An ensemble mixed spatial model in estimating long-term and diurnal variations of PM2.5 in Taiwan

Wong, Pei-Yi; Su, Huey‐Jen; Lung, Shih-Chun Candice; Wu, Chih-Da

doi:10.1016/j.scitotenv.2022.161336

Cited by 15 publications

(6 citation statements)

References 61 publications

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“…To evaluate the associations between cluster persistence and sociodemographic and environmental features, we employed the eXtreme Gradient Boosting (XGBoost), a widely popular machine learning algorithm that has been used in many supervised classification and regression applications ( 48–50 ), including for COVID-19 research ( 51 , 52 ). XGBoost is a gradient boosting algorithm that iteratively ensembles decision trees using gradient descent algorithm to minimize model error ( 53 ).…”

Section: Methodsmentioning

confidence: 99%

Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis

De Ridder,

Ladoy,

Choi

et al. 2024

Front. Public Health

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IntroductionSince 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.MethodsThis 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.ResultsOur 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.DiscussionOverall, 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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Section: Methodsmentioning

confidence: 99%

Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis

De Ridder,

Ladoy,

Choi

et al. 2024

Front. Public Health

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“…The survey results summarize the various source variables and individual chemical constituents of the data set used for the PM 2.5 study. The chemical sources were divided into the categories of natural and anthropogenic-biogenic [42] : 3) Anthropic variables: According to existing research on PM 2.5 forecasting, road and rail density, population density, and proportion of land use (agricultural land and forest land) as human influencing factors of PM 2.5 [56], [57] . Land use variables have always been the conventional choice in PM 2.5 driving research, representing the degree of landscape modification by humans and as a proxy for local emissions and background air pollution levels.…”

Section: ) Aerosol Optical Depthmentioning

confidence: 99%

“…Understanding the variables that trigger PM 2.5 is essential. The study by Su [56], adopted spatial autocorrelation analysis to explain the spatial correlation of PM 2.5 in the study area and period. This study uses spatial cluster and outlier methods to analyze the distribution and spatial-temporal variation of the PM 2.5 surface.…”

Section: Analysis Of Variables Affecting Pm 25mentioning

confidence: 99%

“…To obtain values for unknown spatial data, a spatial interpolation approach can be used. Various researchers have referred to standard spatial interpolation methods such as Trend Surface (TS) interpolation, Collaborative Kriging (CK), Inverse Distance Weighted (IDW) interpolation, Ordinary Kriging (OK) interpolation [56], and radial basis function.…”

Section: Missing Valuesmentioning

confidence: 99%

“…Where Z(x) is the measurement of position i, λi is the unknown weight of the measurement value at a position i, is the predicted position, and n is the number of measurements. Wong [56] used the OK method to generate continuous air pollutant concentrations and meteorological factors covering Taiwan.…”

Section: Missing Valuesmentioning

confidence: 99%

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PM2.5 Estimation using Machine Learning Models and Satellite Data: A Literature Review

Unik,

Sitanggang,

Syaufina

et al. 2023

IJACSA

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Most researchers are beginning to appreciate the use of remote sensing satellites to assess PM 2.5 levels and use machine learning algorithms to automate the collection, make sense of remote sensing data, and extract previously unseen data patterns. This study reviews delicate particulate matter (PM 2.5 ) predictions from satellite aerosol optical depth (AOD) and machine learning. Specifically, we review the characteristics and gap-filling methods of satellite-based AOD products, sources and components of PM 2.5 , observable AOD products, data mining, and the application of machine learning algorithms in publications of the past two years. The study also included functional considerations and recommendations in covariate selection, addressing the spatiotemporal heterogeneity of the PM 2.5 -AOD relationship, and the use of cross-validation, to aid in determining the final model. A total of 79 articles were included out of 112 retrieved records consisting of articles published in 2022 totaling 43 articles, as of 2023 (until February) totaling 19 articles, and other years totaling 18 articles. Finally, the latest method works well for monthly PM 2.5 estimates, while daily PM 2.5 and hourly PM 2.5 can also be achieved. This is due to the increased availability and computing power of large datasets and increased awareness of the potential benefits of predictors working together to achieve higher estimation accuracy. Some key findings are also presented in the conclusion section of this article. Keywords-AOD; machine learning; PM 2.5; remote sensing; pollutant Eligibility: Eligibility was determined by reading the main findings, use of data, results, and discussion. The authors considered journal articles and books published by reputable publishers as high-quality research and included them in summary. The authors used "Scimago Journal & Country Rank" to check the rankings of the included articles. Inclusion: The research then lists literature articles that correspond to the main topic.Our initial search yielded 112 articles. After passing the initial screening to eligibility assessment, this study used 61 primary and 18 other articles.

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Data analysis and preprocessing techniques for air quality prediction: a survey

Yu,

Tan,

Cheng

et al. 2024

Stoch Environ Res Risk Assess

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An ensemble mixed spatial model in estimating long-term and diurnal variations of PM2.5 in Taiwan

Cited by 15 publications

References 61 publications

Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis

Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis

PM2.5 Estimation using Machine Learning Models and Satellite Data: A Literature Review

Data analysis and preprocessing techniques for air quality prediction: a survey

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