Marc Guevara
email: marc.guevara@bsc.es

Oriol Jorba

Carles Tena

Hugo Denier Van Der Gon

Jeroen Kuenen

Nellie Elguindi-Solmon

Sabine Darras

Claire Granier

Carlos Pérez García-Pando

Nellie Elguindi

CAMS-TEMPO: global and European emission temporal profile maps for atmospheric chemistry modelling

come

Introduction

Spatially and temporally resolved atmospheric emission inventories are key to investigate and predict the transport and chemical transformation of pollutants, as well as to develop effective mitigation strategies (e.g. Pouliot et al., 2015;Galmarini et al., 2017). During the last decade, global and regional inventories have substantially increased spatial resolution from ~50 by 50 km (e.g. MACCity; [START_REF] Granier | Evolution of anthropogenic and biomass burning emissions of air pollutants at global and regional scales during the 1980-2010 period[END_REF]EMEP-50km, Mareckova et al., 2013) to ~10 by 10 km or less (e.g. EMEP-0.1deg, [START_REF] Mareckova | Review of emission data reported under the LRTAP Convention and the NEC Directive Stage 1 and 2 review, Status of gridded and LPS data[END_REF]; TNO-MACC; Kuenen et al., 2014). Several datasets even provide emission maps for selected pollutants or study regions with resolutions as fine as 1 by 1km (e.g. ODIAC2016, Oda et al., 2018;Hestia-LA;[START_REF] Nassar | Improving the temporal and spatial distribution of CO2 emissions from global fossil fuel emission data sets[END_REF]Super et al., 2020). This improvement is largely due to the emergence of new detailed, satellite-based and open-access spatial proxies such as the population maps at 1 by 1 km proposed by the Global Human Settlement Layer (GHSL) project [START_REF] Florczyk | GHSL Data Package 2019,EUR 29788EN[END_REF], the global land cover maps at 300mx300m provided by the European Spatial Agency Climate Change Initiative (ESA CCI, https://www.esa-landcover-cci.org/) or the georeferenced road traffic network distributed by Open Street Maps (OSM, www.openstreetmap.org). While a clear evolution is observed in terms of spatial resolution, the improvement of the temporal representation in current state-of-the-art emission datasets has not been addressed much [START_REF] Reis | Earth System Science MODSIM 2011-19th International Congress on Modelling and Simulation Sustaining Our Future: Understanding and Living with Uncertainty[END_REF].

Using global and regional emission inventories in atmospheric chemistry models requires the original aggregated annual emissions to be broken down into fine temporal resolutions (ideally hourly) using emission temporal profiles (e.g. [START_REF] Borge | Development of a high-resolution emission inventory for Spain using the SMOKE modelling system: A case study for the years 2000 and 2010[END_REF]Bieser et al., 2011;Mues et al., 2014). In practice, temporal profiles are normalized weight factors for each hour of the day, day of the week and month of the year. At the global scale, the most commonly used emission temporal profiles are the monthly factors provided by the Air Pollutants and Greenhouse Gases Emission Database for Global Atmospheric Research inventory (EDGARv4.3.2; Janssens-Maenhout et al., 2019) and the Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants inventory (ECLIPSEv5.a; [START_REF] Klimont | Global anthropogenic emissions of particulate matter including black carbon[END_REF]. Also at the global level, the Temporal Improvements for Modeling Emissions by Scaling (TIMES) dataset was produced to represent the weekly and hourly variability for global CO2 emission inventories [START_REF] Nassar | Improving the temporal and spatial distribution of CO2 emissions from global fossil fuel emission data sets[END_REF]. More recently, [START_REF] Crippa | High resolution temporal profiles in the Emissions Database for Global Atmospheric Research[END_REF] developed a new set of high-resolution temporal profiles for the EDGAR inventory, which allows producing monthly and hourly emission time series and grid maps.

At European level, the temporal factors provided by the University of Stuttgart (IER) as part of the Generation of European Emission Data for Episodes (GENEMIS) project are still considered as the main reference [START_REF] Ebel | Tropospheric Modelling and Emission Estimation: Chemical Transport and Emission Modelling on Regional, Global and Urban Scales[END_REF]Friedrich and Reis, 2004). The original GENEMIS profiles were later used as a basis to derive two independent datasets: (i) the EMEP temporal profiles, which provide monthly, weekly and hourly weight factors that vary per emission sector, country and pollutant [START_REF] Simpson | The EMEP MSC-W chemical transport model -technical description[END_REF], and (ii) the TNO temporal profiles, which provide monthly, weekly and hourly weight factors that vary per emission sector (Denier van der Gon et al., 2011). These two sets of profiles have become over time the reference datasets under the framework of several European air quality modelling activities, including the earlier Monitoring Atmospheric Composition and Climate (MACC) project and the current Copernicus Atmosphere Monitoring Service (CAMS), 2 Methodology

Overview

The CAMS-TEMPO dataset consists of a collection of global and regional temporal factors that follow the domain description and sector classification reported by the CAMS-GLOB_ANT and CAMS-REG_AP/GHG emission inventories. In order to better distinguish between the two sets of profiles, we refer to them as CAMS-GLOB-TEMPO (https://doi.org/10.24380/ks45-9147, global temporal profiles associated to the CAMS-GLOB_ANT inventory) and CAMS-REG-TEMPO (https://doi.org/10.24380/1cx4-zy68, regional European temporal profiles associated to the CAMS-REG_AP/GHG inventory).

Depending on the pollutant source and temporal resolution (i.e. monthly, weekly, daily, hourly), the resulting profiles are reported as spatially invariant (i.e. a unique set of temporal weights for all the domain) or gridded values (i.e. temporal weights vary per grid cell). Similarly, depending on the characteristics of the input data used and approaches to compute the profiles, these can be yearly dependent and/or pollutant dependent. The spatial resolution of the gridded profiles is 0.1x0.1 deg for CAMS-GLOB-TEMPO and 0.1x0.05 deg for CAMS-REG-TEMPO. In the case of CAMS-REG-TEMPO, the domain covered by the dataset is: 30° W -60° E and 30° N -72°N.

Table 1 and Table 2 summarise the characteristics of each temporal profile included in CAMS-GLOB-TEMPO and CAMS-REG-TEMPO datasets, respectively. The sector classification for each case corresponds to those used in CAMS-GLOB_ANT and CAMS-REG_AP/GHG. For both temporal profile datasets, the sum of all weight factors is equal to 12 for monthly profiles, 7 for weekly profiles, 365 or 366 (in case of a leap year) for daily profiles and 24 for hourly profiles.

The following subsections describe the input data and methodologies used to compute the emission temporal profiles for each targeted sector: (i) energy industry (Sect. 2.2), (ii) residential/commercial combustion (Sect. 2.3), (iii) manufacturing industry (Sect. 2.4), (iv) road transport (Sect. 2.5), (v) aviation (Sect. 2.6) and (vi) agriculture (Sect. 2.7). The specificity of the computed profiles depends upon the degree of sectoral disaggregation used in the original CAMS inventories. For example, the CAMS-GLOB_ANT dataset reports emissions from power and heat plants and refineries under the same sector ("ene", see Table 1) and therefore a common set of temporal profiles had to be assumed for the two types of facilities. In contrast, the CAMS-REG_AP/GHG inventory reports power and heat plants under the GNFR_A category and refineries under sector GNFR_B, together with all manufacturing industries (Table 2). All the assumptions made regarding this topic are clearly stated in each subsection.

Energy industry

The temporal profiles computed for the energy industry are reported under the ene sector in CAMS-GLOB-TEMPO and the GNFR_A category in CAMS-REG-TEMPO. The temporal variability of emissions from this sector was estimated from electricity production statistics under the assumption that it largely depends upon the combustion of fossil fuels in power and heat plants. This approximation is consistent with the definition of the GNFR_A sector in the CAMS-REG_AP/GHG dataset.

The representativeness of the computed profiles is likely lower in CAMS-GLOB_TEMPO because the ene sector also includes other facilities such as refineries.

As shown in Table 1 andTable 2, the profiles reported for this sector include pollutant and country dependent monthly, weekly and hourly factors. The electricity production dataset compiled to derive profiles for this sector were as follows:

•

The ENTSO-E Transparency Platform [START_REF] Hirth | The ENTSO-E Transparency Platform -A review of Europe's most ambitious electricity data platform[END_REF][START_REF] Entso-E | Transparency Platform[END_REF] Figure 1.a illustrates the spatial coverage of the compiled dataset by source of information (i.e. ENTSO-E, US EPA, IEA and MBS). Overall, main emission producers (e.g. China, India, Europe and North America) are covered, while most of the countries with no information available are located in South America and Africa. For those countries with no data, the TNO profiles reported under the energy sector (Denier van der Gon et al., 2011) are used.

The compiled data was first analysed to assess whether interannual variability is important for this sector. Seasonal cycles were computed for different years (2010 -2017) and countries using the IEA statistics. In the majority of the countries analysed, the monthly profiles were found to be consistent through the different years and to present small interannual variations (Fig. S1). Although some studies have pointed out a temperature dependence of the monthly electricity generation in power plants [START_REF] Thiruchittampalam | Entwicklung und Anwendung von Methoden und Modellen zur Berechnung von räumlich und zeitlich hochaufgelösten Emissionen in Europa[END_REF], we neglected it at present. Consequently, we assume the monthly temporal profiles for this sector to be the average over all the available years of data.

