Size-Resolved Field Performance of Low-Cost Sensors for Particulate Matter Air Pollution

Airborne particulate matter (PM) is a major public health concern. Exposure to PM contributes to over a million premature deaths worldwide, and populations subject to long-term exposure suffer from significantly higher cardiovascular and respiratory morbidity. (1−3) The atmospheric fate and transport of PM is largely determined by particle size (d_p, in μm), as is the penetration and deposition of PM within the human respiratory tract. (4) How particle size relates to PM health effects remains an active area of research; human exposure to size modes such as ultrafine (d_p < 0.1 μm), fine (d_p < 2.5 μm), and coarse (2.5 < d_p < 10 μm) PM have each been associated with adverse health outcomes. (5,6) Traditional (reference) methods for measuring size-resolved particle concentrations are expensive (e.g., instrument costs from $10,000 to $100,000 each) and resource intensive, (7) limiting their use to spatially sparse outdoor monitoring networks in high-income countries and research studies with short sampling durations and small samples sizes.

The emergence of low-cost PM sensors (miniaturized, mass-produced devices that cost ∼$15 to $50 each) has facilitated the deployment of cheaper (∼$250/each) monitors in crowdsourced measurement networks (e.g., PurpleAir, Clarity) that are denser than traditional national/regional-scale networks. (8) These networks are being leveraged to support growing interest in PM exposure and health science globally. Most low-cost PM sensors operate on the principle of aerosol light scattering: a fan draws PM into a small housing, the PM passes through a focused beam of light, and a photodetector measures the intensity of the light scattered by the particles. (8) Most low-cost PM sensors report mass and number concentrations across a range of sizes (e.g., PM_0.3–0.5 PM_0.5–1.0, PM_1.0–2.5 PM_2.5, PM₁₀). By including concentrations across multiple-sized bins as sensor outputs, manufacturers specify, either explicitly or implicitly, that their sensors can classify particles by size. However, manufacturer documentation often omits the working principle of the sensor (i.e., whether it functions as an optical particle counter or a nephelometer) and the bias/precision of size-resolved outputs.

Particle sizing is challenging even for reference-grade instruments, especially those that rely only on light scattering. (9) For example, the signal produced by a given particle can be below the limit of detection, saturated, ambiguous (e.g., when two particles coincide in the detector), or inherently biased by aspiration or transmission errors during sampling. Sensor evaluations carried out by manufacturers and third parties generally involve evaluating the accuracy and precision of cumulative mass concentrations (e.g., PM_1.0, PM_2.5, PM₁₀). This approach does not provide sufficient information to assess a sensor’s ability to quantify and classify particles in specific size ranges. Laboratory experiments and physics-based models have shown that most low-cost PM sensors detect particles larger than 1 μm with low efficiency. (10−12) Additionally, field-based data suggest that low-cost PM sensors underestimate ambient concentrations of wind-blown dust (which likely includes many particles >1 μm). (13,14)

Here, we compare differential mass concentrations (e.g., PM_1.0–2.5) from three low-cost PM sensors to differential mass concentrations measured by a federal equivalent method (FEM) PM monitor in an outdoor urban environment to better understand the ability of low-cost sensors to detect PM in different size fractions. We focus on the most common PM size ranges (<1.0, 1.0–2.5, and 2.5–10 μm) and compare performance metrics for differential mass to cumulative mass to show how the latter can mask sensor limitations.

Marked differences were evident in the low-cost sensors’ responses to PM of different size fractions (Figures 1 and 2). The low-cost sensors measured PM_1.0 with relatively low bias (mean absolute error [MAE] ranging from 1.2 to 2.4 μg/m³; Table 1) and strong linear correlation (R² from 0.83 to 0.93). For PM_2.5, the sensors showed slightly worse agreement with the reference monitor. MAE increased by a factor of 2 for all sensors (3.0 to 3.9 μg/m³), and linear correlation decreased (0.65 ≤ R² ≤ 0.76 compared to 0.83 ≤ R² ≤ 0.93, as stated above). A larger disparity was evident for PM₁₀ concentrations reported between the low-cost sensors and reference monitor (R² from 0.07 to 0.23). Consistently low PM₁₀ concentrations reported by the low-cost sensors, relative to the reference monitor, indicated that larger particles (i.e., d_p > 1.0 μm) were not adequately detected by the sensor models we evaluated. Differential mass concentrations provided further evidence of this limitation. The PM_1.0–2.5 and PM_2.5–10 signals were noisy and nearly uncorrelated with the reference measurement throughout the experiment. Some of these patterns (or lack thereof) were clearly identifiable from a simple visual assessment of the time series plots (Figure 1). The readings from all sensors lost accuracy when going from PM₁ to PM_2.5 and more so when going from PM_2.5 to PM₁₀. In those same plots, the PM_2.5–10 signals from the low-cost sensors remained flat and nearly zero throughout the campaigns, despite the reference monitor reporting PM_2.5–10 concentrations of ∼10–50 μg/m³. This unresponsiveness is consistent with a fundamental inability to sense and/or classify particles in that size range (2.5–10 μm), which has been demonstrated previously for some of these same low-cost sensor models in laboratory experiments (11) and physical optical models. (12) The regression plots in Figure 2 and performance metrics shown in Table 1 confirm that all the low-cost sensors tested here miss almost all particle mass in the PM_2.5–10 size range under the real-world, outdoor conditions in which these sensors are often used.

Additionally, every sensor demonstrated poor detection of the intermediate differential size fraction (1.0–2.5 μm). As shown in Figure 2, PM_1.0–2.5 errors appear random with no clear trends or consistent bias. Qualitatively, this was seen as large scattering of data points in the regression plots (Figure 2) and the REU plots (Figure S3). Quantitatively, the very low coefficients of determination (R² < 0.1 for all linear regression models) and large errors were coherent with a signal composed mostly of noise. Our results indicate that these sensors can estimate ambient PM mass concentrations in the accumulation mode (0.1 < d_p < 1 μm), but that they cannot reliably detect particle mass in the 1.0–2.5 μm or the 2.5–10 μm size ranges. Our results are consistent with Ouimette et al., (12) who developed a physical–optical model of the PMS5003 and theorized that this device would struggle to size classify particles due to signal misclassification from multiangle scattering within the instrument’s detection zone. Laboratory evaluations of the SPS30 and the PMS5003 have found that size detection ranges do not adhere to manufacturer specifications and that size distribution data from these sensors are unreliable. (10,11) Our results under real-world conditions confirm these reports. The combined evidence suggests that the size-resolved data reported by these sensors is based on mathematical artifices rather than on real measurements of size distribution. There are several reasons why these optically based, low-cost sensors fail to respond adequately to particles larger than 1 μm. First, the sensing zones in these instruments have truncated viewing angles that fail to capture forward light scattering from particles. (12) Such truncation error has been shown to produce a dramatic loss in signal as particle size increases from 0.5 to 5 μm. Second, these devices are likely to experience inertial losses during aspiration and transmission of particles from ambient air to their respective sensing zones. (15) Finally, the shape and refractive index of aerosol can differ between the coarse mode and the accumulation mode (and/or the factory calibration aerosol), which leads to differential light-scattering response between these size ranges during real-world use. (9,16−19)

Ideally, the sensors should provide linear response with relatively low noise (i.e., good precision). Nonzero intercepts and nonunity slopes in the regression calibration models are not a major concern because optical instruments are sensitive to aerosol characteristics such as shape and refractive index, which means that they need to be calibrated to specific sampling conditions in applications where high accuracy is needed. (20) For PM_1.0, most points on the regression plots are close to the 1:1 line indicating good agreement with the reference monitor, and all the sensors fit the linear model reasonably well (R² ≥ 0.83; see also Figures S8, S9). Additionally, the relatively small scatter in the PM_1.0 regression plots and the narrow band patterns in the REU plots (Figure S3) denote low noise. The PM_2.5 estimates combine the good performance of PM_1.0 with the random noise of PM_1.0–2.5. This translates to more scattering, larger errors, and poorer fit of the linear models (0.65 ≤ R² ≤ 0.76), but depending on the application, a calibrated sensor could produce usable PM_2.5 estimates, as has been reported previously. (21−23) For instance, to evaluate PM_2.5 data quality, the uncertainty could be analyzed as the European Commission recommends, which specifies a 50% REU upper bound as the data quality objective (DQO) for low-cost sensors. (24) Alternatively, PM_2.5 could be estimated from the more accurate PM_1.0 output by developing ad hoc conversion equations for a combination of sensor model and use case.

