Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism

An unexpected increase in PM2.5 levels in Xi'an during the COVID-19 pandemic restrictions: The interplay of anthropogenic and natural factors

This study investigated the variations in summer and winter PM2.5 concentrations and chemical composition in urban Xi'an before and during the COVID-19 pandemic restrictions. During the pandemic restrictions, summer daytime PM2.5 concentrations remained comparable to pre-pandemic levels, while a reduction was noted at nighttime. Conversely, winter experienced a significant increase in both daytime and nighttime PM2.5 concentrations. Chemical composition analysis revealed reductions in secondary inorganic ion concentrations but notable increases in crustal matter concentrations during the pandemic restrictions, particularly evident in winter. The reductions in secondary inorganic ion concentrations were likely due to decreased emissions of corresponding anthropogenic precursors in summer, while linked to reductions in transformation efficiencies in winter. The heightened crustal matter concentrations were likely attributed to increased contributions of long-range air mass transport from dusty regions, especially prevalent in winter. Source apportionment using positive matrix factorization analysis provided quantitative insights into the distinct source profiles contributing to PM2.5 before and during the pandemic restrictions, with secondary inorganic-rich sources decreasing and dust-related sources increasing during the pandemic restrictions. Additionally, combustion sources, primarily from coal and biomass burning, showed higher contributions during winter. In conclusion, this study underscores the complex interplay between anthropogenic and natural factors influencing PM2.5 levels in Xi'an. Efforts to mitigate PM2.5 pollution should prioritize reducing anthropogenic emissions and implementing measures to control dust emissions, particularly when dust-related sources significantly contribute to elevated PM2.5 concentrations. These findings provide valuable insights into developing effective strategies for addressing the PM2.5 pollution problem in Xi'an.

Disparities in precipitation effects on PM2.5 mass concentrations and chemical compositions: Insights from online monitoring data in Chengdu

Precipitation plays a pivotal role in wet deposition, significantly affecting aerosol purification. The efficacy of precipitation in removing aerosols depends on its type and the characteristics of the particulates involved. However, further research is necessary to fully understand how precipitation impacts PM2.5 components. This study utilized high-temporal-resolution data on PM2.5, its components and meteorological factors to examine varying responses influenced by precipitation intensity and duration. The findings indicate that increased rainfall intensity and duration enhance PM2.5 and its constituents removal efficiency. Specifically, longer precipitation periods significantly improve PM2.5 purification, especially with drizzle and light rain. Moreover, there is a direct correlation between pre-precipitation PM2.5 levels and its scavenging rates, with drizzle potentially exacerbating PM2.5 pollution under cleaner conditions (≤ 35 µg/m3). Seasonally, the efficacy of removing PM2.5 components varies notably in response to drizzle and light rain. In spring, higher PM2.5 levels after drizzle were primarily due to increased organic carbon concentrations favored by higher relative humidity and lower pH conditions compared to other seasons, conducive to secondary organic aerosol production. Lower wind speeds and higher temperatures further contribute to water-soluble organic carbon accumulation. Daytime and nighttime precipitation exerted differing influences on PM2.5 components, particularly in spring where daytime drizzle and light rain significantly increased PM2.5 and its constituents, notably NO3-, potentially associated with phase distribution changes between gas and aerosol phases in low-temperature, high-RH conditions compared to nighttime. These results propose a dual-impact mechanism of precipitation on PM2.5 and provide scientific basis for designing effective control strategies.

Influences of synoptic circulations on regional transport, local accumulation and chemical transformation for PM2.5 heavy pollution over Twain-Hu Basin, central China

The Twain-Hu Basin (THB), located in Central China, serves as a key juncture where the northerly "polluted" airflows of the East Asian winter monsoon meet the southerly warm and humid airflows. Using the T-PCA (T-mode Principal Component Analysis) objective synoptic pattern classification, Flexible Particle-Weather Research and Forecasting (FLEXPART-WRF) model, and Random Forest model, we investigate the influences of synoptic circulations on regional transport, local accumulation, and chemical transformation of PM2.5 during heavy air pollution over the THB in January of 2015-2022. The results show that the transport-type synoptic pattern accounts for 65.16% of heavy PM2.5 pollution, indicating that regional transport of PM2.5 dominates the THB's heavy air pollution. The PM2.5/CO ratio is higher in the transport-type pattern and positively correlated with PM2.5 concentrations, reflecting a higher efficiency of chemical transformation to secondary PM2.5 in transport-type pollution compared with the accumulation-type pollution. Transport-type heavy PM2.5 pollution is predominantly influenced by upstream anomalous northerly and easterly airflows at the bottom of the high-pressure system, converging with the southern wind in the receptor area over the THB. Accumulation-type heavy pollution exhibits weak wind anomalies in central and eastern China under the control of a uniform pressure field. Furthermore, thermally-induced vertical circulations with sinking airflows in the middle and lower troposphere suppress the vertical air pollutant dispersions. The relative contributions of atmospheric factors for transport-type PM2.5 heavy pollution events are 38.0% for dynamical driver, 26.8% for thermal driver, and 35.1% for chemical transformation, while in accumulation-type, the contribution rates are 33.9%, 36.3%, and 29.7%, respectively. This study elucidates the influences of synoptic patterns on regional transport, local accumulation, and chemical transformation of PM2.5 for heavy air pollution, with implications for understanding changes of air quality in the receptor region of regional transport.

Does distance from the vehicle headlight change the properties of particulate matter?

Understanding factors affecting particulate matter (PM), particularly light exposure from vehicle headlights, is essential for addressing air quality challenges. However, no comprehensive studies are available. This research, therefore, aims to investigate whether light produced by the vehicle headlight (Halogen type; 12V-55w) at various distances (120 cm, 70 cm, and 20 cm) for 1 hr affects the physical and thermal features of PM obtained from a gasoline vehicle at idle speed. The findings indicate that light can significantly change the properties of PM, especially at a close distance of 20 cm. However, as the distance increases, the effectiveness of light decreases, showing almost no impact at 120 cm. At 20 cm, PM shows higher primary particle diameters (42.1 %), core size/particle size ratio (15.2 %), fringe separation distance (12.4 %), low-volatile substances (9.4 %), non-volatile substances (21.4 %), heat required for 5 %, 50 %, and 95 % reductions in PM mass (14.4 %, 41.7 %, and 13.7 %, respectively), and frequency factor (80.3 %), as well as lower shell size/core size ratio (17.4 %), fringe length (9.9 %), PM mass (21.4 %) and high-volatile substances (8.1 %) compared to no light condition. While, fractal dimension, mean radius of gyration, length/width ratio, roundness, fringe tortuosity, soot ignition temperature, and activation energy are almost identical.

Quantitative estimation and influencing factors of transboundary air pollution from the perspective of regional heterogeneity

Transboundary air pollution (TAP) imposes high health and economic burdens on neighboring regions, further causing the issue of environmental injustice. In this study, changes in PM2.5 concentrations resulting from TAP across various provinces in China were quantitatively analyzed, and the quadratic assignment procedure (QAP) method was employed to explore influencing factors from a regional heterogeneity perspective, with the aim of reducing the impact of transboundary pollution. The results reveal the following: (1) The impact of TAP continued to decrease in all provinces. Compared with those in other regions, inland central cities were more significantly affected. (2) Hebei and Jiangsu were identified as the primary pollution sources, and they exported significant amounts of pollutants to neighboring provinces. In contrast, Shaanxi and Chongqing were the main recipients. The reason for this finding is that TAP is influenced by both geographical proximity and regional development differences. (3) Disparities in the total factor energy efficiency played a crucial role in determining pollution spillover in the short term. In the long term, reducing the gaps in environmental regulations between regions constituted the core element for mitigating TAP. Additionally, the subregional regression analysis results indicated that differences in the industrial structure positively affected the broker and net spillover sectors. On the basis of these findings, targeted policy recommendations for regional collaboration, balanced spatial development, and differentiated governance were formulated.

Identifying the determinants of natural, anthropogenic factors and precursors on PM1 pollution in urban agglomerations in China: Insights from optimal parameter-based geographic detector and robust geographic weighted regression models

Revealing the spatial disparities and driving factors of PM1 pollution is crucial for controlling atmospheric pollution. However, the nexus between PM1 pollution and driving forces has rarely been examined, traditional geographic detector models ignore the shortcomings of data discretization methods determined by experience, and the spatial nonstationary effect of dominant factors on PM1 has not been considered. In this study, a comprehensive modelling framework was proposed that integrated optimal parameter-based geographic detector (OPGD) and robust geographic weighted regression (RGWR) models to explore the influence intensities, interactions and spatial heterogeneity effects of natural factors, anthropogenic factors and precursors on PM1 at the urban agglomeration scale in China. There w significant differences in the distribution of PM1 pollution across China. Socioeconomic and combustion emissions were the dominant anthropogenic factors causing PM1 pollution in northern areas, whereas vegetation and meteorological factors played critical roles as natural determinants in southern regions. However, precursors generated complex effects. The interactions of meteorology with natural and anthropogenic factors had bivariate and nonlinear enhancement effects. The associations between PM1 pollution and influencing factors demonstrated significant spatial heterogeneity. This key knowledge provided scientific guidance for understanding the mechanisms driving PM1 pollution, controlling particulate pollutants and achieving sustainable urban management.

