Impact of randomly assigned "pay-as-you-go" liquefied petroleum gas prices on energy use for cooking: Experimental pilot evidence from rural Rwanda.

The disease burden related to air pollution from traditional solid-fuel cooking practices in low- and middle-income countries impacts millions of people globally. Although the use of liquefied petroleum gas (LPG) fuel for cooking can meaningfully reduce household air pollution concentrations, major barriers, including affordability and accessibility, have limited widespread adoption. Using a randomized controlled trial, our objective was to evaluate the association between the cost and use of LPG among 23 rural Rwandan households. We provided a 2-burner LPG stove with accessories and incorporated a "pay-as-you-go" (PAYG) LPG service model that included fuel delivery. PAYG services remove the large up-front cost of cylinder refills by integrating "smart meter" technology that allows participants to pay in incremental amounts, as needed. We assigned three randomized discounted prices for LPG to each household at ~4-week intervals over a 12-week period. We modeled the relationship between randomized PAYG LPG price and use (standardized to monthly periods), analyzing effect modification by relative household wealth. A 1000 Rwandan Franc (about 1 USD at the time of the study) increase in LPG price/kg was associated with a 4.1 kg/month decrease in use (95% confidence interval [CI]: -6.7, -1.6; n=69 observations). Wealth modified this association; we observed a 9.7 kg/month reduction (95% CI: -14.8, -4.5) among wealthier households and a 2.5 kg/month reduction (95% CI: -5.3, 0.3) among lower-wealth households (p-interaction=0.01). The difference in price sensitivity was driven by higher LPG use among wealthier households at more heavily discounted prices; from an 80% to 10% discount, wealthy households used 17.5 to 5.3 kg/month and less wealthy households used 6.2 to 3.1 kg/month. Our pilot-level experimental evidence of PAYG LPG in a rural low-resource setting suggests that further exploration of subsidized pricing varied by household wealth is needed to ensure future policy initiatives can achieve targets without exacerbating inequities.

Application of an Ultra-Low-Cost Passive Sampler for Light-Absorbing Carbon in Mongolia.

Low-cost, long-term measures of air pollution concentrations are often needed for epidemiological studies and policy analyses of household air pollution. The Washington passive sampler (WPS), an ultra-low-cost method for measuring the long-term average levels of light-absorbing carbon (LAC) air pollution, uses digital images to measure the changes in the reflectance of a passively exposed paper filter. A prior publication on WPS reported high precision and reproducibility. Here, we deployed three methods to each of 10 households in Ulaanbaatar, Mongolia: one PurpleAir for PM2.5; two ultrasonic personal aerosol samplers (UPAS) with quartz filters for the thermal-optical analysis of elemental carbon (EC); and two WPS for LAC. We compared multiple rounds of 4-week-average measurements. The analyses calibrating the LAC to the elemental carbon measurement suggest that 1 µg of EC/m3 corresponds to 62 PI/month (R2 = 0.83). The EC-LAC calibration curve indicates an accuracy (root-mean-square error) of 3.1 µg of EC/m3, or ~21% of the average elemental carbon concentration. The RMSE values observed here for the WPS are comparable to the reported accuracy levels for other methods, including reference methods. Based on the precision and accuracy results shown here, as well as the increased simplicity of deployment, the WPS may merit further consideration for studying air quality in homes that use solid fuels.

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

Particulate matter (PM) air pollution is a major health hazard. The health effects of PM are closely linked to particle size, which governs its deposition in (and penetration through) the respiratory tract. In recent years, low-cost sensors that report particle concentrations for multiple-sized fractions (PM1.0, PM2.5, PM10) have proliferated in everyday use and scientific research. However, knowledge of how well these sensors perform across the full range of reported particle size fractions is limited. Unfortunately, erroneous particle size data can lead to spurious conclusions about exposure, misguided interventions, and ineffectual policy decisions. We assessed the linearity, bias, and precision of three low-cost sensor models, as a function of PM size fraction, in an urban setting. Contrary to manufacturers' claims, sensors are only accurate for the smallest size fraction (PM1). The PM1.0-2.5 and PM2.5-10 size fractions had large bias, noise, and uncertainty. These results demonstrate that low-cost aerosol sensors (1) cannot discriminate particle size accurately and (2) only report linear and precise measures of aerosol concentration in the accumulation mode size range (i.e., between 0.1 and 1 µm). We recommend that crowdsourced air quality monitoring networks stop reporting coarse (PM2.5-10) mode and PM10 mass concentrations from these sensors.

