Abhijith Kooloth Valappil

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.