Pub Date : 2024-04-01Epub Date: 2024-01-05DOI: 10.4209/aaqr.230203
Silver Onyango, Crystal M North, Hatem A Ellaithy, Paul Tumwesigye, Choong-Min Kang, Vasileios Matthaios, Martin Mukama, Nuriat Nambogo, J Mikhail Wolfson, Stephen Ferguson, Stephen Asiimwe, Lynn Atuyambe, Data Santorino, David C Christiani, Petros Koutrakis
Air pollution is the leading environmental cause of death globally, and most mortality occurs in resource-limited settings such as sub-Saharan Africa. The African continent experiences some of the worst ambient air pollution in the world, yet there are relatively little African data characterizing ambient pollutant levels and source admixtures. In Uganda, ambient PM2.5 levels exceed international health standards. However, most studies focus only on urban environments and do not characterize pollutant sources. We measured daily ambient PM2.5 concentrations and sources in Mbarara, Uganda from May 2018 through February 2019 using Harvard impactors fitted with size-selective inlets. We compared our estimates to publicly available levels in Kampala, and to World Health Organization (WHO) air quality guidelines. We characterized the leading PM2.5 sources in Mbarara using x-ray fluorescence and positive matrix factorization. Daily PM2.5 concentrations were 26.7 μg m-3 and 59.4 μg m-3 in Mbarara and Kampala, respectively (p<0.001). PM2.5 concentrations exceeded WHO guidelines on 58% of days in Mbarara and 99% of days in Kampala. In Mbarara, PM2.5 was higher in the dry as compared to the rainy season (30.8 vs 21.3, p<0.001), while seasonal variation was not observed in Kampala. PM2.5 concentrations did not vary on weekdays versus weekends in either city. In Mbarara, the six main ambient PM2.5 sources identified included (in order of abundance): traffic-related, biomass and secondary aerosols, industry and metallurgy, heavy oil and fuel combustion, fine soil, and salt aerosol. Our findings confirm that air quality in southwestern Uganda is unsafe and that mitigation efforts are urgently needed. Ongoing work focused on improving air quality in the region may have the greatest impact if focused on traffic and biomass-related sources.
{"title":"Ambient PM<sub>2.5</sub> temporal variation and source apportionment in Mbarara, Uganda.","authors":"Silver Onyango, Crystal M North, Hatem A Ellaithy, Paul Tumwesigye, Choong-Min Kang, Vasileios Matthaios, Martin Mukama, Nuriat Nambogo, J Mikhail Wolfson, Stephen Ferguson, Stephen Asiimwe, Lynn Atuyambe, Data Santorino, David C Christiani, Petros Koutrakis","doi":"10.4209/aaqr.230203","DOIUrl":"10.4209/aaqr.230203","url":null,"abstract":"<p><p>Air pollution is the leading environmental cause of death globally, and most mortality occurs in resource-limited settings such as sub-Saharan Africa. The African continent experiences some of the worst ambient air pollution in the world, yet there are relatively little African data characterizing ambient pollutant levels and source admixtures. In Uganda, ambient PM<sub>2.5</sub> levels exceed international health standards. However, most studies focus only on urban environments and do not characterize pollutant sources. We measured daily ambient PM<sub>2.5</sub> concentrations and sources in Mbarara, Uganda from May 2018 through February 2019 using Harvard impactors fitted with size-selective inlets. We compared our estimates to publicly available levels in Kampala, and to World Health Organization (WHO) air quality guidelines. We characterized the leading PM<sub>2.5</sub> sources in Mbarara using x-ray fluorescence and positive matrix factorization. Daily PM<sub>2.5</sub> concentrations were 26.7 μg m<sup>-3</sup> and 59.4 μg m<sup>-3</sup> in Mbarara and Kampala, respectively (p<0.001). PM<sub>2.5</sub> concentrations exceeded WHO guidelines on 58% of days in Mbarara and 99% of days in Kampala. In Mbarara, PM<sub>2.5</sub> was higher in the dry as compared to the rainy season (30.8 vs 21.3, p<0.001), while seasonal variation was not observed in Kampala. PM<sub>2.5</sub> concentrations did not vary on weekdays versus weekends in either city. In Mbarara, the six main ambient PM<sub>2.5</sub> sources identified included (in order of abundance): traffic-related, biomass and secondary aerosols, industry and metallurgy, heavy oil and fuel combustion, fine soil, and salt aerosol. Our findings confirm that air quality in southwestern Uganda is unsafe and that mitigation efforts are urgently needed. Ongoing work focused on improving air quality in the region may have the greatest impact if focused on traffic and biomass-related sources.</p>","PeriodicalId":7402,"journal":{"name":"Aerosol and Air Quality Research","volume":"24 ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11212479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141465431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2024-01-01Epub Date: 2023-11-22DOI: 10.4209/aaqr.230202
Frederic T Lu, Robert J Laumbach, Alicia Legard, Nirmala T Myers, Kathleen G Black, Pamela Ohman-Strickland, Shahnaz Alimokhtari, Adriana de Resende, Leonardo Calderón, Gediminas Mainelis, Howard M Kipen
