Impact of California Fires on Local and Regional Air Quality: The Role of a Low-Cost Sensor Network and Satellite Observations

A comprehensive review on advancements in sensors for air pollution applications

Air pollution, originating from both natural and human-made sources, presents significant threats to human health and the environment. This review explores the latest technological advancements in air quality sensors focusing on their applications in monitoring a wide range of pollution sources from volcanic eruptions and wildfires to industrial emissions, transportation, agricultural activities and indoor air quality. The review categorizes these sources and examines the operational principles, system architectures, and effectiveness of various air quality monitoring instruments including low-cost sensors, gas analyzers, weather stations, passive sampling devices and remote sensing technologies such as satellite and LiDAR. Key insights include the rapid evolution of sensor technology driven by the need for more accurate, real-time monitoring solutions that are both cost-effective and widely accessible. Despite significant advancements, challenges such as sensor calibration, standardization, and data integration remain critical for ensuring reliable air quality assessments. The manuscript concludes by emphasizing the need for continued innovation and the integration of advanced sensor technologies with regulatory frameworks to enhance environmental management and public health protection.

Advancements in remote sensing for active fire detection: A review of datasets and methods

This study comprehensively and critically reviews active fire detection advancements in remote sensing from 1975 to the present, focusing on two main perspectives: datasets and corresponding instruments, and detection algorithms. The study highlights the increasing role of machine learning, particularly deep learning techniques, in active fire detection. Looking forward, the review outlines current challenges and future research opportunities in remote sensing for active fire detection. These include exploring data quality management and multi-modal learning, developing spatiotemporally explicit models, investigating self-supervised learning models, improving explainable and interpretable models, integrating physical-process based models with machine learning, and building digital twins to replicate wildfire dynamics and perform what-if scenario analysis. The review aims to serve as a valuable resource for informing natural resource management and enhancing environmental protection efforts through the application of remote sensing technology.

Seasons of smoke and fire: preparing health systems for improved performance before, during, and after wildfires

Increased frequency, intensity, and duration of wildfires are intensifying exposure to direct and smoke-related hazards in many areas, leading to evacuation and smoke-related effects on health and health systems that can affect regions extending over thousands of kilometres. Effective preparation and response are currently hampered by inadequate training, continued siloing of disciplines, insufficient finance, and inadequate coordination between health systems and governance at municipal, regional, national, and international levels. This Review highlights the key health and health systems considerations before, during, and after wildfires, and outlines how a health system should respond to optimise population health outcomes now and into the future. The focus is on the implications of wildfires for air quality, mental health, and emergency management, with elements of international policy and finance also addressed. We discuss commonalities of existing climate-resilient health care and disaster management frameworks and integrate them into an approach that addresses issues of financing, leadership and governance, health workforce, health information systems, infrastructure, supply chain, technologies, community interaction and health-care delivery, before, during, and after a wildfire season. This Review is a practical briefing for leaders and health professionals facing severe wildfire seasons and a call to break down silos and join with other disciplines to proactively plan for and fund innovation and coordination in service of a healthier future.

High-resolution urban air pollution mapping

Variation in urban air pollution arises because of complex spatial, temporal, and chemical processes, which profoundly affect population exposure, human health, and environmental justice. This Review highlights insights from two popular in situ measurement methods-mobile monitoring and dense sensor networks-that have distinct but complementary strengths in characterizing the dynamics and impacts of the multidimensional urban air quality system. Mobile monitoring can measure many pollutants at fine spatial scales, thereby informing about processes and control strategies. Sensor networks excel at providing temporal resolution at many locations. Increasingly sophisticated studies leveraging both methods can vividly identify spatial and temporal patterns that affect exposures and disparities and offer mechanistic insight toward effective interventions. This Review summarizes the strengths and limitations of these methods and discusses their implications for understanding fine-scale processes and impacts.

Evaluating low-cost monitoring designs for PM2.5 exposure assessment with a spatiotemporal modeling approach

Determining the most feasible and cost-effective approaches to improving PM2.5 exposure assessment with low-cost monitors (LCMs) can considerably enhance the quality of its epidemiological inferences. We investigated features of fixed-site LCM designs that most impact PM2.5 exposure estimates to be used in long-term epidemiological inference for the Adult Changes in Thought Air Pollution (ACT-AP) study. We used ACT-AP collected and calibrated LCM PM2.5 measurements at the two-week level from April 2017 to September 2020 (N of monitors [measurements] = 82 [502]). We also acquired reference-grade PM2.5 measurements from January 2010 to September 2020 (N = 78 [6186]). We used a spatiotemporal modeling approach to predict PM2.5 exposures with either all LCM measurements or varying subsets with reduced temporal or spatial coverage. We evaluated the models based on a combination of cross-validation and external validation at locations of LCMs included in the models (N = 82), and also based on an independent external validation with a set of LCMs not used for the modeling (N = 30). We found that the model's performance declined substantially when LCM measurements were entirely excluded (spatiotemporal validation R2 [RMSE] = 0.69 [1.2 μg/m3]) compared to the model with all LCM measurements (0.84 [0.9 μg/m3]). Temporally, using the farthest apart measurements (i.e., the first and last) from each LCM resulted in the closest model's performance (0.79 [1.0 μg/m3]) to the model with all LCM data. The models with only the first or last measurement had decreased performance (0.77 [1.1 μg/m3]). Spatially, the model's performance decreased linearly to 0.74 (1.1 μg/m3) when only 10% of LCMs were included. Our analysis also showed that LCMs located in densely populated, road-proximate areas improved the model more than those placed in moderately populated, road-distant areas.

Cooking as an organic aerosol source leading to urban air quality degradation

Air quality degradation events in the urban environment are often attributed to anthropogenic aerosol sources related to combustion of liquid or solid fuels in various activities. The effects of massive cooking emissions during Greek nationwide traditional festivities were investigated by a combined characterization of particulate matter (PM) levels and organic aerosol (OA) sources. Focus was centered on periods around two major festivities, namely "Fat Thursday" and Easter Sunday along six different years. OA sources were apportioned through Positive Matrix Factorization (PMF) on Aerosol Chemical Speciation Monitor (ACSM) mass spectra, while the spatial characteristics of the episodes were assessed through a low-cost, sensor-based PM2.5 monitoring network operating in Athens and other Greek cities. Contrasts were examined by considering a 15-day period around each event, while the effect of the 2020-2021 mobility restrictions, related to COVID-19, was also assessed. An episode-specific cooking organic aerosol (COA) spectral profile was delineated, and can be considered as a reference for ambient COA from meat grilling. Severe pollution episodes that affected the entire Athens basin were recorded, with PM2.5 concentrations exceeding 300 μg m-3 on occasions. COA contributions dominated primary organic aerosol (POA) and made up almost half of OA concentrations. During "Fat Thursday" COA concentrations and contributions peaked during night-time (23.2 μg m-3 and 46 %, respectively) while for Easter Sunday COA maxima were recorded in the early afternoon (27.4 μg m-3 and 39 %). Analyzing a full-year OA source dataset, revealed a pronounced recreational cooking pattern in central Athens, with COA concentrations rising towards the weekend, reflecting the impact of the food service sector. In view of the upcoming review of the EU air quality directive, foreseeing stricter annual PM2.5 limits as well as 24-h limit values and related alerts, the mitigation of cooking emissions appears as a potent instrument for achieving tangible air quality benefits.

Wildland Fires Worsened Population Exposure to PM2.5 Pollution in the Contiguous United States

As wildland fires become more frequent and intense, fire smoke has significantly worsened the ambient air quality, posing greater health risks. To better understand the impact of wildfire smoke on air quality, we developed a modeling system to estimate daily PM2.5 concentrations attributed to both fire smoke and nonsmoke sources across the contiguous U.S. We found that wildfire smoke has the most significant impact on air quality in the West Coast, followed by the Southeastern U.S. Between 2007 and 2018, fire smoke contributed over 25% of daily PM2.5 concentrations at ∼40% of all regulatory air monitors in the EPA's air quality system (AQS) for more than one month per year. People residing outside the vicinity of an EPA AQS monitor (defined by a 5 km radius) were subject to 36% more smoke impact days compared with those residing nearby. Lowering the national ambient air quality standard (NAAQS) for annual mean PM2.5 concentrations to between 9 and 10 μg/m3 would result in approximately 35-49% of the AQS monitors falling in nonattainment areas, taking into account the impact of fire smoke. If fire smoke contribution is excluded, this percentage would be reduced by 6 and 9%, demonstrating the significant negative impact of wildland fires on air quality.

