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Gharehchahi E, Hashemi H, Yunesian M, Samaei M, Azhdarpoor A, Oliaei M, Hoseini M. Geospatial analysis for environmental noise mapping: A land use regression approach in a metropolitan city. ENVIRONMENTAL RESEARCH 2024; 257:119375. [PMID: 38871270 DOI: 10.1016/j.envres.2024.119375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 05/20/2024] [Accepted: 06/05/2024] [Indexed: 06/15/2024]
Abstract
Environmental noise can lead to adverse health outcomes. Understanding the spatial variability of environmental noise is crucial for mitigating potential health risks and developing influential urban strategies for reducing noise levels. This study aimed to measure noise levels and develop a land use regression (LUR) model to determine the spatial variability of environmental noise in Shiraz, Iran. A grid-based technique was used to establish 191 noise measurement sites (summer) across the city to generate the LUR model based on two noise metrics: Lden and Lnight. Leave-one-out cross-validation (LOOCV) and 38 additional measurement sites (winter) were used for the LUR model assessment. The mean values of Lden and Lnight during summer were 68.20 (±8.05) and 58.95 (±9.55), respectively, while during winter, the corresponding values were 69.46 (±5.46) and 58.81 (±6.79). The LUR models explained 67% and 65% of the spatial variability in Lden and Lnight, respectively. LOOCV analysis demonstrated R2 values of 0.64 and 0.61. Moreover, findings indicated mean absolute error (MAE) values of 3.96 dB(A) for Lden and 4.74 dB(A) for Lnight. Validation based on an additional set of 38 measurement sites revealed R2 values of 0.62 for both Lden and Lnight, with MAE of 2.78 and 3.31, respectively. In addition, the adjusted R2 values were 0.54 and 0.53. The results indicated no significant temporal variations between summer and winter. The results revealed that road-related variables significantly influenced noise levels. Moreover, the results indicated that Lden and Lnight levels were higher than the World Health Organization recommendations for exposure to road traffic noise. The results of our study showed that the LUR modeling approach based on geographical predictors is an effective tool for assessing changes in ambient noise levels in other cities in Iran and around the globe.
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Affiliation(s)
- Ehsan Gharehchahi
- Department of Environmental Health Engineering, School of Public Health, Shiraz University of Medical Sciences, Shiraz, Iran; Department of Environmental Health Engineering, School of Public Health, Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Hassan Hashemi
- Department of Environmental Health Engineering, School of Public Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Masud Yunesian
- Department of Environmental Health Engineering, School of Public Health Institute of Public Health Research, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammadreza Samaei
- Department of Environmental Health Engineering, School of Public Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Abooalfazl Azhdarpoor
- Department of Environmental Health Engineering, School of Public Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad Oliaei
- Department of Occupational Health Engineering, School of Public Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad Hoseini
- Department of Environmental Health Engineering, School of Public Health, Shiraz University of Medical Sciences, Shiraz, Iran.
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Mann S, Singh G. Random effect generalized linear model-based predictive modelling of traffic noise. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:168. [PMID: 38236358 DOI: 10.1007/s10661-023-12285-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 12/29/2023] [Indexed: 01/19/2024]
Abstract
Noise pollution is one of the negative consequences of growth and development in cities. Traffic noise pollution due to traffic growth is the main aspect that worsens city quality of life. Therefore, research around the world is being conducted to manage and reduce traffic noise. A number of traffic noise prediction models have been proposed employing fixed effect modelling approach considering each observation as independent; however, observations may have spatial and temporal correlations and unobserved heterogeneity. Random effect models overcome these problems. This study attempts to develop a random effect generalized linear model (REGLM) along with a machine learning random forest (RF) model to validate the results, concerning the parameters related to road, traffic and environmental conditions. Models were developed based on the experimental quantities in Delhi in year 2022-2023. Both the models performed comparably well in terms of coefficient of determination. Random forest models with R2= 0.75, whereas random effect generalized linear model had an R2= 0.70. REGLM model has the ability to quantify the effects of explanatory variables over traffic noise pollution and will be more helpful in prioritizing of resources and chalking out control strategies.
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Affiliation(s)
- Suman Mann
- Civil Engineering Department, DCRUST Murthal, Haryana, India.
| | - Gyanendra Singh
- Civil Engineering Department, DCRUST Murthal, Haryana, India
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Hsu CY, Wong PY, Chern YR, Lung SCC, Wu CD. Evaluating long-term and high spatiotemporal resolution of wet-bulb globe temperature through land-use based machine learning model. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2023:10.1038/s41370-023-00630-1. [PMID: 38104232 DOI: 10.1038/s41370-023-00630-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND The increase in global temperature and urban warming has led to the exacerbation of heatwaves, which negatively affect human health and cause long-term loss of work productivity. Therefore, a global assessment in temperature variation is essential. OBJECTIVE This paper is the first of its kind to propose land-use based spatial machine learning (LBSM) models for predicting highly spatial-temporal variations of wet-bulb globe temperature (WBGT), which is a heat stress indicator used to assess thermal comfort in indoor and outdoor environments, specifically for the main island of Taiwan. METHODS To develop spatiotemporal prediction models for both the working period and noon period, we calculated the WBGT of each weather station from 2001 to 2019 using temperature, humidity, and solar radiation data. These WBGT estimations were then used as the dependent variable for developing the spatiotemporal prediction models. To enhance model performance, we used innovative approaches that combined SHapley Additive exPlanations (SHAP) values for the selection of non-linear variables, along with machine learning algorithms for model development. RESULTS When incorporating temperature along with other land-use/land cover predictor variables, the performance of LBSM models was excellent, with an R2 value of up to 0.99. The LBSM models explained 98% and 99% of the spatial-temporal variations in WBGT for the working and noon periods, respectively, within the complete models. In the temperature-excluded models, the explained variances were 94% and 96% for the working and noon periods, respectively. IMPACT WBGT is a common method used by many organizations to access the impact of heat stress on human beings. However, limited studies have mentioned the association between WBGT and health impacts due to the absence of spatiotemporal databases. This study develops a new approach using land-use-based spatial machine learning (LBSM) models to better predict the fine spatial-temporal WBGT levels, with a 50-m × 50-m grid resolution for both working time and noontime. Our proposed methodology could be used in future studies aimed at evaluating the potential long-term loss of work productivity due to the effects of global warming or urban heat island.
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Affiliation(s)
- Chin-Yu Hsu
- Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, New Taipei City, Taiwan
- Center for Environmental Sustainability and Human Health, Ming Chi University of Technology, New Taipei City, Taiwan
| | - Pei-Yi Wong
- Department of Environmental and Occupational Health, National Cheng Kung University, Tainan, Taiwan
| | - Yinq-Rong Chern
- Department of Geomatics, National Cheng Kung University, Tainan, Taiwan
| | - Shih-Chun Candice Lung
- Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan
- Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan
| | - Chih-Da Wu
- Department of Geomatics, National Cheng Kung University, Tainan, Taiwan.
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan.
- Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Tainan, Taiwan.
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Chen Y, Hansell AL, Clark SN, Cai YS. Environmental noise and health in low-middle-income-countries: A systematic review of epidemiological evidence. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 316:120605. [PMID: 36347406 DOI: 10.1016/j.envpol.2022.120605] [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: 06/30/2022] [Revised: 10/14/2022] [Accepted: 11/03/2022] [Indexed: 06/16/2023]
Abstract
Evidence of the health impacts from environmental noise has largely been drawn from studies in high-income countries, which has then been used to inform development of noise guidelines. It is unclear whether findings in high-income countries can be readily translated into policy contexts in low-middle-income-countries (LMICs). We conducted this systematic review to summarise noise epidemiological studies in LMICs. We conducted a literature search of studies in Medline and Web of Science published during 2009-2021, supplemented with specialist journal hand searches. Screening, data extraction, assessment of risk of bias as well as overall quality and strength of evidence were conducted following established guidelines (e.g. Navigation Guide). 58 studies were identified, 53% of which were from India, China and Bulgaria. Most (92%) were cross-sectional studies. 53% of studies assessed noise exposure based on fixed-site measurements using sound level meters and 17% from propagation-based noise models. Mean noise exposure among all studies ranged from 48 to 120 dB (Leq), with over half of the studies (52%) reporting the mean between 60 and 80 dB. The most studied health outcome was noise annoyance (43% of studies), followed by cardiovascular (17%) and mental health outcomes (17%). Studies generally reported a positive (i.e. adverse) relationship between noise exposure and annoyance. Some limited evidence based on only two studies showing that long-term noise exposure may be associated with higher prevalence of cardiovascular outcomes in adults. Findings on mental health outcomes were inconsistent across the studies. Overall, 4 studies (6%) had "probably low", 18 (31%) had "probably high" and 36 (62%) had "high" risk of bias. Quality of evidence was rated as 'low' for mental health outcomes and 'very low' for all other outcomes. Strength of evidence for each outcome was assessed as 'inadequate', highlighting high-quality epidemiological studies are urgently needed in LMICs to strengthen the evidence base.
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Affiliation(s)
- Yingxin Chen
- Centre for Environmental Health and Sustainability, University of Leicester, Leicester, UK; The National Institute of Health Research (NIHR) Health Protection Research Unit (HPRU) in Environmental Exposure and Health at the University of Leicester, Leicester, UK.
| | - Anna L Hansell
- Centre for Environmental Health and Sustainability, University of Leicester, Leicester, UK; The National Institute of Health Research (NIHR) Health Protection Research Unit (HPRU) in Environmental Exposure and Health at the University of Leicester, Leicester, UK
| | - Sierra N Clark
- Noise and Public Health, Radiation Chemical and Environmental Hazards, Science Group, UK Health Security Agency, UK
| | - Yutong Samuel Cai
- Centre for Environmental Health and Sustainability, University of Leicester, Leicester, UK; The National Institute of Health Research (NIHR) Health Protection Research Unit (HPRU) in Environmental Exposure and Health at the University of Leicester, Leicester, UK
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Clark SN, Alli AS, Ezzati M, Brauer M, Toledano MB, Nimo J, Moses JB, Baah S, Hughes A, Cavanaugh A, Agyei-Mensah S, Owusu G, Robinson B, Baumgartner J, Bennett JE, Arku RE. Spatial modelling and inequalities of environmental noise in Accra, Ghana. ENVIRONMENTAL RESEARCH 2022; 214:113932. [PMID: 35868576 PMCID: PMC9441709 DOI: 10.1016/j.envres.2022.113932] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 06/20/2022] [Accepted: 07/16/2022] [Indexed: 06/02/2023]
Abstract
Noise pollution is a growing environmental health concern in rapidly urbanizing sub-Saharan African (SSA) cities. However, limited city-wide data constitutes a major barrier to investigating health impacts as well as implementing environmental policy in this growing population. As such, in this first of its kind study in West Africa, we measured, modelled and predicted environmental noise across the Greater Accra Metropolitan Area (GAMA) in Ghana, and evaluated inequalities in exposures by socioeconomic factors. Specifically, we measured environmental noise at 146 locations with weekly (n = 136 locations) and yearlong monitoring (n = 10 locations). We combined these data with geospatial and meteorological predictor variables to develop high-resolution land use regression (LUR) models to predict annual average noise levels (LAeq24hr, Lden, Lday, Lnight). The final LUR models were selected with a forward stepwise procedure and performance was evaluated with cross-validation. We spatially joined model predictions with national census data to estimate population levels of, and potential socioeconomic inequalities in, noise levels at the census enumeration-area level. Variables representing road-traffic and vegetation explained the most variation in noise levels at each site. Predicted day-evening-night (Lden) noise levels were highest in the city-center (Accra Metropolis) (median: 64.0 dBA) and near major roads (median: 68.5 dBA). In the Accra Metropolis, almost the entire population lived in areas where predicted Lden and night-time noise (Lnight) surpassed World Health Organization guidelines for road-traffic noise (Lden <53; and Lnight <45). The poorest areas in Accra also had significantly higher median Lden and Lnight compared with the wealthiest ones, with a difference of ∼5 dBA. The models can support environmental epidemiological studies, burden of disease assessments, and policies and interventions that address underlying causes of noise exposure inequalities within Accra.
