Responses of surface O3 and PM2.5 trends to changes of anthropogenic emissions in summer over Beijing during 2014-2019: A study based on multiple linear regression and WRF-Chem

Impacts of different vehicle emissions on ozone levels in Beijing: Insights into source contributions and formation processes

Beijing, with the highest number of motor vehicles in China, significantly contributes to O3 pollution through substantial NOx and VOC emissions in the on-road transportation sector. Understanding the unique impact of emissions from different vehicle types on O3 levels is crucial for developing targeted strategies for O3 pollution. This study applied the Community Multiscale Air Quality Modeling System (CMAQ) to comprehensively investigate the impacts of emissions from different vehicle types on O3 levels in various regions of Beijing and to provide valuable insights into source contributions and formation processes. The results revealed that various vehicle types exhibited different spatial-temporal emission patterns, with medium-heavy duty trucks (HDT) and mini-light passenger vehicles (LDPV) identified as the primary contributors to NOx (36.1 %) and VOC (57.6 %) emissions. Using the Integrated Source Apportionment Method (ISAM) coupled in CMAQ, we found the total vehicle emissions contributed to over 20 % of daily maximum 8-h average O3 (MDA8 O3) concentration, ranked as the second largest contributor after regional transport. Contributions to O3 formation from LDPV and medium-large passenger vehicles (MDPV) were 2.6-4.0 and 4.2-6.8 ppb and mainly concentrated in urban areas, while the contributions from mini-light duty trucks (LDT) and HDT were 3.5-4.8 and 3.7-6.2 ppb and mainly concentrated in suburban areas. Through scenario analysis that removed emissions from specific types of vehicles, we found removing LDPV emissions led to decreases in daytime O3 concentration by 0.3-3.8 ppb. In contrast, removing MDPV emissions led to notable O3 increases by 4.0-11.8 ppb at rush hours. Removing LDT and HDT emissions resulted in 0.6-8.0 ppb increases in nocturnal O3 concentrations while 0.8-2.0 ppb decreases during the afternoon. This research highlights the necessity of tailoring control strategies for different vehicle types to effectively reduce O3 levels in Beijing and provides useful information for decision-makers to formulate effective measures of vehicle management in the future.

Understanding the spatial and seasonal variation of the ground-level ozone in Southeast China with an interpretable machine learning and multi-source remote sensing

Ground-level ozone (O3) pollution poses significant threats to both human health and air quality. This study uses ground observations and satellite retrievals to explore the spatiotemporal characteristics of ground-level O3 in Zhejiang Province, China. We created data-driven machine learning models that include meteorological, geographical and atmospheric parameters from multi-source remote sensing products, achieving good performance (Pearson's r of 0.81) in explaining regional O3 dynamics. Analyses revealed the crucial roles of temperature, relative humidity, total column O3, and the distributions and interactions of precursor (volatile organic compounds and nitrogen oxides) in driving the varied O3 patterns observed in Zhejiang. Furthermore, the interpretable modeling quantified multifactor interactions that sustain high O3 levels in spring and autumn, suppress O3 levels in summer, and inhibit O3 formation in winter. This work demonstrates the value of a combined approach using satellite and machine learning as an effective novel tool for regional air quality assessment and control.

Comparison of the ozone formation mechanisms and VOCs apportionment in different ozone pollution episodes in urban Beijing in 2019 and 2020: Insights for ozone pollution control strategies

Ground-level ozone (O3) pollution has been a tough issue in urban areas of China in the past decade. Clarifying the formation mechanisms of O3 and the sources of its precursors is necessary for the effective prevention of O3 pollution. In this study, a comparative analysis of O3 formation mechanisms and VOCs apportionment for five O3 pollution episodes was carried out at two urban sites (CRAES and CGZ) in Beijing in 2019 and 2020 by applying an observation-based modeling approach in order to obtain insights into O3 pollution control strategies. Results indicated that O3 pollution levels were generally more severe in 2019 than in 2020 during the observation periods. O3 formation at the two sites was both VOCs-limited on O3 polluted days and non-O3 polluted days. Stronger atmospheric oxidation capacity and ROx radicals cycling processes were found on O3 polluted days which could accelerate the local production of O3, and local photochemical production dominated the observed O3 concentrations at the two sites even on non-O3 polluted days. Emission reduction of VOCs should be a priority for mitigating O3 pollution, and alkenes and biogenic VOCs was the priority species at the CRAES and CGZ sites, respectively. Additionally, the reduction of oxygenated VOCs should also be important for the ozone control. Gasoline exhaust at the CRAES site, and solvent utilization and fuel evaporation at the CGZ site were main anthropogenic sources of VOCs. Therefore, local control measures should be further strengthened and differentiated control strategies of VOCs in the aspects of area, time, sources and species should be adopted in urban Beijing in the future. Overall, the findings of this study could provide a scientific understanding of the causes of O3 pollution and significant guidelines for formulating O3 control strategies from the perspective of different ozone pollution episodes in urban Beijing.

