Global Surface HCHO Distribution Derived from Satellite Observations with Neural Networks Technique
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 resea...
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MDPI AG
2021-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/20/4055 |
| _version_ | 1797513264191504384 |
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| author | Jian Guan Bohan Jin Yizhe Ding Wen Wang Guoxiang Li Pubu Ciren |
| author_facet | Jian Guan Bohan Jin Yizhe Ding Wen Wang Guoxiang Li Pubu Ciren |
| author_sort | Jian Guan |
| collection | DOAJ |
| description | 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 μg/m<sup>3</sup>. 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. |
| first_indexed | 2024-03-10T06:14:13Z |
| format | Article |
| id | doaj.art-5a25c4636c5d4e31bb7440b3daecf7b4 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T06:14:13Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj.art-5a25c4636c5d4e31bb7440b3daecf7b42023-11-22T19:53:28ZengMDPI AGRemote Sensing2072-42922021-10-011320405510.3390/rs13204055Global Surface HCHO Distribution Derived from Satellite Observations with Neural Networks TechniqueJian Guan0Bohan Jin1Yizhe Ding2Wen Wang3Guoxiang Li4Pubu Ciren5Center for Spatial Information, School of Environment and Natural Resources, Renmin University of China, Beijing 100872, ChinaCenter for Spatial Information, School of Environment and Natural Resources, Renmin University of China, Beijing 100872, ChinaSchool of Statistics and Data Science, Nankai University, Tianjin 300071, ChinaCenter for Spatial Information, School of Environment and Natural Resources, Renmin University of China, Beijing 100872, ChinaSchool of Information, Renmin University of China, Beijing 100872, ChinaI.M. System Group Inc. & NOAA/NESDIS/STAR, 5825 University Research Ct., Suite 3250 M Square, College Park, MD 20740, USAFormaldehyde (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 μg/m<sup>3</sup>. 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.https://www.mdpi.com/2072-4292/13/20/4055surface formaldehydeneural network modelinterval estimationTROPOMIglobal distribution |
| spellingShingle | Jian Guan Bohan Jin Yizhe Ding Wen Wang Guoxiang Li Pubu Ciren Global Surface HCHO Distribution Derived from Satellite Observations with Neural Networks Technique Remote Sensing surface formaldehyde neural network model interval estimation TROPOMI global distribution |
| title | Global Surface HCHO Distribution Derived from Satellite Observations with Neural Networks Technique |
| title_full | Global Surface HCHO Distribution Derived from Satellite Observations with Neural Networks Technique |
| title_fullStr | Global Surface HCHO Distribution Derived from Satellite Observations with Neural Networks Technique |
| title_full_unstemmed | Global Surface HCHO Distribution Derived from Satellite Observations with Neural Networks Technique |
| title_short | Global Surface HCHO Distribution Derived from Satellite Observations with Neural Networks Technique |
| title_sort | global surface hcho distribution derived from satellite observations with neural networks technique |
| topic | surface formaldehyde neural network model interval estimation TROPOMI global distribution |
| url | https://www.mdpi.com/2072-4292/13/20/4055 |
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