## 5 Conclusions  
Through integrating ground-based PM\({}_{2.5}\) measurements and MAIAC AOT, this study has preliminarily demonstrated that the LMEM model can be used effectively for estimating surface PM\({}_{2.5}\) at high spatial resolution (1 km) for heavily polluted urban areas with high accuracy. The results indicated that LMEM models fitted using the PM\({}_{2.5}\) 24 h average have better performance than those using the PM\({}_{2.5}\) period average. The high spatial resolution of MAIAC AOT data sets has a substantial advantage over other satellite AOT data sets at coarser resolutions for estimating surface PM\({}_{2.5}\) by providing more spatial variation details of PM\({}_{2.5}\) in large urban areas, which is valuable for pollution warning and health impact analysis. The spatial patterns and trends revealed by the annual mean PM\({}_{2.5}\) concentrations generated by our models show good consistency with those revealed by the ground-based measurements, and both have indicated that the surface PM\({}_{2.5}\) levels have decreased from 2013 to 2017, thereby verifying the effectiveness of government air quality control measures in recent years. This study generated 1 km PM\({}_{2.5}\) data sets at different temporal resolutions (daily, weekly, monthly, seasonal, and annual) for the Beijing metropolitan area. These data products can be provided to the government environmental management agencies and urban air quality and public health research institutions for applications in relevant research and policy decision-making support.  
The PM\({}_{2.5}\) data products at 1 km spatial resolution in this study may work well to estimate the incidence of specific diseases or relevant mortalities in large areas but are still not adequate to estimate PM\({}_{2.5}\) effects at community levels. Hotspots like urban street canyons are not detectable at such a resolution. For better health impact studies, AOT data products at higher spatial resolutions still need to be incorporated with the models in the future.  
MAIAC AOT data have been available twice per day since 2000 and therefore have the potential to be applied in health impact analysis for long-term exposure to PM\({}_{2.5}\). Given the higher R\({}^{2}\) values for both site-specific and year-specific model estimation statistics, other explanatory factors are unlikely to play a dominant role in the models, but it is worth future investigation to improve the models. The methodology can be applied to other large and heavily polluted urban areas in China or other regions of the world based on further tests.  
The following materials are available online at [[http://www.mdpi.com/2076-3417/8/12/2624/s1](http://www.mdpi.com/2076-3417/8/12/2624/s1)]([http://www.mdpi.com/2076-3417/8/12/2624/s1](http://www.mdpi.com/2076-3417/8/12/2624/s1)), Figure S1: Scatterplots of Daily PM\({}_{2.5}\) vs. Daily AOT, Table S1: Geolocations of the 35 Monitoring Sites in Beijing, Table S2: Statistical Summary of Daily PM\({}_{2.5}\) 24 h Average for Each Monitoring Site, Table S3: Statistical Summary of Daily PM\({}_{2.5}\) Period Average for Each Monitoring Site, Table S4: Statistical Summary of Daily AOT Average for Each Monitoring Site.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON] analyzed the data; [PERSON] wrote the original draft of the paper, which was revised by [PERSON], [PERSON], [PERSON] and [PERSON].  
This research was funded by the National Natural Science Foundation of China (Grant No. 41571333), Advanced Research Project (Grant No. 30102060301), China Scholarship Council (Grant No. 201606070063) and the National Natural Science Foundation of China (Grant No. 41771435). Additional support was provided by the US NIEHS (ES009089).  
The authors would like to thank BMEMC for offering ground-based PM\({}_{2.5}\) data and the MAIAC algorithm team for offering AOT data.  
The authors declare no conflict of interest.