the cluster analysis in this study shows that the main air masses arriving Hanoi during the winter period originated from the northeast direction, with cluster C1, C2 and C3 accounting for approximately 70% of the total number of back-trajectories.

Figure 4 -

 4 Figure 4-5 (a). Mean 96-h backward air mass trajectories ending at Hanoi resulting from the clustering from October to March during the winter period of 2006 -2020 and (b). mean altitudes for each of these mean trajectoriesThese air masses were divided into three groups: Clusters C3 came from Siberia and passed directly through mainland China, bringing dry and cold air to northern Vietnam. In

Figure 4 - 6 .

 46 Figure 4-6. Frequency of occurrences for each cluster and variation of AOD over Hanoi during 14 winter seasons from period 2006/07 to period 2019/20.

Chapter 4 .

 4 Intricate behavior of the winter pollution in Hanoi over the 2006 -2020 semi-climatic period 130 northeastern air masses, was used in the rest of the study as a tracer of RT and transboundary LRT in order to investigate the impact of the Northeast monsoon on air quality in Hanoi.

Figure 4 - 7 .

 47 Figure 4-7. The schematic diagram of the classification of winter days. The winter days include "Stagnant conditions" -SC, "Northeast high AOD" -NEHA, "Northeast low AOD" -NELA and "Other conditions" -OC. (iii) Sulfate AOD: During the Northeast monsoon's days, sulfate AOD from the NAAPs model is used as a tracer for RT and transboundary LRT of emission. The average sulfate AOD value of 0.63 during the four winters (2016/17 -2019/20) is used as a threshold to determine how the Northeast monsoon affects Hanoi's air quality. Particularly, the days, which are accompanied by relatively high sulfate AOD (> 0.63), are classified as "Northeast high AOD"

Chapter 4 .

 4 Intricate behavior of the winter pollution in Hanoi over the 2006 -2020 semi-climatic period 133

Figure 4 -

 4 Figure 4-8. (a) The daily average of temperature (Temp), pressure, relative humidity (RH), wind speed (WS), wind direction (WD), visibility, PM2.5 concentration and Sulfate AOD over the winter period 2016/17. Black triangles on the horizontal axis mark the cold surges. The winter days include "Stagnant conditions" -SC, "Northeast high AOD" -NEHA, "Northeast low AOD" -NELA and "Other conditions" -OC. Phase 1, phase 2 and phase 3 were marked as red, grey and blue areas, respectively.

Figure 4 -

 4 Figure 4-8. (b) The daily average of temperature (Temp), pressure, relative humidity (RH), wind speed (WS), wind direction (WD), visibility, PM2.5 concentration and Sulfate AOD over the winter period 2017/18. Black triangles on the horizontal axis mark the cold surges. The winter days include "Stagnant conditions" -SC, "Northeast high AOD" -NEHA, "Northeast low AOD" -NELA and "Other conditions" -OC. Phase 1, phase 2 and phase 3 were marked as red, grey and blue areas, respectively.

Figure 4 -

 4 Figure 4-8. (c) The daily average of temperature (Temp), pressure, relative humidity (RH), wind speed (WS), wind direction (WD), visibility, PM2.5 concentration and Sulfate AOD over the winter period 2018/19. Black triangles on the horizontal axis mark the cold surges. The winter days include "Stagnant conditions" -SC, "Northeast high AOD" -NEHA, "Northeast low AOD" -NELA and "Other conditions" -OC. Phase 1, phase 2 and phase 3 were marked as red, grey and blue areas, respectively.

Figure 4 -

 4 Figure 4-8. (d) The daily average of temperature (Temp), pressure, relative humidity (RH), wind speed (WS), wind direction (WD), visibility, PM2.5 concentration and Sulfate AOD over the winter period 2019/20. Black triangles on the horizontal axis mark the cold surges. The winter days include "Stagnant conditions" -SC, "Northeast high AOD" -NEHA, "Northeast low AOD" -NELA and "Other conditions" -OC. Phase 1, phase 2 and phase 3 were marked as red, grey and blue areas, respectively.

