Remote sensing of air pollution from satellite and MAX-DOAS network in China
Abstract
With the fast industrialization and urbanization in China, environmental pollution has become more serious and complex. Precise and real-time monitoring is the prerequisite for knowing the distribution characteristics and the evolution mechanism of the atmospheric pollutants. Over the last few years, we have successfully monitored atmospheric composition by using remote sensing from different platform, including satellite, ground and mobile vehicle, which have been validated to have good performance.Remote sensing by satellite can describe the global distribution of various pollutants, which can also locate the emission point sources, such as factories etc. Ground-based MAX-DOAS monitor the vertical evolutions of these trace gases and aerosol at a fixed position, the column density of pollutants was divided into different layers, so we could detect transport plum in all altitude. Up to now, we have established a notional monitoring network with more than 30 MAX-DOAS, which could provide sufficient validation for satellite products and conduct scientific researches. Combining these two methods, which could provide precise horizontal and vertical distribution of pollutant, we could get a 3-D distribution of pollutants and the transport flux. Here, we analyzed the spatial distribution and temporal trends of satellite-observed air pollutants over eastern China during 2005-2017. We found significant decreasing trends in NO2 and SO2 since 2011 over most regions. Furthermore, we used the generalized additive models to clarify the relative contribution of local emissions and meteorological conditions. Our results show that meteorological determines daily changes in pollutants, while long-term, inter annual changes are determined by emissions. Emission reduction has played a decisive role in the recent reduction of the pollution!
- Publication:
-
EGU General Assembly Conference Abstracts
- Pub Date:
- May 2020
- DOI:
- 10.5194/egusphere-egu2020-1757
- Bibcode:
- 2020EGUGA..22.1757L