31Sanitarno inženirstvo / International Journal of Sanitary Engineering Research Vol. 15 1/2022 SANITARNO INŽENIRSTVO / International Journal of Sanitary Engineering Research 2022;15(1): 31-46. DOI: 10.2478/ijser-2022-0004 State of the Art Emission Inventory and Their Application: Literature review 1University of Ljubljana, Faculty of Civil and Geodetic Engineering, Jamova 2, 1000 Ljubljana, Slovenia 2 Slovenian Environment Agency, State of the Environment Office, Air Quality Division, Vojkova 1b 1000 Ljubljana 3University of Ljubljana, Faculty of Medicine, Vrazov trg 2, 1000 Ljubljana, Slovenia *Corresponding author: Ms Petra Dolšak Lavrič, MSc University of Ljubljana, Faculty of Civil and Geodetic Engineering, Jamova 2, 1000 Ljubljana, Slovenia Slovenian Environment Agency, State of the Environment Office, Air Quality Division, Vojkova 1b 1000 Ljubljana Email: petra.dolsak-lavric@gov.si © 2022 Petra Dolšak Lavrič, Andreja Kukec, Rahela Žabkar. This is an open access article licenced under the Creative Commons Attribution NonCommercial- NoDerivs license as currently displayed on http://creativecommons.org/licenses/by-nc- nd/4.0/. Petra Dolšak Lavrič* , Andreja Kukec, Rahela Žabkar ABSTRACT Literature Review article Currently, the complex bottom-up emissions inventories are in rise. Its development is essential for both understanding the sources of air pollution and designing effective air pollution control measures. Anyway, the main challenge to get the most reliable emissions evidence is the variety of contributing sources, the complexity of the technology mix and the lack of reliable emission factors. The input data bases are improving constantly, by more reliable statistics and survey-based data. Our study reveals the strengths and deficiency of currently published scientific papers on the topic of emission inventory. With that purpose, 40 crucial scientific papers were selected. We first highlight the period and geographic region, when and where the inventories were made for. We then summarize the sector-based estimates of emissions of different species contained by SNAP sectors in selected inventories. Additionally, the resolution of inventories is analysed. Finally, the last section summarizing common ways of assessing and validating inventories and their main purpose. This review shows that there is still a lot of chance to improve emissions inventories in a way to develop input data and emission factors for different technologies and activities or to develop inventories on fine grids. Those efforts will give us wider knowledge about pollution sources and will lead to accepted better air quality policy. Key words: Air Quality, Emission Inventory, State of the Art, Validation Process, SNAP Nomenclature Received: 21. 11. 2022 Accepted: 22. 12. 2022 Published: 31. 12. 2022 3 21,2 32Sanitarno inženirstvo / International Journal of Sanitary Engineering Research Vol. 15 1/2022 INTRODUCTIONS Air pollution poses important risk on our population, because it causes 6,5 million deaths per year or 1/8 of all deaths [1], [2]. According to the United Nation (UN) [3] in year 2018 around 55% of the global population lived in the urban areas. Those percentage will even increase by 60% until year 2030 and by 80% in year 2050. Consequently, the good quality of air in urban area will be important challenge in the future. Air quality can be measured on monitoring stations using reference methods for different pollutants. Representative locations and minimum density of measurement network are determined by European commission. The accuracy of measurements obtained depends on the maintenance and monitoring of measurement equipment. [4]. The long-term observed and analysed data on permanent locations enable monitoring of improvement or deterioration of local air quality [5]. On the other hand, the spatial distribution of air quality can be assessed by using mathematical air quality models. In complex air quality models the dispersion model is coupled with detailed meteorological model, which take into account meteorological measurements and fine grid terrain information like land-use and altitude. The important part of air quality models is capability to calculate chemical transformations, which enables the model to represent the formation of secondary pollutants [6]. The crucial part, which modellers have impact on, is recognizing the emissions sources and their release, which is still currently the highest uncertainty of those models [6], [7]. Those sources are defined as points such as chimneys, lines such as roads or areas such as fields. Emission inventory is defined as a comprehensive list of pollutants from all sources in a geographical area during a selected period of time. To get the most novel emission inventory the constant development and accuracy of input data are crucial [8]. Emission inventory can be developed on local, regional or national scale. Broad and precise inventory helps us to manage air quality through applying the most proper policy in the area. It can be used to recognize the highest sources of pollutant emissions and to determine the most endangered areas [9]. Moreover, emission inventory is