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the Creative Commons Attribution 4.0 License.
| 27 Aug 2018
The influence of humidity on the performance of a low-cost air particle mass sensor and the effect of atmospheric fog
While low-cost particle sensors are increasingly beingused in numerous applications, most of them have no heater or dryer at theinlet to remove water from the sample before measurement. Deliquescentgrowth of particles and the formation of fog droplets in the atmosphere canlead to significant increases in particle number concentration (PNC) andmass concentrations reported by such sensors. We carried out a detailedstudy using a Plantower PMS1003 low-cost particle sensor, both in thelaboratory and under actual ambient field conditions, to investigate itsresponse to increasing humidity and the presence of fog in the air. We foundsignificant increases in particle number and mass concentrations at relativehumidity above about 75 %. During a period of fog, the total PNC increasedby 28 %, while the PNC larger than 2.5 µm increased by over 50 %.The PM10 concentration reported by the PMS1003 was 46 % greater thanthat on the standard monitor with a charcoal dryer at the inlet. While thereis a causal link between particle pollution and adverse health effects, thepresence of water on the particles is not harmful to humans. Therefore, airquality standards for particles are specifically limited to solid particlesand standard particle monitoring instruments are fitted with a heater ordryer at the inlet to remove all liquid material from the sample before theconcentrations are measured. This study shows that it is important tounderstand that the results provided by low-cost particle sensors, such asthe PMS1003, cannot be used to ascertain if air quality standards are beingmet.
The rapid technological advancements in the fields of material science,digital electronics, and wireless communication have given rise to a widerange of low-cost air quality sensors that are now readily available on themarket. These sensors are increasingly being used in many applications thatwere previously not achievable with conventional expensive equipment (Kumaret al., 2015; Rai et al., 2017; Snyder et al., 2013). Some of theseapplications are the monitoring of personal exposure and indoor airpollution and the gathering of high-resolution spatio-temporal air pollutiondata by means of extensive sensor networks. The data thus derived are beingutilised for a variety of air pollution management tasks such assupplementing conventional air pollution monitoring, understanding the linkbetween pollutant exposure and human health, emergency response management,hazardous leak detection and source compliance monitoring. In the process,they also serve to increase the community's awareness and engagement towardsair quality issues (Snyder et al., 2013; Jovasevic-Stojanovic et al., 2015;Rai et al., 2017).
However, there are many questions regarding the reliability and, inparticular, the accuracy of these low-cost sensors and their suitability inthe applications that they are being used (Lewis and Edwards, 2016). Many ofthese sensors have serious limitations. For example, while many particlesensors respond well to high concentrations, they fail to do so at lowerlevels such as typical ambient concentrations (Jayaratne et al., 2018; Raiet al., 2017; Kelly et al., 2017). Single gas sensors are very oftenaffected by other interfering gases (Fine et al., 2010; Piedrahita et al.,2014), while environmental parameters, such as temperature and humidity, canalso affect the performance of these sensors under certain conditions(Holstius et al., 2014; Rai et al., 2017; Crilley et al., 2018; Jayaratne etal., 2018).
In this paper, we investigate the effect of atmospheric relative humidity onthe performance of a low-cost particulate matter sensor. Humid conditionscan affect the performance of a sensor in several ways. For example, sensorsthat operate on the principle of light scattering are affected, as theparticle refractive indices are dependent on relative humidity (Hänel,1972; Hegg et al., 1993). High humidity can cause condensation to form onelectrical components, leading to resistive bridges across components. In gassensors, condensation on the sensor surfaces can affect the reactions thatgive rise to the measurable electric currents.
