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An assessment of health risks posed by consumption of pesticide residues in fruits and vegetables among residents in the Kampala Metropolitan Area in Uganda

An assessment of health risks posed by consumption of pesticide residues in fruits and vegetables... Background: Pesticide use for fruits and vegetable production in Uganda may result in presence of residues on pro- duce which may pose health risks to consumers. Uganda does not have an established system for monitoring pesti- cide residues in fruits and vegetables and assessing potential health risks. This research aimed to conduct a health risk assessment of presence of pesticide residues in fruits and vegetables in the Kampala Metropolitan Area in Uganda. Method: Pesticides were measured in 160 fruits and vegetables samples collected at farms, markets, street vendors, restaurants and homes; and analysed using liquid chromatography-tandem mass spectrometry and gas chromatog- raphy-mass spectrometry. Fruit and vegetable consumption information was collected from 2177 people. Pesticide concentrations were compared with European Union maximum residual limits (MRLs). Mean values of pesticide con- centration residues found in the sample of fruits and vegetables; and fruits and vegetables intake and body weight were used to calculate the estimated daily intake (EDI) of pesticide residues. EDI values were compared with accept- able daily intakes (ADI) to calculate the hazard quotient by age group, and stage at which consumption happens along the chain. Results: Overall, 57 pesticides were detected in fruits and vegetables from farm to fork. Of the 57, 39 pesticides were detected in all the fruits and vegetables studied. Concentrations of fonofos, fenitrothion and fenhexamid were above the European Union MRLs in some samples. Hazard quotients based on dietary ingestion scenarios for 18 pesticides, including dichlorvos (444) alanycarb (314), fonofos (68), fenitrothion (62), dioxacarb (55) and benfuracarb (24) and others, were above 1, indicating the possibility of chronic health risk to consumers. Chronic health risk decreased with age but was stable for stage at which consumption happens along the food chain. The number of pesticides with EDI greater than the ADI decreased with increase in age; with 18, 13, 9, 11, 8, 9, and 9 pesticides for age groups < 5, 5-12, 13-19, 20-25, 36-49 and ≥ 50 respectively. Conclusion: Chronic dietary pesticide exposures to Ugandans are likely common, and for some pesticides result in exposure exceeding health-based benchmarks. Risks were highest for younger participants. There is an urgent need *Correspondence: cssemugabo@gmail.com Department of Disease Control and Environmental Health, School of Public Health, Makerere University College of Health Sciences, Kampala, Uganda Full list of author information is available at the end of the article © The Author(s) 2022. 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The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Ssemugabo et al. International Journal of Food Contamination (2022) 9:4 Page 2 of 14 to increase monitoring and regulation of pesticides in fruits and vegetables in order to protect consumers, especially the children who are vulnerable to the adverse effects of pesticides. Keywords: Maximum residual limits, Hazard quotient, Estimated daily intake, Acceptable daily intake, Uganda Introduction The use of these chemicals in agriculture may result Pesticides are widely used in agriculture to control in residues in food and expose consumers. Events where pests and disease in crops to improve the quality of pro- high levels of pesticide contamination has occurred have duce (Aktar et  al. 2009). Some commonly used classes resulted in acute health risks including nausea, exces- of pesticides include organophosphates, carbamates, sive sweating and salivation, diarrhoea and vomiting, pyrethroids and neonicotinoids (Matowo et  al. 2020; inhibition of blood clotting, and paralysis of the respira- Maggi et  al. 2019; Fuhrimann et  al. 2021; Staudacher tory and circulatory systems (PAN 2018). Several studies et  al. 2020). Most of these pesticide chemical groups, have shown that chronic exposure to low levels of some such as organophosphates, are broad spectrum insec- neurotoxic pesticides are associated with poorer learn- ticides, fungicides or herbicides used to control many ing and behavioral problems in children, memory loss, different pests, diseases or weeds in different crops loss of coordination, reduced speed of response to stim- (Hill et al. 2017). Many organophosphates, carbamates, uli, reduced visual ability, altered or uncontrolled mood pyrethroids and neonicotinoids, all neurotoxic pes- and general weakness; reproductive defects and cancers ticides, are registered for use in Uganda, (Ministry of (Nicolopoulou-Stamati et  al. 2016; Coker et  al. 2018; Agriculture Animal Industry and Fisheries, 2018) and Chiu et al. 2018). use is increasing with increasing consumption of fruits In Uganda, the volume of pesticides used has increased and vegetables including tomatoes, cabbage and water- from 338 t in the 1960s to 18,928.16 t in 2019 (FOA: melons, to name but a few (Ngabirano and Birungi FAOSTAT 2021). Many farmers do not follow recom- 2020). mended mixing concentrations on label instructions and Organophosphates and carbamates pesticides are gen- pre-harvest intervals (Kaye et  al. 2015). Such improper erally not persistent because they degrade when exposed pesticide use practices may result in higher levels of to sunlight, air and soils, but they often have high solu- pesticide residues in fruits and vegetables (Grewel et  al. bility and volatility and are heavily used in many farming 2017) that leave the farm to the final consumer. While systems (Akkad and Schwack 2010). Organophosphates they are important sources of minerals, vitamins, and and carbamates inhibit cholinesterase and may impact other healthful nutrients, consumption of fruits and neurodevelopment by other mechanisms, including vegetables contaminated with pesticide can be a route interference in synaptogenesis and myelin sheath forma- of exposure to hazardous chemicals. Fruit and vegetable tion (Vale and Lotti 2015; Sagiv et  al. 2019). Pyrethroids consumption is a protective factor for noncommunica- and neonicotinoids are often systemic pesticides with a ble diseases such as diabetes (World Health Organisa- higher affinity to soil and, especially for neonicotinoids, tion 2013), and consumption is rising among Ugandans, have the potential to bioaccumulate. They also have low which consume an average 260 g of fruits and vegetables volatility (Laskowski 2002; Bonmatin et  al. 2015). Pyre- each day (Ssemugabo et  al. 2021a). Fruit and vegetable throids act by altering the function of voltage-gated consumption has grown among residents of the Kam- sodium channel and consequently disrupt electrical pala Metropolitan Area (KMA) (Kabwama et  al. 2019), signalling in the nervous system (Soderlund 2010) and and organophosphate, carbamate, pyrethroid and neoni- are generally less acutely toxic than organophosphates cotinoid pesticides have been previously detected in the (Simaremare et  al. 2019). However, they are neurotoxi- tomatoes, watermelon, cabbages among others in this cants and have been associated with confusion, lacri- market (Kaye et  al. 2015; Ssemugabo et  al. 2021b; Atu- mation and salivation (Bradberry et  al. 2005) and also haire et al. 2017). poorer development and asthma in children (Pitzer et al. In the current study, we assessed potential pesticide 2021; Vester et  al. 2019). The mechanism of toxicity for exposures and health risks from consumption of fruit and neonicotinoids is based on selective binding and interac- vegetables by residents of the KMA, in Uganda. tion with nicotinic acetylcholine receptor sites of a target organism causing paralysis that leads to death (Taillebois Materials and methods et  al. 2018; Cartereau et  al. 2021; Houchat et  al. 2020), Study area and they have also been associated with development or This study was conducted in Kampala, Wakiso and neurological disorders (Cimino et al. 2017) in humans. Mukono Districts, three of the 5 districts that make up Ssemugabo  et al. International Journal of Food Contamination (2022) 9:4 Page 3 of 14 the KMA in Uganda. The 3 districts have a population and extracted to determine of pesticide residues (Ana- of 10,812,700 people (UBOS 2018) and cover an area stassiades et al. 2002). Briefly, 1-2 kgs of fruit or vegetable of 1000 km (Kasimbazi 2016). Agriculture is the larg- was chopped, grinded and blended to homogenize the est economic activity in Central Uganda within which sample. Of the homogenized sample, 200 g was put into the KMA is located, supporting 39.3% of the population containers and immediately frozen in order to minimize (UBOS 2018). This region has many large fresh produce the risk of degradation of any pesticide residues present. markets, restaurants, fruit and vegetable vending along Ten grams of homogenized sample was mixed with 3 g of the streets, as well as many of the farms where fruits and sodium bicarbonate (NaHCO3) and 20.0 mL acetonitrile, vegetables consumed within central Uganda are grown. vortexed and placed on a mechanical shaker at 300 rpm/ Kampala, Wakiso and Mukono are inhabited by 15% min for 15 min to improve extractability of pesticide resi- of Uganda’s population and contain Uganda’s districts dues and then centrifuged for 3 min at 3200 rpm. To this, that consume a large volume of the fruit and vegetables 10 g of anhydrous sodium sulphate (Na2SO4) was then produced. added, vortexed and centrifuged for 3 min at 3200 rpm. Ethical clearance to conduct the study was obtained We filtered the crude extract using a 0.2-μm PTFE from the Makerere University School of Public Health syringe filter. The final supernatant layer (0.50 g /mL) was Higher Degrees, Research and Ethics Committee; and transferred into the vials and injected into the LC-MS/ registered by Uganda National Council for Science and MS for analysis of pesticide residues (Ssemugabo et  al. Technology (SS 5203). Participation in the study was 2021b). voluntary and participants (farmers, restaurants market managers, street fruit and vegetable vendors, and house- Pesticide analysis hold heads) provided informed written consent to collect Liquid chromatography – Tandem mass spectrometry samples and fruit and vegetable dietary intake informa- (LC-MS/MS) analysis was carried to detect and ensure tion. All samples and questionnaire were coded with an quality of the pesticides residue measurements. A zorbax anonymous identification number. eclipse plus C18 capillary column (150 mm with 2.1 mm internal diameter and 1.8 μm particle size) operating at Pesticide residue data 35 °C to 360 °C was used with the internal temperature Sampling of fruits and vegetables set at 35 °C for 1 min, then ramped to 120 °C per minute Fruits and vegetables samples were collected from key and 375 °C per minute. This process was run over two stages along the supply chain including farms (50), mar- mobile phases. Phase A involved – water (0.1% formic kets (50), street vendors (20), restaurants (20) and homes acid, 5 mM ammonium formate, and 2% MeOH). Phase B (20), totaling 160 samples. The detailed methodology involved – methanol (0.1% formic acid, 5 mM ammonium used to collect the fruits and vegetable samples has been formate, 2% water). The injector temperature was 120 °C previously described (Ssemugabo et  al. 2021b). Briefly, and carrier gas was helium at a flow rate of 13 L/minute fresh fruit and vegetable samples were purchased and with splitless injection. The injection volume was 5 μL at collected in sterile polythene bags or PET (polyethylene a pressure of 45 psi. The MS ion source temperature was terephthalate) plastic containers from selected farms, 120 °C for a minute and raised at a rate of 35 °C per min- markets, and street vendors. Samples of ready-to-eat ute to 375 °C. Confirmation analysis utilised LC-MS/MS foods were bought from restaurants and homes, espe- which requires two product ions. Compounds with only cially juices and salads that do not contain fat-soluble one product ion were quantified and confirmed using the substances. Three replicate fruit and vegetable samples second ion. For confirmation, the relative ion intensity for were collected at each location measuring at least 1 kg for a pesticide in a sample was calculated and the value com- small and 2 kg for large produce as suggested by Codex pared to the equivalence for a calibration standard. For