Mitigating PM2.5 and O3 pollution remains a protracted and critical challenge for China due to their synergistic public health threats. Here we investigate the changes in future meteorological and anthropogenic emission contributions to PM2.5 and O3, and evaluate related premature mortality and economic losses under three Shared Socioeconomic Pathways during 2020-2060 by integrating a chemical transport model, the Global Exposure Mortality Model, concentration-response function and willingness-to-pay method. Results showed that national average PM2.5 concentrations declined steadily from 30.2 & micro;g/m3 in 2020-4.7 & micro;g/m3 in 2060, while O3 concentrations decreased from 86.3 & micro;g/m3 to 72.5 & micro;g/m3 with fluctuations, exhibiting regional variations. Anthropogenic emissions became the dominant driver of PM2.5 reduction, with contribution rising from 55.7% to 81.6%. Meteorological changes' contribution to O3 variations fell from 49.8% to 37.4%, with anthropogenic emissions accounting for 50%-60% of O3 changes in major regions by 2060. By 2060, PM2.5-attributable premature deaths decreased by 3.197 (95% CI: 2.813-3.772) million (51.8% from Ischemic heart disease and Stroke), avoiding CNY 1.0107 (95% CI: 0.8591-1.1623) trillion in economic losses. O3-related deaths dropped by 167,000 (95% CI: 146,960-197,060) (90.0% from Cardiovascular disease), reducing losses by CNY 85.7 (95% CI: 72.8-98.6) billion. Anthropogenic emission reductions contributed to 2.571 (95% CI: 2.262-3.034) million fewer PM2.5-attributable deaths, while meteorological changes reduced O3-attributable mortalities by 101,000 (95% CI: 88,880-119,180). Findings underscore the necessity of sustained and region-specific emission control strategies, providing critical scientific insights for mitigating environmental pollution, advancing air quality management, and informing climate-adaptive policy design.

Infectious viruses continue to cause severe impacts on the global economy and public health. Although existing studies have demonstrated that meteorological factors and air pollutants are related to virus transmission and mortality, most of them are limited to analyses of specific regions or single factors, often using correlation methods that are not suitable for nonlinear data. Therefore, this study proposes a causal analysis framework based on Convergent Cross-Mapping (CCM) to systematically explore the relationships between environmental factors and the confirmed cases and mortality rates of infectious diseases. First, the simplex projection method was applied to determine the optimal parameters for constructing a causal relationship analysis model. Using COVID-19 as a representative infectious disease, multiple environmental factors and their causal relationships across 39 cities worldwide were investigated. The results show that temperature and humidity consistently ranked first and second among all factors in terms of causal coefficients. Moreover, the causal relationships between humidity, O3, and PM2.5 and new confirmed cases increased with higher annual averages, while the causal relationships between temperature, NO2, PM10, PM2.5, and CO and new deaths became more significant as the annual averages increased. This study validates, on a global scale, the nonlinear causal links between environmental factors and COVID-19, thereby providing new empirical evidence for epidemic prevention, urban environmental governance, and public health policymaking.

Abstract. This paper describes efforts to establish aerosol data assimilation capabilities for NOAA's National Air Quality Forecasting Capability (NAQFC), a regional online air quality modeling (AQM) system under NOAA's Unified Forecast System (UFS), by assimilating measurements of fine particulate matter (PM2.5, particles with aerodynamic diameters less than 2.5 µm). PM2.5 assimilation is developed within the Joint Effort for Data assimilation Integration (JEDI) framework and tested using its 3D-Var data assimilation (DA) component. The PM2.5 observation operator is constructed by combining newly developed PM2.5 transformation recipes in the JEDI Variable Derivation Repository (VADER) with a general spatial interpolation operator in the Unified Forward Operator (UFO). Cycled DA and forecast experiments were conducted from 1–21 September 2020, during a period of Western US wildfires, to assess the impact of assimilating PM2.5 observations from the AirNow and PurpleAir networks. The control and analysis variables include individual aerosol species, with background error standard deviations generated by scaling their respective background values. Prognostic variables such as aerosol particle number and total particulate surface area are updated accordingly following each analysis update. All DA experiments use a 3-hourly cycling interval, with PM2.5 observations assimilated every 3 h. The control experiment uses the same configuration but without any data assimilation. Results show that assimilating either AirNow or PurpleAir PM2.5 data reduces 1–24 h forecast errors in terms of mean absolute error (MAE) and root mean square error (RMSE) compared to the control run over Continental United States (CONUS). Substantial improvements are in regions where fire events took place and largely affected by transported smoke. Overall, the assimilation of PurpleAir observations in addition to AirNow data leads to a slight reduction in 3–24 h MAE.

