Main content area

To what extent can the below-cloud washout effect influence the PM2.5? A combined observational and modeling study

Lu, Xingcheng, Chan, Siu Chung, Fung, Jimmy C.H., Lau, Alexis K.H.
Environmental pollution 2019 v.251 pp. 338-343
air quality, data collection, models, particulates, pollutants, rain, rain intensity
The below-cloud washout (BCW) effect on PM2.5 concentration during periods of rain is still a subject of debate. Existing BCW schemes for PM2.5 have large deficiencies that influence its simulation in 3D chemical transport models (CTMs). In this study, a 7-year dataset with high temporal resolution (in minutes) sampled from a pristine rural site is used to calculate the BCW coefficient during the rain events. The data used for the BCW coefficient calculation cover a wide range of rain intensity from 2 mm h−1 to 60 mm h−1. The BCW coefficient linearly correlates with the rain intensity, with a correlation coefficient of 0.82. The coefficient has a magnitude of 10−5 to 10−4 s−1 when the rain intensity ranges from 1 to 40 mm h−1. After implementing the updated BCW scheme into the Comprehensive Air Quality Model with Extensions (CAMx) model, the performance of PM2.5 simulation improves for the two months of heavy rain. Apart from the CAMx model, our scheme can be easily implemented into other 3D CTMs to improve PM2.5 simulation during rainy days. The BCW effect can clean around 10–40% of the PM2.5 over our study region, which can help to reduce the PM2.5 exposure level for residents, and the health burdens caused by this pollutant can thus be reduced. Rainmaking is a potential way to decrease PM2.5 concentration, but it cannot be the key method to reduce the PM2.5 level to the standard during episodic cases (e.g., >200 μg/m3).