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Worsening wildfire seasons in recent years are reversing decadal progress on the reduction of harmful air pollutants in the US, particularly in Western states. Measurements of the contributions of wildfire smoke to ambient air pollutants, such as fine particulate matter (PM2.5), at fine resolution scales would be valuable to public health research on climate vulnerable populations and compound climate risks. We estimate the influence of wildfire smoke emissions on daily PM2.5 at fine-resolution, 3 km, for California 2011–2020, using a geostatistical modeled ambient PM2.5 estimate and wildfire smoke plume data from NOAA Hazard Mapping System. Additionally, we compare this product with the US Environmental Protection Agency (EPA) daily and annual standards for PM2.5 exposure. Our results show wildfires significantly influence PM2.5 in California and nearly all exceedances of the daily US EPA PM2.5 standard were influenced by wildfire smoke, while annual exceedances were increasingly attributed to wildfire smoke influence in recent years. This wildfire-influenced PM2.5 product can be applied to public health research to better understand source-specific air pollution impacts and assess the combination of multiple climate hazard risks. [ABSTRACT FROM AUTHOR]

Coupled fire‐atmosphere models often struggle to simulate important fire processes like fire generated flows, deep flaming fronts, extreme updrafts, and stratospheric smoke injection during large wildfires. This study uses the coupled fire‐atmosphere model, WRF‐Fire, to examine the sensitivities of some of these phenomena to the modeled total fuel load and its consumption. Specifically, the 2020 Bear Fire and 2021 Caldor Fire in California's Sierra Nevada are simulated using three fuel loading scenarios (1X, 4X, and 8X LANDFIRE derived surface fuel), while controlling the fire rate of spread using observations. This approach helps isolate the fuel loading and consumption needed to produce fire‐generated winds and plume rise comparable to radar observations of these events. Increasing fuel loads and corresponding fire residence time in WRF‐Fire leads to deep plumes in excess of 10 km, strong vertical velocities of 40–45 m s−1, and combustion fronts several kilometers in width (in the along wind direction). These results indicate that LANDFIRE‐based surface fuel loads in WRF‐Fire likely under‐represent fuel loading, having significant implications for simulating landscape‐scale wildfire processes, associated impacts on spread, and fire‐atmosphere feedbacks. Plain Language Summary: Coupled fire‐atmosphere models poorly depict fire‐generated winds and deep smoke plumes during extreme wildfires. In this study, the 2020 Bear Fire and 2021 Caldor Fire in California are simulated under various fuel scenarios. The simulations show that fuel characteristics used in the fire‐atmosphere model under‐represent observed conditions and thus, produce inadequate fire‐generated winds and plume characteristics. When the modeled fuels are augmented to match proxy observations of fuel characteristics, simulated fire‐atmosphere feedbacks better resemble fire generated winds and deep convective plumes seen in radar observations. The results of these simulations will help inform future improvements to coupled fire‐atmosphere models to better simulate large wildland fires. Key Points: Coupled fire‐atmosphere models struggle to simulate critical fire‐generated winds and plume rise during large wildland firesDeficient fire‐generated winds are linked to inadequate fuel loads and burnout timescale in the modelAdjustment of the fuel characteristics results in more realistic simulated plumes and fire‐generated winds [ABSTRACT FROM AUTHOR]

In December 2017, one of the largest wildfires in California history, the Thomas Fire, created a large smoke and ash plume that extended over the northeastern Pacific Ocean. Here, we explore the impact of Thomas Fire ash deposition on seawater chemistry and the growth and composition of natural microbial communities. Experiments conducted in coastal California waters during the Thomas Fire revealed that leaching of ash in seawater resulted in significant additions of dissolved nutrients including inorganic nitrogen (nitrate, nitrite and ammonium), silicic acid, metals (iron, nickel, cobalt and copper), organic nitrogen and organic carbon. After exposure to ash leachate at high (0.25 g ash l−1) and low (0.08 g ash l−1) concentrations for 4 days, natural microbial communities had 59–154% higher particulate organic carbon concentrations than communities without ash leachate additions. Additionally, a diverse assemblage of eukaryotic microbes (protists) responded to the ash leachate with taxa from 11 different taxonomic divisions increasing in relative abundance compared with control treatments. Our results suggest that large fire events can be important atmospheric sources of nutrients (particularly nitrogen) to coastal marine systems, where, through leaching of various nutrients, ash may act as a 'food for all' in protist communities. [ABSTRACT FROM AUTHOR]

