NAT'L INST. OF STAND S TECH

A11107 DSfiEEM

NBSIR 00-2137 R

Procedure for Assessing Impact on

Public Health and Welfare Due to

Noise Emitted By Household and

Consumer Products

Simone V. Yaniv

Daniel R. Flynn

Daniel M. Corley

Marilyn A. Cadoff

National Engineering Laboratory

National Bureau of Standards

U S. Department of Commerce

Washington, D.C. 20234

September 1980

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vRTMENT OF COMMERCE

IUREAU OF STANDARDS

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NBSIR 80-2137 (R)

PROCEDURE FOR ASSESSING IMPACT ON

PUBLIC HEALTH AND WELFARE DUE TO

NOISE EMITTED BY HOUSEHOLD AND

CONSUMER PRODUCTS

Simone Y. Yaniv

Daniel R. Flynn

Daniel M. Corley

Marilyn A. Cadoff

National Engineering Laboratory

National Bureau of Standards

U.S. Department of Commerce

Washington, D.C. 20234

September 1980

U.S. DEPARTMENT OF COMMERCE, Philip M. Klutznick, Secretary

Luther H. Hodges, Jr., Deputy Secretary

Jordan J. Baruch, Assistant Secretary for Productivity. Technology, and Innovation

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director
. .. , ' V

'• I
ABSTRACT

This report documents the development of a conceptual model, and a computer

program for its implementation, for assessing the aggregate impact of noise

from household and consumer products on the health and welfare of the

national population. The computer program requires input data on population, time utilization, dwelling type, noise isolation in dwellings, product noise emission, and product utilization. From the input data, the

"weighted population," corresponding to each of a number of different noise

descriptors, is computed. The report describes the assumptions involved in

the model, reviews available input data, and documents the computer program.

iii
TABLE OF CONTENTS

Page

ABSTRACT iii

ACKNOWLEDGEMENTS vii

EXECUTIVE SUMMARY viii

1. INTRODUCTION ...... 1

2. CONSUMER PRODUCT NOISE EMISSION , , . , 3

2.1 Products Included and Excluded 3

2.2 Product Classification 3

2.3 Required Noise Emission Data 4

2.4 Available Noise Emission Data 5

2.5 Measurement Procedures 6

2.6 Market Saturation Data 10

2.7 Information Gaps 11

3. NOISE ISOLATION 25

3.1 Prediction of Sound Levels at Listener Locations 25

3.2 Characteristics of Typical Dwellings 28

3.3 Information Gaps and Research Requirements 29

4. DESCRIPTION OF EXPOSED POPULATIONS 34

4.1 Population Sub-Groups 34

4.2 Time Utilization of Potentially Exposed Population Groups . . 34

4.3 Product Utilization 35

4.4 Information Gaps 35

5. CRITERIA USED TO ASSESS NOISE IMPACT FROM CONSUMER PRODUCTS ON

PUBLIC HEALTH AND WELFARE 47

5.1 Criteria for Hearing Damage Risk 49

5.1.1 Fractional Exposure 51

5.1.2 Potential Hearing Damage for Products that Exceed

the Allowable Exposure Time 53

5.2 General Adverse Response 54

5.3 Speech Interference 60

5.4 Sleep Interference 66

6. IMPACT ASSESSMENT MODEL 71

6.1 Input Data Parameters 71

6.2 Input Data Required for Implementation of the Model 72

6.3 Flow Chart for Computer Program 74

6.4 Format of Data Inputs 87

7. REFERENCES 91

Appendix A Existing Measurement Procedures 95

Appendix B Correlations Among Ratings for Different Spectra 96

Appendix C Listing of Computer Program for Assessing the Impact of

Noise From Consumer Products 117

Appendix D Sample Calculation 133

iv
LIST OF TABLES Page

Table 1. Classification of consumer products that emit noise which

may adversely affect public health and welfare 12

Table 2. List of references and types of physical data available

on consumer products 16

Table 3. Range, mean, and standard deviation of the A-weighted sound

levels (at 1 meter from the source) for certain household

products 21

Table 4. Saturation data currently available on household consumer

products 23

Table 5. Percentage of employed men engaged in various activities

at given times during a 24-hour period 37

Table 6. Percentage of employed women engaged in various activities

at given times during a 24-hour period 38

Table 7. Percentage of housewives engaged in various activities at

given times during a 24-hour period 39

Table 8. Percentage of time children spend at home by mother’s

employment 40

Table 9. The distribution of time spent on household obligations,

according to different social contacts 41

Table 10. Usage data currently available on household and consumer

products 42

Table 11. Presumed or potential primary exposure (in hours) for a variety of consumer products 44

v
LIST OF FIGURES

Page

Figure 1. Floor plan of characteristic single family house 30

Figure 2. Floor plan of characteristic townhouse 31

Figure 3. Floor plan of characteristic low-rise apartment 32

Figure 4. Floor plan of characteristic high-rise apartment 33

Figure 5. Percentage of people being at home during various periods

throughout the day 46

Figure 6. Average noise-induced permanent threshold shift expected

as a function of the continuous A-weighted equivalent

sound level 50

Figure 7. Weighting function for assessing the general adverse

response to noise 56

Figure 8. Weighting functions proposed by Alexandre and Barde .... 58

Figure 9. Relation between the Articulation Index and various

measures of speech intelligibility ... 63

Figure 10. Relationship between A-weighted sound pressure level and

fractional sentence unintelligibility 65

Figure 11. Weighting factor for sleep disruption as a function of

sound exposure level 69

Figure 12. Weighting factor for frequency of arousal or awakening

from sleep as a function of sound exposure level 70

vi
ACKNOWLEDGEMENTS

The authors acknowledge the valuable contributions of the following persons

to this report: Thomas L. Quindry and William F. Danner for their assistance in compiling and reducing the consumer product noise emission data,

Paul R. Donavan for his assistance in drafting parts of this report, and

Raelyn Janssen and Jeffrey Goldstein, U. S. Environmental Protection Agency,

for their encouragement and support during the course of this program.

vii
EXECUTIVE SUMMARY

The objective of the present study was the development of a conceptual model

and a practical procedure for assessing the aggregate impact of noise from

household and consumer products on the health and welfare of the national

population. A secondary objective of this study was to determine the availability of data on household and consumer products, for use in application

of the impact assessment model, and identification of essential gaps in

current information.

The assessment of the impact of noise exposure on the population requires

consideration of the intensity , or severity, of the effect and consideration

of the extent , or number of people affected. Thus, to assess the total

impact of noise it is necessary to determine:

o individual noise exposures,

o response criteria — cause-effect relationships in which a given

noise exposure is expressed in terms of its effects,

o the number of people who experience various levels of noise

exposure,

o a procedure for combining the properties of intensity and extent*

Because of the large amount of input data required, the complexity of the

calculation procedures, and the number of computations required, a computer

program was developed to assess the population impact due to noise from

household products. The concepts underlying this computer model, the criteria used to assess impacts, the required input data, and the use of the

model are discussed in the body of the report. The computer program itself

is contained in Appendix C.

A large number of noisy products are used in and around the home. Thus, it was important to classify consumer products into a limited and manageable

number of categories. Since the manner, place, and time of use are dependent upon the function performed by the product, products were grouped

according to their function. The proposed categorization allows various

products to be treated as groups.

The main objective in obtaining noise emission data for consumer products is

to provide information needed for predicting, quantitatively and accurately,

the effects of the noise on people. For noise sources that emit steadystate noise over their operating cycle, the average noise emission over the

period of operation suffices. For those sources with several modes of operation, each with its own characteristics, the noise emission must be averaged

over all modes of operation. The computer program requires information on

the octave-band spectrum shape for each product class and the probability

distributions for the noise emission from the products within a class.

viii
Few noise emission data for consumer products are available. Those data

which are available are frequently unreliable, chiefly due to the lack

of well-defined and uniform measurement procedures. A similar situation

exists with respect to market saturation data and usage patterns needed

to derive estimates of the population exposed to the noise from consumer

products.

In order to assess the noise exposure resulting from a given product at

each potential listener location, it is necessary, in addition to having

noise emission data for the source, to obtain information on the degree

of noise isolation between the source and each listener location. Accordingly, the computer model requires data on the noise isolation, as a function of frequency, between rooms within a given dwelling, between rooms in one dwelling and rooms in a neighboring dwelling, and between rooms and

the location(s) of an outdoor source. In addition, data are needed on the

total sound absorption in the room where a listener is located as well as data regarding the location of the listener relative to a source located in

the same room.

Once information has been compiled on the noise emission characteristics

and market saturation of a consumer product and the sound levels have been

predicted at the listener location(s), it is necessary to have further information in order to predict the population impact of that product. First,

it is necessary to know what the population groupings are and how many

persons in each group are potentially exposed to consumer product noise.

Second, data on the time utilization for each group need to be obtained.

Finally, product utilization information must be obtained for each consumer

product. Using all these data, estimates can be made of the population

exposed to each consumer product and how long that population is exposed at

various levels. Accordingly, the computer program requires data on time

and product utilization. The time utilization data currently available do

not cover all of the potentially exposed population groups. The categories

of activities considered are so broad that it is difficult to establish in

sufficient detail the amount of exposure to appliance noise sustained by

each population grouping.

Assuming that input data described above could be obtained, the computer

program calculates the "Weighted Population, " which is the summation of the

results of multiplying a weighting factor (as a function of noise exposure,

for each response of concern) by the population subjected to that exposure.

Twenty different weighting factors are available for use in computing population impact for hearing loss, general adverse response, speech interference, and sleep interference.

In addition to the Weighted Population, another quantity computed, useful

in comparing the relative impact of one noise exposure to another, is the

Noise Impact Index. This is the Weighted Population divided by the number

of people potentially exposed to the noise from each class of products.

ix
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.••
1. INTRODUCTION

Through the Noise Control Act of 1972 (PL.92-574), Congress established a national policy "to promote an environment for all Americans free from

noise that jeopardizes their health and welfare." This policy has been

reaffirmed through the Quiet Communities Act of 1978 (PL95-609). In pursuit

of that policy, Congress stated in Section 2 of the Act that "while the primary responsibility for the control of noise rests with state and local

governments, Federal action is essential to deal with major noise sources

in commerce, control of which requires national uniformity of treatment."

As part of this essential Federal action, Section 5(b) of the Noise Control

Act requires the Administrator of the Environmental Protection Agency to

publish, after consultation with appropriate Federal agencies a report or series of reports "identifying products (or classes of products) which in

his judgment are major sources of noise." Section 6 of the Act requires

that the Administrator of EPA publish proposed regulations for each product

identified as a major source of noise and "for which in his judgment noise

standards are feasible and are requisite to protect the public health and

welfare.

In addition, Section 8 of the Act directs the Administrator of EPA "to designate any product or class thereof which emits noise capable of adversely

affecting the public health or welfare and to require that notice be given

to the prospective user of the level of noise the product emits” through a labeling action.

The Noise Control Act requires EPA to develop uniform procedures for assessing the public health and welfare impact of noise from various products.

The main objective of the present study was the development of a conceptual

model and a practical procedure for assessing the impact of noise from

household and consumer products on the health and welfare of the national

population. A secondary objective of the study was to ascertain the availability of data on household and consumer products for use as input to an impact assessment model, and to identify gaps in that information.

As pointed out in previous EPA documents [1-5]*, assessment of the population impact due to noise exposure requires consideration of the intensity , or severity, of the effect and consideration of the extent , or number of

people affected. Therefore, to assess the total impact of noise, it is

necessary to determine:

* Figures in brackets indicate the literature references in Section 7 of

this report.

1
o individual noise exposures,

o response criteria — cause-effect relationships in which a given noise exposure is expressed in terms of its effects,

o the number of people experiencing various levels of noise

exposure,

o a procedure for combining the properties of intensity and

extent

.

In order to estimate individual exposures, it is necessary to have data on

the noise emission for each class of product, the length of time each product is operated, the noise isolation between the location of the product

and the location(s) of the listeners, and the length of time listeners spend

in various locations. Criteria are needed that relate each effect of con- cern to the noise exposure which produces that effect. Data are needed on the number of people in each category of "population at risk." Finally, a procedure is needed to combine all of these data into a single measure of

the total population impact. These data and criteria requirements are discussed in the sections that follow.

2
2. CONSUMER PRODUCT NOISE EMISSION

2.1. PRODUCTS INCLUDED AND EXCLUDED

Much attention has been focused on the impact of noise from sources such as aircraft, vehicular traffic, and construction activities. In the past,

little attention has been given to noise sources encountered in and around

the home. The present study focuses only on household appliances and con- sumer products (referred to hereafter as "consumer products"). These include typical built-in interior equiment such as heating and cooling systems, dishwashers, and garbage disposers. Also included are portable

appliances, such as electric shavers, food blenders, hair dryers, vacuum

cleaners, shop tools, and lawn-care equipment.

Consumer products whose sound level is under the control of the operator —

radios, television sets, and stereo systems — are not included in the present study. Plumbing systems and built-in mechanical equipment, such as elevators and trash chutes, also are not included because they normally

are not purchased directly by people for use in their own homes.

Also excluded are those consumer products which are used either very infrequently, or by very few people, and products that produce noise levels that can be considered to be below the levels of immediate concern with respect

to public health or welfare. Included among such products are rotisseries,

refrigerators, freezers, and hot water heaters. The decision to exclude

these products from consideration was arbitrary and does not imply that

under no condition can they be noisy. The assessment model could be used

with these products provided that appropriate input data were available.

2.2. PRODUCT CLASSIFICATION

Because of the large number of noisy products used in and around the home,

it was important to classify consumer products into a limited and manageable

number of categories. Since the manner, place, and time of use depend on

the function performed by the product, products were grouped according to

their function. For example, products that are used for food preparation,

disposal, or storage, and may operate for varying amounts of time, are most

frequently used in the kitchen by the person in the family who is responsible for food preparation. Products designed for personal hygiene and grooming are normally used in the bathroom or bedroom by various members of the

family but usually only for brief amounts of time.

This rationale led to a practical, although arbitrary, classification scheme

that specifies nine classes of products, each of which is subdivided (when

appropriate) according to the specific function associated with a given product. Table 1 summarizes the classification format used and separately

lists those products emitting noise that (1) potentially could or (2) pro3
welfare.* bably could not adversely affect public health and * These latter

products have been excluded from further consideration in this report.

The proposed categorization allows various products to be treated as groups

for selection of noise measurement procedures. For example, appliances

used in the kitchen normally affect the user most, although they can be

heard elsewhere, but to a lesser degree, by people located in either adjacent rooms or adjacent dwellings. The important parameters affecting the

exposure of the user are the sound power radiated, the directivity pattern

of an appliance, and, to a lesser extent, the acoustical properties of the room. For counter-top appliances such as blenders, coffee grinders, and

mixers, the noise exposure may be sensitive to the presence of nearby

walls and corners. These should be considered when measurement procedures

are developed. For built-in kitchen appliances, the noise emission will

be influenced by the counter top and cabinet construction and the manner

in which the appliance is installed. In this latter case, these factors

also may affect greatly the noise received along structure-borne paths.

Such products may require different measurement procedures to account for

this factor. As another example, appliances used for grooming and personal

hygiene are often used in proximity to the ear, thus requiring different

measurement procedures from those used for other classes of products.

2.3 REQUIRED NOISE EMISSION DATA

The chief application of noise emission data for consumer products is to

provide information needed to predict, quantitatively and with a satisfactory degree of accuracy, the effects of the noise on people. The

accumulated evidence of research on human response to sound indicates that

the intensity of a sound, as a function of time and frequency, is the basic

indicator of probable human response. For this reason, specification of

the noise emission data from consumer products is desired in terms that permit computation of the average sound pressure level, over the period of

operation of the product, at various listener locations.

Two basic approaches can be used to characterize the noise emission from

a source. The average sound pressure level at some reference distance

from the source can be determined or the sound power level and the directivity pattern of the source can be measured.

Since the human ear is not equally sensitive to sound of different frequencies, the spectrum shape for each class of products is required. Since the

range of sound levels for any given class of product can be large, the distribution of 1/ 3-octave-band and A-weighted levels over the population of

products contained within a class is also required.

* In order to avoid interfering with the continuity of the text, tables

*and figures are located at the end of each Section.

4
2.4 AVAILABLE NOISE EMISSION DATA

Published noise emission data on consumer products are scarce. Those which

are available do not lend themselves to the kind of analyses required to

provide a basis for comparisons among products. This stems mainly from a lack of adequate definition and general acceptance of measurement methods

for most products. As a result most published data were obtained by measurement procedures that either are not fully reported or not documented at

all.

Measurement procedures that are reported in the literature vary considerably, even for the same product. In many instances, each researcher

devised his own test procedure. Sometimes a controlled environment, such

as an anechoic or reverberation chamber was used; other tests were performed in situ or outdoors. Terms such as "acoustically average kitchen"

might be all the information given to specify the environment. Operating

conditions might be described as "normal" or "simulated." For hand-held

appliances, some were resiliently suspended; others were not. A counter

top may be specified as part of the test for mounted appliances or for

appliances which rest on a counter top under actual use conditions, but this was not always the case. Often it was impossible to know whether the data

reported for a product were obtained in situ or in the laboratory, whether

mounting conditions were actual or simulated, and whether or not precautions

were taken to control the influences of structure-borne interactions. In

addition, some products were measured in their noisiest modes of operation,

while others were measured in all operating modes and the average of all

modes reported. All of these factors combine to make the published physical

data very difficult to interpret, and intercomparisons virtually impossible.

To further complicate the situation, since there are no standard measurement

methods for most products, data are not reported in a standardized way.

Some authors report their data in terms of 1/3-octave-band levels while

others report octave-band data, narrow-band data, A-weighted levels, Cweighted levels, sones, N-ratings, or some other metric. To illustrate the

variability in the available data, Table 2 summarizes the type of physical

data reported by various authors for a variety of consumer products.

In a few instances either data are reported in terms of sound power, levels, or a measurement distance accompanies a reported sound pressure level. In cases such as these, it is possible to convert the reported data to levels

at a single reference distance. To provide some insight into the range of

data associated with consumer products, whenever possible the range of Aweighted levels and the average A-weighted level at a distance of 1 meter

were computed (assuming spherical spreading). These data are reported in

Table 3. Note that the tabulated values in Table 3 are illustrative only and

should not be construed as representative of the noise emission of actual

consumer products . This disclaimer is made for two reasons. First is the

weakness associated with the basic data from which they were derived. Second, products found in homes throughout the United States are of different

ages and brands and are in various states of wear (all factors affecting

5
noise emission). These products were not sampled, and thus the levels

reported would not necessarily be representative of actual conditions found

in the home.

2.5 MEASUREMENT PROCEDURES

Most of the published physical data on consumer products were obtained by

diverse measurement procedures. This diversity is due to the lack of wellaccepted and standardized measurement procedures. At least two exceptions

are worth noting — fans and lawn mowers. Data for these products usually

have been acquired by methods prescribed in voluntary standards.

Emission data on fans often have been obtained in accordance with either the

measurement methodology recommended by the American Society of Heating,

Refrigerating and Air Conditioning Engineers (ASHRAE) [6] or by that of the

Air Moving and Conditioning Association [7]. Both procedures call for measurement of the sound power level for eight octave-bands in a reverberation

room calibrated using a reference sound source. The ASHRAE procedure further

specifies that 1/3-octave-band data must be obtained to estimate whether pure

tones may be present for a given product.

In the case of lawn mowers, measurement data usually were obtained by a procedure developed by the Society of Automotive Engineers [8]. This procedure

calls for measurements to be made outdoors over artificial turf.

The general lack of standard measurement methods for household products causes acute problems. Although this matter is receiving attention, particularly at the international level, considerable disagreement remains

concerning how to measure the noise emission of each product, what constitutes a suitable test environment and how to report the data. In order to

obtain the essential data base required by EPA, suitable measurement standards must be available.

Although it is not the purpose of the present section to address all the

problems involved in the development of measurement procedures, an illustrative example of questions that emerge may be useful.

One of the key problems is that of defining and choosing the acoustical

quantity to be measured: sound pressure at one or more selected locations,

total sound power, or sound power and directivity. Furthermore, should this

quantity be expressed in terms of a weighted level, such as an A-weighted

sound pressure level or an A-weighted sound power level?

Some acousticians strongly prefer noise ratings expressed in terms of sound

power rather than sound pressure. Since sound power is almost always computed from measured sound pressures it is difficult to argue that sound power

is inherently superior to sound pressure. However, the proponents of sound

power argue that the sound pressure values observed from a given source depend strongly upon the point at which the measurement is made, and can depend upon the environment in which both the source and the measuring

6
microphone are located. Sound power level is a measure of the total sound

power radiated by the source in all directions and directivity is a measure

of the spatial intensity distribution. Sound power and directivity are basic characteristics of the noise source and hence need not be specified

in terms of any particular measurement distance, except that measurements

ought to be made in the far field. Sound power depends less on the environment in which the source is located than does sound pressure. However, both

the radiated sound power and the directivity are influenced by nearby reflecting surfaces, such as floors and walls. In enclosed spaces, the sound power

usually is less affected by the environment than is the sound pressure observed at distances far enough from the source to be in the reverberant field.

As was mentioned above, sound power usually must be computed from sound pressure measurements. This is accomplished simply and accurately in two limiting

cases: (1) in a free field beyond the near field, and (2) in a reverberant

(diffuse) sound field. In such fields the sound power can be calculated from

the mean-square sound pressure, averaged over an appropriate surface enclosing

the source (free field) or averaged over the volume of the room (reverberant

field). Close approximations to free-field conditions can be achieved in

anechoic chambers, hemianechoic chambers (i.e., free field over a reflecting

plane), or outdoors. Approximately diffuse sound fields can be obtained in

large, hard-walled reverberation chambers. To determine the directivity of

the source, an essentially free-field environment must be used.

Sound power proponents further argue that whenever acoustical data are expressed in terms of sound power, one should be able to assume that the data

correspond to the far field around the source, and the the sound power is

based either on measurements at a sufficient number of angular positions to

sample the field adequately in all directions or by adequately sampling a reverberant field. If on the other hand, acoustical data are expressed in

terms of sound pressure, these data must be accompanied with sufficient information regarding the measurement location and test environment to enable one

to infer the extent to which the data may be used to predict sound pressure

at other locations and in other locations and in other environments.

Proponents of sound pressure level argue that one disadvantage of sound power

is that the human ear does not respond directly to sound power but rather to

sound pressure. Thus, in cases where human response is of concern, they

believe that sound power may not be the most suitable measure. So long as the sound pressure levels reported are accompanied with sufficient information

regarding the measurement location and test environment, the data can be useful in predicting sound pressure levels at other locations and/or other environments.

Recently, the relationship between source emission and sound pressure levels

has been challenged by Schultz [9] who suggests that some of the underlying

assumptions of theoretical acoustics are "valid only under certain limited

conditions." These conditions may not necessarily be present for many noise

sources found in dwellings since even "at distances far from the source, real

rooms do not behave like the classical reverberant rooms of theoretical acoustics, but more like lined ducts."

7
The most important criterion in terms of consumer product noise emission is

relating the data to human response. If the operator of the product stays

at a particular location the sound pressure at the position of his head may

be the most useful quantity to measure. If the operator moves around and/or

if the concern is with potential bystanders that move about the noise source,

a spatial average of sound pressure levels over the total region of interest

may be the most appropriate quantity. In cases where several products operate at the same time it may be necessary to know the sound pressure level at

the ear of the operator for a particular product plus the sound power contributed by the other products in order to ascertain the total noise level at

the operator position.

As can be seen by the above discussion the problem of choosing which quantity

to measure is a difficult one to resolve. In general, sound pressure level

at the location of a listener is required to assess the effect of noise from

a product. How this quantity is obtained is less clear. Sound pressure

level is appropriate if the listener location is well defined, the transmission to the listener does not differ much in various situations, and if the

data can be obtained and are reported in sufficient detail to be easily

extrapolated to other locations. Sound power, on the other hand, may be more appropriate if information is needed to predict sound pressure in a variety

of environments which significantly affect the resultant sound pressure.

In addition to the problem of what is to be measured, many other choices

must be made when developing standard measurement procedures. These include

the choice of the measurement environment (e.g., anechoic, hemianechoic,

reverberant, and in situ conditions), how the measurements are to be made,

where the product is located, how it is to be installed and operated, how

much precision is required, and how the data are to be reported. Obviously,

answers to such questions are not independent; for example, sound pressure

level measurements for a given device are obtained by measuring at a specified distance from the source in essentially free-field conditions. For

sound power determinations, in an acoustically controlled environment, the source is usually located near the center of the room for anechoic measurements, near the center of the floor for hemi -anechoic measurements, for

reverberant measurements the source could be located at various locations.

For devices normally mounted on or against a wall, products should be tested

in their use configuration.

The noise level produced by a specific device depends not only on the sound

radiating characteristics of the product itself but also on the way the

appliance is operated and/or installed and the specific environment in which

it is used. In setting noise limits for such devices through noise emission

or labeling standards, test procedures and measurement methods should include

such items as loading, operating speeds, installation requirements, and the

location and specification of needed auxiliary equipment.

8
Thus it can be seen that standard measurement methods should specify at

least the following:

1. the quantity to be measured

2. the environment in which the measurements are to be made

3. the mode of installation and operation of the source, which includes

the following information:

o the device under test should be located in its use

configuration or, alternatively, the choice of a location should be governed by the test environment and

the quantity to be measured; e.g., the source should

be located near the center of the room for anechoic

measurements of sound power,

o the device under test should be mounted under conditions similar to those recommended for normal installation. Care should be taken to ensure that adequate

isolation is provided to minimize extraneous airborne

noise due to vibration excitation,

o the operational modes under which tests are to be

carried out,

o the extent of loading and the manner of application

of the load to the source under test, these should be

similar to actual use conditions wherever possible.

4. the actual measurement procedures should include:

o the location of all microphones,

o the position of the source with respect to the test

environment,

o the number of observations, and the averaging time,

necessary for each sound level measurement,

o criteria which determine whether the use of diffusers

is indicated in reverberation room measurements,

o procedures for determining background noise,

o techniques and procedures for characterizing the

adequacy of the test environment,

o instrumentation and facility calibration requirements

and procedures.

9
5. the information to be recorded should include:

o the size, dimensions, design characteristics, and noise performance

claims for the source under test,

o the location, mounting, and/or installation details of the source,

o the operational and loading characteristics of the source during the

test,

o a description of the acoustic environment, including test facility,

background noise levels, and environmental conditions,

o identification of instrumentation used,

o documentation of unavoidable deviations from the prescribed test

procedures

,

o a maintenance and calibration record to indicate the current calibration status of all instrumentation (calibration methods and

periodicity, accuracy, and traceability of the calibration devices

need to be detailed),

o all significant data collected during the test,

o documentation of calculation procedures used in transforming the raw data into its final form,

o an indication of the accuracy and precision of the data.

2.6 MARKET SATURATION DATA

In order to derive estimates of the national population exposed to the noise

from consumer products, it is desirable to have market saturation data —

information as to how many households possess one or more of each product

class. Table 4 lists all of the household products included in this study,

indicates whether or not saturation data were found, and if so, shows the

percentage of wired households containing each product. Practically, what

this means is that, of all the wired households in the United States, X% of

the households contain at least one of the product of interest. The data do

not account for households which may have more than one particular product

(such as electric razors, vacuum cleaners, etc.). Moreover, there is probably no practical way to get this information from exiscing data. Saturation data are available for only about 25% of the products included in this

study

Even though three different references have been cited in Table 4 as primary

sources of data, References 11 and 12 both used Reference 10 as their primary

primary source. The U. S. Census Bureau also uses statistics from Merchandising magazine in compiling the product data tables for the Statistical

10
Abstract of the United States. The statistical and marketing reports of

Merchandising magazine are considered reliable, accordingly, their saturation data have been used to make estimates of potential consumer exposure to

household appliance and product noise.

There are major problems, however, with using market saturation data. First,

for many appliances there are no data readily available. In fact, there are more noise-producing appliances for which there are little or no data than

there are appliances for which data are available. In order to determine the

actual impact of household appliances and consumer products, hard data will

be required in the future.

Second, for a product such as an electric shaver there is little problem in

deciding whether it can be categorized as a home appliance. It is not always

this clear-cut. Appliances such as a vacuum cleaner or a room air conditioner seem to qualify as home appliances (and probably are, predominantly);

however, these are also used and have an impact in numerous situations outside of the home. There appears to be no way to identify and quantify these

situations from the data currently available. For example, office and public

buildings have to be cleaned, and there are numerous vacuum cleaners used for

this purpose. In older public buildings with no central air conditioning,

window air conditioning units are often used. When categorizing lawn care and home shop products, the situation is worse. Who can tell, for example,

whether a circular saw has been purchased for home use, or for use at a con- struction site? Thus, in order to assess the impact of such products, even

if complete saturation data were to exist, there would still be a need to

determine how and where these products are used.

2.7 INFORMATION GAPS

Large information gaps exist in the available noise emission data, product

measurement procedures, and market saturation data. In connection with the

noise emission data, it must first be stated that quantitative data do not

exist for all of the products considered. Moreover, for those products for

which data are available, these data are weak because, in general, no standardized measurements methods were utilized. In addition, there is no way

to assess whether or not, for any product, reported data represent the product class. Finally, where data are found in the literature they appear

in a variety of metrics, thus making comparisons among data difficult, if

not impossible.

Market saturation data are problematic also. First, there is no way of

determining for each household and each particular appliance how many of

these are owned. It can only be known that if a household is included among

those owning an appliance, it contains at least one such appliance. Even if

a household contains an appliance, it cannot be said with any certainty that

the product is actually used. Product usage is merely assumed. Second, for

most household products, there are no readily available saturation data.

11
Table 1. Classification of consumer products that emit noise which may

adversely affect public health and welfare.

Potentially

Hazardous

Potentially Less

3. / HazardousFOOD PREPARATION, DISPOSAL AND STORAGE

a. Mixing, Straining, Crushing

Blender/ liquidizer

Electric mixer

Coffee grinder

Ice crusher

Electric knife

Slicer

Knife sharpener

c. Opening

Electric can opener

d. Cooking

Rotisserie

e. Clean Up/Disposal

Garbage disposer

Dishwasher

Trash compactor

f. Storage

Juicer

Electric ice cream freezer

Electric meat grinder

b. Cutting, Slicing

Ref rigerator/f reezer

PERSONAL HYGIENE AND GROOMING

Oral hygiene device Electric shoe polisher

Hair dryer/blower

Electric razor Electric hair clipper

CLIMATE AND ENVIRONMENTAL CONTROL

Exhaust fan, range hood Sump pump

Heating and air conditioning (central) Vaporizer

Air conditioner (room) Gas water heater

a/ Inclusion of products in this column does not imply that under no conditions can they be noisy.

