Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space, presented at Tackling Climate Change with Machine Learning workshop at ICML 2021.

Code repository for "Toward Global Estimation of Ground-Level NO2 Pollution With Deep Learning and Remote Sensing", IEEE TGSRS, 2022

Arduino project on a ESP32 for NO2 measurement sending the data with LoRaWan to TheThingsNetwork.

Python scripts and Jupyter Notebooks to download and preprocess Sentinel-5P NO2 data.

Python script downloading NO2 pollution datas from the European satellite Sentinel5 and aggregating by country, regions, states, cities. This work is carried out within the framework of the juanporrasl/AMSECovid19 project.

Website (startpage and map)

BEVO Beacon with more robust air quality monitoring

This Python script automates the retrieval and visualization of tropospheric NO2 data from Sentinel-5P satellite's TROPOMI instrument, enabling efficient monitoring of atmospheric pollution patterns through automated data processing and visualization.

air quality model benchmarking

Shared code regarding S5P/TROPOMI data

Core code for the TROPOMI lightning NO2 retrievals

MICS4514 Breakout board sensor for CO2 and NO2 only 3.3v

Some notebooks for [S5P-LNO2](https://github.com/zxdawn/S5P-LNO2).

This is a COVID-19 Air Pollution tracking program that looks at the correlation between COVID-19 Lockdowns and the overall pollution impact in certain European cities. The program utilses graphs and other visual stats to allow visual representations of significant or minimal difference in pollution levels with as a result to the on going pandemic.

A DFRobot_MultigasSensor Library for Micropython

Comparing the NO2 air pollution during COVID-19 in different cities to see what measures help most in reducing air pollution

Satellite observations showed a negligible reduction in NO2 pollution due to COVID-19 lockdown over Poland

Scripts for urban NO2 air quality analysis. Funded by the Air, Climate, and Health Lab at George Washington University

Australia SA3-level analysis of relationship between child mortality/morbidity and climate conditions/air pollution