Impact of ambient air pollution exposure during pregnancy on adverse birth outcomes: generalized structural equation modeling approach.

BACKGROUND: Air pollution and several prenatal factors, such as socio-demographic, behavioural, physical activity and clinical factors influence adverse birth outcomes. The study aimed to investigate the impact of ambient air pollution exposure during pregnancy adjusting prenatal risk factors on adverse birth outcomes among pregnant women in MACE birth cohort. METHODS: Data for the study was obtained from the Mother and Child in the Environment (MACE) birth cohort study in Durban, South Africa from 2013 to 2017. Land use regression models were used to determine household level prenatal exposure to PM2.5, SO2 and NOx. Six hundred and fifty-six births of pregnant females were selected from public sector antenatal clinics in low socio-economic neighbourhoods. We employed a Generalised Structural Equation Model with a complementary log-log-link specification. RESULTS: After adjustment for potential prenatal factors, the results indicated that exposure to PM2.5 was found to have both significant direct and indirect effects on the risk of all adverse birth outcomes. Similarly, an increased level of maternal exposure to SO2 during pregnancy was associated with an increased probability of being small for gestational age. Moreover, preterm birth act a mediating role in the relationship of exposure to PM2.5, and SO2 with low birthweight and SGA. CONCLUSIONS: Prenatal exposure to PM2.5 and SO2 pollution adversely affected birth outcomes after controlling for other prenatal risk factors. This suggests that local government officials have a responsibility for better control of air pollution and health care providers need to advise pregnant females about the risks of air pollution during pregnancy.

Maternal exposure to indoor PM2.5 and associated adverse birth outcomes in low socio-economic households, Durban, South Africa.

The association between in utero exposure to indoor PM2.5 and birth outcomes is not conclusive. We assessed the association between in utero exposure to indoor PM2.5 , birth weight, gestational age, low birth weight, and/or preterm delivery. Homes of 800 pregnant women were assessed using a structured walkthrough questionnaire. PM2.5 measurements were undertaken in 300 of the 800 homes for a period of 24 h. Repeated sampling was conducted in 30 of these homes to determine PM2.5 predictors that can reduce within-and/or between-home variability. A predictive model was used to estimate PM2.5 levels in unmeasured homes (n = 500). The mean (SD) for PM2.5 was 37 µg/m3 (29) with a median of 28µg/m3 . The relationship between PM2.5 exposure, birth weight, gestational age, low birth weight, and preterm delivery was assessed using multivariate linear and logistic regression models. We explored infant sex as a potential effect modifier, by creating an interaction term between PM2.5 and infant sex. The odds ratio of low birth weight and preterm delivery was 1.75 (95%CI: 1.47, 2.09) and 1.21 (95%CI: 1.06, 1.39), respectively, per interquartile increase (18 µg/m3 ) in PM2.5 exposure. The reduction in birth weight and gestational age was 75 g (95%CI: 107.89, 53.15) and 0.29 weeks (95%CI: 0.40, 0.19) per interquartile increase in PM2.5 exposure. Infant sex was an effect modifier for PM2.5 on birth weight and gestational age, and the reduction in birth weight and gestational age was 103 g (95%CI: 142.98, 64.40) and 0.38 weeks (95% CI: 0.53, 0.23), respectively, for boys, and 54 g (95%CI: 91.78,15.62) and 0.23 weeks (95%CI:0.37, 0.08), respectively, for girls. Exposure to PM2.5 is associated with adverse pregnancy outcomes. To protect the population during their reproductive period, public health policy should focus on indoor PM2.5 levels.

A hybrid air pollution / land use regression model for predicting air pollution concentrations in Durban, South Africa.

