November 2004

NASA/CR-2004-213249

Effects of Meteorological Conditions on

Reactions to Noise Exposure

James M. Fields

Wyle Laboratories, El Segundo, California
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November 2004

NASA/CR-2004-213249

Effects of Meteorological Conditions on

Reactions to Noise Exposure

James M. Fields

Wyle Laboratories, El Segundo, California
Available from:

NASA Center for AeroSpace Information (CASI) National Technical Information Service (NTIS)

7121 Standard Drive 5285 Port Royal Road

Hanover, MD 21076-1320 Springfield, VA 22161-2171

(301) 621-0390 (703) 605-6000
-iiiTABLE OF CONTENTS

LIST OF FIGURES ......................................................... iv

LIST OF TABLES ......................................................... iv

ABBREVIATIONS AND SYMBOLS .......................................... v

EXECUTIVE SUMMARY .................................................... vi

1.0 INTRODUCTION ..................................................... 1

1.1 Basis for expecting meteorological effects on noise reactions ............... 2

1.2 Overview of data available for analyses ................................ 3

1.2.1 The Netherlands Continuous Life Information Survey (DLO) ......... 4

1.2.2 The day-linked data set ...................................... 8

1.2.3 The season-linked data set .................................... 8

1.2.4 Features of both the day-linked and season-linked data sets .......... 15

2.0 SEASONAL DIFFERENCES IN NOISE REACTIONS: THE NETHERLANDS

CONTINUOUS LIFE INFORMATION STUDY (DLO) ....................... 17

2.1 Annoyance and meteorological variables for the DLO analysis ............. 17

2.2 Noise annoyance reactions ........................................ 18

2.3 Model for the seasonal reaction pattern ............................... 22

2.4 Explaining seasonal differences ..................................... 23

3.0 METEOROLOGICAL EFFECTS: PERIOD OF IMPACT ..................... 28

3.1 Data: day-linked data set ......................................... 28

3.2 Analysis of reactions ............................................. 28

4.0 ASSESSING METEOROLOGICAL EFFECTS ............................. 34

4.1 Data: Season-linked data set ....................................... 34

4.2 Analysis of reactions ............................................. 34

4.3 Discussion of climatic effects ...................................... 36

5.0 RELATIONSHIP BETWEEN TEMPERATURE AND OTHER METEOROLOGICAL

CONDITIONS AND NOISE ANNOYANCE REACTIONS .................... 42

5.1 Available meteorological data ...................................... 42

5.2 Relationships with temperature ..................................... 44

5.3 Relationships with precipitation .................................... 46

5.4 Relationships with sunshine ....................................... 47

5.5 Relationships with wind speed ..................................... 47

6.0 FINDINGS ABOUT SEASONAL DIFFERENCES FROM PREVIOUS SURVEYS . 49
-iv7.0 SUMMARY AND CONCLUSIONS ...................................... 52

7.1 Summary of findings ............................................. 52

7.2 The role of seasonal and climate effects in research and noise policy ......... 52

7.3 Strategies for acquiring information through future research ............... 54

REFERENCES ............................................................ 55

ACKNOWLEDGMENTS .................................................... 56

APPENDIX A: METHODS FOR THE NETHERLANDS CONTINUOUS LIFE

INFORMATION SURVEY (DLO) ....................................... 57

A.1 Wording of questions in English .................................... 57

A.2 Wording of questions in Dutch ..................................... 58

LIST OF FIGURES

Figure 1 Annoyance reactions by month (DLO survey) ............................ 19

Figure 2 Meteorological conditions by month (continuous DLO survey) ............... 20

Figure 3 Comparison of sine model and reactions (DLO survey) ..................... 24

LIST OF TABLES

Table 1: Characteristics of noise, meteorological and social survey data in three data sets ... 6

Table 2: Codes for climates in the Köppen Climate Classification System ............... 9

Table 3: Noise annoyance surveys used in the day-linked and season-linked analyses ..... 10

Table 4: Seven-year average of noise reactions and meteorological conditions for the DLO

survey (by month) ................................................. 21

Table 5: Parameters of sine functions for the continuous DLO survey ................. 23

Table 6: Correlation coefficients for the continuous DLO survey .................... 25

Table 7: Regression coefficients for four meteorological variables from regressions of noise

annoyance [three noise sources and combined annoyance index] (DLO survey) ... 26

Table 8: Regression analyses for varying lengths of aggregated meteorological data (Daylinked data set) ................................................... 29

Table 9: Partial regression coefficients from linear and logistic regressions of noise annoyance

on DNL, noise source and nine alternative integration periods for temperature and

precipitation (Day-linked data set) .................................... 31

Table 10: Correlations between noise annoyance and meteorological variables in the seasonlinked data set .................................................... 35

Table 11: Regressions for the season-linked data set ............................... 38

Table 12: Numbers of responses available for analysis for each type of meteorological data . 43

Table 13: Estimates of the impact of temperature on noise annoyance .................. 45

Table 14: Findings on seasonal differences from seven studies ....................... 51
-vTable 15: The Netherlands continuous DLO survey questions about noise (English translation)

............................................................... 57

Table 16: The DLO survey questions about noise (in Dutch) ........................ 58

ABBREVIATIONS AND SYMBOLS

Exact definitions of noise indices and scales for acoustical measurements can be found in general

acoustical reference publications (Bennett and Pearsons 1981). The units in which quantities are

expressed are enclosed in square brackets.

a Intercept in a regression equation

dB Decibel

BL Annoyance associated with a 1-decibel difference in steady-state noise exposure (positive

values indicate that annoyance increases with noise level) [Annoyance scale units]

Bx Annoyance associated with a 1-unit change in variable x, calculated in a regression

analysis [Annoyance scale units]

dBEAU Decibel Equivalent Units. This expresses annoyance effects in terms of the number

of decibels that would create an equivalent effect by dividing an effect (expressed

in annoyance units) by the amount of increase in annoyance (expressed in

annoyance units) that is associated with a one-decibel increase in steady-state noise

exposure.

DNL Day-night Average Sound Level, [dB(A)]

GDCN Global Daily Climatology Network 

KNMI Royal Netherlands Meteorological Institute
-vi-

 

EXECUTIVE SUMMARY

 

This report analyzes three sets of data to determine whether meteorological conditions, including

temperature, amount of precipitation and wind speed affect residents' reactions to environmental

noise. The first data set is based on seven years of continuous surveys of the population of the

Netherlands. The second and third data sets are based on 41previous community surveys from 14

countries that contain measures of noise exposure and social survey data for more than 53,000

responses to that noise. The continuous Netherlands survey shows a clear seasonal pattern in

residents' annoyance with road traffic, aircraft and railway noise in which the greatest annoyance

is expressed around the summer months and the least in the winter months. Five of seven

previously published studies have also found evidence of seasonal effects.

All three data sets analyzed in this report find a pattern of increasing noise annoyance with

increasing temperature but this pattern is not statistically significant. Other mixed evidence

suggests that annoyance is also increased by more hours of sunshine, decreasing precipitation, and

decreasing wind speeds. One explanation for such meteorological factors' effects is that they

encourage opening windows and more outdoor activities and thus could lead to higher noise

exposure to environmental noise such as aircraft and road traffic.

The analyses of these data sets provide some weak evidence on other issues. The analyses find

some evidence that noise annoyance is affected by the average temperature conditions in the

months preceding residents' interviews, but find no evidence that noise annoyance is particularly

strongly affected by meteorological conditions in the days immediately preceding residents'

interviews. The best estimate from these data is that a 15E (C) difference in temperature has

about the same effect on noise annoyance as does a one to three-decibel difference in noise

exposure. Estimates from the DLO Netherlands data are relatively precise, but are based on only

the particular set of correlated climatic conditions found in the Netherlands. All estimates of

meteorological effects from the combined data from the 41 season-linked studies are imprecise

and significance tests indicate that these analyses cannot exclude either the possibility that

temperature has little or no effect or that temperature has an important effect, with a 15E

difference in temperature having the same effect as a five to ten decibel difference in noise

exposure. 

Each of the data sets used in these analyses has different weaknesses that limit the accuracy of the

results it could provide. Stronger results could be expected in the future if these and other data

sets were developed that include noise exposure data and date of interview for all interviews;

closely link the respondent's location with permanent weather stations; include a wide range of

meteorological conditions that are not highly correlated; and use uniform social survey methods

and noise annoyance measures.
-1-

 

1.0 INTRODUCTION

 

Residents’ reactions to the noise environments at their residences have been widely studied. 

Several hundred studies have been conducted in which residents’ reactions, measured in social

surveys, have been related to the long-term noise environments, as measured in acoustical surveys

and noise estimation programs (Fields 2001). Estimates of reactions to different types of noise

sources have been published based on the mean estimates from diverse surveys and are widely

available for environmental noise planning purposes (Fidell et al. 1991;Miedema and Vos

1998;Schultz 1978). Considerable attention has been directed at ways in which residents'

characteristics and the acoustical environment can affect residents' reactions to noise. Relatively

little attention has been devoted to other types of factors that are normally relatively uniform

within any one social survey such as meteorological and cultural conditions.

This report examines the effects of two types of meteorological conditions on residents'

annoyance with environmental noise. Short-term, meteorological effects (weather effects) are

generally referred to as "seasonal" effects in this report. Long-term meteorological conditions are

generally referred to as "climate" effects in the report. Either type of meteorological effect, if

present, could have important implications for both noise research and noise policy. For noiseannoyance survey design purposes, seasonal effects are a potentially confounding factor for any

comparisons between studies or, for longitudinal studies, within a study if different waves of

surveys are conducted during periods with different meteorological conditions. For example,

longitudinal studies of reactions to changes in noise environments could easily confound the

effects of a change in noise conditions with differences in meteorological conditions at the time of

the survey. Such meteorological and climatic differences become especially important if attempts

are made to set uniform noise policies for different countries or regions that vary greatly in their

meteorological conditions. For example, if colder temperatures are associated with reduced

reactions to noise, policy issues arise about whether the same noise annoyance thresholds are

appropriate for different countries in the European Union.

Although meteorological effects, primarily temperature, have been considered in at least seven

noise response surveys, these surveys have been limited to a small number of communities and a

limited range of climatic conditions. Each survey has compared reactions at only a few points in

time (for example between one winter survey and one summer survey). Most of these surveys

have found some evidence for seasonal differences in response, but due to study designs that

make observations under only two conditions, they have not attempted to assess the independent

effects of different aspects of the meteorological conditions or attempted to model seasonal

responses for an entire year.

The present report analyzes existing data from social surveys to study reactions under different

meteorological conditions. Two distinct analytic approaches are taken to overcome the previous
-2-

studies' limitations. The first approach is to study a single population over a long period of time. 

This approach is pursued with an analysis of seven years of data from the Netherlands Continuous

Life Information Survey (DLO). The second approach is to study a set of diverse, independentlydesigned studies that are available from a single archive but which were conducted under very

different meteorological conditions. This approach is pursued through the analysis of two data

sets that were obtained in cooperation with the Netherlands TNO noise data archive. One of the

data sets, the day-linked data set, consists of 12 surveys in which individuals' responses can be

related to the meteorological conditions on the date of their interview. The second data set, the

season-linked data set, adds additional surveys from the data archive to construct a data base of

41 surveys where the individuals' responses are linked to only the season of the interview

(identified by month) and to a thirty-year average of the meteorological conditions for a month in

that season. In analyses of this season-linked data set, the day and the year of the interview are

not considered in the analyses.

1.1 Basis for expecting meteorological effects on noise reactions

This report does not attempt to provide a formal model for meteorological effects. This section

presents, however, a few of the factors that might be considered for such a model. A previous

publication contains an attempt to describe some of these factors and to present them in a

graphical format (Fields et al. 2000).

A simple acoustical basis for the effect of meteorological conditions is provided by the

observation that residents' exposure to exterior transportation noises will vary depending upon the

amount of time they spend out of doors and the amount of time their windows are open. As a

result the noise exposure would be expected to increase as temperature increases, the extent of

precipitation decreases, and the wind speed decreases. Of course there could be regional and

national variations in outdoor activity patterns and window opening practices that could affect

these exposure patterns. For example, the residential air conditioning in some countries may

result in a high level of acoustical insulation in warm seasons, some cultures value open windows

for "fresh air" in the nighttime in almost any season, and particular seasons may have

accompanying outdoor activities that are likely to occur with little regard to the meteorological

conditions. None-the-less the concept of individual acoustical exposure provides a possible basis

for combining the effects of a variety of meteorological conditions.

This simple acoustical model may need to be supplemented by other considerations. There may

be some adaptation of populations with different long term climatic conditions. For example,

populations in warmer countries may be less affected by warm weather noise exposure because

they experience such exposure for a longer period of the year. A similar pattern of warm/cold

climate differences would be generated if residents in colder climates attach special importance to

outdoor activities during their relative short periods of outdoor exposure. In addition, there could

be cultural factors that would attach more or less importance to noise-related disturbances

regardless of season. The possibility of general seasonal variations in people's moods and

sensitivity to environmental nuisances could also be considered.
-3-

Of course the possibility of variations in differences in acoustical source emissions for particular

local areas should also be considered. These can be substantial for specific, communities with

major seasonal variations in population or for short periods of time (for example, resort

communities). It is possible, but unlikely, that such differences have an important effect on much

of the residential population. Pavements are not usually wet enough, even during wet seasons, to

substantially effect noise exposures that are averaged over month-long periods. Variations in

numbers of aircraft or road traffic vehicles are not usually of substantial acoustical importance in

residential areas for noise indices such as DNL for which a doubling of numbers of events

generates only a three-decibel increase in noise exposure. Perhaps the main possibility for such

variations might be for local road traffic and neighborhood noise in European countries where a

large proportion of the population takes vacations within the same small number of weeks in the

summer.

1.2 Overview of data available for analyses

The strongest data sets for analyses of meteorological effects on noise annoyance consist of three

linked components. Those components and a few of the characteristics that distinguish strong

from weak components for meteorological effect surveys are :

* Social survey data with each resident's reaction to specified noise sources.

Strong social surveys will: 1) contain identical noise-annoyance questions, 2) be

designed with interviews spread over a broad range of noise levels, 3) be

conducted under a wide range of meteorological conditions, 4) be designed so that

meteorological conditions are not correlated with noise levels, methodological

procedures, or other variables.

* Acoustical data on the exposure of residents' dwelling to the noise sources.

Acoustical data should: 1) be expressed in the same unit for all respondents, 2)

characterize a standard position at all respondents' dwellings, and 3) be of a

known level of accuracy for the estimation of the exposure at the respondents'

dwelling

* Meteorological data about the conditions for the area on dates preceding each resident's

interview.

Meteorological data should: 1) be available on a daily basis during the days and

months preceding the interview, 2) be available on a daily or monthly aggregated

basis for several years before the interview, and 3) be representative of the

meteorological conditions present at the individuals' residences.

Since the meteorological data come from weather stations that may be unfamiliar to the

investigator, correctly linking the meteorological data and the interview data may be difficult. In

preparing the data sets for the analyses in this report, the two parts of this linking that were found

to be most difficult or impossible were:

* Determining the exact date or, in many cases, even the year of the interview
-4-

* Determining which, if any, meteorological station should be linked to a specific

study area

The three data sets used in this report differed in the sources of the social survey, acoustical, and

meteorological data. Table 1 summarizes some of these differences. More details about the

meteorological data are available in Table 12.

1.2.1 The Netherlands Continuous Life Information Survey (DLO)

The Central Bureau of Statistics of the Netherlands has conducted the Continuous Life

Information Survey (DLO) for more than a decade. The present analysis is based on seven years

of data, the 84 months from January 1990 through December 1996. The DLO survey is

conducted on a continuous basis throughout the year with a nationally-representative sample (18

years of age and older) that is redrawn every four weeks. In the seven-year period over 35,000

residents were interviewed with an average of about 400 a month. The response rates were less

than 50%.

The individual data were not available for the DLO data set. Instead, the Netherlands Statistical

Agency (CSB) provided the mean annoyance reaction for each month. This mean reaction was

based on weighted data that adjusted for non-response. The approximate number of respondents

for each month were indicated on the dataset for each month. All of the statistical tests for the

DLO data are conducted with monthly values (n = 84 = 7 years x 12 months) as the unit of

analysis and simple random sampling assumptions as it is not possible with the existing data to

take into account the details of the within-month sample design.

This general-purpose survey has included questions about noise annoyance since at least April

1989. For the seven-year study period in this report, the questions remained similar with revisions

being made at only one point, in January of 1994. The basic annoyance questions are reproduced

in full in Appendix A in English (Table 15) and in Dutch (Table 16 ). 

This survey provides the only source of information about the same population's reaction to noise

in every month of the year. Since the survey methodology has remained the same over the seven

years and the only question wording change was made in January of 1994, there are no

methodological differences to be confounded with meteorological conditions. The nationwide

sample design means that there should only be random differences in noise exposure between the

respondents in different time periods. 

While the survey is valuable for examining seasonal differences, the information in Table 1 shows

that its value is limited by three major aspects of the design: 1) the respondents' noise exposures

are not known, 2) the location of the respondents within the Netherlands is not known, and 3)

only the month (not the day) of the interview is known. These limitations mean that the data

cannot be used to test for the effect of the weather in the days immediately preceding the
-5-

interview and that the data cannot clearly test the relative importance of seasonal effects and noise

exposure effects. The absence of information about interview locations is less important for the

Netherlands than it would be for larger, more geographically diverse countries. All respondents

were assumed to have experienced the same meteorological conditions as were measured at a

central location in the Netherlands (De Bilt). An analysis of the variation in monthly

meteorological indices over the 84 months at 6 locations in the Netherlands determined that there

was not a statistically significant difference between the temperature and precipitation indices at

the six sites, but that here were distinct differences in the wind speed and sunshine indices with the

two coastal stations having distinctly higher wind speeds and numbers of hours of sunlight. As a

result, the effects of these variables may be somewhat underestimated in the analyses or may be

confounded with the effects of correlated variables. The averaging of reactions across different

noise exposures and inability to link interviews to particular day-linked meteorological data, may

have also attenuated the observed relationships for this survey.
-6-

Table 1: Characteristics of noise, meteorological and social survey data in three data sets

Characteristic

Data set

Day-linked data set Season-linked data set Netherlands Continuous Life

Information survey (DLO)

Social survey characteristics

Number of

responsesa

15,107

(7,713 with precipitation data)

53,130

(47,825 with wind speed data)

35,000 

(approximate)

Number of

respondents

 13,100

 (approximate)

49,400

 (approximate)

35,000

 (approximate)

Number of

studies

12

 (16 noise sources evaluated)

41

(48 noise sources evaluated)

1

(4 noise sources evaluated)

Number of

sample PSUs 

 28 (cities or other

areas within the 12 studies)

41 

(studies)

84

( months)

Settings 7 countries 

(5 languages)

14 countries

(9 languages)

Netherlands

(one language)

Reaction

question

Intensity of annoyance

(Diverse wordings)

Intensity of annoyance

(Diverse wordings)

Frequency of annoyancee

(1990-93 & 1994-96 versions)

Study

methods Methods differ between studies Methods differ between studies Uniform for data set

Noise sources

studied

Road traffic

Aircraft

Road traffic

Aircraft

Railway

Road traffic

Aircraft

Railway

(Neighbors)

Aggregation Individual interviews Individual interviews Group averages of each

month's interviews

Within-study

variation in

weather

Small Small Large

Acoustical data

Acoustical

index DNL DNL (No noise data)

Estimation &

measurement

methods

Methods differ between studies Methods differ between studies (No noise data, but same

population for all years.)

