Climate Change and Sea Level Rise Projections for Boston

While the broad outlines of how climate change would impact Boston have been known for some time, it is only recently that we have developed a more definitive understanding of what lies ahead. That understanding was advanced considerably with the publication of Climate Change and Sea Level Rise Projections for Boston by the Boston Research Advisory Group (BRAG).The BRAG report is the first major product of "Climate Ready Boston," a project led by the City of Boston in partnership with the Green Ribbon Commission and funded in part by the Barr Foundation. The BRAG team includes 20 leading experts from the region's major universities on subjects ranging from sea level rise to temperature extremes. University of Massachusetts Boston professors Ellen Douglas and Paul Kirshen headed the research.The BRAG report validates earlier studies, concluding Boston will get hotter, wetter, and saltier in the decades ahead (see figures below). But the group has produced a much more definitive set of projections than existed previously, especially for the problem of sea level rise. BRAG also concluded that some of the effects of climate change will come sooner than expected, accelerating the urgency of planning and action

Global climate change and solutions for urban sustainability of Ho CHi Minh City, Vietnam

Ho Chi Minh City (HCMC), the largest city in Vietnam, is steadily growing, certainly towards a mega city in the near future. Like other mega cities at the boom stage, it has to face with serious environmental matters insolvable for many years. The situation may be worse under the effects of global climate change, geological subsidence due to non-standard construction and sea level rise. The situation of HCMC can be damaged or even broken by resonant effects of unsolved environmental matters and latent impacts of climate change. This article shows the challenges to the urban sustainable development under the duo effect of urban environmental matters and climate change in Ho Chi Minh City. Opportunities and strategic directions to overcome the challenges are also analyzed and recommended

Optimal speed limit for shared-use roadways

Motor vehicle crashes are a serious social problem in the United States. Each year a large number of motor vehicle crashes occur and many people are killed or injured, resulting in substantial economic costs. To minimize economic costs, it is necessary to determine optimal speed limits on roadways because of the strong relationship among posted speed limit, crash frequency, and crash injury severity. A comprehensive literature review about the relationship among posted speed limit, crash frequency, and crash injury severity level was conducted. Crash frequency prediction models and crash injury severity models are developed to obtain crash frequency and injury severity of victims in motor vehicle crashes at different posted speed limits. Model tests were also performed to verify the model fitness of data. Crash costs were then calculated based on crash frequency, injury severity level, and unit cost of each severity level. In addition, CORSIM simulation was used under various posted speed limits to obtain parameters related to operational cost. Total cost curves were then built to show the relationship between posted speed limit and total economic cost. Using the developed crash frequency models, injury severity models and CORSIM simulation results, case studies were conducted to determine optimal speed limits on selected roadways. The results determined optimal speed limits on specific roadways on the basis of total cost

Climate Change Impact Assessment for Surface Transportation in the Pacific Northwest and Alaska

WA-RD 772.

A good practice guide on the sources and magnitude of uncertainty arising in the practical measurement of environmental noise

A brief introduction to measurement uncertainty, uncertainty budgets, and inter-comparison exercises (repeated measurements), is provided in Chapter 2. The procedure forformulating an uncertainty budget and evaluating magnitudes is outlined in greater detail in Chapter 3. A flow chart summarising this process, and a checklist for the identification of sources of measurement uncertainty are included at the end of the chapter. Two example measurement exercises with corresponding uncertainty budgets are presented in Chapter 4. Some of the more commonly encountered sources of measurement uncertainty are outlined in Chapter5. Where possible, information on magnitudes or pointers to where that information can be found are included. The more important sources of uncertainty are highlighted, and “good practice guidelines” provided to help the practitioner identify means of reducing their effect. Case studies illustrating some of the points made in Chapter 5,and listing of relevant guidelines and further reading are provided in the Appendices

Vehicular CO emission prediction using support vector regression model and GIS

© 2018 by the authors. Transportation infrastructures play a significant role in the economy as they provide accessibility services to people. Infrastructures such as highways, road networks, and toll plazas are rapidly growing based on changes in transportation modes, which consequently create congestions near toll plaza areas and intersections. These congestions exert negative impacts on human health and the environment because vehicular emissions are considered as the main source of air pollution in urban areas and can cause respiratory and cardiovascular diseases and cancer. In this study, we developed a hybrid model based on the integration of three models, correlation-based feature selection (CFS), support vector regression (SVR), and GIS, to predict vehicular emissions at specific times and locations on roads at microscale levels in an urban areas of Kuala Lumpur, Malaysia. The proposed model comprises three simulation steps: first, the selection of the best predictors based on CFS; second, the prediction of vehicular carbon monoxide (CO) emissions using SVR; and third, the spatial simulation based on maps by using GIS. The proposed model was developed with seven road traffic CO predictors selected via CFS (sum of vehicles, sum of heavy vehicles, heavy vehicle ratio, sum of motorbikes, temperature, wind speed, and elevation). Spatial prediction was conducted based on GIS modelling. The vehicular CO emissions were measured continuously at 15 min intervals (recording 15 min averages) during weekends and weekdays twice per day (daytime, evening-time). The model's results achieved a validation accuracy of 80.6%, correlation coefficient of 0.9734, mean absolute error of 1.3172 ppm and root mean square error of 2.156 ppm. In addition, the most appropriate parameters of the prediction model were selected based on the CFS model. Overall, the proposed model is a promising tool for traffic CO assessment on roads

Multi-Criteria Evaluation in Support of the Decision-Making Process in Highway Construction Projects

The decision-making process in highway construction projects identifies and selects the optimal alternative based on the user requirements and evaluation criteria. The current practice of the decision-making process does not consider all construction impacts in an integrated decision-making process. This dissertation developed a multi-criteria evaluation framework to support the decision-making process in highway construction projects. In addition to the construction cost and mobility impacts, reliability, safety, and emission impacts are assessed at different evaluation levels and used as inputs to the decision-making process. Two levels of analysis, referred to as the planning level and operation level, are proposed in this research to provide input to a Multi-Criteria Decision-Making (MCDM) process that considers user prioritization of the assessed criteria. The planning level analysis provides faster and less detailed assessments of the inputs to the MCDM utilizing analytical tools, mainly in a spreadsheet format. The second level of analysis produces more detailed inputs to the MCDM and utilizes a combination of mesoscopic simulation-based dynamic traffic assignment tool, and microscopic simulation tool, combined with other utilities. The outputs generated from the two levels of analysis are used as inputs to a decision-making process based on present worth analysis and the Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Situation) MCDM method and the results are compared

Development of multi-functional streetscape green infrastructure using a performance index approach

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper presents a performance evaluation framework for streetscape vegetation. A performance index (PI) is conceived using the following seven traits, specific to the street environments – Pollution Flux Potential (PFP), Carbon Sequestration Potential (CSP), Thermal Comfort Potential (TCP), Noise Attenuation Potential (NAP), Biomass Energy Potential (BEP), Environmental Stress Tolerance (EST) and Crown Projection Factor (CPF). Its application is demonstrated through a case study using fifteen street vegetation species from the UK, utilising a combination of direct field measurements and inventoried literature data. Our results indicate greater preference to small-to-medium size trees and evergreen shrubs over larger trees for streetscaping. The proposed PI approach can be potentially applied two-fold: one, for evaluation of the performance of the existing street vegetation, facilitating the prospects for further improving them through management strategies and better species selection; two, for planning new streetscapes and multi-functional biomass as part of extending the green urban infrastructure