Estimating Wildfire Smoke Concentrations during the October 2017 California Fires through BME Space/Time Data Fusion of Observed, Modeled, and Satellite-Derived PM2.5

Wildland fire smoke adds to disproportionate PM2.5 exposure in the United States

Wildland fire (i.e., prescribed fire and wildfire) smoke exposure is an emerging public health threat, in part due to climate change. Previous research has demonstrated disparities in ambient fine particulate matter (PM2.5) exposure, with Black people, among others, exposed to higher concentrations; yet it remains unclear how wildland fire smoke may contribute to additional disproportionate exposure. Here, we investigate the additional PM2.5 burden contributed by wildland fire smoke in the contiguous United States by race and ethnicity, urbanicity, median household income, and language spoken at home, using modeled total, non-fire, and fire PM2.5 concentrations from 2007 to 2018. Wildland fires contributed 7% to 14% of total population weighted PM2.5 concentrations annually, while non-fire PM2.5 concentrations declined by 24% over the study period. Wildland fires contributed to greater PM2.5 exposure for Black and American Indian or Alaska Native people, and those who live in non-urban areas. Disproportionate mean non-fire PM2.5 concentrations for Black people (9.1 μg/m3, compared to 8.7 μg/m3 overall) were estimated to be further exacerbated by additional disproportionate concentrations from fires (1.0 μg/m3 , compared to 0.9 μg/m3 overall). These results can inform equitable strategies by public health agencies and air quality managers to reduce smoke exposure in the United States.

Evaluation of Bayesian Maximum Entropy Data Fusion Approaches to Estimate Styrene, Benzene, Toluene, Ethylbenzene, and Xylenes and to Inform Epidemiological Analyses in the US Gulf States

The Gulf States are home to industries emitting styrene, benzene, toluene, ethylbenzene, and xylenes (SBTEX). Presently, adverse health effects of ambient SBTEX exposure in highly polluted regions, such as the Gulf States, must be evaluated. Epidemiologists, however, are limited by inadequate estimates of ambient SBTEX. Using Bayesian Maximum Entropy, SBTEX estimation methods of varying resource intensity were evaluated, including simple kriging (least intense), incorporation of observational and emissions data trends (moderately intense), and data fusion of observed and Comprehensive Air quality Model with extensions (CAMx) data (most intense). Generally, as resource intensity increased, so did SBTEX estimation performance, where SBTEX Spearman R values increased by 0.48 on average from the least to most intense methods. Data fusion of observed and CAMx data was identified as the best ambient SBTEX estimation method in the Gulf States. Exposure estimates revealed that Gulf States residences within commuting distance of high industrial activity experienced 1.64 times higher 97.5th percentile daily exposures to SBTEX on average than those living in less industrialized areas, which could contribute to total occupational and ambient exposure disparities. Furthermore, ambient benzene exposure was greater than the acceptable one-in-a-million excess cancer risk threshold for 75% of estimated residence locations in the Gulf States.

Evaluating Chemical Transport and Machine Learning Models for Wildfire Smoke PM2.5: Implications for Assessment of Health Impacts

Growing wildfire smoke represents a substantial threat to air quality and human health. However, the impact of wildfire smoke on human health remains imprecisely understood due to uncertainties in both the measurement of exposure of population to wildfire smoke and dose-response functions linking exposure to health. Here, we compare daily wildfire smoke-related surface fine particulate matter (PM2.5) concentrations estimated using three approaches, including two chemical transport models (CTMs): GEOS-Chem and the Community Multiscale Air Quality (CMAQ) and one machine learning (ML) model over the contiguous US in 2020, a historically active fire year. In the western US, compared against surface PM2.5 measurements from the US Environmental Protection Agency (EPA) and PurpleAir sensors, we find that CTMs overestimate PM2.5 concentrations during extreme smoke episodes by up to 3-5 fold, while ML estimates are largely consistent with surface measurements. However, in the eastern US, where smoke levels were much lower in 2020, CTMs show modestly better agreement with surface measurements. We develop a calibration framework that integrates CTM- and ML-based approaches to yield estimates of smoke PM2.5 concentrations that outperform individual approach. When combining the estimated smoke PM2.5 concentrations with county-level mortality rates, we find consistent effects of low-level smoke on mortality but large discrepancies in effects of high-level smoke exposure across different methods. Our research highlights the differences across estimation methods for understanding the health impacts of wildfire smoke and demonstrates the importance of bench-marking estimates with available surface measurements.