useful tool for identification of the most appropriate monitoring locations and to identify the most problematic pollutants to be measured [10], [11]. The emission inventory could be conducted by two different methods, top-down or bottom-up. The more basic method is top-down, which holds information about average statistic activities, usually based on national level data, and basic emission factors for those activities. This method is used for rigid spatial distribution and to analyse national or regional emissions [12]. Meanwhile, the more progressive method is bottom-up, which includes information about activity and technology for each particulate source individually and is in addition generated to the desired spatial resolution; local, regional or even national level [13]. Emission factors for different activities are collected in emission inventory guidebook and are based on the previous studies about measurement emissions during the different activities and technologies. In Europe the most common known Emission Inventory Guidebook is EMEP/EEA Emissions Inventory Guidebook from year 2019 [14]. The guidebook holds information about emission factors on three different complex stages. Stage 1 or TIER 1 holds information about emission factors for the most basic activities and technologies. More advanced is stage 2 or TIER 2 method which includes more advanced information about activities, emission factors and technologies. P. Dolšak Lavrič, A. Kukec, R. Žabkar 33Sanitarno inženirstvo / International Journal of Sanitary Engineering Research Vol. 15 1/2022 The most advances method is TIER 3, which presents the most detailed emissions input data. The stage used depends on the availability of input data and the importance of a particular source [7]. The general equation for emission estimations according to bottom-up method is the following: Where E = calculated emissions; A = activity rate needs for develop emissions; EF = emissions factor and ER = overall emission reduction efficiency (%). Development of emission inventory following these steps: 1. Collecting the data about the sources such as vehicle fleet, national building register, national chimneys database, number and location of livestock or amount of solvent use in household; 2. determine the types of air pollutant emissions from each of the listed sources; 3. find out the emission factors for each of the concerned pollutant, which could be found in EMEP/EEA Emissions Inventory Guidebook [15]; 4. determine the number and size of specific sources in the area; 5. repeat steps 3. and 4. to obtain the total emissions; 6. sum up the similar emissions and aggregate them on a desired resolution; 7. validate, analyse and interpretate given results [10]. Aim of our study is to provide an extensive literature review based on a multitude of studies on emission inventories. To the best of our knowledge, there is not yet a review covering all the aspects mentioned here. Throughout the first search the 15.157 articles were given and 40 of those were finally selected for the additional analyse. All the articles included inventory developed by bottom-up methods for anthropogenic sources. This comprehensive article is organized as follows. Section 1 categorizes the 40 collected articles according to the period of time for which the inventory is reported. Section 2 discusses the analysed geographic area, which can be on local, regional, or national scale. Next section presents the sectors involved in the analysed inventory, which are mostly those which emitted the majority of analyzed pollutants. In this article the SNAP nomenclature was used to report sectors. The section 4 categorizes chosen articles by pollutants included in the inventory. Meanwhile, section 5 analyses the articles by their resolution. Finally, the last section summarizes common ways of assessing and validating inventories and their main purpose. Last section also includes all the additional interesting data about the particulate article. Overall view of the articles is summarized in the discussion chapter. We believe that this literature review of emission inventories conducted on a global scale from multidisciplinary viewpoints will enable the recommendation of targeted environmental policies for maintaining good air quality, leading to healthier living in cities. METHODS The study is focused on the systematic review of the literature addressing the bottom-up emission inventory. The scientific articles were selected from the ScienceDirect database. The Advanced Search Builder was used and the keywords were searched in the title or abstract of the paper. We have filtered only research articles published in English language and selected the following keywords: »emission AND inventory OR evidence OR database«. P. Dolšak Lavrič, A. Kukec, R. Žabkar 34Sanitarno inženirstvo / International Journal of Sanitary Engineering Research Vol. 15 1/2022 The first research found out the 15.157 articles, which are present in figure 1 by years of publication. To eliminate unfitted articles the keywords »transport AND small combustion« were added. The small combustions and transport are known as two main sources