Pub Date : 2025-03-18DOI: 10.1016/j.apr.2025.102513
K.B. Betsy, Sanjay Kumar Mehta
The extreme heatwave event is a major threat to living beings in the warming climate which demands immediate quantification of the meteorological factors triggering its amplification. In this study, we explored the role of absorbing and scattering aerosols in the occurrence of extreme heatwave events as well as changes in the atmospheric boundary layer (ABL) over the northwest (NW) and east coast (EC) India during March–June 2017–2022. Ten dry (RH < 33 %) and nine moist (RH > 55 %) heatwave events are observed over the study period. Among these cases, a dry heatwave over NW region prolonged from 27 May to June 11, 2019 is explored in detail. In this case, the increased ABL height from ∼2.0 to 3.0 km to ∼4.0–5.0 km is observed and the entire ABL depth shows enhanced temperature by ∼4 K. The latent and sensible heat fluxes are found to be reduced by 50 W/m2 and enhanced by 80 W/m2 respectively during heatwave. The total aerosol optical depth (AOD) is gradually enhanced to 0.6 leading to enhanced atmospheric warming of ∼8.5–11.5 W/m2 during the heatwave event. Furthermore, the heating rates for moist heatwave cases (∼2 K/day) are higher than those for dry heatwave cases (∼1.8 K/day). In addition, the moist heatwaves exhibit a higher concentration of PM2.5 (∼80–120 μg/m3) compared to the dry heatwave (∼60–100 μg/m3) posing a greater threat to public health and air quality.
{"title":"Role of aerosols on prolonged extreme heatwave event over India and its implication to atmospheric boundary layer","authors":"K.B. Betsy, Sanjay Kumar Mehta","doi":"10.1016/j.apr.2025.102513","DOIUrl":"10.1016/j.apr.2025.102513","url":null,"abstract":"<div><div>The extreme heatwave event is a major threat to living beings in the warming climate which demands immediate quantification of the meteorological factors triggering its amplification. In this study, we explored the role of absorbing and scattering aerosols in the occurrence of extreme heatwave events as well as changes in the atmospheric boundary layer (ABL) over the northwest (NW) and east coast (EC) India during March–June 2017–2022. Ten dry (RH < 33 %) and nine moist (RH > 55 %) heatwave events are observed over the study period. Among these cases, a dry heatwave over NW region prolonged from 27 May to June 11, 2019 is explored in detail. In this case, the increased ABL height from ∼2.0 to 3.0 km to ∼4.0–5.0 km is observed and the entire ABL depth shows enhanced temperature by ∼4 K. The latent and sensible heat fluxes are found to be reduced by 50 W/m<sup>2</sup> and enhanced by 80 W/m<sup>2</sup> respectively during heatwave. The total aerosol optical depth (AOD) is gradually enhanced to 0.6 leading to enhanced atmospheric warming of ∼8.5–11.5 W/m<sup>2</sup> during the heatwave event. Furthermore, the heating rates for moist heatwave cases (∼2 K/day) are higher than those for dry heatwave cases (∼1.8 K/day). In addition, the moist heatwaves exhibit a higher concentration of PM2.5 (∼80–120 μg/m<sup>3</sup>) compared to the dry heatwave (∼60–100 μg/m<sup>3</sup>) posing a greater threat to public health and air quality.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102513"},"PeriodicalIF":3.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725785","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 : 2025-03-15DOI: 10.1016/j.apr.2025.102505
Mingquan Ren , Lili Guo , Yang Cui , Qiusheng He , Dongsheng Ji , Yuesi Wang
Ammonia (NH3) is a significant precursor for secondary inorganic aerosol, in order to better study the impacts of NH3 on PM2.5 pollution in Fenwei Plain in China, hourly-resolved NH3 and water-soluble ions (WSI) were measured at an urban site in Taiyuan from 1 December 2021 to 30 November 2022. Hourly NH3 concentrations ranged from 0.7 to 40.2 μg m−3, with an average concentration of 10.2 ± 5.0 μg m−3. Due to the impacts of meteorology and emission sources, NH3 exhibited apparent seasonal variations: summer > autumn > spring > winter. Diurnal variations of NH3 concentrations showed higher values during the daytime except in autumn. Cluster analysis of backward trajectories suggested that the southern short-distance air mass from Taiyuan Basin had the highest concentrations of TNHx (NH3+NH4+) and PM2.5. The analysis by conditional probability function and weighted concentration weighted trajectory function showed the rough consistency between the distribution of the TNHx and PM2.5 in four seasons. The analysis of hourly excess NH3 showed that Taiyuan's atmosphere was always ammonia-sufficient. SOR (nSO42−/(nSO42− + nSO2)) and NOR (nNO3-/(nNO3- + nNO2)) increased with NHR (nNH3/(nNH4++nNH3); n denotes the molar concentration) and RH in four seasons, indicating that the gas-particle conversion of NH3 promoted the formation of SO42− and NO3− under high RH condition. The critical total ammonia concentrations (CTACs) in spring, summer, autumn, and winter were 63 %, 61 %, 60 %, and 53 %, respectively. Considering the current difficulty in reducing NH3 and WSI concentration decreased linearly with the reduction of TNO3 (NO3− + HNO3), controlling NOx emissions is more effective for PM2.5 pollution mitigation in Taiyuan.
