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

Spatial variation and factors associated of solid fuel use in Ethiopia a multilevel and spatial analysis based on EDHS 2016

Cooking and heating using solid fuels, such as dung, wood, agricultural residues, grass, straw, charcoal, and coal, is a main source of household air pollution. This indoor combustion encompasses a diversity of health detrimental pollutants, especially for people from low-income countries like Ethiopia since solid fuels are accessible easily at a lesser cost. Limited studies done showing factors affecting in choosing fuel type and no study, which revealed spatial heterogeneity of solid fuel used based on such nationally representative data. Therefore, this study, aimed at investigating spatial variation and determinants of solid fuel use in Ethiopia. This study was done using the data from the Ethiopian Demographic and Health Survey 2016, a national representative sample (16,650) households were included. Spatial and Multi-level logistic regression analysis was done by considering the DHS data hierarchal nature. Variables in the final model with a p-value < 0.05 were reported as significant predictors of using solid fuel. All analyses were done using ArcGIS V.10.7.1 and STATA V.14 software. The finding of this study revealed that 90.8% (95% CI (87.9%, 91.2%)) of households depend on solid fuel for cooking. Based on the final model ;Male household head (AOR 1.38, 95% CI (1.12-1.71)), age of household head (AOR 1.61, 95% CI (1.20, 2.17)), and 1.49 (OR 1.49, 95% CI (1.12, 1.99)) respectively for the age classes of < 30, and 30-40, education attainment no education (OR 3.14, 95% CI (1.13, 8.71)) and primary education (AOR 2.16, 95% CI (2.78, 5.96), wealth index Poorest (AOR 11.05, 95% CI (5.68, 15.78)), Poorer (OR 5.19, 95% CI (5.43, 13.19)), Middle (OR 3.08, 95% CI (2.44, 8.73)), and Richer (OR 1.30, 95IC (1.07, 13.49)) compared to richest, and not accessibility of electricity (AOR 31.21, 95% CI (35.41, 42.67)), were individual-level factors significantly associated with using solid fuel. Community-level factors like households found at large city (AOR 2.80, 95CI (1.65, 4.77)), small city (AOR 2.58, 95% CI (1.55, 4.32)) town (AOR 4.02, 95% CI (2.46, 6.55)), and countryside (AOR 14.40, 95% CI (6.23, 21.15)) compared households found in capital city, community level media exposure (AOR 6.00, 95% CI (4.61, 7.82)) were statistically predictors in using solid fuel for cooking. This finding revealed that a large proportion of households in Ethiopia heavily depend on biomass, especially wood, for cooking. There was a greater disparity on solid fuel use for cooking in Ethiopia. Implementing major policy interventions should be introduced to reduce solid fuel use for cooking and inequalities in accessing clean fuel in Ethiopia.

Indoor air pollutants and respiratory symptoms among residents of an informal urban settlement in Uganda: A cross-sectional study

BACKGROUND

Indoor air pollutants (IAP) and household conditions such as dampness, crowding and chemical exposures have been associated with acute and chronic respiratory infections. In Uganda, literature on the effects of IAP on respiratory outcomes in informal settlements is limited.

METHODS

We describe the baseline household characteristics of 284 adults and their children in an informal settlement in Uganda from April to May 2022. We monitored same-day indoor concentrations of particulate matter PM2.5, PM10, Carbon monoxide (CO), relative humidity %, and temperature from 9 am to 2 pm and interviewed caregivers/mothers about their respiratory symptoms and those of their children in the previous 30 days. We employed robust Poisson regressions to evaluate the associations between indoor air indicators and respiratory health symptoms.

RESULTS

Approximately 94.7% of households primarily used biomass fuels and 32.7% cooked from inside their dwelling rooms. The median PM2.5, PM10 and CO levels were 49.5 (Interquartile range (IQR) = 31.1,86.2) μg/m3, 73.6 (IQR = 47.3,130.5) μg/m3 and 7.70 (IQR = 4.1,12.5) ppm respectively. Among adults, a 10 unit increase in PM2.5 was associated with cough (Prevalence Ratio (PR) = 3.75, 95%CI 1.15-1.55). Dwelling unit dampness was associated with phlegm (PR = 2.53, 95%CI = 1.39-4.61) and shortness of breath (PR = 1.78, 95% CI 1.23-2.54) while cooking from outside the house was protective against shortness of breath (PR = 0.62, 95% CI = 0.44-0.87). In children, dampness was associated with phlegm (PR = 13.87, 95% CI 3.16-60.91) and cough (PR = 1.62, 95% CI 1.12-2.34) while indoor residual spraying was associated with phlegm (PR = 3.36, 95%CI 1.71-6.61).

CONCLUSION

Poor indoor air conditions were associated with respiratory symptoms in adults and children. Efforts to address indoor air pollution should be made to protect adults and children from adverse health effects.

Construction and evaluation of hourly average indoor PM2.5 concentration prediction models based on multiple types of places

Background

People usually spend most of their time indoors, so indoor fine particulate matter (PM2.5) concentrations are crucial for refining individual PM2.5 exposure evaluation. The development of indoor PM2.5 concentration prediction models is essential for the health risk assessment of PM2.5 in epidemiological studies involving large populations.

Methods

In this study, based on the monitoring data of multiple types of places, the classical multiple linear regression (MLR) method and random forest regression (RFR) algorithm of machine learning were used to develop hourly average indoor PM2.5 concentration prediction models. Indoor PM2.5 concentration data, which included 11,712 records from five types of places, were obtained by on-site monitoring. Moreover, the potential predictor variable data were derived from outdoor monitoring stations and meteorological databases. A ten-fold cross-validation was conducted to examine the performance of all proposed models.

