Pub Date : 2025-11-06DOI: 10.1016/j.apr.2025.102813
Tao Niu , Congwu Huang , Yiliang Jiang , Qi Jiang , Bihui Zhang , Hongli Liu , Rong Li , Yuzhan Xie , Tijian Wang
In this study, the maximum correlation coefficient (MCC) method was employed to fuse Fengyun (FY) 3D and 4A satellite aerosol optical depth (AOD) products, and an enhanced particulate matter remote sensing (PMRS) method was subsequently utilised for inverting near-surface fine particulate matter (PM2.5) and inhalable particle (PM10) concentrations. Comparative experiments with and without assimilation revealed that the combination of the nudging emission inversion method and machine learning increased the accuracy of the Chinese Unified Atmospheric Chemistry Environment (CUACE) model in predicting the air quality index (AQI), PM2.5, and PM10 in the China, Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions, whereas the positive assimilation effect on emission sources did not decrease rapidly over time. Assimilation using satellite fusion revision data in January 2022 reduced the root mean square errors of the CUACE forecasts of the AQI, PM2.5, and PM10 by 46 %, 45 %, 36 % compared to nonassimilation forecasts, and 13 %, 13 %, 8 % compared to assimilation using only ground-based observations, respectively. The average threat score (TS) increased by up to 32 % compared with that under the no-assimilation scenario. This demonstrates that the accuracy of the CUACE model for the PM2.5 and PM10 concentrations can be notably enhanced by employing the nudging emission inversion and machine learning methods developed in this study to assimilate PM2.5 and PM10 data inverted from FY satellite fused AOD products.
{"title":"Study of the machine learning emission inversion method of the CUACE model on the basis of fused Fengyun observations","authors":"Tao Niu , Congwu Huang , Yiliang Jiang , Qi Jiang , Bihui Zhang , Hongli Liu , Rong Li , Yuzhan Xie , Tijian Wang","doi":"10.1016/j.apr.2025.102813","DOIUrl":"10.1016/j.apr.2025.102813","url":null,"abstract":"<div><div>In this study, the maximum correlation coefficient (MCC) method was employed to fuse Fengyun (FY) 3D and 4A satellite aerosol optical depth (AOD) products, and an enhanced particulate matter remote sensing (PMRS) method was subsequently utilised for inverting near-surface fine particulate matter (PM<sub>2.5</sub>) and inhalable particle (PM<sub>10</sub>) concentrations. Comparative experiments with and without assimilation revealed that the combination of the nudging emission inversion method and machine learning increased the accuracy of the Chinese Unified Atmospheric Chemistry Environment (CUACE) model in predicting the air quality index (AQI), PM<sub>2.5</sub>, and PM<sub>10</sub> in the China, Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions, whereas the positive assimilation effect on emission sources did not decrease rapidly over time. Assimilation using satellite fusion revision data in January 2022 reduced the root mean square errors of the CUACE forecasts of the AQI, PM<sub>2.5</sub>, and PM<sub>10</sub> by 46 %, 45 %, 36 % compared to nonassimilation forecasts, and 13 %, 13 %, 8 % compared to assimilation using only ground-based observations, respectively. The average threat score (TS) increased by up to 32 % compared with that under the no-assimilation scenario. This demonstrates that the accuracy of the CUACE model for the PM<sub>2.5</sub> and PM<sub>10</sub> concentrations can be notably enhanced by employing the nudging emission inversion and machine learning methods developed in this study to assimilate PM<sub>2.5</sub> and PM<sub>10</sub> data inverted from FY satellite fused AOD products.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102813"},"PeriodicalIF":3.5,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135704","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}
Pub Date : 2025-11-05DOI: 10.1016/j.apr.2025.102819
Renato S. Pacaldo , Miraç Aydın , Randell Keith Amarille
Alleviating soil CO2 pollution after forest fires is challenging, especially for large tracts of post-fire forest land. Thus, a granular management approach prioritizing CO2 hotspot areas must be accounted for in CO2 pollution management and the efficient implementation of post-fire forest rehabilitation programs. However, identifying precisely CO2 hotspot areas requires understanding the effects of multiple factors on soil CO2 fluxes because the interaction among multiple variables magnifies the effects of forest fires. This study addresses a critical question of how fire types (crown and surface fires), aspects (north- and south-facing slopes), and soil moisture extremes (extremely wet and dry soils) affect soil CO2 effluxes (FCO2). We simultaneously measured FCO2, soil, and air temperatures, as well as soil moisture, in post-fire black pine (Pinus nigra Arnold) forests using an automated soil respiration system (LI-8100 A). The analysis revealed significant effects of the aforementioned factors and their interaction on FCO2 (p < 0.05), with the highest emissions (2.55 μmol s−1 m−2) occurring at the water-saturated surface fire on the south-facing slope, suggesting that CO2 pollution management efforts should prioritize this location. Although the water-drought crown fire areas at the south-facing slope generate the significantly lowest FCO2 (1.21 μmol s−1 m−2), offsetting CO2 emissions during wet periods, this site should be given priority in rehabilitation efforts to accelerate recovery. The FCO2 correlates positively with temperatures but negatively with soil moisture. Our findings highlight the importance of accounting for multiple factors in quantifying the FCO2 and identifying CO2 pollution hotspots in post-fire forest ecosystems.
