In China, the intensive supervision mechanism (ISM) has been implemented to combat air pollution, yet its effectiveness remains under debate. This study systematically evaluated the effectiveness of the ISM using a multisource data set integrating the CHAP data set, meteorological factors, and ISM data, with the iron and steel industry as a case study. The results indicated that the number of issues identified and the number of enterprises involved from 2018 to 2024 exhibited spatiotemporal heterogeneity. The environmental issues identified by ISM data across different processes were grouped into three main categories: automatic monitoring of pollution sources and data management, pollutant emission control and compliance, and fugitive dust and particulate matter control. Linear regression model revealed that ISM contributed to air quality improvements, influenced by baseline pollutant concentrations, meteorological factors, and the implementation intensity of the ISM. Provincial-level spatial autocorrelation indicated positive clustering between ISM implementation intensity and reductions in pollutant concentrations, particularly in Hebei, Henan, Shanxi, and Shandong Provinces. Enterprise-level evaluation revealed that even in regions with overall strong performance, significant heterogeneity existed among individual enterprises, highlighting the need for refined enterprise-level assessment and differentiated management. These findings underscored ISM's tangible effectiveness and provided empirical support for its long-term optimization.

Tracking emission changes throughout the Corona Virus Disease 2019 (COVID-19) pandemic is critical for revealing the impact of the pandemic on pollutant and carbon emissions; yet detailed assessments across this period remain limited. In this study, we developed integrative emission inventories for China in 2020 and 2023, and combined them with the inventory of 2018 to analyze national emissions of CO2 and nine air pollutants before, during, and after the COVID-19 pandemic. Emissions were mainly concentrated in economically industrialized and developed provinces like Hebei, Shandong and Jiangsu, with higher emission intensities in energy-dependent provinces. 2020 saw notable declines in SO2 (-18.1 %), VOCs (-32.6 %), and CO (-8.8 %) emissions compared to 2018 levels due to pandemic lockdowns and emission reduction policies, while CO2 (11.6 %), NOx (16.8 %), and PM10 (21.9 %) increased owing to expanded power generation and metal production. As economic activities rebounded in 2023, emissions of CO2 (16.6 %) and nine air pollutants (3.2 %-15.1 %) increased compared with those in 2020, although adjustments in the energy structure helped reduce residential emissions of certain pollutants like PM and CO. Regionally, CO2 emissions in the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), and Fenwei Plain (FWP) regions continued to rise, while VOCs declined during the pandemic but rebounded afterward. The FWP region, dominated by heavy industry and fossil energy production, showed persistent PM2.5 growth, facing greater challenges for emission reduction. These findings reveal the regional and sectoral emission dynamics throughout the COVID-19 pandemic, supporting integrated air pollutants and carbon management policies in China.

The heavy industry sector is a major source of gaseous pollutants, including heavy metals (HMs), which disperse beyond enterprise boundaries and contaminate surrounding soils. However, soil heavy metal source tracing and multi-factor quantification of cross-media transport remain insufficient. To address these research gaps, an integrated model for heavy metal source tracing in soil beyond enterprise boundaries and multi-factor quantification was developed, with a case study of a typical mega iron and steel enterprise in Tangshan, Hebei Province, China. The results indicated that the CALPUFF model can effectively capture the cross-media transport of heavy metals and identify the spatial distribution patterns of heavy metal hotspots. Moreover, distance from pollution sources, soil physicochemical properties, and atmospheric deposition had different influences on HMs because of the variability in the properties of different HMs, with atmospheric deposition identified as the primary factor affecting As and Ni. The integrated ecological risk index (with an average RI value of 87.12) reflected a low cumulative ecological risk, with Cd, Tl, and Pb identified as the priority contributors over the other heavy metals. The probabilistic health risk assessment revealed that Cr was the primary contributor to carcinogenic risk, accounting for 69.80 % of the risk in adult males, 69.40 % of that in adult females, and 64.10 % of that in children, whereas Tl and Cr were the two major elements responsible for non-carcinogenic risk. This study provides a scientific basis for characterizing the source-sink dynamics of heavy metals and quantifying their cross-media transport under multiple influencing factors, thereby supporting the development of more effective pollution control and environmental management strategies in heavy industries, particularly the steel sector.