Monthly profiles

For European countries, monthly profiles were derived using the ENTSO-E dataset. The analysis of the data showed that the seasonality of electricity production varies significantly by fuel type (Fig. S1). The different use of energy sources (i.e. lignite, hard coal, natural gas, biomass and oil) implies that temporal patterns will also vary from one pollutant to the other. For each month, country and pollutant, profiles were calculated following Eq. ( 1):

! !,#,$ = ∑ ! !,#,% &' !,# * )& #,$ ∑ &' !,# * )& #,$ % #&' + %,- (1)
Where ! !,#,$ is the monthly factor for month m, country c and pollutant p; ! !,#,% is the is the monthly factor for month m, country c and fuel f; %& #,% is the fuel share factor for country c and fuel f and '% %,$ is the emission factor for fuel f and pollutant p. Fuel share factors were obtained by averaging the ENTSO-E production data for years 2010 to 2017 per country, and the emission factors were taken from the EMEP/EEA 2016 emission inventory guidebook for the priority air pollutants (EMEP/EEA, 2016; 1.A.1 Energy industries, Table 3-2, 3-3, 3-4, 3-5 and 3-7) and from the IPPC guidelines (IPCC, 2006; Volume 2: Energy, Table 2.2) for GHGs (Table 3). We note that only fuels with shares larger than 10% were considered. For instance, in the case of Austria, only hard coal (25%) and natural gas (65%) were used, and the original shares were normalised so that their sum equalled 100%. This was done to avoid introducing errors due to residual fuels, which may be related to few (or even just one) power plants.

For other countries, monthly factors by pollutant could not be developed as both the IEA and the MSB datasets do not report electricity production split by fuel type. Hence, monthly factors were derived by averaging the available production data per month and relating them to the total production in the year. For the US, NOx and SOx monthly profiles were derived from the corresponding hourly measured emissions reported by the EPA. The seasonality for the other pollutants (i.e. NMVOC, NH3, CO, PM10, PM2.5, CO2, CH4) was linked to the measured heat input, following EPA's recommendations.

Weekly profiles

Weekly profiles were developed for Europe and the US using the hourly electricity production data reported by ENTSO-E and US EPA, respectively. As in the case of the monthly profiles, weekly scale factors were found to significantly vary according to the type of fuel (Fig. S1). These results are in line with the conclusions of Adolph (1997), which identified three generic weekly profiles-base, medium and peak load-as a function of the type of power plant.

Pollutant-related weekly profiles were developed following the same methodology applied for obtaining the monthly weight factors (Sect. 2.2.1). For countries with no information on daily electricity production data, we used the weekly profile reported in the TNO dataset for the energy sector.

Hourly profiles

Hourly profiles were developed for Europe and the US using the hourly electricity production data reported by ENTSO-E and US EPA, respectively. As previously seen, large differences are observed between fuels. Profiles related to the so-called base peak load power plants (i.e. annual useful life of more than 4000 hours) present a rather flat distribution, whereas in other cases the change in energy production between day and night is relatively high (Fig. S1).

Pollutant-related hourly profiles were developed following the same methodology applied for obtaining the monthly weight factors (Sect. 2.2.1). For countries with no information on hourly electricity production data, we assumed the hourly profile reported in the TNO dataset. Some studies have suggested that the hourly variation of power plant activities may vary according to the season of the year [START_REF] Thiruchittampalam | Entwicklung und Anwendung von Methoden und Modellen zur Berechnung von räumlich und zeitlich hochaufgelösten Emissionen in Europa[END_REF]. This feature is not considered in the present version of the CAMS-TEMPO profiles, and will be addressed in future releases.

Residential/commercial combustion

The temporal profiles computed for the residential/commercial sector are reported under the res sector in CAMS-GLOB-TEMPO and the GNFR_C category in CAMS-REG-TEMPO. The temporal variability of emissions for this sector is assumed to be dominated by the stationary combustion of fossil fuels in households and commercial/public service buildings. These categories are also assumed to be the main contributors to the total emissions reported by CAMS-GLOB_ANT and CAMS-REG_AP/GHG. Other combustion installations activities included under this sector (i.e. plants in agriculture/forestry/aquaculture and other stationary including military) are assumed to follow the same temporal profile.

The temporal weight factors developed for this sector include monthly, daily and hourly profiles. The monthly and daily profiles depend upon year and region, and were derived using meteorological parametrisations (Sect. 2.3.1). The hourly profiles depend upon pollutant and region (Sect. 2.3.2).

Monthly and daily profiles

Gridded daily temporal profiles were derived according to the heating Degree Day (HDD) concept, which is an indicator used as a proxy variable to reflect the daily energy demand for heating a building [START_REF] Quayle | Heating degree day data applied to residential heating energy consumption[END_REF]. This method has been proven to be successful in previous emission modelling works (e.g. Mues et al., 2014;Terrenoire et al., 2015).

The heating degree day factor (())(+, -)) for grid cell x and day d is defined relative to a threshold temperature (/ . ) above which a building needs no heating (i.e. heating appliances will be switched off), following Eq. ( 2):

())(+, -) = max (/ . -/ /! (+, -), 1) (2)
Where / /! (+, -) is the daily mean 2m outdoor temperature for grid cell x and day d [ºC]. This information was obtained from the ERA5 reanalysis dataset for the period 2010 -2017 (C3S, 2017). As shown in Eq. ( 2), ())(+, -) increases with the difference between the threshold and actual outdoor temperatures. A minimum value of 1 is assumed instead of 0 to avoid numerical problems when used in Eq. (3).

A challenge when using this method is to set the threshold or comfort temperature (/ . ). The choice of / . depends on local climate and building characteristics, among other. When dealing with an extended area like Europe or even the whole world, it is difficult to choose a unique / . . This value is usually set to 18°C (e.g., Mues et al. 2014), 15.5°C (e.g. [START_REF] Spinoni | European degree-day climatologies and trends for the period 1951-2011[END_REF] or even 15ºC (e.g. Stohl et al., 2013). Following the work by [START_REF] Spinoni | European degree-day climatologies and trends for the period 1951-2011[END_REF], which developed gridded European degreeday climatologies, we assumed that / . = 15.5°C, a value also suggested by the UK MET-Office. A first guess of the daily temporal factor (%)(+, -)) for grid cell x and day d is (Eq. 3):

%)(+, -) = 011(3,4) 011 6666666( 3) (3)
Where ()) 5555555 (+) is the yearly average of the heating degree day factor per grid cell x (Eq. 4)

()) 5555555 (+) = ∑ 011(3,4) ( ' 7 (4)
Where N = 365 or 366 days (leap or non-leap year). Considering that residential combustion processes are not only related to space heating but also to other activities that remain constant throughout the year such as water heating or cooking, a second term is introduced to Eq. 5 by means of a constant offset (f) (Eq. 5): 9 %)(+, -) = 011(3,4)8% * 011 6666666( 3) (-8%) * 011 6666666( 3)

(5)

Where f = 0.2 based on the European household energy statistics reported by Eurostat (2018) (Table 4). As observed, this share may vary depending by fuel. In the case of biofuels or coal products, which dominate the contribution to total PM emissions, space heating represents 89.1% of the residential combustion processes (f ~ 0.1), whereas in the case of natural gas, which is the main contributor to NOx, the share is 77.7% (f ~ 0.2). Significant differences between countries are also observed for specific fuels. For instance, in Norway 100% of solid fuels are used for space heating (f ~ 0), whereas in Greece this share is only 65% (f ~ 0.35), with the remainder being attribute to water heating (27%) and cooking (8%) (Eurostat, 2018). More significant differences can be found in developing regions (e.g. Tibetan Plateau) where the share of solid biofuels used for cooking can go up to 80%. Despite these variations, a generic value of f = 0.2 is assumed for all regions. The investigation and proposal of different f values (as well as different / . values) for different regions of the world will be addressed in future works.

Gridded daily temporal profiles were developed for eight years (2010 to 2017). A climatological daily profile based on the average of each day over all the available years was also produced. Monthly gridded factors were derived from the daily profiles for all the years available. We interpolated the estimated gridded daily factors from the ERA5 working domain (approx. 0.3x0.3 deg.) onto the CAMS-GLOB_ANT (0.1x0.1 deg.) and CAMS-REG_AP/GHG (0.05x0.1 deg.) grids applying a nearest neighbour approach.

Hourly profiles

The hourly distribution of residential/commercial combustion activities has typically been described following the profile A presented in Fig. 2, used in both EMEP and TNO datasets. This hourly distribution presents one peak in the morning and another one in the afternoon, when energy consumption is supposedly higher due to increased space heating or cooking activities. We evaluated this profiles with real-world measurements of natural gas consumption for residential houses in the UK (Retrofit for the Future project, https://retrofit.innovateuk.org/), US Texas (Data Port -Pecan Street dataset, https://dataport.cloud/) and a house in Canada [START_REF] Makonin | AMPds2-Almanac of Minutely Power dataset: Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014[END_REF]. The measurements show the two peaks in all the profiles, although their occurrence and intensity varies due to the specific energy consumption behaviour of each house (Fig. S2). This comparison suggests that profile A is representative of emissions related to natural gas combustion.

We created a second hourly profile (Fig. 2, profile B) linked to the combustion of residential wood for space-heating purposes using as a basis information derived from citizen interviews performed in Norway and Finland [START_REF] Finstad | Wood consumption, fire habits and particulate matter. Results from Folke and housing census 2001, Living Conditions Survey 2002 and Survey of wood consumption and firing habits in Oslo 2002Statistics Norway[END_REF][START_REF] Gröndahl | Use of fireplaces and discharges from small houses in the Helsinki metropolitan area[END_REF] as well as from long-term measurements of the wood burning fraction of black carbon in Athens (Athanasopoulou et al., 2017). As shown in the Fig. 2, the resulting profile B presents an intense peak during the evening hours, but not during the morning, in contrast to Profile A. It is actually a common practice in developed countries to use fireplaces and other types of wood-burning appliances mainly in the evening.

As reported by the World Health Organization (WHO), most of developing countries use wood not only for heating space purposes but also for cooking activities [START_REF] Bonjour | Solid fuel use for household cooking: country and regional estimates for[END_REF]. We created a third profile that represents these activities (Fig.

2, profile C) based on information derived from continuous indoor PM2.5 measurements performed in households in the eastern Tibetan Plateau (Carter et al., 2016). The profile is influenced by heating and cooking practices and therefore presents three peaks that correspond to typical morning, midday, and evening mealtimes.

