Characterizing Uncertainty in Air Pollution Damage Estimates

This study uses Monte Carlo methods to characterize the uncertainty associated with per-ton damage estimates for 100 power plants in the contiguous United States (U.S.) This analysis focuses on damage estimates produced by an Integrated Assessment Model (IAM) for emissions of two local air pollutants: sulfur dioxide (SO2) and .ne particulate matter (PM2:5). For each power plant, the Monte Carlo procedure yields an empirical distribution for the damage per ton of SO2 and PM2:5:For a power plant in New York, one ton of SO2 produces $5,160 in damages with a 90$1,000 and $14,090. A ton of PM2:5 emitted from the same facility causes$17,790 worth of damages with a 90% percentile interval of $3,780 and$47,930. Results for the sample of 100 fossil-fuel .red power plants shows a strong spatial pattern in the marginal damage distributions. The degree of variability increases by plant location from east to west. This result highlights the importance of capturing uncertainty in air quality modeling in the empirical marginal damage distributions. Further, by isolating uncertainty at each module in the IAM we .nd that uncertainty associated with the dose-response parameter, which captures the in.uence of exposure to PM2:5 on adult mortality rates, the mortality valuation parameter, and the air quality model exert the greatest in.uence on cumulative uncertainty. The paper also demonstrates how the marginal damage distributions may be used to guide regulators in the design of more efficient market-based air pollution policy in the U.S.Monte Carlo, Air Pollution, Market-based Pollution Policy

Sinusoidal Modeling Applied to Spatially Variant Tropospheric Ozone Air Pollution

This paper demonstrates how parsimonious models of sinusoidal functions can be used to fit spatially variant time series in which there is considerable variation of a periodic type. A typical shortcoming of such tools relates to the difficulty in capturing idiosyncratic variation in periodic models. The strategy developed here addresses this deficiency. While previous work has sought to overcome the shortcoming by augmenting sinusoids with other techniques, the present approach employs station-specific sinusoids to supplement a common regional component, which succeeds in capturing local idiosyncratic behavior in a parsimonious manner. The experiments conducted herein reveal that a semi-parametric approach enables such models to fit spatially varying time series with periodic behavior in a remarkably tight fashion. The methods are applied to a panel data set consisting of hourly air pollution measurements. The augmented sinusoidal models produce an excellent fit to these data at three different levels of spatial detail.Air Pollution, Idiosyncratic component, Regional variation, Semiparametric model, Sinusoidal function, Spatial-temporal data, Tropospheric Ozone

The Ancillary Benefits from Climate Policy in the United States

This study investigates the benefits to human health that would occur in the United States (U.S.) due to reductions in local air pollutant emissions stemming from a federal policy to reduce greenhouse gas emissions (GHG). In order to measure the impacts of reduced emissions of local pollutants, this study considers a representative U.S. climate policy. Specifically, the climate policy modeled in this analysis is the Warner-Lieberman bill (S.2191) of 2008 and the paper considers the impacts of reduced emissions in the transport and electric power sectors. This analysis provides strong evidence that climate change policy in the U.S. will generate significant returns to society in excess of the benefits due to climate stabilization. The total health-related co-benefits associated with a representative climate policy over the years 2006 to 2030 range between $90 and$725 billion in present value terms depending on modeling assumptions. The majority of avoided damages are due to reduced emissions of SO2 from coal-fired power plants. Among the most important assumptions is whether remaining coal-fired generation capacity is permitted to “backslide” up to the Clean Air Interstate Rule (CAIR) cap on emissions. This analysis models two scenarios specifically related to this issue. Co-benefits increase from $90 billion, when the CAIR cap is met, to$256 billion if SO2 emissions are not permitted to exceed current emission rates. On a per ton basis, the co-benefit per ton of GHG emissions is projected to average between $2 and$14 ($2006). The per ton marginal abatement cost for the representative climate policy is estimated at$9 ($2006).

A novel prostate cancer biomarker

Prostate cancer is the most common non-cutaneous malignancy in men and is the second leading cause of cancer death in American men, trailing only lung cancer. About 1 man in 7 will be diagnosed with prostate cancer and about 1 in 38 will die of prostate cancer. Prostate cancer does not usually present any symptoms until it has advanced or metastasized and thus screening for prostate cancer is an arduous task. Three of the most common techniques used to screen for prostate cancer includes digital rectal exam, transrectal ultrasound, and the use of biomarkers, specifically Prostate Specific Antigen (PSA), which has proven controversial. Due to the need for a more rapid, specific marker for the early detection of prostate cancer, this study aims to identify a new biomarker for prostate cancer. A novel strategy to identify a protein biomarker for prostate cancer was explored, a highly specific hybridoma against the novel biomarker was generated, the efficacy of the biomarker detection tools in prostate cancer was observed and an attempt to identify the biomarker protein sequence was made. Every time a prostate cancer specimen was tested, it was found that the clone 164 antibody that was generated was able to identify unique antigens in the prostate cancer tissue that were not evident in normal tissue. In addition, it was noticed that the clone 164 antibody could identify the marker protein in urine as well. It is believed that the clone 164 antibody is highly specific for early stage prostate cancer diagnosis. Finally, using mass spectrometry, four candidate protein biomarkers that clone 164 recognizes were isolated, with the closest match being Ig alpha-1 chain C region. It is believed that the antigens recognized by clone 164 promises great potential as a future biomarker for prostate cancer. Since the protein is only seen in the urine of patients with prostate cancer, it appears that the clone 164 antibody is suitable to include in a device that can be used in a urine-based, rapid diagnostics point of care kit. Future steps involve animal studies before proceeding to the next step of clinical trials. If the clone 164 antibody identified biomarker proves successful, the respective biomarker protein can be analyzed in detail. Once the expression profile of this biomarker is elucidated, it can be compared to the normal prostate DNA and may help in determining the location in the DNA, which may eventually lead to the idea of treating prostate cancer through gene therapy or the possibility of preventing or curing prostate cancer. Also, the specific antibody against this biomarker can be used as a preventive agent by humanizing this antibody and using it as a therapeutic vaccine

