Pub Date : 2025-10-23DOI: 10.1016/j.apr.2025.102784
Hamidreza Abediasl , Masoud Aliramezani , Charles Robert Koch , Mahdi Shahbakhti
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.
{"title":"Machine learning-based estimation of vehicular emissions using on-board diagnostics data for intelligent fleet management","authors":"Hamidreza Abediasl , Masoud Aliramezani , Charles Robert Koch , 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":"2025-10-23","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}
Pub Date : 2025-10-21DOI: 10.1016/j.apr.2025.102793
Athanasios Besis , Marco Wietzoreck , Eleni Serafeim , Benjamin A. Musa Bandowe , Stefanie Hildmann , Rong Jin , Jun-Tae Kim , Athanasios Kouras , Gerhard Lammel , Constantini Samara
Size-resolved samples (<0.49, 0.49–0.95, 0.95–1.5, 1.5–3.0, 3.0–7.2 and > 7.2 μm) of atmospheric particulate matter (PM) were collected at an urban Mediterranean and a rural central European site and analyzed for the mass fraction of water-soluble organic carbon (WSOC), humic-like substances (HULIS) and water-soluble elements. In addition, the total mass fractions of several polycyclic aromatic hydrocarbon (PAH) derivatives i.e., nitrated-PAHs (NPAHs), oxygenated-PAHs (OPAHs), chlorinated-PAHs (ClPAHs), and brominated PAHs (BrPAHs), as well as the bioaccessible fraction (extracted with simulated epithelial lining fluid) of OPAHs and NPAHs were analyzed in the same PM samples. The oxidative potential (OP) of PM was determined using the dithiothreitol (DTT) assay. Total concentrations of PM, WSOC, most water-soluble elements and ∑16NPAHs were higher at the urban site, whereas those of ∑20BrPAHs, Cr, As, as well as of the mass-normalized and the air volume-normalized OP (OPmDTT, OPVDTT) were higher at the rural site. OPAHs’ bioaccessibility ranged 0.7 %–25 % and NPAHs’ from 4.2 % to 100 %. Multiple linear regression analysis (MLR) indicated OPmDTT to be mostly driven by Cu, Fe, and 11H-benzo(a)fluoren-11-one at the urban site, and by water-soluble Co and 2-methyl-1,4-naphthoquinone at the rural site. The OP of the PM collected from the two sites in this study could have been influenced by redox active constituents not determined in this study in addition to possible differences in the photochemical age of their secondary organic aerosol content.
{"title":"Oxidative potential of size-resolved PM related to water-soluble components and total and bioaccessible mass fractions of PAH derivatives","authors":"Athanasios Besis , Marco Wietzoreck , Eleni Serafeim , Benjamin A. Musa Bandowe , Stefanie Hildmann , Rong Jin , Jun-Tae Kim , Athanasios Kouras , Gerhard Lammel , Constantini Samara","doi":"10.1016/j.apr.2025.102793","DOIUrl":"10.1016/j.apr.2025.102793","url":null,"abstract":"<div><div>Size-resolved samples (<0.49, 0.49–0.95, 0.95–1.5, 1.5–3.0, 3.0–7.2 and > 7.2 μm) of atmospheric particulate matter (PM) were collected at an urban Mediterranean and a rural central European site and analyzed for the mass fraction of water-soluble organic carbon (WSOC), humic-like substances (HULIS) and water-soluble elements. In addition, the total mass fractions of several polycyclic aromatic hydrocarbon (PAH) derivatives i.e., nitrated-PAHs (NPAHs), oxygenated-PAHs (OPAHs), chlorinated-PAHs (ClPAHs), and brominated PAHs (BrPAHs), as well as the bioaccessible fraction (extracted with simulated epithelial lining fluid) of OPAHs and NPAHs were analyzed in the same PM samples. The oxidative potential (OP) of PM was determined using the dithiothreitol (DTT) assay. Total concentrations of PM, WSOC, most water-soluble elements and ∑<sub>16</sub>NPAHs were higher at the urban site, whereas those of ∑<sub>20</sub>BrPAHs, Cr, As, as well as of the mass-normalized and the air volume-normalized OP (OP<sub>m</sub><sup>DTT</sup>, OP<sub>V</sub><sup>DTT</sup>) were higher at the rural site. OPAHs’ bioaccessibility ranged 0.7 %–25 % and NPAHs’ from 4.2 % to 100 %. Multiple linear regression analysis (MLR) indicated OP<sub>m</sub><sup>DTT</sup> to be mostly driven by Cu, Fe, and 11H-benzo(a)fluoren-11-one at the urban site, and by water-soluble Co and 2-methyl-1,4-naphthoquinone at the rural site. The OP of the PM collected from the two sites in this study could have been influenced by redox active constituents not determined in this study in addition to possible differences in the photochemical age of their secondary organic aerosol content.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102793"},"PeriodicalIF":3.5,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135622","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}
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.
