Wout Van Echelpoel

Design of waste stabilization pond systems: A review

A better design instruction for waste stabilization ponds is needed due to their growing application for wastewater purification, increasingly strict environmental regulations, and the fact that most of previous design manuals are outdated. To critically review model-based designs of typical pond treatment systems, this paper analyzed more than 150 articles, books, and reports from 1956 to 2016. The models developed in these publications ranged from simple rules and equations to more complex first-order and mechanistic models. From a case study on all four approaches, it appeared that rules of thumb is no longer a proper tool for pond designs due to its low design specification and very high output variability and uncertainty. On the other hand, at the beginning phase of design process or in case of low pressure over land and moderate water quality required, regression equations can be useful to form an idea for pond dimensions. More importantly, mechanistic models proved their capacity of generating more precise and comprehensive designs but still need to overcome their lack of calibration and validation, and overparameterization. In another case study, an essential but often overlooked role of uncertainty analysis in pond designs was investigated via a comparison between deterministic and uncertainty-based approaches. Unlike applying a safety factor representing all uncertainty sources, probabilistic designs quantify the uncertainty of model outputs by including prior uncertainty of inputs and parameters, which generates more scientifically reliable outcomes for decision makers. Based on these findings, we advise engineers and designers to shift from the conventional approaches to more innovative and economic tools which are suitable for dealing with large variations of natural biological systems.

Species distribution models for sustainable ecosystem management

Abstract Reactions to ongoing loss of biodiversity include a variety of restoration actions and are characterised by high costs and uncertainty. Related decision-making can be supported by developing species distribution models (SDMs) that link predictors (both abiotic and biotic) with biotic response variables (e.g., abundance, occurrence, etc.). SDMs can fill in the gaps of current ecological knowledge and predict the potential impact of environmental (including climate) change on species distributions. As climate change already resulted in species shifting their range and an increased risk of extinction, invasion, and disease propagation, SDMs can act as a valuable tool to estimate future species distributions and their effects on ecosystem functioning and related services. Among the variety of modelling techniques used to predict future species distributions, five modelling techniques are selected: decision trees, generalised linear models, artificial neural networks, fuzzy logic, and Bayesian belief networks. The unique advantages of each modelling technique allow the modeller to choose the most appropriate technique in each particular situation. In turn, each modelling technique is characterised by specific drawbacks and is restricted by the limited ecological knowledge related to biotic interactions. Gathering additional ecological knowledge provides the possibility to go beyond simple pattern recognition and to establish more ecologically sound models.

Analysing the effects of water quality on the occurrence of freshwater macroinvertebrate taxa among tropical river basins from different continents

Macroinvertebrates are globally used in environmental monitoring and assessment. However, due to environmental and biological evolution, local adaptations of species might occur. This can contribute to uncertainties in the extrapolation of familyspecific ecological models developed from one region to another. Thus, we aimed to determine if models can be extrapolated to other regions with similar climatic conditions and if a reliable model can be developed from a pooled dataset (consisting of data from different regions). The occurrence of five families was modelled based on physical-chemical water quality variables with classification trees using the data from three tropical river basins (Chaguana in Ecuador, Gilgel Gibe in Ethiopia and Cau in Vietnam). The relevance of each model was tested on complementary data from both the same and other river basins, to test specificity and universality. Furthermore, models with a pooled dataset were developed and tested. Model reliability was assessed based on chance-corrected agreement (Cohens kappa, kappa) and percent agreement (correctly classified instances, CCI). Values of higher than 0.4 (kappa) and 70% (CCI) were used to classify models as good. Only the pollution sensitive taxon (Leptophlebiidae) resulted in reliable models for most cases. In general, responses of macroinvertebrates towards pollution were different among countries except for the pollution sensitive taxa. Thus, extrapolation of ecological models for sensitive taxa to another river basin with similar climatic and environmental conditions is possible. Nevertheless, this type of systematic analyses for all families is necessary to determine and minimize uncertainty in ecological assessment.

Distribution of agricultural pesticides in the freshwater environment of the Guayas river basin (Ecuador)

The rapid increase and transition to more intensive agricultural activities in developing nations are often leading to misuse and overuse of pesticides, making their environment vulnerable for pesticide accumulation. In the present study, the Guayas river basin was taken as a representative case study to evaluate pesticide contamination of the Ecuadorean freshwater environment. Pesticide contamination was determined at 181 sampling sites by a multi-residue method using solid phase extraction (SPE) and pesticide residues were linked with agricultural land use activities to identify the main pollution sources. Moreover, the biological water quality status based on macroinvertebrate communities was determined at every location and the relation with the occurrence of pesticide residues was further investigated. Results showed that pesticide contamination of the freshwater environment was widely present in the Guayas river basin with detections at 108 sampling sites (60%). A total of 26 pesticide products were identified. Most frequently detected pesticides included cadusafos (62 locations), butachlor (21 locations) and pendimethalin (21 locations), with concentrations up to 0.081, 2.006 and 0.557 μg·L-1 respectively. Pesticide residues detected in this study did not significantly influence the biological water quality (p = 0.69), but were observed to be positively correlated with ammonium concentrations, supporting the assumed combined application of chemical fertilizers and pesticides in agriculture. These pesticide residues were also associated with one or more agricultural crops, with in particular the banana and rice industries identified as major pollution sources. Both high consumption rates and non-specific application methods, such as aerial spraying of banana plantations and application directly into the water layer of irrigated rice fields, may attribute to pesticide contamination of the freshwater environment of the Guayas river basin. It is therefore suggested that measures, e.g. legal regulations and awareness campaigns, taken to prevent environmental pollution and accumulation of pesticides primarily focus on these industries.

