Comparison of PM2.5 Measurements Using the AirVisual Monitor and Beta Attenuation Monitor

Comparison of PM2.5 Measurements Using the AirVisual Monitor and Beta Attenuation Monitor

Measurements collected using AirVisual’s continuous light scattering sensor were compared to the measurements made using reference Beta Attenuation Monitor (BAM) data, computed by the US embassy and the Chinese government, between the 1st of June to the 30th of June 2015. The aim of this investigation was to analyze the accuracy and precision of the AirVisual (AV) sensor in measuring the mass concentration of airborne particles with an aerodynamic diameter of less than 2.5 𝝁m. The PM2.5 measurements made by the AV sensor and the BAM sensor were found to correlate well, with a daily and hourly correlation efficient of 0.96 and 0.83 respectively. As such, the AV sensor appears to be suitable and reliable for real time continuous monitoring of PM 2.5.

ResearchJanuary 4, 2016Jacqueline Ng
Understanding AirVisual's Forecasting Method - Deep Machine Learning
ResearchSeptember 15, 2015Cheyne Jolley

Understanding AirVisual's Forecasting Method - Deep Machine Learning

AirVisual applies artificial intelligence, big data and cloud computing to map complex, nonlinear air pollution trends accurately and efficiently. The method employs deep machine learning - a process of simplifying data by learning multiple levels of representations and abstractions (Deng & Yu, 2013). This type of advanced technology is often hard to define in exact terms due to its wide spectrum. As such, it is this paper’s purpose to explain AirVisual’s deep learning technology in a clear, simplistic manner.

Air Quality Forecast Methods
ResearchSeptember 1, 2015Cheyne Jolley

Air Quality Forecast Methods

A detailed analysis of the methods used to forecast air quality - with an explanation of the benefits and shortcomings of each.

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