The escalating frequency and severity of global wildfires necessitate an in-depth understanding and monitoring of wildfire smoke impacts, specifically its contribution to fine particulate matter (PM 2.5 ). We propose a data-fusion method to study wildfire contribution to PM 2.5 using satellite-derived smoke plume indicators and PM 2.5 monitoring data. Our study incorporates two types of monitoring data, the high-quality but sparse Air Quality System (AQS) stations and the abundant but less accurate PurpleAir (PA) sensors that are gaining popularity among citizen scientists. We propose a multi-resolution spatiotemporal model specified in the spectral domain to calibrate the PA sensors against accurate AQS measurements, and leverage the two networks to estimate wildfire contribution to PM 2.5 in California in 2020 and 2021. A Bayesian approach is taken to incorporate all uncertainties and our prior intuition that the dependence between networks, as well as the accuracy of PA network, vary by frequency. We find that 1% to 3% increase in PM 2.5 concentration due to wildfire smoke, and that leveraging PA sensors improves accuracy. [ABSTRACT FROM AUTHOR]

With global wildfires becoming more widespread and severe, tracking their emissions of greenhouse gases and air pollutants is becoming increasingly important. Wildfire emissions have primarily been characterized by in situ laboratory and field observations at fine scales. While this approach captures the mechanisms relating emissions to combustion phase and fuel properties, their evaluation on regional-scale plumes has been limited. In this study, we report remote observations of total column trace gases and aerosols during the 2020 wildfire season from smoke plumes in the Sierra Nevada of California with an EM27/SUN solar Fourier transform infrared (FTIR) spectrometer. We derive total column aerosol optical depth (AOD), emission factors (EFs) and modified combustion efficiency (MCE) for these fires and evaluate relationships between them, based on combustion phase at regional scales. We demonstrate that the EM27/SUN effectively detects changes in CO, CO 2 , and CH 4 in the atmospheric column at ∼10 km horizontal scales that are attributed to wildfire emissions. These observations are used to derive total column EF CO of 120.5±12.2 and EF CH4 of 4.3±0.8 for a regional smoke plume event in mixed combustion phases. These values are consistent with in situ relationships measured in similar temperate coniferous forest wildfires. FTIR-derived AOD was compared to a nearby AERONET (AErosol RObotic NETwork) station and observed ratios of X CO to AOD were consistent with those previously observed from satellites. We also show that co-located X CO observations from the TROPOspheric Monitoring Instrument (TROPOMI) satellite-based instrument are 9.7±1.3 % higher than our EM27/SUN observations during the wildfire period. Finally, we put wildfire CH 4 emissions in context of the California state CH 4 budget and estimate that 213.7±49.8 Gg CH 4 were emitted by large wildfires in California during 2020, about 13.7 % of the total state CH 4 emissions in 2020. Our work demonstrates a novel application of the ground-based EM27/SUN solar spectrometers in wildfire monitoring by integrating regional-scale measurements of trace gases and aerosols from smoke plumes. [ABSTRACT FROM AUTHOR]

Wildfires and their resulting smoke are an increasing problem in many regions of the world. However, identifying the contribution of smoke to pollutant loadings in urban regions can be challenging at low concentrations due to the presence of the usual array of anthropogenic pollutants. Here we propose a method using the difference in PM 2.5 to CO emission ratios between smoke and typical urban pollution. For temperate wildfires, the mean emission ratio of PM 2.5 to CO is in the range of 0.14–0.18 g PM 2.5 g CO -1 , whereas typical urban emissions have a PM 2.5 to CO emissions ratio that is lower by a factor of 2–20. This gives rise to the possibility of using this ratio as an indicator of wildfire smoke. We use observations at a regulatory surface monitoring site in Sparks, NV, for the period of May–September 2018–2021. There were many smoke-influenced periods from numerous California wildfires that burned during this period. Using a PM 2.5 / CO threshold of 30.0 µgm-3ppm-1 , we can split the observations into smoke-influenced and no-smoke periods. We then develop a Monte Carlo simulation, tuned to local conditions, to derive a set of PM 2.5 / CO values that can be used to identify smoke influence in urban areas. From the simulation, we find that a smoke enhancement ratio of 140 µgm-3ppm-1 best fits the observations, which is significantly lower than the ratio observed in fresh smoke plumes (e.g., 200–300 µgm-3ppm-1). The most likely explanation for this difference is loss of PM 2.5 during dilution and transport to warmer surface layers. We find that the PM 2.5 / CO ratio in urban areas is an excellent indicator of smoke and should prove to be useful to identify biomass burning influence on the policy-relevant concentrations of both PM 2.5 and O 3. Using the results of our Monte Carlo simulation, this ratio can also quantify the influence of smoke on urban PM 2.5. [ABSTRACT FROM AUTHOR]