On the other hand, the PM₁₀ signals of these sensors are clearly flawed, incorporating the noise of PM_1.0–2.5 and the systematic bias of PM_2.5–10. The regression plots of PM₁₀ show high scattering, large bias and no linear or other type of trend. The REU plots of PM₁₀ show extremely large uncertainty with a combination of bias and noise across all concentrations (Figure S3). Consequently, the PM₁₀ signal of the sensors we tested should be disregarded, as it appears to provide no meaningful output. A field evaluation and calibration by Kosmopoulos et al. reached similar conclusions and recommended the removal of high PM_coarse events from data sets collected with PurpleAir to improve the accuracy of the PM₁ and PM_2.5 signals, but the authors did not find a way to obtain adequate PM₁₀ estimates. (25)

Some studies where only cumulative concentrations (e.g., PM_1.0, PM_2.5) were analyzed have found good agreement between low-cost sensors and FEM monitors for PM_2.5 and even for PM_10. (26,27) Nevertheless, our analysis of the differential concentrations (i.e., PM_1.0–2.5 and PM_2.5–10) shows that the PM_2.5 performance is driven largely by the performance in the PM_1.0 range. If sensors are tested in environments where PM_1.0 constitutes a substantial proportion of PM_2.5 and PM₁₀, or where the particle size distributions resemble the conditions that the manufacturer used to calibrate the devices, the accuracy of PM_2.5 and PM₁₀ signals can be artificially high. Hence, evaluations do not provide a full picture of sensor performance if a wide range of conditions is not covered and if only cumulative concentrations are analyzed.

Our work does not focus on calibration schemes for improving low-cost sensor data, for which there are many potential strategies. (14,20,21,28,29) Further, we tested only three different sensor models; however, these models typify the state-of-the-art models for low-cost light-scattering devices and represent the majority of in-use technologies for crowdsourced measurement networks around the world. (30−32) Although our results are limited to a single geographic location and two seasons, they are consistent with previous theoretical and laboratory investigations. (9,11,12)

The limitations of low-cost sensors presented here should be acknowledged by sensor manufacturers (or product integrators), and the networks that leverage these sensors should cease reporting PM₁₀ mass concentrations. Use of inaccurate particle size distribution data for research applications such as source apportionment or outdoor-to-indoor air penetration (33) could lead to wrong conclusions and, ultimately, to misguided public health interventions. For everyday applications where people want to understand local PM₁, PM_2.5, and PM₁₀ sources and levels, it is important to understand how effective (or ineffective) low-cost sensors are at detecting particulate matter depending on the primary sources (e.g., wildfire smoke, vehicle exhaust, cooking aerosol, wind-blown dust) and sizes of the particles. In all cases, clear guidelines for using (or disregarding) sensor outputs based on their trustworthiness (i.e., accuracy, precision, uncertainty) will benefit the user community. Specific applications where low-cost sensors have been shown to produce reliable estimates of aerosol mass concentration include calibration schemes for which the following conditions hold: (1) The aerosol of interest is stable in terms of size and refractive index. (2) The environmental conditions are accurately recorded (especially relative humidity). (3) The aerosol mass median diameter (MMD) falls within the accumulation mode (i.e., 0.1 < MMD < 1 μm). Applications where these conditions have been met may include urban fine particulate matter, (34,35) wildfire smoke, (36,37) and household solid fuel burning. (38) When calibration schemes fail to account for changes in particle size, particle refractive index, and ambient relative humidity, the responses from these sensors will be uncertain and subject to bias. (20,39) Whenever possible, calibration schemes should be designed to account for potential variability in particle characteristics and ambient conditions, as noted above. Environmental conditions (i.e., temperature, relative humidity, barometric pressure) during calibration should ideally span ranges typical of the environmental conditions under which the sensors will be deployed in their intended setting(s). Sensor calibration should also occur in the presence of air pollution sources that are similar in composition and magnitude to those that the sensors will experience during deployment. Regardless of the calibration scheme, fundamental safeguards for the use of sensors should include protocols that address data handling and initial processing, outlier detection and removal, sensor detection limit, and data completeness. Further, calibration schemes should disclose the kind of reference air quality monitor used, the duration of the colocation experiment and ambient conditions, the time-averaging intervals used for processing the data, and the statistical model(s) selected with appropriate justification (e.g., evidence of model assumptions being met). (20,29)

In summary, we conclude that low-cost PM sensors commonly used in crowdsourced measurement networks are best described as accumulation mode PM (PM_0.1–1.0) sensors with little-to-no sizing ability or measurement reliability outside this size range. None of the devices evaluated here could detect coarse mode PM (PM_2.5–10), and most struggled to measure the 1.0–2.5 μm range when compared to a size-resolved reference monitor. Therefore, PM_2.5 estimates from these low-cost sensors should be interpreted with caution, and PM₁₀ estimates from these sensors should be seen only as a proxy representing the contribution of the accumulation mode to the PM₁₀ fraction.

This article references 39 other publications.

1. 1

   Anderson, J. O.; Thundiyil, J. G.; Stolbach, A. Clearing the Air: A Review of the Effects of Particulate Matter Air Pollution on Human Health. J. Med. Toxicol. 2012, 8 (2), 166– 175,  DOI: 10.1007/s13181-011-0203-1

   Google Scholar

   1

   Clearing the Air: A Review of the Effects of Particulate Matter Air Pollution on Human Health

   Anderson, Jonathan O.; Thundiyil, Josef G.; Stolbach, Andrew

   Journal of Medical Toxicology (2012), 8 (2), 166-175CODEN: JMTOAG; ISSN:1556-9039. (Springer)

   A review. The World Health Organization ests. that particulate matter (PM) air pollution contributes to approx. 800,000 premature deaths each year, ranking it the 13th leading cause of mortality worldwide. However, many studies show that the relationship is deeper and far more complicated than originally thought. PM is a portion of air pollution that is made up of extremely small particles and liq. droplets contg. acids, org. chems., metals, and soil or dust particles. PM is categorized by size and continues to be the fraction of air pollution that is most reliably assocd. with human disease. PM is thought to contribute to cardiovascular and cerebrovascular disease by the mechanisms of systemic inflammation, direct and indirect coagulation activation, and direct translocation into systemic circulation. The data demonstrating PM's effect on the cardiovascular system are strong. Populations subjected to long-term exposure to PM have a significantly higher cardiovascular incident and mortality rate. Short-term acute exposures subtly increase the rate of cardiovascular events within days of a pollution spike. The data are not as strong for PM's effects on cerebrovascular disease, though some data and similar mechanisms suggest a lesser result with smaller amplitude. Respiratory diseases are also exacerbated by exposure to PM. PM causes respiratory morbidity and mortality by creating oxidative stress and inflammation that leads to pulmonary anat. and physiol. remodeling. The literature shows PM causes worsening respiratory symptoms, more frequent medication use, decreased lung function, recurrent health care utilization, and increased mortality. PM exposure has been shown to have a small but significant adverse effect on cardiovascular, respiratory, and to a lesser extent, cerebrovascular disease. These consistent results are shown by multiple studies with varying populations, protocols, and regions. The data demonstrate a dose-dependent relationship between PM and human disease, and that removal from a PM-rich environment decreases the prevalence of these diseases. While further study is needed to elucidate the effects of compn., chem., and the PM effect on susceptible populations, the preponderance of data shows that PM exposure causes a small but significant increase in human morbidity and mortality. Most sources agree on certain "common sense" recommendations, although there are lonely limited data to support them. Indoor PM exposure can be reduced by the usage of air conditioning and particulate filters, decreasing indoor combustion for heating and cooking, and smoking cessation. Susceptible populations, such as the elderly or asthmatics, may benefit from limiting their outdoor activity during peak traffic periods or poor air quality days. These simple changes may benefit individual patients in both short-term symptomatic control and long-term cardiovascular and respiratory complications.

2. 2

   Kim, K. H.; Kabir, E.; Kabir, S. A Review on the Human Health Impact of Airborne Particulate Matter. Environ. Int. 2015, 74, 136– 143,  DOI: 10.1016/j.envint.2014.10.005

   Google Scholar

   2

   A review on the human health impact of airborne particulate matter

   Kim, Ki-Hyun; Kabir, Ehsanul; Kabir, Shamin

   Environment International (2015), 74 (), 136-143CODEN: ENVIDV; ISSN:0160-4120. (Elsevier Ltd.)

   Particulate matter (PM) is a key indicator of air pollution brought into the air by a variety of natural and human activities. As it can be suspended over long time and travel over long distances in the atm., it can cause a wide range of diseases that lead to a significant redn. of human life. The size of particles has been directly linked to their potential for causing health problems. Small particles of concern include "inhalable coarse particles" with a diam. of 2.5 to 10 μm and "fine particles" smaller than 2.5 μm in diam. As the source-effect relationship of PM remains unclear, it is not easy to define such effects from individual sources such as long-range transport of pollution. Because of the potent role of PM and its assocd. pollutants, detailed knowledge of their human health impacts is of primary importance. This paper summarizes the basic evidence on the health effects of particulate matter. An in-depth anal. is provided to address the implications for policy-makers so that more stringent strategies can be implemented to reduce air pollution and its health effects.

3. 3

   Yang, Y.; Ruan, Z.; Wang, X.; Yang, Y.; Mason, T. G.; Lin, H.; Tian, L. Short-Term and Long-Term Exposures to Fine Particulate Matter Constituents and Health: A Systematic Review and Meta-Analysis. Environ. Pollut. 2019, 247, 874– 882,  DOI: 10.1016/j.envpol.2018.12.060

   Google Scholar

   3

   Short-term and long-term exposures to fine particulate matter constituents and health: A systematic review and meta-analysis

   Yang, Yang; Ruan, Zengliang; Wang, Xiaojie; Yang, Yin; Mason, Tonya G.; Lin, Hualiang; Tian, Linwei

   Environmental Pollution (Oxford, United Kingdom) (2019), 247 (), 874-882CODEN: ENPOEK; ISSN:0269-7491. (Elsevier Ltd.)