{"title":"Characteristics, regional transport and control strategies of atmospheric ammonia in urban Taiyuan, Fenwei Plain, China","authors":"Mingquan Ren , Lili Guo , Yang Cui , Qiusheng He , Dongsheng Ji , Yuesi Wang","doi":"10.1016/j.apr.2025.102505","DOIUrl":"10.1016/j.apr.2025.102505","url":null,"abstract":"<div><div>Ammonia (NH<sub>3</sub>) is a significant precursor for secondary inorganic aerosol, in order to better study the impacts of NH<sub>3</sub> on PM<sub>2.5</sub> pollution in Fenwei Plain in China, hourly-resolved NH<sub>3</sub> and water-soluble ions (WSI) were measured at an urban site in Taiyuan from 1 December 2021 to 30 November 2022. Hourly NH<sub>3</sub> concentrations ranged from 0.7 to 40.2 μg m<sup>−3</sup>, with an average concentration of 10.2 ± 5.0 μg m<sup>−3</sup>. Due to the impacts of meteorology and emission sources, NH<sub>3</sub> exhibited apparent seasonal variations: summer > autumn > spring > winter. Diurnal variations of NH<sub>3</sub> concentrations showed higher values during the daytime except in autumn. Cluster analysis of backward trajectories suggested that the southern short-distance air mass from Taiyuan Basin had the highest concentrations of TNHx (NH<sub>3</sub>+NH<sub>4</sub><sup>+</sup>) and PM<sub>2.5</sub>. The analysis by conditional probability function and weighted concentration weighted trajectory function showed the rough consistency between the distribution of the TNHx and PM<sub>2.5</sub> in four seasons. The analysis of hourly excess NH<sub>3</sub> showed that Taiyuan's atmosphere was always ammonia-sufficient. SOR (nSO<sub>4</sub><sup>2−</sup>/(nSO<sub>4</sub><sup>2−</sup> + nSO<sub>2</sub>)) and NOR (nNO<sub>3</sub><sup>-</sup>/(nNO<sub>3</sub><sup>-</sup> + nNO<sub>2</sub>)) increased with NHR (nNH<sub>3</sub>/(nNH<sub>4</sub><sup>+</sup>+nNH<sub>3</sub>); n denotes the molar concentration) and RH in four seasons, indicating that the gas-particle conversion of NH<sub>3</sub> promoted the formation of SO<sub>4</sub><sup>2−</sup> and NO<sub>3</sub><sup>−</sup> under high RH condition. The critical total ammonia concentrations (CTACs) in spring, summer, autumn, and winter were 63 %, 61 %, 60 %, and 53 %, respectively. Considering the current difficulty in reducing NH<sub>3</sub> and WSI concentration decreased linearly with the reduction of TNO<sub>3</sub> (NO<sub>3</sub><sup>−</sup> + HNO<sub>3</sub>), controlling NOx emissions is more effective for PM<sub>2.5</sub> pollution mitigation in Taiyuan.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102505"},"PeriodicalIF":3.9,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642040","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 : 2025-03-13DOI: 10.1016/j.apr.2025.102503
Xiaoxia Wang , Zhihai Fan , Xiaolong Yue , Qianqian Zhou , Danting Lin , Hong Zou
Since PM2.5 pollution poses a serious threat to the environment and health, understanding its interaction with the urban built environment (UBE) is essential for effective mitigation. To assess the impact of UBE on PM2.5 pollution, this study quantitatively evaluates the relationship between them. First, given the limitation that current PM2.5 concentration collection mainly relies on fixed monitoring stations, this study set up a taxi mobile monitoring system. Second, aiming at the deficiency of traditional extraction mostly based on remote sensing imagery, this study proposed a deep learning-based method to calculate the green and sky visibility index. Pearson's preliminary correlation analysis showed that climate factors were most correlated to changes in PM2.5 concentration. Furthermore, the prediction effects of nine mainstream machine learning methods were compared. The results showed that (1) The overall prediction performance of summer (R2 = 0.92) and autumn (R2 = 0.93) outperformed the one of spring (R2 = 0.88) and winter (R2 = 0.86) seasons. (2) The Random Forest and LightGBM models obtained optimal predictions with R2 of 0.907 and 0.916, respectively. (3) The complex nonlinear relationship between the UBE and PM2.5 concentration needed to be captured by the Shapley additive explanations method. The findings suggested controlling the space enclosure index between 0.08 and 0.15, plot area ratio within 0.5, and building density within 0.2. This study provided a general analytical framework for understanding the diffusion mechanism of PM2.5 concentrations and a theoretical basis for green urban design.
