IntroductionProstate cancer incidence ranks 2nd among men worldwide. Farming and pesticide use are associated with prostate cancer risk. Triazole fungicides are widely used in agriculture to fight ...against several crop diseases such as odiumn scab, rot… Some are carcinogenic on animals, many are endocrine disruptors and most of them were never studied in epidemiology.Material and MethodsData on pesticide use on 10 crops, including years of beginning and ending, were collected in 2005–2007 for 81,960 men from the enrolment questionnaire of AGRICAN. Incident prostate cancer cases were identified through linkage with cancer registries. Exposure to 26 triazole fungicides was assessed using the crop-exposure matrix PESTIMAT (Baldi, 2017). Hazard Ratios (HR and 95%CI) were estimated using Cox models with attained age as time scale.ResultsUntil 2017, we identified 4,654 incident prostate cancer cases among AGRICAN men. 42,316 men were exposed to pesticides and 21,645 to at least one of the 26 triazole fungicides. An elevated prostate cancer risk was found with azaconazole -used on fruit growing beteween 1995 and 2003 (HR=1.21, p=0.12), with no duration effect. We also found a tendency of excess risk with exposure to myclobutanil (used on vineyard and fruit growing) for use exceeding 30 years (HR=1.43, p=0.20), and to penconazole (used on vineyard, fruit growing and tobacco) for a duration of use between 30 and 40 years (HR=1.65, p=0.16). In contrast, we found a tendency of decreased risk for exposure to tebuconazole during more than 40 years (HR=0.70, p=0.19), and for short exposure duration to triadimenol (HR<10 years=0.88, p=0.18).ConclusionsConsidering ever/never exposure, we found few associations between prostate cancer and triazoles. We will assess exposure more in depth with the calculation of life-long cumulated scores score exposure (probability x frequency x intensity), especially for triazoles associated with prostate cancer in the present analysis.
Intensive and widespread use of pesticides raises serious environmental and human health concerns. The presence and levels of 209 pesticide residues (active substances and transformation products) in ...625 environmental samples (201 soil, 193 crop, 20 outdoor air, 115 indoor dust, 58 surface water, and 38 sediment samples) have been studied. The samples were collected during the 2021 growing season, across 10 study sites, covering the main European crops, and conventional and organic farming systems. We profiled the pesticide residues found in the different matrices using existing hazard classifications towards non-target organisms and humans. Combining monitoring data and hazard information, we developed an indicator for the prioritization of pesticides, which can support policy decisions and sustainable pesticide use transitions. Eighty-six percent of the samples had at least one residue above the respective limit of detection. One hundred residues were found in soil, 112 in water, 99 in sediments, 78 in crops, 76 in outdoor air, and 197 in indoor dust. The number, levels, and profile of residues varied between farming systems. Our results show that non-approved compounds still represent a significant part of environmental cocktails and should be accounted for in monitoring programs and risk assessments. The hazard profiles analysis confirms the dominance of compounds of low-moderate hazard and underscores the high hazard of some approved compounds and recurring "no data available" situations. Overall, our results support the idea that risk should be assessed in a mixture context, taking environmentally relevant mixtures into consideration. We have uncovered uncertainties and data gaps that should be addressed, as well as the policy implications at the EU approval status level. Our newly introduced indicator can help identify research priority areas, and act as a reference for targeted scenarios set forth in the Farm to Fork pesticide reduction goals.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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Pesticide exposure of workers in apple growing in France Bureau, Mathilde; Béziat, Béatrix; Duporté, Geoffroy ...
International archives of occupational and environmental health,
05/2022, Volume:
95, Issue:
4
Journal Article
Peer reviewed
Open access
Objective
Although apple trees are heavily sprayed, few studies have assessed the pesticide exposure of operators and workers in apple orchards. However, these data are crucial for assessing the ...health impact of such exposures. The aim of this study was to measure pesticide exposure in apple growing according to tasks and body parts.
Methods
A non-controlled field study was conducted in apple orchards in 4 regions of France during the 2016 and 2017 treatment seasons. Workers’ external contamination and their determinants were assessed over 156 working days corresponding to 30 treatment days, 68 re-entry days and 58 harvesting days. We measured pesticide dermal contamination during each task and made detailed observations of work characteristics throughout the day. Captan and dithianon were used as markers of exposure.