12
Humidifier

Dehumidifier

Electric portable and window fan

Electronic air cleaner

Space heater

INDOOR MAINTENANCE

Vacuum cleaner

Rug cleaner/floor waxer

Floor scrubber

CRAFTS, HOBBIES, AND NON-WORKSHOP TOOLS

Sewing machine

Electric typewriter

Movie/slide projector

LAUNDRY EQUIPMENT

Washing machine

Clothes dryer

LAWN AND GARDEN CARE AND MAINTENANCE

Lawn mower (gas, electric, riding)

Edger/trimmer/grass shears

Chain saw Incinerator

Lawn thatcher

Weed eater

Leaf blower

Leaf sweeper/mulcher/shredder

Rotor tiller

Snow blower

Hedge trimmer

Garden tractor

HOME WORKSHOP TOOLS

a. Cutting

Power shears

Sabre saw Pneumatic power chisel

Circular saw Abrasive cut-off machine

Radial arm saw Table saw Power hacksaw

Electric scissors

Calculator

Antenna rotor

Pool filter

Aerator

Insect fogger

Power washer

Electronic bug killer

Electric engraving pen

13
Scroll saw Band saw Reciprocating saw Motorized mitre box

b. Shaping

Router

Lathe

Shaper

c. Drilling

Drill press

Portable electric drill

Impact drill

Pneumatic air drill

d. Driving

Nail gun Electric screwdriver

Electric staple gun Roto hammer

Pneumatic chipping hammer

e. Sanding/Planing

Belt sander

Pneumatic sander

Vibration sander

Sander/polisher

Disc sander

Planer/ jointer

Sand blaster

f. Sharpening

Grinder

Saw blade sharpener

Drill bit sharpener

g.

Fastening

Impact wrench

Molder

Spot welder

Arc welder

h. Unfastening

Flameless heat gun

i. Pumping

14

Liquid pump

Vacuum pump
Air compressor

Power generator

Motor

j.

Power Generating

k. Cleaning

Shop vacuum Spark plug cleaner

Chipper cleaner

1.

Finishing

Paint sprayer

Spray gun

15
Food

Preparation,

Disposal,

Storage

A.

Mixing,

Straining,

u

D

"Oou

CO

CO TJ

COu

W

SI

CL

a)a.

X!C

CO

16
Type

of

Spectral

Information

o co U

a 73

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73 a>

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CM

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CM 32

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nd •H G G

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4= G •H G \ <v 73 n3 H 3 H T3 (U 60 G a G ai G G o 60 G 43 c o

G g ai G G 43 rH 3 u 4) G G G •H G

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*H C3 0) cx •H X CJ 14 X CX G G G G CX rH (X n3 •H G G u G & 43 a, *H CL *H 4= <u 2 u 4) O rH rH G H U 60 43 ^ G AJ G u »H <u <3 o

43 o 4= 3 O >> c ai 0 <3 0 43 60 41 3 n> 0) 60 a; <u D G rH G 60 G 0) G 73 6o a o u

<3 O 43 a 44 CT" 2 H <D o u 0 a 0 CO C H CJ *H »H T3 C 4J 43 cx •H rH AJ G 4J >-l CJ TJ d) C c o

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G x 60 CO G c o 4J G 3 44 o AJ CO U 03 H OQ G erf hJ CO •H Q Oj C M > Q CO G o 0 3 <e 2

•H G o 00 >4 u c <D O <0 0 G 3 3 43 u G 43 3 o

rH AJ 3 c AJ 73 •H 4= AJ U G <3 CJ CO a CO CO X PH CL 4-1 1 *H o (3 4= AJ G C c <3 •H 4h 60 ai cl G e 3 <0 3 CO o 5 *H IS 60 G G x a

<3 M o <u «H G G rH G G <0 73 4= <u CD O • • • • • • • U 2 CO W hJ 3 CJ J s XI w CJ XJ 33 33 <! m u Q w £xh

17

Miscellaneous

Small

equipment

cooling

fan

25,27

27(NR,

SIL)
Table 2 References

1. Anon., Noise from construction equipment and operations, building

equipment, and home appliances, Report No. NTID 300.1 (U. S. Environmental Protection Agency, Washington, D.C., Dec. 1971).

2. Weissler, P. G., Zerdy, G. A., and Revoile, S., Consumer product noise:

A basis for regulation, NBSIR 74-606 (National Bureau of Standards,

Washington, D. C., Nov. 1974).

3. Anon., Lawn mowers: Noise and cost of abatement. Report No. 550/9-74-011

(U. S. Environmental Protection Agency, Washington, D. C., June 1974).

4. Clark, W. D., and Williams, K. C., Rotary power lawn mower noise, presented at the 87th meeting of the Acoustical Society of America,

New York, N.Y . (April 1974).

5. Hart, F. D., O’Brien, D. M. , and Ferioli, D. A., Investigation of noise

radiated from a circular saw, Proceedings of Noise-Con 73 (Noise Control

Foundation, Poughkeepsie, N.Y., October 1973).

6. Bragden, C. R., Quiet product emphasis in consumer advertising, S)V

Sound and Vib. 8^(9), 33-36 (Sept. 1974).

7. Kirschner, F., Vibration damping systems for rotary cutting tools, Proceedings of the Eighth International Congress for Acoustics,

London, England (1974).

8. Lalwani, R. J., Domestic appliances and noise, Proceedings of the Eighth

International Congress on Acoustics, London, England (1974).

9. Jackson, G. M. and Jeatt, K. E., Domestic appliance noise, Proceedings

of the Eighth International Congress on Acoustics, London, England (1974).

10. Baade, P. K., Household noise problems, J. Acous. Soc. Am. _50(5), 1233-

1235 (November 1971).

11. Tsao, M C. C., and Musa, R. S., Noise control in home appliances, S)V

Sound and Vib. 1( 3 ), 31-33 (March 1973).

12. Jackson, G. M. and Leventhall, H. G., Household appliance noise, Appl.

Acous. 8 (2), 101-118 (April 1975).

13. Farr, L. E., Medical consequences of environmental home noises, J. Am.

Med. Assn. 202 (3) (October 1967).

14. Bloomfield, B. C., and Hay, D. C., The auditory environment in the home,

Environmental Design Department, Univ. of Wisconsin, M adison, Wisconsin.

15. Cudworth, A. L., Quieting circular saws. Noise Control ^6 (1), 26-30, 52

(January/February 1970).

18
References (continued)

16. Hirschorn, M., The 90 dBA guideline for noise control engineering, S)V

Sound and Vibration ^ ( 4) 20-24 (April 1970).

17. Harrison, J., Melling, G. F., adn Konowicz, P. J., Problems of measuring and assessing electrical appliance noise, Paper No. 73/02, Presented

at the British Acoustical Society Meeting, Chelsea College, London,

England (1973).

18. Slone, R. M., Jr., and Robertson, J. E., Investigation of aerodynamic

noise from circular saw blades, Proceedings of Noise-Expo 1975, Atlanta,

Georgia, 121-124 (April/May 1975).

19. Mikeska, E. E., Noise in the modern home, Noise Control h (3), 38-41,

52 (May 1958).

20. Christie, D. H. and Graham, J. B., Sound power level measurements of

fan noise, ASHRAE Trans. _7()> Chap. 1890, 278-290 (1964).

21. Petrusewicz, S. A., Pump noise in domestic heating systems, Paper No.

73/04, Presented at the British Acoustical Society meeting, Chelsea

College, London, England (1973).

22. Clark, R. S., and Petrusewicz, S. A., Noise and vibration problems of

small central heating circulating water pumps, ASHRAE Journal 1_3 (2),

23-

33 (February 1971); 13 (3), 53-59 (March 1971).

23. Anon., Electric drills, Consumer Reports ^0(10) , 600-605 (October 1975).

24. Blazier, W. E., Jr., A measurement study of sound levels produced outdoors by residential air-conditioning equipment, ASHRAE Journal, j) (5),

35-39 (May 1967).

25. Tseo, G. G., Noise reduction of a miniature fan by using blade treatment, J. Sound and Vib. _32 (2), 153-158 (January 197 4).

26. Graham, J. B., How to estimate fan noise, S)V Sound and Vib. 6_ (5),

24-

27 (May 1972).

27. Dennson, E. E., Sound power level measurement of small fans, S)V Sound

and Vib. 10 (3), 44-45 (March 1976).

28. Ostergaard, P). B., Sound in buildings. Building Systems Design, ^8 (6),

25-

30 (June 1971).

29. Lipscomb, D. M., Noise: The Unwanted Sounds, Chapter 9, The castle

crumbles (Nelson-Hall Company, Chicago, Illinois, 1974).

30. Finkelstein, W., Preliminary computations of fan noise, Building

Services Engineer 41, 268-275 (March 1974).

19
References (continued)

31. Bayless, W. S., Noise considerations in the application and installation

of outdoor air conditioning equipment, ASHRAE Journal j) ( 4) , 52-56

(April 1967).

32. Ellison, A. J., The noise of small electric motors. Paper 73/07 Presented

at the British Acoustical Society meeting, Chelsea College, London,

England (1973).

33. Epp, S. and Kong, S., Home appliance noise: annoyance and speech interference, J. Home Economics Research J3(3) (March 1975).

34. Anon. , Sound in apartments (NAHB Research Foundation, Inc. , 627 Southlawn

Lane, Rockville, Maryland, 1965).

20
Table 3. Range, mean, and standard deviation of the A-weighted sound levels

(at 1 meter from the source) for certain household products.

Appliances

Range of

A-Weighted

Levels

(re 20 pPa)

(dB)

Number

of

Products

Tested

Mean

A-Weighted

Level

(re 20 pPa)

(dB)

Standard

Deviation

of

Levels

(dB)

1 . FOOD PREPARATION, DISPOSAL, AND STORAGE

a. Mixing, Straining,

Crushing

Blender 62-91 15 75 8

Liquidizer 61-89 4 75 12

Electric mixer 45-75 17 64 8

b. Cutting, Slicing

Electric knife 64-74 3 70 6

c. Opening

Electric can opener 64-80 3 70 7 d. Clean up/Disposal

Garbage disposer 59-70 8 67 4

Dishwasher 66-74 4 69 3

2. PERSONAL HYGIENE AND

GROOMING

Hair dryer/blower 47-61 6 55 6

Electric razor 44-71 12 58 8

3. CLIMATE AND ENVIRONMENTAL CONTROL

Exhaust fan 46-63 4 58 8

Central heat 32-51 5 47 10

Room air conditioner 59-66 3 64 4

Humidifier 41-64 3 52 12

Electric fan 45-66 12 54 7

Heater 41-53 10 47 4

4. INDOOR MAINTENANCE

Vacuum cleaner

Rug cleaner/floor 64-85 12 73 6

waxer 65-67 — — —

5. CRAFTS, HOBBIES, NONWORKSHOP TOOLS

Sewing machine 66-72 3 69 3

Electric typewriter-' 65-74

21
Range of Number Mean Standard

A-Weighted of A-Weighted Deviation

Levels Products Level of

(re 20 MPa) Tested (re 20 uPa) Levels

Appliances (dB) (dB) (dB)

6. LAUNDRY EQUIPMENT

Washing Machine 55-75

Clothes dryer 52-68 7 59 6

7. LAWN AND GARDEN CARE

AND MAINTENANCE

Powered lawn mowers 80-106 - - -

8. HOME WORKSHOP TOOLS

a. Cutting

Sabre saw 84-90 2 87 4

Circular saw 89-102 2 96 9 Table saw 72-91 3 79 11

b. Shaping

Router 80-87 2 84 5

Lathe

Shapers-'

72-73 2 73 1

85-90 5 88 3

c. Drilling

Drill press

Portable electric

78-86 2 83 6

drill 77-86 3 82 5

d. Sanding /Planing

Belt sander

Sander/polisher-'

Disc sander-'

85-88 2 87 2

80-82

93 — —

e. Sharpening

Grinder 84-91 3 91 7

f

.

Pumping 73-93 — —

8- Power Generating

Motors 79-102 —

Si / - Measurements made at the operator's ear.

No measurement distance given.

22
Table 4. Saturation data currently available on household consumer products.

Product Saturation Data Percentage of

Available Wired Homes with

FOOD PREPARATION/DISPOSAL

Blender Yes [11] 45

Electric mixer Yes [11]

Coffee grinder No

Ice crusher No

Electric knife Yes [11] 41

Slicer No

Can opener/knife sharpener Yes [11] 55

Garbage disposer Yes [11] 39

Dishwasher Yes [11] 38

Trash compactor Yes [11] 2

PERSONAL HYGIENE/GROOMING

Oral hygiene device Yes [11] 14

Hair dryer/blower Yes [11] 47

Electric razor Yes [12] 40

Electric hair clipper No

CLIMATE/ENVIRONMENTAL CONTROL

Exhaust fan, range hood No

Central heat/air conditioning No

Room air conditioner Yes [11] 53

Humidifier Yes [12] 7 Dehumidifier Yes [12] 7 Electric fan No

Electronic air cleaner No

Heater No

Space heater No

INDOOR MAINTENANCE

Vacuum cleaner Yes [11] 99

Rug cleaner/floor waxer No

Floor scrubber No

CRAFTS, HOBBIES, NON-WORKSHOP TOOLS

Sewing machine Yes [12] 50

Electric typewriter No

LAUNDRY EQUIPMENT

Washing machine Yes [11] 70

Clothes dryer Yes [11] 58

LAWN AND GARDEN CARE AND MAINTENANCE

Powered lawn mower Yes [12] 59

Edger/trimmer/grass shears Yes [12] 8

Chain saw Yes [13] 3

Incinerator No

Lawn t hatcher No

Weed eater No • Leaf blower No

Leaf sweeper/mulcher/shredder No

Rotor tiller No

23
Table 4. (cont.)

Snow blower

Hedge trimmer

Garden tractor

HOME WORKSHOP TOOLS

Power shears

Sabre saw Pneumatic power chisel

Circular saw Abrasive cut-off machine

Radial arm saw Table saw Power hacksaw

Scroll saw Band saw Reciprocating saw Motorized mitre box

Router

Lathe

Shaper

Drill press

Portable electric drill

Impact drill

Pneumatic air drill

Nail gun

Electric staple gun

Pneumatic chipping hammer

Belt sander

Pneumatic sander

Vibration sander

Sander/polisher

Disc sander

Planer/ jointer

Sand blaster

Grinder

Saw blade sharpener

Drill bit sharpener

Impact wrench

Molder

Air compressor

Power generator

Motor

Shop vacuum Chipper cleaner

Paint sprayer

Spray gun

No

No

No

Yes [12] 20

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

24
3. NOISE ISOLATION

In addition to having noise emission data on the source in order to assess

the noise exposure at each potential listener location, it is necessary to

obtain information on the degree of noise isolation between the source and

each listener location. The methods used to predict or estimate the noise

exposure for various listener locations are described in the following section.

3.1. PREDICTION OF SOUND LEVELS AT LISTENER LOCATIONS

The spatially-averaged mean-square sound pressure in a room is, within the

limitations of certain simplifying assumptions, proportional to the total

sound power entering the room and inversely proportional to the total sound

absorption in the room. In a typical dwelling this acoustic power can be

generated by sources: (1) within a room, (2) elsewhere in the building, or

(3) exterior to the building. Therefore, to predict sound levels at potential listener locations, each of these three conditions must be examined.

When a source is located in the same room as a listener the sound energy

from the source is largely confined by the boundaries of the room. If

there were no absorption by walls and other objects the sound energy density in the room theoretically would increase indefinitely so long as the

source were operated. However, since some energy gets absorbed by surfaces such as walls, carpets, draperies, etc. , the sound pressure in the room builds up only to the point where as much energy (per unit time) gets

absorbed by these boundaries as is being supplied by the source (per unit

time). The more absorption there is in a room, the lower the build-up.

In the region close to the source and for a source away from reflecting

walls or floors, the sound behaves as it would outdoors. This occurs when

the boundaries of a room are far enough from the source so that they do not

influence the local sound behavior. In such (direct-field) cases the sound

pressure level for a listener located near the source depends upon the

power output of the source, the directivity of the source, and the distance

of the observer from the acoustic center of the source (it decreases at a rate of 6 dB per doubling of distance for an idealized source).

In the reverberant field the sound pressure level is no longer determined

by the direct field; rather it is determined by the sound power emitted by

the source and the amount of absorptive material in the room. In the rever- berant field, the sound pressure level is more or less the same everywhere

and can be computed using the following equation:

SPL = PWL - 10 log A + 6, (3.1) O where SPL = space-averaged sound pressure level, re 20 MPa, in dB

,

o

o A = total absorption in the room, in m , and

PWL = sound power level of the source, re 1 pW, in dB.

25
In the case where a noise source is located in one room and the listener

is in another room, the sound pressure level in the listener room is

given by:

SPL

r

= SPLg - NR , (3.2)

where SPLR

= space-averaged sound pressure level in the receiving room

(i.e., room where listener is located), re 20 pPa, in dB,

SPLg = space-averaged sound pressure level in the source room, re 20 pPa, in dB,

NR = noise reduction, or noise isolation, between the two rooms for each specified frequency band, in dB.

In many instances the actual noise reduction between two rooms is unknown

and must be computed from transmission loss data, usually obtained from

laboratory measurements performed according to a procedure standardized

by the American Society for Testing and Materials [13] . The transmission

loss of a material is defined by the following relationship:

TL 10 (3.3)

where TL = transmission loss of the partition in a specified frequency

band, in dB,

t = sound transmission coefficient of the wall in the same frequency band (fraction of the incident power that is

transmitted through the wall).

To estimate the noise reduction from a knowledge of the transmission loss

tEe following equation is used:

S

NR = TL - 10 log - ^

where NR = noise reduction in a specified frequency band, in dB

TL = transmission loss of the particular partition in the same frequency band, in dB,

2

S = surface area of the wall, in m , A = total absorption in the receiving room in the same frequency

band, in m^.

26
When a partition is composed of several elements, such as wall, windows

doors and cracks, each element provides a parallel pathway through which

sound can be transmitted. The overall transmission loss of such an assembly may be computed from a knowledge of the TL associated with each

element and its area. This computation is carried out in terms of the

transmission coefficient, x, of each element, which itself is obtained

according to the following equation:

-TL./ 10

t ± = 10 , (3.5)

where x^ = transmission coefficient in a specified frequency band

for the i-th element,

TL^ = corresponding transmission loss.

Once the transmission coefficient of each element has been computed, the

transmission coefficient of the whole assembly, x , can be computed using

the following expression:

whe re

and so

x S + t S + X S +.

1 1 2 2 3 3

t S n n To =

S-l + S

2 + S 3 +. n

= transmission coefficient of the first element,

2 S = surface area of the first element, in m , 1

x = transmission coefficient of the second element,

2

2 S = surface area of the second element, in m , 2

forth.

(3.6)

Having determined the transmission coefficient of the whole assembly, the

transmission loss (TL

q

) of the whole assembly is computed according to the

following:

TL

o = 10 1ogi , (3.7)

where x

Q = transmission coefficient of the whole assembly.

The principles involved in computing the sound pressure level in a listening room resulting from a source located outdoors are basically similar.

However, in the absence of significant excess attenuation (i.e., for a source close by), the sound pressure level from an exterior point source

is assumed to fall off by 6 decibels per doubling of the distance from

the source until the sound wave impinges upon the exterior wall. The

27
average sound pressure level in a room having an exterior wall on which

sound is incident is given by:

SPLr

= PWL - 20 log r - LR - 8 , (3.8)

where SPLR and PWL are as above,

r = distance from outside source to the center of receiving room, m,

and

LR = level reduction due to structure, in dB

.

The level reduction, in a specified frequency band, is the decrease in

sound pressure level due to the building shell. Note that the level re- duction is a function of the direction from which the incident wave impinges upon the partition. The maximum transmission loss occurs for a nor- mally incident sound wave and decreases significantly as the sound

approaches grazing incidence.

3.2. CHARACTERISTICS OF TYPICAL DWELLINGS

As mentioned previously, the sound pressure level to which a listener is

exposed when indoors may arise from a source located in the same room or from a source located either in another room or outdoors. In the latter

two cases it is useful to have a general idea of typical construction

styles in the United States. From this information, the noise isolation

between rooms located within a dwelling, between dwellings within a multifamily building, or outdoor-to-indoor noise isolation may be estimated.

Figures 1 through 4 show floor plans of four different types of dwellings —

a single family residence, a townhouse, a low-rise apartment unit, and a high-rise apartment unit. These floor plans originally appeared in studies carried out by Hittman Associates [14,15] for the Department of Housing

and Urban Development to determine residential energy consumption patterns

in the Baltimore/Washington area.

Because of the wide variety of combinations of style, size, and construction materials that occurs in actual buildings, it was necessary in that

study to reduce various residential types into a finite number of general

plans which are purported to represent "typical" design for each type of

dwelling. In performing this reduction, the data base used by Hittman

Associates was supplemented by data obtained from the U. S. Census Bureau,

trade associations, and industrial sources. Additional information was derived from a survey of building and development companies in the

Balt imo re-Washington area, as well as field observation of completed dwellings and dwellings under construction throughout the study area [14,15]

.

28
The floor plans presented In the Hittman studies may or may not be "representative" of "typical" current architectural trends throughout the United

States. Moreover, "typical" design does not imply that any one of the plans

presented therein would be found in an actual dwelling but, rather, that

actual dwelling units would be variations of the general characteristics

shown in the floor plans.

There are numerous reports (e.g., see [16-18]) in the literature that provide data' on the sound transmission loss of walls, floor-ceiling assemblies,

and windows. However, these data must be used with great caution in attempting to infer the noise reduction between rooms and the level reduction due to

the exterior shell of a dwelling. Very few data are available on noise or level reduction, including the effects of openings and common construction

practices. In the sample calculations of Appendix D, fictitious but not

unreasonable values were assumed for the noise reductions between spaces.

3.3. INFORMATION GAPS AND RESEARCH REQUIREMENTS

The procedures described in Section 3.1 for determining the sound pressure

level at listener locations from noise emission data are based upon "classical" theory presented in textbooks. Recently Schultz [9] has questioned the

applicability of the assumptions inherent in classical theory and has stated

that in fact "most kinds of equipment that will be considered for labelling

are large enough that within the direct field they are point sources, the

attenuation with distance is more like 3 dB than 6 dB per distance doubled.

Moreover, at distances far from the source, real rooms do not behave like the

classical reverberant rooms of theoretical acoustics, but more like lined

ducts." These observations suggest that there is a need to determine the

acoustic behavior of real and furnished rooms, of the types found in dwellings. It would appear that conventional room acoustics theory may not be adequate for determining the sound pressure level at a listener's ear.

Another research need is the validation of the assumption that most dwellings

within the United States fall within one of the "typical" floor plans shown

above since these floor plans may be representative only of middle class

dwellings along the eastern seaboard. Finally, reliable data must be obtained

on noise isolation within and between dwellings and on level reduction from

outside to inside a dwelling. Such data are needed for representative dwellings selected in different climates throughout the country.

29
GO

00

QOo

GO

ce

30

Figure

1.

Floor

plan

of

characteristic

single

family

home

[15].
A

Figure

'i ,

A A A A A lA~ \A~

(a) UNIT ARRANGEMENT

FIRST FLOOR

(b) UNIT FLOOR PLAN

. Floor plan of characteristic townhouse [16]

31
(b) UNIT FLOOR PLAN

Figure 3. Floor plan of characteristic low-rise apartment [16].
(a) UNIT ARRANGEMENT

1-BEDROOM

(b) UNIT FLOOR PLANS

Figure 4. Floor plan of characteristic high-rise apartment [16].

33
4. DESCRIPTION OF EXPOSED POPULATIONS

Once information has been compiled on the noise emission characteristics and

market saturation of a consumer product and sound levels have been predicted

at the listener location(s), it is necessary to have further information in

order to predict the impact of the product on the population. Exposure patterns of sub-groups of the population, for each product class, and the number

of people potentially in each sub-group must be determined. This section dis

cusses the type of available information on population groupings and product

utilization patterns.

4.1. POPULATION SUB-CROUPS

For the purpose of this study, the population of the United States was classified into six categories based upon time utilization. These categories are:

o adult male employed outside the home,

o unemployed adult male or adult male working at home,

o adult female employed outside the home,

o unemployed adult female or adult female working at home,

o school-age or day care child,

o preschool child at home.

These particular groups were chosen because, in terms of time utilization,

they each show their own distinct patterns. (The model could easily be

modified to utilize other groupings than these and other time periods than

those described below.

4.2. TIME UTILIZATION OF POTENTIALLY EXPOSED POPULATION GROUPS

A detailed break-down of the time utilization of potentially exposed population groups, in terms of consumer product usage, was not found in the literature. This is not merely our conclusion, but that reached by other authors

in a report to the Environmental Protection Agency [12]

There is information in the literature as to how certain groups of persons

divide their time among certain broad categories of activities (some of these

data are presented in Tables 5-7 along with references as to their origin).

Based on the data contained in these tables, certain behavioral patterns may

be inferred.

A limited amount of data was found in the literature for some groups of people

which show either when people are at home and during what hours (see Fig. 5 [19]) or for how many hours/day they are at home (see Table 8 [20]). This

34
information was used to examine time utilization patterns for some population

groups. However, the data do not cover all of the potentially exposed population groups in sufficient detail for the purposes of this study.*

Another type of time utilization datum Which may aid in making inferences

about persons, other than the primary operator, who are exposed to household

product noise, is the distribution of time for household obligations for certain categories of people as a function of their social contacts. The distribution for four different groups of adults is shown in Table 9, where the

results of different surveys [19] are summarized.

4.3 PRODUCT UTILIZATION

The information available on product utilization is, at best, scarce. Table

10 gives a list of the household products under consideration in this study,

and identifies those for which usage information is available. The primary

exposure for each appliance is given in Table 11. This table was, for the

most part, obtained from a study conducted by Bolt Beranek and Newman in 1976

for the U. S. Environmental Protection Agency [12].

The major problem in developing data on exposure time is the scarity of usage

information and the fact that which is available is usually incomplete. For

example, one study states that a vacuum cleaner is used an average of 2-3

times per week in the home [20] . However, no data were found on the average

amount of time the vacuum cleaner is operated when in use. Likewise, for a clothes washer, several sources [21,22] state that 8-10 loads/week/home is the

average number of loads washed. The average time of the entire wash cycle can

be calculated. Yet, from this information there is no way to determine how

long the operator is in the same room with the machine and thus primarily

exposed to its noise and how long the person is subjected to secondary exposure. Likewise, since there are no data regarding the time of day when a particular appliance is likely to be used, it is virtually impossible to estimate

how many people may be subjected to its noise as bystanders (e.g., in another

room or in an adjacent dwelling unit), what activities they may be engaged in

during the time the product is operating (sleep, speech, watching TV, etc.),

and for how long. Such information is required, however, to assess the overall impact of noise from consumer products.

4.4 INFORMATION GAPS

In summary, information gaps exist in both the time and product utilization

areas. With regard to time utilization, the main problems are that the data

* Subsequent to writing this report, another publication was located on time utilization of Americans. In this report a survey method was used

that is similar to that used and reported earlier in Ref. 2. Further,

its results help to substantiate the results of the earlier study. The

results of the later study are summarized in Robinson, John P.,

How Americans Use Time (MacMillan, New York, New York, 1977).

35
do not cover all of the potentially exposed population groups and that the

activity categories considered are so broad that it is not possible to know,

in sufficient detail, how people actually use their time. Second, data for

computing exposure to appliance noise are, for all practical purposes, nonexistent. The unverified information available is generally not suitable;

consequently many assumptions must be relied upon.

36
Table 5. Percentage of employed men engaged in various activities at given

times during a 24-hour period. (Survey made in Jackson, Michigan,

in 1965 [19]).

Time

of Day

Percentage Engaged in

Sleep Eat Work Travel

Home and

Family Leisure

Mass

Media TV

00:00 73 0 11 5 1 4 1 5 01:00 79 2 9 1 1 3 0 5

02:00 88 0 5 0 0 6 0 1

03:00 90 2 6 0 0 1 1 0

04:00 92 0 6 0 0 2 0 0

05:00 91 0 7 0 0 2 0 0

06:00 68 7 9 1 1 12 1 1

07:00 38 9 23 12 4 12 1 1

08:00 14 14 45 8 5 12 1 1

09:00 13 5 70 1 2 6 2 1

10:00 10 3 69 1 7 8 0 2

11:00 5 3 71 2 8 7 2 2

12:00 4 19 55 5 6 7 2 2

13:00 5 11 60 6 7 7 2 2

14:00 6 5 70 3 5 7 1 3

15:00 3 4 71 7 6 6 1 2

16:00 1 1 60 12 8 13 3 2

17:00 2 7 46 13 7 15 7 3

18:00 2 18 29 10 10 12 10 9 19:00 2 12 23 10 11 12 12 18

20:00 3 7 20 6 10 17 12 25

21:00 6 3 21 3 9 18 10 30

22:00 12 1 22 7 3 15 7 33

23:00 38 0 15 3 3 10 5 26

37
Table 6. Percentage of employed women engaged in various activities at

given times during a 24-hour period. (Survey made in Jackson,

Michigan, in 1965 [19]).

Time

of Day

Percentage Engaged in

Sleep Eat Work Travel

Home and

Family Leisure Media TV

00:00 84 2 0 3 3 3 5 0

01:00 92 2 0 2 2 1 1 0

02:00 97 1 0 0 1 0 1 0

03:00 97 1 0 0 1 0 1 0

04:00 95 1 2 0 2 0 0 0

05:00 92 1 1 0 1 3 1 1

06:00 59 3 3 0 4 29 2 0

07:00 30 7 25 0 20 18 0 0

08:00 10 8 32 21 12 15 0 2

09:00 7 2 63 5 12 9 1 1

10:00 4 0 69 6 10 9 1 1

11:00 3 1 72 9 4 7 2 2

12:00 2 26 49 5 9 6 2 1

13:00 2 10 60 9 12 5 1 1

14:00 2 8 62 7 12 4 0 5

15:00 2 1 68 9 12 5 1 2

16:00 2 0 51 12 18 13 2 2

17:00 0 4 36 13 23 18 3 3

18:00 1 17 13 16 35 11 5 2

19:00 1 17 10 6 26 18 12 10

20:00 2 5 10 7 32 16 16 12

21:00 3 0 7 9 32 24 8 17

22:00 16 2 3 8 16 24 12 19

23:00 40 0 1 8 10 20 11 10

38
Table 7 . Percentage of housewives engaged in various activities at given

times during a 24-hour period. (Survey made in Jackson,

Michigan, in 1965 [19]).

