Ozone Monitoring Instrument Tropospheric NO2 Research Articles

Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality research in Africa

Abstract. The Multi-Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) is a new community modeling infrastructure that enables the study of atmospheric composition and chemistry across all relevant scales. We develop a MUSICAv0 grid with Africa refinement (∼ 28 km × 28 km over Africa). We evaluate the MUSICAv0 simulation for 2017 with in situ observations and compare the model results to satellite products over Africa. A simulation from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), a regional model that is widely used in Africa studies, is also included in the analyses as a reference. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Both models underestimate carbon monoxide (CO) compared to in situ observations and satellite CO column retrievals from the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument. MUSICAv0 tends to overestimate ozone (O3), likely due to overestimated stratosphere-to-troposphere flux of ozone. Both models significantly underestimate fine particulate matter (PM2.5) at two surface sites in East Africa. The MUSICAv0 simulation agrees better with aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and tropospheric nitrogen dioxide (NO2) column retrievals from the Ozone Monitoring Instrument (OMI) than WRF-Chem. MUSICAv0 has a consistently lower tropospheric formaldehyde (HCHO) column than OMI retrievals. Based on model–satellite discrepancies between MUSICAv0 and WRF-Chem and MOPITT CO, MODIS AOD, and OMI tropospheric NO2, we find that future field campaign(s) and more in situ observations in the East African region (5∘ S–5∘ N, 30–45∘ E) could substantially improve the predictive skill of atmospheric chemistry model(s). This suggested focus region exhibits the largest model–in situ observation discrepancies, as well as targets for high population density, land cover variability, and anthropogenic pollution sources.

Characterization of errors in satellite-based HCHO ∕ NO2 tropospheric column ratios with respect to chemistry, column-to-PBL translation, spatial representation, and retrieval uncertainties

Abstract. 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.

Evaluation of TROPOMI and OMI Tropospheric NO2 Products Using Measurements from MAX-DOAS and State-Controlled Stations in the Jiangsu Province of China

The tropospheric vertical column density of NO2 (Trop NO2 VCD) can be obtained using satellite remote sensing, but it has been discovered that the Trop NO2 VCD is affected by uncertainties such as the cloud fraction, terrain reflectivity, and aerosol optical depth. A certain error occurs in terms of data inversion accuracy, necessitating additional ground observation verification. This study uses surface NO2 mass concentrations from the China National Environmental Monitoring Center (CNEMC) sites in Jiangsu Province, China in 2019 and the Trop NO2 VCD measured by MAX-DOAS, respectively, to verify the Trop NO2 VCD product (daily and monthly average data), that comes from the TROPOspheric Monitoring Instrument (TROPOMI) and Ozone Monitoring Instrument (OMI). The results show that the spatial distributions of NO2 in TROPOMI and OMI exhibit a similar tendency and seasonality, showing the characteristics of being high in spring and winter and low in summer and autumn. On the whole, the concentration of NO2 in the south of Jiangsu Province is higher than that in the north. The Pearson correlation coefficient (r) between the monthly average TROPOMI VCD NO2 and the CNEMC NO2 mass concentration is 0.9, which is greater than the r (0.78) between OMI and CNEMC; the r (0.69) between TROPOMI and the MAX-DOAS VCD NO2 is greater than the r (0.59) between OMI and the MAX-DOAS. As such, the TROPOMI is better than the previous generation of OMI at representing the spatio-temporal distribution of NO2 in the regional scope. On the other hand, the uncertainties of the satellite products provided in this study can constrain regional air quality forecasting models and top-down emission inventory estimation.

Evaluating the impact of spatial resolution on tropospheric NO2 column comparisons within urban areas using high-resolution airborne data.

