Abstract Accurate estimations of the concentration of ambient fine-particle matter with aerodynamic diameters of less than 2.5 μm (PM2.5) are necessary for human health studies. In this study, individual city-scale linear mixed effect models (LME) were employed to accurately estimate ground PM2.5 concentrations considering the spatiotemporal variability of the relationship between PM2.5 and atmospheric, meteorological, and land observations. The contributions of diverse influential factors including aerosol optical depth, planetary boundary layer height, relative humidity, vegetation index, and wind on local PM2.5 pollution were also determined. High correlation coefficient (R2 = 0.89) and low root mean square error (RMSE = 13.1 μg/m3) ensured satisfactory LME model performances in estimating ground-level PM2.5 concentrations. Spatiotemporal analyses of satellite-based PM2.5 showed high concentrations in eastern, southern, and northern Hubei, and low concentrations in the northwest and southeast because of unbalanced development. These analyses also displayed a mitigation trend of PM2.5 concentrations with a mean annual decline rate of 3–12% from 2016 to 2018. Moreover, from the statistical results of the model, the influential factor of aerosol optical depth was positively correlated with PM2.5 concentration, while planetary boundary layer height, relative humidity, and the normalized difference vegetation index were negatively correlated to local PM2.5 pollution. However, the winds had contradictory contributions on PM2.5 pollution; the northerly wind in western Hubei and the southerly and northeasterly winds in eastern Hubei alleviated local PM2.5 pollution, while the westerly wind in eastern Hubei facilitated PM2.5 diffusion between cities and aggravated PM2.5 pollution. The analysis of the spatiotemporal trend of local PM2.5 pollution at a city scale and the identification of the influence of wind on PM2.5 pollution provide a theoretical reference for regional pollution warnings and controls.

中文翻译：

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湖北省大气PM2.5浓度卫星时空变化趋势及影响因素

摘要 空气动力学直径小于 2.5 μm (PM2.5) 的环境细颗粒物浓度的准确估计对于人类健康研究是必要的。在本研究中，考虑到 PM2.5 与大气、气象和陆地观测之间关系的时空变异性，采用单个城市尺度线性混合效应模型 (LME) 来准确估计地面 PM2.5 浓度。还确定了包括气溶胶光学深度、行星边界层高度、相对湿度、植被指数和风在内的多种影响因素对局部 PM2.5 污染的贡献。高相关系数 (R2 = 0.89) 和低均方根误差 (RMSE = 13.1 μg/m3) 确保 LME 模型在估算地面 PM2.5 浓度方面具有令人满意的性能。PM2.5卫星时空分析显示，由于发展不平衡，鄂东、鄂南、鄂北地区PM2.5浓度较高，西北、东南部PM2.5浓度较低。这些分析还显示出 PM2.5 浓度在 2016 年至 2018 年间呈年均下降 3%～12% 的减缓趋势。此外，从模型的统计结果来看，气溶胶光学深度的影响因素与 PM2 .5 浓度，而行星边界层高度、相对湿度和归一化差异植被指数与当地 PM2.5 污染呈负相关。然而，风对 PM2.5 污染的贡献相互矛盾；鄂西偏北风和鄂东偏南偏东北风缓解了当地PM2.5污染，而鄂东的西风则促进了城市间PM2.5的扩散，加重了PM2.5的污染。城市尺度局部PM2.5污染时空趋势分析及风对PM2.5污染影响的识别为区域污染预警与控制提供理论参考。