smartphone app for residential testing of formaldehyde ... · 83 smartphone-based measurement....
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Smartphone App for Residential Testing of Formaldehyde
(SmART-Form)
Journal: Indoor Air
Manuscript ID Draft
Manuscript Type: Original Article
Date Submitted by the Author: n/a
Complete List of Authors: Zhang, Siyang; Ohio State University, Civil Environmental and Geodetic Engineering Shapiro, Nicholas Gehrke, Gretchen Castner, Jessica Liu, Zhenlei; Syracuse University, Mechanical and Aerospace Engineering
Guo, Beverly; Syracuse University, Mechanical and Aerospace Engineering Satish Prasad , Romesh ; Syracuse University, Mechanical and Aerospace Engineering Zhang, Jianshun; Syracuse University, Mechanical and Aerospace Engineering Haines, Sarah; Ohio State University, Civil Environmental and Geodetic Engineering Kormos, David; Ohio State University, Civil Environmental and Geodetic Engineering Frey, Paige; Ohio State University, Civil Environmental and Geodetic Engineering Qin, Rongjun; Ohio State University, Civil Environmental and Geodetic
Engineering Dannemiller, Karen; Ohio State University, Civil Environmental and Geodetic Engineering; Ohio State University, Environmental Health Sciences
Keywords: Sensor, colorimetric, detection, citizen science, education, exposure
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Smartphone App for Residential Testing of Formaldehyde 1
(SmART-Form) 2
3
4
5
Siyang Zhang1, Nicholas Shapiro
2, Gretchen Gehrke
2, Jessica Castner
3 Zhenlei Liu
4, Beverly 6
Guo4, Romesh Prasad
4, Jianshun Zhang
4, Sarah Haines
5,6,7, David Kormos
6, Paige Frey
8, 7
Rongjun Qin6,9, Karen C. Dannemiller
6,7* 8
9
10
1 Department of Computer Science and Engineering, Ohio State University 11
2 Public Laboratory for Open Technology & Science 12
3 Castner Incorporated 13
4 Department of Mechanical & Aerospace Engineering, Syracuse University 14
5 Environmental Science Graduate Program, Ohio State University 15
6 Department of Civil, Environmental and Geodetic Engineering, College of Engineering, 16
Ohio State University 17 7 Department of Environmental Health Sciences, College of Public Health, Ohio State 18
University 19 8 Strand Associates, Incorporated 20
9 Department of Electrical and Computer Engineering, Ohio State University 21
22
23 *Corresponding author: [email protected], 470 Hitchcock Hall, 2070 Neil Ave, 24
Columbus, OH 43210, 614-292-4031 25
26
27
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ABSTRACT 28
Chemical contamination is ubiquitous in the indoor environment, but measurement options 29
are often limited outside of research studies. This is especially true for formaldehyde, a known 30
carcinogen and irritant. The goal of this project was to develop a novel screening tool: a 31
smartphone-based app that can be paired with low-cost colorimetric badges for detection of 32
indoor formaldehyde. The target users include citizen scientists, concerned citizens, public 33
health nurses visiting homes, and researchers with relevant measurement needs. The user 34
interface was designed using a collaborative development model. Badges are exposed to air 35
for 72 hours, and the user takes images that are analyzed in the phone. The app itself measures 36
illumination (lightness) to determine color change, which was associated with formaldehyde 37
concentration (R² = 0.8811, P<0.0001). The detectable range is 20-120 ppb and the standard 38
deviation of readings was 10.9 ppb. Warnings were integrated into the app to address current 39
limitations, including sensitivity to extreme lighting conditions and elevated (>80%) relative 40
humidity. Co-exposure to acetaldehyde or a VOC mixture did not interfere with measurement 41
(P=0.93, P=0.07, respectively). Overall, this screening tool can provide inexpensive, 42
accessible information to users about their formaldehyde exposure, which can inform further 43
testing and potential remediation activities. 44
45
KEYWORDS 46
Sensor, colorimetric, detection, citizen science, education, exposure 47
48
PRACTICAL IMPLICATIONS 49
Smartphone technology can be paired with colorimetric chemistry to design low-cost 50
screening tools for chemical contaminants in the indoor environment. Here, we demonstrate 51
this technology for measurement of formaldehyde and utilize a community-based approach 52
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for app development. This process will enhance contaminant measurement options for citizen 53
scientists, concerned members of the general public, and clinicians who conduct home 54
exposure assessments in the future, and enable extensive data collection from many locations 55
compared to other methods. 56
57
INTRODUCTION 58
Formaldehyde is ubiquitous in the indoor environment, where this contaminant is 59
released from various consumer products such as pressed wood, adhesives, paints, cleaners, 60
and more 1. Exposure to this compound can result in eye, nose, and throat irritation, and a 61
range of other non-clinical ailments. Formaldehyde is also a known human carcinogen. 62
Research began demonstrating the toxicity of this compound more than 100 years ago 2. 63
Recent concerns have arisen due to elevated presence in Federal Emergency Management 64
Agency (FEMA) trailers such as those provided after Hurricane Katrina in 2005-2006 and in 65
flooring retailed by Lumber Liquidators in 2015. 66
Formaldehyde measurement options. Currently, measurement options for citizen 67
scientists and concerned members of the general public are limited. A commonly-used method 68
for formaldehyde measurement involves collecting samples in 2,4-dinitrophenylhydrazine 69
(DNPH) cartridges to derivatize formaldehyde with later laboratory-based detection using 70
high performance liquid chromatography 3,4. This technique requires specific sampling 71
equipment, uses expensive and complex analytical instruments, is prone to contamination, and 72
is nearly impossible for a citizen scientist to perform. Other sampling technologies have 73
recently been developed 1,5–9
, but to our knowledge, are not commercially available or widely 74
used by citizen scientists, and may also suffer from contamination either during transport or 75
due to other compounds. Right now, we are missing a low-cost, accessible screening tool for 76
formaldehyde in homes that may not require the same level of precision as the DNPH method. 77
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Our previous survey of 147 individuals indicated broad public interest in formaldehyde testing 78
using smartphone technology. Poor reported health status was associated with increased desire 79
for testing (odds ratio for very good to excellent health 0.31, 95% confidence interval 0.12-80
0.81). Location in our targeted area of community engagement was also associated with 81
interest in testing 10. 82
Smartphone-based measurement. Smartphone technologies provide a new 83
opportunity for formaldehyde detection. One downside to the currently available colorimetric 84
badges is the low accuracy due to visible color comparisons by the human eye. This allows 85
for concentration “binning” but not accurate, quantitative results. Fortunately, the use of 86
digital image processing technologies provide the opportunity to more accurately read the 87
color change on the badges that results from formaldehyde exposure as a continuous variable. 88
This is especially important for a compound such as formaldehyde that is ubiquitous in the 89
indoor environment, such that concentration level is more informative than simple detection 90
