Atmospheric Environment最新文献

{"title":"Estimating global anthropogenic carbon dioxide emissions using satellite observations and machine learning methods","authors":"Farhan Mustafa ,&nbsp;Ming Xu","doi":"10.1016/j.atmosenv.2025.121423","DOIUrl":"10.1016/j.atmosenv.2025.121423","url":null,"abstract":"<div><div>Several countries are working to reduce their anthropogenic CO₂ emissions to meet the goals of the Paris Agreement. However, evaluation of the carbon reduction efforts is hindered by the larger uncertainties in the currently available datasets. Therefore, it is imperative to explore new efficient and reliable methods to estimate carbon emissions accurately. This study proposed a novel method to estimate global gridded anthropogenic CO₂ emissions using satellite datasets. The methodology included the development and integration of two machine learning models, i.e., RXCO₂ (Reconstruct XCO₂) and REMI (Reconstruct EMIssion), to achieve the objective. RXCO₂ utilized the CO₂ products from the Copernicus Atmosphere Monitoring Service (CAMS) model and the Orbiting Carbon Observatory 2 (OCO-2) satellite to produce a daily global long-term regular gridded column-averaged dry-air model fraction of CO₂ (XCO₂) dataset with a spatial resolution of 1°. The predicted XCO₂ dataset was thoroughly validated against the ground-based and satellite-derived XCO₂ observations, and good consistency was observed among the datasets. Further, the XCO₂ anomalies were derived using the predicted XCO₂ dataset and were utilized in the second model (REMI) along with tropospheric NO₂ column, nighttime light, and population density to predict annual gridded anthropogenic CO₂ emissions at a spatial resolution of 1° for 2021 and 2022. The model achieved high accuracy with a coefficient of determination (R²) of 0.96 and a root mean squared error (RMSE) of 10^0.3 tons. The predicted results were comprehensively compared with the anthropogenic CO₂ emissions provided by established inventories and good agreement was observed among the datasets.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"360 ","pages":"Article 121423"},"PeriodicalIF":4.2,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intercomparison of multiple chemical mechanisms in simulating severe haze event over the North China plain","authors":"Zhen Li ,&nbsp;Yicong He ,&nbsp;Shaocai Yu ,&nbsp;Hongkui Wang ,&nbsp;Fan Fan ,&nbsp;Chuang Qin ,&nbsp;Ye Chen ,&nbsp;Weiwei Dai ,&nbsp;Zixi Jin ,&nbsp;Dongliang Zhao","doi":"10.1016/j.atmosenv.2025.121439","DOIUrl":"10.1016/j.atmosenv.2025.121439","url":null,"abstract":"","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"361 ","pages":"Article 121439"},"PeriodicalIF":3.7,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Changes in gas-to-aerosol-phase partitioning ratio of semi-volatile products affect secondary organic aerosol formation from α-pinene photooxidation","authors":"Shijie Liu ,&nbsp;Xinbei Xu ,&nbsp;Si Zhang ,&nbsp;Rongjie Li ,&nbsp;Zheng Li ,&nbsp;Can Wu ,&nbsp;Rui Li ,&nbsp;Feiyong Chen ,&nbsp;Guiqin Zhang ,&nbsp;Gehui Wang","doi":"10.1016/j.atmosenv.2025.121427","DOIUrl":"10.1016/j.atmosenv.2025.121427","url":null,"abstract":"<div><div>α-Pinene is one of the most important precursors of secondary organic aerosols (SOA). The formation of α-pinene derived SOA is strongly affected by NOx. However, the effects of NOx on α-pinene derived SOA formation, especially the enhancing effect of NOx on SOA yield, are still not comprehensively understood. A series of α-pinene photooxidation experiments were performed at different NOx concentrations through an atmospheric chamber in this study. The yields of α-pinene SOA initially increased with rising NOx concentrations but subsequently decreased at higher levels. The maximum SOA yields were 8.0 % and 26.2 % in 115 ppb and 250 ppb α-pinene experiments, respectively. It is found that the fitted curves of SOA mass concentration (M₀) versus SOA yield shift downward with increasing NOx, which means the volatility of the oxidation products gradually increases. However, the higher SOA yields observed with the increasing M₀ during each photooxidation process, which were attributed to the enhanced gas-to-aerosol-phase partitioning ratio. The relationship of SOA yields with M₀ for different NOx experiments shows that, under low-NOx conditions, the elevation in M₀ which was