Pub Date : 2025-02-20DOI: 10.1016/j.atmosenv.2025.121115
Lian Duan , Huimin Yu , Fengwen Wang , Tareq Hussein , Tian Lin , Zhigang Guo
The COVID-19 pandemic, particularly the Omicron-22 lockdown in Shanghai, provided a unique opportunity to examine the influence of lockdown and decreasing human activities on the urban atmosphere environment. Here, we explored the chemical compositions and sources of carbonaceous aerosols during the pre-lockdown, lockdown, and post-lockdown periods in 2022, Shanghai. The mean concentrations of organic carbon (OC) and element carbon (EC) were 4.40 ± 1.58 and 1.05 ± 0.41 μg/m3 pre-lockdown, 1.60 ± 0.99 and 0.30 ± 0.13 μg/m3 during lockdown, and 2.66 ± 2.01 and 0.47 ± 0.19 μg/m3 post-lockdown, respectively. Normal alkanes (n-alkanes) and polycyclic aromatic hydrocarbons (PAHs) concentrations were 27.5 ± 9.77 and 1.81 ± 1.34 ng/m3 pre-lockdown, 10.6 ± 5.84 and 1.50 ± 0.67 ng/m3 during lockdown, and 7.27 ± 3.86 and 1.44 ± 0.23 ng/m3 post lockdown, respectively, with a further decline noted post-lockdown. A notable decrease in carbonaceous aerosols was observed during the lockdown. Carbonaceous aerosols showed a marked decrease during the lockdown, with OC and EC increasing by 60% post-lockdown, while n-alkanes and PAHs continued to decline. Although the composition of OC, EC, and PAHs remained stable, n-alkanes, particularly C29-C34, significantly increased due to plant wax emissions. Source apportionment indicated that coal combustion and industrial emissions were the primary contributors across all periods, with reduced transport emissions having limited impact on OC, EC, and PAH sources. This study highlights the air quality improvements from reduced anthropogenic activities, offering insights for future pollution mitigation policies.
{"title":"Revealing the chemical composition and sources of carbonaceous aerosols in PM2.5: Insights from the Omicron-22 lockdown in Shanghai","authors":"Lian Duan , Huimin Yu , Fengwen Wang , Tareq Hussein , Tian Lin , Zhigang Guo","doi":"10.1016/j.atmosenv.2025.121115","DOIUrl":"10.1016/j.atmosenv.2025.121115","url":null,"abstract":"<div><div>The COVID-19 pandemic, particularly the Omicron-22 lockdown in Shanghai, provided a unique opportunity to examine the influence of lockdown and decreasing human activities on the urban atmosphere environment. Here, we explored the chemical compositions and sources of carbonaceous aerosols during the pre-lockdown, lockdown, and post-lockdown periods in 2022, Shanghai. The mean concentrations of organic carbon (OC) and element carbon (EC) were 4.40 ± 1.58 and 1.05 ± 0.41 μg/m<sup>3</sup> pre-lockdown, 1.60 ± 0.99 and 0.30 ± 0.13 μg/m<sup>3</sup> during lockdown, and 2.66 ± 2.01 and 0.47 ± 0.19 μg/m<sup>3</sup> post-lockdown, respectively. Normal alkanes (n-alkanes) and polycyclic aromatic hydrocarbons (PAHs) concentrations were 27.5 ± 9.77 and 1.81 ± 1.34 ng/m<sup>3</sup> pre-lockdown, 10.6 ± 5.84 and 1.50 ± 0.67 ng/m<sup>3</sup> during lockdown, and 7.27 ± 3.86 and 1.44 ± 0.23 ng/m<sup>3</sup> post lockdown, respectively, with a further decline noted post-lockdown. A notable decrease in carbonaceous aerosols was observed during the lockdown. Carbonaceous aerosols showed a marked decrease during the lockdown, with OC and EC increasing by 60% post-lockdown, while n-alkanes and PAHs continued to decline. Although the composition of OC, EC, and PAHs remained stable, n-alkanes, particularly C<sub>29</sub>-C<sub>34</sub>, significantly increased due to plant wax emissions. Source apportionment indicated that coal combustion and industrial emissions were the primary contributors across all periods, with reduced transport emissions having limited impact on OC, EC, and PAH sources. This study highlights the air quality improvements from reduced anthropogenic activities, offering insights for future pollution mitigation policies.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"348 ","pages":"Article 121115"},"PeriodicalIF":4.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
The limited number of PM2.5 monitoring stations from the Environmental Protection Agency (EPA) across the Contiguous United States (CONUS) restricts PM2.5 monitoring and associated policymaking efforts. Low-cost PM2.5 stations, such as those from the PurpleAir network, offer a vital alternative to expand coverage in regions not monitored by the EPA. However, the accuracy of PurpleAir measurements has been questioned. This study introduces a deep learning (DL) approach, specifically a deep convolutional neural network (DeepCNN), to align hourly PM2.5 data from PurpleAir with EPA PM2.5 observations across the CONUS for the year 2021. Utilizing over nine million samples from 1595 PurpleAir stations located within 5 km of EPA stations, the DeepCNN demonstrates significant improvements in the agreement between PurpleAir and EPA observations. It increases the correlation coefficient (R) with EPA observations from 0.58 to 0.85 and reduces the mean absolute bias (MAB) from 4.99 to 2.98 μg/m3, achieving a 40% reduction in bias. The state-wise cross-validation also underscores the model's generalizability, with an average 11% improvement in R values and a 13% reduction in bias between PurpleAir and EPA PM2.5 measurements in various states. Comparative analysis reveals that the accuracy of our DL-enhanced PurpleAir PM2.5 (PM-DL) data significantly surpasses that of five previously established PurpleAir correction models, which show low R values of 0.55–0.58 and MABs ranging from 4.21 to 6.43 μg/m3 when validated against EPA data. This study underscores the need for more sophisticated models to better align PurpleAir PM2.5 measurements to EPA standards. The PM-DL data can substantially mitigate the scarcity of reliable institutional PM2.5 observations across the CONUS. By aligning PurpleAir PM2.5 data with EPA observations, our model has the potential to augment the existing network with over ten thousand accurate monitoring stations, significantly expanding upon the nearly one thousand EPA stations currently in operation.
