air quality monitoring using mobile microscopy and machine learning
by：Kenwei
2019-08-17
Fast, accurate, high
Throughput size and quantification of particulate matter (PM)
Air is essential for monitoring and improving air quality.
In fact, particles in the air with a diameter of ≤ 2.
It is listed as a carcinogenic substance by the World Health Organization.
We\'re showing an area here. portable cost-
Efficient platform
Throughput quantification of particulate matter using computational lenses-
Free microscope and machinelearning.
This platform is called c-
Air also integrates with smartphone apps for device control and display of results.
This mobile device can be quickly screened.
Produce 5 liters of air in 30 seconds and produce microscopic images of the aerosol in the air.
It provides statistics on particle size and density distribution with a dimensional accuracy of about 93%.
We test this mobile platform by measuring the air quality and measurement time of different indoor and outdoor environments and align our results with the beta-based approved by the Environmental Protection Agency-
Attenuation monitoring display and c-
Air measurement.
Also, we used c-
Air quality map around Los Angeles International Airport (LAX)
More than 24 hours to confirm that even in places more than 7 kilometres from the airport, especially in the direction of the landing flight, the effect of slack on the increase in PM concentration exists.
Its machinelearning-
C- computing microscope interface based
The air can be adapted to detect specific particles in the air, such as various types of pollen and mold, and provide costs
Efficient mobile solutions for high-precision distributed perception of air quality.
Throughput size and quantification of particulate matter (PM)
Air is essential for monitoring and improving air quality.
In fact, particles in the air with a diameter of ≤ 2.
It is listed as a carcinogenic substance by the World Health Organization.
We\'re showing an area here. portable cost-
Efficient platform
Throughput quantification of particulate matter using computational lenses-
Free microscope and machinelearning.
This platform is called c-
Air also integrates with smartphone apps for device control and display of results.
This mobile device can be quickly screened.
Produce 5 liters of air in 30 seconds and produce microscopic images of the aerosol in the air.
It provides statistics on particle size and density distribution with a dimensional accuracy of about 93%.
We test this mobile platform by measuring the air quality and measurement time of different indoor and outdoor environments and align our results with the beta-based approved by the Environmental Protection Agency-
Attenuation monitoring display and c-
Air measurement.
Also, we used c-
Air quality map around Los Angeles International Airport (LAX)
More than 24 hours to confirm that even in places more than 7 kilometres from the airport, especially in the direction of the landing flight, the effect of slack on the increase in PM concentration exists.
Its machinelearning-
C- computing microscope interface based
The air can be adapted to detect specific particles in the air, such as various types of pollen and mold, and provide costs
Efficient mobile solutions for high-precision distributed perception of air quality.
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