Environmental project, 1005 – Time Series Study of Air Pollution Health Effects in COPSAC Children

Time Series Study of Air Pollution Health Effects in COPSAC Children

Summary and conclusions

We studied associations between daily levels of ambient air pollutants at monitoring stations and daily airway symptoms among 411 children with atopic predisposition from the COPSAC (COpenhagen Prospective Study on Atopy in Children) cohort. The children were followed from birth to the age of 18 month and if present, wheezing symptoms were recorded daily by the parents. CO, NO_x, NO₂, O₃, PM₁₀, and total number concentration of ultrafine particles (TON) were measured at one urban background station at the H.C. Ørsted institute (HCØ) and at two street stations at Jagtvej and H.C. Andersens Boulevard (HCAB).

A total of 963 episodes of wheezing with a total duration of 7287 days were recorded. This correspond 0.6 new episodes per day and 4.4 children with symptoms on average per day.

Analyses were performed separately for children from central Copenhagen

(n=115), Copenhagen suburbs (n=134) and the rest of Zealand (n=186). Single day exposure and unconstrained distributed lag generalized additive models were used for this.

We found consistent associations between daily ambient levels of air pollutants and daily incidence of respiratory symptoms in terms of wheezing during the first 18 month of life of children with atopic predisposition and living in Copenhagen. Among children from central Copenhagen the associations were statistically significant and positive with respect to street levels of CO and NO_x, and negative with respect to street levels of O₃, whereas positive associations with urban background levels of PM₁₀, CO and NO₂were borderline significant. Among children living in Copenhagen suburbs or the rest of Zealand similar, but much weaker associations with the gases were seen, only significant for street levels of NO₂ and O₃ at one station and only for children from outside Copenhagen, whereas there were no sign of associations with PM₁₀ levels. These apparently differential associations related to distance from the sources of pollutants and monitoring sites supports causal relationships. Moreover, positive associations with the street levels of CO and NO_x and negative with street levels of ozone, which is consumed by NO from diesel emission, suggest traffic as the important source of pollutants relevant for airway symptoms. Associations with total number concentrations of ultrafine particles, which are mainly traffic generated, would also be expected, although these were not significant, but that may be due to the low number of days with measurements. We found furthermore that air pollution has small or no effect on development of the outcome on the concurrent day, but that the effect on airway symptoms comes with a delay of 2-4 days for different pollutants and that the effect is accumulated over several days.

Our results confirm our hypothesis that children living in central Copenhagen (postcode ≤ 2450) are the most relevant choice of population, as it is most representative of pollution levels measured in urban background and in the street at Jagtvej and HCAB. The association between symptoms and in particular CO and NO_x and inverse association with ozone suggest a relationship with traffic related air pollution as there is no other significant sources, where CO is mainly associated with petrol driven cars whereas NO_x is associated with diesel powered vehicles. Thus, ultrafine particles, which are emitted from particularly diesel vehicles, would also be expected to show associations but showed less consistency, Our results for particles were borderline significant for PM₁₀ which have other main sources than traffic, but completely consistent with the only published similar study from Santiago, Chile, where traffic may be more important for fine particles (Pino et al. 2004). Only the data for CO, NO_x and NO₂ are close to complete for the study periods, whereas data on PM₁₀ and ultrafine number concentrations are very incomplete. Thus, lack of significant associations with symptoms may also be related to low statistical power as can be seen from the large standard errors for most of the coefficient estimates.