{"title":"Deep learning calibration model for PurpleAir PM2.5 measurements: Comprehensive Investigation of the PurpleAir network","authors":"Masoud Ghahremanloo , Yunsoo Choi , Mahmoudreza Momeni","doi":"10.1016/j.atmosenv.2025.121118","DOIUrl":"10.1016/j.atmosenv.2025.121118","url":null,"abstract":"<div><div>The limited number of PM<sub>2.5</sub> monitoring stations from the Environmental Protection Agency (EPA) across the Contiguous United States (CONUS) restricts PM<sub>2.5</sub> monitoring and associated policymaking efforts. Low-cost PM<sub>2.5</sub> stations, such as those from the PurpleAir network, offer a vital alternative to expand coverage in regions not monitored by the EPA. However, the accuracy of PurpleAir measurements has been questioned. This study introduces a deep learning (DL) approach, specifically a deep convolutional neural network (DeepCNN), to align hourly PM<sub>2.5</sub> data from PurpleAir with EPA PM<sub>2.5</sub> observations across the CONUS for the year 2021. Utilizing over nine million samples from 1595 PurpleAir stations located within 5 km of EPA stations, the DeepCNN demonstrates significant improvements in the agreement between PurpleAir and EPA observations. It increases the correlation coefficient (R) with EPA observations from 0.58 to 0.85 and reduces the mean absolute bias (MAB) from 4.99 to 2.98 μg/m<sup>3</sup>, achieving a 40% reduction in bias. The state-wise cross-validation also underscores the model's generalizability, with an average 11% improvement in R values and a 13% reduction in bias between PurpleAir and EPA PM<sub>2.5</sub> measurements in various states. Comparative analysis reveals that the accuracy of our DL-enhanced PurpleAir PM<sub>2.5</sub> (PM-DL) data significantly surpasses that of five previously established PurpleAir correction models, which show low R values of 0.55–0.58 and MABs ranging from 4.21 to 6.43 μg/m<sup>3</sup> when validated against EPA data. This study underscores the need for more sophisticated models to better align PurpleAir PM<sub>2.5</sub> measurements to EPA standards. The PM-DL data can substantially mitigate the scarcity of reliable institutional PM<sub>2.5</sub> observations across the CONUS. By aligning PurpleAir PM<sub>2.5</sub> data with EPA observations, our model has the potential to augment the existing network with over ten thousand accurate monitoring stations, significantly expanding upon the nearly one thousand EPA stations currently in operation.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"348 ","pages":"Article 121118"},"PeriodicalIF":4.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-02-18DOI: 10.1016/j.atmosenv.2025.121117
S. Itahashi , N.K. Kim , Y.P. Kim , M. Song , C.H. Kim , K.S. Jang , K.Y. Lee , H.J. Shin , J.Y. Ahn , J.S. Jung , Z. Wu , J.Y. Lee , Y. Sadanaga , S. Kato , N. Tang , A. Matsuki
In 2020, the Fine Particle Research Initiative in East Asia considering National Differences (FRIEND) project was launched to understand air quality issues over Northeast Asia better. In the FRIEND project, high-temporal-resolution measurements of gas and aerosol concentrations were taken simultaneously at five key sites in Northeast Asia. In this study, we used the dataset at Beijing in China, Seoul in Republic of Korea, and Noto in Japan. The second FRIEND campaign was conducted in early summer from June 1 to 30, 2021. Compared with the results of the first FRIEND campaign conducted in winter, it was revealed that the fraction of sulfate aerosol (SO42−) had dramatically increased in the upwind region of Northeast Asia (Beijing and Seoul). This period corresponds to the early rainy season in Northeast Asia; therefore, the role of the aqueous-phase oxidation process could be increased in SO42− production. However, accurate modeling of precipitation is still challenging because of the parameterization in the meteorological model. Thus, we investigated the microphysics and cumulus schemes in the meteorological model and conducted 10 simulations. All schemes underestimated the precipitation amount and the cloud fraction. Hence, SO42− concentration was underestimated with a lower conversion ratio from sulfur dioxide (SO2) to SO42− (FS) at Beijing, Seoul, and Noto. At Seoul, the SO42− concentration was underestimated with the aerosol ion monitor (AIM) measurements, corresponding to PM2.5, but had an acceptable performance level. The SO42− concentration at Seoul was sensitive to microphysics and cumulus schemes. However, the SO42− concentration was compared with aerosol chemical speciation monitors (ACSMs), corresponding to PM1.0, in Beijing and Noto, and showed greater underestimation. The sensitivities of SO42− concentration to the precipitation schemes were small at Beijing and Noto. The simulated aerosol diameter shifted to a coarser range (1–2.5 μm) in the second campaign compared with the first campaign dataset with increasing temperature and relative humidity. The international measurement network in the FRIEND project demonstrates that the modeled aerosol diameter treatment must be revised carefully.