{"title":"Forest fire types, soil moisture extremes, and aspects and their interactions significantly affect soil CO2 effluxes in post-fire black pine forests","authors":"Renato S. Pacaldo , Miraç Aydın , Randell Keith Amarille","doi":"10.1016/j.apr.2025.102819","DOIUrl":"10.1016/j.apr.2025.102819","url":null,"abstract":"<div><div>Alleviating soil CO<sub>2</sub> pollution after forest fires is challenging, especially for large tracts of post-fire forest land. Thus, a granular management approach prioritizing CO<sub>2</sub> hotspot areas must be accounted for in CO<sub>2</sub> pollution management and the efficient implementation of post-fire forest rehabilitation programs. However, identifying precisely CO<sub>2</sub> hotspot areas requires understanding the effects of multiple factors on soil CO<sub>2</sub> fluxes because the interaction among multiple variables magnifies the effects of forest fires. This study addresses a critical question of how fire types (crown and surface fires), aspects (north- and south-facing slopes), and soil moisture extremes (extremely wet and dry soils) affect soil CO<sub>2</sub> effluxes (F<sub>CO2</sub>). We simultaneously measured F<sub>CO2</sub>, soil, and air temperatures, as well as soil moisture, in post-fire black pine (<em>Pinus nigra</em> Arnold) forests using an automated soil respiration system (LI-8100 A). The analysis revealed significant effects of the aforementioned factors and their interaction on F<sub>CO2</sub> (p < 0.05), with the highest emissions (2.55 μmol s<sup>−1</sup> m<sup>−2</sup>) occurring at the water-saturated surface fire on the south-facing slope, suggesting that CO<sub>2</sub> pollution management efforts should prioritize this location. Although the water-drought crown fire areas at the south-facing slope generate the significantly lowest F<sub>CO2</sub> (1.21 μmol s<sup>−1</sup> m<sup>−2</sup>), offsetting CO<sub>2</sub> emissions during wet periods, this site should be given priority in rehabilitation efforts to accelerate recovery. The F<sub>CO2</sub> correlates positively with temperatures but negatively with soil moisture. Our findings highlight the importance of accounting for multiple factors in quantifying the F<sub>CO2</sub> and identifying CO<sub>2</sub> pollution hotspots in post-fire forest ecosystems.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102819"},"PeriodicalIF":3.5,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135744","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}
Pub Date : 2025-11-04DOI: 10.1016/j.apr.2025.102809
Meltem Apaydın Üstün , Can Burak Özkal
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.
{"title":"Predicting odor nuisance levels using meteorological data and citizen complaints records: A machine learning approach","authors":"Meltem Apaydın Üstün , 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":"2025-11-04","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}
Pub Date : 2025-11-04DOI: 10.1016/j.apr.2025.102815
Bianca Wernecke , Caradee Y. Wright , Kristy Langerman , Angela Mathee , Nada Abdelatif , Marcus A. Howard , Nkosana Jafta , Christiaan Pauw , Shumani Phaswana , Kareshma Asharam , Ishen Seocharan , Hendrik Smith , Rajen N. Naidoo
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 PM2.5 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 (>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.
