.history/README_20250328225853.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with=100>
+
+### Files
+`data` is the folder contening all data use in this work, including the predictions of station cotegories form Machine Learning (ML) model.
+
+`TOAR-classifier_v2.ipynb` is the notebook contening the code
+
+`requirements.txt` contains all neccessary packages.
+
+### Run code
+NB: This has been test on Ubuntu 24.04
+clone the project by running the following
+`https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+change directory to ml_toar_station_classification, `cd ml_toar_station_classification`
+creat virtual environment `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+activate the created venv `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
\ No newline at end of file

.history/README_20250328225934.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with=100>
+
+### Files
+`data` is the folder contening all data use in this work, including the predictions of station cotegories form Machine Learning (ML) model.
+
+`TOAR-classifier_v2.ipynb` is the notebook contening the code
+
+`requirements.txt` contains all neccessary packages.
+
+### Run code
+NB: This has been test on Ubuntu 24.04
+clone the project by running the following
+`https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+change directory to ml_toar_station_classification, `cd ml_toar_station_classification`
+creat virtual environment `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+activate the created venv `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+

.history/README_20250328230624.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with=100>
+
+### Files
+`data` is the folder contening all data use in this work, including the predictions of station cotegories form Machine Learning (ML) model.
+
+`TOAR-classifier_v2.ipynb` is the notebook contening the code
+
+`requirements.txt` contains all neccessary packages.
+
+### Run code
+NB: This has been test on Ubuntu 24.04 
+Install if needed python3, most Linux system already have python install
+Install jupyter notebook `pip install notebook`  or jupyter lab `pip install jupyterlab`
+clone the project by running the following
+`https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+change directory to ml_toar_station_classification, `cd ml_toar_station_classification`
+creat virtual environment `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+activate the created venv `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+

.history/README_20250328230629.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with=100>
+
+### Files
+`data` is the folder contening all data use in this work, including the predictions of station cotegories form Machine Learning (ML) model.
+
+`TOAR-classifier_v2.ipynb` is the notebook contening the code
+
+`requirements.txt` contains all neccessary packages.
+
+### Run code
+NB: This has been test on Ubuntu 24.04 
+Install if needed python3, most Linux system already have python install
+Install jupyter notebook `pip install notebook`  or jupyter lab `pip install jupyterlab`
+clone the project by running the following
+`https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+change directory to ml_toar_station_classification, `cd ml_toar_station_classification`
+creat virtual environment `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+activate the created venv `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+

.history/README_20250328230631.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with=100>
+
+### Files
+`data` is the folder contening all data use in this work, including the predictions of station cotegories form Machine Learning (ML) model.
+
+`TOAR-classifier_v2.ipynb` is the notebook contening the code
+
+`requirements.txt` contains all neccessary packages.
+
+### Run code
+NB: This has been test on Ubuntu 24.04 
+Install if needed python3, most Linux system already have python install
+Install jupyter notebook `pip install notebook`  or jupyter lab `pip install jupyterlab`
+clone the project by running the following
+`https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+change directory to ml_toar_station_classification, `cd ml_toar_station_classification`
+creat virtual environment `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+activate the created venv `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+

.history/README_20250328231832.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with=100>
+
+### Files
+`data` is the folder contening all data use in this work, including the predictions of station cotegories form Machine Learning (ML) model.
+
+`TOAR-classifier_v2.ipynb` is the notebook contening the code
+
+`requirements.txt` contains all neccessary packages.
+
+### Run code
+NB: This has been test on Ubuntu 24.04 
+Install if needed python3, most Linux system already have python install
+Install jupyter notebook `pip install notebook`  or jupyter lab `pip install jupyterlab`
+clone the project by running the following
+`https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+change directory to ml_toar_station_classification, `cd ml_toar_station_classification`
+creat virtual environment `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+activate the created venv `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+

.history/README_20250328232455.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with=100>
+
+### Files
+`data` is the folder contening all data use in this work, including the predictions of station cotegories form Machine Learning (ML) model.
+
+`TOAR-classifier_v2.ipynb` is the notebook contening the code
+
+`requirements.txt` contains all neccessary packages.
+
+### Run code
+NB: This has been test on Ubuntu 24.04 
+
+Install if needed python3, most Linux system already have python install
+
+Install jupyter notebook `pip install notebook`  or jupyter lab `pip install jupyterlab`
+
+clone the project by running the following
+`https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+
+change directory to ml_toar_station_classification, `cd ml_toar_station_classification`
+
+creat virtual environment `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+
+activate the created venv `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+

