Development and evaluation of an innovative Enhanced River Pollution Index model for holistic monitoring and management of river water quality

Global risk assessment of river pollution stress based on nighttime light remote sensing data

Rivers play a crucial role in the development of human civilization, and river pollution is a significant environmental issue that accompanies with intensified human activity. However, the evaluation of river pollution at a global scale is difficult because of the limitations of long-term pollution-related datasets. As human activities are the main factor causing river pollution, nighttime light (NTL) remote sensing data can be used as an alternative indicator of the risk of river pollution stress(RPS), which is closely related to human activities and refers to the amount of pollutants within the confluence range of river reaches. In this study, we propose a river pollution pressure index (PI) to indicate risk of RPS by considering the accumulation effect of water flow. Then we calculated PI of over 0.67 million reaches global with annual total flow >100 million m3/s from 2000 to 2022, which was validated using water quality data of >2000 river sections in China. The results show that, from 2000 to 2022, the spatial variations of the risk of RPS are uneven, with a migration trend from west to east. The risk of RPS continues to increase globally, especially rapidly after 2010. Central Asia, Southeast Asia, East Asia, and eastern China are the regions with the fastest growth rates. In most developed countries, developing countries, and underdeveloped countries, the risk of RPS is high and increasing slowly, moderate and increasing rapidly, and low and increasing slowly, respectively. However, in some special cases, such as Japan, the risk of RPS continues to decrease. These spatiotemporal variations of the risk of RPS correlate with global events, such as quantitative easing of global economy after 2008, China's "Belt and Road Initiative", and COVID-19. This study demonstrates that NTL data can be applied to evaluate the global risk of RPS.

Development of AI-based hybrid soft computing models for prediction of critical river water quality indicators

Prediction of river water quality indicators (RWQIs) using artificial intelligence (AI)-based hybrid soft computing modeling techniques could provide essential predictions required for efficient river health planning and management. The study described the development of a novel AI-based relative weighted ensemble (AIRWE) hybrid model for predicting critical RWQIs, i.e., biochemical oxygen demand (BOD) and total coliform (TC). The study involved comprehensive water quality (WQ) monitoring from 30 locations along the Damodar River to establish the baseline data and delineate the WQ. The representative input features showing a strong association with BOD and TC were identified using Spearman's rank-coupled orthogonal linear transformation (SOT). The relative weighted ensemble (RWE) method was applied to determine the relative weights for base learners in the AIRWE model. The statistical analysis of the developed model revealed that it was most efficient and accurate for predicting BOD (R2, 0.97; RMSE, 0.06; MAE, 0.04) and TC (R2, 0.98; RMSE, 0.06; MAE, 0.05) over the traditional techniques. The tstat (BOD 0.02 and TC 0.47) was lesser than tcrit (1.672), confirming its unbiased predictions. The SOT technique removed the data noise and multicollinearity, whereas RWE curtailed the individual model's limitations and predicted more reliable results. The model resulted 97% accuracy with high precision (96%) in classifying the river water quality for various end uses. The study describes a novel approach for researchers, scientists, and decision-makers for modeling and predicting various environmental attributes.

Evaluation and monitoring of the water quality of an Argentinian urban river applying multivariate statistics

In this work, we present the water quality assessment of an urban river, the San Luis River, located in San Luis Province, Argentina. The San Luis River flows through two developing cities; hence, urban anthropic activities affect its water quality. The river was sampled spatially and temporally, evaluating ten physicochemical variables on each water sample. These data were used to calculate a Simplified Index of Water Quality in order to estimate river water quality and infer possible contamination sources. Data were statistically analyzed with the opensource software R, 4.1.0 version. Principal component analysis, cluster analysis, correlation matrices, and heatmap analysis were performed. Results indicated that water quality decreases in areas where anthropogenic activities take place. Robust inferential statistical analysis was performed, employing an alternative of multivariate analysis of variance (MANOVA), MANOVA.wide function. The most statistically relevant physicochemical variables associated with water quality decrease were used to develop a multiple linear regression model to estimate organic matter, reducing the variables necessary for continuous monitoring of the river and, hence, reducing costs. Given the limited information available in the region about the characteristics and recovery of this specific river category, the model developed is of vital importance since it can quickly detect anthropic alterations and contribute to the environmental management of the rivers. This model was also used to estimate organic matter at sites located in other similar rivers, obtaining satisfactory results.

Application of Monte Carlo simulation for carcinogenic and non-carcinogenic risks assessment through multi-exposure pathways of heavy metals of river water and sediment, India

