A Multivariate and Spatiotemporal Analysis of Water Quality in Code River, Indonesia

Abstract

The efficacy of a water quality management strategy highly depends on the analysis of water quality data, which must be intensively analyzed from both spatial and temporal perspectives. This study aims to analyze spatial and temporal trends in water quality in Code River in Indonesia and correlate these with land use and land cover changes over a particular period. Water quality data consisting of 15 parameters and Landsat image data taken from 2011 to 2017 were collected and analyzed. We found that the concentrations of total dissolved solid, nitrite, nitrate, and zinc had increasing trends from upstream to downstream over time, whereas concentrations of parameter biological oxygen demand, cuprum, and fecal coliform consistently undermined water quality standards. This study also found that the proportion of natural vegetation land cover had a positive correlation with the quality of Code River’s water, whereas agricultural land and built-up areas were the most sensitive to water pollution in the river. Moreover, the principal component analysis of water quality data suggested that organic matter, metals, and domestic wastewater were the most important factors for explaining the total variability of water quality in Code River. This study demonstrates the application of a GIS-based multivariate analysis to the interpretation of water quality monitoring data, which could aid watershed stakeholders in developing data-driven intervention strategies for improving the water quality in rivers and streams.

Antibiotic Resistomes and Microbiomes in the Surface Water along the Code River in Indonesia Reflect Drainage Basin Anthropogenic Activities

“…Water quality parameters of the Code River were analyzed in previous studies, and it was found that both rural and city areas contribute to the bacterial load of the river. , One of the previous analyses revealed differences between the rural and city areas: A rural area roughly corresponding to Spring Water and Rural + Cattle Farm sites had lower concentrations of total dissolved solids, NO 2 – , NO 3 – , Zn, Cu, Pb, and lower biological and chemical oxygen demand than downstream sampling sites, which correspond to the city sampling area of this study. Detergent concentration and the abundance of fecal coliforms were higher in the city area (covering Hospital and City sites) than upstream (Spring Water and Rural + Cattle Farm sites) or downstream of the city (City Downstream).…”

“…Detergent concentration and the abundance of fecal coliforms were higher in the city area (covering Hospital and City sites) than upstream (Spring Water and Rural + Cattle Farm sites) or downstream of the city (City Downstream). The increased metal and detergent concentrations could partly explain the elevation of MGEs and related ARGs we observed in the city area, since MGEs commonly carry metal and quaternary ammonium compound resistance genes. ,, Concentrations of PO 4 3– and total suspended solids were higher in the rural area (Spring Water and Rural + Cattle Farm sites) than in the city area, possibly due to soil erosion . Although making statistical connections between previously analyzed water quality parameters , and the data of this study was not possible due to different numbers of samples and sampling times, the river quality parameters revealed that different sources contributed to the river contaminant load through different mechanisms.…”

“…Although making statistical connections between previously analyzed water quality parameters , and the data of this study was not possible due to different numbers of samples and sampling times, the river quality parameters revealed that different sources contributed to the river contaminant load through different mechanisms. In the rural area, due to higher concentrations of PO 4 3– and total suspended solids, it can be postulated that ARG contamination arises from soil erosion and agricultural runoffs containing feces of the production animals, whereas in the city area, the ARG contamination is most likely increased due to direct flushing of wastes to the river , with elevated NO 2 – , NO 3 – , and detergent concentrations . Thus, decreasing the resistance load of the river could be possible with measures reducing soil erosion in the rural area and by improving wastewater treatment in the city area.…”

“…9,10,62 Concentrations of PO 4 3− and total suspended solids were higher in the rural area (Spring Water and Rural + Cattle Farm sites) than in the city area, possibly due to soil erosion. 60 Although making statistical connections between previously analyzed water quality parameters 60,68 and the data of this study was not possible due to different numbers of samples and sampling times, the river quality parameters revealed that different sources contributed to the river contaminant load through different mechanisms. In the rural area, due to higher concentrations of PO 4…”

“…The abundance of most ARGs and MGEs increased significantly from rural sampling sites to the city sampling sites (66 vs 38, adjusted p -value <0.05, gamma distribution GLMs) (Figure , Table S3). It is plausible that discharges of various wastes (e.g., metals, use of personal care products, drug residues, and feces) caused the elevation of these genes in the city area. ,− However, even though the abundance of MGEs and related ARGs increased in the city sampling sites, the increase can be caused by a shift in the taxa from rural agroecosystem-adapted bacteria to bacteria that are better adapted to humans and built environments. , It is common that human-associated bacteria carry MGEs that are packed with multiple features, including various resistance genes. ,, There were more ARGs co-occurring with MGEs in the city area than in the rural area sites (24 MGEs and 107 ARGs in the city area vs 19 MGEs and 70 ARGs in the rural area, adjusted p -value <0.05, ρ > 0.8) (Figure S2). There were also more ARGs that correlated with integrons in the city area (Figure S2); thus, the city area resistome had increased mobility potential compared to the rural area resistome.…”