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Chen Q, Shao K, Zhang S. Enhanced PM2.5 estimation across China: An AOD-independent two-stage approach incorporating improved spatiotemporal heterogeneity representations. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 368:122107. [PMID: 39126840 DOI: 10.1016/j.jenvman.2024.122107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 07/02/2024] [Accepted: 08/03/2024] [Indexed: 08/12/2024]
Abstract
In China, population growth and aging have partially negated the public health benefits of air pollution control measures, underscoring the ongoing need for precise PM2.5 monitoring and mapping. Despite its prevalence, the satellite-derived Aerosol Optical Depth (AOD) method for estimating PM2.5 concentrations often encounters significant spatial data gaps. Additionally, current research still needs better representation of PM2.5 spatiotemporal heterogeneity. Addressing these challenges, we developed a two-stage model employing the Extreme Gradient Boosting (XGBoost) algorithm. By incorporating improved spatiotemporal factors, we achieved high-precision and full-coverage daily 1-km PM2.5 mappings across China for the year 2020 without utilizing AOD products. Specifically, Model 1 develops improved temporal encodings and a terrain classification factor (DC), while Model 2 constructs an enhanced spatial autocorrelation term (Ps) by integrating observed and estimated values. Notably, Model 2 excelled in 10-fold sample-based cross-validation, achieving a coefficient of determination of 0.948, a mean absolute error of 3.792 μg/m³, a root mean square error of 7.144 μg/m³, and a mean relative error of 14.171%. Feature importance and Shapley Additive exPlanations (SHAP) analyses determined the relative importance of predictors in model training and outcome prediction, while correlation analysis identified strong links between improved temporal encodings, PM2.5 concentrations, and significant meteorological factors. Two-way Partial Dependence Plots (PDPs) further explored the interactions among these factors and their impact on PM2.5 levels. Compared to traditional methods, improved temporal encodings align more closely with seasonal variations and synergize more effectively with meteorological factors. Besides, the structured nature of DC aids in model training, while the improved Ps more effectively captures PM2.5's spatial autocorrelation, outperforming traditional Ps. Overall, this study effectively represents spatiotemporal information, thereby boosting model accuracy and enabling seamless large-scale PM2.5 estimations. It provides deep insights into variables and models, providing significant implications for future air pollution research.
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Affiliation(s)
- Qingwen Chen
- College of Surveying and Geo-informatics, Tongji University, Shanghai, 200092, China.
| | - Kaiwen Shao
- College of Surveying and Geo-informatics, Tongji University, Shanghai, 200092, China.
| | - Songlin Zhang
- College of Surveying and Geo-informatics, Tongji University, Shanghai, 200092, China.
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2
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Araki S, Shimadera H, Chatani S, Kitayama K, Shima M. Long-term spatiotemporal variation of benzo[a]pyrene in Japan: Significant decrease in ambient concentrations, human exposure, and health risk. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 360:124650. [PMID: 39111529 DOI: 10.1016/j.envpol.2024.124650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 07/24/2024] [Accepted: 07/30/2024] [Indexed: 08/15/2024]
Abstract
Although Benzo[a]pyrene (BaP) is considered carcinogenic to humans, the health effects of exposure to ambient levels have not been sufficiently investigated. This study estimated the long-term spatiotemporal variation of BaP in Japan over nearly two decades at a fine spatial resolution of 1 km. This study aimed to obtain an accurate spatiotemporal distribution of BaP that can be used in epidemiological studies on the health effects of ambient BaP exposure. The annual BaP concentrations were estimated using an ensemble machine learning approach using various predictors, including the concentrations and emission intensities of the criteria air pollutants, and meteorological, land use, and traffic-related variables. The model performance, evaluated by location-based cross-validation, exhibited satisfactory accuracy (R2 of 0.693). Densely populated areas showed higher BaP levels and greater temporal reduction, whereas BaP levels remained higher in some industrial areas. The population-weighted BaP in 2018 was 0.12 ng m-3, a decrease of approximately 70% from its 2000 value of 0.44 ng m-3, which was also reflected in the estimated excess number of lung cancer incidences. Accordingly, the proportion of BaP exposure below 0.12 ng m-3, which is the BaP concentration associated with an excess lifetime cancer risk of 10-5, reached 67% in 2018. Our estimates can be used in epidemiological studies to assess the health effects of BaP exposure at ambient concentrations.
