Land Use Regression Modeling To Estimate Historic (1962−1991) Concentrations of Black Smoke and Sulfur Dioxide for Great Britain

Gulliver, J; Perkins, C; Lee, K; Vienneau, D; Briggs, D; Hansell, A; (2011) Land Use Regression Modeling To Estimate Historic (1962−1991) Concentrations of Black Smoke and Sulfur Dioxide for Great Britain. Environmental science & technology, 45 (8). pp. 3526-3532. ISSN 0013-936X DOI:

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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.

Item Type: Article
Keywords: Agriculture, statistics & numerical data, Air Pollutants, analysis, Air Pollution, statistics & numerical data, Environmental Monitoring, Geographic Information Systems, Models, Chemical, Population Density, Regression Analysis, Smoke, analysis, Sulfur Dioxide, analysis, United Kingdom
Faculty and Department: Faculty of Public Health and Policy > Dept of Global Health and Development
Faculty of Epidemiology and Population Health > Dept of Population Health (2012- )
PubMed ID: 21446726
Web of Science ID: 289341300048


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