Atmospheric Pollution Research最新文献

{"title":"Quantitative analysis to define baseline criteria for introducing reduced-emission firecrackers","authors":"Shilpa Kumari ,&nbsp;Rahul Wadichar ,&nbsp;Payal Mane ,&nbsp;Sadhana Rayalu ,&nbsp;Penumaka Nagababu","doi":"10.1016/j.apr.2025.102740","DOIUrl":"10.1016/j.apr.2025.102740","url":null,"abstract":"<div><div>This research paper emphasizes the crucial role of statistical methods in validating the proposed methodology for the state-of-the-art emission testing facility. This facility is specifically designed for monitoring emissions from developed reduced-emission firecrackers and commercial crackers. Establishing baseline values, derived through statistical analysis of data collected by the emission testing facility, is pivotal in ensuring the production of less polluting firecrackers by the fireworks industry. This, in turn, supports sustainable festival celebrations and events in the future. Statistical techniques such as frequency distribution, regression equations, and the Spearman correlation coefficient were employed to understand the significance of the data and its distribution for calculating standard error and deviation. The baseline values, identified through this statistical analysis, serve as crucial parameters in the evaluation of emission levels. According to the study's findings, the P-value indicates a significant result at P &lt; 0.05. Furthermore, the correlation coefficient between PM₁₀ and PM_2.5 is reported to have an R² value of 0.99, highlighting a strong correlation. This robust statistical foundation strengthens the credibility of the proposed methodology and underscores its importance in advancing the development and monitoring of environmentally sustainable firecrackers.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102740"},"PeriodicalIF":3.5,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044271","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":"Enhancing Air Quality forecasting with functional neural networks: A case study of PM2.5 in Seoul","authors":"Yaeji Lim ,&nbsp;Yeonjoo Park","doi":"10.1016/j.apr.2025.102732","DOIUrl":"10.1016/j.apr.2025.102732","url":null,"abstract":"<div><div>Reliable prediction of PM_2.5 levels is essential due to their substantial impacts on public health, the environment, and society. This is especially critical in regions like South Korea, where air quality is often compromised by elevated PM_2.5 concentrations resulting from domestic emissions and transboundary pollution. This study considers a Functional Neural Network (FNN) model that combines Functional Data Analysis (FDA) with deep learning techniques to predict PM_2.5 levels. The FNN model is applied to data from 13 monitoring stations in Seoul and compared with traditional multivariate-based neural networks (NN) and functional regression (FM) models. The enhanced predictive accuracy was observed from the FNN model by integrating dynamic temporal patterns in pollutant and meteorological trajectories as functional inputs. Additionally, this study proposes a model selection procedure within the FNN framework to identify a subset of functional inputs that significantly enhances prediction performance. Comprehensive comparison studies confirm that the proposed FNN, combined with the input selection procedure, offers a reliable tool for PM_2.5 prediction. This functional approach holds potential for supporting air quality management and protecting public health.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102732"},"PeriodicalIF":3.5,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044272","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":"Development of a spatiotemporal resolution enhancement method for satellite-observed XCH4 products based on spatiotemporal information and LightGBM-Bayesian Integration","authors":"Yong Wan,&nbsp;Yuhang Liu,&nbsp;Yu Liu,&nbsp;Qian Xiao","doi":"10.1016/j.apr.2025.102736","DOIUrl":"10.1016/j.apr.2025.102736","url":null,"abstract":"<div><div>High spatiotemporal resolution XCH₄ observational data are crucial for the comprehensive prevention and control of CH₄ pollution. Satellite remote sensing has emerged as a key approach for XCH₄ monitoring; however, its effectiveness is constrained by satellite observation tracks, atmospheric disturbances, and sensor limitations. Consequently, data