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9. Characteristic Analysis and Health Risk Assessment of PM 2.5 and VOCs in Tianjin Based on High-Resolution Online Data.

Author

Huangfu, Yanqi, Wang, Feng, Dai, Qili, Liang, Danni, Shi, Guoliang, and Feng, Yinchang

Subjects
HEALTH risk assessment, VOLATILE organic compounds, EMISSIONS (Air pollution), AIR quality management, AIR pollution
Abstract

This study leveraged 2019 online data of particulate matter (PM2.5) and volatile organic compounds (VOCs) in Tianjin to analyze atmospheric pollution characteristics. PM2.5 was found to be primarily composed of water-soluble ions, with nitrates as the dominant component, while VOCs were predominantly alkanes, followed by alkenes and aromatic hydrocarbons, with notable concentrations of propane, ethane, ethylene, toluene, and benzene. The receptor model identified six major sources of PM2.5 and seven major sources of VOCs. The secondary source is the main contribution source, while motor vehicles and coal burning are important primary contribution sources in PM2.5. And, industrial processes and natural gas volatilization were considered major contributors for VOCs. A health risk assessment indicated negligible non-carcinogenic risks but potential carcinogenic risks from trace metals As and Cr, and benzene within VOCs, underscoring the necessity for focused public health measures. A risk attribution analysis attributed As and Cr in PM to coal combustion and vehicular emissions. Benzene in VOCs primarily originates from fuel evaporation, and industrial and vehicular emissions. These findings underscore the potential for reducing health risks from PM and VOCs through enhanced regulation of emissions in coal, industry, and transportation. Such strategies are vital for advancing air quality management and safeguarding public health. [ABSTRACT FROM AUTHOR]

Published
2024
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19. The effect of emission source chemical profiles on simulated PM2.5 components: sensitivity analysis with the Community Multiscale Air Quality (CMAQ) modeling system version 5.0.2.

Author

Luo, Zhongwei, Han, Yan, Hua, Kun, Zhang, Yufen, Wu, Jianhui, Bi, Xiaohui, Dai, Qili, Liu, Baoshuang, Chen, Yang, Long, Xin, and Feng, Yinchang

Subjects
PARTICULATE matter, AIR quality, AIR pollution control, HEALTH risk assessment, EMISSION inventories
Abstract

A chemical transport model (CTM) is an essential tool for air quality prediction and management, widely used in air pollution control and health risk assessment. However, the current models do not perform very well in reproducing the observations of some major chemical components, for example, sulfate, nitrate, ammonium and organic carbon. Studies have suggested that the uncertainties in the model chemical mechanism, source emission inventory and meteorological field can cause inaccurate simulation results. Still, the emission source profile (used to create speciated emission inventories for CTMs) of PM 2.5 has not been fully taken into account in current numerical simulation. Based on the characteristics and variation rules of chemical components in typical PM 2.5 sources, different simulation scenarios were designed and the sensitivity of simulated PM 2.5 components to the source chemical profile was explored. Our findings showed that the influence of source profile changes on simulated PM 2.5 components' concentrations cannot be ignored. Simulation results of some components were sensitive to the adopted source profile in CTMs. Moreover, there was a linkage effect: the variation in some components in the source profile would bring changes to the simulated results of other components. These influences are connected to chemical mechanisms of the model since the variation in species allocations in emission sources can affect the potential composition and phase state of aerosols, chemical reaction priority, and multicomponent chemical balance in thermodynamic equilibrium systems. We also found that the perturbation of the PM 2.5 source profile caused variation in simulated gaseous pollutants, which indirectly indicated that the perturbation of source profile would affect the simulation of secondary PM 2.5 components. Our paper highlights the necessity of paying enough attention to the representativeness and timeliness of the source profile when using CTMs for simulation. [ABSTRACT FROM AUTHOR]

Published
2023
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24. The Characteristics of Heavy Ozone Pollution Episodes and Identification of the Primary Driving Factors Using a Generalized Additive Model (GAM) in an Industrial Megacity of Northern China

Author

Diao, Liuli, primary, Bi, Xiaohui, additional, Zhang, Wenhui, additional, Liu, Baoshuang, additional, Wang, Xuehan, additional, Li, Linxuan, additional, Dai, Qili, additional, Zhang, Yufen, additional, Wu, Jianhui, additional, and Feng, Yinchang, additional

Published
2021
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30. Global Air Quality and COVID-19 Pandemic: Do We Breathe Cleaner Air?

