Abstract. Soils have been identified as a major source (∼ 15 %) of global nitrogen oxide (NO x ) emissions. Parameterizations of soil NO x emissions (S NO x ) commonly used in the current generation of chemical transport models were designed to capture mean seasonal behaviour. These parameterizations do not, however, respond quantitatively to the meteorological triggers that are observed to result in pulsed S NO x . Here we present a new parameterization of S NO x implemented within a global chemical transport model (GEOSChem). The parameterization represents available nitrogen (N) in soils using biome specific emission factors, online wet-and dry-deposition of N, and fertilizer and manure N derived from a spatially explicit dataset, distributed using seasonality derived from data obtained by the Moderate Resolution Imaging Spectrometer. Moreover, it represents the functional form of emissions derived from point measurements and ecosystem scale experiments including pulsing following soil wetting by rain or irrigation, and emissions that are a smooth function of soil moisture as well as temperature between 0 and 30 • C. This parameterization yields global above-soil S NO x of 10.7 Tg N yr −1 , including 1.8 Tg N yr −1 from fertilizer N input (1.5 % of applied N) and 0.5 Tg N yr −1 from atmospheric N deposition. Over the United States (US) Great Plains region, S NO x are predicted to comprise 15-40 % of the tropospheric NO 2 column and increase column variability by a factor of 2-4 during the summer months due to chemical fertilizer application and warm temperatures. S NO x enhancements of 50-80 % of the simulated NO 2 column are predicted over the African Sahel during the monsoon onset (April-June). In this region the day-to-day variability of column NO 2 is increased by a factor of 5 due to pulsed-N emissions. We evaluate the model by comparison with observations of NO 2 column density from the Ozone Monitoring Instrument (OMI). We find that the model is able to reproduce the observed interannual variability of NO 2 (induced by pulsed-N emissions) over the US Great Plains. We also show that the OMI mean (median) NO 2 observed during the overpass following first rainfall over the Sahel is 49 % (23 %) higher than in the five days preceding. The measured NO 2 on the day after rainfall is still 23 % (5 %) higher, providing a direct measure of the pulse's decay time of 1-2 days. This is consistent with the pulsing representation used in our parameterization and much shorter than 5-14 day pulse decay length used in current models.

We use observations of fire radiative power (FRP) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and tropospheric NO 2 column measurements from the Ozone Monitoring Instrument (OMI) to derive NO 2 wildfire emission coefficients (g MJ −1 ) for three land types over California and Nevada. Retrieved emission coefficients were 0.279±0.077, 0.342±0.053, and 0.696±0.088 g MJ −1 NO 2 for forest, grass and shrub fuels, respectively. These emission coefficients reproduce ratios of emissions with fuel type reported previously using independent methods. However, the magnitude of these coefficients is lower than prior estimates. While it is possible that a negative bias in the OMI NO 2 retrieval over regions of active fire emissions is partly responsible, comparison with several other studies of fire emissions using satellite platforms indicates that current emission factors may overestimate the contributions of flaming combustion and underestimate the contributions of smoldering combustion to total fire emissions.Our results indicate that satellite data can provide an extensive characterization of the variability in fire NO x emissions; 67 % of the variability in emissions in this region can be accounted for using an FRP-based parameterization.

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 (PM2.5) sensors with reference instruments near multiple fires in the western and eastern United States (maximum hourly PM2.5 = 295 µg/m3). Sensors were moderately to strongly correlated with reference instruments (hourly averaged r2 = 0.52–0.95), but overpredicted PM2.5 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 PM2.5 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 PM2.5 concentrations when using a sensor-specific smoke correction equation.

Abstract. Biomass burning represents both a significant and highly variable source of NOx to the atmosphere. This variability stems from both the episodic nature of fires, and from fire conditions such as the modified combustion efficiency of the fire, the nitrogen content of the fuel and possibly other factors that have not been identified or evaluated by comparison with observations. Satellite instruments offer an opportunity to observe emissions from wildfires, providing a large suite of measurements which allow us to study mean behavior and variability on the regional scale in a statistically rigorous manner. Here we use space-based measurements of fire radiative power from the Moderate Resolution Imaging Spectroradiometer in combination with NO2 tropospheric column densities from the Ozone Monitoring Instrument to measure mean emission coefficients (ECs in g NO MJ−1) from fires for global biomes, and across a wide range of smaller-scale ecoregions, defined as spatially-distinct clusters of fires with similar fuel type. Mean ECs for all biomes fall between 0.250–0.362 g NO MJ−1, a range that is smaller than found in previous studies of biome-scale emission factors. The majority of ecoregion ECs fall within or near this range, implying that under most conditions, mean fire emissions of NOx per unit energy are similar between different regions regardless of fuel type or spatial variability. In contrast to these similarities, we find that about 24% of individual ecoregion ECs deviate significantly (with 95% confidence) from the mean EC for the associated biome, and a similar number of ecoregion ECs falls outside the range of all mean biome ECs, implying that there are some regions where fuel type-specific global emission parameterizations fail to capture local fire NOx emissions.

We use observations of fire radiative power (FRP) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and tropospheric NO₂ column measurements from the Ozone Monitoring Instrument (OMI) to derive NO₂ wildfire emission coefficients (g MJ⁻¹) for three land types over California and Nevada. Retrieved emission coefficients were 0.279 ± 0.077, 0.342 ± 0.053, and 0.696 ± 0.088 g MJ⁻¹ NO₂ for forest, grass and shrub fuels, respectively. These emission coefficients reproduce ratios of emissions with fuel type reported previously using independent methods. However, the magnitude of these coefficients is lower than prior estimates, which suggests either a negative bias in the OMI NO₂ retrieval over regions of active emissions, or that the average fire observed in our study has a smaller ratio of flaming to smoldering combustion than measurements used in prior estimates of emissions. Our results indicate that satellite data can provide an extensive characterization of the variability in fire NO_x emissions; 67% of the variability in emissions in this region can be accounted for using an FRP-based parameterization