{"title":"Modeling comparison of precipitation schemes and implications on aerosol diameter treatment for better sulfate aerosol production in the early summer rainy season over Northeast Asia","authors":"S. Itahashi , N.K. Kim , Y.P. Kim , M. Song , C.H. Kim , K.S. Jang , K.Y. Lee , H.J. Shin , J.Y. Ahn , J.S. Jung , Z. Wu , J.Y. Lee , Y. Sadanaga , S. Kato , N. Tang , A. Matsuki","doi":"10.1016/j.atmosenv.2025.121117","DOIUrl":"10.1016/j.atmosenv.2025.121117","url":null,"abstract":"<div><div>In 2020, the Fine Particle Research Initiative in East Asia considering National Differences (FRIEND) project was launched to understand air quality issues over Northeast Asia better. In the FRIEND project, high-temporal-resolution measurements of gas and aerosol concentrations were taken simultaneously at five key sites in Northeast Asia. In this study, we used the dataset at Beijing in China, Seoul in Republic of Korea, and Noto in Japan. The second FRIEND campaign was conducted in early summer from June 1 to 30, 2021. Compared with the results of the first FRIEND campaign conducted in winter, it was revealed that the fraction of sulfate aerosol (SO<sub>4</sub><sup>2−</sup>) had dramatically increased in the upwind region of Northeast Asia (Beijing and Seoul). This period corresponds to the early rainy season in Northeast Asia; therefore, the role of the aqueous-phase oxidation process could be increased in SO<sub>4</sub><sup>2−</sup> production. However, accurate modeling of precipitation is still challenging because of the parameterization in the meteorological model. Thus, we investigated the microphysics and cumulus schemes in the meteorological model and conducted 10 simulations. All schemes underestimated the precipitation amount and the cloud fraction. Hence, SO<sub>4</sub><sup>2−</sup> concentration was underestimated with a lower conversion ratio from sulfur dioxide (SO<sub>2</sub>) to SO<sub>4</sub><sup>2−</sup> (F<sub>S</sub>) at Beijing, Seoul, and Noto. At Seoul, the SO<sub>4</sub><sup>2−</sup> concentration was underestimated with the aerosol ion monitor (AIM) measurements, corresponding to PM<sub>2.5</sub>, but had an acceptable performance level. The SO<sub>4</sub><sup>2−</sup> concentration at Seoul was sensitive to microphysics and cumulus schemes. However, the SO<sub>4</sub><sup>2−</sup> concentration was compared with aerosol chemical speciation monitors (ACSMs), corresponding to PM<sub>1.0</sub>, in Beijing and Noto, and showed greater underestimation. The sensitivities of SO<sub>4</sub><sup>2−</sup> concentration to the precipitation schemes were small at Beijing and Noto. The simulated aerosol diameter shifted to a coarser range (1–2.5 μm) in the second campaign compared with the first campaign dataset with increasing temperature and relative humidity. The international measurement network in the FRIEND project demonstrates that the modeled aerosol diameter treatment must be revised carefully.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"349 ","pages":"Article 121117"},"PeriodicalIF":4.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
Chromophoric dissolved organic matter (CDOM) in atmospheric PM significantly impacts climate and health. However, China's air pollution control on CDOM is unclear. To evaluate the effect of the Blue Sky Defense Battle (BSDB) on the pollution characteristics and sources of CDOM, we comprehensively analyzed 1,428 PM2.5 samples in Xi'an (2016-2022). Compared with the pre-BSDB period, we found a 17% increase in the air quality index and a 42% reduction in CDOM pollution levels. Furthermore, in winter, carbonaceous aerosol concentrations notably declined, with OC decreased by 25% and EC by 74%. Additionally, CDOM in Xi'an was predominantly composed of humic chromophores (80%), and the highly oxygenated humic-like substances (HO-HULIS) declined by 34%, effectively reducing the potential oxidative toxicity risks. Source apportionment results from the excitation-emission matrix fluorescence fingerprinting technique with the non-negative matrix factorization (NMF) model demonstrated that the effective control of combustion sources was the main driver of changes in atmospheric concentrations, chemical composition, and optical properties. Based on the NMF model analysis of carbonaceous aerosol, the source contributions of atmospheric oxidation and photochemical reactions increased after the BSDB indicating the limited control of the secondary sources. Additionally, the ineffective control of dust sources and the increasing contribution of external transport to CDOM after the BSDB highlighted the importance of effective dust management and regional collaborative control. Despite the decline in PM2.5 levels, the rise in toxic components (HO-HULIS) indicated ongoing challenges for health-focused pollution control.
