Formation of an Air Pollution Index

Abstract

An air pollution index is a quantitative tool through which air pollution data can be reported uniformly. There have been efforts to describe overall air pollution by an aggregation of pollutant subindices. When ambiguous, these aggregations raise unnecessary alarm by declaring a less polluted air to be highly polluted. Similarly, when eclipsed, a false sense of security is provided by indicating highly polluted air as less polluted. Linear sum and root sum square forms in vogue suffer from ambiguity. Whereas the maximum operator aggregation does not consider change in the remaining pollutants, it is not a good tool for management purposes. In this paper, an ambiguity-and eclipsicity-free function has been presented for aggregation of air pollution subindices. For computer adaptation of the aggregation process, the subindices have been expressed as full range functions of the pollutant concentrations.

Evaluating the application of multipollutant exposure metrics in air pollution health studies

“…To avoid the potential for overstating the risk associated with low concentrations of several pollutants, some metrics have used a power sum (e.g., root-mean-square) rather than a linear sum (Kyrkilis et al, 2007;Swamee and Tyagi, 1999). The method has been extended to account for variability in the composition of the pollutant mixture and to calculate metrics using data from multiple sites (Plaia et al, 2013;Ruggieri and Plaia, 2012).…”

“…Levy et al (2014) Marker Species  Compares mobile measurements of NO 2 to different particulate and gaseous traffic-related pollutants  Finds nitrogen oxide species, including NO 2 , to be a good marker of traffic based on high spatial correlation among measured traffic species Lobscheid et al (2012) Intake Fraction  Calculates the intake fraction of conserved pollutants emitted from on-road mobile sources utilizing AERMOD for the conterminous United States  Population-weighted mean  Finds intake fractions for populous urban counties are about two orders of magnitude greater than for sparsely populated rural counties with 75% of the intake occuring in the same county as emissions. Maciejczyk et al (2010) Source Apportionment  Uses FA to identify major sources of PM 2.5 in urban area in toxicological study  Observes a strong association between metals and cellular oxidant generation Mar et al (2006) Source Penttinen et al (2006) Source Apportionment  Uses PCA and multiple linear regressions to identify PM 2.5 sources associated with adverse health outcomes  Determines combustion sources are largely linked to negative respiratory outcomes Plaia et al (2013) Risk-based  Develops multi-site, multipollutant index for PM 10 , NO 2 , CO, and SO 2 by aggregating pollutant concentrations across sites using PCA, then aggregating across pollutants using a power sum with exponent 2  Using simulated data, shows that method is sensitive to highly variable pollutants, particularly those at low concentrations Ruggieri and Plaia (2012) Risk-based  Develops power-sum index with exponent 2 for PM 10 , NO 2 , O 3 , CO, and SO 2 and a variability index to account for situations when one pollutant is much higher than the others  Combines air quality and variability indexes to clarify whether high power-sum index values are due to one or multiple pollutants Sarnat et al (2008) Source Stieb et al (2005) Risk-based  Develops AQHI by weighting pollutant concentrations by epidemiologic effect estimate, summing across pollutants, and scaling to an arbitrary scale of 1-10  Uses mortality effect estimates from a multi-city Canadian study for CO, NO 2 , O 3 , SO 2 , and PM 2.5 Stieb et al (2008) Risk-baseed  Conducts sensitivity analyses on pollutants included in AQHI and appropriateness of using multicity effect estimates  NO 2 , O 3 , and PM 2.5 main drivers of index values; multicity formulation in good agreement with single-city effect estimates Suh et al (2011) Chemical Property  Develops a new approach to link chemical properties of air pollution to adverse health outcomes  Observes an association between adverse health effects and alkanes, transition metals, aromatics, and oxides Swamee and Tyagi (1999) Risk-based  Analyzes methods of summing weighted pollutant concentrations to generate a multipollutant index  Suggests a power-sum method with exponent 2.5 as an To et al (2013) Risk-based  Evaluates association between AQHI and asthma morbidity in Ontario  Observes consistent associations between AQHI and asthma hospitalizations, despite AQHI being developed from mortality studies …”

Air-quality and Climatic Consequences of Bioenergy Crop Cultivation

“…[103][104][105] Swamee and Tyagi (1999) created a modified aggregate index which combined multiple standards using a formula meeting the following conditions: a) perfect zero scores for one criteria pollutant should not affect poor scores in another; and b)…”

A Fuzzy Expression Way for Air Quality Index with More Comprehensive Information

“…According to the suggestions put forward by Swamee and Tyagi [4], Kyrkilis et al [12] developed an aggregate AQI for Athens, Greece. Since the aggregate AQI does not clearly explain the established exposure-response relationships among the pollutants, Cairncross et al [13] proposed a health-risk-based AQI.…”

“…Afterwards, Ott and Hunt [3] proposed a non-linear standardization to quantify the impact of each pollutant on air quality, whose transformation was used by the U.S. EPA later. In addition, Swamee and Tyagi [4] put forward its linear standardization. Based on their research, these methods were diffusely applied into the practical air quality evaluation.…”