Long-term influence of red mud on As mobility and soil physico-chemical and microbial parameters in a polluted sub-acidic soil

Garau, Giovanni; Silvetti, Margherita; Deiana, S.; Deiana, Pietrino; Castaldi, Paola

doi:10.1016/j.jhazmat.2010.10.037

Cited by 87 publications

(66 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the experimental conditions, such as a relatively small amount, incubation at room temperature and irrigation at regular intervals may also contribute to a decrease in microbial activity. Otherwise the AWCD values in the acidic sandy soil were small (0.13 after 120 h), which indicates an originally low microflora activity in the acidic sandy soil (S), typical for degraded sandy soils (Garau et al, 2011).…”

Section: Discussionmentioning

confidence: 97%

“…Most of the carbohydrates are intermediates of soil organic matter degradation which explains the high affinity of bacteria to them (Kenarova et al, 2014). However, the utilization of some substrate groups (carboxylic acids and polymers with the highest utilization percentage) was maintained at a higher level in the treated soils (at up to 20% RM and 10% RM, respectively) than in the untreated acidic sandy soil (S) over the long term, suggesting that RM addition created a more favorable environment for certain microbial groups in the soil to utilize specific substrates (Garau et al, 2011). At 30-50% RM dose all values clearly indicated deteriorating effect on the microbial activity of the acidic sandy soil.…”

Section: Discussionmentioning

confidence: 99%

“…Negative values were set to 0. The OD values for data evaluation were applied at 120 h since these represented the optimal range of OD readings (Frąc et al, 2012, Nagy et al, 2013, Gryta et al, 2014 and were best suited for comparison with other experiments with red mud as soil amendment (Garau et al, 2007(Garau et al, , 2011. The endpoints calculated from the corrected data were the following: average well color development (AWCD), substrate average well color development (SAWCD), Shannon-index (H), substrate richness (SR) and Shannon evenness (E).…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Influence of red mud on soil microbial communities: Application and comprehensive evaluation of the Biolog EcoPlate approach as a tool in soil microbiological studies

Feigl

Ujaczki

Vaszita

et al. 2017

Science of The Total Environment

132

View full text Add to dashboard Cite

Red mud can be applied as soil ameliorant to acidic, sandy and micronutrient deficient soils. There are still knowledge gaps regarding the effects of red mud on the soil microbial community. The Biolog EcoPlate technique is a promising tool for community level physiological profiling. This study presents a detailed evaluation of Biolog EcoPlate data from two case studies. In experiment "A" red mud from Ajka (Hungary) was mixed into acidic sandy soil in soil microcosms at 5-50 w/w%. In experiement "B" red mud soil mixture was mixed into low quality subsoil in a field experiment at 5-50 w/w%. According to average well color development, substrate average well color development and substrate richness 5-20% red mud increased the microbial activity of the acidic sandy soil over the short term, but the effect did not last for 10 months. Shannon diversity index showed that red mud at up to 20% did not change microbial diversity over the short term, but the diversity decreased by the 10 th month. 30-50% red mud had deteriorating effect on the soil microflora. 5-20% red mud soil mixture in the low quality subsoil had a long lasting enhancing effect on the microbial community based on all Biolog EcoPlate parameters. However, 50% red mud soil mixture caused a decrease in diversity and substrate richness. With the Biolog EcoPlate we were able to monitor the changes of the microbial community in red mud affected soils and to assess the amount of red mud and red mud soil mixture applicable for soil treatment in these cases.

show abstract

Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Influence of red mud on soil microbial communities: Application and comprehensive evaluation of the Biolog EcoPlate approach as a tool in soil microbiological studies

Feigl

Ujaczki

Vaszita

et al. 2017

Science of The Total Environment

132

View full text Add to dashboard Cite

show abstract

“…In previous studies, numerous amendments have been incorporated into soils polluted with metal (metalloids) to immobilize pollutants. These amendments include individual additions of amendments (Walker et al, 2004;Schwab et al, 2007;Derome, 2009;González-Alcaraz et al, 2011;Melamed et al, 2003;Brown et al, 2005;Ford, 2002;Trivedi and Axe, 2001;Hartley and Lepp, 2008;Gupta and Sharma., 2002;Garau et al, 2011;Brown et al, 2003;Torri and Lavado, 2008) and combinations of different amendments including organic and liming materials (Alvarenga et al,2008;Farrell and Jones, 2010;Bes and Mench, 2008), iron oxides and lime (Warren and Alloway, 2003) and compost and iron oxide (Gadepalle et al, 2008).…”