Hygroscopic growth occurs when the relative humidity exceeds thedeliquescence point of a substance. There are many hygroscopic salts such assodium chloride, that absorb water and grow at relative humidity as low as70 %, present in the atmosphere, especially in marine environments (Hu etal., 2010). Jamriska et al. (2008) found a significant effect of relativehumidity on traffic emission particles in the size range 150–880 nm andattributed it to hygroscopic particle growth. Crilley et al. (2018)demonstrated a significantly large positive artefact in measured particlemass by an Alphasense OPC-N2 sensor during times of high ambient relativehumidity. Manikonda et al. (2016) cautioned against using PM sensors inoutdoor locations at high humidity due to hygroscopic growth of particles.In circumstances where the relative humidity approaches 100 %, there isthe possibility of mist or fog droplets that are detected as particles.While there is a causal link between particle pollution and adverse humanhealth effects, the presence of water on the particles plays no part in it.Therefore, air quality standards for particles are based on the dry, solidmaterial only, and stipulate that the liquid portion must be eliminated whenmeasuring particle mass for regulatory purposes. In order to achieve this,many conventional particle mass monitors, such as the standard taperedelement oscillating microbalance (TEOM), employ a charcoal heater at itsinlet to remove all liquids from the particles that are being measured(Charron et al., 2004; Alexandrova et al., 2003). Thus, sensors with nodrying facility at the inlet measure what is actually present in theenvironment rather than what is required under regulatory protocols.
The composition of particles in the atmosphere of Brisbane, as derived fromHarrison (2007), is shown in Fig. 1. The subtropical, near-coastalenvironment is characterised by the presence of several hygroscopic saltssuch as sodium chloride, ammonium sulfate, and ammonium nitrate that havedeliquescence relative humidities in the range of 70–80 % (Hu etal., 2010). Many particles in the air in Brisbane contain these salts invarying concentrations. Once the relative humidity exceeds the respectivedeliquescence values, those salts begin to absorb water, resulting inparticle growth and the excess water registered by PM sensors, unlessthey are removed at the instrument inlets by heating or drying (Alexandrovaet al., 2003). While more expensive instruments, such as the TEOM, havebuilt-in drying features at the sample inlets, it is not standard onlow-cost sensors and even in many other mid-cost monitors such as the TSIDustTrak (Kingham et al., 2006).
Figure 1Composition of particles in the atmosphere of Brisbane, as derivedfrom Harrison (2007).
There have been very few studies of the effect of relative humidity on theperformance of low cost sensors. Wang et al. (2015) investigated theperformance of three low cost particle sensors based on light scattering andconcluded that the absorption of infrared radiation by a film of water on aparticle can cause an overestimation of the derived particle massconcentration due to the reduced intensity of light received by thephototransistor. Hojaiji et al. (2017) showed that the particle massconcentration reported by a Sharp PM sensor increased when the humidity wasincreasing but not when it was decreasing. While several studies have drawnattention to a possible effect of humidity on the performance of low costsensors, no study has reliably quantified the effect. This study was carriedout to investigate and to assess the magnitude of the effect of relativehumidity on the performance of a low-cost particle sensor and to understandthe mechanisms involved.
In this study, we focussed on the effect of relative humidity on theperformance of a low-cost particle sensor in the laboratory and under realworld conditions in an outdoor location at an air quality monitoring stationwith standard instrumentation.
2.1The test sensor
Prior to commencing this study we tested a range of low-cost particlesensors, including the Sharp GP2Y, Shinyei PPD42NS, Plantower PMS1003,Innociple PSM305 and the Nova SDS011 (Jayaratne et al., 2018). All of themwere found to be affected to some degree by humidity with the Sharp andShinyei being affected at relative humidity as low as 50 % while the otherthree showed deviations from the standard instruments when the relativehumidity exceeded 75–80 %. Considering their performance characteristics,the Plantower PMS1003 was selected as the most suitable sensor for thisstudy. This sensor was selected because it is freely available, low-cost(around AUD 20) and its performance characteristics have been previouslyinvestigated extensively in our laboratories and found to be superior to theother sensors tested (Jayaratne et al, 2018). The PMS1003 is a compactparticle sensor that monitors particles larger than 0.3 µm indiameter. It operates by drawing the sample air, using a miniature fan, into asmall inbuilt chamber, where the particles are exposed to a fine laser beam.The scattered light is detected by a photodetector which produces anelectrical output. The signal is processed using a complex algorithm toprovide real-time readings of particle mass concentration in three ranges –PM1, PM2.5, and PM10, together with particle numberconcentrations (PNC) in six size ranges – greater than 0.3, 0.5, 1.0, 2.5,5, and 10 µm, at intervals down to 2 s. All three PM values arereported in units ofµg m−3, while the PNCs are reported as per0.1 L or dL−1.