Portable air cleaners (PACs) equipped with HEPA filters are gaining attention as cost-effective means of decreasing indoor particulate matter (PM) air pollutants and airborne viruses. However, the performance of PACs in naturalistic settings and spaces beyond the room containing the PAC is not well characterized. We conducted a single-blinded randomized cross-over interventional study between November 2020 and May 2021 in the homes of adults who tested positive for COVID-19. The intervention was air filtration with PAC operated with the HEPA filter set installed ("filter" condition) versus removed ("sham" condition, i.e., control). Sampling was performed in 29 homes for two consecutive 24-hour periods in the primary room (containing the PAC) and a secondary room. PAC effectiveness, calculated as reductions in overall mean PM2.5 and PM10 concentrations during the filter condition, were for the primary rooms 78.8% and 63.9% (n = 23), respectively, and for the secondary rooms 57.9% and 60.4% (n = 22), respectively. When a central air handler (CAH) was reported to be in use, filter-associated reductions of PM were statistically significant during the day (06:00-22:00) and night (22:01-05:59) in the primary rooms but only during the day in the secondary rooms. Our study adds to the literature evaluating the real-world effects of PACs on a secondary room and considering the impact of central air systems on PAC performance.
{"title":"Real-World Effectiveness of Portable Air Cleaners in Reducing Home Particulate Matter Concentrations.","authors":"Frederic T Lu, Robert J Laumbach, Alicia Legard, Nirmala T Myers, Kathleen G Black, Pamela Ohman-Strickland, Shahnaz Alimokhtari, Adriana de Resende, Leonardo Calderón, Gediminas Mainelis, Howard M Kipen","doi":"10.4209/aaqr.230202","DOIUrl":"https://doi.org/10.4209/aaqr.230202","url":null,"abstract":"<p><p>Portable air cleaners (PACs) equipped with HEPA filters are gaining attention as cost-effective means of decreasing indoor particulate matter (PM) air pollutants and airborne viruses. However, the performance of PACs in naturalistic settings and spaces beyond the room containing the PAC is not well characterized. We conducted a single-blinded randomized cross-over interventional study between November 2020 and May 2021 in the homes of adults who tested positive for COVID-19. The intervention was air filtration with PAC operated with the HEPA filter set installed (\"filter\" condition) versus removed (\"sham\" condition, i.e., control). Sampling was performed in 29 homes for two consecutive 24-hour periods in the primary room (containing the PAC) and a secondary room. PAC effectiveness, calculated as reductions in overall mean PM<sub>2.5</sub> and PM<sub>10</sub> concentrations during the filter condition, were for the primary rooms 78.8% and 63.9% (n = 23), respectively, and for the secondary rooms 57.9% and 60.4% (n = 22), respectively. When a central air handler (CAH) was reported to be in use, filter-associated reductions of PM were statistically significant during the day (06:00-22:00) and night (22:01-05:59) in the primary rooms but only during the day in the secondary rooms. Our study adds to the literature evaluating the real-world effects of PACs on a secondary room and considering the impact of central air systems on PAC performance.</p>","PeriodicalId":7402,"journal":{"name":"Aerosol and Air Quality Research","volume":"24 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11014421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140846846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
Shovan Kumar Sahu, Lei Chen, Song Liu, Jia Xing, Rohit Mathur
Future estimates of atmospheric pollutant concentrations serve as critical information for policy makers to formulate current policy indicators to achieve future targets. Tropospheric burden of O3 is modulated not only by anthropogenic and natural precursor emissions, but also by the downward transport of O3 associated with stratosphere to troposphere exchange (STE). Hence changes in the estimates of STE and its contributions are key to understand the nature and intensity of future ground level O3 concentrations. The difference in simulated O3 mixing ratios with and without the O3-Potential Vorticity (PV) parameterization scheme is used to represent the model estimated influence of STE on tropospheric O3 distributions. Though STE contributions remain constant in Northern hemisphere as a whole, regional differences exist with Europe (EUR) registering increased STE contribution in both spring and winter while Eastern China (ECH) reporting increased contribution in spring in 2050 (RCP8.5) as compared to 2015. Importance of climate change can be deduced from the fact that ECH and EUR recorded increased STE contribution to O3 in RCP8.5 compared to RCP4.5. Comparison of STE and non-STE meteorological process contributions to O3 due to climate change revealed that contributions of non-STE processes were highest in summer while STE contributions were highest in winter. EUR reported highest STE contribution while ECH reported highest non-STE contribution. None of the 3 regions show consistent low STE contribution due to future climate change (< 50%) in all seasons indicating the significance of STE to ground level O3.