The results summarised in Fig. 2 indicate that the hourly behaviour of residential combustion emissions varies according not only to the fuel type but also to the type of end-use (i.e. space heating or cooking). Both the CAMS-GLOB_ANT and CAMS-REG_AP/GHG reports total residential/commercial emission as a unique sector, without discriminating by type of fuel or enduse. Therefore, several decisions were made in order to assignm the three proposed profiles to different pollutants and regions:

• Profile A: NOx and SOx emissions in urban/rural areas of developed and developing countries.

• Profile B: PM10, PM2.5, CO, CO2, NMVOC and NH3 in urban/rural areas of developed countries.

• Profile C: All pollutants in rural areas of developing countries

The assumptions made behind this assignment are:

• Natural gas/diesel heating combustion is the main contributor to total NOx, SOx and CH4 emissions • Wood combustion is the main contributor to total PM10, PM2.5, CO, CO2, NMVOC and NH3 emissions

•

In the urban and rural areas of developed countries wood is mainly used for heating purposes.

•

In the urban areas of developing countries wood is mainly used for heating purposes.

•

In the rural areas of developing countries all fuels are used both for heating and/or cooking purposes (i.e. the two activities occur at the same time).

The list of developing countries was obtained from the World Bank Country Classifications (World Bank, 2014). The discrimination of human settlements between urban and rural areas was derived from the Global Human Settlement Layer (GHSL) project [START_REF] Florczyk | GHSL Data Package 2019,EUR 29788EN[END_REF][START_REF] Florczyk | GHSL Data Package 2019,EUR 29788EN[END_REF]. The GHSL provides a global classification of human settlements on the base of the built-up and population density at a resolution of 1kmx1km corresponding to four epochs (2015, 2000, 1990, and 1975). The 2015 epoch was selected and the original raster was remapped onto the CAMS-GLOB_ANT (0.1x0.1 deg) and CAMS-REG_AP/GHG (0.1x0.05 deg) grids.

Manufacturing industry

The temporal variability of industrial emissions is reported under the sectors indu (CAMS-GLOB-TEMPO) and GNFR_B (CAMS-REG-TEMPO). Both in the CAMS-GLOB_ANT and the CAMS-REG_AP/GHG inventories, all industrial manufacturing emissions are reported under these single categories. Hence, the same temporal pattern has to be assumed for all types of facilities (e.g. cement plants, iron and steel plants, food and beverage). For this sector, only country-dependent monthly profiles were developed due to the lack of more detailed data.

Monthly profiles

Country-specific monthly profiles were estimated using the Industrial Production Index (IPI), which measures the monthly evolution of the productive activity of different industrial branches, including manufacturing activities. The IPI as a monthly surrogate for industrial emissions has been used in previous studies (e.g. [START_REF] Pham | Development of an inventory and temporal allocation profiles of emissions from power plants and industrial facilities in Thailand[END_REF]Markakis et al., 2010).

The IPI data was obtained from the MBS database (MBS, 2018) which provides monthly information per country and general industrial branch (i.e. mining, manufacturing, electricity, gas and steam and water supply) for the year 2015. The manufacturing branch, which includes several divisions such as iron and steel industries, chemical industries and food and beverage products, was used to derive country-specific monthly profiles. Figure 1b shows the spatial coverage of the compiled dataset. As in the case of the energy industry sector (Fig. 1a), the lack of information mostly affects Africa and South America. For those countries without available information, the monthly profile reported in the TNO dataset under the industry sector was used (Denier van der Gon et al., 2011). In the case of China, the monthly profile reported in the MIX inventory under the industry sector was used (Li et al., 2017).

The time profiles are based on IPI information from 2015 and are assumed to be representative for other years. Our assumption is supported by the low inter-annual variability observed in the IPI values collected from different national statistical offices including Italy (ISTAT, 2018), Norway (SSB, 2018), Spain (INE, 2018) and the UK (ONS, 2018) (Fig. S3). Another implicit assumption made is that the constructed monthly profiles can be equally applied to all the different industrial activities reported under the ind and GNFR_B sectors. The national IPI values collected for Italy and Spain were used to compare the seasonality of individual industrial divisions against to the general manufacturing IPI monthly profile. For both countries, and up to a certain extent, it was found that all the industrial divisions (except food/beverages and petrochemical industry) follow the seasonality of the general manufacturing profile (Fig. S4), which allow concluding that the assumption made is reasonable.

Weekly and hourly profiles

Due to the lack of country-specific data, the fixed weekly and hourly temporal profiles provided in the TNO dataset for industry sector is used. The weekly profile assumes a flat distribution during the working days and a slight decrease during weekends.

On the other hand, the hourly profile includes an increase of the activity during the central hours of the day.

Road transport

Temporal profiles for road transport emissions are reported under the tro sector in CAMS-GLOB-TEMPO and the GNR_F1 (exhaust gasoline), GNR_F2 (exhaust diesel), GNFR_F3 (exhaust LPG gas) and GNFR_F4 (non-exhaust) categories in CAMS-REG-TEMPO. The fact that CAMS-REG_AP/GHG traffic-related emissions are classified into four different categories (discriminated by type of process and fuel), which allows considering specific temporal features associated to each one of them (e.g. temperature dependence of CO gasoline exhaust emissions and of NMVOC non-exhaust emissions). On the other hand, in CAMS-GLOB_ANT all traffic emissions are reported under a single sector and subsequently the approach used for the development of the temporal profiles is more simplistic.

As summarised in Table 1, the CAMS-GLOB-TEMPO monthly and weekly profiles constructed for this sector are regiondependent, whereas the hourly profiles vary per region and day of the week (i.e. weekday, Saturday and Sunday). In the case of CAMS-REG-TEMPO, the constructed profiles can vary by region, pollutant, day of the week and/or year, as a function of the source sector and temporal resolution (Table 2).

Depending on the dataset and sector category, temporal emission variability is assumed to be either exclusively driven by the traffic activity data (CAMS-GLOB-TEMP, all cases) or by a combination of traffic activity data and changes in ambient temperature (e.g. CAMS-REG-TEMP, CO and NMVOC GNR_F1). For the first case, temporal profiles were developed using traffic count data compiled from multiple sources of information (Sect. 2.5.1). As listed in Table 5, the information was obtained from local and national open data portals, publications or through personal communications. The spatial coverage of the compiled dataset is illustrated in Fig. 1c. For the second group of profiles, the temporal variability of traffic activity was combined with meteorological parametrisations available in the literature (Sect. 2.5.2).

Considering that for each traffic count dataset the reference years are different (Table 5), we analysed the data in view of differences in the resulting profiles as a function of the year. We took the Paris city traffic data as an example since it covers a wide range of years (2013 to 2017). For each year, monthly, weekly and hourly (i.e. Wednesday and Saturday) profiles were constructed (Fig. S5). The results suggest that temporal patterns in vehicle activity do not change much over long time scales.

Consequently, and following the assumptions made for the energy and manufacturing industry sectors, we assumed that the vehicles, LDV and heavy-duty vehicles, HDV). The monthly, weekly and hourly profiles as a function of the vehicle type showed significant differences, especially for weekly and hourly profiles (Fig. S6). HDV traffic presents a larger decrease on the weekend than LDV. Moreover, the hourly LDV profile exhibits two distinct (morning and evening) commuter-related peaks, whereas HDV shows a single midday peak. These results highlight the importance of applying separate temporal profiles to characterize traffic and associated emissions for LDV and HDV. However, in the present work this disaggregation was not considered since both the CAMS-GLOB_ANT and CAMS-REG_AP/GHG inventories report LDV and HDV-related emissions under the same pollutant sector.

Meteorology-independent monthly profiles

A comparison between monthly variation in traffic patterns at urban and rural locations (i.e. urban streets and highways) was performed for selected countries/regions including California, Germany, Spain and UK. For UK and California, the original traffic statistics were already discriminated by type of location. For the German and Spanish datasets, each traffic station was classified as urban or rural considering its geographical location and the GHSL human settlement classification dataset (Sect.

2.3.2).

As shown in Fig. 3, while there is little seasonal variation in German urban locations, rural areas tend to exhibit a stronger seasonality, with a peak occurring during summertime, presumably due to increased recreational and vacation-related driving. The results derived from California, Spain and UK are consistent with these patterns (Fig. S7).

The datasets collected from several cities (i.e. Athens, Barcelona, Berlin, Copenhagen, London, Madrid, Mexico City, Milano, Oslo and Paris) were also used to construct monthly profiles (Fig. 4). For comparison purposes, the TNO road transport profile is also included (Denier van der Gon et al., 2011). The three southern European cities (i.e. Athens, Madrid and Milano) together with Paris present a similar pattern, with a significant decrease of the activity during the month of August due to the summer holidays. Similarly, Northern European cities (i.e. Copenhagen and Oslo) also present a decrease in summer, but of a lower intensity and during July. On the other hand, the seasonality observed in London, Berlin and Mexico City is rather flat and closer to the TNO profile.

Results in Fig. 3 and Fig. 4 showed that: (i) monthly variations can significantly differ among countries and (ii) within a country, traffic regimes show differences according to the location (urban, rural). Considering all of the above, country and region (urban, rural) specific monthly profiles were constructed based on the traffic information compiled. For countries without any available temporal factors, assumptions were made considering geographical proximity. For instance, the urban profile for Scandinavian countries without data (i.e. Finland, Sweden and Iceland) was constructed by averaging the profiles of Oslo and Copenhagen. On the other hand, the rural profile constructed for Spain was assigned to other Southern European countries (i.e.

Italy, Greece, Malta, Croatia, Bosnia and Herzegovina, Montenegro, Albania, Slovenia, Cyprus and Portugal). Similarly, the seasonality in Canada was assumed to be equal to the one observed in the US. For all the countries not listed in the table, the urban/rural profiles developed for Germany were assumed. This approach may be further improved as more traffic count data becomes available. In the case of China and India, profiles were derived from the MIX emission inventory (Li et al., 2017).

