Air Quality Monitoring Using Mobile Microscopy And Machine Learning

Tech ID: 29262 / UC Case 2017-513-0

Summary

UCLA researchers have developed a novel method to monitor air quality using mobile microscopy and machine learning.

Background

Air quality is an increasing concern in the industrialized world. Particulate matter (PM) is a mixture of solid and liquid particles in air and forms a significant form of air pollution. PM comes in a range of sizes which can cause serious health problems by entering the lings and bloodstream. Some PM has even been linked to be carcinogenic. Monitoring PM air quality as a function of space and time is critical for understanding the effects of industrial activities, studying atmospheric models, and providing regulatory and advisory guidelines for transportation, residents, and industries. There is a need for a low-cost, accurate, easy to use, mobile method to sample and analyze particulate matter in the field. Current solutions, such as conventional microscope-based screening of aerosols, cannot be conducted in the field and are cumbersome, heavy, expensive, and require specialized skills to operate.

Innovation

Field-portable cost-effective platform for high-throughput quantification of particulate matter (PM) using computational lens-free microscopy and machine-learning

Applications

  • Field particulate matter/air monitoring

Advantages

  • Field-portable/ mobile solution 
  • Cost-effective platform  
  • High-throughput quantification of particulate matter (air) 
  • Uses computational lens-free microscopy and machine-learning 
  • High accuracy
  • Easy to use

State Of Development

The invention was demonstrated on 2/1/2015

Patent Status

Country Type Number Dated Case
United Kingdom Published Application GB2574357 12/04/2019 2017-513
United States Of America Published Application GB2574357 12/04/2019 2017-513
 

Additional Patents Pending

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Inventors

  • Ozcan, Aydogan

Other Information

Keywords

Air monitoring, particulate matter, machine learning, mobile, air quality, mobile microscopy

Categorized As