Using Indoor Positioning and Mobile Sensing for Spatial Exposure

Subscriber access provided by La Trobe University Library

Environmental Measurements Methods

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

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 14

Environmental Science & Technology Letters

1

Using Indoor Positioning and Mobile Sensing for Spatial Exposure and Environmental

2

Characterizations: Pilot Demonstration of PM2.5 Mapping

3

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

4

1Civil

5

*corresponding author: [email protected]

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

6 7

ABSTRACT

8

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

9

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

10

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

11

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

12

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

13

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

14

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

15

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

16

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

17

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

18

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

19

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

20

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

21

photometer).

22

INTRODUCTION

23

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

24

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

25

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

26

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

27

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

28

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

29

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

30

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

31

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


32

The stationary mapping method typically involves deploying sensors near expected source

33

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

34

placement becomes less straightforward.

35

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

36

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

37

nodes, allowing unknown sources inside buildings to be pinpointed.

38

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

39

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

40

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

41

mapping PM2.5 distributions for smoke intrusion.

42

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

43

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

44

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

45

MATERIALS AND METHODS

46

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

47

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

48

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

129

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

130

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

131

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

132

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

133

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

134

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

135

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

136

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

137

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

138

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

139

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

140

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

141

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

142

SidePak.

143

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

144

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

145

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

146

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

147

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

148

corresponding exposures.

149

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

150

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

151

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

152

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

153

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

154

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


155

Despite PTQS shortcomings in capturing concentration variations, it identified the

156

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

157

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

158

detection will be unlikely.20

159

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

160

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

161

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

162

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

163

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

164

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

165

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

166

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

167

expected value (1).

168

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

169

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

170

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

171

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

172

as near the air purifier.

173

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

174

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

175

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

176

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

177

distributions, using the MATLAB surface function.

178

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

179

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

180

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

181

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

182

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

183

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

184

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

185

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

Page 6 of 14

Page 7 of 14


186

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

187

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

188

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

189

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

190

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

191

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

192

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

193

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

194

quickly respond to rapid changes in concentrations.

195

IMPLICATIONS

196

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

197

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

198

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

199

concentration spikes occurred as the occupant walked around.

200

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

201

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

202

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

203

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

204

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

205

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

206

directions from sources).

207

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

208

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

209

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

210

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

211

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

212

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

213

location) would be valuable.

214

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

215

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

216

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


217

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

218

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

219

conservation and human health protection in the built indoor environment.

220

ACKNOWLEDGMENTS

221

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

222

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

223 224

REFERENCES

225

(1) McBride, S. J.; Ferro, A.; Ott, W. R.; Switzer, P.; Hildemann, L. M. Investigations of the

226

Proximity Effect for Pollutants in the Indoor Environment. Journal of Exposure Analysis and

227

Environmental Epidemiology 1999, 9, 602-621.

228 229

(2) Ferro, A. R.; Kopperud, R. J.; Hildemann, L. M. Elevated Personal Exposure to Particulate

230

Matter from Human Activities in a Residence. Journal of Exposure Analysis and

231

Environmental Epidemiology 2004, 14, S34-S40.

232 233

(3) Cheng, K. C.; Acevedo-Bolton, V.; Jiang, R. T.; Klepeis, N. E.; Ott, W. R.; Fringer, O. B.;

234

Hildemann, L. M. Modeling Exposure Close to Air Pollution Sources in Naturally Ventilated

235

Residences: Association of Turbulent Diffusion Coefficient with Air Change Rate.

236

Environmental Science and Technology 2011, 45, 4016-4022.

237 238

(4) Acevedo-Bolton, V.; Cheng, K. C.; Jiang, R. T.; Ott, W. R.; Klepeis, N. E.; Hildemann, L.

239

M. Measurement of the Proximity Effect for Indoor Air Pollutant Sources in Two Homes.

240

Journal of Environmental Monitoring 2012, 14, 94-104.

241 242

(5) Zhao, T.; Nguyen, C.; Lin, C. H.; Middlekauff, H. R.; Peters, K.; Moheimani, R.; Guo, Q;

243

Zhu, Y. Characteristics of Secondhand Electronic Cigarette Aerosols from Active Human

244

Use. Aerosol Science and Technology 2017, 51, 1368-1376.

245

8 ACS Paragon Plus Environment

Page 8 of 14

Page 9 of 14


246

(6) Acevedo-Bolton, V.; Ott, W. R.; Cheng, K. C.; Jiang, R. T.; Klepeis, N. E.; Hildemann, L.

247

M. Controlled Experiments Measuring Personal Exposure to PM2.5 in Close Proximity to

248

Cigarette Smoking. Indoor Air 2014, 24, 199-212.

249 250

(7) Cheng K. C.; Zheng, D.; Hildemann, L. M. Impact of Mechanical Mixing on Air Pollutant

251

Concentration near Sources in Unoccupied Rooms: Connecting Peak Exposure with Energy

252

Input. Environmental Science and Technology 2018 (submitted).

253 254

(8) Patel, S.; Li, J.; Pandey, A.; Pervez, S.; Chakrabarty, R. K.; Biswas, P. Spatio-temporal

255

Measurement of Indoor Particulate Matter Concentrations Using a Wireless Network of

256

Low-cost Sensors in Households Using Solid Fuels. Environmental Research 2017, 152, 59-

257

65.

258 259

(9) Li, J.; Li, H.; Ma, Y.; Wang, Y.; Abokifa, A. A.; Lu, C.; Biswas, P. Spatiotemporal

260

Distribution of Indoor Particulate Matter Concentration with a Low-cost Sensor Network.