{"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, Wenxiao Jia, Yutong Wang, Qiaoan Chen, Yang Liu, Zixiang Gao, 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/m<sup>3</sup> in summer and 5.35 ± 0.73 μg/m<sup>3</sup> 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":"2025-10-21","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}
Pub Date : 2025-10-19DOI: 10.1016/j.apr.2025.102777
José María Cordero , Jing Li , David de la Paz , Petros Koutrakis , Rafael Borge
{"title":"Corrigendum to “A two-stage algorithm to estimate ground-level PM2.5 concentration levels in Madrid (Spain) from AOD satellite data and surface proxies” [Atmos. Pollut. Res. 16/12 (2025) 102678]","authors":"José María Cordero , Jing Li , David de la Paz , Petros Koutrakis , Rafael Borge","doi":"10.1016/j.apr.2025.102777","DOIUrl":"10.1016/j.apr.2025.102777","url":null,"abstract":"","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 2","pages":"Article 102777"},"PeriodicalIF":3.5,"publicationDate":"2025-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962828","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 : 2025-10-14DOI: 10.1016/j.apr.2025.102783
Marios Panagi , Roberto Sommariva , Zoë L. Fleming , Paul S. Monks , Gongda Lu , Eloise A. Marais , James R. Hopkins , Alastair C. Lewis , Qiang Zhang , James D. Lee , Freya A. Squires , Lisa K. Whalley , Eloise J. Slater , Dwayne E. Heard , Robert Woodward-Massey , Chunxiang Ye , Joshua D. Vande Hey
Volatile organic compounds (VOCs) are important precursors to the formation of ozone (O3) and secondary organic aerosols (SOA) and can also have direct human health impacts. The emissions of VOCs remain poorly characterized due to the complexity and variability of their sources. The VOC levels in Beijing during the winter campaign (APHH) were investigated using a dispersion model (NAME), and a chemical box model (AtChem2) in order to understand how chemistry and transport affect the VOC concentrations in Beijing. Emissions of VOCs in Beijing and contributions from outside Beijing were modelled using the NAME dispersion model combined with the emission inventories and were used to initialize the AtChem2 box model. The modelled concentrations of VOCs from the NAME-AtChem2 combination were then compared to the output of a chemical transport model (GEOS-Chem). The results from the emission inventories and the NAME air mass pathways suggest that industrial sources to the south of Beijing and within Beijing during the winter campaign are very important in controlling the VOC levels in Beijing. A number of scenarios with different nitrogen oxides to ozone ratios (NOx/O3) and hydroxyl (OH) levels were simulated to determine the changes in VOC levels. In Beijing over 80 % of VOC are emitted locally during winter. Most scenarios are in good agreement with daily GEOS-Chem simulations, with the best agreements seen for the modelled concentrations of ethanol, benzene and propane with correlation coefficients of 0.67, 0.63 and 0.64 respectively. Furthermore, the production of formaldehyde in an air mass within 24 h of travel from Beijing was investigated, and it was estimated that 90 % of formaldehyde in Beijing is secondary, produced from oxidation of non-methane volatile organic compounds (NMVOCs). The benzene/CO and toluene/CO ratios during the campaign are very similar to the ratio derived from literature for 2014 in Beijing, however more data are needed to enable investigation of more species over longer timeframes to determine whether this ratio can be applied to predicting VOCs in Beijing. The results suggest that VOC concentrations in Beijing are driven predominantly by sources within Beijing and by local atmospheric chemistry during the winter. Moreover, the relationship of the NOx/VOC and O3 shows that the VOCs during the winter campaign are possibly emitted from similar sources as NOx.