(yes vs. no). 91
Others have also recognized the opportunity to pair colorimetric sensors with 92
Smartphone detection for accurate measurements 11–14
. In this paired system, the camera 93
function of the smartphone is utilized to record and analyze the color changes on the badge 94
that occur as a result of contaminant exposure. An image formation model describes how the 95
color images are produced by the camera that received light reflected from the badge surface. 96
Colors are dependent upon both the material reflectance and the intensity of the environmental 97
light. It is generally impossible to measure the exact environmental lighting conditions, so the 98
colorimetric change is quantified using a ratio of the lightness (illumination) of badge to a 99
calibration patch 15,16
. Here, we refer to this as the color-change ratio. In this case, the variant 100
of the environment lighting is the same on the reaction and calibration areas, leaving the 101
material property as the only variant accounting for the colorimetric change. This material 102
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property, also known as the surface albedo, can be easily computed by analyzing digital 103
photos through extracting the color values of the reacting and referencing area. The surface 104
albedo correlates with the measured formaldehyde concentration. 105
Goal of this work. The goal of the work described here was to develop a novel 106
screening tool for formaldehyde detection by pairing a color-changing badge with a 107
smartphone app (Figure 1). The target user is a citizen scientist or person concerned about 108
formaldehyde exposure in their home environment. The system may also be useful for visiting 109
nurses in asthma homecare programs 17, environmental health professionals, or in certain 110
epidemiological research studies depending on measurement requirements. User input was 111
invited at every stage of development of the low-cost smartphone-based formaldehyde 112
measurement system to ensure that this technology is accessible and useful to the public. 113
Community engagement was sought from both a national network of citizen scientists with 114
high technological literacy and a community environmental group in a small city in southern 115
Georgia with average to low technological literacy. Existing colorimetric badges used in 116
occupational settings were modified by our industry partner for enhanced sensitivity in the 117
residential environment and for an app-friendly configuration. The system was calibrated on 118
both Android and iOS devices using exposure to known concentrations of formaldehyde. 119
Community feedback had been collected previously to assess public interest in residential 120
formaldehyde and home testing, willingness to pay for test kits, smartphone access, and 121
capacity to implement a multi-day sampling protocol 10. Upon completion of a beta version of 122
the app, community members were solicited to provide feedback about the design, clarity, and 123
functionality of this novel system; that feedback was then incorporated into the app. 124
125
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METHODS 126
Our goal was to develop a smartphone app to measure changes in color (illumination 127
or lightness) of low-cost colorimetric badges due to exposure to formaldehyde. Steps in this 128
process included badge modification, prototype app development, quantification of color 129
change (also see information on the lighting model in supporting information), system 130
calibration, beta testing with feedback integration, and statistical analysis. 131
Badge modification 132
We used modified SafeAir® formaldehyde detection badges (Morphix Technologies, 133
Virginia Beach, VA) to detect formaldehyde concentrations typically found in the indoor 134
environment. The badges are commonly used in occupational settings and were modified for 135
use with our system. We requested that the badges display a greater intensity of color change 136
for a lower range of formaldehyde exposure concentrations more common in residential 137
environments. We also requested adjustments to the layout of the badges to be more easily 138
read with the app and incorporate a calibration area that does not change color. We also 139
evaluated a modified version of the ChromAir® badges (referred to as “version 2” of the 140
badge), but selected the SafeAir® badges for use with our system due to increased precision 141
and a lower detection limit. 142
Prototype App Development 143
The first steps of app development were to create a back-end image processing method 144
to measure formaldehyde concentration based on substrate color change, and a front-end user 145
interface for the app. The required functionalities included: create multiple tests, take badge 146
image via camera before and after badge exposure, convert badge image into intensity ratio 147
and calculate the concentration result, save multiple tests containing badge images and 148
formaldehyde concentration results, request test data and user experience feedback from users 149
and incorporate relevant changes. 150
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Prototype. The first step of app design was creating the framework of our prototype. 151
To facilitate organizing multiple tests, the main layout was designed with a master-detail two-152
panel structure: table view and detail view. The table view contains all existing tests with 153
titles and dates, and the detail view displays the whole process of a badge test, including user 154
input parameters, images taken by the camera, and access to data upload surveys (Figure S1). 155
Our design strategy was closely related to the functional requirements and utilized object-156
oriented design, which is the process of planning a system of interacting objects. The object-157
oriented design structure was focused on the badge test, and for each test, we included the 158
unique ID, title, date, images as inputs, and result as output. For memory efficiency, the test 159
objects were stored in JSON format and images were referred by path in the internal storage. 160
The main functional requirements fall into two categories: test management and image 161
processing. As shown in Figure 2, the app allowed the user to create a new test by inputting 162
the test title, and the start time was recorded after taking the “before” image. Afterward, the 163
user has access to view all existing tests via the table view and check details by clicking a test 164
in the list. For camera setup, the original plan was to use the default camera embedded with 165
the smartphone. However, default parameters vary by phone manufacturer and model, and 166
many of the smartphone cameras in different systems are rather restrictive in the customizable 167
function settings (e.g. ISO level, aperture control). Thus it is challenging to realize 168
homogeneity in terms of colors and lighting sensitivity across different smartphone cameras in 169
different systems. Therefore, in our app, we experimented with taking images with different 170
parameters on different phones, and selected a set of the most common parameters each for 171
Android and IOS systems to minimize variation (see Lighting Experiments section below). 172
Our mathematic model uses the calibration patch on the badge to account for environmental 173
variation, and thus we expect the differences in camera parameters can be partially addressed. 174
