Spatial distribution of sediment pesticides concentrations in the Maritime Region of Canada

Spatial distribution of sediment pesticides concentrations in the Maritime Region of Canada | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial distribution of sediment pesticides concentrations in the Maritime Region of Canada Benoit A. Lalonde This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6916507/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study measured pesticide concentrations at 51 distinct sampling sites in freshwater streams in three maritime provinces of Canada (New Brunswick, Nova Scotia and Prince Edward Island) in 2020–2021. There were 50 different pesticides analysed in each sediment samples. The majority of those pesticides had concentrations below the detection limit of the laboratory. However, nine pesticides had detections that ranged from one to nine sites. Terbufos and parathion were the two most detected pesticides of this study. Surprisingly, terbufos has not been registered for use in Canada since 2012 which showcases the prevalence of some legacy contamination in sediments. The results align with other recent Canadian research, suggesting that pesticide occurrence in sediment is complex and not explained by a single factor like organic matter or chemical properties alone. Instead, it is a result of multiple interacting factors, including land use, the pesticide's solubility, its concentration in the surrounding water, and the persistence of legacy products. Introduction The Maritime region of Canada possesses several areas of intense agricultural production, with land use and crop selection governed by a combination of soil characteristics, temperature regimes, and topography. For instance, the fertile, well-drained soils and temperate climate of Prince Edward Island and areas of New Brunswick, like the Saint John River Valley, create ideal conditions for potato cultivation. Concurrently, the unique microclimate of Nova Scotia’s Annapolis Valley, which offers shelter from harsh weather, is particularly well-suited for fruit production, including apples, berries, and increasingly, grapes for wine (Xing et al. 2012 ). Across the entire Maritime region, root vegetables, corn, and cereals are also significant components of the agricultural landscape. The type and variety of produce grown directly dictates the pesticide regimens required to protect crops and maximize yields. This intensity of use varies significantly by crop. For example, orchard operations may require numerous applications of fungicides and insecticides throughout a growing season to manage persistent pest and disease pressure (Ernst 2009 ). Potato production is similarly pesticide-intensive, often involving seed piece treatments, herbicides for weed control, and repeated fungicide applications to combat late blight, potentially totaling over a dozen distinct applications in a single year (AAFC 2022; Murphy 2006 ). In contrast, crops like field corn typically require fewer applications, primarily targeting early-season weed competition and specific insect pests (Ernst 2009 ). Once applied, pesticides can be transported from agricultural fields into adjacent aquatic ecosystems. The primary transport pathways are surface runoff following precipitation events, erosion of pesticide-laden soil particles, and atmospheric spray drift during application (Vryzas 2018 ). Waterways in the Maritime region located within these agricultural zones range from small, first-order streams to major rivers like the Wolastoq (Saint John) River. A comprehensive study by Lalonde and Garron ( 2020 ) provided a crucial baseline for the region by describing the temporal and spatial distribution of numerous pesticides in surface water, yet this represents only one part of the environmental contamination picture. In contrast, there is a notable paucity of data regarding pesticide contamination within the sediments of agricultural streams in Atlantic Canada and other areas such as the UK (Ramage et al 2025 ). Sediments are too often overlooked, despite being a critical component of aquatic ecosystems and a major reservoir for pesticide residues (Malaj et al., 2014 ). Due to the physicochemical properties of many pesticides, particularly their hydrophobicity, they readily adsorb to organic matter and mineral surfaces, causing them to accumulate in bottom sediments (U.S. Geological Survey, 2000 ). These sediments can thus become a long-term source of contamination, resuspending stored pesticides back into the water column through bioturbation or high-flow events, leading to prolonged exposure for aquatic life (Eggleton and Thomas 2004 ). Sediment sampling may provide a time-integrated measure of contamination in comparison to surface water samples. This chronic exposure, even at low concentrations, can have significant, long-term negative impacts on the community structure and health of benthic invertebrates, which form the base of the aquatic food web (Hunt et al., 2017 ; Nowell et al., 2016 ). Mahler et al. ( 2020 ) suggested that more hydrophobic pesticides are likely to remain bound to sediment and resist desorption, creating a persistent in-situ risk for sediment-dwelling organisms. The potential for an inverse relationship between pesticide detection in water versus sediment, where less soluble compounds are missed by water sampling but are concentrated in sediment (Toth et al 2024), highlights the critical need to analyze this environmental compartment. This study is therefore essential for establishing a more complete portrait of pesticide contamination in the agricultural streams of the Maritime region. The objective of this study was to determine the rate of detection and spatial distribution of pesticides accumulated in the sediments of agricultural streams across the Maritime region of Canada. Method Fifty one sampling sites were distributed throughout the agricultural areas of the three Maritime provinces of Canada. The majority of sites were located in Nova Scotia and Prince Edward Island while New Brunswick contained the fewest number of sites (6 sites). Sampling sites metadata such as coordinates, range of median flows (when available) and stream orders are described in Supplementary Material Table 1. Stream orders ranged from 1 to 4 while median flows ranged from < 1 to 50m 3 /s. Sample Collection Pesticide and grain size samples were collected in laboratory certified clean 500mL amber glass jars with Teflon caps. The method used to sample the sediment was dependant on the size and/or depth of the water at each watercourse. For smaller watercourses, a stainless steel spoon was used to carefully lift the top 3 cm of the surface sediment in depositional areas of the brooks. For deeper or larger systems, an Eckman was lowered into the water column onto a depositional area and once retrieved, the top 3 cm of the sample was collected with a stainless steel spoon into the jars. At all times, the samplers wore clean polyethylene gloves during the sampling procedure. Immediately after the samples were put into the jars, they were kept in the dark, refrigerated until overnight delivery to the analytical laboratory (Atlantic Laboratory Environmental Testing, ALET) in Moncton, New Brunswick. Samples were frozen at -20C and thawed prior to sample preparation. Samples were extracted within 12 months of sample collection. Finale extracts are stored at < -10C until analysis and were analysed within 12 months of extraction. Laboratory Analysis Grain size Particle size analysis for sediments was performed at ECCC’s Prairie and Northern Laboratory for Environmental Testing under laboratory method 180.4. The samples were dried, weighed and passed through a series of sieves decreasing in size to2.0 mm. The portion of samples retained on each sieve is weighed and computed as a percentage of the total sample. A representative portion of the material passing though the 2.0 mm sieve is treated with 30% hydrogen peroxide, dried, then dispersed in a sodium metaphophate solution and diluted in reagent water for the sample handling unit on the Horiba Partica Laser (LA-950V2) scattering particle size distribution analyser, Organics The ECCC Atlantic Laboratory Environmental Testing (ALET 2024) method covers the quantitative determination of pesticides currently used in agriculture applications. This method was developed by ECCC-ALET using QuEChERS methodology for sample preparation paired with top of the line instrumentation. The samples are extracted using a QuEChERS extraction procedure developed in order to extract all traces of contaminants found in the samples. The type of analysis used for the quantification is selected based on the best response possible for each compound using either gas or liquid chromatography. Measures were put in place in order to maximize the recovery of the compounds as well as reduce the risk of loss during the sample processing. The different families of pesticides covered in this application are organophosphates & Aquatic Toxins, Fungicides, Carbamates, Herbicides, Neonicotinoids. The acquisition methods were developed using extracts prepared into Acetonitrile. The concentrated sample extracts undergo QuEChERS cleanup and are concentrated to the final volume of 1mL in Acetonitrile. The extracts are split and one portion is acidified. The use of either acidified or un-acidified extracts during the data acquisition is based on the best fit for the compounds. Using the un-acidified portion of the extract, a 1:1 dilution is prepared using Toluene. This diluted extract analysed using a hot Acetonitrile:Mesitylene (ACN:MES) injection, customized Gas Chromatography (GC) followed by Multiple Residue Monitoring (MRM) detection. MRM acquisition parameters are listed in Table 1 in the supplementary information. This data acquisition is referred to as un-acidified GC-MSMS analysis. Using the acidified portion of the extract, the analysis known as LC-MSMS analysis is composed of a Flow-Through-Needle (FTN) injection, customized Liquid Chromatography (LC) followed by Multiple Residue Monitoring (MRM) detection. Tables 2 –6 of the supplementary information indicates the detection limits (MDLs) in sediments. The MDLs ranged from 1.45 to 2.9 ng/g dry. The detection limits