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Strict air pollution control measures were conducted during the Youth Olympic Games (YOG) period at Nanjing city and surrounding areas in August 2014. This event provides a unique chance to evaluate the effect of government control measures on regional atmospheric pollution and greenhouse gas emissions. Many previous studies have observed significant reductions of atmospheric pollution species and improvement in air quality, while no study has quantified its synergism on anthropogenic CO2 emissions, which can be co-reduced with air pollutants. To better understand to what extent these pollution control measures have reduced anthropogenic CO2 emissions, we conducted atmospheric CO2 measurements at the suburban site in Nanjing city from 1st July to 30th September 2014 and 1st August to 31st August 2015, obvious decrease in atmospheric CO2 was observed between YOG and the rest period. By coupling the a priori emission inventory with atmospheric transport model, we applied the scale factor Bayesian inversion approach to derive the posteriori CO2 emissions in YOG period and regular period. Results indicate CO2 emissions from power industry decreased by 45%, and other categories also decreased by 16% for manufacturing combusting, and 37% for non-metallic mineral production. Monthly total anthropogenic CO2 emissions were 9.8 (±3.6) × 109 kg/month CO2 for regular period and decreased to 6.2 (±1.9) × 109 kg/month during the YOG period in Nanjing city, with a 36.7% reduction. When scaling up to whole Jiangsu Province, anthropogenic CO2 emissions were 7.1 (±2.4) × 1010 kg/month CO2 for regular period and decreased to 4.4 (±1.2) × 1010 kg/month CO2 during the YOG period, yielding a 38.0% reduction.

Summary Continuous expansion of fossil fuel-based energy infrastructure can be one of the key obstacles in delivering the Paris Agreement goals. The oil refinery is the world's third-largest stationary emitter of greenhouse gases (GHGs), but the historical mapping of the regional-specific refining industry, their CO2 emission patterns, and mitigation potentials remain understudied. This study develops a plant-level, technical-specific, and time-series global refinery CO2 emission inventory, covering 1,056 refineries from 2000 to 2018. The CO2 emissions of the refinery industry were about 1.3 gigatonnes (Gt) in 2018, representing 4% of the total. If current technical specifications continue, the global refineries will cumulatively emit 16.5 Gt of CO2 during 2020–2030. The refineries vary in operation age, refining configuration structure, and geographical location, leading to the demand for specific mitigation strategies, such as improving refinery efficiency and upgrading heavy oil processing technologies, which could potentially reduce global cumulative emissions by 10% during 2020–2030.

India initiated prudent measures voluntarily in the last two decades to combat against excessive carbon emissions. Acknowledging these initiatives, the study conjoins the policy of ‘border carbon adjustment (BCA)’ by the developed countries on Indian export with the ‘domestic carbon adjustment (DCA)’ by India to evaluate its impact on emission reduction and the macroeconomy. The study also raises an inconsistency in this dual carbon adjustment under the production-based accounting (PBA) of the ‘national emission inventory (NEI)’ and conducts simulation experiments under the PBA and alternatively proposed consumption-based accounting (CBA) framework. The results reveal that the closer the rates of BCA and DCA the more effective the carbon adjustment schemes are. The dual carbon adjustment also found giving better outcome under the CBA than PBA. The result of carbon-revenue recycling schemes suggest direct compensation to the enterprises for making the economy recover from the carbon tax-distortions in the most effective way.

New challenges are emerging in fine-scale air quality modeling in China due to a lack of high-resolution emission maps. Currently, only a few emission sources have accurate geographic locations (point sources), while a large part of sources, including industrial plants, are estimated as provincial totals (area sources) and spatially disaggregated onto grid cells based on proxies; this approach is reasonable to some extent but is highly questionable at fine spatial resolutions. Here, we compile a new comprehensive point source database that includes nearly 100,000 industrial facilities in China. We couple it with the frame of Multi-resolution Emission Inventory for China (MEIC), estimate point source emissions, combine point and area sources, and finally map China’s anthropogenic emissions of 2013 at the spatial resolution of 30″×30″ (~1 km). Consequently, the percentages of point source emissions in the total emissions increase from less than 30% in the MEIC up to a maximum of 84% for SO2 in 2013. The new point source-based emission maps show the uncoupled distribution of emissions and populations in space at fine spatial scales, however, such a pattern cannot be reproduced by any spatial proxy used in the conventional emissions mapping. This new accurate high-resolution emission mapping approach reduces the modeled biases of air pollutant concentrations in the densely populated areas compared to the raw MEIC inventory, thus improving the assessment of population exposure.

Atmospheric pollutant emissions from industry and traffic are causing pressing air quality and climate issues in urban areas. This study aims to estimate the emissions from road transport and small industry in Kuala Lumpur, and to review the spatial distribution of the estimated emissions. The emission data for 2015 from small industries and road traffic for PM10, PM2.5, SO2, NO2 and NOx was collated, processed and then aggregated at a 1 km2 resolution using the Geographic Information System (GIS) and emissions inventory database software tools: ArcGIS and the Emission Inventory Toolkit (EMIT) respectively. The results from this study show that the total emissions for PM10, PM2.5, SO2, NO2 and NOx are 8234.4, 3991.7, 2589.3, 5168.8, and 56927 t/y respectively, where close to 99% of the total estimated emissions originated from road traffic sources. On average, Kuala Lumpur population emit 4.55, 2.21, 1.43, 2.86 and 31.5 kg/y/person of PM10, PM2.5, SO2, NO2 and NOx respectively which are in range with per capita emission of pollutants in other cities in the world such as Kolkata and Ho Chi Minh City. The spatial resolution of estimated emissions are mainly concentrated in the central grid cells of Kuala Lumpur and in good agreement with road pollution sources. The results show that the majority of the emissions arise from road sources, mainly because emission from other source types have been neglected and data is unavailable.

Understanding Pakistan's greenhouse gas (GHG) emission status is critical for identifying the national targets for GHG mitigation with respect to the nationally determined contributions (NDCs). This study focuses on the development of Pakistan's GHG (CO2, CH4, and N2O) inventories for agriculture, forestry and other land use (AFOLU) and waste sectors using 2006 IPCC Guidelines. This study should be seen as a direct continuation of the preceding one (Part I [ Available online at: https://doi.org/10.1016/j.accre.2020.05.002 ]) which discusses energy and industrial processes and product use in compliance with the 2006 and 1996 IPCC Guidelines. It also provides sector-specific comparative time series (1994–2017) analysis of GHG inventories, identification of key categories, and national GHG emissions trend for Pakistan. The results indicate an average relative difference (on average for time series 1994–2017) of 19% and 6% in total GHG emissions (CO2-eq) from AFOLU and waste sector respectively between 2006 and 1996 IPCC Guidelines. The absolute difference over the entire time series for AFOLU and waste sector was in the range of 3–67 Mt CO2-eq and 1–7 Mt CO2-eq respectively. Findings further reveal that the quantity of national GHG emissions by 2006 IPCC Guidelines is 10% lower on average for complete time series compared to 1996 IPCC Guidelines. The average relative difference for total national emissions of CO2, CH4 and N2O is −1%, 9%, and 48% respectively. Key category analysis based on 2006 IPCC Guidelines estimates identified three categories, each contributing ≥10% to the level assessment in the latest year 2017 and accounting for approximately half of the national GHG emissions. In order to further improve the reliability of GHG inventories, Pakistan needs to move from 1996 to 2006 IPCC Guidelines under a higher Tier approach particularly for identified key categories.

It was well recognized that the pollutant emissions from real-world residential coal combustion contributed significantly to air pollution and human health, resulting in an urgent need for a better understanding of the pollutant emission factors (EFs) of residential coal combustion. However, field-based researches on residential coal combustion emissions are still limited at this stage. In this study, based on carbon balance method, emission factors (EFs) of CO2 and a series of pollutants, including gaseous pollutants (CO, NOX, CH4, and SO2), particulate matters (PM2.5 and PM10), elemental carbon (EC), organic carbon (OC), and polycyclic aromatic hydrocarbons (PAHs) from residential honeycomb briquette (HB) and coal chunk (CC) combustions were measured in real-world cooking stoves. As a result, apart from SO2, the burning of HB produced much less pollutants than CC. The median of emission reduction ratios estimated from Monte Carlo simulation ranged from 2.4% (SO2) to 98.8% (EC) by replacing CC with HB in residential combustion in Shanxi province in 2012. EFs of CO, PM2.5 and PAHs derived from the present field measurement were around 1–50 times higher than those from laboratory tests; furthermore, larger variations were found in our field measurement compared to that in the laboratory studies. This result called for more field measurements to reduce uncertainties of emission inventory estimations. As for the PAH emissions, particle phase PAHs dominated in both HB and CC burning and PAH emissions from HB burning had lower toxicity than CC combustion. Hence, HB is an ideal substituted fuel compared with CC. Previous studies suggested that PAH isomer ratios of FLA/(FLA+PYR) and IcdP/(IcdP+BghiP) above 0.5 represented source of coal combustion, which did not fit with our study well, indicating PAH isomer ratios might not be valid enough for source apportionment.