Meteorological data

Aggregation

in base data Daily data 30-year averages for eachmonth (total of 12 values) Monthly data
Characteristic

Data set

Day-linked data set Season-linked data set Netherlands Continuous Life

Information survey (DLO)

-7-

Integration

time

(default)b

91 days up to and including

the interview day

3 months up to and including

the interview month

2 months preceding the

interview month

Meteorologic

al data

(means of

daily values)

Temperature (C)c

Precipitation (total - mm per24

hours)

Temperature(C)c

Precipitation (total (mm) &

number of days with

precipitation)

Sunshine (minutes per 24

hours)

Wind speed (mean - meter/s)

Temperature(C)c

Precipitation (total (mm) &

number of hours with

precipitation)

Sunshine (minutes & % of

daylight hours with sunshine)

Wind speed (mean & average

of hourly maximums)

Sourcesd

 UK Met office, for British

sites, GDCN Version 1.0 for

all other sites

Wereld Klimaat Informatie

(WKI) data base from KNMI KNMI (Web site)

Information for linking meteorological and social survey data

Locationf City of survey or nearby city

 City of survey or nearest

location in the same country

within the same climate zone

One central location in the

Netherlands (De Bilt)

Data on date

of survey

interviews

Day

Month

Year

Month

(Within 3- month period)

Month

Year

a. If respondents are asked about two noise sources for which noise data are available, a single interview usually

provides two responses in the day-linked and season-linked data sets

b. This "default" integration period is used in the analyses in this article unless otherwise noted. The interview

month is excluded from the DLO data because respondents interviewed early in a month would not have

experienced the entire month's conditions. The nominal "interview" month is included for the season-linked

data set because this "nominal" month is sometimes in the middle of a two or three-month interviewing period.

c. Three measures of temperature (in Celsius) are available for all three data sets:average of the 24 hourly

values, and the maximum and minimum daily values. The algorithm for combining the 24 hourly values may

vary between studies.

d. The sources of the meteorological data for the three data sets are:

- Day-linked data set: Data for most studies come from the Global Daily Climatology Network (GDCN)

Version 1.0 data set that was produced in July 2002 by the Climate Analysis Branch of the National Climate

Data Center of the National Oceanic and Atmospheric Administration (NOAA)

(www.ncdc.noaa.gov/gden.html). Daily weather data for the UK were ordered from the UK MET Office

(www.meto.gov.uk).

- Season-linked data set: In 2003 the Wereld Klimaat Informatie (WKI) data base from KNMI (Royal

Netherlands Meteorological Institute at http://www.knmi.nl) provided 30-year averages (1961-1990) for each

month of the year for a large number of sites around the world in a graphic product (in Dutch).

- Continuous DLO survey: The KNMI data set (De Bilt) includs data on mean air pressure (hPa), maximum

wind gusts, wind direction, and cloud cover (octants of sky visible) that were not examined in any analyses. In

January 2003 the definitions appear on http://www.knmi.nl/voor/kd/lijsten/daggem/uitleg_dagklim_en_html. 

e. The annoyance questions for the DLO survey are shown in Dutch and an English translation in Appendix A.

f. For two UK surveys (UKD-072, UKD-116) and all of The Netherlands' surveys a single central

meteorological station's data were used to represent the meteorological conditions for the entire country.
-8-

1.2.2 The day-linked data set

A search of the TNO archived noise annoyance surveys identified 13 studies for which the exact

date of each interview was known. The twelve of these data sets (listed in Table 3) that permitted

a close match between survey locations and meteorological station data constitute the datespecific data set with 15,107 responses. In some cases some interviews from a selected study

were excluded from the day-linked data set. Interviews were excluded, for example, if they were

conducted in periods after a recent change in noise conditions.

Although this data set shares many of its strengths and weakness with that of the season-linked

data set that also comes from the TNO data archive, it has one major strength: temperature

indices are merged on the social survey data set for each of the 15,107 responses for the date of

interview and for every day preceding the interview for the previous 11 years (i.e. approximately

4,000 days of temperature data per respondent). Precipitation data are available on a similar basis

for a lesser 7,713 responses because precipitation data were not available for some meteorological

stations. This day-linked meteorological data set provides the data to examine the effects of

meteorological conditions in the days, weeks, and months immediately preceding the interview. 

The meteorological data for Great Britain were purchased from the United Kingdom Met office. 

Meteorological data for all other countries for the day-linked analysis were purchased from the

National Climate Data Center of the National Oceanic and Atmospheric Administration (NOAA)

of the United States. In each case, the longitude, latitude, and altitude of the meteorological

stations were examined before linking the station to the study sites for the survey respondents.

1.2.3 The season-linked data set

The search of the TNO data archive identified a total of 41 surveys, listed in Table 3 that contain

53,130 responses for which the months of the study field period can be determined and the study

location could be linked to a meteorological station's data. These 41 surveys include reactions to 

48 noise sources. These 41 surveys include the 12 surveys and 15,107 responses from the daylinked data set. These 41 surveys provide social survey and acoustical data with most of the same

types of strengths and weaknesses as were described in the previous section for the day-linked

data set. The primary difference between the two data sets is in the specificity of the interview

date information and the accompanying types of meteorological indices that are available for

analyses.

Table 3 displays the range in climatic and meteorological conditions found in the study sites. 

Reactions are studied in areas that extend over approximately a 15 degree range in temperature

(Celsius), 5 millimeter range in daily precipitation and 5 meter/sec range in wind velocity. The

combination of seasonal temperature and precipitation patterns are captured by the codes of the

Köppen Climate Classification System in Table 2, the most widely used system for classifying the

world's climates.(Critchfield 1983). In Table 3 it is seen that about two-thirds of the studies come

from moist mid latitude climates (C -__) while such sites with warm summers and no dry period

(Code Cfa) account for about half of the study sites.
-9-

Table 2: Codes for climates in the Köppen Climate Classification System

Character in Köppen climate classification system (1st, 2nd and 3rd character codes)

1st 2nd 3rd

A=tropical moist (all months>18E Celsius)

w =dry winter

s = dry summer

f = no dry period

B=dry climates h=hot (mean>18EC)

k =cool (<18EC)

C=moist mid-latitude with mild winter a = hot summer

b = warm summers

D=moist mid-latitude with cold winter c = cool summers

E=polar - extremely cold winter and summer

 For most of the season-linked studies, the date-of-survey information comes from publications or

personal communications with researchers that more-or-less precisely define the social survey

field period. To link these data to the meteorological data a nominal "interview" month is assigned

to each respondent. When the interviews with groups of respondents were spread over two or

three months, a month in which most interviews were concentrated or the month in the middle of

the interviewing period was chosen. No studies or interviews were included if the interview could

not be placed within a three-month period. In some cases, the precise year of the study's field

work was also uncertain. At least one study was conducted at the same season in two years but

interviews were not identified by the year of interview in the data set. These uncertainties and the

ready availability of long-term meteorological data lead to the decision to use thirty-year averages

(1961 - 1990) to characterize the meteorological conditions for each month of the year. The

primary meteorological index for each response to this survey is the average of three of these 30-

year averaged months, the month of the survey and the two preceding months. The data were

obtained from the Wereld Klimaat Informatie (WKI) data base, an electronic publication that

includes a graphical presentation format (in Dutch) that is available from the Royal Netherlands

Meteorological Institute (KNMI). 

The primary weakness of the season-linked data set, relative to the day-linked data set, is the less

precise linkage between the interview date and the meteorological conditions. The primary

strength is the larger number of interviews, studies, and climatic conditions. This advantage

provides data to attempt to determine whether there is both a climatic effect (i.e. differences

between reactions in different locations even under similar acoustical and short-term

meteorological conditions.) as well as a seasonal effect (i.e. variations in reactions at different

times of year in a single location).
-10-

Table 3: Noise annoyance surveys used in the day-linked and season-linked analyses

Description of study Climate

(Köppen

classifica

tion)

Conditions for survey period - avr. over sites 

 (N of int's)

Yearly range over site(s) (N of

interviews) Response

scale

points &

type Type a

Study [Catalog number 

(Fields 2001)]

Noise

source

Study

year 

Temperature

(90 days

before)

Temperature

(3-month,

30-year avr.)

Precipitation

(3-month,

30-yr avr.)

 Wind speed

(3-month,

30-yr avr.)

Temperature

(Coldest

month)

Temperature

(Warmest

month)

Temperature

(range)

 Daylinked

studies

(also in

Seasonlinked

data set)

1967 USA Four-Airport

(Phase I Tracor ) (USA-022) Aircraft 1967 Cfa, Csa,

Dfa

 13.5

 (3499)

 15.4

 (3499)

 3.2

 (3499)

 5.3

 (3499)

 2.8

 (3499)

 24.2

 (3499)

 21.4

 (3499)

5-Annoy:

Unipolar

1973 Los Angeles Airport

Night (USA-082) Aircraft 1973 Csa 14.2

 (702)

 14.7

 (702)

 4.5

 (702)

 4.1

 (702)

 13.8

 (702)

 20.6

 (702)

 6.8

 (702)

5-Annoy:

Unipolar

1974 Dordrecht Home Sound

Insulation (NET-106) Road 1974 Cfb 6.1

 (420)

 5.6

 (420)

 1.7

 (420)

 5.6

 (420)

 2.8

 (420)

 16.8

 (420)

 14.0

 (420)

6>5Annoy

:Unipolarb

1975-76 Southern Ontario

Community (CAN-121) Road 1975-

76 Dfb 11.1

 (1303)

 15.1

 (1303)

 2.2

 (1303)

 4.1

 (1303)

 -4.5

 (1304)

 22.1

 (1304)

 26.6

 (1304)

9>5Annoy

: Bipolar

1979 Burbank Aircraft Noise

Change (USA-203) Aircraft 1979 Csa 20.9

 (924)

 20.6

 (924)

 6.6

 (924)

 4.0

 (924)

 13.8

 (924)

 20.6

 (924)

 6.8

 (924)

5-Annoy:

Unipolar

1984 Glasgow Combined

Aircraft/Road Traffic (UKD238)

Aircraft 1984 Cfb 8.2

 (588)

 9.6

 (597)

 2.1

 (597)

 .

 (0)

 2.8

 (598)

 14.4

 (598)

 11.6

 (598)

10-Annoy:

Unipolar

Road 1984 Cfb 8.2

 (527)

 9.6

 (535)

 2.1

 (535)

 .

 (0)

 2.8

 (536)

 14.4

 (536)

 11.6

 (536)

10-Annoy:

Unipolar

1984-1986 French Combined

Aircraft/Road Traffic (FRA239)

Aircraft 1984 Cfb 14.4

 (564)

 15.4

 (565)

 1.9

 (565)

 3.7

 (565)

 3.4

 (565)

 18.4

 (565)

 15.0

 (565)

10-Annoy:

Unipolar

Road 1984 Cfb 14.5

 (523)

 15.4

 (524)

 1.9

 (524)

 3.7

 (524)

 3.4

 (524)

 18.4

 (524)

 15.0

 (524)

10-Annoy:

Unipolar

1984 Schiphol Combined

Aircraft/Road Traffic (NET240)

Aircraft 1984 Cfb 14.9

 (569)

 12.9

 (573)

 2.3

 (573)

 4.5

 (573)

 2.4

 (573)

 16.6

 (573)

 14.2

 (573)

10-Annoy:

Unipolar

Road 1984 Cfb 14.7

 (471)

 12.7

 (473)

 2.3

 (473)

 4.6

 (473)

 2.4

 (473)

 16.6

 (473)

 14.2

 (473)

10-Annoy:

Unipolar

1982 United Kingdom

Aircraft Noise Index (UKD242)

Aircraft 1980,

1982 Cfb 15.1 (1993)

 14.4

 (1993)

 1.7

 (1993)

 3.6

 (1993)

 3.5

 (1993)

 16.5

 (1993)

 13.0

 (1993)

4-Annoy:

Unipolar
Description of study Climate

(Köppen

classifica

tion)

Conditions for survey period - avr. over sites 

 (N of int's)

Yearly range over site(s) (N of

interviews) Response

scale

points &

type Type a

Study [Catalog number 

(Fields 2001)]

Noise

source

Study

year 

Temperature

(90 days

before)

Temperature

(3-month,

30-year avr.)

Precipitation

(3-month,

30-yr avr.)

 Wind speed

(3-month,

30-yr avr.)

Temperature

(Coldest

month)

Temperature

(Warmest

month)

Temperature

(range)

-11-

Road 1982 Cfb 14.8

 (410)

 14.1

 (410)

 1.7

 (410)

 3.6

 (410)

 3.5

 (410)

 16.5

 (410)

 13.0

 (410)

4-Annoy:

Unipolar

1975 Amsterdam Home

Sound Insulation (NET-258) Road 1975 Cfb 4.9

 (364)

 4.5

 (364)

 1.6

 (364)

 5.8

 (364)

 2.4

 (365)

 16.6

 (365)

 14.2

 (365)

6>5Annoy

:Unipolarb

1989 Oslo Airport (NOR311) Aircraft 1989 Dfb 3.8

 (1548)

 .1

 (1548)

 1.4

 (1548)

 2.1

 (1548)

 -4.3

 (1548)

 16.4

 (1548)

 20.7

 (1548)

2 Questionsb

#31

1992-93 Bodoe Aircraft

Military Exercise (NOR-328) Aircraft 1992 Cfc 1.1

 (702)

 -1.5

 (702)

 2.8

 (702)

 .

 (0)

 -2.2

 (702)

 12.5

 (702)

 14.7

 (702)

4-Annoy:

Unipolar

 Subtotal 11.8

 (15107)

 12.0

 (15132)

 2.6

 (15132)

 4.2

 (13298)

 2.5

 (15136)

 19.0

 (15136)

 16.5

 (15136)

Season-

(only)-

linked

studies

1967 Heathrow Aircraft (2nd

Heathrow) (UKD-024) Aircraft 1967 Cfb .

 (0)

 15.5

 (4515)

 1.8

 (4515)

 3.3

 (4515)

 3.5

 (4515)

 16.5

 (4515)

 13.0

 (4515)

4-Annoy:

Unipolar

1969 USA Three-Airport

(Phase II Tracor) (USA-032) Aircraft 1969 Aw, Cfa,

Dfb

 .

 (0)

 21.7

 (1166)

 2.5

 (1166)

5.7

 (1166)

 -1.8

 (1166)

 23.1

 (1166)

24.9

 (1166)

5-Annoy:

Unipolar

1970 USA Small City

Airports (USA-044) Aircraft 1970-

71 Bsk, Cfa .

 (0)

 7.9

 (1954)

 2.5

 (1954)

 2.7

 (1954)

 1.9

 (1954)

 24.2

 (1954)

 22.3

 (1954)

5-Annoy:

Unipolar

1971 Swiss Three-City (SWI053)

Aircraft 1971-

72 Cfb .

 (0)

8.8

 (3934)

3.1

 (3934)

 3.1

 (3934)

 -1.0

 (3934)

 16.7

 (3934)

 17.7

 (3934)

11-Annoy:

Unipolar

Road 1972 Cfb .

 (0)

11.6

 (945)

 3.7

 (945)

 3.0

 (945)

 -1.0

 (945)

 16.7

 (945)

 17.7

 (945)

11-Annoy:

Unipolarb

1972 Paris Area Railway

(FRA-063) Railway 1972 Cfb .

 (0)

 6.8

 (344)

 1.6

 (344)

 4.3

 (344)

 3.4

 (344)

 18.4

 (344)

 15.0

 (344)

7-Annoy:

Unipolarb

1972 B.R.S. London Traffic

(UKD-071) Road 1972 Cfb . (0)

 11.2

 (2903)

 1.8

 (2903)

 3.8

 (2903)

 3.5

 (2903)

 16.5

 (2903)

 13.0

 (2903)

7>6Annoy

:Bipolar

1972 English Road Traffic

(UKD-072) Road 1972 Cfb . (0)

 12.7

 (1031)

 1.7

 (1031)

 4.1

 (1031)

 3.1

 (1040)

 15.8

 (1040)

 12.7

 (1040)

7>6Annoy

:Bipolar
Description of study Climate

(Köppen

classifica

tion)

Conditions for survey period - avr. over sites 

 (N of int's)

Yearly range over site(s) (N of

interviews) Response

scale

points &

type Type a

Study [Catalog number 

(Fields 2001)]

Noise

source

Study

year 

Temperature

(90 days

before)

Temperature

(3-month,

30-year avr.)

Precipitation

(3-month,

30-yr avr.)

 Wind speed

(3-month,

30-yr avr.)

Temperature

(Coldest

month)

Temperature

(Warmest

month)

Temperature

(range)

-12-

1973 French Ten-City

Traffic (FRA-092) Road 1973-

74 Cfb, Csa .

 (0)

 17.1

 (975)

 1.9

 (975)

 3.1

 (897)

 3.1

 (975)

 20.1

 (975)

 17.0

 (975)

4-Annoy:

Unipolar

1975 British National

Railway (UKD-116) Railway 1975-

76 Cfb .

 (0)

 8.5

 (1382)

 1.9

 (1382)

 4.3

 (1382)

 3.1

 (1385)

 15.8

 (1385)

 12.7

 (1385)

4-Annoy:

Unipolar

1975 Western Ontario

University Traffic (CAN-120) Road 1976 Dfb .

 (0)

 -.4

 (1149)

 2.3

 (1149)

 5.9

 (1149)

 -4.5

 (1149)

 22.1

 (1149)

 26.6

 (1149)

7-Annoy:

Unipolar

1977 Netherlands

Railway (NET-153)

Railway 1977 Cfb .

