Low-cost Optical Particle Counter Research Articles

Socioeconomic inequity of measured indoor and outdoor exposure to PM2.5: 5 Years of data from 14,000 low-cost particle monitors

Multiple studies have considered socioeconomic or ethnic group inequities in outdoor fine particle (PM2.5) concentrations. Due to the lack of indoor measurements, these studies are forced to assume that indoor exposures are directly related to outdoor concentrations. In general, this assumption may be reasonable, but it is violated when indoor-generated fine particles form a substantial contribution to total potential indoor exposure. We now have for the first time access to long-term (months or years) indoor potential exposures, made possible by the development of low-cost optical particle counters. A large database of 120 million hourly PM2.5 indoor and outdoor concentrations measured by 10,000 outdoor and >4000 indoor PurpleAir monitors over a 5-year period (2017–2021) in three West Coast states (Washington, Oregon, California) has been used to compare with US Census 2021 estimates of median household income, educational attainment, housing characteristics, and ethnic groups. Clear evidence of inequities is found using indoor as well as outdoor PM2.5 concentrations.

Small-Scale Diffusion Dryer on an Optical Particle Counter for High-Humidity Aerosol Measurements with an Uncrewed Aircraft System

Abstract The negative effects of relative humidity to measurements of particulate matter (PM) due to hygroscopic growth are often not inherently handled by low-cost optical particle counters (OPCs). This study presents a new approach in constructing a miniaturized diffusion dryer, for use with an OPC mounted on an uncrewed aircraft system (UAS), namely, the DJI S900 (weight of 7.5 kg and flight endurance of 20 min) for short-term measurements under humid conditions. In this work, an OPC of type N3 (Alphasense) was employed alongside the dryer, with experiments both in the laboratory and outdoors. Evaluation of the dryer’s performance in a fog tank showed effective drying from almost saturated air to 41% relative humidity for 35 min, which is longer than the endurance of the UAS, and therefore sufficient. Changes in the flow rate through the OPC-N3 with the dryer showed a 17% reduction compared to an absent dryer, but the measured PM values remained unaffected. Airborne measurements were taken from four hovering flights near a governmental air pollution station (Mannheim-Nord, Germany) under humid conditions (88%–93%) where the system gave agreeable concentrations when the dryer was in place, but significantly overestimated all PM types without it. At a rural area near the Boundary Layer Field Site Falkenberg (Lindenberg, Germany), operated by the German Meteorological Service (DWD), vertical profiles inside a low-altitude cloud showed sharp increase in concentrations when the UAS entered the cloud layer, demonstrating its capability to accurately detect the layer base.

Machine learning methods for low-cost pollen monitoring – Model optimisation and interpretability

Pollen is a major issue globally, causing as much as 40 % of the population to suffer from hay fever and other allergic conditions. Current techniques for monitoring pollen are either laborious and slow, or expensive, thus alternative methods are needed to provide timely and more localised information on airborne pollen concentrations. We have demonstrated previously that low-cost Optical Particle Counter (OPC) sensors can be used to estimate pollen concentrations when machine learning methods are used to process the data and learn the relationships between OPC output data and conventionally measured pollen concentrations.This study demonstrates how methodical hyperparameter tuning can be employed to significantly improve model performance. We present the results of a range of models based on tuned hyperparameter configurations trained to predict Poaceae (Barnhart), Quercus (L.), Betula (L.), Pinus (L.) and total pollen concentrations. The results achieved here are a significant improvement on results we previously reported: the average R2 scores for the total pollen models have at least doubled compared to using previous parameter settings.Furthermore, we employ the explainable Artificial Intelligence (XAI) technique, SHAP, to interpret the models and understand how each of the input features (i.e. particle sizes) affect the estimated output concentration for each pollen type. In particular, we found that Quercus pollen has a strong positive correlation with particles of optical diameter 1.7–2.3 μm, which distinguishes it from other pollen types such as Poaceae and may suggest that type-specific subpollen particles are present in this size range.There is much further work to be done, especially in training and testing models on data obtained across different environments to evaluate the extent of generalisability. Nevertheless, this work demonstrates the potential this method can offer for low-cost monitoring of pollen and the valuable insight we can gain from what the model has learned.

Applicability of the low-cost OPC-N3 optical particle counter for microphysical measurements of fog

Abstract. Low-cost devices for particulate matter measurements are characterised by small dimensions and a light weight. This advantage makes them ideal for UAV measurements, where those parameters are crucial. However, they also have some issues. The values of particulate matter from low-cost optical particle counters can be biased by high ambient humidity. In this article, we evaluate the low-cost Alphasense OPC-N3 optical particle counter for measuring the microphysical properties of fog. This study aimed to show that OPC-N3 not only registers aerosols or humidified aerosols but also registers fog droplets. The study was carried out on the rooftop of the Institute of Geophysics, University of Warsaw, Poland, during autumn–winter 2021. To validate the results, the data from OPC-N3 were compared with the data obtained from the reference instrument, the Oxford Lasers VisiSize D30. VisiSize D30 is a shadowgraph device able to register photos of individual droplets. Considering the effective radius of droplets, it is possible to differentiate low-visibility situations between fog conditions (which are not hazardous for people) from haze events, when highly polluted air can cause health risks to people. The compared microphysical properties were liquid water content (LWC), number concentration (Nc), effective radius reff and statistical moments of radius. The Pearson correlation coefficient between both devices for LWC was 0.92, Nc was 0.95 and reff was 0.63. Overall, these results suggest good compliance between instruments. However, the OPC-N3 has to be corrected regarding professional equipment.

39 Investigation of Workplace Silica Exposures Using Novel Samplers and Methods

Abstract Scientists from the Health and Safety Executive visited a range of workplaces where there was the potential for exposure to silica. The aim of the visits was to determine potential worker exposures to dust and respirable crystalline silica by static air monitoring and exhaled breath condensate (EBC). To evaluate potential airborne exposures low-cost particle monitors were deployed alongside more established scientific equipment.  On site airborne dust concentrations were measured at different locations using two photometers, an optical particle counter (OPC) and a respirable cyclone. The size distribution was measured in real-time using a low-cost OPC and by offline analysis of a polycarbonate membrane filter housed inside an inhalable sampler using a Coulter Counter III. Pre and post shift samples of EBC were taken from 29 workers, with samples analysed by single particle inductively coupled plasma tandem mass spectrometry to measure Si-containing particles.  The real-time air monitoring data showed sustained increased concentrations or peaks corresponding to specific areas or activities, therefore indicating the tasks and/or locations most likely to contribute to worker exposure. The low-cost instruments were also effective in highlighting site locations and tasks most likely to contribute to worker exposure. An approach combining multiple sampling techniques provided a good characterisation of airborne, silica containing dusts. The EBC results consistently showed increased numbers of silicon containing particles post-shift compared with pre-shift.

106 Investigation of Workplace Silica Exposures Using Novel Samplers and Methods

Abstract Scientists from the Health and Safety Executive visited a manufacturing workplace where coatings are prepared by loading powders, frit and water into various sized mills. The aim of the visit was to determine potential worker exposures to dust and respirable crystalline silica by static air monitoring and exhaled breath condensate. Low-cost particle monitors were deployed alongside more established scientific equipment. Airborne dust concentrations were measured at three site locations using two photometers, an optical particle counter (OPC) and a respirable cyclone. The size distribution was measured in real-time using a low-cost OPC and by offline analysis of a polycarbonate membrane filter housed inside an inhalable sampler. Pre and post shift samples of exhaled breath condensate were collected from 6 workers, with samples analysed by single particle inductively coupled plasma mass spectrometry to measure the number and size of silicon containing particles present. The real-time air monitoring data at all three locations showed peaks in airborne concentrations corresponding to the loading activities and indicating the tasks most likely to contribute to worker exposure. The average concentration of respirable dust measured closest to the process was 0.13 mg.m-3 for the 8-hour shift. The low-cost monitors provided good information about the changes in concentration over time relating to worker activities.

