Development of ASTM International D8405-Standard Test Method for Evaluating PM2.5 Sensors or Sensor Systems Used in Indoor Applications.

Sensors and sensor systems for monitoring fine particles with aerodynamic diameters smaller than 2.5 µm can provide real-time feedback on indoor air quality and thus can help guide actions to manage indoor air pollutant concentrations. Standardized verification of the performance and accuracy of sensors and sensor systems is crucial for predicting the efficacy of such monitoring. A new ASTM International standard test method (ASTM D8405) was created for this need and is the most exacting laboratory protocol published to date for evaluating indoor air quality sensors and sensor systems measuring particles smaller than 2.5 µm in diameter. ASTM D8405 subjects sensors and sensor systems to five test phases: (1) an initial particle concentration ramp; (2) exposure to various temperature and humidity conditions; (3) exposure to interfering particles; (4) temperature cycling; and (5) a final particle concentration ramp to assess drift. This paper discusses the development of the standard test method, key aspects of the testing process, example evaluation results, and a comparison of this standard test method against peer evaluation protocols.

Towards the Development of a Sensor Educational Toolkit to Support Community and Citizen Science.

As air quality sensors increasingly become commercially available, a deeper consideration of their usability and usefulness is needed to ensure effective application by the public. Much of the research related to sensors has focused on data quality and potential applications. While this information is important, a greater understanding of users' experience with sensors would provide complementary information. Under a U.S. EPA-funded Science to Achieve Results grant awarded to the South Coast Air Quality Management District in California, titled "Engage, Educate, and Empower California Communities on the Use and Applications of Low-Cost Air Monitoring Sensors", approximately 400 air quality sensors were deployed with 14 California communities. These communities received sensors and training, and they participated in workshops. Widely varying levels of sensor installation and engagement were observed across the 14 communities. However, despite differences between communities (in terms of participation, demographics, and socioeconomic factors), many participants offered similar feedback on the barriers to sensor use and strategies leading to successful sensor use. Here, we assess sensor use and participant feedback, as well as discuss the development of an educational toolkit titled "Community in Action: A Comprehensive Toolkit on Air Quality Sensors". This toolkit can be leveraged by future community and citizen science projects to develop networks designed to collect air quality information that can help reduce exposure to and the emissions of pollutants, leading to improved environmental and public health.

Long-term evaluation of a low-cost air sensor network for monitoring indoor and outdoor air quality at the community scale.

Given the growing interest in community air quality monitoring using low-cost sensors, 30 PurpleAir II sensors (12 outdoor and 18 indoor) were deployed in partnership with community members living adjacent to a major interstate freeway from December 2017- June 2019. Established quality assurance/quality control techniques for data processing were used and sensor data quality was evaluated by calculating data completeness and summarizing PM2.5 measurements. To evaluate outdoor sensor performance, correlation coefficients (r) and coefficients of divergence (CoD) were used to assess temporal and spatial variability of PM2.5 between sensors. PM2.5 concentrations were also compared to traffic levels to assess the sensors' ability to detect traffic pollution. To evaluate indoor sensors, indoor/outdoor (I/O) ratios during resident-reported activities were calculated and compared, and a linear mixed-effects regression model was developed to quantify the impacts of ambient air quality, microclimatic factors, and indoor human activities on indoor PM2.5. In general, indoor sensors performed more reliably than outdoor sensors (completeness: 73% versus 54%). All outdoor sensors were highly temporally correlated (r > 0.98) and spatially homogeneous (CoD<0.06). The observed I/O ratios were consistent with existing literature, and the mixed-effects model explains >85% of the variation in indoor PM2.5 levels, indicating that indoor sensors detected PM2.5 from various sources. Overall, this study finds that community-maintained sensors can effectively monitor PM2.5, with main data quality concerns resulting from outdoor sensor data incompleteness.

Development of a Performance Evaluation Protocol for Air Sensors Deployed on a Google Street View Car.

Performance evaluation studies of low-cost sensors (LCS) measuring air pollutants have been conducted by academic and governmental groups for stationary applications. In contrast, evaluation protocols are nonexistent for LCS used in mobile deployments, though LCS are used in this manner by research groups and may be employed to complement regulatory directives for community monitoring. Mobile measurements with LCS are a nascent but growing use-case, and questions of data quality will become increasingly important. The South Coast Air Quality Management District's Air Quality Sensor Performance Evaluation Center has developed the first evaluation protocol in which LCS are compared to reference- or research-grade instruments while deployed on a ground-based mobile platform. LCS are assessed in test scenarios of various degrees of environmental control, ranging from placement in a controlled flow sampling duct to unsheltered mounting on a vehicle rooftop. The testing procedures aim to quantify the performance of LCS and the effects of sensor siting, orientation, and vehicle velocity, the results of which can guide users on appropriate LCS and configurations for their applications. Unexpected performance effects have been revealed through pilot-testing of this evaluation protocol that would likely have not been known from stationary field and laboratory testing.