Fine particulate matter (PM2.5) predictions at a high spatial resolution (i.e., neighborhood scale) are critically needed to better understand the health impacts of air pollution, especially at neighborhood scales. This work develops a statistical downscaling approach to predict PM2.5 at a 1-km grid resolution over the contiguous United States (CONUS) under baseline and future energy transition scenarios and estimate health benefits utilizing the Environmental Benefits Mapping and Analysis Program (BenMAP). To this end, we incorporate the satellite-based high-resolution aerosol optical depth (AOD), land use data, and PM2.5 composition predicted by the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) at 36-km into daily multi-linear regressions over different climate regions of the CONUS. Compared to the WRF-Chem baseline predictions in 2008-2012, 1-km PM2.5 estimates enhance the accuracy by increasing the yearly correlation coefficients from ~0.4 to ~0.8 and reducing normalized mean errors from ~47 % to ~27 %. Future 1-km PM2.5 is projected by combining the baseline 5-yr (2008-2012) monthly-averaged training coefficients with high-resolution statistically improved projected AOD and PM2.5 subsets from WRF-Chem. BenMAP with WRF-Chem predictions under future energy scenarios shows an average of 2478 fewer deaths per year in 2050 in New York City and Boston due to PM2.5, while the downscaled PM2.5 shows less PM2.5 reduction and about half the health benefit of the WRF-Chem projections. The downscaling approach is more computationally efficient than running the 3-D air quality model with a 1-km spatial grid resolution. This work uniquely combines WRF-Chem outputs and statistical downscaling to provide high-resolution and high-fidelity PM2.5 predictions.

The complex pore structure and organic matter composition of coal significantly affect the storage and transportation characteristics of gas, and the role of soluble organic matter is still lacking in in-depth research. This study, represented by tetrahydrofuran-2-ol (C4H8O2), explores the effect of small molecule organic compounds on coal adsorption of CH4 and CO2 through quantum chemical simulations. The static potential of a single molecule was determined through quantum chemistry calculations. Detailed analysis was conducted on the adsorption heat, mean square displacement, radial distribution function, and adsorption energy distribution during the adsorption process. The results indicate that the excessive adsorption capacity of coal for CO2 is always higher than that for CH4. Organic small molecules significantly reduce the gas adsorption capacity and adsorption heat of coal, weaken the interaction between heteroatoms and adsorbate molecules, and have a significant impact on CO2 adsorption, thereby significantly reducing the interaction between CO2 and coal molecules and weakening the displacement effect of CO2 on methane. At 6 MPa, its impact on CO2 adsorption is minimal. The results of this study contribute to a better understanding of the occurrence mechanism of coalbed methane, providing theoretical support for optimizing pre extraction gas technology and assisting in coal mine safety and efficient production.

Source inversion optimization using sensor observations is a key method for rapidly and accurately identifying unknown source parameters (source strength and location) in abrupt hazardous gas leaks. Sensor number and location distribution both play important roles in source inversion; however, their combined impacts on source inversion optimization remain poorly understood. In our study, the optimization inversion method is established based on the Gaussian plume model and the generation algorithm. A research strategy combining random sampling and coefficient of variation methods was proposed to simultaneously quantify their combined impacts in the case of a single emission source. The sensor layout impact difference was analyzed under varying atmospheric conditions (unstable, neutral, and stable) and source location information (known or unknown) using the Prairie Grass experiments. The results indicated that adding sensors improved the source strength estimation accuracy more when the source location was known than when it was unknown. The impacts of sensor location distribution were strongly negatively correlated (r ≤ −0.985) with the number of sensors across scenarios. For source strength estimation, the impacts of the sensor location distribution difference decreased non-linearly with more sensors for known locations but linearly for unknown ones. The impacts of sensor number and location distribution on source strength estimation were amplified under stable atmospheric conditions compared to unstable and neutral conditions. The minimum number of randomly scattered sensors required for stable source strength inversion accuracy was 11, 12, and 17 for known locations under unstable, neutral, and stable atmospheric conditions, respectively, and 24, 9, and 21 for unknown locations. The multi-layer arc distribution outperformed rectangular, single-layer arc, and downwind-axis distributions in source strength estimation. This study enhances the understanding of factors influencing source inversion optimization and provides valuable insights for optimizing sensor layouts.

