Geomatics and Environmental Engineering, vol. 19, no. 5

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Vol. 19 No. 5 (2025): Geomatics and Environmental Engineering

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Submitted
May 28, 2024
Accepted
Jul 3, 2025
Published
Sep 8, 2025
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This work is licensed under a Creative Commons Attribution 4.0 International License.

DRIMERA: New Model of Airborne Pesticide Dispersion by Eulerian Approach Coupled with Weibull’s Law and Wind Rose

https://doi.org/10.7494/geom.2025.19.5.23
Saint-Pierre Kouadio
Laboratoire de Nutrition et de Technologie Alimentaire, Unité Mixte de Recherche et d’Innovation en Sciences Agronomiques et Procédés de Transformation, Institut National Polytechnique Félix HOUPHOUËT-BOIGNY, Yamoussoukro, Ivory Coast
https://orcid.org/0009-0007-2120-1580
Nogbou Emmanuel Assidjo
Laboratoire de Nutrition et de Technologie Alimentaire, Unité Mixte de Recherche et d’Innovation en Sciences Agronomiques et Procédés de Transformation, Institut National Polytechnique Félix HOUPHOUËT-BOIGNY, Yamoussoukro, Ivory Coast
https://orcid.org/0000-0003-4515-0961

Corresponding Author(s) : Saint-Pierre Kouadio

kouadiostpierre@gmail.com

Geomatics and Environmental Engineering, Vol. 19 No. 5 (2025): Geomatics and Environmental Engineering

Abstract

To solve the constraints that are commonly faced in risk assessment, research on the modeling of the dispersion and distribution of pollutants in the environment are emerging, and software is being developed. This paper presents a tool that is based on the Eulerian approach coupled with Weibull’s law and the wind rose approach (called DRIMERA). This physically detailed modeling-based software can accurately predict pesticide drift under different weather conditions and calculate aerially applied pesticide concentrations in the environment at a threshold of p = 0.05; the values of the coefficient of determination r² vary between 0.6331 and 0.9876. This support thus helps facilitate the wider use and adaptation of atmospheric models.