Emission Inventory serves as a valuable tool for effectively characterizing pollution profiles and greenhouse gas emissions in the study area, offering a robust scientific foundation for developing pollutant prevention and control measures and collaborative emission reduction strategies. Amid global efforts to promote green and low-carbon development, the iron and steel industry, one of the key industrial sectors, has drawn increasing attention for its CO2 and air pollutant emissions. The low-carbon transformation of this industry has become an inevitable trend. As low-carbon innovative technologies continue to develop and be implemented, emission inventories for the iron and steel industry are progressing toward greater precision and comprehensiveness. The emergence of new technologies, such as electric arc furnace steelmaking, hydrogen metallurgy, and carbon capture, utilization, and storage, offers innovative solutions for reducing carbon emissions while simultaneously increasing demands for more accurate emission inventories. Traditionally, emission inventories have relied on generalized emission factors for estimating emissions. However, the advancement of low-carbon technologies introduces greater diversity in emission sources and variations in emission characteristics, necessitating the development of more refined emission inventories. Therefore, research on air pollutants and CO2 emission inventories in the iron and steel industry is crucial. Drawing on extensive domestic and international literature, this study summarizes and organizes the development of emission inventories in this sector across four key dimensions: (1) accounting methods for air pollutants and greenhouse gases, with greenhouse gas accounting primarily focusing on CO2; (2) emissions at global, intercontinental, national, urban, and urban agglomeration scales; spatial distribution of inventories; (3) uncertainty analysis, along with identified limitations and (4) future prospects for improvements. This study highlights several critical issues in China’s iron and steel industry, including challenges related to the diversity and quality of emission inventory data, the construction of an unorganized emission accounting system, deficiencies in CO2 accounting systems and methodologies, challenges in characterizing component spectra of volatile organic compounds (VOCs) and particulate matter profiles for key processes, obstacles in developing detailed time series for steel enterprises using continuous emission monitoring system (CEMS), and concerns in the practical application of emission inventories. Future efforts should prioritize diversifying the types of emission inventory data in the steel industry and enhancing the collection and analysis of measured data to improve the accuracy of foundational data. In addition, establishing a comprehensive, unorganized emission accounting system and optimizing the CO2 accounting framework and methods are essential. Incorporating real-time CEMS monitoring data and integrating composition profiles of VOCs and particulate matter characteristics from key processes can remarkably enhance the resolution of emission inventories. Furthermore, expanding the applications of emission inventories, fostering interdisciplinary and international collaborations, and promoting green and low-carbon development in the iron and steel industry are vital for achieving the synergistic control of global air pollutants and greenhouse gas emissions.

Cangzhou is an important city in the Beijing-Tianjin-Hebei (BTH) region for assessing air quality. Based on the data from the Continuous Emissions Monitoring System (CEMS) and other sources, in this study, we used a combination of bottom-up and top-down approaches to establish an emission inventory of nine atmospheric pollutants in Cangzhou City in 2018. The results showed that the emission of nitrogen oxides (NOx), sulfur dioxide (SO2), particulate matter with aerodynamic diameters less than 10 μm and 2.5 μm (PM10 and PM2.5), volatile organic compounds (VOCs), carbon monoxide (CO), ammonia (NH3), black carbon (BC), and organic carbon (OC) were 20.29, 82.67, 116.41, 54.87, 702.81, 87.66, 62.00, 4.15, and 3.83 kt, respectively. Most of the emitted pollutants were concentrated in the eastern and southern areas of Cangzhou, but NH3 was emitted the most in the eastern region. In this study, we revealed the emission characteristics of air pollutants and put forward the air pollution control suggestions, which can not only provide a scientific basis for pollution control efforts in the city and similar industrial structure of the city but also offer valuable insights and references for environmental governance across the BTH region. Analyzing emission characteristics and mitigation measures in Cangzhou enhanced understanding of pollution status and management needs in the BTH region, thereby supporting regional coordinated governance with data-backed policy recommendations.