 (0)

 13.7

 (671)

 2.3

 (671)

 3.0

 (671)

 2.2

 (671)

 16.8

 (671)

 14.6

 (671)

2 Questionsb

1977 London Area Panel

(UKD-157) Road 1977-

78 Cfb .

 (0)

 6.2

 (364)

 2.4

 (364)

 3.9

 (364)

 3.5

 (364)

 16.5

 (364)

 13.0

 (364)

4-Annoy:

Unipolar

1978 Canadian Four-Airport

(CAN-168)

Aircraft 1978 Dfb .

 (0)

 15.0

 (630)

 2.2

 (630)

 4.1

 (630)

 -4.5

 (631)

 22.1

 (631)

 26.6

 (631) 11-Annoy:

Unipolar

Road 1978 Dfb .

 (0)

 15.0

 (567)

 2.2

 (567)

 4.1

 (567)

 -4.5

 (568)

 22.1

 (568)

 26.6

 (568)

1981 John Wayne Airport

Operation Change (USA-204) Aircraft 1981 Csa .

 (0)

 21.0

 (601)

 6.5

 (601)

 4.0

 (601)

 13.8

 (601)

 20.6

 (601)

 6.8

 (601)

2 Questionsb

1980 Australian Five-Airport

(AUL-210) Aircraft 1980 Bsh, Cfb,

Csa

 .

 (0)

 18.7

 (3288)

 3.2

 (3288)

 4.1

 (3288)

 11.7

 (3288)

 22.3

 (3288)

 -10.6

 (3288)

5-Annoy:

Unipolar

1983 Netherlands

Tram/Road Traffic (NET276)

Road 1983 Cfb .

 (0)

 14.7

 (697)

 2.1

 (697)

 5.0

 (697)

 2.8

 (697)

 16.8

 (697)

 14.0

 (697) 2 Questionsb

Railway 1983 Cfb .

 (0)

 14.7

 (265)

 2.1

 (265)

 5.0

 (265)

 2.8

 (265)

 16.8

 (265)

 14.0

 (265)

1989 Austrian Alps Road

Traffic (AUS-329) Road 1989 Cfb . (0)

 10.0

 (826)

 2.0

 (826)

 1.2

 (826)

 -1.1

 (826)

 18.7

 (826)

 19.8

 (826)

4-Annoy:

Unipolar

1990 Modena Traffic (ITL350) Road 1990 Cfa .

 (0)

 17.6

 (908)

 2.0

 (908)

 .

 (0)

 2.4

 (908)

 24.6

 (908)

 22.2

 (908)

2 Questionsb
Description of study Climate

(Köppen

classifica

tion)

Conditions for survey period - avr. over sites 

 (N of int's)

Yearly range over site(s) (N of

interviews) Response

scale

points &

type Type a

Study [Catalog number 

(Fields 2001)]

Noise

source

Study

year 

Temperature

(90 days

before)

Temperature

(3-month,

30-year avr.)

Precipitation

(3-month,

30-yr avr.)

 Wind speed

(3-month,

30-yr avr.)

Temperature

(Coldest

month)

Temperature

(Warmest

month)

Temperature

(range)

-13-

1993 Netherlands National

Environmental (NET-361) Road 1993 Cfb .

 (0)

 15.5

 (880)

 2.3

 (880)

 3.0

 (880)

 2.2

 (880)

 16.8

 (880)

 14.6

 (880)

10-Annoy:

Unipolar

1984-85 Arnhem Trolley Bus

Introduction (NET-362) Road 1984 Cfb .

 (0)

 8.2

 (293)

 2.1

 (293)

 4.7

 (293)

 1.6

 (293)

 16.6

 (293)

 15.0

 (293)

2 Questionsb

1993-94 French 18-Site

Time-Of-Day (FRA-364) Road 1993-

94 Cfb, Csa .

 (0)

 11.2

 (895)

 2.2

 (895)

 3.2

 (573)

 2.7

 (895)

 19.0

 (895)

 16.3

 (895)

11Annoy:

Unipolar

1989-93 Swedish Railway

(SWE-365) Railway 1989-

93 Cfb .

 (0)

 6.0

 (2532)

 1.5

 (2532)

4.2

 (1540)

-2.4

 (2532)

 15.9

 (2532)

 18.3

 (2532)

4-Annoy:

Unipolar

1990-91 Vaernes Aircraft

Military Exercise (NOR-366) Aircraft 1990 Cfc .

 (0)

 13.3

 (391)

 2.7

 (391)

 2.4

 (391)

 -3.2

 (391)

 13.9

 (391)

 17.1

 (391)

4-Annoy:

Unipolar

 Istanbul Trans-European

Motorway (TRK-367) Road 1996 Csa .

 (0)

 22.0

 (154)

 .9

 (154)

 .

 (0)

 5.6

 (154)

 23.2

 (154)

 17.6

 (154)

5-Annoy:

Unipolar

1996 Gothenburg Road

Traffic (SWE-368) Road 1996 Cfb .

 (0)

 -1.0

 (1316)

 2.0

 (1316)

 4.5

 (1316)

 -1.6

 (1316)

 16.2

 (1316)

 17.8

 (1316)

4-Annoy:

Unipolar

1996 Kumamoto Road

Traffic (JPN-369) Road 1996 Dfa .

 (0)

 18.6

 (823)

 5.8

 (823)

 2.9

 (823)

 5.8

 (823)

 26.9

 (823)

 21.1

 (823)

4-Annoy:

Unipolar

1985-86 Ratingen Dusseldorf

Aircraft/Road (GER-372) Road 1986 Cfb .

 (0)

 3.9

 (559)

 2.1

 (559)

 .

 (0)

 2.2

 (559)

 18.1

 (559)

 15.9

 (559)

11-Annoy:

Unipolar

1987 Dusseldorf/Ratingen

Aircraft/ Road (GER-373) Road 1987 Cfb .

 (0)

 6.9

 (440)

 2.0

 (440)

 .

 (0)

 2.2

 (440)

 18.1

 (440)

 15.9

 (440)

11-Annoy:

Unipolar
Description of study Climate

(Köppen

classifica

tion)

Conditions for survey period - avr. over sites 

 (N of int's)

Yearly range over site(s) (N of

interviews) Response

scale

points &

type Type a

Study [Catalog number 

(Fields 2001)]

Noise

source

Study

year 

Temperature

(90 days

before)

Temperature

(3-month,

30-year avr.)

Precipitation

(3-month,

30-yr avr.)

 Wind speed

(3-month,

30-yr avr.)

Temperature

(Coldest

month)

Temperature

(Warmest

month)

Temperature

(range)

-14-

1993 Greifswald Traffic

(GER-374) Road 1993 Cfb .

 (0)

 15.5

 (582)

 1.8

 (582)

 4.9

 (582)

 -1.0

 (582)

 18.1

 (582)

 19.1

 (582)

5-Annoy:

Unipolar

 Subtotal .

 (0)

 11.9

 (37980)

 2.4

 (37980)

 3.7

 (34527)

 2.1

 (37994)

18.7

 (37994)

14.7

 (37994)

 Total 11.8

 (15107)

 11.9

 (53112)

 2.5

 (53112)

 3.9

 (47825)

 2.2

 (53130)

 18.8

 (53130)

 15.2

 (53130)

a

 Unless otherwise noted all scales ask about general, not specific annoyance or disturbance, and assign numeric scores that are separated by equal intervals with the end points of

each scale being at the center of the first and last category (see text). The ">" in an expression "A>B" indicates that the original "A" points in the question were collapsed into "B"

points when the points are scored. Unless otherwise noted in footnote "b" the "Unipolar" scales in the questionnaire were anchored with equally extreme positive and negative end

positions and included a neutral word (e.g. "Not annoyed") in the center of the scale. The scoring of many of these studies' scales is available in a TNO report (Miedema 1992).

b The scoring for the annoyance scales on the following studies departed from the general description given in footnote "a".

SWI-053 The only available data collapses the 11-point, unipolar numeric scale into four categories that are assigned the mid-point of their combined categories resulting in the

following scores being assigned: point 1=4.55, points 2,3,4=22.73, points 5,6,7,8=54.55, points 9,10,11=86.36.

ITL-350 - Two 11-point numeric scales asking about weekday and weekend annoyance are averaged to create a 21-point scale with equal intervals

NOR-311-Two 4-point verbal scales asking about indoor and outdoor annoyance are expanded into a 7-point scale with equal intervals

USA-204 - An answer of "not" on a screening annoyance question is scored 11.25. Other respondents were scored on a unipolar 5-point scale without equal intervals (11.25,

21.25, 43.75, 66.25, 88.75).

NET-106, NET-258 A 6-point verbal scale is collapsed to form five equally-spaced categories by collapsing the two lowest points ("definitely not annoying" and "not annoying").

NET-153 - An answer of "never" or "seldom" on a 4-point frequency of annoyance screening question was scored 10. Other respondents were scored on their answers to a 7-point

verbal scale. The 7-point scale is collapsed into five equally spaced categories by scoring 10 for the lowest two categories ("definitely not annoying", "not annoying") and scoring

20 for either of the next two points ("just not annoying" "neutral") .

NET-362 and NET-376 - An answer of "never" or "seldom" on a 4-point frequency of annoyance screening question was scored 10. Other respondents were scored on answers to

a 5-point verbal scale that was scored with five equal intervals

UKD-071, UKD-072 - A two lowest points of a bipolar 7-point numeric scale with ends labeled as "definitely satisfactory" and "definitely unsatisfactory" are collapsed to form 6

equally-spaced points..

FRA-063 A 7-point numeric scale is considered to be a unipolar scale on the assumption that the lowest point ("Trés supportable" - Quite acceptable) is not a positive rating.
-15-

1.2.4 Features of both the day-linked and season-linked data sets

Important strengths that the day-linked data set shares with the season-linked data set include:

1) Environmental noise data expressed in a common index (Day Night Noise Level - DNL)

that have been normalized to the noisiest facade of each respondent's dwelling (after

removing reflection effects)

2) Response scales that measure the intensity of annoyance

3) A large number of responses

4) A wide range of meteorological conditions (e.g. The maximum temperatures in the

warmest month range from 12.5 degrees for a survey in Norway to 26.9 for one Japan in

Table 3)

5) Relatively low correlations between the two different meteorological conditions

(temperature and precipitation) because the studies come from different climates.

The primary weaknesses for both the day-linked and season-linked data sets are that:

1) The estimates of meteorological effects are almost entirely dependent upon comparisons

between studies (not within studies) because there is relatively little variation in

meteorological conditions within any of the 41 component studies, largely because

interviews tend to be conducted within a short time period

2) There are considerable between-survey differences in language (9 languages), cultures (14

countries), annoyance question wording, questionnaire design, noise sources (road traffic,

aircraft, and railway), and other aspects of the survey methods.

3) The effective sample size is very much smaller than the number of responses (53,130 for

the season-linked data set) would seem to indicate because; 1] more than one response is

sometimes given by a single individual (e.g. when a survey includes separate responses -

and noise data- for more than one noise source), and 2] the errors in measurements within

the twelve surveys are not independent.

The noise annoyance questions and response scales differ in wording and numbers of scale points. 

For the comparative analysis in this report each answer scale is expressed as a numeric quantity

within a range of scores that are greater than zero and less than one-hundred by assuming that

each of the response scale points is the mid-point of an interval along the same 0 - 100 point scale.

A single simple rule is used for creating the scores when the survey response scale is unipolar

scale. At unipolar scale only measures negative reactions and extends from a low point of "no

annoyance" to a high point (i.e. "extremely annoyed"). A bipolar scale includes responses for 

both positive and negative reactions to the sound. For the simple unipolar scale, the definition of

the scale points is expressed as scorei = 100 (i-1/2)/m where "i" is the rank order of the scale point

in the questionnaire and "m" is the number of points on the scale. As a result, the three points for

the continuous DLO survey question are assigned scores of 16.7, 50.0, and 84.3. The rationale

for this procedure is provided in (Miedema and Vos 1998).
-16-

When a balanced bipolar scale with "r" points is used with the middle point labeled with a neutral

label (i.e. "no annoyance") then the positive responses are merged into a single category with the

"no annoyance" responses and the resulting scale is scored just as for a unipolar scale that has

(r+1)/2 points. For example the 11-point bipolar scale becomes a 6-point scale with the six points

assumed to be the midpoints of six equal-sized intervals. The words that are used to describe the

scale points for the unipolar scales and the negative end of the bipolar scales differ between

studies. The simple rule presented in the previous paragraph is based on the assumptions that the

variations between the wordings will not affect respondents' answers and thus that respondents

react to the position of the points on the scale and that those points are spaced at equal intervals

on some underlying, unobserved annoyance scale.

More complex rules that involve investigator judgment are required for other instances when, for

example, screening questions are used, separate questions are used for annoyance experienced

indoors and outdoors, separate questions were used for weekday and weekend, or the bipolar

scale does not have a clearly labeled neutral point. Studies in which these types of judgments

were required are listed in footnote b of Table 3. The investigator's judgement is occasionally

invoked to decide when the scale point words create a bipolar scale or the words are so unusual

as to violate the equal interval assumption. For example, for studies UKD-071 and UKD-072

respondents are asked to chose one of seven points on a numeric scale upon which the endpoints

are labeled "definitely satisfactory" and "definitely unsatisfactory" and the mid point is not labeled. 

To recognize the bi-polar nature of the words, the lowest two points are collapsed and, with the

remaining five points, are assumed to, as for other scales, be the midpoints of six equal sized

intervals. For other scales it is assumed that differences in wordings of two scales should not alter

the scoring. For example scores of 10, 30, 50, 70, 90 are assigned to both the 5-point scale for

USA-203 with the five points being labeled "not at all, a little, moderately, very, extremely"

annoying and for the 5-point scale from NET-362 with the labels "not annoying, just not

annoying, just annoying, annoying, and very annoying". More details on the scoring and the

rationale for scoring many questions are given in a TNO report (Miedema 1992).

Since the individual interviews are clustered within countries, studies, and cities, the interview

observations are not statistically independent. As a result sampling errors have been assessed

using a technique, jackknife repeated replication (Model JK1), that recognizes the dependence

between observations in the sample (Brick et al. 2000;Frankel 1983). For the purposes of these

sampling error calculations, the day-linked sample of 15,107 responses from 12 studies, are

assumed to be clustered into 28 independent primary sampling units (PSU). The jackknife model

used here does not assume that the sample of PSU's is stratified. A single study was divided into

several PSU's when, for example, the study had been conducted in several cities. For the seasonlinked analysis the PSU's are assumed to be the 41 studies. Sampling errors using these

techniques indicate that inferential statistics such as confidence intervals, are several times larger

than would be estimated using incorrect simple random sampling assumptions.
-17-

 

2.0 SEASONAL DIFFERENCES IN NOISE REACTIONS: THE NETHERLANDS

CONTINUOUS LIFE INFORMATION STUDY (DLO)

 

The strongest evidence about seasonal effects comes from the year-round Continuous Life

Information Survey (DLO) in the Netherlands. Interviews that are available from seven years for

every season give a firm basis for averaging over idiosyncratic aspects of particular years or

months and, as a result, provide a more stable estimate of the reactions at different times of year.

2.1 Annoyance and meteorological variables for the DLO analysis

The analyses in this report are based on noise annoyance questions about four noise sources. 

Respondents were asked about being bothered at home by three transportation noise sources;

aircraft noise; noise from trains, trams or subways; and noise from cars, lorries, motorcycles, or

mopeds. The noise from these three sources comes from outside the home and, as a result, the

levels experienced by residents could vary with the time of year as window opening or outside

activity patterns differ. The noise from the fourth source, neighbors, could include interior noise

from adjacent neighbors in multi-unit dwellings as well as noise from outdoors. The noise

annoyance answer scale has three points and, unlike other noise annoyance surveys, has a middle

point "sometimes" that refers to the frequency of annoyance. The scales are given in Appendix A 

in English (Table 15) and Dutch (Table 16 ). 

In the analyses the respondents' answers are presented as the percentage annoyed (i.e. giving any

answer other than the lowest point) and as three-point annoyance scores with the three points

being scored 16.7, 50.0, or 83.3 (the midpoints of three intervals that extend from 0 to 100), as

was explained earlier. In several analyses the three transportation noise sources are combined into

a single index by averaging each individual's scores for the three transportation noise sources.

The meteorological variables that appear in the tables and figures in the DLO analyses are defined

similarly and all are constrained by the fact that the month, but not the day, of the interview is

known. Each respondent is assigned the average of the daily values for the two months preceding

the interviewing month. This is consistent with the finding reported in the section on the daylinked analysis that reactions appear to be linked as closely to conditions over the preceding 90

days as they are to the conditions in the month of the survey. The meteorological conditions for

the month of the interview are not included since respondents interviewed at the beginning of the

month would not have experienced the conditions from the remainder of the month. Other

analyses examined the effects of meteorological conditions when indices were formed using

periods defined as the previous three months, the interview month, the two months including the

interview month, the three months including the interview month, and the 90 days extending back

from the middle of the interview month. Although values of regression and correlation

coefficients changed somewhat, none of these indices was consistently more closely related to
-18-

noise annoyance than was the two-preceding-month index used here. The meteorological

conditions for the DLO analyses are monthly averages of the following daily indices:

Temperature Mean = Mean of 24 hourly temperature values (degrees Celsius)

Temperature Max = Maximum temperature reading in the day.

Temperature Min = Minimum temperature reading in the day.

Wind speed = Mean of wind speed during 24 hours (meters per second)

Sunshine = Percent of the possible daylight hours with sunlight

Precipitation (mm.) = Total of the precipitation during the 24 hours (millimeters)

Precipitation (hrs.) = Number of hours during which there is precipitation.

2.2 Noise annoyance reactions

The average values for each month for both annoyance and meteorological indices from the

survey appear in Table 4 and Figures 1 and 2. The first panel in Table 4 gives the percentage of

the respondents who express annoyance by selecting either of the top two points on the three

point scale. The middle panel averages the scores on the three-point annoyance scale where the

three points are scored 16.67, 50.00, and 83.33 respectively. Annoyance with road traffic,

aircraft, and railway noise all follow a general seasonal pattern with annoyance being highest in

the summer and early autumn and lowest in the winter. Annoyance with neighbors does not

follow the same pattern and is not clearly linked to season. The transportation noise annoyance

patterns can be clearly discerned in Figure 1 if each of the four sets of annoyance reactions' data

points is compared to their respective reference lines (dotted straight lines) at the yearly mean

annoyance score. For example, road traffic annoyance, the most frequent source of annoyance,

has reactions that are above the yearly average in June, July, August, September, and October and

reactions that are below the yearly average in December, January, February, and March. The

corresponding change in mean temperature is seen in Figure 1 to range between about 18 degrees

(C) in the summer to about 3 in the winter. 