Smoke from the 2018 Camp Fire in Northern California blanketed a large part of the region for 2 weeks, creating poor air quality in the "unhealthy" range for millions of people. The NOAA Global System Laboratory's HRRR-Smoke model was operating experimentally in real time during the Camp Fire. Here, output from the HRRR-Smoke model is compared to surface observations of PM2.5 from AQS and PurpleAir sensors as well as satellite observation data. The HRRR-Smoke model at 3-km resolution successfully simulated the evolution of the plume during the initial phase of the fire (8-10 November 2018). Stereoscopic satellite plume height retrievals were used to compare with model output (for the first time, to the authors' knowledge), showing that HRRR-Smoke is able to represent the complex 3D distribution of the smoke plume over complex terrain. On 15-16 November, HRRR-Smoke was able to capture the intensification of PM2.5 pollution due to a high pressure system and subsidence that trapped smoke close to the surface; however, HRRR-Smoke later underpredicted PM2.5 levels due to likely underestimates of the fire radiative power (FRP) derived from satellite observations. The intensity of the Camp Fire smoke event and the resulting pollution during the stagnation episodes make it an excellent test case for HRRR-Smoke in predicting PM2.5 levels, which were so high from this single fire event that the usual anthropogenic pollution sources became insignificant. The HRRR-Smoke model was implemented operationally at NOAA/NCEP in December 2020, now providing essential support for smoke forecasting as the impact of U.S. wildfires continues to increase in scope and magnitude. [ABSTRACT FROM AUTHOR]

Biomass burning is an important source of trace gases and particles, and can influence air quality on local, regional, and global scales. With the threat of wildfire events increasing due to changes in land use, increasing population, and climate change, the importance of characterizing wildfire emissions is vital. In this work we characterize trace gas emissions from 12 wildfires and 1 prescribed fire in California between 2013 and 2017, in some cases with multiple measurements performed during different burn periods of a specific fire. Airborne measurements of carbon dioxide, methane, ozone, formaldehyde, water vapor, temperature, and three‐dimensional winds were made by the Alpha Jet Atmospheric eXperiment and have been published at NASA's Atmospheric Science Data Center (https://doi.org/10.5067/ASDC/AJAX/wildfire, Iraci et al., 2021). The majority of these measurements were made as close as possible to each fire and represent fresh emissions from known fire sources. This set of observations from 13 different fires offers the opportunity to explore trace gas emissions over a range of meteorology, fire conditions, and to a lesser extent, vegetation type and drought, and adds to the body of knowledge collected by other investigators and field campaigns. Plain Language Summary: Biomass burning is an important source of trace gases and particles which can influence air quality on local, regional, and global scales. This set of airborne observations of trace pollutants and winds from 13 different fires broadens the range of fire types and conditions for which observations have been made near the top of visible smoke plumes. Calibrated and quality‐controlled observations of methane, formaldehyde, ozone, carbon dioxide, water vapor, and 3‐D winds near to a variety of active fires offer the opportunity to explore emissions over a range of meteorology, fire conditions, and to a lesser extent, vegetation type, and drought. This data set can also be an asset for bridging the gap of knowledge between models and in situ data collected by other investigators and field campaigns. Key Points: Trace gas observations near/around 12 wildfires and one prescribed fire (2013–2017) highlight variability of many fires over multiple yearsFive flights to Soberanes fire, providing trace gas and wind measurements over the lifetime of the megafireSeveral downwind legs and regional transits also included, making possible contributions to studies of human exposure at regional scales [ABSTRACT FROM AUTHOR]

