Atmospheric Pollution Research最新文献

{"title":"Long-term seasonal variability in the water-soluble inorganic composition of coarse and fine aerosols over the northeast Arabian sea","authors":"Garima Shukla ,&nbsp;Ashwini Kumar ,&nbsp;Ankush Kaushik","doi":"10.1016/j.apr.2025.102794","DOIUrl":"10.1016/j.apr.2025.102794","url":null,"abstract":"<div><div>The chemical composition of atmospheric aerosols plays a crucial role in understanding air quality, climate interactions, and oceanic biogeochemical cycles. This study presents a comprehensive long-term seasonal analysis (December 2017–May 2022) of water-soluble inorganic composition (WSIC) in coarse (PM₁₀) and fine (PM_2.5) aerosols over a tropical coastal site located in the northeast Arabian Sea (Goa, India). A total of 583 aerosol samples (290 p.m.₁₀ and 293 p.m._2.5) were simultaneously collected and analysed for major cations (Na⁺, NH₄⁺, K⁺, Mg²⁺, Ca²⁺) and anions (Cl⁻, NO₃⁻, SO₄²⁻). The seasonal variability in WSIC was found to be strongly influenced by continental outflows, monsoonal dynamics, and marine sources. The highest WSIC concentrations were observed during winter (23.1 μg m⁻³ for PM₁₀ and 16.2 μg m⁻³ for PM_2.5) season which was attributed to anthropogenic emissions and secondary inorganic aerosol formation, while lower values were observed during summer months mainly due to dilution effects caused by strong marine air intrusion. Non-sea-salt sulphate (nss-SO₄^2-) dominated both aerosol fractions (&gt;98 %) while nitrate (NO₃⁻) was dominant particularly in summer due to its interaction with long-range transported mineral dust. Ammonium (NH₄⁺) correlated strongly with sulphate (r &gt; 0.9) in winter and post-monsoon, indicating ammonium sulphate as a major component of secondary aerosols. Crustal elements (Ca²⁺, Mg²⁺) were more prominent in PM₁₀, peaking in summer due to long-range dust transport. Biomass burning tracers (nss-K⁺) were significantly higher (p &lt; 0.05) in PM_2.5 during winter, emphasizing regional agricultural residue burning as a key source from the northern regions in the Indo-Gangetic Plains. Global reanalysis datasets (MERRA-2, CAMS) effectively captured sulphate trends but overestimated sea salt concentrations. These findings provide critical insights into aerosol chemistry in a tropical coastal environment and its implications for air quality and climate modeling. Regional scale chemical characterisation of aerosols is important for their better parameterization in chemical transport models.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102794"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Investigating temporal PM2.5 exposure in relation to rapid lung-function decline among COPD patients: A retrospective study","authors":"Kang-Yun Lee ,&nbsp;Rachel Chien ,&nbsp;Kuan-Yuan Chen ,&nbsp;Ying-Ying Chen ,&nbsp;Wen-Te Liu ,&nbsp;Po-Hao Feng ,&nbsp;Kun-Ta Lee ,&nbsp;Shu-Chuan Ho ,&nbsp;Arnab Majumdar ,&nbsp;Jiunn-Horng Kang ,&nbsp;Wun-Hao Cheng ,&nbsp;I-Jung Liu ,&nbsp;Yen-Ling Chen ,&nbsp;Cheng-Yu Tsai","doi":"10.1016/j.apr.2025.102822","DOIUrl":"10.1016/j.apr.2025.102822","url":null,"abstract":"<div><div>Fine particulate matter (PM) is associated with chronic obstructive pulmonary disease (COPD) development, particularly the rapid-decline phenotype. However, its differential impacts across exposure periods remain unclear. In this study, we investigated associations between temporally stratified PM_2.5 exposure and lung-function decline in COPD.</div><div>A retrospective analysis was performed on data from COPD cohorts (2016–2023), including subjects with three pulmonary function tests (PFTs) conducted over at least 18 months. PM_2.5 exposure was estimated using inverse distance weighting based on residential proximity to monitoring stations. Temporal exposure was modeled via an exponentially weighted moving average (EWMA) across baseline (year before cohort entry and first PFT), early (first to second PFT), and late follow-up (second to third PFT) exposure periods. A multivariable regression was used to assess associations with lung-function decline and rapid-decline risk. The rapid-decline group (<em>n</em> = 59) showed significantly higher annual reductions in the forced expiratory volume in 1 s (FEV₁) and forced vital capacity (FVC) than the non-rapid-decline group (<em>n</em> = 183). Models assigning the highest weight to the baseline exposure period demonstrated significant negative associations between PM_2.5 and annualized FEV₁ (−291.19 mL/year, <em>p</em> &lt; 0.01) and FVC (−225.89 mL/year, <em>p</em> &lt; 0.05). Additionally, PM_2.5 exposure was significantly linked to higher odds of a rapid decline in FEV₁ when emphasizing the baseline or late follow-up periods.</div><div>Collectively, baseline PM_2.5 exposure was associated with accelerated lung-function decline and increased risk of rapid decline, underscoring the cumulative impact of early exposure and the importance of exposure timing in COPD progression.