Although maximum weighted traffic within 150 m was not associated with SAB in the analysis without the temporal adjustment, adjusting for NO2 levels during the first month of pregnancy increased the association between SAB and that traffic metric (Table 3).
As such, it is the relationship of the session relative to the seasonal average at the reference site(s) that determines the temporal adjustment, which can therefore adjust distributed concentration to both lesser and greater values.
We found that the temporal adjustment approach predicted measured values in the validation well.
The logistic regression analysis was performed both with and without the temporal adjustment for NO2 levels.
This suggests that misclassification of the temporal adjustment was probably small in our study.
In addition, for pollutants without a consistent city-wide temporal trend (e.g., more localized or sparse sources) the temporal adjustment approach may not be appropriate.
The temporal adjustment of LUR spatial estimates of pollutant levels assumed that the city spatial surface and the spatial distribution of pollutants remained constant over the study period.
NO2 concentrations increased with number of inversion mornings per 5-day sampling session under the temporal adjustment method drawing information from both regional and local temporal trends (Equation 2), but not under regional-only adjustment (Equation 1).
After the temporal adjustment, the exposure variation increased, widening the IQR by > 50% for all pollutants (Table 2), thus giving greater statistical power to detect associations in the spatiotemporal models compared with the spatial models.
As a validation, we predicted the raw two-week concentrations at the 150 NYCCAS locations in Year 2 (a total of four predictions at each of 150 locations – one for each season) using the temporal adjustment approach discussed above.
