4.7 Article

Characteristics of PM2.5 concentrations across Beijing during 2013-2015

期刊

ATMOSPHERIC ENVIRONMENT
卷 145, 期 -, 页码 104-114

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.atmosenv.2016.08.060

关键词

Particulate matter; PM2.5; Spatial variation; Temporal variation; Beijing; Episode

资金

  1. Plan of Environmental Protection Science and Technology, Fujian Province, P.R.China [2013R003]

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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 mu g m(-3), well above Chinese and international standards. Across the region, annual average concentrations varied by 20 mu g 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 mu g 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 mu g m(-3)), and 2 to 5 episodes occurred each month, including summer months. The longest episode lasted 9 days and daily concentrations exceeded 450 mu g m(-3). Daily PM2.5 levels were autocorrelated (r(lag1) = 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. (C) 2016 Elsevier Ltd. All rights reserved.

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