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McCracken T, Chen P, Metcalf A, Fan C. Quantifying the impacts of Canadian wildfires on regional air pollution networks. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 928:172461. [PMID: 38615767 DOI: 10.1016/j.scitotenv.2024.172461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 04/16/2024]
Abstract
Wildfire smoke greatly impacts regional atmospheric systems, causing changes in the behavior of pollution. However, the impacts of wildfire smoke on pollution behavior are not easily quantifiable due to the complex nature of atmospheric systems. Air pollution correlation networks have been used to quantify air pollution behavior during ambient conditions. However, it is unknown how extreme pollution events impact these networks. Therefore, we propose a multidimensional air pollution correlation network framework to quantify the impacts of wildfires on air pollution behavior. The impacts are quantified by comparing two time periods, one during the 2023 Canadian wildfires and one during normal conditions with two complex network types for each period. In this study, the value network represents PM2.5 concentrations and the rate network represents the rate of change of PM2.5 concentrations. Wildfires' impacts on air pollution behavior are captured by structural changes in the networks. The wildfires caused a discontinuous phase transition during percolation in both network types which represents non-random organization of the most significant spatiotemporal correlations. Additionally, wildfires caused changes to the connectivity of stations leading to more interconnected networks with different influential stations. During the wildfire period, highly polluted areas are more likely to form connections in the network, quantified by an 86 % and 19 % increase in the connectivity of the value and rate networks respectively compared to the normal period. In this study, we create novel understandings of the impacts of wildfires on air pollution correlation networks, show how our method can create important insights into air pollution patterns, and discuss potential applications of our methodologies. This study aims to enhance capabilities for wildfire smoke exposure mitigation and response strategies.
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Affiliation(s)
- Teague McCracken
- School of Civil and Environmental Engineering, Clemson University, 455 Bracket Hall, Clemson, SC 29631, USA.
| | - Pei Chen
- Department of Computer Science and Engineering, Texas A&M University, L.F. Peterson Building, College Station, TX 77843, USA.
| | - Andrew Metcalf
- School of Civil and Environmental Engineering, Clemson University, 455 Bracket Hall, Clemson, SC 29631, USA.
| | - Chao Fan
- School of Civil and Environmental Engineering, Clemson University, 455 Bracket Hall, Clemson, SC 29631, USA.
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Abstract
Air pollution is a complex mixture of gases and particulate matter, with adsorbed organic and inorganic contaminants, to which exposure is lifelong. Epidemiological studies increasingly associate air pollution with multiple neurodevelopmental disorders and neurodegenerative diseases, findings supported by experimental animal models. This breadth of neurotoxicity across these central nervous system diseases and disorders likely reflects shared vulnerability of their inflammatory and oxidative stress-based mechanisms and a corresponding ability to produce brain metal dyshomeo-stasis. Future research to define the responsible contaminants of air pollution underlying this neurotoxicity is critical to understanding mechanisms of these diseases and disorders and protecting public health.
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Affiliation(s)
- Deborah A Cory-Slechta
- Department of Environmental Medicine, University of Rochester School of Medicine, Rochester, New York, USA;
| | - Alyssa Merrill
- Department of Environmental Medicine, University of Rochester School of Medicine, Rochester, New York, USA;
| | - Marissa Sobolewski
- Department of Environmental Medicine, University of Rochester School of Medicine, Rochester, New York, USA;
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Research on the Spatial Heterogeneity and Influencing Factors of Air Pollution: A Case Study in Shijiazhuang, China. ATMOSPHERE 2022. [DOI: 10.3390/atmos13050670] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Rapid urbanization causes serious air pollution and constrains the sustainable development of society. The influencing factors of urban air pollution are complex and diverse. Multiple factors act together to interact in influencing air pollution. However, most of the existing studies on the influencing factors of air pollution lack consideration of the interaction mechanisms between the factors. Using multisource data and geographical detectors, this study analyzed the spatial heterogeneity characteristics of air pollution in Shijiazhuang City, identified its main influencing factors, and analyzed the interaction effects among these factors. The results of spatial heterogeneity analysis indicate that the distribution of aerosol optical depth (AOD) has obvious agglomeration characteristics. High agglomeration areas are concentrated in the eastern plain areas, and low agglomeration areas are concentrated in the western mountainous areas. Forests (q = 0.620), slopes (q = 0.616), elevation (q = 0.579), grasslands (q = 0.534), and artificial surfaces (q = 0.506) are the main individual factors affecting AOD distribution. Among them, natural factors such as topography, ecological space, and wind speed are negatively correlated with AOD values, whereas the opposite is true for human factors such as roads, artificial surfaces, and population. Each factor can barely affect the air pollution status significantly alone, and the explanatory power of all influencing factors showed an improvement through the two-factor enhanced interaction. The associations of elevation ∩ artificial surface (q = 0.625), elevation ∩ NDVI (q = 0.622), and elevation ∩ grassland (q = 0.620) exhibited a high explanatory power on AOD value distribution, suggesting that the combination of multiple factors such as low altitude, high building density, and sparse vegetation can lead to higher AOD values. These results are conducive to the understanding of the air pollution status and its influencing factors, and in future, decision makers should adopt different strategies, as follows: (1) high-density built-up areas should be considered as the key areas of pollution control, and (2) a single-factor pollution control strategy should be avoided, and a multi-factor synergistic optimization strategy should be adopted to take full advantage of the interaction among the factors to address the air pollution problem more effectively.
