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The San Joaquin Valley is an agricultural region in California that suffers from poor air quality. Since traffic emissions are decreasing, other sources of volatile organic compounds (VOCs) are gaining importance in the formation of secondary air pollutants. Using airborne eddy covariance, we conducted direct, spatially resolved flux observations of a wide range of VOCs in the San Joaquin Valley during June 2021 at 23–36 °C. Through landcover-informed footprint disaggregation, we were able to attribute emissions to sources and identify tracers for distinct source types. VOC mass fluxes were dominated by alcohols, mainly from dairy farms, while oak isoprene and citrus monoterpenes were important sources of reactivity. Comparisons with two commonly used inventories showed that isoprene emissions in the croplands were overestimated, while dairy and highway VOC emissions were generally underestimated in the inventories, and important citrus and biofuel VOC point sources were missing from the inventories. This study thus presents unprecedented insights into the VOC sources in an intensive agricultural region and provides much needed information for the improvement of inventories, air quality predictions and regulations.

Nitrogen oxides (NOx ≡ NO + NO2) are involved in most atmospheric photochemistry, including the formation of tropospheric ozone (O3). While various methods exist to accurately measure NOx concentrations, it is still a challenge to quantify the source and flux of NOx emissions. We present airborne measurements of NOx and winds used to infer the emission of NOx across Los Angeles. The measurements were obtained during the research aircraft campaign RECAP-CA (Re-Evaluating the Chemistry of Air Pollutants in CAlifornia) in June 2021. Geographic allocations of the fluxes are compared to the NOx emission inventory from the California Air Resources Board (CARB). We find that the NOx fluxes have a pronounced weekend effect and are highest in the Eastern part of the San Bernardino valley. The comparison of the RECAP-CA and the modeled CARB NOx fluxes suggest the modeled emissions are too high near the coast and in downtown Los Angeles and too low further inland in the Eastern part of the San Bernardino valley.

The lifetime and concentration of nitrogen oxides (NOx) are susceptible to nonlinear production and loss and to the resolution of a chemical transport model (CTM). This is due to the strong spatial gradients of NOx and the dependence of its own chemical loss on such gradients. In this study, we use the GEOS-Chem CTM in its high-performance implementation (GCHP) to investigate NOx simulations over the eastern United States across a wide range of spatial model resolutions (six different horizontal grids from 13 to 181 km). Following increasing grid size, afternoon surface NOx mixing ratios over July 2015 generally decrease over the Great Lakes region (GL) and increase over the southern states of the US region (SS), yielding regional differences (181 km vs. 13 km) of −16 % (in the GL) to 7 % (in the SS); meanwhile, hydrogen oxide radicals (HOx) increase over both regions, consistent with their different chemical regimes (i.e., NOx-saturated in the GL and NOx-limited in the SS). Nighttime titration of ozone by surface nitric oxide (NO) was found to be more efficient at coarser resolutions, leading to longer NOx lifetimes and higher surface mixing ratios of nitrogen dioxide (NO2) over the GL in January 2015. The tropospheric NO2 column density at typical afternoon satellite overpass time has spatially more coherent negative biases (e.g., −8 % over the GL) at coarser resolutions in July, which reversed the positive biases of surface NOx over the SS. The reduced NOx aloft (>1 km altitude) at coarser resolutions was attributable to the enhanced HOx that intrudes into the upper troposphere. Application of coarse-resolution simulations for interpreting satellite NO2 columns will generally underestimate surface NO2 over the GL and overestimate surface NO2 over the SS in summer, but it will uniformly overestimate NOx emissions over both regions. This study significantly broadens understanding of factors contributing to NOx resolution effects and the role of fine-resolution data in accurately simulating and interpreting NOx and its relevance to air quality.

