Add image

.history/README_20250328222519.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>
+
+### Run code
\ No newline at end of file

.history/README_20250328222601.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>
+
+
+
+### Run code
\ No newline at end of file

.history/README_20250328223018.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>
+
+`data` is the folder contening all data use in this work and the prediction of station cotegory form Machine Learning (ML) model.
+
+### Run code
\ No newline at end of file

.history/README_20250328223037.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>
+
+`data` is the folder contening all data use in this work and the prediction of station cotegory form Machine Learning (ML) model.
+
+### Run code
\ No newline at end of file

.history/README_20250328223328.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>
+
+`data` is the folder contening all data use in this work and the prediction of station cotegory form Machine Learning (ML) model.
+`TOAR-classifier_v2.ipynb` is the notebook contening the code
+`requirements.txt` containts 
+
+### Run code
\ No newline at end of file

.history/README_20250328223358.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>
+
+`data` is the folder contening all data use in this work and the prediction of station cotegory form Machine Learning (ML) model.
+`TOAR-classifier_v2.ipynb` is the notebook contening the code
+`requirements.txt` containts all neccessary packages.
+
+### Run code
\ No newline at end of file

.history/README_20250328223446.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 and the prediction of station cotegory form Machine Learning (ML) model.
+`TOAR-classifier_v2.ipynb` is the notebook contening the code
+`requirements.txt` containts all neccessary packages.
+
+### Run code
\ No newline at end of file

.history/README_20250328223627.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
\ No newline at end of file

README.md

@@ -2,3 +2,9 @@
 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
\ No newline at end of file