of emissions. In that way the 226 articles were selected. Additionally, the 47 articles were duplicate and eliminated. The full-text articles were assessed for eligibility. One of the criteria was the impact factor of the journals, which should not be less than 2.5. The total of 40 eligible published research articles were obtained in their final version. Figure 1: The number of articles search by keywords »emission AND inventory OR evidence OR database« in database ScienceDirect by year. The selected scientific articles were categorized by 7 main categories and 2 subcategories. First category is the year or time period for which the inventory was developed and can be from a month to a few years long. The next category was the area where the inventory was conducted, it can be on local, regional, or national scale. Followed by sectors included in the inventory and reported in SNAP categorization, as briefly describe below. Collection of pollutants included in the study give us information about the most popular pollution covered by inventory. The important information is also the spatial resolution of the model, the most often data is in the grid form. The validation process gave us information about the most frequently used type of inventory validation. Lastly, the information about the purpose of the inventory was collected. This section additionally includes other interesting specific information about the inventories. However, to reach the information the category of article, published year and the journal, where the article was published, was added. Categorized selected articles are present in table 2. It was decided that pollution sectors in this study will be reported using SNAP nomenclature. The English acronym SNAP stands for Selected Nomenclature for Air Pollution, that was developed under the EMEP/EEA organization in year 2001 with the purpose to synchronise the IPCC/OECD (Integrated Pollution Prevention and Control) nomenclature of source categories for activities resulting in emissions. The SNAP nomenclature is also the official nomenclature for inventory reported under the CLRTAP (Convention on Long- range Transboundary Air Pollution) convention [16]. Table 1 presents the SNAP codes and their description [14], [17]. RESULTS P. Dolšak Lavrič, A. Kukec, R. Žabkar SNAP Code SNAP Description 01 Combustion in the production and transformation of energy 02 Non-industrial combustion plants 03 Industrial combustion plants 04 Industrial processes without combustion 05 Extraction and distribution of fossil fuels and geothermal energy 06 Use of solvents and other products 07 Road Transport 08 Other mobile sources and machinery 09 Waste treatment and disposal 10 Agriculture 11 Other sources and sinks (nature) 35Sanitarno inženirstvo / International Journal of Sanitary Engineering Research Vol. 15 1/2022 Table 1: SNAP nomenclature and their description. In this study, the SNAP codes 3 and 4 are usually treated as common sources, therefore those sources are label as number 34. In the case when all SNAP sectors were used it is signed by “all SNAP sectors”, meanwhile where only subcategories were used it is noted. Table 2: Summary table reporting reviewed results on the topic of Emission Inventory. P. Dolšak Lavrič, A. Kukec, R. Žabkar 36Sanitarno inženirstvo / International Journal of Sanitary Engineering Research Vol. 15 1/2022 P. Dolšak Lavrič, A. Kukec, R. Žabkar 37Sanitarno inženirstvo / International Journal of Sanitary Engineering Research Vol. 15 1/2022 It can be note that 1 article was published in year 2022 and 2017, 2 articles in year 2014 and 3 articles in years 2015, 2016 and 2019. 5 articles were from years 2018 and 2019. The majority, 17 articles, were published in year 2020. Most of the articles, 18 altogether, were published in Atmospheric Environment with 4.5 impact factor in year 2021 and 9.2 rated Cite Store[58]. 6 articles were from Atmospheric Pollution Research, 3 articles from Journal of Cleaner Production, whilejournals Atmospheric Chemistry and Physics and Environmental Pollution had 2 articles each. One paper per journal was from Air Quality, Atmosphere & Health, Chemosphere, Environment International, Geoscientific Model Development, Transportation Research Part D: Transport and Environment, Urban Climate and Journal of Environmental Sciences. Figure 2: Published years (left) and journals (right) of selected articles. P. Dolšak Lavrič, A. Kukec, R. Žabkar 38Sanitarno inženirstvo / International Journal of Sanitary Engineering Research Vol. 15 1/2022 ... 3.1 Period of time More than half of all inventories (29.) used one-year long time period as shown in figure 3. The most represented years were 2010 and 2016. 8 inventories were prepared for longer time periods, the longest assessed period was 18 years long [40]. One article includes 16 years long period [50] and 2 articles include 14 [41], [57] and 6 years long periods [21], [24]. 1 article each refers to a 10 [56] and 2 year long period [20]. 1 article included only one month, December 2017 [37], besides another article refers on month August in two different years [46]. Figure 3: Distribution of one year long period inventories. Time lag between the year of inventory and year of paper publication was usually at least 4 years. The highest time lag was 12 years. 