One new development to come to light this month has done so through the help of a group of UCLA researchers as they've developed a mobile device to measure the air quality that's cost-effective and could potentially save the lives of millions across the world. The way in which this new device works by detecting pollutants and figuring out their exact size and concentration using a smartphone with a mobile microscope attached to it. There's also a machine learning algorithm that analyzes the images of the pollutants automatically. Connecting to a smartphone the device uses a machine learning algorithm to size and analyze the concentration of the particle from the image.
Rapid, accurate and high-throughput sizing and quantification of particulate matter (PM) in air is crucial for monitoring and improving air quality. Here we present a field-portable cost-effective platform for high-throughput quantification of particulate matter using computational lens-free microscopy and machine-learning. This mobile device rapidly screens 6.5 L of air in 30 s and generates microscopic images of the aerosols in air. We tested this mobile platform by measuring the air quality at different indoor and outdoor environments and measurement times, and compared our results to those of an Environmental Protection Agency–approved device based on beta-attenuation monitoring, which showed strong correlation to c-Air measurements.
They have developed quite a unique mechanism to measure air quality for cheap -- a mobile microscope connected to a smartphone which will detect the air quality using a machine-learning algorithm. The aim of the invention is to provide users the ability to accurately assess dangerous airborne particulate matter and avoid health hazards, which kill 7 million people prematurely annually according to the World Health Organization. Accurate analysis of air quality is very important for improving this air quality and the researchers claim this device might be capable of such analysis. Cheaper air quality analysis might help create better air quality solutions.
Here's what we came up with by extrapolating technology and journalism trends highlighted in AP's report: The sensors send an alert to his vehicle's smart dashboard: "There has been a 10 percent decrease in air quality in Springfield." He downloads images from a series of robotic cameras posted throughout the region and uses computer vision (an algorithm able to view and comprehend a photo or video with enhanced accuracy) to compare photos of the area around the factory over time. The representative, the journalist suspects, may be hiding something; voice analysis technology declares the tone of the person on the phone is "tentative" and "nervous." Sitting in his car on the way back to the newsroom, the journalist runs a voice recording of the interview through his sentiment analysis system, which determines the mother's tone to be "genuine" and "analytical."
Beneath the kid-friendly kit are the robust features we've seen in Netgear's Arlo Q indoor security camera: 1080p full HD video, night vision, sound and motion detection, two-way audio, 24/7 recording, and free cloud storage. Lastly, a pair of sensors monitor indoor temperature, humidity, and air quality levels and alert you when they're out of range, so you can maintain an optimum nursery environment. Along the top of the streaming window are Wi-Fi, battery, and sound and motion alert indicators; access a to timeline for use with CVR plans; and a counter displaying how many unwatched video clips are in your library. Under the window are the camera controls: buttons for pausing the stream, recording video on demand, taking a snapshot of the live feed and toggling the mic and speaker on and off.
Inspired by the problem, Chiwewe got to work on a solution: an air quality forecasting platform, developed in collaboration with South Africa's Council for Scientific and Industrial Research, that's undergoing trials in Johannesburg. With an accurate understanding of pollution patterns, the city could identify –and prosecute – major polluters, plan the location of future roads and settlements, and tailor intervention strategies. While Beijing has a network of more than 30 air quality monitoring stations, and Johannesburg has eight, many African cities have no stations to measure air pollution at all. "We can combine satellite data, weather data and climate chemistry models using machine learning and AI," Chiwewe says.
Coway makes Airmega air filtration systems that suck allergens out of the air in your home. And now it's making the devices easier to control, with the integration of Amazon Alexa voice commands, starting in mid-January. With Alexa, you can now ask Airmega for updates on indoor air quality, filter lifetime, fan speed changes, timer setup, and more. In its automated smart mode, Airmega quietly goes about filtering your air with a high-efficiency particulate air filter (HEPA).
The latest is Johannesburg, South Africa, where computer engineer Tapiwa M. Chiwewe at the newly opened IBM Research lab is adapting IBM's air quality prediction software to local needs and adding new capabilities. Last month, Chiwewe presented some of the Johannesburg lab's first results, involving ground-level ozone level predictions, at the 14th International Conference on Industrial Informatics in Poitiers, France. "You can do a lot of physics to understand how ozone is found in different places," he says, "but what we did is we just collected a lot of data and trained these machines on it and they were able to predict [local ozone levels] without any knowledge of how ozone works in the atmosphere." While Chiwewe says that he and his South African colleagues were able to re-use some of the air quality forecasting tool developed by their colleagues in China, they must also adapt it to local particularities.