Characteristics analysis of water pollutants in Cihu Lake, China, based on a multivariate statistical analysis method

Innovative lake pollution profiling: unveiling pollutant sources through advanced multivariate clustering techniques

In many developed and developing nations, lakes are the primary source of drinking water. In the current scenario, due to rapid mobilization in anthropogenic activities, lakes are becoming increasingly contaminated. Such practices not only destroy lake ecosystems but also jeopardize human health through water-borne diseases. This study employs advanced hierarchical clustering through multivariate analysis to establish a novel method for concurrently identifying significantly polluted lakes and critical pollutants. A systematic approach has been devised to generate rotating component matrices, dendrograms, monoplots, and biplots by combining R-mode and Q-mode analyses. This enables the identification of contaminant sources and their grouping. A case study analyzing five lakes in Bengaluru, India, has been conducted to demonstrate the effectiveness of the proposed methodology. Additionally, one pristine lake from Jammu & Kashmir, India, has been included to validate the findings from the aforementioned five lakes. The study explored correlations among various physical, chemical, and biological characteristics such as temperature, pH, dissolved oxygen, conductivity, nitrates, biological oxygen demand (BOD), fecal coliform (FC), and total coliform (TC). Critical contaminants forming clusters included conductivity, nitrates, BOD, TC, and FC. Factor analysis identified four primary components that collectively accounted for 85% of the overall variance. Following identification of pollution hotspots, the study recommends source-based pollution control and integrated watershed management, which could significantly reduce lake pollution levels. Continuous monitoring of lake water quality is essential for identifying actual contaminant sources. These findings provide practical recommendations for maximizing restoration efforts, enforcing regulations on pollutant sources, and improving water quality conditions to ensure sustainable development of lakes.

Development and implementation of a novel mechanized planting method for ecological restoration of aquatic environments using degradable nutrition pots

Submerged plants play a significant role in the remediation and purification of polluted water bodies. Reconstruction of submerged plants has been considered as an important ecological method to restore aquatic ecosystems. However, large-scale and efficient plantation of submerged plants in water restoration is a huge challenge. This paper proposes a novel mechanized planting method for submerged plants utilizing nutrition pots as planting units. Firstly, the details of the mechanized planting method were introduced. The mechanized planting method involves pre-planting the reproductive bodies of submerged plants in degradable nutrition pots, and then implanting them into the underwater soil through a planting device. Secondly, the interaction force between the nutrition pot and the soil was measured. It was found that the implantation force of nutrition pots increases with planting velocity. The planting force shows a significant increase trend when the water content in the soil decreases. Thirdly, the deformation of the nutrition pot was studied through simulations. It was discovered that the deformation of the nutrition pot mainly occurs at the bottom and the side walls near the bottom, and the limited deformation ensures the integrity of the nutrition pot. Finally, a planting device with a linear motion mechanism was designed, and a typical submerged plant, Vallisneria natans was tested, using agricultural paper seedling containers as the nutrition pots. It was demonstrated that the mechanized device successfully planted submerged plant nutrition pots into the soil, and the submerged plants survived and showed a clear growth trend. The mechanized planting method of submerged plants proposed in this article is expected to provide a new and friendly technology for ecological restoration of water source.

Method for screening water physicochemical parameters to calculate water quality index based on these parameters' correlation with water microbiota

Water quality index (WQI) plays a crucial role in guiding water resource management. However, WQI calculation methods are not uniform, especially the selection of water parameters and the weighting given to each water parameter (P_i). To optimize WQI calculation, 132 water samples from seven rivers and from Chaohu Lake (33 sampling sites in Chaohu Lake Basin) in four seasons were collected, and the water parameters and microbiota composition were analyzed using high-throughput sequencing of 16 S rDNA. The correlation coefficient R² between water parameters and microbiota composition using redundancy analysis with the Monte Carlo method were calculated, and the water parameters that significantly correlated with the microbiota composition were selected to calculate WQI_min. The results showed that TP, COD, DO, and Chl a correlated significantly with water microbiota composition. WQI_b calculated by substituting R² for P_i was more consistent with the similarity between the microbiota compositions. WQI_minb calculated using TP, COD, and DO was consistent with WQI_b. The results of WQI_b and WQI_minb were more consistent than those of WQI and WQI_min. These results imply that using R² instead of P_i could help obtain a more stable WQI_b that could better reflect the biological characteristics of the Chaohu Lake Basin.

Effects of rainfall and land use on nutrient responses in rivers in the Brazilian semiarid region

This paper investigated whether rainfall promotes dilution or increase in nutrient concentrations and which land use indicators are the main predictors of nutrients in intermittent rivers in a large Brazilian semiarid region. The total phosphorus (TP) and total inorganic nitrogen (TIN) were monitored between 2013 and 2018 at 92 river water quality monitoring sites. The monthly rainfall (Rn) was obtained from 575 rain gauges. Pearson's correlation (R) between Rn and nutrient concentration was performed. The correlation patterns were also analysed based on land use data: urban area (%), agricultural field area (%), demographic density (inhabitants/km2), sewer system coverage (%), and reservoir density (reservoir/km2). Backward stepwise regression was performed to identify predictors of nutrient concentrations. The results revealed a marginal effect of rainfall on nutrients when the effects of urbanisation outweigh all other aspects. However, in regions with greater accumulated rainfall and lower reservoir density, the rainfall was related to a linear increase in nutrient concentrations (R > 0.8). Contrastingly, in the basins with less accumulated rainfall and greater inter-basin hydrological disconnection, there was a linear reduction in nutrient concentration (R < - 0.5). In the backward stepwise regression, sewer system coverage and Rn had the greatest influence for TP, and the urban area was the strongest predictor for TIN. Importantly, our results demonstrated that in semiarid rivers in densely populated regions, there is no single pattern of variability in nutrient concentration, on a wide scale of assessment. Therefore, adaptative and decentralised management can be more effective in improving water quality in these regions.