The Impact of Ambient and Wildfire Air Pollution on Rhinosinusitis and Olfactory Dysfunction

PURPOSE OF REVIEW

With increasing industrialization, exposure to ambient and wildfire air pollution is projected to increase, necessitating further research to elucidate the complex relationship between exposure and sinonasal disease. This review aims to summarize the role of ambient and wildfire air pollution in chronic rhinosinusitis (CRS) and olfactory dysfunction and provide a perspective on gaps in the literature.

RECENT FINDINGS

Based on an emerging body of evidence, exposure to ambient air pollutants is correlated with the development of chronic rhinosinusitis in healthy individuals and increased symptom severity in CRS patients. Studies have also found a robust relationship between long-term exposure to ambient air pollutants and olfactory dysfunction. Ambient air pollution exposure is increasingly recognized to impact the development and sequelae of sinonasal pathophysiology. Given the rising number of wildfire events and worsening impacts of climate change, further study of the impact of wildfire-related air pollution is a crucial emerging field.

Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution-Part B-Particle Number Concentrations

Low-cost Particulate Matter (PM) sensors offer an excellent opportunity to improve our knowledge about this type of pollution. Their size and cost, which support multi-node network deployment, along with their temporal resolution, enable them to report fine spatio-temporal resolution for a given area. These sensors have known issues across performance metrics. Generally, the literature focuses on the PM mass concentration reported by these sensors, but some models of sensors also report Particle Number Concentrations (PNCs) segregated into different PM size ranges. In this study, eight units each of Alphasense OPC-R1, Plantower PMS5003 and Sensirion SPS30 have been exposed, under controlled conditions, to short-lived peaks of PM generated using two different combustion sources of PM, exposing the sensors' to different particle size distributions to quantify and better understand the low-cost sensors performance across a range of relevant environmental ranges. The PNCs reported by the sensors were analysed to characterise sensor-reported particle size distribution, to determine whether sensor-reported PNCs can follow the transient variations of PM observed by the reference instruments and to determine the relative impact of different variables on the performances of the sensors. This study shows that the Alphasense OPC-R1 reported at least five size ranges independently from each other, that the Sensirion SPS30 reported two size ranges independently from each other and that all the size ranges reported by the Plantower PMS5003 were not independent of each other. It demonstrates that all sensors tested here could track the fine temporal variation of PNCs, that the Alphasense OPC-R1 could closely follow the variations of size distribution between the two sources of PM, and it shows that particle size distribution and composition are more impactful on sensor measurements than relative humidity.

Low-cost PM2.5 sensors can help identify driving factors of poor air quality and benefit communities

Air quality is critical for public health. Residents rely chiefly on government agencies such as the Environmental Protection Agency (EPA) in the United States to establish standards for the measurement of harmful contaminants including ozone, sulfur dioxide, carbon monoxide, volatile organic chemicals (VOCs), and fine particulate matter at or below 2.5 μm. According to the California Air Resources Board [1], "short-term PM2.5 exposure (up to 24-h duration) has been associated with premature mortality, increased hospital admissions for heart or lung causes, acute and chronic bronchitis, asthma attacks, emergency room visits, respiratory symptoms, and restricted activity days". While public agency resources may provide guidance, it is often inadequate relative to the widespread need for effective local measurement and management of air quality risks. To that end, this paper explores the use of low-cost PM2.5 sensors for measuring air quality through micro-scale (local) analytical comparisons with reference grade monitors and identification of potential causal factors of elevated sensor readings. We find that a) there is high correlation between the PM2.5 measurements of low-cost sensors and reference grade monitors, assessed through calibration models, b) low-cost sensors are more prevalent and provide more frequent measurements, and c) low-cost sensor data enables exploratory and explanatory analytics to identify potential causes of elevated PM2.5 readings. This understanding should encourage community scientists to place more low-cost sensors in their neighborhoods, which can empower communities to demand policy changes that are necessary to reduce particle pollution, and provide a basis for subsequent research.

Evaluation of the Little Rock Green Schoolyard initiative: a quasi-experimental study protocol

BACKGROUND

Evidence suggests that access to green schoolyards may facilitate vigorous play and lead to increased physical activity, which could lead to improved academic outcomes and reduce excess childhood weight gain. Greener schoolyards can also provide additional outdoor amenities that help the community at large. The Little Rock Green Schoolyard Initiative, a program aiming to promote outdoor learning and play in two of the city's community schools, provides a natural experiment to evaluate the role of such interventions. This article presents the protocols and study plans that will be used to evaluate this community-led initiative on several outcomes including physical activity, sleep quality, use of schoolgrounds, and perceptions of the school environment. Administrative datasets will be used to assess exposure to green schoolyard improvements on academic achievement, attendance, and disciplinary referrals during elementary school.

METHODS

Data will be gathered in two community schools where the green schoolyard improvements are taking place and in two demographically-matched comparison schools located elsewhere within the Little Rock School District. Data will be collected before, during, and after the green schoolyard improvements go into effect. Physical activity and sleep quality will be measured using actigraphy. Physical activity will also be assessed through direct playground observations during recess and outside of school hours. During the final year of the study, administrative data will be assembled and evaluated using difference-in-differences estimation and synthetic controls, two causal inference methods from the program evaluation literature.

DISCUSSION

The study is designed to provide new insights into the design, implementation, and evaluation of playgrounds among schoolchildren, especially those who are at risk of developing severe obesity during their elementary school years. The research herein will develop empirical data, elucidate potential mechanisms, and practical experience for future study, policymaking, and health services.

Summary of PM2.5 measurement artifacts associated with the Teledyne T640 PM Mass Monitor under controlled chamber experimental conditions using polydisperse ammonium sulfate aerosols and biomass smoke

Particulate matter (PM) is a major primary pollutant emitted during wildland fires that has the potential to pose significant health risks to individuals/communities who live and work in areas impacted by smoke events. Limiting exposure is the principle measure available to mitigate health impacts of smoke and therefore the accurate determination of ambient PM concentrations during wildland fire events is critical to protecting public health. However, monitoring air pollutants in smoke impacted environments has proven challenging in that measurement interferences or sampling conditions can result in both positive and negative artifacts. The EPA has performed research on methods for the measurement of PM2.5 in a series of laboratory-based studies including evaluation in smoke. This manuscript will summarize the results of the laboratory-based evaluation of federal equivalent method (FEM) monitors for PM2.5 with particular attention being given to the Teledyne-API Model T640 PM Mass monitor, as compared to the filter-based federal reference method (FRM). The T640 is an optical-based PM monitor and has been gaining wide use by state and local agencies in monitoring for PM2.5 U.S. National Ambient Air Quality Standards (NAAQS) attainment. At present, the T640 (includes both T640 and T640×) comprises ~44% of the PM2.5 FEM monitors in U.S. regulatory monitoring networks. In addition, the T640 has increasingly been employed for the higher time resolution comparison/evaluation of low-cost PM sensors including during smoke impacted events. Results from controlled non-smoke laboratory studies using generated ammonium sulfate aerosols demonstrated a generally negative T640 measurement artifact that was significantly related to the PM2.5 concentration and particle size distribution. Results from biomass burning chamber studies demonstrated positive and negative artifacts significantly associated with PM2.5 concentration and optical wavelength-dependent absorption properties of the smoke aerosol.Implications: The results detailed in this paper will provide state and local air monitoring agencies with the tools and knowledge to address PM2.5 measurement challenges in areas frequently impacted by wildland fire smoke. The observed large positive and negative artifacts in the T640 PM mass determination have the potential to result in false exceedances of the PM2.5 NAAQS or in the disqualification of monitoring data through an exceptional event designation. In addition, the observed artifacts in smoke impacted air will have a detrimental effect on providing reliable public information when wildfires occur and also in identifying reference measurements for small sensor evaluation studies. Other PM2.5 FEMs such as the BAM-1022 perform better in smoke and are comparable to the filter-based FRM. Care must be taken in choosing high time resolution FEM monitors that will be operated at smoke impacted sites. Accurate methods, such as the FRM and BAM-1022 will reduce the burden of developing and reviewing exceptional event request packages, data loss/disqualification, and provide states with tools to adequately evaluate public exposure risks and provide accurate public health messaging during wildfire/smoke events.