driven by enhanced VOC consumption would still promote SOA yield with increasing NOx concentrations, despite the position of the Odum curve shift downward. That is to say, the change of M₀ leading to the variation gas-to-aerosol-phase partitioning ratio should be taken into account in the facilitation of NOx on SOA yield. The relation of nitrogen-containing organic compound (NOCs) concentrations with NOx was also quantified in this study. The rapid increase in NOCs formation under low NOx conditions is another factor contributing to the increase of SOA yields. This study greatly enhances our understanding of the mechanisms by which NOx promotes SOA yields, and provides crucial information for improving the accurate simulation of SOA formation.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"360 ","pages":"Article 121427"},"PeriodicalIF":4.2,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unexpected benefits of agricultural greenhouses in mitigating ozone pollution on crop yields in China","authors":"Jingwei Zhang ,&nbsp;Yuanqi Huang ,&nbsp;Xiaoye Tong ,&nbsp;Hailin Wu ,&nbsp;Haiyan Ran ,&nbsp;Wenxuan Fan ,&nbsp;Yuanzhe Li ,&nbsp;Shijing Dong ,&nbsp;Shengsen Zhou ,&nbsp;Jiangping Liu ,&nbsp;Dawei Lu ,&nbsp;Huizhi Liu ,&nbsp;Junling An","doi":"10.1016/j.atmosenv.2025.121438","DOIUrl":"10.1016/j.atmosenv.2025.121438","url":null,"abstract":"<div><div>Ozone (O₃) is a major photochemical pollutant that harms crops and reduces yields. While its impact on open-field crops is well-documented, research on O₃ levels in agricultural greenhouses is limited. This study provides the first real-time measurements of O₃ concentrations inside and outside a lettuce-growing tunnel greenhouse in Kunming, China, revealing an average indoor/outdoor (I/O) O₃ ratio of 0.55 ± 0.15. We assessed Accumulated Ozone exposure over a Threshold of 40 ppb (AOT40) and relative yield losses (RYLs) for lettuce as an example, estimating the benefits of greenhouse cultivation across China. In ten major greenhouse farming regions, outdoor O₃ levels consistently exceeded the AOT40 threshold, while indoor levels remained mostly below it. Over five lettuce growing seasons (1.5 months each) from March to October, with I/O ratios of 1.0, 0.70, 0.55, and 0.40, AOT40 values were 7.21 ± 2.71, 2.16 ± 1.15, 0.67 ± 0.46, and 0.03 ± 0.05 ppm h, respectively. Corresponding RYLs were −0.08 ± 0.03, −0.02 ± 0.01, −0.01 ± 0.00, and −0.00 ± 0.00. Greenhouses prevented a 6–8 % yield reduction by lowering internal O₃ levels. This mitigation translated into an economic benefit of 4.9–6.5 billion USD, equivalent to 103.2–147.6 USD per person for China's 55 million greenhouse farmers in 2019, or 3.4–4.6 % of annual greenhouse vegetable production benefits, assuming planting lettuce. This single-crop approximation introduced merely 8.3 % ± 0.7 % overestimation versus Monte Carlo simulations with 17-crop combinations (2–16 species). Our findings show that greenhouses significantly reduce O₃-induced crop damage and offer substantial economic advantages. With rising O₃ levels and the rapid growth of facility agriculture globally, this study underscores the importance of adopting greenhouse cultivation practices.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"360 ","pages":"Article 121438"},"PeriodicalIF":3.7,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144737983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-term exposure to air pollution and lung cancer incidence in the Danish Nurse Cohort study","authors":"Sophie MA. Effing ,&nbsp;Jiawei Zhang ,&nbsp;Stéphane Tuffier ,&nbsp;Thomas Cole-Hunter ,&nbsp;Marie Bergmann ,&nbsp;George Maria Napolitano ,&nbsp;Rina So ,&nbsp;Jørgen Brandt ,&nbsp;Matthias Ketzel ,&nbsp;Steffen Loft ,&nbsp;Jaime E. Hart ,&nbsp;Youn-Hee Lim ,&nbsp;Zorana Jovanovic Andersen","doi":"10.1016/j.atmosenv.2025.121430","DOIUrl":"10.1016/j.atmosenv.2025.121430","url":null,"abstract":"<div><h3>Background</h3><div>Although the link between air pollution and lung cancer is well established, recent evidence from low-pollution areas is mixed. We investigated the association of long-term exposure to air pollution with lung cancer incidence in Denmark.