Interpreting hourly mass concentrations of PM2.5 chemical components with an optimal deep-learning model

PM2.5 constitutes a complex and diverse mixture that significantly impacts the environment, human health, and climate change. However, existing observation and numerical simulation techniques have limitations, such as a lack of data, high acquisition costs, and multiple uncertainties. These limitations hinder the acquisition of comprehensive information on PM2.5 chemical composition and effectively implement refined air pollution protection and control strategies. In this study, we developed an optimal deep learning model to acquire hourly mass concentrations of key PM2.5 chemical components without complex chemical analysis. The model was trained using a randomly partitioned multivariate dataset arranged in chronological order, including atmospheric state indicators, which previous studies did not consider. Our results showed that the correlation coefficients of key chemical components were no less than 0.96, and the root mean square errors ranged from 0.20 to 2.11 µg/m3 for the entire process (training and testing combined). The model accurately captured the temporal characteristics of key chemical components, outperforming typical machine-learning models, previous studies, and global reanalysis datasets (such as Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service ReAnalysis (CAMSRA)). We also quantified the feature importance using the random forest model, which showed that PM2.5, PM1, visibility, and temperature were the most influential variables for key chemical components. In conclusion, this study presents a practical approach to accurately obtain chemical composition information that can contribute to filling missing data, improved air pollution monitoring and source identification. This approach has the potential to enhance air pollution control strategies and promote public health and environmental sustainability.

Temporally boosting neural network for improving dynamic prediction of PM2.5 concentration with changing and unbalanced distribution

Increasing medical research evidence suggests that even low PM2.5 concentrations may trigger significant health issues. Hence, an accurate prediction of PM2.5 holds immense significance in securing public health safety. However, current data-drive predictive methods exhibit seasonal model performance decline and difficulties in predicting extremely high values. Those issues may stem from neglecting two crucial features in PM2.5 data streams, i.e., concept drift and imbalanced distribution. In this study, we validate this hypothesis by conducting an in-depth analysis of the characteristics of the PM2.5 data stream and the prediction errors of three mainstream models trained on this PM2.5 data stream, i.e., random forest, convolutional neural network and transformer. Based on the identified types of concept drift and the patterns of imbalanced distribution, we introduce the Temporally boosting neural network (Temp-boost), a novel ensemble learning method designed to enhance predictive accuracy by integrating static and dynamic models. Static models, which are trained on balanced historical datasets, typically receive infrequent updates. Conversely, dynamic models are trained on newly arrived data and undergo more frequent updates. We evaluated the performance of Temp-boost and the three mentioned models in predicting gridded PM2.5 concentrations across the North China Plain in 2019. Compared to the three models, the Temp-boost shows improved prediction accuracy for different seasons, with notable enhancements in high-pollution levels. Specifically, for pollution levels above lightly polluted, the Temp-boost effectively reduces the average MAE by 13.22 μgm-3, RMSE by 13.32 μgm-3 , with reductions peaking MAE at 26.45 μgm-3,RMSE at 25.76 μgm-3 in more severe case.

Machine learning-based quantification and separation of emissions and meteorological effects on PM2.5 in Greater Bangkok

This study presents the first-ever application of machine learning (ML)-based meteorological normalization and Shapley additive explanations (SHAP) analysis to quantify, separate, and understand the effect of meteorology on PM2.5 over Greater Bangkok (GBK). Six ML models namely random forest (RF), adaptive boosting (ADB), gradient boosting (GB), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and cat boosting (CB) were used with meteorological factors, fire activity, land use, and socio-economic data as predictor variables. The LGBM outperformed other models achieving ρ = 0.9 (0.95), MBE = 0 (- 0.01), MAE = 5.5 (3.3) μg m-3, and RMSE = 8.7 (4.9) μg m-3 for hourly (daily) PM2.5 prediction. LGBM was used for spatiotemporal PM2.5 estimation, and meteorological normalization was applied to calculate PM2.5_emis (emission-related PM2.5) and PM2.5_met (meteorology-related PM2.5). Diurnal variation reveals higher PM2.5 levels in the morning (08-10 LT) due to increased traffic emissions and thermal inversion and a decrease in PM2.5 as the day progresses due to decreased emission and inversion dissipation. Monthly variation suggests higher PM2.5 in winter (December and January) due to emissions and stagnant meteorological conditions. Negative PM2.5_met during November, March, and April values show meteorology improves air quality, while positive values from December to February indicate stagnant winter conditions worsen it. During winter, PM2.5_emis and PM2.5 showed an increasing trend in 15.6% and 67.8% of the area while decreasing trends fell from 23.2 to 1.9%. In summer, the percentage of areas with an increasing trend rose from 18.7 to 34.6%, and decreasing areas fell from 12.6 to 6.5%. Increase in PM2.5 despite decreasing emission over a larger area, indicating limited effectiveness of mitigation measures. Winter exhibits greater PM2.5 variability due to episodic increases from changing meteorological conditions. In Bangkok and nearby areas, higher variability is mainly driven by meteorology, with more consistent emissions in Bangkok compared to rural areas affected by agricultural burning. PM2.5 and PM2.5_emis showed stronger persistence in winter than in summer, with weaker effects in Bangkok. Hurst exponent averages were 0.75, 0.76, and 0.72 for PM2.5 and 0.79, 0.8, and 0.73 for PM2.5_emis in dry, winter, and summer seasons, respectively. SHAP analysis suggested relative humidity, planetary boundary layer height, v wind, temperature, u wind, global radiation, and aerosol optical depth as the key variables affecting PM2.5 with mean absolute SHAP values of 5.29, 4.79, 4.29, 3.68, 2.37, 2.22, and 2.03, respectively. Based on these findings, some policy recommendations have been proposed.

Spatial characterization of periodic behaviors of ground PM2.5 concentration across the Yangtze River Delta and the North China Plain during 2014-2024: A new insight on driving processes of regional air pollution

Fine particulate matter (PM2.5) has long been the major air pollutant across China, but its periodic behaviors are still not comprehensively understood at a regional scale. Spatial characterization of the periodic behaviors can sufficiently extract the spatiotemporal information of PM2.5 time series and provide additional insights to understand key drivers of regional air pollution. This study collected PM2.5 hourly concentrations at 168 sites across the Yangtze River Delta (YRD) and the North China Plain (NCP) during 2014-2024 and identified PM2.5 periodic behaviors based on a fast Fourier transform (FFT) algorithm with a pseudo F statistic extraction and harmonic regression analysis with a linear trend term. Spatial characterization of PM2.5 concentration periodicity was explored in long-term trend, daily and annual cycles. Results showed more local emissions and atmospheric upward dissipation in the NCP resulted in a stronger daily vibration of PM2.5 concentration rather than in the YRD. Greater annual amplitude in the NCP than in the YRD reflected the significantly-elevated emissions from coal combustion for domestic heat in cold season. A strongly negative correlation between annual amplitude of PM2.5 concentrations and spatial latitude of monitoring sites across the NCP was attributed to the decreasing washing-out effect of precipitation from southeast to northwest. PM2.5 concentration across both regions was experienced a long-term decreasing trend in 2014-2024 and the trend has slowed down after 2021. This study provided a new insight on driving PM2.5 concentrations across the NCP and the YRD of China and underlined the importance of periodic behaviors as complementing PM2.5 characterization.

Metabolic Disruptions and Non-Communicable Disease Risks Associated with Long-Term Particulate Matter Exposure in Northern Thailand: An NMR-Based Metabolomics Study

Background/Objectives: Particulate matter (PM) is a primary health hazard associated with metabolic pathway disruption. Population characteristics, topography, sources, and PM components contribute to health impacts. Methods: In this study, NMR-based metabolomics was used to evaluate the health impacts of prolonged exposure to PM. Blood samples (n = 197) were collected from healthy volunteers in low- (control; CG) and high-exposure areas (exposure; EG) in Northern Thailand. Non-targeted metabolite analysis was performed using proton nuclear magnetic resonance spectroscopy (1H-NMR). Results: Compared to CG, EG showed significantly increased levels of dopamine, N6-methyladenosine, 3-hydroxyproline, 5-carboxylcytosine, and cytidine (p < 0.05), while biopterin, adenosine, L-Histidine, epinephrine, and norepinephrine were significantly higher in CG (p < 0.05). These metabolic disturbances suggest that chronic exposure to particulate matter (PM) impairs energy and amino acid metabolism while enhancing oxidative stress, potentially contributing to the onset of non-communicable diseases (NCDs) such as cancer and neurodegenerative conditions. Conclusions: This study highlighted the connection between sub-chronic PM2.5 exposure, metabolic disturbances, and an increased risk of non-communicable diseases (NCDs), stressing the critical need for effective PM2.5 reduction strategies in Northern Thailand.

Estimating the seasonal and spatial variation of urban vegetation's PM2.5 removal capacity

Fine particulate matter (PM2.5) is one of the most severe factors contributing to urban air pollution, posing significant risks to human health and environmental quality. Urban vegetation, acting as a natural method for pollution mitigation, can effectively reduce harmful air particle concentrations through processes like adsorption and deposition. While much research has quantified urban vegetation's role in PM2.5 removal, the spatial variability and seasonal fluctuations of this process in urban environments remain poorly understood. Furthermore, few studies have quantitatively explored the environmental factors that influence this capability. Using Shanghai as a case study, this research estimates the PM2.5 reduction by urban vegetation in 2022, integrating the i-Tree Eco model with Local Climate Zones (LCZs) classification. The results indicate that vegetation plays a significant role in PM2.5 removal, with a total annual removal of 835 tons and an average removal rate of 0.51 g⋅m-2⋅year-1 per unit leaf area. The maximum annual air quality improvement reached 21.7%, with an average of 4.09%. The removal flux exhibited a clear "double peak" pattern throughout the year, with peaks occurring in late spring and late summer. Significant spatial variations in PM2.5 removal capacity were observed across different LCZs, ranked as follows: Dense Trees > Open Lowrise > Large Lowrise > Bush/Shrub > Scattered Trees > Others. Notably, Open Lowrise areas demonstrated considerable potential in both removal flux and total removal. The 38-42 mm evapotranspiration range was found to be the most effective for PM2.5 removal. However, when evapotranspiration exceeded 50 mm, removal efficiency showed a clear diminishing marginal effect, closely linked to the regulation of leaf stomatal opening and closing. The findings of this study underscore the importance of vegetation in improving air quality and provide valuable insights for urban planning and environmental policy.