Characterization of summertime single aerosol particles in Chengdu (China): Interannual evolution and impact of COVID-19 lockdown

To investigate the interannual evolution of air pollution in summer and the impact of the COVID-19 lockdown on local pollution in Chengdu, China, single aerosol particles were continuously measured in three summer periods: the regular period in 2020 (RP2020); the regular period in 2022 (RP2022); and the lockdown period in 2022 (LP2022). It was found that, from RP2020 to RP2022, the mass concentrations of PM2.5, PM10, SO2 and NO2 decreased by 25.6 %, 24.7 %, 28.8 % and 38.5 %, respectively, while the concentration of O3 increased by 11.0 %. Affected by regional transport, there was no significant decrease in the concentrations of various pollutants during LP2022. All single aerosol particles could be classified into seven categories: vehicle emissions (VE), dust, biomass burning (BB), coal combustion (CC), K mixed with sulfate (KSO4), K mixed with nitrate (KNO3) and K mixed with sulfate and nitrate (KSN) particles. From RP2020 to RP2022, the contributions of BB and CC particles decreased by 12.1 % and 0.9 %, respectively, while VE and dust particles increased by 3.6 % and 2.5 %, respectively; and compared to RP2022, the contributions of VE, dust and CC particles in LP2022 decreased by 22.2 %, 11.0 % and 12.7 %, respectively. The high PM2.5 pollution events in RP2020 and RP2022 were mainly caused by combustion sources (BB and CC, 51.6 %) and VE (38.3 %) particles, respectively, while the pollution event in LP2022 was contributed by BB (27.0 %) and secondary inorganic (KSO4, KNO3 and KSN, 60.2 %) particles. The formation mechanisms of different pollution events were further validated by WRF-Chem results. Although the potential source areas of particles showed a shrinking trend from RP2020 to RP2022, regional transport still caused high PM2.5 pollution events during LP2022. Photochemical processes dominated the formation of KSO4 particles, while the KNO3 and KSN particles were mainly generated by liquid-phase reactions, and this effect increased year by year.

Reduction of the concentration of particulate material at a sampling point in Cusco city at the beginning of the pandemic

The pandemic produced by SARS-CoV-2 generated various impacts on public health, the environment and other anthropogenic activities. The purpose of this study was to evaluate the reduction of air pollution due to [Formula: see text] and [Formula: see text] particulate matter in Cusco city at the beginning of the pandemic. Social confinement in Peru began on March 16, 2020, until the end of June. These health measures caused strict isolation that resulted in a significant decrease in vehicle flow on the streets and avenues of the city of Cusco. In the first days of May, even during the time of confinement, we managed to measure air quality at a sampling point located on the campus of the Universidad Nacional de San Antonio Abad de Cusco; a reduction in air pollution due to particulate matter was observed. The evaluation was carried out using an high-volume (HiVol) 3000 particulate matter sampler and the mass of particulate matter adhered to the filters was determined by gravimetry. The concentrations of particulate matter [Formula: see text] and [Formula: see text] obtained pre-pandemic were compared with those recorded at the beginning of the pandemic. The results revealed a significant average reduction in the concentration of [Formula: see text] and [Formula: see text], reaching - 57.43% and - 59.52%, respectively, compared to pre-pandemic values. At the same time, its relationship with meteorological parameters and Google mobility data was evaluated and it was concluded that these parameters did not significantly affect the reduction of particulate matter. This study reveals the positive effects of the pandemic in reducing air pollution and the confinement measures had as a secondary effect on the decrease in air pollution in Cusco City.

