Spatial air pollution modelling for a West-African town

Gebreab, Sirak Zenebe; Vienneau, Danielle; Feigenwinter, Christian; Ba, Hampâté; Cissé, Guéladio; Tsai, Ming-Yi

doi:10.4081/gh.2015.321

Cited by 11 publications

(16 citation statements)

References 35 publications

(39 reference statements)

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“…With this, the models are suitable for use in local epidemiological studies. These levels of explanatory degree are also in the same magnitude as earlier models conducted in South Africa and Mauritania [18,19,34]. In Durban, South Africa, for instance, a LUR model could explain 73% of the variance of the NO x levels (30).…”

Section: Lur Modelssupporting

confidence: 72%

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Measurements of NOx and Development of Land Use Regression Models in an East-African City

et al. 2021

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Air pollution causes premature mortality and morbidity globally, but these adverse health effects occur over proportionately in low- and middle-income countries. Lack of both air pollution data and knowledge of its spatial distribution in African countries have been suggested to lead to an underestimation of health effects from air pollution. This study aims to measure nitrogen oxides (NOx), as well as nitrogen dioxide (NO2), to develop Land Use Regression (LUR) models in the city of Adama, Ethiopia. NOx and NO2 was measured at over 40 sites during six days in both the wet and dry seasons. Throughout the city, measured mean levels of NOx and NO2 were 29.0 µg/m3 and 13.1 µg/m3, respectively. The developed LUR models explained 68% of the NOx variances and 75% of the NO2. Both models included similar geographical predictor variables (related to roads, industries, and transportation administration areas) as those included in prior LUR models. The models were validated by using leave-one-out cross-validation and tested for spatial autocorrelation and multicollinearity. The performance of the models was good, and they are feasible to use to predict variance in annual average NOx and NO2 concentrations. The models developed will be used in future epidemiological and health impact assessment studies. Such studies may potentially support mitigation action and improve public health.

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Section: Lur Modelssupporting

confidence: 72%

“…Its methodology has even been applied in some parts of Asia [33]. To the best of our knowledge, only a few applications of the LUR model have been performed within the African continent to estimate NO x or NO 2 , as well as to assess the association between long-term ambient air pollution and health effects [18,19,34].…”

Section: Introductionmentioning

confidence: 99%

Measurements of NOx and Development of Land Use Regression Models in an East-African City

et al. 2021

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“…On the other side, this study is the first to give rise to noise measurements over several days in South Africa, more especially in informal settings. In addition, no LUR model for noise exposure had yet been developed with data from the African continent, one previous LUR model having been developed for air pollution in Western Africa [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

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

Sieber

Ragettli

Brink

et al. 2017

IJERPH

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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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“…Global population growth contributes to the destruction, modification and fragmentation of wildlife habitat, reduced genetic diversity, threats from pathogens, the spread of exotic and invasive species, air, noise and light pollution, alteration in natural hydrologic and fire regimes, and a rapidly changing climate [33–37]. The consequences of these environmental changes for most species include a reduced ability to forage, reduced prey or food availability, altered immune function, and diminished breeding success [38–45]. Changes to any of these life traits can compromise the persistence of native fauna populations in the wild.…”

Section: Introductionmentioning

confidence: 99%

The impact of human activities on Australian wildlife

et al. 2019

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Increasing human population size and the concomitant expansion of urbanisation significantly impact natural ecosystems and native fauna globally. Successful conservation management relies on precise information on the factors associated with wildlife population decline, which are challenging to acquire from natural populations. Wildlife Rehabilitation Centres (WRC) provide a rich source of this information. However, few researchers have conducted large-scale longitudinal studies, with most focussing on narrow taxonomic ranges, suggesting that WRC-associated data remains an underutilised resource, and may provide a fuller understanding of the anthropogenic threats facing native fauna. We analysed admissions and outcomes data from a WRC in Queensland, Australia Zoo Wildlife Hospital, to determine the major factors driving admissions and morbidity of native animals in a region experiencing rapid and prolonged urban expansion. We studied 31,626 admissions of 83 different species of native birds, reptiles, amphibians, marsupials and eutherian mammals from 2006 to 2017. While marsupial admissions were highest (41.3%), admissions increased over time for all species and exhibited seasonal variation (highest in Spring to Summer), consistent with known breeding seasons. Causes for admission typically associated with human influenced activities were dominant and exhibited the highest mortality rates. Car strikes were the most common reason for admission (34.7%), with dog attacks (9.2%), entanglements (7.2%), and cat attacks (5.3%) also high. Admissions of orphaned young and overt signs of disease were significant at 24.6% and 9.7%, respectively. Mortality rates were highest following dog attacks (72.7%) and car strikes (69.1%) and lowest in orphaned animals (22.1%). Our results show that WRC databases offer rich opportunities for wildlife monitoring and provide quantification of the negative impacts of human activities on ecosystem stability and wildlife health. The imminent need for urgent, proactive conservation management to ameliorate the negative impacts of human activities on wildlife is clearly evident from our results.

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Spatial air pollution modelling for a West-African town

Cited by 11 publications

References 35 publications

Measurements of NOx and Development of Land Use Regression Models in an East-African City

Measurements of NOx and Development of Land Use Regression Models in an East-African City

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

The impact of human activities on Australian wildlife

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