Two main assumptions underlie these profiles. First, the differences among cities within a country are assumed to be small and therefore we use a unique urban profile therein. The second hypothesis was to assume that all the streets/highways located in urban/rural areas present the same seasonality. While this is a reasonable assumption overall, individual traffic count stations can show particular features. For instance, on certain highways near city entrances or crossing urban areas, traffic intensity shows a flat distribution without the typical summer anomalies. This level of detail, which would require a specific temporal profile per road segment, is out of the scope of this dataset but may be explored in future works.

Meteorology-dependent monthly profiles

The We used available parametrizations (Eq. 6 to Eq. 8) to account for the meteorological drivers of the seasonality of CO and NMVOC gasoline-related emissions (GNFR_F1) (US EPA, 2015) and NOx diesel-related emissions (GNFR_F2) [START_REF] Keller | HBEFA version 3.3. Background documentation[END_REF]:

%! 9: (+, 6) = 7 8 (;<.<>? * (@ )* (3,!);AB) , / /! (+, 6) ≤ 75℉ 1, / /! (+, 6) > 75℉ (6)
%! 7DE:9 (+, 6) = 7 8 (;<.<F? * (@ )* (3,!);AB) , / /! (+, 6) ≤ 75℉ 1, / /! (+, 6) > 75℉ (7)

Where %! 9: (+, 6), %! 7DE:9 (+, 6) and %! 7:3 (+, 6) are the CO, NMVOC, and NOx gridded monthly profiles for grid cell

x and month m; and / /! (+, 6) is the monthly mean 2m outdoor temperature for grid cell x and month m. Note that / /! (+, 6)

is expressed in ºF in Eq. 6 and Eq. 7 and in ºC in Eq. 8. This difference is due to the fact that the first two expression are derived from the North American MOtor Vehicle Emission Simulator (MOVES), while the last one is derived from the European Handbook Emission Factors for Road Transport (HBEFA) model. Monthly gridded 2m temperature was taken from ERA5 between 2010 and 2017 (CS3, 2017). The obtained monthly profiles where normalised so that their total sum equals 12

The meteorology-dependent monthly profiles were then combined with the meteorology-independent ones (Sect. 2.5.1) so that the resulting seasonality accounts for both temperature influences and traffic activity. For CO, we used a weight factor of 45%

for the temperature-dependent profiles and of 55% for the traffic activity ones, following the UK National Atmospheric

Emissions Inventory (NAEI), which reports road transport annual emissions and distinguishes between cold start and hot exhaust. Due to the lack of information, we assumed the same share for all countries. Likewise, for NMVOC profiles we assumed a 33% weight for the temperature-dependent temporal factors (i.e. emissions related to cold-start) and 67% for the ones derived from traffic counts (i.e. emissions related to hot exhaust). Finally, for NOx we assumed a 50% and 50% split, since the "diesel low temperature NOx emission penalty" affects total exhaust diesel emissions (cold start and hot).

In addition to the meteorological-dependent profiles described above, we created a specific monthly profile for NMVOC evaporative emissions (GNFR_F4) based on recent results obtained with the High-Elective Resolution Modelling Emission System (HERMESv3) (Guevara et al., 2020c). The HERMESv3 model computes hourly gasoline evaporative emissions from standing cars (diurnal losses) using the Tier 2 approach reported in the EMEP/EEA emission inventory guidelines 2016.

Weekly profiles

Country-and region-(urban, rural) specific weekly profiles were constructed based on the traffic information summarised in Table 5. In contrast to urban areas, rural traffic activity is lower during weekdays and decrease relatively less during the weekend, specially on Sundays (Fig. 3 and Fig. S7). Figure 4 shows how, depending on the location, the intensity of the weekend decrease is relatively higher (e.g. Madrid) or lower (e.g. Mexico City), which is likely due to different sociodemographic patterns. We used the weekly profile provided in the TNO dataset as the urban profile for the countries where local information is not available. Similarly, we used an average profile including data from Germany, Spain and UK as the rural profile for countries without data. The resulting profiles were assigned equally to all the traffic-related categories of both CAMS-GLOB-TEMPO and CAMS-REG-TEMPO, with the exception of NMVOC gasoline evaporation (GNFR_4), for which a flat profile is proposed.

Hourly profiles

The analysis of hourly profiles constructed per day of the week for six cities (i.e. Berlin, Madrid, Milan, Oslo, Paris and Utrecht) clearly highlights the need to create hourly profiles per day type (Fig. S9). Weekdays (i.e. Monday to Friday) tend to exhibit strong similarities and reflect commuting patterns that are typically bimodal with morning and afternoon volume peaks. Hourly variations during weekdays at urban and rural locations were compared for selected countries and regions (Fig. 3 and Fig. S8). Morning traffic peaks associated with commuting are found in and near cities, but not in rural areas (see California and Guangzhou in Fig. S8). Lunch time peaks tend to be higher in rural areas, mainly due to the activity of HDV (Spain and Germany). In contrast, hourly variations on Saturday and Sunday were found to be very similar in urban and rural areas for all the available datasets (not shown). Consequently, only for weekdays we differentiated the hourly profiles of urban and rural areas. Also in this case, the GHSL dataset was used to assign the respective profiles to either urban or rural grid cells.

Weekday hourly profiles constructed for different cities are shown in Fig. 4 (bottom-left). Two groups of profiles (showed in red and blue) with similar behaviours were identified. For the first group (in red), the rush hours in the morning and in the evening can be clearly identified. The occurrence of the peaks varies from one city to another due to different sociodemographic patterns. In the second group (in blue), a maximum level of activity is reached in the morning (between 07:00 and 08:00h) that largely remains for the rest of the day-time period (i.e. 07:00 to 19:00h) and through part of the night time (i.e. 19:00 to 21:00h). The hour when traffic activity reaches the maximum level also varies from one city to the other.

Besides the potential effect of different social habits, the difference between the two groups of profiles could be also associated with differences in the vehicle densities. For instance, Oslo, Utrecht and Berlin (first group) have vehicle densities of 0.6, 1.1 and 1.3 veh/km 2 (in thousands), whereas in Barcelona, Madrid and Milano (second group) the densities are much higher (i.e.

5.2, 2.2 and 3.9, respectively) (AB, 2017).

Figure 4 (bottom-right) shows the constructed Saturdays hourly profiles for selected cities. As before, two groups of profiles showing similar patterns are highlighted. The profiles related to the first group (in red) tend to present larger activity levels during day time (between 09:00h and 18:00h), whereas in the second group (in blue) weight factors are higher during night time (between 21:00 and 03:00h). A similar pattern is observed with Sunday hourly profiles (not shown).

The resulting profiles were assigned equally to all the traffic-related categories with the exception of NMVOC evaporative emissions (GNFR_4), in which we use a specific hourly profile based on HERMESv3 (Sect. 2.5.2). The resulting profile is shown in Fig. 4 (bottom-left and bottom-right, in yellow).

For countries without any available temporal factors, assumptions were made considering geographical proximity. For instance, the urban profile for Scandinavian countries without data (i.e. Finland, Sweden and Iceland) was constructed by averaging the profiles of Oslo and Copenhagen. On the other hand, the rural profile constructed for Spain was assigned to other Southern European countries (i.e. Italy, Greece, Malta, Croatia, Bosnia and Herzegovina, Montenegro, Albania, Slovenia, Cyprus and Portugal). Similarly, the seasonality in Canada was assumed to be equal to the one observed for the US. For other countries , the urban/rural profiles developed for Germany were assumed as default. This approach may, of course, be improved but was constrained in this study by the traffic count data availability.

Aviation

The temporal profiles developed for air traffic emissions during Landing and Take-Off cycles (LTO) in airports are reported under the GNFR_H category in the CAMS-REG-TEMPO dataset. Country-dependent monthly temporal profiles were constructed using airport traffic data, as described below. We could not consider this sector for CAMS-GLOB-TEMPO since it is excluded in the CAMS-GLOB_ANT inventory. Aviation emissions are reported in a separate inventory called CAMS-GLOB-AIR, in which emission from LTO cycles are reported together with climbing, descent and cruise airplane operations.

Monthly profiles

We collected monthly airport traffic data by reporting airport for the years 2011 to 2017 from the Eurostat statistics (Eurostat, 2019). Year 2010 was excluded from the data gathering process due to the air travel disruption in North and Central Europe caused by the Eyjafjallajökull eruption. An analysis of the seasonality observed in several airports for each individual year allowed to confirm a low interannual variability (Fig. S10). Consequently, the constructed temporal profiles were based on the average data of all the available years. Country-dependent monthly profiles were derived by aggregating the respective national airports available in the Eurostat dataset.

Weekly and hourly profiles

We assumed flat weekly profiles for this sector as no clear patterns could be found in the available datasets. We use a fixed hourly profile based on airport traffic from the Madrid-Barajas and Barcelona-El Prat airports (AENA, personal communication). The computed fixed profile was found to be broadly consistent with the hourly variations reported by other studies (e.g. [START_REF] Unal | Airport related emissions and impacts on air quality: application to the Atlanta International Airport[END_REF]Zhou et al., 2019).

Agriculture

Global and regional temporal profiles for the agricultural emissions are reported in two separate sectors: mma and agr in CAMS-GLOB-TEMPO, and GNFR_K and GNFR_L in CAMS-REG-TEMPO. In both cases, the former category only includes emissions from livestock (enteric fermentation, manure management), whereas the latter reports emissions from several activities but mainly fertilizer applications and agricultural waste burning.

For the livestock sector (mma and GNFR_K), both in CAMS-GLOB-TEMPO and the CAMS-REG-TEMPO we assumed NH3

and NOx to arise from the excreta of the animals and follow a meteorology-dependent temporal profile. The rest of pollutants are considered to depend upon of the animal activity (e.g. emissions of PM arise mainly from feed, CH4 from enteric fermentation) and therefore we assumed a flat temporal profile. In the other agricultural categories (agr and GNFR_L), NH3

emissions are assumed to be mainly related to fertilizer application, while the other criteria pollutants (i.e. NOx, SOx, NMVOC, CO, PM10, PM2.5) and CO2 are dominated by agricultural waste burning. For the particular case of CH4, which is mostly emitted from rice fields, no particular profiles are proposed yet, this will be addressed in future works. All in all, the profiles created for agricultural emissions depend on the pollutant, year and specific sector.

