Design and testing of a low-cost sensor and sampling platform for indoor air quality

Tryner, Jessica; Phillips, M.F.; Quinn, Casey; Neymark, Gabe; Wilson, Ander; Jathar, Shantanu H.; Carter, Ellison; Volckens, John

doi:10.1016/j.buildenv.2021.108398

Cited by 46 publications

(23 citation statements)

References 38 publications

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“…A previous study has found that the SCD30 could underestimate the CO 2 concentration measurement under reduced pressure (approximately 85 kPa). 1 However, we didn't correct the SCD30 sensor's CO 2 data because the atmospheric pressure in Edmonton was 90 to 96 kPa during the study period (December 2021 to April 2022). 52 The graph of inter-comparison of SCD30 sensors using dry ice can be found in ESI, Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Air pollution is a leading risk factor for human morbidity and mortality. 1 According to the world health organization (WHO), 2 household air pollution exposure contributes 2 million deaths annually. This number surpasses those due to the use of tobacco, AIDS, tuberculosis, and malaria.…”

Section: Introductionmentioning

confidence: 99%

“…14 These devices are portable, affordable, and user-friendly. 1,15 In the last few decades, numerous studies have demonstrated applications and limitations of LCS, such as guidelines for implementing an LCS network, improving the resolution of concentration coverage, air quality monitoring in the indoor environment, and outdoor measurement, e.g., detecting wildre emissions. 7,16,17 LCS needs to be calibrated, and data must be interpreted with care to avoid inaccuracies.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of indoor air quality in university residences using low-cost sensors

Afroz

Guo

Chen

et al. 2023

Environ. Sci.: Atmos.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigation of indoor air quality in university residences using low-cost sensors

Afroz

Guo

Chen

et al. 2023

Environ. Sci.: Atmos.

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“…The development of low-cost sensors (LCS) to measure air pollutants brings unprecedented opportunities to study air quality, as their lower cost and improved portability allow for a higher spatiotemporal resolution when compared to that of conventional research-grade instruments. − Recently, LCS have been used to monitor air quality at varying temporal (hourly, daily, and weekly) and spatial (local and regional) scales, − and there have been active efforts to improve accuracy − and test the reliability of LCS data. ,− …”

Section: Introductionmentioning

confidence: 99%

Investigating the Sources of Urban Air Pollution Using Low-Cost Air Quality Sensors at an Urban Atlanta Site

Yang

Hagan²,

Rivera-Rios

et al. 2022

Environ. Sci. Technol.

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Advances in low-cost sensors (LCS) for monitoring air quality have opened new opportunities to characterize air quality in finer spatial and temporal resolutions. In this study, we deployed LCS that measure both gas (CO, NO, NO 2 , and O 3 ) and particle concentrations and co-located research-grade instruments in Atlanta, GA, to investigate the capability of LCS in resolving air pollutant sources using non-negative matrix factorization (NMF) in a moderately polluted urban area. We provide a comparison of applying the NMF technique to both normalized and nonnormalized data sets. We identify four factors with different temporal trends and properties for both normalized and nonnormalized data sets. Both normalized and non-normalized LCS data sets can resolve primary organic aerosol (POA) factors identified from research-grade instruments. However, applying normalization provides factors with more diverse compositions and can resolve secondary organic aerosol (SOA). Results from this study demonstrate that LCS not only can be used to provide basic mass concentration information but also can be used for in-depth source apportionment studies even in an urban setting with complex pollution mixtures and relatively low aerosol loadings.

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“…The scale of accurate sensor-based monitoring required to achieve this can come at a huge cost and thus inhibit building a dense fine-grained pollution sensing map. The deployment of low-cost particulate matter sensors to replace or augment reference grade pollution air quality monitoring systems has been studied extensively recently, and have addressed issues of calibration 4 – 6 , design 7 , 8 , data selection 9 , and personal exposure quantification 10 , 11 . However, building a highly accurate large scale fine-grained pollution sensing and monitoring map that leverages the size of a pollution network has been largely unexplored.…”

Section: Introductionmentioning

confidence: 99%

Modeling fine-grained spatio-temporal pollution maps with low-cost sensors

et al. 2022

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The use of air quality monitoring networks to inform urban policies is critical especially where urban populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments’ ability to deploy reference grade air quality monitors at scale; for instance, only 33 reference grade monitors are available for the entire territory of Delhi, India, spanning 1500 sq km with 15 million residents. In this paper, we describe a high-precision spatio-temporal prediction model that can be used to derive fine-grained pollution maps. We utilize two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors covering a dense region of Delhi. The model uses a combination of message-passing recurrent neural networks combined with conventional spatio-temporal geostatistics models to achieve high predictive accuracy in the face of high data variability and intermittent data availability from low-cost sensors (due to sensor faults, network, and power issues). Using data from reference grade monitors for validation, our spatio-temporal pollution model can make predictions within 1-hour time-windows at 9.4, 10.5, and 9.6% Mean Absolute Percentage Error (MAPE) over our low-cost monitors, reference grade monitors, and the combined monitoring network respectively. These accurate fine-grained pollution sensing maps provide a way forward to build citizen-driven low-cost monitoring systems that detect hazardous urban air quality at fine-grained granularities.

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Design and testing of a low-cost sensor and sampling platform for indoor air quality

Cited by 46 publications

References 38 publications

Investigation of indoor air quality in university residences using low-cost sensors

Investigation of indoor air quality in university residences using low-cost sensors

Investigating the Sources of Urban Air Pollution Using Low-Cost Air Quality Sensors at an Urban Atlanta Site

Modeling fine-grained spatio-temporal pollution maps with low-cost sensors

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