{"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 , Caradee Y. Wright , Kristy Langerman , Angela Mathee , Nada Abdelatif , Marcus A. Howard , Nkosana Jafta , Christiaan Pauw , Shumani Phaswana , Kareshma Asharam , Ishen Seocharan , Hendrik Smith , 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<sub>2.5</sub> levels exceeded national daily standards (40 μg/m<sup>3</sup>). 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 (>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":"2025-11-04","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}
Pub Date : 2025-11-03DOI: 10.1016/j.apr.2025.102817
Mengbo Wu , Na Liu , Jingyi Wei , Xinxin Xia , Tianqi Li , Junyi Li , Zhipeng Zhu
Urban morphology and meteorological conditions jointly influence the spatial and temporal dynamics of PM2.5 pollution. Although previous studies have examined their individual effects, the synergistic impacts of three-dimensional (3D) morphology and seasonal meteorological variations remain insufficiently explored. To address this gap, this study integrated 2D/3D urban morphological factors—2D (planar) factors such as road density (RD), edge density (ED), and NDVI, and 3D (volumetric) factors such as building volume density (BVD) and floor area ratio (FAR)—with background meteorological data, employing a generalized additive model (GAM) combined with shapley additive explanations (SHAP), a game-theory-based approach used to quantify variable importance and interpret nonlinear effects, to analyze the spatiotemporal distribution and driving mechanisms of PM2.5 pollution within the Fourth Ring of Zhengzhou, North China Plain. Results revealed distinct seasonal “hot-spot” and “cold-spot” patterns. During spring and summer, large-scale (≥1000 m) 3D morphological factors dominated PM2.5 accumulation, with building height density (BHD) and building height standard deviation (BHSD) explaining 66.3 % and 56.3 % of variance, respectively. In contrast, during autumn and winter, small-scale (<300 m) 2D landscape metrics such as landscape shape index (LSI) and Shannon's evenness index (SHEI) contributed 88.1 % and 54.1 %. Annually, RD and BVD explained 43.1 % and 45.0 % of PM2.5 variation, indicating the persistent influence of urban road networks. Meteorological factors—temperature (Temp), relative humidity (RH), and wind speed (AWS)—modulated these relationships through atmospheric mixing and accumulation. These findings provide insights for targeted, seasonal pollution control strategies and optimized urban design.
{"title":"Modulation mechanisms of seasonal PM2.5 by 2D/3D urban morphology under background meteorological conditions: Insights from a GAM-based analysis","authors":"Mengbo Wu , Na Liu , Jingyi Wei , Xinxin Xia , Tianqi Li , Junyi Li , Zhipeng Zhu","doi":"10.1016/j.apr.2025.102817","DOIUrl":"10.1016/j.apr.2025.102817","url":null,"abstract":"<div><div>Urban morphology and meteorological conditions jointly influence the spatial and temporal dynamics of PM<sub>2.5</sub> pollution. Although previous studies have examined their individual effects, the synergistic impacts of three-dimensional (3D) morphology and seasonal meteorological variations remain insufficiently explored. To address this gap, this study integrated 2D/3D urban morphological factors—2D (planar) factors such as road density (RD), edge density (ED), and NDVI, and 3D (volumetric) factors such as building volume density (BVD) and floor area ratio (FAR)—with background meteorological data, employing a generalized additive model (GAM) combined with shapley additive explanations (SHAP), a game-theory-based approach used to quantify variable importance and interpret nonlinear effects, to analyze the spatiotemporal distribution and driving mechanisms of PM<sub>2.5</sub> pollution within the Fourth Ring of Zhengzhou, North China Plain. Results revealed distinct seasonal “hot-spot” and “cold-spot” patterns. During spring and summer, large-scale (≥1000 m) 3D morphological factors dominated PM<sub>2.5</sub> accumulation, with building height density (BHD) and building height standard deviation (BHSD) explaining 66.3 % and 56.3 % of variance, respectively. In contrast, during autumn and winter, small-scale (<300 m) 2D landscape metrics such as landscape shape index (LSI) and Shannon's evenness index (SHEI) contributed 88.1 % and 54.1 %. Annually, RD and BVD explained 43.1 % and 45.0 % of PM<sub>2.5</sub> variation, indicating the persistent influence of urban road networks. Meteorological factors—temperature (Temp), relative humidity (RH), and wind speed (AWS)—modulated these relationships through atmospheric mixing and accumulation. These findings provide insights for targeted, seasonal pollution control strategies and optimized urban design.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102817"},"PeriodicalIF":3.5,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135743","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}
Pub Date : 2025-10-30DOI: 10.1016/j.apr.2025.102810
Ku Mohd Kalkausar Ku Yusof , Nurul Najwa Zulkarnain , Sabiqah Tuan Anuar , Mohd Nizam Lani , Noorlin Mohamad , Elham Taghavi , Yusof Shuaib Ibrahim
Microplastics (<5 mm) exhibit intrinsic characteristics, including density, hydrophobic surfaces, and a high surface-to-volume ratio, that determine their airborne deposition and subsequent exposure within food systems. Their presence has affected humans in several aspects, namely, air quality, public health, and food safety. However, limited information on microplastic contamination and microplastic-related issues as a carrier in airborne contamination at various air quality levels (suburban, urban, and industrial areas) can be investigated at food hawker stalls in coastal environments. This study aims to determine the significant differences in Airborne Microplastic (AMP) abundance among hawker stalls located in suburban, urban, and industrial areas of dry deposition exposure across seven locations in the coastal state of Terengganu, Malaysia. The microplastic particles were collected in an airborne environment using Whatman glass filter paper (0.2 μm). They were then manually sorted under a digital stereomicroscope and identified based on a functional group polymer's physical characteristics (color, shape) and chemical characteristics (ATR-FTIR). The findings provide strong evidence that individuals frequenting coastal hawker stalls are likely exposed to and may ingest atmospheric microplastics, with deposition rates ranging from 0.48 to 17.44 n/m2/d. Microplastic fiber was the dominant microplastic found in the air compared to fragment types in Malaysia. In particular, it was found that transparent microplastics were the most dominant, followed by black, purple, and brown. Two polymers have been identified, namely polyester and polyamide (nylon). This study confirms the dry deposition distribution of atmospheric microplastics associated with hawker stalls in suburban, urban, and industrial populations.