Time

of Day

Percentage Engaged in

Sleep Eat Wo rk Travel

Home and

Family Leisure

Mass

Media TV

00:00 85 0

— 0 4 4 3 4

01:00 91 0 - 0 1 4 2 2

02:00 96 0

- 0 1 1 0 2

03:00 96 0 - 0 1 2 0 1

04:00 95 0

- 0 3 2 0 0

05:00 88 0

- 0 9 2 0 1

06:00 71 4 - 0 15 8 1 1

07:00 55 5 - 0 28 10 1 1

08:00 27 11 - 2 34 23 2 1

09:00 10 9 - 2 60 15 2 2

10:00 2 3 - 5 58 23 7 2

11:00 0 2 - 7 62 20 8 1

12:00 0 13 - 8 58 14 5 2

13:00 2 11 - 8 48 21 3 7 14:00 5 5 - 5 53 14 7 11

15:00 5 3 - 10 48 22 4 8

16:00 1 4 - 9 47 23 9 7 17:00 2 9 - 8 58 9 10 4

18:00 0 17 - 8 46 11 7 11

19:00 0 11 - 8 42 12 10 17

20:00 0 4 - 9 30 24 12 21

21:00 2 2 - 5 26 25 10 30

22:00 10 2 - 5 15 28 9 31

23:00 43 3 0 8 14 4 28

39
Table 8. Percentage of time children spend at home by mother's

employment [20]

.

Hours Spent Mother Employed Mother Not Employed

Children aged

6-11 yrs.

Children aged

12-17 yrs.

Children aged

6-11 yrs.

Children aged

12-17 yrs.

16-24 79% 58% 84% 64%

7-15 20% 40% 16% 32%

<7 1% 2% — 4%

40
Table 9. Distribution of time spent on household obligations, according

to different social contacts [19]

.

Percentage distribution of daily time

Forty-four Jackson,

Cities, U.S.A. Michigan

A. Single men

total minutes = 100% 52.1 min 62.0 min

alone 43.0% 43.3%

with spouse (fiancee) 2.7 1.0

with other members of households 13.6 21.0

with friends and neigh bo rds 16.7 24.2

with work colleagues 0.8 0.5

with others 23.2 10.0

B. Married men

total minutes = 100% 68.8 min 79.2 min

alone 36.5% 43 . 3%

with spouse 12.2 11.4

with other members of household 33.1 23.2

with friends and neighbors 9.7 7.1

with work colleagues 0.0 0.1

with others 8.5 14.9

C. Employed married women

total minutes = 100% 223.9 min 216.5 min

alone 59.0% 49.6%

with spouse 9.6 9.5

with other members of household 25.1 27.1

with friends and neighbors 5.8 8.5

with work colleagues 0.2 0.9

with others 0.3 4.4

D. Married housewives

total minutes = 100% 358.1 min 350.3 min

alone 55.2% 55.3%

with spouse 6.8 5.7

with other members of household 26.8 29.0

with friends and neighbors 6.4 6.9

with others 4.8 3.1

cL / Data are weighted to ensure equality of days of the week and number of

eligible respondents per household.

The data may total more than 100% because of overlapping categories in case of simultaneous presence of several family members.

41
Table 10. Usage data currently available on household and consumer products.

Product Usage Data Available

FOOD PREPARATION/DISPOSAL

Blender Yes [13]

Electric mixer Yes [13]

Coffee grinder Yes [13]

Ice crusher No

Electric knife Yes [13]

Slicer No

Can opener/knife sharpener Yes [13]

Garbage disposer Yes [13]

Dishwasher Yes [12]

Trash compactor No

PERSONAL HYGIENE/GROOMING

Oral hygiene device Yes [13]

Hair dryer/blower Yes [13]

Electric razor Yes [13]

Electric hair clipper No

CLIMATE/ENVIRONMENTAL CONTROL

Exhaust fan, range hood Yes [13]

Central heat/air conditioning No

Room air conditioner Yes [13]

Humidifier Yes [13]

Dehumidifier Yes [13]

Electric fan Yes [13]

Electric air cleaner No

Heater No

Space heater Yes [13]

INDOOR MAINTENANCE

Vacuum cleaner Yes [13]

Rug cleaner/floor waxer Yes [22]

Floor scrubber Yes [22]

CRAFTS, HOBBIES, NON-WORKSHOP TOOLS

Sewing machine Yes [13]

Electric typewriter No

LAUNDRY EQUIPMENT

Washing machine Yes [13]

Clothes dryer Yes [13]

LAWN AND GARDEN CARE AND MAINTENANCE

Powered lawn mower Yes [13]

Edger/trimmer/grass shears Yes [13]

Chain saw Yes [13]

Incinerator No

Lawn thatcher No

Weed eater No

Leaf blower Yes [13]

Leaf sweeper/mulcher/shredder Yes [13]

Rotor tiller Yes [13]

42
Product Usage Data Available

Snow blower Yes [13]

Hedge trimmer Yes [13]

Garden tractor No

HOME WORKSHOP TOOLS

Power shears No

Sabre saw Yes [13]

Pneumatic power chisel No

Circular saw Yes [13]

Abrasive cut-off machine No

Radial arm saw Yes [13]

Table saw No

Power hacksaw No

Scroll saw No

Band saw No

Reciprocating saw . No

Motorized mitre box No

Router No

Lathe No

Shapers No

Drill press No

Portable electric drill Yes [13]

Impact drill No

Pneumatic air drill No

Nail gun No

Electric staple gun ' No

Pneumatic chipping hammer No

Belt sander No

Pneumatic sander No

Vibration sander No

Sander/polisher No

Disc sander Yes [13]

Planer/ jointer No

Sand blaster No

Grinder No

Saw blade sharpener No

Drill bit sharpener No

Impact wrench No

Molder No

Air compressor No

Power generator Yes [13]

Motor No

Shop vacuum No

Chipper cleaner No

Paint sprayer No

Spray gun No

43
Table 11. Presumed or potential primary exposure (in hours) for a variety of consumer products. [12]

Appliances Presumed or Potential

Primary Exposure

(Hours of Use per day)

1. FOOD PREPARATION, DISPOSAL, STORAGE

a. Mixing, straining, crushing

Blender 0.25

Food mixer 0.25

Coffee grinder 0.25

b. Cutting, slicing

Electric knife 0.1

c. Opening

Electric can opener 0.02

d. Clean up/disposal

Garbage disposer 0.1

Dishwasher 0.7

2. PERSONAL HYGIENE/GROOMING

Hair dryers/blowers 0.3

Electric razor 0.1

Oral hygiene devices

Electric toothbrush 0.1

Dental irrigator 0.5

3. CLIMATE AND ENVIRONMENTAL CONTROL

Exhaust fan 1.5

Room air conditioner 6.0

Humidifier 6.0

Dehumidifier 6.0

Electric window and floor fans 12.0

Space heater 6.0

4. INDOOR MAINTENANCE

Vacuum cleaner 0.2

Rug cleaner/floor waxer —

Floor scrubber —

5. CRAFTS, HOBBIES, NON-WORKSHOP TOOLS

Sewing machine 0.5

6 . LAUNDRY EQUIPMENT

Clothes washer 1.0

Clothes dryer 1.0

7 . LAWN AND GARDEN CARE AND MAINTENANCE

Electric lawn mower 0.1

Gasoline-powered lawn mower 0.1

Riding mower 0.1

Electric edger/t rimmer 0.05

44
Lawn edger 0.05

Chain saw 0.05

Leaf blower 0.05

Shredder 0.05

Rotor tiller 0.05

Snow blower 0.02

Hedge clippers 0.05

8. HOME WORKSHOP TOOLS

a. Cutting

Sabre saw 0.02

Circular saw 0.02

Radial saw 0.02

b. Drilling

Portable drill 0.02

c. Sanding

Sander 0.02

d. Power generating

Generator 0.25

45
% ‘3W0H IV 31d03d

46

Figure

5.

Percentage

of

people

being

at

home

during

various

periods

throughout

the

day

[19j

.
5. CRITERIA USED TO ASSESS NOISE IMPACT FROM CONSUMER PRODUCTS ON PUBLIC

HEALTH AND WELFARE

People react to noise in a complex manner which depends both on the physical

nature of the noise and on less well-defined psychological and sociological

factors. The problem is further complicated by the fact that, for any given

socio-economic level, each individual may react differently to the same noise

environment. For this reason what are sought, and have been for many years, are "single-figure ratings" of the noise environment which can be used to

predict, with a reasonable degree of certainty, the average response of groups

of people. This is not to say that individuals have the same susceptibility

to noise; they do not. Even different groups of people may vary in response,

depending upon previous exposure, age, socio-economic status, political cohesiveness, and other psycho-social variables. However, at the present time the

state-of-the-art is such that these individual differences cannot be predicted a priori.

Once the "average response" is known, a "population impact" can be derived

from a knowledge of the number of people subjected to various noise exposures.

By population impact is meant some metric that includes considerations of how

many people are impacted and how severely.

In order to compare the effects on the public health and welfare of noise

emission from various products or the consequences of regulatory actions, it

is desirable to derive a single number descriptor of the impact of the noise

relative to each effect of concern. In doing so, certain assumptions are usually made which are often not explicit in spite of the fact that they may

have significant implications. Usually the assumptions behind such analyses

are:

* o that the intensity of the human response to the physical characteristics of- the noise is a function of the sound pressure level and the

time of exposure, whether this response is expressed in terms of

hearing loss, speech interference, or the general adverse response.

o that the impact of high noise levels on a small number of individuals

can be statistically treated as being "equivalent" to the impact of

moderate noise levels on a large number of people. Accordingly, the

Accordingly, the properties of extent and intensity can be combined

through some mathematical function.

Once such a function is defined, a "partial" impact can be assigned to each

segment of the population subjected to a specific yearly exposure level.

Once the various degrees of impact have been identified, the total impact may

be taken as the summation of these partial impacts. Specifically, a "Weighted

Population," WP(k), is defined as

N

WP(k). £ W

1 (k)P

l , (5.1)

i

47
where

= the sub-population subjected to the i-th level of yearly

exposure,

i W^(k) = a weighting factor derived from a function describing the

cause-effect relationship for the k-th effect (e.g., hearing

loss, speech interference, etc.) for the i-th level of

yearly exposure,

and the summation is taken over the N sub-populations potentially exposed to

the noise emission from various classes of products.

Implicit in such analyses is the assumption that, for instance, a 10 dB threshold shift in X people is equivalent to a 20 dB hearing threshold shift in

only Y people, where Y is significantly smaller than X. However, the individual effect of a hearing threshold shift may be quite different, depending on the original threshold of the person experiencing the shift. Thus a 10 dB

shift occurring in a person who has already incurred a severe loss may be

devastating, while a 10 dB shift occurring in a person who has "good" hearing

may be barely noticeable.

As additional data on the relative impacts on public health and welfare of

various noise exposures become available, it may be necessary to refine the

various relationships used to combine the properties of intensity and extent.

The impact of product noise upon the public health and welfare can only be

assessed with respect to those responses for which cause-effect relationships

are relatively well-defined. Accordingly, in this report, attention is

directed to the following human responses:

i o physiological response, which in study is treated as synonymous with

hearing damage (owing to lack of well-defined criteria regarding

extra-auditory physiological effects),

o the general adverse response to noise, often referred to as annoyance,

which itself may result from a combination of several factors including

speech interference, sleep interference, desire for a quiet environment, and the ability to use TV, telephone and radio satisfactorily,

o speech communication;

o sleep interference.

In the model used in the present report, the Weighted Population, WP(k), as defined in Eq. (5.1), is computed using several cause-effect relationships for

the responses listed above. In addition, in order to compare the relative

impact of one noise exposure to another, it is convenient to define the Noise

Impact Index, NII(k), an average degree of impact for the defined population

group, defined mathematically as:

48
NII(k) , WP(k) (5.2)

PR

where

PR * the unweighted population at risk, i.e., the number of people that

could potentially be exposed to the noise emitted by a particular class

of products.

Several weighting factors, W^(k) are available. These are described below.

5.1 CRITERIA FOR HEARING DAMAGE RISK

The EPA Levels Document [23] identifies an A-weighted equivalent level (LEQ)

of 70 dB averaged over a 24-hour period on a long-term basis as the level

below which no risk to hearing exists for virtually the entire population.*

This level represents a yearly average level. Above this level a potential

risk to hearing could exist. This risk is normally expressed in terms of

noise-induced permanent threshold shift (NIPTS) that is predicted for various

segments of the population over an exposure of 40 years.

Generally speaking, the risk to hearing accelerates rapidly as the noise

exposure increases above an LEQ level of 70 dB (the EPA criterion level).

Table C-l of the EPA Levels Document shows the change in hearing threshold

that may be expected for an average of four audiometric frequencies (.5, 1,

2, and 4 kHz) and* at 4 kHz as a function of exposure level for various segments of the population. These data are presented in Figure 6 in terms of

the average over the' -'total population for 4 kHz and for the average of the

4 audiometric frequncles*

The EPA Levels Documents thus provides a useful measure for assessing hearing

risk for noise exposures that are above the identified criterion level. However, inherent in the EPA criterion is the assumption that the A-weighted

sound pressure level provides an adequate means of assessing noise impact

with respect to its potential to produde hearing damage. Although the Aweighted level is widely accepted as an adequate ^ay* of -weighting noise spectra with respect to' hearing damage, most of the data.upon which various

damage risk criteria rest are based upon studies of populations exposed to

industrial noises. , .

Industrial noises by and large tend to have- spectra that display little

acoustic energy in the high frequency region. While many consumer products,

such as lawn mowers and clothes washers, have spectra similar to industrial

spectra, many others do not. For example, electric shavers, blenders and

some other products have significant amounts of energy in the 2-4 kHz region

where the ear is most sensitive.

* For consistency the notation "LEQ" rather than the more common "L " is

used in this report since- the computer printer cannot handle lower4 case letters.

49
15

I-Z

LU

70 75 80 85

24 - HOUR A - WEIGHTED EQUIVALENT

SOUND LEVEL, dB

Figure 6. Average noise-induced permanent threshold shift expected as a function of the continuous A-weighted equivalent sound level. The data

points are from the ELPA Levels Document [23] . The lower curve cor- responds to Eq. (5.4) while the upper curve is for Eq . (5.5).

50
Miller [24] reports that studies of temporary threshold shift (TTS) show that

noises with concentrations of energy in the 2-4 kHz region produce greater

TTS than noises concentrated elsewhere in the audible range. He also notes

that people differ in their susceptibility to TTS and that these susceptibilities are not uniform across the audio frequency range. Thus, some people

are especially sensitive to high frequencies while others are especially sen- ensitive to low frequencies. Although there is some controversy regarding

the ability of TTS to predict NIPTS, the possibility does exist that "measurements on the A-weighting scale may underestimate hazard to hearing” [25] for

those noises. that either contain a large amount of energy in the high frequency

region or else have strong tonal components. For this reason, the possibility

should be considered that the A-weighted level may not be sensitive enough to

assess the risk to hearing associated with those products that either contain

a significant amount of energy in the high frequency region or else have strong

tonal components.

Until additional research data become available, however, there is no choice

but to base the analyses developed in this study on an A-weighted level, provided it is recognized that the risk to hearing may be underestimated in cer- tain instances.

Also inherent in the EPA criterion is the use of the "equal energy" concept,

wherein doubling the time of exposure at a given sound level is equivalent to

holding the time of exposure constant but increasing the sound level by 3 dB.

There is ongoing controversy over the best way to account for time-varying

noise exposure. If a different procedure were selected, the impact assessment model could be modified accordingly.

In this report hearing damage risk due to noise from consumer products is

evaluated in three ways, each using as a basis the criterion level and a method derived from the information contained in the EPA Levels Document.

5.1.1. Fractional Exposure

To account for the fact that most noise exposures are not steady but vary

with time, the Levels Document recommended that hearing damage risk be evaluated in terms of the whole time-varying pattern of sound levels. Accordingly, in the EPA document, an equivalent sound level (LEQ) was defined and

used to arrive at the criterion level which assesses hearing damage risk from

environmental noise.

The level identified by the Environmental Protection Agency, when combined

with the equal energy hypothesis which states that equal amounts of acoustic

energy will cause equal amounts of noise-induced hearing damage, provides a convenient way of comparing the exposures of people to different noise levels

and durations. This is done by comparing the exposure time due to the operation of a given product to the allowable safe exposure of an individual exposed to a steady state noise for 24 hours at the criterion level. (For an example of the use of a similar concept, see Reference 26.) The reference

level is an A-weighted level of 70 dB; thus an exposure to a steady level of

70 dB for 24 hours would have a fractional exposure of 1.0.

51
Similarly an exposure to a level of 73 dB for 12 hours would yield a fractional exposure of 1.0, as would an exposure lasting 6 hours at 76 dB. What

this means practically is that an exposure of 6 hours to a level of 76 dB uses up 100 percent of the daily allowable dose, as would an exposure of 12

hours to 73 dB.

Since the criterion level identified by EPA represents the safe level of

exposure and is computed on a yearly basis, the equivalent level for a product

must also be computed on a yearly basis and take into account the length of

each exposure and the number of exposures that occur during the whole year.

The weighting factor (see Eq. (5.1) and accompanying text) for computing the

fractional exposure is defined by:

W

± (l)

10 (LEQi - 70) /10

, LEQ.^ < 70 dB

1 , LEQ

1 > 70 dB

(5.3)

where LEQ^ represents the yearly average level for the 1-th sub-population

due to the noise from the product of concern. The resulting value for W^(l)

is to be substituted into Eq. (5.1) in order to obtain the corresponding

weighted population.

Whenever the fractional exposure exceeds 1.0 a potential impact on hearing

exists since 100 percent of the daily dose has been used. However, when a fractional exposure is less than 1.0 it may not have a direct impact upon

hearing but a certain amount of the allowable daily dose is consumed, there

by decreasing the remaining amount of exposure allowed for the rest of the

day. A combination of exposures, each less than 1.0, may result in a com- bined equivalent sound level greater than 70 dB and thus present a hearing

damage risk (see Section 5.1.2).

For example, suppose a person operates a home tool that produces an Aweighted level of 90 dB at the ear of the operator for 2 hours a day, 25

days per year. Then, the yearly equivalent level for the tool alone is

given by:

LEQ = 90 + 10 log 2 X --Hc - 67.6 dB.

24 x 365

A priori the tool would appear to be safe. However, another way to look at

the risk involved is to consider, from Eq. (5.3), that use of this tool for

only 2 hours a day, 25 days a year, consumes 57% of the allowable daily dose on an energy basis leaving, therefore, little room for other exposures.

An alternative way to approach this problem is to use the concept of an equivalent daily exposure time (cf. Reference 26), and multiply W^(l), as defined

above, by 1440, the number of minutes in a day. Thus in the example above,

the use of this product, as described, is equivalent to exposure to a sound

level of 70 dB for (0.57 x 1440 =) 821 minutes per day, on each day of the

year.

52
5.1.2. Potential Hearing Damage for Products That Exceed The Allowable

Exposure Time

For those products that produce high noise levels and are used for prolonged

periods of time, the fractional exposure time is likely to exceed unity —

that is the dose used by exposure to these products may exceed 100 per cent

and thus a risk of hearing damage may exist. In such cases, the potential

degradation in hearing acuity is to be assessed.

In the present impact assessment model, two different weighting functions are available to examine potential hearing damage. The first of these, corresponding to the lower curve in Figure 6, is the equation developed for this

purpose by the NAS-NRC Committee on Hearing, Acoustics, and Bioacoustics

(CHABA), Working Group 69 [27]. This weighting function represents the average threshold shift for 4 audiometric frequencies (i.e., 0.5, 1, 2, and 4 kHz).

It is the mean value for the entire exposed population. Accordingly, this

weighting function cannot be used to predict the threshold shift that would

occur at a particular frequency, in a particular individual and at a particular time.

In the nomenclature of the present report, the weighting function for the

average NIPTS for the 4 audiometric frequencies is given by the expression*:

( 0.025(LEQi - 70) 2

, LEQ± > 70 dB

Wi(2) = (5.4)

(0 , LEQ

i < 70 dB.

This weighting factor represents the average NIPTS over a 40-year period

for the i-th subpopulation. As in the previous case, and for the remaining

responses to be considered, the value derived from Eq. (5.4) is the weighting factor to be used in Eq. (5.1)

Since in the 4-kHz region the ear is very susceptible to noise-induced hearing loss, the hearing level at 4 kHz is an early indication of potential loss

of hearing acuity. Accordingly, another weighting factor has been derived,

based upon the data given in Table C-l of the EPA Levels Document [23] for the

average hearing loss at 4 kHz. These data are well approximated by the upper

curve in Figure 6, which corresponds to

l 0.6(LEQ. - 70) 1,2

, LEQ± > 70 dB

W

± (3) = (5.5)

(0 , LEQ

1 <70dB.

Again the weighting factor represents the expected average NIPTS (at 4 kHz)

over a 40-year period for the i-th subpopulation.

* In the CHABA document [29], the yearly average level is approximated by

the yearly day-night average sound level.

53
5.2. GENERAL ADVERSE RESPONSE

The EPA Levels Document [23] defines the general adverse response to noise as

a combination of responses to several factors such as speech interference,

sleep interference, the desire for a quiet environment, and the ability to use TV, telephone, and radio satisfactorily. General adverse response is

frequently expressed in terms of the percentage of the population that would

be expected to express a high degree of annoyance, in a social survey, as a result of a specific exposure level. Since people differ significantly in

their responses to noise, only the average responses for groups of people can

be considered.

As was shown in EPA documents [23-25] , the magnitude of this general adverse

response is stable and can be related to a cumulative measure of noise, such

as the A-weighted day-night average level (LDN) . For this reason this basic

metric is used in the present document to assess, in terms of the general

adverse response, noise impact to be expected from consumer products.

The Levels Document further specifies that for noise exposures not exceeding

a yearly LDN of 55 dB outdoors and 45 dB indoors, no significant effects

occur. These levels are used in the present report to define safe limits for

assessing the general adverse response. Above these levels noise becomes a factor that may lead to a generalized adverse response, the magnitude of which

must be assessed both in terms of its severity and extent (i.e., number of

people affected).

As noted in Section 3.1, in many instances people are located at different

places relative to a noise source. They may either be at different locations

within the source room, or in different rooms, or indoors relative to an outdoor source. Consequently, in computing the level at a listener’s position

it is necessary to account for either indoor-to-indoor isolation, or, for sources located outdoors, outdoor-to-indoor isolation.

Even within a given room the levels may vary from one location to another,

depending on whether the listener is in the near field or the reverberant

field of the source. Procedures for computing sound levels at listeners’

locations have been described in Section 3.1.

Thus for each source several levels of exposure must be computed, each corre- sponding to locations of listeners.

Recently EPA has used the fractional impact methodology in which the intensity of the general adverse response to a given noise exposure is expressed

in terms of the Fractional Impact [3, 28-30], which is analogous to the

weighting factors used in the present report; specifically:

( 0.05(LDN1 - 55), LDN, > 55 dB

Wi(6) =

1

(5.6)

' 0 , LD^ < 55 dB ,

54
where

LDN^ = the i-th (A-weighted) yearly day-night average sound level.

More recently, the functional relationship between general adverse response

and noise exposure has been further refined [27,31]. This function, arbitrarily normalized to unity at 75 dB for evaluation of population impact,

is shown in Figure 7. This weighting factor has been expressed [27] as:

Wi(4) = 3.36 x 10 6

/ 0. 03LDNi

\

0.2 ^10 ) +

/ 0.103LDNi\

V 10 /

/ 0.08LDN.

1.43 x 10~4 Uo

(5.7)

Since the function represented by Eq. (5.7) was derived from studies of outdoor environmental noise, for sources that are located outdoors the function

is used as given. For sources that are located indoors, it may be appropriate

for the function to be shifted by 15 decibels to account for the "typical"

outdoor-to-indoor noise isolation. This value is based upon current but scare knowledge regarding noise isolation; accordingly, as more data become available regarding outdoor-to-indoor noise isolation provided by various building

envelopes there may be a need to revise the value used in this report. It

follows, that in the above equation and for indoor sources the term LDN^ might

be replaced by the term (LDN^ + 15) such that the equation reads as follows:

Wi(5)

/ 0.103(LDN±+15)

3.36 x 10~6 \10

( 0.03(LDN-+15) \

0.2 V 10 / + 1.43 x 10"4

0.08(LDNi+15)

)

(5.8)

Other industrialized nations have also been concerned with the assessment of

the population impact of noise. Alexandre and Barde [32] have proposed sev- eral additional weighting factors. In the computer program it was decided to

incorporate three of these weighting factors, each expressed as a function of

the (A-weighted) yearly day-night average sound level.

The first of these, in the present nomenclature, is

W

± (7)

0. 01 [l0

(LDNi - 55)/10

-l] , LDNi > 55

, LDN

1 < 55

(5.9)

This weighting factor varies for 0 to 1 as LDN goes from 55 to 75 dB. In

this regard, the normalization is similar to that for Fractional Impact (i.e.,

Wj (6)). However, the growth of this weighting function parallels the growth

of acoustical energy (i.e., similar to the growth for W^(l)).

Another of Alexandre and Barde' s weighting functions can be expressed as:

55
WEIGHTING

FACTOR,

Wj(4)

3.0

DAY-NIGHT AVERAGE SOUND LEVEL, dB

Figure 7. Weighting function for assessing the general adverse response

to noise [27 , 31].

56
(5.10)

1

[ ^(LDNj - 55)/10 LDNj > 55

( 3 * J*

Wl(8) =

i 0 , LDN

i < 55 •

This function also is normalized to zero at 55 dB and unity at 75 dB. However, the function approximates the growth of loudness with increasing sound

level.

The third of Alexandre and Barde' s weighting functions included in the

present model is:

/ 0.0125(LDN

i - 55) x 2

(LDNi " 55)/1°

} LDN^ > 55

W.(9) = { (5.11)

( 0 , LD^ < 55 .

This function lies between that derived on the basis of energy growth (Wi (7))

and that based on loudness growth (W^(8)), hence, it is a compromise between

these two functions.

The three weighting factors proposed by Alexandre and Barde are shown in

Figure 8 as functions of day-night average level. Each of the relations

developed by Alexandre and Barde can be used with Eqs. (5.1) and (5.2) to

assess noise impact.

Although the day-night average level is probably a good predictor of general

adverse response to noise, it uses as a basic metric the A-weighted level.

Some questions have been raised as to the degree to which the A-weighted level

accounts for either tonal components or significant variation in their spectral shapes. Accordingly, in the course of the development of this report,

the question of the order in which different rating schemes would rank order

products having either similar and/or significantly varied spectral shapes was investigated. The results of these investigations are described in

Appendix C. Here, it is sufficient to say that:

(1) the A-, D-, and E- weighted levels agree well, both in terms of differentiating among products contained within a class and among products

belonging to different classes.

(2) the complex schemes examined, including several loudness indices and

perceived noise levels, do not appear to yield results that are very

different from those obtained using the simple A-, D- , or E-weighted

levels

.

* These conclusions should be viewed with caution because of the limited

quantity of data available and because it was not possible to conduct

meaningful statistical tests of significance.

57
WEIGHTING

FACTORS,

Wj

55 60 65 70 75

DAY-NIGHT AVERAGE SOUND LEVEL, dB

Figure 8. Weighting functions proposed by Alexandre and Barde [32].

58
For the sake of simplicity and consistency, the A-weighted level appears to

be a reasonable choice for assessing consumer products. However, this con- clusion is based on limited information and is therefore only tentative. For

that reason, and because the application of A-weighted LDN to consumer products is questionable, a number of weighting functions based upon loudness

or perceived noisiness have been incorporated into the model. These are sum- marized below. In computing the loudness or loudness level, in the impact

assessment model, the yearly equivalent sound level for each frequency band

of interest is computed and the appropriate frequency weighting is applied to

these yearly average levels.

At present, two standardized procedures and one proposed procedure are available for computing loudness and loudness level (auditory magnitude) [33-35].

The Federal Aviation Administration has established [36] a procedure for

computing perceived noisiness and perceived noise level (unwantedness/

annoyance due to sound). One can hypothesize that the corresponding weighting functions should grow in a manner that is proportional to the growth of

either loudness (noisiness) or of loudness level (perceived noise level).

The weighting functions, shown below and derived using these hypotheses, have

been arbitrarily normalized to unity for a sound having the same loudness

level, or perceived noise level, as would a band of noise centered at 1 kHz

presented at a sound pressure level, re 20 PPa, of 75 dB.

The weighting factors based upon these functions are:

Widl) =

!

(1/35)(LLMK6

1 - 40)

0

where

LLMK6

± > 40 dB

LLMK6

i < 40 dB

(5.12)

LLMK6^ = loudness level computed using the Stevens Mark VI

procedure [33] for the i-th sub-population;

! (LLMK6-. - 40) /10 x 2

{

11.31 , LLMK6. > 40 dB

0 LLMK6

i < 40 dB

where LLMK6^ is as above.

( iy(LLMK7 - 32)

W

± (13) = <

* 0

where

LLML7 > 32 dB

LLMK7

± < 32 dB

(5.13)

(5.14)

LLMK.7^ = loudness level computed using the Stevens Mark VII procedure

[34], for the i-th sub-population;

59
1

1 2 (LLMK7 - 32)/9 ,

( 14.81

X ^ 1

LLMK7 i > 32 dB

W

± (14) = <(o LLMK7

i < 32 dB , (5.15)

where LLMK7 i is as above;

j

f (LLZi- 40)

1 LLZi > 40 dB

W

± (15) =

j

t o LLZ

i < 40 dB,

(5.16)

where LLZ^ = loudness level, for diffuse field conditions, computed using

the Zwicker procedure [35], for the i-th sub-population,

1 „ 0 (LLZ, - 40) /10

H3I x W

± (16) =

where LLZ^ is as above;

'33 (PN

4

- 40)

W

± (17) - ( 0

LLZ

i > 40 dB

LLZ

i < 40 dB,

PNL > 40

i

PNL

i < 40,

(5.17)

(5.18)

where PNL^ = Perceived Noise Level, as defined in FAR-36 [36] but with

no tone corrections included, for the i-th sub-population, and

|

n.

W

± (18) = )^ 0

1 x 2

( PNL i - *0) /10

PNL

± > 40 dB

PNLi < 40 dB,

(5.19)

where PNL^ is as above.

5.3 SPEECH INTERFERENCE

The general adverse response to noise reflects reactions against communications interference, but the effect of noise on speech communication needs

to be addressed in its own right, independent of the general adverse

response. Criteria for speech communication typically are based on three

factors:

(1) vocal power, as a function of frequency and time, achieved by various

speakers under various conditions;

(2) the degree of speech recognition for speech material of different

degrees of difficulty in the presence of various types of noise;

60
(3) the definition of "acceptable" speech communication for both speaker and

listener.

The EPA Levels Document has identified a criterion level for speech in terms

of the A-weighted average sound level, for both indoor and outdoor situations.

At an indoor level of 45 dB the intelligibility of speech is considered to be

good enough for normal conversation. At or below this level it is considered

that noise has little effect on ordinary speech communication. At higher

levels, speech communication starts to deteriorate to a degree that is related

to the intensity of the noise and the unfamiliarity of the speech material.

In this report, the impact of the noise is assessed in terms of the speech

unintelligibility. However, before a specific method is defined for assessing

noise impact on speech communication, it is desirable to review the basic

parameters on which speech criteria are built.

