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Atmospheric Effects on Traffic Noise Propagation

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Atmospheric Effects on Traffic Noise Propagation TRANSPORTATION RESEARCH RECORD 1255 59 Atmospheric Effects on Traffic Noise Propagation ROGER L. WAYSON AND WILLIAM BOWLBY Atmospheric effects on traffic noise propagation have largely L 0 sound level at a reference distance, been ignored during measurements and modeling, even though Ageo attenuation due to geometric spreading, it has generally been accepted that the effects may produce large Ab insertion loss due to diffraction, changes in receiver noise levels. Measurement of traffic noise at L, level increases due to reflection, and multiple locations concurrently with measurement of meteoro­ Ac attenuation due to ground characteristics and envi­ logical data is described. Statistical methods were used to evaluate the data. Atmospheric effects on traffic noise levels were shown ronmental effects. to be significant, even at very short distances; parallel components It should be noted that all levels in dB in this paper are of the wind (which are usually ignored) were important at second 2 row receivers; turbulent scattering increased noise levels near the referenced to 2 x 10-s N/m • ground more than refractive ray bending for short-distance prop­ The last term on the right side of Equation 1, A,, consists agation; and temperature lapse rates were not as important as of three parameters: ground attenuation, attenuation due to wind shear very near the highway. A statistical model was devel­ atmospheric absorption, and attenuation due to atmospheric oped to predict excess attenuations due to atmospheric effects. refraction. (2) Outdoor noise propagation has been studied since the time of the Greek philosopher Chrysippus (240 B. C.). Modern where prediction models have become accurate, and the advent of computers has increased the capabilities of models. However, attenuation due to ground interference, primarily because of their dynamic nature, atmospheric effects attenuation due to atmospheric absorption, and on traffic noise propagation have not been predicted well. attenuation due to refraction. A research effort involving quantitative analysis of data and correlation of measured meteorological effects on traffic noise The effects of rain, sleet, snow, and fog are not considered propagation at relatively short distances common to first and here. With the careful site selection used for this research, L, second row homes along heavily traveled roadways is described. and Ab were considered negligible, so Equation 1 could be Project planning and the collection, reduction, and analysis written of data are described. (3) METHOD OF RESEARCH To evaluate the relationship between Lx and A,er, the other The problem, simply stated, is to determine the physical variables needed to be known; this was done by normalizing mechanisms that cause atmospheric (weather) effects on traffic the data for refractive effects. After all terms in Equation 3 noise levels and to predict these levels accurately. The solution except A,er were determined in various ways, allowing the is complicated by the interacting effects of geometric spread­ data to be normalized, excess attenuation from atmospheric ing, shielding (diffraction), reflection, ground impedance, refraction was calculated. Once sample data were on a com­ atmospheric absorption, and atmospheric refraction, all of mon basis, comparison of each sample period for changes in which must be considered in the modeling process. excess attenuation due to atmospheric variables could be These effects may be considered to act separately on the determined. These relationships were then evaluated to deter­ noise levels received by an observer as reported by many mine statistical correlation. sources including the well-read text by Beranek (1) and the Once data were normalized, the statistical approaches pre­ FHWA methodology (2). Using this concept, the receiver sented a realistic way to correlate the effects of random atmos­ noise level may be defined as pheric motion. Statistical methods used were regression anal­ ysis, Gaussian statistics, and hypothesis testing. (1) where DATA COLLECTION Lx = time-averaged sound level at some distance x (in dB), Data were collected in March and April 1987 along 1-10 in Houston, Tex. 1-10 at this location consisted of three main Vanderbilt Engineering Center for Transportation Operations and Research, Vanderbilt University, Box 1625, Station B, Nashville, lanes in each direction, two frontage roads in each direction, Tenn. 37235. and a center, high-occupancy vehicle (HOV) lane, all at grade. 