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Olgun R, Karakuş N, Selim S, Eyileten B. Assessment and mapping of noise pollution in recreation spaces using geostatistic method after COVID-19 lockdown in Turkey. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:33428-33442. [PMID: 38684610 PMCID: PMC11136750 DOI: 10.1007/s11356-024-33434-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/18/2024] [Indexed: 05/02/2024]
Abstract
Increased use of recreational areas after the lifting of COVID-19 pandemic restrictions has led to increased noise levels. This study aims to determine the level of noise pollution experienced in recreational areas with the increasing domestic and international tourism activities after the lifting of pandemic lockdowns, to produce spatial distribution maps of noise pollution, and to develop strategic planning suggestions for reducing noise pollution in line with the results obtained. Antalya-Konyaaltı Beach Recreation Area, the most important international tourism destination of Turkey, is determined as the study area. To determine the existing noise pollution, 31 measurement points were marked at 100 m intervals within the study area. Noise measurements were taken during the daytime (07:00-19:00), evening (19:00-23:00), and nighttime (23:00-07:00) on weekdays (Monday, Wednesday, Friday) and weekends (Sunday) over 2 months in the summer when the lockdown was lifted. In addition, the sound level at each measurement point was recorded for 15 min, while the number of vehicles passing through the area during the same period was determined. The database created as a result of measurements and observations was analyzed using statistical and geostatistical methods. After the analysis of the data, it was found that the co-kriging-stable model showed superior performance in noise mapping. Additionally, it was revealed that there is a high correlation between traffic density and noise intensity, with the highest equivalent noise level (Leq) on weekdays and weekend evenings due to traffic and user density. In conclusion, regions exposed to intense noise pollution were identified and strategic planning recommendations were developed to prevent/reduce noise sources in these identified regions.
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Affiliation(s)
- Rifat Olgun
- Vocational School of Serik G-S. Sural, Akdeniz University, Antalya, 07500, Turkey.
- The Design School, Arizona State University, Tempe, 85287, USA.
| | - Nihat Karakuş
- Vocational School of Serik G-S. Sural, Akdeniz University, Antalya, 07500, Turkey
- Institute of Science, Akdeniz University, 07058, Antalya, Turkey
| | - Serdar Selim
- Faculty of Science, Akdeniz University, 07058, Antalya, Turkey
| | - Buket Eyileten
- Institute of Science, Akdeniz University, 07058, Antalya, Turkey
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2
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Zhao N, Prieur JF, Liu Y, Kneeshaw D, Lapointe EM, Paquette A, Zinszer K, Dupras J, Villeneuve PJ, Rainham DG, Lavigne E, Chen H, van den Bosch M, Oiamo T, Smargiassi A. Tree characteristics and environmental noise in complex urban settings - A case study from Montreal, Canada. ENVIRONMENTAL RESEARCH 2021; 202:111887. [PMID: 34425113 DOI: 10.1016/j.envres.2021.111887] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 08/03/2021] [Accepted: 08/11/2021] [Indexed: 05/17/2023]
Abstract
Field studies have shown that dense tree canopies and regular tree arrangements reduce noise from a point source. In urban areas, noise sources are multiple and tree arrangements are rarely dense. There is a lack of data on the association between the urban tree canopy characteristics and noise in complex urban settings. Our aim was to investigate the spatial variation of urban tree canopy characteristics, indices of vegetation abundance, and environmental noise levels. Using Light Detection and Ranging point cloud data for 2015, we extracted the characteristics of 1,272,069 public and private trees across the island of Montreal, Canada. We distinguished needle-leaf from broadleaf trees, and calculated the percentage of broadleaf trees, the total area of the crown footprint, the mean crown centroid height, and the mean volume of crowns of trees that were located within 100m, 250m, 500m, and 1000m buffers around 87 in situ noise measurement sites. A random forest model incorporating tree characteristics, the normalized difference vegetation index (NDVI) values, and the distances to major urban noise sources (highways, railways and roads) was employed to estimate variation in noise among measurement locations. We found decreasing trends in noise levels with increases in total area of the crown footprint and mean crown centroid height. The percentages of increased mean squared error of the regression models indicated that in 500m buffers the total area of the crown footprint (29.2%) and the mean crown centroid height (12.6%) had a stronger influence than NDVI (3.2%) in modeling noise levels; similar patterns of influence were observed using other buffers. Our findings suggest that municipal initiatives designed to reduce urban noise should account for tree features, and not just the number of trees or the overall amount of vegetation.