guidelines (El-Zaher et  al. 2011; Food and Agriculture positive confirmation, the retention times were matched Organisation 1999); processed food samples were at least to the calibration standard as well the relative ion inten- 1 kg or 1 l in case of juice. The samples were stored in a sities according to the recommended maximum toler- cooler and transported to the laboratory within 8 h and ances. Limits of detection (LOD) was determined during stored at − 20 °C until analysis. the method validation and measurements of uncertainty. The method developed by Keppel et  al. at the United Sample preparation and extraction States Food and Drug Administration (U.S. FDA) (Kab- A total of 93 pesticides residues were screened in the wama et  al. 2019; Ssemugabo et  al. 2021b) was used to fruit and vegetable samples (Supplementary Table  1). measure dithiocarbamates (mancozeb, maneb, dithane, Using the Quick, Easy, Cheap, Effective, Rugged and Safe thiram, metam sodium and propineb. Frozen sub-sam- (QuEChERS) approach, samples were prepared, cleaned ples of 10 g were placed into a Duran bottle (250 ml) and Ssemugabo et al. International Journal of Food Contamination (2022) 9:4 Page 4 of 14 mixed with isooctane (20 ml) followed by stannous chlo- following interviews with farmers and agricultural exten- ride (reducing solution) in hydrochloric acid (100 ml), sion. Workers. and sealed immediately with a septum and cap. The sam - ple was incubated at 80 °C in a water bath for 1.5 h with Health risk assessment frequent shaking. The Duran bottles were removed and We first prepared descriptive statistics of the pesticide left at ambient temperature for approximately 1 h. The residue levels in the produce samples. The mean pesti - bottles were frozen for 30 min to allow the generated cide concentrations were then compared with European carbon disulphide gas to condense. The samples were Union maximum residual limits (EU MRLs) obtained shaken and left for 5 min. The organic phase (iso-octane) from the pesticide residue database (https:// ec. europa. was removed and placed in a vial prior to the quantita- e u/ f o o d/ pl ant/ p e sti c ide s/ e u- p e sti c ide s - d at a b a s e/ mr l s/? tion of carbon disulphide by Gas Chromatography-Mass event= search. pr) (EUROPEAN UNION 2021). EU spectrometry (GC–MS). Spiking was done twice, once at MRLs were used because they provided comprehensive the limit of quantitation (LOQ) (50 μg/kg) and another standard values for all fruits studied; they have also been at the expected residue level (1000 μg/kg), as obtained used in other African studies (Fosu et al. 2017; Issa et al. from previous runs during instrument optimization 2018). We also used the mean pesticide concentrations (mean recoveries for individual pesticides in the range 60 to calculate the  estimated pesticide intake to compare – 140%) and precision (RSD ≤ 12%). A 5-point calibra- with acceptable daily intakes (ADIs). Estimated daily tion was used, ranging from 0.125–5 μg/ml. The method’s intake (EDI) (mg/kg/bw/day) was calculated by multiply- LOQ was set at 0.05 mg/kg which equates to the calibra- ing the mean concentration of each pesticide (C) and the tion standard of 0.125 μg/ml. All extracts were analyzed fruits and vegetable consumption rate (FVCR) (g/day) using GC-MS. Final pesticide residues concentrations and dividing this by body weight (BW) using the follow- were expressed in mg/kg of food. ing formula EDI = (C x FVCR)/BW (Gad Alla et al. 2015; Chen et al. 2011). The FVCR used was obtained from the Dietary consumption data dietary consumption survey (Ssemugabo et  al. 2021a). A modified semi-structured food frequency question - FVCR was calculated as mean consumption of fruits and naire from the World Health Organization’s (WHO) vegetables studied for the sample population as well as STEPwise approach to surveillance, standardized for different age groups studied that is < 5, 5-12, 13-19, method of collecting data on risk factors for noncom- 20-24, 25-35, 36-49 and 50+. BW used was measured municable diseases (NCDs) (WHO 2017) was used during the dietary consumption survey with the mean was used to interview 2177 participants to assess fruit for general sample population and age groups calculated and vegetable consumption over a 24-h dietary recall accordingly. The ADI (mg/kg/bw/day) for the different period and their body weight was concurrently meas- pesticides was obtained from the EU pesticide residue ured using a weighing scale. The detailed methodology database (EUROPEAN UNION 2021). The chronic risk on this has been described elsewhere (Ssemugabo et  al. assessment for pesticide residue was calculated by com- 2021a). Briefly, based on typical Ugandan diets, a food paring EDI with the ADI to get the hazard quotient (HQ) album was developed with different quantities of selected using the following equation; HQ = EDI/ADI. A hazard fruits and vegetables. Each research assistant was given a quotient (HQ) > 1 indicates exposures over the health- copy of the food album as a guide during the interview. based benchmarks and the potential to induce unaccep- Respondents were asked to identify the quantities they table health risks among consumers. consume per serving to determine the amounts con- sumed. Based on portion size in the food album, we esti- Results mated intake in grams of each fruit and vegetable each Pesticide residue concentrations day of the week. For children under 18 years, their par- The mean concentration of organophosphates, carba - ents or caretakers were interviewed. Participant’s weight mates, pyrethroids and neonicotinoids among other pes- was also measured thrice and the average calculated. For ticides detected in watermelons, passion fruit, tomato, children below 2 years who cannot stand, their weight cabbage and eggplants and comparisons with their was obtained by reviewing their immunization chart or respective MRLs are shown in Table 1. Out of the 62 pes- asking their parents or caregivers the measurement from ticide active ingredients detected, 5 were excluded due to their last weighing event. Socio-demographics data was the lack of verified maximum residue levels (MRLs) in the also obtained using the study questionnaire. Consump- EU database for the studied fruits and vegetables. There - tion data was collected for five commonly consumed fore, 57 pesticides were considered for the risk assess- and pesticide intensive fruits and vegetables, that is: ment. Of the 57 pesticides, 39 pesticides were detected watermelon, passion fruit, tomato, cabbage and eggplant in all the fruits and vegetable types. Dimethoate was Ssemugabo  et al. International Journal of Food Contamination (2022) 9:4 Page 5 of 14 Table 1 Concentration of pesticide residues per fruit and vegetable type compared with the MRLs Pesticide residues LOD (mg/kg) Water melon (mg/ Passion fruit (mg/ Tomato (mg/kg) Cabbage (mg/kg) Eggplant (mg/ kg) kg) kg) Mean MRL Mean MRL Mean MRL Mean MRL Mean MRL Dithiocarbamate 0.000006 0.001 1.5 0.00007 0.05 0.0002 3 0.0006 3 0.0004 3 Omethoate 0.00002 0.0004 0.01 0.0002 0.01 0.0003 0.01 BDL 0.01 0.00007 0.01 Acephate 0.00003 0.001 0.01 0.001 0.01 0.0001 0.01 0.00008 0.01 0.0002 0.01 Monocrotophos 0.00001 0.00003 0.01 0.00004 0.01 BDL 0.01 0.00003 0.01 0.00002 0.01 Vamidothion 0.00001 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 0.00008 0.01 Dimethoate 0.000008 0.0007 0.01 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 Mevinphos 0.00003 BDL 0.01 BDL 0.01 BDL 0.01 0.00004 0.01 0.00005 0.01 Phosphamidon 0.00002 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 0.00005 0.01 a a a a Fonofos 0.00001 0.03 0.01 0.2 0.01 0.2 10 0.1 0.01 0.1 0.01 Azamethiphos 0.000005 0.00002 0.01 0.000007 0.01 BDL 0.01 BDL 0.01 BDL 0.01 Dichlorvos 0.00002 0.003 0.01 0.007 0.01 0.0001 0.01 0.0002 0.01 0.0007 0.01 Malaoxon 0.00001 BDL 0.02 BDL 0.02 0.00004 0.02 0.00002 0.02 BDL 0.02 Methidathion 0.00001 BDL 0.02 0.000003 0.02 0.00002 0.02 BDL 0.02 0.000001 0.02 Malathion 0.00002 BDL 0.02 BDL 0.02 BDL 0.02 BDL 0.02 0.00007 0.02 Methacrifos 0.000005 BDL 0.01 0.00003 0.01 BDL 0.01 BDL 0.01 BDL 0.01 Ethoprophos 0.00008 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 Fenamiphos 0.000009 BDL 0.02 BDL 0.02 BDL 0.04 BDL 0.04 BDL 0.02 Quinalphos 0.00003 0.0001 0.01 0.0001 0.01 0.00003 0.01 BDL 0.01 0.0001 0.01 Coumaphos 0.00002 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 Chlorpyriphos-methyl 0.000008 BDL 0.01 0.00004 0.01 0.00009 0.01 0.00005 0.01 0.00002 0.01 Temephos 0.000008 BDL 0.01 0.00001 0.01 BDL 0.01 0.000009 0.01 BDL 0.01 Profenofos 0.00001 0.003 0.01 0.00002 0.01 0.04 10 0.003 0.01 0.005 0.01 Pirimiphosmethyl 0.00002 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 0.00001 0.01 a a a a Fenitrothion 0.00001 0.02 0.01 0.004 0.01 0.01 0.01 0.03 0.01 0.03 0.01 Aminocarb 0.00002 BDL 0.01 0.0007 0.01 BDL 0.01 0.00008 0.01 0.00002 0.01 Methomyl 0.00003 0.00006 0.015 0.00003 0.01 0.00003 0.01 BDL 0.01 0.00003 0.01 Aldicarbfragment 0.00001 0.00002 0.02 0.00002 0.02 0.00004 0.02 0.00002 0.02 BDL 0.02 Pirimicarb 0.00003 0.00004 0.5 0.00004 0.01 BDL 0.5 BDL 0.5 BDL 0.5 Dioxacarb 0.00001 0.004 0.01 0.003 0.01 0.003 0.01 0.003 0.01 0.004 0.01 Carbaryl 0.000008 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 0.00001 0.01 Carbofuran 0.000009 0.00003 0.01 BDL 0.01 BDL 0.002 BDL 0.002 0.00003 0.002 Alanycarb 0.00001 0.0001 0.02 0.00006 0.02 0.08 0.02 0.01 0.02 0.006 0.02 Benfuracarb 0.00005 0.0005 0.01 BDL 0.01 0.004 0.002 0.07 0.002 0.004 0.002 Methiocarb 0.00004 BDL 0.03 BDL 0.03 BDL 0.03 0.00005 0.03 BDL 0.03 Imidacloprid 0.00003 0.0007 0.2 0.0008 0.05 0.0004 0.5 0.0004 0.5 0.0002 0.5 Acetamiprid 0.00002 0.004 0.2 0.002 0.01 0.008 0.5 0.005 0.4 0.001 0.2 Thiacloprid 0.00001 BDL 0.2 BDL 0.01 BDL 0.5 BDL 0.3 BDL 0.7 Bifenthrin 0.00002 0.0001 0.01 0.00004 0.01 0.0004 0.3 0.00005 0.4 BDL 0.3 Lambda-Cyhalothrin 0.00002 0.0002 0.06 0.0001 0.01 0.0002 0.07 0.0002 0.15 0.0002 0.3 Deltamethrin 0.00001 BDL 0.02 BDL 0.01 BDL 0.07 0.00006 0.1 BDL 0.4 Cypermethrin 0.00001 0.0002 0.2 BDL 0.05 0.001 0.5 0.0004 1 0.0004 0.5 Carbendazim 0.00002 BDL 0.1 0.0001 0.1 BDL 0.3 0.0001 0.1 BDL 0.5 Imazalil 0.00001 0.0005 0.01 0.0004 0.01 0.0001 0.3 0.0003 0.01 0.0003 0.01 Metazachlor 0.00001 0.00001 0.02 0.00004 0.02 0.00006 0.02 0.00002 0.4 0.00002 0.02 Metalaxyl 0.00002 BDL 0.2 BDL 0.01 0.00005 0.3 BDL 0.06 BDL 0.01 Azaconazole 0.000006 0.000009 0.01 0.0001 0.01 0.000008 0.01 0.00007 0.01 0.000008 0.01 Clomazone 0.000007 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 Ssemugabo et al. International Journal of Food Contamination (2022) 9:4 Page 6 of 14 Table 1 (continued) Pesticide residues LOD (mg/kg) Water melon (mg/ Passion fruit (mg/ Tomato (mg/kg) Cabbage (mg/kg) Eggplant (mg/ kg) kg) kg) Mean MRL Mean MRL Mean MRL Mean MRL Mean MRL Azoxystrobin 0.000007 BDL 1 BDL 4 0.00456 3 0.003 5 0.003 3 Pyrimethanil 0.00002 0.0001 0.01 0.00008 0.01 0.0001 1 0.00008 0.01 0.00006 1 Spirotetramat 0.00002 0.00003 0.2 BDL 0.1 0.00009 2 0.00001 2 BDL 2 a a a Fenhexamid 0.00001 0.01 0.01 0.07 0.01 BDL 2 0.03 0.01 0.009 2 Fenarimol 0.00001 0.0006 0.05 0.0003 0.02 0.0002 0.02 0.0003 0.02 0.0004 0.02 Fluazifop 0.00002 0.005 0.01 BDL 0.01 BDL 0.06 0.0004 0.01 BDL 1 Flufenoxuron 0.00002 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 Pyriproxyfen 0.000007 BDL 0.05 BDL 0.05 BDL 1 BDL 0.05 BDL 1 Quinoxyfen 0.00003 BDL 0.05 0.00005 0.02 0.00004 0.02 BDL 0.02 0.00003 0.02 Proquinazid 0.00001 BDL 0.02 BDL 0.02 0.001 0.15 0.0003 0.02 0.00009 0.02 BDL Below detection limits, LOD Limit of Detection Above the MRLs detected only in watermelon with a mean concentration may occur in 16 of the 57 pesticides assessed. EDIs of 0.0007 mg/kg. Fonofos was detected in all fruits and for dichlorvos, fenitrothion, alanycarb and benfura- vegetables with concentrations above the MRLs in water- carb were above the ADI at all stages of consumption. melon (0.03 mgkg), passion fruit (0.02 mg/kg), cabbages EDIs for fonofos and profenofos exceeded the ADI at (0.11 mg/kg) and eggplants (0.14 mg/kg). Methidathion four stages of consumption. Fonofos, dichlorvos, feni- was not detected in watermelon and cabbages and mala- trothion, dioxacarb, alanycarb and benfuracarb pre- thion was not detected in passion fruit. Methacrifos was sented the highest risk levels with HQs of 27.5, 442.6, detected in passion fruit at 0.00003 mg/kg and cabbages 23.6, 29.5, 118.0 and 23.6 respectively, at the farm and at 0.000002 mg/kg. Ethoprophos was not detected in throughout the entire supply chain (See supplementary vegetables but only in eggplants at 0.0003 mg/kg. Cou- Table 2). Overall, pesticide concentration at street ven- maphos and pirimiphos-methyl