Abstract
Air quality, like weather, can affect everyone, but responses differ depending on the sensitivity and health condition of a given individual. To help protect exposed populations, many countries have put in place real-time air quality nowcasting and forecasting capabilities. We present in this paper an optimal combination of air quality measurements and model outputs and show that it leads to significant improvements in the spatial representativeness of air quality. The product is referred to as multi-pollutant surface objective analyses (MPSOAs). Moreover, based on MPSOA, a geographical mapping of the Canadian Air Quality Health Index (AQHI) is also presented which provides users (policy makers, public, air quality forecasters, and epidemiologists) with a more accurate picture of the health risk anytime and anywhere in Canada and the USA. Since pollutants can also behave as passive atmospheric tracers, they provide information about transport and dispersion and, hence, reveal synoptic and regional meteorological phenomena. MPSOA could also be used to build air pollution climatology, compute local and national trends in air quality, and detect systematic biases in numerical air quality (AQ) models. Finally, initializing AQ models at regular time intervals with MPSOA can produce more accurate air quality forecasts. It is for these reasons that the Canadian Meteorological Centre (CMC) in collaboration with the Air Quality Research Division (AQRD) of Environment Canada has recently implemented MPSOA in their daily operations.Electronic supplementary materialThe online version of this article (doi:10.1007/s11869-015-0385-9) contains supplementary material, which is available to authorized users.
Highlights
Air quality (AQ), like weather, can affect everyone, but responses differ depending on the sensitivity and health condition of a given individual
High-resolution multi-pollutant surface objective analyses (MPSOAs) are important since they provide users with a more accurate and detailed picture of the true state of a given chemical species as compared to mapping based on observations or model output alone
Models are generally characterized by known deficiencies for prediction of many pollutants, whereas measurement systems suffer from representativeness problems and lack of sufficient coverage and, are often best suited to providing local air quality information
Summary
Air quality (AQ), like weather, can affect everyone, but responses differ depending on the sensitivity and health condition of a given individual. Health effects related to air pollution include eye irritation, asthma, chronic obstructive pulmonary disease (COPD) heart attacks, lung cancer, diabetes, premature death and damage to the body’s immune, neurological, and reproductive systems (Pope et al 2002; EEA-WHO 2002; WHO 2003; Sun et al 2005; Ebtekar 2006; Pope and Dockery 2006; Georgopoulos and Lioy 2006; Institute for Risk Research 2007; Reeves 2011; Crouse et al 2015). It has recently been estimated, using coupled climate-chemistry global models with concentration-response functions, that up to 3.7 million premature deaths occur annually worldwide due to outdoor air pollution as compared to a reference year before widespread industrialization, i.e. year 1850 (Silva et al 2013). To overcome the difficulty of application of the HL86, the methodology proposed in S14 but modified as described below with the effective model resolution has been adopted, σ20 1⁄4 σ2instr
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