Evaluation of two collocated federal equivalent method PM2.5 instruments over a wide range of concentrations in Sarajevo, Bosnia and Herzegovina.

Two widely used PM2.5 monitors in the United States (U.S.) designated as federal equivalent methods (FEMs) by the U.S. Environmental Protection Agency were collocated for 15 months in Sarajevo, Bosnia and Herzegovina (BiH) to evaluate their comparability. With differing measurement principles, the FEMs (Met One BAM-1020 and Teledyne API T640) exhibited unique responses to the significant range in PM2.5 over the study period. During the winter months when concentrations greatly increased (e.g., daily PM2.5 > 100 µg m-3), the BAM-1020 had intermittent malfunctioning nozzle contact to the collection tape, resulting in periods of data invalidation. Increased operator observation and doubling the cleaning frequency were required to maintain proper operation. The hourly data from the BAM-1020, which detects PM2.5 via beta-attenuation of particles loaded to the collection tape, indicated higher noise at concentrations below 40 µg m-3 relative to the T640, which detects PM2.5 via an optical method. Above this concentration threshold, the two instruments appear to have comparable hourly fluctuations in the data. Relative to the BAM-1020, the T640 reported higher concentrations when PM2.5 is above 80 µg m-3. A linear regression equation was developed and applied to adjust T640 PM2.5 high concentration values, resulting in 24-hr average T640adj PM2.5 values closely matching that from the BAM-1020 for the full concentration range. Based on the T640adj values, the annual average for Sarajevo was calculated at the site to be 42 µg m-3, with significant seasonality resulting in over 7-fold higher concentrations in the months of December-January compared to June-July.

Creating Clean Air Spaces During Wildland Fire Smoke Episodes: Web Summit Summary.

Effective strategies to reduce indoor air pollutant concentrations during wildfire smoke events are critically needed. Worldwide, communities in areas prone to wildfires may suffer from annual smoke exposure events lasting from days to weeks. In addition, there are many areas of the world where high pollution events are common and where methods employed to reduce exposure to pollution may have relevance to wildfire smoke pollution episodes and vice versa. This article summarizes a recent virtual meeting held by the United States Environmental Protection Agency (EPA) to share research, experiences, and other information that can inform best practices for creating clean air spaces during wildland fire smoke events. The meeting included presentations on the public health impacts of wildland fire smoke; public health agencies' experiences and resilience efforts; and methods to improve indoor air quality, including the effectiveness of air filtration methods [e.g., building heating ventilation and air conditioning (HVAC) systems and portable, free-standing air filtration systems]. These presentations and related research indicate that filtration has been demonstrated to effectively improve indoor air quality during high ambient air pollution events; however, several research questions remain regarding the longevity and maintenance of filtration equipment during and after smoke events, effects on the pollution mixture, and degree to which adverse health effects are reduced.

Three Years of High Time-resolution Air Pollution Monitoring in the Complex Multi-source Harbor of New York and New Jersey.

In densely developed port areas with numerous emissions sources, relating measured air quality changes to emissions is challenging given the geographic density of sources without unique pollutant composition signatures. To better understand air quality during increasing emission controls at the Port of New York and New Jersey ("Port"), an air monitoring station was sited to minimize collinearity of sources along ordinal directions. The study area includes an international airport, interstate highway, port terminals and shipping lanes, and industrial sources, as well as typical urban emissions of a megacity. Because air flow travel time from sources to the monitor were usually much less than one hour, minute-by-minute, high-precision data were collected for three years (2013-2015) for sulfur dioxide (SO2), carbon monoxide (CO), oxides of nitrogen (NO, NO2), black carbon (BC), fine particulate matter (PM2.5), and meteorology (wind speed, wind direction, temperature, humidity). From summer 2014 to spring 2015, hourly metals data were also collected. A high degree of temporal variability was observed for pollutants associated with direct emissions, with highest hourly average coefficient of variation observed for NO (2.65), SO2 (1.45) and BC (1.21). Nonparametric trajectory analysis (NTA) was utilized to separate the source areas influencing the monitoring data and observe how they changed over time, with over 1.6 million trajectories computed in total. Comparing the last 5 quarters of the study to the first 5 quarters, concentrations at the monitoring site associated with three port-related geographic areas decreased by 34-41%, 11-17%, and 28-41% for SO2, NOx, and BC, respectively. Over the same period, indicators of shipping and cargo activity at the port remained relatively constant; therefore, a shift in emission factors is likely the cause of the change. This study demonstrates the value of high-time resolution, accurate monitoring data along with careful siting to understand source area influences.