Predicting hourly PM2.5 concentrations in wildfire-prone areas using a SpatioTemporal Transformer model

Globally, wildfires are becoming more frequent and destructive, generating a significant amount of smoke that can transport thousands of miles. Therefore, improving air pollution forecasts from wildfires is essential and informing citizens of more frequent, accurate, and interpretable updates related to localized air pollution events. This research proposes a multi-head attention-based deep learning architecture, SpatioTemporal (ST)-Transformer, to improve spatiotemporal predictions of PM_2.5 concentrations in wildfire-prone areas. The ST-Transformer model employed a sparse attention mechanism that concentrates on the most useful contextual information across spatial, temporal, and variable-wise dimensions. The model includes critical driving factors of PM_2.5 concentrations as predicting factors, including wildfire perimeter and intensity, meteorological factors, road traffic, PM_2.5, and temporal indicators from the past 24 h. The model is trained to conduct time series forecasting on PM_2.5 concentrations at EPA's air quality stations in the greater Los Angeles area. Prediction results were compared with other existing time series forecasting methods and exhibited better performance, especially in capturing abrupt changes or spikes in PM_2.5 concentrations during wildfire situations. The attention matrix learned by the proposed model enabled interpretation of the complex spatial, temporal, and variable-wise dependencies, indicating that the model can differentiate between wildfires and non-wildfires. The ST-Transformer model's accurate predictability and interpretation capacity can help effectively monitor and predict the impacts of wildfire smoke and be applicable to other complex spatiotemporal prediction problems.

Wildfire-induced pollution and its short-term impact on COVID-19 cases and mortality in California

Globally, wildfires have seen remarkable increase in duration and size and have become a health hazard. In addition to vegetation and habitat destruction, rapid release of smoke, dust and gaseous pollutants in the atmosphere contributes to its short and long-term detrimental effects. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has emerged as a public health concern worldwide that primarily target lungs and respiratory tract, akin to air pollutants. Studies from our lab and others have demonstrated association between air pollution and COVID-19 infection and mortality rates. However, current knowledge on the impact of wildfire-mediated sudden outburst of air pollutants on COVID-19 is limited. In this study, we examined the association of air pollutants and COVID-19 during wildfires burned during August-October 2020 in California, United States. We observed an increase in the tropospheric pollutants including aerosols (particulate matter [PM]), carbon monoxide (CO) and nitrogen dioxide (NO2) by approximately 150%, 100% and 20%, respectively, in 2020 compared to the 2019. Except ozone (O3), similar proportion of increment was noticed during the peak wildfire period (August 16 - September 15, 2020) in the ground PM2.5, CO, and NO2 levels at Fresno, Los Angeles, Sacramento, San Diego and San Francisco, cities with largest active wildfire area. We identified three different spikes in the concentrations of PM2.5, and CO for the cities examined clearly suggesting wildfire-induced surge in air pollution. Fresno and Sacramento showed increment in the ground PM2.5, CO and NO2 levels, while San Diego recorded highest change rate in NO2 levels. Interestingly, we observed a similar pattern of higher COVID-19 cases and mortalities in the cities with adverse air pollution caused by wildfires. These findings provide a logical rationale to strategize public health policies for future impact of COVID-19 on humans residing in geographic locations susceptible to sudden increase in local air pollution.

Efficacy of Low-Cost Sensor Networks at Detecting Fine-Scale Variations in Particulate Matter in Urban Environments

The negative health impacts of air pollution are well documented. Not as well-documented, however, is how particulate matter varies at the hyper-local scale, and the role that proximal sources play in influencing neighborhood-scale patterns. We examined PM2.5 variations in one airshed within Indianapolis (Indianapolis, IN, USA) by utilizing data from 25 active PurpleAir (PA) sensors involving citizen scientists who hosted all but one unit (the control), as well as one EPA monitor. PA sensors report live measurements of PM2.5 on a crowd sourced map. After calibrating the data utilizing relative humidity and testing it against a mobile air-quality unit and an EPA monitor, we analyzed PM2.5 with meteorological data, tree canopy coverage, land use, and various census variables. Greater proximal tree canopy coverage was related to lower PM2.5 concentrations, which translates to greater health benefits. A 1% increase in tree canopy at the census tract level, a boundary delineated by the US Census Bureau, results in a ~0.12 µg/m3 decrease in PM2.5, and a 1% increase in "heavy industry" results in a 0.07 µg/m3 increase in PM2.5 concentrations. Although the overall results from these 25 sites are within the annual ranges established by the EPA, they reveal substantial variations that reinforce the value of hyper-local sensing technologies as a powerful surveillance tool.

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.

Understanding Air Quality Changes after Implementation of Mitigation Measures during a Pandemic: A Scoping Review of Literature in the United States

Traffic-related emissions continue to be a significant source of air pollution in the United States (US) and around the globe. Evidence has shown that previous policies implemented to restrict-traffic flows have affected air pollution levels. Thus, mitigation strategies associated with the COVID-19 pandemic that modified population-level mobility patterns provide a unique opportunity to study air pollution change across the US. For instance, to slow the spread of the pandemic, state and local governments started implementing various mitigation actions, including stay-at-home directives, social distancing measures, school closures, and travel restrictions. This scoping review aimed to summarize the existing evidence about how air quality changed through mitigation practices throughout the pandemic in the US. We found 66 articles that fit our inclusion criteria. Generally, the consolidated results revealed that nitrogen dioxide (NO2) and carbon monoxide (CO) decreased across the country. Studies observed mixed directions and magnitudes of change for fine and coarse particulate matter (PM2.5, PM10), ozone (O3), and sulfur dioxide (SO2). Few articles tried to explain this notable heterogeneity in air quality changes by associating contextual factors, such as mobility, traffic flow, and demographic factors. However, all studies agreed that the change in air pollution was nonuniform across the US and even varied within a city.

Association between PM2.5 and respiratory hospitalization in Rio Branco, Brazil: Demonstrating the potential of low-cost air quality sensor for epidemiologic research

BACKGROUND

There is currently a scarcity of air pollution epidemiologic data from low- and middle-income countries (LMICs) due to the lack of air quality monitoring in these countries. Additionally, there is limited capacity to assess the health effects of wildfire smoke events in wildfire-prone regions like Brazil's Amazon Basin. Emerging low-cost air quality sensors may have the potential to address these gaps.

OBJECTIVES

We investigated the potential of PurpleAir PM2.5 sensors for conducting air pollution epidemiologic research leveraging the United States Environmental Protection Agency's United States-wide correction formula for ambient PM_2.5.

METHODS

We obtained raw (uncorrected) PM_2.5 concentration and humidity data from a PurpleAir sensor in Rio Branco, Brazil, between 2018 and 2019. Humidity measurements from the PurpleAir sensor were used to correct the PM_2.5 concentrations. We established the relationship between ambient PM_2.5 (corrected and uncorrected) and daily all-cause respiratory hospitalization in Rio Branco, Brazil, using generalized additive models (GAM) and distributed lag non-linear models (DLNM). We used linear regression to assess the relationship between daily PM_2.5 concentrations and wildfire reports in Rio Branco during the wildfire seasons of 2018 and 2019.

RESULTS

We observed increases in daily respiratory hospitalizations of 5.4% (95%CI: 0.8%, 10.1%) for a 2-day lag and 5.8% (1.5%, 10.2%) for 3-day lag, per 10 μg/m³ PM_2.5 (corrected values). The effect estimates were attenuated when the uncorrected PM_2.5 data was used. The number of reported wildfires explained 10% of daily PM2.5 concentrations during the wildfire season.

DISCUSSION

Exposure-response relationships estimated using corrected low-cost air quality sensor data were comparable with relationships estimated using a validated air quality modeling approach. This suggests that correcting low-cost PM_2.5 sensor data may mitigate bias attenuation in air pollution epidemiologic studies. Low-cost sensor PM_2.5 data could also predict the air quality impacts of wildfires in Brazil's Amazon Basin.

Using wildland fire smoke modeling data in gerontological health research (California, 2007-2018)

Widespread population exposure to wildland fire smoke underscores the urgent need for new techniques to characterize fire-derived pollution for epidemiologic studies and to build climate-resilient communities especially for aging populations. Using atmospheric chemical transport modeling, we examined air quality with and without wildland fire smoke PM2.5. In 12-km gridded output, the 24-hour average concentration of all-source PM2.5 in California (2007-2018) was 5.16 μg/m3 (S.D. 4.66 μg/m3). The average concentration of fire-PM2.5 in California by year was 1.61 μg/m3 (~30% of total PM2.5). The contribution of fire-source PM2.5 ranged from 6.8% to 49%. We define a "smokewave" as two or more consecutive days with modeled levels above 35 μg/m3. Based on model-derived fire-PM2.5, 99.5% of California's population lived in a county that experienced at least one smokewave from 2007 to 2018, yet understanding of the impact of smoke on the health of aging populations is limited. Approximately 2.7 million (56%) of California residents aged 65+ years lived in counties representing the top 3 quartiles of fire-PM2.5 concentrations (2007-2018). For each year (2007-2018), grid cells containing skilled nursing facilities had significantly higher mean concentrations of all-source PM2.5 than cells without those facilities, but they also had generally lower mean concentrations of wildland fire-specific PM2.5. Compared to rural monitors in California, model predictions of wildland fire impacts on daily average PM2.5 carbon (organic and elemental) performed well most years but tended to overestimate wildland fire impacts for high-fire years. The modeling system isolated wildland fire PM2.5 from other sources at monitored and unmonitored locations, which is important for understanding exposures for aging population in health studies.

