Understanding the dynamics of pesticide transport in the Malewa River and Lake Naivasha, a major fresh water resource, is critical to safeguard water quality in the basin. In this study, the soil and water assessment tool (SWAT) model was used to simulate the discharge of sediment and pesticides (notably the organochlorine residues of lindane, methoxychlor and endosulfan) into the Malewa River Basin. Model sensitivity analysis, calibration and validation were performed for both daily and monthly time steps using the sequential uncertainty fitting version 2 (SUFI-2) algorithm of the SWAT-CUP tool. Water level gauge data as well as a digital turbidity sensor (DTS-12) for suspended sediment transport were used for the SWAT calibration. Pesticide residues were measured at Upper and Down Malewa locations using a passive sampling technique and their quantity was determined using laboratory gas chromatography. The sensitivity analysis results showed that curve number (CN2), universal soil loss equation erodibility factor (USLE-K) and pesticide application efficiency (AP_EF) formed the most sensitive parameters for discharge, sediment and pesticide simulations, respectively. In addition, SWAT model calibration and validation showed better results for monthly discharge simulations than for daily discharge simulations. Similarly, the results obtained for the monthly sediment calibration demonstrated more match between measured and simulated data as compared to the simulation at daily steps. Comparison between the simulated and measured pesticide concentrations at upper Malewa and down Malewa locations demonstrated that although the model mostly overestimated pesticide loadings, there was a positive association between the pesticide measurements and the simulations. Higher concentrations of pesticides were found between May and mid-July. The similarity between measured and simulated pesticides shows the potential of the SWAT model as initial evaluation modelling tool for upstream to downstream suspended sediment and pesticide transport in catchments.

Passive sampling techniques can improve the discovery of low concentrations by continuous collecting the contaminants, which usually go undetected with classic and once-off time-point grab sampling. The aim of this study was to evaluate organochlorine pesticide (OCP) residues in the aquatic environment of the Lake Naivasha river basin (Kenya) using passive sampling techniques. Silicone rubber sheet and Speedisk samplers were used to detect residues of α-HCH, β-HCH, γ-HCH, δ-HCH, heptachlor, aldrin, heptachlor epoxide, pp-DDE, endrin, dieldrin, α-endosulfan, β-endosulfan, pp-DDD, endrin aldehyde, pp-DDT, endosulfan sulfate, and methoxychlor in the Malewa River and Lake Naivasha. After solvent extraction from the sampling media, the residues were analyzed using gas chromatography electron capture detection (GC-ECD) for the OCPs and gas chromatography-mass spectrometry (GC-MS) for the PCB reference compounds. Measuring the OCP residues using the silicone rubber samplers revealed the highest concentration of residues (∑OCPs of 81 (± 18.9 SD) μg/L) to be at the Lake site, being the ultimate accumulation environment for surficial hydrological, chemical, and sediment transport through the river basin. The total OCP residue sums changed to 71.5 (± 11.3 SD) μg/L for the Middle Malewa and 59 (± 12.5 SD) μg/L for the Upper Malewa River sampling sites. The concentration sums of OCPs detected using the Speedisk samplers at the Upper Malewa, Middle Malewa, and the Lake Naivasha sites were 28.2 (± 4.2 SD), 31.3 (± 1.8 SD), and 34.2 (± 6.4 SD) μg/L, respectively. An evaluation of the different pesticide compound variations identified at the three sites revealed that endosulfan sulfate, α-HCH, methoxychlor, and endrin aldehyde residues were still found at all sampling sites. However, the statistical analysis of one-way ANOVA for testing the differences of ∑OCPs between the sampling sites for both the silicone rubber sheet and Speedisk samplers showed that there was no significant difference from the Upper Malewa to the Lake site (P < 0.05). Finally, the finding of this study indicated that continued monitoring of pesticides residues in the catchment remains highly recommended.

Distribution of pesticide residues in the environment and their transport to surface water bodies is one of the most important environmental challenges. Fate of pesticides in the complex environments, especially in aquatic phases such as lakes and rivers, is governed by the main properties of the contaminants and the environmental properties. In this study, a multimedia mass modeling approach using the Quantitative Water Air Sediment Interaction (QWASI) model was applied to explore the fate of organochlorine pesticide residues of methoxychlor, α-HCH and endosulfan–sulfate in the lake Naivasha (Kenya). The required physicochemical data of the pesticides such as molar mass, vapor pressure, air–water partitioning coefficient (K_AW), solubility, and the Henry’s law constant were provided as the inputs of the model. The environment data also were collected using field measurements and taken from the literature. The sensitivity analysis of the model was applied using One At a Time (OAT) approach and calibrated using measured pesticide residues by passive sampling method. Finally, the calibrated model was used to estimate the fate and distribution of the pesticide residues in different media of the lake. The result of sensitivity analysis showed that the five most sensitive parameters were K_OC, logKow, half-life of the pollutants in water, half-life of the pollutants in sediment, and K_AW. The variations of outputs for the three studied pesticide residues against inputs were noticeably different. For example, the range of changes in the concentration of α-HCH residue was between 96% to 102%, while for methoxychlor and endosulfan-sulfate it was between 65% to 125%. The results of calibration demonstrated that the model was calibrated reasonably with the R² of 0.65 and RMSE of 16.4. It was found that methoxychlor had a mass fraction of almost 70% in water column and almost 30% of mass fraction in the sediment. In contrast, endosulfan–sulfate had highest most fraction in the water column (>99%) and just a negligible percentage in the sediment compartment. α-HCH also had the same situation like endosulfan–sulfate (e.g., 99% and 1% in water and sediment, respectively). Finally, it was concluded that the application of QWASI in combination with passive sampling technique allowed an insight to the fate process of the studied OCPs and helped actual concentration predictions. Therefore, the results of this study can also be used to perform risk assessment and investigate the environmental exposure of pesticide residues.