Nitrogen oxides (NOx) are principle components of air pollution and serve as important ozone precursors. As the San Joaquin Valley (SJV) experiences some of the worst air quality in the United States, reducing NOx emissions is a pressing need, yet quantifying current emissions is complicated due to a mixture of mobile and agriculture sources. We performed airborne eddy covariance flux measurements during the Re-Evaluating the Chemistry of Air Pollutants in CAlifornia (RECAP-CA) field campaign in June 2021. Combining footprint calculations and land cover statistics, we disaggregate the observed fluxes into component fluxes characterized by three different land cover types. On average we find emissions of 2.95 mg m-2 h-1 over highways, 1.24 mg m-2 h-1 over urban areas and 0.79 mg m-2 h-1 over croplands. The calculated NOx emissions using flux observations are utilized to evaluate anthropogenic emission inventories and soil NOx emission schemes. We show that two anthropogenic inventories for mobile sources, EMFAC (EMssion FACtor) and FIVE (Fuel-based Inventory for Vehicle Emissions), yield similar agreement with emissions derived from measured fluxes over urban regions with 24 % and 22 % low bias, respectively. Three soil NOx schemes, including MEGAN v3 (Model of Emissions of Gases and Aerosols from Nature), BEIS v3.14 (Biogenic Emission Inventory System) and BDISNP (Berkeley Dalhousie Iowa Soil NO Parameterization), show substantial underestimates over the study domain. Compared to the cultivated soil NOx emissions derived from measured fluxes, MEGAN and BEIS are lower by more than one order of magnitude and BDISNP is lower by a factor of 2.7. Despite the low bias, observed soil NOx emissions and BDISNP present a similar spatial pattern as well as temperature dependence. We conclude that soil NOx is a key feature of the NOx emissions in the SJV and that a state-of-the-science model of these emissions is needed to simulate emissions for modeling air quality in the region.

The availability of formaldehyde (HCHO) (a proxy for volatile organic compound reactivity) and nitrogen dioxide (NO2) (a proxy for nitrogen oxides) tropospheric columns from ultraviolet–visible (UV–Vis) satellites has motivated many to use their ratios to gain some insights into the near-surface ozone sensitivity. Strong emphasis has been placed on the challenges that come with transforming what is being observed in the tropospheric column to what is actually in the planetary boundary layer (PBL) and near the surface; however, little attention has been paid to other sources of error such as chemistry, spatial representation, and retrieval uncertainties. Here we leverage a wide spectrum of tools and data to quantify those errors carefully. Concerning the chemistry error, a well-characterized box model constrained by more than 500 h of aircraft data from NASA's air quality campaigns is used to simulate the ratio of the chemical loss of HO2 + RO2 (LROx) to the chemical loss of NOx (LNOx). Subsequently, we challenge the predictive power of HCHO/NO2 ratios (FNRs), which are commonly applied in current research, in detecting the underlying ozone regimes by comparing them to LROx/LNOx. FNRs show a strongly linear (R2=0.94) relationship with LROx/LNOx, but only on the logarithmic scale. Following the baseline (i.e., ln(LROx/LNOx) = −1.0 ± 0.2) with the model and mechanism (CB06, r2) used for segregating NOx-sensitive from VOC-sensitive regimes, we observe a broad range of FNR thresholds ranging from 1 to 4. The transitioning ratios strictly follow a Gaussian distribution with a mean and standard deviation of 1.8 and 0.4, respectively. This implies that the FNR has an inherent 20 % standard error (1σ) resulting from not accurately describing the ROx–HOx cycle. We calculate high ozone production rates (PO3) dominated by large HCHO × NO2 concentration levels, a new proxy for the abundance of ozone precursors. The relationship between PO3 and HCHO × NO2 becomes more pronounced when moving towards NOx-sensitive regions due to nonlinear chemistry; our results indicate that there is fruitful information in the HCHO × NO2 metric that has not been utilized in ozone studies. The vast amount of vertical information on HCHO and NO2 concentrations from the air quality campaigns enables us to parameterize the vertical shapes of FNRs using a second-order rational function permitting an analytical solution for an altitude adjustment factor to partition the tropospheric columns into the PBL region. We propose a mathematical solution to the spatial representation error based on modeling isotropic semivariograms. Based on summertime-averaged data, the Ozone Monitoring Instrument (OMI) loses 12 % of its spatial information at its native resolution with respect to a high-resolution sensor like the TROPOspheric Monitoring Instrument (TROPOMI) (> 5.5 × 3.5 km2). A pixel with a grid size of 216 km2 fails at capturing ∼ 65 % of the spatial information in FNRs at a 50 km length scale comparable to the size of a large urban center (e.g., Los Angeles). We ultimately leverage a large suite of in situ and ground-based remote sensing measurements to draw the error distributions of daily TROPOMI and OMI tropospheric NO2 and HCHO columns. At a 68 % confidence interval (1σ), errors pertaining to daily TROPOMI observations, either HCHO or tropospheric NO2 columns, should be above 1.2–1.5 × 1016 molec. cm−2 to attain a 20 %–30 % standard error in the ratio. This level of error is almost non-achievable with the OMI given its large error in HCHO. The satellite column retrieval error is the largest contributor to the total error (40 %–90 %) in the FNRs. Due to a stronger signal in cities, the total relative error (< 50 %) tends to be mild, whereas areas with low vegetation and anthropogenic sources (e.g., the Rocky Mountains) are markedly uncertain (> 100 %). Our study suggests that continuing development in the retrieval algorithm and sensor design and calibration is essential to be able to advance the application of FNRs beyond a qualitative metric.

