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Earth systems models (ESMs), which can simulate the complex feedbacks between climate and fires, struggle to predict fires well for tropical rainforests. This study provides equations that predict historic carbon monoxide emissions from Amazon rainforest fires for 2003–2018, which could be implemented within ESMs' current structures. We also include equations to convert the predicted emissions to burned area. Regressions of varying mathematical forms are fitted to one or both of two fire CO emission inventories. Equation accuracy is scored on r2, bias of the mean prediction, and ratio of explained variances. We find that one equation is best for studying smoke consequences that scale approximately linearly with emissions, or for a fully coupled ESM with online meteorology. Compared to the deforestation fire equation in the Community Land Model ver. 4.5, this equation's linear-scale accuracies are higher for both emissions and burned area. A second equation, more accurate when evaluated on a log scale, may better support studies of certain health or cloud process consequences of fires. The most accurate recommended equation requires that meteorology be known before emissions are calculated. For all three equations, both deforestation rates and meteorological variables are key groups of predictors. Predictions nevertheless fail to reproduce most of the variation in emissions. The highest linear r2s for monthly and annual predictions are 0.30 and 0.41, respectively. The impossibility of simultaneously matching both emission inventories limits achievable fit. One key cause of the remaining unexplained variability appears to be noise inherent to pan-tropical data, especially meteorology. [ABSTRACT FROM AUTHOR]

Global modeling of aerosol‐particle number and size is important for understanding aerosol effects on Earth's climate and air quality. Fine‐resolution global models are desirable for representing nonlinear aerosol‐microphysical processes, their nonlinear interactions with dynamics and chemistry, and spatial heterogeneity. However, aerosol‐microphysical simulations are computationally demanding, which can limit the achievable global horizontal resolution. Here, we present the first coupling of the TwO‐Moment Aerosol Sectional (TOMAS) microphysics scheme with the High‐Performance configuration of the GEOS‐Chem model of atmospheric composition (GCHP), a coupling termed GCHP‐TOMAS. GCHP's architecture allows massively parallel GCHP‐TOMAS simulations including on the cloud, using hundreds of computing cores, faster runtimes, more memory, and finer global horizontal resolution (e.g., 25 km × 25 km, 7.8 × 105 model columns) versus the previous single‐node capability of GEOS‐Chem‐TOMAS (tens of cores, 200 km × 250 km, 1.3 × 104 model columns). GCHP‐TOMAS runtimes have near‐ideal scalability with computing‐core number. Simulated global‐mean number concentrations increase (dominated by free‐tropospheric over‐ocean sub‐10‐nm‐diameter particles) toward finer GCHP‐TOMAS horizontal resolution. Increasing the horizontal resolution from 200 km × 200–50 km × 50 km increases the global monthly mean free‐tropospheric total particle number by 18.5%, and over‐ocean sub‐10‐nm‐diameter particles by 39.8% at 4‐km altitude. With a cascade of contributing factors, free‐tropospheric particle‐precursor concentrations increase (32.6% at 4‐km altitude) with resolution, promoting new‐particle formation and growth that outweigh coagulation changes. These nonlinear effects have the potential to revise current understanding of processes controlling global aerosol number and aerosol impacts on Earth's climate and air quality. Plain Language Summary: Small particles in the air have important effects on Earth's climate and air quality. Representing the number and size of these particles in global models is challenging because their processes are complex. This factor has often limited global‐model horizontal resolution because fine global resolution models (e.g., 25 km × 25 km or smaller) generally ran too slowly but would be useful for representing details missed at traditional coarse resolution (e.g., 200 km × 250 km). We start with a detailed particle scheme that previously only ran at coarse global resolution because fine resolution would take too long. We present the initial use of this scheme in an updated model version, with a structure allowing a fast‐running, high‐memory model with fine resolution, by using hundreds to thousands of computer cores. In the updated structure, model speed increases with the number of cores used. We find that the total number of particles in the model is more with fine compared to coarse model resolution. These increases are most in Earth's remote regions and for particles which come from gas. Using fine model resolution globally when representing particles could change our understanding of how they impact Earth's climate and air quality. Key Points: We couple aerosol microphysics with GEOS‐Chem's High‐Performance configuration for fine (25 km × 25 km) global‐resolution capabilityGlobal‐mean aerosol number increases with model resolution, dominated by particles smaller than 10 nm in the over‐ocean free troposphereToward finer horizontal resolution, enhanced particle precursor loading in the free troposphere promotes particle formation and growth [ABSTRACT FROM AUTHOR]

