Ground-level NO2 concentration estimation based on OMI tropospheric NO2 and its spatiotemporal characteristics in typical regions of China

Abstract

Although the ground-level NO2 measurement from air quality monitoring sites is relatively accurate, it is a challenge to obtain continuous spatial coverage due to the discrete distribution of sites. Thus, the tropospheric column NO2 amount from satellites with wide spatial and temporal coverage and higher resolution has been increasingly used to estimate ground-level NO2. However, most estimation methods were performed spontaneously using a simple linear model throughout the study period. These simplified models improve the efficiency of model development and enhance the generality of the model application, but they ignore the fact that contributors to changes of ground-level NO2 are not always consistent with time. This study considered the fixed and random effects of influencing factors and developed a mixed effect model (MEM) to estimate the ground-level NO2. By using the data of tropospheric NO2 in China from January 1, 2014 to June 30, 2020 and other multivariate auxiliary data such as meteorological elements and terrain elevation, the reliability of daily ground-level NO2 in typical populated areas of China estimated by the MEM was evaluated. The average of monthly R2 of 10-fold CV in each study area during 2014–2020 is greater than 0.60 and the proportion of R2 greater than 0.7 is about 71%, suggesting the reliability of MEM. It is found that the ground-level NO2 distribution characteristics of each study area are more distinct, and the influential factors are also different. In addition, associated with the air quality control policies and emission reduction measures in various regions, the ground-level NO2 in each study area has shown an overall downward trend during 2014–2019. The uncertainty of daily-scale meteorological elements and boundary layer conditions can lead to varying degrees of deviations in daily-scale predictions of ground-level NO2. Validation with the station NO2 observations demonstrates that the ground-level NO2 prediction at seasonal time scale (R2 = 0.81, RMSE = 3.86 μg/m3) performs better than those at time scales of daily and monthly (R2 = 0.65, and 0.75, RMSE = 7.92, and 6.24 μg/m3). Therefore, the method of averaging can be used to improve the accuracy of ground-level NO2 predictions on individual dates. In summary, this study shows that MEM is a promising ground-level NO2 modeling method, and is effective for air pollution mapping in a large geographic region.