Algorithm of Atmospheric Self-Purification Capacity and Its Critical Physical Factors

The air’s capacity in removing air pollutants during December 2017 was scientifically quantified by using NAQPMS (Nested Air Quality Prediction Modeling System). Moreover, critical influential factors were compared to analyze the difference of the atmosphere’s self-cleaning ability between using the Modified A-value Algorithm (MAA) and the Atmospheric Self-Purification Capacity Algorithm (ASPCA). The air self-purification capacity margin and its contribution were also evaluated for the air pollution process. The results show that the planetary boundary layer height, wind profile, and wet scavenging coefficient could describe the atmosphere’s self-cleaning ability more properly than the mixing layer height, wind speed at the height of 10 m, and rain-washing intensity. Both the MAA and ASPCA can represent the atmospheric removal capacity, but the former is highly affected by the city area, whose result is much higher than that of the latter. The atmospheric self-purification capacity margin is negatively correlated with the trend of the PM2.5 concentration, where advection-diffusion contributes the most, followed by the chemical transformation process and finally by the wet deposition processes.