   A review. Fine particulate matter (Particulate matter with diam. ≤ 2.5μm) is assocd. with multiple health outcomes, with varying effects across seasons and locations. It remains largely unknown that which components of PM2.5 are most harmful to human health. We systematically searched all the relevent studies published before August 1, 2018, on the assocns. of fine particulate matter constituents with mortality and morbidity, using Web of Science, MEDLINE, PubMed and EMBASE. Studies were included if they explored the assocns. between short term or long term exposure of fine particulate matter constituents and natural, cardiovascular or respiratory health endpoints. The criteria for the risk of bias was adapted from OHAT and New Castle Ottawa. We applied a random-effects model to derive the risk ests. for each constituent. We performed main analyses restricted to studies which adjusted the PM2.5 mass in their models. Significant assocns. were obsd. between several PM2.5 constituents and different health endpoints. Among them, black carbon and org. carbon were most robustly and consistently assocd. with all natural, cardiovascular mortality and morbidity. Other potential toxic constituents including nitrate, sulfate, Zinc, silicon, iron, nickel, vanadium, and potassium were assocd. with adverse cardiovascular health, while nitrate, sulfate and vanadium were relevant for adverse respiratory health outcomes. Our anal. suggests that black carbon and org. carbon are important detrimental components of PM2.5, while other constituents are probably hazardous to human health. However, more studies are needed to further confirm our results.

4. 4

   Brown, J. S.; Gordon, T.; Price, O.; Asgharian, B. Thoracic and Respirable Particle Definitions for Human Health Risk Assessment. Part. Fibre Toxicol. 2013, 10 (12), 1– 12,  DOI: 10.1186/1743-8977-10-12

5. 5

   Brook, R. D.; Rajagopalan, S.; Pope, C. A.; Brook, J. R.; Bhatnagar, A.; Diez-Roux, A. V.; Holguin, F.; Hong, Y.; Luepker, R. V.; Mittleman, M. A.; Peters, A.; Siscovick, D.; Smith, S. C.; Whitsel, L.; Kaufman, J. D. Particulate Matter Air Pollution and Cardiovascular Disease: An Update to the Scientific Statement from the American Heart Association. Circulation 2010, 121 (21), 2331– 2378,  DOI: 10.1161/CIR.0b013e3181dbece1

   Google Scholar

   5

   Particulate Matter Air Pollution and Cardiovascular Disease: An Update to the Scientific Statement From the American Heart Association

   Brook, Robert D.; Rajagopalan, Sanjay; Pope, C. Arden, III; Brook, Jeffrey R.; Bhatnagar, Aruni; Diez-Roux, Ana V.; Holguin, Fernando; Hong, Yuling; Luepker, Russell V.; Mittleman, Murray A.; Peters, Annette; Siscovick, David; Smith, Sidney C., Jr.; Whitsel, Laurie; Kaufman, Joel D.

   Circulation (2010), 121 (21), 2331-2378CODEN: CIRCAZ; ISSN:0009-7322. (Lippincott Williams & Wilkins)

   A review. In 2004, the first American Heart Assocn. scientific statement on "Air Pollution and Cardiovascular Disease" concluded that exposure to particulate matter (PM) air pollution contributes to cardiovascular morbidity and mortality. In the interim, numerous studies have expanded our understanding of this assocn. and further elucidated the physiol. and mol. mechanisms involved. The main objective of this updated American Heart Assocn. scientific statement is to provide a comprehensive review of the new evidence linking PM exposure with cardiovascular disease, with a specific focus on highlighting the clin. implications for researchers and healthcare providers. The writing group also sought to provide expert consensus opinions on many aspects of the current state of science and updated suggestions for areas of future research. On the basis of the findings of this review, several new conclusions were reached, including the following: Exposure to PM < 2.5 μm in diam. (PM2.5) over a few hours to weeks can trigger cardiovascular disease-related mortality and nonfatal events; longer-term exposure (eg, a few years) increases the risk for cardiovascular mortality to an even greater extent than exposures over a few days and reduces life expectancy within more highly exposed segments of the population by several months to a few years; redns. in PM levels are assocd. with decreases in cardiovascular mortality within a time frame as short as a few years; and many credible pathol. mechanisms have been elucidated that lend biol. plausibility to these findings. It is the opinion of the writing group that the overall evidence is consistent with a causal relationship between PM2.5 exposure and cardiovascular morbidity and mortality. This body of evidence has grown and been strengthened substantially since the first American Heart Assocn. scientific statement was published. Finally, PM2.5 exposure is deemed a modifiable factor that contributes to cardiovascular morbidity and mortality.

6. 6

   Kelly, F. J.; Fussell, J. C. Air Pollution and Public Health: Emerging Hazards and Improved Understanding of Risk. Environ. Geochem. Health 2015, 37 (4), 631– 649,  DOI: 10.1007/s10653-015-9720-1

   Google Scholar

   6

   Air pollution and public health: emerging hazards and improved understanding of risk

   Kelly, Frank J.; Fussell, Julia C.

   Environmental Geochemistry and Health (2015), 37 (4), 631-649CODEN: EGHEE3; ISSN:0269-4042. (Springer)

   Despite past improvements in air quality, very large parts of the population in urban areas breathe air that does not meet European stds. let alone the health-based World Health Organization Air Quality Guidelines. Over the last 10 years, there has been a substantial increase in findings that particulate matter (PM) air pollution is not only exerting a greater impact on established health endpoints, but is also assocd. with a broader no. of disease outcomes. Data strongly suggest that effects have no threshold within the studied range of ambient concns., can occur at levels close to PM2.5 background concns. and that they follow a mostly linear concn.-response function. Having firmly established this significant public health problem, there has been an enormous effort to identify what it is in ambient PM that affects health and to understand the underlying biol. basis of toxicity by identifying mechanistic pathways-information that in turn will inform policy makers how best to legislate for cleaner air. Another intervention in moving towards a healthier environment depends upon the achieving the right public attitude and behavior by the use of optimal air pollution monitoring, forecasting and reporting that exploits increasingly sophisticated information systems. Improving air quality is a considerable but not an intractable challenge. Translating the correct scientific evidence into bold, realistic and effective policies undisputedly has the potential to reduce air pollution so that it no longer poses a damaging and costly toll on public health.

7. 7

   Lowther, S. D.; Jones, K. C.; Wang, X.; Whyatt, J. D.; Wild, O.; Booker, D. Particulate Matter Measurement Indoors: A Review of Metrics, Sensors, Needs, and Applications. Environ. Sci. Technol. 2019, 53 (20), 11644– 11656,  DOI: 10.1021/acs.est.9b03425

   Google Scholar

   7

   Particulate Matter Measurement Indoors: A Review of Metrics, Sensors, Needs, and Applications

   Lowther, Scott D.; Jones, Kevin C.; Wang, Xinming; Whyatt, J. Duncan; Wild, Oliver; Booker, Douglas

   Environmental Science & Technology (2019), 53 (20), 11644-11656CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)

   A review is given. Many populations spend ∼90% of their time indoors, with household particulate matter being linked to millions of premature deaths worldwide. Particulate matter is currently measured using particle mass, particle no., and particle size distribution metrics, with other metrics, such as particle surface area, likely to be of increasing importance in the future. Particulate mass is measured using gravimetric methods, tapered element oscillating microbalances, and beta attenuation instruments and is best suited to use in compliance monitoring, trend anal., and high spatial resoln. measurements. Particle no. concn. is measured by condensation particle counters, optical particle counters, and diffusion chargers. Particle no. measurements are best suited to source characterization, trend anal. and ultrafine particle investigations. Particle size distributions are measured by gravimetric impactors, scanning mobility particle sizers, aerodynamic particle sizers, and fast mobility particle sizers. Particle size distribution measurements are most useful in source characterization and particulate matter property investigations, but most measurement options remain expensive and intrusive. However, we are on the cusp of a revolution in indoor air quality monitoring and management. Low-cost sensors have potential to facilitate personalized information about indoor air quality (IAQ), allowing citizens to reduce exposures to PM indoors and to resolve potential dichotomies between promoting healthy IAQ and energy efficient buildings. Indeed, the low cost will put this simple technol. in the hands of citizens who wish to monitor their own IAQ in the home or workplace, to inform lifestyle decisions. Low-cost sensor networks also look promising as the soln. to measuring spatial distributions of PM indoors, however, there are important sensor/data quality, technol., and ethical barriers to address with this technol. An improved understanding of epidemiol. is essential to identify which metrics correlate most with health effects, allowing indoor specific PM stds. to be developed and to inform the future of exptl. applications.

8. 8

   Morawska, L.; Thai, P. K.; Liu, X.; Asumadu-Sakyi, A.; Ayoko, G.; Bartonova, A.; Bedini, A.; Chai, F.; Christensen, B.; Dunbabin, M.; Gao, J.; Hagler, G. S. W.; Jayaratne, R.; Kumar, P.; Lau, A. K. H.; Louie, P. K. K.; Mazaheri, M.; Ning, Z.; Motta, N.; Mullins, B.; Rahman, M. M.; Ristovski, Z.; Shafiei, M.; Tjondronegoro, D.; Westerdahl, D.; Williams, R. Applications of Low-Cost Sensing Technologies for Air Quality Monitoring and Exposure Assessment: How Far Have They Gone?. Environ. Int. 2018, 116, 286– 299,  DOI: 10.1016/j.envint.2018.04.018

   Google Scholar

   8

   Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?

