Using geographical semi-variogram method to quantify the difference between NO₂ and PM_2.5 spatial distribution...

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Spatial Analysis

Background:

• Urban air quality, specifically nitrogen dioxide (NO2) and fine particulate matter (PM2.5), is critical for environmental and health studies.
• NO2 and PM2.5 are key indicators of photochemical smog and haze pollution in urban environments.
• Understanding the spatial distribution of these pollutants is essential for effective environmental management.

Purpose of the Study:

• To quantify seasonal differences in the spatial distribution of urban NO2 and PM2.5 concentrations in Foshan, China.
• To analyze the spatial variation and autocorrelation of NO2 and PM2.5 using geographical semi-variogram analysis.
• To provide scientific evidence for air pollution control policies and land use regression models.

Main Methods:

• Collected daily NO2 and PM2.5 concentration data from 38 monitoring sites in Foshan.
• Employed geographical semi-variogram analysis to delineate spatial variations and autocorrelation.
• Calculated total spatial variance, random spatial variance, nugget effect, and spatial autocorrelation distances.

Main Results:

• Total spatial variance of NO2 was 38.5% higher than PM2.5; random spatial variance of NO2 was 1.6 times that of PM2.5.
• Nugget effects for NO2 and PM2.5 were 29.7% and 20.9%, respectively, indicating NO2 is influenced by local/regional factors, while PM2.5 is primarily regional.
• NO2 exhibited a larger spatial autocorrelation distance (48km) than PM2.5 (33km), with seasonal variations in spatial range for both pollutants.

Conclusions:

• Geographical semi-variogram analysis effectively reveals seasonal differences in NO2 and PM2.5 spatial distributions.
• Findings support targeted policies for reducing NO2 and PM2.5 pollution.
• Results aid in selecting appropriate buffering radii for spatial predictors in land use regression models.