Outdoor agricultural workers often work in harsh environmental conditions, including high temperatures and poor air quality. This paper studies how these factors impact worker productivity, which can have implications for worker health, well‐being, and income as well as farm payroll, production, and profitability. Our analysis uses 6 years of payroll records of harvesters on two large farms combined with pollution and weather monitor data from multiple sources. We address simultaneity issues by exploring pollution measurements from nearby upwind and downwind monitors and incorporating an alternative PM2.5 measure that better captures ambient or regional concentration. Across all specifications, results suggest that heightened concentrations of ground‐level ozone and PM2.5 are associated with reduced productivity. In our main specification, we find that one standard deviation increases in ozone and PM2.5 are associated with reductions in productivity of 2% and 1.1%, respectively. [ABSTRACT FROM AUTHOR]

In the Flint Hills region of eastern Kansas, there is a long tradition of spring prescribed burns. However, air quality concerns in downwind communities have sparked conversation regarding the environmental and social impacts of these burns. This study aimed to identify the risk perceptions associated with prescribed burns using two theoretical frameworks: the social amplification of risk framework and the protective action decision model. In April 2022, we conducted 18 in-depth, semi-structured interviews with Flint Hills community members from different social stations. Participants identified several benefits of prescribed burns: cattle production gains, invasive species management, prairie ecological health maintenance, and wildfire prevention. Some participants viewed prescribed burning as a protective response. However, risk perceptions differed between rural and urban participants: rural community members were primarily concerned with prescribed fires that went out of control, while those in downwind cities were primarily concerned with smoke exposure. Participants sometimes used protective actions to mitigate their risks, but also explained the complexities of changing burn practices that are integral to the local culture, economies, and greater society. Additionally, formal communication of health and safety risks from prescribed burns is not uniform across Kansas counties. We therefore recommend systematic county and statewide communication of burn practices and protective behaviors. Understanding community perceptions of the risks and effects of prescribed burns, and any protective actions taken, can inform how professional communicators approach burning in similar agricultural and ranching communities. [ABSTRACT FROM AUTHOR]

Aerosol optical depth (AOD) is used to characterize aerosol loadings within Earth's atmosphere. Sun photometers measure AOD from the Earth's surface based on direct-sunlight intensity readings by spectrally narrow light detectors. However, when the solar disk is partially obscured by cloud cover, sun photometer measurements can be biased due to the interaction of sunlight with cloud constituents. We present a novel deep transfer learning model on all-sky images to support more accurate AOD retrievals. We used three independent image datasets for training and testing: the novel Northern Colorado All-Sky Image (NCASI), the Whole Sky Image SEGmentation (WSISEG), and the METCRAX-II datasets from the National Center for Atmospheric Research (NCAR). We visually partitioned all-sky images into three categories: 1) clear sky around the solar disk, 2) thin cirrus obstructing the solar disk, and 3) thick, non-cirrus clouds obstructing the solar disk. Two-thirds of the images were allocated for training and one-third were allocated for testing. We trained models based on all possible combinations of the training sets. The best-performing model successfully classified 95.5%, 96.9%, and 89.1% of testing images from NCASI, METCRAX-II and WSISEG datasets, respectively. Our results demonstrate that all-sky imaging with deep transfer learning can be applied toward cloud screening, which would aid ground-based AOD measurements. [ABSTRACT FROM AUTHOR]

Accurate sub-city fine particulate matter (PM2.5) estimates could improve epidemiological and health-impact studies in cities with heterogeneous distributions of PM2.5, yet most cities globally lack the monitoring density necessary for sub-city-scale estimates. To estimate spatiotemporal variability in PM2.5, we use machine learning (Random Forests; RFs) and concurrent PM2.5 and AOD measurements from the Citizen Enabled Aerosol Measurements for Satellites (CEAMS) low-cost sensor network as well as PM2.5 measurements from the Environmental Protection Agency's (EPA) reference monitors during wintertime in Denver, CO, USA. The RFs predicted PM2.5 in a 5-fold cross validation (CV) with relatively high skill (95% confidence interval R2=0.74-0.84 for CEAMS; R2=0.68-0.75 for EPA) though the models were aided by the spatiotemporal autocorrelation of the PM2.5 measurements. We found that the most important predictors of PM2.5 were factors associated with pooling of pollution in wintertime, such as low planetary boundary layer heights (PBLH), stagnant wind conditions, and, to a lesser degree, elevation. In general, spatial predictors were less important than spatiotemporal predictors because temporal variability exceeded spatial variability in our dataset. Finally, although concurrent AOD was an important predictor in our RF model for hourly PM2.5, it did not improve model performance with high statistical significance. Regardless, we found that low-cost PM2.5 measurements incorporated into an RF model were useful in interpreting meteorological and geographic drivers of PM2.5 over wintertime Denver. We also explored how the RF model performance and interpretation changes based on different model configurations and data processing. [ABSTRACT FROM AUTHOR]

