Oath

French version

En présence de mes pairs. Je, Sanjeev BISTA, parvenu(e) à l'issue de mon doctorat en santé publique et épidémiologie, et ayant ainsi pratiqué, dans ma quête du savoir, l'exercice d'une recherche scientifique exigeante, en cultivant la rigueur intellectuelle, la réflexivité éthique et dans le respect des principes de l'intégrité scientifique, je m'engage, pour ce qui dépendra de moi, dans la suite de ma carrière professionnelle quel qu'en soit le secteur ou le domaine d'activité, à maintenir une conduite intègre dans mon rapport au savoir, mes méthodes et mes résultats.

English Version

In the presence of my peers. I, Sanjeev Bista, With the completion of my doctorate in public health and epidemiology, in my quest for knowledge, I have carried out demanding research, demonstrated intellectual rigour, ethical reflection, and respect for the principles of research integrity. As I pursue my professional career, whatever my chosen field, I pledge, to the greatest of my ability, to continue to maintain integrity in my relationship to knowledge, in my methods and in my results.

i To my supervisor, Dr. Basile Chaix, thank you for your constant motivation and exceptional research guidance and expertise throughout all stages of my Ph.D. project. I am grateful for your timely feedback, even when you were extremely busy with your research. It was a pleasure of mine to have you as my supervisor, and I wish to collaborate with you in the near future.

Thank you so much for being there for me throughout the project.

I am very grateful to the experts from the chemistry department of Cambridge University, Dr.

Lia Chatzidiakou, and Prof. Roderic L Jones, for their constant support. It would have been nearly impossible to complete my thesis without their timely and adequate support in postprocessing the data collected with a particular air quality sensor device. Moreover, I would like to thank Dr. Tarik Benmarhnia for providing methodological input to this project. I am lucky to have Clélie Dureau and grateful for her timely feedback and near-instantaneous email replies whenever I had queries related to data storage and information on the project. My most profound appreciation to Magali Moulé, the administrative staff at doctoral school ED 393, who eased every administrative step I had to go through in all these years. I am thankful to my supervisor for providing me with an opportunity to autonomously supervise two interns during my stay on the research team. That opportunity helped me embrace my skills, such as supervisory skills, collaboration skills, and problem-solving skills, particularly in the research field.

I am fortunate to have my beloved wife and son by my side while going through ups and downs over the past 3 years of living in Paris. Thanks to my family members who were not physically present but supported me emotionally throughout the period. A special thanks to my father, Nabaraj Bista, for believing in me and encouraging me throughout my academic career. It was his wish to see me as a scholar, and I am delighted today to have his dream fulfilled.

expected to reach 1.56 billion by 2025, which is a 60% increase compared to 2020. In 2018, the WHO estimated that 9 out of 10 people worldwide breathe polluted air, and ambient air pollution accounts for 4.2 million deaths yearly. While the long-term (annual exposure) effect of air pollution on BP has been extensively studied and is of great concern, the short-term (minutes to hourly exposure) effect is still poorly understood. Furthermore, previous studies assessing the relationship did not consider the synergistic effect that air pollution mixtures may have on BP.

Objective: Based on the data collected by the MobiliSense sensor-based study, our first project was to quantify the level of personal exposure (concentration and inhaled doses) to black carbon (BC) across different transport modes at the trip stage level (n = 7497) in 283 participants in the Grand Paris region. Secondly, we evaluated the association between short-term exposures to BC and ambulatory BP (n = 6772) as 245 individuals go about their ordinary lives. Finally, we assessed the joint effect of air pollution mixture on ambulatory BP (n = 3319) among 221 participants.

Methods:

The MobiliSense study used several sensor devices to monitor personal exposures (air pollutants and noise) and to measure different physiological parameters (BP, heart rate, breathing rate, and physical activity). We tracked individuals' whereabouts by processing the personally collected GPS data parallel to the phone-based mobility survey. Inhaled doses of air pollutants were derived from the ventilation rate estimated based on metabolic equivalents from accelerometry. The methods used include multilevel linear regressions, binomial multilevel model (log link) estimated with the Markov chain Monte Carlo method, random forest prediction model, and quantile G-computation models.

Results: Three articles are incorporated into this thesis. We found that trip stages involving public transport, private motorized vehicles, and biking exposed participants to high black carbon (BC) concentration compared to walking trip stages. In contrast, biking trip stages had higher inhaled doses of BC per 30 minutes compared to all other modes due to the increased level of ventilation rate during biking. We observed that the magnitude of associations between BC exposure and BP gradually decreased with the increase in exposure averaging period from 5-minute to 1 hour before BP measurements, where previous 5-minute BC exposure increased systolic and diastolic BP by 0.57 mmHg and 0.36 mmHg per 1-μg/m 3 respectively. Likewise, per quartile increase in air pollutants concentration (BC, nitrogen dioxide, nitrogen monoxide, carbon monoxide and ozone) mixture in the previous 5-minute windows was associated with with a 1.92 mmHg higher SBP, while 30-minute and 1-hour exposures were not associated with SBP.

Conclusion:

Our findings support that using inhaled doses over concentrations might help overcome the exposure misclassification and improve the accuracy of effect estimates while assessing associations of air pollution exposure in the transport microenvironment with health risks. These findings are the first of a kind, as most previous studies evaluated dose-response relationships between air pollution exposure and health events only considering concentrations of air pollutants, assuming that the volume of air pollution entering individuals' lungs is independent of ventilation rate. Overall, these results highlight the importance of reducing anthropogenic air pollution in urban settings to promote cardiovascular health among the adult population.

v Résume Introduction : La pollution atmosphérique est connue comme un déterminant de la pression artérielle, et peut avoir des effets néfastes sur la santé humaine via sa contribution sur l'incidence d'une hypertension artérielle chronique. Si son effet long terme sur la pression artérielle a été largement étudié, l'effet à court terme, qui a une importance mécanistique, est encore mal compris. De plus, les études précédentes n'ont pas pris en compte l'effet synergique que les mélanges différents polluants de l'air peuvent avoir sur la pression artérielle.

Objectif : Notre premier projet était de quantifier le niveau d'exposition personnelle (concentrations et doses inhalées) au carbone suie dans différents modes de transport étudiés au niveau de trajets avec un mode unique (n=7497) chez 283 participants. Dans une seconde étude, nous avons évalué l'association entre les expositions à court terme au carbone suie et la pression artérielle ambulatoire (n = 6772) chez 245 individus. Dans une troisième analyse, nous avons évalué l'effet conjoint d'un mélange de polluants de l'air sur la pression artérielle ambulatoire (n = 3319 mesures ) chez 221 participants.

Méthodes : Nous avons étudié les déplacements des individus en traitant des données collectées au moyen de traceurs GPS et d'une enquête de mobilité par téléphone. Les concentrations de polluants ont été mesurées au moyen de capteurs ambulatoires. Les doses inhalées de polluants atmosphériques ont été dérivées en tenant compte du taux de ventilation estimé au moyen de données d'accélérométrie. Les méthodes utilisées pour l'analyse comprennent des régressions linéaires multiniveaux, un modèle de prédiction par forêt aléatoire et des modèles de quantile G computation.

Résultats : Nous avons constaté que les étapes de déplacement impliquant les transports publics, les véhicules motorisés privés et le vélo exposaient les participants à une concentration plus élevée de carbone suie par rapport aux à pied. En revanche, les étapes de déplacement en vélo présentaient des doses inhalées de carbone suie plus élevées pour chaque 30 minutes de vi déplacement par rapport à tous les autres modes, en raison de l'augmentation du taux de ventilation pendant la pratique du vélo. Nous avons observé que l'amplitude des associations positives entre l'exposition au carbone suie et la pression artérielle diminuait progressivement avec l'augmentation de la période d'exposition prise en compte de 5 minutes à 1 heure avant les mesures de pression artérielle. Une augmentation de 1μg/m 3 de l'exposition au carbone suie pendant 5 minutes augmentait la pression artérielle systolique et diastolique de 0,57 mmHg et 0,36 mmHg. L'augmentation d'un quartile du mélange de concentrations de polluants atmosphériques (carbone suie, monoxyde et dioxyde d'azote, monoxyde de carbone et ozone) dans des fenêtres d'exposition dans les 5 minutes précédentes était associée à une pression artérielle systolique plus élevée de 1,92 mmHg.

Conclusion :

Nos résultats montrent que la prise en compte de doses inhalées plutôt que de concentrations pourrait aider à surmonter les erreurs de classification de l'exposition en particulier dans le microenvironnement des transports. Ces résultats sont les premiers du genre, car la majorité des études précédentes ont évalué les relations dose-réponse entre l'exposition à des polluants de l'air singuliers et la pression artérielle et sans prendre en compte les mélanges de polluants. Dans l'ensemble, ces résultats permettent de formuler l'hypothèse que l'exposition à la pollution de l'air jour après jour sur des mois et des années entraîne des réponses aiguës répétées de la pression artérielle susceptibles de conduire à une hypertension artérielle chronique. Cela soulignent l'importance de réduire la pollution atmosphérique environnementale anthropique en milieu urbain pour promouvoir la santé cardiovasculaire de la population adulte.

List of figures

List of tables

Introduction

This section incorporates various sub-sections, starting with the definition of air pollution characteristics in this thesis. A sub-section then divides the air pollution and health relationship into historical and current evidence, followed by a separate sub-section discussing the air quality standards set by European and French authorities and compares those to the standards set by the World Health Organisation (WHO). This sub-section further reflects the current trend of air quality levels in France and particularly in Paris (our study area). After that, we discuss the public health importance of blood pressure (BP). Then, we present a literature review on air pollution and BP, including sub-sections such as procedures of BP measurements used, methods adopted in assessing air pollution levels, and study designs used by previous studies. This subsection of the literature review ends with enumerating the findings of earlier studies. Eventually, we discuss the limitation of prior studies evaluating the associations between air pollution exposure and BP. Then, we discuss the importance, challenges, and issues associated with using personal sensors in air quality assessment. Finally, this section highlights this thesis's rationale, aim, and objectives.

Air pollution: sources and geographical distribution

Air pollution is the contamination of the indoor or outdoor environment by any chemical, physical or biological agent that modifies the natural characteristics of the atmosphere [START_REF]World health statistics 2016: monitoring health for the SDGs sustainable development goals[END_REF]. The significant sources of air pollutants are motorized vehicles, industrial facilities, domestic sources, waste management, and agriculture. Air pollutants have been classified into two broad categories; those directly emitted into the atmosphere are labeled as primary pollutants, and those formed within the atmosphere with a chain of chemical reactions are known as secondary pollutants. Air pollutants recognized as detrimental to human health include particular matter (PMx), carbon monoxide (CO), ozone (O3), oxides of nitrogen (NOx), and sulfur dioxide (SO2) [START_REF]World health statistics 2016: monitoring health for the SDGs sustainable development goals[END_REF]. The concentrations of air pollutants are usually measured either in a mass of pollutant per unit volume of air and expressed as µg/m 3 or in mixing ratios and expressed as part per million (ppm) or part per billion (ppb). In 2016, the WHO reported that 90% of the world's population breathes air that exceeds the WHO air quality guidelines [START_REF]World health statistics 2019: monitoring health for the SDGs, sustainable development goals[END_REF]. In 2020, AirParif, the association dedicated to monitoring the air quality in Paris, flagged PMx and nitrogen dioxide (NO2) concentration levels as a problematic issue in the Paris region for exceeding the European Union limit values of 40 µg/m 3 (3).

Black carbon

Black carbon (BC) is a principal atmospheric constituent responsible for climate change and air pollution. Furthermore, BC emissions contribute highly to current global warming, second to carbon monoxide emissions (4). BC present in an outdoor environment is primarily due to the incomplete combustion of fossil fuels, wood, and biomass and specific industrial processes such as producing carbon black for automobile tires and printing inks. Indoors, it is due to cooking with biofuels, residential heating, and burning candles (4). BC is one of the significant contributors, 5% to 20% depending on the source, to fine particulate matter < 2.5 μm (PM2.5) [START_REF] Kirrane | A systematic review of cardiovascular responses associated with ambient black carbon and fine particulate matter[END_REF], with a diameter between 20 and 150 nanometers. These particles absorb scattered solar radiation at a very high capacity. BC may contain other components, such as aliphatic hydrocarbons, polycyclic aromatic hydrocarbons, and several volatile compounds [START_REF] Dentener | Emissions of primary aerosol and precursor gases in the years 2000 and 1750 prescribed data-sets for AeroCom[END_REF]. It is suggested that urban cities have a higher BC concentration than rural sites, indicating that urban city traffic and commercial activities contribute heavily to ambient BC [START_REF] Gramsch | Particle size distribution and its relationship to black carbon in two urban and one rural site in Santiago de Chile[END_REF].

PM 2.5

Vast sources are responsible for outdoor PM2.5 emissions, such as energy production, households, industries, transport, waste, agriculture, desert dust, and forest fires. Indoor PM2.5 are derived during home heating, candle burning, open fireplaces, and cooking. Furthermore, it is worth mentioning that outdoor particles penetrating the indoors can contribute mainly to indoor environment concentration levels. Urban residents consume relatively more energy and use more motorized vehicles daily than rural residents, leading to higher PM2.5 levels in metropolitan cities [START_REF] Liu | Urban-rural disparity of the short-term association of PM2.5 with mortality and its attributable burden[END_REF]. These air particles can travel to hundred kilometers, and their physical and chemical composition varies significantly according to geographical settings and time [START_REF] Shaddick | Half the world's population are exposed to increasing air pollution[END_REF].

The major components of PM2.5 are sulfate, nitrate, ammonium, organic matter, and BC [START_REF] Sun | Exposure to air pollutant mixture and gestational diabetes mellitus in Southern California: Results from electronic health record data of a large pregnancy cohort[END_REF].

The contribution of individual components to overall PM2.5 mass depends on the emission source [START_REF] Adams | Particulate matter components, sources, and health: Systematic approaches to testing effects[END_REF].

Nitrogen oxides

Nitrogen dioxide (NO2) belongs to a larger group of related gases known as nitrogen oxides (NOx) and is regarded as the "indicator gas" for the group [START_REF]WHO guidelines for indoor air quality: selected pollutants[END_REF]. The familiar sources of ambient NO2 are burning fossil fuels, especially vehicle emissions, power plants, and microbial activity [START_REF]WHO guidelines for indoor air quality: selected pollutants[END_REF]. NO2 can also form indoors when burning the fossil fuels like wood or natural gas. In the presence of oxygen, NO2 reduces to NO. NO2 in the outer environment reacts with other chemicals to form PMx and O3, other forms of air pollutants [START_REF]WHO guidelines for indoor air quality: selected pollutants[END_REF]. The chain reaction of interconversion of O3, NO, and NO2 in the atmosphere is given as [START_REF] Han | Analysis of the Relationship between O3, NO and NO2 in Tianjin, China[END_REF]:

NO2 + energy of a photon → NO + O (1) O + O2 → O3 + M (2) O3 + NO → NO2 + O2 (3)
Here: M (usually N2 or O2) represents a molecule that stabilizes the newly formed O3 molecule absorbing the excess energy produced in the process. According to the report of the ministry of France, in 2020, motorized transport and industries contributed substantially, 54% and 20%, respectively, to the outdoor NOx, suggesting that French urban cities have higher NOx concentrations than less urbanized cities.

Ozone

Ozone falls under the category of secondary air pollutants, as its formation and loss in the atmosphere depend on different mechanisms. The production of O3 in the troposphere (the lowest layer of the earth's atmosphere extending about 10-16 km from the earth's surface) is dependent on the photochemical oxidation of volatile organic compounds and carbon monoxide in the presence of NO2 (CO+2O2 = CO2+O3) [START_REF] Han | Analysis of the Relationship between O3, NO and NO2 in Tianjin, China[END_REF]. This complex process depends on temperature, radiation, precursor concentrations, spectral distribution, and other atmospheric factors [START_REF] Seinfeld | Atmospheric Chemistry and Physics: From Air Pollution to Climate Change[END_REF]. Therefore, the O3 concentration gradually increases after sunrise, reaching a peak in the daytime and then reducing until the following day [START_REF] Han | Analysis of the Relationship between O3, NO and NO2 in Tianjin, China[END_REF]. Since O3 reacts with NO to produce NO2 [START_REF] Han | Analysis of the Relationship between O3, NO and NO2 in Tianjin, China[END_REF], the level of O3 is lower in the area with a higher concentration of NO or NO2. The warm and moist airflow in the summer season suppresses the photochemical production of O3. In contrast, this process peaks in spring and autumn, thereby contributing to the geographical distribution of O3 seasonality [START_REF] Yin | Geographical distribution of ozone seasonality over China[END_REF].

Carbon monoxide

Carbon monoxide (CO) is an odorless, colorless gas. Internal combustion engines, especially motor vehicles, are a significant source of CO, and other sources are sea vessels, electric generators, and forest fires. The indoor sources of CO are leakage of gas supply or the combustion of the gas/fuel in the indoor heating system. Incomplete combustion reactions produce CO due to the lack of oxygen or low mixing. Therefore, the emission of CO is lower in the presence of a higher air ratio. CO level in the air decreases with the increase in oxygen level since oxygen reacts with CO and forms carbon dioxide and ozone (CO+2O2 =CO2+O3).

CO level has increased rapidly over the past decades due to the rapid growth in industrialization and urbanization [START_REF] Fang | Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models[END_REF]. The CO concentrations in rural areas are substantially lower than in populated urban areas due to the great use of combustion engine vehicles and many industries in urban areas [START_REF] Yashiro | Temporal and spatial variations of carbon monoxide over the western part of the Pacific Ocean[END_REF]. The chemical lifetime of CO is about 2 months [START_REF] Yashiro | Temporal and spatial variations of carbon monoxide over the western part of the Pacific Ocean[END_REF].

Air pollution and health

Historical epidemiological evidence

For centuries, air pollution has been a public health concern. A landmark in air pollution epidemiology was the fog in 1930 in the Meuse valley, Belgium causing 60 deaths within less than a week [START_REF] Nemery | The Meuse Valley fog of 1930: An air pollution disaster[END_REF]. This episode is considered the first epidemiological evidence that helped researchers understand the potentiality of atmospheric pollution to cause mortality and morbidity, identifying the most likely causes [START_REF] Nemery | The Meuse Valley fog of 1930: An air pollution disaster[END_REF]. The industrial emissions and the weather condition, characterized by anticyclonic conditions with high atmospheric pressures, were responsible for the Meuse valley fog [START_REF] Nemery | The Meuse Valley fog of 1930: An air pollution disaster[END_REF]. Another vital historical incident was the London smog in 1952, with a concentration of 4.5 mg/m 3 for smoke and 3.8 mg/m 3 for SO2, caused mainly due to domestic heating and weather conditions [START_REF] Bell | Reassessment of the lethal London fog of 1952: novel indicators of acute and chronic consequences of acute exposure to air pollution[END_REF]. The smog episode was responsible for above 12000 deaths [START_REF] Bell | Reassessment of the lethal London fog of 1952: novel indicators of acute and chronic consequences of acute exposure to air pollution[END_REF]. The mortality rate increased within a short time lag after the smog started and persistently increased over the few days even after the fog had already disappeared from the atmosphere, suggesting that the lagged effect of air pollution exposure is more detrimental to human health [START_REF] Bell | Reassessment of the lethal London fog of 1952: novel indicators of acute and chronic consequences of acute exposure to air pollution[END_REF]. The episode of London smog is considered one of the landmarks of air pollution epidemiology because it allowed epidemiologists to conduct the first detailed analysis of the relationship between the levels of air pollutants and increased morbidity and mortality [START_REF] Nemery | The Meuse Valley fog of 1930: An air pollution disaster[END_REF]. In January 1985, the 5-day smog in the Rhine-Ruhr (western Germany) area increased daily mortality by 8%, respiratory and cardiovascular related hospital admissions by 15%, outpatient visits by 12%, and ambulance transports by 28% [START_REF] Wichmann | What can we learn today from the Central European smog episode of 1985 (and earlier episodes)?[END_REF]. An inversion of a vertical temperature gradient from January 16 to 20 with a wind velocity below 1.5 m/sec caused the episode. The maximum 24-hour concentrations recorded during the episode for SO2 and total suspended particles were 0.8 mg/m 3 and 0.6 mg/m 3 , respectively [START_REF] Wichmann | What can we learn today from the Central European smog episode of 1985 (and earlier episodes)?[END_REF]. The Harvard six-city study commenced in 1975 was one of the oldest cohort studies and a major successful project in history to quantify the effects of air pollutants on morbidity and mortality [START_REF] Dockery | An Association between Air Pollution and Mortality in Six U.S. Cities[END_REF]. The study followed more than 8000 people over 14-16 years in six different US cities to investigate the association between air pollution exposure and health outcomes [START_REF] Dockery | An Association between Air Pollution and Mortality in Six U.S. Cities[END_REF]. Figure 1.1 illustrates the city-specific estimated mortality-rate ratios plotted against the mean air pollution concentration of the city; the estimates were adjusted for personal behaviors, such as smoking habits, level of education, body mass index, along with the day of the week, and temperature. More strong associations of mortality were documented with the levels of fine and sulfate particles than with the levels of O3, SO2 and NO2, and among the six cites air pollutant specific morality rates were higher in Steubenville, Ohio and Harriman, Tennessee compared to others (figure 1.1).

In summary, these historical events and the Harvard study successfully created awareness among policymakers and researchers about the potential of atmospheric pollution to cause morbidity and mortality. Concretely, ecological evidence from London and Rhine-Ruhr incidents encouraged air pollution epidemiologists to formulate hypotheses to test the associations between air pollution exposure and specific cardiovascular and respiratory diseases at the individual level. Soon after these historical events of smog episodes, air pollution successfully attracted substantial attention in public health research [START_REF] Laden | Reduction in Fine Particulate Air Pollution and Mortality[END_REF], leading to many related publications in the following decades (figure 1.2). Massachusetts;L St. Louis;H Harriman, Tennessee;andS Steubenville, Ohio. Figure 1.2. Publications related to air pollution (1955-2006). Comparison of results in "Web of Science" and "PubMed."

Current epidemiological evidence

Air pollution and mortality

Laden and colleagues did an extended follow-up of the Harvard Six Cities study for over 8 years (1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988) and documented that a 10-mg/m 3 increase in city-specific annual PM2.5 was associated with a 1.16 mortality rate ratio (95% CI: 1.07, 1.26) [START_REF] Laden | Reduction in Fine Particulate Air Pollution and Mortality[END_REF]. A prospective study (1982 to 1989) among 552,138 adults residing in the metropolitan US found that the relative risk ratio of all-cause mortality was 1.15 (95% CI: 1.09, 1.22) and 1.17 (95% CI: 1.09, 1.26) for sulfate and fine particle polluted area respectively when compared with the least polluted areas [START_REF] Pope | Particulate Air Pollution as a Predictor of Mortality in a Prospective Study of U.S. Adults[END_REF].

