A grid-free deep learning model predicts high-resolution surface PM2.5 concentrations in near real time by combining sparse EPA station data with topographic, meteorological, and land-use inputs.
Title resolution pending
2 Pith papers cite this work, alongside 8,151 external citations. Polarity classification is still indexing.
2
Pith papers citing it
8,151
external citations · OpenAlex
years
2026 2representative citing papers
A grid-free attention-based deep learning model interpolates surface-level PM2.5 concentrations across the US using sparse sensor data and auxiliary geospatial features.
citing papers explorer
-
Ground-Level Near Real-Time Modeling for PM2.5 Pollution Prediction
A grid-free deep learning model predicts high-resolution surface PM2.5 concentrations in near real time by combining sparse EPA station data with topographic, meteorological, and land-use inputs.