gaps persist in certain regions. Machine learning models have demonstrated remarkable success in generating high spatiotemporal resolution data for gases such as CO₂, yet research on their application to XCH₄ remains limited. Moreover, most existing studies fail to fully capture the complex spatiotemporal characteristics of XCH₄ due to insufficient feature selection. Therefore, this study proposes a novel spatiotemporal resolution enhancement method for satellite-derived XCH₄ data, integrating spatiotemporal information with a LightGBM-Bayesian framework. This approach establishes the latent relationships between satellite-derived XCH₄ measurements, auxiliary data, and precise spatiotemporal information. Using this method, we generated XCH₄ distribution maps for the Beijing-Tianjin-Hebei region from 2020 to 2022. Experimental results indicate that: (1) The LightGBM-Bayesian model outperforms traditional models such as LightGBM, XGBoost, and RF, demonstrating superior accuracy; (2) Model predictions exhibit strong consistency with TCCON station observations, validating its high precision; (3) Incorporating precise spatiotemporal information as input features significantly enhances the model's predictive performance; and (4) The spatiotemporal distribution of XCH₄ in the Beijing-Tianjin-Hebei region from 2020 to 2022 reveals a seasonal trend of higher concentrations in summer and autumn and lower concentrations in spring and winter, along with a year-on-year increase. Spatial patterns indicate elevated levels in the southwest and lower levels in the northeast.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102736"},"PeriodicalIF":3.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044270","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":"Variations in the characteristics and sources of PM2.5 during the COVID-19 lockdown in Xiangyang, Hubei Province, central China","authors":"Yao Mao ,&nbsp;Tianpeng Hu ,&nbsp;Weijie Liu ,&nbsp;Mingming Shi ,&nbsp;Ying Liu ,&nbsp;Changlin Zhan ,&nbsp;Jiaquan Zhang ,&nbsp;Xinli Xing ,&nbsp;Shihua Qi","doi":"10.1016/j.apr.2025.102723","DOIUrl":"10.1016/j.apr.2025.102723","url":null,"abstract":"<div><div>This study investigated the variations in the characteristics and sources of fine particulate matter (PM_2.5) during the COVID-19 lockdown. The research was conducted in Xiangyang, a city in Hubei Province, central China, which is influenced by air pollutant transport from northern China and was the last city in Hubei to implement lockdown measures following the COVID-19 outbreak. PM_2.5 samples were obtained between 19 January and April 19, 2020. A significant reduction in the average PM_2.5 concentration was observed, falling from 148 ± 33 μg m⁻³ prior to the lockdown to 93 ± 40 μg m⁻³ after its implementation. Consistent with expectations, concentrations of polycyclic aromatic hydrocarbons (PAHs) and elemental carbon (EC) also gradually declined. Interestingly, 1 p.m._2.5 pollution event was observed during the lockdown, attributed to enhanced atmospheric oxidation capacity, as indicated by the elevated sulfur oxidation ratio (SOR), nitrogen oxidation ratio (NOR) and secondary organic carbon (SOC) levels. Results of diagnostic ratios and principal component analysis multiple linear regression (PCA-MLR) indicated an increased contribution from secondary aerosol formation and residential combustion emission during the lockdown, while vehicle emissions decreased. Geographical and meteorological data further suggested non-negligible influence from regional transport. Our findings reveal a complicated evolution of PM_2.5 species and sources amidst the enforcement of pollution control strategies. This underscores the necessity for regional coordinated source management and synergistic control of O₃ and PM_2.5, and provides a critical case for the design of differentiated emission control strategies.