Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments
Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments
复制标题
改进东亚 OMI 对流层 NO2 反演气溶胶校正：来自 CALIOP 气溶胶垂直剖面的约束
DOI：
10.5194/amt-12-1-2019
复制
发表时间：
2019-08-30
影响因子：
3.8
通讯作者：
Jones, Roderic L.
中科院分区：
文献类型：
--
作者：
Chatzidiakou, Lia;Krause, Anika;Jones, Roderic L.
对个人暴露在空气污染中的不准确量化在健康评估中引入了错误和偏见，严重限制了世界各地流行病学研究中的因果推断。负担得起的小型化空气污染传感器技术的快速进步提供了解决这一限制的可能性，方法是以前所未有的空间和时间分辨率在大规模研究中捕捉日常生活中个人暴露的高度可变性。然而，对于新型传感技术是否适用于科学和政策目的，人们仍然感到关切。在这篇文章中，我们描述了便携式个人空气质量监测仪(PAM)的性能，它集成了多个微型传感器，用于测量氮氧化物(NOx)、一氧化碳(CO)、臭氧(O-3)和颗粒物(PM)，以及温度、相对湿度、加速度、噪声和GPS传感器。总体而言，空气污染传感器在不同季节和不同地理环境的室外、室内和通勤微环境中表现出很高的重复性(Mean(R)over bar(2)=0.93，min-max：0.80-1.00)，并与标准仪器(Mean(R)over bar(2)=0.82，min-max：0.54-0.99)非常一致。这项研究的一个重要结果是，PAM的误差明显小于基于稀疏分布的室外固定监测站估计个人暴露时引入的误差。因此，像这里展示的这样的新型传感技术可以通过在大范围内提供高分辨率的可靠暴露指标来调查空气污染对健康影响的潜在机制，从而使健康研究发生革命性变化。
The inaccurate quantification of personal exposure to air pollution introduces error and bias in health estimations, severely limiting causal inference in epidemiological research worldwide. Rapid advancements in affordable, miniaturised air pollution sensor technologies offer the potential to address this limitation by capturing the high variability of personal exposure during daily life in large-scale studies with unprecedented spatial and temporal resolution. However, concerns remain regarding the suitability of novel sensing technologies for scientific and policy purposes. In this paper we characterise the performance of a portable personal air quality monitor (PAM) that integrates multiple miniaturised sensors for nitrogen oxides (NOx), carbon monoxide (CO), ozone (O-3) and particulate matter (PM) measurements along with temperature, relative humidity, acceleration, noise and GPS sensors. Overall, the air pollution sensors showed high reproducibility (mean (R) over bar (2) = 0.93, min-max: 0.80-1.00) and excellent agreement with standard instrumentation (mean (R) over bar (2) = 0.82, min-max: 0.54-0.99) in outdoor, indoor and commuting microenvironments across seasons and different geographical settings. An important outcome of this study is that the error of the PAM is significantly smaller than the error introduced when estimating personal exposure based on sparsely distributed outdoor fixed monitoring stations. Hence, novel sensing technologies such as the ones demonstrated here can revolutionise health studies by providing highly resolved reliable exposure metrics at a large scale to investigate the underlying mechanisms of the effects of air pollution on health.