{"title":"New insights into the dry deposition distribution of atmospheric microplastics in suburban, urban, and industrial areas: A focus on hawker stalls in the East Coast of Peninsular Malaysia","authors":"Ku Mohd Kalkausar Ku Yusof , Nurul Najwa Zulkarnain , Sabiqah Tuan Anuar , Mohd Nizam Lani , Noorlin Mohamad , Elham Taghavi , Yusof Shuaib Ibrahim","doi":"10.1016/j.apr.2025.102810","DOIUrl":"10.1016/j.apr.2025.102810","url":null,"abstract":"<div><div>Microplastics (<5 mm) exhibit intrinsic characteristics, including density, hydrophobic surfaces, and a high surface-to-volume ratio, that determine their airborne deposition and subsequent exposure within food systems. Their presence has affected humans in several aspects, namely, air quality, public health, and food safety. However, limited information on microplastic contamination and microplastic-related issues as a carrier in airborne contamination at various air quality levels (suburban, urban, and industrial areas) can be investigated at food hawker stalls in coastal environments. This study aims to determine the significant differences in Airborne Microplastic (AMP) abundance among hawker stalls located in suburban, urban, and industrial areas of dry deposition exposure across seven locations in the coastal state of Terengganu, Malaysia. The microplastic particles were collected in an airborne environment using Whatman glass filter paper (0.2 μm). They were then manually sorted under a digital stereomicroscope and identified based on a functional group polymer's physical characteristics (color, shape) and chemical characteristics (ATR-FTIR). The findings provide strong evidence that individuals frequenting coastal hawker stalls are likely exposed to and may ingest atmospheric microplastics, with deposition rates ranging from 0.48 to 17.44 n/m<sup>2</sup>/d. Microplastic fiber was the dominant microplastic found in the air compared to fragment types in Malaysia. In particular, it was found that transparent microplastics were the most dominant, followed by black, purple, and brown. Two polymers have been identified, namely polyester and polyamide (nylon). This study confirms the dry deposition distribution of atmospheric microplastics associated with hawker stalls in suburban, urban, and industrial populations.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102810"},"PeriodicalIF":3.5,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135700","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}
Pub Date : 2025-10-30DOI: 10.1016/j.apr.2025.102801
Yinwei Luo , Wanqi Sun , Zhen Li , Yan Wang , Bo Yang , Qirun Li , Jiawei Dong , Sitong Chen , Jianing Wang , Pengbo Li , Guozhong Wei , Yingying Ding , Yu Wang
Transient and spatially heterogeneous methane (CH4) emissions hamper the formulation of effective abatement policies. Here we construct an integrated “Monitoring-Identification-Modeling” framework that couples a self-developed off-axis integrated cavity-output spectrometer (OA-ICOS), a meter-resolution vehicle-mounted platform (40 m × 1 s), and CALPUFF inverse dispersion modeling to map and quantify CH4 sources across the industrial-agricultural city of Binzhou, China. Compared with fixed-site and satellite products, our mobile observations improve spatiotemporal resolution by ≥ 102 and ≥103 times, respectively, enabling the first fine-scale depiction of urban CH4 heterogeneity in this region. Three classes of emitters were differentiated through simultaneous CH4-C2H6 fingerprinting: (i) fossil-fuel-dominated hotspots such as the Zhonghai Asphalt Industrial Park (peak 2911 ppb; 33.7 g s−1) and a bus-terminal CNG hub (CH4/C2H6 r = 0.92); (ii) Fossil-fuel-related emission sources, including East Suburb Reservoir, whose methane flux (56.7 g s−1) is primarily driven by natural gas leakage from aging infrastructure; and (iii) agricultural sources represented by Yiliyuan Livestock Farm (11.7 g s−1). Although super-emitters occupied <10 % of the surveyed area, they accounted for ∼55 % of the total flux. Model-observation comparison returned an overall RMSE of 45 ppb (±22 %), confirming the robustness of the mobile-inversion paradigm in the absence of detailed bottom-up inventories. Our results demonstrate that targeted leak-detection-and-repair (LDAR) at a handful of high-intensity sites can deliver disproportionate climate benefits, and that the proposed framework is readily apply to other mixed-source regions for near-real-time CH4 mitigation planning.
