Air Pollution during Pregnancy and Childhood Autism Spectrum Disorder in Taiwan

Pre-pregnancy ozone and ultrafine particulate matter exposure during second year of life associated with decreased cognitive and adaptive functioning at aged 2-5 years

BACKGROUND

This study sought to investigate the association of prenatal and early life exposure to a mixture of air pollutants on cognitive and adaptive outcomes separately in children with or without autism spectrum disorder (ASD).

METHODS

Utilizing data from the CHARGE case-control study (birth years: 2000-2016), we predicted daily air concentrations of NO2, O3, and particulate matter <0.1 μm (PM0.1), between 0.1 and 2.5 μm (PM0.1-2.5), and between 2.5 and 10 μm (PM2.5-10) using chemical transport models with ground-based monitor adjustments. Exposures were evaluated for pre-pregnancy, each trimester, and the first two years of life. Individual and combined effects of pollutants were assessed with Vineland Adaptive Behavior Scales (VABS) and Mullen Scales of Early Learning (MSEL), separately for children with ASD (n = 660) and children without ASD (typically developing (TD) and developmentally delayed (DD) combined; n = 753) using hierarchical Bayesian Kernel Machine Regression (BKMR) models with three groups: PM size fractions (PM0.1, PM0.1-2.5, PM2.5-10), NO2, and O3.

RESULTS

Pre-pregnancy Ozone was strongly negatively associated with all scores in the non-ASD group (group posterior inclusion probability (gPIP) = 0.83-1.00). The PM group during year 2 was also strongly negatively associated with all scores in the non-ASD group (gPIP = 0.59-0.93), with PM0.1 driving the group association (conditional PIP (cPIP) = 0.73-0.96). Weaker and less consistent associations were observed between PM0.1-2.5 during pre-pregnancy and ozone during year 1 and VABS scores in the ASD group.

CONCLUSIONS

These findings prompt further investigation into ozone and ultrafine PM as potential environmental risk factors for neurodevelopment.

Exploring the association between early-life air pollution exposure and autism spectrum disorders in children: A systematic review and meta-analysis

The objective of this meta-analysis is to investigate the association between air pollution and the vulnerability of children to autism spectrum disorders (ASD). A thorough examination and analysis of data obtained from a compilation of 14 studies was undertaken, with a particular emphasis on investigating the effects of nitrogen dioxide (NO2), oxide of nitrogen (NOx), ozone (O3), and particulate matter (PM10 and PM2.5) on individuals diagnosed with ASD. The findings demonstrate a moderate association between exposure to nitrogen dioxide (NO2) and ASD, as indicated by a combined odds ratio (OR) of 1.13 and a 95% confidence interval (CI) spanning from 0.77 to 1.549. O3 shows a combined odds ratio (OR) of 0.82, along with a 95% confidence interval (CI) ranging from 0.49 to 1.14. NOx shows a moderate level of heterogeneity (I² = 75.9%, p = 0.002), suggesting that the impact of NOx on the risk of ASD. There is a statistically significant relationship between exposure to O3 and ASD, although the strength of this relationship is diminished. The findings demonstrated a noteworthy correlation between exposure to PM10 and PM2.5 and the occurrence of ASD. The study found a significant correlation, in relation to PM2.5, with a combined odds ratio (OR) of 1.22 and a 95% confidence interval (CI) ranging from 1.11 to 1.34. The findings have significant implications for the formulation of programs aimed at reducing exposure to harmful chemicals, especially among vulnerable groups such as children.

Effect of In utero Exposure to Air Pollution on Adulthood Hospitalizations

Empirical analyses have demonstrated that individuals exposed to severe air pollution in utero have worse health outcomes during childhood. However, there is little evidence on the long-term health impacts of air pollution exposure. The objective of this paper is to estimate the effect of in utero exposure to the Great London Smog of 1952 (GLS) on five health outcomes identified through a scoping review to be those most likely affected: respiratory, circulatory, neoplasms, mental health, and nervous system conditions. We use the GLS, an extreme air pollution event in December 1952, as a quasi-natural experiment to estimate the effect of exposure to air pollution in utero on adulthood health. Data from the UK Biobank is analysed for a cohort of participants born from December 1952 to July 1956. Differences in health outcomes between adults exposed and not exposed to the GLS due to their birth dates, born inside and outside London, were explored. Our primary focus is hospitalization events between 1997 and 2020 (corresponding to ages 40 to 69), as recorded in linked administrative data from the National Health Service (NHS). Specifically, the five primary outcomes are binary variables indicating that the individual had at least one hospitalization where the main cause of hospitalization is related to respiratory, circulatory, neoplasms, mental health, or nervous system conditions. The analytical sample comprised 36,281 individuals. A positive effect on adulthood hospitalizations due to respiratory conditions was observed. If exposed to the GLS in utero, the probability of at least one respiratory health-related hospitalization between 1997 and 2020 increased by 2.58 percentage points (95% CI 0.08, 4.30, p = 0.03), a 23% increase relative to the sample mean. Small effects were found for all other outcomes, suggesting that these conditions were not affected by the GLS. We do not find heterogeneous effects by sex or childhood socioeconomic status. This study found that a 5-day pollution exposure event while in utero significantly increased respiratory-related hospitalizations at ages 40 to 69 but had no impact on hospitalizations due to circulatory, neoplasms, mental health, and nervous system conditions.

