Abstract
This study employs the correlation coefficients technique to support emission sources detection for indoor environments. Unlike existing methods analyzing merely primary pollution, we consider alternatively the secondary pollution (i.e., chemical reactions between pollutants in addition to pollutant level), and calculate intra pollutants correlation coefficients for characterizing and distinguishing emission events. Extensive experiments show that seven major indoor emission sources are identified by the proposed method, including (1) frying canola oil on electric hob, (2) frying olive oil on an electric hob, (3) frying olive oil on a gas hob, (4) spray of household pesticide, (5) lighting a cigarette and allowing it to smoulder, (6) no activities, and (7) venting session. Furthermore, our method improves the detection accuracy by a support vector machine compared to without data filtering and applying typical feature extraction methods such as PCA and LDA.
Highlights
Some studies on indoor air quality were motivated by the desire to understand the origins of the risks to the health of householders and the contribution of indoor emission sources relative to outdoor sources, as both imply quite different intervention strategies
Linear discriminant analysis (LDA) [6,7,8], principle component analysis (PCA) [9,10,11] and genetic algorithms (GA) [12] have been used as feature-extraction methods, to magnify the main orthogonal contributions that explain most of the pollutants of an emission source
The proposed method to define representative indoor events was based on the processing of air quality time series and consisted of three steps: (i) selecting an appropriate continuously sliding window and fitting the range of the continuous sequence of air quality network data, (ii) removing short-term fluctuations associated with the influence of local emission sources from the original measurements, taking into account the correlation determined from the correlation coefficient analysis, and (iii) mapping the correlation factors into an nonlinear space for emission source recognition
Summary
Some studies on indoor air quality were motivated by the desire to understand the origins of the risks to the health of householders and the contribution of indoor emission sources relative to outdoor sources, as both imply quite different intervention strategies. Identifying the contribution of each source, and the exposure to it, is central to the effort to understand health effects and manage the risks. There is huge potential for large variations in indoor emissions, air quality and exposures among homes, as well as among occupants. For this reason, a technique was sought to identify and quantify indoor emission sources in a form that could be deployed rapidly with ease in multiple homes at low cost. Capturing the reaction among pollutants is necessary for emission sources’ detection
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
