See your city's air pollution measured in daily cigarettes. iOS/Android.

基于Keras的LSTM多变量时间序列预测

Air Pollution Image Dataset from India and Nepal

Public Available Code and Data to Reproduce Analyses in "Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis."

Resource Watch features hundreds of data sets all in one place on the state of the planet’s resources and citizens. Users can visualize challenges facing people and the planet, from climate change to poverty, water risk to state instability, air pollution to human migration, and more.

A spatiotemporal causal convolutional network for predicting air pollution.

Predicting Air Pollution in Beijing, China using Deep Neural Nets

Python scripts to download and preprocess air pollution concentration level data aquired from the Sentinel-5P mission

Time Series Analysis of Air Pollutants(PM2.5) using LSTM model

Air pollution constitutes a critical public health challenge in rapidly urbanising sub-Saharan African cities, where industrial activity, vehicular emissions, and meteorological dynamics converge to produce complex spatiotemporal pollution patterns. This study presents the first comprehensive geospatial machine learning analysis of multivariate

:octocat: Detection and Prediction of Air quality Index :octocat:

Forecasting air pollution using temporal attention mechanism in Beijing

A visualization of Beijing air pollution

☁Air pollution visualization and forecasting platform based on Spring Cloud(基于Spring Cloud的环境污染物数据分析与预测平台)

Air pollution is a major environmental issue that affects human health and well being. To combat this issue, a smart air monitoring system has been developed to provide real-time air quality data. The system uses various sensors to monitor the levels of pollutants in the air, such as particulate matter (PM), carbon monoxide (CO).

Stacking a machine learning ensemble for multivariate time series forecasting, with the goal of predicting the one-period ahead PM 2.5 air pollution level, as published in Towards Data Science on Medium.com

This project uses an LSTM neural network to predict air quality (PM2.5) from synthetic time-series data. It includes data generation, normalization, model training, and prediction visualization. The results demonstrate how deep learning can forecast pollution levels

Deep learning for prediction of air pollution trend in India

🤢 Air 🥶 Pollution 🤡 Analysis is 🤖 a data 👿 driven 👽 that 🙈 explores 😹 analyzes 🍔 and 🍅 visualizes 🍏 air 🍊quality data 🌰 to ✈ understand 🚁 pollution 🚀 trends 🛬 and their 🚟 impact on ⛱ the 🚞 environment 🚋 and 🛼 human 🚂 health 🏩 Using 🕌 data 🚒 science 🏭 machine 🚈 actionable 🏥 insights 🏦 policymakers researchers and citizens

watchOS app for checking air pollution levels, created for my blog post series

Predicting air pollution level in a specific city

Developed a novel algorithm to predict air pollution levels with state-of-art accuracy using deep learning and GoogleMaps satellite images

KDD2018 CUP - Predicting air pollutants for next 48 hours in London and Beijing using Deep Learning

The dataset focuses on air quality assessment across various regions. The dataset contains 5000 samples and captures critical environmental and demographic factors that influence pollution levels.

Air pollution model

DIY IoT project for air-pollution measurement and real-time analysis

R scripts that analyze sulfate and nitrate pollution data from sensors monitoring fine particulate matter air pollution at 332 locations in the United States.

🗺️ Open source tools to create maps of air pollution data

Time series modeling for air pollution monitoring with a focus on the calibration of low-cost sensors

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.