3.2 Geographic Area of Inventory The geographic area of inventories varied from global, national, regional or local scale. Study by Winijkul et. al. [42]included the whole world and collected emission data on the global scale. The same applies to the study by Kuenen et. al. [24], where 51 countries from Europe and North America or countries which reported their emissions under the CLRTAP convention were included [16]. Interesting areas were also discussed in the study by Shi et. al. [50], where main focus was on the tropical area of America, Asia and Africa. From national point of view, the 14 articles were based on the countries within the Europe, 19 stayed in Chine’s region, 7 articles were located in Asia and at least 4 articles include the region within the America. Majority of those were made for cities area, which are the highest sources of anthropogenic emission [59]. The research geographic area depends on the input database accessed [11]. P. Dolšak Lavrič, A. Kukec, R. Žabkar 39Sanitarno inženirstvo / International Journal of Sanitary Engineering Research Vol. 15 1/2022 Figure 4: The geographic area of selected articles. Green colours indicate the countries, while purple colours show the cities. 3.3 Including Sectors in the Inventories 9 of all analysed scientific papers covered all SNAP sectors. Extended sectors, but not full SNAP nomenclatures, have been considered in 16 articles. Nevertheless, all of them included SNAP 02 – non-industrial combustion plants or mainly small combustions, 03 – industrial combustion plants and 07 – road transport. Only small combustion sector is involved in 3 studies, while road transport sectors is only included in 5 studies. These two sectors can be found in study by Elessa Etuman et. al [60], while Azhari et. al [38] included road transport sector and small industry. There are two outstanding studies [50] and [41], one focused on biomass burning from fires and another one on burning of crop residual. 3.4 Pollutants included in Inventories The most common pollutants to be investigated are NOx, SOx and PM10. NOx emissions were included in 25 studies, while SOx was investigated in 20 studies. PM10 emissions can be found in 18 and PM2.5 in 17 studies. CO emissions were researched in 17, NMVOC emissions in 9 studies and NH3 in 10 studies. The minority of articles, merely in 1 or 2, emissions of O3, dioxins, metals, PAH and PCB were represented, which could be a consequence of less availability and variability of emission factors for certain sectors and technologies [11]. Most studies analyse only one pollutant, but in some cases the precursors of secondary pollutants were investigated, such as VOC [54], [29], [30]. In the study by Z. Zhou et al. [30], where main focus was on VOC emissions, there were 45 VOC profiles and 519 species included, with the purpose of VOC specifications. The aim of the study by E. Winijkul et al. [42] was to analyse size distribution of PM on worldwide scale. In study by A. K. Pathak et al. [35] the toxicity of PM2,5 was researched in the area of Delphi, Greece. On European scale, study by A. Leclerc et al. [57]included the emissions of NMVOC, metals, PAH’s, dioxins and PCB’s. 3.5 Spatial Resolution of the Inventory The range of resolution emission’s inventories was from 500 × 500 meters [47] to 111 × 111 kilometres or 1° [32]. Majority of them used resolution of 1 ×1 kilometres. Some of the studies have results as common emissions on particulate territories, such as city, municipality, province, or region. P. Dolšak Lavrič, A. Kukec, R. Žabkar 40Sanitarno inženirstvo / International Journal of Sanitary Engineering Research Vol. 15 1/2022 Papers with the main purpose of emission inventory validation, in general did not provide information about the spatial resolution of the inventories, the purpose of those studies was the final result’s validation with other methods. 3.6 Validation process and the purpose of inventory Accuracy of emission inventory is guaranteed through the validation process. Validation can be performed in different ways. In case of previously developed emission inventories, using either top-down or bottom-up principle, for a particular area a comparison between old and new estimated emissions can be done. Even though this approach is a bit rough, it was used in 10 studies [61]. In the case of both bottom-up and top-down inventory availability the comparison with the Diamond graph [62] can be used. This method was developed by The Forum for Air quality Modelling (FAIRMODE), which was lunched under the initiative of the European Environment Agency (EEA) and the European Commission Joint Research Centre (JRC) and is currently chaired by the Joint Research Centre [63]. The Diamond graph recognizes differences between the input data based on the activities and emission factors. This method was used in 4 discussed articles [7], [26], [39] in [13] conducted in European area. 5 of the analysed studies compared emissions based on measurement network or satellite data. The main disadvantage of