Pub Date : 2024-11-13DOI: 10.1016/j.atmosenv.2024.120929
S. Stoulos , E. Ioannidou , P. Koseoglou , E. Vagena , A. Ioannidou
Wood combustion was the key heating source in Greece during the first years at the beginning of the financial crisis. Signals of 137Cs were detected in Thessaloniki during the winter of 2013–2014 on weekends and holidays when the residents were at home burning the biggest amount of wood all day. 137Cs signals were >6–21 μBq m−3 detected using high-volume air filters and γ-spectrometry. No signals have been detected since then, as gas has replaced oil for residential heating, reducing forest wood. Besides, signal <6 μBq m−3 is undetectable because this is the minimum detectable activity. 40K concentrations were also measured, revealing a constant value of 143 ± 16 μBq m−3. The Cs-to-K ratio in air was 0.04–0.14 compared to 0.05 ± 0.01 measured before and after. Higher levels were measured when the air temperature was the lowest, but no correlation was observed with wind or pressure. Simulations using the HYSLIT model were applied on the dates on which the ratio was the highest. The model confirms the experimental results observed. 137Cs signals detected and related to the Chernobyl-contaminated biomass used for central heating indicate that contaminated forest ecosystems remain a source of unwanted radioactivity in the environment.
{"title":"137Cs in outdoor air due to Chernobyl-contaminated wood combustion for residential heating in Thessaloniki, North Greece","authors":"S. Stoulos , E. Ioannidou , P. Koseoglou , E. Vagena , A. Ioannidou","doi":"10.1016/j.atmosenv.2024.120929","DOIUrl":"10.1016/j.atmosenv.2024.120929","url":null,"abstract":"<div><div>Wood combustion was the key heating source in Greece during the first years at the beginning of the financial crisis. Signals of <sup>137</sup>Cs were detected in Thessaloniki during the winter of 2013–2014 on weekends and holidays when the residents were at home burning the biggest amount of wood all day. <sup>137</sup>Cs signals were >6–21 μBq m<sup>−3</sup> detected using high-volume air filters and γ-spectrometry. No signals have been detected since then, as gas has replaced oil for residential heating, reducing forest wood. Besides, signal <6 μBq m<sup>−3</sup> is undetectable because this is the minimum detectable activity. <sup>40</sup>K concentrations were also measured, revealing a constant value of 143 ± 16 μBq m<sup>−3</sup>. The Cs-to-K ratio in air was 0.04–0.14 compared to 0.05 ± 0.01 measured before and after. Higher levels were measured when the air temperature was the lowest, but no correlation was observed with wind or pressure. Simulations using the HYSLIT model were applied on the dates on which the ratio was the highest. The model confirms the experimental results observed. <sup>137</sup>Cs signals detected and related to the Chernobyl-contaminated biomass used for central heating indicate that contaminated forest ecosystems remain a source of unwanted radioactivity in the environment.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"341 ","pages":"Article 120929"},"PeriodicalIF":4.2,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662800","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 : 2024-11-12DOI: 10.1016/j.atmosenv.2024.120888
Rosa D. García , África Barreto , Celia Rey , Eugenio Fraile-Nuez , Alba González-Vega , Sergio F. León-Luis , Antonio Alcantara , A. Fernando Almansa , Carmen Guirado-Fuentes , Pablo González-Sicilia , Victoria E. Cachorro , Frederic Bouchar
This study presents a comprehensive 5-year period assessment of aerosol optical depth (AOD) and Å ngströn Exponent (AE) data from a hand-held Calitoo sun photometer on board the Ángeles Alvariño research vessel. Observations spanned March 2018 to September 2023, focusing on key maritime regions such as the Canary Islands, coasts of North Africa, the Mediterranean, Portugal, the Cantabrian, and the Bay of Biscay. The Calitoo device measures solar irradiance at three wavelengths (465, 540, and 619 nm). Uncertainty analysis for Calitoo AOD retrievals was performed using the Monte Carlo method, yielding an expanded uncertainty (U) ranging between 0.008 and 0.050 with a mean and standard deviation of 0.032 ± 0.008 for the three wavelengths. Our results also highlight the remarkable calibration stability of the Calitoo ( 2.6%) over this 5-year period. Calitoo AOD values were assessed using reference AOD data from Santa Cruz de Tenerife (the Canary Islands), El Arenosillo (Huelva), and Palma de Mallorca (the Balearic Islands) AERONET (Aerosol Robotic Network) stations. The comparison revealed a good agreement with correlation coefficients ranging from 0.727 to 0.917 and mean bias ranging from -0.030 to -0.001. Additionally, the Calitoo AOD data were compared with MODIS (Moderate Resolution Imaging Spectroradiometer) and CAMS-ECMWF (Copernicus Atmosphere Monitoring Service-European Centre for Medium-Range Weather Forecasts) aerosol products obtaining that Calitoo AOD values were generally lower, showing negative mean bias of -0.063 and -0.024, respectively.