Figure 2 graphs the six meteorological indices that are presented in the bottom panel of Table 4. 

To more clearly show the patterns, all the scales except temperature are shown as 10 times their

actual value (e.g. the maximum precipitation of about 3 mm/day in January is plotted as 30 in the

figure).
-19-

Figure 1 Annoyance reactions by month (DLO survey)
-20-

Figure 2 Meteorological conditions by month (continuous DLO survey)
-21-

Table 4: Seven-year average of noise reactions and meteorological conditions for the DLO survey (by month)

Variablea Month of interview Average

(12

April May June July Aug. Sept. Oct. Nov. Dec. Jan. Feb. March months)

Annoyance (% with any degree of annoyance)

Road-Annoyed (%)

Air -Annoyed (%)

Rail-Annoyed (%)

Neighbors-Annoyed (%)

31.9%

21.0%

5.4%

25.9%

31.9%

22.5%

5.0%

25.5%

33.3%

23.7%

5.9%

25.8%

34.4%

25.3%

5.9%

24.6%

33.0%

22.9%

6.5%

23.6%

32.1%

25.1%

6.5%

25.9%

33.3%

19.8%

6.2%

25.3%

31.9%

20.4%

5.9%

25.7%

31.0%

20.1%

5.1%

27.0%

30.3%

20.5%

4.8%

25.1%

29.4%

22.0%

4.9%

25.8%

31.3%

21.7%

5.9%

24.8%

32.0%

22.1%

5.7%

25.4%

Annoyance score (3-point scale)

Road-Annoyance (Score)

Air -Annoyance (Score)

Rail-Annoyance (Score)

Neighbors-Annoyance (Score)

31.43

25.36

19.06

28.53

31.49

26.20

18.89

28.01

32.42

26.71

19.39

28.06

33.02

27.11

19.48

27.67

32.09

25.83

19.60

27.18

32.08

27.00

19.58

28.46

32.66

25.04

19.62

28.13

31.78

25.15

19.36

28.36

31.36

24.84

19.01

28.70

30.53

25.13

18.88

28.04

30.12

25.81

18.79

28.25

30.91

25.50

19.25

27.51

31.66

25.81

19.24

28.08

Meteorological variables

Temperature (Max C)

Temperature (Mean C)

Wind speed (Avr. m/s)

Sunshine (%/day)

Precipitation (mm.)

Precipitation (hrs.)

13.93

9.15

3.65

41.0%

1.30

1.6

17.74

12.82

3.36

43.1%

1.68

1.2

19.59

15.18

3.17

34.7%

2.44

1.8

23.17

18.29

3.07

45.0%

2.23

1.3

22.88

17.73

2.92

43.3%

2.00

1.2

18.11

13.74

3.02

31.6%

2.93

2.2

14.42

10.29

3.29

35.2%

2.40

1.9

9.13

6.36

3.61

23.0%

2.89

2.6

5.59

3.13

3.95

20.5%

2.49

2.3

6.17

3.65

4.70

24.0%

2.10

2.2

6.34

3.28

4.09

27.1%

1.77

2.1

10.44

6.56

4.26

37.5%

1.52

1.7

13.96

10.02

3.59

33.8%

2.15

1.8

a

 Variables for the continuous DLO study are defined in the Annoyance and Meteorological section in the beginning of this chapter and in Table 12.
-22-

2.3 Model for the seasonal reaction pattern

The seasonal variation can be summarized with a function that assumes that the variation over the

seven-year period is represented by a continuous 12-month curve in which the growth and decay

curves are symmetrical, but exactly reversed. The sine function defines one such curve as:

annoyance score = a + b sin[(month-c)*(2B/12)]

where:

Month= Month of year (1=Jan., 2=Feb., 3=Mar., . . . . 12=Dec)

a = is the mean yearly reaction

b = a multiplier determining the size of the most extreme deviations from the mean

reaction

c = a locational parameter that gives the month identification number for one of two

months at which the function predicts the mean yearly reaction 

2B/12 = 0.5236

The parameters of the function are estimated with a Marquardt procedure (Draper 1981). This

function has been estimated for each of the four noise sources and for a combined index for the

three transportation noise sources, all of which display the seasonal variation. The analysis is

based on 84 observations, one for each month in each of the seven years. The values of the

parameters are given for each noise source in Table 5. Each function is plotted as a solid line and

compared to the monthly annoyance scores in Figure 3. 

The analysis based on the sine function supports the conclusion that there is a statistically

significant effect of season on transportation noise annoyance, but that there is not a statistically

significant effect of season on annoyance with neighbors. The fact that the amplitude parameter is

significantly different than zero for all the transportation noise sources in Table 5 shows that there

is a significant relationship with season for all the transportation noise sources. The maximum

annoyance for these transportation noise functions is reached at the month with the value of c +

3. With values of c ranging from c=3 to c=5 in Table 5, the point of maximum annoyance for the

transportation noise sources is thus in the interval from June (Month 6) to August. 

The reactions to neighbors are strikingly different and do not show a seasonal effect: the

amplitude parameter (b) is not significantly different than zero. The explanation for the lack of a

seasonal pattern for neighbor noise has not been explored. It is hypothesized that any reduction in

neighbor noise that is achieved in the winter by closing windows is counterbalanced in multiple

unit dwellings when an apartment neighbor's noise becomes more noticeable in the winter when

noise from exterior sources are reduced by the closed windows. The low point for neighbor noise

is in August. It is possible that this may be related to the fact that many residents are away from

their homes for vacations during the period of the summer school holidays with the likely result

that the neighborhood is quieter for other residents.
-23-

Parameters of sine function

(Standard errors given in parentheses) Predict

maximum

annoyance in: Reaction measure Mean reaction

 (a)

Amplitude 

(b)

Average month

(c)

R

2

Road traffic 31.658

(0.168 )

1.049

 (0.238)

4.932

 (0.434)

0.19 July

Aircraft 25.808

(0.153)

0.826

 (0.216)

3.666

 (0.499)

0.15 June

Rail 19.243

(0.068)

0.356

 (0.096)

5.446

 (0.513)

0.15 August

Index(Road, Air,

Rail)

25.570

(0.088)

0.695

 (0.124)

4.550

 (0.342)

0.28 July

Neighbors 28.077

(0.132)

-0.273

 (0.187)

3.372

 (1.310)

0.03 December

Table 5: Parameters of sine functions for the continuous DLO survey

Although there is a seasonal pattern for the transportation noise sources, the patterns vary and are

not precisely explained by the time of year. The point of maximum annoyance is estimated to be

almost two months earlier for aircraft than for railway noise. The 95 percent confidence intervals

for the point of maximum annoyance [(c + 3) x ±1.96( standard error of c)] for each of the noise

sources extends over more than a three-week period. The variation in predicted points of

maximum annoyance would be expected to be a function of random variations in respondents'

sensitivity to noise, random differences in the noise exposures of the samples each year and to

genuine differences in meteorological conditions each year. The variations in the timing of

maximum annoyance between sources might reflect genuine differences or may indicate that the

actual sampling errors are being underestimated.

2.4 Explaining seasonal differences

Definitive explanations for the seasonal differences in reactions to transportation noise sources are

not obvious from an examination of Figures 1 and 2 or the associated meteorological data in

Table 4. Although it is seen that the reactions roughly parallel the mean temperature curve in

Figure 1, the display of other meteorological data for the same period in Figure 2 shows that the

amount of sunshine is closely correlated with temperature and that the wind speed follows a

correlated seasonal pattern (negative correlation) in the Netherlands. Both temperature and wind

conditions are such that they would both encourage the least window opening and outside activity

levels in the winter months. The correlations in Table 6 show that there are significant

relationships of most of the noise annoyance measures with most of the meteorological indices. 
-24-

Figure 3 Comparison of sine model and reactions (DLO survey)

The analysis of the relationship of these variables with reactions is presented in the regression

analysis in Table 7 and discussed in more detail in a later section of this report . 

The contrast between the reaction to neighbors' noise and to the transportation noise sources

does, however, eliminate one possible explanation for the seasonal variation in noise annoyance

reactions. The absence of a similar seasonal response pattern for neighbor noise suggests that the

seasonal response pattern to transportation noise is not due to an overall, undifferentiated

response bias that might apply to all types of feelings or judgments during the summer or winter

months. 
-25-

AnnoyRoadAnnoyAircraftAnnoyRailwayAnnoyNeighborsTemperat

ure-mean

Sunshine

(% day)

Precipitati

on-(mm)

Precipitat

ion (hrs.)

 Wind

speed

AnnoyRoad 1.000 .294

(.007)

 .370

(.001)

 .225

(.040)

 .459

(.000)

 .393

(.000)

 -.050

(.654)

 -.246

(.024)

 -.344

(.001)

AnnoyAircraft .294

(.007) 1.000 .001(.996)

 .209

(.056)

 .262

(.016)

 .297

(.006)

 -.160

(.145)

 -.288

(.008)

 -.218

(.046)

AnnoyRailway .370

(.001)

 .001

(.996) 1.000 .131(.233)

 .358

(.001)

 .171

(.119)

 .099

(.371)

 -.118

(.285)

 -.330

(.002)

AnnoyNeighbors .225

(.040)

 .209

(.056)

 .131

(.233) 1.000 -.035(.751)

 -.065

(.558)

 -.062

(.573)

 -.090

(.413)

 -.266

(.015)

Temperat

ure-mean

 .459

(.000)

 .262

(.016)

 .358

(.001)

 -.035

(.751) 1.000 .661(.000)

 .108

(.328)

 -.300

(.006)

 -.576

(.000)

Sunshine 

% day

 .393

(.000)

 .297

(.006)

 .171

(.119)

 -.065

(.558)

 .661

(.000) 1.000 -.375(.000)

 -.576

(.000)

 -.386

(.000)

Precipitati

on (mm.) 

 -.050

(.654)

 -.160

(.145)

 .099

(.371)

 -.062

(.573)

 .108

(.328)

 -.375

(.000) 1.000 .833(.000)

 .310

(.004)

Precipitati

on -(hrs.) 

 -.246

(.024)

 -.288

(.008)

 -.118

(.285)

 -.090

(.413)

 -.300

(.006)

 -.576

(.000)

 .833

(.000) 1.000 .645(.000)

Wind

speed

 -.344

(.001)

 -.218

(.046)

 -.330

(.002)

 -.266

(.015)

 -.576

(.000)

 -.386

(.000)

 .310

(.004)

 .645

(.000) 1.000

Notes to table: The statistical significance is given in parentheses.

Variables for the continuous DLO study are defined in the Annoyance and Meteorological section in the beginning of this

chapter. The meteorological variables are thus defined as two-month averages.

Table 6: Correlation coefficients for the continuous DLO survey
-26-

Table 7: Regression coefficients for four meteorological variables from regressions of noise

annoyance [three noise sources and combined annoyance index] (DLO survey)

Type of

analysis

Independent variables in the regression equationb:

Intercept Mean temperature (C) Sunshine (% of daylight

hours)

Precipitation (mm) Wind speed (m/s)

B Std Err (B) B Std Err (B) B Std Err (B) B Std Err (B)

Road traffic noise annoyance

Univariate a

a

0.148* 0.032 0.072* 0.018 -0.519* 0.226 -0.824* 0.248

Multivariate

analyses

(two to four

meteorological

variables in

the equation)

30.765 0.088 0.057 0.032 0.032 0.052 0.363 -0.326 0.408

31.192 0.129* 0.039 -0.188 0.274 -0.137 0.362

29.525 0.114* 0.042 0.029 0.024

30.737 0.137* 0.033 -0.251 0.217

31.419 0.126* 0.039 -0.288 0.287

29.447 0.069* 0.023 -0.060 0.264

31.711 0.056* 0.020 -0.542* 0.258

34.537 -0.085 0.288 -0.762* 0.327

Aircraft noise annoyance

Univariate a

a

0.076* 0.030 0.048* 0.017 -0.539* 0.198 -0.462* 0.228

Multivariate

analyses

(two to four

meteorological

variables in

the equation)

25.219 0.055 0.054 0.009 0.030 -0.456 0.344 0.154 0.386

25.342 0.067 0.370 -0.526* 0.258 0.208 0.341

24.271 0.033 0.040 0.035 0.023

26.033 0.055 0.031 -0.43* 0.205

25.977 0.059 0.037 -0.212 0.277

25.328 0.032 0.021 -0.327 0.240

25.360 0.040 0.018 -0.257 0.242

27.083 -0.472 0.260 -0.117 0.295
Type of

analysis

Independent variables in the regression equationb:

Intercept Mean temperature (C) Sunshine (% of daylight

hours)

Precipitation (mm) Wind speed (m/s)

B Std Err (B) B Std Err (B) B Std Err (B) B Std Err (B)

-27-

Railway noise annoyance

Univariate a

a

0.046* 0.013 0.012 0.008 -0.097 0.090 -0.308* 0.097

Multivariate

analyses

(two to four

meteorological

variables in

the equation)

19.724 0.035 0.023 -0.004 0.013 0.083 0.148 -0.240 0.167

19.675 0.030 0.016 0.110 0.111 -0.261 0.147

18.969 0.055* 0.017 -0.008 0.010

18.811 0.045* 0.014 -0.010 0.090

19.543 0.031* 0.016 -0.173 0.117

18.914 0.011 0.010 -0.024 0.110

20.153 0.004 0.008 -0.289 0.106

20.451 0.134 0.112 -0.406 0.127

Mean of three annoyance scores (Road-Air-Rail)

Univariate a

a

0.090* 0.017 0.044* 0.010 -0.385* 0.122 -0.531* 0.134

Multivariate

analyses (2

to 4 meteorological

variables in

the equation)

25.236 0.059 0.030 0.013 0.017 -0.107 0.193 -0.137 0.216

25.403 0.075* 0.021 -0.201 0.145 -0.063 0.192

24.255 0.067* 0.023 0.019 0.013

25.194 0.078* 0.018 -0.23* 0.115

25.646 0.072* 0.021 -0.224 0.153

24.563 0.037* 0.012 -0.137 0.142

* An asterisk indicates that the regression coefficient is statistically significantly different from zero (p<0.05)

a The "Univariate" rows contain the results from four univariate regressions of noise annoyance on each meteorological

condition. The intercept for each of the regression equations is not shown here but is, of course, different for each of the four

regressions.

b Variables for the continuous DLO study are defined in the Annoyance and Meteorological section in the beginning of this

chapter. The period of integration for the meteorological variables is the two months preceding the interview. The

parameters for each independent variable are: B = The unstandardized regression coefficient, Std Err(B) = Standard Error of

B. (The 95% confidence interval for B is 1.96 * Std Err(B).)
-28-

 

3.0 METEOROLOGICAL EFFECTS: PERIOD OF IMPACT

 

The analysis of the continuous DLO population study of the Netherlands in the previous section

clearly demonstrates that reactions to similar noise environments differ by season with residents

being more annoyed during warmer times of year. The data from the DLO study are not,

however, suitable for closely examining the length of the period that should be considered for

understanding noise reactions. The only noise reaction data available in the DLO study are for 

the average of the noise reactions over entire months of 28 to 31 days. As a result the exposure

on the date of an interview could not be determined and analyses could not examine the

relationship between meteorological conditions and reactions on particular dates. Other data are

analyzed in this section to obtain information about the period during which the temperature could

affect reactions.

3.1 Data: day-linked data set

The day-linked data set consists of 15,107 respondents' reactions to aircraft and road traffic noise

from 12 studies conducted in 7 countries (see Table 3). The meteorological data have been

individualized to each respondent's interview date by integrating the meteorological data over nine

proximate periods of increasing lengths ranging from one day (the date of the interview) to 91

days (the average over the date of the interview and the 90 days leading up to the interview). The

long-term meteorological conditions for the site have been calculated by averaging the data for

the previous ten calendar years (not including the year of the interview). Although many complex

models might be posited for the relative importance of immediate and remote meteorological

conditions, the analyses in this report are based on a simple model in which all days during a

period are given the same weight. Other, more theoretically satisfying models (for example: an

exponential decay model) have not been tested because, as will be seen, the evidence does not

support a model that weights recent events more heavily.

3.2 Analysis of reactions

Table 8 compares the strength of the effect of meteorological conditions on annoyance as the

number of days integrated increases from the single day of the interview to all 90 days before the

interview. This comparison is first made for a base analysis in the first column (Column A) and

then for variations on that base analysis in the other columns. The effect of temperature is

examined for the base analysis (Column A in Table 8) by regressing noise level and the average

daily mean temperature on annoyance in the first data column (A). The analyses that provide

evidence about the period of integration for the meteorological effect are in the lowest panel of

the table but the earlier rows provide the context for understanding that panel.
-29-

Table 8: Regression analyses for varying lengths of aggregated meteorological data (Day-linked data set)

Information in row Temperature analyses Precipitation

Model >>> Base

(DNL & 90-

day met)

(A)

Base &

Source of

noise a

(B)

Base & 10-

year average

temperaturea

(C)

Subset

(DNL from

45 to 75)

(D)

Base analysis

- but

maximum

temp. (E)

Base analysis

- but

minimum

temp (F)

Discrete

periods (not

cumulative)

(G)

Precipitation

(Total daily --

in mm.) 

