环境科学
北京
高度(三角形)
广义加性模型
人口
大气科学
空间分布
中国
自然地理学
地理
统计
人口学
数学
遥感
地质学
社会学
考古
几何学
作者
Runmei Ma,Jie Ban,Qing Wang,Yayi Zhang,Yang Yang,Mike Z. He,Shenshen Li,Wenjiao Shi,Tiantian Li
标识
DOI:10.1016/j.envpol.2021.116635
摘要
Ambient ozone (O3) 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 O3 (O3-8hmax) concentration, daily average O3 (O3-mean) concentration, and daily maximum 1h O3 (O3-1hmax) concentration from 2010 to 2017. Model features included meteorological variables, chemical transport model output variables, geographic variables, and population data. The test-R2 of sample-based O3-8hmax, O3-mean and O3-1hmax models were all greater than 0.80, while the R2 of site-based and date-based model were 0.68–0.87. From 2010 to 2017, O3-8hmax, O3-mean, and O3-1hmax concentrations in the BTH region increased by 4.18 μg/m3, 0.11 μg/m3, and 4.71 μg/m3, 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 O3 concentrations indicated a similar pattern as altitude, where high concentration levels were distributed in regions with higher altitude.
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