'Climate Healing Stones': Common Minerals Offer Substantial Climate Change Mitigation Potential.

This review proposes that mineral-based greenhouse gas (GHG) mitigation could be developed into a substantial climate change abatement tool. This proposal was evaluated via three objectives: (1) synthesise literature studies documenting the effectiveness of geological minerals at mitigating GHG emissions; (2) quantify, via meta-analysis, GHG magnitudes that could be abated by minerals factoring-in the carbon footprint of the approach; and (3) estimate the global availability of relevant minerals. Several minerals have been effectively harnessed across multiple sectors-including agriculture, waste management and coal mining-to mitigate carbon dioxide/CO2 (e.g., olivine), methane/CH4 (e.g., allophane, gypsum) and nitrous oxide/N2O (e.g., vermiculite) emissions. High surface area minerals offer substantial promise to protect soil carbon, albeit their potential impact here is difficult to quantify. Although mineral-based N2O reduction strategies can achieve gross emission reduction, their application generates a net carbon emission due to prohibitively large mineral quantities needed. By contrast, mineral-based technologies could abate ~9% and 11% of global CO2 and CH4 anthropogenic emissions, respectively. These estimates conservatively only consider options which offer additional benefits to climate change mitigation (e.g., nutrient supply to agricultural landscapes, and safety controls in landfill operations). This multi-benefit aspect is important due to the reluctance to invest in stand-alone GHG mitigation technologies. Minerals that exhibit high GHG mitigation potential are globally abundant. However, their application towards a dedicated global GHG mitigation initiative would entail significant escalation of their current production rates. A detailed cost-benefit analysis and environmental and social footprint assessment is needed to ascertain the strategy's scale-up potential.

Modelling of the adsorption of Pb, Cu and Ni ions from single and multi-component aqueous solutions by date seed derived biochar: Comparison of six machine learning approaches.

Biochar is an effective material for the removal of heavy metals from wastewater. Operational conditions, such as metal initial concentration, temperature, contact time as well as the presence of competing ions can impact the effectiveness of the treatment process. While several models have been proposed for modelling the adsorption process, no model currently exists that accounts for the mutual interactions of key process parameters on the adsorption capacity in multi-solute systems. The aim of this study is to address this gap in knowledge by formulating a multi-input multi-output (MIMO) model, which takes into account the effect of mutual interactions of key factors while predicting heavy metals adsorption capacity of the biochar in single and multi-solute systems. In this study, we use machine learning models, specifically several ANN models, radial basis and gradient boosting algorithms to model the MIMO process. The results of our models provide highly accurate predictions (R2 > 0.99). The generalized regression network provided the best match to the experimental data. This approach can allow operators to predict how the adsorption system will respond to changes in the operations and hence provide them with a tool for process optimization.

Comparative life cycle assessment of aerobic treatment units and constructed wetlands as onsite wastewater treatment systems in Australia.

Life Cycle Assessment was used to evaluate onsite wastewater treatment systems (OWTS): aerobic treatment unit (ATU) with reinforced concrete (C.ATU) and HDPE (H.ATU) tank; and constructed wetland (CW) with three biochar concentrations in the substrate (0%; 10, and 20% v:v), dubbed CW.BC0, CW.BC10 and CW.BC20, respectively. CML 2001 in SimaPro® was used to evaluate the impacts of the treatment of 1 m3 wastewater. The OWTS were compared on their overall environmental performance scores (OEP). ATUs have higher impacts on human toxicity, eutrophication, freshwater and marine ecotoxicity. The CW.BC20 has the lowest global warming impact (GWP) while CW.BC0 has the highest. Electricity consumption was the largest contributor to the impacts of the ATUs. PVC pipes, coir peat, geomembrane, and electronic devices were the biggest contributors to the impacts of the CWs. The OEP of the CWs were almost a third of the ATUs' (6.07E-03). Changes in electricity sources were tested according to the 2030-Australian targets; increasing renewables share improves the OEP of ATUs by 39%; nevertheless, CWs continue to outperform the ATUs. Variations in biochar biodegradation had a small effect on the OEP of CWs; being relevant only to GWP. This study provides a reference to policy makers for better evaluation of OWTS.

Electro-assisted adsorption of heavy metals from aqueous solutions by biochar.