</div></div><div><h3>Methods</h3><div>We analyzed data from 28,731 female nurses in the Danish Nurse Cohort, followed from 1993/1999 until 2020 for lung cancer incidence. We estimated residential annual mean concentrations of particulate matter (PM₁₀ and PM_2.5), nitrogen dioxide (NO₂), black carbon (BC), and ozone (O₃) using the DEHM/UBM/AirGIS modelling system. Time-varying Cox regression models evaluated the associations between these exposures and lung cancer incidence.</div></div><div><h3>Results</h3><div>After excluding participants who did not meet the criteria for our study, we were left with 23,706 participants, 450 of whom developed lung cancer during the study period. The mean PM_2.5, PM₁₀, NO₂, BC, and O₃ concentrations at the participants' baseline addresses were 14.65, 21.02, 20.25, 0.92, and 50.77 μg/m³, respectively. Our study found no association between the pollutants investigated and lung cancer incidence. Hazard ratios (HRs) and 95 % confidence intervals for lung cancer incidence associated with 5-year moving average exposures to PM_2.5, PM₁₀, NO₂, BC, and O₃ were 0.95 (0.75–1.20) per 2.68 μg/m³, 0.93 (0.78–1.12) per 2.81 μg/m³, 0.95 (0.83–1.08) per 7.98 μg/m³, 0.96 (0.86–1.08) per 0.34 μg/m³, and 1.05 (0.90–1.21) per 7.04 μg/m³, respectively, in the fully adjusted model. These findings were robust across various statistical models and sensitivity analyses.</div></div><div><h3>Discussion</h3><div>We found no association between long-term air pollution exposure and lung cancer incidence in Danish female nurses, contrasting with established links in other contexts but aligning with recent studies in low-exposure settings.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"360 ","pages":"Article 121430"},"PeriodicalIF":4.2,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The evolution of physical and chemical properties of PM2.5 in the developing stage of pollution events in a coastal megacity, South Korea","authors":"Yongmi Park ,&nbsp;Myounghwa Byun ,&nbsp;Jaehun Park ,&nbsp;Subin Han ,&nbsp;Jae-Jin Kim ,&nbsp;Youn-Suk Son ,&nbsp;Taehyoung Lee ,&nbsp;Wonsik Choi","doi":"10.1016/j.atmosenv.2025.121435","DOIUrl":"10.1016/j.atmosenv.2025.121435","url":null,"abstract":"<div><div>Concentrations of PM_2.5 (particulate matter smaller than 2.5 μm in diameter) vary depending on regional emissions and meteorological conditions. This study investigates the physical and chemical characteristics of PM_2.5 and its formation mechanisms in Busan, a coastal megacity in Korea with significant port-related emissions. An increase in PM_2.5 concentrations was associated with a rising proportion of nitrate (NO₃⁻), highlighting the role of nitrate formation in elevated wintertime PM_2.5 levels. Throughout the measurement period, an ammonium-rich environment persisted, with the molar ratio of excess-NH₄⁺ to NO₃⁻ approaching 1:1, indicating that the formed nitrates were effectively neutralized by ammonium ions. In diurnal variations during high PM_2.5 periods, the peak concentrations in PM_2.5 occurred at night, closely following the daily maxima in relative humidity and aerosol liquid water content (ALWC). Moreover, case studies revealed that increases in relative and specific humidity preceded the augmentation of ALWC, coinciding with the growth of the major aerosol mode larger than 200 nm. This increase in ALWC likely facilitated the efficient conversion of nitrate into a condensed phase, promoting heterogeneous nitrate formation. Consequently, this nighttime heterogeneous formation of nitrate, driven by the increase in ALWC, plays a significant role in the formation of fine particulate matter in a coastal megacity of Korea during winter.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"360 ","pages":"Article 121435"},"PeriodicalIF":3.7,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Formation pathways of particulate NO3− and sources of its precursor over the northwest India: Insights through dual isotopes","authors":"Chandrima Shaw ,&nbsp;Neeraj Rastogi ,&nbsp;Ritwick Mandal ,&nbsp;Prasanta Sanyal","doi":"10.1016/j.atmosenv.2025.121426","DOIUrl":"10.1016/j.atmosenv.2025.121426","url":null,"abstract":"<div><div>NO_x plays a vital role in tropospheric ozone formation, OH radical recycling, and acts as a precursor to the formation of particulate nitrate (pNO₃^-), a major reactive nitrogen species. pNO₃^- mainly forms via four pathways: oxidation of NO₂ by OH (P₁), N₂O₅ hydrolysis (P₂), reactions with VOCs (P₃), and ClO (P₄). However, studies on its sources and