{"title":"Daily evolution of VOCs in Beijing: chemistry, emissions, transport, and policy implications","authors":"Marios Panagi , Roberto Sommariva , Zoë L. Fleming , Paul S. Monks , Gongda Lu , Eloise A. Marais , James R. Hopkins , Alastair C. Lewis , Qiang Zhang , James D. Lee , Freya A. Squires , Lisa K. Whalley , Eloise J. Slater , Dwayne E. Heard , Robert Woodward-Massey , Chunxiang Ye , Joshua D. Vande Hey","doi":"10.1016/j.apr.2025.102783","DOIUrl":"10.1016/j.apr.2025.102783","url":null,"abstract":"<div><div>Volatile organic compounds (VOCs) are important precursors to the formation of ozone (O<sub>3</sub>) and secondary organic aerosols (SOA) and can also have direct human health impacts. The emissions of VOCs remain poorly characterized due to the complexity and variability of their sources. The VOC levels in Beijing during the winter campaign (APHH) were investigated using a dispersion model (NAME), and a chemical box model (AtChem2) in order to understand how chemistry and transport affect the VOC concentrations in Beijing. Emissions of VOCs in Beijing and contributions from outside Beijing were modelled using the NAME dispersion model combined with the emission inventories and were used to initialize the AtChem2 box model. The modelled concentrations of VOCs from the NAME-AtChem2 combination were then compared to the output of a chemical transport model (GEOS-Chem). The results from the emission inventories and the NAME air mass pathways suggest that industrial sources to the south of Beijing and within Beijing during the winter campaign are very important in controlling the VOC levels in Beijing. A number of scenarios with different nitrogen oxides to ozone ratios (NO<sub>x</sub>/O<sub>3</sub>) and hydroxyl (OH) levels were simulated to determine the changes in VOC levels. In Beijing over 80 % of VOC are emitted locally during winter. Most scenarios are in good agreement with daily GEOS-Chem simulations, with the best agreements seen for the modelled concentrations of ethanol, benzene and propane with correlation coefficients of 0.67, 0.63 and 0.64 respectively. Furthermore, the production of formaldehyde in an air mass within 24 h of travel from Beijing was investigated, and it was estimated that 90 % of formaldehyde in Beijing is secondary, produced from oxidation of non-methane volatile organic compounds (NMVOCs). The benzene/CO and toluene/CO ratios during the campaign are very similar to the ratio derived from literature for 2014 in Beijing, however more data are needed to enable investigation of more species over longer timeframes to determine whether this ratio can be applied to predicting VOCs in Beijing. The results suggest that VOC concentrations in Beijing are driven predominantly by sources within Beijing and by local atmospheric chemistry during the winter. Moreover, the relationship of the NO<sub>x</sub>/VOC and O<sub>3</sub> shows that the VOCs during the winter campaign are possibly emitted from similar sources as NO<sub>x</sub>.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102783"},"PeriodicalIF":3.5,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135676","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 : 2025-10-13DOI: 10.1016/j.apr.2025.102785
Youngkwon Kim , Cinoo Kang , Seung-Muk Yi , JongBae Heo , Hwajin Kim , Woojoo Lee , Ho Kim , Philip K. Hopke , Young Su Lee , Hye-Jung Shin , Jungmin Park , Myungsoo Yoo , Kwonho Jeon , Jieun Park
Missing observations of particulate matter (PM2.5) can distort exposure data, thereby modifying associated mortality risks. This study assessed whether imputing missing data with statistical-learning estimates reduces such modifications. Two types of hourly PM2.5 datasets were used: PM2.5 from 25 districts and chemical constituents from one district in Seoul, South Korea. Each dataset was apportioned into area- and source-specific PM2.5 concentrations. Baseline relative risks (RRs) of all-cause mortality for cumulative lag days 0-1 and 0-5, respectively, associated with each type of PM2.5 were estimated using the daily-averaged datasets. Subsequently, some concentrations in each dataset were masked to create eight realistic