(H)

Precipitation

(% of days

precipitate) 

(I)

Meteorological descriptor

(avr. of daily values)

Temperature - mean Temperature

- high

Temperature -

minimum

Temperature -

mean

Precipitation -

total

Precipitation -

% of days

N of reactions>> 15,107 15,107 15,107 12,770 15,107 15,107 15,107 7,713 7,713

First regression analysis (Regression with 90-day meteorological variable)

Regression coefficients 

(not standardized) (BDNL is

statistically significant

p<.05)

a= -36.618

BDNL= 1.200

BM-90= .312

a= -44.784

BDNL=1.215

BAir=11.497

BM-90= .204

a= -35.926

BDNL= 1.137

BT10-Yr= .778

BM-90= -.115

a= -56.657

BDNL=1.533

BM-90= .299

a= -36.054

BDNL= 1.228

BM-90= 0.079

a= -35.808

BDNL= 1.179

BM-90= .581

a= -38.565

BDNL= 1.202

BM-90= .564

a= -17.375

BDNL= 1.166

BM-90= -7.855

a= -43.482

BDNL= 1.257

BM-90= 0.070

Std. error for BM-90

Significance

0.340

(NS)

0.364

(NS)

0.573

(NS)

 0.278

 (NS)

0.292

(NS)

0.355

(NS)

0.331

(NS)

2.288

p<0.05

0.341

NS

Multiple R2= R2=0.152 R

2=0.179 R2=0.155 R2=0.132 R2=0.149 R2=0.158 R2=0.159 R2=0.169 R2=0.114

Second regression analysis (Regression without 90-day meteorological variable)

Regression coefficients

 (not standardized) (BDNL is

always significant p<.05)

a= -35.545

BDNL= 1.240

a= -44.282

BDNL=1.242

BAir=11.768

a= -36.177

BDNL= 1.141

BT10-Yr= 0.650

a= -54.307

BDNL=1.550

a= -35.545

BDNL= 1.240

a= -35.545

BDNL= 1.240

a= -35.545

BDNL= 1.240

a= -39.369

BDNL= 1.234

a= -39.369

BDNL= 1.234

Addendum to 2nd analysis: Beta (standardized regression)coefficient for each period (other periods are not in the equation)

Earliest day from

which daily

meteorological

data are averaged

0

1

2

3

7

14

30

60

90

-.025

-.021

-.015

-.010

.001

.000

.002

.025

.055

-.024

-.016

-.013

-.008

.002

.000

-.002

.014

.036

-.104

-.096

-.091

-.084

-.076

-.090

-.104

-.077

-.022

-.036

-.025

-.021

-.014

-.003

-.005

-.003

.019

.053

-.051

-.048

-.045

-.039

-.030

-.034

-.032

-.013

.015

.011

.015

.022

.027

.039

.039

.041

.066

.097

-.025

-.021

-.010

.002

.009

-.002

.004

.044

.104

-.007

-.047

-.056

-.054

-.082

-.097

-.089

-.136

-.172

-.025

-.037

-.049

-.055

-.064

-.056

-.048

.009

.028

a. The DNL-only regression equation for columns B and C is a=-35.545, BDNL=1.240.
-30-

The results from two regression equations are shown in each column. In all cases the dependent

variable is the annoyance scale. The first three rows are based on the regression of annoyance on

DNL, a meteorological variable (the 90-day mean value for that variable) and, in Columns B and

C, one other variable (a dummy variable representing aircraft surveys in Column B, a 10-year

mean temperature variable in Column C.) The coefficients in the rows in the first panel are

unstandardized regression coefficients. The second panel gives the standard error for the 90-day

meteorological variable. The coefficients in the first panel of the first column show that each

increase of 1 degree centigrade (mean temperature averaged over the date of the interview and

the previous 90 days) is estimated to increase noise annoyance by approximately 0.312

(BM90=0.312) annoyance score points. This would indicate that, as expected, annoyance increases

as temperature increases. However, the standard error of 0.340 in the next panel shows that the

0.312 effect is surrounded by a confidence interval of +/- 0.666 (0.666= 0.340 * 1.96) and thus is

not significantly different than zero. The third panel containing the multiple R2, gives the

proportion of the variance in annoyance explained by the equation.

The second regression in each column more directly addresses the period-of-integration issue. 

The coefficients are given in the first row for an equation that does not include the 90-day

temperature variable. The primary finding with respect to the period of integration is presented in

the lowest panel of the table where the Beta coefficient is given for each of nine time periods. 

Each of these is the beta coefficient that would apply to each meteorological variable if it were

included in the regression analysis presented immediately above. These beta coefficients are

standardized regression coefficients (standardized to the variances in the annoyance scores and

meteorological scores) and can be expressed as a transformation of partial correlation coefficients

and the unstandardized regression coefficients. The value of beta = 0.055 for the 90 period (last

line in the first column) is the transformation of the unstandardized regression coefficient that

appeared in the first regression in the column (BM90=0.312 ) that indicates that a one standard

deviation change in temperature (90-day mean temperature index) generates a 0.055 standard

deviation change in annoyance. Thus the beta values given in the lowest panel are a measure of

the effect of each of nine noise periods when only one of them is included in a regression equation

as the sole index for the meteorological condition (temperature, in the first seven columns;

precipitation in the last two columns).

The analyses based on temperature data are all consistent (Columns A through G) with higher

temperatures increasing noise annoyance (the coefficients for the 90-day temperature variable are

positive), but the "NS" in each column shows that the 95% confidence for the estimates of the 90-

day regression coefficient are so large that the effect of temperature is not statistically significant. 

For precipitation, the measure of total daily precipitation is statistically significant (Column H),

but not the measure of the percent of days that have any precipitation (Column I). The large

standard errors for the meteorological variables mean that these data will not, therefore, give

definitive estimates of the effect of these two meteorological variables. Some of the trends in the

data do, however, give the best information available. Seven of the nine columns in the table

provide this information in a similar format. The two columns with shaded Beta coefficients are

less relevant; Column C is included for a different purpose (examining the effect of the year-
-31-

Table 9: Partial regression coefficients from linear and logistic regressions of noise

annoyance on DNL, noise source and nine alternative integration periods for

temperature and precipitation (Day-linked data set) 

around average temperature) and Column G contains a different definition of the meteorological

variables in the Beta coefficient panel at the bottom of the table.

The values of the Beta coefficients in Column G give some insight into the pattern that is

displayed in the other columns that assess the effect of temperature (Columns A, B, D, E, F). 

Column G is the only column in which the temperature variables in the lowest panel are based on

mutually-exclusive, discrete periods. Thus the 90-day variable (beta=0.104) is an average for 30

days (days 61 to 90) not for 90 days. The Beta coefficients in this column (Column G) show that

the coefficients are positive (i.e. consistent with the seasonal effect found in the continuous

Netherlands DLO data) from the third day backward (except for day 8-14), but that they are

negative for the first three days (the interview day, and day 1 and day 2). The beta coefficients,

negative or positive, are very small for the day 2 to day 30 time periods, but become stronger in

the expected direction for the 60-day (i.e. 31-60 day period) and 90-day period (i.e. 61-90 day). 

None of these coefficients are statistically significant. There is certainly no indication that the

temperature on the day of the interview or on the immediately preceding days has an especially

strong effect; the coefficients are small, the relationships are not statistically significant and the

relationships are in the wrong direction relative to the strong evidence from the Netherlands DLO

survey. With all of the uncertainties it is concluded that an integration over a period that is more

than a month is most likely to be correct.

The same conclusion is consistent with the variety of patterns of the Beta coefficients for six of

the seven remaining columns all of which are based on cumulative meteorological data. The three

columns based on mean daily temperatures (Columns A, B, D) show the same tendency for weak

negative relationships in the first days, but increasing positive relationships as additional days of

meteorological information are integrated into the index with the 90 day meteorological variable

showing the strongest relationship. These three columns include the base case with only a DNL

predictor (Column A), an equation that adds a dummy variable for type of noise source (Column

B), or an analysis that is restricted to the 45-75 DNL exposure range (Column D). The two other

temperature analyses show slight variations on the same pattern. For the maximum temperature

column (E) all beta coefficients are negative except for the 90-day coefficient that is finally in the

expected direction. For the minimum temperature (Column F) all beta coefficients are positive

with the value steadily increasing as more days are cumulated. The total precipitation variable

shows the same pattern (Column H): the longer the integration period the stronger the

relationship. This is the only column in which the meteorological variables have statistically

significant relationships. The only column that does not support the general pattern of stronger

relations with longer time periods is the last column in which the coefficients for the days-ofprecipitation variable do not follow a clear pattern. There is some evidence that this days-ofprecipitation index is so weak that the pattern is of relative little importance. An examination of

the standard errors shows that the standard error for the 90-day coefficient is almost five times the

size of the estimate (5=0.341/0.0700), greater than for any other column.
-32-

Meteorological

variable

(mean of daily

values)a

Controls

(DNL, noise

source)

Earliest day from which daily meteorological data are averaged b

0 1 2 3 7 14 30 60 90

Linear regression

Temperature (C) 

DNL -0.12 -0.10 -0.08 -0.05 0.01 0.00 0.01 0.13 0.31

DNL& source -0.11 -0.08 -0.06 -0.04 0.01 0.00 -0.01 0.07 0.20

Precipitation (mm)

DNL -0.04 -0.05 -0.08 -0.09 -0.12 -0.12 -0.14 -0.19 -0.19

DNL & source -0.04 -0.04 -0.06 -0.07 -0.09 -0.09 -0.10 -0.09 -0.03

Logistic regression with moderate annoyance (50% or more of scale = 1, under 50%= 0)

Temperature (C) 

DNL 0.000 0.001 0.004 0.006 0.010 0.009 0.009 0.016 0.026

DNL & source -0.001 0.002 0.004 0.005 0.009 0.008 0.006 0.010 0.017

Precipitation (mm)

DNL -0.003 -0.003 -0.005 -0.006 -0.008 -0.009 -0.011 -0.017 -0.017

DNL & source -0.003 -0.003 -0.004 -0.005 -0.007 -0.007 -0.007 -0.010 -0.007

Logistic regression with high annoyance (72% or more of scale = 1 ("High annoyance"), 71% or less =0)c

Temperature (C) 

DNL -0.014 -0.014 -0.013 -0.012 -0.009 -0.010 -0.011 -0.003 0.007

DNL & source -0.016 -0.015 -0.014 -0.014 -0.011 -0.012 -0.014 -0.010 -0.003

Precipitation (mm)

DNL -0.002 -0.003 -0.004 -0.004 -0.005 -0.005 -0.006 -0.006 -0.006

DNL & source -0.001 -0.002 -0.003 -0.003 -0.003 -0.002 -0.003 0.000 0.003

Notes for Table 9: None of the coefficients for the meteorological variables in this table are statistically

significant at p<0.05]. Coefficients are not shown for DNL but are statistically significant for all analyses. The

dummy variable coefficients for noise source (aircraft or road) are not statistically significant for any regressions

including the precipitation variable or for any of the regressions for moderate annoyance. The dummy variable

coefficients for noise source are statistically significant for all linear regressions in the second line of the table

(temperature, line) and for about half the logistic regression analyses for the high annoyance equations that

include temperature.

a

 In this table temperature is the average daily temperature. Precipitation is the total daily amount.

b

 The meteorological data for day "0" are for the interview day. The data for day 3, for example include the

interview day (0) and the 3 prior days of meteorological data.

c

 Since the response scales have been transformed as explained in the text, high annoyance is the top point on a 4

or 5-point scale, the top 2 points on a 6 or 7-point scale, and top 3 on a 10 or 11-point scale.

The regression coefficients (unadjusted) for different length-of-integration metrics are provided in

Table 9. (The other regression equation parameters from the regression equations are not

presented.) The first three lines of Table 9, repeat the analyses in columns A, B and H of the

lowest panel of Table 8. For example, the beta=0.055 for the 90 day period at the bottom of

column A (Table 8) is shown in Table 9 to be the standardized value of the unadjusted partial

regression coefficient of B=0.31 (last column of the first row ). The analyses in Table 9 all draw

on the same data for one linear and two logistic regression analyses. The temperature variable
-33-

coefficients in five of the six analyses in Table 9 show the same tendency for stronger relations

with annoyance as the meteorological information is integrated over the longer period. Only the

high annoyance logistic regression analysis with controls for noise source does not find the

expected positive relationship between annoyance and temperature. For the precipitation analyses

based on either linear regression or logistic regression (for moderate annoyance), all relationships

are in the expected direction (noise annoyance increases when there is less precipitation) and the

strongest relationship is found for coefficients for the 7, 30, 60 or 90-day aggregation period. 

The relationships are not consistent for the logistic regression using the high annoyance measure. 

None of the meteorological variables for any of the day-linked analyses in Table 9 are statistically

significant (p<0.05). 

3.3 Conclusions about the period of impact

The best available evidence about the period of impact, weak though it is, comes from the

analyses in this section. There is no evidence for noise annoyance being strongly impacted by the

temperature or precipitation in the days immediately preceding the interview. The available

evidence suggests that the impact comes from a longer period, possibly including the 90 days

before the interview. This is the evidence that supports the decision to cumulate the

meteorological data for 90 days for the analyses presented in the remainder of this report. Given

the tentative nature of the conclusion, however, most of other analyses were repeated for other

integration periods as well even though they are not reported here.

The DLO data that seem to indicate that annoyance is highest in the warmest months does not

provide a good basis for conclusions about the period of integration because the exact interview

dates are unknown and because the meteorological conditions in the DLO survey are highly

correlated. For example, the noise reaction data in Figure 3 closely follow the sine curves that

show the highest annoyance is in the warmest months (June, July, August) rather than in the early

Autumn as would be expected from a 90-day cumulation period. However the seasonal

meteorological patterns in Figure 2 show that both precipitation and wind speed increase in the

Autumn and may thus counterbalance the residual effect of temperature from the summer months.
-34-

 

4.0 ASSESSING METEOROLOGICAL EFFECTS WITH THE SEASON-LINKED DATA

SET

 

The continuous DLO study showed that season is related to noise annoyance reactions in the

Netherlands and that the pattern of reactions is consistent with an effect of temperature. 

However, the high correlations between the major meteorological variables, especially wind speed

and temperature (r= -0.68) within the Netherlands limits the precision of the estimates and the

conclusions drawn about the independent effects of the correlated variables. Data from a wider

range of climatic conditions in a season-linked data set are used in this section to evaluate the

independent effects of these meteorological variables. This data set relates noise reactions to the

meteorological conditions that normally occur at the time of year at the site where the study

interviews were conducted as well as to the average yearly meteorological conditions at the site.

4.1 Data: Season-linked data set

The season-linked data set includes 53,130 responses from 41 studies in 14 countries (Table 3). 

These include all of the 15,107 responses from the day-linked analysis for which the exact date of

interview is known as well as about 38,000 additional responses for which the exact date of the

interview is not known. 

Three months of meteorological data were averaged to characterize the weather for each

interview, the nominal interview month and the two preceding months. The meteorological

conditions for each month are, as was explained earlier, based on the average of thirty years of

meteorological data (from 1961 to 1990) for the specified months.

4.2 Analysis of reactions

Four meteorological conditions are considered in the season-linked analysis. Correlations

between some of the indices representing these conditions and annoyance and noise level are

presented in Table 10. Sunshine is the average of the hours of sunshine per day. This is, as a

result, a function of both the amount of cloud cover and the number of hours of day-light that are

possible at the particular time of year. As a result, it is seen that there is a rather high correlation

between sunshine and the five temperature indices in Table 10 (from r=0.577 to r=0.826). 

Precipitation ("Precip." in Table 10) is represented by the average of the daily total amount of

precipitation (millimeters). Wind speed is the average of the daily mean wind speed expressed in

meters/second. The primary temperature variable used in these analyses (T-3MoMn =

Temperature-3 Month Mean)is the average of the daily mean temperatures for three months that

include the nominal interview month and two previous months (just as for the other variables). 
-35-

Table 10: Correlations between noise annoyance and meteorological variables in the season-linked data set

Variables Annoyance DNL Aircraft Railway T-3MoMn T-3MoMx T-3MoMi T-2MoMn T-2-4Mo Precip. Sunshine Wind

Annoyance (100-Point

Scale)

 1.000 .382 .122 -.191 .128 .125 .132 .136 .095 .102 .161 .031

DNL Noise level (DNL) .382 1.000 -.041 -.039 .092 .096 .088 .129 .011 .138 .212 .222

Aircraft noise source

 (Aircraft=1, others=0)

 .122 -.041 1.000 -.342 .233 .269 .211 .231 .198 .269 .360 -.110

Railway noise source

 (Railway=1, others = 0)

-.191 -.039 -.342 1.000 -.205 -.222 -.195 -.237 -.114 -.194 -.274 .075

T-3MoMnv= Temperature

3-Month Daily means(Avr)

 .128 .092 .233 -.205 1.000 .988 .983 .969 .881 .358 .787 .057

T-3MoMx = Temperature

3-Month Maximum(Avr.)

 .125 .096 .269 -.222 .988 1.000 .946 .962 .863 .297 .821 .026

T-3MoMi v=Temperature

3-Month Minimum (Avr.)

 .132 .088 .211 -.195 .983 .946 1.000 .953 .866 .438 .724 .073

T-2MoMn = Temperature

2-Month Mean

 .136 .129 .231 -.237 .969 .962 .953 1.000 .738 .353 .826 .085

T-2-4Mo = Temperature

2-4 Months before Mean

 .095 .011 .198 -.114 .881 .863 .866 .738 1.000 .315 .577 -.004

Precipitation 3-Month

Mean Daily

 .102 .138 .269 -.194 .358 .297 .438 .353 .315 1.000 .329 .008

Sunshine hours 3-Month

Mean daily hours

 .161 .212 .360 -.274 .787 .821 .724 .826 .577 .329 1.000 .096

Wind Speed 3-Month Mean .031 .222 -.110 .075 .057 .026 .073 .085 -.004 .008 .096 1.000
-36-

In Table 10 it is seen that this mean temperature index is highly correlated with three other

temperature indices: 3-month average of the daily maximum temperatures (T-3MoMx) at

r=0.988, 3-month average of the daily minimum temperature (T-3MoMi) at r=0.983, two-month

average (interview month and preceding month) of the daily mean temperature (T-2MoMn) at

r=0.969, and a 3-month index that excludes the interview month (i.e. includes the last three

months before the interview month) for the daily mean temperature (T-2-4Mo) at r=0.881. Since

the maximum, minimum and mean temperature indices are so highly correlated, only the mean

temperature indices are included in the remaining analyses.

The correlations in Table 10, also show that noise level happens to be correlated with many of the

meteorological indices across these data sets, presumably because some of the studies that were

designed with especially high noise exposure zones happened to have been conducted in seasons

and countries where there were warmer, sunnier, windier and higher precipitation conditions. As

a result all further analyses control for noise level in assessing the impact of meteorological

conditions on noise annoyance.

Information on precipitation and wind speed does not help to reveal the relative role of seasonal

conditions and long-term climatic conditions because the correlations are too high between annual

and the 3-month indices for precipitation (r=0.93) and annual and 3-month indices for wind speed

(r=0.91). The two remaining variables, temperature and sunshine, provide only a moderately

stronger basis for these analyses due to the high correlations between the 3-month average

conditions and the year-long average conditions (r=0.62 for temperature, r=0.67 for sunshine).

The effects of meteorological conditions on noise annoyance are examined in regression analyses

of noise annoyance on the six meteorological indices, each of which is represented by a column in

Table 11. All regressions contain DNL and at least one meteorological variable. The regressions

in the second analysis section (Section #2), also contain dummy variables to represent type of

noise source (aircraft and rail traffic are set to the value of one). The coefficients for the aircraft

and rail traffic dummy variables represent the deviations from the road traffic condition. 