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102723"},"PeriodicalIF":3.5,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145026992","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":"Evaluating the impact of anthropogenic drivers and meteorological factors on air pollutants by explainable machine learning in Shandong Province, China","authors":"Yue Yuan ,&nbsp;Fuzhen Shen ,&nbsp;Chunyan Sheng ,&nbsp;Zeming Zhang ,&nbsp;Weihua Guo ,&nbsp;Wengang Zhu ,&nbsp;Hui Zhu","doi":"10.1016/j.apr.2025.102694","DOIUrl":"10.1016/j.apr.2025.102694","url":null,"abstract":"<div><div>Unexpected haze in North China Plain during the COVID-19 lockdown has been regarded as a natural window to explore the meteorological impact on formatting PM_2.5 pollution but with limitations in quantifying weather elements’ contributions. In this study, daily data of six air pollutants (including PM_2.5, PM₁₀, SO₂, NO₂, O₃, and CO) and six meteorological factors (including temperature, pressure, relative humidity (RH), wind speed (WS), wind direction (WD), and precipitation) from 2015 to 2020 across 16 capital cities in Shandong province, China, was used to drive the Machine Learning and the SHapley Additive exPlanation (SHAP) models. By applying these models, contributions from anthropogenic drivers to pollutant reductions and contributions from meteorological factors to the haze event were investigated. Results show that the COVID-19 lockdown measures reduced concentrations of NO₂, PM_2.5, PM₁₀, CO and SO₂ by −52.1 %, −40.0 %, −45.5 %, −29.4 % and −38.7 % respectively. On average, an 18.9 % increase in O₃ was observed. PM_2.5 pollution was mainly driven by temperature with a SHAP value of 19.7 μg/m³, followed by RH (5.8 μg/m³), precipitation (0.9 μg/m³), WD (0.3 μg/m³), pressure (0.1 μg/m³) and WS (0.1 μg/m³) during the haze period. Relative to the post-haze period, high-pressure systems coupled with lower temperatures and weakened surface winds hindered the dispersion of PM_2.5 whilst higher RH was in favour of PM_2.5 production during the haze period. This study underscores the intricate interplay between emissions, meteorological conditions, and regulatory measures in air pollution, offering critical insights into future air quality management strategies by air pollution prediction.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102694"},"PeriodicalIF":3.5,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866351","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":"Biomonitoring of airborne magnetic particles over time: an in situ magnetic susceptibility-based methodology","authors":"Daniela Buitrago Posada ,&nbsp;Marcos A.E. Chaparro ,&nbsp;Harald N. Böhnel ,&nbsp;José Duque-Trujillo","doi":"10.1016/j.apr.2025.102687","DOIUrl":"10.1016/j.apr.2025.102687","url":null,"abstract":"<div><div>We introduce a novel methodology that utilizes <em>in situ</em> magnetic susceptibility (κ_is) measurements through transplants of <em>Tillandsia capillaris</em>, which facilitates the quantification of (sub)micron-sized magnetite particles that may pose health risks. Scanning Electron Microscopy with Energy Dispersive Spectroscopy analysis reveals the presence of iron-rich spherical, semi-spherical, and irregular particles, along with potentially toxic elements such as chromium, cobalt, and manganese. Over one year, we tested two <em>in situ</em> measuring protocols—direct-contact methodology (DCM) and Petri-wood methodology (PWM)—on thirty-nine samples. The κ_is values obtained were comparable; however, the DCM exhibited a higher coefficient of variation (CV ≈ 1–97 %) compared to the PWM (CV ≈ 0–10 %). The PWM demonstrated low dispersion in its results, with a standard error of the mean of 0–3 × 10⁻⁷ SI, which is comparable to the instrument's sensitivity of 1 × 10⁻⁷ SI. The maximum change in κ_is observed in the transplants during the year of exposure across various sites ranged from 2.8 to 13.1 × 10⁻⁶ SI, indicating an accumulation of airborne magnetic particles (AMP) between 0.13 and 0.63 mg on the transplants. The analysis over one year suggests that traffic avenues corresponded with high AMP accumulation, while most other sites exhibited moderate accumulation. This insight is crucial for developing more accurate <em>in situ</em> measurement protocols and for understanding the role of epiphytic plants as biomonitors of air particle pollution.