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For details on the mathematical model used, please refer to the supporting information (Figure 175
S1). 176
User Interface design. The initial user interface for the detail page contained multiple 177
functionalities, such as taking a photo of the surroundings of the test, taking pre-exposure 178
(“before”) and post-exposure (“after”) images, and uploading data to a survey platform 179
(Qualtrics). Surveys were intended to engage users in creating a database of formaldehyde 180
exposure information, and these included a survey about personal health and a survey about 181
environmental characteristics of the sampling location. A unique non-identifiable code is 182
automatically copied to user phone's clipboard when leaving the app and can be pasted into 183
the ID field in the survey, which allows multiple survey and formaldehyde test entries to be 184
linked together without additional personal identifying information. 185
186
Quantification of color change 187
Integration of user warnings. We identified key conditions that could lead to 188
erroneous readings throughout app development (Table 1). For each condition, we analyzed 189
images taken under both suitable (no error) and unsuitable (may cause an error) conditions. 190
The parameter associated with each error, such as lightness or saturation, was chosen based on 191
the value with the most substantial difference between conditions. Boundaries for warnings 192
were selected to be about halfway between extreme points measured in suitable and unsuitable 193
conditions. 194
Lighting experiments. We also conducted lighting experiments to determine limits on 195
lighting conditions in the indoor environment for accurate calculation of illumination values 196
for the badge. When developing this application, we needed to take clear images with no 197
shadows and correct color composition. We also tested the accuracy of the automatic camera 198
settings on the iOS and Android phones and compared to alternative settings. To do this we 199
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purchased different types of lightbulbs and a desk lamp, setting up the lamp 30.5 cm from the 200
badge with the lampshade horizontal to avoid shadows covering the badge. The types of 201
lightbulbs purchased were: 25W Soft White, 40W Clear, 40W LED, 50W Soft White, 60W 202
Crystal Clear and 60W LED Soft White. 203
We photographed the color grid paper under three different manual camera settings 204
and auto adjust. Four settings (Table S2) were tested using special color grid paper (Figure 3). 205
After each image was taken, they were imported into Matlab and cropped by reaction area and 206
calibration area. These cropped bitmaps from the orange and yellow blocks were converted 207
into HSI (Hue, Saturation, and Intensity) color model 18. We found that the color-changing 208
reaction of the badge changes the color lightness. Lightness is correlated with the intensity 209
value of the HSI transformation, so we then calculate the ratios of the means of color intensity 210
(I) of each image. We used the color grid paper instead of the actual badge because the printed 211
color will not change, thus providing a good reference to determine the influence caused by 212
the camera settings. 213
Algorithm. The mathematical model is based on the color change ratio (illumination 214
or lightness) between the calibration patch and the chemical badge. Figure 4 highlights the 215
areas in green and red, respectively. These two blocks of bitmap were cropped and calculated 216
for the mean value of the color intensity I, and the ratio = I(red)/I(green). We confirmed a 217
linear relationship between exposure (formaldehyde concentration in ppb·hr) and the color 218
change ratio. 219
220
Calibration 221
Calibration was performed by subjecting the badge sensor to several known 222
concentrations of formaldehyde maintained in a 50 L small-scale environmental chamber (0.5 223
m by 0.4 m by 0.25 m, Figure S2). The test system included a pressurized clean air supply and 224
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a Dynacalibrator containing a permeation tube at a constant temperature and flow rate to 225
provide the desired formaldehyde generation rate (Figure S3). Different concentrations in the 226
chamber were obtained by adjusting the total airflow rate through the chamber, and were 227
verified by air sampling with DNPH cartridge followed by HPLC analysis (Table 2). For each 228
test, a set of three badge sensors were placed inside the chamber (Figure S4), and their color 229
change was measured over time. Images were taken before the badges were placed in the 230
chamber, after exposure inside the chamber over the different time periods through a pane of 231
glass, and after they were removed from the chamber by using the mobile phone app (Huawei 232
Mate 8 Android 7.0 and iPhone 6s iOS 11.2) and a high resolution digital camera (Casio 233
Exilim- F1). Data were analyzed to obtain the mean color change ratio of the three badges as a 234
function of the amount of cumulative exposure (i.e., concentration multiplied by the 235
cumulated exposure time, (E = C × T): CCR = �() = �(� × �), where CCR is the color 236
change ratiom, E is total exposure, C is formaldehyde concentration, and T is time. Given this 237
calibration curve, the concentration can be determined from the color change ratio and 238
exposure time. The uncertainty of the badge response was quantified by the standard deviation 239
of the color change ratio measured by the three badge sensors. Taking images through the 240
glass had a small effect on the measured values, so the mathematical model was adjusted for 241
this “glass effect” by comparing images taken without the glass (out of the chamber) and 242
through the glass (in the chamber) at the same time point, both before and after testing. 243
244
245
Beta Testing 246
We conducted a user test of the app’s beta version to ensure that the SmART-Form 247
app is useful for and accessible to interested members of the public. We followed the 248
principles of user-centered design 19. We asked participants to download and use the app with 249
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an image of an exposed badge, and to provide feedback on the app’s design, clarity, and 250
functionality. Participants provided feedback to study staff through a survey in Qualtrics with 251
questions about whether or not they had trouble at each step of the app, from initial 252
downloading through survey completion, and were asked for general feedback and 253
suggestions. We recruited beta test participants through: (1) email solicitation of people who 254
had participated in the initial feasibility assessment study, (2) social media solicitation of an 255
environmental community science network, (3) solicitation on the project website. 256
Recruitment targeted both a national network of citizen scientists with high technological 257
literacy and a community environmental group in a small city in southern Georgia with 258
average to low technological literacy. One purpose of the beta test was to evaluate image 259
collection on different smartphone systems. Another core component of the beta test was to 260
investigate whether or not the intended flow of the app, including exiting the app in order to 261
provide health and environmental data through a secure browser-based platform Qualtrics, 262
was intuitive, confusing, or caused technical challenges for users. 263
The user feedback portion of the beta test was conducted through an analogous survey 264
