NO₂ pollution forecasting for Essen using XGBoost & ARIMA | Python · ML · OpenAQ API | Top grade — FOM University 🏆

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

Geostatistical modeling of urban air pollutants (NO₂ and PM₁₀) across Los Angeles County using the Hidden Dynamic Geostatistical Model (HDGM) framework implemented in MATLAB with the D-STEM package. Includes full data processing, model fitting, cross-validation, and spatial prediction workflow.

Library for Freshtech Air6814 I2C Sensor Module.

An interactive Shiny dashboard using R that analyzes and forecasts urban air pollution levels—specifically PM2.5, PM10, and NO₂.

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.

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

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

Website (startpage and map)

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

Core code for the TROPOMI lightning NO2 retrievals

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.

Shared code regarding S5P/TROPOMI data

BEVO Beacon with more robust air quality monitoring

This repository implements Inverse Distance Weighting (IDW) interpolation in R to create prediction surfaces for NO₂, PM₂.₅, and O₃ concentrations.

ME975 - Satellite Data Assimilation and Analysis - Assignment 2021/22

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

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.

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.

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