Abstract
Effective assessments of air-pollution exposure depend on the ability to accurately predict pollutant concentrations at unmonitored locations, which can be achieved through spatial interpolation. However, most interpolation approaches currently in use are based on the Euclidean distance, which cannot account for the complex nonlinear features displayed by air-pollution distributions in the wind-field. In this study, an interpolation method based on the shortest path distance is developed to characterize the impact of complex urban wind-field on the distribution of the particulate matter concentration. In this method, the wind-field is incorporated by first interpolating the observed wind-field from a meteorological-station network, then using this continuous wind-field to construct a cost surface based on Gaussian dispersion model and calculating the shortest wind-field path distances between locations, and finally replacing the Euclidean distances typically used in Inverse Distance Weighting (IDW) with the shortest wind-field path distances. This proposed methodology is used to generate daily and hourly estimation surfaces for the particulate matter concentration in the urban area of Beijing in May 2013. This study demonstrates that wind-fields can be incorporated into an interpolation framework using the shortest wind-field path distance, which leads to a remarkable improvement in both the prediction accuracy and the visual reproduction of the wind-flow effect, both of which are of great importance for the assessment of the effects of pollutants on human health.
Highlights
Public health studies of air-pollution exposure require accurate predictions of concentrations at unmonitored locations to minimize the misclassification of exposure levels [1]
This study demonstrates the potential of incorporating windfield into interpolation using the IDW based on the SWPD (IDWS) approach
Incorporating wind-fields into the spatial interpolation of air-pollution distributions serves to enhance the predictive capability of such interpolation
Summary
Public health studies of air-pollution exposure require accurate predictions of concentrations at unmonitored locations to minimize the misclassification of exposure levels [1]. Areas downwind of highways are more heavily exposed to trafficrelated pollutants than are upwind areas This effect illustrates the necessity of incorporating wind-field into spatial interpolation. There have been several attempts to incorporate long-term, large-scale wind-fields into corresponding air-pollution estimations, short-term, small-scale wind-fields have not been extensively used for this purpose, because no direct numerical relations exist between the angle of the wind-direction and the concentration level in such cases. As a result, these approaches fail to capture the expected short-term effects of the wind flow
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