 pollution control days. Median (25–75% range, number of sample analysed) 1-OHP levels corrected for creatinine were: Autumn control 0.0195 (0.009–0.036, n = 88) μmol OHP/mol creatinine; Winter control 0.025 (0.013–0.038, n = 77) μmol OHP/mol creatinine; Pollution day 1 0.043 (0.030–0.073, n = 79) μmol OHP/mol creatinine; Pollution day 2 0.042 (0.022–0.064, n = 73); p < 0.0001 for all comparisons of high pollution days vs controls. Differences in 1-OHP levels between the two control days did not reach statistical significance. Similarly, the 1-OHP levels on the two high pollution days were not significantly different.
EBC pH readings are shown in Figure 4. There was a broad range of pH readings on all sampling days. pH differences between autumn and winter controls reached statistical significance, and pH at the first high pollution day was significantly higher than the second study day, and the winter control day, but not the autumn control day. There was no independent effect in students with asthma in comparison with healthy students.
EBC hydrogen peroxide
EBC H2O2 readings are shown in Figure 5. Detectable hydrogen peroxide was present in most samples. There was no difference in measured H2O2 between any of the sampling days, either control or high pollution. There was also no difference in proportions of samples with detectable H2O2. There was no independent effect of asthma in comparison with healthy subjects. There was no significant correlation between EBC levels of hydrogen peroxide, and pH, and no correlation with either of these measurements and urinary 1-OHP.
Lung function data
FEV1 data was available for a median of 129.5 time points per student (Range 4–189). PEFR data was available for a median of 128 time points (Range 2–186). Univariate analysis identified a number of biologically plausible correlations between lung function data and PM10 levels which reached statistical significance. There was a significant correlation between morning FEV1 and 24 hour average outdoor air pollution levels the previous day (Spearman's rho -0.201, p = 0.034). When analysing for the presence of asthma, asthmatic subjects showed correlations between FEV1 and 24 hour average outdoor air pollution levels which just failed to reach statistical significance (Spearman's rho -0.187, p = 0.06), while non-asthmatic subjects showed no significant correlations. There was no significant correlation between peak PM10 levels and lung function in either asthmatics or non-asthmatics. In the asthmatic subjects, there were a number of associations noted between pollution and temperature variables and the use of any reliever medication which reached statistical significance. These were daytime indoor pollution – one day lag; night-time indoor pollution – same night and one day lag; temperature; minimum and maximum inside temperature, and relative humidity. There was no significant relationship between the use of any reliever medication and any of the lung function variables in the asthmatic group.
When multivariate analysis was undertaken, using six dependent lung function variables (FEV1 and PEF, morning and afternoon and daily variation in both measures), and lagging air pollution measures out to seven days, no pollution variable was retained in a regression model, with only minor temperature effects being retained. When asthmatics were studied separately, small but statistically significant associations between maximum outdoor air pollution levels and variability in FEV1, and night time PEFR were noted. With higher maximum out door pollution, FEV1 variability between morning and afternoon dropped, and afternoon PEFR dropped. Effect size was small, in the case of afternoon PEFR having an R2 of 3.6%. Reliever use was not retained in the regression models for any of the lung function data, except for daily variability in PEF.
When comparing median morning FEV1 between moderate/high pollution days (>20 μg/m3) and low pollution days, students with asthma demonstrated lower readings on high pollution days (p = 0.043). No effect was seen in non-a