The water industry is committed to reducing its carbon emissions and one of the ‘Big Questions’ posed by UKWIR to help inform the strategic programme of research is: How do we become carbon neutral by 2050? To achieve this, we must develop a better understanding of the greenhouse gas emissions that are specific to our treatment and disposal processes.

Greenhouse gas emissions from our treatment processes are the second highest driver of our industry carbon footprint after energy. As other elements of the industry footprint are reduced, the process emissions become more important. Currently, we are doing little to reduce this component of our footprint, because the science behind our understanding of these emissions and how to reduce them is poor.

Overall, industry Greenhouse Gas (GHG) emissions are falling. Much of the reduction seen is as a result of lower emissions linked to grid electricity. This is partly because the UK grid mix is using less fossil fuels and partly because water companies are generating or procuring renewable energy directly. Historically around 70% of industry emissions have been linked to the use of grid electricity. As this becomes a lesser part of the total, the other elements of the industry footprint become more significant.

Removing surface water from foul or combined sewers offers a number of benefits, both in terms of reducing operational expenditure and flows (and subsequent flooding and spill frequency), as well as offering wider community benefits.

There is an increasing appetite across the water industry to consider and deliver such interventions to help manage a range of drivers, but this appetite is not matched by an in depth understanding of the scenarios and catchment characteristics that make surface water removal options more cost beneficial than traditional engineering solutions.

Increased Antimicrobial Resistance (AMR) remains a concern for both Governments and water companies alike. AMR bacteria have been found downstream of Wastewater Treatment Works’ (WwTW) discharges in rivers and in the marine environment. It seems likely that this is not solely due to the discharge of antibiotic residues in effluent (although these may contribute to some lesser extent) but more simply, to the release of bacteria that are already resistant, or the release of genetic material containing antibiotic resistance genes (ARG) which are then incorporated by environmental bacteria.

A better understanding of the extent of resistant bacteria discharged from the WwTW and the link to anti-microbial resistance in the environment is needed, allied to quantifying the relevance of these environmental reservoirs in the context of human health. This knowledge also needs to be placed in context of the UK and Irish Governments’ AMR strategies and plans, to help to increase understanding of the contribution from the environment to the wider problem of AMR spread. Similarly, understanding the contribution of WwTW discharges relative to other environmental sources of AMR, for example, agricultural discharges, is key if we are as a society to prioritise our efforts to limit the spread of AMR.

Given the increasing importance and public concern over AMR, one of the key issues (after development of new antimicrobials) is how to prevent or limit the spread and dissemination of AMR; of specific interest to the water industry is the release of resistant organisms to the environment, and how this might impact society . WwTWs are one of a number of obvious release sources and although they achieve significant bacterial reductions, this may not be sufficient. This is particularly pertinent to bathing water discharges; although such effluents are routinely disinfected the probability of direct human exposure is far greater than for inland freshwaters.

Source Apportionment Geographical Information System (SAGIS) is a tool developed by UKWIR that helps in quantifying the pollutant load from different sources in UK surface waters. It is primarily used by the industry and regulators in Asset Management Planning (AMP) and River Basin Management Planning (RBMP) and will remain so until at least the year 2027. It is used to determine permits for wastewater treatment works (WWTW) discharges and identify future investment needs.

The value of SAGIS and associated decision supporting tools is related to the data contained within the tool and the extent to which it incorporates the latest knowledge (scientific and political). This project will therefore focus on preparing the system for the future by including assessments of climate change, PR 24, RBMP cycle 3 and Brexit.

The water industry is committed to reducing its carbon emissions and one of the ‘Big Questions’ posed by UKWIR to help inform the strategic programme of research is: How do we become carbon neutral by 2050? To achieve this, we must develop a better understanding of the greenhouse gas emissions that are specific to our treatment and disposal processes.

Greenhouse gas emissions from our treatment processes are the second highest driver of our industry carbon footprint after energy. As other elements of the industry footprint are reduced, the process emissions become more important. Currently, we are doing little to reduce this component of our footprint, because the science behind our understanding of these emissions and how to reduce them is poor.

Overall, industry Greenhouse Gas (GHG) emissions are falling. Much of the reduction seen is as a result of lower emissions linked to grid electricity. This is partly because the UK grid mix is using less fossil fuels and partly because water companies are generating or procuring renewable energy directly. Historically around 70% of industry emissions have been linked to the use of grid electricity. As this becomes a lesser part of the total, the other elements of the industry footprint become more significant.

SAGIS-SIMCAT is the most widely used catchment scale water quality model in the UK. The traction it has gained is based on its capability to apportion the contribution from polluting sectors, such as water companies, arable and livestock farming, urban run-off and septic tanks, and the impact they have upon concentrations within the receiving rivers. As such it is used to support regulators and water companies alike make decisions designed to improve water quality. For PR19, SAGIS-SIMCAT is driving expenditure across the country of c.£4bn which is focused fairly on water companies’ share.
Prior to the generation of models and outputs, SAGIS which processes the data used to support investment decisions, needs to be updated with contemporaneous data. This task to-date has been a long and laborious one. For PR24 a new set of regional models will be required, necessitating an extensive update of the data within SAGIS and to the features of the GIS interface. To do this manually would involve considerable resourcing from both regulators and water companies.
The aim of this project proposes to generate a new tool, or collection of tools that will allow users to automatically update the SAGIS models with contemporaneous data. This tool/tools will need to take raw data provided by multiple sources including regulators such as the Environment Agency (EA), Natural Resources Wales (NRW) and the Scottish Environmental Protection Agency (SEPA) as well as water companies and process the data into a format needed to generate a new SAGIS model. From the raw data, new data tables and geo-referencing will be required. Effectively producing a new set of models.
The tool/tools also need(s) to report on the quality of the data, in addition to generating the input tables for new models and summarise the data to be used within the model. It is fundamental that decision makers understand the uncertainty within the source data and its implications upon investment. As such, the tool will be required to assess the raw data used and report on, but not limited to the following:
· Step changes in data
· The proportion of ‘less than’ observations within the data
· The number of observations
· The temporal coverage of the data
· The statistical distribution of the data
· Whether default values/assumption have been used in parameter estimation.

For every data set a quality score should be assigned taking account of these factors.
The tool will need to be able to identify data associated with features not already included in the model and will need to be able to add new features to the model all in one go. The project should consider either developing new tools or adapting existing tools.

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