Search

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).

We examine O3 production and its sensitivity to precursor gases and boundary layer mixing in Korea by using a 3-D global chemistry transport model and extensive observations during the KORea-US cooperative Air Quality field study in Korea, which occurred in May–June 2016. During the campaign, observed aromatic species onboard the NASA DC-8 aircraft, especially toluene, showed high mixing ratios of up to 10 ppbv, emphasizing the importance of aromatic chemistry in O3 production. To examine the role of VOCs and NOx in O3 chemistry, we first implement a detailed aromatic chemistry scheme in the model, which reduces the normalized mean bias of simulated O3 mixing ratios from –26% to –13%. Aromatic chemistry also increases the average net O3 production in Korea by 37%. Corrections of daytime PBL heights, which are overestimated in the model compared to lidar observations, increase the net O3 production rate by ~10%. In addition, increasing NOx emissions by 50% in the model shows best performance in reproducing O3 production characteristics, which implies that NOx emissions are underestimated in the current emissions inventory. Sensitivity tests show that a 30% decrease in anthropogenic NOx emissions in Korea increases the O3 production efficiency throughout the country, making rural regions ~2 times more efficient in producing O3 per NOx consumed. Simulated O3 levels overall decrease in the peninsula except for urban and other industrial areas, with the largest increase (~6 ppbv) in the Seoul Metropolitan Area (SMA). However, with simultaneous reductions in both NOx and VOCs emissions by 30%, O3 decreases in most of the country, including the SMA. This implies the importance of concurrent emission reductions for both NOx and VOCs in order to effectively reduce O3 levels in Korea.

We examine O3 production and its sensitivity to precursor gases and boundary layer mixing in Korea by using a 3-D global chemistry transport model and extensive observations during the KORea-US cooperative Air Quality field study in Korea, which occurred in May-June 2016. During the campaign, observed aromatic species onboard the NASA DC-8 aircraft, especially toluene, showed high mixing ratios of up to 10 ppbv, emphasizing the importance of aromatic chemistry in O3 production. To examine the role of VOCs and NOx in O3 chemistry, we first implement a detailed aromatic chemistry scheme in the model, which reduces the normalized mean bias of simulated O3 mixing ratios from -26% to -13%. Aromatic chemistry also increases the average net O3 production in Korea by 37%. Corrections of daytime PBL heights, which are overestimated in the model compared to lidar observations, increase the net O3 production rate by ~10%. In addition, increasing NOx emissions by 50% in the model shows best performance in reproducing O3 production characteristics, which implies that NOx emissions are underestimated in the current emissions inventory. Sensitivity tests show that a 30% decrease in anthropogenic NOx emissions in Korea increases the O3 production efficiency throughout the country, making rural regions ~2 times more efficient in producing O3 per NOx consumed. Simulated O3 levels overall decrease in the peninsula except for urban and other industrial areas, with the largest increase (~6 ppbv) in the Seoul Metropolitan Area (SMA). However, with simultaneous reductions in both NOx and VOCs emissions by 30%, O3 decreases in most of the country, including the SMA. This implies the importance of concurrent emission reductions for both NOx and VOCs in order to effectively reduce O3 levels in Korea.

The San Joaquin Valley (SJV) of California experiences high concentrations of particulate matter NH4NO3 during episodes of meteorological stagnation in winter. A rich data set of observations related to NH4NO3 formation was acquired during multiple periods of elevated NH4NO3 during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) field campaign in SJV in January and February 2013. Here NH4NO3 is simulated during the SJV DISCOVER-AQ study period with the Community Multiscale Air Quality (CMAQ) model, diagnostic model evaluation is performed using the DISCOVER-AQ data set, and integrated reaction rate analysis is used to quantify HNO3 production rates. Simulated NO3- generally agrees well with routine monitoring of 24-hr average NO3-, but comparisons with hourly average NO3- measurements in Fresno revealed differences at higher time resolution. Predictions of gas-particle partitioning of total nitrate (HNO3 + NO3-) and NHx (NH3 + NH4+) generally agree well with measurements in Fresno, although partitioning of total nitrate to HNO3 is sometimes overestimated at low relative humidity in afternoon. Gas-particle partitioning results indicate that NH4NO3 formation is limited by HNO3 availability in both the model and ambient. NH3 mixing ratios are underestimated, particularly in areas with large agricultural activity, and additional work on the spatial allocation of NH3 emissions is warranted. During a period of elevated NH4NO3, the model predicted that the OH + NO2 pathway contributed 46% to total HNO3production in SJV and the N2O5 heterogeneous hydrolysis pathway contributed 54%. The relative importance of the OH + NO2 pathway for HNO3 production is predicted to increase as NOx emissions decrease.