The eastern region is an important economic hinterland and energy region of China; however, the rapid economic development and the decoupling of environmental pressure have resulted in a significant increase in volatile organic compound (VOC) emissions, and the region is facing the air pollution problem of high-concentration formaldehyde accumulation. The Aura/Ozone Monitoring Instrument (OMI) remote sensing satellite can perform long-term and large-scale dynamic monitoring of tropospheric formaldehyde, and its data can reflect the distribution of formaldehyde column concentration in the study area. This article discusses the spatial and temporal characteristics of formaldehyde in eastern China from 2009 to 2018 based on OMI's formaldehyde column concentration data daily products. In addition, based on the Multi-resolution Emission Inventory for China (MEIC), the potential contributions of natural sources (isoprene) and anthropogenic sources (industrial sources, transportation sources, and residential sources) to formaldehyde are analyzed. The results show that the formaldehyde column concentration is higher in eastern China, and the high-value areas are mainly concentrated in regions with close human activities, such as Jing-Jin-Ji (JJJ), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD), and the concentration is predicted to increase slowly in the future. One of the main sources of formaldehyde is isoprene emitted by plants, but the impact of human activities on formaldehyde cannot be ignored. The OMI data can objectively reflect the spatiotemporal distributions and change characteristics of formaldehyde in large areas and effectively characterize the accumulation of atmospheric pollutants; thus, the use of OMI can provide an important theoretical basis and reference value for the improvement of regional ecological environment and atmospheric quality.

This comprehensive study aims to examine the relationships between ambient air quality (AAQ) and respiratory health of the participants residing in three different towns of Canakkale city, Turkey between August 2013 and 2014. AAQ measurements for bioaerosols, volatile organic compounds (VOCs), total particulate matter (PM) count, carbon dioxide (CO2), and carbon monoxide (CO) were performed and monthly pulmonary function test (PFT) was applied to volunteer participants residing in the study sites (n = 121) for one year. Additionally, concentrations of air pollutants for PM2.5/PM10, sulfur dioxide (SO2), nitrogen oxides, and ozone were gathered from AAQ monitoring stations. To estimate the contributions of the emission sources and their effects on human health in the region, an emission inventory was also prepared in the region and AERMOD modelling system was applied for the year of 2013. Accordingly, the industry was the most polluting sector for NOx and SO2, while road traffic and residential heating were the most polluting sectors for CO and PM10. Factor analysis revealed that organics, combustion, bioaerosol propagation, and ozone accumulation contributed to AAQ, in agreement with AQ modelling results. Can had the worst air quality (industrialized site), which affected the respiratory health indicators of the participants. Generalized linear model estimated that PF of the participants varied according to the residing district, gender, suffering from asthma, and lifelong smoking or environmental tobacco smoke exposure (p < 0.05). Also, PF of the participants were linked with VOCs levels in the Central town, total PM count in Lapseki, and ozone levels in Can (p < 0.05).

The pollution levels, composition characteristics and sources of atmospheric PM2.5 were investigated based on field measurement at a rural site in the North China Plain (NCP) from pre-heating period to heating period in winter of 2017. The hourly average concentrations of PM2.5 frequently exceeded 150 µg/m3 and even achieved 400 µg/m3, indicating that the PM2.5 pollution was still very serious despite the implementation of stricter control measures in the rural area. Compared with the pre-heating period, the mean concentrations of organic carbon (OC), element carbon (EC) and chlorine ion (Cl-) during the heating period increased by 20.8%, 36.6% and 38.8%, accompanying with increments of their proportions in PM2.5 from 37.5%, 9.8% and 5.5% to 42.9%, 12.7% and 7.2%, respectively. The significant increase of both their concentrations and proportions during the heating period was mainly ascribed to the residential coal combustion. The proportions of sulfate, nitrate and ammonium respectively increased from 9.9%, 10.9% and 9.0% in nighttime to 13.8%, 16.2% and 11.1% in daytime, implying that the daytime photochemical reactions made remarkable contributions to the secondary inorganic aerosols. The simulation results from WRF-Chem revealed that the emission of residential coal combustion in the rural area was underestimated by the current emission inventory. Six sources identified by positive matrix factorization (PMF) based on the measurement were residential coal combustion, secondary formation of inorganic aerosols, biomass burning, vehicle emission and raising dust, contributing to atmospheric PM2.5 of 40.5%, 21.2%, 16.4%, 10.8%, 8.6% and 2.5%, respectively.

The present study is primarily aimed to establish a detailed exhaustive emission inventory of urban motor vehicles in a medium-sized city, i.e. Isfahan. The International Vehicle Emission (IVE) model was utilized to estimate the motor vehicle Emission Factors (EFs) in different areas of Isfahan. IVE was adjusted using the results obtained by on-board real-world EF measurements. A high resolution 1 km × 1 km vehicle emission inventory for the year 2018 was then developed in a bottom-up approach. The method was employed based on the influencing factors such as the collected hourly-data on road network, traffic flow, vehicle driving cycles, and the vehicle population in Isfahan. Moreover, the variations of EFs with vehicle speed were investigated for different vehicle categories to assess the emission rates through Isfahan transportation fleet. The results showed that EFs of all pollutants emitted from vehicle exhausts usually decrease with vehicle speed. Also, gasoline vehicles have larger CO and VOCs EFs than the others, whereas vehicles fueled by diesel have the largest NOx, SOx, and PM10 EFs. The established emission inventory revealed that the annual amount of CO, VOCs, NOx, SOx, and PM10 emitted from Isfahan on-road mobile sources in 2018 are 267.6, 12.6, 20.3, 0.3, and 2.3 kt, respectively. The gasoline vehicles including cars and motorcycles were the main sources of CO, VOCs, and SOx, whereas the heavy-duty vehicles and buses equipped with diesel engines were the main sources of NOx and PM10. The hourly variations of EFs ensure an acceptable consistency with the hourly variation of the traffic flow at different areas. Moreover, the high-resolution spatial distribution of vehicle emission inventory represented that emissions were concentrated on the downtown, esp. on underdeveloped historical area of the city, where the most of the governmental and administrative departments are concentrated.

Low- and middle-income countries (LMICs) often lack the necessary tools, guidance, and capacity for compiling an emission inventory (EI) for air pollutants. A reliable EI is an important prerequisite for the identification of key emissions sources, as an input to modelling atmospheric transport and impacts of air pollutants, and the identification of appropriate mitigation policies. The publicly-available Global Atmospheric Pollution Forum Emission Inventory (GAPF-EI) tool meets the need of LMICs for a user-friendly tool allowing in-country practitioners to compile their own EIs. The species covered are SO2, NOX, CO, NMVOC, CH4, NH3, PM10, PM 2.5, black carbon, organic carbon and CO2. Output from the tool can therefore support the development of integrated air quality and climate change mitigation strategies. This tool incorporates default emission factors and inventory methods conforming with internationally recognised approaches. The GAPF-EI tool enables emissions to be estimated for technologies or practices that are often of little or no relevance to developed countries, but may represent key sources in LMICs. This paper describes the GAPF-EI tool and its application to Cote d’Ivoire where emissions from traditional biomass cookstoves, vegetation fires, traditional charcoal manufacture, road transport (including dust from unpaved roads) and open burning of municipal solid waste were found to be particularly important components of the inventory. The application of the GAPF-EI approach to Cote d’Ivoire has demonstrated its utility in addressing sources of particular relevance to LMICs in addition to providing a user-friendly, transparent and flexible EI preparation tool.

Accurate and gridded emission inventories are crucial for better air quality modeling and air pollution policy making. As the economic center of the Shandong province and part of the first batch of open coastal cities in China, Qingdao has been frequently plagued by volatile organic compound (VOC) pollution along with its rapid economic development and urbanization in recent years. In this study, a high spatiotemporal resolution VOC emission inventory for Qingdao was established for the year 2016 using updated source-specific emission factors and the latest activity data for different sources. The anthropogenic sources considered in this study are classified into seven major sources and 46 subcategories. The results indicated that a total of 151.5 kt of VOCs were emitted in Qingdao in 2016. The three largest emission sources, industrial process, on-road mobile, and solvent use sources accounted for 49.9% 20.1%, and 14.1% of the total VOC emissions, respectively. Spatially, the Huangdao District (57.9 kt), the Chengyang District (15.4 kt), and Pingdu City (15.1 kt) were the top three emitters of VOCs. The high concentration of VOCs was mainly distributed in the central areas of Qingdao city and the emissions distribution was highly consistent with its road network. The maximum monthly VOC emissions occurred in June (14.4 kt) and the minimum monthly VOC emissions occurred mainly in February (9.5 kt). The uncertainties in the emission inventory were quantified using a Monte Carlo simulation provided by the Oracle Crystal Ball software. There exist relatively high uncertainties in the on-road mobile (-84.79%, 252.07%), biomass burning (-65.38%, 134.77%) and industrial process (-36.08%, 133.62%) sources. For the complicated species and different reaction rates of ozone formation, the control of the amount of VOCs emitted and the highly reactive VOCs are equally important. Based on the emissions of individual VOC species and the corresponding maximum incremental reactivity (MIR), the ozone formation potential (OFP) profiles of Qingdao City were constructed. The results indicated that alkenes/alkynes and aromatics contributed the most to the OFP and that m/p-xylene, ethylene, propylene, formaldehyde, toluene, trans-2-butene, cis-2-butene, 1-butene, o-xylene, and 1-pentene were the top 10 individual VOC species contributing to the OFP. The contribution of industrial process and on-road mobile sources accounted for 37.5% and 24.5% of the total OFP in Qingdao City, respectively. Both the large VOC emission and OFP indicate that the focus of VOC emission control should be on industrial process and on-road mobile sources in Qingdao City.