{"title":"Unexpected changes in occurrence and sources of chromophoric dissolved organic matter in PM2.5 driven by the clean air action over Xi'an, China","authors":"Xin Zhu , Qingcai Chen , Tong Sha , Yue Yin , Jinwen Li , Zimeng Zhang , Jiale Ding , Tengfei Xu","doi":"10.1016/j.atmosenv.2025.121116","DOIUrl":"10.1016/j.atmosenv.2025.121116","url":null,"abstract":"<div><div>Chromophoric dissolved organic matter (CDOM) in atmospheric PM significantly impacts climate and health. However, China's air pollution control on CDOM is unclear. To evaluate the effect of the Blue Sky Defense Battle (BSDB) on the pollution characteristics and sources of CDOM, we comprehensively analyzed 1,428 PM<sub>2.5</sub> samples in Xi'an (2016-2022). Compared with the pre-BSDB period, we found a 17% increase in the air quality index and a 42% reduction in CDOM pollution levels. Furthermore, in winter, carbonaceous aerosol concentrations notably declined, with OC decreased by 25% and EC by 74%. Additionally, CDOM in Xi'an was predominantly composed of humic chromophores (80%), and the highly oxygenated humic-like substances (HO-HULIS) declined by 34%, effectively reducing the potential oxidative toxicity risks. Source apportionment results from the excitation-emission matrix fluorescence fingerprinting technique with the non-negative matrix factorization (NMF) model demonstrated that the effective control of combustion sources was the main driver of changes in atmospheric concentrations, chemical composition, and optical properties. Based on the NMF model analysis of carbonaceous aerosol, the source contributions of atmospheric oxidation and photochemical reactions increased after the BSDB indicating the limited control of the secondary sources. Additionally, the ineffective control of dust sources and the increasing contribution of external transport to CDOM after the BSDB highlighted the importance of effective dust management and regional collaborative control. Despite the decline in PM<sub>2.5</sub> levels, the rise in toxic components (HO-HULIS) indicated ongoing challenges for health-focused pollution control.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"348 ","pages":"Article 121116"},"PeriodicalIF":4.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-02-16DOI: 10.1016/j.atmosenv.2025.121114
Haiyan Ran , Jingwei Zhang , Yu Qu , Juan Yang , Yong Chen , Yele Sun , Chaoyang Xue , Yujing Mu , Junling An
Nitrous acid (HONO) is a critical precursor of the hydroxyl radical (OH) and plays a pivotal role in atmospheric photochemistry. Although nitrogen dioxide (NO2) heterogeneous reactions (HET) on ground and aerosol surfaces are widely recognized as major paths of HONO production, their influencing factors are not well characterized in air quality models, limiting the understanding of HONO formation and the quantification of their regional impact. In this study, a novel parameterization scheme for the NO2 uptake coefficient, including the effects of solar radiation, relative humidity (RH) and ammonia (NH3), was developed and coupled into the Weather Research and Forecasting model with Chemistry. Nine simulation scenarios were designed to assess the impacts of RH and NH3 on HONO chemistry and O3 levels in the North China Plain (NCP). The results showed that the RH-impacted HET contributed 10−25% of HONO, with a significant increase of more than 35% during the haze periods; whereas the NH3-impacted HET contributed 15% of nighttime HONO and <5% of noontime HONO, playing a more significant role in rural areas. Vertically, the RH-impacted HET contribution to nighttime HONO concentrations remained 26−31% at an altitude of 700–900 m due to the higher RH levels (50−60%) during the haze periods; whereas the NH3-impacted HET contribution was minor above 500 m owing to the fast-decreasing NH3 concentrations with height. When RH exceeded the turning point (70%), nighttime HONO was suppressed by up to 1 ppb in eastern NCP. The combination of RH and NH3 produced a ground daily maximum 8h averaged O3 enhancement of 6–14 μg m−3 during the haze periods, exceeding the effect of solar radiation. These findings deepen our understanding of the role of RH and NH3 in HONO chemistry and imply the importance of reasonably expressing HET in air quality models.
{"title":"HONO chemistry affected by relative humidity and ammonia in the North China Plain during winter","authors":"Haiyan Ran , Jingwei Zhang , Yu Qu , Juan Yang , Yong Chen , Yele Sun , Chaoyang Xue , Yujing Mu , Junling An","doi":"10.1016/j.atmosenv.2025.121114","DOIUrl":"10.1016/j.atmosenv.2025.121114","url":null,"abstract":"<div><div>Nitrous acid (HONO) is a critical precursor of the hydroxyl radical (OH) and plays a pivotal role in atmospheric photochemistry. Although nitrogen dioxide (NO<sub>2</sub>) heterogeneous reactions (HET) on ground and aerosol surfaces are widely recognized as major paths of HONO production, their influencing factors are not well characterized in air quality models, limiting the understanding of HONO formation and the quantification of their regional impact. In this study, a novel parameterization scheme for the NO<sub>2</sub> uptake coefficient, including the effects of solar radiation, relative humidity (RH) and ammonia (NH<sub>3</sub>), was developed and coupled into the Weather Research and Forecasting model with Chemistry. Nine simulation scenarios were designed to assess the impacts of RH and NH<sub>3</sub> on HONO chemistry and O<sub>3</sub> levels in the North China Plain (NCP). The results showed that the RH-impacted HET contributed 10−25% of HONO, with a significant increase of more than 35% during the haze periods; whereas the NH<sub>3</sub>-impacted HET contributed 15% of nighttime HONO and <5% of noontime HONO, playing a more significant role in rural areas. Vertically, the RH-impacted HET contribution to nighttime HONO concentrations remained 26−31% at an altitude of 700–900 m due to the higher RH levels (50−60%) during the haze periods; whereas the NH<sub>3</sub>-impacted HET contribution was minor above 500 m owing to the fast-decreasing NH<sub>3</sub> concentrations with height. When RH exceeded the turning point (70%), nighttime HONO was suppressed by up to 1 ppb in eastern NCP. The combination of RH and NH<sub>3</sub> produced a ground daily maximum 8h averaged O<sub>3</sub> enhancement of 6–14 μg m<sup>−3</sup> during the haze periods, exceeding the effect of solar radiation. These findings deepen our understanding of the role of RH and NH<sub>3</sub> in HONO chemistry and imply the importance of reasonably expressing HET in air quality models.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"348 ","pages":"Article 121114"},"PeriodicalIF":4.2,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-02-13DOI: 10.1016/j.atmosenv.2025.121111