were detected only cab- dors presented lower HQs and consequently lower like- bages at 0.0000005 mg/kg and eggplants at 0.00001 mg/kg lihood for health risks compared to other stages along respectively. Apart from passion fruit, fenitrothion con- the chain (Fig. 1). centration was above the MRLs in watermelon (0.02 mg/ kg), tomato (0.013 mg/kg), cabbage (0.03 mg/kg) and eggplant (0.03 mg/kg). Neonicotinoids were detected in Health risk assessment by age group almost all fruits and vegetables apart from thiacloprid We evaluated the risk of consumption of pesticide res- that was only detected in passion fruit 0.000007 mg/kg idues by age of consumers as shown in Table  3. EDIs and tomato 0.000002 mg/kg. Deltamethrin, azoxystrobin for fonofos, dichlorvos, profenofos, fenitrothion, diox- and proquinazid were only detected in vegetables with acarb, alanycarb, benfuracarb, cypermethrin and flu - concentrations below the MRLs. Although not detected azifop exceeded ADIs throughout all age groups and in tomato, fenhexamid’s concentration was above the consequently pose chronic health risks. The number MRLs in watermelon (0.01 mg/kg), passion fruit (0.07 mg/ of pesticides with EDIs greater than the ADI decreased kg) and cabbage (0.03 mg/kg). with age with 18, 13, 9, 11, 8, 9, and 9 for age groups under 5 years, 5-12, 13-19, 20-25, 36-49 and 50+ years respectively. Dichlorvos had the highest risk with a Health risk assessment by stage of consumption HQ of 444 followed by alanycarb (314), Fonofos (68), along the chain fenitrothion (62), dioxacarb (55) and benfuracarb (24) The risk of exposures to pesticides residues in fruits among children under 5 with a similar trend across age and vegetables are evaluated by the stage at which con- groups (see supplementary Table 2). Overall, HQ values sumption may occur along the chain including at the decreased across age groups with children under 5 pre- farm, market, street vendor, restaurant and home as senting highest risks and adults 50+ having the lowest shown in Table  2. The EDI was higher than the ADI chronic health risks for the nine pesticides as shown in in at least one of the stages at which consumption Fig. 2. Ssemugabo  et al. International Journal of Food Contamination (2022) 9:4 Page 7 of 14 Table 2 Estimated daily intake (mgkg/bw/day) for fruits and vegetables by stage along the chain Pesticides ADI (mg/kg/bw/ EDI (mg/kg/bw/day) day) Farm Market Street Restaurant Home Dithiocarbamate 0.05 0.002 0.003 0.005 0.002 0.002 a a Omethoate 0.002 4.7E-06 0.002 BDL 0.002 2.9E-06 Acephate 0.03 0.005 0.004 0.0004 0.002 0.0006 Monocrotophos 0.0006 0.0001 0.0002 0.0002 0.0002 0.0001 Vamidothion 0.008 0.0002 0.0002 5.9E-07 3.54E-06 2.95E-06 Dimethoate 0.002 BDL 0.003 BDL BDL BDL Mevinphos 0.001 0.0002 0.0002 2.4E-06 2.4E-06 5.9E-05 Phosphamidon 0.0005 1.2E-06 1.18E-06 1.18E-06 BDL 1.77E-06 a a a a Fonofos 0.03 0.8 0.9 0.006 1.2 0.4 Azamethiphos 0.025 2.4E-06 5.9E-05 1.2E-06 5.9E-06 BDL a a a a a Dichlorvos 0.00008 0.04 0.0006 0.0004 0.002 0.003 Malaoxon 0.03 4.1E-06 4.1E-06 0.0003 5.9E-05 5.9E-05 Methidathion 0.001 BDL BDL 1.2E-06 BDL 0.0002 Malathion 0.03 0.0003 BDL BDL BDL 2.9E-06 Methacrifos 0.006 BDL 0.0001 BDL BDL BDL Ethoprophos 0.0004 BDL 0.0001 BDL 0.0001 0.0002 Fenamiphos 0.0008 5.9E-08 1.2E-06 5.9E-05 1.8E-06 BDL Quinalphos 0.001 0.0001 0.0005 0.0004 0.001 0.0006 Coumaphos 0.001 1.8E-07 BDL BDL BDL BDL Chlorpyriphos-methyl 0.01 0.0005 BDL 2.9E-06 0.0006 BDL Temephos 0.001 BDL 3.5E-06 BDL 2.9E-06 0.0001 a a a a Profenofos 0.03 0.1 0.04 0.04 0.06 0.004 Pirimiphosmethyl 0.03 BDL 4.7E-06 BDL BDL BDL a a a a a Fenitrothion 0.005 0.1 0.05 0.2 0.2 0.02 Aminocarb 0.001 0.0002 0.003 0.0001 0.0001 5.9E-05 Methomyl 0.0025 0.0002 0.0003 4.1E-06 0.0002 2.9E-06 Aldicarbfragment 0.001 0.0001 0.0001 0.000177 5.9E-05 0.0001 Pirimicarb 0.035 0.0001 0.0001 BDL 0.0004 5.9E-05 a a a Dioxacarb 0.001 0.03 0.02 0.02 BDL BDL Carbaryl 0.0075 2.4E-06 2.4E-06 1.8E-06 BDL 0.0001 a a Carbofuran 0.00015 4.1E-06 5.9E-07 0.0003 0.0002 2.9E-07 a a a a a Alanycarb 0.001 0.1 0.1 0.06 0.1 0.2 a a a a Benfuracarb 0.01 0.2 0.05 0.02 4.7E-13 0.02 Methiocarb 0.00025 0.0002 5.9E-05 0.0002 4.1E-06 5.9E-07 Imidacloprid 0.06 0.003 0.002 0.005 0.006 0.001 Acetamiprid 0.025 0.04 0.02 0.01 0.01 0.005 Thiacloprid 0.01 3.5E-06 BDL BDL BDL BDL Bifenthrin 0.015 0.0006 0.002 4.7E-06 0.0002 2.4E-06 Lambda-Cyhalothrin 0.0012 0.001 0.001 0.001 0.0006 0.0006 Deltamethrin 0.01 2.4E-06 0.0002 BDL BDL BDL a a a Cypermethrin 0.0016 0.004 0.002 0.005 0.001 0.0006 Carbendazim 0.02 1.2E-06 0.001 2.4E-06 5.9E-05 4.7E-07 Imazalil 0.025 0.002 0.001 0.004 0.002 0.002 Metazachlor 0.08 0.0002 0.0002 0.0002 0.0002 0.0001 Metalaxyl 0.08 1.2E-06 0.0002 BDL BDL BDL Azaconazole 0.001 4.7E-06 0.0005 0.0004 BDL 0.0006 Clomazone 0.133 1.2E-06 BDL BDL BDL BDL Azoxystrobin 0.2 0.02 BDL BDL 0.01 0.04 Ssemugabo et al. International Journal of Food Contamination (2022) 9:4 Page 8 of 14 Table 2 (continued) Pesticides ADI (mg/kg/bw/ EDI (mg/kg/bw/day) day) Farm Market Street Restaurant Home Pyrimethanil 0.17 0.0002 0.0006 0.001 0.0005 0.0006 Spirotetramat 0.05 0.0003 0.0002 0.0002 2.9E-06 0.0001 Fenhexamid 0.2 0.1 0.2 0.6 0.006 0.05 Fenarimol 0.01 0.002 0.0006 0.002 0.002 0.005 Fluazifop 0.004 0.0006 0.02 BDL BDL BDL Flufenoxuron 0.01 1.8E-06 2.4E-06 1.8E-07 1.2E-06 4.1E-07 Pyriproxyfen 0.05 BDL BDL 2.9E-06 BDL BDL Quinoxyfen 0.2 0.0002 5.9E-05 0.0003 1.2E-06 0.0003 Proquinazid 0.01 0.003 0.0006 0.002 0.001 BDL BDL Below detection limit, ADI Acceptable Daily Intake, EDI Estimated Daily Intake EDI greater than ADI (HQ > 1) Fig. 1 Hazard quotients for various pesticide residuals, for fruits and vegetables by stage of consumption along the chain compared to other risk assessment studies (Szpyrka et al. Discussion 2013; Lozowicka et al. 2015; Mebdoua et al. 2017). When Pesticides were detected in all studied fruits and veg- calculated by stage along the supply chain and age group, etables, with 39 active ingredients (AIs) detected in all 16 and 18 pesticides respectively had high EDIs are above samples and 18 AIs in at least some of the food sam- their ADI. As discussed by JA Vaccaro and FG Huffman ples. Fonofos, fenitrothion and fenhexamid concentra- (Vaccaro and Huffman 2017), age is a key dietary risk fac - tions were above the MRLs in watermelon, passion fruit, tor that should be considered while performing health tomato, cabbage and eggplant. Risk assessment calcula- risk assessment Several fruit and vegetable surveillance tions show that EDIs for 18 pesticides were above the studies have estimated EDI and similar EDIs. Studies in ADI in some cases, with HQs that ranged from 1 up to Chile, Poland and Kazakhstan had EDIs ranging from 443 and thus may pose chronic health risks. Children < 0.001 to 5.2 (Lozowicka et al. 2015; Elgueta et al. 2017, experienced the highest HQs and therefore potentially 2019, 2020; Si et  al. 2021; Szpyrka and Słowik-Borowiec higher chronic health risks from pesticide residues in fruits and vegetables. 2019), which is within the range of our findings. Overall, 29% of the pesticides we tested for had EDIs Many pesticides were detected in all studied fruits over an ADI. This is a high proportion of exceedances and vegetables with levels below the EU MRLs except Ssemugabo  et al. International Journal of Food Contamination (2022) 9:4 Page 9 of 14 Table 3 Estimated daily intake (mgkg/bw/day) for fruits and vegetables by age group Pesticides ADI (mg/kg EDI (mgkg/bw/day) bw/day) General < 5 5-12 13-19 20-24 25-35 36-49 50+ population Dithiocarbamate 0.05 0.003 0.007 0.004 0.002 0.003 0.002 0.002 0.002 Omethoate 0.002 0.001 0.003 0.002 0.001 0.001 0.001 0.001 0.001 Acephate 0.03 0.003 0.008 0.005 0.003 0.003 0.003 0.002 0.003 Monocrotophos 0.0006 0.0002 0.0005 0.0003 0.0004 0.0002 0.0002 0.0001 0.0002 Vamidothion 0.008 0.0001 0.0003 0.0002 0.0001 0.0001 0.0001 9.4E-05 0.0001 Dimethoate 0.002 0.0008 0.002 0.001 0.0008 0.0009 0.0008 0.0007 0.0007 Mevinphos 0.001 0.0001 0.0003 0.0002 0.0001 0.0001 0.0001 9.4E-05 0.0001 Phosphamidon 0.0005 5.9E-06 1.7E-05 9.4E-06 5.8E-06 6.7E-06 5.4E-06 4.7E-06 5.1E-06 a a a a a a a a Fonofos 0.03 0.7 2.0 1.2 0.7 0.8 0.8 0.6 0.6 Azamethiphos 0.025 4.1E-05 0.0001 6.6E-05 4.1E-05 4.7E-05 3.8E-05 3.3E-05 3.5E-05 a a a a a a a a Dichlorvos 0.00008 0.01 0.04 0.02 0.01 0.01 0.01 0.01 0.01 Malaoxon 0.03 5.9E-05 0.0002 9.4E-05 5.8E-05 6.7E-05 5.4E-05 4.7E-05 5.1E-05 Methidathion 0.001 3.0E-05 8.2E-05 4.7E-05 2.9E-05 3.3E-05 2.7E-05 2.3E-05 2.5E-05 Malathion 0.03 0.0001 0.0003 0.0002 0.0001 0.0001 0.0001 9.4E-05 0.0001 Methacrifos 0.006 4.1E-05 0.0001 6.6E-05 4.1E-05 4.7E-05 3.8E-05 3.3E-05 3.5E-05 Ethoprophos 0.0004 5.9E-05 0.0002 9.4E-05 5.8E-05 6.7E-05 5.4E-05 4.7E-05 5.1E-05 Fenamiphos 0.0008 1.2E-05 3.3E-05 1.9E-05 1.2E-05 1.3E-05 1.1E-05 9.4E-06 1.0E-05 Quinalphos 0.001 0.0005 0.001 0.0008 0.0005 0.0005 0.0004 0.0004 0.0004 Coumaphos 0.001 5.3E-07 1.5E-06 8.4E-07 5.2E-07 6.0E-07 4.9E-07 4.2E-07 4.5E-07 Chlorpyriphos-methyl 0.01 0.0002 0.0007 0.0004 0.0002 0.0003 0.0002 0.0002 0.0002 Temephos 0.001 3.0E-05 8.2E-05 4.7E-05 2.9E-05 3.3E-05 2.7E-05 2.3E-05 2.5E-05 a a a a a a a a Profenofos 0.03 0.06 0.2 0.1 0.06 0.07 0.06 0.05 0.05 Pirimiphosmethyl 0.03 1.8E-05 4.9E-05 2.8E-05 1.7E-05 2E-05 1.6E-05 1.4E-05 1.5E-05 a a a a a a a a Fenitrothion 0.005 0.1 0.3 0.2 0.1 0.1 0.1 0.09 0.1 a a a Aminocarb 0.001 0.0009 0.003 0.002 0.0009 0.001 0.0009 0.0007 0.0008 Methomyl 0.0025 0.0002 0.0005 0.0003 0.0002 0.0002 0.0002 0.0001 0.0001 Aldicarbfragment 0.001 0.0001 0.0003 0.0002 0.0001 0.0001 0.0001 9.4E-05 0.0001 Pirimicarb 0.035 0.0001 0.0003 0.0002 0.0001 0.0001 0.0001 9.4E-05 0.0001 a a a a a a a a Dioxacarb 0.001 0.02 0.06 0.03 0.02 0.02 0.02 0.02 0.02 Carbaryl 0.0075 3.0E-05 8.2E-05 4.7E-05 2.9E-05 3.3E-05 2.7E-05 2.3E-05 2.5E-05 Carbofuran 0.00015 5.9E-05 0.0002 9.4E-05 5.8E-05 6.7E-05 5.4E-05 4.7E-05 5.1E-05 a a a a a a a a Alanycarb 0.001 0.1 0.3 0.2 0.1 0.1 0.1 0.09 0.1 a a a a a a a a Benfuracarb 0.01 0.09 0.2 0.1 0.09 0.1 0.08 0.07 0.08 Methiocarb 0.00025 0.0001 0.0003 0.0002 0.0001 0.0001 0.0001 9.4E-05 0.0001 Imidacloprid 0.06 0.003 0.008 0.005 0.003 0.003 0.003 0.002 0.003 a a a Acetamiprid 0.025 0.02 0.06 0.04 0.02 0.03 0.02 0.02 0.02 Thiacloprid 0.01 1.2E-05 3.3E-05 1.9E-05 1.2E-05 1.3E-05 1.1E-05 9.4E-06 1.0E-05 Bifenthrin 0.015 0.0007 0.0015 0.001 0.0007 0.0008 0.0007 0.0006 0.0006 a a Lambda-Cyhalothrin 0.0012 0.001 0.003 0.002 0.001 0.001 0.0009 0.0008 0.0009 Deltamethrin 0.01 5.9E-05 0.0002 9.4E-05 5.8E-05 6.7E-05 5.4E-05 4.7E-05 5.1E-05 a a a a a a a a Cypermethrin 0.0016 0.003 0.008 0.004 0.003 0.003 0.002 0.002 0.002 Carbendazim 0.02 0.0003 0.0008 0.0005 0.0003 0.0003 0.0003 0.0002 0.0003 Imazalil 0.025 0.002 0.005 0.003 0.002 0.002 0.002 0.002 0.002 Metazachlor 0.08 0.0002 0.0005 0.0003 0.0002 0.0002 0.0002 0.0001 0.0002 Metalaxyl 0.08 5.9E-05 0.0002 9.4E-05 5.8E-05 6.7E-05 5.4E-05 4.7E-05 5.1E-05 Azaconazole 0.001 0.0002 0.0007 0.0004 0.0002 0.0003 0.0002 0.0002 0.0002 Clomazone 0.133 3.5E-06 9.9E-06 5.6E-06 3.5E-06 4E-06 3.3E-06 2.8E-06 3.0E-06 Ssemugabo et al. International Journal of Food Contamination (2022) 9:4 Page 10 of 14 Table 3 (continued) Pesticides ADI (mg/kg EDI (mgkg/bw/day) bw/day) General < 5 5-12 13-19 20-24 25-35 36-49 50+ population Azoxystrobin 0.2 0.01 0.04 0.02 0.01 0.01 0.01 0.01 0.01 Pyrimethanil 0.17 0.0006 0.002 0.0009 0.0006 0.0007 0.0005 0.0005 0.0005 Spirotetramat 0.05 0.0002 0.0005 0.0003 0.0002 0.0002 0.0002 0.0001 0.0002 a a Fenhexamid 0.2 0.1 0.4 0.2 0.1 0.2 0.1 0.1 0.1 Fenarimol 0.01 0.002 0.006 0.003 0.002 0.002 0.002 0.002 0.002 a a a a a a a a Fluazifop 0.004 0.007 0.02 0.01 0.007 0.008 0.006 0.006 0.006 Flufenoxuron 0.01 1.8E-05 4.9E-05 2.8E-05 1.7E-05 2E-05 1.6E-05 1.4E-05 1.5E-05 Pyriproxyfen 0.05 3.5E-06 9.9E-06 5.6E-06 3.5E-06 4E-06 3.3E-06 2.8E-06 3.0E-06 Quinoxyfen 0.2 0.0002 0.0005 0.0003 0.0002 0.0002 0.0002 0.0001 0.0002 Proquinazid 0.01 0.002 0.005 0.003 0.002 0.002 0.002 0.001 0.001 BDL Below detection limit, ADI Acceptable Daily Intake, EDI Estimated Daily Intake EDI greater than ADI (HQ > 1) Fig. 2 Hazard quotients for various pesticide residuals, for fruits and vegetables by age group for Fonofos, fenitrothion and fenhexamid. Our find - et  al. 2015). For example, recent studies in Ghana and ings are consistent with existing literature showing Nigeria also found that many pesticides residue lev- detection of many pesticides in fruits and vegetables els in produce were above the respective MRLs (Fosu (Elgueta et  al. 2019, 2020; Jallow et  al. 2017; López- et  al. 2017; Adeleye et  al. 2019a). The most frequently Dávila et  al. 2021). Like our findings, many past stud - detected pesticides that have exceeded