In Asia, anthropogenic emissions have increased substantially over the last decade from various sectors, including power generation (PG), industries, road transportation (RT), and residential. This study analyzed different regional (REAS, MIX-Asia) and global (EDGAR) emission inventory (EI) datasets to provide insight into ASEAN's comprehensive emission status (emission trend, sectoral and country-specific emissions, changes in spatial distribution) during 2000–2015. The study observed a considerable increase in SO 2 , NO x , CO, CO 2 , and particulate matter (PM) emissions in ASEAN during this period. Results analyzed from the EDGAR EI dataset (2015) show that among the pollutants, SO 2 , CO 2 , and N 2 O were substantially contributed by the PG sector (43.4–56%), while CO, NO x , NMVOC, and CH 4 were from the RT sector (35.6–61.5%), and PM and NH 3 emissions were from the residential sector (50–80.6%). Similar contributions were also observed in 2000 and 2010. It is apparent that these sectors contributed noticeably to the total Asian emission (i.e., 14–34% in 2010, based on the MIX-Asian dataset). We have observed increasing annual emission trends for most pollutants in ASEAN countries, with more significant emission growth in Vietnam (e.g., SO 2 and NO x emissions increased by 232% and 145%, respectively). Considerable changes in spatial emission distributions over the ASEAN between that period were also observed caused by the shifting of sparse development into concentrated urban expansion surrounding large metropolitan clusters. The information from this study will be vital for the ASEAN governments to review and update their approved/planned regulations on emission control with prioritizing the sectors aimed at air quality management and environmental sustainability.
{"title":"Review of Decadal Changes in ASEAN Emissions Based on Regional and Global Emission Inventory Datasets","authors":"S. Roy, Y. Lam, S. S. Chopra, M. Hoque","doi":"10.4209/aaqr.220103","DOIUrl":"https://doi.org/10.4209/aaqr.220103","url":null,"abstract":"In Asia, anthropogenic emissions have increased substantially over the last decade from various sectors, including power generation (PG), industries, road transportation (RT), and residential. This study analyzed different regional (REAS, MIX-Asia) and global (EDGAR) emission inventory (EI) datasets to provide insight into ASEAN's comprehensive emission status (emission trend, sectoral and country-specific emissions, changes in spatial distribution) during 2000–2015. The study observed a considerable increase in SO 2 , NO x , CO, CO 2 , and particulate matter (PM) emissions in ASEAN during this period. Results analyzed from the EDGAR EI dataset (2015) show that among the pollutants, SO 2 , CO 2 , and N 2 O were substantially contributed by the PG sector (43.4–56%), while CO, NO x , NMVOC, and CH 4 were from the RT sector (35.6–61.5%), and PM and NH 3 emissions were from the residential sector (50–80.6%). Similar contributions were also observed in 2000 and 2010. It is apparent that these sectors contributed noticeably to the total Asian emission (i.e., 14–34% in 2010, based on the MIX-Asian dataset). We have observed increasing annual emission trends for most pollutants in ASEAN countries, with more significant emission growth in Vietnam (e.g., SO 2 and NO x emissions increased by 232% and 145%, respectively). Considerable changes in spatial emission distributions over the ASEAN between that period were also observed caused by the shifting of sparse development into concentrated urban expansion surrounding large metropolitan clusters. The information from this study will be vital for the ASEAN governments to review and update their approved/planned regulations on emission control with prioritizing the sectors aimed at air quality management and environmental sustainability.","PeriodicalId":7402,"journal":{"name":"Aerosol and Air Quality Research","volume":"1 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70292265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
Jarl Tynan Collado, J. G. Abalos, Imee de los Reyes, M. Cruz, G. Leung, Katrina Abenojar, Carlos Rosauro Manalo, Bernell Go, Christine L. Chan, Charlotte Kendra Gotangco Gonzales, J. Simpas, E. Porio, J. Wong, S. Lung, M. Cambaliza
Drivers of open-air public utility jeepneys (PUJs) in the Philippines are regularly exposed to severe levels of fine particulate pollution (PM 2.5 ), making them the appropriate sub-population for investigating the health impacts of PM 2.5 on populations chronically exposed to these kinds of unique sources. Real-time PM 2.5 exposures of PUJ drivers for a high-traffic route in Metro Manila, Philippines were assessed using Academia Sinica-LUNG (AS_LUNG) portable sensing devices. From all 15-second measurements obtained, the mean concentration of PM 2.5 is 36.4 µ g m –3 , seven times greater than the mean annual guideline value (5.0 µ g m –3 ) set by the World Health Organization (WHO). Elevated levels of PM 2.5 were observed at key transportation microenvironments (TMEs) such as a transport terminal and near a shopping mall. The occurrence of hotspots along the route is mainly attributed to traffic-promoting factors like stoplights and traffic rush hours. Multiple linear regression (MLR) analysis revealed that the area by the shopping mall had the highest contribution ( β = 52 µ g m –3 ) to PUJ driver exposure. To the best of our knowledge, this study is the first in the country to perform a detailed characterization of the exposure of a high-risk occupational group to PM 2.5 . These results reveal information that is normally undetected by fixed site monitoring (FSM), underscoring the importance of mobile measurements as a complement to FSM in assessing the exposure of urban populations to air pollution more extensively. Furthermore, this study demonstrates the heavy influence of traffic-promoting factors on air pollution, and the feasibility of high-resolution mobile sensing for quantifying pollution characteristics in rapidly developing nations with unique air pollution sources. Gaps in our knowledge of their health impacts may be closed through quantifying exposure using reliable sensing devices and methods presented in this work.
