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Wang J, Alli AS, Clark SN, Ezzati M, Brauer M, Hughes AF, Nimo J, Moses JB, Baah S, Nathvani R, D V, Agyei-Mensah S, Baumgartner J, Bennett JE, Arku RE. Inequalities in urban air pollution in sub-Saharan Africa: an empirical modeling of ambient NO and NO 2 concentrations in Accra, Ghana. ENVIRONMENTAL RESEARCH LETTERS : ERL [WEB SITE] 2024; 19:034036. [PMID: 38419692 PMCID: PMC10897512 DOI: 10.1088/1748-9326/ad2892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 02/04/2024] [Accepted: 02/12/2024] [Indexed: 03/02/2024]
Abstract
Road traffic has become the leading source of air pollution in fast-growing sub-Saharan African cities. Yet, there is a dearth of robust city-wide data for understanding space-time variations and inequalities in combustion related emissions and exposures. We combined nitrogen dioxide (NO2) and nitric oxide (NO) measurement data from 134 locations in the Greater Accra Metropolitan Area (GAMA), with geographical, meteorological, and population factors in spatio-temporal mixed effects models to predict NO2 and NO concentrations at fine spatial (50 m) and temporal (weekly) resolution over the entire GAMA. Model performance was evaluated with 10-fold cross-validation (CV), and predictions were summarized as annual and seasonal (dusty [Harmattan] and rainy [non-Harmattan]) mean concentrations. The predictions were used to examine population distributions of, and socioeconomic inequalities in, exposure at the census enumeration area (EA) level. The models explained 88% and 79% of the spatiotemporal variability in NO2 and NO concentrations, respectively. The mean predicted annual, non-Harmattan and Harmattan NO2 levels were 37 (range: 1-189), 28 (range: 1-170) and 50 (range: 1-195) µg m-3, respectively. Unlike NO2, NO concentrations were highest in the non-Harmattan season (41 [range: 31-521] µg m-3). Road traffic was the dominant factor for both pollutants, but NO2 had higher spatial heterogeneity than NO. For both pollutants, the levels were substantially higher in the city core, where the entire population (100%) was exposed to annual NO2 levels exceeding the World Health Organization (WHO) guideline of 10 µg m-3. Significant disparities in NO2 concentrations existed across socioeconomic gradients, with residents in the poorest communities exposed to levels about 15 µg m-3 higher compared with the wealthiest (p < 0.001). The results showed the important role of road traffic emissions in air pollution concentrations in the GAMA, which has major implications for the health of the city's poorest residents. These data could support climate and health impact assessments as well as policy evaluations in the city.
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Affiliation(s)
- Jiayuan Wang
- Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, United States of America
| | - Abosede S Alli
- Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, United States of America
| | - Sierra N Clark
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Majid Ezzati
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Regional Institute for Population Studies, University of Ghana, Accra, Ghana
- Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - Michael Brauer
- School of Population and Public Health, The University of British Columbia, Vancouver, Canada
| | | | - James Nimo
- Department of Physics, University of Ghana, Accra, Ghana
| | | | - Solomon Baah
- Department of Physics, University of Ghana, Accra, Ghana
| | - Ricky Nathvani
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Vishwanath D
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Samuel Agyei-Mensah
- Department of Geography and Resource Development, University of Ghana, Accra, Ghana
- Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom
| | - 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, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Raphael E Arku
- Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, United States of America
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Nathvani R, D V, Clark SN, Alli AS, Muller E, Coste H, Bennett JE, Nimo J, Moses JB, Baah S, Hughes A, Suel E, Metzler AB, Rashid T, Brauer M, Baumgartner J, Owusu G, Agyei-Mensah S, Arku RE, Ezzati M. Beyond here and now: Evaluating pollution estimation across space and time from street view images with deep learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 903:166168. [PMID: 37586538 PMCID: PMC7615099 DOI: 10.1016/j.scitotenv.2023.166168] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/07/2023] [Accepted: 08/07/2023] [Indexed: 08/18/2023]
Abstract
Advances in computer vision, driven by deep learning, allows for the inference of environmental pollution and its potential sources from images. The spatial and temporal generalisability of image-based pollution models is crucial in their real-world application, but is currently understudied, particularly in low-income countries where infrastructure for measuring the complex patterns of pollution is limited and modelling may therefore provide the most utility. We employed convolutional neural networks (CNNs) for two complementary classification models, in both an end-to-end approach and as an interpretable feature extractor (object detection), to estimate spatially and temporally resolved fine particulate matter (PM2.5) and noise levels in Accra, Ghana. Data used for training the models were from a unique dataset of over 1.6 million images collected over 15 months at 145 representative locations across the city, paired with air and noise measurements. Both end-to-end CNN and object-based approaches surpassed null model benchmarks for predicting PM2.5 and noise at single locations, but performance deteriorated when applied to other locations. Model accuracy diminished when tested on images from locations unseen during training, but improved by sampling a greater number of locations during model training, even if the total quantity of data was reduced. The end-to-end models used characteristics of images associated with atmospheric visibility for predicting PM2.5, and specific objects such as vehicles and people for noise. The results demonstrate the potential and challenges of image-based, spatiotemporal air pollution and noise estimation, and that robust, environmental modelling with images requires integration with traditional sensor networks.
