Use of geographic information systems for assessing groundwater pollution potential by pesticides in central Thailand.

摘要

This study employed geographic information systems (GIS) technology to evaluate the vulnerability of groundwater to pesticide pollution. The study area included three provinces (namely, Kanchana Buri, Ratcha Buri, and Suphan Buri) located in the western part of central Thailand. Factors used for this purpose were soil texture, percent slope, primary land use, well depth, and monthly variance of rainfall. These factors were reclassified to a common scale showing potential to cause groundwater contamination by pesticides. This scale ranged from 5 to 1 which means high to low pollution potential. Also, each factor was assigned a weight indicating its influence on the movement of pesticides to groundwater. Well depth, the most important factor in this study, had the highest weight of 0.60 while each of the remaining factors had an equal weight of 0.10. These factors were superimposed by a method called “arithmetic overlay” to yield a composite vulnerability map of the study area.; Maps showing relative vulnerability of groundwater to contamination by pesticides were produced. Each of them represented the degree of susceptibility of groundwater to be polluted by the following pesticides: 2,4-D, atrazine, carbofuran, dicofol, endosulfan, dieldrin & aldrin, endrin, heptachlor & heptachlor epoxide, total BHC, and total DDT. These maps were compared to groundwater quality data derived from actual observations. However, only the vulnerability maps of atrazine, endosulfan, total BHC, and heptachlor & heptachlor epoxide showed the best approximation to actual data. It was found that about 7 to 8%, 83 to 88% and 4.9 to 8.7% of the study area were highly, moderately, and lowly susceptible to pesticide pollution in groundwater, respectively.; In this study a vulnerability model was developed, which is expressed as follow: V = 0.60C_W + 0.10C_S + 0.10C_R + 0.10C_L + 0.10C_SL. Its function is to calculate a vulnerability score for a certain area. The factor “V” in the model represents the vulnerability score of a certain area, whereas C_W, C_S , C_R, C_L, and C_SL represent the values or classes assigned to well depth, soil texture, monthly variance of rainfall, primary land use, and percent slope in that area.