Measuring secondhand smoke in homes in Malaysia: A feasibility study comparing indoor fine particulate (PM_2.5) concentrations following an educational feedback intervention to create smoke-free homes during the COVID-19 pandemic

INTRODUCTION

Extensive regulations have been introduced to reduce secondhand smoke (SHS) exposure among non-smokers in Malaysia. However, there is still a need to encourage behavior change of smokers in relation to making homes smoke-free. This feasibility study aimed to use low-cost air pollution monitors to quantify SHS concentrations in Malaysian households and to explore the practicality of using personalized feedback in educating families to make their homes smoke-free.

METHODS

A total of 35 smokers in three states in Malaysia were recruited via snowball and convenience sampling methods. Indoor fine particulate (PM_2.5) concentrations in participants’ homes were measured for 7 days before and after educational intervention using a pre-defined template, which included personalized air-quality feedback, and information on SHS impacts were given. The feedback was delivered over two 20-minute phone calls or in-person sessions following the completion of the air-quality measurements. Data were corrected for outdoor PM_2.5 concentrations from the nearest environmental monitor.

RESULTS

Despite the challenges in conducting the project during COVID-19 pandemic, the delivery of the intervention was found to be feasible. Twenty-seven (77%) out of 35 participants completed PM_2.5 measurements and received a complete intervention. The median (IQR: 25th –75th percentile concentrations) SHS-PM_2.5 concentrations at baseline and follow-up were 18.3 µg/m³ (IQR: 13.3–28.3) and 16.2 µg/m³ (IQR: 10.4 – 25.6), respectively. There was a reduction of SHS-PM_2.5 concentrations at follow-up measurement in the houses of 17 participants (63%). The change in corrected indoor PM_2.5 concentrations between baseline and follow-up was not statistically significant (Z= -1.01, p=0.29).

CONCLUSIONS

This educational intervention, combining the use of a low-cost air particle counter with personalized air-quality feedback, was found to be feasible in the Malaysian setting. It has potential to trigger behavior change among smokers, reducing indoor smoking and consequent SHS concentrations, and increasing smoke-free home implementation. A large-scale trial is needed.

Calibration of PurpleAir PA-I and PA-II Monitors Using Daily Mean PM2.5 Concentrations Measured in California, Washington, and Oregon from 2017 to 2021

Large quantities of real-time particle data are becoming available from low-cost particle monitors. However, it is crucial to determine the quality of these measurements. The largest network of monitors in the United States is maintained by the PurpleAir company, which offers two monitors: PA-I and PA-II. PA-I monitors have a single sensor (PMS1003) and PA-II monitors employ two independent PMS5003 sensors. We determine a new calibration factor for the PA-I monitor and revise a previously published calibration algorithm for PA-II monitors (ALT-CF3). From the PurpleAir API site, we downloaded 83 million hourly average PM_2.5 values in the PurpleAir database from Washington, Oregon, and California between 1 January 2017 and 8 September 2021. Daily outdoor PM_2.5 means from 194 PA-II monitors were compared to daily means from 47 nearby Federal regulatory sites using gravimetric Federal Reference Methods (FRM). We find a revised calibration factor of 3.4 for the PA-II monitors. For the PA-I monitors, we determined a new calibration factor (also 3.4) by comparing 26 outdoor PA-I sites to 117 nearby outdoor PA-II sites. These results show that PurpleAir PM_2.5 measurements can agree well with regulatory monitors when an optimum calibration factor is found.

Investigating the Sources of Urban Air Pollution Using Low-Cost Air Quality Sensors at an Urban Atlanta Site

Advances in low-cost sensors (LCS) for monitoring air quality have opened new opportunities to characterize air quality in finer spatial and temporal resolutions. In this study, we deployed LCS that measure both gas (CO, NO, NO₂, and O₃) and particle concentrations and co-located research-grade instruments in Atlanta, GA, to investigate the capability of LCS in resolving air pollutant sources using non-negative matrix factorization (NMF) in a moderately polluted urban area. We provide a comparison of applying the NMF technique to both normalized and non-normalized data sets. We identify four factors with different temporal trends and properties for both normalized and non-normalized data sets. Both normalized and non-normalized LCS data sets can resolve primary organic aerosol (POA) factors identified from research-grade instruments. However, applying normalization provides factors with more diverse compositions and can resolve secondary organic aerosol (SOA). Results from this study demonstrate that LCS not only can be used to provide basic mass concentration information but also can be used for in-depth source apportionment studies even in an urban setting with complex pollution mixtures and relatively low aerosol loadings.

Outdoor and indoor fine particulate matter at skilled nursing facilities in the western United States during wildfire and non-wildfire seasons

Wildfire activity is increasing in parts of the world where extreme drought and warming temperatures contribute to fireprone conditions, including the western United States. The elderly are among the most vulnerable, and those in long-term care with preexisting conditions have added risk for adverse health outcomes from wildfire smoke exposure. In this study, we report continuous co-located indoor and outdoor fine particulate matter (PM_2.5 ) measurements at four skilled nursing facilities in the western United States. Throughout the year 2020, over 8000 h of data were collected, which amounted to approximately 300 days of indoor and outdoor sampling at each facility. The highest indoor 24 h average PM_2.5 recorded at each facility was 43.6 µg/m³ , 103.2 µg/m³ , 35.4 µg/m³ , and 202.5 µg/m³ , and these peaks occurred during the wildfire season. The indoor-to-outdoor PM_2.5 ratio and calculated infiltration efficiencies indicated high variation in the impact of wildfire events on Indoor Air Quality between the four facilities. Notably, infiltration efficiency ranged from 0.22 to 0.76 across the four facilities. We propose that this variability is evidence that PM_2.5 infiltration may be impacted by modifiable building characteristics and human behavioral factors, and this should be addressed in future studies.

Evaluation of Cairpol and Aeroqual Air Sensors in Biomass Burning Plumes

Cairpol and Aeroqual air quality sensors measuring CO, CO₂, NO₂, and other species were tested in fresh biomass burning plumes in field and laboratory environments. We evaluated sensors by comparing 1-minute sensor measurements to collocated reference instrument measurements. Sensors were evaluated based on the coefficient of determination (r ²) between the sensor and reference measurements, by the accuracy, collocated precision, root mean square error (RMSE), and other metrics. In general, CO and CO₂ sensors performed well (in terms of accuracy and r ² values) compared to NO₂ sensors. Cairpol CO and NO₂ sensors had better sensor-versus-sensor agreement (e.g., collocated precision) than Aeroqual CO and NO₂ sensors of the same species. Tests of other sensors (e.g., NH₃, H₂S, VOC, NMHC) provided more inconsistent results and need further study. Aeroqual NO₂ sensors had an apparent O₃ interference that was not observed in the Cairpol NO₂ sensors. Although the sensor accuracy lags that of reference-level monitors, with location-specific calibrations they have the potential to provide useful data about community air quality and personal exposure to smoke impacts.

Intercomparison of PurpleAir Sensor Performance over Three Years Indoors and Outdoors at a Home: Bias, Precision, and Limit of Detection Using an Improved Algorithm for Calculating PM2.5

Low-cost particle sensors are now used worldwide to monitor outdoor air quality. However, they have only been in wide use for a few years. Are they reliable? Does their performance deteriorate over time? Are the algorithms for calculating PM_2.5 concentrations provided by the sensor manufacturers accurate? We investigate these questions using continuous measurements of four PurpleAir monitors (8 sensors) under normal conditions inside and outside a home for 1.5–3 years. A recently developed algorithm (called ALT-CF3) is compared to the two existing algorithms (CF1 and CF_ATM) provided by the Plantower manufacturer of the PMS 5003 sensors used in PurpleAir PA-II monitors. Results. The Plantower CF1 algorithm lost 25–50% of all indoor data due in part to the practice of assigning zero to all concentrations below a threshold. None of these data were lost using the ALT-CF3 algorithm. Approximately 92% of all data showed precision better than 20% using the ALT-CF3 algorithm, but only approximately 45–75% of data achieved that level using the Plantower CF1 algorithm. The limits of detection (LODs) using the ALT-CF3 algorithm were mostly under 1 µg/m³, compared to approximately 3–10 µg/m³ using the Plantower CF1 algorithm. The percentage of observations exceeding the LOD was 53–92% for the ALT-CF3 algorithm, but only 16–44% for the Plantower CF1 algorithm. At the low indoor PM_2.5 concentrations found in many homes, the Plantower algorithms appear poorly suited.