using satellite data is misleading the secondary emissions, which is the main source of uncertainty [64]. The comparison method was used in 6 papers, either comparison of input data or comparison of new inventories with the results from previous studies. Indispensable was the uncertainty analysis of emission models, based on the description of model uncertainty or with the use of Monte Carlo methods. The last approach was used in 6 studies. Study by Zhang et. al. [56], conducted in China area, used mathematical program tool AuvToolPro [55] to analyse uncertainty as part of validation process. Figure 5: The validation process used in different studies. The main goal of the collected studies was to develop validation of emission inventories. In 8 studies the results of emissions inventories were used in air quality models. Comparison of air quality model results with measurement network data still provide some discrepancy. For instance, vehicle emission factors for NOx emissions were typically underestimated, especially during the rush hours [65]. Moreover, emissions of PM can be underestimated due to the disregard of secondary emissions, resuspension and long-range emissions [20]. One of the disadvantage of this validation model is also, that dispersion models more preciously predict the average values of modelled pollution, meanwhile the maximum hourly or daily values are underestimated or overrated [19]. The main focus of five studies was to create different scenarios of fuel use, use of different technologies or changes in activity. In this way, the certain measures to improve local air quality were analysed. P. Dolšak Lavrič, A. Kukec, R. Žabkar 41Sanitarno inženirstvo / International Journal of Sanitary Engineering Research Vol. 15 1/2022 Fine spatial resolution emission inventories are useful tools to briefly analyse different emission sources. They can also be used as an input to air quality models. Emission inventory enables to analyse different scenarios for different technologies used and activity changes. Consequently, it represents the effective tool to accept different measurements, which goal is to reach the most appropriate balance between human activities and quality of urban air. This study found out, that the most covered areas with emission inventories are Europe and China, which could be result of diversity and availability of input data. The need for better spatial resolution, i.e. emission distribution on finer grid, based on the top-down method was shown [13]. The comparison of both methods, bottom-up and top-down, recognized the overrated emissions from top-down methods [66]. The detailed bottom-up emission model is achievable in cases of available detailed input activity and technology data, accessible only in countries or regions with transparent database centres, more common in developed countries [20]. The solution for lack of available activity input data from small combustion and transport on local scale offers the OLYMPUS model [27]. Model considers everyday citizen activities and defines their mobility needs around the city. Sum of each activity represents the common activity in city Paris. The spatial distribution of mobility is based on the proximity of service facilities or private buildings and use of different transport vehicles. The model also considers small combustion based on the energy use of buildings. The main model input data are population density, location of service facilities, road network, public transport and meteorological characteristics that have an impact on emissions [60]. Our study recognized that emission inventories need improvements of source identification and specification in the urban environment. Additionally, there is a need to broader knowledge about the formation of secondary emissions, emissions from resuspension and chemical-physical processes, which could be implemented in the inventory [20]. Furthermore, additional studies focused on discrepancies between bottom- up and top-down emissions on smaller area are needed. A tendency to make inventories with higher spatial resolution is evident [20], [67]. The requirement to acquire more precise emission factors for dominant technologies used in all SNAP sectors in a given area, was shown. More studies are needed with the focus on emission factors related to different conditions. Consequently, it is recommended to fund more studies, which purpose will be research emission factors obtain from different conditions [57], [34]. This study shown that almost all analysed inventories deal with anthropogenic emission sources. The natural emission sources can as well contribute to higher emissions, such as occurrence of desert dust. Last but not least, the important step in emission inventory development is analysis of model uncertainties and sensitivities. In the most research papers included in our study, the uncertainty analysis and Monte Carlo method was used [13]. In the future, we suggest the improvement in comparison methodology to enable two inventories to be compared for the same area by different parts like sectors, activity use, emission factors, and population density [26]. Currently the