Antibiotics in ambient fine particulate matter from two metropolitan cities in China: Characterization, source apportionment, and health risk assessment

Excessive production and widespread application of antibiotic has led to residues in environmental matrices worldwide. There is limited knowledge of the concentrations of antibiotics bound to ambient fine particulate matter (PM2.5) and their health risks. We investigated the occurrence, sources, environmental driving factors, and health risks of antibiotics in PM2.5 samples collected from Beijing and Shijiazhuang, China, during periods of high air pollution. Using ultra-high performance liquid chromatography-tandem mass spectrometry, 25 antibiotics were detected in PM2.5 at concentrations ranging from undetectable to 774.7 pg/m3. These compounds were predominantly tetracyclines and macrolides. The positive matrix factorization model was used to pinpoint the main sources of these antibiotics as pharmaceutical and medical waste, sewage treatment plants, and livestock emissions, with contributions of 39.1 %, 31.7 %, and 29.2 % respectively, to the total concentrations. Crucial environmental driving factors were determined using a linear mixed-effects model and random forest model. Most antibiotics showed a positive correlation with gaseous pollutants and a negative correlation with meteorological factors. PM2.5, PM10, and CO had the highest influence. The estimated daily intake and hazard quotient (HQ) were calculated to assess the human inhalation exposure risks for these antibiotics, and children aged 0-6 years had the highest intake of 102.8 pg/kg/day. Although the calculated health risk of antibiotic inhalation was low (HQ < 1), considering that exposure to antibiotics via inhalation occurs over long periods and these compounds accumulate, further attention should be given to health risks associated with this exposure. Our results provide valuable insight for environmental planning and policymaking concerning antibiotic pollution and its associated health risks.

Long-Term Retrospective Predicted Concentration of PM2.5 in Upper Northern Thailand Using Machine Learning Models

This study aims to build, for the first time, a model that uses a machine learning (ML) approach to predict long-term retrospective PM2.5 concentrations in upper northern Thailand, a region impacted by biomass burning and transboundary pollution. The dataset includes PM10 levels, fire hotspots, and critical meteorological data from 1 January 2011 to 31 December 2020. ML techniques, namely multi-layer perceptron neural network (MLP), support vector machine (SVM), multiple linear regression (MLR), decision tree (DT), and random forests (RF), were used to construct the prediction models. The best ML prediction model was selected considering root mean square error (RMSE), mean prediction error (MPE), relative prediction error (RPE) (the lower, the better), and coefficient of determination (R2) (the bigger, the better). Our study found that the ML model-based RF technique using PM10, CO2, O3, fire hotspots, air pressure, rainfall, relative humidity, temperature, wind direction, and wind speed performs the best when predicting the concentration of PM2.5 with an RMSE of 6.82 µg/m3, MPE of 4.33 µg/m3, RPE of 22.50%, and R2 of 0.93. The RF prediction model of PM2.5 used in this research could support further studies of the long-term effects of PM2.5 concentration on human health and related issues.

Spatiotemporal correlations of PM2.5 and O3 variations: A street-scale perspective on synergistic regulation

PM2.5 and O3 are major pollutants affecting air quality and posing serious health risks in China. While many studies focus on their control at urban and regional scales, their co-regulation at the street level-closely tied to traffic emissions and commuting patterns-remains unexplored. This study addressed the gap by using nonlinear statistical methods to analyze the spatiotemporal evolution of PM2.5 and O3 from street-scale mobile measurements in Fuzhou, China. A random forest (RF) model was applied to elucidate factors influencing PM2.5-O3 synchronicity. Key findings revealed that street-scale variations in PM2.5 and O3 exhibited multifractality and long-term persistence. Co-directional changes between PM2.5 and O3 peaked at noon, compared to traffic peak hours and midnight. An 800 m threshold was identified for analyzing PM2.5-O3 synchronicity-below this spatial scale, local factors weaken their concordance, while beyond it, the concordance strengthened. RF models showed that PM2.5 was primarily influenced by precursor substances in winter and meteorological conditions in summer, while O3 was consistently affected by meteorological conditions across both seasons. Road traffic and construction disrupted the co-directional changes of PM2.5 and O3, whereas high humidity partially mitigated high concentrations of both pollutants but enhanced their synchronicity.

Association between annual concentration of air pollutants and incidence of metabolic syndrome among Korean adults: Korean Genome and Epidemiology Study (KoGES)

BACKGROUND

Air pollution is a global public health concern and incidence rates of metabolic syndrome (MetS) are increasing. To evaluate the effect of long-term air pollution exposure, we examined the association between long-term exposure to ambient air pollution and the incidences of MetS among Korean adults.

METHODS

We used data from the Korean Genome and Epidemiology Study's Cardiovascular Disease Association Study, a population-based cohort consisting of community-dwelling Korean adults between 2005 and 2011, who were followed up with until 2016 (n = 7,428). Air pollution exposure was estimated using the Congestion Mitigation and Air Quality model based on the participants' addresses. The participants had a physical examination at every visit during follow-up, and MetS was defined based on the National Institute of Health's National Cholesterol Education Program-Adult Treatment Panel III. We used Cox proportional hazard model to analyze the association between long-term air pollution exposure and incidences of MetS per interquartile range (IQR) increment of the annual concentration after adjusting for potential confounders using single and two-pollutant analysis.

RESULTS

The hazard ratios (HR) of MetS per IQR increment in PM2.5, SO2, NO2, and CO were 1.19 (95% CI: 1.12-1.27), 1.57 (95% CI: 1.47-1.68), 1.11 (95% CI: 1.03-1.20), and 1.63 (95% CI: 1.48-1.78), respectively. The incidences of MetS components, which are high blood pressure, elevated fasting glucose, abdominal obesity, high fasting triglyceride (TG), and low fasting high-density lipoprotein (HDL-C), were significantly associated with an IQR increment especially in SO2 and CO. In subgroup analysis, males had higher risk of MetS than females. The HR was the highest in the 60-69 year old age group for all pollutants.

CONCLUSION

In the present study, we found that long-term ambient air pollution exposure increased the incidences of MetS and its components among Korean adults, especially in males and the elderly population.

Spatiotemporal evolution of PM2.5 and its components and drivers in China, 2000-2023: effects of air pollution prevention and control actions in China

This study evaluated the Air Pollution Prevention and Control Action Plan (APPCAP) in China using 2000-2023 data. The average annual PM2.5 concentration dropped from 46.11 ± 16.18 µg/m3 to 31.75 ± 14.22 µg/m3 (P < 0.05) after APPCAP, with components showing a similar decline. Temporal analysis via Mann-Kendall test indicated a decreasing trend (Z < 0, P < 0.05), seasonally peaking in winter and lowest in summer. Spatially, APPCAP reduced concentration distribution, with key regions improving but areas like Shandong and Henan still facing severe pollution. The main PM2.5 driver shifted from human (e.g., population density) to meteorological (e.g., temperature) factors post-APPCAP, and anthropogenic influence varied across regions. In summary, APPCAP has curbed PM2.5 pollution, yet SO42-, NO3-, and NH4+ remain relatively high, and the increasing human impact in central and southeastern China demands attention in future policies.

Characterization and Source Apportionment Analysis of PM2.5 and Ozone Pollution over Fenwei Plain, China: Insights from PM2.5 Component and VOC Observations

PM2.5 and volatile organic compounds (VOCs) have been identified as the primary air pollutants affecting the Fenwei Plain (FWP), necessitating urgent measures to improve its air quality. To gain a deeper understanding of the formation mechanisms of these pollutants, this study employed various methods such as HYSPLIT, PCT, and PMF for analysis. Our results indicate that the FWP is primarily impacted by PM2.5 from the southern Shaanxi air mass and the northwestern air mass during winter. In contrast, during summer, it is mainly influenced by O3 originating from the southern air mass. Specifically, high-pressure fronts are the dominant weather pattern affecting PM2.5 pollution in the FWP, while high-pressure backs predominately O3 pollution. Regarding the sources of PM2.5, secondary nitrates, vehicle exhausts, and secondary sulfates are major contributors. As for volatile organic compounds, liquefied petroleum gas sources, vehicle exhausts, solvent usage, and industrial emissions are the primary sources. This study holds crucial scientific significance in enhancing the regional joint prevention and control mechanism for PM2.5 and O3 pollution, and it provides scientific support for formulating effective strategies for air pollution prevention and control.