The role of anthropogenic NOx emissions in secondary organic aerosol (SOA) production is not fully understood but is important for understanding the contribution of emissions to air quality. Here, we examine the role of organic nitrates (RONO2) in SOA formation over the Korean Peninsula during the Korea-United States Air Quality field study in Spring 2016 as a model for RONO2 aerosol in cities worldwide. We use aircraft-based measurements of the particle phase and total (gas + particle) RONO2 to explore RONO2 phase partitioning. These measurements show that, on average, one-fourth of RONO2 are in the condensed phase, and we estimate that ≈15% of the organic aerosol (OA) mass can be attributed to RONO2. Furthermore, we observe that the fraction of RONO2 in the condensed phase increases with OA concentration, evidencing that equilibrium absorptive partitioning controls the RONO2 phase distribution. Lastly, we model RONO2 chemistry and phase partitioning in the Community Multiscale Air Quality modeling system. We find that known chemistry can account for one-third of the observed RONO2, but there is a large missing source of semivolatile, anthropogenically derived RONO2. We propose that this missing source may result from the oxidation of semi- and intermediate-volatility organic compounds and/or from anthropogenic molecules that undergo autoxidation or multiple generations of OH-initiated oxidation.

Biomass burning emits an estimated 25 % of global annual nitrogen oxides (NOx), an important constituent that participates in the oxidative chemistry of the atmosphere. Estimates of NOx emission factors, representing the amount of NOx per mass burned, are primarily based on field or laboratory case studies, but the sporadic and transient nature of wildfires makes it challenging to verify whether these case studies represent the behavior of the global fires that occur on earth. Satellite remote sensing provides a unique view of the earth, allowing for the study of emissions and downwind evolution of NOx from a large number of fires. We describe direct estimates of NOx emissions and lifetimes for fires using an exponentially modified Gaussian analysis of daily TROPOspheric Monitoring Instrument (TROPOMI) retrievals of NO2 tropospheric columns. We update the a priori profile of NO2 with a fine-resolution (0.25∘) global model simulation from NASA's GEOS Composition Forecasting System (GEOS-CF), which largely enhances NO2 columns over fire plumes. We derive representative NOx emission factors for six fuel types globally by linking TROPOMI-derived NOx emissions with observations of fire radiative power from Moderate Resolution Imaging Spectroradiometer (MODIS). Satellite-derived NOx emission factors are largely consistent with those derived from in situ measurements. We observe decreasing NOx lifetime with fire emissions, which we infer is due to the increase in both NOx abundance and hydroxyl radical production. Our findings suggest promise for applying space-based observations to track the emissions and chemical evolution of reactive nitrogen from wildfires.

