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  • Open Access

    ARTICLE

    Micro-Locational Fine Dust Prediction Utilizing Machine Learning and Deep Learning Models

    Seoyun Kim1,#, Hyerim Yu2,#, Jeewoo Yoon1,3, Eunil Park1,2,*

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 413-429, 2024, DOI:10.32604/csse.2023.041575

    Abstract Given the increasing number of countries reporting degraded air quality, effective air quality monitoring has become a critical issue in today’s world. However, the current air quality observatory systems are often prohibitively expensive, resulting in a lack of observatories in many regions within a country. Consequently, a significant problem arises where not every region receives the same level of air quality information. This disparity occurs because some locations have to rely on information from observatories located far away from their regions, even if they may be the closest available options. To address this challenge, a novel approach that leverages machine… More >

  • Open Access

    ARTICLE

    Impacts of Torrefaction on PM10 Emissions from Biomass Combustion

    Zihao Wang, Dunxi Yu*, Jingkun Han, Jianqun Wu

    Energy Engineering, Vol.118, No.5, pp. 1267-1276, 2021, DOI:10.32604/EE.2021.016107

    Abstract Typical biomass torrefaction is a mild pyrolysis process under conditions of ordinary pressure, low temperature (200–300°C) and inert atmosphere. Torrefaction is considered to be a competitive technology for biomass pretreatment, but its impacts on the emissions of particulate matter from biomass combustion are worthy of further study. In this paper, three kinds of biomass, i.e., bagasse, wheat straw and sawdust were selected for torrefaction pretreatment and the impacts of torrefaction on the emission characteristics of PM10 from biomass combustion were investigated. The combustion experiments were carried out on a drop tube furnace. The combustion-generated particulate and bulk ash samples were… More >

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