A novel mathematical template for developing fDOM probe fluorescence signal correction models for freshwaters.

The reliable application of field deployable fluorescent dissolved organic matter (fDOM) probes is hindered by several influencing factors which need to be compensated. This manuscript describes the corrections of temperature, pH, turbidity and inner filter effect on fluorescence signal of a commercial fDOM probe (fDOMs). For this, Australian waters with wide ranging qualities were selected, e.g. dissolved organic carbon (DOC) ranging from ∼1 to ∼30 mg/L, specific UV absorbance at 254 nm from ∼1 to ∼6 L/m/mg and turbidity from ∼1 to ∼ 350 FNU. Laboratory-based model calibration experiments (MCEs) were performed. A model template was developed and used for the development of the correction models. For each factor, data generated through MCEs were used to determine model coefficient (α) values by fitting the generated model to the experimental data. Four discrete factor models were generated by determination of a factor-specific α value. The α values derived for each water of the MCEs subset were consistent for each factor model. This indicated generic nature of the four α values across wide-ranging water qualities. High correlation between fDOMs and DOC were achieved after applying the four-factor compensation models to new data (r, 0.96, p < 0.05). Also, average biases (and %) between DOC predicted through fDOMs and actual DOC were decreased by applying the four-factor compensation model (from 3.54 (60.9%) to 1.28 (16.7%) mg/L DOC). These correction models were incorporated into a Microsoft EXCEL-based software termed EXOf-Correct for ready-to-use applications.

Effects of wind-driven current and thermal dynamics in a temperate monomictic reservoir: Implications for manganese transport and treatment in water supply systems.

Increasing manganese (Mn) concentrations in source water contribute to aesthetic and health-related concerns in drinking water. The challenges with Mn in drinking water primarily arise from elevated Mn concentrations in the water supply reservoir, with the inefficacy of Mn treatment largely attributed to fluctuating Mn levels in the water source. A three-dimensional Mn cycle model in a temperate monomictic reservoir, Tarago Reservoir, and a decision support system reflecting Mn variations in the local water treatment plant have been established in previous research. This study aimed to examine Mn variations from the reservoir to raw water and treated water under the influence of wind conditions during different stages of thermal structure, and discover valuable recommendations for Mn treatment in the local water supply system. We crafted 12 scenarios to scrutinize the impact of varying intensities of offshore and onshore winds on hydrodynamic processes and Mn transport during strong thermal stratification, weak thermal stratification, and turnover. The scenario analysis revealed that, during the gradual weakening of thermal stratification, offshore wind induced a substantial amount of Mn to the upper layers near the water intake point. Conversely, onshore wind hindered the upward transport of Mn. The simulated Mn in the raw water under the 12 scenarios indicated that the timing of turnover in the Tarago Reservoir is the primary concern for Mn treatment in the water treatment plant. Additionally, close attention should be given to the frequency and intensity of offshore winds during the weakening of thermal stratification.

Satellite-based estimation of total suspended solids and chlorophyll-a concentrations for the Gold Coast Broadwater, Australia.

Satellite retrieval of total suspended solids (TSS) and chlorophyll-a (chl-a) was performed for the Gold Coast Broadwater, a micro-tidal estuarine lagoon draining a highly developed urban catchment area with complex and competing land uses. Due to the different water quality properties of the rivers and creeks draining into the Broadwater, sampling sites were grouped in clusters, with cluster-specific empirical/semi-empirical prediction models developed and validated with a leave-one-out cross validation approach for robustness. For unsampled locations, a weighted-average approach, based on their proximity to sampled sites, was developed. Confidence intervals were also generated, with a bootstrapping approach and visualised through maps. Models yielded varying accuracies (R2 = 0.40-0.75). Results show that, for the most significant poor water quality event in the dataset, caused by summer rainfall events, elevated TSS concentrations originated in the northern rivers, slowly spreading southward. Conversely, high chl-a concentrations were first recorded in the southernmost regions of the Broadwater.