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Affiliation(s)
- Sierra N Clark
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Abosede S Alli
- Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, Amherst, USA
| | - Majid Ezzati
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK; Regional Institute for Population Studies, University of Ghana, Accra, Ghana; Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Michael Brauer
- School of Population and Public Health, The University of British Columbia, Vancouver, Canada
| | - Mireille B Toledano
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK; Mohn Centre for Children's Health and Wellbeing, School of Public Health, Imperial College London, London, UK
| | - James Nimo
- Department of Physics, University of Ghana, Accra, Ghana
| | | | - Solomon Baah
- Department of Physics, University of Ghana, Accra, Ghana
| | - Allison Hughes
- Department of Physics, University of Ghana, Accra, Ghana
| | | | - Samuel Agyei-Mensah
- Department of Geography and Resource Development, University of Ghana, Accra, Ghana
| | - George Owusu
- Institute of Statistical, Social & Economic Research, University of Ghana, Accra, Ghana
| | - Brian Robinson
- Department of Geography, McGill University, Montreal, Canada
| | - Jill Baumgartner
- Institute for Health and Social Policy, McGill University, Montreal, Canada; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada
| | - James E Bennett
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.
| | - Raphael E Arku
- Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, Amherst, USA.
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Kalisa E, Irankunda E, Rugengamanzi E, Amani M. Noise levels associated with urban land use types in Kigali, Rwanda. Heliyon 2022; 8:e10653. [PMID: 36164533 PMCID: PMC9508508 DOI: 10.1016/j.heliyon.2022.e10653] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 08/04/2022] [Accepted: 09/09/2022] [Indexed: 11/30/2022] Open
Abstract
Noise pollution poses a serious threat to public health and continues to grow in extent, frequency, and severity due to the rapid population growth and urbanization, and this is of particular concern in developing countries such as Rwanda. However, data on noise pollution levels, noise laws and regulations are, however, lacking in Rwanda. We assessed the effect of land-use type during a two-month period at nine sites: three commercial sites, three passenger-car parking sites, two road junction sites, and one reference site (Car-Free Zone) in Rwanda. We collected data on noise pollution during weekdays (Monday, Wednesday, and Friday) and Weekends (Saturday and Sunday) in the morning (7h00–10h00), around noon (11h00–14h00), and in the evening (15h00–18h00). The mean noise levels were higher during weekdays (60–80) A-weighted decibels (dB) (A)) than during weekends (50–70 dB (A)). We recorded the lowest noise level at Kigali car-free zone in the morning (34.4 dB (A)) and the highest noise level at Nyabugogo passenger-car parks in the evening (111.2dB (A)). Spatial variation of noise levels interpolated for Kigali City shows higher noise levels (hotspot) in the outskirts of Kigali, Remera and Kimironko. Noise levels recorded in Kigali exceeded the World Health Organization permissible daytime limits during both weekdays and weekends at all land-use types except the car-free zone site. Our results indicate that Kigali residents are exposed to high levels of noise, and urgent development of noise pollution monitoring programs and control measures in Rwanda is required.
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Affiliation(s)
- Egide Kalisa
- University of Rwanda, Center of Excellence in Biodiversity and Natural Resource Management, College of Science and Technology, Kigali, P.O. BOX 3900 Kigali, Rwanda
| | - Elisephane Irankunda
- The East African University (T.E.A.U.), School of Computer Science and Information Technology, Kenya
| | - Eulade Rugengamanzi
- Muhimbili University of Health and Applied Sciences, Dar es Salaam, Tanzania
| | - Mabano Amani
- Department de Biologia Evolutiva, Ecologia i Ciències Ambientals (BEECA), Universitat de Barcelona (UB), Av.Diagonal 643, 08028 Barcelona, Spain
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Staab J, Schady A, Weigand M, Lakes T, Taubenböck H. Predicting traffic noise using land-use regression-a scalable approach. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2022; 32:232-243. [PMID: 34215843 PMCID: PMC8920888 DOI: 10.1038/s41370-021-00355-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 06/09/2021] [Accepted: 06/10/2021] [Indexed: 05/05/2023]
Abstract
BACKGROUND In modern societies, noise is ubiquitous. It is an annoyance and can have a negative impact on human health as well as on the environment. Despite increasing evidence of its negative impacts, spatial knowledge about noise distribution remains limited. Up to now, noise mapping is frequently inhibited by the necessary resources and therefore limited to selected areas. OBJECTIVE Based on the assumption, that prevalent noise is determined by the arrangement of sources and the surrounding environment in which the sound propagates, we build a geostatistical model representing these parameters. Aiming for a large-scale noise mapping approach, we utilize publicly available data, context-aware feature engineering and a linear land-use regression (LUR) model. METHODS Compliant to the European Noise Directive 2002/49/EG, we work at a high spatial granularity of 10 × 10-m resolution. As reference, we use the day-evening-night noise level indicator Lden. Therewith, we carry out 2000 virtual field campaigns simulating different sampling schemes and introduce spatial cross-validation concepts to test the transferability to new areas. RESULTS The experimental results suggest the necessity for more than 500 samples stratified over the different noise levels to produce a representative model. Eventually, using 21 selected variables, our model was able to explain large proportions of the yearly averaged road noise (Lden) variability (R2 = 0.702) with a mean absolute error of 4.24 dB(A), 3.84 dB(A) for build-up areas, respectively. In applying this best performing model for an area-wide prediction, we spatially close the blank spots in existing noise maps with continuous noise levels for the entire range from 24 to 106 dB(A). SIGNIFICANCE This data is new, particular for small communities that have not been mapped sufficiently in Europe so far. In conjunction, our findings also supplement conventionally sampled studies using physical microphones and spatially blocked cross-validations.
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Affiliation(s)
- Jeroen Staab
- German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Weßling, Germany.
- Geography Department, Humboldt-University Berlin, Berlin, Germany.
| | - Arthur Schady
- German Aerospace Center (DLR), Institute of Atmospheric Physics (IPA), Weßling, Germany
| | - Matthias Weigand
- German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Weßling, Germany
- Department of Remote Sensing, Institute of Geography and Geology, University of Würzburg, Würzburg, Germany
| | - Tobia Lakes
- Geography Department, Humboldt-University Berlin, Berlin, Germany
- Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys), Berlin, Germany
| | - Hannes Taubenböck
- German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Weßling, Germany
- Department of Remote Sensing, Institute of Geography and Geology, University of Würzburg, Würzburg, Germany
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Xu X, Ge Y, Wang W, Lei X, Kan H, Cai J. Application of land use regression to map environmental noise in Shanghai, China. ENVIRONMENT INTERNATIONAL 2022; 161:107111. [PMID: 35121497 DOI: 10.1016/j.envint.2022.107111] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 01/09/2022] [Accepted: 01/21/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Urban environment noise has been linked with wide adverse effects on health; however, noise epidemiological researches are hindered by the lack of large-scale population-based exposure assessment. OBJECTIVE We aimed to measure noise levels over multiple seasons and to establish an LUR model to assess the spatial variability of intra-urban noise and identify its potential sources in Shanghai, China. METHODS Forty-minute (LAeq, 40 min) measurements of environmental noise were collected at 144 fixed sites, and each was visited three times (morning, afternoon, and evening) in winter, spring, and summer in 2019. Noise measurements were then integrated with land-use types, road networks, socioeconomic variables, and geographic information systems to construct LUR models. Ten-fold cross-validation was used to test the model performance. RESULTS A total of 1296 measurements and 29 predicting variables were used to estimate the spatial variation in environmental noise. The annual mean (±standard deviation) of LAeq, 40min, was 62 ± 8 dB (A). Significant variations were observed among monitoring sites but not between seasons or time of day. The LUR model explained 79% of the spatial variability of the noise, and the R2 of the ten-fold cross-validation was 0.75. The most contributory predictors of noise level were road-related variables all within the 50-m buffers, followed by urban area within a 50-m buffer, total area of buildings within a 1000-m buffer, and number of restaurant clusters within a 50-m buffer. Farmland area within a 100-m buffer was the only negative variable in the model. A 50-m resolution noise prediction map was produced and suggested high noise level in urban areas and near traffic arteries. CONCLUSION LUR can be a robust method for reflecting noise variability in megacities such as Shanghai and may provide an efficient solution for noise exposure assessment in areas where noise maps are not available.
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Affiliation(s)
- Xueyi Xu
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Yihui Ge
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Weidong Wang
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Xiaoning Lei
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China; Department of Environmental Health, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Haidong Kan
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Jing Cai
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China.
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Kim AR, Bang JH, Lee SH, Lee J. What Is the Role of Night-Time Noise Exposure in Childhood Allergic Disease? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19052748. [PMID: 35270440 PMCID: PMC8910617 DOI: 10.3390/ijerph19052748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/17/2022] [Accepted: 02/24/2022] [Indexed: 01/25/2023]
Abstract
The cause of the allergic disease is known to be multifactorial, and there is growing evidence of environmental factors triggering the disease. Indeed, it is essential to find modifiable environmental factors related to allergic disease. Noise is an environmental pollutant causing various health problems, especially when exposed during the night-time. This study assessed the impact of night-time noise exposure in allergic disease. Subjects were selected from a panel data survey containing questions on allergic disease and related factors. Incidence of allergic disease, covariates, and addresses was derived from survey questionnaires. By applying the Land Use Regression modeling method, each subject’s night-time noise estimates were elicited based on the night-time noise level collected from the noise monitoring site. Association between night-time noise difference rate and incidence of asthma were analyzed by Cox proportional hazard regression. Incidence of allergic disease increased when night-time noise difference was positive compared to the negative difference. Additionally, the incidence of allergic disease increased by per interquartile range of night-time noise difference rate. The result showed that exposure to night-time noise tends to increase the risk of allergic disease. With further studies, the result of our study may serve as supplementary data when determining noise limits.
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Affiliation(s)
- A-Ram Kim
- Department of Occupational and Environmental Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan 44033, Korea; (A.-R.K.); (S.-H.L.)
| | - Jin-Hee Bang
- Environmental Health Center, University of Ulsan College of Medicine, Ulsan 44033, Korea;
| | - Sung-Hee Lee
- Department of Occupational and Environmental Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan 44033, Korea; (A.-R.K.); (S.-H.L.)
| | - Jiho Lee
- Department of Occupational and Environmental Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan 44033, Korea; (A.-R.K.); (S.-H.L.)
- Correspondence: ; Tel.: +82-52-250-7288
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Huang YK, Mitchell UA, Conroy LM, Jones RM. Community daytime noise pollution and socioeconomic differences in Chicago, IL. PLoS One 2021; 16:e0254762. [PMID: 34347815 PMCID: PMC8336802 DOI: 10.1371/journal.pone.0254762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 07/03/2021] [Indexed: 11/18/2022] Open
Abstract
Environmental noise may affect hearing and a variety of non-auditory disease processes. There is some evidence that, like other environmental hazards, noise may be differentially distributed across communities based on socioeconomic status. We aimed to a) predict daytime noise pollution levels and b) assess disparities in daytime noise exposure in Chicago, Illinois. We measured 5-minute daytime noise levels (Leq, 5-min) at 75 randomly selected sites in Chicago in March, 2019. Geographically-based variables thought to be associated with noise were obtained, and used to fit a noise land-use regression model to estimate the daytime environmental noise level at the centroid of the census blocks. Demographic and socioeconomic data were obtained from the City of Chicago for the 77 community areas, and associations with daytime noise levels were assessed using spatial autoregressive models. Mean sampled noise level (Leq, 5-min) was 60.6 dBA. The adjusted R2 and root mean square error of the noise land use regression model and the validation model were 0.60 and 4.67 dBA and 0.51 and 5.90 dBA, respectively. Nearly 75% of city blocks and 85% of city communities have predicted daytime noise level higher than 55 dBA. Of the socioeconomic variables explored, only community per capita income was associated with mean community predicted noise levels, and was highest for communities with incomes in the 2nd quartile. Both the noise measurements and land-use regression modeling demonstrate that Chicago has levels of environmental noise likely contributing to the total burden of environmental stressors. Noise is not uniformly distributed across Chicago; it is associated with proximity to roads and public transportation, and is higher among communities with mid-to-low incomes per capita, which highlights how socially and economically disadvantaged communities may be disproportionately impacted by this environmental exposure.