Author

Amouei Torkmahalleh, Mehdi, Akhmetvaliyeva, Zarina, Omran, Ali Darvishi, Faezeh Darvish Omran, Faezeh, Kazemitabar, Mohadeseh, Naseri, Mahtab, Naseri, Motahareh, Sharifi, Hamed, Malekipirbazari, Milad, Kwasi Adotey, Enoch, Gorjinezhad, Soudabeh, Eghtesadi, Neda, Sabanov, Sergei, Alastuey, Andrés, De Fátima Andrade, María, Buonanno, Giorgio, Carbone, Samara, Cárdenas-fuentes, Diego Ernesto, Cassee, Flemming R., Dai, Qili, Henríquez, Andrés, Hopke, Philip K., Keronen, Petri, Khwaja, Haider Abbas, Kim, Jong, Kulmala, Markku, Kumar, Prashant, Kushta, Jonilda, Kuula, Joel, Massagué, Jordi, Mitchell, Tamsin, Mooibroek, Dennis, Morawska, Lidia, Niemi, Jarkko V., Ngagine, Soulemane Halif, Norman, Michael, Oyama, Beatríz, Oyola, Pedro, Öztürk, Fatma, Petäjä, Tuukka, Querol, Xavier, Rashidi, Yousef, Reyes, Felipe, Ross-jones, Matthew, Salthammer, Tunga, Savvides, Chrysanthos, Stabile, Luca, Sjöberg, Karin, Söderlund, Karin, Sunder Raman, Ramya, Timonen, Hilkka, Umezawa, Masakazu, Viana, Mar, Xie, Shanju, Amouei Torkmahalleh, Mehdi, Akhmetvaliyeva, Zarina, Omran, Ali Darvishi, Faezeh Darvish Omran, Faezeh, Kazemitabar, Mohadeseh, Naseri, Mahtab, Naseri, Motahareh, Sharifi, Hamed, Malekipirbazari, Milad, Kwasi Adotey, Enoch, Gorjinezhad, Soudabeh, Eghtesadi, Neda, Sabanov, Sergei, Alastuey, Andrés, De Fátima Andrade, María, Buonanno, Giorgio, Carbone, Samara, Cárdenas-fuentes, Diego Ernesto, Cassee, Flemming R., Dai, Qili, Henríquez, Andrés, Hopke, Philip K., Keronen, Petri, Khwaja, Haider Abbas, Kim, Jong, Kulmala, Markku, Kumar, Prashant, Kushta, Jonilda, Kuula, Joel, Massagué, Jordi, Mitchell, Tamsin, Mooibroek, Dennis, Morawska, Lidia, Niemi, Jarkko V., Ngagine, Soulemane Halif, Norman, Michael, Oyama, Beatríz, Oyola, Pedro, Öztürk, Fatma, Petäjä, Tuukka, Querol, Xavier, Rashidi, Yousef, Reyes, Felipe, Ross-jones, Matthew, Salthammer, Tunga, Savvides, Chrysanthos, Stabile, Luca, Sjöberg, Karin, Söderlund, Karin, Sunder Raman, Ramya, Timonen, Hilkka, Umezawa, Masakazu, Viana, Mar, and Xie, Shanju

