Recommendations for environmental baseline monitoring in areas of shale gas development

Environmental monitoring plays a key role in risk assessment and management of industrial operations where there is the potential for the release of contaminants to the environment (i.e. air and water) or for structural damage (i.e. seismicity). The shale-gas industry is one such industry. It is also new to the UK and so specific environmental regulation and other controls have been introduced only recently. Associated with this is a need to carry out monitoring to demonstrate that the management measures to minimise the risk to the environment are being effective. While much of the monitoring required is common to other industries and potentially polluting activities, there are a number of requirements specific to shale gas and to what is a new and undeveloped industry. This report presents recommendations for environmental monitoring associated with shale-gas activities and in particular the monitoring required to inform risk assessment and establish the pre-existing environmental conditions at a site and surrounding area. This baseline monitoring is essential to provide robust data and criteria for detecting any future adverse environmental changes caused by the shale-gas operations. Monitoring is therefore required throughout the lifecycle of a shale gas operation. During this lifecycle, the objectives of the monitoring will change, from baseline characterisation to operational and post-operational monitoring. Monitoring requirements will also change. This report focusses on good practice in baseline monitoring and places it in the context of the longer-term environmental monitoring programme, recognising the need to transition from the baseline condition and to establish criteria for detecting any changes within the regulatory framework. The core suite of environmental monitoring activities currently required to support regulatory compliance, i.e. meet environmental and other permit conditions, encompasses monitoring of seismicity, water quality (groundwater and surface water) and air quality. Recommendations for each of these are included in this report. Additionally, recommendations for a number of other types of environmental monitoring are included – radon in air, soil gas and ground motion (subsidence/uplift). These are not associated directly with regulatory compliance but can provide information to support interpretation of statutory monitoring results. They are also considered important for public reassurance. Health impacts arising from radon and damage caused by ground motion are both issues of public concern in relation to shale gas

Full Proceedings, 2018

Full conference proceedings for the 2018 International Building Physics Association Conference hosted at Syracuse University

SynerCrete’18: interdisciplinary approaches for cement-based materials and structural concrete: synergizing expertise and bridging scales of space and time, vol. 2

info:eu-repo/semantics/publishedVersio

INNOVATIVE DEVELOPMENT OF RESOURCE-SAVING TECHNOLOGIES AND SUSTAINABLE USE OF NATURAL RESOURCES. 3rd INTERNATIONAL SCIENTIFIC AND TECHNICAL CONFERENCE

Місце збереження: https://www.upet.ro/cercetare/manifestari/Krivoi_Rog_2020_Book_of_Abstracts.pd

Iron and manganese accumulation potential in water distribution networks

The occurrence of discoloured drinking water at customers’ taps, which is mainly caused by the deposition and release of iron (Fe) and manganese (Mn) in water distribution networks (WDNs), is a major concern for both customers and water companies. Increased concentrations of Fe and Mn in WDNs can lead to penalisation by the Drinking Water Inspectorate (DWI) and Water Services Regulation Authority in England and Wales (Ofwat). These high concentration levels can cause aesthetic problems such as giving water an unpleasant metallic taste and staining of laundry. It has also been found that increased Mn concentrations in drinking water can reduce intellectual function of children. Despite efforts by water companies to comply with standards for drinking water, they continue to receive customer complaints related to water discolouration. Currently, most water companies identify high-discolouration-risk regions in WDNs by either selecting areas in the network with high concentrations of Fe and Mn from their routine sampling, or using data obtained from customer complaints related to discolouration. However, these risk assessment methods are imprecise, because only few selected nodes are sampled and not all customers who experience water discolouration complain. Moreover, considering that the water mains in England and Wales span approximately 315,000 km, monitoring Fe and Mn concentrations will always be a difficult and expensive task. It is therefore imperative for water companies to gain a practical understanding of the processes and mechanisms that lead to water discolouration, and to develop a model to identify the high-risk areas in WDNs so that remedial measures can be effectively implemented.The factors that influence Fe and Mn accumulation from post-treatment to customers’ taps through WDNs can be categorised into physical, chemical and biological. However, to date, researchers have only studied these factors partially or separately, but never in combination. None of the current models are able to predict discolouration/Fe and Mn accumulation potential for every node in WSZs using chemical, biological, and hydraulic/physical variables. This study took a holistic approach in investigating these factors. A five-year data set comprising of 36 water quality, hydraulic, and pipe-related variables covering 176 different district metered areas (DMAs) were analysed to identify relevant variables that influence Fe and Mn accumulation potential. Customer complaint data were also investigated for seasonal trends. Majority of the DMAs (67.44%) showed significant peaks in customer complaints during summer. These spikes may be attributed to increased water consumption and warmer water temperatures during this period. An artificial neural network (ANN) model was developed using relevant variables identified through the data analysis. The model could predict Fe and Mn accumulation potential values for every node in a given water supply zone (WSZ). From the risk maps generated by the ANN model, it was observed that most of the regions in the network with high Fe and Mn accumulation potential also had high levels of customer complaints related to discolouration. Although the ANN model could predict Fe and Mn accumulation potential failures in WSZs, its black-box nature made it difficult to explain the causes of the failures, unless they were manually investigated.To overcome the limitation in the ANN model, a fuzzy inference system (FIS) was developed to predict Fe and Mn accumulation potential for every node in WDNs and also capture the chemical, biological and physical processes as water travels through the network. The rules and weights of the rules for the FIS were calibrated using a genetic algorithm. The FIS is also able to determine the causes of the Fe and Mn accumulation potential failures. The ability of the developed models in this research to predict and indicate the causes of high Fe and Mn accumulation potential at the node level make them a unique and practical tool to detect high risk nodes in all regions in WDNs, including regions which have not been sampled. Both models could be of great benefit to water resource engineers and drinking water supply companies in managing water discolouration. They could also be used to investigate variables that influence physical, chemical and biological processes in WDNs

