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Xia Y, McCracken T, Liu T, Chen P, Metcalf A, Fan C. Understanding the Disparities of PM2.5 Air Pollution in Urban Areas via Deep Support Vector Regression. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:8404-8416. [PMID: 38698567 DOI: 10.1021/acs.est.3c09177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
In densely populated urban areas, PM2.5 has a direct impact on the health and quality of residents' life. Thus, understanding the disparities of PM2.5 is crucial for ensuring urban sustainability and public health. Traditional prediction models often overlook the spillover effects within urban areas and the complexity of the data, leading to inaccurate spatial predictions of PM2.5. We propose Deep Support Vector Regression (DSVR) that models the urban areas as a graph, with grid center points as the nodes and the connections between grids as the edges. Nature and human activity features of each grid are initialized as the representation of each node. Based on the graph, DSVR uses random diffusion-based deep learning to quantify the spillover effects of PM2.5. It leverages random walk to uncover more extensive spillover relationships between nodes, thereby capturing both the local and nonlocal spillover effects of PM2.5. And then it engages in predictive learning using the feature vectors that encapsulate spillover effects, enhancing the understanding of PM2.5 disparities and connections across different regions. By applying our proposed model in the northern region of New York for predictive performance analysis, we found that DSVR consistently outperforms other models. During periods of PM2.5 surges, the R-square of DSVR reaches as high as 0.729, outperforming non-spillover models by 2.5 to 5.7 times and traditional spatial metric models by 2.2 to 4.6 times. Therefore, our proposed model holds significant importance for understanding disparities of PM2.5 air pollution in urban areas, taking the first steps toward a new method that considers both the spillover effects and nonlinear feature of data for prediction.
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Affiliation(s)
- Yuling Xia
- School of Mathematics, Southwest Jiaotong University, Sichuan province Chengdu 611756, China
| | - Teague McCracken
- School of Civil and Environmental Engineering and Earth Sciences, Clemson University, Clemson, South Carolina 29634, United States
| | - Tong Liu
- School of Civil and Environmental Engineering and Earth Sciences, Clemson University, Clemson, South Carolina 29634, United States
| | - Pei Chen
- Department of Computer Science and Engineering, Texas A&M University, College Station, Texas 77843, United States
| | - Andrew Metcalf
- School of Civil and Environmental Engineering and Earth Sciences, Clemson University, Clemson, South Carolina 29634, United States
| | - Chao Fan
- School of Civil and Environmental Engineering and Earth Sciences, Clemson University, Clemson, South Carolina 29634, United States
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Zhang W, Huang W, Tan J, Huang D, Ma J, Wu B. Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives. CHEMOSPHERE 2023; 311:137044. [PMID: 36330979 DOI: 10.1016/j.chemosphere.2022.137044] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/22/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.
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Affiliation(s)
- Wentao Zhang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China
| | - Wenguang Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China.
| | - Jie Tan
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Dawei Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Jun Ma
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Bingdang Wu
- School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, People's Republic of China; Key Laboratory of Suzhou Sponge City Technology, Suzhou, 215002, People's Republic of China.
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Zhang H, Liu Y, Yang D, Dong G. PM 2.5 Concentrations Variability in North China Explored with a Multi-Scale Spatial Random Effect Model. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191710811. [PMID: 36078527 PMCID: PMC9518430 DOI: 10.3390/ijerph191710811] [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: 07/21/2022] [Revised: 08/23/2022] [Accepted: 08/26/2022] [Indexed: 05/16/2023]
Abstract
Compiling fine-resolution geospatial PM2.5 concentrations data is essential for precisely assessing the health risks of PM2.5 pollution exposure as well as for evaluating environmental policy effectiveness. In most previous studies, global and local spatial heterogeneity of PM2.5 is captured by the inclusion of multi-scale covariate effects, while the modelling of genuine scale-dependent variabilities pertaining to the spatial random process of PM2.5 has not yet been much studied. Consequently, this work proposed a multi-scale spatial random effect model (MSSREM), based a recently developed fixed-rank Kriging method, to capture both the scale-dependent variabilities and the spatial dependence effect simultaneously. Furthermore, a small-scale Monte Carlo simulation experiment was conducted to assess the performance of MSSREM against classic geospatial Kriging models. The key results indicated that when the multiple-scale property of local spatial variabilities were exhibited, the MSSREM had greater ability to recover local- or fine-scale variations hidden in a real spatial process. The methodology was applied to the PM2.5 concentrations modelling in North China, a region with the worst air quality in the country. The MSSREM provided high prediction accuracy, 0.917 R-squared, and 3.777 root mean square error (RMSE). In addition, the spatial correlations in PM2.5 concentrations were properly captured by the model as indicated by a statistically insignificant Moran's I statistic (a value of 0.136 with p-value > 0.2). Overall, this study offers another spatial statistical model for investigating and predicting PM2.5 concentration, which would be beneficial for precise health risk assessment of PM2.5 pollution exposure.