Harbin-Changchun Megalopolis (HCM), as one of the vehicle production centers in China, has rapidly increasing vehicles on road supported by people’s increasing purchasing power, having directly and indirectly led to air pollution. This study systematically analyzes tempo-spatial characteristics of vehicle emissions by combining International Vehicle Emissions (IVE) model with the Technical guidelines for road motor vehicle emission inventory of air pollutants (Guideline) in HCM. This research further analyzes emissions from various emissions sources and projected emissions based on scenarios. The results show that the CO and HC emissions from mini passenger cars (MiniPC) and ordinary motorcycles using gasoline account for 86.4 % and 82.3 % of total emissions. Diesel-fueled heavy-duty trucks (HDT) are the main sources of NOX and PM, accounting for 86.5 % and 89.7 % of the total emissions, respectively. Three emission reduction scenarios are developed to analyze the vehicle emissions in HCM in 2020. This study concluded that phasing out the old vehicles is an effective strategy to mitigate air pollutions in HCM, with reduction rates of pollutants CO, HC, NOX, PM2.5 and PM10 being 19.8 %, 19.6 %, 8.6 %, 18.3 % and 18.8 %, respectively.

On-road traffic emissions of Greater Yangon in 2015 were estimated using IVE model. Local surveys revealed the engine technology distributions, driving activities and flows for the fleets of bus, personal car (PC), pickup (PU), taxi, van and light duty truck. Vehicles in Greater Yangon were relatively new but recent use of leaded gasoline caused deactivation of catalytic converters that bring about high emission factors (EFs) of CO, VOC and NOx from gasoline-powered vehicles. Pre-Euro diesel-powered buses and trucks were high PM emitters while pre-Euro and Euro1 gasoline vehicles were high emitters of CO and VOC. Composite EFs of CO and VOC were the highest for PU (45.1 and 3.2 g/km, respectively) followed by taxi (34.2 and 2.6 g/km, respectively). CNG vehicles with their small number and low EFs contributed the least to total on-road traffic emissions. The bus fleet, despite being diesel-powered at only 28%, still had the highest EFs of PM and NOx, 1.6 and 2.7 g/km, respectively. Total emissions for 2015 (base case) in Greater Yangon from six surveyed fleets and the motorcycle fleet of CO; VOC; NOx; PM; BC; OC; CO2; CH4 and air toxics, in Gg/year, were 358; 41; 24; 3.8; 1.6; 0.9; 5358, 6.8 and 3.1, respectively. If all vehicles in this domain at least comply with Euro3 standard, the collective emission reduction from the base case of the air pollutants would be 71% while that of GWP (GHGs and short-lived climate pollutants) 43%, hence showing significant potential co-benefits.

The proportion of steel produced using electric arc furnaces (EAFs) is increasing. In this study, polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) concentrations and patterns in stack gas samples from three typical EAFs in steelmaking plants were determined to allow the contributions of EAFs in steelmaking plants to PCDD/Fs emissions in China to be assessed. Samples collection was accomplished by adopting the automatic isokinetic sampling technique, then the PCDD/Fs congeners concentrations were analyzed qualitatively and quantitatively by isotope dilution high-resolution gas chromatography high-resolution mass spectrometry (HRGC-HRMS). The mean PCDD/Fs mass concentrations in stack gases emitted during the scrap preheating phase of plants E1, E2, and E3 were 0.48, 385.55, and 0.34 ng Nm−3, respectively, and the total toxic equivalent (WHO2005-TEQ) concentrations were 0.05, 33.16, and 0.03 ng Nm−3, respectively. The PCDD/Fs mass concentration in stack gases emitted during the melting phase at plant E3 was 0.12 ng Nm−3 (0.02 ng WHO2005-TEQ Nm−3). The operational temperature of the bag filter of the preheating in the E2 EAF was as high as 230 °C, which is conducive to the generation of PCDD/Fs, whether the temperature in the bag filter of E1 (150 °C) and E3 (120 °C) are not. Moreover, the bag filter temperature is suggested to be best controlled to below 150 °C. The emission factors (EF) for PCDD/Fs released into the air during the scrap preheating phase for plants E1, E2, and E3 were 0.13, 52, and 0.10 μg WHO2005-TEQ t−1, respectively, and the EF for the smelting phase from plant E3 was 0.14 μg WHO2005-TEQ t−1. The emission factor in E2 is 5 times higher than the highest current UNEP toolkit factor for EAFs (10 μg TEQ/tonne steel). Therefore, an update for the UNEP toolkit emission factor for a few case plants is suggested. Several congeners, including 2,3,7,8-TeCDF, 1,2,3,4,6,7,8-HpCDF, and 1,2,3,4,6,7,8-HpCDD, were dominant during the preheating phase, while 2,3,7,8-TeCDF, 1,2,3,7,8-PeCDF, and 2,3,4,7,8-PeCDF were dominant during the smelting phase. The monitoring data from the case plants helps to better understand the PCDD/Fs emissions of EAFs using preheating process stages. However, a more complete emission inventory requires more case investigations on the PCDD/Fs emissions from representative EAFs steelmaking plants.

This study aims at estimating the carbon emission from biomass carbon stock change in tropical forests of Sarawak, Malaysia, within the years 2005–2014 as a consequence of wood harvesting. Emission inventory has been developed in the study, based on Tier 1 methodology of 2006 IPCC (Intergovernmental Panel on Climate Change). To the best of our knowledge, this paper is among the first papers to utilize 2006 IPCC Guidelines for computing change in biomass carbon stocks. From the results obtained, it was found that overall carbon emission was a net gain for Sarawak. During the period of 2005–2014 net gain in biomass carbon stocks was observed with values ranging from 28.76 Mt C or 105.44 CO2 equivalent to 39.03 Mt C or 143.11 CO2 equivalent. Whereas emission from wood removal has started to decrease since 2008 from 6.28 Mt C to 2013 at 4.64 Mt C due to lesser wood removed resulting from global economic crisis. Rapid changes in areas of tropical forests was and varying evaluation method were identified to be the major problem. High uncertainty of data currently in existence for this tropic region highlights the need for additional field investigations in the effort of evaluating future biomass carbon stock. Keywords: Biomass carbon stock, Gain-loss method, Forest remain forest, Tier 1, Wood removal

The purpose of this paper is to develop an emission inventory and source profile database for volatile organic compounds (VOCs) in Sichuan Province. Based on the test data of the research literatures, the source profiles of species containing no oxygenated volatile organic compounds (OVOCs) were revised and reconstructed to obtain the normalized VOCs source profile. In addition, the source profile-based 1 km × 1 km speciated VOCs emission inventory was established in accordance with the 2015 Air Pollution Source Emission Inventory in Sichuan Province with the ozone formation potential (OFP) estimated to assess the impact on ozone formation. There were 45 profiles and 519 species included in the established VOCs source profile database. Since the profiles were established mainly based on domestic test data, foreign research results were employed to supplement the profiles of some sources that had not been actually tested or had been researched less in China. In addition, the profiles of some sources such as biomass burning and transportation with emissions containing rich OVOCS have been revised and reconstructed, and the profiles including redundant data of many tested sources were simplified accordingly as well. Therefore, the established source profile database is well applicable to the establishment of the speciated VOCs emission inventory and the source apportionment. The VOCs emission inventory showed that the anthropogenic emissions of alkanes, alkenes, alkynes, aromatics, OVOCs, halohydrocarbons and other VOCs in Sichuan Province were 167.1 kt, 77.4 kt, 13.2 kt, 216.7 kt, 202.7 kt, 32.5 kt and 64.2 kt respectively; the total OFP in Sichuan was 2584.9 kt, and the above-mentioned VOCs contributed 6.9%, 26.1%, 0.5%, 42.3%, 23.2%, 0.4% and 0.5% to the total OFP, respectively. Therefore, alkenes, aromatics and OVOCs were the major species for ozone formation among the VOCs emitted in Sichuan Province, and controlling the emissions of such species plays a key role in reducing ozone formation. The major VOCs species emitted in various cities of Sichuan Province were aromatics, OVOCs and alkanes; however, different cities have their own remarkable characteristics. VOCs emissions and major OFP contributing species in Sichuan Province were mainly distributed in the Sichuan Basin as well as some areas in Liangshan and Panzhihua, where the population was relatively dense and the industry was relatively developed. The main source of m-Xylene and toluene was solvent use; therefore, m-Xylene and toluene were more intensively distributed in urban built-up areas. Furthermore, since biomass burning contributed a lot to ethene and formaldehyde emissions, ethene and formaldehyde were substantially distributed in cultivated areas in eastern Sichuan and southern Sichuan.