Fire Smoke Elevated the Carbonaceous PM2.5 Concentration and Mortality Burden in the Contiguous U.S. and Southern Canada

Despite emerging evidence on the health impacts of fine particulate matter (PM2.5) from wildland fire smoke, the specific effects of PM2.5 composition on health outcomes remain uncertain. We developed a three-level, chemical transport model-based framework to estimate daily full-coverage concentrations of smoke-derived carbonaceous PM2.5, specifically Organic Carbon (OC) and Elemental Carbon (EC), at a 1 km2 spatial resolution from 2002 to 2019 across the contiguous U.S. (CONUS) and Southern Canada (SC). Cross-validation demonstrated that the framework performed well at both the daily and monthly levels. Modeling results indicated that increases in wildland fire smoke have offset approximately one-third of the improvements in background air quality. In recent years, wildland fire smoke has become more frequent and carbonaceous PM2.5 concentrations have intensified, especially in the Western CONUS and Southwestern Canada. Smoke exposure is also occurring earlier throughout the year, leading to more population being exposed. We estimated that long-term exposure to fire smoke carbonaceous PM2.5 is responsible for 7,462 and 259 non-accidental deaths annually in the CONUS and SC, respectively, with associated annual monetized damage of 68.4 billion USD for the CONUS and 1.97 billion CAD for SC. The Southeastern CONUS, where prescribed fires are prevalent, contributed most to these health impacts and monetized damages. Given the challenges posed by climate change for managing prescribed and wildland fires, our findings offer critical insights to inform policy development and assess future health burdens associated with fire smoke exposure.

Estimated Impacts of Prescribed Fires on Air Quality and Premature Deaths in Georgia and Surrounding Areas in the US, 2015-2020

Smoke from wildfires poses a substantial threat to health in communities near and far. To mitigate the extent and potential damage of wildfires, prescribed burning techniques are commonly employed as land management tools; however, they introduce their own smoke-related risks. This study investigates the impact of prescribed fires on daily average PM2.5 and maximum daily 8-h averaged O3 (MDA8-O3) concentrations and estimates premature deaths associated with short-term exposure to prescribed fire PM2.5 and MDA8-O3 in Georgia and surrounding areas of the Southeastern US from 2015 to 2020. Our findings indicate that over the study domain, prescribed fire contributes to average daily PM2.5 by 0.94 ± 1.45 μg/m3 (mean ± standard deviation), accounting for 14.0% of year-round ambient PM2.5. Higher average daily contributions were predicted during the extensive burning season (January-April): 1.43 ± 1.97 μg/m3 (20.0% of ambient PM2.5). Additionally, prescribed burning is also responsible for an annual average increase of 0.36 ± 0.61 ppb in MDA8-O3 (approximately 0.8% of ambient MDA8-O3) and 1.3% (0.62 ± 0.88 ppb) during the extensive burning season. We estimate that short-term exposure to prescribed fire PM2.5 and MDA8-O3 could have caused 2665 (95% confidence interval (CI): 2249-3080) and 233 (95% CI: 148-317) excess deaths, respectively. These results suggest that smoke from prescribed burns increases the mortality. However, refraining from such burns may escalate the risk of wildfires; therefore, the trade-offs between the health impacts of wildfires and prescribed fires, including morbidity, need to be taken into consideration in future studies.

A model for rapid PM2.5 exposure estimates in wildfire conditions using routinely available data: rapidfire v0.1.3

Urban smoke exposure events from large wildfires have become increasingly common in California and throughout the western United States. The ability to study the impacts of high smoke aerosol exposures from these events on the public is limited by the availability of high-quality, spatially resolved estimates of aerosol concentrations. Methods for assigning aerosol exposure often employ multiple data sets that are time-consuming to create and difficult to reproduce. As these events have gone from occasional to nearly annual in frequency, the need for rapid smoke exposure assessments has increased. The rapidfire (relatively accurate particulate information derived from inputs retrieved easily) R package (version 0.1.3) provides a suite of tools for developing exposure assignments using data sets that are routinely generated and publicly available within a month of the event. Specifically, rapidfire harvests official air quality monitoring, satellite observations, meteorological modeling, operational predictive smoke modeling, and low-cost sensor networks. A machine learning approach, random forest (RF) regression, is used to fuse the different data sets. Using rapidfire, we produced estimates of ground-level 24 h average particulate matter for several large wildfire smoke events in California from 2017-2021. These estimates show excellent agreement with independent measures from filter-based networks.

