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Air Quality and Emissions Assessment
Published in Wayne T. Davis, Joshua S. Fu, Thad Godish, Air Quality, 2021
Wayne T. Davis, Joshua S. Fu, Thad Godish
Relatively good air quality (i.e. reported AQIs of <100) is common in many metropolitan areas in the United States. However, values between 101 and 200 are reported in many communities several times a year, particularly the summertime high-O3 season. High AQI values based on CO may occur in some cities during winter. AQI values for O3 of more than 101 commonly occur in the Los Angeles Basin and Houston, TX, where smog is a significant problem. Values that are higher than 300 in the United States are extremely rare. They are likely to be common, however (if the AQI were calculated), in many other countries such as China since 2012, European Union since 2006, India since 2014, Mexico since 2006, United Kingdom since 1998, in the different scales. Air Quality Health Index is used in Canada and Hong Kong to indicate the impact of air pollution on human health.
Evaluating an Air Quality Health Index (AQHI) amendment for communities impacted by residential woodsmoke in British Columbia, Canada
Published in Journal of the Air & Waste Management Association, 2020
Jeffrey Trieu, Jiayun Yao, Kathleen E. McLean, Dave M. Stieb, Sarah B. Henderson
The Air Quality Health Index (AQHI) was developed in Canada as a tool for communicating the acute health risks of current and forecasted air quality conditions (Government of Canada 2017). The AQHI is expressed on a scale of 1 to 10+, with values categorized as low (1–3), moderate (4–6), high (7–10), and very high (10+) health risk. Each category has an associated health message to communicate how individuals can modify their behavior to reduce personal exposure. The AQHI is normally calculated using the 3-hour running average concentrations of nitrogen dioxide (NO2), ground-level ozone (O3), and PM2.5. This formulation was based on its association with mortality in twelve urban Canadian cities (Stieb et al. 2008). One ongoing concern with the AQHI is that PM2.5 has the smallest coefficient in the equation, but PM2.5 is the dominant pollutant in biomass smoke from sources such as wildfires, slash burning, and residential wood burning. These sources all have relatively smaller impacts on ambient NO2 and O3 concentrations, which have larger coefficients in the AQHI equation.
Associations between air pollution and cardio-respiratory physiological measures in older adults exercising outdoors
Published in International Journal of Environmental Health Research, 2021
David Stieb, Robin H. Shutt, Lisa M. Kauri, Sarah Mason-Renton, Li Chen, Mieczyslaw Szyszkowicz, Nina A. Dobbin, Marc Rigden, Branka Jovic, Marie Mulholland, Martin S. Green, Ling Liu, Guillaume Pelletier, Scott A. Weichenthal, Robert E. Dales, Julie Andrade, Isaac Luginaah
Air quality indices (AQIs) and advisories are provided based on the premise that they furnish information that people can use to reduce their exposure to air pollution and, as a consequence, the risk of adverse health effects. The Air Quality Health Index (AQHI) is an aggregate measure of nitrogen dioxide (NO2), ozone (O3) and fine particulate matter (PM2.5) which was developed to address identified deficiencies in existing AQIs, notably their inability to reflect potentially additive effects among multiple pollutants and the occurrence of adverse effects at low levels of exposure, i.e. without a threshold (Stieb et al. 2008a). We previously reported associations of the AQHI and individual air pollutants with cardio-respiratory physiological measures in panel studies of summer and winter outdoor physical activity in older adults conducted in a predominantly rural area and small northern industrial city, respectively (Stieb et al. 2017, 2018). These findings provided evidence supporting the utility of the AQHI in predicting health risks for diverse health outcomes and types of communities not accounted for in developing the AQHI, which was based on the effects of air pollution on mortality in large urban centres. However, empirical evidence to support the effectiveness of AQIs and advisories in actually reducing exposures and health risks is mixed (Bickerstaff and Walker 2001; Semenza et al. 2008; Stieb et al. 2008b; Smallbone 2009, 2015; Wen et al. 2009; Neidell 2009; Maheswaran et al. 2010; Licskai et al. 2013; Mullins and Bharadwaj 2015; Radisic et al. 2016; Lyons et al. 2016; D’Antoni et al. 2017; Chen et al. 2018) and to our knowledge there have been no previous experimental studies based on individual-level data. The present study was designed as a randomized controlled trial of the AQHI in which the intervention comprised advising participants to exercise indoors rather than outdoors on days when the maximum AQHI was forecast to be 5 or higher. We hypothesized that as a result of reducing exposure to outdoor air pollution on designated days, associations of physiological measures with air pollution in the intervention group would be attenuated relative to the control group. During the study period of approximately 70 days, however, there were only 2 intervention days, substantially fewer than anticipated based on historical data. Nonetheless, we analyzed the data according to original group assignment, in keeping with intention to treat analysis. Given this limitation, our results cannot readily address our hypothesis but may provide additional evidence of the AQHI as a predictor of health risk in diverse settings.