Deliberating performance targets workshop: Potential paths for emerging PM2.5 and O3 air sensor progress

Investigating Use of Low-Cost Sensors to Increase Accuracy and Equity of Real-Time Air Quality Information

U.S. Environmental Protection Agency (EPA) air quality (AQ) monitors, the "gold standard" for measuring air pollutants, are sparsely positioned across the U.S. Low-cost sensors (LCS) are increasingly being used by the public to fill in the gaps in AQ monitoring; however, LCS are not as accurate as EPA monitors. In this work, we investigate factors impacting the differences between an individual's true (unobserved) exposure to air pollution and the exposure reported by their nearest AQ instrument (which could be either an LCS or an EPA monitor). We use simulations based on California data to explore different combinations of hypothetical LCS placement strategies (e.g., at schools or near major roads), for different numbers of LCS, with varying plausible amounts of LCS device measurement errors. We illustrate how real-time AQ reporting could be improved (or, in some cases, worsened) by using LCS, both for the population overall and for marginalized communities specifically. This work has implications for the integration of LCS into real-time AQ reporting platforms.

Daily Associations of Air Pollution and Pediatric Asthma Risk Using the Biomedical REAI-Time Health Evaluation (BREATHE) Kit

Background: Exposure to air pollution is associated with acute pediatric asthma exacerbations, including reduced lung function, rescue medication usage, and increased symptoms; however, most studies are limited in investigating longitudinal changes in these acute effects. This study aims to investigate the effects of daily air pollution exposure on acute pediatric asthma exacerbation risk using a repeated-measures design. Methods: We conducted a panel study of 40 children aged 8−16 years with moderate-to-severe asthma. We deployed the Biomedical REAI-Time Health Evaluation (BREATHE) Kit developed in the Los Angeles PRISMS Center to continuously monitor personal exposure to particulate matter of aerodynamic diameter < 2.5 µm (PM2.5), relative humidity and temperature, geolocation (GPS), and asthma outcomes including lung function, medication use, and symptoms for 14 days. Hourly ambient (PM2.5, nitrogen dioxide (NO2), ozone (O3)) and traffic-related (nitrogen oxides (NOx) and PM2.5) air pollution exposures were modeled based on location. We used mixed-effects models to examine the association of same day and lagged (up to 2 days) exposures with daily changes in % predicted forced expiratory volume in 1 s (FEV1) and % predicted peak expiratory flow (PEF), count of rescue inhaler puffs, and symptoms. Results: Participants were on average 12.0 years old (range: 8.4−16.8) with mean (SD) morning %predicted FEV1 of 67.9% (17.3%) and PEF of 69.1% (18.4%) and 1.4 (3.5) puffs per day of rescue inhaler use. Participants reported chest tightness, wheeze, trouble breathing, and cough symptoms on 36.4%, 17.5%, 32.3%, and 42.9%, respectively (n = 217 person-days). One SD increase in previous day O3 exposure was associated with reduced morning (beta [95% CI]: −4.11 [−6.86, −1.36]), evening (−2.65 [−5.19, −0.10]) and daily average %predicted FEV1 (−3.45 [−6.42, −0.47]). Daily (lag 0) exposure to traffic-related PM2.5 exposure was associated with reduced morning %predicted PEF (−3.97 [−7.69, −0.26]) and greater odds of “feeling scared of trouble breathing” symptom (odds ratio [95% CI]: 1.83 [1.03, 3.24]). Exposure to ambient O3, NOx, and NO was significantly associated with increased rescue inhaler use (rate ratio [95% CI]: O3 1.52 [1.02, 2.27], NOx 1.61 [1.23, 2.11], NO 1.80 [1.37, 2.35]). Conclusions: We found significant associations of air pollution exposure with lung function, rescue inhaler use, and “feeling scared of trouble breathing.” Our study demonstrates the potential of informatics and wearable sensor technologies at collecting highly resolved, contextual, and personal exposure data for understanding acute pediatric asthma triggers.

Key Concerns and Drivers of Low-Cost Air Quality Sensor Use

Low-cost sensors are revolutionizing air pollution monitoring by providing real-time, highly localized air quality information. The relatively low-cost nature of these devices has made them accessible to the broader public. Although there have been several fitness-of-purpose appraisals of the various sensors on the market, little is known about what drives sensor usage and how the public interpret the data from their sensors. This article attempts to answer these questions by analyzing the key themes discussed in the user reviews of low-cost sensors on Amazon. The themes and use cases identified have the potential to spur interventions to support communities of sensor users and inform the development of actionable data-visualization strategies with the measurements from such instruments, as well as drive appropriate ‘fitness-of-purpose’ appraisals of such devices.