Surface wind speed(SWS)not only plays a crucial role in regulating the Earth's energy and hydrological cycle,but also is an important source of sustainable renewable energy.This study assesses the credibility of SWSs in three reanalyses(ERA5,MERRA2,and JRA-55)in East Asia using both satellite and in-situ observations.Results show all three reanalyses can capture the spatial pattern of SWS as in observations,yet there are notable differences in magnitude.On land,ERA5 and MERRA2 overestimate the SWS by about 0.6 and 1.5 m s-1,respectively,whereas JRA-55 underestimates it.The biases over the oceans are opposite to those on land and are relatively small due to the assimilation of observations of oceanic surface winds.Overall,JRA-55 and ERA5 offer better estimates of seasonal means and variances of SWS than MERRA2.The observed SWS shows a negative trend of-0.08 m s-1/10 yr on land and a positive trend of 0.09 m s-1/10 yr in the western North Pacific.Only JRA-55 shows similar trends to observations over both land and ocean,while ERA5 and MERRA2 show varying degrees of deviation from the observations.Further investigation shows that there is a strong link between the trend of SWS and that of the large-scale circulation,and that a large part of the SWS trend can be attributed to changes in large-scale circulations.

Abstract The National Oceanic and Atmospheric Administration (NOAA) has developed an advanced regional air quality prediction system (AQPS) within the Unified Forecast System (UFS) framework to improve representations of wildfire emissions and their impacts on air quality predictions. This innovative system integrates the Environmental Protection Agency’s (EPA) Community Multiscale Air Quality (CMAQ) model as a column chemistry model with the UFS-based atmospheric model, operating in an online mode. The calculation of wildfire gas and particulate emissions relies on satellite-derived fire products, high-resolution Regional Hourly Advanced Baseline Imager (ABI) and Visible Infrared Imaging Radiometer Suite (VIIRS) Emissions (RAVE). A period in June and July 2023 with Quebec Canadian wildfires, which severely impacted air quality in the United States (US), was chosen as a case study to assess the predictive capability of the UFS-AQM system. The UFS-AQM predictions of fine particulate (PM2.5) and ozone (O3) were evaluated against AirNow observations from June 15 to July 14, 2023. The results indicate a substantial improvement in PM2.5 predictions when compared to the previous operational forecast. Meanwhile, the system demonstrates a strong ability of predicting O3 exceedance events during the dissipation phase of the wildfire. Furthermore, the online system shows more realistic predictions of aerosol optical depth (AOD) as compared to the previous operational forecast and satellite retrieval data. Finally, this study outlines a plan for further advancing a comprehensive regional AQPS at NOAA.

This paper considers the government, electric power enterprises, and digital service providers as the primary stakeholders in the digital transformation of the electric power sector and constructs a game model to explore paths and strategies in the evolutionary process. The results show that the synergistic income between electric power enterprises and digital service providers significantly impacts whether the enterprises undergo digital transformation. The intensity of government subsidies and the severity of penalties influence the willingness to participate in the digital transformation and directly impact cooperative relationships. Cost is the fundamental reason for restricting the digital transformation of electric power enterprises.

Ni-Co bimetal is a considerably promising catalyst to apply in DRM process, and the unique Ni@Co core-shell structure maybe improve the activity and stability, but the reaction pathways and underlying mechanism on Ni@Co surface at the micro level need to be further uncovered. In current work, the adsorption of intermediates and the elementary reactions were systematically investigated based on Density Functional Theory calculations, and reason for Ni@Co core-shell structure enhancing the catalytic performance was obtained by comparing the related energies on Ni (111), Ni@Co (111) and Co (111) surface. Results suggested that CH4 dehydrogenation is the rate-determining step for CH4 activation, and the activation energy barrier is 1.43 eV on Ni@Co (111) surface, higher than 1.27 eV on Ni (111) and 1.31 eV on Co (111) surface. Compared with pure Ni and Co catalyst, Ni@Co core-shell catalyst could reduce the carbon formation by inhibiting the CH4 dissociation into C. Moreover, CO2 direct dissociation is dominating path for CO2 activation process. The activation energy barrier is 0.56 eV on Ni (111) surface, and it is further decreased to 0.36 eV on Co (111) surface and 0.22 eV on Ni@Co (111) surface. Ni@Co core-shell structure could promote the CO2 activation to produce more oxygen species to the oxidize CH and C. Moreover, Ni@Co core-shell structure is also contribute to the elimination of carbon deposited by promote C oxidation and hydrogenation. Furthermore, Ni@Co core-shell structure could also reduce the activation energy barrier of CO and H2 formation, and increase the activation energy barrier of H2O formation, which could suppress the generation of by-products and improve the selectivity of target products.

