Evaluation of Particle sensors for indoor air quality monitoring ......TSI Aerodynamic Particle...

Evaluation of Particle Sensors for Indoor Air Quality

Monitoring and Smart Building Systems – KPI

Justification (ASHRAE 1756-RP)

Matthew Young, Graduate Research Assistant

Yangyang Zou, Graduate Research Assistant Dr. Andrew May, PI

Dr. Jordan Clark, Co-PI

Submitted to: ASHRAE RP-1756 Project Evaluation Subcommittee

Submitted on: December 20th, 2018

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EVALUATION OF PARTICLE SENSORS FOR INDOOR AIR QUALITY MONITORING AND SMART BUILDING SYSTEMS

Introduction Because of the difficulty in predicting and controlling indoor PM based on an a priori

understanding of sources and transport, a data-driven approach is an exciting prospect and one

that could have tangible positive consequences for human health. The quickly growing fields of

sensor science, data analytics, and intelligent control of buildings promise numerous paradigm-

shifting advantages over traditional means of predicting and controlling PM in both residential

and commercial buildings. Until recently, monitoring indoor PM concentrations was more

expensive than other less direct means of quantifying indoor air quality.

The low-cost sensors we are testing are exclusively optical sensors. Optical sensors operate by

detecting light originating from light emitting diode (LED) or laser that is scattered as it passes

through a stream of particles passing between the LED/Laser and a photocell. Briefly, light

scattering by particles is a function of particle size distributions, particle concentrations, particle

refractive index, particle shape, environmental variables, wavelength of light source, and other

variables. To convert light signals sensed at the photocell to mass concentration, additional

information, such as the density of the particles, is required.

However, the ability to observe events and produce signals is only the first step needed for a

sensor to be operational. The second step is a calibration/correlation process that relates sensor

outputs (voltages, pulse occupancy ratios, or digital signals) and particle concentrations.

Kulkarni, Baron, & Willeke (2011) suggest that reliable and accurate calibration requires:

“sufficient knowledge of the capabilities and limitations of the instrument, adequate information

on the where the information will be used, appropriate test facilities, proper selection of a desired

test aerosol, a thorough investigation of relevant parameters, and a quality assurance program

that is followed throughout the test.” Once calibration is conducted, in most cases a linear

equation is developed and integrated into sensors software or provided by manufacturers so that

bare sensor outputs can be converted to real concentrations or densities.

However, while PM sensors have become less expensive, the reliability of these inexpensive

sensors is very much in question. After consulting with the Indoor Environment Group at

Lawrence Berkeley National Laboratory (LBNL) and reviewing relevant literature and standards

including IEEE_2700-2014 and the US EPA’s Air Sensor Guidebook (2014), we developed a list

of quantitative and qualitative key performance indicators (KPI’s) for assessing the ability of a

particle sensing device to be used in building applications. The list was further refined with close

consultation with the ASHRAE Project Monitoring Subcommittee, and our final KPI’s include:

Functional Size-specific Range, Accuracy and Precision within this range, Peak-catching Ability,

Meteorological Sensitivity (temperature and humidity), Size Effects, Stability Metrics

(hysteresis, long-term degradation, response to extreme conditions), Manufacturing Consistency,

and Ease of Use. The following sections describe each of these KPI’s.

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Functional Size-specific Range Both the minimum and maximum limits of quantification that can be accurately reported by a

device may prove invaluable in determining a sensor’s usefulness in building applications. In

continuous monitoring applications, in which there is a cumulative effect of relatively low levels

of exposure, a sensor must be able to report low readings. In other cases, such as event detection

applications, it may be necessary to measure large concentrations for brief periods. The ideal

sensor will operate reliably across all conditions, but these limits must be evaluated, and a

functional range must be defined.