60 TRANSPORTATION RESEARCH RECORD 1255 The t:rontage roads we re eparated by a small grassy median An on-site mobile laboratory housed required instrumen­ from 1hc main lanes, whereas the I lOV lane wa . eparated tation and provided shelter and convenience. Meteorological by Jer ey era h barriers. he south side of the highway facilit)', sensors and microphones were connected by long shielded where sampling wa don , coo ·i ·ted of a large open field with cables to the mobile laboratory. All cables were carefully mown grass. Figure 1 shows general site layout and the mea­ checked, and calibrations were conducted with the cables in surement site locations in regards to 1-10. Table 1 presents a place. Reco1diug was uuut:: uu sluuiu 4uality tapes using a complete listing of the data collected. precision RACAL tape recorder at a speed of 15 in./sec to During data collection, specific sets of atmospheric con­ ensure high-quality recording. Proper, careful calibrations were ditions were desi red. A total of 29 periods of dam were finally recorded on each tape. Precise calibrations were repeated for collected, ranging in duration from 4.2 to 24.7 min (2 to 148 each instrument. To quantify the noise data, the tapes were 1.0-sec averages). Table 2 present the average weather c()n­ analyzed using a Norwegian Electronics real-time analyzer. ditions for each sample period. Weather data were collected The selected output of this noise analyzer was in one-third concurrently; in this way, a comprehensive spatial data base octave bands from 16 to 10,000 Hz for each microphone. A was developed. Periods 24 and 29 were deleted due to incom­ data-averaging time of 10 sec was used because the atmos­ pleteness of data. pheric changes and effects on noise data are minimized on DISTANCE TO CENTERLINE OF INTERSTATE I 0 AXIS U 38 .I M 61 M 122 M v~ m tiilJ llD (vertical) W T / ~TDWER3(SITESF,G ANO~~ I L TO'w'ER 1 (SITES A ANO B) -sLoM TO'w'ER 2 (SITES c, o,4\NO E) TO---- INTERSTATE I 0 r ALTERNATE RELATIVE HUMIDITY MEASUREMENT SITE mobH•fl I DEAL I ZED PLAN VIEW lab INOT TO SCALE I .-- 10M - c F H E I G H T 3M- ~A D G +-- I .SM- -e E H TO INT ERST ATE I 0 TO'w'ER I TOW'ER 2 TO'w'ER 3 I 38.1 M 61 M 122M 0 IST ANCE TO CENTERLINE OF INT ERST ATE I 0 IDEALIZED SECTION VIEW (MEASUREMENT SITES SHO'w' BY LETTER DESIGNATIONS A-H) FIGURE 1 Highway and site detail. Wayson and Bowlby 61 TABLE 1 DATA COLLECTED BY LOCATION Measurement Traffic u-v-w Asperated Shielded Relative Station Noise Wind Speed Temp. Temp. Humidity A x B x c x x x D x x x x E x x E** x x F x x x G x x H x x H** x MEL III x A,B: Tower 1 C,D,E,E**: Tower 2 (E** at 0.5 meters) F,G,H,H**: Tower 3 (H** at 0.5 meters) *Also collected manually were: - soil type and relative moisture content - traffic data vehicle counts (by lane classification) vehicle average speeds vehicle types - cloud cover - relative humidity (sling psychometer) - unusual noises this time scale. Weather data were collected using a Balconies tical testing package (5). Figure 2 displays graphically the minicomputer with half-sec recording intervals of all weather series of events needed to combine and analyze the data. data and output to nine-track computer tapes. Each data file was reviewed for accuracy and completeness. A series of FORTRAN computer programs was written, tested, ANALYSIS and run for each sample period to format these VAX­ compatible, ASCII data files. Indirectly measured parameters After formatting was accomplished, data were mathematically were calculated such as lapse rate "/, vertical wind gradient adjusted to normalize for traffic, distance, ground interfer­ du/dz, turbulent intensities iu, iv, iw, standard deviations, ence, atmospheric absorption, and the reference microphone. Richardson number Ri (3), and Tatarski's refractive index Formatting also allowed combinations of various data sets for function ( 4). A mathematical description of Ri and Tatarski's statistical testing. Logarithmic averaging was done for each refractive index function is given in the appendix at the end sample period. The following discussion explains how each of this paper. The meteorological data were averaged in 10- term in Equation 3 was determined or calculated. sec intervals to match the noise data averaging procedure. From the final meteorological and noise data files, various data combinations were sorted and combined. These files Reference Level (L 0) were manipulated to contain specific information of interest for correlation analysis. Statistical testing, as well as corre­ Noise levels measured at Site B were used as the reference lation analysis, was done using a commercial software statis- levels L 0 for data normalization. Site B presented a measured 62 TRANSPORTATION RESEARCH RECORD 1255 TABLE 2 AVERAGE WEATHER CONDITIONS BY SAMPLE PERIOD Sample Avg. Avg. Avg. V Cloud l:'/U Lapse Wind Period RH(%) Temp (C) 10 M (m/s) Cover Class Rate (C/m) Shear (m/s/m) RI# 1 78 23 2.10 0.4 B -0.036 0.031 -1.64 2 80 23 2.42 0.4 B -0.030 0.049 -0.56 3 79 23 2.21 0.4 B -0.044 0.056 -0.57 4 49 14 2.80 0.2 A -0.140 -0.017 -17.60 5 47 14 2.93 0.2 A -0.137 -0.054 -1.71 6 37 19 1.29 0.9 B -0.010 -0.019 -1.75 7 37 19 2.37 0.5 B -0.145 -0.048 -2.24 8 32 22 2.70 0.8 c -0.094 -0.044 -1.83 9 41 20 0.29 0.8 B O.D35 0.027 1.15 10 45 20 0.44 0.8 E 0.052 O.Q28 1.75 11 49 19 3.30 0.9 c -0.080 -0.086 -0.40 12 48 20 4.10 0.9 c -0.095 -0.092 -0.41 13 44 21 1.66 0.1 A -0.026 -0.073 -0.23 14 31 22 2.93 0.0 B -0.123 -0.024 -7.59 15 33 22 0.37 0.0 A 0.007 O.Q18 -0.26 16 50 19 0.23 0.0 A 0.261 0.002 2708.10 17 28 26 1.38 0.0 n.
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