Exploring the influence of meteorological conditions on the performance of a waste stabilization pond at high altitude with structural equation modeling

Algal photosynthesis plays a key role in the removal mechanisms of waste stabilization ponds (WSPs), which is indicated in the variations of three parameters, dissolved oxygen, pH, and chlorophyll a. These variations can be considerably affected by extreme climatic conditions at high altitude. To investigate these effects, three sampling campaigns were conducted in a high-altitude WSP in Cuenca (Ecuador). From the collected data, the first application of structure equation modeling (SEM) on a pond system was fitted to analyze the influence of high-altitude characteristics on pond performance, especially on the three indicators. Noticeably, air temperature appeared as the highest influencing factors as low temperature at high altitude can greatly decrease the growth rate of microorganisms. Strong wind and large diurnal variations of temperature, 7-20 °C, enhanced flow efficiency by improving mixing inside the ponds. Intense solar radiation brought both advantages and disadvantages as it boosted oxygen level during the day but promoted algal overgrowth causing oxygen depletion during the night. From these findings, the authors proposed insightful recommendations for future design, monitoring, and operation of high-altitude WSPs. Moreover, we also recommended SEM to pond engineers as an effective tool for better simulation of such complex systems like WSPs.

Functional Response (FR) and Relative Growth Rate (RGR) Do Not Show the Known Invasiveness of Lemna minuta (Kunth)

Growing travel and trade threatens biodiversity as it increases the rate of biological invasions globally, either by accidental or intentional introduction. Therefore, avoiding these impacts by forecasting invasions and impeding further spread is of utmost importance. In this study, three forecasting approaches were tested and combined to predict the invasive behaviour of the alien macrophyte Lemna minuta in comparison with the native Lemna minor: the functional response (FR) and relative growth rate (RGR), supplemented with a combined biomass-based nutrient removal (BBNR). Based on the idea that widespread invasive species are more successful competitors than local, native species, a higher FR and RGR were expected for the invasive compared to the native species. Five different nutrient concentrations were tested, ranging from low (4 mgN.L-1 and 1 mgP.L-1) to high (70 mgN.L-1 and 21 mgP.L-1). After four days, a significant amount of nutrients was removed by both Lemna spp., though significant differences among L. minor and L. minuta were only observed at lower nutrient concentrations (lower than 17 mgN.L-1 and 6 mgP.L-1) with higher nutrient removal exerted by L. minor. The derived FR did not show a clear dominance of the invasive L. minuta, contradicting field observations. Similarly, the RGR ranged from 0.4 to 0.6 d-1, but did not show a biomass-based dominance of L. minuta (0.5 ± 0.1 d-1 versus 0.63 ± 0.09 d-1 for L. minor). BBNR showed similar results as the FR. Contrary to our expectations, all three approaches resulted in higher values for L. minor. Consequently, based on our results FR is sensitive to differences, though contradicted the expectations, while RGR and BBNR do not provide sufficient power to differentiate between a native and an invasive alien macrophyte and should be supplemented with additional ecosystem-based experiments to determine the invasion impact.

Variable importance for sustaining macrophyte presence via random forests: data imputation and model settings

Data sets plagued with missing data and performance-affecting model parameters represent recurrent issues within the field of data mining. Via random forests, the influence of data reduction, outlier and correlated variable removal and missing data imputation technique on the performance of habitat suitability models for three macrophytes (Lemna minor, Spirodela polyrhiza and Nuphar lutea) was assessed. Higher performances (Cohen’s kappa values around 0.2–0.3) were obtained for a high degree of data reduction, without outlier or correlated variable removal and with imputation of the median value. Moreover, the influence of model parameter settings on the performance of random forest trained on this data set was investigated along a range of individual trees (ntree), while the number of variables to be considered (mtry), was fixed at two. Altering the number of individual trees did not have a uniform effect on model performance, but clearly changed the required computation time. Combining both criteria provided an ntree value of 100, with the overall effect of ntree on performance being relatively limited. Temperature, pH and conductivity remained as variables and showed to affect the likelihood of L. minor, S. polyrhiza and N. lutea being present. Generally, high likelihood values were obtained when temperature is high (>20 °C), conductivity is intermediately low (50–200 mS m−1) or pH is intermediate (6.9–8), thereby also highlighting that a multivariate management approach for supporting macrophyte presence remains recommended. Yet, as our conclusions are only based on a single freshwater data set, they should be further tested for other data sets.