A Multisite-Multipollutant Air Quality Index

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). Another scaled approach has recently been proposed using satellite data for PM 2.5 and NO 2 instead of using monitoring data from a limited number of sampling sites.…”

“…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 Pollution Monitoring: A Case Study from Romania

“…Reference [24] shows how a multi-pollutant and multi-site AQI could be designed in order to get an aggregate measure of air pollution. However, the AQI has the advantage to concentrate multiple and multi-scale measurements in a unique indicator and allows to follow the evolution of air quality in a given region or city providing timely and understandable information for population and supporting local authorities governments in decisions to prevent and avoid adverse health effects.…”

Assessing Air Quality in Turkey: A Proposed, Air Quality Index

“…In this study, "Distance to Reference Value Normalization" method [24] is applied since this approach is widely used in environmental applications [25][26][27]. In this method, distance to a reference measures the relative position of a given indicator vis-à-vis a reference.…”