Land use regression modeling to estimate historic (1962-1991) concentrations of black smoke and sulfur dioxide for Great Britain.

TitleLand use regression modeling to estimate historic (1962-1991) concentrations of black smoke and sulfur dioxide for Great Britain.
Publication TypeJournal Article
Year of Publication2011
AuthorsGulliver J, Morris C, Lee K, Vienneau D, Briggs D, Hansell A
JournalEnviron Sci Technol
Volume45
Issue8
Pagination3526-32
Date Published2011 Apr 15
ISSN1520-5851
KeywordsAgriculture, Air Pollutants, Air Pollution, Environmental Monitoring, Geographic Information Systems, Great Britain, Models, Chemical, Population Density, Regression Analysis, Smoke, Sulfur Dioxide
Abstract

Land-use regression modeling was used to develop maps of annual average black smoke (BS) and sulfur dioxide (SO(2)) concentrations in 1962, 1971, 1981, and 1991 for Great Britain on a 1 km grid for use in epidemiological studies. Models were developed in a GIS using data on land cover, the road network, and population, summarized within circular buffers around air pollution monitoring sites, together with altitude and coordinates of monitoring sites to consider global trend surfaces. Models were developed against the log-normal (LN) concentration, yielding R(2) values of 0.68 (n = 534), 0.68 (n = 767), 0.41 (n = 771), and 0.39 (n = 155) for BS and 0.61 (n = 482), 0.65 (n = 733), 0.38 (n = 756), and 0.24 (n = 153) for SO(2) in 1962, 1971, 1981, and 1991, respectively. Model evaluation was undertaken using concentrations at an independent set of monitoring sites. For BS, values of R(2) were 0.56 (n = 133), 0.41 (n = 191), 0.38 (n = 193), and 0.34 (n = 37), and for SO(2) values of R(2) were 0.71 (n = 121), 0.57 (n = 183), 0.26 (n = 189), and 0.31 (n = 38) for 1962, 1971, 1981, and 1991, respectively. Models slightly underpredicted (fractional bias: 0∼-0.1) monitored concentrations of both pollutants for all years. This is the first study to produce historic concentration maps at a national level going back to the 1960s.

DOI10.1021/es103821y
Alternate JournalEnviron. Sci. Technol.
PubMed ID21446726