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Zhao E, Kuo YM, Chen N. Assessment of water quality under various environmental features using a site-specific weighting water quality index. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 783:146868. [PMID: 33866161 DOI: 10.1016/j.scitotenv.2021.146868] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 03/26/2021] [Accepted: 03/27/2021] [Indexed: 06/12/2023]
Abstract
In a multiregional river system, environmental features such as natural conditions and anthropogenic activities vary among regions, resulting in spatiotemporal variations in water quality. Therefore, a robust water quality assessment method (e.g., water quality index [WQI]) that considers various environmental features is essential for water resources management. This study developed a min/max autocorrelation factor analysis (MAFA) based WQI framework (MAFAWQI). The statistical procedure reduces the bias of expert opinions. The MAFAWQI characterizes impaired water quality variables as indicators and assesses appropriate weighting values of indicators at each sampling site to reflect site-specific environmental features. The MAFAWQI was successful for assessing water quality in the middle and down streams of Han River in central China with site-specific pollution features such as nitrogen and phosphorus pollution related to multiple-source in tributaries, impacts of tributaries on the main stream, and phosphorus pollution related to nonpoint-source in agricultural regions. The MAFAWQI exhibited a balanced rating of water quality compared to the strict assessment method using a single indicator and the lenient assessment method using stationary weighting values of indicators. The MAFAWQI scores indicated that the water quality in tributaries and during the spring were significantly worse than those in and during the other regions and seasons in the middle and down streams of Han River, respectively. The framework and application of the MAFAWQI may provide a new perspective for developing WQIs.
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Affiliation(s)
- Enmin Zhao
- School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
| | - Yi-Ming Kuo
- School of Environmental Studies, China University of Geosciences, Wuhan 430074, China.
| | - Nengwang Chen
- Key Laboratory of the Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China; State Key Laboratory of Marine Environment Science, Xiamen University, Xiamen 361102, China
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Zhou D, Lin Z, Liu L, Qi J. Spatial-temporal characteristics of urban air pollution in 337 Chinese cities and their influencing factors. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:36234-36258. [PMID: 33751379 DOI: 10.1007/s11356-021-12825-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 02/02/2021] [Indexed: 06/12/2023]
Abstract
Urban air pollution, especially in the form of haze events, has become a serious threat to socio-economic development and public health in most developing countries. It is of great importance to assess the frequency of urban air pollution occurrence and its influencing factors. The objective of our study is to develop consistent methodologies for constructing an index system and for assessing the influencing factors of the urban air pollution occurrence based on the Driver-Pressure-State-Impact-Response (DPSIR) framework by incorporating spatial analysis, geographical detector, and geographically weighted regression models. The 27 influencing factors were selected for assessing their influences on the urban air pollution occurrence in 337 Chinese cities. The results indicate that the spatial pattern of the urban air pollution in China was mostly consistent with the Chinese population-based Hu Line. Urban air pollution frequently occurred in North China, Central China, Northeast China, and East China, and displayed strong seasonality. The influencing factors of urban air pollution were complex and diverse, varying from season to season. Influencing factor analysis also shows that the explanatory power between any two influencing factors was greater than that of a single influencing factor of the urban air pollution. Furthermore, most influencing factors had both positive and negative effects and local effects on urban air pollution. Finally, we put forward five suggestions on reducing urban air pollution occurrence, which can provide the basis and reference for the government to make policies on urban air pollution control in China.
