Testing and comparison of three pesticide risk indicator models under Norwegian conditions—A case study in the Skuterud and Heiabekken catchments

Abstract

In recent years, there has been an increased concern for the risk of non-target impacts of pesticides. It has been widely acknowledged that weight and volume measures are not adequate proxies for assessing this risk, and many pesticide risk indicator models have been developed and are in use throughout the world. They all have their strengths and weaknesses, and here we have evaluated the Norwegian Environmental Risk Indicator (NERI) in relation to the Environmental Impact Quotient (EIQ), both models weighting potential effects on the environment in relation to risk of exposure, and the leaching model Surface Water Attenuation (SWAT). We have utilized reported use of pesticides within two catchments in Norway to be able to compare the model outputs under realistic pest management regimes. In general, the NERI-model gives much weight to substances of long persistence in soil and/or high risk of bioaccumulation, while the EIQ-model risk estimates are largely governed by amount of pesticide used. As the general focus when considering pesticide use and the environment has shifted from purely focusing on reducing amounts to risk reduction, the NERI-model might deliver a more up-to-date output with the more toxic and persistent compounds dominating. However, the absence of mobile pesticides like MCPA from the top of the ranking raises some concern, and points out some of the lacks in such simple models when it comes to use for farmer guidance. They do not include any site specific data on soil characteristics and weather conditions, factors of great importance for pesticide leaching and, hence, governing the risk of exposure. The SWAT-model focuses on the risk of exposure due to leaching, and does not include an effect factor (e.g. toxicity to terrestrial and aquatic organisms). However, for the purpose of farmer guidance, presentations of leaching risk of individual pesticides based on pre-simulated model results under some representative environmental conditions, give the opportunity for an initial screening of the more suitable pesticides regarding risk of exposure.