Optimizing BenMAP health impact assessment with meteorological factor driven machine learning models

This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited meteorological factor data and missing pollutant data. By employing data increment strategies and multiple machine learning m...

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Bibliographic Details
Published in:The Science of the total environment Vol. 949; p. 175246
Main Authors: Wu, Juncheng, Dai, Qili, Song, Shaojie
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 01.11.2024
Subjects:
air pollutants
Air pollution
atmospheric pressure
BenMap
cardiovascular diseases
China
decision support systems
dewpoint
environment
health effects assessments
Health impact assessment
Machine learning
Meteorological factors
mortality
prediction
Prediction accuracy
relative humidity
respiratory tract diseases
China
Prediction accuracy
Air pollution
Health impact assessment
BenMap
Meteorological factors
Machine learning
ISSN:0048-9697, 1879-1026, 1879-1026
Online Access:Get full text
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Summary:This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited meteorological factor data and missing pollutant data. By employing data increment strategies and multiple machine learning models, this research explores the effects of data volume, time steps, and meteorological factors on model prediction performance using several years of data from Tianjin City as an example. The findings indicate that increasing training data volume enhances the performance of Random Forest Regressor (RF) and Decision Tree Regressor (DT) models, especially for predicting CO, NO2, and PM2.5. The optimal prediction time step varies by pollutant, with the DT model achieving the highest R2 value (0.99) for CO and O3. Combining multiple meteorological factors, such as atmospheric pressure, relative humidity, and dew point temperature, significantly improves model accuracy. When using three meteorological factors, the model achieves an R2 of 0.99 for predicting CO, NO2, PM10, PM2.5, and SO2. Health impact assessments using BenMap demonstrated that the predicted all-cause mortality and specific disease mortalities were highly consistent with actual values, confirming the model's accuracy in assessing health impacts from air pollution. For instance, the predicted and actual all-cause mortality for PM2.5 were both 3120; for cardiovascular disease, both were 1560; and for respiratory disease, both were 780. To validate its generalizability, this method was applied to Chengdu, China, using several years of data for training and prediction of PM2.5, CO, NO2, O3, PM10, and SO2, incorporating atmospheric pressure, relative humidity, and dew point temperature. The model maintained excellent performance, confirming its broad applicability. Overall, we conclude that the machine learning and BenMap-based methods show high accuracy and reliability in predicting air pollutant concentrations and health impacts, providing a valuable reference for air pollution assessment. [Display omitted] •Utilize BenMAP and ML to improve air pollution assessment.•Find optimal prediction steps and key meteorological factors.•High accuracy in CO and O3 prediction with Decision Tree.•Demonstrated the generalizability of the method for application to different cities.
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ISSN:0048-9697
1879-1026
1879-1026
DOI:10.1016/j.scitotenv.2024.175246