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Affiliation(s)
- De Zhou
- Department of Land Resources Management, School of Public Administration, Zhejiang Gongshang University, 18 Xuezheng St., Xiasha University Town, Hangzhou, 310018, China.
| | - Zhulu Lin
- Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND, 58108, USA
| | - Liming Liu
- College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jialing Qi
- Department of Land Resources Management, School of Public Administration, Zhejiang Gongshang University, 18 Xuezheng St., Xiasha University Town, Hangzhou, 310018, China
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Lin YC, Shih HS, Lai CY, Tai JK. Investigating a Potential Map of PM 2.5 Air Pollution and Risk for Tourist Attractions in Hsinchu County, Taiwan. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E8691. [PMID: 33238515 PMCID: PMC7700626 DOI: 10.3390/ijerph17228691] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 11/17/2020] [Accepted: 11/19/2020] [Indexed: 01/04/2023]
Abstract
In the past few years, human health risks caused by fine particulate matters (PM2.5) and other air pollutants have gradually received attention. According to the Disaster Prevention and Protection Act of Taiwan's Government enforced in 2017, "suspended particulate matter" has officially been acknowledged as a disaster-causing hazard. The long-term exposure to high concentrations of air pollutants negatively affects the health of citizens. Therefore, the precise determination of the spatial long-term distribution of hazardous high-level air pollutants can help protect the health and safety of residents. The analysis of spatial information of disaster potentials is an important measure for assessing the risks of possible hazards. However, the spatial disaster-potential characteristics of air pollution have not been comprehensively studied. In addition, the development of air pollution potential maps of various regions would provide valuable information. In this study, Hsinchu County was chosen as an example. In the spatial data analysis, historical PM2.5 concentration data from the Taiwan Environmental Protection Administration (TWEPA) were used to analyze and estimate spatially the air pollution risk potential of PM2.5 in Hsinchu based on a geographic information system (GIS)-based radial basis function (RBF) spatial interpolation method. The probability that PM2.5 concentrations exceed a standard value was analyzed with the exceedance probability method; in addition, the air pollution risk levels of tourist attractions in Hsinchu County were determined. The results show that the air pollution risk levels of the different seasons are quite different. The most severe air pollution levels usually occur in spring and winter, whereas summer exhibits the best air quality. Xinfeng and Hukou Townships have the highest potential for air pollution episodes in Hsinchu County (approximately 18%). Hukou Old Street, which is one of the most important tourist attractions, has a relatively high air pollution risk. The analysis results of this study can be directly applied to other countries worldwide to provide references for tourists, tourism resource management, and air quality management; in addition, the results provide important information on the long-term health risks for local residents in the study area.
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Affiliation(s)
- Yuan-Chien Lin
- Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan; (H.-S.S.); (C.-Y.L.); (J.-K.T.)
- Research Center for Hazard Mitigation and Prevention, National Central University, Taoyuan 32001, Taiwan
| | - Hua-San Shih
- Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan; (H.-S.S.); (C.-Y.L.); (J.-K.T.)
| | - Chun-Yeh Lai
- Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan; (H.-S.S.); (C.-Y.L.); (J.-K.T.)
| | - Jen-Kuo Tai
- Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan; (H.-S.S.); (C.-Y.L.); (J.-K.T.)
- Fire Bureau, Hsinchu County Government, Hsinchu County 30295, Taiwan
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Zhu Y, Zhan Y, Wang B, Li Z, Qin Y, Zhang K. Spatiotemporally mapping of the relationship between NO 2 pollution and urbanization for a megacity in Southwest China during 2005-2016. CHEMOSPHERE 2019; 220:155-162. [PMID: 30583207 DOI: 10.1016/j.chemosphere.2018.12.095] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Revised: 12/08/2018] [Accepted: 12/11/2018] [Indexed: 06/09/2023]
Abstract
Nitrogen dioxide (NO2) significantly contributes to air pollution. Long-term NO2 exposure is harmful to human health. The NO2 pollution in China has surpassed developed countries and attracts international attention. To understand the spatial and temporal distributions of NO2 across Chengdu in Southwest China, a random forest (RF) model was developed based on NO2 environmental monitoring data, the Ozone Monitoring Instrument (OMI) satellite retrievals, and geographic covariates. The RF model showed good performance with a cross validation R2 of 0.77, and a root mean square error (RMSE) of 11.0 μg/m3. The ground-level NO2 concentrations of Chengdu for 2005-2016 were predicted using the developed model with the multiyear population weighted NO2 concentration being 41.7 ± 11.7 μg/m3. The predicted NO2 concentrations exhibited a clear seasonal variation trend with winter being the highest and summer being the lowest. Furthermore, higher NO2 concentrations in the downtown areas were observed than that in the rural areas indicating the former being attributed to more anthropogenic sources. The population weighted NO2 concentrations with deseasonlization were relatively high during 2011-2013. The NO2 concentration increased at a rate of 0.81 μg/m3/year before 2011 (43.4 ± 11.2 μg/m3) and decreased at a rate of -1.03 μg/m3/year after 2013 (44.8 ± 12.8 μg/m3).
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Affiliation(s)
- Yijing Zhu
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Yu Zhan
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Bin Wang
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Zhi Li
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Yuanqing Qin
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Kaishan Zhang
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China.
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