A novel algorithm for generating multi-component, high-resolution emission inventories for air quality models (AQMs) was developed, enhancing the 0.01 degrees spatial allocation scheme for points, lines, and surfaces based on land use, population density, and road networks. It incorporated localized chemical species allocation based on recent Volatile Organic Compounds (VOCs) and Particulate Matter (PM) composition, along with time allocation scheme utilizing monthly and hourly non-uniformity coefficients. The algorithm consists of four modules for grid calculation, refined inventory calculation, coarse grid interpolation, and model-ready inventory production. It achieved highly integrated and one-step workflow from raw parameter settings to direct model-ready emission files for simulations with CMAQ and CAMx. The Beijing-Tianjin-Hebei (BTH) region was used as example to introduce the production process and research characteristics of refined emission inventory. The results demonstrated that this algorithm effectively captures the spatiotemporal distribution and dynamics of atmospheric pollutants, offering significant support for emission and simulation research.

Coking industry is usually regarded as a high pollution and high energy consumption industry. China is accelerating its efforts to reduce pollution and carbon emissions in the industrial sector, which has received little attention as the world's largest producer of coke. Therefore, in this study, the trend of air pollution and carbon emissions in China's coking industry and the path of coordinated emission reduction were studied. The results indicate that the average annual emissions of PM, SO2, 2 , NOx, VOCs, and CO2 2 in China's coking industry from 2012 to 2022 amount to 205.98, 69.47, 193.45, 599.80 Gg and 191.10 Tg, respectively. The main sources of PM, SO2, 2 , NOx, VOCs and CO2 2 in coking industry were coal preparation (51.5 %), charge and pushing (39.5 %), coke oven gas (99.8 %), byproduct recovery (47.0 %) and fuel combustion (87.5 %). The emissions from coking plants in central and southern Shanxi, eastern and southern Hebei, and central Shandong are the most concentrated. Ultra-low emission transformation and deep treatment of VOCs have greatly reduced pollutant emissions in key areas of air pollutant control, but the actual emission reduction effect of these measures has been weakened by the additional emissions caused by the increase of coke production in other non-key areas. The research on synergetic emission reduction path shows that there is a great synergistic benefit between air pollutants and CO2 2 emission reduction in coking industry. It is estimated that the APeq (air pollutants and carbon equivalent) of China's coking industry in 2025, 2028 and 2030 will decrease by 38.2 %, 63.5 % and 70.8 % respectively compared with 2022. With the continuous promotion of pollution reduction and carbon reduction measures, the emission reduction potential of China's coking industry will gradually shift from key areas to non-key areas.

Two air quality models, the U. S. EPA Community Multiscale Air Quality (CMAQ) model and ENVIRON's Comprehensive Air Quality Model with extensions (CAMx), are evaluated for their applications in simulating ambient air quality, in particular, the fate and transport of agriculturally-emitted NH3 over an area in the southeastern U. S. in January and July 2002 using a fine-scale horizontal grid resolution of 4-km. Both models moderately overpredict maximum 1-hr and 8-hr ozone (O-3) and fine particulate matter (PM2.5) in January, due likely to a weaker vertical mixing and insufficient dry and wet removal of PM2.5 species simulated by the models. They either slightly underpredict or overpredict O-3 but significantly underpredict PM2.5 in July. The large underprediction in PM2.5 is due to an excess wet deposition removal of sulfate, an excess dry deposition removal of precursors, and an underestimation of emissions of primary PM and precursors of secondary PM and secondary organic aerosol concentrations. Both models show large biases in the simulated concentrations of several gases (e. g., CO in CAMx, NO in CMAQ, NO2 in both models in both months and NH3 by both models in July) and PM species (in particular, nitrate in both months and carbonaceous PM in July), visibility indices, and dry and wet deposition fluxes. They also show some inaccuracies in reproducing temporal variations of NH3, PM2.5, dry and wet deposition fluxes. Differences in model performance between the two models are attributed to different model treatments such as vertical mixing, wet and dry deposition, SOA formation, and PM size representations. These results indicate a need to improve accuracies of the emissions and measurements of NH3, the emissions of primary PM and precursors of secondary PM, as well as model treatments of vertical mixing and dry and wet removal processes.

