Comparison of Three Nonparametric Kriging Methods for Delineating Heavy‐Metal Contaminated Soils

• Published in: Journal of environmental quality Vol. 29; no. 1; pp. 197 - 205
• Main Authors: Juang, Kai‐Wei, Lee, Dar‐Yuan
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Madison, WI American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America 01.01.2000; Crop Science Society of America; American Society of Agronomy
• Subjects: Analysis methods; Applied sciences; CADMIUM; COMPARATIVE EVALUATIONS; Earth sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; ENVIRONMENTAL SCIENCES; Exact sciences and technology; Heavy metals; KRIGING; LEAD; Pollution; Pollution, environment geology; REMEDIAL ACTION; Soil and sediments pollution; Soil contamination; SOILS; Asia; Taiwan; Case study; Statistical method; Cartography; Geostatistics; Distribution function; Probability; Soil pollution; Spatial variation; Kriging; Concentration distribution; Heavy metal
• ISSN: 0047-2425; 1537-2537
• DOI: 10.2134/jeq2000.00472425002900010025x

The probability of pollutant concentrations greater than a cutoff value is useful for delineating hazardous areas in contaminated soils. It is essential for risk assessment and reclamation. In this study, three nonparametric kriging methods [indicator kriging, probability kriging, and kriging with the cumulative distribution function (CDF) of order statistics (CDF kriging)] were used to estimate the probability of heavy‐metal concentrations lower than a cutoff value. In terms of methodology, the probability kriging estimator and CDF kriging estimator take into account the information of the order relation, which is not considered in indicator kriging. Since probability kriging has been shown to be better than indicator kriging for delineating contaminated soils, the performance of CDF kriging, which we propose, was compared with that of probability kriging in this study. A data set of soil Cd and Pb concentrations obtained from a 10‐ha heavy‐metal contaminated site in Taoyuan, Taiwan, was used. The results demonstrated that the probability kriging and CDF kriging estimations were more accurate than the indicator kriging estimation. On the other hand, because the probability kriging was based on the cokriging estimator, some unreliable estimates occurred in the probability kriging estimation. This indicated that probability kriging was not as robust as CDF kriging. Therefore, CDF kriging is more suitable than probability kriging for estimating the probability of heavy‐metal concentrations lower than a cutoff value.