A systematic review of the global burden of disease, including studies conducted between 1990 and 2016, reported that air pollution accounted for 72% of global morbidity [START_REF] Gakidou | Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016[END_REF]. Air pollution is one of the major health risk factors worldwide, responsible for roughly about 7 million premature deaths in 2019, which was 3.8 million in 2015 [START_REF] Anenberg | A global snapshot of the air pollution-related health impacts of transportation sector emissions in 2010 and 2015[END_REF][START_REF] Scheres | The Ten Threats to Global Health in 2018 and 2019. A welcome and informative communication of WHO to everybody[END_REF]. Likewise, in 2019, PM2.5, NO2, and O3 were responsible for 29,775, 4,974, and 2,053 premature deaths in France [START_REF]France -Air pollution country fact sheet -European Environment Agency[END_REF]. In 2018, the WHO estimated that 9 out of 10 people worldwide breathe polluted air, and ambient air pollution accounts for 4.2 million deaths yearly (28), ranking air pollution as the fourth leading cause of global mortality [START_REF] Brauer | The global burden of disease from air pollution[END_REF]. Recently in 2019, The WHO estimated that 7.6% of total global mortality is alone caused by ambient air pollution (30).

Air pollution and cardiovascular diseases

Most cohort studies have linked long-term (annual) exposure to air pollution with an increased risk of coronary artery disease [START_REF] Laden | Reduction in Fine Particulate Air Pollution and Mortality[END_REF][START_REF] Miller | Long-Term Exposure to Air Pollution and Incidence of Cardiovascular Events in Women[END_REF][START_REF] Chen | Long-Term Exposure to Traffic-Related Air Pollution and Cardiovascular Mortality[END_REF]. For instance, a UK-based cohort study following 836,557 people aged 40 to 89 years for five years concluded that long-term exposure to ambient PM and NO2 was associated with an increased risk of heart failure [START_REF] Atkinson | Long-term exposure to outdoor air pollution and incidence of cardiovascular diseases[END_REF]. A systematic review and meta-analysis including 195 studies concluded that heart failure was associated with increased carbon monoxide (3.52% per 1 ppm; 95% CI: 2.52, 4.54), sulphur dioxide (2.36% per 10 ppb; 95% CI: 1.35, 3.38), nitrogen dioxide (1.70% per 10 ppb; 95% CI: 1.25, 2.16) and PM2.5 (2.21% per 10 μg/m 3 ; 95% CI: 1.42, 2.82) concentrations [START_REF] Shah | Global association of air pollution and heart failure: a systematic review and meta-analysis[END_REF]. A Korean study analyzing the mortality record between 1995 to 1998 documented that a 2-day lag of outdoor NO2, CO, and O3 was associated with stroke-related mortality [START_REF] Hong | Effects of air pollutants on acute stroke mortality[END_REF]. A Canadian cohort study with a 5year exposure period (January 1994-December 1998) concluded that one interquartile increase in residential BC concentration (modeled with land use regression) increased the risk of coronary heart disease mortality by 4% (95% CI: 1, 8) [START_REF] Gan | Association of Long-term Exposure to Community Noise and Traffic-related Air Pollution With Coronary Heart Disease Mortality[END_REF]. A prospective study including 11European cohorts (follow-up: 2002-2008) found that a 5-μg/m 3 increase in annual mean PM2.5 (modeled outside the residence) was associated with a 13% increased risk of myocardial infarction (hazard ratio: 1.13; 95% CI: 0.98, 1.30) [START_REF] Cesaroni | Long term exposure to ambient air pollution and incidence of acute coronary events: prospective cohort study and meta-analysis in 11 European cohorts from the ESCAPE Project[END_REF]. A Canadian cohort study (1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004) including 205,440 adults aged 35-85 years reported that a 5-ppb increase in ambient NO2 (fixed-station-monitored) increased the risk of ischemic heart disease-related mortality by 15% (95% CI: 8, 21) [START_REF] Chen | Long-Term Exposure to Traffic-Related Air Pollution and Cardiovascular Mortality[END_REF].

Air pollution and respiratory diseases

A meta-analysis including 7 prospective longitudinal studies, considering at least three years of exposure, documented that a 10-μg/m 3 increase in annual ambient PM2.5 and NO2 (fixedstation-monitored) was associated with an 18% (95% CI : 13, 23) and with a 7% (95% CI: 0, 16) increased risk of chronic obstructive pulmonary disease (COPD), respectively [START_REF] Park | Impact of long-term exposure to ambient air pollution on the incidence of chronic obstructive pulmonary disease: A systematic review and meta-analysis[END_REF]. A prospective study including three European cohorts had consistent findings for PM2.5 and NO2 exposures as above, and added that 0.5 10 -5 m -1 , expressed as absorbance units, increase in annual BC (modeled outside the residence) increased the risk of COPD by 11 % (95% CI: 6, 15) [START_REF] Liu | Long-term exposure to low-level air pollution and incidence of chronic obstructive pulmonary disease: The ELAPSE project[END_REF]. A meta-analysis including 13 cohort studies (at least with 2 years of follow-up) found that higher annual ambient NO2 (fixed-station-monitored) was associated with higher asthma incidence (odds ratio: 1.07 (95% CI: 1.02 to 1.13) per 10 μg/m 3 ) [START_REF] Anderson | Long-term exposure to air pollution and the incidence of asthma: metaanalysis of cohort studies[END_REF]. The same study did a meta-analysis with five studies and concluded that a 10-μg/m 3 increase in annual PM2.5 was associated with a 1.16 higher odds (95% CI: 0.98 to 1.37) of asthma [START_REF] Anderson | Long-term exposure to air pollution and the incidence of asthma: metaanalysis of cohort studies[END_REF]. A prospective study including 307,550 cohort participants with a mean follow-up of 18 years indicated that the risk of lung cancer increases by 13% (95% CI: 5, 23) per 5 µg/m 3 increase in annual mean of PM2.5 (modeled outside the residence), no association was documented for NO2, O3, and BC [START_REF] Hvidtfeldt | Long-term low-level ambient air pollution exposure and risk of lung cancer -A pooled analysis of 7 European cohorts[END_REF]. A review article published in 2020 listed cough, shortness of breath, wheezing, asthma, and respiratory disease as short-term effects of air pollution exposure, while chronic asthma and pulmonary insufficiency were reported as long-term effects [START_REF] Manisalidis | Environmental and Health Impacts of Air Pollution: A Review[END_REF]. Another review article published in 2016 suggested that exposure to air pollution is associated with lung function impairment, such as lower forced expiratory volume in 1 second and forced vital capacity [START_REF] Paulin | Particulate air pollution and impaired lung function[END_REF].

On top of that, evidence from more recent studies has provided suggestive links between air pollution exposure and an increasingly wide range of adverse health outcomes, including neurological issues [START_REF] Power | Traffic-Related Air Pollution and Cognitive Function in a Cohort of Older Men[END_REF], psychiatric conditions [START_REF] Buoli | Is there a link between air pollution and mental disorders?[END_REF], premature birth, low birth weight, mental health conditions and diabetes [START_REF] Thurston | A joint ERS/ATS policy statement: what constitutes an adverse health effect of air pollution? An analytical framework[END_REF]. Furthermore, a recent global review even suggested that almost every cell in the human body may be affected by exposure to air pollution [START_REF] Schraufnagel | Air Pollution and Noncommunicable Diseases[END_REF]. Figure 1.3 gives an overview of the harmful effects related to air pollution exposure. The bold type indicates conditions currently included in the Global Burden of Disease categories [START_REF] Thurston | A joint ERS/ATS policy statement: what constitutes an adverse health effect of air pollution? An analytical framework[END_REF].

European and French Ambient Air Quality Standards and current air quality levels

Learnings from historical smog episodes in different parts of Europe, such as in the Meuse valley, Germany, and the Smog of London, led to the formation of air quality guidelines and legislative acts across the globe to control air pollution emissions. The US Air Pollution Control Act of 1955 is regarded as the world's first-ever documented legislation at the country level concerning air pollution research [START_REF]Evolution of the Clean Air Act | US EPA[END_REF]. This Act allocated funds to federal studies into air pollution. The Clean Air Act of 1963 in the US, formed based on the findings from the Act of 1955, was the world's first legislation to control air pollution [START_REF]Evolution of the Clean Air Act | US EPA[END_REF]. The Act governed the establishment of the federal program within the U.S. public health service and was primarily dedicated to monitoring and controlling air pollution. Lessons from the London Smog led to the formation of the 1956 Clean Air Act in the UK to control air pollution [START_REF] Williams | Air pollution and policy-1952-2002[END_REF].

Air pollution has been a European Union's (EU) political concern since the late 1970s [START_REF]Review of the EU Air policy -Environment -European Commission[END_REF]. The European Environment Agency (EEA), located in Copenhagen, is the principal advising agency all over Europe [START_REF]Review of the EU Air policy -Environment -European Commission[END_REF]. The main agenda of the EEA is to develop and drive appropriate measures to improve air quality across Europe. EEA functions with a focus on controlling emissions from mobile sources, improving fuel quality, and promoting environmental protection requirements in the energy and transport system [START_REF]Review of the EU Air policy -Environment -European Commission[END_REF].

Two examples of long-lasting EEA policies that were successful in reducing the level of air pollution in Europe were: (i) the Convention on Long-range Transboundary Air Pollution (LRTAP) in the 1980s [START_REF] Byrne | Convention on Long-Range Transboundary Air Pollution: Assessing its Effectiveness as a Multilateral Environmental Regime after 35 Years[END_REF]; and (ii) the European emission standards passed in the European Union in the early 1990s [START_REF] Crippa | Forty years of improvements in European air quality: regional policy-industry interactions with global impacts[END_REF]. Recently, the Thematic Strategy on Air Pollution (TSAP) of 2005, formed under the 6 th Environmental Action Program (6EAP), established air quality objectives with supporting policy measures spanning 2020 in Europe [START_REF]EU: Air Quality Standards | Transport Policy[END_REF]. National Emission Ceilings Directive (NECD) 2008 is the latest official document setting standards for the EU's ambient air quality [START_REF]EU: Air Quality Standards | Transport Policy[END_REF] to achieve the objectives set by TSAP.

As a member state of the EU, France follows all the EEA policies related to air pollution regulations. Table 1 shows that France adopted NECD 2008's air quality standards until 2020, except for PM2.5, but started setting its air quality standards in 2021 [START_REF]Bilan de la qualité de l'air extérieur en France en[END_REF]. As depicted in table 1, French air quality standards set in 2021 resembles the WHO 2021 standards [START_REF]What are the WHO Air quality guidelines?[END_REF], while the NECD 2008 standards are less strict than the French standards.

Current air quality levels

According to the latest EEA report (2021), 97% of Europeans are exposed to O3 concentrations above the WHO standards [START_REF]France -Air pollution country fact sheet -European Environment Agency[END_REF]. In 2019, 17.4% and 1.9% of French urban citizens were exposed to annual outdoor O3 and NO2 concentrations above EU standards [START_REF]France -Air pollution country fact sheet -European Environment Agency[END_REF]. Air quality assessment in France in 2020 confirmed that outdoor air quality of that year improved due to a reduction in pollution emissions, where the Covid-19 lockdown further intensified the progress [START_REF]Bilan de la qualité de l'air extérieur en France en[END_REF]. Since 2000, annual average outdoor NO2 concentrations have consistently remained above the NECD 2008 standard (<40 μg/m 3 ) in traffic-dense urban cities of France (except for the years 2015-2020) [START_REF]Bilan de la qualité de l'air extérieur en France en[END_REF]. For other semi-urban towns in France, outdoor NO2 concentration has never risen above the EU standard since 2000 [START_REF]Bilan de la qualité de l'air extérieur en France en[END_REF]. Likewise, annual PM2.5 concentration has never been a problem for France since 2000 [START_REF]Bilan de la qualité de l'air extérieur en France en[END_REF]. In 2020, the NO2 concentrations for trafficdense urban cities and semi-urban areas were 25 μg/m 3 and 13 μg/m 3 , whereas, for PM2.5, they were 10 μg/m 3 and 9 μg/m

Blood pressure and its importance in public health

In simple terms, BP is the pressure applied by the circulating blood on the arterial walls during cardiac contraction and relaxation, measured as systolic BP (SBP) and diastolic BP (DBP), respectively [START_REF] Guzman-Limon | Pediatric Hypertension Diagnosis, Evaluation, and Treatment PHYSIOLOGY OF NORMAL BLOOD PRESSURE[END_REF]. SBP measures pressure in the arteries when the heart beats, and DBP is when the heart rests between beats. BP at a given time depends on a complex relationship between several physiologic mechanisms, such as vascular tone (contraction experienced by a blood vessel), and circulating blood quantity [START_REF] Guzman-Limon | Pediatric Hypertension Diagnosis, Evaluation, and Treatment PHYSIOLOGY OF NORMAL BLOOD PRESSURE[END_REF]. Abnormal blood pressure results from a disturbance in one of these regulating factors [START_REF] Guzman-Limon | Pediatric Hypertension Diagnosis, Evaluation, and Treatment PHYSIOLOGY OF NORMAL BLOOD PRESSURE[END_REF]. Individual genetic makeup, combined with behavioral factors, leads to heart rate variability, blood coagulation, and vascular tone, which results in changes in BP, and frequent increases in BP may lead to hypertension [START_REF] Sanidas | Air pollution and arterial hypertension. A new risk factor is in the air[END_REF][START_REF] Brook | Environmental Hypertensionology" the effects of environmental factors on blood pressure in clinical practice and research[END_REF] [START_REF] Andrade | Effects of acute blood pressure elevation on biochemical-metabolic parameters in individuals with hypertensive crisis[END_REF][START_REF] Marik | Hypertensive Crises[END_REF]. The HC with organ damage is classified as a hypertensive emergency, and that without it is known as hypertensive urgency [START_REF] Salkic | Clinical Presentation of Hypertensive Crises in Emergency Medical Services[END_REF]. The symptoms of hypertensive urgency include headache, dyspnea, nausea, vomiting, epistaxis, and pronounced anxiety [START_REF] Backer | European guidelines on cardiovascular disease prevention in clinical practice Third Joint Task Force of European and other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of eight societies and by invite[END_REF]. Unfortunately, hypertensive emergency and urgency are still the least understood and the worst-treated acute medical issues [START_REF] Salkic | Clinical Presentation of Hypertensive Crises in Emergency Medical Services[END_REF].

A cross-sectional study among 76 adults suggested that acute BP elevations were associated with hyperglycemia, dyslipidemia, higher potassium and creatinine levels and lower renal function [START_REF] Andrade | Effects of acute blood pressure elevation on biochemical-metabolic parameters in individuals with hypertensive crisis[END_REF]. A cohort study following 316, 675 adults without baseline kidney disease in Northern California over 20 years insisted that even a slight elevation in BP can be an independent risk factor for end-stage renal disease, with the odds ratio of 1.62 (95% CI: 1.27, 2.07) for BP of 120 to 129 (SBP) / 80 to 84 (DBP) mmHg compared to lesser BP levels [START_REF] Leary | The influence of physical activity on the variability of ambulatory blood pressure[END_REF]. It is also hypothesized that a large number of repeated episodes of acute increase in BP may lead to hypertension [START_REF] Timothy | Acute Effects of Ambient Particulate Matter on Blood Pressure: Differential Effectsacross Urban Communities[END_REF] and other cardiovascular events in the future [START_REF] Clement | Prognostic Value of Ambulatory Blood-Pressure Recordings in Patients with Treated Hypertension[END_REF], especially among the susceptible population (people with low immune response systems and with co-morbidity) [START_REF] Tofler | Triggering of acute cardiovascular disease and potential preventive strategies[END_REF].

Effect of long-term elevation in blood pressure on human health

High BP has long been understood as a severe public health problem, and extensive studies such as the Framingham study have classified it as a significant contributor to cardiovascular disease (CVD) and mortality [START_REF] Kannel | Role of Blood Pressure in Cardiovascular Disease: the Framingham Study[END_REF]. In 2010, high BP was marked as one of the leading causes of mortality, responsible for more than 9 million adult deaths worldwide [START_REF] Lim | A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010[END_REF]. A systematic analysis of the global burden of disease, including studies conducted between 1990 and 2017, indicated that high SBP was the first leading risk factor for mortality and morbidity globally, corresponding to 10•4 million deaths and 218 million disability-adjusted life years [START_REF] Stanaway | Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Stu[END_REF]. It is documented that a 20 mmHg increase in SBP or even a 10 mmHg increase in DBP raises the risk of cardiovascular mortality by more than two-fold [START_REF] Lewington | Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies[END_REF][START_REF] Choi | Reconsidering the cut-off diastolic blood pressure for predicting cardiovascular events: a nationwide population-based study from Korea[END_REF]. A study including 4 observational cohorts following 22,626 CVD-free individuals aged 45 to 84 years for 10 years concluded that SBP greater than 130 mmHg is responsible for 28% of coronary heart disease (CHD) incidence [START_REF] Pencina | Quantifying Importance of Major Risk Factors for Coronary Heart Disease[END_REF]. A systematic review including eight prospective studies in 17,698 adults with follow-up time ranging from 3.2-10.9 years documented the following findings [START_REF] Korotkoff | Should Physicians Still Measure their Patients' Blood Pressure or Should Patients Measure their own Blood Pressure at Home?[END_REF]. The risk of all-cause mortality for home and office-measured BP was 1. [START_REF] Zhou | Uncontrolled hypertension increases risk of all-cause and cardiovascular disease mortality in US adults: the NHANES III Linked Mortality Study[END_REF]. Even individual observational studies have recognized BP as a prime risk factor for renal failure [START_REF] Pickering | Definitions, natural histories and consequences[END_REF], stroke, and cardiovascular disease [START_REF] Fuchs | High Blood Pressure and Cardiovascular Disease[END_REF].

Potential risk factors for blood pressure

According to the European Society of Hypertension guidelines (2018) and other several epidemiological studies, behavioral factors including, salt intake, sedentary habits, alcohol abuse, depression, and obesity [START_REF] Williams | ESC/ESH Guidelines for the management of arterial hypertension[END_REF], and environmental factors, such as temporal trend, ambient temperature, and relative humidity [START_REF] Williams | ESC/ESH Guidelines for the management of arterial hypertension[END_REF][START_REF] Brook | Environmental Hypertensionology" The Effects of Environmental Factors on Blood Pressure in Clinical Practice and Research[END_REF][START_REF] Whelton | Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Pr[END_REF][START_REF] Forman | Diet and Lifestyle Risk Factors Associated With Incident Hypertension in Women[END_REF] are potential risk factors for elevated BP. Several experimental studies have also reported a positive association between air pollution exposure and BP [START_REF] Bellavia | DNA Hypomethylation, Ambient Particulate Matter, and Increased Blood Pressure: Findings From Controlled Human Exposure Experiments[END_REF][START_REF] Urch | Acute Blood Pressure Responses in Healthy Adults During Controlled Air Pollution Exposures[END_REF][START_REF] Brook | Inhalation of Fine Particulate Air Pollution and Ozone Causes Acute Arterial Vasoconstriction in Healthy Adults[END_REF].

Public health management of elevated blood pressure

Healthy lifestyle choices can help control elevated BP and reduce the risk of cardiovascularrelated morbidity and mortality [START_REF] Williams | ESC/ESH Guidelines for the management of arterial hypertension[END_REF]. Effective lifestyle changes may be sufficient to prevent drug treatment in people with early stages of hypertension. The recommended lifestyle measures that are shown effective in reducing BP are high consumption of vegetables and fruits, moderation in alcohol consumption, regular physical activity, salt restriction, smoking cessation, and maintaining ideal body weight [START_REF] Williams | ESC/ESH Guidelines for the management of arterial hypertension[END_REF]. There is a catchphrase in hypertension management called "rule of halves". This rule states that half of the people with hypertension are not known or are undiagnosed (rule 1), half of those diagnosed are not treated (rule 2), and half of those receiving medical attention are not controlled (rule 3) (89). Although the concept was coined in the 90s, communities involved in hypertension management argue that the rule is still applicable today [START_REF] Zhou | Association between Undiagnosed Hypertension and Health Factors among Middle-Aged and Elderly Chinese Population[END_REF].

Table 3 compares practices recommended by the European society of hypertension and the American heart association in managing BP according to its levels.

Literature review on air pollution and blood pressure

In this sub-section, we first brief on different methods of measuring BP and estimating air pollution exposure applied by past epidemiological studies investigating the relationship between air pollution exposure and BP. We then elaborate on different study designs adopted by those past studies and enumerate their findings. Resting blood pressure in epidemiological studies is usually measured in two ways: in the clinical setting by scheduling an appointment with each participant [START_REF] Day | Association of Ozone Exposure With Cardiorespiratory Pathophysiologic Mechanisms in Healthy Adults[END_REF][START_REF] Rabito | The association between short-term residential black carbon concentration on blood pressure in a general population sample[END_REF][START_REF] Weichenthal | Exposure to traffic-related air pollution during physical activity and acute changes in blood pressure, autonomic and micro-vascular function in women: a cross-over study[END_REF][START_REF] Pun | Blood pressure and pulmonary health effects of ozone and black carbon exposure in young adult runners[END_REF][START_REF] Baumgartner | Indoor Air Pollution and Blood Pressure in Adult Women Living in Rural China[END_REF] or at participants' residences by home-visiting medical personnel [START_REF] Wu | Blood Pressure Changes and Chemical Constituents of Particulate Air Pollution: Results from the Healthy Volunteer Natural Relocation (HVNR) Study[END_REF][START_REF] Mirowsky | Repeated measures of inflammation, blood pressure, and heart rate variability associated with traffic exposures in healthy adults[END_REF][START_REF] Baumgartner | Highway proximity and black carbon from cookstoves as a risk factor for higher blood pressure in rural China[END_REF]. In some studies, participants are handed a valid oscillometric device during the study period and asked to use it on themselves to measure their own BP at a scheduled time [START_REF] Chaix | MobiliSense cohort study protocol: do air pollution and noise exposure related to transport behaviour have short-term and longer-term health effects in Paris, France?[END_REF]100). This sort of study trains the participants to use the device and techniques to verify the measurements. Usually, three consecutive measurements are taken after the participants have been sitting in a relaxed position for 5 to 10 minutes, and the average of the 2 nd and 3 rd measurements are recorded as a valid resting BP (101).

