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A Multi-Pollutant Air Quality Health Index (AQHI) Based on Short-Term Respiratory Effects in Stockholm, Sweden

In this study, an Air Quality Health Index (AQHI) for Stockholm is introduced as a tool to capture the combined effects associated with multi-pollutant exposure. Public information regarding the expected health risks associated with current or forecasted concentrations of pollutants and pollen can b...

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Detalles Bibliográficos
Autores principales: Olstrup, Henrik, Johansson, Christer, Forsberg, Bertil, Tornevi, Andreas, Ekebom, Agneta, Meister, Kadri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339148/
https://www.ncbi.nlm.nih.gov/pubmed/30609753
http://dx.doi.org/10.3390/ijerph16010105
Descripción
Sumario:In this study, an Air Quality Health Index (AQHI) for Stockholm is introduced as a tool to capture the combined effects associated with multi-pollutant exposure. Public information regarding the expected health risks associated with current or forecasted concentrations of pollutants and pollen can be very useful for sensitive persons when planning their outdoor activities. For interventions, it can also be important to know the contribution from pollen and the specific air pollutants, judged to cause the risk. The AQHI is based on an epidemiological analysis of asthma emergency department visits (AEDV) and urban background concentrations of NO(x), O(3), PM(10) and birch pollen in Stockholm during 2001–2005. This analysis showed per 10 µg·m(–3) increase in the mean of same day and yesterday an increase in AEDV of 0.5% (95% CI: −1.2–2.2), 0.3% (95% CI: −1.4–2.0) and 2.5% (95% CI: 0.3–4.8) for NO(x), O(3) and PM(10), respectively. For birch pollen, the AEDV increased with 0.26% (95% CI: 0.18–0.34) for 10 pollen grains·m(–3). In comparison with the coefficients in a meta-analysis, the mean values of the coefficients obtained in Stockholm are smaller. The mean value of the risk increase associated with PM(10) is somewhat smaller than the mean value of the meta-coefficient, while for O(3), it is less than one fifth of the meta-coefficient. We have not found any meta-coefficient using NO(x) as an indicator of AEDV, but compared to the mean value associated with NO(2), our value of NO(x) is less than half as large. The AQHI is expressed as the predicted percentage increase in AEDV without any threshold level. When comparing the relative contribution of each pollutant to the total AQHI, based on monthly averages concentrations during the period 2015–2017, there is a tangible pattern. The AQHI increase associated with NO(x) exhibits a relatively even distribution throughout the year, but with a clear decrease during the summer months due to less traffic. O(3) contributes to an increase in AQHI during the spring. For PM(10), there is a significant increase during early spring associated with increased suspension of road dust. For birch pollen, there is a remarkable peak during the late spring and early summer during the flowering period. Based on monthly averages, the total AQHI during 2015–2017 varies between 4 and 9%, but with a peak value of almost 16% during the birch pollen season in the spring 2016. Based on daily mean values, the most important risk contribution during the study period is from PM(10) with 3.1%, followed by O(3) with 2.0%.