missing scenarios (spatial-PM2.5: S0, constituents: S1-S7). In each scenario, the missing concentrations were handled by imputation, exclusion, or replacement with means or medians. Imputations were performed using estimates (r² = 0.609-0.940). Baseline RRs were re-estimated using each missing scenario with both imputation and conventional handling methods. Resulting RRs were compared with baseline RRs, and percentage errors for matching days were calculated. Baseline RRs of PM2.5 specific to two areas and three sources were significantly higher than 1.00 (95% CI): northwestern and southwestern-western combined areas, sulfate, coal combustion, and district heating-incineration. Although some statistical significance was lost when missing data were handled, these losses were least frequent when imputation was applied in most scenarios. Even when significance was retained, RRs showed the lowest error (7.0%) compared with conventional methods (8.7-12%). However, losses occurred more frequently in S5 and S7 (carbon species and trace elements; all constituents), where median replacement partly restored significance.
{"title":"Imputing missing data with statistical-learning estimates: impacts on mortality risks attributable to area- and source-specific PM2.5.","authors":"Youngkwon Kim , Cinoo Kang , Seung-Muk Yi , JongBae Heo , Hwajin Kim , Woojoo Lee , Ho Kim , Philip K. Hopke , Young Su Lee , Hye-Jung Shin , Jungmin Park , Myungsoo Yoo , Kwonho Jeon , Jieun Park","doi":"10.1016/j.apr.2025.102785","DOIUrl":"10.1016/j.apr.2025.102785","url":null,"abstract":"<div><div>Missing observations of particulate matter (PM<sub>2.5</sub>) can distort exposure data, thereby modifying associated mortality risks. This study assessed whether imputing missing data with statistical-learning estimates reduces such modifications. Two types of hourly PM<sub>2.5</sub> datasets were used: PM<sub>2.5</sub> from 25 districts and chemical constituents from one district in Seoul, South Korea. Each dataset was apportioned into area- and source-specific PM<sub>2.5</sub> concentrations. Baseline relative risks (RRs) of all-cause mortality for cumulative lag days 0-1 and 0-5, respectively, associated with each type of PM<sub>2.5</sub> were estimated using the daily-averaged datasets. Subsequently, some concentrations in each dataset were masked to create eight realistic missing scenarios (spatial-PM<sub>2.5</sub>: S0, constituents: S1-S7). In each scenario, the missing concentrations were handled by imputation, exclusion, or replacement with means or medians. Imputations were performed using estimates (<em>r</em>² = 0.609-0.940). Baseline RRs were re-estimated using each missing scenario with both imputation and conventional handling methods. Resulting RRs were compared with baseline RRs, and percentage errors for matching days were calculated. Baseline RRs of PM<sub>2.5</sub> specific to two areas and three sources were significantly higher than 1.00 (95% CI): northwestern and southwestern-western combined areas, sulfate, coal combustion, and district heating-incineration. Although some statistical significance was lost when missing data were handled, these losses were least frequent when imputation was applied in most scenarios. Even when significance was retained, RRs showed the lowest error (7.0%) compared with conventional methods (8.7-12%). However, losses occurred more frequently in S5 and S7 (carbon species and trace elements; all constituents), where median replacement partly restored significance.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102785"},"PeriodicalIF":3.5,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135723","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 : 2025-10-13DOI: 10.1016/j.apr.2025.102779
Kamaljeet Kaur , Jenna R. Krall , Cesunica Ivey , Heather Holmes , Kerry E. Kelly