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.

Characterization of Spatial Air Pollution Patterns Near a Large Railyard Area in Atlanta, Georgia.

Railyards are important transportation hubs, and they are often situated near populated areas with high co-located density of manufacturing, freight movement and commercial enterprises. Emissions occurring within railyards can affect nearby air quality. To better understand the air pollution levels in proximity to a major railyard, an intensive mobile air monitoring study was conducted in May 2012 around a major railyard area in Atlanta, GA, constituted of two separate facilities situated side-by-side. A total of 19 multi-hour mobile monitoring sessions took place over different times of day, days of the week, and under a variety of wind conditions. High time resolution measurements included black carbon (BC), particle number concentration (PN), particle optical extinction (EXT), oxides of nitrogen (NO, NO2, NOy), carbon monoxide (CO), and speciated air toxics. Urban background was estimated to contribute substantially (>70%) to EXT and CO, whereas BC, oxides of nitrogen (NOx) and toluene had comparably low background contributions (<30%). Mobile monitoring data were aggregated into 50 meter spatial medians by wind categories, with categories including low speed wind conditions (<0.5 m s-1) and, for wind speeds above that threshold, by wind direction relative to the railyard. Spatial medians of different pollutants measured had a wide range of correlation-gas-phase air toxics (benzene, toluene, acetaldehyde) had moderate correlation with each other (r = 0.46⁻0.59) and between toluene and CO (r = 0.53), but lower correlation for other pairings. PN had highest correlation with oxides of nitrogen (r = 0.55⁻0.66), followed by BC (r = 0.4), and lower correlation with other pollutants. Multivariate regression analysis on the full set of 50 m medians found BC and NO as having the strongest relationship to railyard emissions, in comparison to their respective background levels. This was indicated by an increase associated with transiting through the yard and inverse relationship with distance from the railyard; NO and BC decreased by a factor of approximately 0.5 and 0.7 over 1 km distance of the railyard boundary, respectively. Low speed, variable wind conditions were related to higher concentrations of all measured parameters.

Examining spatiotemporal variability of urban particulate matter and application of high-time resolution data from a network of low-cost air pollution sensors.

Traditional air monitoring approaches using regulatory monitors have historically been used to assess regional-scale trends in air pollutants across large geographical areas. Recent advances in air pollution sensor technologies could provide additional information about nearby sources, support the siting of regulatory monitoring stations, and improve our knowledge of finer-scale spatiotemporal variation of ambient air pollutants and their associated health effects. Sensors are now being developed that are much smaller and lower cost than traditional ambient air monitoring systems and are capable of being deployed as a network to provide greater coverage of a given area. The CitySpace project conducted by the US EPA and the Shelby County Health Department included the deployment of a network of 17 sensor pods using Alphasense OPC-N2 particulate matter (PM) sensors integrated with meteorological sensors in Memphis, TN for six months. Sensor pods were collocated with a federal equivalent method (FEM) tapered element oscillating microbalance (TEOM) monitor both before and after the primary study period. Six of the sensor pods were found to meet the data quality objective (DQO) of coefficient of determination (R2) greater than 0.5 when collocated with the TEOM. Seven pods were decommissioned before the end of the study due to mechanical failure. The six pods meeting the DQO were used to examine the spatiotemporal variability of fine PM (PM2.5) across the Memphis area. One site was found to have higher relative PM2.5 concentrations when compared to the other sites in the network. The 1-min data from this sensor pod were evaluated to quantify the regional urban background and local-scale contributions to PM2.5 at that monitoring location. This method found that approximately 20% of the PM2.5 was attributed to local sources at this location, compared to 9% at a local regulatory monitoring site. Additionally, the 1-min data were combined with 1-min wind speed and wind direction data to examine potential sources in the area using the nonparametric trajectory analysis (NTA) technique. This method geographically identified local source areas that contributed to the measured concentrations at the high reading sensor location throughout the course of the study.

Solar-powered air quality monitor applied under subtropical conditions in Hong Kong: Performance evaluation and application for pollution source tracking.

