Ronald Johannes van der A

Detection and emission estimates of NOx sources over China North Plain using OMI observations

ABSTRACT We designed a fast procedure to detect the nitrogen oxides (NOx) sources in the China North Plain and to estimate their NOx emissions through a two-dimensional Gaussian fitting method applied to averaged Ozone Monitoring Instrument (OMI) observations of nitrogen dioxide (NO2) column concentration. The Northern China Plain is a region that has one of the highest densities of anthropogenic NOx sources in the world and therefore the sources are difficult to distinguish. With our procedure we still found 94 individual NOx emission sources. Of these sources Tangshan city has the strongest NOx emission rate (92 Gg N year–1), while the weakest that we are still able to detect is Zhangjiakou city, with a NOx emission rate of 0.4 Gg N year–1. Using the fitting results, we reconstruct the NO2 column concentration distribution map, which matches the OMI observations with an R2 = 0.85 and a slope of 0.78. The derived NOx emission rates for cities and provinces level show good agreement with former studies.

MAX-DOAS NO 2 observations over Guangzhou, China; ground-based and satellite comparisons

In this study, the tropospheric NO2 vertical column density (VCD) over an urban site in Guangzhou megacity in China is investigated by means of MAX-DOAS measurements during a campaign from late March 2015 to mid-March 2016. A MAX-DOAS system was deployed at the Guangzhou Institute of Geochemistry of the Chinese Academy of Sciences and operated there for about 1 year, during the spring and summer months. The tropospheric NO2 VCDs retrieved by the MAX-DOAS are presented and compared with space-borne observations from GOME-2/MetOpA, GOME-2/MetOp-B and OMI/Aura satellite sensors. The comparisons reveal good agreement between satellite and MAX-DOAS observations over Guangzhou, with correlation coefficients ranging between 0.795 for GOME-2B and 0.996 for OMI. However, the tropospheric NO2 loadings are underestimated by the satellite sensors on average by 25.1, 10.3 and 5.7 %, respectively, for OMI, GOME-2A and GOME2B. Our results indicate that GOME-2B retrievals are closer to those of the MAX-DOAS instrument due to the lower tropospheric NO2 concentrations during the days with valid GOME-2B observations. In addition, the effect of the main coincidence criteria is investigated, namely the cloud fraction (CF), the distance (d) between the satellite pixel center and the ground-based measurement site, as well as the time period within which the MAX-DOAS data are averaged around the satellite overpass time. The effect of CF and time window criteria is more profound on the selection of OMI overpass data, probably due to its smaller pixel size. The available data pairs are reduced to half and about one-third for CF≤ 0.3 and CF≤ 0.2, respectively, while, compared to larger CF thresholds, the correlation coefficient is improved to 0.996 from about 0.86, the slope value is very close to unity (∼ 0.98) and the mean satellite underestimation is reduced to about half (from ∼ 7 to ∼ 3.5× 1015 molecules cm−2). On the other hand, the distance criterion affects mostly GOME-2B data selection, because GOME-2B pixels are quite evenly distributed among the different radii used in the sensitivity test. More specifically, the number of collocations is notably reduced when stricter radius limits are applied, the r value is improved from 0.795 (d ≤ 50 km) to 0.953 (d ≤ 20 km), and the absolute mean bias decreases about 6 times for d ≤ 30 km compared to the reference case (d ≤ 50 km).