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. 2024 May 21;1(8):767-779.
doi: 10.1021/acsestair.3c00105. eCollection 2024 Aug 9.

Low-Cost Indoor Sensor Deployment for Predicting PM2.5 Exposure

Shahar Tsameret  1 , Daniel Furuta  1 , Provat Saha  2   3 , Nohhyeon Kwak  1 , Aliaksei Hauryliuk  4 , Xiang Li  5 , Albert A Presto  2 , Jiayu Li  1
Affiliations

Low-Cost Indoor Sensor Deployment for Predicting PM2.5 Exposure

Shahar Tsameret et al. ACS EST Air. .

Abstract

Indoor air quality is critical to human health, as individuals spend an average of 90% of their time indoors. However, indoor particulate matter (PM) sensor networks are not deployed as often as outdoor sensor networks. In this study, indoor PM2.5 exposure is investigated via 2 low-cost sensor networks in Pittsburgh. The concentrations reported by the networks were fed into a Monte Carlo simulation to predict daily PM2.5 exposure for 4 demographics (indoor workers, outdoor workers, schoolchildren, and retirees). Additionally, this study compares the effects of 4 different correction factors on reported concentrations from the PurpleAir sensors, including both empirical and physics-based corrections. The results of the Monte Carlo simulation show that mean PM2.5 exposure varied by 1.5 μg/m3 or less when indoor and outdoor concentrations were similar. When indoor PM concentrations were lower than outdoor, increasing the time spent outdoors on the simulation increased exposure by up to 3 μg/m3. These differences in exposure highlight the importance of carefully selecting sites for sensor deployment and show the value of having a robust low-cost sensor network with both indoor and outdoor sensor placement.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Daily schedules that were programmed into the Monte Carlo simulation. Red text shows an example of what the simulation looks like for the “indoors employee” demographic.
Figure 2
Figure 2
Box plots of the RAMPs (A) and PurpleAir (B) datasets with the hygroscopic, EPA, two-piece, and wildfire correction factors, and the raw data. Indoor (home and workplace) data is shown in blue, outdoor in orange. Outliers not shown. Data is from the entire duration of the study. Box boundaries represent the 1st and 3rd quartiles, with the median indicated by a bar in the center. The whiskers show the rest of the distribution, excluding outliers.
Figure 3
Figure 3
Sample diurnal patterns from sensors located at a: RAMPs workplace (A), outdoor RAMPs location (B), RAMPs home (C), PurpleAir workplace (D), outdoor PurpleAir location (E), and PurpleAir home (F). Diurnal patterns represent averages of selected sites throughout the whole sampling period.
Figure 4
Figure 4
Top: violin plot of Monte Carlo simulation results for the RAMPs dataset (A-D) Bottom: PurpleAir dataset (E-H). The plots show the probability density of exposure concentration of four demographics, with a box plot in the center. Circles indicate the medians, and each box encompasses the interquartile range. (If there was a notable probability of an exposure of 0 μg/m3, there is an appearance of a flat bottom). The black lines represent the average outdoor concentrations.
Figure 5
Figure 5
A-D: histogram of the Monte Carlo simulation results for the RAMPs dataset using the hygroscopic correction factor. E-H: PurpleAir dataset. The blue lines show the simulations’ average exposure and the black lines show the average concentrations of the outdoor sensors. The count depicted on the y axis represents the number of times exposure in a given bin was obtained in the simulation.
See this image and copyright information in PMC

References

    1. Kim K.-H.; Kabir E.; Kabir S. A Review on the Human Health Impact of Airborne Particulate Matter. Environ. Int. 2015, 74, 136–143. 10.1016/j.envint.2014.10.005. - DOI - PubMed
    1. Tian F.; Qi J.; Wang L.; Yin P.; Qian Z.; Ruan Z.; Liu J.; Liu Y.; McMillin S. E.; Wang C.; Lin H.; Zhou M. Differentiating the Effects of Ambient Fine and Coarse Particles on Mortality from Cardiopulmonary Diseases: A Nationwide Multicity Study. Environ. Int. 2020, 145, 10609610.1016/j.envint.2020.106096. - DOI - PubMed
    1. Pope C. A.; Burnett R. T.; Thun M. J.; Calle E. E.; Krewski D.; Ito K.; Thurston G. D. Lung Cancer, Cardiopulmonary Mortality, and Long-Term Exposure to Fine Particulate Air Pollution. JAMA 2002, 287 (9), 1132–1141. 10.1001/jama.287.9.1132. - DOI - PMC - PubMed
    1. Brook R. D.; Rajagopalan S.; Pope C. A.; Brook J. R.; Bhatnagar A.; Diez-Roux A. V.; Holguin F.; Hong Y.; Luepker R. V.; Mittleman M. A.; Peters A.; Siscovick D.; Smith S. C.; Whitsel L.; Kaufman J. D. Particulate Matter Air Pollution and Cardiovascular Disease: An Update to the Scientific Statement from the American Heart Association. Circulation 2010, 121 (21), 2331–2378. 10.1161/CIR.0b013e3181dbece1. - DOI - PubMed
    1. Allen G.; Sioutas C.; Koutrakis P.; Reiss R.; Lurmann F. W.; Roberts P. T. Evaluation of the TEOM ® Method for Measurement of Ambient Particulate Mass in Urban Areas. J. Air Waste Manage Assoc 1997, 47 (6), 682–689. 10.1080/10473289.1997.10463923. - DOI - PubMed

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