MRLs have ies have pesticide residue levels that are above MRL been organophosphates, carbamates, pyrethroids and values, especially organophosphates like fenitrothion neonicotinoids based on studies in Uganda, Ghana, (Szpyrka et al. 2013; Mebdoua et al. 2017; Si et al. 2021; Egypt, Poland and Chile (Fuhrimann et  al. 2021; Stau- Szpyrka and Słowik-Borowiec 2019; Eslami et  al. 2021; dacher et al. 2020; Kaye et al. 2015; Atuhaire et al. 2017; Kazar Soydan et  al. 2021; Toptanci et  al. 2021; Akoto Fosu et  al. 2017; Issa et  al. 2018; Szpyrka et  al. 2013; Ssemugabo  et al. International Journal of Food Contamination (2022) 9:4 Page 11 of 14 Akomea-Frempong et al. 2017), especially in leafy veg- vegetable was measured using a contextualised food etables (Elgueta et  al. 2019; 2020). Given that MRLs album and thus presents a true reflection of the study are determined based on good agricultural practices community. We used mean residue concentrations to (GAPs) in field experiments and not necessarily health assess likely average exposures to consumers, but indi- risks (Fothergill and Abdelghani 2013; Salazar 2011), vidual variability in eating patterns may result in higher consumption of pesticides below the MRLs might or lower chronic exposures (Szpyrka et  al. 2015). Addi- exceed health-based exposure benchmarks depending tionally, we computed hazard quotients for consumption on individual consumption patterns. of individual foods. It is likely that consumers ate several Our findings confirm similar findings to other studies different fruits or vegetables on any given day. In future carried out in Poland, Nigeria and Saudi Arabia which analyses, we will use probabilistic methods to assess the found that many pesticides had a HQ > 1 (Szpyrka et  al. range of potential exposures and health risks from more 2013; Odewale et al. 2021; Picó et al. 2018). On the other realistic diet patterns. We will also apply relative potency hand, literature from Turkey, Poland, Ghana, China and factors (RPFs) to assess cumulative health risks for pes- South Korea showed no chronic health risk associated ticide classes with established RPFS (U.S. Environmental with pesticide residues in fruits and vegetables (Si et  al. Protection Agency 2002). Fruits and vegetables were not 2021; Szpyrka and Słowik-Borowiec 2019; Kazar Soydan tracked from farm to fork during sampling due cost and et  al. 2021; Akoto et  al. 2015; Szpyrka 2015; Park et  al. time challenges. Future studies examining pesticide resi- 2021; Zhang et al. 2021; Yi et al. 2020). Using probabilis- dues along the farm to fork chain should track and sam- tic modelling, Z Eslami, V Mahdavi and B Tajdar-Oranj ple individual produce lots from harvest to the consumer. (Eslami et  al. 2021) in Iran found that pesticide residues Additionally, this study was carried out in a primarily did not pose health risks to adults and children. When urban community and may not represent a typical Ugan- assessed by stage along the supply chain, some pesticide dan rural setting. Finally, dietary consumption meas- showed a low HQ and consequently lower risk when con- urement did not cover the broad spectrum of fruits and sumed at farm than at other stages further along the sup- vegetables but rather focussed on commonly consumed ply chain, such as restaurants and homes. Our findings items within the study area (watermelon, passion fruit, are similar to those from previous studies which have tomato, cabbage and eggplant). However, the study area shown a higher chronic health risk for stages upstream represents a large proportion of the Ugandan population along the chain (Akomea-Frempong et  al. 2017; Jacxs- and several commonly eaten foods. ens et al. 2017). When HQ was assessed by age, children more frequently experienced higher hazard quotients Conclusion (18-13) compared with adults (11-9) with HQS up to 443, Sixty-two (62) pesticide residues were detected in fruits compared with a maximum HQ for adults at XX. Our and vegetables from farm to fork. Concentrations of fon- findings are similar to findings from studies from Chile, ofos, fenitrothion and fenhexamid were above EU MRLs Nigeria and China that assessed risk by age which found in watermelon, passion fruit, tomato, cabbages and egg- that chronic health risks were higher in children com- plant. Exposures to 16 and 18 pesticides exceeded health- pared to adults (Elgueta et al. 2020; Si et al. 2021; Zhang based benchmarks and potentially pose chronic health et al. 2021; Adeleye et al. 2019b). risks to consumers, especially to children. The study Our findings have implications on policy and future findings demonstrate the urgent need for routine pesti - research. We used the EU MRLs and ADIs to evaluate cide monitoring and surveillance and risk assessment for exposures and risks, these benchmarks are lower and fruits and vegetables in local Ugandan markets. There is hence more sensitive than other guidelines. For example, also need to regulate the levels of pesticide in fruits and Codex Alimentarius guidelines are higher, which would vegetables in order to protect consumers, especially the suggest lower health risks based on the exposure we eval- children who present higher chronic health risks. uated. There is a need to develop Ugandan standards for MRLs and ADI based on local studies and context. The Abbreviations high HQs demonstrate in our study also demonstrate the ADI: Acceptable Daily Intake; AIs: Active Ingredients; BDL: Below Detection Limits; need for routine monitoring and surveillance of pesticide BW: Body Weight; C: Mean concentration of each Pesticide; EDI: Estimated Daily Intake; EU MRLs: European Union Maximum Residual Limits; FVCR: Fruit and Veg- residues in foods, especially in fruits and vegetables. etable Intake Rate; GAPs: Good Agricultural Practices; GC – MS: Gas Chromatog- This study has several strengths and limitations. This raphy – Mass Spectrometry; HQ: Hazard Quotient; KMA: Kampala Metropolitan study is the largest in Uganda to examine pesticide Area; LC – MS/MS: Liquid Chromatography – Tandem Mass Spectrometry; LOD: Limit of Detection; LOQ: Limit of Quantification; MRLs: Maximum Residual Limits; residues in fruits and vegetables; and we interviewed NCDs: Noncommunicable Diseases; QuEchERS: Quick, Easy, Cheap, Eec ff tive, Rug- over 2000 residents to obtain information on dietary ged and Safe; RPFs: Relative Potency Factors; U.S. FDA: United States Food and intake patterns. Dietary consumption data for fruit and Drugs Authority; WHO: World Health Organisation. Ssemugabo et al. International Journal of Food Contamination (2022) 9:4 Page 12 of 14 Author details Supplementary Information Department of Disease Control and Environmental Health, School of Public The online version contains supplementary material available at https:// doi. Health, Makerere University College of Health Sciences, Kampala, Uganda. org/ 10. 1186/ s40550- 022- 00090-9. Department of Public Health, School of Social Sciences, Humanities and Arts, University of California Merced, Merced, CA 95343, USA. Center for Chil- dren’s Environmental Health Research, School of Public Health, University Additional file 1: Table 1A. Hazard quotient for pesticides with EDI of California, Berkeley, CA 94704, USA. Department of Environmental Health greater than the ADI at different stages along the chain. This file contains and Engineering, The Johns Hopkins University Bloomberg School of Public pesticide that presented a high hazardous quotient at different stages Health, Baltimore, MD 21205, USA. Department of Epidemiology and Biosta- along the chain from farm to fork that can potentially put the health of tistics, School of Public Health, Makerere University College of Health Sciences, fruits and vegetable consumers at risk. Table 2A. Hazard quotient for Kampala, Uganda. pesticides with EDI greater than the ADI by age group. This file contains pesticide that presented a high hazardous quotient by age group that can Received: 15 February 2022 Accepted: 9 April 2022 potentially put the health of fruits and vegetable consumers at risk. Acknowledgements The authors wish to thank farmers, market vendors, street vendors, restaurants References and homes from whose premises study samples were collected. The authors Adeleye AO, Sosan MB, Oyekunle JAO (2019a) Dietary exposure assessment of would also like to thank the pesticide laboratory team at the Government organochlorine pesticides in two commonly grown leafy vegetables in Analytic Laboratory (GAL) especially Evarist Natugonza and Oscar Kibirango South-western Nigeria. Heliyon 5(6):e01895 for their support during sample collection and analysis as well as Mr. Aggrey Adeleye AO, Sosan MB, Oyekunle JAO (2019b) Occurrence and human health Atuhaire from Uganda National Association of Community and Occupational risk of dichlorodiphenyltrichloroethane (DDT ) and hexachlorocyclohex- Health for his support with sampling and sample collection. We would also ane (HCH) pesticide residues in commonly consumed vegetables in like to thank the study participants and research assistants that took part in southwestern Nigeria. J Health Pollut 9(23):190909 the fruit and vegetable intake survey. Akkad R, Schwack W (2010) Multi-enzyme inhibition assay for the detection of insecticidal organophosphates and carbamates by high-performance Authors’ contributions thin-layer chromatography applied to determine enzyme inhibition CS: conceived of the study; participated in the design, coordination, and factors and residues in juice and water samples. J Chromatogr B Anal implementation of all study field activities; conducted the statistical analysis; Technol Biomed Life Sci 878(17-18):1337–1345 and drafted the manuscript; AB: conceived of the study; participated in the Akomea-Frempong S, Ofosu IW, Owusu-Ansah ED, Darko G (2017) Health risks design, and helped to draft the manuscript; JCS: conceived of the study; par- due to consumption of pesticides in ready-to-eat vegetables (salads) in ticipated in the design, and helped to draft the manuscript; FS: participated in Kumasi, Ghana. Int J Food Contam 4(1):13 the design, and helped to draft the manuscript; DG: conceived of the study; Akoto O, Gavor S, Appah MK, Apau J (2015) Estimation of human health risk participated in the design, and helped to draft the manuscript. All authors associated with the consumption of pesticide-contaminated vegetables read and approved the final manuscript. from Kumasi, Ghana. Environ Monit Assess 187(5):244 Aktar MW, Sengupta D, Chowdhury A (2009) Impact of pesticides use in agri- Funding culture: their benefits and hazards. Interdiscip Toxicol 2(1):1–12 This research was supported by the Consortium for Advanced Research Anastassiades M, Lehotay S, Štajnbaher D (2002) Quick, easy, cheap, effective, Training in Africa (CARTA). CARTA is jointly led by the African Population and rugged, and safe (QuEChERS) approach for the determination of pesti- Health Research Center and the University of the Witwatersrand, South Africa cide residues and is funded by Sida (Grant No: 54100113), Carnegie Corporation of New York Atuhaire A, Kaye E, Mutambuze IL, Matthews G, Friedrich T, Jørs E (2017) (Grant No. G-19-57145), the DELTAS Africa Initiative (Grant No: 107768/Z/15/Z). Assessment of dithiocarbamate residues on tomatoes conventionally The DELTAS Africa Initiative is an independent funding scheme of the African grown in Uganda and the effect of simple washing to reduce exposure Academy of Sciences (AAS)’s Alliance for Accelerating Excellence in Science risk to consumers. Environ Health Insights 11:1178630217712218 in Africa (AESA) and supported by the New Partnership for Africa’s Develop- Bonmatin JM, Giorio C, Girolami V, Goulson D, Kreutzweiser DP, Krupke C, ment Planning and Coordinating Agency (NEPAD Agency) with funding from Liess M, Long E, Marzaro M, Mitchell EAD et al (2015) Environmental the Wellcome Trust (UK) and the UK government. The statements made and fate and exposure; neonicotinoids and fipronil. Environ Sci Pollut Res Int views expressed are solely the responsibility of the Authors. Research reported 22(1):35–67 in this publication was partially support by the Fogarty International Center Bradberry SM, Cage SA, Proudfoot AT, Vale JA (2005) Poisoning due to pyre- of the National Institutes of Health under Award Number D43TW009340. The throids. Toxicol Rev 24(2):93–106 content is solely the responsibility of the authors and does not necessarily Cartereau A, Taillebois E, Le Questel JY, Thany SH (2021) Mode of action of represent the official views of the National Institutes of Health. neonicotinoid insecticides Imidacloprid and Thiacloprid to the cockroach Pameα7 nicotinic acetylcholine receptor. Int J Mol Sci 22(18):9880 Availability of data and materials Chen C, Qian Y, Chen Q, Tao C, Li C, Li Y (2011) Evaluation of pesticide The dataset used during the study is available from the corresponding author residues in fruits and vegetables from Xiamen, China. 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An assessment of health risks posed by consumption of pesticide residues in fruits and vegetables among residents in the Kampala Metropolitan Area in Uganda