   Morawska, Lidia; Thai, Phong K.; Liu, Xiaoting; Asumadu-Sakyi, Akwasi; Ayoko, Godwin; Bartonova, Alena; Bedini, Andrea; Chai, Fahe; Christensen, Bryce; Dunbabin, Matthew; Gao, Jian; Hagler, Gayle S. W.; Jayaratne, Rohan; Kumar, Prashant; Lau, Alexis K. H.; Louie, Peter K. K.; Mazaheri, Mandana; Ning, Zhi; Motta, Nunzio; Mullins, Ben; Rahman, Md. Mahmudur; Ristovski, Zoran; Shafiei, Mahnaz; Tjondronegoro, Dian; Westerdahl, Dane; Williams, Ron

   Environment International (2018), 116 (), 286-299CODEN: ENVIDV; ISSN:0160-4120. (Elsevier Ltd.)

   A review. Over the past decade, a range of sensor technologies became available on the market, enabling a revolutionary shift in air pollution monitoring and assessment. With their cost of up to three orders of magnitude lower than std./ref. instruments, many avenues for applications have opened up. In particular, broader participation in air quality discussion and utilization of information on air pollution by communities has become possible. However, many questions have been also asked about the actual benefits of these technologies. To address this issue, we conducted a comprehensive literature search including both the scientific and gray literature. We focused upon two questions: (1) Are these technologies fit for the various purposes envisaged and (2) How far have these technologies and their applications progressed to provide answers and solns. Regarding the former, we concluded that there is no clear answer to the question, due to a lack of: sensor/monitor manufacturers' quant. specifications of performance, consensus regarding recommended end-use and assocd. minimal performance targets of these technologies, and the ability of the prospective users to formulate the requirements for their applications, or conditions of the intended use. Numerous studies have assessed and reported sensor/monitor performance under a range of specific conditions, and in many cases the performance was concluded to be satisfactory. The specific use cases for sensors/monitors included outdoor in a stationary mode, outdoor in a mobile mode, indoor environments and personal monitoring. Under certain conditions of application, project goals, and monitoring environments, some sensors/monitors were fit for a specific purpose. Based on anal. of 17 large projects, which reached applied outcome stage, and typically conducted by consortia of organizations, we obsd. that a sizable fraction of them (~ 30%) were com. and/or crowd-funded. This fact by itself signals a paradigm change in air quality monitoring, which previously had been primarily implemented by government organizations. An addnl. paradigm-shift indicator is the growing use of machine learning or other advanced data processing approaches to improve sensor/monitor agreement with ref. monitors. There is still some way to go in enhancing application of the technologies for source apportionment, which is of particular necessity and urgency in developing countries. Also, there has been somewhat less progress in wide-scale monitoring of personal exposures. However, it can be argued that with a significant future expansion of monitoring networks, including indoor environments, there may be less need for wearable or portable sensors/monitors to assess personal exposure. Traditional personal monitoring would still be valuable where spatial variability of pollutants of interest is at a finer resoln. than the monitoring network can resolve.

9. 9

   Hagan, D.; Kroll, J. Assessing the Accuracy of Low-Cost Optical Particle Sensors Using a Physics-Based Approach. Atmos. Meas. Technol. Discuss. 2020, 13, 6343– 6355,  DOI: 10.5194/amt-13-6343-2020

10. 10

    Tryner, J.; Mehaffy, J.; Miller-Lionberg, D.; Volckens, J. Effects of Aerosol Type and Simulated Aging on Performance of Low-Cost PM Sensors. J. Aerosol Sci. 2020, 150 (June), 105654  DOI: 10.1016/j.jaerosci.2020.105654

    Google Scholar

    10

    Effects of aerosol type and simulated aging on performance of low-cost pm sensors

    Tryner, Jessica; Mehaffy, John; Miller-Lionberg, Daniel; Volckens, John

    Journal of Aerosol Science (2020), 150 (), 105654CODEN: JALSB7; ISSN:0021-8502. (Elsevier Ltd.)

    Studies that characterize the performance of low-cost particulate matter (PM) sensors are needed to help practitioners understand the accuracy and precision of the mass and no. concns. reported by different models. We evaluated Plantower PMS5003, Sensirion SPS30, and Amphenol SM-UART-04L PM sensors in the lab. by exposing them to: (1) four different polydisperse aerosols (ammonium sulfate, Arizona road dust, NIST Urban PM, and wood smoke) at concns. ranging from 10 to 1000μg m-3, (2) hygroscopic and hydrophobic aerosols (ammonium sulfate and oil) in an environment with varying relative humidity (15%-90%), (3) polystyrene latex spheres (PSL) ranging from 0.1 to 2.0μm in diam., and (4) extremely high concns. of Arizona road dust (18-h mean total PM = 33,000μg m-3; 18-h mean PM2.5 = 7300μg m-3). Linear models relating PMS5003- and SPS30-reported PM2.5 concns. to TEOM-reported ammonium sulfate concns. up to 1025μg m-3, nebulized Arizona road dust concns. up to 540μg m-3, and NIST Urban PM concns. up to 330μg m-3 had R2 ≥ 0.97; however, an F-test identified a significant lack of fit between the model and the data for each sensor/aerosol combination. Ratios of filter-derived to PMS5003-reported PM2.5 concns. were 1.4, 1.7, 1.0, 0.4, and 4.3 for ammonium sulfate, nebulized Arizona road dust, NIST Urban PM, wood smoke, and oil mist, resp. For SPS30 sensors, these ratios were 1.6, 2.1, 2.1, 0.6, and 2.2, resp. Collocated PMS5003 sensors were less precise than collocated SPS30 sensors when measuring ammonium sulfate, nebulized Arizona road dust, NIST Urban PM, oil mist, or PSL. Our results indicated that particle count data reported by the PMS5003 were not reliable. The no. size distribution reported by the PMS5003 (a) did not agree with APS data and (b) remained roughly const. whether the sensors were exposed to 0.1μm PSL, 0.27μ m PSL, 0.72μ m PSL, 2.0μ m PSL, or any of the other lab.-generated aerosols. The size distribution reported by the SPS30 did not always agree with APS data, but did shift towards larger particle sizes when the sensors were exposed to 0.72 PSL, 2.0μm PSL, oil mist, or Arizona road dust from a fluidized bed generator. The proportions of PM mass assigned as PM1, PM2.5, and PM10 by all three sensor models shifted as the PSL size increased. After the sensors were exposed to high concns. of Arizona road dust for 18 h, PM2.5 concns. reported by SPS30 sensors remained consistent, whereas 3/8 PMS5003 sensors and 2/7 SM-UART-04L sensors began reporting erroneously high values.

11. 11

    Kuula, J.; Mäkelä, T.; Aurela, M.; Teinilä, K.; Varjonen, S.; González, Ó.; Timonen, H. Laboratory Evaluation of Particle-Size Selectivity of Optical Low-Cost Particulate Matter Sensors. Atmos. Meas. Technol. 2020, 13 (5), 2413– 2423,  DOI: 10.5194/amt-13-2413-2020

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    11

    Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors

    Kuula, Joel; Makela, Timo; Aurela, Minna; Teinila, Kimmo; Varjonen, Samu; Gonzalez, Oscar; Timonen, Hilkka

    Atmospheric Measurement Techniques (2020), 13 (5), 2413-2423CODEN: AMTTC2; ISSN:1867-8548. (Copernicus Publications)

    Low-cost particulate matter (PM) sensors have been under investigation as it has been hypothesized that the use of low-cost and easy-to-use sensors could allow costefficient extension of the currently sparse measurement coverage. While the majority of the existing literature highlights that low-cost sensors can indeed be a valuable addn. to the list of commonly used measurement tools, it often reiterates that the risk of sensor misuse is still high and that the data obtained from the sensors are only representative of the specific site and its ambient conditions. This implies that there are underlying reasons for inaccuracies in sensor measurements that have yet to be characterized. The objective of this study is to investigate the particle-size selectivity of lowcost sensors. Evaluated sensors were Plantower PMS5003, Nova SDS011, Sensirion SPS30, Sharp GP2Y1010AU0F, Shinyei PPD42NS, and Omron B5W-LD0101. The investigation of size selectivity was carried out in the lab. using a novel ref. aerosol generation system capable of steadily producing monodisperse particles of different sizes (from ~ 0.55 to 8.4μm) online. The results of the study show that none of the low-cost sensors adhered to the detection ranges declared by the manufacturers; moreover, cursory comparison to a mid-cost aerosol size spectrometer (Grimm 1.108, 2020) indicates that the sensors can only achieve independent responses for one or two size bins, whereas the spectrometer can sufficiently characterize particles with 15 different size bins. These observations provide insight into and evidence of the notion that particle-size selectivity has an essential role in the anal. of the sources of errors in sensors.