Measuring resting BP is cost-effective since a single device can be used in monitoring the BP of several participants. Resting BP provides a lot of information obtained under fixed conditions and times. Therefore resting BP has high reproducibility and might be superior to ambulatory BP in that case (102). Resting BP assists patients in monitoring ongoing disease prognosis and improves adherence to antihypertensive treatment. One limitation of using resting BP is that it requires specific skills to perform the measurement. Another limitation is that the device used in measuring resting BP cannot be used on the participants in an ambulatory context, as the person has to rest for several minutes before the measurement is taken.

Furthermore, since the device is always used when the person is entirely at rest, it cannot capture the acute variation in BP caused by different life circumstances, including stress, exercise, and exposure to certain pollutants. Over-consciousness of the person undergoing the examination might introduce a bias in the resting BP measurement, referred to as white-coat hypertension (103). White-coat hypertension is defined as having a high reading of BP only when a clinician measures it (103).

Ambulatory blood pressure

Ambulatory blood pressure monitoring is a new method used in assessing a person's BP as the individual goes about their daily life. This technique of measuring BP is getting popular in the research field (104-107). It is measured with a device consisting of a cuff tied to a participant's upper arm and a small recording device. This device can be worn on the participant's belt (108).

Depending on the device's battery life, it can function continuously for several days without interruption (109). Throughout the wear period, the device records an individual's blood pressure periodically (usually at every 30-minute to 1 hour), based on the configuration given by the person using it (110).

BP monitored in a clinical setting (resting blood pressure) does not necessarily reflect the levels that individuals experience when they go about in their daily lives (110). Thus, guidelines suggest that a clinically classified hypertensive person should also be confirmed with . Moreover, some studies show that ambulatory-measured BP is more closely related to organ damage than isolated measurements in clinical settings (112).

Ambulatory BP monitoring might effectively study the changes in BP corresponding with participants' daily activities and sleep patterns. As ambulatory BP is taken throughout the day, in the morning, in the evening, and at night during sleep, it can be used as an essential tool for diagnosing white-coat and masked hypertension (103). Masked hypertension is when a person's BP is lower in clinical settings but higher at other times (103). Limitations of ambulatory BP measurements include validity and reliability of the device being used, post-processing of the collected data, and needing experts to interpret it. Furthermore, ambulatory readings are often used in analyzing the acute changes in BP and thus may not help study the long-term effect on BP. Besides, the interpretation of ambulatory BP always demands experts since it has to be done taking into account the concurrent physical activity levels, considering that the relationship between ambulatory BP and physical activity varies widely between individuals (69).

In conclusion, it is tough to justify which BP measuring technique is best, resting or ambulatory, as they have advantages and limitations. Therefore, the choice of the BP measuring method should depend on the purpose and logistic constraints of the study. For example, researchers interested in acute BP changes as an effect of air pollution exposure may choose the ambulatory technique. In contrast, others determined to evaluate an association between long-term exposure to air pollutants and BP might prefer resting BP. Whether using resting or ambulatory BP, it is widely recommended for studies to follow authentic guidelines, such as "2021 European Society of Hypertension practice guidelines for office and out-of-office blood pressure measurement" (113), to improve the accuracy of the measurements. One of the most common traditional approaches in past epidemiological studies evaluating associations between air pollution exposure and BP is using the air pollution measurements recorded by a nearby stationary air quality monitoring station equipped with a certified reference device (114). A single station of this kind is responsible for estimating the outdoor exposure level for a large geographical region. Station-monitored measurements are primarily used in studies analyzing the long-term effect of air pollution exposure on health events, such as annual exposure to air pollution and BP (115). Since measurements reported by a static station represent air pollution exposure for a larger geographical area, they are mostly used in ecological studies, e.g., evaluating the association between daily ambient pollution levels and hospital visits with particular health events in a city (116).

Ambient measurements from fixed stations neglect the spatial and temporal variations in personal exposure due to mobility and other life circumstances, which is far higher than the variations captured by these conventional methods (117). Therefore, air quality levels retrieved from fixed stations may not represent the actual personal exposure levels while estimating the acute effect of air pollution on health events (such as BP). Furthermore, a study comparing the personal vs. outdoor measured air pollution exposure reported that air pollution measured in a person's breathing zone usually exceeds ambient concentrations (measured by a fixed station), and indoor sources appeared responsible for the observed differences (118).

Measuring and modeling air quality at residence

There are several methods used under this broad heading to estimate the air pollution concentration at participants' residences when investigating associations between air pollution exposure and BP, which are as follows;

Satellite

This method uses optical instruments attached to satellites in order to measure air pollution concentrations. It is believed that the aerosol optical thickness can be derived from images taken over the land and can be used to estimate the aerosol concentration (119). The spatial resolution reported with this method is usually regarded as low-resolution results and presented in degrees of latitude and longitude (120). The discrepancies in conclusions between satellite-measured air pollution and air pollution reported with other methods enabling high spatial resolutions indicate that more accuracy is needed in exposure classification at the personal level to develop reliable associations between air pollution exposure and health events (120).

Air pollution concentrations can vary significantly in space and time because they are influenced by different factors such as temperature, humidity, nearby emission sources, traffic density, and ventilation (121). Thus, the correlation between ambient pollution level and personal exposure can be very low (R 2 = 0.09-0.83, median 0.54) (121), suggesting that measurements from nearby fixed monitoring stations or satellite readings applied at the residence could be poor predictors for personal air pollution exposure.

Land use regression

Land use regression combines air pollution monitoring at a small number of locations and the development of stochastic models, including variables obtained through geographic information systems to predict the air pollution concentration of a large number of un-sampled areas (122). This method also includes other factors as explanatory variables, such as altitude and meteorological parameters. In standard practice, it is used to predict outdoor air pollution exposure. This method might help estimate the long-term (annual) exposure to air pollution.

Still, it might not be a good proxy for short-term (hourly) personal exposure, as it doesn't consider a person's mobility and indoor emission sources (118).

Air quality models

Air quality models are based on ambient air pollution measurements reported by either satellites or static monitoring stations. They report air pollution concentration in high spatial resolutions by considering atmospheric dispersion, transport dynamics, local emission sources, meteorological factors, industrial facilities, atmospheric chemistry, and temporal information (123). This method is considered quite reliable in estimating air pollution exposure for large numbers of people and is very successful in predicting future exposures.

Mobility-based personal sampling

Measuring an individual's breathing zone concentration for targeted air pollutants with personal sensors is the only personal sampling method used in past observational epidemiological studies investigating the adverse effect of air pollution exposure on BP.

Based on the characteristics of targeted air pollutants, several devices are used. Light scattering devices measure the particle concentration by detecting the amount of light scattered while particles pass through a sensor chamber. Another personal device used is an electrochemical sensor, which measures gaseous pollutants based on the redox reactions of the specific pollutant at the sensor electrode surface that produces a measurable current proportional to the pollution concentration. These sensors are affected by relative humidity and temperature; therefore, they need correction afterward during post-processing. The pros and cons of using a personal sensor in air pollution epidemiology are discussed in section 1.7.

Commonly used study designs in the past studies

Based on the type of the effect under investigation, chronic or acute, epidemiologists have adopted several study designs in order to explore the association between air pollution exposure and BP.

Experimental study design: a) clinical randomized study

This study design randomly assigns blinded participants either to exposure or a control group, where the control group is exposed to particle-free air (PFA) and the exposure group to a specific air pollutant at a certain concentration level (non-lethal) [START_REF] Urch | Acute Blood Pressure Responses in Healthy Adults During Controlled Air Pollution Exposures[END_REF]124). After the decided exposure period, all participants' resting BP is recorded in clinical settings to test the association. A case cross-over is another experimental design commonly used in air pollution research. A certain number of healthy participants with no prior history of respiratory symptoms are recruited and randomly exposed to a particular type of air pollutant at a specific concentration (non-lethal) for the decided period or to particle-free air (control) in randomized order with a certain washout period [START_REF] Bellavia | DNA Hypomethylation, Ambient Particulate Matter, and Increased Blood Pressure: Findings From Controlled Human Exposure Experiments[END_REF]125). Resting BP is immediately taken after the period of exposure for analysis purposes. In the experimental study design, ethical approval and informed consent are most before conducting the study. This study design is popular in clinical settings to explore the short-term (within hours) effect of air pollution because the usual duration of exposure is concise due to health and ethical reasons.

b) Non-clinical randomized study

This study design is commonly adopted in the non-laboratory setting among healthy adults with no air pollution-related medical conditions. For an equal duration, people are randomly assigned to different scenarios with different levels of air pollution exposure, such as randomly assigning people wearing air pollution sensors to different walking tracks [START_REF] Pun | Blood pressure and pulmonary health effects of ozone and black carbon exposure in young adult runners[END_REF][START_REF] Mirowsky | Repeated measures of inflammation, blood pressure, and heart rate variability associated with traffic exposures in healthy adults[END_REF] or monitoring the level of personal air pollution exposure among women during cooing sessions (104,126). Some studies even adopt a case cross-over design within this study type [START_REF] Pun | Blood pressure and pulmonary health effects of ozone and black carbon exposure in young adult runners[END_REF][START_REF] Mirowsky | Repeated measures of inflammation, blood pressure, and heart rate variability associated with traffic exposures in healthy adults[END_REF]104). Usually, in this study type, resting BP is measured after the period of exposure, or ambulatory BP is measured throughout the exposure period at specific intervals. This study design is more prevalent in studying the acute (within hours) effect of air pollution, as the period of exposure assignment is very short.

Observational study design: a) Cohort study

Researchers often use the data from well-established cohort (a group of people with similar characteristics) studies to evaluate the long-term adverse effect of air pollution on BP (115,(127)(128)(129)(130)(131), where each participant's resting BP is measured periodically. In this study type, average yearly concentrations of air pollutants from the nearby stationary monitoring station are generally assigned as a proxy for individuals' exposure metrics (115). Another popular method adopted for exposure assessment is estimating air pollution concentration at participants' homes or office/school addresses using different techniques, such as land-use regression models (128,130), satellite-based models using geographic information (129), or air quality models (131,132).

b) Panel study

In a panel study, randomly selected participants are usually followed up for several days or weeks, where BP measurements are taken at specific intervals of the follow-up duration. These studies used resting [START_REF] Weichenthal | Exposure to traffic-related air pollution during physical activity and acute changes in blood pressure, autonomic and micro-vascular function in women: a cross-over study[END_REF][START_REF] Baumgartner | Indoor Air Pollution and Blood Pressure in Adult Women Living in Rural China[END_REF][START_REF] Wu | Blood Pressure Changes and Chemical Constituents of Particulate Air Pollution: Results from the Healthy Volunteer Natural Relocation (HVNR) Study[END_REF](133)(134)(135) or ambulatory BP measurements (104-106). Most studies in this category used personal monitors to measure personal air pollution exposure [START_REF] Weichenthal | Exposure to traffic-related air pollution during physical activity and acute changes in blood pressure, autonomic and micro-vascular function in women: a cross-over study[END_REF][START_REF] Baumgartner | Indoor Air Pollution and Blood Pressure in Adult Women Living in Rural China[END_REF](104)(105)(106)135), while few of the studies used air quality levels reported by the nearby fixed station [START_REF] Wu | Blood Pressure Changes and Chemical Constituents of Particulate Air Pollution: Results from the Healthy Volunteer Natural Relocation (HVNR) Study[END_REF]133,134).

C) Cross-sectional study

A cross-sectional study is a less popular study design adopted to analyze the association between air pollution exposure and BP. Usually, this study design relies on a single resting BP measurement per individual and extracts the air pollution exposure by matching the time of BP recording with the nearby air quality station (114,136,137).

Irrespective of the study design adopted, most studies investigated the delayed effect of air pollution exposure on BP developing daily or hourly exposure lags.

Evidence linking air pollution exposure to blood pressure

Elevated BP has been understood as a possible pathophysiological pathway linking exposure to air pollution and the increased risk of cardiovascular morbidity and mortality [START_REF] Sanidas | Air pollution and arterial hypertension. A new risk factor is in the air[END_REF].

Accumulated epidemiological and experimental evidence establishes the association between air pollution exposure and BP. Many studies reviewed in this section provided lag patterns of exposure to investigate the immediate and delayed effect of air pollutants on BP. While 0-day lag meant the present-day exposure (averaged over 24 hours before BP measurement), 1-day lag meant only the day before exposure (averaged over 24 to 48 hours before the BP measurement), and so on. Studies also tested cumulated lags, e.g., 0-2 days lag meant averaging the exposure of the current day and the day before, while1-2 days lag meant the previous 2 days averaged exposure without including the present day. The same theory of explanation applies to hourly exposures as well.

Furthermore, air pollution concentration measured with the nearby fixed station is referred to as station-measured air pollution exposure, air pollution concentration estimated at individuals' residential address applying different methods mentioned in the previous sub-section, 1.5.2.2, is referred to as residential exposure, while air pollution concentration measured with a personal device/sensor is referred to as personal exposure in the following paragraphs.

Annual air pollution exposure and BP

A recent review paper suggested that short (daily) and long-term (annual) exposure to stationmeasured particulate matter are associated with increased BP (138). A cohort study including A population-based study in Teheran concluded that 0-day lag to 14-day lag exposure to stationmeasured NO2 exposure raised resting SBP, where 6-day lag had the strongest effect (β =3.00 mmHg, per 10-μg/m 3 ), while no association was found with resting DBP (146). Nevertheless, a cohort study documented no association between the previous 7-day averaged residential NO2 exposure and resting BP ( 128). An area-level randomized controlled case cross-over study including 66 healthy adults found no association between 1-day lag station-measured NO2 and resting BP (147). A Chinese study conducted among 9,354 children reported positive associations of station-measured pollutants (NO2 and O3) exposure, averaged over different periods before BP measurements (1 day to 5 days), with both resting BP (148).

A study among type 2 diabetes mellitus patients reported that previous 5-day averaged exposure to station-measured O3 was associated with decreased resting SBP (β =5.2 mmHg 95% CI: -8.60, -1.80, per 13 ppb), whereas no association was documented with resting DBP (134). A

Taiwanese study concluded that an IQR increase in station-measured O3 was associated with a 0.37 mmHg (95%CI: 0.04 to 0.69) increase in resting DBP but not with resting SBP 3-day after exposure (100). A Teheran's study among 4,580 healthy adults aged 20-69 years documented that station-measured O3 exposure (0-day to 14-day lag) decreased resting SBP, where 3-day lag had the strongest effect (β =-0.35 mmHg, per 10-μg/m 3 ), while reported no association with resting DBP (146). A randomized controlled case cross-over study including 61 healthy adults found no association between 1-day lag station-measured O3 and resting BP (147).

A study in Teheran concluded that 0-day lag to 14-day lag exposure to station-measured CO had no effect on resting SBP but decreased resting DBP, where 9-day lag had the strongest effect on DBP (β =-0. [START_REF] Baumgartner | Indoor Air Pollution and Blood Pressure in Adult Women Living in Rural China[END_REF].

A pooled analysis between three European counties showed that a 10 mg/m 3 increase in prior 24-hour averaged exposure to station-measured PM2.5 decreased resting DBP by 0.27 mmHg (95% CI: -0.5, -0.03), whereas no association was documented with resting SBP (151). A randomized cross-over study found no association between previous 24-hour averaged personal PM2.5 exposure and resting BP [START_REF] Mirowsky | Repeated measures of inflammation, blood pressure, and heart rate variability associated with traffic exposures in healthy adults[END_REF]. Likewise, a Chinese panel study among 280 women reported no association between previous 24-hour averaged personal PM2.5 exposure and resting BP [START_REF] Baumgartner | Highway proximity and black carbon from cookstoves as a risk factor for higher blood pressure in rural China[END_REF].

Among 89 healthy adults, a cohort study found no association between previous 24-hour averaged personal NO2 exposure and resting BP [START_REF] Day | Association of Ozone Exposure With Cardiorespiratory Pathophysiologic Mechanisms in Healthy Adults[END_REF]. A cross-sectional study documented that previous 24-hour averaged station-measured NO2 exposure was positively associated with resting SBP only in the warm season (July-September), whereas no association was found with resting DBP in either warm or cold seasons (October-December) (137). Similarly, A randomized controlled case cross-over study including 61 healthy adults found no association between 24-hour averaged station-measured NO2 exposure and resting BP (147).

A cohort study conducted among 89 healthy individuals identified that a 10-ppb increase in previous 24-hour averaged personal O3 exposure was associated with a 2.8% (95% CI: 0.6%, 5.1%) increase in resting DBP but did not report an association with resting SBP [START_REF] Day | Association of Ozone Exposure With Cardiorespiratory Pathophysiologic Mechanisms in Healthy Adults[END_REF] [START_REF] Urch | Acute Blood Pressure Responses in Healthy Adults During Controlled Air Pollution Exposures[END_REF].

A Canadian cross-over study among 53 healthy non-smoking women applied multi-pollutant models to evaluate the associations between exposure to air pollutants and resting BP and reported the following findings [START_REF] Weichenthal | Exposure to traffic-related air pollution during physical activity and acute changes in blood pressure, autonomic and micro-vascular function in women: a cross-over study[END_REF]. Each 24 ppb increase in the previous 3-hour averaged personal O3 exposure was associated with a 2.49% (95% CI: 0.141, 4.84) increase in resting SBP and with a 3.26% (95% CI: 0.0117, 6.51) increase in resting DBP, whereas the study did not document any associations between other air pollutants (NO2 and PM2.5) and resting BP [START_REF] Weichenthal | Exposure to traffic-related air pollution during physical activity and acute changes in blood pressure, autonomic and micro-vascular function in women: a cross-over study[END_REF].

A randomized controlled crossover study reported a decreasing effect of personal NO2 exposure (from domestic gas cooking) on resting SBP and DBP after 45 and 90 minutes, relative to a control group (126). Among 30 healthy adult runners, a case cross-over study reported an association between the previous 30-minute averaged personal BC exposure and resting BP (only with SBP), whereas no association was reported between personal O3 exposure and resting BP [START_REF] Pun | Blood pressure and pulmonary health effects of ozone and black carbon exposure in young adult runners[END_REF].

A panel study conducted on 65 subjects with metabolic syndrome documented that a 1-μg/m 3 increase in personal exposure to BC during the previous 10 hours was related to a 0.53 (95% CI: 0.17, 0.89) mmHg increased ambulatory SBP, and a 0.37 (95% CI: 0. Thus more studies are needed to understand better the potential acute effect of air pollutants on BP.

Limitations of previous epidemiological studies evaluating the adverse effect of air pollution exposure on blood pressure

Historical events such as Meuse valley fog and the London Smog have helped epidemiologists understand the correlation between air pollution exposure and mortality; however, numerous exposure-related questions have been left unanswered. Decades after the piling of these ecological studies, scientists have made much progress in studying the relationship between personal air pollution exposure and acute health events (such as BP), overcoming the inherent limitation of past epidemiological studies (152). Several conventional methods such as stationary stations, air quality models, land use regression, satellite imaging, etc. (explained in sub-section 1.5.2) have been applied in past epidemiological studies to assess air pollution exposure. These approaches estimate the exposure data for large cohorts, resulting in exposure misclassification at the individual level and increasing the uncertainties of their health effects in epidemiological studies (153). Another limitation observed in past studies is not adopting the appropriate statistical techniques to consider the high correlation between air pollutants while estimating the independent effect of each air pollutant on BP. Furthermore, even though individuals are simultaneously exposed to more than one air pollutant, no study, to our knowledge, has attempted to estimate a joint effect of targeted air pollutants mixtures on BP.

These two limitations, choice of inappropriate statistical methods for handling correlated pollutants and exposure misclassification are discussed at length in the following paragraphs.

Limitations in statistical methods for analyzing highly correlated air pollutants

Usually, several air pollutants are highly correlated in the atmosphere as they share common sources (154). For example, the China National Environmental Monitoring Center reported that areas with a high mean concentration of NO2 also had a high concentration of PM2.5, whereas NO2 and O3 were highly negatively correlated (155). It is also suggested that it is challenging to identify the causal link between a specific air pollutant and a health outcome due to simultaneous exposure to all or a subset of outdoor pollution species (152). Despite the clear association between air pollution and mortality in historical epidemiological episodes (e.g.,

London smog), it is tough to understand which particular air pollutant was responsible for the increase in daily deaths because the study population was simultaneously exposed to more than one air pollutant, which were highly correlated with each other. A re-analysis of the Harvard six city study concluded that more than one agent in the ambient pollution maybe responsible for increased death rates (156). Likewise, a recent review of studies that used multi-pollutant models to determine the combined health effect of two or more air pollutants reported that the health effect of two pollutants was similar to or a little stronger than the single-pollutant relationship reported with either of the pollutants (157). This finding suggests that adding up the detrimental effect of the individual pollution species obtained from a standard regression analysis would massively overestimate the health effect of the mixture of the ambient pollutants. Furthermore, it is suggested that the individual effect of air pollutants on health outcomes cannot be readily determined due to the interrelationships among pollutants (such as NO2 formed from O3), the influence of meteorological conditions, and the sharing of common sources (158).

Most previous studies have focused on understanding the effects of air pollutants, PM2.5, NO2, O3, CO, and BC, supposing that each pollutant has an isolated impact on BP. This single pollutant approach ignores the fact that these air pollutants are highly correlated and may interact synergistically or antagonistically, resulting in effect modification (158,159).

Consequently, findings from single-pollutant models might be misleading or hard to interpret (159). Therefore, it is highly encouraged to adopt a multi-pollutant approach to estimate the health effect of ambient pollution, which is always encountered as a complex mixture (158,160,161).

One of the methods suggested under the multi-pollutant approach, which a few previous studies have also adopted, is using multi-pollutant regression models to estimate the individual effect of air pollutants on BP by adjusting for others (161). In this approach, interactions are evaluated by adding product terms among targeted air pollutants into the models and testing for the fitness of such models (158). These multi-pollutant regression models are pretty helpful in identifying the independent as well as interacting effects of air pollutants if the correlation between them is not very high (161). However, based on the study sample, such analyses lack statistical power to test all the possible interactions when more than two air pollutants are of interest.

Furthermore, the interpretation of how air pollutants may interact depends more on how the interaction terms were specified in the regression models rather than how they actually interact in the atmosphere (158). In addition, effect estimates and interaction terms obtained from these regression models become highly unreliable when incorporating two or more pollutants that are highly correlated (such as PM2.5 and NO2, or O3 and NO2) (161), referred to as multicollinearity issue (162).