The aerosol characterizations using AE vs. AOD plots in the three maritime study regions using 5-years of non-routine Calitoo data are similar to the corresponding aerosol characterizations performed with simultaneous AERONET-Cimel data.
These findings underscore Calitoo’s reliability for aerosol studies in regions where AERONET instruments or other aerosol networks are unavailable. Likewise, given the low cost of Calitoo photometers, they could be deployed onboard a large number of merchant and passenger ships or in other remote or under-monitored areas, providing near real-time AOD/AE data to enhance our understanding of aerosols processes or for model or satellite assimilation/validation.
{"title":"Aerosol retrievals derived from a low-cost Calitoo sun-photometer taken on board a research vessel","authors":"Rosa D. García , África Barreto , Celia Rey , Eugenio Fraile-Nuez , Alba González-Vega , Sergio F. León-Luis , Antonio Alcantara , A. Fernando Almansa , Carmen Guirado-Fuentes , Pablo González-Sicilia , Victoria E. Cachorro , Frederic Bouchar","doi":"10.1016/j.atmosenv.2024.120888","DOIUrl":"10.1016/j.atmosenv.2024.120888","url":null,"abstract":"<div><div>This study presents a comprehensive 5-year period assessment of aerosol optical depth (AOD) and Å ngströn Exponent (AE) data from a hand-held Calitoo sun photometer on board the <em>Ángeles Alvariño</em> research vessel. Observations spanned March 2018 to September 2023, focusing on key maritime regions such as the Canary Islands, coasts of North Africa, the Mediterranean, Portugal, the Cantabrian, and the Bay of Biscay. The Calitoo device measures solar irradiance at three wavelengths (465, 540, and 619 nm). Uncertainty analysis for Calitoo AOD retrievals was performed using the Monte Carlo method, yielding an expanded uncertainty (U<span><math><msub><mrow><msub><mrow></mrow><mrow><mi>A</mi></mrow></msub><msub><mrow></mrow><mrow><mi>O</mi></mrow></msub></mrow><mrow><mi>D</mi></mrow></msub></math></span>) ranging between 0.008 and 0.050 with a mean and standard deviation of 0.032 ± 0.008 for the three wavelengths. Our results also highlight the remarkable calibration stability of the Calitoo (<span><math><mo><</mo></math></span> 2.6%) over this 5-year period. Calitoo AOD values were assessed using reference AOD data from Santa Cruz de Tenerife (the Canary Islands), El Arenosillo (Huelva), and Palma de Mallorca (the Balearic Islands) AERONET (Aerosol Robotic Network) stations. The comparison revealed a good agreement with correlation coefficients ranging from 0.727 to 0.917 and mean bias ranging from -0.030 to -0.001. Additionally, the Calitoo AOD data were compared with MODIS (Moderate Resolution Imaging Spectroradiometer) and CAMS-ECMWF (Copernicus Atmosphere Monitoring Service-European Centre for Medium-Range Weather Forecasts) aerosol products obtaining that Calitoo AOD values were generally lower, showing negative mean bias of -0.063 and -0.024, respectively.</div><div>The aerosol characterizations using AE vs. AOD plots in the three maritime study regions using 5-years of non-routine Calitoo data are similar to the corresponding aerosol characterizations performed with simultaneous AERONET-Cimel data.</div><div>These findings underscore Calitoo’s reliability for aerosol studies in regions where AERONET instruments or other aerosol networks are unavailable. Likewise, given the low cost of Calitoo photometers, they could be deployed onboard a large number of merchant and passenger ships or in other remote or under-monitored areas, providing near real-time AOD/AE data to enhance our understanding of aerosols processes or for model or satellite assimilation/validation.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"341 ","pages":"Article 120888"},"PeriodicalIF":4.2,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663223","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}
Pub Date : 2024-11-12DOI: 10.1016/j.atmosenv.2024.120928
Chao-Lu Xie , Hao Yang , Bo Long
Peracetic acid (PAA, CH3C(O)OOH) is one of the most abundant organic peroxyacid in the atmosphere. PAA is often assumed to be removed by hydroxyl radical in the gas phase of troposphere, but its reaction rate is quite low. Here, we investigated the new reaction between PAA and carbonyl oxide (CH2OO) by using quantum chemical methods, reaction kinetics in combination with atmospheric modeling. We first performed W3X-L calculations close to CCSDT(Q)/CBS accuracy with the reaction systems containing eight carbon and oxygen atoms. The present findings show that the post-CCSD(T) contribution is about 0.50 kcal/mol, which is important for obtaining quantitative relative enthalpy of activation at 0 K. We find that the recrossing effect reduces the rate constant by an order of magnitude for the mechanism of the hydrogen-shift coupled carbon-oxygen addition at low temperature. The calculated results reveal that the anharmonicity increases the rate constants of CH2OO + CH3C(O)OOH by a factor of 6.27 at 298 K. The present findings uncover that the PAA + CH2OO reaction is a dominant pathway for PAA sinks in the gas phase of troposphere at the lower nighttime OH concentrations at 298 K, since the rate of PAA + CH2OO is even an order of magnitude higher than the rate of the PAA + OH reaction. Moreover, atmospheric modeling simulations unveil that CH2OO can make certain contribution to the reduction of PAA in the Amazon.