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Affiliation(s)
- Yu-Kai Huang
- Division of Environmental and Occupational Health Sciences, School of Public Health, University of Illinois at Chicago, Chicago, Illinois, United States of America
| | - Uchechi A. Mitchell
- Division of Community Health Sciences, School of Public Health, University of Illinois at Chicago, Chicago, Illinois, United States of America
| | - Lorraine M. Conroy
- Division of Environmental and Occupational Health Sciences, School of Public Health, University of Illinois at Chicago, Chicago, Illinois, United States of America
| | - Rachael M. Jones
- Division of Environmental and Occupational Health Sciences, School of Public Health, University of Illinois at Chicago, Chicago, Illinois, United States of America
- Department of Family and Preventive Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, United States of America
- * E-mail:
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11
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Raess M, Brentani A, Ledebur de Antas de Campos B, Flückiger B, de Hoogh K, Fink G, Röösli M. Land use regression modelling of community noise in São Paulo, Brazil. ENVIRONMENTAL RESEARCH 2021; 199:111231. [PMID: 33971126 DOI: 10.1016/j.envres.2021.111231] [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: 01/08/2021] [Revised: 04/20/2021] [Accepted: 04/22/2021] [Indexed: 06/12/2023]
Abstract
Noise pollution has negative health consequences, which becomes increasingly relevant with rapid urbanization. In low- and middle-income countries research on health effects of noise is hampered by scarce exposure data and noise maps. In this study, we developed land use regression (LUR) models to assess spatial variability of community noise in the Western Region of São Paulo, Brazil.We measured outdoor noise levels continuously at 42 homes once or twice for one week in the summer and the winter season. These measurements were integrated with various geographic information system variables to develop LUR models for predicting average A-weighted (dB(A)) day-evening-night equivalent sound levels (Lden) and night sound levels (Lnight). A supervised mixed linear regression analysis was conducted to test potential noise predictors for various buffer sizes and distances between home and noise source. Noise exposure levels in the study area were high with a site average Lden of 69.3 dB(A) ranging from 60.3 to 82.3 dB(A), and a site average Lnight of 59.9 dB(A) ranging from 50.7 to 76.6 dB(A). LUR models had a good fit with a R2 of 0.56 for Lden and 0.63 for Lnight in a leave-one-site-out cross validation. Main predictors of noise were the inverse distance to medium roads, count of educational facilities within a 400 m buffer, mean Normalized Difference Vegetation Index (NDVI) within a 100 m buffer, residential areas within a 50 m (Lden) or 25 m (Lnight) buffer and slum areas within a 400 m buffer. Our study suggests that LUR modelling with geographic predictor data is a promising and efficient approach for noise exposure assessment in low- and middle-income countries, where noise maps are not available.
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Affiliation(s)
- Michelle Raess
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Alexandra Brentani
- Department of Pediatrics at the Medical School of São Paulo University, São Paulo, Brazil
| | - Bartolomeu Ledebur de Antas de Campos
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Benjamin Flückiger
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Kees de Hoogh
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Günther Fink
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Martin Röösli
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland.
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Space-time characterization of community noise and sound sources in Accra, Ghana. Sci Rep 2021; 11:11113. [PMID: 34045545 PMCID: PMC8160008 DOI: 10.1038/s41598-021-90454-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 05/04/2021] [Indexed: 11/30/2022] Open
Abstract
Urban noise pollution is an emerging public health concern in growing cities in sub-Saharan Africa (SSA), but the sound environment in SSA cities is understudied. We leveraged a large-scale measurement campaign to characterize the spatial and temporal patterns of measured sound levels and sound sources in Accra, Ghana. We measured sound levels and recorded audio clips at 146 representative locations, involving 7-days (136 locations) and 1-year measurements between 2019 and 2020. We calculated metrics of noise levels and intermittency and analyzed audio recordings using a pre-trained neural network to identify sources. Commercial, business, and industrial areas and areas near major roads had the highest median daily sound levels (LAeq24hr: 69 dBA and 72 dBA) and the lowest percentage of intermittent sound; the vice-versa was found for peri urban areas. Road-transport sounds dominated the overall sound environment but mixtures of other sound sources, including animals, human speech, and outdoor music, dominated in various locations and at different times. Environmental noise levels in Accra exceeded both international and national health-based guidelines. Detailed information on the acoustical environmental quality (including sound levels and types) in Accra may guide environmental policy formulation and evaluation to improve the health of urban residents.
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Statistical Road-Traffic Noise Mapping Based on Elementary Urban Forms in Two Cities of South Korea. SUSTAINABILITY 2021. [DOI: 10.3390/su13042365] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Statistical models that can generate a road-traffic noise map for a city or area where only elementary urban design factors are determined, and where no concrete urban morphology, including buildings and roads, is given, can provide basic but essential information for developing a quiet and sustainable city. Long-term cost-effective measures for a quiet urban area can be considered at early city planning stages by using the statistical road-traffic noise map. An artificial neural network (ANN) and an ordinary least squares (OLS) model were developed by utilizing data on urban form indicators, based on a 3D urban model and road-traffic noise levels from a normal noise map of city A (Gwangju). The developed ANN and OLS models were applied to city B (Cheongju), and the resultant statistical noise map of city B was compared to an existing normal road-traffic noise map of city B. The urban form indicators that showed multi-collinearity were excluded by the OLS model, and among the remaining urban forms, road-related urban form indicators such as traffic volume and road area density were found to be important variables to predict the road-traffic noise level and to design a quiet city. Comparisons of the statistical ANN and OLS noise maps with the normal noise map showed that the OLS model tends to under-estimate road-traffic noise levels, and the ANN model tends to over-estimate them.
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Yankoty LI, Gamache P, Plante C, Goudreau S, Blais C, Perron S, Fournier M, Ragettli MS, Fallah-Shorshani M, Hatzopoulou M, Liu Y, Smargiassi A. Manuscript title: Long─term residential exposure to environmental/transportation noise and the incidence of myocardial infarction. Int J Hyg Environ Health 2020; 232:113666. [PMID: 33296779 DOI: 10.1016/j.ijheh.2020.113666] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 11/17/2020] [Accepted: 11/18/2020] [Indexed: 10/22/2022]
Abstract
BACKGROUND Cardiovascular effects of environmental noise are a growing concern. However, the evidence remains largely limited to the association between road traffic noise and hypertension and coronary heart diseases. OBJECTIVES To investigate the association between long-term residential exposure to environmental/transportation noise and the incidence of myocardial infarction (MI) in the adult population living in Montreal. METHODS An open cohort of adults aged 45 years old and over, living on the island of Montreal and free of MI before entering the cohort was created for the years 2000-2014 with the Quebec Integrated Chronic Disease Surveillance System; a systematic surveillance system from the Canadian province of Quebec starting in 1996. Residential noise exposure was calculated in three ways: 1) total ambient noise levels estimated by Land use regression (LUR) models; 2) road traffic noise estimated by a noise propagation model CadnaA and 3) distances to transportation sources (roads, airport, railways). Incident MI was based on diagnostic codes in hospital admission records. Cox models with time-varying exposures (age as the time axis) were used to estimate the associations with various adjustments (material deprivation indicator, calendar year, nitrogen dioxide, stratification for sex). Indirect adjustment based on ancillary data for smoking was performed. RESULTS 1,065,414 individuals were followed (total of 9,000,443 person-years) and 40,718 (3.8%) developed MI. We found positive associations between total environmental noise, estimated by LUR models and the incidence of MI. Total noise LUR levels ranged from ~44 to ~79 dBA and varied slightly with the metric used. The adjusted hazard ratios (HRs) (also adjusted for smoking) were 1.12 (95% Confidence Intervals [CI]: 1.08-1.15), 1.11 (95%CI: 1.07-1.14) and 1.10 (95%CI: 1.06-1.14) per 10 dBA noise levels increase respectively in Level Accoustic equivalent 24 h (LAeq24 h), Level day-evening-night (Lden) and night level (Lnight). We found a borderline negative association between road noise levels estimated with CadnaA and MI (HR: 0.99 per 10 dBA; 95%CI: 0.98-1.00). Distances to major roads and highways were not associated with MI while the proximity to railways was positively associated with MI (HR for ≤100 vs > 1000 m: 1.07; 95%CI: 1.01-1.14). A negative association was found with the proximity to the airport noise exposure forecast (NEF25); HR (<1 vs >1000 m) = 0.88 (95%CI: 0.81-0.96). CONCLUSIONS These associations suggest that exposure to total environmental noise at current urban levels may be related to the incidence of MI. Additional studies with more accurate road noise estimates are needed to explain the counterintuitive associations with road noise and specific transportation sources.
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Affiliation(s)
- Larisa I Yankoty
- School of Public Health, Centre of Public Health Research, University of Montreal and CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada
| | | | - Céline Plante
- Montreal Regional Department of Public Health, Canada
| | | | - Claudia Blais
- Quebec National Institute of Public Health National, Canada; Faculty of Pharmacy, Laval University, Canada
| | - Stéphane Perron
- School of Public Health, Centre of Public Health Research, University of Montreal and CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada; Quebec National Institute of Public Health National, Canada
| | | | - Martina S Ragettli
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | | | | | - Ying Liu
- School of Public Health, Centre of Public Health Research, University of Montreal and CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada
| | - Audrey Smargiassi
- School of Public Health, Centre of Public Health Research, University of Montreal and CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montreal, Canada; Quebec National Institute of Public Health National, Canada.
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15
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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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Yin X, Fallah-Shorshani M, McConnell R, Fruin S, Franklin M. Predicting Fine Spatial Scale Traffic Noise Using Mobile Measurements and Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:12860-12869. [PMID: 32930589 DOI: 10.1021/acs.est.0c01987] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Environmental noise has been associated with a variety of health endpoints including cardiovascular disease, sleep disturbance, depression, and psychosocial stress. Most population noise exposure comes from vehicular traffic, which produces fine-scale spatial variability that is difficult to characterize using traditional fixed-site measurement techniques. To address this challenge, we collected A-weighted, equivalent noise (LAeq in decibels, dB) data on hour-long foot journeys around 16 locations throughout Long Beach, California and trained four machine learning models, linear regression, random forest, extreme gradient boosting, and a neural network, to predict noise with 20 m resolution. Input variables to the models included traffic metrics, road network features, meteorological conditions, and land use type. Among all machine learning models, extreme gradient boosting had the best results in validation tests (leave-one-route-out R2 = 0.71, root mean square error (RMSE) of 4.54 dB; 5-fold R2 = 0.96, RMSE of 1.8 dB). Local traffic volume was the most important predictor of noise; road features, land use, and meteorology including humidity, temperature, and wind speed also contributed. We show that a novel, on-foot mobile noise measurement method coupled with machine learning approaches enables highly accurate prediction of small-scale spatial patterns in traffic-related noise over a mixed-use urban area.
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Affiliation(s)
- Xiaozhe Yin
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90032, United States
| | - Masoud Fallah-Shorshani
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90032, United States
| | - Rob McConnell
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90032, United States
| | - Scott Fruin
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90032, United States
| | - Meredith Franklin
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90032, United States
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17
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Clark SN, Alli AS, Brauer M, Ezzati M, Baumgartner J, Toledano MB, Hughes AF, Nimo J, Bedford Moses J, Terkpertey S, Vallarino J, Agyei-Mensah S, Agyemang E, Nathvani R, Muller E, Bennett J, Wang J, Beddows A, Kelly F, Barratt B, Beevers S, Arku RE. High-resolution spatiotemporal measurement of air and environmental noise pollution in Sub-Saharan African cities: Pathways to Equitable Health Cities Study protocol for Accra, Ghana. BMJ Open 2020; 10:e035798. [PMID: 32819940 PMCID: PMC7440835 DOI: 10.1136/bmjopen-2019-035798] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
INTRODUCTION Air and noise pollution are emerging environmental health hazards in African cities, with potentially complex spatial and temporal patterns. Limited local data are a barrier to the formulation and evaluation of policies to reduce air and noise pollution. METHODS AND ANALYSIS We designed a year-long measurement campaign to characterise air and noise pollution and their sources at high-resolution within the Greater Accra Metropolitan Area (GAMA), Ghana. Our design uses a combination of fixed (year-long, n=10) and rotating (week-long, n =~130) sites, selected to represent a range of land uses and source influences (eg, background, road traffic, commercial, industrial and residential areas, and various neighbourhood socioeconomic classes). We will collect data on fine particulate matter (PM2.5), nitrogen oxides (NOx), weather variables, sound (noise level and audio) along with street-level time-lapse images. We deploy low-cost, low-power, lightweight monitoring devices that are robust, socially unobtrusive, and able to function in Sub-Saharan African (SSA) climate. We will use state-of-the-art methods, including spatial statistics, deep/machine learning, and processed-based emissions modelling, to capture highly resolved temporal and spatial variations in pollution levels across the GAMA and to identify their potential sources. This protocol can serve as a prototype for other SSA cities. ETHICS AND DISSEMINATION This environmental study was deemed exempt from full ethics review at Imperial College London and the University of Massachusetts Amherst; it was approved by the University of Ghana Ethics Committee (ECH 149/18-19). This protocol is designed to be implementable in SSA cities to map environmental pollution to inform urban planning decisions to reduce health harming exposures to air and noise pollution. It will be disseminated through local stakeholder engagement (public and private sectors), peer-reviewed publications, contribution to policy documents, media, and conference presentations.