Results

The final predictor variables incorporated in the MLR model were outdoor PM2.5 concentration, type of place, season, wind direction, surface wind speed, hour, precipitation, air pressure, and relative humidity. The ten-fold cross-validation results indicated that both models constructed had good predictive performance, with the determination coefficients (R2) of RFR and MLR were 72.20 and 60.35%, respectively. Generally, the RFR model had better predictive performance than the MLR model (RFR model developed using the same predictor variables as the MLR model, R2 = 71.86%). In terms of predictors, the importance results of predictor variables for both types of models suggested that outdoor PM2.5 concentration, type of place, season, hour, wind direction, and surface wind speed were the most important predictor variables.

Conclusion

In this research, hourly average indoor PM2.5 concentration prediction models based on multiple types of places were developed for the first time. Both the MLR and RFR models based on easily accessible indicators displayed promising predictive performance, in which the machine learning domain RFR model outperformed the classical MLR model, and this result suggests the potential application of RFR algorithms for indoor air pollutant concentration prediction.

Kitchen Characteristics and Practices Associated with Increased PM2.5 Concentration Levels in Zimbabwean Rural Households

Household air pollution (HAP) from biomass fuels significantly contributes to cardio-respiratory morbidity and premature mortality globally. Particulate matter (PM), one of the pollutants generated, remains the most accurate indicator of household air pollution. Determining indoor air concentration levels and factors influencing these levels at the household level is of prime importance, as it objectively guides efforts to reduce household air pollution. This paper describes household factors associated with increased PM_2.5 levels in Zimbabwean rural household kitchens. Our HAP and lung health in women study enrolled 790 women in rural and urban households in Zimbabwe between March 2018 and December 2019. Here, we report data from 148 rural households using solid fuel as the primary source of fuel for cooking and heating and where indoor air samples were collected. Data on kitchen characteristics and practices were collected cross-sectionally using an indoor walk-through survey and a modified interviewer-administered questionnaire. An Air metrics miniVol Sampler was utilized to collect PM_2.5 samples from the 148 kitchens over a 24 h period. To identify the kitchen features and practices that would likely influence PM_2.5 concentration levels, we applied a multiple linear regression model. The measured PM_2.5 ranged from 1.35 μg/m³ to 1940 μg/m³ (IQR: 52.1-472). The PM_2.5 concentration levels in traditional kitchens significantly varied from the townhouse type kitchens, with the median for each kitchen being 291.7 μg/m³ (IQR: 97.2-472.2) and 1.35 μg/m³ (IQR: 1.3-97.2), respectively. The use of wood mixed with other forms of biomass was found to have a statistically significant association (p < 0.001) with increased levels of PM_2.5 concentration. In addition, cooking indoors was strongly associated with higher PM_2.5 concentrations (p = 0.012). Presence of smoke deposits on walls and roofs of the kitchens was significantly associated with increased PM_2.5 concentration levels (p = 0.044). The study found that kitchen type, energy type, cooking place, and smoke deposits were significant predictors of increased PM₂.₅ concentrations in the rural households. Concentrations of PM_2.5 were high as compared to WHO recommended exposure limits for PM_2.5. Our findings highlight the importance of addressing kitchen characteristics and practices associated with elevated PM_2.5 concentrations in settings where resources are limited and switching to cleaner fuels may not be an immediate feasible option.

Childhood lower respiratory tract infections linked to residential airborne bacterial and fungal microbiota

Residential microbial composition likely contributes to the development of lower respiratory tract infections (LRTI) among children, but the association is poorly understood. We aimed to study the relationship between the indoor airborne dust bacterial and fungal microbiota and childhood LRTI in Ibadan, Nigeria. Ninety-eight children under the age of five years hospitalized with LRTI were recruited and matched by age (±3 months), sex, and geographical location to 99 community-based controls without LRTI. Participants' homes were visited and sampled over a 14-day period for airborne house dust using electrostatic dustfall collectors (EDC). In airborne dust samples, the composition of bacterial and fungal communities was characterized by a meta-barcoding approach using amplicons targeting simultaneously the bacterial 16S rRNA gene and the internal-transcribed-spacer (ITS) region-1 of fungi in association with the SILVA and UNITE database respectively. A 100-unit change in house dust bacterial, but not fungal, richness (OR 1.06; 95%CI 1.03-1.10) and a 1-unit change in Shannon diversity (OR 1.92; 95%CI 1.28-3.01) were both independently associated with childhood LRTI after adjusting for other indoor environmental risk factors. Beta-diversity analysis showed that bacterial (PERMANOVA p < 0.001, R² = 0.036) and fungal (PERMANOVA p < 0.001, R² = 0.028) community composition differed significantly between homes of cases and controls. Pair-wise differential abundance analysis using both DESEq2 and MaAsLin2 consistently identified the bacterial phyla Deinococcota (Benjamini-Hochberg (BH) adjusted p-value <0.001) and Bacteriodota (BH-adjusted p-value = 0.004) to be negatively associated with LRTI. Within the fungal microbiota, phylum Ascomycota abundance (BH adjusted p-value <0.001) was observed to be directly associated with LRTI, while Basidiomycota abundance (BH adjusted p-value <0.001) was negatively associated with LRTI. Our study suggests that early-life exposure to certain airborne bacterial and fungal communities is associated with LRTI among children under the age of five years.