China's iron and steel industry has achieved notable progress in reducing pollution following the implementation of ultralow emission (ULE) standards. However, the extent of pollutant reduction, compliance with ULE standards, and influencing factors, particularly during the prepandemic and midpandemic periods, remain insufficiently explored. In this study, high-resolution, nationwide, process-based, and facility-level emission inventories for China's iron and steel industry for 2018-2020 were developed on the basis of real-time measurements from Continuous Emission Monitoring Systems (CEMS). The emission inventories covered air pollutant emissions from 996 iron and steel enterprises, accounting for 87.98 to 96.43% of China's crude steel production between 2018 and 2020, and encompassed key production processes, including coking, sintering, pelletizing, blast furnaces, basic oxygen furnaces, electric arc furnaces, and steel rolling. Compared to 2018, emissions of SO2, NO x , and PM from the iron and steel industry in 2020 decreased by 32.56, 47.02, and 9.05%, respectively, despite a 14.74% increase in production. Furthermore, average hourly concentration compliance rates for SO2, NO x , and PM at the sintering machine head, sintering machine tail, and pellet roasting showed significant improvement in 2020. Structural equation modeling identified product output, emission factors, ULE standards, and emission intensity as the key determinants of pollutant emissions. This study highlighted the necessity of strengthening the implementation of ULE standards in regions and fostering regional cooperation for emission reduction. This study identified substantial reductions in SO2, NO x , and PM emissions from China's iron and steel sector after the introduction of ULE standards during the pre- and midpandemic periods and revealed the underlying drivers using the structural equation model.

Volatile organic compounds (VOCs) are considered as important precursors of ozone in the air, while the contribution of VOCs from pesticide application (PVOCs) to ozone production is unknown. Utilizing data from the Ministry of Agriculture and Rural Affairs of the People's Republic of China and ChinaCropPhen1km, this paper developed PVOC emission inventories with a resolution of 1 km for the main crops (rice, maize, and wheat) from 2012 to 2019 in China. The results revealed that pesticide application is an important VOC emission source in China. Specially, the PVOC emissions from the major grain-producing regions in June accounted for approximately 30% of the annual total PVOC emissions in the local regions. The simulation with the Weather Research and Forecasting Community Multiscale Air Quality model (WRF-CMAQ) indicated that the PVOC emissions increased the mean maximum daily 8-hour average (MDA8) ozone concentration across China by 2.5 ppb in June 2019. During the same period, PVOCs in the parts of North China Plain contributed 10% of the ozone formation. Under the comprehensive emission reduction scenario, it is anticipated that by 2025, the joint implementation of measures including reducing pesticide application, improving pesticide utilization efficiency and promoting solvent substitution will decrease PVOC emissions by 60% compared with 2019, thereby mitigating ozone pollution.

The widespread application of pesticides in China has led to the accumulation of residues in soil. However, few regional studies have fully elucidated the characteristics of pesticide residues in soil (PRS) and the associated risks to the ecosystem and human health on a national level. Therefore, this study aims to compile a dataset on PRS in China from 2006 to 2020 and analyze the interactions and impacts between PRS and the environment. The average concentration of PRS in China was 243.96 μg/kg which was lower than the levels reported in Euro-Americans and other nations. This study revealed PRS in China predominantly originates from organochlorine pesticide residues, with DDTs and HCHs being significant contributors. Despite the high intensity of pesticide application in the Southeast China, PRS concentrations were comparable to those in the Northeast, due to environmental factors that favor pesticide degradation in the Southeast. Both legacy and in-use pesticides were transported by surface runoff or air current, resulting in their accumulation in soil of the lower Yangtze River basin or the piedmont soil of Qinling Mountains, respectively. The average soil environment carrying capacity of PRS in China was -69.5 kg. The ecological risk contributed by PRS in China was mainly at a negligible level. Carcinogenic risks of PRS to adults (4.6 ×10-4) and children (6 ×10-4) exceeded the tolerable thresholds (10-5) by a small margin.