Speech can be analyzed into a finite number of speech sounds which differ from

each other in terms of their total intensity, characteristics of buildup and

decay, and distribution of intensity with respect to frequency. For example,

the vowels as a group carry relatively large amounts of energy which are distributed into harmonics of the fundamental frequency of the voice. These

harmonics have distinguishable frequency regions which differ for each vowel

and which are in the low frequencies. The consonants, on the other hand,

carry much less energy but that which they do carry is in the higher frequency

region (relative to the vowels). In general it is known that the frequency

range of speech covers the region between 0.1 and 10 kHz. However, most of the

information contained in speech is carried by the consonants, which, because

they carry little energy, are easily masked.

When speech sounds are spoken, the various basic sounds are combined into

orderly sequences to form syllables which in turn are grouped into words and

sentences. Speech is an acoustical signal that constantly undergoes very

rapid fluctuations both in intensity and frequency. In order for a listener

to understand speech he must be able not only to detect the various sounds

but also to integrate and recognize the constantly shifting patterns. When

noise is present, some of the sounds are lost and the shifting patterns

become more difficult to resolve. As a result, speech intelligibility deteriorates in amounts related to the intensity of the noise and its bandwidth

relative to the speech signal.

Observations such as those described above are the basis of the Articulation

Index developed by French and Steinberg [37] as a means of predicting speech

intelligibility from a knowledge of speech and noise spectra. This index

represents a measure of the portion of speech which is available to the listener when communication occurs in a noisy environment. In effect, the

Articulation Index takes into account the sound level differential between

speech and noise (i.e., signal-to-noise ratio) in 20 contiguous bands located

between 0.2 and 6 kHz which, under optimal conditions, contribute equal

amounts to the Articulation Index.

The basic assumptions underlying the Articulation Index can be summarized as follows:

61
o the total variation in intensity levels of successive speech sounds

is constant throughout each frequency region and roughly equal to

30 dB,

o the relative occurrences of intervals of different intensities are roughly identical for each frequency region for both men and women,

o the average (1/8 second) peak levels of single speech phonemes exceed

the long-term average of the speech levels by about 12 dB for 10

percent of the time.

The Articulation Index is at present the best available predictor of speech

intelligibility in the presence of steady state noise, it also contains provisions for predicting the effects of noise having a definite duty cycle.

Thus, it would be a good candidate for rating consumer products in terms of

their ability to interfere with speech while they are on. However, the

method is complex and thus not very practical for the purposes of this study.

Nevertheless the relationships between sentence intelligibility and the

Articulation Index for listeners with good hearing are shown in Figure 9.

By and- large, speech criteria have been derived from studies involving young

adults with good hearing and speaking the same dialect. In the home environment these criteria may underestimate the effects of noise on speech. Children, particularly young children, have a more limited vocabulary than adults.

This, combined with their relative lack of knowledge of language, often makes

them less able to understand or "hear" speech when some of the cues in the

speech stream are lost. Likewise, older listeners with presbyacusis or persons with some hearing impairment are less able to understand partiallymasked speech than the young adult subjects for which speech criteria were

derived. Finally, in the United States several dialects are spoken; this in

turn could affect the estimates of speech unintelligibility based on present

criteria. However, little, if any quantitative data exist to date to account

for such effects.

The Speech Interference Level [38-39] is a simple method for estimating the

speech-interfering aspects of noise based on physical measurements of the

noise. Unlike the Articulation Index, SIL does not include specific consideration of the level and spectrum of the speech but employs a table or a nomogram for estimating the noise levels that will seriously restrict speech

communication in terms of general voice level and the distance between communicators. Indoors, however, the distance between the speaker and the listener

is less critical, since the listener often is likely to be in a reverberant

sound field and the speech levels do not decrease as rapidly as they would

outdoors. This is particularly true in cases where the listener is at distances equal to or greater than 1 meter — a situation typical of the home

environment.

The Speech Interference Level is also considered to be a good predictor of the

selective ranking of noises with respect to their speech interfering properties. However, this procedure is not appropriate for noise spectra with con- siderably more energy at high frequencies than at low or when any of the

62
Figure 9. Relation between the Articulation Index and various measures of

speech intelligibility [39]. (On the figure, "PB" designates

phonetically balanced.

)

63
following conditions exist: (1) the level of the noise is not of a steadystate nature, (2) the frequency spectrum of the noise is not constant with

time, and (3) the speech and noise are subject to perceptible echo or rever- beration (as could be the case in some rooms).

For many types of noise spectra, the Speech Interference Level can be approximated fairly well from the A-weighted sound level. Because the A-weighted

sound level can be read directly from a sound level meter, it is easier to

obtain than SIL. However, since consumer products may have unusual and uneven spectra containing acoustical energy in the high frequency region where the

consonants lie, it is possible that use of the A-weighted level to predict

speech interference from household products may underestimate the actual

interference with speech (as would also be the case with Speech Interference

Level)

For the purposes of this study, the data of Figure D-l of the EPA Level Documents [23] were used to estimate fractional (sentence) unintelligibility for

steady noises in an indoor situation. The curve thus derived is shown in

Figure 10. This curve also is approximately correct for predicting sentence

interference outdoors for normal voice and 2 meter separation between speaker

and listener — see Figure D-4 of the Levels Document. The severity of speech

interference for a particular noise exposure is assumed to be the product of

the fractional unintelligibility and the fraction of the time that the listener is exposed to that sound level.

The intensity of speech interference over an entire day ( 0700-2200 only ) is

assumed to be the summation of the severities, as defined above, of single

exposures. In other words, it is a measure of the fraction of sentences* lost

during an average day.

The weighting factor to be used in Eq. (5.1) is given by

W

i (10) = I Zu(L

j

)T.

j

(5.20)

where

U(Lj) = the fractional unintelligibility for the j-th noise level, as shown in Figure 10,

T^j = the time during which a given listener in the i-th population

sub-group is exposed to the j-th noise level, and

T = the total daytime period (i.e., 15 hours).

* Sentences are assumed to contain words that are both familiar and

unfamiliar to the listener.

64
FRACTIONAL

UNINTELLIGIBILITY,

A-WEIGHTED SOUND LEVEL, dB

Figure 10. Relationship between A-weighted sound pressure level and

fractional sentence unintelligibility ([23], Fig. D-l).

65
5.4 SLEEP INTERFERENCE

The accumulated evidence of research on the effects of noise on sleep indicates

that noise may arouse a person from sleep, and/or prevent a person from falling

asleep. At sub-arousal levels, noise may shift a person's sleep from a deep,

dreamless stage to a lighter stage of sleep. However, much of what is known

about the effects of noise on sleep comes from laboratory observations on a few

people. Therefore, caution must be exercised in making generalizations about

people under widely differing circumstances.

Sleep is a very complex phenomenon, comprising several stages. It is generally

agreed that as a person relaxes, the electroencephalographic (EEG) pattern

changes from rapid irregular waves to a pattern that is regular. This is followed by a prolonged reduction in the amplitude and frequency of the waves. Later this pattern changes to one where bursts of waves (spindle waves) are mixed with relatively large amplitude and single slow waves known as Kcomplexes. Typically, this sleep stage lasts 30 to 45 minutes (depending on

age) and then yields to a new pattern characterized by bursts of relatively

high amplitude and slow waves. The deepest sleep occurs when these later

waves, known as delta waves, occur for about 50 percent of the recording

period. Approximately one and a half hours after this deep stage of sleep has

occurred, the EEG pattern starts to change again and to resemble that seen at

the beginning of the sleep cycle except that, typically, electrodes located

near the eye indicate rapid eye movements. Dreaming occurs during this period.

In the laboratory, a person normally goes through the whole progression

described above, although occasional reversals occur. The amount of time

which a person spends in each sleep stage varies with his age and psychophysiological and motivational state. It is generally believed that all

stages of sleep are important for good "health", at least in the short term,

since, typically, when sleep is severely disturbed the subject reports the

next day that he feels lethargic, nervous, and unable to perform work. Some

unanswered questions exist, however, regarding the long-range effects of

sleep disturbance on physiological and psychological health since little

experimental data are available from prolonged studies.

Survey data indicate that sleep disturbance is often one of the principal

complaints about noise [40]

.

Recently Lukas [41] has reviewed the experimental sleep and noise literature for EPA and derived a method for assessing

sleep disturbance due to noise. In the context of that study, sleep disturbance includes either of two phenomena:

o a change in the electroencephalographic pattern to at least one "shallower" sleep stage,

o a complete behavioral awakening.

In addition, the Lukas study yielded some functional relationships between

noise levels and probabilities of sleep disruptions. These relationships

are shown in Figures 11 and 12, and provide the basis for the method developed by EPA [42,43] for assessing the impact of noise on sleep. It must be

66
recognized, however, that in addition to the fact that most of the data upon

which these relationships rest were obtained under laboratory conditions (and

thus may not accurately represent real life conditions), about 75 percent of

the test stimuli used were from transportation systems, in particular from

subsonic and/or supersonic aircraft. Accordingly, caution must be exercised

in applying the results of these studies to noises of other types. In the

absence of other data, however, one can only assume that relationships based

on transportation noises hold for other types of noise.

The curve shown in Figure 11 indicates the frequency of sleep disturbance (as

measured by both sleep change rate and awakening) as a function of the Sound

Exposure Level (SEL) of the intruding noise. This curve can be used to

approximate the degree of partial impact which results from a specified noise

exposure level for an individual.

For the purposes of this study the Sound Exposure Level was defined as

!

L, + 10 log ( t j/t ) , t. < 120 sec

J J J (5.21)

Lj + 20.8 , tj > 120 sec ,

where

L . = the steady j-th noise level produced in a bedroom by the product

of concern,

tj = the duration (sec) of exposure due to a single operation of the

product, and

t Q = reference time = 1 sec.

At the request of EPA, an upper limit of 2 min (120 sec) was placed on the

integration time for SEL. It was felt that it would be unreasonable to

extrapolate Lukas' data beyond this duration. Thus, it is implicitly

assumed that if sleep were not disrupted within the first two minutes, it

would be unlikely that any disruption would occur.

Two weighting factors, corresponding to Figures 11 and 12, respectively,

are incorporated into the model.

The weighting factor corresponding to sleep disruption is defined as I X (1-35 x SEL, - 50) , SEL, > 37 dB

( 100 j J J W

± (19) = < (5.22)

(0 , SEL.. < 37 dB

where SEL is as defined in Eq. (5.22) and the summation is only over the

nightime hours (2200-0700). In the computation of the Weighted Population, using Eq. (5.1), only persons in bedrooms are included.

67
The weighting factor corresponding to behavioral awakening is defined as — £(1.01 x SEL, - 49.5), SEL. _> 50 dB

!

100 j

J J (5.23)

0 , SEL

j < 50 dB

where SEL, is as defined above, the summation again is only over the nighttime hours, and in the computation of weighted population only persons in

bedrooms are included.

68
100

SOUND EXPOSURE LEVEL, dB

Figure 11. Weighting factor for sleep disruption as a function of sound

exposure level [42-43] . (Note: In this figure W

i (19) is given

in percent; in the computer program it is used as a fraction

going from 0.0 to 1.0.)

69
50 60 70 80 90 100 110 120 130

SOUND EXPOSURE LEVEL, dB

Figure 12. Weighting factor for frequency of arousal or awakening from

sleep as a function of sound exposure level [42-43]. (Note: in

this figure W

i (20) is given in percent, in the computer program

it is used as a fractions group from 0.0 to 1.0).

70
6. IMPACT ASSESSMENT MODEL

As noted in the introduction of this report, the main objective of the present

study was the development of a conceptual approach and a procedure for assessing the impact of noise from consumer products on the health and welfare of

the national population. The complexity of the calculations involved, and the

large quantity of data to be handled, convinced the authors that the only practical procedure for using this conceptual model was to embody it into a com- puter program. This program, the imputs required, and the outputs obtained

are described below.

Section 6.1 defines the categories of dwellings, rooms, people, days, and time

periods used in the program.

Section 6.2 provides an overview of the type of data required in order to use

the model. These data requirements are commensurate with the discussions in

Sections 2, 3, and 4.

Section 6.3 provides a conceptual flowchart of the FORTRAN program, indicating

how these data are read in and manipulated, using the procedures outlined in

Section 5, to yield the desired descriptors of population impact.

Section 6.4 details the specific formats for the data that are required for

use in the program, which itself is contained in Appendix C.

6.1 INPUT DATA PARAMETERS

In the development of the FORTRAN program, categorization of identified parameters has been made. These categories, the subscript variable used in the

FORTRAN program to designate each category, and the specific parameter subscripts are:

o Dwellings — (I):

1. single family

2. townhouse

3. multifamily (i.e., apartments)

o Living spaces — (J), (K), or (L) for source room, receiving room

in primary dwelling, or room in secondary dwelling, respectively:

1. kitchen

2. living room/dining room/family room

3. bathroom

4. bedroom

5. basement/utility room/ garage

6. outdoors

71
o People — (M):

1 . unemployed or working-at-home adult male

2. employed adult male

3. unemployed or working-at-home adult female

4. employed adult female

5. school age or nursery child

6. preschool child at home

Day — (N):

1 . weekday with school

2. weekday with no school

3. weekend and holidays

Period of day — (P):

1 . 0700-0900 hours

2. 0900-1700 hours

3. 1700-2200 hours

4. 2200-0700 hours

Octave band center frequency — (S):

1 . 63 Hz

2. 125 Hz

3. 250 Hz

4. 500 Hz

5. 1000 Hz

6. 2000 Hz

7. 4000 Hz

8. 8000 Hz

6.2 INPUT DATA REQUIRED FOR IMPLEMENTATION OF THE MODEL

In assessing the impact of noise from consumer products, several categories

of data inputs are required. These include: population parameters., sound

isolation data, and product noise emission data. In these categories, the

following input data are required (in order of input):

1. Population Parameters

o Length of time, in minutes, spent in different rooms as a function of

building type, room type, person type, period of the day, and type of

day. TIMEIN(I,K,M,P,N)

o The number of people of each type that lives in each type of dwelling.

PEOPLE( I ,M)

72
2. Sound Isolation

o Noise reduction from room to room for the primary dwelling for each

pair of source and receiving rooms, for each octave band, as a function of building type. NRP(S,K,J,I)

o Noise reduction from the primary to the secondary dwelling for each

combination of source room and receiving room, for each octave band, as a function of building type. NRS(S,L,J,I).

3. Product Noise Emission (for each product)

o Title describing the appliance.

o Distribution of operators, in percent, among person types. OPTYP(I,M)

o The fraction of each dwelling type having the appliance. PERC(I)

o Relative length of time, in minutes/day, of operation as a function

of dwelling type and day. ONTIM(I,N)

o The length of time, in minutes, of each operation as a function of

dwelling type and day type. TIM(I,N)

o The relative probability of an appliance being operated in a given

time period in a given house type on a given day type. TP9(_P,I,N)

(The sum of TP9 (P,I,N) over P must equal one.)

o The relative probability of an appliance being operated in a given

room type in a given house type on a given day type. RMP9 (K,I,N)

(The sum of RMP9 (K,I,N) over K must equal one).

o Octave band sound level spectrum (in decibels) of the product class.

SPECTR(S)

o The minimum and maximum source sound power level and step size (in

decibels). MIN,MAX,IDB

o The difference (assumed to be constant) between the sound power level

and the sound pressure level at the operator position. OPDIFF

o The probability of the power level in the source room being in a particular decibel range (specified by MIN,MAX,IDB from above).

73
6.3 Flow Chart for Computer Program

74
75
76
YES

NO

For operators:

Calculate the yearly average equivalent level for each octave band spectrum, for each I,M

from: Leq(S,I ,M,1)=10* log (X) where

X = L0NTIM(I,N)*XDAYS(N)*0PTYP(M)/1440

N where 1440 is the number of minutes in a day

NO

For persons in the primary, secondary

dwelling (other than operators) calculate

the yearly average equivalent level from:

Leq(S,I,M,IK) = £

XDAYS(N) (zi2>*B*10C/10

)

where

A = SIMEIN(I ,K,M,P,N)

B = PR0B0N(I,J,P,N)

C = SPECTR(S) - NRP(S,K,J,I)(primary)

= SPECTR(S) - NRS(S,K,J,I)(secondary)

and C = C+10 if ITST=2 (LDN calculations)

YES

77
Add the source levels to the normalized

spectrum

A(S) = Leq(S,I ,M,IK)+MIN+(T-1)*IDB+0PDIFF( IK)

A(S) is the octave band spectrum level for operators ( I K= 1 ) or for primary (IK=2)

YES

Sum operators S primary dwelling

AA1 = A + B + C; AA2 = A + B where

A = Leq(S, I ,M,IK)

B = MIN+(T-1)*IDB)

C = OPDIFF(IK)

A(Sl = IOLOgCiQ^171^!^^10

)

Call WWIGHT( ICA,A(S) ,X)

WWIGHT selects the correct

spectral weighting function

which calculates the weighted

level X(T)

Call WE(W,X,ICA)

WE selects the correct weighting for W(Leq)

Calculate the population impact from

PI ( IK) = PI ( IK) + SEOPLE ( I ,M) •W*0ISLEV(T)

78
77
78
C

Secondary >v & total J

A1(S) —A6(S)=0 S=1 ,8 AA1—AA6=0 SAV2=0

AA = Leq(S,l)+MIN+( IT5-1) *IDB +0PDIFF( 1) BB = Leq(S,2)+MIN+( IT5-1) • IDB A5(S) = 10AA/10+10B8/10

A1(S) = 10 CLeqtS,3}+MIN+(ITl-l).IDB)/10

YES

79
80
81
82
L

83
(

Speech inter- N

fere nee (cont)J

X1=0

X2=0

A(5) = SPECTR(S)-NRP(S,K,J ,1)

YES

Call WWIGHT(ICA,A(5),A)

Calculate:

CC = U(L)

U = Speech interference

function

L = A+MIN+(T-1) • IDB

X2 = X2+SIMEIN( I ,K,M,P,N) • PROBON ( I ,J ,P,N) *CC

YES

YES

YES

c

Return

n|o

YES

rftO XI = X1(2)+XDAYS(N) *X2 A

YES

PI (2) = P I ( 2

) +SE0PLE ( I ,M) •X1*0ISLEV(T)

NO

84
(

Subroutine 'N SLEEP J

85
86
6.4 FORMAT OF DATA INPUTS

The format and order of data inputs for the FORTRAN program is:

1. (20 cards)

TITCAL-Titles associated with W^Cl) - W^(20).

2. (72 cards)

TIMEIN(I,K,M,P,N) - Length of time, in minutes, spent in various room

types. Input on each card the time spent in each of I = 1 to 3 building

types for each of K 3 1 to 6 room types. Each card corresponds to one of

M = 1 to 6 person types, for each of P = 1 to 4 periods of the day, for

each of N = 1 to 3 day types. The subscripts should be varied systematically in the order stated with data input on each card in FORMAT (18F4.0).

3. (1 card)

People(I,M) - The total number of people of each type in each dwelling

(in millions). Input using FORMAT (18F4.0)

4. (54 cards)

NRP(S,K,J,I) - Room-to-room noise reductions in the primary dwelling.

Input data for each of the eight octave bands centered at frequencies

from 0.063 to 8 kHz with S=1 corresponding to the 0.063 kHz band, S=2 to

the 0.125 kHz band, S=3 to the 0.25 kHz band, etc. Input two rooms, with

values for S=1 to 8 on each card (16 values) for each combination of K=l,

2; 3,4; 5,6 (3 cards) receiver rooms, J=1 to 6 source rooms, and 1=1 to

to 3 building types, with each of these subcripts varied systematically

in the stated order. The input should be made using FORMAT (16F5.0).

5. (54 cards)

NRS(S,L,J,I) - Noise reductions between rooms in primary dwellings and rooms in secondary dwelling. Input two rooms with values for the octave

bands corresponding to S=1 to 8 on each card (16 values) for each combination of L=1 to 6 rooms in the primary dwelling, and 1=1 to 3 building

types. Input is to be made in FORMAT (16F5.0).

6. (1 card)

TITLE - Title to include product name and description. Input limited to one card with 60 characters; read in using FORMAT (10A6).

7. (1 card)

NCANUM - Indicator of which weighting factors are to be used to determine

Weighted Population and which type of calculation is to be done. Input

integers 1 through 4 in free format. If it is desired not to calculate a

87
a particular weighting factor W^(k) for the k-th weighting factor, the

k-th integer should be zero. If the integer is 1, 2, 3, 4 the calculation is LEQ, LDN, speech interference, sleep interference. The ordering

of the weighting factors (which are discussed in detail in Section 5) is

Wi (l) - Hearing: Fractional Exposure (Criterion Level - 70 dB)

W^(2) - Hearing: Four-Frequency Average NIPTS (corresponds to PHL from

CHABA WG 69 report)

W

± (3)

W

± (4)

W± (5)

W± (6)

W± (7)

Hearing: Average NIPTS at 4 kHZ

General Adverse Response: CHABA WG 69 report, LWP(LDN)

General Adverse Response: CHABA WG 69 report, LWP(LDN+15)

General Adverse Response: Fractional impact

General Adverse Response: Alexandre and Barde, energy indicator

(LDN)

W^(8) - General Adverse Response: Alexandre and Barde, loudness

indicator (LDN)

W^(9) - General Adverse Response: Alexandre and Barde, synthetic

indicator (LDN)

W

i (10) - Speech Interference: Fractional unintelligibility based on Alevel

W^(ll) - General

W^(12) - General

W^(13) - General

W^(14) - General

W^(15) - General

Adverse Response:

Adverse Response:

Adverse Response:

Adverse Response:

Adverse Response:

Stevens Mark VI loudness level

Stevens Mark VI loudness

Stevens Mark VII loudness level

Stevens Mark VII loudness

Zwicker loudness level

W^(16) - General Adverse Response: Zwicker loudness

W^(17) - General Adverse Response: Perceived noise level (FAR 36)

W^(18) - General Adverse Response: Noisiness (FAR 36)

W^(19) - Sleep Interference: Disruption (from A-weighted Sound Exposure

Level)

W^(20) - Sleep Interference: Awakening (from A-weighted Sound Exposure

Level)

88
8. (1 card)

OPTYP(M) - Distribution of operators among person types. Input percent

for each of M=1 to 6 operator types in FORMAT (12F6.0).

9. (1 card)

Perc(I) - Percentage of each dwelling type having the appliance. Input

for each of I=*l to 3 dwelling types in FORMAT (12F6.2).

10. (1 card)

0NTIM(I,N) - Length of time, in minutes/day, that appliance operates.

If ONTIM is independent of N, input only first three numbers; program

fills in the blanks. FORMAT (9F6.0).

11. (1 card)

TIM(I,N) - Length of each use in each building on each day. If TIM is

independent of N, input only first three numbers; program fills in the

blanks. FORMAT (9F6.0).

12. (1 card)

NT,MT - If NT equals 3, input three cards for TP9(P,I,N), N =*1,3. If

NT =1, TP9(P,I,N) is independent of N and input one card for TP9.

(NT=1 for test case.) Program fills in the blanks. If MT equals 3

input three cards for RMP9(J,I,N), N =1,3. MT=1 if RMP9(P,I,N) is

independent of N and input one card for RMP9. (MT=1 for test case.)

Program fills in blanks. Free format (integers separated by commas).

13. (1 card)

TP9(P,I , 1)-The relative probability of the appliance being operated in

a given time period on a given type of day (The sum of TP9(P,I,1) over

P must equal one). Input 1 card FORMAT (12F6.0).

14. (1 card)

RMP9 (J,I,1)- The relative probability of the appliance being operated

in a given room type on a given type of day. (the sum of RMP9(J,I,1)

over J must equal one.) Input 1 card FORMAT (18F6.0).

15. (1 card)

SPECTR(S) - Octave band levels (re arbitrary reference level) for the

product in the particular mode of operation. The same spectrum shape

is used for the sound power levels and the sound pressure levels at

the operator position. Input data for each of the eight octave bands

centered at frequencies from 0.063 to 8 kHz with S=1 corresponding to

the 0.063 kHz band, etc. Input values for S=1 to 8 in FORMAT (12F6.2).

89
16. (1 card)

IDB, MIN, MAX, OPDFF - The stepsize (IDB), the minimum (MIN), and the

maximum (MAX) sound power level of the source. OPDFF is the constant

difference between the sound power level of source and the sound pressure level at the operator position. Input four numbers in free format

(3 integers, 1 floating point number).

17. (1 card)

DISLEV(T) - The probability of the power level being in a particular

decibel range specified by MIN, MAX, IDB (above). The number of levels

is given by ( (MAX-MIN)/IDB) + 1. Maximum of 18 values. Input in

FORMAT (18F4.0).

Many of the data inputs required by the FORTRAN program will not necessarily

change from product to product. These inputs are those corresponding to

population parameters, data arrays TIMEIN and PEOPLE, and those corresponding

to sound isolation, NRP and NRS. The data input has been, therefore, structured so that data which are not related to the product are input first, thus

segregating them from product-related data inputs which will necessarily

change with each class of products.

In addition to the data which must be input to the program on cards, limited

data are in the program through FORTRAN DATA statements. These data include:

o U(ID) - Percent of speech unintelligibility as a function of discrete

indoor A-weighted sound levels. The subscript ID runs from ID=1 to

16

.

o LEV(IC) - A-weighted sound levels corresponding to the percent of

speech unintelligibility defined by U. The subscript ID runs from

IC=1 to 16.

o XDAYS(N) - Fraction of types of days in a year for N=1 to 3 day types.

o TPER(P) - Number of hours in each of the periods of the day for P=1 to

4 period types.

90
7 . REFERENCES

[1]

. Identification of Products as Major Sources of Noise, AO Federal

Register 23069 (May 28, 1975). [2]

. Noise Standards for Civil Subsonic Turbojet Engine-Powered Airplanes

(Retrofit and Fleet Noise Level) (U.S. Environmental Protection

Agency, Washington, D. C., December 1974).

[3]

. Yaniv, S. L., Evaluation of the effects of motor vehicle noise regulations on population. Motor Vehicle Noise Control, Special Report 152,

pp. 77-84 (Transportation Research Board, National Research Council,

Washington, D.C., 1975).

[4]

. Noise Emission Standards for Construction Equipment: Background

Document for Portable Air Compressors, Report No. EPA 550/9-76-004

(U.S. Environmental Protection Agency, Washington, D.C., December

1975).

[5]

. Background Document for Medium and Heavy Truck Noise Emission Regulations, Report No. EPA 550/9-76-008 (U. S. Environmental Protection

Agency, Washington, D.C., March 1976).

[6]

. American Society of Heating, Refrigerating, and Air-Conditioning

Engineers Methods of Testing for Sound Rating Heating, Refrigerating,

and Air-Conditioning Equipment, 36-72 (American Society of Heating,

Refrigerating, and Air-Conditioning Engineers, New York, New York,

1972).

[7]

. Air Moving and Conditioning Association Test Code for Sound Rating,

Standard 300-67 (Air Moving and Conditioning Association, Arlington,

Heights, Illinois, 1967).

[8]

. Society of Automotive Engineers Recommended Practice for Exterior

Sound Level Measurement Procedures for Small Engine Powered Equipment,

J1046 (Society of Automotive Engineers, Inc. , Warrendale, Pennsylvania,

1974).

[9]

. Schultz, T. J., Development of EPA Noise Labeling Regulations: Rating

Schemes for Noise Producers, BBN Report No. 3196 (Bolt Beranek and

Newman Inc. , Cambridge, Massachusetts, November 1975).

[10]

. 1976 Statistical and Marketing Reports, Merchandising (March 1976).

[11]

. Noise from Construction Equipment and Operations, Building Equipment,

and Home Appliances, Report No. NTID300.1 (U.S. Environmental Protection Agency, Washington, D. C., December 31, 1971).

[12]

. Noise in America, Report No. 3318 (Bolt Beranek and Newman, Inc,

Cambridge, Massachusetts, June 1976).

91
[13]

. American Society for Testing and Materials Standard Recommended

Practice for Laboratory Measurement of Airborne Sound Transmission

Loss of Building Partitions, E90-75 (American Society for Testing

and Materials, Philadelphia, Pennsylvania, 1975). [14]

. Hittman Assoc., Inc., Residential Energy Consumption — Single Family

Housing: Final Report (Department of Housing and Urban Development,

Washington, D. C., June 1974).

[16]

. Sabine, H. J., Lacher, M. B., Flynn, D. R. , and Quindry, T. L.,

Acoustical and Thermal Performance of Exterior Residential Walls,

Doors and Windows, NBS Building Science Series 77 (National Bureau

of Standards, Washington, D. C., November 1975).

[17]

. Berendt, R. D., Winzer, G. E., and Burroughs, C. B., A Guide to

Airborne, Impact, and Structureborne Noise Control in Multifamily

Dwellings, Report No. FT/TS-24, (U. S. Department of Housing and

Urban Development, Washington, D. C., September 1967).

[18]

. Compendium of Materials for Noise Control, HEW Publication No. (NIOSH)

75-165 (U. S. Department of Health, Education, and Welfare, Cincinnati,

Ohio, June 1975).

[19]

. Szalai, A. (Ed.), The Use of Time: Daily Activities of Urban and

Suburban Populations in Twelve Countries (Mouton, The Hague,

The Netherlands, 1972).

[20]

. Walker, K. E., and Woods, M. E., Time Use: A Measure of Household

Production of Family Goods and Services (Center for the Family of

the American Home Economics Association, Washington, D. C., 1976).

[21]

. Johnston, B. J., Home laundering; A study of equipment and procedures,

J. Home Economics 50(1), 36-39 (January 1958).

[22]

. Conversation between C. P. Howard, NBS, and M. A. Cadoff, NBS

(November 1976).

[23]

. Information on Levels of Environmental Noise Requisite to Protect

Public Health and Welfare with an adequate Margin of Safety,

Report No. EPA 550/9-74-004 (U. S. Environmental Protection Agency,

Washington, D. C., March 1974).

[2.4] . Miller J.D., Effect of Noise on People, Report No. NTID 300.7

(U.S. Environmental Protection Agency, Washington, D. C.,

December 1971).

[25]

Public Health and Welfare Criteria for Noise, Report No.

EPA 550/9-73-002 (U.S. Environmental Protection Agency,

Washington, D. C., July 27, 1973).

92
[26]

. Hazardous Noise Exposure, Air Force Regulation 160-3 (U. S. Department

of the Air Force, Washington, D. C. October 1956).

[27]

. Guidelines For Preparing Environmental Impact Statements on Noise,

Report of the Committee on Hearing, Acoustics, and Bioacoustics,

Working Group 69 (National Research Council, National Academy of

Sciences, Washington, D. C., December 1976). [28]

. Medium and Heavy Duty Trucks Technology and Cost Information, Report

No. EPA 550/9-74-015 (U. S. Environmental Protection Agency, Washington, D.C., September 1974).

[29]

. Noise Emission Standards for Construction Equipment: Background

Document for Portable Air Compressors, Report No. EPA 550/9-76-004

(U.S. Environmental Protection Agency, Washington, D.C. December

1975).

[30]

. Kugler, A. B., Commins, D. E., and Galloway, W. J., Highway Noise:

A Design Guide for Highway Noise Prediction and Control, NCHRP

Report 174 (Transportation Research Board, National Research Council,

Washington, D.C. 1976).

