Land use regression modelling estimating nitrogen oxides exposure in industrial south Durban, South Africa

Muttoo, Sheena; Ramsay, Lisa; Brunekreef, Bert; Beelen, Rob; Meliefste, Kees; Naidoo, Rajen N.

doi:10.1016/j.scitotenv.2017.07.278

Cited by 50 publications

(35 citation statements)

References 41 publications

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“…The annual NO 2 LUR model explained a large component of the spatial variability (76%), which is comparable to other studies of annual NO 2 LUR models, such as for example in Europe (median R 2 of 0.82 across 36 study areas) [ 28 ], in California, US (R 2 0.71), Toronto, Canada (R 2 0.69) [ 32 ], and Taiwan (R 2 0.74) [ 33 ]. A recent study in Durban in the KwaZulu-Natal province of South Africa also developed a NOx LUR model explaining 73% of variance [ 16 ]. However, very few studies have developed seasonal models.…”

Section: Discussionmentioning

confidence: 99%

“…A study from 2015 applied LUR modelling in Africa to investigate the spatial variation of NO 2 in Mauritania [ 15 ]. Recently, Muttoo et al used LUR to predict NOX levels in Durban, South Africa [ 16 ]. The studies demonstrated that the same method as used in Western countries settings can be applied in African towns and provide consistent models and predictions.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…The model was developed using the European Study of Cohorts for Air Pollution Effects (ESCAPE) approach (Beelen et al, 2013), which entailed monitoring NOx levels at selected locations within the study area and regressing these measurements against a predefined set of geographical predictor variables in a multivariate regression model. The model parameters were then used to derive estimates of NOx exposure at the participant's home, and data from this study have been published (Muttoo et al, 2017). (b) NBW: 2,500-4,000 g; (c) HBW: >4,000 g; (d) NGA: >37 weeks;…”

Section: Nox Exposure Characterizationmentioning

confidence: 99%

“…body mass index, human immunodeficiency virus, hypertension, low birthweight, nitrogen oxide, p53 Pro72Arg the aforementioned into consideration and based on studies linking maternal p53 rs1042522, HIV infection and HAART exposure, BMI and NOx air pollution exposure with oxidative stress, DNA damage and ABO, the present study investigated whether HIV infection, higher-than-normal BMI and NOx exposure during pregnancy, hinders maternal health and foetal growth in rs1042522 genotyped South African pregnant women living in Durban, a city with high traffic volumes and high NOx pollution levels (Matooane & Diab, 2001;Muttoo et al, 2017;Naidoo, Gqaleni, Batterman, & Robins, 2006). In this study, pregnant women were genotyped for the rs1042522 and subdivided based on HIV status, and infants' birthweight and gestational age.…”

Section: Introductionmentioning

confidence: 99%

Effect of maternal HIV infection, BMI and NOx air pollution exposure on birth outcomes in South African pregnant women genotyped for the p53 Pro72Arg (rs1042522)