Liu Yang , Xiaoyan Hu , Zhenxing Shen , Yiming Yang , Hongmei Xu , Jian Sun
Indoor air quality in rural households significantly impacts public health, yet bioaerosol characteristics in these environments remain poorly understood. This study investigates the characteristics of bioaerosols and bacterial communities in rural households of the Fenwei Plain, China, comparing indoor and outdoor environments and contrasting rural-urban differences. The peak concentrations of total airborne microbes (TAMs), viable bacteria, non-viable bacteria, and viability showed pronounced indoor/outdoor variations, meanwhile, rural areas exhibited significantly lower bioaerosol concentrations than urban areas. The bacterial communities displayed distinct indoor-outdoor patterns: Actinobacteria (40.5% and 27.3%) and Proteobacteria (34.5% and 40.3%) were the predominant phyla detected in kitchen and living room, respectively, while Bacteroidetes (41% and 51.5) for chimney and outdoor environment. Notably, rural areas showed 2.8 and 8 times higher relative abundances of Firmicutes and Bacteroidetes compared to urban areas, indicating fundamentally different microbial ecosystems. At the genus level, the top two bacteria were Vibrio and Chloroplast in indoor areas, whereas the predominant genera in outdoor areas included Prevotella, Faecalibacterium, and Bacteroides. Bacterial communities in urban and rural areas displayed significant heterogeneity. The peak and valley relative abundance of potential pathogenic bacteria in rural areas appeared in the chimney area (62.7%) and in the living room (18.3%), respectively. Rhodococcus and Prevotella were the indicator pathogenic bacteria for urban and rural sites, respectively, with links to pulmonary infections and intestinal diseases. This study provides valuable insights into the characteristics of bioaerosols and their implications for human health protection in rural areas.
{"title":"Insights into bacteria characteristics and potential pathogen in rural indoor households in Fenwei Plain, China","authors":"Liu Yang , Xiaoyan Hu , Zhenxing Shen , Yiming Yang , Hongmei Xu , Jian Sun","doi":"10.1016/j.atmosenv.2025.121111","DOIUrl":"10.1016/j.atmosenv.2025.121111","url":null,"abstract":"<div><div>Indoor air quality in rural households significantly impacts public health, yet bioaerosol characteristics in these environments remain poorly understood. This study investigates the characteristics of bioaerosols and bacterial communities in rural households of the Fenwei Plain, China, comparing indoor and outdoor environments and contrasting rural-urban differences. The peak concentrations of total airborne microbes (TAMs), viable bacteria, non-viable bacteria, and viability showed pronounced indoor/outdoor variations, meanwhile, rural areas exhibited significantly lower bioaerosol concentrations than urban areas. The bacterial communities displayed distinct indoor-outdoor patterns: <em>Actinobacteria</em> (40.5% and 27.3%) and <em>Proteobacteria</em> (34.5% and 40.3%) were the predominant phyla detected in kitchen and living room, respectively, while <em>Bacteroidetes</em> (41% and 51.5) for chimney and outdoor environment. Notably, rural areas showed 2.8 and 8 times higher relative abundances of <em>Firmicutes</em> and <em>Bacteroidetes</em> compared to urban areas, indicating fundamentally different microbial ecosystems. At the genus level, the top two bacteria were <em>Vibrio</em> and <em>Chloroplast</em> in indoor areas, whereas the predominant genera in outdoor areas included <em>Prevotella</em>, <em>Faecalibacterium</em>, and <em>Bacteroides</em>. Bacterial communities in urban and rural areas displayed significant heterogeneity. The peak and valley relative abundance of potential pathogenic bacteria in rural areas appeared in the chimney area (62.7%) and in the living room (18.3%), respectively. <em>Rhodococcus</em> and <em>Prevotella</em> were the indicator pathogenic bacteria for urban and rural sites, respectively, with links to pulmonary infections and intestinal diseases. This study provides valuable insights into the characteristics of bioaerosols and their implications for human health protection in rural areas.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"347 ","pages":"Article 121111"},"PeriodicalIF":4.2,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-02-12DOI: 10.1016/j.atmosenv.2025.121102
Ishmael Mutanda , Masashi Inafuku , Hirosuke Oku
The Guenther 1993 (G-93) formula is the most extensively used algorithm for predicting leaf-scale isoprene emissions driven by temperature and light intensity, and has been incorporated into many isoprene emission models. The temperature and light response variables of the G-93 define the rate of increase (ascend) and decrease (descend) of emissions as driven by temperature and light intensity. Our previous study on the tropical tree Ficus septica noted that hot weather in the previous days impacted unevenly the ascend and descend changes of isoprene emission, causing a significant deviation between the G-93 prediction and observations. Separate parameterization of the ascend and the descend phases successfully ameliorated this deviation, however, the relationship between weather history and parameters for individual ascend and descend phase still warrants more detailed studies to inform their reliable use in emission algorithms. We herein further examined the relationship between weather history and G-93 parameters for individual ascending and descending phase responses. We found that among the G-93 parameters, CT1 and α correlated with cumulative temperature or PPFD, whilst CT2 essentially remained constant for both the ascending and descending phases. These correlations allowed us to parameterize the G-93 formula based on weather history for the first time, and direct modification of CT1 and α in terms of cumulative temperature and light intensity captured 96.6% of variation in the ascending and 98.1% in the descending phase in the study period. Separate parameterization of the upward and the downward changes was found to be effective in improving our ability to predict isoprene emission from plants that experienced hot weather in the previous days. More importantly, this result implies that the assumption of a symmetric response of isoprene emission across the maxima temperature and light intensity needs revision especially under circumstances of a warming climate.