Towards nutrient neutrality: A review of agricultural runoff mitigation strategies and the development of a decision-making framework.

Nutrient runoff from agriculture practices poses a significant risk to waterway health and can have long-lasting and complex implications for the environment, ecosystems, and the human population. Consequently, a systematic quantitative literature review (SQLR) was conducted to identify different nutrient runoff mitigation strategies (NRMS) that are currently used globally to prevent or remediate environmental damage from excessive agricultural fertilisation. Empirical data on the outcomes from various NRMS from the reviewed studies were used to evaluate the strategies based on environmental benefit, implementation cost, and practicality perspectives. An overall assessment of the feasibility of NRMS was determined, and a macro-level assessment of the reported barriers preventing the widespread implementation of NRMS was provided. Identified research gaps and issues included a dearth of literature covering nutrient runoff mitigation, scepticism from agricultural landowners to voluntarily adopt policy without substantial incentives, and a general lack of cost/benefit analyses, including an understanding of the uncertainty associated with NRMS that can inform decision-makers about effective and efficient strategies for different site situations. Synthesis of SQLR data facilitated the development of a comprehensive nutrient runoff decision-making framework which addresses present limitations and provides site-specific NRMS recommendations for policymakers to implement.

A probabilistic decision support tool for prediction and management of rainfall-related poor water quality events for a drinking water treatment plant.

A data-driven Bayesian Network (BN) model was developed for a large Australian drinking water treatment plant, whose raw water comes from a river into which a number of upstream dams outflow water and smaller tributaries flow. During wet weather events, the spatial distribution of rainfall has a crucial role on the incoming raw water quality, as runoff from specific sub-catchments usually causes significant turbidity and conductivity issues, as opposed to larger dam outflows which have typically better water quality. The BN relies on a conceptual model developed following expert consultation, as well as a combination of different types (e.g. water quality, flow, rainfall) and amount (e.g. high-frequency, daily, scarce depending on variable) of historical data. The validated model proved to have acceptable accuracy in predicting the probability of different incoming raw water quality ranges, and can be used to assess different scenarios (e.g. timing, flow) of dam water releases, for the purpose of achieving dilution of the tributary's poor-quality water and mitigate related drinking water treatment challenges.

Chlorophyll and phycocyanin in-situ fluorescence in mixed cyanobacterial species assemblages: Effects of morphology, cell size and growth phase.

Cyanobacteria harmful blooms can represent a major risk for public health due to potential release of toxins and other noxious compounds in the water. A continuous and high-resolution monitoring of the cyanobacteria population is required due to their rapid dynamics, which has been increasingly done using in-situ fluorescence of phycocyanin (f-PC) and chlorophyll a (f-Chl a). Appropriate in-situ fluorometers calibration is essential because f-PC and f-Chl a are affected by biotic and abiotic factors, including species composition. Measurement of f-PC and f-Chl a in mixed species assemblages during different growth phases - representative of most field conditions - has received little attention. We hypothesized that f-PC and f-Chl a of mixed assemblages of cyanobacteria may be accurately estimated if taxa composition and fluorescence characteristics are known. We also hypothesized that species with different morphologies would have different fluorescence per unit cell and biomass. We tested these hypotheses in a controlled culture experiment in which photosynthetic pigment fluorescence, chemical pigment extraction, optical density and microscopic enumeration of four common cyanobacteria species (Aphanocapsa sp, Microcystis aeruginosa, Dolichospermum circinale and Raphidiopsis raciborskii) were quantified. Both monocultures and mixed cultures were monitored from exponential to late stationary growth phases. The sum of fluorescence of individual species calculated for mixed samples was not significantly different than measured fluorescence of mixed cultures. Estimated and measured f-PC and f-Chl a of mixed cultures had higher correlations and smaller absolute median errors when estimations were based on fluorescence per biomass instead of fluorescence per cell. Largest errors were overestimations of measured fluorescence for species with different morphologies. Fluorescence per cell was significantly different among most species, while fluorescence per unit biomass was not, indicating that conversion of fluorescence to biomass reduces species-specific bias. This study presents new information on the effect of species composition on cyanobacteria fluorescence. Best practices of deployment and operation of fluorometers, and data-driven models supporting in-situ fluorometers calibration are discussed as suitable solutions to minimize taxa-specific bias in fluorescence estimates.