甲烷（CH4）的瞬态和空间异质性排放阻碍了有效减排政策的制定。本文构建了一个集成的“监测-识别-建模”框架，结合自主开发的离轴集成腔输出光谱仪（OA-ICOS）、米分辨率车载平台（40 m × 1 s）和CALPUFF逆色散模型，对中国滨州工农业城市的CH4源进行了映射和量化。与固定站点和卫星产品相比，移动观测的时空分辨率分别提高了≥102倍和≥103倍，首次实现了该地区城市CH4异质性的精细尺度描述。通过同时进行CH4-C2H6指纹识别，将排放源划分为三类：(i)以化石燃料为主的热点地区，如中海沥青工业园区（峰值2911 ppb； 33.7 g s−1）和公交终端CNG枢纽（CH4/C2H6 r = 0.92）；（二）与化石燃料有关的排放源，包括东郊水库，其甲烷通量（56.7 g s - 1）主要由老化基础设施的天然气泄漏驱动；（iii）以亿利源畜牧场为代表的农业来源（11.7 g s−1）。虽然超级排放者占调查面积的10%，但它们占总通量的55%。模型-观测比较的总体RMSE为45 ppb（±22%），证实了在没有详细的自下而上清单的情况下移动反演范式的稳健性。我们的研究结果表明，在少数高强度地区，有针对性的泄漏检测和修复（LDAR）可以提供不成比例的气候效益，并且所提出的框架很容易应用于其他混合源地区的近实时CH4缓解规划。
{"title":"Integrated mobile monitoring and atmospheric modeling for methane emission assessment in an industrial-agricultural hub: A case study of Binzhou, China","authors":"Yinwei Luo , Wanqi Sun , Zhen Li , Yan Wang , Bo Yang , Qirun Li , Jiawei Dong , Sitong Chen , Jianing Wang , Pengbo Li , Guozhong Wei , Yingying Ding , Yu Wang","doi":"10.1016/j.apr.2025.102801","DOIUrl":"10.1016/j.apr.2025.102801","url":null,"abstract":"<div><div>Transient and spatially heterogeneous methane (CH<sub>4</sub>) emissions hamper the formulation of effective abatement policies. Here we construct an integrated “Monitoring-Identification-Modeling” framework that couples a self-developed off-axis integrated cavity-output spectrometer (OA-ICOS), a meter-resolution vehicle-mounted platform (40 m × 1 s), and CALPUFF inverse dispersion modeling to map and quantify CH<sub>4</sub> sources across the industrial-agricultural city of Binzhou, China. Compared with fixed-site and satellite products, our mobile observations improve spatiotemporal resolution by ≥ 10<sup>2</sup> and ≥10<sup>3</sup> times, respectively, enabling the first fine-scale depiction of urban CH<sub>4</sub> heterogeneity in this region. Three classes of emitters were differentiated through simultaneous CH<sub>4</sub>-C<sub>2</sub>H<sub>6</sub> fingerprinting: (i) fossil-fuel-dominated hotspots such as the Zhonghai Asphalt Industrial Park (peak 2911 ppb; 33.7 g s<sup>−1</sup>) and a bus-terminal CNG hub (CH<sub>4</sub>/C<sub>2</sub>H<sub>6</sub> r = 0.92); (ii) Fossil-fuel-related emission sources, including East Suburb Reservoir, whose methane flux (56.7 g s<sup>−1</sup>) is primarily driven by natural gas leakage from aging infrastructure; and (iii) agricultural sources represented by Yiliyuan Livestock Farm (11.7 g s<sup>−1</sup>). Although super-emitters occupied <10 % of the surveyed area, they accounted for ∼55 % of the total flux. Model-observation comparison returned an overall RMSE of 45 ppb (±22 %), confirming the robustness of the mobile-inversion paradigm in the absence of detailed bottom-up inventories. Our results demonstrate that targeted leak-detection-and-repair (LDAR) at a handful of high-intensity sites can deliver disproportionate climate benefits, and that the proposed framework is readily apply to other mixed-source regions for near-real-time CH<sub>4</sub> mitigation planning.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102801"},"PeriodicalIF":3.5,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135686","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}
Pub Date : 2025-10-30DOI: 10.1016/j.apr.2025.102812
Zhiheng Liao , Youjun Dou , Weiwei Pu , Zhiqiang Ma
