TY - JOUR

T1 - Wireless distributed environmental sensor networks for air pollution measurement-the promise and the current reality

AU - Broday, David M.

AU - Arpaci, Alexander

AU - Bartonova, Alena

AU - Castell-Balaguer, Nuría

AU - Cole-Hunter, Tom

AU - Dauge, Franck R.

AU - Fishbain, Barak

AU - Jones, Rod L.

AU - Galea, Karen

AU - Jovasevic-Stojanovic, Milena

AU - Kocman, David

AU - Martinez-Iñiguez, Tania

AU - Nieuwenhuijsen, Mark

AU - Robinson, Johanna

AU - Svecova, Vlasta

AU - Thai, Phong

AU - Citi-Sense Project Collaborators

N1 - Funding Information: Acknowledgments: The research was done at the Technion Center of Excellence in Exposure Science and Environmental Health (TCEEH) and supported by Citi-Sense—a FP7 European Commission grant agreement no. 308524 and by the Leona H. and Harry B. Helmsley Charitable Trust grant no. 2015PG-ISL006. Publisher Copyright: © 2017 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2017/10

Y1 - 2017/10

N2 - The evaluation of the effects of air pollution on public health and human-wellbeing requires reliable data. Standard air quality monitoring stations provide accurate measurements of airborne pollutant levels, but, due to their sparse distribution, they cannot capture accurately the spatial variability of air pollutant concentrations within cities. Dedicated in-depth field campaigns have dense spatial coverage of the measurements but are held for relatively short time periods. Hence, their representativeness is limited. Moreover, the oftentimes integrated measurements represent time-averaged records. Recent advances in communication and sensor technologies enable the deployment of dense grids ofWireless Distributed Environmental Sensor Networks for air quality monitoring, yet their capability to capture urban-scale spatiotemporal pollutant patterns has not been thoroughly examined to date. Here, we summarize our studies on the practicalities of using data streams from sensor nodes for air quality measurement and the required methods to tune the results to different stakeholders and applications. We summarize the results from eight cities across Europe, five sensor technologies-three stationary (with one tested also while moving) and two personal sensor platforms, and eight ambient pollutants. Overall, few sensors showed an exceptional and consistent performance, which can shed light on the fine spatiotemporal urban variability of pollutant concentrations. Stationary sensor nodes were more reliable than personal nodes. In general, the sensor measurements tend to suffer from the interference of various environmental factors and require frequent calibrations. This calls for the development of suitable field calibration procedures, and several such in situ field calibrations are presented.

AB - The evaluation of the effects of air pollution on public health and human-wellbeing requires reliable data. Standard air quality monitoring stations provide accurate measurements of airborne pollutant levels, but, due to their sparse distribution, they cannot capture accurately the spatial variability of air pollutant concentrations within cities. Dedicated in-depth field campaigns have dense spatial coverage of the measurements but are held for relatively short time periods. Hence, their representativeness is limited. Moreover, the oftentimes integrated measurements represent time-averaged records. Recent advances in communication and sensor technologies enable the deployment of dense grids ofWireless Distributed Environmental Sensor Networks for air quality monitoring, yet their capability to capture urban-scale spatiotemporal pollutant patterns has not been thoroughly examined to date. Here, we summarize our studies on the practicalities of using data streams from sensor nodes for air quality measurement and the required methods to tune the results to different stakeholders and applications. We summarize the results from eight cities across Europe, five sensor technologies-three stationary (with one tested also while moving) and two personal sensor platforms, and eight ambient pollutants. Overall, few sensors showed an exceptional and consistent performance, which can shed light on the fine spatiotemporal urban variability of pollutant concentrations. Stationary sensor nodes were more reliable than personal nodes. In general, the sensor measurements tend to suffer from the interference of various environmental factors and require frequent calibrations. This calls for the development of suitable field calibration procedures, and several such in situ field calibrations are presented.

KW - Air pollution

KW - In situ field calibration

KW - Micro sensing units

KW - Multi-sensor nodes

KW - Spatiotemporal variability

KW - Wireless distributed environmental sensor networks

U2 - 10.3390/s17102263

DO - 10.3390/s17102263

M3 - Journal article

C2 - 28974042

AN - SCOPUS:85030686460

VL - 17

JO - Sensors

JF - Sensors

SN - 1424-3210

IS - 10

M1 - 2263

ER -