Health burden evaluation of industrial parks caused by PM2.5 pollution at city scale

Industrial park is an important emission sector of PM2.5 pollution. Previous studies have provided valuable information on the impact of PM2.5 from industrial parks on human health, but relevant studies at city scale are limited. In this study, the health burden of industrial parks was evaluated based on PM2.5-related premature deaths and economic contributions. The premature deaths were calculated in terms of a novel research model by integrating the Bayesian maximum entropy (BME) model, weighted concentration-weighted trajectory (WCWT), and integrated exposure-response function (IER). Take Tianjin City for example, it was found that since the main diffusion direction of PM2.5 in Tianjin is from south to north, the industrial parks in the south of Tianjin and close to the central city with high population density have high health burden. These industrial parks need to be focused on or even relocated in the future. The research model can provide scientific basis for the health burden evaluation of industrial parks at city scale, so as to help local governments optimize the layout of industrial parks and formulate environmental responsibility management policies for industrial parks.

Evaluation of Model-Based PM2.5 Estimates for Exposure Assessment during Wildfire Smoke Episodes in the Western U.S

Investigating the health impacts of wildfire smoke requires data on people's exposure to fine particulate matter (PM2.5) across space and time. In recent years, it has become common to use machine learning models to fill gaps in monitoring data. However, it remains unclear how well these models are able to capture spikes in PM2.5 during and across wildfire events. Here, we evaluate the accuracy of two sets of high-coverage and high-resolution machine learning-derived PM2.5 data sets created by Di et al. and Reid et al. In general, the Reid estimates are more accurate than the Di estimates when compared to independent validation data from mobile smoke monitors deployed by the US Forest Service. However, both models tend to severely under-predict PM2.5 on high-pollution days. Our findings complement other recent studies calling for increased air pollution monitoring in the western US and support the inclusion of wildfire-specific monitoring observations and predictor variables in model-based estimates of PM2.5. Lastly, we call for more rigorous error quantification of machine-learning derived exposure data sets, with special attention to extreme events.

The impact of fire-specific PM2.5 calibration on health effect analyses

The quantification of PM_2.5 concentrations solely stemming from both wildfire and prescribed burns (hereafter referred to as 'fire') is viable using the Community Multiscale Air Quality (CMAQ), although CMAQ outputs are subject to biases and uncertainties. To reduce the biases in CMAQ-based outputs, we propose a two-stage calibration strategy that improves the accuracy of CMAQ-based fire PM_2.5 estimates. First, we calibrated CMAQ-based non-fire PM_2.5 to ground PM_2.5 observations retrieved during non-fire days using an ensemble-based model. We estimated fire PM_2.5 concentrations in the second stage by multiplying the calibrated non-fire PM_2.5 obtained from the first stage by location- and time-specific conversion ratios. In a case study, we estimated fire PM_2.5 during the Washington 2016 fire season using the proposed calibration approach. The calibrated PM_2.5 better agreed with ground PM_2.5 observations with a 10-fold cross-validated (CV) R² of 0.79 compared to CMAQ-based PM_2.5 estimates with R² of 0.12. In the health effect analysis, we found significant associations between calibrated fire PM_2.5 and cardio-respiratory hospitalizations across the fire season: relative risk (RR) for cardiovascular disease = 1.074, 95% confidence interval (CI) = 1.021-1.130 in October; RR = 1.191, 95% CI = 1.099-1.291 in November; RR for respiratory disease = 1.078, 95% CI = 1.005-1.157 in October; RR = 1.153, 95% CI = 1.045-1.272 in November. However, the results were inconsistent when non-calibrated PM_2.5 was used in the analysis. We found that calibration affected health effect assessments in the present study, but further research is needed to confirm our findings.