Air monitoring is desirable in many places to understand dynamic pollution trends and sources and improve knowledge of population exposure. While highly miniaturized low cost sensor technology is quickly evolving, there is also a need for the advancement of mid-tier systems that are closer to reference-grade technologies in their longevity and performance, but also feature compactness that requires less significant infrastructure. This project evaluated the performance of a prototype solar-powered air monitoring system known as a Village Green Project (VGP) system with wireless data transmission that was deployed on a school rooftop in Hong Kong and operated for over one year. The system provided highly time-resolved and long-term data utilizing mid-tier cost ozone, PM2.5 and meteorological instruments. It operated with very minimal maintenance but shading by a nearby building reduced solar radiation, thus battery run time, over the 16-months measurement period, approximately 330,000 1-min observations were recorded (data completeness of ~62%). The monitoring data were evaluated by comparison with a nearby Hong Kong Environment Protection Department (EPD) station and exhibited good performance for 1-h resolution (R 2 = 0.74 for PM2.5 and R 2 = 0.76 for ozone). Furthermore as a demonstration, a nonparametric regression (NPR) model was applied for identifying the location of pollution source, combining air pollution and meteorological measurements. In addition, based on the high time-resolution wind data, local-scale back-trajectories were calculated as an input for receptor-oriented Nonparametric Trajectory Analysis (NTA) model. The combination of the VGP air monitoring system and NTA model identified apparent local sources in urban area. The demonstration was largely successful and operational improvements are clearly suggested to insure better siting and configurations to insure adequate power and air flow.

Opportunities and Challenges for Filling the Air Quality Data Gap in Low- and Middle-Income Countries.

Given the millions of people suffering from air pollution, filling the air quality monitoring gap in low- and middle-income countries has been recognized as a global challenge. To meet this challenge and make it work will require private enterprise, multiple levels of government, international organizations, academia and civil society to work together toward the common goal of characterizing, understanding better, and then reducing, the air pollution that causes sickness and preventable death for millions of people each year in lowand middle-income countries around the world. This article offers concrete next steps on how to make progress toward increasing air quality monitoring using a combination of emerging technologies, adaptation to country-specific conditions, and building capacity towards the development of lasting institutions.

A Call for an Aloft Air Quality Monitoring Network: Need, Feasibility, and Potential Value.

Changing precursor emission patterns in conjunction with stringent health protective air quality standards necessitate accurate quantification of nonlocal contributions to ozone pollution at a location due to atmospheric transport, that by nature predominantly occurs aloft nocturnally. Concerted efforts to characterize ozone aloft on a continuous basis to quantify its contribution to ground-level concentrations, however, are lacking. By applying our classical understanding of air pollution dynamics to analyze variations in widespread surface-level ozone measurements, in conjunction with process-based interpretation from a comprehensive air pollution modeling system and detailed backward-sensitivity calculations that quantitatively link surface-level and aloft pollution, we show that accurate quantification of the amount of ozone in the air entrained from aloft every morning as the atmospheric boundary layer grows is the key missing component for characterizing background pollution at a location, and we propose a cost-effective continuous aloft ozone measurement strategy to address critical scientific gaps in current air quality management. Continuous aloft air pollution measurements can be achieved cost-effectively through leveraging advances in sensor technology and proliferation of tall telecommunications masts. Resultant improvements in ozone distribution characterization at 400-500 m altitude are estimated to be 3-4 times more effective in characterizing the surface-level daily maximum 8-h average ozone (DM8O3) than improvements from surface measurements since they directly quantify the amount of pollution imported to a location and furnish key missing information on processes and sources regulating background ozone and its modulation of ground-level concentrations. Since >80% of the DM8O3 sensitivity to tropospheric ozone is potentially captured through measurements between 200 and 1200 m altitude (a possible design goal for future remote sensing instrumentation), their assimilation will dramatically improve air quality forecast and health advisories.

Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?

