AirPen: A Wearable Monitor for Characterizing Exposures to Particulate Matter and Volatile Organic Compounds.

Exposure to air pollution is a leading risk factor for disease and premature death, but technologies for assessing personal exposure to particulate and gaseous air pollutants, including the timing and location of such exposures, are limited. We developed a small, quiet, wearable monitor, called the AirPen, to quantify personal exposures to fine particulate matter (PM2.5) and volatile organic compounds (VOCs). The AirPen combines physical sample collection (PM onto a filter and VOCs onto a sorbent tube) with a suite of low-cost sensors (for PM, VOCs, temperature, pressure, humidity, light intensity, location, and motion). We validated the AirPen against conventional personal sampling equipment in the laboratory and then conducted a field study to measure at-work and away-from-work exposures to PM2.5 and VOCs among employees at an agricultural facility in Colorado, USA. The resultant sampling and sensor data indicated that personal exposures to benzene, toluene, ethylbenzene, and xylenes were dominated by a specific workplace location. These results illustrate how the AirPen can be used to advance our understanding of personal exposure to air pollution as a function of time, location, source, and activity, even in the absence of detailed activity diary data.

Design and Testing of a Low-Cost Sensor and Sampling Platform for Indoor Air Quality.

Americans spend most of their time indoors at home, but comprehensive characterization of in-home air pollution is limited by the cost and size of reference-quality monitors. We assembled small "Home Health Boxes" (HHBs) to measure indoor PM2.5, PM10, CO2, CO, NO2, and O3 concentrations using filter samplers and low-cost sensors. Nine HHBs were collocated with reference monitors in the kitchen of an occupied home in Fort Collins, Colorado, USA for 168 h while wildfire smoke impacted local air quality. When HHB data were interpreted using gas sensor manufacturers' calibrations, HHBs and reference monitors (a) categorized the level of each gaseous pollutant similarly (as either low, elevated, or high relative to air quality standards) and (b) both indicated that gas cooking burners were the dominant source of CO and NO2 pollution; however, HHB and reference O3 data were not correlated. When HHB gas sensor data were interpreted using linear mixed calibration models derived via collocation with reference monitors, root-mean-square error decreased for CO2 (from 408 to 58 ppm), CO (645 to 572 ppb), NO2 (22 to 14 ppb), and O3 (21 to 7 ppb); additionally, correlation between HHB and reference O3 data improved (Pearson's r increased from 0.02 to 0.75). Mean 168-h PM2.5 and PM10 concentrations derived from nine filter samples were 19.4 µg m-3 (6.1% relative standard deviation [RSD]) and 40.1 µg m-3 (7.6% RSD). The 168-h PM2.5 concentration was overestimated by PMS5003 sensors (median sensor/filter ratio = 1.7) and underestimated slightly by SPS30 sensors (median sensor/filter ratio = 0.91).

Bayesian Nonparametric Monotone Regression.

In many applications there is interest in estimating the relation between a predictor and an outcome when the relation is known to be monotone or otherwise constrained due to the physical processes involved. We consider one such application-inferring time-resolved aerosol concentration from a low-cost differential pressure sensor. The objective is to estimate a monotone function and make inference on the scaled first derivative of the function. We proposed Bayesian nonparametric monotone regression which uses a Bernstein polynomial basis to construct the regression function and puts a Dirichlet process prior on the regression coefficients. The base measure of the Dirichlet process is a finite mixture of a mass point at zero and a truncated normal. This construction imposes monotonicity while clustering the basis functions. Clustering the basis functions reduces the parameter space and allows the estimated regression function to be linear. With the proposed approach we can make closed-formed inference on the derivative of the estimated function including full quantification of uncertainty. In a simulation study the proposed method performs similar to other monotone regression approaches when the true function is wavy but performs better when the true function is linear. We apply the method to estimate time-resolved aerosol concentration with a newly-developed portable aerosol monitor. The R package bnmr is made available to implement the method.

Effects of Fuel Moisture Content on Emissions from a Rocket-Elbow Cookstove.

