Understanding spatiotemporal variability of in-stream water quality in urban environments – A case study of Melbourne, Australia

Shi, Baiqian; Bach, Peter M.; Lintern, Anna; Zhang, Kefeng; Coleman, Rhys A.; Metzeling, Leon; McCarthy, David Thomas; Deletić, Ana

doi:10.1016/j.jenvman.2019.06.006

Cited by 36 publications

(19 citation statements)

References 63 publications

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“…The variations in NSF-WQI and IPI values along river sections are thus related mainly to illegal or untreated domestic sewage water and small industrial spills and solid wastes, most of which were verified during fieldwork in 2017. A similar situation is reported by Shi et al [68] in Melbourne, Australia, where water quality decreases are likely caused by phosphates in detergents, construction waste, illegal wastewater discharge, and industrial spills. In a study in Campo Grande, Brazil, de Souza et al [66] found that land use and occupation, increasing population density, and lack of sanitation are related to water pollution.…”

Section: Discussionsupporting

confidence: 83%

Spatial and Temporal Variations in Water Quality and Land Use in a Semi-Arid Catchment in Bolivia

Gossweiler

Wesström

Messing

et al. 2019

Water

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Increasing pressures caused by human activities pose a major threat to water availability and quality worldwide. Water resources have been declining in many catchments during recent decades. This study investigated patterns of river water quality status in a peri-urban/rural catchment in Bolivia in relation to land use during a 26 year period. Satellite images were used to determine changes in land use. To assess water quality, data in the dry season from former studies , complemented with newly collected data (2017), were analysed using the National Sanitation Foundation-Water Quality Index method and the Implicit Pollution Index method. The highest rates of relative increase in land use area were observed for forest, urban, and peri-urban areas, whereas relative decreases were observed for water infiltration zones, bare soil, shrubland, and grassland areas. The water quality indices revealed clear water quality deterioration over time, and from catchment headwaters to outlet. Statistical analyses revealed a significant relationship between decreasing water quality and urban expansion. These results demonstrate the need for an effective control programme, preferably based on water quality index approaches as in the present study and including continuous monitoring of runoff water, mitigation of pollution, and water quality restoration, in order to achieve proper water management and quality.

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Section: Discussionsupporting

confidence: 83%

Spatial and Temporal Variations in Water Quality and Land Use in a Semi-Arid Catchment in Bolivia

Gossweiler

Wesström

Messing

et al. 2019

Water

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“…High demand for domestic water mostly results in an increased volume of household waste. Moreover, in urban areas, some economic activities also produce municipal waste (Shi et al 2019). Belik Sub-watershed is mostly built-up land covering an area of 621.41 ha or 91% of the total area of the study site (i.e., 683.79 ha).…”

Section: Regional Characteristicsmentioning

confidence: 99%

Analyzing the Characteristics of Domestic Wastes in Belik River, the Special Region of Yogyakarta, Indonesia

Suprayogi

Marfai

Cahyadi

et al. 2019

AJSTD

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The multiplying number of population in the City of Yogyakarta has resulted in a larger volume of wastes in the region. People living on the riverbanks are unfortunately in the habit of discarding domestic waste directly to the river channel, worsening the already polluted water. This study was intended to analyze the characteristics of the municipal wastewater contaminating Belik River. During the water quality test, a rapid investigation method and laboratory analysis were employed. The sampling in the field was based on river segments and travel time of river water. Based on the laboratory test results, the concentrations of phosphate, BOD, and COD in the water bodies had exceeded the standard for Class II water quality indicating pollution due to frequent disposal of household wastes like detergents. The higher the BOD and COD levels, the more unsuitable the water for fisheries and agricultural practices.

