Development of a Universal Water Quality Index (UWQI) for South African River Catchments

Why we should have a universal air quality index?

Air pollution is known to be one of the major risk factors for premature morbidity and mortality globally, which are preventable. Therefore, the public is informed about air pollution through the Air Quality Index (AQI). The AQI represents integrated data of selected pollutants and produces a combined overall index for specific locations and time. The AQI algorithm generates a range of numbers and categories of colors that indicate likely health risks from air pollutants and the public's actions to minimize the risks. However, it lacks emerging evidence on chemical toxicity or composition. Hence, policymakers may also consider addinga toxicity matrix of fine particles to refine the algorithm, such as oxidative potential. Further, the risk is commonly communicated in numbers and not in color as dictated by the AQI. The AQI values and categories vary significantly between countries for the same pollution concentration, leading to confusion. Hence, we recommend developing a universal AQI (UAQI) with a consistent relationship between colors, concentrations, and toxicity to communicate air pollution risks to the public. Further, communication media should be encouraged to use universal color-coding rather than AQI values, i.e., numbers. Therefore, a global policy framework for regulatory authorities and policymakers is needed to communicate air pollution risk information consistently and to minimize public health exposure.

Impact of anthropogenic and land use pattern change on spatio-temporal variations of groundwater quality in Odisha, India

The groundwater physicochemical parameters were studied to understand their spatiotemporal variations and groundwater quality, using the statistical and entropy weights methods. Out of ten parameters that were considered, F-, TDS, and Cl- appear to be the major contributors influencing the quality of groundwater. The Principal Component Analysis (PC1, PC2) indicates that the majority of ions are derived from both natural and anthropogenic sources. Studies of saturation indices of gypsum, Halite, dolomite, and calcite indicate that dissolution of these salts also affects groundwater salinization. In coastal areas, a few of the water samples also appear to be contaminated by the mixing of seawater. The entropy weights, which are free from subjective biases were used to estimate the water quality index. The entropy-based water quality index (EWQI) varies from excellent to good quality for the year 2012-13, and appears to degrade after 2015 onwards. For the year 2018-19 and 2021-22, 71.42% and 68.42% of the study areas show excellent water quality, followed by 25.33% and 24.33% (good), 2.54% and 3.54% (average), 0.7% and 3.7% study area shows as poor quality respectively. The groundwater quality, particularly in the western, central, northern, and eastern parts of the region, appears to be average, poor, and very poor in several small patches, respectively. Industrial developments, mining activities, irrigation, changing land use patterns, agricultural activities, and increased anthropogenic activities may be contributing to the degradation of the water quality.

A comprehensive review of water quality indices for lotic and lentic ecosystems

Freshwater resources play a pivotal role in sustaining life and meeting various domestic, agricultural, economic, and industrial demands. As such, there is a significant need to monitor the water quality of these resources. Water quality index (WQI) models have gradually gained popularity since their maiden introduction in the 1960s for evaluating and classifying the water quality of aquatic ecosystems. WQIs transform complex water quality data into a single dimensionless number to enable accessible communication of the water quality status of water resource ecosystems. To screen relevant articles, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed to include or exclude articles. A total of 17 peer-reviewed articles were used in the final paper synthesis. Among the reviewed WQIs, only the Canadian Council for Ministers of the Environment (CCME) index, Irish water quality index (IEWQI) and Hahn index were used to assess both lotic and lentic ecosystems. Furthermore, the CCME index is the only exception from rigidity because it does not specify parameters to select. Except for the West-Java WQI and the IEWQI, none of the reviewed WQI performed sensitivity and uncertainty analysis to improve the acceptability and reliability of the WQI. It has been proven that all stages of WQI development have a level of uncertainty which can be determined using statistical and machine learning tools. Extreme gradient boosting (XGB) has been reported as an effective machine learning tool to deal with uncertainties during parameter selection, the establishment of parameter weights, and determining accurate classification schemes. Considering the IEWQI model architecture and its effectiveness in coastal and transitional waters, this review recommends that future research in lotic or lentic ecosystems focus on addressing the underlying uncertainty issues associated with the WQI model in addition to the use of machine learning techniques to improve the predictive accuracy and robustness and increase the domain of application.