The re-emergence of COVID-19 in late spring (April 29 to June 5) of 2022 compelled the Beijing government to implement a stringent lockdown policy to curb the spread of the virus. In comparison to the first lockdown in the winter of 2020, the late spring lockdown provided a more suitable opportunity to examine how ozone (O3) responds to substantial emission reductions during a photochemically active season. This study investigates the meteorological and chemical mechanisms underlying the surface O3 enhancement during the 2022 late spring lockdown in Beijing, using a combination of ground-based and satellite observations, along with three meteorology normalization models (Random Forest, Long Short Term Memory, and eXtreme Gradient Boosting). The results indicate that the surface O3 concentration in Beijing increased by 4.9 ppbv during the 2022 lockdown (compared to the same period in 2021 and 2023). The multiple meteorology normalization models reveal that on average 14.3 % (0.7 ppbv) of surface ozone enhancement was attributed to adverse meteorological conditions, and the remaining 85.7 % (4.2 ppbv) attributed to unfavorable emission factors, including a substantial reduction in nitrogen oxides (NOx) and a slight increase in volatile organic compounds (VOCs). Despite substantial NOx reductions during the lockdown, the O3 formation sensitivity remained VOC-limited, rather than shifting to NOx-limited as expected, highlighting the priority of VOC-targeted management for controlling O3 pollution at the current stage.
{"title":"Meteorology-normalized ozone enhancement during the 2022 late-spring COVID-19 lockdown in Beijing","authors":"Zhiheng Liao , Youjun Dou , Weiwei Pu , Zhiqiang Ma","doi":"10.1016/j.apr.2025.102812","DOIUrl":"10.1016/j.apr.2025.102812","url":null,"abstract":"<div><div>The re-emergence of COVID-19 in late spring (April 29 to June 5) of 2022 compelled the Beijing government to implement a stringent lockdown policy to curb the spread of the virus. In comparison to the first lockdown in the winter of 2020, the late spring lockdown provided a more suitable opportunity to examine how ozone (O<sub>3</sub>) responds to substantial emission reductions during a photochemically active season. This study investigates the meteorological and chemical mechanisms underlying the surface O<sub>3</sub> enhancement during the 2022 late spring lockdown in Beijing, using a combination of ground-based and satellite observations, along with three meteorology normalization models (Random Forest, Long Short Term Memory, and eXtreme Gradient Boosting). The results indicate that the surface O<sub>3</sub> concentration in Beijing increased by 4.9 ppbv during the 2022 lockdown (compared to the same period in 2021 and 2023). The multiple meteorology normalization models reveal that on average 14.3 % (0.7 ppbv) of surface ozone enhancement was attributed to adverse meteorological conditions, and the remaining 85.7 % (4.2 ppbv) attributed to unfavorable emission factors, including a substantial reduction in nitrogen oxides (NO<sub>x</sub>) and a slight increase in volatile organic compounds (VOCs). Despite substantial NO<sub>x</sub> reductions during the lockdown, the O<sub>3</sub> formation sensitivity remained VOC-limited, rather than shifting to NO<sub>x</sub>-limited as expected, highlighting the priority of VOC-targeted management for controlling O<sub>3</sub> pollution at the current stage.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102812"},"PeriodicalIF":3.5,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135702","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}
Pub Date : 2025-10-28DOI: 10.1016/j.apr.2025.102808
Bhupendra Das , Hishila Sujakhu , Suvekshya Sitaula , K.C. Sheela , Meera Prajapati , James Hall , James Robert Hodgson , Bijaya Maharjan , Rejina M. Byanju
This cross-sectional study investigates the health impact of air pollutants from brick kilns in the Kathmandu Valley, Nepal's most urbanized region. A mixed-methods approach was used, combining quantitative and qualitative data. Air quality data was collected from working environment, exposed and control households using devices (Air Visual Pro, calibrated with GRIMM sensors and Gas meter).
