Monitoring pesticides residues in water resources of the Lake Naivasha catchment using passive sampling

Abstract

Passive samplers are able to recover the detection of low concentrations using continuous gathering the pollutants. While these contaminants might be undetected with the conventional grab sampling which is an once-off time-point sampling. In this study, Silicone rubber sheet samplers were used as the passive sampler to monitor the residue of some organochlorine pesticides, notably; 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 Sulphate and Methoxychlor in the Lake Naivasha basin, Kenya. The samplers were deployed in the water for one month after which the concentration of the pesticides was measured by analyzing the extraction of samplers using the GC-ECD. Determining the organochlorine pesticides residues by means of the Silicone rubber samplers demonstrated that the maximum contamination occurred at the lake site with the total sum concentration of 81ng/L which is the final accumulation location for surficial hydrological, chemical and sediment transport through the river basin. The total organochlorine residue changed to 71.5ng/L for the Middle Malewa and 59ng/L for the Upper Malewa river sampling sites. Finally, comparing the concentration of the studied pesticides with the maximum standard limit showed that the concentrations were below the limit. However, because of the risk of these pesticides continued monitoring of pesticides residues in the catchment remains highly recommended.

Evaluating and Monitoring Environmental Exposure to Pesticide Residues in the Lake Naivasha River Basin (Kenya)

“…The D values (mol. Pa -1 h -1 ) are presented in Figure (4)(5)(6). These values are parameters which are based on the fugacity amounts and show the process rate (mol/h) or the transformation of the chemicals (Webster et al 2006).…”

“…The diagram of the calibration (2007-2012) and validation (2013 -2017) periods shown in Figure (3)(4)(5)(6) demonstrates agreement between discharge and precipitation with slight underestimation by the model in the wet seasons and overestimation in the dry seasons. The statistical evaluation measurements of the model's monthly simulations are also acceptable (R 2 >0.46 and NSE>0.44), which means that the selected range of model parameters matches the basin condition.…”

“…Although data on organochlorine pesticides loading in the rivers of the Lake Naivasha catchment were limited for a full model calibration and validation , the attempt at simulating the fate of the pesticide residues of Methoxychlor, a-HCH and endosulfan-sulfate using in-situ measured concentrations could help gain insight into pesticide application and mobility in the study area. The sensitive parameters in the model calibration of pesticide residues are presented in Table (3)(4). The results of this calibration demonstrate that application efficiency (AP_EF) forms the most sensitive parameter when simulating the pesticides.…”

“…HLIFE_S and HLIFE_F that show half-life duration (days) of the pesticides in the soil and on the foliage, respectively, also proved highly sensitive in the calibration. Correlation between the simulated and measured pesticide residue concentrations at the Upper Malewa and Down Malewa sites is represented in Table and Figure (3)(4)(5). The results of pesticide transport modeling are based on data converted from masstransported amounts to pesticide concentrations.…”