Tropospheric nitrogen dioxide (NO2) is an important component of urban atmospheric chemistry. It is one of the major pollutants affecting humans and the biosphere (Chauhan et al., 2003; Kampa and Castanas, 2008), and works as an important precursor in tropospheric ozone chemistry and aerosol formation. Continuous monitoring of tropospheric NO2 is important for understanding urban air quality and changes in anthropogenic emissions. NO2 is also used as an important indicator for traffic and urbanization (Rijnders et al., 2001; Ross et al., 2006; Studinicka et al., 1997).

Tropospheric NO2 has been measured from space since the mid-1990s; the Global Ozone Monitoring Experiment (GOME; 1996–2003, onboard the European Remote Sensing-2), Scanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY; 2002–2012, onboard ENVISAT), Ozone Monitoring Instrument (OMI; 2004–present, onboard Aura), and GOME-2 (2007–present, onboard MetOp-A and 2013–present on MetOp-B) have all been used for the detection of NOx emission from natural and anthropogenic sources (Beirle et al., 2004; Boersma et al., 2007; Kim et al., 2006, 2009; Konovalov et al., 2006; Lamsal et al., 2008; Martin et al., 2003; Napelenok et al., 2008; Richter et al., 2005; van der A et al., 2006, 2008).

NO2 plumes from urban anthropogenic sources, especially from point and mobile sources, usually have a fine structure, as small as a few hundred meters and as large as 10–20 km, as reported in comparisons of column NO2 based on in situ observations and modeled calculations (Heue et al., 2008; Valin et al., 2011; Ryerson et al., 2013). Heue et al. (2008) used an airborne instrument based on imaging Differential Optical Absorption Spectroscopy (iDOAS) to build a two-dimensional (2-D) distribution model of urban plumes. By comparing NO2 column densities over the industrialized South African Highveld with OMI and SCIAMACHY measurements, they demonstrated that iDOAS shows strong enhancements close to industrial areas, 4–9 times higher than measurements from OMI and SCIAMACHY. Previous studies have demonstrated that modeled ozone production depends strongly on the spatial scale of the modeling grid due to the nonlinear dependence of ozone production on NOx concentration (e.g., Cohan et al., 2006; Gillani and Pleim, 1996; Liang and Jacobson, 2000; Sillman et al., 1990); therefore, an accurate comparison of urban NO2 plumes in fine scale is crucial for understanding surface ozone chemistry and air pollution over urban cities. Using 1-D and 2-D models, Valin et al. (2011) computed the resolution-dependent bias in the predicted NO2 column, demonstrating large negative biases over large sources and positive biases over small sources at coarse model resolution.

The inhomogeneity of urban NO2 plumes within the scale of satellite footprint pixels is of rising interest as satellite-based measurements are being compared with fine-scale modeling (Beirle et al., 2004, 2011; Hilboll et al., 2013). Richter et al. (2005) showed that there are considerable differences between GOME and SCIAMACHY observations for locations with steep gradients in the tropospheric NO2 columns; on the other hand, these observations agree very well over large areas of relatively homogeneous NO2 signals. Hilboll et al. (2013) argued that these effects result from spatial smoothing that differs depending on the ground resolution of the instruments; therefore, the inherent spatial heterogeneity of the NOx fields must be considered when studying them over small, localized areas. Hilboll et al. (2013) also presented approaches to account for instrumental differences while preserving individual instruments' spatial resolutions. In comparing GOME and SCIAMACHY, they used an explicit climatological correction factor to convolve GOME pixels (40 km × 320 km) with better-resolution SCIAMACHY (30 km × 60 km) data, producing a combined data set for studying long-term trends.

In this study, we try to investigate and to quantify the uncertainty resulting from the geometry of OMI satellite-based Nitrogen dioxide vertical column density (NO2 VCD) measurements by comparing these data to a fine-scale regional quality model. First, a pseudo-OMI data set is built from the outputs of fine-scale model simulations, and then these results are compared to model data in order to quantify the impact from pure differences in geometry. Second, we extend the basic concept of Hilboll et al. (2013) to apply spatial-distribution information from the fine-scale model to the OMI measurements, and demonstrate how the new approach adjusts the original OMI measurements. Satellite and model data are described in Sect. 2. Construction of pseudo-OMI data and the quantification of the impact of pixel geometry are discussed in Sect. 3. In Sect. 4, the downscaling approach is discussed; Sect. 5 concludes and discusses the implications of findings for emission policy decision-making.

OMI footprint-pixel size increases as the viewing angle deviates from the nadir direction to the edge of swaths. Figure 1 shows the actual size distributions of OMI pixels collected during September 2013. The blue line indicates size distribution counts for each 50 km2 bin, while the red line indicates the cumulative distribution of the OMI pixel sizes. The size distribution has high occurrences near 300 km2, as expected from the OMI's resolution at the nadir (that is, 13 × 24 = 312). However, many pixels still have larger sizes; around half of total pixels are larger than 500 km2, and 20 % of total pixels are larger even than 1000 km2. Geographical coverage rapidly increases with pixel size, so deciding a threshold for footprint-pixel sizes and available coverage may present a serious dilemma.

