Air Quality Index Revisited from a Compositional Point of View

Abstract

The so-called Air Quality Index (AQI), expresses the quality of atmospheric air. The overall AQI is determined from the AQIs of some reference air pollutants, which are calculated by a transform of the respective concentrations. Concentrations of air pollutants are compositional data; they are expressed as part of mass of each pollutant in a total air volume or mass. Therefore, air pollution concentration data, as compositional data, just provide ratio information between concentrations of pollutants. Operations involved in the computation of overall AQI are not admissible operations in the framework of compositional data analysis, as they destroy the original ratio information. Consequently, the standard methodology should be reviewed for such calculations, taking into account the principles and operations of compositional data analysis. The objective of this article is to present a first approach to incorporate compositional perspective to air quality expression. For this, it is proposed to use a balance log-contrast of concentrations expressed in µg/m 3 to define a new kind of air quality indicator. Furthermore, the geometric mean of the concentrations is applied to obtain a new and simple scale air quality index, avoiding definition of piecewise linear interpolations used in the standard AQI computation. As an illustrative example, statistical analysis of atmospheric pollution data series (2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) of the city of Madrid (Spain) has been carried out.

The Quality of Urban Air in Barcelona: A New Approach Applying Compositional Data Analysis Methods

“…The filling-up value of air residual component is almost never reported, its computation is very difficult and the second term in the right-hand of Equation 3 is almost constant ( [10]). Therefore, air pollution can be expressed using the considered air pollutants with Equation 3 or its approximation  …”

“…The best known is the proposal by the USA Environmental Protection Agency (EPA), which is based on a piecewise linear function that transforms the concentrations into AQI values in a certain scale. However, all the methodologies applied until now, handle concentrations of air pollutants ignoring their compositional nature and therefore committing some errors, such as calculating arithmetic averages [8][9][10].…”

“…Although there are other authors who use methodology that has to do with indexes [11,12], we believe that our proposition based on compositional data is more consistent with the nature of the kind of data under study. The most recent proposal for an Air Quality Index that takes into account the compositional nature of the data is, as far as we know, the one developed in [10]. The proposal is based on the concept of logcontrast and an air quality index is defined (AQI*) as a function of the geometric mean of concentrations of six air pollutants (O3, NO2, CO, SO2, PM10, PM2.5).…”

SAM Smart Asthma Monitoring: Focus on Air Quality Data and Internet of Things (IoT)

“…IV. DATA ANALYSIS Previously, AQI has been calculated mathematically, which is currently replaced by data analysis practices [22]. The data analysis is done using MATLAB, which analyses the collected data from outdoor regional areas of NSW and the indoor UTS buildings that are embedded to provide the personalized feedback to the individuals.…”

Spatio-temporal air pollution modelling using a compositional approach

“…Modelling and simulation works in air quality considering a compositional approach are still scarce ( Sánchez-Balseca and Pérez-Foguet, 2019 ), with the few studies related to AQI ( Jarauta-Bragulat et al., 2016 ; AL-Dhurafi et al., 2018 ). In contrast, this is not the case for issues related to water ( Buccianti and Pawlowsky-Glahn, 2005 ; Blake et al., 2016 ; Owen et al., 2016 ; Pérez-Foguet et al., 2017 ; Ezbakhe and Pérez-Foguet, 2019 ; Quispe-Coica and Pérez-Foguet, 2020 ) or to soil ( Reimann et al., 2012 ; Shi-wen et al., 2013 ; López-Abente et al., 2018 ; Petrika et al., 2018 ).…”