Regional PM2.5 prediction with hybrid directed graph neural networks and Spatio-temporal fusion of meteorological factors

Traditional statistical prediction methods on PM2.5 often focus on a single temporal or spatial dimension, with limited consideration for regional transport interactions among adjacent cities. To address this limitation, we propose a hybrid directed graph neural network method based on deep learning, which utilizes domain features to quantify the influence of neighboring cities and construct a directed graph. The model comprises a historical feature extraction module and a future transmission prediction module, and each module integrates a Graph Neural Network (GNN) and a Long Short-Term Memory Network (LSTM) for spatiotemporal encoding. Compared to other neural network models, our model improves the prediction accuracy of PM2.5 concentration and demonstrates superior performance for 48-h prediction in the North China Plain. For 3- to 48-h prediction tasks, the proposed model achieves mean absolute error (MAE) at 7.64 - 14.04 μg/m3. In addition, by expanding the modeling scope from different directions and integrating domain information, the model significantly enhances its ability to predict PM2.5 trends, seasonal variations, and PM2.5 exceedances in heavily polluted urban areas. The proposed model represents a promising advancement in optimizing air quality forecasting and management.

Emerging threats in Сentral Asia: Comparative characterization of organic and elemental carbon in ambient PM2.5 in urban cities of Kazakhstan

This study (June 2022-July 2023) investigates the atmospheric concentrations of carbonaceous species, including organic carbon (OC) and elemental carbon (EC), in PM2.5 in two major cities in Kazakhstan. Samples were collected from two sites in Almaty (Seifullin and KazNU) and one in Astana. The results showed that Almaty had significantly higher annual average concentrations of OC (10.8 and 10.5 μg/m3) and EC (1.68 and 1.87 μg/m3) compared to Astana (OC: 7.1 μg/m3, EC: 0.61 μg/m3). Both cities exhibited pronounced seasonal variations, with significantly elevated concentrations (1.5-3.4 times for OC, 2.1-4.8 times for EC) during the heating season compared to the non-heating season. This indicates a significant influence of coal and biomass combustion for heating on carbonaceous aerosol concentrations. Both cities' OC/EC ratios varied widely (2.6-39.4), showing strong positive correlations (0.61-0.94) across all seasons except summer, suggesting a common primary emission source. Primary organic carbon dominated OC levels in winter (71-74%), whereas secondary organic carbon contributed significantly to OC concentrations in summer (43-50%). Higher OC-EC concentrations correlated with lower atmospheric visibility values. The OC-EC contributions to the total light extinction coefficient were estimated to be 15.3-15.9% for Almaty and 12.0% for Astana stations.

Influence of meteorological conditions on atmospheric microplastic transport and deposition

Atmospheric microplastics are of great concern because of their potential impact on the environment and human health. Although several studies have shown the presence of large quantities of microplastics in the air, questions about the transport and deposition of microplastics in the atmosphere remain unanswered. Based on these shortcomings, this review provides a comprehensive overview of the influence of meteorological conditions on atmospheric microplastic fate. Dry and wet deposition are the main removal mechanisms for atmospheric microplastic. Furthermore, by exploring how wind facilitates the long-range transport of microplastics between terrestrial and marine ecosystems, establishing a global microplastic cycle. Besides, this review also examines the effects of other meteorological conditions on atmospheric microplastic transport. Characteristics of current atmospheric microplastic models are summarized, particularly with respect to the consideration of meteorological conditions. Finally, we propose future research directions and mitigation measures for atmospheric microplastic pollution, which are necessary for mitigating atmospheric microplastic pollution and protecting ecosystems and human health.

Application of the Lasso regularisation technique in mitigating overfitting in air quality prediction models

As a significant global concern, air pollution triggers enormous challenges in public health and ecological sustainability, necessitating the development of precise algorithms to forecast and mitigate its impacts, which has led to the development of many machine learning (ML)-based models for predicting air quality. Meanwhile, overfitting is a prevalent issue with ML algorithms that decreases their efficacy and generalizability. The present investigation, using an extensive collection of data from 16 sensors in Tehran, Iran, from 2013 to 2023, focuses on applying the Least Absolute Shrinkage and Selection Operator (Lasso) regularisation technique to enhance the forecasting precision of ambient air pollutants concentration models, including particulate matter (PM2.5 and PM10), CO, NO2, SO2, and O3 while decreasing overfitting. The outputs were compared using the R-squared (R2), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and normalised mean square error (NMSE) indices. Despite the preliminary findings revealing that Lasso dramatically enhances model reliability by decreasing overfitting and determining key attributes, the model's performance in predicting gaseous pollutants against PM remained unsatisfactory (R2PM2.5 = 0.80, R2PM10 = 0.75, R2CO = 0.45, R2NO2 = 0.55, R2SO2 = 0.65, and R2O3 = 0.35). The minimal degree of missing data presumably explained the strong performance of the PM model, while the high dynamism of gases and their chemical interactions, in conjunction with the inherent characteristics of the model, were the primary factors contributing to the poor performance of the model. Simultaneously, the successful implementation of the Lasso regularisation approach in mitigating overfitting and selecting more important features makes it highly suggested for application in air quality forecasting models.

The capacity of human interventions to regulate PM2.5 concentration has substantially improved in China

The rapid urbanization in China has brought about serious air pollution problems, which are likely to persist for a considerable period as the urbanization process continues. In urban areas, the spatial distribution of air pollutants represented by PM2.5 has been proved mainly affected by emission, urban landscape pattern (short as ULP), as well as meteorological conditions. However, the contributions of these factors can seriously vary with different periods of urban development. Based on multi-source data, 304 prefecture-level cities in China were chosen as study areas, and we used the Geographically and Temporally Weighted Regression (GTWR) model to quantify the relative contributions of three factors-emission, ULP, and meteorological condition-to PM2.5 concentration variation in different periods, namely, the Slow Ascending Period (SAP, 2000-2007), the Stable High-level Period (SHP, 2007-2013), and the Rapid Decline Period (RDP, 2013-2020). During SAP, the relative contribution of emission remained low and the relative contribution of ULP decreased, while the contribution of meteorological factors to PM2.5 concentration variation becoming the dominant factor. During SHP and RDP, the relative contribution of emission notably increased (The largest increase is 28 %), while the relative contribution of meteorological factors significantly decreased (The largest decrease is 16 %). Spatially, the key regions for air pollution control in China, such as the Beijing-Tianjin-Hebei, the Fenwei Plain, the Yangtze River Delta, and the Pearl River Delta, experienced a significantly greater decrease (The largest decrease is 39 %) in the meteorological contribution and increase in the emission contribution (The largest increase is 66 %) compared to other regions. In general, we found that 27 cities in southwest China become increasingly sensitive to meteorological conditions, while the majority of cities (277 in total), particularly in key regions, have shown a growing sensitivity to emission during the whole period. These results prove that the ability of anthropogenic influence on air quality is gradually more effective, indicating the air pollution prevention and control policies in China in recent years have achieved satisfactory results. It is worthy to notice that the PM2.5 level in most cities is still sensitive to emissions. Therefore, strict emission reduction measures still needs to implemented in the future to further improve air quality.

Characterization and regional linkage analysis of PM2.5 emissions driven by energy consumption in mainland China, 2007-2017

Fine particulate matter (PM2.5) pollution poses a serious threat to public health, and there has been a recent resurgence in PM2.5 pollution levels in China. Inter-provincial trade has further complicated the allocation of responsibility for PM2.5 emissions. An in-depth analysis of the Air Pollution Prevention and Control Action Plan (APPCAP), a highly effective environmental policy, offers new perspectives and avenues for reflection. Using the multi-regional input-output model and structural decomposition analysis model, this study provides insights into the interlinkages of PM2.5 emissions, and their influencing mechanisms among different regions from the perspective of source emissions by quantifying the dynamics of production-related PM2.5 emissions (PEp) associated with energy consumption and the key driving socio-economic factors in the pre-and post-APPCAP phases. The results indicate that PEp initially increased and then decreased over the study period. In the pre-policy stage, only five provinces exhibited a decrease in PEp, and this number increased to 21 provinces post-policy. Manufacturing and energy utilities consistently account for significant PEp contributions, particularly in Shanghai, Inner Mongolia, and Shanxi. This study finds that pre-policy, the industrial structure effect, the demographic effect, and the level of affluence effect primarily drove PEp increases. The post-policy decrease was influenced by industrial structure and consumption pattern effects. Although China's PEp remains higher than the consumption-based PM2.5 emissions (PEc), significant provincial variations exist. Notably, while changes in PEp do not always align with PM2.5 concentration changes, simultaneous reductions following policy implementation signal positive progress in pollution control. This underscores the necessity of continuously optimizing policy strategies to accommodate regional characteristics.

The Interactive and Joint Associations of Ambient PM2.5 and Temperature on the Onset of Acute Coronary Syndrome: Findings from The Chinese Cardiovascular Association (CCA) Database-Chest Pain Center Registry

Environmental factors are important exposures that trigger acute coronary syndrome (ACS) onset. However, the interactive and joint associations of multiple exposures on ACS onset remain unknown. A time-stratified case-crossover study was conducted including 1,292,219 ACS patients who were selected from 1,895 districts/counties across China during 2015-2020. The ACS conditions included ST-segment-elevation myocardial infarction (STEMI), non-ST-segment-elevation myocardial infarction (NSTEMI), and unstable angina (UA). Conditional logistic regression models were applied to estimate the interactive and joint associations of particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) and temperature (TM) with the ACS onset. The ACS onset risks increased by 0.38% for each 10 μg/m3 increment in PM2.5 concentration, and an inverse U-shaped curve of TM and risk of ACS onset was observed. The associations of PM2.5 with the ACS onset were greater on colder days. The jointly attributable fractions (AF) of PM2.5 and nonoptimal TM was 9.93% in all ACS patients, 10.31% in females, 12.91% in patients aged ≥65 years, 17.54% in NSTEMI patients, and 12.43% in Southern China. This study suggested that joint short-term exposures to ambient PM2.5 and moderate cold TM may substantially increase the onset of ACS. Furthermore, there are synergistic interactions among higher PM2.5 and lower TM peaks on the ACS onset.