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Abstract

Background: Pesticide use for fruits and vegetable production in Uganda may result in presence of residues on pro- duce which may pose health risks to consumers. Uganda does not have an established system for monitoring pesti- cide residues in fruits and vegetables and assessing potential health risks. This research aimed to conduct a health risk assessment of presence of pesticide residues in fruits and vegetables in the Kampala Metropolitan Area in Uganda. Method: Pesticides were measured in 160 fruits and vegetables samples collected at farms, markets, street vendors, restaurants and homes; and analysed using liquid chromatography-tandem mass spectrometry and gas chromatog- raphy-mass spectrometry. Fruit and vegetable consumption information was collected from 2177 people. Pesticide concentrations were compared with European Union maximum residual limits (MRLs). Mean values of pesticide con- centration residues found in the sample of fruits and vegetables; and fruits and vegetables intake and body weight were used to calculate the estimated daily intake (EDI) of pesticide residues. EDI values were compared with accept- able daily intakes (ADI) to calculate the hazard quotient by age group, and stage at which consumption happens along the chain. Results: Overall, 57 pesticides were detected in fruits and vegetables from farm to fork. Of the 57, 39 pesticides were detected in all the fruits and vegetables studied. Concentrations of fonofos, fenitrothion and fenhexamid were above the European Union MRLs in some samples. Hazard quotients based on dietary ingestion scenarios for 18 pesticides, including dichlorvos (444) alanycarb (314), fonofos (68), fenitrothion (62), dioxacarb (55) and benfuracarb (24) and others, were above 1, indicating the possibility of chronic health risk to consumers. Chronic health risk decreased with age but was stable for stage at which consumption happens along the food chain. The number of pesticides with EDI greater than the ADI decreased with increase in age; with 18, 13, 9, 11, 8, 9, and 9 pesticides for age groups < 5, 5-12, 13-19, 20-25, 36-49 and ≥ 50 respectively. Conclusion: Chronic dietary pesticide exposures to Ugandans are likely common, and for some pesticides result in exposure exceeding health-based benchmarks. Risks were highest for younger participants. There is an urgent need *Correspondence: cssemugabo@gmail.com Department of Disease Control and Environmental Health, School of Public Health, Makerere University College of Health Sciences, Kampala, Uganda Full list of author information is available at the end of the article © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visithttp:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Ssemugabo et al. International Journal of Food Contamination (2022) 9:4 Page 2 of 14 to increase monitoring and regulation of pesticides in fruits and vegetables in order to protect consumers, especially the children who are vulnerable to the adverse effects of pesticides. Keywords: Maximum residual limits, Hazard quotient, Estimated daily intake, Acceptable daily intake, Uganda Introduction The use of these chemicals in agriculture may result Pesticides are widely used in agriculture to control in residues in food and expose consumers. Events where pests and disease in crops to improve the quality of pro- high levels of pesticide contamination has occurred have duce (Aktar et  al. 2009). Some commonly used classes resulted in acute health risks including nausea, exces- of pesticides include organophosphates, carbamates, sive sweating and salivation, diarrhoea and vomiting, pyrethroids and neonicotinoids (Matowo et  al. 2020; inhibition of blood clotting, and paralysis of the respira- Maggi et  al. 2019; Fuhrimann et  al. 2021; Staudacher tory and circulatory systems (PAN 2018). Several studies et  al. 2020). Most of these pesticide chemical groups, have shown that chronic exposure to low levels of some such as organophosphates, are broad spectrum insec- neurotoxic pesticides are associated with poorer learn- ticides, fungicides or herbicides used to control many ing and behavioral problems in children, memory loss, different pests, diseases or weeds in different crops loss of coordination, reduced speed of response to stim- (Hill et al. 2017). Many organophosphates, carbamates, uli, reduced visual ability, altered or uncontrolled mood pyrethroids and neonicotinoids, all neurotoxic pes- and general weakness; reproductive defects and cancers ticides, are registered for use in Uganda, (Ministry of (Nicolopoulou-Stamati et  al. 2016; Coker et  al. 2018; Agriculture Animal Industry and Fisheries, 2018) and Chiu et al. 2018). use is increasing with increasing consumption of fruits In Uganda, the volume of pesticides used has increased and vegetables including tomatoes, cabbage and water- from 338 t in the 1960s to 18,928.16 t in 2019 (FOA: melons, to name but a few (Ngabirano and Birungi FAOSTAT 2021). Many farmers do not follow recom- 2020). mended mixing concentrations on label instructions and Organophosphates and carbamates pesticides are gen- pre-harvest intervals (Kaye et  al. 2015). Such improper erally not persistent because they degrade when exposed pesticide use practices may result in higher levels of to sunlight, air and soils, but they often have high solu- pesticide residues in fruits and vegetables (Grewel et  al. bility and volatility and are heavily used in many farming 2017) that leave the farm to the final consumer. While systems (Akkad and Schwack 2010). Organophosphates they are important sources of minerals, vitamins, and and carbamates inhibit cholinesterase and may impact other healthful nutrients, consumption of fruits and neurodevelopment by other mechanisms, including vegetables contaminated with pesticide can be a route interference in synaptogenesis and myelin sheath forma- of exposure to hazardous chemicals. Fruit and vegetable tion (Vale and Lotti 2015; Sagiv et  al. 2019). Pyrethroids consumption is a protective factor for noncommunica- and neonicotinoids are often systemic pesticides with a ble diseases such as diabetes (World Health Organisa- higher affinity to soil and, especially for neonicotinoids, tion 2013), and consumption is rising among Ugandans, have the potential to bioaccumulate. They also have low which consume an average 260 g of fruits and vegetables volatility (Laskowski 2002; Bonmatin et  al. 2015). Pyre- each day (Ssemugabo et  al. 2021a). Fruit and vegetable throids act by altering the function of voltage-gated consumption has grown among residents of the Kam- sodium channel and consequently disrupt electrical pala Metropolitan Area (KMA) (Kabwama et  al. 2019), signalling in the nervous system (Soderlund 2010) and and organophosphate, carbamate, pyrethroid and neoni- are generally less acutely toxic than organophosphates cotinoid pesticides have been previously detected in the (Simaremare et  al. 2019). However, they are neurotoxi- tomatoes, watermelon, cabbages among others in this cants and have been associated with confusion, lacri- market (Kaye et  al. 2015; Ssemugabo et  al. 2021b; Atu- mation and salivation (Bradberry et  al. 2005) and also haire et al. 2017). poorer development and asthma in children (Pitzer et al. In the current study, we assessed potential pesticide 2021; Vester et  al. 2019). The mechanism of toxicity for exposures and health risks from consumption of fruit and neonicotinoids is based on selective binding and interac- vegetables by residents of the KMA, in Uganda. tion with nicotinic acetylcholine receptor sites of a target organism causing paralysis that leads to death (Taillebois Materials and methods et  al. 2018; Cartereau et  al. 2021; Houchat et  al. 2020), Study area and they have also been associated with development or This study was conducted in Kampala, Wakiso and neurological disorders (Cimino et al. 2017) in humans. Mukono Districts, three of the 5 districts that make up Ssemugabo  et al. International Journal of Food Contamination (2022) 9:4 Page 3 of 14 the KMA in Uganda. The 3 districts have a population and extracted to determine of pesticide residues (Ana- of 10,812,700 people (UBOS 2018) and cover an area stassiades et al. 2002). Briefly, 1-2 kgs of fruit or vegetable of 1000 km (Kasimbazi 2016). Agriculture is the larg- was chopped, grinded and blended to homogenize the est economic activity in Central Uganda within which sample. Of the homogenized sample, 200 g was put into the KMA is located, supporting 39.3% of the population containers and immediately frozen in order to minimize (UBOS 2018). This region has many large fresh produce the risk of degradation of any pesticide residues present. markets, restaurants, fruit and vegetable vending along Ten grams of homogenized sample was mixed with 3 g of the streets, as well as many of the farms where fruits and sodium bicarbonate (NaHCO3) and 20.0 mL acetonitrile, vegetables consumed within central Uganda are grown. vortexed and placed on a mechanical shaker at 300 rpm/ Kampala, Wakiso and Mukono are inhabited by 15% min for 15 min to improve extractability of pesticide resi- of Uganda’s population and contain Uganda’s districts dues and then centrifuged for 3 min at 3200 rpm. To this, that consume a large volume of the fruit and vegetables 10 g of anhydrous sodium sulphate (Na2SO4) was then produced. added, vortexed and centrifuged for 3 min at 3200 rpm. Ethical clearance to conduct the study was obtained We filtered the crude extract using a 0.2-μm PTFE from the Makerere University School of Public Health syringe filter. The final supernatant layer (0.50 g /mL) was Higher Degrees, Research and Ethics Committee; and transferred into the vials and injected into the LC-MS/ registered by Uganda National Council for Science and MS for analysis of pesticide residues (Ssemugabo et  al. Technology (SS 5203). Participation in the study was 2021b). voluntary and participants (farmers, restaurants market managers, street fruit and vegetable vendors, and house- Pesticide analysis hold heads) provided informed written consent to collect Liquid chromatography – Tandem mass spectrometry samples and fruit and vegetable dietary intake informa- (LC-MS/MS) analysis was carried to detect and ensure tion. All samples and questionnaire were coded with an quality of the pesticides residue measurements. A zorbax anonymous identification number. eclipse plus C18 capillary column (150 mm with 2.1 mm internal diameter and 1.8 μm particle size) operating at Pesticide residue data 35 °C to 360 °C was used with the internal temperature Sampling of fruits and vegetables set at 35 °C for 1 min, then ramped to 120 °C per minute Fruits and vegetables samples were collected from key and 375 °C per minute. This process was run over two stages along the supply chain including farms (50), mar- mobile phases. Phase A involved – water (0.1% formic kets (50), street vendors (20), restaurants (20) and homes acid, 5 mM ammonium formate, and 2% MeOH). Phase B (20), totaling 160 samples. The detailed methodology involved – methanol (0.1% formic acid, 5 mM ammonium used to collect the fruits and vegetable samples has been formate, 2% water). The injector temperature was 120 °C previously described (Ssemugabo et  al. 2021b). Briefly, and carrier gas was helium at a flow rate of 13 L/minute fresh fruit and vegetable samples were purchased and with splitless injection. The injection volume was 5 μL at collected in sterile polythene bags or PET (polyethylene a pressure of 45 psi. The MS ion source temperature was terephthalate) plastic containers from selected farms, 120 °C for a minute and raised at a rate of 35 °C per min- markets, and street vendors. Samples of ready-to-eat ute to 375 °C. Confirmation analysis utilised LC-MS/MS foods were bought from restaurants and homes, espe- which requires two product ions. Compounds with only cially juices and salads that do not contain fat-soluble one product ion were quantified and confirmed using the substances. Three replicate fruit and vegetable samples second ion. For