[Show abstract][Hide abstract] ABSTRACT: The Ozone Monitoring Instrument (OMI) onboard NASA's Aura satellite has been providing global observations of the ozone layer and key atmospheric pollutant gases, such as nitrogen dioxide (NO2) and sulfur dioxide (SO2), since October 2004. The data products from the same instrument provide consistent spatial and temporal coverage and permit the study of anthropogenic and natural emissions on local-to-global scales. In this paper we examine changes in SO2 and NO2 over some of the world's most polluted industrialized regions during the first decade of OMI observations. In terms of regional pollution changes, we see both upward and downward trends, sometimes in opposite directions for NO2 and SO2, for the different study areas. The trends are, for the most part, associated with economic and/or technological changes in energy use, as well as regional regulatory policies. Over the eastern US, both NO2 and SO2 levels decreased dramatically from 2005 to 2014, by more than 40 and 80 %, respectively, as a result of both technological improvements and stricter regulations of emissions. OMI confirmed large reductions in SO2 over eastern Europe's largest coal power plants after installation of flue gas desulfurization devices. The North China Plain has the world's most severe SO2 pollution, but a decreasing trend has been observed since 2011, with about a 50 % reduction in 2012–2014, due to an economic slowdown and government efforts to restrain emissions from the power and industrial sectors. In contrast, India's SO2 and NO2 levels from coal power plants and smelters are growing at a fast pace, increasing by more than 100 and 50 %, respectively, from 2005 to 2014. Several SO2 hot spots observed over the Persian Gulf are probably related to oil and gas operations and indicate a possible underestimation of emissions from these sources in bottom-up emission inventories. Overall, OMI observations have proved to be very valuable in documenting rapid changes in air quality over different parts of the world during the last decade. The baseline established during the first 10 years of OMI is indispensable for the interpretation of air quality measurements from current and future satellite atmospheric composition missions.
[Show abstract][Hide abstract] ABSTRACT: We estimate ground-level nitrogen dioxide (NO2) concentrations from the OMI (Ozone Monitoring Instrument) over North America for the period 2005–2012. A chemical transport model (GEOS-Chem) is used to account for effects of the NO2 profile on the column retrieval, and to relate OMI NO2 columns to ground-level concentrations. The magnitude of the period-mean OMI-derived NO2 concentrations is evaluated versus in situ measurements from air quality networks yielding a significant spatial correlation (r = 0.81) but OMI-derived values are lower with a slope of 0.4. Comparison of the in situ concentrations versus spatially resolved concentrations estimated from land use regression models reveals that this difference partially arises from representativeness difference due to preferential placement of in situ monitors at locations with enhanced NO2, coupled with the OMI horizontal resolution. In situ observations provide information about local concentrations while OMI offers area-averaged information. The remaining difference is less readily explained and appears to include a combination of the effects of local unresolved geophysical processes affecting both the NO2 retrieval and the vertical profile used to relate the column to ground level. We also evaluate trends over North America from OMI and in situ measurements for the period of 2005–2012. OMI derived ground-level NO2 well reproduces the spatial pattern of the in situ trends (r = 0.77) and the slope of 0.4 versus the trend from in situ monitors is consistent with the slope versus mean concentrations. Absolute regional trends inferred from in situ measurements alone may overestimate area average changes. Nonetheless coincidently sampled ground-level NO2 concentrations from OMI and in situ measurements for 2005–2012 exhibit similar relative decreases over Eastern (−6.5 ± 2.0%/yr, −7.1 ± 1.3%/yr), Western (−4.5 ± 1.1%/yr, −6.5 ± 0.7%/yr) and Central (−3.3 ± 2.3%/yr, −4.1 ± 0.8%/yr) North America.