{"title":"Effect of Future Climate Change on Stratosphere-to-Troposphere-Exchange Driven Ozone in the Northern Hemisphere.","authors":"Shovan Kumar Sahu, Lei Chen, Song Liu, Jia Xing, Rohit Mathur","doi":"10.4209/aaqr.220414","DOIUrl":"10.4209/aaqr.220414","url":null,"abstract":"<p><p>Future estimates of atmospheric pollutant concentrations serve as critical information for policy makers to formulate current policy indicators to achieve future targets. Tropospheric burden of O<sub>3</sub> is modulated not only by anthropogenic and natural precursor emissions, but also by the downward transport of O<sub>3</sub> associated with stratosphere to troposphere exchange (STE). Hence changes in the estimates of STE and its contributions are key to understand the nature and intensity of future ground level O<sub>3</sub> concentrations. The difference in simulated O<sub>3</sub> mixing ratios with and without the O<sub>3</sub>-Potential Vorticity (PV) parameterization scheme is used to represent the model estimated influence of STE on tropospheric O<sub>3</sub> distributions. Though STE contributions remain constant in Northern hemisphere as a whole, regional differences exist with Europe (EUR) registering increased STE contribution in both spring and winter while Eastern China (ECH) reporting increased contribution in spring in 2050 (RCP8.5) as compared to 2015. Importance of climate change can be deduced from the fact that ECH and EUR recorded increased STE contribution to O<sub>3</sub> in RCP8.5 compared to RCP4.5. Comparison of STE and non-STE meteorological process contributions to O<sub>3</sub> due to climate change revealed that contributions of non-STE processes were highest in summer while STE contributions were highest in winter. EUR reported highest STE contribution while ECH reported highest non-STE contribution. None of the 3 regions show consistent low STE contribution due to future climate change (< 50%) in all seasons indicating the significance of STE to ground level O<sub>3</sub>.</p>","PeriodicalId":7402,"journal":{"name":"Aerosol and Air Quality Research","volume":"1 1","pages":"1-15"},"PeriodicalIF":2.5,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10802885/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70295186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2023-10-01Epub Date: 2023-07-28DOI: 10.4209/aaqr.230011
Austin Close, Jane Blackerby, Heather Tunnell, Jack Pender, Eric Soule, Sinan Sousan
Electronic cigarettes (ECIGs) generate high concentrations of particulate matter (PM), impacting the air quality inhaled by humans through secondhand exposure. ECIG liquids are available commercially and some users create their own "do-it-yourself" liquids, and these liquids often vary in the amounts of their chemical ingredients, including propylene glycol (PG) and vegetable glycerin (VG). Previous studies have quantified PM concentrations in ECIG aerosol generated from liquids containing different PG/VG ratios. However, the effects of these ratios on aerosol instrument filter correction factors needed to measure PM concentrations accurately have not been assessed. Thus, ECIG aerosol filter correction factors for multiple aerosol instruments (SMPS + APS, MiniWRAS, pDR, and SidePak) were determined for five different PG/VG ratios 1) 0PG/100VG, 2) 15PG/85VG, 3) 50PG/50VG, 4) 72PG/28VG, and 5) 90PG/10VG and two different PM sizes, PM1 (1 μm and smaller) and PM2.5 (2.5 μm and smaller). ECIG aerosols were generated inside a controlled exposure chamber using a diaphragm pump and a refillable ECIG device for all the ratios. In addition, the aerosol size distribution and mass median diameter were measured for all five ECIG ratios. PM2.5 correction factors (5-7.6) for ratios 1, 2, 3, and 4 were similar for the SMPS + APS combined data, and ratios 1, 2, 3 were similar for the MiniWRAS (~2), pDR (~0.5), and SidePak (~0.24). These data suggest different correction factors may need to be developed for aerosol generated from ECIGs with high PG content. The higher correction factor values for the 90PG/10VG ratio may have resulted from greater PG volatility relative to VG and sensor losses. The correction factors (ratios 1-4) for PM2.5 were SMPS + APS data (4.96-7.62), MiniWRAS (2.02-3.64), pDR (0.50-1.07), and SidePak (0.22-0.40). These data can help improve ECIG aerosol measurement accuracy for different ECIG mixture ratios.