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Affiliation(s)
- Shin Araki
- Graduate School of Engineering, Osaka University, Suita, 565-0871, Japan.
| | - Hikari Shimadera
- Graduate School of Engineering, Osaka University, Suita, 565-0871, Japan.
| | - Satoru Chatani
- National Institute for Environmental Studies, Tsukuba, 305-8506, Japan.
| | - Kyo Kitayama
- National Institute for Environmental Studies, Tsukuba, 305-8506, Japan.
| | - Masayuki Shima
- Department of Public Health, School of Medicine, Hyogo Medical University, Nishinomiya, 663-8501, Japan.
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Asri AK, Newman GD, Tao Z, Zhu R, Chen HL, Lung SCC, Wu CD. What is the spatiotemporal pattern of benzene concentration spread over susceptible area surrounding the Hartman Park community, Houston, Texas? JOURNAL OF HAZARDOUS MATERIALS 2024; 474:134666. [PMID: 38815389 DOI: 10.1016/j.jhazmat.2024.134666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Revised: 05/07/2024] [Accepted: 05/19/2024] [Indexed: 06/01/2024]
Abstract
The Hartman Park community in Houston, Texas-USA, is in a highly polluted area which poses significant risks to its predominantly Hispanic and lower-income residents. Surrounded by dense clustering of industrial facilities compounds health and safety hazards, exacerbating environmental and social inequalities. Such conditions emphasize the urgent need for environmental measures that focus on investigating ambient air quality. This study estimated benzene, one of the most reported pollutants in Hartman Park, using machine learning-based approaches. Benzene data was collected in residential areas in the neighborhood and analyzed using a combination of five machine-learning algorithms (i.e., XGBR, GBR, LGBMR, CBR, RFR) through a newly developed ensemble learning model. Evaluations on model robustness, overfitting tests, 10-fold cross-validation, internal and stratified validation were performed. We found that the ensemble model depicted about 98.7% spatial variability of benzene (Adj. R2 =0.987). Through rigorous validations, stability of model performance was confirmed. Several predictors that contribute to benzene were identified, including temperature, developed intensity areas, leaking petroleum storage tank, and traffic-related factors. Analyzing spatial patterns, we found high benzene spread over areas near industrial zones as well as in residential areas. Overall, our study area was exposed to high benzene levels and requires extra attention from relevant authorities.
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Affiliation(s)
- Aji Kusumaning Asri
- Department of Geomatics, College of Engineering, National Cheng Kung University, Tainan 701, Taiwan, ROC.
| | - Galen D Newman
- Department of Landscape Architecture and Urban Planning, School of Architecture Texas A&M University, 3137 TAMU, College Station, TX 77843, USA
| | - Zhihan Tao
- Department of Landscape Architecture and Urban Planning, School of Architecture Texas A&M University, 3137 TAMU, College Station, TX 77843, USA
| | - Rui Zhu
- Department of Landscape Architecture and Urban Planning, School of Architecture Texas A&M University, 3137 TAMU, College Station, TX 77843, USA
| | - Hsiu-Ling Chen
- Department of Food Safety Hygiene and Risk Management, National Cheng Kung University, Tainan 701, Taiwan, ROC
| | - Shih-Chun Candice Lung
- Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan, ROC; Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan, ROC; Institute of Environmental Health, School of Public Health, National Taiwan University, Taipei, Taiwan, ROC
| | - Chih-Da Wu
- Department of Geomatics, College of Engineering, National Cheng Kung University, Tainan 701, Taiwan, ROC; National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan, ROC; Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung City 402, Taiwan, ROC; Research Center for Precision Environmental Medicine, Kaohsiung Medical University, Kaohsiung 804, Taiwan, ROC.
