Estimating the global abundance of ground level presence of particulate matter (PM2.5)
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Summary
A suite of remote sensing and meteorological data products together with ground-based observations of particulate matter from 8,329 measurement sites in 55 countries taken 1997-2014 are used to train a machine-learning algorithm to estimate the daily distributions of PM2.5 from 1997 to the present.
- Type
- article
- Published
- 2014-12-01
- Cited by
- 90
- References
- 150
- Access
- Open access
- OpenAlex
- https://openalex.org/W2020487351
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:752267
Keywords
Particulates, Environmental science, Abundance (ecology), Suite, Product (mathematics)
References
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- Prolonged Exposure to Particulate Pollution, Genes Associated with Glutathione Pathways, and DNA Methylation in a Cohort of Older Men
- Estimating ground-level PM 2.5 using aerosol optical depth determined from satellite remot
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- Using Machine Learning to Estimate Global PM2.5 for Environmental Health Studies
- The Neighborhood Scale Variability of Airborne Particulates
- Asthmatic symptoms and air pollution: a panel study on children living in the Italian Po Valley.
- Review on recent progress in observations, source identifications and countermeasures of PM2.5.
- Interpretation of satellite retrievals of PM2.5 over the southern African Interior
- Machine learning in geosciences and remote sensing
- The Ignite Distributed Collaborative Scientific Visualization System
- The Ignite Distributed Collaborative Visualization System
- Improving the Accuracy of Daily PM2.5 Distributions Derived from the Fusion of Ground-Level Measurements with Aerosol Optical Depth Observations, a Case Study in North China.
- Air quality classification and its temporal trend in Tehran, Iran, 2002-2012.
- How much incident lung cancer was missed globally in 2012? An ecological country-level study.
- GEOGRAPHIC MEDICAL HISTORY: ADVANCES IN GEOSPATIAL TECHNOLOGY PRESENT NEW POTENTIALS IN MEDICAL PRACTICE
- Thin client collaborative visualizations using the distributed cloud
- PROnet: A programmable optical network prototype
- A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth
- Desert dust hazards: A global review
- Artificial neural network forecast application for fine particulate matter concentration using meteorological data
- LiveTalk: A Framework for Collaborative Browser-Based Replicated-Computation Applications
- Base64Geo: an efficient data structure and transmission format for large, dense, scalar GIS datasets
- Insights Into the Morphology of the East Asia PM2.5 Annual Cycle Provided by Machine Learning
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