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Stormwater contamination can threaten the health of aquatic ecosystems and human exposed to runoff via nutrient and pathogen influxes. In this study, the concentrations of 11 bacterial pathogens and 47 antibiotic resistance genes (ARGs) were determined by using high-throughput microfluidic qPCR (MFQPCR) in several storm drain outfalls (SDOs) during dry and wet weather in Tampa Bay, Florida, USA. Data generated in this study were also compared with the levels of fecal indicator bacteria (FIB) and sewage-associated molecular markers (i.e., Bacteroides HF183 and crAssphage markers) in same SDOs collected in a recent study (Ahmed et al., 2018). Concentration of FIB, sewage-associated markers, bacterial pathogens and many ARGs in water samples were relatively high and SDOs may be potentially hotspots for microbial contamination in Tampa Bay. Mean concentrations of culturable E. coli and Enterococcus spp. were tenfold higher in wet compared to dry weather. The majority of microbiological contaminants followed this trend. E. coli eaeA, encoding the virulence factor intimin, was correlated with levels of 20 ARGs, and was more frequently detected in wet weather than dry weather samples. The blaKPC gene associated with carbapenem resistant Enterobacteriaceae and the beta-lactam resistant gene (blaNPS) were only detected in wet weather samples. Frequency of integron genes Intl2 and Intl3 detection increased by 42% in wet weather samples. Culturable E. coli and Enterococcus spp. significantly correlated with 19 of 47 (40%) ARG tested. Sewage-associated markers crAssphage and HF183 significantly correlated (p

The Coal Seam Gas (CSG) extraction industry is developing rapidly within the Surat Basin in southern Queensland, Australia, with licenses already approved for tenements covering more than 24,000 km2. Much of this land is used for a broad range of agricultural purposes and the need for coexistence between the farm and gas industries has been the source of much conflict. Whilst much research has been undertaken into the environmental and economic impacts of CSG, little research has looked into the issues of coexistence between farmers and the CSG industry in the shared space that is a farm business, a home and a resource extraction network. We conducted three workshops with farmers from across a broad region undergoing CSG development to explore farmers’ perceptions of some of the issues arising from large scale land use change. Workshops explored the importance of place identity and landscape aesthetics for farmers, farmers’ acceptance and coping with change, and possible benefits from off-farm income. We found that farmers believed that place identity was not well understood by CSG staff from non-rural backgrounds and that farmers struggled to explain some concerns because of the different way they interpreted their landscape. Furthermore, high staff turnover, and the extensive use of contractors also impacted on communications. These factors were the cause of much frustration and farmers felt that this has led to severe impacts on mental health and wellbeing. Farmers felt that a change in culture within the CSG companies will be required if engagement with farmers is to improve and that efforts to employ local people in these communications was helping this. The workshops also identified a range of issues perceived by farmers arising from increased traffic volumes, impacts to mental health and wellbeing, place identity and loss of water resources for farmers. Finally, it was suggested that scientists and agricultural industry groups will need to work closely with farmers to develop understanding of these emerging issues and to develop solutions that are timely and relevant.

There is an ever growing demand for energy worldwide and the demand for gas alone is predicted to double between 2010 and 2035. This demand together with concurrent advances in drilling technologies caused the production of unconventional natural gas such as shale gas and coal seam gas (CSG), which is in the focus of this paper, to grow rapidly in the last decades. With the gas bearing coal seams extending across vast areas within their respective basins and with CSG production having to follow these seams through a network of production wells, pipelines and access roads, CSG activity affects large areas and therefore interferes with existing land uses, predominantly agriculture. For the eastern Australian Surat Basin and the southern Bowen Basin alone there are projected well numbers in excess of 15,000 to 20,000 between the years 2020 and 2030. The interference of CSG with agriculture on a large scale has raised concerns about the impact of CSG on farmland, food security, water resources and the socio-economic environment within the affected regions and beyond. This paper presents a newly developed spatial model which provides order of magnitude figures of the impact of CSG activity on gross economic returns of current agricultural land uses in a given region over the time of CSG production. The estimated gross figures do not account for any compensation payments received by farmers. The model is capable of accounting for a variation in a variety of parameters including impact frequency of distinct infrastructure elements, differences in soil types and associated varying responses of soil productivity, varying length of the CSG production phase and more. The model is flexible in that it can be transferred and applied in other regions as well. Based upon a literature review and given that CSG is an industry that started operating at larger scales relatively recently, we claim that the presented model is the first of its kind to provide these important agro-economic indicators.