Augmenting the Standard Operating Procedures of Health and Air Quality Stakeholders With NASA Resources

The combination of air quality (AQ) data from satellites and low-cost sensor systems, along with output from AQ models, have the potential to augment high-quality, regulatory-grade data in countries with in situ monitoring networks and provide much needed AQ information in countries without them, including Low and Moderate Income Countries (LMICs). We demonstrate the potential of free and publicly available USA National Aeronautics and Space Administration (NASA) resources, which include capacity building activities, satellite data, and global AQ forecasts, to provide cost-effective, and reliable AQ information to health and AQ professionals around the world. We provide illustrative case studies that highlight how global AQ forecasts along with satellite data may be used to characterize AQ on urban to regional scales, including to quantify pollution concentrations, identify pollution sources, and track the long-range transport of pollution. We also provide recommendations to data product developers to facilitate and broaden usage of NASA resources by health and AQ stakeholders.

Development and Application of a United States wide correction for PM2.5 data collected with the PurpleAir sensor

PurpleAir sensors, which measure particulate matter (PM), are widely used by individuals, community groups, and other organizations including state and local air monitoring agencies. PurpleAir sensors comprise a massive global network of more than 10,000 sensors. Previous performance evaluations have typically studied a limited number of PurpleAir sensors in small geographic areas or laboratory environments. While useful for determining sensor behavior and data normalization for these geographic areas, little work has been done to understand the broad applicability of these results outside these regions and conditions. Here, PurpleAir sensors operated by air quality monitoring agencies are evaluated in comparison to collocated ambient air quality regulatory instruments. In total, almost 12,000 24-hour averaged PM2.5 measurements from collocated PurpleAir sensors and Federal Reference Method (FRM) or Federal Equivalent Method (FEM) PM2.5 measurements were collected across diverse regions of the United States (U.S.), including 16 states. Consistent with previous evaluations, under typical ambient and smoke impacted conditions, the raw data from PurpleAir sensors overestimate PM2.5 concentrations by about 40% in most parts of the U.S. A simple linear regression reduces much of this bias across most U.S. regions, but adding a relative humidity term further reduces the bias and improves consistency in the biases between different regions. More complex multiplicative models did not substantially improve results when tested on an independent dataset. The final PurpleAir correction reduces the root mean square error (RMSE) of the raw data from 8 μg m-3 to 3 μg m-3 with an average FRM or FEM concentration of 9 μg m-3. This correction equation, along with proposed data cleaning criteria, has been applied to PurpleAir PM2.5 measurements across the U.S. in the AirNow Fire and Smoke Map (fire.airnow.gov) and has the potential to be successfully used in other air quality and public health applications.

High-Resolution Assessment of Air Quality in Urban Areas—A Business Model Perspective

The increasing availability of low-cost air quality sensors has led to novel sensing approaches. Distributed networks of low-cost sensors, together with data fusion and analytics, have enabled unprecedented, spatiotemporal resolution when observing the urban atmosphere. Several projects have demonstrated the potential of different approaches for high-resolution measurement networks ranging from static, low-cost sensor networks over vehicular and airborne sensing to crowdsourced measurements as well as ranging from a research-based operation to citizen science. Yet, sustaining the operation of such low-cost air quality sensor networks remains challenging because of the lack of regulatory support and the lack of an organizational framework linking these measurements to the official air quality network. This paper discusses the logical inclusion of lower-cost air quality sensors into the existing air quality network via a dynamic field calibration process, the resulting sustainable business models, and how this expansion can be self-funded.

On the distribution of low-cost PM2.5 sensors in the US: demographic and air quality associations

BACKGROUND

Low-cost sensors have the potential to democratize air pollution information and supplement regulatory networks. However, differentials in access to these sensors could exacerbate existing inequalities in the ability of different communities to respond to the threat of air pollution.

OBJECTIVE

Our goal was to analyze patterns of deployments of a commonly used low-cost sensor, as a function of demographics and pollutant concentrations.

METHODS

We used Wilcoxon rank sum tests to assess differences between socioeconomic characteristics and PM_2.5 concentrations of locations with low-cost sensors and those with regulatory monitors. We used Kolomogorov-Smirnov tests to examine how representative census tracts with sensors were of the United States. We analyzed predictors of the presence, and number of, sensors in a tract using regressions.