The majority of global anthropogenic CO2 emissions originate in cities. We have proposed that dense networks are a strategy for tracking changes to the processes contributing to urban CO2 emissions and suggested that a network with ∼ 2 km measurement spacing and ∼ 1 ppm node-to-node precision would be effective at constraining point, line, and area sources within cities. Here, we report on an assessment of the accuracy of the Berkeley Environmental Air-quality and CO2 Network (BEACO2N) CO2 measurements over several years of deployment. We describe a new procedure for improving network accuracy that accounts for and corrects the temperature-dependent zero offset of the Vaisala CarboCap GMP343 CO2 sensors used. With this correction we show that a total error of 1.6 ppm or less can be achieved for networks that have a calibrated reference location and 3.6 ppm for networks without a calibrated reference.

The availability of formaldehyde (HCHO) (a proxy for volatile organic compound reactivity) and nitrogen dioxide (NO2) (a proxy for nitrogen oxides) tropospheric columns from Ultraviolet-Visible (UV-Vis) satellites has motivated many to use their ratios to gain some insights into the near-surface ozone sensitivity. Strong emphasis has been placed on the challenges that come with transforming what is being observed in the tropospheric column to what is actually in the planetary boundary layer (PBL) and near to the surface; however, little attention has been paid to other sources of error such as chemistry, spatial representation, and retrieval uncertainties. Here we leverage a wide spectrum of tools and data to carefully quantify those errors. Concerning the chemistry error, a well-characterized box model constrained by more than 500 hours of aircraft data from NASA’s air quality campaigns is used to simulate the ratio of the chemical loss of HO2+RO2 (LROx) to the chemical loss of NOx (LNOx). Subsequently, we challenge the predictive power of HCHO / NO2 ratios (FNRs), which are commonly applied in current research, at detecting the underlying ozone regimes by comparing them to LROx / LNOx. FNRs show a strongly linear (R2=0.94) relationship to LROx / LNOx in the log-log scale. Following the baseline (i.e., ln(LROx / LNOx) = -1.0±0.2) with the model and mechanism (CB06, r2) used for segregating NOx-sensitive from VOC-sensitive regimes, we observe a broad range of FNR thresholds ranging from 1 to 4. The transitioning ratios strictly follow a Gaussian distribution with a mean and standard deviation of 1.8 and 0.4, respectively. This implies that FNR has an inherent 20 % standard error (1-sigma) resulting from not being able to fully describe the ROx-HOx cycle. We calculate high ozone production rates (PO3) dominated by large HCHO×NO2 concentration levels, a new proxy for the abundance of ozone precursors. The relationship between PO3 and HCHO×NO2 becomes more pronounced when moving towards NOx-sensitive regions due to non-linear chemistry; our results indicate that there is fruitful information in the HCHO×NO2 metric that has not been utilized in ozone studies. The vast amount of vertical information on HCHO and NO2 concentration from the air quality campaigns enables us to parameterize the vertical shapes of FNRs using a second-order rational function permitting an analytical solution for an altitude adjustment factor to partition the tropospheric columns to the PBL region. We propose a mathematical solution to the spatial representation error based on modeling isotropic semivariograms. With respect to a high-resolution sensor like TROPOspheric Monitoring Instrument (TROPOMI) (>5.5×3.5 km2), Ozone Monitoring Instrument (OMI) loses 12 % of spatial information at its native resolution. A pixel with a grid size of 216 km2 fails at capturing ~65 % of the spatial information in FNRs at a 50 km length scale comparable to the size of a large urban center (e.g., Los Angeles). We ultimately leverage a large suite of in-situ and ground-based remote sensing measurements to draw the error distributions of daily TROPOMI and OMI tropospheric NO2 and HCHO columns. At 68 % confidence interval (1 sigma) errors pertaining to daily TROPOMI observations, either HCHO or tropospheric NO2 columns should be above 1.2–1.5×1016 molec.cm-2 to attain 20–30 % standard error in the ratio. This level of error is almost non-achievable with OMI given its large error in HCHO. The satellite column retrieval error is the largest contributor to the total error (40–90 %) in the FNRs. Due to a stronger signal in cities, the total relative error (100 %). Our study suggests that continuing development in the retrieval algorithm and sensor design and calibration is essential to be able to advance the application of FNRs beyond a qualitative metric.

