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Can pollution source apportionment tools help deliver integrated catchment management?

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‘Source Apportionment Methods’ (SAMs) are a way of estimating sources of water pollution and so inform efforts to improve water quality. This year the Scottish Environment Protection Agency (SEPA) is launching a source apportionment method called SAGIS for rivers in Scotland. 

For over 10 years I have been researching water quality and pollution source apportionment at catchment scale, so this seems a good time for me to reflect on its pros and cons of SAMs.

SAMs usually involve a mixture of data collection and modelling of pollutant transport to identify how different activities cause pollution into the environment. For example, the graph below provides estimates of sources of soluble phosphate discharged into a catchment in northeast Scotland called the Lunan Water, as part of a Scottish Government funded study on mitigating sources of diffuse pollution. What I want to focus on is how SAMs may help us discuss responsibility, target actions, and so design schemes to reduce pollution. This is important because improving water quality is a major policy goal for Scotland, which aims to be a “Hydronation”.

Can SAMs help attribute responsibility for pollution?  SAM can help to identify point sources of pollution (e.g. factories and sewage treatment works), and hence decide about permits, fees and fines to control pollution caused by those sources. However, with more diffuse sources (e.g. pollution that arises cumulatively from many small inputs and activities), there are large uncertainties about the extent and timing of pollution. This makes it harder to identify responsibility for the problem.  For example, in the Lunan Water, in 2000-2002 there was a big decrease in phosphate levels in the river. The reason why is unclear: it could be due to the decline in livestock numbers associated with introduction of the single farm payment scheme, but sewage inputs also declined over the same period.   Therefore, it is important to use knowledge of a site and context to complement the data collection and modelling.  For example, when studying the Cuiaba river in central Brazil in 2010, I found dead fish floating in its lower reaches.  It was easy to assume that this was caused by sewage inputs from upstream cities but it was actually caused by natural drainage upstream, which had carried soluble organic matter off flooded natural grasslands.  So we should not regard  SAMs  as “the truth”, but instead be continually informed by on the ground observations.

Can SAMs help target action?  SAMs can help to identify priority sectors or activities to target in order to reduce pollution. For example, should we spend money on upgrades of water treatment works or on rural Sustainable Drainage Systems, such as sediment treatment bunds or buffer strips, to mitigate sediment runoff from farmland?  SAMs, together with information about the costs of interventions, can help to answer such questions.  For example, in the Lunan Water, both sewage treatment and erosion control could deliver water quality improvements. My work indicates this can be worth about £30 for every kilogramme of phosphorus saved, so it can be worthwhile to invest in both erosion control and sewage treatment.

Can SAMs help us design schemes to reduce pollution?  I think SAMs can be very useful to help us to reduce pollution. It can also help inform development of economic instruments to incentivise mitigation.   However, there is uncertainty in both the effectiveness and costs of many interventions to reduce pollution. We also need to remember that timelags mean the results of some actions will take a long time to become visible.  For example, reducing phosphorus inputs on farmland may take decades to impact surface water quality because of the large phosphorus reserves within the soil. Moreover, if on the ground verification of SAMs were to be linked to intervention in every catchment, this will greatly increase costs of a scheme, so stakeholder confidence, or at least a readiness to accept estimates of pollution contributions, is vital.  

Therefore it will be interesting to see how the forthcoming SAGIS tool gets used in Scotland.  I expect that SAMs, together with economic information, can help us to target activities and also develop ways to incentivise or motivate these activities, but we will also need other sources of information to work alongside,  to achieve cost-effective improvements in water quality.

 

Disclaimer: The views expressed in this blog post are the views of the author, and not an official position of the institute or funder.

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Printed from /blog/segs/pollution-apportionment on 24/08/19 06:42:14 PM

The James Hutton Research Institute is the result of the merger in April 2011 of MLURI and SCRI. This merger formed a new powerhouse for research into food, land use, and climate change.