CO₂ is a well-known gas but a fine resolution to confirm a daily trend is far from easy. In this one, we will see a clear trend of CO₂ concentration driven by photosynthesis/respiration

analyze high-resolution of CO₂ hourly concentration provided by NOAA from selected stations in the U.S.

prove a trivial thesis that CO₂ concentration are reducing as the intake height increases

produce a high-quality graph like the one below.

This series of CO₂ data analysis was a part of my effort to interpreting CO₂ data from lowcost sensors. The sensors are located in a high-rise building near the center of Hanoi.

In this post, we will work with ESRL/NOAA data taken daily from several sites over the world. The sample was taken daily using flask sampling. The site was selected to refect various conditions of high photosynthesis, on a tiny island, a reference station in Mauna Loa (Hawaii)...

Experiment with requests and feedparser package to extract update on CO₂ concentration from NOAA website

Exercise with web scraping, manipulate string and convert to Python object such as datetime, float and pack the data into dataframe. Finally, the data is plotted using matplotlib.pyplot

Taking a variety of tools to embed images to the Jupyter Notebook. A few are: Using img tag in HTML; converting image to a base64 string representation; using selenium to render map created by folium

Convert images into base64 string is handy to avoid missing attached files and pack the document into one single file