Heavy metal contamination has severe detrimental impacts on the entire river ecosystem's quality and causes potential risks to human health. An integrated approach comprising deterministic and probabilistic (Monte Carlo simulation) models with sensitivity analysis was adopted to determine heavy metals' chronic daily intake (CDI) and their associated health risks from the riverine ecosystem. Both carcinogenic and non-carcinogenic risks of water and sediment were estimated through multi-exposure pathways. The analytical results indicated that the concentration patterns of heavy metals in sediment (Fe > Mn > Sr > Zn > Cr > Cu > Cd) were slightly different and higher than in water (Fe > Zn > Cr > Sr > Mn > Cu > Cd). The potential carcinogenic risks of Cr and Cd in sediment (5.06E-02, 5.98E-04) were significantly (p < 0.05) higher than in water (9.08E-04, 8.97E-05). Moreover, 95th percentile values of total cancer risk (TCR) for sediment (1.80E-02, 3.37E-02) were about 22 and 143 times higher than those of water (8.10E-04, 2.36E-04) for adults and children, respectively. The analysis of non-carcinogenic risk revealed a significantly higher overall hazard index (OHI) for both sediment (adults: 1.26E+02, children: 1.11E+03) and water (adults: 3.26E+00, children: 9.85E+00) than the USEPA guidelines (OHI ≤ 1). The sensitivity analysis identified that the concentration of heavy metals was the most influencing input factor in health risk assessment. Based on the reasonable maximum exposure estimate (RME), the study will be advantageous for researchers, scientists, policymakers, and regulatory authorities to predict and manage human health risks.

Development of fuzzy leachate pollution index for treatability-based classification of solid waste landfills

The fuzzy leachate pollution index (FLPI) was established to classify the landfill sites on the basis of their leachate pollution potential by considering the limitations of traditional methods. The FLPI was developed adopting 9 critical input parameters, i.e., TDS, pH, Cl, Cu, Pb, Cr, Zn, BOD, and COD, from 22 major landfill sites across India. Using these critical parameters, 3 groups, i.e., inorganic leachate strength (INLS), organic leachate strength (ORLS), and heavy metal leachate strength (HMLS), were generated to estimate the FLPI. The regression analysis, ANOVA, and sensitivity analysis were also performed to determine the significance and uncertainty of the index. The results showed that among all MFs, the triangular with overlapping open ends (TOO) MF was best fitted (R = 0.90) for FLPI estimation. Accordingly, 41% of the landfill sites showed less treatment while the others (59%) required moderate degree of treatment. The regression (R² = 0.92) and ANOVA (F value = 15.003, p = 0.000031) analyses described that the developed tool was significant (p < 0.05). The sensitivity analysis showed that Zn (R = 0.99) was the most influencing factor followed by BOD > COD > pH > Cr > Cu > Cl > Pb > TDS. The study provides an important tool that can also be used by researchers and scientists for investigating and evaluating various environmental problems.

Multifunctional resilience of river health to human service demand in an alluvial quarried reach: a comparison amongst fuzzy logic, entropy, and AHP-based MCDM models

Riverine ecosystem services to human beings are dynamically evaluated by harmonic relationships; however, over growing human service demands (HSDs) are leading to deteriorate the river health resilience. In this study, an assessment index system of river health involving pressure-state-response (PSR) based on twenty indicators of riparian, channel geomorphic, hydroecological, and social attributes was developed to detect the multifunctional reliability and resilience of river system integrity for HSDs at upper (US), middle (MS), and lower segments (LS) of Kangsabati River using fuzzy logic, analytical hierarchical process (AHP), and entropy weight-based multi-criteria decision matrix (MCDM) methods. Borda integrating MCDM results revealed that overall indicator performance is high health score in US (77), medium score in MS (69), but mostly unhealthy score in LS (34); thus, entropy-MCDM models give highest rank to US, medium rank to MS, and least rank to LS, while AHP and fuzzy MCDM models assigned as high priority rank to MS, medium rank to US, and least rank to LS, respectively. According to model validation performances, entropy-MCDM models (RMSE < 2.48) are rationalized to the harmonic relationship of riverine system, whereas fuzzy and AHP-MCDM models (RMSE < 2.79) are signified to HSDs, and these results are closer to real problems. With the acceptability of AHP-MCDM models through the percentage change (73.89%) and intensity change (17.16) assessment, it points that over HSDs are crucial factors for river health degradation. Moreover, final outcome of the present research helps to find out the sick river health sites for ecological restoration.

An assessment of hospital wastewater and biomedical waste generation, existing legislations, risk assessment, treatment processes, and scenario during COVID-19

Hospitals release significant quantities of wastewater (HWW) and biomedical waste (BMW), which hosts a wide range of contaminants that can adversely affect the environment if left untreated. The COVID-19 outbreak has further increased hospital waste generation over the past two years. In this context, a thorough literature study was carried out to reveal the negative implications of untreated hospital waste and delineate the proper ways to handle them. Conventional treatment methods can remove only 50%-70% of the emerging contaminants (ECs) present in the HWW. Still, many countries have not implemented suitable treatment methods to treat the HWW in-situ. This review presents an overview of worldwide HWW generation, regulations, and guidelines on HWW management and highlights the various treatment techniques for efficiently removing ECs from HWW. When combined with advanced oxidation processes, biological or physical treatment processes could remove around 90% of ECs. Analgesics were found to be more easily removed than antibiotics, β-blockers, and X-ray contrast media. The different environmental implications of BMW have also been highlighted. Mishandling of BMW can spread infections, deadly diseases, and hazardous waste into the environment. Hence, the different steps associated with collection to final disposal of BMW have been delineated to minimize the associated health risks. The paper circumscribes the multiple aspects of efficient hospital waste management and may be instrumental during the COVID-19 pandemic when the waste generation from all hospitals worldwide has increased significantly.