Global-Scale Patterns and Trends in Tropospheric NO2 Concentrations, 2005–2018

Research output: Contribution to journalArticle


Nitrogen dioxide (NO2) is an important air pollutant with both environmental and epidemiological effects. The main aim of this study is to analyze spatial patterns and temporal trends in tropospheric NO2 concentrations globally using data from the satellite-based Ozone Monitoring Instrument (OMI). Additional aims are to compare the satellite data with ground-based observations, and to find the timing and magnitude of greatest breakpoints in tropospheric NO2 concentrations for the time period 2005–2018. The OMI NO2 concentrations showed strong relationships with the ground-based observations, and inter-annual patterns were especially well reproduced. Eastern USA, Western Europe, India, China and Japan were identified as hotspot areas with high concentrations of NO2. The global average trend indicated slightly increasing NO2 concentrations (0.004 × 1015 molecules cm−2 y−1) in 2005–2018. The contribution of different regions to this global trend showed substantial regional differences. Negative trends were observed for most of Eastern USA, Western Europe, Japan and for parts of China, whereas strong, positive trends were seen in India, parts of China and in the Middle East. The years 2005 and 2007 had the highest occurrence of negative breakpoints, but the trends thereafter in general reversed, and the highest tropospheric NO2 concentrations were observed for the years 2017–2018. This indicates that the anthropogenic contribution to air pollution is still a major issue and that further actions are necessary to reduce this contribution, having a substantial impact on human and environmental health.


External organisations
  • Golder Associates AB
  • University of Copenhagen
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Physical Geography
  • Meteorology and Atmospheric Sciences


  • tropospheric NO2 concentrations; nitrogen dioxide; OMI; spatio-temporal trends; DBEST; PolyTrend; time-series analysis; breakpoint detection
Original languageEnglish
Article number3526
Number of pages18
JournalRemote Sensing
Issue number21
Publication statusPublished - 2020 Oct 28
Publication categoryResearch