The emissions from the civil aviation sector are a significant source of CO2 and air pollutants, which represent a serious threat to ambient air quality and public health. To gain a deeper understanding of civil aviation airport emissions, it is imperative to develop a precise and comprehensive emission inventory of China's civil aviation airports. However, there are limited studies dedicated to analyzing and verifying the accuracy and completeness of China's civil aviation emission inventory. Here, this study explored pollution characteristics from temporal trends and spatial distribution perspectives based on a previously developed 2019–2020 high-resolution air pollution and CO2 emission inventory of the landing and take-off (LTO) cycle of civil aviation airports in China and the ChinaHighAirPollutants (CHAP) dataset. Besides, this study established an empirical model to evaluate the relationship between the air pollutant emissions of China's civil aviation sector in 2019–2020 and the pollutant concentration from the CHAP dataset. Compared to those in 2019, the total NOx, CO, PM, and SO2 emissions during the LTO phase in China's civil aviation sector in 2020 decreased by 14.29%∼24.32%, and the concentrations of NO2, CO, PM10, and SO2 in 2020 decreased by 6.33%∼9.45%. The eastern, central, and southern regions of China are characterized by high emissions of pollutants, a phenomenon closely related to the economic prosperity and tourism development in these areas. They tend to boast higher route densities, increasing air transport activity and consequently resulting in elevated emissions. In addition, NOx had the highest correlation coefficient in the empirical model, with a correlation coefficient of 0.603 in 2019. Our findings provide new insights into civil aviation emissions in China from the analysis of the emission inventory of air pollutants and the CHAP dataset and provide a new method for verifying the accuracy and completeness of China's civil aviation emission inventory.

Since 2018,Shandong Province has successively issued a series of pollution prevention and control programs for air pollutants from the coking industry,which plays a key role in coke production in China. However,a comprehensive assessment of the effectiveness of these measures is lacking. For a better understanding of this issue,based on the published air pollutant emission inventory for the independent coking industry and related policies formulated in Shandong in 2018,this study developed business-as-usual scenarios (BAU-2018,BAU-2025,and BAU-2035) and emission reduction optimization scenarios (ERO-2025 and ERO-2035). The emission reduction potential of PM10,SO2,NOx,PM2.5,and CO2 and their corresponding contributions to Shandong's air quality with the air quality model (CALPUFF) were assessed under different scenarios. In terms of scenario settings,the gross domestic product of the secondary industry and the coke output of independent coking enterprises were used for linear regression to predict the coke output in 2025 and 2035. The mesoscale atmospheric data model,Weather Research and Forecasting,provided simulated three-dimensional meteorological field data for this study. Regional terrain data (90 m) were obtained from the United States Geological Survey. The resolution of the land use type data was 30 m,according to our previous research results. We adopted the MESOPUFF Ⅱ chemical mechanism to simulate SO2,NOx,SO42−,NO3−,HNO3,PM10,and PM2.5 pollutants. To ensure the accuracy of the enterprise location information,we examined the enterprise latitude and longitude information one by one using Google Earth location recognition and manual visual inspection. The results showed that in the BAU-2018 scenario,the annual contribution ratios of SO2,NOx,PM10,and PM2.5 in Shandong Province were 0.06％-0.84％,0.01％-0.63％,0.04％-0.19％,and 0.07％-0.21％,respectively. Linyi has the highest contribution of SO2 and NOx concentrations to air quality,whereas Jining has the highest contribution of PM10 and PM2.5 concentrations to air quality. Compared with the current scenario (BAU-2018),the emission and contribution concentration of each pollutant showed an alarming decrease in the ERO-2025 scenario. Under the ERO-2035 scenario,the results showed that SO2,NOx,PM10,PM2.5,and CO2 emissions would be reduced by 60.12％,78.24％,75.07％,74.20％,and 37.47％,respectively,compared with those under the BAU-2018 scenario. In the ERO-2035 scenario,the average contribution concentrations of SO2,NOx,PM10,and PM2.5 decreased by 60.74％,78.56％,75.00％,and 74.53％,respectively. Moreover,the results of this study showed that the comprehensive implementation of ultralow emission standards in Shandong Province had a considerable impact on air pollutant reduction in the independent coking industry,and the synergistic reduction of air pollutants and carbon dioxide showed huge potential.

