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<p>The fast growth of urbanization and increases in urban populations have greatly affected air quality, creating a significant challenge for both the environment and public health in urban areas. Consequently, it has become essential to understand and evaluate the levels and impacts of air pollution in these regions for the purpose of sustainable urban development. This study explores recent advancements in spatial air quality research spanning from 2010 to 2023, synthesizing findings from various sources. To accomplish this, the study employs a comprehensive methodology that includes bibliometric analysis to measure research output and identify publication trends, content analysis to extract thematic insights from the literature, case studies to analyze applied models in practical situations, and survey analysis. The findings emphasize important research directions in the field of spatial air quality modeling, with a particular focus on health impact assessments, which are a recurring theme throughout the studies reviewed. Other essential areas of emphasis include predictions concerning emerging pollutants, assessments of indoor air quality in urban settings, and the effects of natural disasters&#xa0;specifically forest fires and dust storms&#xa0;on air quality. The study emphasizes progress in high-precision modeling, the incorporation of machine learning techniques, and the integration of climate change projections. Despite these advancements, notable gaps remain in the existing literature, especially concerning the scarcity of studies on emerging contaminants such as nanoparticles and persistent organic pollutants, and limited attention given to rural and developing regions. These gaps emphasize the necessity for interdisciplinary models that combine real-time data related to health, environmental concerns, and socioeconomic factors to improve the robustness and applicability of models. This paper emphasizes the significance of sustained access to real-time air quality data in assisting knowledgeable decision-making processes. It accentuates the essential necessity of enhancing awareness regarding the relationships among air quality, public health, and environmental well-being.</p>

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Recent trends in spatial modeling studies of air quality (2010–2023)

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月20日

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  • Hesham Badawy

摘要

The fast growth of urbanization and increases in urban populations have greatly affected air quality, creating a significant challenge for both the environment and public health in urban areas. Consequently, it has become essential to understand and evaluate the levels and impacts of air pollution in these regions for the purpose of sustainable urban development. This study explores recent advancements in spatial air quality research spanning from 2010 to 2023, synthesizing findings from various sources. To accomplish this, the study employs a comprehensive methodology that includes bibliometric analysis to measure research output and identify publication trends, content analysis to extract thematic insights from the literature, case studies to analyze applied models in practical situations, and survey analysis. The findings emphasize important research directions in the field of spatial air quality modeling, with a particular focus on health impact assessments, which are a recurring theme throughout the studies reviewed. Other essential areas of emphasis include predictions concerning emerging pollutants, assessments of indoor air quality in urban settings, and the effects of natural disasters specifically forest fires and dust storms on air quality. The study emphasizes progress in high-precision modeling, the incorporation of machine learning techniques, and the integration of climate change projections. Despite these advancements, notable gaps remain in the existing literature, especially concerning the scarcity of studies on emerging contaminants such as nanoparticles and persistent organic pollutants, and limited attention given to rural and developing regions. These gaps emphasize the necessity for interdisciplinary models that combine real-time data related to health, environmental concerns, and socioeconomic factors to improve the robustness and applicability of models. This paper emphasizes the significance of sustained access to real-time air quality data in assisting knowledgeable decision-making processes. It accentuates the essential necessity of enhancing awareness regarding the relationships among air quality, public health, and environmental well-being.