Integration of Data and Predictive Models for the Evaluation of Air Quality and Noise in Urban Environments

This work addresses assessing air quality and noise in urban environments by integrating predictive models and Internet of Things technologies. For this, a model generated heat maps for PM2.5 and noise levels, incorporating traffic data from open sources for precise contextualization. This approach reveals significant correlations between high pollutant/noise concentrations and their proximity to industrial zones and traffic routes. The predictive models, including convolutional neural networks and decision trees, demonstrated high accuracy in predicting pollution and noise levels, with correlation values such as R2 of 0.93 for PM2.5 and 0.90 for noise. These findings highlight the need to address environmental issues in urban planning comprehensively. Furthermore, the study suggests policies based on the quantitative results, such as implementing low-emission zones and promoting green spaces, to improve urban environmental management. This analysis offers a significant contribution to scientific understanding and practical applicability in the planning and management of urban environments, emphasizing the relevance of an integrated and data-driven approach to inform effective policy decisions in urban environmental management.

Significantly mitigating PM2.5 pollution level via reduction of NOx emission during wintertime

Despite considerable decreases in fine particulate matter (PM2.5) in Chinese megacities over the past decade, many second- and third-tier cities that distribute abundant industrial enterprises are still facing great challenges for PM2.5 further reduction under the recent policy background of eliminating heavily-polluted weather. In view of core effects of NOx on PM2.5, the deeper reductions of NOx in these cities are expected to break the plateau of PM2.5 decline, however, the link between NOx emission and PM2.5 mass loading is currently lacking. Herein, we progressively construct an evaluation system for PM2.5 productions based on daily NOx emissions in a typical industrial city (Jiyuan), considering a sequence of nested parameters involving evolutions of NO2 into nitric acid and then nitrate, and contributions of nitrate to PM2.5. The evaluation system was subsequently validated to better reproduce real increasing processes for PM2.5 pollution based on 19 pollution cases, with root mean square errors of 19.2 ± 16.4 %, suggesting the feasibility of developing NOx emission indicators linked to goals of mitigating atmospheric PM2.5. Additionally, further comparative results reveal that currently high NOx emissions in this industrial city severely hinder the achievement of atmospheric PM2.5 environmental capacity targets, especially in the scenarios of high initial PM2.5 level, low planetary boundary layer height and long pollution duration. It is anticipated that these methodologies and findings would supply guidelines for further regional PM2.5 mitigation, in which source-oriented NOx indicators could also provide some orientations for industrial cleaner production such as denitrification and low nitrogen combustion.

Roles of ambient temperature and relative humidity on the relationship between fine particulate matter and gaseous pollutants in the largest industrial city of Ulsan, South Korea

Although meteorological conditions play a significant role in air pollution, research on their effects on the relationship between air pollutants is limited. In this study, trends of six criteria air pollutants were investigated from 15 air quality monitoring stations (AQMSs) in Ulsan, a multi-industrial city in South Korea, during 2015-2019. Unlike CO and O3, SO2, NO2, PM10, and PM2.5 showed statistically significant decreasing trends over the period. The companion relationship between PM2.5 and gaseous pollutants was evaluated by their correlations [R (PM2.5-GPs)]. R (PM2.5-NO2) was relatively high at almost all AQMSs, whereas high R (PM2.5-SO2) was observed near the petrochemical industrial complex, suggesting a great influence of local emissions (vehicles and industries). R (PM2.5-CO) and the standardized regression coefficients of CO obtained from the multiple linear regression model were the highest, indicating that combustion processes may significantly contribute to PM2.5. The effect of temperature (T) was more apparent on R (PM2.5-GPs) than that of relative humidity, with significant values under T > 15 °C. Moreover, R (PM2.5-O3) was positive at the T range of 12-18 °C, suggesting that reducing GPs emitted by industrial facilities during May-June may control PM2.5 and O3 in Ulsan. The methodology demonstrated in this study can be further used for a better understanding of the influences of environmental factors on the secondary PM2.5 formation from gaseous precursors and the R (PM2.5-O3).

Enhanced enzymatic saccharification and ethanol production of corn stover via pretreatment with urea and steam explosion

Enhancing the degradation of lignocellulosic structure is essential for the efficient use of corn stover. This study investigated the effects of using urea combined with steam explosion on the enzymatic hydrolysis and ethanol production of corn stover. The results demonstrated that 4.87% urea addition and 1.22 MPa steam pressure were optimal for ethanol production. The highest reducing sugar yield (350.12 mg/g) was increased by 116.42% (p < 0.05), and the corresponding degradation rates of cellulose, hemicellulose, and lignin in pretreated corn stover were increased by 40.26%, 45.89% and 53.71% compared with the untreated corn stover (p < 0.05). Moreover, the maximal sugar alcohol conversion rate was approximately 48.3%, and the ethanol yield reached 66.5%. In addition, the key functional groups in corn stover lignin under combined pretreatment were identified. These findings offer new insights into corn stover pretreatment and can help develop feasible technologies to enhance ethanol production.