From Raising Awareness to a Behavioural Change: A Case Study of Indoor Air Quality Improvement Using IoT and COM-B Model

The topic of indoor air pollution has yet to receive the same level of attention as ambient pollution. We spend considerable time indoors, and poorer indoor air quality affects most of us, particularly people with respiratory and other health conditions. There is a pressing need for methodological case studies focusing on informing households about the causes and harms of indoor air pollution and supporting changes in behaviour around different indoor activities that cause it. The use of indoor air quality (IAQ) sensor data to support behaviour change is the focus of our research in this paper. We have conducted two studies-first, to evaluate the effectiveness of the IAQ data visualisation as a trigger for the natural reflection capability of human beings to raise awareness. This study was performed without the scaffolding of a formal behaviour change model. In the second study, we showcase how a behaviour psychology model, COM-B (Capability, Opportunity, and Motivation-Behaviour), can be operationalised as a means of digital intervention to support behaviour change. We have developed four digital interventions manifested through a digital platform. We have demonstrated that it is possible to change behaviour concerning indoor activities using the COM-B model. We have also observed a measurable change in indoor air quality. In addition, qualitative analysis has shown that the awareness level among occupants has improved due to our approach of utilising IoT sensor data with COM-B-based digital interventions.

Exposure-response relationship of residential dampness and mold damage with severe lower respiratory tract infections among under-five children in Nigeria

Previous epidemiological studies demonstrated an increased risk of respiratory health effects in children and adults exposed to dampness or mold. This study investigated associations of quantitative indicators of indoor dampness and mold exposure with severe lower respiratory tract infections (LRTI) among children aged 1-59 months in Ibadan, Nigeria.

Methods

In-home visits were conducted among 178 children hospitalized with LRTI matched by age (±3 months), sex, and geographical location with 180 community-based children without LRTI. Trained study staff evaluated the indoor environment using a standardized home walkthrough checklist and measured visible dampness and mold damage. Damp-moldy Index (DMI) was also estimated to quantify the level of exposure. Exposure-response relationships of dampness and mold exposure with severe LRTI were assessed using multivariable restricted cubic spline regression models adjusting for relevant child, housing, and environmental characteristics.

Results

Severe LRTI cases were more often male than female (61.8%), and the overall mean (SD) age was 7.3 (1.35) months. Children exposed to dampness <0.3 m² (odds ratio [OR] = 2.11; 95% confidence interval [CI] = 1.05, 4.36), and between 0.3 and 1.0 m² (OR = 2.34; 95% CI = 1.01, 7.32), had a higher odds of severe LRTI compared with children not exposed to dampness. The restricted cubic spline showed a linear exposure-response association between severe LRTI and residential dampness (P < 0.001) but a nonlinear relationship with DMI (P = 0.01).

Conclusions

Residential dampness and DMI were exposure-dependently associated with higher odds of severe LRTI among under-five children. If observed relationships were causal, public health intervention strategies targeted at reducing residential dampness are critically important to mitigate the burden of severe LRTI among under-five children.

DNA methylation as a potential mediator of the association between indoor air pollution and neurodevelopmental delay in a South African birth cohort

BACKGROUND

Exposure to indoor air pollution during pregnancy has been linked to neurodevelopmental delay in toddlers. Epigenetic modification, particularly DNA methylation (DNAm), may explain this link. In this study, we employed three high-dimensional mediation analysis methods (HIMA, DACT, and gHMA) followed by causal mediation analysis to identify differentially methylated CpG sites and genes that mediate the association between indoor air pollution and neurodevelopmental delay. Analyses were performed using data from 142 mother to child pairs from a South African birth cohort, the Drakenstein Child Health Study. DNAm from cord blood was measured using the Infinium MethylationEPIC and HumanMethylation450 arrays. Neurodevelopment was assessed at age 2 years using the Bayley Scores of Infant and Toddler Development, 3rd edition across four domains (cognitive development, general adaptive behavior, language, and motor function). Particulate matter with an aerodynamic diameter of 10 μm or less (PM₁₀) was measured inside participants' homes during the second trimester of pregnancy.

RESULTS

A total of 29 CpG sites and 4 genes (GOPC, RP11-74K11.1, DYRK1A, RNMT) were identified as significant mediators of the association between PM₁₀ and cognitive neurodevelopment. The estimated proportion mediated (95%-confidence interval) ranged from 0.29 [0.01, 0.86] for cg00694520 to 0.54 [0.11, 1.56] for cg05023582.

CONCLUSIONS

Our findings suggest that DNAm may mediate the association between prenatal PM₁₀ exposure and cognitive neurodevelopment. DYRK1A and several genes that our CpG sites mapped to, including CNKSR1, IPO13, IFNGR1, LONP2, and CDH1, are associated with biological pathways implicated in cognitive neurodevelopment and three of our identified CpG sites (cg23560546 [DAPL1], cg22572779 [C6orf218], cg15000966 [NT5C]) have been previously associated with fetal brain development. These findings are novel and add to the limited literature investigating the relationship between indoor air pollution, DNAm, and neurodevelopment, particularly in low- and middle-income country settings and non-white populations.

Indoor air quality in day-care centres: a global review

A healthy indoor environment is critical for children due to the severe effect of poor indoor air quality (IAQ) on their overall well-being. Day-care centres (DCCs) are important indoor microenvironments for children apart from their homes. Therefore, monitoring IAQ in this microenvironment is vital because of the vulnerability of the occupants. This review gives a global overview of the predominant indoor chemical pollutant levels monitored in DCCs, compares their concentration with available regulations for IAQ, evaluates the sources and health risk effects of chemical pollutants and proposes strategies for enhancing IAQ in DCCs. Thirty-seven (37) articles were used based on specific stated inclusion and exclusion criteria. Continents like Europe and Asia have the most published studies in indoor DCCs. The decreasing trend of pollutants examined in most studies include particulate matter > carbon dioxide > formaldehyde > carbon monoxide > total volatile organic compounds > volatile organic compounds > nitrogen dioxide > ozone > benzene > sulphur dioxide = radon. Particulate matter in the size and mass concentration range of PM₁₀ (0.116-1920.71 μg/m³) > PM_2.5 (0.279.2-260.74 μg/m³) was the most investigated pollutant. While nitrogen dioxide, radon and carbon monoxide were consistent with the existing national and international reference values for IAQ across the continents, exceedances occurred in other pollutants. The limited number of indoor chemical pollutant studies suggests the need for more comprehensive studies on IAQ in DCC globally. Further studies should highlight the availability of low-cost sensors and mobile analytical equipment that will promote affordable ground-level data accessibility.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11869-023-01320-5.