摘要
{"title":"Spatiotemporal Assessment of PM2.5 Exposure of a High-risk Occupational Group in a Southeast Asian Megacity","authors":"Jarl Tynan Collado, J. G. Abalos, Imee de los Reyes, M. Cruz, G. Leung, Katrina Abenojar, Carlos Rosauro Manalo, Bernell Go, Christine L. Chan, Charlotte Kendra Gotangco Gonzales, J. Simpas, E. Porio, J. Wong, S. Lung, M. Cambaliza","doi":"10.4209/aaqr.220134","DOIUrl":"https://doi.org/10.4209/aaqr.220134","url":null,"abstract":"Drivers of open-air public utility jeepneys (PUJs) in the Philippines are regularly exposed to severe levels of fine particulate pollution (PM 2.5 ), making them the appropriate sub-population for investigating the health impacts of PM 2.5 on populations chronically exposed to these kinds of unique sources. Real-time PM 2.5 exposures of PUJ drivers for a high-traffic route in Metro Manila, Philippines were assessed using Academia Sinica-LUNG (AS_LUNG) portable sensing devices. From all 15-second measurements obtained, the mean concentration of PM 2.5 is 36.4 µ g m –3 , seven times greater than the mean annual guideline value (5.0 µ g m –3 ) set by the World Health Organization (WHO). Elevated levels of PM 2.5 were observed at key transportation microenvironments (TMEs) such as a transport terminal and near a shopping mall. The occurrence of hotspots along the route is mainly attributed to traffic-promoting factors like stoplights and traffic rush hours. Multiple linear regression (MLR) analysis revealed that the area by the shopping mall had the highest contribution ( β = 52 µ g m –3 ) to PUJ driver exposure. To the best of our knowledge, this study is the first in the country to perform a detailed characterization of the exposure of a high-risk occupational group to PM 2.5 . These results reveal information that is normally undetected by fixed site monitoring (FSM), underscoring the importance of mobile measurements as a complement to FSM in assessing the exposure of urban populations to air pollution more extensively. Furthermore, this study demonstrates the heavy influence of traffic-promoting factors on air pollution, and the feasibility of high-resolution mobile sensing for quantifying pollution characteristics in rapidly developing nations with unique air pollution sources. Gaps in our knowledge of their health impacts may be closed through quantifying exposure using reliable sensing devices and methods presented in this work.","PeriodicalId":7402,"journal":{"name":"Aerosol and Air Quality Research","volume":"1 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70292792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
Martin Adrian, Puji Lestari, Ferry Iskandar, M. Munir
The COVID-19 outbreak impacted the people's lives in the world. Lockdown is one way of controlling the spread of the virus. In Indonesia, the government would rather implement public activity restriction than lockdown. The detailed comprehension of the effect of lockdown or similar policies on air pollution is valuable for making future policies about the control of pandemics as well as its effect on air quality. To understand the effect of public activity restriction (PAR) and its correlation with air pollution, mobile monitoring (MM) of particulate matter (PM 2.5 ) was performed in the urban area of Bandung, Indonesia, in July 2021. Based on MM using a bicycle, we found that a PAR had an impact on air pollution. Our result showed that there was a decrease between 20% and 30% in 3 of 6 sub-districts. The advantage of MM was highlighted by the prominent visualization of the concentration of PM 2.5 MM data at the level of the road. Localization of polluted roads could be seen clearly through the MM method. The uncovering effect of PAR on air pollution using the MM method will provide important insights for government and policymakers to develop future policy that controls air pollution for better citizen health.
