Characterization of allergens and airborne fungi in low and middle-income homes of primary school children in Durban, South Africa

Indoor Household Exposures and Associated Morbidity and Mortality Outcomes in Children and Adults in South Africa

Human exposure to indoor pollution is one of the most well-established ways that housing affects health. We conducted a review to document evidence on the morbidity and mortality outcomes associated with indoor household exposures in children and adults in South Africa. The authors conducted a scientific review of the publicly available literature up to April 2022 using different search engines (PubMed, ProQuest, Science Direct, Scopus and Google Scholar) to identify the literature that assessed the link between indoor household exposures and morbidity and mortality outcomes in children and adults. A total of 16 studies with 16,920 participants were included. Bioaerosols, allergens, dampness, tobacco smoking, household cooking and heating fuels, particulate matter, gaseous pollutants and indoor spray residue play a significant role in different morbidity outcomes. These health outcomes include dental caries, asthma, tuberculosis, severe airway inflammation, airway blockage, wheeze, rhinitis, bronchial hyperresponsiveness, phlegm on the chest, current rhinoconjunctivitis, hay fever, poor early life immune function, hypertensive disorders of pregnancy, gestational hypertension, and increased incidence of nasopharyngeal bacteria, which may predispose people to lower respiratory tract infections. The findings of this research highlight the need for more initiatives, programs, strategies, and policies to better reduce the negative consequences of indoor household exposures.

Indoor air quality of low and middle income urban households in Durban, South Africa

INTRODUCTION

Elevated levels of indoor air pollutants may cause cardiopulmonary disease such as lower respiratory infection, chronic obstructive lung disease and lung cancer, but the association with tuberculosis (TB) is unclear. So far the risk estimates of TB infection or/and disease due to indoor air pollution (IAP) exposure are based on self-reported exposures rather than direct measurements of IAP, and these exposures have not been validated.

OBJECTIVE

The aim of this paper was to characterize and develop predictive models for concentrations of three air pollutants (PM₁₀, NO₂ and SO₂) in homes of children participating in a childhood TB study.

METHODS

Children younger than 15 years living within the eThekwini Municipality in South Africa were recruited for a childhood TB case control study. The homes of these children (n=246) were assessed using a walkthrough checklist, and in 114 of them monitoring of three indoor pollutants was also performed (sampling period: 24h for PM₁₀, and 2-3 weeks for NO₂ and SO₂). Linear regression models were used to predict PM₁₀ and NO₂ concentrations from household characteristics, and these models were validated using leave out one cross validation (LOOCV). SO₂ concentrations were not modeled as concentrations were very low.

RESULTS

Mean indoor concentrations of PM₁₀ (n=105)_, NO₂ (n=82) and SO₂ (n=82) were 64μg/m³ (range 6.6-241); 19μg/m³ (range 4.5-55) and 0.6μg/m³ (range 0.005-3.4) respectively with the distributions for all three pollutants being skewed to the right. Spearman correlations showed weak positive correlations between the three pollutants. The largest contributors to the PM₁₀ predictive model were type of housing structure (formal or informal), number of smokers in the household, and type of primary fuel used in the household. The NO₂ predictive model was influenced mostly by the primary fuel type and by distance from the major roadway. The coefficients of determination (R²) for the models were 0.41 for PM₁₀ and 0.31 for NO₂. Spearman correlations were significant between measured vs. predicted PM₁₀ and NO₂ with coefficients of 0.66 and 0.55 respectively.

CONCLUSION

Indoor PM₁₀ levels were relatively high in these households. Both PM₁₀ and NO₂ can be modeled with a reasonable validity and these predictive models can decrease the necessary number of direct measurements that are expensive and time consuming.

Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control

Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM2.5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM2.5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web.

The association of respiratory symptoms and indoor housing conditions among migrant farmworkers in eastern North Carolina

Farm labor housing has been described as among the worst in the nation, oftentimes with poor and unsanitary indoor living conditions. The objective of this study was to evaluate the association between indoor environmental risk factors and respiratory health among migrant farmworker occupants (N = 352) living in employer-provided housing. A cross-sectional sample of adult Latino male farm laborers were administered a questionnaire to identify the prevalence of major respiratory symptoms. Self-reported and independent observations were made to evaluate environmental respiratory risk factors and indoor housing conditions, including but not limited to, the presence of cockroaches, rodents, pesticides, and visible signs of mold. Spirometry was performed to evaluate lung function using FEV1 (forced expiratory volume in 1 second), FVC (forced vital capacity), and FEV1 /FVC ratio. Bivariate analysis was applied to evaluate associations between respiratory symptoms and selected indoor environmental risk factors. Findings for respiratory health included prevalence of wheeze (11.4%), coughing up phlegm (17.3%), tightness of chest (16.8%), and runny or stuffy nose (34.4%). Respiratory risks identified inside the dwellings included the use of pesticides or bug sprays for cockroaches (31.5%), rat or mouse poison (19.5%), visible signs of water damage in the bathroom (22.5%), and mold in the sleeping room (11.1%). Spirometry values were normal for most occupants, although statistically significant associations were found between mold and coughing up phlegm when not having a cold (P = .0262); presence of mold and asthma (P = .0084); pesticides used in the home and tightness of chest (P = .0001); and use of tobacco and coughing up phlegm (P = .0131). Although causal inference can be difficult to establish from a cross-sectional study, findings from this study represents suggestive evidence that indoor environmental risk factors may be contributory factors for respiratory health problems among this vulnerable workgroup population.