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102687"},"PeriodicalIF":3.5,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866445","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":"Unveiling the dynamics of multi-scale carbon emissions in urban agglomerations: A hierarchical causal framework for China's strategic economic corridor","authors":"Jianan Wang ,&nbsp;Wei Fang ,&nbsp;Haizhong An ,&nbsp;Yujia Fu","doi":"10.1016/j.apr.2025.102698","DOIUrl":"10.1016/j.apr.2025.102698","url":null,"abstract":"<div><div>Understanding multi-scale spatiotemporal dynamics of urban carbon emissions is critical for crafting targeted decarbonization strategies. However, existing studies predominantly examine emissions at singular scales, overlooking cross-scale interactions and causal spatial dependencies. This study proposes a hierarchical analytical framework integrating urban agglomeration, county, and 500m grid levels to dissect carbon emission patterns across China's Yangtze River economic belt (YREB) from 2010 to 2020. Leveraging convergent advances in satellite-derived NPP-VIIRS-like nighttime light data and provincial energy inventories, we develop an ensemble approach combining geographical convergent cross mapping (GCCM) with multi-scale geographically weighted regression (MGWR) to unravel causal mechanisms and scale-dependent drivers. Our findings reveal three insights: (1) Emission trajectories exhibit strong path dependency, with the Yangtze River delta agglomeration contributing 60.4 % of total YREB emissions through 2020, while emerging hotspots demonstrate spatial decoupling from traditional economic cores; (2) Causal analysis identifies technology-intensity and tertiary sector growth as dominant mitigation factors, contrasting with persistent carbon lock-in effects from legacy infrastructure; (3) MGWR exposes paradoxical regional dynamics where urbanization drives emission reductions in advanced economies yet accelerates emissions in developing regions. The framework advances spatial econometrics by reconciling Simpson's paradox in cross-scale analysis while providing actionable intelligence for tiered carbon governance. This contribution establishes a replicable paradigm for transboundary emission management in mega-economic corridors globally.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102698"},"PeriodicalIF":3.5,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866350","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":"Online observation of PM2.5 during a persistent haze in the Yangtze River Delta: chemical components, health effect and light extinction","authors":"Zelin Ao ,&nbsp;Honglei Wang ,&nbsp;Yinglong Zhang ,&nbsp;Li Li ,&nbsp;Yue Ke ,&nbsp;Zihao Wu ,&nbsp;Zhizhen Peng","doi":"10.1016/j.apr.2025.102695","DOIUrl":"10.1016/j.apr.2025.102695","url":null,"abstract":"<div><div>Haze impacts visibility and health. To understand the effects of haze chemical components on health risks and atmospheric optics, PM_2.5 chemical components during a haze event in Jiaxing (Dec 15, 2023–Jan 8, 2024) were analyzed. NO₃⁻ was the main component of water-soluble ions (WSIs) (45.07 %–58.20 %). Primary organic carbon (POC) was the main component of total carbon (TC) (55.12 %–78.21 %). In the clean and developing stages, Fe was the main component of metal. K was the main component in the maintenance and dissipating stages. In the developing stage, NO₃⁻ and secondary organic carbon (SOC) concentrations increased sharply, 3.86 and 18.75 times that of the previous stage. Among them, the increase of NO₃⁻ played an essential role in light extinction. In the maintenance stage, SO₄²⁻ played a more critical role. Its contribution to PM_2.5 was 1.68 times that of the previous stage, and the contribution of sulfate (Sul) to light extinction was 1.98 times that of the previous stage. The decrease in NO₃⁻ concentration mainly caused haze dissipating, but the contribution of Nitrate (Nit) to light extinction increased (62.30 %). From clean to maintenance stage, the contribution of WSIs to PM_2.5 increased, while that of TC decreased. At the same time, the enrichment factors (EF) of heavy metals such as Fe, Ca, and Zn, as well as the non-carcinogenic and carcinogenic risk for kids, decreased but increased in the dissipating stage. The non-carcinogenic and carcinogenic risk for adults increased in the developing stage. Kids were more sensitive to PM_2.5 than adults.