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2020
DOI: 10.1016/j.envpol.2019.113486
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A sensitivity analysis of pesticide concentrations in California Central Valley vernal pools

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Cited by 12 publications
(13 citation statements)
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References 34 publications
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“…Unfortunately, as model complexity increases to allow for more realistic scenarios, so does the variability associated with its predictions. The Pesticide in Water Calculator, a model regularly used to estimate pesticide concentrations in surface and ground water, may predict concentrations that span >10 orders of magnitude using realistic estimates for over 50 parameters (Sinnathamby et al, 2020). As such, stakeholders should be prepared for modeled exposure estimates to span large ranges, and minimal monitoring data for validation.…”
Section: Uncertainty In Erasmentioning
confidence: 99%
“…Unfortunately, as model complexity increases to allow for more realistic scenarios, so does the variability associated with its predictions. The Pesticide in Water Calculator, a model regularly used to estimate pesticide concentrations in surface and ground water, may predict concentrations that span >10 orders of magnitude using realistic estimates for over 50 parameters (Sinnathamby et al, 2020). As such, stakeholders should be prepared for modeled exposure estimates to span large ranges, and minimal monitoring data for validation.…”
Section: Uncertainty In Erasmentioning
confidence: 99%
“…Thus, weather patterns including spatial and temporal trends in temperature and precipitation were important in population model development (i.e., in estimation of vital rates). Data collected for population and spatial characteristics included density dependence, population size, metapopulation structure, movement, geographic range and habitat measures (features and classification/suitability) [1,5,8,24,31,32,34,35,[43][44][45][46][47] (see Table S2). Density dependence (e.g., based upon pond volume) was important to consider due to potential impact on fitness [35].…”
Section: Phase 2: Data Collectionmentioning
confidence: 99%
“…For example, climate changes could exacerbate impacts due to chemical exposure if the vernal pools do not get charged as frequently and/or may not stay inundated for as long [50]. Data acquired for chemical exposure includes estimated environmental concentrations from the Pesticide Water Calculator (PWC version 1.59), which has been used to predict temporal trends of organophosphate pesticide concentrations (including diazinon and malathion) in three CA vernal pools based upon exposure duration and representative of nearby crop application [8]. Chemical effects characteristics included representation of toxic effects using standard crustacean surrogates (e.g., Daphnia magna, Thamnocephalus platyurus) and examination of effects by life stage or size as well as exposure route [4,7,8,[15][16][17][18][19][51][52][53][54][55][56][57] (see Table S4).…”
Section: Phase 2: Data Collectionmentioning
confidence: 99%
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“…Screening models do not consider the influence of weather conditions, differences in soil topography, and product use patterns on different crops (USEPA, 2022a). On the other hand, a scenario‐dependent model includes data such as crop growth stages, soil properties, weather patterns, field hydrology, and pesticide use patterns and fate (Sinnathamby et al, 2020; Young & Fry, 2019). In general, scenario‐dependent models have been built for the regulatory agencies of the US (USEPA, 2022b) and the European Union (European Food Safety Authority [EFSA], 2013) in partnership with industry and academics, whereas for other countries, especially tropical regions, there is need for scientific knowledge development including potential adaptations especially regarding the development of local scenarios.…”
Section: Introductionmentioning
confidence: 99%