Multi-factor analysis of algal blooms in gate-controlled urban water bodies by data mining

Are more data always better? - Machine learning forecasting of algae based on long-term observations

Bloom-forming algae present a unique challenge to water managers as they can significantly impair provision of important ecosystem services and cause health risks to humans and animals. Consequently, effective short-term algae forecasts are important as they provide early warnings and enable implementation of mitigation strategies. In this context, machine learning (ML) emerges as a promising forecasting tool. However, the performance of ML models is heavily dependent on the availability of appropriate training data. Consequently, it is essential to determine the volume of data necessary to develop reliable ML forecasts. Understanding this will guide future monitoring strategies, optimize resource allocation, and set realistic expectations for management outcomes. In this study, we used 30 years of fortnightly measurements of 13 different parameters from a lake in the English Lake District (UK) to examine the impact of training data duration on the performance of ML models for forecasting chlorophyll-a two weeks in advance. Once training data availability exceeded four years, a Random Forest model was found to consistently outperform naive benchmarks (mean absolute percentage error 16.4 % lower than the best-performing benchmark). With more than 5 years of training data, model performance generally continued to improve, but with diminishing returns. Furthermore, it was found that equivalent and, in some cases, better performance could be achieved by only using a subset of the most important input features. Additionally, it was found that reducing the sampling frequency had negative impacts on performance, both due to the reduced number of training observations available, and increased forecast horizon. Our findings demonstrate that for lakes ecologically similar to the study site, a consistent and regular sampling programme focused on monitoring a limited number of key parameters can provide sufficient observations for generating short-term algae forecasts after approximately five years of data collection. Importantly, this result provides justification for the initiation of new monitoring programmes for sites where algal blooms are a concern, and suggests that there are likely many pre-existing monitoring datasets which would be suitable for training algae forecast models.

Unveiling the earthworm-associated preferential remediation of emerging organic pollutants and heavy metals in MSW-based vermicomposting systems: Insights through the lens of multivariate techniques and novel empirical models

Studies on the efficacies of vermicomposting and composting in countering the toxic impacts of pollutant cocktails in municipal solid waste (MSW) are scarce. Moreover, further research is needed to explore earthworms' remediation preferences for various pollutants in heterogeneous vermicomposting feedstocks, such as MSW. Therefore, removal dynamics of pesticides (chlorpyrifos, cypermethrin, and carbofuran), pharmaceuticals (diclofenac and carbamazepine), and heavy metals (Pb, Zn, Cu, and Mn) in MSW-based vermicomposting (Eisenia fetida and Eudrilus eugeniae) and composting systems were evaluated through multivariate analytical techniques (principal component (PCA) and multi-factor (MFA)) on the R-platform. Both earthworms satisfactorily increased their population and augmented NPK (nitrogen, phosphorous, and potassium) availability, cation exchange, microbial biomass C&N, and their metabolic activity 2-3 folds more than composting, accompanied by a 3-4 folds reduction of organic C, pH, and bulk density. Correspondingly, heavy metals, pesticides, and pharmaceuticals decreased by 8-10-folds via earthworm's significant pollutant removal efficiencies that subsided MSW-driven ecological risks by 60-90%. PCA and MFA revealed that N, P, and K-availability, organic C, and microbial activity were the indicative attributes for heavy metal and emerging organic micropollutant (EOMP)-removal during biocomposting; however, earthworms remove pesticides faster than pharmaceuticals and heavy metals. PCA-based novel empirical models demonstrated that in MSW-only feedstock, earthworm-mediated pollutant detoxification followed the order of pesticides > pharmaceuticals > heavy metals. However, in MSW combined with cow dung (1:1 ratio) feedstock, the detoxification order shifted to pharmaceuticals > heavy metals > pesticides. Therefore, this study provides fresh insights into pollutant-focused feedstock optimization for vermicomposting through model-based approaches, advancing the eco-friendly valorization of toxic MSW.