Development of a Network of Accurate Ozone Sensing Nodes for Parallel Monitoring in a Site Relocation Study.

Recent technological advances in both air sensing technology and Internet of Things (IoT) connectivity have enabled the development and deployment of remote monitoring networks of air quality sensors. The compact size and low power requirements of both sensors and IoT data loggers allow for the development of remote sensing nodes with power and connectivity versatility. With these technological advancements, sensor networks can be developed and deployed for various ambient air monitoring applications. This paper describes the development and deployment of a monitoring network of accurate ozone (O3) sensor nodes to provide parallel monitoring in an air monitoring site relocation study. The reference O3 analyzer at the station along with a network of three O3 sensing nodes was used to evaluate the spatial and temporal variability of O3 across four Southern California communities in the San Bernardino Mountains which are currently represented by a single reference station in Crestline, CA. The motivation for developing and deploying the sensor network in the region was that the single reference station potentially needed to be relocated due to uncertainty that the lease agreement would be renewed. With the implication of siting a new reference station that is also a high O3 site, the project required the development of an accurate and precise sensing node for establishing a parallel monitoring network at potential relocation sites. The deployment methodology included a pre-deployment co-location calibration to the reference analyzer at the air monitoring station with post-deployment co-location results indicating a mean absolute error (MAE) < 2 ppb for 1-h mean O3 concentrations. Ordinary least squares regression statistics between reference and sensor nodes during post-deployment co-location testing indicate that the nodes are accurate and highly correlated to reference instrumentation with R2 values > 0.98, slope offsets < 0.02, and intercept offsets < 0.6 for hourly O3 concentrations with a mean concentration value of 39.7 ± 16.5 ppb and a maximum 1-h value of 94 ppb. Spatial variability for diurnal O3 trends was found between locations within 5 km of each other with spatial variability between sites more pronounced during nighttime hours. The parallel monitoring was successful in providing the data to develop a relocation strategy with only one relocation site providing a 95% confidence that concentrations would be higher there than at the current site.

Secondary particles formed from the exhaust of vehicles using ethanol-gasoline blends increase the production of pulmonary and cardiac reactive oxygen species and induce pulmonary inflammation.

BACKGROUND: Ethanol vehicles release exhaust gases that contribute to the formation of secondary organic aerosols (SOA). OBJECTIVE: To determine in vivo toxicity resulting from exposure to SOA derived from vehicles using different ethanol-gasoline blends (E0, E10, E22, E85W, E85S, E100). METHODS: Exhaust emissions from vehicles using ethanol blends were delivered to a photochemical chamber and reacted to produce SOA. The aerosol samples were collected on filters, extracted, and dispersed in an aqueous solutions and intratracheally instilled into Sprague Dawley rats in doses of 700 µg/0.2 ml. After 45 min and 4 h pulmonary and cardiac chemiluminescence (CL) was measured to estimate the amount of reactive oxygen species (ROS) produced in the lungs and heart. Inflammation was measured by differential cell count in bronchoalveolar lavages (BAL). RESULTS: Statistically and biologically significant differences in response to secondary particles from the different fuel formulations were detected. Compared to the control group, animals exposed to SOA from gasoline (E0) showed a significantly higher average CL in the lungs at 45 min. The highest CL averages in the heart were observed in the groups exposed to SOA from E10 and pure ethanol (E100) at 45 min. BAL of animals exposed to SOA from E0 and E85S had a significant increased number of macrophages at 45 min. BAL neutrophil count was increased in the groups exposed to E85S (45 min) and E0 (4 h). Animals exposed to E0 and E85W had increased BAL lymphocyte count compared to the control and the other exposed groups. DISCUSSION: Our results suggest that SOA generated by gasoline (E0), followed by ethanol blends E85S and E85W, substantially induce oxidative stress measured by ROS generation and pulmonary inflammation measured by the recruitment of white blood cells in BAL.

Inhaled ambient-level traffic-derived particulates decrease cardiac vagal influence and baroreflexes and increase arrhythmia in a rat model of metabolic syndrome.

BACKGROUND: Epidemiological studies have linked exposures to ambient fine particulate matter (PM2.5) and traffic with autonomic nervous system imbalance (ANS) and cardiac pathophysiology, especially in individuals with preexisting disease. It is unclear whether metabolic syndrome (MetS) increases susceptibility to the effects of PM2.5. We hypothesized that exposure to traffic-derived primary and secondary organic aerosols (P + SOA) at ambient levels would cause autonomic and cardiovascular dysfunction in rats exhibiting features of MetS. Male Sprague Dawley (SD) rats were fed a high-fructose diet (HFrD) to induce MetS, and exposed to P + SOA (20.4 ± 0.9 µg/m3) for 12 days with time-matched comparison to filtered-air (FA) exposed MetS rats; normal diet (ND) SD rats were separately exposed to FA or P + SOA (56.3 ± 1.2 µg/m3). RESULTS: In MetS rats, P + SOA exposure decreased HRV, QTc, PR, and expiratory time overall (mean effect across the entirety of exposure), increased breathing rate overall, decreased baroreflex sensitivity (BRS) on three exposure days, and increased spontaneous atrioventricular (AV) block Mobitz Type II arrhythmia on exposure day 4 relative to FA-exposed animals receiving the same diet. Among ND rats, P + SOA decreased HRV only on day 1 and did not significantly alter BRS despite overall hypertensive responses relative to FA. Correlations between HRV, ECG, BRS, and breathing parameters suggested a role for autonomic imbalance in the pathophysiologic effects of P + SOA among MetS rats. Autonomic cardiovascular responses to P + SOA at ambient PM2.5 levels were pronounced among MetS rats and indicated blunted vagal influence over cardiovascular physiology. CONCLUSIONS: Results support epidemiologic findings that MetS increases susceptibility to the adverse cardiac effects of ambient-level PM2.5, potentially through ANS imbalance.

