Impact of population growth and land use and land cover (LULC) changes on water quality in tourism-dependent economies using a geographically weighted regression approach.

This paper aims to assess the influence of land use and land cover (LULC) indicators and population density on water quality parameters during dry and rainy seasons in a tourism area in Indonesia. This study applies least squares regression (OLS) and Pearson correlation analysis to see the relationship among factors, and all LULC and population density were significantly correlated with most of water quality parameter with P values of 0.01 and 0.05. For example, DO shows high correlation with population density, farm, and built-up in dry season; however, each observation point has different percentages of LULC and population density. The concentration value should be different over space since watershed characteristics and pollutions sources are not the same in the diverse locations. The geographically weighted regression (GWR) analyze the spatially varying relationships among population density, LULC categories (i.e., built-up areas, rice fields, farms, and forests), and 11 water quality indicators across three selected rivers (Ayung, Badung, and Mati) with different levels of tourism urbanization in Bali Province, Indonesia. The results explore that compared with OLS estimates, GWR performed well in terms of their R2 values and the Akaike information criterion (AIC) in all the parameters and seasons. Further, the findings exhibit population density as a critical indicator having a highly significant association with BOD and E. Coli parameters. Moreover, the built-up area has correlated positively to the water quality parameters (Ni, Pb, KMnO4 and TSS). The parameter DO is associated negatively with the built-up area, which indicates increasing built-up area tends to deteriorate the water quality. Hence, our findings can be used as input to provide a reference to the local governments and stakeholders for issuing policy on water and LULC for achieving a sustainable water environment in this region.

Removal and monitoring acetaminophen-contaminated hospital wastewater by vertical flow constructed wetland and peroxidase enzymes.

Hospital wastewater contains acetaminophen (ACT) and nutrient, which need adequate removal and monitoring to prevent impact to environment and community. This study developed a pilot scale vertical flow constructed wetland (CW) to (1) remove high-dose ACT and pollutants in hospital wastewater and (2) identify the correlation of peroxidase enzyme extruded by Scirpus validus and pollutants removal efficiency. By that correlation, a low-cost method to monitor pollutants removal was drawn. Plants, such as Scirpus validus, generated peroxidase enzymes to alleviate pollutants' stress. Results showed that the CW removed 3.5 to 6 logs of initial concentration 10 mg ACT/L to a recommended level for drinking water. The CW eliminated COD, TKN and TP efficiently, meeting the wastewater discharged standards of Thailand and Vietnam. By various multivariable regression models, concentrations of ACT in CW effluent and enzymes in S. validus exhibited a significant correlation (p < 0.01, R2 = 68.3%). These findings suggested that (i) vertical flow CW could remove high-dose ACT and nutrient and (ii) peroxidase enzymes generated in S. validus, such as soluble and covalent ones, could track ACT removal efficiency. This would help to reduce facilities and analytical cost of micro-pollutants.

High-throughput DNA microarray detection of pathogenic bacteria in shallow well groundwater in the Kathmandu Valley, Nepal.

Because of heavy dependence on groundwater for drinking water and other domestic use, microbial contamination of groundwater is a serious problem in the Kathmandu Valley, Nepal. This study investigated comprehensively the occurrence of pathogenic bacteria in shallow well groundwater in the Kathmandu Valley by applying DNA microarray analysis targeting 941 pathogenic bacterial species/groups. Water quality measurements found significant coliform (fecal) contamination in 10 of the 11 investigated groundwater samples and significant nitrogen contamination in some samples. The results of DNA microarray analysis revealed the presence of 1-37 pathogen species/groups, including 1-27 biosafety level 2 ones, in 9 of the 11 groundwater samples. While the detected pathogens included several feces- and animal-related ones, those belonging to Legionella and Arthrobacter, which were considered not to be directly associated with feces, were detected prevalently. This study could provide a rough picture of overall pathogenic bacterial contamination in the Kathmandu Valley, and demonstrated the usefulness of DNA microarray analysis as a comprehensive screening tool of a wide variety of pathogenic bacteria.

Characterization of microbial communities distributed in the groundwater pumped from deep tube wells in the Kathmandu Valley of Nepal.

Although groundwater is a major water supply source in the Kathmandu Valley of Nepal, it is known that the groundwater has significant microbial contamination exceeding the drinking water quality standard recommended by the World Health Organization (WHO), and that this has been implicated in causing a variety of diseases among people living in the valley. However, little is known about the distribution of pathogenic microbes in the groundwater. Here, we analysed the microbial communities of the six water samples from deep tube wells by using the 16S rRNA gene sequences based culture-independent method. The analysis showed that the groundwater has been contaminated with various types of opportunistic microbes in addition to fecal microbes. Particularly, the clonal sequences related to the opportunistic microbes within the genus Acinetobacter were detected in all samples. As many strains of Acinetobacter are known as multi-drug resistant microbes that are currently spreading in the world, we conducted a molecular-based survey for detection of the gene encoding carbapenem-hydrolysing ß-lactamase (bla(oxa-23-like) gene), which is a key enzyme responsible for multi-drug resistance, in the groundwater samples. Nested polymerase chain reaction (PCR) using two specific primer sets for amplifying bla(oxa-23-like) gene indicated that two of six groundwater samples contain multi-drug resistant Acinetobacter.