Paul Agnew

Interactions of physical, chemical, and biological weather calling for an integrated approach to assessment, forecasting, and communication of air quality.

This article reviews interactions and health impacts of physical, chemical, and biological weather. Interactions and synergistic effects between the three types of weather call for integrated assessment, forecasting, and communication of air quality. Today’s air quality legislation falls short of addressing air quality degradation by biological weather, despite increasing evidence for the feasibility of both mitigation and adaptation policy options. In comparison with the existing capabilities for physical and chemical weather, the monitoring of biological weather is lacking stable operational agreements and resources. Furthermore, integrated effects of physical, chemical, and biological weather suggest a critical review of air quality management practices. Additional research is required to improve the coupled modeling of physical, chemical, and biological weather as well as the assessment and communication of integrated air quality. Findings from several recent COST Actions underline the importance of an increased dialog between scientists from the fields of meteorology, air quality, aerobiology, health, and policy makers.

Quantifying the impact of current and future concentrations of air pollutants on respiratory disease risk in England

BackgroundEstimating the long-term health impact of air pollution in a spatio-temporal ecological study requires representative concentrations of air pollutants to be constructed for each geographical unit and time period. Averaging concentrations in space and time is commonly carried out, but little is known about how robust the estimated health effects are to different aggregation functions. A second under researched question is what impact air pollution is likely to have in the future.MethodsWe conducted a study for England between 2007 and 2011, investigating the relationship between respiratory hospital admissions and different pollutants: nitrogen dioxide (NO2); ozone (O3); particulate matter, the latter including particles with an aerodynamic diameter less than 2.5 micrometers (PM2.5), and less than 10 micrometers (PM10); and sulphur dioxide (SO2). Bayesian Poisson regression models accounting for localised spatio-temporal autocorrelation were used to estimate the relative risks (RRs) of pollution on disease risk, and for each pollutant four representative concentrations were constructed using combinations of spatial and temporal averages and maximums. The estimated RRs were then used to make projections of the numbers of likely respiratory hospital admissions in the 2050s attributable to air pollution, based on emission projections from a number of Representative Concentration Pathways (RCP).ResultsNO2 exhibited the largest association with respiratory hospital admissions out of the pollutants considered, with estimated increased risks of between 0.9 and 1.6% for a one standard deviation increase in concentrations. In the future the projected numbers of respiratory hospital admissions attributable to NO2 in the 2050s are lower than present day rates under 3 Representative Concentration Pathways (RCPs): 2.6, 6.0, and 8.5, which is due to projected reductions in future NO2 emissions and concentrations.ConclusionsNO2 concentrations exhibit consistent substantial present-day health effects regardless of how a representative concentration is constructed in space and time. Thus as concentrations are predicted to remain above limits set by European Union Legislation until the 2030s in parts of urban England, it will remain a substantial health risk for some time.

Mortality and emergency hospitalizations associated with atmospheric particulate matter episodes across the UK in spring 2014.

Exposure to particulate air pollution is known to have negative impacts on human health. Long-term exposure to anthropogenic particulate matter is associated with the equivalent of around 29,000 deaths a year in the UK. However, short-lived air pollution episodes on the order of a few days are also associated with increased daily mortality and emergency hospital admissions for respiratory and cardiovascular conditions. The UK experienced widespread high levels of particulate air pollution in March-April 2014; observations of hourly mean PM2.5 concentrations reached up to 83μgm-3 at urban background sites. We performed an exposure and health impact assessment of the spring air pollution, focusing on two episodes with the highest concentrations of PM2.5 (12-14 March and 28 March-3 April 2014). Across these two episodes of elevated air pollution, totalling 10days, around 600 deaths were brought forward from short-term exposure to PM2.5, representing 3.9% of total all-cause (excluding external) mortality during these days. Using observed levels of PM2.5 from other years, we estimate that this is 2.0 to 2.7 times the mortality burden associated with typical urban background levels of PM2.5 at this time of year. Our results highlight the potential public health impacts and may aid planning for health care resources when such an episode is forecast.