Infinite hidden Markov models for multiple multivariate time series with missing data.

Exposure to air pollution is associated with increased morbidity and mortality. Recent technological advancements permit the collection of time-resolved personal exposure data. Such data are often incomplete with missing observations and exposures below the limit of detection, which limit their use in health effects studies. In this paper, we develop an infinite hidden Markov model for multiple asynchronous multivariate time series with missing data. Our model is designed to include covariates that can inform transitions among hidden states. We implement beam sampling, a combination of slice sampling and dynamic programming, to sample the hidden states, and a Bayesian multiple imputation algorithm to impute missing data. In simulation studies, our model excels in estimating hidden states and state-specific means and imputing observations that are missing at random or below the limit of detection. We validate our imputation approach on data from the Fort Collins Commuter Study. We show that the estimated hidden states improve imputations for data that are missing at random compared to existing approaches. In a case study of the Fort Collins Commuter Study, we describe the inferential gains obtained from our model including improved imputation of missing data and the ability to identify shared patterns in activity and exposure among repeated sampling days for individuals and among distinct individuals.

Disparities in Air Pollutants Across Racial, Ethnic, and Poverty Groups at US Public Schools.

We investigate socioeconomic disparities in air quality at public schools in the contiguous US using high resolution estimates of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations. We find that schools with higher proportions of people of color (POC) and students eligible for the federal free or reduced lunch program, a proxy for poverty level, are associated with higher pollutant concentrations. For example, we find that the median annual NO2 concentration for White students, nationally, was 7.7 ppbv, compared to 9.2 ppbv for Black and African American students. Statewide and regional disparities in pollutant concentrations across racial, ethnic, and poverty groups are consistent with nationwide results, where elevated NO2 concentrations were associated with schools with higher proportions of POC and higher levels of poverty. Similar, though smaller, differences were found in PM2.5 across racial and ethnic groups in most states. Racial, ethnic, and economic segregation across the rural-urban divide is likely an important factor in pollution disparities at US public schools. We identify distinct regional patterns of disparities, highlighting differences between California, New York, and Florida. Finally, we highlight that disparities exist not only across urban and non-urban lines but also within urban environments.

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).

Correction: Dynamic classification of personal microenvironments using a suite of wearable, low-cost sensors.

The original version of this Article featured an incorrect supplementary figure file. This error has been rectified in the PDF and HTML versions of this Article.

Dynamic classification of personal microenvironments using a suite of wearable, low-cost sensors.

Human exposure to air pollution is associated with increased risk of morbidity and mortality. However, personal air pollution exposures can vary substantially depending on an individual's daily activity patterns and air quality within their residence and workplace. This work developed and validated an adaptive buffer size (ABS) algorithm capable of dynamically classifying an individual's time spent in predefined microenvironments using data from global positioning systems (GPS), motion sensors, temperature sensors, and light sensors. Twenty-two participants in Fort Collins, CO were recruited to carry a personal air sampler for a 48-h period. The personal sampler was retrofitted with a GPS and a pushbutton to complement the existing sensor measurements (temperature, motion, light). The pushbutton was used in conjunction with a traditional time-activity diary to note when the participant was located at "home", "work", or within an "other" microenvironment. The ABS algorithm predicted the amount of time spent in each microenvironment with a median accuracy of 99.1%, 98.9%, and 97.5% for the "home", "work", and "other" microenvironments. The ability to classify microenvironments dynamically in real time can enable the development of new sampling and measurement technologies that classify personal exposure by microenvironment.