12. 12

    Ouimette, J.; Malm, W.; Schichtel, B.; Sheridan, P.; Andrews, E.; Ogren, J.; Arnott, W. P. Evaluating the PurpleAir Monitor as an Aerosol Light Scattering Instrument. Atmos. Meas. Technol. Discuss. 2022, 15, 655– 676,  DOI: 10.5194/amt-15-655-2022

13. 13

    Sayahi, T.; Butterfield, A.; Kelly, K. E. Long-Term Field Evaluation of the Plantower PMS Low-Cost Particulate Matter Sensors. Environ. Pollut. 2019, 245, 932– 940,  DOI: 10.1016/j.envpol.2018.11.065

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    13

    Long-term field evaluation of the Plantower PMS low-cost particulate matter sensors

    Sayahi, T.; Butterfield, A.; Kelly, K. E.

    Environmental Pollution (Oxford, United Kingdom) (2019), 245 (), 932-940CODEN: ENPOEK; ISSN:0269-7491. (Elsevier Ltd.)

    The low-cost and compact size of light-scattering-based particulate matter (PM) sensors provide an opportunity for improved spatiotemporally resolved PM measurements. However, these inexpensive sensors have limitations and need to be characterized under realistic conditions. This study evaluated two Plantower PMS (particulate matter sensor) 1003s and two PMS 5003s outdoors in Salt Lake City, Utah over 320 days (1/2016-2/2016 and 12/2016-10/2017) through multiple seasons and a variety of elevated PM2.5 events including wintertime cold-air pools (CAPs), fireworks, and wildfires. The PMS 1003/5003 sensors generally tracked PM2.5 concns. compared to co-located ref. air monitors (one tapered element oscillating microbalance, TEOM, and one gravimetric federal ref. method, FRM). The different PMS sensor models and sets of the same sensor model exhibited some intra-sensor variability. During winter 2017, the two PMS 1003s consistently overestimated PM2.5 by a factor of 1.89 (TEOM PM2.5<40 μg/m3). However, compared to the TEOM, one PMS 5003 overestimated PM2.5 concns. by a factor of 1.47 while the other roughly agreed with the TEOM. The PMS sensor response also differed by season. In two consecutive winters, the PMS PM2.5 measurements correlated with the hourly TEOM measurements (R2 > 0.87) and 24-h FRM measurements (R2 > 0.88) while in spring (March-June) and wildfire season (June-Oct.) 2017, the correlations were poorer (R2 of 0.18-0.32 and 0.48-0.72, resp.). The PMS 1003s maintained high intra-sensor agreement after one year of deployment during the winter seasons, however, one PMS 1003 sensor exhibited a significant drift beginning in March 2017 and continued to deteriorate through the end of the study. Overall, this study demonstrated good correlations between the PMS sensors and ref. monitors in the winter season, seasonal differences in sensor performance, some intra-sensor variability, and drift in one sensor. These types of factors should be considered when using measurements from a network of low-cost PM sensors.

14. 14

    McFarlane, C.; Raheja, G.; Malings, C.; Appoh, E. K. E.; Hughes, A. F.; Westervelt, D. M. Application of Gaussian Mixture Regression for the Correction of Low Cost PM2.5 Monitoring Data in Accra, Ghana. ACS Earth Sp. Chem. 2021, 5 (9), 2268– 2279,  DOI: 10.1021/acsearthspacechem.1c00217

15. 15

    Pawar, H.; Sinha, B. Humidity, Density, and Inlet Aspiration Efficiency Correction Improve Accuracy of a Low-Cost Sensor during Field Calibration at a Suburban Site in the North-Western Indo-Gangetic Plain (NW-IGP). Aerosol Sci. Technol. 2020, 54 (6), 685– 703,  DOI: 10.1080/02786826.2020.1719971

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    15

    Humidity, density, and inlet aspiration efficiency correction improve accuracy of a low-cost sensor during field calibration at a suburban site in the North-Western Indo-Gangetic plain (NW-IGP)

    Pawar, Harshita; Sinha, Baerbel

    Aerosol Science and Technology (2020), 54 (6), 685-703CODEN: ASTYDQ; ISSN:0278-6826. (Taylor & Francis, Inc.)

    Low-cost particulate matter (PM) sensors are now widely used by concerned citizens to monitor PM exposure despite poor validation under field conditions. Here, we report the field calibration of a modified version of the Laser Egg (LE), against Class III US EPA Federal Equiv. Method PM10 and PM2.5 β-attenuation analyzers. The calibration was performed at a site in north-western Indo-Gangetic Plain from 27 Apr. 2016 to 25 July 2016. At ambient PM mass loadings ranging from <1-838 μg m-3 and <1-228 μg m-3 for PM10 and PM2.5, resp., measurements of PM10, PM2.5 from the LE were precise, with a Pearson correlation coeff. (r) >0.9 and a percentage coeff. of variance <12%. The original Mean Bias Error (MBE) of ∼-90 μg m-3 decreased to -30.9 μg m-3 (Sensor 1) and -23.2 μg m-3 (Sensor 2) during the summer period (27 Apr.-15 June 2016) after correcting for particle d. and aspiration losses. The corrections reduced the overall MBE to <20 μg m-3 for PM10 and <3 μg m-3 for PM2.5, indicating that modified version of the LE could be used for ambient PM monitoring with appropriate correction and meteorol. observations. However, users of the original product may underestimate their PM10 exposure.

16. 16

    Hussein, T.; Puustinen, A.; Aalto, P. P.; Mäkelä, J. M.; Hämeri, K.; Kulmala, M. Urban Aerosol Number Size Distributions. Atmos. Chem. Phys. 2004, 4 (2), 391– 411,  DOI: 10.5194/acp-4-391-2004

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    16

    Urban aerosol number size distributions

    Hussein, T.; Puustinen, A.; Aalto, P. P.; Maekelae, J. M.; Haemeri, K.; Kulmala, M.

    Atmospheric Chemistry and Physics (2004), 4 (2), 391-411CODEN: ACPTCE; ISSN:1680-7316. (European Geosciences Union)

    Aerosol no. size distributions have been measured since 5 May 1997 in Helsinki, Finland. The presented aerosol data represents size distributions within the particle diam. size range 8-400 nm during the period from May 1997 to Mar. 2003. The daily, monthly and annual patterns of the aerosol particle no. concns. were investigated. The temporal variation of the particle no. concn. showed close correlations with traffic activities. The highest total no. concns. were obsd. during workdays; esp. on Fridays, and the lowest concns. occurred during weekends; esp. Sundays. Seasonally, the highest total no. concns. were obsd. during winter and spring and lower concns. were obsd. during June and July. More than 80% of the no. size distributions had three modes: nucleation mode (Dp<30 nm), Aitken mode (20-100 nm) and accumulation mode (Dp>90 nm). Less than 20% of the no. size distributions had either two modes or consisted of more than three modes. Two different measurement sites were used; in the first (Siltavuori, 5.5.1997-5.3.2001), the arithmetic means of the particle no. concns. were 7000 cm-3, 6500 cm-3, and 1000 cm-3 resp. for nucleation, Aitken, and accumulation modes. In the second site (Kumpula, 6.3.2001-28.2.2003) they were 5500 cm-3, 4000 cm-3, and 1000 cm-3. The total no. concn. in nucleation and Aitken modes were usually significantly higher during workdays than during weekends. The temporal variations in the accumulation mode were less pronounced. The lower concns. at Kumpula were mainly due to building construction and also the slight overall decreasing trend during these years. During the site changing a period of simultaneous measurements over two weeks were performed showing nice correlation at both sites.

17. 17

    Raut, J. C.; Chazette, P. Retrieval of Aerosol Complex Refractive Index from a Synergy between Lidar, Sunphotometer and in Situ Measurements during LISAIR Experiment. Atmos. Chem. Phys. 2007, 7 (11), 2797– 2815,  DOI: 10.5194/acp-7-2797-2007

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    17

    Retrieval of aerosol complex refractive index from a synergy between lidar, sunphotometer and in situ measurements during LISAIR experiment

    Raut, J.-C.; Chazette, P.

    Atmospheric Chemistry and Physics (2007), 7 (11), 2797-2815CODEN: ACPTCE; ISSN:1680-7316. (European Geosciences Union)

    Particulate pollutant exchanges between the streets and the Planetary Boundary Layer (PBL), and their daily evolution linked to human activity were studied in the framework of the LIdar pour la Surveillance de l"AIR (LISAIR) expt. This program lasted from 10 to 30 May 2005. A synergetic approach combining dedicated active (lidar) and passive (sunphotometer) remote sensors as well as ground based in situ instrumentation (nephelometer, aethalometer and particle sizers) was used to investigate urban aerosol optical properties within Paris. Aerosol complex refractive indexes were assessed to be 1.56-0.034 i at 355 nm and 1.59-0.040 i at 532 nm, thus leading to single-scattering albedo values between 0.80 and 0.88. These retrievals are consistent with soot components in the aerosol arising from traffic exhausts indicating that these pollutants have a radiative impact on climate. We also discussed the influence of relative humidity on aerosol properties. A good agreement was found between vertical extinction profile derived from lidar backscattering signal and retrieved from the coupling between radiosounding and ground in situ measurements.

