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Real-time particle monitoring of pesticide drift from an axial fan airblast orchard sprayer


In Washington State, a majority of reported pesticide-related illnesses and application-related complaints involve drift. We employed real-time particle monitors (Dylos) during a series of experimental spray events investigating drift. Sections of an orchard block were randomly sprayed by an axial fan airblast sprayer, while monitors sampled particulate matter above and below the canopy at various downwind locations. We found elevated particle mass concentrations (PMC) at all distances (16–74 m). The 75th percentile PMC while spraying was significantly greater than the control periods by 107 (95% CI 94–121) μg/m3, after adjusting for sampler height and wind speed. The 75th percentile PMC below the canopy was significantly greater than above the canopy by 9.4 (95% CI 5.2–12) μg/m3, after adjusting for spraying and wind speed. In a restricted analysis of the spray events, the 75th percentile PMC significantly decreased by 2.6 (95% CI −3.2 to −1.7) μg/m3 for every additional meter away from the edge of the spray quadrant, after adjusting for canopy height and wind speed. Our results were consistent with a larger study that performed passive sampling during the same spray events, suggesting that real-time monitoring can be used as a screening tool for pesticide drift. Compared with traditional methods of drift sampling, real-time monitoring is overall an easily employed, affordable sampling technique, and it can provide minute-by-minute measurements that can be coupled with meteorological measurements to better understand how changes in wind speed and direction affect drift.

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  1. Calvert GM, Karnik J, Mehler L, Beckman J, Morrissey B, Sievert J, et al. Acute pesticide poisoning among agricultural workers in the United States, 1998–2005. Am J Ind Med. 2008;51:883–98. [accessed on 6 June 2017].

    Article  Google Scholar 

  2. WADOH. 2013 Pesticide Data Reports - Summary of 2010-2011 Data [Internet]. 2013.

  3. Stokes L, Stark A, Marshall E, Narang A. Neurotoxicity among pesticide applicators exposed to organophosphates. Occup Environ Med. 1995;52:648–53.

    Article  CAS  Google Scholar 

  4. De Roos A, Blair A, Rusiecki J, Hoppin JA, Svec M, Dosemeci M, et al. Cancer incidence among glyphosate-exposed pesticide applicators in the Agricultural Health Study. Environ Health Perspect. 2005;113:49–54.

    Article  Google Scholar 

  5. Alavanja MCR, Sandler DP, McDonnell CJ, Lynch CF, Pennybacker M, Zahm SH, et al. Characteristics of pesticide use in a pesticide applicator cohort: The Agricultural Health Study. Environ Res. 1999;80:172–9.

    Article  CAS  Google Scholar 

  6. Lee S-J, Mehler L, Beckman J, Diebolt-Brown B, Prado J, Lackovic M, et al. Acute Pesticide Illnesses Associated with Off-Target Pesticide Drift from Agricultural Applications: 11 States, 1998–2006. Environ Health Perspect. 2011;119:1162–9.

    Article  Google Scholar 

  7. Fox R, Derksen R, Zhu H, Brazee R, Svensson S. A history of air-blast sprayer development and future prospects. Trans ASABE. 2008;51:405–10.

    Article  Google Scholar 

  8. US EPA. Introduction to Pesticide Drift [Internet]. United States Environmental Protection Agency (US EPA); 2016.

  9. Keen R. Development of a low-cost vertical patternator. 2010.

  10. Steiner P. The Distribution of Spray Material Between Target and Non-target Areas of a Mature Apple Orchard by Airblast Equipment. Cornell University, New York; 1969, 106 p.

  11. Nuyttens D. Drift from field crop sprayers: The influence of spray application technology determined using indirect and direct drift assessment means [Internet]. Katholieke Universiteit Leuven. 2007.

  12. Butler Ellis M, Lane A, O’Sullivan C, Miller P, Glass C. Bystander exposure to pesticide spray drift: New data for model development and validation. Biosyst Eng [Internet]. 2010;107:162–8.

    Article  Google Scholar 

  13. Butler Ellis M, Lane A, O’Sullivan C, Alanis R, Harris A, Stallinga H, et al. Bystander and resident exposure to spray drift from orchard applications: field measurements, including a comparison of spray drift collectors. Asp Appl Biol. 2014;122:187–94.

    Google Scholar 

  14. Butler Ellis M, van de Zande J, van den Berg F, Kennedy M, O’Sullivan C, Jacobs CM, et al. The BROWSE model for predicting exposures of residents and bystanders to agricultural use of plant protection products: An overview. Biosyst Eng. 2017;154:92–104.