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Lin YC, Fan KC, Wu CD, Pan WC, Chen JC, Chao YP, Lai YJ, Chiu YL, Chuang YF. Yearly change in air pollution and brain aging among older adults: A community-based study in Taiwan. ENVIRONMENT INTERNATIONAL 2024; 190:108876. [PMID: 39002330 DOI: 10.1016/j.envint.2024.108876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 07/02/2024] [Accepted: 07/04/2024] [Indexed: 07/15/2024]
Abstract
BACKGROUND Air pollution is recognized as a modifiable risk factor for dementia, and recent evidence suggests that improving air quality could attenuate cognitive decline and reduce dementia risk. However, studies have yet to explore the effects of improved air quality on brain structures. This study aims to investigate the impact of air pollution reduction on cognitive functions and structural brain differences among cognitively normal older adults. METHODS Four hundred and thirty-one cognitively normal older adults were from the Epidemiology of Mild Cognitive Impairment study in Taiwan (EMCIT), a community-based cohort of adults aged 60 and older, between year 2017- 2021. Annual concentrations of PM2.5, NO2, O3, and PM10 at participants' residential addresses during the 10 years before enrollment were estimated using ensemble mixed spatial models. The yearly rate of change (slope) in air pollutants was estimated for each participant. Cognitive functions and structural brain images were collected during enrollment. The relationships between the rate of air pollution change and cognitive functions were examined using linear regression models. For air pollutants with significant findings in relation to cognitive function, we further explored the association with brain structure. RESULTS Overall, all pollutant concentrations, except O3, decreased over the 10-year period. The yearly rates of change (slopes) in PM2.5 and NO2 were correlated with better attention (PM2.5: r = -0.1, p = 0.047; NO2: r = -0.1, p = 0.03) and higher white matter integrity in several brain regions. These regions included anterior thalamic radiation, superior longitudinal fasciculus, inferior longitudinal fasciculus, corticospinal tract, and inferior fronto-occipital fasciculus. CONCLUSIONS Greater rate of reduction in air pollution was associated with better attention and attention-related white matter integrity. These results provide insight into the mechanism underlying the relationship between air pollution, brain health, and cognitive aging among older adults.
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Affiliation(s)
- Ying-Cen Lin
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Kang-Chen Fan
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chih-Da Wu
- Department of Geomatics, National Cheng Kung University, Tainan, Taiwan; National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan; Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung, Taiwan; Research Center for Precision Environmental Medicine, Koahsiung Medical University, Koahsiung, Taiwan
| | - Wen-Chi Pan
- Institute of Environmental and Occupational Health Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jiu-Chiuan Chen
- Departments of Population & Public Health Sciences and Neurology, University of Southern California, Keck School of Medicine, Los Angeles, CA, USA
| | - Yi-Ping Chao
- Department of Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan; Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan; Department of Otorhinolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Yen-Jun Lai
- Division of Medical Imaging, Department of Radiology, Far Eastern Memorial Hospital, New Taipei, Taiwan
| | - Yen-Ling Chiu
- Department of Medical Research, Far Eastern Memorial Hospital, Taipei, Taiwan; Graduate Program in Biomedical Informatics and Graduate Institute of Medicine, Yuan Ze University, Taoyuan, Taiwan; Graduate Institute of Clinical Medicine, National Taiwan University, Taipei, Taiwan
| | - Yi-Fang Chuang
- Institute of Public Health, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; International Health Program, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Psychiatry, Far Eastern Memorial Hospital, New Taipei, Taiwan; Health Innovation Center, National Yang Ming Chao Tung Univeristy, Taipei, Taiwan.
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De Ridder D, Ladoy A, Choi Y, Jacot D, Vuilleumier S, Guessous I, Joost S, Greub G. Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis. Front Public Health 2024; 12:1298177. [PMID: 38957202 PMCID: PMC11217542 DOI: 10.3389/fpubh.2024.1298177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 06/03/2024] [Indexed: 07/04/2024] Open
Abstract
Introduction Since its emergence in late 2019, the SARS-CoV-2 virus has led to a global health crisis, affecting millions and reshaping societies and economies worldwide. Investigating the determinants of SARS-CoV-2 diffusion and their spatiotemporal dynamics at high spatial resolution is critical for public health and policymaking. Methods This study analyses 194,682 georeferenced SARS-CoV-2 RT-PCR tests from March 2020 and April 2022 in the canton of Vaud, Switzerland. We characterized five distinct pandemic periods using metrics of spatial and temporal clustering like inverse Shannon entropy, the Hoover index, Lloyd's index of mean crowding, and the modified space-time DBSCAN algorithm. We assessed the demographic, socioeconomic, and environmental factors contributing to cluster persistence during each period using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), to consider non-linear and spatial effects. Results Our findings reveal important variations in the spatial and temporal clustering of cases. Notably, areas with flatter epidemics had higher total attack rate. Air pollution emerged as a factor showing a consistent positive association with higher cluster persistence, substantiated by both immission models and, to a lesser extent, tropospheric NO2 estimations. Factors including population density, testing rates, and geographical coordinates, also showed important positive associations with higher cluster persistence. The socioeconomic index showed no significant contribution to cluster persistence, suggesting its limited role in the observed dynamics, which warrants further research. Discussion Overall, the determinants of cluster persistence remained across the study periods. These findings highlight the need for effective air quality management strategies to mitigate air pollution's adverse impacts on public health, particularly in the context of respiratory viral diseases like COVID-19.