{"title":"Weather history-based parameterization of the G-93 isoprene emission formula for the tropical plant Ficus septica","authors":"Ishmael Mutanda , Masashi Inafuku , Hirosuke Oku","doi":"10.1016/j.atmosenv.2025.121102","DOIUrl":"10.1016/j.atmosenv.2025.121102","url":null,"abstract":"<div><div>The Guenther 1993 (G-93) formula is the most extensively used algorithm for predicting leaf-scale isoprene emissions driven by temperature and light intensity, and has been incorporated into many isoprene emission models. The temperature and light response variables of the G-93 define the rate of increase (ascend) and decrease (descend) of emissions as driven by temperature and light intensity. Our previous study on the tropical tree <em>Ficus septica</em> noted that hot weather in the previous days impacted unevenly the ascend and descend changes of isoprene emission, causing a significant deviation between the G-93 prediction and observations. Separate parameterization of the ascend and the descend phases successfully ameliorated this deviation, however, the relationship between weather history and parameters for individual ascend and descend phase still warrants more detailed studies to inform their reliable use in emission algorithms. We herein further examined the relationship between weather history and G-93 parameters for individual ascending and descending phase responses. We found that among the G-93 parameters, <em>C</em><sub><em>T1</em></sub> and <em>α</em> correlated with cumulative temperature or PPFD, whilst <em>C</em><sub><em>T2</em></sub> essentially remained constant for both the ascending and descending phases. These correlations allowed us to parameterize the G-93 formula based on weather history for the first time, and direct modification of <em>C</em><sub><em>T1</em></sub> and <em>α</em> in terms of cumulative temperature and light intensity captured 96.6% of variation in the ascending and 98.1% in the descending phase in the study period. Separate parameterization of the upward and the downward changes was found to be effective in improving our ability to predict isoprene emission from plants that experienced hot weather in the previous days. More importantly, this result implies that the assumption of a symmetric response of isoprene emission across the maxima temperature and light intensity needs revision especially under circumstances of a warming climate.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"347 ","pages":"Article 121102"},"PeriodicalIF":4.2,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-02-10DOI: 10.1016/j.atmosenv.2025.121101
Marek Kopáček , Petr Porcal , Jiří Kopáček , Yuliya Vystavna
The effects of local climate parameters, seasonality and sources of air masses on the stable water isotopes [hydrogen (δ2H) and oxygen (δ1⁸O)] in precipitation were investigated along an altitudinal gradient (381–1118 m a.s.l.) in southern Bohemia, Central Europe, from December 2021 to November 2023. The relationship between the observed δ2H and δ18O values was consistent with the Global Meteoric Water Line. The isotopic composition of precipitation changed with increasing altitude by −6.5 and −1.2‰ km−1 for δ2H and δ18O, respectively. The δ values differed between seasons, with the values being most enriched in summer and most depleted in winter. An analysis of the air mass trajectories using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model showed that the main sources of precipitation were the North Atlantic (from 44% in spring to 70% in fall and winter), the Arctic Ocean (from 15% in summer to 38% in spring) and the Mediterranean Sea (from 12% in winter to 34% in summer). Throughout the study period, average δ values differed significantly between air masses (p < 0.05) and along the altitudinal gradient, with the most enriched values (from −56 to −37‰ for δ2H and from −8.2 to −5.5‰ for δ18O) and the most depleted values (from −91 to −84‰ for δ2H and from −12.7 to −12.0‰ for δ18O) occurring in the Mediterranean and Arctic air masses, respectively. However, for individual daily samples, strong correlations occurred between the δ values and air temperature (the strongest), humidity, precipitation, time of sunshine and solar radiation, while the influence of air mass directions was not significant. A stepwise regression analysis showed that most of the variation in daily δ values (43–48% and 47–52% of δ2H and δ18O, respectively) was explained by the combination of air temperature and humidity, precipitation amount, and season.