NASA deployed the GeoTASO airborne UV-Visible spectrometer in May-June 2017 to produce high resolution (approximately 250 × 250 m) gapless NO2 datasets over the western shore of Lake Michigan and over the Los Angeles Basin. The results collected show that the airborne tropospheric vertical column retrievals compare well with ground-based Pandora spectrometer column NO2 observations (r2=0.91 and slope of 1.03). Apparent disagreements between the two measurements can be sensitive to the coincidence criteria and are often associated with large local variability, including rapid temporal changes and spatial heterogeneity that may be observed differently by the sunward viewing Pandora observations. The gapless mapping strategy executed during the 2017 GeoTASO flights provides data suitable for averaging to coarser areal resolutions to simulate satellite retrievals. As simulated satellite pixel area increases to values typical of TEMPO, TROPOMI, and OMI, the agreement with Pandora measurements degraded, particularly for the most polluted columns as localized large pollution enhancements observed by Pandora and GeoTASO are spatially averaged with nearby less-polluted locations within the larger area representative of the satellite spatial resolutions (aircraft-to-Pandora slope: TEMPO scale=0.88; TROPOMI scale=0.77; OMI scale=0.57). In these two regions, Pandora and TEMPO or TROPOMI have the potential to compare well at least up to pollution scales of 30×1015 molecules cm-2. Two publicly available OMI tropospheric NO2 retrievals are both found to be biased low with respect to these Pandora observations. However, the agreement improves when higher resolution a priori inputs are used for the tropospheric air mass factor calculation (NASA V3 Standard Product slope = 0.18 and Berkeley High Resolution Product slope=0.30). Overall, this work explores best practices for satellite validation strategies with Pandora direct-sun observations by showing the sensitivity to product spatial resolution and demonstrating how the high spatial resolution NO2 data retrieved from airborne spectrometers, such as GeoTASO, can be used with high temporal resolution ground-based column observations to evaluate the influence of spatial heterogeneity on validation results.

Detection of Strong NOX Emissions from Fine-scale Reconstruction of the OMI Tropospheric NO2 Product

Satellite-retrieved atmospheric NO2 column products have been widely used in assessing bottom-up NOX inventory emissions emitted from large cities, industrial facilities, and power plants. However, the satellite products fail to quantify strong NOX emissions emitted from the sources less than the satellite’s pixel size, with significantly underestimating their emission intensities (smoothing effect). The poor monitoring of the emissions makes it difficult to enforce pollution restriction regulations. This study reconstructs the tropospheric NO2 vertical column density (VCD) of the Ozone Monitoring Instrument (OMI)/Aura (13 × 24 km2 pixel resolution at nadir) over South Korea to a fine-scale product (grid resolution of 3 × 3 km2) using a conservative spatial downscaling method, and investigates the methodological fidelity in quantifying the major Korean area and point sources that are smaller than the satellite’s pixel size. Multiple high-fidelity air quality models of the Weather Research and Forecast-Chemistry (WRF-Chem) and the Weather Research and Forecast/Community Multiscale Air Quality modeling system (WRF/CMAQ) were used to investigate the downscaling uncertainty in a spatial-weight kernel estimate. The analysis results showed that the fine-scale reconstructed OMI NO2 VCD revealed the strong NOX emission sources with increasing the atmospheric NO2 column concentration and enhanced their spatial concentration gradients near the sources, which was accomplished by applying high-resolution modeled spatial-weight kernels to the original OMI NO2 product. The downscaling uncertainty of the reconstructed OMI NO2 product was inherent and estimated by 11.1% ± 10.6% at the whole grid cells over South Korea. The smoothing effect of the original OMI NO2 product was estimated by 31.7% ± 13.1% for the 6 urbanized area sources and 32.2% ± 17.1% for the 13 isolated point sources on an effective spatial resolution that is defined to reduce the downscaling uncertainty. Finally, it was found that the new reconstructed OMI NO2 product had a potential capability in quantifying NOX emission intensities of the isolated strong point sources with a good correlation of R = 0.87, whereas the original OMI NO2 product failed not only to identify the point sources, but also to quantify their emission intensities (R = 0.30). Our findings highlight a potential capability of the fine-scale reconstructed OMI NO2 product in detecting directly strong NOX emissions, and emphasize the inherent methodological uncertainty in interpreting the reconstructed satellite product at a high-resolution grid scale.

Top-Down NOX Emissions of European Cities Based on the Downwind Plume of Modelled and Space-Borne Tropospheric NO₂ Columns.