A comprehensive review of water quality indices (WQIs): history, models, attempts and perspectives

Water quality index (WQI) is one of the most used tools to describe water quality. It is based on physical, chemical, and biological factors that are combined into a single value that ranges from 0 to 100 and involves 4 processes: (1) parameter selection, (2) transformation of the raw data into common scale, (3) providing weights and (4) aggregation of sub-index values. The background of WQI is presented in this review study. the stages of development, the progression of the field of study, the various WQIs, the benefits and drawbacks of each approach, and the most recent attempts at WQI studies. In order to grow and elaborate the index in several ways, WQIs should be linked to scientific breakthroughs (example: ecologically). Consequently, a sophisticated WQI that takes into account statistical methods, interactions between parameters, and scientific and technological improvement should be created in order to be used in future investigations.

Advances on Water Quality Detection by UV-Vis Spectroscopy

Water resources are closely linked to human productivity and life. Owing to the deteriorating water resources environment, accurate and rapid determination of the main water quality parameters has become a current research hotspot. Ultraviolet-visible (UV-Vis) spectroscopy offers an effective tool for qualitative analysis and quantitative detection of contaminants in a water environment. In this review, the principle and application of UV-Vis technology in water quality detection were studied. The principle of UV-Vis spectroscopy for detecting water quality parameters and the method of modeling and analysis of spectral data were presented. Various UV-Vis technologies for water quality detection were reviewed according to the types of pollutants, such as chemical oxygen demand, heavy metal ions, nitrate nitrogen, and dissolved organic carbon. Finally, the future development of UV-Vis spectroscopy for the determination of water quality was discussed.

Water Quality Index of Suceava River in Suceava City Metropolitan Area

The water quality of rivers worldwide is of persistent interest due to its impact on human life. Five streamwater quality parameters of Suceava River were monitored in 2019 upstream and downstream of Suceava city, Romania: dissolved oxygen, specific conductivity, pH, oxidation-reduction potential, and temperature. Data was recorded at a high temporal frequency, every hour, and produced Water Quality Index (WQI) time series of similar resolution. Our additive WQI has variants with particular advantages. Water quality of Suceava city exhibits a diurnal cycle. Upstream, WQI values indicate a quasi-permanent good water quality; downstream, the water quality oscillates around the average WQI value because of the various sources of water contaminants, especially the wastewaters from the wastewater treatment plant. Parameters from this point source of pollution are taken into account to explain the decaying streamwater quality towards the end of 2019. WQI is useful for detecting time intervals when water self-purification events have a high chance of occurrence.

Application of Multivariate Statistical Analysis in the Development of a Surrogate Water Quality Index (WQI) for South African Watersheds

Water quality indices (WQIs) are customarily associated with heavy data input demand, making them more rigorous and bulky. Such burdensome attributes are too taxing, time-consuming, and command a significant amount of resources to implement, which discourages their application and directly influences water resource monitoring. It is then imperative to focus on developing compatible, simpler, and less-demanding WQI tools, but with equally matching computational ability. Surrogate models are the best fitting, conforming to the prescribed features and scope. Therefore, this study attempts to provide a surrogate WQI as an alternative water quality monitoring tool that requires fewer inputs, minimal effort, and marginal resources to function. Accordingly, multivariate statistical techniques which include principal component analysis (PCA), hierarchical clustering analysis (HCA) and multiple linear regression (MLR) are applied primarily to determine four proxy variables and establish relevant model coefficients. As a result, chlorophyll-a, electrical conductivity, pondus Hydrogenium and turbidity are the final four proxy variables retained. A vital feature of the proposed surrogate index is that the input parameters qualify for inclusion into remote monitoring systems; henceforth, the model can be applied in remote monitoring programs. Reflecting on the model validation results, the proposed surrogate WQI is considered scientifically stable, with a minimum magnitude of divergence from the ideal water quality values. More importantly, the model displayed a predictive pattern identical to the ideal graph, matching on both index scores and classification values. The established surrogate model is an important milestone with the potential of promoting water resource monitoring and assisting in capturing of spatial and temporal changes in South African river catchments. This paper aims at outlining the methods used in developing the surrogate water quality index and document the results achieved.