Improving traffic-related air pollution estimates by modelling minor road traffic volumes.

Accurately estimating annual average daily traffic (AADT) on minor roads is essential for assessing traffic-related air pollution (TRAP) exposure, particularly in areas where most people live. Our study assessed the direct and indirect external validity of three methods used to estimate AADT on minor roads in Melbourne, Australia. We estimated the minor road AADT using a fixed-value approach (assuming 600 vehicles/day) and linear and negative binomial (NB) models. The models were generated using road type, road importance index, AADT and distance of the nearest major road, population density, workplace density, and weighted road density. External measurements of traffic counts, as well as black carbon (BC) and ultrafine particles (UFP), were conducted at 201 sites for direct and indirect validation, respectively. Statistical tests included Akaike information criterion (AIC) to compare models' performance, the concordance correlation coefficient (CCC) for direct validation, and Spearman's correlation coefficient for indirect validation. Results show that 88.5% of the roads in Melbourne are minor, yet only 18.9% have AADT. The performance assessment of minor road models indicated comparable performance for both models (AIC of 1,023,686 vs. 1,058,502). In the direct validation with external traffic measurements, there was no difference between the three methods for overall minor roads. However, for minor roads within residential areas, CCC (95% confidence interval [CI]) values were -0.001 (-0.17; 0.18), 0.47 (0.32; 0.60), and 0.29 (0.18; 0.39) for the fixed-value approach, the linear model, and the NB model, respectively. In the indirect validation, we found differences only on UFP where the Spearman's correlation (95% CI) for both models and fixed-value approach were 0.50 (0.37; 0.62) and 0.34 (0.19; 0.48), respectively. In conclusion, our linear model outperformed the fixed-value approach when compared against traffic and TRAP measurements. The methodology followed in this study is relevant to locations with incomplete minor road AADT data.

The impact of Traffic-Related air pollution on child and adolescent academic Performance: A systematic review.

BACKGROUND: The negative health impacts of traffic-related air pollution (TRAP) have been investigated for many decades, however, less attention has been paid to the effect of TRAP on children's academic performance. Understanding the TRAP-academic performance relationship will assist in identifying mechanisms for improving students' learning and aid policy makers in developing guidance for protecting children in school environments. METHODS: This systematic review assessed the relationship between TRAP and academic performance. Web of Science, PubMed, CINAHL, Medline, PsycINFO, SPORTDiscus, Scopus and ERIC databases were searched for relevant, peer reviewed, articles published in English. Articles assessing exposure to TRAP pollutants (through direct measurement, local air quality monitoring, modelling, or road proximity/density proxy measures) and academic performance (using standardised tests) in children and adolescents were included. Risk of bias was assessed within and between studies. RESULTS: Of 3519 search results, 10 relevant articles were included. Nine studies reported that increased exposure to some TRAP was associated with poorer student academic performance. Study methodologies were highly heterogeneous and no consistent patterns of association between specific pollutants, age groups, learning domains, exposure windows, and exposure locations were established. There was a serious risk of bias within individual studies and confidence in the body of evidence was low. CONCLUSIONS: This review found evidence suggestive of a negative association between TRAP and academic performance. However, the quality of this evidence was low. The existing body of literature is small, lacks the inclusion of high-quality exposure measures, and presents limitations in reporting. Future research should focus on using valid and reliable exposure measures, individual-level data, consistent controlling for confounders and longitudinal study designs.