TY - JOUR
T1 - Probabilistic indicators for soil and groundwater contamination risk assessment
AU - la Cecilia, Daniele
AU - Porta, Giovanni M.
AU - Tang, Fiona H.M.
AU - Riva, Monica
AU - Maggi, Federico
N1 - Funding Information:
We acknowledge the support of the University of Sydney through the SREI2020 EnviroSphere research program, the University of Sydney Mid-career Research Award and SOAR Fellowship supporting F. Maggi. G.M. Porta and M. Riva acknowledge the EU and MIUR for funding, in the frame of the collaborative international Consortium (WE-NEED) financed under the ERA-NET WaterWorks2014 Cofunded Call. This ERA-NET is an integral part of the 2015 Joint Activities developed by the Water Challenges for a Changing World Joint Programme Initiative (Water JPI). The authors acknowledge the Sydney Informatics Hub and the University of Sydney's high performance computing cluster Artemis for providing the high performance computing resources that have contributed to the research results reported within this paper.
Funding Information:
We acknowledge the support of the University of Sydney through the SREI2020 EnviroSphere research program, the University of Sydney Mid-career Research Award and SOAR Fellowship supporting F. Maggi. G.M. Porta and M. Riva acknowledge the EU and MIUR for funding, in the frame of the collaborative international Consortium (WE-NEED) financed under the ERA-NET WaterWorks2014 Cofunded Call. This ERA-NET is an integral part of the 2015 Joint Activities developed by the Water Challenges for a Changing World Joint Programme Initiative (Water JPI). The authors acknowledge the Sydney Informatics Hub and the University of Sydney’s high performance computing cluster Artemis for providing the high performance computing resources that have contributed to the research results reported within this paper.
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/8
Y1 - 2020/8
N2 - Deterministic assessments of whether, when, and where environmental safety thresholds are exceeded by pollutants are often unreliable due to uncertainty stemming from incomplete knowledge of the properties of environmental systems and limited sampling. We present a global sensitivity analysis to rank the contribution of uncertain parameters to the probability, P, of a target quantity to exceed user-defined environmental safety thresholds. To this end, we propose a new index (AMAP) which quantifies the impact of a parameter on P and can be readily employed in probabilistic risk assessment. We apply AMAP, along with existing moment-based sensitivity indices, to quantify the sensitivity of soil and aquifer contamination following herbicide glyphosate (GLP) dispersal to soil hydraulic parameters. Target quantities are GLP and its toxic metabolite aminomethylphosphonic acid (AMPA) concentrations in the top soil as well as their leaching below the root zone. The global sensitivity analysis encompasses six scenarios of managed water amendments and rainfall events. The biodegradation of GLP and AMPA varies slightly across scenarios, while leaching below the root zone is greatly affected by the assumed hydrologic boundary conditions. AMAP shows that, among the tested uncertain parameters, absolute permeability, air-entry suction, and porosity have the greatest impact on GLP and AMPA probability to pollute the aquifer by exceeding the aqueous concentration thresholds. Our results show that AMAP is effective to thoroughly explore time histories arising from model-based predictions of environmental pollution hazards. The proposed methodology may support informed decision making in risk assessments and help assessing ecological indicators through threshold-based analyses.
AB - Deterministic assessments of whether, when, and where environmental safety thresholds are exceeded by pollutants are often unreliable due to uncertainty stemming from incomplete knowledge of the properties of environmental systems and limited sampling. We present a global sensitivity analysis to rank the contribution of uncertain parameters to the probability, P, of a target quantity to exceed user-defined environmental safety thresholds. To this end, we propose a new index (AMAP) which quantifies the impact of a parameter on P and can be readily employed in probabilistic risk assessment. We apply AMAP, along with existing moment-based sensitivity indices, to quantify the sensitivity of soil and aquifer contamination following herbicide glyphosate (GLP) dispersal to soil hydraulic parameters. Target quantities are GLP and its toxic metabolite aminomethylphosphonic acid (AMPA) concentrations in the top soil as well as their leaching below the root zone. The global sensitivity analysis encompasses six scenarios of managed water amendments and rainfall events. The biodegradation of GLP and AMPA varies slightly across scenarios, while leaching below the root zone is greatly affected by the assumed hydrologic boundary conditions. AMAP shows that, among the tested uncertain parameters, absolute permeability, air-entry suction, and porosity have the greatest impact on GLP and AMPA probability to pollute the aquifer by exceeding the aqueous concentration thresholds. Our results show that AMAP is effective to thoroughly explore time histories arising from model-based predictions of environmental pollution hazards. The proposed methodology may support informed decision making in risk assessments and help assessing ecological indicators through threshold-based analyses.
KW - AMPA
KW - Environmental risk assessment
KW - Global sensitivity analysis
KW - Glyphosate
KW - Groundwater
KW - Modeling
KW - Pollution
KW - Soil
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=85084059498&partnerID=8YFLogxK
U2 - 10.1016/j.ecolind.2020.106424
DO - 10.1016/j.ecolind.2020.106424
M3 - Article
AN - SCOPUS:85084059498
SN - 1470-160X
VL - 115
JO - Ecological Indicators
JF - Ecological Indicators
M1 - 106424
ER -