Current status and prospects of algal bloom early warning technologies: A Review

In recent years, frequent occurrences of algal blooms due to environmental changes have posed significant threats to the environment and human health. This paper analyzes the reasons of algal bloom from the perspective of environmental factors such as nutrients, temperature, light, hydrodynamics factors and others. Various commonly used algal bloom monitoring methods are discussed, including traditional field monitoring methods, remote sensing techniques, molecular biology-based monitoring techniques, and sensor-based real-time monitoring techniques. The advantages and limitations of each method are summarized. Existing algal bloom prediction models, including traditional models and machine learning (ML) models, are introduced. Support Vector Machine (SVM), deep learning (DL), and other ML models are discussed in detail, along with their strengths and weaknesses. Finally, this paper provides an outlook on the future development of algal bloom warning techniques, proposing to combine various monitoring methods and prediction models to establish a multi-level and multi-perspective algal bloom monitoring system, further improving the accuracy and timeliness of early warning, and providing more effective safeguards for environmental protection and human health.

Exorbitant signatures of pesticides and pharmaceuticals in municipal solid wastes (MSWs): Novel insights through risk analysis, dissolution dynamics, and model-based source identification

Studies on the occurrence and fates of emerging organic micropollutants (EOMPs) like pharmaceuticals and pesticides in MSWs are scarce in the literature. Therefore, MSWs were sampled from 20 Indian landfills and characterized for five widely consumed EOMPs (chlorpyrifos, cypermethrin, carbofuran, carbamazepine, and sodium diclofenac), physicochemical, and biological properties. The pesticide (median: 0.17-0.44 mg kg-1) and pharmaceutical (median: 0.20-0.26 mg kg-1) concentrations significantly fluctuated based on landfill localities. Eventually, principal component and multi-factor (MFA) models demonstrated close interactions of EOMPs with biological (microbial biomass and humification rates) and chemical (N, P, K, Ca, S, etc.) properties of MSWs. At the same time, the MFA resolved that EOMPs' fates in MSWs significantly differ from bigger cosmopolitan cities to smaller rural townships. Correspondingly, the concentration-driven ecological risks were high in 15 MSWs with EOMP-toxicity ranks of diclofenac > carbofuran = chlorpyrifos > cypermethrin > carbamazepine. The EOMPs' dissolution dynamics and source apportionments were evaluated using the positive matrix factorization (PMF) model for the first time on experimental data, extracting four anthropogenic sources (households, heterogeneous business centers, agricultural, and open drains). The most significant contribution of EOMPs to MSWs was due to heterogeneous business activity. Notably, the aging of soluble chemical fractions seems to influence the source characteristics of EOMPs strongly.

Quantifying the contribution of external loadings and internal hydrodynamic processes to the water quality of Lake Okeechobee

The water quality of a waterbody is determined by internal hydrodynamic processes as well as external loadings. Understanding the interaction between the external loading and internal process of a waterbody is essential for efficient water management and water quality improvement. Studies and efforts have focused on water and nutrient loading from drainage watersheds, but the contribution of the waterbody's internal process to water quality is often ignored and not well documented. This study investigated how the water quality of Lake Okeechobee is controlled by external and internal factors using statistical and numerical modeling approaches. Water quantity and quality observed at the outlets of the Lake Okeechobee drainage basins and 19 monitoring sites located within the lake were statistically analyzed using multilinear regression. A three-dimensional numerical model, namely Environmental Fluid Dynamics Code (EFDC), was calibrated to the observations to mathematically represent the lake's internal hydrodynamic process. The multilinear regression found that the water quality was the most sensitive to air temperature, the total phosphorus (TP) concentration of inflow entering the lake from the Kissimmee River basins, and the amount of outflow discharged from the lake among external factors. However, the regression models and their explanatory power were substantially varied by the monitoring stations. The model parameter sensitivity analysis of the calibrated EFDC model showed that model parameters related to the lake's internal algal processes including algal growth, predation, and basal metabolism rates had greater impacts on algal biomass than other model parameters controlling nutrient-related processes such as nutrient half-saturation and hydrolysis rates. The EFDC input data sensitivity analysis found that wind (speed) is the major driving force for the internal hydrodynamic processes; its impact on algal biomass was greater than those of the external loadings. In addition, the algal biomass was found to have an inverse relationship with wind-induced horizontal currents. The results demonstrate the dynamic contribution of the internal and external drivers to the water quality of Lake Okeechobee, suggesting the need to consider both internal hydrodynamic and external loading processes for efficient water quality improvement of the lake.