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102695"},"PeriodicalIF":3.5,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866349","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":"Environmental disinfection to inactivate airborne viruses by aerosolized surfactants","authors":"Isaura Yáñez Noguez ,&nbsp;Ignacio Monje Ramírez ,&nbsp;Jesús Gracia Fadrique ,&nbsp;Lidia Alicia López Vega ,&nbsp;María Teresa Orta Ledesma","doi":"10.1016/j.apr.2025.102684","DOIUrl":"10.1016/j.apr.2025.102684","url":null,"abstract":"<div><div>Providing effective alternatives for the detection and inactivation of airborne viruses is the focus of this research in response to the emergence of new variants of the virus, such as SARS-CoV-2. The use of environmentally friendly surfactants has led to the development of a promising technique with high efficacy for the inactivation of viruses in indoor bioaerosols, with no by-products and no health risks. Environments contaminated with infectious bioaerosols were simulated under controlled laboratory conditions (contact chamber) and in test rooms. The Sampling Bio-Aerosol Button by Double Agar Layer (SBAB-DAL) was developed and validated for virus recovery from infectious bioaerosols. Three new custom surfactants (CS) (patent pending): CS formulated with benzalkonium chloride (CSBC), CS based on pyridinium (CSPB) and CS prepared from cetyltrimethylammonium chloride (CSCTAC), were evaluated for inactivation of bacteriophage MS2 ATCC 15597-B1 as a surrogate for SARS-CoV-2. Non-inactivated viruses were analysed by SBAB-DAL to assess reduction percentages (% reduction) in contact chamber. CSBC, CSPB and CSCTAC were consistently able to inactivate up to 99.810, 99.986 and 99.500 % of the MS2 surrogate respectively in a 5-min contact time. The three surfactants were able to inactivate up to and 99.999 % over a 10-min contact time. The highest inactivation (up to 5 log₁₀ reduction) by application of the customised surfactant treatments supplied in aerosol form has a high potential for the inactivation of environmental viruses such as SARS-CoV-2. The SBAB-DAL methodology is a simple and effective test that can be applied to the monitoring of infectious bioaerosols as vehicles of primary virus transmission in indoor environments.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102684"},"PeriodicalIF":3.5,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830464","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":"The impact of different spatial sampling strategies on the spatial extrapolation prediction accuracy of PM2.5 concentrations in the Beijing-Tianjin-Hebei region, China","authors":"Lihang Fan ,&nbsp;Guangjian Wu ,&nbsp;Weimiao Li ,&nbsp;Wei Wang ,&nbsp;Fuxing Li","doi":"10.1016/j.apr.2025.102685","DOIUrl":"10.1016/j.apr.2025.102685","url":null,"abstract":"<div><div>PM_2.5 monitoring station networks play a critical role in retrieving ground-level PM_2.5 concentrations using satellite remote sensing technology. However, the optimal spatial distribution of PM_2.5 monitoring stations is frequently overlooked during satellite-based PM_2.5 retrieval. Here, we employ the space-time linear mixed effects (STLME) model to assess the impact of different spatial sampling strategies on the spatial extrapolation prediction accuracy of PM_2.5 concentrations in the Beijing-Tianjin-Hebei region in 2020. Results demonstrate that the all-station strategy (national + provincial stations) achieves superior performance, with station- and county/district-based CV-R² values measuring 0.87 and 0.78 respectively, compared to the national-station strategy's corresponding values of 0.80 and 0.73. These findings suggest that both the all-station and national-station strategies generally reflect the model's spatial extrapolation prediction accuracy at the county and city scales. Furthermore, six strategies based on the all-station framework were developed through spatial stratified sampling strategy, demonstrating that strategic expansion of monitoring stations enhances model performance. However, model performance exhibits limited improvement when the number of stations exceeds 300. That indicate the all-station strategy can meet the estimation accuracy requirements for spatial extrapolation prediction models in this area. These findings suggest that the all-station strategy offers a relatively robust framework for PM₂.₅ extrapolation modeling in the BTH region, making it a preferred choice for supporting future air quality management and monitoring network optimization.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102685"},"PeriodicalIF":3.5,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841566","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}