Improving spatial nitrogen dioxide prediction using diffusion tubes: A case study in West Central Scotland

Spatiotemporal variability of nitrogen dioxide (NO2) pollution in Manchester (UK) city centre (2017-2018) using a fine spatial scale single-NOx diffusion tube network

Nitrogen dioxide (NO₂) is linked to poor air quality and severe human health impacts, including respiratory and cardiovascular diseases and being responsible annually for approximately 23,500 premature deaths in the UK. Automated air quality monitoring stations continuously record pollutants in urban environments but are restricted in number (need for electricity, maintenance and trained operators), only record air quality proximal to their location and cannot document variability of airborne pollutants at finer spatial scales. As an alternative, passive sampling devices such as Palmes-type diffusion tubes can be used to assess the spatial variability of air quality in greater detail, due to their simplicity (e.g. small, light material, no electricity required) and suitability for long-term studies (e.g. deployable in large numbers, useful for screening studies). Accordingly, a one passive diffusion tube sampling approach has been adapted to investigate spatial and temporal variability of NO₂ concentrations across the City of Manchester (UK). Spatial and temporal detail was obtained by sampling 45 locations over a 12-month period (361 days, to include seasonal variability), resulting in 1080 individual NO₂ measurements. Elevated NO₂ concentrations, exceeding the EU/UK limit value of 40 µg m^-3, were recorded throughout the study period (N = 278; 26% of individual measurements), particularly during colder months and across a wide area including residential locations. Of 45 sampling locations, 24% (N = 11) showed annual average NO₂ above the EU/UK limit value, whereas 16% (N = 7) showed elevated NO₂ (> 40 µg m^-3) for at least 6 months of deployment. Highest NO₂ was recorded in proximity of highly trafficked major roads, with urban factors such as surrounding building heights also influencing NO₂ dispersion and distribution. This study demonstrates the importance of high spatial coverage to monitor atmospheric NO₂ concentrations across urban environments, to aid identification of areas of human health concern, especially in areas that are not covered by automated monitoring stations. This simple, reasonably cheap, quick and easy method, using a single-NO_x diffusion tube approach, can aid identification of NO₂ hotspots and provides fine spatial detail of deteriorated air quality. Such an approach can be easily transferred to comparable urban environments to provide an initial screening tool for air quality and air pollution, particularly where local automated air quality monitoring stations are limited. Additionally, such an approach can support air quality assessment studies, e.g. lichen or moss biomonitoring studies.

How robust are the estimated effects of air pollution on health? Accounting for model uncertainty using Bayesian model averaging

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We explored the sensitivity of the pollution-health effect to three factors.

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Estimation of NO2, choice of deprivation and choice of spatial autocorrelation model.

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Choice of these factors leads to a wide variation in pollution-health effects.

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BMA is utilised to estimate an overall effect while accounting for model uncertainty.

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Overall, a positive but borderline pollution-health effect was obtained.

The long-term impact of air pollution on human health can be estimated from small-area ecological studies in which the health outcome is regressed against air pollution concentrations and other covariates, such as socio-economic deprivation. Socio-economic deprivation is multi-factorial and difficult to measure, and includes aspects of income, education, and housing as well as others. However, these variables are potentially highly correlated, meaning one can either create an overall deprivation index, or use the individual characteristics, which can result in a variety of pollution-health effects. Other aspects of model choice may affect the pollution-health estimate, such as the estimation of pollution, and spatial autocorrelation model. Therefore, we propose a Bayesian model averaging approach to combine the results from multiple statistical models to produce a more robust representation of the overall pollution-health effect. We investigate the relationship between nitrogen dioxide concentrations and cardio-respiratory mortality in West Central Scotland between 2006 and 2012.