Top-down estimates of surface NOX emissions were derived for 23 European cities based on the downwind plume decay of tropospheric nitrogen dioxide (NO2) columns from the LOTOS-EUROS (Long Term Ozone Simulation-European Ozone Simulation) chemistry transport model (CTM) and from Ozone Monitoring Instrument (OMI) satellite retrievals, averaged for the summertime period (April–September) during 2013. Here we show that the top-down NOX emissions derived from LOTOS-EUROS for European urban areas agree well with the bottom-up NOX emissions from the MACC-III inventory data (R2 = 0.88) driving the CTM demonstrating the potential of this method. OMI top-down NOX emissions over the 23 European cities are generally lower compared with the MACC-III emissions and their correlation is slightly lower (R2 = 0.79). The uncertainty on the derived NO2 lifetimes and NOX emissions are on average ~55% for OMI and ~63% for LOTOS-EUROS data. The downwind NO2 plume method applied on both LOTOS-EUROS and OMI tropospheric NO2 columns allows to estimate NOX emissions from urban areas, demonstrating that this is a useful method for real-time updates of urban NOX emissions with reasonable accuracy.

Comparing OMI-based and EPA AQS in situ NO<sub>2</sub> trends: towards understanding surface NO<sub><i>x</i></sub> emission changes

Abstract. With the improved spatial resolution of the Ozone Monitoring Instrument (OMI) over earlier instruments and more than 10 years of service, tropospheric NO2 retrievals from OMI have led to many influential studies on the relationships between socioeconomic activities and NOx emissions. Previous studies have shown that the OMI NO2 data show different relative trends compared to in situ measurements. However, the sources of the discrepancies need further investigations. This study focuses on how to appropriately compare relative trends derived from OMI and in situ measurements. We retrieve OMI tropospheric NO2 vertical column densities (VCDs) and obtain the NO2 seasonal trends over the United States, which are compared with coincident in situ surface NO2 measurements from the Air Quality System (AQS) network. The Mann–Kendall method with Sen's slope estimator is applied to derive the NO2 seasonal and annual trends for four regions at coincident sites during 2005–2014. The OMI-based NO2 seasonal relative decreasing trends are generally biased low compared to the in situ trends by up to 3.7 % yr−1, except for the underestimation in the US Midwest and Northeast during December, January, and February (DJF). We improve the OMI retrievals for trend analysis by removing the ocean trend, using the Moderate Resolution Imaging Spectroradiometer (MODIS) albedo data in air mass factor (AMF) calculation. We apply a lightning flash filter to exclude lightning-affected data to make proper comparisons. These data processing procedures result in close agreement (within 0.3 % yr−1) between in situ and OMI-based NO2 regional annual relative trends. The remaining discrepancies may result from inherent difference between trends of NO2 tropospheric VCDs and surface concentrations, different spatial sampling of the measurements, chemical nonlinearity, and tropospheric NO2 profile changes. We recommend that future studies apply these procedures (ocean trend removal and MODIS albedo update) to ensure the quality of satellite-based NO2 trend analysis and apply the lightning filter in studying surface NOx emission changes using satellite observations and in comparison with the trends derived from in situ NO2 measurements. With these data processing procedures, we derive OMI-based NO2 regional annual relative trends using all available data for the US West (−2.0 % ± 0.3 yr−1), Midwest (−1.8 % ± 0.4 yr−1), Northeast (−3.1 % ± 0.5 yr−1), and South (−0.9 % ± 0.3 yr−1). The OMI-based annual mean trend over the contiguous United States is −1.5 % ± 0.2 yr−1. It is a factor of 2 lower than that of the AQS in situ data (−3.9 % ± 0.4 yr−1); the difference is mainly due to the fact that the locations of AQS sites are concentrated in urban and suburban regions.

Estimating Ground Level NO2 Concentrations over Central-Eastern China Using a Satellite-Based Geographically and Temporally Weighted Regression Model

People in central-eastern China are suffering from severe air pollution of nitrogen oxides. Top-down approaches have been widely applied to estimate the ground concentrations of NO2 based on satellite data. In this paper, a one-year dataset of tropospheric NO2 columns from the Ozone Monitoring Instrument (OMI) together with ambient monitoring station measurements and meteorological data from May 2013 to April 2014, are used to estimate the ground level NO2. The mean values of OMI tropospheric NO2 columns show significant geographical and seasonal variation when the ambient monitoring stations record a certain range. Hence, a geographically and temporally weighted regression (GTWR) model is introduced to treat the spatio-temporal non-stationarities between tropospheric-columnar and ground level NO2. Cross-validations demonstrate that the GTWR model outperforms the ordinary least squares (OLS), the geographically weighted regression (GWR), and the temporally weighted regression (TWR), produces the highest R2 (0.60) and the lowest values of root mean square error mean (RMSE), absolute difference (MAD), and mean absolute percentage error (MAPE). Our method is better than or comparable to the chemistry transport model method. The satellite-estimated spatial distribution of ground NO2 shows a reasonable spatial pattern, with high annual mean values (>40 μg/m3), mainly over southern Hebei, northern Henan, central Shandong, and southern Shaanxi. The values of population-weight NO2 distinguish densely populated areas with high levels of human exposure from others.