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 y relationships to constrain emission inventories. We derive ambient and modeled ΔCO:ΔNO y ratios that have previously been interpreted to represent CO:NO y ratios in emissions from local sources. Modeled and measured ΔCO:ΔNO y are similar; however, measured ΔCO:ΔNO y has much more daily variability than modeled values. Sector-based tagging shows that regional transport, on-road gasoline vehicles, and nonroad equipment are the major contributors to modeled CO mixing ratios in the Baltimore area. In addition to those sources, on-road diesel vehicles, soil emissions, and power plants also contribute substantially to modeled NO y in the area. The sector mix is important because emitted CO:NO x ratios vary by several orders of magnitude among the emission sources. The model-predicted gasoline/diesel split remains constant across all measurement locations in this study. Comparison of ΔCO:ΔNO y to emitted CO:NO y is challenged by ambient and modeled evidence that free tropospheric entrainment, and atmospheric processing elevates ambient ΔCO:ΔNO y above emitted ratios. Specifically, modeled ΔCO:ΔNO y from tagged mobile source emissions is enhanced 5-50% above the emitted ratios at times and locations of aircraft measurements. We also find a correlation between ambient formaldehyde concentrations and measured ΔCO:ΔNO y suggesting that secondary CO formation plays a role in these elevated ratios. This analysis suggests that ambient urban daytime ΔCO:ΔNO y values are not reflective of emitted ratios from individual sources.

Gas phase elemental mercury (Hg°) was measured aboard the NASA DC-8 research aircraft during the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) campaign conducted in spring 2008 primarily over the North American Arctic. We examined the vertical distributions of Hg° and ozone (O3) together with tetrachloroethylene (C2Cl4), ethyne (C2H 2), and alkanes when Hg°- and O3-depleted air masses were sampled near the surface (

In this study, the WRF-Chem v4.4 model was utilized to evaluate three bottom-up emission inventories (EDGAR-HTAP v2, v3, and KORUS v5) using surface and aircraft data in East Asia during the Korea-United States Air Quality (KORUS-AQ) campaign period in 2016. All emission inventories were found to reproduce the diurnal variations of O3 and NO2 as compared to the surface monitor data. However, the spatial distributions of the daily maximum 8-hour average (MDA8) O3 in the model do not completely align with the observations. The model MDA8 O3 had a negative (positive) bias north (south) of 30°N over China. All simulations underestimated the observed CO by 50-60% over China and South Korea. In the Seoul Metropolitan Area (SMA), EDGAR-HTAP v2, v3, and KORUS v5 simulated the vertical shapes and diurnal patterns of O3 and other precursors effectively, but the model underestimated the observed O3, CO and HCHO concentrations. Notably, the model aromatic VOCs were significantly underestimated with the three bottom-up emission inventories, although the KORUS v5 shows improvements. The model isoprene estimations had a positive bias relative to the observations, suggesting that the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.04 overestimated isoprene emissions. Additional model simulations were conducted by doubling CO and VOC emissions over China and South Korea to investigate the causes of the model O3 biases and the effects of the long-range transport on the O3 over South Korea. The doubled CO and VOC emission simulations improved the model O3 simulations for the local emission dominant case, but led to the model O3 overestimations for the transport dominant case, which emphasizes the need for accurate representations of the local VOC emissions over South Korea. [ABSTRACT FROM AUTHOR]

The availability of formaldehyde (HCHO) (a proxy for volatile organic compound reactivity) and nitrogen dioxide (NO 2) (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 HO 2 + RO 2 (LROx) to the chemical loss of NO x (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 (PO 3) dominated by large HCHO × NO 2 concentration levels, a new proxy for the abundance of ozone precursors. The relationship between PO 3 and HCHO × NO 2 becomes more pronounced when moving towards NOx -sensitive regions due to nonlinear chemistry; our results indicate that there is fruitful information in the HCHO × NO 2 metric that has not been utilized in ozone studies. The vast amount of vertical information on HCHO and NO 2 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 km 2). A pixel with a grid size of 216 km 2 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 NO 2 and HCHO columns. At a 68 % confidence interval (1σ), errors pertaining to daily TROPOMI observations, either HCHO or tropospheric NO 2 columns, should be above 1.2–1.5 × 10 16 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. [ABSTRACT FROM AUTHOR]

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 (<50 %) tends to be mild, whereas areas with low vegetation and anthropogenic sources (e.g., 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 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 (<50 %) tends to be mild, whereas areas with low vegetation and anthropogenic sources (e.g., 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. [ABSTRACT FROM AUTHOR]