Geoinformatics & Geostatistics: An OverviewISSN: 2327-4581

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Lary DJ Author

david.lary@utdallas.edu

Subjects of specialization
Environmental Sensing, Robotic Teams, Biometric Sensing

Affiliation
Hanson Center for Space Sciences, University| Remote Sensing, Health Applications, Unmanned Aerial Vehicles, Dallas, TX, USA

Biography

David John Lary is working at Hanson Center for Space Sciences in the University of Texas at Dallas, Dallas, TX, USA. His research interest are Smart Sensing in Service of Society, Preemptive Human Protection, Autonomy, Atmospheric Chemistry, BigData, Machine Learning, Smart Cities.

Publications

Short Communication Subscription

Using Machine Learning to Estimate Global PM2.5 for Environmental Health Studies

Author(s):

Lary DJ, Lary T and Sattler B

There is an increasing awareness of the health impacts of particulate matter and a growing need to quantify the spatial and temporal variations of the global abundance of ground level airborne particulate matter (PM2.5). In March 2014, the World Health Organization (WHO) released a report that in 2012 alone, a staggering 7 million people died as a result of air pollution exposure , one in eight of the total global deaths. A major component of this pollution is airborne particulate matter (e.g. PM2.5 & PM10).

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DOI: 10.4172/2327-4581.1000149

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