on Qualtrics. The survey was available from September 2017 to May 2018. Feedback was 265
then integrated into app design. To support improvements, a user interface consultant was 266
hired to provide guidance, as the usability of the app is of fundamental importance. We also 267
tested our app in a field test that will be reported in a future publication and solicited some 268
verbal user feedback there. 269
This study was approved by the Ohio State Institutional Review Board (IRB). 270
271
Statistical analysis. 272
Statistical analysis was conducted in SAS, version 9.4 and Microsoft Excel. 273
Formaldehyde concentrations were evaluated as ppb·hr and badge color change was evaluated 274
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as the illumination ratio of the color changing area to the calibration area (referred to 275
throughout as the color change ratio). The method detection limit was calculated as 3 times 276
the standard deviation of blank readings. The standard deviation of all the data was calculated 277
by comparing the actual formaldehyde concentration to the calculated formaldehyde 278
concentration based on the color change ratio and our mathematical model. Cross-279
contamination of acetaldehyde and VOCs were evaluated using the GENMOD procedure in 280
SAS and considering the interaction term based on grouping reading based on presence or 281
absence of acetaldehyde and using the “no intercept” option. 282
283
RESULTS 284
Our testing of the badges demonstrated the ability of smartphone cameras to accurately 285
determine changes in illumination (color change) associated with formaldehyde exposure. 286
287
App Development 288
The app user interface was developed and refined using a collaborative development model to 289
allow both measurements of the color change of the badges and access to surveys that can be 290
made publicly available (Figure 3,4). Modifications were made to the user interface 291
throughout the app design process (Table 3). 292
293
Necessary user warnings and education 294
Integration of user warnings. The image output relies on the camera settings and the 295
lighting conditions, and it is important to reduce the possibility of overexposure or low 296
lighting conditions. To avoid such test errors, we identified a list of warnings to test and 297
subsequently notify the user to instruct them on the appropriate use of the app (Table 1). 298
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The values in Table 1 were determined from a series of experiments on the badges. 299
Different experimental conditions were tested, and boundaries for warnings were selected to 300
be about halfway between extreme points measured in suitable and unsuitable conditions. 301
For instance, we noted that high relative humidity conditions above about 75-80% will 302
interfere with the color change of the badge and can potentially cause erroneous readings in 303
the app. Fortunately, under these conditions the badge also develops a blue/purple tint that is 304
detectable in the image. We incubated badges at various relative humidity conditions to 305
determine when an interfering blue color appeared on the badge. The lowest saturation value 306
detected in suitable conditions was 0.8008 and the highest saturation value detected in 307
unsuitable conditions was 0.5185. Therefore, we selected 0.6 as the boundary for activation of 308
this warning. We also subjected badges to long-term, high-temperature storage conditions to 309
produce contamination and quantified detection with the app. 310
For exposure time, we selected a 72 hour period to balance detection capabilities with 311
user time and consistency in the color-changing area of the badge. The badge can theoretically 312
be read at different times that allow for sufficient color change (at least about 12 hours), but 313
those parameters were not validated here. Taking an image in a very short amount of time (for 314
instance, 5 minutes) will result in an artificially high value by dividing by a very small time 315
value due to the algorithm used, and thus we also wanted to prevent this error. We also placed 316
a limit on the reported values so that high and low values will be reported as >120 ppb and 317
<20 ppb, respectively. This range represents the calibration range above the method detection 318
limit. Values above 120 ppb may have also experienced saturation on the badge, but this 319
needs further evaluation to confirm. 320
User education. We integrated a formaldehyde information sheet into the app, with 321
the intention of providing necessary information to users to interpret their results and take 322
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appropriate action if necessary. The informational page lists typical formaldehyde 323
concentrations and potential sources and remediation strategies. 324
325
Camera Parameters and Lighting Conditions. Our results at different lighting 326
conditions and camera settings informed our decisions related to app design. Overexposure 327
was a challenge that could potentially result in inaccurate readings, but fortunately, we could 328
prevent these inaccuracies by incorporating warning messages into the app. Under our 329
experimental conditions with a close light source 30.5 cm from the badge, the ratio of color 330
intensity between “orange” and “yellow” is quite stable with different camera settings under 331
25W, while the results of 60W show more variations (Figure S5). In our setup, the 25W or 332
40W non-LED light bulb worked best (Figure S6), and we were able to determine the optimal 333
bounds for lighting conditions. The variations are possibly caused by overexposure as well, so 334
the ideal camera settings should eliminate such overexposure. We also determined that it was 335
necessary to use fixed camera settings as opposed to automatic settings, which vary by phone. 336
We selected White Balance: 4000K, ISO: 200, Exposure Duration: 1/60s or 1/125s depending 337
on external lighting conditions, to avoid any overexposure (Table S2). Other uncontrolled 338
camera settings may cause small differences in image quantifications, especially under 339
extreme lighting conditions. However, we expect the system to perform well under most 340
“normal” indoor lighting situations. We incorporated error messages into the app to alert the 341
user if lighting conditions exceed acceptable bounds, allowing them to take a better picture for 342
more accurate results. 343
344
Calibration 345
The results of color change ratio as a function of exposure time, CCR = f(t), for the 346
three different levels of formaldehyde concentrations under the same temperature (23 °C) and 347
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relative humidity (50% RH) are shown in Figure S8. The badge sensors did not appear to 348
suffer from saturation up to the 72 hours tested. The sensors of the low concentration test 349
showed only minimal color change at 72 h because this was close to the limit of detection. In 350
order to test the limit of sensor under the low indoor concentration level, we extended the test 351
of the low concentration test to 360 h until it reached the saturation state. The results show 352
that the function CCR = f(t) shows the trend of linear relation for all the three levels of 353
formaldehyde exposure conditions (Figure S8). The results also show that the color change of 354
version1 (SafeAir®, Morphix Technologies, Virginia Beach, Virginia) of the badge sensor is 355
more sensitive to the formaldehyde exposure than version2 (ChromAir®, Morphix 356
