Estimating monthly PM2.5 concentrations from satellite remote sensing data, meteorological variables, and land use data using ensemble statistical modeling and a random forest approach

Chen, Chu-Chih; Wang, Yin Ru; Yeh, Hung Yi; Lin, Tang Huang; Huang, Chun Sheng; Wu, Chang Fu

doi:10.1016/j.envpol.2021.118159

Cited by 41 publications

(8 citation statements)

References 30 publications

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“…Chen et al [35] obtained a RMSE of 13.55 µg/m 3 for the prediction of surface Ozone in a large area of China, using meteorological data between September 2015 and August 2021. As regards PM s , Chu-Chih Chen et al [36] presented a machine learning framework to forecast the monthly PM 2.5 concentrations of Taiwan at different spatial resolutions obtaining a R 2 of 0.80, comparable with the results of our XGBoost model. Peddle et al [37] used Aerosol Optical Depth data to predict concentrations of PM 2.5 and PM 10 for six US urban areas: Los Angeles, CA; Chicago, IL; St. Paul, MN; Baltimore, MD; New York, NY; Winston-Salem, NC.…”

Section: Discussionsupporting

confidence: 76%

Estimation of Daily Ground Level Air Pollution in Italian Municipalities with Machine Learning Models Using Sentinel-5P and ERA5 Data

Fania,

Monaco,

Pantaleo

et al. 2024

Remote Sensing

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Recent years have witnessed an increasing interest in air pollutants and their effects on human health. More generally, it has become evident how human, animal and environmental health are deeply interconnected within a One Health framework. Ground level air monitoring stations are sparse and thus have limited coverage due to high costs. Satellite and reanalysis data represent an alternative with high spatio-temporal resolution. The idea of this work is to build an Artificial Intelligence model for the estimation of surface-level daily concentrations of air pollutants over the entire Italian territory using satellite, climate reanalysis, geographical and social data. As ground truth we use data from the monitoring stations of the Regional Environmental Protection Agency (ARPA) covering the period 2019–2022 at municipal level. The analysis compares different models and applies an Explainable Artificial Intelligence approach to evaluate the role of individual features in the model. The best model reaches an average R2 of 0.84 ± 0.01 and MAE of 5.00 ± 0.01 μg/m3 across all pollutants which compare well with the body of literature. The XAI analysis highlights the pivotal role of satellite and climate reanalysis data. Our work can facilitate One Health surveys and help researchers and policy makers.

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Section: Discussionsupporting

confidence: 76%

Estimation of Daily Ground Level Air Pollution in Italian Municipalities with Machine Learning Models Using Sentinel-5P and ERA5 Data

Fania,

Monaco,

Pantaleo

et al. 2024

Remote Sensing

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“…Thus, based on the points data of barbecues and Chinese restaurants, the impact of cooking emissions was calculated by using the buffering analysis, separately. In addition, more than 2 million people in Hong Kong, follow the Buddhist/Taoist religion, and during the periods of prayer incense and joss paper burnt in temples both during festival periods and as a common part of daily life (C. Chen et al., 2021 ; Hsu et al., 2020 ). Based on the points data of temples, the impact of incense/joss paper burning was calculated using buffering analysis.…”

Section: Methodsmentioning

confidence: 99%

Estimation of On‐Road PM_2.5 Distributions by Combining Satellite Top‐of‐Atmosphere With Microscale Geographic Predictors for Healthy Route Planning

