Monitoring & indicators
In this section of FAIRWAYiS we identify, evaluate and further develop transparent agri-environmental indicators to monitor and assess the impact of measures and good practices on drinking water quality. Appropriate monitoring tools are needed to help develop and implement governance models to preserve the quality of drinking water resources. Here we:
- review, select and prioritize pressure agri-drinking water quality indicators on farm level;
- review, select and prioritize state and impact agri-drinking water quality indicators, including IT/sensor techniques for participative monitoring by farmers and citizens;
- develop a database containing all the data necessary to compute selected indicators in the case studies and to compile a harmonized dataset on pesticide and fertiliser contamination of drinking water resources.
Agri-drinking water quality indicators and IT/sensor techniques
|Main authors:||Susanne Klages, Nicolas Surdyk, Christophoros Christophoridis, Birgitte Hansen, Claudia Heidecke, Abel Henriot, Hyojin Kim, Sonja Schimmelpfennig|
|FAIRWAYiS Editor:||Jane Brandt|
|Source document:||»Klages, S. et al. 2018. Review report of Agri-Drinking Water quality Indicators and IT/sensor techniques, on farm level, study site and drinking water source. FAIRWAY Project Deliverable 3.1, 180 pp
Results from this research task have also been published as scientific papers:
- Klages, S.; Heidecke, C.; Osterburg, B. The Impact of Agricultural Production and Policy on Water Quality during the Dry Year 2018, a Case Study from Germany. Water 2020, 12, 1519. https://www.mdpi.com/2073-4441/12/6/1519
- Klages, S.; Heidecke, C.; Osterburg, B.; Bailey, J.; Calciu, I.; Casey, C.; Dalgaard, T.; Frick, H.; Glavan, M.; D’Haene, K.; Hofman, G.; Leitão, I.A.; Surdyk, N.; Verloop, K.; Velthof, G. Nitrogen Surplus—A Unified Indicator for Water Pollution in Europe? Water 2020, 12, 1197. https://www.mdpi.com/2073-4441/12/4/1197
Nitrogen and pesticide cycles in the agri-hydrogeochemical system
We start this section by defining 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
Data and indicators to regulate and monitor the use of nitrates and pesticides
We then 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 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
Developing FAIRWAY agri-drinking water quality indicators (ADWIs)
The DPSIR model is defined as “causal framework for the description of interactions between society and the environment”. It was adopted by the European Environment Agency (EEA 2018). According to its terminology, 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 (Smeets and Weterings, 1999; Gabrielsen and Bosch, 2003). In FAIRWAY we consider ADWIs within the DPSIR-framework. The adjusted DPSLIR-framework contains a new element, the Link Indicator.
»Developing FAIRWAY agri-drinking water quality indicators (ADWIs)
Agri-drinking water quality indicators at farm and drinking water levels
Agri-environmental indicators (AEI), as developed by OECD and Eurostat, are implemented and further developed for the monitoring and evaluation of the negative and positive impacts of agricultural activities on the environment. AEIs are used on European/national level (28 AEI are listed in fact sheets related to COM final 0508/2006 (Eurostat, 2018). The AEI are applied e. g. to evaluate/benchmark the transcript of EU-legislation at Member State level), at regional level (to monitor the impact of agriculture on environment, identify hotspots or focus subjects and areas for the agricultural advisory service) and at farm level (as decision aid tool for the farmer). Agri-drinking water quality indicators (ADWIs) to be developed in FAIRWAY are defined as indicators for the quality of drinking water. As drinking water may be produced from groundwater or surface water, ADWIs aim at the quality of both. As done for the 28 harmonised AEI (COM 2006, Eurostat 2018), we classified all ADWIs, which the case studies reported into the adjusted DPSLIR framework. We added further ADWIs according to a literature review. The ADWIs listed in the table may work as indicators by themselves or they are elements of compound indicators. Indicators for both, nitrates and pesticides, are listed in the same table, in order to avoid redundance as far as possible.