RESULTS

Census tracts with low-cost sensors were higher income more White and more educated than the US as a whole and than tracts with regulatory monitors. For all states except for California they are in locations with lower annual-average PM_2.5 concentrations than regulatory monitors. The existing presence of a regulatory monitor, the percentage of people living above the poverty line and PM_2.5 concentrations were associated with the presence of low-cost sensors in a tract.

SIGNIFICANCE

Strategies to improve access to low-cost sensors in less-privileged communities are needed to democratize air pollution data.

Assessment and Improvement of Two Low-Cost Particulate Matter Sensor Systems by Using Spatial Interpolation Data from Air Quality Monitoring Stations

Two low-cost fine particulate matter (PM2.5) sensor systems have been established by the government and community in Taiwan. Each system combines hundreds of PM2.5 sensors through an Internet of Things architecture. Since these sensors have not been calibrated, their performance has been questioned. In this study, the spatial interpolation data from air quality monitoring stations (AQMSs) was used to quantify the performances of the two sensor systems. The linearity, sensitivity, offset, precision, accuracy, and bias of the two sensor systems were estimated. The results indicate that the linearity of the government’s sensor system was higher than that of the community sensor system. However, the sensitivity of the government’s system was lower than that of the community system. The relative standard deviation, relative error, offset, and bias of the community sensor system were higher than those of the government sensor system. However, the government sensor system exhibited superior spatial interpolation results for the AQMS data than the community sensor system did. The precision and accuracy of the two sensor systems were poor during a period of low PM2.5 concentrations. A working platform of improvements consisting of monitoring the operation loop and automatic correction loop is proposed. The monitoring operation loop comprises five modules, namely outlier detection, temporal anomaly analysis, spatial anomaly analysis, spatiotemporal anomaly analysis, and trajectory analysis modules. The automatic correction loop contains spatial interpolation module, a sensor performance detection module, and a correction module. The proposed working platform can enhance the performance of low-cost sensor systems, especially as alert systems for reportable events.

Plume analysis from field evaluations of a portable air quality monitoring system

Near-road measurements in Rochester, NY with a Portable Air Quality Monitoring System indicate a significant plume control of PM_2.5 black carbon (BC) concentrations. This study evaluates the performance of two portable air quality enclosures deployed at collocated research sites to determine their accuracy and usefulness in field deployments, and specifically in pollution plume analysis. One system deployed collocated sensors for measurement of particulate matter mass concentration (Thermo pDR 1500 against Tapered Element Oscillating Microbalance (TEOM) measurement) and the second system deployed sensors for measurement of black carbon (Magee AE33 aethalometer and Brechtel Tricolor Absorption Photometer) in ambient and near-road locations in Rochester, New York, respectively. While the optical PM_2.5 sensors tended to be biased in their determination of concentration by ~15%, they followed changes and trends in concentration very well. The black carbon sensors in the portable systems agreed very well with each other and with the collocated sensor. As a case study to determine the contribution from statistically significant short-lived excursions of pollutant concentration, Morlet wavelet analysis was performed on data from the portable system sensors. Black carbon was found to be strongly influenced by plume behavior with significant plume excursions representing just over 12% of all data points and contributing on average 1 µg/m³ of black carbon above ambient concentrations. Implications: This paper first evaluates two air pollutant monitoring enclosures with wide applicability including near-road detection of pollutants. Then, we present a novel method to designate isolate statistically significant excursions in air pollution concentration which can be used to determine the impact of pollutant plumes as observed in PM and black carbon behavior near road.

Deliberating Performance Targets: Follow-on workshop discussing PM10, NO2, CO, and SO2 air sensor targets

The use of air sensor technology is increasing worldwide for a variety of applications, however, with significant variability in data quality. The United States Environmental Protection Agency held a workshop in July 2019 to deliberate possible performance targets for air sensors measuring particles with aerodynamic diameters of 10 μm or less (PM10), nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2). These performance targets were discussed from the perspective of non-regulatory applications and with the sensors operating primarily in a stationary mode in outdoor environments. Attendees included representatives from multiple levels of government organizations, sensor developers, environmental nonprofits, international organizations, and academia. The workshop addressed the current lack of sensor technology requirements, discussed fit-for-purpose data quality needs, and debated transparency issues. This paper highlights the purpose and key outcomes of the workshop. While more information on performance and applications of sensors is available than in past years, the performance metrics, or parameters used to describe data quality, vary among the studies reports and there is a need for more clear and consistent approaches for evaluating sensor performance. Organizations worldwide are increasingly considering, or are in the process of developing, sensor performance targets and testing protocols. Workshop participants suggested that these new guidelines are highly desirable, would help improve data quality, and would give users more confidence in their data. Given the wide variety of uses for sensors and user backgrounds, as well as varied sensor design features (e.g., communication approaches, data tools, processing/adjustment algorithms and calibration procedures), the need for transparency was a key workshop theme. Suggestions for increasing transparency included documenting and sharing testing and performance data, detailing best practices, and sharing data processing and correction approaches.