{"title":"Research on the impact of urban built environments on PM2.5 pollution based on machine learning methods","authors":"Xiaoxia Wang , Zhihai Fan , Xiaolong Yue , Qianqian Zhou , Danting Lin , Hong Zou","doi":"10.1016/j.apr.2025.102503","DOIUrl":"10.1016/j.apr.2025.102503","url":null,"abstract":"<div><div>Since PM<sub>2.5</sub> pollution poses a serious threat to the environment and health, understanding its interaction with the urban built environment (UBE) is essential for effective mitigation. To assess the impact of UBE on PM<sub>2.5</sub> pollution, this study quantitatively evaluates the relationship between them. First, given the limitation that current PM<sub>2.5</sub> concentration collection mainly relies on fixed monitoring stations, this study set up a taxi mobile monitoring system. Second, aiming at the deficiency of traditional extraction mostly based on remote sensing imagery, this study proposed a deep learning-based method to calculate the green and sky visibility index. Pearson's preliminary correlation analysis showed that climate factors were most correlated to changes in PM<sub>2.5</sub> concentration. Furthermore, the prediction effects of nine mainstream machine learning methods were compared. The results showed that (1) The overall prediction performance of summer (<em>R</em><sup>2</sup> = 0.92) and autumn (<em>R</em><sup>2</sup> = 0.93) outperformed the one of spring (<em>R</em><sup>2</sup> = 0.88) and winter (<em>R</em><sup>2</sup> = 0.86) seasons. (2) The Random Forest and LightGBM models obtained optimal predictions with <em>R</em><sup>2</sup> of 0.907 and 0.916, respectively. (3) The complex nonlinear relationship between the UBE and PM<sub>2.5</sub> concentration needed to be captured by the Shapley additive explanations method. The findings suggested controlling the space enclosure index between 0.08 and 0.15, plot area ratio within 0.5, and building density within 0.2. This study provided a general analytical framework for understanding the diffusion mechanism of PM<sub>2.5</sub> concentrations and a theoretical basis for green urban design.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102503"},"PeriodicalIF":3.9,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679303","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 : 2025-03-13DOI: 10.1016/j.apr.2025.102501
Peng Cheng , Baobin Han , Zhilin Tian , Zhen Liu , Wenda Yang , Jianwei Gu , Xiaofang Yu , Hongli Wang , Min Zhou
Nitrous acid (HONO) greatly impacts tropospheric chemistry by producing hydroxyl radical (OH) through photolysis, and yet our knowledge about sources of HONO remains elusive. Emissions of nitrogen (N) containing gases from soils have long been a subject of research in biogeochemistry. Soil emissions of HONO have received greater attention recently, helping explain a missing source of observed atmospheric HONO. We conducted laboratory experiments to simultaneously measure emission fluxes of HONO along with other N containing gases including nitric oxide (NO), nitrous oxide (N2O), and ammonia (NH3) from lateritic red soil samples, and evaluated the contributions of microbiological processes to HONO emissions by conducting process inhibiting experiments. Results from monitoring emissions during a full wet-drying cycle showed that the emissions of HONO, NO and N2O have a strong dependance on soil water content, with maximum fluxes for HONO (125 ± 17 ng N m−2 s−1), NO (115 ± 11 ng N m−2 s−1) and N2O (453 ± 100 ng N m−2 s−1) observed at 17 % (HONO), 42 % (NO) and 94 % (N2O) water filled pore space (WFPS), respectively, while NH3 emission remains at ∼16 ng N m−2 s−1 in majority of the WFPS range. Results from process inhibiting experiments suggested ammonia oxidation to be the dominant pathway for HONO production in the low water-content range, while reduction of nitrate to NO2− appeared dominant in the high water-content range. Our study demonstrates the feasibility of studying emissions of HONO along with other N containing gases as connected network of processes as a whole.