The PMS1003 was mounted on a custom interface board including a low-powermicrocontroller with multiple serial interfaces, a high-resolution 16-bitanalog to digital converter, and a real-time clock that provided accuratetime-stamping of the measurements. The PMS1003 was attached to a frame alongwith the interface board, allowing unobstructed airflow into and out of thedevice. The microcontroller was programmed to perform the necessary signalprocessing and power management. The time-stamped data were transferred inreal-time via USB serial communications to a computer and logged into a textfile for post-analysis.
2.2Standard instrumentation
In the laboratory experiments, we used a TSI 8530 DustTrak DRX aerosolmonitor with a PM2.5 impactor. The instrument has an inbuilt datalogger. The sample air is drawn through the inlet which has no dryingfacility to remove the liquid portions of the particles, if any. Prior tothe study, the DustTrak was calibrated against a standard TEOM in thelaboratory. With dry ambient aerosols, the PM2.5 concentrationsreported by the two instruments agreed to within 10 % (Jayaratne et al.,2018). With normal ambient aerosols, the readings again agreed closely untilthe relative humidity exceeded about 75 % when the DustTrak readings weresignificantly greater than that of the TEOM. The air quality monitoringstation, where the field study was conducted, contained two TEOMs providingaccurate 5 min readings of PM2.5 and PM10, together withaccurate measurements of air temperature and relative humidity.
The station also included a nephelometer to monitor atmospheric visibilityin terms of the particle back-scatter (BSP) coefficient, reported in unitsof Mm−1. The BSP corresponds to the concentration of particles in theair and provides an estimate of the visibility. Observations have shown thatits value typically ranges from about 5–15 Mm−1 on a “clean” day toabout 50 Mm−1 on polluted days with, for example, traces of smoke inthe atmosphere. However, during periods of fog, the value is generally muchhigher. Careful visual observations over a period of several weeks inBrisbane confirmed that the presence of mist or fog in the air generallyresulted in BSP readings greater than 100 Mm−1. Where visualobservations were not possible, such as during the night, this value of BSPwas used in this study as an indicator of fog in the atmosphere.
2.3Laboratory experiments
The laboratory experiments were carried out in a 1 m3 chamber. Ambientair from outside the building was drawn into the chamber by means of a lowpower air pump at a flow rate of about 1 L min−1 so that the particleconcentration in the chamber was maintained at a relatively steady valueclose to that of the outdoor air. The interface board with the PMS1003 wasplaced on a raised platform inside the chamber and directly connected to thecomputer which was placed outside. Readings were obtained in real-time atintervals of 5 s. The DustTrak monitor was located outside the chamber,sampling the air through a short length of conductive rubber tubing. A smallfan on the floor of the chamber was used to ensure that the air was wellmixed to give uniform particle concentrations throughout its volume. Thehumidity in the chamber was increased by introducing moist tissue paper. Therelative humidity was monitored with a TSI 7545 Indoor Air Quality meter.
2.4Field experiments
The field measurements were carried out at an air quality monitoringstation, situated close to a busy road, carrying approximately 100 vehiclesper min during the day. The PMS1003 was housed in a sealed weather-proof boxof dimensions mm, and the built-in fan was used to draw ambientair from the outside through an aperture in the box. Readings were obtainedat 5 min intervals over a continuous period of 24 days between 21 July and14 August 2017.