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Affiliation(s)
- Naizhuo Zhao
- Centre d'étude de la forêt, Département des Sciences Biologiques, Université du Québec à Montréal, Montréal, QC, Canada; Division of Clinical Epidemiology, McGill University Health Centre, Montreal, QC, Canada
| | - Jean-François Prieur
- Centre d'étude de la forêt, Département des Sciences Biologiques, Université du Québec à Montréal, Montréal, QC, Canada
| | - Ying Liu
- Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, QC, Canada; Centre of Public Health Research, University of Montreal and CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, QC, Canada
| | - Daniel Kneeshaw
- Centre d'étude de la forêt, Département des Sciences Biologiques, Université du Québec à Montréal, Montréal, QC, Canada
| | - Eugénie Morasse Lapointe
- Centre d'étude de la forêt, Département des Sciences Biologiques, Université du Québec à Montréal, Montréal, QC, Canada
| | - Alain Paquette
- Centre d'étude de la forêt, Département des Sciences Biologiques, Université du Québec à Montréal, Montréal, QC, Canada
| | - Kate Zinszer
- Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, QC, Canada; Centre of Public Health Research, University of Montreal and CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, QC, Canada
| | - Jérôme Dupras
- Institut des Sciences de la Forêt Tempérée, Université du Québec en Outaouais, Ripon, QC, Canada
| | - Paul J Villeneuve
- School of Mathematics and Statistics and Department of Neuroscience, Carleton University, Ottawa, ON, Canada
| | - Daniel G Rainham
- School of Health and Human Performance and the Healthy Populations Institute, Dalhousie University, Halifax, NS, Canada
| | - Eric Lavigne
- Air Health Science Division, Health Canada, Ottawa, ON, Canada; School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Hong Chen
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Matilda van den Bosch
- School of Population and Public Health, Faculty of Medicine, The University of British Columbia, BC, Canada; Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, BC, Canada; ISGlobal, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; Centro de Investigación Biomédica en Red Instituto de Salud Carlos III, Madrid, Spain
| | - Tor Oiamo
- Department of Geography and Environmental Studies, Ryerson University, Toronto, ON, Canada
| | - Audrey Smargiassi
- Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, QC, Canada; Centre of Public Health Research, University of Montreal and CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, QC, Canada.
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Barrigón Morillas JM, Rey Gozalo G, Montes-González D, Vílchez-Gómez R, Gómez Escobar V. Variability of traffic noise pollution levels as a function of city size variables. ENVIRONMENTAL RESEARCH 2021; 199:111303. [PMID: 34019896 DOI: 10.1016/j.envres.2021.111303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 05/05/2021] [Accepted: 05/05/2021] [Indexed: 06/12/2023]
Abstract
Noise levels measured in 27 cities with different areas (from 0.6 km2 to 59.27 km2) and populations (from approximately 2000 to 70,000 inhabitants) were compared with respect to five different urban characteristics (population, area, total street length, density, and linear density). Comparisons were conducted for both overall city noise levels and noise registered on five types of roads with different functionality using the Categorisation Method. The results showed that four of the five cities' characteristics presented a significant correlation with the noise levels (all except for density). The calculated correlations were better for noise levels in the different categories than the overall noise values, with higher explained variability on the streets with more traffic. Therefore, the road categorisation method can be used not only to assess the noise variations within cities, but also to better explain the effect of noise on the analysed city characteristics. The results of the calculated relationships enable the estimation of noise levels both currently and in future urban developments of noise values on different types of streets.