confirmation, the relative ion intensity for were collected at each location measuring at least 1 kg for a pesticide in a sample was calculated and the value com- small and 2 kg for large produce as suggested by Codex pared to the equivalence for a calibration standard. For guidelines (El-Zaher et  al. 2011; Food and Agriculture positive confirmation, the retention times were matched Organisation 1999); processed food samples were at least to the calibration standard as well the relative ion inten- 1 kg or 1 l in case of juice. The samples were stored in a sities according to the recommended maximum toler- cooler and transported to the laboratory within 8 h and ances. Limits of detection (LOD) was determined during stored at − 20 °C until analysis. the method validation and measurements of uncertainty. The method developed by Keppel et  al. at the United Sample preparation and extraction States Food and Drug Administration (U.S. FDA) (Kab- A total of 93 pesticides residues were screened in the wama et  al. 2019; Ssemugabo et  al. 2021b) was used to fruit and vegetable samples (Supplementary Table  1). measure dithiocarbamates (mancozeb, maneb, dithane, Using the Quick, Easy, Cheap, Effective, Rugged and Safe thiram, metam sodium and propineb. Frozen sub-sam- (QuEChERS) approach, samples were prepared, cleaned ples of 10 g were placed into a Duran bottle (250 ml) and Ssemugabo et al. International Journal of Food Contamination (2022) 9:4 Page 4 of 14 mixed with isooctane (20 ml) followed by stannous chlo- following interviews with farmers and agricultural exten- ride (reducing solution) in hydrochloric acid (100 ml), sion. Workers. and sealed immediately with a septum and cap. The sam - ple was incubated at 80 °C in a water bath for 1.5 h with Health risk assessment frequent shaking. The Duran bottles were removed and We first prepared descriptive statistics of the pesticide left at ambient temperature for approximately 1 h. The residue levels in the produce samples. The mean pesti - bottles were frozen for 30 min to allow the generated cide concentrations were then compared with European carbon disulphide gas to condense. The samples were Union maximum residual limits (EU MRLs) obtained shaken and left for 5 min. The organic phase (iso-octane) from the pesticide residue database (https:// ec. europa. was removed and placed in a vial prior to the quantita- e u/ f o o d/ pl ant/ p e sti c ide s/ e u- p e sti c ide s - d at a b a s e/ mr l s/? tion of carbon disulphide by Gas Chromatography-Mass event= search. pr) (EUROPEAN UNION 2021). EU spectrometry (GC–MS). Spiking was done twice, once at MRLs were used because they provided comprehensive the limit of quantitation (LOQ) (50 μg/kg) and another standard values for all fruits studied; they have also been at the expected residue level (1000 μg/kg), as obtained used in other African studies (Fosu et al. 2017; Issa et al. from previous runs during instrument optimization 2018). We also used the mean pesticide concentrations (mean recoveries for individual pesticides in the range 60 to calculate the  estimated pesticide intake to compare – 140%) and precision (RSD ≤ 12%). A 5-point calibra- with acceptable daily intakes (ADIs). Estimated daily tion was used, ranging from 0.125–5 μg/ml. The method’s intake (EDI) (mg/kg/bw/day) was calculated by multiply- LOQ was set at 0.05 mg/kg which equates to the calibra- ing the mean concentration of each pesticide (C) and the tion standard of 0.125 μg/ml. All extracts were analyzed fruits and vegetable consumption rate (FVCR) (g/day) using GC-MS. Final pesticide residues concentrations and dividing this by body weight (BW) using the follow- were expressed in mg/kg of food. ing formula EDI = (C x FVCR)/BW (Gad Alla et al. 2015; Chen et al. 2011). The FVCR used was obtained from the Dietary consumption data dietary consumption survey (Ssemugabo et  al. 2021a). A modified semi-structured food frequency question - FVCR was calculated as mean consumption of fruits and naire from the World Health Organization’s (WHO) vegetables studied for the sample population as well as STEPwise approach to surveillance, standardized for different age groups studied that is < 5, 5-12, 13-19, method of collecting data on risk factors for noncom- 20-24, 25-35, 36-49 and 50+. BW used was measured municable diseases (NCDs) (WHO 2017) was used during the dietary consumption survey with the mean was used to interview 2177 participants to assess fruit for general sample population and age groups calculated and vegetable consumption over a 24-h dietary recall accordingly. The ADI (mg/kg/bw/day) for the different period and their body weight was concurrently meas- pesticides was obtained from the EU pesticide residue ured using a weighing scale. The detailed methodology database (EUROPEAN UNION 2021). The chronic risk on this has been described elsewhere (Ssemugabo et  al. assessment for pesticide residue was calculated by com- 2021a). Briefly, based on typical Ugandan diets, a food paring EDI with the ADI to get the hazard quotient (HQ) album was developed with different quantities of selected using the following equation; HQ = EDI/ADI. A hazard fruits and vegetables. Each research assistant was given a quotient (HQ) > 1 indicates exposures over the health- copy of the food album as a guide during the interview. based benchmarks and the potential to induce unaccep- Respondents were asked to identify the quantities they table health risks among consumers. consume per serving to determine the amounts con- sumed. Based on portion size in the food album, we esti- Results mated intake in grams of each fruit and vegetable each Pesticide residue concentrations day of the week. For children under 18 years, their par- The mean concentration of organophosphates, carba - ents or caretakers were interviewed. Participant’s weight mates, pyrethroids and neonicotinoids among other pes- was also measured thrice and the average calculated. For ticides detected in watermelons, passion fruit, tomato, children below 2 years who cannot stand, their weight cabbage and eggplants and comparisons with their was obtained by reviewing their immunization chart or respective MRLs are shown in Table 1. Out of the 62 pes- asking their parents or caregivers the measurement from ticide active ingredients detected, 5 were excluded due to their last weighing event. Socio-demographics data was the lack of verified maximum residue levels (MRLs) in the also obtained using the study questionnaire. Consump- EU database for the studied fruits and vegetables. There - tion data was collected for five commonly consumed fore, 57 pesticides were considered for the risk assess- and pesticide intensive fruits and vegetables, that is: ment. Of the 57 pesticides, 39 pesticides were detected watermelon, passion fruit, tomato, cabbage and eggplant in all the fruits and vegetable types. Dimethoate was Ssemugabo  et al. International Journal of Food Contamination (2022) 9:4 Page 5 of 14 Table 1 Concentration of pesticide residues per fruit and vegetable type compared with the MRLs Pesticide residues LOD (mg/kg) Water melon (mg/ Passion fruit (mg/ Tomato (mg/kg) Cabbage (mg/kg) Eggplant (mg/ kg) kg) kg) Mean MRL Mean MRL Mean MRL Mean MRL Mean MRL Dithiocarbamate 0.000006 0.001 1.5 0.00007 0.05 0.0002 3 0.0006 3 0.0004 3 Omethoate 0.00002 0.0004 0.01 0.0002 0.01 0.0003 0.01 BDL 0.01 0.00007 0.01 Acephate 0.00003 0.001 0.01 0.001 0.01 0.0001 0.01 0.00008 0.01 0.0002 0.01 Monocrotophos 0.00001 0.00003 0.01 0.00004 0.01 BDL 0.01 0.00003 0.01 0.00002 0.01 Vamidothion 0.00001 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 0.00008 0.01 Dimethoate 0.000008 0.0007 0.01 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 Mevinphos 0.00003 BDL 0.01 BDL 0.01 BDL 0.01 0.00004 0.01 0.00005 0.01 Phosphamidon 0.00002 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 0.00005 0.01 a a a a Fonofos 0.00001 0.03 0.01 0.2 0.01 0.2 10 0.1 0.01 0.1 0.01 Azamethiphos 0.000005 0.00002 0.01 0.000007 0.01 BDL 0.01 BDL 0.01 BDL 0.01 Dichlorvos 0.00002 0.003 0.01 0.007 0.01 0.0001 0.01 0.0002 0.01 0.0007 0.01 Malaoxon 0.00001 BDL 0.02 BDL 0.02 0.00004 0.02 0.00002 0.02 BDL 0.02 Methidathion 0.00001 BDL 0.02 0.000003 0.02 0.00002 0.02 BDL 0.02 0.000001 0.02 Malathion 0.00002 BDL 0.02 BDL 0.02 BDL 0.02 BDL 0.02 0.00007 0.02 Methacrifos 0.000005 BDL 0.01 0.00003 0.01 BDL 0.01 BDL 0.01 BDL 0.01 Ethoprophos 0.00008 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 Fenamiphos 0.000009 BDL 0.02 BDL 0.02 BDL 0.04 BDL 0.04 BDL 0.02 Quinalphos 0.00003 0.0001 0.01 0.0001 0.01 0.00003 0.01 BDL 0.01 0.0001 0.01 Coumaphos 0.00002 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 Chlorpyriphos-methyl 0.000008 BDL 0.01 0.00004 0.01 0.00009 0.01 0.00005 0.01 0.00002 0.01 Temephos 0.000008 BDL 0.01 0.00001 0.01 BDL 0.01 0.000009 0.01 BDL 0.01 Profenofos 0.00001 0.003 0.01 0.00002 0.01 0.04 10 0.003 0.01 0.005 0.01 Pirimiphosmethyl 0.00002 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 0.00001 0.01 a a a a Fenitrothion 0.00001 0.02 0.01 0.004 0.01 0.01 0.01 0.03 0.01 0.03 0.01 Aminocarb 0.00002 BDL 0.01 0.0007 0.01 BDL 0.01 0.00008 0.01 0.00002 0.01 Methomyl 0.00003 0.00006 0.015 0.00003 0.01 0.00003 0.01 BDL 0.01 0.00003 0.01 Aldicarbfragment 0.00001 0.00002 0.02 0.00002 0.02 0.00004 0.02 0.00002 0.02 BDL 0.02 Pirimicarb 0.00003 0.00004 0.5 0.00004 0.01 BDL 0.5 BDL 0.5 BDL 0.5 Dioxacarb 0.00001 0.004 0.01 0.003 0.01 0.003 0.01 0.003 0.01 0.004 0.01 Carbaryl 0.000008 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 0.00001 0.01 Carbofuran 0.000009 0.00003 0.01 BDL 0.01 BDL 0.002 BDL 0.002 0.00003 0.002 Alanycarb 0.00001 0.0001 0.02 0.00006 0.02 0.08 0.02 0.01 0.02 0.006 0.02 Benfuracarb 0.00005 0.0005 0.01 BDL 0.01 0.004 0.002 0.07 0.002 0.004 0.002 Methiocarb 0.00004 BDL 0.03 BDL 0.03 BDL 0.03 0.00005 0.03 BDL 0.03 Imidacloprid 0.00003 0.0007 0.2 0.0008 0.05 0.0004 0.5 0.0004 0.5 0.0002 0.5 Acetamiprid 0.00002 0.004 0.2 0.002 0.01 0.008 0.5 0.005 0.4 0.001 0.2 Thiacloprid 0.00001 BDL 0.2 BDL 0.01 BDL 0.5 BDL 0.3 BDL 0.7 Bifenthrin 0.00002 0.0001 0.01 0.00004 0.01 0.0004 0.3 0.00005 0.4 BDL 0.3 Lambda-Cyhalothrin 0.00002 0.0002 0.06 0.0001 0.01 0.0002 0.07 0.0002 0.15 0.0002 0.3 Deltamethrin 0.00001 BDL 0.02 BDL 0.01 BDL 0.07 0.00006 0.1 BDL 0.4 Cypermethrin 0.00001 0.0002 0.2 BDL 0.05 0.001 0.5 0.0004 1 0.0004 0.5 Carbendazim 0.00002 BDL 0.1 0.0001 0.1 BDL 0.3 0.0001 0.1 BDL 0.5 Imazalil 0.00001 0.0005 0.01 0.0004 0.01 0.0001 0.3 0.0003 0.01 0.0003 0.01 Metazachlor 0.00001 0.00001 0.02 0.00004 0.02 0.00006 0.02 0.00002 0.4 0.00002 0.02 Metalaxyl 0.00002 BDL 0.2 BDL 0.01 0.00005 0.3 BDL 0.06 BDL 0.01 Azaconazole 0.000006 0.000009 0.01 0.0001 0.01 0.000008 0.01 0.00007 0.01 0.000008 0.01 Clomazone 0.000007 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 Ssemugabo et al. International Journal of Food Contamination (2022) 9:4 Page 6 of 14 Table 1 (continued) Pesticide residues LOD (mg/kg) Water melon (mg/ Passion fruit (mg/ Tomato (mg/kg) Cabbage (mg/kg) Eggplant (mg/ kg) kg) kg) Mean MRL Mean MRL Mean MRL Mean MRL Mean MRL Azoxystrobin 0.000007 BDL 1 BDL 4 0.00456 3 0.003 5 0.003 3 Pyrimethanil 0.00002 0.0001 0.01 0.00008 0.01 0.0001 1 0.00008 0.01 0.00006 1 Spirotetramat 0.00002 0.00003 0.2 BDL 0.1 0.00009 2 0.00001 2 BDL 2 a a a Fenhexamid 0.00001 0.01 0.01 0.07 0.01 BDL 2 0.03 0.01 0.009 2 Fenarimol 0.00001 0.0006 0.05 0.0003 0.02 0.0002 0.02 0.0003 0.02 0.0004 0.02 Fluazifop 0.00002 0.005 0.01 BDL 0.01 BDL 0.06 0.0004 0.01 BDL 1 Flufenoxuron 0.00002 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 BDL 0.01 Pyriproxyfen 0.000007 BDL 0.05 BDL 0.05 BDL 1 BDL 0.05 BDL 1 Quinoxyfen 0.00003 BDL 0.05 0.00005 0.02 0.00004 0.02 BDL 0.02 0.00003 0.02 Proquinazid 0.00001 BDL 0.02 BDL 0.02 0.001 0.15 0.0003 0.02 0.00009 0.02 BDL Below detection limits, LOD Limit of Detection Above the MRLs detected only in watermelon with a mean concentration may occur in 16 of the 57 pesticides assessed. EDIs of 0.0007 mg/kg. Fonofos was detected in all fruits and for dichlorvos, fenitrothion, alanycarb and benfura- vegetables with concentrations above the MRLs in water- carb were above the ADI at all stages of consumption. melon (0.03 mgkg), passion fruit (0.02 mg/kg), cabbages EDIs for fonofos and profenofos exceeded the ADI at (0.11 mg/kg) and eggplants (0.14 mg/kg). Methidathion four stages of consumption. Fonofos, dichlorvos, feni- was not detected in watermelon and cabbages and mala- trothion, dioxacarb, alanycarb and benfuracarb pre- thion was not detected in passion fruit. Methacrifos was sented the highest risk levels with HQs of 27.5, 442.6, detected in passion fruit at 0.00003 mg/kg and cabbages 23.6, 