Vertical distribution of aerosols and association with atmospheric boundary layer structures during regional aerosol transport over central China

Atmospheric boundary layer (ABL) structure was a crucial factor in altering the vertical aerosol distribution and modulating the impact of regional aerosol transport on the atmospheric environment in the receptor region. The long-term characteristics of ABL structures for different vertical aerosol distributions and the distinct influencing mechanisms between daytime and nighttime aerosol transport interacting with the diurnal ABL transition have rarely been studied in the receptor regions. Based on 9-year (2013-2021) satellite-retrieved profiles of aerosol extinction coefficients and meteorological sounding data, we targeted Wuhan, an urban city with noteworthy transport contribution in central China, to reveal the general wintertime transport height of ∼500 m and the corresponding unstable ABL structure during regional transport. By comparing typical daytime and nighttime aerosol transport with high-resolution Lidar observations, the aerosol transport near the ABL top coupled with intense mechanical mixing provided sufficient meteorological conditions for heavy aerosol pollution formation in the receptor regions, which was more favorable during nighttime transport followed by the adequate ABL development after sunrise. These findings enhance our comprehension of the ABL impact on air pollution in the receptor regions, which have implications for the precise prevention and control of the regional atmospheric environment.

Increased impacts of aircraft activities on PM2.5 concentration and human health in China

The rapid development of China's aviation industry has caused a rapid increase in airport PM2.5 emissions. This study uses the Global Exposure Mortality Model (GEMM) to evaluate the monthly deaths caused by aircraft activities at 164 airports in China from 2015 to 2023, based on the PM2.5 concentration of airport aircraft activities and the detection data of the China National Environmental Monitoring Center, including twenty age groups, six diseases, and gender. This paper presents three main conclusions. Firstly, aviation PM2.5 emissions significantly impact mortality, with notable variations by year and season. The highest cumulative deaths are recorded in 2023, particularly in the third quarter, which peaked at 8,305 deaths. Despite the comparatively modest total of 11,604 deaths in 2022, a mere 0.2965 μg/m3 increase in PM2.5 concentration would precipitate an additional 39,138 deaths, representing a 1.05-fold rise from 2015. Secondly, the 80-84 age bracket exhibited the highest death proportion (16.51 %-18.73 %), while the 5-9 and 10-14 age groups had the lowest (0 %-0.13 %). Males aged 80-84 are the most affected demographic, with each 1 μg/m3 increase in PM2.5 leading to an additional 87 male deaths monthly in 2023, primarily from stroke and ischemic heart disease. In contrast, females only experienced 67 additional deaths per month from the same concentration increase. Lastly, airports in the economically vibrant Beijing-Shanghai-Guangzhou-Shenzhen region showed the highest mortality rates due to PM2.5 emissions. Airports in eastern coastal areas are more severely impacted than those in central and western China, revealing a spatial clustering of high death tolls in developed regions.

Spatiotemporal distribution and source analysis of PM2.5 and its chemical components in national industrial complexes of Korea: a case study of Ansan and Siheung

This study investigated the sources and distribution characteristics of PM2.5 and its chemical components (ions, carbons, elements) at five locations within the Banwal and Sihwa National Industrial Complexes in Ansan and Siheung. These large-scale industrial clusters, comprising 7642 businesses across sectors such as petrochemicals, steel, machinery, and electronics, operate throughout the year. From 2020 to 2023, the average PM2.5 concentration in the study area was 28.66 ± 16.72 μg/m3, with notable seasonal differences observed across the five measurement points. Ionic components were the primary contributors to PM2.5, while carbon and trace element concentrations fluctuated with the seasons. The coefficient of divergence (COD) analysis indicated that emission source differences between sites were insignificant, with COD values consistently below the threshold of 0.3. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) identified secondary aerosols and vehicle emissions as the main sources of PM2.5, alongside additional contributions from Asian dust, industrial emissions, road dust, coal combustion, metal processing, biomass burning, and soil dust. These results highlight the need for systematic and economical air pollution control strategies in complex industrial areas, using COD to identify source differences and quantify contributions at different sites.

Integrating Doppler LiDAR and machine learning into land-use regression model for assessing contribution of vertical atmospheric processes to urban PM2.5 pollution

Air pollution has been recognized as a global issue, through adverse effects on environment and health. While vertical atmospheric processes substantially affect urban air pollution, traditional epidemiological research using Land-use regression (LUR) modeling usually focused on ground-level attributes without considering upper-level atmospheric conditions. This study aimed to integrate Doppler LiDAR and machine learning techniques into LUR models (LURF-LiDAR) to comprehensively evaluate urban air pollution in Hong Kong, and to assess complex interactions between vertical atmospheric processes and urban air pollution from long-term (i.e., annual) and short-term (i.e., two air pollution episodes) views in 2021. The results demonstrated significant improvements in model performance, achieving CV R2 values of 0.81 (95 % CI: 0.75-0.86) for the long-term PM2.5 prediction model and 0.90 (95 % CI: 0.87-0.91) for the short-term models. Approximately 69 % of ground-level air pollution arose from the mixing of ground- and lower-level (105 m-225 m) particles, while 21 % was associated with upper-level (825 m-945 m) atmospheric processes. The identified transboundary air pollution (TAP) layer was located at ~900 m above the ground. The identified Episode one (E1: 7 Jan-22 Jan) was induced by the accumulation of local emissions under stable atmospheric conditions, whereas Episode two (E2: 13 Dec-24 Dec) was regulated by TAP under instable and turbulent conditions. Our improved air quality prediction model is accurate and comprehensive with high interpretability for supporting urban planning and air quality policies.

Towards Cleaner Cities: Estimating Vehicle-Induced PM2.5 with Hybrid EBM-CMA-ES Modeling

In developing countries, vehicle emissions are a major source of atmospheric pollution, worsened by aging vehicle fleets and less stringent emissions regulations. This results in elevated levels of particulate matter, contributing to the degradation of urban air quality and increasing concerns over the broader effects of atmospheric emissions on human health. This study proposes a Hybrid Explainable Boosting Machine (EBM) framework, optimized using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), to predict vehicle-related PM2.5 concentrations and analyze contributing factors. Air quality data were collected from Open-Seneca sensors installed along the Nairobi Expressway, alongside meteorological and traffic data. The CMA-ES-tuned EBM model achieved a Mean Absolute Error (MAE) of 2.033 and an R2 of 0.843, outperforming other models. A key strength of the EBM is its interpretability, revealing that the location was the most critical factor influencing PM2.5 concentrations, followed by humidity and temperature. Elevated PM2.5 levels were observed near the Westlands roundabout, and medium to high humidity correlated with higher PM2.5 levels. Furthermore, the interaction between humidity and traffic volume played a significant role in determining PM2.5 concentrations. By combining CMA-ES for hyperparameter optimization and EBM for prediction and interpretation, this study provides both high predictive accuracy and valuable insights into the environmental drivers of urban air pollution, providing practical guidance for air quality management.

Trace elements in PM2.5 from 2016 to 2021 in Shenzhen, China: Concentrations, temporal and spatial distribution, and related human inhalation exposure risk

The prevalence of trace elements from industrial and traffic activities poses potential health risks through inhalation exposure. Prior studies have focused on trace elements in water, food, and dust, and less attention has been paid to their occurrence in fine particulate matter (PM2.5). In this study, 1424 air samples were collected from three districts (Nanshan, Longgang, and Yantian) in Shenzhen from 2016 to 2021, and we analyzed the concentrations, temporal trends, and spatial distributions of PM2.5 and associated trace elements. Both PM2.5 and trace elements exhibited decreasing trends and similar seasonal variations, with high levels in cold seasons and low levels in warm seasons. In terms of spatial distributions, the concentrations of PM2.5 and trace elements in Nanshan and Longgang were significantly higher than those in Yantian, likely due to the industrial structure and traffic activities. It is worth noting that PM2.5 was identified as a potential mediator of the effect of meteorological parameters on trace element levels. Besides, the values of estimated daily intake (EDI) and uptake (EDU) suggested that infants and young children experienced an elevated risk of exposure to trace elements. While the annual average excess hazard indexes (R) were below the safety threshold (10-6), carcinogenic trace elements like arsenic (As) and chromium (Cr) posed a greater potential threat to human health compared to non-carcinogenic trace elements.

Understanding the drivers of PM2.5 concentrations in Chinese cities: A comprehensive study of anthropogenic and environmental factors

Understanding the factors that drive PM2.5 concentrations in cities with varying population and land areas is crucial for promoting sustainable urban population health. This knowledge is particularly important for countries where air pollution is a significant challenge. Most existing studies have investigated either anthropogenic or environmental factors in isolation, often in limited geographic contexts; however, this study fills this knowledge gap. We employed a multimethodological approach, using both multiple linear regression models and geographically weighted regression (GWR), to assess the combined and individual effects of these factors across different cities in China. The variables considered were urban built-up area, land consumption rate (LCR), population size, population growth rate (PGR), longitude, and latitude. Compared with other studies, this study provides a more comprehensive understanding of PM2.5 drivers. The findings of this study showed that PGR and population size are key factors affecting PM2.5 concentrations in smaller cities. In addition, the extent of urban built-up areas exerts significant influence in medium and large cities. Latitude was found to be a positive predictor for PM2.5 concentrations across all city sizes. Interestingly, the northeast, south, and southwest regions demonstrated lower PM2.5 levels than the central, east, north, and northwest regions. The GWR model underscored the importance of considering spatial heterogeneity in policy interventions. However, this research is not without limitations. For instance, international pollution transfers were not considered. Despite the limitation, this study advances the existing literature by providing an understanding of how both anthropogenic and environmental factors, in conjunction with city scale, shape PM2.5 concentrations. This integrated approach offers invaluable insights for tailoring more effective air pollution management strategies across cities of different sizes and characteristics.