.history/README_20250328232554.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with=100>
+
+### Files
+`data` is the folder contening all data use in this work, including the predictions of station cotegories form Machine Learning (ML) model.
+
+`figures` 
+
+`TOAR-classifier_v2.ipynb` is the notebook contening the code
+
+`requirements.txt` contains all neccessary packages.
+
+### Run code
+NB: This has been test on Ubuntu 24.04 
+
+Install if needed python3, most Linux system already have python install
+
+Install jupyter notebook `pip install notebook`  or jupyter lab `pip install jupyterlab`
+
+clone the project by running the following
+`https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+
+change directory to ml_toar_station_classification, `cd ml_toar_station_classification`
+
+creat virtual environment `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+
+activate the created venv `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+

.history/README_20250328232619.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with=100>
+
+### Files
+`data` is the folder contening all data use in this work, including the predictions of station cotegories form Machine Learning (ML) model.
+
+`figures` contains all figures
+
+`TOAR-classifier_v2.ipynb` is the notebook contening the code
+
+`requirements.txt` contains all neccessary packages.
+
+### Run code
+NB: This has been test on Ubuntu 24.04 
+
+Install if needed python3, most Linux system already have python install
+
+Install jupyter notebook `pip install notebook`  or jupyter lab `pip install jupyterlab`
+
+clone the project by running the following
+`https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+
+change directory to ml_toar_station_classification, `cd ml_toar_station_classification`
+
+creat virtual environment `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+
+activate the created venv `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+

.history/README_20250328232709.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with=100>
+
+### Files
+`data` is the folder contening all data use in this work, including the predictions of station cotegories form Machine Learning (ML) model.
+
+`figures` contains all figures
+
+`TOAR-classifier_v2.ipynb` is the notebook contening the code
+
+`requirements.txt` contains all neccessary packages.
+
+### Run code
+NB: This has been test on Ubuntu 24.04 
+
+Install if needed python3, most Linux system already have python install
+
+Install jupyter notebook `pip install notebook`  or jupyter lab `pip install jupyterlab`
+
+clone the project by running the following
+`git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+
+change directory to ml_toar_station_classification, `cd ml_toar_station_classification`
+
+creat virtual environment `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+
+activate the created venv `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+

.history/README_20250329004405.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with=100>
+
+### Files
+- `data` is the folder containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+
+clone the project by running the following
+`git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+
+change directory to ml_toar_station_classification, `cd ml_toar_station_classification`
+
+creat virtual environment `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+
+activate the created venv `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+

.history/README_20250329004519.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with=100>
+
+### Files
+- `data` is the folder containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+
+3. clone the project by running the following
+`git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+
+change directory to ml_toar_station_classification, `cd ml_toar_station_classification`
+
+creat virtual environment `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+
+activate the created venv `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+

.history/README_20250329004717.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with=100>
+
+### Files
+- `data` is the folder containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+
+3. clone the project by running the following
+   - `git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+
+change directory to ml_toar_station_classification
+-  `cd ml_toar_station_classification`
+
+creat virtual environment 
+- `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+
+activate the created venv 
+- `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+

.history/README_20250329004822.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with=100>
+
+### Files
+- `data` is the folder containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+
+3. clone the project by running the following
+   - `git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+
+change directory to ml_toar_station_classification
+-  `cd ml_toar_station_classification`
+
+creat virtual environment 
+- `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+
+activate the created venv 
+- `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+- open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+- Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+

.history/README_20250329004932.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with=100>
+
+### Files
+- `data` is the folder containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+
+3. clone the project by running the following
+   - `git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+
+4. Change directory to ml_toar_station_classification
+-  `cd ml_toar_station_classification`
+
+5. Creat virtual environment 
+- `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+
+6. Activate the created venv 
+- `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+1. open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+2. Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+

.history/README_20250329005041.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with="200">
+
+### Files
+- `data` is the folder containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+
+3. clone the project by running the following
+   - `git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+
+4. Change directory to ml_toar_station_classification
+-  `cd ml_toar_station_classification`
+
+5. Creat virtual environment 
+- `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+
+6. Activate the created venv 
+- `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+1. open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+2. Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+