Electro-assisted adsorption was investigated for Pb2+, Cu2+ and Ni2+ removal using date seed biochar (DSB-Electro). Compared with pristine biochar, the results showed that DSB-Electro effectively increased the adsorption capacity of Pb2+, Cu2+ and Ni2+ by 21% to 94%. Significant differences were observed between Pb2+ and Cu2+ adsorption compared with Ni2+, which could be explained based on ion polarizing power. Under the same voltage, Ni2+ solution shows the highest electric conductivity; thereby more Ni2+ is transported to the biochar anode, giving them a greater chance to interact with the surface groups. Electro-assisted adsorption occurred rapidly as around 88% of Pb2+ and Ni2+ adsorbed within the first 3 h, while 96% of Cu2+ occurred within the first hour of contact. Reversing the polarity did not seem to cause significant desorption of the adsorbed ions as the amount released from reversing polarity was less than 38%, indicating that only a small fraction of the ions was held by the electrostatic charge introduced by the current. It was likely that the enhanced charge facilitated other adsorption mechanisms by bringing the ions in contact with the biochar initially via electrostatic force. Electro-assisted adsorption can improve the biochar economic feasibility for metals removal (particularly Ni2+) from industrial streams.

Carbon abatement via treating the solid waste from the Australian olive industry in mobile pyrolysis units: LCA with uncertainty analysis.

The olive oil industry in Australia has been growing at a rapid rate over the past decade. It is forecast to continue growing due to the steady increase in demand for olive oil and olive products in the local and regional market. However, the olive oil extraction process generates large amounts of solid waste called olive husk which is currently underutilized. This paper uses life-cycle methodology to analyse the carbon emission reduction potential of utilizing olive husk as a feedstock in a mobile pyrolysis unit. Four scenarios, based on different combinations of pyrolysis technologies (slow versus fast) and end-use of products (land application versus energy utilization), are constructed. The performance of each scenario under conditions of uncertainty was also investigated. The results show that all scenarios result in significant carbon emission abatement. Processing olive husk in mobile fast pyrolysis units and the utilization of bio-oil and biochar as substitutes for heavy fuel oil and coal is likely to realize a carbon offset greater than 32.3 Gg CO2-eq annually in 90% of the time. Likewise, more than 3.2 Gg-C (11.8 Gg CO2-eq) per year could be sequestered in the soil in the form of fixed carbon if slow mobile pyrolysis units were used to produce biochar.

Strategies for the municipal waste management system to take advantage of carbon trading under competing policies: The role of energy from waste in Sydney.

Climate change is a driving force behind some recent environmental legislation around the world. Greenhouse gas emission reduction targets have been set in many industrialised countries. A change in current practices of almost all greenhouse-emitting industrial sectors is unavoidable, if the set targets is to be achieved. Although, waste disposal contributes around 3% of the total greenhouse gas emissions in Australia (mainly due to fugitive methane emissions from landfills), the carbon credit and trading scheme set to start in 2010 presents significant challenges and opportunities to municipal solid waste practitioners. Technological advances in waste management, if adopted properly, allow the municipal solid waste sector to act as carbon sink, hence earning tradable carbon credits. However, due to the complexity of the system and its inherent uncertainties, optimizing it for carbon credits may worsen its performance under other criteria. We use an integrated, stochastic multi-criteria decision-making tool that we developed earlier to analyse the carbon credit potential of Sydney municipal solid waste under eleven possible future strategies. We find that the changing legislative environment is likely to make current practices highly non-optimal and increase pressures for a change of waste management strategy.

Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg.

The development and application of multi-criteria decision-making tool with consideration of uncertainty: the selection of a management strategy for the bio-degradable fraction in the municipal solid waste.

A modified version of the multi-criteria decision aid, ELECTRE III has been developed to account for uncertainty in criteria weightings and threshold values. The new procedure, called ELECTRE-SS, modifies the exploitation phase in ELECTRE III, through a new definition of the pre-order and the introduction of a ranking index (RI). The new approach accommodates cases where incomplete or uncertain preference data are present. The method is applied to a case of selecting a management strategy for the bio-degradable fraction in the municipal solid waste of Sydney. Ten alternatives are compared against 11 criteria. The results show that anaerobic digestion (AD) and composting of paper are less environmentally sound options than recycling. AD is likely to out-perform incineration where a market for heating does not exist. Moreover, landfilling can be a sound alternative, when considering overall performance and conditions of uncertainty.