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Prediction of pollutant emission characteristics in ISO50001 energy management in the Americas: Uni and multivariate machine learning approach.
de Oliveira Neves, Fábio; Salgado, Eduardo Gomes; de Figueiredo, Eduardo Costa; Sampaio, Paulo; Marafão, Fernando Pinhabel.
Afiliación
  • de Oliveira Neves F; Exact Science Institute, Environmental Science Department, Federal University of Alfenas, Alfenas, Minas Gerais State, Brazil. Electronic address: fabiooneves@gmail.com.br.
  • Salgado EG; Exact Science Institute, Federal University of Alfenas, Alfenas, Minas Gerais State, Brazil.
  • de Figueiredo EC; Faculty of Pharmaceutical Sciences, Federal University of Alfenas, Alfenas, Minas Gerais State, Brazil.
  • Sampaio P; Department of University of Minho, School of Engineering, ALGORITMI Research Centre, Portugal.
  • Marafão FP; Institute of Science and Technology, São Paulo State University (UNESP), Sorocaba, São Paulo State, Brazil.
Sci Total Environ ; 949: 174797, 2024 Nov 01.
Article en En | MEDLINE | ID: mdl-39038677
ABSTRACT
The American continent is experiencing significant economic and industrial development driven by sustainability principles. In this context, discussions on improving energy consumption have become increasingly frequent and dynamic across various sectors of civil society, including the implementation of energy efficiency measures as advocated by the ISO50001 energy management standard. However, there is a pressing need to investigate which socioeconomic aspects are responsible for the issuance of this certification in the Americas and how these factors relate to characteristic industrial emissions, especially particulate matter. This study aims to evaluate the socioeconomic factors influencing ISO50001 standard issuance and how these adjusted factors correlate with particulate matter of 2.5 µm and 10 µm dimensions. To achieve this, machine learning techniques were employed, considering the complex nature and risk of data overfitting. Model fitting was performed through multiple lasso regression, and the relationship between the adjusted factors was examined through cross-correlation analysis. The analyses indicate a strong correlation of adjusted macroeconomic indicators, especially with PM2.5, suggesting an association with cardiorespiratory problems and methane-related origins. This work is of great relevance to academia as it proposes new concepts regarding the interaction between energy efficiency standards and particulate matter. For the industrial sector, the adjusted factors provide guidance for standard implementation while also helping to mitigate health issues. Additionally, for the government, these results can assist in formulating policies to address specific health problems related to this area.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Monitoreo del Ambiente / Contaminantes Atmosféricos / Contaminación del Aire / Material Particulado / Aprendizaje Automático Idioma: En Revista: Sci Total Environ Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Monitoreo del Ambiente / Contaminantes Atmosféricos / Contaminación del Aire / Material Particulado / Aprendizaje Automático Idioma: En Revista: Sci Total Environ Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos