Estimation of High-Resolution Daily Ground-Level PM2.5 Concentration in Beijing 2013–2017 Using 1 km MAIAC AOT Data

Abstract

High-spatiotemporal-resolution PM2.5 data are critical to assessing the impacts of prolonged exposure to PM2.5 on human health, especially for urban areas. Satellite-derived aerosol optical thickness (AOT) is highly correlated to ground-level PM2.5, providing an effective way to reveal spatiotemporal variations of PM2.5 across urban landscapes. In this paper, Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOT and ground-based PM2.5 measurements were fused to estimate daily ground-level PM2.5 concentrations in Beijing for 2013–2017 at 1 km resolution through a linear mixed effect model (LMEM). The results showed a good agreement between the estimated and measured PM2.5 and effectively revealed the characteristics of its spatiotemporal variations across Beijing: (1) the PM2.5 level is higher in the central and southern areas, while it is lower in the northern and northwestern areas; (2) the PM2.5 level is higher in autumn and winter, while it is lower in spring and summer. Moreover, annual PM2.5 concentrations decreased by 24.03% for the whole of Beijing and 31.46% for the downtown area from 2013 to 2017. The PM2.5 data products we generated can be used to assess the long-term impacts of PM2.5 on human health and support relevant government policy decision-making, and the methodology can be applied to other heavily polluted urban areas.

Assessment of urban aerosol pollution over the Moscow megacity by the MAIAC aerosol product

“…Over Europe, a perma-E. Y. Zhdanova et al: Assessment of urban aerosol pollution nently elevated aerosol loading was observed over several industrial regions, with particularly high values found over the Netherlands, Belgium, the Ruhr area, the Po Valley, northern Germany and the former East Germany, Poland, and parts of central European countries. Elevated aerosol loading usually correlates with suspended particulate matter associated with the poor air quality (Wang and Christopher, 2003;Hoff and Christopher, 2009;Chudnovsky et al, 2012;van Donkelaar et al, 2015). Recently a high 1 km resolution aerosol MA-IAC satellite product has been used for estimating relationships between AOT and particulate matter Hu et al, 2014;Kloog et al, 2015;Xiao et al, 2017;Beloconi et al, 2018;Liang et al, 2018;Han et al, 2018).…”

Application of Low-Cost Fine Particulate Mass Monitors to Convert Satellite Aerosol Optical Depth Measurements to Surface Concentrations in North America and Africa

“…An online or (in Bayesian terminology) posterior approach can also be adopted, in which this relationship is modified as additional data are available. This approach has been proposed by Lee et al (2011) and evaluated by Han et al (2018), and allows for the potentially time-varying relationship between satellite AOD and surface PM2.5 concentration to be accounted for. In the online approach, for a time during the application phase, a new data set consisting of in, and in, is created by combining all data available from the in ground sites together with satellite AOD data for that time:…”

“…In China, the r 2 between surface PM2.5 estimates derived from satellite AOD, meteorological, and land use information and measured surface PM2.5 was found to be about 0.7, corresponding to a root mean square error (RMSE) of about 30 µg/m 3 in resulting satellite-derived surface concentration estimates (Ma et al, 2014). A method that updates the relationships between AOD and surface PM2.5 on a daily basis (Lee et al, 2011) was able to improve these results, increasing r 2 above 0.8 while reducing RMSE to about 20 µg/m 3 (Han et al, 2018). This method, however, relies on local ground-based measurements to provide the data necessary to perform this daily updating.…”

Prediction of Air Pollution Concentration Based on mRMR and Echo State Network

“…The 5 variables in the optimal subset are expressed as bold in the table. As shown in Table 1, the delay time for the obtained optimal subset is [8,8,6,4,4], and the embedding dimension is [2,2,2,4,4] for PM2.5, PM10, CO, H and WS respectively.…”

“…Therefore, modeling and forecasting the air quality index (e.g., PM 2.5 concentration) has become an effective way to prevent and control air pollution, and it also provides a scientific basis for the development of effective measures [3]. The implementation of this idea can effectively reduce the health hazard of air pollution, thus achieving early warning and rational planning [4].…”