{"title":"Soil emissions of HONO and other nitrogen-containing gases: Insights into microbial pathways and moisture effects","authors":"Peng Cheng , Baobin Han , Zhilin Tian , Zhen Liu , Wenda Yang , Jianwei Gu , Xiaofang Yu , Hongli Wang , Min Zhou","doi":"10.1016/j.apr.2025.102501","DOIUrl":"10.1016/j.apr.2025.102501","url":null,"abstract":"<div><div>Nitrous acid (HONO) greatly impacts tropospheric chemistry by producing hydroxyl radical (OH) through photolysis, and yet our knowledge about sources of HONO remains elusive. Emissions of nitrogen (N) containing gases from soils have long been a subject of research in biogeochemistry. Soil emissions of HONO have received greater attention recently, helping explain a missing source of observed atmospheric HONO. We conducted laboratory experiments to simultaneously measure emission fluxes of HONO along with other N containing gases including nitric oxide (NO), nitrous oxide (N<sub>2</sub>O), and ammonia (NH<sub>3</sub>) from lateritic red soil samples, and evaluated the contributions of microbiological processes to HONO emissions by conducting process inhibiting experiments. Results from monitoring emissions during a full wet-drying cycle showed that the emissions of HONO, NO and N<sub>2</sub>O have a strong dependance on soil water content, with maximum fluxes for HONO (125 ± 17 ng N m<sup>−2</sup> s<sup>−1</sup>), NO (115 ± 11 ng N m<sup>−2</sup> s<sup>−1</sup>) and N<sub>2</sub>O (453 ± 100 ng N m<sup>−2</sup> s<sup>−1</sup>) observed at 17 % (HONO), 42 % (NO) and 94 % (N<sub>2</sub>O) water filled pore space (WFPS), respectively, while NH<sub>3</sub> emission remains at ∼16 ng N m<sup>−2</sup> s<sup>−1</sup> in majority of the WFPS range. Results from process inhibiting experiments suggested ammonia oxidation to be the dominant pathway for HONO production in the low water-content range, while reduction of nitrate to NO<sub>2</sub><sup>−</sup> appeared dominant in the high water-content range. Our study demonstrates the feasibility of studying emissions of HONO along with other N containing gases as connected network of processes as a whole.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102501"},"PeriodicalIF":3.9,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679304","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 : 2025-03-13DOI: 10.1016/j.apr.2025.102502
Yilong Wang , Haoran Wang , Junjie Chen , Yigang Wei , Yan Li
Affected by numerous uncertainties, climate change is a critical issue linked to carbon emissions that warm the planet. Although scholars have conducted detailed research on carbon emissions and established predictive models for them, there are few models specifically designed for predicting carbon emissions during public health emergencies. With the concentrated outbreak of various uncertain factors, organizations and institutions urgently need a model capable of predicting carbon emissions during public health emergencies. This study introduces a novel self-attention multi-neuron time series (SAMNTS) model to evaluate the previously unexplored impact of public health emergencies on carbon emissions. Specifically, we have designed a more comprehensive deep learning prediction framework that can effectively utilize a large amount of relevant data to conduct detailed reasoning and analysis on the issue of carbon emissions, enabling more accurate predictions of daily carbon emissions. To better test its effectiveness, we used COVID-19 as an example to test the model. The results proved that the model can effectively make predictions and analyze various factors that affect carbon emissions.