A geospatial modeling approach to quantifying the risk of exposure to environmental chemical mixtures via a common molecular target

In the real world, individuals are exposed to chemicals from sources that vary over space and time. However, traditional risk assessments based on in vivo animal studies typically use a chemical-by-chemical approach and apical disease endpoints. New approach methodologies (NAMs) in toxicology, such as in vitro high-throughput (HTS) assays generated in Tox21 and ToxCast, can more readily provide mechanistic chemical hazard information for chemicals with no existing data than in vivo methods. In this paper, we establish a workflow to assess the joint action of 41 modeled ambient chemical exposures in the air from the USA-wide National Air Toxics Assessment by integrating human exposures with hazard data from curated HTS (cHTS) assays to identify counties where exposure to the local chemical mixture may perturb a common biological target. We exemplify this proof-of-concept using CYP1A1 mRNA up-regulation. We first estimate internal exposure and then convert the inhaled concentration to a steady state plasma concentration using physiologically based toxicokinetic modeling parameterized with county-specific information on ages and body weights. We then use the estimated blood plasma concentration and the concentration-response curve from the in vitro cHTS assay to determine the chemical-specific effects of the mixture components. Three mixture modeling methods were used to estimate the joint effect from exposure to the chemical mixture on the activity levels, which were geospatially mapped. Finally, a Monte Carlo uncertainty analysis was performed to quantify the influence of each parameter on the combined effects. This workflow demonstrates how NAMs can be used to predict early-stage biological perturbations that can lead to adverse health outcomes that result from exposure to chemical mixtures. As a result, this work will advance mixture risk assessment and other early events in the effects of chemicals.

Progression of a large syphilis outbreak in rural North Carolina through space and time: Application of a Bayesian Maximum Entropy graphical user interface

In 2001, the primary and secondary syphilis incidence rate in rural Columbus County, North Carolina was the highest in the nation. To understand the development of syphilis outbreaks in rural areas, we developed and used the Bayesian Maximum Entropy Graphical User Interface (BMEGUI) to map syphilis incidence rates from 1999-2004 in seven adjacent counties in North Carolina. Using BMEGUI, incidence rate maps were constructed for two aggregation scales (ZIP code and census tract) with two approaches (Poisson and simple kriging). The BME maps revealed the outbreak was initially localized in Robeson County and possibly connected to more urban endemic cases in adjacent Cumberland County. The outbreak spread to rural Columbus County in a leapfrog pattern with the subsequent development of a visible low incidence spatial corridor linking Roberson County with the rural areas of Columbus County. Though the data are from the early 2000s, they remain pertinent, as the combination of spatial data with the extensive sexual network analyses, particularly in rural areas gives thorough insights which have not been replicated in the past two decades. These observations support an important role for the connection of micropolitan areas with neighboring rural areas in the spread of syphilis. Public health interventions focusing on urban and micropolitan areas may effectively limit syphilis indirectly in nearby rural areas.

Assessing Perception of Wildfires and Related Impacts among Adult Residents of Southern California

Major wildfires and their smoke pose a threat to public health and are becoming more frequent in the United States, particularly in California and other populated, fire-prone states. Therefore, it is crucial to understand how California residents view wildfires and engage in risk-reducing behaviors during wildfire events. Currently, there is a knowledge gap concerning this area of inquiry. We disseminated a 40-question cross-sectional survey to explore wildfire perception and knowledge along with related risk-reducing measures and policies among 807 adult residents in the fire-prone region of Orange County, California. Results demonstrated that nearly all (>95%) participants had (or knew someone who had) previously experienced a wildfire. Female gender, knowing a wildfire victim and reporting to have a general interest/passion for environmental issues were the three factors most strongly associated with (1) wildfires (and smoke) being reported as a threat, (2) participants' willingness to evacuate if threatened by a nearby wildfire, and (3) participants' willingness to support a wildfire-related tax increase (p < 0.05). The majority (57.4%) of participants agreed that the occurrence of wildfires is influenced by climate change, with the most commonly reported risk-reducing actions (by 44% of participants) being informational actions (e.g., tracking the news) rather than self-motivated physical safety actions (e.g., using an air purifier) (29%). The results of this study can help to inform decision- and policy-making regarding future wildfire events as well as allow more targeted and effective public health messaging and intervention measures, in turn helping to reduce the risk associated with future wildfire/smoke episodes.