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Affiliation(s)
- Ricky Nathvani
- 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.
| | - Vishwanath D
- 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
| | - 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
| | - Emily Muller
- 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
| | - Henri Coste
- 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
| | - 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
| | - 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
| | - Esra Suel
- 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; Centre for Advanced Spatial Analysis, University College London, London, UK
| | - Antje Barbara Metzler
- 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
| | - Theo Rashid
- 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
| | - Michael Brauer
- School of Population and Public Health, University of British Columbia, Vancouver, Canada
| | - Jill Baumgartner
- Institute for Health and Social Policy, McGill University, Montreal, Canada; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada
| | - George Owusu
- Institute of Statistical, Social & Economic Research, University of Ghana, Accra, Ghana
| | - Samuel Agyei-Mensah
- Department of Geography and Resource Development, University of Ghana, Accra, Ghana
| | - Raphael E Arku
- 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
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Metzler AB, Nathvani R, Sharmanska V, Bai W, Muller E, Moulds S, Agyei-Asabere C, Adjei-Boadi D, Kyere-Gyeabour E, Tetteh JD, Owusu G, Agyei-Mensah S, Baumgartner J, Robinson BE, Arku RE, Ezzati M. Phenotyping urban built and natural environments with high-resolution satellite images and unsupervised deep learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 893:164794. [PMID: 37315611 PMCID: PMC7615085 DOI: 10.1016/j.scitotenv.2023.164794] [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: 02/09/2023] [Revised: 05/05/2023] [Accepted: 06/08/2023] [Indexed: 06/16/2023]
Abstract
Cities in the developing world are expanding rapidly, and undergoing changes to their roads, buildings, vegetation, and other land use characteristics. Timely data are needed to ensure that urban change enhances health, wellbeing and sustainability. We present and evaluate a novel unsupervised deep clustering method to classify and characterise the complex and multidimensional built and natural environments of cities into interpretable clusters using high-resolution satellite images. We applied our approach to a high-resolution (0.3 m/pixel) satellite image of Accra, Ghana, one of the fastest growing cities in sub-Saharan Africa, and contextualised the results with demographic and environmental data that were not used for clustering. We show that clusters obtained solely from images capture distinct interpretable phenotypes of the urban natural (vegetation and water) and built (building count, size, density, and orientation; length and arrangement of roads) environment, and population, either as a unique defining characteristic (e.g., bodies of water or dense vegetation) or in combination (e.g., buildings surrounded by vegetation or sparsely populated areas intermixed with roads). Clusters that were based on a single defining characteristic were robust to the spatial scale of analysis and the choice of cluster number, whereas those based on a combination of characteristics changed based on scale and number of clusters. The results demonstrate that satellite data and unsupervised deep learning provide a cost-effective, interpretable and scalable approach for real-time tracking of sustainable urban development, especially where traditional environmental and demographic data are limited and infrequent.
