Aerosol and Air Quality Research最新文献

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"title":"Prevalence, Dispersion and Nature of Bioaerosols over a Solid Landfill Site in Central India","authors":"Dayaram Bhoyar Priyanka, Pooja Kamdi, Amit P. Bafana, P. Devara, Servana Sivanesan, Amrit Kumar, K. Krishnamurthi","doi":"10.4209/aaqr.220431","DOIUrl":"https://doi.org/10.4209/aaqr.220431","url":null,"abstract":"Bioaerosols (or biological aerosols) consist of aerosol particles that originate biologically either as fully active component or as whole or part of inactive fragments. They are ubiquitously present in the atmospheric environment. They are the least investigated pollutants due to their complex structure and composition. The effects of bioaerosols, originating due to the processes, such as wastewater management, handling of sludge, composting, municipal solid waste","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":"70295521","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":"Strategy Design of PM2.5 Controlling for Northern Thailand","authors":"Karuna Jainontee, Prapat Pongkiatkul, Ying-Lin Wang, Roy J.F. Weng, Yi-Ting Lu, Ting Wang, Wang-Kun Chen","doi":"10.4209/aaqr.220432","DOIUrl":"https://doi.org/10.4209/aaqr.220432","url":null,"abstract":"The emission of fine particulate matter (PM 2.5 ) in dry season from the open biomass burning has caused a long-term negative impact on residents’ health in Northern Thailand. This study takes Chiang Mai and Chiang Rai provinces in Northern Thailand as the study areas to identify pollution episodes, analyze PM 2.5 source trajectories, and finally propose pollution control strategies accordingly. PM 2.5 levels during 2019–2021 of three representative air pollution monitoring stations (i.e., Chaing Mai-35T, Chiang Rai-57T, and Mae Sai-73T) in these two provinces were collected and analyzed. The Air Quality Index (AQI) defined by PM 2.5 level higher than 91 µ g m –3 causing serious adverse health effects was adopted to define periods having pollution levels, and the days of the air pollution episodes were identified. Based on these episodes, we applied the backward trajectory model to identify the sources of pollutants. Results showed that PM 2.5 levels were significantly higher between February to April compared with other months during 2019– 2021 at all three monitoring stations, indicating the severity of PM 2.5 episode during the dry season. The backward trajectory demonstrated that air mass transported through the Northern Thailand or nearby mountain areas (categorized as long-or short-transport-distance) contributed up to 21.6% and 75.9% of the total air mass, respectively. Although residents in these mountainous areas are accustomed to the biomass burning, we suggested that there should be urgent needs for the improvement of the long-term air quality in these two provinces. Therefore, this study proposes some control strategies including improvement of prevention knowledge, increase of the risk perception, cultivation of the protection behavior, and intensification of the social influence. In addition to reducing biomass burning pollution, this improvement plan also has a co-benefit of achieving resources recycling concomitantly. Providing effective management strategies may reduce the adverse health effects to Thai residents.","PeriodicalId":7402,"journal":{"name":"Aerosol and Air Quality Research","volume":"10 23 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70295758","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":"Sources of PM2.5 Oxidative Potential during Haze and Non-haze Seasons in Chiang Mai, Thailand","authors":"Pitakchon Ponsawansong, T. Prapamontol, K. Rerkasem, S. Chantara, Kraichat Tantrakarnapa, S. Kawichai, Guoxing Li, Cao Fang, Xiaochuan Pan, Yanlin Zhang","doi":"10.4209/aaqr.230030","DOIUrl":"https://doi.org/10.4209/aaqr.230030","url":null,"abstract":"Dithiothreitol (DTT) assay is an acellular technique used to investigate the oxidative potential (OP) of chemical substances bound on PM, which may potentially lead to oxidative stress after exposure. In this study, the source contributions of 16 high priority polycyclic aromatic hydrocarbons (PAHs), designated by the United States Environmental Protection Agency (U.S. EPA), and 10 species of water-soluble inorganic ions bound on PM 2.5 and their OP were investigated using DTT assay. The 24-hr ambient PM 2.5 samples were collected throughout 2018–2019 and the analyzed OP was compared during haze episodes, which generally occurs in the dry season, and non-haze rainy season in the Chiang Mai-Lamphun basin. During haze episodes, DTTv activity was positively correlated with 4–5 rings PAHs including fluoranthene (Fla) pyrene (Pyr), benzo[a]anthracene (BaA), chrysene (Chr), benzo[b]fluoranthene (BbF) and benzo[k]fluoranthene (BkF) with coefficient ranging from 0.327 to 0.545, p = 0.002 to 0.009 (Pearson’s correlation). Inorganic ions, particularly NH 4+ , SO 42– , and NO 3– , which are the tracers of secondary inorganic aerosol (SIA), were positively correlated with DTTv activity (r = 0.394 to 0.659. p = 0.000 to 0.047; Spearman’s correlation). Positive matrix factorization (PMF) indicated the biomass burning factor had the highest contribution (57.9%) to PM 2.5 during haze episodes, followed by SIA (26.2%), and vehicle exhausts (7.8%). Furthermore, multiple linear regression (MLR) showed that biomass burning was the highest contributor to DTTv (43.0%). These results suggest that during haze episodes, higher levels of PM 2.5 and its chemical compositions play a crucial role on OP, particularly DTTv activity, which may induce oxidative stress in human body.","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":"70296784","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":"Optical Performance of Sodium Tungsten Bronze Particles in Transparent Matrix: An Ensemble Particle Modeling Study","authors":"Hao Tu, Da-Ren Chen","doi":"10.4209/aaqr.230085","DOIUrl":"https://doi.org/10.4209/aaqr.230085","url":null,"abstract":"Abstract","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":"70297218","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}