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102822"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Investigation of temporal PCDD/F concentration levels and gas/particle partitioning employing recent models in the ambient air of a wastewater treatment plant site","authors":"Berke Gülegen,&nbsp;Abdul Alim Noori,&nbsp;Yücel Taşdemir","doi":"10.1016/j.apr.2025.102823","DOIUrl":"10.1016/j.apr.2025.102823","url":null,"abstract":"<div><div>Polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs) are persistent organic pollutants posing a serious threat to human health and the environment. The present work measured PCDD/F atmospheric concentrations in the ambient air of Bursa's largest wastewater treatment plant over four seasons. The average PCDD/F concentration was 410 ± 178 fg/m³ (30.83 ± 4.85 fg WHO TEQ/m³). This value was in agreement with many studies conducted worldwide. Ambient air PCDD/Fs were found to originate mainly from sewage sludge in the aeration basin and motor vehicle emissions. However, the risk values were less than 10⁻⁶, indicating that PCDD/F pollution in the site does not pose a health risk. The gas/particle (G/P) distribution is a significant concern from many different perspectives, including the mechanisms of atmospheric deposition, long-distance transport and health risks. Traditionally, adsorption and absorption-based models have been employed to define the PCDD/F G/P partitioning. In the present study, five different models (Li-Ma-Yang, Dachs-Eisenreich, QSPR, pp-LFER, mp-pp-LFER) were utilized along with these two models. Among these models, the Li-Ma-Yang model presented successful performance and revealed the importance of absorption and deposition mechanisms for the G/P transitions. Also, the model of pp-LFER generally performed well and showed that it could predict PCDD/F partitioning.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102823"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Joint estimation and driving mechanism analysis of NO2 and PM2.5 in Northwest China based on a hybrid modeling approach","authors":"Wenbo Zhu,&nbsp;Ning Du,&nbsp;Li Wang,&nbsp;Yujun Zuo,&nbsp;Xiaodong Deng,&nbsp;Yang Geng,&nbsp;Ling Qian","doi":"10.1016/j.apr.2025.102787","DOIUrl":"10.1016/j.apr.2025.102787","url":null,"abstract":"<div><div>Comprehensive understanding of the spatiotemporal distribution of air pollutants and their driving mechanisms is crucial for formulating scientifically grounded strategies for atmospheric environmental management. Taking Shaanxi, Gansu, and Ningxia in Northwest China as the study area, this study constructed a hybrid modeling framework integrating LightGBM and Lasso to overcome the limitations of conventional machine learning models in feature selection and nonlinear relationship capturing, aiming to simultaneously improve prediction accuracy and model interpretability. Furthermore, the model was coupled with Geodetector and SHapley Additive exPlanations (SHAP) to systematically investigate the spatial distribution patterns and driving mechanisms of nitrogen dioxide (NO₂) and fine particulate matter (PM_2.5). The results demonstrate that the model exhibits high predictive accuracy, with R² values of 0.91 and 0.94 for NO₂ and PM_2.5, respectively; corresponding MAE values are 3.57 μg/m³ and 4.49 μg/m³, and RMSE values are 5.01 μg/m³ and 8.20 μg/m³. Spatial analysis reveals that concentrations of NO₂ and PM_2.5 are higher in the Guanzhong Plain and surrounding urbanized areas, indicating that population density, traffic emissions, and industrial activities are the primary sources of pollution. The driving mechanism analysis further shows that factors such as topographic factors, temperature, elevation, and transportation infrastructure significantly affect pollutant distribution, exhibiting marked spatial heterogeneity and nonlinear interactions. This study not only enhances the accuracy of regional air pollution modeling but also provides a scientific basis for pollution control and precision management in the region.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102787"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Ozone pollution in Taiyuan Basin during summer: the impact of atmospheric boundary layer structure and synoptic patterns","authors":"Shutong Liu ,&nbsp;Yan Yan ,&nbsp;Xuhui Cai ,&nbsp;Yu Song ,&nbsp;Hongsheng Zhang ,&nbsp;Xiaobin Wang","doi":"10.1016/j.apr.2025.102791","DOIUrl":"10.1016/j.apr.2025.102791","url":null,"abstract":"<div><div>Taiyuan Basin has attracted much attention due to its serious ozone (O₃) pollution in summer. However, the combined effects of synoptic patterns and atmospheric boundary layer processes on ozone pollution remain unclear. In this study, the high-resolution (1 km) ozone concentration distribution datasets during the summer seasons from 2015 to 2020 were analyzed, together with the Weather Research and Forecasting (WRF) model simulations. Four dominant synoptic patterns were identified based on the obliquely rotated T-mode Principal Component Analysis (T-PCA). Results revealed three key dynamic mechanisms regulating ozone pollution within Taiyuan Basin by synoptic patterns and boundary layer processes. 1) Easterly winds partially alleviated urban pollution through the air-flushing process while advecting ozone air mass southward. 