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Using Indoor Positioning and Mobile Sensing for Spatial Exposure and Environmental Characterizations: Pilot Demonstration of PM2.5 Mapping Kai-Chung Cheng, Ching-Hao Tseng, and Lynn M. Hildemann Environ. Sci. Technol. Lett., Just Accepted Manuscript • DOI: 10.1021/acs.estlett.8b00694 • Publication Date (Web): 04 Jan 2019 Downloaded from http://pubs.acs.org on January 5, 2019

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Environmental Science & Technology Letters

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Using Indoor Positioning and Mobile Sensing for Spatial Exposure and Environmental

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Characterizations: Pilot Demonstration of PM2.5 Mapping

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Kai-Chung Cheng,1* Ching-Hao Tseng, Lynn M. Hildemann1

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1Civil

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*corresponding author: [email protected]

& Environmental Engineering Dept., Stanford University, Stanford, CA 94305

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ABSTRACT

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New indoor positioning technology makes it possible to use air measuring devices carried by

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occupants to characterize spatiotemporal patterns of exposure and air quality inside buildings.

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This pilot study investigated the potential of a mobile monitoring method to map highly variable

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PM2.5 distributions inside an occupied one-bedroom apartment, coupling a new ultrasound

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localization method with a pair of collocated research-grade and low-cost sensors (SidePak and

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PTQS). Measuring the position of the mobile sensing node every 1 s, down to the centimeter

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scale, this method generated detailed occupant moving trajectories and location histories on a

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floorplan map, allowing identification of microenvironments causing transient peak exposures.

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Utilizing a random walking approach throughout the apartment, it identified smoke intrusion

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sources and captured the spatial distributions of PM2.5 using interpolated 2-D concentration

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fields. The correlations between SidePak and PTQS were evaluated when used as portable

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devices for (i) occupant exposure and (ii) indoor environmental mapping applications. This new

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mobile sensing method is most effective with a rapidly responding air monitor (e.g., SidePak

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photometer).

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INTRODUCTION

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Indoor air pollution levels close to sources such as cooking and smoking are substantially

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higher than farther away – this “proximity effect” leads to high spatial variance and sizable

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differences between personal and stationary measurements (e.g., 1-5).

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Stationary research-grade sensors have evaluated spatial distributions of fine particulate

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matter (PM2.5) inside two residences6 and a hospital5 with a smoker, and for a smoke source in

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an office and a garage.7 Low-cost sensor arrays have measured PM in two households using

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solid fuels,8 a shop with wood dust emissions,9 and a vehicle manufacturing facility.10 Using

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sufficient numbers of sensors and suitable interpolation methods (e.g., kriging), concentration

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fields can be determined with high spatial resolution. 1 ACS Paragon Plus Environment


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The stationary mapping method typically involves deploying sensors near expected source

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locations. However, for buildings with unknown emissions (e.g., smoke intrusion), sensor array

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placement becomes less straightforward.

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With recent advances in indoor positioning technologies, portable monitors potentially can be

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used for indoor spatial mapping. Occupants carrying monitors could serve as mobile sensing

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nodes, allowing unknown sources inside buildings to be pinpointed.

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Using a new indoor positioning technique involving ultrasound, this study measured locations

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of an occupant-worn SidePak to ±2 cm resolution. This mobile monitoring method was

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investigated for (i) tracking occupant time-location patterns and PM2.5 exposures and (ii)

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mapping PM2.5 distributions for smoke intrusion.

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By integrating this indoor positioning with wireless PM2.5 sensing, we evaluated how well a

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low-cost sensor carried by the occupant can capture, in the field, the time-varying location of

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exposure and spatial distribution for PM2.5, compared with a research-grade monitor (SidePak).

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MATERIALS AND METHODS

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Equipment. A pair of collocated AM510 SidePak (TSI Inc., Shoreview, MN, USA) and

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PTQS1005 (Plantower Co., Ltd., Beijing, China) monitors sampled PM2.5 in real time (Figure

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1(a)), with short response times (~0.8) slightly lower than previously reported values (>~0.9)13 comparing 3 low-cost sensors

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vs. SidePak for incense burning under well-mixed conditions. Besides differences in sensors and

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sources, our study involved more rapid decays (ventilated vs. well-sealed chamber) and shorter

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averaging times (1 versus 30 s). The ratio varied greatly between sources (Table S2), consistent

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with previous studies.17-19 These ratios were used to rescale PTQS measurements for each source

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emission and subsequent decay period. For concentrations comparable to background levels,

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daily pre-emission scaling factors (1.14-1.28) were used.