This existing methodological limitation could be overcome using multi-pollutant mixture methods (158). To our knowledge, this method has never been adopted in evaluating the associations between air pollution exposure and BP. Statistical methods, including weighted sum quantile regression (WQS) ( 163), quantile G-computation (164), and Bayesian kernel machine regression (BKMR) (165), have long been used in evaluating other health effects of air pollution mixture. Such approaches typically estimate a joint effect of the mixture components while considering multicollinearities and interactions between targeted air pollutants. Methods such as quantile G-computation and BKMR, as opposed to WQS, are also capable of handling exposures that may be related to the outcome in either direction (both positive and negative) and are known for modeling non-linear and additive effects (164,165).

The methodological detail on the multi-pollutant mixture method, i.e., quantile G-computation, used in this thesis is described fully in the method section.

Exposure misclassification

Standardizing the concept of exposure to air pollution, Wayne Ott defined exposure as an event that happens when an individual comes in contact with air pollution of a concentration c at a particular time t (166). Exposure misclassification is generally considered as the difference between the exposure status assigned and the actual exposure of the study participants (157).

Differences between residential and work microenvironments, choice of transport modes and route while commuting, time spent indoors, and sources of indoor pollution make personal exposure very different from those monitored by static stations. Generally, epidemiological studies assign exposure to air pollutants at the residence, estimated from one or several nearby air monitoring stations or air dispersion models (114,167). This approach of exposure assessment might not be suitable for evaluating the very short-term effect (within minutes) of air pollution, as measurements reported by static stations are generally averaged over a long period (1hr-24hr) and represent large underlying spatial distribution (168). Thus, this method neglects the spatial and temporal variations in personal exposure (due to mobility and other life circumstances), which is far higher than the variations captured at fixed stations and results in exposure misclassification at the individual level (117). Besides, these stations are located above the ground level, away from the main highways, railroads, and other identified sources, and therefore fail to represent the values that a person is exposed to while being present in different microenvironments with various pollution sources (169).

The methods estimating air pollution levels at participants' residential addresses at daily or hourly levels do not consider indoor emission sources, building ventilation, and participant mobility, thus resulting in exposure misclassification at the individual level (118). This sort of exposure misclassification might hugely bias the effect estimates when modeling the association between air pollution exposure and health outcomes, and the bias can be towards the null or away from it, depending on the direction and magnitude of the exposure misclassification among diseased and non-diseased people (170). Therefore, past studies seem to have paid less attention to classifying exposure status than measuring disease status and other confounding factors (170). For example, a study in the UK illustrated that NO2 measured 50 meters apart differed by a two to a three-fold ratio in concentration (172), while the concentrations measured by air quality stations are used to infer exposure status over several square kilometers. A review including 18 studies concluded that the correlation coefficient ranged between 0.09-0.83 (median: 0.56) between station-measured and personally measured PM2.5 (121). Another review even found very poor correlations (often approaching the null) between these two exposure versions (173). Even though exposure misclassification is a prevalent issue in epidemiological studies, it has rarely been adequately accounted for while evaluating the association between air pollution and health outcomes (174). In future studies, eliminating the existing error in exposure classification might help better understand the relationships between air pollution and health events (175).

Goldman et al. documented that "Health risks per unit increase in pollutant concentration associated with primary air pollutants are predicted to be attenuated by up to 80% when air pollution levels measured by fixed stations are used [instead of spatially refined exposure metrics]" (176). Thus, using a portable device for continuous measurement of person-level exposure reduces exposure misclassification (117,177), especially when studying in-vehicle exposure or exposures related to transport behavior, which varies highly in space and time (178).

The inhaled dose corresponds to the volume of air pollution entering the lungs while an individual comes in contact with the concentration c over time t (166). Physiological doses are affected by several factors, such as an individual's health conditions, the level of physical activity a person is involved with, body weight, and environmental determinants, including temperature and humidity, along with the chemical composition of the air pollutant.

Furthermore, the inhaled dose not only depends on the air pollution concentration to which an individual is exposed, but it is also directly proportional to the minute ventilation. Estimating minute ventilation is a complex process as it usually is derived stochastically as a function of an individual's characteristics, such as age, sex, breathing rate, heart rate, or Metabolic Equivalents/intensity of physical activity (179) (see article 3.1). Considering inhaled doses over personally measured concentrations may help overcome measurement errors and improve accuracy while estimating air pollution-related health risks (180). This statement is particularly true when the aim is to establish the health benefits of cycling and walking, as the higher inhaled dose of pollutants may offset some of the health benefits gained through physical activity (181).

Furthermore, it is emphasized that using personally monitored air pollution measurements over

Importance, limitations, and challenges of personal monitors in air pollution exposure measurements

The previous section highlighted that despite their interest, conventional epidemiological studies on air pollution are flawed with large uncertainties because of exposure misclassification. In addition to being costly, monitoring stations are installed at strategic locations and report reliable pollution concentration readings but only for restricted geographical areas. Using air quality levels from fixed monitoring stations is noted as a major issue when monitoring air pollution over a larger spatial area in heterogeneous environments with different emission sources (184). Therefore, measuring air pollution at high spatial and temporal resolution is essential because of the high location-time dependency of atmospheric pollution concentrations, especially in the urban setting, where the variability of point pollution concentrations is very high (185). In addition, to be able to investigate the very acute (within an hour or minutes) effect of air pollution on intermediary health outcomes (such as ambulatory BP), exposure to air pollution should be measured personally at high temporal and spatial resolutions (117). This is possible with portable and flexible devices that can be attached to individuals' bodies during the study period with minimal disturbances to their daily routines.

These sensors/devices are manufactured by applying micro-fabrication techniques and using microfluidic, optical, and nanostructured elements, making them lightweight, inexpensive, compact, and low-power-consuming products (184).

While air quality data obtained from fixed stations and modeling techniques have helped epidemiologists understand the long-term effect of air pollution exposure on human health both at ecological and individual levels, the use of personal sensors might allow researchers to understand further the acute effect (at several temporal intervals; minutes, hours and days)

produced by air pollution exposure on human health at the individual level. Moreover, some studies have highlighted that personal exposure to air pollution may induce different responses in humans than background ambient levels due to the variation in sources and chemical compositions (186-188). Thus, using personal sensors in studies may also help understand better the acute effect caused by personal air pollution exposure on human health, distinct from exposure to background ambient pollution. Moreover, using these modern sensors improves accuracy in measuring personal air pollution exposure in various settings (outdoors/indoors), such as during sports, cooking, and commuting, thus helping researchers understand the sourcerelated health effects of air pollution in different microenvironments. Plus, these modern devices are often complemented with data management software packages provided by the manufacturer to simplify post-processing and data visualization (189).

A significant drawback of these air quality sensors is that their accuracy is low compared to standard devices (184). In addition, the detection limit of these personal sensors is higher than that of the reference instrument. The massive use of these devices by non-experts, unfamiliar with the sensor's limitation, might result in a large volume of inaccurate air quality measurements, which may steer policy decisions in the wrong direction (190). Furthermore, scientists have remarkably raised the requirement of a standardized calibration process to ensure sensors' data quality before being deployed to participants (191)(192)(193). Some sensors, such as MicroAeth AE51 for monitoring BC, may produce error readings when the sensors are exposed to mechanical shocks or intense vibrations (194). Some optical particle counter sensors for measuring PM2.5 may be affected by temperature and humidity (195). Therefore, expert advice is essential during data post-processing to overcome these sensor-specific shortcomings.

One of the major challenges in using air pollution sensors is ensuring not to overburden the participants with many sensors in the name of monitoring a wide range of air pollutants. A US-based study reported that manufacturers avoided the technical framework set by the US Environmental Protection Agency for benchmarking their air quality sensors against the most accurate monitors (192), which eventually increased the use of non-calibrated sensors in pollution-related studies. Another challenge is correctly identifying when the device was not worn by participants and left at home because not being able to do so would result in massive exposure misclassification when cross-referencing the air quality data with mobility patterns.

In the case of using more than one sensor simultaneously, the research team might have to spend more time with participants to educate them on the dos and don'ts required to handle each personal sensor. More importantly, these personal sensors may not be practically useful in studying the long-term effect (annual) of air pollution exposure for several reasons; 1) personal sensors may not work continuously for more extended periods (more than a week or two due to power backup issues, 2) it would not be practically possible for participants to wear sensors for several months, and 3) the research budget would go very high compared to using free-available data from fixed stations due to logistic and staffing costs.

Rationale, aim, and objectives of this thesis

We have sufficient evidence that air pollution has an adverse impact on human health globally;

however, an inappropriate choice of exposure monitoring technique resulted in exposure misclassification in some studies estimating short-term (hourly or daily) health effects.

Likewise, improper statistical methods applied in past studies in analyzing correlated air pollutants hindered the production of reliable and trustworthy epidemiological evidence.

Despite a longstanding interest of scientists in air pollution epidemiology, the progress of widening the knowledge in this field is impeded, among other aspects, by the difficulties in measuring personal air pollution exposure in free-living conditions. In this introduction, we have discussed the paradigm shift in air quality monitoring with the evolution of personal sensors. However, there are still critical challenges to using personal sensors in epidemiological studies. One of which is validating and calibrating the devices in the study context before deploying them in real-life settings.

Although traveling accounts for a large share of individuals' daily total personal exposure and inhaled doses, data about transport-related air pollution are challenging to collect. Until now, probably for technical and logistic difficulties in estimating ventilation rate in real-life settings from physiological measurements (heart rate, breathing rate, and/or acceleration), limited studies have reported inhaled doses of air pollutants in non-experimental settings especially considering peoples' mobility.

The present thesis takes one step forward in developing this new paradigm by undertaking a comprehensive study of personal air pollution exposure in real-life conditions, its causes and effects, accounting for a relatively large spectrum of air pollutants in all microenvironments and at all periods of the day. In order to make this possible, we analyzed data from the MobiliSense multi-sensor study, comprising physiological and behavioral measurements (ambulatory BP, heart rate, breathing rate, and physical activity) as well as information on mobility, transport modes taken, places visited, and personal exposure to noise and several air pollutants in more than 280 participants. We adopted different methods appropriate to our data and study type to estimate minute ventilation from the accelerometer, heart rate, and breathing rate, in order to compute inhaled doses of air pollutants (detailed in article 3.1). More concretely, we tabulated air pollution exposure according to mobility data (places visited and transport modes used) obtained using GPS and mobility surveys. This approach of classifying participants' whereabouts was conducted to a) isolate the time segments when people were traveling and evaluate the air pollution exposure levels across different transport modes at the trip stage level; b) control for the confounding effect of microenvironments (in-home, out-ofhome, and transport) on the relationship between air pollution exposure and BP outcomes; and c) assess the associations between air pollution exposure and BP stratified according to different microenvironments.

Humans are simultaneously exposed to a complex mixture of air pollutants depending on the variation of pollution sources in the present microenvironment. Thus, it is crucial to estimate the independent effects of air pollutants and the joint effects of cocktails of air pollutants using a mixture modeling approach. Therefore, we evaluated the joint effect of air pollution mixture on BP across several exposure windows, which has never been explored before, by taking advantage of recent developments in 1) statistical methodologies and 2) state-of-the-art sensor technologies. Notably, quantile G-computation (164) offers new capabilities in estimating the joint effect of complex exposure mixtures on health outcomes, considering collinearities and interactions between components. Additionally, rapid advancement in sensor technologies enables the collection of highly resolved personal exposure and BP measurements as individuals go about their daily lives.

Our study entirely focuses on ambulatory BP measurements because it is believed that frequent acute increases in BP may lead to hypertension [START_REF] Timothy | Acute Effects of Ambient Particulate Matter on Blood Pressure: Differential Effectsacross Urban Communities[END_REF] and other adverse cardiovascular conditions in the future [START_REF] Clement | Prognostic Value of Ambulatory Blood-Pressure Recordings in Patients with Treated Hypertension[END_REF]. Furthermore, it is suggested that ambulatory BP can capture small variations in BP and minimize the effect of other factors on measurements by virtue of intensive recording in real-time, making ambulatory BP readings more reliable than clinical measurements (196).

Moreover, some studies show that ambulatory BP is more closely related to organ damage than isolated measurements in clinical settings (112).

While the long-term (annual) as well as short-term (within hours) effects of air pollution exposure on resting BP are of great concern and have been extensively studied, the short-term (within minutes) effect of air pollution exposure on ambulatory BP is less understood. This thesis aims to apply sensor-based approaches to better estimate the relationship between the exposure to air pollutants and the acute ambulatory blood pressure response.

The specific empirical research objectives were to:

2 Analyze the relationship between personal use of transport modes and personally measured BC exposure at the level of trip stages (article 3.1).

3 Evaluate the effect of very short-term exposure (less than an hour) to several air pollutants (NO2, NO, CO, O3, BC, and PM2.5) on ambulatory BP in everyday life conditions (articles 3.2. and 3.3).

4 Estimate the joint effect of the mixture of air pollutants (NO2, NO, CO, O3, and BC) on ambulatory BP (article 3.3).

Moreover, the air pollution sources and the chemical composition/toxicological profile of the air pollutants vary according to the microenvironments encountered during daily travel.

Therefore, we extended objectives 2 and 3 to answer how the exposure context (microenvironment) could modulate the associations between exposures to specific air pollutants or air pollution as a mixture and ambulatory BP. Exposure to air pollution in our study was defined in two ways a) personal breathing zone concentration and b) inhalation.

Methods

Sampling database and selected participants

This Ph.D. project uses the data from the first wave of the MobiliSense study conducted in the Grand Paris (Paris City and some surrounding municipalities) from May 2018 to October 2020.

To ensure that diversity in neighbourhood conditions from the Grand Paris area would be In order to model the determinants of participation in the study, we created a database comprising all the individuals who were finally not recruited and all the recruited participants.

If the recruited participants were new members of a household already recorded in the 2013-2014 censuses, they were added to the database. If they were from a new household in the dwelling, former household members were removed from the database, and only the participating members from the new household (but not eligible non-participating members for which we had no information) were added to the database.

For all non-participating individuals from the sampling database, socioeconomic and demographic characteristics, including date of birth, sex, education, employment status, nationality, and marital status, were assessed from the census database, whereas for participating individuals, this information was updated at the time of recruitment. Participants were requested to list their current household members during the baseline survey. It helped us to identify that 50 individuals listed in the 2013-2014 census were no longer living in these dwellings, so we deleted them from the database. Likewise, in the households with at least one recruited participant, few members moved in after the census; we could add them to the database if they participated in our study. Nevertheless, we could not add them if they refused to participate, as we had no socio-demographic information about them. Regarding nonparticipating dwellings, we had no way to obtain the current list of household members, so we could not apply the aforementioned correction and update process to those. After applying the processing steps described above, our database had 49027 individuals living in 31970 dwellings from 234 neighborhoods, of which 289 individuals living in 259 dwellings participated in our study. Every individual agreeing to participate in the study was asked to sign an informed consent form before joining the study.

Modeling the probability of participating in the study

The recruitment of participants in our study likely implied selection biases. To investigate them, a binomial multilevel model (log link) estimated with the Markov chain Monte Carlo method with random intercepts at dwelling and neighborhood levels was run with 400,000 iterations to investigate determinants of the probability of participating in the study.

Table 1 indicates the characteristics of the 289 participants involved in the study and reports associations with the probability of participating in the MobiliSense Study. Table 1 shows that the probability of participating in our study was not different between genders. It also shows that older participants, people with higher education, inactive people and to a lower extent, selfemployed people and people with stable and unstable jobs (compared to unemployed people), and French citizens had a higher probability of participating in our study.

Overview of the study protocol

Upon enrolment, participants were requested to fill in a standardized questionnaire to collect information on potential confounders such as age, alcohol consumption units, household income, education attainment, employment status, marital status, nationality, and gender, as well as many other factors not used in the present thesis. Participants' body mass index (BMI) was calculated at the time of enrolment as weight in kilogram per square of height in meters, based on measurements conducted by research assistants at the dwelling of participants. As illustrated in Figure 1.6, participants were followed-up for 6 continuous days, where each day started at 3:00 am on the present day and ended at 2:59 am on the very next day. Over these days, participants carried a different set of sensors with them on different study days. On all study days, participants were followed with a BT-Q1000XT GPS receiver and an accelerometer (ActiGraph wGT3X+). Participants carried a MicroAethalometer sensor (MicroAeth AE51) to monitor the BC concentration over the first four days. Participants wore a PAM sensor (personal air quality monitor) over 4 days (days 1, 5, and 6) to measure air pollutants such as NO2, NO, CO, O3, and PM2.5. This sensor was also able to measure surrounding temperature and relative humidity. The noise exposure was monitored with an SV 104A dosimeter over the last 4 days of the study. Likewise, the ambulatory BP of participants was measured with an Arteriograph 24 (AG24) device only on the 1 st and 3 rd day of the study, whereas the breathing rate and heart rate of the participants were monitored with a Biopatch sensor over 4 days of the study (days 2, 4, 5 and 6). Participants were requested to carry all devices on the days specified in figure 1.6 from wakeup to bedtime. In addition, participants were asked to maintain a travel diary throughout the study period detailing the timetable of places they had visited and trips they had taken, along with information on their choice of transport mode. In article 3.2, we analyzed the association between short-term BC exposure and ambulatory BP.

As the devices named PAM, used for monitoring air pollutants such as NO2, NO, CO, O3, and PM2.5, were under calibration process at The University of Cambridge during this time, we could not use these air pollutants in article 3.2. Therefore, this article was developed only with personal exposure to BC. Another sensor-monitored data used in article 3.2 was vector magnitude derived from the accelerometer as a measure of physical activity. Ambulatory BP was measured on the 1 st and 3 rd day of our study, so those people having no BC data or vector magnitude (accelerometer) data or people not taking part in the mobility survey on these days were totally excluded from the analyses. Other reasons for excluding BP measurements from article 3.2 included the failure of any of the devices monitoring BC, noise and accelerometry at the time of the BP measurement, non-wear of any of the devices mentioned above, and the closest meteorological station being 15 km away or more from the participant's location.

Overall, 6772 ambulatory BP measurements from 245 participants were retained in article 3.2.

Article 3.3 studied the short-term joint effect of exposure to air pollution mixture on ambulatory BP. Personal exposure to NO2, NO, CO, O3, and PM2.5 were monitored with PAM. After successfully calibrating three selected PAM devices against the reference instrument at The University of Cambridge, we were finally able to calibrate other PAMs at our lab using those three pre-calibrated devices. Although ambulatory BP was collected on the 1 st and 3 rd day of the protocol, we could only use the data from day 1 as day 3 did not have data from PAM (figure

Data collection and processing

In this section, we define the large set of variables used throughout the three articles presented in chapter 3. The first part of this section summarizes the procedure involved in collecting personal exposure to air pollution along with the steps involved in post-processing the data. In addition, this section briefly presents the technique used in identifying the trip stages and the two approaches for classifying the identified trip stages in terms of the choice of transport modes. This information on trip stages was explicitly used in article 3.1. This section also summarizes the extraction process of background concentration for NO2 and PM2.5 from the nearest monitoring station, which was used in article 3.1, and the meteorological variables used in article 3.2.

Black carbon

Participants were followed for 4 continuous days from day 1 (3 am) to day 5 (3 am) with a MicroAethalometer (MicroAeth AE51, AethLabs, CA, USA) (197). BC concentration around participants' breathing zone was continuously measured at a 10s time resolution with the AE51 strapped on participants' shoulders with the tube's inlet at the neck level. The post-processing of BC data was done using the Optimized Noise Reduction Averaging (ONA) algorithm (194) (see details of the ONA algorithm in Appendix 2 of article 3.1). BC concentration measured by AE51 can be substantially underestimated when the filter is overloaded. Thus, to avoid filter saturation and preserve measurement integrity, participants were asked to change the filter on the second day at 8 pm when they were recruited in winter, and the filter was changed in any case by a new one whenever the device indicated the necessity of filter replacement (when the optical density reached a certain level). The ONA algorithm takes into account filter changes (194). Filter changes were identified from an incremental light attenuation (ΔATN) of at least -5 between two successive data points, and these pre-identified candidates for a filter change were manually verified.

Electronic or instrumental optical noise results in no change of ATN coefficient or even drop of ATN for some successive observations, as opposed to the general concept of the device (194). This results in flawed negative concentrations or constant concentrations of BC for a relatively long period, even after applying the ONA algorithm. Therefore, the ONA-processed BC concentrations that remained negative for longer than 5 minutes or BC concentrations that remained constant for more than 4 hours, although the person was switching between microenvironments, were deleted from the analysis. All the articles presented in this Ph.D.

thesis used BC exposure. Article 3.1 defined exposure to BC in two ways: mean breathing zone BC concentration at trip stage level, and inhalation of BC per 30 minutes of the trip-stage duration. Articles 3.2 and 3.3 used breathing zone concentration as well as inhalation of BC that was averaged/cumulated in 5-minute, 15-minute, 30-minute, and one-hour time windows before each ambulatory BP measurement.

Gaseous pollutants and PM 2.5

Upon enrolment, participants received a personal air quality monitor (PAM) (183) with multiple sensors for temperature, relative humidity, gaseous pollutants, and particulate matter with wellcharacterized accuracy. The breathing zone concentration of gaseous pollutants (CO, NO, NO2, O3) was collected at 10s time resolution with electrochemical sensors, whereas PM2.5 was measured at a 1-minute interval with an optical particle counter. The PAM is an autonomous platform that incorporates multiple sensors for physical and chemical parameters. The data are stored in a secure digital card inside the monitor and uploaded through a general packet radio service (GPRS) to a secure access FTP server.

Colleagues from The University of Cambridge (chemistry department) have already tested the validity and reliability of this personal sensor in real-life settings, and they have published an entire article discussing the working modality of this device (195). We had a technical collaboration with them in order to receive support from them in calibrating these sensors as well as to ease the technical aspect of post-processing the air pollution data collected. As per the agreement, 3 well-performing PAM devices were calibrated by colocating them for a month with the reference instrument at their department. Then these three devices were used to calibrate other remaining PAMs at our lab, where they were also co-located for a month. All the co-locations were done outdoors.

The calibration process of gaseous pollutants

Out of 21 PAMs of the study, 3 PAMs were located on the roof of the Department of Chemistry (Cambridge University), outdoor next to the reference instruments. The three calibrated PAMs were then used as a "gold standard" to calibrate the rest of the 21 PAMs used in this study with an outdoor co-location in the local environment at the Medical Faculty of Sorbonne Université.

