State 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.
State 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.
Sci Total Environ. 2024 Nov 1;949:175246. doi: 10.1016/j.scitotenv.2024.175246. Epub 2024 Aug 3.
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, NO, and PM. The optimal prediction time step varies by pollutant, with the DT model achieving the highest R value (0.99) for CO and O. 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 R of 0.99 for predicting CO, NO, PM, PM, and SO. 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 PM, CO, NO, O, PM, and SO, 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.
本研究旨在解决环境效益制图与分析规划程序（BenMap）在评估空气污染健康影响时所面临的精度挑战，该挑战源于气象因素数据有限和污染物数据缺失。本研究通过采用数据增量策略和多种机器学习模型，以天津市多年的数据为例，探讨了数据量、时间步长和气象因素对模型预测性能的影响。研究结果表明，增加训练数据量可提高随机森林回归（RF）和决策树回归（DT）模型的性能，特别是对 CO、NO 和 PM 的预测。最佳预测时间步长因污染物而异，DT 模型对 CO 和 O 的预测取得了最高的 R 值（0.99）。结合多个气象因素，如大气压、相对湿度和露点温度，可显著提高模型精度。当使用三个气象因素时，模型对 CO、NO、PM、PM 和 SO 的预测达到了 0.99 的 R 值。使用 BenMap 进行的健康影响评估表明，预测的全因死亡率和特定疾病死亡率与实际值高度一致，证实了模型在评估空气污染对健康的影响方面的准确性。例如，PM2.5 的预测和实际全因死亡率均为 3120；心血管疾病均为 1560；呼吸疾病均为 780。为了验证其通用性，本方法应用于中国成都，使用多年的数据进行训练和预测 PM、CO、NO、O、PM 和 SO，并结合大气压、相对湿度和露点温度。该模型保持了出色的性能，证实了其广泛的适用性。总体而言，我们得出结论，基于机器学习和 BenMap 的方法在预测空气污染物浓度和健康影响方面具有高精度和可靠性，为空气污染评估提供了有价值的参考。
