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

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.

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

“…Measurements were performed by trained fieldworkers in these locations as well as in one school in Marconi-Beam and Masiphumulele, two schools in Khayelitsha and at the official air pollution monitoring station in Khayelitsha. The selected sites were additionally monitored for noise, which led to predictive models of noise levels for the study participants, as described by Sieber et al, (2017) [ 20 ].…”

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

“…Previous studies have used the Noise Sentry SLM in diverse settings. 12 14 Our prepilot tests of monitor–to-monitor precision showed good agreement (more details in online supplementary information 1 (SI 1)). Our Noise Sentry SLMs were also validated in a separate aircraft noise study conducted in San Francisco against a Type I industry standard instrument (DUO 01 dB), 37 and the agreement was high (mean and median second by second difference between the instruments was −0.42 and −0.38 dBA, respectively).…”

“… 48 49 To date, most applications of LUR models for air and noise pollution have been in high-income countries, 13 14 48 with an emerging number in Asian cities and a limited number in African cities. 12 50–52 To generate high-resolution estimates of air and noise pollution in the GAMA, we will build LUR models with spatial and temporal predictor variables. The models will also include terms that allow for the capturing of systematic temporal patterns, for example, random intercepts for hour of the day or month of the year, and terms that use pollution levels at fixed sites to remove weekly temporal changes.…”

“…There are even less data on noise pollution, and none on its health burden. The limited noise data show much higher levels compared with cities in highincome countries, [10][11][12][13][14][15] which may be associated with hearing loss, sleep disturbance, impaired cognitive function, and cardiovascular disease. [16][17][18] Air and noise pollution in SSA have a complex mix of local and regional sources: these include informal industries; transportation predominantly from old imported vehicles for commercial and private use; biomass use for household and commercial activities; household trash burning; resuspended dust from unpaved roads; dust from regional dust storms; and noise from road traffic, small road-side businesses, and religious practices, to name a few.…”

Slums, Space, and State of Health—A Link between Settlement Morphology and Health Data

“…Non-specific studies were mentioned 106 times, with most of the studies also being conducted on the city level (50%). These are, for example, studies on aging and the related effects or questions [76,77], studies on the influence of community centers [78], noise exposure [79], the impact of climate change on health [80], the influence of smoke from cooking stations [81], and general studies on sanitation that do not investigate its influence on a specific infectious disease.…”