Figure 2 shows the relationship between OMI footprint-pixel size and actual geographical coverage over the CONUS. With 1 July 2011 data, 25 % of OMI pixel sizes are less than 342 km2, and they cover 1.4 % of the CONUS domain. CONUS coverage changes to 11.5, 24.0, and 58.8 % when 50, 75, and 100 % of OMI pixels are used, respectively. Using only finer data may provide detailed information, but they represent only a small part of all the data. If we also use coarser-resolution data, they provide more coverage but tend to be biased over areas with spatial gradient, as discussed in the previously mentioned studies (Hilboll et al., 2013). We therefore estimated the theoretical range of biases deriving from this geometric effect by constructing a pseudo-OMI data set out of a fine-scale model. Using the fine-scale regional CMAQ simulations and assuming this model represents a true world, we constructed a data set to mimic OMI instrument measurement of this modeled world.

In order to construct the pseudo-OMI data, we utilized a conservative spatial regridding technique to perform a lossless conversion of gridded modeling outputs into actual OMI footprint pixels. Figure 3 demonstrates the concepts of conservative regridding. The gray grid cells are 12 km grid cells for modeling – zoomed on the Houston region as an example – and the blue lines are actual OMI pixel coverage. The blue, shaped pixel is an example of an actual OMI pixel, while the pink boxes are model grid cells overlaid by the example OMI pixel. The numbers in the grid cells are calculations of the fractional area overlaid by the OMI pixel for each cell using the Sutherland–Hodman polygon-clipping algorithms available from the Interactive Data Language (IDL)-based Geospatial Data Processor (Kim et al., 2013); 0.74 means the OMI pixel covers 74 % of the corresponding grid cell. The pseudo-OMI value for the blue OMI pixel area in Fig. 3 can be estimated as Pj=∑pi⋅fi,j∑fi,j, where i and j are indices for the model grid cell and OMI pixel, respectively, and fi,j indicates the fractional area of cell i overlaid by OMI pixel j.

Figure 4 compares the spatial distributions of CMAQ NO2 VCDs (assumed to be a true world) and pseudo-OMI (pOMI) NO2 VCDs, along with the difference and percentage difference, (pOMI-CMAQ)/CMAQ × 100, over the northeastern US. It is evident that there are prominent differences between the original fine-scale modeled NO2 VCDs and reconstructed pseudo-OMI distribution, especially over and near urban locations. As expected from the smoothing effects of larger pixel sizes, pOMI shows a slightly smoothed transition from urban cores to suburban, and most of the sharp peaks near small cities are gone in the pOMI distribution. As already mentioned, this is purely a result of geometry. We can see that, for all the major cities, pOMI underestimates the actual NO2 VCD values while overestimating at the boundaries of major cities, as clearly seen in the New York, Pittsburgh, Philadelphia, Baltimore, and Washington D.C. areas. This effect is also prominent in locations with small but strong NOx emission sources, such as power plants, or small cities such as Norfolk, VA. It should be noted that these discrepancies result from purely geometric effects deriving from the OMI's designed pixel sizes and are around ±5–10 × 1015 molec cm2, with 20–30 % under- or overestimation biases for major cities and more than 100 % under- or overestimation for local cities like Norfolk and Richmond, VA. In the next section, we introduce a new approach – the conservative downscaling method – to reduce this effect of resolution due to varying OMI footprint-pixel sizes.

As described in the previous section, urban NO2 plumes usually have too fine of a spatial structure compared to OMI's measuring footprints. In this section, we introduce a new approach for adjusting those geometric effects. Downscaling is a common concept in meteorological simulations, used especially in global circulation models to provide initial and boundary conditions for regional models. We use a similar concept, describing a downscaling method in data processing as a special case of spatial regridding that provides further details through the incorporation of additional information into a set of coarse-resolution data. This approach differs from simply increasing the resolution, as the raw, coarse data are restructured using a set of logics, analogous to a regional meteorological model that downscales global meteorology using its own set of physical and thermal field balances. Conceptually, we use a calculation process reversed from that used to construct the pseudo-OMI data set.