{"title":"The Impact of Public Activity Restriction during COVID-19 to Air Quality in Urban Area of Bandung Measured Using Mobile Monitoring","authors":"Martin Adrian, Puji Lestari, Ferry Iskandar, M. Munir","doi":"10.4209/aaqr.220215","DOIUrl":"https://doi.org/10.4209/aaqr.220215","url":null,"abstract":"The COVID-19 outbreak impacted the people's lives in the world. Lockdown is one way of controlling the spread of the virus. In Indonesia, the government would rather implement public activity restriction than lockdown. The detailed comprehension of the effect of lockdown or similar policies on air pollution is valuable for making future policies about the control of pandemics as well as its effect on air quality. To understand the effect of public activity restriction (PAR) and its correlation with air pollution, mobile monitoring (MM) of particulate matter (PM 2.5 ) was performed in the urban area of Bandung, Indonesia, in July 2021. Based on MM using a bicycle, we found that a PAR had an impact on air pollution. Our result showed that there was a decrease between 20% and 30% in 3 of 6 sub-districts. The advantage of MM was highlighted by the prominent visualization of the concentration of PM 2.5 MM data at the level of the road. Localization of polluted roads could be seen clearly through the MM method. The uncovering effect of PAR on air pollution using the MM method will provide important insights for government and policymakers to develop future policy that controls air pollution for better citizen health.","PeriodicalId":7402,"journal":{"name":"Aerosol and Air Quality Research","volume":"1 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70293566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 “Coal to Gas” (CTG) policy in north China markedly altered the characteristics of polycyclic aromatic hydrocarbons (PAHs) in PM 2.5 . Existing researches about CTG impacts on components, sources, and health risks of PM 2.5 -bound PAHs mainly focused on metropolitan area, whereas they were lacking in rural area of north China. Here, we deployed an intensive observation in winter of 2020 at a rural site in the central area of the Beijing-Tianjin-Hebei (BTH) region. A positive matrix factorization (PMF) model and an incremental lifetime cancer risk (ILCR) model were utilized to examine the PAH sources and health risks. Higher daily average PM 2.5 of 81.5 µ g m –3 in the sampling period than 75 µ g m –3 of the National Air Quality Standard Grade II indicated the air pollution in rural area was still serious. The total PAHs increased obviously from diurnal 86.2 ng m –3 to nocturnal 151 ng m –3 because of the nocturnal high intensity of heating, with the increases of 20.7%, 85.5%, and 76.3% for low, medium, and high molecular weight PAHs, respectively. Vehicular exhaust (VE), coal burning (CB), industrial source (IS), biomass burning (BB), and oil spill and leakages (OSL) were the main PAH contributors, with the average daily contributions of 32.7%, 21.5%, 18.3%, 15.9%, and 11.6%, respectively. Lower CC contribution of 27.6% in winter of 2020 than 27.6% in winter of 2019 indicated the positive role of CTG policy. However, the nocturnal CC fraction increased by 680% compared with the diurnal value, and CC had become the largest contributor in the nighttime. BB contribution was up to 18.3%, evidencing that biomass utility should be managed in term of the biomass burning was prohibited in BTH rural area. Moreover, the nocturnal average BaPeq equivalent concentration exhibited higher levels than those in the daytime. The nocturnal ILCR values of adults and children was 9.35 × 10 –6 and 2.66 × 10 –6 , exceeding the acceptable threshold, suggesting there was a potential carcinogenic risk.