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Affiliation(s)
| | - Guillermo Rey Gozalo
- INTERRA, Lambda, Departamento de Física Aplicada, Universidad de Extremadura, Cáceres, Spain; Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, 5 Poniente 1670, 3460000, Talca, Chile.
| | - David Montes-González
- INTERRA, Lambda, Departamento de Física Aplicada, Universidad de Extremadura, Cáceres, Spain; ISISE, Departamento de Engenharia Civil, Universidade de Coimbra, Luis Reis dos Santos 290, Coimbra, Portugal
| | - Rosendo Vílchez-Gómez
- INTERRA, Lambda, Departamento de Física Aplicada, Universidad de Extremadura, Cáceres, Spain
| | - Valentín Gómez Escobar
- INTERRA, Lambda, Departamento de Física Aplicada, Universidad de Extremadura, Cáceres, Spain
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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. ENVIRONMENTAL RESEARCH 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] [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.
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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.
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Barrigón Morillas JM, Montes González D, Gómez Escobar V, Rey Gozalo G, Vílchez-Gómez R. A proposal for producing calculated noise mapping defining the sound power levels of roads by street stratification. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 270:116080. [PMID: 33234367 DOI: 10.1016/j.envpol.2020.116080] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 10/21/2020] [Accepted: 11/12/2020] [Indexed: 06/11/2023]
Abstract
The European Noise Directive proposes using strategic noise maps as tools to assess populations affected by environmental noise. It recommends using computational methods instead of in situ measurements when possible. A sound source's emission power is an important factor in the calculation of noise indicators. For traffic noise, this parameter is usually defined based on vehicle flow considering an emission spectrum that depends on the type of vehicle and its speed. This study analysed the possibility of using the categorisation method to propose an alternative method of defining a sound source's emission power to develop noise maps. This was accomplished using previously published values of the emission power per unit length. Another method is also proposed that estimates traffic flows. To verify their estimation capacity, the results of both methods were compared with the values obtained from in situ measurements. The results demonstrated similar uncertainties in both methods and were in the range of expected average uncertainties compared to the results obtained by calculating a noise map with the measured experimental values. In particular, for the differences between calculations and measurements, in absolute values, the mean uncertainties were approximately 2 dBA in estimating different long-term noise indicators. For the differences, the mean of the uncertainties obtained via the categorisation method did not present significant differences for the null value for all the analysed noise indicators. Street stratification is a rapid and low-cost approach for road traffic noise mapping without increasing uncertainties.
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Affiliation(s)
| | - David Montes González
- INTERRA, Lambda, Departamento de Física Aplicada, Universidad de Extremadura, Cáceres, Spain; ISISE, Departamento de Engenharia Civil, Universidade de Coimbra, Luis Reis Dos Santos 290, Coimbra, Portugal.
| | - Valentín Gómez Escobar
- INTERRA, Lambda, Departamento de Física Aplicada, Universidad de Extremadura, Cáceres, Spain
| | - Guillermo Rey Gozalo
- INTERRA, Lambda, Departamento de Física Aplicada, Universidad de Extremadura, Cáceres, Spain
| | - Rosendo Vílchez-Gómez
- INTERRA, Lambda, Departamento de Física Aplicada, Universidad de Extremadura, Cáceres, Spain
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Abstract
Noise pollution must be considered to achieve sustainable cities because current levels of exposure to environmental noise are a considerable risk to the health and quality of life of citizens. Urban features and sound levels were registered in 150 streets in the Chilean cities of Talca and Valdivia to analyze the relationship between both types of variables. Urban variables related to street location, urban land use, street geometry, road traffic control, and public and private transportation showed very significant correlations with the noise levels, and multiple regression models were developed from these variables for each city. Models using only urban variables in Valdivia and Talca explained 71% and 73%, respectively, of the variability of noise. The prediction error was similar in the different types of urban roads and did not exhibit significant differences between models developed in different cities. The urban models developed in one city could, therefore, be used in other similar cities. Considering the usefulness of these variables in urban planning, these models can be a useful tool for urban planners and decision-makers to implement action plans regarding noise pollution.