{"title":"How to forecast daily carbon emissions during public health emergencies: A novel self-attention multi-neuron time series model","authors":"Yilong Wang , Haoran Wang , Junjie Chen , Yigang Wei , Yan Li","doi":"10.1016/j.apr.2025.102502","DOIUrl":"10.1016/j.apr.2025.102502","url":null,"abstract":"<div><div>Affected by numerous uncertainties, climate change is a critical issue linked to carbon emissions that warm the planet. Although scholars have conducted detailed research on carbon emissions and established predictive models for them, there are few models specifically designed for predicting carbon emissions during public health emergencies. With the concentrated outbreak of various uncertain factors, organizations and institutions urgently need a model capable of predicting carbon emissions during public health emergencies. This study introduces a novel self-attention multi-neuron time series (SAMNTS) model to evaluate the previously unexplored impact of public health emergencies on carbon emissions. Specifically, we have designed a more comprehensive deep learning prediction framework that can effectively utilize a large amount of relevant data to conduct detailed reasoning and analysis on the issue of carbon emissions, enabling more accurate predictions of daily carbon emissions. To better test its effectiveness, we used COVID-19 as an example to test the model. The results proved that the model can effectively make predictions and analyze various factors that affect carbon emissions.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102502"},"PeriodicalIF":3.9,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725784","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 : 2025-03-13DOI: 10.1016/j.apr.2025.102500
Keyu Luo , Huagui Guo , Weifeng Li , Jiansheng Wu
The increasing global incidence of lung cancer, which now ranks first among all cancer types, along with the highest risk of lung cancer mortality in East Asia and the narrowing gender gap in incidence since the turn of the century, presents a significant and growing public health concern in Chinese cities. This research investigated how greenness affects the relationships between the incidence of lung cancer and PM1, PM2.5 and PM10 concentrations via a linear mixed model (LMM) and a generalized linear mixed model (GLMM). The findings revealed that particulate matter was associated with increased incidence of lung cancer, with the most substantial changes observed for PM1 (4.92), followed by PM2.5 (4.57) and PM10 (4.22). Our study also revealed that counties with higher levels of greenness experienced a decrease in the incidence of lung cancer among both males and females compared with counties with lower greenness levels, suggesting a protective effect of greenness against lung cancer. The joint associational analysis of particulate matter and NDVI greenness revealed elevated RRs of lung cancer incidence (male: 33 % for PM1, 40 % for PM2.5, 30 % for PM10; female: 43 % for PM1, 51 % for PM2.5, 42 % for PM10) in high particulate matter and low greenness (the highest-impacted group) relative to those exposed to low particulate matter and high greenness (the least-impacted group). The moderating role of greenness was stronger in females than in males (PM1: RERIfemale = 0.106; PM2.5: RERIfemale = 0.208, RERImale = 0.043; and PM10: RERIfemale = 0.139, RERImale = 0.017) and more pronounced in areas with medium greenness than in those with high greenness. These findings remained consistent in the smoking-adjusted and region-adjusted models and with an alternative index of the lung cancer mortality rate and greenness. These findings underscored the importance of urban greenness in the development of healthy cities.
{"title":"How does greenness contribute to reducing lung cancer risks associated with particulate matter exposure?","authors":"Keyu Luo , Huagui Guo , Weifeng Li , Jiansheng Wu","doi":"10.1016/j.apr.2025.102500","DOIUrl":"10.1016/j.apr.2025.102500","url":null,"abstract":"<div><div>The increasing global incidence of lung cancer, which now ranks first among all cancer types, along with the highest risk of lung cancer mortality in East Asia and the narrowing gender gap in incidence since the turn of the century, presents a significant and growing public health concern in Chinese cities. This research investigated how greenness affects the relationships between the incidence of lung cancer and PM<sub>1</sub>, PM<sub>2.5</sub> and PM<sub>10</sub> concentrations via a linear mixed model (LMM) and a generalized linear mixed model (GLMM). The findings revealed that particulate matter was associated with increased incidence of lung cancer, with the most substantial changes observed for PM<sub>1</sub> (4.92), followed by PM<sub>2.5</sub> (4.57) and PM<sub>10</sub> (4.22). Our study also revealed that counties with higher levels of greenness experienced a decrease in the incidence of lung cancer among both males and females compared with counties with lower greenness levels, suggesting a protective effect of greenness against lung cancer. The joint associational analysis of particulate matter and NDVI greenness revealed elevated RRs of lung cancer incidence (male: 33 % for PM<sub>1</sub>, 40 % for PM<sub>2.5</sub>, 30 % for PM<sub>10</sub>; female: 43 % for PM<sub>1</sub>, 51 % for PM<sub>2.5</sub>, 42 % for PM<sub>10</sub>) in high particulate matter and low greenness (the highest-impacted group) relative to those exposed to low particulate matter and high greenness (the least-impacted group). The moderating role of greenness was stronger in females than in males (PM<sub>1</sub>: RERI<sub>female</sub> = 0.106; PM<sub>2.5</sub>: RERI<sub>female</sub> = 0.208, RERI<sub>male</sub> = 0.043; and PM<sub>10</sub>: RERI<sub>female</sub> = 0.139, RERI<sub>male</sub> = 0.017) and more pronounced in areas with medium greenness than in those with high greenness. These findings remained consistent in the smoking-adjusted and region-adjusted models and with an alternative index of the lung cancer mortality rate and greenness. These findings underscored the importance of urban greenness in the development of healthy cities.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102500"},"PeriodicalIF":3.9,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-10DOI: 10.1016/j.apr.2025.102499
Tianhao Wang , Jiansen Wang , Ning Hu , Ruonan Li , Meng Shan , Qun Lin , Longlong Chen , Jun Wang , Yuxin Jiang , Zhonghao Yang , Wei Xiao
On the basis of the vehicle-carried mobile observation method, we conducted CO2 and CH4 observations in Hangzhou city during different periods before, during and after the Asian Games in autumn 2023. Both the difference between urban and rural monitoring station data and the difference between mobile observation and urban background station data during the period of emission reduction implementation were used as quantitative indicators of policy effectiveness. The differences in the CO2 and CH4 concentrations between the mobile observations and the background values exhibited the order of during the Asian Games < before the Asian Games < after the Asian Games, and the differences between the urban and rural observation station values decreased during the Asian Games, indicating the effectiveness of the emission reduction measures. Additionally, the differences in the CO2 and CH4 concentrations between the mobile observations and background values revealed different spatial variation characteristics before, during and after the Asian Games. This demonstrates that emission reduction measures do not yield exactly the same effectiveness for greenhouse gases from diverse emission sources. Moreover, the wind speed, wind direction and boundary layer height may negatively affect the effectiveness of emission reduction. To obtain better results, emission reduction measures must continue over a longer period.