Association between recent COVID-19 diagnosis, depression and anxiety symptoms among slum residents in Kampala, Uganda

BACKGROUND

Despite the known link between poor living conditions and mental health, there has been little research on the mental health of slum dwellers worldwide. Although the Coronavirus disease 2019 (COVID-19) pandemic has led to an increase in mental health issues, little focus has been given to the impact on slum dwellers. The study aimed to investigate the association between recent COVID-19 diagnosis and the risk of depression and anxiety symptoms among people living in an urban slum in Uganda.

METHODS

A cross-sectional study was conducted among 284 adults (at least 18 years of age) in a slum settlement in Kampala, Uganda between April and May 2022. We assessed depression symptoms and anxiety using validated Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder assessment tool (GAD-7) questionnaires respectively. We collected data on sociodemographic characteristics, and self-reported recent COVID-19 diagnosis (in the previous 30 days). Using a modified Poisson regression, adjusted for age, sex, gender and household income, we separately provided prevalence ratios and 95% confidence intervals for the associations between recent COVID-19 diagnosis and depressive and anxiety symptoms.

RESULTS

Overall, 33.8% and 13.4% of the participants met the depression and generalized anxiety screening criteria respectively and 11.3% were reportedly diagnosed with COVID-19 in the previous 30 days. People with recent COVID-19 diagnosis were more likely to be depressed (53.1%) than those with no recent diagnosis (31.4%) (p<0.001). Participants who were recently diagnosed with COVID-19 reported higher prevalence of anxiety (34.4%) compared to those with no recent diagnosis of COVID-19 (10.7%) (p = 0.014). After adjusting for confounding, recent diagnosis with COVID-19 was associated with depression (PR = 1.60, 95% CI 1.09-2.34) and anxiety (PR = 2.83, 95% CI 1.50-5.31).

CONCLUSION

This study suggests an increased risk of depressive symptoms and GAD in adults following a COVID-19 diagnosis. We recommend additional mental health support for recently diagnosed persons. The long-term of COVID-19 on mental health effects also need to be investigated.

Indoor bacterial and fungal aerosols as predictors of lower respiratory tract infections among under-five children in Ibadan, Nigeria

BACKGROUND

This study aimed to investigate the association between exposure to diverse indoor microbial aerosols and lower respiratory tract infections (LRTI) among children aged 1 to 59 months in Ibadan, Nigeria.

METHODS

One hundred and seventy-eight (178) hospital-based LRTI cases among under-five children were matched for age (± 3 months), sex and geographical location with 180 community-based controls (under-five children without LRTI). Following consent from caregivers of eligible participants, a child's health questionnaire, clinical proforma and standardized home-walkthrough checklist were used to collect data. Participant homes were visited and sampled for indoor microbial exposures using active sampling approach by Anderson sampler. Indoor microbial count (IMC), total bacterial count (TBC), and total fungal count (TFC) were estimated and dichotomized into high (> median) and low (≤ median) exposures. Alpha diversity measures including richness (R), Shannon (H) and Simpson (D) indices were also estimated. Conditional logistic regression models were used to test association between exposure to indoor microbial aerosols and LRTI risk among under-five children.

RESULTS

Significantly higher bacterial and fungal diversities were found in homes of cases (R = 3.00; H = 1.04; D = 2.67 and R = 2.56; H = 0.82; D = 2.33) than homes of controls (R = 2.00; H = 0.64; D = 1.80 and R = 1.89; H = 0.55; D = 1.88) p < 0.001, respectively. In the multivariate models, higher categories of exposure to IMC (aOR = 2.67, 95% CI 1.44-4.97), TBC (aOR = 2.51, 95% CI 1.36-4.65), TFC (aOR = 2.75, 95% CI 1.54-4.89), bacterial diversity (aOR = 1.87, 95% CI 1.08-3.24) and fungal diversity (aOR = 3.00, 95% CI 1.55-5.79) were independently associated with LRTI risk among under-five children.

CONCLUSIONS

This study suggests an increased risk of LRTI when children under the age of five years are exposed to high levels of indoor microbial aerosols.

Estimating the burden of disease attributable to household air pollution from cooking with solid fuels in South Africa for 2000, 2006 and 2012

Background. Household air pollution (HAP) due to the use of solid fuels for cooking is a global problem with significant impacts on human health, especially in low- and middle-income countries. HAP remains problematic in South Africa (SA). While electrification rates have improved over the past two decades, many people still use solid fuels for cooking owing to energy poverty.Objectives. To estimate the disease burden attributable to HAP for cooking in SA over three time points: 2000, 2006 and 2012. Methods. Comparative risk assessment methodology was used. The proportion of South Africans exposed to HAP was assessed and assigned the estimated concentration of particulate matter with a diameter <2.5 μg/m3(PM2.5) associated with HAP exposure. Health outcomes and relative risks associated with HAP exposure were identified. Population-attributable fractions and the attributable burden of disease due to HAP exposure (deaths, years of life lost, years lived with disability and disability-adjusted life years (DALYs)) for SA were calculated. Attributable burden was estimated for 2000, 2006 and 2012. For the year 2012, we estimated the attributable burden at provincial level.Results. An estimated 17.6% of the SA population was exposed to HAP in 2012. In 2012, HAP exposure was estimated to have caused 8 862 deaths (95% uncertainty interval (UI) 8 413 - 9 251) and 1.7% (95% UI 1.6% - 1.8%) of all deaths in SA, respectively. Loss of healthy life years comprised 208 816 DALYs (95% UI 195 648 - 221 007) and 1.0% of all DALYs (95% UI 0.95% - 1.0%) in 2012, respectively. Lower respiratory infections and cardiovascular disease contributed to the largest proportion of deaths and DALYs. HAP exposure due to cooking varied across provinces, and was highest in Limpopo (50.0%), Mpumalanga (27.4%) and KwaZulu-Natal (26.4%) provinces in 2012. Age standardised burden measures showed that these three provinces had the highest rates of death and DALY burden attributable to HAP.Conclusion. The burden of disease from HAP due to cooking in SA is of significant concern. Effective interventions supported by legislation and policy, together with awareness campaigns, are needed to ensure access to clean household fuels and improved cook stoves. Continued and enhanced efforts in this regard are required to ensure the burden of disease from HAP is curbed in SA.