Local- and regional-scale air pollution modelling (PM 10 ) and exposure assessment for pregnancy trimesters, infancy, and childhood to age 15 years: Avon Longitudinal Study of Parents And Children (ALSPAC)

We established air pollution modelling to study particle (PM10) exposures during pregnancy and infancy (1990–1993) through childhood and adolescence up to age ~15 years (1991–2008) for the Avon Longitudinal Study of Parents And Children (ALSPAC) birth cohort. For pregnancy trimesters and infancy (birth to 6 months; 7 to 12 months) we used local (ADMS-Urban) and regional/long-range (NAME-III) air pollution models, with a model constant for local, non-anthropogenic sources. For longer exposure periods (annually and the average of birth to age ~8 and to age ~15 years to coincide with relevant follow-up clinics) we assessed spatial contrasts in local sources of PM10 with a yearly-varying concentration for all background sources. We modelled PM10 (μg/m3) for 36,986 address locations over 19 years and then accounted for changes in address in calculating exposures for different periods: trimesters/infancy (n = 11,929); each year of life to age ~15 (n = 10,383). Intra-subject exposure contrasts were largest between pregnancy trimesters (5th to 95th centile: 24.4–37.3 μg/m3) and mostly related to temporal variability in regional/long-range PM10. PM10 exposures fell on average by 11.6 μg/m3 from first year of life (mean concentration = 31.2 μg/m3) to age ~15 (mean = 19.6 μg/m3), and 5.4 μg/m3 between follow-up clinics (age ~8 to age ~15). Spatial contrasts in 8-year average PM10 exposures (5th to 95th centile) were relatively low: 25.4–30.0 μg/m3 to age ~8 years and 20.7–23.9 μg/m3 from age ~8 to age ~15 years. The contribution of local sources to total PM10 was 18.5%–19.5% during pregnancy and infancy, and 14.4%–17.0% for periods leading up to follow-up clinics. Main roads within the study area contributed on average ~3.0% to total PM10 exposures in all periods; 9.5% of address locations were within 50 m of a main road. Exposure estimates will be used in a number of planned epidemiological studies.

Improving Air Quality Forecasts Using High Resolution Pollutant Climatologies and Surface Observations

An existing bias correction technique has been extended to intelligently incorporate urban centre and roadside observations by using high (1 km) resolution pollution climatologies. The results show that this can give important improvements in forecast skill, particularly during rush hours where a clear distinction between urban and rural areas becomes more apparent.

Modelling UK Air Quality for AQMEII2 with the Online Forecast Model AQUM

AQUM is an online air quality modelling system which is used to provide the operational Met Office air quality forecast for the UK. The standard model configuration runs at a resolution of 12 km and covers a domain including the UK and part of Western Europe. The model is routinely verified against near-real-time surface pollutant measurements from the UK Automatic Urban and Rural Network (AURN) to provide a continuous evaluation of model performance. We have developed a new configuration of AQUM to run on the AQMEII (Air Quality Modelling Evaluation International Initiative) Phase 2 European domain at a resolution of 22 km, using the prescribed AQMEII emission datasets. This latter dataset contains wildfire emissions which are not included in the standard AQUM emissions. An initial analysis is conducted to compare the emissions over the UK used as input to both models. Model simulations for 2010 from the AQUM standard and AQMEII configurations are compared to AURN surface observations and an analysis of the effect of the differing model domain, resolution and emissions is made. In 2010 Russian wildfires constituted a significant additional source of pollutants in Eastern Europe; we evaluate the impact of these wildfire emissions on UK air quality.

Evaluation of a Year-Long Ozone Hindcast for 2006 as Part of a DEFRA Model Intercomparison

A variant of the operational forecast configuration of the Met Office’s newly developed Eulerian Air Quality Forecast Model was used to generate an air quality hindcast for 2006 as part of a DEFRA model intercomparison. Verification of predicted ozone concentrations was carried out by comparing against hourly observations from 15 rural and urban background sites spread over the UK. Models were primarily assessed statistically using standard metrics including bias, mean error, correlation, and fraction of predictions within a factor of 2 of observations for (a) all observations, and (b) periods of elevated ozone (>100 μg/m3). We will present results showing that the Met Office model is competitive with other models for hourly ozone, but is best in class at modelling episodes of elevated ozone. The results indicate that the availability of high quality met data and interactive treatment of chemistry and meteorology are both important in modelling ozone episodes.