|Main authors:||Matjaž Glavan, Gregor Kramberger and Katarina Kresnik
|FAIRWAYiS Editor:||Jane Brandt|
|Source documents:||»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
»Laurencelle, M. et al 2021. (Short note for the) database containing harmonised datasets, FAIRWAY Project Deliverable 3.3, 28 pp
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 Dravsko Polje case study was used as described here.
|1. Agri-drinking water quality indicators and IT/sensor techniques|
|2. 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 of the issue of the identification and use of appropriate indicators and how all the FAIRWAY case studies, including Dravsko Polje, 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 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)
- Dravsko Polje together with the other FAIRWAY case studies provided information about the agri-drinking water quality indicators (ADWIs) used in their local area. Because drinking water may be produced from groundwater or surface water, ADWIs aim at quantifying the quality of both. 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 Dravsko Polje 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
- Statistical analyses of data of La 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
- Monitoring has evolved considerably over the past ten years and even more in recent years. A review showed that many tools (some are prototypes) and methods are being developed to improve measures for both nitrates and pesticides.
»IT/sensor and automatic sampler techniques for pesticide and nitrate sampling
- 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 Dravsko Polje 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 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 test site 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.
2. Harmonized indicator database
2.1 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 Dravsko Polje 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 Dravsko Polje, and describe it in terms of its general structure,
- describe its development from the data supplied by Dravsko Polje 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. In Dravsko Polje, the correlation analyses yielded significant results between the amount of fertilizers applied on the catchment (in tons of nitrogen) and the nitrate concentrations (i.e. the average concentration of nitrate in groundwater wells in the catchment). The best correlation was found for a lag time of 4 years. This lag time could seem short considering that the depth to the water table could reach 15 m in the northern part of the study area. But the Slovenian case study leader (University of Ljubljana) estimated that this lag time could be correctly calculated. In the upper terrace, the proportion of rock/gravel/pebbles is 40%. The proportion go higher with depth. On lower terrace, soils contain a high share of sand. Lag times have also connection to drought and precipitation periods (dry and wet years) that impacts nitrate leaching trough soil profile.
»Using the database
- Finally, the major problems and limitations encountered throughout this work are discussed (including definition of boundaries, local data through European databases (publicly available data for Dravsko Polje were downloaded to test whether this data could easily be used), timescale of monitoring data and institutions with different operational aims.
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.