Modeled source attribution information from the Community Multiscale Air Quality model was coupled with ambient data from the 2011 Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality Baltimore field study. We assess source contributions and evaluate the utility of using aircraft measured CO and NO

Observations of winds in the planetary boundary layer remain sparse making it challenging to simulate and predict atmospheric conditions that are most important for describing and predicting urban air quality. Short-lived chemicals are observed as plumes whose location is affected by boundary layer winds and whose lifetime is affected by boundary layer height and mixing. Here we investigate the application of data assimilation of NO2 columns as will be observed from geostationary orbit to improve predictions and retrospective analysis of wind fields in the boundary layer.

Tropospheric ozone (O

Air quality policy in the Los Angeles megacity is a guidepost for other megacities. Over the last 2 decades, the policy has substantially reduced aerosol (OA) concentrations and the frequency of high aerosol events in the region. During this time, the emissions contributing to, and the temperature associated with, high aerosol events have changed. Early in the record, aerosol concentrations responded to a variety of different sources. We show that emission control has been effective with a strong decrease in temperature-independent sources. As a result, the response of aerosol to temperature has become a dominant feature of high aerosol events in the basin. The organic fraction of the aerosol (OA) increases with the temperature approaching 35% at 40 °C. We describe a simple conceptual model of aerosol in Los Angeles, illustrating how benzene, toluene, ethylbenzene, and xylenes (BTEX) and isoprene, along with molecules for which these are plausible surrogates such as monoterpenes, are sufficient to explain the observed temperature dependence of PM 2.5.

Ozone, a major contributor to poor air quality, has an array of adverse effects on human, animal, and plant health. In the Los Angeles basin, a megacity that has pursued cleaner air for decades, unhealthy levels of ozone have decreased but remain stubbornly frequent even as the ozone precursors NOx (nitrogen oxides ≡ NO2 + NO) and VOC (volatile organic compounds) have decreased. We describe a combined analysis of decadal trends in these precursors, differences in emissions with day-of-week, and of the impact of temperature to assess the role of VOC and NOx and the likely effects of additional emission reductions on the occurrence of high ozone in the region.

Both canopy-level field measurements and laboratory studies suggest that uptake of NO2 through the leaf stomata of vegetation is a significant sink of atmospheric NOx. However, the mechanisms of this foliar NO2 uptake and their impact on NOx lifetimes remain incompletely understood. To understand the leaf-level processes affecting ecosystem-scale atmosphere–biosphere NOx exchange, we have conducted laboratory experiments of branch-level NO2 deposition fluxes to six coniferous and four broadleaf native California trees using a branch enclosure system with direct laser-induced fluorescence (LIF) detection of NO2. We report NO2 foliar deposition that demonstrates a large degree of inter-species variability, with maximum observed deposition velocities ranging from 0.15 to 0.51 cm s−1 during the daytime, as well as significant stomatal opening during the night. We also find that the contribution of mesophyllic processing to the overall deposition rate of NO2 varies by tree species but has an ultimately inconsequential impact on NOx budgets and lifetimes. Additionally, we find no evidence of any emission of NO2 from leaves, suggesting an effective unidirectional exchange of NOx between the atmosphere and vegetation.

