AI & Environment Resource HubAir pollutants and associated exposure outside greenhouse gas accounting.
Browse all 13 resources →Counts reflect provisional metadata classification, not measured environmental impact. A record may belong to multiple topics.
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Research papers and scholarly literature on impacts, applications, and methods.
AI's growing energy demand worsens air quality, imposing significant public health costs, especially on disadvantaged communities, necessitating better reporting and mitigation strategies.
The project explores innovative ways to address Barcelona’s sustainability challenges, such as carbon emissions, air pollution, and energy inefficiency. By leveraging AI, IoT sensors, and Nature-Based Solutions (NBS), the project aims to optimize municipal budgets for interventions that transform underutilized urban spaces into productive assets.
This study applies machine learning, including deep learning with LSTM, to classify and predict Dhaka's Air Quality Index (AQI), incorporating daily temperature as a parameter and demonstrating optimal AQI forecasting performance.
This study demonstrates that Random Forest-based spatial interpolation can accurately estimate urban PM2.5 levels in Brookline, MA, even with reduced sensor networks, offering a cost-effective approach to enhance air quality monitoring.
Missing metadata is a curation task, not evidence of absent research or activity. The Resource Hub does not currently contain a validated incident dataset, expert survey, or measurements of net environmental benefit.