Air Pollution Monitoring for Health Research and Patient Care. An Official American Thoracic Society Workshop Report.

Air quality data from satellites and low-cost sensor systems, together with output from air quality models, have the potential to augment high-quality, regulatory-grade data in countries with in situ monitoring networks and provide much-needed air quality information in countries without them. Each of these technologies has strengths and limitations that need to be considered when integrating them to develop a robust and diverse global air quality monitoring network. To address these issues, the American Thoracic Society, the U.S. Environmental Protection Agency, the National Aeronautics and Space Administration, and the National Institute of Environmental Health Sciences convened a workshop in May 2017 to bring together global experts from across multiple disciplines and agencies to discuss current and near-term capabilities to monitor global air pollution. The participants focused on four topics: 1) current and near-term capabilities in air pollution monitoring, 2) data assimilation from multiple technology platforms, 3) critical issues for air pollution monitoring in regions without a regulatory-quality stationary monitoring network, and 4) risk communication and health messaging. Recommendations for research and improved use were identified during the workshop, including a recognition that the integration of data across monitoring technology groups is critical to maximizing the effectiveness (e.g., data accuracy, as well as spatial and temporal coverage) of these monitoring technologies. Taken together, these recommendations will advance the development of a global air quality monitoring network that takes advantage of emerging technologies to ensure the availability of free, accessible, and reliable air pollution data and forecasts to health professionals, as well as to all global citizens.

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.

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.

Assessment of increased sampling pump flow rates in a disposable, inhalable aerosol sampler.

A newly designed, low-cost, disposable inhalable aerosol sampler was developed to assess workers personal exposure to inhalable particles. This sampler was originally designed to operate at 10 L/min to increase sample mass and, therefore, improve analytical detection limits for filter-based methods. Computational fluid dynamics modeling revealed that sampler performance (relative to aerosol inhalability criteria) would not differ substantially at sampler flows of 2 and 10 L/min. With this in mind, the newly designed inhalable aerosol sampler was tested in a wind tunnel, simultaneously, at flows of 2 and 10 L/min flow. A mannequin was equipped with 6 sampler/pump assemblies (three pumps operated at 2 L/min and three pumps at 10 L/min) inside a wind tunnel, operated at 0.2 m/s, which has been shown to be a typical indoor workplace wind speed. In separate tests, four different particle sizes were injected to determine if the sampler's performance with the new 10 L/min flow rate significantly differed to that at 2 L/min. A comparison between inhalable mass concentrations using a Wilcoxon signed rank test found no significant difference in the concentration of particles sampled at 10 and 2 L/min for all particle sizes tested. Our results suggest that this new aerosol sampler is a versatile tool that can improve exposure assessment capabilities for the practicing industrial hygienist by improving the limit of detection and allowing for shorting sampling times.

Low-Cost Reusable Sensor for Cobalt and Nickel Detection in Aerosols Using Adsorptive Cathodic Square-Wave Stripping Voltammetry.

A low-cost electrochemical sensor with Nafion/Bi modification using adsorptive stripping voltammetry for Co and Ni determination in airborne particulate matter and welding fume samples is described. Carbon stencil-printed electrodes (CSPEs) manufactured on low-cost PET films were utilized. Dimethylglyoxime (DMG) was used as a Co(II) and Ni(II) chelator with selective chemical precipitation for trace electrochemical analysis. Electrochemical studies of the Nafion/Bi-modified CSPE indicated a diffusion-controlled redox reaction for Co and Ni measurements. The Nafion coating decreased the background current and enhanced the measured peak current. Repeatability tests based on changes in percent relative standard deviation (RSD) of peak current showed the electrode could be used at least 15 times before the RSD exceeded 15% (the reported value of acceptable repeatability from Association of Official Analytical Chemists (AOAC)) due to deterioration of electrode surface. Limits of detection were 1 µg L-1 and 5 µg L-1 for Co and Ni, respectively, which were comparable to electrochemical sensors requiring more complicated modification procedures. The sensor produced a working range of 1-250 and 5-175 µg L-1 for Co and Ni, respectively. Interference studies showed no other metal species interfered with Co and Ni measurements using the optimized conditions. Finally, the developed sensors were applied for Co and Ni determination in aerosol samples generated from Co rods and a certified welding-fume reference material, respectively. Validation with ICP-MS showed no statistically different results with 95% confidence between sensor and the ICP methods.