4.3 Discussion of climatic effects

The season-linked data set has been used to explore the possibility of climate effects; that is,

whether reactions are affected by only the transitory conditions in the months preceding the

interview or whether reactions are somewhat moderated by the base, average climatic conditions

in a location. Do residents in all climates find, for example, that summer is more annoying but

adjust to the average climatic conditions so that average of the reactions over the entire year is the

same in all climates? In Part E of Table 11 a measure of the yearly average for the

meteorological condition at each site [Met. Var.(Year)] is included in each of the regression

equations. The analyses do not provide clear answers to the question. When both the seasonal

meteorological measure (Met. Var) and the yearly average [Met. Var.(Year)] are included in the

same equations in Sections #13 and #14, the coefficients for the yearly average are never

statistically significant and the coefficient for the seasonal measure is statistically significant in
-37-

only two instances. The expected correlations between seasonal and yearly meteorological

conditions clearly introduce imprecision in the estimates. 

There is, however, some indication that reactions to similar meteorological conditions in different

climates in different countries may not be as different as would be expected from the differences

between the meteorological conditions in the countries imply. The correlations with the seasonal

meteorological conditions (Sections #1 and 2) are about the same as the correlations with only the

yearly meteorological variables (Sections #15 and 16). Similarly when both the seasonal and

yearly average meteorological variables are included in the same equations (Sections #13 and #14)

the seasonal variable and yearly variable have opposite signs and thus suggest that seasonal

differences may be partially compensated for by a tendency for a similar average yearly reaction. 

Thus, for example, if there is a 10 degree difference in the average annual temperature at two

locations, the regression equation for temperature in the second column of Section #13 predicts

that those in the warmer location will be less annoyed at the same temperature by 0.6 points (-0.6

= -0.06*10) on the annoyance scale. However, if the seasonal temperature in a particular month

is 8 degrees higher in the warmer climate then the annoyance scores in the warmer climate that

month will actually be 3.1 annoyance score points higher than the colder climate because the 0.6

point annoyance score reduction due to the climate is more than offset by a 3.7 annoyance score

increase due to the differences in temperature (3.7 = 0.46 * 8). These results are subject to such

high standard errors, however, that the results actually provide little useful information about the

true relationship. This is due to the high correlations between the yearly and seasonal values; 

r=0.93 for precipitation, 0.91 for wind speed, 0.67 for sunshine, and 0.62 for temperature.

In general, the relationships of noise annoyance with meteorological conditions are not accurately

estimated in Table 11, as demonstrated by the standard errors that are large relative to the sizes of

the regression coefficients for the meteorological conditions. It is unlikely that any other analyses

of these data would yield more definitive results about the relative impact of seasonal and meanyear conditions. The standard errors of the partial regression coefficients for seasonal variables

do not show increased precision over the regressions shown in Section # 2 that introduce noise

source type as a control. Relationships are similar or weaker for the regressions in Sections #3

and 4 that are based on interviews in the 45 to 75 DNL range where there is a more nearly linear

relationship between noise annoyance and noise level. The relations are not systematically more

likely to be statistically significant in Sections #5 to 8 where the logistic regression allows for

non-linear relationships and does not rely on the assumption of a linear reaction scale. In addition

to characterizing a climate by the yearly average temperature, the temperature in the warmest

month (July in the Northern Hemisphere, January in the Southern Hemisphere) as well as the

coldest month, was also examined. Neither of these variables has a statistically significant

correlation with noise annoyance.Although the estimates of all the parameters are too imprecise to

draw conclusions from these data, they do indicate that some caution should be exercised in

extrapolating observed differences in seasonal reactions to presumed differences in reactions to

yearly climatic differences. 
-38-

Table 11: Regressions for the season-linked data set

Variables in equation Meteorological variable examined 

(The "Met. Var" is

defined in the column

heading.)

Temperature 

3-Month Daily Means

(Average) [T-3MoMnv]

Temperature

2,3,4 Prior Months Daily

Mean (Avr.) [T-2-4Mo ]

Sunshine

3-Month Mean daily

hours

Precipitation

 3-Month Mean Daily

Precipitation

 2-Month Mean Daily

Wind speeda

 3-Month Mean

Statistics> B Std.Er. Sig. B Std.Er. Sig. B Std.Er. Sig. B Std.Er. Sig. B Std.Er. Sig. B Std.Er. Sig.

Part A: Examination of monthly meteorological variables - linear regression

#1: [ Intercept]

Met. Var.

DNL

R

2=

 -37.39

0.46* 0.228 0.05

1.19* 0.169 0.00

R

2=0.155 

 -38.21

0.44* 0.220 0.05

1.21* 0.166 0.00

R

2=0.155 

 -37.00

1.16 0.638 0.08

1.16* 0.175 0.00

R

2=0.153 

 -35.37

1.26 0.964 0.20

1.20* 0.179 0.00

R

2=0.149

 -35.33

1.17 0.884 0.19

1.20* 0.180 0.00

R

2= 0.149

 -25.77

-1.56 1.268 0.22

1.20* 0.173 0.00

R

2=0.139 

#2: [Intercept]

Met. Var.

DNL

Aircraft 

Railway

R

2=

 -36.14

0.23 0.166 0.18

1.20* 0.146 0.00

4.70 3.173 0.15

-13.97* 2.843 0.00

R

2=0.186 

 -37.14

0.29 0.153 0.07

1.21* 0.142 0.00

4.59 3.065 0.14

-14.30* 2.634 0.00

R

2=0.187 

 -34.86

0.15 0.618 0.80

1.20* 0.142 0.00

5.03 3.383 0.14

-14.44* 3.118 0.00

R

2=0.184 

 -34.48

-0.05 0.879 0.96

1.21* 0.147 0.00

5.26 3.284 0.12

-14.63* 3.219 0.00

R

2=0.184 

 -34.35

-0.17 0.773 0.83

1.21* 0.146 0.00

5.35 3.269 0.11

-14.69* 3.233 0.00

R

2=0.184 

 -28.81

-0.99 1.686 0.56

1.18* 0.147 0.00

5.06 3.516 0.16

-13.49* 2.845 0.00

R

2=0.169 

#3: [Intercept]

Met. Var.

DNL(45-75)

R

2=

 -48.99

0.40 0.215 0.07

1.38* 0.157 0.00

R

2=0.124 

 -49.94

0.40 0.205 0.06

1.40* 0.153 0.00

R

2=0.124 

 -49.15

1.01 0.574 0.08

1.37* 0.160 0.00

R

2=0.122 

 -47.93

1.24 0.981 0.21

1.39* 0.164 0.00

R

2=0.119 

 -47.86

1.15 0.902 0.21

1.39* 0.164 0.00

R

2=0.119 

 -36.16

-1.71 1.222 0.17

1.36* 0.163 0.00

R

2=0.107 

#4: [Intercept]

Met. Var.

DNL(45-75)

Aircraft 

Railway

R

2=

 -47.76

0.18 0.156 0.26

1.38* 0.136 0.00

5.25 2.912 0.08

-13.87* 2.779 0.00

R

2=0.158 

 -48.67

0.25 0.145 0.10

1.39* 0.132 0.00

5.09 2.798 0.08

-14.10* 2.476 0.00

R

2=0.160 

 -46.75

0.04 0.591 0.94

1.40* 0.132 0.00

5.61 3.126 0.08

-14.31* 3.055 0.00

R

2=0.157 

 -46.61

-0.04 0.723 0.96

1.40* 0.135 0.00

5.68* 2.876 0.05

-14.37* 3.185 0.00

R

2=0.157 

 -46.46

-0.16 0.640 0.81

1.40* 0.134 0.00

5.75* 2.861 0.05

-14.44* 3.210 0.00

R

2=0.157 

 -39.56

-1.01 1.548 0.52

1.35* 0.143 0.00

5.41 3.146 0.09

-13.23* 2.715 0.00

R

2=0.139 
Variables in equation Meteorological variable examined 

(The "Met. Var" is

defined in the column

heading.)

Temperature 

3-Month Daily Means

(Average) [T-3MoMnv]

Temperature

2,3,4 Prior Months Daily

Mean (Avr.) [T-2-4Mo ]

Sunshine

3-Month Mean daily

hours

Precipitation

 3-Month Mean Daily

Precipitation

 2-Month Mean Daily

Wind speeda

 3-Month Mean

Statistics> B Std.Er. Sig. B Std.Er. Sig. B Std.Er. Sig. B Std.Er. Sig. B Std.Er. Sig. B Std.Er. Sig.

-39-

Part B: Examination of monthly meteorological variables - Logistic regression with a Moderate annoyance division (50% or more of scale = 1, under 50%= 0)

#5: [Intercept]

Met. Var.

DNL

R

2

 analog

 -5.98

0.03* 0.016 0.05

0.09* 0.012 0.00

0.101 

 -6.03

0.03* 0.015 0.05

0.09* 0.011 0.00

0.101 

 -5.99

0.10* 0.043 0.03

0.08* 0.012 0.00

0.102 

 -5.85

0.11 0.071 0.13

0.08* 0.013 0.00

0.098 

 -5.86

0.11 0.069 0.12

0.08* 0.013 0.00

0.099 

 -5.15

-0.06 0.082 0.49

0.08* 0.012 0.00

0.088 

#6: [Intercept]

Met. Var.

DNL

Aircraft 

Railway

R

2

 analog

 -6.11

0.01 0.012 0.24

0.09* 0.010 0.00

0.38* 0.177 0.04

-1.32* 0.369 0.00

0.129 

 -6.18

0.02* 0.008 0.04

0.09* 0.010 0.00

0.37* 0.172 0.04

-1.35* 0.355 0.00

0.130 

 -6.05

0.02 0.042 0.60

0.09* 0.010 0.00

0.39* 0.192 0.05

-1.34* 0.394 0.00

0.128 

 -6.02

0.01 0.048 0.79

0.09* 0.011 0.00

0.41* 0.178 0.03

-1.36* 0.381 0.00

0.128 

 -6.02

0.01 0.042 0.80

0.09* 0.011 0.00

0.41* 0.178 0.03

-1.36* 0.381 0.00

0.128 

 -5.64

-0.01 0.107 0.90

0.09* 0.010 0.00

0.42* 0.194 0.03

-1.21* 0.291 0.00

0.115 

Part C: Examination of monthly meteorological variables - Logistic regression with a High annoyance division (72% or more of scale = "High" annoyance)b

#7: [Intercept]

Met. Var.

DNL

R

2

 analog

 -7.45

0.02 0.015 0.22

0.09* 0.010 0.00

0.98

 -7.43

0.01 0.014 0.34

0.09* 0.010 0.00

0.097 

 -7.39

0.04 0.044 0.31

0.09* 0.010 0.00

0.098 

 -7.33

0.04 0.067 0.59

0.09* 0.010 0.00

0.097 

 -7.31

0.02 0.062 0.70

0.09* 0.010 0.00

0.096 

 -6.87

-0.05 0.114 0.66

0.09* 0.009 0.00

0.091 

#8: [Intercept]

Met. Var.

DNL

Aircraft 

Railway

R

2

 analog

 -7.39

0.01 0.014 0.64

0.09* 0.010 0.00

0.14 0.254 0.58

-1.38* 0.269 0.00

0.113 

 -7.39

0.01 0.012 0.65

0.09* 0.010 0.00

0.14 0.245 0.57

-1.40* 0.271 0.00

0.113 

 -7.32

-0.01 0.041 0.80

0.09* 0.009 0.00

0.17 0.259 0.51

-1.42* 0.261 0.00

0.113 

 -7.30

-0.03 0.070 0.67

0.09* 0.010 0.00

0.18 0.247 0.48

-1.42* 0.277 0.00

0.113 

 -7.29

-0.04 0.065 0.49

0.09* 0.010 0.00

0.19 0.246 0.44

-1.43* 0.277 0.00

0.114 

 -7.00

-0.03 0.132 0.83

0.09* 0.009 0.00

0.12 0.285 0.68

-1.29* 0.280 0.00

0.105 
Variables in equation Meteorological variable examined 

(The "Met. Var" is

defined in the column

heading.)

Temperature 

3-Month Daily Means

(Average) [T-3MoMnv]

Temperature

2,3,4 Prior Months Daily

Mean (Avr.) [T-2-4Mo ]

Sunshine

3-Month Mean daily

hours

Precipitation

 3-Month Mean Daily

Precipitation

 2-Month Mean Daily

Wind speeda

 3-Month Mean

Statistics> B Std.Er. Sig. B Std.Er. Sig. B Std.Er. Sig. B Std.Er. Sig. B Std.Er. Sig. B Std.Er. Sig.

-40-

Part D: Examination of the combined effect of three meteorological variables - linear regression (Each of the two sections of cells represents a single regression equation)

#9: [Intercept]

Met. Var.

DNL

R

2=

 -30.28

0.46 0.320 0.16

1.17* 0.162 0.00

R

2=0.148 

0.30 1.065 0.78 -1.68 1.488 0.27

#10: [Intercept]

Met. Var.

DNL

Aircraft 

Railway

R

2=

 -30.14

0.24 0.247 0.34

1.18* 0.143 0.00

4.51 3.726 0.23

-12.67* 2.898 0.00

R

2=0.171 

-0.27 1.018 0.79 -1.11 1.709 0.52

#11: [Intercept]

Met. Var.

DNL

Aircraft 

Railway

R

2=

 -35.87

0.25 0.192 0.20

1.20* 0.143 0.00

4.89 3.264 0.14

-14.09* 2.907 0.00

R

2=0.186 

-0.43 0.973 0.66

#12: [Intercept]

Met. Var.

DNL

Aircraft 

Railway

R

2=

 -30.28

0.22 0.206 0.29

1.18* 0.145 0.00

4.41 3.675 0.24

-12.61* 2.822 0.00

R

2=0.171 

-1.10 1.705 0.52
Variables in equation Meteorological variable examined 

(The "Met. Var" is

defined in the column

heading.)

Temperature 

3-Month Daily Means

(Average) [T-3MoMnv]

Temperature

2,3,4 Prior Months Daily

Mean (Avr.) [T-2-4Mo ]

Sunshine

3-Month Mean daily

hours

Precipitation

 3-Month Mean Daily

Precipitation

 2-Month Mean Daily

Wind speeda

 3-Month Mean

Statistics> B Std.Er. Sig. B Std.Er. Sig. B Std.Er. Sig. B Std.Er. Sig. B Std.Er. Sig. B Std.Er. Sig.

-41-

Part E: Examination of the both seasonal and year-round indicators for meteorological variables - linear regression 

#13: [Intercept]

Met. Var.

DNL

Met. Var.(Year)

R

2=

 -37.41

0.45 0.311 0.15

1.19* 0.167 0.00

0.01 0.491 0.99

R

2=0.155 

 -38.07

0.46 0.333 0.17

1.22* 0.158 0.00

-0.06 0.573 0.91

R

2=0.155 

 -35.57

1.77* 0.811 0.03

1.18* 0.162 0.00

-1.23 1.016 0.23

R

2=0.155 

 -38.25

-2.31 2.703 0.40

1.18* 0.176 0.00

5.17 3.805 0.18

R

2=0.152 

 -37.89

-1.49 1.870 0.43

1.18* 0.177 0.00

4.25 2.903 0.15

R

2=0.152 

 -25.45

-1.23 3.891 0.75

1.19* 0.177 0.00

-0.37 4.613 0.94

R

2= 0.139

#14: [Intercept]

Met. Var.

DNL

Aircraft 

Railway

Met. Var.(Year)

R

2=

 -35.02

0.34 0.270 0.21

1.22* 0.136 0.00

5.00 3.048 0.11

-14.26* 3.157 0.00

-0.37 0.421 0.39

R

2=0.187 

 -35.75

0.51* 0.226 0.03

1.25* 0.123 0.00

5.09 2.847 0.08

-15.13* 2.893 0.00

-0.65 0.373 0.09

R

2=0.190 

 -33.21

1.02 0.731 0.17

1.25* 0.119 0.00

6.44* 3.160 0.05

-14.06* 2.931 0.00

-1.98* 0.994 0.05

R

2=0.190 

 -36.27

-2.27 3.108 0.47

1.20* 0.148 0.00

5.06 3.325 0.13

-14.39* 3.351 0.00

3.28 4.771 0.50

R

2=0.185 

 -36.40

-2.26 2.180 0.31

1.20* 0.147 0.00

5.29 3.264 0.11

-14.48* 3.380 0.00

3.36 3.761 0.38

R

2=0.186 

 -28.33

-0.50 4.376 0.91

1.18* 0.151 0.00

5.07 3.745 0.18

-13.49* 2.935 0.00

-0.55 4.579 0.90

R

2=0.169 

#15: [Intercept]

- - - -

DNL

Met. Var.(Year)

R

2=

 -36.78

1.18* 0.179 0.00

0.52 0.382 0.18

R

2=0.150 

 -36.78

1.18* 0.179 0.00

0.52 0.382 0.18

R

2=0.150 

 -34.47

1.20* 0.172 0.00

0.31 0.864 0.72

R

2=0.146 

 -37.09

1.18* 0.178 0.00

2.29 1.277 0.08

R

2=0.151 

 -37.09

1.18* 0.178 0.00

2.29 1.277 0.08

R

2=0.151 

 -24.94

1.18* 0.181 0.00

-1.52 1.678 0.37

R

2=0.138 

#16: [Intercept]

- - - - - 

DNL

Aircraft 

Railway

Met. Var.(Year)

R

2=

 -34.53

1.21* 0.145 0.00

5.24 3.075 0.10

-14.62* 3.236 0.00

0.00 0.245 1.00

R

2=0.184 

 -34.53

1.21* 0.145 0.00

5.24 3.075 0.10

-14.62* 3.236 0.00

0.00 0.245 1.00

R

2=0.184 

 -32.43

1.26* 0.124 0.00

6.77* 3.124 0.04

-14.97* 2.846 0.00

-1.18 0.884 0.19

R

2=0.187 

 -35.09

1.20* 0.151 0.00

5.03 3.408 0.15

-14.45* 3.192 0.00

0.45 1.365 0.74

R

2=0.184 

 -35.09

1.20* 0.151 0.00

5.03 3.408 0.15

-14.45* 3.192 0.00

0.45 1.365 0.74

R

2=0.184 

 -28.15

1.18* 0.152 0.00

5.09 3.473 0.15

-13.51* 2.863 0.00

-1.01 1.829 0.58

R

2=0.169 

* A single asterisk ("*") indicates that a partial regression coefficient is significant at p<0.05.