Results
The median dermal contamination per day was 5.50 mg of captan and 3.33 mg of dithianon for operators, 24.39 mg of captan and 1.84 mg of dithianon for re-entry workers, and 5.82 mg of captan and 0.74 mg of dithianon for harvesters. Thus, workers performing re-entry tasks, especially thinning and anti-hail net opening, presented higher contamination, either equal to or higher than in operators. For these last ones, mixing/loading and equipment cleaning were the most contaminating tasks. Most of the contamination was observed on workers’ hands in all tasks, except for net-opening in which their heads accounted for the most daily contamination.
Conclusions
This study highlights the importance of taking indirect exposures into account during re-entry work in apple growing.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
IntroductionGreenspace workers are exposed to many occupational hazards, including pesticides. Few epidemiological studies have been conducted among these workers, most of them using rough exposure ...parameters. Thus, improvement in exposure assessment is crucial to expand our knowledge on greenspace workers’ health. Our objectives were to i) define past pesticide exposures in greenspace workers and ii) characterise present exposure levels in real work conditions. This project has received funding from the French National Research Agency.MethodsTo identify pesticides used in the past, we explored various historical data sources that enabled to increment the crop-exposure matrix PESTIMAT (France) with molecules used in greenspaces since 1950. For each molecule, the probability, the frequency and the intensity of use were estimated on an annual basis. To characterise present exposures, field studies in real work conditions were conducted among municipal workers and gardeners in 2011 and in a plant nursery in 2022 during treatment tasks. Dermal exposure was assessed using patches placed onto the skin and cotton gloves. Detailed information on the tasks were collected to identify determinants of exposure.ResultsFifty active ingredients used in greenspaces (including pyrethrinoids, organochlorines, carbamates, dinitroanilines, etc.) have been added into PESTIMAT to date. The field studies were conducted among 24 municipal workers applying glyphosate with knapsacks and among six plant nursery workers applying insecticides (bifenazate, azoxystrobine and difenoconazole), fungicides (boscalid and pyraclostrobine) and the herbicide glyphosate. Analyses of dosimeters are ongoing and will be linked with the detailed characteristics we collected on workers, tasks, conditions of work, equipment and practices.ConclusionThe data we generated on the types of molecules and exposure levels will help to build metrics for pesticide exposures in greenspace workers, usable in epidemiological studies. Associations between the use of specific active ingredients and cancer occurrence will be studied in existing cohorts.
Abstract Introduction Pesticide exposure increases the risk of chronic disease among farmers. Understanding exposure is necessary for epidemiological and regulatory purposes. Since 2014, worker ...exposure has been assessed in the registration process by EFSA, using the OPEX model. Data specific to fruit-growing workers is limited to five European studies conducted in the 1980s by pesticide companies, among others. We compared exposure predicted in the regulations with that measured in field studies. Methods In 2016-2017, dermal exposure to captan and dithianon was measured in French farmers during 121 days of re-entry (net folding and deployment, thinning) and harvest, using patches and cotton gloves. Exposure was calculated using several parameters (task, personal protective equipment (PPE), treatment schedules). Exposure was recalculated from dislodgeable foliar residues (RDF) measured 2 to 312 days after application in 20 observations. Relationships between measured and calculated exposures were studied by linear regression. Results Exposure depended on PPE worn and tasks performed (thinning, net folding > harvest, net deployment) due to differences in pesticide accumulation on plants over the season. Most exposures calculated using default settings were 100 times higher than measured exposure. The model underestimated exposure recalculated with measured DFR in all observations for dithianon and almost all for captan. Discussion In the regulatory process, re-entry exposure is only calculated when it occurs immediately after application. Exposure measured up to 300 days after application was never zero. When re-entry was not immediate after application, the model underestimated exposure. Conclusion This demonstrates the importance of using field studies in the registration process to ensure a truly conservative approach.