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Exposure Characterization. Figure 2(a) shows 5 clusters of spikes from the collocated

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mobile SidePak and PTQS (rescaled). While the SidePak had many maxed-out readings

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(replaced with 20 mg/m3 in the plot), the PTQS (maximum limit ~5 mg/m3) had none. This

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reflects the SidePak’s more rapid response to concentration changes. The ratios of SidePak to

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PTQS (unrescaled) time-averaged concentrations over each emission period were systematically

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higher than scaling factors involving gradually-varying concentrations (Table S2). Overall time-

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averaged exposure measured by PTQS was 32% (rescaled) and 52% (unrescaled) lower than

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SidePak.

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To identify the spatial regions (microenvironments) for these 5 clusters, PM2.5 time series

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(Figure 2(a)) were connected with occupant location data measured by ultrasound beacons.

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Figure 2(b) and 2(c) show occupant locations (second by second) as circles on the floorplan, with

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PM2.5 levels (from SidePak and PTQS, respectively) on a color scale. The plots visualize the

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patterns for occupant movement around the furniture and between rooms, along with the

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corresponding exposures.

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Coupling SidePak with positioning measurements (Figure 2(b)), specific indoor locations

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(kitchen stove, living room desk, and chair near main entrance) were identified for the

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concentration spikes (Figure 2(a)). The peak for the spraying activity (chair near main entrance,

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Figure 2(b)) is less visible due to its much shorter emission period (~5 s versus 10-43 min for

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cooking/vaping). Higher concentrations along trajectories between the stove and living room

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table were measured during the decay period after cooking brunch (1st cluster in Figure 2(a)). 5 ACS Paragon Plus Environment


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Despite PTQS shortcomings in capturing concentration variations, it identified the

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microenvironments with elevated exposures, except for spraying at the main entrance chair

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(Figure 2(c)). This is not surprising – for emission period (~5 s) ≤ sensor response time (≤10 s),

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detection will be unlikely.20

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Rescaling PTQS increased its correlation with SidePak PM2.5 along the moving trajectories

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(R2 = 0.57 versus 0.49) - this is because it normalized for varying sensor responses to different

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emissions. The correlation for rescaled PTQS is much lower than in chamber experiments with

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gradually varying well-mixed concentrations (R2 = 0.78-0.93 here; 0.8-1.0 (typically) in previous

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studies13,16,17). The more rapid concentration fluctuations weaken the correlation due to different

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monitor response times. This is supported by the high SidePak to PTQS (rescaled) ratio (1.86) –

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while the SidePak was able to sense transient peaks, the PTQS was not. Excluding all periods

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with transient spikes gave a stronger correlation (R2 = 0.90) with a slope (1.05) close to the

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expected value (1).

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Environmental Characterization. The moving trajectories of the occupant (blue lines) and

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measurements at 751 different locations (black dots) in Figure 3(a) covered the three rooms. All

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3 hidden sources (red stars, Figure 3(a)) were identified by the occupant (either by SidePak

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display or smell of smoke); therefore, more data points are in proximity to these sources as well

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as near the air purifier.

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A 2-D linear interpolation for each room used MATLAB’s (MathWorks Inc., Natick, MA,

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USA) griddata function, and 2×2 cm grid size (consistent with positioning resolution), to test

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whether a linear approach (simpler than Kriging) can capture high concentration locations, given

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much more dense spatial data. Figure 3(b) and 3(c) plot the SidePak and PTQS (rescaled) spatial

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distributions, using the MATLAB surface function.

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Figure 3(b) visualizes the locations of three incense sources and the air purifier (localized

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minimum in bedroom). Concentration gradients close to the sources reveal air flow patterns

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indoors (e.g., plume moving from the open window to the living room sofa).

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The PTQS also captured the three emission source locations and the air purifier (Figure 3(c)).

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However, high concentration regions looked more dispersed (e.g., plumes in the bedroom).

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Unlike the exposure experiment, in this experiment the sampling point was constantly moving.

