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• We present a multi-disciplinary collaborative approach for developing a regional scale climate adaptation plan with the health and wellbeing sector. • This includes an innovative engagement and policy mapping strategy. • We demonstrate the application of this approach for developing climate change adaptation plan for health and wellbeing sector in Queensland, Australia. • We find that direct engagement between various key stakeholders is a critical element of translating scientific evidence of climate change impacts on human health into a regional adaptation policy for the health and wellbeing sector. The science of climate change and its impacts on health makes it clear that human health and wellbeing will be increasingly negatively impacted as a result of climate change. The health and wellbeing sector must respond to these growing pressures in order to continue to provide safe, quality care. Adaptation and mitigation policies need to be developed at different scales, including at a regional government level. Numerous challenges exist; for example, the necessity for collaboration between multiple agencies across scales, the tailoring of policies to the health issues specific to regions, and constraints on existing regional and local resources and adaptive capacities, to name a few. This paper presents a multi-disciplinary collaborative approach used to develop a regional scale climate adaptation plan with the health sector. Starting from a scientific understanding of climate change impact on the health sector in Queensland, Australia, the approach used an innovative engagement strategy to a) better understand awareness of relevant stakeholders about current and future climate change impact on the health of the population and on service provision, b) identify on-ground barriers to effective adaptation faced by the sector stakeholders, c) identify opportunities and benefits which would arise from adaptation, and d) identify what conditions or support stakeholders required to overcome those barriers, take advantage of opportunities, and achieve benefits from adaptation. Analysis of these findings guided the development of specific policy directions for the sector. We found direct engagement between various key stakeholders such as health service providers (e.g. hospitals), critical infrastructure providers, academics, local government authorities, and sub-sectors such as aged care and early childhood care facilities, was a critical element of translating scientific evidence of climate change impacts on human health into a regional adaptation policy for the health and wellbeing sector. The resulting policy, grounded in the reality and experience of health and wellbeing sector stakeholders, reflects their insights and concerns, and served to develop a level of sectoral 'ownership' (not 'top-down' imposition) which will be important for its successful ongoing development and implementation. [ABSTRACT FROM AUTHOR]

• A PV variability tolerant multifunctional control strategy for BESS is proposed. • Four control functions of BESS with possible user-defined extensions were formulated. • A use-case to control PV plant's power ramp rate is demonstrated using real data. • Validation is performed in real-time software-in-loop platform using real-field data. • Evaluation of proposed strategy shows the potential for real-world deployment. This paper proposes a multifunctional control strategy for battery energy storage systems (BESSs) in solar photovoltaic (PV) plants to avoid the unacceptable PV-power ramp-rate caused by PV variability. In addition to the PV variability tolerant ramp-rate control, the proposed multifunctional BESS control strategy is capable of maintaining a user-specified charging profile to store excess PV power while providing backup power support and charge maintenance, by accommodating diverse control functions time to time. Four control functions, namely, Rest (RST), Surplus Energy Storage (STR), Back-up Power Support (SPT) and Charge Maintenance (CMT) functions, are proposed in this article. Besides, extendibility of the proposed multifunctional strategy is presented by adding two action strategies – one with the STR function and another with the SPT function. Validation of the proposed control strategy is conducted by using real historical data from a utility-scale solar PV plant located in Queensland, Australia. Numerical analyses show that the strategy can successfully keep the PV ramp-rate within specified limit along with the user specified charge profile. Further, exploiting the real-field data, the proposed strategy is demonstrated through a real-time co-simulation study conducted in a MATLAB interfaced RSCAD-RTDS platform that can be used as Digital Twin (DT) for the PV plant. [ABSTRACT FROM AUTHOR]