3.1Laboratory experiments
With the steady introduction of ambient air, the PM2.5 concentration inthe chamber was maintained at about10±1 µg m−3. PNCswere typically about 1000 and 50 dL−1 in the size bins larger than 0.3and 1.0 µm, respectively. As the humidity in the chamber wasgradually increased, the particle mass concentrations reported by thePMS1003 did not show a significant change until the relative humidityreached about 78 %. Figure 2 shows the corresponding PM2.5concentrations reported by the PMS1003 and the DustTrak. The criticalrelative humidity beyond which the PM2.5concentration reported by thePMS1003 begins to deviate from the previous ambient value is indicated bythe broken line in the figure. Beyond this value, the PM2.5 readingsindicated by the PMS1003 increased steadily from about 9 µg m−3at a relative humidity of 78 % to about 16 µg m−3 at themaximum relative humidity of 89 % achieved in this experiment, anincrease of almost 80 %. Interestingly, the corresponding increase in thenumber concentration of particles in the smallest size bin, 0.3 to 0.5 µm, was of the order of 10 %, suggesting that the increase inPM2.5 was mainly as a result of particle growth by water absorptionand not due to the formation of new water droplets. Thereafter, graduallyallowing the relative humidity to decrease resulted in a hysteresis effectwith no significant reduction in PM2.5 concentration until therelative humidity had decreased to about 50 %. The DustTrak aerosolmonitor also showed a similar trend, with no change in PM2.5concentration reading until the relative humidity exceeded about 75 % andthen a steady increase in concentration as the humidity was increasedfurther (Fig. 2).
Figure 2The PM2.5 concentration reported by the PMS1003 and theDustTrak as the relative humidity was increased in the laboratory chamber.
As observed in the figure, the PM2.5 readings of the particularPMS1003 sensor used in this experiment were consistently higher than thereadings on the DustTrak. In general, the readings of the PMS1003 sensorsdiffered between the individual units. The differences depended on the typeof aerosol and the concentration being measured. At the low concentrationsfound in the ambient environment of Brisbane, the coefficient of variationbetween “identical” PMS1003 sensor units was about 0.07, as reported indetail in Jayaratne et al. (2018).
3.2Field experiments
Figure 3 shows the time series of the PM2.5 concentrations reported by thePMS1003 and the standard TEOM during the entire duration of the study. Alsoshown is the relative humidity during this period. The relative humidityexhibited a daily cycle with a minimum in the early afternoon and a maximumat night. Note that the peak PM2.5 concentrations indicated by bothinstruments generally coincided with the time when the relative humidityreached its maximum value near dawn each day. The maximum value oftencoincided with episodes of fog, although its value did not reach 100 %. Itis likely that this was a consequence of a limitation of the instrument. Atsuch times, the PMS1003 reading was generally higher than the TEOM. However,from Fig. 3, it is observed that on many days, the readings on bothinstruments increased during times when the relative humidity was high,suggestive that the TEOM did not remove all of the liquid portion of theaerosols. In the afternoon, the TEOM reading was often higher than thePMS1003. This is probably because most of the aerosols in the atmosphere atthis time were ultrafine particles from motor vehicle emissions. The size ofthese particles are below the minimum detectable size limit of the PMS1003which is 0.3 µm.
Figure 3Time series of the PM2.5 concentrations reported by thePMS1003 and the standard TEOM, together with the relative humidity, duringthe entire duration of the study.
Figure 4 shows the hourly PM2.5 concentrations reported by the PMS1003and TEOM on the night of 6–7 August, which was relatively humid at theair quality monitoring station. On this night, the relative humidityreported by the monitoring station increased steadily through the night from76 % at 18:00 h, exceeding 90 % at 05:00 h the next morning. Fog wasvisually observed at the site during the early morning hours. The TEOMshowed little variation in PM2.5 concentration over this period but thevalue reported by the PMS1003 increased sharply and doubled by the morning.
The PNC values reported by the PMS1003 in all size bins were also higherduring periods of fog. Under stable conditions, the PNCs reported by thePMS1003 in the various size bins are generally linearly related. In Fig. 5,we show the number concentration of particles larger than 1.0 µmagainst the corresponding number in the lowest size bin, 0.3 to 0.5 µm on the 31 August when there was an episode of fog visually observedduring the early morning. The points under the broken line in the graphcorrespond to the daytime and the first half of the night when there was nofog observed. A linear relationship is evident at this time as illustratedby the straight line in Fig. 5. However, there is a departure from thistrend in the section of the graph above the broken line which coincides withthe period when the relative humidity was above 75 %. As indicated, thepoints at the upper end of this graph correspond to the early morning hoursduring the presence of fog, clearly suggesting that the PMS1003 detectswater droplets in the air.
