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Nitrogen oxides (NOx≡ NO + NO2) are of central importance for air quality, climate forcing, and nitrogen deposition to ecosystems. The Geostationary Environment Monitoring Spectrometer (GEMS) is now providing hourly NO2 satellite observations over East Asia, offering the first direct measurements of NO2 diurnal variation from space to guide understanding of NOx emissions and chemistry. The NO2 retrieval requires independent vertical profile information from a chemical transport model (CTM) to compute the air mass factor (AMF) that relates the NO2 column measured along the line of sight to the NO2 vertical column. Here, we use aircraft observations from the Korea-United States Air Quality (KORUS-AQ) campaign over the Seoul metropolitan area (SMA) and around the Korean Peninsula in May–June 2016 to better understand the factors controlling the NO2 vertical profile, its diurnal variation, the implications for the AMFs, and the ability of the GEOS-Chem CTM to compute the NO2 vertical profiles used for AMFs. Proper representation of oxidant chemistry is critical for the CTM simulation of NO2 vertical profiles and is achieved in GEOS-Chem through new model developments, including aerosol nitrate photolysis, reduced uptake of hydroperoxy (HO2) radicals by aerosols, and accounting for atmospheric oxidation of volatile chemical products (VCPs). We find that the tropospheric NO2 columns measured from space in the SMA are mainly contributed by the planetary boundary layer (PBL) below 2 km altitude, reflecting the highly polluted conditions. Repeated measurements of NO2 vertical profiles over the SMA at different times of day show that diurnal change in mixing depth affecting the NO2 vertical profile induces a diurnal variation in AMFs of comparable magnitude to the diurnal variation in the NO2 column. GEOS-Chem captures this diurnal variation in AMFs and more generally the variability in the AMFs for the KORUS-AQ NO2 vertical profiles (2.7 % mean bias, 7.6 % precision), with some outliers in the morning due to errors in the timing of mixed-layer growth.

This study reports measurements of size-resolved aerosol composition at a site in Incheon along with other aerosol characteristics for contrast between Incheon (coastal) and Seoul (inland), South Korea, during a transboundary pollution event during the early part of an intensive sampling period between 4 and 11 March 2019. Anthropogenic emissions were dominant in the boundary layer over the study region between 4 and 6 March, with much smaller contributions from dust, smoke, and sea salt. The meteorology of this period (shallow boundary layer, enhanced humidity, and low temperature) promoted local heterogeneous formation of secondary inorganic and organic species, including high nitrate (NO3-) relative to sulfate (SO42-). Seoul exhibited higher PM2.5 levels than Incheon, likely due to local emissions. The following findings point to secondary aerosol formation and growth sensitivity to water vapor during this pollution event: (i) significant concentrations of individual inorganic and organic acids in the supermicrometer range relative to their full size range (∼40 %) at higher humidity; (ii) high correlation (r=0.95) between oxalate and SO42-, a marker of secondary aqueous production of oxalate; (iii) increased sulfur and nitrogen oxidation ratios as a function of humidity; and (iv) matching composition apportionment (for soluble ions) between the PM1 and PM2.5−1 size fractions. The last finding confirms that PM1 aerosol composition measurements fully capture PM2.5 composition apportionment (for soluble ions) during haze events and may therefore be reliably applied in modeling studies of such events over the full PM2.5 size range. However, the differences evident in the periods following the haze event imply that under other atmospheric conditions PM1 composition measurements will not fully reflect the apportionment of PM2.5 aerosols. The study period was marked by relatively low temperatures that made NO3- the most abundant species detected, pointing to the sensitivity of PM2.5 levels and composition as a function of season during such transboundary events. For instance, other such events in previous studies exhibited more comparable levels between SO42- and NO3- coincident with higher temperatures than the current study. This dataset can contribute to future evaluation of model PM2.5 composition to better support regulatory efforts to control PM2.5 precursors.

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.