Keywords

modeling software DRIMERA risk assessment pollutant dispersion
Kouadio, S.-P., & Assidjo, N. E. (2025). DRIMERA: New Model of Airborne Pesticide Dispersion by Eulerian Approach Coupled with Weibull’s Law and Wind Rose. Geomatics and Environmental Engineering, 19(5), 23–47. https://doi.org/10.7494/geom.2025.19.5.23
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References
  1. United Nations Environment Programme: Environmental and health impacts of pesticides and fertilizers and ways of minimizing them – envisioning a chemical-safe world. Summary for policymakers. 2022. https://www.unep.org/resources/report/environmental-and-health-impacts-pesticides-and-fertilizers-and-ways-minimizing [access: 2.05.2022].
  2. Deram A., Van Staevel E.: Évaluation et acceptabilité des risques environnementaux. Synthèse de l’étude. Étude RECORD n°04-0810//0811/1A, Juillet 2006. https://record-net.org/media/etudes/90/public/synthese/synth-record04-0810-0811-1a.pdf [access: 15.11.2021].
  3. Assidjo E., Sadat A., Akmel C., Akaki D., Elleingand E., Yao B.: L’analyse des risques: Outils innovant d’amélioration de la sécurité sanitaire des aliments. Revue Africaine de Santé et de Productions Animales, no. 11, 2013, pp. 3–13.
  4. Kouadio K.S.-P., Assidjo N.E.: Review of the approach to modeling pesticides dispersion in environment for determining the concentrations to which organisms are exposed as part of risk assessment. Journal of Applied Science and Process Engineering, vol. 10(2), 2023, pp. 94–108. https://doi.org/10.33736/jaspe.5489.2023.
  5. Carrier G., Bard D.: Analyse du risque toxicologique, [in:] Gérin M., Gosselin P., Cordier S., Viau C., Quénel P., Dewailly É. (réd.), Environnement et santé publique: Fondements et pratiques, Édisem, Tec & Doc, Acton Vale, Paris 2003, pp. 203–226.
  6. Einax J. (ed.): Chemometrics in Environmental Chemistry – Statistical Methods. The Handbook of Environmental Chemistry (ed. O. Hutzinger ), vol. 2, part G, Springer, Berlin, Heidelberg 1995.
  7. U.S. Department of the Navy: Handbook for Statistical Analysis of Environmental Background Data. Southwest Division and Expeditionary Force Ashore West of Naval Facilities Engineering Command, San Diego 1999.
  8. Rong Y.: Statistical methods and pitfalls in environmental data analysis. Environmental Forensics, vol. 1(4), 2000, pp. 213–220. https://doi.org/10.1006/enfo.2000.0022.
  9. Alkarkhi A.F.M., Alqaraghuli W.A.A.: Multivariate data, [in:] Applied Statistics for Environmental Science with R, Elsevier, 2020, pp. 1–10. https://doi.org/10.1016/B978-0-12-818622-0.00001-0.
  10. Unsworth J.B., Wauchope R.D., Klein A.W., Dorn E., Zeeh B., Yeh S.M., Akerblom M., Racke K.D., Rubin B.: Significance of the long-range transport of pesticides in the atmosphere. Pure and Applied Chemistry, vol. 71(7), 1999, pp. 1359–1383. https://doi.org/10.1351/pac199971071359.
  11. Gil Y., Sinfort C.: Emission of pesticides to the air during sprayer application: A bibliographic review. Atmospheric Environment, vol. 39(28), 2005, pp. 5183–5193. https://doi.org/10.1016/j.atmosenv.2005.05.019.
  12. Chahine A.: Modélisation de la dispersion aérienne de pesticides des échelles locales aux échelles régionales, influence des aménagements et quantification des niveaux d’exposition. Centre International d’Études Supérieures en Sciences Agronomiques, Montpellier SupAgro 2011 [PhD thesis].
  13. Brunet Y., Dupont S., Chahine A., Sinfort C.: MODAPEX: modélisation de la dispersion aérienne des pesticides et des niveaux d’exposition à l’échelle du paysage – Programme «Évaluation et réduction des risques liés à l’utilisation des pesticides». Institut National de la Recherche Agronomique (INRA), Bordeaux 2013 [research report].
  14. Meyer M.: Technique for measurement of inert gases in liquids by gas chromatography. Pflügers Archiv European Journal of Physiology, vol. 375(2), 1978, pp. 161–165. https://doi.org/10.1007/BF00584239.
  15. Holland C.D., Sielken R.L., Jr.: Quantitative Cancer Modeling and Risk Assessment. PTR Prentice-Hall, Englewood Cliffs 1993.
  16. Addae-Mensah K.A., Wikswo J.P.: Measurement techniques for cellular biomechanics in vitro. Experimental Biology and Medicine, vol. 233(7), 2008, pp. 792–809. https://doi.org/10.3181/0710-MR-278.
  17. DeCaprio A.P.: Biomarkers: coming of age for environmental health and risk assessment. Environmental Science & Technology, vol. 31(7), 1997, pp. 1837–1848. https://doi.org/10.1021/es960920a.
