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

Juncheng Wu¹, Qili Dai¹, Shaojie Song¹

¹State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China.

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Summary

This summary is machine-generated.

This study enhances air pollution health impact assessments by using machine learning to improve pollutant prediction accuracy. The findings show that more data and meteorological factors significantly boost model performance and reliability.

Keywords:

Air pollution BenMap Health impact assessment Machine learning Meteorological factors Prediction accuracy

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Area of Science:

Environmental Science
Data Science
Public Health

Background:

Assessing air pollution health impacts is crucial but challenged by limited meteorological and pollutant data.
Existing models like the Environmental Benefits Mapping and Analysis Program (BenMap) require more accurate input data for reliable health impact assessments.

Purpose of the Study:

To improve the accuracy of air pollution health impact assessments by addressing data limitations.
To explore the influence of data volume, time steps, and meteorological factors on machine learning model performance for pollutant prediction.

Main Methods:

Employed data increment strategies and machine learning models (Random Forest Regressor, Decision Tree Regressor).
Utilized several years of air quality and meteorological data from Tianjin City for model training and validation.
Incorporated multiple meteorological factors (atmospheric pressure, relative humidity, dew point temperature) to enhance prediction accuracy.

Main Results:

Increased training data volume improved prediction performance for CO, NO2, and PM2.5.
Optimal prediction time steps varied by pollutant; Decision Tree Regressor achieved R²=0.99 for CO and O3.
Integrating three meteorological factors resulted in R²=0.99 for predicting CO, NO2, PM10, PM2.5, and SO2.
BenMap health impact assessments showed high consistency between predicted and actual mortality rates.

Conclusions:

Machine learning models, enhanced with sufficient data and meteorological factors, accurately predict air pollutant concentrations.
The developed methods provide reliable assessments of air pollution's health impacts, validated in both Tianjin and Chengdu.
This approach offers a valuable reference for improving air pollution assessment strategies.