Over the past decade, a range of sensor technologies became available on the market, enabling a revolutionary shift in air pollution monitoring and assessment. With their cost of up to three orders of magnitude lower than standard/reference instruments, many avenues for applications have opened up. In particular, broader participation in air quality discussion and utilisation of information on air pollution by communities has become possible. However, many questions have been also asked about the actual benefits of these technologies. To address this issue, we conducted a comprehensive literature search including both the scientific and grey literature. We focused upon two questions: (1) Are these technologies fit for the various purposes envisaged? and (2) How far have these technologies and their applications progressed to provide answers and solutions? Regarding the former, we concluded that there is no clear answer to the question, due to a lack of: sensor/monitor manufacturers' quantitative specifications of performance, consensus regarding recommended end-use and associated minimal performance targets of these technologies, and the ability of the prospective users to formulate the requirements for their applications, or conditions of the intended use. Numerous studies have assessed and reported sensor/monitor performance under a range of specific conditions, and in many cases the performance was concluded to be satisfactory. The specific use cases for sensors/monitors included outdoor in a stationary mode, outdoor in a mobile mode, indoor environments and personal monitoring. Under certain conditions of application, project goals, and monitoring environments, some sensors/monitors were fit for a specific purpose. Based on analysis of 17 large projects, which reached applied outcome stage, and typically conducted by consortia of organizations, we observed that a sizable fraction of them (~ 30%) were commercial and/or crowd-funded. This fact by itself signals a paradigm change in air quality monitoring, which previously had been primarily implemented by government organizations. An additional paradigm-shift indicator is the growing use of machine learning or other advanced data processing approaches to improve sensor/monitor agreement with reference monitors. There is still some way to go in enhancing application of the technologies for source apportionment, which is of particular necessity and urgency in developing countries. Also, there has been somewhat less progress in wide-scale monitoring of personal exposures. However, it can be argued that with a significant future expansion of monitoring networks, including indoor environments, there may be less need for wearable or portable sensors/monitors to assess personal exposure. Traditional personal monitoring would still be valuable where spatial variability of pollutants of interest is at a finer resolution than the monitoring network can resolve.

Influential factors affecting black carbon trends at four sites of differing distance from a major highway in Las Vegas.

Elevated air pollution levels adjacent to major highways are an ongoing topic of public health concern worldwide. Black carbon (BC), a component of particulate matter (PM) emitted by diesel and gasoline vehicles, was measured continuously via a filter-based light absorption technique over ~ 16 months at four different stations positioned on a perpendicular trajectory to a major highway in Las Vegas, NV. During downwind conditions (winds from the west), BC at 20 m from the highway was 32 and 60% higher than concentrations at 100 and 300 m from the roadway, respectively. Overall highest roadside (20-m site) BC concentrations were observed during the time period of 4 a.m.-8 a.m. under low-speed variable winds (3.02 µg/m3) or downwind conditions (2.84 µg/m3). The 20-m site BC concentrations under downwind conditions are 85% higher on weekday periods compared to weekends during the time period of 4 a.m.-8 a.m. Whereas total traffic volume was higher on weekdays versus weekends and differed by approximately 3% on weekdays versus weekends, similarly, the detected heavy-duty fraction was higher on weekdays versus weekends and differed by approximately 21% on weekdays versus weekend. Low wind speeds predominated during early morning hours, leading to higher BC concentrations during early morning hours despite the maximum traffic volume occurring later in the day. No noticeable impact from the airport or nearby arterial roadways was observed, with the 300-m site remaining the lowest of the four-site network when winds were from the east. Multivariate linear regression analysis revealed that heavy-duty traffic volume, light-duty traffic volume, wind speed, weekday versus weekend, surface friction velocity, ambient temperature, and the background BC concentration were significant predictors of roadside BC concentrations. Comparison of BC and PM2.5 downwind concentration gradients indicates that the BC component contributes substantially to the PM2.5 increase in roadside environments. These results suggest that BC is an important indicator to assess the contribution of primary traffic emissions to near-road PM2.5 concentrations, providing opportunities to evaluate the feasibility and effectiveness of mitigation strategies.

Field Test of Several Low-Cost Particulate Matter Sensors in High and Low Concentration Urban Environments.

Detailed quantification of the spatial and temporal variability of ambient fine particulate matter (PM2.5) has, to date, been limited due to the cost and logistics involved with traditional monitoring approaches. New miniaturized particle sensors are a potential strategy to gather more time- and spatially-resolved data, to address data gaps in regions with limited monitoring and to address important air quality research priorities in a more cost-effective manner. This work presents field evaluations and lab testing of three models of low-cost (< $200) PM sensors (SHINYEI: models PPD42NS, PPD20V, PPD60PV) in three locations: urban background (average PM2.5: 8 µg m-3) and roadside in Atlanta, Georgia, USA (average PM2.5: 21 µg m-3), and a location with higher ambient concentrations in Hyderabad, India (average PM2.5: 72 µg m-3). Sensor measurements were compared against reference monitors in the lab using one-minute averages and in field locations using one-hour averages. At the Atlanta sites the sensors were weakly correlated with a tapered element oscillating microbalance (TEOM) at best (R2 ≤ 0.30). In Hyderabad, the PPD20V sensors had the highest correlation with the environmental beta attenuation monitor (E-BAM) (R2 > 0.80), however the same sensors had poor agreement if the comparison was restricted to lower concentrations (R2 = ~0, < 40 µg m-3). The results of this work indicate the potential usefulness of these sensors, including the PPD20V, for higher concentration applications (< ~250 µg m-3). These field- testing results provide important insights into the varying performance of low-cost PM sensors under highly contrasting atmospheric conditions. The inconsistent performance results underscore the need for rigorous evaluation of optical particle sensors in the laboratory and in diverse field environments.