  18. Zhang J., Yin J., Wang R.: Basic framework and main methods of uncertainty quantification. Mathematical Problems in Engineering, vol. 2020(1), 2020, 068203. https://doi.org/10.1155/2020/6068203.
  19. Abdar M., Pourpanah F., Hussain S., Rezazadegan D., Liu L., Ghavamzadeh M., Fieguth P., Cao X., Khosravi A., Acharya U.R., Makarenkov V., Nahavandi S.: A review of uncertainty quantification in deep learning: techniques, applications and challenges. Information Fusion, vol. 76, 2021, pp. 243–297. https://doi.org/10.1016/j.inffus.2021.05.008.
  20. Chicco D., Warrens M.J., Jurman G.: The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, vol. 7, 2021, e623. https://doi.org/10.7717/peerj-cs.623.
  21. Sinfort C., Vallet A.: Modélisation de la dispersion des pesticides pendant les applications. Ingénieries: Eau, Agriculture, Territoires, no. spéc., 2003, pp. 85–94. https://hal.inrae.fr/hal-02582989.
  22. Sinfort C., Bonicelli B.: Mesure et modélisation de la dispersion des pesticides dans l’air au voisinage des parcelles agricoles. Bulletin de Veille Scientifique, no. 14, 2011, pp. 18–21. https://hal.inrae.fr/hal-02598450v1.
  23. Hilz E., Vermeer A.: Spray drift review: The extent to which a formulation can contribute to spray drift reduction. Crop Protection, vol. 44, 2013, pp. 75–83. https://doi.org/10.1016/j.cropro.2012.10.020.
  24. FOCUS: Landscape and Mitigation Factors in Aquatic Risk Assessment. Volume 1: Extended Summary and Recommendations. Final Report of the FOCUS Working Group on Landscape and Mitigation Factors in Ecological Risk Assessment, EC Document Reference SANCO/10422/2005 v2.0, 2007.
  25. FOCUS: Landscape and Mitigation Factors in Aquatic Risk Assessment. Volume 2: Detailed Technical Reviews. Final Report of the FOCUS Working Group on Landscape and Mitigation Factors in Ecological Risk Assessment, EC Document Reference SANCO/10422/2005 v2.0, 2007.
  26. Ganzelmeier H., Rautmann D., Spangenberg R., Streloke M.: Studies on spray drift of plant protection products. Mitteilungen aus der Biologischen Bundesanstalt für Landund Forstwirtschaft, Berlin-Dahlem 1995.
  27. Reichard D., Zhu H., Fox R., Brazee R.: Wind tunnel evaluation of a computer program to model spray drift. Transactions of the ASAE, vol. 35(3), 1992, pp. 755–758. https://doi.org/10.13031/2013.28658.
  28. Holterman H., van de Zande J., Porskamp H.J., Huijmans J.: Modelling spray drift from boom sprayers. Computers and Electronics in Agriculture, vol. 19(1), 1997, pp. 1–22. https://doi.org/10.1016/S0168-1699(97)00018-5.
  29. Elliott J.G., Wilson B.J.: The Influence of Weather on the Efficiency and Safety of Pesticide Application: The Drift of Herbicides. Occasional Publication, vol. 3, British Crop Protection Council, 1983.
  30. Thistle H.: The role of stability in fine pesticide droplet dispersion in the atmosphere: A review of physical concepts. Transactions of the ASAE, vol. 43(6), 2000, pp. 1409–1413. https://doi.org/10.13031/2013.3038.
  31. Burton T., Jenkins N., Sharpe D., Bossanyi E.: Wind Energy Handbook. Wiley, Chichester 2011.
  32. Saleh H., Aly A.A.E.-A., Abdel-Hady S.: Assessment of different methods used to estimate Weibull distribution parameters for wind speed in Zafarana wind farm, Suez Gulf, Egypt. Energy, vol. 44(1), 2012, pp. 710–719. https://doi.org/10.1016/j.energy.2012.05.021.
  33. Calvert J.: Glossary of atmospheric chemistry terms (Recommendations 1990). Pure and Applied Chemistry, vol. 62(11), 1990, pp. 2167–2219. https://doi.org/10.1351/pac199062112167.
  34. Koné D., Tuo S., Coulibaly K., Gore B.O., Dje K., Traoré M., Aka B.: Assessment of wind potential energy of four major cities in Cote d’Ivoire using satellite data from 2015 to 2022. Journal of King Saud University, vol. 36(11), 2024, 103579. https://doi.org/10.1016/j.jksus.2024.103579.
  35. Cerruto E., Aglieco C., Failla S., Manetto G.: Parameters influencing deposit estimation when using water sensitive papers. Journal of Agricultural Engineering, vol. 44(2), 2013, pp. 62–70. https://doi.org/10.4081/jae.2013.e9.
  36. Privitera S., Manetto G., Pascuzzi S., Pessina D., Cerruto E.: Drop size measurement techniques for agricultural sprays: a state-of-the-art review. Agronomy, vol. 13(3), 2023, 678. https://doi.org/10.3390/agronomy13030678.
  37. Grisso R., Hipkins P., Askew S., Hipkins L., McCall D.: Nozzles: Selection and Sizing. Publication 442–032, Virginia Cooperative Extension, Virginia Tech, 2019.