Spatial and Temporal Exposure Assessment to PM2.5 in a Community Using Sensor-Based Air Monitoring Instruments and Dynamic Population Distributions

This research was to conduct a pilot study for two consecutive days in order to assess fine particulate matter (PM2.5) exposure of an entire population in a community. We aimed to construct a surveillance system by analyzing the observed spatio-temporal variation of exposure. Guro-gu in Seoul, South Korea, was divided into 2,204 scale grids of 100 m each. Hourly exposure concentrations of PM2.5 were modeled by the inverse distance weighted method, using 24 sensor-based air monitoring instruments and the indoor-to-outdoor concentration ratio. Population distribution was assessed using mobile phone network data and indoor residential rates, according to sex and age over time. Exposure concentration, population distribution, and population exposure were visualized to present spatio-temporal variation. The PM2.5 exposure of the entire population of Guro-gu was calculated by population-weighted average exposure concentration. The average concentration of outdoor PM2.5 was 42.1 µg/m3, which was lower than the value of the beta attenuation monitor measured by fixed monitoring station. Indoor concentration was estimated using an indoor-to-outdoor PM2.5 concentration ratio of 0.747. The population-weighted average exposure concentration of PM2.5 was 32.4 µg/m3. Thirty-one percent of the population exceeded the Korean Atmospheric Environmental Standard for PM2.5 over a 24 h average period. The results of this study can be used in a long-term aggregate and cumulative PM2.5 exposure assessment, and as a basis for policy decisions on public health management among policymakers and stakeholders.

A Statistical Calibration Framework for Improving Non-Reference Method Particulate Matter Reporting: A Focus on Community Air Monitoring Settings

Recent advancement in lower-cost air monitoring technology has resulted in an increased interest in community-based air quality studies. However, non-reference monitoring (NRM; e.g., low-cost sensors) is imperfect and approaches that improve data quality are highly desired. Herein, we illustrate a framework for adjusting continuous NRM measures of particulate matter (PM) with field-based comparisons and non-linear statistical modeling as an example of instrument evaluation prior to exposure assessment. First, we collected continuous measurements of PM with a NRM technology collocated with a US EPA federal equivalent method (FEM). Next, we fit a generalized additive model (GAM) to establish a non-linear calibration curve that defines the relationship between the NRM and FEM data. Then, we used our fitted model to generate calibrated NRM PM data. Evaluation of raw NRM PM2.5 data revealed strong correlation with FEM (R = 0.9) but an average bias (AB) of −2.84 µg/m3 and a root mean square error (RMSE) of 2.85 µg/m3, with 406 h of data. Fitting of our GAM revealed that the correlation structure was maintained (r = 0.9) and that average bias (AB = 0) and error (RMSE = 0) were minimized. We conclude that field-based statistical calibration models can be used to reduce bias and improve NRM data used for community air monitoring studies.

Bayesian Proxy Modelling for Estimating Black Carbon Concentrations using White-Box and Black-Box Models

Black carbon (BC) is an important component of particulate matter (PM) in urban environments. BC is typically emitted from gas and diesel engines, coal-fired power plants, and other sources that burn fossil fuel. In contrast to PM, BC measurements are not always available on a large scale due to the operational cost and complexity of the instrumentation. Therefore, it is advantageous to develop a mathematical model for estimating the quantity of BC in the air, termed a BC proxy, to enable widening of spatial air pollution mapping. This article presents the development of BC proxies based on a Bayesian framework using measurements of PM concentrations and size distributions from 10 to 10,000 nm from a recent mobile air pollution study across several areas of Jordan. Bayesian methods using informative priors can naturally prevent over-fitting in the modelling process and the methods generate a confidence interval around the prediction, thus the estimated BC concentration can be directly quantified and assessed. In particular, two types of models are developed based on their transparency and interpretability, referred to as white-box and black-box models. The proposed methods are tested on extensive data sets obtained from the measurement campaign in Jordan. In this study, black-box models perform slightly better due to their model complexity. Nevertheless, the results demonstrate that the performance of both models does not differ significantly. In practice, white-box models are relatively more convenient to be deployed, the methods are well understood by scientists, and the models can be used to better understand key relationships.

