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Updated: May 3, 2026

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使用移动监控和机器学习,高分辨率的街道空气污染的时空估计

Qi Long1, Jun Ma1, Cui Guo1

  • 1Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China.

Journal of environmental management
|February 22, 2025
PubMed
概括

这项研究引入了一种新的机器学习框架,以高空间和时间分辨率准确估计街道水平空气污染 (SLAP). 该方法有效地填补了数据缺口,为城市空气质量管理提供了关键的见解.

关键词:
空气质量 空气质量低排放区是一个低排放区.移动监控 移动监控 移动监控多任务机器学习填补时空空间差距的方法在街道层面的空气污染

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Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
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