Chongqing, a metropolitan with over 32 million residents in southwest China, has suffered from SO2 pollution since 1980s. The emission inventory is an important tool to evaluate the SO2 pollution and to design the effective emission reduction policies. The present work developed a scheme to update the obsolescent SO2 emission inventory in Chongqing obtained from Multi-resolution Emission Inventory for China in 2008 (MEIC2008). The updated emission inventory was estimated by integrating the a priori knowledge of the baseline emissions and the current observations based on Bayesian inference, in which the source-receptor sensitivities were calculated by the Decoupled Direct Method in Three Dimensions in the Community Multiscale Air Quality Modeling System (CMAQ DDM-3D). An analytical solution of the Bayesian theorem was derived based on the linear response assumption and applied to estimate the actual SO2 emissions. The updated emission inventory was comparable with the most recent MEIC emission inventory in 2016 and 2017, and was in line with the decline trend of SO2 emissions in Chongqing in the last decade. The adjustment of the emissions improved the accuracy in predicting SO2 concentrations with the developed method., Atmospheric Environment, 294, ISSN:1352-2310, ISSN:0004-6981, ISSN:1352-3260

Central Plains region of China, represented by Henan Province, is facing serious air pollution problems. Vehicular exhaust emissions had adverse impacts on the atmospheric environment. The first comprehensive and novel vehicle emission inventory for Henan Province using vehicle kilometers traveled, localized emission factors, and activity data at city-level was developed. Furthermore, 3 km × 3 km gridded emission and temporal variations were determined by using localized information. Results show that the total emissions of sulfur dioxide (SO2), nitrogen oxides (NOx), carbon monoxide (CO), particular matter with aerodynamic diameter

Primary microplastics are mostly produced as part of the daily plastic product use. The emission process is often invisible but poses potential ecological hazards. Thus, primary microplastics deserve public attention. Due to China's huge population base and its rapid economic development, primary microplastics emissions are of both regional and global significance. This study is the first to establish the emission inventory of primary microplastics in mainland China. It was estimated that the primary microplastic waste from mainland China amounts to 737.29 Gg, and one-sixth of this amount entered the aquatic environment in 2015. The highest proportion of this waste was attributable to tire dust and synthetic fiber, accounting for 53.91% and 28.77% of the total respectively, in mainland China. The primary microplastics emissions mainly depend on the population, followed by the level of economic development. It was roughly estimated that 538 g of microplastics is produced by each person in China. At the grid scale, the spatial difference in the total primary microplastics emissions in mainland China primarily depends on the population density distribution and transportation network. We studied the entire life cycle of several sources of microplastics, from production to discharge into the aquatic environment. We suggested different control measures under different nodes. Increasing microplastics treatment in sewage treatment plants should be a short-term viable way to achieve some measure of reduction in their entry to the environment in mainland China. Our research can not only raise public awareness about primary microplastics, but can also guide the development of environmental policies to reduce plastic pollution.

With the implementation of the ultra-low emission policy in China, emission factors (EFs) of power plant pollutants are constantly changing. Emission inventories developed using the recommended EFs contain high levels of uncertainty and it is difficult to achieve a high temporal resolution. Detailed sulfur dioxide (SO 2 ), nitrogen oxides (NOx), and particulate matter (PM) emission data based on a continuous emission monitoring system (CEMS) were obtained from 33 units at 13 power plants in Shanghai in 2017. The data were used to develop an hourly unit-based emission inventory and to devise updated EFs for coal-fired power plants. Emissions of SO 2 , NOx, and PM typically met the ultra-low emission limit, with total emissions of SO 2 , NOx, and PM of 2895.0, 5348.3, and 503.8 tons, respectively. Emission proportions of SO 2 and NOx for 300–600, 600–1000, and above 1000 MW units were similar, while the emission proportion of PM decreased with an increase in unit capacity. Emissions of SO 2 , NOx, and PM displayed similar monthly variations, peaking in winter and summer. Diurnal hourly variations of SO 2 , NOx, and PM emissions displayed a bimodal trend, with higher emissions at night on weekends than on weekdays. EFs based on CEMS (EF C ) of SO 2 , NOx, and PM were 0.10, 0.36, and 0.04 g kg −1 of coal, respectively, which were one or two orders of magnitude lower than the widely-used EFs and 4–30 times lower than EFs based on the mass balance approach. After replacing the recommended fixed decontamination efficiencies with individually fitted values, the calculated EFs were consistent with the corresponding EF C and discrepancies were further reduced. The new inventory and updated EFs will enable a better understanding of the temporal variations of power plant emissions and reduce the uncertainty caused by the overestimation of EFs after the implementation of ultra-low emissions technology.

With rapid economic growth and urbanization, the Yangtze River Delta (YRD) region in China has experienced serious air pollution challenges. In this study, we analyzed the air pollution characteristics and their relationship with emissions and meteorology in the YRD region during 2014-2016. In recent years, the concentrations of all air pollutants, except O3, decreased. Spatially, the PM2.5, PM10, SO2, and CO concentrations were higher in the northern YRD region, and NO2 and O3 were higher in the central YRD region. Based on the number of non-attainment days (i.e., days with air quality index greater than 100), PM2.5 was the largest contributor to air pollution in the YRD region, followed by O3, PM10, and NO2. However, particulate matter pollution has declined gradually, while O3 pollution worsened. Meteorological conditions mainly influenced day-to-day variations in pollutant concentrations. PM2.5 concentration was inversely related to wind speed, while O3 concentration was positively correlated with temperature and negatively correlated with relative humidity. The air quality improvement in recent years was mainly attributed to emission reductions. During 2014-2016, PM2.5, PM10, SO2, NOx, CO, NH3, and volatile organic compound (VOC) emissions in the YRD region were reduced by 26.3%, 29.2%, 32.4%, 8.1%, 15.9%, 4.5%, and 0.3%, respectively. Regional transport also contributed to the air pollution. During regional haze periods, pollutants from North China and East China aggravated the pollution in the YRD region. Our findings suggest that emission reduction and regional joint prevention and control helped to improve the air quality in the YRD region.

China's room air conditioning (RAC) sector is now undergoing a substitution of HCFC-22 (CHClF2) by HFC-410A (a blend of HFC-125 (C2HF5) and HFC-32 (CH2F2)) as refrigerant. However, HFC-410A is also one of the strong greenhouse gases (GHGs) and will be regulated under the Kigali Amendment to the Montreal Protocol. In this study, an updated HFC-410A emission inventory for China's RAC sector is established for the period of 2006–2017 with a projection to 2050 based on a bottom-up method. Our estimation indicates that the HFC-410A emissions increased from 0.0092 Gg yr−1 to 12.0 Gg yr−1 with an annual increase of 115.7%/yr during 2006–2017. More than 95% of the emissions were from end-of-life process. Under a business-as-usual (BAU) scenario, projected emissions will continuously grow to 89.2 (54.8–126.0) Gg yr−1 in 2050. However, under a mitigation scenario with implementation of the Kigali Amendment, emissions will peak at 63.8 Gg yr−1 in 2039 and then decline to 43.1 (26.1–62.0) Gg yr−1 in 2050, and about 373 Gg of cumulative emission could be curbed during 2018–2050. Additionally, we find that the Beijing-Tianjin-Hebei region, the Yangzi River Delta, the Pearl River Delta and provincial capital cities show greater HFC-410A emission intensities per unit of area than those of the rest of China during 2009–2017.

To evaluate the potential benefits of biomass use for air pollution control, this paper identified and quantified the emissions of major reactive organic compounds anticipated from biomass-fired industrial boilers. Wood pellets (WP) and straw pellets (SP) were burned to determine the volatile organic compound emission profiles for each biomass-boiler combination. More than 100 types of volatile organic compounds (VOCs) were measured from the two biomass boilers. The measured VOC species included alkanes, alkenes and acetylenes, aromatics, halocarbons and carbonyls. A single coal-fired boiler (CB) was also studied to provide a basis for comparison. Biomass boiler 1 (BB1) emitted relatively high proportions of alkanes (28.9%–38.1% by mass) and alkenes and acetylenes (23.4%–40.8%), while biomass boiler 2 (BB2) emitted relatively high proportions of aromatics (27.9%–29.2%) and oxygenated VOCs (33.0%–44.8%). The total VOC (TVOC) emission factors from BB1 (128.59–146.16 mg/kg) were higher than those from BB2 (41.26–85.29 mg/kg). The total ozone formation potential (OFP) ranged from 6.26 to 81.75 mg/m3 with an average of 33.66 mg/m3 for the two biomass boilers. The total secondary organic aerosol potential (SOAP) ranged from 61.56 to 211.67 mg/m3 with an average of 142.27 mg/m3 for the two biomass boilers. The emission factors (EFs) of TVOCs from biomass boilers in this study were similar to those for industrial coal-fired boilers with the same thermal power. These data can supplement existing VOC emission factors for biomass combustion and thus enrich the VOC emission inventory.

Crop residue burning is an important source of air pollutants and strongly affects the regional air quality and global climate change. This study presents a detailed emission inventory of major air pollutants from crop residue burning for the year of 2014 in China by the bottom-up method. Activity data were investigated for 296 prefecture-level cities. Emission factors were determined for indoor and in-field burning separately. Regional differences were considered for the proportion of residue burned (PCRB), the ratio between indoor and in-field burning, and the ratio of straw to grain production. The emissions were estimated at prefecture-city level as the first step; then they were redistributed within a city based on 1-km resolution land-use, MODIS fire counts, and rural population. Temporal variation was determined according to farming practices in different regions and MODIS fire counts. Uncertainties were estimated using the Monte Carlo method. The total emissions from crop residue burning in China were estimated to be 0.13 (−47–92%) for BC, 0.71 (−48–92%) for OC, 1.77 (−48–91%) for PM2.5, 2.04 (−50–100%) for PM10, 0.16 (−59–133%) for SO2, 0.53 (−55–105%) for NOX, 0.12 (−47–93%) for NH3, 1.07 (−55–102%) for CH4, 1.85 (−43–74%) for NMVOC, 18.33 (−46–85%) for CO and 305.20 (−45–80%) for CO2, in unit of Tg yr−1. Our results are remarkably lower than those reported in previous studies, mainly because of the PCRB has decreased significantly in recent years. For most of the pollutants, indoor burning accounted for about 50–70% of the emissions. Rice, wheat and corn contributed more than 85% of the emissions, but their relative contributions varied a lot with region and season. High emissions were mostly located in the eastern China, central China and northeastern China, and temporally peaked in April, June and October, with different intensities in the north and the south. This study provides a useful basis for air quality modeling and the policy making of pollution control strategies.