煤改气政策显著改变了华北地区pm2.5中多环芳烃(PAHs)的特征。现有的CTG对pm2.5结合多环芳烃组分、来源和健康风险的影响研究主要集中在城市地区，而对华北农村地区的研究较少。在此，我们于2020年冬季在京津冀中部地区的一个农村站点进行了密集观测。采用正矩阵分解(PMF)模型和终生癌症风险增量(ILCR)模型检测多环芳烃来源和健康风险。采样期内日均pm2.5为81.5µg m -3，高于国家空气质量二级标准75µg m -3，说明农村地区空气污染依然严重。由于夜间高强度的加热，多环芳烃总量从白天的86.2 ng m -3明显增加到夜间的151 ng m -3，低、中、高分子量多环芳烃总量分别增加了20.7%、85.5%和76.3%。汽车尾气(VE)、燃煤(CB)、工业源(IS)、生物质燃烧(BB)和溢油泄漏(OSL)是多环芳烃的主要贡献源，日均贡献分别为32.7%、21.5%、18.3%、15.9%和11.6%。2020年冬季的碳排放贡献率为27.6%，低于2019年冬季的27.6%，表明CTG政策发挥了积极作用。然而，夜间CC比白天增加了680%，夜间CC已成为最大的贡献者。BB贡献度高达18.3%，说明北京市农村禁止生物质燃烧，应当对生物质利用进行管理。夜间平均BaPeq当量浓度高于白天。成人和儿童夜间ILCR分别为9.35 × 10 -6和2.66 × 10 -6，均超过可接受阈值，存在潜在的致癌风险。
{"title":"Sources, Compositions, and Health Risks of PM2.5-bound PAHs at the Rural Area along with the “Coal to Gas” Law","authors":"Zhiyong Li, Ziyuan Yue, Wenjia Zhu, Wenquan Liu, Jintao Gao, Jiaqiang Zhang, Ziyi Zhan, Lan Chen, Huiying Gao, Jihong Wei","doi":"10.4209/aaqr.220352","DOIUrl":"https://doi.org/10.4209/aaqr.220352","url":null,"abstract":"The “Coal to Gas” (CTG) policy in north China markedly altered the characteristics of polycyclic aromatic hydrocarbons (PAHs) in PM 2.5 . Existing researches about CTG impacts on components, sources, and health risks of PM 2.5 -bound PAHs mainly focused on metropolitan area, whereas they were lacking in rural area of north China. Here, we deployed an intensive observation in winter of 2020 at a rural site in the central area of the Beijing-Tianjin-Hebei (BTH) region. A positive matrix factorization (PMF) model and an incremental lifetime cancer risk (ILCR) model were utilized to examine the PAH sources and health risks. Higher daily average PM 2.5 of 81.5 µ g m –3 in the sampling period than 75 µ g m –3 of the National Air Quality Standard Grade II indicated the air pollution in rural area was still serious. The total PAHs increased obviously from diurnal 86.2 ng m –3 to nocturnal 151 ng m –3 because of the nocturnal high intensity of heating, with the increases of 20.7%, 85.5%, and 76.3% for low, medium, and high molecular weight PAHs, respectively. Vehicular exhaust (VE), coal burning (CB), industrial source (IS), biomass burning (BB), and oil spill and leakages (OSL) were the main PAH contributors, with the average daily contributions of 32.7%, 21.5%, 18.3%, 15.9%, and 11.6%, respectively. Lower CC contribution of 27.6% in winter of 2020 than 27.6% in winter of 2019 indicated the positive role of CTG policy. However, the nocturnal CC fraction increased by 680% compared with the diurnal value, and CC had become the largest contributor in the nighttime. BB contribution was up to 18.3%, evidencing that biomass utility should be managed in term of the biomass burning was prohibited in BTH rural area. Moreover, the nocturnal average BaPeq equivalent concentration exhibited higher levels than those in the daytime. The nocturnal ILCR values of adults and children was 9.35 × 10 –6 and 2.66 × 10 –6 , exceeding the acceptable threshold, suggesting there was a potential carcinogenic risk.","PeriodicalId":7402,"journal":{"name":"Aerosol and Air Quality Research","volume":"100 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70294705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
Zeynep Ece Kuloğlu, Zeynep Bengi Eren, Bedirhan Haykar, Cansel Vatansever, Tayfun Barlas, M. Kuşkucu, Gülen Güney-Esken, F. Can
The Omicron variant spreads quicker than the earlier variants and can evade the immune response. The behavior changes and waning immunity could cause large numbers of COVID-19 infections and potential hospitalizations. Here, we present a cross-sectional/longitudinal study conducted in a tertiary hospital in Istanbul. In the cross-sectional part, we collected aerosol samples from clinical and public areas in the hospital. We performed qPCR and viral culture. In the pediatrics, outpatient clinic waiting room, where the children were without masks, and unvaccinated, 66% of the samples were positive for viral RNA. However, the positivity rate was 14% in the staff dining hall where everybody was without masks but fully vaccinated. The viral RNA was positive in 50% of the COVID-19 patient rooms, 33% of the febrile disease outpatient clinic, and 33% of the clinical laboratory waiting room. There was no viral growth in the culture of all samples. The highest viral load was detected in the COVID-19 patient room (3.60 × 10 10 PFU m –3 ), followed by the pediatrics outpatient clinic waiting room (2.8 × 10 8 PFU m –3 ). In the longitudinal study, samples were collected 0, 2.5, 4.5, and 24 hours after a meeting in which all attendees were wearing masks and three participants were diagnosed with COVID-19. Only one sample collected 24 hours after the meeting was weakly positive for the viral RNA (1.12 × 10 2 PFU m –3 ). In conclusion, mask use and vaccination are still the main effective methods for preventing the COVID-19 Omicron variant in indoor environments. Unvaccinated children are a significant source of air contamination and risk further transmission of COVID-19.