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.

Determinants of Solid Fuel Use and Emission Risks among Households: Insights from Limpopo, South Africa

Emissions from residential solid fuels reduce ambient air quality and cause indoor air pollution resulting in adverse human health. The traditional solid fuels used for cooking include coal, straws, dung, and wood, with the latter identified as the prevalent energy source in developing countries. Emissions from such fuel sources appear to be significant hazards and risk factors for asthma and other respiratory diseases. This study aimed at reporting factors influencing the choice of dominant solid fuel for cooking and determine the emission risk from such solid fuel in three villages of Phalaborwa, Limpopo province, South Africa. The study used descriptive analysis to show the relationship between the socio-economic variables and the choice of cooking fuel at the household level. Multiple correspondence analysis (MCA) was used further to detect and represent underlying structures in the choice of dominant fuels. MCA shows the diversity and existing relationship of how variables are related analytically and graphically. Generalised linear logistic weight estimation procedure (WLS) was also used to investigate the factors influencing choice of fuel used and the inherent emission risks. In the three villages, wood was the prevalent cooking fuel with 76.8% of participant households using it during the summer and winter seasons. Variables such as low monthly income, level of education, and system of burning are revealed as strong predictors of wood fuel usage. Moreover, income, water heating energy, types of wood, and number of cooking hours are significant (p ≤ 0.05) in influencing emission from wood fuel in the community. A notable conclusion is that variables such as income, education status and system of burning are determinants of wood fuel usage in the three villages, while income, water heating energy, types of wood and number of hours influence vulnerability to household emission and possible health risks in the use of solid energy sources.

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 PM_2.5 and birth outcomes is not conclusive. We assessed the association between in utero exposure to indoor PM_2.5 , birth weight, gestational age, low birth weight, and/or preterm delivery. Homes of 800 pregnant women were assessed using a structured walkthrough questionnaire. PM_2.5 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 PM_2.5 predictors that can reduce within-and/or between-home variability. A predictive model was used to estimate PM_2.5 levels in unmeasured homes (n = 500). The mean (SD) for PM_2.5 was 37 µg/m³ (29) with a median of 28µg/m³ . The relationship between PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 exposure. Infant sex was an effect modifier for PM_2.5 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 PM_2.5 is associated with adverse pregnancy outcomes. To protect the population during their reproductive period, public health policy should focus on indoor PM_2.5 levels.

Spatiotemporal variation in residential PM2.5 and PM10 concentrations in China: National on-site survey

BACKGROUND

Significant efforts have been directed toward addressing the adverse health effects of particulate matter, while few data exist to evaluate indoor exposure nationwide in China.

OBJECTIVES

This study aimed to investigate dwellings particulate matter levels in the twelve cities in China and provide large data support for policymakers to accelerate the legislative process.

METHODS

The current study was based on the CIEHS 2018 study and conducted in 12 cities of China. A total of 2128 air samples were collected from 610 residential households during the summer and winter. Both PM₁₀ and PM_2.5 were detected with a light-scattering dust meter in both the living room and bedroom. The Wilcoxon rank-sum test was performed to evaluate the correlations between PM_2.5 and PM₁₀ concentrations and both sampling season and site. Ratios of the living room to bedroom were calculated to evaluate the particulate matter variation between rooms. Hierarchical clustering was used to probe the question of whether the concentration varies between cities throughout China.

RESULTS

The geometric means of the PM_2.5 in living rooms and bedrooms were 39.80 and 36.55 μg/m³ in the summer, and 70.97 and 67.99 μg/m³ in the winter, respectively. In the summer, approximately 70 % of indoor dwelling PM_2.5 exceeded the limit of 25 μg/m³, and for PM₁₀ approximately 60 % of dwellings demonstrated levels higher than 50 μg/m³; the corresponding values were over 90 % and 80 % in winter, respectively. In Shijiazhuang, Lanzhou, Luoyang and Qingdao, the geometric means of the PM_2.5 concentrations were observed to be 1.5 to 4.3 times higher during winter than during summer; similar concentrations in summer and winter were observed in Harbin, Wuxi, and Shenzhen, while the PM_2.5 concentrations in Panjin were approximately 1.5 times higher in summer than in winter. There was no significant difference in particulate matter concentrations between the living rooms and bedrooms. Scatter plots showed that cities with low GDP and a small population had higher concentrations, while Shenzhen, which has a higher GDP and a large permanent population, had a relatively low concentration of particulate matter.

CONCLUSIONS

Our results suggest that indoor air pollution is a severe problem in China. It is necessary to continue monitoring indoor air quality to observe the changing trend under the tremendous effort of the Chinese government.