Impact of aerosols on the OMI tropospheric NO<sub>2</sub> retrievals over industrialized regions: how accurate is the aerosol correction of cloud-free scenes via a simple cloud model?

Abstract. The Ozone Monitoring Instrument (OMI) has provided daily global measurements of tropospheric NO2 for more than a decade. Numerous studies have drawn attention to the complexities related to measurements of tropospheric NO2 in the presence of aerosols. Fine particles affect the OMI spectral measurements and the length of the average light path followed by the photons. However, they are not explicitly taken into account in the current operational OMI tropospheric NO2 retrieval chain (DOMINO – Derivation of OMI tropospheric NO2) product. Instead, the operational OMI O2 − O2 cloud retrieval algorithm is applied both to cloudy and to cloud-free scenes (i.e. clear sky) dominated by the presence of aerosols. This paper describes in detail the complex interplay between the spectral effects of aerosols in the satellite observation and the associated response of the OMI O2 − O2 cloud retrieval algorithm. Then, it evaluates the impact on the accuracy of the tropospheric NO2 retrievals through the computed Air Mass Factor (AMF) with a focus on cloud-free scenes. For that purpose, collocated OMI NO2 and MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua aerosol products are analysed over the strongly industrialized East China area. In addition, aerosol effects on the tropospheric NO2 AMF and the retrieval of OMI cloud parameters are simulated. Both the observation-based and the simulation-based approach demonstrate that the retrieved cloud fraction increases with increasing Aerosol Optical Thickness (AOT), but the magnitude of this increase depends on the aerosol properties and surface albedo. This increase is induced by the additional scattering effects of aerosols which enhance the scene brightness. The decreasing effective cloud pressure with increasing AOT primarily represents the shielding effects of the O2 − O2 column located below the aerosol layers. The study cases show that the aerosol correction based on the implemented OMI cloud model results in biases between −20 and −40 % for the DOMINO tropospheric NO2 product in cases of high aerosol pollution (AOT ≥ 0.6) at elevated altitude. These biases result from a combination of the cloud model error, used in the presence of aerosols, and the limitations of the current OMI cloud Look-Up-Table (LUT). A new LUT with a higher sampling must be designed to remove the complex behaviour between these biases and AOT. In contrast, when aerosols are relatively close to the surface or mixed with NO2, aerosol correction based on the cloud model results in an overestimation of the DOMINO tropospheric NO2 column, between 10 and 20 %. These numbers are in line with comparison studies between ground-based and OMI tropospheric NO2 measurements in the presence of high aerosol pollution and particles located at higher altitudes. This highlights the need to implement an improved aerosol correction in the computation of tropospheric NO2 AMFs.

Seasonal, interannual, and long-term variabilities in biomass burning activity over South Asia.

The seasonal, interannual, and long-term variations in biomass burning activity and related emissions are not well studied over South Asia. In this regard, active fire location retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS), the retrievals of aerosol optical depth (AOD) from MODIS Terra, and tropospheric column NO2 from Ozone Monitoring Instrument (OMI) are used to understand the effects of biomass burning on the tropospheric pollution loadings over South Asia during 2003-2013. Biomass burning emission estimates from Global Fire Emission Database (GFED) and Global Fire Assimilation System (GFAS) are also used to quantify uncertainties and regional discrepancies in the emissions of carbon monoxide (CO), nitrogen oxide (NOx), and black carbon (BC) due to biomass burning in South Asia. In the Asian continent, the frequency of fire activity is highest over Southeast Asia, followed by South Asia and East Asia. The biomass burning activity in South Asia shows a distinct seasonal cycle that peaks during February-May with some differences among four (north, central, northeast, and south) regions in India. The annual biomass burning activity in north, central, and south regions shows an increasing tendency, particularly after 2008, while a decrease is seen in northeast region during 2003-2013. The increase in fire counts over the north and central regions contributes 24 % of the net enhancement in fire counts over South Asia. MODIS AOD and OMI tropospheric column NO2 retrievals are classified into high and low fire activity periods and show that biomass burning leads to significant enhancement in tropospheric pollution loading over both the cropland and forest regions. The enhancement is much higher (110-176 %) over the forest region compared to the cropland (34-62 %) region. Further efforts are required to understand the implications of biomass burning on the regional air quality and climate of South Asia.