[31]

. Schultz, T. J., Synthesis of social surveys on noise annoyance, J.

Acoust. Soc. Am. 64^, 377-405 (1978).

[32]

. Alexandre, A., and Barde, J. P., Noise charges: An international

assessment, Noise Control Engineering 10, 5259 (1978). (The forms of

the expressions used for W.(7), W^(8), and W.,(9) corresponds to an earlier private communication from A. Alexandre.)

[33]

. American National Standard Procedure for the Computation of Loudness

of Noise, ANSI S3. 4-1968 (American National Standards Institute, New

York, New York, 1972).

[34]

. Stevens, S. S., Perceived level of noise by Mark VII and decibels(E),

J. Acous. Soc. Am. 5^, 575-601 (1972).

[35]

. International Organization for Standardization Standard on Acoustics — Method for Calculating Loudness Level, ISO 532-1975 (International

Organization for Standardization, Geneva, Switzerland, 1975).

[36]

. Federal Aviation Administration Noise Standards, Title 14, Code of

Federal Regulations, Chapter I, Part 36, 34 Federal Register 1864;

(November 18, 1969); as amended at 38 FR 29574; October 26, 1973;

39 FR 43832, December 19, 1974; 40 FR 1034, January 6, 1975, 40 FR

2174, January 10, 1975; 40 FR 6346, February 11, 1975 (correction);

41 FR 12360, March 3, 1977; 43 FR 8722, March 2, 1978; 43 FR 28406,

June 29, 1978.

[37]

. French, N. R., and Steinberg, J. C., Factors governing the intelligibility of speech sounds, J. Acous. Soc. Am. 19, 90-119 (1947).

93
[38]

. Beranek, L. L., The design of speech communication systems, Proc.

IRE 35, 880-890 (1947).

[39]

. American National Standard for Rating Noise with Respect to Speech

Intelligibility, S3. 14-1977 (American National Standards Institute,

New York, New York, 1977). [40]

. Alexandre, A., and Barde, J. P., Le Temps du Bruit, (Flammarion,

Paris, France, 1973).

[41]

. Lukas, J. S., Measures of Noise Level: Their Relative Accuracy in

Predicting Objective and Subjective Responses to Noise During Sleep,

Report No. EPA 600/1-77-010 (U. S. Environmental Protection Agency,

Washington, D. C., February 1977).

[42]

. Goldstein, J., Assessing the impact of transportation noise: human

response measures, Proc. Inter-Noise 77, pp. 79-98 (Noise Control

Foundation, New York, 1977).

[43]

. Information in Support of the Proposed Regulation for Truck-Mounted

Solid Waste Compactors, Rept. No. EPA 550/9-77-204 (U.S. Environmental

Protection Agency, Washington, D. C., August 1977).

94
APPENDIX A

EXISTING MEASUREMENT PROCEDURES

As was noted in the main body of the report, very few standards currently

exist which specify how consumer product noise emission is to be measured.

In addition, even though there is keen interest in developing such standards, there are such large differences among the various concerned parties

regarding what ought to be measured, where, how and to what level of accuracy, that relatively few measurement standards have been proposed. A number of measurement standards which do exist are listed in one or both of the

following publications:

Quindry, T. L. (Ed.), Standards on Noise Measurements, Rating Schemes,

and Definitions: A Compilation, NBS Special Publication 386,

(National Bureau of Standards, Washington, D.C. 1976).

Brenig, A., and Corcoran, P. A. (eds.), Index to Noise Standards, ASA

STDS INDEX 1-1976 (Acoustical Society of America, New York, N.Y.,

1976).

Later information on new or pending measurement standards can be obtained

from the various organizations responsible for different classes of equipment. Addresses for most such organizations are listed in the two publications just cited.

95
APPENDIX B

CORRELATIONS AMONG RATINGS FOR DIFFERENT SPECTRA

A viable method for assigning noise ratings to consumer products will depend

primarily upon:

o the identification of those physical parameters of the noise

that are of concern from a public health and welfare viewpoint;

o a well-defined procedure for quantifying these noise parameters,

and

o the ability to relate the results of measurement to human response

to noise.

As demonstrated, for example, in several EPA documents [1-5]*, the accumulated evidence of research on human response to noise indicates that the

magnitude of sound and its variation as a function of frequency and time are

the primary physical parameters affecting human response.

Much psychoacoustic research has focused on schemes for combining the

frequency content and overall intensity of the noise into a metric related

to the perceived magnitude (e.g., loudness) of the noise, presumably as experienced by a person.

Although various investigators disagree somewhat as to the exact functional

relationship between a noise stimulus and the perceived magnitude, there

appears to be a consensus regarding the general form of the function. Loudness is generally thought to grow as a power function of sound pressure

[6,8]. Each time the sound pressure level is increased by 10 dB, the loud- ness experienced increases by approximately a factor of two.

Psychoacoustic research has established that the human ear is not equally

sensitive to sounds at all frequencies. Generally, people are more senstive

to sounds in the middle of the audible frequency range (i.e., 1-4 kHz) than

to either low or high frequency sounds. Two basic approaches are used to

obtain physical measurements that account for the differential sensitivity of

the ear to sounds of various frequencies. The simpler approach consists of

applying a single filter designed such that the contributions from various

frequencies are weighted in a manner that approximates the way in which the

ear hears them. The more involved approach consists of analyzing the noise

sound pressure level in each of a number of bands and then combining these

band pressure levels using a computational procedure that yields a predictor

of perceived magnitude.

The simpler A-,B-, and C- networks of sound level meters were originally

intended to represent the frequency response of the ear to low, moderate

and high sound pressure levels, respectively. However, over the years it

* Numbers in square brackets refer to the references listed at the end of

this appendix.

96
became apparent that, in real-life situations, the A-weighted sound pressure

level is a relatively good predictor of human response to environmental

noise [9, 10] over a large range of levels.

Kryter [11] has indicated that in many situations it is not how "loud" a sound is that is of concern, but rather how "noisy" and unwanted it is.

Inherent is this statement is the assumption that "loudness" and "noisiness"

represent somewhat different attributes of the human response to noise.

Investigations carried out in the late 1950's by Kryter [12-15] suggest that

this may be the case and that, although loudness is a major contributor to

noisiness (the unwantedness of a given noise), the two concepts are not

synonymous. In later work, however, Stevens [7] argued that loudness and

noisiness are not distinguishable.

Since the criteria used in developing the A-weighted network were first

reported in the 1930's [16], psychoacoustic research has indicated that,

for certain types of noises, particularly those associated with aircraft,

a different frequency weighting which increases the relative weight of the

1-4 kHz region may be more representative of human response than A-weighting.

Several alternatives to this weighting function have been proposed and are usually referred to as "D-weightings". Very recently one of the D-weighting

networks was standardized [17] for use in sound level meters. This network

is currently restricted to the measurement of aircraft noise, since most of

the experimental evidence behind D-weighting comes from studies involving

aircrat noises. Some suggestions have been made that D-weighting may be

more reliable than A-weighting in predicting human response to a greater

variety of noises. However, no firm evidence exists to support the general

application of D-weighting to noises other than aircraft noises. Consideration should be given to the possibility that a D-weighting may be appropriate

for rating consumer products.

At the present time, however, A-weighted sound level is most widely used for

measurements made with a sound level meter. However, the A-weighted sound

level is only an approximate predictor of human response. In particular,

one of the major difficulties with the A-weighted sound level is that tonal

components are sometimes not adequately accounted for when data are obtained

with the simple sound level meter. For these reasons various investigators

have attempted to improve the accuracy of prediction by using more detailed

descriptors than the simple frequency-weighted sound level obtained with a sound level meter.

Generally, refined schemes are based on segmentation of the sound pressure

spectrum of a noise into a series of contiguous frequency bands by means of

electrical networks so as to display the distribution of sound energy over

the audible frequency range. From data thus obtained, a "loudness level"

is computed by: (1) assigning to each frequency band a loudness index

designed to represent the potential contribution to the perceived loudness

of that band; (2) correcting this index by applying a weighting to account

for the fact that bands with higher loudness indices may inhibit or mask

the contributions of other bands; and (3) summing up the loudness indices

to estimate the overall loudness level of the noise. A number of variants

97
to this basic procedure are now in existence. These include the Stevens Mark

VI [18] and Mark VII [19] loudness computations and procedures developed by

Zwicker [20] .

Kryter [11-13], following the computational schemes derived from loudness

experiments, developed a new scale for assessing noise called the Perceived

Noise Level. The difference between loudness computations and Kryter'

s

method is that instead of assigning loudness indices to each measured band

level, a perceived noisiness index is assigned. The unit of perceived noisiness is the noy and values are obtained from contours of equal "noisiness,"

rather than contours of equal loudness.

Since it was originally proposed in the late 1950's and early 1960's, the

Perceived Noise Level methodology has been further refined to account for

discrete frequency components of tones associated with aircraft noise as well as to account for the fact that, all else being equal, long duration

flyovers are more annoying than short duration flyovers [14,15]. All of

these developments involve detailed studies of noise spectra and complex

computational procedures which are embodied in a rating procedure known as the Effective Perceived Noise Level [13]

.

Detailed frequency analyses are complex and require sophisticated equipment

that must be operated by trained personnel. Obviously, noise measurements

with a sound level meter are far less complicated. Thus, there is some merit in examining the feasibility of rating consumer products in terms of one of the weighted levels obtainable with a sound level meter. Furthermore, whatever rating scheme is used to rate consumer product noise, this

rating must be referable to the criteria discussed in Section 5. However,

such an approach would be justified only if the differences obtained between

appliance ratings derived from complex measurements do not differ significantly from those derived from sound level meter measurements.

In addition to the type of approach chosen to perform frequency analysis,

there is the added difficulty of choosing the rating scheme to be used.

For example, should a product be rated in terms of A-weighted sound level

or D-weighted sound level? Or, by assuming that detailed frequency analysis

is performed, should one then compute loudness level or Perceived Noise

Level? Moreover, which of the various computational procedures should be

utilized? Should tonal components be accounted for in all cases?

Finally, another type of question must be answered. While ultimately it

may not matter which scheme is used to rate similar spectra (as would be

the case for products contained within one class), some rating schemes

might be more sensitive than others to differences among significantly

different spectra, such as those contained in different product classes

(e.g., blender vs. dishwasher). Consequently, the rating schemes must be

evaluated in terms of their sensitivity to spectral differences in the

noise emission both between and within classes of products.

98
In the present study, an attempt has been made to address some of the questions discussed above. Several appliances, for which spectral data were available in the literature, were selected, and Pearson correlation coefficients between rating schemes were computed for each product. The six products chosen for these analyses were: garbage disposals, electric shavers,

dishwashers, food mixers, blenders, and centrifugal fans. The actual spectra used to make the computations reported here are presented in Figures

B-l through B-6. Observations of these spectra reveal that, within a given

class of product, spectra are similar in shape; but, significant differences

exist in spectral shapes between classes of products.

From the spectral data shown above, A-,B-, C-, D-, and E-weighted sound

levels were computed, as were loudness levels using the Stevens Mark VI [18]

and Mark VII [19] procedures. In addition, two perceived noise levels were

computed: one with a tone correction (using the method described in FAR-36)

and one without tone corrections. These computations were performed for

each set of appliances. From these data the average levels and their ranges

were also computed for each rating scheme. In addition, the Pearson correlation coefficients were computed among rating schemes. The results of these

calculations are presented in Tables B-l through B-6. Three decimal places are shown in these tables to facilitate comparisons. However, the data do

not warrant such precision.

As can be observed through inspection of the above tables, the correlation

coefficients among all ratings for a given product are very high — usually

above 0.85 — except for the garbage disposal where B- and C-weighted sound

levels have lower correlation coefficients with other ratings than the other

schemes. These data suggest that it does not matter which scheme is used to

rate products which are in the same class, since the correlation among the

various schemes is high. Of particular interest is the fact that, for the

sets of spectral data examined, the more complicated (loudness or noisiness)

schemes, whether they make use of tone corrections or not, correlate highly

with A-, D-, and E-weighted levels. Thus, on the basis of these analyses

it could be argued that, for rating consumer products, any rating scheme is as good as any other, except for B- and C-weighting (see Tables B-l through

B-6). Consequently, for simplicity's sake, as well as for consistency with

other regulatory actions involving other types of noise sources (e.g.,

trucks, air compressors) and with the criteria discussed in the EPA Levels

Document, the A-weighted level would appear to be as reasonable a choice as any other for rating household products. (Note that these are correlations

among ratings and not between ratings and subjective responses. Thus high

correlations merely indicate that a pair of ratings are either about equally

good or about equally bad in terms of their ability to predict human

responses.

)

Although correlation coefficients are often used to justify the use of a rating scheme, it should be observed that this approach in conjunction with

this particular study could be somewhat misleading. For the range of intensity and frequency which are of concern in this study, if the overall sound

pressure level is raised by, say, 10 dB, then any of the ratings would also

be raised by a similar amount for any spectrum shape. The reason for

99
this is that in the region between approximately 0.05 to 8 kHz and for

low to moderate noise levels, the loudness contours (which are the

basis for most of the rating schemes) are roughly parallel. Thus, the

ratings are not independent variables. As a consequence, the larger the

range of sound levels, the larger the correlation coefficent will be among

the rating schemes. In terms of practicality, what this means is that if

loudness contours are exactly parallel and if the spectra of consumer

products are identical, then any two rating schemes could be related by an additive constant. One could predict what one rating would be from

the actual rating obtained by simply adding some constant (i.e., the

E-weighted sound level could be predicted from the A-weighted sound level

by adding a constant).

However, since the loudness contours are only roughly parallel and since

spectra within a given class of products are not exactly identical, the

additive constant used to predict one rating from another will vary around

some mean value. The standard deviation around this mean value is a measure of how well one can predict one rating scheme from another, for either

similar spectra or significantly different spectra. Figure B-7 shows

schematically what is meant by the above discussion in terms of predicting

loudness level computed according to Stevens Mark VI method from a knowledge

of the A-weighted level.

Using the rationale given above, standard deviations were computed for the

additive constants that would be used to predict one rating from another

(the Zwicker loudness [20] was added for these calculations). The spectra

which were used to compute these standard deviation are those shown in

Figures B-l through B-6. The results of these computations for each product are given in Tables B-7 through B-12.

As can be seen from inspection of these tables, the A-, D-, and E-weighted

sound levels can be predicted from each other well since the standard deviation is usually small. On the other hand, the B- and C-weighted sound

levels cannot be predicted as well from the other weighted levels, as demonstrated by the fact that the standard deviation is usually larger.

The loudness levels as computed by the Stevens Mark VI, Mark VII, and

Zwicker procedures and the Perceived Noise Levels, whether tone corrected

or not, do not appear to improve significantly predictability relative to

the simple weighted levels (A, D and E). Thus in terms of a given product

class, A-, D-, and E-weighted sound levels are as "good" as more complex

methods to predict one rating from another.

Table B-13 is a summary of the same kind of computations, except that it

displays the standard deviation around the mean of additive constants used

to predict one scheme from another for all the products considered within

this study. This table was derived by aggregating all spectra for the 6

products studied, thus looking at the effect of significantly varied spectral shapes. The results of Table B-13 are similar to those discussed for

each individual product and indicate that use of either A-, D-, and Eweighted sound level is appropriate also for differentiating among several

types of products that have significant spectral differences. After the

analyses described in the appendix were completed, a parallel EPA-funded

study [21] was published that compares various noise rating procedures.

100
TABLE B-l. 8 GARBAGE DISPOSALS (MEASURED AT 1 m)

MEAN

(dB)

RANGE

(dB)

PEARSON CORRELATION COEFFICIENT

l

a

l

b

L

C ld

l

e LL(VI) LL(VII) l

pn

lpnt

la

67.6 10.6 1 .674 .587 .966 .946 .844 .908 .913 .961

l

b 71.9 10.5 .674 1 .972 .656 .744 .723 .596 .736 .648

L

c 74.3 10.4 .587 .972 1 .610 .696 .701 .566 .686 .608

l

d 74.8 12.2 .966 .656 .610 1 .985 • .830 .918 .888 .988

l

e 72.9 11.3 .946 .744 .696 .985 1 .794 .866 .857 .966

LL(VI) 80.8 9.6 .844 .723 .701 .830 .794 1 .941 .988 .853

LL(VII) 68.6 10.6 .908 .596 .566 .918 .866 .941 1 .957 .9 45

l

pn

81.1 11.7 .913 .736 .686 .888 .857 .988 .957 1 .902

lpnt 83.6 12.7 .961 .648 .608 .988 .966 .853 .945 .902 1

TABLE B-2. 12 ELECTRIC SHAVERS (MEASURED AT 1 m)

MEAN

(dB)

RANGE

(dB) la

l

b LC

PEARSON CORRELATION

l

d l

e LL(VI)

COEFFICIENT

LL(VII) l

pn

LPNT

la

58.8 28.0 1 .998 .996 .996 .993 .977 .966 .979 .993

l

b 57.8 27.8 .998 1 .999 .988 .984 .969 .953 .971 .991

L

c 58.0 27.8 .996 .999 1 .987 .983 .972 .953 .973 .990

l

d 66.9 29.0 .996 .988 .987 1 .998 .984 .975 .982 .990

l

e 64.3 28.4 .993 .984 .983 .998 1 .972 .964 .969 .991

LL(VI) 70.7 25.5 .977 .969 .972 .984 .972 1 .993 .998 .959

LL(VII) 60.1 23.7 .966 .953 .953 .975 .964 .993 1 .994 .943

l

pn

70.3 29.1 .979 .971 .973 .982 .969 .998 .994 1 .959

lpnt 73.6 30.4 .993 .991 .990 .990 .991 .959

*

.9 43 .959 1

101
TABLE B-3. 6 DISHWASHERS (MEASURED AT 1 m)

MEAN

(dB)

RANGE

(dB)

PEARSON CORRELATION COEFFICIENT

l

a

l

b L

C l

d l

e

LL(VI) LL(VII) l

pn

lpnt

la

65.9 16.0 1 .942 .921 .966 .971 .982 .952 .983 .963

l

b

72.7 21.1 .942 1 .989 .996 .994 .952 .837 .966 .975

Lc 75.8 22.6 .921 .989 1 .986 .976 .950 .839 .960 .970

ld 72.7 20.0 .966 .996 .986 1 .998 .971 .877 .982 .984

l

e 71.3 19.2 .971 .994 .976 .998 1 .968 .875 .980 .981

LL(VI) 80.1 14.9 .982 .952 .950 .971 .968 1 .957 .997 .979

LL(VII) 66.3 12.9 .952 ,837 .839 .877 .875 .957 1 .934 .884

l

pn

79.9 18.1 .983 .996 .960 .982 .980 .997 .934 1 .992

lpnt 82.1 19.2 .963 .975 .970 .984 .981 .979 .844 .992 1

TABLE B-4. 7 FOOD MIXERS (MEASURED AT 1 m)

MEAN

(dB)

RANGE

(dB)

PEARSON CORRELATION COEFFICIENT

lal

b LC

l

d l

e LL(VI) LL(VII) l

pn

lpnt

la69.1 15.5 1 .992 .982 .999 .994 .958 .973 .958 .993

l

b 69.5 15.4 .992 1 .998 .989 .991 .951 .971 • .945 .993

L

C 70.1 .5.5 .982 .998 1 .979 .987 .936 .961 .926 .988

ld 76.1 16.1 .999 .989 .979 1 .996 .953 .969 .953 .992

l

e 73.4 17.5 .994 .991 .987 .996 1 .932 .956 .930 .997

LL(VI) 80.4 16.5 .958 .951 .936 .953 .932 1 .995 .997 .928

LL(VII) 69.4 17.4 .973 .971 .961 .969 .956 .995 1 .989 .952

l

pn

81.4 18.2 .958 .945 .926 .953 .930 .997 .989 1 .927

lpnt 84.3 13.6 .993 .993 .988 .992 .997 .928 .952 .927 1

102
TABLE B-5 8 BLENDERS (MEASURED AT 1 m)

MEAN

(dB)

RANGE

(dB)

PEARSON CORRELATION COEFFICIENT

l

a

l

b L

C

lDl

e > M

N—

^(VII) l

pn

lpnt

LA

79.2 29.9 1 .994 .989 .992 .994 .984 .988 .980 .994

l

b 78.9 31.1 .994 1 .999 .973 .980 .963 .973 .957 .989

L

c 79.1 31.2 .989 .999 1 .965 .974 .956 .966 .949 .985

ld 86.5 29.8 .992 .973 .965 1 .998 .982 .987 .980 .981

l

e 83.5 29.9 .994 .980 .974 .998 1 .975 .983 .971 .981

LL(VI) 89.0 27.3 .984 .963 .956 .992 .975 1 .998 .999 .973

LL(VII) 77.4 27.8 .989 .973 .966 .987 .983 .998 1 .995 .976

l

pn

91.5 29.4 .980 .957 .949 .980 .971 .999 .995 1 .973

lpnt 94.8 29.4 .994 .989 .985 .981 .981 .973 .976 .973 1

TABLE B-6 . 16 CENTRIFUGAL FANS (MEASURED AT 1 m)

MEAN

(dB)

RANGE

(dB)

PEARSON CORRELATION COEFFICIENT

la

l

b LC ld

L

E LL(VI) LL(VII) lpn LPNT

la

43.5 59.2 1 .997 .993 .999 .999 .993 .997 .998 .999

l

b 47.6 59.5 .977 1 .999 .997 .998 .989 .991 .994 .995

L

C 50.4 58.5 .993 .999 1 .994 .994 .984 .986 .990 .990

l

d 49.6 59.9 .999 .997 .994 1 1.000 .995 .997 .999 .999

l

e 47.9 59.5 .999 .998 .994 1.000 1 .994 .996 .998 .999

LL(VI) 50.9 68.5 .993 .989 .984 .995 .994 1 .997 .996 .996

LL(VII) 39.8 66.0 .997 .991 .986 .997 .996 .997 1 .999 .998

l

pn

51.0 67.8 .998 .994 .990 .999 .998 .996 .999 1 .999

lpnt 57.3 64.5 .999 .995 .990 .999 .999 .996 .998 .999 1

103
TABLE B-7 . STANDARD DEVIATIONS for 8 GARBAGE DISPOSERS (MEASURED AT 1 m)

STANDARD DEVIATION (dB)

MEAN

(dB)

RANGE

(dB) l

a

l

b LC ld

l

e

LL(VI) LL(VII) LL(Z) l

pn

lPNt

la

67.6 10.6 0 2.6 3.0 1.1 1.1 1.8 1.6 1.6 1.5 1.3

l

b 71.9 10.5 2.6 0 0.8 3.0 2.4 2.4 2.4 3.2 2.5 3.2

L

c 74.3 10.4 3.0 0.8 0 3.2 2.7 2.5 2.6 3.3 2.8 3.4

ld 74.8 12.2 1.1 3.0 3.2 0 0.7 2.2 2.0 1.5 1.8 0.7

l

e 72.9 11.3 1.1 2.4 2.7 0.7 0 2.1 2.0 1.8 1.9 1.2

LL(VI) 80.8 9.6 1.8 2.4 2.5 2.2 2.1 0 0.3 1.8 0.8 2.2

LL(VII) 72.2 9.6 1.6 2.4 2.6 2.0 2.0 0.3 0 1.6 0.8 2.0

LL(Z) 85.7 9.95 1.6 3.2 3.3 3.3 1.5 1.8 1.6 0 1.7 1.2

l

pn

81.1 11.7 1.5 2.5 2.8 1.8 1.9 0.8 0.8 1.7 0 1.8

LpNT 83.6 12.7 1.3 3.2 3.4 0.7 1.2 2.2 2.0 1.2 1.8 0

TABLE B-8 . STANDARD DEVIATIONS for 8 BLENDERS (MEASURED AT 1 m)

mean

(dB)

RANGE

(dB)

STANDARD DEVIATION (dB)

la

l

b L

C l

d

l

e LL(VI) LL(VII) LL(Z) Lpn LPNT

la79.2 29.9 0 1.0 1.4 1.2 1.0 1.7 1.4 1.7 1.9 1.1

l

b 78.9 31.1 1.0 0 0.3 2.2 1.8 2.5 2.3 1.5 2.8 1.4

L

C 79.1 31.3 1.4 0.3 0 2.5 2.1 2.7 2.5 1.5 3.1 1.7

l

d 86.5 29.8 1.2 2.2 2.5 0 0.6 1.9 1.5 2.4 1.9 1.8

l

e 83.5 29.9 1.0 1.8 2.1 0.6 0 2.1 1.6 1.9 2.3 1.8

LL(VI) 89.0 27.3 1.7 2.5 2.7 1.9 2.1 0 0.5 2.6 1.0 2.3

LL(VII) 80.9 28.6 1.4 2.3 2.5 1.5 1.6 0.5 0 2.4 1.0 2.1

LL(Z) 94.1 27.8 1.7 1.5 1.5 2.4 1.9 2.6 2.4 0 3.3 2.4

l

pn

91.5 29.4 1.9 2.8 3.1 1.9 2.3 1.0 1.0 3.3 0 2.2

l pnt 94.8 29.4 1.1 1.4 1.7 1.8 1.8 2.3 2.1 2.4 2.2 0

104
TABLE B-9. STANDARD DEVIATIONS FOR FANS (MEASURED AT lm)

MEAN

(dB)

RANGE

(dB)

STANDARD DEVIATION (dB)

l

a

l

b L

C ld

l

e LL(VI) LL(VII) LL(Z) l

pn

lPNt

la

59.8 15.7 0 2.1 3.5 1.4 1.0 1.4 0.6 1.2 2.0 2.3

l

b 66.6 19.5 2.1 0 1.6 0.8 1.2 1.0 1.9 1.2 0.7 0.7

L

c 69.9 21.1 3.5 1.6 0 2.3 2.7 2.2 3.2 2.5 2.0 1.9

l

d 66.6 18.5 1.4 0.8 2.3 0 0.4 0.8 1.2 0.8 0.7 1.0

l

e 65.0 17.3 1.0 1.2 2.7 0.4 0 0.9 1.0 0.8 1.0 1.3

LL(VI) 75.3 17.4 1.4 1.0 2.2 0.8 0.9 0 1.1 0.4 1.2 1.4

LL(VII) 65.9 15.4 0.6 1.9 3.2 1.2 1.0 1.1 0 0.8 1.9 2.2

LL(Z) 79.1 16.8 1.2 1.2 2.5 0.8 0.8 0.4 0.8 0 1.3 1.6

l

pn 74.3 20.2 2.0 0.7 2.0 0.7 1.0 1.2 1.9 1.3 0 0.4

lpnt 76.2 20.5 2.3 0.7 1.9 1.0 1.3 1.4 2.2 1.6 0.4 0

TABLE B-10 STANDARD DEVIATIONS FOR 12 ELECTRIC SHAVERS (MEASURED AT lm)

MEAN

(dB)

RANGE

(dB)

STANDARD DEVIATION (dB)

l

a

l

b L

C ld

l

e LL(VI) LL(VII) LL(Z) l

pn

lpnt

la

58.8 28.0 0 0.6 0.7 0.9 1.0 1.7 2.2 1.1 1.9 1.2

l

b 57.8 27.8 0.6 0 0.3 1.4 1.5 2.0 2.4 0.9 2.2 1.4

L

C 58.0 27.8 0.7 0.3 0 1.4 1.6 1.9 2.4 0.7 2.2 1.4

l

d 66.9 29.0 0.9 1.4 1.4 0 0.6 1.6 2.2 1.7 1.7 1.2

l

e 64.3 28.4 1.0 1.5 1.6 0.6 0 2.0 2.6 1.8 2.2 1.2

LL(VI) 70.7 25.7 1.7 2.0 1.9 1.6 2.0 0 0.8 1.6 1.2 2.5

LL(VII) 62.5 24.4 2.2 2.4 2.4 2.2 2.6 0.8 0 2.0 1.4 3.1

LL(Z) 75.0 26.6 1.1 0.9 0.7 1.7 1.8 1.6 2.0 0 2.0 1.9

l

pn 70.3 29.1 1.9 2.2 2.2 1.7 2.2 1.2 1.4 2.0 0 2.5

LPNt 73.6 30.4 1.2 1.4 1.4 1.2 1.2 2.5 3.1 1.9 2.5 0

105
TABLE B-ll. STANDARD DEVIATIONS FOR 6 DISHWASHERS (MEASURED AT 1 m)

MEAN

(dB)

RANGE

(dB)

STANDARD DEVIATION (dB)

l

a

l

b L

C l

d

l

e LL(VI) LL(VII) LL(Z) l

pn

lPNt

la

65.9 16.0 0 2.6 3.5 2.0 1.7 1.1 1.1 1.7 1.3 1.9

l

b 72.7 21.1 2.6 0 1.3 0.7 1.0 2.6 3.0 3.7 1.9 1.7

L

c 75.8 22.6 3.5 1.3 0 1.7 2.1 3.2 3.6 4.0 2.5 2.2

l

d

72.5 20.0 2.0 0.7 1.7 0 0.5 1.9 2.3 3.0 1.3 1.2

l

e 71.3 19.2 1.7 1.0 2.1 0.5 0 1.8 2.2 2.9 1.3 1.3

LL(VI) 80.1 14.9 1.1 2.6 3.2 1.9 1.8 0 0.7 1.3 1.0 1.6

LL(VII) 71.1 13.7 1.3 3.0 3.6 2.3 2.2 0.7 0 1.1 1.6 2.3

LL(Z) 84.1 13.3 1.7 3.7 4.0 3.0 2.9 1.3 1.1 0 2.0 2.7

l

pn

79.9 18.1 1.3 1.9 2.5 1.3 1.3 1.0 1.6 2.0 0 0.8

lPNt 82.1 19.2 1.9 1.7 2.2 1.2 1.3 1.6 2.3 2.7 0.8 0

TABLE B-12. STANDARD DEVIATIONS FOR 7 FOOD MIXERS (MEASURED AT 1 m)

MEAN

(dB)

RANGE

(dB)

STANDARD DEVIATION (dB)

l

a

l

b L

C ld

l

e J3 >

LL(VII) LL(Z) l

pn

lpnt

la

69.1 15.5 0 0.8 1.3 0.5 0.8 1.8 1.9 1.0 2.0 0.7

l

b 69.5 15.4 0.8 0 0.5 1.1 1.0 2.0 1.9 0.3 2.3 0.8

L

C 70.1 15.5 1.3 0.5 0 1.5 1.3 2.2 2.2 0.3 2.6 1.1

l

d 76.1 16.1 0.5 1.1 1.5 0 0.6 2.1 2.1 1.2 2.1 0.9

l

e 73.4 17.5 0.8 1.0 1.3 0.6 0 2.5 2.5 1.1 2.6 0.7

LL(VI) 80.4 16.5 1.8 2.0 2.2 2.1 2.5 0 0.3 2.1 0.8 2.4

LL(VII) 72.1 16.9 1.9 1.9 2.2 2.1 2.5 0.3 0 2.1 0.8 2.4

L L(Z) 86.3 15.3 1.0 0.3 0.3 1.2 1.1 2.1 2.1 0 2.5 0.9

l

pn

81.4 18.2 2.0 2.3 2.6 2.1 2.6 0.8 0.8 0.8 0 2.6

l pnt 84.3 16.4 0.7 0.8 1.1 0.9 0.7 2.4 2.4 2.4 2.6 0

106
TABLE B-13 STANDARD DEVIATION FOR THE 6 PRODUCTS OF TABLES B-7 THROUGH B-12

MEAN

(dB)

STANDARD DEVIATION (dB)

l

a

l

b L

C

ldl

e LL(VI) LL(VII) LL ( Z ) l

pn

lpnt

la66.3 0 3.6 5.0 1.2 1.2 2.4 2.1 1.9 2.0 1.5

l

b 68.6 3.6 0 1.5 4.0 3.5 2.8 3.2 2.9 3.0 3.2

L

c 70.0 5.0 1.5 0 5.4 4.8 3.8 4.3 4.0 4.3 4.5

ld

73.7 1.2 4.0 5.4 0 0.8 2.7 2.5 2.5 2.2 1.6

l

e 71.4 1.2 3.5 4.8 0.8 0 2.4 2.3 2.2 2.2 1.4

LL(VI) 78.7 2.4 2.8 3.8 2.7 2.4 0 0.8 1.8 1.5 2.6

L

L(VII) 70.2 2.1 3.2 4.3 2.5 2.3 0.8 0 1.8 1.5 2.6

LL(Z) 83.4 1.9 2.9 4.0 2.5 2.2 1.8 1.8 0 2.2 2.1

l

pn

79.1 2.0 3.0 4.3 2.2 2.2 1.5 1.5 2.2 0 2.0

lpnt 81.9 1.5 3.2 4.5 1.6 1.4 2.6 2.6 2.1 2.0 0

107
Figure B-l. Frequency spectra for dishwashers.

108
Figure B-2 . Frequency spectra for garbage disposals.

109
Figure B-3. Frequency spectra for electric shavers.

110
Figure B-4. Frequency spectra for food blenders.

Ill
Figure B-5. Frequency spectra for food mixers.