Diamond graph is effective tool for comparisons of bottom – up and top – down emission inventories [26]. Above listed improvements will lead to the effective tools for assessment of measures targeting urban air quality. DISCUSSION P. Dolšak Lavrič, A. Kukec, R. Žabkar 42Sanitarno inženirstvo / International Journal of Sanitary Engineering Research Vol. 15 1/2022 CONSLUSION Air quality can be assessed by measurement network composed of representative monitoring sites equipped with certain measurement equipment. On the other hand, air quality models represent reality with uncertainties. Their accuracy also heavily depends on the accuracy of input data. Emission inventories with fine spatial distribution and temporal emissions release are essential for defining effective air-pollution-control measures. They are help finding the compromise between human goods and health environment. Literature review showed there is still deficiency of available good quality input data to analyse sources. Furthermore, emission factors should be researched more, especially for new and widely used technologies. Currently the most covered states are China and Europe. Slovenia still has a lot of space to improve national Emission Inventory based on the bottom-up methods [68]. P. Dolšak Lavrič, A. Kukec, R. Žabkar 43Sanitarno inženirstvo / International Journal of Sanitary Engineering Research Vol. 15 1/2022 REFERENCES [1] 71 World Health Assembly, “Health, environment and climate change: report by the Director-General,” World Heal. Organ., vol. 2016, no. SEVENTY- FIRSTWORLD HEALTH ASSEMBLY, pp. 1–7, 2018. [2] I. Manisalidis, E. Stavropoulou, A. Stavropoulos, and E. Bezirtzoglou, “Environmental and Health Impacts of Air Pollution: A Review,” Front. Public Heal., vol. 8, no. February, pp. 1–13, 2020. [3] P. D. United Nations, Department of Economic and Social Affairs, “The World ’s Cities in 2018,” World’s Cities 2018 - Data Bookl. (ST/ESA/ SER.A/417), p. 34, 2018. [4] S. Gani et al., “Systematizing the approach to air quality measurement and analysis in low and middle income countries,” Environ. Res. Lett., vol. 17, no. 2, Feb. 2022. [5] V. F. McNeill, “Addressing the Global Air Pollution Crisis: Chemistry’s Role,” Trends Chem., vol. 1, no. 1, pp. 5–8, 2019. [6] K. Karroum et al., “A Review of Air Quality Modeling,” Mapan - J. Metrol. Soc. India, vol. 35, no. 2, pp. 287–300, 2020. [7] S. López-Aparicio, M. Guevara, P. Thunis, K. Cuvelier, and L. Tarrasón, “Assessment of discrepancies between bottom-up and regional emission inventories in Norwegian urban areas,” Atmos. Environ., vol. 154, pp. 285–296, 2017. [8] M. Li et al., “Special Topic: Air Pollution and Control Anthropogenic emission inventories in China: a review,” Natl. Sci. Rev., vol. 4, pp. 834–866, 2017. [9] B. Xu et al., “The Study of Emission Inventory on Anthropogenic Air Pollutants and Source Apportionment of PM 2.5 in the Changzhutan Urban Agglomeration, China.” [10] H. Q. Bang and V. H. N. Khue, Air Pollution: Monitoring, Quantification and Removal of Gases and Particles. IntechOpen, 2019. [11] D. A. Vallero, Fundamentals of Air Pollution, Fifth. 2014. [12] M. Satt, M. De Almeida, D. Agosto, F. Gonçalves, H. Beatriz, and B. Cybis, “The evolution of city-scale GHG emissions inventory methods : A systematic review,” Environ. Impact Assess. Rev., vol. 80, no. September 2019, p. 106316, 2020. [13] M. Trombetti et al., “Spatial inter-comparison of Top-down emission inventories in European urban areas,” Atmos. Environ., vol. 173, no. May 2017, pp. 142–156, 2018. [14] European Environment Agency, EMEP/EEA air pollutant emission inventory guidebook 2019, 13/2019. Luxembourg: European Environment Agency, 2019. [15] et. al. Ole-Kenneth Nielsen, “EMEP/EEA air pollutant emission inventory guidebook 2019 Small combustion,” 2019. [16] T. Kuokkanen, “The convention on long-range transboundary air pollution,” Mak. Treaties Work Hum. Rights, Environ. Arms Control, pp. 161–178, 2007. [17] Eutat, “Definition SNAP Nomenclature,” Eustat official statistical information of the Basque Country, 2022. [Online]. Available: https://en.eustat.eus/documentos/elem_13173/definicion.html. [Accessed: 06- Jul-2022]. [18] L. Khazini, M. J. Kalajahi, Y. Rashidi, and S. M. M. M. Ghomi, “Real-world and bottom-up methodology for emission inventory development and scenario design in medium-sized cities,” J. Environ. Sci., no. xxxx, 2022. [19] L. Sartini, M. Antonelli, E. Pisoni, and P. Thunis, “From emissions to source allocation: Synergies and trade-offs between top-down and bottom-up information,” Atmos. Environ. X, vol. 7, no. July, p. 100088, 2020. [20] E. Terrenoire et al., “High-resolution air quality simulation over Europe with the chemistry transport model CHIMERE,” Geosci. Model Dev., vol. 8, no. 1, pp. 21– 42, 2015. P. Dolšak Lavrič, A. Kukec, R. Žabkar 44Sanitarno inženirstvo / International Journal of Sanitary Engineering Research Vol. 15 1/2022 REFERENCES [21] K. M. Fameli and V. D. Assimakopoulos, “The new open Flexible Emission Inventory for Greece and the Greater Athens Area (FEI-GREGAA): Account of pollutant sources and their importance from 2006 to 2012,” Atmos. Environ., vol. 137, pp. 