This study evaluates the impact of satellite soil moisture (SM) data assimilation (DA) on regional weather and ozone (O3) modeling over the southeastern US during the summer. Satellite SM data are assimilated into the Noah land surface model using an ensemble Kalman filter approach within National Aeronautics and Space Administration's Land Information System framework, which is semicoupled with the Weather Research and Forecasting model with online Chemistry (WRF-Chem; standard version 3.9.1.1). The DA impacts on the model performance of SM, weather states, and energy fluxes show strong spatiotemporal variability. Dense vegetation and water use from human activities unaccounted for in the modeling system are among the factors impacting the effectiveness of the DA. The daytime surface O3 responses to the DA can largely be explained by the temperature-driven changes in biogenic emissions of volatile organic compounds and soil nitric oxide, chemical reaction rates, and dry deposition velocities. On a near-biweekly timescale, the DA modified the mean daytime and daily maximum 8 h average surface O3 by up to 2–3 ppbv, with the maximum impacts occurring in areas where daytime surface air temperature most strongly (i.e., by ∼2 K) responded to the DA. The DA impacted WRF-Chem upper tropospheric O3 (e.g., for its daytime-mean, by up to 1–1.5 ppbv) partially via altering the transport of O3 and its precursors from other places as well as in situ chemical production of O3 from lightning and other emissions. Case studies during airborne field campaigns suggest that the DA improved the model treatment of convective transport and/or lightning production. In the cases that the DA improved the modeled SM, weather fields, and some O3-related processes, its influences on the model's O3 performance at various altitudes are not always as desirable. This is in part due to the uncertainty in the model's key chemical inputs, such as anthropogenic emissions, and the model representation of stratosphere–troposphere exchanges. This can also be attributable to shortcomings in model parameterizations (e.g., chemical mechanism, natural emission, photolysis and deposition schemes), including those related to representing water availability impacts. This study also shows that the WRF-Chem upper tropospheric O3 response to the DA has comparable magnitudes with its response to the estimated US anthropogenic emission changes within 2 years. As reductions in anthropogenic emissions in North America would benefit the mitigation of O3 pollution in its downwind regions, this analysis highlights the important role of SM in quantifying air pollutants' source–receptor relationships between the US and its downwind areas. It also emphasizes that using up-to-date anthropogenic emissions is necessary for accurately assessing the DA impacts on the model performance of O3 and other pollutants over a broad region. This work will be followed by a Noah-Multiparameterization (with dynamic vegetation)-based study over the southeastern US, in which selected processes including photosynthesis and O3 dry deposition will be the foci.

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

The 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field experiment obtained a diverse set of in-situ and remotely-sensed measurements before and during a pyrocumulonimbus (pyroCb) event over the Williams Flats fire in Washington State. This unique dataset confirms that pyroCb activity is an efficient vertical smoke transport pathway into the upper troposphere and lower stratosphere (UTLS). The magnitude of smoke plumes observed in the UTLS has increased significantly in recent years, following unprecedented wildfire and pyroCb activity observed worldwide. The FIREX-AQ pyroCb dataset is therefore extremely relevant to a broad community, providing the first measurements of fresh smoke exhaust in the upper-troposphere, including from within active pyroCb cloud tops. High-resolution remote sensing reveals that three plume cores linked to localized fire fronts, burning primarily in dense forest fuels, contributed to four total pyroCb “pulses”. Rapid changes in fire geometry and spatial extent dramatically influenced the magnitude, behavior, and duration of pyroCb activity. Cloud probe measurements and weather radar identify the presence of large ice particles within the pyroCb and hydrometers below cloud base, indicating precipitation development. The resulting feedbacks suggest that vertical smoke transport efficiency was reduced slightly when compared with intense pyroCb events reaching the lower stratosphere. Physical and optical aerosol property measurements in pyroCb exhaust are compared with previous assumptions. A large suite of aerosol and gas-phase chemistry measurements sets a foundation for future studies aimed at understanding the composition of smoke plumes lifted by pyroconvection into the UTLS and their role in the climate system.

The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and temporal resolution from a geostationary Earth orbit (GEO) for the first time. With the development of UV–visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO, and aerosols) can be obtained. To date, all the UV–visible satellite missions monitoring air quality have been in low Earth orbit (LEO), allowing one to two observations per day. With UV–visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be on board the Geostationary Korea Multi-Purpose Satellite 2 (GEO-KOMPSAT-2) satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager 2 (GOCI-2). These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) and ESA’s Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS).