PM2.5 concentration around brick kilns was 151.2 μg/m3 compared to control grocery stores (8.8 μg/m3), while households to the brick kilns (<1 km) was 84.6 μg/m3 compared to control households (>1 km) 7.5 μg/m3. The correlation between PM2.5 and self-reported respiratory symptoms was greater in the exposed communities compared to control one with a strong positive correlation for breathlessness (Pearson correlation coefficient r = 0.68, p < 0.05), moderate correlation for persistent cough (r = 0.53, p < 0.05), asthmatic symptoms (r = 0.55, p < 0.05), phlegm (r = 0.58, p < 0.05), wheeze (r = 0.44, p < 0.05) and bronchitis (r = 0.41, p < 0.05). Around brick kiln workers, PM2.5 concentrations showed a strong correlation with breathlessness (r = 0.56, p < 0.05), phlegm (r = 0.70, p < 0.05), and wheeze (r = 0.82, p < 0.05), and weak correlation with persistent cough (r = 0.18, p > 0.05) and asthmatic symptoms (r = 0.24, p > 0.05). The findings suggest high PM2.5 concentrations at brick kiln sites are associated with respiratory symptoms among residents living in local communities. This study emphasizes better quality management through various interventions.
这项横断面研究调查了尼泊尔城市化程度最高的地区加德满都谷地砖窑空气污染物对健康的影响。采用定量和定性数据相结合的混合方法。空气质量数据来自工作环境、暴露环境和控制家庭，使用设备（Air Visual Pro，使用GRIMM传感器和燃气表校准），砖窑周围的pm2.5浓度为151.2 μg/m3，而控制杂货店的pm2.5浓度为8.8 μg/m3，砖窑周围（<；1公里）的pm2.5浓度为84.6 μg/m3，而控制家庭（>；1公里）的pm2.5浓度为7.5 μg/m3。与对照组相比，暴露社区PM2.5与自报呼吸症状的相关性更大，其中呼吸困难（Pearson相关系数r = 0.68, p < 0.05）、持续性咳嗽（r = 0.53, p < 0.05）、哮喘症状（r = 0.55, p < 0.05）、痰（r = 0.58, p < 0.05）、喘息（r = 0.44, p < 0.05）和支气管炎（r = 0.41, p < 0.05）的相关性较强。砖窑工人周围PM2.5浓度与呼吸困难（r = 0.56, p < 0.05）、痰多（r = 0.70, p < 0.05）、喘息（r = 0.82, p < 0.05）呈强相关，与持续咳嗽（r = 0.18, p < 0.05）、哮喘症状（r = 0.24, p < 0.05）呈弱相关。研究结果表明，砖窑工地的高PM2.5浓度与当地社区居民的呼吸道症状有关。本研究强调透过各种干预措施改善品质管理。
{"title":"Assessment of brick kiln’s air pollutants impact on human health in industrial areas of Kathmandu Valley, Nepal","authors":"Bhupendra Das , Hishila Sujakhu , Suvekshya Sitaula , K.C. Sheela , Meera Prajapati , James Hall , James Robert Hodgson , Bijaya Maharjan , Rejina M. Byanju","doi":"10.1016/j.apr.2025.102808","DOIUrl":"10.1016/j.apr.2025.102808","url":null,"abstract":"<div><div>This cross-sectional study investigates the health impact of air pollutants from brick kilns in the Kathmandu Valley, Nepal's most urbanized region. A mixed-methods approach was used, combining quantitative and qualitative data. Air quality data was collected from working environment, exposed and control households using devices (Air Visual Pro, calibrated with GRIMM sensors and Gas meter).</div><div>PM<sub>2.5</sub> concentration around brick kilns was 151.2 μg/m<sup>3</sup> compared to control grocery stores (8.8 μg/m<sup>3</sup>), while households to the brick kilns (<1 km) was 84.6 μg/m<sup>3</sup> compared to control households (>1 km) 7.5 μg/m<sup>3</sup>. The correlation between PM<sub>2.5</sub> and self-reported respiratory symptoms was greater in the exposed communities compared to control one with a strong positive correlation for breathlessness (Pearson correlation coefficient r = 0.68, p < 0.05), moderate correlation for persistent cough (r = 0.53, p < 0.05), asthmatic symptoms (r = 0.55, p < 0.05), phlegm (r = 0.58, p < 0.05), wheeze (r = 0.44, p < 0.05) and bronchitis (r = 0.41, p < 0.05). Around brick kiln workers, PM<sub>2.5</sub> concentrations showed a strong correlation with breathlessness (r = 0.56, p < 0.05), phlegm (r = 0.70, p < 0.05), and wheeze (r = 0.82, p < 0.05), and weak correlation with persistent cough (r = 0.18, p > 0.05) and asthmatic symptoms (r = 0.24, p > 0.05). The findings suggest high PM<sub>2.5</sub> concentrations at brick kiln sites are associated with respiratory symptoms among residents living in local communities. This study emphasizes better quality management through various interventions.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102808"},"PeriodicalIF":3.5,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135698","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}
Pub Date : 2025-10-28DOI: 10.1016/j.apr.2025.102811
Yasutomo Hoshika , Barbara Baesso Moura , Robert Haensch , Jacopo Manzini , Andrea Viviano , Elena Marra , Cesare Garosi , Matheus Casarini Siqueira , Ryoji Tanaka , Bin Hu , Heinz Rennenberg , Elena Paoletti