Simulation and Analysis of Indoor Air Quality in Florida Using Time Series Regression (TSR) and Artificial Neural Networks (ANN) Models

Exposures to air pollutants have been associated with various acute respiratory diseases and detrimental human health. Analysis and further interpretation of air pollutant patterns are correspondingly important as monitoring them. In the present study, the 24-h and four-month indoor and outdoor PM2.5, PM10, NO2, relative humidity, and temperature were measured simultaneously for a laboratory in Gainesville city, Florida. The indoor PM2.5, PM10, and NO2 concentrations were predicted using multiple linear regression (MLR), time series regression (TSR), and artificial neural networks (ANN) models. The modeling conducted in this study aims to perform a cross comparison study between these models in a symmetric environment. The value of root-mean-square error was improved by 18.33% in comparison with the MLR model. In addition, the value of the coefficient of determination was improved by 24.68%. The ANN model had the best performance and could predict the target air pollutants at 10-min intervals of the studied building with 90% accuracy levels. The TSR model showed slightly better performance compared to the MLR model. These results can be accordingly referred for studies analyzing indoor air quality in similar building types and climate zones.

Major climate change-induced risks to human health in South Africa

There are many climatic changes facing South Africa which already have, or are projected to have, a detrimental impact on human health. Here the risks to health due to several alterations in the climate of South Africa are considered in turn. These include an increase in ambient temperature, causing, for example, a significant rise in morbidity and mortality; heavy rainfall leading to changes in the prevalence and occurrence of vector-borne diseases; drought-associated malnutrition; and exposure to dust storms and air pollution leading to the potential exacerbation of respiratory diseases. Existing initiatives and strategies to prevent or reduce these adverse health impacts are outlined, together with suggestions of what might be required in the future to safeguard the health of the nation. Potential roles for the health and non-health sectors as well as preparedness and capacity development with respect to climate change and health adaptation are considered.

Epidemiological characteristics of tuberculosis and effects of meteorological factors and air pollutants on tuberculosis in Shijiazhuang, China: A distribution lag non-linear analysis

BACKGROUND

Tuberculosis (TB) is a serious public health problem in China. There is evidence to prove that meteorological factors and exposure to air pollutants have a certain impact on TB. But the evidence of this relationship is insufficient, and the conclusions are inconsistent.

METHODS

Descriptive epidemiological methods were used to describe the distribution characteristics of TB in Shijiazhuang in the past five years. Through the generalized linear regression model (GLM) and the generalized additive model (GAM), the risk factors that affect the incidence of TB are screened. A combination of GLM and distribution lag nonlinear model (DLNM) was used to evaluate the lag effect of environmental factors on the TB. Results were tested for robustness by sensitivity analysis.

RESULTS

The incidence of TB in Shijiazhuang showed a downward trend year by year, with seasonality and periodicity. Every 10 μg/m³ of PM₁₀ changes, the RR distribution is bimodal. The first peak of RR occurs on the second day of lag (RR = 1.00166, 95% CI: 1.00023, 1.00390); the second risk period starts from 13th day of lag and peaks on15th day (RR = 1.00209, 95% CI: 1.00076, 1.00341), both of which are statistically significant. The cumulative effect of increasing 10 μg/m³ showed a similar bimodal distribution. Time zones where the RR makes sense are days 4-6 and 13-20. RR peaked on the 18th day (RR = 1.02239, 95% CI: 1.00623, 1.03882). The RR has a linear relationship with the concentration. Under the same concentration, the RR peaks within 15-20 days.

CONCLUSION

TB in Shijiazhuang City showed a downward trend year by year, with obvious seasonal fluctuations. The air pollutant PM₁₀ increases the risk of TB. The development of TB has a short-term lag and cumulative lag effects. We should focus on protecting susceptible people from TB in spring and autumn, and strengthen the monitoring and emission management of PM₁₀ in the atmosphere.

Indoor Exposure to Selected Air Pollutants in the Home Environment: A Systematic Review

(1) Background: There is increasing awareness that the quality of the indoor environment affects our health and well-being. Indoor air quality (IAQ) in particular has an impact on multiple health outcomes, including respiratory and cardiovascular illness, allergic symptoms, cancers, and premature mortality. (2) Methods: We carried out a global systematic literature review on indoor exposure to selected air pollutants associated with adverse health effects, and related household characteristics, seasonal influences and occupancy patterns. We screened records from six bibliographic databases: ABI/INFORM, Environment Abstracts, Pollution Abstracts, PubMed, ProQuest Biological and Health Professional, and Scopus. (3) Results: Information on indoor exposure levels and determinants, emission sources, and associated health effects was extracted from 141 studies from 29 countries. The most-studied pollutants were particulate matter (PM_2.5 and PM₁₀); nitrogen dioxide (NO₂); volatile organic compounds (VOCs) including benzene, toluene, xylenes and formaldehyde; and polycyclic aromatic hydrocarbons (PAHs) including naphthalene. Identified indoor PM_2.5 sources include smoking, cooking, heating, use of incense, candles, and insecticides, while cleaning, housework, presence of pets and movement of people were the main sources of coarse particles. Outdoor air is a major PM_2.5 source in rooms with natural ventilation in roadside households. Major sources of NO₂ indoors are unvented gas heaters and cookers. Predictors of indoor NO₂ are ventilation, season, and outdoor NO₂ levels. VOCs are emitted from a wide range of indoor and outdoor sources, including smoking, solvent use, renovations, and household products. Formaldehyde levels are higher in newer houses and in the presence of new furniture, while PAH levels are higher in smoking households. High indoor particulate matter, NO₂ and VOC levels were typically associated with respiratory symptoms, particularly asthma symptoms in children. (4) Conclusions: Household characteristics and occupant activities play a large role in indoor exposure, particularly cigarette smoking for PM_2.5, gas appliances for NO₂, and household products for VOCs and PAHs. Home location near high-traffic-density roads, redecoration, and small house size contribute to high indoor air pollution. In most studies, air exchange rates are negatively associated with indoor air pollution. These findings can inform interventions aiming to improve IAQ in residential properties in a variety of settings.

