Assessment of Water Quality and Identification of Pollution Risk Locations in Tiaoxi River (Taihu Watershed), China

Diffuse pollution of phosphorus (P) from agriculture is a major pressure on water quality in Ireland. The Agricultural Catchments Programme (ACP) was initiated to evaluate the Good Agricultural Practice measures implemented under the EU Nitrates Directive. Within the ACP, extensive monitoring and research has been made to understand the drivers and controls on nutrient loss in the agricultural landscape. However, tapering P pollution in agricultural catchments also requires informed decisions about the likely effectiveness of measures as well as their spatial targeting.&#160; There is a need to develop Decision Support Tools (DST) that can account for the uncertainty inherently present in both data and water quality models.Bayesian Belief Networks (BBNs) are probabilistic graphical models that allow the integration of both quantitative and qualitative information from different sources (experimental data, model outputs and expert opinion) all in one model. Moreover, these models can be easily updated with new knowledge and can be applied with scarce datasets. BBNs have previously been used in multiple decision-making settings to understand causal relationships in different contexts. Recently, BBNs were used to support ecological risk-based decision making.In this study, a prototype BBN was implemented with the Genie software to develop a DST for understanding the influence of land management and P pollution risk in four ACP catchments dominated by intensively farmed land with contrasting hydrology and land use. In the fist stage of the study, the spatial BBN was constructed visualising the &#8216;source-mobilisation-transport-continuum&#8217;, identifying the main drivers of P pollution based on previous findings from the ACP catchments. A second step involved the consultation of experts and stakeholders through a series of workshops aimed at eliciting their input. These stakeholders have expertise ranging from hydrology and hydrochemistry, land management and farm consulting, to policy and environmental modelling.At present, the BBN is being parameterized for a 12km2 catchment with mostly grassland on poorly drained soils, using a high temporal and spatial resolution dataset that includes hydro-chemo-metrics, mapped soil properties (drainage class and Soil Morgan P), landscape characteristics (i.e. land use and management, presence of mitigation measures and presence of point pollution sources). Preliminary results show that the model captures the difference in P loss risk between catchments, probably caused by contrasting hydrological characteristics and soil P sources.Future research will be focussed on parameterizing and testing the BBN in three other ACP catchments. Such parametrization will be pivotal to testing the model in data sparse catchments and possibly upscaling the tool to regional and national scale. Moreover, climate change and land use change modelled scenarios will be crucial to inform targeting of mitigation measures. &#160;

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Pengaruh Sanitasi dan Konstruksi terhadap Kualitas Sumur Gali di Desa Sembubuk Kecamatan Jambi Luar Kota Kabupaten Muaro Jambi

Jurnal Daur Lingkungan ◽

10.33087/daurling.v3i1.36 ◽

2020 ◽

Vol 3 (1) ◽

pp. 5

Author(s):

Dirga Rangga Malindo ◽

G M Saragih ◽

Anggrika Riyanti

Keyword(s):

Water Quality ◽

Drinking Water ◽

Public Works ◽

Quality Analysis ◽

Well Water ◽

Pollution Risk ◽

Laboratory Test Results ◽

Management Procedures ◽

Dug Wells ◽

Dug Well

Efforts to monitor and supervise the quality of dug well water is an important part in meeting the need for clean water. This study is to determine the effect of sanitation and dug well construction on well water quality. Samples of well water quality were taken at wells with poor and poor sanitation and construction conditions based on SNI 03-2916-1992 concerning Dug Well Specifications. Water quality analysis was carried out in Jambi Province Regional Laboratory in accordance with Permenkes 32 of 2017 concerning About the environmental health quality standards and water health requirements for the needs of Sanitary Hygiene, Swimming Pool, Solus per Aqua, and Public Baths. Sanitation inspection to see the level of pollution risk refers to Permenkes Number 736 of 2010 concerning Management Procedures for Drinking Water Quality at dug well facilities. Laboratory test results show that wells with good sanitation and construction have good water quality compared to wells with poor sanitation and construction. However, both water quality samples still meet the Minister of Health Regulation 32 of 2017. Based on the results of sanitation inspection in the field as many as 63% of wells have a high level of pollution risk (High TRP), most of the dug wells have problems in their construction, to reduce the level of risk of dug well pollution needs improvement construction. In the planning and preparation of the Budget Plan for repairs to construction required a fee of Rp. 1,057,703,764, - these costs refer to Minister of Public Works Regulation No. 11 / PRT / M / 2013 Concerning guidelines for unit price analysis in the field of public works and repair of dug wells in accordance with SNI 03-2916-1992 Dug Well Specifications for Drinking Water Sources.

