John D. MacDonald

Estimating the Lower Heating Values of Hazardous and Solid Wastes

A new equation is proposed to predict the lower heating value of hazardous and non-hazardous materials. The equation was developed by a statistical correlation of heating value and composition data for a variety of materials as reported in a number of sources. The model takes into account the carbon, hydrogen, oxygen, chlorine, and sulfur content of the material being combusted.

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.

Florida Noise Barrier Evaluation and Computer Model Validation

The results of a project that investigated the effectiveness of in situ noise barriers in Florida are presented. The prediction accuracy of the FHWA Traffic Noise Model (TNM) is compared with STAMINA 2.0 and 2.1 (Florida-specific). A total of 20 barrier sites were visited during a 3-year period that resulted in 844 discrete 20-min equivalent sound level (Leq) measurements behind the barriers. Barrier insertion loss was determined using the ANSI indirect barrier method. A methodology was developed to estimate shadow zone length created behind highway noise barriers. All of the barriers tested were effective (>5 dB:LAeq insertion loss at distances equivalent to the first row of homes, where LAeq is the A-weighted Leq) except one site because of marginal additional shielding from a berm–barrier combination. Only three sites had an insertion loss of less than 5 dB at distances representative of the second row of homes. Overall, measurements indicate that the barriers provide substantial sound level reduction for residents along the highway. TNM was the best prediction model when considering all test sites; however, the STAMINA models were more accurate at predicting source level. TNM predictions using the Average pavement input overpredicted the reference sound levels measured at these sites. TNM predictions using the OGAC (open-graded asphalt concrete) input were improved (under 2 dB:LAeq of error) over those using the Average pavement type input. This result is expected because Florida uses an open-graded asphalt friction mix.

Sound Levels and Shadow Zones Behind Barriers in Florida

A description is given of new results based on measurements of 19 noise barriers in the state of Florida and the innovative techniques used during data analysis. This work is a continuation of an ongoing study of noise barrier effectiveness in Florida. A new empirical method was developed to estimate the length of shadow zones behind highway noise barriers. This new method can lead to more effective design of future highway noise barriers. The method required an estimate of location and strength of a simulated background source to determine a more realistic edge of the shadow zone. This work also produced custom software that estimates and graphs the formation of shadow zones behind barriers, given readily available site information.

On‐board sound intensity measurements and results in Florida.

This paper describes an investigation of on‐board sound intensity (OBSI) and concurrent wayside sound levels along multiple roadway surfaces in the state of Florida. By conducting concurrent sampling of the OBSI and wayside noise not only were insights into the pavement texture noise generation at the tire/pavement interface possible but direct comparisons allowed findings on the propagation characteristics as well. Measurement values, pavement rankings, multiple findings, and preliminary statistical modeling will be shown.

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 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.