The response of surface ozone to climate change over the Eastern United States

International audienceWe investigate the response of surface ozone (O3) to future climate change in the eastern United States by performing simulations corresponding to present (1990s) and future (2050s) climates using an integrated model of global climate, tropospheric gas-phase chemistry, and aerosols. A future climate has been imposed using ocean boundary conditions corresponding to the IPCC SRES A2 scenario for the 2050s decade. Present-day anthropogenic emissions and CO2/CH4 mixing ratios have been used in both simulations while climate-sensitive emissions were allowed to vary with the simulated climate. The severity and frequency of O3 episodes in the eastern U.S. increased due to future climate change, primarily as a result of increased O3 chemical production. The 95th percentile O3 mixing ratio increased by 5 ppbv and the largest frequency increase occured in the 80?90 ppbv range; the US EPA's current 8-h ozone primary standard is 80 ppbv. The increased O3 chemical production is due to increases in: 1) natural isoprene emissions; 2) hydroperoxy radical concentrations resulting from increased water vapor concentrations; and, 3) NOx concentrations resulting from reduced PAN. The most substantial and statistically significant (p3 season over the eastern U.S. in a future climate to include late spring and early fall months. Increased chemical production and shorter average lifetime are two consistent features of the seasonal response of surface O3, with increased dry deposition loss rates contributing most to the reduced lifetime in all seasons except summer. Significant interannual variability is observed in the frequency of O3 episodes and we find that it is necessary to utilize 5 years or more of simulation data in order to separate the effects of interannual variability and climate change on O3 episodes in the eastern United States

Cultural and review characteristics in the formation of trust in online product reviews: A multinational investigation

Recent changes in web technologies have given a voice to consumers in online discussion of products and services. While the web has long been a source of information about products and services, web content was controlled by those who knew how to develop for the web, or those who could hire web developers. The trend toward web software that permits novice users to contribute to conversations about products has been embraced by online retailers, who facilitate and encourage online user reviews of products. Researchers are just starting to understand the relationship between online user reviews and purchase intention, however have determined that trust is central to the development of purchase intention. In this study, we report the results of a simulation based web purchase experiment that included subjects in Colombia, the People’s Republic of China and the United States. The experiment included manipulations for both information quality and a social component of the review, and espoused culture scores of subjects where measured. We find that information quality, the social component and espoused uncertainty avoidance influence trust in the review. We were not able to support an interaction effect between information quality and uncertainty avoidance and trust, nor an interaction effect between the social component and collectivism

Evaluation of a three-dimensional chemical transport model (PMCAMx) in the European domain during the EUCAARI May 2008 campaign

PMCAMx-2008, a detailed three-dimensional chemical transport model (CTM), was applied to Europe to simulate the mass concentration and chemical composition of particulate matter (PM) during May 2008. The model includes a state-of-the-art organic aerosol module which is based on the volatility basis set framework treating both primary and secondary organic components as semivolatile and photochemically reactive. The model performance is evaluated against high time resolution aerosol mass spectrometer (AMS) ground and airborne measurements. Overall, organic aerosol is predicted to account for 32% of total PM<sub>1</sub> at ground level during May 2008, followed by sulfate (30%), crustal material and sea-salt (14%), ammonium (13%), nitrate (7%), and elemental carbon (4%). The model predicts that fresh primary OA (POA) is a small contributor to organic PM concentrations in Europe during late spring, and that oxygenated species (oxidized primary and biogenic secondary) dominate the ambient OA. The Mediterranean region is the only area in Europe where sulfate concentrations are predicted to be much higher than the OA, while organic matter is predicted to be the dominant PM<sub>1</sub> species in central and northern Europe. The comparison of the model predictions with the ground measurements in four measurement stations is encouraging. The model reproduces more than 94% of the daily averaged data and more than 87% of the hourly data within a factor of 2 for PM<sub>1</sub> OA. The model tends to predict relatively flat diurnal profiles for PM<sub>1</sub> OA in many areas, both rural and urban in agreement with the available measurements. The model performance against the high time resolution airborne measurements at multiple altitudes and locations is as good as its performance against the ground level hourly measurements. There is no evidence of missing sources of OA aloft over Europe during this period

Identifying diachronic topic-based research communities by clustering shared research trajectories

Communities of academic authors are usually identified by means of standard community detection algorithms, which exploit ‘static’ relations, such as co-authorship or citation networks. In contrast with these approaches, here we focus on diachronic topic-based communities –i.e., communities of people who appear to work on semantically related topics at the same time. These communities are interesting because their analysis allows us to make sense of the dynamics of the research world –e.g., migration of researchers from one topic to another, new communities being spawn by older ones, communities splitting, merging, ceasing to exist, etc. To this purpose, we are interested in developing clustering methods that are able to handle correctly the dynamic aspects of topic-based community formation, prioritizing the relationship between researchers who appear to follow the same research trajectories. We thus present a novel approach called Temporal Semantic Topic-Based Clustering (TST), which exploits a novel metric for clustering researchers according to their research trajectories, defined as distributions of semantic topics over time. The approach has been evaluated through an empirical study involving 25 experts from the Semantic Web and Human-Computer Interaction areas. The evaluation shows that TST exhibits a performance comparable to the one achieved by human experts

