Urban form and seasonal PM2.5 dynamics: Enhancing air quality prediction using interpretable machine learning and IoT sensor data

IF 10.5 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Sustainable Cities and Society Pub Date : 2024-11-10 DOI:10.1016/j.scs.2024.105976
Jeongwoo Lee , Caryl Anne M. Barquilla , Kitae Park , Andy Hong
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Abstract

This study investigates the critical issue of how urban form characteristics influence PM2.5 concentrations, a key concern for public health in densely populated cities. Traditional monitoring methods have faced data gaps and methodological limitations. To address this, we employed interpretable machine learning (ML) models with data from 1,069 Internet-of-Things (IoT) sensors across Seoul, South Korea (September 2020–August 2023). Over 80 urban form variables—including density, transportation, road design, building morphology, and land use—were analyzed using Recursive Feature Elimination to identify key factors affecting PM2.5 concentrations within three buffer zones (300-m, 500-m, 1-km). The random forest model demonstrated the highest accuracy, with an R² of 95 % for autumn and 96 % for spring. Our findings show higher PM2.5 levels in colder months, driven by road width and building density in autumn and traffic and industrial activity in winter. In summer, green spaces and meteorological conditions were primary factors, while spring air quality was notably impacted by localized traffic emissions around highways and bus stops. This study offers robust predictions and actionable insights for urban planning and air quality management. Future research could integrate additional environmental variables and expand sensor coverage to further refine predictive models.
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城市形态与 PM2.5 的季节性动态:利用可解释的机器学习和物联网传感器数据加强空气质量预测
这项研究探讨了城市形态特征如何影响 PM2.5 浓度这一关键问题,PM2.5 浓度是人口稠密城市公共卫生的一个主要问题。传统的监测方法面临数据缺口和方法限制。为解决这一问题,我们利用韩国首尔(2020 年 9 月至 2023 年 8 月)1069 个物联网(IoT)传感器的数据,建立了可解释的机器学习(ML)模型。使用递归特征消除法分析了 80 多个城市形态变量,包括密度、交通、道路设计、建筑形态和土地利用,以确定影响三个缓冲区(300 米、500 米和 1 公里)内 PM2.5 浓度的关键因素。随机森林模型的准确度最高,秋季和春季的 R² 分别为 95% 和 96%。我们的研究结果表明,秋季道路宽度和建筑密度以及冬季交通和工业活动导致寒冷月份的 PM2.5 水平较高。在夏季,绿地和气象条件是主要因素,而春季空气质量则明显受到高速公路和公交车站周围局部交通排放物的影响。这项研究为城市规划和空气质量管理提供了可靠的预测和可行的见解。未来的研究可以整合更多的环境变量,扩大传感器的覆盖范围,以进一步完善预测模型。
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来源期刊
Sustainable Cities and Society
Sustainable Cities and Society Social Sciences-Geography, Planning and Development
CiteScore
22.00
自引率
13.70%
发文量
810
审稿时长
27 days
期刊介绍: Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including: 1. Smart cities and resilient environments; 2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management; 3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management); 4. Energy efficient, low/zero carbon, and green buildings/communities; 5. Climate change mitigation and adaptation in urban environments; 6. Green infrastructure and BMPs; 7. Environmental Footprint accounting and management; 8. Urban agriculture and forestry; 9. ICT, smart grid and intelligent infrastructure; 10. Urban design/planning, regulations, legislation, certification, economics, and policy; 11. Social aspects, impacts and resiliency of cities; 12. Behavior monitoring, analysis and change within urban communities; 13. Health monitoring and improvement; 14. Nexus issues related to sustainable cities and societies; 15. Smart city governance; 16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society; 17. Big data, machine learning, and artificial intelligence applications and case studies; 18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems. 19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management; 20. Waste reduction and recycling; 21. Wastewater collection, treatment and recycling; 22. Smart, clean and healthy transportation systems and infrastructure;
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