29.5, 118.0 and 23.6 respectively, at the farm and at 0.000002 mg/kg. Ethoprophos was not detected in throughout the entire supply chain (See supplementary vegetables but only in eggplants at 0.0003 mg/kg. Cou- Table 2). Overall, pesticide concentration at street ven- maphos and pirimiphos-methyl were detected only cab- dors presented lower HQs and consequently lower like- bages at 0.0000005 mg/kg and eggplants at 0.00001 mg/kg lihood for health risks compared to other stages along respectively. Apart from passion fruit, fenitrothion con- the chain (Fig. 1). centration was above the MRLs in watermelon (0.02 mg/ kg), tomato (0.013 mg/kg), cabbage (0.03 mg/kg) and eggplant (0.03 mg/kg). Neonicotinoids were detected in Health risk assessment by age group almost all fruits and vegetables apart from thiacloprid We evaluated the risk of consumption of pesticide res- that was only detected in passion fruit 0.000007 mg/kg idues by age of consumers as shown in Table  3. EDIs and tomato 0.000002 mg/kg. Deltamethrin, azoxystrobin for fonofos, dichlorvos, profenofos, fenitrothion, diox- and proquinazid were only detected in vegetables with acarb, alanycarb, benfuracarb, cypermethrin and flu - concentrations below the MRLs. Although not detected azifop exceeded ADIs throughout all age groups and in tomato, fenhexamid’s concentration was above the consequently pose chronic health risks. The number MRLs in watermelon (0.01 mg/kg), passion fruit (0.07 mg/ of pesticides with EDIs greater than the ADI decreased kg) and cabbage (0.03 mg/kg). with age with 18, 13, 9, 11, 8, 9, and 9 for age groups under 5 years, 5-12, 13-19, 20-25, 36-49 and 50+ years respectively. Dichlorvos had the highest risk with a Health risk assessment by stage of consumption HQ of 444 followed by alanycarb (314), Fonofos (68), along the chain fenitrothion (62), dioxacarb (55) and benfuracarb (24) The risk of exposures to pesticides residues in fruits among children under 5 with a similar trend across age and vegetables are evaluated by the stage at which con- groups (see supplementary Table 2). Overall, HQ values sumption may occur along the chain including at the decreased across age groups with children under 5 pre- farm, market, street vendor, restaurant and home as senting highest risks and adults 50+ having the lowest shown in Table  2. The EDI was higher than the ADI chronic health risks for the nine pesticides as shown in in at least one of the stages at which consumption Fig. 2. Ssemugabo  et al. International Journal of Food Contamination (2022) 9:4 Page 7 of 14 Table 2 Estimated daily intake (mgkg/bw/day) for fruits and vegetables by stage along the chain Pesticides ADI (mg/kg/bw/ EDI (mg/kg/bw/day) day) Farm Market Street Restaurant Home Dithiocarbamate 0.05 0.002 0.003 0.005 0.002 0.002 a a Omethoate 0.002 4.7E-06 0.002 BDL 0.002 2.9E-06 Acephate 0.03 0.005 0.004 0.0004 0.002 0.0006 Monocrotophos 0.0006 0.0001 0.0002 0.0002 0.0002 0.0001 Vamidothion 0.008 0.0002 0.0002 5.9E-07 3.54E-06 2.95E-06 Dimethoate 0.002 BDL 0.003 BDL BDL BDL Mevinphos 0.001 0.0002 0.0002 2.4E-06 2.4E-06 5.9E-05 Phosphamidon 0.0005 1.2E-06 1.18E-06 1.18E-06 BDL 1.77E-06 a a a a Fonofos 0.03 0.8 0.9 0.006 1.2 0.4 Azamethiphos 0.025 2.4E-06 5.9E-05 1.2E-06 5.9E-06 BDL a a a a a Dichlorvos 0.00008 0.04 0.0006 0.0004 0.002 0.003 Malaoxon 0.03 4.1E-06 4.1E-06 0.0003 5.9E-05 5.9E-05 Methidathion 0.001 BDL BDL 1.2E-06 BDL 0.0002 Malathion 0.03 0.0003 BDL BDL BDL 2.9E-06 Methacrifos 0.006 BDL 0.0001 BDL BDL BDL Ethoprophos 0.0004 BDL 0.0001 BDL 0.0001 0.0002 Fenamiphos 0.0008 5.9E-08 1.2E-06 5.9E-05 1.8E-06 BDL Quinalphos 0.001 0.0001 0.0005 0.0004 0.001 0.0006 Coumaphos 0.001 1.8E-07 BDL BDL BDL BDL Chlorpyriphos-methyl 0.01 0.0005 BDL 2.9E-06 0.0006 BDL Temephos 0.001 BDL 3.5E-06 BDL 2.9E-06 0.0001 a a a a Profenofos 0.03 0.1 0.04 0.04 0.06 0.004 Pirimiphosmethyl 0.03 BDL 4.7E-06 BDL BDL BDL a a a a a Fenitrothion 0.005 0.1 0.05 0.2 0.2 0.02 Aminocarb 0.001 0.0002 0.003 0.0001 0.0001 5.9E-05 Methomyl 0.0025 0.0002 0.0003 4.1E-06 0.0002 2.9E-06 Aldicarbfragment 0.001 0.0001 0.0001 0.000177 5.9E-05 0.0001 Pirimicarb 0.035 0.0001 0.0001 BDL 0.0004 5.9E-05 a a a Dioxacarb 0.001 0.03 0.02 0.02 BDL BDL Carbaryl 0.0075 2.4E-06 2.4E-06 1.8E-06 BDL 0.0001 a a Carbofuran 0.00015 4.1E-06 5.9E-07 0.0003 0.0002 2.9E-07 a a a a a Alanycarb 0.001 0.1 0.1 0.06 0.1 0.2 a a a a Benfuracarb 0.01 0.2 0.05 0.02 4.7E-13 0.02 Methiocarb 0.00025 0.0002 5.9E-05 0.0002 4.1E-06 5.9E-07 Imidacloprid 0.06 0.003 0.002 0.005 0.006 0.001 Acetamiprid 0.025 0.04 0.02 0.01 0.01 0.005 Thiacloprid 0.01 3.5E-06 BDL BDL BDL BDL Bifenthrin 0.015 0.0006 0.002 4.7E-06 0.0002 2.4E-06 Lambda-Cyhalothrin 0.0012 0.001 0.001 0.001 0.0006 0.0006 Deltamethrin 0.01 2.4E-06 0.0002 BDL BDL BDL a a a Cypermethrin 0.0016 0.004 0.002 0.005 0.001 0.0006 Carbendazim 0.02 1.2E-06 0.001 2.4E-06 5.9E-05 4.7E-07 Imazalil 0.025 0.002 0.001 0.004 0.002 0.002 Metazachlor 0.08 0.0002 0.0002 0.0002 0.0002 0.0001 Metalaxyl 0.08 1.2E-06 0.0002 BDL BDL BDL Azaconazole 0.001 4.7E-06 0.0005 0.0004 BDL 0.0006 Clomazone 0.133 1.2E-06 BDL BDL BDL BDL Azoxystrobin 0.2 0.02 BDL BDL 0.01 0.04 Ssemugabo et al. International Journal of Food Contamination (2022) 9:4 Page 8 of 14 Table 2 (continued) Pesticides ADI (mg/kg/bw/ EDI (mg/kg/bw/day) day) Farm Market Street Restaurant Home Pyrimethanil 0.17 0.0002 0.0006 0.001 0.0005 0.0006 Spirotetramat 0.05 0.0003 0.0002 0.0002 2.9E-06 0.0001 Fenhexamid 0.2 0.1 0.2 0.6 0.006 0.05 Fenarimol 0.01 0.002 0.0006 0.002 0.002 0.005 Fluazifop 0.004 0.0006 0.02 BDL BDL BDL Flufenoxuron 0.01 1.8E-06 2.4E-06 1.8E-07 1.2E-06 4.1E-07 Pyriproxyfen 0.05 BDL BDL 2.9E-06 BDL BDL Quinoxyfen 0.2 0.0002 5.9E-05 0.0003 1.2E-06 0.0003 Proquinazid 0.01 0.003 0.0006 0.002 0.001 BDL BDL Below detection limit, ADI Acceptable Daily Intake, EDI Estimated Daily Intake EDI greater than ADI (HQ > 1) Fig. 1 Hazard quotients for various pesticide residuals, for fruits and vegetables by stage of consumption along the chain compared to other risk assessment studies (Szpyrka et al. Discussion 2013; Lozowicka et al. 2015; Mebdoua et al. 2017). When Pesticides were detected in all studied fruits and veg- calculated by stage along the supply chain and age group, etables, with 39 active ingredients (AIs) detected in all 16 and 18 pesticides respectively had high EDIs are above samples and 18 AIs in at least some of the food sam- their ADI. As discussed by JA Vaccaro and FG Huffman ples. Fonofos, fenitrothion and fenhexamid concentra- (Vaccaro and Huffman 2017), age is a key dietary risk fac - tions were above the MRLs in watermelon, passion fruit, tor that should be considered while performing health tomato, cabbage and eggplant. Risk assessment calcula- risk assessment Several fruit and vegetable surveillance tions show that EDIs for 18 pesticides were above the studies have estimated EDI and similar EDIs. Studies in ADI in some cases, with HQs that ranged from 1 up to Chile, Poland and Kazakhstan had EDIs ranging from 443 and thus may pose chronic health risks. Children < 0.001 to 5.2 (Lozowicka et al. 2015; Elgueta et al. 2017, experienced the highest HQs and therefore potentially 2019, 2020; Si et  al. 2021; Szpyrka and Słowik-Borowiec higher chronic health risks from pesticide residues in fruits and vegetables. 2019), which is within the range of our findings. Overall, 29% of the pesticides we tested for had EDIs Many pesticides were detected in all studied fruits over an ADI. This is a high proportion of exceedances and vegetables with levels below the EU MRLs except Ssemugabo  et al. International Journal of Food Contamination (2022) 9:4 Page 9 of 14 Table 3 Estimated daily intake (mgkg/bw/day) for fruits and vegetables by age group Pesticides ADI (mg/kg EDI (mgkg/bw/day) bw/day) General < 5 5-12 13-19 20-24 25-35 36-49 50+ population Dithiocarbamate 0.05 0.003 0.007 0.004 0.002 0.003 0.002 0.002 0.002 Omethoate 0.002 0.001 0.003 0.002 0.001 0.001 0.001 0.001 0.001 Acephate 0.03 0.003 0.008 0.005 0.003 0.003 0.003 0.002 0.003 Monocrotophos 0.0006 0.0002 0.0005 0.0003 0.0004 0.0002 0.0002 0.0001 0.0002 Vamidothion 0.008 0.0001 0.0003 0.0002 0.0001 0.0001 0.0001 9.4E-05 0.0001 Dimethoate 0.002 0.0008 0.002 0.001 0.0008 0.0009 0.0008 0.0007 0.0007 Mevinphos 0.001 0.0001 0.0003 0.0002 0.0001 0.0001 0.0001 9.4E-05 0.0001 Phosphamidon 0.0005 5.9E-06 1.7E-05 9.4E-06 5.8E-06 6.7E-06 5.4E-06 4.7E-06 5.1E-06 a a a a a a a a Fonofos 0.03 0.7 2.0 1.2 0.7 0.8 0.8 0.6 0.6 Azamethiphos 0.025 4.1E-05 0.0001 6.6E-05 4.1E-05 4.7E-05 3.8E-05 3.3E-05 3.5E-05 a a a a a a a a Dichlorvos 0.00008 0.01 0.04 0.02 0.01 0.01 0.01 0.01 0.01 Malaoxon 0.03 5.9E-05 0.0002 9.4E-05 5.8E-05 6.7E-05 5.4E-05 4.7E-05 5.1E-05 Methidathion 0.001 3.0E-05 8.2E-05 4.7E-05 2.9E-05 3.3E-05 2.7E-05 2.3E-05 2.5E-05 Malathion 0.03 0.0001 0.0003 0.0002 0.0001 0.0001 0.0001 9.4E-05 0.0001 Methacrifos 0.006 4.1E-05 0.0001 6.6E-05 4.1E-05 4.7E-05 3.8E-05 3.3E-05 3.5E-05 Ethoprophos 0.0004 5.9E-05 0.0002 9.4E-05 5.8E-05 6.7E-05 5.4E-05 4.7E-05 5.1E-05 Fenamiphos 0.0008 1.2E-05 3.3E-05 1.9E-05 1.2E-05 1.3E-05 1.1E-05 9.4E-06 1.0E-05 Quinalphos 0.001 0.0005 0.001 0.0008 0.0005 0.0005 0.0004 0.0004 0.0004 Coumaphos 0.001 5.3E-07 1.5E-06 8.4E-07 5.2E-07 6.0E-07 4.9E-07 4.2E-07 4.5E-07 Chlorpyriphos-methyl 0.01 0.0002 0.0007 0.0004 0.0002 0.0003 0.0002 0.0002 0.0002 Temephos 0.001 3.0E-05 8.2E-05 4.7E-05 2.9E-05 3.3E-05 2.7E-05 2.3E-05 2.5E-05 a a a a a a a a Profenofos 0.03 0.06 0.2 0.1 0.06 0.07 0.06 0.05 0.05 Pirimiphosmethyl 0.03 1.8E-05 4.9E-05 2.8E-05 1.7E-05 2E-05 1.6E-05 1.4E-05 1.5E-05 a a a a a a a a Fenitrothion 0.005 0.1 0.3 0.2 0.1 0.1 0.1 0.09 0.1 a a a Aminocarb 0.001 0.0009 0.003 0.002 0.0009 0.001 0.0009 0.0007 0.0008 Methomyl 0.0025 0.0002 0.0005 0.0003 0.0002 0.0002 0.0002 0.0001 0.0001 Aldicarbfragment 0.001 0.0001 0.0003 0.0002 0.0001 0.0001 0.0001 9.4E-05 0.0001 Pirimicarb 0.035 0.0001 0.0003 0.0002 0.0001 0.0001 0.0001 9.4E-05 0.0001 a a a a a a a a Dioxacarb 0.001 0.02 0.06 0.03 0.02 0.02 0.02 0.02 0.02 Carbaryl 0.0075 3.0E-05 8.2E-05 4.7E-05 2.9E-05 3.3E-05 2.7E-05 2.3E-05 2.5E-05 Carbofuran 0.00015 5.9E-05 0.0002 9.4E-05 5.8E-05 6.7E-05 5.4E-05 4.7E-05 5.1E-05 a a a a a a a a Alanycarb 0.001 0.1 0.3 0.2 0.1 0.1 0.1 0.09 0.1 a a a a a a a a Benfuracarb 0.01 0.09 0.2 0.1 0.09 0.1 0.08 0.07 0.08 Methiocarb 0.00025 0.0001 0.0003 0.0002 0.0001 0.0001 0.0001 9.4E-05 0.0001 Imidacloprid 0.06 0.003 0.008 0.005 0.003 0.003 0.003 0.002 0.003 a a a Acetamiprid 0.025 0.02 0.06 0.04 0.02 0.03 0.02 0.02 0.02 Thiacloprid 0.01 1.2E-05 3.3E-05 1.9E-05 1.2E-05 1.3E-05 1.1E-05 9.4E-06 1.0E-05 Bifenthrin 0.015 0.0007 0.0015 0.001 0.0007 0.0008 0.0007 0.0006 0.0006 a a Lambda-Cyhalothrin 0.0012 0.001 0.003 0.002 0.001 0.001 0.0009 0.0008 0.0009 Deltamethrin 0.01 5.9E-05 0.0002 9.4E-05 5.8E-05 6.7E-05 5.4E-05 4.7E-05 5.1E-05 a a a a a a a a Cypermethrin 0.0016 0.003 0.008 0.004 0.003 0.003 0.002 0.002 0.002 Carbendazim 0.02 0.0003 0.0008 0.0005 0.0003 0.0003 0.0003 0.0002 0.0003 Imazalil 0.025 0.002 0.005 0.003 0.002 0.002 0.002 0.002 0.002 Metazachlor 0.08 0.0002 0.0005 0.0003 0.0002 0.0002 0.0002 0.0001 0.0002 Metalaxyl 0.08 5.9E-05 0.0002 9.4E-05 5.8E-05 6.7E-05 5.4E-05 4.7E-05 5.1E-05 Azaconazole 0.001 0.0002 0.0007 0.0004 0.0002 0.0003 0.0002 0.0002 0.0002 Clomazone 0.133 3.5E-06 9.9E-06 5.6E-06 3.5E-06 4E-06 3.3E-06 2.8E-06 3.0E-06 Ssemugabo et al. International Journal of Food Contamination (2022) 9:4 Page 10 of 14 Table 3 (continued) Pesticides ADI (mg/kg EDI (mgkg/bw/day) bw/day) General < 5 5-12 13-19 20-24 25-35 36-49 50+ population Azoxystrobin 0.2 0.01 0.04 0.02 0.01 0.01 0.01 0.01 0.01 Pyrimethanil 0.17 0.0006 0.002 0.0009 0.0006 0.0007 0.0005 0.0005 0.0005 Spirotetramat 0.05 0.0002 0.0005 0.0003 0.0002 0.0002 0.0002 0.0001 0.0002 a a Fenhexamid 0.2 0.1 0.4 0.2 0.1 0.2 0.1 0.1 0.1 Fenarimol 0.01 0.002 0.006 0.003 0.002 0.002 0.002 0.002 0.002 a a a a a a a a Fluazifop 0.004 0.007 0.02 0.01 0.007 0.008 0.006 0.006 0.006 Flufenoxuron 0.01 1.8E-05 4.9E-05 2.8E-05 1.7E-05 2E-05 1.6E-05 1.4E-05 1.5E-05 Pyriproxyfen 0.05 3.5E-06 9.9E-06 5.6E-06 3.5E-06 4E-06 3.3E-06 2.8E-06 3.0E-06 Quinoxyfen 0.2 0.0002 0.0005 0.0003 0.0002 0.0002 0.0002 0.0001 0.0002 Proquinazid 0.01 0.002 0.005 0.003 0.002 0.002 0.002 0.001 0.001 BDL Below detection limit, ADI Acceptable Daily Intake, EDI Estimated Daily Intake EDI greater than ADI (HQ > 1) Fig. 2 Hazard quotients for various pesticide residuals, for fruits and vegetables by age group for Fonofos, fenitrothion and fenhexamid. Our find - et  al. 2015). For example, recent studies in Ghana and ings are consistent with existing literature showing Nigeria also found that many pesticides residue lev- detection of many pesticides in