Wu Sensen, Huang Bo, Wang Jionghua, He Lijie, Wang Zhongyi, Yan Zhen, Lao Xiangqian, Zhang Feng, Liu Renyi, Du Zhenhong
School of Earth Sciences, Zhejiang University, Hangzhou, 310027, China; Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, 310028, China; Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, NT, Hong Kong.
Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, NT, Hong Kong; Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, NT, Hong Kong.
Environ Pollut. 2021 Jan 8;273:116456. doi: 10.1016/j.envpol.2021.116456.
Nitrogen dioxide (NO) is an important air pollutant that causes direct harms to the environment and human health. Ground NO mapping with high spatiotemporal resolution is critical for fine-scale air pollution and environmental health research. We thus developed a spatiotemporal regression kriging model to map daily high-resolution (3-km) ground NO concentrations in China using the Tropospheric Monitoring Instrument (TROPOMI) satellite retrievals and geographical covariates. This model combined geographically and temporally weighted regression with spatiotemporal kriging and achieved robust prediction performance with sample-based and site-based cross-validation R values of 0.84 and 0.79. The annual mean and standard deviation of ground NO concentrations from June 1, 2018 to May 31, 2019 were predicted to be 15.05 ± 7.82 μg/m, with that in 0.6% of China's area (10% of the population) exceeding the annual air quality standard (40 μg/m). The ground NO concentrations during the coronavirus disease (COVID-19) period (January and February in 2020) was 14% lower than that during the same period in 2019 and the mean population exposure to ground NO was reduced by 25%. This study was the first to use TROPOMI retrievals to map fine-scale daily ground NO concentrations across all of China. This was also an early application to use the satellite-estimated ground NO data to quantify the impact of the COVID-19 pandemic on the air pollution and population exposures. These newly satellite-derived ground NO data with high spatiotemporal resolution have value in advancing environmental and health research in China.
二氧化氮（NO）是一种重要的空气污染物，会对环境和人类健康造成直接危害。以高时空分辨率绘制地面NO分布图对于精细尺度的空气污染和环境健康研究至关重要。因此，我们开发了一种时空回归克里金模型，利用对流层监测仪器（TROPOMI）卫星反演数据和地理协变量，绘制中国每日高分辨率（3公里）地面NO浓度图。该模型将地理加权回归和时间加权回归与时空克里金相结合，通过基于样本和基于站点的交叉验证，R值分别为0.84和0.79，实现了稳健的预测性能。预测2018年6月1日至2019年5月31日地面NO浓度的年均值和标准差为15.05±7.82μg/m³，中国0.6%的地区（10%的人口）超过了年空气质量标准（40μg/m³）。冠状病毒病（COVID-19）期间（2020年1月和2月）的地面NO浓度比2019年同期低14%，平均人群暴露于地面NO的情况减少了25%。本研究首次利用TROPOMI反演数据绘制全中国精细尺度的每日地面NO浓度图。这也是利用卫星估算的地面NO数据量化COVID-19大流行对空气污染和人群暴露影响的早期应用。这些新的具有高时空分辨率的卫星衍生地面NO数据对于推进中国的环境与健康研究具有价值。
