Predicting carbonaceous aerosols and identifying their source contribution with advanced approaches

Jun-Jie Zhu; Yu-Cheng Chen; Ruei-Hao Shie; Zhen-Shu Liu; Chin-Yu Hsu

doi:10.1016/j.chemosphere.2020.128966

Predicting carbonaceous aerosols and identifying their source contribution with advanced approaches

Chemosphere. 2021 Mar:266:128966. doi: 10.1016/j.chemosphere.2020.128966. Epub 2020 Nov 13.

Authors

Jun-Jie Zhu¹, Yu-Cheng Chen², Ruei-Hao Shie³, Zhen-Shu Liu⁴, Chin-Yu Hsu⁵

Affiliations

¹ Department of Civil, Architectural and Environmental Engineering, Illinois Institute of Technology, Chicago, IL, 60616-3793, USA; Current Affiliation: Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, 08544, USA.
² National Institute of Environmental Health Sciences, National Health Research Institute, 35 Keyan Road, Zhunan Town, Miaoli, 35053, Taiwan; Department of Occupational Safety and Health, China Medical University, 91 Hsueh-Shih Road, Taichung, 40402, Taiwan.
³ Green Energy and Environment Research Laboratories, Industrial Technology Research Institute, 321Guangfu Road, East District, Hsinchu City, 30011, Taiwan.
⁴ Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, 84 Gungjuan Rd., Taishan Dist., New Taipei City, 24301, Taiwan; Center for Environmental Sustainability and Human Health, Ming Chi University of Technology, 84 Gungjuan Rd., Taishan Dist., New Taipei City, 24301, Taiwan; Chronic Diseases and Health Promotion Research Center, Chang Gung University of Science and Technology, Puzi, Chiayi, 61363, Taiwan.
⁵ Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, 84 Gungjuan Rd., Taishan Dist., New Taipei City, 24301, Taiwan; Center for Environmental Sustainability and Human Health, Ming Chi University of Technology, 84 Gungjuan Rd., Taishan Dist., New Taipei City, 24301, Taiwan. Electronic address: gracecyhsu@mail.mcut.edu.tw.

PMID: 33243573
DOI: 10.1016/j.chemosphere.2020.128966

Abstract

Organic carbon (OC) and elemental carbon (EC) play important roles in various atmospheric processes and health effects. Predicting carbonaceous aerosols and identifying source contributions are important steps for further epidemiological study and formulating effective emission control policies. However, we are not aware of any study that examined predictions of OC and EC, and this work is also the first study that attempted to use machine learning and hyperparameter optimization method to predict concentrations of specific aerosol contaminants. This paper describes an investigation of the characteristics and sources of OC and EC in fine particulate matter (PM_2.5) from 2005 to 2010 in the City of Taipei. Respective hourly average concentrations of OC and EC were 5.2 μg/m³ and 1.6 μg/m³. We observed obvious seasonal variation in OC but not in EC. Hourly and daily OC and EC concentrations were predicted using generalized additive model and grey wolf optimized multilayer perceptron model, which could explain up to about 80% of the total variation. Subsequent clustering suggests that traffic emission was the major contribution to OC, accounting for about 80% in the spring, 65% in the summer, and 90% in the fall and winter. In the Taipei area, local emissions were the dominant sources of OC and EC in all seasons, and long-range transport had a significant contribution to OC and in PM_2.5 in spring.

Keywords: Clustering; Elemental carbon; Hyperparameter optimization method; Machine learning; Organic carbon; Source apportionment.

MeSH terms

Aerosols / analysis
Air Pollutants* / analysis
Carbon / analysis
China
Cities
Environmental Monitoring
Particle Size
Particulate Matter / analysis
Seasons

Substances

Aerosols
Air Pollutants
Particulate Matter
Carbon