• Presents a typology of emergent risk that highlights the iterative and inter-related nature of climate change risk, and propensity for impacts, such as legal action and reduced debt access, to emerge subsequent to, yet far removed from, initial physical impacts. • Highlights current practice and gaps in local government risk management concerning emergent risk. • Highlights the potential and need for local government to consider, the application of risk transfer mechanisms such as catastrophe bonds in the face of historical and conceivable climate change-perpetuated reduced access to, and increased cost of, traditional mechanisms such as insurance. • Provides insight into barriers and enablers to local government agencies gaining an appreciation of the potential application of alternative risk transfer mechanisms. Insurance represents an integral part of local government risk management strategy. As climate change progresses, increased loss and risk related to extreme weather events such as tropical cyclones, could motivate insurers to withdraw from certain markets. In some regions, such a withdrawal represents an emergent risk that, when coupled with increasing populations and other climate change impacts, could leave local governments and ratepayers particularly vulnerable. This paper investigates such a scenario and its ramifications in the context of a region particularly vulnerable to climate change, through an exploration of the degree that consideration of emergent risks, such as loss of insurance, and the potential application of insurance alternatives influence Queensland local government risk management. The study finds little appreciation amongst government officials of emergent risk implicit in extreme weather events such as cyclones, little understanding of the nuances of risk transfer mechanisms beyond familiar traditional insurance and disaster funding mechanisms, and by default, a lack of appreciation of the relationship between the two. A lack of resource and leadership with respect to emergent risk, and an absence of dialogue between insurance brokers and local government concerning climate change risk arise as the main reasons for this result. This research is significant because it challenges current local government risk management practice through an exploration of the risks inherent in the process itself. This has potential social, economic and ecological ramifications in drawing attention to aspects of possible "uninsurability" and prospects of alleviation thereto. Further research is recommended to consider the insurance industry's part in this study's findings in order to inform current industry practice and thinking and further enlighten the causes of local government disengagement in this critical area. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) or particle pollution include the tiny particles of dust and fly ash particles are expelled from coal-burning power plants. Coal combustion is an extremely prevalent source of air pollution, and resulting PM has substantial impacts on human health, especially in industrial zones. This paper aims to design a novel hybrid deep learning framework based on long short-term memory (LSTM) integrated with a two-stage data filtering technique to accurately predict the air quality indices (i.e., PM 2.5 and PM 10) in a chosen study region 'Miles Airport, Queensland,' that meets the needs of the coal seam gas industry in Australia. The data used to construct the novel hybrid two-stage data filtering technique based on LSTM comprising of six meteorological parameters (i.e., wind direction, wind speed, air temperature, relative humidity, solar radiation, and rainfall) and two environmental factors (i.e., ozone and total suspended particulate). In the first stage, two robust feature selection methods, namely, extreme gradient boosting (XGBoost) and the classification and regression tree (CART) approach, were adopted to explore the most significant predictors. Then in the second stage, the best subset regression (BSR) technique is utilized to determine the best subsets input combinations i.e., C1, C2, and C3 based on several particular metrics. The three BSR-based input combinations were employed in the LSTM model to estimate the PM 2.5 and PM 10. Furthermore, to validate the main hybrid framework, two advanced machine learning (ML) methods (i.e., LightGBM and ridge kernel regression (KRR)) and two traditional ML methods (i.e., Adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron neural network (MLP)) were hybridized with the multi-level data filtering strategy by examining the optimal input combinations. Several statistical metrics, graphical tools, and diagnostic analyses evaluated the hybrid models. The outcomes of the PM 2.5 simulation based on 2375 data samples showed that the LSTM-C3 containing all the selected predictors yielded the most promising accuracy, followed by the LightGBM-C3 and MLP-C3 models. On the other hand, the simulation of PM 10 distribution demonstrated that the LSTM-C3 was superior to other models, followed by the KRR-C3 and LightGBM-C3 models. [ABSTRACT FROM AUTHOR]

