Background: Ambient fine particulate matter (PM2.5) pollution is becoming increasingly serious in China, and is a major risk factor for various cancers. Moreover, recent studies have indicated that there is an impact of PM2.5 from regional transport. We aimed to predict spatial distribution of cancers related to PM2.5, and draw a series of forecasting maps of these cancers.
Methods: A 1000-loops simulation was done to choose the optimal forecasting model between five alternative models: ridge regression, partial least square regression, regression tree, model tree, and the combined forecasting model. Then a kriging interpolation method was used to draw the forecasted cancer maps.
Results: The trend showed a gradual increase in the mortality and morbidity of breast cancer, pancreatic cancer, and all-cause cancer. We found a significant spatial autocorrelation between cancer incidence and PM2.5. Our results from forecasting showed a constant growth in mortality and morbidity of all cancers, and the kriging method suggested a similar spatial pattern. High morbidity and mortality areas were mainly in central-east and south-east China. We found a similar distribution pattern between PM2.5 concentration and mortality and morbidity associated with PM2.5-related cancers.
Conclusions: These findings serve as a valuable reference for the development of effective policies to reduce air pollution emissions, with the efforts from governments in high-risk areas.