Abstract

The global spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has challenged most countries worldwide. It was quickly recognized that reduced activities (lockdowns) during the Coronavirus Disease of 2019 (COVID-19) pandemic produced major changes in air quality. Our objective was to assess the impacts of COVID-19 lockdowns on ground-level PM2.5, NO2, and O3 concentrations on a global scale. We obtained data from 34 countries, 141 cities, and 458 air monitoring stations on 5 continents (few data from Africa). On a global average basis, a 34.0% reduction in NO2 concentration and a 15.0% reduction in PM2.5 were estimated during the strict lockdown period (until April 30, 2020). Global average O3 concentration increased by 86.0% during this same period. Individual country and continent-wise comparisons have been made between lockdown and business-as-usual periods. Universally, NO2 was the pollutant most affected by the COVID-19 pandemic. These effects were likely because its emissions were from sources that were typically restricted (i.e., surface traffic and non-essential industries) by the lockdowns and its short lifetime in the atmosphere. Our results indicate that lockdown measures and resulting reduced emissions reduced exposure to most harmful pollutants and could provide global-scale health benefits. However, the increased O3 may have substantially reduced those benefits and more detailed health assessments are required to accurately quantify the health gains. At the same, these restrictions were obtained at substantial economic costs and with other health issues (depression, suicide, spousal abuse, drug overdoses, etc.). Thus, any similar reductions in air pollution would need to be obtained without these extensive economic and other consequences produced by the imposed activity reductions.

Published
2021

31. Global Air Quality and COVID-19 Pandemic: Do We Breathe Cleaner Air?

Author

Alastuey, Andrés [0000-0002-5453-5495], Querol, Xavier [0000-0002-6549-9899], Viana, Mar [0000-0002-4073-3802], Torkmahalleh, Mehdi Amouei, Akhmetvaliyeva, Zarina, Darvishi Omran, Ali, Darvish Omran, Faezeh, Kazemitabar, Mohadeseh, Naseri, Mahtab, Naseri, Motahareh, Sharifi, Hamed, Malekipirbazari, Milad, Kwasi Adotey, Enoch, Gorjinezhad, Soudabeh, Eghtesadi, Neda, Sabanov, Sergei, Alastuey, Andrés, Andrade, María de Fátima, Buonanno, Giorgio, Carbone, Samara, Cárdenas-Fuentes, Diego Ernesto, Cassee, Flemming R., Dai, Qili, Henríquez, Andrés, Hopke, Philip K., Keronen, Petri, Khwaja, Haider Abbas, Kim, Jong, Kulmala, Markku, Kumar, Prashant, Kushta, Jonilda, Kuula, Joel, Massagué, Jordi, Mitchell, Tamsin, Mooibroek, Dennis, Morawska, Lidia, Niemi, Jarkko V., Ngagine, Soulemane Halif, Norman, Michael, Oyama, Beatriz, Oyola, Pedro, Öztürk, Fatma, Petäjä, Tuukka, Querol, Xavier, Rashidi, Yousef, Reyes, Felipe, Ross-Jones, Matthew, Salthammer, Tunga, Savvides, Chrysanthos, Stabile, Luca, Sjöberg, Karin, Söderlund, Karin, Sunder Raman, Ramya, Timonen, Hilkka, Umezawa, Masakazu, Viana, Mar, Xie, Shanju, Alastuey, Andrés [0000-0002-5453-5495], Querol, Xavier [0000-0002-6549-9899], Viana, Mar [0000-0002-4073-3802], Torkmahalleh, Mehdi Amouei, Akhmetvaliyeva, Zarina, Darvishi Omran, Ali, Darvish Omran, Faezeh, Kazemitabar, Mohadeseh, Naseri, Mahtab, Naseri, Motahareh, Sharifi, Hamed, Malekipirbazari, Milad, Kwasi Adotey, Enoch, Gorjinezhad, Soudabeh, Eghtesadi, Neda, Sabanov, Sergei, Alastuey, Andrés, Andrade, María de Fátima, Buonanno, Giorgio, Carbone, Samara, Cárdenas-Fuentes, Diego Ernesto, Cassee, Flemming R., Dai, Qili, Henríquez, Andrés, Hopke, Philip K., Keronen, Petri, Khwaja, Haider Abbas, Kim, Jong, Kulmala, Markku, Kumar, Prashant, Kushta, Jonilda, Kuula, Joel, Massagué, Jordi, Mitchell, Tamsin, Mooibroek, Dennis, Morawska, Lidia, Niemi, Jarkko V., Ngagine, Soulemane Halif, Norman, Michael, Oyama, Beatriz, Oyola, Pedro, Öztürk, Fatma, Petäjä, Tuukka, Querol, Xavier, Rashidi, Yousef, Reyes, Felipe, Ross-Jones, Matthew, Salthammer, Tunga, Savvides, Chrysanthos, Stabile, Luca, Sjöberg, Karin, Söderlund, Karin, Sunder Raman, Ramya, Timonen, Hilkka, Umezawa, Masakazu, Viana, Mar, and Xie, Shanju

