One of FAIRWAY's major research themes is »Monitoring & indicators in which we evaluate and develop transparent agri-drinking water indicators (ADWIs) to monitor and assess the impact of measures and good practices on drinking water quality.
Data and information on ADWIs collected from the Aalborg case study was used as described here.
|1. Agri-drinking water quality indicators and IT/sensor techniques|
|2. Evaluating agri-drinking water quality indicators in three case studies|
|3. Harmonized indicator database|
Agri-drinking water quality indicators and IT/sensor techniques
1.1 Research highlights
In »Agri-drinking water quality indicators and IT/sensor techniques we describe the issue of the identification and use of appropriate indicators and how all the FAIRWAY case studies, including Aalborg, have contributed information about the local use of indicators and availability of data.
- We define the agri-hydrogeochemical system and looking at the pathways that nitrates and pesticides follow from the agricultural system to the drinking water supplies. We consider the challenges in monitoring and regulation, particularly of pesticides, and how contaminated water is treated in water works.
»Nitrogen and pesticide cycles in the agri-hydrogeochemical system
- We look at what data and statistics there are available on the regulation, marketing and use of nitrogen and pesticides, what indicators are used to monitor them and how indicators are intended to support central and local administration and policy-makers, water companies in analysing the situation of diffuse pollution and selecting measures to protect drinking water resources.
»Data and indicators to regulate and monitor the use of nitrates and pesticides
- The DPSIR indicators model is defined as “causal framework for the description of interactions between society and the environment” where: social and economic developments (Driving forces, D), exert Pressures (P) on the environment and, as a consequence, the State (S) of the environment changes. This leads to Impacts (I) on ecosystems, human health and society, which may elicit a societal Response (R) that feeds back on Driving forces, on State or on Impacts via various mitigations, adaptations or curative actions. In FAIRWAY we consider ADWIs within the DPSIR-framework and add a new Link element, L. The adjusted DPSLIR-framework contains a new element, the Link indicator.
»Developing FAIRWAY agri-drinking water quality indicators (ADWIs)
- Aalborg together with the other FAIRWAY case studies provided information about the agri-drinking water quality indicators (ADWIs) used in their local area where drinking water is abstracted from groundwater. All the ADWIs were classified according to the DPSLIR framework and additional indicators according to a literature review.
»Agri-drinking water quality indicators at farm and drinking water levels
- Because Driving force and Pressure indicators are particularly numerous, the survey of ADWIs used in Aalborg and the other case studies provided information to prioritise D and P indicators used in the agricultural sector, in order to focus on those that are the most significant, prevalent, effective and easy to use indicators.
»Prioritisation of agri-drinking water quality indicators
- Finding the proper, statistically based link between agricultural Driving forces and Pressure indicators and the State/impact indicators might supply ADWIs on a reliable basis.
»Further prioritisation and evaluation of agri-drinking water quality indicators
- Participatory monitoring, although old in its concept, has become much more developed during the last decades. Several types of participative monitoring systems can be characterised in relation to the intended goal of the promoter.
»Participative monitoring: involvement of citizens
From the survey of indicator use in Aalborg and the other FAIRWAY case studies, together with the other information in »Agri-drinking water quality indicators and IT/sensor techniques, the following can be deduced:
- Regarding the two kinds of pollutants – nitrates and pesticides – the framing conditions are quite different:
- Nitrate is one single substance, being mobilised and immobilised, leached, transported by runoff, denitrified in the subsurface and emitted. It is essential for plant growth and omnipresent, even under “natural” conditions.
- In contrast, around 250 so called “active substances” of pesticides are authorised by EFSA. Placement on the market of pesticide product needs national approvement. They may only consist of the registered active substances registered on EU-level, pure or in mixture, and of additives, for a better handling of the pesticide. Pesticides are supposed to be – to the greatest possible extent - harmless. They are supposed to degrade or at least to be absorbed by the soil matrix, but not to leach into groundwaters. Improper handling may however lead to runoff or drift and therefore to pollution of surface water and groundwater.
- ADWI are useful on all levels: at farm level as an aid in farmer’s consultation, at local or even national level as an evaluation and monitoring tool for administration work and for policy-makers. However, as more aggregated data show less standard deviation than the single datasets, correlation of ADWI with water quality could be stronger between data on a regional level than on farm level.
- ADWIs which act in the agricultural sector as Driving forces and as Pressure indicators are far more numerous than State or Impact indicators; this indicates how many factors from the agricultural side may influence water pollution. State indicators which are used for the evaluation of the water quality are – on the contrary – far more standardised, like the water quality standards they are supposed to monitor.
- Aim, size and structure of the different case studies are different, and so are the ADWIs in use; very few ADWIs are uniformly used throughout Europe.
- Common indicators on nitrate risk in use are rather simple statistics on fertiliser use, animal density or yield, but also N-budgets are applied.
- Pesticide risk indicators in use are compound/composite indicators, like the Treatment Frequency Index and Pesticide Load Index.