{"title":"Factors controlling variation of δ2H and δ18O in precipitation in Southern Bohemia, Central Europe","authors":"Marek Kopáček , Petr Porcal , Jiří Kopáček , Yuliya Vystavna","doi":"10.1016/j.atmosenv.2025.121101","DOIUrl":"10.1016/j.atmosenv.2025.121101","url":null,"abstract":"<div><div>The effects of local climate parameters, seasonality and sources of air masses on the stable water isotopes [hydrogen (δ<sup>2</sup>H) and oxygen (δ<sup>1</sup>⁸O)] in precipitation were investigated along an altitudinal gradient (381–1118 m a.s.l.) in southern Bohemia, Central Europe, from December 2021 to November 2023. The relationship between the observed δ<sup>2</sup>H and δ<sup>18</sup>O values was consistent with the Global Meteoric Water Line. The isotopic composition of precipitation changed with increasing altitude by −6.5 and −1.2‰ km<sup>−1</sup> for δ<sup>2</sup>H and δ<sup>18</sup>O, respectively. The δ values differed between seasons, with the values being most enriched in summer and most depleted in winter. An analysis of the air mass trajectories using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model showed that the main sources of precipitation were the North Atlantic (from 44% in spring to 70% in fall and winter), the Arctic Ocean (from 15% in summer to 38% in spring) and the Mediterranean Sea (from 12% in winter to 34% in summer). Throughout the study period, average δ values differed significantly between air masses (<em>p</em> < 0.05) and along the altitudinal gradient, with the most enriched values (from −56 to −37‰ for δ<sup>2</sup>H and from −8.2 to −5.5‰ for δ<sup>18</sup>O) and the most depleted values (from −91 to −84‰ for δ<sup>2</sup>H and from −12.7 to −12.0‰ for δ<sup>18</sup>O) occurring in the Mediterranean and Arctic air masses, respectively. However, for individual daily samples, strong correlations occurred between the δ values and air temperature (the strongest), humidity, precipitation, time of sunshine and solar radiation, while the influence of air mass directions was not significant. A stepwise regression analysis showed that most of the variation in daily δ values (43–48% and 47–52% of δ<sup>2</sup>H and δ<sup>18</sup>O, respectively) was explained by the combination of air temperature and humidity, precipitation amount, and season.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"347 ","pages":"Article 121101"},"PeriodicalIF":4.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-02-10DOI: 10.1016/j.atmosenv.2025.121079
Sujan Ghimire , Ravinesh C. Deo , Ningbo Jiang , A.A. Masrur Ahmed , Salvin S. Prasad , David Casillas-Pérez , Sancho Salcedo-Sanz , Zaher Mundher Yaseen
<div><div>Total Suspended Particles (<span><math><mrow><mi>T</mi><mi>S</mi><mi>P</mi></mrow></math></span>) is an important indicator of air quality, yet traditional prediction models lack comprehensive consideration of spatio-temporal interactions of different meteorological and air pollution phenomena. To address these limitations, this study introduces an explainable (X) deep hybrid (H) network, integrating Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (BGRU), for hourly <span><math><mrow><mi>T</mi><mi>S</mi><mi>P</mi></mrow></math></span> concentration prediction. The model was trained and evaluated using meteorological and air quality data from Canon Hill, Australia. By combining CNN’s spatial feature extraction capabilities with BGRU’s temporal dependencies, the model effectively captures complex spatial–temporal patterns in the data. The X-H-CBGRU model outperforms fifteen competing benchmark models such as deep neural network, extreme learning machine, multilayer perceptron, support vector regression, random forest regression, light gradient boosting, gradient boosting regression, long short-term memory network, as well as their hybrid CNN counterparts in terms of the accuracy evidenced by a lower Root Mean Square Error (<span><math><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow></math></span> <span><math><mrow><mo>≈</mo><mspace></mspace><mn>6</mn><mo>.</mo><mn>302</mn><mspace></mspace><mi>μ</mi><msup><mrow><mi>g/m</mi></mrow><mrow><mn>3</mn></mrow></msup></mrow></math></span>) and higher Correlation Coefficient (<span><math><mi>r</mi></math></span> <span><math><mrow><mo>≈</mo><mspace></mspace><mn>0</mn><mo>.</mo><mn>91</mn></mrow></math></span>) compared to other models. Moreover, the model demonstrates strong probabilistic performance with a high Prediction Interval Coverage Probability (<span><math><mrow><mi>P</mi><mi>I</mi><mi>C</mi><mi>P</mi></mrow></math></span> <span><math><mrow><mo>≈</mo><mn>0</mn><mo>.</mo><mn>98</mn></mrow></math></span>) and low Prediction Interval Normalized Average Width (<span><math><mrow><mi>P</mi><mi>I</mi><mi>N</mi><mi>A</mi><mi>W</mi></mrow></math></span> <span><math><mrow><mo>≈</mo><mspace></mspace><mn>0</mn><mo>.</mo><mn>18</mn></mrow></math></span>), indicating its reliable prediction intervals. To enhance model interpretability, Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) methods were employed, revealing <span><math><mrow><mi>P</mi><msub><mrow><mi>M</mi></mrow><mrow><mn>10</mn></mrow></msub></mrow></math></span> concentration, relative humidity, air temperature, and wind speed as key predictors of <span><math><mrow><mi>T</mi><mi>S</mi><mi>P</mi></mrow></math></span> concentrations. The Diebold–Mariano statistical test further confirmed the model’s superior performance. This study contributes towards advancing <span><math><mrow><mi>T</mi><mi>S</mi><mi>P</mi></mrow></math></span> prediction by providing a robust, ac