Figure 4 - 9 .

 49 Figure 4-9. The daily average (a) temperature (Temp, o C), (b) pressure (Pa), (c) relative humidity (RH, %), (d) wind speed (WS, m s -1 ), (e) wind direction (WD), (f) visibility (km), (g) PM2.5 concentration (μg m -3 ) and Sulfate AOD from 19 th December 2019 to 05 th February 2020. Black triangles on the horizontal axis were marked as the cold surges. Phase 1, phase 2 and phase 3 were marked as red, grey and blue areas, respectively.

  . Moreover, wind speeds were alsoChapter 4. Intricate behavior of the winter pollution in Hanoi over the 2006 -2020 semi-climatic period 139 significantly attenuated because of the impact of the high-pressure system, resulting in stagnant conditions and severe pollution episodes as the PM2.5 level rose (Figure4-9.g) and the visibility decreased (Figure4-9.f). Unlike phase 1, the variations of PM2.5 and sulfate AOD seemed to be more correlated during phase 2, suggesting that aerosols in the boundary-layer represented most of the AOD.Phases 3 were recorded along with the occurrence southeast trade winds, bringing warmer and more humid air (increased temperature and humidity; Figure). These cases are associated with the low levels of PM2.5 and sulfate AOD values (Figure4-9.g) as trade winds might bring clean-marine air masses over Hanoi; it lasted normally from 4 to 6 days. In this phase, the downward trend in pressure was clearly seen due to the attenuation of the high-pressure system (Figure4-9.b).

Figure 4 -

 4 Figure 4-10. The averaged PM2.5 concentration for the different types of winter days during the four winter periods (period 2016 -2017 to period 2019 -2020). Error bars represent +/-1 standard deviation of uncertainty.

•

  La phase 1 correspond à l'arrivée d'une masse d'air anticyclonique du nord-est, c'est-àdire au démarrage d'une vague de froid. C'est pendant cette phase que les polluants émis en Chine continentale sont susceptibles d'être transportés jusqu'à Hanoi : le transport à longue distance est possible, mais pas systématique. Au contraire, les polluants émis dans le delta du Fleuve Rouge seront très probablement transportés jusqu'à Hanoi en raison des distances plus courtes à parcourir : le transport régional est très probable. Enfin, les polluants émis localement seront, pour une large part, transportés hors de Hanoi par les vents plus forts prévalant pendant cette phase.• La phase 2 correspond à la persistance de la vague de froid avec des conditions stagnantes.

117 Figure 4-2 (g), (h), (i), (j), (k) and (l). Time

  ). Time-averaged total AOD distribution during the winter periods from 2006/07 to 2011/12 over Red River Delta, northern Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina Peninsula region -green square and Cambodia -cyan square). ................................................................................... ...................................................................................

	-averaged total AOD distribution during the winter
	periods from 2012/13 to 2017/18 over Red River Delta, northern Vietnam (blue square) and
	the neighboring areas (mainland China -purple square, Indochina Peninsula region -green
	square and Cambodia -cyan square).

118 Figure 4-2 (m) and (n). Time

  .......................................................................................................

	-averaged total AOD distribution from October to March during
	the winter periods from 2018/19 to 2019/20 over Red River Delta, Vietnam (blue square) and
	the neighboring areas (mainland China -purple square, Indochina region -green square and
	Cambodia -cyan square)

119 Figure 4-3 (a), (b), (c), (d), (e) and (f). Time

  Cambodia -cyan square). ......................................................................... 120 Figure

	-averaged smoke AOD distribution during the
	winter periods from 2006/07 to 2011/12 over Red River Delta, northern Vietnam (blue square)
	and the neighboring areas (mainland China -purple square, Indochina Peninsula region -
	green square and

) and (l). Time

  -averaged smoke AOD distribution during the winter periods from 2012/13 to 2017/18 over Red River Delta, northern Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina Peninsula regiongreen square and Cambodia -cyan square). .........................................................................