124 Laboratory Evaluation of Low-Cost Optical Particle Counters for Occupational Respirable Exposure Measurements

Abstract Direct-reading, time-resolved devices, such as optical particle counters (OPCs) and photometers, offer a unique insight into the temporal and spatial distribution of airborne particles. They can provide a comprehensive picture of changes in concentration of airborne particles in occupational settings and therefore can be used to investigate failures in engineering control systems as well as identify exposures driven by working procedures and methods. In recent years, new developments have led to the commercialisation of low- cost optical-based sensors, which provide particle matter (PM) mass concentrations including PM2.5 and PM10 for environmental monitoring. TNO, NIOSH, and HSE are investigating their application to occupational settings with the aim to produce guidelines for calibration and use. This study evaluated the performance and accuracy of six commercially available low-cost sensors (Airbeam 2, Airveda, Omni Awair, OPC-N3, OPC-R1 and PATS+), in calm air test chambers, against reference devices including an Aerodynamic Particle Sizer (APS 3320), GK2.69 respirable cyclones, and pDR-1500 photometers. Several factors were considered: type of dust (particles having different size distribution, shape and refractive index), within- and between-device variations and exposure pattern (peak and constant concentrations). The devices were subjected to relatively high respirable concentrations (greater than 1 mg/m3) in addition to low concentrations. This presentation will present the results of the laboratory testing with particular focus on their accuracy, response, and calibration for quantitative exposure measurements. The low-cost sensor devices are also being deployed in the workplace for further evaluation and practicality of use.

Constructing a pollen proxy from low-cost Optical Particle Counter (OPC) data processed with Neural Networks and Random Forests

Pollen allergies affect a significant proportion of the global population, and this is expected to worsen in years to come. There is demand for the development of automated pollen monitoring systems. Low-cost Optical Particle Counters (OPCs) measure particulate matter and have attractive advantages of real-time high time resolution data and affordable costs. This study asks whether low-cost OPC sensors can be used for meaningful monitoring of airborne pollen.We employ a variety of methods, including supervised machine learning techniques, to construct pollen proxies from hourly-average OPC data and evaluate their performance, holding out 40 % of observations to test the proxies. The most successful methods are supervised machine learning Neural Network (NN) and Random Forest (RF) methods, trained from pollen concentrations collected from a Hirst-type sampler. These perform significantly better than using a simple particle size-filtered proxy or a Positive Matrix Factorisation (PMF) source apportionment pollen proxy. Twelve NN and RF models were developed to construct a pollen proxy, each varying by model type, input features and target variable. The results show that such models can construct useful information on pollen from OPC data. The best metrics achieved (Spearman correlation coefficient = 0.85, coefficient of determination = 0.67) were for the NN model constructing a Poaceae (grass) pollen proxy, based on particle size information, temperature, and relative humidity. Ability to distinguish high pollen events was evaluated using F1 Scores, a score reflecting the fraction of true positives with respect to false positives and false negatives, with promising results (F1 ≤ 0.83). Model-constructed proxies demonstrated the ability to follow monthly and diurnal trends in pollen. We discuss the suitability of OPCs for monitoring pollen and offer advice for future progress. We demonstrate an attractive alternative for automated pollen monitoring that could provide valuable and timely information to the benefit of pollen allergy sufferers.