The production of coal seam gas (CSG) in Australia is set to increase, driven by increasing global demand for energy and in response to the transition to a lower carbon economy through greater use of gas for electricity generation. Despite the many economic benefits delivered by the CSG industry, concerns have been raised over the potential environmental impacts associated with CSG production, particularly the long-term effects on the soil resource. Therefore, this work was conducted to: (1) assess the extent of damage to agricultural soil caused by the various elements of CSG development and (2) estimate the likely impact of soil compaction, caused during establishment of CSG infrastructure, on crop productivity. The study was undertaken using a paired-sites approach by comparing measurements conducted on selected soil properties in areas around and including well-head sites with measurements in neighboring agricultural fields. These spatial areas are referred to as “lease” and “field” areas, respectively. Measurements were used to guide parameterization and application of the Agricultural Production Systems Simulator (APSIM) model to assess the likely effects of changed soil conditions on crop productivity. To achieve this, the APSIM model was used to simulate wheat ( L.) yields for 115 years on Grey Vertosols in the Darling Downs region of Queensland. Simulations were conducted with soil properties representing: (1) field area conditions not affected by CSG activities, (2) lease area conditions in which soil had been impacted by CSG activities during the development phase, and (3) lease area conditions where soils had been rehabilitated. Results showed that soil compaction within lease areas in the top 300 mm of the profile was approximately 15% higher compared with field areas (p -3 for the 0 to 350 mm and 350 to 700 mm depth intervals, respectively, are suggested as references for CSG-rehabilitated soil. These critical values may be used as guidance until further studies are undertaken. The assessment of soil chemical properties indicated that these were affected to a lesser extent by the establishment of CSG infrastructure. However, a general requirement is careful manipulation of sodium-rich subsoil and avoidance of soil blending during reinstatement operations. The dataset acquired and the simulation approach employed in this study can be used to further develop soil management guidelines relevant to the Australian CSG industry. Cost-benefit analyses of soil management practices for reinstatement, development of soil quality standards, and industry best management practices are required.

This study aimed to determine the prevalence and abundance of sewage and animal fecal contamination of sediment at seven estuarine locations in Sydney, NSW, Australia. Sediment samples were tested for the occurrence of microbial targets including molecular marker genes of enterococci (ENT), Bacteroides HF183 (HF183), Methanobrevibacter smithii (nifH), human adenovirus (HAdV) and emerging sewage-associated marker genes crAssphage (CPQ_056) and Lachnospiraceae (Lachno3) and animal feces-associated marker genes, including avian feces-associated Helicobacter spp. (GFD), canine-feces associated Bacteroides (DogBact), cattle-feces associated (cowM2) and horse feces-associated Bacteroides (HoF597). Results from this study showed that urban estuarine sediment can act as a reservoir of fecal indicator bacteria (FIB) and several microbial source tracking (MST) marker genes, including previously unreported Lachno3. The sewage-associated marker gene CPQ_056 was most prevalent, in 63.8% of sediment samples, while the avian associated marker gene GFD had the highest mean abundance. The GFD marker gene was highly abundant and widely detected in sediment samples from all seven locations compared to the other animal feces-associated marker genes. In all, 31 (44.9%) sediment samples were positive for at least two sewage-associated marker genes. However, the non-quantifiable detection of the HAdV marker gene did not always align with the detection of two or more sewage-associated marker genes. In addition, the most frequent wet weather overflow exposure occurred at locations that did not have a consistent pattern of detection of the sewage-associated marker genes, suggesting sediments may not be a suitable measure of recent sewage contamination. To assist water quality and public health managers better understand past microbial contamination of estuarine sediment, further studies seem justified to explore the role of decay of MST marker genes in sediment. Further work is also needed on the role of resuspension of MST marker genes from sediment during storm events to the water column as a source of contamination for both the GFD and sewage-associated marker genes.