{"title":"Effects of E-Cigarette Liquid Ratios on the Gravimetric Filter Correction Factors and Real-Time Measurements.","authors":"Austin Close, Jane Blackerby, Heather Tunnell, Jack Pender, Eric Soule, Sinan Sousan","doi":"10.4209/aaqr.230011","DOIUrl":"10.4209/aaqr.230011","url":null,"abstract":"<p><p>Electronic cigarettes (ECIGs) generate high concentrations of particulate matter (PM), impacting the air quality inhaled by humans through secondhand exposure. ECIG liquids are available commercially and some users create their own \"do-it-yourself\" liquids, and these liquids often vary in the amounts of their chemical ingredients, including propylene glycol (PG) and vegetable glycerin (VG). Previous studies have quantified PM concentrations in ECIG aerosol generated from liquids containing different PG/VG ratios. However, the effects of these ratios on aerosol instrument filter correction factors needed to measure PM concentrations accurately have not been assessed. Thus, ECIG aerosol filter correction factors for multiple aerosol instruments (SMPS + APS, MiniWRAS, pDR, and SidePak) were determined for five different PG/VG ratios 1) 0PG/100VG, 2) 15PG/85VG, 3) 50PG/50VG, 4) 72PG/28VG, and 5) 90PG/10VG and two different PM sizes, PM<sub>1</sub> (1 μm and smaller) and PM<sub>2.5</sub> (2.5 μm and smaller). ECIG aerosols were generated inside a controlled exposure chamber using a diaphragm pump and a refillable ECIG device for all the ratios. In addition, the aerosol size distribution and mass median diameter were measured for all five ECIG ratios. PM<sub>2.5</sub> correction factors (5-7.6) for ratios 1, 2, 3, and 4 were similar for the SMPS + APS combined data, and ratios 1, 2, 3 were similar for the MiniWRAS (~2), pDR (~0.5), and SidePak (~0.24). These data suggest different correction factors may need to be developed for aerosol generated from ECIGs with high PG content. The higher correction factor values for the 90PG/10VG ratio may have resulted from greater PG volatility relative to VG and sensor losses. The correction factors (ratios 1-4) for PM<sub>2.5</sub> were SMPS + APS data (4.96-7.62), MiniWRAS (2.02-3.64), pDR (0.50-1.07), and SidePak (0.22-0.40). These data can help improve ECIG aerosol measurement accuracy for different ECIG mixture ratios.</p>","PeriodicalId":7402,"journal":{"name":"Aerosol and Air Quality Research","volume":"1 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10947168/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70296064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
Didem Han Yekdeş, Ali Cem Yekdeş, Ülfiye Çelikkalp, Pelin Sarı Serin, Miraç Çağlayan, Galip Ekuklu
Approximately seven million premature deaths occured due to several health problems caused by air pollution. In this study, we aimed to calculate the mortality rates of lung cancer and Chronic Obstructive Pulmonary Disease (COPD) attributed to PM2.5 in Türkiye in 2019. The universe of the research consists of the entire Türkiye region. Air quality data was obtained from the official website of the Ministry of Environment, Urbanization and Climate Change of the Republic of Türkiye. Lung cancer and COPD mortality data were collected from the official website of the Turkish Statistical Institute by a special request. Mortality rates attributed to PM2.5 were calculated with the WHO AIRQ+ program, and the monthly percent change (MPC) in air pollution level was computed by the JP regression method. The annual average values of PM2.5 and PM10 for 2019 in Türkiye were calculated to be 28.82 µg m-3 and 48.08 µg m-3, respectively. The mortality rate attributed to PM2.5 for lung cancer is 15% whereas the mortality rate attributed to PM2.5 for COPD is 22%. Except two Nomenclature d'Unités Territoriales Statistiques (NUTS) regions (TR1, TR7) all other regions have statisitcally significant one joinpoint. As a conclusion, the PM2.5 average values for 2019 in Türkiye are over the limits for both the national legislation and the World Health Organization (WHO). Taking precautions to control air pollution sources and determination of legitinate national PM2.5 limits should be prioritized. Thus, one out of every six deaths from lung cancer and one out of every five deaths from COPD can be prevented.