Linyi, a typical industrial city, has historically experienced severe air pollution. However, since the environmental protection inspection in 2014, there has been a significant improvement in air quality. The question remains as to whether this improvement has reduced the incidence of cardiovascular disease (CVD). This study examines the associations between inhalable particulate matter (PM2.5 and PM10) and CVD in Linyi from 2014 to 2019. A distributed lag non-linear model (DLNM) was used to analyze the exposure-response relationship between particulate matter and CVD. The results confirmed that exposure to PM2.5 and PM10 significantly increases the risk of CVD. The highest relative risk (RR) was observed in severe PM2.5 pollution (PM2.5 = 217 µg m−3, RR = 1.64, 95

Dioxins (including 2,3,7,8-tetrachlorodibenzo-p-dioxin, as Group 1 Carcinogen) in the atmosphere mainly originate from incomplete combustion during municipal solid waste (MSW) incineration. To significantly reduce dioxins emission from the MSW incineration industry, China has promulgated a set of ambitious plans regulating MSW-related pollution; however, the emission reduction potentials and concomitant environmental and health impacts associated with the implementation of these programs on a national scale remain unknown. Here, we use real measurements from official environmental impact assessment systems and continuous emissions monitoring systems (covering 96.6% of national MSW incinerators) to estimate unit-level dioxins emission and concomitant environmental and health impacts. We find that in 2018, 99.3% and 66.7% of Chinese incinerators met such concentration and temperature standards, respectively, controlling the total emissions to 19.6 g toxic equivalency quantity and maintaining carcinogenic and noncarcinogenic risks significantly below safety levels nationwide. Fully achieving both current standards and future regulations will reduce emissions and health risks by 67.7% and 62.6%, respectively, with waste sorting program contributing the majority. This study reveals substantial benefits from curbing MSW-related dioxins pollution and underscores the promise of ongoing management.

China fully implemented the new emission standards in 2016 to further reduce the emissions of air pollutants from the municipal solid waste (MSW) incineration industry; however, the implementation effect of the new standards remains unknown. This study developed the first nationwide air pollutant emission inventory of MSW incineration plants in China based on the measured concentration data from China's continuous emissions monitoring systems (CEMS) network, and activity level data from the China Urban Construction Statistical Yearbook, to evaluate the effectiveness of implementing the new emission standards and estimate the future reduction potentials. Our results demonstrated that the overall standard-reaching proportions of particulate matter (PM), sulfur dioxide (SO2), nitrogen oxide (NOX), hydrogen chloride (HCl) and carbon monoxide (CO) were 98.8%, 99.3%, 99.4%, 99.4% and 97.6%, respectively, by comparing with the corresponding concentration limits of new emission standards. The total emissions of PM, SO2, NOX, HCl and CO from 412 MSW incineration plants in 2019 were 1.9, 6.2, 50.8, 4.3 and 6.6 kt yr(-1), respectively, which is 33.6-75.8% lower than those in 2015, mainly due to the sharp decrease in emission factors. Pollutant emission hotspots were mainly concentrated in eastern and central and southern regions with large populations and well-developed economies. The analysis of future scenario results shows that despite the continuous increase of MSW incineration amount in the future, if the government strengthens pollutant emission standards and comprehensively implements waste sorting, total emissions and emission factors of air pollutants could be further reduced by 25.8-72.7% and 59.8-81.2%, respectively, by 2050. These findings provide helpful insights into future policymaking and technology selection for China and other countries seeking to reduce pollutant emissions from the MSW incineration industry.

As the world’s largest industrial producer, China has generated large amount of industrial atmospheric pollution, particularly for particulate matter (PM), SO 2 and NO x emissions. A nationwide, time-varying, and up-to-date air pollutant emission inventory by industrial sources has great significance to understanding industrial emission characteristics. Here, we present a nationwide database of industrial emissions named Chinese Industrial Emissions Database (CIED), using the real smokestack concentrations from China’s continuous emission monitoring systems (CEMS) network during 2015–2018 to enhance the estimation accuracy. This hourly, source-level CEMS data enables us to directly estimate industrial emission factors and absolute emissions, avoiding the use of many assumptions and indirect parameters that are common in existing research. The uncertainty analysis of CIED database shows that the uncertainty ranges are quite small, within ±7.2% for emission factors and ±4.0% for emissions, indicating the reliability of our estimates. This dataset provides specific information on smokestack concentrations, emissions factors, activity data and absolute emissions for China’s industrial emission sources, which can offer insights into associated scientific studies and future policymaking.

