Development and Back-Extrapolation of NO₂ Land Use Regression Models for Historic Exposure Assessment in Great Britain

Modeling historic air pollution exposures is often restricted by availability of monitored concentration data. We evaluated back-extrapolation of land use regression (LUR) models for annual mean NO₂ concentrations in Great Britain for up to 18 years earlier. LUR variables were created in a geographic information system (GIS) using land cover and road network data summarized within buffers, site coordinates, and altitude. Four models were developed for 2009 and 2001 using 75% of monitoring sites (in different groupings) and evaluated on the remaining 25%. Variables selected were generally stable between models. Within year, hold-out validation yielded mean-squared-error-based <i>R</i>² (MSE-<i>R</i>²) (i.e., fit around the 1:1 line) values of 0.25–0.63 and 0.51–0.65 for 2001 and 2009, respectively. Back-extrapolation was conducted for 2009 and 2001 models to 1991 and for 2009 models to 2001, adjusting to the year using two background NO₂ monitoring sites. Evaluation of back-extrapolated predictions used 100% of sites from an historic national NO₂ diffusion tube network (<i>n</i> = 451) for 1991 and 70 independent sites from automatic monitoring in 2001. Values of MSE-<i>R</i>² for back-extrapolation to 1991 were 0.42–0.45 and 0.52–0.55 for 2001 and 2009 models, respectively, but model performance varied by region. Back-extrapolation of LUR models appears valid for exposure assessment for NO₂ back to 1991 for Great Britain

Additional file 1 of Long-term exposure to transportation noise and diabetes mellitus mortality: a national cohort study and updated meta-analysis

Supplementary Material 1

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

Land-use regression modeling was used to develop maps of annual average black smoke (BS) and sulfur dioxide (SO₂) 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 R² values of 0.68 (<i>n</i> = 534), 0.68 (<i>n</i> = 767), 0.41 (<i>n</i> = 771), and 0.39 (<i>n</i> = 155) for BS and 0.61 (<i>n</i> = 482), 0.65 (<i>n</i> = 733), 0.38 (<i>n</i> = 756), and 0.24 (<i>n</i> = 153) for SO₂ in 1962, 1971, 1981, and 1991, respectively. Model evaluation was undertaken using concentrations at an independent set of monitoring sites. For BS, values of R² were 0.56 (<i>n</i> = 133), 0.41 (<i>n</i> = 191), 0.38 (<i>n</i> = 193), and 0.34 (<i>n</i> = 37), and for SO₂ values of R² were 0.71 (<i>n</i> = 121), 0.57 (<i>n</i> = 183), 0.26 (<i>n</i> = 189), and 0.31 (<i>n</i> = 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

Additional file 1: of Air pollution modelling for birth cohorts: a time-space regression model

Figure S1. Spatial distribution of the BECO (rural) and AFU (urban) measurement locations in the canton of Bern, displayed on background NO2 from the 2007 dispersion model. Figure S2. NO2 levels measured in a sample of urban monitoring sites during the year 2007. Figure S3. Internal validation Bland-Altman plot of predicted and measured values in the rural region (top) and urban area (bottom) in the log scale. Figure S4. Bland-Altman plot for external validation in μg/m3 (rural or urban model without intercept, corrected for backyard measurements). Table S1. Potential predictors of NO2. Table S2. Variance Inflation Factors (VIF) of main predictors in the rural and urban model. Table S3. Kappa statistics for External validation – measured vs estimated concentration in quartiles. (DOCX 1047 kb

Additional file 1: of Risk factors for schistosomiasis in an urban area in northern Côte d’Ivoire

Multilingual abstracts in the six official working languages of the United Nations. (PDF 594 kb

Western European Land Use Regression Incorporating Satellite- and Ground-Based Measurements of NO₂ and PM₁₀

Land use regression (LUR) models typically investigate within-urban variability in air pollution. Recent improvements in data quality and availability, including satellite-derived pollutant measurements, support fine-scale LUR modeling for larger areas. Here, we describe NO₂ and PM₁₀ LUR models for Western Europe (years: 2005–2007) based on >1500 EuroAirnet monitoring sites covering background, industrial, and traffic environments. Predictor variables include land use characteristics, population density, and length of major and minor roads in zones from 0.1 km to 10 km, altitude, and distance to sea. We explore models with and without satellite-based NO₂ and PM_2.5 as predictor variables, and we compare two available land cover data sets (global; European). Model performance (adjusted <i>R</i>²) is 0.48–0.58 for NO₂ and 0.22–0.50 for PM₁₀. Inclusion of satellite data improved model performance (adjusted <i>R</i>²) by, on average, 0.05 for NO₂ and 0.11 for PM₁₀. Models were applied on a 100 m grid across Western Europe; to support future research, these data sets are publicly available

Simple^a and adjusted^b associations of cord blood cells with perinatal risk factors.

<p>Simple<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0200236#t004fn002" target="_blank">^a</a> and adjusted<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0200236#t004fn003" target="_blank">^b</a> associations of cord blood cells with perinatal risk factors.</p

Simple^a and adjusted^b associations of cord blood cells with environmental risk factors.

<p>Simple<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0200236#t003fn002" target="_blank">^a</a> and adjusted<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0200236#t003fn003" target="_blank">^b</a> associations of cord blood cells with environmental risk factors.</p

a+b. Adjusted^a effect of perinatal stress factors on mDCs, pDCs, and pDCs/ mDCs ratio in multivariable models using a) NO₂ and b) PM₁₀ exposure during last 14 days before the birth.

<p>^a adjusted for sex, gestational age, birth order, gestational age, mode of delivery, CTG, maternal smoking during pregnancy, maternal atopy, change in the gating strategy, and season of birth.</p

Population characteristics.

<p>Population characteristics.</p