The hydroxyl radical (OH) is the primary cleansing agent in the atmosphere. The abundance of OH in cities initiates the removal of local pollutants; therefore, it serves as the key species describing the urban chemical environment. We propose a machine learning (ML) approach as an efficient alternative to OH simulation using a computationally expensive chemical transport model. The ML model is trained on the parameters simulated from the WRF-Chem model, and it suggests that six predictive parameters are capable of explaining 76% of the OH variability. The parameters are the tropospheric NO₂ column, the tropospheric HCHO column, J(O¹D), H₂O, temperature, and pressure. We then use observations of the tropospheric NO₂ column and HCHO column from OMI as input to the ML model to enable measurement-based prediction of daily near surface OH at 1:30 pm local time across 49 North American cities over the course of 10 years between 2005 and 2014. The result is validated by comparing the OH predictions to measurements of isoprene, which has a source that is uncorrelated with OH and is removed rapidly and almost exclusively by OH in the daytime. We demonstrate that the predicted OH is, as expected, anticorrelated with isoprene. We also show that this ML model is consistent with our understanding of OH chemistry given the solely data-driven nature.

Acyl peroxynitrates are formed in the atmosphere through the oxidation of NOx and are treated as temporary NOx sinks because they typically decompose to rerelease NOx on the time scale of a few hou...

NASA's Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ, conducted in 2011–2014) campaign in the United States and the joint NASA and National Institute of Environmental Research (NIER) Korea–United States Air Quality Study (KORUS-AQ, conducted in 2016) in South Korea were two field study programs that provided comprehensive, integrated datasets of airborne and surface observations of atmospheric constituents, including nitrogen dioxide (NO2), with the goal of improving the interpretation of spaceborne remote sensing data. Various types of NO2 measurements were made, including in situ concentrations and column amounts of NO2 using ground- and aircraft-based instruments, while NO2 column amounts were being derived from the Ozone Monitoring Instrument (OMI) on the Aura satellite. This study takes advantage of these unique datasets by first evaluating in situ data taken from two different instruments on the same aircraft platform, comparing coincidently sampled profile-integrated columns from aircraft spirals with remotely sensed column observations from ground-based Pandora spectrometers, intercomparing column observations from the ground (Pandora), aircraft (in situ vertical spirals), and space (OMI), and evaluating NO2 simulations from coarse Global Modeling Initiative (GMI) and high-resolution regional models. We then use these data to interpret observed discrepancies due to differences in sampling and deficiencies in the data reduction process. Finally, we assess satellite retrieval sensitivity to observed and modeled a priori NO2 profiles. Contemporaneous measurements from two aircraft instruments that likely sample similar air masses generally agree very well but are also found to differ in integrated columns by up to 31.9 %. These show even larger differences with Pandora, reaching up to 53.9 %, potentially due to a combination of strong gradients in NO2 fields that could be missed by aircraft spirals and errors in the Pandora retrievals. OMI NO2 values are about a factor of 2 lower in these highly polluted environments due in part to inaccurate retrieval assumptions (e.g., a priori profiles) but mostly to OMI's large footprint (>312 km2).

Foliar deposition of NO2 removes a large fraction of the global soil-emitted NOx. Understanding the mechanisms of NOx foliar loss is important for constraining surface ozone, constraining NOx mixing ratios, and assessing the impacts of nitrogen inputs to ecosystems. We have constructed a 1-D multibox model with representations of chemistry and vertical transport to evaluate the impact of leaf-level processes on canopy-scale concentrations, lifetimes, and canopy fluxes of NOx. Our model is able to closely replicate canopy fluxes and above-canopy NOx daytime mixing ratios observed during two field campaigns, one in a western Sierra Nevada pine forest (BEARPEX-2009) and the other in a northern Michigan mixed hardwood forest (UMBS-2012). We present a conceptual argument for the importance of NO2 dry deposition and demonstrate that NO2 deposition can provide a mechanistic explanation for the canopy reduction of NOx. We show that foliar deposition can explain observations suggesting as much as ∼60 % of soil-emitted NOx is removed within forest canopies. Stomatal conductances greater than 0.1 cm s−1 result in modeled canopy reduction factors in the range of those used in global models, reconciling inferences of canopy NOx reduction with leaf-level deposition processes. We show that incorporating parameterizations for vapor pressure deficit and soil water potential has a substantial impact on predicted NO2 deposition in our model, with the percent of soil NOx removed within one canopy increasing by ∼15 % in wet conditions compared to dry conditions. NO2 foliar deposition was also found to have a significant impact on ozone and nitrogen budgets under both high- and low-NOx conditions.