A hybrid semantic approach to building dynamic maps of research communities

In the last ten years, ontology-based recommender systems have been shown to be effective tools for predicting user preferences and suggesting items. There are however some issues associated with the ontologies adopted by these approaches, such as: 1) their crafting is not a cheap process, being time consuming and calling for specialist expertise; 2) they may not represent accurately the viewpoint of the targeted user community; 3) they tend to provide rather static models, which fail to keep track of evolving user perspectives. To address these issues, we propose Klink UM, an approach for extracting emergent semantics from user feedbacks, with the aim of tailoring the ontology to the users and improving the recommendations accuracy. Klink UM uses statistical and machine learning techniques for finding hierarchical and similarity relationships between keywords associated with rated items and can be used for: 1) building a conceptual taxonomy from scratch, 2) enriching and correcting an existing ontology, 3) providing a numerical estimate of the intensity of semantic relationships according to the users. The evaluation shows that Klink UM performs well with respect to handcrafted ontologies and can significantly increase the accuracy of suggestions in content-based recommender systems

Smart Tourism Destinations: Can the Destination Management Organizations Exploit Benefits of the ICTs? Evidences from a Multiple Case Study

Recent developments of ICTs enable new ways to experience tourism and conducted to the concept of smart tourism. The adoption of cutting-edge technologies and its combination with innovative organizational models fosters cooperation, knowledge sharing, and open innovation among service providers in tourism destination. Moreover, it offers innovative services to visitors. In few words, they become smart tourism destinations. In this paper, we report first results of the SMARTCAL project aimed at conceiving a digital platform assisting Destination Management Organizations (DMOs) in providing smart tourism services. A DMO is the organization charged with managing the tourism offer of a collaborative network, made up of service providers acting in a destination. In this paper, we adopted a multiple case studies approach to analyze five Italian DMOs. Our aims were to investigate (1) if, and how, successful DMOs were able to offer smart tourism services to visitors; (2) if the ICTs adoption level was related to the collaboration level among DMO partners. First results highlighted that use of smart technologies was still in an embryonic stage of development, and it did not depend from collaboration levels

Understanding uncertainties in future Colorado River streamflow

Artículo -- Universidad de Costa Rica. Centro de Investigaciones Geofísicas, 2014The Colorado River is the primary water source for more than 30 million people in the United States and Mexico. Recent studies that project streamflow changes in the Colorado River all project annual declines, but the magnitude of the projected decreases range from less than 10% to 45% by the mid-twenty-first century. To understand these differences, we address the questions the management community has raised: Why is there such a wide range of projections of impacts of future climate change on Colorado River streamflow, and how should this uncertainty be interpreted? We identify four major sources of disparities among studies that arise from both methodological and model differences. In order of importance, these are differences in 1) the global climate models (GCMs) and emission scenarios used; 2) the ability of land surface and atmospheric models to simulate properly the high-elevation runoff source areas; 3) the sensitivities of land surface hydrology models to precipitation and temperature changes; and 4) the methods used to statistically downscale GCM scenarios. In accounting for these differences, there is substantial evidence across studies that future Colorado River streamflow will be reduced under the current trajectories of anthropogenic greenhouse gas emissions because of a combination of strong temperature-induced runoff curtailment and reduced annual precipitation. Reconstructions of preinstrumental streamflows provide additional insights; the greatest risk to Colorado River streamflows is a multidecadal drought, like that observed in paleoreconstructions, exacerbated by a steady reduction in flows due to climate change. This could result in decades of sustained streamflows much lower than have been observed in the ~100 years of instrumental record.Universidad de Costa Rica. Centro de Investigaciones GeofísicasLamont-Doherty Earth Observatory of Columbia UniversityUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI

Ranking online consumer reviews

YesProduct reviews are posted online by the hundreds and thousands for popular products. Handling such a large volume of continuously generated online content is a challenging task for buyers, sellers and researchers. The purpose of this study is to rank the overwhelming number of reviews using their predicted helpfulness scores. The helpfulness score is predicted using features extracted from review text, product description, and customer question-answer data of a product using the random-forest classifier and gradient boosting regressor. The system classifies reviews into low or high quality with the random-forest classifier. The helpfulness scores of the high-quality reviews are only predicted using the gradient boosting regressor. The helpfulness scores of the low-quality reviews are not calculated because they are never going to be in the top k reviews. They are just added at the end of the review list to the review-listing website. The proposed system provides fair review placement on review listing pages and makes all high-quality reviews visible to customers on the top. The experimental results on data from two popular Indian e-commerce websites validate our claim, as 3–4 newer high-quality reviews are placed in the top ten reviews along with 5–6 older reviews based on review helpfulness. Our findings indicate that inclusion of features from product description data and customer question-answer data improves the prediction accuracy of the helpfulness score.Ministry of Electronics and Information Technology (MeitY), Government of India for financial support during research work through “Visvesvaraya PhD Scheme for Electronics and IT”