Technologies, Virginia Beach, Virginia). 357
The results of high relative humidity test are plotted in Figure S9. Under high RH 358
condition, the CCR value increased instead of decreased with time, indicating a RH 359
interference. This was partially due to a blue/purple tint that occurs under this condition. The 360
CCR at the high RH also varied with time or exposure amount, but in a non-linear fashion. It 361
is therefore recommended not to use the current sensor at high RH, and a warning was 362
integrated into the app that detects this blue/purple color. 363
Figure 7 shows the exposure as a function of CCR and time for all the tests conducted 364
at 23 C and 50% RH without potential contaminants, including a linear correlation function 365
that was implemented in the app. Illumination (lightness) from the color change was 366
associated with formaldehyde concentration (R² = 0.8811, P<0.0001). 367
We calculated the method detection limit to be 20 ppb at 72 hours. We also placed an 368
upper limit of 120 ppb detection due to concerns about saturation of the reaction area beyond 369
what was tested. The standard deviation of the data was calculated to be equivalent to 10.9 370
ppb if measured at 72 hours of exposure. Assuming a normal distribution, we expect 68% of 371
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the measured values to fall within 10.9 ppb of the true value at 72 hours of exposure, and 95% 372
of the data will be within 21.8 ppb of the true value. 373
The data in Figure 7 are from the Android system, and a similar graph with nearly 374
equivalent linear model resulted from the DLSR camera images. The iPhone that was used in 375
testing developed an artificial blue tint only when images were taken through the glass that 376
interfered with analysis, and thus the Android results were used for all versions of the app. 377
Further testing indicated that Android and iOS systems yielded similar results within error 378
limits when imaging the same badge under appropriate lighting conditions (P=0.70). We do 379
not expect the blue tint to interfere in future use of the app on iOS systems because 1) it 380
appeared to be limited to the phone used and 2) we do not expect users to be taking images 381
through glass under normal circumstances. 382
We also tested the badges under two additional conditions at the mid-concentration 383
level to examine the possible interface effect of the co-existence of acetaldehyde, which is 384
another major VOC contaminant in buildings, and other VOCs from the composite wood 385
product source such as a particleboard. The results were not statistically different with and 386
without co-exposure to acetaldehyde (P=0.93) or with and without co-exposure to a VOC 387
mixture from the particleboard (P=0.07) (Figure 7B). 388
Beta Testing 389
We received 10 responses to our beta test survey that both consented and completed at 390
least one question on the survey. Four users reported their age with a mean age of 33 years. 391
We received three other responses where users did not consent to the consent form, and we 392
are unable to determine how many people in total attempted the beta test. Users rated the 393
usability of the app at the time of testing with a mean of 8.1 on a 1 (difficult) to 10 (easy) 394
scale. Users cited “ease of use,” “simple use,” and “clean layout and intuitive” as the main 395
strength of the app (Table 4). The biggest challenge occurred with accessing the survey. 396
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Importantly, beta testers reported a bug that impeded the Unique ID assigned to their app from 397
being automatically copied to the Qualtrics survey, which is essential for contextual data 398
collection while protecting personally identifiable information. Because of this issue, many 399
users had difficulty submitting their feedback within the beta test survey and 9 additional beta 400
testers emailed feedback directly to research team members. Other more minor technical 401
glitches included compatibility with down market and older phones. These glitches were 402
addressed to ensure the app functioned properly. 403
Another suggestion received from three beta testers and others who reviewed the app 404
was the need for more clear instructions, both with an overview instruction page and step-by-405
step instructions for each phase. The user interface was substantially transformed following 406
beta testing to address the app’s clarity by adding instructional pages, improving the screen 407
layout to emphasize key information, utilize familiar icons, and ensuring more intuitive user 408
pathways. 409
Overall, the app and beta test had global appeal. There were 136 app downloads from 410
September 2017 to May 2018, although a small but unknown number of those included from 411
the study team. Country of download included the United States, India, South Korea, South 412
Africa, Mexico, China, Italy, Canada, United Kingdom, Turkey, Afghanistan, Brazil, Czech 413
Republic, Chile, Germany, Japan, Namibia, Malawi, Niger, Taiwan, Ukraine, and Russia. 414
415
DISCUSSION 416
We have developed a novel measurement system for formaldehyde in the indoor 417
environment. Our current results indicate that this system can provide accurate results and is 418
amenable to use by citizen scientists and concerned members of the general public, as well as 419
visiting nurses and researchers 10,20
. Additional field testing in a community showed color 420
change of the badge and measurement by the system, and will be described in detail in a 421
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future publication. Eventually, this technology, which is openly-licensed and open sourced 422
(https://github.com/publiclab/SmART-Form), can be expanded to detect additional 423
compounds of concern in the indoor environment. Integration into a smartphone app also 424
provides an important venue for user education. 425
Formaldehyde is present in most indoor environments 1,4,21,22
, and is notoriously 426
difficult to measure. The contaminant is too volatile to effectively adhere to many common 427
sorbents, and also requires derivitization for detection by gas chromatography/mass 428
spectrometry. Ubiquitous presence also increases the possibility for ambient contamination 429
during sampling. 430
Challenges. Uncontrolled lighting conditions present the greatest challenge for use of 431
this system. Shadows, overexposure, multiple light scattering, and low light are situations that 432
could affect the reading of the badge. Previous work has considered these challenges in 433
applications of surface material and optical property measurement. Normally, measuring the 434
albedo of a surface requires a lab-based reflectance measurement 23,24
, where a single light 435
source and an object with known shape (i.e. a perfect sphere) in a dark room. In a natural or 436
indoor environment, surface albedo measurement requires physical-model based disturbance 437
correction (e.g. atmosphere and light scattering) with in situ information such as scene 438
geometry (walls, tables, etc. in the room) or humidity/weather conditions 25–27
. In our system, 439
we know that the badge is a flat surface, while the environment and its lighting are 440
unpredictable. We designed the badge to include a calibration patch, where we assumed that 441
this portion in the smartphone image encodes variants of the environment. Using this, we are 442
able to approximate the albedo computation through calculating the ratio of lightness of the 443
