1
|
Liu Y, Oiamo T, Rainham D, Chen H, Hatzopoulou M, Brook JR, Davies H, Goudreau S, Smargiassi A. Integrating random forests and propagation models for high-resolution noise mapping. Environ Res 2021; 195:110905. [PMID: 33631139 DOI: 10.1016/j.envres.2021.110905] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 02/08/2021] [Accepted: 02/17/2021] [Indexed: 06/12/2023]
Abstract
The adverse effects of long-term exposure to environmental noise on human health are of increasing concern. Noise mapping methods such as spatial interpolation and land use regression cannot capture complex relationships between environmental conditions and noise propagation or attenuation in a three-dimension (3D) built environment. In this study, we developed a hybrid approach by combining a traffic propagation model and random forests (RF) machine learning algorithm to map the total environment noise levels for daily average, daytime, nighttime, and day-evening-nighttime at 30 m × 30 m resolution for the island of Montreal, Canada. The propagation model was used to predict traffic noise surfaces using road traffic flow, 3D building information, and a digital elevation model. The traffic noise estimates were compared with ground-based sound-level measurements at 87 points to extract residuals between total environmental noise and traffic noise. Residuals at these points were fit to RF models with multiple environmental and geographic predictor variables (e.g., vegetation index, population density, brightness of nighttime lights, land use types, and distances to noise contour around the airport, bus stops, and road intersections). Using the sound-level measurements as baseline data, the prediction errors, i.e., mean error, mean absolute error, and root mean squared error of daily average noise levels estimated by our hybrid approach was -0.03 dB(A), 2.67 dB(A), and 3.36 dB(A). Combining deterministic and stochastic models can provide accurate total environmental noise estimates for large geographic areas where sound-level measurements are available.
Collapse
Affiliation(s)
- Ying Liu
- Canadian Urban Environmental Health Research Consortium, Canada; Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, QC H3C 3J7, Canada
| | - Tor Oiamo
- Canadian Urban Environmental Health Research Consortium, Canada; Department of Geography and Environmental Studies, Ryerson University, Toronto, ON M5B 2K3, Canada
| | - Daniel Rainham
- Canadian Urban Environmental Health Research Consortium, Canada; School of Health and Human Performance, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Hong Chen
- Canadian Urban Environmental Health Research Consortium, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
| | - Marianne Hatzopoulou
- Canadian Urban Environmental Health Research Consortium, Canada; Department of Civil Engineering, University of Toronto, Toronto, ON M5S 1A4, Canada
| | - Jeffrey R Brook
- Canadian Urban Environmental Health Research Consortium, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
| | - Hugh Davies
- Canadian Urban Environmental Health Research Consortium, Canada; School of Population and Public Health, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Sophie Goudreau
- Canadian Urban Environmental Health Research Consortium, Canada; Montreal Department of Public Health, Montreal, QC H2L 1M3, Canada
| | - Audrey Smargiassi
- Canadian Urban Environmental Health Research Consortium, Canada; Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, QC H3C 3J7, Canada; Institut National de Santé Publique du Québec (INSPQ), Montréal, QC, Canada; Centre de Recherche en Santé Publique de l'Université de Montréal (CReSP), Montréal, QC, Canada.
| |
Collapse
|