»Agri-drinking water quality indicators at farm and drinking water levels
Prioritisation of agri-drinking water quality indicators
All the ADWIs that are the subject of the survey among the case studies, those proposed by the case study leaders to be included in a further evaluation and those which, according to a literature review, are used for pesticide and nitrate monitoring/risk assessment are listed and described. Indicators which act in the agricultural sector as Driving forces and as Pressure indicators, are far more numerous than State respectively Impact indicators. The large number of agricultural Driving forces and Pressure ADWIs also explains, that from this part of the DPSLIR-model, many factors 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. A prioritisation of ADWI is therefore above all necessary for the Driving forces and Pressure indicators in the agricultural sector, in order to focus on the most significant, prevalent, effective and easy to use indicators. The survey on ADWIs already used in case studies and the most promising indicators leads to a first weighting of indicators.
»Prioritisation of agri-drinking water quality indicators
Further prioritisation and evaluation of agri-drinking water quality indicators
In order to further drive forward the proiritisation of the selected ADWIs in FAIRWAY, we intend to connect ADWIs from the agricultural and the water work side, using statistical methods. We also intend to further investigate on the Link indicator, especially how this ADWI fits in between the other indicators. We intend to examine
- the feasibility of indicators calculation,
- the link between indicators, and
- the relevance of some indicators, as statistical calculations give the mathematical expression for the link that exists between them.
For this purpose, a database of ADWI-data on catchments-level will be established by collecting data from the FAIRWAY-case studies. Preparatory work has been carried out, using the Voulzie case study, in order to specify the data request to the case studies. Statistical analyses of data of the Voulzie case study showed, that the spring discharge time series can be rather well explained by the evolution of the recharge of the year before. The first attempt to build this database enabled the calculations of indicators as well as the first links between Pressure indicators and State 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
IT/sensor and automatic sampler techniques for pesticide and nitrate sampling
Monitoring has evolved considerably over the past ten years and even more in recent years. There are broad avenues for innovation and, as part of the FAIRWAY project, a review of in situ monitoring methods has been achieved, in accordance with the chapter on participatory monitoring. Many methods can also be applied in the laboratory. A review showed that many tools (some are prototypes) and methods are being developed to improve measures for both nitrates and pesticides. The developed methods are based e. g. on optical sensors and paper based sensors. These tools make it possible to improve the confidence in the measurement while improving the analytic capacities of the devices (limits of measurements and types of molecules). In addition, relays with smartphones can be developed to facilitate the reading of the results and to trust them.
»IT/sensor and automatic sampler techniques for pesticide and nitrate sampling
Participatory monitoring: involvement of citizens
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 promotor. Participatory monitoring initiatives can often be considered successful as they allow measurement of phenomena at frequencies and locations that are not reachable by a team of researchers alone. On the other hand, associated difficulties have been identified. First, it is not always easy to find the right number of participants to complete a large program, some "site-specific" programs may be canceled due to lack of participants. Moreover, in our field of water and environment, participatory programs can only hope to change behaviors if educational tasks have been planned in the projects. Lastly, participatory monitoring programs generally only work with a coherent method to analyse the data (computer infrastructure and/or scientific manpower) that must be anticipated. If the educational tasks and IT tasks are taken into account, participatory monitoring programs are not necessary less expensive than the institutional programs. The review of in situ monitoring tools in development (even prototypical) suggests possibilities of access to increasingly simple and robust tools or new probes attached to smartphones. Thanks to these tools, some problems, such as the lack of participants and some analysis bias, could be resolved.
»Participative monitoring: involvement of citizens
From a survey among the FAIRWAY case studies on indicator use and from the the information in this section of FAIRWAYiS, the following aspects 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 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 waters.
- 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).
- Correlation analysis with data of the testsite showed, that the compound/composite indicators (field budget or Cassis-N surplus) were not the ones with the best correlation: budgets calculate N-losses from the root zone, and therefore do not take into account the N-losses in the unsaturated zone beneath the root zone (this is the reason why we introduce the Link indicator for the DPSLIR-framework). Composite indicators may show a low relative sensitivity for changing conditions (Buczko and Kuchenbuch, 2010).
- 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.