Section: Figurementioning

confidence: 99%

Application of an inverse neural network model for the identification of optimal amendment to reduce copper toxicity in phytoremediated contaminated soils

Hattab

Motelica-Heino

2014

Journal of Geochemical Exploration

View full text Add to dashboard Cite

To cite this version:Nour Hattab, Mikael Motelica-Heino. Application of an inverse neural network model for the identification of optimal amendment to reduce Copper toxicity in phytoremediated contaminated soils. Journal of Geochemical Exploration, Elsevier, 2014, 136, pp.14-23 AbstractArtificial neural network ANN prediction approaches applied to the modeling of soil behavior are often solved in the forward direction, by measuring the response of the soil (outputs) to a given set of soil inputs. Conversely, one may be interested in the assessment of a given set of soil inputs that leads to given (target) soil outputs. This is the inverse of the former problem. In this study, we develop and test an inverse artificial neural network model for the prediction of the optimal soil treatment to reduce copper ( Cu) toxicity assessed by a given target concentration of Cu in dwarf bean leaves (BL) from selected soil inputs. In this study the inputs are the soil pH, electrical conductivity (EC), dissolved organic carbon (DOC) and a given target toxicity value of Cu, whereas the output is the best treatment to reduce the given toxicity level. It is shown that the proposed method can successfully identify the best soil treatment from the soil properties (inputs).Two important challenges for optimal treatment prediction using neural networks are the nonuniqueness of the solution of the inverse problem and the inaccuracies in the measurement of the soil properties (inputs). It is shown that the neural network prediction model proposed can overcome both these challenges. It is also shown that the proposed inverse neural network method 2 can potentially be applied with a high level of success to the phytoremediation of contaminated soils. Before large-scale application, further validation is needed by performing several experiments and investigations including additional factors and their combinations to capture the complex soil behavior.

show abstract

“…The S\S process could be applied both in laboratory and in situ showing good results against the risk with the remarkable benefit of immobilizing heavy metal inside natural minerals, such as clays and zeolite or soil-compatible materials. Among these, fly ash and red muds are widely used (Apak et al, 1998;Castaldi et al, 2010;Ciccu et al, 2003;Coruh & Nur Ergun, 2010;Dermatas & Meng, 2003;Garau et al, 2011;Glenister & Thornber, 1985;Gray et al, 2006;Lombi et al, 2002a;McPharlin et al, 1994;Summers et al, 1996). In particular, many authors show that amendment of contaminated soil with red mud results in a durable reduction in metal mobility and also in a smaller risk of metal remobilization if soil pH were to decrease (Gray et al, 2006;Lombi et al, 2002a).…”

Section: Solidification/stabilization Technologies For Soil Remediatimentioning

confidence: 99%

Multi-Technique Application for Waste Material Detection and Soil Remediation Strategies: The Red Mud Dust and Fly Ash Case Studies

Belviso¹,

Pascucci²,

Cavalcante³

et al. 2011

Soil Contamination

View full text Add to dashboard Cite

Long-term influence of red mud on As mobility and soil physico-chemical and microbial parameters in a polluted sub-acidic soil

Cited by 87 publications

References 42 publications

Influence of red mud on soil microbial communities: Application and comprehensive evaluation of the Biolog EcoPlate approach as a tool in soil microbiological studies

Influence of red mud on soil microbial communities: Application and comprehensive evaluation of the Biolog EcoPlate approach as a tool in soil microbiological studies

Application of an inverse neural network model for the identification of optimal amendment to reduce copper toxicity in phytoremediated contaminated soils

Multi-Technique Application for Waste Material Detection and Soil Remediation Strategies: The Red Mud Dust and Fly Ash Case Studies

Contact Info

Product

Resources

About