are based on a sample volume of 3g initial dry weight for solid matrices. The final extract volume for all matrices is 1.0mL. The un-acidified GC-MSMS acquisition and analysis were developed in-house using a HP-5ms Ultra Inert chromatographic column, a hot 5uL Multi-Mode-Inlet (MMI) injection as well as custom GC temperature program designed to maximize the sensitivity and peak resolution of the various Pesticides in this analysis. The instrumentation used for the GC-MSMS analysis were Agilent GC model 7890B, PN G3440B, Agilent QQQ model 7010B EI mainframe, PN G7013B, Agilent Auto-injector model 7693A, PN G4513A. The LC-MSMS acquisition and analysis were developed in-house using a 3uL injection, custom gradient and collecting MSMS data specific to the compounds of interest. The sensitivity and peak resolution of each compound is maximized with custom MRM transitions and detector conditions for each compound. The instrumentation used for this LC-MSMS analysis were; Waters Xevo TQS-micro, Binary Solvent Manager ACQUITY I-Class and Waters Sample Manager FTN. Pesticides analyses use a 10-point calibration curve for quantification. The calibration standards used are listed in Tables 7 and 8 of the supplementary information. The QuEChERS cleanup procedures associated with solid matrices require a correction factor of 4 to be applied to the results at the time of quantification (ALET 2024). Calibration check standards are included in the sequence. Check standards, or QCs LOW, MID and HIGH evaluate the calibration curve throughout the range. The concentration range used for the different QCs are listed in Tables 9 and 10 in the supplementary information. All procedural blanks and spikes are analyzed within the sample sequence. The various spiking levels used during the method validation and processing of routine samples are listed in Table 11 of the supplementary information. Results and Discussion Overall, fifty pesticides were monitored during this study. However the majority of samples did not contain quantifiable concentrations of 41 of those pesticides. The following list of pesticides had no detections at our sampling sites; atrazine, thiacloprid, azinphos-methyl, aatrazine desethyl, imidaclprid, atrazine desisopropyl, thiamethoxam, benzolyprop ethyl, bromacil, dinotefuran, acetaprimid, butylate, coumaphos, flupyradifurone, clomazone, flonicamid, cyanazine, dimethoate, spirodiclofen, diallate, fonofos, spiromesifen, diclofop-methyl, ivermectin, ethalfluralin, malathion, spirotetramat, sulfoxaflor, cyantraniliprole, hexazinone, phorate, metobromuron, phosmet, disulfoton, metribuzin, ethion, simazine, piperonyl butoxide, abamectin, triallate, pyriproxyfen and azamethiphos. The following list of pesticides were detected in our samples (Table 1 ); Terbufos, parathion, chlorantraniliprole, chlorpyrifos, pendimethalin, clothianidin, linuron, chlorpyrifos, metolachlor trifluralin. Terbufos and parathion were the most detected pesticides with nine and eight sites respectively while trifluralin was only detected at one site (Table 1 ). None of the 6 sites in New Brunswick had pesticide detections. Overall, the Dunk River site had the highest frequency of pesticides detected at 4 while both sites on the Wilmot as well as Lawrence (NS) and Watton (NS) having each two pesticides detected. Table 1 Detection frequency, average concentration detected and province where pesticides were measured during this study. Pesticide # detections Range (ng/g) Average concentration (ng/g) Avg detection limit (ng/g) Province Trifluralin 1 2.01 2.01 1.97 PEI Linuron 2 1.45–2.02 1.7 1.7 NS, PEI Chlorpyrifos 2 2.11–3.35 2.73 1.99 NS, PEI Metolachlor 2 3.38–9.91 6.65 2.5 NS Clothianidin 2 1.99–2.24 2.12 1.98 PEI Pendimethalin 4 2.27–16.1 9.41 2.16 NS Chlorantraniliprole 5 3.53–9.21 5.13 2.2 NS, PEI Parathion 8 1.5–2.9 2.49 1.99 NS, PEI Terbufos 9 2.3–5.82 3.69 2.41 NS Pendimethalin is a selective herbicide used to control annual grasses and some broadleaf weeds in field used for produce as well as stone and berry fruit. EPA ( 1997 ) describes Pendimethalin has as having a high affinity to bind to soil and sediment which will limit its concentrations into surface waters. Pendimethalin was only detected at four sites all located in Nova Scotia with concentrations ranging from 2.27–16.1 ng//g (Table 1 ). Two of those sites also contained the highest silt concentrations measured from all the sites in Nova Scotia. Chlorantraniliprole was detected in 5 out of the 51 sites of this study and they were are all located on Prince Edward Island during the 2020 sampling campaign but was detected at two sites in Nova Scotia in 2021 (Table 1 ). It is used as an insecticide and commonly found in surface water in PEI (Chalis 2023 ). On Prince Edward Island, 2 out of the three sites contained high concentrations of silt (above 40%), the third site with a detection had a lower silt value (27%). In contrast, chlorantraniliprole was detected in 35% of all water samples in the Lalonde and Garron ( 2020 ) study which occurred in the same region as those of this study. Chlorantraniliprole has been described as moderately persistent in the aquatic environment and will partition to sediment while it is expected to accumulate in aquatic systems (HC 2014) Chlorpyrifos in PEI was measured at two sites including Dunk River which has the fifth highest silt concentration out of our 51 sampling sites of this study. The use of chlorpyrifos has been revoked in Canada since December 2023. It was originally used as an insecticide in field used to grow fruits and vegetables, cereals and turf grass (HC 2019). Chlorpyrifos has a low water solubility and is characterised as readily migrating from the water column to the sediments (HC 2019). However, in a prior study, it was detected in 17% of the water samples (0.7 to 601ng/L) in Maritime Canada (Lalonde and Garron 2020 ) while in this study it was detected in 5% of the sites (2 sites) and at concentrations that were very close to the detection limit of the laboratory (Table 1 ). A recent study by Toth et al (2024) did not detects any chlopyrifos in 113 sediment samples from Quebec. The authors of that study link the lack of detection to the pesticide ban. Trifluralin is used as a pre-emergent soil herbicide to control broadleaved weeds and annual grasses. Trifluarin has a low water solubility but is somewhat volatile (HC 1992). The low water solubility may explain the low detection of it from the meta-analysis of 248 discrete water samples in the Maritime region, where trifluralin was only detected in 4 instances with a range of concentration from 4.36 to 3230 ng/L. (Lalonde and Garron 2020 ). Trifluralin has been described as having a high affinity to absorb to soils with high organic content (HC 1992) and the site at Dunk River contains the fifth highest silt proportion of all the sampling sites of this study which may explain in part its presence. However, trifluralin was only detected at one out of 51 sites samples as [art of this study (Table 1 ). Parathion is used as an insecticide on fruit, cereals and vegetables. It was the pesticide with the second highest number of detections at 16% of all sites sampled. Richardson ( 2005 ) describes parathion has binding tightly to soil particles and little or no potential for groundwater contamination. It is not surprising then that parathion was not detected in any of the 248 water samples in the Lalonde and Garron ( 2020 ) study in some of the same streams where parathion was detected in the sediments as part of this study. The high affinity to soil particles did not translate to a direct correlation to silt concentrations at our sampling sites since it was detected at sites ranging in silt concentrations from 14 to 70%. Terbufos is used as an organophosphorus insecticide which was used on corn, sugar beets and rutabagas in Canada (HC 1995). It has a low water solubility and is slightly to moderately persistent in soil and sediment. The physical and chemical properties of terbufos indicate that in aquatic systems it will partition into sediments (PMRA 2003 ) with a slow transformation rate. Terbufos has been shown to have a higher concentration in sediment than water during runoff events due to the relative magnitude of the absorption coefficient to sediment in comparison to its solubility in water (Mamo et al. 2006 ). According to Pest Management Regulatory Agency (PMRA), the use of products containing terbufos in Canada was prohibited after August 1 2012. However, it was detected at nine sites in Nova Scotia with a range of concentrations from 2.3 to 4.35 ng/g (Table 1 ). There was no direct correlation to silt with sampling sites containing terbufos having sediment ranging from 14.7 to 21.3% silt. The Lalonde and Garron ( 2020 ) study did not detect any terbufos in the 248 water samples obtained between 2013–2018. Terbufos was the pesticide that was detected the most often in this study. Clothianidin is a neonicotinoid pesticide used on various crops and for seed treatments. PMRA published various risk reduction measures such as decreasing the number of applications per season and increasing buffer zones in 2023. Environmental Protection Agency (EPA) described clothianidin as persistent and mobile and its high persistence could lead to accumulate in soils with repeated uses (EPA 2007 ). HC (2018) described clothianidin as moderately persistent to persistent in water systems containing sediment. Clothianidin was the most frequently detected pesticides in stream of Maritime Canada with a proportion of detection of 90% in water samples (Lalonde and Garron 2020 ) which is in contrast to a detection frequency of only 4% in sediment in this study. Clothianidin was only detected in PEI (1.99-2.24ng/g) and upstream but not downstream on the Wilmot River (Table 1 ). This is in sharp contrast to the review study of sediments in Chinese rivers where frequency of detection in sediments ranged from 73 to 100% with a range of concentrations from 0.01 to 12.7 ng/g (dw) (Zhang et al 2025). The large difference in detection frequency may be explained by the higher detection limit of clothianidin in this study. However, a Canadian study on neonicotinoid pesticide concentrations in wetlands sediments and water revealed a frequency of