Tropospheric ozone (O3) is an air pollutant with phytotoxic effects on plants. This research aimed to evaluate the impacts of O3 on Robinia pseudoacacia L., a tree species introduced worldwide due to its ability for symbiotic nitrogen fixation, and to assess if rhizobia root inoculation could alleviate the effects from O3. Using a free-air O3 exposure system, plants either inoculated with Mesorhizobium or left uninoculation (Inoculated vs. Control) were subjected to ambient, 1.5 × , and 2 × ambient O3 levels over a 129-day period. Measurements were made of visible foliar injury (VFI), leaf color index (SPAD), biomass growth and stomatal O3 uptake. In the presence of elevated O3, rhizobia inoculation significantly decreased VFI and fine root biomass loss. This protective mechanism was associated with a 22 % reduction in maximum stomatal conductance (gmax), which restricted stomatal O3 uptake. Dose-response relationships showed that flux-based indices (PODy) better explained VFI and biomass development than exposure-based indices (AOT40). Accordingly, critical levels (CLs) for O3 were set at 24.5 mmol m−2 POD0 causing the first appearance of VFI, and 7.1 mmol m−2 POD4 resulting in a 4 % reduction in biomass. These CLs are higher than those for O3-sensitive species, suggesting moderate O3 tolerance of R. pseudoacacia to O3. Overall, the present results highlight that rhizobia inoculation can increase tree resistance to O3 stress, possibly by greater limitation of stomatal O3 uptake and by improving tolerance to oxidative stress. As climate change intensifies abiotic stressors, further research should investigate the combined stress mitigation potential of rhizobial symbioses.
{"title":"Rhizobia inoculation alleviates ozone-induced foliar damage and root biomass loss for Robinia pseudoacacia L.","authors":"Yasutomo Hoshika , Barbara Baesso Moura , Robert Haensch , Jacopo Manzini , Andrea Viviano , Elena Marra , Cesare Garosi , Matheus Casarini Siqueira , Ryoji Tanaka , Bin Hu , Heinz Rennenberg , Elena Paoletti","doi":"10.1016/j.apr.2025.102811","DOIUrl":"10.1016/j.apr.2025.102811","url":null,"abstract":"<div><div>Tropospheric ozone (O<sub>3</sub>) is an air pollutant with phytotoxic effects on plants. This research aimed to evaluate the impacts of O<sub>3</sub> on <em>Robinia pseudoacacia</em> L., a tree species introduced worldwide due to its ability for symbiotic nitrogen fixation, and to assess if rhizobia root inoculation could alleviate the effects from O<sub>3</sub>. Using a free-air O<sub>3</sub> exposure system, plants either inoculated with <em>Mesorhizobium</em> or left uninoculation (Inoculated <em>vs.</em> Control) were subjected to ambient, 1.5 × , and 2 × ambient O<sub>3</sub> levels over a 129-day period. Measurements were made of visible foliar injury (VFI), leaf color index (SPAD), biomass growth and stomatal O<sub>3</sub> uptake. In the presence of elevated O<sub>3</sub>, rhizobia inoculation significantly decreased VFI and fine root biomass loss. This protective mechanism was associated with a 22 % reduction in maximum stomatal conductance (<em>g</em><sub>max</sub>), which restricted stomatal O<sub>3</sub> uptake. Dose-response relationships showed that flux-based indices (POD<sub>y</sub>) better explained VFI and biomass development than exposure-based indices (AOT40). Accordingly, critical levels (CLs) for O<sub>3</sub> were set at 24.5 mmol m<sup>−2</sup> POD<sub>0</sub> causing the first appearance of VFI, and 7.1 mmol m<sup>−2</sup> POD<sub>4</sub> resulting in a 4 % reduction in biomass. These CLs are higher than those for O<sub>3</sub>-sensitive species, suggesting moderate O<sub>3</sub> tolerance of <em>R. pseudoacacia</em> to O<sub>3</sub>. Overall, the present results highlight that rhizobia inoculation can increase tree resistance to O<sub>3</sub> stress, possibly by greater limitation of stomatal O<sub>3</sub> uptake and by improving tolerance to oxidative stress. As climate change intensifies abiotic stressors, further research should investigate the combined stress mitigation potential of rhizobial symbioses.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102811"},"PeriodicalIF":3.5,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135701","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号