261

Building and Environment 2018, 127, 138-147.

262 263

(10) Thomas, G. W.; Sousan, S.; Tatum, M.; Liu, X.; Zuidema, C.; Fitzpatrick, M.; Koehler, K.

264

A.; Peters, T. M. Low-cost, Distributed Environmental Monitors for Factory Worker Health.

265

Sensors 2018, 18, 1441.

266 267 268

(11) Koyuncu, H; Yang, S. H. A Survey of Indoor Positioning and Object Locating Systems. International Journal of Computer Science and Network Security 2010, 10, 121-128.

269 270

(12) Brena, R. F.; García-Vázquez, J. P.; Galván-Tejada, C. E.; Muñoz-Rodriguez, D.; Vargas-

271

Rosales, C.; Frangmeyer, J. Evolution of Indoor Positioning Technologies: A Survey.

272

Journal of Sensors 2017, 2630413.

273 274

(13) Wang,Y.; Li, J.; Jing, H.; Zhang, Q.; Jiang, J.; Biswas, P. Laboratory Evaluation and

275

Calibration of Three Low-cost Particle Sensors for Particulate Matter Measurement. Aerosol

276

Science and Technology 2015, 49, 1063-1077. 9 ACS Paragon Plus Environment


277 278

(14) Jiang, R. T.; Acevedo-Bolton, V.; Cheng, K. C.; Klepeis, N. E.; Ott, W. R.; Hildemann, L.

279

M. Determination of Response of Real-time SidePak AM510 Monitor to Secondhand Smoke,

280

Other Common Indoor Aerosols, and Outdoor Aerosol. Journal of Environmental

281

Monitoring 2011, 13, 1695-1702.

282 283

(15) Dacunto, P. J.; Cheng, K. C.; Acevedo-Bolton, V.; Jiang, R. T.; Klepeis, N. E.; Repace, J. L.;

284

Ott, W. R.; Hildemann, L. M. Real-time Particle Monitor Calibration Factors and PM2.5

285

Emission Factors for Multiple Indoor Sources. Environmental Science: Processes & Impacts

286

2013, 15:1511-1519.

287 288 289

(16) Singer, B. C.; Delp, W. W. Response of Consumer and Research Grade Indoor Air Quality Monitors to Residential Sources of Fine Particles. Indoor Air 2018, 28, 624-639.

290 291 292

(17) Manikonda, A.; Zíková, N.; Hopke, P. K.; Ferro, A. R. Laboratory Assessment of Low-cost PM Monitors. Journal of Aerosol Science 2016, 102, 29-40.

293 294 295

(18) Sousan, S.; Koehler, K.; Hallett, L.; Peters, T. M. Evaluation of Consumer Monitors to Measure Particulate Matter. Journal of Aerosol Science 2017, 107, 123-133.

296 297

(19) Zikova, N.; Hopke, P. K.; Ferro, A. R. Evaluation of New Low-cost Particle Monitors for

298

PM2.5 Concentrations Measurements. Journal of Aerosol Science 2017, 105, 24-34.

299 300

(20) Cheng, K. C.; Acevedo-Bolton, V.; Jiang, R.T.; Klepeis, N. E.; Ott, W. R.,; Hildemann, L.

301

M. Model-based Reconstruction of the Time Response of Electrochemical Air Pollutant

302

Monitors to Rapidly Varying Concentrations. Journal of Environmental Monitoring 2010,

303

12, 846-853.

304 305

(21) Austin, E.; Novosselov, I.; Seto, E.; Yost, M. G. Laboratory Evaluation of the Shinyei

306

PPD42NS Low-cost Particulate Matter Sensor. PLOS ONE 2015, 10, e0141928.

307 10 ACS Paragon Plus Environment

Page 10 of 14

Page 11 of 14

308


(22) Kelly, K. E.; Whitaker, J.; Petty, A.; Widmer, C.; Dybwad, A.; Sleeth, D.; Martin, R.;

309

Butterfield, A. Ambient and Laboratory Evaluation of a Low-cost Particulate Matter Sensor.

310

Environmental Pollution 2017, 221, 491-500.

311 312

(23) Pummakarnchana, O.; Tripathi, N..; Dutta, J. Air Pollution Monitoring and GIS Modeling: A

313

New Use of Nanotechnology Based Solid State Gas Sensors. Science and Technology of

314

Advanced Materials 2005, 6, 251-255.

315 316

(24) Steinle, S.; Reis, S.; Sabel, C. E. Quantifying Human Exposure to Air Pollution – Moving

317

from Static Monitoring to Spatio-temporally Resolved Personal Exposure Assessment.

318

Science of the Total Environment 2013, 443, 184-193.

319 320

(25) Steinle, S.; Reis, S.; Sabel, C. E.; Semple, S.; Twigg, M. M.; Braban, C. F.; Leeson, S. R.;

321

Heal, M. R.; Harrison, D.; Lin, C., Wu, H. Personal Exposure Monitoring of PM2.5 in Indoor

322

and Outdoor Microenvironments. Science of the Total Environment 2015, 508, 383-394.

323



324 325

Figure 1(a) and (b)

326 327 328 329 330 331

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.


Page 12 of 14

Page 13 of 14

332


Figure 2(a)-(c)

333 334 335 336 337

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.

338 339



340

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)).


Page 14 of 14

Using Indoor Positioning and Mobile Sensing for Spatial Exposure

Recommend Documents