Socioeconomic Disparities of Low-Cost Air Quality Sensors in California, 2017-2020

Objectives. To (1) examine the disparity in availability of PurpleAir low-cost air quality sensors in California based on neighborhood socioeconomic status (SES) and exposure to fine particulate matter smaller than 2.5 micrometers (PM2.5), (2) investigate the temporal trend of sensor distribution and operation, and (3) identify priority communities for future sensor distribution. Methods. We obtained census tract-level SES variables and PM2.5 concentrations from the CalEnviroScreen4.0 data set. We obtained real-time PurpleAir sensor data (July 2017-September 2020) to examine sensor distribution and operation. We conducted spatial and temporal analyses at the census tract level to investigate neighborhood SES and PM2.5 concentrations in relation to sensor distribution and operation. Results. The spatial coverage and the number of PurpleAir sensors increased significantly in California. Fewer sensors were distributed in census tracts with lower SES, higher PM2.5, and higher proportions of racial/ethnic minority populations. Furthermore, a large proportion of existing sensors were not in operation at a given time, especially in disadvantaged communities. Conclusions. Disadvantaged communities should be given access to low-cost sensors to fill in spatial gaps of air quality monitoring and address environmental justice concerns. Sensor purchasing and deployment must be paired with regular maintenance to ensure their reliable performance. (Am J Public Health. 2022;112(3):434-442. https://doi.org/10.2105/AJPH.2021.306603).

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

Given the growing interest in community air quality monitoring using low-cost sensors, 30 PurpleAir II sensors (12 outdoor and 18 indoor) were deployed in partnership with community members living adjacent to a major interstate freeway from December 2017- June 2019. Established quality assurance/quality control techniques for data processing were used and sensor data quality was evaluated by calculating data completeness and summarizing PM_2.5 measurements. To evaluate outdoor sensor performance, correlation coefficients (r) and coefficients of divergence (CoD) were used to assess temporal and spatial variability of PM_2.5 between sensors. PM_2.5 concentrations were also compared to traffic levels to assess the sensors' ability to detect traffic pollution. To evaluate indoor sensors, indoor/outdoor (I/O) ratios during resident-reported activities were calculated and compared, and a linear mixed-effects regression model was developed to quantify the impacts of ambient air quality, microclimatic factors, and indoor human activities on indoor PM_2.5. In general, indoor sensors performed more reliably than outdoor sensors (completeness: 73% versus 54%). All outdoor sensors were highly temporally correlated (r > 0.98) and spatially homogeneous (CoD<0.06). The observed I/O ratios were consistent with existing literature, and the mixed-effects model explains >85% of the variation in indoor PM_2.5 levels, indicating that indoor sensors detected PM_2.5 from various sources. Overall, this study finds that community-maintained sensors can effectively monitor PM_2.5, with main data quality concerns resulting from outdoor sensor data incompleteness.

Low-Cost Sensors for Air Quality Monitoring - the Current State of the Technology and a Use Overview

In recent years the monitoring of air quality using cheap sensors has become an interesting alternative to conventional analytical techniques. Apart from vast price differences conventional techniques need to be performed by the trained personnel of commercial or research laboratories. Sensors capable of measuring dust, ozone, nitrogen and sulphur oxides, or other air pollutants are relatively simple electronic devices, which are comparable in size to a mobile phone. They provide the general public with the possibility to monitor air quality which can contribute to various projects that differ in regional scale, commercial funding or community-base. In connection with the low price of sensors arises the question of the quality of measured data. This issue is addressed by a number of studies focused on comparing the sensor data with the data of reference measurements. Sensory measurement is influenced by the monitored analyte, type and design of the particular sensor, as well as by the measurement conditions. Currently sensor networks serve as an additional source of information to the network of air quality monitoring stations, where the density of the network provides concentration trends in the area that may exceed specific measured values of pollutant concentrations and low uncertainty of reference measurements. The constant development of all types of sensors is leading to improvements and the difference in data quality between sensors and conventional monitoring techniques may be reduced.

Publicly available low-cost sensor measurements for PM2.5 exposure modeling: Guidance for monitor deployment and data selection

High-resolution, high-quality exposure modeling is critical for assessing the health effects of ambient PM_2.5 in epidemiological studies. Using sparse regulatory PM_2.5 measurements as principal model inputs may result in two issues in exposure prediction: (1) they may affect the models' accuracy in predicting PM_2.5 spatial distribution; (2) the internal validation based on these measurements may not reliably reflect the model performance at locations of interest (e.g., a cohort's residential locations). In this study, we used the PM_2.5 measurements from a publicly available commercial low-cost PM_2.5 network, PurpleAir, with an external validation dataset at the residential locations of a representative sample of participants from the Adult Changes in Thought - Air Pollution (ACT-AP) study, to improve the accuracy of exposure prediction at the cohort participant locations. We also proposed a metric based on principal component analysis (PCA) - the PCA distance - to assess the similarity between monitor and cohort locations to guide monitor deployment and data selection. The analysis was based on a spatiotemporal modeling framework with 51 "gold-standard" monitors and 58 PurpleAir monitors for model development, as well as 105 home monitors at the cohort locations for model validation, in the Puget Sound region of Washington State from June 2017 to March 2019. After including calibrated PurpleAir measurements as part of the dependent variable, the external spatiotemporal validation R² and root-mean-square error, RMSE, for two-week concentration averages improved from 0.84 and 2.22 μg/m³ to 0.92 and 1.63 μg/m³, respectively. The external spatial validation R² and RMSE for long-term averages over the modeling period improved from 0.72 and 1.01 μg/m³ to 0.79 and 0.88 μg/m³, respectively. The exposure predictions incorporating PurpleAir measurements demonstrated sharper urban-suburban concentration gradients. The PurpleAir monitors with shorter PCA distances improved the model's prediction accuracy more substantially than the monitors with longer PCA distances, supporting the use of this similarity metric.

Uncertainty in Health Impact Assessments of Smoke From a Wildfire Event

Wildfires cause elevated air pollution that can be detrimental to human health. However, health impact assessments associated with emissions from wildfire events are subject to uncertainty arising from different sources. Here, we quantify and compare major uncertainties in mortality and morbidity outcomes of exposure to fine particulate matter (PM2.5) pollution estimated for a series of wildfires in the Southeastern U.S. We present an approach to compare uncertainty in estimated health impacts specifically due to two driving factors, wildfire-related smoke PM2.5 fields and variability in concentration-response parameters from epidemiologic studies of ambient and smoke PM2.5. This analysis, focused on the 2016 Southeastern wildfires, suggests that emissions from these fires had public health consequences in North Carolina. Using several methods based on publicly available monitor data and atmospheric models to represent wildfire-attributable PM2.5, we estimate impacts on several health outcomes and quantify associated uncertainty. Multiple concentration-response parameters derived from studies of ambient and wildfire-specific PM2.5 are used to assess health-related uncertainty. Results show large variability and uncertainty in wildfire impact estimates, with comparable uncertainties due to the smoke pollution fields and health response parameters for some outcomes, but substantially larger health-related uncertainty for several outcomes. Consideration of these uncertainties can support efforts to improve estimates of wildfire impacts and inform fire-related decision-making.

Framework for nontargeted investigation of contaminants released by wildfires into stormwater runoff: Case study in the northern San Francisco Bay area

Wildfires can be extremely destructive to communities and ecosystems. However, the full scope of the ecological damage is often hard to assess, in part due to limited information on the types of chemicals introduced to affected landscapes and waterways. The objective of this study was to establish a sampling, analytical, and interpretive framework to effectively identify and monitor contaminants of emerging concern in environmental water samples impacted by wildfire runoff. A nontargeted analysis consisting of comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC/TOF-MS) was conducted on stormwater samples from watersheds in the City of Santa Rosa and Sonoma and Napa Counties, USA, after the three most destructive fires during the October 2017 Northern California firestorm. Chemicals potentially related to wildfires were selected from the thousands of chromatographic features detected through a screening method that compared samples from fire-impacted sites versus unburned reference sites. This screening led to high confidence identifications of 76 potentially fire-related compounds. Authentic standards were available for 48 of these analytes, and 46 were confirmed by matching mass spectra and GC × GC retention times. Of these 46 compounds, 37 had known commercial and industrial uses as intermediates or ingredients in plastics, personal care products, pesticides, and as food additives. Nine compounds had no known uses or sources and may be oxidation products resulting from burning of natural or anthropogenic materials. Preliminary examination of potential toxicity associated with the 46 compounds, conducted via online databases and literature review, indicated limited data availability. Regional comparison suggested that more structural damage may yield a greater number of unique, potentially wildfire-related compounds. We recommend further study of post-wildfire runoff using the framework described here, which includes hypothesis-driven site selection and nontargeted analysis, to uncover potentially significant stormwater contaminants not routinely monitored after wildfires and inform risk assessment. Integr Environ Assess Manag 2021;17:1179-1193. © 2021 SETAC.

