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Soil quality indices for metal(loid) contamination: An enzymatic perspective
Author(s) -
Aponte Humberto,
Medina Jorge,
Butler Benjamin,
Meier Sebastian,
Cornejo Pablo,
Kuzyakov Yakov
Publication year - 2020
Publication title -
land degradation and development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 81
eISSN - 1099-145X
pISSN - 1085-3278
DOI - 10.1002/ldr.3630
Subject(s) - environmental chemistry , urease , chemistry , soil contamination , contamination , arylsulfatase , soil water , catalase , metalloid , metal , enzyme , biochemistry , biology , ecology , organic chemistry
Soil degradation by metal and metalloid (metal[loid]) contamination represents a widespread environmental threat. Most studies investigating soil metal(loid) contamination have disregarded the soil quality concept. Enzyme activities (EAs) are good soil quality indicators due to their direct connection to functions related to C, N, P, and S cycles and their sensitivity to metal(loid) contamination. This review (a) provides an overview of the development of soil quality indices (SQIs) based on EAs in metal(loid) contaminated soils, and (b) evaluates the effects of individual metal(loid)s and their combinations on the activities of the most common enzymes involved in the C, N, P, and S cycles. Four intracellular mechanisms dominate the inhibition of EAs by metal(loid)s: (a) transcription inhibition, (b) protein denaturation, (c) cell division inhibition, (d) cell membrane disruption. These mechanisms can also be further exacerbated in soil by nonspecific exoenzyme inhibition. The main EAs used as indicators for metal(loid) contaminated soils are dehydrogenase (DHA), arylsulfatase (ARY), urease (UA), acid phosphatase (Pacid), alkaline phosphatase (Palk), and catalase (CAT). These enzymes are sensitive to metal(loid) contamination, with DHA and ARY being the most sensitive indicators (62 and 56% inhibition, respectively, when averaged over all metal[loid] contamination levels). Other enzymes are inhibited by 32% (UA), 23% (Palk), 18% (Pacid), and 18% (CAT). We suggest principles for the development of SQIs considering biotic soil functions via the use of EAs. This review is the first presenting SQIs based on EAs for metal(loid) contaminated soils, and represents the first quantitative analysis of metal(loid) effects on EAs.