Comparing Spatial Interpolation Methods for PM2.5 as Inputs to Urban Decision-Making in Greater Boston
Lee et al. · 2025-04-23
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.
Lee et al.. 2025-04-23. Comparing Spatial Interpolation Methods for PM2.5 as Inputs to Urban Decision-Making in Greater Boston. https://s3.us-east-1.amazonaws.com/climate-change-ai/papers/iclr2025/6/paper.pdf (AI & Environment Resource Hub; record paper-251; collection snapshot 2026-09-15).
Classification and context
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
AI is already reshaping the way we live and work, but what role can it play in tackling the effects of climate change? This short, practical course analyses how we can harness the power of AI to drive climate solutions, from planning resilient cities to protecting nature and powering the energy transition. Along the way, you'll hear from Stanford experts, analyse global case studies and reflect on what this could mean in a public sector context.
Apolitical
Shared topics: Climate and greenhouse gases; Biodiversity and ecosystems; Weather, hazards, and adaptation; Built environment and infrastructure
This study presents an AI-driven framework that uses Ant Colony Optimisation, species-specific thermal traits, and high-resolution climate simulations to strategically place and select urban trees, achieving significant reductions in extreme heat and improved thermal comfort at the neighbourhood scale.
ScienceDirect
Shared topics: Climate and greenhouse gases; Biodiversity and ecosystems; Weather, hazards, and adaptation; Built environment and infrastructure
This study uses random forest-based AI and Landsat imagery to assess how urban growth in Baghdad from 1985 to 2021 has significantly increased land surface temperatures, primarily due to reduced vegetation and moisture.
Asian Geographer
Shared topics: Climate and greenhouse gases; Biodiversity and ecosystems; Weather, hazards, and adaptation; Built environment and infrastructure
This study demonstrates that machine learning models, particularly Random Forest Classifiers, can effectively simulate and classify precipitation and extreme weather patterns across North Indian states, offering valuable insights for disaster preparedness and water resource management.
