ArticleResearch square2026
Particulate matter removal by precipitation under normal and wildfire conditions across the United States using double machine learning.
Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Authors and funding
3 authors.
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Abstract
Precipitation scavenging is an important sink for particulate matter (PM) and can therefore have direct consequences for human and ecosystem health. Controlled experiments and modeling studies quantifying precipitation-driven removal have been conducted in the past, but the literature lacks large-scale observational-based estimates of precipitation influence on PM concentrations. This gap has become more important as wildfire smoke now contributes a growing share of PM pollution across the United States, and climate change drives rainfall pattern shifts. We analyzed hourly PM
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.