Emissions and secondary photochemical products from the Daesan petrochemical complex (DPCC), on the west coast of South Korea, were measured from the NASA DC-8 research aircraft during the Korea-United States Air Quality campaign in 2016. The chemical evolution of petrochemical emissions was examined utilizing near-source and downwind plume transects. Small alkenes, such as ethene (C2H4), propene (C3H6), and 1,3-butadiene (C4H6), dominated the hydroxyl (OH) radical reactivity near the source region. The oxidation of these alkenes in the petrochemical plumes led to efficient conversion of nitrogen oxides (NOx) to nitric acid (HNO3), peroxycarboxylic nitric anhydrides (PANs), and alkyl nitrates (ANs), where the sum of the speciated reactive nitrogen contributes more than 80% of NOy within a few hours. Large enhancements of short-lived NOx oxidation products, such as hydroxy nitrates (HNs) and peroxyacrylic nitric anhydride, were observed, in conjunction with high ozone levels of up to 250 ppb, which are attributed to oxidation of alkenes such as 1,3-butadiene. Instantaneous ozone production rates, P(O3), near and downwind of the DPCC ranged from 9 to 24 ppb h−1, which were higher than those over Seoul. Ozone production efficiencies ranged from 6 to 10 downwind of the DPCC and were lower than 10 over Seoul. The contributions of alkenes to the instantaneous secondary organic aerosol (SOA) production rate, P(SOA), were estimated to be comparable to those of more common SOA precursors such as aromatics at intermediate distances from the DPCC. A model case study constrained to an extensive set of observations provided a diagnostic of petrochemical plume chemistry. The simulated plume chemistry reproduced the observed evolution of ozone and short-lived reactive nitrogen compounds, such as PANs and HNs as well as the rate and efficiency of ozone production. The simulated peroxy nitrates (PNs) budget included large contributions (approximately 30%) from unmeasured PNs including peroxyhydroxyacetic nitric anhydride and peroxybenzoic nitric anhydride. The large, predicted levels of these PAN compounds suggest their potential importance in chemical evolution of petrochemical plumes. One unique feature of the DPCC plumes is the substantial contribution of 1,3-butadiene to ozone and potentially SOA production. This work suggests that reductions in small alkene, especially 1,3-butadiene, emissions from the DPCC should be a priority for reducing downwind ozone.

Description, While ozone increases rapidly in wildfire plumes, downwind its production rate slows dramatically as nitrogen oxide levels decline., Wildfires are a substantial but poorly quantified source of tropospheric ozone (O3). Here, to investigate the highly variable O3 chemistry in wildfire plumes, we exploit the in situ chemical characterization of western wildfires during the FIREX-AQ flight campaign and show that O3 production can be predicted as a function of experimentally constrained OH exposure, volatile organic compound (VOC) reactivity, and the fate of peroxy radicals. The O3 chemistry exhibits rapid transition in chemical regimes. Within a few daylight hours, the O3 formation substantially slows and is largely limited by the abundance of nitrogen oxides (NOx). This finding supports previous observations that O3 formation is enhanced when VOC-rich wildfire smoke mixes into NOx-rich urban plumes, thereby deteriorating urban air quality. Last, we relate O3 chemistry to the underlying fire characteristics, enabling a more accurate representation of wildfire chemistry in atmospheric models that are used to study air quality and predict climate.

Accurate fire emissions inventories are crucial to predict the impacts of wildland fires on air quality and atmospheric composition. Two traditional approaches are widely used to calculate fire emissions: a satellite-based top-down approach and a fuels-based bottom-up approach. However, these methods often considerably disagree on the amount of particulate mass emitted from fires. Previously available observational datasets tended to be sparse, and lacked the statistics needed to resolve these methodological discrepancies. Here, we leverage the extensive and comprehensive airborne in situ and remote sensing measurements of smoke plumes from the recent Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign to statistically assess the skill of the two traditional approaches. We use detailed campaign observations to calculate and compare emission rates at an exceptionally high-resolution using three separate approaches: top-down, bottom-up, and a novel approach based entirely on integrated airborne in situ measurements. We then compute the daily average of these high-resolution estimates and compare with estimates from lower resolution, global top-down and bottom-up inventories. We uncover strong, linear relationships between all of the high-resolution emission rate estimates in aggregate, however no single approach is capable of capturing the emission characteristics of every fire. Global inventory emission rate estimates exhibited weaker correlations with the high-resolution approaches and displayed evidence of systematic bias. The disparity between the low-resolution global inventories and the high-resolution approaches is likely caused by high levels of uncertainty in essential variables used in bottom-up inventories and imperfect assumptions in top-down inventories.

