Estimating ground level NO2 concentrations over central-eastern China using a satellite-based geographically and temporally weighted regression model

People in central-eastern China are suffering from severe air pollution of nitrogen oxides. Top-down approaches have been widely applied to estimate the ground concentrations of NO2 based on satellite data. In this paper, a one-year dataset of tropospheric NO2 columns from the Ozone Monitoring Instrument (OMI) together with ambient monitoring station measurements and meteorological data from May 2013 to April 2014, are used to estimate the ground level NO2. The mean values of OMI tropospheric NO2 columns show significant geographical and seasonal variation when the ambient monitoring stations record a certain range. Hence, a geographically and temporally weighted regression (GTWR) model is introduced to treat the spatio-temporal non-stationarities between tropospheric-columnar and ground level NO2. Cross-validations demonstrate that the GTWR model outperforms the ordinary least squares (OLS), the geographically weighted regression (GWR), and the temporally weighted regression (TWR), produces the highest R2 (0.60) and the lowest values of root mean square error mean (RMSE), absolute difference (MAD), and mean absolute percentage error (MAPE). Our method is better than or comparable to the chemistry transport model method. The satellite-estimated spatial distribution of ground NO2 shows a reasonable spatial pattern, with high annual mean values (>40 μg/m3), mainly over southern Hebei, northern Henan, central Shandong, and southern Shaanxi. The values of population-weight NO2 distinguish densely populated areas with high levels of human exposure from others

Spatially and temporally coherent reconstruction of tropospheric NO2 over China combining OMI and GOME-2B measurements

Tropospheric NO2 columns retrieved from OMI are widely used, even though there is a significant loss of spatial coverage due to multiple factors. This work introduces a framework for reconstructing gaps in the OMI NO2 data over China by using machine learning and an adaptive weighted temporal fitting method with NO2 measurements from GOME-2B, and surface measurements. The reconstructed NO2 has four important characteristics. First, there is improved spatial and temporal coherence on a day-to-day basis, allowing new scientific findings to be made. Second, the amount of data doubled, with 40% more data available. Third, the results are reliable overall, with a good agreement with MAX-DOAS measurements (R: 0.75-0.85). Finally, the mean of reconstructed NO2 vertical columns during 2015 and 2018 is consistent with the original data in the spatial distribution, while the standard deviation decreases in most places over mainland China. This novel finding is expected to contribute to both air quality and climate studies

The estimation of ground-level nitrogen dioxide (NO2) and ozone (O3) concentrations using Real-Time Learning (RTL)-based machine learning approach

Department of Urban and Environmental Engineering (Environmental Science and Engineering)Nitrogen dioxide (NO2) and ozone (O3) are the significant components of gaseous air pollutants that have harmful effects on human health. The monitoring and analysis of air pollutant exposure and persistence, and short-term forecasts are necessary for efficient public health management. In this study, the estimation model for the ground-level O3 and NO2 concentrations was developed which are spatially continuous over the land and ocean. The ground-level estimation was developed using the RTL-based machine learning technique with various satellite data and numerical model data as input variables. Three models were tested to build an accurate model using the most available data. 1) the ocean model using only ocean variables that have values for all regions2) the land model using all available data with assigning constant values to ocean variables3) the combined model that combines the results of the ocean model for sea area and the results of the land model for land area. Since NO2 and O3 have a relatively short lifespan, the real-time learning model is effective in estimating accurate ground-level concentrations.ope