Evaluation of a low-cost dryer for a low-cost optical particle counter

Abstract. The use of low-cost sensors for air quality measurements has become very popular in the last few decades. Due to the detrimental effects of particulate matter (PM) on human health, PM sensors like photometers and optical particle counters (OPCs) are widespread and have been widely investigated. The negative effects of high relative humidity (RH) and fog events in the mass concentration readings of these types of sensors are well documented. In the literature, different solutions to these problems – like correction models based on the Köhler theory or machine learning algorithms – have been applied. In this work, an air pre-conditioning method based on a low-cost thermal dryer for a low-cost OPC is presented. This study was done in two parts. The first part of the study was conducted in the laboratory to test the low-cost dryer under two different scenarios. In one scenario, the drying efficiency of the low-cost dryer was investigated in the presence of fog. In the second scenario, experiments with hygroscopic aerosols were done to determine to which extent the low-cost dryer reverts the growth of hygroscopic particles. In the second part of the study, the PM10 and PM2.5 mass concentrations of an OPC with dryer were compared with the gravimetric measurements and a continuous federal equivalent method (FEM) instrument in the field. The feasibility of using univariate linear regression (ULR) to correct the PM data of an OPC with dryer during field measurement was also evaluated. Finally, comparison measurements between an OPC with dryer, an OPC without dryer, and a FEM instrument during a real fog event are also presented. The laboratory results show that the sensor with the low-cost dryer at its inlet measured an average of 64 % and 59 % less PM2.5 concentration compared with a sensor without the low-cost dryer during the experiments with fog and with hygroscopic particles, respectively. The outcomes of the PM2.5 concentrations of the low-cost sensor with dryer in laboratory conditions reveal, however, an excess of heating compared with the FEM instrument. This excess of heating is also demonstrated in a more in-depth study on the temperature profile inside the dryer. The correction of the PM10 concentrations of the sensor with dryer during field measurements by using ULR showed a reduction of the maximum absolute error (MAE) from 4.3 µg m−3 (raw data) to 2.4 µg m−3 (after correction). The results for PM2.5 make evident an increase in the MAE after correction: from 1.9 µg m−3 in the raw data to 3.2 µg m−3. In light of these results, a low-cost thermal dryer could be a cost-effective add-on that could revert the effect of the hygroscopic growth and the fog in the PM readings. However, special care is needed when designing a low-cost dryer for a PM sensor to produce FEM similar PM readings, as high temperatures may irreversibly change the sampled air by evaporating the most volatile particulate species and thus deliver underestimated PM readings. New versions of a low-cost dryer aiming at FEM measurements should focus on maintaining the RH at the sensor inlet at 50 % and avoid reaching temperatures higher than 40 ∘C in the drying system. Finally, we believe that low-cost dryers have a very promising future for the application of sensors in citizen science, sensor networks for supplemental monitoring, and epidemiological studies.

Mass concentration measurements of autumn bioaerosol using low-cost sensors in a mature temperate woodland free-air carbon dioxide enrichment (FACE) experiment: investigating the role of meteorology and carbon dioxide levels

Abstract. Forest environments contain a wide variety of airborne biological particles (bioaerosols), including pollen, fungal spores, bacteria, viruses, plant detritus, and soil particles. Forest bioaerosol plays a number of important roles related to plant and livestock health, human disease and allergenicity, and forest and wider ecology and are thought to influence biosphere–atmosphere interactions via warm and cold cloud formation. Despite the importance of bioaerosols, there are few measurements of forest aerosol, and there is a lack of understanding of how climate change will affect forest bioaerosol in the future. We installed low-cost optical particle counters (OPCs) to measure particles in the size range between 1 and 10 µm (PM10–PM1) for a period of 2 months in autumn 2018 at the Birmingham Institute of Forest Research (BIFoR) free-air carbon dioxide enrichment (FACE) facility. In the paper, we propose that the PM10–PM1 (particulate matter) metric is a good proxy for bioaerosols because of the bioaerosol representative size range, the location of the study site (a woodland in a rural location), the field measurement taking place during the season of peak fungal activity, and the low hygroscopicity of the particles measured. The BIFoR FACE facility fumigates three 700 m2 areas of the forest with an additional 150 ppm CO2 above ambient levels with minimal impacts on other potential environmental drivers such as temperature, humidity, and wind. This experimental set-up enabled us to investigate the effect of environmental variables, including elevated CO2 (eCO2), on bioaerosol proxy concentrations and to evaluate the performance of the low-cost OPCs in a forested environment. Operating the low-cost OPCs during autumn 2018, we aimed to capture predominantly the fungal bioaerosol season. Across the experimental duration, the OPCs captured both temporal and spatial variation in bioaerosol concentrations. Aerosol concentrations were affected by changing temperatures and wind speeds but, contrary to our initial hypothesis, not by relative humidity. We detected no effect of the eCO2 treatment on total bioaerosol concentrations, but a potential suppression of high-concentration bioaerosol events was detected under eCO2. In-canopy atmospheric dispersion modelling indicates that the median spore dispersion distance is sufficiently small that there is little mixing between treatment and control experiments. Our data demonstrate the suitability of low-cost OPCs, interpreted with due caution, for use in forests and so opens the possibility of forest bioaerosol monitoring in a wider range of habitats to a wider range of researchers at a modest cost.