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Bai L, Shin S, Oiamo TH, Burnett RT, Weichenthal S, Jerrett M, Kwong JC, Copes R, Kopp A, Chen H. Exposure to Road Traffic Noise and Incidence of Acute Myocardial Infarction and Congestive Heart Failure: A Population-Based Cohort Study in Toronto, Canada. ENVIRONMENTAL HEALTH PERSPECTIVES 2020; 128:87001. [PMID: 32783534 PMCID: PMC7422718 DOI: 10.1289/ehp5809] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
BACKGROUND Epidemiological evidence for the association between traffic-related noise and the incidence of major cardiovascular events such as acute myocardial infarction (AMI) and congestive heart failure (CHF) is inconclusive, especially in North America. OBJECTIVES We evaluated the associations between long-term exposure to road traffic noise and the incidence of AMI and CHF. METHODS Our study population comprised ∼1 million people 30-100 years of age who lived in Toronto, Canada, from 2001 to 2015 and were free of AMI (referred to as the AMI cohort) or CHF (the CHF cohort) at baseline. Outcomes were ascertained from health administrative databases using validated algorithms. Annual average noise levels were estimated as the A-weighted equivalent sound pressure level over the 24-h period (LAeq24) and during nighttime (LAeqNight), respectively, using propagation modeling, and assigned to participants' annual six-digit postal code addresses during follow-up. We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for incident AMI and CHF in relation to LAeq24 and LAeqNight using random-effects Cox proportional hazards models adjusting for individual- and census tract-level covariates, including traffic-related air pollutants [e.g., ultrafine particles (UFPs) and nitrogen dioxide]. RESULTS During follow-up, there were 37,441 AMI incident cases and 95,138 CHF incident cases. Each interquartile range change in LAeq24 was associated with an increased risk of incident AMI (HR=1.07; 95% CI: 1.06, 1.08) and CHF (HR=1.07; 95% CI: 1.06, 1.09). Similarly, LAeqNight was associated with incident AMI (HR=1.07; 95% CI: 1.05, 1.08) and CHF (HR=1.06; 95% CI: 1.05, 1.07). These results were robust to various sensitivity analyses and remained elevated after controlling for long-term exposure to UFPs and nitrogen dioxide. We found near-linear relationships between noise and the incidence of AMI and CHF with no evidence of threshold values. CONCLUSION In this large cohort study in Toronto, Canada, chronic exposure to road traffic noise was associated with elevated risks for AMI and CHF incidence. https://doi.org/10.1289/EHP5809.
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Affiliation(s)
- Li Bai
- ICES, Toronto, Ontario, Canada
| | - Saeha Shin
- Public Health Ontario, Toronto, Ontario, Canada
| | - Tor H. Oiamo
- Department of Geography and Environmental Studies, Ryerson University, Toronto, Ontario, Canada
| | | | - Scott Weichenthal
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
- Air Health Science Division, Health Canada, Ottawa, Ontario, Canada
| | - Michael Jerrett
- Department of Environmental Health Sciences, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, California, USA
| | - Jeffrey C. Kwong
- ICES, Toronto, Ontario, Canada
- Public Health Ontario, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Ray Copes
- ICES, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | | | - Hong Chen
- ICES, Toronto, Ontario, Canada
- Public Health Ontario, Toronto, Ontario, Canada
- Health Canada, Ottawa, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
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Shin S, Bai L, Oiamo TH, Burnett RT, Weichenthal S, Jerrett M, Kwong JC, Goldberg MS, Copes R, Kopp A, Chen H. Association Between Road Traffic Noise and Incidence of Diabetes Mellitus and Hypertension in Toronto, Canada: A Population-Based Cohort Study. J Am Heart Assoc 2020; 9:e013021. [PMID: 32146894 PMCID: PMC7335534 DOI: 10.1161/jaha.119.013021] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Background Exposure to road traffic noise has been linked to cardiometabolic complications, such as elevated blood pressure and glucose dysregulation. However, epidemiologic evidence linking road traffic noise to diabetes mellitus and hypertension remains scarce. We examined associations between road traffic noise and the incidence of diabetes mellitus and hypertension in Toronto, Canada. Methods and Results Using the Ontario Population Health and Environment Cohort, we conducted a retrospective, population-based cohort study of long-term residents of Toronto, aged 35 to 100 years, who were registered for provincial publicly funded health insurance, and were without a history of hypertension (n=701 174) or diabetes mellitus (n=914 607). Road traffic noise exposure levels were assessed by the equivalent continuous A-weighted sound pressure level (dBA) for the 24-hour day and the equivalent continuous A-weighted sound pressure level for the night (11 pm-7am). Noise exposures were assigned to subjects according to their annual residential postal codes during the 15-year follow-up. We used random-effect Cox proportional hazards models adjusting for personal and area-level characteristics. From 2001 to 2015, each interquartile range increase in the equivalent continuous A-weighted sound pressure level (dBA) for the 24-hour day (10.0 dBA) was associated with an 8% increase in incident diabetes mellitus (95% CI, 1.07-1.09) and a 2% increase in hypertension (95% CI, 1.01-1.03). We obtained similar estimates with the equivalent continuous A-weighted sound pressure level for the night (11 pm-7am). These results were robust to all sensitivity analyses conducted, including further adjusting for traffic-related air pollutants (ultrafine particles and nitrogen dioxide). For both hypertension and diabetes mellitus, we observed stronger associations with the equivalent continuous A-weighted sound pressure level (dBA) for the 24-hour day among women and younger adults (aged <60 years). Conclusions Long-term exposure to road traffic noise was associated with an increased incidence of diabetes mellitus and hypertension in Toronto.
