Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements
The environments in which we live, work, and play are subject to enormous variability in air pollutant concentrations. To adequately characterize air quality (AQ), measurements must be fast (real time), scalable, and reliable (with known accuracy, precision, and stability over time). Lower-cost...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-09-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://www.atmos-meas-tech.net/10/3575/2017/amt-10-3575-2017.pdf |
| Summary: | The environments in which we live, work, and play are subject to
enormous variability in air pollutant concentrations. To adequately
characterize air quality (AQ), measurements must be fast (real time), scalable,
and reliable (with known accuracy, precision, and stability over time).
Lower-cost air-quality-sensor technologies offer new opportunities for
fast and distributed measurements, but a persistent characterization gap
remains when it comes to evaluating sensor performance under realistic
environmental sampling conditions. This limits our ability to inform the
public about pollution sources and inspire policy makers to address
environmental justice issues related to air quality. In this paper, initial
results obtained with a recently developed lower-cost air-quality-sensor
system are reported. In this project, data were acquired with the ARISense
integrated sensor package over a 4.5-month time interval during which the
sensor system was co-located with a state-operated (Massachusetts, USA) air
quality monitoring station equipped with reference instrumentation measuring
the same pollutant species. This paper focuses on validating electrochemical
(EC) sensor measurements of CO, NO, NO<sub>2</sub>, and O<sub>3</sub> at an urban neighborhood
site with pollutant concentration ranges (parts per billion by volume, ppb; 5 min averages, ±1<i>σ</i>):
[CO] = 231 ± 116 ppb (spanning 84–1706 ppb),
[NO] = 6.1 ± 11.5 ppb (spanning 0–209 ppb),
[NO<sub>2</sub>] = 11.7 ± 8.3 ppb (spanning 0–71 ppb), and
[O<sub>3</sub>] = 23.2 ± 12.5 ppb (spanning 0–99 ppb). Through
the use of high-dimensional model representation (HDMR), we show that
interference effects derived from the variable ambient gas concentration mix
and changing environmental conditions over three seasons (sensor flow-cell
temperature = 23.4 ± 8.5 °C,
spanning 4.1 to
45.2 °C; and
relative humidity = 50.1 ± 15.3 %, spanning
9.8–79.9 %) can be effectively modeled for the Alphasense CO-B4,
NO-B4, NO2-B43F, and Ox-B421 sensors, yielding (5 min average) root mean
square errors (RMSE) of 39.2, 4.52, 4.56, and 9.71 ppb, respectively. Our
results substantiate the potential for distributed air pollution measurements
that could be enabled with these sensors. |
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| ISSN: | 1867-1381 1867-8548 |