Concentrated landfill leachate treatment with a combined system including electro-ozonation and composite adsorbent augmented sequencing batch reactor process

Mojiri, Amin; Liu, Ziyang; Wang, Hui; Ahmad, Zakiah; Tajuddin, Ramlah Mohd; Amr, Salem S. Abu; Kindaichi, Tomonori; Aziz, Hamidi Abdul; Farraji, Hossein

doi:10.1016/j.psep.2017.07.013

Cited by 60 publications

(18 citation statements)

References 34 publications

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“…Chinese municipal solid waste has increased from 31.3 to 113.0 million tons from 1980 to 1998, following an annual increase rate of 3-10%. Chinese municipal solid waste categories in China comprise kitchen wastes, paper, plastic, glass, batteries, metal, brick and stones, fabric, pottery, and discarded domestic appliances [14].…”

Section: Literature Reviews Of Packaging Waste and Recycling Behaviormentioning

confidence: 99%

Are Chinese Residents Willing to Recycle Express Packaging Waste? Evidence from a Bayesian Regularized Neural Network Model

Dong¹,

Hua²

2018

Sustainability

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While enriching people’s lives, the rapid development of online shopping has posed a severe challenge to the environment. Questionnaires focusing on the intention to recycle packaging waste are designed. These questionnaires contain first-level variables such as recycling behavior attitude, recycling behavior cognition, situational factors, historical recycling behavior, and recycling behavior intention. With the collected questionnaire data, a regression analysis is first conducted on the selection of variables and the effect of variable prediction. After ensuring the validity of the variables, 15 second-level variables are extracted into eight principal components using principal component analysis. These components serve as input to a Bayesian regularized neural network. Subsequently, a three-layer (8-15-1) neural network model is constructed; the trained neural network model achieves a high degree of fit between the predicted and measured values of the test set, thus further proving the rationality of the selected variables and the neural network model. Finally, this study uses the connection weights matrix of the neural network model and the Garson formula to analyze in depth the specific impact of each second-level variable on the intention to recycle packaging waste. Note that given the particularity of packaging waste recycling behavior, the impact on social norms, recycling behavior knowledge, values, and publicity on behavioral intentions in second-level variables is different from that obtained in similar previous studies.

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Section: Literature Reviews Of Packaging Waste and Recycling Behaviormentioning

confidence: 99%

Are Chinese Residents Willing to Recycle Express Packaging Waste? Evidence from a Bayesian Regularized Neural Network Model

Dong¹,

Hua²

2018

Sustainability

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“…As an electrolyte, 1 g/L of Na 2 S 2 O 8 added to the samples before each experiment [ 3 ]. The current and voltage were fixed at 50 mA and 7 V, and were set according to the ranges that were reported by Bashir et al [ 3 ] and Mojiri et al [ 12 ], respectively.…”

Section: Methodsmentioning

confidence: 99%

“…First, 20 mg/L of each contaminant were added to 200-mL beakers containing various concentrations (0–2.5 g/L) of adsorbent. Then, the beakers were shaken at 200 rpm for 30 min [ 12 ].…”

Section: Methodsmentioning

confidence: 99%

“…Researchers often recommend combined techniques to treat the high pollutant concentrations in industrial wastewater. For example, adsorption is a common wastewater treatment method and is typically combined with other techniques [ 12 ]. Several researchers have investigated the use of adsorption to treat various types of wastewater [ 13 , 14 ], and several studies [ 15 ] have verified that adsorbents can eliminate considerable amounts of contaminants, especially heavy metals.…”

Section: Introductionmentioning

confidence: 99%

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Pollutant Removal from Synthetic Aqueous Solutions with a Combined Electrochemical Oxidation and Adsorption Method

Mojiri

Ohashi

Ozaki

et al. 2018

IJERPH

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Eliminating organic and inorganic pollutants from water is a worldwide concern. In this study, we applied electrochemical oxidation (EO) and adsorption techniques to eliminate ammonia, phenols, and Mo(VI) from aqueous solutions. We analyzed the first stage (EO) with response surface methodology, where the reaction time (1–3 h), initial contaminant concentration (10–50 mg/L), and pH (3–6) were the three independent factors. Sodium sulfate (as an electrolyte) and Ti/RuO2–IrO2 (as an electrode) were used in the EO system. Based on preliminary experiments, the current and voltage were set to 50 mA and 7 V, respectively. The optimum EO conditions included a reaction time, initial contaminant concentration, and pH of 2.4 h, 27.4 mg/L, and 4.9, respectively. The ammonia, phenols, and Mo elimination efficiencies were 79.4%, 48.0%, and 55.9%, respectively. After treating water under the optimum EO conditions, the solution was transferred to a granular composite adsorbent column containing bentonite, limestone, zeolite, cockleshell, activated carbon, and Portland cement (i.e., BAZLSC), which improved the elimination efficiencies of ammonia, phenols, and molybdenum(VI) to 99.9%. The energy consumption value (8.0 kWh kg−1 N) was detected at the optimum operating conditions.

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“…For example, inorganic pollution could be treated by phytostabilization, phytovolatilization and rhizoremediation methods (Cunningham et al, 1996;Nwoko, 2013) based on their physicochemical characteristics and concentration as well as their biodegradability. Phytoremediation process can be enhanced through soil conditioners, such as compost (Mangkoedihardjo, et al, 2008), cow dung (Essien et al, 2015;Njoku et al, 2012), olive waste (Nogales and Benítez, 2006), rhizobacteria (Gurska et al, 2009;Langella et al, 2014), biochar (Houben et al, 2013), adsorbent (Mojiri et al, 2016), and plant growth promoters (Vamerali, 2011). The main target of this review is to present different merit aspects of phytoremediation as green technology and their eligibility for environmental cleaning.…”

Section: Farraji Et Al Phytoremediation: Green Technology For Improvmentioning

confidence: 99%

Phytoremediation: green technology for improving aquatic and terrestrial environments

Farraji¹,

Robinson²,

Mohajeri³

et al. 2020

Nipp J Environ Sci

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Environmental remediation by traditional physicochemical methods has several drawbacks, the most important of which is the high cost. Phytoremediation is the use of plants and associated microorganisms to improve degraded environments, Plant sensitivity to climate, seasonal growing, resistant to toxic compounds and suspended solids are limitations of this cost effective technology. Costs of phytoremediation can be offset by the production of valuable biomass for timber, fuel, essential oils and other uses. In some cases, the biomass may contain high concentrations of valuable trace elements such as nickel. Successful phytoremediation depends on suitable plants selection and application of appropriate agronomic techniques. In most cases, locally native plants are the most suitable phytoremediator for phytoremediation. This review collected 20 phytoremediation related terms containing phytomining,

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Concentrated landfill leachate treatment with a combined system including electro-ozonation and composite adsorbent augmented sequencing batch reactor process

Cited by 60 publications

References 34 publications

Are Chinese Residents Willing to Recycle Express Packaging Waste? Evidence from a Bayesian Regularized Neural Network Model

Are Chinese Residents Willing to Recycle Express Packaging Waste? Evidence from a Bayesian Regularized Neural Network Model

Pollutant Removal from Synthetic Aqueous Solutions with a Combined Electrochemical Oxidation and Adsorption Method

Phytoremediation: green technology for improving aquatic and terrestrial environments

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