A linear regression equation was applied to the co-location data to obtain the calibration parameters in order to convert the raw signals (mV) to mixing ratios (ppb) of corresponding air pollutant concentrations. Temperature effects were corrected through the auxiliary electrode, which might have a different sensitivity than the working electrode. The cross-sensitivities between the NO2 and O3 measurements were corrected via a specific parameter (crosssensitivity for gas NO2 is O3 and vice versa). As the CO and NO sensors were found to be sufficiently selective, the cross-sensitivity parameter was set to zero for the calibration of these gaseous pollutants. The detail of the calibration equation is defined elsewhere (195). This way, we obtained the device-specific calibration equations for all of the PAMs in two stages; First in Cambridge for 3 PAMs by co-locating them against the reference device and second in Paris by co-locating the rest of the PAMs against the 3 pre-calibrated PAMs. Next, the device-specific calibration equation was applied to the entire study period data to predict the corresponding air pollutant concentrations.

The calibration process of PM 2.5

The PAM device used in this study is equipped with a commercially available miniaturized optical particle counter (Alphasense OPC-N2) capable of counting air particles of different diameters (198). A detailed explanation of its functioning and validity is discussed elsewhere (195). In brief, the OPC-N2 classifies air particles into 16 different bins according to the size of their diameter in the range of 0.38 to 17 μm.

The use of optical PM sensors has its limitations (defined below), which may result in differences in mass estimation compared to the reference gravimetric method.

1) Overestimation of PM mass due to the hygroscopic growth of particles when exposed to high relative humidity (199).

2) Optical methods cannot assess the physical and chemical parameters of aerosols (such as density, volatility, and hygroscopicity), which might change considerably when people move from one microenvironment to another with different air pollution sources, additionally contributing to the uncertainty in mass calculations.

3) Slight variations in the sensitivities of the photodetector may lead to systematic errors specific to each optical particle counter sensor. Furthermore, after particles enter the optical chamber, they may deposit on the internal surface, reducing the measured scattered light and introducing device-specific measurement error.

4) Devices using optical methods are unable to detect particles with diameters below 200-400 nm (200).

In order to mitigate the effects of these limitations, we first corrected for the effect of relative humidity by applying an algorithm proposed by Di Antonio et al. (199) to the 16 bins of particles specified above. The changing aerosol composition (limitation 2 above) is a challenge; therefore, we took a constant density of 1.65 g/cm 3 for the study period for all the sensors used in our study while converting the 16-relative humidity corrected bin counts into PM2.5 mass. To compensate for the third limitation, we determined a scaling factor for each sensor that corrects the sensor-specific variability. This factor was estimated from a linear regression between relative humidity-corrected PM2.5 mass and the reference instrument measurements. This step also helps in correcting the sensor-specific sensitivities that may vary over time. The limit of detection of reference devices is lower than that of these optical particle counters, which may lead to under-estimation of PM2.5 mass while using these sensors. This problem is partially solved by the scaling factor estimated above. The detail of the calibration equation for PM2.5

is explained elsewhere (195).

In summary, the calibrating equations used in this study had a high mean R 2 ; 0.90 for NO2, 0.92 for NO, 0.94 for CO, 0.91 for O3, and 0.88 for PM2.5, showing strong agreement between PAMs and the reference device.

Accelerometer and physical activity

ActiGraph wGT3X+ devices (201) worn by participants on their waist throughout the study period collected accelerometer data in 1-second epochs. We used a 3-axes count of 0 for at least 1 hour with a spike tolerance of 2 minutes of non-zero epochs, a default setting in Actilife, to identify the non-wear time of the accelerometer (202,203) and excluded this portion of the data during the analysis. proposed by Crouter (204) was used in Actilife to calculate the METs in 10-second epochs using raw accelerometer data. Likewise, in articles 3.2 and 3.3, accelerometer-derived vector magnitude, as a measure of physical activity, was also adjusted for while modelling the association between air pollution exposure and ambulatory BP.

Calculation of minute ventilation and inhaled doses

We estimated the ventilation rate using three methods in article 3.1. They were based on METs, heart rate and breathing rate, respectively (appendix 3, article 3.1). Heart and breathing rate data were continuously collected on the 2nd and 4th days by a portable Zephyr BioHarness 3.0 device (207) at 1-second intervals. The heart rate and breathing rate data processing followed the steps suggested by Bigazzi and Figliozzi (205). Since accelerometer data were collected throughout the study, they allowed us to include a larger number of trip stages with minute ventilation than when breathing rate and heart rate measurements were used. Therefore, the method using METs was chosen to report inhaled doses in the main text of article 3.1. This technique computes the ventilation rate as the product of METs with the energy conversion factor derived stochastically as a function of an individual's gender and age. After merging gaseous pollution and BC data with ventilation data to the nearest 5-second data point, inhaled

doses were calculated at a 10-second resolution as the cross-product of the corresponding breathing zone concentration of air pollutants (µg/m 3 ) and minute ventilation. For PM2.5, as it was collected at the 1-minute resolution, the inhaled doses were also calculated at the minute level.

Processing GPS data and mobility survey data

Classification of trip stages

In article 3.1, we applied two different approaches in classifying trip stages in terms of choice of transport modes. First, a crude classification of transport modes was defined as follows:

entirely walking, other active modes (skateboarding, roller skating, biking, etc.), personalized motorized vehicles (including taxis and motorbikes), and public transport. The second was a more detailed classification including entirely walking, other active modes, driver of personal motorized vehicle, passenger of private motorized vehicle (including taxi), bus/coach, tramway, metro (within Paris city), and suburban trains.

Time-activity patterns

For articles 3.2 and 3.3, we calculated the proportion of time spent in-home, out-of-home, and in motorized and non-motorized transport (walking, biking, skateboarding, etc.) for 5-minute to 1-hour periods preceding each BP measurement. This was possible using the timestamped information on visited places and trip segments (with information on transport modes) from the detailed timetable developed by processing the GPS and mobility survey data (section 2.5.4).

Noise exposure

We monitored personal exposure to noise at a 1-second level with an SV 104A dosimeter (Svantek, Warszawa, Poland) attached to the belt, with a microphone fixed at the collar near the participant's ear. The noise level was measured in A-weighted decibels [dB (A)] to capture the sound corresponding to human hearing. As a confounder, noise exposure was only adjusted for in article 3.2 while modeling the association between BC exposure and BP. Therefore, knot of the tree. Secondly, sub-samples of the observations (68%) are randomly selected for growing each tree, and predictions are obtained for the remaining observations (32%), known as out-of-bag data, using the data from the tree. With this technique, random forest overcomes overfitting, a common problem faced in classical regression analysis.

Although we analyzed the relationship between exposure to multiple air pollutants and BP in article 3.3, we did not adjust for noise exposure. The reason behind not doing so was that we were only using observations from day 1 for article 3.3, and day 1 lacked observed noise (figure 1. [START_REF] Dentener | Emissions of primary aerosol and precursor gases in the years 2000 and 1750 prescribed data-sets for AeroCom[END_REF]. Even though we predicted noise for day 1, we did not use it in article 3.3 because BC, one of the independent variables for article 3.3, was a major predictor of noise. Thus, we believed involving the major predictor (BC) and predicted variable (noise) in the same regression analysis would introduce multi-collinearity issues and would not add any new information to the model. The situation is different in article 3.2 because we relied on data from day 1 and day 3. Thus, the noise variable considered for adjustment in this article was a mix between the observed variable in day 3 and the predicted variable in day 1.

Background concentration of NO 2 and PM 2.5

In article 3.1, while comparing the BC exposure across different transport modes at the trip stage level, we considered that adjusting for background PM2.5 concentration would help to control for the background BC concentration, given that BC is a major contributor to PM2.5 in the atmosphere (210). Likewise, background NO2 was taken into account as a proxy for vehicle density as it is a good measure of vehicle emission (211), the major source of BC. By adjusting for these background air pollutants in a regression analysis, we estimated the effect of the individual transport mode per se on personal BC exposure by controlling for the influence of background BC concentration. The background concentration of PM2.5 and NO2 adjusted for in article 3.1 were extracted from the nearest monitoring station following the steps indicated below.

Background concentrations of PM2.5 and NO2 were retrieved for all the geographic coordinates continuously recorded with GPS receivers and the mobility survey by matching their timestamps to those of the closest Airparif air quality monitoring stations. To minimize the misclassification in personal exposure to background concentrations of air pollutants, we ignored those points that were more than 15 km away from the nearest station. These PM2.5 and NO2 background concentrations at the level of mobility points were aggregated at the trip stage level using the start and end times of the trip stages. For the trip stages without continuous mobility points (n =121 which were often short trips), we took the trips' extremities (start and end point), retrieved the PM2.5 and NO2 concentrations from the nearest monitoring station at those points and corresponding timestamps, and determined the trip stage background concentrations as the average of the start and end points concentrations.

Meteorological measurements

The relationship between BC exposure and choice of transport modes (article 3.1) could be confounded by meteorological conditions because personal exposure to BC can vary substantially depending on prevailing meteorological conditions, and it is obvious that the choice of transport modes can be influenced by the meteorological condition. Therefore, in article 3.1, we decided to adjust our models for seasons and time of the day as morning (6 am to 11 am), afternoon (12 am to 5 pm), evening (6 pm to 9 pm), and night (10 pm to 5 am) in addition to other factors (weekdays vs. weekend and mean background concentration of PM2.5

and NO2 assessed along GPS tracks from nearby monitoring station data).

Regarding articles 3.2 and 3.3, a systematic review including 14 studies concluded that a decrease in outdoor temperature increases both SBP and DBP (212), and some individual papers also add to the evidence that ambient temperature and relative humidity are related to BP outcomes [START_REF] Brook | Environmental Hypertensionology" The Effects of Environmental Factors on Blood Pressure in Clinical Practice and Research[END_REF]151). Given that meteorological conditions influence both the level of air pollution and BP outcomes, temperature and humidity, in our study, were conceptualized as potential confounders in the association between air pollution exposure and BP. Unlike in article 3.1, we decided to be more precise in measuring the meteorological variables in articles 3.2 and 3.3 while examining the association between air pollution exposure and BP. For article 3.3, we could use the temperature and relative humidity measurements reported by the sensor (PAM), whereas for article 3.2 this option was not available because the days of observation analysed in this article were not systematically covered by the PAM sensor. Therefore, values for ambient temperature and relative humidity, for article 3.2, were obtained for all the geographic coordinates continuously recorded with GPS receivers and the mobility survey by matching their timestamps to those of the hourly measurements of the closest Météo-France (https://meteofrance.com) weather monitoring station. Participants' locations were not constant during the ambulatory BP monitoring, resulting in a change of their nearby station as a function of time. Adjusting for the absolute temperature and humidity measured at each station may not be appropriate, since there are deltas in temperature across stations depending on their location (e.g., near the traffic, in a park, etc.). Thus, as a method of data standardization between different weather stations and in order to make them comparable, z-scores were calculated for each station's measurements (temperature and humidity) retaining only the measurements taken within the study period (May 2018 to October 2020). To minimize misclassification in personal exposure to temperature and humidity, we ignored those points which were more than 15 km away from the nearest station. These z-scores of temperature and relative humidity were aggregated in the corresponding time windows before each ambulatory BP measurement, as the personal exposures to air pollutants, to adjust for in the regression models.

Ambulatory blood pressure

Arteriograph 24 (AG24) ambulatory blood pressure monitor (TensioMed, Budapest, Hungary) was used on day 1 and day 3 to measure participants' blood pressure from wakeup to bedtime.

The device was set to measure every 30 minutes during the wear time. This device is user independent and can monitor the ambulatory BP with a single upper arm cuff (213). Details on its functioning and invasive validity are explained elsewhere (214). Article 3.2 explored the association between BC exposure and ambulatory BP measured on day 1 and day 3. However, when analyzing the relationship between air pollution exposure (NO2, NO, CO, O3, PM2.5, and BC) and ambulatory BP (article 3.3), we could only use the BP data from day 1 because the PAM device used for monitoring most of the aforementioned air pollutants was not used on day 3.

Other covariates

In article 3.1, we considered that sociodemographic characteristics should not be associated with particular exposure during trips conditional on the chosen transport mode. In addition, controlling for sociodemographic characteristics would yield an abstract estimate considering a similar sociodemographic structure across transport modes, which is unrealistic. However, in articles 3.2 and article 3.3, age was coded as a continuous variable and sex as a binary variable (male vs. female). Monthly alcohol drinks from the questionnaire and body mass index (BMI)

from measured height and weight were coded as continuous variables. Educational attainment was coded into three categories, lower than Baccalauréat, equal to Baccalauréat, and higher than Baccalauréat. The employment status of participants was classified into five categories; retired, stable job, unstable job, unemployment, and other residual categories. Annual household income was divided by the number of units in the household, considering members younger than 14 years old as 0.5 units. Household income per consumption unit was divided into three categories based on tertiles. Individuals without income information were coded as missing (N = 6, article 3.2 and N = 4, article 3.3) so that we could retain all the observations for the main analysis. To control for the neighborhood socioeconomic effect on BP, we adjusted for the per-person winsorized living standards (source: Insee, 2019) of both the residential neighborhood and the neighborhood where participants were when each BP measurement was taken in the analyses as continuous variables. Place of residence was further assessed by distinguishing between Paris, close suburbs, and far suburbs.

Ethical approval of the MobiliSense project

The sampling and data collection protocol for the MobiliSense study was approved by the National Council for Statistical Information, the French Data Protection Authority, and the Ethical Committee of Inserm.

Statistical analysis

In this section, we first describe the multilevel linear model, which was a common method used across all the articles produced during this Ph.D. project. Lastly, we discuss the mixture model used to examine the joint effect of air pollutants on ambulatory BP.

Multilevel linear regression

A multilevel model is a statistical method that efficiently examines the relationship between the dependent and the independent variables collected at more than one level of hierarchical structure. This analysis technique offers an advantage in estimating the relative influence of a particular level of hierarchy (individual in our case) on the outcome variable and allows the statistician to detect variables at each level that are associated with the outcome (215). In multilevel models, the variability produced by each hierarchical level is modeled using random coefficients. In other words, this model permits regression parameters, in any case, the intercept and, in some models, the slopes, to vary between levels of the hierarchal structure. Therefore, this model has been regarded as a statistical procedure to address the known limitations of standard regression, as it accounts for the variability of observation between level-2 units (individuals in our case) (215).

A basic assumption made by linear regression is that the level 1 residuals are uncorrelated, i.e., independent from each other. In article 3.1 of this Ph.D. project, analysis was done at the trip level with trips nested within participants. Therefore, while evaluating the association between transport modes and BC exposure at the trip level, we used multilevel models with a random intercept at the individual level to take into account the within-person correlation in the outcome.

In some circumstances, especially when observations of the same entity are made close in time, such as blood pressure measurements in our study, it is crucial to take care of the correlation structure while modeling the exposure-outcome association by employing multilevel analysis (216). If not, residuals of level-1 observations grouped within level-2 units will tend to be correlated, and this violates the basic assumption of linear regression of independence of observations, resulting in the inaccurate estimation of the standard error of parameters in the model ( 216). Therefore, in articles 3.2 and 3.3, we used multilevel linear models with a random intercept at the individual level and temporal autocorrelation structure of order 1 (AR1) to handle the temporal correlation among residual errors within individuals (216) while examining the association between short-term air pollution exposure and ambulatory BP. In both articles, the short-term time trend (the clock hour of the day when the BP was measured) was modeled as a random slope varying between participants. All the multilevel linear models used in articles 3.1, 3.2, and 3.3 were adjusted for potential confounders (details are specified in respective sections).

In article 3.2, to increase the robustness of findings, we eliminated confounding by unmeasured time-invariant personal level variables, by modelling the BC-BP associations with fixed-effect models (estimating effects within individuals) that control for one dummy variable related to each participant.

Mixture models

Ambient pollutants are highly correlated with each other for sharing common sources (154).

Therefore, individuals are usually exposed to air pollutants in a mixture rather than to an individual air pollution species (152). However, to our knowledge, to date, no study has explored the joint effect of multiple air pollutants on ambulatory BP. Methods have been recently developed to consider complex chemical mixtures that can be easily adapted to study air pollution mixtures [START_REF] Sun | Exposure to air pollutant mixture and gestational diabetes mellitus in Southern California: Results from electronic health record data of a large pregnancy cohort[END_REF]217). Such approaches typically estimate a joint effect of the mixture components while considering multicollinearities and interactions. Notably, quantile Gcomputation (164) offers new capabilities in estimating the effect of complex exposure mixtures on health outcomes.

In article 3.3, we analyzed the combined effect of various air pollutants (NO2, NO, CO, O3, and BC) on ambulatory BP by applying quantile G-computation models with 1000 bootstrapped samples considering the clustered nature of our data and adjusting for potential confounders.

Quantile G-computation has been recently developed as an extension of weighted quantile sum (WQS) regression by further relaxing the directional homogeneity, non-linearity, and non-additivity assumptions (218). This method uses a quantized exposure index with empirical weights for each exposure derived from quantiles of the exposures (219). First, this method transforms the exposures into quantized versions, i.e., cutting them into categorical variables using quartiles of the exposures as cut points. Then, it fits a linear model between the transformed exposures and the outcome (219). G-computation models are capable of estimating both: a) the individual effect of per quartile increase in each exposure component (β regression coefficient), and b) the joint effect of all the exposure components (ψ) on the outcome when all of them are increased by one quartile simultaneously (164). This joint effect is the sum of all β regression coefficients of individual components in the model. Thus, quantile G-computation helps estimate a causal dose-response parameter of the entire exposure mixture in air pollution studies. In our case, the estimated coefficient ψ from the model is interpretable as the change in ambulatory BP for increasing all air pollution exposure variables by one quartile simultaneously.

Given that the sources of air pollutants widely differ across microenvironments and exposure related to vehicle emissions has a stronger effect on BP than exposure from home-based sources (220,221), we performed multi-pollutant multilevel analyses as well as G-computation analyses stratified by time spent in different microenvironments across all exposure windows. In article 3.3, we stratified the observations into two microenvironments to investigate changes in ambulatory BP in relation to personal exposure in various microenvironments: when participants were entirely at home (in-home) and out-of-home. We did not have a sufficient sample size to analyze the transport microenvironment separately (N <120). Therefore we combined visiting non-home places and transport microenvironments as a single stratum, outof-home. Methods: Personal exposure to BC was continuously measured at 10-sec resolution with MicroAethalometer (MicroAeth AE51) portable monitors. The post-processing of BC data was done using the Optimized Noise Reduction Averaging (ONA) algorithm proposed by [START_REF] Hagler | Post-processing Method to Reduce Noise while Preserving High Time Resolution in Aethalometer Real-time Black Carbon Data[END_REF]. Inhaled doses in our study were derived from more than one ventilation estimating methods, such as metabolic equivalents from accelerometry, heart rate, and breathing rate.

Since accelerometer data were collected throughout the study, they allowed us to include a larger number of trip stages with minute ventilation than when breathing rate and heart rate measurements recorded only on the 2 nd and 4 th days were used. Therefore, we chose the method of applying metabolic equivalents to report the inhaled doses in the main text. This preidentified information on each trip stage obtained from the TripBuilder application was crossverified with participants during a mobility survey via phone. Our study classified transport modes at the trip stage level into crude and detailed types. Breathing zone concentration (µg/m 3 ) and inhaled doses (µg/minute) of BC were averaged throughout each trip stage. Multilevel models with a random intercept at the individual level were built to test the association between choices of transport modes and BC exposure (breathing zone concentration or inhaled doses) at trip stage level (n=7495, 283 participants), adjusting for seasons, weekdays vs. weekend, time of the day (morning, afternoon, evening, and night), and background concentration of PM2.5 and NO2. Observations were weighted according to trip stage durations.

Results: Variations of BC breathing zone concentrations and inhaled doses were documented across transport modes at the trip stage level, where the use of public transport (especially metro, bus, and suburban train), private motorized transport, and biking were associated with a higher breathing zone concentration of BC compared to entirely walking trip stages. When modeled with inhaled doses rather than concentrations, biking appeared to have a higher level of inhaled doses of BC per 30 min due to the high ventilation rate assigned to it than other modes.

Full article

The rest of the section consists of the article published in Environmental International. However, a large number of observational studies evaluating the adverse effect of air pollution exposure on BP reported mixed results. A few methodological limitations could explain such inconsistencies in findings. Firstly, most of the previous air pollution studies used air quality data obtained from outdoor fixed stations or modeling techniques as proxies for personal exposure, resulting in exposure misclassification and erroneous associations. Secondly, inconsistencies in exposure assessment windows, with some studies using yearly exposure levels and others using short-term exposure periods, such as exploring the effects on BP after days or hours of exposure. This article evaluated the association between short-term exposure to personally measured BC (averaged over the previous hour or less) and ambulatory BP outcomes.

Article

Methods: As stated in article 3.1, the post-processing of BC data collected at 10s resolution was done using the Optimized Noise Reduction Averaging (ONA) algorithm proposed by Hagler et al. 2011. Arteriograph 24 ambulatory BP monitor (TensioMed, Budapest, Hungary) was used to measure participants' BP at 30 minutes intervals from wakeup to bedtime (n=6772, participants = 245). Personal exposure to BC was averaged over 5-minute to 1-hour time windows before each ambulatory BP measurement. We employed linear multilevel models with a random intercept at the participant level and time auto-correlation structure to estimate relationships between BC exposure averaged over the previous 5 minutes to 1 hour and ambulatory BP measurements accounting for potential confounders, such as noise, physical activity, temperature, and relative humidity, participants' whereabouts and participants' sociodemographic characteristics. In order to increase the robustness of findings, we eliminated confounding by unmeasured time-invariant personal level variables by modeling the associations with fixed-effect models (estimating effects within individuals). We tested the nonlinear BC-BP associations by adding 6 th -degree polynomials splines for each BC exposure window separately into the multilevel models and estimated piece-wise regressions to obtain interpretable coefficients by choosing an appropriate cutoff value for BC concentrations. As a sensitivity analysis, we tested the effect modification of noise by adding interactive terms between BC exposure and noise exposure into the multilevel models.

Results:

The multilevel models suggest that per 1-μg/m 3 increase in BC exposure in the previous 5-minute corresponded to a 0.57 mmHg and a 0.36 mmHg evaluated SBP and DBP, respectively. The magnitude of the association decreased gradually with the increase in the length of BC exposure averaging period, where 30-minute and 1-hour BC exposure were not related to BP (both SBP and DBP). Similar to the results from multi-level models, in fixedeffect models, we found that a 1-μg/m 3 increase in BC exposure averaged over the previous 5 minutes resulted in the greatest changes in both SBP (0.42 mmHg) and DBP (0.31 mmHg) across all the exposure averaging windows. Findings from the piecewise regression suggest that previous 5-minute BC exposure when less than 5-μg/m 3 in concentration was only associated with high SBP and DBP. Similar patterns were witnessed for both 10 and 15-minute averaged BC exposure: they were associated with SBP and DBP only below the cutoff value of 4-μg/m 3 .