Effects of Optical Configuration on the Accuracy and Response of Low-Cost Optical Particle Counters

Effects of Optical Configuration on the Accuracy and Response of Low-Cost Optical Particle Counters

Use of Low-Cost Particle Counters for Cotton Dust Exposure Assessment in Textile Mills in Low- and Middle-Income Countries.

ObjectiveThere is a lack of consensus on methods for cotton dust measurement in the textile industry, and techniques vary between countries—relying mostly on cumbersome, traditional approaches. We undertook comparisons of standard, gravimetric methods with low-cost optical particle counters for personal and area dust measurements in textile mills in Pakistan.MethodsWe included male textile workers from the weaving sections of seven cotton mills in Karachi. We used the Institute of Occupational Medicine (IOM) sampler with a Casella Apex 2 standard pump and the Purple Air (PA-II-SD) for measuring personal exposures to inhalable airborne particles (n = 31). We used the Dylos DC1700 particle counter, in addition to the two above, for area-level measurements (n = 29).ResultsThere were no significant correlations between the IOM and PA for personal dust measurements using the original (r = −0.15, P = 0.4) or log-transformed data (r = −0.32, P = 0.07). Similarly, there were no significant correlations when comparing the IOM with either of the particle counters (PA and Dylos) for area dust measurements, using the original (r = −0.07, P = 0.7; r = 0.10, P = 0.6) or log-transformed data (r = −0.09, P = 0.6; r = 0.07, P = 0.7).ConclusionOur findings show a lack of correlation between the gravimetric method and the use of particle counters in both personal and area measurements of cotton dust, precluding their use for measuring occupational exposures to airborne dust in textile mills. There continues to be a need to develop low-cost instruments to help textile industries in low- and middle-income countries to perform cotton dust exposure assessment.

Laboratory Evaluation of Low-Cost Optical Particle Counters for Environmental and Occupational Exposures.

Low-cost optical particle counters effectively measure particulate matter (PM) mass concentrations once calibrated. Sensor calibration can be established by deriving a linear regression model by performing side-by-side measurements with a reference instrument. However, calibration differences between environmental and occupational settings have not been demonstrated. This study evaluated four commercially available, low-cost PM sensors (OPC-N3, SPS30, AirBeam2, and PMS A003) in both settings. The mass concentrations of three aerosols (salt, Arizona road dust, and Poly-alpha-olefin-4 oil) were measured and compared with a reference instrument. OPC-N3 and SPS30 were highly correlated (r = 0.99) with the reference instrument for all aerosol types in environmental settings. In occupational settings, SPS30, AirBeam2, and PMS A003 exhibited high correlation (>0.96), but the OPC-N3 correlation varied (r = 0.88–1.00). Response significantly (p < 0.001) varied between environmental and occupational settings for most particle sizes and aerosol types. Biases varied by particle size and aerosol type. SPS30 and OPC-N3 exhibited low bias for environmental settings, but all of the sensors showed a high bias for occupational settings. For intra-instrumental precision, SPS30 exhibited high precision for salt for both settings compared to the other low-cost sensors and aerosol types. These findings suggest that SPS30 and OPC-N3 can provide a reasonable estimate of PM mass concentrations if calibrated differently for environmental and occupational settings using site-specific calibration factors.

Assessing the sources of particles at an urban background site using both regulatory instruments and low-cost sensors – a comparative study

Abstract. Measurement and source apportionment of atmospheric pollutants are crucial for the assessment of air quality and the implementation of policies for their improvement. In most cases, such measurements use expensive regulatory-grade instruments, which makes it difficult to achieve wide spatial coverage. Low-cost sensors may provide a more affordable alternative, but their capability and reliability in separating distinct sources of particles have not been tested extensively yet. The present study examines the ability of a low-cost optical particle counter (OPC) to identify the sources of particles and conditions that affect particle concentrations at an urban background site in Birmingham, UK. To help evaluate the results, the same analysis is performed on data from a regulatory-grade instrument (SMPS, scanning mobility particle sizer) and compared to the outcomes from the OPC analysis. The analysis of the low-cost sensor data manages to separate periods and atmospheric conditions according to the level of pollution at the site. It also successfully identifies a number of sources for the observed particles, which were also identified using the regulatory-grade instruments. The low-cost sensor, due to the particle size range measured (0.35 to 40 µm), performed rather well in differentiating sources of particles with sizes greater than 1 µm, though its ability to distinguish their diurnal variation, as well as to separate sources of smaller particles, at the site was limited. The current level of source identification demonstrated makes the technique useful for background site studies, where larger particles with smaller temporal variations are of significant importance. This study highlights the current capability of low-cost sensors in source identification and differentiation using clustering approaches. Future directions towards particulate matter source apportionment using low-cost OPCs are highlighted.

