A Simple New Method for Calculating Precipitation Scavenging Effect on Particulate Matter: Based on Five-Year Data in Eastern China

Abstract

A “rain-only” method is proposed to find out the precipitation effect on particle aerosol removal from the atmosphere, and this method is not only unique and novel but also very simple and can be easily adapted to predict aerosol particle scavenging over any region across the world irrespective of the topographical, orographical, and climatic features. By using this simple method, the influences of the rain intensity and particle mass concentration on the aerosol scavenging efficiency are discussed. The results show that a higher concentration, a higher rain intensity, and a larger particle size lead to a higher scavenging efficiency and a higher scavenging rate. The greater the rain intensity, the higher the scavenging efficiency. The scavenging efficiency of PM10 by precipitation is better than that of PM2.5. When the rain intensity is 10 mm h−1, the scavenging efficiency of PM2.5 reaches 5.1 μg m−3 h−1, and the scavenging efficiency of PM10 reaches 15.8 μg m−3 h−1. The scavenging rate increases faster when accumulative precipitation is below 15 mm. The scavenging rate has obvious monthly variation, and the scavenging rate of coastal areas is less than that of inland Jiangsu. The growth of the particle mass concentration after precipitation is divided into two stages: the rapid growth stage after precipitation ends, and the slow growth stage about 24 h after precipitation ends.

Spatiotemporal Patterns and Dominant Factors of Urban Particulate Matter Islands: New Evidence from 240 Cities in China

“…Cities with positive correlations were mainly located in the northeastern and northwestern parts of China. The existing literature shows that a small influence of rainfall on PM2.5 mitigations has been observed in dryer climates, such as Jiangsu province [7] and Beijing [12]; moreover, rainfall, to some extent, can increase PM2.5 concentrations in dry climatic settings.…”

“…In order to explore the influence of different driving factors on UPIs, we employed the GWR model to quantify human-environment interactions in relation to shifts and spatial variations in PM 2.5 concentrations (details can be found in Equation ( 8)). The driving factor data were standardized based on Equation (7). The weighted least squares method was used to calculate the regression coefficient, as shown in Equation ( 9).…”

“…In studies that explored influential factors concerning PM 2.5 concentrations, Zhou et al utilizing a regression method, investigated the relationship between precipitation and particulate pollution in Jiangsu, China. They found that an increase in rainfall helped mitigate particulate pollution and that the mitigation effect was more pronounced in regions with abundant rainfall [7]. Wang et al estimated PM 2.5 concentrations in China using Timely Structure Adaptive Modeling (TSAM) and chose four environmental factors and eight anthropogenic factors to conduct driving analyses.…”

A Movable Fog-Haze Boundary Layer Conceptual Model Over Jianghuai Area, China

“…Local emissions, pollutants transported from the North China Plain, and boundary layer change can all affect the intensity and duration of the fog-haze episode (Liang et al, 2017;Wang et al, 2016;Yu et al, 2016). The atmosphere boundary layer conditions in the Jianghuai area, which covers Jiangsu and Anhui provinces in this article, can affect not only local pollutant concentration but also pollutant transportation between North China and the Yangtze River Delta (Dai et al, 2020;Gu et al, 2020;Liu et al, 2018a;Liu et al, 2018b;Peng et al, 2016;Shen et al, 2021a;Shen et al, 2021b;Xie et al, 2021;Zhou et al, 2021).…”

Seasonal variations of metals and metalloids in atmospheric particulate matter (PM2.5) in the urban megacity Hanoi

“…Although wet deposition is considered a major process for removing particulate-bound MM, transfer mechanisms depend on particle size and metal solubility (Pan and Wang 2014;Jaffrezo and Colin, 1988). PM10 is more readily recovered than PM2.5 (Cheng et al, 2021;Zhou et al, 2021) and only the soluble fraction of MM is cleaned from particles (Vithanage et al, 2022). These considerations may explain the lack of correlation between PM2.5-bound MM and precipitation.…”