Black carbon (BC) emissions in China have been changing significantly due to rapid evolution of fuel consumption. In this study, BC emissions from fossil fuel consumption in different sectors were estimated in 2015 based on up-to-date activity data and emission factors (EFs). While for the future scenarios in 2020 and 2030, it was estimated according to relative changes in the activity level and EFs. In 2015, total BC emissions were estimated to be 1.48 Tg, mostly from the industrial, residential, and transportation sectors. Emission fluxes were found to have remarkable spatial features, where high fluxes generally located in eastern China. About 25% of the terrestrial area of China showed an annual flux above 0.2 t km−2. Total BC emissions will be decreased to 1.33 (2020) and 1.16 Tg (2030). Most reduction will occur in the industrial and residential sectors, while the transportation sector will see an obvious increase. The dramatic reduction of BC emissions in future is a new indication to our current understanding of the emission and its effects as well, and it may provide guidance for future scientific research and policy making in the field of climate change and air quality control.

The emission characteristics of VOCs in the rubber footwear industry (RFI) and its effect on human health are poorly understood to date. Herein, up to 68 VOCs, sorted into seven classes including alkanes, alkenes, acetylene, aromatics, halocarbons, carbon disulfide, and oxygenated VOCs, were monitored. VOCs emitted from three main processing stages of RFI, including shaping, painting and vulcanizing, were 383, 1507 and 1026 mg/m3, respectively. The top 10 VOCs contributing to the concentration and ozone formation potential were identified. Generally, alkanes were the major component emitted from three stages, contributing 48.58%–63.07% of the total VOCs. Alkenes contributed most to the OFP, accounting for 37.2%–69.1%. Based on the risk assessment, a definite cancer risk for workers in shaping workshop should be noticed. Several VOCs with a life carcinogenic risk higher than 10−4, especially benzene, bromodichloromethane, ethylbenzene and 1,1,2-trichloroethane, should be focused on. Therefore, more attention should be taken for the extended-ranges of VOCs in subordinate RFI, except for the publicly concerned aromatics in rubber industry. A VOCs emission inventory from the production process of Chinese RFI in 2000–2016 was compiled. It is estimated that Chinese RFIs have emitted a total of 319 × 104 t VOCs in those past 17 years.

Particulate matter is the main air pollutant in China, especially in Xi'an in recent years. Since 2013, the WRF-SMOKE-CMAQ model system has been used to build an air quality model system for daily air quality forecasting in Xi'an. The emission inventory was built based on several anthropogenic emission inventories and open access emission datasets, and the model evaluation is presented to verify the emission inventory for particulate matter in Xi'an. Comparing the daily observed and simulated fine particulate (PM2.5) concentrations for four winters in different years (from 2014 to 2017), the model performs well in all studied time periods. The correlation coefficient of the simulated daily PM2.5 concentration data are all larger than 0.58, reaches 0.80 in 2016, and the fraction of predictions within a factor of two of observations (FAC2) are all above 66%. The differences of simulated results based on emission-unchanged system between 2014 and 2015 indicate that the slightly deteriorating air quality of 2015 is affected by the unfavorable air diffusion condition. The PM10 concentration increases from 95.9 μg/m3 to 110.3 μg/m3, and the PM2.5 from 82.4 μg/m3 to 95.4 μg/m3. According to the error analysis in model performance, the serious polluted situation of 2016 is mostly because of the sharp increased dust emissions. The emission-unchanged simulated particulate matter concentrations have little variation from 2015 to 2016, but the observation data increase obviously, that results in dramatically change of Mean Bias (MB). The absolute MB of PM10 increase from 70.3 μg/m3 to 135.2 μg/m3, and PM2.5 from 0.46 μg/m3 to 69.9 μg/m3. While the improved air quality in 2017 is attributed to both the better weather condition and the emission-reductions. The emission-unchanged simulated results decrease, and the absolute MB even have bigger decrease, that of the PM10 concentration reduce by 47μg/m3, and PM2.5 by 37μg/m3.

It is important to understand the emission characteristics of PM and its precursors for the iron and steel industry in high resolution, considering its significant contribution to the total national emissions and the pressing need for more scientific and specific strategies of air pollution control in China. We developed a unit-based emission inventory of SO2, NOx, PM2.5, PM10 and TSP for the Chinese iron and steel industry from 2010 to 2015 by integrating source-specific unabated emission factors with unit-based information of both the activity rate and abatement technology from ~4900 production facilities in mainland China. This is the first study that accomplished a unit-based and source-specific emission inventory of SO2, NOx, PM2.5, PM10 and TSP updated to 2015 for the Chinese iron and steel industry. Based on our estimates, despite the rise of 26.1% in crude steel production from 2010 to 2015, the annual emissions of SO2, PM2.5, PM10, TSP were reduced by 27.9%, 27.4%, 36.1% and 50.4% respectively, and the NOx had increased by 12.4% for the Chinese iron and steel industry. The emissions of SO2 and PM from sintering, pelletizing, BF, BOF, and EAF all significantly decreased. Hebei, Jiangsu, Shandong, Liaoning, Shanxi, Henan, and Hubei are hot spots for the air pollutant emissions of the iron and steel industry. Our results suggest that the strengthened capacity policy and emission control policy were two major human driving forces of the emission mitigation of SO2 and PM for the Chinese iron and steel industry, with the latter playing a more important role during the period of 2010-2015.

Volatile organic compounds (VOCs) pollution, which is closely linked to photochemical smog and secondary organic aerosols, has become a severe concern in China. Therefore, we compiled a new high-resolution emission inventory for the industrial non-methane Volatile organic compounds (NMVOCs) using “bottom-up” approaches throughout 2010 and 2016. In this work, the industrial sources were divided into five major categories, and 108 specific sources, as well as an emission factor database, was developed for industrial NMVOCs. Results indicated that the total NMVOCs emissions from industrial sources increased from 16.88 Tg in 2010 to 21.04 Tg in 2016 at an annual average rate of 3.7%. The five major source categories including “production of VOCs”, “storage and transportation”, “industrial processes using VOCs as raw material”, “processes using VOCs-containing products”, and “fossil fuel combustion” generated 1.92 Tg, 0.94 Tg, 6.54 Tg, 10.04 Tg, and 1.60 Tg NMVOCs, respectively, in 2016. Coke production, plastic manufacturing, raw medicine industry, and architectural decoration were the primary sources of industrial NMVOCs and emissions of these sources increased by 140 Gg, 190 Gg, 640 Gg, and 700 Gg between 2010 and 2016. The emissions displayed distinct spatial characteristics, with significantly higher emissions in the Beijing-Tianjin-Hebei region, the Pearl River Delta, the Yangtze River Delta, and the Cheng-Yu region than in other areas. Shandong, Guangdong, Jiangsu, Zhejiang, and Henan were the top five provinces with the highest NMVOCs emissions, while the emission hotspots in the county-level were mainly distributed in Guangzhou urban area, Shanghai Pudong New Area, Hangzhou urban area, and Shenzhen urban area. The emissions in Henan province, Hubei province, and Cheng-Yu region increased significantly during the study period. Instead, emissions in some counties of Zhejiang province and Hebei province decreased than in 2010.

Currently, atmospheric emission inventory that includes multi-type pollutants and sources is still limited at the city level. This study developed a high-resolution emission inventory of eight pollutants based on detailed investigated data for Wuxi city in 2015. Results show that the total emissions of sulfur dioxide (SO2), nitrogen oxides (NOx), total suspended particulates (TSP), particulate matter with an aerodynamic diameter of 10 μm or less (PM10), particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5), carbon monoxide (CO), ammonia (NH3), and volatile organic compounds (VOCs) were 36.9 kt, 95.2 kt, 287.3 kt, 127.8 kt, 46.0 kt, 626.4 kt, 10.9 kt, and 104.9 kt, respectively. Power plants were the major contributors of SO2 and NOx, accounting for 34.6% and 36.3% of the total emissions, respectively. Road dust was the dominant source of particulate matter, contributing 63.1%, 57.8% and 38.2% to the total TSP, PM10 and PM2.5 emissions, respectively. Around 67.0% of CO emissions were from iron and steel plants, while 50.1% of NH3 emissions were from livestock and poultry sectors. VOCs emissions largely came from oil refinery and chemical industry, with a share of 41.3%. High NH3 emissions were found in the southwest and northeast of Wuxi where high-intensity agriculture was located, while other pollutants were mainly concentrated on the eastern Wuxi where heavy industries, crowded traffic and highly dense population were located. Most pollutants from the industrial source exhibited the lowest value in February due to the Chinese Spring Festival with the exception of CO and NH3. This study helps to formulate the targeted air pollution control measures for Wuxi city by providing detailed information regarding the spatial-temporal distribution of multi-type air pollutants.

