Abstract :
Groundwater is one of the major sources of exploitation in mid-eastern China. Thus for protecting groundwater quality, data on spatial and temporal distribution is important. Geostatistics methods are one of the most advanced techniques for interpolation of groundwater quality. In this research, ordinary kriging was used for predicting spatial distribution of some groundwater characteristics including pH, salinity, total hardness, ammonia nitrogen, volatile phenol, CODMn, SO42-, Cl-, F-, nitrate nitrogen, and nitrite nitrogen. Sampling data were collected and statistical analyses from 45 wells even distributing in Huaihe river basin. After statistical analysis of data, for selecting suitable model for fitness on experimental variograme, a cross validation indicators and additional model parameters was used to select the best method for interpolation. Results showed that ordinary kriging methods are suit to spatial interpolation of groudwater quality. Finally, filled contour maps generating from their semivariograms are drawn and predict the change of groundwater quality in the basin.
Keywords :
ammonia; chlorine; fluorine; groundwater; hydrological techniques; interpolation; organic compounds; rivers; statistical analysis; sulphur compounds; water quality; CODMn characteristic; Cl- characteristic; F- characteristic; Huaihe River basin distribution; SO42- characteristic; advanced techniques; ammonia nitrogen characteristic; best interpolation method; cross validation indicators; experimental variograme; filled contour maps; geostatistics methods; groundwater characteristics; groundwater quality change prediction; groundwater quality interpolation; groundwater quality protection; major exploitation sources; mid-eastern China; model parameters; nitrate nitrogen characteristic; nitrite nitrogen characteristic; ordinary kriging methods; ordinary kriging research; pH characteristic; salinity characteristic; sampling data; semivariograms; spatial distribution analysis; spatial distribution data; spatial distribution prediction; spatial groudwater quality interpolation; statistical data analysis; temporal distribution data; total hardness characteristic; volatile phenol characteristic; well statistical analyses; Interpolation; Mathematical model; Nitrogen; Rivers; Standards; Water pollution; Water resources; Huaihe River Basin; Interpolation; cross-validation; geostatistic analysis; groundwater quality; saptial distribution;
