Blond et al., 2007 - Google Patents
Intercomparison of SCIAMACHY nitrogen dioxide observations, in situ measurements and air quality modeling results over Western EuropeBlond et al., 2007
View PDF- Document ID
- 10098934390143700985
- Author
- Blond N
- Boersma K
- Eskes H
- Van Der A R
- Van Roozendael M
- De Smedt I
- Bergametti G
- Vautard R
- Publication year
- Publication venue
- Journal of Geophysical Research: Atmospheres
External Links
Snippet
The Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) satellite spectrometer provides detailed information on the nitrogen dioxide (NO2) content in the planetary boundary layer. NO2 tropospheric column retrievals of …
- JCXJVPUVTGWSNB-UHFFFAOYSA-N nitrogen dioxide O=[N]=O 0 title abstract description 72
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0073—Control unit therefor
- G01N33/0075—Control unit therefor for multiple spatially distributed sensors, e.g. for environmental monitoring
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/26—Investigating or analysing materials by specific methods not covered by the preceding groups oils; viscous liquids; paints; inks
- G01N33/28—Oils, i.e. hydrocarbon liquids
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0036—Specially adapted to detect a particular component
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/84—Systems specially adapted for particular applications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/22—Fuels, explosives
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/02—Devices for withdrawing samples
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N25/00—Investigating or analyzing materials by the use of thermal means
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V9/00—Prospecting or detecting by methods not provided for in groups G01V1/00 - G01V8/00
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Blond et al. | Intercomparison of SCIAMACHY nitrogen dioxide observations, in situ measurements and air quality modeling results over Western Europe | |
Ye et al. | Constraining fossil fuel CO2 emissions from urban area using OCO‐2 observations of total column CO2 | |
Borsdorff et al. | Measuring carbon monoxide with TROPOMI: First results and a comparison with ECMWF‐IFS analysis data | |
Jin et al. | Spatial and temporal variability of ozone sensitivity over China observed from the Ozone Monitoring Instrument | |
Lassman et al. | Spatial and temporal estimates of population exposure to wildfire smoke during the Washington state 2012 wildfire season using blended model, satellite, and in situ data | |
Benedetti et al. | Aerosol analysis and forecast in the European centre for medium‐range weather forecasts integrated forecast system: 2. Data assimilation | |
Miller et al. | Precision requirements for space‐based data | |
Xu et al. | Constraints on aerosol sources using GEOS‐Chem adjoint and MODIS radiances, and evaluation with multisensor (OMI, MISR) data | |
Nakajima et al. | Overview of the Atmospheric Brown Cloud East Asian Regional Experiment 2005 and a study of the aerosol direct radiative forcing in east Asia | |
Pant et al. | Aerosol characteristics at a high‐altitude location in central Himalayas: Optical properties and radiative forcing | |
Arellano Jr et al. | Time‐dependent inversion estimates of global biomass‐burning CO emissions using Measurement of Pollution in the Troposphere (MOPITT) measurements | |
Mahowald et al. | Interannual variability in atmospheric mineral aerosols from a 22‐year model simulation and observational data | |
Xiao et al. | Atmospheric acetylene and its relationship with CO as an indicator of air mass age | |
Taubman et al. | Aircraft vertical profiles of trace gas and aerosol pollution over the mid‐Atlantic United States: Statistics and meteorological cluster analysis | |
Lee et al. | Transport of NOx in East Asia identified by satellite and in situ measurements and Lagrangian particle dispersion model simulations | |
Park et al. | Hydrocarbons in the upper troposphere and lower stratosphere observed from ACE‐FTS and comparisons with WACCM | |
Roy et al. | A comparison of CMAQ‐based aerosol properties with IMPROVE, MODIS, and AERONET data | |
Ukhov et al. | Study of SO 2 Pollution in the Middle East Using MERRA‐2, CAMS Data Assimilation Products, and High‐Resolution WRF‐Chem Simulations | |
Vautard et al. | A synthesis of the Air Pollution Over the Paris Region (ESQUIF) field campaign | |
Ford et al. | An A‐train and model perspective on the vertical distribution of aerosols and CO in the Northern Hemisphere | |
Choi et al. | Springtime transitions of NO2, CO, and O3 over North America: Model evaluation and analysis | |
Chatterjee et al. | Background error covariance estimation for atmospheric CO2 data assimilation | |
DiGangi et al. | Seasonal variability in local carbon dioxide biomass burning sources over central and eastern US using airborne in situ enhancement ratios | |
Amnuaylojaroen | Prediction of PM2. 5 in an urban area of northern Thailand using multivariate linear regression model | |
Yang et al. | Use of hourly Geostationary Operational Environmental Satellite (GOES) fire emissions in a Community Multiscale Air Quality (CMAQ) model for improving surface particulate matter predictions |