Combining physiological, environmental and locational sensors for citizen-oriented health applications.

This work investigates the potential of combining the outputs of multiple low-cost sensor technologies for the direct measurement of spatio-temporal variations in phenomena that exist at the interface between our bodies and the environment. The example used herein is the measurement of personal exposure to traffic pollution, which may be considered as a function of the concentration of pollutants in the air and the frequency and volume of that air which enters our lungs. The sensor-based approach described in this paper removes the 'traditional' requirements either to model or interpolate pollution levels or to make assumptions about the physiology of an individual. Rather, a wholly empirical analysis into pollution exposure is possible, based upon high-resolution spatio-temporal data drawn from sensors for NO2, nasal airflow and location (GPS). Data are collected via a custom smartphone application and mapped to give an unprecedented insight into exposure to traffic pollution at the individual level. Whilst the quality of data from low-cost miniaturised sensors is not suitable for all applications, there certainly are many applications for which these data would be well suited, particularly those in the field of citizen science. This paper demonstrates both the potential and limitations of sensor-based approaches and discusses the wider relevance of these technologies for the advancement of citizen science.

Spatially-varying surface roughness and ground-level air quality in an operational dispersion model.

Urban form controls the overall aerodynamic roughness of a city, and hence plays a significant role in how air flow interacts with the urban landscape. This paper reports improved model performance resulting from the introduction of variable surface roughness in the operational air-quality model ADMS-Urban (v3.1). We then assess to what extent pollutant concentrations can be reduced solely through local reductions in roughness. The model results suggest that reducing surface roughness in a city centre can increase ground-level pollutant concentrations, both locally in the area of reduced roughness and downwind of that area. The unexpected simulation of increased ground-level pollutant concentrations implies that this type of modelling should be used with caution for urban planning and design studies looking at ventilation of pollution. We expect the results from this study to be relevant for all atmospheric dispersion models with urban-surface parameterisations based on roughness.

Assessment of uncertainties in a long range atmospheric transport model: Methodology, application and implications in a UK context.

Acid deposition models are inherently simplified representations of real world behaviour and their performance is best evaluated by comparison with observations. National and international acid rain policy assessments handle observed and modelled deposition fields in different ways. Here, both the observed and modelled deposition fields are seen as uncertain and the Generalised Likelihood Uncertainty Estimation (GLUE) framework is used to choose acceptable sets of model input parameters that minimise the differences between them. These acceptable sets of model parameters are then used to estimate deposition budgets to the UK and to provide a probabilistic treatment of excess deposition over environmental quality standards (critical loads).