Link between agricultural pressure and drinking water quality state: lessons learned in Denmark and France
|Main authors:||Hyojin Kim, Nicolas Surdyk, Ingelise Møller, Abel Henriot, Birgitte Hansen|
|FAIRWAYiS Editor:||Jane Brandt|
|Source document:||Kim H., Surdyk N., Møller, I., Henriot A., Hansen B. 2019. The link between agricultural pressure and drinking water quality state: lessons learned in Denmark and France. Water, 11 pp. FAIRWAY Project Deliverable 3.2
Database containing harmonized data sets
|Main authors:||Marc Laurencelle, Nicolas Surdyk, Matjaž Glavan, Birgitte Hansen, Claudia Heidecke, Hyojin Kim, Susanne Klages|
|FAIRWAYiS Editor:||Jane Brandt|
|Source document:||»Laurencelle, M. et al 2021. (Short note for the) database containing harmonised datasets, 28 pp. FAIRWAY Project Deliverable 3.3
In this section of FAIRWAYiS 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.
A large range of environmental indicators has been considered for monitoring the quality of drinking water. Our focus in FAIRWAY has mainly been on indicators related to the monitoring of nitrate and pesticide application (»Agri-drinking water quality indicators and IT/sensor techniques) and the transport and fate in the hydrogeological system and in drinking water (»Link between agricultural pressure and drinking water quality state - lessons learned in Denmark and France).
The development of the database has mainly been driven by existing datasets coming from each of the FAIRWAY case studies (»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 and describe it in terms of its general structure,
describe its development,
»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.
»Using the database
Finally, the major problems and limitations encountered throughout this work are discussed.
Some major challenges identified throughout this work 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.
Evaluating agri-drinking water quality indicators in three case studies
|Main authors:||Birgitte Hansen, Hyojin Kim, Ingelise Møller, Abel Henriot, Marc Laurencelle, Tommy Dalgaard, Morten Graversgaard, Susanne Klages, Claudia Heidecke and Nicolas Surdyk|
|FAIRWAYiS Editor:||Jane Brandt|
|Source document:||»Birgitte Hansen, Hyojin Kim, Ingelise Møller, Abel Henriot, Marc Laurencelle, Tommy Dalgaard, Morten Graversgaard, Susanne Klages, Claudia Heidecke and Nicolas Surdyk 2021. Evaluation of ADWIs: agri-drinking water quality indicators in three case studies (FAIRWAY Project Deliverable 3.2)
Results from this research task have also been published
- as a scientific paper: »Kim, H.; Surdyk, N.; Møller, I.; Graversgaard, M.; Blicher-Mathiesen, G.; Henriot, A.; Dalgaard, T.; Hansen, B. Lag Time as an Indicator of the Link between Agricultural Pressure and Drinking Water Quality State. Water 2020, 12, 2385
- and for a more general readership in an article in The Conversation: »Moins de nitrates dans l’eau, une vraie course de fond (English summary »Reducing nitrates in water, a real long-distance race)
In this section of FAIRWAYiS we build on the literature review, expert and database consultation that was used to develop a preliminary selection of indicators that is described in »Agri-drinking water quality indicators and IT/sensor techniques. Here we take the next step in the development of agri-drinking water indicators (ADWIs), based on statistical analyses of available data from 3 of the 13 FAIRWAY case studies. This information is used in the »Harmonised indicator database.
Take home messages
ADWIs are defined within the DPLSIR-framework including a new 'link' type of indicator. The link indicator is developed to better explain the relationship between pressure from agriculture and state of water quality. At the time of writing this report, three of the FAIRWAY case studies, two in Denmark (Aalborg and Tunø) and one in France (La Voulzie) had sufficient data (i.e. long-term series of water quality in groundwater in combination with nitrogen (N) pressure indicators) for the required analysis of a shortlist of nitrogen, pesticide and link indicators.
»Framework, case studies and indicators
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 and as a 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. However, in this study, nitrate leaching from pore water data were available from Tunø, Denmark. This is an exceptional case and here we show how they can be used in combination with the N surplus and groundwater nitrate data. 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 specific case of La Voulzie, the analyses of the two other pressure indicators (area of main crop type and amount of application of pesticides) regarding pesticide contamination of groundwater were appropriate choices of indicators. These indicators are transparent and easy to use and to communicate to stakeholders. However, they cannot be abundant indicators because it is rare that a single pesticide product is used on all the agricultural fields having the same crop type in a catchment. Therefore at specific moments, when some pesticides are intrinsically linked to the growth of crops, these two pressure indicators (area of main crop type and amount of application of pesticides) could be usable indicators of potential pesticide contamination. 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 when long time series of data are available. This link shows the joint increase of nitrate and pesticide during the rise of modern agriculture.
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 the 3 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