Identifying and quantifying catchment pesticide hotspots in Mediterranean first-flush stormwaters: A synchronized watershed-scale grab sampling approach.

BACKGROUND

Mediterranean watersheds are characterized by extreme hydrological variability, where intense "first-flush" events mobilize accumulated agrochemicals. Conventional monitoring programs often fail to capture these high-energy, episodic pulses, resulting in a systemic underestimation of non-point source pollution and a critical lack of data-driven mitigation strategies.

OBJECTIVES

This study established a diagnostic framework to identify and quantify pesticide "hotspots" across the 1100 km2 Kishon River basin. By employing a synchronized, watershed-scale sampling approach, we evaluated relationships between land-use intensity and pesticide richness to provide a monitoring framework for strategic catchment management.

METHODS

Synchronized grab sampling was conducted at 25 stations during the initial winter storms of 2020 and 2021. Samples were screened for 110 compounds using LC-MS/MS. Statistical analysis utilized Linear Mixed Models (LMM) to evaluate the influence of agricultural intensity, while a dual-metric "Hotspot Index"-integrating concentration magnitude and compound richness-prioritized sub-basins for urgent intervention.

RESULTS

Pesticides were ubiquitous (100% detection). High agriculture intensity was significantly associated (p < 0.001) with increased pesticide concentrations, with field crops exerting more influence on chemical richness than orchards. Notably, banned legacy compounds (e.g., Simazine) were detected at over 50% of stations, indicating high environmental persistence or unauthorized contemporary use. Four specific sub-basins met the hotspot criteria, consistently exceeding the 75th percentile for multiple pollution metrics.

CONCLUSIONS

The synchronized framework effectively captured pesticide peaks typically underrepresented in routine monitoring. We propose a management shift with storm-triggered protocols. This transferable approach identifies critical spatial contamination patterns, providing a robust framework for prioritizing mitigation in Mediterranean agroecosystems globally.