Tri-clustering–based exploration of spatio-temporal heterogeneity of six criteria air pollutants and their relationships in China

Abstract

Severe air pollution in China has become a challenging issue because of its adverse health effects. The distribution of air pollutants and their relationships exhibits spatio-temporal heterogeneity due to influences by meteorological and socioeconomic factors. Investigation of spatio-temporal variations of criteria air pollutants and their relationships, thus, helps understand the current status and further assist pollution prevention and control. Even though many studies have been conducted, relationships among pollutants are non-linear due to complicated chemical reactions and were difficult to model by linear analyses in previous studies. Here, we presented a tri-clustering–based method, the Bregman cuboid average tri-clustering algorithm with I-divergence (BCAT_I), to explore spatio-temporal heterogeneity of air pollutants and their relationships in China. Concentrations of PM2.5, PM10, CO, SO2, NO2, and O3 in 31 provincial cities in 2021 were used as the case study dataset. Results showed that air pollutants except O3 exhibited spatial and seasonal variations, i.e., low in summer in southern cities and high in winter in northern cities. Variations of PMs were more similar to those of CO than other pollutants in southern cities in 2021. Results also found that relationships among these air pollutants were heterogeneous in different regions and time periods in China. Moreover, with the increasing level of NO2 from summer to winter in northern cities, concentrations of O3 first decreased and then increased. This is because the response of O3 to NO2 was negative at the low pollution level due to the titration reaction, which, however, changed to positive when concentrations of NO2 became high.