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+## 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
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+## 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
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+## 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
diff --git a/.history/README_20250328223037.md b/.history/README_20250328223037.md
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index 0000000000000000000000000000000000000000..ae41b641ca9b630fd1bda965895f25861b198c2d
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@@ -0,0 +1,7 @@
+## 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
diff --git a/.history/README_20250328223328.md b/.history/README_20250328223328.md
new file mode 100644
index 0000000000000000000000000000000000000000..8e04628fc4180e2c66f1c7daafd1ea79b14aa74f
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+++ b/.history/README_20250328223328.md
@@ -0,0 +1,9 @@
+## 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
diff --git a/.history/README_20250328223358.md b/.history/README_20250328223358.md
new file mode 100644
index 0000000000000000000000000000000000000000..0c79a57031dcbbcfc6ba5dd0c5a9a21a0af2d2dd
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+++ b/.history/README_20250328223358.md
@@ -0,0 +1,9 @@
+## 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
diff --git a/.history/README_20250328223446.md b/.history/README_20250328223446.md
new file mode 100644
index 0000000000000000000000000000000000000000..d561c5964628066a7eea2b754dcc2ba6a6ba39b1
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+++ b/.history/README_20250328223446.md
@@ -0,0 +1,10 @@
+## 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
diff --git a/.history/README_20250328223627.md b/.history/README_20250328223627.md
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index 0000000000000000000000000000000000000000..bc18d1efb3498ffe002ee86cdee3b422a1e7625c
--- /dev/null
+++ b/.history/README_20250328223627.md
@@ -0,0 +1,10 @@
+## 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
diff --git a/README.md b/README.md
index 10ccc5df0f4b1f753566ab8e8203c1d830e4a0f3..bc18d1efb3498ffe002ee86cdee3b422a1e7625c 100644
--- a/README.md
+++ b/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