Compound Risk of Air Pollution and Heat Days and the Influence of Wildfire by SES across California, 2018-2020: Implications for Environmental Justice in the Context of Climate Change

Major wildfires and heatwaves have begun to increase in frequency throughout much of the United States, particularly in western states such as California, causing increased risk to public health. Air pollution is exacerbated by both wildfires and warmer temperatures, thus adding to such risk. With climate change and the continued increase in global average temperatures, the frequency of major wildfires, heat days, and unhealthy air pollution episodes is projected to increase, resulting in the potential for compounding risks. Risks will likely vary by region and may disproportionately impact low-income communities and communities of color. In this study, we processed daily particulate matter (PM) data from over 18,000 low-cost PurpleAir sensors, along with gridMET daily maximum temperature data and government-compiled wildfire perimeter data from 2018-2020 in order to examine the occurrence of compound risk (CR) days (characterized by high temperature and high PM2.5) at the census tract level in California, and to understand how such days have been impacted by the occurrence of wildfires. Using American Community Survey data, we also examined the extent to which CR days were correlated with household income, race/ethnicity, education, and other socioeconomic factors at the census tract level. Results showed census tracts with a higher frequency of CR days to have statistically higher rates of poverty and unemployment, along with high proportions of child residents and households without computers. The frequency of CR days and elevated daily PM2.5 concentrations appeared to be strongly related to the occurrence of nearby wildfires, with over 20% of days with sensor-measured average PM2.5 > 35 μg/m3 showing a wildfire within a 100 km radius and over two-thirds of estimated CR days falling on such days with a nearby wildfire. Findings from this study are important to policymakers and government agencies who preside over the allocation of state resources as well as organizations seeking to empower residents and establish climate resilient communities.

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.

Development of a screening protocol to identify persons who are responsive to wood smoke particle-induced airway inflammation with pilot assessment of GSTM1 genotype and asthma status as response modifiers

BACKGROUND

We are currently screening human volunteers to determine their sputum polymorphonuclear neutrophil (PMN) response 6- and 24-hours following initiation of exposure to wood smoke particles (WSP). Inflammatory responders (≥10% increase in %PMN) are identified for their subsequent participation in mitigation studies against WSP-induced airways inflammation. In this report we compared responder status (<i>N</i> = 52) at both 6 and 24 hr time points to refine/expand its classification, assessed the impact of the GSTM1 genotype, asthma status and sex on responder status, and explored whether sputum soluble phase markers of inflammation correlate with PMN responsiveness to WSP.

RESULTS

Six-hour responders tended to be 24-hour responders and vice versa, but 24-hour responders also had significantly increased IL-1beta, IL-6, IL-8 at 24 hours post WSP exposure. The GSTM1 null genotype significantly (<i>p</i> &lt; 0.05) enhanced the %PMN response by 24% in the 24-hour responders and not at all in the 6 hours responders. Asthma status enhanced the 24 hour %PMN response in the 6- and 24-hour responders. In the entire cohort (not stratified by responder status), we found a significant, but very small decrease in FVC and systolic blood pressure immediately following WSP exposure and sputum %PMNs were significantly increased and associated with sputum inflammatory markers (IL-1beta, IL-6, IL-8, and PMN/mg) at 24 but not 6 hours post exposure. Blood endpoints in the entire cohort showed a significant increase in %PMN and PMN/mg at 6 but not 24 hours. Sex had no effect on %PMN response.

CONCLUSIONS

The 24-hour time point was more informative than the 6-hour time point in optimally and expansively defining airway inflammatory responsiveness to WSP exposure. GSTM1 and asthma status are significant effect modifiers of this response. These study design and subject parameters should be considered before enrolling volunteers for proof-of-concept WSP mitigation studies.

Short-Term Exposure to Wildfire Smoke and PM2.5 and Cognitive Performance in a Brain-Training Game: A Longitudinal Study of U.S. Adults

BACKGROUND

There is increasing evidence that long-term exposure to fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] may adversely impact cognitive performance. Wildfire smoke is one of the biggest sources of PM2.5 and concentrations are likely to increase under climate change. However, little is known about how short-term exposure impacts cognitive function.

