Pan Jinghu, Li Xuexia, Zhu Shixin
College of Geography and Environmental Science, Northwest Normal University, No. 967 Anning East Road, Lanzhou, Gansu Province, People's Republic of China.
Environ Monit Assess. 2024 Feb 10;196(3):249. doi: 10.1007/s10661-024-12416-5.
Considering the spatial and temporal effects of atmospheric pollutants, using the geographically and temporally weighted regression and geo-intelligent random forest (GTWR-GeoiRF) model and Sentinel-5P satellite remote sensing data, combined with meteorological, emission inventory, site observation, population, elevation, and other data, the high-precision ozone concentration and its spatiotemporal distribution near the ground in China from March 2020 to February 2021 were estimated. On this basis, the pollution status, near-surface ozone concentration, and population exposure risk were analyzed. The findings demonstrate that the estimation outcomes of the GTWR-GeoiRF model have high precision, and the precision of the estimation results is higher compared with that of the non-hybrid model. The downscaling method enhances estimation results to some extent while addressing the issue of limited spatial resolution in some data. China's near-surface ozone concentration distribution in space shows obvious regional and seasonal characteristics. The eastern region has the highest ozone concentrations and the lowest in the northeastern region, and the wintertime low is higher than the summertime high. There are significant differences in ozone population exposure risks, with the highest exposure risks being found in China's eastern region, with population exposure risks mostly ranging from 0.8 to 5.
考虑到大气污染物的时空效应，利用地理加权回归和地理智能随机森林（GTWR-GeoiRF）模型以及哨兵-5P卫星遥感数据，结合气象、排放清单、站点观测、人口、海拔等数据，估算了2020年3月至2021年2月中国地面附近高精度臭氧浓度及其时空分布。在此基础上，分析了污染状况、近地面臭氧浓度和人口暴露风险。研究结果表明，GTWR-GeoiRF模型的估算结果具有较高精度，与非混合模型相比，估算结果的精度更高。降尺度方法在一定程度上提高了估算结果，同时解决了一些数据空间分辨率有限的问题。中国近地面臭氧浓度的空间分布呈现出明显的区域和季节特征。东部地区臭氧浓度最高，东北地区最低，冬季低值高于夏季高值。臭氧人口暴露风险存在显著差异，中国东部地区暴露风险最高，人口暴露风险大多在0.8至5之间。