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Affiliation(s)
- Saeha Shin
- Public Health Ontario Toronto Ontario Canada
| | - Li Bai
- ICES Toronto Ontario Canada
| | - Tor H Oiamo
- Department of Geography and Environmental Studies Ryerson University Toronto Ontario Canada
| | - Richard T Burnett
- Environmental Health Science and Research Bureau Population Studies Division Health Canada Ottawa Ontario Canada
| | - Scott Weichenthal
- Department of Epidemiology, Biostatistics, and Occupational Health and Gerald Bronfman Department of Oncology McGill University Montreal Québec Canada.,Air Health Science Division Health Canada Ottawa Ontario Canada
| | - Michael Jerrett
- Department of Environmental Health Sciences Fielding School of Public Health University of California Los Angeles CA
| | - Jeffrey C Kwong
- Public Health Ontario Toronto Ontario Canada.,ICES Toronto Ontario Canada.,Dalla Lana School of Public Health University of Toronto Ontario Canada.,Department of Family and Community Medicine University of Toronto Ontario Canada
| | - Mark S Goldberg
- Department of Medicine McGill University Montreal Québec Canada.,Centre for Outcomes Research and Evaluation Research Institute of the McGill University Health Centre Montreal Québec Canada
| | - Ray Copes
- Public Health Ontario Toronto Ontario Canada.,Dalla Lana School of Public Health University of Toronto Ontario Canada
| | | | - Hong Chen
- Public Health Ontario Toronto Ontario Canada.,ICES Toronto Ontario Canada.,Environmental Health Science and Research Bureau Population Studies Division Health Canada Ottawa Ontario Canada.,Dalla Lana School of Public Health University of Toronto Ontario Canada
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9
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Time-Series Prediction of Environmental Noise for Urban IoT Based on Long Short-Term Memory Recurrent Neural Network. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10031144] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Noise pollution is one of the major urban environmental pollutions, and it is increasingly becoming a matter of crucial public concern. Monitoring and predicting environmental noise are of great significance for the prevention and control of noise pollution. With the advent of the Internet of Things (IoT) technology, urban noise monitoring is emerging in the direction of a small interval, long time, and large data amount, which is difficult to model and predict with traditional methods. In this study, an IoT-based noise monitoring system was deployed to acquire the environmental noise data, and a two-layer long short-term memory (LSTM) network was proposed for the prediction of environmental noise under the condition of large data volume. The optimal hyperparameters were selected through testing, and the raw data sets were processed. The urban environmental noise was predicted at time intervals of 1 s, 1 min, 10 min, and 30 min, and their performances were compared with three classic predictive models: random walk (RW), stacked autoencoder (SAE), and support vector machine (SVM). The proposed model outperforms the other three existing classic methods. The time interval of the data set has a close connection with the performance of all models. The results revealed that the LSTM network could reflect changes in noise levels within one day and has good prediction accuracy. Impacts of monitoring point location on prediction results and recommendations for environmental noise management were also discussed in this paper.