Monthly and daily profiles for livestock emissions

For the livestock sector (mma and GNFR_K), the temporal variation of NH3 and NOx emissions are assumed to be depend on temperature and ventilations rates [START_REF] Gyldenkaerne | A dynamical ammonia emission parameterization for use in air pollution models[END_REF][START_REF] Skjøth | Spatial and temporal variations in ammonia emissions -a freely accessible model code for Europe[END_REF] (Eq. 9):

%) ! (+, -) = @ * (3,4) +.-. * E * (3,4) +.)/ ∑ @ * (3,4) +.-. * E * (3,4) +.)/ 0/1 2&' (9)
where %) ! (+, -) is the daily temporal profile for manure management practice m, grid cell x and day d; / ! (+, -) is the average daily temperature associated to manure management practice m, grid cell x and day d (in ºC), and J ! (+, -) is the Since both the CAMS-GLOB_ANT and CAMS-REG_AP/GHG inventories report livestock emissions under a unique category, with no discrimination by manure management practice, the computed %) ! (+, -) values had to be averaged in order to derive general daily factor values (%)(+, -)). The averaging was performed considering country-and grid-dependent weight factors following Eq. 10:

%)(+, -) = ∑ %) ! (+, -) > !,- * K(+) ! (10)
The weight factors (K(+) ! ) were constructed using as a basis: (i) the national EMEP emissions reported by the Centre on Emission Inventories and Projections (EMEP/CEIP, 2019) for countries that are Members of the EMEP programme and (ii)

the global bottom-up inventory of NH3 emissions (MASAGE_NH3) (Paulot et al., 2014) for the rest of the world. Table S1 summarises the weight factors used for each country. For countries not included in the list, an average weight factor is used.

The resulting gridded daily temporal profiles were developed for eight years (2010 to 2017). Using these time series as a basis, and following with the procedure described in Sect. 2.3.1, a climatological daily profile and monthly gridded factors were also produced.

Monthly and daily profiles for fertilizer-related emissions

The seasonality of NH3 emissions from fertilizer application depends mainly on the magnitude and timing of fertilizer application over different crop categories (i.e. planting schedule for each crop). The proposed gridded monthly profiles for this pollutant sector are based on a mosaic of multiple bottom-up agricultural emission inventories, which include information on local crop calendars in their emission estimations. The datasets included in the mosaic are: (i) the global bottom-up gridded MASAGE_NH3 inventory (Paulot et al., 2014), (ii) the regional gridded Chinese emission inventory reported by Zhang et al.

(2018), (iii) the regional gridded North American National Emission Inventory (NEI) reported by US EPA (2019b) and (iv)

the regional European emission inventories reported for Denmark, Germany (Skjoth et al., 2011), (v) Poland (Werner et al., 2015) and (vi) Netherlands, France and Belgium [START_REF] Backes | Ammonia emissions in Europe, part I: Development of a dynamical ammonia emission inventory[END_REF].

20

With the objective of computing daily variations, the gridded monthly profiles obtained using the aforementioned mosaic approach were combined with the daily meteorological parametrizations reported by [START_REF] Gyldenkaerne | A dynamical ammonia emission parameterization for use in air pollution models[END_REF] The resulting gridded daily temporal profiles includes eight years of data (2010 to 2017) and a climatological profile.

Monthly profiles for agricultural waste burning emissions

For all the other criteria pollutants (i.e. NOx, SOx, NMVOC, CO, PM10, PM2.5) included in the agr and GNFR_L sectors, we used the monthly gridded profiles reported by Klimont et al. (2016) for the agricultural waste burning category. This temporal representation was developed based on the timing and location of active fires on agricultural land in the Global Fire Database (GFEDv3.1) combined with annual emissions from the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) model. The original monthly weights were remapped from the ECLIPSE source grid (0.5x0.5 deg) onto the CAMS-GLOB_ANT and CAMS-REG_AP grids applying a nearest neighbour approach.

Hourly profiles

The hourly distribution of agricultural emissions has typically relied on the profile reported by both TNO and EMEP datasets (Fig. 2, bottom). The profile is constructed on the idea that NH3 emission rates from agricultural practices (fertilizer application and manure management) tend to vary with temperature, usually showing a peak in the middle of the day. Figure 2 (bottom) compares this profile with the hourly distributions derived from flux measurements performed in a fertilized corn canopy in North Carolina (Walker et al., 2013) and from direct measurements performed in a mechanically ventilated swine barn [START_REF] James | Characterizing ammonia emissions from a commercial mechanically ventilated swine finishing facility and an anaerobic waste lagoon in North Carolina[END_REF]. The similarity observed between the TNO profile and the two measurement-based temporal factors is significantly high (i.e. emissions peak during the afternoon in each case, starting from 13:00 to 15:00h Local Time). We therefore maintained the TNO profile for describing the hourly variation of NH3 agricultural emissions (both for livestock and fertilizer application).

Regarding the other criteria pollutants (i.e. NOx, SOx, NMVOC, CO, PM10, PM2.5) included in the agr and GNFR_L sectors, a new hourly fixed hourly temporal profile for agricultural waste burning emissions is proposed based on the work by Mu et al. (2011), where climatological mean hourly cycles were constructed using GOES WF_ABBA active fire satellite observations from full hemisphere scans during 2007-2009. The constructed profile (Fig. 2, bottom) shows a maximum peak at midday, which is consistent with high levels of midday fire emissions.

Results and discussion

In this section, we discuss the obtained temporal profiles for CAMS-GLOB-TEMPO and CAMS-REG-TEMPO. In Sect. 3.1 the profiles are compared to independent observational datasets and in Sect. 3.2 to other existing sets of temporal profiles currently used under the framework of CAMS.

Figure 5 shows the 0.1x0.1 deg CAMS-GLOB-TEMPO gridded January and August profiles constructed for NMVOC and SOx energy industry emissions and for manufacturing industry emissions. For the energy sector, several countries such as Spain and UK show how the seasonality significantly varies as a function of the pollutant. It is also observed that in many

European countries the manufacturing industrial activity decreases during August due to summer holidays, while an increase is observed in other countries such as China.

Figure 6 shows two examples of CAMS-GLOB-TEMPO gridded daily profiles for the residential/commercial sector along with the times series at four geographically or climatically different locations (i.e. Athens; Barcelona; Buenos Aires and Oslo)

for years 2010 and 2017. As expected, the largest factors occur in winter and the lowest ones in summer at all four locations.

According to the results, emissions in Athens, Barcelona or Buenos Aires can be 3 to 5 times higher during the cold periods (i.e. January in Barcelona and Athens, June in Argentina) than during warm periods (August in Barcelona and Athens, January in Argentina). In Oslo both the seasonal cycle and daily variability are less pronounced than in the other locations because the differences between daily and annual mean temperatures are generally lower. There is a large inter-annual variability in the four locations. In winter of 2010, Barcelona experienced three cold outbreaks of similar intensity (in January, February and March), whereas in 2017 only one significant episode can be observed (mid-January). Similarly, in 2010 three major peaks are observed in Athens in mid-January, beginning of February and mid-December, whereas in 2017 only one episode stands out above the rest. Results clearly highlight that extreme weather events can strongly affect the temporal profiles and thereby the resulting emissions.

Comparison to independent observational datasets

Figure 9 shows the CAMS-TEMPO monthly weight factors for SOx energy industry in Spain and manufacturing industry in Italy compared against two independent temporal profiles based on SOx measured emissions from all Spanish power plants (CIEMAT, personal communication) and real-world measurements of industrial natural gas consumption provided by the European Network of Transmission System Operators for Gas [START_REF] Entsog | Transparency platform[END_REF], respectively. Correlations between the two time series are high in both cases (i.e. r= 0.84 and 0.61, respectively). In the case of energy industry in Spain both profiles show minimum levels during spring and an increase during summer due to the intensive use of air conditioning systems. In Italy, both manufacturing industry profiles present a strong decrease during August and December due to summer and Christmas holidays.

In Fig. 10 we compare our profiles for the residential sector with daily factors derived from minute-resolved measurements of natural gas from a residential house in Canada during 2013 [START_REF] Makonin | AMPds2-Almanac of Minutely Power dataset: Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014[END_REF]. We compared our daily factors estimated using the HDD approach for the grid cell closer to the house and considering two different values of / . (18 and 15.5°C). In both cases, CAMS-TEMPO reproduces the temporal variation of the locally measured profile. Both at the beginning and the end of the year, the measured and HDD-based profiles show similar maximum values, which correspond to the periods when outdoor temperature reached the minimum levels (not shown). The results also show that using / . = 15.5°C we obtain a slightly higher correlation than when assuming / . = 18°C (0.81 versus 0.76).

In Fig. 11 The monthly variability of CO and NO2 measured concentrations in Milano and Madrid were compared against the meteorology-dependent gridded monthly profiles created for CO GNFR_F1 (gasoline exhaust) and NOx GNFR_F2 (diesel exhaust) at these locations. Weight factors from the closest grid cells closest to each station were selected for the comparison.

As shown in Fig. 11 (top-left and top-right), the traffic decrease occurring during summertime in Milano and Madrid is also reproduced in the CO and NO2 levels. At the same time, both the CAMS-REG-TEMPO profiles and the measurement-based profiles show a V shape, indicating higher emissions during winter due to the low temperatures. The correlation between the two times series is of r= 0.82 for Milano and of r= 0.74 for Madrid. we note that the seasonality of the CO and NO2

measurements may be also influenced by other factors, including meteorology and the influence of other emission sources such as residential combustion, and subsequently a perfect correlation between the two datasets cannot be expected.