[Show abstract][Hide abstract] ABSTRACT: Satellite remote sensing of tropospheric nitrogen dioxide (NO2) can provide valuable information for estimating surface nitrogen oxides (NOx) emissions. Using an exponentially-modified Gaussian (EMG) method and taking into account the effect of wind on observed NO2 distributions, we estimate three-year moving-average emissions of summertime NOx from 35 US urban areas directly from NO2 retrievals of the Ozone Monitoring Instrument (OMI) during 2005–2014. Following the conclusions of previous studies that the EMG method provides robust and accurate emission estimates under strong-wind conditions, we derive top-down NOx emissions from each urban area by applying the EMG method to OMI data with wind speeds greater than 3–5 m s−1. Meanwhile, we find that OMI NO2 observations under weak-wind conditions (i.e., < 3 m s−1) are qualitatively better correlated with the surface NOx source strength in comparison to all-wind OMI maps; and therefore we use them to calculate the satellite-observed NO2 burdens of urban areas and compare with NOx emission estimates. The EMG results show that OMI-derived NOx emissions are highly correlated (R > 0.93) with weak-wind OMI NO2 burdens as well as bottom-up NOx emission estimates over 35 urban areas, implying a linear response of the OMI observations to surface emissions under weak-wind conditions. The simultaneous, EMG-obtained, effective NO2 lifetimes (~3.5 ± 1.3 h), however, are biased low in comparison to the summertime NO2 chemical lifetimes. In general, isolated urban areas with NOx emission intensities greater than ~ 2 Mg h−1 produce statistically significant weak-wind signals in three-year average OMI data. From 2005 to 2014, we estimate that total OMI-derived NOx emissions over all selected US urban areas decreased by 49%, consistent with reductions of 43, 47, 49, and 44% in the total bottom-up NOx emissions, the sum of weak-wind OMI NO2 columns, the total weak-wind OMI NO2 burdens, and the averaged NO2 concentrations, respectively, reflecting the success of NOx control programs for both mobile sources and power plants. The decrease rates of these NOx-related quantities are found to be faster (i.e., −6.8 to −9.3% yr−1) before 2010 and slower (i.e., −3.4 to −4.9% yr−1) after 2010. For individual urban areas, we calculate the R values of pair-wise trends among the OMI-derived and bottom-up NOx emissions, the weak-wind OMI NO2 burdens, and ground-based NO2 measurements; and high correlations are found for all urban areas (median R = 0.8), particularly large ones (R up to 0.97). The results of the current work indicate that using the EMG method and considering the wind effect, the OMI data allow for the estimation of NOx emissions from urban areas and the direct constraint of emission trends with reasonable accuracy.
[Show abstract][Hide abstract] ABSTRACT: Nitrogen dioxide retrievals from the Aura/Ozone Monitoring Instrument (OMI) have been used extensively over the past decade, particularly in the study of tropospheric air quality. Recent comparisons of OMI NO2 with independent data sets and models suggested that the OMI values of slant column density (SCD) and stratospheric vertical column density (VCD) in both the NASA OMNO2 and Royal Netherlands Meteorological Institute DOMINO products are too large, by around 10–40%. We describe a substantially revised spectral fitting algorithm, optimized for the OMI visible light spectrometer channel. The most important changes comprise a flexible adjustment of the instrumental wavelength shifts combined with iterative removal of the ring spectral features; the multistep removal of instrumental noise; iterative, sequential estimates of SCDs of the trace gases in the 402–465 nm range. These changes reduce OMI SCD(NO2) by 10–35%, bringing them much closer to SCDs retrieved from independent measurements and models. The revised SCDs, submitted to the stratosphere-troposphere separation algorithm, give tropospheric VCDs ∼10–15% smaller in polluted regions, and up to ∼30% smaller in unpolluted areas. Although the revised algorithm has been optimized specifically for the OMI NO2 retrieval, our approach could be more broadly applicable.