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Affiliation(s)
- Sierra N Clark
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC Center for Environment and Health, Imperial College London, London, UK
| | - Abosede S Alli
- Department of Environmental Health Sciences, University of Massachusetts Amherst, Amherst, Massachusetts, USA
| | - Michael Brauer
- School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Majid Ezzati
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC Center for Environment and Health, Imperial College London, London, UK
- Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
- Regional Institute for Population Studies, University of Ghana, Legon, Accra, Ghana
| | - Jill Baumgartner
- Institute for Health and Social Policy, McGill University, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Mireille B Toledano
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC Center for Environment and Health, Imperial College London, London, UK
| | | | - James Nimo
- Department of Physics, University of Ghana, Legon, Accra, Ghana
| | | | | | - Jose Vallarino
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Samuel Agyei-Mensah
- Department of Geography and Resource Development, University of Ghana, Legon, Accra, Ghana
| | - Ernest Agyemang
- Department of Geography and Resource Development, University of Ghana, Legon, Accra, Ghana
| | - Ricky Nathvani
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC Center for Environment and Health, Imperial College London, London, UK
| | - Emily Muller
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC Center for Environment and Health, Imperial College London, London, UK
| | - James Bennett
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC Center for Environment and Health, Imperial College London, London, UK
| | - Jiayuan Wang
- Department of Environmental Health Sciences, University of Massachusetts Amherst, Amherst, Massachusetts, USA
| | - Andrew Beddows
- MRC Center for Environment and Health, Imperial College London, London, UK
| | - Frank Kelly
- MRC Center for Environment and Health, Imperial College London, London, UK
- NIHR HPRU in Environmental Exposures and Health, Imperial College London, London, UK
| | - Benjamin Barratt
- MRC Center for Environment and Health, Imperial College London, London, UK
- NIHR HPRU in Environmental Exposures and Health, Imperial College London, London, UK
| | - Sean Beevers
- MRC Center for Environment and Health, Imperial College London, London, UK
| | - Raphael E Arku
- Department of Environmental Health Sciences, University of Massachusetts Amherst, Amherst, Massachusetts, USA
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Taubenböck H, Schmich P, Erbertseder T, Müller I, Tenikl J, Weigand M, Staab J, Wurm M. [Satellite data for recording health-relevant environmental conditions: examples and interdisciplinary potential]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2020; 63:936-944. [PMID: 32617643 DOI: 10.1007/s00103-020-03177-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Environmental conditions influence human health and interact with other factors such as DNA, lifestyle, or the social environment. Earth observations from space provide data on the most diverse manifestations of these environmental conditions and make it possible to quantify them spatially. Using two examples - the availability of open and recreational space and the spatial distribution of air pollution - this article presents the potential of Earth observations for health studies. In addition, possible applications for health-related issues are discussed. To this end, we try to outline key points for an interdisciplinary approach that meets the conceptual, data technology, and ethical challenges.
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Affiliation(s)
- Hannes Taubenböck
- Earth Observation Center (EOC) Weßling, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, Münchener Str. 20, 82234, Weßling, Deutschland.
- Institut für Geographie und Geologie, Julius-Maximilians-Universität Würzburg, Würzburg, Deutschland.
| | | | - Thilo Erbertseder
- Earth Observation Center (EOC) Weßling, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, Münchener Str. 20, 82234, Weßling, Deutschland
| | - Inken Müller
- Earth Observation Center (EOC) Weßling, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, Münchener Str. 20, 82234, Weßling, Deutschland
| | - Julia Tenikl
- Earth Observation Center (EOC) Weßling, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, Münchener Str. 20, 82234, Weßling, Deutschland
| | - Matthias Weigand
- Earth Observation Center (EOC) Weßling, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, Münchener Str. 20, 82234, Weßling, Deutschland
| | - Jeroen Staab
- Earth Observation Center (EOC) Weßling, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, Münchener Str. 20, 82234, Weßling, Deutschland
| | - Michael Wurm
- Earth Observation Center (EOC) Weßling, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, Münchener Str. 20, 82234, Weßling, Deutschland
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Dzhambov AM, Browning MHEM, Markevych I, Hartig T, Lercher P. Analytical approaches to testing pathways linking greenspace to health: A scoping review of the empirical literature. ENVIRONMENTAL RESEARCH 2020; 186:109613. [PMID: 32668553 DOI: 10.1016/j.envres.2020.109613] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 04/26/2020] [Accepted: 04/26/2020] [Indexed: 05/04/2023]
Abstract
BACKGROUND Inadequate translation from theoretical to statistical models of the greenspace - health relationship may lead to incorrect conclusions about the importance of some pathways, which in turn may reduce the effectiveness of public health interventions involving urban greening. In this scoping review we aimed to: (1) summarize the general characteristics of approaches to intervening variable inference (mediation analysis) employed in epidemiological research in the field; (2) identify potential threats to the validity of findings; and (3) propose recommendations for planning, conducting, and reporting mediation analyses. METHODS We conducted a scoping review, searching PubMed, Scopus, and Web of Science for peer-reviewed epidemiological studies published by December 31, 2019. The list of potential studies was continuously updated through other sources until March 2020. Narrative presentation of the results was coupled with descriptive summary of study characteristics. RESULTS We found 106 studies, most of which were cross-sectional in design. Most studies only had a spatial measure of greenspace. Mental health/well-being was the most commonly studied outcome, and physical activity and air pollution were the most commonly tested intervening variables. Most studies only conducted single mediation analysis, even when multiple potentially intertwined mediators were measured. The analytical approaches used were causal steps, difference-of-coefficients, product-of-coefficients, counterfactual framework, and structural equation modelling (SEM). Bootstrapping was the most commonly used method to construct the 95% CI of the indirect effect. The product-of-coefficients method and SEM as used to investigate serial mediation components were more likely to yield findings of indirect effect. In some cases, the causal steps approach thwarted tests of indirect effect, even though both links in an indirect effect were supported. In most studies, sensitivity analyses and proper methodological discussion of the modelling approach were missing. CONCLUSIONS We found a persistent pattern of suboptimal conduct and reporting of mediation analysis in epidemiological studies investigating pathways linking greenspace to health; however, recent years have seen improvements in these respects. Better planning, conduct, and reporting of mediation analyses are warranted.
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Affiliation(s)
- Angel M Dzhambov
- Department of Hygiene and Ecomedicine, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria.
| | - Matthew H E M Browning
- Department of Park, Recreation, and Tourism Management, Clemson University, Clemson, USA
| | - Iana Markevych
- Institute of Psychology, Jagiellonian University, Krakow, Poland
| | - Terry Hartig
- Institute for Housing and Urban Research, Uppsala University, Uppsala, Sweden
| | - Peter Lercher
- Institute for Highway Engineering and Transport Planning, Graz University of Technology, Graz, Austria
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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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21
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Dzhambov AM, Tilov B, Makakova-Tilova D, Dimitrova DD. Pathways and contingencies linking road traffic noise to annoyance, noise sensitivity, and mental Ill-Health. Noise Health 2019; 21:248-257. [PMID: 32978362 PMCID: PMC7986452 DOI: 10.4103/nah.nah_15_20] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 07/07/2020] [Indexed: 11/12/2022] Open
Abstract
CONTEXT Traffic noise may contribute to depression and anxiety through higher noise annoyance (NA). However, little is known about noise sensitivity (NS) and mental health status as contextual factors. OBJECTIVE We tested three hypotheses: (1) Traffic noise is associated with mental ill-health through higher NA; (2) Mental ill-health and NS moderate the association between traffic noise and NA; and (3) NS moderates the indirect effect of traffic noise on mental ill-health. SUBJECTS AND METHODS We used a convenience sample of 437 undergraduate students from the Medical University in Plovdiv, Bulgaria (mean age 21 years; 35% male). Residential road traffic noise (LAeq; day equivalent noise level) was calculated using a land use regression model. Depression and anxiety symptoms were measured with the Patient Health Questionnaire 9-item (PHQ-9) and the Generalized Anxiety Disorder 7-item (GAD-7) scale, respectively. NA was measured using a 5-point verbal scale. The Noise Sensitivity Scale Short Form (NSS-SF) was used to measure NS. To investigate how these variables intertwine, we conducted mediation, moderation and moderated mediation analyses. RESULTS LAeq was indirectly associated with higher PHQ-9/GAD-7 scores through higher NA, but only in the low NS group. The relationship between LAeq and NA was stronger in students reporting depression/anxiety. While high NS was associated with high NA even at low noise levels, LAeq contributed to NA only in students low on NS. CONCLUSIONS We found complex conditional relationships between traffic noise, annoyance and mental ill-health. Understanding respective vulnerability profiles within the community could aid noise policy and increase efficacy of interventions.
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Affiliation(s)
- Angel M. Dzhambov
- Department of Hygiene and Ecomedicine, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Boris Tilov
- Medical College, Medical University of Plovdiv, Plovdiv, Bulgaria
- Department of Management, Faculty of Economics and Management, University of Agribusiness and Rural Development, Plovdiv, Bulgaria
| | - Desislava Makakova-Tilova
- Department of Operative Dentistry and Endodontics, Faculty of Dental Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Donka D. Dimitrova
- Department of Health Management and Healthcare Economics, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
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Chang TY, Liang CH, Wu CF, Chang LT. Application of land-use regression models to estimate sound pressure levels and frequency components of road traffic noise in Taichung, Taiwan. ENVIRONMENT INTERNATIONAL 2019; 131:104959. [PMID: 31284109 DOI: 10.1016/j.envint.2019.104959] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 06/06/2019] [Accepted: 06/21/2019] [Indexed: 05/22/2023]
Abstract
Few studies have applied land-use regression to predict road traffic noise exposure, and there are few predictive models for different frequencies. This study aimed to measure 24-h average road traffic noise levels and to analyze the frequency components over one year to establish land-use regression models of noise exposure. Fifty monitoring stations were set up to conduct 3 measurements for A-weighted equivalent sound pressure levels over 24 h (Leq,24h) and night equivalent sound pressure levels (Lnight), as well as octave-band analyses, during the 2013-2014 period. Noise measurements were integrated with land-use types, road and traffic information, meteorological data and geographic information systems to construct land-use regression models. Leave-one-out cross-validation was performed to test the validity of the predictive models. The annual means of Leq,24h and Lnight were 66.4 ± 4.7 A-weighed decibels (dBA) and 62.1 ± 6.0 dBA, respectively. Octave-band frequency analyses revealed that the highest means over 24 h and at night were 61.4 ± 5.3 decibels (dB) and 56.7 ± 6.6 dB (both at 1000 Hz), respectively. The model-explained variance (R2) of the full-frequency noise was 0.83 for Leq,24h and 0.79 for Lnight. The R2 values for octave-band-frequency noise ranged from 0.67 to 0.88 for Leq,24h and 0.65 to 0.85 for Lnight, with the highest R2 at 250 Hz for Leq,24h and at 125 Hz for Lnight. The differences between the model R2 and the leave-one-out cross-validation R2 ranged from 5% to 15% for both Leq,24h and Lnight at all frequencies. In the validation, the root mean squared error was 2.09 dBA and 2.80 dBA for the full-frequency Leq,24 and Lnight, respectively, and ranged from 1.89 to 2.62 dB and from 2.51 to 3.28 dB for the octave-band-frequency Leq,24h and Lnight, respectively. This study observed that the annual means of the measured Leq,24h and Lnight in Taichung were both above 60 dBA and had the highest level at 1000 Hz. The developed land-use regression models of Leq,24 and Lnight both had good predictive capacity for the full frequency spectrum and within octave bands and can therefore be applied for epidemiological studies.