Estimating the spatiotemporal distribution of PM2.5 concentrations in Tianjin during the Chinese Spring Festival: Impact of fireworks ban

SETTING

off fireworks during the Spring Festival (SF) is a traditional practice in China. However, because of its environmental impact, the Chinese government has banned this practice completely. Existing evaluations of the effectiveness of firework prohibition policies (FPPs) lack spatiotemporal perspectives, making it difficult to comprehensively assess their effects on air quality. Consequently, this study used remote sensing technology based on aerosol optical depth and multiple variables, compared nine statistical learning methods, and selected the optimal model, transformer, to estimate daily spatiotemporal continuous PM2.5 concentration datasets for Tianjin from 2016 to 2020. The overall model accuracy reached a root mean square error of 15.30 μg/m³, a mean absolute error of 9.55 μg/m³, a mean absolute percentage error of 21.07%, and an R2 of 0.88. Subsequently, we analysed the variations in PM2.5 concentrations from three time dimensions-the entire year, winter, and SF periods-to exclude the impact of interannual variations on the experimental results. Additionally, we quantitatively estimated firework-specific PM2.5 concentrations based on time-series forecasting. The results showed that during the three years following the implementation of the FPPs, firework-specific PM2.5 concentrations decreased by 52.70%, 49.76%, and 86.90%, respectively, compared to the year before the implementation of the FPPs. Spatially, the central urban area and industrial zones are more affected by FPPs than the suburbs. However, daily variations of PM2.5 concentrations during the SF showed that high concentrations of PM2.5 produced in a short period will return to normal rapidly and will not cause lasting effects. Therefore, the management of fireworks needs to consider both environmental protection and the public's emotional attachment to traditional customs, rather than simply imposing a blanket ban on fireworks. We advocate improving firework policies in four aspects-production, sales, supervision, and control-to promote sustainable development of the ecological environment and human society.

Assessing the effectiveness of PM2.5 pollution control from the perspective of interprovincial transport and PM2.5 mitigation costs across China

Due to the transboundary nature of air pollutants, a province's efforts to improve air quality can reduce PM2.5 concentration in the surrounding area. The inter-provincial PM2.5 pollution transport could bring great challenges to related environmental management work, such as financial fund allocation and subsidy policy formulation. Herein, we examined the transport characteristics of PM2.5 pollution across provinces in 2013 and 2020 via chemical transport modeling and then monetized inter-provincial contributions of PM2.5 improvement based on pollutant emission control costs. We found that approximately 60% of the PM2.5 pollution was from local sources, while the remaining 40% originated from outside provinces. Furthermore, about 1011 billion RMB of provincial air pollutant abatement costs contributed to the PM2.5 concentration decline in other provinces during 2013-2020, accounting for 41.2% of the total abatement costs. Provinces with lower unit improvement costs for PM2.5, such as Jiangsu, Hebei, and Shandong, were major contributors, while Guangdong, Guangxi, and Fujian, bearing higher unit costs, were among the main beneficiaries. Our study identifies provinces that contribute to air quality improvement in other provinces, have high economic efficiency, and provide a quantitative framework for determining inter-provincial compensations. This study also reveals the uneven distribution of pollution abatement costs (PM2.5 improvement/abatement costs) due to transboundary PM2.5 transport, calling for adopting inter-provincial economic compensation policies. Such mechanisms ensure equitable cost-sharing and effective regional air quality management.

Does COVID-19 lockdown matter for air pollution in the short and long run in China? A machine learning approach to policy evaluation

This paper leverages a data-driven two-step approach to effectively evaluate the effects of COVID-19 lockdown on air pollution in both the short and long-term in China. Using air pollution, meteorological conditions, and air mass clusters from 34 air quality monitoring stations in Beijing from 2015 to 2022, this study first employs a deweathering machine learning technique to decouple the confounding effects of meteorological on the air pollution. Furthermore, a detrending percentage change indictor is applied to remove the influence of seasonal variations on air pollution. The findings reveal that: (1) Human interventions are the primary drivers of changes in air pollution concentrations, whereas meteorological factors have a relatively minor impact. (2) During the COVID-19 lockdown, significant variations in air pollution levels are observed, with the effects of city lockdown ranging from a decrease of 40.11% ± 14.81% to an increase of 20.28% ± 14.36%. Notably, there is a decline in concentrations of NO2, PM2.5, CO, and PM10, while the levels of O3 and SO2 increase even during the strictest lockdown period. (3) In the year following the COVID-19 lockdown, there is a rebound in overall air pollution levels. However, by the second year, a general decline in air pollution is observed, except for O3. Therefore, it is imperative to integrate the confounding effects of meteorological factors into air quality management policies under various future scenarios: adopt high-intensity control measures for sudden air quality deteriorations, advance green recovery initiatives for long-term emission reductions, and coordinate efforts to reduce composite atmospheric pollution.

Spatial source, simulating improvement, and short-term health effect of high PM2.5 exposure during mutation event in the key urban agglomeration regions in China

Air quality in China has significantly improved owing to the effective implementation of pollution control measures. However, mutation events caused by short-term spikes in PM2.5 in urban agglomeration regions continue to occur frequently. Identifying the spatial sources and influencing factors, as well as improving the prediction accuracy of high PM2.5 during mutation events, are crucial for public health. In this study, we firstly introduced discrete wavelet transform (DWT) to identify the mutation events with high PM2.5 concentration in the four key urban agglomerations, and evaluated the spatial sources for the polluted scenario using Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Additionally, DWT was combined with a widely used artificial neural network (ANN) to improve the prediction accuracy of PM2.5 concentration seven days in advance (seven-day forecast). Results indicated that mutation events commonly occurred in the northern regions during winter time, which were under the control of both short-range transportation of dirty airmass as well as negative meteorology conditions. Compared with the ANN model alone, the average band errors decreased by 9% when using DWT-ANN model. The average correlation coefficient (R) and root mean square error (RMSE) obtained using the DWT-ANN improved by 10% and 12% compared to those obtained using the ANN, indicating the efficiency and accuracy of simulating PM2.5, by combining the DWT and ANN. The short-term mortality during mutation events was then calculated, with the total averted all-cause, cardiovascular, and respiratory deaths in the four regions, being 4751, 2554, and 582 persons, respectively. A declining trend in prevented deaths from 2018 to 2020 demonstrated that the pollution intensity during mutation events gradually decreased owing to the implementation of the Three-Year Action Plan to Win the Blue Sky Defense War. The method proposed in this study can be used by policymakers to take preventive measures in response to a sudden increase in PM2.5, thereby ensuring public health.

Long-term spatial distribution and implication of black and brown carbon in the Tibetan Plateau

Black carbon (BC) and brown carbon (BrC) over the high-altitude Tibetan Plateau (TP) can significantly influence regional and global climate change as well as glacial melting. However, obtaining plateau-scale in situ observations is challenging due to its high altitude. By integrating reanalysis data with on-site measurements, the spatial distribution of BC and BrC can be accurately estimated using the random forest algorithm (RF). In our study, the on-site observations of BC and BrC were successively conducted at four sites from 2018 to 2021. Ground-level BC and BrC concentrations were then obtained at a spatial resolution of 0.25° × 0.25° for three periods (including Periods-1980, 2000, and 2020) using RF and multi-source data. The highest annual concentrations of BC (1363.9 ± 338.7 ng/m3) and BrC (372.1 ± 96.2 ng/m3) were observed during Period-2000. BC contributed a dominant proportion of carbonaceous aerosol, with concentrations 3-4 times higher than those of BrC across the three periods. The ratios of BrC to BC decreased from Period-1980 to Period-2020, indicating the increasing importance of BC over the TP. Spatial distributions of plateau-scale BC and BrC concentrations showed heightened levels in the southeastern TP, particularly during Period-2000. These findings significantly enhance our understanding of the spatio-temporal distribution of light-absorbing carbonaceous aerosol over the TP.