The objective of this paper was to incorporate source-meteorological interaction information from two commonly employed atmospheric dispersion models into the land use regression technique for predicting ambient nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter (PM10). The study was undertaken across two regions in Durban, South Africa, one with a high industrial profile and a nearby harbour, and the other with a primarily commercial and residential profile. Multiple hybrid models were developed by integrating air pollution dispersion modelling predictions for source specific NO2, SO2, and PM10 concentrations into LUR models following the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to characterise exposure, in Durban. Industrial point sources, ship emissions, domestic fuel burning, and vehicle emissions were key emission sources. Standard linear regression was used to develop annual, summer and winter hybrid models to predict air pollutant concentrations. Higher levels of NO2 and SO2 were predicted in south Durban as compared to north Durban as these are industrial related pollutants. Slightly higher levels of PM10 were predicted in north Durban as compared to south Durban and can be attributed to either traffic, bush burning or domestic fuel burning. The hybrid NO2 models for annual, summer and winter explained 60%, 58% and 63%, respectively, of the variance with traffic, population and harbour being identified as important predictors. The SO2 models were less robust with lower R2 annual (44%), summer (53%) and winter (46%), in which industrial and traffic variables emerged as important predictors. The R2 for PM10 models ranged from 80% to 85% with population and urban land use type emerging as predictor variables.

Harbor and Intra-City Drivers of Air Pollution: Findings from a Land Use Regression Model, Durban, South Africa.

Multiple land use regression models (LUR) were developed for different air pollutants to characterize exposure, in the Durban metropolitan area, South Africa. Based on the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology, concentrations of particulate matter (PM10 and PM2.5), sulphur dioxide (SO2), and nitrogen dioxide (NO2) were measured over a 1-year period, at 41 sites, with Ogawa Badges and 21 sites with PM Monitors. Sampling was undertaken in two regions of the city of Durban, South Africa, one with high levels of heavy industry as well as a harbor, and the other small-scale business activity. Air pollution concentrations showed a clear seasonal trend with higher concentrations being measured during winter (25.8, 4.2, 50.4, and 20.9 µg/m3 for NO2, SO2, PM10, and PM2.5, respectively) as compared to summer (10.5, 2.8, 20.5, and 8.5 µg/m3 for NO2, SO2, PM10, and PM2.5, respectively). Furthermore, higher levels of NO2 and SO2 were measured in south Durban as compared to north Durban as these are industrial related pollutants, while higher levels of PM were measured in north Durban as compared to south Durban and can be attributed to either traffic or domestic fuel burning. The LUR NO2 models for annual, summer, and winter explained 56%, 41%, and 63% of the variance with elevation, traffic, population, and Harbor being identified as important predictors. The SO2 models were less robust with lower R2 annual (37%), summer (46%), and winter (46%) with industrial and traffic variables being important predictors. The R2 for PM10 models ranged from 52% to 80% while for PM2.5 models this range was 61-76% with traffic, elevation, population, and urban land use type emerging as predictor variables. While these results demonstrate the influence of industrial and traffic emissions on air pollution concentrations, our study highlighted the importance of a Harbor variable, which may serve as a proxy for NO2 concentrations suggesting the presence of not only ship emissions, but also other sources such as heavy duty motor vehicles associated with the port activities.

Predictors of urban household variability of indoor PM2.5 in low socio-economic communities.

In epidemiological studies, levels of PM2.5 need to be estimated over time and space. Because of logistical constraints, very few studies have been conducted to assess the variability within and across homes and the predictors of this variability. This study evaluated within- and between-home variability of indoor PM2.5 and identified predictors for PM2.5 in homes of mothers participating in the urban Mother and Child in the Environment birth cohort study in Durban, South Africa. Thirty homes were selected from 300 homes that were previously sampled for PM2.5. Two measurements of PM2.5 levels were conducted in each home within a 1 week interval in both warm and cold seasons (four samplings per home) using Airmetrics MiniVol samplers. A linear mixed-effect model was used to evaluate within- and between-home variability and to identify fixed effects (predictors) that result in reduced variability. The PM2.5 levels in the 30 homes ranged from 2 to 303 µg m-3. The within-home variability accounted for 94% of the total variability in the log-transformed PM2.5 levels for the 30 homes. The fixed effects extracted from the repeated samplings in the present study were used to improve a previously developed multivariable linear regression model for 300 homes, and thereby increased the R2 from 0.50 to 0.54. Inclusion of fixed-effects in multivariable linear regression models resulted in a reasonably robust model that can be used to predict PM2.5 levels in unmeasured homes of the cohort.