Long-term source attribution studies help evaluate the effectiveness of PM2.5 reduction strategies, but inconsistencies in chemical speciation methods complicate source attribution. This study addresses these challenges by integrating Positive Matrix Factorization and Chemical Mass Balance to assess source contributions to PM2.5 over a 20-year period (2000–2020) at 12 monitoring sites in five western U.S. states: Utah (Bountiful, Hawthorne, Lindon), California (Fresno, Bakersfield, Modesto, Visalia, Sacramento), Nevada (Reno, Las Vegas), Idaho (Boise), and Colorado (Commerce City). At each site, this study identified five to eight source factors including secondary ammonium nitrate (AN), secondary ammonium sulfate (AS), dust, chloride, organic carbon (OC) rich, elemental carbon (EC) rich, EC Cu rich, Cl Zn rich, and aged sea salt. Over two decades, PM2.5 concentrations significantly declined (−0.38 to −0.037 μg/m3 per year) at all sites except Boise and Commerce City. Declines in AN, AS, EC rich, and EC Cu rich concentrations suggest the role of stringent national regulations on mobile, point, and area sources, resulting in reductions in NOx, SO2, and direct PM2.5 emissions. Despite increasing vehicle miles traveled (VMT), EC rich (vehicle) concentrations decreased, indicating the importance of lower per-vehicle emissions. Winter OC rich concentrations, linked to biomass burning, saw the largest seasonal decline due to national and local efforts to curb residential wood combustion emissions. In contrast, dust concentrations generally increased, likely driven by rising regional aridity and VMT. These findings underscore the long-term effectiveness of air-quality policies in reducing PM2.5 concentrations.
{"title":"Long-term PM2.5 source apportionment at 12 sites in the western United States from 2000 to 2020","authors":"Kamaljeet Kaur , Jenna R. Krall , Cesunica Ivey , Heather Holmes , Kerry E. Kelly","doi":"10.1016/j.apr.2025.102779","DOIUrl":"10.1016/j.apr.2025.102779","url":null,"abstract":"<div><div>Long-term source attribution studies help evaluate the effectiveness of PM<sub>2.5</sub> reduction strategies, but inconsistencies in chemical speciation methods complicate source attribution. This study addresses these challenges by integrating Positive Matrix Factorization and Chemical Mass Balance to assess source contributions to PM<sub>2.5</sub> over a 20-year period (2000–2020) at 12 monitoring sites in five western U.S. states: Utah (Bountiful, Hawthorne, Lindon), California (Fresno, Bakersfield, Modesto, Visalia, Sacramento), Nevada (Reno, Las Vegas), Idaho (Boise), and Colorado (Commerce City). At each site, this study identified five to eight source factors including secondary ammonium nitrate (AN), secondary ammonium sulfate (AS), dust, chloride, organic carbon (OC) rich, elemental carbon (EC) rich, EC Cu rich, Cl Zn rich, and aged sea salt. Over two decades, PM<sub>2.5</sub> concentrations significantly declined (−0.38 to −0.037 μg/m<sup>3</sup> per year) at all sites except Boise and Commerce City. Declines in AN, AS, EC rich, and EC Cu rich concentrations suggest the role of stringent national regulations on mobile, point, and area sources, resulting in reductions in NO<sub>x</sub>, SO<sub>2</sub>, and direct PM<sub>2.5</sub> emissions. Despite increasing vehicle miles traveled (VMT), EC rich (vehicle) concentrations decreased, indicating the importance of lower per-vehicle emissions. Winter OC rich concentrations, linked to biomass burning, saw the largest seasonal decline due to national and local efforts to curb residential wood combustion emissions. In contrast, dust concentrations generally increased, likely driven by rising regional aridity and VMT. These findings underscore the long-term effectiveness of air-quality policies in reducing PM<sub>2.5</sub> concentrations.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 2","pages":"Article 102779"},"PeriodicalIF":3.5,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962813","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 : 2025-10-13DOI: 10.1016/j.apr.2025.102787
Wenbo Zhu, Ning Du, Li Wang, Yujun Zuo, Xiaodong Deng, Yang Geng, Ling Qian
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 (PM2.5). The results demonstrate that the model exhibits high predictive accuracy, with R2 values of 0.91 and 0.94 for NO2 and PM2.5, 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 PM2.5 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.