OBJECTIVES

We aimed to evaluate the associations between daily and subdaily (hourly) PM2.5 and wildfire smoke exposure and cognitive performance in adults.

METHODS

Scores from 20 plays of an attention-oriented brain-training game were obtained for 10,228 adults in the United States (U.S.). We estimated daily and hourly PM2.5 exposure through a data fusion of observations from multiple monitoring networks. Daily smoke exposure in the western U.S. was obtained from satellite-derived estimates of smoke plume density. We used a longitudinal repeated measures design with linear mixed effects models to test for associations between short-term exposure and attention score. Results were also stratified by age, gender, user behavior, and region.

RESULTS

Daily and subdaily PM2.5 were negatively associated with attention score. A 10 μg/m3 increase in PM2.5 in the 3 h prior to gameplay was associated with a 21.0 [95% confidence interval (CI): 3.3, 38.7]-point decrease in score. PM2.5 exposure over 20 plays accounted for an estimated average 3.7% (95% CI: 0.7%, 6.7%) reduction in final score. Associations were more pronounced in the wildfire-impacted western U.S. Medium and heavy smoke density were also negatively associated with score. Heavy smoke density the day prior to gameplay was associated with a 117.0 (95% CI: 1.7, 232.3)-point decrease in score relative to no smoke. Although differences between subgroups were not statistically significant, associations were most pronounced for younger (18-29 y), older (≥70y), habitual, and male users.

DISCUSSION

Our results indicate that PM2.5 and wildfire smoke were associated with reduced attention in adults within hours and days of exposure, but further research is needed to elucidate these relationships. https://doi.org/10.1289/EHP10498.

Use of Low-Cost Sensors to Characterize Occupational Exposure to PM2.5 Concentrations Inside an Industrial Facility in Santa Ana, CA: Results from a Worker- and Community-Led Pilot Study

PM2.5 is an air contaminant that has been widely associated with adverse respiratory and cardiovascular health, leading to increased hospital admissions and mortality. Following concerns reported by workers at an industrial facility located in Santa Ana, California, workers and community leaders collaborated with experts in the development of an air monitoring pilot study to measure PM2.5 concentrations to which employees and local residents are exposed during factory operating hours. To detect PM2.5, participants wore government-validated AtmoTube Pro personal air monitoring devices during three separate workdays (5 AM–1:30 PM) in August 2021. Results demonstrated a mean PM2.5 level inside the facility of 112.3 µg/m3, nearly seven-times greater than outdoors (17.3 µg/m3). Of the eight workers who wore personal indoor sampling devices, five showed measurements over 100 μg/m3. Welding-related activity inside the facility resulted in the greatest PM2.5 concentrations. This study demonstrates the utility of using low-cost air quality sensors combined with employee knowledge and participation for the investigation of workplace air pollution exposure as well as facilitation of greater health-related awareness, education, and empowerment among workers and community members. Results also underscore the need for basic measures of indoor air pollution control paired with ongoing air monitoring within the Santa Ana facility, and the importance of future air monitoring studies aimed at industrial facilities.

Estimating Associations Between Annual Concentrations of Particulate Matter and Mortality in the United States, Using Data Linkage and Bayesian Maximum Entropy

BACKGROUND

Exposure to fine particulate matter (PM2.5) is an established risk factor for human mortality. However, previous US studies have been limited to select cities or regions or to population subsets (e.g., older adults).

METHODS

Here, we demonstrate how to use the novel geostatistical method Bayesian maximum entropy to obtain estimates of PM2.5 concentrations in all contiguous US counties, 2000-2016. We then demonstrate how one could use these estimates in a traditional epidemiologic analysis examining the association between PM2.5 and rates of all-cause, cardiovascular, respiratory, and (as a negative control outcome) accidental mortality.

RESULTS

We estimated that, for a 1 log(μg/m3) increase in PM2.5 concentration, the conditional all-cause mortality incidence rate ratio (IRR) was 1.029 (95% confidence interval [CI]: 1.006, 1.053). This implies that the rate of all-cause mortality at 10 µg/m3 would be 1.020 times the rate at 5 µg/m3. IRRs were larger for cardiovascular mortality than for all-cause mortality in all gender and race-ethnicity groups. We observed larger IRRs for all-cause, nonaccidental, and respiratory mortality in Black non-Hispanic Americans than White non-Hispanic Americans. However, our negative control analysis indicated the possibility for unmeasured confounding.

