Abstract
In environmental studies, it is important to assess how regulatory standards for air pollutants affect public health. High ozone levels contribute to harmful air pollutants. The EPA regulates ozone levels by setting ozone standards to protect public health. It is thus crucial to assess how various regulatory ozone standards affect non-accidental mortality related to respiratory deaths during the ozone season. The original rollback approach provides an adjusted ozone process under a new regulation scenario in a deterministic fashion. Herein, we consider a statistical rollback approach to allow for uncertainty in the rollback procedure by adopting the quantile matching method so that it provides flexible rollback sets. Hierarchical Bayesian models are used to predict the potential effects of different ozone standards on human health. We apply the method to epidemiologic data.
Highlights
We shall describe our results that mortality decreases as limits of acceptable ozone level get lower through the statistical rollback approach
Relative mortality rates associated with exposure to ozone over the past few days can be estimated in a specific community by constrained or unconstrained distributed-lag models as daily ozone levels are readily available
We investigate how the rollback transformation predicts that ozone series change under new ozone regulation standards possibly affecting public health
Summary
Based on the reviews of the air quality criteria for ozone (O3 ) and related photochemical oxidants and the NAAQS for O3 , a modification in the current ozone regulatory standards provides the required protection for public health and welfare (see [5,6]). To assess how changes in the ozone regulations affect mortality, ground-level ozone must be adjusted by strengthening the air quality standards. Air quality adjustment procedures proposed by the EPA [9], can be useful for adjusting the ozone process, it cannot introduce sufficient variability in the rollback adjustment as the adjustment is deterministic and the EPA regulatory standards are based on the average of three consecutive years’ AQI values. To conduct the risk assessment, we consider hierarchical Bayesian models that provide uncertainty quantification for relevant parameters to predict the potential effects of different regulatory ozone standards. We shall describe our results that mortality decreases as limits of acceptable ozone level get lower through the statistical rollback approach
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