Large wildfires influence regional atmospheric composition, but chemical complexity challenges model predictions of downwind impacts. Here, we elucidate key connections within gas-phase photochemistry and assess novel chemical processes via a case study of the 2013 California Rim Fire plume. Airborne in situ observations, acquired during the NASA Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC 4 RS) mission, illustrate the evolution of volatile organic compounds (VOCs), oxidants, and reactive nitrogen over 12 h of atmospheric aging. Measurements show rapid formation of ozone and peroxyacyl nitrates (PNs), sustained peroxide production, and prolonged enhancements in oxygenated VOCs and nitrogen oxides (NO x). Observations and Lagrangian trajectories constrain a 0-D puff model that approximates plume photochemical history and provides a framework for evaluating process interactions. Simulations examine the effects of (1) previously unmeasured reactive VOCs identified in recent laboratory studies and (2) emissions and secondary production of nitrous acid (HONO). Inclusion of estimated unmeasured VOCs leads to a 250 % increase in OH reactivity and a 70 % increase in radical production via oxygenated VOC photolysis. HONO amplifies radical cycling and serves as a downwind NO x source, although impacts depend on how HONO is introduced. The addition of initial HONO (representing primary emissions) or particulate nitrate photolysis amplifies ozone production, while heterogeneous conversion of NO 2 suppresses ozone formation. Analysis of radical initiation rates suggests that oxygenated VOC photolysis is a major radical source, exceeding HONO photolysis when averaged over the first 2 h of aging. Ozone production chemistry transitions from VOC sensitive to NO x sensitive within the first hour of plume aging, with both peroxide and organic nitrate formation contributing significantly to radical termination. To simulate smoke plume chemistry accurately, models should simultaneously account for the full reactive VOC pool and all relevant oxidant sources. [ABSTRACT FROM AUTHOR]

Air quality models are used to assess the impact of smoke from wildland fires, both prescribed and natural, on ambient air quality and human health. However, the accuracy of these models is limited by uncertainties in the parametrisation of smoke plume injection height (PIH) and its vertical distribution. We compared PIH estimates from the plume rise method (Briggs) in the Community Multiscale Air Quality (CMAQ) modelling system with observations from the 2013 California Rim Fire and 2017 prescribed burns in Kansas. We also examined PIHs estimated using alternative plume rise algorithms, model grid resolutions and temporal burn profiles. For the Rim Fire, the Briggs method performed as well or better than the alternatives evaluated (mean bias of less than ±5-20% and root mean square error lower than 1000mcompared with the alternatives). PIH estimates for the Kansas prescribed burns improved when the burn window was reduced from the standard default of 12 h to 3 h. This analysis suggests that meteorological inputs, temporal allocation and heat release are the primary drivers for accurately modelling PIH. [ABSTRACT FROM AUTHOR]

Remote sensing techniques have been used to study and track wildfire smoke plume structure and evolution; however, knowledge gaps remain because of the limited availability of observational datasets aimed at understanding finescale fire--atmosphere interactions and plume microphysics. Meteorological radars have been used to investigate the evolution of plume rise in time and space, but highly resolved plume observations are limited. In this study, we present a new mobile millimeter-wave (Ka band) Doppler radar system acquired to sample the fine-scale kinematics and microphysical properties of active wildfire smoke plumes from both wildfires and large prescribed fires. Four field deployments were conducted in autumn of 2019 during two wildfires in California and one prescribed burn in Utah. Radar parameters investigated in this study include reflectivity, radial velocity, Doppler spectrum width, differential reflectivity ZDR, and copolarized correlation coefficient ρHV. Observed radar reflectivity ranged between 215 and 20 dBZ in plume, and radial velocity ranged from 0 to 16m s-1. Dual-polarimetric observations revealed that scattering sources within wildfire plumes are primarily nonspherical and oblate-shaped targets as indicated by ZDR values measuring above 0 and rHV values below 0.8 within the plume. Doppler spectrum width maxima were located near the updraft core region and were associated with radar reflectivity maxima. [ABSTRACT FROM AUTHOR]