The interactive term of BC and noise exposure (P-value>0.05) showed no evidence of BC effect modifications by noise exposure on ambulatory BP.

Full article

The rest of the section consists of the article published in Science of the total environment.

pollutant fixed-effect linear models to estimate within-person relationships between air pollutants exposure averaged over the previous 5 minutes to 1 hour and ambulatory BP measurements accounting for potential confounders, including physical activity, temperature, and relative humidity, participants' whereabouts and participants' socio-demographic characteristics. We applied a novel mixture method, fixed-effect quantile G-computation, to estimate the within-person joint effect of targeted air pollutants on BP. This approach can handle collinearities and additive as well as non-additive interactions between exposure of interests. As little is known about the source-related health effects of air pollution, this study further evaluated the air pollution and BP associations in different microenvironments, namely;

in-home and out-of-home microenvironments.

Results:

In multi-pollutant fixed-effect linear models, we found that exposure to O3 (5 to 15minute windows), BC (5-minute window), and NO2 (5-minute window) increased SBP, whereas 5, 15, and 30-minute averaged CO exposure decreased DBP. Nevertheless, the results

were not consistent with inhaled doses. In mixture models, a quartile increase in air pollutant concentrations (BC, NO2, NO, CO, and O3) in the previous 5 minutes was associated with a 1.92 mmHg (95% CI: 0.63, 3.20) higher systolic BP (SBP), while 30-minute and 1-hour exposures were not associated with SBP. However, the effects on diastolic BP (DBP) were inconsistent across exposure windows. Unlike concentration mixtures, inhalation mixtures in the previous 5 minutes to 1 hour were associated with increased SBP. Our stratified analyses showed that out-of-home BC and O3 concentrations had stronger positive effects on BP outcomes than in-home exposure. In contrast, only the in-home concentration of CO reduced DBP in stratified analyses.

Conclusion:

Our study found that very short-term simultaneous exposure (in terms of concentrations or inhaled doses) to air pollutants (NO2, NO, CO, O3, and BC) was positively associated with SBP. O3 and BC drove the associations between the mixture of targeted air pollutants and ambulatory BP. Interventions targeting a reduction of air pollution exposure and early screening could potentially reduce the future burden of hypertension. We highly encourage air pollution epidemiologists to adopt a mixture approach to investigation in order to improve the scientific evidence on the adverse health effects of air pollution and, therefore, to guide the governmental authorities in formulating better regulations.

Discussion

Air pollution: a new method of exposure assessment

Concentrations monitored by fixed stations and, to a lesser extent, modeled concentrations can be made available to researchers through partnerships with institutions involved in this monitoring. In this context, those institutions specialized in air quality monitoring control how these data are collected, how the data are processed and analyzed, and mainly the purpose for collecting the data. The paradigm of air pollution exposure assessment is shifting with the development of low-cost, handy, and portable personal air pollution sensors that offer high temporal and spatial resolution data (Figure 1. [START_REF] Gramsch | Particle size distribution and its relationship to black carbon in two urban and one rural site in Santiago de Chile[END_REF]. A single sensor device now available on the market can monitor a broad range of air pollutant species with little need for calibration in reference to the validated device (198), such as the PAM sensor used in our project. These portable air quality devices provide researchers with opportunities to complement the existing work with conventional air pollution monitors and could further serve as a modern approach to measuring exposure at the personal level. Special software packages and services provided by sensor developers in processing and delivering usable data enhance the availability and accessibility of air quality data. In fact, the widespread collection and sharing of data are already happening in air pollution projects using newly available low-cost sensors, even before validating the sensor performance, which can be a significant concern (http://airqualityegg.com/) (222). Assessing air pollution exposure with newly introduced sensors requires a clear understanding of the working mechanism of the sensors. It also requires new scientific tools and methods in processing the collected data. In population studies, air quality has to be monitored using a validated personal sensor capable of measuring pollution concentration at a high temporal and spatial resolution to capture the actual variability of air pollution exposure as participants go through different microenvironments. Sensors used in these studies should have a high battery backup so that there is no interruption in data collection for several days when being deployed to participants. Researchers using these techniques should be well aware of the sensor-specific calibrating processes. Furthermore, scientists should also be aware of the sensor-specific limitations, such as the need for sensors with electrochemical cells to be frequently recalibrated due to occasional drifts (183), the fact that sensors using direct particulate mass are sensitive to changes in temperature and humidity (183), and the fact that sensors using light absorption techniques require periodic filter changes and filter loading correction (194). The research community should continue to develop sensor-specific post-processing algorithms and integrate them in a data processing software, which is yet to be done for several sensors, in order to trip stage level considering a large number of geographic settings and a larger number of participants. Thus, this paper may interest researchers interested in quantifying personal exposure to air pollutants while commuting and might help them choose an appropriate ventilation calculation method for their study to estimate the inhaled doses.

Prediction of personal noise exposure

Although it was not a primary objective of our thesis, yet we were able to develop a machinelearning algorithm to predict noise exposure. In article 3.2, while investigating the association of BC exposure with BP, noise exposure was identified as a potential confounder and adjusted for in the regression models. Among the two days (days 1 and 3) of observations used in article 3.2, personal noise exposure was not measured on the 1 st day of our study; therefore, observed data from days 3 and 4 were used to predict the personal noise exposure for day 1. BC concentration, mode of transport, the combination of departure place and arrival place (when the person was traveling), visited place id and activity type (when the person was at a place), week vs. weekend, and time of the day (morning, noon, evening, and night) defined at the minute level were used in predicting noise exposure (variables related to departure and arrival places of trips and visited place ids corresponded to wide sets of binary variables specific to each participant). These variables were created using the detailed mobility survey timetable discussed in section 2.5.4. The R 2 of the random forest algorithm used in predicting noise exposure for day 1 was 81%, showing that this important momentary co-exposure could be predicted relatively reliably.

Application of mixture models in estimating the joint effect of air pollutants on blood pressure

Considering that the individuals are simultaneously exposed to a complex mixture of a wide range of air pollutants while going through their daily routine, this project aimed to estimate the joint effect of a mixture of air pollutant exposures on ambulatory BP. The corresponding article 3.3 evaluated the combined effect of air pollutants (BC, NO2, NO, CO and O3) averaged over 5 minutes to 1 hour before each BP measurement on ambulatory BP using quantile Gcomputation models. Results from the quantile G-computation method are reported in two parts; β coefficients represent the individual effect of each component, while the ψ estimate is the sum of all β coefficients in the model, corresponding to the joint effect of per quartile simultaneous increase in all exposure components on the outcome. We found that very shortterm simultaneous exposure (concentration or inhaled doses) to air pollutants (BC, NO2, NO, CO, and O3) had an increasing effect on SBP, with 5-minute averaged mixture exposure having the strongest effect. Across all the exposure windows, O3 and BC were the strongest contributors to the effect on SBP. This study is the first attempt ever made to investigate the joint effect of an air pollution mixture on BP. The application of mixture models is very important in future air pollution studies because individuals are simultaneously exposed to more than one pollutant, and the synergistic effect of multiple air pollutants on health events might differ from the linear combination of their individual effects obtained from the standard regression analysis. In addition, this approach may help design public health interventions targeting specific exposure sources, such as road traffic for BC the major driver of the air pollution-BP associations in our findings. in our dataset, our study is unlikely to suffer from major biases that compromises the causal inference: the preferences or opinions of a given participant using a given transport mode are unlikely to affect her or his exposure. The particular research question of our thesis took the advantage of the high-resolution data for linking the momentary exposure to BC with choice of transport modes at the trip stage level, avoiding major selection biases and important measurements errors, common pitfalls in previous related studies.

Empirical findings

In article 3.1, we showed that commuting through public transport, private motorized vehicles, and biking results in exposure to higher BC concentration than walking. These results are consistent with the findings from a previous study done in Shanghai (226) and several other studies (224,238). When considering inhaled doses instead of breathing zone BC concentration, it was observed that biking was related to a higher inhaled dose of BC per 30 minutes. This finding is in line with some previous findings (224,225). The increased level of physical activity that bikers are involved with results in a higher ventilation rate and higher inhalation (179,181,239), even though their breathing zone concentration of BC is relatively lower. It suggests that dose-response relationships of transport-related air pollution exposure with health status should be explored taking into account inhaled doses rather than only measured concentrations.

Article 3.1 is the first study to assess inhaled doses of BC with a large number of real-life nonexperimental trip stages where ventilation rates were computed using physiological parameters (heart rate, breathing rate, and accelerometry). In addition, we have trips made in different seasons of the year, all possible hours of the day, and included various transport modes commonly used in the study area.

Association between air pollution exposure and blood pressure

In article 3.2, we analyzed the association between BC exposure and ambulatory BP estimating single-pollutant models based on measurements from day 1 and day 3. In contrast, article 3.3 investigated the joint and individual effects of various air pollutants (NO2, NO, CO, O3, and PM2.5) on BP using multi-pollutant models, including observations only from day 1. Article 3.3 had observations only from a single day because day 1 was the only common day of our study having BP measurements and exposure assessment for air pollutants, including NO2, NO, CO, O3, and PM2.5. It must be noted that day 1 of our study was weekends for most participants and spent at home to some extent.

Very few studies measured both air pollution exposure and ambulatory BP across the multiple daily environments visited by the general population. Our study successfully addresses the existing gap in measuring air pollution exposure in various microenvironments that participants are involved in their everyday settings. On top of that, our study is the first of its kind to calculate inhaled doses of air pollutants using a personal behavioral parameter (physical activity) and test their association with ambulatory BP in different temporal structures.

Exposure to air pollution in our study was measured at a very high temporal resolution with personal sensors, which allowed us to capture the high spatio-temporal variability in exposure happening at the personal level and to test the associations between air pollution exposure and BP in varying temporal structures (5-minute to 1-hour periods). Our study is the first to crossreference air pollution exposure with mobility data (types of places visited and modes of transport used) obtained using GPS and mobility surveys to calculate the proportion of time spent in different microenvironments across all exposure windows and to adjust for it as a confounder while testing the associations. Furthermore, we also stratified our samples based on the microenvironments visited and tested the BC-BP association within the different microenvironments.

Humans are simultaneously exposed to a complex mixture of air pollutants depending on the variation of pollution sources in the present microenvironment. Thus, it is very crucial to estimate the joint effects of air pollution exposure using a mixture modeling approach. We applied a very new approach to evaluating the combined effect of different air pollutants on ambulatory BP across several exposure windows, which has never been explored before.

This section first discusses the individual association of each air pollutant with ambulatory BP, then continues with the discussion of the joint effect of the air pollution mixture on BP, then with the result from the stratified analysis conducted in the different microenvironments, and ends with reflecting on the inconsistencies between the findings of our and past studies.

Association between individual air pollutants and blood pressure

Findings from article 3.2 are that the magnitude of the positive dose-response relationship between BC concentration and BP gradually decreased with the increase in the exposure averaging period from 5 minutes to 1 hour, where 30-minute and 1-hour averaged BC concentrations were no longer associated with both SBP and DBP. When analyzed in multi-pollutant models in article 3.3 (only on day 1), only the previous 5-minute BC concentration positively affected SBP. We believe that the associations between 15-minute averaged BC and BP disappeared in multi-pollutant models for two reasons. Firstly and primarily, this may be due to the restriction of the analysis to day 1 and to the difference in air pollution exposure patterns between day 1 and day 3 of our study, where day 1 was more often on weekends and with more time spent at home. A second reason may be that we performed further adjustment for other air pollutants in this second study. An Indian study reported that the previous 5-minute averaged breathing zone BC concentrations had no effect on SBP and DBP (104), which is different from our finding. Coherent to our finding, two studies reported no association between 1-hour averaged BC concentration and BP (105,106). A case cross-over study including 30 adults found no association between 30-minute breathing zone BC concentration and BP [START_REF] Pun | Blood pressure and pulmonary health effects of ozone and black carbon exposure in young adult runners[END_REF], which mirrors our result.

PM2.5, across all exposure windows, was not associated with BP. Results from previous studies of daily PM2.5 levels and BP have been inconsistent, with some studies reporting positive associations [START_REF] Baumgartner | Indoor Air Pollution and Blood Pressure in Adult Women Living in Rural China[END_REF][START_REF] Wu | Blood Pressure Changes and Chemical Constituents of Particulate Air Pollution: Results from the Healthy Volunteer Natural Relocation (HVNR) Study[END_REF] and others reporting null associations [START_REF] Mirowsky | Repeated measures of inflammation, blood pressure, and heart rate variability associated with traffic exposures in healthy adults[END_REF]151). Previous studies documented a stronger association between PM2.5 concentration and BP than with gaseous pollutants, while our study showed that short-term effects on BP were greater for gaseous pollutants. In the literature, evidence supporting the longer-term effects of PM2.5 on BP is more convincing. Thus, longer exposure windows (one-day exposure window or even more) might be required to find an effect on BP.

Only the previous 5-minute averaged NO2 concentration positively affected both SBP and DBP, and NO was not associated with SBP and DBP across all exposure windows. Past studies have reported mixed findings regarding the short-term effect of NOx (NO +NO2) on BP [START_REF] Day | Association of Ozone Exposure With Cardiorespiratory Pathophysiologic Mechanisms in Healthy Adults[END_REF]107,128,136,137,148) exposure and BP [START_REF] Weichenthal | Exposure to traffic-related air pollution during physical activity and acute changes in blood pressure, autonomic and micro-vascular function in women: a cross-over study[END_REF]. A randomized controlled crossover study reported a decreasing effect of personal NO2 exposure (from domestic gas cooking) on resting SBP and DBP after 45 and 90 minutes, relative to a control group (126).

Our study showed a negative association between exposure to CO concentration and DBP, concordant with a previous study investigating the adverse effect of nearby station-monitored CO concentrations on resting BP (136). Another study also reported that ambient CO concentration (station monitored) at 0-day to 14-day lag reduced DBP but had no effect on SBP (146). Experimental studies have suggested that a decrease in BP due to a higher concentration of CO may be due to vasorelaxation (reduction in tension of the blood vessel walls) in the cerebrovascular circulation (240). According to an animal study, CO exposure leads to arterial hypotension and reduced systemic vascular resistance (241). Furthermore, a human study also observed that acute CO exposure was responsible for reduced peripheral resistance (242).

O3 concentration was positively associated with SBP and DBP in several exposure windows.

Although it reported weaker associations in magnitude than ours, a cross-sectional study concluded that a 3-5 lag hour concentration of ambient (station monitored) O3 increased BP (136). Several other quasi-experimental [START_REF] Day | Association of Ozone Exposure With Cardiorespiratory Pathophysiologic Mechanisms in Healthy Adults[END_REF][START_REF] Weichenthal | Exposure to traffic-related air pollution during physical activity and acute changes in blood pressure, autonomic and micro-vascular function in women: a cross-over study[END_REF] and observational studies (100,105,137) confirmed the association using 24-hour averaged O3 concentration. Some potential mechanisms linking O3 and increased BP are an elevated level of oxidative stress, serotonininduced vasoconstriction, and decreased acetylcholine-induced vasodilation (243).

Our study is the first to evaluate the association between inhaled doses of air pollution and BP; therefore, we could not elaborate in our discussion section by comparing our findings with previous studies. In summary, results from our study suggested that some of the air pollutants associated with BP outcomes with their concentrations were no longer related to the outcomes when modeled with their inhaled doses. A reason may be that the calculations of inhaled doses in our study involved several approximations in computing METs from accelerometers and then ventilation rates from METs, which likely introduced intrinsic error in estimated doses and resulted in non-differential misclassification of inhalation. It is a well-known fact that this sort of misclassification can bias the association towards null.

Association between air pollution mixture and blood pressure

In article 3. Pollutants such as O3 and BC (concentration and inhaled doses) were contributing strongly and positively to the joint effect, whereas CO had a consistently negative effect on SBP across all exposure windows.

Therefore, interventions acting on the corresponding air pollution sources, at least in Grand Paris region, may help reduce the incidence of hypertension over the long term and reduce the incidence of cardiovascular events triggered by air pollution exposure in the near future. The mixture was not found to be associated with DBP, while in the multi-pollutant fixed-effect linear models some associations were documented for DBP (with NO2 and O3, although weaker than for SBP). This may be due to some extent to the strong negative effect of CO on DBP which cancelled the association with the mixture. To our knowledge, none of the previous studies has analyzed the association between air pollution and BP by applying multi-pollutants mixture methods. Therefore, we could not compare our findings from mixture models to previous studies.

Association between air pollution exposure and blood pressure across microenvironments

Being in different microenvironments plays a significant role in air pollution exposure, and the sources of air pollutants vastly differ between microenvironments and geographical settings (e.g., lower exposure to vehicle emissions while at home or in green spaces). This might explain the effect modification by microenvironments observed in our stratified analyses, both in linear models and mixture models. For instance, BC concentration had a stronger effect on SBP in out-of-home than in the in-home microenvironment. One potential reason is that exposure to transport-related emissions has a stronger effect on BP than exposure from home-based sources because of the differences in chemical composition and toxicological profile of air pollutants (220,221).

The major reason that may explain the inconsistencies between our findings and previous studies is the time frame, 5 minutes to 1 hour after exposure, which is much more acute compared to previous short-term studies considering day lag models. Another potential explanation that may help understand the discrepancies between our results and those of previous studies is that most of them used ambient air pollution levels measured at the nearest station or modeled air pollution levels as a proxy for breathing zone concentration. Several studies have highlighted that personal exposure to air pollutants may induce different responses in humans than exposure to background ambient pollutants due to variations in sources and chemical compositions (136,187,188). Furthermore, in the past studies, only a few studies estimated multi-pollutant linear models with BP as an outcome, mostly including two pollutants in the models [START_REF] Day | Association of Ozone Exposure With Cardiorespiratory Pathophysiologic Mechanisms in Healthy Adults[END_REF][START_REF] Weichenthal | Exposure to traffic-related air pollution during physical activity and acute changes in blood pressure, autonomic and micro-vascular function in women: a cross-over study[END_REF][START_REF] Pun | Blood pressure and pulmonary health effects of ozone and black carbon exposure in young adult runners[END_REF], whereas we have taken into account five air pollutants to estimate the independent effect of each air pollutant on BP as well as the effect of the mixture. Moreover, almost all past studies used resting BP, whereas our study totally relied on ambulatory measurements. It is believed that ambulatory BP can capture small variations in BP and minimize the effect of other factors at measurements by virtue of intensive recording in realtime, making ambulatory BP readings more reliable than clinical measurements (196). In summary, to date, the association between exposure to individual air pollutant and BP still remain to be clarified in studies due to large variation in exposure levels and sources, differences in assessment methods and exposure windows, use of resting vs. ambulatory BP measurements as well as heterogeneity of study populations, study designs and covariates adjusted for in previous studies.

Strengths and limitations

The major strength of this work lies in using onboard sensors to measure both the outcome, exposures, and potential confounders: BP, personal exposure to air pollutants, noise, location, breathing and heart rates, and physical activity. Furthermore, these measurements were supplemented with precise information about the participants' movements, obtained by Another strength of this thesis lies in the modeling technique adopted. The use of multilevel models allowed us to take into account the basal individual heterogeneity in BP and the unequal number of observations across individuals. This approach to data modeling made it possible for us to efficiently handle the repeated number of measurements per person, also taking into account the temporal autocorrelation among residual errors within individuals. To increase the robustness of findings, we eliminated confounding by unmeasured time-invariant personal level variables by modeling the associations with fixed-effect models (estimating effects within individuals) that control for one dummy variable related to each participant. Another modeling approach used in this thesis was quantile G-computation. This novel technique estimates the joint effect of air pollution mixture on BP while considering components' multicollinearities, interactions, and individual contributions.

We acknowledged that our studies were subject to several limitations. The major limitation of this project is that, by design, we had a few common days where every sensor was worn, such as noise being measured only on one of the two ambulatory BP monitoring days. Thus, we predicted noise exposure for day one of our study using a random forest model and adjusted for it in the associations. This technique might have resulted in residual confounding by noise in our findings from article 3.2. However, the variability explained by our noise prediction model was likely high enough (81%) to capture the distribution of noise exposure. Again, the device measuring the gaseous pollutants and PM2.5 was carried by participants only on one of the two BP monitoring days, which left us with single-day data to analyze the association between gaseous pollutants and BP. Furthermore, noise, a potential confounder, was not measured on that common day, so we could not adjust for it while evaluating the associations in article 3.3.

Future research with multiple days of BP measurements along with personal monitoring of several air pollutants and potential confounders (including noise) might help better understand the association between air pollution and BP outcomes, especially when the effect of an air pollution mixture (simultaneous exposure to different air pollutant species) is of interest.

However, our experience shows that collecting such data will be challenging. Another limitation of our study, article 3.3, was the lack of statistical power to test the association by isolating only the transport microenvironment. Furthermore, the wide range of confidence intervals of the effect estimates associating concentrations mixtures with SBP, especially in the be generalized cautiously. It should be considered that by design, we only recruited older adults (35-64 years) and focused on the metropolitan area of Paris, which is specific to some extent.

Perspectives

Social and policy implications

It should be, however, mentioned that the inhalation of air pollution is only one aspect of the detrimental or beneficial effects of transport modes on human health, with other effects including noise exposure (246), anxiety (247), and gain in physical activity, especially on commutes via bike and/or walking but also public transport (248). Walking in our study was associated with relatively lower concentrations of air pollutants compared to biking, suggesting that pedestrians are further away from traffic than where bikes are used (bike lanes or other).

Furthermore, the finding also implies that more bike lanes separated from traffic should be developed and that the existing bike lanes should be shifted further away than they are from motorized traffic. Reducing dependency on cars and promoting cycling for daily trips is one of the suggested pathways to reducing ambient pollution and improving cardiovascular health (249). It is indicated that the development of polycentric cities with an expansion of public transport systems would encourage the public to shift from personal vehicles to public transport and may imply a significant reduction in air pollution emissions (250). On the other hand, using public transport or bikes by the wider public in their daily lives would eventually reduce the need for parking spaces, resulting in free spaces that authorities could use to promote cities' greenery.

