Elucidating the mechanism by which weather conditions cause changes in the concentration of fine particles in the atmosphere

A new study published in the journal Atmospheric and Oceanic Science Letters reports major advances in the study of the relationship between weather conditions and atmospheric particulate matter (PM2.5) concentrations.

Air pollution is a major global issue, and PM2.5 is a serious pollutant. Fluctuations in PM2.5 concentration not only affect the quality of the air environment, but also directly affect human health. Therefore, exploring the causes of PM2.5 concentration changes, especially the influence of meteorological conditions, has emerged as a hot topic in scientific research.

This new study used two sets of data to capture meteorological fields and analyze differences in simulated PM2.5 concentrations. The results showed that the meteorological field had a strong influence on the concentration level and spatial distribution of simulated pollution. One of the data sources also resulted in relatively smaller simulation errors, allowing for more accurate modeling of PM2.5 concentrations.

Based on the joint data assimilation system they built, the research team quantitatively evaluated the influence of various meteorological fields on simulated PM2.5 concentrations, including wind speed, temperature, relative humidity, and boundary layer height. We revealed the important role of specific meteorological factors. in the accumulation, maintenance, and dissipation of pollutants.

In particular, temperature at 2 m height was positively correlated with PM2.5 concentration in northern China and negatively correlated in southern China.

The joint assimilation system has demonstrated the ability to effectively and easily assimilate observations at multiple moments. Experimental results using the assimilation system showed that the uncertainty of PM2.5 prediction during pollution occurrence can be effectively reduced by simultaneously optimizing the initial mass concentration and emission rate.

According to Professor Tian, ​​corresponding author of the study, this discovery provides new ideas and methods to improve the accuracy of PM2.5 prediction. Moreover, it is of great importance in developing more effective air pollution prevention and control strategies.