detection very similar to that of this study with a detection rate of 6% and a range of values close to that of this study (2.6–4.4 ng/g) (Main et al. 2014 ). Since clothianidin has a relatively low soil-water organic carbon coefficient and high water solubility, it seems likely that the low proportion of detection in this study is due to those characteristics. Linuron is an herbicide used to control annual and perennial broadleaf and grasses. PMRA ( 2020 ) describes Linuron as slightly to moderately persistent and carryover into the following growing season is not expected to be of concern. In the Lalonde and Garron ( 2020 ) study, Linuron was only detected in 4% of all the surface water samples obtained (at concentrations almost equal to the detection limit of the laboratory) which is the same to that of the frequency of detection in sediment of this study. In this study, Linuron seems to have affinity to high silt concentrations with both detections occurring at sites with over 40% sit in NS and PEI however another mechanism could be involved since our site containing 70% silt did not have a detection. The detections at these two sites might be strictly a reflection of the usage of this product on crops that are deemed to meet standards for human health and environment (carrots, parsnips, potato, asparagus and shelterbelts) (PMRA 2020 ) Metolachlor is an herbicide used to control broadleaved weeds and annual grasses ands is heavily used on corn and soyabean crops (Toth et al 2024). In Canada, it is registered for use on various field crops, vegetables, fruit trees and outdoor ornamentals (PMRA 2024 ). Metolachlor was detected in sediment at two sites located in Nova Scotia (4% frequency of detection). In contrast, a prior study in the Maritimes of Canada, metolachlor was detected at a frequency of 22% of all water samples with a range of values between 4.64–270 ng/L (Lalonde and Garron 2020 ) which is not surprising since metolachlor is only mildly persistent in soil but highly persistent in water due to a low photodegradation rate (EXTOXNET 2019 ). In contrast, the study in Québec by Toth et al (2024) revealed that metolachlor was detected in 34% of all sediments analysed. This difference between the detection rate between the studies might be explained by the overwhelming domination of corn and soya crops in the watersheds of the Toth et al (2024) study in comparison to our sites. The grain size analysis of the sediment at each sites revealed 7 sampling sites which had 30% or more silt with Valleyfield (PEI) ranking highest at 70.8%. The four highest concentrations of silt were sites on Prince Edward Island while two sites in Nova Scotia had 41 and 36% silt (Lawrence and Armstrong respectively) and a last site in PEI (Cains Bk) came last of the seven highest at 33.4% silt. The affinity of pesticides to the smallest fraction grain size such as silt has been described before (Vryzas 2018 , Toth et al 2024). In our study there are similarities where high silt sites such as Dunk and Wilmot have some of the highest detection frequencies of pesticides. However, our site with the highest silt (Valleyfield) only had one detection of pesticide while and another site with over 40% silt (Knox) had no detection at all. These results seem to indicate that while silt is a good indicator of potential pesticide detection in sediment, there are other factors which may correlate better with pesticide detections. Toth et al (2024) describe how a high n-octanol-water partition coefficient (Kow) gauges the hydrophobicity of organic molecules which may lead to a higher affinity of binding to sediment. Pesticides such as chlopyrifos, metolachlor and linuron all have Kow > 3 (Toth and Yargeau 2024 ). However, the results they presented did not correlate a high Kow and the detection of insecticides in freshwater sediments of Qc (Toth et al 2024). The frequency of detection for lower Kow pesticides such as atrazine and carbaryl were 46 and 37% respectively which is significantly higher that the detection for chlorantraniliprole and linuron both at 1%. The results from the Toth et al (2024) study conclude in part that since the three most detected pesticides in sediment mirror those most frequently found in water samples, the presence of the pesticide in the water samples may contribute to the concentrations in sediments given there would be less of a tendency for desorption in order to reach equilibrium. Comparison to other studies The results of our study would benefit from a comparison to previously published concentrations of pesticides in freshwater sediments in the Maritime. However, the author of this study was not able to find any prior study in the Maritime region of Canada. Giroux et al (2023) published a large-scale study of pesticides occurring in sediments in the province of Québec. Similarly to our results only 18/77 pesticides analysed had detections. The two most frequently detected pesticides in sediments in the Giroux et al (2023) study was glysophate and AMPA with rates of detections of 8 and 78% respectively. Unfortunately, these two pesticides were not part of the suites analysed as part of this study. However, the third most detected pesticides in sediment in Giroux et al (2023) was chlorantraniliprole at 15% detection which is similar to the results from this study (detection rate of 10%) (Table 2 ). The only statistically significant correlation in the Giroux et al study (2023) was between the number of detections and % land maraichère (land for vegetable) within the watershed. Although Giroux et al (2023) described how the concentration of pesticides in sediment was somewhat related to the concentration of organic matter in sediment, the relationship was not statistically significant. However, Ramage et al ( 2025 ) described how total organic carbon had a statistically significant (p < 0.01, R 2 = 0.645) positive influence on total pesticide concentrations in UK sediments. These conflicting results would suggest that the presence of pesticide in sediment is complex and not easily predicted by single factor like organic matter or chemical properties alone. Toth et al (2024) also published a large scale study of pesticide concentrations in Québec (Canada) sediments. Frequency of detections of pesticides in sediments occurred in 119 out of 232 samples with 47% of the 30 pesticides detected at least once in a sample. Atrazine, carbaryl and metolachlor had the three highest proportion of detection at 46, 37 and 34% respectively while chlorantraniliprole had a low frequency of detection of 1% (Toth et al 2024) (Table 2 ). Toth et al (2024) had suggested that a high Kow would suggest an affinity of a pesticide to bind to sediment but no such correlations were detected within their dataset which again suggests that pesticide presence depends on complex interacting factors rather than just a single factor like Kow. Wei et al ( 2021 ) present sediment pesticide concentrations in China (Table 2 ) for both wet and dry seasons with higher detection frequency and range of concentrations occurring in the dry season. While chlorpyrifos concentrations are like that of this study, the highest terbufos concentrations in the Wei et al ( 2021 ) study was a full order of magnitude higher than that of this study. Interestingly, the use of Terbufos was prohibited in Canada as of 2012. It’s moderate persistence in soil/ sediment may explain some of the detections in this study and may explain this legacy contamination. Similarly, Prajapati et al ( 2022 ) had a detection frequency of 67 and 100% (in 2020) for methoxychlor and lindane both of which have been banned in Canada since the 1970s which suggests legacy contamination. The legacy contamination may stem from the substance’s high lipophilicity and recalcitrance to degradation by both biotic and abiotic processes (Prajapati et al. 2022 ). Similar to our study, pp’-DDT and dieldrin were detected in United States freshwater sediments 32 and 17 years, respectively after their last legal applications. (Knight at al 2007). Prajapati et al ( 2022 ) measured pesticides in sediments in Saskatchewan with a 3% detection frequency for parathion (Table 2 ) which is in stark contrast to the 16% detection in this study. Concentrations for parathion were up to 3 times higher in this study compared to the results from Prajapati et al ( 2022 ). Perhaps the different crops grown in Saskatchewan in comparison to Atlantic Canada led to the difference in usage and therefore detection of parathion in sediments. Table 2 Range of pesticide concentrations, detection frequency in sediment from other studies. Pesticide Range (ng/g) Detection Frequency (%) Locations Study Chlorantraniliprole Nd-102 1 Canada Toth et al 2024 Metolachlor ND-1237 34 Canada Toth et al 2024 Chlorantraniliprole 0.5–9.2 15 Canada Giroux et al 2023 Clothianidin 0.5–24 6.1 Canada Giroux et al 2023 Clothianidin 2.6–4.4 6.0 Canada Main et al 2014 Parathion 0-0.91 3.7 Canada Prajapati et al 2022 Clothianidin ND-1.91 61–100 China Zhang et al 2019 Chlorpyrifos Nd- 6.31 27.6–37.9 China Wei et al. 2021 Terbufos ND-30.6 20.7 41.4 China Wei et al 2021 Clothianidin < 0.2-11.93 31–55 United States Kuechle et al 2019 Metolachlor ND-107.1 69 United States Knight et al 2007 Trifluralin ND-1.2 19 United States Knight et al 2007 Pendimethalin ND-39.4 11 United States Knight et al 2007 Chlorpyrifos ND-34.3 33 United States Knight et al 2007 Chlorpyrifos ND-440 n/a United States Smalling et al 2012 Pendimethalin ND-13.2 n/a United States Smalling et al 2012 Clothianidin Nd-1.22 26–30 United Kingdom Ramage et al. 2025 Nowell et al ( 2016 ) developed sediment likely and threshold effects benchmarks for 48 pesticides. The benchmarks are based on pesticide concentrations corrected for organic carbon (ug/g OC). The sediment organic carbon normalization in Nowell et al ( 2016 ) assumes that this fraction predicts the bioavailable pesticide concentration. The likely effect benchmarks for chlorpyrifos, parathion and pendimethalin were 4.1, 5.2 and 3800 ug/g which is three order of magnitude or more above the total concentrations found in this study. This suggests that the concentrations of these three compounds in this study were not close to the likely effect benchmark suggested by the calculation of Nowell et al ( 2016 ). Conclusion The result from this study found an inconsistent relationship between the percentage of silt in sediment and the frequency of pesticide detection. This suggests that while silt content can be an indicator for potential pesticide presence, it is not the sole or primary driver for sites in the Maritime region of Canada. Although the n-octanol-water partition coefficient (Kow) which measures a chemical’s tendency to bind to sediment can be a good indicator of potential pesticide presence in sediments, the results from this study do not follow this rule. The findings of this study highlight some environmental risk from pesticides that might have not been detected unless sediment monitoring is conducted in addition to surface water monitoring. Lastly, legacy contamination form pesticides which are not currently approved for use in Canada is still prevalent in sediments of the Maritime region of Canada. Overall, the results from this study indicate that sediment pesticide occurrence is not easily explained by single factors such as organic matter or Kow but rather by multiple interacting factors including land use, solubility/ lipophilicity of the pesticide and the persistence of the pesticides. Declarations The author declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The author has no relevant financial or non-financial interests to disclose. Author Contribution Only one author contributed to this manuscript. Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Agriculture and Agri Food Canada (AAFC) (2022) Crop Profile for Potato. Ottawa, Canada. Catalogue No.: A118-10/22-2020E. Atlantic Laboratory Environmental Testing (ALET) (2024). Agriculture Application Analyses by GC-MSMS & LC-MSMS (Liquid-Liquid Extraction for Water and/or QuEChERS Extraction for Solid Matrices, QuEChERS Extract clean-up GC-MSMS and LC-MSMS Analyses). Document ID MET-CH-66, Moncton, Canada. Chalis J (2023) Pesticide occurrence and dynamics in surface waters of two distinct agricultural and ecological regions of Canada. https://www.ecotoxcan.ca/_files/ugd/f18aa9_1bd4c27aff6045dfad0e68ec9dab9e97.pdf ECCC (2023) Prairie and Northen Laboratory for Environmental Testing. Method 180.4 Particle Size Analysis for sediment. Rev10. October 12, 2023. Eggleton J Thomas KV (2004) A review of factors affecting the release and bioavailability of contaminants during sediment disturbance. Env Intl 30(7): 973-980 EPA (1997) R.E.D. Facts; Pendimethalin. EPA-738-F-97-007 EPA (2007) EFED section 3 registration for a clothianidin and Beta-cyfluthrin combination product for use on sugar beets as a seed treatment. DP barcode: D335254. Ernst B (2009) Surveillance of pesticide residues in surface water, sediment and groundwater in agricultural regions of Prince Edward Island, New Brunswick and Nova Scotia. Environment Canada, Ottawa EXTOXNET (2019) Extension Toxicology Network. Pesticide Information Profiles. EXTOXNET PIP - METOLACHLOR. Accessed May 2025. Giroux I (2023) Pesticides dans les cours d'eau au Québec. Québec, ministère de l’Environnement, de la lutte contre les changements climatiques, de la Faune et des Parcs. 38p. ISBN; 978-255094620-5 Health Canada (HC) (1992) Guidelines for Canadian Drinking water quality. ; Trifluralin. Accessible at; Guidelines for Canadian Drinking Water Quality: Guideline Technical Document – Trifluralin - Canada.ca Health Canada (HC) (2014) Chlorantraniliprole. ISSN 1925-0940. Catalogue Number. H113-25/2014-26E-PDF. Available at; https://www.canada.ca/content/dam/hc-sc/migration/hc-sc/cps-spc/alt_formats/pdf/pubs/pest/_decisions/rd2014-26/rd2014-26-eng.pdf Health Canada (HC)(2018) Special review of clothianidin risk to aquatic invertebrates: proposed decision for consultation. PRSD 2018-01. Ottawa, Canada. ISSN: 2561-6366 Health Canada (HC) (2019) Chlorpyrifos et préparations commerciales connexes. Évaluation des risques pour l’environnement mise à jour. PRVD2019-05. ISSN; 1925-0983. Catalogue H113-27/2019-5F-PDF. Available at Chlorpyrifos et préparations commerciales connexes : évaluation des risques pour l’environnement mise à jour, document de consultation.: H113-27/2019-5F-PDF - Government of Canada Publications - Canada.ca Hunt L Bonetto C Marrochi N Scalise A Fanelli S Liess M (2017) Species at risk index indicates effects of pesticides on stream invertebrate communities in soy production region of Argentine Pampas. Sci. Tot. Environ. 580, 699-709 Knight SS Lizotte RE Smith S Bryant CT (2007) Distribution and spatial variation in surface sediment pesticides of Mississipi alluvial plain. J. Int. Env. Appl Sci 2(3/4):40-50. Kuchle KJ Webb EB Mengel D. Main AR (2019). Factors influencing neonicotinoid insecticide concentrations in floodplain wetland sediments across Missouri. Env. Sci. Tech. 53:10591-10600. DOI: 10.1021/acs.est.9b01799 Lalonde B Garron C (2020) Temporal and spatial analysis of surface water pesticide occurrences in the Maritime region of Canada. Arch. Env. Contam. Toxicol. 79(1): 12-22 Mahler BJ Schmidt TS Nowell LG Qi SL Van Metre PC Hladik ML (2020) Biofilms provide new insights into pesticide occurrence in streams and links to aquatic ecological communities, Environ. Sci Tech. 54: 5506-5519 Main AR Headley JV Peru KM Michel NL Cessna AJ Morrissey CA (2014) Widespread Use and Frequent Detection of Neonicotinoid Insecticides in Wetlands of Canada's Prairie Pothole Region. PLOS ONE 9(6): 1-12 Malaj E von der Ohe P Grote M Kuhne R Mondy CP Usseglio-Polatera P Brack W Schafer RB (2014) Organic chemicals jeopardize the health of freshwater ecosystems on the continental scale. Proc Nat Academy Sci. 111(26): 9549-9554. Mamo M, Kranz WL, Douskey ER, Kamble ST, Witkowski JF (2006) Impact of tillage and placement f terbufos insecticide runoff. Biological Systems Engineering: Papers and publications. 182 Matus FJ (2021) Fine silt and clay content is the main factor defining maximal C and N accumulations in soils: a meta analysis. Scientific Reports 11 (6438) Murphy C (2006) Multi-media pesticide monitoring programs in Prince Edward Island, New Brunswick and Nova Scotia – 3 year monitoring program. No 2003/04-2005/06. Environment Canada Nowell LH, Norman JE, Ingersoll CG, Moran PW (2016) Development and application of freshwater sediment-toxicity benchmarks for currently used pesticides. Sci. Tot. Environ. 550, 835-850 Prajapati S Challis JK Jardine TD Brinkmann M (2022) Levels of pesticides and trace metals in water, sediment, and fish of large, agriculturally-dominated river. Chem 308: 136236 PMRA (2003) Re-evaluation of Terbufos. PACR2003-02. Ottawa, Canada. ISBN: 0-662-32860-4. Catalogue number: H113-18/2003-2E-IN PMRA (2020) Re-evaluation Decision RVD2020-10, Linuron and its associated end-use products. Ottawa, Canada. ISSN: 1925-1025 (PDF version). Catalogue number: H113-28/2020-10E-PDF (PDF version) PMRA (2024) Preposed re-evaluation decision PRVD2024-01, S-metolachlor and R-enantiomer and its associated end-use [products. Ottawa, Canada. ISSN: 1925-0967. Catalogue number: H113-27/2024-1E-PDF. Ramage CI Lopes dos Santos RA Yon L Johnson MF Vane CH (2025) Widespread pesticide poluution in two English river catchments of contrasting alnd-use: from sediments to fish. Env Poll 375:126371 Richardson JR (2005) Parathion. Encyclopedia of Toxicology (second Edition). P331-333 Smalling KL Orlando JL Calhoun D Battaglin WA Kuivila KM (2012) Occurrence of Pesticides in Water and Sediment Collected from Amphibian Habitats Located Throughout the United States, 2009–2010. U.S. Geological Survey Data Series 707, 40 p. Toth J Yargeau V (2024) Multiresidue method for the fast and efficient analysis of current-use pesticides in streambed sediments using pressurized liquid extraction. Sci. Tot. Env. 906 Toth J Fugère V Yargeau V (2024) Relationship between stream size, watershed land use, and pesticide concentrations in headwater streams. Env. Poll. 349:123940 U.S. Geological Survey (2000) Pesticides in Stream Sediment and Aquatic Biota . Fact Sheet 092-00. Available at; https://pubs.usgs.gov/publication/fs09200 Vryzas Z (2018) Pesticides fate in soil-sediment-water environment in relation to contamination preventing actions. Current opinion in Env Sci Health. 4: 5-9 Wei G Wang C Niu W Huan Q Tian T Zou S Huang D (2021) Occurrence and risk assessment of currently used organophosphate pesticides in overlying water and surface sediments in Guangzhou urban waterways, China. Env Sci Poll Res 28:48194-48206 Xing Z Chow L Cook A Benoy G Rees H Ernst B Meng F Li S Zha T Murphy C Batchelor S Hewitt M (2012) Pesticide application and detection in variable agricultural intensity watersheds and their river systems in the Maritime region of Canada. Arch Environ Contam Toxicol 63:471–483 Zhang S Jiuang JQ (2025) The occurrence and distribution of neonicotinoids in sediments, soil and other environmental media in China. Environments. 12(5):150 Supplementary Files Supplementaryinformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6916507","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":473128192,"identity":"ef4e4f5c-0f22-4390-98c4-8471294f08af","order_by":0,"name":"Benoit A. Lalonde","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYBACAxCRwMAgw8AOZPAw2BCrJQGomBmsJY1ILQwILYcJazFn7z344OEPBh7+ZuZjD95UnJfjb2C/+IGhxg6nFsuec8kGIIdJHGZLN5xz5raxxAGeYgmGY8m4HXYjx0wC7JfDPGbSvG23Ezcw8KQxMDYw49Zy/w1EizxYy79z9VAt9Xhs4YFoMQBraTiQYMDAfgyoBXc4WPbkGBskpEnwGB5mS5OccyzZcMZhHmaJhGPHcWoxZz9j+PCHjY2c3PHmYxJvauzk+dvbH374UFONUwsUSCCxmXkMQOmBJMD+gEQNo2AUjIJRMMwBAGBtSCOwzpzIAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-4366-8444","institution":"Environment and Climate Change Canada","correspondingAuthor":true,"prefix":"","firstName":"Benoit","middleName":"A.","lastName":"Lalonde","suffix":""}],"badges":[],"createdAt":"2025-06-17 16:57:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6916507/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6916507/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91126850,"identity":"0800fbb1-5bcb-4a51-999e-24e40e010f7c","added_by":"auto","created_at":"2025-09-11 22:01:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":520969,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6916507/v1/daa148b8-3ba1-4f0e-804f-3e53e21abaf2.pdf"},{"id":85071953,"identity":"1c370688-9ab6-45fe-87c7-c0f2856f7529","added_by":"auto","created_at":"2025-06-20 15:46:10","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":80180,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6916507/v1/90ba3d6182c9c7e35f040576.docx"}],"financialInterests":"","formattedTitle":"Spatial distribution of sediment pesticides concentrations in the Maritime Region of Canada","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Maritime region of Canada possesses several areas of intense agricultural production, with land use and crop selection governed by a combination of soil characteristics, temperature regimes, and topography. For instance, the fertile, well-drained soils and temperate climate of Prince Edward Island and areas of New Brunswick, like the Saint John River Valley, create ideal conditions for potato cultivation. Concurrently, the unique microclimate of Nova Scotia\u0026rsquo;s Annapolis Valley, which offers shelter from harsh weather, is particularly well-suited for fruit production, including apples, berries, and increasingly, grapes for wine (Xing et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Across the entire Maritime region, root vegetables, corn, and cereals are also significant components of the agricultural landscape.