17–37, 2016. [22] L. Pallavidino, R. Prandi, A. Bertello, E. Bracco, and F. Pavone, “Compilation of a road transport emission inventory for the Province of Turin: Advantages and key factors of a bottom–up approach,” Atmos. Pollut. Res., vol. 5, no. 4, pp. 648–655, 2014. [23] H. A. C. Denier Van Der Gon et al., “Particulate emissions from residential wood combustion in Europe - revised estimates and an evaluation,” Atmos. Chem. Phys., vol. 15, no. 11, pp. 6503–6519, 2015. [24] J. J. P. Kuenen, A. J. H. Visschedijk, M. Jozwicka, and H. A. C. Denier Van Der Gon, “TNO-MACC-II emission inventory; A multi-year (2003-2009) consistent high-resolution European emission inventory for air quality modelling,” Atmos. Chem. Phys., vol. 14, no. 20, pp. 10963–10976, 2014. [25] P. Thunis et al., “Sensitivity of air quality modelling to different emission inventories: A case study over Europe,” Atmos. Environ. X, vol. 10, no. October 2020, p. 100111, 2021. [26] M. Guevara, S. Lopez-Aparicio, C. Cuvelier, L. Tarrason, A. Clappier, and P. Thunis, “A benchmarking tool to screen and compare bottom-up and top-down atmospheric emission inventories,” Air Qual. Atmos. Heal., vol. 10, no. 5, pp. 627–642, 2017. [27] A. Elessa Etuman, I. Coll, and V. Rivera Salas, “OLYMPUS: An emission model to connect urban form, individual practices and atmospheric pollutant release,” Atmos. Environ., vol. 245, no. 118013, 2021. [28] “Volatile chemical products emerging as largest petrochemical source of urban organic emissions,” Atmos. Chem., vol. 764, no. February, pp. 760–764, 2018. [29] S. Zhu, M. Mac Kinnon, B. P. Shaffer, and G. S. Samuelsen, “An uncertainty for clean air: Air quality modeling implications of underestimating VOC emissions in urban inventories,” Atmos. Environ., vol. 211 pp. 256–267, 2019. [30] Z. Zhou et al., “Compilation of emission inventory and source profile database for volatile organic compounds : A case study for Sichuan , China,” Atmos. Pollut. Res., vol. 11, no. September, pp. 105–116, 2019. [31] C. Gao, C. Gao, K. Song, Y. Xing, and W. Chen, “Vehicle emissions inventory in high spatial – temporal resolution and emission reduction strategy in Harbin-Changchun Megalopolis,” Process Saf. Environ. Prot., vol. 138, pp. 236– 245, 2020. [32] I. Bouarar et al., “Influence of anthropogenic emission inventories on simulations of air quality in China during winter and summer 2010,” Atmos. Environ., vol. 198, no. September 2018, pp. 236–256, 2019. [33] H. Hua, S. Jiang, H. Sheng, Y. Zhang, X. Liu, and L. Zhang, “A high spatial- temporal resolution emission inventory of multi-type air pollutants for Wuxi city,” J. Clean. Prod., vol. 229, pp. 278–288, 2019. [34] P. Jiang, X. Chen, Q. Li, H. Mo, and L. Li, “High-resolution emission inventory of gaseous and particulate pollutants in Shandong Province, eastern China,” J. Clean. Prod., vol. 259, p. 120806, 2020. [35] A. K. Pathak, M. Sharma, and P. K. Nagar, “Chemosphere A framework for PM 2 . 5 constituents-based ( including PAHs ) emission inventory and source toxicity for priority controls : A case study of,” Chemosphere, vol. 255, p. 126971, 2020. [36] M. Zhu, L. Liu, S. Yin, J. Zhang, K. Wang, and R. Zhang, “County-level emission inventory for rural residential combustion and emission reduction potential by technology optimization : A case study of Henan, China,” Atmos. Environ., vol. 228, p. 117436, 2020. [37] Y. Kwon, H. Lim, Y. Lim, and H. Lee, “Implication of activity-based vessel emission to improve regional air inventory in a port area,” Atmos. Environ., vol. 203, no. June 2018, pp. 262–270, 2019. P. Dolšak Lavrič, A. Kukec, R. Žabkar 45Sanitarno inženirstvo / International Journal of Sanitary Engineering Research Vol. 15 1/2022 REFERENCES [38] A. Azhari et al., “Highly spatially resolved emission inventory of selected air pollutants in Kuala Lumpur ’ s urban environment,” Atmos. Pollut. Res., vol. In Press, 2020. [39] A. Clappier and P. Thunis, “A probabilistic approach to screen and improve emission inventories,” Atmos. Environ., vol. 242, no. July, p. 117831, 2020. [40] T. Coudon et al., “A national inventory of historical dioxin air emissions sources in France,” Atmos. Pollut. Res., vol. 10, no. 4, pp. 1211–1219, 2019. [41] B. Das, P. V Bhave, S. P. Puppala, K. Shakya, B. Maharjan, and R. M. Byanju, “A model-ready emission inventory for crop residue open burning in the context of Nepal,” Environ. Pollut., vol. 266, p. 115069, 2020. [42] E. Winijkul, F. Yan, Z. Lu, D. G. Streets, T. C. Bond, and Y. Zhao, “Size-resolved global emission inventory of primary particulate matter from energy-related combustion sources *,” vol. 107, pp. 137–147, 2015. [43] A. De Sousa, L. Hoinaski, T. Barros, and R. Castelan, “A methodology for high resolution vehicular emissions inventories in metropolitan areas : Evaluating the effect of automotive technologies improvement,” Transp. Res. Part D, vol. 77, pp. 303–319, 2019. [44] N. Huneeus et al., “Evaluation of anthropogenic air pollutant emission inventories for South America at national and city scale,” Atmos. Environ., vol. 235, p. 117606, 2020. [45] D. Majumdar, P. Purohit, A. D. Bhanarkar, P. S. Rao, and P. Rafaj, “Managing future air