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Estimation of inter-seasonal differences in NO2 concentrations using a dispersion ADMS-Urban model and measurements

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Abstract

Air pollution dispersion modeling is one of the methods for the ambient air quality assessment. The mathematical models are widely used tools for predicting air pollution and accurately assessing the spatial distribution of air pollutants in the city. Road transport is one of the main sources of pollution affecting air quality in urban areas, and nitrogen dioxide is considered to be one of the most common with traffic emission-related pollutant, which concentrations are strongly correlated to the distance from the roadways. The study aim was to determine the dispersion of nitrogen dioxide (NO2) concentration and to estimate the inter-seasonal differences in these concentrations at different sites (rural, urban, traffic) using a ADMS-Urban model and measurements in Kaunas city. For the modeling of nitrogen dioxide pollution, the year 2011 was selected. Average concentrations of nitrogen dioxide for the winter, summer, and intermediate spring seasons were modeled. Nitrogen dioxide concentrations calculated by model have been verified against 41 Ogawa passive sampling results. The modeling results showed that the highest average seasonal NO2 concentration was modeled in the winter season (21.79 μg/m3), while the lowest concentration was determined in summer (12.28 μg/m3). Correlation coefficient between modeled NO2 concentrations using the ADMS-Urban model and measured by Ogawa passive samplers was significantly high for all seasons and confirmed the generally good performance of the model.

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Authors and Affiliations

  1. Department of Environmental Sciences, Vytautas Magnus University, 8 Vileikos street, LT-44404, Kaunas, Lithuania

    Audrius Dėdelė & Auksė Miškinytė

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  1. Audrius Dėdelė
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  2. Auksė Miškinytė
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Correspondence to Audrius Dėdelė.

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Dėdelė, A., Miškinytė, A. Estimation of inter-seasonal differences in NO2 concentrations using a dispersion ADMS-Urban model and measurements. Air Qual Atmos Health 8, 123–133 (2015). https://doi.org/10.1007/s11869-014-0272-9

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  • DOI: https://doi.org/10.1007/s11869-014-0272-9

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