欧米克隆变异比之前的变异传播得更快，可以逃避免疫反应。行为改变和免疫力下降可能导致大量COVID-19感染和潜在的住院治疗。在这里，我们提出了横断面/纵向研究在伊斯坦布尔的三级医院进行。在横断面部分，我们从医院的临床和公共区域收集气溶胶样本。我们进行了qPCR和病毒培养。在儿科门诊候诊室，孩子们没有戴口罩，也没有接种疫苗，66%的样本病毒RNA呈阳性。然而，在员工食堂，每个人都没有戴口罩，但都接种了疫苗，阳性率为14%。50%的COVID-19病房、33%的发热门诊和33%的临床实验室候诊室病毒RNA阳性。在所有样本的培养中都没有病毒生长。病毒载量最高的是病房(3.60 × 10 10 PFU m -3)，其次是儿科门诊候诊室(2.8 × 10 8 PFU m -3)。在纵向研究中，在会议结束后0、2.5、4.5和24小时收集样本，所有与会者都戴着口罩，三名参与者被诊断出患有COVID-19。会议后24小时采集的样本中只有一个病毒RNA呈弱阳性(1.12 × 10 2 PFU m -3)。综上所述，在室内环境中，使用口罩和接种疫苗仍然是预防COVID-19欧米克隆变异的主要有效方法。未接种疫苗的儿童是空气污染的重要来源，有进一步传播COVID-19的风险。
{"title":"Omicron Positivity in Air of Hospital Settings Gathered COVID-19 Patients, Vaccinated/Unvaccinated Populations","authors":"Zeynep Ece Kuloğlu, Zeynep Bengi Eren, Bedirhan Haykar, Cansel Vatansever, Tayfun Barlas, M. Kuşkucu, Gülen Güney-Esken, F. Can","doi":"10.4209/aaqr.220388","DOIUrl":"https://doi.org/10.4209/aaqr.220388","url":null,"abstract":"The Omicron variant spreads quicker than the earlier variants and can evade the immune response. The behavior changes and waning immunity could cause large numbers of COVID-19 infections and potential hospitalizations. Here, we present a cross-sectional/longitudinal study conducted in a tertiary hospital in Istanbul. In the cross-sectional part, we collected aerosol samples from clinical and public areas in the hospital. We performed qPCR and viral culture. In the pediatrics, outpatient clinic waiting room, where the children were without masks, and unvaccinated, 66% of the samples were positive for viral RNA. However, the positivity rate was 14% in the staff dining hall where everybody was without masks but fully vaccinated. The viral RNA was positive in 50% of the COVID-19 patient rooms, 33% of the febrile disease outpatient clinic, and 33% of the clinical laboratory waiting room. There was no viral growth in the culture of all samples. The highest viral load was detected in the COVID-19 patient room (3.60 × 10 10 PFU m –3 ), followed by the pediatrics outpatient clinic waiting room (2.8 × 10 8 PFU m –3 ). In the longitudinal study, samples were collected 0, 2.5, 4.5, and 24 hours after a meeting in which all attendees were wearing masks and three participants were diagnosed with COVID-19. Only one sample collected 24 hours after the meeting was weakly positive for the viral RNA (1.12 × 10 2 PFU m –3 ). In conclusion, mask use and vaccination are still the main effective methods for preventing the COVID-19 Omicron variant in indoor environments. Unvaccinated children are a significant source of air contamination and risk further transmission of COVID-19.","PeriodicalId":7402,"journal":{"name":"Aerosol and Air Quality Research","volume":"1 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70295162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
Yi-bo Zhao, N. Hayeck, N. Saliba, C. Schreiner, M. Zennegg, Fuze Jiang, R. Figi, Davide Bleiner, Jing Wang
The Beirut port explosion in 2020 released a huge amount of chemicals including ammonium nitrate, however, the long-term effects of the explosion on air quality and public health remain unclear. In this study, particulate matter (PM 10 ) samples were collected in Beirut, Lebanon 1 month and 3 months after the explosion. The average concentrations of main anions measured in 2020 (one and three months after the explosion) were compared with those in 2009–2015 by calculating the percentage of difference, and the average concentrations of cations and anions in September (one month after the explosion) and November (three months after the explosion) 2020 were also compared to identify any abnormal values, indicating insignificant effects on the post-explosion PM in terms of component