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A pilot study has produced 31 groundwater samples from a coal seam gas (CSG) exploration well located in Maramarua, New Zealand. This paper describes sources of CSG water chemistry variations, and makes sampling and analytical recommendations to minimize these variations. The hydrochemical character of these samples is studied using factor analysis, geochemical modelling, and a sparging experiment. Factor analysis unveils carbon dioxide (CO(2)) degassing as the principal cause of sample variation (about 33%). Geochemical modelling corroborates these results and identifies minor precipitation of carbonate minerals with degassing. The sparging experiment confirms the effect of CO(2) degassing by showing a steady rise in pH while maintaining constant alkalinity. Factor analysis correlates variations in the major ion composition (about 17%) to changes in the pumping regime and to aquifer chemistry variations due to cation exchange reactions with argillaceous minerals. An effective CSG water sampling program can be put into practice by measuring pH at the wellhead and alkalinity at the laboratory; these data can later be used to calculate the carbonate speciation at the time the sample was collected. In addition, TDS variations can be reduced considerably if a correct drying temperature of 180 °C is consistently implemented., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

The need for improved cost planning methods for solid waste management (SWM) is particularly strong in emerging economies where problems are severe, expectations for improvements are high, but finances are constrained. Estimating cost functions is suggested as an improved cost planning method. The research uses 1999 data from 298 Indian municipalities, covering over 140 million people. Stepwise multiple regression involving eight predictor variables was conducted on costs to detect any statistically significant correlations. The average costs on either a per tonne or per capita basis are most influenced by the total number of staff employed per capita or per tonne. The results are believed to be due to labour costs being such a high fraction of total costs in developing countries. Due to high variability in labour intensity between municipalities the data showed no clear correlation between per tonne or per capita costs and population, indicating no economy-of-scale. The data used here are subject to significant conjecture over their quality and age; however, the unique nature of the study should help future researchers investigating costs in emerging economies.

The importance of cost planning for solid waste management (SWM) in industrialising regions (IR) is not well recognised. The approaches used to estimate costs of SWM can broadly be classified into three categories - the unit cost method, benchmarking techniques and developing cost models using sub-approaches such as cost and production function analysis. These methods have been developed into computer programmes with varying functionality and utility. IR mostly use the unit cost and benchmarking approach to estimate their SWM costs. The models for cost estimation, on the other hand, are used at times in industrialised countries, but not in IR. Taken together, these approaches could be viewed as precedents that can be modified appropriately to suit waste management systems in IR. The main challenges (or problems) one might face while attempting to do so are a lack of cost data, and a lack of quality for what data do exist. There are practical benefits to planners in IR where solid waste problems are critical and budgets are limited., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Depending on their nature and severity, disasters can create large volumes of debris and waste. The waste can overwhelm existing solid waste management facilities and impact on other emergency response and recovery activities. If poorly managed, the waste can have significant environmental and public health impacts and can affect the overall recovery process. This paper presents a system overview of disaster waste management based on existing literature. The main literature available to date comprises disaster waste management plans or guidelines and isolated case studies. There is ample discussion on technical management options such as temporary storage sites, recycling, disposal, etc.; however, there is little or no guidance on how these various management options are selected post-disaster. The literature does not specifically address the impact or appropriateness of existing legislation, organisational structures and funding mechanisms on disaster waste management programmes, nor does it satisfactorily cover the social impact of disaster waste management programmes. It is envisaged that the discussion presented in this paper, and the literature gaps identified, will form a basis for future comprehensive and cohesive research on disaster waste management. In turn, research will lead to better preparedness and response to disaster waste management problems., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Bioreactors represent an emerging technology for removing metals and sulfate commonly found in acid mine drainage (AMD). Six continuously fed anaerobic bioreactors employing organic and alkaline waste materials were operated to investigate relationships between metal and sulfate removal from AMD. Median AMD influent chemistry was 65.8mg/L Fe (49.7-113mg/L), 46.5mg/L Al (33.5-72.4mg/L) and 608mg/L sulfate (493-1007mg/L). Bioreactors containing mussel shells as an alkaline substrate amendment were more effective at removing metals and sulfate than those containing limestone. Experimental results indicated bioreactor design and operation should be dependent on treatment goals. These include 0.3mol sulfate loading/m(3)/day for sulfate removal (mean of 94.1% (87.6-98.0%), 0.4mol metals/m(3)/day for metal (mean of 99.0% (98.5-99.9%)) and partial sulfate (mean of 46.0% (39.6-57.8%)) removal and 0.8mol metals/m(3)/day for metal (mean of 98.4% (98.2-98.6%) and minimal sulfate (mean of 16.6% (11.9-19.2%)) removal. Aluminum removal efficiency was on average 1.72% (0.04-3.42%) greater than Fe during stable operating conditions.

Variations in solid waste composition data are necessary as inputs to solid waste planning, yet uncertainty exists regarding which probability distributions might be generally valuable to describe the variability. Twenty-two detailed analyses of solid waste from British Columbia, Canada, were fitted to distributions using the BestFit software. Alternative distributions were ranked based on three goodness-of-fit parameters and twelve waste fractions. The log-logistic distribution was found to be the most able to fit over the wide range of composition types considered. The results were demonstrated to be insensitive to the number of waste components or to the choice of a two- or three-parameter distribution. Although other distributions were able to better match the waste composition for individual waste types, the log-logistic distribution was demonstrated to fit, overall, a wide variety of waste composition types.