121 Figure 4-3 (m) and (n). Time

  -averaged smoke AOD distribution from October to March during the winter periods from 2018/19 to 2019/20 over Red River Delta, Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina region -green square and Cambodia -cyan square) .......................................................................................................

122 Figure 4-4 (a), (b), (c), (d), (e) and (f). Time

  .........................................................................

	-averaged sulfate AOD distribution during the
	winter periods from 2006/07 to 2011/12 over Red River Delta, northern Vietnam (blue square)
	and the neighboring areas (mainland China -purple square, Indochina Peninsula region -
	green square and Cambodia -cyan square).

123 Figure 4-4 (g), (h), (i), (j), (k) and (l). Time

	-averaged sulfate AOD distribution during the
	winter periods from 2012/13 to 2017/18 over Red River Delta, northern Vietnam (blue square)
	and the neighboring areas (mainland China -purple square, Indochina Peninsula region -
	green square and Cambodia -

cyan square

  ). ........................................................................

124 Figure 4-4 (m) and (n). Time-averaged sulfate AOD distribution from October to March during the winter periods from 2018/19 to 2019/20 over Red River Delta, Vietnam (blue square) and the neighboring areas (mainland China -purple square, Indochina region -green square and Cambodia -cyan square

  ) .................................................................................... 125

Table 1 -1. Economic

 1 features of the Red River Delta region in the period of2011 -2020 .... 34

Table 1 -2. Type

 1 of emissions from various manufacturing industry group over the RRD ...... 52

Table 1 -3. A comparison

 1 between WHO air quality guidelines, EU air quality guidelines and Vietnam air quality guidelines(QCVN). Alert threshold values are put in parentheses. ........ 55

Table 2 -1. Summary

 2 of implemented instruments during VASP campaign ............................. 62

Table 2 -2.

 2 Properties of the Doppler Lidar (Wind-cube 2) used for the observation of wind profiles and turbulence. ........................................................................................................... 64

Table 2 -3.

 2 Properties of the SLIM Lidar used for the observation of boundary-layer height 68

Table 2 -4. The

 2 existing satellite-based aerosol retrievals assimilated in the NAAPS model .... 81

Table 3 -1.

 3 Availability of the SLIM data during the campaign. (white) no availability, (orange) partial availability, (green) 24-h availability and (grey) no Lidar operations. ...................... 86

Table 3 -2. Statistical summary

 3 (mean ± one standard deviation) of meteorological parameters for each month during the VASP campaign. ............................................................................ 86

Table 4 -1. The mean

 4 and standard deviation of meteorological data and sulfate AOD data in each winter period from the winter period 2016/17 to period 2019/20. ............................... 130

Table 4 -2. Mean

 4 and standard deviation of meteorological data, ground-based PM2.5 and sulfate AOD in each phase during the 4-winter periods from 2016/17 to 2019/20. The mean and standard deviation are calculated after computing the delta between the actual values and the monthly average for each month of the 4 winter periods. ............................................... 132

Economical features Unit Period 2011 -2015 2016 -2020

	Contribution to national GDP by the end of the period	%	27,7	29,5
	Contribution to national exports	%	31,0	35,6
	GDP per capita by the end of the period	USD	3.061	4.458
	Economic structure		100,00	100,00
	-Agriculture	%	7,94	6,40
	-Industry and construction		39,27	45,12
	-Service		52,78	48,48

Table 1 -

 1

1. Economic features of the Red River Delta region in the period of 2011 -2020 Source: Vietnam Economic Institute, (2022) Chapter 1. Atmospheric dynamics and aerosols in Hanoi and its surrounding area 35 1.1

Table 1 -2. Type

 1

of emissions from various manufacturing industry group over the RRD Source:

MONRE (2016)

,

[START_REF] Monre | Ministry of Natural Resources and Environment[END_REF]

Table 1 -

 1 3. A comparison between WHO air quality guidelines, EU air quality guidelines and Vietnam air quality guidelines (QCVN). Alert threshold values are put in parentheses.