The Effect of Maternal Exposure to Air Pollutants and Heavy Metals during Pregnancy on the Risk of Neurological Disorders Using the National Health Insurance Claims Data of South Korea

The objective of this study was to evaluate the effects of high levels of maternal exposure to ambient air pollution and heavy metals on risks of autism spectrum disorder (ASD) and epilepsy using the National Health Insurance claims data of South Korea. The data of mothers and their newborns from 2016 to 2018 provided by the National Health Insurance Service were used (n = 843,134). Data on exposure to ambient air pollutants (PM2.5, CO, SO₂, NO₂, and O₃) and heavy metals (Pb, Cd, Cr, Cu, Mn, Fe, Ni, and As) during pregnancy were matched based on the mother's National Health Insurance registration area. SO₂ (OR: 2.723, 95% CI: 1.971-3.761) and Pb (OR: 1.063, 95% CI: 1.019-1.11) were more closely associated with the incidence of ASD when infants were exposed to them in the third trimester of pregnancy. Pb (OR: 1.109, 95% CI: 1.043-1.179) in the first trimester of pregnancy and Cd (OR: 2.193, 95% CI: 1.074-4.477) in the third trimester of pregnancy were associated with the incidence of epilepsy. Thus, exposure to SO₂, NO₂, and Pb during pregnancy could affect the development of a neurologic disorder based on the timing of exposure, suggesting a relationship with fetal development. However, further research is needed.

Association between Long-Term Ambient PM2.5 Exposure and under-5 Mortality: A Scoping Review

Studies have established a link between exposure to fine particles (PM2.5) and mortality in infants and children. However, few studies have explored the association between post-birth exposure to PM2.5 and under-5 mortality. We conducted a scoping review to identify relevant epidemiological evidence on the association between post-birth ambient PM2.5 exposure and under-5 mortality. We searched PubMed and Web of Science for articles published between 1970 and the end of January 2022 that explicitly linked ambient PM2.5 and under-5 mortality by considering the study area, study design, exposure window, and child age. Information was extracted on the study characteristics, exposure assessment and duration, outcomes, and effect estimates/findings. Ultimately, 13 studies on infant and child mortality were selected. Only four studies measured the effect of post-birth exposure to PM2.5 on under-5 mortality. Only one cohort study mentioned a positive association between post-birth ambient PM2.5 exposure and under-5 mortality. The results of this scoping review highlight the need for extensive research in this field, given that long-term exposure to ambient PM2.5 is a major global health risk and child mortality remains high in some countries.

Particulate matter exposure during pregnancy and infancy and risks of autism spectrum disorder in children: A systematic review and meta-analysis

PURPOSE

This meta-analysis aimed to clarify the relationship between particulate matter (PM) and autism spectrum disorder (ASD) in detail.

METHODS

A systematic literature search was performed using eight databases before April 9, 2022. The estimated effects were combined separately according to the PM type. Subgroup analyses were conducted in terms of the study design type, study location, exposure window, birth year, and sex.

RESULTS

PM_2.5 was associated with an increased risk of ASD, while PM₁₀ was not. PMc, PM₁, and diesel particulate matter (DPM) were also associated with an increased risk of ASD. Specifically, a 10 μg/m³ increase in PM_2.5 was associated with a 1.337-fold increased risk of ASD in children, and a 10 μg/m³ increase in PMc and PM₁ may increase the risk of ASD by 1.062 and 3.643 times, respectively. PM_2.5 exposure may increase the risk of ASD in boys. Exposure to PMc might increase the risk of ASD in children born after the year 2000. The combined results of different PM differed between studies with continuous and non-continuous data for different study design type, study location, and birth year. The sensitive window for PM_2.5 exposure to increase the risk of ASD may be from the first, second, and third trimesters to the first year of the postnatal period. Exposure to PMc during pregnancy was significantly associated with ASD.

CONCLUSION

Exposure to PM_2.5 may increase the risk of ASD in boys. Exposure to PM_2.5 during the first, second, and third trimesters and postnatally increased the risk of ASD.