quality in megacities: Emission inventory and scenario analysis for the Kolkata Metropolitan City, India1,” Atmos. Environ., vol. 222, p. 117135, 2020. [46] S. Mentese et al., “A comprehensive assessment of ambient air quality in Çanakkale city : Emission inventory , air quality monitoring , source apportionment , and respiratory health indicators,” Atmos. Pollut. Res., vol. 11, pp. 2282–2296, 2020. [47] H. Shahbazi, S. Taghvaee, V. Hosseini, and H. Afshin, “Urban Climate A GIS based emission inventory development for Tehran,” UCLIM, vol. 17, pp. 216–229, 2016. [48] P. S. Enrique, B. Tomas, B. Lucas, and P. Romina, “High resolution inventory of atmospheric emissions from livestock production, agriculture, and biomass burning sectors of Argentina,” Atmos. Environ., vol. 223, p. 117248, 2020. [49] O. Ghaffarpasand, M. R. Talaie, H. Ahmadikia, A. T. Khozani, and M. D. Shalamzari, “A high-resolution spatial and temporal on-road vehicle emission inventory in an Iranian metropolitan area, Isfahan, based on detailed hourly traffic data,” Atmos. Pollut. Res., vol. 11, pp. 1598–1609, 2020. [50] Y. Shi, S. Zang, T. Matsunaga, and Y. Yamaguchi, “A multi-year and high- resolution inventory of biomass burning emissions in tropical continents from 2001–2017 based on satellite observations,” J. Clean. Prod., vol. 270, p. 122511, 2020. [51] V. V. Paunu et al., “Spatial distribution of residential wood combustion emissions in the Nordic countries: How well national inventories represent local emissions?,” Atmos. Environ., vol. 264, no. October 2020, 2021. [52] P. Jiang, X. Zhong, and L. Li, “On-road vehicle emission inventory and its spatio-temporal variations,” Environ. Pollut., vol. 267, p. 115639, 2020. [53] Y. Zhao, Y. Xia, and Y. Zhou, “Assessment of a high-resolution NO X emission inventory using satellite observations : A case study of southern Jiangsu , China,” Atmos. Environ., vol. 190, no. X, pp. 135–145, 2018. [54] M. Zhou, W. Jiang, W. Gao, B. Zhou, and X. Liao, “A high spatiotemporal resolution anthropogenic VOC emission inventory for Qingdao City in 2016 and its ozone formation potential analysis,” Process Saf. Environ. Prot., vol. 139, pp. 147– 160, 2020. P. Dolšak Lavrič, A. Kukec, R. Žabkar 46Sanitarno inženirstvo / International Journal of Sanitary Engineering Research Vol. 15 1/2022 REFERENCES [55] H. C. Frey and J. Zheng, “Quantification of Variability and Uncertainty in Air Pollutant Emission Inventories: Method and Case Study for Utility NO x Emissions Quantification of Variability and Uncertainty in Air Pollutant Emission Inventories : Method and Case Study for Utility N,” J. Air Waste Manage. Assoc., no. 52, pp. 1083–1095, 2011. [56] R. Zhang, “High-resolution ammonia emission inventories with comprehensive analysis and evaluation in Henan, China, 2006–2016,” Atmos. Environ., vol. 193, pp. 11–23, 2018. [57] A. Leclerc, S. Sala, M. Secchi, and A. Laurent, “Building national emission inventories of toxic pollutants in Europe,” Environ. Int., vol. 130, no. March, p. 104785, 2019. [58] Clarivate, “Journal Impact Factor,” Journal Citation Reports, Web of Science Group, 2021. [Online]. Available: https://clarivate.com/webofsciencegroup/solutions/journal-citation-reports/? gclid=CjwKCAjwtcCVBhA0EiwAT1fY7xpDAokCMFI1PD5UUTpJC3ZTXvEfYOtdSa0qZ AOezhytcv2hWGbW1BoCLDkQAvD_BwE. [59] K. Chen et al., “Summertime O 3 and related health risks in the north China plain : A modeling study using two anthropogenic emission inventories,” Atmos. Environ., no. October, p. 118087, 2020. [60] A. Elessa Etuman and I. Coll, “OLYMPUS v1.0: Development of an integrated air pollutant and GHG urban emissions model-methodology and calibration over greater Paris,” Geosci. Model Dev., vol. 11, no. 12, pp. 5085–5111, 2018. [61] T. L. Vaughn et al., “Temporal variability largely explains top- down/bottom-up difference in methane emission estimates from a natural gas production region,” Proc. Natl. Acad. Sci. U. S. A., vol. 115, no. 46, pp. 11712– 11717, 2018. [62] P. Thunis, B. Degraeuwe, K. Cuvelier, M. Guevara, L. Tarrason, and A. Clappier, “A novel approach to screen and compare emission inventories,” Air Qual. Atmos. Heal., no. March, pp. 325–333, 2016. [63] JRC - Joint Research Centre, “FAIRMODE - Forum for Air quality Modeling.” [Online]. Available: https://fairmode.jrc.ec.europa.eu/home/index. [Accessed: 07- Jul-2022]. [64] D. G. Streets et al., “Emissions estimation from satellite retrievals: A review of current capability,” Atmos. Environ., vol. 77, pp. 1011–1042, Oct. 2013. [65] O. E. Salmon et al., “Top-Down Estimates of NOxand CO Emissions FromWashington, D.C.-Baltimore During theWINTER Campaign,” J. Geophisical Res. Atmos., no. 10.1029/2018JD028539, pp. 7705–7724. [66] M. Hoogwijk et al., “Sectoral Emission Mitigation Potentials: Comparing Bottom-Up and Top-Down Approaches,” Toshihiko Masui. [67] P. Jiang, X. Chen, Q. Li, H. Mo, and L. Li, “High-resolution emission inventory of gaseous and particulate pollutants in Shandong Province, eastern China,” J. Clean. Prod., vol. 259, p. 120806, 2020. [68] Slovenian Environment Agency, “Slovenian Informative Inventory Report 2020,” Ljubljana, 2020. P. Dolšak Lavrič, A. Kukec, R. Žabkar