Biomass burning is a major source of trace gases and aerosols that can ultimately impact health, air quality, and climate. Global and regional-scale three-dimensional Eulerian chemical transport models (CTMs) use estimates of the primary emissions from fires and can unphysically mix them across large-scale grid boxes, leading to incorrect estimates of the impact of biomass burning events. On the other hand, plume-scale process models allow for explicit simulation and examination of the chemical and physical transformations of trace gases and aerosols within biomass burning smoke plumes, and they may be used to develop parameterizations of this aging process for coarser grid-scale models. Here we describe the coupled SAM-ASP plume-scale process model, which consists of coupling the large-eddy simulation model, the System for Atmospheric Modelling (SAM) version 1.0, with the detailed gas and aerosol chemistry model, the Aerosol Simulation Program (ASP). We find that the SAM-ASP version 1.0 model is able to correctly simulate the dilution of CO in a California chaparral smoke plume, as well as the chemical loss of NOx, HONO, and NH3 within the plume, the formation of PAN and O3, the loss of OA, and the change in the size distribution of aerosols as compared to measurements and previous single-box model results. The newly coupled model is able to capture the cross-plume vertical and horizontal concentration gradients as the fire plume evolves downwind of the emission source. The integration and evaluation of SAM-ASP presented here will support the development of parameterizations of near-source biomass burning chemistry that can be used to more accurately simulate biomass burning chemical and physical transformations of trace gases and aerosols within coarser grid-scale CTMs. [ABSTRACT FROM AUTHOR]

Increasingly, adverse health effects from wildfire exposure are not limited to select populations in the wildland-urban interface. As wildfires continue to grow in frequency and intensity, they are also continuing to encroach on urban areas, putting larger and larger populations at risk. In this study we develop an innovative research design in the wildfire and health literature, and report results for a small scale implementation on California's central coast. Instead of focusing on smoke exposure and PM 2.5 or PM 10 as the primary physiological pathway linking wildfire and adverse cardiovascular health outcomes, we draw on a stress pathway as a potential link between stress and heart health. We use a novel spatiotemporal definition of wildfire exposure that is directly measurable at the individual level and acutely stressful: evacuation orders. Combining longitudinal health data from the dominant local hospital system in southern Santa Barbara County, California, we directly determine exposure to an evacuation order and smoke plumes from three large fires in the 2017–2019 wildfire period. Controlling for additional known risk factors, such as diabetes status and smoking status, we model the risk of secondary cardiovascular events (CVE) for 2411 patients with existing cardiovascular disease. Roughly 16.2% of patients (n = 391) were exposed to an evacuation order. We found evacuation order exposure was not significantly associated with an increased risk of cardiac events for the CVD population, but estimates hovered between 12.5 and 16.3% over un-evacuated cohorts. Smoke exposures were not significantly associated with CVE risk in models adjusted for evacuation orders nor unadjusted models, and estimates of effects varied widely. Both the method and the findings have implications for public health departments, clinicians and wildfire researchers. [ABSTRACT FROM AUTHOR]

The 2013 Rim Fire, which burned over 104,000 ha, was one of the most severe fire events in California's history, in terms of its rapid growth, intensity, overall size, and persistent smoke plume. At least two large pyrocumulonimbus (pyroCb) events were observed, allowing smoke particles to extend through the upper troposphere over a large portion of the Pacific Northwest. However, the most extreme fire spread was observed on days without pyroCb activity or significant regional convection. A diverse archive of ground, airborne, and satellite data collected during the Rim Fire provides a unique opportunity to examine the conditions required for both extreme spread events and pyroCb development. Results highlight the importance of upper-level and nocturnal meteorology, as well as the limitations of traditional fire weather indices. The Rim Fire dataset also allows for a detailed examination of conflicting hypotheses surrounding the primary source of moisture during pyroCb development. All pyroCbs were associated with conditions very similar to those that produce dry thunderstorms. The current suite of automated forecasting applications predict only general trends in fire behavior, and specifically do not predict 1) extreme fire spread events and 2) injection of smoke to high altitudes. While these two exceptions are related, analysis of the Rim Fire shows that they are not predicted by the same set of conditions and variables. The combination of numerical weather prediction data and satellite observations exhibits great potential for improving automated regional-scale forecasts of fire behavior and smoke emissions. [ABSTRACT FROM AUTHOR]