Characteristics and sources analysis of ambient volatile organic compounds in a typical industrial park: Implications for ozone formation in 2022 Asian Games

In this study, volatile organic compounds (VOCs) at a major industrial park in Yangtze River Delta Region, China, along with an urban site had been investigated for three years (2018-2020). The daily-mean concentration of total 97 VOCs in the industrial park (224.3 ± 139.1 μg/m³) was about twice that of urban site (112.0 ± 64.2 μg/m³). Halohydrocarbons were predominant VOCs species at both sites accounting for 39.0 % and 32.2 % in industrial and urban sites, respectively. Annual-average concentrations of total VOCs slowed down gradually in industrial park, while that of the urban site increased annually. Evident seasonal and diurnal variations were observed for VOCs concentration in both sites. Higher VOCs concentrations appeared in summer for industrial park, and high concentrations generally appeared at 8:00 and 19:00-20:00 in two sites. Diagnostic ratios of m/p-xylene to ethylbenzene indicated vehicle emissions and solvent volatilization were main sources of VOCs in industrial site during winter. Further positive matrix factorization identified fuel usage and industry source as major sources in industrial park and urban site, respectively. Ozone formation potential calculations showed aromatics contributed most to ozone formation, and benzyl chloride was a key species when its concentration was high. Further empirical kinetic modeling approach revealed ozone formation in industrial park was in VOCs-limited regime. Through air mass trajectory analysis, air pollutants especially ozone from industrial park will be transported to stadiums by northeast wind during the 2022 Asian Games. The reductions in VOCs emissions from industrials are highly recommended for ozone control in 2022 Asian Games.

How a winding-down oil refinery park impacts air quality nearby?

It is always difficult to compare, let alone estimate, the difference of air pollutant concentrations before and after closure of a major source because the pollutants cannot be traced or predicted after entering the ambient. Indeed, we are not aware of any studies specifically related to the air pollutants impacted by a winding-down source. In this work, we applied nine years (2010-2018) online measurement of air pollutants (including PM₁₀, PM_2.5, NO₂, SO₂, O₃ and VOCs) to investigate (i) the temporal behavior of air pollutants before and after closure of an oil refinery park by using pair-wise statistics and correlations between wind speed and direction, and (ii) the source impacts on O₃ concentrations using PMF coupled with multiple linear regression (MLR) analysis (PMF-MLR). Example applications are presented at two monitoring sites (A and B) close to the Kaohsiung Oil Refinery (KOR), located in the southern industrial city of Taiwan. The results show that the KOR shutdown changed air pollutant concentrations to a certain extent in these study areas. We also conclude that, instead of using propylene-equivalent and ozone formation potential (OFP) concentrations, it is better to estimate the formation of O₃ based on PMF-MLR analysis as developed in this study. The PMF analysis has identified various VOCs sources at both sites including solvent usage, petrochemical industrial sources, industrial emissions, vehicle-related sources, vegetation emissions and aged air-masses. Also, the MLR model shows that both the background sources and petrochemical industrial sources may significantly change O₃ concentrations.

Study on the mechanism of the black crust formation on the ancient marble sculptures and the effect of pollution in Beijing area

In Beijing area, the precious stone objects often suffer from the black crusts on the specific parts of the objects, in order to understand the forming mechanism of the black crusts, samples from the stone sculptures in Beijing Stone Carving Art Museum, ZHIHUA Temple and Museum of Western Zhou Yandu Relics were taken and studied. Nondestructive measurement was carried out firstly to acquire main elements of the samples by portable X-ray spectrum (pXRF). Morphology and microstructure of typical black crust samples were examined by ultra-depth of field microscope (UDFM) and scanning electron microscopy coupled with energy dispersive spectroscopy (SEM-EDS). Compositions of black crusts and body rocks were evaluated with X-ray diffraction (XRD), Raman spectra and mapping. Inductively coupled plasma optical emission spectrometry (ICP-OES) and pyrolysis-gas chromatography/mass spectrometry (Py-GCMS) were used to identify the major pollution sources leading to the black crusts.