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Affiliation(s)
- Ta-Yuan Chang
- Department of Occupational Safety and Health, College of Public Health, China Medical University, Taichung, Taiwan.
| | - Chih-Hsiang Liang
- Department of Occupational Safety and Health, College of Public Health, China Medical University, Taichung, Taiwan
| | - Chang-Fu Wu
- Institute of Occupational Medicine and Industrial Hygiene, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Li-Te Chang
- Department of Environmental Engineering and Science, Feng Chia University, Taichung, Taiwan
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Noise Disturbances and Calls for Police Service in València (Spain): A Logistic Model with Spatial and Temporal Effects. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16162815. [PMID: 31394778 PMCID: PMC6720910 DOI: 10.3390/ijerph16162815] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 07/30/2019] [Accepted: 08/04/2019] [Indexed: 11/17/2022]
Abstract
The purpose of this paper is to explore the presence of spatial and temporal effects on the calls for noise disturbance service reported to the Local Police of València (Spain) in the time period from 2014 to 2015, and investigate how some socio-demographic and environmental variables affect the noise phenomenon. The analysis is performed at the level of València’s boroughs. It has been carried out using a logistic model after dichotomization of the noise incidence variable. The spatial effects consider first- and second-order neighbors. The temporal effects are included in the model by means of one- and two-week temporal lags. Our model confirms the presence of strong spatio-temporal effects. We also find significant associations between noise incidence and specific age groups, socio-economic status, land uses, and recreational activities, among other variables. The results suggest that there is a problem of "social" noise in València that is not exclusively a consequence of coexistence between local residents. External factors such as the increasing number of people on the streets during weekend nights or during summer months severely increase the chances of expecting a noise incident.
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Ahmed AA, Pradhan B. Vehicular traffic noise prediction and propagation modelling using neural networks and geospatial information system. ENVIRONMENTAL MONITORING AND ASSESSMENT 2019; 191:190. [PMID: 30809746 DOI: 10.1007/s10661-019-7333-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Accepted: 02/18/2019] [Indexed: 06/09/2023]
Abstract
This study proposes a neural network (NN) model to predict and simulate the propagation of vehicular traffic noise in a dense residential area at the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia. The proposed model comprises of two main simulation steps: that is, the prediction of vehicular traffic noise using NN and the simulation of the propagation of traffic noise emission using a mathematical model. First, the NN model was developed with the following selected noise predictors: the number of motorbikes, the sum of vehicles, car ratio, heavy vehicle ratio (e.g. truck, lorry and bus), highway density and a light detection and ranging (LiDAR)-derived digital surface model (DSM). Subsequently, NN and its hyperparameters were optimised by a systematic optimisation procedure based on a grid search approach. The noise propagation model was then developed in a geographic information system (GIS) using five variables, namely road geometry, barriers, distance, interaction of air particles and weather parameters. The noise measurement was conducted continuously at 15-min intervals and the data were analysed by taking the minimum, maximum and average values recorded during the day. The measurement was performed four times a day (i.e. morning, afternoon, evening, and midnight) over two days of the week (i.e. Sunday and Monday). An optimal radial basis function NN was used with 17 hidden layers. The learning rate and momentum values were 0.05 and 0.9, respectively. Finally, the accuracy of the proposed method achieved 78.4% with less than 4.02 dB (A) error in noise prediction. Overall, the proposed models were found to be promising tools for traffic noise assessment in dense urban areas.
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Affiliation(s)
- Ahmed Abdulkareem Ahmed
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, 2007, Australia
| | - Biswajeet Pradhan
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, 2007, Australia.
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25
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Vienneau D, Héritier H, Foraster M, Eze IC, Schaffner E, Thiesse L, Rudzik F, Habermacher M, Köpfli M, Pieren R, Brink M, Cajochen C, Wunderli JM, Probst-Hensch N, Röösli M. Façades, floors and maps - Influence of exposure measurement error on the association between transportation noise and myocardial infarction. ENVIRONMENT INTERNATIONAL 2019; 123:399-406. [PMID: 30622064 DOI: 10.1016/j.envint.2018.12.015] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2018] [Revised: 12/07/2018] [Accepted: 12/08/2018] [Indexed: 05/20/2023]
Abstract
BACKGROUND Epidemiological research on transportation noise uses different exposure assessment strategies based on façade point estimates or regulatory noise maps. The degree of exposure measurement error and subsequent potentially biased risk estimates related to exposure definition is unclear. We aimed to evaluate associations between transportation noise exposure and myocardial infarction (MI) mortality considering: assumptions about residential floor, façade point selection (loudest, quietest, nearest), façade point vs. noise map estimates, and influence of averaging exposure at coarser spatial scales (e.g. in ecological health studies). METHODS Lden from the façade points were assigned to >4 million eligible adults in the Swiss National Cohort for the best match residential floor (reference), middle floor, and first floor. For selected floors, the loudest and quietest exposed façades per dwelling, plus the nearest façade point to the residential geocode, were extracted. Exposure was also assigned from 10 × 10 m noise maps, using "buffers" from 50 to 500 m derived from the maps, and by aggregating the maps to larger areas. Associations between road traffic and railway noise and MI mortality were evaluated by multi-pollutant Cox regression models, adjusted for aircraft noise, NO2 and socio-demographic confounders, following individuals from 2000 to 2008. Bias was calculated to express differences compared to the reference. RESULTS Hazard ratios (HRs) for the best match residential floor were 1.05 (1.02-1.07) and 1.03 (1.01-1.05) per IQR (11.3 and 15.0 dB) for road traffic and railway noise, respectively. In most situations, comparing the alternative exposure definitions to this reference resulted in attenuated HRs. For example, assuming everyone resided on the middle or everyone on first floor introduced little bias (%Bias in excess risk: -1.9 to 4.4 road traffic and -4.4 to 10.7 railway noise). Using the noise grids generated a bias of approximately -26% for both sources. Averaging the maps at a coarser spatial scale led to bias from -19.4 to -105.1% for road traffic and 17.6 to -34.3% for railway noise and inflated the confidence intervals such that some HRs were no longer statistically significant. CONCLUSION Changes in spatial scale introduced more bias than changes in residential floor. Use of noise maps to represent residential exposure may underestimate noise-induced health effects, in particular for small-scale heterogeneously distributed road traffic noise in urban settings.
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Affiliation(s)
- Danielle Vienneau
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland.
| | - Harris Héritier
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Maria Foraster
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland; ISGlobal, Barcelona, Spain
| | - Ikenna C Eze
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Emmanuel Schaffner
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Laurie Thiesse
- Centre for Chronobiology, Psychiatric Hospital of the University of Basel, Basel, Switzerland; Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, Switzerland
| | - Franziska Rudzik
- Centre for Chronobiology, Psychiatric Hospital of the University of Basel, Basel, Switzerland; Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, Switzerland
| | | | | | - Reto Pieren
- Empa, Laboratory for Acoustics/Noise control, Swiss Federal Laboratories for Materials Science and Technology, Dubendorf, Switzerland
| | - Mark Brink
- Federal Office for the Environment, Bern, Switzerland
| | - Christian Cajochen
- Centre for Chronobiology, Psychiatric Hospital of the University of Basel, Basel, Switzerland; Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, Switzerland
| | - Jean Marc Wunderli
- Empa, Laboratory for Acoustics/Noise control, Swiss Federal Laboratories for Materials Science and Technology, Dubendorf, Switzerland
| | - Nicole Probst-Hensch
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Martin Röösli
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
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26
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Remote Sensing in Environmental Justice Research—A Review. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2019. [DOI: 10.3390/ijgi8010020] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Human health is known to be affected by the physical environment. Various environmental influences have been identified to benefit or challenge people’s physical condition. Their heterogeneous distribution in space results in unequal burdens depending on the place of living. In addition, since societal groups tend to also show patterns of segregation, this leads to unequal exposures depending on social status. In this context, environmental justice research examines how certain social groups are more affected by such exposures. Yet, analyses of this per se spatial phenomenon are oftentimes criticized for using “essentially aspatial” data or methods which neglect local spatial patterns by aggregating environmental conditions over large areas. Recent technological and methodological developments in satellite remote sensing have proven to provide highly detailed information on environmental conditions. This narrative review therefore discusses known influences of the urban environment on human health and presents spatial data and applications for analyzing these influences. Furthermore, it is discussed how geographic data are used in general and in the interdisciplinary research field of environmental justice in particular. These considerations include the modifiable areal unit problem and ecological fallacy. In this review we argue that modern earth observation data can represent an important data source for research on environmental justice and health. Especially due to their high level of spatial detail and the provided large-area coverage, they allow for spatially continuous description of environmental characteristics. As a future perspective, ongoing earth observation missions, as well as processing architectures, ensure data availability and applicability of ’big earth data’ for future environmental justice analyses.
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Influence of Contextual Factors on Soundscape in Urban Open Spaces. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8122524] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The acoustic environment in urban open spaces has played a key role for users. This study analyzed the different effects of contextual factors, including shop openness, season, and commercial function, on the soundscape in two typical commercial pedestrian streets. The following observations were based on a series of measurements, including crowd measurements, acoustic environment measurements, and a questionnaire survey. First, the number of talkers in Central Avenue was greater than the number of talkers in Kuan Alley in cases with the same crowd density, while there was no significant difference in the sound pressure level. When the crowd density increased, acoustic comfort trended downward in Kuan Alley, while the value of acoustic comfort in Central Avenue took a parabolic shape. Second, there was no significant difference between the number of talkers in summer and the number of talkers in winter; however, when crowd density increased by 0.1 persons/m2, the level of sound pressure increased by 1.3 dBA in winter and 2.2 dBA in summer. Acoustic comfort took a parabolic shape that first increased and then decreased in both winter and summer. Regarding commercial function, as the crowd density increased, the number of talkers and the level of sound pressure both increased, while acoustic comfort decreased in three zones with different commercial functions. In addition, a cross-tab analysis was used to discuss the relationship between the number of talkers and the level of sound pressure, and it was found to be positive.
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Harouvi O, Ben-Elia E, Factor R, de Hoogh K, Kloog I. Noise estimation model development using high-resolution transportation and land use regression. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2018; 28:559-567. [PMID: 29789670 DOI: 10.1038/s41370-018-0035-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2017] [Revised: 01/09/2018] [Accepted: 02/26/2018] [Indexed: 05/22/2023]
Abstract
Noise pollution is a common phenomenon of the 21st century. Noise prediction models tend to estimate noise levels mainly from road traffic sources (such as cars, public transportation etc.). This paper describes the adoption of land use regression (LUR) modeling methodology to assess noise pollution in two periods of the day (rush hour and off-peak), in two major cities in Israel (Tel Aviv and Beer Sheva). For both rush hour and off-peak times, 20 min short term measurements were used to develop a LUR noise estimation model. We used GIS-based predictors alongside commonly used traffic predictors. The findings show good fits for our model, with rush hour "out of sample" ten folds cross-validated R² of 0.79 (Tel Aviv) and 0.52 (Beer Sheva). The Tel Aviv model performance was also tested with independent monitoring data in an adjacent city (Bat Yam), presenting a good performance as well (R² of 0.93). The findings demonstrate the viability of using a LUR approach for applying high-resolution spatial data to estimate and map noise pollution for environmental noise assessment.
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Affiliation(s)
- Omer Harouvi
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Eran Ben-Elia
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Roni Factor
- The Institute of Criminology, Faculty of Law, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Kees de Hoogh
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Itai Kloog
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel.