Sensitivity of middle Miocene climate and regional monsoon to palaeo-altimetry

It is well-known that plate motions and the elevation of mountains belts have played a major role in palaeoclimate evolution. The present day monsoon in southeast Asia and northern Australia is associated with the Tibetan plateau. We investigate how the Miocene Climate Optimum (MCO) developed in response to altimetry changes in Eurasia and South America impacting changes in regional monsoon, wind stress and precipitation. We carried out a number of numerical experiments with alternative paleo-altimetries, using an updated NCAR coupled climate model, CCSM3, and CAM3.1 and CLM3 with slab ocean and ice models, validated with proxies. Our model results explore the sensitivities of regional climate change to plate motions and rising mountain belts as well as sea-level change. Especially, the model simulations ground-truth the monsoon evolution in the southeast Asia, northern Australia and South America

Sinusoidal Modeling Applied to Spatially Variant Tropospheric Ozone Air Pollution

This paper demonstrates how parsimonious models of sinusoidal functions can be used to fit spatially variant time series in which there is considerable variation of a periodic type. A typical shortcoming of such tools relates to the difficulty in capturing idiosyncratic variation in periodic models. The strategy developed here addresses this deficiency. While previous work has sought to overcome the shortcoming by augmenting sinusoids with other techniques, the present approach employs station-specific sinusoids to supplement a common regional component, which succeeds in capturing local idiosyncratic behavior in a parsimonious manner. The experiments conducted herein reveal that a semi-parametric approach enables such models to fit spatially varying time series with periodic behavior in a remarkably tight fashion. The methods are applied to a panel data set consisting of hourly air pollution measurements. The augmented sinusoidal models produce an excellent fit to these data at three different levels of spatial detail

Unsupervised extraction of semantic relations using discourse cues

International audienceThis paper presents a knowledge base containing triples involving pairs of verbs associated with semantic or discourse relations. The relations in these triples are marked by discourse connectors between two adjacent instances of the verbs in the triple in the large French corpus, frWaC. We detail several measures that evaluate the relevance of the triples and the strength of their association. We use manual annotations to evaluate our method, and also study the coverage of our resource with respect to the discourse annotated corpus Annodis. Our positive results show the potential impact of our resource for discourse analysis tasks as well as other semantically oriented tasks like temporal and causal information extractio

Complex discourse units and their semantics

International audienc

Extraction non supervisée de relations sémantiques lexicales

International audienceNous présentons une base de connaissances comportant des triplets de paires de verbes associés avec une relation sémantique/discursive, extraits du corpus français frWaC par une méthode s’appuyant sur la présence d’unconnecteur discursif reliant deux verbes. Nous détaillons plusieurs mesures visant à évaluer la pertinence des triplets et la force d’association entre la relation sémantique/discursive et la paire de verbes. L’évaluation intrinsèque est réalisée par rapport à des annotations manuelles. Une évaluation de la couverture de la ressource est également réalisée par rapport au corpus Annodis annoté discursivement. Cette étude produit des résultats prometteurs démontrant l’utilité potentielle de notre ressource pour les tâches d’analyse discursive mais aussi des tâches de nature sémantique

Constrained decoding for text-level discourse parsing

International audienceThis paper presents a novel approach to document-based discourse analysis by performing a global A* search over the space of possible structures while optimizing a global criterion over the set of potential coherence relations. Existing approaches to discourse analysis have so far relied on greedy search strategies or restricted themselves to sentence-level discourse parsing. Another advantage of our approach, over other global alternatives (like Maximum Spanning Tree decoding algorithms), is its flexibility in being able to integrate constraints (including linguistically motivated ones like the Right Frontier Constraint). Finally, our paper provides the first discourse parsing system for French; our evaluation is carried out on the Annodis corpus. While using a lot less training data than earlier approaches than previous work on English, our system manages to achieve state-of-the-art results, with F1-scores of 66.2 and 46.8 when compared to unlabeled and labeled reference structures