concentrations. That is, PM and gases directly induced by the explosion might be subject to rapid atmospheric transport and deposition. Hence, the results imply that investigations of the chemical contaminations in soil and water are urgently needed. Long-term monitoring is necessary to avoid subsequent air pollution caused by possible particle resuspension. The continuous demolition and reconstruction after the explosion are possibly the main long-term effect of the Beirut port explosion, causing an elevated concentration of PM 2.5 at ground level 400% higher than the recommended concentrations (15 µ g m –3 for 24-hour mean). Protective measures must be taken to reduce the exposure risks by controlling the PM release from demolition and construction, traffic, and diesel generators. The cancer risk in Beirut based on PAHs measurements in 2021 was also estimated and discussed
2020年贝鲁特港口爆炸释放了包括硝酸铵在内的大量化学物质，但爆炸对空气质量和公众健康的长期影响尚不清楚。本研究采集了黎巴嫩贝鲁特爆炸后1个月和3个月的颗粒物(pm10)样本。将2020年(爆炸后1个月和3个月)测得的主要阴离子的平均浓度与2009-2015年进行对比，计算差值百分比，并将2020年9月(爆炸后1个月)和11月(爆炸后3个月)的阳离子和阴离子的平均浓度进行对比，发现异常值，从成分浓度上看，对爆炸后PM的影响不显著。也就是说，爆炸直接引起的PM和气体可能会受到快速的大气输送和沉积。因此，研究结果表明迫切需要对土壤和水的化学污染进行调查。长期监测是必要的，以避免可能的颗粒再悬浮造成随后的空气污染。爆炸后持续的拆除和重建可能是贝鲁特港口爆炸的主要长期影响，导致地面pm2.5浓度比推荐浓度(24小时平均值15µg m -3)高出400%。必须采取防护措施，通过控制拆迁施工、交通和柴油发电机释放PM来降低暴露风险。还根据2021年多环芳烃测量结果对贝鲁特的癌症风险进行了估计和讨论
{"title":"Any Long-term Effect of the Beirut Port Explosion on the Airborne Particulate Matter?","authors":"Yi-bo Zhao, N. Hayeck, N. Saliba, C. Schreiner, M. Zennegg, Fuze Jiang, R. Figi, Davide Bleiner, Jing Wang","doi":"10.4209/aaqr.220395","DOIUrl":"https://doi.org/10.4209/aaqr.220395","url":null,"abstract":"The Beirut port explosion in 2020 released a huge amount of chemicals including ammonium nitrate, however, the long-term effects of the explosion on air quality and public health remain unclear. In this study, particulate matter (PM 10 ) samples were collected in Beirut, Lebanon 1 month and 3 months after the explosion. The average concentrations of main anions measured in 2020 (one and three months after the explosion) were compared with those in 2009–2015 by calculating the percentage of difference, and the average concentrations of cations and anions in September (one month after the explosion) and November (three months after the explosion) 2020 were also compared to identify any abnormal values, indicating insignificant effects on the post-explosion PM in terms of component concentrations. That is, PM and gases directly induced by the explosion might be subject to rapid atmospheric transport and deposition. Hence, the results imply that investigations of the chemical contaminations in soil and water are urgently needed. Long-term monitoring is necessary to avoid subsequent air pollution caused by possible particle resuspension. The continuous demolition and reconstruction after the explosion are possibly the main long-term effect of the Beirut port explosion, causing an elevated concentration of PM 2.5 at ground level 400% higher than the recommended concentrations (15 µ g m –3 for 24-hour mean). Protective measures must be taken to reduce the exposure risks by controlling the PM release from demolition and construction, traffic, and diesel generators. The cancer risk in Beirut based on PAHs measurements in 2021 was also estimated and discussed","PeriodicalId":7402,"journal":{"name":"Aerosol and Air Quality Research","volume":"1 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70295433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
