Black carbon (BC) emissions in urban areas are primarily driven by traffic, with hotspots near major roads disproportionately affecting marginalized communities. Because BC monitoring is typically performed using costly and specialized instruments. there is little to no available data on BC from local traffic sources that could help inform policy interventions targeting local factors. By contrast, traffic monitoring systems are widely deployed in cities around the world, highlighting the imbalance between what we know about traffic conditions and what do not know about their environmental consequences. To bridge this gap, we propose a machine learning-driven system that extracts visual information from traffic video to capture vehicles behaviors and conditions. Combining these features with weather data, our model estimates BC at street level, achieving an R-squared value of 0.72 and RMSE of 129.42 ng/m3 (nanogram per cubic meter). From a sustainability perspective, this work leverages resources already supported by urban infrastructure and established modeling techniques to generate information relevant to traffic emission. Obtaining BC concentration data provides actionable insights to support pollution reduction, urban planning, public health, and environmental justice at the local municipal level.

翻译：城市区域的黑碳（BC）排放主要由交通驱动，主要道路附近的热点区域对边缘化社区造成不成比例的影响。由于黑碳监测通常依赖昂贵且专业的仪器，关于本地交通源黑碳的可用数据极少甚至没有，这限制了针对本地因素的政策干预的制定。相比之下，交通监控系统在全球城市中广泛部署，突显了我们对交通状况的了解与对其环境后果的无知之间的不平衡。为弥合这一差距，我们提出一种基于机器学习的系统，从交通视频中提取视觉信息以捕捉车辆行为与状态。将这些特征与气象数据结合，我们的模型实现了街道级别的黑碳浓度估算，R平方值达到0.72，均方根误差为129.42纳克/立方米。从可持续性视角看，本研究利用城市基础设施已支持的资源和成熟的建模技术，生成与交通排放相关的信息。获取黑碳浓度数据为地方市政层面的污染减排、城市规划、公共卫生和环境公正提供了可操作的见解。