Relevant factors in the eutrophication of the Uruguay River and the Río Negro

Beretta-Blanco, Andrés; Carrasco-Letelier, Leónidas

doi:10.1016/j.scitotenv.2020.143299

Cited by 30 publications

(21 citation statements)

References 29 publications

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“…The strong daily and annual seasonality that characterizes this variable makes its simulation and, therefore, its imputation less difficult. The correlation that exists between Tw and EC (an increase in Tw leads to an increase in EC) [43,44] is reflected in the "good" performance of this variable at the six monitoring sites ("satisfactory" at SLC01 and PS01; "good" at SLC02; "very good" at PS03, PS04 and PS02). The imputation process for the other waterquality variables shows different results.…”

Section: Imputation Resultsmentioning

confidence: 99%

“…An increase in T w may also produce an increment in the number of ions due to molecule dissociation. As the EC depends on these factors, an increase in T w leads to an increase in EC [43,44]. Furthermore, EC represents the ability of a liquid to conduct an electric charge; this ability depends on dissolved ion concentration, which is usually measured as total dissolved solids (TDS) [45].…”

Section: Helper Variables For the Imputation Processmentioning

confidence: 99%

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Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach

et al. 2021

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The monitoring of surface-water quality followed by water-quality modeling and analysis are essential for generating effective strategies in surface-water-resource management. However, worldwide, particularly in developing countries, water-quality studies are limited due to the lack of a complete and reliable dataset of surface-water-quality variables. In this context, several statistical and machine-learning models were assessed for imputing water-quality data at six monitoring stations located in the Santa Lucía Chico river (Uruguay), a mixed lotic and lentic river system. The challenge of this study is represented by the high percentage of missing data (between 50% and 70%) and the high temporal and spatial variability that characterizes the water-quality variables. The competing algorithms implement univariate and multivariate imputation methods (inverse distance weighting (IDW), Random Forest Regressor (RFR), Ridge (R), Bayesian Ridge (BR), AdaBoost (AB), Hubber Regressor (HR), Support Vector Regressor (SVR) and K-nearest neighbors Regressor (KNNR)). According to the results, more than 76% of the imputation outcomes are considered “satisfactory” (NSE > 0.45). The imputation performance shows better results at the monitoring stations located inside the reservoir than those positioned along the mainstream. IDW was the model with the best imputation results, followed by RFR, HR and SVR. The approach proposed in this study is expected to aid water-resource researchers and managers in augmenting water-quality datasets and overcoming the missing data issue to increase the number of future studies related to the water-quality matter.

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Section: Imputation Resultsmentioning

confidence: 99%

Section: Helper Variables For the Imputation Processmentioning

confidence: 99%

Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach

et al. 2021

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“…In this framework, the current information pointed out that Uruguay has the feedstock availability to hold a biorefinery and first results about environmental impacts. However, the current approach is not enough to avoid the impacts on its soils and waters [20,98]. In the future, the improvement in the information about water and biodiversity footprints would be required.…”

Section: Eroi Carbon Footprint and Other Footprintsmentioning

confidence: 99%

Availability and Environmental Performance of Wood for a Second-Generation Biorefinery

Rachid-Casnati

Resquín

Carrasco-Letelier

2021

Forests

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The current global climate change, the 2030 Agenda, and the planetary boundaries have driven new development strategies, such as the circular economy, bioeconomy, and biorefineries. In this framework, this study analyzes the potential availability and sustainability of the wood supply chain for a small-scale biorefinery aiming at producing 280–300 L of bioethanol per ton of dry biomass, consuming 30,000 t of dry biomass per year harvested in a 50 km radius. This wood production goal was assessed from Eucalyptus grandis stands planted for solid wood in northeastern Uruguay. Moreover, to understand the environmental performance of this biomass supply chain, the energy return on investment (EROI), carbon footprint (CF), and potential soil erosion were also assessed. The results showed that the potential wood production would supply an average of 81,800 t of dry mass per year, maintaining the soil erosion below the upper threshold recommended, an EROI of 2.3, and annual CF of 1.22 kg CO2−eq m−3 (2.6 g CO2−eq MJ−1). Combined with the environmental performance of the bioethanol biorefinery facility, these results would show acceptable values of sustainability according to EU Directive 2009/28/ec because the bioethanol CF becomes 1.7% of this petrol’s CF.

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“…Phosphorus is the main factor of eutrophication in urban rivers [1] and comes from industry, agriculture and transportation activities [2], being mainly spread by urban stormwater runoff, a kind of non-point pollution of surface water. To manage stormwater runoff, developers typically use bioretention facilities, whose primary goal is to reduce floods by reducing the volume of overland flow during a storm event and reinstating natural stormwater infiltration in the developed area to its pre-developmental capacity [3].…”

Section: Introductionmentioning

confidence: 99%

Phosphorus Release and Adsorption Properties of Polyurethane–Biochar Crosslinked Material as a Filter Additive in Bioretention Systems

Meng

Wang

2021

Polymers

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Bioretention systems are frequently employed in stormwater treatment to reduce phosphorus pollution and prevent eutrophication. To enhance their efficiency, filter additives are required but the currently used traditional materials cannot meet the primary requirements of excellent hydraulic properties as well as outstanding release and adsorption capacities at the same time. In this research, a polyurethane-biochar crosslinked material was produced by mixing the hardwood biochar (HB) with polyurethane to improve the performance of traditional filter additives. Through basic parameter tests, the saturated water content of polyurethane-biochar crosslinked material (PCB) was doubled and the permeability coefficient of PCB increased by two orders of magnitude. Due to the polyurethane, the leaching speed of phosphorus slowed down in the batching experiments and fewer metal cations leached. Moreover, PCB could adsorb 93–206 mg/kg PO43− at a typical PO43− concentration in stormwater runoff, 1.32–1.58 times more than HB, during isothermal adsorption experiments. In the simulating column experiments, weaker hydropower reduced the PO43− leaching quantities of PCB and had a stable removal rate of 93.84% in phosphate treatment. This study demonstrates the potential use of PCB as a filter additive in a bioretention system to achieve hydraulic goals and improve phosphate adsorption capacities.

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Relevant factors in the eutrophication of the Uruguay River and the Río Negro

Cited by 30 publications

References 29 publications

Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach

Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach

Availability and Environmental Performance of Wood for a Second-Generation Biorefinery

Phosphorus Release and Adsorption Properties of Polyurethane–Biochar Crosslinked Material as a Filter Additive in Bioretention Systems

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