{"title":"Reaction between peracetic acid and carbonyl oxide: Quantitative kinetics and insight into implications in the atmosphere","authors":"Chao-Lu Xie , Hao Yang , Bo Long","doi":"10.1016/j.atmosenv.2024.120928","DOIUrl":"10.1016/j.atmosenv.2024.120928","url":null,"abstract":"<div><div>Peracetic acid (PAA, CH<sub>3</sub>C(O)OOH) is one of the most abundant organic peroxyacid in the atmosphere. PAA is often assumed to be removed by hydroxyl radical in the gas phase of troposphere, but its reaction rate is quite low. Here, we investigated the new reaction between PAA and carbonyl oxide (CH<sub>2</sub>OO) by using quantum chemical methods, reaction kinetics in combination with atmospheric modeling. We first performed W3X-L calculations close to CCSDT(Q)/CBS accuracy with the reaction systems containing eight carbon and oxygen atoms. The present findings show that the post-CCSD(T) contribution is about 0.50 kcal/mol, which is important for obtaining quantitative relative enthalpy of activation at 0 K. We find that the recrossing effect reduces the rate constant by an order of magnitude for the mechanism of the hydrogen-shift coupled carbon-oxygen addition at low temperature. The calculated results reveal that the anharmonicity increases the rate constants of CH<sub>2</sub>OO + CH<sub>3</sub>C(O)OOH by a factor of 6.27 at 298 K. The present findings uncover that the PAA + CH<sub>2</sub>OO reaction is a dominant pathway for PAA sinks in the gas phase of troposphere at the lower nighttime OH concentrations at 298 K, since the rate of PAA + CH<sub>2</sub>OO is even an order of magnitude higher than the rate of the PAA + OH reaction. Moreover, atmospheric modeling simulations unveil that CH<sub>2</sub>OO can make certain contribution to the reduction of PAA in the Amazon.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"341 ","pages":"Article 120928"},"PeriodicalIF":4.2,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662799","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 : 2024-11-12DOI: 10.1016/j.atmosenv.2024.120927
Ping Du , Xinghui Liu , Xiaoling Nie , Tao Li , Haoran He , Jianing Zhang , Xinfeng Wang , Yan Wang , Jianmin Chen
Online detection of cloud water chemistry is a pressing issue in atmospheric outfield observation, with online detection modules representing a significant development direction for cloud water observation. Addressing the common problem of time-delayed errors in manual detection, particularly in the context of cloud water acidity, has remained challenging, with limited understanding and effective solutions available. We developed an Online Cloud Fog Monitor (OCFM) featuring automatic pH and electrical conductivity (EC) detection capabilities, and conducted comprehensive laboratory and field tests. The OCFM utilizes a peristaltic pump, water pipe, and diversion chamber to direct cloud samples to distinct detection chambers, enabling real-time analysis. The diversion chamber is equipped with dual liquid level sensors to segregate and preserve samples once the volume exceeds a predetermined threshold. Calibration results indicate that the instrument's background metal elements do not affect cloud water analysis, and detection occurs within the designed response time. Field tests demonstrate that the OCFM can collect over 50 ml of cloud water, with a response accuracy exceeding 63.6%, though influenced by meteorological conditions. The time-delay error for pH was notably larger than for EC. Comparative analysis with the Caltech Active Strand Cloudwater Collector (CASCC) revealed that the OCFM's sampling process does not introduce errors, and the online detection accuracy of pH and EC is comparable to manual methods. Additionally, water-soluble ions in samples collected by the OCFM showed no significant differences compared to those collected by CASCC. Overall, the OCFM effectively replaces manual testing, mitigating time-delay errors in chemical property testing. The introduction of this cloud water detector promises to significantly reduce labor costs and economic consumption associated with cloud water observation, thereby facilitating long-term, multi-site observation of cloud water chemistry.