Detection and Localisation of Pipe Bursts in a District Metered Area Using an Online Hydraulic Model

This thesis presents a research work on the development of new methodology for near-real-time detection and localisation of pipe bursts in a Water Distribution System (WDS) at the District Meters Area (DMA) level. The methodology makes use of online hydraulic model coupled with a demand forecasting methodology and several statistical techniques to process the hydraulic meters data (i.e., flows and pressures) coming from the field at regular time intervals (i.e. every 15 minutes). Once the detection part of the methodology identifies a potential burst occurrence in a system it raises an alarm. This is followed by the application of the burst localisation methodology to approximately locate the event within the District Metered Area (DMA). The online hydraulic model is based on data assimilation methodology coupled with a short-term Water Demand Forecasting Model (WDFM) based on Multi-Linear Regression. Three data assimilation methods were tested in the thesis, namely the iterative Kalman Filter method, the Ensemble Kalman Filter method and the Particle Filter method. The iterative Kalman Filter (i-KF) method was eventually chosen for the online hydraulic model based on the best overall trade-off between water system state prediction accuracy and computational efficiency. The online hydraulic model created this way was coupled with the Statistical Process Control (SPC) technique and a newly developed burst detection metric based on the moving average residuals between the predicted and observed hydraulic states (flows/pressures). Two new SPC-based charts with associated generic set of control rules for analysing burst detection metric values over consecutive time steps were introduced to raise burst alarms in a reliable and timely fashion. The SPC rules and relevant thresholds were determined offline by performing appropriate statistical analysis of residuals. The above was followed by the development of the new methodology for online burst localisation. The methodology integrates the information on burst detection metric values obtained during the detection stage with the new sensitivity matrix developed offline and hydraulic model runs used to simulate potential bursts to identify the most likely burst location in the pipe network. A new data algorithm for estimating the ‘normal’ DMA demand and burst flow during the burst period is developed and used for localisation. A new data algorithm for statistical analysis of flow and pressure data was also developed and used to determine the approximate burst area by producing a list of top ten suspected burst location nodes. The above novel methodologies for burst detection and localisation were applied to two real-life District Metred Areas in the United Kingdom (UK) with artificially generated flow and pressure observations and assumed bursts. The results obtained this way show that the developed methodology detects pipe bursts in a reliable and timely fashion, provides good estimate of a burst flow and accurately approximately locates the burst within a DMA. In addition, the results obtained show the potential of the methodology described here for online burst detection and localisation in assisting Water Companies (WCs) to conserve water, save energy and money. It can also enhance the UK WCs’ profile customer satisfaction, improve operational efficiency and improve the OFWAT’s Service Incentive Mechanism (SIM) scores.This STREAM project is funded by the Engineering and Physical Sciences Research Council and Industrial Collaborator, United Utilities