Predicting PM2.5 in Well-Mixed Indoor Air for a Large Office Building Using Regression and Artificial Neural Network Models

Although the exposure to PM_2.5 has serious health implications, indoor PM_2.5 monitoring is not a widely applied practice. Regulations on the indoor PM_2.5 level and measurement schemes are not well established. Compared to other indoor settings, PM_2.5 prediction models for large office buildings are particularly lacking. In response to these challenges, statistical models were developed in this paper to predict the PM_2.5 concentration in well-mixed indoor air in a commercial office building. The performances of different modeling methods, including multiple linear regression (MLR), partial least squares regression (PLS), distributed lag model (DLM), least absolute shrinkage selector operator (LASSO), simple artificial neural networks (ANN), and long-short term memory (LSTM), were compared. Various combinations of environmental and meteorological parameters were used as predictors. The root-mean-square error (RMSE) of the predicted hourly PM_2.5 was 1.73 μg/m³ for the LSTM model and in the range of 2.20-4.71 μg/m³ for the other models when regulatory ambient PM_2.5 data were used as predictors. The LSTM models outperformed other modeling approaches across the performance metrics used by learning the predictors' temporal patterns. Even without any ambient PM_2.5 information, the developed models still demonstrated relatively high skill in predicting the PM_2.5 levels in well-mixed indoor air.

Exposure assessment of indoor particulate matter during pregnancy: a narrative review of the literature

OBJECTIVE

The aim of this review was to summarize the evidence of the exposure assessment approaches of indoor particulate matter (PM) during pregnancy and to recommend future focus areas.

CONTENT

Exposure to indoor PM during pregnancy is associated with adverse birth outcomes. However, many questions remain about the consistency of the findings and the magnitude of this effect. This may be due to the exposure assessment methods used and the challenges of characterizing exposure during pregnancy. Exposure is unlikely to remain constant over the nine-month period. Pregnant females' mobility and activities vary - for example, employment status may be random among females, but among those employed, activities are likely to be greater in the early pregnancy than closer to the delivery of the child.

SUMMARY

Forty three studies that used one of the five categories of indoor PM exposure assessment (self-reported, personal air monitoring, household air monitoring, exposure models and integrated approaches) were assessed. Our results indicate that each of these exposure assessment approaches has unique characteristics, strengths, and weaknesses. While questionnaires and interviews are based on self-report and recall, they were a major component in the reviewed exposure assessment studies. These studies predominantly used large sample sizes. Precision and detail were observed in studies that used integrated approaches (i. e. questionnaires, measurements and exposure models).

OUTLOOK

Given the limitations presented by these studies, exposure misclassification remains possible because of personal, within and between household variability, seasonal changes, and spatiotemporal variability during pregnancy. Therefore, using integrated approaches (i. e. questionnaire, measurements and exposure models) may provide better estimates of PM levels across trimesters. This may provide precision for exposure estimates in the exposure-response relationship.

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.

Home Assessment of Indoor Microbiome (HAIM) in Relation to Lower Respiratory Tract Infections among Under-Five Children in Ibadan, Nigeria: The Study Protocol

The association between household air pollution and lower respiratory tract infections (LRTI) among children under five years of age has been well documented; however, the extent to which the microbiome within the indoor environment contributes to this association is uncertain. The home assessment of indoor microbiome (HAIM) study seeks to assess the abundance of indoor microbiota (IM) in the homes of under-five children (U-5Cs) with and without LRTI. HAIM is a hospital- and community-based study involving 200 cases and 200 controls recruited from three children’s hospitals in Ibadan, Nigeria. Cases will be hospital-based patients with LRTI confirmed by a pediatrician, while controls will be community-based participants, matched to cases on the basis of sex, geographical location, and age (±3 months) without LRTI. The abundance of IM in houses of cases and controls will be investigated using active and passive air sampling techniques and analyzed by qualitative detection of bacterial 16SrRNA gene (V3–V4), fungal ITS1 region, and viral RNA sequencing. HAIM is expected to elucidate the relationship between exposure to IM and incidence of LRTI among U-5Cs and ultimately provide evidence base for strategic interventions to curtail the burgeoning burden of LRTI on the subcontinent.

Environmental Health Research in Africa: Important Progress and Promising Opportunities

The World Health Organization in 2016 estimated that over 20% of the global disease burden and deaths were attributed to modifiable environmental factors. However, data clearly characterizing the impact of environmental exposures and health endpoints in African populations is limited. To describe recent progress and identify important research gaps, we reviewed literature on environmental health research in African populations over the last decade, as well as research incorporating both genomic and environmental factors. We queried PubMed for peer-reviewed research articles, reviews, or books examining environmental exposures and health outcomes in human populations in Africa. Searches utilized medical subheading (MeSH) terms for environmental exposure categories listed in the March 2018 US National Report on Human Exposure to Environmental Chemicals, which includes chemicals with worldwide distributions. Our search strategy retrieved 540 relevant publications, with studies evaluating health impacts of ambient air pollution (n=105), indoor air pollution (n = 166), heavy metals (n = 130), pesticides (n = 95), dietary mold (n = 61), indoor mold (n = 9), per- and polyfluoroalkyl substances (PFASs, n = 0), electronic waste (n = 9), environmental phenols (n = 4), flame retardants (n = 8), and phthalates (n = 3), where publications could belong to more than one exposure category. Only 23 publications characterized both environmental and genomic risk factors. Cardiovascular and respiratory health endpoints impacted by air pollution were comparable to observations in other countries. Air pollution exposures unique to Africa and some other resource limited settings were dust and specific occupational exposures. Literature describing harmful health effects of metals, pesticides, and dietary mold represented a context unique to Africa. Studies of exposures to phthalates, PFASs, phenols, and flame retardants were very limited. These results underscore the need for further focus on current and emerging environmental and chemical health risks as well as better integration of genomic and environmental factors in African research studies. Environmental exposures with distinct routes of exposure, unique co-exposures and co-morbidities, combined with the extensive genomic diversity in Africa may lead to the identification of novel mechanisms underlying complex disease and promising potential for translation to global public health.