Comprehensive modelling and cost-benefit optimization for joint regulation of algae in urban water system

Algal blooms in urban water system is an international concern, which especially in China, have become a major obstacle to the urban water environment improvement since the preliminary achievements were made in the treatment of black and odorous water bodies. The complex blooming mechanisms require a joint regulation plan. This study established a framework that consisted of three steps, i.e., simulation, optimization, and verification, to build an optimal joint regulation plan. By taking the urban river network in Suzhou Pingjiang Xincheng as a case study, the cost-benefits of six alternative regulation measures were assessed using an algal bloom mechanism model and the discounted cash flow model based on 70 regulation scenarios. The joint regulation plan was optimized using the marginal-cost-based greedy strategy on the basis of the cost-benefits of different measures. The optimized joint plans, which were verified to be global optima, were more cost-effective than the designed regulation scenarios, and reduced the average chlorophyll-a concentrations by 55.3%-60.1% compared with the status quo. Applying the optimized cost allocation ratios of each measure to adjust the existing regulation scheme of another similar case verified that the optimization results had great generalizability.

Using physicochemical and biological parameters for the evaluation of water quality and environmental conditions in international wetlands on the southern part of Lake Urmia, Iran

The Kani Barazan and Yadegarlou wetlands in the southern part of Lake Urmia (Iran) have been substantially modified due to human activities and anthropogenic use. In recent years, freshwater-based eco-biological studies to recognize the quality of water resources have been greatly expanded. Microalgae and Cyanophyta are considered important bioindicators for the evaluation of water quality and wetland health worldwide. Herein, 22 microalgae and 5 Cyanophyta genera were identified in both wetlands, in which Cyanophyta has mainly caused blooms. Principal components analysis (PCA) was carried out based on links between the distribution of microalgae and Cyanophyta with physical and chemical parameters. The data showed that depth, turbidity, and the temperature had a significant influence on the microalga and Cyanophyta communities in both wetlands. Based on the biological properties, it seems that the Kani Barazan and Yadegarlou international wetlands experience meso-eutrophic conditions. The integration of the physical, chemical and biological parameters with the water quality index (WQI) revealed that both wetlands were polluted as a consequence of human activities. Moreover, a close relationship between WQI and the biological parameters was documented. Thus, we concluded that microalgae and Cyanophyta communities, their abundance patterns, and water quality changes could provide valuable data for the conservation of the Kani Barazan and Yadegarlou international wetlands.

Impacts of storm events on chlorophyll-a variations and controlling factors for algal bloom in a river receiving reclaimed water

Harmful algal bloom is prevalent in the reclaimed-water-source (RWS) river caused by the excessive nutrient's inputs. Rainfall water may be the sole nutrient-diluted water source for the RWS river. However, the effects of storm events on the algal bloom in the RWS river are poorly understood. This study presents chlorophyll-a (Chl-a) variations before, during, and after the initial storm events (Pre-storm, In-storm, and Post-storm) at four representative sites with distinct hydraulic conditions in a dam-regulated RWS river system, Beijing. The response of Chl-a to the initial storm events mostly depends on the ecosystem status that caused by the river hydraulic properties. The upstream is more river-like and downstream is more lake-like. In the river-like system, elevated water temperature (WT, increased by 2 %) could support the dominating algae (diatom) growth (Chl-a increased by 130 %) from Pre-storm to In-storm period. In the lake-like system, the dominant algae (blue algae) declined (Chl-a sharply decreased by 96%-99 %) due to the lower WT (decreased by 3%-7%) and increased flow velocities from Pre-storm to In-storm period. During the Post-storm period, the dominant algae break out (Chl-a surged by 20%-319 %) in the lake-like system caused by the recovered WT (increased by 3%-6%) and flow velocity. The occurrence of algal bloom can be predicted by the Random Forest (RF) model based on water quality parameters such as total nitrogen (TN). The thresholds of algal bloom for the Pre-storm, In-storm, and Post-storm periods were identified as 30 μg/L, 10 μg/L, and 10 μg/L, respectively. The two driven factors were WT and nitrate (NO₃-N) for the Pre-storm period and were WT and TN for the In- & Post-storm periods. A higher risk of algal bloom is highlighted during the initial storm events in the RWS river. We propose recommendations for improving water quality in the RWS river systems under the climatic change.