I started my master’s degree in 1971 and completed Ph.D. degree in 1976 under the mentorship of Prof. Benjamin Liu. During these years, we worked on bipolar charging and established the criteria to neutralize charged aerosols (Liu and Pui, 1974a, 1974b) (Fig. 1). An electrical aerosol analyzer (EAA) was developed to measure atmospheric particle size distributions and led to the successful commercialization of TSI 3030 EAA (Liu and Pui, 1975). TSI founding aerosol instrument manager Gilmore Sem wrote that without the success of the EAA, TSI would have gotten out of the aerosol instrument business (Schmidt et al., 2022). Professor Kenneth Whitby and Dr. William Wilson of EPA invited me to participate in the LA smog measuring campaign (Whitby et al., 1975) which provided results, together with other field measurements, to help EPA set up the PM2.5
我于1971年开始攻读硕士学位，1976年在Benjamin Liu教授的指导下获得博士学位。在这些年中，我们致力于双极充电，并建立了中和带电气溶胶的标准(Liu and Pui, 1974a, 1974b)(图1)。开发了一种电子气溶胶分析仪(EAA)来测量大气粒径分布，并导致TSI 3030 EAA成功商业化(Liu and Pui, 1975)。TSI创始气溶胶仪器经理Gilmore Sem写道，如果没有EAA的成功，TSI可能已经退出了气溶胶仪器业务(Schmidt et al.， 2022)。环保署的Kenneth Whitby教授和William Wilson博士邀请我参加洛杉矶烟雾测量活动(Whitby et al.， 1975)，该活动提供的结果与其他现场测量一起，帮助环保署建立PM2.5
{"title":"Reflection on 50 Years of Friendship and Collaboration on Aerosol Science and Technology","authors":"D. Pui","doi":"10.4209/aaqr.220400","DOIUrl":"https://doi.org/10.4209/aaqr.220400","url":null,"abstract":"I started my master’s degree in 1971 and completed Ph.D. degree in 1976 under the mentorship of Prof. Benjamin Liu. During these years, we worked on bipolar charging and established the criteria to neutralize charged aerosols (Liu and Pui, 1974a, 1974b) (Fig. 1). An electrical aerosol analyzer (EAA) was developed to measure atmospheric particle size distributions and led to the successful commercialization of TSI 3030 EAA (Liu and Pui, 1975). TSI founding aerosol instrument manager Gilmore Sem wrote that without the success of the EAA, TSI would have gotten out of the aerosol instrument business (Schmidt et al., 2022). Professor Kenneth Whitby and Dr. William Wilson of EPA invited me to participate in the LA smog measuring campaign (Whitby et al., 1975) which provided results, together with other field measurements, to help EPA set up the PM2.5","PeriodicalId":7402,"journal":{"name":"Aerosol and Air Quality Research","volume":"1 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70295484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
Accurate prediction of air pollution is a difficult problem to be solved in atmospheric environment research. An Artificial Neural Network (ANN) is exploited to predict hourly PM 2.5 and PM 10 concentrations in Chongqing City. We take PM 2.5 (PM 10 ), time and meteorological elements as the input of the ANN, and the PM 2.5 (PM 10 ) of the next hour as the output to build an ANN model. Thirteen kinds of training functions are compared to obtain the optimal function. The research results display that the ANN model exhibits good performance in predicting hourly PM 2.5 and PM 10 concentrations. Trainbr is the best function for predicting PM 2.5 concentrations compared to other training functions with R value (0.9783), RMSE (1.2271), and MAE (0.9050). Trainlm is the second best with R value (0.9495), RMSE (1.8845), and MAE (1.3902). Similarly, trainbr is also the best in forecasting PM 10 concentrations with R value (0.9773), RMSE value (1.8270), and MAE value (1.4341). Trainlm is the second best with R value (0.9522), RMSE (2.6708), and MAE (1.8554). These two training functions have good generalization ability and can meet the needs of hourly PM 2.5 and PM 10 prediction. The forecast results can support fine management and help improve the ability to prevent and control air pollution in advance, accurately and scientifically.
