EGU2020-13635
https://doi.org/10.5194/egusphere-egu2020-13635
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Estimating surface nitrogen dioxide and ozone concentrations using satellite-based and numerical model-based data

Minso Shin and Jungho Im
Minso Shin and Jungho Im
  • Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea (msshin1125@unist.ac.kr)

Prolonged exposure to high concentrations of nitrogen dioxide (NO2) and ozone (O3) at ground level could be harmful to human health. In-situ air pollutant concentration data observed from ground monitoring stations are limited in providing spatially continuous information. Since there are only a few stations installed above the sea, it is difficult to monitor the concentrations of air pollutants over the sea. In this study, machine learning-based models were developed to estimate ground-level NO2 and O3 concentrations using satellite-based remote sensing data and model-based meteorological and emission data over East Asia during 2015-2017, to overcome such limitations. NO2 and O3 vertical column density products from the Aura Ozone Monitoring Instrument (OMI) were used as essential predictors to estimate NO2 and O3 concentrations. Missing pixels of OMI products due to row anomalies were filled using a temporal convolution approach to generate the spatiotemporally continuous distribution of NO2 and O3 concentrations. In order to estimate the air pollutant concentrations in both land and ocean, specific values were assigned to the ocean for land-only variables. Random forest (RF) was used to develop the estimation models for NO2 and O3 concentrations. The RF-based models showed the results with R2 values of 0.72 and 0.75, and RMSEs of 6.24 ppb and 10.56 ppb for NO2 and O3, respectively. The estimated results over the ocean were validated using coastal stations that are located within a 1 km distance from the coast. Compared to the model without land-only variables, the models using all variables had slightly better results. The satellite-based NO2 and O3 vertical column density were identified as significant variables in both models. Besides, urban land cover ratio, wind-related variables such as wind vectors, and stacked maximum wind speed had relatively high variable importance. The spatial variation of NO2 and seasonal variation of O3 were well shown in the estimated spatiotemporal distribution.

How to cite: Shin, M. and Im, J.: Estimating surface nitrogen dioxide and ozone concentrations using satellite-based and numerical model-based data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13635, https://doi.org/10.5194/egusphere-egu2020-13635, 2020

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