CONCLUSION

We used a novel method that allowed us to estimate PM2.5 concentrations in all contiguous US counties and obtained estimates of the association between PM2.5 and mortality comparable to previous studies. Our analysis provides one example of how Bayesian maximum entropy could be used in epidemiologic analyses; future work could explore other ways to use this approach to inform important public health questions.

Environmental Nanoparticles Reach Human Fetal Brains

Anthropogenic ultrafine particulate matter (UFPM) and industrial and natural nanoparticles (NPs) are ubiquitous. Normal term, preeclamptic, and postconceptional weeks(PCW) 8–15 human placentas and brains from polluted Mexican cities were analyzed by TEM and energy-dispersive X-ray spectroscopy. We documented NPs in maternal erythrocytes, early syncytiotrophoblast, Hofbauer cells, and fetal endothelium (ECs). Fetal ECs exhibited caveolar NP activity and widespread erythroblast contact. Brain ECs displayed micropodial extensions reaching luminal NP-loaded erythroblasts. Neurons and primitive glia displayed nuclear, organelle, and cytoplasmic NPs in both singles and conglomerates. Nanoscale Fe, Ti, and Al alloys, Hg, Cu, Ca, Sn, and Si were detected in placentas and fetal brains. Preeclamptic fetal blood NP vesicles are prospective neonate UFPM exposure biomarkers. NPs are reaching brain tissues at the early developmental PCW 8–15 stage, and NPs in maternal and fetal placental tissue compartments strongly suggests the placental barrier is not limiting the access of environmental NPs. Erythroblasts are the main early NP carriers to fetal tissues. The passage of UFPM/NPs from mothers to fetuses is documented and fingerprinting placental single particle composition could be useful for postnatal risk assessments. Fetal brain combustion and industrial NPs raise medical concerns about prenatal and postnatal health, including neurological and neurodegenerative lifelong consequences.

Particulate Air Pollution and Risk of Neuropsychiatric Outcomes. What We Breathe, Swallow, and Put on Our Skin Matters

We appraise newly accumulated evidence of the impact of particle pollution on the brain, the portals of entry, the neural damage mechanisms, and ultimately the neurological and psychiatric outcomes statistically associated with exposures. PM pollution comes from natural and anthropogenic sources such as fossil fuel combustion, engineered nanoparticles (NP ≤ 100 nm), wildfires, and wood burning. We are all constantly exposed during normal daily activities to some level of particle pollution of various sizes-PM_2.5 (≤2.5 µm), ultrafine PM (UFP ≤ 100 nm), or NPs. Inhalation, ingestion, and dermal absorption are key portals of entry. Selected literature provides context for the US Environmental Protection Agency (US EPA) ambient air quality standards, the conclusions of an Independent Particulate Matter Review Panel, the importance of internal combustion emissions, and evidence suggesting UFPs/NPs cross biological barriers and reach the brain. NPs produce oxidative stress and neuroinflammation, neurovascular unit, mitochondrial, endoplasmic reticulum and DNA damage, protein aggregation and misfolding, and other effects. Exposure to ambient PM_2.5 concentrations at or below current US standards can increase the risk for TIAs, ischemic and hemorrhagic stroke, cognitive deficits, dementia, and Alzheimer's and Parkinson's diseases. Residing in a highly polluted megacity is associated with Alzheimer neuropathology hallmarks in 99.5% of residents between 11 months and ≤40 y. PD risk and aggravation are linked to air pollution and exposure to diesel exhaust increases ALS risk. Overall, the literature supports that particle pollution contributes to targeted neurological and psychiatric outcomes and highlights the complexity of the pathophysiologic mechanisms and the marked differences in pollution profiles inducing neural damage. Factors such as emission source intensity, genetics, nutrition, comorbidities, and others also play a role. PM_2.5 is a threat for neurological and psychiatric diseases. Thus, future research should address specifically the potential role of UFPs/NPs in inducing neural damage.