{"title":"Development of an online cloud fog monitor: Design, laboratory, and field deployment at an unoccupied coastal site in Eastern China","authors":"Ping Du , Xinghui Liu , Xiaoling Nie , Tao Li , Haoran He , Jianing Zhang , Xinfeng Wang , Yan Wang , Jianmin Chen","doi":"10.1016/j.atmosenv.2024.120927","DOIUrl":"10.1016/j.atmosenv.2024.120927","url":null,"abstract":"<div><div>Online detection of cloud water chemistry is a pressing issue in atmospheric outfield observation, with online detection modules representing a significant development direction for cloud water observation. Addressing the common problem of time-delayed errors in manual detection, particularly in the context of cloud water acidity, has remained challenging, with limited understanding and effective solutions available. We developed an Online Cloud Fog Monitor (OCFM) featuring automatic pH and electrical conductivity (EC) detection capabilities, and conducted comprehensive laboratory and field tests. The OCFM utilizes a peristaltic pump, water pipe, and diversion chamber to direct cloud samples to distinct detection chambers, enabling real-time analysis. The diversion chamber is equipped with dual liquid level sensors to segregate and preserve samples once the volume exceeds a predetermined threshold. Calibration results indicate that the instrument's background metal elements do not affect cloud water analysis, and detection occurs within the designed response time. Field tests demonstrate that the OCFM can collect over 50 ml of cloud water, with a response accuracy exceeding 63.6%, though influenced by meteorological conditions. The time-delay error for pH was notably larger than for EC. Comparative analysis with the Caltech Active Strand Cloudwater Collector (CASCC) revealed that the OCFM's sampling process does not introduce errors, and the online detection accuracy of pH and EC is comparable to manual methods. Additionally, water-soluble ions in samples collected by the OCFM showed no significant differences compared to those collected by CASCC. Overall, the OCFM effectively replaces manual testing, mitigating time-delay errors in chemical property testing. The introduction of this cloud water detector promises to significantly reduce labor costs and economic consumption associated with cloud water observation, thereby facilitating long-term, multi-site observation of cloud water chemistry.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"341 ","pages":"Article 120927"},"PeriodicalIF":4.2,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663224","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 : 2024-11-09DOI: 10.1016/j.atmosenv.2024.120925
Jinxing Liu , Hui Yu , Yaqing Zhang , Junjun Chen , Shiyuan Feng , Rui Guo , Feng Wang , Bo Xu , Guoliang Shi , Yinchang Feng
Ambient fine particulate matter (PM2.5) is affected by many factors, such as source emissions, meteorological conditions, and chemical reactions. Revealing the effects of these factors on PM2.5 is essential to understand the causes of PM2.5 pollution. The machine learning method can establish the non-linear relationship between influencing factors and PM2.5. Here, a coupling model of machine learning and interpretation method was constructed to comprehensively quantify the importance of influencing factors to PM2.5 from multiple dimensions and analyze the sensitivity of influencing factors. Among the primary indicators of influencing factors, the importance of emission, meteorological conditions, and atmospheric chemical reaction to PM2.5 is 49%, 29%, and 22%, respectively. In the secondary indicator of influencing factors, the transmission effect is the most important meteorological condition, with an important degree of 15%. The liquid phase reaction is the most important atmospheric chemical reaction, with an importance of 7%. Among the three levels of influencing factors, emission, transport distance, liquid phase reaction coefficient, aerosol acidity, and accumulation promotion coefficient are important factors. The sensitivity of a single factor is complex and changeable, and the interaction between emission and other important factors is the strongest among the two factors. Of which the interaction between transmission distance and emission during the observation period is the strongest, and the interaction coefficient is 1.82. Our study focuses on the effect of influencing factors on PM2.5, provides a basis for the analysis of the causes of PM2.5 pollution, and technical support for the treatment of PM2.5.