reaction and calibration areas. We also integrated warnings to the user within the app to 444
account for some of these conditions, but potential still remains for introduced error. Future 445
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enhancements of the system can focus on further improving mathematical models used for 446
color calculation, or potentially improving conditions under which images are taken. 447
The standard deviation of our data was 10.9 ppb if the badge has been exposed for 72 448
hours. For instance, this means that a reading of 35 ppb is 68% likely to have a true value 449
between 24-46 ppb, and a reading of 85 ppb is 68% likely to have a true value between 74-96 450
ppb. It is always desirable to obtain a more precise reading. However, the precision available 451
here is most likely acceptable to citizen scientists or concerned citizens who want to quickly 452
determine the general range of their formaldehyde exposure with an inexpensive and easily-453
accessible method. This device is best used as a screening tool to determine if their exposure 454
is high or low, and can inform decisions about further testing or potential remediation. 455
Future use and interventions. This system is not as precise as the gold-standard 456
DNPH cartridge sampling and HPLC analysis method 28, but it does serve as an important 457
screening tool for formaldehyde in the environment. By comparison, the DNPH cartridge 458
method has a reported coefficient of variation of 1.02% and a method standard deviation of 459
0.71 µg/m3 (approximately 0.57 ppb)
29. Such extreme precision is not necessary in all 460
situations, especially when other factors such as ventilation changes or occupant activity may 461
alter formaldehyde levels over different periods of time. An interested user will need to weigh 462
cost, usability, accessibility, and accuracy/precision when deciding which measurement tool 463
to employ. In some cases, the smartphone-based system described here may provide the best 464
combination of these factors, and also provides additional information to the user related to 465
interpretation of results and options for remediation. After use, the user can decide to either 1) 466
pursue more expensive/difficult but more precise testing, 2) conduct additional tests with the 467
SmART-Form system, 3) do nothing, or 4) take remedial action to reduce exposure. More 468
precise and more expensive/cumbersome methods can be employed if desired. Additionally, 469
future improvements can potentially increase precision in future iterations of this system. 470
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The use of a smartphone for measurement of environmental exposures presents a 471
novel opportunity for user education and engagement. Increasing viability of low-cost and 472
easy-to-use monitoring systems is already changing the current paradigm of air quality 473
monitoring, according to the U.S. Environmental Protection Agency and others. The historical 474
monitoring model relied on highly expensive and complex instruments with limited input 475
from the public on where they are deployed and limited access of the data once collected. A 476
new paradigm that is variously influenced by citizen science, participatory civic engagement, 477
and the popularity of ‘quantified self’ technologies is seen to bear the possibility of bringing 478
communities and members of the general public into closer dialog with scientists about air 479
quality, enhancing source compliance monitoring and yielding more robust understandings of 480
personal exposures 30,31
. Most of the “next generation” air quality monitors are electronic real-481
time sensors that pose ongoing calibration and drift issues, and are relatively inexpensive (150 482
USD or more) but still too expensive for the communities that bear the highest burdens of 483
environmental exposure. Even highly motivated and scientifically literate users are finding 484
that they are “drowning in data” due to the large volumes of data produced by real time 485
sensors 32,33
. For smartphone colorimetric systems, the analytical technology is already owned 486
by 77% of the U.S. population (with higher rates in some Asian, Middle Eastern, and 487
European countries), allowing a truly accessible price point with badges costing 488
approximately 5 USD and the app being free. Further, these badges represent discrete 489
averages of exposure that are more easily understandable, meaningful, and actionable for the 490
general public than thousands of real-time data points. Information in the app can also assist 491
users in result interpretation and decision-making related to remediation. 492
Fortunately, there are interventions available that can reduce formaldehyde exposure 493
once it is detected. These include primarily source removal or avoidance, but may also 494
involve increasing ventilation rates 1. Future work might explore how occupants respond to 495
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detecting high levels of formaldehyde with this app, and if they are likely to take action to 496
reduce their exposure. 497
Limitations. Limitations of our system include potential error introduced due to 498
uncontrolled and potentially untested lighting conditions. We expect our system to function 499
properly under most indoor lighting conditions and provide a warning to the user when 500
lighting conditions are outside of system requirements. However, it is still possible that 501
particular shadows or other untested conditions could affect results. We tested the badges 502
under various lighting conditions, but the lightbulbs in our test were placed close to the badge 503
(30.5) and this will generally not reflect the typical light distance in a given room. Thus, the 504
bulbs used are reported here for reproducibility, but may not indicate the bulbs that are 505
necessary to use in a room where the light source is placed further away from the badge, 506
Additionally, error in the final reading could potentially be reduced with future badge 507
enhancements such as by controlling the position in which the user takes the image, taking 508
multiple images from which to calculate the final value, and further refining our mathematical 509
model. We tested and did not see interference from co-exposure to acetaldehyde or other 510
compounds present in pressed wood products. However, it is also possible that other untested 511
compounds, such as acetone or ammonia, may cause interference in the color change of the 512
badge. Our system is not able to be used under conditions of elevated relative humidity above 513
about 75-80%, which causes an artificial blue tint to the color changing area of the badge. 514
Additional work needs to be done to ensure sufficient communication of measurement 515
precision and error within the app. Further development and testing of this technology may be 516
able to overcome some of the limitations listed here. 517
Strengths. Strengths of this study include the development of a novel screening tool to 518
use a smartphone and color-changing badge to measure formaldehyde concentration in the air. 519
This system was developed in conjunction with end-users across the spectrum of 520
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technological literacy to expand potential use. We found a strong correlation between 521
exposure to formaldehyde and color change in the badge as read by the smartphone camera. 522
Thus, the badge is useful as an inexpensive indicator that formaldehyde is present and 523