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Liu Y, Goudreau S, Oiamo T, Rainham D, Hatzopoulou M, Chen H, Davies H, Tremblay M, Johnson J, Bockstael A, Leroux T, Smargiassi A. Comparison of land use regression and random forests models on estimating noise levels in five Canadian cities. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2020; 256:113367. [PMID: 31662255 DOI: 10.1016/j.envpol.2019.113367] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 10/07/2019] [Accepted: 10/08/2019] [Indexed: 05/22/2023]
Abstract
Chronic exposure to environment noise is associated with sleep disturbance and cardiovascular diseases. Assessment of population exposed to environmental noise is limited by a lack of routine noise sampling and is critical for controlling exposure and mitigating adverse health effects. Land use regression (LUR) model is newly applied in estimating environmental exposures to noise. Machine-learning approaches offer opportunities to improve the noise estimations from LUR model. In this study, we employed random forests (RF) model to estimate environmental noise levels in five Canadian cities and compared noise estimations between RF and LUR models. A total of 729 measurements and 33 built environment-related variables were used to estimate spatial variation in environmental noise at the global (multi-city) and local (individual city) scales. Leave one out cross-validation suggested that noise estimates derived from the RF global model explained a greater proportion of variation (R2: RF = 0.58, LUR = 0.47) with lower root mean squared errors (RF = 4.44 dB(A), LUR = 4.99 dB(A)). The cross-validation also indicated the RF models had better general performance than the LUR models at the city scale. By applying the global models to estimate noise levels at the postal code level, we found noise levels were higher in Montreal and Longueuil than in other major Canadian cities.
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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
| | - Sophie Goudreau
- Canadian Urban Environmental Health Research Consortium, Canada; Montreal Regional Department of Public Health, Montreal, QC H2L 1M3, 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; Department of Earth and Environmental Sciences, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Marianne Hatzopoulou
- Canadian Urban Environmental Health Research Consortium, Canada; Department of Civil Engineering, University of Toronto, Toronto, ON M5S 1A4, Canada
| | - Hong Chen
- Canadian Urban Environmental Health Research Consortium, Canada; Population Studies Division, Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada; Public Health Ontario, Toronto, ON M5G 1V2, Canada; Institute for Clinical Evaluative Sciences, Toronto, ON M4N 3M5, 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
| | - Mathieu Tremblay
- Department of Public Health of Montérégie, Longueuil, QC J4K 2M3, Canada
| | - James Johnson
- Canadian Urban Environmental Health Research Consortium, Canada; Public Health Ontario, Toronto, ON M5G 1V2, Canada
| | - Annelies Bockstael
- School of Speech-Language Pathology and Audiology, University of Montreal, QC H3N 1X7, Canada
| | - Tony Leroux
- National Institute of Public Health of Quebec, Montreal, QC H2P 1E2, 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; National Institute of Public Health of Quebec, Montreal, QC H2P 1E2, Canada.
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11
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Fallah-Shorshani M, Minet L, Liu R, Plante C, Goudreau S, Oiamo T, Smargiassi A, Weichenthal S, Hatzopoulou M. 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. ENVIRONMENTAL RESEARCH 2018; 167:662-672. [PMID: 30241005 DOI: 10.1016/j.envres.2018.08.021] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 07/25/2018] [Accepted: 08/14/2018] [Indexed: 06/08/2023]
Abstract
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 LAeq (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 R2 (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.
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Affiliation(s)
| | - Laura Minet
- Civil Engineering, University of Toronto, Canada.
| | - Rick Liu
- Civil Engineering, University of Toronto, Canada.
| | - Céline Plante
- Direction régionale de santé publique du CIUSS du Centre-Sud-de-l'Île-de Montréal, Canada.
| | - Sophie Goudreau
- Direction régionale de santé publique du CIUSS du Centre-Sud-de-l'Île-de Montréal, Canada.
| | - Tor Oiamo
- Department of Geography and Environmental Studies, Faculty of Arts, Ryerson University, Canada.
| | - Audrey Smargiassi
- Department of Environmental Health and Occupational Health, School of Public Health, Universtiy of Montreal, Canada.
| | - Scott Weichenthal
- Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine, McGill University, Canada.
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