{"title":"Explainable deep learning hybrid modeling framework for total suspended particles concentrations prediction","authors":"Sujan Ghimire , Ravinesh C. Deo , Ningbo Jiang , A.A. Masrur Ahmed , Salvin S. Prasad , David Casillas-Pérez , Sancho Salcedo-Sanz , Zaher Mundher Yaseen","doi":"10.1016/j.atmosenv.2025.121079","DOIUrl":"10.1016/j.atmosenv.2025.121079","url":null,"abstract":"<div><div>Total Suspended Particles (<span><math><mrow><mi>T</mi><mi>S</mi><mi>P</mi></mrow></math></span>) is an important indicator of air quality, yet traditional prediction models lack comprehensive consideration of spatio-temporal interactions of different meteorological and air pollution phenomena. To address these limitations, this study introduces an explainable (X) deep hybrid (H) network, integrating Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (BGRU), for hourly <span><math><mrow><mi>T</mi><mi>S</mi><mi>P</mi></mrow></math></span> concentration prediction. The model was trained and evaluated using meteorological and air quality data from Canon Hill, Australia. By combining CNN’s spatial feature extraction capabilities with BGRU’s temporal dependencies, the model effectively captures complex spatial–temporal patterns in the data. The X-H-CBGRU model outperforms fifteen competing benchmark models such as deep neural network, extreme learning machine, multilayer perceptron, support vector regression, random forest regression, light gradient boosting, gradient boosting regression, long short-term memory network, as well as their hybrid CNN counterparts in terms of the accuracy evidenced by a lower Root Mean Square Error (<span><math><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow></math></span> <span><math><mrow><mo>≈</mo><mspace></mspace><mn>6</mn><mo>.</mo><mn>302</mn><mspace></mspace><mi>μ</mi><msup><mrow><mi>g/m</mi></mrow><mrow><mn>3</mn></mrow></msup></mrow></math></span>) and higher Correlation Coefficient (<span><math><mi>r</mi></math></span> <span><math><mrow><mo>≈</mo><mspace></mspace><mn>0</mn><mo>.</mo><mn>91</mn></mrow></math></span>) compared to other models. Moreover, the model demonstrates strong probabilistic performance with a high Prediction Interval Coverage Probability (<span><math><mrow><mi>P</mi><mi>I</mi><mi>C</mi><mi>P</mi></mrow></math></span> <span><math><mrow><mo>≈</mo><mn>0</mn><mo>.</mo><mn>98</mn></mrow></math></span>) and low Prediction Interval Normalized Average Width (<span><math><mrow><mi>P</mi><mi>I</mi><mi>N</mi><mi>A</mi><mi>W</mi></mrow></math></span> <span><math><mrow><mo>≈</mo><mspace></mspace><mn>0</mn><mo>.</mo><mn>18</mn></mrow></math></span>), indicating its reliable prediction intervals. To enhance model interpretability, Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) methods were employed, revealing <span><math><mrow><mi>P</mi><msub><mrow><mi>M</mi></mrow><mrow><mn>10</mn></mrow></msub></mrow></math></span> concentration, relative humidity, air temperature, and wind speed as key predictors of <span><math><mrow><mi>T</mi><mi>S</mi><mi>P</mi></mrow></math></span> concentrations. The Diebold–Mariano statistical test further confirmed the model’s superior performance. This study contributes towards advancing <span><math><mrow><mi>T</mi><mi>S</mi><mi>P</mi></mrow></math></span> prediction by providing a robust, ac","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"347 ","pages":"Article 121079"},"PeriodicalIF":4.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-02-10DOI: 10.1016/j.atmosenv.2025.121097
Mae Sexauer Gustin , David A. Gay , Nicole Choma
The National Atmospheric Deposition Program established the Mercury (Hg) Litterfall Network in 2007 to assist with estimating changes in dry deposition of Hg. These measurements represent primarily gaseous elemental mercury (Hg0) taken up by foliage actively during the growing season through stomata. Hg deposition is driven by litterfall mass; thus concentrations are a better indicator of trends. Previous work assessed trends from 2007 to 2014 from 27 locations in the eastern U.S. and found that litterfall total Hg concentrations declined. Here, data from the same area representing 2017 to 2021, 2013 to 2021, and 2007 to 2021 were compiled. For the first two time periods no significant trends in litter concentrations were observed; however, values measured at locations impacted by local/regional sources had higher concentrations and showed increasing trends, but these were not significant. Using all sites for which data were available from 2017 to 2021, total Hg concentration in litterfall for 2017 to 2018 was significantly greater than 2020 to 2021. Using all data from 2007 to 2021 Hg concentrations in litter have declined, as have precipitation concentrations. In general, from 2013 to 2021 Mid-Atlantic, East Coast, and Mid-Western concentration in foliage declined due to controls on sources; while the Great Lakes Region and Southeast did not change. Methylmercury was measured in litterfall at all locations. MeHg concentrations generally declined from 2007 to 2021, but have not changed since 2017. However, concentrations for 2021 were higher than for 2020 for most sites. Methylmercury in litterfall has been demonstrated to bioaccumulate in terrestrial ecosystems raising concerns for songbirds.
{"title":"Assessment of recent mercury trends associated with the National Atmospheric Deposition Program Mercury Litterfall Network","authors":"Mae Sexauer Gustin , David A. Gay , Nicole Choma","doi":"10.1016/j.atmosenv.2025.121097","DOIUrl":"10.1016/j.atmosenv.2025.121097","url":null,"abstract":"<div><div>The National Atmospheric Deposition Program established the Mercury (Hg) Litterfall Network in 2007 to assist with estimating changes in dry deposition of Hg. These measurements represent primarily gaseous elemental mercury (Hg<sup>0</sup>) taken up by foliage actively during the growing season through stomata. Hg deposition is driven by litterfall mass; thus concentrations are a better indicator of trends. Previous work assessed trends from 2007 to 2014 from 27 locations in the eastern U.S. and found that litterfall total Hg concentrations declined. Here, data from the same area representing 2017 to 2021, 2013 to 2021, and 2007 to 2021 were compiled. For the first two time periods no significant trends in litter concentrations were observed; however, values measured at locations impacted by local/regional sources had higher concentrations and showed increasing trends, but these were not significant. Using all sites for which data were available from 2017 to 2021, total Hg concentration in litterfall for 2017 to 2018 was significantly greater than 2020 to 2021. Using all data from 2007 to 2021 Hg concentrations in litter have declined, as have precipitation concentrations. In general, from 2013 to 2021 Mid-Atlantic, East Coast, and Mid-Western concentration in foliage declined due to controls on sources; while the Great Lakes Region and Southeast did not change. Methylmercury was measured in litterfall at all locations. MeHg concentrations generally declined from 2007 to 2021, but have not changed since 2017. However, concentrations for 2021 were higher than for 2020 for most sites. Methylmercury in litterfall has been demonstrated to bioaccumulate in terrestrial ecosystems raising concerns for songbirds.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"347 ","pages":"Article 121097"},"PeriodicalIF":4.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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