Rank regression analysis of correlated water quality data from South East Queensland

With growing population and fast urbanization in Australia, it is a challenging task to maintain our water quality. It is essential to develop an appropriate statistical methodology in analyzing water quality data in order to draw valid conclusions and hence provide useful advices in water management. This paper is to develop robust rank-based procedures for analyzing nonnormally distributed data collected over time at different sites. To take account of temporal correlations of the observations within sites, we consider the optimally combined estimating functions proposed by Wang and Zhu (Biometrika, 93:459-464, 2006) which leads to more efficient parameter estimation. Furthermore, we apply the induced smoothing method to reduce the computational burden. Smoothing leads to easy calculation of the parameter estimates and their variance-covariance matrix. Analysis of water quality data from Total Iron and Total Cyanophytes shows the differences between the traditional generalized linear mixed models and rank regression models. Our analysis also demonstrates the advantages of the rank regression models for analyzing nonnormal data.

Nonparametric rank regression for analyzing water quality concentration data with multiple detection limits

Environmental data usually include measurements, such as water quality data, which fall below detection limits, because of limitations of the instruments or of certain analytical methods used. The fact that some responses are not detected needs to be properly taken into account in statistical analysis of such data. However, it is well-known that it is challenging to analyze a data set with detection limits, and we often have to rely on the traditional parametric methods or simple imputation methods. Distributional assumptions can lead to biased inference and justification of distributions is often not possible when the data are correlated and there is a large proportion of data below detection limits. The extent of bias is usually unknown. To draw valid conclusions and hence provide useful advice for environmental management authorities, it is essential to develop and apply an appropriate statistical methodology. This paper proposes rank-based procedures for analyzing non-normally distributed data collected at different sites over a period of time in the presence of multiple detection limits. To take account of temporal correlations within each site, we propose an optimal linear combination of estimating functions and apply the induced smoothing method to reduce the computational burden. Finally, we apply the proposed method to the water quality data collected at Susquehanna River Basin in United States of America, which dearly demonstrates the advantages of the rank regression models.

Statistical tools for analyzing water quality data

Water quality data are often collected at different sites over time to improve water quality management. Water quality data usually exhibit the following characteristics: non-normal distribution, presence of outliers, missing values, values below detection limits (censored), and serial dependence. It is essential to apply appropriate statistical methodology when analyzing water quality data to draw valid conclusions and hence provide useful advice in water management. In this chapter, we will provide and demonstrate various statistical tools for analyzing such water quality data, and will also introduce how to use a statistical software R to analyze water quality data by various statistical methods. A dataset collected from the Susquehanna River Basin will be used to demonstrate various statistical methods provided in this chapter. The dataset can be downloaded from website http://www.srbc.net/programs/CBP/nutrientprogram.htm.

Chemical and bioanalytical assessment of coal seam gas associated water

A comprehensive study was undertaken involving chemical (inorganic and organic) and bioanalytical (a suite of 14 in vitro bioassays) assessments of coal seam gas (coal bed methane) associated water (CSGW) in Queensland, Australia. CSGW is a by-product of the gas extraction process and is generally considered as water of poor quality. This was done to better understand what is known about the potential biological and environmental effects associated with the organic constituents of CSGW in Australia. In Queensland, large amounts of associated water must be withdrawn from coal seams to allow extraction of the gas. CSGW is disposed of via release to surface water, reinjected to groundwater or reused for irrigation of crops or pasture, supplied for power station cooling and or reinjected specifically to augment drinking water aquifers. Groundwater samples were collected from private wells tapping into the Walloon Coal Measures, the same coal aquifer exploited for coal seam gas production in the Surat Basin, Australia. The inorganic characteristics of these water samples were almost identical to the CSGW entering the nearby gas company operated Talinga-Condabri Water Treatment Facility. The water is brackish with a pH of 8 to 9, high sodium, bicarbonate and chloride concentrations but low calcium, magnesium and negligible sulphate concentrations. Only low levels of polyaromatic hydrocarbons (PAHs) were detected in the water samples, and neither phenols nor volatile organic compounds were found. Results from the bioassays showed no genotoxicity, protein damage, or activation of hormone receptors (with the exception of the estrogen receptor). However, five of the 14 bioassays gave positive responses: an arylhydrocarbon-receptor gene activation assay (AhR-CAFLUX), estrogenic endocrine activity (ERα-CALUX), oxidative stress response (AREc32), interference with cytokine production (THP1-CPA) and non-specific toxicity (Microtox). The observed effects were benchmarked against known water sources and were similar to secondary treated wastewater effluent, stormwater and surface water. As mixture toxicity modelling demonstrated, the detected PAHs explained less than 5% of the observed biological effects.

