The Effects of Traffic Air Pollution in and around Schools on Executive Function and Academic Performance in Children: A Rapid Review
Abstract
:1. Introduction
1.1. Traffic-Related Air Pollution
1.2. Air Pollution and Health
1.3. Executive Function
- Is executive function related to TRAP in and around schools?
- Is academic achievement related to TRAP in and around schools?
2. Methods
3. Results
3.1. General Description of the Studies
3.2. Quality Assessment
3.3. Effects of Pollution on Executive Function
3.3.1. Working Memory
3.3.2. Attention
3.3.3. Additional Cognitive Measures
3.3.4. Effects of Pollution on Test Scores/Academic Attainment
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ANT | Attentional Network Test |
API | Academic Performance Index |
BC | black carbon |
CI | confidence interval |
CO | carbon monoxide |
EC | elemental carbon |
GPA | grade point average |
HRT | hit reaction time |
HRT-SE | standard error of hit reaction time |
IQR | inter-quartile range |
NOx | nitrogen oxides |
NO2 | nitrogen dioxide |
O3 | ozone |
PAH | polycyclic aromatic hydrocarbon |
PM | particulate matter |
ROS | reactive oxygen species |
SES | socioeconomic status |
TRAP | traffic-related air pollution |
UFP | ultra-fine particle |
VOC | volatile organic compound |
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Author and Country | Design | n | Age Range | Air Pollutants Investigated | Pollution Estimated/Measured | Outcome Measures | Control Variables | Results |
---|---|---|---|---|---|---|---|---|
1. Alemany et al., (2018) Barcelona, Spain [42] | Cohort | 2897 | 7–11 | Schoolyard pollution: Polycyclic aromatic hydrocarbons (PAHs) Elemental carbon (EC) Nitrogen dioxide (NO2) | Measured Summary measure | Behavioural problems Inattentiveness Working memory | age sex maternal education level residential neighbourhood SES | IQR increases in PAHs: Inattentiveness (β = 4.44; 95% CI: 0.48, 8.40) 2-back numbers d’ values (WM) (β = −0.08; 95% CI: −0.14, −0.02) 2-back words d’ values (WM) (β = −0.02; 95% CI: −0.07, 0.04) |
2. Alvarez-Pedrerol et al., (2017) Barcelona, Spain [43] | Cohort | 1234 | 7–10 | Pollutants from walking commute to school: Average particulate matter ≤2.5 µm (PM2.5) Black carbon (BC) Nitrogen dioxide (NO2) | Estimated Summary measure | Inattentiveness Working memory | age sex maternal education level residential neighbourhood SES commuting time school and home air pollution | IQR increase in PM2.5: WM (β = −9.0, 95% CI (−15.5, −2.6), p < 0.01) IQR increase in BC: WM (β = −7.8, 95% CI (−13.4, −2.3), p < 0.01) No significant associations for inattentiveness. |
3. Basagana et al., (2016) Barcelona, Spain [44] | Cohort | 2618 | 7–10 | Indoor and outdoor PM2.5 pollution at schools: Sulfate Nitrate Chloride Ammonium Organic carbon (OC) Elemental carbon (EC) | Measured Summary measure | Inattentiveness Working memory Superior working memory | age sex maternal education level residential neighbourhood SES air pollution exposure at home | IQR increase in indoor traffic source: WM (β = –5.6; 95% CI: –10.7, –0.5) SWM (β = –5.1; 95% CI: –9.2, –1.1) Inattentiveness (β = 3.6; 95% CI: 0.0, 7.1) |
4. Clark et al., (2012) London, UK [45] | Cross-sectional | 719 | 9–10 | Outdoor pollution levels linked to school postcodes: NO2 | Estimated Summary measure | Reading comprehension Episodic memory Working memory | age sex maternal education level parental employment status crowding in the home home ownership long-standing illness main language spoken at home parental support for schoolwork classroom window glazing | NO2 levels not significantly associated with reading comprehension, recognition memory, information recall, conceptual recall, or working memory (per 1-point increase in nitrogen dioxide (μg/m3)). |
5. Forns et al., (2017) Barcelona, Spain [46] | Cohort | 1439 | 11.4 (SD0.6) at last follow-up (3.5 years post-baseline) | Indoor and outdoor pollution at schools: Elemental carbon (EC) Nitrogen dioxide (NO2) Particulate matter (PM2.5) from traffic sources Ultra-fine particles (UFP) | Measured Summary measure | Working memory | age sex grade maternal education level Urban Vulnerability Index at home address air pollution exposure at home address (NO2) | IQR increase in NO2: WM (indoor β = −2.11; 95% CI: −3.54, −0.68; outdoor β = −4.22; 95% CI: −6.22, −2.22) IQR increase in EC: WM (indoor β = −2.92; 95% CI: −4.53, −1.31; outdoor β = −2.13; 95% CI: −3.26, −0.99) IQR increase in PM2.5: WM (indoor β = −3.38; 95% CI: −5.81, −0.95; outdoor β = −2.30; 95% CI: −3.65, −0.96) IQR increase in UFP: WM (indoor β = −4.12; 95% CI: −6.51, −1.73; outdoor β = −3.75; 95% CI: −5.68, −1.83) |