Performance and Deployment of Low-Cost Particle Sensor Units to Monitor Biomass Burning Events and Their Application in an Educational Initiative

Biomass burning smoke is often a significant source of airborne fine particles in regional areas where air quality monitoring is scarce. Emerging sensor technology provides opportunities to monitor air quality on a much larger geographical scale with much finer spatial resolution. It can also engage communities in the conversation around local pollution sources. The SMoke Observation Gadget (SMOG), a unit with a Plantower dust sensor PMS3003, was designed as part of a school-based Science, Technology, Engineering and Mathematics (STEM) project looking at smoke impacts in regional areas of Victoria, Australia. A smoke-specific calibration curve between the SMOG units and a standard regulatory instrument was developed using an hourly data set collected during a peat fire. The calibration curve was applied to the SMOG units during all field-based validation measurements at several locations and during different seasons. The results showed strong associations between individual SMOG units for PM2.5 concentrations (r2 = 0.93-0.99) and good accuracy (mean absolute error (MAE) < 2 μg m-3). Correlations of the SMOG units to reference instruments also demonstrated strong associations (r2 = 0.87-95) and good accuracy (MAE of 2.5-3.0 μg m-3). The PM2.5 concentrations tracked by the SMOG units had a similar response time as those measured by collocated reference instruments. Overall, the study has shown that the SMOG units provide relevant information about ambient PM2.5 concentrations in an airshed impacted predominantly by biomass burning, provided that an adequate adjustment factor is applied.

Using Low-Cost Sensors to Assess Fine Particulate Matter Infiltration (PM2.5) during a Wildfire Smoke Episode at a Large Inpatient Healthcare Facility

Wildfire smoke exposure is associated with a range of acute health outcomes, which can be more severe in individuals with underlying health conditions. Currently, there is limited information on the susceptibility of healthcare facilities to smoke infiltration. As part of a larger study to address this gap, a rehabilitation facility in Vancouver, Canada was outfitted with one outdoor and seven indoor low-cost fine particulate matter (PM_2.5) sensors in Air Quality Eggs (EGG) during the summer of 2020. Raw measurements were calibrated using temperature, relative humidity, and dew point derived from the EGG data. The infiltration coefficient was quantified using a distributed lag model. Indoor concentrations during the smoke episode were elevated throughout the building, though non-uniformly. After censoring indoor-only peaks, the average infiltration coefficient (range) during typical days was 0.32 (0.22–0.39), compared with 0.37 (0.31–0.47) during the smoke episode, a 19% increase on average. Indoor PM_2.5 concentrations quickly reflected outdoor conditions during and after the smoke episode. It is unclear whether these results will be generalizable to other years due to COVID-related changes to building operations, but some of the safety protocols may offer valuable lessons for future wildfire seasons. For example, points of building entry and exit were reduced from eight to two during the pandemic, which likely helped to protect the building from wildfire smoke infiltration. Overall, these results demonstrate the utility of indoor low-cost sensors in understanding the impacts of extreme smoke events on facilities where highly susceptible individuals are present. Furthermore, they highlight the need to employ interventions that enhance indoor air quality in such facilities during smoke events.

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.

Evaluation of MODIS Aerosol Optical Depth and Surface Data Using an Ensemble Modeling Approach to Assess PM2.5 Temporal and Spatial Distributions

The use of statistical models and machine-learning techniques along satellite-derived aerosol optical depth (AOD) is a promising method to estimate ground-level particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), mainly in urban areas with low air quality monitor density. Nevertheless, the relationship between AOD and ground-level PM2.5 varies spatiotemporally and differences related to spatial domains, temporal schemes, and seasonal variations must be assessed. Here, an ensemble multiple linear regression (EMLR) model and an ensemble neural network (ENN) model were developed to estimate PM2.5 levels in the Monterrey Metropolitan Area (MMA), the second largest urban center in Mexico. Four AOD-SDSs (Scientific Datasets) from MODIS Collection 6 were tested using three spatial domains and two temporal schemes. The best model performance was obtained using AOD at 0.55 µm from MODIS-Aqua at a spatial resolution of 3 km, along meteorological parameters and daily scheme. EMLR yielded a correlation coefficient (R) of ~0.57 and a root mean square error (RMSE) of ~7.00 μg m−3. ENN performed better than EMLR, with an R of ~0.78 and RMSE of ~5.43 μg m−3. Satellite-derived AOD in combination with meteorology data allowed for the estimation of PM2.5 distributions in an urban area with low air quality monitor density.

Sub-City Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground Measurements

While multiple information sources exist concerning surface-level air pollution, no individual source simultaneously provides large-scale spatial coverage, fine spatial and temporal resolution, and high accuracy. It is, therefore, necessary to integrate multiple data sources, using the strengths of each source to compensate for the weaknesses of others. In this study, we propose a method incorporating outputs of NASA's GEOS Composition Forecasting model system with satellite information from the TROPOMI instrument and ground measurement data on surface concentrations. Although we use ground monitoring data from the Environmental Protection Agency network in the continental United States, the model and satellite data sources used have the potential to allow for global application. This method is demonstrated using surface measurements of nitrogen dioxide as a test case in regions surrounding five major US cities. The proposed method is assessed through cross-validation against withheld ground monitoring sites. In these assessments, the proposed method demonstrates major improvements over two baseline approaches which use ground-based measurements only. Results also indicate the potential for near-term updating of forecasts based on recent ground measurements.

Application and evaluation of a low-cost PM sensor and data fusion with CMAQ simulations to quantify the impacts of prescribed burning on air quality in Southwestern Georgia, USA

Prescribed burning (PB) is a prominent source of PM_2.5 in the southeastern US and exposure to PB smoke is a health risk. As demand for burning increases and stricter controls are implemented for other anthropogenic sources, PB emissions tend to be responsible for an increasing fraction of PM_2.5 concentrations. Here, to quantify the effect of PB on air quality, low-cost sensors are used to measure PM_2.5 concentrations in Southwestern Georgia. The feasibility of using low-cost sensors as a supplemental measurement tool is evaluated by comparing them with reference instruments. A chemical transport model, CMAQ, is also used to simulate the contribution of PB to PM_2.5 concentrations. Simulated PM_2.5 concentrations are compared to observations from both low-cost sensors and reference monitors. Finally, a data fusion method is applied to generate hourly spatiotemporal exposure fields by fusing PM_2.5 concentrations from the CMAQ model and all observations. The results show that the severe impact of PB on local air quality and public health may be missed due to the dearth of regulatory monitoring sites. In Southwestern Georgia PM_2.5 concentrations are highly non-homogeneous and the spatial variation is not captured even with a 4-km horizontal resolution in model simulations. Low-cost PM sensors can improve the detection of PB impacts and provide useful spatial and temporal information for integration with air quality models. R² of regression with observations increases from an average of 0.09 to 0.40 when data fusion is applied to model simulation withholding the observations at the evaluation site. Adding observations from low-cost sensors reduces the underestimation of nighttime PM_2.5 concentrations and reproduces the peaks that are missed by the simulations. In the future, observations from a dense network of low-cost sensors could be fused with the model simulated PM_2.5 fields to provide better estimates of hourly exposures to smoke from PB.Implications: Prescribed burning emissions are a major cause of elevated PM_2.5 concentrations, posing a risk to human health. However, their impact cannot be quantified properly due to a dearth of regulatory monitoring sites in certain regions of the United States such as Southwestern Georgia. Low-cost PM sensors can be used as a supplemental measurement tool and provide useful spatial and temporal information for integration with air quality model simulations. In the future, data from a dense network of low-cost sensors could be fused with model simulated PM_2.5 fields to provide improved estimates of hourly exposures to smoke from prescribed burning.

