Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Global Surface HCHO Distribution derived from Satellite Observations with Neural Networks Technique

Version 1 : Received: 24 May 2021 / Approved: 25 May 2021 / Online: 25 May 2021 (08:58:59 CEST)
Version 2 : Received: 28 August 2021 / Approved: 30 August 2021 / Online: 30 August 2021 (10:30:42 CEST)

How to cite: Guan, J.; Jin, B.; Ding, Y.; Wang, W.; Li, G.; Ciren, P. Global Surface HCHO Distribution derived from Satellite Observations with Neural Networks Technique. Preprints 2021, 2021050595 (doi: 10.20944/preprints202105.0595.v2). Guan, J.; Jin, B.; Ding, Y.; Wang, W.; Li, G.; Ciren, P. Global Surface HCHO Distribution derived from Satellite Observations with Neural Networks Technique. Preprints 2021, 2021050595 (doi: 10.20944/preprints202105.0595.v2).

Abstract

Formaldehyde (HCHO) is one of the most important carcinogenic air contaminants. However, the lack of global surface concentration of HCHO monitoring is currently hindering research on outdoor HCHO pollution. Traditional methods are either restricted to small areas or data- demanding for a global scale of research. To alleviate this issue, we adopted neural networks to estimate surface HCHO concentration with confidence intervals in 2019, where HCHO vertical column density data from TROPOMI, in-situ data from HAPs (harmful air pollutants) monitoring network and ATom mission are utilized. Our result shows that the global surface HCHO average concentration is 2.30 μg/m3. Furthermore, in terms of regions, the concentration in Amazon Basin, Northern China, South-east Asia, Bay of Bengal, Central and Western Africa are among the highest. The results from our study provides a first dataset of the global surface HCHO concentration. In addition, the derived confidence interval of surface HCHO concentration adds an extra layer for the confidence to our results. As a pioneer work in adopting confidence interval estimation into AI-driven atmospheric pollutant research and the first global HCHO surface distribution dataset, our paper will pave the way for the rigorous study on global ambient HCHO health risk and economic loss, thus providing a basis for pollutant controlling policies worldwide.

Keywords

surface formaldehyde; neural network model; interval estimation; TROPOMI; global distribution

Comments (1)

Comment 1
Received: 30 August 2021
Commenter: Wen Wang
Commenter's Conflict of Interests: Author
Comment: Rewritten the discussion section and fixed syntax errors
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