Prospective aquatic risk assessment for chemical mixtures in agricultural landscapes

Abstract

Environmental risk assessment of chemical mixtures is challenging because of the multitude of possible combinations that may occur. Aquatic risk from chemical mixtures in an agricultural landscape was evaluated prospectively in 2 exposure scenario case studies: at field scale for a program of 13 plant‐protection products applied annually for 20 yr and at a watershed scale for a mixed land‐use scenario over 30 yr with 12 plant‐protection products and 2 veterinary pharmaceuticals used for beef cattle. Risk quotients were calculated from regulatory exposure models with typical real‐world use patterns and regulatory acceptable concentrations for individual chemicals. The results could differentiate situations when there was concern associated with single chemicals from those when concern was associated with a mixture (based on concentration addition) with no single chemical triggering concern. Potential mixture risk was identified on 0.02 to 7.07% of the total days modeled, depending on the scenario, the taxa, and whether considering acute or chronic risk. Taxa at risk were influenced by receiving water body characteristics along with chemical use profiles and associated properties. The present study demonstrates that a scenario‐based approach can be used to determine whether mixtures of chemicals pose risks over and above any identified using existing approaches for single chemicals, how often and to what magnitude, and ultimately which mixtures (and dominant chemicals) cause greatest concern. Environ Toxicol Chem 2018;37:674–689. © 2017 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Prospective mixture risk assessment and management prioritizations for river catchments with diverse land uses

“…For agriculture in the present study, cypermethrin, pendimethalin, and chlorothanonil were found to be important regarding peak exposure levels, ranking first, second, and third, respectively, using AA‐EQS to define HI. Those also ranked high in the agriculture study, with regulatory acceptable concentrations (RAC) as assessment criteria (Holmes et al 2018). The rankings according to exposure time also showed similar results.…”

“…Each scenario layout was modeled for 20 yr, with daily quantifications of PECs for the each of the studied chemicals. Details are in the Supplemental Data (section 1) and the scenario reviews (Holmes et al 2018; Diamond et al 2018; de Zwart et al 2018). …”

“…The agriculture scenario incorporated time dependency of emissions related to PPP use on row crops. A 20‐yr time period was modeled on a daily basis by using actual pesticide usage application data for a large arable farm in eastern England (see Holmes et al 2018) and actual rain events from the FOCUS R1 scenario meteorological data set (used in European Union regulatory modeling for PPPs), which is directly applicable to United Kingdom agricultural conditions. The selected agriculture scenario used a winter wheat exposure scenario, with 13 active ingredients applied on known dates and rates.…”

“…The aims of the present study were to combine the land‐use scenarios of the associated manuscripts of the Pellston workshop, references Holmes et al (2018), Diamond et al (2018), and de Zwart et al (2018), to investigate these questions for catchments with different combinations of land use.…”

“…Prospective, catchment‐scale prioritization of chemical mixture risks can assist decision‐making regarding risk‐mitigation strategies (Ginebreda et al 2013; Coppens et al 2015; Sobek et al 2016; Brack et al 2017). The present study expands on and integrates 3 detailed analyses of land use–related scenarios, investigating the specific chemical signatures of an agriculture scenario (emissions from agricultural land dictated by rainfall, soils, and PPP use [Holmes et al 2018]), a treated domestic wastewater scenario (daily use of household chemicals [Diamond et al 2018]), and an urban runoff scenario (rainfall‐mediated emissions from city surface areas [de Zwart et al 2018]).…”