Tinkering with tools in my laboratory has helped me more than I expected. Tinkering with tools in my laboratory has helped me more than I expected.

Data repository for paper: "Simulation of radon-222 with the GEOS-Chem global model: Emissions, seasonality, and convective transport" by Bo Zhang, Hongyu Liu, James H. Crawford, Gao Chen, T. Duncan Fairlie, Scott Chambers, Chang-Hee Kang, Alastair G. Williams, Kai Zhang, David B. Considine, Daniel J. Jacob, Melissa P. Sulprizio, and Robert M. Yantosca, submitted to Atmos. Chem. Phys., July 2020 Created by Bo Zhang (bzhang@nianet.org), July 2020 -> Data -> Rn_surface Contains site measurement files -> Please refer to Zhang et al. (2011) for most of the surface site measurements. -> MLO_radon.xlsx Long-term Rn-222 measurements at Mauna Loa. -> Gosan_2001-2010.xlsx Long-term Rn-222 measurements at Gosan. -> Rn_profile Contains no data here. Please refer to Liu et al. (1984), Kritz et al. (1998), Zaucker et al. (1996), and Williams et al. (2011) for the four Rn-222 profile datasets. -> Model_modified -> Code_1101f_Rn_paper The folder contains a complete code directory of GEOS-Chem v11-01f. -> hcox_gc_RnPbBe_mod.F90.ZKCHA120NASWred This file replaces HEMCO/Extensions/hcox_gc_RnPbBe_mod.F90 under GEOS-Chem version 11.01f code directory to read the updated emission files (Rn_emis_ZKCHA120_NASWred). Search 'bz' in the file for modifications. -> Rn_emis_ZKCHA120_NASWred: updated global Rn-222 emission fluxes. -> ZKC_EMIS_ARR_ATOM_2X25_MONTHLY.sav IDL save-file with saved monthly mean Rn-222 emission fluxes. (gridded 2x2.5 array ZKC_EMIS_ARR_ATOM_2X25_MONTHLY) Unit is atom Rn-222 per cm2 per second. -> Rn_emis_ZKCHA120_NASWred_2x25_std01-12 Text files need to be put under GEOS-Chem run directory. Each monthly file contains a single column array. The data are placed in a longitude and latitude sequence starting from (180W, 89.5S) to (178W, 89.5S) .... and until (177.5E, 89.5N). The entire folder needs to be placed under GEOS-Chem run directory. -> For the other three emission scenarios used in the paper please refer to Jacob et al. (1997), Schery and Wasiolek (1998), and Zhang et al. (2011). -> Model_output -> tracerinfo.dat, diaginfo.dat: GEOS-Chem dat files needed for IDL-GAMAP -> rpbbe2_1101f_merra_2013_JA97.bpch: model output with emission JA97. -> rpbbe2_1101f_merra_2013_SW98.bpch: model output with emission SW98. -> rpbbe2_1101f_merra_2013_ZK11.bpch: model output with emission ZK11. -> rpbbe2_1101f_merra_2013_ZKCHA120NASWred.bpch: model output with the updated emission in this work. Reference: Jacob, D. J., Prather, M. J., Rasch, P. J., Shia, R. L., Balkanski, Y. J., Beagley, S. R., ... & Chipperfield, M. P. (1997). Evaluation and intercomparison of global atmospheric transport models using 222Rn and other short‐lived tracers. Journal of Geophysical Research: Atmospheres, 102(D5), 5953-5970. Kritz, M. A., Rosner, S. W. and Stockwell, D. Z.: Validation of an off-line three-dimensional chemical transport model using observed radon profiles: 1. Observations, J. Geophys. Res, 103, 8425–8432, 1998. Liu, S. C., Mcafee, J. R. and Cicerone, R. J.: Radon 222 and Tropospheric Vertical Transport, J. Geophys. Res., 89(20), 7291–7297, doi:10.1029/JD089iD05p07291, 1984. Schery, S. D. and Wasiolek, M. A.: Modeling radon flux from the Earth’s surface, in: Radon and Thorn in the Human Environment, in: Proceedings of the 7th Tohwa University International Symposium, edited by: Katase, A. and Shimo, M., World Scientific Publishing Co. Pre. Ltd., Singapore, 73–78, 1998. Williams, A. G., Zahorowski, W., Chambers, S., Griffiths, A., Hacker, J. M., Element, A. and Werczynski, S.: The vertical distribution of radon in clear and cloudy daytime terrestrial boundary layers, J. Atmos. Sci., 68(1), 155–174, doi:10.1175/2010JAS3576.1, 2011. Zaucker, F., PH, and D., U, W., Berkowitz, C., nd Kromer, B. and Broecker, W. S.: Atmospheric 222Rn measurements during the 1993 NARE Intensive., J. Geophys. Res., 101, 29149–29164, 1996. Zhang, K., Feichter, J., Kazil, J., Wan, H., Zhuo, W., Griffiths, A., ... & Yver, C. (2011). Radon activity in the lower troposphere and its impact on ionization rate: a global estimate using different radon emissions. Atmospheric Chemistry and Physics, 11(15), 7817-7838. &nbsp

