Studies of Brisbane municipal water quality using Inductively Coupled Plasma - Mass Spectrometry and Chemometrics

This research established innovative methods and a predictive model to evaluate water quality using the trace element and heavy metal concentrations of drinking water from the greater Brisbane area. Significantly, the combined use of Inductively Coupled Plasma - Mass Spectrometry and Chemometrics can be used worldwide to provide comprehensive, rapid and affordable analyses of elements in drinking water that can have a considerable impact on human health.

Source characterisation of urban road surface pollutants for enhanced water quality predictions

This study developed a comprehensive research methodology for identification and quantification of sources responsible for pollutant build-up and wash-off from urban road surfaces. The study identified soil and asphalt wear, and non-combusted diesel fuel as the most influential sources for metal and hydrocarbon pollution respectively. The study also developed mathematical models to relate contributions from identified sources to underlying site specific factors such as land use and traffic. Developed mathematical model will play a key role in urban planning practices, enabling the implementation of effective water pollution control strategies.

Robust principal component analysis in water quality index development

Some statistical procedures already available in literature are employed in developing the water quality index, WQI. The nature of complexity and interdependency that occur in physical and chemical processes of water could be easier explained if statistical approaches were applied to water quality indexing. The most popular statistical method used in developing WQI is the principal component analysis (PCA). In literature, the WQI development based on the classical PCA mostly used water quality data that have been transformed and normalized. Outliers may be considered in or eliminated from the analysis. However, the classical mean and sample covariance matrix used in classical PCA methodology is not reliable if the outliers exist in the data. Since the presence of outliers may affect the computation of the principal component, robust principal component analysis, RPCA should be used. Focusing in Langat River, the RPCA-WQI was introduced for the first time in this study to re-calculate the DOE-WQI. Results show that the RPCA-WQI is capable to capture similar distribution in the existing DOE-WQI.

No-reference image quality assessment using modified extreme learning machine classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images ('No Reference' metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.

Fuzzy optimization model for water quality management of a river system - Closure

A fuzzy waste-load allocation model, FWLAM, is developed for water quality management of a river system using fuzzy multiple-objective optimization. An important feature of this model is its capability to incorporate the aspirations and conflicting objectives of the pollution control agency and dischargers. The vagueness associated with specifying the water quality criteria and fraction removal levels is modeled in a fuzzy framework. The goals related to the pollution control agency and dischargers are expressed as fuzzy sets. The membership functions of these fuzzy sets are considered to represent the variation of satisfaction levels of the pollution control agency and dischargers in attaining their respective goals. Two formulations—namely, the MAX-MIN and MAX-BIAS formulations—are proposed for FWLAM. The MAX-MIN formulation maximizes the minimum satisfaction level in the system. The MAX-BIAS formulation maximizes a bias measure, giving a solution that favors the dischargers. Maximization of the bias measure attempts to keep the satisfaction levels of the dischargers away from the minimum satisfaction level and that of the pollution control agency close to the minimum satisfaction level. Most of the conventional water quality management models use waste treatment cost curves that are uncertain and nonlinear. Unlike such models, FWLAM avoids the use of cost curves. Further, the model provides the flexibility for the pollution control agency and dischargers to specify their aspirations independently.

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.

Assessing the cost-effectiveness of water quality interventions in South-East Queensland

The focus of this article is on the cost-effectiveness of mitigation strategies to reduce pollution loads and improve water quality in South-East Queensland. Scenarios were developed about the types of catchment interventions that could be considered, and the resulting changes in water quality indicators that may result. Once these catchment scenarios were modelled, the range of expected outcomes was assessed and the costs of mitigation interventions were estimated. Strategies considered include point and non-point source interventions. Predicted reductions in pollution levels were calculated for each action based on the expected population growth. The cost of the interventions included the full investment and annual running costs as well as planned public investment by the state agencies. Cost-effectiveness of strategies is likely to vary according to whether suspended sediments, nitrogen or phosphorus loads are being targeted.

Runoff, sediment loss and water quality from forest roads in a southeast Queensland coastal plain Pinus plantation

Runoff and sediment loss from forest roads were monitored for a two-year period in a Pinus plantation in southeast Queensland. Two classes of road were investigated: a gravelled road, which is used as a primary daily haulage route for the logging area, and an ungravelled road, which provides the main access route for individual logging compartments and is intensively used as a haulage route only during the harvest of these areas (approximately every 30 years). Both roads were subjected to routine traffic loads and maintenance during the study. Surface runoff in response to natural rainfall was measured and samples taken for the determination of sediment and nutrient (total nitrogen, total phosphorus, dissolved organic carbon and total iron) loads from each road. Results revealed that the mean runoff coefficient (runoff depth/rainfall depth) was consistently higher from the gravelled road plot with 0.57, as compared to the ungravelled road with 0.38. Total sediment loss over the two-year period was greatest from the gravelled road plot at 5.7 t km−1 compared to the ungravelled road plot with 3.9 t km−1. Suspended solids contributed 86% of the total sediment loss from the gravelled road, and 72% from the ungravelled road over the two years. Nitrogen loads from the two roads were both relatively constant throughout the study, and averaged 5.2 and 2.9 kg km−1 from the gravelled and ungravelled road, respectively. Mean annual phosphorus loads were 0.6 kg km−1 from the gravelled road and 0.2 kg km−1 from the ungravelled road. Organic carbon and total iron loads increased in the second year of the study, which was a much wetter year, and are thought to reflect the breakdown of organic matter in roadside drains and increased sediment generation, respectively. When road and drain maintenance (grading) was performed runoff and sediment loss were increased from both road types. Additionally, the breakdown of the gravel road base due to high traffic intensity during wet conditions resulted in the formation of deep (10 cm) ruts which increased erosion. The Water Erosion Prediction Project (WEPP):Road model was used to compare predicted to observed runoff and sediment loss from the two road classes investigated. For individual rainfall events, WEPP:Road predicted output showed strong agreement with observed values of runoff and sediment loss. WEPP:Road predictions for annual sediment loss from the entire forestry road network in the study area also showed reasonable agreement with the extrapolated observed values.