{"title":"Atmospheric CO2 and CH4 observations in Hangzhou before, during, and after the 2023 Asian Games: Insights from vehicle-carried and fixed stations","authors":"Tianhao Wang , Jiansen Wang , Ning Hu , Ruonan Li , Meng Shan , Qun Lin , Longlong Chen , Jun Wang , Yuxin Jiang , Zhonghao Yang , Wei Xiao","doi":"10.1016/j.apr.2025.102499","DOIUrl":"10.1016/j.apr.2025.102499","url":null,"abstract":"<div><div>On the basis of the vehicle-carried mobile observation method, we conducted CO<sub>2</sub> and CH<sub>4</sub> observations in Hangzhou city during different periods before, during and after the Asian Games in autumn 2023. Both the difference between urban and rural monitoring station data and the difference between mobile observation and urban background station data during the period of emission reduction implementation were used as quantitative indicators of policy effectiveness. The differences in the CO<sub>2</sub> and CH<sub>4</sub> concentrations between the mobile observations and the background values exhibited the order of during the Asian Games < before the Asian Games < after the Asian Games, and the differences between the urban and rural observation station values decreased during the Asian Games, indicating the effectiveness of the emission reduction measures. Additionally, the differences in the CO<sub>2</sub> and CH<sub>4</sub> concentrations between the mobile observations and background values revealed different spatial variation characteristics before, during and after the Asian Games. This demonstrates that emission reduction measures do not yield exactly the same effectiveness for greenhouse gases from diverse emission sources. Moreover, the wind speed, wind direction and boundary layer height may negatively affect the effectiveness of emission reduction. To obtain better results, emission reduction measures must continue over a longer period.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102499"},"PeriodicalIF":3.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629286","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}
We investigate the impact of the Russian invasion and military activities on aerosol parameters and air quality in the atmosphere over Kyiv and Ukraine. This study analyzes changes in pollutants such as black carbon, particulate matter PM2.5 and PM10, and sulfates (SO2 and SO4) using MERRA-2 reanalysis data. Black carbon concentration surged in eastern and western Ukraine during pre-invasion times, attributed to industrial emissions and solid fuel heating. During invasion, black carbon levels decreased overall, except in conflict-affected areas like Kyiv and southeastern regions, reflecting reduced industrial activities in the battle region. Similarly, PM2.5 levels increased in eastern conflict zones, correlating with military intensity. Shifts in SO2 and SO4 concentrations indicated increased emissions in southeastern Ukraine due to military activities and infrastructure damage. Also, this research aims to analyze aerosol properties using AERONET data. Sun photometer observations reveal changes in the annual dynamics of the Ångstrom exponent, with lower values observed in 2022 and a decrease in the fine aerosol fraction. Analysis of the aerosol complex refractive index and single scattering albedo indicate a shift in the dominant aerosol type present in the atmosphere in 2022–2024. According to the GRASP algorithm, a significant increase in the black carbon fraction was registered in 2022. Air contamination in Kyiv through PM2.5 and PM10 in 2021 and 2022 revealed substantial increases during critical conflict periods attributed to military actions. Despite initial declines just after the invasion, PM levels remained elevated compared to pre-invasion years, indicating ongoing air quality challenges exacerbated by war-related factors.