	Source: EEA, (2020); QCVN 05:2013/BTNMT, (2013); WHO, (2021)

  used the Weather Research and Forecasting/Community Multiscale Air Quality Modeling (WRF/CMAQ) system to simulate the contribution of different sources to the total PM2.5 concentration in December 2015. The authors reported that Chapter 1. Atmospheric dynamics and aerosols in Hanoi and its surrounding area 59 local and non-local sources accounted respectively for 57 % and 43 % during high pollution events, and 42 % and 58 % during lower pollution periods.

Table 2 - 1 .

 21 Summary of implemented instruments during VASP campaign

Doppler Lidar (Wind-cube 2)

	Pulse duration:	175 ns
	Emission power :	7 mW (eye-safe)
	Minimum range:	60 m
	Laser wavelength:	1.543 μm
	Radial wind accuracy:	± 0.1 m/s
	Direction accuracy:	2 o
	Accumulation time:	1 sec/beam
	Meas. Frequency:	5 s (10 min averaging)
	Spatial resolution:	20 m

Table 2 - 2 .

 22 Properties of the Doppler Lidar (Wind-cube 2) used for the observation of wind profiles and turbulence.

Table 2 - 3 .

 23 Properties of the SLIM Lidar used for the observation of boundary-layer height

	2.1.3.2 Overlap function correction

Table 2 - 4 .

 24 The existing satellite-based aerosol retrievals assimilated in the NAAPS model

	Source: https://www.nrlmry.navy.mil/aerosol_web/Docs/nrlmryonrprop.html

Table 3 -

 3 1 shows the SLIM data availability during the campaign.

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
	January
	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31
	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
	February
	16	17	18	19	20	21	22	23	24	25	26	27	28
	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
	March
	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31

Table 3 - 1 .

 31 Availability of the SLIM data during the campaign. (white) no availability, (orange) partial availability, (green) 24-h availability and (grey) no Lidar operations.

Table 3 -

 3

		January	February	March
	T (°C)	18.3 ± 2.5	21.9 ± 3.2	21.4 ± 2.8
	RH (%)	83.1 ± 6.7	85.2 ± 4.9	85.9 ± 7.2
	WS (m s -1 )	3.1 ± 1.2	3.9 ± 0.8	3.5 ± 0.87
	Visibility (km)	5.4 ± 2.4	7.1 ± 1.6	6.5 ± 2.1
	PM2.5 (μg m -3 )	56.8 ± 33.2	36.4 ± 14.2	44.4 ± 23.5

2. Statistical summary (mean ± one standard deviation) of meteorological parameters for each month during the VASP campaign.

Winter period Number of cold surges Temperature (°C) Humidity (%) Wind speed (m s -1 )

							Wind direction
									Sulfate
						NE	SE	Other	AOD
						(%)	(%)	(%)
	2016/17	14	22.1 ± 4.0	74.9 ± 15.1 3.0 ± 0.9	27.4	51.1	21.5	0.46 ± 0.27
	2017/18	10	20.5 ± 4.1	73.4 ± 12.8 2.9 ± 0.8	26.1	56.2	17.7	0.64 ± 0.27
	2018/19	16	21.5 ± 4.1	80.6 ± 10.5 3.1 ± 0.9	34.6	54.9	10.5	0.65 ± 0.24
	2019/20	15	21.5 ± 3.6	76.9 ± 11.1	3 ± 0.9	29.7	51.9	18.4	0.65 ± 0.26
	Average		21.3 ± 4.1	76.6 ± 12.7 3.1 ± 0.9	29.5	53.4	17.1	0.63 ± 0.28

Table 4 - 1 .

 41 The mean and standard deviation of meteorological data and sulfate AOD data in each winter period from the winter period 2016/17 to period 2019/20.

Table 4 - 2 .