    Article  Google Scholar 

  15. Butler Ellis M, van den Berg F, van de Zande J, Kennedy M, Charistou A, Arapaki NS, et al. The BROWSE model for predicting exposures of residents and bystanders to agricultural use of pesticides: Comparison with experimental data and other exposure models. Biosyst Eng. 2017;154:122–36.

    Article  Google Scholar 

  16. US EPA. Worker protection standard application exclusion zone requirements. US Environmental Protection Agency. 2016.

  17. Kasner E On preventing farmworker exposure to pesticide drift in Washington orchards. Dissertation. [Internet]. University of Washington, 2017.

  18. ISO. ISO 22866:2005 - equipment for crop protection - methods for field measurement of spray drift. International Organization for Standardization (ISO). 2005.

  19. ASABE. Procedure for measuring drift deposits from ground, orchard, and aerial sprayers. American Society of Agricultural and Biological Engineers (ASABE). 2004.

  20. AgWeatherNet. Station Details - AgWeatherNet at Washington State University. 2017.

  21. US EPA. PRN 2001-X draft: spray and dust drift label statements for pesticide products. US Environmental Protection Agency. 2017.

  22. Brantley HL, Hagler GSW, Kimbrough ES, Williams RW, Mukerjee S, Neas LM. Mobile air monitoring data-processing strategies and effects on spatial air pollution trends. Atmos Meas Tech. 2014;7:2169–83.

    Article  Google Scholar 

  23. Bukowiecki N, Dommen J, Prévôt A, Richter R, Weingartner E, Baltensperger U. A mobile pollutant measurement laboratory - measuring gas phase and aerosol ambient concentrations with high spatial and temporal resolution. Atmos Environ. 2002;36:5569–79.

    Article  CAS  Google Scholar 

  24. Rodriguez R, Yao Y. Five Things You Should Know about Quantile Regression. SAS Institute Inc. 2017.

  25. Cade BS, Noon BR. A gentle introduction to quantile regression for ecologists. Front Ecol Environ. 2003;1:412–20.[0412:AGITQR]2.0.CO;2.

    Article  Google Scholar 

  26. Bradman A, Salvatore AL, Boeniger M, Castorina R, Snyder J, Barr DB, et al. Community-based intervention to reduce pesticide exposure to farmworkers and potential take-home exposure to their families. J Expo Sci Environ Epidemiol. 2009;19:79–89.

    Article  CAS  Google Scholar 

  27. Rydbeck F, Bottai M, Tofail F, Persson L-Å, Kippler M. Urinary iodine concentrations of pregnant women in rural Bangladesh: a longitudinal study. J Expo Sci Environ Epidemiol. 2013;24:504.

    Article  Google Scholar 

  28. Schlink U, Thiem A, Kohajda T, Richter M, Strebel K. Quantile regression of indoor air concentrations of volatile organic compounds (VOC). Sci Total Environ. 2010;408:3840–51.

    Article  CAS  Google Scholar 

  29. Richards SM, McClure GYH, Lavy TL, Mattice JD, Keller RJ, Gandy J. Propanil (3,4-Dichloropropionanilide) Particulate Concentrations Within and Near the Residences of Families Living Adjacent to Aerially Sprayed Rice Fields. Arch Environ Contam Toxicol. 2001;41:112–6.

    Article  CAS  Google Scholar 

  30. Felsot AS, Unsworth JB, Linders JBHJ, Roberts G, Rautman D, Harris C, et al. Agrochemical spray drift; assessment and mitigation--a review. J Environ Sci Health B. 2011;46:1–23.

    Article  CAS  Google Scholar 

  31. Kennedy M, Butler Ellis M. Probabilistic modelling for bystander and resident exposure to pesticides using the Browse software. Biosyst Eng. 2017;154:105–21.

    Article  Google Scholar 

  32. Reichard DL, Fox RD, Brazee RD, Hall FR. Air velocities delivered by orchard air sprayers. Trans ASAE Am Soc Agric Eng USA. 1979;

  33. Fenske R, Yost M, Galvin K, Tchong M, Negrete M, Palmendez pablo, et al. Organophosphorous Pesticide Air Monitoring. Washignton State Department of Health Pesticide Program; 2009.

