预测公路空气污染 合街景图像和机器学习:对最佳战略的定量分析
Hui Zhong1,2, Di Chen3, Pengqin Wang4
1Intelligent Transportation Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511455, China.
Environmental science & technology
|January 29, 2025
概括
将移动监测数据与街景图像 (SVIs) 集成,可以改善空气污染预测. 最佳策略涉及多个角度和100米的缓冲区,最大限度地减少错误,以获得更好的环境模型.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 数据科学数据科学数据科学
背景情况:
- 使用移动监测和街景图像 (SVIs) 预测当地空气污染显示出潜在的潜力.
- 现有的方法缺乏可靠的引用来量化算法,采样和图像质量的错误.
研究的目的:
- 使用SVI量化算法,采样策略和图像质量对空气污染预测的影响.
- 为准确的空气质量建模,确定集成移动监测数据和SVI的最佳策略.
主要方法:
- 部署了314辆出租车来实时监测NO,NO2,PM2.5和PM10.
- 从多个角度和缓冲半径 (100-500米) 的约382,000个SVI中提取了特征.
- 将机器学习算法 (随机森林,XGBoost,神经网络) 与土地利用回归 (LUR) 模型进行比较.
主要成果:
- 机器学习方法通常优于线性LUR模型.
- 平均化策略有效地减少了因功能捕获不足而导致的偏差.
- 最佳采样需要在100米的缓冲区内整合多个视角,产生误差<2.5μg/m3或ppb.
- 图像质量问题 (过度/不足曝光,模糊) 导致错误识别特征.
结论:
- 该研究为优化基于SVI的空气质量模型提供了宝贵的见解.
- 这些发现支持开发更准确,更可靠的环境监测系统.
- 最好的做法包括使用多个视角和适当的缓冲区大小来进行特征提取.
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