This study proposes a new hybrid deep learning (DL) model, the called CSVR, for Global Solar Radiation (GSR) predictions by integrating Convolutional Neural Network (CNN) with Support Vector Regression (SVR) approach. First, the CNN algorithm is used to extract local patterns as well as common features that occur recurrently in time series data at different intervals. Then, the SVR is subsequently adopted to replace the fully connected CNN layers to predict the daily GSR time series data at six solar farms in Queensland, Australia. To develop the hybrid CSVR model, we adopt the most pertinent meteorological variables from Global Climate Model and Scientific Information for Landowners database. From a pool of Global Climate Models variables and ground-based observations, the optimal features are selected through a metaheuristic Feature Selection algorithm, an Atom Search Optimization method. The hyperparameters of the proposed CSVR are optimized by mean of the HyperOpt method, and the overall performance of the objective algorithm is benchmarked against eight alternative DL methods, and some of the other Machine Learning approaches (LSTM, DBN, RBF, BRF, MARS, WKNNR, GPML and M5TREE) methods. The results obtained shows that the proposed CSVR model can offer several predictive advantages over the alternative DL models, as well as the conventional ML models. Specifically, we note that the CSVR model recorded a root mean square error/mean absolute error ranging between ≈ 2.172–3.305 MJ m2/1.624–2.370 MJ m2 over the six tested solar farms compared to ≈ 2.514–3.879 MJ m2/1.939–2.866 MJ m2 from alternative ML and DL algorithms. Consistent with this predicted error, the correlation between the measured and the predicted GSR , including the Willmott's, Nash-Sutcliffe's coefficient and Legates & McCabe's Index was relatively higher for the proposed CSVR model compared to other DL and Machine Learning methods for all of the study sites. Accordingly, this study advocates the merits of CSVR model to provide a viable alternative to accurately predict GSR for renewable energy exploitation, energy demand or other forecasting-based applications. [Display omitted] • This paper presents a hybrid CSVR algorithm for global solar radiation prediction. • The proposed approach combines Convolutional Neural Network with a Support Vector Machine. • An Atom Search-based feature selection is applied to improve the performance of the system. • Experiments are carried out in data at six solar energy farms in Queensland, Australia. • The results show the efficacy of deep learning CSVR for solar energy prediction problems. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a practical job grouping approach, which aims to enhance the time related performance metrics of container transfers in the Patrick AutoStrad container terminal, located in Brisbane, Australia. It first formulates a mathematical model of the automated container transfers in a relatively complex environment. Apart from the consideration on collision avoidance of a fleet of large vehicles in a confined area, it also deals with many other difficult practical challenges such as the presence of multiple levels of container stacking and sequencing, variable container orientations, and vehicular dynamics that require finite acceleration and deceleration times. The proposed job grouping approach aims to improve the makespan of the schedule for yard jobs, while reducing straddle carrier waiting time by grouping jobs using a guiding function. The performance of the current sequential job allocation method and the proposed job grouping approach are evaluated and compared statistically using a pooled t-test for 30 randomly generated yard configurations. The experimental results show that the job grouping approach can effectively improve the schedule makespan and reduce the total straddle carrier waiting time. [Copyright &y& Elsevier]

Abstract: This paper presents an integrated modeling method for multi-criteria land-use suitability assessment (LSA) using classification rule discovery (CRD) by ant colony optimisation (ACO) in ArcGIS. This new attempt applies artificial intelligent algorithms to intelligentise LSA by discovering suitability classification rules. The methodology is implemented as a tool called ACO–LSA. The tool can generate rules which are straightforward and comprehensible for users with high classification accuracy and simple rule list in solving CRD problems. A case study in the Macintyre Brook Catchment of southern Queensland in Australia is proposed to demonstrate the feasibility of this new modeling technique. The results have addressed the major advantages of this novel approach. [Copyright &y& Elsevier]