Health Outcomes in Children Associated with Prenatal and Early-Life Exposures to Air Pollution: A Narrative Review

(1) Background: The developmental origins of health and disease (DOHaD) hypothesis links adverse fetal exposures with developmental mal-adaptations and morbidity later in life. Short- and long-term exposures to air pollutants are known contributors to health outcomes; however, the potential for developmental health effects of air pollution exposures during gestation or early-childhood have yet to be reviewed and synthesized from a DOHaD lens. The objective of this study is to summarize the literature on cardiovascular and metabolic, respiratory, allergic, and neuropsychological health outcomes, from prenatal development through early childhood, associated with early-life exposures to outdoor air pollutants, including traffic-related and wildfire-generated air pollutants. (2) Methods: We conducted a search using PubMed and the references of articles previously known to the authors. We selected papers that investigated health outcomes during fetal or childhood development in association with early-life ambient or source-specific air pollution exposure. (3) Results: The current literature reports that prenatal and early-childhood exposures to ambient and traffic-related air pollutants are associated with a range of adverse outcomes in early life, including cardiovascular and metabolic, respiratory and allergic, and neurodevelopmental outcomes. Very few studies have investigated associations between wildfire-related air pollution exposure and health outcomes during prenatal, postnatal, or childhood development. (4) Conclusion: Evidence from January 2000 to January 2022 supports a role for prenatal and early-childhood air pollution exposures adversely affecting health outcomes during development. Future studies are needed to identify both detrimental air pollutants from the exposure mixture and critical exposure time periods, investigate emerging exposure sources such as wildfire, and develop feasible interventional tools.

The epidemiological evidence linking exposure to ambient particulate matter with neurodevelopmental disorders: A systematic review and meta-analysis

BACKGROUND

There has been increasing attention on the associations between ambient particulate matter (PM) in early-life and neurodevelopmental disorders (NDDs). However, the associations remained unclear when considering different types of NDDs and different sizes of PM, and vulnerable exposure windows during early-life were not identified yet.

OBJECTIVE

To synthesize the published literature on the associations between ambient particulate matter (PM) and risk of different types of neurodevelopmental disorders (NDDs) in a systematic review and meta-analysis.

METHODS

A systematic search of Medline, Embase, PubMed, Cochrane Library, and Web of Science was performed from inception through 24 January 2022. Two reviewers conducted the study selection, data extraction, and quality appraisal. A random-effects model was used for meta-analyses with two quality-of-evidence assessments (the Grading of Recommendations Assessment, Development, and Evaluation system and the best evidence synthesis system).

RESULTS

A total of 6554 articles were screened, of which 31 were included in the review, and 20 provided adequate data for meta-analyses. Exposures to particulate matter of 2.5 μm or less (PM_2.5) during prenatal periods (OR, 1.32 [95%CI, 1.03-1.69]), the first year after birth (OR, 1.62 [95%CI, 1.22-2.15]) and the second year after birth (OR, 3.13 [95%CI, 1.47-6.67]) were associated with increased risk of autism spectrum disorders (ASD) in children. The quality of evidence for these associations during early postnatal periods was somewhat moderate with limited studies. We found inconsistent evidence when considering other types of NDDs and different sizes of PM.

CONCLUSIONS AND RELEVANCE

Current evidence indicated that there might be an association between PM_2.5 exposure and higher risk of ASD, and early postnatal periods appeared to be the critical exposure window. High-quality studies are needed to assess the evidence for other types of NDDs.

Features Importance Analysis of Diesel Vehicles' NOx and CO2 Emission Predictions in Real Road Driving Based on Gradient Boosting Regression Model

In order to have an accurate and fast prediction of the artificial intelligence (AI) model, the choice of input features is at least as important as the choice of model. The effect of input features selection on the emission models of light diesel vehicles driven on real roads was investigated in this paper. The gradient boosting regression (GBR) model was used to train and to predict the emissions of nitrogen oxide (NO_x), carbon dioxide (CO₂), and the fuel consumption of real driving diesel vehicles in urban scenarios, the suburbs, and on highways. A portable emissions measurement system (PEMS) system was used to collect data of vehicles as well as environmental conditions. The vehicle was run on two routes. The model was trained with the first route data and was used to predict the emissions of the second route. There were ten features related to the NO_x model and nine features associated with the CO₂ model. The importance of each feature was sorted, and a different number of features were used as input to train the models. The best NO_x model had the coefficient of determination (R²) values of 0.99, 0.99, and 0.99 in each driving pattern (urban, suburbs, and highways). Predictions of the second route had the R² values of 0.88, 0.89, and 0.96 respectively. The best CO₂ model had the R² values of 0.98, 0.99, and 0.99 in each driving pattern, respectively. Predictions of the second route had the R² values are 0.79, 0.82, and 0.83, respectively. The most important features for the NO_x model are mass air flow rate (g/s), exhaust flow rate (m³/min), and CO₂ (ppm), while the important features for the CO₂ model are exhaust flow rate (m³/min) and mass air flow rate (g/s). It is noted that the regression models based on the top three features may give predictions very close to the measured data.