Bioindicators assessing water quality and environmental impacts of water treatment plant sludge

Costa, Talitha R. Alves Abreu Da; Weber, Olmar Baller; Pestana, Carlos J.; Neto, José Capelo

doi:10.1504/ijesd.2017.080837

Cited by 2 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, we used this scale for comparative purposes only, as the index was developed for rivers and temperate systems, where diversity is naturally higher. Finally, we selected three of the most abundant of sensitive taxa of different orders to analyze the potential changes in their spatial distribution resulting from rehabilitation measures: the Ephemeroptera Caenidae, the Trichoptera Polycentropodidae and the Diptera Chironomidae Zavreliella (Butakka, Ragonha, & Takeda, 2014; Costa, Alves, Weber, Pestana, & Capelo‐Neto, 2017; Mazzoni, Lanzer, & Schafer, 2014).…”

Section: Methodsmentioning

confidence: 99%

Rehabilitation scenarios for reservoirs: Predicting their effect on invertebrate communities through machine learning

Azevêdo

Bezerra‐Neto

Molozzi

et al. 2020

River Research & Apps

View full text Add to dashboard Cite

Reservoirs are ecosystems that provide important services and reserves of aquatic biodiversity, especially in arid and semiarid regions. However, they are also highly vulnerable to pollution, water abstraction and land use change. Here, we aimed to develop a “dirty‐water” predictive model that could be used to create recovery scenarios and analyze the effect of measures on the biological quality of reservoirs. Accordingly, we constructed a machine learning predictive model that was trained with environmental and macroinvertebrate community data from 129 sites, sampled in six reservoirs in Northeast Brazil. Two different rehabilitation scenarios were simulated (D1 = lower improvement, 25% change; and D2 = higher improvement, 75% change), and three initial levels of disturbance were considered based on PCA analyses: least disturbed, moderately disturbed and severely disturbed. The effects were analyzed in terms of changes in expected taxa (E), observed/expected taxon ratio (OE), biotic and trophic status indices and the spatial distribution of sensitive taxa. The simulations resulted in significant improvements (p < .001) for all disturbance levels and indicators analyzed. Furthermore, rehabilitation measures in artificial systems, such as reservoirs, could result in a higher biodiversity, biological quality and water quality, also in the least disturbed sites. Thus, this approach has the potential to provide information on management and conservation measures, since the simultaneous predictions of many taxa can be used, including: mapping the distribution of species; monitor the population dynamics of species at risk of extinction; and determining the best rehabilitation measures to improve disturbed ecosystems, allowing the determination of the most cost‐effective restoration measures.

show abstract

Section: Methodsmentioning

confidence: 99%

Rehabilitation scenarios for reservoirs: Predicting their effect on invertebrate communities through machine learning

Azevêdo

Bezerra‐Neto

Molozzi

et al. 2020

River Research & Apps

View full text Add to dashboard Cite

show abstract

Monitoring water input quality: early screening and system support through the application of an adapted multiple criteria decision making method

De¹,

Majumder²

2017

Desalination and Water Treatment

View full text Add to dashboard Cite

a b s t r a c tThe WHO estimates that, on average, dehydration caused by water borne illnesses claims up to 1.5 million lives a year, with a disproportionate number of casualties located in developing nations. In order to mitigate risks to public health, previous studies have helped to gain extensive insights and create management techniques to ensure that water quality standards are maintained. The present study will investigate a relatively neglected field, which is the need for the prioritization of monitoring the quality of a water treatment plant's inflow, which may vary significantly in quantity and quality throughout the day. The technique proposed for this investigation is a novel application of the multiple criteria decision making (MCDM) method, adapted particularly for the purposes of decision making for optimal scenarios, called the multi variable temporal decision making method for the selection of optimal solutions (MVTDMSOS). By cascading self-selecting neural network algorithms, which are implicit in such systems, this method is designed to eliminate human bias and aims to identify the priority parameters based on optimal, rather than normal, scenarios. The introduction of polynomial neural networks ensures adaptability and alacrity of the modeling framework. Test results encourage further application of the proposed technique.

show abstract

Bioindicators assessing water quality and environmental impacts of water treatment plant sludge

Cited by 2 publications

References 13 publications

Rehabilitation scenarios for reservoirs: Predicting their effect on invertebrate communities through machine learning

Rehabilitation scenarios for reservoirs: Predicting their effect on invertebrate communities through machine learning

Monitoring water input quality: early screening and system support through the application of an adapted multiple criteria decision making method

Contact Info

Product

Resources

About