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Affiliation(s)
- A Barbara Metzler
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, Imperial College London, London, UK
| | - Ricky Nathvani
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, Imperial College London, London, UK
| | - Viktoriia Sharmanska
- Department of Informatics, University of Sussex, UK; Department of Computing, Imperial College London, London, UK
| | - Wenjia Bai
- Department of Computing, Imperial College London, London, UK; Department of Brain Sciences, Imperial College London, London, UK
| | - Emily Muller
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, Imperial College London, London, UK
| | - Simon Moulds
- School of Geography and the Environment, University of Oxford, UK
| | | | - Dina Adjei-Boadi
- Department of Geography and Resource Development, University of Ghana, Legon, Accra, Ghana
| | - Elvis Kyere-Gyeabour
- Department of Geography and Resource Development, University of Ghana, Legon, Accra, Ghana
| | - Jacob Doku Tetteh
- Department of Geography and Resource Development, University of Ghana, Legon, Accra, Ghana
| | - George Owusu
- Institute of Statistical, Social & Economic Research, University of Ghana, Accra, Ghana
| | - Samuel Agyei-Mensah
- Department of Geography and Resource Development, University of Ghana, Legon, Accra, Ghana
| | - Jill Baumgartner
- Department of Epidemiology and Biostatistics, McGill University, Montreal, Québec, Canada; Department of Equity, Ethics and Policy, McGill University, Montreal, Québec, Canada
| | - Brian E Robinson
- Department of Geography, McGill University, Montreal, Québec, Canada
| | - Raphael E Arku
- 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, 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.
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Gemmell E, Adjei-Boadi D, Sarkar A, Shoari N, White K, Zdero S, Kassem H, Pujara T, Brauer M. "In small places, close to home": Urban environmental impacts on child rights across four global cities. Health Place 2023; 83:103081. [PMID: 37506630 PMCID: PMC7615291 DOI: 10.1016/j.healthplace.2023.103081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/03/2023] [Accepted: 06/28/2023] [Indexed: 07/30/2023]
Abstract
Urban environments influence child behaviours, exposures and experiences and may affect health, development, achievement and realization of fundamental human rights. We examined the status of eleven UN Convention on the Rights of the Child articles, in a multi-case study across four global cities. Within all study cities, children experienced unequal exposure to urban environmental risks and amenities. Many violations of child rights are related to car-based transportation systems and further challenged by pressures on urban systems from rapid population increases in the context of climate change. A child rights framework provides principles for a collective, multi-sectoral re-imagination of urban environments that support the human rights of all citizens.
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Affiliation(s)
- Emily Gemmell
- School of Population and Public Health, University of British Columbia, Vancouver, 2206 West Mall, Vancouver, BC, V6T 1Z4, Canada.
| | - Dina Adjei-Boadi
- Department of Geography and Resource Development, University of Ghana, MR28+9MQ, Doutor J.B. Danquah Avenue, Accra, Ghana.
| | - Asesh Sarkar
- Department of Architecture and Planning, Indian Institute of Technology, Haridwar Highway, Roorkee, Uttarakhand, 247667, India.
| | - Niloofar Shoari
- MRC Centre for Environment & Health, Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, United Kingdom.
| | - Katherine White
- School of Population and Public Health, University of British Columbia, Vancouver, 2206 West Mall, Vancouver, BC, V6T 1Z4, Canada.
| | - Svetlana Zdero
- School of Population and Public Health, University of British Columbia, Vancouver, 2206 West Mall, Vancouver, BC, V6T 1Z4, Canada.
| | - Hallah Kassem
- School of Population and Public Health, University of British Columbia, Vancouver, 2206 West Mall, Vancouver, BC, V6T 1Z4, Canada.
| | - Tina Pujara
- Department of Architecture and Planning, Indian Institute of Technology, Haridwar Highway, Roorkee, Uttarakhand, 247667, India.
| | - Michael Brauer
- School of Population and Public Health, University of British Columbia, Vancouver, 2206 West Mall, Vancouver, BC, V6T 1Z4, Canada; Institute for Health Metrics and Evaluation, Population Health Building, Hans Rosling Center, 3980 15th Ave. NE, Seattle, WA, 98195, USA.