{"title":"Impact of military activity on atmospheric aerosol characteristics in Ukraine and Kyiv city","authors":"Xuanyi Wei , Yuliia Yukhymchuk , Vassyl Danylevsky , Gennadi Milinevsky , Philippe Goloub , Ihor Fesianov , Ivan Syniavskyi , Olena Turos , Tetiana Maremukha , Arina Petrosian , Volodymyr Kyslyi , Yu Shi","doi":"10.1016/j.apr.2025.102496","DOIUrl":"10.1016/j.apr.2025.102496","url":null,"abstract":"<div><div>We investigate the impact of the Russian invasion and military activities on aerosol parameters and air quality in the atmosphere over Kyiv and Ukraine. This study analyzes changes in pollutants such as black carbon, particulate matter PM<sub>2.5</sub> and PM<sub>10</sub>, and sulfates (SO<sub>2</sub> and SO<sub>4</sub>) using MERRA-2 reanalysis data. Black carbon concentration surged in eastern and western Ukraine during pre-invasion times, attributed to industrial emissions and solid fuel heating. During invasion, black carbon levels decreased overall, except in conflict-affected areas like Kyiv and southeastern regions, reflecting reduced industrial activities in the battle region. Similarly, PM<sub>2.5</sub> levels increased in eastern conflict zones, correlating with military intensity. Shifts in SO<sub>2</sub> and SO<sub>4</sub> concentrations indicated increased emissions in southeastern Ukraine due to military activities and infrastructure damage. Also, this research aims to analyze aerosol properties using AERONET data. Sun photometer observations reveal changes in the annual dynamics of the Ångstrom exponent, with lower values observed in 2022 and a decrease in the fine aerosol fraction. Analysis of the aerosol complex refractive index and single scattering albedo indicate a shift in the dominant aerosol type present in the atmosphere in 2022–2024. According to the GRASP algorithm, a significant increase in the black carbon fraction was registered in 2022. Air contamination in Kyiv through PM<sub>2.5</sub> and PM<sub>10</sub> in 2021 and 2022 revealed substantial increases during critical conflict periods attributed to military actions. Despite initial declines just after the invasion, PM levels remained elevated compared to pre-invasion years, indicating ongoing air quality challenges exacerbated by war-related factors.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102496"},"PeriodicalIF":3.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679300","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 : 2025-03-06DOI: 10.1016/j.apr.2025.102498
Lee Gunwon , Han Yuhan , Geunhan Kim
Prediction models ranging from statistical probability to machine learning techniques have been employed to improve and manage urban air quality. However, the number of air quality monitoring stations (AQMS) for the collection of air quality information is limited. This study established a model that explains the relationship between six air pollutants–SO2, CO, O3, NO2, PM10, and PM2.5–measured by approximately 443 AQMS in South Korea and factors, such as the vegetation index, topography, and land cover elements. The model analyzed the impact of land cover changes on air pollutant concentrations and derived scenarios predicting changes in the air quality due to land use changes. Despite the relatively small sample size of approximately 360 AQMS, multiple regression analysis demonstrated higher explanatory power compared with Xtreme Gradient Boosting, a representative machine learning technique. The optimal spatial range for explaining air pollutant concentrations varied for each air pollutant. The highest R2 in the multiple regression analysis was 0.34 at a distance of 12,000 m for SO2; 0.27 at 11,000 m for CO; 0.50 at 6000 m for O3; 0.70 at 18,000 m for NO2; 0.49 at 18,000 m for PM10; and 0.48 at 11,000 m for PM2.5. Certain land cover characteristics were found to significantly affect air quality, whereas small-scale restoration had a minimal impact on air quality improvement, and large-scale development substantially increased pollutant concentrations. This study provides essential information for urban planning and policymaking aimed at improving urban air quality.