Similarly, the hourly profiles proposed for urban locations in Spain and Germany during weekdays and Sundays were compared to the hourly variation of NO2 concentrations for Barcelona and Berlin (Fig. 11, bottom-left and bottom-right). In all cases the hourly evolution of NO2 is mostly driven by variations accounted for in the CAMS-TEMPO profiles. For weekdays, the shift of the traffic morning/evening peaks observed between cities is also reproduced in NO2 concentrations.

Traffic and NO2 levels in Berlin reach maximum levels between 07:00h and 08:00h LT, whereas in Barcelona the peak occurs between 08:00 and 09:00h LT. On the other hand, Sunday night time and morning NO2 concentrations (between 23:00 and 08:00h LT) are relative higher in Barcelona than in Berlin (in relative terms), which is related to the more intense traffic activity registered in the streets.

Comparison to other temporal profile datasets

We compared the CAMS-TEMPO profiles against the profiles reported by other existing datasets. The comparison is focussed on the profiles that are currently being used for air quality modelling purposes under the framework of CAMS, namely: The global EDGARv4.3.2 (Janssens-Maenhout et al., 2019) monthly profiles (used in the CAMS global production) and the European EMEP [START_REF] Simpson | The EMEP MSC-W chemical transport model -technical description[END_REF] and TNO (Denier van der Gon et al., 2011) profiles (used in CAMS regional production).

The comparison of monthly, weekly and hourly profiles for the energy industry sector in selected countries is shown in Fig.

12. It is worth noting that both TNO and EDGAR report the same profile for all countries. At the monthly level, significant differences are observed between CAMS-TEMPO and EDGAR in China and USA. In both cases, the summer peak observed in CAMS-TEMPO (presumably due to the intensive use of air conditioning systems) does not show up in EDGAR profile. In the case of Romania, all profiles show important decreases but at different times of the year: April in CAMS-TEMPO (presumably due to low use of heating/cooling devices), July in TNO/EDGAR and September in EMEP. In the UK, the patterns between the different datasets are more similar, all of them reproducing a V shape, except for the relatively flatter NMVOC emissions profiles in CAMS-TEMPO. Concerning the weekly and hourly profiles, important discrepancies are observed between CAMS-TEMPO and the factors proposed by TNO and EMEP for certain countries. In the CAMS-TEMPO weekly profiles for Austria the intensity of the weekend decrease is relatively higher, while the hourly profiles for Spain are flatter than the ones reported by TNO and EMEP.

The comparison of monthly weight factors for the residential/combustion sector indicates generally low discrepancies between the different datasets (Fig. 13). The largest difference is observed in Greece, where both CAMS-TEMPO and EMEP allocate most of the emissions during wintertime while TNO and EDGAR propose a smoother transition between this season of the year and spring/fall. Both EDGAR and CAMS-TEMPO datasets propose an almost flat profile for India, where residential fuel is mainly used for cooking activities, an activity that can be considered constant throughout the year. For the daily temporal disaggregation of emissions, TNO and EMEP propose a fixed weekly profile (not shown), which disregards the daily dynamics inferred by the heating degree day approach considered in CAMS-TEMPO (Fig. 6).

For road transport, the differences between CAMS-TEMPO and other datasets are quite significant. The monthly profiles reported by TNO, EMEP and EDGAR are almost flat, while the pollutant and meteorological dependent profiles developed in the present work suggest important decreases during summertime, especially for the case of CO (Fig. 14). At the weekly level, the TNO profile is in line with most of the city-level constructed profiles, although in some cases differences in the intensity of the weekend decrease are observed (Fig. 4,. At the hourly level, the main discrepancies are observed when comparing TNO with the Saturday and NMVOC evaporative profiles of CAMS-TEMPO (Fig. 4, bottom right). It is worth noting that the hourly weekday profile constructed for Utrecht is almost perfectly correlated with TNO (R 2 = 0.97). This is explained by the fact that the TNO profile was estimated using Dutch traffic data.

Figure 15 shows a comparison of the monthly profiles for the agricultural sector. All the datasets selected for comparison, except for TNO, propose a unique profile for all the different agricultural activities (i.e. livestock, use of fertilizers, agricultural waste burning), which is equally applied to all emissions. The CAMS-TEMPO profiles constructed for NH3 fertilizer emissions show how the peak significantly varies among countries (i.e. May in China, June in India, April in Spain). In the case of India and China, these peaks are consistent with the NH3 satellite-derived seasonality shown by Warner et al. (2017). It is also worth noting the triple peak in Poland, which is related to the application timings of fertilizers (spring) and manure (summer and autumn) on crops (Werner et al., 2017). In contrast, the profiles reported by TNO and EMEP show a unique peak in April in all countries. In the case of NH3 from livestock, the profiles show a weaker seasonality, but relevant discrepancies in the intensities and occurrences of the emission peaks are also observed (i.e. EDGAR-India, EMEP-Spain). In the case of PM10 emissions related to agricultural waste burning activities, the results suggest a good agreement between EMEP and CAMS-TEMPO, with most emissions occurring during fall, after the harvesting period. On the other hand, the profile proposed by TNO presents a double peak, a first one occurring during spring (March and April) and a second one during summer (August).

There are several features that makes the CAMS-TEMPO profiles a major step when compared to the datasets currently being used for air quality modelling under the framework of CAMS:

• Pollutant-dependency: For some sectors, profiles were computed for all species independently in order to account for the variability of the activity patterns and the specific processes that drive their release to the atmosphere. As an example, a distinction was made between the temporal distribution of traffic emissions from non-exhaust PM mainly driven by traffic activity and non-exhaust NMVOC evaporation mainly driven by outdoor temperature.

• Spatial variability: For nearly all sectors, the temporal profiles are made country or even country and region-specific in order to take into account the effects of e.g., different sociodemographic patterns and climatological conditions.

For instance, differences between urban and rural traffic activity patterns are considered in the profiles constructed for road transport emissions.

• Meteorological influence: For the residential/commercial combustion, road transport and agriculture sector, the profiles were constructed using meteorology-dependent parametrizations (e.g. heating degree day, temperature effect on exhaust traffic emissions) that account for the emissions variability driven by temperature or wind speed. The resulting profiles are year-dependent and cover a timespan that goes from 2010 to 2017.

Several CAMS-TEMPO profiles are analyzed in this paper to illustrate their main characteristics and potential. Moreover, an inter-comparison exercise against independent observational datasets (e.g. real-world measurements of natural gas consumptions, emissions and pollutant concentrations) and other existing sets of temporal profiles was also performed. The comparison between CAMS-TEMPO temporal weight factors and the measurement-based profiles showed in general a high degree of correlation. Despite the scarcity of independent measurements, our comparison suggests a high level of representativeness of the developed profiles. On the other hand, the comparison against other sets of temporal profiles showed important discrepancies, especially for the traffic and agricultural sectors. This comparison highlights some shortcomings of the global and regional profiles currently used in the framework of CAMS, namely the omission of meteorological influences and the neglection of the temporal variation of emissions across sectors, species and/or countries/regions.

It is important to highlight that the continuous growth of the open data movement has been a key element for the successful development of the CAMS-TEMPO data. Services such as the European Open Data Portal (https://data.europa.eu/euodp/en/data) -and similar initiatives at the city level -or the ENTSO-E Transparency platform (ENTSO-E, 2018) are delivering very valuable data for the development of emission-related databases. Despite all the efforts, there are, however, some limitations associated to the current version of the CAMS-TEMPO profiles. First, and most importantly, the specificity of the computed profiles depends upon the degree of sectoral disaggregation used to report the original CAMS inventories. For instance, monthly profiles per industrial divisions could not be considered, since all manufacturing emissions are reported under a unique sector in both CAMS-GLOB_ANT and CAMS-REG_AP/GHG. On the https://doi.org/10.5194/essd-2020-175

Open other hand, the split by fuel type of traffic emissions in the CAMS-REG_AP/GHG inventory allowed to consider meteorological influences associated to e.g. CO gasoline and NOx diesel emissions, but specific traffic activity dynamics associated to light-duty and heavy-duty vehicles could not be accounted for. A second limitation is related to the assumptions made for certain sources, mainly the residential/commercial combustion sector. The application of the Heating Degree Day approach was done considering a threshold temperature and a fraction of non-space heating activities homogenous for all the world (15.5ºC and 0.2, respectively). Several studies have highlighted that these two values can vary across regions due to changes in local climate, building characteristics and socio-demographic aspects (e.g. Grythe et al., 2019). Another important shortcoming of the current CAMS-TEMPO dataset is related to the scarcity of available information in developing countries (i.e. Africa, Latin America and Asia).

The CAMS-TEMPO dataset represents an effort to improve the temporal characterisation of emission data to be used for atmospheric chemistry modelling. Future works will include the evaluation of these temporal profiles when used for modelling activities. Through close cooperation with air quality modelers, we expect to obtain feedback on the dataset as well as suggestions for future improvements. Besides that, a number of future updates have also been identified during the present work, including:

• The extension of the year dependent temporal profiles to be in line with the current timespan considered in the CAMS- GLOB_ANT (2000[START_REF] Entso-E | Transparency Platform[END_REF] and CAMS-REG_AP/GHG (2000-2017) inventories.

•

The development of new temporal profiles for certain sectors/pollutants that have not currently been considered, including CH4 emissions from agriculture (rice fields) and waste management (landfills).

•

The refinement of the heating degree day approach considering region-dependent threshold temperature values.

Besides the identified updates, the investigation of monthly and seasonal changes of emissions using satellite data will be also explored in future works. Global and high-resolution observations of the atmospheric composition provided by missions such as the Copernicus Sentinel-5 Precursor (S5P) can be of great value to improve the description of the spatio-temporal distribution of emissions (Lorente et al., 2019). Finally, understanding and quantifying the impact of the current COVID-19 lockdowns upon the temporal distribution of emissions will be also an important topic to be studied.