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29
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Oiamo TH, Davies H, Rainham D, Rinner C, Drew K, Sabaliauskas K, Macfarlane R. A combined emission and receptor-based approach to modelling environmental noise in urban environments. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2018; 242:1387-1394. [PMID: 30138831 DOI: 10.1016/j.envpol.2018.08.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2018] [Revised: 08/01/2018] [Accepted: 08/05/2018] [Indexed: 05/23/2023]
Abstract
The state of practice for noise assessment utilizes established standards for emission and propagation modelling of linear and point sources. Recently, land use regression (LUR) modelling has emerged as an alternative method due to relatively low data and computing resource demands. However, a limitation of LUR modelling is that is does not account for noise attenuation and reflections by features of the built environment. This study demonstrates and validates a method that combines the two modelling frameworks to exploit their respective strengths: Emission and propagation based prediction of traffic noise, the predominant source of noise at the level of streetscapes, and a LUR-based correction for noise sources that vary on spatial scales beyond the streetscape. Multi-criteria analysis, location-allocation modelling and stakeholder consultation identified 220 monitoring sites with optimal coverage for a 1-week sampling period. A subset of sites was used to validate a road traffic noise emission and propagation model and to specify a LUR model that predicted the contribution of other sources. The equivalent 24-h sound pressure level (LAeq) for all sites was 62.9 dBA (SD 6.4). This varied by time of day, weekday, types of roads and land uses. The traffic noise emission model demonstrated a high level of covariance with observed noise levels, with R2 values of 0.58, 0.60 and 0.59 for daytime, nighttime and 24-h periods, respectively. Combined with LUR models to correct for other noise sources, the hybrid models R2 values were 0.64, 0.71 and 0.67 for the respective time periods. The study showed that road traffic noise emissions account for most of the variability of total environmental noise in Toronto. The combined approach to predict fine resolution noise exposures with emission and receptor-based models presents an effective alternative to noise modelling approaches based on emission and propagation or LUR modelling.
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Affiliation(s)
- Tor H Oiamo
- Department of Geography and Environmental Studies, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada.
| | - Hugh Davies
- School of Population and Public Health, Faculty of Medicine, The University of British Columbia, Canada
| | - Daniel Rainham
- Healthy Populations Institute, Dalhousie University, Canada
| | - Claus Rinner
- Department of Geography and Environmental Studies, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada
| | - Kelly Drew
- Toronto Public Health, Healthy Public Policy, City of Toronto, Canada
| | | | - Ronald Macfarlane
- Toronto Public Health, Healthy Public Policy, City of Toronto, Canada
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30
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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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Dzhambov AM, Markevych I, Hartig T, Tilov B, Arabadzhiev Z, Stoyanov D, Gatseva P, Dimitrova DD. Multiple pathways link urban green- and bluespace to mental health in young adults. ENVIRONMENTAL RESEARCH 2018; 166:223-233. [PMID: 29890427 DOI: 10.1016/j.envres.2018.06.004] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 06/01/2018] [Accepted: 06/03/2018] [Indexed: 05/23/2023]
Abstract
BACKGROUND A growing body of scientific literature indicates that urban green- and bluespace support mental health; however, little research has attempted to address the complexities in likely interrelations among the pathways through which benefits plausibly are realized. OBJECTIVES The present study examines how different plausible pathways between green/bluespace and mental health can work together. Both objective and perceived measures of green- and bluespace are used in these models. METHODS We sampled 720 students from the city of Plovdiv, Bulgaria. Residential greenspace was measured in terms of the Normalized Difference Vegetation Index (NDVI), tree cover density, percentage of green areas, and Euclidean distance to the nearest green space. Bluespace was measured in terms of its presence in the neighborhood and the Euclidean distance to the nearest bluespace. Mental health was measured with the 12-item form of the General Health Questionnaire (GHQ-12). The following mediators were considered: perceived neighborhood green/bluespace, restorative quality of the neighborhood, social cohesion, physical activity, noise and air pollution, and environmental annoyance. Structural equation modelling techniques were used to analyze the data. RESULTS Higher NDVI within a 300 m buffer around the residence was associated with better mental health through higher perceived greenspace; through higher perceived greenspace, leading to increased restorative quality, and subsequently to increased physical activity (i.e., serial mediation); through lower noise exposure, which in turn was associated with lower annoyance; and through higher perceived greenspace, which was associated with lower annoyance. Presence of bluespace within a 300 m buffer did not have a straightforward association with mental health owing to competitive indirect paths: one supporting mental health through higher perceived bluespace, restorative quality, and physical activity; and another engendering mental ill-health through higher noise exposure and annoyance. CONCLUSIONS We found evidence that having more greenspace near the residence supported mental health through several indirect pathways with serial components. Conversely, bluespace was not clearly associated with mental health.
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Affiliation(s)
- Angel M Dzhambov
- Department of Hygiene and Ecomedicine, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria.
| | - Iana Markevych
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Munich, Germany; Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Terry Hartig
- Institute for Housing and Urban Research, Uppsala University, Uppsala, Sweden
| | - Boris Tilov
- Medical College, Medical University of Plovdiv, Plovdiv, Bulgaria; Department of Management, Faculty of Economics and Management, University of Agribusiness and Rural Development, Plovdiv, Bulgaria
| | - Zlatoslav Arabadzhiev
- Department of Psychiatry and Medical Psychology, Faculty of Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Drozdstoj Stoyanov
- Department of Psychiatry and Medical Psychology, Faculty of Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Penka Gatseva
- Department of Hygiene and Ecomedicine, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Donka D Dimitrova
- Department of Health Management and Healthcare Economics, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
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Dzhambov AM, Markevych I, Tilov B, Arabadzhiev Z, Stoyanov D, Gatseva P, Dimitrova DD. Pathways linking residential noise and air pollution to mental ill-health in young adults. ENVIRONMENTAL RESEARCH 2018; 166:458-465. [PMID: 29940479 DOI: 10.1016/j.envres.2018.06.031] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 06/04/2018] [Accepted: 06/13/2018] [Indexed: 05/27/2023]
Abstract
BACKGROUND Recent years have seen growing, but still tentative, evidence of the potential associations of environmental noise and air pollution with mental disorders. In the present study, we aimed to examine the associations between residential noise and air pollution exposures and general mental health in young adults with a focus on underlying processes METHODS: We sampled 720 students (18-35 years) from one university in the city of Plovdiv, Bulgaria. Residential noise (LAeq; day equivalent noise level) and air pollution (NO2) were assessed at participant's residential address by land use regression models. General mental health was measured with a short form of the General Health Questionnaire (GHQ). The following putative mediators were considered: annoyance from environmental pollution, sleep disturbance, restorative quality of the neighborhood, neighborhood social cohesion, and commuting/leisure time physical activity. Structural equation modeling was used to analyze the theoretically-indicated interplay between exposures, mediators, and GHQ. RESULTS We observed an association between higher LAeq and GHQ, in which environmental annoyance and neighborhood restorative quality emerged as key mediators. First, LAeq was associated with higher annoyance, and through it with lower restorative quality, and then in turn with lower physical activity, and thus with higher GHQ. Simultaneously, higher annoyance was associated with higher sleep disturbance, and thereby with higher GHQ. NO2 had no overall association with GHQ, but it was indirectly associated with it through higher annoyance, lower restorative quality, and lower physical activity working in serial. CONCLUSION We found evidence that increased residential noise was related to mental ill-health through several indirect pathways. Air pollution was associated with mental health only indirectly.
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Affiliation(s)
- Angel M Dzhambov
- Department of Hygiene and Ecomedicine, Faculty of Public Health, Medical University of Plovdiv, 15A Vassil Aprilov Blvd., 4002 Plovdiv, Bulgaria.
| | - Iana Markevych
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Munich, Germany; Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Boris Tilov
- Medical College, Medical University of Plovdiv, Plovdiv, Bulgaria; Department of Management, Faculty of Economics and Management, University of Agribusiness and Rural Development, Plovdiv, Bulgaria
| | - Zlatoslav Arabadzhiev
- Department of Psychiatry and Medical Psychology, Faculty of Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Drozdstoj Stoyanov
- Department of Psychiatry and Medical Psychology, Faculty of Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Penka Gatseva
- Department of Hygiene and Ecomedicine, Faculty of Public Health, Medical University of Plovdiv, 15A Vassil Aprilov Blvd., 4002 Plovdiv, Bulgaria
| | - Donka D Dimitrova
- Department of Health Management and Healthcare Economics, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
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Khan J, Ketzel M, Kakosimos K, Sørensen M, Jensen SS. Road traffic air and noise pollution exposure assessment - A review of tools and techniques. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 634:661-676. [PMID: 29642048 DOI: 10.1016/j.scitotenv.2018.03.374] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 03/30/2018] [Accepted: 03/30/2018] [Indexed: 05/27/2023]
Abstract
Road traffic induces air and noise pollution in urban environments having negative impacts on human health. Thus, estimating exposure to road traffic air and noise pollution (hereafter, air and noise pollution) is important in order to improve the understanding of human health outcomes in epidemiological studies. The aims of this review are (i) to summarize current practices of modelling and exposure assessment techniques for road traffic air and noise pollution (ii) to highlight the potential of existing tools and techniques for their combined exposure assessment for air and noise together with associated challenges, research gaps and priorities. The study reviews literature about air and noise pollution from urban road traffic, including other relevant characteristics such as the employed dispersion models, Geographic Information System (GIS)-based tool, spatial scale of exposure assessment, study location, sample size, type of traffic data and building geometry information. Deterministic modelling is the most frequently used assessment technique for both air and noise pollution of short-term and long-term exposure. We observed a larger variety among air pollution models as compared to the applied noise models. Correlations between air and noise pollution vary significantly (0.05-0.74) and are affected by several parameters such as traffic attributes, building attributes and meteorology etc. Buildings act as screens for the dispersion of pollution, but the reduction effect is much larger for noise than for air pollution. While, meteorology has a greater influence on air pollution levels as compared to noise, although also important for noise pollution. There is a significant potential for developing a standard tool to assess combined exposure of traffic related air and noise pollution to facilitate health related studies. GIS, due to its geographic nature, is well established and has a significant capability to simultaneously address both exposures.
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Affiliation(s)
- Jibran Khan
- Department of Environmental Science, Aarhus University, Roskilde, Denmark; Department of Chemical Engineering, Texas A&M University at Qatar (TAMUQ), Doha, Qatar
| | - Matthias Ketzel
- Department of Environmental Science, Aarhus University, Roskilde, Denmark
| | | | - Mette Sørensen
- Danish Cancer Society Research Center, Copenhagen, Denmark
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Yang WT, Wang VS, Chang LT, Chuang KJ, Chuang HC, Liu CS, Bao BY, Chang TY. Road Traffic Noise, Air Pollutants, and the Prevalence of Cardiovascular Disease in Taichung, Taiwan. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:ijerph15081707. [PMID: 30096942 PMCID: PMC6121634 DOI: 10.3390/ijerph15081707] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Revised: 08/03/2018] [Accepted: 08/07/2018] [Indexed: 11/16/2022]
Abstract
Background: A few studies have investigated the interaction between exposure to road traffic noise, air pollutants, and cardiovascular disease (CVD), but their results were inconsistent. This cross-sectional study investigated whether road traffic noise, particulate matter with dynamic diameter less than 10 μm (PM10) and nitrogen dioxides (NO₂) exposure were independently associated with the risk of CVD. Methods: We recruited 663 volunteers who had been living near main roads for more than three years in 2008. Information concerning the subjects' home addresses was combined with noise measurements at 42 locations and annual average of air pollutants from 2 monitoring stations to estimate individual exposure. Multivariate logistic regression was used to calculate the odds ratio (OR) for diagnosed CVD, adjusting for potential confounders and co-exposure. Results: Only per 5-dBA increase in road traffic noise was significantly associated with elevated risk of CVD (adjusted OR = 2.23, 95% confidence interval (CI) = 1.26⁻3.93) in the single-exposure models. Such association was aggravated (adjusted OR = 2.96, 95% CI = 1.41⁻6.23) after adjustment for total traffic and PM10 or NO₂ in the two-exposure models. Conclusions: Road traffic noise exposure may be associated with the increasing prevalence of CVD. No synergistic association was observed between co-exposure to noise and air pollutants and the risk of CVD.