Machine learning and statistical models for predicting indoor air quality

Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically based mechanistic models or statistical models that are driven by measured data. In comparison with mechanistic models mostly used in unoccupied or scenario-based environments, statistical models have great potential to explore IAQ captured in large measurement campaigns or in real occupied environments. The present study carried out the first literature review of the use of statistical models to predict IAQ. The most commonly used statistical modeling methods were reviewed and their strengths and weaknesses discussed. Thirty-seven publications, in which statistical models were applied to predict IAQ, were identified. These studies were all published in the past decade, indicating the emergence of the awareness and application of machine learning and statistical modeling in the field of IAQ. The concentrations of indoor particulate matter (PM_2.5 and PM₁₀ ) were the most frequently studied parameters, followed by carbon dioxide and radon. The most popular statistical models applied to IAQ were artificial neural networks, multiple linear regression, partial least squares, and decision trees.

The health and social implications of household air pollution and respiratory diseases

Approximately three billion individuals are exposed to household air pollution (HAP) from the burning of biomass fuels worldwide. Household air pollution is responsible for 2.9 million annual deaths and causes significant health, economic and social consequences, particularly in low- and middle-income countries. Although there is biological plausibility to draw an association between HAP exposure and respiratory diseases, existing evidence is either lacking or conflicting. We abstracted systematic reviews and meta-analyses for summaries available for common respiratory diseases in any age group and performed a literature search to complement these reviews with newly published studies. Based on the literature summarized in this review, HAP exposure has been associated with acute respiratory infections, tuberculosis, asthma, chronic obstructive pulmonary disease, pneumoconiosis, head and neck cancers, and lung cancer. No study, however, has established a causal link between HAP exposure and respiratory disease. Furthermore, few studies have controlled for tobacco smoke exposure and outdoor air pollution. More studies with consistent diagnostic criteria and exposure monitoring are needed to accurately document the association between household air pollution exposure and respiratory disease. Better environmental exposure monitoring is critical to better separate the contributions of household air pollution from that of other exposures, including ambient air pollution and tobacco smoking. Clinicians should be aware that patients with current or past HAP exposure are at increased risk for respiratory diseases or malignancies and may want to consider earlier screening in this population.

Association of childhood pulmonary tuberculosis with exposure to indoor air pollution: a case control study

Background

Crude measures of exposure to indicate indoor air pollution have been associated with the increased risk for acquiring tuberculosis. Our study aimed to determine an association between childhood pulmonary tuberculosis (PTB) and exposure to indoor air pollution (IAP), based on crude exposure predictors and directly sampled and modelled pollutant concentrations.

Methods

In this case control study, children diagnosed with PTB were compared to children without PTB. Questionnaires about children’s health; and house characteristics and activities (including household air pollution) and secondhand smoke (SHS) exposure were administered to caregivers of participants. A subset of the participants’ homes was sampled for measurements of PM₁₀ over a 24-h period (n = 105), and NO₂ over a period of 2 to 3 weeks (n = 82). IAP concentrations of PM₁₀ and NO₂ were estimated in the remaining homes using predictive models. Logistic regression was used to look for association between IAP concentrations, crude measures of IAP, and PTB.

Results

Of the 234 participants, 107 were cases and 127 were controls. Pollutants concentrations (μg/m³) for were PM₁₀ median: 48 (range: 6.6–241) and NO₂ median: 16.7 (range: 4.5–55). Day-to-day variability within- household was large. In multivariate models adjusted for age, sex, socioeconomic status, TB contact and HIV status, the crude exposure measures of pollution viz. cooking fuel type (clean or dirty fuel) and SHS showed positive non-significant associations with PTB. Presence of dampness in the household was a significant risk factor for childhood TB acquisition with aOR of 2.4 (95% CI: 1.1–5.0). The crude exposure predictors of indoor air pollution are less influenced by day-to-day variability. No risk was observed between pollutant concentrations and PTB in children for PM₁₀ and NO₂.

Conclusion

Our study suggests increased risk of childhood tuberculosis disease when children are exposed to SHS, dirty cooking fuel, and dampness in their homes. Yet, HIV status, age and TB contact are the most important risk factors of childhood PTB in this population.

Developing a predictive model for fine particulate matter concentrations in low socio-economic households in Durban, South Africa

In low-resource settings, there is a need to develop models that can address contributions of household and outdoor sources to population exposures. The aim of the study was to model indoor PM_2.5 using household characteristics, activities, and outdoor sources. Households belonging to participants in the Mother and Child in the Environment (MACE) birth cohort, in Durban, South Africa, were randomly selected. A structured walk-through identified variables likely to generate PM_2.5 . MiniVol samplers were used to monitor PM_2.5 for a period of 24 hours, followed by a post-activity questionnaire. Factor analysis was used as a variable reduction tool. Levels of PM_2.5 in the south were higher than in the north of the city (P < .05); crowding and dwelling type, household emissions (incense, candles, cooking), and household smoking practices were factors associated with an increase in PM_2.5 levels (P < .05), while room magnitude and natural ventilation factors were associated with a decrease in the PM_2.5 levels (P < .05). A reasonably robust PM_2.5 predictive model was obtained with model R² of 50%. Recognizing the challenges in characterizing exposure in environmental epidemiological studies, particularly in resource-constrained settings, modeling provides an opportunity to reasonably estimate indoor pollutant levels in unmeasured homes.