Search NASA⌕ Search

Engineering topics

Martin, R. V.

Publications and source records attributed to Martin, R. V..

12 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Improved Satellite Retrievals of NO2 and SO2 over the Canadian Oil Sands and Comparisons with Surface Measurements

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 greater than 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.

surface concentration↗
NASA NTRS2013

Application of OMI, SCIAMACHY and GOME-2 Satellite SO2 Retrievals for Detection of Large Emission Sources

Retrievals of sulfur dioxide (SO2) from space-based spectrometers are in a relatively early stage of development. Factors such as interference between ozone and SO2 in the retrieval algorithms often lead to errors in the retrieved values. Measurements from the Ozone Monitoring Instrument (OMI), Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), and Global Ozone Monitoring Experiment-2 (GOME-2) satellite sensors, averaged over a period of several years, were used to identify locations with elevated SO2 values and estimate their emission levels. About 30 such locations, detectable by all three sensors and linked to volcanic and anthropogenic sources, were found after applying low and high spatial frequency filtration designed to reduce noise and bias and to enhance weak signals to SO2 data from each instrument. Quantitatively, the mean amount of SO2 in the vicinity of the sources, estimated from the three instruments, is in general agreement. However, its better spatial resolution makes it possible for OMI to detect smaller sources and with additional detail as compared to the other two instruments. Over some regions of China, SCIAMACHY and GOME-2 data show mean SO2 values that are almost 1.5 times higher than those from OMI, but the suggested spatial filtration technique largely reconciles these differences.

SO2 retrievals↗
NASA NTRS2013

Impact of NO2 Profile Shape in OMI Tropospheric NO2 Retrievals

Nitrogen oxides (NOx NO + NO2) are key actors in air quality and climate change. Tropospheric NO2 columns from the nadir-viewing satellite sensors have been widely used to understand sources and chemistry of NOx. We have implemented several improvements to the operational algorithm developed at NASA GSFC and retrieved tropospheric NO2 columns. We present tropospheric NO2 validation studies of the new OMI Standard Product version 2.1 using ground-based and in-situ aircraft measurements. We show how vertical profile of scattering weight and a-priori NO2 profile shapes, which are taken from chemistry-transport models, affect air mass factor (AMF) and therefore tropospheric NO2 retrievals. Users can take advantage of scattering weights information that is made available in the operational NO2 product. Improved tropospheric NO2 data retrieved using thoroughly evaluated high spatial resolution NO2 profiles are helpful to test models.

OMI↗
NASA NTRS2012

Using Satellite Remote Sensing and Modelling for Insights into N02 Air Pollution and NO2 Emissions

Nitrogen oxides (NO(x)) are key actors in air quality and climate change. Satellite remote sensing of tropospheric NO2 has developed rapidly with enhanced spatial and temporal resolution since initial observations in 1995. We have developed an improved algorithm and retrieved tropospheric NO2 columns from Ozone Monitoring Instrument. Column observations of tropospheric NO2 from the nadir-viewing satellite sensors contain large contributions from the boundary layer due to strong enhancement of NO2 in the boundary layer. We infer ground-level NO2 concentrations from the OMI satellite instrument which demonstrate significant agreement with in-situ surface measurements. We examine how NO2 columns measured by satellite, ground-level NO2 derived from satellite, and NO(x) emissions obtained from bottom-up inventories relate to world's urban population. We perform inverse modeling analysis of NO2 measurements from OMI to estimate "top-down" surface NO(x) emissions, which are used to evaluate and improve "bottom-up" emission inventories. We use NO2 column observations from OMI and the relationship between NO2 columns and NO(x) emissions from a GEOS-Chem model simulation to estimate the annual change in bottom-up NO(x) emissions. The emission updates offer an improved estimate of NO(x) that are critical to our understanding of air quality, acid deposition, and climate change.