Light-induced fluorescence quenching leads to errors in sensor measurements of phytoplankton chlorophyll and phycocyanin.

Optical sensors for fluorescence of chlorophyll a (f-Chl a) and phycocyanin (f-PC) are increasingly used as a proxy for biomass of algae and cyanobacteria, respectively. They provide measurements at high-frequency and modest cost. These sensors require site-specific calibration due to a range of interferences. Light intensity affects the fluorescence yield of cyanobacteria and algae through light harvesting regulation mechanisms, but is often neglected as a potential source of error for in-situ f-Chl a and f-PC measurements. We hypothesised that diel light variations would induce significant f-Chl a and f-PC suppression when compared to dark periods. We tested this hypothesis in a controlled experiment using three commercial fluorescence probes which continuously measured f-Chl a and f-PC from a culture of the cyanobacterium Dolichospermum variabilis as well as f-Chl a from a culture of the green alga Ankistrodesmus gracilis in a simulated natural light regime. Under light, all devices showed a significant (p<0.01) suppression of f-Chl a and f-PC compared to measurements in the dark. f-Chl a decreased by up to 79% and f-PC by up to 59% at maximum irradiance compared to dark-adapted periods. Suppression levels were higher during the second phase of the diel cycle (declining light), indicating that quenching is dependent on previous light exposure. Diel variations in light intensity must be considered as a significant source of bias for fluorescence probes used for algal monitoring. This is of high relevance as most monitoring activities take place during daytime and hence f-Chl a and f-PC are likely to be systematically underestimated under bright conditions. Compensation models, design modifications to fluorometers and sampling design are discussed as suitable alternatives to overcome light-induced fluorescence quenching.

A systematic literature review of forecasting and predictive models for cyanobacteria blooms in freshwater lakes.

Cyanobacteria harmful blooms (CyanoHABs) in lakes and reservoirs represent a major risk for water authorities globally due to their toxicity and economic impacts. Anticipating bloom occurrence and understanding the main drivers of CyanoHABs are needed to optimize water resources management. An extensive review of the application of CyanoHABs forecasting and predictive models was performed, and a summary of the current state of knowledge, limitations and research opportunities on this topic is provided through analysis of case studies. Two modelling approaches were used to achieve CyanoHABs anticipation; process-based (PB) and data-driven (DD) models. The objective of the model was a determining factor for the choice of modelling approach. PB models were more frequently used to predict future scenarios whereas DD models were employed for short-term forecasts. Each modelling approach presented multiple variations that may be applied for more specific, targeted purposes. Most models reviewed were site-specific. The monitoring methodologies, including data frequency, uncertainty and precision, were identified as a major limitation to improve model performance. A lack of standardization of both model output and performance metrics was observed. CyanoHAB modelling is an interdisciplinary topic and communication between disciplines should be improved to facilitate model comparisons. These shortcomings can hinder the adoption of modelling tools by practitioners. We suggest that water managers should focus on generalising models for lakes with similar characteristics and where possible use high frequency monitoring for model development and validation.

In-situ fluorescence monitoring of cyanobacteria: Laboratory-based quantification of species-specific measurement accuracy.