2) Southwesterly flows facilitated northward transport of industrial emissions to urban areas, combined with favorable photochemical conditions (e.g., elevated temperatures and reduced relative humidity) to amplify ozone production. 3) Strong northwest winds invaded the whole Taiyuan Basin and enhanced atmospheric ventilation capacity, achieving effective pollutant removal while entrapping residual ozone in southern piedmont regions. Furthermore, under weak synoptic conditions, terrain-induced mountain-valley circulation emerged and generated subsidence compensation flows that interacted with organized descending motions from upper large-scale weather systems. This multi-scale vertical coupling mechanism significantly enhanced surface ozone accumulation. This study provided insight for designing localized ozone control strategies, which may apply to other cities with complex terrain worldwide.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102791"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Spatial monitoring of black carbon aerosols and their driving factors based on street view images in a developing city of China","authors":"Wenjun Yang,&nbsp;Wenxiao Jia,&nbsp;Yutong Wang,&nbsp;Qiaoan Chen,&nbsp;Yang Liu,&nbsp;Zixiang Gao,&nbsp;Jizu Wu","doi":"10.1016/j.apr.2025.102796","DOIUrl":"10.1016/j.apr.2025.102796","url":null,"abstract":"<div><div>Black carbon (BC) aerosols accelerate global warming and prompt adverse health effects for urban dwellers. Current research is with limited spatial resolution data for cities with high spatial heterogeneity. Based on MicroAeth 200-installed mobile monitoring platform, this study identified the spatial pattern of BC aerosols, assessed the health risk and analyzed its sources and influencing factors in Yangling, one developing city of the Fenwei Basin in China. Results showed that near surface atmospheric BC in Yangling were high in the northeast and low in the southwest, with average of 2.43 ± 0.71 μg/m³ in summer and 5.35 ± 0.73 μg/m³ in winter. Winter BC exposure posed health risk equivalent to ∼3.5 times passively smoked cigarettes than summer exposure. The emission source differed for summer and winter (summer BC was mainly from fossil fuel combustion emissions, while winter BC was mainly from biomass combustion emissions). High buildings were unfavorable to the ease of atmospheric BC dispersion. Vegetation plantings should consider both the absorption effect and diffusion impeding effect. To reduce the health hazards of BC-induced exposure, restriction policies should be urgent for atmospheric BC control and prevention.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102796"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Machine learning-based estimation of vehicular emissions using on-board diagnostics data for intelligent fleet management","authors":"Hamidreza Abediasl ,&nbsp;Masoud Aliramezani ,&nbsp;Charles Robert Koch ,&nbsp;Mahdi Shahbakhti","doi":"10.1016/j.apr.2025.102784","DOIUrl":"10.1016/j.apr.2025.102784","url":null,"abstract":"<div><div>Stringent regulations on real-driving emissions have been introduced to reduce the effect of tailpipe vehicular emissions on environmental pollution. The need to monitor emissions in real-driving conditions and across different driving cycles has underscored the importance of models for estimating emission rates. In this study, machine learning is employed to model commonly regulated tailpipe emissions (CO, UHC, NOx) based on real-time data obtained through on-board diagnostics (OBD) of vehicles. The models are trained using real-world tailpipe emission data and engine/vehicle operation data collected from three vehicles with various powertrains, including conventional gasoline engine, hybrid electric, and plug-in hybrid electric, under different ambient temperatures. Emphasis is placed on developing models capable of effectively estimating emissions during the cold phase of operation, which accounts for a significant portion of vehicular emissions, particularly in cold climates. The models are subsequently integrated into an intelligent fleet management system to enable real-time estimation of emissions using OBD data received from Internet of Things (IoT) modules installed on fleet vehicles.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102784"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Predicting odor nuisance levels using meteorological data and citizen complaints records: A machine learning approach","authors":"Meltem Apaydın Üstün ,&nbsp;Can Burak Özkal","doi":"10.1016/j.apr.2025.102809","DOIUrl":"10.1016/j.apr.2025.102809","url":null,"abstract":"<div><div>Understanding and predicting odor nuisance in industrial areas is vital for public health and quality of life. In Çorlu, an industrial city with unique topography, we analyzed citizen-reported odor complaints collected via the Geographic Information System-integrated mobile application Çorlu KODER (October 2020–August 2022). Using machine learning models incorporating meteorological factors like mixed-layer height, temperature, pressure, and humidity, along with seasonal and diurnal variations, we addressed significant class imbalance in the dataset. Ensemble methods such as Random Forest and Adaptive Boosting combined with synthetic minority oversampling and edited nearest neighbors achieved macro-averaged mean absolute error scores of 0.232 and 0.276. Our findings demonstrate the potential of machine learning for proactive odor prediction, aiding urban management in improving air quality and community well-being.