detectible, while additional modification is required to enhance the precision of the 524
quantitative results. Given the wide range of plausible values from each reading, and levels of 525
uncertainty, we recommend use by well-informed users, professionals, or paraprofessionals 526
with risk communication training to interpret results and recommended actions until greater 527
quantitative precision can be achieved. However, information in the app can also assist with 528
interpretation of results and user education. This app is currently available for download on 529
both Android and iOS devices. This system can be further improved upon to provide results 530
that are more precise in the future. Our system makes formaldehyde measurement more 531
accessible to community scientists and other interested uses and also provides a new 532
opportunity for both scientific engagement and education. 533
534
CONCLUSIONS 535
Here, we developed a novel system for smartphone-based formaldehyde measurement 536
system for testing of residential formaldehyde levels. This new method can expand 537
measurement options for a range of future users and potentially also be applied to additional 538
compounds of interest. This current system is best used as a screening tool to determine the 539
general level of formaldehyde in an environment when cost, usability, and accessibility are the 540
most important considerations. We expect that future improvements to this and similar 541
systems can improve precision. The use of a smartphone encourages engagement of 542
community scientists and concerned citizens, and also provides an additional opportunity for 543
education about formaldehyde exposure. This platform can also be used by researchers in 544
epidemiological studies as well as medical providers conducting home visits for vulnerable 545
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populations, such as asthmatics. Finally, this system can be used to respond to future 546
widespread concerns that might occur related to formaldehyde exposure. 547
548
ACKNOWLEDGEMENT 549
Funding was provided by National Science Foundation (NSF) grant 1645048. Content has not 550
been reviewed by NSF. We also thank our industry collaborator, Morphix Technologies, for 551
manufacturing and providing novel home testing badges for the research team’s testing in this 552
study. We also want to acknowledge the community scientists who have contributed to the 553
project. A special thanks to Joan Tibor for her efforts related to use of this app. The authors 554
are grateful for user interface design advice from Kevin Nguyen, who also provided the 555
wireframe diagram included in this manuscript. 556
557
558
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655
656
657
658
TABLES 659 660
Table 1. SmART-Form app warning list. Boundary levels are listed in Table S1. 661
Warning Type Condition Solution Suggested to User
Overexposure Average lightness > 0.8 Retake the badge photo with less light
Low-light Average lightness < 0.4 Retake the badge photo with more light
Badge Contamination Lightness ratio > 1.0 Redo the test with a new badge
Blue tint due to high
relative humidity
Average saturation < 0.4 Redo the test with a new badge
Short exposure time Exposure time < 12 hours Wait for 72 hours of total exposure
Below detection limit Concentration < 20 ppb Concentration too low to detect
Above detection limit Concentration > 120 ppb Concentration higher than detectable
limit
662
663
664
665
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Table 2. Calibration test conditions. All tests were conducted at 23±0.5 °C for 72 hours. The 666
“Low Concentration” test was extended to measure continued color change. The “VOC co-667
exposure” test was used to simulate a realistic indoor condition with VOC emissions from a 668
piece of particleboard placed inside the chamber. 669
Test name
Formaldehyde
Concentration (ppb) Co-exposure conditions
Relative
humidity
Target
Actual
(HPLC)
Acetaldehyde
(ppb, HPLC) VOCs
High Concentration 100 109 0 No 50 ±5 %
Medium Concentration 50 68 0 No 50 ±5 %
Low Concentration 25 18 0 No 50 ±5 %
Acetaldehyde co-
exposure 50 68 26 No 50 ±5 %
VOC co-exposure 50 54 18 Yes 50 ±5 %
High relative humidity 50 67 0 No 75 ±5 %
670
671
672
Table 3. Comparison of app layouts before and after refinement. 673
Initial Version Final Version
Two layouts, with many features located on
test page
Three layouts, move surveys to additional
page
Simple list view, only titles and dates Add intuitive color signs and result
information
Three ambiguous steps to take photos Remove capability to take image of
surroundings, highlight the two before/after
steps
Evenly distributed user interface spaces for
all elements
Assign weighted space to each element
Simplified camera process, hard to handle Redesign camera function, show badge icons
674
675
676
677
678
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Table 4. Beta test results. A total of 10 people consented to the survey and completed at least 679
one question. 680
Question Yes (n) No (n) No response (n)
Did you have trouble opening the app? 0 10 0
Did you have trouble taking images? 0 9 1
Did you have trouble taking survey? 6 3 1
Was any part confusing?1 2 6 2
Were you able to get a formaldehyde concentration? 8 0 2
Did you receive an error taking survey? 3 1 6
1. Comments indicated confusion was related to survey access or questions. 681
682
FIGURE LEGENDS 683
Figure 1. System Overview. 684
685
Figure 2. Flow within app. Note that the research version also contained a mandatory consent 686
form upon the application start. If the user did not consent, then the application would end. 687
688
Figure 3. Color grid paper used to evaluate lighting conditions. 689
690
Figure 4. The cropped badge image from the SmART-Form application camera. The 691
calibration patch noted in green and the color-changing area is noted in red. Each block 692
contains 50×50 pixels in the 250×250 cropped badge image. 693
694
Figure 5. Final wireframe. 695
696
Figure 6. Screenshots from the app. The first screen is the test display screen. Each test will 697
bring the user to the test detail page (second screenshot). The third screen is accessed via the 698
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“data survey” link and will send the user to the external survey after clicking the “go to 699
survey” link. 700
701
Figure 7. Calibration of the app. A. The best-fit line to the data was y = -36301x + 36671 (R² 702
= 0.8811 and P<0.0001) and is shown in small blue dots. Standard deviation lines (dashed 703
outer lines) are shown in green around data. The standard deviation of the data was equivalent 704
to 10.9 ppb at 72 hours of exposure. B. Co-exposure to acetaldehyde or a VOC mixture did 705
not interfere with measurement (P=0.93, P=0.07, respectively). 706
707
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96x79mm (300 x 300 DPI)
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1
Supporting Information for: 1
2
Smartphone-based App for Residential Testing of 3
Formaldehyde (SmART-Form) 4
5
Siyang Zhang1, Nicholas Shapiro
2, Gretchen Gehrke
2, Jessica Castner
3 Zhenlei Liu
4, Beverly 6
Guo4, Romesh Prasad
4, Jianshun Zhang
4, Sarah Haines
5,6,7, David Kormos
6, Paige Frey
8, 7
Rongjun Qin6,9
, Karen C. Dannemiller6,7*
8
9
10 1
Department of Computer Science and Engineering, Ohio State University 11 2
Public Laboratory for Open Technology & Science 12 3
Castner Incorporated 13 4
Department of Mechanical & Aerospace Engineering, Syracuse University 14 5
Environmental Science Graduate Program, Ohio State University 15 6