{"title":"The coupling model of random forest and interpretable method quantifies the response relationship between PM2.5 and influencing factors","authors":"Jinxing Liu , Hui Yu , Yaqing Zhang , Junjun Chen , Shiyuan Feng , Rui Guo , Feng Wang , Bo Xu , Guoliang Shi , Yinchang Feng","doi":"10.1016/j.atmosenv.2024.120925","DOIUrl":"10.1016/j.atmosenv.2024.120925","url":null,"abstract":"<div><div>Ambient fine particulate matter (PM<sub>2.5</sub>) is affected by many factors, such as source emissions, meteorological conditions, and chemical reactions. Revealing the effects of these factors on PM<sub>2.5</sub> is essential to understand the causes of PM<sub>2.5</sub> pollution. The machine learning method can establish the non-linear relationship between influencing factors and PM<sub>2.5</sub>. Here, a coupling model of machine learning and interpretation method was constructed to comprehensively quantify the importance of influencing factors to PM<sub>2.5</sub> from multiple dimensions and analyze the sensitivity of influencing factors. Among the primary indicators of influencing factors, the importance of emission, meteorological conditions, and atmospheric chemical reaction to PM<sub>2.5</sub> is 49%, 29%, and 22%, respectively. In the secondary indicator of influencing factors, the transmission effect is the most important meteorological condition, with an important degree of 15%. The liquid phase reaction is the most important atmospheric chemical reaction, with an importance of 7%. Among the three levels of influencing factors, emission, transport distance, liquid phase reaction coefficient, aerosol acidity, and accumulation promotion coefficient are important factors. The sensitivity of a single factor is complex and changeable, and the interaction between emission and other important factors is the strongest among the two factors. Of which the interaction between transmission distance and emission during the observation period is the strongest, and the interaction coefficient is 1.82. Our study focuses on the effect of influencing factors on PM<sub>2.5</sub>, provides a basis for the analysis of the causes of PM<sub>2.5</sub> pollution, and technical support for the treatment of PM<sub>2.5</sub>.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"341 ","pages":"Article 120925"},"PeriodicalIF":4.2,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663227","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 : 2024-11-07DOI: 10.1016/j.atmosenv.2024.120922
Changlin Zhan , Chong Wei , Ziguo Liu , Hongxia Liu , Xuefen Yang , Jingru Zheng , Shan Liu , Jihong Quan , Yong Zhang , Qiyuan Wang , Nan Li , Junji Cao
This study investigates the concentrations, chemical compositions, and sources of PM2.5 in Huangshi, China. Daily average PM2.5 levels ranged from 8.43 to 193.08 μg m−3, with an annual mean of 54.13 μg m−3, exceeding China's annual secondary standard of 35 μg m−3. Seasonal mean concentrations peaked in winter and were lowest in summer. Organic carbon (OC) and elemental carbon (EC) had annual means of 4.89 μg m−3 and 0.94 μg m−3, respectively. Water-soluble inorganic ions (WSIIs) accounted for 52.17% of PM2.5, with NO3−, SO42−, and NH4+ being the major components. The NO3−/SO42− ratio averaged 1.65, indicating a transition from coal combustion to vehicle emissions as the primary pollution source. Chemical mass reconstruction revealed that NH4NO3, (NH4)2SO4, and organic matter (OM) accounted for 65.3% of PM2.5 mass. Seasonal variations in light extinction (bext) highlighted the impact of secondary inorganic salts on visibility, with an annual average bext of 346.30 ± 246.98 Mm−1. Airmass clusters and potential source region analysis suggested PM2.5 and its components were primarily originated from local and nearby regions. These findings underscore the effectiveness of local pollution control measures, changing pollution sources, and the necessity for targeted emission controls to improve air quality and visibility in urban areas.