* Analyses are based on 53,130 reactions except for those including wind speed analyses for which 47,825 reactions are available. which 47,825 reactions are available.

b

 Since the scale has been transformed (see text) high annoyance is the top point on a 4 or 5-point scale, top 2 on a 6 or 7-point scale and top 3 on a 10 or 11-point scale.
-42-

 

5.0 RELATIONSHIP BETWEEN TEMPERATURE AND OTHER METEOROLOGICAL

CONDITIONS AND NOISE ANNOYANCE REACTIONS

 

The Netherlands Continuous Life Information Survey (DLO) clearly finds a seasonal effect in the

Netherlands with annoyance being higher around the summer months and lower around the winter

months. The analysis of the day-linked annoyance/meteorological data found no evidence that the

meteorological conditions in the few days immediately preceding an interview has an especially

strong effect on annoyance. In fact, it appears that the best predictor is the average of the

meteorological conditions over several months preceding the interview. As a result,

the analyses in this section proceed with the assumption that there can be a seasonal effect and

that the average of the meteorological conditions in the three months preceding the interview will

be the most effective predictor of reactions for the existing data sets.

5.1 Available meteorological data

This section attempts to determine which meteorological characteristics of the weather impact

noise annoyance and how large those effects are. All three of the data sets provide some evidence

on these issues. The meteorological data that are available from each data set are summarized in

Table 12. It should be recalled that there are other potentially important differences between the

data sets that were previously summarized in Table 1. The sources of the meteorological data are

also given in Table 1. The day-linked and season-linked meteorological data come from many

measurement stations located in different countries. Although the latitude, longitude and

elevations of all measurement stations are known, the precise methods used to measure and

summarize meteorological observations at these stations are not known, but are presumed to

introduce only minor variations between stations.

All of the meteorological indices used in this report are simple averages of daily values. The

single maximum value for a month or longer period for temperature and wind speed were

examined in some analyses of the DLO data, but did not reveal stronger relationships. The

possibility of weighting the days immediately prior to the interview with a more complex scheme

to define multi-month meteorological indices was examined in the earlier analysis of the daylinked data set. However the lack of evidence for a strong relationship with proximate

interviewing days, suggests that a more complex weighting of daily meteorological data would

not provide stronger relationships than those observed with the simple averages used in this

report.
-43-

Table 12: Numbers of responses available for analysis for each type of meteorological data

Indicatora Data set 

(Each cell contains the number of interview reactions with data)

Condition Description

Day-linked data set

[Means of

meteorological data

for interview day

and 1,2,3,7,14,30,60

or 90 previous days]

Season-linked set

[Mean for 3 months

(interview &

preceding 2 months)

for 30 year avr.

(1961 to 1990)]

Continuous (DLO)

Survey

[Mean for 2 months

before the month of

the interview]

Temperatureb

(degrees

Celsius)

Daily mean (average of 24

hourly values or mean of

highest and lowest hours)

15,107 53,130 35,000

Maximum in 24 hours 15,107 53,130 35,000

Minimum in 24 hours 15,107 53,130 35,000

Precipitation

Daily total (millimeters) 7,713 53,130 35,000

Number of hours with

precipitation (hrs) 0 0 35,000

Percent of days with any

precipitation (%) 7,713 0 0

Sunshine 

Percent of time in daylight

hours with sunshine (%) 0 0 35,000

Number of hours with

sunshine (hours) 0 53,130 35,000

Wind speed

(meters per

second)

Average over day 0 47,825 35,000

Average of hourly maximums 0 0 35,000

a. The height of measurement is not specified for all measurement stations. KNMI temperature data for De Bilt

were taken at 1.5 meters. KNMI wind data are measured at 10 meters above the surface.

b. Temperature: The precise method for averaging temperatures over the 24 hours could vary between different

weather stations.

The effects of alternative meteorological indices are analyzed with slightly different techniques for

the continuous Netherlands DLO study and the day-linked and season-linked data sets. The DLO

study does not include noise data. As a result of consistently surveying a single national study

population, it is assumed that noise emissions effects should not be confounded with seasonal

conditions. As a result, these DLO data are analyzed with simple bivariate correlation or

regression coefficients. However, for the particular combination of studies examined in the daylinked and season-linked data sets, noise levels are correlated with some meteorological

conditions. To control for these correlations, regression analyses are performed that include a

noise exposure index (DNL) to remove the effects of correlated noise levels from the analyses. 

For the season-linked analyses, correlations between DNL and meteorological conditions in Table
-44-

10 are r=0.092 for 3-month mean temperature and r=0.222 for 3-month mean wind speed. For

the day-linked analysis, correlations are r=0.231 with 90-day mean temperature and r=-0.218 for

precipitation (percent of time with precipitation).

5.2 Relationships with temperature

Analyses of all three data sets have found more consistent evidence for an effect of temperature

than for the effect of precipitation or wind speed. Although the effects of temperature are not

usually statistically significant, it is possible to estimate the range of a possible temperature effect

from various analyses in the three data sets.

Table 13 presents the estimated effect of a 15E (C) change in temperature from 17 analyses

performed on the three data sets in this report. Fifteen degrees is approximately the average

difference between the coldest and warmest months of the year for these survey sites and about

the same as the difference between the various sites' summer temperatures observed in Table 3. 

About 3/4's of the 48 evaluations in Table 3 are with a fifteen degree range in mean temperatures

from 7.9 E (C) to 22.7 E (C) during the survey period. Each part of Table 13 presents the

results for a different data set. The first three columns of results provide the basic regression

coefficients for noise level and temperature and the standard error of the temperature regression

coefficient. The decibel equivalent of the one degree increase in the next column is the ratio of

the temperature to the noise exposure regression coefficients. The last three columns indicate the

number of decibels of change in noise exposure that are estimated to create the same increase in

annoyance as a 15E (C) increase in temperature. The estimates in the "best estimate" column thus

indicate this 15E difference in temperature is estimated to produce a change in annoyance that is

equivalent to that produced by a zero to seven decibel increase in noise exposure An approximate

estimate of the 95 percent confidence intervals of the "best" estimates is formed in the last two

columns where only the variability in the temperature regression coefficient has been considered. 

The findings in these columns suggest that the results from the DLO survey are very exact: the

15E (C) increase in temperature is equivalent to a 1 decibel increase in noise level. However, this

confidence interval does not take into account other non-sampling errors such as the fact that the

estimate of the effect of noise level (BDNL= 1.15)comes from other studies that used a different

type of noise annoyance scale (a degree-of-annoyance rather than frequency-of-annoyance scale)

and the fact that the temperature effects in the Netherlands climatic setting may be confounded

with other effects that are not present in other countries. When the results from the wider range

of conditions are examined with the day-linked or season-linked data sets, the confidence intervals

for the estimates are very large. The lower 95% confidence interval is negative or zero for all

estimates. The upper 95% confidence interval is from 6 to 16 decibels. If the analyses are

restricted to those using the mean temperature (not maximum or minimum) and the those

including controls for type of noise source, the range of estimates is considerably reduced. The

best estimates range from the equivalent of 1 to 3 decibels and the upper 95% confidence interval

ranges from 1 to 11 decibels 
-45-

Table 13: Estimates of the impact of temperature on noise annoyance

Unique features of analysis 

(The standard, base analysis is a linear

regression with temperature represented by the

daily means using all interviews.)

Regression equation Decibel

equivalent

of 1E (C)

increase

Decibel equivalent of

15E (C) increase

BDNL BTemp Std. Error

(BTemp)

Best

est.

Lower

(95%)

Upper 

(95%)

Part A: Day-linked data set (Temperature is average of previous 90 days)

Base analysis (DNL, daily mean termperatures) 1.200* 0.312 0.340 0.26 4 -4 12

Noise source 1.215* 0.204 0.364 0.17 3 -6 11

(45-75 dB, DNL) 1.533* 0.299 0.209 0.20 3 -1 7

Noise source (45-75 dB, DNL) 1.660* 0.209 0.317 0.13 2 -4 8

(temperature=daily maixmums) 1.228* 0.079 0.291 0.06 1 -6 8

Noise source (temperature=daily maximums) 1.241* 0.004 0.340 0.00 0 -8 8

(temperature=daily minimums) 1.179* 0.581 0.355 0.49 7 -1 16

Noise source (temperature=daily minimums) 1.194* 0.447 0.376 0.37 6 -4 15

Part B: Season-linked (Temperature is averaged over 30 years for three months)

Base analysis (DNL, daily mean termperatures) 1.337* 0.468* 0.223 0.35 5 0 10

Noise source 1.281* 0.241 0.191 0.19 3 -2 7

(45-75 dB, DNL) 1.474* 0.431 0.221 0.29 4 0 9

Noise source (45-75 dB, DNL) 1.386* 0.193 0.189 0.14 2 -2 6

Logistic: Moderate annoyancea 0.092* 0.033 0.019 0.36 5 -1 11

Logistic: Moderate annoyancea, Noise source 0.092* 0.009 0.015 0.10 2 -3 6

Logistic: High annoyancea 0.097* 0.025 0.018 0.26 4 -2 9

Logistic: High annoyancea, Noise source 0.097* 0.014 0.017 0.14 2 -3 7

Part C: Continuous DLO survey (Temperature is mean of previous two months)

Noise source [No noise data] 1.150 0.090* 0.011 0.08 1 1 1

a See footnotes in previous table for definitions of moderate and high annoyance.

b The estimate of the regression coefficient for noise exposure for the DLO survey comes from analyses of other

surveys what, unlike the DLO survey, use intensity-of-annoyance not frequency-of-annoyance questions.

Several alternative approaches to assessing temperature effects were considered but it was

concluded that none would change the conclusions provided here. Maximum or minimum daily

temperatures were evaluated, but the correlations between maximum, minimum, and mean daily

temperatures are so high that any differences are likely to be of no consequence. The correlation

in the day-linked data set between the mean and maximum 90-day temperatures is r=0.98 and

between the mean and minimum temperatures is r=0.97. In the season-linked data set the

comparable correlations are r=0.99 and r=0.98. The possibility that there might be strong nonlinear relationships was explored by plotting the residuals from regressions of annoyance on noise
-46-

exposure. These residuals were visually inspected and found to be approximately linear with no

strong, consistent non-linear components. Logistic regression models were considered in the

season-linked analyses in Table 11, but the temperature coefficients were found to be significant

statistically even less often than were the coefficients from linear regression analyses.

When precipitation and wind speed and temperature are simultaneously included as predictors in

an equation with temperature in Sections #10 to #12 of Table 11 and each of the variable's

regression coefficients are compared with earlier single meteorological variable analyses in the

table, it is seen that the relationships are not weakened. The regression coefficient for

temperature of BMet.Var=0.23 (Section #2) that is controlled for only DNL and noise source is

almost the same as the value of BMet.Var=0.24 (Section #10) when both precipitation and wind

speed are simultaneously considered. The continuous DLO survey displays a similar pattern for

temperature for the analyses that do not include sunshine in Table 7. As a result it appears the

effects of temperature, wind speed, and precipitation are not being confounded. However, the

effects of sunshine and temperature may still be partially confounded in these analyses due to their

high correlations The number of hours of sunshine is highly correlated with temperature in both

data sets (r=0.661 for the continuous DLO data and r=0.787 for the season-linked data set). No

attempt has been made to separate the effects of hours of sunshine and temperature in this report.

5.3 Relationships with precipitation

One precipitation index is included in all three data sets: the amount of precipitation (millimeters). 

Two other indices of the duration of precipitation are each used in a data set; the percent of a 24-

hour day with precipitation (continuous DLO data set) and the percent of days with any

precipitation (day-linked data set). The findings, while mixed, suggest that precipitation may have

some effect.

The season-linked data set provides only weak evidence that precipitation reduces annoyance

(Table 11). The precipitation is not estimated to reduce annoyance unless the control for noise

source is introduced. Even with this control in Table 11, the probability that the relationship

could arise from chance is very high (p>0.96).

The analysis for the day-linked data set in Table 8 (Column H), however, provides stronger

evidence of a precipitation effect (statistically significant, p<0.05) with the multiple correlation

coefficient being about the same for the precipitation and the temperature variables. If the

comparison is restricted to only those 7,713 interviews with precipitation data, the multiple

correlation coefficient of R2=0.17 (next-to-last column in Table 8) for precipitation and DNL is

again greater than the correlation of R2=0.14 for an equation with DNL and mean temperature

(not shown in Table 8). The same pattern is present when a control for noise source is also

included: R2=0.17 for DNL, noise source, and 90 day total precipitation; R2=0.16 for DNL,

noise source, and 90 day mean temperature. 

The DLO data set (Table 6) shows a similar relationship in the expected direction for the index of

the amount of precipitation that is statistically significant (p<0.05) for road traffic and aircraft

noise but not rail traffic. When controlled for temperature, the relation remains statistically

significant for road traffic, but is substantially reduce for aircraft and even changes direction for

railway noise.
-47-

The mean daily precipitation levels during the survey period in Table 3 cover a 5.7 mm range (0.9

mm to 6.6 mm. About 3/4 of the sites are included with in a narrow 1.2 mm range (1.6 mm to 2.8

mm). The coefficients for the precipitation variables are so small, that the analyses presented in

Tables 7 and 11 estimate that even a 5 mm difference in precipitation implies no more than the

equivalent of a 2-decibel reduction in annoyance. The estimates are so imprecise, however, that

the season-linked data cannot reject the possibility of as much as the equivalent of a 7-decibel

reduction.

5.4 Relationships with sunshine

Sunshine is measured as the number of hours a day during which the sun appears in both the

season-linked data-set and the continuous DLO survey data set. The hours a day the sun could be

visible varies, of course, with the latitude and time of year. A second index in the DLO survey

accounts for this with an index of the percent of the daylight hours during which the sun is visible. 

Although this variable is not used in the analysis in Table 6, the correlation with the number-ofhours-of-sunshine variable is so high (r=0.94) that the relative strengths of the two variables could

not be evaluated.

Sunshine is, like temperature, positively correlated with noise annoyance. The relationship is

slightly weaker than the relationship with temperature for the season-linked analysis in Table 11

(the multiple correlation coefficient is almost identical or slightly lower for most analyses) and is

weaker for two of the three noise annoyance sources in the continuous DLO analyses (Table 6). 

The number of hours of sunshine is highly correlated with temperature in both data sets (r=0.661

for the continuous DLO data and r=0.787 for the season-linked data set). No attempt has been

made to separate the effects of hours of sunshine and temperature in this report.

5.5 Relationships with wind speed

The daily average wind speed (meters/sec) is available for two data sets, the season-linked and

DLO data sets. The DLO data set also includes indices of the highest mean wind speed measured

in any hourly period and the highest wind gust measured during each day but these variables have

not been analyzed here. In the DLO survey, the relationship between seasonal patterns for

temperature and wind conditions seen in Figure 2 create a correlation of r= -0.58 between the

temperature and wind speed (Table 6). In the season-linked data set, however, a wider variety of

climatic conditions results in a correlation of only r=0.06 (Table 10). Average wind speeds for

the study periods examined in this report included one site at 1.2 m/s with the rest contained

within a 3.8 m/s range from 2.1 m/s to 5.9 m/s (see Table 3). About 3/4 of the locations fell

within about a 2.7 m/s range.

In both data sets, wind speed is consistently negatively related to noise annoyance (as would be

expected) with the impact of wind speed on annoyance being about the same or somewhat weaker

than is that of temperature (Tables 6, 11). The relationship is generally modest and not

statistically significant. In Sections #2 and #4 of the last column of Table 11 it is seen that each 1

m/s increase in wind speed reduces annoyance by approximately one annoyance score point in

analyses that include controls for noise source A 4 m/s increase in wind speed is estimated to

have about the same effect as a 3-decibel decrease in noise exposure for the linear analyses with

controls for noise level and noise source in Table 11. Similar logistic regression analyses provide
-48-

estimates of no effect or 1 decibel effect. Analyses that do not control for noise source, provide

somewhat higher estimates with as much as a 5-decibel impact for linear regression analyses. 

Using the same methods as were used in Table 13, the DLO data provide estimates that a 4 m/s

change in wind speed would reduce annoyance by the equivalent of a one or two decibel increase

in noise exposure. The estimates are subject to considerable sampling error for the season-linked

data set. In the season-linked data set, the correlations are not statistically significant (p>0.05). 

As a result the 95% confidence interval for the effect of a 4 m/s increase in wind speed extends

over a range from the equivalent of more than a 10-decibel increase in annoyance to nearly a 10-

decibel decrease in annoyance. The simple bivariate correlations between wind speed and

annoyance are statistically significant in the DLO data set, but are considerably reduced and not

statistically significant when controlled for temperature. The confidence intervals based on

sampling error are small for the DLO data with estimates that, for example, the 4 m/s wind speed

range could generate no greater reduction in annoyance than would a three decibel change in

annoyance. However, unmeasured non-sampling errors introduce uncertainty in the evaluation of

the DLO results. For example, the single, centrally-located meteorological station at de Bilt is

used to characterize the wind conditions for all of the Netherlands DLO survey when in fact the

wind speeds tend to vary by location and be, for example, systematically higher in costal areas. 

Also, as for temperature, the possible effect of other correlated seasonal factors introduce

uncertainty as to whether the estimate of the effect of wind speed is being affected by other

factors. In Table 7, for example, it is seen that introducing the control for temperature reduces

the estimated effect of wind speed by more than 50 percent for road traffic, aircraft and the

combined annoyance indices.
-49-

 

6.0 FINDINGS ABOUT SEASONAL DIFFERENCES FROM PREVIOUS SURVEYS

 

Although meteorological effects, primarily temperature, have been considered in at least seven

noise response surveys, these surveys have been limited to a small number of communities and a

limited range of climatic conditions. Most of these surveys have found some evidence for

seasonal differences in response. The surveys and their findings are listed in Table 14. Additional

background information about each survey can be located through their catalog identification

number (first Column of Table 14) in a noise survey catalog (Fields 2001).

The seven noise annoyance studies have evaluated the effects of season by comparing their

respondents' answers at two times of year, usually at the same location. The first five of these

seven studies in Table 14 found that respondents who were interviewed in warm months were

more annoyed than respondents who were interviewed in colder months. The remaining two

studies report no difference. The results in the fourth column of Table 14 show seasonal effects

for the five studies that are the equivalent of at least a five-percentage point difference in

annoyance responses or a three-decibel difference in noise level. The narrative descriptions in the

next-to-last column of the table document differences as large as 15 percentage points (catalog

ID=USA-143) or the equivalent of five decibels (USA-022/USA-032/USA-044). Most of the

surveys include information about dose/response relationships that could, theoretically, be used to

more closely estimate the decibel equivalence of the differences in annoyance reactions in different

seasons. This has not been done due to concerns about the effects of correlated variables and

biases introduced by publication practices. These concerns are related to the conclusions drawn

about seasonal effects, a minor goal for most surveys, and not for conclusions about the surveys’

other, usually more important, goals.