Limit of detection (LOD) for continuous PM sensors is one such indicator. Establishment of

the lower bound of a sensor’s functional range (which we are using interchangeably with the

term LOD) is important because under relatively clean indoor environments under normal

operating conditions, overall sensor performance may be a strong function of their ability to

accurately measure low concentrations. However, there is no standardized method for defining

the LOD for continuous PM monitoring by low-cost sensors (Wallace et al. 2011). LOD has

been previously defined as what can be detected with reasonable certainty using an analytical

method (Wallace et al. 2011). Wang et al (2015) used the LOD equation LOD = 3σblk/k, where

σblk is the standard deviation of a sensor’s response under “blank” conditions maintained in a

cleaned air chamber, and k is the slope of linear regression slope using incense as the particle

source. Zikova et al. (2017) define LOD as the least sensor concentration when the ratio of the

mean to the standard deviation exceeds 3. Sun et al. (2016) define LOD for gas sensors as LOD

=3σblk/k, similar to Wang et al. (2015). Similarly, Northcross et al. (2013) defined LOD as three

times the standard deviation when a sensor is exposed to a near-zero particle environment

(Northcross et al., 2013).

While the lower LOD has been studied extensively, there is no consensus method for

determining the upper limit of detection. In some cases, sensors can fail to read particle

concentrations far below the manufactures reported range, and in other cases the sensor may

outperform the manufactures reported range. Wang et al. (2016) observed a Syhitech DSM

sensor showed a saturation level at 5,000 µg/m3 even though the manufacture lists an operational

range of 0-1000 µg/m3. Austin et al. (2015) defined the upper limit of detection, or saturation

point, as the concentration at which a 10 µg/m3 increase in the reference monitor (TSI APS

3321) resulted in a less than 0.2 change in the Lo Pulse occupancy of the low-cost sensor

(Shinyei PPD42NS).

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Precision and Accuracy

Sensor precision will be assessed on both size-resolved and non-size-resolved bases. Factors

contributing to lack of precision include: different wavelengths of light sources, orientation of

the light source and detector, mode of particle transfer from inlet to sensors, and air flow rate of

different instruments (Manikonda et al., 2016).

Researchers use a variety of metrics to define a sensors accuracy, such as: R2, slope,

intercepts, linearity, or an integrated approach. Even if sensor accuracy is poor, there is often a

linear relationship between low-cost sensors and research-grade equipment. Thus, low accuracy

can be accounted for by using a linear model to develop a calibration curve for the sensor. The

calibration curve is created by comparing a low-cost sensor to a reference sensor and exposing

both sensors to known concentrations. Comparing the sensor’s outputs gives the ability to

generate a best-fit curve, which may be either linear or non-linear equation, that allows the user

to obtain more accurate concentrations from the low-cost sensor outputs. However, in most cases

a linear regression analysis has proven to be the most used method for developing a calibration

equation (Budde et al. 2013, Sousan et al. 2016a, Sousan et al. 2016b, Taylor 2016, Sousan et al.

2017). Sousan et al. (2016b) suggests that any sensor can be used to accurately estimate mass

concentrations once calibrated for a specific aerosol type.

One influential factor in determining a sensor’s accuracy is the time-step used during

analysis. As shown below (Figure 1), Budde et al. (2013) tested a Sharp GP2Y1010 (GP2Y)

against a high-accuracy reference device (TSI Dustrak DRX 8533) and found no correlation

when initially comparing raw-sensor output from the Sharp GP2Y to the TSI DustTrak.

However, when samples were averaged over 1 second intervals the correlation improved

dramatically. And, when samples were averaged over 60 second intervals, there was an even

higher correlation between the sensors.

Zheng et al. (2018) tested five uncalibrated Plantower PMS50003 over various time scales.

The results showed that 1 hour aggregated data had a mean R2 of 0.4, 6 hour aggregated data had

a mean R2 of 0.80, 12 hour aggregated data had a mean R2 of 0.84, and 24 hour aggregated data

had a mean R2 of 0.93. This shows that there are immense improvements in correlations from <1

hour windows up to 6 hour windows, and then gradual improvements for larger windows sizes.

While increasing the time averaging interval may increase agreement in most cases, it may not

be suitable for some end user applications. For example, using a 24 hour time average might be

adequate for gauging outdoor air pollution, but it may fail to be used for specific event detection

applications.