- Concerning pesticides, the DPSLIR-model can only be used, if data on the Driving force and Pressure side on the use of specific pesticides are available and can be linked to the State/Impact side. Since a regional differentiated data compilation of application data and a consequential estimation of the pesticide inputs is missing, pesticides found in drinking water can only sporadically be related to application data (SRU, 2016).
- Calibration and validation of ADWIs against field data is of high importance (Buczko and Kuchenbuch, 2010a).
- The data acquisition scale may be a problem, because readily available data categories at the national level are difficult to access at the local level. Due to uncertainties related to the new regulation on data protection (EU 2016/679), but also due to a tightening of fertiliser legislation in some member states, questions on confidentiality of farm data arise in conjunction with the survey.
2. Evaluating agri-drinking water quality indicators in three case studies
2.1 Research highlights
In »Evaluating agri-drinking water quality indicators in three case studies we take the next step in the development of agri-drinking water indicators (ADWIs), based on statistical analyses of available data from Aalborg and two other case studies
- We describe how long-term series of water quality in groundwater in combination with nitrogen pressure indicators data were used to develop the new link type of indicator in the DPLSIR framework.
»Framework, case studies and indicators,
- and how analysis reveals the relative significance of the nitrogen and pesticide indicators
»Analyses and results.
Of the nitrogen indicators the agricultural N surplus pressure indicator is identified and reconfirmed as a suitable indicator as it is the most significant, prevalent, effective, and easy to use indicator regarding nitrate contamination of water. The nitrate leaching below the soil zone would be the most appropriate state indicator but is seldom collected because sampling equipment to measure leaching is very costly to install and to maintain for monitoring, and the results can be difficult to upscale. In general, the more abundant state indicator such as nitrate concentrations in groundwater is recommended as this is the standard state quality indicator.
Selecting directly appropriate pesticide indicators are much more difficult than for nitrogen due to the lack of long time series of both pesticide application pressure and pesticide concentration state data. In the case of lack of direct appropriate pesticide pressure data, an attempt can be made by using N surplus as the pressure indicator of intensive agriculture and probable use of pesticides. It is suggested that the use of nitrogen fertilizers and pesticides is positively correlated.
Lag times may provide a valuable insight into the mode of contaminant transport because they represent the shortest travel time that delivers the agricultural signal to the water sample collection point. In contrast, the groundwater age represents the mean residence time of the existing groundwater at the collection point. Therefore, knowledge of both groundwater age and lag time are important for protection of the aquatic environment.
»Conclusions and recommendations
A leaflet has been prepared to disseminate the importance of linking agricultural impact and drinking water quality response using examples from Aalborg and the other two case studies. Workshops and presentations have highlighted the importance of coherency and consistency in agri-environmental measures since, in some hydrological context, only long-term coherent policies will produce sufficient effects. Passive samplers have been used to both involve local stakeholders in monitoring and improve water quality monitoring itself by adding an integrative sampling to point sampling.
»Dissemination to stakeholders
3. Harmonized indicator database
3.1 Research highlights
In »Harmonized indicator database we describe the preparation of harmonized datasets for water quality monitoring of drinking water resources, and the development of a readily usable database from these harmonized datasets.
The development of the database has mainly been driven by existing datasets coming from Aalborg and the other FAIRWAY case studies. The database contains near 390,000 rows of data from the 13 case study sites, with more than 65 parameters and more than 500 sub-parameters. One of the challenges throughout the task of database development has been to find ways to harmonize as much as possible the datasets obtained from those various sources.
- We provide access to the database, including the data supplied by Aalborg, and describe it in terms of its general structure,
- describe its development from the data supplied by Aalborg and the other case studies,
»Database development process
- and detailed structure.
»Detailed structure of the database
- Possible uses of the database are then mentioned along with examples of some interesting data series and instructions on using the database efficiently (e.g. lag-time and groundwater-age data sets from Aalborg were added to the database as “Link” indicators).
»Using the database
- Finally, the major problems and limitations encountered throughout this work are discussed, local data through European databases, timescale of monitoring data and institutions with different operational aims although in Aalborg the FAIRWAY partner responsible for the case study was involved in pre-existing projects that had already begun to collect data, so there was little difficulty providing data to the database.
Some of the major challenges identified in building the database are that:
- Definitions of ‘boundary’ are different from the pressure and state perspectives. The catchment area defines the hydrogeological boundary, but the agricultural boundary is an administrative boundary (at least are displayed as that). Moreover, there is generally a lag time (delay) between pressure and state indicators. Consequently, pressure data and state data do not overlap in most cases, and thus they cannot be linked directly.
- Because of the difference in those definitions, the scale of the collected data is also different. The state data (mainly hydrogeochemical data on water quality) can be point or catchment scale while the pressure data is ideally at the field plot scale but actually most often at administrative levels (municipal, regional, or even national level).
- Therefore, it is time-consuming to collect these large sets of data and process the data to a comparable form between state and pressure for a case study and between the case studies.