Our Fast Emission Retrieval at High Resolution

Tropospheric nitrogen oxides (NO_x = NO + NO₂) are an important air pollutant and a major precursor to PM_2.5 and ozone, posing severe risks to human and environmental health. NO_x is emitted mainly in the form of nitric oxide (NO) from fossil fuel combustion, biomass burning, lightning, and microbial processes. The emitted NO undergoes fast chemical exchange with nitrogen dioxide (NO₂) in the atmosphere which can be detected by satellite sensors. Knowledge remains poor on both anthropogenic and natural emission sources at high horizontal resolutions such as 5 km.

For more than a decade, our group has been devoted to retrieval of tropospheric NO₂ vertical column density (VCD) from satellite remote sensing and subsequent estimate of NO emissions at high resolutions. As NO_x undergoes atmospheric transport and nonlinear chemistry, it was quite a challenge to achieve high-resolution NO emission retrieval with low computational cost while considering these two effects. In 2019, we developed an algorithm named PHLET for fast retrieval of NO emissions at a 5-km resolution and applied it to Yangtze River Delta as a case study. Later in 2022, we established the second version of PHLET to achieve fast, accurate NO emission retrieval covering China on a 5-km grid (Fig. 1)¹. The resulting emission dataset offers crucial information for monitoring anthropogenic and natural emission sources, exploring their climatic, environmental and ecological effects, and supporting targeted emission control.

Emissions from Tibetan Lakes: They Are Not Noises

At the early stage of our study on emission retrieval, our main concern was anthropogenic sources. Back in the summer of 2021 when we obtained a preliminary high-resolution map of NO emissions over China, we adopted proxies for human activities (e.g. nighttime light, road network) to filter out the emissions that appear not to be anthropogenic sources. In the meantime, my supervisor, Prof. Lin, was curious about the emissions filtered out,

“There must be new findings waiting for us with high-quality emission retrieval at such a high resolution, not only in the term of anthropogenic sources. There are many unexpected hotspots appearing in our emission retrieval data, for example, over the Tibetan Plateau. I believe that they are too strong to be just noises. You should check on that and we might be able to find something interesting.”

After a few modifications and tests on to the algorithm to finally obtain the retrieved emissions, we found that many of the unexpected emissions and hotspots of NO₂ VCDs over the Tibetan Plateau coincided well with the lakes in both location and shape (Fig. 2), which suggested potential emissions from the lakes. Then, we selected the lakes away from human activities and found that NO₂ VCDs over those lakes peaked in summer and reached the minimum in winter, suggesting their NO emissions to be caused by natural microbial processes.

These results are important, because inland water bodies far from human activities were traditionally thought to cause trivial amounts of natural microbial-sourced NO emissions. As we further link the emissions to rapid warming over the Plateau, our study points to previously unknown feedbacks between climate change, lake ecology and nitrogen emissions.

What Is Next?

In-situ measurements of NO fluxes and laboratory experiments are necessary to provide further evidence of such emissions and their exact microbial mechanisms and processes. Such work is difficult because of the unique location with tough environment for both humans and measurement instruments. While keeping on my research based on satellite, I hope to participate in a team survey with professional equipment to uncover the underlining mechanism behind the unexpected NO emissions from the TP lakes.

References

1. Kong, H., Lin, J.-T.*, Chen, L.-L., Zhang, Y.-H., Yan, Y.-Y., Liu, M.-Y., Ni, R.-J., Liu, Z., and Weng, H.-J.: Considerable unaccounted local sources of NO_x emissions in China revealed from satellite, Environmental Science & Technology, 56, 7131-7142, doi:10.1021/acs.est.1c07723, 2022