  38. Chung T.: Computational Fluid Dynamics. Cambridge University Press, 2002.
  39. Perry R., Green D., Maloney J.: Perry’s Chemical Engineers’ Handbook. McGrawHill Professional, New York 1984.
  40. Tuo S., Camara B., Kassi K.F.J.-M., Kamaté K., Ouédraogo S.L., Koné D.: Actualisation de la distribution géographique des cercosporioses des bananiers en Côte d’Ivoire: Diversité et incidence de l’agent pathogène. Journal of Applied Biosciences, vol. 166, 2021, pp. 17188–17211.
  41. FIRCA (Fond Interprofessionnel pour la Recherche et le Conseil Agricoles): Etude d’impact sanitaire et environnemental de l’épandage des pesticides dans les exploitations de banane et ses environs. Rapport provisoire. 2019.
  42. International Organization for Standardization (ISO): Equipment for crop protection – Methods for field measurement of spray drift (ISO 22866:2005). June 2005.
  43. Fritz B.K., Hoffmann W.C., Bagley W.E., Hewitt A.: Field scale evaluation of spray drift reduction technologies from ground and aerial application systems. Journal of ASTM International, vol. 5(8), 2011, pp. 1–11. https://doi.org/10.1520/JAI103457.
  44. Pitt J.J.: Principles and applications of liquid chromatography-mass spectrometry in clinical biochemistry. Clinical Biochemist Reviews, vol. 30(1), 2009, pp. 19–34. https://pubmed.ncbi.nlm.nih.gov/19224008/.
  45. Thode H.C.: Testing for Normality. CRC Press, Boca Raton 2002.
  46. Snedecor G.W., Cochran W.G.: Statistical Methods. 8th ed. Iowa State University Press, Ames 1989.
  47. Leon A.C.: Descriptive and inferential statistics, [in:] Bellack A.S., Hersen M. (eds.), Comprehensive Clinical Psychology. Volume 3: Research and Methods. Section III: Statistical Methodology, Pergamon, 1998, pp. 243–285. https://doi.org/10.1016/B0080-4270(73)00264-9.
  48. Bach C., Brangier E., Scapin D.L.: Comment s’assurer de la facilité d’utilisation d’une nouvelle technologie?, [in:] Lévy-Leboyer C., Louche C., Rolland J.-P. (eds.), RH: les apports de la psychologie du travail. Tome 2: Management des organisations, Éditions d’Organisation, Paris 2006, pp. 413–428.
  49. Jelassi K., Herault S.: Continuité d’usage et appropriation de l’Internet mobile: Un essai de modélisation. Management & Avenir, vol. 78(4), 2015, pp. 59–77. https://doi.org/10.3917/mav.078.0059.
  50. Renaudo C.A., Bertin D.E., Bucalá V.: A hybrid Lagrangian-dispersion model for spray drift prediction applied to horizontal boom sprayers. Journal of Aerosol Science, vol. 173, 2023, 106210. https://doi.org/10.1016/j.jaerosci.2023.106210.
  51. Huang J., Bou-Zeid E.: Turbulence and vertical fluxes in the stable atmospheric boundary layer. Part I: A large-eddy simulation study. Journal of the Atmospheric Sciences, vol. 70, 2013, pp. 1513–1527. https://doi.org/10.1175/JAS-D-12-0167.1.
  52. Kruger G.R., Klein R.N., Ogg C.L.: Spray Drift of Pesticide. University of Nebraska, 2013.
  53. Katul G.G., Mahrt L., Poggi D., Sanz C.: ONEand TWO-Equation Models for Canopy Turbulence. Boundary-Layer Meteorology, vol. 113(1), 2004, pp. 81–109. https://doi.org/10.1023/B:BOUN.0000037333.48760.e5.
  54. Finnigan J.J.: Turbulence in plant canopies. Annual Review of Fluid Mechanics, vol. 32(1), 2000, pp. 519–571. https://doi.org/10.1146/annurev.fluid.32.1.519.
  55. Thomson S.J., Womac A.R., Mulrooney J.E., Deck S.: Evaluation of upwind/ downwind boom switching and propeller direction on drift of aerially applied spray, [in:] Proceedings of the International Conference on Pesticide Application for Drift Management, Waikoloa, HI, October 27–29, 2004, American Society of Agricultural and Biological Engineers, 2004, pp. 340–347.
  56. Thomson S.J., Womac A.R., Mulrooney J.E.: Reducing pesticide drift by considering propeller rotation effects from aerial application near buffer zones. Sustainable Agriculture Research, vol. 2(3), 2013, pp. 41–51. https://doi.org/10.5539/sar.v2n3p41.
  57. Bub S., Schad T., Gao Z.: XDrift – An R package to simulate spatially explicit pesticide spray-drift exposure of non-target-species habitats at landscape scales. SoftwareX, vol. 12, 2020, 100610. https://doi.org/10.1016/j.softx.2020.100610.
  58. Teske M.E., Bird S.L., Esterly D.M., Curbishley T.B., Ray S.L., Perry S.G.: AgDRIFT®: A model for estimating near-field spray drift from aerial applications. Environmental Toxicology and Chemistry, vol. 21(3), 2002, pp. 659–671. https://doi.org/10.1002/etc.5620210327.
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