Using Non-Parametric Regression Methods to Analyze the Impact of air Pollutants on Psychiatric & Neurological Illnesses

Abstract

While industrial pollutions cause changes in the environment and gradually has a strong impact on human physiologies, the relationship between air pollutants and disease occurrences is a subject worthy of exploration. Therefore, based on the nationwide datasets, this study would use non-parametric regression methods to analyze the impact of air pollutants on various psychiatric & neurological illnesses. Through these regression models, the time lag effect of environmental factors on the target diseases would also be taken into account. According to the evaluation outcomes of correlation coefficients, the targets diseases were mainly associated with air pressure, CH4, and SO2. Moreover, observing the coefficients of non-parametric regression models, influences from the environmental factors, i.e. meteorological items and air pollutants, were not limited to the current occurrence (0~1-day lag) but might also accumulate after a period of time (5~7-day lag). In summary, the relationships between air pollutants and psychiatric/neurological illnesses have been verified in this study.