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Using data from rural monitoring sites across the contiguous United States, we evaluated fine particulate matter (PM2.5) trends for 1988–2016. We calculate trends in the policy-relevant 98th quantile of PM2.5 using Quantile Regression. We use Kriging and Gaussian Geostatistical Simulations to interpolate trends between observed data points. Overall, we found positive trends in 98th quantile PM2.5 at sites within the Northwest United States (average 0.21 ± 0.12 μg·m−3·y−1; ±95% confidence interval). This was in contrast with sites throughout the rest of country, which showed a negative trend in 98th quantile PM2.5, likely due to reductions in anthropogenic emissions (average −0.66 ± 0.10 μg·m−3·y−1). The positive trend in 98th quantile PM2.5 is due to wildfire activity and was supported by positive trends in total carbon and no trend in sulfate across the Northwest. We also evaluated daily moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) for 2002–2017 throughout the United States to compare with ground-based trends. For both Interagency Monitoring of Protected Visual Environments (IMPROVE) PM2.5 and MODIS AOD datasets, we found positive 98th quantile trends in the Northwest (1.77 ± 0.68% and 2.12 ± 0.81% per year, respectively) through 2016. The trend in Northwest AOD is even greater if data for the high-fire year of 2017 are included. These results indicate a decrease in PM2.5 over most of the country but a positive trend in the 98th quantile PM2.5 across the Northwest due to wildfires.

Relationships between various optical, physical, and chemical properties of biomass-combustion-derived particles are characterized for particles produced in the laboratory from a wide range of fuels and burn conditions. The modified combustion efficiency (MCE), commonly used to parameterize biomass particle emissions and properties, is shown to generally have weak predictive capabilities, especially for more efficient combustion conditions. There is, however, a strong relationship between many intensive optical properties (e.g., single-scatter albedo, Ångström absorption exponent, mass absorption efficiency) and the organic aerosol-to-black carbon ([OA] ∕ [BC]) mass ratio over a wider range than previously considered (0.3 to 105). The properties of brown carbon (BrC, i.e., light-absorbing organic carbon) also vary with [OA] ∕ [BC]. Coating-induced enhancements (i.e., “lensing” effects) contribute only a minor amount to BC absorption for all of the burns despite some burns producing particles having large ensemble-average coating-to-core mass ratios. The BC–OA mixing state varies strongly with [OA] ∕ [BC]; the fraction of OA that is internally mixed with BC decreases with [OA] ∕ [BC] while the relative amount of OA coated on BC increases. In contrast, there is little relationship between many OA bulk chemical properties and [OA] ∕ [BC], with the O : C and H : C atomic ratios and the relative abundance of a key marker ion (m/z=60, linked to levoglucosan) all showing no dependence on [OA] ∕ [BC]. In contrast, both the organic nitrate fraction of OA and the OA volatility do depend on the [OA] ∕ [BC]. Neither the total particle nor BC-specific size distributions exhibit any clear dependence on the burn conditions or [OA] ∕ [BC], although there is perhaps a dependence on fuel type. Overall, our results expand on existing knowledge to contribute new understanding of the properties of particles emitted from biomass combustion.

Background: Fire research and management applications, such as fire behaviour analysis and emissions modelling, require consistent, highly resolved spatiotemporal information on wildfire growth progression.Aims: We developed a new fire mapping method that uses quality-assured sub-daily active fire/thermal anomaly satellite retrievals (2003–2020 MODIS and 2012–2020 VIIRS data) to develop a high-resolution wildfire growth dataset, including growth areas, perimeters, and cross-referenced fire information from agency reports.Methods: Satellite fire detections were buffered using a historical pixel-to-fire size relationship, then grouped spatiotemporally into individual fire events. Sub-daily and daily growth areas and perimeters were calculated for each fire event. After assembly, fire event characteristics including location, size, and date, were merged with agency records to create a cross-referenced dataset.Key results: Our satellite-based total fire size shows excellent agreement with agency records for MODIS (R2 = 0.95) and VIIRS (R2 = 0.97) in California. VIIRS-based estimates show improvement over MODIS for fires with areas less than 4047 ha (10 000 acres). To our knowledge, this is the finest resolution quality-assured fire growth dataset available.Conclusions and Implications: The novel spatiotemporal resolution and methodological consistency of our dataset can enable advances in fire behaviour and fire weather research and model development efforts, smoke modelling, and near real-time fire monitoring.

Forest fire smoke influence in urban areas is relatively easy to detect at high concentrations but more challenging to detect at low concentrations. In this study, we present a simplified method that can reliably quantify smoke tracers in an urban environment at relatively low cost and complexity. For this purpose, we used dual-bed thermal desorption tubes with an auto-sampler to collect continuous samples of volatile organic compounds (VOCs). We present the validation and evaluation of this approach using thermal desorption gas chromatography mass spectrometry (TD-GC-MS) to detect VOCs at ppt to ppb concentrations. To evaluate the method, we tested stability during storage, interferences (e.g., water and O3), and reproducibility for reactive and short-lived VOCs such as acetonitrile (a specific chemical tracer for biomass burning), acetone, n-pentane, isopentane, benzene, toluene, furan, acrolein, 2-butanone, 2,3-butanedione, methacrolein, 2,5- dimethylfuran, and furfural. The results demonstrate that these VOCs can be quantified reproducibly with a total uncertainty of ≤30% between the collection and analysis, and with storage times of up to 15 days. Calibration experiments performed over a dynamic range of 10–150 ng loaded on to each thermal desorption tube at different relative humidity showed excellent linearity (r2 ≥ 0.90). We utilized this method during the summer 2019 National Oceanic and Atmospheric Administration (NOAA) Fire Influence on Regional to Global Environments Experiment–Air Quality (FIREX-AQ) intensive experiment at the Boise ground site. The results of this field study demonstrate the method’s applicability for ambient VOC speciation to identify forest fire smoke in urban areas.

The Southeast Atmosphere Studies (SAS), which included the Southern Oxidant and Aerosol Study (SOAS); the Southeast Nexus (SENEX) study; and the Nitrogen, Oxidants, Mercury and Aerosols: Distributions, Sources and Sinks (NOMADSS) study, was deployed in the field from 1 June to 15 July 2013 in the central and eastern United States, and it overlapped with and was complemented by the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC⁴RS) campaign. SAS investigated atmospheric chemistry and the associated air quality and climate-relevant particle properties. Coordinated measurements from six ground sites, four aircraft, tall towers, balloon-borne sondes, existing surface networks, and satellites provide in situ and remotely sensed data on trace-gas composition, aerosol physicochemical properties, and local and synoptic meteorology. Selected SAS findings indicate 1) dramatically reduced NOx concentrations have altered ozone production regimes; 2) indicators of “biogenic” secondary organic aerosol (SOA), once considered part of the natural background, were positively correlated with one or more indicators of anthropogenic pollution; and 3) liquid water dramatically impacted particle scattering while biogenic SOA did not. SAS findings suggest that atmosphere–biosphere interactions modulate ambient pollutant concentrations through complex mechanisms and feedbacks not yet adequately captured in atmospheric models. The SAS dataset, now publicly available, is a powerful constraint to develop predictive capability that enhances model representation of the response and subsequent impacts of changes in atmospheric composition to changes in emissions, chemistry, and meteorology.