112
CM

50 - —• 1/3 OCTAVE ANALYSIS

o- —ol/t OCTAVE ANALYSIS

40

30

20

I5s"ate3&-=».

1000

FREQUENCY, Hz

10000

Figure B-6. Frequency spectra for centrifugal fans.

113
NUMBER

L

—16

Figure B-7 Schematic presentation of the variation in the

standard deviation of the additive constant used

to predict one rating from another, e.g., loudness

level from A-weighted level.

114
References for Appendix B

[1] Noise Standards for Civil Subsonic Turbojet Engine-Powered Airplanes

(Retrofit and Fleet Noise Level) (U. S. Environmental Protection Agency,

Washington, D. C., December 16, 1974).

[2] Noise Emission Standards for Construction Equipment: Background Document for Portable Air Compressors, Report No. EPA 550/9-76-004 (U.S.

Environmental Protection Agency, Washington, D. C., December 1975).

[3] Background Document for Medium and Heavy Truck Noise Emission Regulation, Report No. EPA 550/9-76-008 (U. S. Environmental Protection

Agency, Washington, D. C., March 1976).

[4] Information on Levels of Environmental Noise Requisite to Protect

Public Health and Welfare with an Adequate Margin of Safety, Report No.

EPA 550/9-74-004 (U. S. Environmental Protection Agency, Washington,

D. C., March 1974).

[5] Public Health and Welfare Criteria for Noise, Report No. EPA 550/9-73-

002 (U. S. Environmental Protection Agency, Washington, D. C.,

July 27, 1973).

[6] Stevens, S. S., The measurement of loudness, J. Acoust. Soc. Am. 27 , 815-829 (1955).

[7] Stevens, S. S., The measurement of loudness, J. Acoust. Soc. Am. 31 , 995-1003 (1959).

[8] Stevens, S. S., Concerning the form of the loudness function, J.

Acoust. Soc. Am. 29_ t 603-606 (1957).

[9] Young, R. W., Don't forget the simple sound level meter, Noise Control

4, 42 (1958).

[10] Young, R. W., Single number criteria for room noise, J. Acoust. Soc.

Am. 36, 289-295 (1964).

[11] Kryter, K. D., Scaling human reactions to the sound from aircraft,

J. Acoust. Soc. Am. 31_, 1415-1429 (1959).

[12] Kryter, K. D., The meaning and measurement of perceived noise level,

Noise Control 6^, 12 (1960).

[13] Kryter, K. D., Concepts of perceived noisiness, their implementation

and application, J. Acoust. Soc. Am. ji3, 344-361 (1968).

[14] Kryter, K. D., and Pearsons, K. S., Some effects of spectral content

and duration on perceived noise level, J. Acoust. Soc. Am. 15, 866-883

(1963).

115
[15] Kryter, K. D., and Pearsons, K. S., Modification of noy tables, J.

Acoust. Soc. Am. _36, 394-397 (1964).

[16] Fletcher, H. , and Munson, W. A., Loudness: Its definition, measurement, and calculation, J. Acoust. Soc. Am. I>, 82-108 (1933).

[17] International Electrotechnical Commission Standard on Frequency

Weighting for Measurement of Aircraft Noise (D-weighting) , Publication

537 (International Electrotechnical Commission, Geneva, Switzerland,

1976).

[18] American National Standard Procedure for the Computation of Loudness

of Noise, S3. 4-1968 (R1972) (American National Standards Institute,

New York, New York, 1972).

[19] Stevens, S. S., Perceived level of noise by Mark VII and decibels (E),

J. Acoust. Soc. Am. 51, 575-601 (1972).

[20] International Organization for Standardization Standard on Acoustics —

Method for Calculating Loudness Level, ISO 532-1975 (International

Organization for Standardization, Geneva, Switzerland, 1975).

[21] Scharf, B., Heilman, R. , and Bauer, J., Comparison of Various Methods

for Predicting the Loudness and Acceptability of Noise, Report No. EPA

550/9-77-101 (U. S. Environmental Protection Agency, Washington, D.C.

,

August 1977).

116
APPENDIX C

LISTING OF COMPUTER PROGRAM FOR ASSESSING THE IMPACT OF

NOISE FROM CONSUMER PRODUCTS

C.l Main Program

l*

2 * 3*

4*

5*

6*

7*

8 * 9*

10 *

1 t*

12 * 13* 14*

15 *

1 6* 17* 18*

19*

20 * 21 * 22* 23*

24*

25*

26*

27*

2 8* 29*

30*

31*

32*

33*

34*

35*

36*

37*

38*

39*

40*

41*

42*

C THIS CODES COMPUTES POPULATION IMPACTS FOR NOISE FROM HOUSEHOLD

C APPLIANCES AND CONSUMER PRODUCTS

C

C I DESIGNATES TYPE OF DWELLING

C 1=1, SINGLE FAMILY 1=2 , TOWNHOUSE I =3 . MULT IFAM ILY

C J DESIGNATES SOURCE ROOM

C J=1 .KIT J=2. LR-DR-FR J=3»BATH J=4 • BEDRM J=5 • BASEMENT-UT I L- GAR

C J=6» OUTDOORS

C K DESIGNATES RECEIVING ROOM WITHIN PRIMARY DWELLING

C K SAME AS J

C L DESIGNATES RECEIVING ROOM WITHIN SECONDARY DWELLING

C L SAME AS J

C M DESIGNATES TYPE OF PERSON

C M=1 • UNEM ADULT MALE M=2,EM ADULT MALE M=3,UNEM ADULT FEM

C M=4 * EM ADULT FEM M=5.SCHOOL AGE CHILD M=6. PRESCHOOL CHILD

C N DESIGNATES TYPE OF DAY

C N= 1 • WEEKDAY ( SCHOOL ) N=2. WEEKDAY! NO SCHOOL) N=3, WEEKEND

C P DESIGNATES TIME-PERIOD DURING DAY

C P=l* 0700-0900 P=2. 0900-1700 P=3, 1700-2200 P=4, 2200-0700

C S DESIGNATES THE OCTAVE BAND(S=1 FOR 63 HZ.S=2 FOR 125 HZ* ETC.)

C T DESIGNATES THE SOUND POWER LEVEL OF THE SOURCE. A MAXIMUM OF 20

C LEVELS IS ALLOWED. E.G. 20 OB IN 1 DE STEPS. 40 DB IN 2 DB STEPS

C NRP (S.K.J.I) DESIGNATES NOISE REDUCTION WITHIN PRIMARY DWELLING

C NRS(S.L.J.I) DESIGNATES NOISE REDUCTION BETWEEN PRIMARY DWELLING

C AND A SECONDARY DWELLING

C TIMEINI I.K.M.P.N) DESIGNATES THE LENGTH OF TIME DIFFERENT TYPES

C OF PEOPLE SPEND IN DIFFERENT TYPES OF ROOMS IN

C DIFFERENT TYPES OF DWELLINGS FOR DIFFERENT TIME

C PERIODS ON DIFFERENT TYPES OF DAY (IN MINUTES)

C PEOPLE ( I. M ) DESIGNATES THE TOTAL NUMBER OF PEOPLE

C OF A GIVEN TYPE THAT LIVE IN A GIVEN TYPE OF

C DWELLING! IN MILLIONS)

C TIM(I.N) IS AVERAGE LENGTH OF EACH USE IN EACH BUILDING

C ONTIM(I.N) IS AVERAGE LENGTH OF TIME IN MINUTES/DAY • C A GIVEN PRODUCT OPERATES IN A GIVEN TYPE OF DWELLING

C ON A GIVEN TYPE OF DAY

C SPECTRIS) DESIGNATES THE OCTAVE BAND LEVEL, RELATIVE TO THE

C A— WE IGHTED LEVEL. FOR A GIVEN TYPE OF PROOUCT.

C IT IS ASSUMED THAT THE

C SPECTRUM SHAPE IS THE SAME FOR SOUND POWER AND FOR

C SOUND PRESSURE AT THE OPERATOR LOCATION

C PERC ! 1 1 ) IS THE FRACTION OF EACH TYPE HOUSE THAT HAVE THE APPLIANCE

117
43 * 44 * 45 * 46 * 47 * 48 * 49 * 50 * 51 * 52 * 53 * 54 * 55 * 56 * 57 * 58 * 59* 60 * 61 * 62* 63 * 64 * 65 * 66 * 67 * 68* 69 * 70 *

7 1 * 72 * 73 * 74* 75 * 76 * 77 * 78* 79 * 80 * 81 * 82 * 83 * 84 * 85 * 86 * 87 * 88 * 89* 90 * 91 * 92 * 93 * 94 * 95 * 96 * 97 * 98 * 99 * 10 0 *

C OISLEV(T) IS THE PROBABILITY OF THE POWER LEVEL IN THE SOURCE

C ROOM BEING IN A PARTICULAR DECIBEL RANGE SPECIFIED RY

C MIN* MAX* ID8(MINI MUM* MAX I MUM* STEPS I ZE IN DB

)

C OPDIFF IS THED IFFERENCE! ASSUMED CONSTANT) BETWEEN THE SOUND LEVEL

C AT THE OPERATOR LOCATION AND THE POWER LEVEL IN THE

C SOURCE ROOM. SPL < OPERATOR ) =PWL+OPD I FF

C PROBONII.J ,P,N) DESIGNATES THE PROBABILITY OF A GIVEN TYPE

C OF PRODUCT BEING OPERATED IN A PARTICULAR TYPE

C OF DWELLING IN A PARTICULAR ROOM IN A GIVEN

C TIME PERIOD ON A GIVEN TYPE DAY. EITHER INPUT DIRECT

C LY(NT=0> OR CALC FROM ONT I M* TPER* TP9* RMP9

C T P9 C P * I * N ) IS RELATIVE PROS OF AN APPLIANCE BEING OPERATED IN A GIVEN

C TIME PERIOD IN A GIVEN TYPE HOUSE ON A GIVEN TYPE OF

C DAY (SUM OVER P=1

)

C RMP9 ( J • I » N ) IS RELATIVE PROB OF AN APPLIANCE BEING OPERATED IN A GIVE

C N ROOM IN A GIVEN HOUSE ON A GIVEN D A Y( SUM OVER J=l)

C OPTYP GIVES THE FRACTION OF OPERATORS AS TO TYPE OF PERSON

C NC A IS THE NUMBER OF DIFFERENT CALC OF POP IM PARAMETER 1 1=3 * J J=6 • KK=6 *LL=6 * MM=6. NN=3 , 1 P=4 * IS = 8,NCA= 20

DIMENSION PEOPLE! II.MM), SEOPLE< 1 1 * MM) * OPTYP ( MM

)

1 * T I ME IN ( I I * KK* MM* IP. NN), S I ME I N ( I I ,KK . MM , I P, NN

)

DIMENSION A1 ( IS) • A2( IS) • A3< IS) *A4(IS) . A5< IS) * A6 ( IS)

1 *NRP( I S* KK* J J* I I ) * NRS ( I S*LL* J J* I I ) *SP£CTR( IS)* XLEQ ( IS. II.MM, 3

)

DIMENSION ONT IM( I I * NN) * PROBON (II.JJ.IP *NN ) * TP9( IP.II.NN)

DIMENSION TPER (IP). XDAYS(NN)*PI (5)*LEV( 16 ) • U ( 16 ) • XT ( 1 6

)

DIMENSION NCANUM(NCA) .DISLEV! 20) .OPDIFF (3 ) .T IM( II. NN

)

DIMENSION TITLE(IO) »PERC (II ) . T ITC AL ( 1 3, NCA ) . RMP9< J J . I I * NN

)

INTEGER P ,S, T.HSWT! 3)

REAL NRP* NRS* LEV

C LEV AND U ARE FOR SPEECH INTERFERENCE CALCS

DATA LEV/50. .55..60..64..65.*66..67..68..69..70.,71.*72.*73.»74..

U75. ,76./

DATA U/0 . *.0I* .02 5* .05*. 08**1* .13* .18*. 265*. 37* .51. .68. .81

U, .905, .99, l .0/

C XD AY S GIVES FRACTION(OUT OF 365) OF EACH TYPE OF DAY

DATA XDAYS/.534* . 181 . .285/

C TPER(IP) GIVES TIME PERIODS AS FCN OF P(MINUTES)

DATA TPER/120..480. * 300. * 540./

C HSWT IS NUMBER OF SECONDARY DWELLINGS AFFECTED ( MINUTES

)

DATA HSWT/2,2.4/

C I I K=4 CALC SINGLE EXP FROM SEC *DONT ADD TO TOTAL

C I IK=5 CALC MULTIPLE EXP FROM SEC .ADD TO TOTAL

I I K=5

C *************

c

c

C THESE INPUTS ARE STANDARD AND SHOULD BE READ FROM FILE IMPACTDAT

A

C TITCAL ARE NAMES OF THE NCANUM CALCS

C ALL ARE READ IN HERE

READ( 5,4) TITCAL

4 FORMAT ( 13 A6

)

C TIMEIN INPUT II BUILD* KK RECEIV RMS/CARD *MM PERSONS*PP PERIODS*NN DAYS

READ( 5* 11 )T I ME IN

11 FORMAT! 18F4.0)

C PEOPLE INPUT 1 CARD I I BU ILD I NGS* MM PERSONS

READ( 5* 11) PEOPLE

C NRP, NRS INPUT 208 CARDS(NOISE REDUCTION MATRICES)

C 16 OCT/CARD*3 SOURCE RMS*6 REC RMS*3 BUILDINGS

118
101 * 102 * 102 * 104*

105*

106*

107*

108*

109*

I 1 10*

111 *

| 112*

j 112*

! 114*

115*

116*

j 117*

118*

119*

120 * 121 * 122 * 123*

124*

125*

i 126*

127*

12e*

129*

130*

133*

134*

135*

136*

137*

138*

139*

140*

141*

142*

i 143*

144*

145*

146*

147*

148*

149*

; iso*

151*

152*

153*

154*

155*

156*

157*

158*

READ (5*5) NRP

READ (5*5 )NRS

5 FORMAT ( 16F5. 1 1 C PEOPLE IK MILLIONS SO CONVERT

00 741 [>1,II

00

741 M=1,MM

741 PEOPLE ( I » M )= 1 E 6* PEOPLE ( I • M

)

C

C

C********* )

C TITLE INCLUOES===APPLI ANCE NAME • OPERATOR REQUIREMENT

1 FORMAT ( ) RE AO (5, 31TITLE

3 FORMAT 1 10 A6

)

2 FORMAT ( 1 H 1 • 1 0A6)

«RITE(6,2 1TITLE

C NCANUM ARE ALL CALC NUMBERS OESIREOIl TO NCA. IF 0 NEGLECT

READ ! 5, 1 1 NCANUM

C OPT V P INPUT 1 CARO MM PERSONS .IF NO OPERATOR ENTER BLANK CARD

READC 5# 10 )OPTYP

10 FORMAT ( 1 2F6 • 2

)

cPTor=o

DO 24 M= 1 • MM

24 OPT OT=QPTQ T 4QPTYP (Ml

C PERC INPUT II BUILDINGS

fi EAD { 5# 10) PERC

C SAV 1 IS TOTAL NUM OF POTENTIALLY EXPOSEO PERSONS IN PRIMARY DUELLING

S A V 1 =0

POPT OT=0

00 29 Is 1 • I I 00 29 M= 1 , MM

SEOPLE < I * M )=PEOPLE( I,M)*PERC( I)

POPTQT-=PQPTOT PEOPLE < I,M ) 29 SAV1=SAV1+SE0PLE( I ,M>

C CNTIM.TIM 1NPUTII BUILDINGS X NN DAYS

C IF CNTIM.TIM INOOF I AND N INPUT 1 NUM F6, IF INDEP OF N INPUT 3 NUM

CALL INPUT(CNTIM, II, NN)

CALL INPUT! TIM, I I ,NN)

C IF NT=1 PROGRAM SETS N = 2.3 TO N=1 (NT=l OR 3)* IF NT=0 READ DIRECTLY

READ! 5* 1 JNTtMT

IF ( NT • EC • 0 ) RE AO (7,11) PROBON

00 22 N= 1 .NT

22 CALL I NPUT (TP9(1.1.N)«IP,II)

C IF MT=1 PROGRAM SETS M=2,3 TO M= 1 (MT-l OR 3)

00 23 N = 1 » MT

23 CALL INPUT! RMP9( 1 .1 *N) • JJ. I I

)

K 1 = 1 K3=l

00 30 N= 1 • NN

IF(NT,EC.3)K1=N

IF ( MT ,EG,3)K3=N

00 30 1=1,11

00 30 J=1,JJ

00 30 P=1,IP

RMP9! J, I , N ) =RMP9 ( J, I • K3

)

TP9! P, I,N)=TP9(P» I • K 1

)

IF ( NT • NE • 0 ) PROBON! I, J, P,N)=ONTIM( I, N)*TP9( P, I , N)*RMP9( J , I , N )/

1 TPER(P)

119
1 59*

160 * 161*

162*

l 63 * 164*

166*

166*

167*

168*

169*

170*

171 *

1 72*

173*

174*

175*

176*

177*

178*

179*

180*

181*

182*

183*

184*

185*

186*

187*

168*

189*

190*

191*

192*

193*

194*

195*

196*

197*

198*

199*

200* 201 * 202 * 203*

204*

205*

206*

207*

20e*

209*

2 10 *

2 11*

212 *

2 13*

21 4*

2 15 *

2 16*

C TIMEIN AS INPUT INCLUDES OPERATOR TIME SO SUBTRACT OFF

DO 30 M=1*MM

CPT I M = T PER ( P 1 *PRUBGN! I • J * P* N ) *OPTYP ( M

)

SIMEIN!I*J*M,P,N)=TIMEIN<I.J*M*P,N)-OPTIM

C ALL OK FOR SMALL DISCREPANCIES IN PROBON VS TIMEIN

IF(SIMEIN( I.J.M.P.N) «OT • — • 2 ) GO TO 30

MRITEI6.60) l i Ji M* P» N

60 FORMAT ( 1H ,' CALC A0ORTED-T I ME I N LESS THAN OPTIM FOR I*J*M*P,N='

U • 5 I 5 ) C-0 TO 999

30 CONTINUE

C SPEC T R INPUT 1 CARD (8 FREQ)

READ ( 5 f 10 ) SPECTR

REA0!5.1)IDB,M IN # MAX • OPD I FF ( 1 ) IT=( MAX-M IN )/IDB+ 1 READ! 5, 11 ) ( D I SLEV ( T ) » T= 1 .IT)

C NORMALIZE SPECTR TO AWT=0

CALL MW IGHT( 1 • SPECTR. A)

DO 21 S= 1 • I

S

21 SPECTR! S)=SPECTR( S)-A

MR ITE (6*12)

12 FORMAT! 1H . 'POPULATION IMPACTS FOR OPERATOR. PRIMARY . SECONDARY

l DWELL INGS ' ) WRITE!6*40)SAVl»P OPT OT

40 FORMAT ! 1H EXPOSED PER SONS= • , E9 .4 ,

• TOTAL POPUL A T l CN= • • E9 . 4

)

IF! OPTO T.EQ. 0) WRI TE! 6*41

)

41 FORMAT ! 1H , • NO OPERATOR FOR THIS APPLIANCE')

ITST=0

C ICA IS REFERRED TO IN PAPER AS KTH EFFECT WP!K)

DO 950 IC A= 1 . NCA

I T ST=NC ANUM ( ICA)

IF(NCANUM! ICA) .EQ.O)GO TO 950

DO 39 IK=l . I IK

IF! IK.EQ.l. AND .OP TOT «EQ • 0 • ) 60 TO 38

IF! IK.EC.4. AND .OPTOT • EQ • 0 • ) GO TO 38

P I ! IK )=0

IF! ITST .EQ.4 ) GO TO 39

DO 28 M= 1 .MM

DO 28 1=1*11

IF! ITST.LE.2 )CALL ALLLEQ

IF ( ITST. EQ. 3) CALL SPEECH

28 CONTINUE

~ 38 CONTINUE

39 IF ! ITST .EQ.4) CALL SLEEP

IF! ITST.EQ.3)PI(4)=PI!1 ) +P I ! 2 ) IF! I IK.EQ.5. AND. ITST .EQ.4) PI ! 5)=P I ! 2 ) +PI! 3)

MR ITE! 6. 1 3) ICA, ! T ITCAL! J. ICA) • J=1 * 1 3)

13 FORMAT! 1H0, 15, 3X . 13A6)

IF! ICA.EQ.10)WRIT£!6,17)

17 FORMAT! IH ***** NO SECONDARY CALC FOR SPEECH INT***')

IF! ICA.GE. 19)MRITE!6, 13)

18 FORMAT! 1H * ****N0 OPERATOR CALC FOR SLEEP INT***')

IF! ICA.GE. 19)WRITE!6* 19)

19 FORMAT! IH ,'***N0 NIGHT OPER FOR VACUUM USE TPER!3) FOR TEST')

IF! IIK.EQ.5)MRITE!6« 16)

IF! I IK .EQ.4 ) MR ITE !6. 20)

20 FORMAT! IH , 28X, 'OPERATOR • ,8X. 'PRIMARY • . 5X .

• SECONDARY • . 8X* 'PR+OP' ) 16 FORMAT! IH *28X* 'OPERATOR ' • 8X* • PRIMARY • . 5X* • SECONDARY • • 8X * • PR I +0P
217* 1 • 8X» • TOTAL •

)

2 16* *RITE(6.14)(PI( I K 1 « I K— 1 . I (K)

219* 14 FORMAT C 1 H ,« WEIGHTED POPULAT I ON • . 5E 1 5 • 4

)

220* 00 740 1=1 1 1 IK

221* 740 PI < I )=PI( I1/SAV1

222* WRITE(6,15)<PI( IK). 1K=1 , IIK)

223* 15 FORMAT ( 1 H ,» NOISE IMPACT INDEX ’•5E15.4)

224* 950 CONTINUE

2 25* 999 STOP

226* C INPUT DETECT S BLANKS ON INPUT AND REPEATS ACCORD

227* SUBROUTINE INPUT(X.N.M)

228* 0 l MENS ICN XIN.M)

229* DIMENSION VF (3) » V A ( 3

)

230* DATA VF/» ( 12F* , *6 • .

• .3) '

/

231* DATA VA/« ( 12A» • *6* •

• ) •/

232* BL ANK= • • 233* IF ( N «LE *4 ) GO TO 10

234* VFC1 »*• (18F«

235* VF (2 ) = * 4 • 236* V A ( 1 )=• ( 1 QA

237* VA(2)=»4»

238* 10 CONTINUE

239* M 1 =0

240* N 1 = 0

241* READ I 5 . VA ) X

242* IF ( X ( 2 » 1) .EQ. BLANK) Nl=l

243* IF ( X ( 1*2) .EQ. BLANK) M 1 = 1 244* RE AD < 0 • VF ) X 245* I F ( N 1 • E Q • 0 ) GO TO 20

246* 00 12 I = l » M

247* DO 12 J= 1 * N

248* 12 X( J, I )=X( 1 , 1 ) 249* GO TO 25

250* 20 IF (Ml .EC.OIGO TO 25

251* DO 14 1=2 iM

252* 00 14 J= 1 •

N

253* 14 X (J, I )=X( J, 1

)

254* 25 CONTINUE

255* RETURN

256* SUBROUTINE ALLLEQ

257* c***

258* IF ( IK.GT.31G0 TO 730

259* I F ( IK.NE. UGO TO 24

260* 00 23 S=1 .IS

261* 23 XLEQIS. I • M . 1 >=-1000

262* IF (CPTYP(M) • EQ« 0 ) RETURN

263* 24 CONTINUE

264* 00 27 S=1 .IS

265* XLEQIS. I • M , IK) =0

266* IFIIK.NE.UGO TO 705

267* c TO 705 POP I M FOR OPERATORS

268* c 24 HR LEO FOR OPERINO D-N PENALTY FOR OPERATORS

)

269* X1=0

270* 00 718 N= 1 • NN

271* 718 X 1 =X 1 -fC NT I M ( I «N)*XDAYS(N) *OPT YP ( M ) 272* XLEQIS. I.M.l ) = SPECTR ( S ) * 1 0 *ALOG 101X1/1440)

273* GO TO 27

274* 705 CONTINUE
275*

27 6*

277* 276*

279*

280*

28 1 * 282*

283*

284*

285*

286*

267*

288*

289*

290*

291*

292*

293*

294*

295*

296*

297*

298*

299*

300*

301*

302*

303*

304*

305*

306*

307*

308*

309*

310*

3 11 * 312*

313*

3 14*

315*

316*

317*

318*

319*

320*

32 1*

322*

323*

324*

325*

326*

327*

328*

329*

330*

331 * 332*

00 39 N=1 *NN

S AV2 =0

00 26 J=1 t JJ

OO 26 K = 1 iKK

00 26 P=1 • IP

AA=SIMEIN(I.K.M,P,N)

BB=PROBCN( I* J.P.N)

IF< IK.EG.2)X1=SPECTR(S)-NRP< S.K. J . I

)

IF<IK.EG.3)X1=SPECTR(S)-NRS(S,K. J. I

)

IF ( ITST.EQ.2. AND.P.EQ.4 ) X1 = X1 MO

CC=10.**<Xl/10)

S AV2= SA V2 + A A*0B*CC

26 CONTINUE

39 XLEQ ( S • I * M . I K ) =XLEQ ( S * I.M, I K ) +XOA YS ( N 1 * SA V2

XLEQ ( St It Mt IK >=10*ALOG10( XLEQI St I.M,IK)/1440)

27 CONTINUE

730 CONTINUE

C POP I M OPERATORS AND PR I MAR Y { TO 717)

IF< I IK.EQ.5.AND. IK.EQ.3) GO TO 328

IF(I IK.EQ.5.AN0.IK.EQ.5) GO TO 828

C IK=1 FOR OPERATORS. IK=2 FOR PRIMARY. IK=3 FOR SECONDARY ,IK=4 FOR OP

C ER+PRIM ,IK=5 FOR TOTAL

00 717 I T 1 = 1 • IT

IFIIK.EG.4) GO TO 825

00 716 S=1.1S

7 16 A1(S )-= XLEQI S. I.M,IK)+MIN+(IT1-1 ) *IDB+OPOIFF (IK)

GO TO 826

825 DO 827 S= 1 . I

S

AA1 =XL£G( S. I *M. 1 ) + MlN+( ITl-l )*IDB+OPO IFF( 1

)

AA2=XLEC(S. I.M. 2)*MIN+( IT 1-1 )*IDB

827 A1 (S)=10* ALOG 1 0 ( 1 0** ( A A l / 1 0 ) + 1 0** ( A A2 / 1 0 ) ) 826 CALL to* I GET ( ICA.A1 .A)

CALL toE(W.A.ICA)

A=SEOPLE< I.M)

717 PI ( IK)=PI ( IK) + A* W*0 I SLEV ( IT1

)

RETURN

828 CONTINUE

C POP I M FOR SECONDARY AND TOTAL TO 714

SAV2=0

00 824 S=1,IS

Al (S)=0

A2(S)=0

A3 ( S ) =0

A 4 ( S ) = 0

A5(S)=0

824 A6(S)=0

A A 1 = 1 A A 2= 1 A A3= 1 AA4= 1 A A5- l IF ( IK.NE.5 )GQ TO 722

1 T 5=0

724 I T 5= I T 5

M

A A5=D I SLEV ( IT5)

00 723 S=1.1S

AA = XLEQ ( S . I . M, 1 ) 4-MIN+I IT 5-1 )* IOB+OPOIFFI 1 ) BB = XLEG(St I.M.2)+MIN+( IT 5— 1 ) * IDS

122
333* 723 A 5 ( S ) = 1 0* * C A A/ 10) * 1 0 * * ( DB/ 10)

334* 722 IF(HS*T( I 1-3)713,712.

