Open Access
| Issue |
E3S Web Conf.
Volume 520, 2024
4th International Conference on Environment Resources and Energy Engineering (ICEREE 2024)
|
|
|---|---|---|
| Article Number | 02010 | |
| Number of page(s) | 5 | |
| Section | Carbon Emission Control and Waste Resource Utilization | |
| DOI | https://doi.org/10.1051/e3sconf/202452002010 | |
| Published online | 03 May 2024 | |
- Solomon, S., Plattner, G.K., Knutti, R., Friedlingstein, P. (2009) Irreversible climate change due to carbon dioxide emissions. Proceedings of the National Academy of Sciences., 106:1704–1709. https://doi.org/10.1073/pnas.0812721106 [CrossRef] [PubMed] [Google Scholar]
- Etheridge, D. M., Steele, L. P., Langenfelds, R. L., Francey, R. J., Barnola, J.M., Morgan, V. I. (1996) Natural and anthropogenic changes in atmospheric CO2 over the last 1000 years from air in Antarctic ice and firn. Journal of Geophysical Research., 101 (D2): 4115–4128. https://doi.org/10.1029/95JD03410 [CrossRef] [Google Scholar]
- Satterthwaite, D. (2008). Cities’ contribution to global warming: notes on the allocation of greenhouse gas emissions. Environment and Urbanization., 20(2): 539–549. https://doi.org/10.1177/0956247808096127 [CrossRef] [Google Scholar]
- Lauvaux T., et al. (2016) High-resolution atmospheric inversion of urban CO2 emissions during the dormant season of the Indianapolis Flux Experiment (INFLUX), J. Geophys. Res. Atmos., 121:5213–5236. DOI: 10.1002/2015JD024473. [CrossRef] [PubMed] [Google Scholar]
- Wu, L., Bocquet, M., Chevallier, F., Lauvaux, T., Davis, K. (2013) Hyperparameter estimation for uncertainty quantification in mesoscale carbon dioxide inversions. Tellus B., 65(1):p.20894. https://doi.org/10.3402/tellusb.v65i0.20894 [CrossRef] [Google Scholar]
- Rasmussen, C.E., Williams, C.K.I. (2006) Model Selection and Adaptation of Hyperparameters. In: Rasmussen, C.E. (Eds.), Gaussian Processes for Machine Learning. The MIT Press, Cambridge. pp.105–128. [Google Scholar]
- Tarantola, A. (2004) The Least-Squares Criterion. In: Mosegaard, K. (Eds.), Inverse problem theory and methods for model parameter estimation. SIAM, Philadelphia. pp.57–75. [Google Scholar]
- Johnson, S.G. The NLopt nonlinear-optimization package. http://github.com/stevengj/nlopt. [Google Scholar]
- Paszke, A. et al., (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32. http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf. [Google Scholar]
- Lin, J.C., Gerbig, C., Wofsy, S. C., Andrews, A. E., Daube, B. C., Davis, K. J., and Grainger, C. A. (2003) A near-field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time-Inverted Lagrangian Transport (STILT) model. Journal of Geophysical ResearchAtmospheres., 108:4493. DOI: 10.1029/2002JD003161. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.