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A Vine Copula-Based Modeling for Identification of Multivariate Water Pollution Risk in an Interconnected River System Network

Water ◽

10.3390/w12102741 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2741

Author(s):

Ruolan Yu ◽

Rui Yang ◽

Chen Zhang ◽

Maria Špoljar ◽

Natalia Kuczyńska-Kippen ◽

...

Keyword(s):

Water Pollution ◽

Water Quality ◽

Technical Assistance ◽

River System ◽

Ammonia Nitrogen ◽

Anthropogenic Pressures ◽

Pollution Risk ◽

Before And After ◽

Vine Copula ◽

Stability Of Water

The Interconnected River System Network (IRSN) has become a popular and useful measure to realize the long-term health and stability of water bodies. However, there are lots of uncertain consequences derived from natural and anthropogenic pressures on the IRSN, especially the water pollution risk. In our study, a Vine Copula-based model was developed to assess the water pollution risk in the IRSN. Taking the ponds around Nanyang station as research objects, we selected five proxy indicators from water quality indexes and eutrophication indexes, which included dissolved oxygen (DO), total nitrogen (TN), total phosphorus (TP), chlorophyll-a (Chla), and ammonia nitrogen (NH3-N). Models based on three classes of vine copulas (C-, D-, and R-vine) were utilized respectively to identify the water quality indicators before and after the operation of the connection project. Our results showed that TN, Chla, and NH3-N should be considered as key risk factors. Moreover, we compared the advantages and prediction accuracy of C-, D-, and R-vine to discuss their applications. The results reveal that the Vine Copula-based modeling could provide eutrophication management reference and technical assistance in IRSN projects.

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A systems approach to modelling phosphorus pollution risk in Scottish rivers using Bayesian Belief Networks

10.5194/egusphere-egu2020-8229 ◽

2020 ◽

Author(s):

Miriam Glendell ◽

Andy Vinten ◽

Samia Richards ◽

Zisis Gagkas ◽

Allan Lilly ◽

...

Keyword(s):

Water Quality ◽

Land Use ◽

Sewage Treatment ◽

Decision Support Tool ◽

Mitigation Measures ◽

Phosphorus Pollution ◽

Support Tool ◽

Pollution Risk ◽

Application Rates ◽

Fertiliser Application

Water pollution is an important reason for the failure of 17 % of Scottish waterbodies to reach Good Ecological Status under the Water Framework Directive (WFD). Among the multiple pressures affecting water quality, phosphorus (P) pollution is a major cause of surface water quality failures. Reducing the P pollution in agricultural catchments requires evidence-based decision-making about the effectiveness of land management mitigation measures and their spatial targeting, under current conditions and future scenarios.Here we introduce a decision-support tool, PhosphoRisk, that uses a Bayesian Belief Network to integrate information on the potential effects of water quality mitigation measures, including data and expert opinion, and parameterizations of the uncertainties in these quantities, in a single model. Specifically, the model integrates spatially distributed geographic information system data about land use and crops, soil erosion risk, topographic connectivity, presence of soil drains, soil hydrological leaching and P binding properties, farm yard locations for incidental P losses, sewage treatment works and septic tank location, with catchment rainfall and runoff data, fertiliser application rates and likely buffer effectiveness. Critical source areas of diffuse and point source pollution risk are mapped on 100x100 m raster grids for two pilot catchments in north-east Scotland &#8211; Lunan Water (124 km2) and Tarland (72 km2). The model simulates the probability of P concentration falling into the WFD high-good-moderate-poor classification categories at the catchment outlet and models P source apportionment alongside the effectiveness of mitigation measures such as buffer strips and fertiliser application rates.Sensitivity analysis of the model reveals the importance of hydrology for the seasonal dilution of P concentrations at the catchment outlet. Diffuse point sources, such as incidental losses from farmyards, are also important for this model of P pollution risk, along with sewage treatment works. The presence/absence of soil drains and septic tanks have a smaller influence on the outputs from the model.The PhosphoRisk decision support tool facilitates system-level thinking about phosphorus pollution and brings together academic and stakeholder communities to co-construct a model structure appropriate to the region it is modelling. The model reveals the causal relationships between the modelled factors driving an understanding of the effects of land use on P pollution risk in Scottish catchments. The modelled scenarios will help to inform and target water quality mitigation measures in high risk areas, while the quantified model uncertainties will inform further research and motivate targeted data collection.

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