Exposure to air pollution from solid-fuel cookstoves is a leading risk factor for premature death; however, the effect of fuel moisture content on air pollutant emissions from solid-fuel cookstoves remains poorly constrained. The objective of this work was to characterize emissions from a rocket-elbow cookstove burning wood at three different moisture levels (5%, 15%, and 25% on a dry mass basis). Emissions of carbon dioxide (CO2), carbon monoxide (CO), methane, fine particulate matter (PM2.5), PM2.5 elemental carbon (EC), PM2.5 organic carbon, formaldehyde, acetaldehyde, benzene, toluene, ethylbenzene, and xylenes were measured. Emission factors (EFs; g·MJdelivered-1) for all pollutants, except CO2 and EC, increased with increasing fuel moisture content: CO EFs increased by 84%, PM2.5 EFs increased by 149%, formaldehyde EFs increased by 216%, and benzene EFs increased by 82%. Both modified combustion efficiency and the temperature at the combustion chamber exit decreased with increasing fuel moisture, suggesting that the energy required to vaporize water in the fuel led to lower temperatures in the combustion chamber and lower gas-phase oxidation rates. These results illustrate that changes in fuel equilibrium moisture content could cause EFs for pollutants such as PM2.5 to vary by a factor of 2 or more across different geographic regions.

A Laboratory Assessment of 120 Air Pollutant Emissions from Biomass and Fossil Fuel Cookstoves.

Cookstoves emit many pollutants that are harmful to human health and the environment. However, most of the existing scientific literature focuses on fine particulate matter (PM2.5) and carbon monoxide (CO). We present an extensive data set of speciated air pollution emissions from wood, charcoal, kerosene, and liquefied petroleum gas (LPG) cookstoves. One-hundred and twenty gas- and particle-phase constituents-including organic carbon, elemental carbon (EC), ultrafine particles (10-100 nm), inorganic ions, carbohydrates, and volatile/semivolatile organic compounds (e.g., alkanes, alkenes, alkynes, aromatics, carbonyls, and polycyclic aromatic hydrocarbons (PAHs))-were measured in the exhaust from 26 stove/fuel combinations. We find that improved biomass stoves tend to reduce PM2.5 emissions; however, certain design features (e.g., insulation or a fan) tend to increase relative levels of other coemitted pollutants (e.g., EC ultrafine particles, carbonyls, or PAHs, depending on stove type). In contrast, the pressurized kerosene and LPG stoves reduced all pollutants relative to a traditional three-stone fire (≥93% and ≥79%, respectively). Finally, we find that PM2.5 and CO are not strong predictors of coemitted pollutants, which is problematic because these pollutants may not be indicators of other cookstove smoke constituents (such as formaldehyde and acetaldehyde) that may be emitted at concentrations that are harmful to human health.

The Firepower Sweep Test: A novel approach to cookstove laboratory testing.

Emissions from solid-fuel cookstoves have been linked to indoor and outdoor air pollution, climate forcing, and human disease. Although task-based laboratory protocols, such as the Water Boiling Test (WBT), overestimate the ability of improved stoves to lower emissions, WBT emissions data are commonly used to benchmark cookstove performance, estimate indoor and outdoor air pollution concentrations, estimate impacts of stove intervention projects, and select stoves for large-scale control trials. Multiple-firepower testing has been proposed as an alternative to the WBT and is the basis for a new standardized protocol (ISO 19867-1:2018); however, data are needed to assess the value of this approach. In this work, we (a) developed a Firepower Sweep Test [FST], (b) compared emissions from the FST, WBT, and in-home cooking, and (c) quantified the relationship between firepower and emissions using correlation analysis and linear model selection. Twenty-three stove-fuel combinations were evaluated. The FST reproduced the range of PM2.5 and CO emissions observed in the field, including high emissions events not typically observed under the WBT. Firepower was modestly correlated with emissions, although the relationship varied between stove-fuel combinations. Our results justify incorporating multiple-firepower testing into laboratory-based protocols but demonstrate that firepower alone cannot explain the observed variability in cookstove emissions.

The Effects of Air Flow Rates, Secondary Air Inlet Geometry, Fuel Type, and Operating Mode on the Performance of Gasifier Cookstoves.

