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Many studies have sought to determine if there is an association between airquality and acute deaths. Many consider it plausible that current levels of airquality cause acute deaths. However, several factors call causation and evenassociation into question. Observational data sets are large and complex.Multiple testing and multiple modeling can lead to false positive findings.Publication, confirmation and other biases are also possible problems.  Moreover, the fact that most data sets used in studies evaluating therelationships among air quality and public health outcomes are not publiclyavailable makes reproducing the claims nearly impossible. Here we have builtand made publicly available a dataset containing daily air quality levels,PM2.5 and ozone, daily temperature levels, minimum and maximum and dailyrelative humidity levels for the eight most populous California air basins. Weanalyzed the dataset using a moving median analysis, a standard time seriesanalysis, and a prediction analysis within the following analysis strategy. Weexamine the eight air basins separately to see if estimates replicate acrosslocations. We use leave one year out cross validation analysis to evaluatepredictions. Both the moving medians analysis and the standard time seriesanalysis found little evidence for association between air quality and acutedeaths. The prediction analysis process was a run as a large factorial designusing different models and holding out one year at a time. Among the variablesused to predict acute death, most of the daily death variability was explainedby time of year or weather variables. In summary, the empirical evidence isthat current levels of air quality, ozone and PM2.5, are not causally relatedto acute deaths for California. An empirical and logical case can be made airquality is not causally related to acute deaths for the rest of the UnitedStates.

Air quality and acute deaths in California, 2000-2012