Discovery of Temporal Variation of Arsenic in a Historical Blackfoot Disease Territory by Time Series Analysis

Time series analysis is useful tool for extracting interesting pattern from ordered sequence of observations. The Chianan Blackfoot disease region was selected as study area, and the monitoring data of arsenic in groundwater during the period of 2003 and 2008 was subjected to time series analysis. This study attempted to discover the temporal trend of arsenic level in groundwater by applying the tool of time series analysis. ARMA and ARIMA, the common time series modelling methods, were employed to interpret the information beneath the monitoring data of groundwater quality. Through further verification, the selected ARMA(1, 1) model fits the data set well over the other three models. The result showed that this developed numerical model can effectively interpret and forecast the arsenic level in groundwater from area affected by salinization and high arsenic level in Chianan Plain based on the known information.