Sub-grid variability (SGV) of atmospheric trace gases within satellite pixels is a key issue in satellite design, and interpretation and validation of retrieval products. However, characterizing this variability is challenging due to the lack of independent high-resolution measurements. Here we use tropospheric NO2 vertical column (VC) measurements from the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument with a spatial resolution of about 250 m 250 m to quantify the normalized SGV (i.e., the standard deviation of the sub-grid GeoTASO values within the sampled satellite pixel divided by their mean of the sub-grid GeoTASO values within the sampled satellite pixel) for different satellite pixel sizes. We use the GeoTASO measurements over the Seoul Metropolitan Area (SMA) and Busan region of South Korea during the 2016 KORUS‐AQ field campaign, and over the Los Angeles Basin, USA during the 2017 SARP field campaign. We find that the normalized SGV of NO2 VC increases with increasing satellite pixel sizes (from ~10 % for 0.5 km × 0.5 km pixel size to ~35 % for 25 km × 25 km pixel size), and this relationship holds for the three study regions, which are also within the domains of upcoming geostationary satellite air quality missions. We also quantify the temporal variability of the retrieved NO2 VC within the same satellite pixels (represented by the difference of retrieved values at two different times of a day). For a given satellite pixel size, the temporal variability within the same satellite pixels increases with the sampling time difference over SMA. For a given small (e.g., 2 VC increases with the increasing spatial resolution over the SMA, Busan region, and the Los Angeles basin. The results of this study have implications for future satellite design and retrieval interpretation, and validation when comparing pixel data with local observations. In addition, the analyses presented in this study are equally applicable in model evaluation when comparing model grid values to local observations. Results from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model indicate that the normalized satellite SGV of tropospheric NO2 VC calculated in this study could serve as an upper bound to the satellite SGV of other species (e.g., CO and SO2) that share common source(s) with NO2 but have relatively longer lifetime.

The Korea-United States Air Quality (KORUS-AQ) field study was conducted during May–June 2016 to understand the factors controlling air quality in South Korea. Extensive aircraft and ground network observations from the campaign offer an opportunity to address issues in current air quality models and reduce model-observation disagreements. This study examines these issues using model evaluation against the KORUS-AQ observations and intercomparisons between models. Six regional and two global chemistry transport models using identical anthropogenic emissions participated in the model intercomparison study and were used to conduct air quality simulations focusing on ozone (O3), aerosols, and their precursors for the campaign. Using the KORUSv5 emissions inventory, which has been updated from KORUSv1, the models successfully reproduced observed nitrogen oxides (NOx) and volatile organic compounds mixing ratios in surface air, especially in the Seoul Metropolitan Area, but showed systematic low biases for carbon monoxide (CO), implying possible missing CO sources in the inventory in East Asia. Although the DC-8 aircraft-observed O3 precursor mixing ratios were well captured by the models, simulated O3 levels were lower than the observations in the free troposphere in part due to too low stratospheric O3 influxes, especially in regional models. During the campaign, the synoptic meteorology played an important role in determining the observed variability of PM2.5 (PM diameter ≤ 2.5 μm) concentrations in South Korea. The models successfully simulated the observed PM2.5 variability with significant inorganic sulfate-nitrate-ammonium aerosols contribution, but failed to reproduce that of organic aerosols, causing a large inter-model variability. From the model evaluation, we find that an ensemble of model results, incorporating individual models with differing strengths and weaknesses, performs better than most individual models at representing observed atmospheric compositions for the campaign. Ongoing model development and evaluation, in close collaboration with emissions inventory development, are needed to improve air quality forecasting.