We investigated the abundance of marker genes of two fecal indicator bacteria (FIB) and eight potential pathogens in fecal samples of humans (n = 14) and 10 domestic and native wild animals (n = 134). For each target animal, between 10 and 14 individual fecal samples were collected (n = 148 individual fecal samples in total). The abundance of FIB and potential pathogens within each sample was determined using quantitative PCR (qPCR) assays. All animals tested were positive for Escherichia coli (EC) and the concentrations ranged from 6.13 (flying fox) to 8.87 (chicken) log10 GC/g of feces. These values for Enterococcus spp. (ENT) were 5.25 log10 GC/g for flying fox and 8.12 log10 GC/g of feces for chicken. Moderate correlations were observed between EC with P. aeruginosa, EC O157 and Cryptosporidium parvum, whereas weak correlations were observed between EC and Salmonella spp. and Giardia lamblia, Mycobacterium avium complex (MAC) and Campylobacter spp. The prevalence of MAC and P. aeruginosa were low in dog (14.3% each) and moderate (57.2%, MAC; 42.9% P. aeruginosa) in Eastern grey kangaroo fecal samples. Cryptosporidium parvum was detected in one cattle and one human fecal sample, while G. lamblia was detected in one dog, one flying fox, and one pig fecal samples. Among the eight potential pathogens tested, five pathogens were detected in chicken and dog fecal samples. The remaining animal species contained up to three potential pathogens in their feces. The data generated in this study may aid in the calculation of pathogen loads in the environment, and hence to assess the risks from human and animal fecal contamination of source waters.

A marginal abatement cost curve (MACC) represents a set of options available to an economy to reduce pollution. MACC are valuable tools in that they help not only understand the costs associated with a variety of abatement options but also provide information about the amount of pollutants which can potentially be reduced. MACC have therefore become increasingly popular as instruments to inform policy discussions and to prioritize investment opportunities to reduce pollution. Surprisingly though, there are not many tools available to produce MACCs with the flexibility and functionality as was required in recent research the authors were involved in. This technical paper presents the new tool `ABATE' which arose from assessing options to improve water quality in the Great Barrier Reef.

Technical efficiency is the effectiveness with which a given set of inputs is used to produce one or more outputs. As such it is an important indicator that helps to differentiate between production entities that represent current best practice (or operate close to it) from those that do not. State of the art methods to estimate technical efficiency of a production entity are econometric frontier techniques such as data envelopment analysis (DEA) or stochastic frontier analysis (SFA) where observational data on inputs and outputs are analysed. While efficiency scores are then known for the observational units, efficiency scores cannot simply be interpolated to estimate efficiency scores for production units that have not been part of a survey. This paper focuses the development of a proxy measure of technical efficiency for specialised grain farms where no observational data are available for. The proxy measure is based upon crop simulation results and technical efficiencies that are estimated from available survey data. A moderate to strong correlation (0.5-0.7) is found between metrics inferred from crop simulation and technical efficiency scores. Fitted linear models with technical efficiency as the predictor variable returned an R^2 lower equal to 0.42 (linear models have zero intercept). To apply the linear model beyond observed locations, the integration of a remote sensing component is discussed and put into a conceptual framework. The suggested method would be suitable to be applied on a regional level and will help improving the regionalisation of technical efficiency. The findings are based on the analysis of a 4year plot level survey dataset from Australian grain specialists. Findings are to be considered preliminary at this stage and more research that involves a remote sensing component is needed to confirm the applicability of the suggested framework.

The provision of new housing for a growing population is a challenge that many cities and regions all over the world are confronted with. The design and management of new housing development is a complex task given it needs to be socially, environmentally and economically sustainable. This paper describes a new method that helps spatially direct new housing development to minimise impact on water quality. Currently, the model accommodates for the pollutants total nitrogen (TN), total phosphorus (TP) and suspended sediments (TSS), but can be extended. The method is based on mathematical programming linked with multi-criteria analysis, and kinematic wave equations. It utilises GIS to underpin multiple spatial constraints on where housing development can take place, and to provide an effective means of visually analysing the impacts of land development scenarios produced by the model. We demonstrate the method using a case study for Hornsby Shire Council, New South Wales, Australia. With water quality and waterway health being a major issue in any urban development the proposed methodology has wide applications beyond the current study area.