Figure 1: Time Averaging Effect (Budde et al., 2013)

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Peak-catching Ability Peak-catching is a sensor’s ability to measure and

react to a short-lived increase in concentration. To

increase applicability, sensors should be able to

accurately detect events that create a rapid increase in

indoor PM concentrations. They should also avoid

reading “false positives” which could inhibit an end-

user’s intended application of the sensor. A sensor’s

peak-catching ability can be measured by how it

responds to predetermined concentrations greater

than its baseline measurement, as well as the time it

takes to reach its peak concentration. Conversely,

sensors should be able to recover after a peak event

and return to accurately reading lower concentrations.

Peak-catching may also differ based on particle

composition. The return to steady-steady is also an

important observation because a sensor should be

able to differentiate whether peaks are linked

events or independent events (e.g., whether there

one event or two).

Several researchers have explored the ability of

low-cost sensors to detect events and defined peak-

catching ability in different ways. Singer & Delp

(2018) showed that peak-catching ability is an area

where low-cost sensors differ dramatically. Some

sensors are able to track quick changes in PM

concentrations with accuracy close to that of a

research-grade instrument, while some miss peaks

completely.

To address peak-catching for personal

exposure in a residential setting, Wallace et al. (2006)

defined an event as a 7 µg/m3 increase above the

baseline concentration. Then, the event was counted as

a single event until the running average fell below 10

µg/m3 and the slope changed from negative to

positive. Chan et al. (2017) identified events as a 10

μg/m3 increase above the baseline concentration.

Event start time was defined as the time when there are three consecutive measurements with

zero or positive change from the previous measurement. The decay period from the event was

defined as three consecutive measurements with zero or negative measurements following the

peak. Events were considered independent if the concentration either (a) dropped more than 50

μg/m3 below the peak concentration, or (b) the concentration dropped by more than 50% of the

peak concentration minus the baseline.

Figure 2: Peak-catching Test of Residential Activities (Wallace et al.,

2006)

Independent

Linked

Figure 3: Linked vs. Independent Events (Chan et al., 2017)

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In other studies, researchers have tested

sensor peak catching ability using

controlled events. Manikonda et al. (2016)

found that during a 6-hour test with Arizona

Test Dust (ATD), 3 expected peaks were

observed. The peaks, as recorded by the

TSI Aerodynamic Particle Sizer (APS)

3321, were approximately 475, 200, and

700 µg/m3. When testing ATD, the APS

3321 reported overall higher concentrations

than the other sensors (Speck, Dylos,

AirAssure, and Grimm PAS-1.109). One of

the Speck monitors reported 30% higher

peak concentrations when compared to the other Speck monitor. On the contrary, all three

AirAssure sensors reported peak concentrations 50% lower in comparison to the APS 3321.

Furthermore, there may be differences in peak-catching quantification based on the concentration

of the peak.

Zikova, Hopke, & Ferro (2017) tested 66 Speck sensors in comparison to a Grimm PAS-

1.109, in an indoor residential setting for 3 days. The Grimm 1.109 identified two peaks

exceeding 20 µg/m3, and in both instances the average of the 66 Specks was greater than the

concentrations reported by the Grimm PAS-1.109. In one instance, the Speck average was five

times greater than the reported concentration from the Grimm PAS-1.109 (20 µg/m3 compared to

100 µg/m3). In the other instance, the Speck average was only 10 µg/m3 greater than the

concentration reported by the Grimm PAS-1.109 (45 µg/m3 compare to 55 µg/m3).

Figure 4: Peak-catching Test with ATD (Manikonda et al., 2016)

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Meteorological Sensitivity For a variety of reasons, sensors

behave differently in different

environmental conditions, namely

temperature and relative humidity (RH).

Most studies have found that

temperature has a negligible effect on

the accuracy of low-cost sensors,

within a reasonable range of

temperatures. However, Budde et al.

(2013) noted a correlation between a

Sharp GP2Y sensor output and

temperature when compared at a

constant concentration (Figure 5).

