Termination of the leprosy isolation policy in the US and Japan : Science, policy changes, and the garbage can model

BACKGROUND: In both the US and Japan, the patient isolation policy for leprosy /Hansen's disease (HD) was preserved along with the isolation facilities, long after it had been proven to be scientifically unnecessary. This delayed policy termination caused a deprivation of civil liberties of the involuntarily confined patients, the fostering of social stigmas attached to the disease, and an inefficient use of health resources. This article seeks to elucidate the political process which hindered timely policy changes congruent with scientific advances. METHODS: Examination of historical materials, supplemented by personal interviews. The role that science played in the process of policy making was scrutinized with particular reference to the Garbage Can model. RESULTS: From the vantage of history, science remained instrumental in all period in the sense that it was not the primary objective for which policy change was discussed or intended, nor was it the principal driving force for policy change. When the argument arose, scientific arguments were employed to justify the patient isolation policy. However, in the early post-WWII period, issues were foregrounded and agendas were set as the inadvertent result of administrative reforms. Subsequently, scientific developments were more or less ignored due to concern about adverse policy outcomes. Finally, in the 1980s and 1990s, scientific arguments were used instrumentally to argue against isolation and for the termination of residential care. CONCLUSION: Contrary to public expectations, health policy is not always rational and scientifically justified. In the process of policy making, the role of science can be limited and instrumental. Policy change may require the opening of policy windows, as a result of convergence of the problem, policy, and political streams, by effective exercise of leadership. Scientists and policymakers should be attentive enough to the political context of policies

Now Hear This! What All Environmental Engineers Should Know About Noise Control

Noise is an is an that affects almost everyone. And even though environmental engineers are often called on to deal with noise-related problems, most of them receive little or no academic training in noise control. This primer suggests why all environmental engineers should know something about noise control, what they need to know, and where they can find the necessary information

Advancement in traffic noise modeling: The AAMA community noise model 4.0

The University of Central Florida developed the AAMA Community Noise Model (CNM) which is a traffic simulation model that determines sound levels at receivers by modeling vehicles as discrete moving point sources. The vehicle energy is determined from acceleration, deceleration, idle and cruise reference energy mean emission level curves. Attenuation of energy from the vehicles for distance, ground adsorption and user input barriers is calculated. The model sums the energy at receivers on user defined time steps from all vehicles and then calculates the Leq noise level at the receivers

RAILWAY NOISE MODEL

The Railway Noise Model (RWNM) was developed at the University of Central Florida and predicts sound levels at receivers near railway operations for analyses used in environmental documents. The RWNM is a simulation model, and trains are modeled as moving point sources of sound. The user can create model objects, tracks, barriers, and receivers, using either the mouse of spreadsheet interfaces. During simulation, the user observes trains moving along railways and the relationships to receiver locations. The RWNM simulates a 24-h period of rail traffic and computes day/night sound pressure level (Ldn), maximum sound pressure level (Lmax), sound exposure level (SEL), and equivalent sound pressure level (Seq) at the receivers. The RWNM uses REMEL (reference energy mean emission levels) curves based on Federal Transit Administration (FTA) reported Lmax pass-by levels for locomotives and rail cars. In addition, the model has the ability to model heavy rail locomotives and rail cars, which makes it applicable to Federal Railroad Administration projects. Testing has shown that the RWNM results match those of the FTA-approved spreadsheet, although heavy rail validation is limited

Railway Noise Model

The Railway Noise Model (RWNM) was developed at the Univ of Central Florida and predicts sound levels at receivers near railway operations for analyses used in environmental documents. The RWNM is a simulation model, and trains are modeled as moving point sources of sound. The user can create model objects, tracks, barriers, and receivers, using either the mouse or spreadsheet interfaces. During simulation, the user observes trains moving along railways and the relationships to receiver locations. The RWNM simulates a 24-h period of rail traffic and computes day/night sound pressure level (Ldn), maximum sound pressure level (Lmax), sound exposure level (SEL), and equivalent sound pressure level (Leq) at the receivers. The RWNM uses REMEL (reference energy mean emission levels) curves based on Federal Transit Administration (FTA) reported Lmax pass-by levels for locomotives and rail cars. In addition, the model has the ability to model heavy rail locomotives and rail cars, which makes it applicable to Federal Railroad Administration projects. Testing has shown that the RWNM results match those of the FTA-approved spreadsheet, although heavy rail validation is limited

Simulation approach to traffic noise modeling: American automobile manufacturers association community noise model version 4.0

Several models are available for predicting traffic noise levels. The FHWA-promulgated model, STAMINA 2.0, is the most widely used noise model in the United States and is used to model free-flow vehicular traffic. STAMINA 2.0 cannot directly model interrupted-flow traffic. Sound levels from interrupted-flow traffic can be approximated with STAMINA 2.0 using the method presented in NCHRP Report 311. This method is time-consuming and difficult to use. These limitations demonstrate the need for a traffic noise model that can model the acceleration and deceleration behavior of interrupted-flow traffic. The University of Central Florida has developed the American Automobile Manufacturers Association Community Noise Model (CNM). The CNM is a traffic simulation model that determines sound levels at receivers by modeling vehicles as discrete moving point sources. The vehicle energy is determined from acceleration, deceleration, idle, and cruise reference energy mean emission level curves. Sound energy attenuation is calculated from distance, ground absorption, and user input barriers. The model sums the energy at receivers from all vehicles and then calculates the Leq noise level at the receivers. It is demonstrated that the CNM predicts receiver Leq levels that are very close to STAMINA 2.0 results for constant-speed traffic. The CNM can also accurately predict sound levels at receivers located before and after intersections. In addition to the advantages of a real simulation model, the CNM is user friendly, allowing the user to place lanes and receivers using the mouse