A better understanding of the chemistry of nitrogen oxides (NOx) is crucial to effectively reducing air pollution and predicting future air quality. The response of NOx lifetime to perturbations in emissions or in the climate system is set in large part by whether NOx loss occurs primarily by the direct formation of HNO3 or through the formation of alkyl and multifunctional nitrates (RONO2). Using 15 years of detailed in situ observations, we show that in the summer daytime continental boundary layer the relative importance of these two pathways can be well approximated by the relative likelihood that OH will react with NO2 or instead with a volatile organic compound (VOC). Over the past decades, changes in anthropogenic emissions of both NOx and VOCs have led to a significant increase in the overall importance of RONO2 chemistry to NOx loss. We find that this shift is associated with a decreased effectiveness of NOx emissions reductions on ozone production in polluted areas and increased transport of NOx from source to receptor regions. This change in chemistry, combined with changes in the spatial pattern of NOx emissions, is observed to be leading to a flatter distribution of NO2 across the United States, potentially transforming ozone air pollution from a local issue into a regional one.

Heavy-duty vehicles (HDV) contribute a significant, but decreasing, fraction of primary aerosol emissions in urban areas. Previous studies have shown spatial heterogeneity in compliance with regulation. Consequently, location-specific emissions factors are necessary to describe primary particulate matter (PM) emissions by HDV. Using near-road observations from the Bay Area Air Quality Management District (BAAQMD) network over the 2009–2020 period in combination with Caltrans measurements of vehicle number and type, we determine primary PM2.5 emission factors from HDV on highways in the San Francisco Bay Area. We demonstrate that HDV primary aerosol emission factors derived using this method are in line with observations by other studies, that they decreased a by a factor of ~7 in the past decade, and that they are still 2–3 times higher than would be expected if all HDV were in compliance with California HDV regulations.

Satellite retrievals of columnar nitrogen dioxide (NO 2 ) are essential for the characterization of nitrogen oxides (NO x ) processes and impacts. The requirements of modeled a priori profiles present an outstanding bottleneck in operational satellite NO 2 retrievals. In this work, we instead use neural network (NN) models trained from over 360,000 radiative transfer (RT) simulations to translate satellite radiances across 390-495 nm to total NO 2 vertical column (NO 2 C). Despite the wide variability of the many input parameters in the RT simulations, only a small number of key variables were found essential to the accurate prediction of NO 2 C, including observing angles, surface reflectivity and altitude, and several key principal component scores of the radiances. In addition to the NO 2 C, the NN training and cross-validation experiments show that the wider retrieval window allows some information about the vertical distribution to be retrieved (e.g., extending the rightmost wavelength from 465 to 495 nm decreases the root-mean-square-error by 0.75%) under high-NO 2 C conditions. Applying to four months of TROPOMI data, the trained NN model shows strong ability to reproduce the NO 2 C observed by the ground-based Pandonia Global Network. The coefficient of determination ( R 2 , 0.75) and normalized mean bias (NMB, -33%) are competitive with the level 2 operational TROPOMI product ( R 2 = 0.77 , NMB = − 29 % ) over clear ( geometric cloud fraction < 0.2 ) and polluted ( N O 2 C ≥ 7.5 × 10 15 molecules/cm 2 ) regions. The NN retrieval approach is ~12 times faster than predictions using high spatial resolution (~3 km) a priori profiles from chemical transport modeling, which is especially attractive to the handling of large volume satellite data.