18. 18

    Petzold, A.; Rasp, K.; Weinzierl, B.; Esselborn, M.; Hamburger, T.; Dörnbrac, A.; Kandler, K.; Schütz, L.; Knippertz, P.; Fiebig, M.; Virkkula, A. Saharan Dust Absorption and Refractive Index from Aircraft-Based Observations during SAMUM 2006. Tellus, Ser. B Chem. Phys. Meteorol. 2022, 61 (1), 118– 130,  DOI: 10.1111/j.1600-0889.2008.00383.x

19. 19

    Rocha-Lima, A.; Vanderlei Martins, J.; Remer, L. A.; Todd, M.; Marsham, J. H.; Engelstaedter, S.; Ryder, C. L.; Cavazos-Guerra, C.; Artaxo, P.; Colarco, P.; Washington, R. A Detailed Characterization of the Saharan Dust Collected during the Fennec Campaign in 2011: In Situ Ground-Based and Laboratory Measurements. Atmos. Chem. Phys. 2018, 18 (2), 1023– 1043,  DOI: 10.5194/acp-18-1023-2018

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    19

    A detailed characterization of the Saharan dust collected during the Fennec campaign in 2011: in situ ground-based and laboratory measurements

    Rocha-Lima, Adriana; Martins, J. Vanderlei; Remer, Lorraine A.; Todd, Martin; Marsham, John H.; Engelstaedter, Sebastian; Ryder, Claire L.; Cavazos-Guerra, Carolina; Artaxo, Paulo; Colarco, Peter; Washington, Richard

    Atmospheric Chemistry and Physics (2018), 18 (2), 1023-1043CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)

    Millions of tons of mineral dust are lifted by the wind from arid surfaces and transported around the globe every year. The phys. and chem. properties of the mineral dust are needed to better constrain remote sensing observations and are of fundamental importance for the understanding of dust atm. processes. Ground-based in situ measurements and in situ filter collection of Saharan dust were obtained during the Fennec campaign in the central Sahara in 2011. This paper presents results of the absorption and scattering coeffs., and hence single scattering albedo (SSA), of the Saharan dust measured in real time during the last period of the campaign and subsequent lab. anal. of the dust samples collected in two supersites, SS1 and SS2, in Algeria and in Mauritania, resp. The samples were taken to the lab., where their size and aspect ratio distributions, mean chem. compn., spectral mass absorption efficiency, and spectral imaginary refractive index were obtained from the UV to the nearinfrared (NIR) wavelengths. At SS1 in Algeria, the time series of the scattering coeffs. during the period of the campaign show dust events exceeding 3500Mm-1, and a relatively high mean SSA of 0.995 at 670 nm was obsd. at this site. The lab. results show for the fine particle size distributions (particles diam.<5μm and mode diam. at 2-3 μm) in both sites a spectral dependence of the imaginary part of the refractive index Im(m) with a bow-like shape, with increased absorption in UV as well as in the shortwave IR. The same signature was not obsd., however, in the mixed particle size distribution (particle diam. <10 μm and mode diam. at 4 μm) in Algeria. Im(m) was found to range from 0.011 to 0.001i for dust collected in Algeria and 0.008 to 0.002i for dust collected in Mauritania over the wavelength range of 350-2500 nm. Differences in the mean elemental compn. of the dust collected in the supersites in Algeria and in Mauritania and between fine and mixed particle size distributions were obsd. from EDXRF measurements, although those differences cannot be used to explain the optical properties variability between the samples. Finally, particles with low-d. typically larger than 10 μm in diam. were found in some of the samples collected at the supersite in Mauritania, but these low-d. particles were not obsd. in Algeria.

20. 20

    Giordano, M. R.; Malings, C.; Pandis, S. N.; Presto, A. A.; McNeill, V. F.; Westervelt, D. M.; Beekmann, M.; Subramanian, R. From Low-Cost Sensors to High-Quality Data: A Summary of Challenges and Best Practices for Effectively Calibrating Low-Cost Particulate Matter Mass Sensors. J. Aerosol Sci. 2021, 158 (July), 105833  DOI: 10.1016/j.jaerosci.2021.105833

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    20

    From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors

    Giordano, Michael R.; Malings, Carl; Pandis, Spyros N.; Presto, Albert A.; McNeill, V. F.; Westervelt, Daniel M.; Beekmann, Matthias; Subramanian, R.

    Journal of Aerosol Science (2021), 158 (), 105833CODEN: JALSB7; ISSN:0021-8502. (Elsevier Ltd.)

    A review. Low-cost sensors for particulate matter mass (PM) enable spatially dense, high temporal resoln. measurements of air quality that traditional ref. monitoring cannot. Low-cost PM sensors are esp. beneficial in low and middle-income countries where few, if any, ref. grade measurements exist and in areas where the concn. fields of air pollutants have significant spatial gradients. Unfortunately, low-cost PM sensors also come with a no. of challenges that must be addressed if their data products are to be used for anything more than a qual. characterization of air quality. The various PM sensors used in low-cost monitors are all subject to biases and calibration dependencies, corrections for which range from relatively straightforward (e.g. meteorol., age of sensor) to complex (e.g. aerosol source, compn., refractive index). The methods for correcting and calibrating these biases and dependencies that have been used in the literature likewise range from simple linear and quadratic models to complex machine learning algorithms. Here we review the needs and challenges when trying to get high-quality data from low-cost sensors. We also present a set of best practices to follow to obtain high-quality data from these low-cost sensors.

21. 21

    Barkjohn, K. K.; Gantt, B.; Clements, A. L. Development and Application of a United States-Wide Correction for PM2.5 Data Collected with the PurpleAir Sensor. Atmos. Meas. Technol. 2021, 14 (6), 4617– 4637,  DOI: 10.5194/amt-14-4617-2021

22. 22

    Malings, C.; Tanzer, R.; Hauryliuk, A.; Saha, P. K.; Robinson, A. L.; Presto, A. A.; Subramanian, R. Fine Particle Mass Monitoring with Low-Cost Sensors: Corrections and Long-Term Performance Evaluation. Aerosol Sci. Technol. 2020, 54 (2), 160– 174,  DOI: 10.1080/02786826.2019.1623863

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    22

    Fine particle mass monitoring with low-cost sensors: Corrections and long-term performance evaluation

    Malings, Carl; Tanzer, Rebecca; Hauryliuk, Aliaksei; Saha, Provat K.; Robinson, Allen L.; Presto, Albert A.; Subramanian, R.

    Aerosol Science and Technology (2020), 54 (2), 160-174CODEN: ASTYDQ; ISSN:0278-6826. (Taylor & Francis, Inc.)

    Low-cost sensors for the measurement of fine particulate matter mass (PM2.5) enable dense networks to increase the spatial resoln. of air quality monitoring. However, these sensors are affected by environmental factors such as temp. and humidity and their effects on ambient aerosol, which must be accounted for to improve the in-field accuracy of these sensors. We conducted long-term tests of two low-cost PM2.5 sensors: Met-One NPM and PurpleAir PA-II units. We found a high level of self-consistency within each sensor type after testing 25 NPM and 9 PurpleAir units. We developed two types of corrections for the low-cost sensor measurements to better match regulatory-grade data. The first correction accounts for aerosol hygroscopic growth using particle compn. and corrects for particle mass below the optical sensor size cut-point by collocation with ref. Beta Attenuation Monitors (BAM). A second, fully-empirical correction uses linear or quadratic functions of environmental variables based on the same collocation dataset. The two models yielded comparable improvements over raw measurements. Sensor performance was assessed for two use cases: improving community awareness of air quality with short-term semi-quant. comparisons of sites and providing long-term reasonably quant. information for health impact studies. For the short-term case, both sensors provided reasonably accurate concn. information (mean abs. error of ∼4μg/m3) in near-real time. For the long-term case, tested using year-long collocations at one urban background and one near-source site, error in the annual av. was reduced below 1μg/m3. Hence, these sensors can supplement sparse networks of regulatory-grade instruments, perform high-d. neighborhood-scale monitoring, and be used to better understand spatial patterns and temporal air quality trends across urban areas.

23. 23

    Wallace, L.; Bi, J.; Ott, W. R.; Sarnat, J.; Liu, Y. Calibration of Low-Cost PurpleAir Outdoor Monitors Using an Improved Method of Calculating PM2.5. Atmos. Environ. 2021, 256 (March), 118432  DOI: 10.1016/j.atmosenv.2021.118432

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    23

    Calibration of low-cost PurpleAir outdoor monitors using an improved method of calculating PM2.5

    Wallace, Lance; Bi, Jianzhao; Ott, Wayne R.; Sarnat, Jeremy; Liu, Yang

    Atmospheric Environment (2021), 256 (), 118432CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)

    PM2.5 hourly av. measurements from 33 outdoor PurpleAir particle monitors were compared with hourly measurements from 27 nearby US EPA Air Quality System (AQS) stations employing Federal Equiv. Method (FEM) monitors in California over an 18-mo (77-wk) period. A transparent and reproducible alternative method (ALT) of calcg. PM2.5 from the particle nos. in three size categories was used in place of the ests. provided by Plantower, the manufacturer of the sensors used in PurpleAir monitors. The ALT method was superior in several ways (better precision, lower limit of detection, improved size distribution) compared to Plantower's CF1 or ATM data series. PurpleAir monitors were strongly correlated with the nearby US EPA Air Quality System AQS stations. A calibration factor (CF) ranging between 2.9 and 3.1 was empirically derived for the PurpleAir ests. using the ALT method. This value was based on comparing the av. value of 177,329 PurpleAir measurements to the value calcd. from the FEM stations. The monitoring period included about 13 wk showing very high outdoor values due to several major fires covering several hundred thousand acres. The CF during these 13 wk averaged 2.39, whereas the CF for the remaining 64 wk averaged about 3.21, suggesting a different response to the smoke from wildfires compared with normal ambient fine particulate matter (PM2.5). The std. Plantower CF1 data series overestimated the FEM values by about 40%, in agreement with several other studies.

