Temporal modeling of nitrogen dioxide levels on Main Street, East Los Angeles: Estimating annual periodic components using the Variable Bandpass Periodic Block Bootstrap

Abstract

In this study we assess periodicities in nitrogen dioxide levels at a location in Los Angeles using a novel Variable Bandpass Periodic Block Bootstrap (VBPBB) method resulting in confidence interval bands for the periodic mean. Nitrogen dioxide (NO2) is an air pollutant primarily produced by the combustion of fossil fuels by power plants and vehicles with internal combustion engines which has been linked with a variety of adverse health outcomes including dementia, breast cancer, decreased cognitive function, increased susceptibility to Covid-19, cardiovascular and respiratory mortality. Previous analysis methods such as block bootstrapping can obscure periodically correlated patterns in time series. The sampling destroys the correlation observed in the data for patterns of different periods, such as the daily, weekly and yearly patterns of nitrogen dioxide levels we wish to investigate. We use the VBPBB method to isolate significant periodicities using a band pass filter before bootstrapping so that the correlations between the data are preserved. Confidence interval bands for VBPBB are compared against existing block bootstrapping. The resulting narrower confidence interval bands created by VBPBB show a significant annual fluctuation in nitrogen dioxide levels while the existing methods do not show it as clearly. Better characterization of pollution patterns will aid in pollution reduction efforts by allowing us to pinpoint times of highest risk and direct mitigation efforts where they will have the greatest impact. This technique exhibits potential for future applications to other areas of environmental and health interest and concern.