Abstract Introduction Preventing farmers’ exposure to pesticides is a major public health issue. Wearing personal protective equipment (PPE) is one effective means of prevention among others. ...However, available data show that PPE are not always used by farmers. The aim of this study is to investigate the structural and psychosociological determinants of PPE use, in order to identify ways of improving farmer training. Methods 163 French farmers (winegrowers or open-field farmers) responded to a telephone survey. The questionnaire was divided into 12 parts, covering pesticide knowledge, psychological constructs (beliefs, attitudes, social norms, self-efficacy, health locus of control), and individual and professional aspects. The association with the PPE use (gloves, body protection) was studied by logistic regression. Results Participants were males, farm owners (82%), average age 50, had a low education level (59%), a pesticide certificate (92%), reported performing all their treatments (80%) and using gloves more systematically (60%) than body protection (20%). PPE use was associated with low barriers perceived to self-protection, high perception that peers protect themselves (descriptive social norms) but not with knowledge levels. Body protection use was associated with being a winegrower, working on small farms, high perceived ability to protect oneself and thinking that peers or family expect them to protect themselves (subjective social norms). Discussion and conclusion Determinants of other behaviours (hand washing, product label reading) will be analysed. This analysis enabled us to consider ways of improving farmer training: practical work to reduce perceived barriers and improve perceived ability to use PPE, discussion groups or peer training to change perceived social norms.
Since 2014, the Agricultural Operator Exposure Model (AOEM) has been the harmonised European model used for estimating non-dietary operator exposure to pesticide. It is based on studies conducted by ...the pesticide companies and it features 13 different crops including non-agricultural areas such as amenity grasslands. The objective of this study was to compare the dermal exposure measured during a field study conducted in a non-agricultural area with the corresponding values estimated by the model AOEM. The non-controlled field study was conducted in France in 2011 and included 24 private and public gardeners who apply glyphosate with knapsack sprayers. Dermal exposure was measured using the whole-body method and cotton gloves. Each measured value had an estimated value given by AOEM and we tested their correlation using linear regression.
The model overestimated body exposure for all observations and there was no correlation between values. However, it underestimated hand exposure by 42 times and it systematically underestimated the exposure when the operators were wearing gloves, especially during the application. The model failed at being conservative regarding hand exposure and highly overestimated the protection afforded by the gloves. At a time of glyphosate renewed approval in Europe, non-controlled field studies conducted by academics are needed to improve AOEM model, especially in the non-agricultural sector. Indeed, among the 34 studies included in the model, none were conducted on a non-agricultural area and only four assessed the exposure when using a knapsack sprayer. Moreover, knapsack sprayers being the main equipment used worldwide in both agricultural and non-agricultural settings, it is also crucial to integrate new data specific to this equipment in the model. Operator exposure should be estimated with accuracy in the registration process of pesticides to ensure proper safety as well as in epidemiological studies to improve exposure assessment.
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•The AOEM gave greater overall estimation of dermal exposure than field measures.•The AOEM underestimates hand exposure, especially when protective gloves are worn.•The AOEM would benefit from studies conducted in real work conditions in non-agricultural areas.•Operator's exposure should be estimated with accuracy to ensure proper safety.
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IntroductionData on pesticide exposure during occupational knapsack spraying is scarce.ObjectiveThis field study assessed its levels and determinants.MethodsPrivate landscapers/gardeners and ...municipal workers in the Normandy region, France, were enrolled between March and May 2011. They were equipped with cotton undergarments and gloves to assess actual dermal exposure to glyphosate, and with cotton coveralls separately for each phase to assess the contribution of mixing/loading and spraying and the distribution on 11 body areas. A field monitor observed the whole workshift and filled in a standardized observation grid. Respiratory samplings were also systematically performed, and additional surface wipes obtained from various equipment.ResultsTwenty-four workers were included, all men, with a median age of 40 years old, and a median experience in pesticide use of 14.5 years. The total work time varied between 110 and 360 min (median 210), and the number of mixing/loading-spraying cycles ranged from 1 to 8 (median 2). Spraying was more exposing than mixing/loading for all body parts except hands. Hands contributed to nearly 90% of body exposure during mixing/loading, and 30% during spraying, followed by back for spraying (14%). The median actual body contamination was 5,256 µg, with a median of 4,620 µg for hands. Dermal PPE use was associated with a decreased actual dermal exposure (estimate -0.81, p=0.001), and the number of mixing/spraying cycles with an increased exposure (more or less than 2 cycles: estimate 0.85, p=0.0006).ConclusionGiven their large contribution to overall dermal exposure, caution should be paid to handwashing and common hygiene rules during knapsack spraying. To our knowledge, our study is the first to report a high contamination of the back during spraying.