Through this study, the composition of the black crusts was revealed. Different gypsum crystals and carbonaceous species were found. Pollutant elements analysis and pyrolysis products provide indicators of the pollution sources. As consequence of strong photochemical oxidation processes and the high temperature from June to September in Beijing, more acid rain precursors can be formed. Frequent sulphation process occurs on the CaCO₃/CaMg(CO₃)₂ surface. Combining morphology results and atmospheric data, the formation of black crusts in Beijing can be deduced.

Deep neural networks for spatiotemporal PM2.5 forecasts based on atmospheric chemical transport model output and monitoring data

Reliable long-horizon PM_2.5 forecasts are crucial and beneficial for health protection through early warning against air pollution. However, the dynamic nature of air quality makes PM_2.5 forecasts at long horizons very challenging. This study proposed a novel machine learning-based model (MCNN-BP) that fused multiple convolutional neural networks (MCNN) with a back-propagation neural network (BPNN) for making spatiotemporal PM_2.5 forecasts for the next 72 h at 74 stations covering the whole Taiwan simultaneously. Model configuration involved an ensemble of massive hourly air quality and meteorological monitoring datasets and the existing publicly-available PM_2.5 simulated (forecasted) datasets from an atmospheric chemical transport (ACT) model. The proposed methodology collaboratively constructed two CNNs to mine the observed data (the past) and the forecasted data from ACT (the future) separately. The results showed that the MCNN-BP model could significantly improve the accuracy of spatiotemporal PM_2.5 forecasts and substantially reduce the forecast biases of the ACT model. We demonstrated that the proposed MCNN-BP model with effective feature extraction and good denoising ability could overcome the curse of dimensionality and offer satisfactory regional long-horizon PM_2.5 forecasts. Moreover, the MCNN-BP model has considerably shorter computational time (5 min) and lower computational load than the compute-intensive ACT model. The proposed approach hits a milestone in multi-site and multi-horizon forecasting, which significantly contributes to early warning against regional air pollution.

Full-Coverage PM2.5 Mapping and Variation Assessment during the Three-Year Blue-Sky Action Plan Based on a Daily Adaptive Modeling Approach

Owing to a series of air pollution prevention and control policies, China’s PM2.5 pollution has greatly improved; however, the long-term spatial contiguous products that facilitate the analysis of the distribution and variation of PM2.5 pollution are insufficient. Due to the limitations of missing values in aerosol optical depth (AOD) products, the reconstruction of full-coverage PM2.5 concentration remains challenging. In this study, we present a two-stage daily adaptive modeling framework, based on machine learning, to solve this problem. We built the annual models in the first stage, then daily models were constructed in the second stage based on the output of the annual models, which incorporated the parameter and feature adaptive tuning strategy. Within this study, PM2.5 concentrations were adaptively modeled and reconstructed daily based on the multi-angle implementation of atmospheric correction (MAIAC) AOD products and other ancillary data, such as meteorological factors, population, and elevation. Our model validation showed excellent performance with an overall R2 = 0.91 and RMSE = 9.91 μg/m3 for the daily models, along with the site-based cross-validation R2s and RMSEs of 0.86–0.87 and 12–12.33 μg/m3; these results indicated the reliability and feasibility of the proposed approach. The daily full-coverage PM2.5 concentrations at 1 km resolution across China during the Three-Year Blue-Sky Action Plan were reconstructed in this study. We analyzed the distribution and variations of reconstructed PM2.5 at three different time scales. Overall, national PM2.5 pollution has significantly improved with the annual average concentration dropping from 33.67–28.03 μg/m3, which demonstrated that air pollution control policies are effective and beneficial. However, some areas still have severe PM2.5 pollution problems that cannot be ignored. In conclusion, the approach proposed in this study can accurately present daily full-coverage PM2.5 concentrations and the research outcomes could provide a reference for subsequent air pollution prevention and control decision-making.