Fine-scale monitoring of catkins reveals an association between catkin concentration and plant community characteristics and microclimate

Catkins, as a significant source of plant-caused pollution, disrupts daily human activities and industrial processes. Despite their impact, catkins have not been included in official environmental quality monitoring indicators, leading to a deficiency in scientifically rigorous collection and monitoring methodologies, as well as a lack of ecological prevention and management strategies. In this study, we introduced a fine-scale monitoring approach for catkins. Qualitative and quantitative relationships between catkin concentrations, plant community characteristics and microclimate factors were elucidated by analyzing on-site catkin concentration data from 33 representative plant communities in Beijing. Furthermore, we summarized the ecological strategies for the prevention and management of these catkins. The results indicated that (1) TS (three-dimensional green volume of trees in the catkin source layer), SB (three-dimensional green volume of shrubs in the catkin barrier layer), GB (three-dimensional green volume of ground cover plants in the catkin barrier layer), T (three-dimensional green volume of trees in the whole plant community), W (three-dimensional green volume of the whole plant community), species diversity, and relative air humidity were key plant community characteristics and microclimate factors influencing catkin concentration. Among these factors, TS, T, W, and relative air humidity showed a significant positive correlation with catkin concentration, while SB, GB, and species diversity exhibited a significant negative correlation with catkin concentration. (2) All seven key factors exhibited nonlinear relationships with catkin concentration. (3) TS served as the primary deciding factor for catkin concentration within the plant community. When TS > 744.0755 m3, the secondary decision factor for catkin concentration was GB. Otherwise, the determinants were SB and species diversity. The results showed that enhancing tree species diversity, enhancing the three-dimensional green volume of shrubs and ground cover plants, and increasing air humidity were practical means to facilitate the sedimentation of catkins. The measures used to obstruct catkins vary depending on the TS. When catkin source plants are abundant within a plant community, it is advisable to prioritize increasing ground cover plants. Conversely, when fewer sources of such plants exist, emphasis can be placed on augmenting mid-layer shrubs and diversifying plant species. These findings provide a scientific foundation for the planting design and stock optimization of communities containing catkin source plants.

Metal-containing nanoparticles in road dust from a Chinese megacity over the last decade: Spatiotemporal variation and driving factors

As a crucial sink of metal-containing nanoparticles (MNPs), road dust can record their spatiotemporal variations in urban environments. In this study, taking Shanghai as a representative megacity in China, a total of 272 dust samples were collected in the winter and summer of 2013 and 2021/2022 to understand the spatiotemporal variations and driving factors of MNPs. The number concentrations of Fe-, Ti-, and Zn-containing NPs were 3.8 × 106 - 8.4 × 108, 2.3 × 106-1.4 × 108, and 6.0 × 105-2.3 × 108 particles/mg, respectively, according to single particle (sp)ICP-MS analysis. These MNPs showed significantly higher number concentrations in summer than in winter. Hotspots of Fe-containing NPs were more concentrated in industrial and traffic areas, Zn-containing NPs were mainly distributed in the central urban areas, while Ti-containing NPs were abundant in areas receiving high rainfall. The structural equation model results indicates that substantial rainfall in summer can help remove MNPs from atmospheric PM2.5 into dust, while in winter industrial and traffic activities were the primary contributors for MNPs. Moreover, the contribution of traffic emissions to MNPs has surpassed industrial one over the last decade, highlighting the urgency to control traffic-sourced MNPs, especially those from non-exhaust emissions by electric vehicles.

Research on characteristics and influencing factors of road dust emission in a southern city in China

One of the primary causes of urban atmospheric particulate matter, which is harmful to human health in addition to affecting air quality and atmospheric visibility, is road dust. This study used online monitoring equipment to examine the characteristics of road dust emissions, the effects of temperature, humidity, and wind speed on road dust, as well as the correlation between road and high-space particulate matter concentrations. A section of a real road in Jinhua City, South China, was chosen for the study. The findings demonstrate that the concentration of road dust particles has a very clear bimodal single-valley distribution throughout the day, peaking between 8:00 and 11:00 and 19:00 and 21:00 and troughing between 14:00 and 16:00. Throughout the year, there is a noticeable seasonal change in the concentration of road dust particles, with the highest concentration in the winter and the lowest in the summer. Simultaneously, it has been discovered that temperature and wind speed have the most effects on particle concentration. The concentration of road dust particles reduces with increasing temperature and wind speed. The particle concentrations of road particles and those from urban environmental monitoring stations have a strong correlation, although the trend in the former is not entirely consistent, and the changes in the former occur approximately 1 h after the changes in the latter.

Temporal change and impact on air quality of an energy recovery plant using the M-BACI design in Gipuzkoa

A significant concern in our society is the potential impact on both health and the environment of air pollutants released during the incineration of waste. Therefore, it is crucial to conduct thorough control and monitoring measures. In this context, the objective of this research was to study the evolution of particulate matter (PM2.5) and associated trace elements during the period before and after the installation of an Energy Recovery Plant (ERP). For that, a descriptive and temporal analysis of PM2.5 concentration and composition were performed on two similar areas (impact/control) using the Before-After/Control-Impact (BACI) design and two periods (before from January 01, 2018 to February 06, 2020 and after from December 10, 2020 to September 30, 2022). Results showed a decrease in the levels of PM2.5 and associated trace elements is observed in the impact zone (IZ) and in the control zone (CZ) throughout the study period. In the case of PM2.5, the most notable decrease occurred in the period of the start-up of the ERP, a period that coincides with the confinement and restrictions of COVID, with a subsequent increase in both zones, without reaching the levels observed in the period prior to the start-up of the ERP. Selenium is the only trace element that increases significantly in the IZ. In conclusion, a decrease is observed for all pollutants except selenium in both zones, although less pronounced in the IZ. Since selenium already showed an upward trend in the phase prior to the start of the ERP, it is necessary to investigate its evolution and find out the possible cause.

Elevating hourly PM2.5 forecasting in Istanbul, Türkiye: Leveraging ERA5 reanalysis and genetic algorithms in a comparative machine learning model analysis

Rapid urbanization and industrialization have intensified air pollution, posing severe health risks and necessitating accurate PM2.5 predictions for effective urban air quality management. This study distinguishes itself by utilizing high-resolution ERA5 reanalysis data for a grid-based spatial analysis of Istanbul, Türkiye, a densely populated city with diverse pollutant sources. It assesses the predictive accuracy of advanced machine learning (ML) models-Multiple Linear Regression (MLR), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LGB), Random Forest (RF), and Nonlinear Autoregressive with Exogenous Inputs (NARX). Notably, it introduces genetic algorithm optimization for the NARX model to enhance its performance. The models were trained on hourly PM2.5 concentrations from twenty monitoring stations across 2020-2021. Istanbul was divided into seven regions based on ERA5 grid distributions to examine PM2.5 spatial variability. Seventeen input variables from ERA5, including meteorological, land cover, and vegetation parameters, were analyzed using the Neighborhood Component Analysis (NCA) method to identify the most predictive variables. Comparative analysis showed that while all models provided valuable insights (RF > LGB > XGB > MLR), the NARX model outperformed them, particularly with the complex dataset used. The NARX model achieved a high R-value (0.89), low RMSE (5.24 μg/m³), and low MAE (2.94 μg/m³). It performed best in autumn and winter, with the highest accuracy in Region-1 (R-value 0.94) and the lowest in Region-5 (R-value 0.75). This study's success in a complex urban setting with limited monitoring underscores the robustness of the NARX model and the methodology's potential for global application in similar urban contexts. By addressing temporal and spatial variability in air quality predictions, this research sets a new benchmark and highlights the importance of advanced data analysis techniques for developing targeted pollution control strategies and public health policies.

Accurate PM2.5 urban air pollution forecasting using multivariate ensemble learning Accounting for evolving target distributions

Over the past decades, air pollution has caused severe environmental and public health problems. According to the World Health Organization (WHO), fine particulate matter (PM2.5), a key component reflecting air quality, is the fourth leading cause of death worldwide after cardiovascular disease, smoking, and diet. Various research efforts have aimed to develop PM2.5 forecasting models that can be integrated into a solution to mitigate the adverse effects of air pollution. However, PM2.5 forecasting is challenging because air pollution data are non-stationary and influenced by multiple random effects. This paper proposes an effective multivariate multi-step ensemble machine learning model for predicting continuous 24-h PM2.5 concentrations, considering meteorological conditions, the rolling mean of PM2.5 time series, and temporal features. PM2.5 is strongly correlated with space and time. Therefore, forecasting results from one location are insufficient to represent the level of air pollution for an entire city. In this study, we established six real-time air quality monitoring sites in different regions, including traffic, residential, and industrial areas in Ho Chi Minh City (HCMC), and generated forecasting results for each station. Various statistical methods are incorporated to evaluate the performance of the model. The experimental results confirm that the model performs well, substantially improving its forecasting accuracy compared to existing PM2.5 forecasting models developed for HCMC. In addition, we analyze to determine the contribution of different feature groups to model performance. The model can serve as a reference for citizens scheduling local travel and for healthcare providers to provide early warnings.

Enhanced PM2.5 estimation across China: An AOD-independent two-stage approach incorporating improved spatiotemporal heterogeneity representations

In China, population growth and aging have partially negated the public health benefits of air pollution control measures, underscoring the ongoing need for precise PM2.5 monitoring and mapping. Despite its prevalence, the satellite-derived Aerosol Optical Depth (AOD) method for estimating PM2.5 concentrations often encounters significant spatial data gaps. Additionally, current research still needs better representation of PM2.5 spatiotemporal heterogeneity. Addressing these challenges, we developed a two-stage model employing the Extreme Gradient Boosting (XGBoost) algorithm. By incorporating improved spatiotemporal factors, we achieved high-precision and full-coverage daily 1-km PM2.5 mappings across China for the year 2020 without utilizing AOD products. Specifically, Model 1 develops improved temporal encodings and a terrain classification factor (DC), while Model 2 constructs an enhanced spatial autocorrelation term (Ps) by integrating observed and estimated values. Notably, Model 2 excelled in 10-fold sample-based cross-validation, achieving a coefficient of determination of 0.948, a mean absolute error of 3.792 μg/m³, a root mean square error of 7.144 μg/m³, and a mean relative error of 14.171%. Feature importance and Shapley Additive exPlanations (SHAP) analyses determined the relative importance of predictors in model training and outcome prediction, while correlation analysis identified strong links between improved temporal encodings, PM2.5 concentrations, and significant meteorological factors. Two-way Partial Dependence Plots (PDPs) further explored the interactions among these factors and their impact on PM2.5 levels. Compared to traditional methods, improved temporal encodings align more closely with seasonal variations and synergize more effectively with meteorological factors. Besides, the structured nature of DC aids in model training, while the improved Ps more effectively captures PM2.5's spatial autocorrelation, outperforming traditional Ps. Overall, this study effectively represents spatiotemporal information, thereby boosting model accuracy and enabling seamless large-scale PM2.5 estimations. It provides deep insights into variables and models, providing significant implications for future air pollution research.