6. Gaffron and Niemeier (2015) California, USA [41] | Ecological | 553 schools with 250,433 students | NA | Outdoor air pollution linked to school location: PM2.5 Diesel particulate matter Traffic density | Estimated Summary measure | Test scores: School-level academic performance index (API) | No control variables. | PM2.5 levels correlated with API (r = −0.21, R2 = 0.04, p < 0.001) |
7. Grineski et al., (2016) Texas, USA [39] | Ecological | 1888 | 8–13 | Outdoor respiratory and diesel particulate matter HAP risk estimates: Total diesel particulate matter (PM) On-road diesel PM Non-road diesel PM | Estimated Summary measure Risk estimate | Grade point average (GPA) | School-level control variables: total enrolment % free/reduced price meals student/teacher ratio % special education % teachers with MA degree Individual-level control variables: sex age free/reduced price meals teen mother mother’s education mother is Hispanic mother is Black mother’s English proficiency | IQR increase in total diesel PM risk: GPA (β = −0.22; 95% CI: −0.37, −0.07) IQR increase in on-road diesel PM risk: GPA (β = −0.16; 95% CI: −0.29, −0.04) IQR increase in non-road diesel PM risk: GPA (β = −0.11; 95% CI: −0.26, −0.05) |
8. Hutter et al. (2013) Austria (rural and urban regions) [47] | Cross-sectional | 436 | 6–8 | Indoor pollution at schools: Particulate matter (PM10 and PM2.5) Carbon dioxide (CO2) Chemical parameters (252 different ones) | Measured | Non-verbal reasoning | Social status (parental education and occupation) gender region (urban/rural; population density) | TCEP (PM10) correlated with cognitive performance (r = −0.147, p = 0.003) TCEP (PM2.5) correlated with cognitive performance (r = −0.149, p = 0.002) No significant correlations between PM10 phenanthrene, benzo(a)pyrene, or TDCPP with cognitive performance. Phenanthrene concentrations (PM2.5) correlated with cognitive performance (r = −0.097, p = 0.047) CO2 correlated with cognitive performance (r = −0.102, p = 0.034). |
9. Marcotte (2017) National data, USA [48] | Cross-sectional | 1450 | 6.75 Mean in months 81.01 SD 11.57 | Outdoor pollution levels linked to school locations: Ozone (O3) Particulate matter (PM2.5) | Estimated Multiple measures | Test scores: Maths score Reading score | family composition poverty status child gender race and ethnicity % students in grade eligible for free/reduced price meals high temperature precipitation common year/season fixed effects | PM2.5 significantly predicts reading score (β = −0.02, SE = 0.01, p < 0.05). No significant effect on maths score. No significant effects of O3 on test scores. |
10. Miller and Vela (2013) Chile (Metropolitan, Valparaiso, O’Higgins) [40] | Cohort | 3880 schools | 10–16 | Outdoor daily pollution levels linked to school locations between 1997 and 2012: Particulate matter (PM10 and PM2.5) Carbon monoxide (CO) Nitrogen oxide (NOx) Ozone (O3) | Estimated Daily measures of PM10, annual averages for other pollutants | Test scores: Maths score Reading score | Total children per class school SES public, private, or charter type | PM10 levels predict test scores (reading: β = −0.07, SE = 0.02, p < 0.01; maths: β = −0.08, SE = 0.02, p < 0.01). No significant effect of PM2.5. Effects in the week of exams: Significant effects on reading for PM10 (β = −0.14, SE = 0.01, p < 0.01), PM2.5 (β = −0.24, SE = 0.04, p < 0.01), and NOx (β = −0.18, SE = 0.04, p < 0.01). Non-significant effects of CO and O3. Significant effects on maths for PM10 (β = −0.12, SE = 0.01, p < 0.01) and NOx (β = −0.16, SE = 0.04, p < 0.01). Non-significant effects of PM2.5, CO and O3. |
11. Saenen et al., (2016) Flanders, Belgium [49] | Cohort (analysed cross-sectionally) | 310 | 8–11 | Indoor classroom PM2.5 and PM10 | Measured Multiple measures | Selective attention Sustained attention Short-term memory Visual information processing speed | sex age (linear and quadratic) education of the mother occupation of the parents passive smoking out-of-school physical activity traffic noise day/night hours of computer screen time per week day of the week relatedness of the examination periods chronic residential pollution exposure | Selective attention: IQR increase in PM2.5: 42.7 ms (95% CI: −0.40 to 85.8, p = 0.05) IQR increase in PM10: 50.2 ms (95% CI: 8.55 to 91.8, p = 0.02). Visual information processing speed: IQR increase in PM2.5 (2.05 s; 95% CI: 0.43, 3.66; p = 0.01). The IQR increase in PM10 was 1.9 s (p = 0.02). No significant associations between classroom PM and sustained attention or short-term memory. |
