Impact of Winter Heating on the Air Quality in China Open Access

Xiao, Qingyang (2014)

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China suffers from severe particulate matter (PM) pollution. Previous studies reported that the highest PM concentrations occur in winter. This high PM concentration is believed to be partly due to heating. This study used both remote sensing techniques and ground measured air pollutant concentrations to analyze the impact of heating on winter air quality through standard statistical tests and multivariate linear regression. Both the satellite retrieved data and ground measured air pollutant concentrations indicate that the air pollution levels increase significantly during the heating period. The average adjusted AOD ratio and the PM10 concentration ratio increase by 2.78 (p-value<0.01) and 0.33 (p-value<0.01) in the heating period, respectively. This increase in air pollution levels is significantly higher in the heating area than in the non-heating area. The increase in adjusted AOD ratio and PM10 ratio are higher in the heating area than in the non-heating area by 2.19 (p-value<0.01) and 0.06 (p-value <0.01), respectively. Heating contribute significantly to the increase in the air pollution level in the heating period and the impact of heating on air pollution is immediate. The linear regression model indicates that heating demand, indicated by local temperature, can explain about 25% of the increase in air pollution levels during the heating seasons. Central heating has a pollution-control effect relative to individual heating. Our study furthers the understanding about spatiotemporal variability of PM pollution in China and provides information to make more effective pollution-control policies.

Table of Contents

Table of Contents

1 Introduction 1

1.1 PM2.5 and its health impact. 1

1.2 PM2.5 in China. 3

1.3 Central heating and its impact on winter air quality in China. 4

1.4 Ground measurement of PM concentrations and its limitations. 7

1.5 Remote sensing techniques and MODIS aerosol optical depth. 7

1.6 Study objectives and hypotheses. 9

2 Data and Methods 10

2.1 Study Area 11

2.2 Datasets and Processing. 11

2.2.1 Remote Sensing Dataset11

2.2.2 Ground Measured Air Pollutant Concentrations. 13

2.2.3 Model simulated Data. 16

2.2.4Socioeconomic Data. 17

2.2.5 Data Integration 18

2.4 Analytical Methods20

3 Results21

3.1 Descriptive Statistics. 21

3.2 Spatiotemporal Variability of Adjusted AOD.. 23

3.3 T-test Results 25

3.4 Linear Regression Model 26

3.5 Spatiotemporal Variability of PM10. 28

3.6 Spatiotemporal Variability of PM2.5 in 2013. 30

4 Discussion 31

4.1 Spatiotemporal Variability of Air Pollution Levels. 31

4.2 Impact of Heating and Other Possible Air Pollution Sources. 33

4.3 Limitations and Future Study Directions. 35

5 Conclusion 37

Acknowledgment 38

References 38

Tables and Figures 48

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