Light-absorbing organic carbon (OC), also termed brown carbon (BrC), from laboratory-based biomass burning (BB) has been studied intensively to understand the contribution of BB to radiative forcing. However, relatively few measurements have been conducted on field-based BB and even fewer measurements have examined BrC from anthropogenic combustion sources like motor vehicle emissions. In this work, the light absorption of methanol-extractable OC from prescribed and laboratory BB and gasoline vehicle emissions was examined using spectrophotometry. The light absorption of methanol extracts showed a strong wavelength dependence for both BB and gasoline vehicle emissions. The mass absorption coefficients at 365 nm (MAC₃₆₅, m² g⁻¹C) – used as a measurement proxy for BrC – were significantly correlated (p < 0.05) to the elemental carbon (EC)/OC ratios when examined by each BB fuel type. No significant correlation was observed when pooling fuels, indicating that both burn conditions and fuel types may impact BB BrC characteristics. The average MAC₃₆₅ of gasoline vehicle emission samples is 0.62 ± 0.76 m² g⁻¹C, which is similar in magnitude to the BB samples (1.27 ± 0.76 m² g⁻¹C). These results suggest that in addition to BB, gasoline vehicle emissions may also be an important BrC source in urban areas.

Until recently, air quality impacts from wildfires were predominantly determined based on data from permanent stationary regulatory air pollution monitors. However, low-cost particulate matter (PM) sensors are now widely used by the public as a source of air quality information during wildfires, although their performance during smoke impacted conditions has not been thoroughly evaluated. We collocated three types of low-cost fine PM (PM_2.5) sensors with reference instruments near multiple fires in the western and eastern United States (maximum hourly PM_2.5 = 295 µg/m³). Sensors were moderately to strongly correlated with reference instruments (hourly averaged r² = 0.52–0.95), but overpredicted PM_2.5 concentrations (normalized root mean square errors, NRMSE = 80–167%). We developed a correction equation for wildfire smoke that reduced the NRMSE to less than 27%. Correction equations were specific to each sensor package, demonstrating the impact of the physical configuration and the algorithm used to translate the size and count information into PM_2.5 concentrations. These results suggest the low-cost sensors can fill in the large spatial gaps in monitoring networks near wildfires with mean absolute errors of less than 10 µg/m³ in the hourly PM_2.5 concentrations when using a sensor-specific smoke correction equation.

PurpleAir particulate matter (PM) sensors are increasingly used in the United States and other countries for real-time air quality information, particularly during wildfire smoke episodes. Uncorrected PurpleAir data can be biased and may exhibit a nonlinear response at extreme smoke concentrations (>300 µg/m³). This bias and nonlinearity result in a disagreement with the traditional ambient monitoring network, leading to the public’s confusion during smoke episodes. These sensors must be evaluated during smoke-impacted times and then corrected for bias, to ensure that accurate data are reported. The nearby public PurpleAir sensor and monitor pairs were identified during the summer of 2020 and were used to supplement the data from collocated pairs to develop an extended U.S.-wide correction for high concentrations. We evaluated several correction schemes to identify an optimal correction, using the previously developed U.S.-wide correction, up to 300 µg/m³, transitioning to a quadradic fit above 400 µg/m³. The correction reduces the bias at each air quality index (AQI) breakpoint; most ambient collocations that were studied met the Environmental Protection Agency’s (EPA) performance targets (twelve of the thirteen ambient sensors met the EPA’s targets) and some smoke-impacted sites (5 out of 15 met the EPA’s performance targets in terms of the 1-h averages). This correction can also be used to improve the comparability of PurpleAir sensor data with regulatory-grade monitors when they are collectively analyzed or shown together on public information websites; the methods developed in this paper can also be used to correct future air-sensor types. The PurpleAir network is already filling in spatial and temporal gaps in the regulatory monitoring network and providing valuable air-quality information during smoke episodes.

This study seeks to understand the compositional details of N-containing aromatic compounds (NACs) emitted during biomass burning (BB) and their contribution to light-absorbing organic carbon (OC), also termed brown carbon (BrC). Three laboratory BB experiments were conducted with two United States pine forest understory fuels typical of those consumed during prescribed fires. During the experiments, submicron aerosol particles were collected on filter media and subsequently extracted with methanol and examined for their optical and chemical properties. Significant correlations (p<0.05) were observed between BrC absorption and elemental carbon (EC)∕OC ratios for individual burns data. However, the pooled experimental data indicated that EC∕OC alone cannot explain the BB BrC absorption. Fourteen NAC formulas were identified in the BB samples, most of which were also observed in simulated secondary organic aerosol (SOA) from photooxidation of aromatic volatile organic compounds (VOCs) with NO_x. However, the molecular structures associated with the identical NAC formula from BB and SOA are different. In this work, the identified NACs from BB are featured by methoxy and cyanate groups and are predominately generated during the flaming phase. The mass concentrations of identified NACs were quantified using authentic and surrogate standards, and their contributions to bulk light absorption of solvent-extractable OC were also calculated. The contributions of identified NACs to organic matter (OM) and BrC absorption were significantly higher in flaming-phase samples than those in smoldering-phase samples, and they correlated with the EC∕OC ratio (p<0.05) for both individual burns and pooled experimental data, indicating that the formation of NACs from BB largely depends on burn conditions. The average contributions of identified NACs to overall BrC absorption at 365 nm ranged from 0.087±0.024 % to 1.22±0.54 %, which is 3–10 times higher than their mass contributions to OM (0.023±0.0089 % to 0.18±0.067 %), so the NACs with light absorption identified in this work from BB are likely strong BrC chromophores. Further studies are warranted to identify more light-absorbing compounds to explain the unknown fraction (>98 %) of BB BrC absorption.