Tri-clustering–based exploration of spatio-temporal heterogeneity of six criteria air pollutants and their relationships in China - Wu, Xiaojing (Frontiers in Earth Science, 2022)
Severe air pollution in China becomes 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 to understand 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 difficult to model using linear analyses in previous studies. Here we presented a tri-clustering based method, 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 at 31 provincial cities in 2021 were used as the case study dataset. Result showed that air pollutants except O3 exhibited spatial and seasonal variations, i.e. low in the summer in southern cities and high in the winter in northern cities. Variations of PMs were more similar with CO than other pollutants in southern cities in 2021. Results also found that relationships among these air pollutants were heterogeneous at 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. That is because the response of O3 to NO2 was negative at the low pollution level due to titration reaction, which however, changed to positive when concentrations of NO2 became high.