Classification of Air Quality Inside Car Cabin using Sensor System

Abstract. 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, NO2, and O3 at an urban neighborhood site with pollutant concentration ranges (parts per billion by volume, ppb; 5 min averages, ±1σ): [CO]  =  231 ± 116 ppb (spanning 84–1706 ppb), [NO]  =  6.1 ± 11.5 ppb (spanning 0–209 ppb), [NO2]  =  11.7 ± 8.3 ppb (spanning 0–71 ppb), and [O3]  =  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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Revised Finnish classification of indoor climate 2018

E3S Web of Conferences ◽

10.1051/e3sconf/201911102017 ◽

2019 ◽

Vol 111 ◽

pp. 02017 ◽

Cited By ~ 1

Author(s):

Mervi Ahola ◽

Jorma Säteri ◽

Laura Sariola

Keyword(s):

Air Quality ◽

Indoor Air Quality ◽

Indoor Air ◽

Indoor Environment ◽

Building Materials ◽

Fine Particles ◽

Indoor Climate ◽

Environmental Classification ◽

Target Values

The Finnish Society of Indoor Air Quality and Climate (FiSIAQ) introduced a Classification of Indoor Climate, Construction Cleanliness, and Finishing Materials in 1995. The Classification of Indoor Climate has been revised to meet the new Decree on indoor air quality and ventilation, European standards and experience from users of the classification. The most significant change is that target values for concentration and the in/out ratio of fine particles have been added. Other adjustments have been made to ensure good indoor environment and energy efficiency, but with reasonable investments. The criteria for emissions from building material and furniture were also updated. The Building Information Foundation RTS sr has run the M1-labelling of building products since 1996. The voluntary approach has been proven to improve the IAQ in new buildings and to reduce emissions from building materials. The Classification of Indoor Environment 2018 is integrated part of the new RTS Environmental Classification system.

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