Comparison Of Measured And Modeled Sound Levels In The Vicinity Of Traffic Noise Barriers

A detailed noise prediction model was used to compare 11 highway noise barrier locations in Florida. Insertion losses, ground effects, shadow zones, and overall trends were determined or analyzed, or both. Each location was modeled using STAMINA2.0 (current FHWA regulatory model), STAMINA2.1 (Florida\u27s version of STAMINA2.0 with state-specific emission levels), the Traffic Noise Model (often referred to as TNM; this model will replace STAMINA2.0 in the year 2002), and the University of Central Florida Community Noise Model (CNM5.0). The modeled results were then statistically compared with the measured results. Statistical evaluation results were similar for all models for overall, absolute prediction compared with the measured value, with STAMINA2.1 being slightly better. All models provided adequate results, but ranges of error were significant. When the propagation components were explored, by comparing reference levels with those behind the barrier, the TNM was significantly better. The results also provided further insight into the benefited regions behind the barrier, a more detailed understanding of how the models perform for this complex interaction with the ground and sound wave, and how background levels change the actual size and shape of the benefited region

Method to determine reasonableness and feasibility of noise abatement at special use locations

Most states have policies in place that determine whether noise abatement is necessary and reasonable/feasible for Type I projects. These policies mirror federal guidance and apply to various land uses near the proposed project. Special land use facilities such as parks, churches, and schools are included in the policy as far as when abatement may be necessary (i.e., FHWA noise abatement criteria), but the determination of whether the abatement is reasonable or feasible may not be adequately addressed. A survey of state Departments of Transportation (DOTs) indicated that states are dealing with this need for reasonable/feasible determination for special land uses but do not have formal policies in place to address the issue. A systematic procedure would eliminate arbitrary decisions. A methodology developed for the Florida DOT to aid in the development of a procedure for special land use cases is presented. This methodology includes a feasibility flowchart that leads an individual through the process of determining whether abatement at a special land use site is feasible. The feasibility flowchart directs the individual to cease analysis because abatement is not feasible or to continue onto a reasonableness worksheet that determines whether abatement at the site is reasonable. The reasonableness worksheet leads the individual through site-specific calculations to derive an ent cost factor used to determine reasonableness of abatement at the site

Sensitivity analysis of the AAMA community noise model

The University of Central Florida has developed the Community Noise Model (CNM) for traffic noise prediction of free flowing (highway use) as well as interrupted flowing (urban use) vehicle movement. The CNM is a true traffic simulation model that predicts sound levels at specified receiver locations by modeling vehicles as discrete moving point sources. Numerous variables are utilized in predicting noise levels, such as vehicle volume and speed, vehicle type mix, and ground surface. This paper reports on an in-depth analysis of the effect of each variable on the output of the model and looks at validation studies that have been done. All variables were exercised in this work during an exhaustive sensitivity/validation study. The model inputs are directly compared to the prediction outputs by tabulating and plotting the results. This study permitted conclusions to be drawn about traffic model inputs and their respective predicted noise levels. The findings of this study are therefore representative of the CNM and traffic noise models in general. The accuracy of the model was determined by comparison to measured data. Noise levels for several test(s) were recorded along with the pertinent input parameter details. The test sites were modeled using the parameter data and prediction noise levels obtained. Predicted versus measured sound levels were plotted for direct visual comparison

Queueing algorithm for calculating idling emissions in FLINT - The FLorida INTersection air quality model

The theoretical development of the queueing model used in the FLINT (FLorida INTersection) air quality model is described. FLINT is an area source model used to predict carbon monoxide concentrations for undersaturated and oversaturated traffic conditions at signalized intersections. In the FLINT model, deterministic queueing is used to estimate the queue length for cases of undersaturated conditions. In oversaturated cases, a cycle failure method has been developed to estimate queue length. In addition, a new concept is presented for calculating idling time for each vehicle\u27s position in the queue during both the red and the green phases of the traffic signal cycle. A limited set of undersaturated cases from monitoring data in Melrose Park, Illinois, was used to compare the predicted queue lengths with the measured queue lengths for several air quality models. It was found that FLINT predicted the queue length within one vehicle of the observed queue length. The same cases were tested using CAL3QHC, TEXIN2 intersection air quality models, and the American Automobile Manufacturers Association (AAMA) simulation model. It was found that predictions of the AAMA and the FLINT models were very close to the measured queue lengths in cases of undersaturated conditions. Moreover, although the FLINT and the AAMA models use a different approach to estimate queue length, their predicted queue lengths were very close in oversaturated cases. However, the predicted queue lengths of CAL3QHC were too long for oversaturated cases