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5
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Characterisation of urban environment and activity across space and time using street images and deep learning in Accra. Sci Rep 2022; 12:20470. [PMID: 36443345 PMCID: PMC9703424 DOI: 10.1038/s41598-022-24474-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 11/15/2022] [Indexed: 11/29/2022] Open
Abstract
The urban environment influences human health, safety and wellbeing. Cities in Africa are growing faster than other regions but have limited data to guide urban planning and policies. Our aim was to use smart sensing and analytics to characterise the spatial patterns and temporal dynamics of features of the urban environment relevant for health, liveability, safety and sustainability. We collected a novel dataset of 2.1 million time-lapsed day and night images at 145 representative locations throughout the Metropolis of Accra, Ghana. We manually labelled a subset of 1,250 images for 20 contextually relevant objects and used transfer learning with data augmentation to retrain a convolutional neural network to detect them in the remaining images. We identified 23.5 million instances of these objects including 9.66 million instances of persons (41% of all objects), followed by cars (4.19 million, 18%), umbrellas (3.00 million, 13%), and informally operated minibuses known as tro tros (2.94 million, 13%). People, large vehicles and market-related objects were most common in the commercial core and densely populated informal neighbourhoods, while refuse and animals were most observed in the peripheries. The daily variability of objects was smallest in densely populated settlements and largest in the commercial centre. Our novel data and methodology shows that smart sensing and analytics can inform planning and policy decisions for making cities more liveable, equitable, sustainable and healthy.
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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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Nguyen PD, Hansen KL, Lechat B, Zajamsek B, Hansen C, Catcheside P. Beyond traditional wind farm noise characterisation using transfer learning. JASA EXPRESS LETTERS 2022; 2:052801. [PMID: 36154064 DOI: 10.1121/10.0010494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
This study proposes an approach for the characterisation and assessment of wind farm noise (WFN), which is based on extraction of acoustic features between 125 and 7500 Hz from a pretrained deep learning model (referred to as deep acoustic features). Using data measured at a variety of locations, this study shows that deep acoustic features can be linked to meaningful characteristics of the noise. This study finds that deep acoustic features can reveal an improved spatial and temporal representation of WFN compared to what is revealed using traditional spectral analysis and overall noise descriptors. These results showed that this approach is promising, and thus it could provide the basis for an improved framework for WFN assessment in the future.
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Affiliation(s)
- Phuc D Nguyen
- College of Science and Engineering, Flinders University, Adelaide, South Australia 5042, Australia
| | - Kristy L Hansen
- College of Science and Engineering, Flinders University, Adelaide, South Australia 5042, Australia
| | - Bastien Lechat
- Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia 5042, Australia
| | - Branko Zajamsek
- College of Science and Engineering, Flinders University, Adelaide, South Australia 5042, Australia
| | - Colin Hansen
- School of Mechanical Engineering, University of Adelaide, Adelaide, South Australia 5005, Australia , , , , ,
| | - Peter Catcheside
- Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia 5042, Australia
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Workers’ Satisfaction vis-à-vis Environmental and Socio-Morphological Aspects for Sustainability and Decent Work. SUSTAINABILITY 2022. [DOI: 10.3390/su14031699] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
This study examines worker satisfaction vis-à-vis outdoor places in terms of their environmental and socio-morphological aspects. Numerous studies have considered decent work as the eighth goal of sustainable development. However, it is worth investigating outdoor workers’ satisfaction with a view to the practical design of the surrounding context that supports their work in outdoor places. Using bibliometric analysis, this study investigates possible approaches toward providing decent work in a public place in Cairo as a case study, focusing on outdoor workers’ satisfaction. In the bibliometric analysis, this study used query settings in the Scimago database to search for manuscripts published in the previous five years. The result yielded 195 manuscripts that were filtered down to 50 manuscripts and then grouped using VOSviewr Software. Environmental noise and heat assessment analyses were performed using noise level measurements, remote sensing, and the Grasshopper platform. Further, we conducted an ethnographic study employing 77 participant observations. The results show that work hours and time affect worker satisfaction, as do environmental conditions, particularly noise and heat. However, unexpected findings from participant observation in this study do not accord with findings in other scholarly sources, where other observers find workers neither satisfied nor dissatisfied with the spatial morphology in the case study. Per this study, the alignment of worker satisfaction with convenient socio-morphological tangible elements of the workplace and with other environmental aspects should be attained in both specified replicable methods to engender decent work for outdoor workers.
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