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First evidence of microplastics in atmospheric deposition over the Gulf of Thailand: Distribution, characteristics, and ecological risk
(2026-09-15)
Chinfak, Narainrit
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Gunasekaran, Kannan
;
Jandang, Suppakarn
;
Li, Qipei
;
Sricharoenvech, Piyaporn
Microplastics (MPs) have been extensively studied in aquatic environments; however, their occurrence in atmospheric deposition remains poorly understood. This study provides the first insights into the distribution of MPs in wet deposition across different locations and evaluates their potential environmental risks in the Gulf of Thailand (GoT). The ocean is likely a sink for MPs, with approximately 10% of the total MPs in atmospheric deposition detected in oceanic rainwater. MP abundances in terrestrial rainwater, including urban and agricultural rainwater (62.70 ± 15.89 items/m2 and 29.37 ± 12.29 items/m2, respectively) were significantly higher than those in the GoT rainwater (9.72 ± 3.70 items/m2) ( p = 0.001), with greater MPs abundance observed in the urban center. MPs exhibited similar morphological (shape and color) and compositional characteristics across all locations. Fibrous MPs (100–500 μm) dominated urban (42%) and agricultural (55%) rainwater, whereas smaller fibers (<100 μm) were predominant in the GoT (69%). White and black were the dominant colors found in both terrestrial and GoT rainwater. Low-density polymers (PE, PP and PS) were the most frequently detected polymers, comprising up to 80% of the total MPs. Among them, PE was predominant, accounting for 48%, 51%, and 62% of all MPs identified in urban, agriculture and the GoT rainwater, respectively. Ecological risk assessment indicated a minor potential ecological risk associated with MPs in wet deposition. Future studies investigating both wet and dry deposition, as well as factors influencing MP distribution and transport in the GoT, are needed to support transport modeling, identify potential sources, and assess the impact of climate forcing on MP distribution.
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Harnessing microbial biosurfactant for agricultural applications
(2024-01-01)
Eshwarnath, V. S.
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Thyagarajan, R.
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Kishorekumar, A.
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Gopikrishnan, V.
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Radhakrishnan, M.
Biosurfactant refers to surface-acting substances that can enhance surface-surface interactions by generating micelles that are derived from natural sources including plants, microorganisms, and animals. Biosurfactants are a structurally diverse group of secondary metabolites with lots of potential to serve mankind. The structural and compositional diversity of biosurfactants is unambiguously substrate-dependent. Microbial surfactants are environment-friendly alternatives to synthetic surfactants. Biosurfactants are surface-active agents produced by microorganisms that have higher efficiency and stability, lower toxicity and higher biocompatibility and biodegradability than chemical surfactants. The differences between biosurfactant production can be attributed to the different compositions of the hydrolyzates.
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How intellectual capital, knowledge management, and the business environment affect thailand’s food industry innovation
(2018-01-01)
Yaklai, Pimsara
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Suwunnamek, Opal
;
Srinuan, Chalita
In 2015, Thailand employed nearly 11% of the population in agriculture, which has always been a stable and prosperous component of the economy. Having a rich natural abundance of resources, combined with significant investments in technology, food safety, and research and development (R&D) have helped contribute to Thailand being labeled as “Kitchen of the World.” Given these priorities, stratified sampling was employed to select 246 individuals from the target population. Confirmatory factor analysis was used, followed by a structural equation model to analyze how intellectual capital, knowledge management, and the business environment affect innovation in Thailand’s entrepreneurial food industry. The research survey was conducted using a questionnaire which contained a 7-level Likert type agreement scale. Results from the study revealed that the food industry’s knowledge management capability was the most important factor (0.60), which was also influenced directly by the organization’s intellectual capital (0.44). Of lesser importance was intellectual capital (0.39) and the business environment (0.39).
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Identification of rice using symbolic regression
(2017-02-23)
Watanachaturaporn, Pakorn
Image classification is a preferred method for agriculture product identification since the product is intact from the process. Many published works have adopted image classification techniques for identifying rice seed varieties. Based on a classification algorithm and given sample features vectors, a classification model is built from a set of optimal parameters and operators. However, the obtained parameters rarely reflect relations among the feature symbolically and, therefore, are meaningless to human. This paper proposes of using a symbolic regression algorithm to search for possible identification solutions. A number of possible solutions and the associated analytical expressions are obtained. As a case study two classifiers are built from two chosen solutions and are applied to identify high quality Jasmine rice, the Khao Dawk Mali 105, from other three alike varieties. The performance of the classifiers is compared with the published work using percent identification accuracy. The experimental results show that the two classifiers obtained from a symbolic regression algorithm accurately identify the Khao Dawk Mali 105 at 86.25% and 90.00% while the compared algorithm accurately identify the rice in the range of 73.85%-83.46%. The most advantage of using a symbolic regression algorithm is that the algorithm also reveals analytical expressions. Such expressions suggest human a potential methodology concealed in the observed data.

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