25. 25

    Kosmopoulos, G.; Salamalikis, V.; Pandis, S. N.; Yannopoulos, P.; Bloutsos, A. A.; Kazantzidis, A. Low-Cost Sensors for Measuring Airborne Particulate Matter: Field Evaluation and Calibration at a South-Eastern European Site. Sci. Total Environ. 2020, 748, 141396  DOI: 10.1016/j.scitotenv.2020.141396

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    25

    Low-cost sensors for measuring airborne particulate matter: Field evaluation and calibration at a South-Eastern European site

    Kosmopoulos, G.; Salamalikis, V.; Pandis, S. N.; Yannopoulos, P.; Bloutsos, A. A.; Kazantzidis, A.

    Science of the Total Environment (2020), 748 (), 141396CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)

    Low-cost sensors are useful tools for the collection of air quality data, augmenting the existing regulatory monitoring networks and providing an unprecedented opportunity to increase their spatial coverage. This study presents a calibration process of a low-cost PM sensor (PurpleAir PA-II, PAir) in ambient conditions in the city of Patras, Greece, during 18 mo of 2017-2018. The hourly PM1 and PM2.5 measurements using the original sensor values were reasonably well correlated (R2 = 0.82 for PM1 and R2 = 0.56 for PM2.5) with the ref. instrument, but with a high mean bias and root mean square error. There was a small improvement of around 10% for the daily avs. For PM1-2.5 (particles with diams. between 1 and 2.5μm), PM2.5-10 (diams. between 2.5 and 10μm) and PM10, the performance of the low-cost sensors was poor in this area with R2 < 0.37 in all cases. The response of the PAir sensor for PM1 and PM2.5 changed significantly compared to the ref. instrument during periods with high dust (or other coarse particle) concns. These periods were excluded and a simple linear calibration was then developed for the rest of the fine PM measurements. A method for the identification of these high dust periods based on regional model predictions is proposed. This calibration reduces the relative mean error for hourly PM1 to 19% (1.1μg m-3) and for PM2.5 to 18% (1.1μg m-3). The corresponding root mean square errors are 25% (1.4μg m-3) for hourly PM1 and 25% (1.6μg m-3) for PM2.5. The biases of the cor. values are, as expected, practically zero. Surprisingly, the relative humidity had a negligible effect on fine PM measurements of the PAir in this location and for the conditions of the study.

26. 26

    Kelly, K. E.; Whitaker, J.; Petty, A.; Widmer, C.; Dybwad, A.; Sleeth, D.; Martin, R.; Butterfield, A. Ambient and Laboratory Evaluation of a Low-Cost Particulate Matter Sensor. Environ. Pollut. 2017, 221, 491– 500,  DOI: 10.1016/j.envpol.2016.12.039

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    26

    Ambient and laboratory evaluation of a low-cost particulate matter sensor

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

    Environmental Pollution (Oxford, United Kingdom) (2017), 221 (), 491-500CODEN: ENPOEK; ISSN:0269-7491. (Elsevier Ltd.)

    Low-cost, light-scattering-based particulate matter (PM) sensors are becoming more widely available and are being increasingly deployed in ambient and indoor environments because of their low cost and ability to provide high spatial and temporal resoln. PM information. Researchers have begun to evaluate some of these sensors under lab. and environmental conditions. In this study, a low-cost, particulate matter sensor (Plantower PMS 1003/3003) used by a community air-quality network is evaluated in a controlled wind-tunnel environment and in the ambient environment during several winter-time, cold-pool events that are assocd. with high ambient levels of PM. In the wind-tunnel, the PMS sensor performance is compared to two research-grade, light-scattering instruments, and in the ambient tests, the sensor performance is compared to two federal equiv. (one tapered element oscillating microbalance and one beta attenuation monitor) and gravimetric federal ref. methods (FEMs/FRMs) as well as one research-grade instrument (GRIMM). The PMS sensor response correlates well with research-grade instruments in the wind-tunnel tests, and its response is linear over the concn. range tested (200-850 μg/m3). In the ambient tests, this PM sensor correlates better with gravimetric methods than previous studies with correlation coeffs. of 0.88. However addnl. measurements under a variety of ambient conditions are needed. Although the PMS sensor correlated as well as the research-grade instrument to the FRM/FEMs in ambient conditions, its response varies with particle properties to a much greater degree than the research-grade instrument. In addn., the PMS sensors overestimate ambient PM concns. and begin to exhibit a non-linear response when PM2.5 concns. exceed 40 μg/m3. These results have important implications for communicating results from low-cost sensor networks, and they highlight the importance of using an appropriate correction factor for the target environmental conditions if the user wants to compare the results to FEM/FRMs.

27. 27

    Mei, H.; Han, P.; Wang, Y.; Zeng, N.; Liu, D.; Cai, Q.; Deng, Z.; Wang, Y.; Pan, Y.; Tang, X. Field Evaluation of Low-Cost Particulate Matter Sensors in Beijing. Sensors (Switzerland) 2020, 20 (16), 4381,  DOI: 10.3390/s20164381

28. 28

    Wallace, L.; Bi, J.; Ott, W. R.; Sarnat, J.; Liu, Y. Calibration of Low-Cost PurpleAir Outdoor Monitors Using an Improved Method of Calculating PM2.5. Atmos. Environ. 2021, 256 (May), 118432  DOI: 10.1016/j.atmosenv.2021.118432

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    28

    Calibration of low-cost PurpleAir outdoor monitors using an improved method of calculating PM2.5

    Wallace, Lance; Bi, Jianzhao; Ott, Wayne R.; Sarnat, Jeremy; Liu, Yang

    Atmospheric Environment (2021), 256 (), 118432CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)

    PM2.5 hourly av. measurements from 33 outdoor PurpleAir particle monitors were compared with hourly measurements from 27 nearby US EPA Air Quality System (AQS) stations employing Federal Equiv. Method (FEM) monitors in California over an 18-mo (77-wk) period. A transparent and reproducible alternative method (ALT) of calcg. PM2.5 from the particle nos. in three size categories was used in place of the ests. provided by Plantower, the manufacturer of the sensors used in PurpleAir monitors. The ALT method was superior in several ways (better precision, lower limit of detection, improved size distribution) compared to Plantower's CF1 or ATM data series. PurpleAir monitors were strongly correlated with the nearby US EPA Air Quality System AQS stations. A calibration factor (CF) ranging between 2.9 and 3.1 was empirically derived for the PurpleAir ests. using the ALT method. This value was based on comparing the av. value of 177,329 PurpleAir measurements to the value calcd. from the FEM stations. The monitoring period included about 13 wk showing very high outdoor values due to several major fires covering several hundred thousand acres. The CF during these 13 wk averaged 2.39, whereas the CF for the remaining 64 wk averaged about 3.21, suggesting a different response to the smoke from wildfires compared with normal ambient fine particulate matter (PM2.5). The std. Plantower CF1 data series overestimated the FEM values by about 40%, in agreement with several other studies.

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    Zimmerman, N. Tutorial: Guidelines for Implementing Low-Cost Sensor Networks for Aerosol Monitoring. J. Aerosol Sci. 2022, 159, 105872  DOI: 10.1016/j.jaerosci.2021.105872

    Google Scholar

    29

    Tutorial: Guidelines for implementing low-cost sensor networks for aerosol monitoring

    Zimmerman, Naomi

    Journal of Aerosol Science (2022), 159 (), 105872CODEN: JALSB7; ISSN:0021-8502. (Elsevier Ltd.)

    A review. Over the past decade, there has been exponential growth in low-cost air pollution sensing technol. While low-cost sensors can provide a path towards more accessible air quality measurement, there are several guidelines that should be considered prior to deploying a low-cost sensor network. In this tutorial guide, we focus on low-cost aerosol sensors (in this case, PM2.5). The tutorial reviews key guidelines for implementing a low-cost PM sensor network. This article is also assocd. with a companion web-tutorial (https://nzimmerman-ubc.github.io/lcs-PM-demo/) on downloading and assessing sample low-cost PM sensor data from the PurpleAir network using the new U. S. EPA Fine Particle Sensor Base Testing Guidelines. While this tutorial does not cover every single consideration a researcher or citizen scientist might undertake, it covers the key areas of evaluation and calibration, siting, data reporting, and post-processing. The aim of the tutorial is to improve outcomes for researchers using low-cost PM sensor networks and develop a broader community of practice.