Figure 4The hourly PM2.5 concentration reported by the PMS1003 andTEOM over a humid night (7 August) at the outdoor monitoring station. Thearrows show the changing trends.
Next, we compare the PM2.5 concentration reported by the PMS1003 andTEOM during a day with no fog and on a day with an episode of fog (Fig. 6).Figure 6a shows the results on the 24 July when the relative humiditydid not exceed 80 % and there were no visual reports of fog. Theconcentrations shown by both instruments remained below 20 µg m−3 during much of the day and never exceeded 30 µg m−3 atany time. Figure 5b is the corresponding graph for the 30 August whenthere was fog observed between 03:00 and 06:30. During the morning, theindicated relative humidity touched 100 % at 03:00 and decreased to90 % soon after the fog dispersed at about 06:30. The PMS1003 showed asharp increase in PM2.5 concentration, almost doubling from midnight to06:30, while the TEOM did not show a significant increase during this timeperiod. Thereafter, the concentrations reported by both instruments showed asteady decline and attained agreement at about 09:00.
Figure 5Graph of PNC > 1.0 µm against the PNC between0.3–0.5 µm during a day that included a period of fog (31 July). Thestraight line represents the best fit through the points under the brokenline only.
Figure 7 shows the corresponding PNCs reported by the PMS1003 at 03:00, 06:00,09:00 and 12:00 h on the day shown in Fig. 6b. The bars represent theparticle number dL−1 at all sizes greater than the values given in thelegend inµm. For example, we see approximately 1000 particles thatare larger than 0.5 µm in 1 dL at 03:00. Note that the fog firstbecame evident at 03:00 and dissipated by 06:30. The relative humidityand PM2.5 concentrations reported by the PMS1003 and TEOM at the fourtimes are given below the figure. During the time of fog, the total PNCincreased by 28 %, while the PNC larger than 2.5 µm increased byover 50 %. Considering the particle mass in the air, the TEOM showed aPM10 concentration increase of about 31 % while the PMS1003 showed asignificantly larger increase of 46 %. All these observations indicate amoderate increase in the number of fog droplets in the air, accompanied by avery strong rate of hygroscopic mass growth.
It is well known that humid air can have a negative effect on theperformance of electronic circuits. For example, moisture in the air candecrease the insulation resistance in electrolytic capacitors and increasethe leakage currents in transistors and integrated circuits, reducing thegain. In our previous tests (Jayaratne et al., 2018), we showed that theperformance of some low-cost particle sensors such as the Sharp GP2Y and theShinyei PPD42NS were affected at relative humidity as low as 50 %. Theadverse effect was a fluctuation of the output signals, rather than a steadyincrease with humidity. This was obviously not due to particle growth, andwe conclude that the electronics or optical characteristics were, in someway, responsible for these effects.
Figure 6Variation of the PM2.5 concentration reported by the PMS1003and TEOM during a day(a) with no fog (24 July) and(b) with early morningfog (30 July).
However, sensors such as the Plantower PMS1003, Innociple PSM305 and theNova SDS011, as well as particle monitors such as the TSI DustTrak, did notshow a marked effect until the relative humidity exceeded about 75 %, whenthey began to show a steady increase. The results of the present study, withthe PMS1003 and the DustTrak showed that this was due to particle growth.When the relative humidity is high, particle growth and fog are detected andreported by particle monitoring instruments that do not have dryingfacilities at the sample inlets. This effect needs to be taken intoconsideration when using low-cost particle sensors, especially inenvironments that contain hygroscopic salts such as near coastal regions.Particles in the air begin to grow once the deliquescence relative humidityis exceeded. For example, two hygroscopic salts that are commonly found inBrisbane air are sodium chloride and ammonium sulfate. These havedeliquescence points of approximately 74 and 79 %, respectively (Hu etal., 2010; Wise et al., 2007). Aerosol particles that contain thesesubstances will absorb moisture and grow when the relative humidity exceedsthese values. Our observations are in good agreement with these studies. Thehigh PM2.5 concentration values reported by the PMS1003 during theearly morning hours in Fig. 6b are due to hygroscopic growth of particlesfollowed by the formation of fog droplets in the air. While the TEOM alsoshows an increase, it does not record an increase as high as the PMS1003. Asfog begins to form, we observe an increase in both the PNC and PM2.5 concentration reported by the PMS1003. The corresponding increase in theTEOM reading, although significantly smaller than the PMS1003, suggeststhat, in the presence of fog, the dryer at its inlet has a limitedefficiency in terms of removing the liquid phase of the particles.
