Unraveling how meteorological conditions cause changes in atmospheric fine-particle concentration

New research published in the journal Atmospheric and Oceanic Science Letters reports significant progress in studying the relationship between meteorological conditions and atmospheric fine-particle (PM_2.5) concentrations.

Air pollution is a major global challenge, in which PM_2.5 is a critical pollutant. The variation in PM_2.5 concentrations not only affects the quality of the atmospheric environment, but also has a direct impact on human health. Thus, exploring the causes of PM_2.5 concentration changes, especially the influence of meteorological conditions, has emerged as a hot topic in scientific research.

In this new study, meteorological fields were obtained using two sets of data to analyze the differences in the simulated PM_2.5 concentration. Results showed that the meteorological field had a strong influence on the concentration levels and spatial distribution of the simulated pollution. Also, one of the data sources resulted in relatively smaller simulation errors, allowing more accurate modeling of PM_2.5 concentrations.

Based on a constructed joint data assimilation system, the research team quantitatively assessed the impact of different meteorological fields on the simulated PM_2.5 concentrations and revealed the key roles of specific meteorological factors such as wind speed, temperature, relative humidity, and boundary layer height in the accumulation, maintenance, and dissipation of pollutants.

Notably, the temperature at a height of 2 m showed a positive correlation with the PM_2.5 concentration in the northern part of China and a negative correlation in the southern part of the country.

The joint assimilation system demonstrated its ability to absorb multi-moment observations effectively and easily. Experimental results with the assimilation system showed that it can effectively reduce the uncertainty in PM_2.5 predictions during pollution episodes by simultaneously optimizing initial mass concentrations and emissions.

According to Prof. Tian, the corresponding author of the study, this discovery provides new ideas and methods for improving the accuracy of PM_2.5 predictions. Moreover, it carries significant importance for formulating more effective air pollution prevention and control strategies.