  34. US EPA. PRN 2001-X Draft: Spray and Dust Drift Label Statements for Pesticide Products. 2016.

  35. Endalew M, Hertog, M, Verboven, P, Baetens, K, Delele, M, Ramon, H, et al. Modelling airflow through 3D canopy structure of orchards. Int Adv Pestic Appl. 2006;

  36. Kuo C-Y, Tzeng C-T, Ho M-C, Lai C-M. Wind Tunnel Studies of a Pedestrian-Level Wind Environment in a Street Canyon between a High-Rise Building with a Podium and Low-Level Attached Houses. Energ. 2015;8:10942–57.

    Google Scholar 

  37. Nordby A, Skuterud R. The effects of boom height, working pressure and wind speed on spray drift. Weed Res. 1974;14:385–95.

    Article  Google Scholar 

  38. Hanna S, Briggs G, Hosker R. Handbook on Atmospheric Diffusion. Ch 4: Gaussian Plume Model for Continuous Sources [Internet]. National Oceanic and Atmospheric Administration (NOAA); 1982.

  39. Jones S, Anthony TR, Sousan S, Altmaier R, Park JH, Peters TM. Evaluation of a low-cost aerosol sensor to assess dust concentrations in a swine building. Ann Occup Hyg. 2016;60:597–607.

    Article  Google Scholar 

  40. Carvlin GN, Lugo H, Olmedo L, Bejarano E, Wilkie A, Meltzer D, et al. Development and field validation of a community-engaged particulate matter air quality monitoring network in Imperial, California, USA. J Air Waste Manag Assoc. 2017;67:1342–52.

    Article  CAS  Google Scholar 

  41. Manikonda A, Zíková N, Hopke PK, Ferro AR. Laboratory assessment of low-cost PM monitors. J Aerosol Sci. 2016;102:29–40.

    Article  CAS  Google Scholar 

  42. Northcross AL, Edwards RJ, Johnson MA, Wang Z-M, Zhu K, Allen T, et al. A low-cost particle counter as a realtime fine-particle mass monitor. Environ Sci Process Impacts. 2013;15:433–9.

    Article  CAS  Google Scholar 

  43. Holstius DM, Pillarisetti A, Smith KR, Seto E. Field calibrations of a low-cost aerosol sensor at a regulatory monitoring site in California. Atmos Meas Tech. 2014;7:1121–31(accessed on 6 June 2017)

    Article  Google Scholar 

  44. Semple S, Ibrahim A, Apsley A, Steiner M, Turner S. Using a new, low-cost air quality sensor to quantify second-hand smoke (SHS) levels in homes. Tob Control. 2015;24:153. (accessed on 4 May 2017).

    Article  Google Scholar 

  45. PennState. 11.3 The story of diurnal boundary layer growth told in vertical profiles of virtual potential temperature. 2017 [accessed on 19 March 2018].

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This study would not have been possible without the Pacific Northwest Agricultural Safety and Health (PNASH) field team’s (Pablo Palmandez, Maria Negrete, Maria Tchong-French, Jane Pouzou, Jose Carmona, Ryan Babadi, Christine Perez Delgado) expertise and time on this project. We would also like to thank Gwen A. Hoheisel from the Center for Precision & Automated Agricultural Systems at Washington State University (WSU) for her contribution to the design of this study. The WSU Tree Fruit Research & Extension Center, Washington Tree Fruit Research Commission and Vine Tech & Equipment were also involved in the logistics of this study. This study was supported by the University of Washington’s (UW) Department of Environmental & Occupational Health Sciences (DEOHS), including their Pacific Northwest Agricultural Safety and Health (PNASH) Center (CDC/NIOSH Cooperative Agreement #5 U54 OH007544), Medical Aid and Accident Fund Initiative, Award Number 5P30 ES007033-23 from the National Institute of Environmental Health Sciences, Award Number 83618501-0 from the US Environmental Protection Agency and Russel L. Castner Endowed Student Research Fund. UW’s Graduate Opportunities Minority Achievement Program (GO-MAP) also supported this study.


This research would not have been possible without the support of the Graduate Opportunities and Minorities Achievement Program (GO-MAP); the University of Washington’s Department of Environmental and Occupational Health Sciences (DEOHS); the Pacific Northwest Agricultural Safety and Health Center (PNASH; CDC/NIOSH Cooperative Agreement #5 U54 OH007544); the DEOHS Washington Medical Aid and Accident Fund (MAAF) Award; and the Russel L. Castner Endowed Student Research Fund.

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Correspondence to Magali N. Blanco.

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Blanco, M.N., Fenske, R.A., Kasner, E.J. et al. Real-time particle monitoring of pesticide drift from an axial fan airblast orchard sprayer. J Expo Sci Environ Epidemiol 29, 397–405 (2019).

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