The use of new generation geosynthetics in water treatment

The importance of clean drinking water in any community is absolutely vital if we as the consumers are to sustain a life of health and wellbeing. Suspended particles in surface waters not only provide the means to transport micro-organisms which can cause serious infections and diseases, they can also affect the performance capacity of a water treatment plant. In such situations pre-treatment ahead of the main plant is recommended. Previous research carried out using non-woven synthetic as a pre-filter materials for protecting slow sand filters from high turbidity showed that filter run times can be extended by several times and filters can be regenerated by simply removing and washing of the fabric ( Mbwette and Graham, 1987 and Mbwette, 1991). Geosynthetic materials have been extensively used for soil retention and dewatering in geotechnical applications and little research exists for the application of turbidity reduction in water treatment. With the development of new materials in geosynthetics today, it was hypothesized that the turbidity removal efficiency can be improved further by selecting appropriate materials. Two different geosynthetic materials (75 micron) tested at a filtration rate of 0.7 m/h yielded 30-45% reduction in turbidity with relatively minor head loss. It was found that the non-woven geotextile Propex 1701 retained the highest performance in both filtration efficiency and head loss across the varying turbidity ranges in comparison to other geotextiles tested. With 5 layers of the Propex 1701 an average percent reduction of approximately 67% was achieved with a head loss average of 4mm over the two and half hour testing period. Using the data collected for the Propex 1701 a mathematical model was developed for predicting the expected percent reduction given the ability to control the cost and as a result the number of layers to be used in a given filtration scenario.

Emerging water treatment technologies for decentralised systems : an overview of selected systems suited for application in towns and settlements in remote and very remote regions of Australia and vulnerable and lagging rural regions in Sri Lanka

The primary purpose of this paper is to overview a selection of advanced water treatment technology systems that are suited for application in towns and settlements in remote and very remote regions of Australia and vulnerable and lagging rural regions in Sri Lanka. This recognises that sanitation and water treatment are inextricably linked and both are needed to reduce risks to environment and population health from contaminated water sources. For both Australia and Sri Lanka only a small fraction of the settlements in rural and remote regions are connected to water treatment facilities and town water supplies. In Australia’s remote/very remote regions raw water is drawn from underground sources and rainwater capture. Most settlements in rural Sri Lanka rely on rivers, reservoirs, wells, springs or carted water. Furthermore, Sri Lanka has more than 25,000 hand pumped tube wells which saved the communities during recent droughts. Decentralised water supply systems offer the opportunity to provide safe drinking water to these remote/very remote and rural regions where centralised systems are not feasible due to socio-cultural, economic, political, technological reasons. These systems reduce health risks from contaminated water supplies. In remote areas centralized systems fail due to low population density and less affordability. Globally, a new generation of advanced water treatment technologies are positioned to make a major impact on the provision of safe potable water in remote/very remote regions in Australia and rural regions in Sri Lanka. Some of these systems were developed for higher income countries. However, with careful selection and further research they can be tailored to match local socio-economic conditions and technical capacity. As such, they can equally be used to provide decentralised water supply in communities in developed and developing countries such as Australia and Sri Lanka.

Brief for GSDR - Emerging Contaminants in urban stormwater: Challenges and perspectives for sustainable water use

Emerging contaminants (ECs) are chemical compounds commonly present in water. It is only recently that this family of compounds is being recognized as significant water pollutants (. ECs include a wide variety of chemicals such as pharmaceutical and personal care products (PPCPs), pesticides, hydrocarbons and hormones, among others, that once released into the environment exert adverse impacts on the human and wildlife endocrine system. Natural attenuation and conventional treatment processes are not capable of removing these micro-pollutants detected in wastewater influent and effluent and surface and drinking water. The main challenges related with presence of ECs in stormwater in the context of reuse are: a) Development of suitable laboratory test methodologies and protocols for ECs identification and quantification b) Identification of the sources of ECs in the urban environment; c) Understanding their impacts on human and/or ecosystem health; and d). Development of cost-effective removal technologies which are appropriate for large as well as small-scale application.