Journal of Geophysical Research Atmospheres 05/2015; 120(11):n/a-n/a. DOI:10.1002/2014JD022913 · 3.43 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Uncertain photolysis rates and emission inventory impair the accuracy of state-level ozone (O-3) regulatory modeling. Past studies have separately used satellite-observed clouds to correct the model-predicted photolysis rates, or satellite-constrained top-down NOx emissions to identify and reduce uncertainties in bottom-up NOx emissions. However, the joint application of multiple satellite-derived model inputs to improve O-3 state implementation plan (SIP) modeling has rarely been explored. In this study, Geostationary Operational Environmental Satellite (GOES) observations of clouds are applied to derive the photolysis rates, replacing those used in Texas SIP modeling. This changes modeled O-3 concentrations by up to 80 ppb and improves O-3 simulations by reducing modeled normalized mean bias (NMB) and normalized mean error (NME) by up to 0.1. A sector-based discrete Kalman filter (DKF) inversion approach is incorporated with the Comprehensive Air Quality Model with extensions (CAMx)-decoupled direct method (DDM) model to adjust Texas NOx emissions using a high-resolution Ozone Monitoring Instrument (OMI) NO2 product. The discrepancy between OMI and CAMx NO2 vertical column densities (VCDs) is further reduced by increasing modeled NOx lifetime and adding an artificial amount of NO2 in the upper troposphere. The region-based DKF inversion suggests increasing NOx emissions by 10-50% in most regions, deteriorating the model performance in predicting ground NO2 and O-3, while the sector-based DKF inversion tends to scale down area and nonroad NOx emissions by 50 %, leading to a 2-5 ppb decrease in ground 8 h O-3 predictions. Model performance in simulating ground NO2 and O-3 are improved using sector-based inversion-constrained NOx emissions, with 0.25 and 0.04 reductions in NMBs and 0.13 and 0.04 reductions in NMEs, respectively. Using both GOES-derived photolysis rates and OMI-constrained NOx emissions together reduces modeled NMB and NME by 0.05, increases the model correlation with ground measurement in O-3 simulations, and makes O-3 more sensitive to NOx emissions in the O-3 non-attainment areas.
[Show abstract][Hide abstract] ABSTRACT: National emission inventories (NEIs) take years to assemble, but they can become outdated quickly, especially for time-sensitive applications such as air quality forecasting. This study compares multi-year NOx trends derived from satellite and ground observations and uses these data to evaluate the updates of NOx emission data by the US National Air Quality Forecast Capability (NAQFC) for next-day ozone prediction during the 2008 Global Economic Recession. Over the eight large US cities examined here, both the Ozone Monitoring Instrument (OMI) and the Air Quality System (AQS) detect substantial downward trends from 2005 to 2012, with a seven-year total of -35% according to OMI and -38% according to AQS. The NOx emission projection adopted by NAQFC tends to be in the right direction, but at a slower reduction rate (-25% from 2005 to 2012), due likely to the unaccounted effects of the 2008 economic recession. Both OMI and AQS datasets display distinct emission reduction rates before, during, and after the 2008 global recession in some cities, but the detailed changing rates are not consistent across the OMI and AQS data. Our findings demonstrate the feasibility of using space and ground observations to evaluate major updates of emission inventories objectively. The combination of satellite, ground observations, and in-situ measurements (such as emission monitoring in power plants) is likely to provide more reliable estimates of NOx emission and its trend, which is an issue of increasing importance as many urban areas in the US are transitioning to NOx-sensitive chemical regimes by continuous emission reductions.
[Show abstract][Hide abstract] ABSTRACT: To improve the trace gas retrieval from Airborne Compact Atmospheric Mapper (ACAM) during the DSICOVER-AQ campaigns, we characterize the signal to noise ratio (SNR) of the ACAM measurement. From the standard deviations of the fitting residuals, the SNRs of ACAM nadir measurements are estimated to vary from ~300 at 310 nm to ~1000 in the blue spectral region; the zenith data are noisier due to reduced levels of illumination and lower system throughput and also show many more pixels with abrupt anomalous values; therefore, a new method is developed to derive a solar irradiance reference at the top of the atmosphere (TOA) from average nadir measurements, at instrument spectral resolution and including instrument calibration characteristics. Using this reference can significantly reduce fitting residuals and improve the retrievals. This approach derives an absolute reference for direct fitting algorithms involving radiative transfer calculations and thus can be applied to both aircraft and ground-based measurements. The comparison of ACAM radiance with simulations using coincident ozonesonde and OMI data shows large wavelength-dependent biases in ACAM data, varying from ~−19% at 310 nm to 5% at 360 nm. Correcting ACAM radiance in direct-fitting based ozone profile algorithm significantly improves the consistency with OMI total ozone.