Figure 7PNCs reported by the PMS1003 in the six size bins at three hourlyintervals during a morning with fog (30 July). Fog was observed between 03:00and 06:30. The table under the figure gives additional information at therespective times.
An obvious question that arises from this work is whether it is possible toderive a correction factor for the particle number and mass concentrationsreported by the low-cost sensors in the presence of high humidity and fog.Our results show that, once the deliquescence point is exceeded, theparticle number and mass concentrations begin to increase and are notdirectly related to the absolute value of the relative humidity. Once theambient temperature reaches the dew point temperature, the conditions becomesuitable for the formation of fog droplets in the air and, as asignificant fraction of these water droplets fall within the detection sizeof the PMS1003 (Fig. 7), they are detected as particles. We also observedthat the PNC and PM concentrations reported by the PMS1003 decreased in thepresence of rain. This is not unexpected as it is known that rain washes outa fraction of airborne particles. More interestingly, our results show thatthe decrease in PNC and PM concentrations reported by the PMS1003 due torain were significantly greater when there was an episode of fog than whenthere was no fog. While a significant number of fog droplets fall within thedetection size range of the PMS1003, almost all the rain drops are largerthan the maximum detection size of particles. We hypothesise that theraindrops were washing out the fog droplets in the air, resulting in anoverall decrease in the reported PNC and PM concentrations reported by thelow-cost particle sensors that have no drying facilities at their sampleinlets. Moreover, the relative humidity of the atmosphere increased duringrain, often approaching 100 %. Raindrops are too large to be detected bymost particle sensors and, as such, they do not show an increase inconcentration during rain. For these reasons, we find that there is nodirect relationship between the relative humidity in the atmosphere and thePNC and PM concentrations reported by a sensor or monitor with no dryingfacility at its inlet and, as such, it is not possible to derive anyappropriate correction factors for this effect.
As they generally do not have drying facilities at their sample inlets,low-cost particle sensors measure what is actually present in the air,including both the solid and liquid phases of the particles. This is a realobservation and not an artefact of the instrument, as suggested by Crilley et al. (2018). This is an important aspect to be kept in mind when usinglow-cost sensors to assess the pollution levels in the atmosphere. What thisillustrates is that it should not be presumed that low-cost sensors aresuited for regulatory applications. For example, while it is reasonable touse low-cost sensors to measure the actual particle mass concentrations thatare present in the air; such observations should not be used to verify ifthe air quality meets the stipulated guidelines or standards for particlepollution.
Data used in this paper may be obtained by contacting the Corresponding Author.
RJ Designed the project, carried out the measurements, analysed the data and wrote the paper.XL carried out the measurements and processed the data.PT assisted with the measurements, provided scientific input.MD designed and constructed the low cost PM sensor package.LM supervised the project and provided scientific input.
The authors declare that they have no conflict ofinterest.
We would like to thank the Queensland Department of the Environment andScience for providing the facilities and data from the air qualitymonitoring station. This study was supported by linkage grant LP160100051from the Australian Research Council. We are grateful for useful discussionswith Graham Johnson and Gavin Fisher. Akwasi Asumadu-Sakyi, Mawutorli Nyarku,and Riki Lamont assisted with the field work.
Edited by: Pierre Herckes
Reviewed by: two anonymous referees
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