To feed a growing world population in the coming decades, agriculture must strive to reduce the gap between the yields that are currently achieved by farmers (Ya) and those potentially attainable in rainfed farming systems (Yw). The first step towards reducing yield gaps (Yg) is to obtain realistic estimates of their magnitude and their spatial and temporal variability. In this paper we describe a new yield gap assessment framework. The framework uses statistical yield and cropping area data, remotely sensed data, cropping system simulation and GIS mapping to calculate wheat yield gaps at scales from 1.1 km cells to regional. The framework includes ad hoc on-ground testing of the calculated yield gaps. This framework was applied to wheat in the Wimmera region of Victoria, Australia. Estimated Yg over the whole Wimmera region varied annually from 0.63 to 4.12 Mg ha −1 with an average of 2.00 Mg ha −1 . Expressed as a relative yield ( Y % ) the range was 26.3–77.9% with an average of 52.7%. Similarly large spatial variability was described in a Wimmera yield gap map. Such maps can be used to show where efforts to bridge the yield gap are likely to have the biggest impacts. Bridging the exploitable yield gap in the Wimmera region by increasing average Y % to 80% would increase average annual wheat production from 1.09 M tonnes to 1.65 M tonnes. Model estimates of Yw and Yg were compared with data from crop yield contests, experimental variety trials, and on-farm water use and yields. These alternative approaches agreed well with the modelling results, indicating that the proposed framework provided a robust and widely applicable method of determining yield gaps. Its successful implementation requires that: (1) Ya as well as the area and geospatial distribution of wheat cropping are well defined; (2) there is a crop model with proven performance in the local agro-ecological zone; (3) daily weather and soil data (such as PAWC) required by crop models are available throughout the area; and (4) local agronomic best practice is well defined.

Policy makers in the agricultural sector are confronted with challenges which might drive land use change and ultimately agricultural profitability to a substantial degree. The challenges include questions around climate variability, demographic changes, use of land for bio-fuel production and ensuring an increase in food production. As profitability triggers many agri-business decisions, knowledge about the existing socio-economic landscape and the economic profile of a region as well as potential impacts on profits provides useful contextual information when agricultural policies are designed. Given the upcoming challenges and their associated uncertainties, it is important to ensure that a map of agricultural profit can be reproduced in a scenario and simulation setting which will allow exploring uncertainties around the impacts on agricultural profits as well. There is however currently no flexible system in operation which allows for a consistent update of a map of agricultural profits in Australia or elsewhere. This paper describes a process that has been developed to produce a map of agricultural profit for Australia for the year 2005/2006. The process involves a complex data architecture that accounts for heterogeneous information that is collected by a variety of institutions across different scales. All information can be comfortably queried and query results can be forwarded for immediate processing and subsequent visualisation in a geographic information system (GIS). To facilitate the production of profit maps in the future, the system provides flexibility regarding an update of new economic information but it can also be linked to maps that show an updated distribution of land use. A map of agricultural profit on a large scale and regular updates thereof will help understand profit trends in time and across space. It will help identifying regions that have a lower economic profile and will inform decisions regarding the design of regulatory policies. As these maps are developed using national scale data, we do not recommend using the results at the farm level but we suggest using separate catchment scale profit assessments to calibrate the national scale profit map. The proposed system is well suited to be used in various land use management and economic scenarios and will represent a step forward regarding a scenario impact assessment on agricultural profits. It will also help understand the economic benefit of land use on a large scale.

Growing urbanisation causes pressures on many environmental assets such as groundwater systems, waterways, atmosphere, ecosystems and others. To manage and mitigate the negative effects of these pressures, environmental programs are typically launched, which comprise of eligible projects in different affected locations. The implementation of individual projects has a cost. However, due to budget constraints, most frequently not all suggested projects can be implemented which makes necessary to choose a subset. In this paper, we use a cost utility approach and a subsequent combinatorial optimisation based on metaheuristics to determine a project portfolio which returns a maximum aggregated utility while keeping the budget constraint. To ensure that the mutual exclusiveness of projects at one particular site is guaranteed, we further developed the employed metaheuristics. We apply this approach within a waterway health program in a river catchment in Brisbane, Australia, and compare its results to a commonly used selection process that does not involve combinatorial optimisation. We find that the use of combinatorial optimisation leads to a considerable improvement of the selection process and can help to more effectively allocate environmental expenditure.

