title: A novel approach for exposure assessment in air pollution epidemiological studies using neuro-fuzzy inference systems: Comparison of exposure estimates and exposure-health associations.
abstract: Many epidemiological studies have used proximity to sources as air pollution exposure assessment method. However, proximity measures are not generally good surrogates because of their complex non-linear relationship with exposures. Neuro-fuzzy inference systems (NFIS) can be used to map complex non-linear systems, but its usefulness in exposure assessment has not been extensively explored. We present a novel approach for exposure assessment using NFIS, where the inputs of the model were easily-obtainable proximity measures, and the output was residential exposure to an air pollutant. We applied it to a case-study on NH3pollution, and compared health effects and exposures estimated from NFIS, with those obtained from emission-dispersion models, and linear and non-linear regression proximity models, using 10-fold cross validation. The agreement between emission-dispersion and NFIS exposures was high (Root-mean-square error (RMSE) =0.275, correlation coefficient (r)=0.91) and resulted in similar health effect estimates. Linear models showed poor performance (RMSE=0.527, r=0.59), while non-linear regression models resulted in heterocedasticity, non-normality and clustered data. NFIS could be a useful tool for estimating individual air pollution exposures in epidemiological studies on large populations, when emission-dispersion data are not available. The tradeoff between simplicity and accuracy needs to be considered.
authors.affiliation: The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark. Electronic address: vbv@mmmi.sdu.dk.
authors.forename: Victoria
authors.initials: V
authors.lastname: Blanes-Vidal
authors.affiliation: The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark.
authors.forename: Manuella Lech
authors.initials: ML
authors.lastname: Cantuaria
authors.affiliation: The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark.
authors.forename: Esmaeil S
authors.initials: ES
authors.lastname: Nadimi
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000641:Ammonia / Q000032:analysis*; D003718:Denmark; D004781:Environmental Exposure / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods*; D016021:Epidemiologic Studies; D017143:Fuzzy Logic; D006801:Humans; D016014:Linear Models; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D052638:Particulate Matter / Q000032:analysis*; D012621:Seasons
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter; D000641:Ammonia
keywords: Air pollution; Exposure assessment; Fuzzy logic; Health; Neural networks