Identification of NOx hotspots from oversampled TROPOMI NO2 column based on image segmentation method

Satellite-based measures of NO2 have become increasingly available for resolving the limitation on insufficient spatial and temporal coverage of ground-level monitoring networks. Oversampled NO2 column density can obtain more detailed features of NO2 column with a spatial resolution as high as 2 km × 2 km, while it is still challenging to identify hotspots of NOx pollution plume in city-scale due to background interference. In this study, we proposed a method for detecting the NOx hotspot grids from oversampled satellite NO2 column based on the image segmentation method, and identifying major source types using Term frequency-inverse document frequency (TF-IDF). A fractal model was used to evaluate and eliminate the background portion of the NO2 column and an adaptive threshold method was adopted to identify the region of interest (ROI) of local hotspot NO2 column. Hot-grid index, counting the frequency of NO2 hotspot ROI in each grid, was conducted to identify the hotspot grids. TF-IDF was used to semantically analyze the major source types of NO2 hotspot grids. Taking Central and Eastern China as the studied domain, the hotspot grids of NO2 and the relevant major source types were identified based on the proposed method. The major non-road mobile sources (such as Beijing Capital International Airport), industrial areas (such as Caofeidian Industrial Park) and urban areas were clearly distinguished. The power plant, Coke and Iron and Steel were identified as major source types in the whole year in the corresponding NOx hotspot grids. Notably, the identification of hotspot grids indicated a higher probability of a local high-intensity NOx pollution plume rather than a quantitative NOx emission; the key source types were the semantic keywords in hotspot grids, which does not mean there were no other exiting emission sources. This proposed method has strong implications on rapidly identifying the NOx hotspot grids based on oversampled TROPOMI NO2 column and the list of industrial enterprises. [Display omitted] •Identify NOx hotspot from oversampled NO2 VCD based on image segmentation.•Identify key sectors by semantic recognition analysis.•Proposed method can identify NOx hotspot area in city-scale.•Iron and steel, Coke and power plant are the key sectors for NOx hot spot.