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A statistical method to estimate PM 2.5 concentrations from meteorology and its application to the effect of climate change
Author(s) -
Lecœur Ève,
Seigneur Christian,
Pagé Christian,
Terray Laurent
Publication year - 2014
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2013jd021172
Subject(s) - environmental science , climatology , climate change , residual , pollutant , meteorology , general circulation model , air quality index , atmospheric sciences , geography , mathematics , oceanography , geology , chemistry , organic chemistry , algorithm
A statistical algorithm was developed to estimate PM 2.5 concentrations over Europe based on a weather‐type representation of the meteorology. We used modeled PM 2.5 concentrations as pseudoobservations, because of a lack of PM 2.5 speciated measurements over Europe, and included four meteorological variables. This algorithm was evaluated on the learning period (2000–2008) to test its ability to reproduce the pseudoobserved data set and then applied for two climatological scenarios (RCP4.5 and RCP8.5) and one historical (1975–2004) and two future periods (2020–2049 and 2070–2099). In Italy, Poland, and northern, eastern, and southeastern Europe, all future scenarios lead to decreases in PM 2.5 , whereas in the Balkans, Benelux, the UK, and northern France, they lead to increases in PM 2.5 . Considering each season separately shows stronger responses, which may vary for a given region and scenario. Decomposing the changes in PM 2.5 concentrations as the sum of intertype and intratype changes, and a residual term shows that (1) the residual term is negligible; (2) intertype changes affect more the regions along the Atlantic Ocean; and (3) in most other regions, intertype and intratype changes are often on the same order of magnitude. The relationship between the atmospheric circulation and weather types evolves and therefore modifies the mean of meteorological variables and PM 2.5 concentrations. This algorithm offers a novel approach to investigate the effect of climate change on air quality and can be applied to other pollutants, regions, and meteorological models. Furthermore, this approach can be applied using actual speciated PM 2.5 observations, if a sufficiently dense monitoring network were available.