Figure 5 graphically depicts the steps of conservative downscaling from OMI pixels. Figure 5a shows actual OMI NO2 VCD measurements over Los Angeles on 4 May 2010, and Fig. 5b shows the corresponding CMAQ NO2 VCDs calculated from NAQFC modeling outputs at the same time and location. As readers can easily see, OMI footprint pixels are much bigger (∼ 650 km2) than the CMAQ grid cells (12 × 12 = 144 km2). As a result, an OMI pixel can overlay more than 10 CMAQ grid cells, as demonstrated in Fig. 5b (black box representing the OMI pixel). We collected those CMAQ pixel values and then normalized them so that the total value of each grid cell sums to one. We call this a spatial-weighting kernel (Fig. 5c), and we apply this weighting kernel to the original OMI measurement. As a result, we generate a reconstructed OMI pixel with a finer structure but without any loss of original quantity. Summing the reconstructed pixels gives the original OMI pixel measurement. It should be noted that we strictly apply this method conservatively; theoretically, if there are no missing or duplicated pixels, the quantity of the original data is numerically preserved. This method can be summarized as fusing a satellite-measured “quantity” with modeled “spatial information”; the strength of the modeled NO2 field does not at all affect the result.

As expected, the accuracy of this method indeed depends on the model's performance, especially regarding its wind-field simulation and inputs of emission source locations, so this method clearly has its own limitation. Considering the uncertainties resulting from emission source locations, the air-quality community has had an excellent archive of geographical information about the geophysical locations of emission sources thanks to the efforts of US EPA, although the strengths of these sources are somewhat highly uncertain. As just described, however, the downscaling method is not affected by emission strength, so we do not think that the uncertainty associated with known emission sources is very high. On the other hand, the use of a downscaling method can be limited when there are uncertainties in emission inventory information such as unknown emission sources or removal of known sources. Wind field is important for simulating NO2 plume transport. With the short lifetime of NO2, especially during summer, the spatial distribution of NO2 plumes is strongly determined by the location of emission sources. Improving information about emission-source locations would somewhat improve the model, but it is more important to note that the downscaling method tends to convert the error characteristics. Near urban cores, OMI's coarse footprint resolution always causes unidirectional, systematic biases, with underestimation near urban cores and overestimation at the urban boundary. Using the downscaling method, these systematic biases from resolution are converted to random bias from wind-field error. Since these biases are random, they may be corrected by averaging over a certain time period, unlike the systematic bias resulting from resolution.

This study reports that satellite footprint sizes might cause a considerable effect on the measurement of fine-scale urban NO2 plumes. Comparing OMI NO2 VCDs over North American urban cities to a 12 km CMAQ simulation from NOAA NAQFC, we found that OMI footprint-pixel sizes are too coarse to resolve urban plumes, resulting in possible underestimation (and overestimation of model NO2 VCDs) over the urban core and overestimation outside. In order to quantify this effect of resolution, we first conducted a perfect-model experiment. Pseudo-OMI data were constructed using fine-scale outputs of a model simulation, assuming that the fine-scale model output is a true measurement. To match the footprint coverage from real OMI pathways, we conducted conservative spatial regridding with the corresponding fine-scale model outputs to generate a set of pseudo OMI pixels.

When compared to the original data, the pseudo-OMI data clearly showed smoothed signals over urban locations, with 20–30 % underestimation over major cities and up to 100 % bias over smaller urban areas. We then introduced conservative downscaling of OMI NO2 VCDs using spatial information from the fine-scale model to adjust the spatial distribution, also applying averaging kernel (AK) information to adjust the vertical structure. Four-way comparisons were conducted between OMI with and without downscaling and CMAQ with and without AK information. Results show that OMI and CMAQ NO2 VCDs show the best agreement when both downscaling and AK methods are applied, with correlation coefficient R = 0.89.

These results should be considered when using satellite data in the evaluation of emission inventories and translating these data into decision-making around emission policy. Table 1 shows a summary of the comparisons between OMI and CMAQ NO2 VCDs described in Figs. 8 and 9. When CMAQ without AK and OMI with DS are compared, the percentage difference is (6.43 - 3.61)/3.61 × 100 = 78 %, implying that the current emission inventory likely overestimates NO2 VCDs. Comparing between OMI with DS and CMAQ without AK or between OMI without DS and CMAQ with AK still implies that the current emission inventory is possibly overestimating. However, when both vertical and spatial profiles are adjusted using the AK and DS methods, a slight underestimation is found, -7 %, in modeled NO2 VCDs over AQS monitoring locations, implying that the current inventory possibly underestimates emissions. This may represent an important implication for how spatial information should be considered when investigating fine-scale phenomena such as urban NO2 plumes.

Without question, satellite observations are very useful with their large coverage supplementing sparse surface-monitoring sites. Interpretation of satellite-based measurement, however, should be performed cautiously with consideration of the instrument's characteristics, especially when translating results into policy-making. We expect our current study to provide a reference for the uncertainty of satellite-based information regarding local or regional pollutants, especially until we have the measurement data at a more enhanced resolution that will be provided by future satellites, such as Tropospheric Emissions: Monitoring of Pollution (TEMPO), Tropospheric Monitoring Instrument (TROPOMI), and Geostationary Environmental Monitoring Spectrometer (GEMS).