The relationships between PM2.5 and meteorological factors in China: Seasonal and regional variations (1708.06072v1)

Abstract: The interactions between PM2.5 and meteorological factors play a crucial role in air pollution analysis. However, previous studies that have researched the relationships between PM2.5 concentration and meteorological conditions have been mainly confined to a certain city or district, and the correlation over the whole of China remains unclear. Whether or not spatial and seasonal variations exit deserves further research. In this study, the relationships between PM2.5 concentration and meteorological factors were investigated in 74 major cities in China for a continuous period of 22 months from February 2013 to November 2014, at season, year, city, and regional scales, and the spatial and seasonal variations were analyzed. The meteorological factors were relative humidity (RH), temperature (TEM), wind speed (WS), and surface pressure (PS). We found that spatial and seasonal variations of their relationships with PM2.5 do exist. Spatially, RH is positively correlated with PM2.5 concentration in North China and Urumqi, but the relationship turns to negative in other areas of China. WS is negatively correlated with PM2.5 everywhere expect for Hainan Island. PS has a strong positive relationship with PM2.5 concentration in Northeast China and Mid-south China, and in other areas the correlation is weak. Seasonally, the positive correlation between PM2.5 concentration and RH is stronger in winter and spring. TEM has a negative relationship with PM2.5 in autumn and the opposite in winter. PS is more positively correlated with PM2.5 in autumn than in other seasons. Our study investigated the relationships between PM2.5 and meteorological factors in terms of spatial and seasonal variations, and the conclusions about the relationships between PM2.5 and meteorological factors are more comprehensive and precise than before.

Relationships Between PM2.5 and Meteorological Factors in China: A Regional and Seasonal Investigation

This paper presents a significant examination of the interactions between PM2.5 concentrations and meteorological factors across China. The scope of the analysis includes 74 major cities over a continuous period of 22 months, providing a comprehensive exploration of spatial and seasonal variations. The meteorological parameters considered include relative humidity (RH), temperature (TEM), wind speed (WS), and surface pressure (PS), all of which are critical in affecting PM2.5 levels.

Key Findings

1. Spatial Variations:
   • The authors found that correlations between PM2.5 and RH varied by region: positive in North China and Urumqi, but negative elsewhere, particularly in South China. This suggests that regional aerosol types, influenced by local sources and climates, significantly impact these correlations.
   • WS consistently displayed a negative correlation with PM2.5 in most regions, except Hainan Island, likely due to differences in baseline air quality and local atmospheric conditions.
   • Surface pressure shows a strong positive correlation with PM2.5 in Northeast and Central China but remains weakly correlated elsewhere. This highlights the role of atmospheric boundary layer dynamics in modulating air pollution levels.
2. Seasonal Variations:
   • The correlation strength between PM2.5 and RH was more pronounced in winter and spring. Conversely, TEM showed seasonal dynamics, with positive correlations in winter and negative correlations in autumn.
   • WS was most negatively correlated with PM2.5 during winter, indicating the significance of horizontal dispersion for air pollution control.
   • PS showed stronger correlations in autumn, suggesting potential impacts of seasonal weather patterns on vertical mixing and pollutant dispersion.

Implications and Future Directions

The findings suggest potential improvements in PM2.5 concentration prediction models by incorporating spatial and seasonal variations. These variations can guide the selection of auxiliary variables depending on region and time of year, thereby enhancing model accuracy. Additionally, recognizing the scale variation phenomenon can offer insights for scale-dependent studies and forecasting models.

Understanding regional and seasonal correlation dynamics is crucial for environmental policy. Tailoring pollution controls to consider these variations could lead to more effective strategies in mitigating air pollution. The paper underscores the complexity of PM2.5 interactions and suggests further research to explore unexplained phenomena, such as pressure correlations in different regions, using extended datasets and additional factors like precipitation.

Overall, this research contributes towards precise environmental management practices and encourages studies leveraging long-term data for deeper insights into atmospheric pollution patterns and control efforts. The investigation offers a comprehensive layer to existing knowledge, pressing the importance of acknowledging heterogeneity in air pollution studies and strategies.