Department of Civil, Environmental and Geodetic Engineering, College of Engineering, 16
Ohio State University 17 7
Department of Environmental Health Sciences, College of Public Health, Ohio State 18
University 19 8
Strand Associates, Incorporated 20 9
Department of Electrical and Computer Engineering, Ohio State University 21
22
23 *Corresponding author: [email protected], 470 Hitchcock Hall, 2070 Neil Ave, 24
Columbus, OH 43210, 614-292-4031 25
26
27
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Contents 28
29
30
Material Page 31
Mathematical Modeling for Color Change Measurement 3 32
Figure S1 3 33
Figure S2 5 34
Figure S3 6 35
Figure S4 6 36
Figure S5 7 37
Figure S6 7 38
Figure S7 8 39
Figure S8 9 40
Figure S9 10 41
Table S1 11 42
Table S2 11 43
44
45
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Mathematical Modeling for Color Change Measurement 46
47 In this system, we measure the change of surface albedo, which is calculated as the 48
ratio between the outlet light and inlet light from a surface. The outlet light reflected from the 49
surface into the camera can be referred as the color intensity computed from the Smartphone 50
image. Figure 1 shows the Lambertian reflectance model, a simple and widely used lighting 51
model. In this model, the inlet light and albedo of the material determine the final output light. 52
However, both inlet light ���� and the actual albedo � and �� are unknown and not directly 53
measurable from the images. We will then derive an alternative measurable variable that 54
could be associated with formaldehyde concentration. 55
� ���
��� (1) 56
57
Figure S1. A simple light model 58
From the badge images we can obtain the color intensities (the outlet light) of both the 59
calibration patch and the badge, denoted as �� �� (for calibrating patch) and �� �� (Chemical 60
badge). Let � and � be the albedo of the calibration patch and the chemical badge, we will 61
have the following relationships given Equation (1): 62
� ������
���� , �� �
�����
���� (2) 63
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where the index � refers to the ��� image that has a corresponding formaldehyde 64
concentration Ci. Note � is a constant that does not vary with the images, as there will not be 65
any chemical reactions in the calibrating patch. In Equation (2), Since only �� ��� and �� ��
� 66
are known and can be measured from the images, we eliminate the unknowns by taking the 67
first equation in (2) and substitute to the second: 68
�����
����� �
��
� (3) 69
Since � is a constant and �� is directly related to the degree of exposure of badge ��, 70
we then define �� ���
��
�����
����� as the variable (we call it relative albedo) for building the 71
regression model with the formaldehyde concentration ��, and now �� can be computed 72
directly from the images and no actual albedo needs to be explicitly calculated. We tested 73
with the lab samples and build a linear/non-linear regression model. In our modeling process, 74
other variants, such as aperture of the camera, as well as ISO sensitivity settings will also be 75
considered as factors for more accurate modeling. To relax the regression modeling and 76
minimize the compacts from camera variants, we set the camera parameters to fixed value, 77
such as ISO 200, White Balance 5K, Exposure time 1/60s etc. This model will be 78
implemented in the Smartphone app for formaldehyde concentration reading computation. 79
80
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81
Figure S2 Diagram of the small environmental chamber system. A formaldehyde permeation 82
tube inside the Dynacalibrator is maintained at a constant temperature with a constant passing 83
flow rate. The formaldehyde-containing airflow from the Dynacalibrator is mixed with the 84
clean air and fed to the 50 L small environmental chamber where the test badges are placed. 85
Different levels of test concentrations are obtained by adjusting the clean airflow rate, and are 86
confirmed by sampling at the chamber exhaust with the DNPH cartridges followed by HPLC 87
analysis (ASTM D5197-16). 88
89
90
91
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92
Figure S3 Small-scale environmental chambers (left) for badge exposure and the 93
Dynacalibrator (right) for formaldehyde generation 94
95
Figure S4 Badge sensor inside the environmental chamber 96
97
98
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99
Figure S5. Badge readings (color change ratio) read over 64 hours using 25W, 40W, 50W, 100
60W, and 60 W LED lightbulbs. 101
102
103
104
105
Figure S6. Color readings over different camera settings using 25W and 60W. For each image 106
the calibration and chemical path were cropped randomly three times so each of the values in 107
the bar graph are averaged from three values. Error bars (very small) represent the standard 108
error of three randomly selected areas from a single image of each condition. 109 110
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111
Figure S7. Screenshots from a previous version of the app. 112
113
114
115
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116
Figure S8. Chamber test results for high, medium, and low concentration tests. V1 and V2 117
refer to the two versions of the app (SafeAir®, version 1, and ChromAir®, version 2) that 118
were tested. Images were taken inside the system (through the glass) and then corrected using 119
the images taken outside the glass immediately before/after comparable internal images. 120
121
122
123
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124
Figure S9. High relative humidity caused inaccuracies in the readings on both Android and 125
iOS devices. 126
127
128
129
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Table S1. Boundary values for SmART-Form application variant warning list. Boundaries 130
were selected to be between the extreme values of measurements taken under suitable and 131
unsuitable conditions. 132
Warning Type Selected Boundary Measurements in test for range of
cutoff value
Overexposure Average lightness > 0.8 0.8258-0.6893
Underexposure Average lightness < 0.4 0.3145-0.5520
Badge Contamination
in “Before” image
Lightness ratio > 1 1.0020-0.9651
Blue tint due to high
relative humidity
Average saturation < 0.6 0.5185-0.8008
Short exposure time Exposure time < 12 hours Not applicable
Below detection limit Concentration < 20 ppb Determined by detection limit
Above detection limit Concentration > 120 ppb Determined by tested conditions
133
134
Table S2. Camera settings used along with the color imaging paper in Experiment 3 on an iOS 135
phone. 136 Lighting Experiment Conditions Final Settings
Setting 1 Setting 2 Setting 3 Auto
Adjust
Android <8.0 Android ≥8.0 iOS
White
Balance1
Locked Locked Locked N/A Fluorescent N/A N/A
Color
Temperature2
4003K 4190K 4318K N/A Locked 4000K 4000K
Shutter Speed 1/61s 1/47s 1/400s N/A 1/125 1/125 1/125
ISO3 208 473 555 N/A 200 200 200
Focus Auto Auto Auto N/A Auto Auto Auto
Aperture Lowest Lowest Lowest N/A Lowest Lowest Lowest
137
1 White Balance: Adjusts colors so that the image looks more natural. Most phone cameras 138
have auto, fluorescent, daylight, cloudy, etc. It it similar to the color temperature. 139
2 Color Temperature: Since white balance is locked on iOS systems, we use 4000K color 140
temperature to indicate fluorescent light conditions, similar to typical indoor conditions. 141
3 ISO: the camera’s sensitivity to the light, a higher value represents higher sensitivity. 142
N/A – Not available to access. 143
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