Revised Iranian Water Quality Index (RIWQI): a tool for the assessment and management of water quality in Iran

Improving the performance of macroinvertebrate based multi-metric indices by incorporating functional traits and an index performance-driven approach

Human-driven multiple pressures impact freshwater ecosystems worldwide, reducing biodiversity, and impacting ecosystem functioning and services provided to human societies. Multi-metric indices (MMIs) are suitable tools for tracking the effects of anthropogenic pressures on freshwater ecosystems because they incorporate various biological metrics responding to multiple pressures at different levels of biological organization. However, the performance and applicability of MMIs depend on their metrics' selection and their calibration against natural environmental gradients. In this study, we aimed to unravel i) how incorporating functional trait-based metrics affects the performance of MMIs, ii) how disentangling the natural environmental gradients from anthropogenic pressures effects affects the performance of MMIs, and iii) how the performance of MMIs developed using a metric performance-driven approach compares with MMIs developed using an index performance-driven approach. We carried out a field survey measuring abiotic and biotic variables at 53 sites in the Karun River basin (Iran) in 2018. For functional trait-based metrics, we used 15 macroinvertebrate traits and calculated community-weighted mean trait values and functional diversity indices. We used random forest modeling to account for the effect of natural environmental gradients on each metric. Based on our results, incorporating functional traits increased the MMI performance significantly and facilitated ecological interpretation of MMIs. Both taxonomic and functional components of macroinvertebrate assemblages co-varied strongly with natural environmental gradients, and accounting for these covariations improved the performance of MMIs. Finally, we found that index performance-driven MMIs performed better in terms of precision, bias, sensitivity, and responsiveness than metric performance-driven MMIs.

Environmental impact assessment and conservation planning of a Middle-Eastern River basin using a fish-based tolerance index

The tolerance of aquatic organisms to stressors is widely used to monitor and evaluate the condition of freshwater ecosystems. Tolerance values (TV) derived from analyses of the relationship between species and their environment are considered to be more objective than those that rely on expert opinion. We used principal component analysis (PCA) to derive a generalized stressor gradient based on physicochemical characteristics and physical habitat quality and structure. Scores of the first principal component axis (PC1) were used to estimate TV for 37 fish species collected from 54 sites in the Karun River basin, Iran. PCA of 17 variables identified stressors that were influential such as total phosphate, total nitrogen, total coliform, and habitat and morphological score. The species were separated into three categories on the stressor gradient: sensitive (18.9%), semi-tolerant (48.6%), and tolerant species (32.4%). Based on these results we developed the Karun Fish Tolerance Index (KFTI) and demonstrated that it performed well in separating the least, moderate, and most disturbed sites in the study area. The discrimination efficiency of the KFTI was 82.5%, which makes it a robust management tool for the protection and conservation of streams and rivers in the Karun River watershed. TV developed here reflect objective characteristics of the sensitivity of fish species to the predominant stressors in the Karun and similar systems.

Distribution and Expansion of Alien Fish Species in the Karun River Basin, Iran

We assessed the distribution of alien fishes in the Karun River Basin, Iran. Fish were collected from 39 sites during the November-December 2018 low-flow period. In total, 39 fish species from nine orders and 14 families were documented. Among these, 10 species were alien to the basin (986 individuals; 15.7%). Four species were the most abundant alien species and primarily in impounded, downstream reaches. Redundancy analysis (RDA) was conducted to identify the extent of changes in alien fish assemblages with environmental parameters. RDA1 and RDA2 accounted for 36.24% and 25.33% of the variation of alien species, respectively. Altitude, depth, electrical conductivity, water temperature, turbidity, dissolved oxygen, and river width were the most significant parameters affecting alien species distributions. We present a dual-pathway cause-and-effect hypothesis proposing that alien fish species presence causes declines in the ecological status of native fish communities. We then explore how human-induced aquatic ecosystem degradation creates opportunities for alien species to invade new ecosystems, further impacting native fish communities. Our study contributes insight into the cause and effect of the presence of alien fish species in the Karun River Basin and emphasizes the urgency of conservation measures to protect this critically endangered watershed.

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 sophisticated model for rating water quality

Here, we present the Irish Water Quality Index (IEWQI) model for assessing transitional and coastal water quality in an effort to improve the method and develop a tool that can be used by environmental regulators to abate water pollution in Ireland. The developed model has been associated with the adoption of water quality standards formulated for coastal and transitional waterbodies according to the water framework directive legislation by the environmental regulator of Irish water. The model consists of five identical components, including (i) indicator selection technique is to select the crucial water quality indicator; (ii) sub-index (SI) function for rescaling various water quality indicators' information into a uniform scale; (iii) indicators' weight method for estimating the weight values based on the relative significance of real-time information on water quality; (iii) aggregation function for computing the water quality index (WQI) score; and (v) score interpretation scheme for assessing the state of water quality. The IEWQI model was developed based on Cork Harbour, Ireland. The developed IEWQI model was applied to four coastal waterbodies in Ireland, for assessing water quality using 2021 water quality data for the summer and winter seasons in order to evaluate model sensitivity in terms of spatio-temporal resolution of various waterbodies. The model efficiency and uncertainty were also analysed in this research. In terms of different spatio-temporal magnitudes of various domains, the model shows higher sensitivity in four application domains during the summer and winter. In addition, the results of uncertainty reveal that the IEWQI model architecture may be effective for reducing model uncertainty in order to avoid model eclipsing and ambiguity problems. The findings of this study reveal that the IEWQI model could be an efficient and reliable technique for the assessment of transitional and coastal water quality more accurately in any geospatial domain.