Variation in urban air pollution arises because of complex spatial, temporal, and chemical processes, which profoundly affect population exposure, human health, and environmental justice. This Review highlights insights from two popular in situ measurement methods—mobile monitoring and dense sensor networks—that have distinct but complementary strengths in characterizing the dynamics and impacts of the multidimensional urban air quality system. Mobile monitoring can measure many pollutants at fine spatial scales, thereby informing about processes and control strategies. Sensor networks excel at providing temporal resolution at many locations. Increasingly sophisticated studies leveraging both methods can vividly identify spatial and temporal patterns that affect exposures and disparities and offer mechanistic insight toward effective interventions. This Review summarizes the strengths and limitations of these methods and discusses their implications for understanding fine-scale processes and impacts.

中文翻译：

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高分辨率城市空气污染测绘


城市空气污染的变化是由于复杂的空间、时间和化学过程而产生的，这深刻影响着人口暴露、人类健康和环境正义。本综述重点介绍了两种流行的现场测量方法（移动监测和密集传感器网络）的见解，这两种方法在表征多维城市空气质量系统的动态和影响方面具有独特但互补的优势。移动监测可以在精细的空间尺度上测量许多污染物，从而了解过程和控制策略。传感器网络擅长在许多位置提供时间分辨率。利用这两种方法的日益复杂的研究可以生动地识别影响暴露和差异的空间和时间模式，并为有效干预提供机制见解。本综述总结了这些方法的优点和局限性，并讨论了它们对理解精细过程和影响的影响。