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102809"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Multiple fuel use in low-income communities: socio-economic determinants and impacts on household air pollution and respiratory health in South Africa","authors":"Bianca Wernecke ,&nbsp;Caradee Y. Wright ,&nbsp;Kristy Langerman ,&nbsp;Angela Mathee ,&nbsp;Nada Abdelatif ,&nbsp;Marcus A. Howard ,&nbsp;Nkosana Jafta ,&nbsp;Christiaan Pauw ,&nbsp;Shumani Phaswana ,&nbsp;Kareshma Asharam ,&nbsp;Ishen Seocharan ,&nbsp;Hendrik Smith ,&nbsp;Rajen N. Naidoo","doi":"10.1016/j.apr.2025.102815","DOIUrl":"10.1016/j.apr.2025.102815","url":null,"abstract":"<div><div>Domestic fuel use contributes significantly to household air pollution levels and to the disease burden in low-income households in South Africa. The link between residential fuel stacking and switching, and respiratory health, mediated by household air pollution, remains underexplored, posing challenges to transition to cleaner fuels. This study identified socio-economic determinants of fuel use patterns in two low-income communities of KwaZamokuhle and eMzinoni in South Africa. It also examined the impacts of these patterns on household air pollution levels and respiratory health outcomes. Over half of households relied on dirty fuels across all needs. Average household PM_2.5 levels exceeded national daily standards (40 μg/m³). Education level and employment status were significant factors in determining fuel choice, with employed participants less likely to rely on dirty fuels. Town-specific characteristics also influenced household fuel use patterns. In terms of health, 9.5 % of participants had obstructive airways disease and 26.9 % tested positive for inhalant allergens. Heating fuels were strongest predictor of obstructive airways disease (&gt;75 %) whereas cooking fuels were the main predictor of allergen sensitivity (∼75 %). The stepwise introduction of cleaner fuels predicted better respiratory health outcomes. The findings of this study suggest that even the partial adoption of cleaner fuels has health benefits and supports the formulation of context-specific mitigation efforts aiming to address negative health effects associated with household air pollution.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102815"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Unraveling the impacts of blue-green-gray landscape patterns on PM2.5 in high-density urban areas: A case study of Xi’an","authors":"Baojun Yang ,&nbsp;Lin Zhang ,&nbsp;Danchen Yang ,&nbsp;HuiYing Ma ,&nbsp;Haifan Xie ,&nbsp;Jiahui Hou ,&nbsp;Ling Qiu ,&nbsp;Tian Gao","doi":"10.1016/j.apr.2025.102788","DOIUrl":"10.1016/j.apr.2025.102788","url":null,"abstract":"<div><div>High-density cities face severe PM_2.5 particulate pollution issues, where landscape patterns significantly influence PM_2.5 concentrations. Currently, there is a notable lack of comprehensive research on the integrated effects of urban blue-green-gray space landscape patterns on PM_2.5. To address this research gap, this study examines the influence of blue-green-gray spatial configurations on PM_2.5 concentrations in 410 high-density urban blocks in Xi’an during its peak pollution period. Using spatial autocorrelation, random forest regression, and correlation analysis, the research delineates the spatio-temporal distribution of PM_2.5, constructs a customized landscape pattern index system, and proposes optimization strategies for urban planning. The results reveal that: (1)PM_2.5 levels peak in January, with elevated nocturnal concentrations, and exhibit significant spatial clustering in the western, southwestern, and southern districts of Xi’an. (2)A set of nine primary landscape indices focused on green and gray spaces, including AI-Green, NP-Green, PD-Green, TE-Green, SHAPE (MN)-Green, CA-Gray, PD-Gray, SPLIT-Gray and SHAPE (MN)-Gray effectively characterize the impact of the urban landscape on air quality. (3) In particular, indicators such as AI-Green, TE-Green, and SHAPE (MN)-Green are significantly negatively correlated with PM_2.5, while metrics such as NP-Green, PD-Green, and CA-Gray show positive associations, with CA-Gray exhibiting a particularly strong link. These findings suggest that urban planning should prioritize enhancing the aggregation and connectivity of green spaces, refining the configuration of built-up areas, and promoting a decentralized distribution of gray spaces. Such strategic spatial configurations can meaningfully lower PM_2.5 concentrations, providing a scientifically grounded framework for improving air quality and public health in Xi’an and other high-density urban environments.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102788"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}