Multi-Method Observation and Numerical Simulation of a PM2.5 Pollution Episode in Beijing in October, 2014

Abstract

Multi-method observation and numerical simulation were applied to analyze a PM 2.5 pollution episode in Beijing in October, 2014. The results of vertical observation showed that surface-level backscatter signal and extinction coefficient increased during the episode, suggesting that air pollutants accumulated near the ground. The main meteorological factors during this episode could be described as calm wind, high relative humidity and low surface pressure. The evolution of PM 2.5 concentrations in this episode was divided into four stages, including two-steps type concentration climbing stages (P1 and P2), high concentration maintenance stage (P3) and rapid cleanup stage (P4). Analysis on ground-based observation, satellite remote sensing and atmospheric general circulation showed that regional transport, including crop residue burning, was the main incentive of this pollution episode. Subsequently, local pollutants emission and regional transport maintained and aggravated the episode under unfavorable meteorological conditions. Temporal variation of OX was in close agreement with that of PM 2.5 and the concentration peaks of OX occurred few hours before those of PM 2.5 , which indicated that strong atmospheric oxidation could promote the formation of secondary PM 2.5 . The results of numerical simulation showed that during 8-10 October, the average contribution of regional transport to PM 2.5 in the five sites exceeded 50%.

Tracking Daily Concentrations of PM2.5 Chemical Composition in China since 2000

“…The national-scale PM 2.5 observations since 2013 were taken from the CNEMC website (). The PM 2.5 chemical composition observations obtained from operational monitoring networks (e.g., NACMON, CAWNET, and SPARTAN , ) and from literature studies, − which have relatively long data records, accurate geocoordinates, clear start and end times of sampling, and relatively complete components in each observation record, covering 2013–2020, were used for model training to develop the conversion factor revision model (Section ). Such a data set has been used for the evaluation of PM 2.5 chemical composition simulations in previous studies. , More details about these data, including the temporal resolution, sampling period, measuring methods, etc., could be found in Table S1.…”