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“…Pollutants and constituents such as nutrients, sediments, salts and metals can come from a variety of sources in both rural and urban catchments, depending on the specific land use (Table 1). Whilst Table 1 provides a summary of the typical and diffuse point sources of pollutants in urban and rural catchments, the importance of these sources depends significantly on factors such as: population density, industrial land use (and industry type), traffic volumes and construction activities (in urban catchments) (Shi et al 2019); and crop type, stocking densities and land management strategies (in rural areas) (Lintern et al 2018a). These contrasting and specific pollution sources need to be captured in water quality models of rural and urban catchments.…”

Section: Pollution Sourcesmentioning

confidence: 99%

“…There are other catchment features that may need to be included in water quality models. Climate, topography, vegetation cover, geology, soil conditions and artificial drainage infrastructure can all affect pollutant runoff processes, sources and transformations (e.g., denitrification, nutrient mineralisation from organic matter) in both urban and rural catchments (Shi et al 2019, Lintern et al 2018a. While the importance of these various catchment characteristics on pollutant levels in receiving water has been assessed in rural and urban catchments, a comprehensive comparison of the relative importance of these landscape features has not been conducted.…”

Section: Other Catchment Featuresmentioning

confidence: 99%

Future Water: Comparing and contrasting approaches to predicting water quality

Guo

Lintern²,

Prodanovic

et al. 2019

El Sawah, S. (Ed.) MODSIM2019, 23rd International Congress on Modelling and Simulation.

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Globally, surface water quality deterioration is an important issue exacerbated by increasing urbanisation, intensified agricultural activities and climate change. To mitigate this increase in waterway pollution there is a strong need for effective catchment management strategies. However, this is currently limited by: (1) our lack of understanding of the key processes and mechanisms driving water quality change in waterways, and (2) our inability to predict future water quality change. To address these, we need to improve our ability to understand and model water quality. This improved capacity will enable us to better identify important water quality changes under various land use, land management and climate scenarios. These improvements could thus assist waterway managers to better prioritise regions which require special management attention, as well as better assess the benefits of different management interventions and preventative actions to reduce pollution levels. Given the above, there is a vital role for predictive models for supporting catchment water quality management. There is a wide spectrum of surface water quality modelling approaches, ranging from purely black-box empirical to detailed process-oriented models; these often have contrasting strengths and weaknesses. This study aims to (1) provide a review of key considerations for designing and developing effective modelling approaches for water quality prediction; and to (2) outline reasons for choices in modelling approaches for rural and urban water quality across Victoria. A fundamental consideration is the modelling purpose which provides preliminary guidelines for other modelling decisions. The core decision in modelling water quality is the conceptualisation of key physical processes, which involves the definition of pollution sources, flow/transport pathways and other catchment features. In addition, practical considerations such as data availability and model development effort are also important. For each consideration we provide recommendations for different types of catchments, and specifically highlight distinctions between natural/rural and urban catchments. We illustrate these considerations with two modelling approaches: a) a data-driven statistical approach informed by conceptual understanding and b) a more process-oriented approach. These models have been applied in rural and urban settings respectively. Both approaches are designed to identify spatio-temporal differences in water quality, and to predict how future water quality will change. Being designed for vastly different environments (rural vs urban), these two models present separate, yet parallel approaches for modelling spatio-temporal variability in water quality. By contrasting these models, we aim to highlight strengths and weaknesses of different approaches, to share practical experiences from implementing these approaches and to identify the ways forward for both approaches. The experiences and recommendations from these modelling approaches can provide valuable r...

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Understanding spatiotemporal variability of in-stream water quality in urban environments – A case study of Melbourne, Australia

Cited by 36 publications

References 63 publications

Spatial and Temporal Variations in Water Quality and Land Use in a Semi-Arid Catchment in Bolivia

Spatial and Temporal Variations in Water Quality and Land Use in a Semi-Arid Catchment in Bolivia

Analyzing the Characteristics of Domestic Wastes in Belik River, the Special Region of Yogyakarta, Indonesia

Future Water: Comparing and contrasting approaches to predicting water quality

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