학위논문 (석사)-- 서울대학교 대학원 : 사회과학대학 지리학과, 2018. 2. Park, Key Ho.In recently decade haze in China has severely hurt its economy and threatened the health of its population. There is often strong demand from the Ministry for the Environment for assessing, predicting, and trying to reduce the levels of PM2.5 around the country. In practice, PM2.5 data is difficult to measure. Monitor sites are not distributed uniformly, most of them built in urban area. Traditional air pollution epidemiology studies being conducted in large cities can be limited by the availability of monitoring. Satellite Aerosol Optical Depth (AOD) measurements offer the possibility of exposure estimates for the entire population. In this situation, the 10 km MODIS Aerosol Optical Depth (AOD) product can be used as predictor since recent studies has proved the statistical relationship between AOD and PM2.5. The traditional statistical study on AOD and PM2.5 are primarily Geographic Weighted Regression. Based on Gaussian process regression, this study developed a new regression approach to predict PM2.5 distribution in a Bayesian hierarchical setting from October 2016 to October 2017. The spatial non-stationarity was modeled by a Gaussian process with exponential covariance function. Parameters to explain factors like AOD, spatial random effects and non-spatial factors were estimated via a Bayesian hierarchical framework. The result illustrated that our model showed a good daily prediction on unknow sites by giving a 0.76 R^2 under 10 cross validation and a precise annual prediction with R^2 equal to 0.90. For daily model, we compared our result with GWR and a machine learning method support vector machine (0.68 and 0.75 respectively), which showed modeling spatial random effects via Gaussian process was able to improve the accuracy PM2.5 predicting using MODIS AOD data.Chapter 1 Introduction 1
1.1 Research Motivation 1
1.2 Problem Description 2
1.3 Research Objective and Research Question 4
1.4 Methodology 4
1.5 Contribution 7
Chapter 2 Literature Review 9
2.1 Introduction to PM2.5 9
2.2 Aerosol Optical Depth 12
2.3 Satellite Data and Algorithms for AOD retrieval 14
2.3.1 The MODIS AOD product 15
2.3.2 Validation on MODIS AOD in China 16
2.4 PM2.5 Estimation based on AOD 20
2.4.1 Theoretical basis 20
2.4.2 Estimation Models 23
2.5 Machine Learning Methods 27
Chapter 3 Study Area and Data 33
3.1 Study Area 33
3.2 Data Acquisition 34
3.2.1 MODIS 10km Products 34
3.2.2 PM2.5 ground monitoring data 35
3.2.3 Supplementary Data 37
Chapter 4 Model 40
4.1 Overview of Workflow 40
4.2 Data Pre-processing 41
4.3 Model Construction 43
4.3.1 Gaussian Process Regression Model 43
4.3.2 Geographically Weighted Regression Model 48
4.3.3 Support Vector Regression Model 49
Chapter 5 Results and Analysis 51
5.1 Descriptive Statistics on dataset 51
5.2 Model validation 52
Chapter 6 Conclusions and Limitations 61
5.1 Conclusion 61
5.2 Limitation of this study 63
Bibliography 64Maste