Abstract: This paper examines the fate of perfluorinated sulfonates (PFSAs) and carboxylic acids (PFCAs) in two water reclamation plants in Australia. Both facilities take treated water directly from WWTPs and treat it further to produce high quality recycled water. The first plant utilizes adsorption and filtration methods alongside ozonation, whilst the second uses membrane processes and advanced oxidation to produce purified recycled water. At both facilities perfluorooctane sulfonate (PFOS), perfluorohexane sulfonate (PFHxS), perfluorohexanoic acid (PFHxA) and perfluorooctanoic acid (PFOA) were the most frequently detected PFCs. Concentrations of PFOS and PFOA in influent (WWTP effluent) ranged up to 3.7 and 16ngL−1 respectively, and were reduced to 0.7 and 12ngL−1 in the finished water of the ozonation plant. Throughout this facility, concentrations of most of the detected perfluoroalkyl compounds (PFCs) remained relatively unchanged with each successive treatment step. PFOS was an exception to this, with some removal following coagulation and dissolved air flotation/sand filtration (DAFF). At the second plant, influent concentrations of PFOS and PFOA ranged up to 39 and 29ngL−1. All PFCs present were removed from the finished water by reverse osmosis (RO) to concentrations below detection and reporting limits (0.4–1.5ngL−1). At both plants the observed concentrations were in the low parts per trillion range, well below provisional health based drinking water guidelines suggested for PFOS and PFOA. [ABSTRACT FROM AUTHOR]

Current ambient air quality standards are mass-based and restricted to PM2.5 and PM10 fractions. The major contribution to both PM2.5 and PM10 fractions is from particles belonging to the coarse mode and generated by mechanical processes. These standards are thus unable to effectively control particle concentrations from combustion sources, such as motor vehicles and power plants, which tend to emit very small particles that are almost entirely respirable and in the submicron range, and dominate the nucleation and accumulation modes, which contribute much less to particle mass concentration. The aim of this work was to examine whether PM1 and PM10 would be a more effective combination of mass standards than PM2.5 (dominant in the nucleation and accumulation modes) and PM10 (dominant in the coarse mode) in controlling combustion-related ambient particles, as well as those originating from mechanical processes. First, a large body of data on particle size distributions in a range of environments in South East Queensland, Australia, was analysed, with an aim of identifying the relation between modality in the distributions and sources of particles belonging to different modes. The analyses included a matrix of the following elements: particle volume and number distributions, type of environment and locations of the modes in the range of PM1, PM2.5 and PM10 fractions. Second, with the same aim, 600 published modal location values relating to number, surface area, volume and mass size distributions for a range of environments worldwide were analysed. The analysis identified a clear and distinct separation between the location of the modes for a substantial number of environments worldwide and particle metrics, which suggests that modality in particle size distributions may be a parameter that has potential to be used in the development of PM1 air quality guidelines and standards. Based on these analyses, implications for choosing different mass standards for airborne particulate matter are discussed in the paper. [Copyright &y& Elsevier]

Abstract: This paper details the distribution of Cu, Pb and Zn in aquatic systems draining Mount Isa Ag-Cu-Pb-Zn Mine in arid northern Queensland, Australia. Sediment-metal concentrations in the <2mm grain-size fraction adjacent to and downstream of the mine significantly exceed background concentrations (Cu, 159; Pb, 36; Zn, 86ppm) as well as Australian government sediment quality low trigger guidelines (Cu, 65; Pb, 50; Zn, 200ppm). Overbank sediments are more contaminated than channel sediments with mean values of Cu, 480; Pb, 540; Zn, 750ppm. Mean concentrations in cut riverbank samples from the <2mm fraction were Cu, 195; Pb, 724; Zn, 807ppm. Corresponding <180μm samples returned concentrations of Cu, 321; Pb, 995; Zn, 1110ppm. Delivery of contaminants during wet seasons from Mount Isa Mine and historically contaminated riverbanks remains an ongoing issue. The ease of dust entrainment in arid zones means that sediments enriched in toxic concentrations of metals may be widely dispersed and ultimately ingested and absorbed by biota. [Copyright &y& Elsevier]