Effects of fresh and aged vehicular exhaust emissions on breathing pattern and cellular responses--pilot single vehicle study.

The study presented here is a laboratory pilot study using diluted car exhaust from a single vehicle to assess differences in toxicological response between primary emissions and secondary products resulting from atmospheric photochemical reactions of gas phase compounds with O3, OH and other radicals. Sprague Dawley rats were exposed for 5 h to either filtered room air (sham) or one of two different atmospheres: (i) diluted car exhaust (P)+Mt. Saint Helens Ash (MSHA); (ii) P+MSHA+secondary organic aerosol (SOA, formed during simulated photochemical aging of diluted exhaust). Primary and secondary gases were removed using a nonselective diffusion denuder. Continuous respiratory data was collected during the exposure, and bronchoalveolar lavage (BAL) and complete blood counts (CBC) were performed 24 h after exposure. ANOVA models were used to assess the exposure effect and to compare those effects across different exposure types. Total average exposures were 363 ± 66 µg/m³ P+MSHA and 212 ± 95 µg/m³ P+MSHA+SOA. For both exposures, we observed decreases in breathing rate, tidal and minute volumes (TV, MV) and peak and median flows (PIF, PEF and EF50) along with increases in breathing cycle times (Ti, Te) compared to sham. These results indicate that the animals are changing their breathing pattern with these test atmospheres. Exposure to P+MSHA+SOA produced significant increases in total cells, macrophages and neutrophils in the BAL and in vivo chemiluminescence of the lung. There were no significant differences in CBC parameters. Our data suggest that simulated atmospheric photochemistry, producing SOA in the P+MSHA+SOA exposures, enhanced the toxicity of vehicular emissions.

Development and evaluation of a countercurrent parallel-plate membrane diffusion denuder for the removal of gas-phase compounds from vehicular emissions.

Diffusion denuders have been commonly used to remove trace gases from an aerosol (mixture of gases and particles), while allowing the particles to remain suspended in air. We present the design and evaluation of a high-flow (16.7 L min⁻¹) countercurrent parallel-plate membrane diffusion denuder that has high removal efficiencies for both non-reactive gases such as carbon monoxide (89%), as well as volatile organic compounds (80-85%) from an automobile exhaust. Particle losses were approximately 15% for particles around 100 nm in diameter. This denuder is suitable for toxicological tests involving both human and animal exposures to combustion aerosols. The denuder may also be used for other applications, for example, to reduce the effect of gas-phase sampling artifacts on particle composition.

Laboratory evaluation of a prototype photochemical chamber designed to investigate the health effects of fresh and aged vehicular exhaust emissions.

Laboratory experiments simulating atmospheric aging of motor vehicle exhaust emissions were conducted using a single vehicle and a photochemical chamber. A compact automobile was used as a source of emissions. The vehicle exhaust was diluted with ambient air to achieve carbon monoxide (CO) concentrations similar to those observed in an urban highway tunnel. With the car engine idling, it is expected that the CO concentration is a reasonable surrogate for volatile organic compounds (VOCs) emissions. Varying the amount of dilution of the exhaust gas to produce different CO concentrations, allowed adjustment of the concentrations of VOCs in the chamber to optimize production of secondary organic aerosol (SOA) needed for animal toxicological exposures. Photochemical reactions in the chamber resulted in nitric oxide (NO) depletion, nitrogen dioxide (NO2) formation, ozone (O3) accumulation, and SOA formation. A stable SOA concentration of approximately 40 µg m⁻³ at a chamber mean residence time of 30 min was achieved. This relatively short mean residence time provided adequate chamber flow output for both particle characterization and animal exposures. The chamber was operated as a continuous flow reactor for animal toxicological tests. SOA mass generated from the car exhaust diluted with ambient air was almost entirely in the ultrafine mode. Chamber performance was improved by using different types of seed aerosol to provide a surface for condensation of semivolatile reaction products, thus increasing the yield of SOA. Toxicological studies using Sprague-Dawley rats found significant increases of in vivo chemiluminescence in lungs following exposure to SOA.