Global Health and Economic Impacts of Future Ozone Pollution

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).We assess the human health and economic impacts of projected 2000-2050 changes in ozone pollution using the MIT Emissions Prediction and Policy Analysis-Health Effects (EPPA-HE) model, in combination with results from the GEOS-Chem global tropospheric chemistry model that simulated climate and chemistry effects of IPCC SRES emissions. We use EPPA to assess the human health damages (including acute mortality and morbidity outcomes) caused by ozone pollution and quantify their economic impacts in sixteen world regions. We compare the costs of ozone pollution under scenarios with 2000 and 2050 ozone precursor and greenhouse gas emissions (SRES A1B scenario). We estimate that health costs due to global ozone pollution above pre-industrial levels by 2050 will be $580 billion (year 2000$) and that acute mortalities will exceed 2 million. We find that previous methodologies underestimate costs of air pollution by more than a third because they do not take into account the long-term, compounding effects of health costs. The economic effects of emissions changes far exceed the influence of climate alone.United States Department of Energy, Office of Science (BER) grants DE-FG02-94ER61937 and DE-FG02-93ER61677, the United States Environmental Protection Agency grant EPA-XA-83344601-0, and the industrial and foundation sponsors of the MIT Joint Program on the Science and Policy of Global Change

A useful new type of random regressions based on biological differences among repeated records, application to longevity.

peer reviewedA major problem in random regression models is that it is not always obvious what type of regressions to use. Different types of functions were identified and use. The first type used where functions that described lactation shapes. These functions did an excellent job to describe the mean, however were very poor in modeling of (co)variance structures. The second group of functions was based on strictly mathematical ones as polynomials. Polynomials were excellent for modeling the (co)variances as long as high order polynomials could be used. Different alternative functions were proposed over time (e.g., splines). Recently another alternative method was proposed by Wiggans and Van Raden (2004) based on the concept of parity differences (PD). Instead of using predefined functions they defined as regressions differences among repeated records. This can be considered an approximation of expected a priory change in genetic merit across those repetitions where the relative size of genetic differences by parity were derived from genetic correlations. We will use the word biological differences as the idea is to base it on individual difference corrected for the environment. The following example might clarify the general idea. Wiggans and Van Raden (2004) defined relative PD among the first five lactation for milk yield as -0.9, 0.1, 0.4, 0.6 and 0.7 which means that differences from second to third, from third to fourth and from fourth to five represent 30%, 20% respectively 10% of the difference from first to second. This has the side effect that (co)variance structures are modelled as quadratic functions of regressors. Through the use of these PD they linearized changes from one lactation to the next. The objective of this paper was to present this idea and to use it for multi-lactation longevity evaluations

Use of a weighted random regression test-day model to better relate observed somatic cell score to mastitis infection likelihood

peer reviewedCurrent methods to analyze somatic cell counts or scores do not differentiate the origins of observed values, they rely on the simple hypothesis that a "higher" value is linked to an intra-mammarian infection. This hypothesis may not be totally valid, but until now proposed alternatives were not usable in large scale genetic evaluations. A simple modification of currently used random regression test-day models is presented that weight observed somatic cell scores by a weight that expresses mastitis infection likelihood. Results showed that despite high correlation with breeding values obtained without the weight, differences that were observed were highly related to mastitis results in Nordic countries. Sire breeding values obtained with this method were also submitted to the March 2003 INTERBULL test-run. They showed the largest average correlations of all populations with mastitis results from Nordic populations. The proposed method is currently implemented and used in routine in the genetic evaluation for somatic cell score in the Walloon Region of Belgium