ARPEGES: A Bayesian Belief Network to Assess the Risk of Pesticide Contamination for the River Network of France

Piffady, Jérémy; Carluer, Nadia; Gouy, Véronique; Hénaff, G. Le; Tormos, T.; Bougon, Nolwenn; Adoir, Emilie; Mellac, Katell

doi:10.1002/ieam.4343

Cited by 18 publications

(12 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A number of detailed mechanistic models have successfully simulated pesticide dynamics at plot and catchment scale (Piffady et al, 2020). However, detailed observational data required for the calibration and validation of detailed models is not widely available to managers in many drinking water catchments.…”

Section: Resultsmentioning

confidence: 99%

“…without available observational data (Piffady et al, 2020), making it difficult to calibrate or validate a risk model. Hence, model credibility and salience (Cash et al, 2005) need to be evaluated by experts and stakeholders.…”

Section: Limitations and Outlookmentioning

confidence: 99%

“…The use of BBNs has gained increasing popularity in environmental modelling and risk assessment (Aguilera et al, 2011;Kaikkonen et al, 2021) with examples including pesticide risk management (Carriger and Newman, 2012;Henriksen et al, 2007) and probabilistic assessments of pesticide exposure and effects (Mentzel et al, 2021). While the integration of Bayesian networks with GIS in environmental risk assessment has also been growing steadily over recent years (Moe et al, 2021), to date spatial BBN has only been used for pesticide risk modelling on a single occasion to assess pesticide runoff risk at a basin scale across France (Piffady et al, 2020). To our knowledge, the present study provides a first application of spatial BBN for probabilistic field-level assessment of intrinsic pesticide pollution risk from 'critical source areas' at a monthly resolution.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Probabilistic modelling of inherent field-level pesticide pollution risk in a small drinking water catchment using spatial Bayesian Belief Networks

Troldborg

Gagkas

Vinten

et al. 2021

Preprint

View full text Add to dashboard Cite

Abstract. Pesticides are contaminants of priority concern that continue to present a significant risk to drinking water quality. While pollution mitigation in catchment systems is considered a cost-effective alternative to costly drinking water treatment, the effectiveness of pollution mitigation measures is uncertain and needs to be able to consider local biophysical, agronomic, and social aspects. We developed a probabilistic decision support tool (DST) based on spatial Bayesian Belief Networks (BBN) that simulates inherent pesticide leaching risk to ground- and surface water quality to inform field-level pesticide mitigation strategies in a small drinking water catchment (3.1 km2) with limited observational data. The DST accounts for the spatial heterogeneity in soil properties, topographic connectivity, and agronomic practices; temporal variability of climatic and hydrological processes as well as uncertainties related to pesticide properties and the effectiveness of management interventions. The rate of pesticide loss via overland flow and leaching to groundwater and the resulting risk of exceeding a regulatory threshold for drinking water was simulated for five active ingredients. Risk factors included climate and hydrology (temperature, rainfall, evapotranspiration, overland and subsurface flow), soil properties (texture, organic matter content, hydrological properties), topography (slope, distance to surface water/depth to groundwater), land cover and agronomic practices, pesticide properties and usage. The effectiveness of mitigation measures such as delayed timing of pesticide application; 10 %, 25 % and 50 % reduction in application rate; field buffers; and presence/absence of soil pan on risk reduction were evaluated. Sensitivity analysis identified the month of application, land use, presence of buffers, field slope and distance as the most important risk factors, alongside several additional influential variables. Pesticide pollution risk from surface water runoff showed clear spatial variability across the study catchment, while groundwater leaching risk was uniformly low, with the exception of prosulfocarb. Combined interventions of 50 % reduced pesticide application rate, management of plough pan, delayed application timing and field buffer installation notably reduced the probability of high-risk from overland runoff and groundwater leaching, with individual measures having a smaller impact. The graphical nature of the BBN facilitated interactive model development and evaluation with stakeholders to build model credibility, while the ability to integrate diverse data sources allowed a dynamic field-scale assessment of ‘critical source areas’ of pesticide pollution in time and space in a data scarce catchment, with explicit representation of uncertainties.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Limitations and Outlookmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic modelling of inherent field-level pesticide pollution risk in a small drinking water catchment using spatial Bayesian Belief Networks

Troldborg

Gagkas

Vinten

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…This balance can be achieved by focusing on key processes in the system and its management, and by using simplified methods for the description of relations among variables (such as probabilistic methods). Models based on BBNs have been widely applied for river management purposes (Marcot et al, 2001;Piffady et al, 2021) and often been used as computational background for risk-based adaptive environmental management (Barton et al, 2020). The BBN technique has often been used in trade-off analyses, since it allows for the clear probabilistic description of systems and processes .…”

Section: Way Forward: Bayesian Belief Network Models To Support the S...mentioning

confidence: 99%

Integrating Bayesian Belief Networks in a toolbox for decision support on plastic clean-up technologies in rivers and estuaries

Leone

Catarino

Pauwels

et al. 2022

Environmental Pollution

View full text Add to dashboard Cite

Current mitigation strategies to offset marine plastic pollution, a global concern, typically rely on preventing floating debris from reaching coastal ecosystems. Specifically, clean-up technologies are designed to collect plastics by removing debris from the aquatic environment such as rivers and estuaries. However, to date, there is little published data on their potential impact on riverine and estuarine organisms and ecosystems. Multiple parameters might play a role in the chances of biota and organic debris being unintentionally caught within a mechanical clean-up system, but their exact contribution to a potential impact is unknown. Here, we identified four clusters of parameters that can potentially determine the bycatch: (i) the environmental conditions in which the clean-up system is deployed, (ii) the traits of the biota the system interacts with, (iii) the traits of plastic items present in the system, and, (iv) the design and operation of the clean-up mechanism itself. To efficiently quantify and assess the influence of each of the clusters on bycatch, we suggest the use of transparent and objective tools. In particular, we discuss the use of Bayesian Belief Networks (BBNs) as a promising probabilistic modelling method for an evidence-based trade-off between removal efficiency and bycatch. We argue that BBN probabilistic models are a valuable tool to assist stakeholders, prior to the deployment of any clean-up technology, in selecting the best-suited mechanism to collect floating plastic debris while managing potential adverse effects on the ecosystem.☆ This paper has been recommended for acceptance by Maria Cristina Fossi.

show abstract

“…However, Bayesian Networks can also be used as machine-learning associative tools that are suitable for deriving patterns in datasets without a specific response variable. It could be argued that pesticide risk, expressed as flux or concentration of pesticide in different potential loss pathways (overland flow or groundwater leaching) is a latent variable without available observational data (Piffady et al, 2020), making it difficult to calibrate or validate a risk model. Hence, model credibility and salience (Cash et al, 2005) need to be evaluated by experts and stakeholders.…”

ARPEGES: A Bayesian Belief Network to Assess the Risk of Pesticide Contamination for the River Network of France

Abstract: This article is part of the special series "Applications of Bayesian Networks for Environmental Risk Assessment and Management" and was generated from a session on the use of Bayesian networks (BNs) in environmental modeling and assessment in 1 of 3 recent conferences:

Cited by 18 publications

References 44 publications

Probabilistic modelling of inherent field-level pesticide pollution risk in a small drinking water catchment using spatial Bayesian Belief Networks

Probabilistic modelling of inherent field-level pesticide pollution risk in a small drinking water catchment using spatial Bayesian Belief Networks

Integrating Bayesian Belief Networks in a toolbox for decision support on plastic clean-up technologies in rivers and estuaries

Comment on hess-2021-477

Contact Info

Product

Resources

About