Naidoo

Ramkaran

et al. 2020

Int J Immunogenetics

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Tumour suppressor protein, p53, plays a role in modulating innate immune responses, DNA repair, cell cycle arrest, senescence and apoptosis. Maternal nitrogen oxide (NOx) air pollution exposure, body mass index (BMI), human immunodeficiency virus (HIV) infection and p53 Pro72Arg (rs1042522) affect foetal growth. We investigated whether the aforementioned factors influence birth outcomes in a South African population. Pregnant women (n = 300; HIV −ve = 194 and HIV +ve = 106) were genotyped for the p53 rs1042522 using polymerase chain reaction–restriction fragment length polymorphism (PCR‐RFLP), and further stratified based on HIV status, infants' birthweight (BW; NBW: normal BW [>2,500 g] and LBW: low BW [<2,500 g]) and gestational age (GA; NGA: normal GA [>37 weeks] and PTB: preterm birth [≤37 weeks]). A land use regression model was developed to characterize maternal NOx exposure. Pearson's correlation and multivariate regression analysis statistical tests were used to determine the effect of rs1042522 genotyped pregnant women's BMI and NOx exposure on maternal blood pressure and haemoglobin and iron levels, and infants' anthropometric measurements and Appearance Pulse Grimace Activity and Respiration (APGAR) scores. The prevalence of LBW and PTB was 14.7% and 18.7%, respectively. The LBW group had a higher frequency of the variant Arg‐allele versus NBW group (47.7% vs. 31.4%, p = .0046, OR = 2.0, 95% CI = 1.26–3.17). No association was observed between NGA and PTB groups. A significant association between BMI and systolic blood pressure (r = .50, p = .00; B = 0.76, p = .002) and birth length (r = −.28, p = .01; B = −0.107, p = .011), and NOx and birth length (r = −.26, p = .08; B = −0.191, p = .046) and birthweight (B = −8.87, p = .048) was observed in HIV‐infected mothers with the variant Pro/Arg + Arg/Arg genotypes. Mothers from the LBW group with the variant genotypes displayed an association between NOx and diastolic blood pressure (r = .58, p = .04), blood iron levels (r = −.60, p = .04; B = −0.204, p = .004), APGAR scores at 1 min (r = −.86, p = .00; B = −0.101, p = .003) and 5 min (r = −.75, p = .01) and birth length (r = −.61, p = .04), and BMI and diastolic blood pressure (r = .72, p = .01). In the PTB group, maternal variant genotypes and NOx were associated with blood haemoglobin levels (B = −0.132, p = .045) and APGAR scores at 1 min (B = −0.161, p = .045) and 5 min (B = −0.147, p = .043). Maternal rs1042522 Arg‐allele, HIV infection, BMI and NOx exposure collectively play a role in lowering blood iron levels, gestational hypertension and LBW outcomes.

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“…So linear regression models will not work well. But the progression of air pollution exposure assessment can be range from ground-based, single site air pollution exposure model to an advanced spatial-temporal model which uses advanced machine learning algorithms [14]- [20]. Recent advances in sensor technology facilitates personal exposure monitoring in a sustainable manner [21], [22].…”

Section: A Air Pollution Exposure Monitoringmentioning

confidence: 99%

Machine Learning for Epidemiological Analysis in The Industrial Area for a Sustainable Life

2019

IJITEE

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Pollution exposure and human health in the industry contaminated area are always a concern. The need for industrialization urges to concentrate on sustainable life of residents in the vicinity of the industrial area rather than opposing the industrialists. Literature in epidemiological studies reveal that air pollution is one of the major problems for health risks faced by residents in the industrial area. Main pollutants in industry related air pollution are particulate matter (PM2.5, PM10), SO2 , NO2 , and other pollutants upon the industry. Data for epidemiological studies obtained from different sources which are limited to public access include residents’ sociodemographic characters, health problems, and air quality index for personal exposure to pollutants. This combined data and limited resources make the analysis more complex so that statistical methods cannot compensate. Our review finds that there is an increase in literature that evaluates the connection between ambient air pollution exposure and associated health events of residents in the industrially polluted area using statistical methods, mainly regression models. A very few applies machine learning techniques to figure out the impact of common air pollution exposure on human health. Most of the machine learning approach to epidemiological studies end up in air pollution exposure monitoring, not to correlate its association with diseases. A machine learning approach to epidemiological studies can automatically characterize the residents’ exposure to pollutants and its associated health effects. Uniqueness of the model depends on the appropriate exhaustive data that characterizes the features, and machine learning algorithm used to build the model. In this contribution, we discuss various existing approaches that evaluate residents’ health effects and the source of irritation in association with air pollution exposure, focuses machine learning techniques and mathematical background for epidemiological studies for residents’ sustainable life.

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Land use regression modelling estimating nitrogen oxides exposure in industrial south Durban, South Africa

Cited by 50 publications

References 41 publications

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

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

Effect of maternal HIV infection, BMI and NOx air pollution exposure on birth outcomes in South African pregnant women genotyped for the p53 Pro72Arg (rs1042522)

Machine Learning for Epidemiological Analysis in The Industrial Area for a Sustainable Life

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