Long-term evaluation of air sensor technology under ambient conditions in Denver, Colorado.

Air pollution sensors are quickly proliferating for use in a wide variety of applications, with a low price point that supports use in high-density networks, citizen science, and individual consumer use. This emerging technology motivates the assessment under real-world conditions, including varying pollution levels and environmental conditions. A seven-month, systematic field evaluation of low-cost air pollution sensors was performed in Denver, Colorado, over 2015-2016; the location was chosen to evaluate the sensors in a high-altitude, cool, and dry climate. A suite of particulate matter (PM), ozone (O3), and nitrogen dioxide (NO2) sensors were deployed in triplicate and were collocated with federal equivalent method (FEM) monitors at an urban regulatory site. Sensors were evaluated for their data completeness, correlation with reference monitors, and ability to reproduce trends in pollution data, such as daily concentration values and wind-direction patterns. Most sensors showed high data completeness when data loggers were functioning properly. The sensors displayed a range of correlations with reference instruments, from poor to very high (e.g., hourly-average PM Pearson correlations with reference measurements varied from 0.01 to 0.86). Some sensors showed a change in response to laboratory audits/testing from before the sampling campaign to afterwards, such as Aeroqual, where the O3 response slope changed from about 1.2 to 0.6. Some PM sensors measured wind-direction and time-of-day trends similar to those measured by reference monitors, while others did not. This study showed different results for sensor performance than previous studies performed by the U.S. EPA and others, which could be due to different geographic location, meteorology, and aerosol properties. These results imply that continued field testing is necessary to understand emerging air sensing technology.

Understanding social and behavioral drivers and impacts of air quality sensor use.

BACKGROUND: Lower-cost air quality sensors (hundreds to thousands of dollars) are now available to individuals and communities. This technology is undergoing a rapid and fragmented evolution, resulting in sensors that have uncertain data quality, measure different air pollutants and possess a variety of design attributes. Why and how individuals and communities choose to use sensors is arguably influenced by social context. For example, community experiences with environmental exposures and health effects and related interactions with industry and government can affect trust in traditional air quality monitoring. To date, little social science research has been conducted to evaluate why or how sensors, and sensor data, are used by individuals and communities, or how the introduction of sensors changes the relationship between communities and air quality managers. OBJECTIVES: This commentary uses a risk governance/responsible innovation framework to identify opportunities for interdisciplinary research that brings together social scientists with air quality researchers involved in developing, testing, and deploying sensors in communities. DISCUSSION: Potential areas for social science research include communities of sensor users; drivers for use of sensors and sensor data; behavioral, socio-political, and ethical implications of introducing sensors into communities; assessing methods for communicating sensor data; and harnessing crowdsourcing capabilities to analyze sensor data. CONCLUSIONS: Social sciences can enhance understanding of perceptions, attitudes, behaviors, and other human factors that drive levels of engagement with and trust in different types of air quality data. New transdisciplinary research bridging social sciences, natural sciences, engineering, and design fields of study, and involving citizen scientists working with professionals from a variety of backgrounds, can increase our understanding of air sensor technology use and its impacts on air quality and public health.

The effects of vegetation barriers on near-road ultrafine particle number and carbon monoxide concentrations.

Numerous studies have shown that people living in near-roadway communities (within 100 m of the road) are exposed to high ultrafine particle (UFP) number concentrations, which may be associated with adverse health effects. Vegetation barriers have been shown to affect pollutant transport via particle deposition to leaves and altering the dispersion of emission plumes, which in turn would modify the exposure of near-roadway communities to traffic-related UFPs. In this study, both stationary (equipped with a Scanning Mobility Particle Sizer, SMPS) and mobile (equipped with Fast Mobility Particle Sizer, FMPS) measurements were conducted to investigate the effects of vegetation barriers on downwind UFP (particle diameters ranging from 14 to 102 nm) concentrations at two sites in North Carolina, USA. One site had mainly deciduous vegetation while the other was primarily coniferous; both sites have a nearby open field without the vegetation barriers along the same stretch of limited access road, which served as a reference. During downwind conditions (traffic emissions transported towards the vegetation barrier) and when the wind speed was above or equal to 0.5m/s, field measurements indicated that vegetation barriers with full foliage reduced UFP and CO concentrations by 37.7-63.6% and 23.6-56.1%, respectively. When the test was repeated at the same sites during winter periods when deciduous foliage was reduced, the deciduous barrier during winter showed no significant change in UFP concentration before and after the barrier. Results from the stationary (using SMPS) and mobile (using FMPS) measurements for UFP total number concentrations generally agreed to within 20%.

