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采用公民科学方法测试用于臭氧污染监测的传感器性能。

Testing the performance of sensors for ozone pollution monitoring in a citizen science approach.

机构信息

Institute of Environmental Assessment and Water Research (IDAEA-CSIC), 08034 Barcelona, Spain.

Institute of Environmental Assessment and Water Research (IDAEA-CSIC), 08034 Barcelona, Spain.

出版信息

Sci Total Environ. 2019 Feb 15;651(Pt 1):1166-1179. doi: 10.1016/j.scitotenv.2018.09.257. Epub 2018 Sep 22.

DOI:10.1016/j.scitotenv.2018.09.257
PMID:30360248
Abstract

Tropospheric ozone (O) is an environmental pollutant of growing concern, especially in suburban and rural areas where the density of air quality monitoring stations is not high. In this type of areas citizen science strategies can be useful tools for awareness raising, but sensor technologies must be validated before sensor data are communicated to the public. In this work, the performance under field conditions of two custom-made types of ozone sensing devices, based on metal-oxide and electrochemical sensors, was tested. A large array of 132 metal-oxide (Sensortech MICS 2614) and 11 electrochemical (Alphasense) ozone sensors, built into 44 sensing devices, was co-located at reference stations in Italy (4 stations) and Spain (5). Mean R between sensor and reference data was 0.88 (0.78-0.96) and 0.89 (0.73-0.96) for Captor (metal-oxide) and Raptor (electrochemical) nodes. The metal-oxide sensors showed an upper limit (approximately 170 μg/m) implying that these sensors may be useful to communicate mean ozone concentrations but not peak episodes. The uncertainty of the nodes was 10% between 100 and 150 μg/m and 20% between 150 and 200 μg/m, for Captors, and 10% for >100 μg/m for Raptors. Operating both types of nodes up to 5 months did not evidence any clear influence of drifts. The use of these sensors in citizen science can be a useful tool for awareness raising. However, significant data processing efforts are required to ensure high data quality, and thus machine learning strategies are advisable. Relative uncertainties should always be reported when communicating ozone concentration data from sensing nodes.

摘要

对流层臭氧 (O) 是一种日益受到关注的环境污染物,特别是在空气质量监测站密度不高的郊区和农村地区。在这种类型的地区,公民科学策略可以是提高认识的有用工具,但在将传感器数据传达给公众之前,必须对传感器技术进行验证。在这项工作中,测试了两种基于金属氧化物和电化学传感器的定制型臭氧传感装置在野外条件下的性能。在意大利(4 个站)和西班牙(5 个站)的参考站共放置了 132 个金属氧化物(Sensortech MICS 2614)和 11 个电化学(Alphasense)臭氧传感器组成的大型传感器阵列,共 111 个传感器。传感器和参考数据之间的平均 R 值为 0.88(0.78-0.96)和 0.89(0.73-0.96),用于 Captor(金属氧化物)和 Raptor(电化学)节点。金属氧化物传感器显示出一个上限(约 170μg/m),这意味着这些传感器可能可用于传达平均臭氧浓度,但不能传达峰值事件。节点的不确定性在 100 到 150μg/m 之间为 10%,在 150 到 200μg/m 之间为 20%,对于 Captors 为 10%,对于 Raptors 为 >100μg/m。将这两种类型的节点运行长达 5 个月,没有明显证据表明存在漂移的任何明显影响。在公民科学中使用这些传感器可以是提高认识的有用工具。然而,需要进行大量的数据处理工作以确保高质量的数据,因此建议采用机器学习策略。在传达来自传感器节点的臭氧浓度数据时,应始终报告相对不确定性。

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