Human health is negatively impacted by poor air quality including increased risk for respiratory and cardiovascular disease. Due to a recent increase in extreme air quality events, both globally and locally in the United States, finer resolution air quality forecasting guidance is needed to effectively adapt to these events. The National Oceanic and Atmospheric Administration provides air quality forecasting guidance for the Continental United States. Their air quality forecasting model is based on a 15 km spatial resolution; however, the goal is to reach a three km spatial resolution. This is currently not feasible due in part to prohibitive computational requirements for modeling the transport of chemical species. In this work, we describe a deep learning transport emulator that is able to reduce computations while maintaining skill comparable with the existing numerical model. We show how this method maintains skill in the presence of extreme air quality events, making it a potential candidate for operational use. We also explore evaluating how well this model maintains the physical properties of the modeled transport for a given set of species.

Using the datasets of Chinese A-share listed firms from 2010 to 2020, this study empirically explores the impact of carbon emissions on corporate social responsibility. We find that carbon emissions have a negative effect on the firm' s CSR. Our conclusion is highly robust after using the instrumental variable and the DID test to alleviate the issue of endogeneity and a series of robustness tests. Further analysis suggests that firm financial distress, financing constraints, and tax avoidance are three plausible mechanisms. Our findings offer enterprises an emission alert and provide a new perspective to enhance their CSR performance.

This work presents new climate and emissions scenarios to investigate changes on future meteorology and air quality in the U.S. Here, we employ a dynamically downscaled Weather Research and Forecasting model coupled with chemistry (WRF/Chem) simulations that use two Intergovernmental Panel on Climate Change scenarios (i.e., A1B and B2) integrated with explicitly projected emissions from a novel Technology Driver Model (TDM). The projected 2046–2055 emissions show widespread reductions in most gas and aerosol species under both TDM/A1B and TDM/B2 scenarios over the U.S. The WRF/Chem simulations show that under the combined effects of the TDM/A1B climate and emission changes, the maximum daily average 8-h ozone (MDA8 h O3) increases by ~3 ppb across the U.S. mainly due to widespread increases in near-surface temperature and background methane concentrations, with some contributions from localized TDM emission changes near urban centers. For the TDM/B2 climate and emission changes, however, the MDA8 h O3 is widely decreased, except near urban centers where the relative TDM emission changes and O3 formation regimes leads to increased O3. The number of O3 exceedance days (i.e., MDA8 h O3 > 70 ppb) for the entire domain is significantly reduced by a grid cell maximum of up to 43 days (domain average ~0.5 days) and 62 days (domain average ~2 days) for the TDM/A1B and TDM/B2 scenarios, respectively, while in the western U.S., larger O3 increases lead to increases in nonattainment areas, especially for the TDM/A1B scenario. The combined effects of climate and emissions (for both A1B and B2 scenarios) will lead to widespread decreases in the daily 24-h average (DA24 h) PM2.5 concentrations, especially in the eastern U.S. (max decrease up to 93 µg m−3). The PM2.5 changes are dominated by decreases in anthropogenic emissions for both the TDM/A1B and TDM/B2 scenarios, with secondary effects on decreasing PM2.5 from climate change. The number of PM2.5 exceedance days (i.e., DA24 h PM2.5 > 35 µg m−3) is significantly reduced over the eastern U.S. under both TDM/A1B and B2 scenarios, which suggests that both climate and emission changes may synergistically lead to decreases in PM2.5 nonattainment areas in the future.