Journal of Spectroscopy 01/2015; 2015:1-7. DOI:10.1155/2015/827160 · 0.54 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The Airborne Compact Atmospheric Mapper (ACAM), an ultraviolet/visible/near-infrared spectrometer, has been flown on board the NASA UC-12 aircraft during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) campaigns to provide remote sensing observations of tropospheric and boundary-layer pollutants from its radiance measurements. To assure the tracegas retrieval from ACAM measurements we perform detailed characterization and verification of ACAM slit functions. The wavelengths and slit functions of ACAM measurements are characterized for the air-quality channel (similar to 304-500 nm) through cross-correlation with a high-resolution solar irradiance reference spectrum after necessarily accounting for atmospheric gas absorption and the ring effect in the calibration process. The derived slit functions, assuming a hybrid combination of asymmetric Gaussian and top-hat slit functions, agree very well with the laboratory-measured slit functions. Comparisons of trace-gas retrievals between using derived and measured slit functions demonstrate that the cross-correlation technique can be reliably used to characterize slit functions for trace-gas retrievals.
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] ABSTRACT: A method is developed to estimate global NO2 and SO2 dry deposition fluxes at high spatial resolution (0.1° × 0.1°) using satellite measurements from the Ozone Monitoring Instrument (OMI) on the Aura satellite, in combination with simulations from the GEOS-Chem global chemical transport model. These global maps for 2005–2007 provide a dataset for use in examining global and regional budgets of deposition. In order to properly assess SO2 on a global scale, a method is developed to account for the geospatial character of background offsets in retrieved satellite columns. Globally, annual dry deposition to land estimated from OMI as NO2 contributes 1.5 ± 0.5 Tg of nitrogen and as SO2 contributes 13.7 ± 4.0 Tg of sulfur. Differences between OMI-inferred NO2 dry deposition fluxes and those of other models and observations vary from excellent agreement to an order of magnitude difference, with OMI typically on the low end of estimates. SO2 dry deposition fluxes compare well with in situ CASTNET-network-inferred flux over North America (slope = 0.98, r = 0.71). The most significant NO2 dry deposition flux to land per area occurs in the Pearl River Delta, China at 13.9 kg N ha−1 yr−1, while SO2 dry deposition has a global maximum rate of 72.0 kg S ha−1 yr−1 to the east of Jinan in China's Shandong province. Dry deposition fluxes are explored in several urban areas, where NO2 contributes on average 9–36% and as much as 85% of total NOy dry deposition.
[Show abstract][Hide abstract] ABSTRACT: Uncertain photolysis rates and emission inventory impair the accuracy of state-level ozone (O3) regulatory modeling. Past studies have separately used satellite-observed clouds to correct the model-predicted photolysis rates, or satellite-constrained top-down NOx emissions to identify and reduce uncertainties in bottom-up NOx emissions. However, the joint application of multiple satellite-derived model inputs to improve O3 State Implementation Plan (SIP) modeling has rarely been explored. In this study, Geostationary Operational Environmental Satellite (GOES) observations of clouds are applied to derive the photolysis rates, replacing those used in Texas SIP modeling. This changes modeled O3 concentrations by up to 80 ppb and improves O3 simulations by reducing modeled normalized mean bias (NMB) and normalized mean error (NME) by up to 0.1. A sector-based discrete Kalman filter (DKF) inversion approach is incorporated with the Comprehensive Air Quality Model with extensions (CAMx)-Decoupled Direct Method (DDM) model to adjust Texas NOx emissions using a high resolution Ozone Monitoring Instrument (OMI) NO2 product. The discrepancy between OMI and CAMx NO2 vertical column densities (VCD) is further reduced by increasing modeled NOx lifetime and adding an artificial amount of NO2 in the upper troposphere. The sector-based DKF inversion tends to scale down area and non-road NOx emissions by 50%, leading to a 2–5 ppb decrease in ground 8 h O3 predictions. Model performance in simulating ground NO2 and O3 are improved using inverted NOx emissions, with 0.25 and 0.04 reductions in NMBs and 0.13 and 0.04 reductions in NMEs, respectively. Using both GOES-derived photolysis rates and OMI-constrained NOx emissions together reduces modeled NMB and NME by 0.05 and increases the model correlation with ground measurement in O3 simulations and makes O3 more sensitive to NOx emissions in the O3 non-attainment areas.