{"title":"Seasonal trends and light extinction effects of PM2.5 chemical composition from 2021 to 2022 in a typical industrial city of central China","authors":"Changlin Zhan , Chong Wei , Ziguo Liu , Hongxia Liu , Xuefen Yang , Jingru Zheng , Shan Liu , Jihong Quan , Yong Zhang , Qiyuan Wang , Nan Li , Junji Cao","doi":"10.1016/j.atmosenv.2024.120922","DOIUrl":"10.1016/j.atmosenv.2024.120922","url":null,"abstract":"<div><div>This study investigates the concentrations, chemical compositions, and sources of PM<sub>2.5</sub> in Huangshi, China. Daily average PM<sub>2.5</sub> levels ranged from 8.43 to 193.08 μg m<sup>−3</sup>, with an annual mean of 54.13 μg m<sup>−3</sup>, exceeding China's annual secondary standard of 35 μg m<sup>−3</sup>. Seasonal mean concentrations peaked in winter and were lowest in summer. Organic carbon (OC) and elemental carbon (EC) had annual means of 4.89 μg m<sup>−3</sup> and 0.94 μg m<sup>−3</sup>, respectively. Water-soluble inorganic ions (WSIIs) accounted for 52.17% of PM<sub>2.5</sub>, with NO<sub>3</sub><sup>−</sup>, SO<sub>4</sub><sup>2−</sup>, and NH<sub>4</sub><sup>+</sup> being the major components. The NO<sub>3</sub><sup>−</sup>/SO<sub>4</sub><sup>2−</sup> ratio averaged 1.65, indicating a transition from coal combustion to vehicle emissions as the primary pollution source. Chemical mass reconstruction revealed that NH<sub>4</sub>NO<sub>3</sub>, (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub>, and organic matter (OM) accounted for 65.3% of PM<sub>2.5</sub> mass. Seasonal variations in light extinction (<em>b</em><sub>ext</sub>) highlighted the impact of secondary inorganic salts on visibility, with an annual average <em>b</em><sub>ext</sub> of 346.30 ± 246.98 Mm<sup>−1</sup>. Airmass clusters and potential source region analysis suggested PM<sub>2.5</sub> and its components were primarily originated from local and nearby regions. These findings underscore the effectiveness of local pollution control measures, changing pollution sources, and the necessity for targeted emission controls to improve air quality and visibility in urban areas.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"341 ","pages":"Article 120922"},"PeriodicalIF":4.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663226","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 : 2024-11-06DOI: 10.1016/j.atmosenv.2024.120917
Paloma Cariñanos , Soledad Ruiz-Peñuela , Andrea Casans , Alberto Cazorla , Fernando Rejano , Alejandro Ontiveros , Pablo Ortiz-Amezcua , Juan Luis Guerrero-Rascado , Francisco José Olmo , Lucas Alados-Arboledas , Gloria Titos
{"title":"Assessment of potential sources of airborne pollen in a high-mountain mediterranean natural environment","authors":"Paloma Cariñanos , Soledad Ruiz-Peñuela , Andrea Casans , Alberto Cazorla , Fernando Rejano , Alejandro Ontiveros , Pablo Ortiz-Amezcua , Juan Luis Guerrero-Rascado , Francisco José Olmo , Lucas Alados-Arboledas , Gloria Titos","doi":"10.1016/j.atmosenv.2024.120917","DOIUrl":"10.1016/j.atmosenv.2024.120917","url":null,"abstract":"","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"340 ","pages":"Article 120917"},"PeriodicalIF":4.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662924","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}
Pub Date : 2024-11-05DOI: 10.1016/j.atmosenv.2024.120921
Shibao Wang , Yanxu Zhang
Machine learning, especially deep learning, can outperform traditional atmospheric models in air quality assessment, offering enhanced efficiency and accuracy without relying on detailed emission inventories and atmospheric chemical mechanisms. Despite their predictive power, deep learning models often grapple with the perception of being “black boxes” due to their intricate architectures. Here, we develop an attention-based convolutional neural network (CNN-attention) model that incorporates observational data, the parallelized large-eddy-simulation model (PALM), and urban morphology data for high-resolution spatial estimation of urban air quality. Our findings indicate that the CNN-attention model outperforms traditional CNN with higher accuracy and efficiency, achieving R2 = 0.987 and root mean square error (RMSE) = 0.15 mg/m3, while significantly reducing training time and memory usage. Compared to traditional machine learning models, the CNN exhibits higher R2 values and lower RMSE, showcasing its adeptness at capturing complex nonlinear patterns. The inclusion of attention layer further improves the model's performance by dynamically assigning attention scores to key features, enabling the model to focus on areas of critical emissions and distinctive urban features such as highways, arterial roads, intersections, and dense building clusters. This approach also reveals fluid dynamical principles, highlighting the significant disparities in pollutant concentration across roadways caused by atmospheric turbulence, and the distinct plume formations influenced by land use and topography. When applied to various urban settings, the CNN-attention model exhibits superior generalizability and transferability. This study provides valuable scientific insights and technical support for urban planning, air quality management, and exposure risk evaluation.