Impact of 4th of July Fireworks on Spatiotemporal PM2.5 Concentrations in California Based on the PurpleAir Sensor Network: Implications for Policy and Environmental Justice

Fireworks are often used in celebration, causing short term, extremely high particulate matter air pollution. In recent years, the rapid development and expansion of low-cost air quality sensors by companies such as PurpleAir has enabled an understanding of air pollution at a much higher spatiotemporal resolution compared to traditional monitoring networks. In this study, real-time PM2.5 measurements from 751 PurpleAir sensors operating from June to July in 2019 and 2020 were used to examine the impact of 4th of July fireworks on hourly and daily PM2.5 concentrations at the census tract and county levels in California. American Community Survey (ACS) and CalEnviroScreen 3.0 data were used to identify correlations between PM2.5 measurements and socioeconomic status (SES). A two-step method was implemented to assure the quality of raw PM2.5 sensor data and sensor calibration against co-located reference instruments. The results showed that over 67% and 81% of counties experienced immediate impacts related to fireworks in 2019 and 2020, respectively. Relative to 2019, the peak PM2.5 concentrations on July 4th and 5th 2020 were, on average, over 50% higher in California, likely due to the COVID-19-related increase in the use of household-level fireworks. This increase was most pronounced in southern counties, which tend to have less strict firework-related regulations and a greater use of illegal fireworks. Los Angeles County experienced the highest July 4th daily PM2.5 levels both in 2019 (29.9 µg·m-3) and 2020 (42.6 µg·m-3). Spatial hot spot analyses generally showed these southern counties (e.g., Los Angeles County) to be regional air pollution hotspots, whereas the opposite pattern was seen in the north (e.g., San Francisco). The results also showed PM2.5 peaks that were over two-times higher among communities with lower SES, higher minority group populations, and higher asthma rates. Our findings highlight the important role that policy and enforcement can play in reducing firework-related air pollution and protecting public health, as exemplified by southern California, where policy was more relaxed and air pollution was higher (especially in 2020 when the 4th of July coincided with the COVID-19-lockdown period), and in disadvantaged communities where disparities were greatest.

Indoor-Generated PM2.5 During COVID-19 Shutdowns Across California: Application of the PurpleAir Indoor-Outdoor Low-Cost Sensor Network

Although evidences showed an overall reduction in outdoor air pollution levels across the globe due to COVID-19-related lockdown, no comprehensive assessment was available for indoor air quality during the period of stay-at-home orders, despite that the residential indoor environment contributes most to personal exposures. We examined temporal and diurnal variations of indoor PM2.5 based on real-time measurements from 139 indoor-outdoor co-located low-cost PurpleAir sensor sets across California for pre-, during, and post-lockdown periods in 2020 and "business-as-usual" periods in 2019. A two-step method was implemented to systematically control the quality of raw sensor data and calibrate the sensor data against co-located reference instruments. During the lockdown period, 17-24% higher indoor PM2.5 concentrations were observed in comparison to those in the 2019 business-as-usual period. In residential sites, a clear peak in PM2.5 concentrations in the afternoon and elevated evening levels toping at roughly 10 μg·m-3 was observed, which reflects enhanced human activity during lunch and dinner time (i.e., cooking) and possibly more cleaning and indoor movement that increase particle generation and resuspension in homes. The contribution of indoor-generated PM2.5 to total indoor concentrations increased as high as 80% during and post-lockdown periods compared to before lockdown.

Characterizing outdoor infiltration and indoor contribution of PM2.5 with citizen-based low-cost monitoring data

Epidemiological research on the adverse health outcomes due to PM_2.5 exposure frequently relies on measurements from regulatory air quality monitors to provide ambient exposure estimates, whereas personal PM_2.5 exposure may deviate from ambient concentrations due to outdoor infiltration and contributions from indoor sources. Research in quantifying infiltration factors (F_inf), the fraction of outdoor PM_2.5 that infiltrates indoors, has been historically limited in space and time due to the high costs of monitor deployment and maintenance. Recently, the growth of openly accessible, citizen-based PM_2.5 measurements provides an unprecedented opportunity to characterize F_inf at large spatiotemporal scales. In this analysis, 91 consumer-grade PurpleAir indoor/outdoor monitor pairs were identified in California (41 residential houses and 50 public/commercial buildings) during a 20-month period with around 650000 h of paired PM_2.5 measurements. An empirical method was developed based on local polynomial regression to estimate site-specific F_inf. The estimated site-specific F_inf had a mean of 0.26 (25^th, 75^th percentiles: [0.15, 0.34]) with a mean bootstrap standard deviation of 0.04. The F_inf estimates were toward the lower end of those reported previously. A threshold of ambient PM_2.5 concentration, approximately 30 μg/m³, below which indoor sources contributed substantially to personal exposures, was also identified. The quantified relationship between indoor source contributions and ambient PM_2.5 concentrations could serve as a metric of exposure errors when using outdoor monitors as an exposure proxy (without considering indoor-generated PM_2.5), which may be of interest to epidemiological research. The proposed method can be generalized to larger geographical areas to better quantify PM_2.5 outdoor infiltration and personal exposure.

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.

Estimating hourly PM2.5 concentrations at the neighborhood scale using a low-cost air sensor network: A Los Angeles case study

Predicting PM_2.5 concentrations at a fine spatial and temporal resolution (i.e., neighborhood, hourly) is challenging. Recent growth in low cost sensor networks is providing increased spatial coverage of air quality data that can be used to supplement data provided by monitors of regulatory agencies. We developed an hourly, 500 × 500 m gridded PM_2.5 model that integrates PurpleAir low-cost air sensor network data for Los Angeles County. We developed a quality control scheme for PurpleAir data. We included spatially and temporally varying predictors in a random forest model with random oversampling of high concentrations to predict PM_2.5. The model achieved high prediction accuracy (10-fold cross-validation (CV) R² = 0.93, root mean squared error (RMSE) = 3.23 μg/m³; spatial CV R² = 0.88, spatial RMSE = 4.33 μg/m³; temporal CV R² = 0.90, temporal RMSE = 3.85 μg/m³). Our model was able to predict spatial and diurnal patterns in PM_2.5 on typical weekdays and weekends, as well as non-typical days, such as holidays and wildfire days. The model allows for far more precise estimates of PM_2.5 than existing methods based on few sensors. Taking advantage of low-cost PM_2.5 sensors, our hourly random forest model predictions can be combined with time-activity diaries in future studies, enabling geographically and temporally fine exposure estimation for specific population groups in studies of acute air pollution health effects and studies of environmental justice issues.

Wildfire smoke impacts respiratory health more than fine particles from other sources: observational evidence from Southern California

Wildfires are becoming more frequent and destructive in a changing climate. Fine particulate matter, PM_2.5, in wildfire smoke adversely impacts human health. Recent toxicological studies suggest that wildfire particulate matter may be more toxic than equal doses of ambient PM_2.5. Air quality regulations however assume that the toxicity of PM_2.5 does not vary across different sources of emission. Assessing whether PM_2.5 from wildfires is more or less harmful than PM_2.5 from other sources is a pressing public health concern. Here, we isolate the wildfire-specific PM_2.5 using a series of statistical approaches and exposure definitions. We found increases in respiratory hospitalizations ranging from 1.3 to up to 10% with a 10 μg m^-3 increase in wildfire-specific PM_2.5, compared to 0.67 to 1.3% associated with non-wildfire PM_2.5. Our conclusions point to the need for air quality policies to consider the variability in PM_2.5 impacts on human health according to the sources of emission.

The U.S. EPA wildland fire sensor challenge: Performance and evaluation of solver submitted multi-pollutant sensor systems

Wildland fires can emit substantial amounts of air pollution that may pose a risk to those in proximity (e.g., first responders, nearby residents) as well as downwind populations. Quickly deploying air pollution measurement capabilities in response to incidents has been limited to date by the cost, complexity of implementation, and measurement accuracy. Emerging technologies including miniaturized direct-reading sensors, compact microprocessors, and wireless data communications provide new opportunities to detect air pollution in real time. The U.S. Environmental Protection Agency (EPA) partnered with other U.S. federal agencies (CDC, NASA, NPS, NOAA, USFS) to sponsor the Wildland Fire Sensor Challenge. EPA and partnering organizations share the desire to advance wildland fire air measurement technology to be easier to deploy, suitable to use for high concentration events, and durable to withstand difficult field conditions, with the ability to report high time resolution data continuously and wirelessly. The Wildland Fire Sensor Challenge encouraged innovation worldwide to develop sensor prototypes capable of measuring fine particulate matter (PM2.5), carbon monoxide (CO), carbon dioxide (CO2), and ozone (O3) during wildfire episodes. The importance of using federal reference method (FRM) versus federal equivalent method (FEM) instruments to evaluate performance in biomass smoke is discussed. Ten solvers from three countries submitted sensor systems for evaluation as part of the challenge. The sensor evaluation results including sensor accuracy, precision, linearity, and operability are presented and discussed, and three challenge winners are announced. Raw solver submitted PM2.5 sensor accuracies of the winners ranged from ~22 to 32%, while smoke specific EPA regression calibrations improved the accuracies to ~75-83% demonstrating the potential of these systems in providing reasonable accuracies over conditions that are typical during wildland fire events.

