Journal
REMOTE SENSING
Volume 13, Issue 20, Pages -Publisher
MDPI
DOI: 10.3390/rs13204055
Keywords
surface formaldehyde; neural network model; interval estimation; TROPOMI; global distribution
Categories
Funding
- National Social Science Foundation of China [16CTJ003]
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Formaldehyde is a significant carcinogenic air pollutant, but the lack of global surface concentration monitoring hinders research on outdoor pollution. Utilizing neural networks, the study estimated the global surface HCHO concentration in 2019, with an average concentration of 2.30 μg/m³ globally and highest concentrations in regions such as the Amazon Basin and Northern China. The study provides the first dataset on global surface HCHO concentration and adds confidence intervals to the results, paving the way for further research on global ambient HCHO health risks and economic losses.
Formaldehyde (HCHO) is one of the most important carcinogenic air contaminants in outdoor air. However, the lack of monitoring of the global surface concentration of HCHO is currently hindering research on outdoor HCHO pollution. Traditional methods are either restricted to small areas or, for research on a global scale, too data-demanding. To alleviate this issue, we adopted neural networks to estimate the 2019 global surface HCHO concentration with confidence intervals, utilizing HCHO vertical column density data from TROPOMI, and in-situ data from HAPs (harmful air pollutants) monitoring networks and the ATom mission. Our results show that the global surface HCHO average concentration is 2.30 mu g/m(3). Furthermore, in terms of regions, the concentrations in the Amazon Basin, Northern China, South-east Asia, the Bay of Bengal, and Central and Western Africa are among the highest. The results from our study provide the first dataset on global surface HCHO concentration. In addition, the derived confidence intervals of surface HCHO concentration add an extra layer of confidence to our results. As a pioneering work in adopting confidence interval estimation to AI-driven atmospheric pollutant research and the first global HCHO surface distribution dataset, our paper paves the way for rigorous study of global ambient HCHO health risk and economic loss, thus providing a basis for pollution control policies worldwide.
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