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

Abstract

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 NOx, O3, PM10 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 NOx, O3 and PM10, 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 PM10 is somewhat smaller than the mean value of the meta-coefficient, while for O3, it is less than one fifth of the meta-coefficient. We have not found any meta-coefficient using NOx as an indicator of AEDV, but compared to the mean value associated with NO2, our value of NOx 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 NOx exhibits a relatively even distribution throughout the year, but with a clear decrease during the summer months due to less traffic. O3 contributes to an increase in AQHI during the spring. For PM10, 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 PM10 with 3.1%, followed by O3 with 2.0%.

Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration

“…Finally, the ninth and last step is the production of air quality services built upon high-quality air pollution models. Such services include more advanced air quality index (AQI) [101] models and green path routing [53] to enhance the quality of life of citizens. We note that while such services are not strictly part of the calibration pipeline, air quality services can have contrasting accuracy, resolution, and other requirements.…”

Field Evaluation of Low-Cost PM Sensors (Purple Air PA-II) Under Variable Urban Air Quality Conditions, in Greece

“…Exposure to airborne particulate matter (PM) has been documented, over the last three decades, as being related to a wide range of human health impacts, including respiratory and cardiovascular disease [1,2], increased mortality, and loss of life expectancy [3,4]. Fine particulate matter (PM 2.5 ) has been recognized as a leading factor in human disease and premature mortality among air pollutants [5] and a causal relationship has been identified between long-term exposure to PM 2.5 and cardiovascular effects and mortality [6,7].…”

EXPLUME v1.0: a model for personal exposure to ambient O<sub>3</sub> and PM<sub>2.5</sub>

“…Furthermore, large variability in population exposure arises from human activity, population mobility, transport usage, and building characteristics (Georgopoulos et al, 2005). Therefore, to study the health risk on specific population groups -such as children, elderly people, asthma patients, or pregnant women (Olsson et al, 2014), or the health effects of co-pollutants (Olstrup et al, 2019b, a;Valari et al, 2011), or the risk associated with living or working near busy roads (Lipfert and Wyzga, 2008;Miranda et al, 2013) -one has to account for pollutant concentration at district level, population dynamics, and exposure indoors and during transport (Franklin et al, 2012;Hodas et al, 2012).…”