formation mechanisms are limited. This study uses dual isotopes (δ¹⁸O and δ¹⁵N) of pNO₃^- to explore the sources of NO_x and dominant pNO₃^- formation pathways over Patiala, a semi-urban site in the northwestern Indo-Gangetic Plain (IGP), during a large-scale paddy residue burning. Day-time δ¹⁵N and δ¹⁸O averaged −5.0 ± 2.4 ‰ and 52.1 ± 6.2 ‰, while night-time values were −0.13 ± 5.7 ‰ and 60.0 ± 8.4 ‰, respectively, reflecting enhanced nighttime partitioning due to cooler temperatures. Further, P₁ (79.6 ± 7.2 %) and P₂ (16.1 ± 7.5 %) dominated pNO₃^- formation; P₃ and P₄ were negligible (&lt;5 %). During the study period, the major sources of NO_x were traffic exhaust (38 ± 18 %), biomass burning (29 ± 18 %), followed by emissions from coal-fired power plants (20 ± 11 %) and soil (13 ± 9 %). Our study, the first of its kind over India provide valuable insight into NO_x transformation processes under specific seasonal and emission conditions. While these results improve the understanding of pNO₃^- formation and may aid in refining regional NO_x inventories, they are representative of the particular location and time frame of sampling and may not reflect source contributions in other regions or during periods without episodic biomass burning influence.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"360 ","pages":"Article 121426"},"PeriodicalIF":4.2,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144712857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Formaldehyde variation in urban Beijing: Levels, sources, budget, and ozone impact","authors":"Ming Wang ,&nbsp;Wenxuan Chai ,&nbsp;Dongyang Liu ,&nbsp;Min Shao","doi":"10.1016/j.atmosenv.2025.121446","DOIUrl":"10.1016/j.atmosenv.2025.121446","url":null,"abstract":"<div><div>As a key precursor of radicals and ozone (O₃), formaldehyde (HCHO) affects the atmospheric oxidative capacity of the troposphere. Herein, we investigated seasonal variation of HCHO levels, sources, sinks, and its impact on O₃ based on 17-month online measurements of HCHO and other trace gases at an urban site in Beijing. The average mixing ratio of HCHO during the warm season was 6.09 ± 3.37 ppbv, marking an increase of approximately 50 % compared to the cold season. During the warm season, HCHO accounted for 26.6 ± 6.9 % of the average mixing ratio and 34.6 ± 8.3 % of the OH reactivity for total volatile organic compounds (VOCs), approximately double the levels observed in 2011. The positive matrix factorization analysis reveals that photochemical production accounted for 71.4 % of the average HCHO level during the warm season, while vehicular exhaust contributed 50.6 % during the cold season. A budget analysis of HCHO production (<em>P</em>_HCHO) and destruction (<em>D</em>_HCHO) rates using a box model based on observations (OBM) shows that during the daytime in the warm season, <em>P</em>_HCHO was close to <em>D</em>_HCHO, suggesting a near closure of the HCHO budget. During the cold season, <em>D</em>_HCHO surpassed <em>P</em>_HCHO, possibly driven by primary emissions. The dominant HCHO production pathways were reactions of alkoxyl radicals with oxygen, while its major destruction processes were the reaction with OH radical and self-photolysis during both seasons. The relative incremental reactivity (RIR) values of nitrogen oxides determined using the OBM were negative during the summer. However, these values showed a significant increase from 2011 to 2022, indicating that while O₃ production in urban Beijing was primarily VOC-limited, the influence of NO titration on O₃ decreased over the decade. The RIR value of HCHO increased by 50 % from 2011 to 2022. Moreover, in 2022, HCHO has become the second most important VOC for O₃ formation, following isoprene. This underscores its crucial role in shaping future O₃ control measures.