{"title":"An attention-based CNN model integrating observational and simulation data for high-resolution spatial estimation of urban air quality","authors":"Shibao Wang , Yanxu Zhang","doi":"10.1016/j.atmosenv.2024.120921","DOIUrl":"10.1016/j.atmosenv.2024.120921","url":null,"abstract":"<div><div>Machine learning, especially deep learning, can outperform traditional atmospheric models in air quality assessment, offering enhanced efficiency and accuracy without relying on detailed emission inventories and atmospheric chemical mechanisms. Despite their predictive power, deep learning models often grapple with the perception of being “black boxes” due to their intricate architectures. Here, we develop an attention-based convolutional neural network (CNN-attention) model that incorporates observational data, the parallelized large-eddy-simulation model (PALM), and urban morphology data for high-resolution spatial estimation of urban air quality. Our findings indicate that the CNN-attention model outperforms traditional CNN with higher accuracy and efficiency, achieving R<sup>2</sup> = 0.987 and root mean square error (RMSE) = 0.15 mg/m<sup>3</sup>, while significantly reducing training time and memory usage. Compared to traditional machine learning models, the CNN exhibits higher R<sup>2</sup> values and lower RMSE, showcasing its adeptness at capturing complex nonlinear patterns. The inclusion of attention layer further improves the model's performance by dynamically assigning attention scores to key features, enabling the model to focus on areas of critical emissions and distinctive urban features such as highways, arterial roads, intersections, and dense building clusters. This approach also reveals fluid dynamical principles, highlighting the significant disparities in pollutant concentration across roadways caused by atmospheric turbulence, and the distinct plume formations influenced by land use and topography. When applied to various urban settings, the CNN-attention model exhibits superior generalizability and transferability. This study provides valuable scientific insights and technical support for urban planning, air quality management, and exposure risk evaluation.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"340 ","pages":"Article 120921"},"PeriodicalIF":4.2,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662935","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 : 2024-11-02DOI: 10.1016/j.atmosenv.2024.120910
Sasha D. Hafner , Johanna Pedersen , Roland Fuß , Jesper Nørlem Kamp , Frederik Rask Dalby , Barbara Amon , Andreas Pacholski , Anders Peter S. Adamsen , Sven Gjedde Sommer
Ammonia volatilization from animal slurry applied to agricultural fields reduces nitrogen use efficiency in agriculture and pollutes the environment. This work presents new versions of a model and database focused on this route of N loss. The public ALFAM2 database (https://github.com/AU-BCE-EE/ALFAM2-data) was expanded with ammonia emission and ancillary measurements for >700 additional field plots. The ALFAM2 model (https://github.com/AU-BCE-EE/ALFAM2, https://zenodo.org/records/13312251) was extended with the addition of an ammonia sink for more plausible predictions over extended durations and to better reflect the expected reduction in emission rate several days after slurry application. A new parameter set was developed for the model taking into account the newly available measurement data. Model efficiency improved to 0.67 for the parameter estimation subset (0.52 for cross-validation) and mean absolute error was around 10% of applied total ammoniacal nitrogen. As in earlier versions, predicted emission is sensitive to application method, slurry dry matter and pH, air temperature, and wind speed. A collection of parameter sets for estimating uncertainty in average predictions was developed using a bootstrap approach. Predicted uncertainty is not trivial, and is high for some variable combinations, highlighting the challenge of making predictions based on available measurement data. Still, this work has resulted in more accurate, comprehensive, transparent, and flexible tools for emission inventory and related work on ammonia loss from field-applied slurry.
{"title":"Improved tools for estimation of ammonia emission from field-applied animal slurry: Refinement of the ALFAM2 model and database","authors":"Sasha D. Hafner , Johanna Pedersen , Roland Fuß , Jesper Nørlem Kamp , Frederik Rask Dalby , Barbara Amon , Andreas Pacholski , Anders Peter S. Adamsen , Sven Gjedde Sommer","doi":"10.1016/j.atmosenv.2024.120910","DOIUrl":"10.1016/j.atmosenv.2024.120910","url":null,"abstract":"<div><div>Ammonia volatilization from animal slurry applied to agricultural fields reduces nitrogen use efficiency in agriculture and pollutes the environment. This work presents new versions of a model and database focused on this route of N loss. The public ALFAM2 database (<span><span>https://github.com/AU-BCE-EE/ALFAM2-data</span><svg><path></path></svg></span>) was expanded with ammonia emission and ancillary measurements for >700 additional field plots. The ALFAM2 model (<span><span>https://github.com/AU-BCE-EE/ALFAM2</span><svg><path></path></svg></span>, <span><span>https://zenodo.org/records/13312251</span><svg><path></path></svg></span>) was extended with the addition of an ammonia sink for more plausible predictions over extended durations and to better reflect the expected reduction in emission rate several days after slurry application. A new parameter set was