The primary concern for RH effect is the hygroscopic growth that will occur in particles,

leading to inaccurate sensor readings. Additionally, water absorbs infrared radiation which

reduces light intensity received by the phototransistor and thus underestimates true

concentrations. High concentrations of water can also cause circuits to fail (Wang et al., 2016).

Different sensors and testing conditions have led to a range of suggested operational RH %, with

some studies indicating 80% RH as the upper limit (McMurry & Stolzenburg, 1989) and others

indicating 60% RH as an upper limit (Han, Symanski, & Stock, 2016).

Jayaratne et al. (2018) tested the Plantower PMS1003 in a chamber setting with a constant

exposure of 10±1 µg/m3. The PMS1003 maintained a steady reading around 9 µg/m3 until the

chamber reached a RH of 78%. At the maximum RH of 89%, the PMS1003 saw an increase to

about 16 µg/m3. Wang et al. (2016) conducted a lab evaluation of low-cost particle sensors,

including Shinyei PPD, Samyoung DSM, and a Sharp GP2Y, compared to the SidePak,

AirAssure, and a scanning mobility particle sizers (SMPS). It was found that as RH increased,

from 20% to 67% to 75% to 90%, sensor outputs would first increase, then begin to decrease

(Figure 6). These results were corroborated with the findings in the review of low-cost sensors

conducted by Rai et al. (2017), which confirmed that sensor outputs would first increase, and

then decrease as RH increased from 20% to 90%.

Figure 6: RH Effects (Wang et al., 2016)

Figure 5: Temperature Effect on Sensor Output (Budde et al., 2013)

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Size Effects Sensors must be able to accurately

detect and register particles of different

sizes to improve their applicability in

various environments. Singer & Delp

(2018) found that in some cases neither

consumer nor research grade sensors

responded accurately to emissions

composed entirely of particles smaller than

0.3µm in diameter because they do not

scatter enough light. Additionally,

different sensors may respond more

accurately to smaller or larger particles

depending on their detection mechanisms

(e.g., lower sensor light source wavelength

results in better ability to detect smaller

particles).

Liu at al. (2017) tested four low-cost sensors (Sharp GP2Y, Shinyei PPD42NS (PPD),

Samyoung DSM501A (DSM), and Oneair CP-15-A4 (CP) compared to a Tapered Element

Oscillating Microbalance, and it was found that the slopes of calibration lines for all given

sensors in the cases excluding particles larger than 2.5 µm were higher than those in the cases

including particles larger than 2.5 µm. The author attributes this unlikely outcome to the fact that

low-cost sensors rely on temperature gradients or small fans to drive intake, and larger particles

require higher flowrates.

Wang et al. (2016) exposed a Shinyei PPD, Samyoung DSM, and a Sharp GP2Y sensor to

particles with sizes of 300, 600, and 900 nm. Saturation points for the PPD and DSM sensors for

900 nm particles can be seen at 100 µg/m3, while GP2Y remains linear. While the GP2Y

performed better in the presence of large particles, the PPD and DSM sensors performed better

with smaller particles. This indicates certain sensors perform better depending on particle size.

Figure 7: r2 Values for Different Particle Diameters (Taylor, 2016)

Figure 8: Sensor Performance for Different Sized Particles (Wang et al., 2016)

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Stability Metrics A sensor must show both stability in general, meaning the precision and accuracy remain

relatively constant over a certain time period, and resilience in the environments in which they

will operate. These environments may be somewhat harsh, as within an air duct, an air intake, or

a kitchen environment. Preliminary evidence shows that fouling of optical chambers is one

mechanism by which sensors may lose accuracy over time (Austin et al., 2015). Poor stability

and poor resilience can both be addressed through frequent calibrations and maintenance

practices, respectively. But, if the frequency of either is too high, it can be burdensome.

Long-term degradation (drift) quantification Because low-cost sensors will be expected to operate continuously over long periods of time

without frequent calibration or maintenance, the ability of a sensor to give repeatable

measurements over its lifetime is of great importance for building applications. Drift refers to a

gradual change in performance over an extended period of time. To measure drift, it is best to

compare sensor readings at ambient levels over extended time to an initial baseline measurement.