Seasonal differences may be correlated with other factors in several studies. Two of the studies

did not explicitly control for differences in noise exposure and did not make efforts to ensure that

such differences were controlled or randomly distributed in the two seasons (UKD-008, CAN078). For one of the remaining studies, the difference between seasons is confounded with study

sites, since the two winter surveys were at different airports than the summer surveys (USA022/USA-032/USA-044). In this particular case the two winter airports had been selected for

study because they were smaller than the previously studied airports. In the analysis phase,

however, it was realized that the observed response difference could not be interpreted because

these two small airports were also the only ones surveyed in the winter. None of the seven

studies were designed to average the seasonal effects for both seasons over several years. Five of

the studies base at least one of the seasonal observations on interviews from a single month with

the result that the effect of season is confounded with any other unique events at that time.

Publication practices could introduced biases if investigators reported findings when seasonal

effects were found but failed to report their findings when no seasonal effects were found. 

Although this is a commonly recognized problem in meta-analysis(Wolf 1986), it could be an

especially serious problem for these surveys that were not designed to test for seasonal effects. 

The two surveys that were designed to test for seasonal effects (UKD-157, SPA-313) were the

only two that did not find seasonal effects. When the list of 282 surveys that had yielded six tests
-50-

of seasonal effects was reexamined, it was found that an additional 12 surveys in the list had

interviews from different seasons that encompassed more than a six-month period, but that the

publications had not reported any tests for seasonal effects (Fields 2001); survey numbers: AUL210, AUL-227, FRA-239, GER-192, GER-278, SWE-021, UKD-268, USA-059, USA-088,

USA-127, USA-129, USA-156). It is not known whether these surveys examined the possibility

of seasonal differences.
-51-

Table 14: Findings on seasonal differences from seven studies

Study 

(Catalog ID)

Seasonal

comparison

[Sample size]

Variables

controlled in

analysis

Annoyance is

highest in:

(basis)

Narrative description of

finding

References

1975 Schiphol/

Marssum and

1975 Leeuwarden

NIPO [Aircraft]]

(NET-115, NET844)

September

compared to

November of

1975 [N=143]

Noise level,

study area

Hot, dry

summer 

(>3dB)

Annoyance is reduced by the

equivalent of 3 dB(A) from

September after a hot summer,

until November after a wet,

cool autumn.

de Jong,

1981:8, Fig.

15

USA Airport [9

Cities] (USA-022,

USA-032, USA044)

 Summer of

1967 (May/

August) &

1969(July/

Nov.) compared

to Oct. 1970 to

Jan. 1971

[N=~8500]

Noise level

(different

airports)

Summer 

(>3dB)

Mean annoyance at 7 large city

airports in summers (1967-69)

is the equivalent of 5 dB

greater than winter surveys at

2 small airports 1 to 4 years

later. However annoyance in 4

of the 7 summer surveys is

similar to the winter surveys.

Connor,

Patterson,

1972:31-33;

Fields, 1983:

966

1961 Heathrow

Aircraft (UKD008)

September

[n=1731]

compared to

Oct. & Nov.

[N=114]of 1961

Airport

(uncertain if

Oct/Nov

sample is of

same

population)

Summer

(>5%)

(Statistically

significant,

Simple random

sampling

assumption)

2% more "Very" and 10%

more "Moderately /Very"

annoyed in early Sept. than in

Oct./Nov. (Barely significant,

p<.05 simple random

sampling assumptions)

McKennell,

1963: 

Appendix R

1977 3-Phase JFK

Concorde (USA143)

Summer to

winter (1971) 

[N=5404]

Distance

from airport

Summer (>5%) At least 15% less "high"

annoyance during the winter

interviews.

Borsky,

1978: 20

1972 Calgary

Community Noise

(CAN-078)

February

compared to

Summer of

1972 [N=~720]

None

(Uncertain if

same

population)

Summer (>5%) At least 10% more are

annoyed in summer than in

February. Self-completion

questionnaires were used.

Dunn,

Posey,

1974:26,27

47,48

1977 London Area

Panel [ROAD

TRAFFIC] (UKD157)

Dec. 1977 to

spring & Sept.

1978 (4 waves,

n=888 interviews by N=222

respondents)

Noise level,

study site,

respondents

No difference

(p<.05)

No significant difference

between noise annoyance in

different seasons though

reports of window opening do

differ. (Panel survey with the

same respondents)

Griffiths,

Langdon,

Swan,

1980:236

1984-85 Gand ́ia

Three-Site Traffic

Noise Survey

(SPA-313)

Summers of

1984 & 1985

compared to

Winter 1984-

85, N=543

Noise level,

study site

No difference

(<5%)

It would have been expected

that the additional traffic in

the summer could have

accentuated a seasonal effect.

García,

RomeroFaus,1987,

175
-52-

 

7.0 SUMMARY AND CONCLUSIONS

 

The data analyzed in this report find that there are seasonal differences in noise annoyance reactions

to similar noise levels, but that the size of these seasonal differences cannot be accurately estimated

and the effect of temperature and other meteorological conditions cannot be accurately estimated. 

Seasonal differences can be important for some policy and methodological purposes. Several

different types of research could improve knowledge about seasonal effects.

7.1 Summary of findings

This report analyzes three sets of data to determine whether meteorological conditions, including

temperature, amount of precipitation, and wind speed affect residents' reactions to environmental

noise. Seven years of continuous surveys of the population of the Netherlands generated the first

data set, one that shows a clear seasonal pattern in residents' annoyance with road traffic, aircraft

and railway noise in which the greatest annoyance is expressed around the summer months and the

least around the winter months. Five of seven previously published studies have found evidence of

similar seasonal effects. Analyses of two multi-national data sets containing more than 53,000

responses in 41 noise-reaction surveys also provide some evidence that reactions to noise increase

with increasing temperature. The analyses find some evidence that noise annoyance is affected by

the average temperature conditions in the preceding months, but find no evidence that noise

annoyance is particularly strongly affected by the current temperature conditions (for example, the

temperatures in the days immediately preceding an interview) Other mixed evidence suggests that

annoyance is also increased by more hours of sunshine, less precipitation, and lower wind speeds. 

One explanation for these meteorological factors' effects is that they lead to higher noise exposure

to exterior noise sources because they encourage the opening of windows and the greater usage of

outdoor living areas around residences. 

The analyses of these data sets provide some weak evidence on other issues. While the evidence

suggests that higher temperatures are associated with greater annoyance at the same noise levels in

warmer climates, there is some weak, but not statistically significant evidence that noise annoyance

reactions in such climates may be less than reactions in colder climates at the identical noise levels

and temperatures. The evidence is also imprecise on the importance of any meteorological effects. 

For example, the three data sets analyzed for this study give different estimates of the extent to

which a 15-degree (Celsius) temperature increase would in turn increase annoyance. The lowest

estimate is that a 15-degree increase in temperature is equivalent to less than a one-decibel (DNL)

increase in noise level while the highest is that a 15-degree temperature difference is equivalent to

almost a 7-decibel increase in noise level. The 95 percent confidence intervals for these estimates 

include effects that are equivalent to more than a 10 decibel difference.

7.2 The role of seasonal and climate effects in research and noise policy

Seasonal and climate effects have received relatively little attention in previous research; probably

because they are not important for many traditional environmental noise research studies or local
-53-

environmental noise policy issues. The effects are, however, of considerable potential importance

for other issues.

Meteorological effects are likely to be of little importance for the findings from most community

noise studies because the variables being studied are not correlated with meteorological conditions. 

Most community noise reaction surveys are conducted in a single city or other small area over a

period of a few days or weeks. Even when interview studies are spread over several months, most

interviews are often conducted in a short time at the beginning of the period. The objectives of

most community noise reaction studies are to estimate the effects of personal or environmental

conditions that vary between the different locations within a city or other small region that,

coincidentally, has uniform meteorological conditions during the study period As a result,

meteorological effects are probably of minor importance for the goals of most community noise

surveys. One minor caveat is needed if, contrary to the indications in the present report, noise

reactions were to be affected by short-term conditions on the day of the interview. If there were

such day-of-interview effects, then even these traditional studies could be affected if interviews in

different areas were clustered on different days.

Meteorological effects are of considerable importance for any types of studies that compare

reactions at different times and are of potential importance for investigations that are based on

comparisons of different studies. Studies of reactions to change in environmental noise conditions

are often designed with study waves before a change in noise exposure and at several points after a

change in noise exposure. The social surveys are timed to follow the noise exposure changes and

thus may occur under quite different meteorological conditions. Seasonal effects could therefore

generate misleading findings in such studies. Considerable attention needs to be given to strategies

that will control for or detect any meteorological effects in such studies. The simple strategy of

conducting the before and after studies at the same time of year may not be sufficient if the

temperature or precipitation patterns are quite different in different years. 

Seasonal or climatic effects could also have a serious effect on research that calculates effects by

examining contrasts between surveys rather than contrasts between conditions within a single

survey. If, for example, reactions to different noise sources are evaluated by comparing a road

traffic survey conducted in the winter with an aircraft survey conducted in the summer, it is possible

that any differences in reactions could be caused by weather differences rather than noise source

differences. The data reviewed in this report do not provide a basis for adjusting for seasonal

differences. Meteorological conditions, thus provide another source of variance for between-survey

comparisons as well as another reason for designing surveys so that important variables' effects can

be estimated using within-survey contrasts. If reactions are heightened under certain

meteorological conditions, then survey planners would need to conduct their field work in the

warmest months or shortly after the warmest times of year to measure the maximum impact of a

noise source on the population.

Seasonal and climatic effects may well have little effect on local noise policy. The seasonal effects

are not subject to manipulation. In addition it is not clear that major transportation sources, the

source of most environmental noise, could be operated in different ways at different times of year.

The major implications of seasonal and climatic effects would seem to be on international policy. If
-54-

there are genuine differences in reactions under different climatic conditions then, policy makers

may need to vary the standards with the climatic conditions. The weak pattern observed in the

present data was for temperature to affect annoyance, but for the effect between climate areas to be

less than would be expected from the seasonal differences within a single climate. This implies that

regulations that are set on equivalent annoyance criteria would impose stricter criteria in a warm

than a cold climate, but that under the identical acoustical and temperature conditions (for example

winter in a warm country and summer in a cold country) the criteria would be stricter in the cold

country. The strong evidence for some types of seasonal effects that are correlated with

meteorological conditions, coupled with the mixed evidence on between-climate effects, would

seem to make climate effects a potentially important issue for international noise regulations.

7.3 Strategies for acquiring information through future research

 Each of the data sets used in these analyses has different weaknesses that limit the accuracy of the

results it could provide. Stronger results in the future could be expected if these and other data sets

were developed that include noise exposure data and date of interview for all respondents; closely

link each respondent's location with permanent weather stations; include a wide range of

meteorological conditions that are not highly correlated; and use uniform social survey methods and

noise annoyance measures.
-55-

 

REFERENCES

 

Bennett,R.L., and Pearsons,K.S. 1981. Handbook of Aircraft Noise Metrics. NASA CR-3406. National Aeronautics

and Space Administration, Washington, D.C.

Borsky,P.N. 1978. Community Annoyance Before and After Concorde Operation at J.F.K. Airport. Noise Research

Unit, Columbia Univ., New York.

Brick,J.M., Morganstein,D., and Valliant,R. 2000. Analysis of complex sample data using replication. Westat,

Rockville, Maryland.

Connor,W.K., and Patterson,H.P. 1972. Community Reaction to Aircraft Noise Around Smaller City Airports.

NASA CR-2104. National Aeronautics and Space Administration, Washington, D.C.

Critchfield,H.J. 1983. General climatology. Prentice Hall,

de Jong,R.G. 1981. Nederlands Onderzoek naar Geluidhinder door Vliegtuigen - een overzicht. (Research on Noise

Annoyance by Planes in the Nederlands - an overview)(An English translation of this paper is available as:

Some Highlights from the Dutch Aircraft Noise Studies. IMG-TNO document number 765). Geluid en

Omgeving (Noise and the Environment) 3, pp. 228-232.

Draper,N. 1981. Applied Regression Analysis. Wiley, New York.

Dunn,B.E., and Posey,M.H. 1974. Calgary Noise Survey Vol. II: The Human Response to Noise. Calgary Univ.,

Calgary.

Fidell,S.A., Barber,D.S., and Schultz,T.J. 1991. Updating a Dosage-effect Relationship for the Prevalence of

Annoyance due to General Transportation Noise. The Journal of the Acoustical Society of America 89, pp.

221-233.

Fields,J.M. 1983. Variability in Individuals' Responses to Noise: Community Differences. Inter-noise 83, pp. 965-

968.

Fields,J.M. 2001. An Updated Catalog of 521 Social Surveys of Residents' Reactions to Environmental Noise (1943-

2000). NASA/CR-2001-211257. National Aeronautics and Space Administration, Washington D.C.

Fields,J.M., Ehrlich,G.E., and Zador,P. 2000. Theory and design tools for studies of reactions to abrupt changes in

noise exposure. NASA CR-2000-210280. National Aeronautics and Space Administration, Washington,

D.C.

Frankel,M. 1983. Sampling theory. In P.H.Rossi, J.D.Wright, and A.B.Anderson (Eds.), Handbook of Survey

Research, pp. 21-68. Academic Press Inc., New York.

García,A.M., and Romero,J. 1987. Road Traffic Noise and Sleep Disturbance. Noise-Con 87, pp. 461-464.

Griffiths,I.D., Langdon,F.J., and Swan,M.A. 1980. Subjective Effects of Traffic Noise Exposure: Reliability and

Seasonal Effects. Journal of Sound and Vibration 71:2, pp. 227-240.

McKennell,A.C. 1963. Aircraft Noise Annoyance Around London (Heathrow) Airport. S.S.337. The Government

Social Survey, Central Office of Information, London.

Miedema,H.M.E. 1992. Response Functions for Environmental Noise in Residential Areas. NIPG Report Nr. 92.021.

NIPG-TNO, Leiden.

Miedema,H.M.E., and Vos,H. 1998. Exposure-response Relationships for Transportation Noise. J.Acoust.Soc.Am.

104:6, pp. 3432-3445.

Schultz,T.J. 1978. Synthesis of social surveys on noise annoyance. The Journal of the Acoustical Society of America

64, pp. 377-405.

Wolf,F.M. 1986. Meta-analysis: Quantitative Methods for ResearchSynthesis. Sage publications, Beverly Hills CA.
56

 

ACKNOWLEDGMENTS

 

This report is part of a joint project with researchers at TNO in the Netherlands. Henk M.E.

Miedema provided the impetus, critical comments, and most of the data for the project. Henk Vos

provided several data sets and the computer code that formed the bases of much of the initial data

manipulation. The contents of this report are solely the responsibility of the author.
57

Part of

question

Survey period*

April 1998 to December 1993 January 1994 to December 1996

Stem of

question

Now I am going to mention a number of issues

to you, which could bother people at home. I

would like to know whether you are bothered by

them, here.

Could you answer according to this card?

Are you here, at home, bothered by:

Now I am going to mention a number of issues to

you, of which I would like to know whether they

bother you in your living environment. 

Are you, in your living environment, bothered by:

Alternative

answers

[on show card:]

YES, OFTEN

YES, SOMETIMES

SELDOM OR NEVER

[No show card]

YES 

SOMETIMES

NO

REFUSES/DOESN’T KNOW 

Noise

sources

c) aircraft noise? 1. aircraft noise?

d) noise from trains, trams or subways? 2. noise from trains, trams, subways?

e) noise from cars, motorcycles or mopeds? 3. noise from cars, lorries, motorcycles or

mopeds?

f) street noise, from loading and unloading,

or from playing children?

4. street noise, from loading and unloading?

5. street noise, from playing children or young

people?

g) noise from industry or businesses? 6. noise from industry or businesses?

h) noise from neighbors? 7. noise from neighbors?

* Italics indicate wording that was changed in January 1994.

Table 15: The Netherlands continuous DLO survey questions about noise (English translation)

APPENDIX A: METHODS FOR THE NETHERLANDS CONTINUOUS LIFE INFORMATION

SURVEY (DLO)

The two tables in this appendix provide the wording of the noise annoyance questions from the

Netherlands Continuous Life Information Survey(DLO).

A.1 Wording of questions in English
58

Table 16: The DLO survey questions about noise (in Dutch)

A.2 Wording of questions in Dutch

Part of

question

Wording for two survey periods

(NOTE: Wording changes appear in italics.)

April 1998 to December 1993 January 1994 to December 1996

Stem of

question

1) INT.: Card 1

Ik ga u nu een aantal zaken noemen waar mensen in

huis last van kunnen hebben. Ik zou graag van u

willen weten of u er hier last van heeft.

Wilt u antwoorden aan de hand van de kaart.

Heeft u hier in huis last van

1. Ik ga u nu een aantal zaken noemen waarvan ik

zou willen weten of u daar in uw woonomgeving

last van heeft.

Heeft u in uw woonomgeving last van:

Alternative

answers

[card:] - 

JA, VAAK

JA, SOMS

ZELDEN OF NOOIT

JA

SOMS

NEE

WEIGERT/WEET NIET

Noise

sources

c) lawaai van vliegtuigen 1. lawaai van vliegtuigen/

d) lawaai van treinen, trams of metro’s 2. lawaai van treinen, trams of metro’s/

e) lawaai van auto’s, motoren of brommers 3. lawaai van auto’s, vrachtauto’s, motoren of

brommers/

f) straatlawaai van laden of lossen, of van spelende

kinderen

4. straatlawaai van laden of lossen?

5. straatlawaai van spelende kinderen of jongeren?

g) lawaii van industrie of bedrijven 6. lawaii van industrie of bedrijven?

h) geluiden van buren 7. geluiden van buren?
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Effects of Meteorological Conditions on Reactions to Noise Exposure

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Fields, James M.

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14. ABSTRACT

More than 80,000 residents' responses to transportation noise at different times of year provide the best, but imprecise, 

statistical estimates of the effects of season and meteorological conditions on community response to noise. Annoyance with 

noise is found to be slightly statistically significantly higher in the summer than in the winter in a seven-year study in the 

Netherlands. Analyses of 41 other surveys drawn from diverse countries, climates, and times of year find noise annoyance is 

increased by temperature, and may be increased by more sunshine, less precipitation, and reduced wind speeds. 

Meteorological conditions on the day of the interview or the immediately preceding days do not appear to have any more 

effect on reactions than do the conditions over the immediately preceding weeks or months.

15. SUBJECT TERMS

Aircraft noise; Environmental noise; Social surveys

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