Advancements in environmental monitoring: Integrating digital tools for enhanced MiniSASS assessment and regional diatom index calculation

Abstract

Freshwater in South Africa is largely supplied by rivers. However, the availability of water is limited by a semi-arid climate, high evaporation and low precipitation rates, and El Niño-Southern Oscillation (ENSO) events that contribute to droughts and floods. Pollution from industrial, mining, agricultural, and domestic activities, including acid mine drainage, nutrient runoff, and organic waste, damages ecosystems and further reduces freshwater availability. To manage freshwater sustainably, continuous monitoring of water resources and public awareness are essential. A holistic approach to managing water resources can be achieved using the tools of Integrated Water Resource Management (IWRM). Through the National Water Act (NWA) and the National Water Resource Strategy (NWRS), IWRM is implemented by Catchment Management Areas (CMA) within Water management Areas (WMA). Monitoring rivers, wetlands and estuaries is essential to inform sustainable water use, pollution control and ecological health. This is done, in part, through biomonitoring using diatoms (phytobenthos), aquatic macroinvertebrates and fish. Biomonitoring provides a holistic view of water quality changes over time using biological indicators. Unlike chemical analysis, biomonitoring integrates the responses of biota in the determination of water quality, ecosystem health and habitat degradation. Thephytobenthos and aquatic macroinvertebrate assemblages together provide an integrated indication of ecosystem resilience. Macroinvertebrates are excellent indicators of ecosystem functioning while diatoms indicate more specific water quality changes. Diatom monitoring is implemented through indices such as the Indice de Polluosensibilité Spécifique (IPS), the Generic Diatom Index (GDI), Trophic Diatom Index (TDI) and the Biological Diatom Index (BDI), whilst aquatic macroinvertebrates are implemented using the South African Scoring System (SASS5) and the mini Stream Assessment Scoring System (miniSASS). These indices are used as proxies for water quality that inform on pollution and water quality changes to, in turn, inform management of aquatic resources. Development of regional diatoms indices, as well as simplifying the available tools can help support water management in South Africa by increasing broader public awareness and providing experts and non-experts with newly revised tools. Implementing digital tools that integrate diatom and macroinvertebrate indices will improve data collection and reduce data loss when communicating with online data repositories and cloud-storage. Additionally, incorporating training tools and machine learning techniques can increase identification accuracy, and in turn increase the reliability of data generated by these tools. Digital tools can bridge knowledge gaps and engage citizen scientists and experts to provide better information on water quality. By combining these tools, South Africans can stand as advocates for sustainable water management and aquatic ecosystem health. This study aimed to develop a miniSASS mobile application that fully incorporates and improves data capturing for miniSASS surveys and incorporating machine learning to automatically identify macroinvertebrates to improve the applicability of miniSASS as a citizen science tool. Furthermore, this study aimed to develop new regional riverine diatom indices by using Weighted Averaging (WA), Generalised Logit Regression (GLR) and inferred knowledge to calculate diatom optimal environmental conditions and tolerance ranges, under nutrient content, organic content and ionic load. Additionally, a diatom index to detect Acid Mine Drainage (AMD) in wetlands surrounding coalmines is also included. The novel diatom indices are further incorporated into digital platforms that provide easy-to-use software for diatom index calculation and data generation without the need for OMNIDIA. The miniSASS mobile application provides a holistic and modern tool to simplify and increase the efficiency of river health assessment. The mobile application includes a landing page with relevant information on how to use the app, a map page where users can explore monitoring sites and a sites creation page which houses the full capability of a miniSASS survey whilst including a newly designed digital classification key to improve identification and classification accuracy. Furthermore, the site creation page also incorporates a machine learning model trained on 13 000 images of macroinvertebrates to help alleviate the bottleneck of manual verification of site scores on the miniSASS website. An additional about page is included that provides tutorials, support and extra resources to educated users. Riverine diatom indices were developed by incorporating optima and tolerances calculated using WA, for electrical conductivity (EC), dissolved inorganic nitrogen (DIN) and orthophosphate (PO43-). Species optima were also inferred from expert knowledge related to nutrient levels, organic load and ionic composition. Indices were accordingly calculated for each parameter and combined into a final multimetric average index using weights. The GLR approach was tested but ultimately rejected due to the nature of the dataset. The available datasets were sparse and zero-inflated, with an infrequent occurrence of species along environmental gradients, ultimately making WA are more feasible approach. The calculated optima and tolerances for many species aligned with those present in the IPS, confirming the use of WA for optima and tolerance calculation The calculated indices correlated strongly with water quality and accurately reflected the ecological condition of the rivers used to create the indices. The indices calculated for the individual parameters reflect specific water quality changes more accurately, where the combined multimetric indices and the IPS reflect overall water quality best. A dedicated AMD index was developed by calculating species optima for sulphate, EC, pH, alkalinity and chloride, as these parameters characterize AMD disturbance in wetlands. These optima were integrated with AMD and osmotic tolerance values together with life-form categories (motile, attached and tube-forming) to calculate a final multimetric index score. The index successfully distinguished between AMD disturbed and non-disturbed sites with pH and sulphate emerging as the strongest environmental drivers. The correlation of the index scores with water quality was confirmed using bootstrapping with 1000 iterations of index scores. Four key taxa were identified as early indicators of AMD disturbance in wetlands. Nitzschia capitellata and Frustulia crassinervia indicate AMD impacted sites with low pH and high sulphate. Amphora veneta and Craticula molestiformis indicate AMD free sites, correlating with increased chloride and alkalinity. The diatom indices developed in this study were fully integrated into digital tools that support efficient and accessible water-quality assessment of rivers and wetlands. A Diatom Indexer was created to house the riverine indices calculated using WA and knowledge inferred, as well as the widely used IPS index. The software generates an interpretable, illustrative graph that allows users to track changes in water quality across sites and provides autecological information on the species included in the index calculation together with the scores for each index. A separate AMD Indexer was created to process a species matrix and produce a summary of AMD index scores. Both digital tools use a separate standard list as a reference for calculated optima and tolerances, and contain the AMD tolerances, osmotic tolerances and life-form scores for AMD index calculation. Together these tools enhance data generation, data throughput and knowledge dissemination, by providing free to use- user friendly software that streamlines the efficiently of diatom index calculation. The miniSASS mobile application helps bridge the gap between public awareness of water quality and scientific knowledge. Coupled with the additional newly developed diatom index tools biomonitoring is made simpler by providing visual, accessible as easyto-use tools to empower citizens and community members to engage and participate in river health assessments. Simultaneously, these digital tools provide a reliable and scientific method that can support decision makers and management practitioners. Therefore, by improving public awareness and bolstering water quality monitoring, these tools help South Africa move closer to achieving SDG6 - ensuring the availability and sustainable management of clean water and sanitation for all by 2030.