Research articleA spatial approach to identify priority areas for pesticide pollution mitigation
Introduction
Pesticide residues frequently occur in surface waters in Europe (Reemtsma et al., 2013, VMM, 2017, VMM, 2015) potentially having an impact on aquatic organisms or communities (Lefrancq et al., 2017). Treatment or targeted mitigation can prevent pesticide pollution from dispersing in the environment (Gregoire et al., 2009). Therefore, there is a need to implement mitigation measures in agriculture to ensure food production while reducing the environmental impact of pesticides and simultaneously achieving good water quality (European Commission, 2000, European Commission, 2009).
Agricultural non-point-source (NPS) pesticide pollution is defined as inputs along the entire watercourse from applications of agrochemicals onto farmland (Holvoet et al., 2007). Once pesticides are applied and released in the environment, their fate is affected by their physical and chemical properties and the interactions with soil (Borggaard and Gimsing, 2008, Chaplain et al., 2011, Maqueda et al., 2017), climatology (Doppler et al., 2014, Leu et al., 2004a, Leu et al., 2004b), and agricultural practices (Alletto et al., 2010, Potter et al., 2015). The most important routes of diffuse pesticide pollution in water bodies are surface runoff and soil erosion, drain flow, leaching and spray drift (Reichenberger et al., 2007, Tang et al., 2012). Knowledge of these pathways and their relative importance is a prerequisite for developing mitigation strategies for polluted surface water (Bereswill et al., 2014, Holvoet et al., 2007, Reichenberger et al., 2007, Tang et al., 2012).
Areas within a catchment pose varying risks of pollution. Critical source areas (CSAs) contribute a considerable fraction of the pollution load to surface water. A CSA is where pesticide sources intersect with areas of high mobilisation potential which have the highest propensity for surface runoff generation, pollutant transport and delivery via hydrologically connected pathways (Doppler et al., 2014, Doppler et al., 2012, Frey et al., 2009). The spatial variability of pesticide losses to waterbodies can be significant (Freitas et al., 2008, Leu et al., 2004a, Leu et al., 2004b) therefore the identification of CSA will help target mitigation measures efficiently to locations where they can strongly reduce pesticide loads into river courses.
The spatial variability within a catchment, e.g. different soils and land uses, different travel times and flow lengths from each parcel to the catchment outlet and different application dates of pesticides increase the complexity and the variables that must be included to determine where mitigation measures should be proposed. Topography (which governs the flow paths of surface water) and the position of landscape elements such as riparian buffer strips, grassed waterways, hedges, ditches, decisively influence if and what fraction of applied pesticide ultimately reaches a watercourse (Reichenberger et al., 2007). The amounts of pesticides reaching water resources vary considerably in time and space and are highly dependent upon application rates and the chemical characteristics of the pesticides, as well as soil and climate conditions (Doppler et al., 2014, Doppler et al., 2012, Freitas et al., 2008, Leu et al., 2004a).
Assessment and identification of areas contributing to non-point source (NPS) pollution by pesticides has been performed in other approaches using hydrological models to approximate contaminant transport (Bach et al., 2002, Lescot et al., 2013, Wohlfahrt et al., 2010), a combination of indicators and multi-criteria analysis (Macary et al., 2014), GIS modelling to prioritise catchments or streams within a watershed (Zhang et al., 2008), or the use of long-term pesticide monitoring data (Di Guardo and Finizio, 2018). These approaches were applied mainly to larger scales (watershed) to identify risk zones. Also, these studies do not consider the microscale required for recommendations within a small catchment. Although the watershed scale is proper to achieve environmental goals for water quality, changes in agricultural practices and the implementation of mitigation measures like field buffer strips take place at field level (McGonigle et al., 2012). Therefore, risk assessment at field scale is useful when the implementation of actions by farmers is needed (Bereswill et al., 2014).
A range of management techniques is available to control agricultural pollutants such as the reduction of pesticide use and the installation of landscape features like buffer zones, hedgerows, retention ponds and wetlands that can capture and degrade pollutants before they reach watercourses (Bereswill et al., 2014, Reichenberger et al., 2007). The use of mitigation measures that minimise the risk of off-site pesticide pollution caused by spray drift, drain flow and runoff could contribute to achieve the good status of water bodies (Aguiar et al., 2015, Maillard et al., 2012).
We propose a robust and spatially explicit model-based (Mb) risk approach to identify priority areas to target landscape mitigation measures in order to reduce pesticide pollution and erosion in surface water. The Mb risk relies on geospatial emission modelling and connectivity of parcel sites towards waterbodies. The impact of crop rotation during five-years is analysed for this catchment. The Mb risk method is applied in a case study in the southeast of Flanders, Belgium. The Mb risk areas are then compared with an observation-based (Ob) approach that includes field observations for relevant processes identified for this catchment by local experts.
Section snippets
Site description
The catchment for this study is located in Sint-Truiden, SE Flanders, Belgium (Fig. 1). The site has an area of 10,7 km2 with altitudes ranging from 51 to 107 m above sea level. The Cicindria river flows from South to North with a length of 6.5 km within the catchment limits. The dominant land use is agriculture covering 72% of the area; 32% with fruit trees (apple, pears and cherries) and 68% arable crops (cereals, beets, maize mainly).
The area is characterised by a hilly topography with
Model-based risk areas
Our purpose was to develop a desktop analysis to identify critical areas that could be included in a mitigation action plan to reduce pesticide loads into surface water. The approach considers the potential pesticide emissions and the hydrological connectivity of each parcel. Fig. 5 shows the resulting Mb risk map for 2012.
The Mb risk map allows the identification and prioritisation of specific fields within the catchment. Once the critical areas are identified, the within-field hotspots need
Conclusions
We developed a GIS-based tool for water resource managers to help in the identification and prioritisation of critical source areas. The tool is relatively simple to apply and uses geospatial data that it is often relatively accessible. We propose the model-based risk method as a valuable approach to detect priority areas for actions against diffuse pesticide pollution. It identifies areas in which mitigation measures seem necessary and could, therefore, contribute to improving water quality.
Acknowledgements
The authors would like to thank Katrien Wouters for the compilation of field observations and Petra Deproost from the Bureau for Environment (Flemish Department of Planning) for the erosion sensitivity maps.
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 675120.
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