Ship emissions contribute significantly to gaseous and particulate pollution worldwide. To better understand the impact of ship emissions on air quality, measurements of the size-resolved chemistry of individual particles in ship emissions were made at the Port of Los Angeles using real-time, single-particle mass spectrometry. Ship plumes were identified through a combination of ship position information and measurements of gases and aerosol particles at a site 500 m from the center of the main shipping channel at the Port of Los Angeles. Single particles containing mixtures of organic carbon, vanadium, and sulfate (OC-V-sulfate) resulted from residual fuel combustion (i.e., bunker fuel), whereas high quantities of fresh soot particles (when OC-V-sulfate particles were not present) represented distinct markers for plumes from distillate fuel combustion (i.e., diesel fuel) from ships as well as trucks in the port area. OC-V-sulfate particles from residual fuel combustion contained significantly higher levels of sulfate and sulfuric acid than plume particles containing no vanadium. These associations may be due to vanadium (or other metals such as iron) in the fuel catalyzing the oxidation of SO2 to produce sulfate and sulfuric acid on these particles. Enhanced sulfate production on OC-V-sulfate ship emission particles would help explain some of the higher than expected sulfate levels measured in California compared to models based on emissions inventories and typical sulfate production pathways. Understanding the overall impact of ships emissions is critical for controlling regional air quality in the many populated coastal regions of the world. [ABSTRACT FROM AUTHOR]

Wildland fire smoke contains large amounts of PM 2.5 that can traverse tens to hundreds of kilometers, resulting in significant deterioration of air quality and excess mortality and morbidity in downwind regions. Estimating PM 2.5 levels while considering the impact of wildfire smoke has been challenging due to the lack of ground monitoring coverage near the smoke plumes. We aim to estimate total PM 2.5 concentration during the Camp Fire episode, the deadliest wildland fire in California history. Our random forest (RF) model combines calibrated low-cost sensor data (PurpleAir) with regulatory monitor measurements (Air Quality System, AQS) to bolster ground observations, Geostationary Operational Environmental Satellite-16 (GOES-16)'s high temporal resolution to achieve hourly predictions, and oversampling techniques (Synthetic Minority Oversampling Technique, SMOTE) to reduce model underestimation at high PM 2.5 levels. In addition, meteorological fields at 3 km resolution from the High-Resolution Rapid Refresh model and land use variables were also included in the model. Our AQS-only model achieved an out of bag (OOB) R2 (RMSE) of 0.84 (12.00 μg/m3) and spatial and temporal cross-validation (CV) R2 (RMSE) of 0.74 (16.28 μg/m3) and 0.73 (16.58 μg/m3), respectively. Our AQS + Weighted PurpleAir Model achieved OOB R2 (RMSE) of 0.86 (9.52 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.75 (14.93 μg/m3) and 0.79 (11.89 μg/m3), respectively. Our AQS + Weighted PurpleAir + SMOTE Model achieved OOB R2 (RMSE) of 0.92 (10.44 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.84 (12.36 μg/m3) and 0.85 (14.88 μg/m3), respectively. Hourly predictions from our model may aid in epidemiological investigations of intense and acute exposure to PM 2.5 during the Camp Fire episode. • Incorporation of PurpleAir low-cost sensors expands spatial modeling domain. • Application of SMOTE reduces model underestimation of wildland fire PM 2.5. • Our model (R2 = 0.92) can estimate hourly PM 2.5 during the Camp Fire episode. [ABSTRACT FROM AUTHOR]

