Personal Exposure to PM2.5 Black Carbon and Aerosol Oxidative Potential using an Automated Microenvironmental Aerosol Sampler (AMAS)

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 (PM_2.5) 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 PM_2.5 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.

Airborne and Dermal Collection Methods of Gunshot Residue for Toxicity Studies

Gunshot residue (GSR) has potential negative health effects on humans as a result of inhalation and dermal exposure to the chemical and physical characteristics of GSR such as Pb, Sb, Ba, nitrocellulose, nitroglycerine, and particulate size fraction. Filter (size selective) and double-sided tape (non-size selective) samples collected airborne GSR during single and triple firing of a 0.22 caliber revolver. Dermal exposures were considered using hand swabs and de-leading wipes, designed to remove the heavy metals. The samples underwent analysis to investigate physical (morphology, size distribution, zeta potential), chemical (black carbon and element concentrations), and potential to induce oxidative stress (oxidative potential via the dithiothreitol (DTT) assay). All sample types detected Pb concentrations higher than national ambient air standards. The de-leading wipes reduced the metal content on the hands of the shooter for Pb (15.57 ± 12.99 ppb and 3.13 ± 4.95 ppb). Filter samples provided health relevant data for airborne PM2.5 for all of the analysis methods except for GSR morphology. This work identified collection and analysis methods for GSR in an outdoor setting, providing protocols and considerations for future toxicological studies related to inhalation and dermal exposures to particulate GSR. Future studies should investigate the influence of meteorological factors on GSR exposure in an outdoor setting.

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).

Indoor black carbon and brown carbon concentrations from cooking and outdoor penetration: insights from the HOMEChem study

Particle emissions from cooking are a major contributor to residential indoor air pollution and could also contribute to ambient concentrations. An important constituent of these emissions is light-absorbing carbon, including black carbon (BC) and brown carbon (BrC). This work characterizes the contributions of indoor and outdoor sources of BC and BrC to the indoor environment by concurrently measuring real-time concentrations of these air pollutants indoors and outdoors during the month-long HOMEChem study. The median indoor-to-outdoor ratios of BC and BrC during the periods of no activity inside the test house were 0.6 and 0.7, respectively. The absorption Ångström exponent was used to characterize light-absorbing particle emissions during different activities and ranged from 1.1 to 2.7 throughout the campaign, with the highest value (indicative of BrC-dominated emissions) observed during the preparation of a simulated Thanksgiving Day holiday style meal. An indoor BC exposure assessment shows that exposure for an occupant present in the kitchen area was ∼4 times higher during Thanksgiving Day experiments (primarily due to candle burning) when compared to the background conditions.

The leading role of adsorbed lead in PM2.5-induced hippocampal neuronal apoptosis and synaptic damage

Neurodegenerative diseases may be caused by air pollution, such as PM_2.5. However, particles still need to be elucidated the mechanism of synergistic neurotoxicity induced by pollutant-loading PM_2.5. In this study, we used a reductionist approach to study leading role of lead (Pb) in PM_2.5-induced hippocampal neuronal apoptosis and synaptic damage both in vivo and in vitro. Pb in PM_2.5 caused neurotoxicity: 1) by increasing ROS levels and thus causing apoptosis in neuronal cells and 2) by decreasing the expression of PSD95 via interfering with the calcium signaling pathway through cAMP/CREB/pCREB/BDNF/PSD95 pathway and reducing the synapse length by 50%. This study clarifies a key factor in PM_2.5-induced neurotoxicity and provides the experimental basis for reducing PM_2.5-induced neurotoxicity.

Individual-level interventions to reduce personal exposure to outdoor air pollution and their effects on people with long-term respiratory conditions

BACKGROUND

More than 90% of the global population lives in areas exceeding World Health Organization air quality limits. More than four million people each year are thought to die early due to air pollution, and poor air quality is thought to reduce an average European's life expectancy by one year. Individuals may be able to reduce health risks through interventions such as masks, behavioural changes and use of air quality alerts. To date, evidence is lacking about the efficacy and safety of such interventions for the general population and people with long-term respiratory conditions. This topic, and the review question relating to supporting evidence to avoid or lessen the effects of air pollution, emerged directly from a group of people with chronic obstructive pulmonary disease (COPD) in South London, UK.