{"title":"Prediction of Hourly PM2.5 and PM10 Concentrations in Chongqing City in China Based on Artificial Neural Network","authors":"Qingchun Guo, Zhenfang He, Zhaosheng Wang","doi":"10.4209/aaqr.220448","DOIUrl":"https://doi.org/10.4209/aaqr.220448","url":null,"abstract":"Accurate prediction of air pollution is a difficult problem to be solved in atmospheric environment research. An Artificial Neural Network (ANN) is exploited to predict hourly PM 2.5 and PM 10 concentrations in Chongqing City. We take PM 2.5 (PM 10 ), time and meteorological elements as the input of the ANN, and the PM 2.5 (PM 10 ) of the next hour as the output to build an ANN model. Thirteen kinds of training functions are compared to obtain the optimal function. The research results display that the ANN model exhibits good performance in predicting hourly PM 2.5 and PM 10 concentrations. Trainbr is the best function for predicting PM 2.5 concentrations compared to other training functions with R value (0.9783), RMSE (1.2271), and MAE (0.9050). Trainlm is the second best with R value (0.9495), RMSE (1.8845), and MAE (1.3902). Similarly, trainbr is also the best in forecasting PM 10 concentrations with R value (0.9773), RMSE value (1.8270), and MAE value (1.4341). Trainlm is the second best with R value (0.9522), RMSE (2.6708), and MAE (1.8554). These two training functions have good generalization ability and can meet the needs of hourly PM 2.5 and PM 10 prediction. The forecast results can support fine management and help improve the ability to prevent and control air pollution in advance, accurately and scientifically.","PeriodicalId":7402,"journal":{"name":"Aerosol and Air Quality Research","volume":"42 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70296005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
Harmonizing the particulate carbon data from the Chemical Speciation Network (CSN) is necessary to perform reliable long-term trend and seasonal variability analyses, clean air regulation assessments, and climate change studies. But it is challenging because the measurement of the carbonaceous fraction of PM 2.5 (particulate matter with a diameter less than or equal to 2.5 µ m) underwent several changes both in samplers and analysis protocols. To address the above issue, field blanks are used to remove artifacts from samples, an outlier filter is applied to remove anomalies from the dataset, and a regression between retired samplers and the current sampler data is used to establish the harmonization between two co-located urban sites in this study. A second comparison between the retired method and Interagency Monitoring of Protected Visual Environments (IMPROVE) network data was carried out at two sites (one urban and one rural) with co-located samplers. These results show no site dependence for organic carbon (OC) concentrations and small but non-negligible differences for elemental carbon (EC), which can be attributed to the relatively greater uncertainty of the low concentration rural EC measurements. An adjustment criterion that harmonizes the data from the beginning of the sampling period to the present is obtained. The harmonized data shows consistent trends and seasonal variability when compared to the reported data with these trends declining over the period 2001–2018.
{"title":"Harmonization of the Long-term PM2.5 Carbon Data from the CSN Sites in New York State","authors":"Hesham Hassan, J. Schwab, Jie Zhang","doi":"10.4209/aaqr.230077","DOIUrl":"https://doi.org/10.4209/aaqr.230077","url":null,"abstract":"Harmonizing the particulate carbon data from the Chemical Speciation Network (CSN) is necessary to perform reliable long-term trend and seasonal variability analyses, clean air regulation assessments, and climate change studies. But it is challenging because the measurement of the carbonaceous fraction of PM 2.5 (particulate matter with a diameter less than or equal to 2.5 µ m) underwent several changes both in samplers and analysis protocols. To address the above issue, field blanks are used to remove artifacts from samples, an outlier filter is applied to remove anomalies from the dataset, and a regression between retired samplers and the current sampler data is used to establish the harmonization between two co-located urban sites in this study. A second comparison between the retired method and Interagency Monitoring of Protected Visual Environments (IMPROVE) network data was carried out at two sites (one urban and one rural) with co-located samplers. These results show no site dependence for organic carbon (OC) concentrations and small but non-negligible differences for elemental carbon (EC), which can be attributed to the relatively greater uncertainty of the low concentration rural EC measurements. An adjustment criterion that harmonizes the data from the beginning of the sampling period to the present is obtained. The harmonized data shows consistent trends and seasonal variability when compared to the reported data with these trends declining over the period 2001–2018.","PeriodicalId":7402,"journal":{"name":"Aerosol and Air Quality Research","volume":"1 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70297155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
{"title":"Sulfurization- Desulfurization of Iron-Calcium Oxygen Carriers during Chemical Looping Combustion of Syngas","authors":"Yu-Lun Chen, Young Ku, Ya-Chun Chang","doi":"10.4209/aaqr.220398","DOIUrl":"https://doi.org/10.4209/aaqr.220398","url":null,"abstract":".","PeriodicalId":7402,"journal":{"name":"Aerosol and Air Quality Research","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135356422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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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