A feasibility evaluation identified chemical reduction and biostimulation as a potential remedy for a plume containing hexavalent chromium (Cr(VI)) and tetrachloroethene (PCE) at an industrial site in southern California. The objectives of this laboratory study were to determine the stoichiometry of calcium polysulfide (CaS x ) reaction with Cr(VI) in the presence of sediment, the effect of CaS x on the potential for in situ biological reductive dechlorination of PCE, and the potential to reduce Cr(VI) and PCE by addition of only an electron donor. Approximately 1 L of CaS x solution (containing 50 g S 2- /L) was required per 1000 L of groundwater containing 45 mg/L of Cr(VI) (i.e., 1.8 mol S 2- per mol Cr(VI)). The sediment also exerted a sulfide demand (≥0.38 g S 2 - per kg sediment), but at a slower rate than the Cr(VI). In microcosms prepared with lactate, corn syrup, soybean oil, or methanol, but no CaS x , the Cr(VI) was biologically reduced in the treatments with lactate and corn syrup, but much more slowly than with CaS x . Even after 20 months of incubation, no significant reductive dechlorination of PCE occurred in any of the microcosms, including those in which the Cr(VI) was removed with CaS x . Bioaugmentation was tested with the microcosms that received lactate and corn syrup (following 20 months of incubation), using an enrichment culture that actively dechlorinates trichloroethene. PCE dechlorination began within 1 month in the lactate-only treatment; in the corn syrup–amended treatment, PCE dechlorination occurred in only one of the three bottles. However, no PCE dechlorination occurred following bioaugmentation of the lactate and corn syrup microcosms that were initially treated with CaS x , indicating that CaS x (and/or its reaction products) exerted a negative impact on the chlororespiring microbes. This outcome highlights the need to evaluate sites on a case-by-case basis when in situ chemical treatment is applied prior to microbial reductive dechlorination. [ABSTRACT FROM AUTHOR]

Aircraft measurements of the Sacramento pollutant plume were obtained over the western Sierra Nevada on 7 days in the summers of 1995 and 1996. These measurements, as well as those documenting the meteorological setting and evolution of surface conditions, are summarized. High concentrations of residual ozone above the surface layer in the early morning hours appear to be necessary to produce afternoon concentrations in excess of 125 ppbv. The moderately polluted days were characterized by low residual pollutant concentrations aloft in the early morning and moderate to weak vertical stratification during the day. The cleanest days had good ventilation with deep layers of near neutral stratification and moderate to strong winds. Horizontal spatial variability of ozone concentrations within the mixed layer was often significant at scales ≤5km. The maximum ozone concentrations were frequently observed in the afternoon, 40-80 km downwind of the city, but these decreased to about one-half those values at distances 120 km downwind. The measured oxides of nitrogen were generally just above detection limits (∼ 1 ppbv), except in the surface layer in the early morning hours. [ABSTRACT FROM AUTHOR]

There is limited population-scale evidence on the burden of exposure to wildfire smoke during pregnancy and its impacts on birth outcomes. In order to investigate this relationship, data on every singleton birth in California 2006–2012 were combined with satellite-based estimates of wildfire smoke plume boundaries and high-resolution gridded estimates of surface PM 2. 5 concentrations and a regression model was used to estimate associations with preterm birth risk. Results suggest that each additional day of exposure to any wildfire smoke during pregnancy was associated with an 0.49 % (95 % CI: 0.41–0.59 %) increase in risk of preterm birth (<37 weeks). At sample median smoke exposure (7 days) this translated to a 3.4 % increase in risk, relative to an unexposed mother. Estimates by trimester suggest stronger associations with exposure later in pregnancy and estimates by smoke intensity indicate that observed associations were driven by higher intensity smoke-days. Exposure to low intensity smoke-days had no association with preterm birth while an additional medium (smoke PM 2. 5 5–10 μg/m3) or high (smoke PM 2. 5 > 10 μg/m3) intensity smoke-day was associated with an 0.95 % (95 % CI: 0.47–1.42 %) and 0.82 % (95 % CI: 0.41–1.24 %) increase in preterm risk, respectively. In contrast to previous findings for other pollution types, neither exposure to smoke nor the relative impact of smoke on preterm birth differed by race/ethnicity or income in our sample. However, impacts differed greatly by baseline smoke exposure, with mothers in regions with infrequent smoke exposure experiencing substantially larger impacts from an additional smoke-day than mothers in regions where smoke is more common. We estimate 6,974 (95 % CI: 5,513–8,437) excess preterm births attributable to wildfire smoke exposure 2007–2012, accounting for 3.7 % of observed preterm births during this period. Our findings have important implications for understanding the costs of growing wildfire smoke exposure, and for understanding the benefits of smoke mitigation measures. [ABSTRACT FROM AUTHOR]