As anthropogenic emissions continue to decline and emissions from landscape (wild, prescribed, and agricultural) fires increase across the coming century, the relative importance of landscape‐fire smoke on air quality and health in the United States (US) will increase. Landscape fires are a large source of fine particulate matter (PM2.5), which has known negative impacts on human health. The seasonal and spatial distribution, particle composition, and co‐emitted species in landscape‐fire emissions are different from anthropogenic sources of PM2.5. The implications of landscape‐fire emissions on the sub‐national temporal and spatial distribution of health events and the relative health importance of specific pollutants within smoke are not well understood. We use a health impact assessment with observation‐based smoke PM2.5 to determine the sub‐national distribution of mortality and the sub‐national and sub‐annual distribution of asthma morbidity attributable to US smoke PM2.5 from 2006 to 2018. We estimate disability‐adjusted life years (DALYs) for PM2.5 and 18 gas‐phase hazardous air pollutants (HAPs) in smoke. Although the majority of large landscape fires occur in the western US, we find the majority of mortality (74%) and asthma morbidity (on average 75% across 2006–2018) attributable to smoke PM2.5 occurs outside the West, due to higher population density in the East. Across the US, smoke‐attributable asthma morbidity predominantly occurs in spring and summer. The number of DALYs associated with smoke PM2.5 is approximately three orders of magnitude higher than DALYs associated with gas‐phase smoke HAPs. Our results indicate awareness and mitigation of landscape‐fire smoke exposure is important across the US. Plain Language Summary: The pollutants from landscape (wild, prescribed, and agricultural) fires are expected to have an increasing impact on air quality and health in the United States (US) across the current century. The implications of landscape‐fire smoke on the regional and seasonal distribution of health events and the relative health importance of specific pollutants within smoke are not well understood. In the present study, we assess the seasonal and regional distribution of the health impacts from US smoke exposure from 2006 to 2018. We also estimate the long‐term health impacts for both fine particles (PM2.5) and gas‐phase hazardous air pollutants (HAPs) in smoke. Although the majority of large landscape fires occur in the western US, we find the majority of deaths (74%) and asthma emergency department visits and hospital admissions (on average 75% across 2006–2018) attributable to smoke occur outside the West. Across the US, smoke‐attributable asthma emergency department visits predominantly occur in spring and summer. The long‐term health impacts associated with smoke PM2.5 are much higher than the estimated long‐term health impacts of gas‐phase smoke HAPs. Our results indicate awareness and mitigation of landscape‐fire smoke exposure is important across the US, not just in regions in proximity to large wildfires. Key Points: While the majority of large fires occur in the United States (US) West, a majority of smoke‐attributable US mortality and morbidity occur east of ∼100 degWA higher percent of mortality and morbidity is attributable to smoke in high fire‐impacted northwestern states, relative to other US statesDisability‐adjusted life years attributable to fine particles in smoke are much higher than that from gas‐phase hazardous air pollutants [ABSTRACT FROM AUTHOR]