Among the various determinants of high BP, our study entirely focused on exposure to air pollutants, with attempts to identify exposure windows responsible for the effects. Promoting tomorrow's generations' cardiovascular and respiratory health requires effective interventions targeting both the reduction of air pollution emissions and early screening of health conditions.

Firstly, political interventions with a special focus on addressing transport-related air pollutants in European cities are required, as it was found that urban transport contributes to 39% to 43% of ambient PM2.5 and 3% to 56% of ambient NO2 in Europe (251). Also, it is emphasized that although a small share of individuals' daily time is spent on traveling, commuting accounts for larger portions of their daily total air pollution exposure (182,252,253). Closure or shifting of big industries responsible for air pollution to non-residential areas ( 254 Secondly, public health programs and strategies at a large scale are required to combat the increasing incidence of hypertension and decrease BP in the population as an action to reduce the burden of cardiovascular diseases and related mortality (258,259). Given that the prevalence of global cases of hypertension is expected to reach 1.56 billion by 2025, which is a 60% increase compared to the prevalence of hypertension in 2020 (260), more epidemiological studies are needed to gather evidence on the determinants of BP and to support the policymaking and strategy development processes. In fact, early detection and management of increasing BP at a young age are crucial to preventing hypertension and cardiovascular diseases in the later stage of life [START_REF] Guzman-Limon | Pediatric Hypertension Diagnosis, Evaluation, and Treatment PHYSIOLOGY OF NORMAL BLOOD PRESSURE[END_REF] and preventing hypertensive end-organ damage related to early hypertension onset (261). Interventions focused on reducing high BP/hypertension-related future complications have to be targeted to the high-risk population group. In 2020, a Chinese study following up 71,245 adults for 6.5 years suggested that the risks of cardiovascular disease and all-cause mortality were higher for the group with hypertension onset at age <45 years than at the 45 to 54 and ≥65-year age groups, although the incidence of hypertension was comparatively lower in people less than 45 years old (262).

We estimated the effect of inhaled doses on BP because using breathing zone concentrations relies on the assumption of a uniform breathing rate, neglecting the fact that the amount of air pollutants inhaled depends on the ventilation rate (179). Also, in article 3.1, we have demonstrated that air pollution inhalation is not directly proportional to the breathing zone concentrations as it is also dependent on the concurrent ventilation rates. This reasoning might explain the inconsistency that we found in article 3.3 regarding the associations of concentration and inhalation mixtures with SBP in 30-minute and 1-hour exposure windows. With these results, we recommend that using inhaled doses instead of using breathing zone concentrations as a proxy for exposure may help researchers understand the true dose-response curve between air pollution exposure and health outcome. It is also known that considering inhaled doses over personally measured concentrations overcomes measurement error and improves accuracy while estimating transport-related health risks (180). However, the measurement or calculation of inhaled doses has to be done using participants' respective physiological parameters such as breathing rate, heart rate and accelerometer instead of assuming a generic ventilation rate for every individuals.

Opportunities and challenges of using sensors

The major benefit of using sensors in monitoring human physiology is that they allow researchers to expand the scope of measurements outside the laboratory and clinical settings.

Still, the generalizability of studies using sensor devices can be questioned because of the potential inadequacy of sensors in real-life situations. Researchers using sensor devices should validate the devices in an ambulatory context before deploying them in real-life settings, specifically regarding the effect of the wearer's activity on both BP and exposure measurements.

Making the measurements possible in real-life settings, sensor devices may assist in assessing the potential interactive effect of different momentary environmental exposures on human health or help control for another environmental exposure as a confounder. Such assessment is only possible using various complementary environmental or behavioral sensors (for air pollution, noise, accelerometry, and location), as in our MobiliSense project. Furthermore, using such sensors makes testing of research hypotheses possible in different temporal structures, as outcomes and exposure are measured over a long period at high temporal resolution. Articles 3.2 and 3.3 of this thesis can be taken as an example where momentary noise exposure, physical activity, and other contextual variables (being at home, outside, or in transport, but also temperature and humidity) were accounted for while examining the effect of air pollution exposure on ambulatory BP in several temporal structures. In addition, further consideration of time-activity-location patterns while using sensors allows researchers to quantify exposures or test for the association between exposure and health outcomes across different contextual settings (such as work-home trips, exposure at home, in different geographical settings, etc.). A demonstration of this opportunity is illustrated in article 3.1 of this thesis, where we examined the air pollution exposure only across the different transport modes, isolating only trip stages out of participants' daily lives. In addition, in article 3.3, although as a sensitivity analysis, we analyzed the relationship between air pollution exposure and BP across different microenvironments (such as in-home or out-of-home microenvironments). Jointly, these data may help formulate policy-relevant conclusions concerning specific air pollutants in relation to health outcomes or activities that could be acted upon to reduce exposure.

Our studies, articles 3.1 and 3.3, advocated the use of inhaled doses over personal concentrations while examining the dose-response curve between air pollution exposure and the health outcome. However, until now, probably for technical and logistic difficulties in estimating ventilation rate in real-life settings from physiological measurements (heart rate, breathing rate, and acceleration), limited studies have reported inhaled doses of air pollutants in non-experimental settings especially across various transport modes.

With a certain investment of financial and human resources, sensors allow us to monitor physiological parameters (such as heart rate and breathing rate) over a more extended period than in laboratory settings. Recently, the concept of measuring ambulatory BP has become more popular among researchers due to the availability of either low-cost or very expensive and accurate sensors like the one we used. Concerning air pollution measurements, multiple ambient pollution species are highly correlated, making it more difficult for epidemiologists to identify a causal link between a specific air pollutant and health events. This correlation is drastically reduced when measuring air pollution exposure with high temporal and spatial resolution at a personal level using sensors, as they capture the exposure at sources and help estimate the impact of individual air pollutants on health outcomes.

A significant challenge often faced while using the newly available sensors is calibrating them with reference devices, which sometimes is highly technical and requires help from experts to conduct this correctly. For example, we had to frequently contact the manufacturer to understand the working mechanisms of the sensors used in article 3.3. Furthermore, we established a technical collaboration with The University of Cambridge's chemistry department to collate these sensors with the reference device present in their department. We also received technical assistance from them in calibrating the sensors.

In the innovative process of taking measurements in real-life settings where participants are equipped with numerous sensors for several follow-up days, it might be challenging but could be beneficial to confirm the determinants of missing data with participants (period of non-wear)

to understand the impact on the overall study truly. Nevertheless, it is essential to pay attention to the impact of these ambulatory measurements on the activity and attitudes of people wearing them. It is necessary not to overburden participants, in the name of research, with an excessive number of sensors in order to ensure that the stress of handling numerous sensors in the right manner and following the proper instructions does not hamper the quality of measurement itself.

Conclusion

This dissertation work was based on the data from the MobiliSense sensor-based study. To our knowledge, it is the largest sensor-based study measuring personal exposure to a relatively wide range of air pollutants and noise, along with monitoring physiological and behavioral parameters as they go about their daily lives. Moreover, the study also identified participants' time-activity patterns (places visited and transport modes taken) by processing the continuous GPS data and verifying the obtained mobility information with participants during a phonebased mobility survey. The primary goal of this thesis was to evaluate the short-term (within a few minutes) effects of exposure to air pollution on BP in a real-life context. Findings from this work are crucial for public health because humans are continuously exposed to a wide range of air pollutants in their everyday life.

The data analyses gave rise to the three scientific articles contributing to this thesis. The first article of this thesis quantified the level of BC exposure across several transport modes in the Grand Paris region of France at the trip stage level. Our first article suggested that public transport, private motorized transport, and other active modes (cycling) were associated with a higher personal concentration of BC compared to transport walking. In our study, particularly high personal concentrations of BC were documented for trip stages involving the metro. On the other hand, when inhaled doses of BC were considered as exposure, the hierarchical ordering of transport modes was completely altered, with other active modes (mostly cycling) and, to some extent walking, being associated with higher inhaled doses of BC per 30-minute than other modes. Such a level of BC exposure in different transport modes, especially in active modes, is a threat to human health, e.g., for BP (150,263) or in terms of reduction of lung function (264). Besides, such findings raise a concern that higher inhaled doses of pollutants may offset some health gains from walking and cycling ( 181). This study adds to the evidence that the choice of transport mode, rate of ventilation, and travel duration are good predictors of inhaled doses of air pollution in transport microenvironments. Our findings suggest that future studies assessing the dose-response curve between transport-related air pollution and health outcome should consider both personal concentrations and inhaled doses. Further studies are needed to better explain the influence of particular trip-related characteristics, such as the ventilation system of the motorized transport modes (car, bus, etc.) used and characteristics of routes taken, on personal exposure to air pollution in transport microenvironments.

The second article evaluated the adverse effect of BC exposure on ambulatory BP at a fine temporal structure (windows of 5 minutes to 1 hour). In the article, we documented that the relationship between BC exposure and BP is very acute in time and that the effect of BC exposure was reduced with the increase in the duration of the window considered for exposure assessment, with the previous 5 minutes having the strongest effect and 30-minute exposure windows and longer having no effect. In the third article, we used a novel approach to evaluate the joint effect of targeted air pollutants on BP. This approach used an innovative quantized exposure index with empirical weights for each exposure derived from quantiles of the exposures, considering the collinearities and additive and non-additive interactions between them. The empirical results from this article suggest that the exposure (concentrations and inhaled doses) to air pollutant mixtures elevated the SBP, where the effect decreased with the duration of the exposure time window, with 5-minute mixture exposure producing the strongest effect. We found that exposure to an increased level of O3 (concentrations and inhaled doses) elevated both SBP and DBP, while exposure to an increased CO concentration decreased DBP.

BC concentration had a stronger effect on SBP in out-of-home (transport/visited places)

contexts than in an in-home microenvironment. Our findings add useful information to the mounting evidence on the relationship between exposure to air pollution and adverse health effects, suggesting that exposure to certain air pollutants may adversely affect BP.

Future research focusing on larger populations with repeated BP measurements along with a continuous assessment of a relatively wide range of air pollutants are required to evaluate the associations between air pollution exposure and BP in various temporal structures and to provide more evidence on potential short and long-term adverse effect of air pollutants on cardiovascular health. Besides, these future studies are highly recommended to follow a mixture model approach to investigate the synergistic effect of targeted air pollutants on cardiovascular health, with the hypothesis that their joint effect might differ from adding individual effects estimated for each pollutant from a standard regression. In addition, studies focused on understanding the more detailed physiological mechanisms by which these mixtures of targeted

Figure 1 . 1 .

 11 Figure 1.1. Estimated adjusted mortality-rate ratios and air pollution levels in six cities in the U.S. P denotes Portage, Wisconsin; T Topeka, Kansas; W Watertown, Massachusetts; L St. Louis; H Harriman, Tennessee; and S Steubenville, Ohio. ...............................................................................................................................

Figure 1 . 2 .

 12 Figure 1.2. Publications related to air pollution (1955-2006). Comparison of results in "Web of Science" and "PubMed." .....................................................................................................................................................

Figure 1 . 3 .

 13 Figure 1.3. Overview of diseases, conditions, and biomarkers affected by outdoor air pollution. ......................

Figure 1 . 4 .

 14 Figure 1.4. Boxplots showing the distribution of ambient concentrations (station-measured) and personal exposures by season and pollutants ((PM 2.5 , O 3, and NO 2 ) in Baltimore . ...................................................

Figure 1 . 5 .

 15 Figure 1.5. Average daily pollution dose inhaled by the AIRLESS participants during the winter deployment ....

Figure 2 . 1 .

 21 Figure 2.1. Configurations of sensors over 6 days of study ..................................................................................

Figure 4 .

 4 Figure 4.1. A paradigm shift in assessing air pollution exposure in epidemiological studies . ............................

  ESC/ESH according to the blood pressure thresholds . ............................................................................... Table 2.1. Determinants of the probability of participating in the MobiliSense Study.........................................

Figure 1 . 1 .

 11 Figure 1.1. Estimated adjusted mortality-rate ratios and air pollution levels in six cities in the U.S. P denotes Portage, Wisconsin; T Topeka, Kansas; W Watertown, Massachusetts; L St. Louis; H Harriman, Tennessee; and S Steubenville, Ohio.

Figure 1 . 3 .

 13 Figure 1.3. Overview of diseases, conditions, and biomarkers affected by outdoor air pollution.

Figure 1 . 4 .

 14 Figure 1.4. Boxplots showing the distribution of ambient concentrations (station-measured) and personal exposures by season and pollutants ((PM2.5, O3, and NO2) in Baltimore (171). PM2.5 of ambient origin was determined by comparing the sulfate ([SO4]) content of personal and ambient PM2.5 filter samples.

Figure 1 .

 1 Figure 1.4 illustrates the discrepancies between personally measured air pollution exposure

  station-monitored or residential-modeled air pollution concentrations also eliminates the misclassification of doses (182). A study in China reported that station-measured concentration overestimated the inhaled doses for CO and NO, whereas it underestimated them for NO2 and O3 (figure 1.5) (183).

Figure 1 . 5 .

 15 Figure 1.5. Average daily pollution dose inhaled by the AIRLESS participants during the winter deployment, estimated using methods A-C (A: station measured +generic inhalation rate; B: Personal monitor + generic inhalation rate; C: Personal monitor + activity adjusted inhalation rate). Averaged over the urban (left) and peri-urban cohort (right); error bars indicate the standard error of dose estimations across the cohort (183).

  represented in the sample, participants in the study were recruited through a two-stage stratified random sampling techniqueStage 1 involved the random selection of neighborhoods in the first and last quartiles of road traffic density (traffic model of the Ministry of Infrastructures) within each quartile of income. The second stage involved randomly selecting dwelling units in the pre-selected neighborhoods from the 2013 and 2014 population censuses with the help of the National Institute of Statistics and Economic Studies.The MobiliSense sampling database, drawn from the 2013-2014 censuses, had 35100 dwellings with 86859 residents from the Grand Paris region (our study area). As defined by our eligibility criteria, we only recruited participants aged between 30 and 64 on January 1, 2016. Therefore, after excluding individuals who did not fall into this age group, the database had 49056 individuals from 31979 dwellings. If the former household residents had moved away between the census and the time we wanted to recruit them, our protocol was to attempt to recruit the new members of the very same dwelling. Postal mail was sent twice to invite residents from the selected dwellings to participate in our study. Two hundred eighty-nine individuals living in 259 dwellings accepted the invitation to participate in our study. Of these participants, 32 were new individuals not listed in the census database (who moved in after the census).

Figure 2 . 1 . 5 2. 4

 2154 Figure 2.1. Configurations of sensors over 6 days of study PAM: Personal air quality monitors to measure NO 2 , NO, CO, O 3 and PM 2.5

Articles 3 .

 3 1 and 3.3 calculated the inhalation of air pollutants, for which we needed to estimate the ventilation rate. One of the ways of calculating the ventilation rate was estimating it from physical activity [Metabolic Equivalents of tasks (METs)]. The refined 2-regression model

While article 3 .

 3 1 compared the BC exposure across several transport modes at the trip stage level, articles 3.2 and 3.3 used information on the proportion of time spent in different microenvironments such as home, places other than home, motorized and non-motorized transport as covariates when evaluating the associations between air pollution exposure and ambulatory BP. Trip stages in our study are segments of trips with a unique transport mode used. A time segment of mode transfer assigned to a fixed location necessarily separates the next trip stages within a trip. GPS data collected from individuals with a BT-Q1000XT GPS receiver at 5-sec resolution were processed with the TripBuilder Web mapping application (206). The inbuilt algorithms in the TripBuilder Web mapping application assisted us in a) identifying the places visited by the participants during the study period; b) decomposing the trips between two visited places into trip segments commuted with unique modes; and c) imputing information on the transport mode used in each trip segment based on speed, survey information from participants about the use of typical modes, and on the availability of public transport stations of the particular mode at the start and end of the subjected trip segment. The transport mode used in each trip stage as well as the type of places that were visited obtained from TripBuilder were further confirmed with the participants during a phone mobility survey. This mobility survey allowed us to add information on those trip segments or visited places missed by the GPS receiver. A SAS program generated a detailed timetable over the study period incorporating places visited and trips subdivided into trip stages.

  BC generated from the incomplete combustion of fossil fuels is responsible for chronic health problems like adverse respiratory outcomes, lung function impairment, neurological issues, or cardiovascular diseases. It is suggested that BC, a major component of particulate matter and a good indicator to quantify exposure to diesel exhaust, is one of the most valuable indicators to study traffic-related health effects. Studies suggest that traveling is responsible for a large share of daily total air pollution exposure, although a very small portion of an individual's daily schedule is spent traveling. Most of the previous literature ignored ventilation rates when quantifying transport-related air pollution exposure, and few of the studies accounting for it did not estimate the ventilation rate from participants' physiological parameters but used a generic ventilation rate for all the individuals in terms of the activity (such as cooking, sitting, walking, etc.) they were involved. Considering inhaled doses over exposure concentrations overcomes measurement error and improves accuracy while estimating transport-related health risks. The objective of this study is to analyze the relationship between personal use of transport modes and personally measured BC exposure (concentration and inhaled doses) at the level of trip stages.

  Accumulated epidemiological studies supporting the association between air pollution exposure and increased cardiovascular morbidity and mortality have suggested that elevated BP might be the biological pathway to explain the relationship at the functional level.

Figure 4 .

 4 Figure 4.1. A paradigm shift in assessing air pollution exposure in epidemiological studies (222).

  3, we documented that the effect of a quartile increase in the mixture of air pollutant concentrations (NO2, NO, CO, O3, and BC) on SBP decreased with the increase in the exposure time window, where only the previous 5-minute [β = 1.92 mmHg (95% CI: 0.63, 3.20)] and 15minute [β = 1.64 mmHg (95% CI: 0.38, 2.90)] windows were associated with the outcome. A quartile increase in the inhalation of the air pollution mixture in the previous 5-minute to 1-hour windows was associated with SBP elevations decreasing from 1.99 mmHg (95% CI: 0.89, 3.09) to 1.47 mmHg (95%CI: 0.31, 2.62) with increasing size of the time window considered. Unlike concentration mixtures, thirty-minute and one-hour air pollution inhalation mixtures were associated with SBP. It is because a one-quartile increase in O3 and BC inhalations in those exposure windows had greater effects on SBP than one quartile increase in their concentrations.

  processing the GPS and the mobility survey data. The processing of the GPS information was advanced enough to automatically identify the places visited and also the transport mode used by the participants in each trip stage (fragmentation of trips), which was cross-validated later with participants during the mobility survey over the phone. This level of accuracy in classifying transport modes at the trip stage level helped us increase the precision in estimating the level of BC exposure across different transport modes in the Grand Paris region (article 3.1 of this thesis), reducing exposure misclassification and selection biases. Furthermore, the detailed assessment of places visited and transport modes used during the study period assisted in controlling for the confounding effect of whereabouts in the association between air pollution exposure and BP outcomes (article3.2 and 3.3). This detailed information on whereabouts further helped us test the potential effect modification of microenvironments in the association between air pollution exposure and BP.Moreover, a sensor-based approach allowed us to evaluate the relationship between air pollution exposure and BP in real-life contexts, monitoring participants' daily lives. The use of sensors made it possible for us to estimate the inhaled doses of air pollutants from individuals' subjective physiological parameters such as physical activity, breathing and heart rate, unlike previous studies using a generic ventilation rate for all the participants. Article 3.1 of this work is the first of its kind to compare different methods for estimating ventilation rate in order to calculate inhaled doses of BC at the trip stage level. Indeed article 3.1 can be taken as an extension of a study published byDons et al. 2017 (179), where the comparison of different ventilation estimation methods was made at the minute level. Furthermore, article 3.1 overcomes the limitation of previous literature by monitoring BC concentration across the trip stages in all possible hours of the day and days of the week and by including all the seasons, whereas previous studies only focused on BC concentration in trip stages made on rush hours or off-hours, or only on weekdays(224)(225)(226)(227)(228)(229)233,234,236), and including less than two seasons of the year (224-229). Article 3.1 integrates data from air quality models with continuous personal measurements of BC concentrations to estimate the personal BC exposure attributed to transport mode (activity) as distinct from background concentrations.Article 3.2 is the first to adjust for noise exposure while evaluating the association between BC exposure and ambulatory BP, considering that noise is positively related to blood pressure (244) and highly correlated with air pollutant exposure (207). Also, articles 3.2 and 3.3 are rare examples of assessing the dose-response relationship of air pollution exposure with ambulatory BP measurements, averaging the personal exposure into short time windows (e.g., 5 minutes preceding each BP measurement) among the general public and in real-life settings. Given the continuous monitoring study design, we took advantage of the repeated BP measurements per participant along with the continuous monitoring of personal air pollution exposure and attempted to estimate associations that were as close as possible from causal relationships through proper consideration of time ordering of data and adjustment for time-varying confounders.

  ), clean fuel use (255), road restructuring (255), comprehensive traffic reduction strategies (255), and revising countryspecific air quality standards periodically while setting emissions standards for motorized vehicles (256) are several demonstrated effective policy interventions suggested by a recent Cochrane review to improve air quality (257).

Table 1 .

 1 1. Comparing French air quality standards for PM 2.5 , NO 2 , CO, and O 3 with EU and WHO standards. ....

Table 1 .

 1 2. Blood pressure classification in the ACC/AHA and ESC/ESH guidelines . .............................................

Table 1 .

 1 3. Lifestyle changes and antihypertensive treatment initiation that is recommended by ACC/AHA and

Table 1 .

 1 1. Comparing French air quality standards for PM2.5, NO2, CO, and O3 with EU and WHO standards.