Evaluation of OMI operational standard NO<sub>2</sub> column retrievals using in situ and surface-based NO<sub>2</sub> observations

Abstract. We assess the standard operational nitrogen dioxide (NO2) data product (OMNO2, version 2.1) retrieved from the Ozone Monitoring Instrument (OMI) onboard NASA's Aura satellite using a combination of aircraft and surface in~situ measurements as well as ground-based column measurements at several locations and a bottom-up NOx emission inventory over the continental US. Despite considerable sampling differences, NO2 vertical column densities from OMI are modestly correlated (r = 0.3–0.8) with in situ measurements of tropospheric NO2 from aircraft, ground-based observations of NO2 columns from MAX-DOAS and Pandora instruments, in situ surface NO2 measurements from photolytic converter instruments, and a bottom-up NOx emission inventory. Overall, OMI retrievals tend to be lower in urban regions and higher in remote areas, but generally agree with other measurements to within ± 20%. No consistent seasonal bias is evident. Contrasting results between different data sets reveal complexities behind NO2 validation. Since validation data sets are scarce and are limited in space and time, validation of the global product is still limited in scope by spatial and temporal coverage and retrieval conditions. Monthly mean vertical NO2 profile shapes from the Global Modeling Initiative (GMI) chemistry-transport model (CTM) used in the OMI retrievals are highly consistent with in situ aircraft measurements, but these measured profiles exhibit considerable day-to-day variation, affecting the retrieved daily NO2 columns by up to 40%. This assessment of OMI tropospheric NO2 columns, together with the comparison of OMI-retrieved and model-simulated NO2 columns, could offer diagnostic evaluation of the model.

DOAS measurements of NO<sub>2</sub> from an ultralight aircraft during the Earth Challenge expedition

Abstract. We report on airborne Differential Optical Absorption Spectroscopy (DOAS) measurements of NO2 tropospheric columns above South Asia, the Arabic peninsula, North Africa, and Italy in November and December 2009. The DOAS instrument was installed on an ultralight aircraft involved in the Earth Challenge project, an expedition of seven pilots flying on four ultralight aircraft between Australia and Belgium. The instrument recorded spectra in limb geometry with a large field of view, a set-up which provides a high sensitivity to the boundary layer NO2 while minimizing the uncertainties related to the attitude variations. We compare our measurements with OMI (Ozone Monitoring Instrument) and GOME-2 (Global Ozone Monitoring Experiment 2) tropospheric NO2 products when the latter are available. Above Rajasthan and the Po Valley, two areas where the NO2 field is homogeneous, data sets agree very well. Our measurements in these areas are 0.1 ± 0.1 to 3 ± 1 × 1015 molec cm−2 and 2.6 ± 0.8 × 1016 molec cm−2, respectively. Flying downwind of Riyadh, our NO2 measurements show the structure of the megacity's exhaust plume with a higher spatial resolution than OMI. Moreover, our measurements are larger (up to 40%) than those seen by satellites. We also derived tropospheric columns when no satellite data were available if it was possible to get information on the visibility from satellite measurements of aerosol optical thickness. This experiment also provides a confirmation for the recent finding of a soil signature above desert.

Growth in NO<sub>x</sub> emissions from power plants in China: bottom-up estimates and satellite observations