Lamsal, L. N.↗
NASA NTRS2010

Global Estimates of Average Ground-Level Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth

Exposure to airborne particles can cause acute or chronic respiratory disease and can exacerbate heart disease, some cancers, and other conditions in susceptible populations. Ground stations that monitor fine particulate matter in the air (smaller than 2.5 microns, called PM2.5) are positioned primarily to observe severe pollution events in areas of high population density; coverage is very limited, even in developed countries, and is not well designed to capture long-term, lower-level exposure that is increasingly linked to chronic health effects. In many parts of the developing world, air quality observation is absent entirely. Instruments aboard NASA Earth Observing System satellites, such as the MODerate resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging SpectroRadiometer (MISR), monitor aerosols from space, providing once daily and about once-weekly coverage, respectively. However, these data are only rarely used for health applications, in part because the can retrieve the amount of aerosols only summed over the entire atmospheric column, rather than focusing just on the near-surface component, in the airspace humans actually breathe. In addition, air quality monitoring often includes detailed analysis of particle chemical composition, impossible from space. In this paper, near-surface aerosol concentrations are derived globally from the total-column aerosol amounts retrieved by MODIS and MISR. Here a computer aerosol simulation is used to determine how much of the satellite-retrieved total column aerosol amount is near the surface. The five-year average (2001-2006) global near-surface aerosol concentration shows that World Health Organization Air Quality standards are exceeded over parts of central and eastern Asia for nearly half the year.

Van Donkelaar, A.↗
NASA NTRS2009

Comparison of Weekly Cycle of NO2 Satellite Retrievals and NO(x) Emission Inventories for the Continental United States

Spatially resolved weekly NO2 variations are obtained from 2003 to 2005 Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) tropospheric NO2 columns for three different types of regions: urban, rural, and rural-point (rural with significant electricity generation unit (EGU) emissions). Regions are compared for magnitudes and weekly profiles. Rural regions do not show any weekly pattern, whereas urban areas show a distinct decrease on the weekends. Rural regions with EGUs show a slight decrease on Sundays. When compared with estimated mobile and stationary nitrogen oxides (NO(x)) emissions from the year 2004 for seven cities, the satellite data have greater variation during weekdays (Monday-Friday). Overall comparisons show that SCIAMACHY derived NO2 correlate well with estimated NO(x) emissions for urban and rural but less for rural-point regions.

Kaynak, B.↗
NASA NTRS2008

The Effect of Lightning NOx Production on Surface Ozone in the Continental United States

Lightning NO(x) emissions calculated using the US National Lightning Detection Network data were found to account for 30% of the total NO(x) emissions for July August 2004, a period chosen both for having higher lightning NO(x) production and high ozone levels, thus maximizing the likelihood that such emissions could impact peak ozone levels. Including such emissions led to modest, but sometimes significant increases in simulated surface ozone when using the Community Multi-scale Air Quality Model (CMAQ). Three model simulations were performed, two with the addition of lightning NO(x) emissions, and one without. Domain-wide daily maximum 8-h ozone changes due to lightning NO(x) were less than 2 ppbv in 71 % of the cases with a maximum of 10 ppbv; whereas the difference in 1-h ozone was less than 2 ppbv in 77% of the cases with a maximum of 6 ppbv. Daily maximum 1-h and 8-h ozone for grids containing O3 monitoring stations changed slightly, with more than 43% of the cases differing less than 2 ppbv. The greatest differences were 42 ppbv for both 1-h and 8-h O3 , though these tended to be on days of lower ozone. Lightning impacts on the season-wide maximum 1-h and 8-h averaged ozone decreased starting from the 1 st to 4th highest values (an average of 4th highest, 8-h values is used for attainment demonstration in the US). Background ozone values from the y-intercept of O3 versus NO(z) curve were 42.2 and 43.9 ppbv for simulations without and with lightning emissions, respectively. Results from both simulations with lightning NO(x) suggest that while North American lightning production of NO(x) can lead to significant local impacts on a few occasions, they will have a relatively small impact on typical maximum levels and determination of Policy Relevant Background levels.