fruits and vegetables els in produce were above the respective MRLs (Fosu (Elgueta et  al. 2019, 2020; Jallow et  al. 2017; López- et  al. 2017; Adeleye et  al. 2019a). The most frequently Dávila et  al. 2021). Like our findings, many past stud - detected pesticides that have exceeded MRLs have ies have pesticide residue levels that are above MRL been organophosphates, carbamates, pyrethroids and values, especially organophosphates like fenitrothion neonicotinoids based on studies in Uganda, Ghana, (Szpyrka et al. 2013; Mebdoua et al. 2017; Si et al. 2021; Egypt, Poland and Chile (Fuhrimann et  al. 2021; Stau- Szpyrka and Słowik-Borowiec 2019; Eslami et  al. 2021; dacher et al. 2020; Kaye et al. 2015; Atuhaire et al. 2017; Kazar Soydan et  al. 2021; Toptanci et  al. 2021; Akoto Fosu et  al. 2017; Issa et  al. 2018; Szpyrka et  al. 2013; Ssemugabo  et al. International Journal of Food Contamination (2022) 9:4 Page 11 of 14 Akomea-Frempong et al. 2017), especially in leafy veg- vegetable was measured using a contextualised food etables (Elgueta et  al. 2019; 2020). Given that MRLs album and thus presents a true reflection of the study are determined based on good agricultural practices community. We used mean residue concentrations to (GAPs) in field experiments and not necessarily health assess likely average exposures to consumers, but indi- risks (Fothergill and Abdelghani 2013; Salazar 2011), vidual variability in eating patterns may result in higher consumption of pesticides below the MRLs might or lower chronic exposures (Szpyrka et  al. 2015). Addi- exceed health-based exposure benchmarks depending tionally, we computed hazard quotients for consumption on individual consumption patterns. of individual foods. It is likely that consumers ate several Our findings confirm similar findings to other studies different fruits or vegetables on any given day. In future carried out in Poland, Nigeria and Saudi Arabia which analyses, we will use probabilistic methods to assess the found that many pesticides had a HQ > 1 (Szpyrka et  al. range of potential exposures and health risks from more 2013; Odewale et al. 2021; Picó et al. 2018). On the other realistic diet patterns. We will also apply relative potency hand, literature from Turkey, Poland, Ghana, China and factors (RPFs) to assess cumulative health risks for pes- South Korea showed no chronic health risk associated ticide classes with established RPFS (U.S. Environmental with pesticide residues in fruits and vegetables (Si et  al. Protection Agency 2002). Fruits and vegetables were not 2021; Szpyrka and Słowik-Borowiec 2019; Kazar Soydan tracked from farm to fork during sampling due cost and et  al. 2021; Akoto et  al. 2015; Szpyrka 2015; Park et  al. time challenges. Future studies examining pesticide resi- 2021; Zhang et al. 2021; Yi et al. 2020). Using probabilis- dues along the farm to fork chain should track and sam- tic modelling, Z Eslami, V Mahdavi and B Tajdar-Oranj ple individual produce lots from harvest to the consumer. (Eslami et  al. 2021) in Iran found that pesticide residues Additionally, this study was carried out in a primarily did not pose health risks to adults and children. When urban community and may not represent a typical Ugan- assessed by stage along the supply chain, some pesticide dan rural setting. Finally, dietary consumption meas- showed a low HQ and consequently lower risk when con- urement did not cover the broad spectrum of fruits and sumed at farm than at other stages further along the sup- vegetables but rather focussed on commonly consumed ply chain, such as restaurants and homes. Our findings items within the study area (watermelon, passion fruit, are similar to those from previous studies which have tomato, cabbage and eggplant). However, the study area shown a higher chronic health risk for stages upstream represents a large proportion of the Ugandan population along the chain (Akomea-Frempong et  al. 2017; Jacxs- and several commonly eaten foods. ens et al. 2017). When HQ was assessed by age, children more frequently experienced higher hazard quotients Conclusion (18-13) compared with adults (11-9) with HQS up to 443, Sixty-two (62) pesticide residues were detected in fruits compared with a maximum HQ for adults at XX. Our and vegetables from farm to fork. Concentrations of fon- findings are similar to findings from studies from Chile, ofos, fenitrothion and fenhexamid were above EU MRLs Nigeria and China that assessed risk by age which found in watermelon, passion fruit, tomato, cabbages and egg- that chronic health risks were higher in children com- plant. Exposures to 16 and 18 pesticides exceeded health- pared to adults (Elgueta et al. 2020; Si et al. 2021; Zhang based benchmarks and potentially pose chronic health et al. 2021; Adeleye et al. 2019b). risks to consumers, especially to children. The study Our findings have implications on policy and future findings demonstrate the urgent need for routine pesti - research. We used the EU MRLs and ADIs to evaluate cide monitoring and surveillance and risk assessment for exposures and risks, these benchmarks are lower and fruits and vegetables in local Ugandan markets. There is hence more sensitive than other guidelines. For example, also need to regulate the levels of pesticide in fruits and Codex Alimentarius guidelines are higher, which would vegetables in order to protect consumers, especially the suggest lower health risks based on the exposure we eval- children who present higher chronic health risks. uated. There is a need to develop Ugandan standards for MRLs and ADI based on local studies and context. The Abbreviations high HQs demonstrate in our study also demonstrate the ADI: Acceptable Daily Intake; AIs: Active Ingredients; BDL: Below Detection Limits; need for routine monitoring and surveillance of pesticide BW: Body Weight; C: Mean concentration of each Pesticide; EDI: Estimated Daily Intake; EU MRLs: European Union Maximum Residual Limits; FVCR: Fruit and Veg- residues in foods, especially in fruits and vegetables. etable Intake Rate; GAPs: Good Agricultural Practices; GC – MS: Gas Chromatog- This study has several strengths and limitations. This raphy – Mass Spectrometry; HQ: Hazard Quotient; KMA: Kampala Metropolitan study is the largest in Uganda to examine pesticide Area; LC – MS/MS: Liquid Chromatography – Tandem Mass Spectrometry; LOD: Limit of Detection; LOQ: Limit of Quantification; MRLs: Maximum Residual Limits; residues in fruits and vegetables; and we interviewed NCDs: Noncommunicable Diseases; QuEchERS: Quick, Easy, Cheap, Eec ff tive, Rug- over 2000 residents to obtain information on dietary ged and Safe; RPFs: Relative Potency Factors; U.S. FDA: United States Food and intake patterns. Dietary consumption data for fruit and Drugs Authority; WHO: World Health Organisation. Ssemugabo et al. International Journal of Food Contamination (2022) 9:4 Page 12 of 14 Author details Supplementary Information Department of Disease Control and Environmental Health, School of Public The online version contains supplementary material available at https:// doi. Health, Makerere University College of Health Sciences, Kampala, Uganda. org/ 10. 1186/ s40550- 022- 00090-9. Department of Public Health, School of Social Sciences, Humanities and Arts, University of California Merced, Merced, CA 95343, USA. Center for Chil- dren’s Environmental Health Research, School of Public Health, University Additional file 1: Table 1A. Hazard quotient for pesticides with EDI of California, Berkeley, CA 94704, USA. Department of Environmental Health greater than the ADI at different stages along the chain. This file contains and Engineering, The Johns Hopkins University Bloomberg School of Public pesticide that presented a high hazardous quotient at different stages Health, Baltimore, MD 21205, USA. Department of Epidemiology and Biosta- along the chain from farm to fork that can potentially put the health of tistics, School of Public Health, Makerere University College of Health Sciences, fruits and vegetable consumers at risk. Table 2A. Hazard quotient for Kampala, Uganda. pesticides with EDI greater than the ADI by age group. This file contains pesticide that presented a high hazardous quotient by age group that can Received: 15 February 2022 Accepted: 9 April 2022 potentially put the health of fruits and vegetable consumers at risk. Acknowledgements The authors wish to thank farmers, market vendors, street vendors, restaurants References and homes from whose premises study samples were collected. The authors Adeleye AO, Sosan MB, Oyekunle JAO (2019a) Dietary exposure assessment of would also like to thank the pesticide laboratory team at the Government organochlorine pesticides in two commonly grown leafy vegetables in Analytic Laboratory (GAL) especially Evarist Natugonza and Oscar Kibirango South-western Nigeria. Heliyon 5(6):e01895 for their support during sample collection and analysis as well as Mr. Aggrey Adeleye AO, Sosan MB, Oyekunle JAO (2019b) Occurrence and human health Atuhaire from Uganda National Association of Community and Occupational risk of dichlorodiphenyltrichloroethane (DDT ) and hexachlorocyclohex- Health for his support with sampling and sample collection. We would also ane (HCH) pesticide residues in commonly consumed vegetables in like to thank the study participants and research assistants that took part in southwestern Nigeria. J Health Pollut 9(23):190909 the fruit and vegetable intake survey. Akkad R, Schwack W (2010) Multi-enzyme inhibition assay for the detection of insecticidal organophosphates and carbamates by high-performance Authors’ contributions thin-layer chromatography applied to determine enzyme inhibition CS: conceived of the study; participated in the design, coordination, and factors and residues in juice and water samples. J Chromatogr B Anal implementation of all study field activities; conducted the statistical analysis; Technol Biomed Life Sci 878(17-18):1337–1345 and drafted the manuscript; AB: conceived of the study; participated in the Akomea-Frempong S, Ofosu IW, Owusu-Ansah ED, Darko G (2017) Health risks design, and helped to draft the manuscript; JCS: conceived of the study; par- due to consumption of pesticides in ready-to-eat vegetables (salads) in ticipated in the design, and helped to draft the manuscript; FS: participated in Kumasi, Ghana. Int J Food Contam 4(1):13 the design, and helped to draft the manuscript; DG: conceived of the study; Akoto O, Gavor S, Appah MK, Apau J (2015) Estimation of human health risk participated in the design, and helped to draft the manuscript. All authors associated with the consumption of pesticide-contaminated vegetables read and approved the final manuscript. from Kumasi, Ghana. Environ Monit Assess 187(5):244 Aktar MW, Sengupta D, Chowdhury A (2009) Impact of pesticides use in agri- Funding culture: their benefits and hazards. Interdiscip Toxicol 2(1):1–12 This research was supported by the Consortium for Advanced Research Anastassiades M, Lehotay S, Štajnbaher D (2002) Quick, easy, cheap, effective, Training in Africa (CARTA). CARTA is jointly led by the African Population and rugged, and safe (QuEChERS) approach for the determination of pesti- Health Research Center and the University of the Witwatersrand, South Africa cide residues and is funded by Sida (Grant No: 54100113), Carnegie Corporation of New York Atuhaire A, Kaye E, Mutambuze IL, Matthews G, Friedrich T, Jørs E (2017) (Grant No. G-19-57145), the DELTAS Africa Initiative (Grant No: 107768/Z/15/Z). Assessment of dithiocarbamate residues on tomatoes conventionally The DELTAS Africa Initiative is an independent funding scheme of the African grown in Uganda and the effect of simple washing to reduce exposure Academy of Sciences (AAS)’s Alliance for Accelerating Excellence in Science risk to consumers. Environ Health Insights 11:1178630217712218 in Africa (AESA) and supported by the New Partnership for Africa’s Develop- Bonmatin JM, Giorio C, Girolami V, Goulson D, Kreutzweiser DP, Krupke C, ment Planning and Coordinating Agency (NEPAD Agency) with funding from Liess M, Long E, Marzaro M, Mitchell EAD et al (2015) Environmental the Wellcome Trust (UK) and the UK government. The statements made and fate and exposure; neonicotinoids and fipronil. Environ Sci Pollut Res Int views expressed are solely the responsibility of the Authors. 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Journal

International Journal of Food ContaminationSpringer Journals

Published: Apr 28, 2022

Keywords: Maximum residual limits; Hazard quotient; Estimated daily intake; Acceptable daily intake; Uganda

References