[Show abstract][Hide abstract] ABSTRACT: To investigate the ability of column (or partial column) information to represent surface air quality, results of linear regression analyses between surface mixing ratio data and column abundances for O3 and NO2 are presented for the July 2011 Maryland deployment of the DISCOVER-AQ mission. Data collected by the P-3B aircraft, ground-based Pandora spectrometers, Aura/OMI satellite instrument, and simulations for July 2011 from the CMAQ air quality model during this deployment provide a large and varied data set, allowing this problem to be approached from multiple perspectives. O3 columns typically exhibited a statistically significant and high degree of correlation with surface data (R2 > 0.64) in the P-3B data set, a moderate degree of correlation (0.16 < R2 < 0.64) in the CMAQ data set, and a low degree of correlation (R2 < 0.16) in the Pandora and OMI data sets. NO2 columns typically exhibited a low to moderate degree of correlation with surface data in each data set. The results of linear regression analyses for O3 exhibited smaller errors relative to the observations than NO2 regressions. These results suggest that O3 partial column observations from future satellite instruments with sufficient sensitivity to the lower troposphere can be meaningful for surface air quality analysis.
[Show abstract][Hide abstract] ABSTRACT: Particulate organic matter is of interest for air quality and climate research, but the relationship between ambient organic mass (OM) and organic carbon (OC) remains ambiguous both in measurements and in modeling. We present a simple method to derive an estimate of the spatially and seasonally resolved global, lower tropospheric, ratio between OM and OC. We assume ambient NO2 concentrations as a surrogate for fresh emission which mostly determines the continental scale OM/OC ratio. For this, we first develop a parameterization for the OM/OC ratio using the primary organic aerosol (POA) fraction of total OM estimated globally from Aerosol Mass Spectrometer (AMS) measurements, and evaluate it with high mass resolution AMS data. Second, we explore the ability of ground-level NO2 concentrations derived from the OMI satellite sensor to serve as a proxy for fresh emissions that have a high POA fraction, and apply NO2 data to derive ambient POA fraction. The combination of these two methods yields an estimate of OM/OC from NO2 measurements. Although this method has inherent deficiencies over biomass burning, free-tropospheric, and marine environments, elsewhere it offers more information than the currently used global-mean OM/OC ratios. The OMI-derived global OM/OC ratio ranges from 1.3 to 2.1 (μg/μgC), with distinct spatial variation between urban and rural regions. The seasonal OM/OC ratio has a summer maximum and a winter minimum over regions dominated by combustion emissions. This dataset serves as a tool for interpreting organic carbon measurements, and for evaluating modeling of atmospheric organics. We also develop an additional parameterization for models to estimate the ratio of primary OM to OC from simulated NOx concentrations.
[Show abstract][Hide abstract] ABSTRACT: Satellite remote sensing is increasingly being used to monitor air quality over localized sources such as the Canadian oil sands. Following an initial study, significantly low biases have been identified in current NO2 and SO2 retrieval products from the Ozone Monitoring Instrument (OMI) satellite sensor over this location resulting from a combination of its rapid development and small spatial scale. Air mass factors (AMFs) used to convert line-of-sight "slant" columns to vertical columns were re-calculated for this region based on updated and higher resolution input information including absorber profiles from a regional-scale (15 km × 15 km resolution) air quality model, higher spatial and temporal resolution surface reflectivity, and an improved treatment of snow. The overall impact of these new Environment Canada (EC) AMFs led to substantial increases in the peak NO2 and SO2 average vertical column density (VCD), occurring over an area of intensive surface mining, by factors of 2 and 1.4, respectively, relative to estimates made with previous AMFs. Comparisons are made with long-term averages of NO2 and SO2 (2005-2011) from in situ surface monitors by using the air quality model to map the OMI VCDs to surface concentrations. This new OMI-EC product is able to capture the spatial distribution of the in situ instruments (slopes of 0.65 to 1.0, correlation coefficients of >0.9). The concentration absolute values from surface network observations were in reasonable agreement, with OMI-EC NO2 and SO2 biased low by roughly 30%. Several complications were addressed including correction for the interference effect in the surface NO2 instruments and smoothing and clear-sky biases in the OMI measurements. Overall these results highlight the importance of using input information that accounts for the spatial and temporal variability of the location of interest when performing retrievals.
[Show abstract][Hide abstract] ABSTRACT: How accurately can the emissions from a coal-fired power plant be measured from space? Might it one day be possible for a satellite to determine whether a plant is in compliance with emission regulations? This article reviews the current capability of space-borne instruments to detect and quantify power plant emissions and comments on the possibility of enhanced capability in the next five to ten years.