• California has high variability in PM 2.5 sources, meteorology and topography. • We used ensemble deep learning with multisource big data to improve PM 2.5 estimates. • We reliably imputed missing satellite AOD and fused wildfire dispersion estimates. • Our model achieved high PM 2.5 prediction performance with uncertainty estimates. Estimating PM 2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g, wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use. Using ensemble-based deep learning with big data fused from multiple sources we developed a PM 2.5 prediction model with uncertainty estimates at a high spatial (1 km × 1 km) and temporal (weekly) resolution for a 10-year time span (2008–2017). We leveraged autoencoder-based full residual deep networks to model complex nonlinear interrelationships among PM 2.5 emission, transport and dispersion factors and other influential features. These included remote sensing data (MAIAC aerosol optical depth (AOD), normalized difference vegetation index, impervious surface), MERRA-2 GMI Replay Simulation (M2GMI) output, wildfire smoke plume dispersion, meteorology, land cover, traffic, elevation, and spatiotemporal trends (geo-coordinates, temporal basis functions, time index). As one of the primary predictors of interest with substantial missing data in California related to bright surfaces, cloud cover and other known interferences, missing MAIAC AOD observations were imputed and adjusted for relative humidity and vertical distribution. Wildfire smoke contribution to PM 2.5 was also calculated through HYSPLIT dispersion modeling of smoke emissions derived from MODIS fire radiative power using the Fire Energetics and Emissions Research version 1.0 model. Ensemble deep learning to predict PM 2.5 achieved an overall mean training RMSE of 1.54 μg/m3 (R2: 0.94) and test RMSE of 2.29 μg/m3 (R2: 0.87). The top predictors included M2GMI carbon monoxide mixing ratio in the bottom layer, temporal basis functions, spatial location, air temperature, MAIAC AOD, and PM 2.5 sea salt mass concentration. In an independent test using three long-term AQS sites and one short-term non-AQS site, our model achieved a high correlation (>0.8) and a low RMSE (<3 μg/m3). Statewide predictions indicated that our model can capture the spatial distribution and temporal peaks in wildfire-related PM 2.5. The coefficient of variation indicated highest uncertainty over deciduous and mixed forests and open water land covers. Our method can be generalized to other regions, including those having a mix of major urban areas, deserts, intensive smoke events, snow cover and complex terrains, where PM 2.5 has previously been challenging to predict. Prediction uncertainty estimates can also inform further model development and measurement error evaluations in exposure and health studies. [ABSTRACT FROM AUTHOR]

Wildland fire smoke is visible and detectable with remote sensing technology. Using this technology to assess ground level pollutants and the impacts to human health and exposure is more difficult. We found the presence of satellite derived smoke plumes for more than a couple of hours in the previous three days has significant impact on the chances of ground level ozone values exceeding the norm. While the magnitude of the impact will depend on characteristics of fires such as size, location, time in transport, or ozone precursors produced by the fire, we demonstrate that information on satellite derived smoke plumes together with site specific regression models provide useful information for supporting causal relationship between smoke from fire and ozone exceedances of the norm. Our results indicated that fire seasons increasing the median ozone level by 15 ppb. However, they seem to have little impact on the metric used for regulatory compliance, in particular at urban sites, except possibly during the 2008 forest fires in California. [ABSTRACT FROM AUTHOR]

On September 28, 2016, an intense dust storm impacted the city of Bakersfield and surrounding areas in California. The dust event coincided with smoke aerosols from the forest fire located in the northwest of Bakersfield. In California, forest fires are frequent during summer and fall seasons. The forest fire smoke plumes were subjected to large dispersion and appeared widespread. In this study, we present a detailed analysis of satellite and surface observations indicating pronounced changes in air quality, aerosol characteristics, trace gases, along the prevailing meteorological conditions over Bakersfield associated with the dust event and its interactions with the forest fire smoke. Back trajectory simulations clearly show inflow of the dust airmass from the Mojave Desert located east of Bakersfield, in contrast to the forward trajectories originating from the forest fire event located in the northwestern region, suggesting possibility of mixing of smoke and dust in the Bakersfield area. In addition, low and strong wavelength dependence of aerosol single scattering albedo also supports the observations of strong aerosol mixing of dust and smoke. [ABSTRACT FROM AUTHOR]