{"title":"Chronic Obstructive Pulmonary Disease and Lung Cancer Mortality Attributed to Air Pollution in Turkey in 2019","authors":"Didem Han Yekdeş, Ali Cem Yekdeş, Ülfiye Çelikkalp, Pelin Sarı Serin, Miraç Çağlayan, Galip Ekuklu","doi":"10.4209/aaqr.230144","DOIUrl":"https://doi.org/10.4209/aaqr.230144","url":null,"abstract":"Approximately seven million premature deaths occured due to several health problems caused by air pollution. In this study, we aimed to calculate the mortality rates of lung cancer and Chronic Obstructive Pulmonary Disease (COPD) attributed to PM2.5 in Türkiye in 2019. The universe of the research consists of the entire Türkiye region. Air quality data was obtained from the official website of the Ministry of Environment, Urbanization and Climate Change of the Republic of Türkiye. Lung cancer and COPD mortality data were collected from the official website of the Turkish Statistical Institute by a special request. Mortality rates attributed to PM2.5 were calculated with the WHO AIRQ+ program, and the monthly percent change (MPC) in air pollution level was computed by the JP regression method. The annual average values of PM2.5 and PM10 for 2019 in Türkiye were calculated to be 28.82 µg m-3 and 48.08 µg m-3, respectively. The mortality rate attributed to PM2.5 for lung cancer is 15% whereas the mortality rate attributed to PM2.5 for COPD is 22%. Except two Nomenclature d'Unités Territoriales Statistiques (NUTS) regions (TR1, TR7) all other regions have statisitcally significant one joinpoint. As a conclusion, the PM2.5 average values for 2019 in Türkiye are over the limits for both the national legislation and the World Health Organization (WHO). Taking precautions to control air pollution sources and determination of legitinate national PM2.5 limits should be prioritized. Thus, one out of every six deaths from lung cancer and one out of every five deaths from COPD can be prevented.","PeriodicalId":7402,"journal":{"name":"Aerosol and Air Quality Research","volume":"35 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":"136303710","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}
Positive Matrix Factorization (PMF) is a commonly used receptor model for source apportionment of PM2.5. However, PMF results often retrieve an individual factor mainly composed of secondary aerosols, making it difficult to link with primary emission sources and formulate effective air pollution control strategies. To overcome this limitation, we employed a two-stage PMF modeling approach with adjustments of the species weighting, which was fused with a robust regression model to better characterize the sources of PM2.5 secondary aerosols. Additionally, organic molecular tracers were incorporated into PMF for source identification. A field campaign was conducted between May and December 2021 in Taichung, Taiwan. An improved PMF model was utilized to resolve the multiple time resolution data of 3-h online and 24-h offline measurements of PM2.5 compositions. Retrieved factors from PMF were averaged over 24-h intervals and then applied in robust regression analysis to re-apportion the contributions. Comparing with conventional PMF, downweighting the secondary aerosol-related species in the model was more effective in linking them to primary emission sources. The results from fusion models showed that the majority of secondary aerosols (sum of secondary aerosol-related species = 2.67 μg m-3) within three hours were mainly contributed by oil combustion, while the largest contributor of secondary aerosols (1.65 μg m-3) over 24 hours was industry, highlighting the need for regulation of these two sources based on various temporal scales. The developed fusion strategy of two-stage PMF and robust regression provided refined results and can aid in the management of PM2.5.
