A general method for evaluating the effects of air pollutants on lung cancer prevalence.

It is widely accepted that some air pollutants are related to lung cancer prevalence. An effective method is proposed to quantitatively evaluate the effects of air pollutants and the interactions between them. The method consisted of three parts: data decomposition, comparable data generation and relationship inference. Firstly, very limited monitoring data published by Geographic Information System were applied to calculate the inhalable air pollution of relatively massive patient samples. Then the investigated area was partitioned into a number of districts, and the comparable data containing air pollutant concentrations and lung cancer prevalence in all districts were generated. Finally, the relationships between pollutants and lung cancer prevalence were concluded by an information fusion tool: Choquet integral. As an example, the proposed method was applied in the investigation of air pollution in Tianjin, China. Overall, SO2 , O3 and PM2.5 were the top three factors for lung cancer. And there was obvious positive interaction between O3 and PM2.5 and negative interaction among SO2 , O3 and PM10 . The effect of SO2 on men was larger than on women. O3 and SO2 were the most important factors for the adenocarcinoma and squamous cell carcinoma, respectively. The effect of SO2 or NO2 on squamous cell carcinoma is obviously larger than that on adenocarcinoma, while the effect of O3 or PM2.5 on adenocarcinoma is obviously larger than that on squamous cell carcinoma. The results provide important suggestions for management of pollutants and improvement of environmental quality. The proposed method without any parameter is general and easily realized, and it sets the foundation for further researches in other cities/countries.

IMPLICATIONS: For total lung cancer prevalence, male and female lung cancer prevalence, and adenocarcinoma and squamous cell carcinoma prevalence, the proposed method not only quantify the effect of single pollutant (SO2 , NO2 , CO, O3 , PM2.5 , and PM10 ) but also reveals the correlations between different pollutants such as positive interaction or negative interaction. The proposed method without any geographic predictor and parameter is much easier to realize, and it sets the foundation for further research in other cities/countries. The study results provide important suggestions for the targeted management of different pollutants and the improvement of human lung health.