Satellite Monitoring for Air Quality and Health

Data from satellite instruments provide estimates of gas and particle levels relevant to human health, even pollutants invisible to the human eye. However, the successful interpretation of satellite data requires an understanding of how satellites relate to other data sources, as well as factors affecting their application to health challenges. Drawing from the expertise and experience of the 2016-2020 NASA HAQAST (Health and Air Quality Applied Sciences Team), we present a review of satellite data for air quality and health applications. We include a discussion of satellite data for epidemiological studies and health impact assessments, as well as the use of satellite data to evaluate air quality trends, support air quality regulation, characterize smoke from wildfires, and quantify emission sources. The primary advantage of satellite data compared to in situ measurements, e.g., from air quality monitoring stations, is their spatial coverage. Satellite data can reveal where pollution levels are highest around the world, how levels have changed over daily to decadal periods, and where pollutants are transported from urban to global scales. To date, air quality and health applications have primarily utilized satellite observations and satellite-derived products relevant to near-surface particulate matter <2.5 μm in diameter (PM_2.5) and nitrogen dioxide (NO₂). Health and air quality communities have grown increasingly engaged in the use of satellite data, and this trend is expected to continue. From health researchers to air quality managers, and from global applications to community impacts, satellite data are transforming the way air pollution exposure is evaluated.

Estimating the Acute Health Impacts of Fire-Originated PM2.5 Exposure During the 2017 California Wildfires: Sensitivity to Choices of Inputs

Exposure to wildfire smoke increases the risk of respiratory and cardiovascular hospital admissions. Health impact assessments, used to inform decision-making processes, characterize the health impacts of environmental exposures by combining preexisting epidemiological concentration-response functions (CRFs) with estimates of exposure. These two key inputs influence the magnitude and uncertainty of the health impacts estimated, but for wildfire-related impact assessments the extent of their impact is largely unknown. We first estimated the number of respiratory, cardiovascular, and asthma hospital admissions attributable to fire-originated PM2.5 exposure in central California during the October 2017 wildfires, using Monte Carlo simulations to quantify uncertainty with respect to the exposure and epidemiological inputs. We next conducted sensitivity analyses, comparing four estimates of fire-originated PM2.5 and two CRFs, wildfire and nonwildfire specific, to understand their impact on the estimation of excess admissions and sources of uncertainty. We estimate the fires accounted for an excess 240 (95% CI: 114, 404) respiratory, 68 (95% CI: -10, 159) cardiovascular, and 45 (95% CI: 18, 81) asthma hospital admissions, with 56% of admissions occurring in the Bay Area. Although differences between impact assessment methods are not statistically significant, the admissions estimates' magnitude is particularly sensitive to the CRF specified while the uncertainty is most sensitive to estimates of fire-originated PM2.5. Not accounting for the exposure surface's uncertainty leads to an underestimation of the uncertainty of the health impacts estimated. Employing context-specific CRFs and using accurate exposure estimates that combine multiple data sets generates more certain estimates of the acute health impacts of wildfires.

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.

A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires

Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8-20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke.Implications: Large wildfires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health.

Disproportionate Impacts of Wildfires among Elderly and Low-Income Communities in California from 2000-2020

Wildfires can be detrimental to urban and rural communities, causing impacts in the form of psychological stress, direct physical injury, and smoke-related morbidity and mortality. This study examined the area burned by wildfires over the entire state of California from the years 2000 to 2020 in order to quantify and identify whether burned area and fire frequency differed across Census tracts according to socioeconomic indicators over time. Wildfire data were obtained from the California Fire and Resource Assessment Program (FRAP) and National Interagency Fire Center (NIFC), while demographic data were obtained from the American Community Survey. Results showed a doubling in the number of Census tracts that experienced major wildfires and a near doubling in the number of people residing in wildfire-impacted Census tracts, mostly due to an over 23,000 acre/year increase in the area burned by wildfires over the last two decades. Census tracts with a higher fire frequency and burned area had lower proportions of minority groups on average. However, when considering Native American populations, a greater proportion resided in highly impacted Census tracts. Such Census tracts also had higher proportions of older residents. In general, high-impact Census tracts tended to have higher proportions of low-income residents and lower proportions of high-income residents, as well as lower median household incomes and home values. These findings are important to policymakers and state agencies as it relates to environmental justice and the allocation of resources before, during, and after wildfires in the state of California.