The Korea–United States Air Quality (KORUS-AQ) field study was conducted during May–June 2016. The effort was jointly sponsored by the National Institute of Environmental Research of South Korea and the National Aeronautics and Space Administration of the United States. KORUS-AQ offered an unprecedented, multi-perspective view of air quality conditions in South Korea by employing observations from three aircraft, an extensive ground-based network, and three ships along with an array of air quality forecast models. Information gathered during the study is contributing to an improved understanding of the factors controlling air quality in South Korea. The study also provided a valuable test bed for future air quality–observing strategies involving geostationary satellite instruments being launched by both countries to examine air quality throughout the day over Asia and North America. This article presents details on the KORUS-AQ observational assets, study execution, data products, and air quality conditions observed during the study. High-level findings from companion papers in this special issue are also summarized and discussed in relation to the factors controlling fine particle and ozone pollution, current emissions and source apportionment, and expectations for the role of satellite observations in the future. Resulting policy recommendations and advice regarding plans going forward are summarized. These results provide an important update to early feedback previously provided in a Rapid Science Synthesis Report produced for South Korean policy makers in 2017 and form the basis for the Final Science Synthesis Report delivered in 2020.

International audience; Satellite ammonia (NH3) observations provide unprecedented insights into NH3 emissions, spatiotemporal variabilities and trends, but validation with in‐situ measurements remains lacking. Here, total columns from the Infrared Atmospheric Sounding Interferometer (IASI) were intercompared to boundary layer NH3 profiles derived from aircraft‐ and surface‐based measurements primarily in Colorado, USA, in the summer of 2014. IASI‐NH3 version 3 near real‐time dataset compared well to in‐situ derived columns (windows ±15 km around centroid, ±1 hour around overpass time) with a correlation of 0.58, a slope of 0.78±0.14, and an intercept of 2.1×1015±1.5×1015 molecules cm‐2. Agreement degrades at larger spatiotemporal windows, consistent with the short atmospheric lifetime of NH3. We also examined IASI version 3R data, which relies on temperature retrievals from the ERA Reanalysis, and a third product generated using aircraft‐measured temperature profiles. The overall agreement improves slightly for both cases, and neither is biased within their combined measurement errors. Thus, spatiotemporal averaging of IASI over large windows can be used to reduce retrieval noise. Nonetheless, sampling artifacts of airborne NH3 instruments result in significant uncertainties of the in‐situ‐derived columns. For example, large validation differences exist between ascent and descent profiles, and the assumptions of the free tropospheric NH3 profiles used above the aircraft ceiling significantly impact the validation. Because short‐lived species like NH3 largely reside within the boundary layer with complex vertical structures, more comprehensive validation is needed across a wide range of environments. More accurate and widespread in‐situ NH3 datasets are therefore required for improved validations of satellite products.

The U.S. National Aeronautics and Space Administration in partnership with Korea’s National Institute of Environmental Research embarked on the Korea-United States Air Quality (KORUS-AQ) study to address air quality issues over the Korean peninsula. Underestimation of volatile organic compound (VOC) emissions from various large facilities on South Korea’s northwest coast may contribute to this problem, and this study focuses on quantifying top-down emissions of formaldehyde (CH2O) and VOCs from the largest of these facilities, the Daesan petrochemical complex, and comparisons with the latest emission inventories. To accomplish this and additional goals discussed herein, this study employed a number of measurements acquired during KORUS-AQ onboard the NASA DC-8 aircraft during three Daesan overflights on June 2, 3, and 5, 2016, in conjunction with a mass balance approach. The measurements included fast airborne measurements of CH2O and ethane from an infrared spectrometer, additional fast measurements from other instruments, and a suite of 33 VOC measurements acquired by the whole air sampler. The mass balance approach resulted in consistent top-down yearly Daesan VOC emission flux estimates, which averaged (61 ± 14) × 103 MT/year for the 33 VOC compounds, a factor of 2.9 ± 0.6 (±1.0) higher than the bottom-up inventory value. The top-down Daesan emission estimate for CH2O and its four primary precursors averaged a factor of 4.3 ± 1.5 (± 1.9) times higher than the bottom-up inventory value. The uncertainty values in parentheses reflect upper limits for total uncertainty estimates. The resulting averaged top-down Daesan emission estimate for sulfur dioxide (SO2) yielded a ratio of 0.81–1.0 times the bottom-up SO2 inventory, and this provides an important cross-check on the accuracy of our mass balance analysis.

