The BErkeley Atmospheric CO<sub>2</sub> Observation Network: field calibration and evaluation of low-cost air quality sensors
-
Published:2018-04-06
Issue:4
Volume:11
Page:1937-1946
-
ISSN:1867-8548
-
Container-title:Atmospheric Measurement Techniques
-
language:en
-
Short-container-title:Atmos. Meas. Tech.
Author:
Kim Jinsol, Shusterman Alexis A.ORCID, Lieschke Kaitlyn J., Newman Catherine, Cohen Ronald C.ORCID
Abstract
Abstract. The newest generation of air quality sensors is small,
low cost, and easy to deploy. These sensors are an attractive option for
developing dense observation networks in support of regulatory activities and
scientific research. They are also of interest for use by individuals to
characterize their home environment and for citizen science. However, these
sensors are difficult to interpret. Although some have an approximately
linear response to the target analyte, that response may vary with time,
temperature, and/or humidity, and the cross-sensitivity to non-target
analytes can be large enough to be confounding. Standard approaches to
calibration that are sufficient to account for these variations require a
quantity of equipment and labor that negates the attractiveness of the
sensors' low cost. Here we describe a novel calibration strategy for a set of
sensors, including CO, NO, NO2, and O3, that makes use of (1) multiple
co-located sensors, (2) a priori knowledge about the chemistry of NO, NO2,
and O3, (3) an estimate of mean emission factors for CO, and (4) the global background of CO. The strategy requires one or more well calibrated
anchor points within the network domain, but it does not require direct
calibration of any of the individual low-cost sensors. The procedure
nonetheless accounts for temperature and drift, in both the sensitivity and
zero offset. We demonstrate this calibration on a subset of the sensors
comprising BEACO2N, a distributed network of approximately 50 sensor
“nodes”, each measuring CO2, CO, NO, NO2, O3 and particulate
matter at 10 s time resolution and approximately 2 km spacing within the
San Francisco Bay Area.
Funder
Health Effects Institute
Publisher
Copernicus GmbH
Subject
Atmospheric Science
Reference30 articles.
1. Bart, M., Williams, D. E., Ainslie, B., Mckendry, I., Salmond, J., Grange, S. K., Alavi-Shoshtari, M., Steyn, D., and Henshaw, G. S.: High Density Ozone Monitoring
Using Gas Sensitive Semi-Conductor Sensors in the Lower Fraser Valley,
British Columbia, Environ. Sci. Technol., 48, 3970–3977, 2014. 2. Borrego, C., Costa, A. M., Ginja, J., Amorim, M., Coutinho, M., Karatzas, K., Sioumis, T., Katsifarakis, N., Konstantinidis, K., De Vito, S.,
Esposito, E., Smith, P., Andre, N., Gerard, P., Francis, L. A., Castell, N., Schneider, P., Viana, M., Minguillon, M. C., Reimringer, W.,
Otjes, R. P., Von Sicard, O., Pohle, R., Elen, B., Suriano, D., Pfister, V., Prato, M., Dipinto, S., and Penza, M.: Assessment of air quality microsensors
versus reference methods?: The EuNetAir joint exercise, Atmos. Environ., 147,
246–263,
https://doi.org/10.1016/j.atmosenv.2016.09.050, 2016. 3. Cox, R. M.: The use of passive sampling to monitor forest exposure to
O3, NO2 and SO2: a review and some case studies, Environ. Pollut., 126,
301–311,
https://doi.org/10.1016/S0269-7491(03)00243-4, 2003. 4. Cross, E. S., Williams, L. R., Lewis, D. K., Magoon, G. R., Onasch, T. B., Kaminsky, M. L., Worsnop, D. R., and Jayne, J. T.:
Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements,
Atmos. Meas. Tech., 10, 3575–3588, https://doi.org/10.5194/amt-10-3575-2017, 2017. 5. Dallmann, T. R., Harley, R. A., and Kirchstetter, T. W.: Effects of
Diesel Particle Filter Retrofits and Accelerated Fleet Turnover on Drayage
Truck Emissions at the Port of Oakland, Environ. Sci. Technol., 10773–10779, 2011.
Cited by
63 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|