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Affiliation(s)
- Hang Zhang
- Key Research Institute of Yellow River Civilization and Sustainable Development, Henan University, Kaifeng 475001, China
| | - Yong Liu
- Key Research Institute of Yellow River Civilization and Sustainable Development, Henan University, Kaifeng 475001, China
- Collaborative Innovation Center on Yellow River Civilization Jointly Built by Henan Province and Ministry of Education, Henan University, Kaifeng 475001, China
- Correspondence: (Y.L.); (G.D.)
| | - Dongyang Yang
- Key Research Institute of Yellow River Civilization and Sustainable Development, Henan University, Kaifeng 475001, China
- Collaborative Innovation Center on Yellow River Civilization Jointly Built by Henan Province and Ministry of Education, Henan University, Kaifeng 475001, China
| | - Guanpeng Dong
- Key Research Institute of Yellow River Civilization and Sustainable Development, Henan University, Kaifeng 475001, China
- Collaborative Innovation Center on Yellow River Civilization Jointly Built by Henan Province and Ministry of Education, Henan University, Kaifeng 475001, China
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Kaifeng 475001, China
- Correspondence: (Y.L.); (G.D.)
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Zeng L, Hang J, Wang X, Shao M. Influence of urban spatial and socioeconomic parameters on PM 2.5 at subdistrict level: A land use regression study in Shenzhen, China. J Environ Sci (China) 2022; 114:485-502. [PMID: 35459511 DOI: 10.1016/j.jes.2021.12.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 11/21/2021] [Accepted: 12/08/2021] [Indexed: 06/14/2023]
Abstract
The intraurban distribution of PM2.5 concentration is influenced by various spatial, socioeconomic, and meteorological parameters. This study investigated the influence of 37 parameters on monthly average PM2.5 concentration at the subdistrict level with Pearson correlation analysis and land-use regression (LUR) using data from a subdistrict-level air pollution monitoring network in Shenzhen, China. Performance of LUR models is evaluated with leave-one-out-cross-validation (LOOCV) and holdout cross-validation (holdout CV). Pearson correlation analysis revealed that Normalized Difference Built-up Index, artificial land fraction, land surface temperature, and point-of-interest (POI) numbers of factories and industrial parks are significantly positively correlated with monthly average PM2.5 concentrations, while Normalized Difference Vegetation Index and Green View Factor show significant negative correlations. For the sparse national stations, robust LUR modelling may rely on a priori assumptions in direction of influence during the predictor selection process. The month-by-month spatial regression shows that RF models for both national stations and all stations show significantly inflated mean values of R2 compared with cross-validation results. For MLR models, inflation of both R2 and R2CV was detected when using only national stations and may indicate the restricted ability to predict spatial distribution of PM2.5 levels. Inflated within-sample R2 also exist in the spatiotemporal LUR models developed with only national stations, although not as significant as spatial LUR models. Our results suggest that a denser subdistrict level air pollutant monitoring network may improve the accuracy and robustness in intraurban spatial/spatiotemporal prediction of PM2.5 concentrations.
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Affiliation(s)
- Liyue Zeng
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China; Key Laboratory of Tropical Atmosphere-Ocean System (Sun Yat-sen University), Ministry of Education, Zhuhai 519000, China; Guangdong Provincial Field Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Guangzhou 510275, China
| | - Jian Hang
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China; Key Laboratory of Tropical Atmosphere-Ocean System (Sun Yat-sen University), Ministry of Education, Zhuhai 519000, China; Guangdong Provincial Field Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Guangzhou 510275, China.
| | - Xuemei Wang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, China
| | - Min Shao
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, China
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