{"title":"Impact of land use characteristics on air pollutant concentrations considering the spatial range of influence","authors":"Lee Gunwon , Han Yuhan , Geunhan Kim","doi":"10.1016/j.apr.2025.102498","DOIUrl":"10.1016/j.apr.2025.102498","url":null,"abstract":"<div><div>Prediction models ranging from statistical probability to machine learning techniques have been employed to improve and manage urban air quality. However, the number of air quality monitoring stations (AQMS) for the collection of air quality information is limited. This study established a model that explains the relationship between six air pollutants–SO<sub>2</sub>, CO, O<sub>3</sub>, NO<sub>2</sub>, PM<sub>10</sub>, and PM<sub>2.5</sub>–measured by approximately 443 AQMS in South Korea and factors, such as the vegetation index, topography, and land cover elements. The model analyzed the impact of land cover changes on air pollutant concentrations and derived scenarios predicting changes in the air quality due to land use changes. Despite the relatively small sample size of approximately 360 AQMS, multiple regression analysis demonstrated higher explanatory power compared with Xtreme Gradient Boosting, a representative machine learning technique. The optimal spatial range for explaining air pollutant concentrations varied for each air pollutant. The highest R<sup>2</sup> in the multiple regression analysis was 0.34 at a distance of 12,000 m for SO<sub>2</sub>; 0.27 at 11,000 m for CO; 0.50 at 6000 m for O<sub>3</sub>; 0.70 at 18,000 m for NO<sub>2</sub>; 0.49 at 18,000 m for PM<sub>10</sub>; and 0.48 at 11,000 m for PM<sub>2.5</sub>. Certain land cover characteristics were found to significantly affect air quality, whereas small-scale restoration had a minimal impact on air quality improvement, and large-scale development substantially increased pollutant concentrations. This study provides essential information for urban planning and policymaking aimed at improving urban air quality.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102498"},"PeriodicalIF":3.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679301","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 : 2025-03-06DOI: 10.1016/j.apr.2025.102497
Kyucheol Hwang , Sechan Park , Jeongho Kim , Jae Young Lee , Jong-Sung Park , Kwangyul Lee , Jungmin Park , Jong Bum Kim
Despite the implementation of various policies worldwide to reduce PM2.5 concentrations, they have remained sufficiently high and cause serious environmental and health problems. Most studies and policies regarding PM2.5 in South Korea have primarily focused on the Seoul Metropolitan Area, including Seoul, and there is a lack of research data necessary for implementing PM2.5 management policies in South Chungcheong Province (SCP). In this study, we used data from the Air Quality Research Center in Seosan, SCP, to conduct a detailed analysis of PM2.5, focusing on its chemical and physical properties as well as the influence of meteorological factors on PM2.5 characteristics. The mean PM2.5 concentrations were 16.7 ± 12.5 μg/m3 in the warm season and 31.1 ± 18.7 μg/m3 in the cold season, showing a twofold increase in the cold season. The ratio of NO3− in the chemical composition of PM2.5 was higher in the cold season (19%) compared to the warm season (15%), while SO42− was 1.75 times higher in the warm season. Using the atmospheric oxidant (Ox) and analyzing PM2.5 concentrations under different photochemical conditions, we found that small particles dominated in the warm season, shifting towards smaller particle sizes in the size distribution with higher temperatures due to secondary particle production. In contrast, higher concentrations of PM2.5 during the cold season were attributed to direct emissions and external influx. Our findings highlight the importance of managing small particles in summer and provide valuable data for South Chungcheong Province, aiding future policy development to reduce PM2.5 levels.
{"title":"Understanding the physicochemical characteristics of PM2.5 under meteorological influence: A study in South Chungcheong Province, South Korea (2021–2022)","authors":"Kyucheol Hwang , Sechan Park , Jeongho Kim , Jae Young Lee , Jong-Sung Park , Kwangyul Lee , Jungmin Park , Jong Bum Kim","doi":"10.1016/j.apr.2025.102497","DOIUrl":"10.1016/j.apr.2025.102497","url":null,"abstract":"<div><div>Despite the implementation of various policies worldwide to reduce PM<sub>2.5</sub> concentrations, they have remained sufficiently high and cause serious environmental and health problems. Most studies and policies regarding PM<sub>2.5</sub> in South Korea have primarily focused on the Seoul Metropolitan Area, including Seoul, and there is a lack of research data necessary for implementing PM<sub>2.5</sub> management policies in South Chungcheong Province (SCP). In this study, we used data from the Air Quality Research Center in Seosan, SCP, to conduct a detailed analysis of PM<sub>2.5</sub>, focusing on its chemical and physical properties as well as the influence of meteorological factors on PM<sub>2.5</sub> characteristics. The mean PM<sub>2.5</sub> concentrations were 16.7 ± 12.5 μg/m<sup>3</sup> in the warm season and 31.1 ± 18.7 μg/m<sup>3</sup> in the cold season, showing a twofold increase in the cold season. The ratio of NO<sub>3</sub><sup>−</sup> in the chemical composition of PM<sub>2.5</sub> was higher in the cold season (19%) compared to the warm season (15%), while SO<sub>4</sub><sup>2−</sup> was 1.75 times higher in the warm season. Using the atmospheric oxidant (Ox) and analyzing PM<sub>2.5</sub> concentrations under different photochemical conditions, we found that small particles dominated in the warm season, shifting towards smaller particle sizes in the size distribution with higher temperatures due to secondary particle production. In contrast, higher concentrations of PM<sub>2.5</sub> during the cold season were attributed to direct emissions and external influx. Our findings highlight the importance of managing small particles in summer and provide valuable data for South Chungcheong Province, aiding future policy development to reduce PM<sub>2.5</sub> levels.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 6","pages":"Article 102497"},"PeriodicalIF":3.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629285","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}
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