 42 Mean and standard deviation of meteorological data, ground-based PM2.5 and sulfate AOD in each phase during the 4-winter periods from 2016/17 to 2019/20. The mean and standard deviation are calculated after computing the delta between the actual values and the monthly average for each month of the 4 winter periods.

  -1.

	AQI	Air Pollution Level	Health Implications	Cautionary Statement (for PM2.5)
	0 -50	Good	Air quality is considered satisfactory, and air pollution poses little or no risk	None
	51 -100	Moderate	Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.	Active children and adults, and people with respiratory disease, such as asthma, should limit prolonged outdoor exertion.
	101 -150	Unhealthy for Sensitive Groups	Members of sensitive groups may experience health effects. The general public is not likely to be affected.	Active children and adults, and people with respiratory disease, such as asthma, should limit prolonged outdoor exertion.
				Active children and adults, and
				people with respiratory disease,
			Everyone may begin to experience health	such as asthma, should avoid
	151 -200	Unhealthy	effects; members of sensitive groups may	prolonged outdoor exertion;
			experience more serious health effects	everyone else, especially
				children, should limit prolonged
				outdoor exertion
				Active children and adults, and
	201 -300	Very Unhealthy	Health warnings of emergency conditions. The entire population is more likely to be affected.	people with respiratory disease, such as asthma, should avoid all outdoor exertion; everyone else, especially children, should limit
				outdoor exertion.
	300+	Hazardous	Health alert: everyone may experience more serious health effects	Everyone should avoid all outdoor exertion

Table A - 1 .

 A1 Air Quality Index scale as defined by the U.S. Environmental Protection Agency standard.

Limit threshold: level of the concentration of the air pollutant in the atmosphere beyond which a short-term exposure represents a risk for human health for susceptible groups, requiring immediate and adequate information.

Alert threshold: level of the concentration of the air pollutant beyond which a short-term exposure represents a risk for human health for the entire population or a degradation of the environment requiring emergency intervention measures.

This study provides coefficient correlation (R) value which was converted from coefficient of determination (R 2 ) retrieval from[START_REF] Chew | Relationship between aerosol optical depth and particulate matter over Singapore: Effects of aerosol vertical distributions[END_REF].

until the arrival of a new cold surge. As expected, this one was associated with an increase in the WS and precipitation on 20 th January, which put an end to this pollution event. Similar mechanism in the P1 was observed in other pollution events (P2 -P3).

"Attitude is a little thing that makes a big difference"

Winston Churchill

Chapter 2.

Material and methods

Chapter 2. Material and methods

The dynamics of PM2.5 concentrations in the boundary-layer is closely related to the height of this layer [START_REF] Altstädter | Airborne observations of newly formed boundary layer aerosol particles under cloudy conditions[END_REF][START_REF] Boyouk | Impact of the mixing boundary layer on the relationship between PM2.5 and aerosol optical thickness[END_REF][START_REF] Cohn | Boundary Layer Height and Entrainment Zone Thickness Measured by Lidars and Wind-Profiling Radars[END_REF][START_REF] Stull | An Introduction to Boundary Layer Meteorology[END_REF]. Therefore, a fine analysis of the relation between the BLH and PM2.5 variations will be performed for these four pollution events (Figure 3 -12). Furthermore, the diurnal vertical mixing measured by Doppler Lidar brings a more accurate view to gain insights into the particulate matter variations under stagnant conditions in Hanoi. These vertical mixing evolutions can vary significantly with the evolution of BLH during daytime, while it can be reinforced during nighttime when low-level jets (LLJs) appear. The variation of the vertical mixing leads to the accumulation or dispersion of PM2.5 at the surface, as observed during the four pollution events in the winter of 2018/19 (Figure 3-13). Furthermore, having simultaneous measurements of the wind profile and BLH allows the determination of the ventilation coefficient (VC) (Figure 3-13), a synthetic parameter characterizing the pollutant dispersion which is of high interest for air pollution monitoring and assessment. The greater the coefficient, the better the air quality and the atmosphere can dispose more efficiently of pollutants. Lower ventilation coefficients, to contrast, result in poor pollutant dispersion at the atmosphere, leading to the poorer air quality. The VC (m 2 s -1 ) is normally defined as the horizontal wind speed integrated over the whole ABL depth; however, it was estimated as the product of hourly BLH and the hourly mean wind speed at 200 m a.g.l in this study. The formula of VC is then computed following the Equation (3-1): VC = BLH × hourly mean wind speed According to [START_REF] Nath | Climatological Analysis To Determine Air Pollution Potential For Different Zones In India[END_REF], The VC is classified into three pollution potential ranges as follow:

(1) VC < 6000 m 2 s -1 => High air pollution potential,

(2) 6000 ≤ VC ≤ 12000 m 2 s -1 => Medium air pollution potential

(3) VC > 12000 m 2 s -1 => Low air pollution potential.

Chapter 4.

Intricate behavior of the winter pollution in Hanoi over the 2006 -2020 semi-climatic period

General Conclusion

General Conclusion

NO2 or O3) are calculated from the hourly-averaged concentrations of the considered gas (𝐶 𝑥 ) using a continuous, monotonously increasing, piecewise linear function:

Ik and BPk are respectively the AQI and the gas concentration values at the k th breakpoint of the function. These values depend on the gas (Table A-2) and the index i used in Equation A1-1 is chosen so that the actual gas concentration 𝐶 𝑥 falls in between the i th and (i+1) th breaking points, i.e. BP 𝑖 < 𝐶 𝑥 ≤ BP 𝑖+1 . This function is used to remap gas concentrations that can be very different, depending on the pollutant considered, on a common AQI scale. For particulate pollution (x = PM2.5 or PM10), the hourly VN_AQIx values of are calculated using a formula similar to Equation A1-1. However, the concentration 𝐶 𝑥 at the time for which the VN_AQIx value is computed is replaced by a so-called "nowcast" concentration 𝑁 𝑥 , which is a weighted average taking into account the concentrations observed over the previous 12 hours:

With:

𝐶 𝑥,0 is the "current" PM concentration, measured at the time for which the VN_AQIx value is computed, while the other 𝐶 𝑥,𝑖 values are the "past" PM concentrations, measured 𝑖 hours before the time for which the VN_AQIx value is computed. The weighting coefficient 𝑤 depends on the variability of the PM concentration over the last 12 hours. If 𝐶 min and 𝐶 max are respectively the minimum and maximum concentration values observed over the last 12 hours, then 𝑤 is is equal to the largest value amongst the two following possibilities:

𝑁 𝑥 and the VN_AQIx value are not computed when there are no "recent" observations of the PM concentration. At least two of the three concentrations values closest from the time for which the VN_AQIx is computed (namely 𝐶 𝑥,0 , 𝐶 𝑥,1 and 𝐶 𝑥,2 ) must have actually been measured. In the reverse case, the VN_AQIx value is not defined. Finally, the composite hourly VN_AQI is the highest value among all the VN_AQIx values computed for the different pollutants. As particles are one of the main contributors to the degradation of air quality in Vietnam, the composite AQI is computed only if at least one of the particulate AQI (VN_AQI PM 2.5 or VN_AQI PM 10 ) is defined.

Daily VN_AQI values are also used. They are defined for all pollutants using the same formula as for the hourly VN_AQI (Equation A1-1). However, the concentration Cx appearing in the formula is no longer the hourly concentration but rather:

-For PM2.5 and PM10: the 24-hour averaged concentration; -For CO, SO2 and NO2: the maximum hourly concentration recorded over the last 24 hours; -For O3; the highest values among (i) the maximum hourly concentration over the last 24 hours or (ii) the maximum 8-hour running average concentration over the last 24 hours.

Similar to the hourly AQI, the composite daily VN_AQI value is the largest AQI value among all the VN_AQIx parameters computed for the different pollutants.