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Affiliation(s)
- Wei-Ting Yang
- Department of Occupational Safety and Health, College of Public Health, China Medical University, No. 91, Hsueh-Shih Road, Taichung 40402, Taiwan.
| | - Ven-Shing Wang
- Department of Occupational Safety and Health, College of Public Health, China Medical University, No. 91, Hsueh-Shih Road, Taichung 40402, Taiwan.
| | - Li-Te Chang
- Department of Environmental Engineering and Science, Feng Chia University, No. 100, Wen-Hwa Road, Taichung 40724, Taiwan.
| | - Kai-Jen Chuang
- Department of Public Health, School of Medicine, College of Medicine, Taipei Medical University, No. 250, Wu-Hsing Street, Taipei 11031, Taiwan.
- School of Public Health, College of Public Health and Nutrition, Taipei Medical University, No. 250, Wu-Hsing Street, Taipei 11031, Taiwan.
| | - Hsiao-Chi Chuang
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, No. 250, Wu-Hsing Street, Taipei 11031, Taiwan.
- Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, No. 250, Wu-Hsing Street, Taipei 11031, Taiwan.
| | - Chiu-Shong Liu
- Department of Family Medicine, China Medical University Hospital, Taichung 40447, Taiwan.
| | - Bo-Ying Bao
- Department of Pharmacy, College of Pharmacy, China Medical University, No. 91, Hsueh-Shih Road, Taichung 40402, Taiwan.
- Department of Nursing, Asia University, No. 500, Lioufeng Road, Wufeng, Taichung 41354, Taiwan.
| | - Ta-Yuan Chang
- Department of Occupational Safety and Health, College of Public Health, China Medical University, No. 91, Hsueh-Shih Road, Taichung 40402, Taiwan.
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Dzhambov AM, Markevych I, Tilov B, Arabadzhiev Z, Stoyanov D, Gatseva P, Dimitrova DD. Lower Noise Annoyance Associated with GIS-Derived Greenspace: Pathways through Perceived Greenspace and Residential Noise. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:ijerph15071533. [PMID: 30029561 PMCID: PMC6068578 DOI: 10.3390/ijerph15071533] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 07/17/2018] [Accepted: 07/19/2018] [Indexed: 11/28/2022]
Abstract
Growing amounts of evidence support an association between self-reported greenspace near the home and lower noise annoyance; however, objectively defined greenspace has rarely been considered. In the present study, we tested the association between objective measures of greenspace and noise annoyance, with a focus on underpinning pathways through noise level and perceived greenspace. We sampled 720 students aged 18 to 35 years from the city of Plovdiv, Bulgaria. Objective greenspace was defined by several Geographic Information System (GIS)-derived metrics: Normalized Difference Vegetation Index (NDVI), tree cover density, percentage of green space in circular buffers of 100, 300 and 500 m, and the Euclidean distance to the nearest structured green space. Perceived greenspace was defined by the mean of responses to five items asking about its quantity, accessibility, visibility, usage, and quality. We assessed noise annoyance due to transportation and other neighborhood noise sources and daytime noise level (Lday) at the residence. Tests of the parallel mediation models showed that higher NDVI and percentage of green space in all buffers were associated with lower noise annoyance, whereas for higher tree cover this association was observed only in the 100 m buffer zone. In addition, the effects of NDVI and percentage of green space were mediated by higher perceived greenspace and lower Lday. In the case of tree cover, only perceived greenspace was a mediator. Our findings suggest that the potential for greenspace to reduce noise annoyance extends beyond noise abatement. Applying a combination of GIS-derived and perceptual measures should enable researchers to better tap individuals’ experience of residential greenspace and noise.
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Affiliation(s)
- Angel M Dzhambov
- Department of Hygiene and Ecomedicine, Faculty of Public Health, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria.
| | - Iana Markevych
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, 80336 Munich, Germany.
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764 Neuherberg, Germany.
| | - Boris Tilov
- Medical College, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria.
| | - Zlatoslav Arabadzhiev
- Department of Psychiatry and Medical Psychology, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria.
| | - Drozdstoj Stoyanov
- Department of Psychiatry and Medical Psychology, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria.
| | - Penka Gatseva
- Department of Hygiene and Ecomedicine, Faculty of Public Health, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria.
| | - Donka D Dimitrova
- Department of Health Management and Healthcare Economics, Faculty of Public Health, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria.
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Drudge C, Johnson J, MacIntyre E, Li Y, Copes R, Ing S, Johnson S, Varughese S, Chen H. Exploring nighttime road traffic noise: A comprehensive predictive surface for Toronto, Canada. JOURNAL OF OCCUPATIONAL AND ENVIRONMENTAL HYGIENE 2018; 15:389-398. [PMID: 29494283 DOI: 10.1080/15459624.2018.1442006] [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] [Indexed: 06/08/2023]
Abstract
Road traffic noise can adversely impact the health of city residents, particularly when it occurs at night. The objective of this study was to evaluate nighttime traffic ambient noise in Toronto, Canada using measured and model-estimated noise levels. Road traffic noise was measured at 767 locations over 3 seasonal sampling campaigns between June 2012 and October 2013 to fully capture noise variability in Toronto. Temporal and campaign-specific spatial models, developed using the noise measurements, were used to build a final predictive surface. The surface was capable of estimating noise across the city over a 24-hr time frame. Measured and surface-estimated noise levels were compared with guidelines from the World Health Organization and the Province of Ontario to identify areas where noise may pose a health risk. Measured mean nighttime noise in Toronto exceeded World Health Organization (40 dBA) guidelines and mean daytime noise exceeded provincial (55 dBA) guidelines. The final predictive surface, incorporating spatial variables and daily cycles in noise levels, provides noise estimates geocoded for the entire study area. This tool could be used for epidemiological studies and to inform noise mitigation efforts. Based on surface-estimated noise levels during the quietest time of night (2 a.m.-2:30 a.m.), 100% of Toronto has nighttime noise exceeding 40 dBA (mean = 57 dBA, range = 49-110 dBA). A predictive surface was developed to estimate geocoded noise levels and facilitate further study of noise in Toronto. This tool can be used to assess road traffic noise, particularly at night, as an environmental health hazard.
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Affiliation(s)
| | - James Johnson
- a Public Health Ontario , Toronto , Ontario , Canada
| | - Elaina MacIntyre
- a Public Health Ontario , Toronto , Ontario , Canada
- b Dalla Lana School of Public Health, University of Toronto , Toronto , Ontario , Canada
| | - Ye Li
- a Public Health Ontario , Toronto , Ontario , Canada
- b Dalla Lana School of Public Health, University of Toronto , Toronto , Ontario , Canada
| | - Ray Copes
- a Public Health Ontario , Toronto , Ontario , Canada
- b Dalla Lana School of Public Health, University of Toronto , Toronto , Ontario , Canada
| | - Stanley Ing
- c Chatham-Kent Public Health Unit , Chatham , Ontario , Canada
| | | | | | - Hong Chen
- a Public Health Ontario , Toronto , Ontario , Canada
- b Dalla Lana School of Public Health, University of Toronto , Toronto , Ontario , Canada
- d Institute for Clinical Evaluative Sciences , Toronto , Ontario , Canada
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Gill SA, Grabarczyk EE, Baker KM, Naghshineh K, Vonhof MJ. Decomposing an urban soundscape to reveal patterns and drivers of variation in anthropogenic noise. THE SCIENCE OF THE TOTAL ENVIRONMENT 2017; 599-600:1191-1201. [PMID: 28514837 DOI: 10.1016/j.scitotenv.2017.04.229] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Revised: 04/24/2017] [Accepted: 04/28/2017] [Indexed: 06/07/2023]
Abstract
Continuous and intermittent noise may have different effects on humans and wildlife, therefore distinguishing temporal patterns of noise and their drivers is important for policy regarding both public health and wildlife management. We visualized patterns and explored land-use drivers of continuous and high-amplitude intermittent sound pressure levels (SPLs) on an urban campus in Michigan, U.S.A. To visualize patterns of SPLs, we introduce decibel duration curves (DDCs), which show the cumulative frequency distribution of SPLs and aid in the interpretation of statistical SPLs (Ln values) that reflect continuous versus intermittent sounds. DDCs and Ln values reveal that our 24 recording locations varied in the intensity of both continuous and intermittent noise, with intermittent high-amplitude sound events in particular contributing to variability in SPLs over the study site. Time of day influenced both continuous and intermittent SPLs, as locations relatively close to manmade structures (buildings, roads and parking lots) experienced higher SPLs as the day progressed. Continuous SPLs increased with decreasing distance to manmade structures, whereas intermittent SPLs increased with decreasing distance to roads and increasing distance to buildings. Thus, different land-use factors influenced patterns of continuous and intermittent noise, which suggests that different policy and strategies may be needed to ameliorate their effects on the public and wildlife.
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Affiliation(s)
- Sharon A Gill
- Department of Biological Sciences, Western Michigan University, Kalamazoo, MI 49008-5410, United States.
| | - Erin E Grabarczyk
- Department of Biological Sciences, Western Michigan University, Kalamazoo, MI 49008-5410, United States
| | - Kathleen M Baker
- Department of Geography, Western Michigan University, Kalamazoo, MI 49008-5424, United States; W.E. Upjohn Center for the Study of Geographical Change, Western Michigan University, Kalamazoo, MI 49008-5424, United States
| | - Koorosh Naghshineh
- Department of Mechanical and Aerospace Engineering, Western Michigan University, Kalamazoo, MI 49008-5343, United States
| | - Maarten J Vonhof
- Department of Biological Sciences, Western Michigan University, Kalamazoo, MI 49008-5410, United States; Institute of the Environment and Sustainability, Western Michigan University, Kalamazoo, MI 49008-5419, United States
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Walker ED, Hart JE, Koutrakis P, Cavallari JM, VoPham T, Luna M, Laden F. Spatial and temporal determinants of A-weighted and frequency specific sound levels-An elastic net approach. ENVIRONMENTAL RESEARCH 2017; 159:491-499. [PMID: 28865401 PMCID: PMC5903552 DOI: 10.1016/j.envres.2017.08.034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2017] [Revised: 08/15/2017] [Accepted: 08/17/2017] [Indexed: 05/10/2023]
Abstract
BACKGROUND Urban sound levels are a ubiquitous environmental stressor and have been shown to be associated with a wide variety of health outcomes. While much is known about the predictors of A-weighted sound pressure levels in the urban environment, far less is known about other frequencies. OBJECTIVE To develop a series of spatial-temporal sound models to predict A-weighted sound pressure levels, low, mid, and high frequency sound for Boston, Massachusetts. METHODS Short-term sound levels were gathered at n = 400 sites from February 2015 - February 2016. Spatial and meteorological attributes at or near the sound monitoring site were obtained using publicly available data and a portable weather station. An elastic net variable selection technique was used to select predictors of A-weighted, low, mid, and high frequency sound. RESULTS The final models for low, mid, high, and A-weighted sound levels explained 59 - 69% of the variability in each measure. Similar to other A-weighted models, our sound models included transportation related variables such as length of roads and bus lines in the surrounding area; distance to road and rail lines; traffic volume, vehicle mix, residential and commercial land use. However, frequency specific models highlighted additional predictors not included in the A-weighted model including temperature, vegetation, impervious surfaces, vehicle mix, and density of entertainment establishments and restaurants. CONCLUSIONS Building spatial temporal models to characterize sound levels across the frequency spectrum using an elastic net approach can be a promising tool for noise exposure assessments within the urban soundscape. Models of sound's character may give us additional important sound exposure metrics to be utilized in epidemiological studies.