[Show abstract][Hide abstract] ABSTRACT: Inverse modeling of nitrogen oxide (NOx) emissions using
satellite-based NO2 observations has become more prevalent in
recent years, but has rarely been applied to regulatory modeling at
regional scales. In this study, OMI satellite observations of
NO2 column densities are used to conduct inverse modeling of
NOx emission inventories for two Texas State Implementation
Plan (SIP) modeling episodes. Addition of lightning, aircraft, and soil
NOx emissions to the regulatory inventory narrowed but did
not close the gap between modeled and satellite-observed NO2
over rural regions. Satellite-based top-down emission inventories are
created with the regional Comprehensive Air Quality Model with
extensions (CAMx) using two techniques: the direct scaling method and
discrete Kalman filter (DKF) with decoupled direct method (DDM)
sensitivity analysis. The simulations with satellite-inverted
inventories are compared to the modeling results using the a priori
inventory as well as an inventory created by a ground-level
NO2-based DKF inversion. The DKF inversions yield conflicting
results: the satellite-based inversion scales up the a priori
NOx emissions in most regions by factors of 1.02 to 1.84,
leading to 3-55% increase in modeled NO2 column densities and
1-7 ppb increase in ground 8 h ozone concentrations, while the
ground-based inversion indicates the a priori NOx emissions
should be scaled by factors of 0.34 to 0.57 in each region. However,
none of the inversions improve the model performance in simulating
aircraft-observed NO2 or ground-level ozone (O3)
[Show abstract][Hide abstract] ABSTRACT: Concern is growing about the effects of urbanization on air pollution and health. Nitrogen dioxide (NO2) released primarily from combustion processes such as traffic is a short-lived atmospheric pollutant that serves as an air quality indicator, and is itself a health concern. We derive a global distribution of ground-level NO2 concentrations from tropospheric NO2 columns retrieved from the OMI satellite instrument. Local scaling factors from a three-dimensional chemistry-transport model (GEOS-Chem) are used to relate the OMI NO2 columns to ground-level concentrations. The OMI-derived surface NO2 data are significantly correlated (r=0.69) with in situ surface measurements. We examine how the OMI-derived ground-level NO2 concentrations, OMI NO2 columns, and bottom-up NOx emission inventories relate to urban population. Emission hot spots such as power plants are excluded to focus on urban relationships. The correlation of surface NO2 with population is significant for the three countries and one continent examined here: United States (r=0.71), Europe (r=0.67), China (r=0.69) and India (r=0.59). Urban NO2 pollution, like other urban properties, is a power law scaling function of the population size: NO2 concentration increases proportional to population raised to an exponent. The value of the exponent varies by region from 0.36 for India to 0.66 for China reflecting regional differences in industrial development and per capita emissions. It has been generally established that energy efficiency increases and therefore per capita NOx emissions decrease with urban population; here we show how outdoor ambient NO2 concentrations depend upon urban population in different global regions.
[Show abstract][Hide abstract] ABSTRACT: We describe a new algorithm for the retrieval of nitrogen dioxide
(NO2) vertical columns from nadir-viewing satellite
instruments. This algorithm (SP2) is the basis for the Version 2.1 OMI
NO2 Standard Product and features a novel method for
separating the stratospheric and tropospheric columns. The approach
estimates the stratospheric NO2 directly from satellite data
without using stratospheric chemical transport models or assuming any
global zonal wave pattern. Tropospheric NO2 columns are
retrieved using air mass factors derived from high-resolution radiative
transfer calculations and a monthly climatology of NO2
profile shapes. We also present details of how uncertainties in the
retrieved columns are estimated. The sensitivity of the retrieval to
assumptions made in the stratosphere-troposphere separation is discussed
and shown to be small, in an absolute sense, for most regions. We
compare daily and monthly mean global OMI NO2 retrievals
using the SP2 algorithm with those of the original Version 1 Standard
Product (SP1) and the Dutch DOMINO product. The SP2 retrievals yield
significantly smaller summertime tropospheric columns than SP1 and are
relatively free of modeling artifacts and negative tropospheric
NO2 values. In a re-analysis of an INTEX-B validation study,
we show that SP2 largely eliminates a ∼20% discrepancy that
existed between OMI and independent in situ springtime NO2