IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i12p2210-d242089.html
   My bibliography  Save this article

Vibration and Noise Transmitted by Agricultural Backpack Powered Machines Critically Examined Using the Current Standards

Author

Listed:
  • Angela Calvo

    (DISAFA (Department of Agricultural, Forest and Food Sciences and Technologies), Largo P. Braccini 2, 10095 Turin, Italy)

  • Christian Preti

    (IMAMOTER Institute for Agricultural and Earth-moving Machines of C.N.R (Italian National Research Council), Strada delle Cacce 73, 10135 Turin, Italy)

  • Maria Caria

    (Dipartimento di Ingegneria del Territorio, Sezione di Meccanizzazione e Impiantistica, Viale Italia 39, 07100 Sassari, Italy)

  • Roberto Deboli

    (IMAMOTER Institute for Agricultural and Earth-moving Machines of C.N.R (Italian National Research Council), Strada delle Cacce 73, 10135 Turin, Italy)

Abstract

European Directives 2002/44/EC and 2003/10/EC establish the exposure limit values for preventing operators’ risks to vibration and noise transmitted by machines. Few studies studied noise and vibration of agricultural backpack powered machines (as mist blowers and blowers), but nobody critically studied them. This work analyzed the field back vibration, hand-arm vibration (HAV), and noise transmitted to ten operators by eight blowers and mist blowers. Unweighted and weighted vibration were analyzed, using the standards ISO 2631-1 (back), and ISO 5349-1 and ISO/TR 18570 (hand-arm system). The noise was evaluated by recording the acoustic pressure level at the operators’ ears using the ISO 9612. With the ISO 2631-1, the vibration to the operators’ back was low (0.38 ms −2 ), but the unweighted vibration measured along y and z -axes (not used by the ISO 2631-1) were high (>11 ms −2 ). HAV were also low when using the ISO 5349-1 (the highest value was 2.51 ms −2 in mist blowers), but high with the ISO/TR 18570 for the onset of vibration white finger (1446 ms −1.5 in blowers). Noise levels were always high: more than 100 dB(A), excluding the blower with the exhaust inside the blower hose. This last machine had noise levels lower than 86 dB(A), but its specific feature could increase environmental pollution.

Suggested Citation

  • Angela Calvo & Christian Preti & Maria Caria & Roberto Deboli, 2019. "Vibration and Noise Transmitted by Agricultural Backpack Powered Machines Critically Examined Using the Current Standards," IJERPH, MDPI, vol. 16(12), pages 1-20, June.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:12:p:2210-:d:242089
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/12/2210/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/16/12/2210/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Matías Salibián-Barrera & Stefan Aelst & Gert Willems, 2008. "Fast and robust bootstrap," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(1), pages 41-71, February.
    2. Salibian-Barrera, Matias & Van Aelst, Stefan, 2008. "Robust model selection using fast and robust bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5121-5135, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Raquel Nieto-Álvarez & María L. de la Hoz-Torres & Antonio J. Aguilar & María Dolores Martínez-Aires & Diego P. Ruiz, 2022. "Proposal of Combined Noise and Hand-Arm Vibration Index for Occupational Exposure: Application to a Study Case in the Olive Sector," IJERPH, MDPI, vol. 19(21), pages 1-23, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. La Vecchia, Davide & Camponovo, Lorenzo & Ferrari, Davide, 2015. "Robust heart rate variability analysis by generalized entropy minimization," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 137-151.
    2. Valéry Dongmo Jiongo & Pierre Nguimkeu, 2018. "Bootstrapping Mean Squared Errors of Robust Small-Area Estimators: Application to the Method-of-Payments Data," Staff Working Papers 18-28, Bank of Canada.
    3. Jiao, Xiyu & Pretis, Felix & Schwarz, Moritz, 2024. "Testing for coefficient distortion due to outliers with an application to the economic impacts of climate change," Journal of Econometrics, Elsevier, vol. 239(1).
    4. La Vecchia, Davide & Moor, Alban & Scaillet, Olivier, 2023. "A higher-order correct fast moving-average bootstrap for dependent data," Journal of Econometrics, Elsevier, vol. 235(1), pages 65-81.
    5. Alfons, Andreas & Croux, Christophe & Gelper, Sarah, 2016. "Robust groupwise least angle regression," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 421-435.
    6. Salibian-Barrera, Matias & Van Aelst, Stefan & Yohai, Víctor J., 2016. "Robust tests for linear regression models based on τ-estimates," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 436-455.
    7. Khan, Jafar A. & Van Aelst, Stefan & Zamar, Ruben H., 2010. "Fast robust estimation of prediction error based on resampling," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3121-3130, December.
    8. Samanta, Mayukh & Welsh, A.H., 2013. "Bootstrapping for highly unbalanced clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 70-81.
    9. O’Shaughnessy, P.Y. & Welsh, A.H., 2018. "Bootstrapping longitudinal data with multiple levels of variation," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 117-131.
    10. Matthias Templ, 2023. "Enhancing Precision in Large-Scale Data Analysis: An Innovative Robust Imputation Algorithm for Managing Outliers and Missing Values," Mathematics, MDPI, vol. 11(12), pages 1-22, June.
    11. Riani, Marco & Atkinson, Anthony C., 2010. "Robust model selection with flexible trimming," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3300-3312, December.
    12. Stefan Van Aelst & Gert Willems, 2010. "Inference for robust canonical variate analysis," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 4(2), pages 181-197, September.
    13. Sanjoy K. Sinha, 2019. "Robust small area estimation in generalized linear mixed models," METRON, Springer;Sapienza Università di Roma, vol. 77(3), pages 201-225, December.
    14. Gottard, Anna & Pacillo, Simona, 2010. "Robust concentration graph model selection," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3070-3079, December.
    15. Andreas Alfons & Wolfgang Baaske & Peter Filzmoser & Wolfgang Mader & Roland Wieser, 2011. "Robust variable selection with application to quality of life research," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(1), pages 65-82, March.
    16. repec:cep:stiecm:/2014/572 is not listed on IDEAS
    17. Lorenzo Camponovo & Taisuke Otsu, 2012. "Breakdown point theory for implied probability bootstrap," Econometrics Journal, Royal Economic Society, vol. 15(1), pages 32-55, February.
    18. Lorenzo Camponovo & Taisuke Otsu, 2015. "Robustness of Bootstrap in Instrumental Variable Regression," Econometric Reviews, Taylor & Francis Journals, vol. 34(3), pages 352-393, March.
    19. Roelant, E. & Van Aelst, S. & Croux, C., 2009. "Multivariate generalized S-estimators," Journal of Multivariate Analysis, Elsevier, vol. 100(5), pages 876-887, May.
    20. Filzmoser, P. & Höppner, S. & Ortner, I. & Serneels, S. & Verdonck, T., 2020. "Cellwise robust M regression," Computational Statistics & Data Analysis, Elsevier, vol. 147(C).
    21. Robert Finger, 2010. "Revisiting the Evaluation of Robust Regression Techniques for Crop Yield Data Detrending," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(1), pages 205-211.

    More about this item

    Keywords

    vibration; noise; blower; mist blower;
    All these keywords.

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:16:y:2019:i:12:p:2210-:d:242089. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.