12. Sunyer et al., (2015) Barcelona, Spain [50] | Cohort | 2715 | 7–10 | Indoor and outdoor pollution at schools: Elemental carbon (EC) Ultra-fine particles (UFP; 10–700 nm) Nitrogen dioxide (NO2) | Measured Summary measure | Inattentiveness Working memory Superior working memory | age sex maternal education residential neighbourhood SES air pollution exposure at home | INDOOR IQR increase in EC: WM (β = −6.2, (95% CI −11, −2), p < 0.05) SWM (β = −5.8, ( 95% CI −9.2, −2.4), p < 0.05) Inattentiveness (β = 3.9, (95% CI 0.79, 6.8), p < 0.05) IQR increase in NO2: WM (β = −5.6, (95% CI −11, −0.44), p < 0.05) SWM (β = −5.1, (95% CI −9.2, −0.91), p < 0.05) inattentiveness (β = 2.6, (95% CI −1.0, 6.3), NS) IQR increase in UFP: WM (β = −7.9, (95% CI −15, −1.3), p < 0.05) SWM (β = −6.0, (95% CI −11, −0.75), p < 0.05) Inattentiveness (β = 4.6, (95% CI −0.13, 9.2), NS) OUTDOOR IQR increase in EC: WM (β = −4.1, (95% CI −8.0, −0.2), p < 0.05) SWM (β = −4.4, (95% CI −7.6, −1.3), p < 0.05) Inattentiveness (β = 3.8, (95% CI 1.0, 6.6), p < 0.05) IQR increase in NO2: WM (β = −6.6, (95% CI −12, −1.2), p < 0.05) SWM (β = −6.7, (95% CI −11, −2.3), p < 0.05) Inattentiveness (β = 3.8, (95% CI −0.10, 7.6), NS) IQR increase in UFP: WM (β = −4.9, (95% CI −10, 0.22), NS) SWM (β = −5.0, (95% CI −9.1, −0.96), p < 0.05) Inattentiveness (β = 3.9, (95% CI 0.31, 7.6), p < 0.05) |
13. van Kempen et al. (2012) Amsterdam, The Netherlands [51] | Cross-sectional | 553 | 9–11 | Outdoor air pollution linked to school: Nitrogen dioxide (NO2) Particulate matter (PM10) | Estimated Summary measure | Reaction time Attention switching Coordination Perceptual coding and attention Working memory | age sex crowding home ownership employment and mother’s education longstanding illness (y/n) parental support main language spoken at home is Dutch (y/n) type of window glazing at school | NO2 at school associated with WM (β = −0.16, 95% CI: −0.28 −0.04) No significant effects on other cognitive outcomes. Insufficient variability in levels of PM10 to test. |
Selection | Comparability | Outcome | |||||||
---|---|---|---|---|---|---|---|---|---|
Representativeness of the Sample | Measurement of Exposure | Modelling of Variation in Exposure | Measurement of Outcome at Start and End of Study Period | Controlling for Confounding Variables | Assessment of Outcome | Appropriate Length of Follow-Up | Adequacy of Follow-Up Sample | Quality Score (Max 9) | |
Alemany et al. [42] | * | * | * | ** | * | 6 | |||
Alvarez-Pedrerol et al. [43] | * | * | ** | * | 5 | ||||
Basagana et al. [44] | * | * | * | ** | * | * | 7 | ||
Clark et al. [45] | * | NA | * | * | NA | NA | 3 | ||
Forns et al. [46] | * | * | * | ** | * | * | 7 | ||
Gaffron and Niemeier [41] | * | NA | * | NA | NA | 2 | |||
Grineski et al. [39] | * | NA | * | NA | NA | 2 | |||
Hutter et al. [47] | * | * | NA | * | * | 4 | |||
Marcotte [48] | * | NA | * | * | NA | NA | 3 | ||
Miller and Vela [40] | * | * | NA | * | * | NA | NA | 4 | |
Saenen et al. [49] | * | * | * | ** | * | * | 7 | ||
Sunyer et al. [50] | * | * | * | ** | * | * | 7 | ||
van Kempen et al. [51] | * | NA | * | * | NA | NA | 3 |
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Gartland, N.; Aljofi, H.E.; Dienes, K.; Munford, L.A.; Theakston, A.L.; van Tongeren, M. The Effects of Traffic Air Pollution in and around Schools on Executive Function and Academic Performance in Children: A Rapid Review. Int. J. Environ. Res. Public Health 2022, 19, 749. https://doi.org/10.3390/ijerph19020749
Gartland N, Aljofi HE, Dienes K, Munford LA, Theakston AL, van Tongeren M. The Effects of Traffic Air Pollution in and around Schools on Executive Function and Academic Performance in Children: A Rapid Review. International Journal of Environmental Research and Public Health. 2022; 19(2):749. https://doi.org/10.3390/ijerph19020749
Chicago/Turabian StyleGartland, Nicola, Halah E. Aljofi, Kimberly Dienes, Luke Aaron Munford, Anna L. Theakston, and Martie van Tongeren. 2022. "The Effects of Traffic Air Pollution in and around Schools on Executive Function and Academic Performance in Children: A Rapid Review" International Journal of Environmental Research and Public Health 19, no. 2: 749. https://doi.org/10.3390/ijerph19020749