COMPARISION OF nirS, cnorB, MCR GENES AGAINST WATER QUALITY PARAMETERS TO MONITOR UNCONTROLLED LANDFILLS

Abstract

This study was conducted to investigate the possible relationship between a molecular biological investigation and water quality parameters in monitoring groundwater pollution at the immediate boundary of uncontrolled landfills and their downgradient aquifers, which may consequently facilitate unbiased monitoring for the sites. Two closed landfills, Jicksan and Taejang in Korea, were chosen for this study, where the diversity of the microbial community was characterized and three specific genes, i.e. nirS (nitrite reductase coding gene), cnorB (nitric oxide reductase coding gene) and MCR (methyl coenzyme M reductase coding gene), were quantified. The quantified genes were then compared with conventional water quality parameters. From the analyzed DNA sequences, Proteobacteria phylum was most dominantly observed. A quantitative analysis revealed that the copy numbers (gene abundance) of denitrification enzyme coding genes, i.e. nirS gene and cnorB gene in Jicksan (J) site, are seven and four times, respectively, higher than Taejang (T) site. This simply implied that denitrification was possibly higher in J site than T site. In addition, a methane production enzyme coding gene, i.e. MCR, in a J1 bore immediately bordering the sources in the J site showed the greatest concentration, but it was precipitously decreased in the downgradient direction toward the outer boundary of landfill. A comparative investigation between the copy numbers of three genes, i.e. nirS, cnorB, and MCR, and conventional monitoring parameters, i.e. Cl−, alkalinity, TOC, NH3‐N, and NO2‐N, showed that they had overall correlation as given by more than 0.99 of the squared correlation coefficient (R 2) for almost all of the concerned bores. It was concluded that the comparison between the molecular biological investigation and the conventional groundwater monitoring parameters showed good relationship between them, so that both tools could be more efficiently used for assessing the levels of contamination and prediction of the fate of pollutants, rather than being applied separately.