Evaluation of Low-Cost Optical Particle Counters for Agricultural Exposure Measurements

HighlightsThe OPC-N3, developed by Alphasense, may be useful in measuring occupational exposure in agricultural settings based on the agreement with mass concentrations measured by gravimetrical filter analysis.The AirBeam2 is better suited for environmental exposure measurements rather than occupational measurements.Particle sizing by the GRIMM Mini-WRAS 1371 and the OPC-N3 show many aerosols that agricultural workers are exposed to follow a bimodal curve and are above 0.1 µm, thereby the respirator used as personal protective equipment is effective in filtering out aerosols in this occupation.Abstract. Prolonged exposure to dust has been shown to have adverse health effects in agricultural workers, primarily with the development of respiratory diseases. Low-cost sensors may be cost-effective tools for farmers to determine when they are exposed to harmful levels of dust during their workday. The purpose of this study was to identify low-cost sensors that may be reliably used in occupational settings to help workers and employers identify respirable particle matter exposure. The study utilized two different low-cost optical particle counters (OPCs) to collect data on dust exposure, which were worn on a belt by the participant: the OPC-N3 developed by Alphasense and the AirBeam2 developed by HabitatMap. Additionally, an AirChek TOUCH air sampling pump fit with a respirable dust aluminum cyclone allowed for the collection of respirable particulate matter (PM4) to determine the true concentration of exposure. Results show that the PM4 measurements made by the OPC-N3 are similar to the gravimetrical filter measurement at concentrations of &amp;lt; 50 µg/m3. In addition, the data analysis suggests that the AirBeam2 may be significantly underestimating the amount of particulate matter that farmworkers are exposed to and therefore may not be suitable for occupational exposure measurements compared to the OPC-N3. Keywords: Aerosols, Agriculture, AirBeam2, Dust, Exposure, Low-cost, Occupational, Optical particle counter, OPC-N3.

A machine learning field calibration method for improving the performance of low-cost particle sensors

Measurements of airborne particles in buildings with low-cost optical particle counters (OPCs) are often inaccurate and subject to uncertainties. This study introduces a methodology to improve the performance of low-cost OPCs in measuring indoor particles through machine learning. A two-month field measurement campaign was conducted in an occupied net-zero energy house. The studied OPCs (OPC–N2, Alphasense Ltd.) report size fractionated concentrations from 0.38 to 17.5 μm. Co-located reference instrumentation included a scanning mobility particle sizer (SMPS: 0.01–0.30 μm) and an optical particle sizer (OPS: 0.30–10 μm). The machine learning field calibration method applies Gaussian Process Regression (GPR) and includes two components: (1.) correction of the size-resolved OPC counting efficiency from 0.38 to 10 μm and (2.) prediction of volume size distributions (mass proxy) below the 0.38 μm detection limit of the OPC. The field calibration method is applicable to OPCs that report size fractionated concentrations. In (1.), a GPR function was used to correct the size-resolved counting efficiency of the OPCs between 0.38 and 10 μm using the OPS as reference. In (2.), a second GPR function was used to predict the volume size distribution below 0.38 μm using the SMPS/OPS as reference. This was done given the significant contribution of sub-0.38 μm particles to volume concentrations in the accumulation mode. The machine learning field calibration method resulted in a significant improvement in the accuracy of size-integrated volume concentrations (PV2.5, PV10) reported by the OPCs as compared to the SMPS/OPS. Improvements were seen in the Pearson coefficient (before correction: 0.59–0.83; after correction: 0.98–0.99); coefficient of determination (before correction: 0.35–0.69; after correction: 0.97–0.98); and mean absolute percentage error (before correction: 35–69%; after correction: 19–25%).