{"title":"Joint estimation and driving mechanism analysis of NO2 and PM2.5 in Northwest China based on a hybrid modeling approach","authors":"Wenbo Zhu, Ning Du, Li Wang, Yujun Zuo, Xiaodong Deng, Yang Geng, 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 (NO<sub>2</sub>) and fine particulate matter (PM<sub>2.5</sub>). The results demonstrate that the model exhibits high predictive accuracy, with R<sup>2</sup> values of 0.91 and 0.94 for NO<sub>2</sub> and PM<sub>2.5</sub>, respectively; corresponding MAE values are 3.57 μg/m<sup>3</sup> and 4.49 μg/m<sup>3</sup>, and RMSE values are 5.01 μg/m<sup>3</sup> and 8.20 μg/m<sup>3</sup>. Spatial analysis reveals that concentrations of NO<sub>2</sub> and PM<sub>2.5</sub> 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":"2025-10-13","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}
Pub Date : 2025-10-13DOI: 10.1016/j.apr.2025.102788
Baojun Yang , Lin Zhang , Danchen Yang , HuiYing Ma , Haifan Xie , Jiahui Hou , Ling Qiu , Tian Gao
High-density cities face severe PM2.5 particulate pollution issues, where landscape patterns significantly influence PM2.5 concentrations. Currently, there is a notable lack of comprehensive research on the integrated effects of urban blue-green-gray space landscape patterns on PM2.5. To address this research gap, this study examines the influence of blue-green-gray spatial configurations on PM2.5 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 PM2.5, constructs a customized landscape pattern index system, and proposes optimization strategies for urban planning. The results reveal that: (1)PM2.5 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 PM2.5, 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 PM2.5 concentrations, providing a scientifically grounded framework for improving air quality and public health in Xi’an and other high-density urban environments.
{"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 , Lin Zhang , Danchen Yang , HuiYing Ma , Haifan Xie , Jiahui Hou , Ling Qiu , 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<sub>2.5</sub> particulate pollution issues, where landscape patterns significantly influence PM<sub>2.5</sub> concentrations. Currently, there is a notable lack of comprehensive research on the integrated effects of urban blue-green-gray space landscape patterns on PM<sub>2.5</sub>. To address this research gap, this study examines the influence of blue-green-gray spatial configurations on PM<sub>2.5</sub> 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<sub>2.5</sub>, constructs a customized landscape pattern index system, and proposes optimization strategies for urban planning. The results reveal that: (1)PM<sub>2.5</sub> 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<sub>2.5</sub>, 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<sub>2.5</sub> 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":"2025-10-13","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}
Pub Date : 2025-10-13DOI: 10.1016/j.apr.2025.102786
Hongrui Li , Xiaoyong Liu , Peng Zhou , Zijian Liu , Mengyang Li , Zhaoxiang Cao