The Chinese Industrial Emissions Database (CIED) provides inventory of particulate matter (PM), SO2 and NOX from Chinese industries from 2015 to 2018, based on direct measurements from China’s continuous emission monitoring systems (CEMS) network. This document provides emissions factors for China’s different industrial emission sources.

The location and layout of enterprises have an important impact on local air quality. However, a few studies on exploring of the optimal layout of gas-related enterprises from the perspective of optimizing the layout of air pollution sources. This study developed a method for the evaluation of air pollution source layout based on air pollutant emission inventory data, atmospheric self-purification capacity data, and satellite remote sensing air quality data. Taking Shaanxi Province as an example, the Moran's I index and GIS spatial analysis techniques were used to evaluate the layout of air pollution sources, analyze the spatial variation characteristics of air pollution sources, and propose specific countermeasures to optimize the layout of air pollution sources. Results showed that northern Shaanxi and Guanzhong Plain are the most unsuitable for the distribution of NOx and CO sources, accounting for 13.78% and 21.77% of the total area, respectively. The most suitable area for the distribution of NOx is southern Shaanxi, accounting for 65.77% of the total area, mainly concentrated in Hanzhong and Ankang regions. The most suitable area for the distribution of CO is southern Shaanxi, accounting for 40.97% of the total area, mainly concentrated in Hanzhong and Shangluo regions. The findings of this study could supplement and improve the evaluation of the layout of industrial enterprises in China from technical and methodological aspects, and provide new insight for local governments to adjust and optimize the layout of air pollution sources.

China has successively put forward ultra-low emission (ULE) transformation plans to reduce the air pollutant emissions of industrial pollutants since 2014. To assess the benefits of the ULE policy on regional air quality for Qinhuangdao, this study developed an emission inventory of nine atmospheric pollutants in 2016 and evaluated the effectiveness of the emission policy in Qinhuangdao's key industries under different scenarios with an air quality model (CALPUFF). The emissions of air pollutants in 2016 were as follows: Sulfur dioxide (SO2) emitted 48.91 kt/year, nitrogen oxide (NOx) emitted 86.83 kt/year, volatile organic compounds (VOCs) emitted 52.69 kt/year, particulate matter (PM10 and PM2.5) emitted 302.01 and 116.85 kt/year, carbon monoxide (CO) emitted 1208.80 kt/year, ammonia (NH3) emitted 62.87 kt/year, black carbon (BC) emitted 3.79 kt/year, and organic carbon (OC) emitted 2.72 kt/year, respectively. The results showed that at the regional level in 2025, the iron and steel industry under the PPC (Peak Production Capacity) scenario had the highest potential for reducing SO2 and NOx emissions, while the cement industry under the PPC scenario excelled in reducing PM10 emissions. As for the industrial level in 2025, the flat glass industry under the ULE scenario would reduce the most SO2 emitted, while the iron and steel industry and the cement industry under the PPC scenario demonstrated the best reduction in NOx and PM10 emissions, respectively. Furthermore, the average annual contribution concentration of SO2, NOx, and PM10 in the air monitoring stations of Qinhuangdao under the PPC scenario was significantly lower than that under the BAU scenario revealed by air quality simulation. It can be concluded that the emission policy in Qinhuangdao will help improve the air quality. This study can provide scientific support for policymakers to implement the ULE policy in industrial undeveloped cities and tourist cities such as Qinhuangdao in the future.

Urbanization and industrial development have resulted in increased air pollution, which is concerning for public health. This study evaluates the effect of meteorological factors and air pollution on hospital visits for respiratory diseases (pneumonia, acute upper respiratory infections, and chronic lower respiratory diseases). The test dataset comprises meteorological parameters, air pollutant concentrations, and outpatient hospital visits for respiratory diseases in Linyi, China, from January 1, 2016 to August 20, 2022. We use support vector regression (SVR) to build models that enable analysis of the effect of meteorological factors and air pollutants on the number of outpatient visits for respiratory diseases. Spearman correlation analysis and SVR model results indicate that NO2, PM2.5, and PM10 are correlated with the occurrence of respiratory diseases, with the strongest correlation relating to pneumonia. An increase in the daily average temperature and daily relative humidity decreases the number of patients with pneumonia and chronic lower respiratory diseases but increases the number of patients with acute upper respiratory infections. The SVR modeling has the potential to predict the number of respiratory-related hospital visits. This work demonstrates that machine learning can be combined with meteorological and air pollution data for disease prediction, providing a useful tool whereby policymakers can take preventive measures.