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Affiliation(s)
- Erica D Walker
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
| | - Jaime E Hart
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Petros Koutrakis
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Jennifer M Cavallari
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Community Medicine and Health Care, UConn Health, Farmington, CT, United States
| | - Trang VoPham
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Marcos Luna
- Department of Geography, Salem State University, Salem, MA, United States
| | - Francine Laden
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
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Sieber C, Ragettli MS, Brink M, Toyib O, Baatjies R, Saucy A, Probst-Hensch N, Dalvie MA, Röösli M. Land Use Regression Modeling of Outdoor Noise Exposure in Informal Settlements in Western Cape, South Africa. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2017; 14:ijerph14101262. [PMID: 29053590 PMCID: PMC5664763 DOI: 10.3390/ijerph14101262] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Revised: 10/09/2017] [Accepted: 10/16/2017] [Indexed: 12/22/2022]
Abstract
In low- and middle-income countries, noise exposure and its negative health effects have been little explored. The present study aimed to assess the noise exposure situation in adults living in informal settings in the Western Cape Province, South Africa. We conducted continuous one-week outdoor noise measurements at 134 homes in four different areas. These data were used to develop a land use regression (LUR) model to predict A-weighted day-evening-night equivalent sound levels (Lden) from geographic information system (GIS) variables. Mean noise exposure during day (6:00–18:00) was 60.0 A-weighted decibels (dB(A)) (interquartile range 56.9–62.9 dB(A)), during night (22:00–6:00) 52.9 dB(A) (49.3–55.8 dB(A)) and average Lden was 63.0 dB(A) (60.1–66.5 dB(A)). Main predictors of the LUR model were related to road traffic and household density. Model performance was low (adjusted R2 = 0.130) suggesting that other influences than those represented in the geographic predictors are relevant for noise exposure. This is one of the few studies on the noise exposure situation in low- and middle-income countries. It demonstrates that noise exposure levels are high in these settings.
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Affiliation(s)
- Chloé Sieber
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel 4002, Switzerland.
- University of Basel, Basel 4003, Switzerland.
| | - Martina S Ragettli
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel 4002, Switzerland.
- University of Basel, Basel 4003, Switzerland.
| | - Mark Brink
- Federal Office for the Environment, CH-3003 Bern, Switzerland.
| | - Olaniyan Toyib
- Centre for Environmental and Occupational Health Research, School of Public Health and Family Medicine, University of Cape Town, Rondebosch, Cape Town 7700, South Africa.
| | - Roslyn Baatjies
- Department of Environmental and Occupational Studies, Faculty of Applied Sciences, Cape Peninsula University of Technology, Cape Town 7700, South Africa.
| | - Apolline Saucy
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel 4002, Switzerland.
- University of Basel, Basel 4003, Switzerland.
| | - Nicole Probst-Hensch
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel 4002, Switzerland.
- University of Basel, Basel 4003, Switzerland.
| | - Mohamed Aqiel Dalvie
- Centre for Environmental and Occupational Health Research, School of Public Health and Family Medicine, University of Cape Town, Rondebosch, Cape Town 7700, South Africa.
| | - Martin Röösli
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel 4002, Switzerland.
- University of Basel, Basel 4003, Switzerland.
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Wang VS, Lo EW, Liang CH, Chao KP, Bao BY, Chang TY. Temporal and spatial variations in road traffic noise for different frequency components in metropolitan Taichung, Taiwan. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2016; 219:174-181. [PMID: 27814533 DOI: 10.1016/j.envpol.2016.10.055] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2016] [Revised: 10/18/2016] [Accepted: 10/18/2016] [Indexed: 06/06/2023]
Abstract
Road traffic noise exposure has been associated with auditory and non-auditory health effects, but few studies report noise characteristics. This study determines 24-h noise levels and analyzes their frequency components to investigate associations between seasons, meteorology, land-use types, and traffic. We set up 50 monitoring stations covering ten different land-use types and conducted measurements at three times of the year to obtain 24-h-average A-weighted equivalent noise levels (LAeq,24h) and frequency analyses from 2013 to 2014 in Taichung, Taiwan. Information on land-use types, road parameters, traffic flow rates, and meteorological variables was also collected for analysis with the annual averages of road traffic noise and its frequency components. The annual average LAeq,24h in Taichung was 66.4 ± 4.7 A-weighed decibels (dBA). Significant differences in LAeq,24h and frequency components were observed between land-use types (all p-values < 0.001), but not between seasons, with the highest two noise levels of 71.2 ± 1.0 dBA and 70.0 ± 2.6 dBA measured in stream-channel and commercial areas, with the highest component being 61.4 ± 5.3 dBA at 1000 Hz. Road width, traffic flow rates, and land-use types were significantly associated with annual average LAeq,24h (all p-values < 0.050). Noise levels at 125 Hz had the highest correlation with total traffic (Spearman's coefficient = 0.795) and the highest prediction in the multiple linear regression (R2 = 0.803; adjusted R2 = 0.765). These findings reveal the spatial variation in road traffic noise exposure in Taichung. The highest correlation and predictive capacity was observed between this variation and noise levels at 125 Hz. We recommend that governmental agencies should take actions to reduce noise levels from traffic vehicles.
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Affiliation(s)
- Ven-Shing Wang
- Department of Occupational Safety and Health, College of Public Health, China Medical University, Taichung, Taiwan
| | - Ei-Wen Lo
- Department of Occupational Safety and Health, College of Public Health, China Medical University, Taichung, Taiwan
| | - Chih-Hsiang Liang
- Department of Occupational Safety and Health, College of Public Health, China Medical University, Taichung, Taiwan
| | - Keh-Ping Chao
- Department of Occupational Safety and Health, College of Public Health, China Medical University, Taichung, Taiwan
| | - Bo-Ying Bao
- Department of Pharmacy, College of Pharmacy, China Medical University, Taichung, Taiwan
| | - Ta-Yuan Chang
- Department of Occupational Safety and Health, College of Public Health, China Medical University, Taichung, Taiwan.
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Ragettli MS, Goudreau S, Plante C, Fournier M, Hatzopoulou M, Perron S, Smargiassi A. Statistical modeling of the spatial variability of environmental noise levels in Montreal, Canada, using noise measurements and land use characteristics. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2016; 26:597-605. [PMID: 26732373 DOI: 10.1038/jes.2015.82] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2015] [Revised: 09/23/2015] [Accepted: 11/02/2015] [Indexed: 05/22/2023]
Abstract
The availability of noise maps to assess exposure to noise is often limited, especially in North American cities. We developed land use regression (LUR) models for LAeq24h, Lnight, and Lden to assess the long-term spatial variability of environmental noise levels in Montreal, Canada, considering various transportation noise sources (road, rail, and air). To explore the effects of sampling duration, we compared our LAeq24h levels that were computed over at least five complete contiguous days of measurements to shorter sampling periods (20 min and 24 h). LUR models were built with General Additive Models using continuous 2-min noise measurements from 204 sites. Model performance (adjusted R2) was 0.68, 0.59, and 0.69 for LAeq24h, Lnight, and Lden, respectively. Main predictors of measured noise levels were road-traffic and vegetation variables. Twenty-minute non-rush hour measurements corresponded well with LAeq24h levels computed over 5 days at road-traffic sites (bias: -0.7 dB(A)), but not at rail (-2.1 dB(A)) nor at air (-2.2 dB(A)) sites. Our study provides important insights into the spatial variation of environmental noise levels in a Canadian city. To assess long-term noise levels, sampling strategies should be stratified by noise sources and preferably should include 1 week of measurements at locations exposed to rail and aircraft noise.
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Affiliation(s)
- Martina S Ragettli
- Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, Quebec, Canada
- Quebec Institute of Public Health, Public Health Department of Montreal, Montreal, Quebec, Canada
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Sophie Goudreau
- Quebec Institute of Public Health, Public Health Department of Montreal, Montreal, Quebec, Canada
| | - Céline Plante
- Quebec Institute of Public Health, Public Health Department of Montreal, Montreal, Quebec, Canada
| | - Michel Fournier
- Quebec Institute of Public Health, Public Health Department of Montreal, Montreal, Quebec, Canada
| | - Marianne Hatzopoulou
- Department of Civil Engineering and Applied Mechanics, McGill University, Montreal, Quebec, Canada
| | - Stéphane Perron
- Quebec Institute of Public Health, Public Health Department of Montreal, Montreal, Quebec, Canada
| | - Audrey Smargiassi
- Department of Environmental and Occupational Health, School of Public Health, University of Montreal, Montreal, Quebec, Canada
- National Institute of Public Health of Quebec, Montreal, Quebec, Canada
- Public Health Research Institute of the University of Montreal (IRSPUM), Montreal, Quebec, Canada
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Perron S, Plante C, Ragettli MS, Kaiser DJ, Goudreau S, Smargiassi A. Sleep Disturbance from Road Traffic, Railways, Airplanes and from Total Environmental Noise Levels in Montreal. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2016; 13:ijerph13080809. [PMID: 27529260 PMCID: PMC4997495 DOI: 10.3390/ijerph13080809] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Revised: 08/02/2016] [Accepted: 08/04/2016] [Indexed: 12/02/2022]
Abstract
The objective of our study was to measure the impact of transportation-related noise and total environmental noise on sleep disturbance for the residents of Montreal, Canada. A telephone-based survey on noise-related sleep disturbance among 4336 persons aged 18 years and over was conducted. LNight for each study participant was estimated using a land use regression (LUR) model. Distance of the respondent’s residence to the nearest transportation noise source was also used as an indicator of noise exposure. The proportion of the population whose sleep was disturbed by outdoor environmental noise in the past 4 weeks was 12.4%. The proportion of those affected by road traffic, airplane and railway noise was 4.2%, 1.5% and 1.1%, respectively. We observed an increased prevalence in sleep disturbance for those exposed to both rail and road noise when compared for those exposed to road only. We did not observe an increased prevalence in sleep disturbance for those that were both exposed to road and planes when compared to those exposed to road or planes only. We developed regression models to assess the marginal proportion of sleep disturbance as a function of estimated LNight and distance to transportation noise sources. In our models, sleep disturbance increased with proximity to transportation noise sources (railway, airplane and road traffic) and with increasing LNight values. Our study provides a quantitative estimate of the association between total environmental noise levels estimated using an LUR model and sleep disturbance from transportation noise.
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Affiliation(s)
- Stéphane Perron
- Public Health Department of Montreal, Montreal, QC H2L 1M3, Canada.
- Department of Social and Preventive Medicine, School of Public Health, University of Montreal, Montreal, QC H3C 3J7, Canada.
| | - Céline Plante
- Public Health Department of Montreal, Montreal, QC H2L 1M3, Canada.
| | - Martina S Ragettli
- Swiss Tropical and Public Health Institute, Basel 4002, Switzerland.
- University of Basel, Basel 4003, Switzerland.
| | - David J Kaiser
- Public Health Department of Montreal, Montreal, QC H2L 1M3, Canada.
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 1A2, Canada.
| | - Sophie Goudreau
- Public Health Department of Montreal, Montreal, QC H2L 1M3, Canada.
| | - Audrey Smargiassi
- National Institute of Public Health Quebec, Montreal, QC H3C 2B9, Canada.
- Public Health Research Institute, University of Montreal, QC H3C 3J7, Canada.
- Department of Environmental Health and Occupational Health, School of Public Health, University of Montreal, Montreal, QC H3C 3J7, Canada.
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Annoyance from Road Traffic, Trains, Airplanes and from Total Environmental Noise Levels. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2015; 13:ijerph13010090. [PMID: 26729143 PMCID: PMC4730481 DOI: 10.3390/ijerph13010090] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2015] [Revised: 12/18/2015] [Accepted: 12/21/2015] [Indexed: 11/16/2022]
Abstract
There is a lack of studies assessing the exposure-response relationship between transportation noise and annoyance in North America. Our aims were to investigate the prevalence of noise annoyance induced by road traffic, trains and airplanes in relation to distance to transportation noise sources, and to total environmental noise levels in Montreal, Canada; annoyance was assessed as noise-induced disturbance. A telephone-based survey among 4336 persons aged >18 years was conducted. Exposure to total environmental noise (A-weighted outdoor noise levels—LAeq24h and day-evening-night equivalent noise levels—Lden) for each study participant was determined using a statistical noise model (land use regression—LUR) that is based on actual outdoor noise measurements. The proportion of the population annoyed by road traffic, airplane and train noise was 20.1%, 13.0% and 6.1%, respectively. As the distance to major roads, railways and the Montreal International Airport increased, the percentage of people disturbed and highly disturbed due to the corresponding traffic noise significantly decreased. When applying the statistical noise model we found a relationship between noise levels and disturbance from road traffic and total environmental noise, with Prevalence Proportion Ratios (PPR) for highly disturbed people of 1.10 (95% CI: 1.07–1.13) and 1.04 (1.02–1.06) per 1 dB(A) Lden, respectively. Our study provides the first comprehensive information on the relationship between transportation noise levels and disturbance in a Canadian city. LUR models are still in development and further studies on transportation noise induced annoyance are consequently needed, especially for sources other than road traffic.
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