From 0d449d08350f2837977a0d5836b2abc9487a6790 Mon Sep 17 00:00:00 2001
From: Karim Mache <k.mache@fz-juelich.de>
Date: Fri, 28 Mar 2025 22:20:31 +0100
Subject: [PATCH] Add image

---
 .history/README_20250328221727.md | 3 +++
 .history/README_20250328221948.md | 3 +++
 .history/README_20250328221958.md | 3 +++
 .history/README_20250328222004.md | 4 ++++
 .history/README_20250328222021.md | 4 ++++
 README.md                         | 4 +++-
 6 files changed, 20 insertions(+), 1 deletion(-)
 create mode 100644 .history/README_20250328221727.md
 create mode 100644 .history/README_20250328221948.md
 create mode 100644 .history/README_20250328221958.md
 create mode 100644 .history/README_20250328222004.md
 create mode 100644 .history/README_20250328222021.md

diff --git a/.history/README_20250328221727.md b/.history/README_20250328221727.md
new file mode 100644
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+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+
+<img src="./figures/toar_classifier_v2.png" alt="My image" with="100">
diff --git a/.history/README_20250328221948.md b/.history/README_20250328221948.md
new file mode 100644
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+++ b/.history/README_20250328221948.md
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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">
diff --git a/.history/README_20250328221958.md b/.history/README_20250328221958.md
new file mode 100644
index 0000000..00262ee
--- /dev/null
+++ b/.history/README_20250328221958.md
@@ -0,0 +1,3 @@
+## 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">
diff --git a/.history/README_20250328222004.md b/.history/README_20250328222004.md
new file mode 100644
index 0000000..e5ed8e9
--- /dev/null
+++ b/.history/README_20250328222004.md
@@ -0,0 +1,4 @@
+## 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">
+
diff --git a/.history/README_20250328222021.md b/.history/README_20250328222021.md
new file mode 100644
index 0000000..10ccc5d
--- /dev/null
+++ b/.history/README_20250328222021.md
@@ -0,0 +1,4 @@
+## 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>
+
diff --git a/README.md b/README.md
index ae43e66..10ccc5d 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,4 @@
 ## TOAR-classifier v2: A data-driven classification tool for global air quality stations
-<img src="./figures/toar_classifier_v2.png" alt="My image" with="100">
+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>
+
-- 
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