Kaynak, B.↗
NASA NTRS2004

Evaluation of GOME Satellite Measurements of Tropospheric NO2 and HCHO using Regional Data from Aircraft Campaigns in the Southeastern United States

We compare tropospheric measurements of nitrogen dioxide (NO2) and formaldehyde (HCHO) from the Global Ozone Monitoring Experiment (GOME) satellite instrument with in situ measurements over eastern Texas and the southeast United States. On aveiage, the GOME and in situ measurements of tropospheric NO2 and HCHO columns are consistent despite pronounced sampling differences. The geometric mean in situ to GOME ratios over the campaign are 1.08 for NO2 and 0.84 for HCHO, with corresponding geometric standard deviations of 1.27 and 1.38. The correlation of the observed column spatial variability between the two NO2 measurement sets is encouraging before (r2 = 0.54, n = 18) and after (r2 = 0.67, n = 18) correcting for a sampling bias. Mean relative vertical profiles of HCHO and NO2 calculated with a global three-dimensional model (GEOS-CHEM) and used in the GOME retrieval are highly consistent with in situ measurements; differences would affect the retrieved NO2 and HCHO columns by a few percent. GOME HCHO columns over eastern Texas include contributions from anthropogenic volatile organic compound (VOC) emissions but are dominated by biogenic VOC emissions at the regional scale in August-September when HCHO columns are within 20% of those over the southeastern United States. In situ measurements show that during summer the lowest 1500 m (the lower mixed layer) contains 75% of the tropospheric NO2 column over Houston and Nashville, and 60% of the HCHO column over Houston. Future validation of space-based measurements of tropospheric NO2 and HCHO columns over polluted regions should include coincident in situ measurements that span the entire satellite footprint, especially in the heterogeneous mixed layer.

Martin, R. V.↗
NASA NTRS2004

Satellite Mapping of Rain-Induced Nitric Oxide Emissions from Soils

We use space-based observations of NO2 columns from the Global Ozone Monitoring Experiment (GOME) to map the spatial and seasonal variations of NOx emissions over Africa during 2000. The GOME observations show not only enhanced tropospheric NO2 columns from biomass burning during the dry season but also comparable enhancements from soil emissions during the rainy season over the Sahel. These soil emissions occur in strong pulses lasting 1-3 weeks following the onset of rain, and affect 3 million sq km of semiarid sub-Saharan savanna. Surface observations of NO2 from the International Global Atmospheric Chemistry (IGAC)/Deposition of Biochemically Important Trace Species (DEBITS)/Africa (IDAF) network over West Africa provide further evidence for a strong role for microbial soil sources. By combining inverse modeling of GOME NO2 columns with space-based observations of fires, we estimate that soils contribute 3.3+/-1.8 TgN/year, similar to the biomass burning source (3.8+/-2.1 TgN/year), and thus account for 40% of surface NO(x) emissions over Africa. Extrapolating to all the tropics, we estimate a 7.3 TgN/year biogenic soil source, which is a factor of 2 larger compared to model-based inventories but agrees with observation-based inventories. These large soil NO(x) emissions are likely to significantly contribute to the ozone enhancement originating from tropical Africa.

Jaegle, L.↗
NASA NTRS2003

Ozone Profiles and Tropospheric Ozone from Global Ozone Monitoring Experiment

Ozone profiles are derived from backscattered radiances in the ultraviolet spectra (290-340 nm) measured by the nadir-viewing Global Ozone Monitoring Experiment using optimal estimation. Tropospheric O3 is directly retrieved with the tropopause as one of the retrieval levels. To optimize the retrieval and improve the fitting precision needed for tropospheric O3, we perform extensive wavelength and radiometric calibrations and improve forward model inputs. Retrieved O3 profiles and tropospheric O3 agree well with coincident ozonesonde measurements, and the integrated total O3 agrees very well with Earth Probe TOMS and Dobson/Brewer total O3. The global distribution of tropospheric O3 clearly shows the influences of biomass burning, convection, and air pollution, and is generally consistent with our current understanding.

Liu, X.↗
NASA NTRS2001

Tropospheric Formaldehyde Measurements from the ESA GOME Instrument

The Global Ozone Monitoring Experiment (GOME) was launched on the European Space Agency's ERS-2 satellite on April 20, 1995. GOME measures the Earth's atmosphere in the nadir geometry, using a set of spectrometers that cover the UV and visible (240-790 nm) at moderate resolution (0.2 nm in the UV, 0.4 nm in the visible), employing silicon diode array detectors. GOME takes some 30,000 spectra per day, obtaining full global coverage in three days. We directly fit GOME radiance spectra using nonlinear least-squares analysis to obtain column amounts of several trace species with significant tropospheric concentrations, including ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and formaldehyde (HCHO). Measurements of HCHO due to biogenic activity in the troposphere are presented here.

Chance, K.↗