Water catchments worldwide are experiencing increasing pressure on the quantity and quality of ground and surface water resources. Water managers are increasingly consulting community and stakeholder groups to ensure their decisions reflect the values and preferences of water users. Growing tensions between different water users require the use of techniques that can enable stakeholders to learn about each others’ positions and deliberate about the costs and benefits of alternative water allocation scenarios. This paper describes the use of scenario development, a small group deliberative process (citizens’ jury) and multi-criteria analysis to assist in water planning for the Howard River catchment in the Northern Territory (NT) of Australia. Water planning processes in the NT are in their infancy. As such, this research provides information about stakeholder preferences where none was previously available and demonstrates the use of a new water planning tool. The research found that the process in this case was most useful in providing information to stakeholders, dispelling some unhelpful myths about water use in the catchment, and coalescing opinion about important criteria for assessing future options.

Land-use decisions are usually made on the basis of a variety of criteria. While it is common practice to integrate economic, ecological and social (triple bottom line) criteria, explicit geoscientific factors are relatively rarely considered. If a planned land use involves an interaction with the geosphere, geoscientific aspects should be playing a more important role in the process. With the objective to facilitate a sustainable land-use decision-making a research project was initiated. The area around the city of Zaragoza, in the Ebro Basin of northern Spain, was chosen due to its high degree of industrialisation and urbanization. The area is exposed to several geohazards (e.g., sinkholes and erosion) that may have significant negative effects on current and future land uses. Geographical Information System (GIS) technologies are used to process the complex geoscientific information. Further GIS analysis comprised the creation of an erosion susceptibility map that follows the ITC (International Institute for Geo-Information Science and Earth Observation) system of terrain analysis. The agricultural capability of the soil was determined using the Microleis System. We identify geomorphologic units that show high susceptibility to erosion and high agricultural potential and suggest a method to implement this information in a land-use planning process. Degraded slopes developed upon Tertiary rocks show the highest susceptibility to erosion and low values of agricultural capability, whereas the flat valley bottoms and irrigated flood plains have the highest values of agricultural capability.

This study borrows concepts from healthcare economics and uses cost utility analysis (CUA) to select an optimum portfolio of water quality enhancement projects in Perth, Western Australia. In CUA, costs are handled via standard discounted cash flow analysis, but the benefits, being intangible, are measured with a utility score. Our novel methodology combines CUA with a binary combinatorial optimisation solver, known as a 'knapsack algorithm', to identify the optimum portfolio of projects. We show how water quality projects can be selected to maximise an aggregate utility score while not exceeding a budget constraint. Our CUA model applies compromise programming (CP) to measure utility over multiple attributes in different units. CUA is shown to provide a transparent and analytically robust method to maximise benefits from water quality remediation investments under a constrained budget.

This paper describes an environmental conservation planning model used in Queensland, Australia to purchase agreements on 66 sites covering 81,046 ha at a cost of A$1.9 million. The model was used as part of a competitive tendering program called “NatureAssist”. The model maximises conservation benefit subject to a cost constraint using binary combinatorial optimisation. Multiple criteria analysis (MCA) is used to capture investor preferences and measure benefit over multiple outcomes. Conservation costs are drawn from real market data; i.e. the prices being offered by sellers (landholders) to supply conservation services to a buyer (the government). This study shows the potential strengths, and weaknesses, of taking an MCA approach to select conservation sites in a real market context.

In the surroundings of Zaragoza, karstification processes are especially intense in covered karst areas where fluvial terraces lie directly on Tertiary evaporites. Since the beginning of Quaternary, these processes have lead to the development of collapse and subsidence dolines with a wide range of sizes, which have significant economic impacts. To reduce economic impact and increase safety, a regional analysis of this phenomenon is needed for spatial management. Therefore, a probability map of dolines was developed using logistic regression and geographic information system (GIS) techniques. This paper covers the selection of input data, manipulation of data using the GIS technology, and the use of logistic regression to generate a doline probability map. The primary variable in the doline development in this area is geomorphology, represented by the location of endorheic areas and different terrace levels. Secondary variables are the presence of irrigation and the water table gradient.