.history/README_20250329005411.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with="200">
+
+### Files
+- `data` is the folder containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+
+3. clone the project by running the following
+   - `git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+
+4. Change directory to ml_toar_station_classification
+-  `cd ml_toar_station_classification`
+
+5. Creat virtual environment 
+- `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+
+6. Activate the created venv 
+- `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+1. open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+2. Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+
+
+### Citation
+
+If you use this please cite
+
+@article{Mache2025TOARClassifier,
+  author = {Ramiyou Karim Mache and Sabine Schröder and Michael Langguth and Ankit Patnala and Martin G. Schultz},
+  title = {TOAR-classifier v2: A data-driven classification tool for global air quality stations},
+  journal = {Jülich Supercomputing Centre, Forschungszentrum Jülich},
+  year = {2025},
+  address = {52425 Jülich, Germany},
+  note = {Correspondence: Ramiyou Karim Mache (k.mache@fz-juelich.de)},
+  url = {https://your-link-here.com}
+}
+

.history/README_20250329005421.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with="200">
+
+### Files
+- `data` is the folder containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+
+3. clone the project by running the following
+   - `git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+
+4. Change directory to ml_toar_station_classification
+-  `cd ml_toar_station_classification`
+
+5. Creat virtual environment 
+- `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+
+6. Activate the created venv 
+- `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+1. open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+2. Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+
+
+### Citation
+
+If you use this please cite
+
+@article{Mache2025TOARClassifier,
+  author = {Ramiyou Karim Mache and Sabine Schröder and Michael Langguth and Ankit Patnala and Martin G. Schultz},
+  title = {TOAR-classifier v2: A data-driven classification tool for global air quality stations},
+  journal = {Jülich Supercomputing Centre, Forschungszentrum Jülich},
+  year = {2025},
+  address = {52425 Jülich, Germany},
+  note = {Correspondence: Ramiyou Karim Mache (k.mache@fz-juelich.de)},
+  url = {}
+}
+

.history/README_20250329005452.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with="200">
+
+### Files
+- `data` is the folder containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+
+3. clone the project by running the following
+   - `git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+
+4. Change directory to ml_toar_station_classification
+-  `cd ml_toar_station_classification`
+
+5. Creat virtual environment 
+- `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+
+6. Activate the created venv 
+- `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+1. open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+2. Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+
+
+### Citation
+
+If you use this please cite
+
+@article{Mache2025TOARClassifier,
+  author = {Ramiyou Karim Mache and Sabine Schröder and Michael Langguth and Ankit Patnala and Martin G. Schultz},
+  title = {TOAR-classifier v2: A data-driven classification tool for global air quality stations},
+  year = {2025},
+  note = {Correspondence: Ramiyou Karim Mache (k.mache@fz-juelich.de)},
+  url = {}
+}
+

.history/README_20250329005459.md

+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+This study develops a machine learning approach to classify 23,974 air quality monitoring stations in the TOAR database as urban, suburban, or rural using K-means clustering and an ensemble of supervised classifiers. The proposed method outperforms existing classifications, improving suburban accuracy and providing a more reliable foundation for air quality assessments.
+<img src="./figures/toar_classifier_v2.png" alt="My image" with="200">
+
+### Files
+- `data` is the folder containing all the data used in this work, including the predictions of station categories from the Machine Learning (ML) model.
+- `figures` contains all the figures.
+- `TOAR-classifier_v2.ipynb` is the notebook containing the code.
+- `requirements.txt` contains all the necessary packages.
+
+### Run the Code
+
+**Note:** This has been tested on Ubuntu 24.04.
+
+1. Install Python 3 if not already installed (most Linux systems have Python pre-installed).
+2. Install Jupyter Notebook:
+   - `pip install notebook` (for Jupyter Notebook) or
+   - `pip install jupyterlab` (for JupyterLab).
+
+3. clone the project by running the following
+   - `git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/ml_toar_station_classification.git`
+
+4. Change directory to ml_toar_station_classification
+-  `cd ml_toar_station_classification`
+
+5. Creat virtual environment 
+- `python -m venv TOAR-classifier_v2` # feel free to change the virtual environment as convenient 
+
+6. Activate the created venv 
+- `python -m ipykernel install --user --name=TOAR-classifier_v2 --display-name "Python (TOAR-classifier_v2)"`
+
+#### Install required package
+1. open jupyter notebook, `jupyter-notebook` and select kernel `TOAR-classifier_v2`
+
+2. Install all the required packages for the project by uncommenting the first cell in the notebook and running the cell
+
+Run the code cell by cell.
+
+
+### Citation
+
+If you use this please cite
+
+@article{Mache2025TOARClassifier,
+  author = {Ramiyou Karim Mache and Sabine Schröder and Michael Langguth and Ankit Patnala and Martin G. Schultz},
+  title = {TOAR-classifier v2: A data-driven classification tool for global air quality stations},
+  year = {2025},
+  note = {Correspondence: Ramiyou Karim Mache (k.mache@fz-juelich.de)},
+  url = {}
+}
+