Estimates of China’s agricultural nitrous oxide (N2O) emission inventory are mainly based on data for intensively managed croplands in the monsoon zone. Observations of N2O fluxes and direct emission factors (EFds) remain limited in the non-monsoon zone in China. In this study, year-round measurements of yields, meteorological and soil environmental factors, and N2O fluxes were conducted in highland barley (HB, with the application of synthetic fertilizers) and oilseed rape (OR, with the mixed application of air-dried sheep manure and synthetic fertilizers) cropping systems with typical extensive management in the Qinghai–Tibetan Plateau. The cumulative N2O emissions during the freeze-thaw period (0.10 and 0.31 kg N ha−1) and annual scale (0.35 and 0.70 kg N ha−1 yr−1 in the HB and OR fields, respectively) were in the low range of globally reported freeze-thaw emissions from seasonally frozen croplands. The annual EFds were 0.19% ± 0.11% and 0.04% ± 0.14% in the HB and OR fields, respectively, which were much lower than the national average and IPCC default values. The extensive management (low fertilization rates, aboveground plant nitrogen removal, and rain-fed agriculture) and alpine climate led to low emissions and EFds in the croplands of the Qinghai–Tibetan Plateau. Although the annual N2O emissions and EFds were relatively low, extensive management still needs to be optimized to maintain the sustainability of agricultural production on the plateau because of the extremely low nitrogen use efficiencies (crop recovery efficiency of applied nitrogen: 0.2 kg N kg−1 N) and negative nitrogen surpluses (–58.7 and –8.0 kg N ha−1 yr−1 in the HB and OR fields, respectively). To reduce the uncertainty in the estimates of global and national emission inventories, more studies should be conducted in the non-monsoon zone owing to the peculiarity of management strategy and climate, and the dramatic expansion of cropland area.

The composition and size of the passenger car fleet is often influenced by legislation and policy, including taxation, scrappage, and vehicle registration policies. Numerous measures at national and international level have been investigated to reduce the air pollution and/or climate change impacts of passenger fleets. However, the objectives of climate change and air pollution policy occasionally are conflicting, and this has resulted in a number of well publicised policy shortcomings. This paper outlines the development of a fleet emissions control (FEC) framework which enables the dynamic and adaptive optimisation of fleet emissions through changes in taxation policy in Ireland. The data for analysis was obtained from national sources including datasets from the national emission inventory and COPERT model. Applications of regression-based modelling and statistical analysis were conducted to predict fleet, mileage, and emissions changes. The results of the FEC framework showed that no increase in NOx emissions, above 2007 levels, could be achieved at a cost of a small increase in CO2 in 2016 (+1.8%). This could be achieved by taxation policy primarily resulting in a shift away from small engine diesel vehicles (

Over recent years, crop residue burning has become a widely practiced agricultural activity in developing countries due to varying economic and social reasons. To estimate current pollution generation from crop residue burning in Pakistan, an emission inventory was developed based on district level crop production data from 2000 to 2014. Spatial distribution of quantified emissions was achieved by using MODIS Active Fire Data (MOD/MYD14A1) at 1-day temporal and 1 × km spatial resolutions. The fire occurrences were extracted over agricultural land using land cover data provided by European Space Agency/Climate Change Initiative (ESA/CCI) at 300 m spatial resolution for 2000 to 2014. Results show that CO emissions have increased by 40% at an average annual rate of 2.7% from 1.16 Tg in 2000 to 1.63 Tg in 2014 and increase in emissions of CO2, CH4, NMVOCS, N2O, NH3, SO2, NOx, PM2.5, PM10, OC and BC ranged between 37%, to 63% during same period. Spatially, CO emissions were more significant in districts of Punjab and Sindh provinces with intensive agricultural activities as compared to other areas of Pakistan with highest emission grids located in Muzaffargarh, Jafferabad, and Noushahro Feroze districts. Crop residue burning exhibited similar trend with highest emissions in June followed by February and November over 15-year period. The emissions for most of the considered pollutants were comparable to the limited previous studies available. Finally, the study conducted provides better understanding regarding spatial and temporal patterns of crop residue burning across Pakistan and can be utilized for air quality modeling and management with higher accuracy.

Carbon monoxide (CO) is regarded as a useful tracer of biomass burning and anthropogenic pollution, so CO measurements can provide valuable information about the intensity of various anthropogenic activities. However, the emission estimates of CO based on inventories are associated with high uncertainties, especially in China. As CO is co-emitted with CO2 in the combustion of carbonaceous fuels, the relationship between CO and CO2 is often used to estimate regional CO emissions. Hefei is located in the area of eastern central China, which is one of the most industrialized regions in China, with severe regional air pollution. The enhancement slopes of ∆CO to ∆CO2 were calculated and compared from ground-based remote sensing observations, surface in-situ measurements, satellite and emission inventory data at the Hefei site during the period from September 2015 to August 2017. Both inventory based ratios of ΔCO to ΔCO2 are significantly larger than the ratios based on the observation data, including Fourier Transform Spectrometer (FTS) data, in-situ data, and satellite data. Further the CO emissions in the central China were estimated from the enhancement slopes of ∆CO/∆CO2 combined with the CO2 emission inventory. The CO emission estimated from the ground-based FTS observations and the Peking University (PKU) inventory based CO2 emission is about 10.96 ± 0.88 and 11.95 ± 0.71 Tg CO yr−1 during the 2015–2016 and the 2016–2017 period, respectively. The CO emission estimated from the ground-based FTS observations and the Emission Database for Global Atmospheric Research (EDGAR) inventory based CO2 emission is about 11.27 ± 0.91 and 12.35 ± 0.74 Tg CO yr−1, respectively. So the CO emissions estimated from the ground-based FTS data and the different inventory based CO2 emission show a good agreement. However, CO emissions derived from FTS data are substantially lower than those calculated directly from the inventories, i.e. there is a large difference between CO emissions derived from FTS and CO emissions directly derived from the two inventories. The phenomenon suggests that the emission inventories greatly overestimate the actual CO emission in the study area. This study estimates the regional CO emissions from ground-based remote sensing observations and investigates how much the difference is between the emissions from inventories and ground-based measurements.

China contributes approximately 10% of the total global methane (CH4) emissions from its livestock sector. However, existing inventories of CH4 emissions from the livestock sector involve significant uncertainty, and the reduction potentials and spatially varying efficiencies of different technical options (adding lipid to diets, acidification, composting, anaerobic digestion, and the combination of composting and anaerobic digestion) have not been systematically assessed at the provincial level. Here, we used a bottom-up approach to compile an up-to-date high-resolution CH4 emission inventory for the livestock sector in China using provincial condition-specific emission factors by considering the gross energy intake and the ambient temperature at the provincial level. A 1-km × 1-km gridded map was produced for 2014, and the temporal trends of the CH4 emissions from 1978 to 2013 were re-visited. The effects of the technical mitigation options that could have been used on CH4 emissions were further evaluated in five different scenarios. Livestock sector CH4 emissions decreased by 1.2 Tg CH4 per year from the period of 1999–2006 to the period of 2007–2014, and this rate was approximately 17–28% lower than previous estimates. During the period of 1978–2014, spatial-temporal emission trends indicated a possible relationship between the Chinese government policies and emissions to some extent and a northward shift of emission hotspots induced by economic and policy incentives. Hotspots with high mitigation potential and efficiency (the achieved reduction under a specific mitigation scenario divided by the baseline emissions from the species affected by the respective technical option) were also identified. The combination of composting and anaerobic digestion is a recommended policy, which can simultaneously address slurry and solid manure and significantly mitigated of CH4 emissions. Overall, this study provides insights into the region-dependent implementation of technical options used to reduce CH4 emissions from the livestock sector in China.

Atmospheric toxic metals (TMs) may cause adverse effects on the environment and human health due to their bioavailability and toxicity. High-resolution TMs emission inventory is important input data for assessing human exposure risks, especially synergistic toxicity of multiple toxic metals. By using the latest city- and enterprise-level environment statistical data, an emission inventory of five TMs (Hg, As, Pb, Cd, Cr) in Guangdong province for the year of 2014 was developed using a bottom-up approach. The total emissions of Hg, As, Pb, Cd and Cr in Guangdong were estimated as 17.70, 32.59, 411.34, 13.13, and 84.16 t, respectively. Major emission sources for each TM were different. Hg emissions were dominated by coal combustion (33%), fluorescent lamp (18%) and cement (17%). 78% of Hg emissions were in the form of Hg0, 19% of Hg2+, and only 3% of Hgp due to strict particulate matter control policies. Coal combustion (48%), nonferrous metal smelting (25%) and iron and steel industry (24%) were the major sources of As. Pb emissions primarily came from battery production (42%), iron and steel industry (21%) and gasoline combustion (17%). Cd and Cr emissions were dominated by nonferrous metal smelting (71%) and iron and steel industry (82%), respectively. Most of these TMs were emitted in the non-Pearl River Delta region, where the newly-built iron and steel industry, nonferrous metal smelting and cement production factories were intense. The uncertainties in the five TM emissions were high, due much to high uncertainties in TM emission factors and limited activity data. Thus, to improve the accuracy of these estimates, we recommend more field tests of TM emissions, especially for the industrial process sector. This study provides scientific support for formulating robust TMs control policies to alleviate the high risk of TMs exposure in Guangdong.