Abstract

The global spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has challenged most countries worldwide. It was quickly recognized that reduced activities (lockdowns) during the Coronavirus Disease of 2019 (COVID-19) pandemic produced major changes in air quality. Our objective was to assess the impacts of COVID-19 lockdowns on ground-level PM2.5, NO2, and O3 concentrations on a global scale. We obtained data from 34 countries, 141 cities, and 458 air monitoring stations on 5 continents (few data from Africa). On a global average basis, a 34.0% reduction in NO2 concentration and a 15.0% reduction in PM2.5 were estimated during the strict lockdown period (until April 30, 2020). Global average O3 concentration increased by 86.0% during this same period. Individual country and continent-wise comparisons have been made between lockdown and business-as-usual periods. Universally, NO2 was the pollutant most affected by the COVID-19 pandemic. These effects were likely because its emissions were from sources that were typically restricted (i.e., surface traffic and non-essential industries) by the lockdowns and its short lifetime in the atmosphere. Our results indicate that lockdown measures and resulting reduced emissions reduced exposure to most harmful pollutants and could provide global-scale health benefits. However, the increased O3 may have substantially reduced those benefits and more detailed health assessments are required to accurately quantify the health gains. At the same, these restrictions were obtained at substantial economic costs and with other health issues (depression, suicide, spousal abuse, drug overdoses, etc.). Thus, any similar reductions in air pollution would need to be obtained without these extensive economic and other consequences produced by the imposed activity reductions.

Published
2021

35. Global Air Quality and COVID-19 Pandemic: Do We Breathe Cleaner Air?

Author

Amouei Torkmahalleh, Mehdi, primary, Akhmetvaliyeva, Zarina, additional, Omran, Ali Darvishi, additional, Faezeh Darvish Omran, Faezeh, additional, Kazemitabar, Mohadeseh, additional, Naseri, Mahtab, additional, Naseri, Motahareh, additional, Sharifi, Hamed, additional, Malekipirbazari, Milad, additional, Kwasi Adotey, Enoch, additional, Gorjinezhad, Soudabeh, additional, Eghtesadi, Neda, additional, Sabanov, Sergei, additional, Alastuey, Andrés, additional, de Fátima Andrade, María, additional, Buonanno, Giorgio, additional, Carbone, Samara, additional, Cárdenas-Fuentes, Diego Ernesto, additional, Cassee, Flemming R., additional, Dai, Qili, additional, Henríquez, Andrés, additional, Hopke, Philip K., additional, Keronen, Petri, additional, Khwaja, Haider Abbas, additional, Kim, Jong, additional, Kulmala, Markku, additional, Kumar, Prashant, additional, Kushta, Jonilda, additional, Kuula, Joel, additional, Massagué, Jordi, additional, Mitchell, Tamsin, additional, Mooibroek, Dennis, additional, Morawska, Lidia, additional, Niemi, Jarkko V., additional, Ngagine, Soulemane Halif, additional, Norman, Michael, additional, Oyama, Beatríz, additional, Oyola, Pedro, additional, Öztürk, Fatma, additional, Petäjä, Tuukka, additional, Querol, Xavier, additional, Rashidi, Yousef, additional, Reyes, Felipe, additional, Ross-Jones, Matthew, additional, Salthammer, Tunga, additional, Savvides, Chrysanthos, additional, Stabile, Luca, additional, Sjöberg, Karin, additional, Söderlund, Karin, additional, Sunder Raman, Ramya, additional, Timonen, Hilkka, additional, Umezawa, Masakazu, additional, Viana, Mar, additional, and Xie, Shanju, additional

