Capturing the spatial variability of noise levels based on a short-term monitoring campaign and comparing noise surfaces against personal exposures collected through a panel study
- Civil Engineering, University of Toronto (Canada)
- Direction régionale de santé publique du CIUSS du Centre-Sud-de-l'Île-de Montréal (Canada)
- Department of Geography and Environmental Studies, Faculty of Arts, Ryerson University (Canada)
- Department of Environmental Health and Occupational Health, School of Public Health, Universtiy of Montreal (Canada)
- Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine, McGill University (Canada)
Highlights: • A Generalized Additive Model (GAM) was developed to generate noise exposure surfaces. • Noise data were collected based on short-term measurements in Toronto, Canada. • Various model specifications were tested in terms of the resulting predictions. • Predictions were validated against data from a panel and from a long-term campaign. • Models that involved manual adjustments resulted in more realistic surfaces. Environmental noise can cause important cardiovascular effects, stress and sleep disturbance. The development of appropriate methods to estimate noise exposure within a single urban area remains a challenging task, due to the presence of various transportation noise sources (road, rail, and aircraft). In this study, we developed a land-use regression (LUR) approach using a Generalized Additive Model (GAM) for LA{sub eq} (equivalent noise level) to capture the spatial variability of noise levels in Toronto, Canada. Four different model formulations were proposed based on continuous 20-min noise measurements at 92 sites and a leave one out cross-validation (LOOCV). Models where coefficients for variables considered as noise sources were forced to be positive, led to the development of more realistic exposure surfaces. Three different measures were used to assess the models; adjusted R{sup 2} (0.44–0.64), deviance (51−72%) and Akaike information criterion (AIC) (469.2–434.6). When comparing exposures derived from the four approaches to personal exposures from a panel study, we observed that all approaches performed very similarly, with values for the Fractional mean bias (FB), normalized mean square error (NMSE), and normalized absolute difference (NAD) very close to 0. Finally, we compared the noise surfaces with data collected from a previous campaign consisting of 1-week measurements at 200 fixed sites in Toronto and observed that the strongest correlations occurred between our predictions and measured noise levels along major roads and highway collectors. Our validation against long-term measurements and panel data demonstrates that manual modifications brought to the models were able to reduce bias in model predictions and achieve a wider range of exposures, comparable with measurement data.
- OSTI ID:
- 23095605
- Journal Information:
- Environmental Research, Vol. 167; Other Information: Copyright (c) 2018 Elsevier Inc. All rights reserved.; Country of input: International Atomic Energy Agency (IAEA); ISSN 0013-9351
- Country of Publication:
- United States
- Language:
- English
Similar Records
Long-term nitrogen dioxide exposure assessment using back-extrapolation of satellite-based land-use regression models for Australia
WE-A-16A-01: International Medical Physics Symposium: Increasing Access to Medical Physics Education/Training and Research Excellence