土壤重金属混合样采样数目和空间变异耦合规律研究进展OACSTPCD

Determining the optimum sampling numbers and analyzing spatial variation characteristics of heavy metals in soil are the premise and foundation for the survey of heavy metals in soil.This study aims to assess the research of sampling numbers and spatial variation of heavy metals in soil.Based on extensive search of relevant literature published in recent years,the current situations of the coupling regularity among the sampling number(i.e.increments and composite samples),errors(i.e.within-errors and between-errors),and spatial variation for the survey of soil heavy metals are reviewed and future research issues are discussed in the this study.The results showed that:Previous researches were more focused on determining the numbers of composite samples by statistics under a normal distribution in the units with known variations.The methods under the log-normal distribution always overestimated or underestimated the optimum sampling numbers,and only estimated the number of composite samples.Based on different scenarios,such as different errors,normal or skewed distributions,known or unknown variations,it was not clear how to determine the optimum numbers of increments and composite samples.The trend inflection points of the relationship between increasing the number of increments and composite samples,and the decreasing of within-errors and between-errors were not clear.The coupling regularity among the number of increments,composite samples,and spatial variation of heavy metals in soil were not clear.It was concluded that exploring the aforementioned trend inflection point,and thus optimizing the relationship among increments,composite samples and overall sampling numbers of soil heavy metals were crucially important.Combined with the prediction models of spatial variation,establishing the coupled model of sampling numbers and spatial variation to reveal the coupling regularity of increments,composite samples and spatial variation could provide theoretical support to improving the data quality and making field sampling decisions of soil heavy metal.