Ma Runmei, Ban Jie, Wang Qing, Zhang Yayi, Yang Yang, He Mike Z, Li Shenshen, Shi Wenjiao, Li Tiantian
China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China.
China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China; Jiangsu Ocean University, Jiangsu, 222000, China.
Environ Pollut. 2021 May 1;276:116635. doi: 10.1016/j.envpol.2021.116635. Epub 2021 Feb 2.
Ambient ozone (O) concentrations have shown an upward trend in China and its health hazards have also been recognized in recent years. High-resolution exposure data based on statistical models are needed. Our study aimed to build high-performance random forest (RF) models based on training data from 2013 to 2017 in the Beijing-Tianjin-Hebei (BTH) region in China at a 0.01 ° × 0.01 ° resolution, and estimated daily maximum 8h average O (O-8hmax) concentration, daily average O (O-mean) concentration, and daily maximum 1h O (O-1hmax) concentration from 2010 to 2017. Model features included meteorological variables, chemical transport model output variables, geographic variables, and population data. The test-R of sample-based O-8hmax, O-mean and O-1hmax models were all greater than 0.80, while the R of site-based and date-based model were 0.68-0.87. From 2010 to 2017, O-8hmax, O-mean, and O-1hmax concentrations in the BTH region increased by 4.18 μg/m, 0.11 μg/m, and 4.71 μg/m, especially in more developed regions. Due to the influence of weather conditions, which showed high contribution to the model, the long-term spatial distribution of O concentrations indicated a similar pattern as altitude, where high concentration levels were distributed in regions with higher altitude.
中国的环境臭氧（O）浓度呈上升趋势，近年来其对健康的危害也已得到认识。需要基于统计模型的高分辨率暴露数据。我们的研究旨在基于中国京津冀（BTH）地区2013年至2017年的训练数据，以0.01°×0.01°的分辨率建立高性能随机森林（RF）模型，并估算2010年至2017年的每日最大8小时平均O（O-8hmax）浓度、每日平均O（O-mean）浓度和每日最大1小时O（O-1hmax）浓度。模型特征包括气象变量、化学传输模型输出变量、地理变量和人口数据。基于样本的O-8hmax、O-mean和O-1hmax模型的检验R均大于0.80，而基于站点和基于日期的模型的R为0.68-0.87。2010年至2017年，BTH地区的O-8hmax、O-mean和O-1hmax浓度分别增加了4.18μg/m、0.11μg/m和4.71μg/m，特别是在较发达地区。由于天气条件对模型的贡献较大，O浓度的长期空间分布呈现出与海拔类似的模式，高浓度水平分布在海拔较高的地区。
