Journal
SCIENCE OF THE TOTAL ENVIRONMENT
Volume 592, Issue -, Pages 627-638Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.scitotenv.2017.03.009
Keywords
Severe haze pollution; PM2.5; Meteorological condition; Atmospheric circulation; WRF-Chem
Categories
Funding
- Beijing Municipal Natural Science Foundation [8152019]
- National Key Technologies RAMP
- D Program of China [2014BAC23B03]
- National Natural Science Foundation of China [41621061]
- Beijing Municipal Science AMP
- Technology Commission [Z161100004516018]
- project PE17010 of the Korea Polar Research Institute [PE17010]
- Korea Polar Research Institute of Marine Research Placement (KOPRI) [PE17010] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Based on the hourly PM2.5 concentrations, meteorological variable records and ERA-Interimreanalysis data, a series of diagnostic analyses were conducted to explore the possible meteorological causes for the severe haze pollution that occurred in Beijing in November-December 2015. Using the online-coupled WRF-Chem model and GFS data, the predictability of hourly and daily PM2.5 concentrations was evaluated. The results showed that, in the context of pollutant emission, the severe haze pollution in downtown Beijing in November-December 2015 was primarily attributed to anomalous local meteorological conditions, which were caused and strengthened by anomalous large-scale atmospheric circulations. The abnormal changes in the upper troposphere appeared to trigger the anomalies in the middle-lower troposphere and the local conditions. The numerical simulations can capture the spatial distribution patterns of the PM2.5 concentrations for predictions of 1 to 10 days in advance. The PM2.5 concentration trends in downtown Beijing were generally consistent with the predictions on both daily and hourly time-scales, although the predictability decreased gradually as the lead times prolonged. The predictability of the daily mean PM2.5 concentration was slightly higher than that of the hourly concentration. The statistical indices suggested that the predictions of daily and hourly mean PM2.5 concentrations were generally skillful and reliable for maximum lead times of 8 and 5 days, respectively. (C) 2017 Elsevier B.V. All rights reserved.
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