Literature DB >> 19843549

MODIS and OMI satellite observations supporting air quality monitoring.

W Di Nicolantonio1, A Cacciari, A Petritoli, C Carnevale, E Pisoni, M L Volta, P Stocchi, G Curci, E Bolzacchini, L Ferrero, C Ananasso, C Tomasi.   

Abstract

Within the framework of air quality monitoring, measurements by Earth-observing satellite sensors are combined here with regional meteorological and chemical transport models. Two satellite-derived products developed within the QUITSAT project, regarding significant pollutants including PM(2.5) and NO(2), are presented. Estimates of PM(2.5) concentrations at ground level were obtained using moderate resolution imaging spectroradiometer (Terra-Aqua/NASA) aerosol optical properties. The semi-empirical approach adopted takes into account PM(2.5) sampling and meteorological descriptions of the area studied, as simulated by MM5, to infer aerosol optical properties to PM projection coefficients. Daily maps of satellite-based PM(2.5) concentrations over northern Italy are derived. Monthly average values were compared with in situ PM(2.5) samplings showing good agreement. Ozone monitoring instrument (OMI) (Aura/NASA) NO(2) tropospheric contents are merged using the GAMES chemical model simulations. The method employs a weighted rescaling of the model column in the troposphere according to the OMI observations. The weightings take into account measurement errors and model column variances within the satellite ground pixel. The obtained ground-level concentrations of NO(2) show good agreement with the environmental agencies' in situ.

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Year:  2009        PMID: 19843549     DOI: 10.1093/rpd/ncp231

Source DB:  PubMed          Journal:  Radiat Prot Dosimetry        ISSN: 0144-8420            Impact factor:   0.972


  2 in total

1.  A GIS-based assessment of the suitability of SCIAMACHY satellite sensor measurements for estimating reliable CO concentrations in a low-latitude climate.

Authors:  Mofoluso A Fagbeja; Jennifer L Hill; Tim J Chatterton; James W S Longhurst
Journal:  Environ Monit Assess       Date:  2015-01-28       Impact factor: 2.513

2.  A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM2.5 Concentration by Integrating Multisource Datasets.

Authors:  Yuan Shi; Alexis Kai-Hon Lau; Edward Ng; Hung-Chak Ho; Muhammad Bilal
Journal:  Int J Environ Res Public Health       Date:  2021-12-29       Impact factor: 3.390

  2 in total

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