Currently, haze events in winter occur more frequently than decades ago, especially in Eastern and Central China, including the Yangtze River Delta (YRD). WRF-Chem is applied in this study to explore the discrepancies of the simulated air pollutants induced by employing different emission inventories, particularly during haze events. Two inventories are involved in this study, MEIC (Multi-resolution Emission Inventory for China) and GlobEmission (inventory from GlobEmission project emission estimates), representing the emission inventories based on quite different ways. We first compared monthly emissions of SO2 and NOx in two inventories during January over YRD, and found the mean differences of SO2 (NOx) are 20% (7%), with the ranges of 0%–47% (−100%–100%). The largest differences are both found in Shanghai, with 47% for SO2 (MEIC in 2010, GlobEmission in 2014), and 45% for NOx (MEIC in 2010, GlobEmission in 2015), respectively, partly because the reduction of emissions was large during the 12th Five-Year Plan (2011–2015) in this area. By comparing the simulated air pollutants mass concentrations using two inventories with in-situ observations during January 2015, we found that the simulated SO2 using MEIC and GlobEmission are both higher than observations, with mean normalized biases of 207% and 121% over YRD, respectively, and much larger in the city cluster of Nanjing-Shanghai, where are nearly four (MEIC) and three times (GlobEmission) higher than observations. In contrast, NO2 simulations using GlobEmission are lower than observations (22%) and MEIC simulations (45%) over YRD. The largest biases of GlobEmission simulation are found in Zhejiang province (over 70%). The biases of simulated monthly mean PM2.5 are 38% (MEIC) and 30% (GlobEmission) over YRD, respectively. A case study during heavy haze event shows that the biases increase to 61% (MEIC) and 39% (GlobEmission), and spatial correlation coefficient in the simulation using GlobEmission increase to 0.69 over YRD. Temporal correlation coefficients in the city cluster of Nanjing-Shanghai increase from 0.5 (MEIC) to 0.7 (GlobEmission).

Haze is one of the most serious environmental problems in China, of which Beijing has been suffered from frequent haze events for years. As the major contributor of haze, PM2.5 emission needs more investigation, especially its driving forces that is crucial for mitigation and pollution control. In this study, the main contributors of PM2.5 emissions in Beijing were identified. By using the emission inventory and environmentally extended input–output framework, a structural decomposition model was employed to figure out the key drivers of the primary PM2.5 emissions in Beijing. The results showed that measures to control air pollution (e.g., the Beijing Clean Air Action Plan (2013–2017)) have caused PM2.5 emissions to decrease in recent years, indicating that emission mitigation strategies in Beijing are having preliminary success. Among all emission sources, due to large amounts of coke consumption, direct emission from residential energy consumption constitutes the largest proportion (31.05%–44.48%). Driven by fast urbanization and economic growth, high contribution of capital was invested to construction activities, which makes capital formation the largest driver of PM2.5 emissions from consumption perspective. PM2.5 emissions embodied in imports and exports also make a large proportion of consumption-based emission. Consumption volume and population growth are the main socioeconomic drivers to PM2.5 emissions increase, while emission intensity and consumption patterns offset the increase of emissions. The results will be useful developing PM2.5 emission mitigation measures and policies for Beijing and similar cities around the world.

The establishment of the air pollutant emission inventory of urban motor vehicles is the key to control motor vehicle pollution; therefore, the purpose of this paper is to develop an air pollutant emission inventory of urban motor vehicles in Chengdu. The IVE model localization including the actual measurement correction was completed, the motor vehicle emission factors on various types of roads in Chengdu's different development areas were calculated, and the 1 km × 1 km on-road mobile source emission inventory for the year of 2016 was established in the bottom-up method based on the collected data on road network, traffic flow, vehicle driving cycle, the vehicle population in Chengdu, etc. The inventory showed that the CO, VOCs, NOx, SO2, PM10 and NH3 emissions of Chengdu's on-road mobile sources had reached 432.2 kt, 44.8 kt, 72.4 kt, 0.4 kt, 11.3 kt and 6.2 kt respectively in 2016. Light-duty vehicle, medium-duty vehicle, and heavy-duty vehicle were the major contributors to CO emissions, and their CO emissions accounted for 44%, 14% and 11% of the total CO emission respectively; VOCs mainly came from light-duty vehicle, motorcycle, and heavy-duty truck, the VOCs emissions of which accounted for 49%, 19% and 10% of the total VOCs emission respectively; NOx and SO2 mainly came from light-duty vehicle and heavy-duty truck, the NOx emissions of which accounted for 25% and 48% of the total NOx emission, and the SO2 emissions of which accounted for 70% and 14% of the total SO2 emission respectively; in addition, the heavy-duty truck caused PM10 emission accounting for 78% of the total PM10 emission, while the light-duty vehicle caused NH3 emission accounting for 81% of the total NH3 emission. The spatial distribution of on-road mobile source pollutant emissions showed that emissions were concentrated in the downtown with a decreasing trend from the downtown to the suburbs and the outer suburbs. Based on the 95% confidence intervals, the uncertainties of CO, VOCs and NOx emission estimations were relatively low with relative errors ranging from - 68% to 87%, 58%–84% and 97%–75%. Considering the uncertainties of the traffic flows and emission factors, the on-road mobile source emission inventory shall be further improved to provide important scientific research supporting for the prevention and control of air pollution in Chengdu.

Hg emission inventory for the coal combustion sector in China during the year 2015 was established according to the most recent emission activity data and emission factors from national official statistics and scientific research. Our results show that Hg emission from coal combustion in China in 2015 reached 264.49 tons, and the emission growth rate during the past 10 years began to slow down due to the extensive use of efficient air pollution control devices and the enhanced proportion of cleaned coal in large power plants. Therein, industry was still the biggest emitter, and the Hg emissions of industry, the thermal power sector, the residential sector and other uses were 154.59, 70.40, 15.67 and 23.83 tons respectively. The five largest Hg-emitting provinces were Shandong, Inner Mongolia, Hebei, Shanxi and Jiangsu, due to their great coal consumption. And five provinces with the biggest per capita Hg emission were Inner Mongolia, Ningxia, Shanxi, Guizhou and Hebei. The monthly variation was analyzed according to the sector characteristic, and results indicate that the industrial sector's emission peak appeared in summer; the thermal power sector had two emission peaks, in January and August; Hg emission from residential and other uses exhibited a weak U shape, and a high-emission-intensity region was mainly concentrated in the east of China year-round.

An emission inventory of carbonyl sulfide (COS) from primary anthropogenic sources in China was compiled from 2010 to 2015. The national total emission was estimated at 174 Gg S yr−1 in 2015, with an annually average sustainable growth rate of 7.2% from 2010 to 2015. Industrial sources, biomass burning, coal combustion, agricultural sources and vehicle exhaust contributed 68.2%, 22.0%, 6.1%, 2.2%, and 1.5%, respectively, of total COS emissions in 2015. Aluminum production, carbon black production, titania production, coke production, pulp and paper industry, were the main industrial sources of COS, with the emission about 118 Gg S yr−1 COS in 2015, accounting for 98.5% of total direct emissions from the Chinese industry. The distribution of COS emissions in China showed significant differences at the provincial level. In Shandong and Xinjiang province, they were much bigger than other provinces, with total emissions of 31 and 15 Gg S yr−1, respectively. Uncertainty analysis of COS emission inventories showed that the emission range was 70–267 Gg S yr−1 at a 95% confidence interval. In this study, COS emissions in China were much higher than prior underestimated estimations, even for Asian and global emissions, which could be resolved part of missing sources in the global COS budget.

The Great Lakes Region of the US continues experiencing exceedances of the ozone (O3) standards, despite years of emissions controls. In part, this is due to interactions between emissions from surrounding large cities (e.g., Chicago) and meteorology, which is heavily influenced by the presence of the Great Lakes. These complex meteorology-emissions interactions pose a challenge to fully capture O3 dynamics, particularly near shores of the lakes, where high O3 levels are often experienced. In a simulation with the Community Multiscale Air Quality (CMAQ) model, using inputs as typically constructed, the model tends to be biased high. A literature review indicated that NOx emissions from mobile sources, possibly overestimated in the 2011 National Emission Inventory (NEI), or the version of the Carbon Bond chemical mechanism used in CMAQ could be responsible for high biases of O3. As such, a series of sensitivity tests was conducted to identify potential causes for this bias, including emissions biases (e.g., biogenic VOCs, anthropogenic NOx), chemical mechanism choice, and O3 dry deposition to fresh water, for high O3 periods in July 2011. The base model/emissions configuration used the following: Carbon Bond mechanism CB05, biogenic emissions using Biogenic Emission Inventory System (BEIS), and anthropogenic emissions from the 2011 National Emissions Inventory (NEI). Meteorological inputs were developed using the Weather Research and Forecasting (WRF) model (version 3.8.1). Simulated daily maximum 8-h average O3 without and with a cutoff of 60 ppb (referred to as MDA8 O3 and elevated MDA8 O3 hereafter, respectively) were evaluated against measurements. The evaluation showed a high bias in MDA8 O3 across the domain, particularly at coastal sites (by ∼6 ppb), while elevated MDA8 O3 (i.e., greater than 60 ppb) was biased low, with exceptions centered along the shore of Lake Michigan. Using the CB6 chemical mechanism or 50% reduction of NOx emissions from on-road mobile sources led to substantial domain-wide decreases in O3 from the base case, and the model performance improved, particularly along the Lake Michigan shoreline and for the western domain. However, elevated MDA8 O3 was more biased against measurements, compared to the model performance in the base case, except at a few sites along the shoreline. Using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) instead of BEIS to estimate biogenic emissions, or increasing dry deposition of O3 to fresh water by a factor of ten (which is unrealistic), had minor impacts on simulated O3 over land. But, combining MEGAN with CB6 resulted in improved elevated MDA8 O3 simulation along the western coast of Lake Michigan. Finally, using CB6 combined with a 30% reduction of on-road mobile NOx emissions and MEGAN led to the best performance. Two companion papers investigate how meteorological modeling can be improved. Together, the recommended modeling system could serve as a starting point for future O3 modeling in the region.

