Department of Health Risk Management, College of Public Health, China Medical University, Taichung, Taiwan; and Department of Medical Research, China Medical University Hospital, Taichung, Taiwan.
Department of Health Risk Management, College of Public Health, China Medical University, Taichung, Taiwan.
J Environ Manage. 2019 Sep 15;246:808-820. doi: 10.1016/j.jenvman.2019.06.038. Epub 2019 Jun 20.
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 PM 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 PM (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 PM than the conventional method. The approach was applied to deduce the exposure of PM 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.
本研究旨在通过模糊推理系统（FIS）提高风险评估空间数据的不确定性，从而绘制台湾地区空气污染环境风险图。在建模过程中，FIS 的特征输入包括地理坐标和时间，而输出则是污染物浓度。与克里金插值相比，输出是对地图上浓度等值线的补充。在我们的模型中，FIS 是使用包括 Pb 和 PM 在内的官方开放空气污染物数据设计的，这些数据是从台湾中南部监测站收集的。该模型在初步方案中涉及数据过滤和插补，以提取用于分析的历史数据。我们使用 Pb（2001-2013 年）和 PM（2006-2013 年）的数据进行训练过程，然后使用 2014 年至 2015 年的数据进行验证。与传统方法相比，我们的模型能够计算出 Pb 和 PM 的推断值和测量值的较小误差。该方法应用于根据政府在 2006-2016 年期间开放的 73 个站点的公开数据来推断 PM 分布在台湾岛上的暴露情况，以生成我们的风险图。设计的基于模糊推理的模型可用于访问不可测量位置的时空暴露的潜在风险，具有环境管理的可行性和适应性。
