Fuzzy inference system for modeling the environmental risk map of air pollutants in Taiwan.

This study aimed to improve the uncertainty in spatial data of risk assessment through a Fuzzy inference system (FIS) as a way to conduct an environmental risk map of air pollution in Taiwan. In modeling, the feature inputs of FIS included the geographic coordinates and time, while the outputs are the pollutant concentrations. The outputs are supplements to the concentration contour on the map in comparison with Kriging interpolation. In our model, the FIS was designed using the official open data of air pollutants, including Pb and PM2.5 that were collected from the monitoring stations in mid-southern Taiwan. The model involved data filtration and imputation in the preliminary scheme to extract the historical data for analysis. We used the data of Pb (2001-2013) and PM2.5 (2006-2013) for the training process, and then used the data from 2014 to 2015 for validation. Our model was able to compute the smaller errors of inferred and measured values of Pb and PM2.5 than the conventional method. The approach was applied to deduce the exposure of PM2.5 distributed over the Taiwan Island in accordance with the governmental open data of seventy-three stations during 2006-2016 in order to produce our risk map. The designed model upon Fuzzy inference accesses potential risks of spatiotemporal exposures in the unmeasured locations with feasibility and adaptability for environmental management.