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Fine particulate matter (PM 2.5 ) resulting from wildland fire is a significant public health risk in the United States (U.S.). The existing stationary monitoring network and the tools used to alert the public of smoke conditions, such as the Air Quality Index or NowCast, are not optimized to capture actual exposure concentrations in impacted communities given that wildland fire smoke plumes have characteristically steep exposure concentration gradients that can vary over fine spatiotemporal scales. In response, we developed and evaluated a lightweight, universally attachable mobile PM 2.5 monitoring system to provide supplemental, real-time air quality information during wildfire incidents and prescribed burning activities. We retroactively assessed the performance of the mobile monitor compared to nearby (100-1500 m) stationary low-cost sensors and regulatory monitors using 1 minute averaged data collected during two large wildfires in the western U.S. and during one small, prescribed burn in the Midwest. The mobile measurements were highly correlated ( R 2 > 0.85) with the stationary network during the large wildfires. Further, 1 minute averaged mobile measurements differed from three collocated stationary instruments by <25% on average for fourteen out of fifteen total passages. For the small, prescribed burn, rapidly changing conditions near the fire border complicated the comparison of mobile and stationary measurements but the spatial maximum concentrations measured by both instruments were consistent. In general, this work highlights the value of using portable sensor technologies to address the monitoring challenges presented by dynamic wildland fire conditions and demonstrates the value in combining mobile monitoring with stationary data where possible., Competing Interests: Conflicts of interest There are no conflicts of interest to declare.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Davison, Barkjohn, Hagler, Holder, Coefield, Noonan and Hassett-Sipple.)

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 (PM 10 ), nitrogen dioxide (NO 2 ), carbon monoxide (CO), and sulfur dioxide (SO 2 ). 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., Competing Interests: Declaration of interest There are no interests to declare. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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., Competing Interests: Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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.

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.

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., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

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 (O 3 ), and nitrogen dioxide (NO 2 ) 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 O 3 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., Competing Interests: Competing interests. The authors declare that they have no conflict of interest.

Detailed quantification of the spatial and temporal variability of ambient fine particulate matter (PM 2.5 ) 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 PM 2.5 : 8 μg m -3 ) and roadside in Atlanta, Georgia, USA (average PM 2.5 : 21 μg m -3 ), and a location with higher ambient concentrations in Hyderabad, India (average PM 2.5 : 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 (R 2 ≤ 0.30). In Hyderabad, the PPD20V sensors had the highest correlation with the environmental beta attenuation monitor (E-BAM) (R 2 > 0.80), however the same sensors had poor agreement if the comparison was restricted to lower concentrations (R 2 = ~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.