A satellite-based model for estimating PM2.5 concentration in a sparsely populated environment using soft computing techniques

We applied three soft computing methods including adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and back-propagation artificial neural network (BPANN) algorithms for estimating the ground-level PM2.5 concentration. These models were trained by comprehensive satellite-based, meteorological, and geographical data. A 10-fold cross-validation (CV) technique was used to identify the optimal predictive model. Results showed that ANFIS was the best-performing model for predicting the variations in PM2.5 concentration. Our findings demonstrated that the CV-R2 of the ANFIS (0.81) is greater than that of the SVM (0.67) and BPANN (0.54) model. The results suggested that soft computing methods like ANFIS, in combination with spatiotemporal data from satellites, meteorological data and geographical information improve the estimate of PM2.5 concentration in sparsely populated areas

Estimating the spatiotemporal variation of NO2 concentration using an adaptive neuro-fuzzy inference system

Statistical modelling has been successfully used to estimate the variations of NO2 concentration, but employing new modelling techniques can make these estimations far more accurate. To do so, for the first time in application to spatiotemporal air pollution modelling, we employed a soft computing algorithm called adaptive neuro-fuzzy inference system (ANFIS) to estimate the NO2 variations. Comprehensive data sets were investigated to determine the most effective predictors for the modelling process, including land use, meteorological, satellite, and traffic variables. We have demonstrated that using selected satellite, traffic, meteorological, and land use predictors in modelling increased the R2 by 21%, and decreased the root mean square error (RMSE) by 47% compared with the model only trained by land use and meteorological predictors. The ANFIS model found to have better performance and higher accuracy than the multiple regression model. Our best model, captures 91% of the spatiotemporal variability of monthly mean NO2 concentrations at 1 km spatial resolution (RMSE 1.49 ppb) in a selected area of Australia

Long-term nitrogen dioxide exposure assessment using back-extrapolation of satellite-based land-use regression models for Australia

Assessing historical exposure to air pollution in epidemiological studies is often problematic because of limited spatial and temporal measurement coverage. Several methods for modelling historical exposures have been described, including land-use regression (LUR). Satellite-based LUR is a recent technique that seeks to improve predictive ability and spatial coverage of traditional LUR models by using satellite observations of pollutants as inputs to LUR. Few studies have explored its validity for assessing historical exposures, reflecting the absence of historical observations from popular satellite platforms like Aura (launched mid-2004). We investigated whether contemporary satellite-based LUR models for Australia, developed longitudinally for 2006–2011, could capture nitrogen dioxide (NO2) concentrations during 1990–2005 at 89 sites around the country. We assessed three methods to back-extrapolate year-2006 NO2 predictions: (1) ‘do nothing’ (i.e., use the year-2006 estimates directly, for prior years); (2) change the independent variable ‘year’ in our LUR models to match the years of interest (i.e., assume a linear trend prior to year-2006, following national average patterns in 2006–2011), and; (3) adjust year-2006 predictions using selected historical measurements. We evaluated prediction error and bias, and the correlation and absolute agreement of measurements and predictions using R2 and mean-square error R2 (MSE-R2), respectively. We found that changing the year variable led to best performance; predictions captured between 41% (1991; MSE-R2 = 31%) and 80% (2003; MSE-R2 = 78%) of spatial variability in NO2 in a given year, and 76% (MSE-R2 = 72%) averaged over 1990–2005. We conclude that simple methods for back-extrapolating prior to year-2006 yield valid historical NO2 estimates for Australia during 1990–2005. These results suggest that for the time scales considered here, satellite-based LUR has a potential role to play in long-term exposure assessment, even in the absence of historical predictor data. © 2018 Elsevier Inc

Independent validation of national satellite-based land-use regression models for nitrogen dioxide using passive samplers

Including satellite observations of nitrogen dioxide (NO2) in land-use regression (LUR) models can improve their predictive ability, but requires rigorous evaluation. We used 123 passive NO2 samplers sited to capture within-city and near-road variability in two Australian cities (Sydney and Perth) to assess the validity of annual mean NO2 estimates from existing national satellite-based LUR models (developed with 68 regulatory monitors). The samplers spanned roadside, urban near traffic (≤100 m to a major road), and urban background (>100 m to a major road) locations. We evaluated model performance using R2 (predicted NO2 regressed on independent measurements of NO2), mean-square-error R2 (MSE-R2), RMSE, and bias. Our models captured up to 69% of spatial variability in NO2 at urban near-traffic and urban background locations, and up to 58% of variability at all validation sites, including roadside locations. The absolute agreement of measurements and predictions (measured by MSE-R2) was similar to their correlation (measured by R2). Few previous studies have performed independent evaluations of national satellite-based LUR models, and there is little information on the performance of models developed with a small number of NO2 monitors. We have demonstrated that such models are a valid approach for estimating NO2 exposures in Australian cities