Spatial and Temporal Analysis of Daily Measurements of PM2.5 Air Pollution in Beijing, China
Tao Tang, Hutong Fan, Qiang Sun, Wenji Zhao
Abstract
Air particulate matter (PM2.5) pollution is a critical environment and human health problem. This research utilizes dailymeasurement data of PM2.5 air pollution of 27 air pollution monitoring stations during five-year period (2014-2018) to analyzethe spatial and temporal distribution patterns in Beijing, China. Five (5) sampling time periods daily were extracted from original hourly monitoring data during the study period, namely:morning peak traffic period (MPT); morning low traffic period (MLT); afternoon low traffic period (ALT); afternoon peak traffic period (APT); and midnight period (MIDN).
Mann-Kendall statistical test andPrincipal Component Analysis (PCA) were used to study temporal trend and variations. Geostatistical method of inverse distance weighted (IDW) interpolations was used to study the spatial distribution patterns. The results of Mann-Kendall Trend Analysis indicate PM2.5 concentrations mainly show declining patterns of the four seasons during the five-year period with the p-values of summer, fall, and winter are smaller than 0.01. Some p-values during spring are between 0.01 to 0.05 indicatingweak decline trend.PCA analysis shows that the dailyPM2.5 concentrations reachthe highest in the winter season, and the lowest concentrations in the summer each year.The general trend during the five-year study period is declining. Results of spatial analysis indicate that north and northwest region encountered the lowest PM2.5 air pollutions. The highest PM2.5 air pollution occurred in the southern suburban areas.The results show that heat energy supply duringwinter season to buildings and houses is the greatest impact factor to PM2.5 air pollutions in Beijing.
Full Text: PDF DOI: 10.15640/jges.v11n1a1
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