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:
Jin, B.; Guan, J.; Ding, Y.; Wang, W.; Li, G. Mapping Global Surface HCHO Distribution with Confidential Interval by Satellite Observation. Preprints2021, 2021050595. https://doi.org/10.20944/preprints202105.0595.v1
Jin, B.; Guan, J.; Ding, Y.; Wang, W.; Li, G. Mapping Global Surface HCHO Distribution with Confidential Interval by Satellite Observation. Preprints 2021, 2021050595. https://doi.org/10.20944/preprints202105.0595.v1
Jin, B.; Guan, J.; Ding, Y.; Wang, W.; Li, G. Mapping Global Surface HCHO Distribution with Confidential Interval by Satellite Observation. Preprints2021, 2021050595. https://doi.org/10.20944/preprints202105.0595.v1
APA Style
Jin, B., Guan, J., Ding, Y., Wang, W., & Li, G. (2021). Mapping Global Surface HCHO Distribution with Confidential Interval by Satellite Observation. Preprints. https://doi.org/10.20944/preprints202105.0595.v1
Chicago/Turabian Style
Jin, B., Wen Wang and Guoxiang Li. 2021 "Mapping Global Surface HCHO Distribution with Confidential Interval by Satellite Observation" Preprints. https://doi.org/10.20944/preprints202105.0595.v1
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 researches on outdoor HCHO pollution. Traditional methods are either too naïve or data-demanding for a global scale research. To alleviate this issue, we trained two fully-connected neural networks respectively for deriving point and interval estimation of surface HCHO concentration in 2019, where 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, North China, South-east Asia, Bay of Bengal, Central and Western Africa are among the highest. Our study makes up for the global shortage of surface HCHO monitoring and helps people have a clearer understanding of surface concentration distribution of HCHO. In addition, with the help of quality-driven algorithm, interval estimation of surface HCHO concentration is believed to bring confidence to our results. As an early work adopting interval estimation in AI-driven atmospheric pollutant research and the first to map global HCHO surface distribution, our paper will pave way for rigorous study on global ambient HCHO health risk and economic loss, thus providing basis for pollutant controlling policies worldwide.
Keywords
surface formaldehyde; neural network model; interval estimation; TROPOMI; global distribution
Subject
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