\u003c/p\u003e \u003cp\u003eThe type and variety of produce grown directly dictates the pesticide regimens required to protect crops and maximize yields. This intensity of use varies significantly by crop. For example, orchard operations may require numerous applications of fungicides and insecticides throughout a growing season to manage persistent pest and disease pressure (Ernst \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Potato production is similarly pesticide-intensive, often involving seed piece treatments, herbicides for weed control, and repeated fungicide applications to combat late blight, potentially totaling over a dozen distinct applications in a single year (AAFC 2022; Murphy \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In contrast, crops like field corn typically require fewer applications, primarily targeting early-season weed competition and specific insect pests (Ernst \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOnce applied, pesticides can be transported from agricultural fields into adjacent aquatic ecosystems. The primary transport pathways are surface runoff following precipitation events, erosion of pesticide-laden soil particles, and atmospheric spray drift during application (Vryzas \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Waterways in the Maritime region located within these agricultural zones range from small, first-order streams to major rivers like the Wolastoq (Saint John) River. A comprehensive study by Lalonde and Garron (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) provided a crucial baseline for the region by describing the temporal and spatial distribution of numerous pesticides in surface water, yet this represents only one part of the environmental contamination picture.\u003c/p\u003e \u003cp\u003eIn contrast, there is a notable paucity of data regarding pesticide contamination within the sediments of agricultural streams in Atlantic Canada and other areas such as the UK (Ramage et al \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Sediments are too often overlooked, despite being a critical component of aquatic ecosystems and a major reservoir for pesticide residues (Malaj et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Due to the physicochemical properties of many pesticides, particularly their hydrophobicity, they readily adsorb to organic matter and mineral surfaces, causing them to accumulate in bottom sediments (U.S. Geological Survey, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). These sediments can thus become a long-term source of contamination, resuspending stored pesticides back into the water column through bioturbation or high-flow events, leading to prolonged exposure for aquatic life (Eggleton and Thomas \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Sediment sampling may provide a time-integrated measure of contamination in comparison to surface water samples.\u003c/p\u003e \u003cp\u003eThis chronic exposure, even at low concentrations, can have significant, long-term negative impacts on the community structure and health of benthic invertebrates, which form the base of the aquatic food web (Hunt et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nowell et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Mahler et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) suggested that more hydrophobic pesticides are likely to remain bound to sediment and resist desorption, creating a persistent in-situ risk for sediment-dwelling organisms. The potential for an inverse relationship between pesticide detection in water versus sediment, where less soluble compounds are missed by water sampling but are concentrated in sediment (Toth et al 2024), highlights the critical need to analyze this environmental compartment. This study is therefore essential for establishing a more complete portrait of pesticide contamination in the agricultural streams of the Maritime region.\u003c/p\u003e \u003cp\u003eThe objective of this study was to determine the rate of detection and spatial distribution of pesticides accumulated in the sediments of agricultural streams across the Maritime region of Canada.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eFifty one sampling sites were distributed throughout the agricultural areas of the three Maritime provinces of Canada. The majority of sites were located in Nova Scotia and Prince Edward Island while New Brunswick contained the fewest number of sites (6 sites). Sampling sites metadata such as coordinates, range of median flows (when available) and stream orders are described in Supplementary Material Table\u0026nbsp;1. Stream orders ranged from 1 to 4 while median flows ranged from \u0026lt;\u0026thinsp;1 to 50m\u003csup\u003e3\u003c/sup\u003e/s.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample Collection\u003c/h2\u003e \u003cp\u003ePesticide and grain size samples were collected in laboratory certified clean 500mL amber glass jars with Teflon caps. The method used to sample the sediment was dependant on the size and/or depth of the water at each watercourse. For smaller watercourses, a stainless steel spoon was used to carefully lift the top 3 cm of the surface sediment in depositional areas of the brooks. For deeper or larger systems, an Eckman was lowered into the water column onto a depositional area and once retrieved, the top 3 cm of the sample was collected with a stainless steel spoon into the jars. At all times, the samplers wore clean polyethylene gloves during the sampling procedure. Immediately after the samples were put into the jars, they were kept in the dark, refrigerated until overnight delivery to the analytical laboratory (Atlantic Laboratory Environmental Testing, ALET) in Moncton, New Brunswick. Samples were frozen at -20C and thawed prior to sample preparation. Samples were extracted within 12 months of sample collection. Finale extracts are stored at \u0026lt; -10C until analysis and were analysed within 12 months of extraction.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLaboratory Analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGrain size\u003c/h2\u003e \u003cp\u003eParticle size analysis for sediments was performed at ECCC\u0026rsquo;s Prairie and Northern Laboratory for Environmental Testing under laboratory method 180.4. The samples were dried, weighed and passed through a series of sieves decreasing in size to2.0 mm. The portion of samples retained on each sieve is weighed and computed as a percentage of the total sample. A representative portion of the material passing though the 2.0 mm sieve is treated with 30% hydrogen peroxide, dried, then dispersed in a sodium metaphophate solution and diluted in reagent water for the sample handling unit on the Horiba Partica Laser (LA-950V2) scattering particle size distribution analyser,\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOrganics\u003c/h3\u003e\n\u003cp\u003eThe ECCC Atlantic Laboratory Environmental Testing (ALET 2024) method covers the quantitative determination of pesticides currently used in agriculture applications. This method was developed by ECCC-ALET using QuEChERS methodology for sample preparation paired with top of the line instrumentation. The samples are extracted using a QuEChERS extraction procedure developed in order to extract all traces of contaminants found in the samples. The type of analysis used for the quantification is selected based on the best response possible for each compound using either gas or liquid chromatography. Measures were put in place in order to maximize the recovery of the compounds as well as reduce the risk of loss during the sample processing. The different families of pesticides covered in this application are organophosphates \u0026amp; Aquatic Toxins, Fungicides, Carbamates, Herbicides, Neonicotinoids.\u003c/p\u003e \u003cp\u003eThe acquisition methods were developed using extracts prepared into Acetonitrile. The concentrated sample extracts undergo QuEChERS cleanup and are concentrated to the final volume of 1mL in Acetonitrile. The extracts are split and one portion is acidified. The use of either acidified or un-acidified extracts during the data acquisition is based on the best fit for the compounds. Using the un-acidified portion of the extract, a 1:1 dilution is prepared using Toluene. This diluted extract analysed using a hot Acetonitrile:Mesitylene (ACN:MES) injection, customized Gas Chromatography (GC) followed by Multiple Residue Monitoring (MRM) detection. MRM acquisition parameters are listed in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e in the supplementary information. This data acquisition is referred to as un-acidified GC-MSMS analysis. Using the acidified portion of the extract, the analysis known as LC-MSMS analysis is composed of a Flow-Through-Needle (FTN) injection, customized Liquid Chromatography (LC) followed by Multiple Residue Monitoring (MRM) detection. Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;6 of the supplementary information indicates the detection limits (MDLs) in sediments. The MDLs ranged from 1.45 to 2.9 ng/g dry. The detection limits are based on a sample volume of 3g initial dry weight for solid matrices. The final extract volume for all matrices is 1.0mL.\u003c/p\u003e \u003cp\u003eThe un-acidified GC-MSMS acquisition and analysis were developed in-house using a HP-5ms Ultra Inert chromatographic column, a hot 5uL Multi-Mode-Inlet (MMI) injection as well as custom GC temperature program designed to maximize the sensitivity and peak resolution of the various Pesticides in this analysis. The instrumentation used for the GC-MSMS analysis were Agilent GC model 7890B, PN G3440B, Agilent QQQ model 7010B EI mainframe, PN G7013B, Agilent Auto-injector model 7693A, PN G4513A. The LC-MSMS acquisition and analysis were developed in-house using a 3uL injection, custom gradient and collecting MSMS data specific to the compounds of interest. The sensitivity and peak resolution of each compound is maximized with custom MRM transitions and detector conditions for each compound. The instrumentation used for this LC-MSMS analysis were; Waters Xevo TQS-micro, Binary Solvent Manager ACQUITY I-Class and Waters Sample Manager FTN. Pesticides analyses use a 10-point calibration curve for quantification. The calibration standards used are listed in Tables\u0026nbsp;7 and 8 of the supplementary information. The QuEChERS cleanup procedures associated with solid matrices require a correction factor of 4 to be applied to the results at the time of quantification (ALET 2024).\u003c/p\u003e \u003cp\u003eCalibration check standards are included in the sequence. Check standards, or QCs LOW, MID and HIGH evaluate the calibration curve throughout the range. The concentration range used for the different QCs are listed in Tables\u0026nbsp;9 and 10 in the supplementary information. All procedural blanks and spikes are analyzed within the sample sequence. The various spiking levels used during the method validation and processing of routine samples are listed in Table\u0026nbsp;11 of the supplementary information.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eOverall, fifty pesticides were monitored during this study. However the majority of samples did not contain quantifiable concentrations of 41 of those pesticides. The following list of pesticides had no detections at our sampling sites; atrazine, thiacloprid, azinphos-methyl, aatrazine desethyl, imidaclprid, atrazine desisopropyl, thiamethoxam, benzolyprop ethyl, bromacil, dinotefuran, acetaprimid, butylate, coumaphos, flupyradifurone, clomazone, flonicamid, cyanazine, dimethoate, spirodiclofen, diallate, fonofos, spiromesifen, diclofop-methyl, ivermectin, ethalfluralin, malathion, spirotetramat, sulfoxaflor, cyantraniliprole, hexazinone, phorate, metobromuron, phosmet, disulfoton, metribuzin, ethion, simazine, piperonyl butoxide, abamectin, triallate, pyriproxyfen and azamethiphos. The following list of pesticides were detected in our samples (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e); Terbufos, parathion, chlorantraniliprole, chlorpyrifos, pendimethalin, clothianidin, linuron, chlorpyrifos, metolachlor trifluralin. Terbufos and parathion were the most detected pesticides with nine and eight sites respectively while trifluralin was only detected at one site (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). None of the 6 sites in New Brunswick had pesticide detections. Overall, the Dunk River site had the highest frequency of pesticides detected at 4 while both sites on the Wilmot as well as Lawrence (NS) and Watton (NS) having each two pesticides detected.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetection frequency, average concentration detected and province where pesticides were measured during this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesticide\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e# detections\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRange (ng/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage concentration (ng/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAvg detection limit (ng/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProvince\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrifluralin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePEI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLinuron\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.45\u0026ndash;2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS, PEI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorpyrifos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.11\u0026ndash;3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS, PEI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetolachlor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.38\u0026ndash;9.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClothianidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.99\u0026ndash;2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePEI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePendimethalin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.27\u0026ndash;16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorantraniliprole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.53\u0026ndash;9.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS, PEI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParathion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026ndash;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS, PEI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerbufos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.3\u0026ndash;5.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePendimethalin is a selective herbicide used to control annual grasses and some broadleaf weeds in field used for produce as well as stone and berry fruit. EPA (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) describes Pendimethalin has as having a high affinity to bind to soil and sediment which will limit its concentrations into surface waters. Pendimethalin was only detected at four sites all located in Nova Scotia with concentrations ranging from 2.27\u0026ndash;16.1 ng//g (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Two of those sites also contained the highest silt concentrations measured from all the sites in Nova Scotia.\u003c/p\u003e \u003cp\u003eChlorantraniliprole was detected in 5 out of the 51 sites of this study and they were are all located on Prince Edward Island during the 2020 sampling campaign but was detected at two sites in Nova Scotia in 2021 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It is used as an insecticide and commonly found in surface water in PEI (Chalis \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). On Prince Edward Island, 2 out of the three sites contained high concentrations of silt (above 40%), the third site with a detection had a lower silt value (27%). In contrast, chlorantraniliprole was detected in 35% of all water samples in the Lalonde and Garron (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) study which occurred in the same region as those of this study. Chlorantraniliprole has been described as moderately persistent in the aquatic environment and will partition to sediment while it is expected to accumulate in aquatic systems (HC 2014)\u003c/p\u003e \u003cp\u003eChlorpyrifos in PEI was measured at two sites including Dunk River which has the fifth highest silt concentration out of our 51 sampling sites of this study. The use of chlorpyrifos has been revoked in Canada since December 2023. It was originally used as an insecticide in field used to grow fruits and vegetables, cereals and turf grass (HC 2019). Chlorpyrifos has a low water solubility and is characterised as readily migrating from the water column to the sediments (HC 2019). However, in a prior study, it was detected in 17% of the water samples (0.7 to 601ng/L) in Maritime Canada (Lalonde and Garron \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) while in this study it was detected in 5% of the sites (2 sites) and at concentrations that were very close to the detection limit of the laboratory (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A recent study by Toth et al (2024) did not detects any chlopyrifos in 113 sediment samples from Quebec. The authors of that study link the lack of detection to the pesticide ban.\u003c/p\u003e \u003cp\u003eTrifluralin is used as a pre-emergent soil herbicide to control broadleaved weeds and annual grasses. Trifluarin has a low water solubility but is somewhat volatile (HC 1992). The low water solubility may explain the low detection of it from the meta-analysis of 248 discrete water samples in the Maritime region, where trifluralin was only detected in 4 instances with a range of concentration from 4.36 to 3230 ng/L. (Lalonde and Garron \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Trifluralin has been described as having a high affinity to absorb to soils with high organic content (HC 1992) and the site at Dunk River contains the fifth highest silt proportion of all the sampling sites of this study which may explain in part its presence. However, trifluralin was only detected at one out of 51 sites samples as [art of this study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eParathion is used as an insecticide on fruit, cereals and vegetables. It was the pesticide with the second highest number of detections at 16% of all sites sampled. Richardson (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) describes parathion has binding tightly to soil particles and little or no potential for groundwater contamination. It is not surprising then that parathion was not detected in any of the 248 water samples in the Lalonde and Garron (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) study in some of the same streams where parathion was detected in the sediments as part of this study. The high affinity to soil particles did not translate to a direct correlation to silt concentrations at our sampling sites since it was detected at sites ranging in silt concentrations from 14 to 70%.