{"title":"Characterizing PM2.5 Secondary Aerosols via a Fusion Strategy of Two-stage Positive Matrix Factorization and Robust Regression","authors":"Chun-Sheng Huang, Ho-Tang Liao, Chia-Yang Chen, Li-Hao Young, Ta-Chih Hsiao, Tsung-I Chou, Jyun-Min Chang, Kuan-Lin Lai, Chang-Fu Wu","doi":"10.4209/aaqr.230121","DOIUrl":"https://doi.org/10.4209/aaqr.230121","url":null,"abstract":"Positive Matrix Factorization (PMF) is a commonly used receptor model for source apportionment of PM2.5. However, PMF results often retrieve an individual factor mainly composed of secondary aerosols, making it difficult to link with primary emission sources and formulate effective air pollution control strategies. To overcome this limitation, we employed a two-stage PMF modeling approach with adjustments of the species weighting, which was fused with a robust regression model to better characterize the sources of PM2.5 secondary aerosols. Additionally, organic molecular tracers were incorporated into PMF for source identification. A field campaign was conducted between May and December 2021 in Taichung, Taiwan. An improved PMF model was utilized to resolve the multiple time resolution data of 3-h online and 24-h offline measurements of PM2.5 compositions. Retrieved factors from PMF were averaged over 24-h intervals and then applied in robust regression analysis to re-apportion the contributions. Comparing with conventional PMF, downweighting the secondary aerosol-related species in the model was more effective in linking them to primary emission sources. The results from fusion models showed that the majority of secondary aerosols (sum of secondary aerosol-related species = 2.67 μg m-3) within three hours were mainly contributed by oil combustion, while the largest contributor of secondary aerosols (1.65 μg m-3) over 24 hours was industry, highlighting the need for regulation of these two sources based on various temporal scales. The developed fusion strategy of two-stage PMF and robust regression provided refined results and can aid in the management of PM2.5.","PeriodicalId":7402,"journal":{"name":"Aerosol and Air Quality Research","volume":"24 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":"135909757","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}
N. Tran, Y. Fujii, V. X. Le, Doan Thien Chi Nguyen, H. Okochi, To Thi Hien, N. Takenaka
PM2.5 was continuously collected in Ho Chi Minh City (HCMC), Vietnam, during the period from September 2019 to August 2020, which included the period of socioeconomic suppression caused by restrictions imposed in the face of the coronavirus disease of 2019. The concentrations of PM2.5 mass, water-soluble ions (WSIs), organic carbon (OC), elemental carbon (EC), and water-soluble organic carbon (WSOC) were determined to evaluate the seasonal variations in PM2.5, the effect of socioeconomic suppression on PM2.5, and potential PM2.5 sources in HCMC. The PM2.5 mass concentration during the sampling period was 28.44 +/- 11.55 mu g m(-3) (average +/- standard deviation). OC, EC, and total WSIs accounted for 30.7 +/- 6.6%, 9.7 +/- 2.9%, and 24.9 +/- 6.6% of the PM2.5 mass, respectively. WSOC contributed 46.4 +/- 10.1% to OC mass. NO3-, SO42-, and NH4+ were the dominant species in WSIs (72.7 +/- 17.7% of the total WSIs' mass). The concentrations of PM2.5 mass and total WSIs during the rainy season were lower than those during the dry season, whereas the concentrations of carbonaceous species during the rainy season were higher. The concentrations of PM2.5 mass and chemical species during the socioeconomic suppression period significantly decreased by 45%-61% compared to the values before this period. The OC/EC ratio (3.28 +/- 0.61) and char-EC/soot-EC (4.88 +/- 2.72) suggested that biomass burning, coal combustion, vehicle emissions, cooking activities are major PM2.5 sources in HCMC. Furthermore, the results of a concentration-weighted trajectory analysis suggested that the geological sources of PM2.5 were in the local areas of HCMC and the northeast provinces of Vietnam (where coal-fired power plants are located).
