Spatiotemporal Estimation of Black Carbon Concentration in Tehran Using Aerosol Optical Depth Remote Sensing Data and Meteorological Parameters: Health Risk Assessment and Relationship with Green Spaces

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society Pub Date : 2024-11-14 DOI:10.1016/j.scs.2024.105986

Samira Norzaee , Majid Kermani , Arsalan Ghorbanian , Ahmad Jonidi jafari , Masud Yunesian , Abbas Shahsavani , Mahdi Farzadkia , Roshanak Rezaei Kalantary

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引用次数: 0

Abstract

Black Carbon (BC) is an atmospheric pollutant with considerable adverse effects on human health, increasing the chance of cardiovascular disorders, respiratory issues, and cancers in exposed individuals. Accordingly, studying BC in urban areas is essential for understanding its associated health risks. In this study, the Multi-Angle Implementation of Atmospheric Correction (MAIAC) retrieved Aerosol Optical Depth (AOD) remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS), along with in situ observations of BC concentration and meteorological parameters were utilized to estimate BC concentration in Tehran. In this regard, an ensemble machine learning algorithm, Gradient Boosting Machine (GBM), was employed to estimate BC concentration from 2010 to 2021, enabling a spatiotemporal analysis of BC levels in Tehran. Subsequently, the carcinogenic and non-carcinogenic effects of BC on children and adults were examined, as well as its relationship to urban green spaces. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Adjusted R-squared (R²_adj), values for BC estimation ranged from 0.80 to 1.59 μg/m³, 0.59 to 1.10 μg/m³, and 0.70 to 0.94, respectively, indicating the promising performance of the GBM algorithm. The estimated annual average BC concentration over 11 years was 6.18± 2.46 μg/m³. Spatial variations in BC concentration and hotspot analysis at 99% and 95% confidence levels, showed that hotspots were primarily concentrated in the central and southern parts of Tehran. In contrast, cold spots were more scattered across the western and northeastern parts of the city. The cancer risk (CR) from BC exposure exceeded the recommended risk levels (1 × 10⁻⁶ to 1 × 10⁻⁴) established by the US Environmental Protection Agency (US EPA), demonstrating severe health risks for people in Tehran exposed to the current levels of BC concentrations. The average Hazard Quotient (HQ) value across all areas of Tehran was below the threshold value of 1, indicating that the non-carcinogenic health risk remains within acceptable limits. Results regarding green spaces indicated that greenery significantly influences BC concentration, revealing a negative correlation between green space coverage and BC concentration.

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利用气溶胶光学深度遥感数据和气象参数估算德黑兰的黑碳时空浓度：健康风险评估及与绿地的关系

黑碳（BC）是一种大气污染物，对人类健康有相当大的不利影响，会增加接触者患心血管疾病、呼吸系统问题和癌症的几率。因此，研究城市地区的 BC 对于了解其相关的健康风险至关重要。在这项研究中，利用中分辨率成像分光仪（MODIS）的多角度大气校正（MAIAC）气溶胶光学深度（AOD）遥感数据，以及对 BC 浓度和气象参数的现场观测，来估算德黑兰的 BC 浓度。在这方面，采用了一种集合机器学习算法--梯度提升机（GBM）来估算 2010 年至 2021 年的 BC 浓度，从而能够对德黑兰的 BC 水平进行时空分析。随后，研究了 BC 对儿童和成人的致癌和非致癌影响，以及 BC 与城市绿地的关系。BC 估算的均方根误差 (RMSE)、平均绝对误差 (MAE) 和调整 R 平方 (R2adj) 值分别为 0.80 至 1.59 μg/m3、0.59 至 1.10 μg/m3、0.70 至 0.94，表明 GBM 算法性能良好。在 11 年的时间里，估计的 BC 年平均浓度为 6.18± 2.46 μg/m3。BC 浓度的空间变化以及 99% 和 95% 置信度的热点分析表明，热点主要集中在德黑兰的中部和南部。相比之下，冷点则更多地分布在城市的西部和东北部。暴露于 BC 的癌症风险 (CR) 超过了美国环境保护局（US EPA）规定的建议风险水平（1 × 10-⁶ 至 1 × 10-⁴），这表明暴露于当前 BC 浓度水平的德黑兰人面临着严重的健康风险。德黑兰所有地区的平均危险商数 (HQ) 值均低于阈值 1，表明非致癌健康风险仍在可接受范围内。有关绿地的结果表明，绿化对 BC 浓度有显著影响，绿地覆盖率与 BC 浓度之间呈负相关。

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来源期刊

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;