Within-microenvironment exposure to particulate matter and health effects in children with asthma: a pilot study utilizing real-time personal monitoring with GPS interface.

BACKGROUND: Most particulate matter (PM) and health studies in children with asthma use exposures averaged over the course of a day and do not take into account spatial/temporal variability that presumably occurs as children move from home, into transit and then school microenvironments. The objectives of this work were to identify increases in morning PM exposure occurring within home, transit and school microenvironments and determine their associations with asthma-related inflammation and rescue medication use. METHODS: In 2007-2008, thirty Denver-area schoolchildren with asthma performed personal PM exposure monitoring using a real-time sensor integrated with a geographic information system (GIS) to apportion exposures to home, transit and school microenvironments. Concurrently, daily monitoring of the airway inflammatory biomarker urinary leukotriene E4 (uLTE4) and albuterol usage was performed. RESULTS: Mean PM exposures each morning were relatively well correlated between microenvironments for subject samples (0.3 < r < 0.8), thus limiting use of this exposure metric to attribute health effects to PM exposure in specific microenvironments. Within-microenvironment increases in exposure, such as would be characterized by one or a series of transient spikes or a sustained increase in concentration (exposure event), however, were not strongly correlated between microenvironments (|r| < 0.25). On days when children were exposed to a ≥ 5µg/m3 exposure event during transit, they demonstrated a 24.0 % increase in uLTE4 (95 % CI: 1.5 %, 51.5 %) and a 9.7 % (-5.9 %, 27.9 %) increase in albuterol usage compared to days without transit exposure events. Associations between exposure events and health outcomes in home and school microenvironments tended to be positive as well, but weaker than for transit. CONCLUSIONS: School children with asthma moving across morning microenvironments experience spatially heterogeneous PM exposures with potentially varying health effects.

A Simple and Disposable Sampler for Inhalable Aerosol.

The state-of-the-art for personal sampling for inhalable aerosol hazards is constrained by issues of sampler cost and complexity; these issues have limited the adoption and use of some samplers by practicing hygienists. Thus, despite the known health effects of inhalable aerosol hazards, personal exposures are routinely assessed for only a small fraction of the at-risk workforce. To address the limitations of current technologies for inhalable aerosol sampling, a disposable inhalable aerosol sampler was developed and evaluated in the laboratory. The new sampler is designed to be less expensive and simpler to use than existing technologies. The sampler incorporates a lightweight internal capsule fused to the sampling filter. This capsule-filter assembly allows for the inclusion of particles deposited on the internal walls and inlet, thus minimizing the need to wash or wipe the interior sampling cassette when conducting gravimetric analyses. Sampling efficiency and wall losses were tested in a low-velocity wind tunnel with particles ranging from 9.5 to 89.5 µm. The results were compared to the proposed low-velocity inhalability criterion as well as published data on the IOM sampler. Filter weight stability and time-to-equilibrium were evaluated as these factors affect the practicality of a design. Preliminary testing of the new sampler showed good agreement with both the IOM and the proposed low-velocity inhalability curve. The capsule and filter assemblies reached equilibrium within 25h of manufacturing when conditioned at elevated temperatures. After reaching equilibrium, the capsule-filter assemblies were stable within 0.01mg.

Rapid detection of transition metals in welding fumes using paper-based analytical devices.