OBJECTIVES

1. To assess the efficacy, safety and acceptability of individual-level interventions that aim to help people with or without chronic respiratory conditions to reduce their exposure to outdoor air pollution. 2. To assess the efficacy, safety and acceptability of individual-level interventions that aim to help people with chronic respiratory conditions reduce the personal impact of outdoor air pollution and improve health outcomes.

SEARCH METHODS

We identified studies from the Cochrane Airways Trials Register, Cochrane Central Register of Controlled Trials, and other major databases. We did not restrict our searches by date, language or publication type and included a search of the grey literature (e.g. unpublished information). We conducted the most recent search on 16 October 2020.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) and non-randomised studies (NRS) that included a comparison treatment arm, in adults and children that investigated the effectiveness of an individual-level intervention to reduce risks of outdoor air pollution. We included studies in healthy individuals and those in people with long-term respiratory conditions. We excluded studies which focused on non-respiratory long-term conditions, such as cardiovascular disease. We did not restrict eligibility of studies based on outcomes.

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methods. Two review authors independently selected trials for inclusion, extracted study characteristics and outcome data, and assessed risk of bias using the Cochrane Risk of Bias tool for RCTs and the Risk Of Bias In Non-randomised Studies - of Interventions (ROBINS-I) as appropriate. One review author entered data into the review; this was spot-checked by a second author. We planned to meta-analyse results from RCTs and NRS separately, using a random-effects model. This was not possible, so we presented evidence narratively. We assessed certainty of the evidence using the GRADE approach. Primary outcomes were: measures of air pollution exposure; exacerbation of respiratory conditions; hospital admissions; quality of life; and serious adverse events.

MAIN RESULTS

We identified 11 studies (3372 participants) meeting our inclusion criteria (10 RCTs and one NRS). Participants' ages ranged from 18 to 74 years, and the duration of studies ranged from 24 hours to 104 weeks. Six cross-over studies recruited healthy adults and five parallel studies included either people with pre-existing conditions (three studies) or only pregnant women (two studies). Interventions included masks (e.g. an N95 mask designed to filter out airborne particles) (five studies), an alternative cycle route (one study), air quality alerts and education (five studies). Studies were set in Australia, China, Iran, the UK, and the USA. Due to the diversity of study designs, populations, interventions and outcomes, we did not perform any meta-analyses and instead summarised results narratively. We judged both RCTs and the NRS to be at risk of bias from lack of blinding and lack of clarity regarding selection methods. Many studies did not provide a prepublished protocol or trial registration. From five studies (184 participants), we found that masks or altered cycle routes may have little or no impact on physiological markers of air pollution exposure (e.g. blood pressure and heart rate variability), but we are very uncertain about this estimate using the GRADE approach. We found conflicting evidence regarding health care usage from three studies of air pollution alerts, with one non-randomised cross-over trial (35 participants) reporting an increase in emergency hospital attendances and admissions, but the other two randomised parallel trials (1553 participants) reporting little to no difference. We also gave the evidence for this outcome a very uncertain GRADE rating. None of our included trials reported respiratory exacerbations, quality of life or serious adverse events. Secondary outcomes were not well reported, but indicated inconsistent impacts of air quality alerts and education interventions on adherence, with some trials reporting improvements in the intervention groups and others reporting little or no difference. Symptoms were reported by three trials, with one randomised cross-over trial (15 participants) reporting a small increase in breathing difficulties associated with the mask intervention, one non-randomised cross-over trial (35 participants) reporting reduced throat and nasal irritation in the lower-pollution cycle route group (but no clear difference in other respiratory symptoms), and another randomised parallel trial (519 participants) reporting no clear difference in symptoms between those who received a smog warning and those who did not.

