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Exploring data analysis and insights on volatile organic compounds for hazardous waste detection
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Orientador(es)
Resumo(s)
Volatile Organic Compounds (VOCs) are critical indicators of environmental contamination, particularly in hazardous waste contexts. While gas chromatography–mass spectrometry (GC–MS) provides high specificity, it struggles with scalability and pattern discovery in large, complex datasets. This study presents a data-driven framework integrating Exploratory Data Analysis (EDA) techniques — including principal component analysis (PCA), hierarchical clustering, and correlation mapping — to uncover emission patterns in compost-derived VOC data.
Using the LCSC VOC 2022 Compost Dataset (141 variables, 90 samples), we identified strong co-emission clusters (e.g., D-Limonene and ω-Pinene) and a temperature-dependent ethanol emission pattern unique to food-and-yard waste samples. Pearson correlation analysis revealed shared emission behavior, and regression confirmed a positive slope (25.6) for ethanol versus temperature.
These findings highlight EDA’s potential to enhance VOC dataset interpretability and source identification. The proposed framework supports practical applications such as early-warning systems, sensor deployment, and data-informed environmental policy.
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Contexto Educativo
Citação
Ahmadi, Mahdia; Méndez-Pérez, Natalia; Cruz, Helena; Igrejas, Getúlio; Rodrigues, Pedro João; Lopes, Rui Pedro (2025). Exploring data analysis and insights on volatile organic compounds for hazardous waste detection. In 5th Symposium of Appied Science for Young Researchers. Bragança. ISBN 978-972-745-360-3
Editora
Instituto Politécnico de Bragança