Combining low-cost, surface-based aerosol monitors with size-resolved satellite data for air quality applications

Abstract. Poor air quality is the world's single largest environmental health risk, and air quality monitoring is crucial for developing informed air quality policies. Efforts to monitor air pollution in different countries are uneven, largely due to the high capital costs of reference air quality monitors (AQMs), especially for airborne particulate matter (PM). In sub-Saharan Africa, for example, few cities operate AQM systems. It is thus important to examine the potential of alternative monitoring approaches. Although PM measurements can be obtained from low-cost optical particle counters (OPCs), data quality can be an issue. This paper develops a new method using raw aerosol size distributions from multiple, surface-based low-cost OPCs to constrain the Multiangle Imaging SpectroRadiometer (MISR) component-specific, column aerosol optical depth (AOD) data, which contain some particle-size-resolved information. The combination allows us to derive surface aerosol concentrations for particles as small as ∼0.1 µm in diameter, which MISR detects but are below the OPC detection limit of ∼0.5 µm. As such, we obtain better constraints on the near-surface particulate matter (PM) concentration, especially as the smaller particles tend to dominate urban pollution. We test our method using data from five low-cost OPCs deployed in the city of Nairobi, Kenya, from 1 May 2016 to 2 March 2017. As MISR passes over Nairobi only once in about 8 d, we use the size-resolved MISR AODs to scale the more frequent Moderate Resolution Imaging Spectrometer (MODIS)-derived AODs over our sites. The size distribution derived from MISR and MODIS agrees well with that from the OPCs in the size range where the data overlap (adjusted-R2∼0.80). We then calculate surface-PM concentration from the combined data. The situation for this first demonstration of the technique had significant limitations. We thus identify factors that will reduce the uncertainty in this approach for future experiments. Within these constraints, the approach has the potential to greatly expand the range of cities that can afford to monitor long-term air quality trends and help inform public policy.

Evaluation of Two Low-Cost Optical Particle Counters for the Measurement of Ambient Aerosol Scattering Coefficient and Ångström Exponent.

The aerosol scattering coefficient and Ångström exponent (AE) are important parameters in the understanding of aerosol optical properties and aerosol direct effect. These parameters are usually measured by a nephelometer network which is under-represented geographically; however, a rapid growth of air-pollution monitoring, using low-cost particle sensors, may extend observation networks. This paper presents the results of co-located measurements of aerosol optical properties, such as the aerosol scattering coefficient and the scattering AE, using low-cost sensors and using a scientific-grade polar Aurora 4000 nephelometer. A high Pearson correlation coefficient (0.94–0.96) between the low-cost particulate matter (PM) mass concentration and the aerosol scattering coefficient was found. For the PM10 mass concentration, the aerosol scattering coefficient relation is linear for the Dfrobot SEN0177 sensor and non-linear for the Alphasense OPC-N2 device. After regression analyses, both low-cost instruments provided the aerosol scattering coefficient with a similar mean square error difference (RMSE) of about 20 Mm−1, which corresponds to about 27% of the mean aerosol scattering coefficient. The relative uncertainty is independent of the pollution level. In addition, the ratio of aerosol number concentration between different bins showed a significant statistical (95% of confidence level) correlation with the scattering AE. For the SEN0177, the ratio of the particle number in bin 1 (radius of 0.15–0.25 µm) to bin 4 (radius of 1.25–2.5 µm) was a linear function of the scattering AE, with a Pearson correlation coefficient of 0.74. In the case of OPC-N2, the best correlation (r = 0.66) was found for the ratio between bin 1 (radius of 0.19–0.27 µm) and bin 2 (radius of 0.27–0.39 µm). Comparisons of an estimated scattering AE from a low-cost sensor with Aurora 4000 are given with the RMSE of 0.23–0.24, which corresponds to 16–19%. In addition, a three-year (2016–2019) observation by SEN0177 indicates that this sensor can be used to determine an annual cycle as well as a short-term variability.