Abstract. Using OMI (Ozone Monitoring Instrument) tropospheric NO2 columns and a nested-grid 3-D global chemical transport model (GEOS-Chem), we investigated the growth in NOx emissions from coal-fired power plants and their contributions to the growth in NO2 columns in 2005–2007 in China. We first developed a unit-based power plant NOx emission inventory for 2005–2007 to support this investigation. The total capacities of coal-fired power generation have increased by 48.8% in 2005–2007, with 92.2% of the total capacity additions coming from generator units with size ≥300 MW. The annual NOx emissions from coal-fired power plants were estimated to be 8.11 Tg NO2 for 2005 and 9.58 Tg NO2 for 2007, respectively. The modeled summer average tropospheric NO2 columns were highly correlated (R2 = 0.79–0.82) with OMI measurements over grids dominated by power plant emissions, with only 7–14% low bias, lending support to the high accuracy of the unit-based power plant NOx emission inventory. The ratios of OMI-derived annual and summer average tropospheric NO2 columns between 2007 and 2005 indicated that most of the grids with significant NO2 increases were related to power plant construction activities. OMI had the capability to trace the changes of NOx emissions from individual large power plants in cases where there is less interference from other NOx sources. Scenario runs from GEOS-Chem model suggested that the new power plants contributed 18.5% and 10% to the annual average NO2 columns in 2007 in Inner Mongolia and North China, respectively. The massive new power plant NOx emissions significantly changed the local NO2 profiles, especially in less polluted areas. A sensitivity study found that changes of NO2 shape factors due to including new power plant emissions increased the summer average OMI tropospheric NO2 columns by 3.8–17.2% for six selected locations, indicating that the updated emission information could help to improve the satellite retrievals.

Rain‐Induced Soil NOx Emission From India During the Onset of the Summer Monsoon: A Satellite Perspective

Rain‐induced soil NOx emission in the rural regions in India has been investigated using satellite observations of daily tropospheric NO2 columns from the Ozone Monitoring Instrument (OMI). We selected three rural regions with low population density where industrial and transportation activities are notably less in comparison to other regions of India. We show that OMI‐derived tropospheric NO2 columns capture the NOx pulses from soil during the onset of the rainy season (June) over the regions considered in our analysis. Strong enhancements in tropospheric NO2 columns after the active spell of precipitation and subsequent decrease during the break spell have been observed. We argue that this enhancement is due to rain‐induced soil NOx pulsing after the onset of summer monsoon rainfall over the dry soil in the study regions. Relating OMI tropospheric NO2 columns to surface NOx emission, soil emission accounts for an average emission flux of ∼23–28 ng N m−1 s−1 during the pulsing event. Lightning is unlikely to account for the enhanced OMI tropospheric NO2 columns over the study regions.

Indirect validation of tropospheric nitrogen dioxide retrieved from the OMI satellite instrument: Insight into the seasonal variation of nitrogen oxides at northern midlatitudes

We examine the seasonal variation in lower tropospheric nitrogen oxides (NOx = NO + NO2) at northern midlatitudes by evaluating tropospheric NO2 columns observed from the Ozone Monitoring Instrument (OMI) satellite instrument with surface NO2 measurements (SouthEastern Aerosol Research and Characterization and Air Quality System) and current bottom‐up NOx emission inventories, using a global model of tropospheric chemistry (GEOS‐Chem). The standard (SP) and DOMINO (DP) tropospheric NO2 column products from OMI exhibit broadly similar spatial and seasonal variation, but differ substantially over continental source regions. A comparison of the two OMI tropospheric NO2 products with in situ surface NO2 concentrations and bottom‐up NOx emissions over the southeast United States indicates that annual mean NO2 columns from the DP are biased high by 21%–33% and those from the SP are biased high by 27%–43%. The bias in SP columns is highly seasonal, 67%–74% in summer compared with −6% to −1% in winter. Similar seasonal differences exist between top‐down and bottom‐up NOx emission inventories over North America, Europe, and East Asia. The air mass factor largely explains the observed seasonal difference between the DP and SP, and in turn the seasonal SP bias. We develop a third product (DP_GC) using averaging kernel information from the DP and NO2 vertical profiles from GEOS‐Chem. This product reduces to 5%–21% the annual mean bias over the southeast United States. We use the seasonal variation in the DP_GC to estimate the seasonal variation in the lifetime of lower tropospheric NOx against oxidation to HNO3 over the eastern United States. The effective NOx lifetime at OMI overpass time (early afternoon) ranges from 7.6 h in summer to 17.8 h in winter, consistent within 3 h of the simulated lifetime. GEOS‐Chem calculations reveal that the seasonal variation in OMI NO2 columns largely reflects gas‐phase oxidation of NO2 in summer with an increasing role for heterogenous chemistry in winter.

