The net decay time of anomalies in concentrations of atmospheric pollutants. (July 2017)

• Record Type: Journal Article
• Authors: Vinnikov, Konstantin Y.; Dickerson, Russell R.; Krotkov, Nickolay A.; Edgerton, Eric S.; Schwab, James J.
• Is Part Of: Atmospheric environment. Volume 160(2017)
• Page Start: 19
• Page End: 26
• Publication Date: 2017-07
• Subjects: Chemical climatology air pollutants -- Temporal variability atmospheric pollutants -- Decay time air pollutants -- Remote sensing atmospheric pollutants; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2017.04.006
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: This paper introduces a new parameter to characterize the random component in temporal variability of atmospheric pollutants and proposes a simple statistical technique for its evaluation. That parameter is the net decay time (or the time scale) of the local anomalies in concentrations of atmospheric pollutants, rather than the traditionally used chemical lifetimes of total amounts of the species. Using widely available data of hourly multi-year surface trace gas pollutant concentrations we demonstrate a simplified way to estimate the net decay time with an exponential approximation of lag-correlation functions. We assessed the decay times of fluctuations in observations of eight atmospheric pollutants (SO2, NO, NO2, NOy, O3, CO, NH3, and HNO3 ) at two urban sites and one cleaner rural site in the Eastern US. The time scales of temporal fluctuations (net decay times) vary from about one hour to slightly more than one day. These scales are generally much shorter in urban environments than in remote regions. We also compared day- and night-time observations in warm and cold seasons. At night in the cold season, time scales of fluctuations in atmospheric pollutants are usually the longest. Such estimates should be useful to air quality prediction, public health, and satellite remote sensing research communities. Highlights: We examine the time for an anomaly in air pollution concentration to decay. Lag correlation statistics are used to determine decay times of air pollutants. Decay times are longest (>1 d) in the cold season, at night, in rural areas. Decay times are shortest (∼1 h) in the warm season, in the daytime.