dataesr/scientific-paragraphs-categorization huggingface dataset:
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  (Makonin et al. , 2016). 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.
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. Pham et al. , 2008; 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.
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).
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.
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.
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. Unal et al. , 2005; Zhou et al. , 2019).
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.
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  (James et al. , 2012). 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).
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.
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  (ENTSOG, 2020) , 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.
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.
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  (Simpson et al. , 2012)  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.
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.
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). Access Earth System Science Data Discussions Preprint. The CAMS-TEMPO dataset represents an effort to improve the temporal characterisation of emission data to be used for atmospheric chemistry modelling.