It was commonly believed that a mirror Chern insulator (MCI) must require spin-orbital coupling, since time-reversal symmetry for spinless systems contradicts with the mirror Chern number. So MCI cannot be realized in spinless systems which include the large field of topological artificial crystals. Here, we disprove this common belief. The first point to clarify is that the fundamental constraint is not from spin-orbital coupling but the symmetry algebra of time reversal and mirror operations. Then, our theory is based on the conceptual transformation that the symmetry algebras will be projectively modified under gauge fields. Particularly, we show that the symmetry algebra of mirror reflection and time-reversal required for MCI can be achieved projectively in spinless systems with lattice $\mathbb{Z}_2$ gauge fields, i.e., by allowing real hopping amplitudes to take $\pm$ signs. Moreover, we propose the basic structure, the twisted $\pi$-flux blocks, to fulfill the projective symmetry algebra, and develop a general approach to construct spinless MCIs based on these building blocks. Two concrete spinless MCI models are presented, which can be readily realized in artificial systems such as acoustic crystals.

The Beijing-Tianjin-Hebei (BTH) region, a highly industrialized area in China, boasts a concentration of coking plants that constitute a vital component of the steel industry. In recent years, the Chinese government has implemented measures including backward production capacity elimination (BPCE), ultra-low emission technology transformation (ULET), and deep treatment of volatile organic compounds (DTV), to promote technological progress in the coking industry and mitigate the impact of pollutant emissions. This study focuses on the emission trends, reduction effects of various measures, and the impact on air quality and human health in the regional scale. The findings reveal that in 2015, the emissions of PM, SO2, NOx and VOCs of the coking industry in BTH region were 29.15, 9.64, 26.62 and 82.99 Gg (1000 tons/year) respectively. However, by 2019, these emissions had significantly decreased by 19.95, 5.78, 18.69, and 22.53 Gg, respectively. Of these reductions, ULET contributed about 80.3 % of NOx and SO2, and 57.4 % of PM. Meanwhile, DTV and BPCE contributed 49.2 % and 50.7 % of VOCs emission reduction, respectively. Despite the improvement effect on PM2.5, SO2, and NO2 is limited, the substantial decrease in VOCs (particularly benzene) resulted in a significant reduction in the coking industry's contribution to the atmospheric benzene concentration, dropping from 15.9 % in 2015 to 11.6 % in 2019. Moreover, the lifetime cancer risk (LCR) contribution of benzene inhalation in the BTH region also decreased from 1.7 × 10-6 to 1.2 × 10-6. Looking ahead to 2025, the continued implementation of DTV will be expected to reduce VOCs emissions by 24.41Gg. This will bring the industry's contribution to the benzene concentration down to 6.8 % and the cancer risk of the population to an acceptable level (LCR < 1 × 10-6). Additionally, the deep treatment of VOCs in coking plants will significantly reduce the health risks faced by people living in the vicinity of the plants.

To effectively reduce the compound pollution of fine particulate matter (PM2.5) and ozone (O3), volatile organic compound (VOC) emission sources are being extensively studied. Studies on VOC emission characteristics have mainly focused on solvent-using and technological sources, while research on the combustion processes of high-energy-consuming industries remains limited. Here, we investigate the emission characteristics of 102 VOC species emitted from eight sources (sintering, pelletizing, steel smelting, a coke oven chimney, the topside of a coke oven, thermal power, and two types of cement kilns) used in four types of high-energy-consuming industries. Our results show significant differences among these sources. The average VOC mass concentrations varied from 0.14 to 7.08 mg/m3. Alkanes had the highest percentage contribution to VOCs from sintering (45.9%) and thermal power (44.6%), while aromatics had the highest percentage contribution from steel smelting (47.8%) and the topside of the coke oven (52.9%). Alkenes were abundant in the coke oven chimney (49.1%), and oxygenated VOCs accounted for 65.3% of the total VOCs from pelletizing. We also observed notable differences between the two types of cement kilns. In general, ethane, ethene, benzene, propane, acetaldehyde, and chloromethane were the dominant VOC species in all sources. The differences between the sources were due to factors such as the applied raw materials, calcination temperature, and end-of-pipe treatment. By reviewing studies from the past 20 years, we found that differences in the number of VOC species and the implementation of ultra-low emissions affect VOC emission characteristics. The coke oven chimney and cement kiln chimney-1 had the highest ozone formation potential and secondary organic aerosol potential, respectively. The VOC emissions from coking, iron and steel, thermal power, and cement industries were 1162.1, 289.9, 311.0, and 470.2 Gg, respectively. Our results highlight the need for more effective control measures to minimize VOC emissions from high-energy-consuming industries.