Abstract: Jakeman et al. (Jakeman, A.J., Letcher, R.A., Norton, J.P., 2006. Ten iterative steps in development and evaluation of environmental models. Environmental Modelling & Software 21, 602–614) outline ten steps in the pursuit of good practice in model development and application to increase the credibility and impact of results from environmental models. This paper shows how the ten steps of model development are relevant to numerical groundwater modelling, using a model of a data-rich coastal groundwater system near Bowen in Queensland, Australia as an example. The model is Geographic Information System-based and estimates the dynamical water balance using Darcy''s Law. The method, which is generally applicable to data-rich aquifers, proved cost and time effective and provided important insights to the groundwater dynamics of the area. [Copyright &y& Elsevier]

Abstract: In this paper, a two-dimensional control volume finite-element computational model is developed for simulating saltwater intrusion in a heterogeneous coastal alluvial aquifer system at Gooburrum located near Bundaberg in Queensland, Australia. The model consists of a coupled system of two non-linear partial differential equations. The first equation describes the flow of a variable-density fluid, and the second equation describes the transport of dissolved salt via a form of the Fokker–Planck equation. The outcomes of the work demonstrate that transport simulation techniques provide excellent tools for hydraulic investigations even when complex transition zones are involved. [Copyright &y& Elsevier]

Plutonium (Pu) interactions in the environment are highly complex. Site-specific variables play an integral role in determining the chemical and physical form of Pu, and its migration, bioavailability, and immobility. This paper aims to identify the key variables that can be used to highlight regions of radioecological sensitivity and guide remediation strategies in Australia. Plutonium is present in the Australian environment as a result of global fallout and the British nuclear testing program of 1952–1958 in central and west Australia (Maralinga and Montebello islands). We report the first systematic measurements of 239+240Pu and 238Pu activity concentrations in distal (≥1000 km from test sites) catchment outlet sediments from Queensland, Australia. The average 239+240Pu activity concentration was 0.29 mBq.g −1 (n = 73 samples) with a maximum of 4.88 mBq.g −1.238Pu/239+240Pu isotope ratios identified a large range (0.02–0.29 (RSD: 74%)) which is congruent with the heterogeneous nuclear material used for the British nuclear testing programme at Maralinga and Montebello Islands. The use of a modified PCA relying on non-linear distance correlation (dCorr) provided broader insight into the impact of environmental variables on the transport and migration of Pu in this soil system. Primary key environmental indicators of Pu presence were determined to be actinide/lanthanide/heavier transition metals, elevation, electrical conductivity (EC), CaO, SiO 2 , SO 3 , landform, geomorphology, land use, and climate explaining 81.7% of the variance of the system. Overall this highlighted that trace level Pu accumulations are associated with the coarse, refractive components of Australian soils, and are more likely regulated by the climate of the region and overall soil type. • 73 outlet catchment sediments analysed for 239+240Pu trace-level activity concentration. • Combined with a large national geochemical dataset to investigate the transport of Pu as a contaminant in the Australian soil surface. • Statistical analysis reveals linear and non-linear interrelationships between Pu activity and environmental variables. • Highlighted an association of Pu with the coarse, refractive components of the soils (heavy minerals). • Trace level Pu accumulations are more likely regulated by the climate of the region and overall soil type. [ABSTRACT FROM AUTHOR]