This study evaluates the impact of satellite soil moisture data assimilation (SM DA) on regional weather and ozone (O3) modeling over the southeastern US during the summer. Satellite SM data are assimilated into the Noah land surface model using an ensemble Kalman filter approach within National Aeronautics and Space Administration's Land Information System framework, which is semicoupled with the Weather Research and Forecasting model with online Chemistry (WRF‐Chem, standard version 3.9.1.1). The SM DA impacts on WRF-Chem performance of weather states and energy fluxes show strong spatiotemporal variability, and many factors such as dense vegetation, complex terrain, and unmodeled water use from human activities may have impacted the effectiveness of the SM DA. The changes in WRF-Chem weather fields due to the SM DA modified various model processes critical to its surface O3 fields, such as biogenic isoprene and soil nitric oxide emissions, photochemical reactions, as well as dry deposition. The SM DA impacted WRF-Chem upper tropospheric O3 partially via altering atmospheric transport and in-situ chemical production of O3 from lightning and other emissions. It is shown that WRF-Chem upper tropospheric O3 response to the SM DA has comparable magnitudes with its response to the estimated US anthropogenic emission changes within two years. As reductions in US anthropogenic emissions would be beneficial for mitigating European O3 pollution, our analysis highlights the important role of SM in quantifying pollutants' transport from the US to Europe. It also emphasizes that using up-to-date anthropogenic emissions is necessary for accurately assessing the SM DA impacts on the model performance of O3 and other pollutants over a broad region. Additionally, this work demonstrates that the SM DA impact on WRF-Chem O3 performance at various altitudes is complicated by not only the model's emission input but also other factors such as the model representation of stratosphere-troposphere exchanges. This work will be followed by a Noah-Multiparameterization (with dynamic vegetation) based study over the southeastern US, in which selected processes including photosynthesis and O3 dry deposition will be the foci.

The Korea-United States Air Quality Study (KORUS-AQ) took place in spring 2016 to better understand air pollution in Korea. In support of KORUS-AQ, 2554 whole air samples (WAS) were collected aboard the NASA DC-8 research aircraft and analyzed for 82 C1–C10 volatile organic compounds (VOCs) using multi-column gas chromatography. Together with fast-response measurements from other groups, the air samples were used to characterize the VOC composition in Seoul and surrounding regions, determine which VOCs are major ozone precursors in Seoul, and identify the sources of these reactive VOCs. (1) The WAS VOCs showed distinct signatures depending on their source origins. Air collected over Seoul had abundant ethane, propane, toluene and n-butane while plumes from the Daesan petrochemical complex were rich in ethene, C2–C6 alkanes and benzene. Carbonyl sulfide (COS), CFC-113, CFC-114, carbon tetrachloride (CCl4) and 1,2-dichloroethane were good tracers of air originating from China. CFC-11 was also elevated in air from China but was surprisingly more elevated in air over Seoul. (2) Methanol, isoprene, toluene, xylenes and ethene were strong individual contributors to OH reactivity in Seoul. However methanol contributed less to ozone formation based on photochemical box modeling, which better accounts for radical chemistry. (3) Positive Matrix Factorization (PMF) and other techniques indicated a mix of VOC source influences in Seoul, including solvents, traffic, biogenic, and long-range transport. The solvent and traffic sources were roughly equal using PMF, and the solvents source was stronger in the KORUS-AQ emission inventory. Based on PMF, ethene and propene were primarily associated with traffic, and toluene, ethylbenzene and xylenes with solvents, especially non-paint solvents for toluene and paint solvents for ethylbenzene and xylenes. This suggests that VOC control strategies in Seoul could continue to target vehicle exhaust and paint solvents, with additional regulations to limit the VOC content in a variety of non-paint solvents.