Urban health advantage and penalty in aging populations: a comparative study across major megacities in China

Background

Urban living is linked to better health outcomes due to a combination of enhanced access to healthcare, transportation, and human development opportunities. However, spatial inequalities lead to disparities, resulting in urban health advantages and penalties. Understanding the relationship between health and urban development is needed to generate empirical evidence in promoting healthy aging populations. This study provides a comparative analysis using epidemiological evidence across diverse major Chinese cities, examining how their unique urban development trajectories over time have impacted the health of their aging residents.

Methods

We tracked changes in air pollution (NO2, PM2.5, O3), green space (measured by NDVI), road infrastructure (ring road areas), and nighttime lighting over 20 years in six major cities in China. We followed a longitudinal cohort of 4992 elderly participants (average age 87.8 years) over 16,824 person-years. We employed Cox proportional hazard regression to assess longevity, assessing 14 variables, including age, sex, ethnicity, marital status, residence, household income, occupation, education, smoking, alcohol consumption, exercise, and points of interest (POI) count of medicine-related facilities, sports, and leisure service-related places, and scenic spots within a 5 km-radius buffer.

Findings

Geographic proximity to points of interest significantly improves survival. Elderly living in proximity of the POI-rich areas had a 34.6%-35.6% lower mortality risk compared to those in POI-poor areas, for the highest compared to the lowest quartile. However, POI-rich areas had higher air pollution levels, including PM2.5 and NO2, which was associated with a 21% and 10% increase in mortality risk for increase of 10 μg/m3, respectively. The benefits of urban living had higher effect estimates in monocentric cities, with clearly defined central areas, compared to polycentric layouts, with multiple satellite city centers.

Interpretation

Spatial inequalities create urban health advantages for some and penalties for others. Proximity to public facilities and economic activities is associated with health benefits, and may counterbalance the negative health impacts of lower green space and higher air pollution. Our empirical evidence show optimal health gains for age-friendly urban environments come from a balance of infrastructure, points of interest, green spaces, and low air pollution.

Funding

Natural Science Foundation of Beijing (IS23105), National Natural Science Foundation of China (82250610230, 72061137004), World Health Organization (2024/1463606-0), Research Fund Vanke School of Public Health Tsinghua University (2024JC002), Beijing TaiKang YiCai Public Welfare Foundation, National Key R&D Program of China (2018YFC2000400).

The effect of cross-regional transport on ozone and particulate matter pollution in China: A review of methodology and current knowledge

China is currently one of the countries impacted by severe atmospheric ozone (O3) and particulate matter (PM) pollution. Due to their moderately long lifetimes, O3 and PM can be transported over long distances, cross the boundaries of source regions and contribute to air pollution in other regions. The reported contributions of cross-regional transport (CRT) to O3 and fine PM (PM2.5) concentrations often exceed those of local emissions in the major regions of China, highlighting the important role of CRT in regional air pollution. Therefore, further improvement of air quality in China requires more joint efforts among regions to ensure a proper reduction in emissions while accounting for the influence of CRT. This review summarizes the methodologies employed to assess the influence of CRT on O3 and PM pollution as well as current knowledge of CRT influence in China. Quantifying CRT contributions in proportion to O3 and PM levels and studying detailed CRT processes of O3, PM and precursors can be both based on targeted observations and/or model simulations. Reported publications indicate that CRT contributes by 40-80 % to O3 and by 10-70 % to PM2.5 in various regions of China. These contributions exhibit notable spatiotemporal variations, with differences in meteorological conditions and/or emissions often serving as main drivers of such variations. Based on trajectory-based methods, transport pathways contributing to O3 and PM pollution in major regions of China have been revealed. Recent studies also highlighted the important role of horizontal transport in the middle/high atmospheric boundary layer or low free troposphere, of vertical exchange and mixing as well as of interactions between CRT, local meteorology and chemistry in the detailed CRT processes. Drawing on the current knowledge on the influence of CRT, this paper provides recommendations for future studies that aim at supporting ongoing air pollution mitigation strategies in China.

Elucidating transport dynamics and regional division of PM2.5 and O3 in China using an advanced network model

Air pollution exhibits significant spatial spillover effects, complicating and challenging regional governance models. This study innovatively applied and optimized a statistics-based complex network method in atmospheric environmental field. The methodology was enhanced through improvements in edge weighting and threshold calculations, leading to the development of an advanced pollutant transport network model. This model integrates pollution, meteorological, and geographical data, thereby comprehensively revealing the dynamic characteristics of PM2.5 and O3 transport among various cities in China. Research findings indicated that, throughout the year, the O3 transport network surpassed the PM2.5 network in edge count, average degree, and average weighted degree, showcasing a higher network density, broader city connections, and greater transmission strength. Particularly during the warm period, these characteristics of the O3 network were more pronounced, showcasing significant transport potential. Furthermore, the model successfully identified key influential cities in different periods; it also provided detailed descriptions of the interprovincial spillover flux and pathways of PM2.5 and O3 across various time scales. It pinpointed major pollution spillover and receiving provinces, with primary spillover pathways concentrated in crucial areas such as the Beijing-Tianjin-Hebei (BTH) region and its surrounding areas, the Yangtze River Delta, and the Fen-Wei Plain. Building on this, the model divided the O3, PM2.5, and synergistic pollution transmission regions in China into 6, 7, and 8 zones, respectively, based on network weights and the Girvan Newman (GN) algorithm. Such division offers novel perspectives and strategies for regional joint prevention and control. The validity of the model was further corroborated by source analysis results from the WRF-CAMx model in the BTH area. Overall, this research provides valuable insights for local and regional atmospheric pollution control strategies. Additionally, it offers a robust analytical tool for research in the field of atmospheric pollution.

Design and optimization of haze prediction model based on particle swarm optimization algorithm and graphics processor

With the rapid expansion of industrialization and urbanization, fine Particulate Matter (PM2.5) pollution has escalated into a major global environmental crisis. This pollution severely affects human health and ecosystem stability. Accurately predicting PM2.5 levels is essential. However, air quality forecasting currently faces challenges in processing vast data and enhancing model accuracy. Deep learning models are widely applied for their superior learning and fitting abilities in haze prediction. Yet, they are limited by optimization challenges, long training periods, high data quality needs, and a tendency towards overfitting. Furthermore, the complex internal structures and mechanisms of these models complicate the understanding of haze formation. In contrast, traditional Support Vector Regression (SVR) methods perform well with complex non-linear data but struggle with increased data volumes. To address this, we developed CUDA-based code to optimize SVR algorithm efficiency. We also combined SVR with Genetic Algorithms (GA), Sparrow Search Algorithm (SSA), and Particle Swarm Optimization (PSO) to identify the optimal haze prediction model. Our results demonstrate that the model combining intelligent algorithms with Central Processing Unit-raphics Processing Unit (CPU-GPU) heterogeneous parallel computing significantly outpaces the PSO-SVR model in training speed. It achieves a computation time that is 6.21-35.34 times faster. Compared to other models, the Particle Swarm Optimization-Central Processing Unit-Graphics Processing Unit-Support Vector Regression (PSO-CPU-GPU-SVR) model stands out in haze prediction, offering substantial speed improvements and enhanced stability and reliability while maintaining high accuracy. This breakthrough not only advances the efficiency and accuracy of haze prediction but also provides valuable insights for real-time air quality monitoring and decision-making.

Triple dip La-Nina, unorthodox circulation and unusual spin in air quality of India

The recent La-Nina phase of the El Nino Southern Oscillation (ENSO) phenomenon unusually lasted for third consecutive year, has disturbed global weather and linked to Indian monsoon. However, our understanding on the linkages of such changes to regional air quality is poor. We hereby provide a mechanism that beyond just influencing the meteorology, the interactions between the ocean and the atmosphere during the retreating phase of the La-Niña produced secondary results that significantly influenced the normal distribution of air quality over India through disturbed large-scale wind patterns. The winter of 2022-23 that coincided with retreating phase of the unprecedented triple dip La-Niña, was marred by a mysterious trend in air quality in different climatological regions of India, not observed in recent decades. The unusually worst air quality over South-Western India, whereas relatively cleaner air over the highly polluted North India, where levels of most toxic pollutant (PM2.5) deviating up to about ±30 % from earlier years. The dominance of higher northerly wind in the transport level forces influx and relatively slower winds near the surface, trapping pollutants in peninsular India, thereby notably increasing PM2.5 concentration. In contrast, too feeble western disturbances, and unique wind patterns with the absence of rain and clouds and faster ventilation led to a significant improvement in air quality in the North. The observed findings are validated by the chemical-transport model when forced with the climatology of the previous year. The novelty of present research is that it provides an association of air quality with climate change. We demonstrate that the modulated large-scale wind patterns linked to climatic changes may have far-reaching consequences even at a local scale leading to unusual changes in the distribution of air pollutants, suggesting ever-stringent emission control actions.