Application of multi-angle spaceborne observations in characterizing the long-term particulate organic carbon pollution in China

Yun Hang; Qiang Pu; Qiao Zhu; Xia Meng; Zhihao Jin; Fengchao Liang; Hezhong Tian; Tiantian Li; Tijian Wang; Junji Cao; Qingyan Fu; Sagnik Dey; Shenshen Li; Kan Huang; Haidong Kan; Xiaoming Shi; Yang Liu

doi:10.21203/rs.3.rs-3734829/v1

Application of multi-angle spaceborne observations in characterizing the long-term particulate organic carbon pollution in China

Res Sq [Preprint]. 2023 Dec 12:rs.3.rs-3734829. doi: 10.21203/rs.3.rs-3734829/v1.

Authors

Yun Hang^{1

2}, Qiang Pu¹, Qiao Zhu¹, Xia Meng³, Zhihao Jin¹, Fengchao Liang⁴, Hezhong Tian⁵, Tiantian Li⁶, Tijian Wang⁷, Junji Cao⁸, Qingyan Fu⁹, Sagnik Dey¹⁰, Shenshen Li¹¹, Kan Huang¹², Haidong Kan³, Xiaoming Shi⁶, Yang Liu¹

Affiliations

¹ Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, United States.
² Department of Environmental and Occupational Health Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, 77030, United States.
³ School of Public Health, Fudan University, Shanghai 200032, China.
⁴ School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, 518055, China.
⁵ State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beiji ng, 100875, China.
⁶ China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China.
⁷ School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China.
⁸ Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100101, China.
⁹ State Ecologic Environmental Scientific Observation and Research Station at Dianshan Lake, Shanghai Environmental Monitoring Center, Shanghai 200235, China.
¹⁰ Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.
¹¹ State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China.
¹² Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.

Abstract

Ambient PM_2.5 pollution is recognized as a leading environmental risk factor, causing significant mortality and morbidity in China. However, the specific contributions of individual PM_2.5 constituents remain unclear, primarily due to the lack of a comprehensive ground monitoring network for constituents. This issue is particularly critical for carbonaceous species such as organic carbon (OC) and elemental carbon (EC), which are known for their significant health impacts, and understanding the OC/EC ratio is crucial for identifying pollution sources. To address this, we developed a Super Learner model integrating Multi-angle Imaging SpectroRadiometer (MISR) retrievals to predict daily OC concentrations across China from 2003 to 2019 at a 10-km spatial resolution. Our model demonstrates robust predictive accuracy, as evidenced by a random cross-validation R² of 0.84 and an RMSE of 4.9 μg/m³, at the daily level. Although MISR is a polar-orbiting instrument, its fractional aerosol data make a significant contribution to the OC exposure model. We then use the model to explore the spatiotemporal distributions of OC and further calculate the EC/OC ratio in China. We compared regional pollution discrepancies and source contributions of carbonaceous pollution over three selected regions: Beijing-Tianjin-Hebei, Fenwei Plain, and Yunnan Province. Our model observes that OC levels are elevated in Northern China due to industrial operations and central heating during the heating season, while in Yunnan, OC pollution is mainly contributed by local forest fires during fire seasons. Additionally, we found that OC pollution in China is likely influenced by climate phenomena such as the El Niño-Southern Oscillation. Considering that climate change is increasing the severity of OC concentrations with more frequent fire events, and its influence on OC formation and dispersion, we suggest emphasizing the role of climate change in future OC pollution control policies. We believe this study will contribute to future epidemiological studies on OC, aiding in refining public health guidelines and enhancing air quality management in China.

Keywords: Fractional AOD; MISR; Organic carbon; PM2.5 constituents; Remote sensing; Super Learner.

Publication types

Preprint

Grants and funding

R01 ES032140/ES/NIEHS NIH HHS/United States