Next-Generation Community Air Quality Sensors for Identifying Air Pollution Episodes

Conventional regulatory air quality monitoring sites tend to be sparsely located. The availability of lower-cost air pollution sensors, however, allows for their use in spatially dense community monitoring networks, which can be operated by various stakeholders, including concerned residents, organizations, academics, or government agencies. Networks of many community monitors have the potential to fill the spatial gaps between existing government-operated monitoring sites. One potential benefit of finer scale monitoring might be the ability to discern elevated air pollution episodes in locations that have not been identified by government-operated monitoring sites, which might improve public health warnings for populations sensitive to high levels of air pollution. In the Imperial Air study, a large network of low-cost particle monitors was deployed in the Imperial Valley in Southeastern California. Data from the new monitors is validated against regulatory air monitoring. Neighborhood-level air pollution episodes, which are defined as periods in which the PM2.5 (airborne particles with sizes less than 2.5 μm in diameter) hourly average concentration is equal to or greater than 35 μg m-3, are identified and corroborate with other sites in the network and against the small number of government monitors in the region. During the period from October 2016 to February 2017, a total of 116 episodes were identified among six government monitors in the study region; however, more than 10 times as many episodes are identified among the 38 community air monitors. Of the 1426 episodes identified by the community sensors, 723 (51%) were not observed by the government monitors. These findings suggest that the dense network of community air monitors could be useful for addressing current limitations in the spatial coverage of government air monitoring to provide real-time warnings of high pollution episodes to vulnerable populations.

Review of the Performance of Low-Cost Sensors for Air Quality Monitoring

A growing number of companies have started commercializing low-cost sensors (LCS) that are said to be able to monitor air pollution in outdoor air. The benefit of the use of LCS is the increased spatial coverage when monitoring air quality in cities and remote locations. Today, there are hundreds of LCS commercially available on the market with costs ranging from several hundred to several thousand euro. At the same time, the scientific literature currently reports independent evaluation of the performance of LCS against reference measurements for about 110 LCS. These studies report that LCS are unstable and often affected by atmospheric conditions—cross-sensitivities from interfering compounds that may change LCS performance depending on site location. In this work, quantitative data regarding the performance of LCS against reference measurement are presented. This information was gathered from published reports and relevant testing laboratories. Other information was drawn from peer-reviewed journals that tested different types of LCS in research studies. Relevant metrics about the comparison of LCS systems against reference systems highlighted the most cost-effective LCS that could be used to monitor air quality pollutants with a good level of agreement represented by a coefficient of determination R2 > 0.75 and slope close to 1.0. This review highlights the possibility to have versatile LCS able to operate with multiple pollutants and preferably with transparent LCS data treatment.

The Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS): Integration of Low-Cost Sensors and Reference Grade Monitoring in a Complex Metropolitan Area. Part 1: Overview of the Project

Emissions from transportation sources can impact local air quality and contribute to adverse health effects. The Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS), conducted over a 1-year period, researched emissions source characterization in the Argentine, Turner, and Armourdale, Kansas (KS) neighborhoods and the broader southeast Kansas City, KS area. This area is characterized as a near-source environment with impacts from large railyard operations, major roadways, and commercial and industrial facilities. The spatial and meteorological effects of particulate matter less than 2.5 μm (PM2.5), and black carbon (BC) pollutants on potential population exposures were evaluated at multiple sites using a combination of regulatory grade methods and instrumentation, low-cost sensors, citizen science, and mobile monitoring. The initial analysis of a subset of these data showed that mean reference grade PM2.5 concentrations (gravimetric) across all sites ranged from 7.92 to 9.34 μg/m3. Mean PM2.5 concentrations from low-cost sensors ranged from 3.30 to 5.94 μg/m3 (raw, uncorrected data). Pollution wind rose plots suggest that the sites are impacted by higher PM2.5 and BC concentrations when the winds originate near known source locations. Initial data analysis indicated that the observed PM2.5 and BC concentrations are driven by multiple air pollutant sources and meteorological effects. The KC-TRAQS overview and preliminary data analysis presented will provide a framework for forthcoming papers that will further characterize emission source attributions and estimate near-source exposures. This information will ultimately inform and clarify the extent and impact of air pollutants in the Kansas City area.