The CAMS-TEMPO profiles are distributed free of charge through the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/). (fixed, per pollutant)

(1) Leap or non-leap years

  : The European Network of Transmission System Operators for Electricity (ENTSO-E) centralizes the collection and publication of the electricity generation per production type for each European Member State. The information published by the Transparency Platform is collected from data providers such as Transmission System Operators (TSOs), power exchanges or other qualified third parties. The information collected included production data (MW) per country and fuel type (i.e. lignite, hard coal, natural gas, oil, biomass) at monthly (years 2010 to 2014) and hourly (years 2015 -2017) levels. • The US EPA emission modelling platform (US EPA, 2019a): The Environmental Protection Agency (EPA) maintains an emission modelling platform that includes, among other, processed and clean hourly emission data derived from a Continuous Emission Monitoring System (CEMS). The information collected includes hourly NOx, SOx and heat input data for individual power plants in years 2011 and 2014. • The IEA electricity statistics (IEA, 2018): The International Energy Agency (IEA) provides consistent electricity statistics split by generation type (i.e. combustible fuels, nuclear, hydro, geothermal/other) and country. The information collected included monthly data for the years 2010 to 2017 for each Member country of the Organisation for Economic Co-operation and Development (OECD). • The MBS Online (MBS, 2018): The Monthly Bulletin of Statistics (MBS) provides monthly data of the total electricity gross production per country. The information collected included data for the year 2015.

  https://doi.org/10.5194/essd-2020-175 started: 4 August 2020 c Author(s) 2020. CC BY 4.0 License. inter-annual variability can be negligible. Hence, all profiles developed only as a function of traffic count data were constructed by averaging the values (per month, day of the week or hour of the day) over all the available years. Some of the compiled datasets (e.g. Germany and California) report the traffic counts classified by vehicle type (i.e. light-duty

  https://doi.org/10.5194/essd-2020-175 started: 4 August 2020 c Author(s) 2020. CC BY 4.0 License.

  seasonality of traffic emissions can be affected by temperature. As shown byZheng et al. (2014), during winter months vehicles in China produce 19% more CO and 11% more NMVOC than in the summer due to the higher contribution of coldstart emissions. This study also showed that the monthly pattern of emissions differs remarkably by latitude, which is explained by the large contribution of cold-start emissions and the relationship between latitude and temperature. More recently,[START_REF] Keller | HBEFA version 3.3. Background documentation[END_REF] and[START_REF] Grange | Strong Temperature Dependence for LightDuty Diesel Vehicle NOx Emissions[END_REF] identified strong temperature dependence for diesel vehicle NOx emissions. On the basis of measurements of real-world vehicle emissions, both studies concluded that light-duty diesel NOx emissions are highly dependent on ambient temperature, with low temperatures resulting in higher NOx emissions (up to +80% for temperatures under 0ºC in EURO5 diesel cars;[START_REF] Keller | HBEFA version 3.3. Background documentation[END_REF].[START_REF] Grange | Strong Temperature Dependence for LightDuty Diesel Vehicle NOx Emissions[END_REF] also highlighted the spatial heterogeneity of the socalled "diesel low temperature NOx emission penalty" throughout Europe due to the different climate conditions.

  034 * / /! (+, 6) + 1.64, 0℃ < / /! (+, 6) < 18℃ 1, / /! (+, 6) ≥ 18℃

  Summer and winter temperature-dependent emission factors are defined for each type of vehicle as a function of the 2-m outdoor temperature obtained from the ERA5 reanalysis. The HERMESv3 model was run over Spain for year 2016 at a spatial resolution of 4x4 km and a temporal resolution of 1 hour. The results aggregated by month and normalized (orange line in Fig.4) show a strong seasonality, with emissions increasing up to 100% during summer. We used this profile as a first step that reflects the different dynamics of exhaust and evaporative emissions. Future works may consider developing region-specific profiles for NMVOC evaporative-related emissions.https://doi.org/10.5194/essd-2020

  Saturday and Sunday generally show the traffic activity to plateau between late morning and early evening, typically due to a decrease in commuting activity. Some studies have developed distinct hourly traffic profiles for Monday to Thursday, Friday, Saturday and Sunday (e.g.[START_REF] Mcdonald | High resolution mapping of motor vehicle carbon dioxide emissions[END_REF], and others have discriminated between weekdays and weekends (e.g.Zheng et al., 2009). CAMS-TEMPO includes hourly profiles that vary among weekdays, Saturdays and Sundays, following other studies such as[START_REF] Menut | Impact of realistic hourly emissions profiles on air pollutants concentrations modelled with CHIMERE[END_REF].

  average daily ventilation rate associated to manure management practice m, grid cell x and day d (in m•s -1 ). The manure management practices considered include housing in open barns, housing in closed barns and storage. For each category, the values of / ! (+, -) and J ! (+, -) are estimated following the expressions reported by Gyldenkaerne et al. (2005) and Skjøth et al. (2011). For instance, in the case of storage, J ! (+, -) and / ! (+, -) are assumed to be equal to the 10m outdoor wind speed and 2m outdoor temperature, respectively. The meteorological information needed to compute J ! (+, -) and / ! (+, -) values was obtained from ERA5 for the period2010[START_REF] Ab | Mobility data[END_REF] (CS3, 2017)).

  / /! (+, -) is the daily mean 2m outdoor temperature for grid cell x and day d [ºC]; and L -<! (+, -) is the daily 10m wind speed for grid cell x and day d [m•s -1 ]. Both parameters were derived from ERA5 for the period 2010 -2017 (C3S, 2017).

Figure 7

 7 Figure7shows examples of the 0.1x0.05 deg CAMS-REG-TEMPO gridded profiles constructed for the different road transport categories, including January and August (monthly) weights for gasoline CO exhaust emissions, and both Friday and Sunday (weekly) and 09:00 and 20:00h (hourly weekday) weights for all traffic sources except NMVOC gasoline evaporation. The monthly gridded profiles clearly show the influence of outdoor temperature, with weight factors up to 2.5 times higher in January than in August in Eastern Europe and Russia. The weekly profile maps clearly show the decrease in emissions from traffic during the weekend, especially in urban areas. The hourly profiles show the high levels of traffic activity during night time in some Mediterranean countries compared of central and North European ones, and also a clear distinction between urban and rural areas.

Figure 8

 8 Figure 8 shows examples of CAMS-GLOB-TEMPO profiles for the different agricultural emission sources, including April and November (monthly) weights for fertilizer NH3 emissions, daily (i.e. 2017/03/06 and 2017/07/16) weights for livestock NH3 and NOx emissions, and daily time series for livestock emissions over Spain and Argentina for 2010 and 2017 (Note the country daily times series are spatially aggregated as the actual patterns are variable spatially.). Concerning the fertilizer monthly factors, it is observed that the spatial resolution is not homogeneous across the domain. This is due to the different spatial resolutions of the original datasets used to construct this gridded profile: The MASAGE_NH3 emission inventory is available at a resolution of 2x2.5 degree, while the datasets used for China and USA are reported at resolutions of 0.25x0.25 deg and 12kmx12km, respectively. Despite the coarse resolution, the profiles broadly capture the different seasonality in different parts of the world, with a peak of emissions occurring in April in Europe and in November in Latin America. For the livestock sector, the largest weight factors occur during the summer and the lowest ones during the winter due to the temperature dependence. Nevertheless, and in contrast to what is observed with the residential/commercial daily factors, the signal for livestock emissions is mostly seasonal and the daily fluctuations are relatively small, which makes the interannual variability small.

  we compare the CAMS-REG-TEMPO road transport profiles to the temporal variation of air pollutant concentrations measured in Madrid, Milano, Barcelona and and Berlin. CO and NO2 hourly concentrations were obtained from the European Environmental Agency (EEA) download service of validated and official air quality data (EEA, 2019). Four urban traffic stations highly influenced by road transport emissions were considered for the analysis: Escuelas Aguirre (Madrid), Milano-Senato (Milano), Eixample (Barcelona) and B Neukölln-Karl-Marx-Str. 76 (Berlin).

  Discussion started: 4 August 2020 c Author(s) 2020. CC BY 4.0 License.

  , daily, weekly, hourly). Per country: indicates that the profiles vary per country; per pollutant: indicates that the profiles vary per pollutant; per region: indicates that the profiles vary per grid cell within a country; fixed: indicates that the profiles are spatially invariant. The symbol "-" denotes that no profile is proposed.

( 2 )

 2 Same profiles as the ones reported by the TNO dataset (Denier van derGon et al., 2011) https://doi.org/10.5194/essd-2020

Figure 1 :

 1 Figure 1: Representation of the spatial coverage of the datasets used to derive temporal profiles for energy industry (a), manufacturing industry (b), road transport (c) and agriculture (use of fertilizers) (d). For energy industry, the legend indicates the different sources of information used: The European Network of Transmission System Operators for Electricity (ENTSO-E), the 1190

Figure 4 :

 4 Figure 4: Monthly, weekly and hourly (for weekdays and Saturdays) temporal profiles derived from measured traffic counts in selected cities (see Table 5 for references). The profile reported by the TNO dataset is plotted for comparison purposes (Denier van der Gon et al., 2011). The monthly and hourly profiles proposed for the gasoline evaporative emissions (Evaporative, yellow line) is also shown.

Figure 8 :

 8 Figure 8: CAMS-GLOB-TEMPO (0.1x0.1 deg) monthly and daily scale factor maps for agriculture (agr) NH3 emissions and livestock (mma) NH3 and NOx emissions for April and November and 2017/03/06 and 2017/07/16, respectively. Temporal plots on the bottom represent the daily factors for livestock emissions (mma) obtained over Spain and Argentina for 2010 and 2017, respectively. Administrative boundaries are derived from the Micro World Data Bank (MWDB2, 2011).

