Land Use Regression Modeling of Outdoor Noise Exposure in Informal Settlements in Western Cape, South Africa

Sieber, Chloé; Ragettli, Martina S.; Brink, Mark; Olaniyan, Toyib; Baatjies, Roslyn; Saucy, Apolline; Probst‐Hensch, Nicole; Dalvie, Mohamed Aqiel

doi:10.20944/preprints201708.0035.v1

2017

DOI: 10.20944/preprints201708.0035.v1

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Land Use Regression Modeling of Outdoor Noise Exposure in Informal Settlements in Western Cape, South Africa

Chloé Sieber

Martina S. Ragettli

Mark Brink

et al.

Abstract: 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 (L den ) from geographi… Show more

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Cited by 7 publications

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Towards Urban Sustainability: Developing Noise Prediction Model in an Informal Setting

et al. 2022

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Noise remains an important challenge, particularly in informal settings where planning and regulation are relatively weak. This study aims at developing a model to predict noise in a largely informal urban Kano, the second most populated city in Nigeria. Sound level meter (SLM) 200 TL was used to measure noise at locations covering different land use: residential, industrial, commercial, educational, and administrative areas. Data were collected for seven days, and each day measurements were taken for six hours: 8–10 a.m., 12–2 p.m. and 4–6 p.m. Land use, population density, residential division, traffic volume, and land cover were used to generate a noise model using weighted geographic regression. The findings revealed that noise in the area is higher than the permissible limits set by the WHO and Nigeria’s regulatory agency. The model identified population density as the most influencing factor, followed by land cover, traffic volume and distance to the road, then land use. Seventy three percent of the model’s residual are below five, indicating a significant association between noise and the variables used. The R2 ranges between 18% and 26% depending on the time of the day. Noise in the area can be effectively control by paying serious attention to city planning and enforcing traffic regulation measures.

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Towards Urban Sustainability: Developing Noise Prediction Model in an Informal Setting

et al. 2022

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Traffic Noise Modelling Using Land Use Regression Model Based on Machine Learning, Statistical Regression and GIS

et al. 2021

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This study estimates the equivalent continuous sound pressure level (Leq) during peak daily periods (‘rush hour’) along the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia, using a land use regression (LUR) model based on machine learning, statistical regression, and geographical information systems (GIS). The research utilises two types of soft computing methods including machine learning (i.e., decision tree, random frost algorithms) and statistical regression (i.e., linear regression, support vector regression algorithms) to determine the best approach to create a prediction Leq map at the NKVE in Shah Alam, Malaysia. The selection of the best algorithm is accomplished by considering correlation, correlation coefficient, mean-absolute-error, mean-square-error, root-mean-square-error, and mean absolute percentage error. Traffic noise level was monitored using three sound level meters (TES 52A), and a traffic tally was done to analyse the traffic flow. Wind speed was gauged using a wind speed meter. The study relied on a variety of noise predictors including wind speed, digital elevation model, land use type (specifically, if it was residential, industrial, or natural reserve), residential density, road type (expressway, primary, and secondary) and traffic noise average (Leq). The above parameters were fed as inputs into the LUR model. Additional noise influencing factors such as traffic lights, intersections, road toll gates, gas stations, and public transportation infrastructures (bus stop and bus line) are also considered in this study. The models utilised parameters derived from LiDAR (Light Detection and Ranging) data, and various GIS (Geographical Information Systems) layers were extracted to produce the prediction maps. The results highlighted the superior performances by the machine learning (random forest) models compared to the statistical regression-based models.

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Land Use Regression Modelling of Outdoor NO2 and PM2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa

Saucy

Röösli

Künzli

et al. 2018

IJERPH

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Air pollution can cause many adverse health outcomes, including cardiovascular and respiratory disorders. Land use regression (LUR) models are frequently used to describe small-scale spatial variation in air pollution levels based on measurements and geographical predictors. They are particularly suitable in resource limited settings and can help to inform communities, industries, and policy makers. Weekly measurements of NO2 and PM2.5 were performed in three informal areas of the Western Cape in the warm and cold seasons 2015–2016. Seasonal means were calculated using routinely monitored pollution data. Six LUR models were developed (four seasonal and two annual) using a supervised stepwise land-use-regression method. The models were validated using leave-one-out-cross-validation and tested for spatial autocorrelation. Annual measured mean NO2 and PM2.5 were 22.1 μg/m3 and 10.2 μg/m3, respectively. The NO2 models for the warm season, cold season, and overall year explained 62%, 77%, and 76% of the variance (R2). The PM2.5 annual models had lower explanatory power (R2 = 0.36, 0.29, and 0.29). The best predictors for NO2 were traffic related variables (major roads, bus routes). Local sources such as grills and waste burning sites appeared to be good predictors for PM2.5, together with population density. This study demonstrates that land-use-regression modelling for NO2 can be successfully applied to informal peri-urban settlements in South Africa using similar predictor variables to those performed in Europe and North America. Explanatory power for PM2.5 models is lower due to lower spatial variability and the possible impact of local transient sources. The study was able to provide NO2 and PM2.5 seasonal exposure estimates and maps for further health studies.

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Land Use Regression Modeling of Outdoor Noise Exposure in Informal Settlements in Western Cape, South Africa

Cited by 7 publications

References 1 publication

Towards Urban Sustainability: Developing Noise Prediction Model in an Informal Setting

Towards Urban Sustainability: Developing Noise Prediction Model in an Informal Setting

Traffic Noise Modelling Using Land Use Regression Model Based on Machine Learning, Statistical Regression and GIS

Land Use Regression Modelling of Outdoor NO2 and PM2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa

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