{"title":"Annual Variation of PM2.5 Chemical Composition in Ho Chi Minh City, Vietnam Including the COVID-19 Outbreak Period","authors":"N. Tran, Y. Fujii, V. X. Le, Doan Thien Chi Nguyen, H. Okochi, To Thi Hien, N. Takenaka","doi":"10.4209/aaqr.220312","DOIUrl":"https://doi.org/10.4209/aaqr.220312","url":null,"abstract":"PM2.5 was continuously collected in Ho Chi Minh City (HCMC), Vietnam, during the period from September 2019 to August 2020, which included the period of socioeconomic suppression caused by restrictions imposed in the face of the coronavirus disease of 2019. The concentrations of PM2.5 mass, water-soluble ions (WSIs), organic carbon (OC), elemental carbon (EC), and water-soluble organic carbon (WSOC) were determined to evaluate the seasonal variations in PM2.5, the effect of socioeconomic suppression on PM2.5, and potential PM2.5 sources in HCMC. The PM2.5 mass concentration during the sampling period was 28.44 +/- 11.55 mu g m(-3) (average +/- standard deviation). OC, EC, and total WSIs accounted for 30.7 +/- 6.6%, 9.7 +/- 2.9%, and 24.9 +/- 6.6% of the PM2.5 mass, respectively. WSOC contributed 46.4 +/- 10.1% to OC mass. NO3-, SO42-, and NH4+ were the dominant species in WSIs (72.7 +/- 17.7% of the total WSIs' mass). The concentrations of PM2.5 mass and total WSIs during the rainy season were lower than those during the dry season, whereas the concentrations of carbonaceous species during the rainy season were higher. The concentrations of PM2.5 mass and chemical species during the socioeconomic suppression period significantly decreased by 45%-61% compared to the values before this period. The OC/EC ratio (3.28 +/- 0.61) and char-EC/soot-EC (4.88 +/- 2.72) suggested that biomass burning, coal combustion, vehicle emissions, cooking activities are major PM2.5 sources in HCMC. Furthermore, the results of a concentration-weighted trajectory analysis suggested that the geological sources of PM2.5 were in the local areas of HCMC and the northeast provinces of Vietnam (where coal-fired power plants are located).","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":"70294364","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}
K. P. Dillon, Romie Tignat-Perrier, M. Joly, S. N. Grogan, Catherine Larose, P. Amato, G. Mainelis
Bioaerosols have impacts on atmospheric processes, as well as ecosystem and human health. Common bioaerosol collection methods include impaction, liquid impingement, filtration
{"title":"Comparison of Airborne Bacterial Populations Determined by Passive and Active Air Sampling at Puy de Dôme, France","authors":"K. P. Dillon, Romie Tignat-Perrier, M. Joly, S. N. Grogan, Catherine Larose, P. Amato, G. Mainelis","doi":"10.4209/aaqr.220403","DOIUrl":"https://doi.org/10.4209/aaqr.220403","url":null,"abstract":"Bioaerosols have impacts on atmospheric processes, as well as ecosystem and human health. Common bioaerosol collection methods include impaction, liquid impingement, filtration","PeriodicalId":7402,"journal":{"name":"Aerosol and Air Quality Research","volume":"301 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70295089","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}
Srishti Singh, Pratyush Agrawal, P. Kulkarni, H. Gautam, Meenakshi Kushwaha, V. Sreekanth
In this study, we combined state-of-the-art data modelling techniques (machine learning [ML] methods) and data from state-of-the-art low-cost particulate matter (PM) sensors (LCSs) to improve the accuracy of LCS-measured PM 2.5 (PM with aerodynamic diameter less than 2.5 microns) mass concentrations. We collocated nine LCSs and a reference PM 2.5 instrument for 9 months, covering all local seasons, in Bengaluru, India. Using the collocation data, we evaluated the performance of the LCSs and trained around 170 ML models to reduce the observed bias in the LCS-measured PM 2.5 . The ML models included (i) Decision Tree, (ii) Random Forest (RF), (iii) eXtreme Gradient Boosting, and (iv) Support Vector Regression (SVR). A hold-out validation was performed to assess the model performance. Model performance metrics included (i) coefficient of determination (R 2 ), (ii) root mean square error (RMSE), (iii) normalised RMSE, and (iv) mean absolute error. We found that the bias in the LCS PM 2.5 measurements varied across different LCS types (RMSE = 8– 29 µ g m –3 ) and that SVR models performed best in correcting the LCS PM 2.5 measurements. Hyperparameter tuning improved the performance of the ML models (except for RF). The performance of ML models trained with significant predictors (fewer in number than the number of all predictors, chosen based on recursive feature elimination algorithm) was comparable to that of the ‘all predictors’ trained models (except for RF). The performance of most ML models was better than that of the linear models. Finally, as a research objective, we introduced the collocated black carbon mass concentration measurements into the ML models but found no significant improvement in the model performance.