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Due to PTQS time response (≤10 s), the effects of peak concentrations persisted after the

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occupant had passed the source location, smearing the plumes near emissions. 6 ACS Paragon Plus Environment

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The correlation of rescaled PTQS with SidePak concentrations at all interpolated grid points

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was R2 = 0.50, similar to grid points ≤2 m from sources (0.48). Excluding these proximity grid

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points, the correlation became much stronger (R2 = 0.74).

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To summarize, as a mobile device, the low-cost PTQS can resolve general spatial distributions

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of exposures (Figure 2(c)) and concentrations (Figure 3(c)) indoors. However, it cannot represent

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pollutant levels as reliably as the SidePak when rapidly varying concentrations occur or source

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types are unknown for scaling adjustments. To resolve detailed variations close to a source, and

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detect transient emission peaks, it is advisable to use a research-grade monitor that can more

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quickly respond to rapid changes in concentrations.

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IMPLICATIONS

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Using research-grade photometers, studies (e.g., 16,17,21,22) have evaluated low-cost

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particulate matter sensors in stationary laboratory settings. This study assesses low-cost sensors

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when used as mobile devices in a real indoor environment with sources, where sporadic

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concentration spikes occurred as the occupant walked around.

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Previous studies (e.g., 23-25) have used GPS and portable monitors to map locations and

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personal exposures outdoors, and indoors at the whole building scale. This study shows this

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mobile mapping method can now be applied by analogy to track locations inside buildings, using

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an indoor positioning system (IPS). In conjunction with traditional activity logs for personal

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exposure assessment, it can track or identify omitted location changes (e.g., leaving kitchen

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temporarily while cooking) and provide more detailed exposure assessments (e.g., distances and

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directions from sources).

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Stationary monitors have been used for indoor air quality spatial mapping. Unlike this

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“Eulerian” approach (tracking concentrations at discrete fixed points), the “Lagrangian” (mobile)

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monitoring method tested here offers a way to measure continuous spatial profiles of

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concentration. Our mapping experiment detected leakage of incense emissions indoors, when the

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investigator lingered nearby. Future studies examining how well this mobile sensing method can

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identify sources in real-world situations (e.g., walking by a mild secondhand smoke intrusion

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location) would be valuable.

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The methodology presented could potentially be useful for large occupational indoor settings

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where spatially tracking workers’ exposures or accidental air toxic leaks are critical to ensure

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occupational safety and health. It can also benefit future research characterizing occupant7 ACS Paragon Plus Environment


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environment interactions - this could be useful for smart building applications, such as

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ventilation, light, and appliance automation based on recursive patterns, to achieve energy

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conservation and human health protection in the built indoor environment.

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ACKNOWLEDGMENTS

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The authors thank the TomKat Center for Sustainable Energy at Stanford University for funding

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this seed research and Dr. Ram Rajagopal for his expert advice on the wireless sensor system.

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REFERENCES

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Figure 1(a) and (b)

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Figure 1(a) Mobile air monitoring setup with a pair of collocated sensors (research-grade SidePak and low-cost PTQS). PTQS is connected with a Diymore development board that wirelessly transmitted real-time PM2.5 data to a computer server; and (b) Indoor positioning setup with 12 stationary and 1 mobile Marvelmind ultrasound beacons deployed in an one-bedroom rental apartment in Santa Clara, CA. Location data were wirelessly transmitted in real-time, from the beacons to the computer server.


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Figure 2(a)-(c)

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Figure 2 (a) 1-s concentration time series of PM2.5 measured by the collocated SidePak and PTQS (Figure 1(a)) carried by the occupant. Occupant location history shown as circles on the floor map with PM2.5 levels on a color scale measured by (b) SidePak and (c) PTQS. PTQS data were rescaled using the source-specific scaling factors in Table S2.

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Figure 3(a)-(c)

341 Figure 3 (a) Moving trajectories of the occupant who performed random walk in the presence of 3 hidden burning incense sources (red stars outside the open living room window, inside the cabinet under the kitchen sink, and inside the storage room in the bedroom). Spatial concentration fields of PM2.5 on a color scale created by 2-D interpolations of (b) SidePak and (c) PTQS measurements collected at 751 positions (black dots in Figure 3(a)).


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