Published
2021
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37. Chemical characteristics and source apportionment of PM2.5 using PMF modelling coupled with 1-hr resolution online air pollutant dataset for Linfen, China

Author

Li, Yafei, primary, Liu, Baoshuang, additional, Xue, Zhigang, additional, Zhang, Yufen, additional, Sun, Xiaoyun, additional, Song, Congbo, additional, Dai, Qili, additional, Fu, Ruichen, additional, Tai, Yonggang, additional, Gao, Jinyu, additional, Zheng, Yajun, additional, and Feng, Yinchang, additional

Published
2020
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41. Chemical nature of PM2.5 and PM10 in Xi'an, China: Insights into primary emissions and secondary particle formation

Author

Dai, Qili, primary, Bi, Xiaohui, additional, Liu, Baoshuang, additional, Li, Liwei, additional, Ding, Jing, additional, Song, Wenbin, additional, Bi, Shiyang, additional, Schulze, Benjamin C., additional, Song, Congbo, additional, Wu, Jianhui, additional, Zhang, Yufen, additional, Feng, Yinchang, additional, and Hopke, Philip K., additional

Published
2018
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42. Effectiveness evaluation of temporary emission control action in 2016 in winter in Shijiazhuang, China

Author

Liu, Baoshuang, primary, Cheng, Yuan, additional, Zhou, Ming, additional, Liang, Danni, additional, Dai, Qili, additional, Wang, Lu, additional, Jin, Wei, additional, Zhang, Lingzhi, additional, Ren, Yibin, additional, Zhou, Jingbo, additional, Dai, Chunling, additional, Xu, Jiao, additional, Wang, Jiao, additional, Feng, Yinchang, additional, and Zhang, Yufen, additional

Published
2018
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43. Air pollution in China: Status and spatiotemporal variations

Author

Song, Congbo, primary, Wu, Lin, additional, Xie, Yaochen, additional, He, Jianjun, additional, Chen, Xi, additional, Wang, Ting, additional, Lin, Yingchao, additional, Jin, Taosheng, additional, Wang, Anxu, additional, Liu, Yan, additional, Dai, Qili, additional, Liu, Baoshuang, additional, Wang, Ya-nan, additional, and Mao, Hongjun, additional

Published
2017
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50. Life form dynamics of the tree layer in evergreen and deciduous broad-leaved mixed forest during 1996–2017 in Tianmu Mountains, eastern China

Author

Bai, Cheng, You, Shixue, Ku, Weipeng, Dai, Qilin, Wang, Zhengyi, Zhao, Mingshui, and Yu, Shuquan

Subjects
Forestry, SD1-669.5
Abstract

In the forest areas of eastern China, there is a change from forest dominated by deciduous broad-leaved trees to forest dominated by evergreen broad-leaved trees as the latitude or altitude decreases. Different life forms have different survival strategies to deal with climate change, and studying the life form dynamics of the tree layers in the mixed forest in eastern China, with increasing temperature, can help us understand how the forest responds. This study was performed in a 1 ha plot in evergreen and deciduous broad-leaved mixed forest in Tianmu Mountain National Nature Reserve. Based on the data from two surveys (1996 and 2017), the changes in life form composition and biodiversity over the past 21 years were analyzed. We obtained the following results: (1) The proportion of evergreen trees increased from 55.0% in 1996 to 67.5% in 2017, and the dominance of evergreen species was enhanced. (2) The diversity of both life forms increased, and the tree species were more abundant. (3) The average annual recruitment rate of the evergreen species was 2.1% greater than their mortality rate, and the average annual recruitment rate of the deciduous species was 0.5% less than their mortality rate. (4) The competition among the trees in the small-diameter class (10 cm ⤠DBH < 20 cm) was fierce for many tree species. The proportion of the evergreen species in the small-diameter class was high. The life forms making up the mixed climax forest community has changed over the past 21 years, with the proportion and dominance of evergreen trees increasing significantly.

Published
2020
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