Role of Native Fish in Integrated Aquatic Weed and Water Quality Management within the Burdekin Irrigation Area

This report summarises work conducted by the QDPI, in partnership with the South Burdekin Water Board (SBWB) and the Burdekin Shire Council (BSC) between 2001 and 2003. The broad aim of the research was to assess the potential of native fish as biocontrol agents for noxious weeds, as part of an integrated program for managing water quality in the Burdekin Irrigation Area. A series of trials were conducted at, or using water derived from, the Sandy Creek Diversion near Groper Creek (lower Burdekin delta). Trials demonstrated that aquatic weeds play a positive role in trapping transient nutrients, until such time that weed growth becomes self-shading and weed dieback occurs, which releases stored nutrients and adversely affects water quality. Transient nutrient levels (av. TN<0.5mg/L; av. TP<0.1mg/L) found in the irrigation channel during the course of this research were substantially lower than expected, especially considering the intensive agriculture and sewage effluent discharge upstream from the study site. This confirms the need to consider the control of weeds rather than complete weed extermination when formulating management plans. However, even when low nutrient levels are available, there is competitive exploitation of habitat variables in the irrigation area leading to succession and eventual domination by certain weed species. During these trials, we have seen filamentous algae, phytoplankton, hyacinth and curled pondweed each hold competitive advantage at certain points. However without intervention, floating weeds, especially hyacinth, ultimately predominate in the Burdekin delta due to their fast propagation rate and their ability to out-shade submerged plants. We have highlighted the complexity of interactions in these highly disturbed ecosystems in that even if the more prevalent noxious weeds are contained, other weed species will exploit the vacant niche. This complexity places stringent requirements on the type of native fish that can be used as biocontrol agents. Of the seven fish species identified with herbivorous trophic niches, most target plankton or algae and do not have the physical capacity to directly eat the larger macrophytes of the delta. We do find however that following mechanical weed harvesting, inoculative releases of fish can slow the rate of hyacinth recolonisation. This occurs by mechanisms in addition to direct weed consumption, such as disturbing growth surfaces by grazing on attached biofilms. Predation by birds and water rats presents another impediment to the efficacy of large-scale releases of fish. However, alternative uses of fish in water quality management in the Burdekin irrigation area are discussed.

Nutrient loss and water quality under extensive grazing in the upper Burdekin river catchment, North Queensland

Increased sediment and nutrient losses resulting from unsustainable grazing management in the Burdekin River catchment are major threats to water quality in the Great Barrier Reef Lagoon. To test the effects of grazing management on soil and nutrient loss, five 1 ha mini-catchments were established in 1999 under different grazing strategies on a sedimentary landscape near Charters Towers. Reference samples were also collected from watercourses in the Burdekin catchment during major flow events.Soil and nutrient loss were relatively low across all grazing strategies due to a combination of good cover, low slope and low rainfall intensities. Total soil loss varied from 3 to 20 kg haˉ¹ per event while losses of N and P ranged from 10 to 1900 g haˉ¹ and from 1 to 71 g haˉ¹ per event respectively. Water quality of runoff was considered moderate across all strategies with relatively low levels of total suspended sediment (range: 8-1409 mg lˉ¹), total N (range: 101-4000 ug lˉ¹) and total P (range: 14-609 ug lˉ¹). However, treatment differences are likely to emerge with time as the impacts of the different grazing strategies on land condition become more apparent.Samples collected opportunistically from rivers and creeks during flow events displayed significantly higher levels of total suspended sediment (range: 10-6010 mg lˉ¹), total N (range: 650-6350 ug lˉ¹) and total P (range: 50-1500 ug lˉ¹) than those collected at the grazing trial. These differences can largely be attributed to variation in slope, geology and cover between the grazing trial and different catchments. In particular, watercourses draining hillier, grano-diorite landscapes with low cover had markedly higher sediment and nutrient loads compared to those draining flatter, sedimentary landscapes.These preliminary data suggest that on relatively flat, sedimentary landscapes, extensive cattle grazing is compatible with achieving water quality targets, provided high levels of ground cover are maintained. In contrast, sediment and nutrient loss under grazing on more erodable land types is cause for serious concern. Long-term empirical research and monitoring will be essential to quantify the impacts of changed land management on water quality in the spatially and temporally variable Burdekin River catchment.

Economically viable land regeneration in Central Queensland and improved water quality outcomes for the Great Barrier Reef

The impact of excessive sediment loads entering into the Great Barrier Reef lagoon has led to increased awareness of land condition in grazing lands. Improved ground cover and land condition have been identified as two important factors in reducing sediment loads. This paper reports the economics of land regeneration using case studies for two different land types in the Fitzroy Basin. The results suggest that for sediment reduction to be achieved from land regeneration of more fertile land types (brigalow blackbutt) the most efficient method of allocating funds would be through extension and education. However for less productive country (narrow leaved ironbark woodlands) incentives will be required. The analysis also highlights the need for further scientific data to undertake similar financial assessments of land regeneration for other locations in Queensland.