\u003c/p\u003e \u003cp\u003eTerbufos is used as an organophosphorus insecticide which was used on corn, sugar beets and rutabagas in Canada (HC 1995). It has a low water solubility and is slightly to moderately persistent in soil and sediment. The physical and chemical properties of terbufos indicate that in aquatic systems it will partition into sediments (PMRA \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) with a slow transformation rate. Terbufos has been shown to have a higher concentration in sediment than water during runoff events due to the relative magnitude of the absorption coefficient to sediment in comparison to its solubility in water (Mamo et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). According to Pest Management Regulatory Agency (PMRA), the use of products containing terbufos in Canada was prohibited after August 1 2012. However, it was detected at nine sites in Nova Scotia with a range of concentrations from 2.3 to 4.35 ng/g (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There was no direct correlation to silt with sampling sites containing terbufos having sediment ranging from 14.7 to 21.3% silt. The Lalonde and Garron (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) study did not detect any terbufos in the 248 water samples obtained between 2013\u0026ndash;2018. Terbufos was the pesticide that was detected the most often in this study.\u003c/p\u003e \u003cp\u003eClothianidin is a neonicotinoid pesticide used on various crops and for seed treatments. PMRA published various risk reduction measures such as decreasing the number of applications per season and increasing buffer zones in 2023. Environmental Protection Agency (EPA) described clothianidin as persistent and mobile and its high persistence could lead to accumulate in soils with repeated uses (EPA \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). HC (2018) described clothianidin as moderately persistent to persistent in water systems containing sediment. Clothianidin was the most frequently detected pesticides in stream of Maritime Canada with a proportion of detection of 90% in water samples (Lalonde and Garron \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) which is in contrast to a detection frequency of only 4% in sediment in this study. Clothianidin was only detected in PEI (1.99-2.24ng/g) and upstream but not downstream on the Wilmot River (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This is in sharp contrast to the review study of sediments in Chinese rivers where frequency of detection in sediments ranged from 73 to 100% with a range of concentrations from 0.01 to 12.7 ng/g (dw) (Zhang et al 2025). The large difference in detection frequency may be explained by the higher detection limit of clothianidin in this study. However, a Canadian study on neonicotinoid pesticide concentrations in wetlands sediments and water revealed a frequency of detection very similar to that of this study with a detection rate of 6% and a range of values close to that of this study (2.6\u0026ndash;4.4 ng/g) (Main et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Since clothianidin has a relatively low soil-water organic carbon coefficient and high water solubility, it seems likely that the low proportion of detection in this study is due to those characteristics.\u003c/p\u003e \u003cp\u003eLinuron is an herbicide used to control annual and perennial broadleaf and grasses. PMRA (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) describes Linuron as slightly to moderately persistent and carryover into the following growing season is not expected to be of concern. In the Lalonde and Garron (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) study, Linuron was only detected in 4% of all the surface water samples obtained (at concentrations almost equal to the detection limit of the laboratory) which is the same to that of the frequency of detection in sediment of this study. In this study, Linuron seems to have affinity to high silt concentrations with both detections occurring at sites with over 40% sit in NS and PEI however another mechanism could be involved since our site containing 70% silt did not have a detection. The detections at these two sites might be strictly a reflection of the usage of this product on crops that are deemed to meet standards for human health and environment (carrots, parsnips, potato, asparagus and shelterbelts) (PMRA \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eMetolachlor is an herbicide used to control broadleaved weeds and annual grasses ands is heavily used on corn and soyabean crops (Toth et al 2024). In Canada, it is registered for use on various field crops, vegetables, fruit trees and outdoor ornamentals (PMRA \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Metolachlor was detected in sediment at two sites located in Nova Scotia (4% frequency of detection). In contrast, a prior study in the Maritimes of Canada, metolachlor was detected at a frequency of 22% of all water samples with a range of values between 4.64\u0026ndash;270 ng/L (Lalonde and Garron \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) which is not surprising since metolachlor is only mildly persistent in soil but highly persistent in water due to a low photodegradation rate (EXTOXNET \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast, the study in Qu\u0026eacute;bec by Toth et al (2024) revealed that metolachlor was detected in 34% of all sediments analysed. This difference between the detection rate between the studies might be explained by the overwhelming domination of corn and soya crops in the watersheds of the Toth et al (2024) study in comparison to our sites.\u003c/p\u003e \u003cp\u003eThe grain size analysis of the sediment at each sites revealed 7 sampling sites which had 30% or more silt with Valleyfield (PEI) ranking highest at 70.8%. The four highest concentrations of silt were sites on Prince Edward Island while two sites in Nova Scotia had 41 and 36% silt (Lawrence and Armstrong respectively) and a last site in PEI (Cains Bk) came last of the seven highest at 33.4% silt. The affinity of pesticides to the smallest fraction grain size such as silt has been described before (Vryzas \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Toth et al 2024). In our study there are similarities where high silt sites such as Dunk and Wilmot have some of the highest detection frequencies of pesticides. However, our site with the highest silt (Valleyfield) only had one detection of pesticide while and another site with over 40% silt (Knox) had no detection at all. These results seem to indicate that while silt is a good indicator of potential pesticide detection in sediment, there are other factors which may correlate better with pesticide detections.\u003c/p\u003e \u003cp\u003eToth et al (2024) describe how a high n-octanol-water partition coefficient (Kow) gauges the hydrophobicity of organic molecules which may lead to a higher affinity of binding to sediment. Pesticides such as chlopyrifos, metolachlor and linuron all have Kow\u0026thinsp;\u0026gt;\u0026thinsp;3 (Toth and Yargeau \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the results they presented did not correlate a high Kow and the detection of insecticides in freshwater sediments of Qc (Toth et al 2024). The frequency of detection for lower Kow pesticides such as atrazine and carbaryl were 46 and 37% respectively which is significantly higher that the detection for chlorantraniliprole and linuron both at 1%. The results from the Toth et al (2024) study conclude in part that since the three most detected pesticides in sediment mirror those most frequently found in water samples, the presence of the pesticide in the water samples may contribute to the concentrations in sediments given there would be less of a tendency for desorption in order to reach equilibrium.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eComparison to other studies\u003c/h2\u003e \u003cp\u003eThe results of our study would benefit from a comparison to previously published concentrations of pesticides in freshwater sediments in the Maritime. However, the author of this study was not able to find any prior study in the Maritime region of Canada.\u003c/p\u003e \u003cp\u003eGiroux et al (2023) published a large-scale study of pesticides occurring in sediments in the province of Qu\u0026eacute;bec. Similarly to our results only 18/77 pesticides analysed had detections. The two most frequently detected pesticides in sediments in the Giroux et al (2023) study was glysophate and AMPA with rates of detections of 8 and 78% respectively. Unfortunately, these two pesticides were not part of the suites analysed as part of this study. However, the third most detected pesticides in sediment in Giroux et al (2023) was chlorantraniliprole at 15% detection which is similar to the results from this study (detection rate of 10%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The only statistically significant correlation in the Giroux et al study (2023) was between the number of detections and % land maraich\u0026egrave;re (land for vegetable) within the watershed. Although Giroux et al (2023) described how the concentration of pesticides in sediment was somewhat related to the concentration of organic matter in sediment, the relationship was not statistically significant. However, Ramage et al (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) described how total organic carbon had a statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.645) positive influence on total pesticide concentrations in UK sediments. These conflicting results would suggest that the presence of pesticide in sediment is complex and not easily predicted by single factor like organic matter or chemical properties alone.\u003c/p\u003e \u003cp\u003eToth et al (2024) also published a large scale study of pesticide concentrations in Qu\u0026eacute;bec (Canada) sediments. Frequency of detections of pesticides in sediments occurred in 119 out of 232 samples with 47% of the 30 pesticides detected at least once in a sample. Atrazine, carbaryl and metolachlor had the three highest proportion of detection at 46, 37 and 34% respectively while chlorantraniliprole had a low frequency of detection of 1% (Toth et al 2024) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Toth et al (2024) had suggested that a high Kow would suggest an affinity of a pesticide to bind to sediment but no such correlations were detected within their dataset which again suggests that pesticide presence depends on complex interacting factors rather than just a single factor like Kow.