Identifying and controlling the drivers of change in the quality of water within distribution systems requires a comprehensive understanding of the individual and interactive effects of relevant factors. This article examines the impact of water temperature, pipe characteristics, and hydraulic conditions on the microbiological, physical, and chemical parameters of water quality in the distribution network using Bayesian Dirichlet process mixture of linear models and random forest method. The study was based on a database of the distribution network for the city of Ålesund in Norway and records of water quality data measured at seven different locations in the network from 2013 to 2019. In both modelling approaches applied, temperature was identified as the main factor that controls the microbiological stability of water in the network. From the minimum to the maximum values of temperature in the pipes (3.35 °C–11.14 °C respectively), the probabilities of occurrence of bacteria in water increased from 0.36 to 0.95. Temperature was also shown to be an important factor that affects the chemical parameters of water quality (pH, alkalinity and electrical conductivity). Among the input parameters included in this study, concentration of residual chlorine was shown to have the strongest growth-inhibiting effect on Total Bacteria in the pipes. The results further showed that changes in the hydraulic conditions in the pipes (residence time and flow) were among the most important determinants of the physical, chemical and microbiological quality of water in the distribution network. The random forest models assigned minimal importance to the pipe characteristics and conditions on changes in the water quality parameters. However, the Bayesian models revealed that these parameters have significant impact on the quality of water in the pipes.

Heavy metal (HM) pollution in soils and sediment is a significant concern, yet its levels and ecological risks in peatland areas remain unexplored. This study evaluates these aspects in three peatland regions of the Long An province in Vietnam. Comparisons of HM concentrations in peatland sediments from Tan Thanh, Thanh Hoa, and Duc Hue provinces in the study locations revealed highest values in the Tan Thanh region. Specifically, Cu and Ni were found in sediments at levels two to three times higher than the threshold effects level (TEL) and the effects range median (ERL) guidelines. The main sources of HM pollution in the study area are predicted to include the production and use of fertilizers and pesticides, metal surface processing, mechanical engineering and electronics manufacturing, and chemical plants. Further, positive correlations between HM concentrations and factors such as pH, total organic carbon (TOC), and clay-silt ratio in the sediments were identified through Spearman correlation analysis. The results obtained from the correlation analysis were further corroborated by Bayesian network analysis, which was also applied in this study. In addition, the contamination factor (CF) index indicated that Ni has a pollution level of "moderate degree" in Thanh Hoa (CF = 1.3) and "considerable degree" in Tan Thanh (CF = 3.2), whereas, Cu has a pollution level of "moderate degree" in both Thanh Hoa (CF = 1.3) and Tan Thanh (CF = 2.4). The modified degree of contamination (mCd) ranked the areas as Tan Thanh > Thanh Hoa > Duc Hue, with mCd indexes of 1.3, 0.7, and 0.4, respectively. The potential ecological risk index (RI) indicated a "low risk" level, with an average RI of 35.6 across all sites. These findings address knowledge gaps in HM pollution in peatlands but also contribute to the development of strategies for the protection of peatlands.