Satellite-derived 1-km estimates and long-term trends of PM2.5 concentrations in China from 2000 to 2018.
Satellite-derived 1-km estimates and long-term trends of PM2.5 concentrations in China from 2000 to 2018.
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DOI：
10.1016/j.envint.2021.106726
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发表时间：
2021-11-01
影响因子：
11.8
通讯作者：
Zhang, Ming
中科院分区：
文献类型：
--
作者：
He, Qingqing;Gao, Kai;Zhang, Ming
暴露于环境PM2.5（细颗粒物）会对人体健康造成不利影响。在过去的二十年里，中国的空气污染发生了巨大的变化。利用卫星气溶胶光学厚度（AOD）统计反演地面PM2.5是为环境监测和PM2.5相关流行病学研究提供PM2.5数据的一种新兴尝试。然而，目前中国全国范围内的数据集一般具有较低的精度和较低的时空分辨率，因为历史年份很少记录地面PM2.5水平（即，2013年之前）。本研究旨在利用先进的卫星数据集和地面测量数据，以1 km的精细尺度重建2000年至2018年中国每日环境PM2.5浓度。利用新发布的多角度实施大气校正（MAIAC）1公里AOD数据集，我们开发了一种新的统计策略，建立一个先进的时空模型依赖于自适应模型结构与线性和非线性预测。使用严格的留一年交叉验证（CV）技术对地表观测结果验证了历史年份的估计值。总体每日离开一年的CV R2和均方根偏差值分别为0.59和27.18 mug/m3。由此产生的每月（R2=0.74）和每年（0.77）的平均预测是高度一致的表面测量。全国PM2.5水平在2001-2007年经历了快速增长，并在2013年至2018年期间大幅下降。大部分明显的下降趋势发生在东部和南部地区，而中国西部的空气质量在最近二十年略有变化。我们的模型可以提供可靠的历史PM2.5估计在中国在一个更精细的时空分辨率比以前的方法，这可以推进流行病学研究的健康影响的短期和长期暴露于PM2.5在中国的大规模和精细。
Exposure to ambient PM2.5 (fine particulate matter) can cause adverse effects on human health. China has been experiencing dramatic changes in air pollution over the past two decades. Statistically deriving ground-level PM2.5 from satellite aerosol optical depth (AOD) has been an emerging attempt to provide such PM2.5 data for environmental monitoring and PM2.5-related epidemiologic study. However, current countrywide datasets in China have generally lower accuracies with lower spatiotemporal resolutions because surface PM2.5 level was rarely recorded in historical years (i.e., preceding 2013). This study aimed to reconstruct daily ambient PM2.5 concentrations from 2000 to 2018 over China at a fine scale of 1km using advanced satellite datasets and ground measurements. Taking advantage of the newly released Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1-km AOD dataset, we developed a novel statistical strategy by establishing an advanced spatiotemporal model relying on adaptive model structures with linear and non-linear predictors. The estimates in historical years were validated against surface observations using a strict leave-one-year-out cross-validation (CV) technique. The overall daily leave-one-year-out CV R2 and root-mean-square-deviation values were 0.59 and 27.18mug/m3, respectively. The resultant monthly (R2=0.74) and yearly (0.77) mean predictions were highly consistent with surface measurements. The national PM2.5 levels experienced a rapid increase in 2001-2007 and significantly declined between 2013 and 2018. Most of the discernable decreasing trends occurred in eastern and southern areas, while air quality in western China changed slightly in the recent two decades. Our model can deliver reliable historical PM2.5 estimates in China at a finer spatiotemporal resolution than previous approaches, which could advance epidemiologic studies on the health impacts of both short- and long-term exposure to PM2.5 at both a large and a fine scale in China.