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"360 ","pages":"Article 121446"},"PeriodicalIF":4.2,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Atmospheric nitrogen deposition to cropland in Fujian province, China","authors":"Ziyin Yu ,&nbsp;Mengjuan Han ,&nbsp;Zhang Wen ,&nbsp;Wenqing Li ,&nbsp;Changzheng Wu ,&nbsp;Xin Ma ,&nbsp;Wen Xu ,&nbsp;Ruotong Si ,&nbsp;Xiaoping Xia ,&nbsp;Aohan Tang ,&nbsp;Lin Zhang ,&nbsp;Goulding Keith ,&nbsp;Xuejun Liu","doi":"10.1016/j.atmosenv.2025.121424","DOIUrl":"10.1016/j.atmosenv.2025.121424","url":null,"abstract":"<div><div>To quantitatively characterize atmospheric nitrogen (N) deposition in a key agricultural region, dry and bulk N deposition were measured at five sites (Fuzhou, Taining, Shanghang, Changting, and Wuyishan), in Fujian province of southeastern China over the past decade. Dry deposition of ammonia (NH₃) and nitrogen dioxide (NO₂) was estimated using the influential method, multiplying gaseous N concentrations by the deposition velocity simulated using the GEOS-Chem model. For bulk deposition the amounts of NH₄⁺-N and NO₃⁻-N were obtained by multiplying the N concentrations in rainwater by the precipitation amount. Long-term in situ observations (from 2010 to 2020) indicated that bulk N deposition decreased by 17.4 % during the monitoring period. Notably, from 2016 to 2020, total N deposition significantly decreased by 22.2 %, with an average value of 27.5 ± 1.23 kg N ha⁻¹ yr⁻¹. This was mostly caused by a reduction in reduced N deposition. N deposition exhibited distinct seasonal variability, determined by the amount of precipitation: dry deposition reached its peak in autumn, whereas bulk deposition was at its highest in spring and summer. The ratios of reduced to oxidized N in dry deposition, bulk deposition, and total N deposition were 3.23, 1.03, and 1.39, respectively, indicating a strong influence of agricultural sources. Our results show that atmospheric N deposition is a significant source of N to cropland and surrounding forest ecosystems in Fujian province. As such, it should be incorporated into N management strategies for crop cultivation to achieve sustainable agricultural and ecological protection.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"360 ","pages":"Article 121424"},"PeriodicalIF":4.2,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variability in the concentration of particulate matter in Delhi-NCR: analysis and prediction using machine learning algorithms","authors":"Nabin Sharma ,&nbsp;Jalpesh A. Dave ,&nbsp;Sarvan Kumar ,&nbsp;Kalpana Patel ,&nbsp;Abhay Kumar Singh","doi":"10.1016/j.atmosenv.2025.121422","DOIUrl":"10.1016/j.atmosenv.2025.121422","url":null,"abstract":"<div><div>The air quality in major cities has declined due to increasing urbanization and industrialization, resulting in serious health and crucial economic impacts. The presence of particulate matter in the atmosphere, specifically PM_2.5 (fine) and PM₁₀ (fine and coarse), creates a substantial threat to human health. Prediction of PM concentration levels using machine learning (ML) might help the government to prepare a better prevention and safety plan that can eventually lower the risk factor. The present study is based on predicting the PM concentration in Delhi–NCR (western Uttar Pradesh) by developing a ML model for PM_2.5 and PM₁₀ under different atmospheric conditions, including precipitation, wind speed, temperature, specific humidity, relative humidity, and surface pressure. We have developed a prediction model for two observational stations: the first industrial area (station 1) and the second urban area (station 2). The analysis of particulate matter indicates that seasonal fluctuation significantly influences air pollution levels in industrial areas. It was observed that 90 days of particulate matter concentration exceeded the permissible threshold established by NAAQS (PM_2.5 = 60 μg/m<strong>³</strong>) during the winter season in 2020 and 61 days during the post-monsoon season in 2021. Meteorological parameters, such as temperature inversions and stagnant wind conditions, significantly contribute to pollutant accumulation, especially during the winter and post-monsoon seasons. The Neural Network and Random Forest models outperformed compared to other ML models, achieving R² values of 0.74 for PM_2.5 at station 1 and 0.84 for PM₁₀ at station 2, indicating enhanced precision in explaining non-linear correlations between meteorological parameters and pollutant concentrations. According to the study and predictions, these innovative methods may improve the accuracy of particulate matter prediction and impact policymakers and decision-makers within pollution control agencies to improve air quality.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"360 ","pages":"Article 121422"},"PeriodicalIF":4.2,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}