Development of biomass cookstoves that reduce emissions of CO and PM2.5 by more than 50% and 95%, respectively, compared to a three-stone fire has been promoted as part of efforts to reduce exposure to household air pollution (HAP) among people that cook with solid fuels. Gasifier cookstoves have attracted interest because some have been shown to emit less CO and PM2.5 than other designs. A laboratory test bed and new test procedure were used to investigate the influence of air flow rates, stove geometry, fuel type, and operating mode on gasifier cookstove performance. Power output, CO emissions, PM2.5 emissions, fuel consumption rates, producer gas composition, and fuel bed temperatures were measured. The test bed emitted <41 mg·MJd­1 PM2.5 and <8 g·MJd­1 CO when operating normally with certain prepared fuels, but order of magnitude increases in emission factors were observed for other fuels and during refueling. Changes in operating mode and fuel type also affected the composition of the producer gas entering the secondary combustion zone. Overall, the results suggest that the effects of fuel type and operator behavior on emissions need to be considered, in addition to cookstove design, as part of efforts to reduce exposure to HAP.

Fundamentals of low-cost aerosol sensor design and operation.

Most evaluations of low-cost aerosol sensors have focused on their measurement bias compared to regulatory monitors. Few evaluations have applied fundamental principles of aerosol science to increase our understanding of how such sensors work and could be improved. We examined the Plantower PMS5003 sensor's internal geometry, laser properties, photodiode responses, microprocessor output, flow rates, and response to mono- and poly-disperse aerosols. We developed a physics-based model of particle light scattering within the sensor, which we used to predict counting and sizing efficiency for 0.30 to 10 µm particles. We found that the PMS5003 counts single particle scattering events, acting like an imperfect optical particle counter, rather than a nephelometer. As particle flow is not focused into the core of the laser beam, >99% of particles that flow through the PMS5003 miss the laser, and those that intercept the laser usually miss the focal point and are subsequently undersized, resulting in erroneous size distribution data. Our model predictions of PMS5003 response to varying particle diameters, aerosol compositions, and relative humidity were consistent with laboratory data. Computational fluid dynamics simulations of the PurpleAir monitor housing showed that for wind-speeds less than 3 m s-1, fine and coarse particles were representatively aspired to the PMS5003 inlet. Our measurements and models explain why the PurpleAir overstates regulatory PM2.5 in some locations but not others; why the PurpleAir PM10 is unresponsive to windblown dust; and why it reports a similar particle size distribution for coarse particles as it does for smoke and ambient background aerosol.

Kitchen concentrations of fine particulate matter and particle number concentration in households using biomass cookstoves in rural Honduras.

Cooking and heating with solid fuels results in high levels of household air pollutants, including particulate matter (PM); however, limited data exist for size fractions smaller than PM2.5 (diameter less than 2.5 µm). We collected 24-h time-resolved measurements of PM2.5 (n = 27) and particle number concentrations (PNC, average diameter 10-700 nm) (n = 44; 24 with paired PM2.5 and PNC) in homes with wood-burning traditional and Justa (i.e., with an engineered combustion chamber and chimney) cookstoves in rural Honduras. The median 24-h PM2.5 concentration (n = 27) was 79 µg/m3 (interquartile range [IQR]: 44-174 µg/m3); traditional (n = 15): 130 µg/m3 (IQR: 48-250 µg/m3); Justa (n = 12): 66 µg/m3 (IQR: 44-97 µg/m3). The median 24-h PNC (n = 44) was 8.5 × 104 particles (pt)/cm3 (IQR: 3.8 × 104-1.8 × 105 pt/cm3); traditional (n = 27): 1.3 × 105 pt/cm3 (IQR: 3.3 × 104-2.0 × 105 pt/cm3); Justa (n = 17): 6.3 × 104 pt/cm3 (IQR: 4.0 × 104-1.2 × 105 pt/cm3). The 24-h average PM2.5 and particle number concentrations were correlated for the full sample of cookstoves (n = 24, Spearman ρ: 0.83); correlations between PM2.5 and PNC were higher in traditional stove kitchens (n = 12, ρ: 0.93) than in Justa stove kitchens (n = 12, ρ: 0.67). The 24-h average concentrations of PM2.5 and PNC were also correlated with the maximum average concentrations during shorter-term averaging windows of one-, five-, 15-, and 60-min, respectively (Spearman ρ: PM2.5 [0.65, 0.85, 0.82, 0.71], PNC [0.74, 0.86, 0.88, 0.86]). Given the moderate correlations observed between 24-h PM2.5 and PNC and between 24-h and the shorter-term averaging windows within size fractions, investigators may need to consider cost-effectiveness and information gained by measuring both size fractions for the study objective. Further evaluations of other stove and fuel combinations are needed.

