Model-Based Analysis of the Potential of Macroinvertebrates as Indicators for Microbial Pathogens in Rivers

Greenhouse gas dynamics in an urbanized river system: influence of water quality and land use

Rivers act as a natural source of greenhouse gases (GHGs). However, anthropogenic activities can largely alter the chemical composition and microbial communities of rivers, consequently affecting their GHG production. To investigate these impacts, we assessed the accumulation of CO₂, CH₄, and N₂O in an urban river system (Cuenca, Ecuador). High variation of dissolved GHG concentrations was found among river tributaries that mainly depended on water quality and land use. By using Prati and Oregon water quality indices, we observed a clear pattern between water quality and the dissolved GHG concentration: the more polluted the sites were, the higher were their dissolved GHG concentrations. When river water quality deteriorated from acceptable to very heavily polluted, the mean value of pCO₂ and dissolved CH₄ increased by up to ten times while N₂O concentrations boosted by 15 times. Furthermore, surrounding land-use types, i.e., urban, roads, and agriculture, could considerably affect the GHG production in the rivers. Particularly, the average pCO₂ and dissolved N₂O of the sites close to urban areas were almost four times higher than those of the natural sites while this ratio was 25 times in case of CH₄, reflecting the finding that urban areas had the worst water quality with almost 70% of their sites being polluted while this proportion of nature areas was only 12.5%. Lastly, we identified dissolved oxygen, ammonium, and flow characteristics as the main important factors to the GHG production by applying statistical analysis and random forests. These results highlighted the impacts of land-use types on the production of GHGs in rivers contaminated by sewage discharges and surface runoff.

Classification and Prediction of Fecal Coliform in Stream Waters Using Decision Trees (DTs) for Upper Green River Watershed, Kentucky, USA

The classification of stream waters using parameters such as fecal coliforms into the classes of body contact and recreation, fishing and boating, domestic utilization, and danger itself is a significant practical problem of water quality prediction worldwide. Various statistical and causal approaches are used routinely to solve the problem from a causal modeling perspective. However, a transparent process in the form of Decision Trees is used to shed more light on the structure of input variables such as climate and land use in predicting the stream water quality in the current paper. The Decision Tree algorithms such as classification and regression tree (CART), iterative dichotomiser (ID3), random forest (RF), and ensemble methods such as bagging and boosting are applied to predict and classify the unknown stream water quality behavior from the input variables. The variants of bagging and boosting have also been looked at for more effective modeling results. Although the Random Forest, Gradient Boosting, and Extremely Randomized Tree models have been found to yield consistent classification results, DTs with Adaptive Boosting and Bagging gave the best testing accuracies out of all the attempted modeling approaches for the classification of Fecal Coliforms in the Upper Green River watershed, Kentucky, USA. Separately, a discussion of the Decision Support System (DSS) that uses Decision Tree Classifier (DTC) is provided.

The use of macroinvertebrate based biotic indices and diversity indices to evaluate the water quality of Lepenci river basin in Kosovo

Benthic macroinvertebrates are used to evaluate water quality in 8 sampling stations in Lepenci river basin in Kosovo. Sampling was performed in spring, summer and fall 2017. Following biotic indices are used: EPT taxa richness, Biological Monitoring Working Party (BMWP), Average Score per Taxon (ASPT), Stroud Water Research Center index (SWRC), Family biotic index (FBI), as well as diversity indices: Shannon-Weaver index (H), Simpsons index (D), Hill's index (Hi), Mergalef index (D_Me) and Menhicnik's index (D_Ma). Our results show the presence of 34 macroinvertebrate taxa in Lepenci river which belong to Insecta, Crustaceans and Annelidae. The water quality along the river show variation from high and good class upstream, to moderate, poor and bad, downstream. The Pearson's bivariate correlation used to analyze the relationship between physicochemical parameters with biotic and diversity indices showed a significant correlation (p < 0.01) of EC, TSS, O₂, COD, BOD, NH_4, and PO₄^3- with biotic indices EPT, BMWP, ASPT, SWRC, FBI. We can conclude that the values of biotic and diversity indices have shown differences in water quality between polluted and unpolluted sites and reflect the ecological status of the river, therefore we can consider them as valuable tools for water quality assessment in rivers in Kosovo.

Integrated ecological modelling for evidence-based determination of water management interventions in urbanized river basins: Case study in the Cuenca River basin (Ecuador)

The growth of urbanization worldwide has contributed to the deterioration of the ecological status of water bodies. Efforts at improving the ecological status have been made either in isolated form or by means of integrated measures by stakeholders, but in many cases, these measures have not been evaluated to determine their benefit. In this study, we implemented a scenario analysis to restore the ecological water quality in the Cuenca River and its tributaries, which are located in the southern Andes of Ecuador. For this analysis, an integrated ecological model (IEM) was developed. The IEM linked an urban wastewater system (IUWS) model, which gave satisfactory results in its calibration and validation processes, with ecological models. The IUWS is a mechanistic model that incorporated the river water quality model, a wastewater treatment plant (WWTP) with activated sludge technology, and discharges from the sewage system. The ecological status of the waterways was evaluated with the Andean Biotic Index (ABI), which was predicted using generalized linear models (GLMs). The GLMs were calculated with physicochemical results from the IUWS model. Four scenarios that would enhance the current ecological water quality were analyzed. In these scenarios, the inclusion of a new WWTP with carbon, and with carbon and nitrogen removal as well as the addition of retention tanks before the discharges of combined sewer overflows (CSOs) were assessed. The new WWTP with carbon and nitrogen removal would bring about a better restoration of the ecological water quality due to better nitrogen removal. The retention tanks would help to enhance the ecological status of the rivers during rainy seasons. The integrated model implemented in this study was shown to be an essential tool to support decisions in the Cuenca River basin management.

Predicting Microbial Species in a River Based on Physicochemical Properties by Bio-Inspired Metaheuristic Optimized Machine Learning

The main goal of the analysis of microbial ecology is to understand the relationship between Earth’s microbial community and their functions in the environment. This paper presents a proof-of-concept research to develop a bioclimatic modeling approach that leverages artificial intelligence techniques to identify the microbial species in a river as a function of physicochemical parameters. Feature reduction and selection are both utilized in the data preprocessing owing to the scarce of available data points collected and missing values of physicochemical attributes from a river in Southeast China. A bio-inspired metaheuristic optimized machine learner, which supports the adjustment to the multiple-output prediction form, is used in bioclimatic modeling. The accuracy of prediction and applicability of the model can help microbiologists and ecologists in quantifying the predicted microbial species for further experimental planning with minimal expenditure, which is become one of the most serious issues when facing dramatic changes of environmental conditions caused by global warming. This work demonstrates a neoteric approach for potential use in predicting preliminary microbial structures in the environment.

Advances in Ecological Water System Modeling: Integration and Leanification as a Basis for Application in Environmental Management

The art of applied modeling is determining an appropriate balance between integration of more processes and variables for the sake of increasing representativeness and reliability of the models, while also avoiding too long development and simulation times. The latter can be achieved via leanification, which can be based on reducing the number of variables and processes by focusing on key processes in the system and its management, but can be as well induced by using simplified methods for the description of relations among variables (such as regression and probabilistic methods) to, for instance, reduce the simulation time. In this way, integration and leanification can be combined and together contribute to models that are more relevant and convenient for use by water managers. In particular, it is crucial to find a good balance between the integration level of ecological processes answering environmental challenges in a relevant manner and costs for data collection and model development (and application).