AUTHORS' CONCLUSIONS

The lack of evidence and study diversity has limited the conclusions of this review. Using a mask or a lower-pollution cycle route may mitigate some of the physiological impacts from air pollution, but evidence was very uncertain. We found conflicting results for other outcomes, including health care usage, symptoms and adherence/behaviour change. We did not find evidence for adverse events. Funders should consider commissioning larger, longer studies, using high-quality and well-described methods, recruiting participants with pre-existing respiratory conditions. Studies should report outcomes of importance to people with respiratory conditions, such as exacerbations, hospital admissions, quality of life and adverse events.

Characterising professional drivers' exposure to traffic-related air pollution: Evidence for reduction strategies from in-vehicle personal exposure monitoring

Professional drivers working in congested urban areas are required to work near harmful traffic related pollutants for extended periods, representing a significant, but understudied occupational risk. This study collected personal black carbon (BC) exposures for 141 drivers across seven sectors in London. The aim of the study was to assess the magnitude and the primary determinants of their exposure, leading to the formulation of targeted exposure reduction strategies for the occupation. Each participant's personal BC exposures were continuously measured using real-time monitors for 96 h, incorporating four shifts per participant. 'At work' BC exposures (3.1 ± 3.5 µg/m³) were 2.6 times higher compared to when 'not at work' (1.2 ± 0.7 µg/m³). Workers spent 19% of their time 'at work driving', however this activity contributed 36% of total BC exposure, highlighting the disproportionate effect driving had on their daily exposure. Taxi drivers experienced the highest BC exposures due to the time they spent working in congested central London, while emergency services had the lowest. Spikes in exposure were observed while driving and were at times greater than 100 µg/m³. The most significant determinants of drivers' exposures were driving in tunnels, congestion, location, day of week and time of shift. Driving with closed windows significantly reduced exposures and is a simple behaviour change drivers could implement. Our results highlight strategies by which employers and local policy makers can reduce professional drivers' exposure to traffic-related air pollution.

Assessing the Impact of Wildfires on the Use of Black Carbon as an Indicator of Traffic Exposures in Environmental Epidemiology Studies

Epidemiological studies frequently use black carbon (BC) as a proxy for traffic-related air pollution (TRAP). However, wildfire smoke (WFS) represents an important source of BC not often considered when using BC as a proxy for TRAP. Here, we examined the potential for WFS to bias TRAP exposure assessments based on BC measurements. Weekly integrated BC samples were collected across the Denver, CO region from May to November 2018. We collected 609 filters during our sampling campaigns, 35% of which were WFS-impacted. For each filter we calculated an average BC concentration. We assessed three GIS-based indicators of TRAP for each sampling location: annual average daily traffic within a 300 m buffer, the minimum distance to a highway, and the sum of the lengths of roadways within 300 m. Median BC concentrations were 9% higher for WFS-impacted filters (median = 1.14 μg/m3, IQR = 0.23 μg/m3) than nonimpacted filters (median = 1.04 μg/m3, IQR = 0.48 μg/m3). During WFS events, BC concentrations were elevated and expected spatial gradients in BC were reduced. We conducted a simulation study to estimate TRAP exposure misclassification as the result of regional WFS. Our results suggest that linear health effect estimates were biased away from the null when WFS was present. Thus, exposure assessments relying on BC as a proxy for TRAP may be biased by wildfire events. Alternative metrics that account for the influence of "brown" carbon associated with biomass burning may better isolate the effects of traffic emissions from those of other black carbon sources.

Wearable Devices in Health Monitoring from the Environmental towards Multiple Domains: A Survey

