Background: The sorption and desorption characteristics of methyl bromide (MB) were determined during and after fumigation of recently harvested pine (Pinus radiata D. Don) logs. The effects of dose (48 or 120 g m ), degree of bark cover (0, 50 or 100%) and end-grain sealing (sealed or unsealed) on sorption and desorption were determined over time.
Results: Sorption of MB was proportional to the dose applied and dependent on the amount of end-grain sealed. After 16 h, an average of 70.7 ± 2.5% of the initial concentration remained in the treated space when end-grains were sealed, whereas only 47.3 ± 2.5% remained when unsealed. During aeration, MB was released from logs, initially ranging from 2.8 to 8.8 g · h m , depending on the treatment. The rate of desorption quickly decreased during aeration.
Conclusion: The surface area of a log is the most important factor influencing MB sorption and desorption rates, with greater surface area resulting in greater (de)sorption rates. Sorption data can now be combined with insect toxicity data to estimate a minimum effective dose of MB for further evaluation, while desorption data can be combined with fumigant plume modelling to assess worker safety. © 2016 Society of Chemical Industry.
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http://dx.doi.org/10.1002/ps.4355 | DOI Listing |
Insects
December 2024
Department of Entomology, Kansas State University, 123 W. Waters Hall, Manhattan, KS 66506, USA.
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Department of Process Engineering and Technology of Polymer and Carbon Materials, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, Wrocław, 50-370, Poland.
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Guangxi Normal University, Chemistry and Pharmaceutical Sciences, CHINA.
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January 2025
Departamento de Agronomia e Ciências Florestais, Universidade Federal Rural do Semi-Árido, Mossoró, AV. Francisco Mota, 572 - Pres. Costa E Silva, RN, Mossoró, 59625-900, Rio Grande do Norte, Brazil. Electronic address:
Generally, herbicides used in Brazil follow manufacturer's recommendations, which often do not consider soil attributes. Statistical models that include the physicochemical properties of the soil involved in herbicide retention processes could enable greater precision in herbicide dose decision-making. This study evaluated the potential of artificial neural networks (ANNs) to predict the sorption and desorption of the herbicide linuron in Brazilian soils with different attributes.
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