Characteristics of PM2.5 concentrations across Beijing during 2013–2015. (November 2016)

• Record Type: Journal Article
• Authors: Batterman, Stuart; Xu, Lizhong; Chen, Feng; Chen, Fang; Zhong, Xuefen
• Is Part Of: Atmospheric environment. Volume 145(2016)
• Page Start: 104
• Page End: 114
• Publication Date: 2016-11
• Subjects: Particulate matter -- PM2.5 -- Spatial variation -- Temporal variation -- Beijing -- Episode; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2016.08.060
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: High concentrations of particulate matter (PM2.5 ) and frequent air pollution episodes in Beijing have attracted widespread attention. This paper utilizes data from the new air pollution network in China to examine the current spatial and temporal variability of PM2.5 at 12 monitoring sites in Beijing over a recent 2-year period (April 2013 to March 2015). The long term (2-year) average concentration was 83 μg m −3, well above Chinese and international standards. Across the region, annual average concentrations varied by 20 μg m −3 (25% of the average level), with lower levels in suburban areas compared to periurban and urban areas, which had similar concentrations. The spatial variation in PM2.5 concentrations was associated with several land use and economic variables, including the fraction of vegetated land and building construction activity, which together explained 71% of the spatial variation. Daily air quality was characterized as "polluted" (above 75 μg m −3 ) on 36–47% of days, depending on site. There were 77 pollution episodes during the study period (defined as two or more consecutive days with Beijing-wide 24-hour average concentrations over 75 μg m −3 ), and 2 to 5 episodes occurred each month, including summer months. The longest episode lasted 9 days and daily concentrations exceeded 450 μg m −3 . Daily PM2.5 levels were autocorrelated (rlag1 = 0.516) and associated with many meteorological variables, including barometric pressure, relative humidity, hours of sunshine, surface and ambient temperature, precipitation and scavenging coefficient, and wind direction. Parsimonious models with meteorological and autoregressive terms explained over 60% of the variation in daily PM2.5 levels. The first autoregressive term and hours of sunshine were the most important variables in these models, however, the latter variable is PM2.5 -dependent and thus not an explanatory variable. The present study can serve as a baseline to compare the improved air quality in Beijing expected in future years. Highlights: Annual average PM2.5 levels at 12 Beijing sites range from 69 to 89 μg m −3 . Pollution episodes are frequent and occur in each month. PM2.5 concentrations show considerable spatial and temporal variation. Vegetated land and building construction explain 71% of the spatial variation. Meteorological and autoregressive variables explain 62% of the daily variation.