Social Inequities in Exposure to Traffic-Related Air and Noise Pollution at Public Schools in Texas

Clustering by chemicals: A novel examination of chemical pollutants and social vulnerability in children and adolescents

BACKGROUND

Inhaled air pollutants are environmental determinants of health with negative impacts on human health. Air pollution has been linked to the incidence and progression of disease, with its effects unequally distributed across the population. Children compared to adults are a highly vulnerable group and suffer disproportionately from systemic environmental inequities exacerbated by social determinants.

OBJECTIVE

To explore air pollution cluster patterns among 6- to 19-year-olds from the 2015-2016 National Health and Nutrition Examination Survey (NHANES) and examine chemical cluster associations with social vulnerability.

METHODS

NHANES data was extracted for 697 children and adolescents. Social vulnerability characteristics from questionnaires were assembled to construct a modified social vulnerability index (SVI). Thirty-four air pollutant exposure chemicals were measured in urine and available from the laboratory sub-sample A data. K-means clustering classified the sample into three groups: low, medium, and high chemical exposure groups. Logistic regression was used to examine associations between high chemical group membership and SVI after adjusting for age, biological sex, and BMI. Complex survey analysis was conducted using SAS v9.4 to reflect population effects.

RESULTS

Air pollution clusters revealed significant differences in mean concentrations between groups for 31 analytes with minimal distinction in mixture profiles. SVI scores differed significantly between the three groups (P = .002), and with each point increase in their SVI, the odds of a child being assigned to the highest-chemical exposure group increased by 11.55% (95% CI: 1.02-1.31), after adjustment.

CONCLUSION

Unsupervised clustering of environmental sub-sample specimens from NHANES provides an innovative, multi-pollutant model that can be used to explore exposure patterns in this population. Utilizing the modified SVI allows for the identification of children that may be highly susceptible to air pollution. It is imperative to interpret the research findings in light of historical structural and discriminatory inequalities to develop beneficial and sustainable solutions.

Environmental noise is positively associated with socioeconomically less privileged neighborhoods in the Netherlands

BACKGROUND

Environmental noise has detrimental effects on various health outcomes. Although disparities in some environmental exposures (e.g., air pollution) are well-documented, there is still a limited and uncertain understanding of the extent to which specific populations are disproportionately burdened by noise.

AIM

To assess whether environmental noise levels are associated with demographic and socioeconomic neighborhood compositions.

METHODS

We cross-sectionally examined long-term noise levels for 9,372 neighborhoods in the Netherlands. We linked these noise levels with administrative data on neighborhood characteristics for the year 2021. Linear and non-linear spatial regression models were fitted to explore the associations between noise, demographic, and socioeconomic neighborhood characteristics.

RESULTS

Our results showed that 46 % of the neighborhoods exhibited noise levels surpassing the recommended threshold of 53 dB to prevent adverse health effects. The regressions uncovered positive and partially non-linear neighborhood-level associations between noise and non-Western migrants, employment rates, low-incomers, and address density. Conversely, we found negative associations with higher-educated neighborhoods and those with a greater proportion of younger residents. Neighborhoods with older populations displayed a U-shaped association.

CONCLUSIONS

This national study showed an inequality in the noise burden, adversely affecting vulnerable, marginalized, and less privileged neighborhoods. Addressing the uneven distribution of noise and its root causes is an urgent policy imperative for sustainable Dutch cities.