Design and evaluation of a portable PM2.5 monitor featuring a low-cost sensor in line with an active filter sampler.

Fine particulate air pollution (PM2.5) is a health hazard with numerous indoor and outdoor sources. Versatile monitors are needed to characterize PM2.5 sources, concentrations, and exposures in a range of locations and applications. Whereas low-cost light-scattering PM sensors provide real-time measurements with limited accuracy, gravimetric samples provide more accurate, albeit time-integrated, measurements. When used together, low-cost sensor data can be corrected to gravimetric samples. Here we describe the development of a portable PM2.5 monitor that features a low-cost sensor in line with an active filter sampler. Laboratory tests were conducted to determine (1) the accuracy and precision of PM2.5 concentrations derived from the filter sample and (2) correction factors for the low-cost sensor response to ammonium sulfate, Arizona road dust, urban particulate matter, and match smoke. Filter samples collected at 0.25 and 1.0 L min-1 had mean biases of -10% and -4%, relative to a tapered element oscillating microbalance, and a relative standard deviation (RSD) that ranged from 1% to 17%. The low-cost sensor correction factor varied with the test aerosol, sample flow rate, and between individual monitors. Gravimetric correction reduced the bias and RSD of ∼1 hour average concentrations measured by low-cost sensors in three collocated monitors. A week-long field experiment was also conducted to investigate how the monitor could be used to learn about sources of residential air pollution. Field data were used to identify: (1) pollution events resulting from cooking and use of a wood furnace and (2) variations in the number of air changes per hour inside the residence.

Variation in gravimetric correction factors for nephelometer-derived estimates of personal exposure to PM2.5.

Many portable monitors for quantifying mass concentrations of particulate matter air pollution rely on aerosol light scattering as the measurement method; however, the relationship between scattered light (what is measured) and aerosol mass concentration (the metric of interest) is a complex function of the refractive index, size distribution, and shape of the particles. In this study, we compared 33-h personal PM2.5 concentrations measured simultaneously using nephelometry (personal DataRAM pDR-1200) and gravimetric filter sampling for working adults (44 participants, 249 samples). Nephelometer- and filter-derived 33-h average PM2.5 concentrations were correlated (Spearman's ρ = 0.77); however, the nephelometer-derived concentration was within 20% of the filter-derived concentration for only 13% of samples. The nephelometer/filter ratio, which is used to correct light-scattering measurements to a gravimetric sample, had a median value of 0.52 and varied by over a factor of three (10th percentile = 0.35, 90th percentile = 1.1). When 33-h samples with >50% of 10-s average nephelometer readings below the nephelometer limit of detection were removed from the dataset during sensitivity analyses, the fraction of nephelometer-derived concentrations that were within 20% of the filter-derived concentration increased to 25%. We also evaluated how much the accuracy of nephelometer-derived concentrations improved after applying: (1) a median correction factor derived from a subset of 44 gravimetric samples, (2) participant-specific correction factors derived from one same from each subject, and (3) correction factors predicted using linear models based on other variables recorded during the study. Each approach independently increased the fraction of nephelometer-derived concentrations that were within 20% of the filter-derived concentration to approximately 45%. These results illustrate the challenges with using light scattering (without correction to a concurrent gravimetric sample) to estimate personal exposure to PM2.5 mass among mobile adults exposed to low daily average concentrations (median = 8 µg m-3 in this study).