Data on Climate-Optimized Aircraft Trajectories in European Airspace for 2022

This repository provides the data and Jupyter Notebooks required to reproduce the analyses and figures presented in the paper, available as a preprint at https://doi.org/10.21203/rs.3.rs-4355046/v1. The paper is currently under review in Communications Earth & Environment from Nature Portfolio.

Below is a detailed description of the content included in the `.zip` file, along with the required software dependencies. 

Contents

Data:

There is a folder in the repository (i.e., Data) containing various datasets, including the results of trajectory optimization over one year, such as flight performance variables, emissions, and climate effects, all in pickle file format. These datasets will be used with the Jupyter notebooks described below.

Jupyter Notebooks:

• Routing_options_0.2%_SOC_different_routing_penalization_limits.ipynb

This notebook analyzes one year of trajectory optimization data, considering a maximum 0.2% increase in operational cost and varying limits on the number of rerouted flights.

• Routing_options_2.0%_SOC_different_routing_penalization_limits.ipynb

This notebook analyzes one year of trajectory optimization data, considering a maximum 2.0% increase in operational cost and varying limits on the number of rerouted flights.

• Routing_options_main_paper.ipynb

This notebook analyzes one year of trajectory optimization data for multiple limits on operational cost increases (i.e., 0.0% (cost-optimal), 0.2%, and 2.0%) and different rerouting constraints. It generates the figures currently included in the paper.

• Routing_options_up_to_3.0%_SOC_unrestricted_rerouting.ipynb

This notebook analyzes one year of trajectory optimization data with operational cost increases of up to 3.0% and no restrictions on rerouted flights.

• Traffic_scenario_sensitivity_analysis.ipynb

This notebook performs sensitivity analysis for scaled-up traffic scenarios (refer to Figure S8).

Solution Picking Algorithm:

The repository also includes a folder named "Solution_Picking," which implements the decision-making algorithm described in the Method section of the paper. An example is provided, corresponding to Supplementary Figure S16.

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Software Requirements

The following Python packages are required to run the Jupyter Notebooks:

Instructions

1.     Ensure Python is installed (Python 3.8.5 is recommended).

2.     Install the required packages using the following command:

```bash
pip install numpy==1.20.3 pandas==1.4.2 matplotlib==3.5.0 pyomo==6.4.3 coopr==4.0.9597 seaborn==0.13.2
```

3.     Install GLPK for optimization. Instructions for GLPK installation can be found on the official GNU GLPK website [here](https://www.gnu.org/software/glpk/).