Wildfire smoke is a growing public health concern in the United States. Numerous studies have documented associations between ambient smoke exposure and severe patient outcomes for single‐fire seasons or limited geographic regions. However, there are few national‐scale health studies of wildfire smoke in the United States, few studies investigating Intensive Care Unit (ICU) admissions as an outcome, and few specifically framed around hospital operations. This study retrospectively examined the associations between ambient wildfire‐related PM2.5 at a hospital ZIP code with total hospital ICU admissions using a national‐scale hospitalization data set. Wildfire smoke was characterized using a combination of kriged PM2.5 monitor observations and satellite‐derived plume polygons from National Oceanic and Atmospheric Administration's Hazard Mapping System. ICU admissions data were acquired from Premier, Inc. and encompass 15%–20% of all U.S. ICU admissions during the study period. Associations were estimated using a distributed‐lag conditional Poisson model under a time‐stratified case‐crossover design. We found that a 10 μg/m3 increase in daily wildfire PM2.5 was associated with a 2.7% (95% CI: 1.3, 4.1; p = 0.00018) increase in ICU admissions 5 days later. Under stratification, positive associations were found among patients aged 0–20 and 60+, patients living in the Midwest Census Region, patients admitted in the years 2013–2015, and non‐Black patients, though other results were mixed. Following a simulated severe 7‐day 120 μg/m3 smoke event, our results predict ICU bed utilization peaking at 131% (95% CI: 43, 239; p < 10−5) over baseline. Our work suggests that hospitals may need to preposition vital critical care resources when severe smoke events are forecast. Plain Language Summary: Wildfire smoke negatively affects people's health. Heavy smoke has been linked to higher rates of hospital admissions, emergency room, admissions, and death. However, we do not know the impact of smoke on Intensive Care Unit (ICU) admissions or on limited hospital resources like ICU beds. To fill this knowledge gap, we linked hospital ICU admissions to smoke levels near those hospitals. We also predicted how many ICU admissions would occur during a simulated severe week‐long smoke event and how many ICU beds would be needed to care for the patients. We found that the link between smoke and ICU admissions was relatively modest, but a severe smoke event could more than double the number of ICU beds needed. Key Points: Associations between smoke PM2.5 and ICU admissions were estimated using a national‐scale cohortSmoke PM2.5 was associated with modest changes in ICU admissionsA simulated smoke wave was found to impose severe strain on ICU resources [ABSTRACT FROM AUTHOR]

Globally, fine particulate matter (PM2.5) air pollution is a leading contributor to death, disease, and environmental degradation. Satellite-based measurements of aerosol optical depth (AOD) are used to estimate PM2.5 concentrations across the world, but the relationship between satellite-estimated AOD and ground-level PM2.5 is uncertain. Sun photometers measure AOD from the Earth's surface and are often used to improve satellite data; however, reference-grade photometers and PM2.5 monitors are expensive and rarely co-located. This work presents the development and validation of the Aerosol Mass and Optical Depth (AMOD) sampler, an inexpensive and compact device that simultaneously measures PM2.5 mass and AOD and was designed specifically to be used in citizen science campaigns. The AMOD utilizes a low-cost light-scattering sensor in combination with a gravimetric filter measurement to quantify ground-level PM2.5. Aerosol optical depth is measured using optically filtered photodiodes at four discrete wavelengths. Field validation studies revealed agreement within 10% for AOD values measured between co-located AMOD and AErosol RObotics NETwork (AERONET) monitors and for PM2.5 mass measured between co-located AMOD and EPA Federal Equivalent Method (FEM) monitors. These results demonstrate that the AMOD can quantify AOD and PM2.5 accurately at a fraction of the cost of existing reference monitors. [ABSTRACT FROM AUTHOR]

Exposure to wildland-fire smoke is associated with negative effects on human health. However, these effects are poorly quantified. Accurately attributing health endpoints to wildland-fire smoke requires determining the locations, concentrations, and durations of smoke events. Most current methods for determining these smoke-event properties (ground-based measurements, satellite observations, and chemical-transport modeling) are limited temporally, spatially, and/or by their level of accuracy. In this work, we explore using social-media posts regarding smoke, haze, and air quality from Facebook to determine population-level exposure for the summer of 2015 in the western US. We compare this de-identified, aggregated Facebook data to several other datasets that are commonly used for estimating exposure, such as satellite observations (MODIS aerosol optical depth and Hazard Mapping System smoke plumes), surface particulate-matter measurements, and model (WRF-Chem) simulated surface concentrations. After adding population-weighted spatial smoothing to the Facebook data, this dataset is well-correlated (R2 generally above 0.5) with these other methods in smoke-impacted regions. Removing days with considerable cloud coverage further improves correlations of Facebook data to traditional exposure datasets, which implies that the population is less aware of smoke on cloudy days relative to sunny days. The Facebook dataset is better correlated with surface measurements of PM2.5 at a majority of monitoring sites (163 of 293 sites) than the satellite observations and our model simulation are. We also present an example case for Washington state in 2015, where we combine this Facebook dataset with MODIS observations and WRF-Chem simulated PM2.5 in a regression model. We show that the addition of the Facebook data improves the regression model's ability to predict surface concentrations. This high correlation of the Facebook data with surface monitors and our Washington state example suggests that this social-media-based proxy can be used to estimate smoke exposure in locations without direct ground-based particulate-matter measurements. [ABSTRACT FROM AUTHOR]