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Affiliation(s)
- David De Ridder
- Geographic Information Research and Analysis in Population Health (GIRAPH) Lab, Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland
- Geospatial Molecular Epidemiology Group (GEOME), Laboratory for Biological Geochemistry (LGB), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Anaïs Ladoy
- Geographic Information Research and Analysis in Population Health (GIRAPH) Lab, Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland
- Geospatial Molecular Epidemiology Group (GEOME), Laboratory for Biological Geochemistry (LGB), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Yangji Choi
- Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Damien Jacot
- Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Séverine Vuilleumier
- La Source School of Nursing, University of Applied Sciences and Arts Western Switzerland (HES-SO), Lausanne, Switzerland
| | - Idris Guessous
- Geographic Information Research and Analysis in Population Health (GIRAPH) Lab, Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland
- Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Stéphane Joost
- Geographic Information Research and Analysis in Population Health (GIRAPH) Lab, Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland
- Geospatial Molecular Epidemiology Group (GEOME), Laboratory for Biological Geochemistry (LGB), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- La Source School of Nursing, University of Applied Sciences and Arts Western Switzerland (HES-SO), Lausanne, Switzerland
| | - Gilbert Greub
- Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Infectious Diseases Service, Lausanne University Hospital, Lausanne, Switzerland
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Babaan J, Wong PY, Chen PC, Chen HL, Lung SCC, Chen YC, Wu CD. Geospatial artificial intelligence for estimating daytime and nighttime nitrogen dioxide concentration variations in Taiwan: A spatial prediction model. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 360:121198. [PMID: 38772239 DOI: 10.1016/j.jenvman.2024.121198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 05/23/2024]
Abstract
Nitrogen dioxide (NO2) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO2 levels throughout the day. This study aimed to address this gap by developing ensemble mixed spatial models (EMSM) using geo-artificial intelligence (Geo-AI) to examine the spatial and temporal variations of NO2 concentrations at a high resolution of 50m. These EMSMs integrated spatial modelling methods, including kriging, land use regression, machine learning, and ensemble learning. The models utilized 26 years of observed NO2 measurements, meteorological parameters, geospatial layers, and social and season-dependent variables as representative of emission sources. Separate models were developed for daytime and nighttime periods, which achieved high reliability with adjusted R2 values of 0.92 and 0.93, respectively. The study revealed that mean NO2 concentrations were significantly higher at nighttime (9.60 ppb) compared to daytime (5.61 ppb). Additionally, winter exhibited the highest NO2 levels regardless of time period. The developed EMSMs were utilized to generate maps illustrating NO2 levels pre and during COVID restrictions in Taiwan. These findings could aid epidemiological research on exposure risks and support policy-making and environmental planning initiatives.
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Affiliation(s)
- Jennieveive Babaan
- Department of Geodetic Engineering, University of the Philippines Diliman, Quezon City, Philippines
| | - Pei-Yi Wong
- Department of Environmental and Occupational Health, National Cheng Kung University, Tainan City, Taiwan
| | - Pau-Chung Chen
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, Taiwan; Institute of Environmental and Occupational Health Sciences, National Taiwan University College of Public Health, Taipei City, Taiwan; Department of Environmental and Occupational Medicine, National Taiwan University Hospital, Taipei City, Taiwan; Department of Public Health, National Taiwan University College of Public Health, Taipei City, Taiwan
| | - Hsiu-Ling Chen
- Department of Food Safety/Hygiene and Risk Management, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Shih-Chun Candice Lung
- Research Center for Environmental Changes, Academia Sinica, Taipei City, Taiwan; Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan; Institute of Environmental Health, School of Public Health, National Taiwan University, Taipei, Taiwan
| | - Yu-Cheng Chen
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
| | - Chih-Da Wu
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, Taiwan; Department of Geomatics, National Cheng Kung University, Tainan City, Taiwan; Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung City, Taiwan; Research Center for Precision Environmental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
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Asri AK, Lee HY, Chen YL, Wong PY, Hsu CY, Chen PC, Lung SCC, Chen YC, Wu CD. A machine learning-based ensemble model for estimating diurnal variations of nitrogen oxide concentrations in Taiwan. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 916:170209. [PMID: 38278267 DOI: 10.1016/j.scitotenv.2024.170209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 01/02/2024] [Accepted: 01/14/2024] [Indexed: 01/28/2024]
Abstract
Air pollution is inextricable from human activity patterns. This is especially true for nitrogen oxide (NOx), a pollutant that exists naturally and also as a result of anthropogenic factors. Assessing exposure by considering diurnal variation is a challenge that has not been widely studied. Incorporating 27 years of data, we attempted to estimate diurnal variations in NOx across Taiwan. We developed a machine learning-based ensemble model that integrated hybrid kriging-LUR, machine-learning, and an ensemble learning approach. Hybrid kriging-LUR was performed to select the most influential predictors, and machine-learning algorithms were applied to improve model performance. The three best machine-learning algorithms were suited and reassessed to develop ensemble learning that was designed to improve model performance. Our ensemble model resulted in estimates of daytime, nighttime, and daily NOx with high explanatory powers (Adj-R2) of 0.93, 0.98, and 0.94, respectively. These explanatory powers increased from the initial model that used only hybrid kriging-LUR. Additionally, the results depicted the temporal variation of NOx, with concentrations higher during the daytime than the nighttime. Regarding spatial variation, the highest NOx concentrations were identified in northern and western Taiwan. Model evaluations confirmed the reliability of the models. This study could serve as a reference for regional planning supporting emission control for environmental and human health.