{"title":"Multiple PM Low-Cost Sensors, Multiple Seasons’ Data, and Multiple Calibration Models","authors":"Srishti Singh, Pratyush Agrawal, P. Kulkarni, H. Gautam, Meenakshi Kushwaha, V. Sreekanth","doi":"10.4209/aaqr.220428","DOIUrl":"https://doi.org/10.4209/aaqr.220428","url":null,"abstract":"In this study, we combined state-of-the-art data modelling techniques (machine learning [ML] methods) and data from state-of-the-art low-cost particulate matter (PM) sensors (LCSs) to improve the accuracy of LCS-measured PM 2.5 (PM with aerodynamic diameter less than 2.5 microns) mass concentrations. We collocated nine LCSs and a reference PM 2.5 instrument for 9 months, covering all local seasons, in Bengaluru, India. Using the collocation data, we evaluated the performance of the LCSs and trained around 170 ML models to reduce the observed bias in the LCS-measured PM 2.5 . The ML models included (i) Decision Tree, (ii) Random Forest (RF), (iii) eXtreme Gradient Boosting, and (iv) Support Vector Regression (SVR). A hold-out validation was performed to assess the model performance. Model performance metrics included (i) coefficient of determination (R 2 ), (ii) root mean square error (RMSE), (iii) normalised RMSE, and (iv) mean absolute error. We found that the bias in the LCS PM 2.5 measurements varied across different LCS types (RMSE = 8– 29 µ g m –3 ) and that SVR models performed best in correcting the LCS PM 2.5 measurements. Hyperparameter tuning improved the performance of the ML models (except for RF). The performance of ML models trained with significant predictors (fewer in number than the number of all predictors, chosen based on recursive feature elimination algorithm) was comparable to that of the ‘all predictors’ trained models (except for RF). The performance of most ML models was better than that of the linear models. Finally, as a research objective, we introduced the collocated black carbon mass concentration measurements into the ML models but found no significant improvement in the model performance.","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":"70295386","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}
Shengli Zhu, Zhaowen Wang, Kai Qu, Jun Xu, Ji Zhang, Haiyi Yang, Wenxin Wang, X. Sui, Minghua Wei, Houfeng Liu
Based on the PM 2.5 concentration in the autumn and winter of 2015–2019, the characteristics of urban air pollution in the eastern monsoon region of China were discussed. The spatial distribution and interregional influence of fine particle pollution under different synoptic weather and topography in the eastern monsoon region of China were illustrated. According to synoptic systems, regional PM 2.5 pollution episodes were classified into three categories, including Uniform Pressure field (UP, 60.00%), Pre-High Pressure (PreHP, 30.91%) and Inverted-Trough (IT, 9.09%). The K-Means algorithm combined with the HYSPLIT backward trajectory clustering analysis indicated four clusters under UP controlled, and under weak pressure field was responsible for the elevation of PM 2.5 concentration, where the Beijing-Tianjin-Hebei and its surrounding areas were the most polluted region. For PreHP, four clusters eased after cold front. For IT, three clusters were ascertained, and the severe PM 2.5 pollution area was in the central and southern of the North China Plain. This study provided a scientific basis for the joint prevention of PM 2.5 pollution based on topographic and meteorological characteristics in Eastern China.
{"title":"Spatial Characteristics and Influence of Topography and Synoptic Systems on PM2.5 in the Eastern Monsoon Region of China","authors":"Shengli Zhu, Zhaowen Wang, Kai Qu, Jun Xu, Ji Zhang, Haiyi Yang, Wenxin Wang, X. Sui, Minghua Wei, Houfeng Liu","doi":"10.4209/aaqr.220393","DOIUrl":"https://doi.org/10.4209/aaqr.220393","url":null,"abstract":"Based on the PM 2.5 concentration in the autumn and winter of 2015–2019, the characteristics of urban air pollution in the eastern monsoon region of China were discussed. The spatial distribution and interregional influence of fine particle pollution under different synoptic weather and topography in the eastern monsoon region of China were illustrated. According to synoptic systems, regional PM 2.5 pollution episodes were classified into three categories, including Uniform Pressure field (UP, 60.00%), Pre-High Pressure (PreHP, 30.91%) and Inverted-Trough (IT, 9.09%). The K-Means algorithm combined with the HYSPLIT backward trajectory clustering analysis indicated four clusters under UP controlled, and under weak pressure field was responsible for the elevation of PM 2.5 concentration, where the Beijing-Tianjin-Hebei and its surrounding areas were the most polluted region. For PreHP, four clusters eased after cold front. For IT, three clusters were ascertained, and the severe PM 2.5 pollution area was in the central and southern of the North China Plain. This study provided a scientific basis for the joint prevention of PM 2.5 pollution based on topographic and meteorological characteristics in Eastern China.","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":"70295423","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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