In recent years, in-situ fluorometers have been extensively deployed to monitor cyanobacteria in near real-time. Acceptable accuracy can be achieved between measured pigments and cyanobacteria biovolume provided the cyanobacteria species are known. However, cellular photosynthetic pigment content and measurement interferences are site and species specific and can dramatically affect sensor reliability. We quantified the accuracy of an in-situ fluorometer compared with traditional methods using mono- and mixed cultures of four different cyanobacterial species. We found: (1) lower pigment content in cultures in stationary phase, (2) higher precision with the sensor compared to traditional pigment quantification methods of measuring phycocyanin and chlorophyll a, (3) species-specific relationships between sensor readings and measurements related to biovolume, (4) overestimation of pigments in mixed compared with mono cultures, (5) dissolved organic matter causing a loss in signal proportional to its degree of aromaticity, and (6) potential to quantify the degree of cell lysis with a fluorescent dissolved organic matter sensor. This study has provided important new information on the strengths and limitations of fluorescence sensors. The sensor readings can provide accurate biovolume quantification and species determination for a number of bloom-forming species when sensors are properly compensated and calibrated.

Fluorescence probes for real-time remote cyanobacteria monitoring: A review of challenges and opportunities.

In recent years, there has been a widespread deployment of submersible fluorescence sensors by water utilities. They are used to measure diagnostic pigments and estimate algae and cyanobacteria abundance in near real-time. Despite being useful and promising tools, operators and decision-makers often rely on the data provided by these probes without a full understanding of their limitations. As a result, this may lead to wrong and misleading estimations which, in turn, means that researchers and technicians distrust these sensors. In this review paper, we list and discuss the main limitations of such probes, as well as identifying the effect of environmental factors on pigment production, and in turn, the conversion to cyanobacteria abundance estimation. We argue that a comprehensive calibration approach to obtain reliable readings goes well beyond manufacturers' recommendations, and should involve several context-specific experiments. We also believe that if such a comprehensive set of experiments is conducted, the data collected from fluorescence sensors could be used in artificial intelligence modelling approaches to reliably predict, in near real-time, the presence and abundance of different cyanobacteria species. This would have significant benefits for both drinking and recreational water management, given that cyanobacterial toxicity, and taste and odour compounds production, are species-dependent.

Analysis and modelling of powdered activated carbon dosing for taste and odour removal.

A series of experiments were undertaken in order to understand and predict the dosage of powdered activated carbon required to remove taste and odour compounds in an Australian drinking water treatment plant. Competitive effects with organic matter removal by aluminium sulphate during coagulation were also quantified. Data on raw and finished water quality following jar tests, as well as chemical dosages and treatment performance, were statistically analysed, and a data-driven prediction model was developed. The developed powdered activated carbon dosage prediction model can be used by the plant operators for rapid dosage assessment and can increase the preparedness of the plant to sudden taste and odour events. It was also found that total organic carbon levels and properties greatly affect the ability of powdered activated carbon to remove taste and odour compounds; on the other hand, total organic carbon removal is not affected by high taste and odour levels, since these were still much lower than organic carbon concentrations.

Evaluating a novel tiered scarcity adjusted water budget and pricing structure using a holistic systems modelling approach.

Population growth, coupled with declining water availability and changes in climatic conditions underline the need for sustainable and responsive water management instruments. Supply augmentation and demand management are the two main strategies used by water utilities. Water demand management has long been acknowledged as a least-cost strategy to maintain water security. This can be achieved in a variety of ways, including: i) educating consumers to limit their water use; ii) imposing restrictions/penalties; iii) using smart and/or efficient technologies; and iv) pricing mechanisms. Changing water consumption behaviours through pricing or restrictions is challenging as it introduces more social and political issues into the already complex water resources management process. This paper employs a participatory systems modelling approach for: (1) evaluating various forms of a proposed tiered scarcity adjusted water budget and pricing structure, and (2) comparing scenario outcomes against the traditional restriction policy regime. System dynamics modelling was applied since it can explicitly account for the feedbacks, interdependencies, and non-linear relations that inherently characterise the water tariff (price)-demand-revenue system. A combination of empirical water use data, billing data and customer feedback on future projected water bills facilitated the assessment of the suitability and likelihood of the adoption of scarcity-driven tariff options for a medium-sized city within Queensland, Australia. Results showed that the tiered scarcity adjusted water budget and pricing structure presented was preferable to restrictions since it could maintain water security more equitably with the lowest overall long-run marginal cost.