[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Tchepel et al., 2013 - Google Patents

Analysis of long-range transport of aerosols for Portugal using 3D chemical transport model and satellite measurements

Tchepel et al., 2013

View PDF
Document ID
12436599233264602133
Author
Tchepel O
Ferreira J
Fernandes A
Basart S
Baldasano J
Borrego C
Publication year
Publication venue
Atmospheric environment

External Links

Snippet

The objective of this work is to assess the contribution of long-range transport of mineral dust from North Africa to the air pollution levels in Portugal based on a combination of a modelling approach and satellite observations. The Comprehensive Air Quality Model …
Continue reading at www.bsc.es (PDF) (other versions)

Classifications

    • 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
    • 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
    • G01N1/00—Sampling; Preparing specimens for investigation
    • G01N1/02—Devices for withdrawing samples
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/16—Measuring atmospheric potential differences, e.g. due to electrical charges in clouds
    • 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
    • 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
McKeen et al. Evaluation of several PM2. 5 forecast models using data collected during the ICARTT/NEAQS 2004 field study
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
Vignati et al. Global scale emission and distribution of sea-spray aerosol: Sea-salt and organic enrichment
Yu et al. Evaluation of real‐time PM2. 5 forecasts and process analysis for PM2. 5 formation over the eastern United States using the Eta‐CMAQ forecast model during the 2004 ICARTT study
Blond et al. Intercomparison of SCIAMACHY nitrogen dioxide observations, in situ measurements and air quality modeling results over Western Europe
Hegarty et al. Evaluation of Lagrangian particle dispersion models with measurements from controlled tracer releases
Hu et al. Ozone variability in the atmospheric boundary layer in Maryland and its implications for vertical transport model
Honoré et al. Predictability of European air quality: Assessment of 3 years of operational forecasts and analyses by the PREV'AIR system
Carmichael et al. MICS-Asia II: The model intercomparison study for Asia Phase II methodology and overview of findings
Generoso et al. Aerosol vertical distribution in dust outflow over the Atlantic: Comparisons between GEOS‐Chem and Cloud‐aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)
Reddy et al. A study of the global cycle of carbonaceous aerosols in the LMDZT general circulation model
Geng et al. Satellite‐based daily PM2. 5 estimates during fire seasons in Colorado
Im et al. Study of a winter PM episode in Istanbul using the high resolution WRF/CMAQ modeling system
Gama et al. Seasonal patterns of Saharan dust over Cape Verde–a combined approach using observations and modelling
Bessagnet et al. Origin of particulate matter pollution episodes in wintertime over the Paris Basin
Jiang et al. Modeling the effects of VOC and NOx emission sources on ozone formation in Houston during the TexAQS 2000 field campaign
Han et al. MICS-Asia II: Model intercomparison and evaluation of ozone and relevant species
Tositti et al. Short-term climatology of PM10 at a high altitude background station in southern Europe
Alvarado et al. Evaluating the use of satellite observations to supplement ground-level air quality data in selected cities in low-and middle-income countries
Zhang et al. Improving surface PM2. 5 forecasts in the United States using an ensemble of chemical transport model outputs: 1. Bias correction with surface observations in nonrural areas
Chai et al. Impact of moderate resolution imaging spectroradiometer aerosol optical depth and AirNow PM2. 5 assimilation on community multi‐scale Air quality aerosol predictions over the contiguous United States
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
Yoshioka et al. Simulation of absorbing aerosol indices for African dust
Hu et al. Improving satellite‐driven PM2. 5 models with moderate resolution imaging spectroradiometer fire counts in the southeastern US
Hodzic et al. Comparison of aerosol chemistry transport model simulations with lidar and Sun photometer observations at a site near Paris