IntroductionPesticide exposures increase the risk of chronic diseases in farmers. Knowledge of exposure levels are needed for epidemiological studies and for regulation. In pesticide registration ...process, operator’s exposure is predicted by the AOEM, set up in 2014 by the European Food Safety Authority, based on thirty studies conducted by the pesticide industry. To date, we are not aware of any field study comparing predicted data with those measured under real working conditions.ObjectiveWe aimed to compare operators’ exposures during treatment days in apple-growing under non-controlled conditions of work and values predicted by AOEM.MethodsThirty apple growers from the French CANEPA study, were observed applying two fungicides (captan/dithianon) in 2016–2017. Dermal exposure was measured by body patches and cotton gloves. Detailed parameters about the farm, operator, application day, spraying equipment and personal protective equipment (PPE) used were recorded. For each observation, the corresponding exposure was calculated by the AOEM, using these parameters. The relationship between measured and calculated exposures was studied by linear regression.ResultsSignificant linear correlation was observed between calculated and measured daily exposures. Overall, the model overestimated daily exposure and exposure during application. However exposure was underestimated at mixing/loading in many observations, especially when the operator wore long working clothes or gloves.ConclusionThe AOEM model did not appear conservative in the sense that it did not overestimate exposures in all circumstances. More specifically: 1) the overestimation at spraying appeared a consequence of the overestimation of daily treated area, 2) the protection provided by PPE appeared overestimated, 3) mixing/loading exposure, a phase in which operators are exposed to concentrated products, appeared underestimated. These discrepancies could be due to optimal working conditions (larger farms, newer equipment) under which industries’ studies are conducted that are not representative of operators’ actual working conditions in fruit growing.
Pesticides exposures could be implicated in the excess of Central Nervous System (CNS) tumors observed in farmers, but evidence concerning individual pesticides remains limited. Carbamate derivative ...pesticides, including herbicides and fungicides (i.e. (thio/dithio)-carbamates), have shown evidence of carcinogenicity in experimental studies in animals. In the French AGRICAN cohort, we assessed the associations between potential exposures to carbamate herbicides and fungicides and the incidence of CNS tumors, overall and by histological subtype.
AGRICAN enrolled 181,842 participants involved in agriculture. Incident CNS tumors were identified by linkage with cancer registries from enrollment (2005–2007) until 2013. Individual exposures were assessed by combining information on lifetime periods of pesticide use on crops and the French crop-exposure matrix PESTIMAT, for each of the 14 carbamate and thiocarbamate herbicides and the 16 carbamate and dithiocarbamate fungicides registered in France since 1950. Associations were estimated using proportional hazard models with age as the underlying timescale, adjusting for gender, educational level and smoking.
During an average follow-up of 6.9 years, 381 incident cases of CNS tumors occurred, including 164 gliomas and 134 meningiomas. Analyses showed increased risks of CNS tumors with overall exposure to carbamate fungicides (Hazard Ratio, HR = 1.88; 95% CI: 1.27–2.79) and, to a lesser extent, to carbamate herbicides (HR = 1.44; 95% CI: 0.94–2.22). Positive associations were observed with specific carbamates, including some fungicides (mancozeb, maneb, metiram) and herbicides (chlorpropham, propham, diallate) already suspected of being carcinogens in humans.
Although some associations need to be corroborate in further studies and should be interpreted cautiously, these findings provide additional carcinogenicity evidence for several carbamate fungicides and herbicides.
•Findings reinforce evidence of carcinogenicity in humans for some (thio/dithio)-carbamate herbicides and fungicides that were already suspected of being carcinogens.•Findings show two to three times higher CNS tumor risks following exposure to the (dithio/thio)-carbamates used by farmers growing vineyards, fruits, potatoes and beets.•The majority of studied active ingredients are no longer marketed in 2018, but some are still on the European market, and others were registered too recently to be studied.
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