The World Health Organization (WHO) recognizes the environmental, behavioral, physiological, and psychological domains that impact adversely human health, well-being, and quality of life (QoL) in general. The environmental domain has significant interaction with the others. With respect to proactive and personalized medicine and the Internet of medical things (IoMT), wearables are most important for continuous health monitoring. In this work, we analyze wearables in healthcare from a perspective of innovation by categorizing them according to the four domains. Furthermore, we consider the mode of wearability, costs, and prolonged monitoring. We identify features and investigate the wearable devices in the terms of sampling rate, resolution, data usage (propagation), and data transmission. We also investigate applications of wearable devices. Web of Science, Scopus, PubMed, IEEE Xplore, and ACM Library delivered wearables that we require to monitor at least one environmental parameter, e.g., a pollutant. According to the number of domains, from which the wearables record data, we identify groups: G1, environmental parameters only; G2, environmental and behavioral parameters; G3, environmental, behavioral, and physiological parameters; and G4 parameters from all domains. In total, we included 53 devices of which 35, 9, 9, and 0 belong to G1, G2, G3, and G4, respectively. Furthermore, 32, 11, 7, and 5 wearables are applied in general health and well-being monitoring, specific diagnostics, disease management, and non-medical. We further propose customized and quantified output for future wearables from both, the perspectives of users, as well as physicians. Our study shows a shift of wearable devices towards disease management and particular applications. It also indicates the significant role of wearables in proactive healthcare, having capability of creating big data and linking to external healthcare systems for real-time monitoring and care delivery at the point of perception.

A Spatiotemporal Prediction Model for Black Carbon in the Denver Metropolitan Area, 2009-2020

Studies on health effects of air pollution from local sources require exposure assessments that capture spatial and temporal trends. To facilitate intraurban studies in Denver, Colorado, we developed a spatiotemporal prediction model for black carbon (BC). To inform our model, we collected more than 700 weekly BC samples using personal air samplers from 2018 to 2020. The model incorporated spatial and spatiotemporal predictors and smoothed time trends to generate point-level weekly predictions of BC concentrations for the years 2009-2020. Our results indicate that our model reliably predicted weekly BC concentrations across the region during the year in which we collected data. We achieved a 10-fold cross-validation R² of 0.83 and a root-mean-square error of 0.15 μg/m³ for weekly BC concentrations predicted at our sampling locations. Predicted concentrations displayed expected temporal trends, with the highest concentrations predicted during winter months. Thus, our prediction model improves on typical land use regression models that generally only capture spatial gradients. However, our model is limited by a lack of long-term BC monitoring data for full validation of historical predictions. BC predictions from the weekly spatiotemporal model will be used in traffic-related air pollution exposure-disease associations more precisely than previous models for the region have allowed.

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.

Investigation on daily exposure to PM2.5 in Bandung city, Indonesia using low-cost sensor

Daily exposure to PM_2.5 in developing countries has not been thoroughly studied partly due to limited resources available. In this research, personal PM_2.5 exposures in urban communities in Indonesia were examined using a low-cost sensor, AS-LUNG. Fifty subjects were recruited in both wet and dry seasons. Their personal PM_2.5 concentrations, environmental temperature, and relative humidity were measured using corrected AS-LUNG Portable worn or placed in their vicinity. Details on their activities and locations, air quality (air pollution sources), and weather conditions during monitoring were recorded in time-activity diaries completed at 30 min intervals. Results revealed mosquito coil burning as the source of highest exposure, reaching 241.5 μg/m³ but with significant difference between wet and dry seasons. With ambient PM_2.5 and relative humidity controlled for, mosquito coil burning contributed 12.02 μg/m³ and 4.84 μg/m³ of personal PM_2.5 exposure in wet and dry season, respectively, which was several times higher than the contribution from vehicle emission. The second most contributive source was factory smoke, which increased 4.99 μg/m³ and 3.17 μg/m³ of exposure in wet and dry season, respectively. Findings on contributive factors of high daily personal exposures can serve as useful references for formulating policies and recommendations on exposure reduction and health protection.

Children's microenvironmental exposure to PM2.5 and ozone and the impact of indoor air filtration

BACKGROUND

In highly polluted urban areas, personal exposure to PM_2.5 and O₃ occur daily in various microenvironments. Identifying which microenvironments contribute most to exposure can pinpoint effective exposure reduction strategies and mitigate adverse health impacts.

METHODS

This work uses real-time sensors to assess the exposures of children with asthma (N = 39) in Shanghai, quantifying microenvironmental exposure to PM_2.5 and O₃. An air cleaner was deployed in participants' bedrooms where we hypothesized exposure could be most efficiently reduced. Monitoring occurred for two 48-h periods: one with bedroom filtration (portable air cleaner with HEPA and activated carbon filters) and the other without.