335* I T 1 =0

336* 725 IT1=IT1+1

337* AAl=OISLEV( IT1

)

338* DO 715 S=1.IS

339* 715 A 1 <S)=1 0**( < XLEQI S, I ,M,3 )+MIN*< IT1-1 > * IDBl/l 0

1

340* 712 I T2= 0

341* 727 I T2= I T2*

1

342* AA2=0 ISLE V ( IT2)

343* DO 719 S=1 • IS

344* 719 A 2 ( S 1 = 1 0* ( ( XLEQ ( S * I .M.3) *MIN*C IT2-1 ) *1CB )/10

)

345* 713 IT3=0

346* 728 IT 3= I T3+1

147* AAlaMSLEVf ITU

J48* DO 720 5=1, IS

349* 720 A3 ( S 1 = 1 0* * ( ( XLEQ ( St I . M. 3 ) *M IN* < I T 3- l ) * I OB 1 / 1 0 > 350* I T 4=0

351 * 726 IT 4= I T4 *1

352* AA4=D1SLEV( IT4)

353* 00 721 S=l, IS

354* A4 < S 1= 1 0* * C ( XLEQ ( S.I ,M,3) +MIN+( IT4-1 >*ICB)/10

1

355* 721 A6 ( S )= 1 0* ALOG 1 0 ( A 1 ( S ) *A 2 ( S > + A3 < S 1 +A4 ( S ) *A 5( S ) ) 356* CALL MM IGHT ( ICA.A6.A)

357* CALL ME ( M » A » ICA)

358* 714 SAV2=SAV2*M*AA1*AA2*AA3*AA4*AA5

359* IF(IT4«LT«IT )GO TO 726

360* IF( IT3.LT. IT1GO TO 728

361* IF(HSMT( I ) .GE.3.AND. IT2.LT. IT1GO TO 727

362* IF(HSMT( I ) .EQ.4. AND. I T1 #LT. IT1G0 TO 725

363* IF( IK.EG.5. AND.IT5.LT, IT 1GO TO 724

364* PI( IK)*PI( IK)*SEOPLE< I,M)*SAV2

365* RETURN

366* SUBROUTINE SLEEP

367* IF(IK.NE.2. AND. IK. NE.3) RETURN

368* 00 37 1=1,3

369* DO 42 J=1,JJ

370* DO 43 S=l • IS

371* IFC IK»EQ,£)A6(S)= SPECTR ( S

) -NRP ( S • 4 . J , I

)

372* 43 IF ( IK.EQ.3) A6C S)=SPECTR ( S )-NRS( S,4, J , I

)

373* 42 CALL MMIGHTIl* A6 , A 1 ( J )

)

374* X 3=0

375* DO 36 T= 1 . IT

376* OO 36 N= 1 . NN

377* DO 36 J=1 , J

J

378* EB=T I M ( I , N ) *60*RMP9 ( J * I , N

)

379* c***

380* c*** TPER( 4 1 CHANGED TO TPER ( 3 ) FOR TEST ONLY

381* c***

382* X2=PROBCN< I, Jt3.N)*60*TPER(4)

383* 38 X2=X2—BB

384* IF (X2.GT.0) X1=BB

385* IF (X2.LE.0) X1=X2*BB

386* I F ( X 1 ,LE • 0 ) GO TO 36

387* IFIXl.GT. 120. 1X1=120

388* AA=A1 <J)*MIN*( T— 1 1*1 DB

389* SEL=AA+ 10*AL0G10( XI

)

390* CALL ME(M ,SEL. ICA)

123
391* X3=X3+XOAVSCN)*W*DISLEVC T)

392* IFCX2.GT.01G0 TO 38

393* 36 CONTINUE

394* X4=0

395* DO 39 M-l »MM

396* 39 X4=X4*SE0PLE ( I. Ml

397* A A

398* IF ( IK.EQ.3) AA=HSWT( I

)

399* 37 P I C IK)=PI ( IK I +X4*X3*AA

400* RETURN

401* SUBROUTINE SPEECH

402* IF( IK.GT.2) RETURN

40 3* I F ( IK • NE • 1 ) GO TO 705

404* IF ( OPTYP (M).EQ.O. IRETUR N

405* C TO 705 SPEECH INT FUR OPER

406* A 2 ( 1 )=0

407* DO 38 T= 1 • IT

406* cc=o

409* X 1=M I N+ (T— 1 )* IDB+OPO IFF C 1

)

410* IF( XI .LT.50JG0 TO 38

41 1* IFCX1.GT.76)X1=75.9

4 12* CC = A IT INTCLEV.U.16.X1 *2 * XT)

413* * • A2( 1 ) = A2( 1 ) +CC*D I SLE V ( T ) 4 14* 38 CONTINUE

415* X II O

416* DO 718 N=1 * NN

417* 7 18 X1 = X1 +0NTIMI I * N ) * XDA YS( N)*OPTYP(M)

418* PI Cl )=PI( 1 > + A2 ( 1 >*Xl*SEOPLE( I.MJ/900

4 19* RETURN

420* 705 CONTINUE

421* DO 36 T = l . IT

422* A2C2 )=0

423* DO 39 N= 1 » NN

424* £ A V 2= 0

425* DO 26 J=1 . JJ

426* DO 26 K-= 1 » KK

427* DO 37 S=l , IS

428* 37 A6(S»=SPECTRCS)-NRP(S.K. J. I)

429* CALL WMIGFTCl.A6.Xli

430* X1=X1 +MIN+CT-1 )*IDB

431* cc=o

432* IFCX1 *LT.50)G0 TO 101

433* IF CXI .GT.76)Xl=75.9

434* CC = AITINT CLEV. U. 16.X1.2* XT)

435* 101 CONTINUE

436* C* EXCLUDE P=4 FOR SPEECH INT

437* DO 26 P=1 .3

438* S A V2=S A V2 + S I ME IN C I . K . M. P , N ) +PROBON C

439* 26 CONTINUE

440* 39 A2C2 )=A2C 2 ) +XDAY S ( N ) *SA V2

441* A2C2)=A2C2)/900

442* PI C2)=PIC 2) + SEOPLEC I • Mi *A2 ( 2 ) *0 I SLE

443* 36 CC NT INUE

444* RETURN

445* END

124
1 * 2 * 3*

4*

5*

e*

7*

e*

9*

10 4

1 1 * 12 * 13*

14*

15*

16 * 17*

16 * 19*

20 * 21 * 22 * 23*

24*

25*

26*

27*

28*

29*

30*

31*

32*

1*

2 * 3*

4*

5*

6*

7*

e*

9*

10 * 11*

12 * 13*

14*

15*

16*

17*

18*

19*

20 * 21 *

SUBROUTINE ME CW.X.ICA)

IF CICA.EQ.l) M=10**( (X-70)/10>

IF CICA.EC.2) W=.025*CX-70)**2

IF C ICA.EQ.3.AND.X.GE.70 ) W=. 6* C X-70 ) ** 1 •

2

IF < ICA. LT.4. OR. ICA.GT. 5 ) GO TO 34

IF CICA.EQ.5) X=X*15

AA=3.26E-6*l 0**( . 103*X)

BB=. 2*1 0**( «03*X)

CC=1 • 436-4* 1 0**{ • 08* X)

M*AA/CEB+CC)

34 IF IICA.EC.6) W= • 05* C X- 5 5

)

IF CICA.EQ.7) W=. 0 1 * C 1 0 * * ( CX-55)/10)-l

)

IF IICA.EQ.8) M=( 2.**( ( X-55) /10)-1 )/3

IF CICA.EQ.9) W=.012S*C X-55 )*2**C (X-55 )/10)

IF ( ICA.EQ.10>W=X

IF(ICA«EQ«11 .OR. ICA.EQ. 1 5 .OR • I C A . EQ . 1 7 ) W= C X- 40 ) / 35

I F ( ICA.EQ.12.OR. ICA.EQ. 1 6 .OR .ICA.EQ .18) W=C 2 .**( { X-40 ) / 10 > )/l 1.31

IFC ICA.EQ.13) M-=( X — 32 ) /4 3

IF( ICA.EQ. 141 M=( 2 .**( (X-321/9 ) 1/14.81

I F ( ICA.EQ.19) M=. 0 1 35* X- •

5

IF ( ICA.EQ.20)M=.0110*X-.495

IF( ICA.EQ. 1 . AND.X.GT.70 ) M=

1

IF ( ICA.EQ.2.AND.X.LT.70) W=0

IF( ICA.EQ.3.ANO.X.LT.70) M=0

IFC ICA.GT.5. AND. ICA.LT. 1 0 . AND.X.LT .55 ) M=0

IF ( ICA.GT. 10. AND. ICA.LT. 1 3 . AND . X .LT . 4 0 J M=0

IF( ICA.GT .13.AND.ICA.lt. 15.AND.X.LT.32) M=0

IF ( I CA.GT. 14. AND. ICA.LT • 1 9 . AND • X .LT • 4 0 ) M = 0

IFC ICA.GT .18 .AND. W.GT.1)M=1

IFCM.LT .0 ) M=0

RETURN

END

SUBROUTINE MW I GHT ( ICA.SPECTR.Y

)

REAL SPL( 24»,NS(240).LT( 28). SPECTR(8)

C ALL ROUTINES INPUT 24 FREQ FROM 50 TO 10K HZ

C CCNVERT OCTAVE BANDS TQ 1/3 CCTAVE BANDS

00 20 1 = 1.8

00 20 J = 1 .

3

K = 3* ( I— 1 )

J

20 SPLCK )=SPECTRCI >-4.77

IFCICA.LT.il ) C ALL M T < SPL • 24 , Y • 1

)

IFCICA.EQ.il .OR. ICA .EQ. 1 2 )CALL S0NE6 ( SPL. 24. Y. IF)

IFC ICA.EQ. 13. OR. ICA.EQ. 14)CALL SONE 7 C SPL . 24 . Y

)

IFC ICA.NE.15 .AND. ICA «NE .16) GO TO 101

DO 10 1=1.3

10 LTC I )=0

DO 11 1=4,27

11 LTC I )=SPLC 1-3)

CALL ZWICKC 1 .LT.27.NS.Y

)

101 IFC ICA.EQ. 17. OR. ICA.EQ. 181CALL PNL3 C SPL. 24. Y. IF

)

IFCICA.EQ.19.gr. ICA.EQ. 20) CALL WTC SPL • 24. Y. 1

)

RETURN

END

125
1 *

2 * 3 * 4 * 5 *

6 * 7 * a*

9 * 10 *

1

1

* 12* 13 * 14* 15 * 16 * 17* 18*

1 9* 20 * 21 * 22 * 23 * 24* 25* 26* 27* 28 * 29*

3 0* 31 * 32 * 33* 34* 35*

SUBROUTINE WT< SPL* NF* A* I ) C** STANDARD WEIGHTING FOR A( I = 1 ) . B< 1 = 2 ) • C ( I =3 ) ,0< I =4 ) , E ( 1 = 5 ) • L I N< I =0

>

C**NF 1/3 OCT BANOS FROM 50 TO 10K HZ(NF=24)

C**SPL=VECTOR WITH DB LEVELS FOR EACH BAND

C * * NE G SPL MEANS MISSING BAND

C**A= ANSWER IN DB(A),OR(B) OR(C)ORCLIN)

DIMENSION SPL ( 30 ) • W ( 30 • 6

)

DAT A ( W( J. 1), J=1.3 )/-44.7*-39.4,-34.6/

DAT A ( W ( J* 1). J=4, 30)/-30.2* -26.2, -22.5,-19.1 , -16. 1 ,-l 3.4

I

0.9. -8

U»—6.6,— 4. 8» — 3.2, — 1.9* — .8,0., .6*1. ,1.2,1.3, 1.2,1. ,.5,— .1, — l.l.— 2.

U-4.3,-6 .6, —9.3/

DATA (W(J,2), J=1 , 3 )/—20. 4, -17.1, —1 4.2/

OAT A(W(J*2|* J=4* 30)/— 11 .6* — 9.3# —7 .4, —5. 6* —4 .2, — 3.#—2.* — 1.3#— .8,

U— ,5,— .3* — • 1 • 4*0 •

, — . I, —• 2, — .4, — .7# — 1 .2 , — 1.9,—2.9 , — 4.3, — 6.1 , — 8.4

,

U-ll.l/

DATA C W( J,3), J=1 ,3)/-4.4,-3..-2./

D AT A ( W ( J* 3 ) * J =4

,

3 0 )/— 1.3,— .8#— .5#— .3*— • 2, — . 1, 9* 0., — .1* — .2. — .3, — . U— . 8 ,— 1.3*—2.*—3 .* —4 . 4 , -6.2, —8 . 5* — 11 .2/

DAT A < W ( J , 4 ) ,J=1 , 30 )/-l 8. 7,-16.7, -14. 7, -12. 8, -10. 9, -9. *-7.2. -5.5*

U — 4 • * —2 .6*— 1 .6*— .8* — .4#— .3* — .5#— .6*0.* 2. *4. 9* 7.9* 10.4* 1 1.6*1 1 .1*

U9.6»7 .6*5.5* 3. 4* 1 .4*— .7* —2 .7/

DATA ( W ( J.5) , J=1 . 30 )/-27. 6. -24 .05* -20. 68.-17. 54, -14. 65* -12.. -9.

6

U»— 7.44*—5.5*—3.79* — 2. 36 * — 1 . 24 * — .48* — . 09* — .04,— . 18, — . 16, .48, 1 .92

U.3.80.5.73, 7. 39*8. 55*8. 89*8. 14*6.29*3 .6 1 • .39 *—3. 12* — 6./

OATACWf J.6). J«l. 30 )/30*0./

A«9

IF( I. EQ. 0)1*6

00 20 J * 1 , NF

K = J+3

A=A410.**(CSPL( J)+W(K* I ) )/10.

)

20 CONTINUE

IFCA.LE.O) A=

1

A=10.*ALOG10(A)

RETURN

END

126
1*

2* C

3* C

4* c 5* c 6* c 7* c 8* c 9*

1 0*

1 14

c

12* u 13*

14*

u

15* u 16* u 17* u 18* u 19* u 20* u 21*

22*

u 23*

24*

25*

26*

27*

28*

29*

30*

31*

32*

33*

34*

35*

31

36* 32

37*

38*

39*

40*

41*

33

42* 34

43*

44*

35

45*

46*

47*

48*

49*

50*

20

SUBROUTINE S0NE6 ( SPL . NF . PHON « IFL AG

)

*** STEVENS* SONES. MARK VI *** NF THIRD OCTAVE BANDS FROM 50 TO 10000 HZ CNF=24)0R 20K HZCNF=27>

*** SPL = VECTOR WITH DB LEVELS FOR EACH BAND

** PHON = ANSWER

*** IFL AG = 2 IF ANY BAND WOULD HAVE LOUDNESS INDEX GREATER THAN

*** 298 SONES* 1 OTHERWISE

*** FOR LEVELS BEYOND 298 SONES THE TABLE IS EXTRAPOLATED

*** LINEARLY, 20 SONES/DB

DIMENSION SONEC 103)* SPL (27) , DBMAX(27)

DATA OB MAX/ 133,, 1 32 . , 1 3 1 . , 1 30 . , 1 29 . , l 28 . , 1 2 7 . , 1 2 6 . , 1 25 . , 1 24 .

.

123.

.

12 2. .121. .120. .119. .118. .117. .116. .115. .114. .113. .112.,

111., 112.45, 1 12.5, 1 12.5, 1 12.5/

DATA SONE/ • 1, .14, .18, .22, .26, .3, • 35 , . 4, .45, .5, .55, .61 , .67 ,.73,

.8, ,87, .94, 1,02, 1.1,1 .18,1.27.1 .35 ,1.44,1.54,1 .64,1.75, 1.87,

1 . 99. 2. 11. 2.24. 2. 38. 2. 53. 2. 68. 2. 84. 3.

.

3. 2. 3. 4. 3. 6. 3. 8. 4.1 *4.3,

4. 6, 4. 9, 5. 2, 5. 5, 5. 8,6. 2 ,6.6 ,7 . ,7 .4, 7. 8. 8. 3, 8. 8, 9. 3, 9 .9, 10 .5,

1 1 .1, 11 .8, 12.6, 13.5,14.4, 1 5 .3, 1 6. 4, 1 7.5, 1 8. 7, 20 • • 2 1 . 4, 2 3. • 24 . 7,

26. 5. 28.5. 30.5.

33..

35 .3. 38. .41.. 44 . . 48 . . 52 ..56.. 61 . .66. .71 .

,

77.

.

83. .90. .97. • 1 OS. , 1 1 3 . , 1 2 1 . , 1 30 . . 1 39 . , 1 49 . . 1 60 . , 1 7 1 , , 1 84 .

,

197..

21 1.»226.« 242., 260., 278.. 298./

IFL AG«

l

PH0N=0

SUM=0.

SMAX=0.

DO 20 1=1 .NF

IF ( SPL ( I ) .LE. 0 ) GO TO 20

IF( SPL ( I ) • GT • DBM AX (1)1 I FLAG=2

I F( I .GE .14) GO TO 31

IF ( SPL ( I ) .GE • 1 08.—74 I ) GO TO 31

X=1 .2*SPLC I )42 .4* 1-35.6

GO TO 33

I F ( I .GE.24) GO TO 32

X=SPL( 1)41-14

GO TO 33

X=SPLC I ) 49 . 5097—4 * I 1-23.5097)

IF ( X .LT .18) GO TO 20

I F ( X .GE • 120 ) GO TO 34

NX=X

X=SONE{ NX-17 )4( X-NX)4(SONE( NX-1 6 ) -SONEC NX-17 ) ) GO TO 35

X=298420*CX-120)

IFC X.GT.SMAX) SMAX=X

SUM=SUM4X

CONTINUE

X=SMAX4 • 15*C SUM-SMAX

)

IF(X.LE.O)RETURN

PHON=4041 0.4ALOG1 OCX )/ALOGlO{ 2.

)

RETURN

ENO

127
1 *

2 *

3 *

4 * 5 * 6*

7*

8 * 9 * 10 * 11 * 12 *

1 3 * 14 * 15 * 16 * 17 * 18 * 19 * 20 * 21 * 22 * 23 * 24 * 25* 26 * 27 * 28* 29 * 30 * 31 * 32 * 33 * 34 * 35 * 36 * 37 * 38 * 39 * 40 *

4 1 * 42 * 43 * 44* 45* 46 * 47* 48* 49* 50 * 51 * 52 * 53 * 54 * 55 * 56 * 57 *

SUBROUTINE S0NE7I LL* NF* PL

C****INPUT NF 1/3 OCTAVE BANDS FROM 50 TO I OK HZ

C *** TO CALCULATE STEVENS MARK7 PLOB

REAL LI 30)* S( 140 )* LEO, SO (95) • F( 95 ) *LL ( 30)

DAT A ( S( I) .1 = 1 .140 )/• 079. .087, .097, .107, .118, • 129. .141* .153* .166*

1.181* .196*. 2 12* .23* .248* .269* .29* • 3 1 4 * . 339* .367* .396* .428* .463*

1 • 5* .54* .583* .63* .68* .735* .794* .8 57* .926* 1 .. 1 . 08. 1 . 1 7. 1.26* 1 .36*

I 1 . 47 * 1 . 59 * 1.71 * 1 • 85 • 2 •• 2 • 16 * 2 . 33* 2 . 52 * 2 . 72* 2 . 94 * 3 . 18 * 3 . 43 * 3 . 7 * 4 «. 14 . 32 * 4 . 67 * 5 . 04 * 5 . 44 * 5 .8 8 * 6 . 35* 6 . 86. 7 • 41 * 8 . * 8 . 64 * 9 . 33 * 10 . 1 * 10 . 9 . I I 1.8* 12.7* 13.7* 14.8* 16.0* 17.3* 18.7* 20.2*21.8* 23.5* 25.4* 27.4* 29.6.

1

32..

34.6* 37.3* 40.3* 43.5* 47. *5 0.8* 54.9*59.3.64.. 69.1* 74.7* 80.6*

187.1 * 94 .1* 102**110.* 119.* 128. *138.* 149. *161. * 174. *188. *203. *219*.

12

37..

2 5 6.. 276.. 299.. 323.. 34 8.. 376.. 40 6 • ,439 • * 47 4 • • 5 1 2 . * 553. * 597 .

.

1645..

69 7.. 752.. 8 1 3.. 878.. 94 8.. 1024.* 1 1 06. » 1 1 94. . 1 290 . * 1393.,

1 1505.* 162 5., 1756. * 1896.* 2048. * 22 1 2. « 2389. *2580. • 2787. * 301 0.* 3251 . . 13511 • .3792.* 4096./

DATA ( SO ( I ) • 1=1*95)/. 181* • 196* • 2 1 2 * • 23. • 248* .269* .29, . 314, .339,

I

.

367* .3 96 * . 428 * . 463 * .5* .54* .583* . 63 * .68*. 735*. 794*. 857. .926,1..

II. 08.1. 17*1. 26 , 1 .36 .1 .47*1 .59 *1 .72* 1 • 85*2 •* 2.16* 2 • 33, 2 • 52* 2 • 72,

‘

' 12^94,3. 18*3.43, 3. 7* 4 • * 4 • 32* 4 • 67, 5 • 04* 5 . 44* 5 • 88, 6.35* 6.86, 7.41*

1 8.* 8.64 .9 .33* 10.1*10. 9* 11.8, 12.7* 13. 7, 14. 8* 16. * 17. 3. 18. 7, 20. 2,

1 21 . 8 , 23 . 5 * 25 . 4 , 27 . 4 * 29 . 6 * 32 •« 34 . 6 * 37 . 3 * 40 . 3 . 43 . 5, 47 •* 50 . 8 , 54 . 9 . 159 . 3 * 64 .

,

69. 1 * 74 . 7* 80 . 6 * 87. 1 . 94 . 1 , 1 02 .

,

1 1

0

.

,

1 19 . , 1 23 . , 1 38 . . 1 49 .

,

1161 •* 174 ., 188 .203 .

2 19 .

,

237 .

,

4000 ./

DAT A ( F ( I), 1=1*95) /.l* • 122, • 14, • 158* .1 74,. 187, .2* . 2 1 2, . 222, . 2 32

.

1 .241, .2 5* .259* .267, • 274 * .28 1 * .287* • 293* .298 • • 30 3. • 30 8, • 3 1 2* . 31 6,

l .319, .3 2, .32 2* .32 2* .32* • 319, • 31 7, .31 4 , • 31 1, .308, .304, .3, .296,

1.292.

.

2 88* .284* .279* ,275, ,27, .266* .262* .258, .253, ,248, .244, • 24,

1 .235, .2 3* ,226, .222* .217* .212* .208*. 204* .2* . 197* . 195* .194, .193,

1.192..

191».19,.19,.19*.19, .19* .19* .191, .191, .192, .19 3, .194, .195,

1.197.

.

199. .201. .203* .205* .208* .210*, 2 12, .21 5, ,2 17, .21 9, .221, .223,

1.224.

.

225* .226, .227* .227* .227/

PL=0

DO 100 1=1* NF

100 L ( I

) =LL ( I ) SUM= 0

SM-0

N 1 = 1 7

N2=NF+ 1

6

DO 110 1=N 1 , N2

J=I-N1+1

IF ( LL ( J )• LE • 0 ) GO TO 110

N=I

I F { I • GT .18) GO TO 101

L ( J ) = 1 6 0— 1 9 • ( 160-LHJ) )/I

N = 1

9

101 IF ( I . GT • 25 ) GO TO 102

DBL=85 .5-1 .5* (N-19)

DBH=DBL+45

IF (L(J) «LT « DBL ) LEQ= 1 07—26 • * ^ 1 15-L(J> ) /N

IF ( L ( J ) .GE.DBL. AND.LC J) . LE. DBH) LEQ=L ( J)-8-l • 5*( 2 6-N)

I F ( L ( J ) . GT. DBH) LEQ=1 52-26. *( 160-LCJ ) ) /N

GO TO 108

102 IFII.LE.31 )LEQ=L < J ) —

8

IF( I .GE.32.AND. I.LE.34)LEQ»L( J)-2*< 35-N)

IFC I .GE .35. AND. I .LE • 39 )LEQ^L( J)

I F ( I . GT ,39 )LEQ=L ( J ) 44*( 39—N

)

128
58* 1 08 I F( LEQ.LT

.

1 . )LEQ= 1 i 59* I 1 =LEQ

60* C *** INTERPOLATE THE SONE TABLE

61* S1 = (S(U + 1)-S(I1) ) * ( LEQ— 1 1 ) + S ( I 1 ) 62* SUM=SUM+S1

63* IF ( Sl.GT. SM ) SM=S1

64* 110 CONTINUE

65* C *** FIND THE F FACTOR IN SONE TABLE

66* DO 120 1=2.95

67* 120 IF(SM,LT,SO( I) ) GO TO 121

68 * 121 N= I 69* c *** INTERPOLATE THE F TABLE

70* F1=F(N-1 )+(F(N)-F(N-l ) )*(SM-S0(N-1 ) )/<SO(N)

71* STOT = SM*F 1 * ( SUM—SM

|

72* IF( STOT.LE.O) RETURN

73* PL=3 2 +9 *ALQG10(ST OT ) / ALOG1 0 ( 2

)

74* RETURN

75* ENO

1* SUBROUTINE ZW I CK( MOO* LT* NF* NS* N

)

2* C ZVICKER PHONS

3 * C**** LT NF THIRD OCT LEVS FROM 50 TO 10K HZ

4* C*** PROG ADDS LT<28)=0

5*

6 * 7*

8 * 9*

10 *

11 * 12 * 13* 14*

15* 16*

17*

18*

19*

20 * 21 * 22* 23*

24* 25*

26*

27*

26*

29*

C MOD PLANE (0). DIFFUSE! 1 ) F I EfcO

C OUTPUT

C N LOUDNESS IN SONES

C NS HAS TO DO WITH BARKS

REAL LT (28) • NS (240 > «N*N 1 *N2

REAL LE *LEHS (20)»LG(3)* KERN (21)

REAL LHS( 3 ) • NI

D I MENS I CN AO ( 20 ) • DLTGI 2 0 ) «DLED( 20 ) • ZG (21) «GRENZ ( 1 6 ) . T ANG( 8 » 16)

DIMENSION T I( l 1

)

DATA LHS/63.0* 54 • 0* 47.0/

DATA LEHS/36.*21.*12.5*9.*7.3*6.*5.«4.4«1 2*4 •/

DATA AO/ 10*0.00*-0.50* -1 .6* -3. 20.-5.40.-5. 60.

A — 4 • 00# — 1 • 50* 2.00*5.00. 12.00/

DATA DLED/O.OO. 0.00. 0.50,0.90.1.20, 1.60, 2.30,

A 2.80* 3.00* 2.00*0.00.-1.40,-2.00,-1.90,

A -1.00. 0.50* 3.00.4.00* 4.30. 4.00/

DATA DLTG/-0. 25* -0. 60* -0 .80 *-0.80.-0.50 *0.0 0* 0.5 0*

A 1.10* 1.50* 1.70* 1.80* 1 .80*1 .70*1.60,

A 1.40. 1.20* 0.80, 0.50* 0.00,-0.50/

DATA ZG/ 0.90* 1.80* 2.80* 3.50* 4.40*5.40*6.60*

A 7.90* 9.20* 1 0*60* 12.30* 13.80*15.20* 1 6 .70*

A 18.10*1 9*30 *20 .60 *21.80 *22.70 *23.60.24.0/

DATA GRENZ / 2 3. 50. 1 9 • 00. I 5. 1 0. 1 1 • 90 • 9.00* 6.60*

A 4.60* 3.20* 2.13* 1.36* 0.82* 0.43. 0.21*

A 0.08, 0.03* 0.00/

129
30 * DATA TANG/13.00. 8.20. 5.70. 5*5. 00.

31 * 2 9 . 00 . 7.50. 6 . 00 . 9.10. 4*4.50.

32* 3 7 . 80 . 6 . 70 . 5 . 60 . 4 . 90 , 4.40. 3* 3 . 90 . 33* 4 6 . 40 . 5 . 50 * 4 . 70 . 4 . 10 . 3 . 60 . 3* 3 . 20 , 34* 5 5 . 60 . 5 . 00 . 4 . 50 . 4 . 30 . 3 . 50 . 3 * 2 . 90 . 35* 6 4 . 20 . 3 . 90 . 3 . 70 . 3 . 30 . 2 . 90 . 3* 2 . 42 , 36* 7 3 . 20 . 2 . 80 . 2 . 50 . 2 . 30 . 2 . 20 . 2 . 20 , 2 . 20 , 2 . 02 , 37* 8 2 . 80. 2 . 10 . 1 . 90 . 1 . 80. 1 . 70 . 1 . 60 . 1 . 60 . 1.41.

38* 9 1 . 60. 1 . 50 . 1 . 40 . 1 . 30 . 1 . 20 . • 1** o • • o <0 • O CM • 39* A 1 . 50 . 1 . 20. 0 . 94 . 5* 0 . 77 . 40* B 0 . 72 . 0 . 66 . 0.61 . 5* 0 . 54 , 41* C 0 . 44 . 0.41 • 0 . 40 , 5* 0 . 39 . 42* D 0 . 29. 0 , 25. 6* 0 . 22 . 43* E 0 . 15 . 7* 0 . 13 , 44* F 0 . 06. 7* 0 . 05 . 45*

46*

47*

48*

49*

50*

51*

52*

53*

54*

55*

56*

57*

58*

59*

60*

61*

62*

63*

64*

65*

66 * 67*

68 * 69*

70*

71 * 72*

73*

74*

75*

76*

77*

78*

79*

80*

8 1 * 82*

83*

84*

85*

86 * 87*

89*

89*

90*

91*

i 8*0.04/

C63=0 • 064*( 1 0 • **{ 0. 025*LEHS ( 1 ) )

)

00 1 1=1.3

LG( I )=LEHS( I

)

HSF=0 • 0 64 * ( 10.**< 0.025*LHS( I))

N I =HSF * ( ( 1 .40.25*10. **( 0. 1*(LT( I)-LHS( I ) ) ) > **0.2 5-1 .

)

GI=4.*< (NI/C6341 • )**4-l •

)

Tl { I ) =0

.

IF ( G I • LE .0 • ) GO TO 1 XP= ALOG 1 0 ( G I >+0. 1*LEHS( I

)

TI ( I >=1 0.**XP

CONT INUE

DO 21=4.11

TI ( 1 )=1 0.+*C0. l*LT( I )

)

GI = T I ( l H-TI (2J4TI (3H-TI (4)+TI (5>+TI (6 ) IF ( G I . GT. 0 • ) LG ( 1 >=1 0.*AL0G10(GI

)

GI=T 1(7 )*TI (8)+TI (9)

IF (GI.GT.O.) LG(2)=10.*ALOG10(GI i GI=TI(10) +T 1(11)

IF (GI.GT.O.) LG( 3)= l 0* AL0G1 0 ( G I

)

DO 3 1=1.20

LE=LT ( 148 > I F ( I .LE.3) LE=LG( I

)

LE=LE-AO( I

)

KERN ( I ) =0

IF ( MOD . EQ . 1 ) LE=LE4DLED( I

)

IF (LE.LE.LEHSI I ) ) GO TO 3

LE=LE-DLTG( I

)

HSF=0 . 0 64 * ( 1 0.**( 0.025*LEHS( I ) )

)

KERN ( I ) =HSF * ( ( 1 .40.25*10 .**(0.1 *(LE-LEHS( I ))))** 0.25-1 .

)

CONTINUE

KERN( 21 )=0.

N=0 .2

21=0.

N 1 = 0 . J = 16

IZ-1

Z = 0.