Validating and Comparing Highly Resolved Commercial "Off the Shelf" PM Monitoring Sensors with Satellite Based Hybrid Models, for Improved Environmental Exposure Assessment

Particulate matter is a common health hazard, and under certain conditions, an ecological threat. While many studies were conducted in regard to air pollution and potential effects, this paper serves as a pilot scale investigation into the spatial and temporal variability of particulate matter (PM) pollution in arid urban environments in general, and Beer-Sheva, Israel as a case study. We explore the use of commercially off the shelf (COTS) sensors, which provide an economical solution for spatio-temporal measurements. We started with a comparison process against an A-grade meteorological station, where it was shown that under specific climatic conditions, a number of COTS sensors were able to produce robust agreement (mean R2=0.93, average SD=17.5). The second stage examined the COTS sensors that were proven accurate in a mobile measurement campaign. Finally, data collected was compared to a validated satellite prediction model. We present how these tests and COTS sensor-kits could then be used to further explain the continuity and dispersion of particulate matter in similar areas.

Review: Strategies for using satellite-based products in modeling PM2.5 and short-term pollution episodes

Short-term air pollution episodes motivate improved understanding of the association between air pollution and acute morbidity and mortality episodes, and triggers required mitigation plans. A variety of methods have been employed to estimate exposure to air pollution episodes, including GIS-based dispersion models, interpolation between sparse monitoring sites, land-use regression models, optimization models, line- or area-dispersion plume models, and models using information from imaging satellites, often including land-use and meteorological variables. There has been increasing use of satellite-borne aerosol products for assessing short-term air quality events. They provide better spatial coverage, but currently at the price of low temporal coverage and rather crude spatial resolution. This is a brief review on using satellite data for modeling short-term air quality and pollution events. The review can be pursued as a practical guide for modeling air quality with satellite-based products, as it includes important questions that should be considered in both the study design as well as the model development stages. Progress in this field is detailed and includes published models and their use in environmental and health studies. Both current and future satellite-borne capabilities are covered. It also provides links to access and download relevant datasets and some example R code for data processing and modeling.

Spatial and Temporal Distribution of PM2.5 Pollution over Northeastern Mexico: Application of MERRA-2 Reanalysis Datasets

Aerosol and meteorological remote sensing data could be used to assess the distribution of urban and regional fine particulate matter (PM2.5), especially in locations where there are few or no ground-based observations, such as Latin America. The objective of this study is to evaluate the ability of Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA-2) aerosol components to represent PM2.5 ground concentrations and to develop and validate an ensemble neural network (ENN) model that uses MERRA-2 aerosol and meteorology products to estimate the monthly average of PM2.5 ground concentrations in the Monterrey Metropolitan Area (MMA), which is the main urban area in Northeastern Mexico (NEM). The project involves the application of the ENN model to a regional domain that includes not only the MMA but also other municipalities in NEM in the period from January 2010 to December 2014. Aerosol optical depth (AOD), temperature, relative humidity, dust PM2.5, sea salt PM2.5, black carbon (BC), organic carbon (OC), and sulfate (SO42−) reanalysis data were identified as factors that significantly influenced PM2.5 concentrations. The ENN estimated a PM2.5 monthly mean of 25.62 μg m−3 during the entire period. The results of the comparison between the ENN and ground measurements were as follows: correlation coefficient R ~ 0.90; root mean square error = 1.81 μg m−3; mean absolute error = 1.31 μg m−3. Overall, the PM2.5 levels were higher in winter and spring. The highest PM2.5 levels were located in the MMA, which is the major source of air pollution throughout this area. The estimated data indicated that PM2.5 was not distributed uniformly throughout the region but varied both spatially and temporally. These results led to the conclusion that the magnitude of air pollution varies among seasons and regions, and it is correlated with meteorological factors. The methodology developed in this study could be used to identify new monitoring sites and address information gaps.

Wildfire Smoke Adjustment Factors for Low-Cost and Professional PM2.5 Monitors with Optical Sensors

Air quality monitors using low-cost optical PM2.5 sensors can track the dispersion of wildfire smoke; but quantitative hazard assessment requires a smoke-specific adjustment factor (AF). This study determined AFs for three professional-grade devices and four monitors with low-cost sensors based on measurements inside a well-ventilated lab impacted by the 2018 Camp Fire in California (USA). Using the Thermo TEOM-FDMS as reference, AFs of professional monitors were 0.85 for Grimm mini wide-range aerosol spectrometer, 0.25 for TSI DustTrak, and 0.53 for Thermo pDR1500; AFs for low-cost monitors were 0.59 for AirVisual Pro, 0.48 for PurpleAir Indoor, 0.46 for Air Quality Egg, and 0.60 for eLichens Indoor Air Quality Pro Station. We also compared public data from 53 PurpleAir PA-II monitors to 12 nearby regulatory monitoring stations impacted by Camp Fire smoke and devices near stations impacted by the Carr and Mendocino Complex Fires in California and the Pole Creek Fire in Utah. Camp Fire AFs varied by day and location, with median (interquartile) of 0.48 (0.44-0.53). Adjusted PA-II 4-h average data were generally within ±20% of PM2.5 reported by the monitoring stations. Adjustment improved the accuracy of Air Quality Index (AQI) hazard level reporting, e.g., from 14% to 84% correct in Sacramento during the Camp Fire.

Assessing the usefulness of dense sensor network for PM2.5 monitoring on an academic campus area

Low-cost sensors provide an opportunity to improve the spatial and temporal resolution of air quality measurements. Networks of such devices may complement the traditional air quality monitoring and provide some useful information about pollutants and their impact on health. This paper describes the network of 20 nodes for ambient PM_2.5 monitoring on a campus area of Wrocław University of Science and Technology (Wrocław, Poland). Sensor nodes were equipped with optical sensors PMS A003 (Plantower), which showed high reproducibility between units. The distribution of the sensor nodes was characterised by both high density (14 devices on the main campus area) and wide spread across the city (6 devices on peripheral campuses). During the measurement campaign, signals from sensor nodes were consistent with results from regulatory monitoring stations and sensor devices were capable of indicating elevated levels of PM_2.5 concentrations. A great advantage of this system was the ability to provide up-to-date air quality information to the public. Furthermore, air quality messaging was site-specific because of the observed differences in PM_2.5 concentrations. Data analysis was aimed at assessing variability between locations using Kendall's τ metric and assessing the statistical significance of the differences in measurement results from neighbouring sensor nodes using the Kolmogorov-Smirnov test. The analysis showed high importance of the nodes in the middle of the main campus and variations of signals from nodes on the peripheries. Differences in signals from sensors located in close proximity to each other were in some cases significant, but only for short-term averaged data. Nevertheless, highly visible variation in PM_2.5 signals was observed in the case of nodes arranged vertically on two buildings. PM_2.5 concentrations were even 2-4 times greater near the top parts of the buildings than near the ground. The effect of stratification of PM_2.5 levels was observed under conditions of temperature inversion.

An Integrated Toolbox for the Engagement of Citizens in the Monitoring of Water Ecosystems

The monitoring of water ecosystems requires consistent and accurate sensor measurements, usually provided from traditional in-situ environmental monitoring systems. Such infrastructure, however, is expensive, hard to maintain and available only in limited areas that had been affected by extreme phenomena and require continuous monitoring. Due to climate change, the monitoring of larger areas and extended water ecosystems is imperative, raising the question of whether this monitoring can be disengaged from the in-situ monitoring systems. Due to climate change and extreme weather phenomena, more citizens are affected by environmental issues and become aware of the need to contribute to their monitoring. As a result, they are willing to offer their time to support the collection of scientific data. Collecting such data from volunteers, with no technical knowledge and while using low-cost equipment such as smart phones and portable sensors, raises the question of data quality and consistency. We present here a novel integrated toolbox that can support the organization of crowd-sourcing activities, ensure the engagement of the participants, the data collection in a consistent way, enforce extensive data quality controls and provide to local authorities and scientists access to the collected information in a uniform way, through widely accepted standards.