Testing and improving OMI DOMINO tropospheric NO2 using observations from the DANDELIONS and INTEX‐B validation campaigns

We present a sensitivity analysis of the tropospheric NO2 retrieval from the Ozone Monitoring Instrument (OMI) using measurements from the Dutch Aerosol and Nitrogen Dioxide Experiments for Validation of OMI and SCIAMACHY (DANDELIONS) and Intercontinental Chemical Transport Experiment‐B (INTEX‐B) campaigns held in 2006. These unique campaigns covered a wide range of pollution conditions and provided detailed information on the vertical distribution of NO2. During the DANDELIONS campaign, tropospheric NO2 profiles were measured with a lidar in a highly polluted region of the Netherlands. During the INTEX‐B campaign, NO2 profiles were measured using laser‐induced fluorescence onboard an aircraft in a range of meteorological and polluted conditions over the Gulf of Mexico and the east Pacific. We present a comparison of measured profiles with a priori profiles used in the OMI tropospheric NO2 retrieval algorithm. We examine how improvements in surface albedo estimates improve the OMI NO2 retrieval. From these comparisons we find that the absolute average change in tropospheric columns retrieved with measured profiles and improved surface albedos is 23% with a standard deviation of 27% and no trend in the improved being larger or smaller than the original. We show that these changes occur in case studies related to pollution in the southeastern United States and pollution outflow in the Gulf of Mexico. We also examine the effects of using improved Mexico City terrain heights on the OMI NO2 product.

Characterization of OMI Tropospheric NO2 Measurements in East Asia Based on a Robust Validation Comparison

In the period from June 2006 to December 2008, we measured the tropospheric nitrogen dioxide (NO2) column by ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) at an urban site in China (Tai’an) and three sites in Japan, covering urban (Yokosuka), suburban (Tsukuba), and remote areas (Hedo). This robust dataset is used to characterize Ozone Monitoring Instrument (OMI) tropospheric NO2 column data (the standard product, version 3). Correlations between MAX-DOAS and OMI data, both of which show very low NO2 at Hedo and moderate/high levels at the other sites, have correlation coefficients (R2) as high as 0.64, indicating that relative changes in OMI NO2 data are reliable. However, OMI data have a negative bias of 31% on average. Assuming that these results are valid for OMI data taken over China, we find an increasing trend in tropospheric column NO2 at about 5% per year on average in the industrial areas of China (30°-40°N and 110°-123°E) over 2005-2008, but its spatial distribution is highly inhomogeneous.

Ground‐level nitrogen dioxide concentrations inferred from the satellite‐borne Ozone Monitoring Instrument

We present an approach to infer ground‐level nitrogen dioxide (NO2) concentrations by applying local scaling factors from a global three‐dimensional model (GEOS‐Chem) to tropospheric NO2 columns retrieved from the Ozone Monitoring Instrument (OMI) onboard the Aura satellite. Seasonal mean OMI surface NO2 derived from the standard tropospheric NO2 data product (Version 1.0.5, Collection 3) varies by more than two orders of magnitude (<0.1–>10 ppbv) over North America. Two ground‐based data sets are used to validate the surface NO2 estimate and indirectly validate the OMI tropospheric NO2 retrieval: photochemical steady‐state (PSS) calculations of NO2 based on in situ NO and O3 measurements, and measurements from a commercial chemiluminescent NO2 analyzer equipped with a molybdenum converter. An interference correction algorithm for the latter is developed using laboratory and field measurements and applied using modeled concentrations of the interfering species. The OMI‐derived surface NO2 mixing ratios are compared with an in situ surface NO2 data obtained from the U.S. Environmental Protection Agency's Air Quality System (AQS) and Environment Canada's National Air Pollution Surveillance (NAPS) network for 2005 after correcting for the interference in the in situ data. The overall agreement of the OMI‐derived surface NO2 with the corrected in situ measurements and PSS‐NO2 is −11–36%. A larger difference in winter/spring than in summer/fall implies a seasonal bias in the OMI NO2 retrieval. The correlation between the OMI‐derived surface NO2 and the ground‐based measurements is significant (correlation coefficient up to 0.86) with a tendency for higher correlations in polluted areas. The satellite‐derived data base of ground level NO2 concentrations could be valuable for assessing exposures of humans and vegetation to NO2, supplementing the capabilities of the ground‐based networks, and evaluating air quality models and the effectiveness of air quality control strategies.