\u003c/p\u003e \u003cp\u003eWei et al (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) present sediment pesticide concentrations in China (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) for both wet and dry seasons with higher detection frequency and range of concentrations occurring in the dry season. While chlorpyrifos concentrations are like that of this study, the highest terbufos concentrations in the Wei et al (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) study was a full order of magnitude higher than that of this study. Interestingly, the use of Terbufos was prohibited in Canada as of 2012. It\u0026rsquo;s moderate persistence in soil/ sediment may explain some of the detections in this study and may explain this legacy contamination. Similarly, Prajapati et al (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) had a detection frequency of 67 and 100% (in 2020) for methoxychlor and lindane both of which have been banned in Canada since the 1970s which suggests legacy contamination. The legacy contamination may stem from the substance\u0026rsquo;s high lipophilicity and recalcitrance to degradation by both biotic and abiotic processes (Prajapati et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similar to our study, pp\u0026rsquo;-DDT and dieldrin were detected in United States freshwater sediments 32 and 17 years, respectively after their last legal applications. (Knight at al 2007). Prajapati et al (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) measured pesticides in sediments in Saskatchewan with a 3% detection frequency for parathion (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) which is in stark contrast to the 16% detection in this study. Concentrations for parathion were up to 3 times higher in this study compared to the results from Prajapati et al (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Perhaps the different crops grown in Saskatchewan in comparison to Atlantic Canada led to the difference in usage and therefore detection of parathion in sediments.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRange of pesticide concentrations, detection frequency in sediment from other studies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesticide\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange (ng/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDetection Frequency (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorantraniliprole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNd-102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eToth et al 2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetolachlor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eND-1237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eToth et al 2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorantraniliprole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u0026ndash;9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGiroux et al 2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClothianidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGiroux et al 2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClothianidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6\u0026ndash;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMain et al \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParathion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePrajapati et al \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClothianidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eND-1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZhang et al 2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorpyrifos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNd- 6.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.6\u0026ndash;37.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWei et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerbufos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eND-30.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.7 41.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWei et al \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClothianidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.2-11.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKuechle et al 2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetolachlor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eND-107.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKnight et al \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrifluralin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eND-1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKnight et al \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePendimethalin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eND-39.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKnight et al \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorpyrifos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eND-34.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKnight et al \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorpyrifos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eND-440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmalling et al \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePendimethalin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eND-13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmalling et al \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClothianidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNd-1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRamage et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNowell et al (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) developed sediment likely and threshold effects benchmarks for 48 pesticides. The benchmarks are based on pesticide concentrations corrected for organic carbon (ug/g OC). The sediment organic carbon normalization in Nowell et al (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) assumes that this fraction predicts the bioavailable pesticide concentration. The likely effect benchmarks for chlorpyrifos, parathion and pendimethalin were 4.1, 5.2 and 3800 ug/g which is three order of magnitude or more above the total concentrations found in this study. This suggests that the concentrations of these three compounds in this study were not close to the likely effect benchmark suggested by the calculation of Nowell et al (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe result from this study found an inconsistent relationship between the percentage of silt in sediment and the frequency of pesticide detection. This suggests that while silt content can be an indicator for potential pesticide presence, it is not the sole or primary driver for sites in the Maritime region of Canada. Although the n-octanol-water partition coefficient (Kow) which measures a chemical\u0026rsquo;s tendency to bind to sediment can be a good indicator of potential pesticide presence in sediments, the results from this study do not follow this rule. The findings of this study highlight some environmental risk from pesticides that might have not been detected unless sediment monitoring is conducted in addition to surface water monitoring. Lastly, legacy contamination form pesticides which are not currently approved for use in Canada is still prevalent in sediments of the Maritime region of Canada. Overall, the results from this study indicate that sediment pesticide occurrence is not easily explained by single factors such as organic matter or Kow but rather by multiple interacting factors including land use, solubility/ lipophilicity of the pesticide and the persistence of the pesticides.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe author declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author has no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnly one author contributed to this manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgriculture and Agri Food Canada (AAFC) (2022) Crop Profile for Potato. 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Environments. 12(5):150\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6916507/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6916507/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study measured pesticide concentrations at 51 distinct sampling sites in freshwater streams in three maritime provinces of Canada (New Brunswick, Nova Scotia and Prince Edward Island) in 2020\u0026ndash;2021. There were 50 different pesticides analysed in each sediment samples. The majority of those pesticides had concentrations below the detection limit of the laboratory. However, nine pesticides had detections that ranged from one to nine sites. Terbufos and parathion were the two most detected pesticides of this study. Surprisingly, terbufos has not been registered for use in Canada since 2012 which showcases the prevalence of some legacy contamination in sediments. The results align with other recent Canadian research, suggesting that pesticide occurrence in sediment is complex and not explained by a single factor like organic matter or chemical properties alone. Instead, it is a result of multiple interacting factors, including land use, the pesticide's solubility, its concentration in the surrounding water, and the persistence of legacy products.\u003c/p\u003e","manuscriptTitle":"Spatial distribution of sediment pesticides concentrations in the Maritime Region of Canada","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-20 15:46:06","doi":"10.21203/rs.3.rs-6916507/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c4e402e4-5778-4cf4-90c3-1d340ef7ab3e","owner":[],"postedDate":"June 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-11T21:53:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-20 15:46:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6916507","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6916507","identity":"rs-6916507","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}