Significant contribution of lightning NOx to summertime surface O3 on the Tibetan Plateau

Lightning generates nitrogen oxides (NO_x) in the troposphere, an important precursor of tropospheric ozone (O₃). The Tibetan Plateau (TP) is considered to be a global atmospheric background location with limited anthropogenic influences. However, the observed summertime surface O₃ concentration on the TP is 25% higher than that in highly polluted regions (e.g., southern China). Previous studies have suggested that lightning-produced NO_x (LNO_x) can affect the concentration of surface O₃. We used the Weather Research and Forecasting coupled with chemistry (WRF-Chem) model combined with satellite, ground-based, and airborne observations to evaluate the contribution of LNO_x to the surface O₃ budget on the TP. Our results showed that LNO_x contributed approximately 15% of the surface NO_x emission on the TP in summer. Accordingly, the contribution of LNO_x to the summertime surface daily maximum 8-h average (MDA8) O₃ on the TP was 9.3 ± 7.1 ppb, which was 17.5% ± 14.5% of the total concentration of the surface MDA8 O₃. In addition, our study found that the number of moles of NO produced per lightning flash (LNO_x production efficiency) significantly affected the surface concentration of NO_x, OH, and MDA8 O₃. Increasing the LNO_x production efficiency (PE) from 0 to 330 mol NO flash^-1 increased the concentration of MDA8 O₃ by up to 20% on the TP. Our study revealed that lightning significantly affects the atmospheric chemical processes involving O₃ on the TP.

Emission trends of industrial VOCs in China since the clean air action and future reduction perspectives

Spatiotemporal change patterns of China's industrial VOCs emissions were explored in response to integrated air quality control policies during 2013-2019, and future emissions predicted under the two different scenarios targeting 2030. China's industrial VOCs emissions were decreased to 15.72 Tg in 2019, of which chemical industry, industrial painting, petroleum industry, coal-coking industry, and other industries respectively accounted for 31.0%, 23.9%, 15.6%, and 13.0%, 16.3%, after peaking at 16.40 Tg in 2016. VOC emissions from the petroleum industry and industrial painting showed a continuous increase, with emissions increasing by 0.46 Tg and 0.71 Tg. VOC emissions from the chemical industries increased by 0.91 Tg during 2013-2016 and decreased by 0.72 Tg during 2016-2019. Industrial VOCs emissions in the Beijing-Tianjin-Hebei, Shandong Peninsula, and Central Plain in 2019 respectively reduced by 12.0%, 3.2%, and 8.7% compared to 2013 due to stringent control measures and closure/relocation of highly polluting enterprises. By contrast, industrial VOCs emissions in the West Coast of the Strait and the Central Guizhou increased by 38.1% and 31.8% during 2013-2019. In summary, China's industrial high VOCs emission areas were shifting from key areas to its surrounding areas, resulting in little change in total VOCs emissions. The coal-coking industry, architectural painting, petroleum refining, and pharmaceutical industry will have the most considerable reduction responsibility to reduce VOCs emissions in the future. Guangdong, Jiangsu, Shandong, and Zhejiang will share the highest reduction responsibility, accounting for approximately 40% of national emission reduction.

Study on the Spatial and Temporal Distribution Characteristics and Influencing Factors of Particulate Matter Pollution in Coal Production Cities in China

In recent years, with the continuous advancement of China's urbanization process, regional atmospheric environmental problems have become increasingly prominent. We selected 12 cities as study areas to explore the spatial and temporal distribution characteristics of atmospheric particulate matter in the region, and analyzed the impact of socioeconomic and natural factors on local particulate matter levels. In terms of time variation, the particulate matter in the study area showed an annual change trend of first rising and then falling, a monthly change trend of "U" shape, and an hourly change trend of double-peak and double-valley distribution. Spatially, the concentration of particulate matter in the central and southern cities of the study area is higher, while the pollution in the western region is lighter. In terms of social economy, PM_2.5 showed an "inverted U-shaped" quadratic polynomial relationship with Second Industry and Population Density, while it showed a U-shaped relationship with Generating Capacity and Coal Output. The results of correlation analysis showed that PM_2.5 and PM₁₀ were significantly positively correlated with NO₂, SO₂, CO and air pressure, and significantly negatively correlated with O₃ and air temperature. Wind speed was significantly negatively correlated with PM_2.5, and significantly positively correlated with PM₁₀. In terms of pollution transmission, the southwest area of Taiyuan City is a high potential pollution source area of fine particles, and the long-distance transport of PM_2.5 in Xinjiang from the northwest also has a certain contribution to the pollution of fine particles. This study is helpful for us to understand the characteristics and influencing factors of particulate matter pollution in coal production cities.