Uncertainty in environmental health impact assessment: Quantitative methods and perspectives

Abstract

Environmental health impact assessment models are subjected to great uncertainty due to the complex associations between environmental exposures and health. Quantifying the impact of uncertainty is important if the models are used to support health policy decisions. We conducted a systematic review to identify and appraise current methods used to quantify the uncertainty in environmental health impact assessment. In the 19 studies meeting the inclusion criteria, several methods were identified. These were grouped into random sampling methods, second-order probability methods, Bayesian methods, fuzzy sets, and deterministic sensitivity analysis methods. All 19 studies addressed the uncertainty in the parameter values but only 5 of the studies also addressed the uncertainty in the structure of the models. None of the articles reviewed considered conceptual sources of uncertainty associated with the framing assumptions or the conceptualisation of the model. Future research should attempt to broaden the way uncertainty is taken into account in environmental health impact assessments.

A systematic review of methods of uncertainty analysis and their applications in the assessment of chemical exposures, effects, and risks

“…However, before our study, only Mesa-Frias et al (2013) aimed to conduct a systematic review that identified and appraised current methods for quantifying the uncertainty in environmental health assessment. They found, nevertheless, a much lower number of papers as compared to ours.…”

“…Furthermore, none of them were systematic, in terms of using an identifiable database, relevant research words, and explicit criteria for selecting and analyzing the papers included. Only Mesa-Frias et al (2013) aimed to conduct a systematic review that identified and appraised current 2 L. Maxim methods for quantifying the uncertainty in environmental health assessment. Yet, the number of papers assessed was quite low (from three databases they identified 18, 243, and 49 papers, although overlap between them and the period covered were not addressed; 19 papers were selected).…”

A Hybrid Fuzzy Inference System Based on Dispersion Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning

“…Similar to many other developing cities, PM 2.5 is generated mainly through transportation systems [43,44] which contribute to an increase in permissible level of Air Quality Index [45][46][47]. Recent studies reveal that even normal concentration of PM 2.5 is far more harmful than the commonly known pollutants, such as SO 2 and CO [48]. Studies and epidemiological evidence concerning the effect of PM 2.5 on health are more extensive than studies made on other pollutants [49,50].…”

“…The city of Isfahan, located in the center of Iran with population of about two million and an urban area of 200 km 2 , is one of the most polluted metropolises. The adverse environmental and health outcomes caused by air pollution have experienced a growing trend.…”

“…These models are not flexible to apply heterogeneous, descriptive, and uncertain data. The amount of required data together with considering their uncertainty and their computational mass are the major contributors to the practicality of air pollution prediction model and health metric [2,33,37,38]. Applying fuzzy inference systems in EHIA provide the possibility of establishing an appropriate model from the available heterogeneous, descriptive, and uncertain data.…”