Tool
Data Center Air Pollution Tracker
This scorecard evaluates the eight hyperscaler tech / AI companies with U.S.-based data centers on one simple question: are their data centers running on clean energy, or fossil fuels like gas, oil, and coal?
Tool · source-verified
Beijing-PM2.5 Air Quality Prediction
GitHub · Publication date unknown
This machine learning project explores air quality in Beijing from 2010 to 2014, focusing on predicting fine particulate matter (PM2.5) and how it evolves over time in relation to meteorological and seasonal factors. The dataset is a time series, with hourly readings of PM2.5 and weather conditions such as temperature, wind speed, and pressure.
Show citation
GitHub. Date unknown. Beijing-PM2.5 Air Quality Prediction. https://github.com/denizyennerr/Beijing-PM2.5-Air-Quality-Prediction (AI & Environment Resource Hub; record atlas-77b172e46e58; collection snapshot 2026-09-15).
Classification and context
Included in the original Tool collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
topic
lifecycle
orientation
Unclassified
Available source metadata
- Author or creator
- Unknown or not supplied
- Publisher
- GitHub
- Publication date
- Unknown or not supplied
- Date precision
- unknown
- Geographic scope
- Unknown
- Access
- free
- Language
- unknown
- Peer review
- unknown
- Source status
- verified
- Last verified
- 2026-09-15T16:45:54.226Z
- Snapshot import
- 2026-09-06
- Legacy domain
- Materials, Waste & Circular Economy
- Legacy subdomain
- Unknown or not supplied
Provenance and review
Source sheet: Tool · Row 146 · Original ID: Not supplied.
Inclusion does not establish effectiveness, maintenance, or endorsement. Check access and current documentation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
Suggest a correction for this record →Related through shared environmental topics
Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
Shared topics: Energy and electricity; Materials and critical minerals; E-waste and circularity; Air pollution
Shared topics: Energy and electricity; Air pollution; Weather, hazards, and adaptation
Scientific Paper · 2025-05-21
A Foundation Model for the Earth System
Aurora is a large-scale AI foundation model that significantly outperforms traditional forecasting systems across multiple Earth system domains, like air quality and cyclone tracking, while using far less computational power, marking a major advancement in accessible, efficient environmental prediction.
Shared topics: Energy and electricity; Materials and critical minerals; E-waste and circularity
Multimedia · 2025-08-03
How Used EV Batteries Are Being Turned Into Data Centers To Power AI
As demand for energy skyrockets amid the rise of AI, one of Tesla’s co-founders is betting on a new solution: giving old EV batteries a second life. JB Straubel, who helped launch Tesla and served as its CTO until 2019, founded Redwood Materials in 2017 to recycle batteries and build a closed-loop supply chain for electric vehicles. Now, Straubel is using EV batteries that still hold usable capacity for grid-scale energy storage. Redwood’s new energy division recently partnered with AI infrastructure company Crusoe to launch its first microgrid, showcasing how repurposed batteries can help power data centers. CNBC visited Redwood’s Nevada operations to see how the company has grown and to learn more about its plans to use second-life batteries to meet the surging energy needs of the AI era.
Shared topics: Energy and electricity; Materials and critical minerals; E-waste and circularity
Scientific Paper · 2025-04-26
SOLID-MAP: Development of a Materials AccelerationPlatform for High-Entropy Alloys
This paper presents SOLID-MAP, an AI-driven material acceleration platform that integrates active learning, thermodynamic modeling, and high-throughput experimentation to rapidly discover and evaluate high-entropy alloys for energy-relevant applications.