Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA 94720, USA.
School of Public Health, University of California, Berkeley, Berkeley, CA 94720, USA.
Science. 2024 Jul 26;385(6707):380-385. doi: 10.1126/science.adq3678. Epub 2024 Jul 25.
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.
由于复杂的空间、时间和化学过程，城市空气污染存在差异，这些过程深刻影响着人群暴露、人类健康和环境公平。这篇综述强调了两种流行的原位测量方法——移动监测和密集传感器网络——的见解，它们在描述多维城市空气质量系统的动态和影响方面具有明显但互补的优势。移动监测可以在精细的空间尺度上测量多种污染物，从而了解过程和控制策略。传感器网络在许多地点提供时间分辨率方面表现出色。越来越复杂的研究利用这两种方法可以生动地识别影响暴露和差异的时空模式，并提供对有效干预措施的机制见解。这篇综述总结了这些方法的优缺点，并讨论了它们对理解细尺度过程和影响的意义。