However, this requires leaving a sensor in one location for periods of time ranging from months

to years. Causes of drift could include changes in weather, malfunctioning mechanical parts,

sensor damage from chemical exposure, or a change in intensity of the sensors light source

(Polidori, Papapostolou, & Zhang, 2016).

To evaluate sensor drift, Crilley et al. (2018) left an Alphasense OPC-N2 co-located with a

Grimm PAS-1.108 at an outdoor location for four months. It was found that after applying a RH

corrective factor, there was little observed drift for either sensor. However, drift may be unique

to specific sensors and uses. A study of 29 Speck sensors that were used in different experiments

over a year and a half showed an average monthly drift of -3.1% (Taylor, 2016). The Speck

sensors were obtained from the lending library for the Carnegie Library of Pittsburgh and thus

their specific uses were not known.

Response to extreme conditions For a sensor to successfully be used in occupational settings, they will need to be able to

endure extreme conditions, meaning consistent exposure to high concentrations and peaks. Most

low-cost sensors list their functional range up to concentrations much greater than what would be

expected in ambient conditions. To test the true functional range of a sensor it must be exposed

to increasing concentrations until it reaches its point of failure. It is unproven as to whether

manufacture listed accuracy is consistent at extreme concentrations.

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Ease of Use/Communication Protocol/Output “Ease of use” is a broad term that we are using to capture the level of effort required to

implement each PM sensor in practice. For example, we are interested in the ease of sensor

integration with building control systems and existing communication protocols; ease of data

transmission to local or cloud-based archives; and ease of setup. Related to ease of use is the

relative autonomy of the sensors (i.e. can they collect, store, and communicate data without an

excessive frequency of maintenance?). There are essentially two varieties of low-cost sensors we

are testing: integrated devices and bare sensors. Bare sensors require the most steps to get from

sensor reading to applicable outputs. Many of the bare sensors output a voltage or other

electronic signal that must be recorded, and the user is required to use manufacturer-reported

calibration curves to determine particle concentrations. Conversely, integrated devices require

limited user input since the manufacturers include both the sensor and an algorithm to convert

the sensor output signal to particle concentrations in an “off-the-shelf” package. Many integrated

devices also offer built in features such Bluetooth or Wi-Fi connectivity, SD card storage,

meteorological sensors, or compatibility with other smart devices like thermostats. However,

with each feature there are pros and cons, for example Wi-Fi connected device may not work

with networks depending on their security.

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Manufacturing Consistency Cost of particle sensors is

often reduced by using less

precise and less consistent

manufacturing methods. For

example, the cheapest sensors are

often die-cast with plastic, leading

to appreciable differences in

critical components such as the

optical chamber. To test a

manufacturers consistency,

multiple sensors (typically three

or more) must be tested at the

same location, and their outputs are then compared. The precision in manufacturing could affect

how sensors perform when used within a connected network.

For example, some studies (Budde et al., 2013; Taylor & Noubakhsh, 2015) suggest that

sensor accuracy can be improved through network average calibration practices. At The Air

Quality Sensor Performance Evaluation Center (AQ-SPEC), Polidori, Papapostolou, & Zhang

(2016) tested sensors in triplicate to determine their intra-modal variability, which is analogous

to our description of manufacturing consistency. The intra-modal variability was calculated

through a set of descriptive statistical parameters such as mean, median, and standard deviation.

If the intra-model variability was less than 20% (better agreement) then the concentrations were

averaged and used for the parameters for which the sensors were being tested (i.e., precision,

accuracy, detection limit, climate susceptibility), but if the intra-modal variability was greater

than 20% (worse agreement) then the sensor concentrations were treated independently for the

parametric evaluation. This methodology for testing manufacturing consistency shows how

consistent manufacturing could be implied to networks containing multiple sensors.

Figure 10: Uncalibrated Performance of Dylos Sensors (Budde et al., 2013)

Figure 11: Comparison of 66 Co-located Speck Sensors (Zikova et al., 2017)

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