Large Scale Air Pollution Estimation Method Combining Land Use Regression and Chemical Transport Modeling in a Geostatistical Framework

Abstract

In recognition that intraurban exposure gradients may be as large as between-city variations, recent air pollution epidemiologic studies have become increasingly interested in capturing within-city exposure gradients. In addition, because of the rapidly accumulating health data, recent studies also need to handle large study populations distributed over large geographic domains. Even though several modeling approaches have been introduced, a consistent modeling framework capturing within-city exposure variability and applicable to large geographic domains is still missing. To address these needs, we proposed a modeling framework based on the Bayesian Maximum Entropy method that integrates monitoring data and outputs from existing air quality models based on Land Use Regression (LUR) and Chemical Transport Models (CTM). The framework was applied to estimate the yearly average NO 2 concentrations over the region of Catalunya in Spain. By jointly accounting for the global scale variability in the concentration from the output of CTM and the intraurban scale variability through LUR model output, the proposed framework outperformed more conventional approaches.

A national fine spatial scale land-use regression model for ozone

“…Formation of a secondary pollutant is not well represented in a land use regression model, except with distance. Inclusion of a large-scale chemical transport model within the LUR framework could improve model fit, possibly using Bayesian approaches as recently applied for NO 2 (Akita et al, 2014). The final model included three variables with a negative slope, reflecting scavenging by primary NO emissions by local traffic (traffic load and major road length both in a 50 m buffer) and the collection of urban sources (address density in a 5 km buffer).…”

Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies

“…However, assimilation and mapping techniques (e.g., bias correction, model output statistics, ensemble Kalman filtering, statistical approaches, geostatistical approaches) can improve the air quality estimations of mechanistic models (e.g., [44][45][46][47][48]). Some studies have generated high-resolution maps of air quality in urban areas by combining regional mechanistic and local dispersion models, which estimate regional (or background) and near-road concentrations, respectively (e.g., [49,50]).…”

Comparison of Land-Use Regression Modeling with Dispersion and Chemistry Transport Modeling to Assign Air Pollution Concentrations within the Ruhr Area

“…Similarly, CTMs are only valid if based on a comprehensive and detailed emission database. To overcome limitations of each of the models and optimally make use of the respective strengths, we propose to combine the two approaches into a hybrid model [43,44]. These hybrid models are usually based on the LUR model since LURs are by design much easier to modify.…”