Abhijith Kooloth Valappil

Green infrastructure (GI) is known to reduce road air pollution exposure, but their implementation in schools and associated benefits remain under-researched. In this study, two GI solutions, green screen and green gate, were co-designed and installed at a primary school in Guildford using collaborative and participatory methods. By assessing changes in air pollution levels, noise, and public perception before and after GI installation, we aimed to understand their impact on reducing children's exposure and evaluate other co-benefits. Without considering wind direction's effect, a maximum reduction of up to 32 %, 10 % and 12 % in the average daily concentration of PM10 (green gate), PM2.5 (green screen) and PM1 (green gate), respectively, when compared with in-front concentration. The decay in concentration decreases with distance from the GI, and different wind directions result in varying percentage reductions in PM concentration. For the green screen, ‘parallel to the screen’ and for the green gate, ‘away from the gate’ wind directions provided the highest PM reduction. The horizontal abatement efficiency of GI varied with PM size, with the highest being PM10. Continuous monitoring behind the green screen revealed a decrease in PM concentration after installation, and this relative concentration varied from 0.29 to 0.90 compared to before installation. The green gate effectively lowered noise by 5 dB(A), and the green screen did not report a noticeable impact on noise levels. Most parents perceived the installation of GI in school as significantly decreasing air pollution exposure and slightly reducing noise levels, resembling the changes in their levels observed in monitoring. The successful co-creation and co-implementation of GI interventions and resulting co-benefits underscore the importance of community engagement and participatory approaches in urban planning and environmental management. This study paves the way for the wider-scale application of innovative strategies involving local communities, stakeholders, and policymakers in implementing GI projects to ensure their sustainability and effectiveness. [Display omitted] •This co-designed and participatory study installed the first living green gate and hedges around the school perimeter.•Green gate reduced PM10, PM2.5, PM1 and noise by 32 %, 19 %, 12 % and 5 dB(A), respectively.•Green screen reduced PM10, PM2.5, and PM1 by 31 %, 10 %, and 6 %, respectively.•PM reduction decreased with distance and had no impact from GI after 25-36 m.•PM reduced by 44 % (wind flowing away from green gate) and 42 % (wind flowing parallel to green screen).

Poor environmental quality in school classrooms can have a detrimental impact on children’s health, nevertheless, the association between air pollutants and physical features of classrooms is poorly understood. We monitored particulate matter (PM), carbon dioxide (CO2) and thermal comfort in sixty classrooms across ten London primary schools using similar equipment to produce a comparable dataset. The overall research objective was to understand the association of classroom air quality with occupancy levels, floor types, classroom locations, classroom volume, ventilation types and different year groups. Average in-classroom PM10 (29±20), PM2.5 (10±2) and PM1 (5±2 μg m-3) during occupied hours were ∼150% (PM10) and 110% (PM2.5) higher compared to non-occupied hours. PM10 concentration was reduced by 30% for dual (mechanical+natural) compared to natural ventilation only; the corresponding reduction was slightly lower for PM2.5 (28%) and PM1 (20%). PM10 almost doubled for wooden floored classrooms compared with those having carpets. During high occupancy (>26 occupants), the average CO2 (935±453 ppm) was ∼140% higher than non-occupancy. The average CO2 in classrooms occupied by younger children (reception and year one) was ∼190% higher than those with older children (years eight and nine). 68% of classrooms exceeded the recommended levels of 40% relative humidity. Low PM10 concentrations coincided with low CO2 concentrations in classrooms across all schools. These findings highlight the importance of simultaneously addressing both thermal comfort and the resuspension of PM10 to achieve comprehensive improvements in classroom air quality. Classroom settings where indoor environment is likely to be compromised can also be identified and addressed.

Noise and air pollutants share many common sources including traffic volume. Noise pollution causes annoyance and disturbs sleep and it is the second risk factor, after air pollution, to the estimated environmental burden of disease in Europe. It can also act as a proxy for some of the air pollutants, to allow building of holistic view of environmental pollution. During the pandemic and the resulting lockdowns in cities across the world, traffic volumes reduced significantly, leading to reduced pollutant concentrations and noise levels. In this work, we present an analysis of the multiple pollutants (e.g., fine particulate matter, nitrogen oxide) and noise data that are monitored continuously during the lockdown at 15-minute resolution at a school site in the UK, which is situated next to a busy road. This talk will present trends of noise and the air pollutants during the lockdown period, explore possible relationship of noise as a proxy for air pollutants; variations between pollutants and the underlining reasons explaining the temporal variations.

During the Covid-19 pandemic and resulting lockdowns, road traffic volumes reduced significantly leading to reduced pollutant concentrations and noise levels. Noise and the air pollution data during the lockdown period and loosening of restrictions through five phases in 2021 are examined for a school site in the UK. Hourly and daily average noise level as well as the average over each phase, correlations between noise and air pollutants, variations between pollutants and underlying reasons explaining the temporal variations are explored. Some strong linear correlations were identified between a number of traffic-sourced air pollutants, especially between the differently sized particulates PM1, PM2.5 and PM10 (0.70 < r < 0.98) in all phases and an expected inverse correlation between Nitrogen Dioxide NO2 and ground-level Ozone O3 (-0.68 < r < -0.78) as NO2 is a precursor of O3. Noise levels exhibit a weak correlation with the measured air pollutants and moderate correlation with meteorological factors, including wind direction, temperature, and relative humidity. There was a consistent and significant increase in noise levels (p < 0.01) of up to 3 dB with initial easing and this was maintained through the remaining phases.