In this paper, we build a preliminary inventory of dissolved phase water emissions of 36 of the 45 chemical priority substances under the European Union's Water Framework Directive. For point sources, we consider the European Pollutant Release and Transfer Register (E-PRTR) containing reported emissions from major industrial facilities. We consider all other sources as diffuse, and we estimate European average chemical emission factors from available measurements of dissolved phase concentrations, assuming simple emission patterns such as population and agricultural land. The emission inventory enables modelling concentrations, which have been compared with independent measurements. Due to the way they are estimated, they cannot withstand a point-by-point comparison. However, predicted concentrations exhibit a frequency distribution and order of magnitude compatible with observations, and match a fair proportion of independently reported exceedances of environmental quality standards for many of the substances studied. While apparently a preliminary picture based on crude simplifications, our representation suggests that simple drivers such as population and agriculture are useful to describe chemical pollution at European scale. From our preliminary inventory, E-PRTR industrial point emissions seem to account for a relatively small share of total emissions. Consequently, apart from specific measures such as upgrades to urban wastewater treatment plants in certain high impact areas, the management of priority substances may require a more strategic approach to emission control, addressing chemical use across sectors and the management of out-phased, legacy chemicals. At the same time, we advocate that improving emission inventories requires monitoring data reflecting the variability of emission patterns across Europe, as presently available monitoring data do not enable a catchment-specific estimation of emissions. © 2019 The Authors, This study was partly funded by, and developed in the context of Project Globaqua within the EU Seventh Framework Programme for research, technological development and demonstration under grant agreement No 603629 . Emission factors presented in this paper were estimated by Rafael Marcé within the scope of a service contract of ICRA for the JRC (ref. 392653) funded under the Danube Water Nexus cluster of activities of the JRC in support to the EU Strategy for the Danube region. The IPChem platform is developed and maintained at the European Commission's DG JRC under the responsibility of S. Kephalopoulos. We wish to particularly thank European Commission colleagues Silvia Dalla Costa for her generous and effective support with IPChem data, and Helen Clayton for her advice on an early version of this paper. Appendix A

India is currently the second-largest emitter of black carbon (BC) in the world, with emissions projected to rise steadily in the coming decades. In view of the large variations associated with BC emission inventories in this region, model outputs of BC mass and radiative forcing (RF) need to be validated against long-term regionally-representative atmospheric measurements. Such measurements are highly scattered spatially as well as temporally in India, and a systematic evaluation of BC data is non-existent so far. To address this issue, we present here a comprehensive review of BC measurements in India from a survey of >140 studies spanning 2002–2018. In addition to summarizing baseline BC levels in urban, semi-urban, rural and remote locations, we report impacts of anomalous environmental and/or emission conditions, e.g., truck/general strikes, firework events, fog/haze episodes, large-scale biomass burning events, etc. We also present a discussion on major BC sources and climate impacts (in terms of direct RF) in major land-use categories, mitigation strategies currently employed on a national scale, and recent advances in measuring brown carbon (BrC) in India. We identify key areas for improvement, such as – i) the need for long-term BC monitoring networks, especially in regions where estimated emissions are high but measurement coverage is low; ii) the general lack of understanding, despite some recent reports, of BC aerosol mixing states, aging and direct climate effects in the Indian context; iii) the need to shift from qualitative approaches of BC source apportionment to robust quantitative measures; and iv) the prospects for coupled chemical-optical characterization of BrC for a better understanding of its sources and climate effects. We list potential research directions for the scientific community to address these knowledge gaps. We also believe that this review will be beneficial to policymakers for prioritizing BC mitigation efforts.

Industrialization and urbanization in China have resulted in a substantial increase in carbon emissions. Spatial and temporal analyses of the carbon emission inventory have become very necessary to policymaking and management. This study corrected inconsistency and uncontinuity of nighttime light (NTL) data based on stable light value pixels, and performed a distribute model of CO2 emissions in Guangdong province during the period of 2005–2013. The model results clearly showed the spatial and temporal variations of CO2 emissions during 2005–2013 in Guangdong, with the Pearl River Delta region contributing the highest CO2 emissions. These results are relevant for understanding the spatiotemporal CO2 emission dynamics at a county level and establishing policies for carbon emission mitigation. This research highlights the importance of spatial spillovers, suggesting that future policies need to encourage inter-regional resource sharing, promote cooperation to ensure energy conservation and emission reductions, and optimize the distribution of urban areas.

As one of the energy-intensive and high-polluted industries, steel industry is facing great challenges of energy-saving and emission reduction. In this study, a bottom-up approach is used to build the inventory of air pollutants in China's steel industry. We analyzed the temporal and spatial characteristics of the atmospheric emission inventory of China's steel industry. The results show that: (1) In 2015, the total amount of SO2, NOx, PM, PM2.5, PM10 and PCDD/Fs emissions from Chinese steel industry were 1697.58kt, 1017.24kt, 4028.01kt, 849.02kt, 1330.53kt and 1216.83g I-TEQ, respectively. SO2, NOx, PM, PM10 and PCDD/Fs emissions mainly came from the sintering process, and PM2.5 came from the converter process. (2) NOx emissions show an “upward” trend, while SO2 emissions depict a “slowdown” trend. PM, PM2.5, PM10 and PCDD/Fs emissions present inverse U-shape trends. (3) The air pollutant inventory of Chinese steel industry indicates obviously spatial characteristics. Taking SO2 emissions in 2015 as an example, SO2 emissions from steel industry in North China and East China reached 1006.63kt, accounting for 65.2% of the total emissions. Hebei, Jiangsu and Shandong provinces where locate intensive traditional steel enterprises with majority of steel products generated SO2 emissions of approximately 404.04kt, 171.83kt and 156.28kt respectively, accounting for 23.80%, 10.12% and 9.20% total emissions of China's steel industry. (4) The simulation model of air pollutants (SO2, PM) was established taking adjusted production capacity and layout of steel industry into consideration, the simulation suggests that SO2 and PM emissions are projected to decrease by 1Mt, 2 Mt, respectively.

This paper has aimed to evaluate the air pollution emissions from maritime transportation arrive in/depart from Port of Incheon using two different approaches; fuel-based (Tier 1) and activity-based (Tier 3) methodologies. So far, Tier 1 or 2 level of estimation approaches, based on the fuel consumption provided by national fuel supply statistics, have been reasonably supported for environmentalists and policy makers at national level of emission inventory analysis, even for the global analysis. When considering the local air quality such as in a port area, however, the fuel based approach would not be properly estimating the shipping air emission sector due to the characteristic of maritime transportation. In this study, the fuel-based air emission inventory provided by Clean Air Policy Support System (CAPSS), South Korea was compared and analyzed with the activity-based emission estimation produced by Portal Air Quality Management System (PAQman©). As a result, there discovered a significant gap in the air pollution emissions between fuel-based and activity-based estimations. The findings in this study implies the necessity and significance of local/regional level focused implementation for air quality improvement and regulatory framework especially in a port-city region e.g. Incheon. Although this paper still contains some limitations and assumption described, the results are confident enough to emphasize the application of the activity-based shipping emission estimation approach to discover the potential limitation, which has been unknown and not listed on the national air emission inventory using fuel-based approach.

High concentrations of PM2.5 have become a serious environmental issue in South Korea, which ranked 1st or 2nd among OECD countries in terms of population exposure to PM2.5. Quantitative understanding of PM2.5 source attribution is thus crucial for developing efficient air quality mitigation strategies. Here we use a suite of extensive observations of PM2.5 and its precursors concentrations during the international KORea-US cooperative Air Quality field study in Korea (KORUS-AQ) in May–June 2016 to investigate source contributions to PM2.5 in South Korea under various meteorological conditions. For the quantitative analysis, we updated a 3-D chemical transport model, GEOS-Chem, and its adjoint with the latest regional emission inventory and other recent findings. The updated model is evaluated by comparing against observed daily PM2.5 and its component concentrations from six ground sites (Bangnyung, Bulkwang, Olympic park, Gwangju, Ulsan, and Jeju). Overall, simulated concentrations of daily PM2.5 and its components are in a good agreement with observations over the peninsula. We conduct an adjoint sensitivity analysis for simulated surface level PM2.5 concentrations at five ground sites (except for Bangnyung because of its small population) under four different meteorological conditions: dynamic weather, stagnant, extreme pollution, and blocking periods. Source contributions by regions vary greatly depending on synoptic meteorological conditions. Chinese contribution accounts for almost 68% of PM2.5 in surface air in South Korea during the extreme pollution period of the campaign, whereas an enhanced contribution from domestic sources (57%) occurs for the blocking period. Results from our sensitivity analysis suggest that the reduction of domestic anthropogenic NH3 emissions could be most effective in reducing population exposure to PM2.5 in South Korea (effectiveness = 14%) followed by anthropogenic SO2 emissions from Shandong region (effectiveness = 11%), domestic anthropogenic NOx emissions (effectiveness = 10%), anthropogenic NH3 emissions from Shandong region (effectiveness = 8%), anthropogenic NOx emissions from Shandong region (effectiveness = 7%), domestic anthropogenic OC emissions (effectiveness = 7%), and domestic anthropogenic BC emissions (effectiveness = 5%).