Table A4 : Fixed hourly temporal profiles per domain, sector and pollutant (Part 2: 13 to 24h). The definition of the sectors can be found in Table 1 and Table 2. 865

 A4

	Domain	Sector	Pollutant	13	14	15	16	17	18	19	20	21	22	23	24
	CAMS-GLOB	ind	All	1.22	1.24 1.25 1.16 1.08 1.01 0.95	0.9	0.85 0.81 0.78 0.75
	CAMS-GLOB	agr	NH3, CH4	1.65	1.75	1.7	1.55 1.35	1.1	0.9	0.75 0.65	0.6	0.6	0.6
	CAMS-GLOB	agr	PM10, PM2.5, CO, SOx, NOx, NMVOC, CO2	3.39	3.39 3.39 1.68 1.68 1.68 0.56 0.56 0.56 0.22 0.22 0.22
	CAMS-GLOB	mma	All	1.65	1.75	1.7	1.55 1.35	1.1	0.9	0.75 0.65	0.6	0.6	0.6
	CAMS-GLOB	fef	All	1	1	1	1	1	1	1	1	1	1	1	1
	CAMS-GLOB	slv	All	1.5	1.5	1.5	1.5	1.5	1.4	1.25	1.1	1	0.9	0.8	0.7
	CAMS-REG CAMS-REG CAMS-REG CAMS-REG NMVOC, CO2, CH4 CAMS-REG GNFR_D All GNFR_E All GNFR_F4 NMVOC GNFR_K PM10, PM2.5, CO, SOx, GNFR_B All CAMS-GLOB tnr All CAMS-REG GNFR_B All CAMS-REG GNFR_F4 NMVOC CAMS-REG GNFR_F4 NMVOC CAMS-REG GNFR_K All CAMS-REG GNFR_K All CAMS-REG GNFR_L NH3, CH4 CAMS-REG GNFR_L NH3, CH4 CAMS-REG GNFR_L CAMS-REG GNFR_L PM10, PM2.5, CO, SOx, NOx, NMVOC, CO2 1.2 1.2 0.95 0.96 0.74 0.69 PM10, PM2.5, CO, SOx, NOx, NMVOC, CO2 CAMS-REG GNFR_H All	1.2 1.02 0.74 0.75 0.75 0.78 0.82 0.88 0.95 1.02 1.09 1.16 1.22 1.28 0.8 0.8 0.8 0.8 0.8 0.8 1.2 1.2 1 1.01 1.03 1.03 1.01 1.04 1.03 1.01 0.91 1.2 0.84 1 1.2 1.54 1.5 1.23 1.03 0.77 0.72 1.22 1.24 1.25 1.16 1.08 1.01 0.95 0.9 0.85 0.81 0.78 0.75 1.3 0.48 0.36 0.29 0.25 0.21 0.25 0.4 1.8 1.86 1.86 1.79 1.6 1.43 1.24 1.03 0.86 0.7 0.61 0.57 0.74 1.01 1.38 1.57 1.71 0.6 0.6 0.6 0.6 0.6 0.65 0.75 0.9 1.1 1.35 1.45 1.65 1.75 1.7 1.55 1.35 1.1 0.9 0.75 0.65 0.6 0.6 0.6 1.6 0.6 0.6 0.6 0.6 0.6 0.65 0.75 0.9 1.1 1.35 1.45 1.65 1.75 1.7 1.55 1.35 1.1 0.9 0.75 0.65 0.6 0.6 0.6 1.6 3.39 3.39 3.39 1.68 1.68 1.68 0.56 0.56 0.56 0.22 0.22 0.22 0.06 0.06 0.06 0.07 0.07 0.07 0.2 0.2 0.2 1.82 1.82 1.82 1.66 1.58 1.47 1.43 1.42 1.46 1.46 1.4 1.39 1.25 0.96 0.58
	CAMS-REG	GNFR_H	All	0.28 0.13	0.1	0.08 0.09 0.09 0.44 0.91 1.18 1.43	1.6	1.61
	CAMS-REG	GNFR_E	All	1.5	1.5	1.5	1.5	1.5	1.4	1.25	1.1	1	0.9	0.8	0.7
	CAMS-REG	GNFR_E	All	0.5	0.35	0.2	0.1	0.1	0.2	0.75 1.25	1.4	1.5	1.5	1.5

Table 1 : Main characteristics of the CAMS global temporal profiles (CAMS-GLOB-TEMPO) reported by sector and temporal

 1

  Leap or non-leap years(2) Same profiles as the ones reported by the TNO dataset (Denier van derGon et al., 2011)

	Sector	Description	Monthly (∑=12)	Daily (∑=365/366) (1)	Weekly (∑=7)	Hourly (∑=24)
	ene	Power generation (power and heat plants, refineries, others)	(per country, pollutant)	-	(per country, pollutant)	(per country, pollutant)
	ind	Industrial process	(per country)	-	(fixed) (2)	(fixed) (2)
		Other stationay
	res	combustion (residential, commercial, agriculture,	(per region, year)	(per region, year)	(fixed) (2)	(per region, pollutant)
		others)
	fef	Fugitives	(fixed) (2)	-	(fixed) (2)	(fixed) (2)
	slv	Solvents	(fixed) (2)	-	(fixed) (2)	(fixed) (2)
	tro	Road transportation	(per region)	-	(per region)	(per day type)
	shp	Ships	(fixed) (2)	-	(fixed) (2)	(fixed) (2)
		Off road transportation
	tnr	(airports, agricultural and construction	(fixed) (2)	-	(fixed) (2)	(fixed) (2)
		machinery, others)
	swd	Solid waste and waste water	(fixed) (2)	-	(fixed) (2)	(fixed) (2)
	mma	Agriculture (livestock)	(per region, year for NH3 and NOx; fixed for others)	(per region, year for NH3 and NOx)	(fixed) (2)	(fixed) (2)
	agr	Agriculture (fertilizers and agricultural waste burning)	(per region)	(per region, year for NH3)	(fixed) (2)	(fixed, per pollutant)
	(1)

Table 2 : Main characteristics of the CAMS regional temporal profiles (CAMS-REG-TEMPO) reported by sector and temporal resolution (monthly, daily, weekly, hourly). Per country: indicates that the profiles vary per country; per pollutant: indicates that the profiles vary per pollutant; per region: indicates that the profiles vary per grid cell within a country; fixed: indicates that the profiles are spatially invariant. The symbol "-" denotes that no profile is proposed.

 2

	1170
	Sector	Description	Monthly (∑=12)	Daily (∑=365/366) (1)	Weekly (∑=7)	Hourly (∑=24)
	GNFR_A	Public Power (power and heat plants)	(per country, pollutant)	-	(per country, pollutant)	(per country, pollutant)
	GNFR_B	Industry	(per country)	-	(fixed) (2)	(fixed) (2)
		Other stationay
	GNFR_C	combustion (residential, commercial,	(per region, year)	(per region, year)	(fixed) (2)	(per region, pollutant)
		agriculture, others)
	GNFR_D	Fugitive	(fixed) (2)	-	(fixed) (2)	(fixed) (2)
	GNFR_E	Solvents	(fixed) (2)	-	(fixed) (2)	(fixed) (2)
	GNFR_F1	Road transport exhaust gasoline	(per year, region for CO and NMVOC; per region for others)	-	(per region)	(per region, day type)
	GNFR_2	Road transport exhaust diesel	(per year, region for NOx; per region for others)	-	(per region)	(per region, day type)
	GNFR_F3	Road transport exhaust LPG	(per region)	-	(per region)	(per region, day type)
	GNFR_F4	Road transport non-exhaust (wear and evaporative)	(per region for PM; fixed for NMVOC)	-	(per region for PM; fixed for NMVOC)	(per region, day type for PM; fixed for NMVOC)
	GNFR_G	Shipping	(fixed) (2)	-	(fixed) (2)	(fixed) (2)
	GNFR_H	Aviation	(per country)	-	(fixed) (2)	(fixed)
		Off road transport
	GNFR_I	(agricultural, and construction	(fixed) (2)	-	(fixed) (2)	(fixed) (2)
		machinery, others)
	GNFR_I	Waste management	(fixed) (2)	-	(fixed) (2)	(fixed) (2)
	GNFR_K	Agriculture (livestock)	(per region, year for NH3 others) and NOx; fixed for	(per region, and NOx) year for NH3	(fixed) (2)	(fixed) (2)
		Agriculture
	GNFR_L	(fertilizers, agricultural waste	(per region)	(per region, year for NH3)	(fixed) (2)
		burning)

TableA3: Fixed hourly temporal profiles per domain, sector and pollutant (Part 1: from 1 to 12h). The definition of the sectors can be found in Table1

Preprint. Discussion started: 4 August 2020 c Author(s) 2020. CC BY 4.0 License.

Table 4: Share of final energy consumption in the residential sector by fuel and type of end-use in Europe (Eurostat, 2018)

Acknowledgements

The present work was funded through the CAMS_81 (CAMS global and regional emissions) contract, coordinated by the

temporal profile datasets include the North American profiles provided data.fr/). For review purposes, ECCAD has set up an anonymous repository where subsets of the CAMS-GLOB

Data availability

Gridded maps with all the temporal factors (monthly, weekly, daily, hourly) per sector and year are available as NetCDF files for the global domain at a resolution of 0.1x0.1 deg (CAMS-GLOB-TEMPOv2.1, https://doi.org/10.24380/ks45-9147, Data Discussions Preprint. Discussion started: 4 August 2020 c Author(s) 2020. CC BY 4.0 License. Data Discussions Preprint. Discussion started: 4 August 2020 c Author(s) 2020. CC BY 4.0 License. Data Discussions Preprint. Discussion started: 4 August 2020 c Author(s) 2020. CC BY 4.0 License. Data Discussions Data Discussions Data Discussions Data Discussions Data Discussions Data Discussions Data Discussions Data Discussions Preprint. Discussion started: 4 August 2020 c Author(s) 2020. CC BY 4.0 License. Data Discussions Data Discussions Data Discussions Data Discussions

Appendix A

 3-2, 3-3, 3-4, 3-5 and3-7