Abstract
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.
Highlights
High ground level nitrogen oxides (NOx = NO + NO2 ) are identified to be deleterious to human health, including decreased lung function and an increased risk of respiratory symptoms [1,2].In addition, NOx can produce other photochemical pollutants like O3 in photochemical reactions, and acts as a gaseous precursor of aerosols and acid rain
A dramatic increase in tropospheric NO2 columns was revealed by the Global OzoneMonitoring Experiment (GOME) and SCIAMACHY observations over China [9,10,11,12], the world’s largest developing country along with the fastest growing economy
We introduced a number of meteorological parameters to the geographically and temporally weighted regression (GTWR), i.e., air temperature at 2 m above the ground (T), relative humidity (RH), wind speed at 10 m above the ground (WS), planetary boundary layer height (PBLH), dew point temperature at 2 m above the ground (Td ), and the ambient pressure near ground (P)
Summary
Cartography (SCIAMACHY), and the Ozone Monitoring Instrument (OMI) have been successfully used to retrieve vertical NO2 columns [4,5,6,7,8]. A dramatic increase in tropospheric NO2 columns was revealed by the GOME and SCIAMACHY observations over China [9,10,11,12], the world’s largest developing country along with the fastest growing economy. Given that the existing ambient monitoring stations are sparse and unevenly distributed, there is a growing interest in the top-down satellite approach to obtain timely map of the spatial variations of surface concentrations of NO2. A close relationship between ground level NO2 concentrations and satellite-retrieved tropospheric NO2 columns is expected based on two facts: (1) ground level
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