Episodic Impacts from California Wildfires Identified in Las Vegas Near-Road Air Quality Monitoring.

Air pollutant concentrations near major highways are usually attributed to a combination of nearby traffic emissions and regional background, and generally presumed to be additive in nature. During a near-road measurement study conducted in Las Vegas, NV, the effects of distant wildfires on regional air quality were indicated over a several day period in the summer of 2009. Area-wide elevated particulate levoglucosan (maximum of 0.83 µg/m(3)) and roadside measurements of ultraviolet light-absorbing particulate matter (UVPM) in comparison to black carbon (Delta-C) were apparent over the three-day period. Back-trajectory modeling and satellite images supported the measurement results and indicated the transport of air pollutants from wildfires burning in southern California. Separating roadside measurements under apparent biomass burning event (Delta-C > 1000 ng m(-3)) and nonevent (Delta-C < 1000 ng m(-3)) periods, and constraining to specific days of week, wind speed range, wind direction from the road and traffic volume range, roadside carbon monoxide, black carbon, total particle number count (20-200 nm), and accumulation mode particle number count (100-200 nm) increased by 65%, 146%, 58%, and 366%, respectively, when biomass smoke was indicated. Meanwhile, ultrafine particles (20-100 nm) decreased by 35%. This episode indicates that the presence of aged wildfire smoke may interact with freshly emitted ultrafine particles, resulting in a decrease of particles in the ultrafine mode.

Community Air Sensor Network (CAIRSENSE) project: evaluation of low-cost sensor performance in a suburban environment in the southeastern United States.

Advances in air pollution sensor technology have enabled the development of small and low-cost systems to measure outdoor air pollution. The deployment of a large number of sensors across a small geographic area would have potential benefits to supplement traditional monitoring networks with additional geographic and temporal measurement resolution, if the data quality were sufficient. To understand the capability of emerging air sensor technology, the Community Air Sensor Network (CAIRSENSE) project deployed low-cost, continuous, and commercially available air pollution sensors at a regulatory air monitoring site and as a local sensor network over a surrounding ∼ 2 km area in the southeastern United States. Collocation of sensors measuring oxides of nitrogen, ozone, carbon monoxide, sulfur dioxide, and particles revealed highly variable performance, both in terms of comparison to a reference monitor as well as the degree to which multiple identical sensors produced the same signal. Multiple ozone, nitrogen dioxide, and carbon monoxide sensors revealed low to very high correlation with a reference monitor, with Pearson sample correlation coefficient (r) ranging from 0.39 to 0.97, 0.25 to 0.76, and 0.40 to 0.82, respectively. The only sulfur dioxide sensor tested revealed no correlation (r < 0.5) with a reference monitor and erroneously high concentration values. A wide variety of particulate matter (PM) sensors were tested with variable results - some sensors had very high agreement (e.g., r = 0.99) between identical sensors but moderate agreement with a reference PM2.5 monitor (e.g., r = 0.65). For select sensors that had moderate to strong correlation with reference monitors (r > 0.5), step-wise multiple linear regression was performed to determine if ambient temperature, relative humidity (RH), or age of the sensor in number of sampling days could be used in a correction algorithm to improve the agreement. Maximum improvement in agreement with a reference, incorporating all factors, was observed for an NO2 sensor (multiple correlation coefficient R2 adj-orig = 0.57, R2 adj-final = 0.81); however, other sensors showed no apparent improvement in agreement. A four-node sensor network was successfully able to capture ozone (two nodes) and PM (four nodes) data for an 8-month period of time and show expected diurnal concentration patterns, as well as potential ozone titration due to nearby traffic emissions. Overall, this study demonstrates the performance of emerging air quality sensor technologies in a real-world setting; the variable agreement between sensors and reference monitors indicates that in situ testing of sensors against benchmark monitors should be a critical aspect of all field studies.