The Quaternary deposits of the Ebro Basin in the surroundings of Zaragoza (Spain) form an unconfined alluvial aquifer with a high degree of permeability and low thickness of unsaturated materials. This fact causes a high degree of vulnerability to contamination, which implies consequences for land-use decisions and the risk management of existing industrial facilities. In addition, in the last decades the intense irrigation and the use of pesticides is threatening the quality of the groundwater and as a consequence the amount of usable groundwater at a low cost. Thus, the development of groundwater vulnerability maps has great importance in a regional planning process. Consequently, groundwater vulnerability maps have been developed following a method proposed by the German State Geological Surveys. In this paper the methodology developed within ArcGIS and Gocad, a two- and three-dimensional software, respectively, is presented. Although the results obtained within GIS (Geographical Information System) are acceptable it is to say that the 3D-model improves considerably the final product.

Multi-criteria analysis techniques are well known decision support methods and are widely applied in various disciplines. However, defining the input criteria values for the basic decision matrix which contains all criteria values for every alternative considered is normally not an easy task. Especially qualitative criteria variables which are frequently represented as linguistic terms may be hard to quantify. Moreover, some criteria cannot be represented by just one crisp value, but they may offer a range of possible values. Stochastic multi-criteria approaches which call for distribution models instead of single numerical values can be used in these cases. Outranking multi-criteria methods proved that simulation based stochastic techniques are well suited to give better insight into the preference structure of a variety of decision alternatives. However, besides the knowledge of the preference structure, it is also important to find out about the similarity of decision alternatives which allows a modeller to categorize a decision alternative as a really unique option or as just one option out of a greater subset of very similar alternatives. To be able to perform this categorization, principal components analysis (PCA) was used. The results of the PCA are compared to the results of a stochastic outranking analysis.

The generation of geological models always goes along with the geotechnical engineers' wish to keep the models as accurate as possible. Consequently, a lot of research has been carried out in the last years to decrease geological model uncertainty. The focus was mainly put on the statistics of the mechanical parameters of soil and rock masses that go directly into the safety calculations. Besides the statistical features of the mechanical parameters, care must also be given to the statistics associated with the geometrical parameters of the geological layers. One way to statistically model the underground geometry is through the use of geostatistical interpolation techniques generally known as kriging. However, the most commonly used ordinary kriging techniques produce strongly smoothed model results that lead to an inaccurate regionalization of geological layers. Since geotechnical models are usually on a large scale, such inaccuracies cannot be tolerated. This paper shows how smoothing effects around zero value zones can be reduced significantly by the use of a combined ordinary–indicator kriging approach.

European legislation calls for a well-planned sustainable development. As such, it has to in‐ clude a social, economic as well as an environmental dimension. According to Agenda 21 (http://www.un.org/esa/dsd/agenda21/), countries should undertake efforts to build up a comprehensive national inventory of their land resources in order to establish land informa‐ tion systems. The overall objective is to provide information for the improvement or the re‐ structuring of land-use decision processes including the consideration of socio-economic and environmental issues.

Typical water management or natural resource management decision problems require decision makers to select a subset of available decision options that return a maximum benefit whilst satisfying limiting constraints, usually a budget constraint. This optimisation problem becomes increasingly difficult to solve if uncertainty aspects are being considered and/or as more decision options are included. To tackle this selection problem the multi-criteria analysis tool (MCAT) which contains both multi-criteria functionality and solution methods was developed. The benefit scores for the decision options are computed with the well known compromise programming technique. To optimise the selection of options subject to the constraints, two heuristics, namely Local Search and Tabu Search were coded. MCAT was primarily developed to optimise water management decision making in Australia. However it can also be used to solve a range of other decision problems in natural resources management as well.

Summary: Sand and gravel are important raw materials which are needed for many civil engineering projects. Due to economic reasons, sand and gravel pits are frequently located in the periphery of metropolitan areas which are often subject to competing land-use interests. As a contribution to land-use conflict solving, the Analytic Hierarchy Process (AHP) is applied within a Geo-Information-System (GIS) environment. Two AHP preference matrix scenario constellations are evaluated and their results are used to create a land-use conflict map.