Meteorology and anthropogenic emissions jointly drive urban fine particulate matter (PM2.5), yet their long-term national-scale contributions remain unclear. We combined satellite-derived daily PM2.5 and ionic species—black carbon (BC), ammonium (NH4+), nitrate (NO3−) and sulfate (SO42−)—for 372 Chinese cities (2001–2024) with ERA5 meteorology to build a four-step framework of multiscale decomposition, meteorological normalization, spatial-source separation and component attribution. Kolmogorov–Zurbenko filtering partitions daily PM2.5 variance into short-term (45.2 %), seasonal (34.5 %) and long-term (13.7 %) bands. Random-forest adjustment removes weather effects and reveals that the long-term emission signal (XEMIS-LT) flipped from a mean + 3 μg/m3 during 2001–2012 to −2 μg/m3 in 2017–2024, except in the Chengdu–Chongqing basin and Fenwei Plain. Empirical orthogonal-function analysis attributes 68 % of XEMIS-LT to weakened regional transport and 32 % to local abatement, forming an east-coast transport belt versus inland local dominance. Gradient-boosting trees interpreted with SHAP indicate that BC and NH4+ drive local trends, whereas NO3− and SO42− control transport-related changes. Short-term and seasonal modes govern day-to-day and intrayear variability, while the long-term mode tracks policy effectiveness. Coordinated SO2–NOx–NH3 cuts and inter-provincial management are required along the eastern seaboard, while enclosed basins should emphasize primary carbon and agricultural ammonia reductions. The results offer a quantitative, component-resolved basis for region-specific PM2.5 mitigation across China.
{"title":"Machine learning–based multiscale quantification of long-term emission contributions to PM2.5 variability in China","authors":"Hongrui Li , Xiaoyong Liu , Peng Zhou , Zijian Liu , Mengyang Li , Zhaoxiang Cao","doi":"10.1016/j.apr.2025.102786","DOIUrl":"10.1016/j.apr.2025.102786","url":null,"abstract":"<div><div>Meteorology and anthropogenic emissions jointly drive urban fine particulate matter (PM<sub>2.5</sub>), yet their long-term national-scale contributions remain unclear. We combined satellite-derived daily PM<sub>2.5</sub> and ionic species—black carbon (BC), ammonium (NH<sub>4</sub><sup>+</sup>), nitrate (NO<sub>3</sub><sup>−</sup>) and sulfate (SO<sub>4</sub><sup>2−</sup>)—for 372 Chinese cities (2001–2024) with ERA5 meteorology to build a four-step framework of multiscale decomposition, meteorological normalization, spatial-source separation and component attribution. Kolmogorov–Zurbenko filtering partitions daily PM<sub>2.5</sub> variance into short-term (45.2 %), seasonal (34.5 %) and long-term (13.7 %) bands. Random-forest adjustment removes weather effects and reveals that the long-term emission signal (X<sub>EMIS-LT</sub>) flipped from a mean + 3 μg/m<sup>3</sup> during 2001–2012 to −2 μg/m<sup>3</sup> in 2017–2024, except in the Chengdu–Chongqing basin and Fenwei Plain. Empirical orthogonal-function analysis attributes 68 % of X<sub>EMIS-LT</sub> to weakened regional transport and 32 % to local abatement, forming an east-coast transport belt versus inland local dominance. Gradient-boosting trees interpreted with SHAP indicate that BC and NH<sub>4</sub><sup>+</sup> drive local trends, whereas NO<sub>3</sub><sup>−</sup> and SO<sub>4</sub><sup>2−</sup> control transport-related changes. Short-term and seasonal modes govern day-to-day and intrayear variability, while the long-term mode tracks policy effectiveness. Coordinated SO<sub>2</sub>–NO<sub>x</sub>–NH<sub>3</sub> cuts and inter-provincial management are required along the eastern seaboard, while enclosed basins should emphasize primary carbon and agricultural ammonia reductions. The results offer a quantitative, component-resolved basis for region-specific PM<sub>2.5</sub> mitigation across China.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 3","pages":"Article 102786"},"PeriodicalIF":3.5,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135724","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