RESULTS

Children spent 91% of their time indoors with the majority spent in their bedroom (47%). Without filtration, the bedroom and classroom environments were the largest contributors to PM_2.5 exposure. With filtration, bedroom PM_2.5 exposure was reduced by 75% (45% of total exposure). Although filtration status did not impact O₃, the largest contribution of O₃ exposure also came from the bedroom.

CONCLUSIONS

Actions taken to reduce bedroom PM_2.5 and O₃ concentrations can most efficiently reduce total exposure. As real-time pollutant monitors become more accessible, similar analyses can be used to evaluate new interventions and optimize exposure reductions for a variety of populations.

Airborne Aerosols and Human Health: Leapfrogging from Mass Concentration to Oxidative Potential

The mass concentration of atmospheric particulate matter (PM) has been systematically used in epidemiological studies as an indicator of exposure to air pollutants, connecting PM concentrations with a wide variety of human health effects. However, these effects can be hardly explained by using one single parameter, especially because PM is formed by a complex mixture of chemicals. Current research has shown that many of these adverse health effects can be derived from the oxidative stress caused by the deposition of PM in the lungs. The oxidative potential (OP) of the PM, related to the presence of transition metals and organic compounds that can induce the production of reactive oxygen and nitrogen species (ROS/RNS), could be a parameter to evaluate these effects. Therefore, estimating the OP of atmospheric PM would allow us to evaluate and integrate the toxic potential of PM into a unique parameter, which is related to emission sources, size distribution and/or chemical composition. However, the association between PM and particle-induced toxicity is still largely unknown. In this commentary article, we analyze how this new paradigm could help to deal with some unanswered questions related to the impact of atmospheric PM over human health.

PM2.5 exposure of various microenvironments in a community: Characteristics and applications

While the measurement of particulate matter (PM) with a diameter of less than 2.5 μm (PM_2.5) has been conducted for personal exposure assessment, it remains unclear how models that integrate microenvironmental levels with resolved activity and location information predict personal exposure to PM. We comprehensively investigated PM_2.5 concentrations in various microenvironments and estimated personal exposure stratified by the microenvironment. A variety of microenvironments (>200 places and locations, divided into 23 components according to indoor, outdoor, and transit modes) in a community were selected to characterize PM_2.5 concentrations. Infiltration factors calculated from microenvironmental/central-site station (M/S) monitoring campaigns with time-activity patterns were used to estimate time-weighted exposure to PM_2.5 for university students. We evaluated exposures using a four-stage modeling approach and quantified the performance of each component. It was found that the SidePak monitor overestimated the concentration by 3.5 times as compared with the filter-based measurements. Higher mean concentrations of PM_2.5 were observed in the Taoist temple and night market microenvironments; in contrast, lower concentrations were observed in air-conditioned offices and car microenvironments. While the exposure model incorporating detailed time-location information and infiltration factors achieved the highest prediction (R² = 0.49) of personal exposure to PM_2.5, the use of indoor, outdoor, and transit components for modeling also generated a consistent result (R² = 0.44).

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.

Attributing Air Pollutant Exposure to Emission Sources with Proximity Sensing

Biomass burning for home energy use contributes to negative health outcomes and environmental degradation. As part of the REACCTING study (Research on Emissions, Air quality, Climate, and Cooking Technologies in Northern Ghana), personal exposure to carbon monoxide (CO) was measured to gauge the effects of introducing two different cookstove types over four intervention groups. A novel Bluetooth Low-Energy (BLE) Beacon system was deployed on a subset of those CO measurement periods to estimate participants’ distances to their most-used cooking areas during the sampling periods. In addition to presenting methods and validation for the BLE Beacon system, here we present pollution exposure assessment modeling results using two different approaches, in which time-activity (proximity) data is used to: (1) better understand exposure and behaviors within and away from homes; and (2) predict personal exposure via microenvironment air quality measurements. Model fits were improved in both cases, demonstrating the benefits of the proximity measurements.