developed for the model taking into account the newly available measurement data. Model efficiency improved to 0.67 for the parameter estimation subset (0.52 for cross-validation) and mean absolute error was around 10% of applied total ammoniacal nitrogen. As in earlier versions, predicted emission is sensitive to application method, slurry dry matter and pH, air temperature, and wind speed. A collection of parameter sets for estimating uncertainty in average predictions was developed using a bootstrap approach. Predicted uncertainty is not trivial, and is high for some variable combinations, highlighting the challenge of making predictions based on available measurement data. Still, this work has resulted in more accurate, comprehensive, transparent, and flexible tools for emission inventory and related work on ammonia loss from field-applied slurry.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"340 ","pages":"Article 120910"},"PeriodicalIF":4.2,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662940","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}
Pub Date : 2024-11-02DOI: 10.1016/j.atmosenv.2024.120920
Linchen He , Zhiheng Hao , Charles J. Weschler , Feng Li , Yinping Zhang , Junfeng Jim Zhang
Low-level outdoor ozone (O3) exposure has been associated with adverse respiratory health effects, whereas substantially higher O3 concentrations have been required to exert measurable effects in controlled studies. This discrepancy remains poorly understood. After entering indoors, a substantial portion of O3 reacts with indoor chemicals to generate ozone reaction products that are potentially more toxic than O3 itself. We hypothesize that ozone reaction product exposures contribute to the adverse respiratory effects associated with low-level outdoor O3 exposure. In a panel study of 70 healthy adults, each was measured four times during a low-ozone season (maximum 8-h average: 29 ± 13 ppb). We found that higher average outdoor O3 concentrations, irrespective of whether participants were outdoors or indoors, were significantly associated with worsened spirometric lung function (i.e., FVC, FEV1, FEF25-75) and airway mechanics (i.e., R5, R20) indicators. Per interquartile range (IQR) increase in average outdoor O3 exposure when participants were indoors with windows closed (exposure proxy for ozone reaction products + indoor O3) was significantly associated with worsening of multiple respiratory function indicators including FVC, FEV1, FEF25-75, Z5, R5, and R20 by 0.56–3.08%. In contrast, per IQR increase in average outdoor O3 exposure when participants were outdoors or indoors with windows open (exposure proxy for O3 without ozone reaction products) was only significantly and adversely associated with worsening of one respiratory function indicator X5 by 1.4%. These findings support our hypothesis and suggest further evaluation of indoor ozone reaction products' contribution to adverse health effects induced by outdoor O3 exposure.
{"title":"Indoor ozone reaction products: Contributors to the respiratory health effects associated with low-level outdoor ozone","authors":"Linchen He , Zhiheng Hao , Charles J. Weschler , Feng Li , Yinping Zhang , Junfeng Jim Zhang","doi":"10.1016/j.atmosenv.2024.120920","DOIUrl":"10.1016/j.atmosenv.2024.120920","url":null,"abstract":"<div><div>Low-level outdoor ozone (O<sub>3</sub>) exposure has been associated with adverse respiratory health effects, whereas substantially higher O<sub>3</sub> concentrations have been required to exert measurable effects in controlled studies. This discrepancy remains poorly understood. After entering indoors, a substantial portion of O<sub>3</sub> reacts with indoor chemicals to generate ozone reaction products that are potentially more toxic than O<sub>3</sub> itself. We hypothesize that ozone reaction product exposures contribute to the adverse respiratory effects associated with low-level outdoor O<sub>3</sub> exposure. In a panel study of 70 healthy adults, each was measured four times during a low-ozone season (maximum 8-h average: 29 ± 13 ppb). We found that higher average outdoor O<sub>3</sub> concentrations, irrespective of whether participants were outdoors or indoors, were significantly associated with worsened spirometric lung function (i.e., FVC, FEV<sub>1</sub>, FEF<sub>25-75</sub>) and airway mechanics (i.e., R<sub>5</sub>, R<sub>20</sub>) indicators. Per interquartile range (IQR) increase in average outdoor O<sub>3</sub> exposure when participants were indoors with windows closed (exposure proxy for ozone reaction products + indoor O<sub>3</sub>) was significantly associated with worsening of multiple respiratory function indicators including FVC, FEV<sub>1</sub>, FEF<sub>25-75</sub>, Z<sub>5</sub>, R<sub>5</sub>, and R<sub>20</sub> by 0.56–3.08%. In contrast, per IQR increase in average outdoor O<sub>3</sub> exposure when participants were outdoors or indoors with windows open (exposure proxy for O<sub>3</sub> without ozone reaction products) was only significantly and adversely associated with worsening of one respiratory function indicator X<sub>5</sub> by 1.4%. These findings support our hypothesis and suggest further evaluation of indoor ozone reaction products' contribution to adverse health effects induced by outdoor O<sub>3</sub> exposure.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"340 ","pages":"Article 120920"},"PeriodicalIF":4.2,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578192","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}