Metals in particulate matter (PM) are considered a driving factor for many pathologies. Despite the hazards associated with particulate metals, personal exposures for at-risk workers are rarely assessed due to the cost and effort associated with monitoring. As a result, routine exposure assessments are performed for only a small fraction of the exposed workforce. The objective of this research was to evaluate a relatively new technology, microfluidic paper-based analytical devices (µPADs), for measuring the metals content in welding fumes. Fumes from three common welding techniques (shielded metal arc, metal inert gas, and tungsten inert gas welding) were sampled in two welding shops. Concentrations of acid-extractable Fe, Cu, Ni, and Cr were measured and independently verified using inductively coupled plasma-optical emission spectroscopy (ICP-OES). Results from the µPAD sensors agreed well with ICP-OES analysis; the two methods gave statistically similar results in >80% of the samples analyzed. Analytical costs for the µPAD technique were ~50 times lower than market-rate costs with ICP-OES. Further, the µPAD method was capable of providing same-day results (as opposed several weeks for ICP laboratory analysis). Results of this work suggest that µPAD sensors are a viable, yet inexpensive alternative to traditional analytic methods for transition metals in welding fume PM. These sensors have potential to enable substantially higher levels of hazard surveillance for a given resource cost, especially in resource-limited environments.

Prospects and pitfalls of occupational hazard mapping: 'between these lines there be dragons'.

Hazard data mapping is a promising new technique that can enhance the process of occupational exposure assessment and risk communication. Hazard maps have the potential to improve worker health by providing key input for the design of hazard intervention and control strategies. Hazard maps are developed with aid from direct-reading instruments, which can collect highly spatially and temporally resolved data in a relatively short period of time. However, quantifying spatial-temporal variability in the occupational environment is not a straightforward process, and our lack of understanding of how to ascertain and model spatial and temporal variability is a limiting factor in the use and interpretation of workplace hazard maps. We provide an example of how sources of and exposures to workplace hazards may be mischaracterized in a hazard map due to a lack of completeness and representativeness of collected measurement data. Based on this example, we believe that a major priority for research in this emerging area should focus on the development of a statistical framework to quantify uncertainty in spatially and temporally varying data. In conjunction with this need is one for the development of guidelines and procedures for the proper sampling, generation, and evaluation of workplace hazard maps.

Flexible low-cost system for small animal aerosol inhalation exposure to drugs, proteins, inflammatory agents, and infectious agents.

The design for a simple, low-cost aerosol generation system for rodent inhalation studies is described here. This system is appropriate for low biohazard-level agents. In this study, two biosafety level 2 agents, Pasturella pneumotropica and Pseudomonas aeruginosa, were tested successfully. This system was also used to immunize mice and guinea pigs in ovalbumin-based models of pulmonary inflammation. This design is appropriate for studies with limited budgets and lower-level biosafety containment.

Development of a method for personal, spatiotemporal exposure assessment.

This work describes the development and evaluation of a high resolution, space and time-referenced sampling method for personal exposure assessment to airborne particulate matter (PM). This method integrates continuous measures of personal PM levels with the corresponding location-activity (i.e. work/school, home, transit) of the subject. Monitoring equipment include a small, portable global positioning system (GPS) receiver, a miniature aerosol nephelometer, and an ambient temperature monitor to estimate the location, time, and magnitude of personal exposure to particulate matter air pollution. Precision and accuracy of each component, as well as the integrated method performance were tested in a combination of laboratory and field tests. Spatial data was apportioned into pre-determined location-activity categories (i.e. work/school, home, transit) with a simple, temporospatially-based algorithm. The apportioning algorithm was extremely effective with an overall accuracy of 99.6%. This method allows examination of an individual's estimated exposure through space and time, which may provide new insights into exposure-activity relationships not possible with traditional exposure assessment techniques (i.e., time-integrated, filter-based measurements). Furthermore, the method is applicable to any contaminant or stressor that can be measured on an individual with a direct-reading sensor.