Tong

Shi

et al. 2022

GeoHealth

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How to reduce the health risks for commuters, caused by air pollution such as PM 2.5 has always been an urgent issue needing to be solved. Proposed in this study, is a novel framework which enables greater avoidance of pollution and hence assists the provision of healthy travel. This framework is based on the estimation of on‐road PM 2.5 throughout the whole city. First, the micro‐scale PM 2.5 is predicted by land use regression (LUR) modeling enhanced by the use of the Landsat‐8 top‐of‐atmosphere (TOA) data and microscale geographic predictors. In particular, the green view index (GVI) factor derived, the sky view factor, and the index‐based built‐up index, are incorporated within the TOA‐LUR modeling. On‐road PM 2.5 distributions are then mapped in high‐spatial‐resolution. The maps obtained can be used to find healthy travel routes with less PM 2.5 . The proposed framework was applied in high‐density Hong Kong by Landsat 8 images. External testing was based on mobile measurements. The results showed that the estimation performance of the proposed seasonal TOA‐LUR Geographical and Temporal Weighted Regression models is at a high‐level with an R 2 of 0.70–0.90. The newly introduced GVI index played an important role in these estimations. The PM 2.5 distribution maps at high‐spatial‐resolution were then used to develop an application providing Hong Kong residents with healthy route planning services. The proposed framework can, likewise, be applied in other cities to better ensure people's health when traveling, especially those in high‐density cities.

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“…Previous studies have shown that meteorological and land cover-related factors have significant positive or negative effects on PM 2.5 concentration [53][54][55][56][57][58]. Therefore, a total of six meteorology-related variables including surface pressure (P), boundary layer height (BLH), surface air temperature (TEMP), surface air relative humidity (RHU), precipitation (PRE), wind speed (WS) were selected for our study.…”

Section: Auxiliary Datamentioning

confidence: 99%

Estimation of PM2.5 Concentration across China Based on Multi-Source Remote Sensing Data and Machine Learning Methods

Yang,

Wang,

Cao

et al. 2024

Remote Sensing

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Long-term exposure to high concentrations of fine particles can cause irreversible damage to people’s health. Therefore, it is of extreme significance to conduct large-scale continuous spatial fine particulate matter (PM2.5) concentration prediction for air pollution prevention and control in China. The distribution of PM2.5 ground monitoring stations in China is uneven with a larger number of stations in southeastern China, while the number of ground monitoring sites is also insufficient for air quality control. Remote sensing technology can obtain information quickly and macroscopically. Therefore, it is possible to predict PM2.5 concentration based on multi-source remote sensing data. Our study took China as the research area, using the Pearson correlation coefficient and GeoDetector to select auxiliary variables. In addition, a long short-term memory neural network and random forest regression model were established for PM2.5 concentration estimation. We finally selected the random forest regression model (R2 = 0.93, RMSE = 4.59 μg m−3) as our prediction model by the model evaluation index. The PM2.5 concentration distribution across China in 2021 was estimated, and then the influence factors of high-value regions were explored. It is clear that PM2.5 concentration is not only related to the local geographical and meteorological conditions, but also closely related to economic and social development.

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Estimating monthly PM2.5 concentrations from satellite remote sensing data, meteorological variables, and land use data using ensemble statistical modeling and a random forest approach

Cited by 41 publications

References 30 publications

Estimation of Daily Ground Level Air Pollution in Italian Municipalities with Machine Learning Models Using Sentinel-5P and ERA5 Data

Estimation of Daily Ground Level Air Pollution in Italian Municipalities with Machine Learning Models Using Sentinel-5P and ERA5 Data

Estimation of On‐Road PM_2.5 Distributions by Combining Satellite Top‐of‐Atmosphere With Microscale Geographic Predictors for Healthy Route Planning

Estimation of PM2.5 Concentration across China Based on Multi-Source Remote Sensing Data and Machine Learning Methods

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Estimating monthly PM2.5 concentrations from satellite remote sensing data, meteorological variables, and land use data using ensemble statistical modeling and a random forest approach

Cited by 41 publications

References 30 publications

Estimation of Daily Ground Level Air Pollution in Italian Municipalities with Machine Learning Models Using Sentinel-5P and ERA5 Data

Estimation of Daily Ground Level Air Pollution in Italian Municipalities with Machine Learning Models Using Sentinel-5P and ERA5 Data

Estimation of On‐Road PM2.5 Distributions by Combining Satellite Top‐of‐Atmosphere With Microscale Geographic Predictors for Healthy Route Planning

Estimation of PM2.5 Concentration across China Based on Multi-Source Remote Sensing Data and Machine Learning Methods

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Estimation of On‐Road PM_2.5 Distributions by Combining Satellite Top‐of‐Atmosphere With Microscale Geographic Predictors for Healthy Route Planning