The Missing Benefits of Clean Water and the Role of Mismeasured Pollution

Although the U.S. spends billions of dollars a year controlling water pollution, there is little empirical evidence of comparable benefits. This study argues that measurement error in pollution data causes benefits to be underestimated. Using upstream concentrations as instrumental variables for local concentrations, the study finds substantial benefits from reducing nutrient pollution. Instrumental variable estimates of the effects of phosphorus on recreational use are an order of magnitude larger than conventional estimates. The study uses a long-term pollution dataset from Iowa to show that this difference is consistent with estimates of measurement error in several U.S. water pollution datasets

Adapting to Climate Change Through Tile Drainage: A Structural Ricardian Analysis

This paper provides the first estimates of the effects of climate change on agriculture while explicitly modeling tile drainage. We show in a simple conceptual model that the value of precipitation should differ between drained and non-drained land, implying that pooling these lands could bias estimates of the effects of climate change on land values. We test this hypothesis by estimating a Structural Ricardian model for U.S. counties east of the 100th meridian. Consistent with our theoretical model, our estimates show that the value of precipitation is higher on non-drained lands

Consequences of the Clean Water Act and the Demand for Water Quality

Since the 1972 U.S. Clean Water Act, government and industry have invested over $1 trillion to abate water pollution, or$100 per person-year. Over half of U.S. stream and river miles, however, still violate pollution standards. We use the most comprehensive set of files ever compiled on water pollution and its determinants, including 50 million pollution readings from 170,000 monitoring sites, to study water pollution\u27s trends, causes, and welfare consequences. We have three main findings. First, water pollution concentrations have fallen substantially since 1972, though were declining at faster rates before then. Second, the Clean Water Act\u27s grants to municipal wastewater treatment plants caused some of these declines. Third, the grants\u27 estimated effects on housing values are generally smaller than the grants\u27 costs

Validation of machine learning models to detect amyloid pathologies across institutions.

Semi-quantitative scoring schemes like the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) are the most commonly used method in Alzheimer's disease (AD) neuropathology practice. Computational approaches based on machine learning have recently generated quantitative scores for whole slide images (WSIs) that are highly correlated with human derived semi-quantitative scores, such as those of CERAD, for Alzheimer's disease pathology. However, the robustness of such models have yet to be tested in different cohorts. To validate previously published machine learning algorithms using convolutional neural networks (CNNs) and determine if pathological heterogeneity may alter algorithm derived measures, 40 cases from the Goizueta Emory Alzheimer's Disease Center brain bank displaying an array of pathological diagnoses (including AD with and without Lewy body disease (LBD), and / or TDP-43-positive inclusions) and levels of Aβ pathologies were evaluated. Furthermore, to provide deeper phenotyping, amyloid burden in gray matter vs whole tissue were compared, and quantitative CNN scores for both correlated significantly to CERAD-like scores. Quantitative scores also show clear stratification based on AD pathologies with or without additional diagnoses (including LBD and TDP-43 inclusions) vs cases with no significant neurodegeneration (control cases) as well as NIA Reagan scoring criteria. Specifically, the concomitant diagnosis group of AD + TDP-43 showed significantly greater CNN-score for cored plaques than the AD group. Finally, we report that whole tissue computational scores correlate better with CERAD-like categories than focusing on computational scores from a field of view with densest pathology, which is the standard of practice in neuropathological assessment per CERAD guidelines. Together these findings validate and expand CNN models to be robust to cohort variations and provide additional proof-of-concept for future studies to incorporate machine learning algorithms into neuropathological practice

Research Needs and Challenges in the Food, Energy and Water System: Findings from an NSF Funded Workshop

In October 2015, the Center for Agricultural and Rural Development at Iowa State University, Ames, Iowa hosted a two-day National Science Foundation-funded workshop exploring the challenges and pitfalls associated with integrating biophysical and economic models. The workshop brought together leading economists, statisticians, crop scientists, hydrologists, climate scientists, and other biophysical modelers, to identify and address the key scientific, engineering, and data challenges associated with understanding our food, energy, and water (FEW) system. Approximately 80 people attended the workshop with about half of them representing social scientists (primarily economists) and the rest from the physical and natural sciences. Economics and social sciences were intentionally emphasized so that the findings would be particularly relevant to research needs in those fields