DO 13 1=1.21

IG=1—

IF ( IG.GT.8) IG=8

IF ( N1-KERN( I ) ) 5.7.9

D06 J = 1 . 16

IF ( GRENZ ( J ) .LT.KERN ( I ) ) GO TO 7

CONTINUE

Z2=ZG( I

)

N2=KERN( I

)

130
92*

93*

94*

95*

96*

97*

98*

99*

100 * 101 * 102 * 103*

104*

105*

106*

107*

1 08*

109*

110 *

1 1 1 * 112 * 113*

114* 115*

116*

117*

118*

119* 120 * 121 *

1*

2 *3*

4*

5*

6* 7*

8 * 9*

l 0*

1 1*

12 * 13*

14*

15*

16*

17*

18*

19*

20 * 21 * 22 * 23*

24*

25*

26*

27*

28*

29*

N=N*N2*(Z2-Z1

)

8 IF(Z.GT.Z2) GO TO 12

NS ( I Z ) =N2

IZ=IZ*1

Z=Z*0.1

GO TO 8

9 N2=GRENZ (J)

IF ( N2 LT .KERN ( I ) ) N2=KERN(

I

) DZ=( N1-N2 )/TANG( IG, J)

Z2—Z 14-DZ

IF (Z2.LE.ZG( I) ) GO TO 10

Z2=ZG< I ) OZ-Z2-Z l N2=N1—DZ*T ANG ( IG# J)

10 N=N + ( ( N1+N21/2. )*DZ

11 IF (Z.GT.Z2) GO TO 12

NS< IZ)=N1-(Z-Z1 )*TANG< IG, J)

IZ=IZ+1

Z=Z+0.

I

GO TO 11

12 IF (N2.EQ.GRENZ ( J) ) J=J+1

IF ( J .G T • 16 ) J=1

6

N1=N2

Z1=Z2

IF (Zl.LT.ZG(I)) GO TO 4

13 CONTINUE

IF(N.LE.0)N=1

N=40 • 1 0* ALOG 1 0 (N )/• 301

RETURN

ENO

SUBROUTINE PNL3(SPL,NF, PNDB, I FLAG)

C *** STANDARO PERCEIVED NOISE LEVEL

C *** 24 THIRD OCTAVE BANOS FROM 50 TO 10000 HZ

C *** SPL = VECTOR WITH DB LEVELS FOR EACH BAND

C *** PND E = ANSWER

C *** I FLAG s 2 IF ANY BAND HAS NOY VALUE OFF THE UPPER END OF THE

C *** TABLE. 1 OTHERWISE

C *** FOR HIGH VALUES THE TABLE IS EXTRAPOLATEO ALONG THE LAST

C *** STRAIGHT LINE SEGMENT

DIMENSION AL1 (24) .AL2(24 ) ,AL3(24) ,AL4(24) • ALC( 24 ) » AMI (24 ) ,AM2( 24

)

DIMENSION A M3 ( 24 ) * AM4 ( 24 ) .SPL ( 24 ) .DBM AX (24)

DATA AL 1/49 .,44., 39 ., 34 ., 30., 27,, 24 .,21., 18 .,16. ,16. ,16. ,16., 16.,

U 15., 12. ,9. ,5. ,4. .5* .6* ,10. ,17. ,21./

DATA AL2/55..51 .,46.. 42. ,39., 36*. 33.. 30. .27., 25.. 25.* 25.* 25.* 25.

»

U 23..2 1. . 18., 15. . 14. , 14. . 15. , 17. ,23. , 29./

DATA AL3/64 • , 60, , 56 • , 53. ,51. ,48. ,46. ,44. ,42. ,40. ,40. ,40. .40. ,40.,

U 38, ,34. ,32. ,30. ,29. ,29., 30, ,31 • ,37 .,41./

DATA AL4/52 .,51,, 49 • , 47 • , 46 • • 45 • • 43 ••42., 41,, 40 . ,40., 40. ,40. ,40.

,

U 33 . « 3 4 « ,32. , 30 . ,29. ,29.,30. ,31. ,34. ,37./

DATA ALC/91 .01,85.68. 87.32, 79.85,79.76, 75.96,73.9 6, 74.91,94.63,

U 13*100., 44.29, 50.72/

DATA AM1/.07952 ,2 *.0681 6 • .05964 ,10*.053013. . 059 64, 2*.053013,

U 2* .04 77 12. 2* .05301 3, .0 68 1 6, • 07952 0, .05964 01/

OATA AM2/2*. 058098. .052288, ,047534, 2*. 043573, .040221 , .037349,

U 7* • 034859 • • 040221 , .037349 ,4 *• 034859 •• 037349 , . 0 37349, .043573/

DATA AM3/.043478, • 040572 , 2* • 03683 1, .035336, 2*. 033333, .032051,

U .030675,6*. 030103, 7*. 02996, 2* .04228 5/

DATA AM4/1 5*. 030 1 03 • 9*. 02996/

DATA DBM AX/ 1 4* 1 50 ., 148., 144., 142., 140 .,2* 139., 140., 141., 144., 147./

131
3 C*

31*

32*

33*

34*

35*

36*

37*

38*

39*

40*

41*

42*

43*

44*

45*

46*

47*

48*

49*

50*

5 1*

52*

53*

54*

55*

56*

57*

IFLAG«1

SUM=0.

PNDB=0

SMAX=0.

00

20 I =1 .NF

IF ( SPL ( I ) .LE.O )GO TO 20

IF(SPL< I) .GT.DBMAXI I ) ) I FLAG=2

IF ( SPL ( I ) .LT • AL1 ( I ) ) GO TO 20

IF(SPL( I ) • G E • AL2 ( I ) ) GO TO 31

X=AM1 ( I ) * ( SPL ( I ) - AL 1 ID)

X = . 1*10. **X

GO TO 35

31 IFISPLI I ) .GE.AL3( I) ) GO TO 32

X=AM2< I ) * ( SPL ( I)~AL3C I)

)

GO TO 34

32 IF(SPL( II .GE.ALCI I) ) GO TO 33

X=AM3< I >*(SPL< I )— AL3 ( I ) ) GO TO 34

33 X = A M4 ( I ) * ( SPL ( I ) — AL4 ( I )

)

34 X=10.**X

35 IF(X.GT.SMAX) SMAX=X

SUM=SUM+X

20 CONTINUE

X = SMAX* • 15*< SUM-SMAX

)

IF(X.LE.O) RETURN

PND B=40 • +33 • 22* ALOG 1 0 ( X

)

RETURN

END

1 * 2 * 3*

4*

5*

6 * 7*

8 * 9*

1 0 *

1 1 * 12 * 13*

14*

15*

1 6 * 17*

l 8*

19*

20 * 21 * 22 * 23*

24*

25*

26*

27*

28*

29*

30*

31*

32*

FUNCTION AI T INT (X.Y.N.XB.K.P)

Cc DIMENSION X ( N ) *Y(N).P(K)

002 J=1 .

N

C

C TRY TO USE AN EQUAL NUMBER OF GIVEN POINTS TO

C THE LEFT AND RIGHT OF THE INTERPOLATION POINT. XP

C

IF(X(J)-XB)2. 1.3

C

C IF THE INTERPOLATION POINT IS A GIVEN POINT, USE

C THE GIVEN FUNCTION VALUE ANO EXIT

C

1 AITINT=Y(J)

RETURN

2 CONTINUE

J = N

3 J = MI N0( MAXO ( 1 • J-< K+l )/2 ) . N—K4- 1 ) IF( J+K.LE.N.AND.X (J+K)-XB.LT.XB-X< J ) ) J= J+ MOD < K , 2 > P( 1 )=Y( J)

C

C AITKIN'S PROCEDURE. USING THE CLOSEST OF THE GIVEN POINTS.

C

0041=2.

K

P( I )=Y( J+ 1-1

)

004L=2*

I

4 P(I ) = (P (L-l )*(X( J + I-l )-XB)-P( I )*CXC J+L-21-XB) >/<<X(J+I-l>-XB>-(X(J

1 4-L-2 > — XB ) > A I T I NT *P ( K

)

RETURN

END

132
APPENDIX D

SAMPLE CALCULATION

In order to demonstrate the use of the FORTRAN program, a test case computation for vacuum cleaners was made. The input data and their sources and

the outputs of the FORTRAN program are presented in this appendix.

Note: the estimated data used for this sample calculation are not sufficiently accurate for use in actual Impact assessments! Thus, the

data presented at the end of this appendix do not reflect an actual

assessment of vacuum cleaner noise impact .

The sources of the input data were:

For data independent of the product -

o XDAYS, the fraction of different day types for a year, was determined from a 1977 calendar.

o LEV and U, the fraction of speech unintelligibility as a function of

A-weighted sound level, was determined from Figure 9 of Section 5.3.

o TPER, the number of hours in each time period, was obtained from

the values of P given in Section 6.3.

o TIMEIN, the length of time spent in different rooms was arrived at

through the the use of tables summarizing the time spent in certain

activities by employed men, employed women, and housewives given in

[1]*. The rooms in which these activities were conducted were estimated based on the nature of each activity. There were no data con- cerning unemployed men, preschool children at home, and school-age

children or day care children. Thus, unemployed men were assumed to

spend their time similarly to housewives, except more time would be

spent in the living room/family room and bedroom areas than a housewife, and more time away from home. Preschool children were assumed

to spend their time much like a housewife, except more time would be

spent in the bedroom for all time periods. The school-age/day care children were assumed to spend their time most like the employed

women, except more time is spent outdoors during the day and more

time in bed for the period 1700-2200.

o PEOPLE, the number of people of each type in the country was obtained

from data in [2]

.

o NPR, the room-to-room noise reductions within the primary dwelling,

were estimated on the basis of typical floor plans and construction

practices.

* Numbers in square brackets refer to the references listed at the end of

this appendix.

133
o NRS, the noise reductions between rooms in the primary dwelling and rooms in the secondary dwelling were estimated on the basis of typical construction plans and practices.

For data dependent on the product -

o OPTYR, the distribution of operators among person types was estimated

from personal observations by the authors.

o PERC, the fraction of dwellings that have the product, was estimated

from data in [2,5,6].

o ONTIM, the length of time the product is operated, was estimated from

two sources. The times per week that a vacuum cleaner is used (three

times/week) was found in [4]. The number of minutes per use was estimated from a survey of personnel in the NBS Sound Building. The

length of time taken to vacuum a dwelling was obtained from this

limited survey, which included people living in all three dwelling

types considered in the present model. Using this information, the

length of time to vacuum each of the three dwelling types was estimated. The total time spend per week vacuuming a particular dwelling

was the product of the number of minutes per day of operation, was calculated assuming each day equally likely.

o TIM, the length of time of a single operation, was obtained as described in the description of ONTIM.

o TP9, the probability that the appliance is operated in a given time

period, was obtained by assigning to each time period a probability

that the vaccum cleaner would be on. The probabilities, based on personal observations, were:

0700-0900 - .05

0900-1700 - .75

1700-2200 - .20

2200-0700 - 0

o RMP9, the probability that the appliance is operated in a given room was obtained, for each of the living areas for each dwelling

type, by assigning a probability based on the (estimated from

personal observations) amount of time the vacuum cleaner would be

used in each area:

Area Single-Family Townhouse Apartment

Kitchen .08 .11 .17

Living/Dining/Family Room .50 .56 .50

Bedroom .34 .33 .33

Bathroom 0 0 0

Basement/Garage/Utility Room .08 0 0

Outdoors 0 0 0

134
o SPECTR, the relative octave band spectrum for vacuum cleaners, was obtained from sound power measurements made at NBS.

o DISLEV, the distribution of the A-weighted sound power levels of

vacuum cleaners, was obtained from data in [6].

Using the sources of information described above, the numerical inputs to the

program are indicated on the following pages.

135
/

D.l Listing of Input Data for Sample Calculation

***NOTE***

These data are not sufficiently accurate for use in

actual population impact studies.

******************

1- J u UJ < >

a UJ X J A M 1 a z «r >0 -1 Z 3

<g < — n J z

i z z j w o o o — a

<r H 3 a o CD G 1- 3 H 11 < a 1- < in — X < o < U < X u cr U 1—4 X

Q u. < CD in w o Q Z > O o — h- -» Z f—

1

i- IV ct — + 7 rs _ —

II X z z UJ z o 3

-I CD a J -4 m -I M

111 _J J j < in H a

> X w w > > UJ 11 —

UJ a a Q. 1—4 o z X 3

J 3 3 3 a Q 1- H a -1 J 3 ui 3 z J

7 t- U z O > J

(T* ui J in UJ

—• c. O' O' z

c: in 0 x o ii UJ IG z

ui UJ o x 4-H X a a —

t- X * 1— r> G 3 z H X < z Z Z 3

oc G N < < J < < < u u X a 3 X X X J

'w' X < < a LI ID UJ <

ui UJ X X a 3 J J z a o 4 u u a < < < g < • •> * * • ft — 3 X h UJ UJ UJ UJ UJ UJ I- o UJ < If) in m in in in •o a > z z z z z z < X < Ifl G c 3 o a o a u 1- a c a a a a UL > a in in in in m in * J u M LU UJ UJ UJ iu UJ UJ

<r z Z ac a IX a a a VI z 111 z G G UJ UJ u UJ Hi ui UJ u — a o in so in in in in a

»- UJ < cr Lt X a a a ii u a IX UJ UJ UJ UJ u u u. < X u > > > > > > UJ X > CD o CD 3 o CD t- X 4 < <r < < < < < z

* • ft H

o c 5 J _J -I J j J z z z < < < < < < I — 4* 1-4 X a a a a V

Cr X C£ UJ 'll UJ u UJ UJ UJ < <r < z 7 z z z z 3

Jj UJ u UJ UJ UJ ll UJ UJ a X X X o 13 o 3 o 3 in

_jUJ>

xlJ

ill a

g

CDax j 3 '0 3

UJ 3 G

> > 3

UJ 3 a Z

3 3 « D

3 G

in in If) 3 3 W in

in in if) <n 3 3

UI u 3 3 > 3 3

z z Z Z 3 > 3

3 3 3 3 3 3 1- 3 3 3 3 I

O O G Q cn in >c 15 3 J 3 3 in 3 n w

11.! UJ in 3

O 3 IV N z z M4 a 3 * y a X 3 3 3 < 1 a a a a 3 3 Z 3 < < < < < 3 O W

X z z z 3 3 3 m z 3 in G m in in 3) a a > 3 a

z z z z 3 3 z 3 u 3 3 3 a a UJ M

> > > > 3 u U in Z

3 3 3 3 •—4 *-4 a i-4 G

1- K 1- 1- * * 3 G •—

»

in in n in N N a z 1- » • • • » • • a

ui 3 3 3 3 UJ 3 3 G

in in in in if) in in n a

z z z z Z z z z in o o o n n n o o

a a a a a a a a Q

in in in ui CO m 3 m

Ui u LU 3 3 3 3 3 3

a X a a a a a a VIz

UJ 3 3 tij '3 U) 3 3 LU

in in f) if) in m m 3 a

a a a a a a a a 13 UI 3 3 3 3 3 u UJ 3 > > > > > > > > a

3 3 3 3 3 3 3 3 3

< < <r < < < < < H

z 3 3 3 3 3 3 3 3

< < < < < < < a a a L

L

X a a a a

IU 3 3 3 u i j 3 3 3

z Z Z z z 7 Z z 3

3 3 LU UJ 3 ui 3 3 3 3 3 o o •3 o O 15 in

• • • • O

• • • • • • if) • O • «-4 o O o o o o o CM it o CM >o a CM 3 o o o o o o o o o o

'O O' rH n 3

• • • • • • • • • 3 -4 N CM • a o <t 3 • • o o

3 o o o o o o CM o n vO •-4 3 o m o o o o o o o

> O' 3 • • t • • • • • • • 3 m N m • O o 3 O O • o o

o o o o o o <t t o o ^4 •H H o n —4 o o o o o o o

XI

a.G

J)O

ax

UJoZ

CDO

'J) a

OJH

X

0

M

u

1

I <

J

D

X

Xo

z

z

UJ*<

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u

z

UJa

iu

u.xU

H

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a

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jX

n n [j 3

o O O o O o N o S N O O 3 o 3 N o o G o O o O r> o

3 3 3

• • • a a • • 3

3 4 4 t t • 4 3

3 O o o o o 3 rH N N o «-4 3 N o o 3 o O o O o 3

3 a N a 3 4 3

rn • • • • • • • • • • N K CO 3 O • O O o 4 o

3 o o 3 o o 3 3 «>4 *•4 o 3 ^4 ^4 —4 •4 o O o O o o 3 3

CM CM rj «-4 a •4 N N t OC X • • • • • • • • • • • • t • • • • • • 3 • • • • o o 1-4 P4 3 a 3 • X H X H —4 X 3 3 3 a a 3 N a N H 3 4 K. «-4 X 3 X * CM 3 3 4- t •4 4 3 3 f*4 3 3 3 3 |4 *4 4 4 4 4 3

CM CM CM a H N N • 00 X • • • • • t • • • • • • • • • • • • t 3 • t • t o O •-4 N ^4 3 a 3 • CO ^4 X •H -4 O 3 3 a a 3 |V a N *4 «-4 3 4 H 4 ^4 X 3 X

-t CM rr 3 3 H 4 3 3 •>4 3 3 3 3 *4 j-4 4 4 4 4 3 4

CM i\J CM *4 a *4 N • CO X o •-4 N «-4 3 a 3 • co t-4 X »-4 •-4 cc 3 3 3 a 3 3 fv a N »*4 o 3 4 •-4 X 3 X

<t CM t n 3 4 4 *4 4 3 ro *4 3 3 0 3 O' *4 4 4 4 4 3 4

•4 ^4 - N S a vM 3 3 3 3 3 3 3 a 4

3 o 3 3 3 3 3 o 3 3 o 3 0 o 3 X X 3 3 3 N • • 3

^4 ^4 rH »4 3 t-4 4 *-4 #-4 —4 —4 3 ^4 ^4 —4 -H -4 —• 44 3 3 ^4 *4 -• rv |V OJ 3 3 rn 3 3 ID a -1 3 o 3 3 3 3 3 3 n 3 • 3 O 3 X X 3 3 3 3 rv • • 3

^4 ^4 H «-4 *4 r4 3 —4 4 —

J

3 H •-4 3 pH H ^4 •—4 ^4 ~4 •4 -4 3 3 i-4 ^4 ^4 S- a 3 3 3 3 3 X 3 ry\ ^4 3 o 3 3 3 o O O 3 3 • 3 3 O 3 © X 3 3 3 3 N • • 3

^4 -4 f4 ^4 3 ^4 •*4 3 *4 ^4 3 •4 ^4 «-4 4 •H ^4 ^4 ^4 3 3 •4 3 3 • a • 3 3 • X • 3 • X a •7 a 3 3

• r, • • • • 3 • 3 • • • a • C' • 0 • • • • • • • •4 • ^4 CO CO r4 N 4 3 3 O 3 o s O a o O 3 3 3 3 3 • 4

3 n •-4 3 •-4 3 *4 4 3 3 H a ^4 N •4 3 L0 3 '3 3 3 O 4

3 3 • • 3 3 • • 3 a O' a 4 a 3

• •o •-n)iH^s^rjo«oootPoifl(\in''rJiKi • o -i rt s to ti m iii w ^ n o n 34 • — • t\j n x • c • 4 4 a x x

• •••••• • -iin-*inooo<tnn'nuDWNO'oinovOU)''»('jinin • — 3 n •4 ^4 3 i-4 3 ~4 3 3 rs a S r4 (V r—i O 3 3 3 3 3 o 3

3 CM 3 N N 3 3 3 3 a a 3 3

3 O 3 - — 3 o 1-4 a 3 3 o 3 Ch 4 3 a 3 • 3 3 O • 3

^4 *4 rj 3 3 3 3 4 X 3 ^4 3 3 3 3 4 3 3 4 N 4 3 -4 o 4

3 3 3 N N 3 3 3 <t 4 3 a ^4 a 3 3

3 O 3 *4 3 o f-4 3 r4 3 3 O 3 O' 3 3 o 3 3 o • 3

H *4 3 OJ 3 3 3 4 X 3 •4 3 3 3 3 3 X 3 14 4 3 -4 o *4 3 CM 3 O X 3 r4 3 3 3 c* 3 n

n •4 • • • • 3 O 3 X X 3 3 3 4 3 3 3 3 3 4 o o • • OJ 3 o e 3

m»4 4 CM i4 ^4 3 3 -• N 3 •4 3 r4 3 3 0 3 3 0 N -4 3 a -4

h N n <f if) J N X O' o OJ 3 4 3 j s c j o h nj x

— <OJC\JOJOJ

4 in x n

<M 3 3 0|

rc3

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D.2 Computer Output

for Sample Calculation

VACUUM CLEANER i CYCLE ONLY OPERATOR REQUIRED EUREKA CANIS

POPULATICN IMPACTS FOR OPERATOR* PRIMARY • SECONDARY DWELLINGS

EXPOSED PERSONS^ .2080+09 TOTAL POPULATION= • 2097*09

I FEARING, FRACTIONAL EXPOSURE (CRITER ION LEVEL=70 DB)

OPERATOR PRIMARY SECONDARY PR I + 0P

WEIGHTED POPULATION •7343+06 •3606+07 • 1908+04 •4340+07

NO ISE IMPACT INDEX .3530-02 .1733-01 .9173-05 •2086-01

2 FEARING, 4 FREQUENCY AVERAGE C CORRESP TO PHL FROM CHABA WG69 > OPERATOR PRIMARY SECONDARY PR I+CP

WEIGHTED POPULATICN .0000 • 0000 • 0000 • 0000

NOISE IMPACT INDEX • 0000 • 0000 • 0000 .0000

3 FEARING, AVERAGE NIPTS AT 4 KHZ

OPERATOR PR IMARY SECONDARY PR I +0P

WEIGHTEC POPULATION • 0000 • 0000 .0000 • 0000

NOISE IMPACT INDEX • 0000 • 0000 • 0000 • 0000

4 GENERAL ADVERSE RESPONSE • CHAE A WG69 LWP(LDN)

OPERATOR PRIMARY SECONDARY PR I+CP

WEIGHTEO POPULATION .4395+07 • 1335+08 •8256+05 • 1478 + 08

NOISE IMPACT INDEX •21 13-01 .6415-01 •3969-03 •7103-01

5 GENERAL ADVERSE RESPONSE* CHABA WG69 LWPILDN+15)

OPERATOR PRIMARY SECONDARY PR I + CP

WEIGHTEO POPULATICN •3176+08 •7731 +08 •9868+06 •8304+08

NOISE IMPACT INOEX • 1527+00 .3716+00 .4744-02 .3992+00

6 GENERAL ADVERSE RESPONSE* POPULATION EQU I VALENT (FRACT I ON AL IMPACT ) OPERATOR PRIMARY SECONDARY PR I +0P

WEIGHTED POPULATION •4284+06 •5764+07 • 0000 .7231+07

NOISE IMPACT INDEX •2059-02 •2771-01 .0000 .3476-0

l

7 GENERAL ADVERSE RESPONSE, ALEXANDRE ENERGY INO I CATOR ( LDN

)

OPERATOR PRIMARY SECONDARY PR I + CP

WEIGHTED POPULATICN •2619+05 •4607+06 • 0000 •6380+06

NOISE IMPACT INDEX • 1259-03 .2215-02 • 0000 .3067-02

8 GENERAL ADVERSE RESPONSE* ALEXANDRE LOUDNESS IND I CATOR (LDN

)

OPERATOR PRIMARY SECONDARY PR I+OP

WEIGHTED POPULATION •2150+06 .3097+07 • 0000 .3980+07

NOISE I MP ACT INOEX • 1033-02 • 1489-01 • 0000 .1913-01

9 GENERAL ADVERSE RESPONSE* ALEXANDRE SYNTHETIC I ND I CATOR (LDN ) OPERATOR PRIMARY SECONDARY PR I+OP

WEIGHTED POPULATION • 1260 + 06 • 1 938+07 • 0000 .2548+07

NOISE IMPACT INDEX •6058-03 .9318-02 • 0000 .1225-01

10 SPEECH INTEF ERENCE, FRACTIONAL UNI NTELL I G I B IL ITY BASED ON A -LEVEL

*** NO SECONDARY CALC FOR SPEECH INT*«*

OPERATOR PRIMARY SECONDARY PR I +CP

WEIGHTED POPULATION •2669+06 •6631 +06 • 0000 .9300+06

NOISE IMPACT INDEX • 1 283-02 .3188-02 • 0000 • 447 1-02

141
II GENERAL ADVERSE RESPCNSE STEVENS MARK6 LOUDNESS LEVEL

OPERATOR PRIMARY SECONDARY PR I 4 OP

WEIGHTED POPULATION •4931 >08 • 1 099409 • 0000 • 1 149409

NOISE IMPACT INDEX •2370400 •5282400 • 0000 •5522400

12 GENERAL ADVERSE RESPCNSE. STEVENS MARK6 LOUDNESS

OPERATOR PRIMARY SECONDARY PR I4CP

WEIGHTED POPULATION •3386408 .7700408 • 0000 •8138408

NO ISE IMPACT INDEX • 1628400 •3701 400 • 0000 .3912400

13 GENERAL ADVERSE RESPCNSE. STEVENS MARK7 LOUDNESS LEVEL

OPERATOR PRIMARY SECONDARY PRI40P

WEIGHTED POPULATION •4791408 •9677408 • 0000 • 1005409

NOISE IMPACT INDEX •2303400 •4652400 • 0000 .4830400

14 GENERAL ADVERSE RESPCNSE .STEVENS MARK7 LOUDNESS

OPERATOR PRIMARY SECONDARY PR I40P

WEIGHTEO POPULATION •3513408 •7693408 • 0000 .8150408

NOISE IMPACT INDEX • 1689400 •3698400 • 0000 .3918400

15 GENERAL ADVERSE RESPONSE. ZW I CKER LOUDNESS LEVEL

OPERATOR PRIMARY SECONDARY PR I 4 OP

WEIGHTED POPULATION •8435408 • 1495409 •4935405 • 1 540409

NO ISE IMPACT INDEX •4055400 •7186400 •2372-03 .7403400

16 GENERAL ADVERSE RESPCNSE .ZW I CKER LOUDNESS

OPERATOR PRIMARY SECONDARY PR 1 4 OP

WEIGHTED POPULATION • 5961408 .1198409 •1608406 • 1263409

NOISE IMPACT INDEX •2875400 •5761400 .7731-03 .6074400

17 GENERAL ADVERSE RESPCNSE .PERCE I VED NOISE LEVEL! FAR 36)

OPERATOR PR IMARY SECONDARY PR I4CP

WEIGHTED POPULATION •4294408 •9698408 • 0000 •1017409

NOISE IMPACT INDEX •2064400 •4662400 • 0000 .4891400

18 GENERAL AOVERSE RESPCNSE. NOISINESS(FAR 36)

OPERATOR PRIMARY SECONDARY PR I40P

WEIGH TED POPULATION •2961408 •6573408 • 0000 .6952408

NOISE IMPACT INDEX . 1423400 •3160400 • 0000 .3342400

19 SLEEP INTERFERENCE. D I SRUPT ION! FROM A-WEIGHTEO SOUND EXPOSURE LEVEL) NO OPERATOR CALC FOR SLEEP IN T NO NIGHT OPER FOR VACUUM USE TPER (3) FOR TEST

OPERATOR PRIMARY SECONDARY PR I4CP

WEIGHTEC POPULATION • 0000 •4203409 •2929409 • 0000

NOISE IMPACT INDEX • 0000 •2020401 • 1408401 • 0000

20 SLEEP INTERFERENCE* AWAKEN I NG(FROM A-WEIGHTEO SOUND EXPOSURE LEVEL

)

NO OPERATOR CALC FOR SLEEP INT444 NO NIGHT OPER FOR VACUUM USE TPER(3) FOR TEST

OPERATOR PRIMARY SECONDARY PR I4QP

WEIGHTEO POPULATION • 0000 •2787409 • 1099409 .0000

NOISE IMPACT INDEX • 0000 • 1340401 .5283400 .0000

8BRKFT PRINT*

1^2
References for Appendix D

[1] Szalai, A. (ed.)> The Use of Time: Daily Activities of Urban and Suburban Populations in Twelve Countries (Mouton & Co., The Netherlands,

1972).

[2] The 1976 Statistical Abstract of the United States (U. S. Bureau of the

Census, Washington, D.C., July 1976).

[3] Real Estate Research Corp., The Costs of Sprawl - Environmental and Economic Costs of Alternative Residential Development Patterns at the Urban

Fringe (U.S. Government Printing Office, Washington, D.C., April 1974).

[4] Walker, K. E., and Woods, M. E., Time Use: A Measure of Household

Production of Family Goods and Services (Center for the Family of the

American Home Economic Association, Washington, D.C. 1976).

[5] 1976 Statistical and Marketing Reports, Merchandising (March 1976).

[6] Bolt Beranek and Newman Inc., Noise from Construction Equipment and

Operations, Building, Equipment and Home Appliances, Report No. NTID

300.1 (U. S. Environmental Protection Agency, Washington, D.C.,

December 31, 1979).

143
MBS-1 14A (REV. 9-78)

U.S. DEPT. OF COMM.

BIBLIOGRAPHIC DATA

SHEET

1. PUBLICATION OR REPORT NO. NBSIR 80-2137 (R)

^smmssissr^

m/m&L

3 Accession No.

4. TITLE AND SUBTITLE 5. Publication Date Procedure for Assessing Impact on Public Health and Welfare

Due to Noise Emitted by Household and Consumer Products

September 1980

Code

7. AUTHORfS)

Simone L. Yaniv, Daniel R. Flynn, Daniel M. Corley and

Marilyn A. Cadoff

8. Performing Organ. Report No.

9. PERFORMING ORGANIZATION NAME AND ADDRESS

NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE

WASHINGTON, DC 20234

^^tWTa9*/Wortt Unit No.

11. Contract/Grant No. NBS/EPA D6 0755-1

12. SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS (Street, City, state, ZIP)

Office of Noise Abatement and Control

Environmental Protection Agency

Washington, D.C. 20460

13. Type of Report & Period Covered Final

15. SUPPLEMENTARY NOTES

| | Document describes a computer program; SF-185, FIPS Software Summary, is attached.

16. ABSTRACT (A 200-word or leaa factual aummary ot moat significant information. It document includea a significant bibliography or

literature survey, mention it here.) This report documents the development of a conceptual model, and a computer program

for its implementation, for assessing the aggregate impact of noise from household and consumer products on the health and welfare of the national population. The computer

program requires input data on population, time utilization, dwelling type, noise

isolation in dwellings, product noise emission, and product utilization. From the

input data, the "weighted population," corresponding to each of a number of different

noise descriptors, is computed. The report describes the assumptions involved in

the model, reviews available input data, and documents the computer program.

17. KEY WORDS (six to twelve entries; alphabetical order; capitalize only the first letter of the first key word unless a proper name;

separated by semicolons

)

, , Acoustics; consumer products; environmental impact; human response criteria; noise;

sound

18. AVAILABILITY Unlimited

For Official Distribution. Do Not Release to NTIS

Order From Sup. of Doc., U.S. Government Printing Office, Washington, DC

' 20402, SD Stock No, SN003-003-

Order From National Technical Information Service (NTIS), Springfield, VA. 22161

19. SECURITY CLASS

(THIS REPORT)

UNCLASSIFIED

20. SECURITY CLASS

(THIS PAGE)

UNCLASSIFIED

21. NO. OF

PRINTED PAGES

22. Price

USCOMM-DC

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