		PM 2.5		NO 2		CO	O 3
		Yearly	24 hour	yearly	Hourly	Maximum	Maximum
						daily 8-hour	daily 8-hour
						mean	mean
	NECD 2008 (53)	25 µg/m 3	NA	40 µg/m 3	200 µg/m 3	10,000 µg/m 3	120 µg/m 3
	WHO 2021 (55)	5 µg/m 3	NA	10 µg/m 3	NA	400 µg/m 3	100 µg/m 3
	France 2020 (54)	10 µg/m 3	NA	40 µg/m 3	200 µg/m 3	10, 000 µg/m 3 120 µg/m 3
	France 2021 (54)	5 µg/m 3	15 µg/m 3	10 µg/m 3	25 µg/m 3	4,000 µg/m 3	100 µg/m 3
					(24 hour)	(24 hour)
	NA: Not applicable

1.4.1 Effect of acute elevation in blood pressure on human health

	and/or diastolic blood pressure values ≥90 mmHg (60). The 2017 guideline of the American
	College of Cardiology/American Heart Association (ACC/AHA) defined Hypertension as
	systolic blood pressure values ≥130 mmHg and/or diastolic blood pressure values ≥80 mmHg
	(61). As per table 2, comparing BP classification practices, European hypertension guidelines
	categorize BP in more classes than American guidelines. A recent report suggests that more
	than 30% of the world's adults have hypertension (BP ≥ 140/90 mmHg) (62), and about 30-
	40% of European citizens are living with this status (63).
	Table 1.2. Blood pressure classification in the ACC/AHA and ESC/ESH
	guidelines (64).
		ACC/AHA				ESC/ECH
	Category	SBP		DBP	Category	SBP		DBP
	Normal	< 120	and < 80	Optimal	< 120	and	< 80
	Elevated	120-29	and < 80	Normal	120-129	and/or	80-84
	Stage 1 hypertension	130-139	or	80-89	High-normal 130-139	and/or	85-89
	Stage 2 hypertension	≥ 140	or	≥ 90	Grade 1 hypertension	140-159	and/or	90-99
					Grade 2 hypertension	160-179	and/or	100-109
					Grade 3 hypertension	≥ 180	and/or	≥ 110
	ACC/AHA: American College of Cardiology (ACC) and the American Heart Association (AHA)
	ESC/ESH: European Society of Cardiology (ESC) and the European Society of Hypertension
	(ESH)
	It is hypothesized that an acute elevation in BP can lead to a hypertensive crisis (HC) (65), a
	severe clinical condition endangering patients' life. HC is represented by hypertensive
	symptoms, which can result in vascular damage to vital organs (kidneys, heart, brain, and
	arteries). This condition is characterized by a level of SBP >180 mmHg or a level of DBP >
	120 mmHg and is especially common among hypertensive people (65). Therefore, timely
	. As per the 2018 diagnosis and adequate clinical attention are significant in preventing the mortality and
	European Society of Hypertension and European Society of Cardiology Hypertension morbidity caused by this acute increase in BP
	(ESC/ESH) guidelines, hypertension is defined as systolic blood pressure values ≥140 mmHg

Table 1 .

 1 [START_REF]AIR QUALITY IN THE PARIS REGION[END_REF]. Lifestyle changes and antihypertensive treatment initiation that is recommended by ACC/AHA and ESC/ESH according to the blood pressure thresholds[START_REF] De La | New American and European Hypertension Guidelines, Reconciling the Differences[END_REF].

		ESC/ECH	ACC/AHA
	BP ≥ 160 and/or ≥ 100	Lifestyle changes and antihypertensive treatment.	Lifestyle changes and antihypertensive treatment.
	BP 140-159 and/or 90-99	Lifestyle changes and antihypertensive	Lifestyle changes and antihypertensive
	with high or very high risk	treatment.	treatment.
	BP 140-159 and/or 90-99	Lifestyle changes and antihypertensive	Lifestyle changesConsider
	with low risk	treatment.	antihypertensive treatment if BP
			remains elevated after 3-6 months
		Lifestyle changes and antihypertensive	Lifestyle changes
	BP 130-139 and/or 80-89	treatment if CVD or elevated CV risk (> 10% in 10 years) a .	Consider antihypertensive treatment in very high-risk patients due to coronary
			heart disease.

  3,629 women, 35-76 years of age, concluded that a 10-μg/m 3 increase in yearly residential Among 65 Chinese adults, a panel study reported that station-measured PM2.5 exposure averaged across the previous 1-day to 7-day was associated with increased resting SBP ranging from 2.00 mmHg (95% CI: 0.30, 3.70) to 2.70 mmHg (95%CI: 0.60, 4.80) per increase in standard deviation (67.2 μg/m 3 ), while no association was documented with resting DBP (144).A systematic review published in 2014 highlighted that the largest magnitude of the effect of short-term exposure to station-measured PM2.5 on resting BP was seen at the lag of the previous5-day exposure (145). A panel study among type 2 diabetes mellitus patients reported that an IQR increase during the last 5-day averaged station-measured PM2.5 exposure (3.54 μg/m 3 )

PM2.5 was positively associated with resting SBP (β = 1.40 mmHg, 95% CI: 0.60, 2.30), but did not report an association with resting DBP (127). However, a cohort study among 2,368 children reported that annual residential PM2.5 levels were not associated with resting BP (129).

A cross-sectional study including 4,291 participants aged 45-75 years documented that one interquartile range (IQR) increase in yearly exposure to residential PM2.5 (2.4 μg/m 3 ) was associated with a 1.40 mmHg higher resting SBP (95% CI: 0.5, 2.3) and with a 0.90 mmHg higher resting DBP (95% CI: 0.40, 1.40) (114).

A Spanish cohort study including 1,926 adults identified that a 10-μg/m 3 increase in annual residential NO2 exposure corresponded to

1.23 mmHg (0.21, 2.25)

increased resting SBP, whereas no association was reported with resting DBP (139). However, two cohort studies conducted among children reported no association between annual residential NO2 exposure and resting

BP (128,129)

. Likewise, the popular nationwide American sister study also found no relationship between annual residential NO2 exposure and resting BP (127). Among 3,762 sleep-disordered breathing patients in Taiwan, a study documented that a 21.20 ppb increase in annual station-measured NO2 decreased resting SBP by 2.43 mmHg (95% CI: -2.

90, -1.96)

and increased resting DBP by 0.45 mmHg (95% CI: 0.12, 0.78) (140). A study among

[START_REF] Gakidou | Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016[END_REF]845

Chinese adults from 11 districts of 3 north-eastern cities reported no association between station-measured NO2 exposure and resting BP (141). Among 9,354 children, a Chinese study identified that a 13.0 μg/m 3 increase in 4-year averaged station-measured NO2 corresponded to a 1.15 mmHg (95% CI: 0.84, 1.46) increased resting SBP and a 1.15 mmHg (95% CI: 0.89, 1.41) increased resting DBP (142).

A Taiwanese study reported that annual station-measured O3 exposure reduced the resting SBP by 1.54 mmHg (95% CI: -2.11, -0.98) but observed no effect on resting DBP among sleepdisordered breathing patients (140). A study among Chinese adults concluded that an IQR increase in 3-year averaged station-measured O3 (22 μg/m 3 ) increased resting SBP by 0.73 mmHg (95% CI: 0.35, 1.11) and resting DBP by 0.37 mmHg (95% CI: 0.14, 0.61) (141). A Chinese study among 9,354 children reported that a 46.3 μg/m 3 increase in 4-year averaged station-measured O3 was associated with a 0.50 mmHg (95% CI: 0.43, 0.57) elevated resting SBP and a 0.58 mmHg (95% CI: 0.52, 0.63) increased resting DBP (142). The same study concluded that a 563.4 μg/m 3 increase in 4-year averaged station-measured CO was associated with elevated resting SBP (β =2.07 mmHg 95% CI: 1.64, 2.49) and resting DBP (β =2.89 mmHg 95% CI: 2.53, 3.24) (143).

A cohort study conducted in Boston city documented that an IQR increases in annual residential BC exposure (0.32 μg/m 3 ) was associated with a 2.64 mmHg (95% CI 1.47 to 3.80) increased resting SBP and a 2.41 mmHg (95% CI 1.77 to 3.05) increased resting DBP among older men (131). However, in an Indian cohort study, no association was observed between annual residential BC exposure and resting BP (130).

Air pollution exposure over several days and BP

increased resting SBP by 1.40 mmHg (95% CI: 0.00, 2.90), but did not report an association with resting DBP (134).

  lag personal BC elevated resting DBP (β= 1.01 mmHg, 95%: CI 0.50, 2.10, per 3.61 μg/m 3 ), while no association was documented with resting SBP (144).

	Previous 24-hour air pollution exposure and BP
	An IQR increase in previous 24-hour station-measured PM2.5 exposure (51.20 μg/m 3 ) was
	positively associated with resting SBP (β = 1.08 mmHg, 95% CI: 0.17, 1.99) and resting DBP
	(β = 0.96 mmHg, 95% CI: 0.31, 1.61) (96). A panel study exploring the association between
	indoor exposure to air pollutants and BP in 280 Chinese women reported that a 1-log-
	previous 5-day averaged personal BC exposure (5.20 mg/m 3 ) increased ambulatory SBP by 8.2
	mmHg (95% CI: 3.0, 13.4) and ambulatory DBP by 5.8 mmHg (95% CI: 3.0, 8.6) (106). A
	study found that a 0.25 μg/m 3 increase in previous 5-day station-measured BC exposure
	corresponded to a 2.20 mmHg (95% CI: 0.40, 4.00) increase in resting SBP among type 2

40 mmHg, per 1-μg/m 3 ), among 4,580 healthy adults

(146)

. A Brazilian panel study in 48 healthy, non-smoker adults found that an IQR increase in the previous 2-day averaged station-measured CO (1.10 ppm) increased 2.60 mmHg (95% CI: 1.00, 4.20) and 1.80 mmHg (95% CI: 0.80, 2.80) ambulatory SBP and DBP respectively (

107

). An area-level randomized controlled case cross-over study including 61 healthy adults found no association between 1-day lag station-measured CO and resting BP (147).

Likewise, two studies conducted in Boston reported a positive association between the previous 7-day mean station-measured BC exposure and resting BP (149,150). Among 64 elderly subjects with coronary artery disease, a panel study documented that an IQR increase in the diabetes mellitus patients (134). A Chinese panel study in 65 adults reported that exposure to 2-day µg/m 3 increase in previous 24-hour averaged exposure to personal PM2.5 increased resting SBP by 2.20 mmHg (95% CI: 0.80, 3.70), but did not report an association with resting DBP

  and BP among women found no association between previous 24-hour personal exposure to BC and resting BP (135). Among 64 elderly patients with coronary artery disease, a study found no association between personal exposure to previous 24-hour averaged BC and ambulatory BP (106).

	. A cross-
	sectional study documented a positive association between station-measured O3 and resting
	DBP only in the cold season 24 hours after exposure (137). Among 64 elderly subjects with Hourly level air pollution exposure and BP
	coronary artery disease, a panel study reported no association between O3 exposure and A cross-sectional study including 98,577 individuals concluded no association between station-
	ambulatory BP after 24 hours of personal exposure (106). measured PM2.5, CO, and O3 and resting BP within the same hour of exposure (136). In the
	A Chinese panel study among 280 women reported that a 1-ln(μg/m 3 ) increase in the previous
	24-hour averaged personal BC exposure corresponded to a 4.00 mmHg (95%CI: 1.70, 6.30)
	increase in resting SBP and a 1.40 mmHg (95%CI: 0.00, 2.80) increase in resting DBP (98). A
	study in a US city including 23 healthy adult participants concluded that previous 24-hour
	averaged exposure to personal BC was negatively associated with resting SBP, but no
	association was reported with resting DBP (97), whereas other studies reported no association

A randomized controlled case cross-over study including 61 healthy adults found no association between previous

. A Brazilian panel study conducted on 48 healthy, non-smoker adults found that a 1.10ppm increase in the previous 24-hour averaged station-measured CO was associated with increased SBP (β= 2.00 mmHg, 95%: CI .

50, 3.50)

and DBP (β=1.50 mmHg, 95% CI: 0.30,

2.70) (107).

A study among elderly patients with coronary artery disease (n =64) found no association between personal exposure to CO and ambulatory BP after 24 hours of personal exposure (106). between previous 24-hour residential and station-measured BC exposure and resting BP

(131,133)

. Similarly, another study evaluating exposure to household air pollution from biomass cookstoves same study, an IQR increase in station-measured NO2 (24 ppb) exposure at 0-2 hours lag increased resting SBP by a 0.42 ± 0.12 mmHg. Likewise, an IQR increase in station-measured O3 (26 ppb) exposure increased resting SBP by 0.55 ± 0.14 mmHg at a 3-5 hour lag (136). In contrast, one IQR increase in station-measured CO (0.2 ppm) corresponded to a 0.26 ± 0.07 mmHg decrease in resting SBP at 3-5 hours lag exposure (

136

). The study also concluded that one IQR

(24 ppb)

increase in station-measured NO2 exposure within the same hour increased the resting SBP by 0.60 mmHg (95% CI: 0.43, 0.76) and resting DBP by 0.35 mmHg (95% CI: 0.22, 4.70) (136). All the analyses in this study were performed with single pollutant models (136). Another randomized control study among 23 healthy adults reported no association between PM2.5 and resting BP 2 hours after personal exposure

  to air pollutants, including station-measured PM2.5(24 hours), stationmeasured NO2 (yearly and hourly), station-measured CO (several days and hours), stationmeasured O3 (yearly and several days), and station-measured BC(24-hour) might have reducing effects on BP, especially on SBP. Extracting air quality levels from a nearby fixed station was the most used exposure assessment method in past studies, followed by modeling air pollution concentration at residential addresses. Only a few studies used personal sensor devices to monitor air quality. While most past studies used resting BP, only 4 studies with short-term exposure used ambulatory BP. A large number of epidemiological studies have evaluated the associations between annual or daily exposure to air pollutants and BP. However, studies assessing the effect within very short-term exposure (in minutes or hourly) are lacking.

10, 0.65)

mmHg increased ambulatory DBP

(105)

. Another panel study reported that only ambulatory DBP was negatively associated with prior 4-minute averaged personal BC exposure among women during cooking hours (104). Among 64 elderly subjects with coronary artery disease, a panel study reported no association between O3, CO, and BC and ambulatory BP after 1, 4, and 8hour of personal exposure (result from single pollutant models) (106). In summary, accumulated epidemiological studies suggest that exposure to air pollutants may have an elevated effect on BP in the short and long-term. It appeared that each targeted air pollutant's effects were relatively stronger on SBP than on DBP, and some studies reported associations only with SBP but not with DBP. In past studies, the association between air pollutants and BP appeared to be greater for particles (BC and PM2.5, in descending order) than for gaseous pollutants, and the magnitude of the associations decreased with the decrease in length of exposure time, where annual exposure had the strongest effect. Very few studies reported that exposure

Table 2 .

 2 1. Determinants of the probability of participating in the MobiliSense Study

	Socioeconomic characteristics	Risk ratio (95% CI)	Numerator (%)
	Sex (vs. female)		162 (56%)
	Male	0.96 (0.90, 1.02)	127 (44%)
	Age group (vs. ≤40 years)		78 (27%)
	>40 -≤50 years	1.20 (1.07, 1.36)	99 (34%)
	>50 -≤64	1.29 (1.13, 1.51)	112 (39%)
	Marital status (vs. being in couple)		85 (29%)
	Being single	0.68 (0.57, 0.80)	204 (71%)
	Nationality (vs. French)		280 (97%)
	Non-French	0.72 (0.57, 0.88)	9 (3%)
	Education (vs. lower than Baccalauréat)		17 (6%)
	Baccalauréat or Baccalauréat + 2 years	1.37 (1.15, 1.67)	71 (25%)
	Baccalauréat + 3 years or above	1.83 (1.40, 2.40)	201 (69%)

4.3.1 Choice of transport modes and exposure to black carbon

  Our study explored this association with a novel approach aiming at causal inference. Relying on geographical information and personal daily logbooks containing activity and location data, we were able to isolate spatio-temporal segments corresponding to each use of transport modes from participants' daily life, reducing measurement error that could be expected in such studies. In article 3.1, we explored exposure to BC as a function of the choice of transport modes at the trip stage level, isolating all daily trips from participants' daily timetable, in the Grand Paris region: since commuting is a necessary part of life and since most transport modes and lines over the territory are represented

	Several studies have investigated the relationship between transport mode and personally
	measured concentrations and inhaled doses of BC. Such studies have been conducted in several
	cities, i.e., in Barcelona (223,224), Lisbon (225), Shanghai (226), London (227), Toronto (228),
	Singapore (229,230), Hong Kong (231), Bogota (232,233), Stockholm (234), California (235),
	Londrina (236), and Mexico City (237).

  . A cross-sectional study reported that station-measured NO2 exposure within the same hour increased the resting SBP by 0.60 mmHg (95% CI: 0.43, 0.76) and resting DBP by 0.35 mmHg (95% CI: 0.22, 4.70) (136). A Canadian cross-over study among 53 healthy women found no association between 3-hour averaged personal NO2

ACC/AHA: American College of Cardiology (ACC) and the American Heart Association (AHA) ESC/ESH: European Society of Cardiology (ESC) and the European Society of Hypertension (ESH) a In the European guidelines, this recommendation also applies to patients with SBP 130 -139 and/or DBP85 -89

Available from: http://www.nature.com/articles/7500388

Leq is the constant sound that would have been generated with the same energy level compared to the noise that was perceived during the period and expressed as dB (207).

Personal noise exposure was not measured on the 1 st day of our study. Therefore, observed data from days 3 and 4 were used in predicting the noise exposure for day 1. Because of a very large number of raw data points, both noise data (every 1 second) and BC measurements (every 10 seconds) were aggregated at the minute level. BC concentration, transport modes, the combination of departure place and arrival place (when the person was traveling), activity place id and activity type (when the person was at a place), week vs. weekend, time of the day (morning, noon, evening, and night) defined at the minute level were used in predicting noise exposure using data from days 3 and 4. Variables related to departure and arrival places of trips and activity place ids are wide sets of binary variables specific to each participant. These variables were created using the detailed mobility survey timetable discussed above (section 2.5.4). We ran a random forest algorithm with 50 trees with the above-mentioned independent variables to predict noise exposure at the minute level in days 3 and 4, and then we applied the resulting model to predict noise on day 1, where there was no observed data. In summary, we could predict the day 1 noise exposure at the minute level for 237 participants, and the R 2 of the prediction model was 81%. Random forest was run from the R Package 'randomForest' (208), which implements Breiman's theory (209). The principle of the random forests method (209) relies on the decision tree method. In order to produce high generalizability, this method applies two sources of randomness to the classic decision tree method and simulates the process repeatedly, thereby leading to a forest of decision trees. The first source of randomness is that the approach considers only random samples of the independent variables while defining each Conclusion: Our study adds to the evidence that using inhaled doses rather than just considering air pollution concentrations helps estimate the unbiased effect estimates while exploring the dose-response curve of transport-related air pollution-induced health outcomes.

Future research evaluating the level of air pollution exposure across different transport modes should consider both surrounding concentration and inhaled doses taking into account an individual's ventilation rate.

Conclusion:

Our findings indicate that BC exposure within a few minutes could elevate BP in the adult population, which may have implications for the development of cardiovascular diseases. We observed that the strength of the relationship between BC exposure and BP decreased with the increase in the length of BC exposure windows, indicating that the effect of BC on BP is acute. Interventions targeting a reduction of air pollution exposure and early screening could potentially reduce the future burden of hypertension. Future studies must confirm whether noise exposure modifies the association between BC exposure and BP. Methods: As stated in article 3.1, the post-processing of BC data collected at 10s resolution was done using the Optimized Noise Reduction Averaging (ONA) algorithm proposed by [START_REF] Hagler | Post-processing Method to Reduce Noise while Preserving High Time Resolution in Aethalometer Real-time Black Carbon Data[END_REF]. Other pollutants, including NO2, NO, CO, O3, and PM2.5, were measured using a personal air quality monitor capable of measuring ambient temperature and relative humidity. The device was calibrated with the reference instrument in the outdoor environment at the University of Cambridge. Arteriograph 24 ambulatory BP monitor (TensioMed, Budapest, Hungary) was used to measure participants' BP at 30 minutes intervals from wakeup to bedtime (n = 3319). Personal exposure to all the air pollution species was averaged over 5minute to 1-hour time windows before each ambulatory BP measurement. We employed multi-

Full article

The rest of the section consists of the article that has been accepted by the journal "Environmental research" for publication. maintain the data integrity and increase the adoption of sensors in assessing personal air pollution exposure.

This thesis aimed to contribute to a better understanding of the association between air pollution exposure and acute ambulatory BP response using the new paradigm of monitoring the exposure at the personal level. We relied solely on the MobiliSense multi-sensor study, an attempt to assess personal air pollution exposure and mobility patterns of adults in the Grand Paris region. Our novel findings from the study will be discussed in the following sections: 1) methodological and 2) empirical findings. This discussion ends with summarising key findings and recommendations for future research.

Methodological findings and developments

Estimating inhaled doses of air pollutants from physiological measurements

Article 3.1 of this thesis includes both an empirical part reporting new findings on personal exposure to BC while commuting in the Grand Paris region and a methodological report adding to the evidence that considering inhaled doses over concentrations helps better assess air pollution exposures in transport microenvironments. In 2017, Dons and colleagues compared different ventilation estimation methods so far available in the literature and concluded that the choice of ventilation method has to be governed by the objective, duration, and size of the study (179). Following the recommendation, we selected five different approaches to estimate minute ventilation rate in our study; 1 approach using physical activity (METs), 2 approaches using heart rate, and 2 using breathing rate. The cross-product of the estimated minute ventilation and the corresponding breathing zone concentration of BC was used to compute the inhaled doses per minute, which were then compared at the trip stage level. Therefore, article 3.1 can be viewed as the extension of a previously published paper by Dons et al. (179), shifting the comparison between different approaches to estimate minute ventilation from minute level to in-home microenvironment, further adds to the limitation. Future studies may overcome this limitation to investigate whether microenvironments act as an effect modifier or not in the association between air pollution exposure and BP.

It is known that waist-worn accelerometers underestimate METs for biking (245), therefore resulting in underestimated ventilation rate for this mode (179). It is worth mentioning that BC inhaled doses documented for biking and other active modes in articles 3.1 and 3.3 may have underestimated the actual air pollution inhaled doses while the participants were using these transport modes. Adopting alternative methods of estimating ventilation rate, mentioned in article 3.1, such as breathing and heart rate, as the main technique would help estimate inhaled air pollutants with greater precision in a physically active mode, such as biking, for which waistworn accelerometry is inadequate. Furthermore, the inhaled doses of air pollutants in our project were estimated following two steps: 1) estimation of minute ventilation from body accelerations and age and sex of the participants and 2) calculation of inhaled doses as a product of estimated minute ventilation with the corresponding air pollution concentration. These calculations involved several approximations, likely introducing intrinsic error in estimated doses in our study.

Given that the concentrations of air pollution in our study were much lower compared to highpolluted countries in Asia, findings from our study may not be directly generalizable to people exposed to a higher level of air pollution but could apply to other European countries with comparable air pollution. Furthermore, our sample had a slight positive bias toward older people, urban citizens, and higher socioeconomic class. Although we have adjusted for these factors in our final analyses with a sufficient sample size in each subgroup, our findings should air pollutants lead to an adverse cardiovascular effect are needed to inform future air pollution mitigation interventions better.

Appendices