Effect of aerosol composition on the performance of low-cost optical particle counter correction factors

Abstract. There is considerable interest in using low-cost optical particle counters (OPCs) to supplement existing routine air quality networks that monitor particle mass concentrations. In order to do this, low-cost OPC data need to be comparable with particle mass reference instrumentation; however, there is currently no widely agreed upon methodology to accomplish this. Aerosol hygroscopicity is known to be a key parameter to consider when correcting particle mass concentrations derived from low-cost OPCs, particularly at high ambient relative humidity (RH). Correction factors have been developed that apply κ-Köhler theory to correct for the influence of water uptake by hygroscopic aerosols. We have used datasets of co-located reference particle measurements and low-cost OPC (OPC-N2, Alphasense) measurements, collected in four cities on three continents, to explore the performance of this correction factor. We provide evidence that the elevated particle mass concentrations, reported by the low-cost OPC relative to reference instrumentation, are due to bulk aerosol hygroscopicity under different RH conditions, which is determined by aerosol composition and, in particular, the levels of hygroscopic aerosols (sulfate and nitrate). We exploit measurements made in volcanic plumes in Nicaragua, which are predominantly composed of sulfate aerosol, as a natural experiment to demonstrate this behaviour in the ambient atmosphere; the observed humidogram from these measurements closely resembles the calculated pure sulfuric acid humidogram. The results indicate that the particle mass concentrations derived from low-cost OPCs during periods of high RH (&gt;60 %) need to be corrected for aerosol hygroscopic growth. We employed a correction factor based on κ-Köhler theory and observed that the corrected OPC-N2 PM2.5 mass concentrations were within 33 % of reference measurements at all sites. The results indicated that a κ value derived in situ (using suitable reference instrumentation) would lead to the most accurate correction relative to co-located reference instruments. Applying a κ values from the literature in the correction factor also resulted in improved OPC-N2 performance, with the measurements being within 50 % of the reference values. Therefore, for areas where suitable reference instrumentation for developing a local correction factor is lacking, using a literature κ value can result in a reasonable correction. For locations with low levels of hygroscopic aerosols and low RH values, a simple calibration against gravimetric measurements (using suitable reference instrumentation) would likely be sufficient. Whilst this study generated correction factors specific for the Alphasense OPC-N2 sensor, the calibration methodology developed is likely amenable to other low-cost PM sensors.

Use of low-cost PM monitors and a multi-wavelength aethalometer to characterize PM2.5 in the Yakama Nation reservation

Rural lower Yakima Valley, Washington is home to the reservation of the Confederated Tribes and Bands of the Yakama Nation, and is a major agricultural region. Episodic poor air quality impacts this area, reflecting sources of particulate matter with a diameter of less than 2.5 μm (PM2.5) that include residential wood smoke, agricultural biomass burning and other emissions, truck traffic, backyard burning, and wildfire smoke. University of Washington partnered with the Yakama Nation Environmental Management Program to investigate characteristics of PM2.5 using 9 months of data from a combination of low-cost optical particle counters and a 5-wavelength aethalometer (MA200 Aethlabs) over 4 seasons and an episode of summer wildfire smoke. The greatest percentage of hours sampled with PM2.5 >12 μg/m3 occurred during the wildfire smoke episode (59%), followed by fall (23%) and then winter (21%). Mean (SD) values of Delta-C (μg/m3), which has been posited as an indicator of wood smoke, and determined as the mass absorbance difference at 375–880 nm, were: summer – wildfire smoke 0.34 (0.52), winter 0.27 (0.32), fall 0.10 (0.22), spring 0.05 (0.11), and summer – no wildfire smoke 0.04 (0.14). Mean (95% confidence interval) values of the absorption Ångström exponent, an indicator of the wavelength dependence of the aerosol, were: winter 1.5 (1.2–1.8), summer – wildfire smoke 1.4 (1.0–1.8), fall 1.3 (1.1–1.4), spring 1.2 (1.1–1.4), and summer – no wildfire smoke 1.2 (1.0–1.3). The trends in Delta-C and absorption Ångström exponents are consistent with expectations that a higher value reflects more biomass burning. These results suggest that biomass burning is an important contributor to PM2.5 in the wintertime, and emissions associated with diesel and soot are important contributors in the fall; however, the variety of emissions sources and combustion conditions present in this region may limit the utility of traditional interpretations of aethalometer data. Further research on the interpretation of aethalometer data in regions with complex emissions would contribute to much-needed understanding in communities impacted by air pollution from agricultural as well as residential sources of combustion.