Domestic food waste (DFW) is an important policy issue because it represents inefficiencies in supply chains and consumption behaviour, creates pressures on urban landfills, and causes downstream environmental impacts such as greenhouse gas (GHG) emissions and odour from landfills. However, the evaluation of proposals to minimise or treat food waste, such as through separate disposal streams, is difficult because those impacts are not registered through normal market price signals. This paper presents non-market valuations for a hypothetical kerbside DFW collection service from a survey of households in two local government areas in the Central Queensland region, Australia. Choice modelling (CM) and contingent valuation methods (CVM) were employed to elicit potential participation and estimate willingness to pay (WTP) of the community for a domestic food waste collection service. Latent class analysis results for the sub-groups supporting an improvement option revealed that cost, odour and fortnightly collection frequency limit both WTP for and participation in the potential kerbside collection program. While there was support because it would extend the lifespans of the local landfills, potential reduction of methane emissions was not a strong driver. No difference in support or willingness to pay could be identified between the two local government populations. While the CVM results showed a WTP of $30.42 and a 58% participation rate for the proposed option, the latent class models showed that potential participation and willingness to pay varied across negative, indifference and positive sub-samples. This study provides a useful example of how a new policy option to improve waste management at a local area level might be evaluated, where the willingness to pay estimates can be factored into a benefit cost analysis and the estimates of likely support or opposition can be inputted into a political economy framework. • The study assessed household preferences to reduce domestic food waste. • Stated preference experiments were used to estimate WTP for reduction options. • Behavioural and attitudinal factors were important in explaining choices. • Two-stage contingent valuation models estimated both participation and WTP. • Latent class analysis models were used to classify responses to key attributes. [ABSTRACT FROM AUTHOR]

• A mathematical programming approach for the multi-action problem is proposed (MAMP). • A new MIP model that includes fragmentation penalty in actions (MAMP-E) is presented. • The models are applied to the Mitchell River basin in Queensland, Australia. • MIP models get shorter execution times and higher quality solutions. • The MAMP-E model obtains higher ecological benefits than the MAMP model. Planning for management actions that address threats to biodiversity is important for securing its long term persistence. However, systematic conservation planning (SCP) has traditionally overlooked this aspect and just focused on identifying priority areas without any recommendation on actions needed. This paper develops a mixed integer mathematical programming (MIP) approach for the multi-action management planning problem (MAMP), where the goal is to find an optimal combination of management actions that abate threats, in an efficient way while accounting for connectivity. An extended version of the MAMP model (MAMP-E) is also proposed that adds an expression for minimizing fragmentation between different actions. To evaluate the efficiency of the two models, they were applied to a case study corresponding to a large area of the Mitchell River in Northern Australia, where 45 species of freshwater fish are exposed to the presence of four threats. The evaluation compares our exact MIP approach with the conservation planning software Marxan and the heuristic approach developed in Cattarino et al. (2015). The results obtained show that our MIP models have three advantages over their heuristic counterparts: shorter execution times, higher solutions quality, and a solution quality guarantee. Hence, the proposed MIP methodology provides a more effective framework for addressing the multi-action conservation problem. [ABSTRACT FROM AUTHOR]

Accurate electrical load forecasting always plays a vital role in power system administration and energy dispatch, which are the foundation of the smooth operation of the national economy and people's daily life. Thinking from this vision, many scholars have made great efforts to seek suitable optimization algorithms to improve the performance of existing forecasting algorithm. However, most of the studies ignore the inherent disadvantages of single optimization algorithm, which leads to sub-optimal forecasting performance. Therefore, a novel electric load forecasting system was successfully proposed in this paper by the combination of data preprocessing, hybrid optimization algorithms, and several single classical forecasting methods, which successfully overcomes the defects of single traditional forecasting models and achieves higher forecasting accuracy than that of single model optimization. Besides, the 30 min interval data of Queensland, Australia from March to April is used as illustrative examples to evaluate the performance of the developed model. The results of tests demonstrate that the proposed hybrid model can better approximate the actual value, and it can also be employed as a useful tool for smart grids dispatching planning. • Develop a novel power load forecasting system for grid safety dispatch. • Extract important information effectively from the original load time series. • Creatively propose two hybrid optimization methods to optimize forecasting models. • Set up a comprehensive evaluation system to verify the effectiveness of the models. [ABSTRACT FROM AUTHOR]