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Quantitative Assessment of Different Air Pollutants (QADAP) Using Daily MODIS Images

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Abstract

Large-scale assessment of the amount of different air pollutants is routinely conducted using satellite images. This information is not useful in urban areas where more precise information is needed. In this work, a model called quantitative assessment of different air pollutants (QADAP) was prepared. The ability of QADAP is to produce images quantifying pollutant distribution throughout the city of Tehran at 500 m resolution. The model is based on surface reflectance changes due to air pollution. For this, the city was classified into various classes using GeoEye images. Then the reflectance of each class was calculated using this classified image along with a Hyperion image of the same region. Next, using this spectral information, a MODIS reflectance reference image in its first seven bands was prepared using MODIS image overlaid with the prepared classified image on a clear and clean sky. Then, some coefficients were calculated using surface reflectance differences between polluted days and the referenced clean day image. Required equations were then obtained by relating the pollution coefficients to the values of air pollutants measured in the pollution monitoring stations. Finally, given the equations and coefficients, pollutant concentration was calculated for each pixel and consequently daily images of different pollutants were produced. Data from ground stations were subsequently used to evaluate the model. The best results were for CO, PM2.5, NO2 and O3, which had lower relative Root Mean Squared Errors (RMSE), and the worst result was for PM10 with a high relative RMSE. The relative error of the model was 13–25% for higher levels of pollution and 150–400% for lower values. Finally highly polluted areas were determined by accumulation of different pollution images acquired on consecutive days; this aggregated image was used to identify the most contaminated regions considerably in agreement with the ground measured values.

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References

  • Alidoost F, Mobasheri MR, Abkar A (2013) Introducing a method for spectral enrichment of the high spatial resolution images. PFG 1:0031–0041. doi:10.1127/1432-8364/2013/0156

    Google Scholar 

  • Bechle MJ, Millet DB, Marshall JD (2013) Remote Sensing of exposure to NO2: satellite versus ground-based measurement in a large urban area. Atmos Environ 69:345–353. doi:10.1016/j.atmosenv.2012.11.046

    Article  CAS  Google Scholar 

  • Bucsela EJ, Celarier EA, Wenig MO (2006) Algorithm for NO2 vertical column retrieval from the ozone monitoring instrument. IEEE Trans Geosci Remote Sens 44(5):1245–1258. doi:10.1109/TGRS.2005.863715

    Article  Google Scholar 

  • Burrows JP, Weber M, Buchwitz M, Rozanov VV, Ladstatter-Weissenmayer A, Richter A, DeBeek R, Hoogen R, Bramstedt K, Eichmann KU (1999) The global ozone monitoring experiment (GOME): mission concept and first scientific results. J Atmos Sci 56:151–175

    Article  Google Scholar 

  • Chance K (2002) OMI algorithm theoretical basis document, OMI Trace Gas Algorithms, 4th edn. Smithsonian Astrophysical Observatory Cambridge

  • Frost GJ, Kim SW, Brioude J, Trainer M, McKeen S, Hsie EY, Angevine W, Lee SH, Granier C, Ryerson T, Peischl J, Fehsenfeld F (2011) Evaluating NOx emissions using satellite observations. NOAA, Earth System Research Laboratory Boulder, CO, USA

  • Georgoulias AK, Balis D, Koukouli ME, Meleti C, Bais A, Zerefos C (2009) A study of the total atmospheric sulfur dioxide load using ground-based measurements and the satellite derived sulfur dioxide index. Atmos Environ 43:1693–1701. doi:10.1016/j.atmosenv.2008.12.012

    Article  CAS  Google Scholar 

  • Hejazi A, Mobasheri MR, Ahmadian Marj A (2014) Optimization of an experimental model using genetic algorithm for assessment of PM2.5 in Tehran using satellite and synoptic data. Geogr Environ Plann 25(54–2):37–50

    Google Scholar 

  • Karimian H, Li Q, Li C, Jin L, Fan J, Li Y (2016) An improved method for monitoring fine particulate matter mass concentrations via satellite remote sensing. Aerosol Air Qual Res 16:1081–1092. doi:10.4209/aaqr.2015.06.0424

    Article  CAS  Google Scholar 

  • Lamsal LN, Krotkov NA, Celarier EA (2014) Evaluation of OMI operational standard NO2 column retrievals using in situ and surface-based NO2 observations. Atmos Chem Phys 14:11587–11609

    Article  Google Scholar 

  • Li L, Yang J, Wang Y (2015) Retrieval of high-resolution atmospheric particulate matter concentrations from satellite-based aerosol optical thickness over the Pearl River Delta Area, China. Remote Sens 7:7914–7937. doi:10.3390/rs70607914

    Article  Google Scholar 

  • McCormick BT, Herzog M, Yang J, Edmonds M, Mather TA, Carn SA, Hidalgo S, Langmann B (2014) A comparison of satellite- and ground-based measurements of SO2 emissions from Tungurahua volcano, Ecuador. J Geophys Res Atmos 119:4264–4285. doi:10.1002/2013JD019771

    Article  CAS  Google Scholar 

  • Noia AD, Sellitto P, Frate FD, Laat J (2013) Global tropospheric ozone column retrievals from OMI data by means of neural networks. Atmos Measure Tech 6:895–915. doi:10.5194/amt-6-895-2013

    Article  Google Scholar 

  • Popp C, Brunner D, Damm A, Roozendael MV, Fayt C, Buchmann B (2012) High-resolution NO2 remote sensing from the Airborne Prism EXperiment (APEX) imaging spectrometer. Atmos Measure Tech 5:2211–2225

    Article  CAS  Google Scholar 

  • Rakitin VS, Shtabkin YA, Elansky NF, Pankratova NV, Skorokhod AI, Grechko EI, Safronov AN (2015) Comparison results of satellite and ground-based spectroscopic measurements of CO, CH4, and CO2 total contents. Remote Sens Atmos Hydros Underlying Surf 28–6:533–542. doi:10.1134/S1024856015060135

    Google Scholar 

  • Streets DG, Canty T, Carmichael GR, Foy B, Dickerson RR, Duncan BN, Edwards DP, Haynes JA, Henze DK, Houyoux MR, Jacob DJ, Krotkov NA, Lamsal LN, Liu Y, Lu Z, Martin RV, Pfister GG, Pinder RW, Salawitch RJ, Kevin J. Wecht KJ (2013) Emissions estimation from satellite retrievals: a review of current capability. Atmos Environ 77:1011–1042. doi:10.1016/j.atmosenv.2013.05.051

    Article  CAS  Google Scholar 

  • Zhang T, Liu G, Zhu Z, Gong W, Ji W, Huang Y (2016) Real-time estimation of satellite-derived PM2.5 based on a semi-physical geographically weighted regression model. Int J Environ Res Public Health 13:974. doi:10.3390/ijerph13100974

    Article  Google Scholar 

  • Zoogman P, Jacob DJ, Chance K, Worden HM, Edwards DP, Zhang L (2014) Improved monitoring of surface ozone by joint assimilation of geostationary satellite observations of ozone and CO. Atmos Environ 85:254–261. doi:10.1016/j.atmosenv.2013.11.048

    Article  Google Scholar 

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Correspondence to Abolfazl Ahmadian Marj.

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Ahmadian Marj, A., Mobasheri, M.R. & Matkan, A.A. Quantitative Assessment of Different Air Pollutants (QADAP) Using Daily MODIS Images. Int J Environ Res 11, 523–534 (2017). https://doi.org/10.1007/s41742-017-0046-y

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  • DOI: https://doi.org/10.1007/s41742-017-0046-y

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