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    Alfano, B.; Barretta, L.; Del Giudice, A.; De Vito, S.; Di Francia, G.; Esposito, E.; Formisano, F.; Massera, E.; Miglietta, M. L.; Polichetti, T. Correction to: A Review of Low-Cost Particulate Matter Sensors from the Developers’ Perspectives (Sensors 2020, 20, 6819). Sensors 2021, 21 (9), 3060,  DOI: 10.3390/s21093060

32. 32

    Lee, C. H.; Wang, Y. B.; Yu, H. L. An Efficient Spatiotemporal Data Calibration Approach for the Low-Cost PM2.5 Sensing Network: A Case Study in Taiwan. Environ. Int. 2019, 130 (June), 104838,  DOI: 10.1016/j.envint.2019.05.032

    Google Scholar

    32

    An efficient spatiotemporal data calibration approach for the low-cost PM2.5 sensing network: A case study in Taiwan

    Lee, Chieh-Han; Wang, Yeuh-Bin; Yu, Hwa-Lung

    Environment International (2019), 130 (), 104838CODEN: ENVIDV; ISSN:0160-4120. (Elsevier Ltd.)

    The rapid growth of Internet of Things has provided a new aspect to air quality monitoring system. In Taiwan, over 5000 PM2.5 sensors have been installed in the last two years. The greatest asset of low-cost sensors is possibly mapping spatiotemporal air pollution with finer resoln. But the data quality of low-cost sensors is the most common question that how to take proper interpretation of the measurements. This study proposes an efficient calibration approach based on generalized additive model which is further applied to a particular low-cost PM2.5 sensor in Taiwan. The study carried out a field calibration that collecting both measurements of low-cost sensors and the regulatory stations, and investigated the space/time bias between the low-cost sensors and regulatory stations. Results show that the proposed approach can explain the variability of the biases from the low-cost sensors with R-square of 0.76. In addn., the present calibration model can quantify the uncertainty of the low-cost sensors observations and the av. std. deviation is about 13.85% with respect to its adjusted levels. This operational spatiotemporal data calibration approach provides an useful information for local communities and governmental agency to face the new era of IoT sensor for air quality monitoring.

33. 33

    Krebs, B.; Burney, J.; Zivin, J. G.; Neidell, M. Using Crowd-Sourced Data to Assess the Temporal and Spatial Relationship between Indoor and Outdoor Particulate Matter. Environ. Sci. Technol. 2021, 55 (9), 6107– 6115,  DOI: 10.1021/acs.est.0c08469

    Google Scholar

    33

    Using Crowd-Sourced Data to Assess the Temporal and Spatial Relationship between Indoor and Outdoor Particulate Matter

    Krebs, Benjamin; Burney, Jennifer; Zivin, Joshua Graff; Neidell, Matthew

    Environmental Science & Technology (2021), 55 (9), 6107-6115CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)

    Using hourly measures across a full year of crowd-sourced data from over 1000 indoor and outdoor pollution monitors in the state of California, we explore the temporal and spatial relation between outdoor and indoor particulate matter (PM) concns. for different particle sizes. The scale of this study offers new insight into both av. penetration rates and drivers of heterogeneity in the outdoor-indoor relation. We find that an increase in the daily outdoor PM concn. of 10% leads to an av. increase of 4.2-6.1% in indoor concns. The penetration of outdoor particles to the indoor environment occurs rapidly and almost entirely within 5 h. We also provide evidence showing that penetration rates are assocd. with building age and climatic conditions in the vicinity of the monitor. Since people spend a substantial amt. of each day indoors, our findings fill a crit. knowledge gap and have significant implications for government policies to improve public health through redns. in exposure to ambient air pollution.

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    Singh, A.; Ng’ang’a, D.; Gatari, M. J.; Kidane, A. W.; Alemu, Z. A.; Derrick, N.; Webster, M. J.; Bartington, S. E.; Thomas, G. N.; Avis, W.; Pope, F. D. Air Quality Assessment in Three East African Cities Using Calibrated Low-Cost Sensors with a Focus on Road-Based Hotspots. Environ. Res. Commun. 2021, 3 (7), 075007,  DOI: 10.1088/2515-7620/ac0e0a

35. 35

    Jaffe, D. A.; Miller, C.; Thompson, K.; Finley, B.; Nelson, M.; Ouimette, J.; Andrews, E. An Evaluation of the U.S. EPA’s Correction Equation for Purple Air Sensor Data in Smoke, Dust and Wintertime Urban Pollution Events. Atmos. Meas. Technol. Discuss. 2022,  DOI: 10.5194/amt-2022-265

36. 36

    Delp, W. W.; Singer, B. C. Wildfire Smoke Adjustment Factors for Low-Cost and Professional Pm2.5 Monitors with Optical Sensors. Sensors (Switzerland) 2020, 20 (13), 3683,  DOI: 10.3390/s20133683

37. 37

    Holder, A. L.; Mebust, A. K.; Maghran, L. A.; McGown, M. R.; Stewart, K. E.; Vallano, D. M.; Elleman, R. A.; Baker, K. R. Field Evaluation of Low-cost Particulate Matter Sensors for Measuring Wildfire Smoke. Sensors (Switzerland) 2020, 20 (17), 4796,  DOI: 10.3390/s20174796

38. 38

    Danek, T.; Zaręba, M. The Use of Public Data from Low-Cost Sensors for the Geospatial Analysis of Air Pollution from Solid Fuel Heating during the COVID-19 Pandemic Spring Period in Krakow, Poland. Sensors 2021, 21 (15), 5208,  DOI: 10.3390/s21155208

    Google Scholar

    38

    The Use of Public Data from Low-Cost Sensors for the Geospatial Analysis of Air Pollution from Solid Fuel Heating during the COVID-19 Pandemic Spring Period in Krakow, Poland

    Danek, Tomasz; Zareba, Mateusz

    Sensors (2021), 21 (15), 5208CODEN: SENSC9; ISSN:1424-8220. (MDPI AG)

    In this paper, we present a detailed anal. of the public data provided by low-cost sensors (LCS), which were used for spatial and temporal studies of air quality in Krakow. A PM (particulate matter) dataset was obtained in spring in 2021, during which a fairly strict lockdown was in force as a result of COVID-19. Therefore, we were able to sep. the effect of solid fuel heating from other sources of background pollution, mainly caused by urban transport. Moreover, we analyzed the historical data of PM2.5 from 2010 to 2019 to show the effect of grassroots efforts and pro-clean-air legislation changes in Krakow. We designed a unique workflow with a time-spatial anal. of PM1, PM2.5, and PM10, and temp. data from Airly(c) sensors located in Krakow and its surroundings. Using geostatistical methods, we showed that Krakow's neighboring cities are the main sources of air pollution from solid fuel heating in the city. Addnl., we showed that the changes in the law in Krakow significantly reduced the PM concn. as compared to neighboring municipalities without a fossil fuel prohibition law. Moreover, our research demonstrates that informative campaigns and education are important initiating factors in order to bring about cleaner air in the future.

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    Curto, A.; Donaire-Gonzalez, D.; Barrera-Gómez, J.; Marshall, J. D.; Nieuwenhuijsen, M. J.; Wellenius, G. A.; Tonne, C. Performance of Low-Cost Monitors to Assess Household Air Pollution. Environ. Res. 2018, 163, 53– 63,  DOI: 10.1016/j.envres.2018.01.024

    Google Scholar

    39

    Performance of low-cost monitors to assess household air pollution

    Curto, A.; Donaire-Gonzalez, D.; Barrera-Gomez, J.; Marshall, J. D.; Nieuwenhuijsen, M. J.; Wellenius, G. A.; Tonne, C.

    Environmental Research (2018), 163 (), 53-63CODEN: ENVRAL; ISSN:0013-9351. (Elsevier)

    Exposure to household air pollution is a leading cause of morbidity and mortality globally. However, due to the lack of validated low-cost monitors with long-lasting batteries in indoor environments, most epidemiol. studies use self-reported data or short-term household air pollution assessments as proxies of long-term exposure. We evaluated the performance of three low-cost monitors measuring fine particulate matter (PM2.5) and carbon monoxide (CO) in a wood-combustion expt. conducted in one household of Spain for 5 days (including the co-location of 2 units of HAPEX and 3 units of TZOA-R for PM2.5 and 3 units of EL-USB-CO for CO; a total of 40 unit-days). We used Spearman correlation (ρ) and Concordance Correlation Coeff. (CCC) to assess accuracy of low-cost monitors vs. equiv. research-grade devices. We also conducted a field study in India for 1 wk (including HAPEX in 3 households and EL-USB-CO in 4 households; a total of 49 unit-days). Correlation and agreement at 5-min were moderate-high for one unit of HAPEX (ρ = 0.73 / CCC = 0.59), for one unit of TZOA-R (ρ = 0.89 / CCC = 0.62) and for three units of EL-USB-CO (ρ = 0.82-0.89 / CCC = 0.66-0.91) in Spain, although the failure or malfunction rate among low-cost units was high in both settings (60% of unit-days in Spain and 43% in India). Low-cost monitors tested here are not yet ready to replace more established exposure assessment methods in long-term household air pollution epidemiol. studies. More field validation is needed to assess evolving sensors and monitors with application to health studies.