Improving Below-Cloud Scavenging Coefficients of Sulfate, Nitrate, and Ammonium in PM_2.5 and Implications for Numerical Simulation and Air Pollution Control

Below-cloud scavenging (BS) is often underestimated in chemical transport models (CTMs) due to inaccurate parameterizations of BS coefficient for fine particle (Lambda) caused by a shortage of high-time resolution field observations. Rainfall ions and related air pollutants were measured hourly in Central China (CC) during 2019. BS contributed to 37%-68% of wet deposition for SO42-, NO3-, and NH4+ (SNA). By a bulk method coupled with brute-force search, the Lambda (10(-2)-10 hr(-1)) was parameterized for SNA in PM2.5, which was 1-3 orders of magnitudes higher than theoretical calculations in CTMs. These chemical-specific Lambda parameterizations were validated by EMEP model. Compared to baselines, updated simulations for annual SNA wet deposition increased by 3.3%-20.4% and for mean PM2.5 SNA concentrations reduced by 1.2%-40%, capturing measurements better. The contributions of scavenged gases to wet deposition below cloud were calculated as 9%-73%, exhibiting discrepancies (2%-17% for HNO3 and 19%-90% for SO2) with previous modeling results as different Lambda schemes adopted in CTMs. The nonlinearity between Lambda and precipitation intensity causes frequency exerting stronger impact on aerosol burden than intensity and duration. Periodic light rain with a precipitation amount of 1-10 mm per event can eliminate 60% of SNA in PM2.5 and is suggested as a routine procedure to improve local air quality. Analyzing a typical washout process after a haze event in CC, BS could reduce PM2.5 SNA concentrations by 44%-54% derived from improved parameterizations.