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Affiliation(s)
- Aji Kusumaning Asri
- Department of Geomatics, College of Engineering, National Cheng Kung University, Tainan, Taiwan.
| | - Hsiao-Yun Lee
- Department of Leisure Industry and Health Promotion, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan.
| | - Yu-Ling Chen
- Department of Geomatics, College of Engineering, National Cheng Kung University, Tainan, Taiwan.
| | - Pei-Yi Wong
- Department of Environmental and Occupational Health, National Cheng Kung University, Tainan, Taiwan.
| | - Chin-Yu Hsu
- Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, Taiwan; Center for Environmental Sustainability and Human Health, Ming Chi University of Technology, Taiwan.
| | - Pau-Chung Chen
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan; Institute of Environmental and Occupational Health Sciences, National Taiwan University College of Public Health, Taipei, Taiwan; Department of Environmental and Occupational Medicine, National Taiwan University Hospital, Taipei, Taiwan; Department of Public Health, National Taiwan University College of Public Health, Taipei, Taiwan.
| | - Shih-Chun Candice Lung
- Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan; Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan; Institute of Environmental Health, School of Public Health, National Taiwan University, Taipei, Taiwan.
| | - Yu-Cheng Chen
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan; Department of Occupational Safety and Health, China Medical University, Taichung, Taiwan.
| | - Chih-Da Wu
- Department of Geomatics, College of Engineering, National Cheng Kung University, Tainan, Taiwan; National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan; Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung City 402, Taiwan.
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Chen HW, Chen CY, Lin GY. Impact assessment of spatial-temporal distribution of riverine dust on air quality using remote sensing data and numerical modeling. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:16048-16065. [PMID: 38308783 DOI: 10.1007/s11356-024-32226-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/24/2024] [Indexed: 02/05/2024]
Abstract
Soil erosion is a severe problem in Taiwan due to the steep terrain, fragile geology, and extreme climatic events resulting from global warming. Due to the rapidly changing hydrological conditions affecting the locations and the amount of transported sand and fine particles, timely impact evaluation and riverine dust control are difficult, particularly when resources are limited. To comprehend the impact of desertification in estuarine areas on the variation of air pollutant concentrations, this study utilized remote sensing technology coupled with an air pollutant dispersion model to determine the unit contribution of potential pollution sources and quantify the effect of riverine dust on air quality. The images of the downstream area of the Beinan River basin captured by Formosat-2 in May 2006 were used to analyze land use and land cover (LULC) composition. Subsequently, the diffusion model ISCST-3 based on Gaussian distribution was utilized to simulate the transport of PM across the study area. Finally, a mixed-integer programming model was developed to optimize resource allocation for dust control. Results reveal that sand deposition in specific river sections significantly influences regional air quality, owing to the unique local topography and wind field conditions. The present optimal plan model for regional air quality control further showed that after implementing engineering measures including water cover, revegetation, armouring cover, and revegetation, total PM concentrations would be reduced by 51%. The contribution equivalent calculation, using the air pollution diffusion model, was effectively integrated into the optimization model to formulate a plan for reducing riverine dust with limited resources based on air quality requirements.
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Affiliation(s)
- Ho-Wen Chen
- Department of Environmental Science and Engineering, Tung-Hai University, Taichung, Taiwan
| | - Chien-Yuan Chen
- Department of Civil and Water Resources Engineering, National Chiayi University, Chiayi, Taiwan
| | - Guan-Yu Lin
- Department of Environmental Science and Engineering, Tung-Hai University, Taichung, Taiwan.
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