China is the largest producer and exporter of coke globally, which means that it is very important to understand the characteristics of air pollutants and carbon emissions from China’s independent coking industry. This study was the first to establish a bottom-up inventory of the air pollutants and carbon emissions of China’s independent coking industry during 2001–2018 based on continuous emission monitoring system online monitoring data and unit-based corporate information. Based on the developed emission inventory, four scenarios were established to analyze potential emissions reduction of air pollutants and carbon dioxide (CO2) in future. The emissions of particulate matter (PM10 and PM2.5), sulfur dioxide (SO2), black carbon (BC) and organic carbon (OC) decreased by 62.11%, 63.41%, 72.85%, 63.41% and 63.41%, respectively. CO2, carbon monoxide (CO), volatile organic compounds (VOCs) and nitrogen oxides (NOX) emissions increased by 355.51%, 355.51%, 355.51% and 99.74%, respectively. In 2018, PM10, PM2.5, SO2, NOx, BC, OC, CO, VOCs and CO2 emissions were, respectively. 45.20, 16.91, 63.84, 117.71, 5.07, 5.92, 554.91, 1026.58 Gg, and 176.88 Tg. Shanxi province made the greatest contributions to the total emissions of air pollutants and CO2 from this industry by 25.01%. The emission source that contributed most to PM2.5 (SO2 and NOX) emissions was coke pushing (quenching and the coke oven chimney respectively) in 2018. Under the ULE scenario (2018–2035), PM2.5 and SO2 emissions will reduce by more than 30%. Under the PCP scenario, PM2.5 and SO2 emissions will reduce by more than 55%. Under the CBP scenario, CO2 emissions will peak at 197.99 Tg in 2025 and decrease to 70% of the peak in 2035. The results showed the emission characteristics of air pollutants and CO2, future emission with several scenarios and cooperative reduction potential in China’s independent coking industry, which provides scientific support for the development of pollution control strategies.

Accurate meteorological fields and applicable air quality models are important ways to optimize air pollution simulations. To improve the accuracy of winter air pollution models in the Sichuan basin, we conducted a meteorological field simulation using 25 sets of parameterized scheme combinations in the Weather Research and Forecasting (WRF) Model. Based on the optimal parameters, the air pollution levels were simulated using AERMOD and CALPUFF models in a local large steel plant, and the data were verified by comparing the data from four National Ambient Air Monitoring Stations (NAAMS). The results indicated that the WRF model parameters had substantial effects on the simulation of the ground wind field, high-altitude wind field, and ground humidity field. In contrast, the parameters had no significant effect on the simulation of the ground temperature field, high-altitude temperature field, and high-altitude humidity field. The combination of the SLAB land surface process scheme and Dudhia shortwave radiation scheme with four boundary layer schemes, namely YSU, ACM2, BouLac, and MRF, could well-simulate the trends of winter surface wind, temperature, and humidity fields in Sichuan basin. The simulation results were analyzed by combining the statistical parameters of high-altitude wind, temperature, and humidity. The group 1 parameter scheme was applicable to simulate the meteorological field of Dazhou. Group 13 and Group 17 parameters were applicable to simulate the meteorological fields in Chengdu during the day and night, respectively. The correlation between CALPUFF simulation and monitoring value was better than that for AERMOD. CALPUFF was more accurate than AERMOD when referring to the monitoring data from NAAMS No.3. In addition, the simulation quality of CALPUFF was slightly better than that of AERMOD with reference to data from NAAMS No.2. Using air pollutant monitoring data from NAAMS as a reference, the simulated results of CALPUFF on NOx and PM10 were improved compared to AERMOD at all four stations. Data from the Q-Q diagram indicated that the simulated results of CALPUFF on SO2, NOx, and PM10 were closer to the monitored values compared to those of AERMOD.