Vytautas Magnus Univrsity Lithuanian Research Centre for Agriculture and Forestry

VYTAUTAS MAGNUS UNIVRSITY LITHUANIAN RESEARCH CENTRE FOR AGRICULTURE AND FORESTRY

Inga BENDOKIENĖ

ESTIMATION OF EXPOSURE TO ROAD TRAFFIC NOISE AND ITS RELATION TO THE RISK OF HYPERTENSION IN WOMEN

Summary of Doctoral Dissertation Biomedical Sciences, Ecology and Environmental Sciences (03 B)

Kaunas, 2011 The right of doctoral studies was granted to Vytautas Magnus University jointly with Lithuanian Research Centre for Agriculture and Forestry on July 15, 2003, by the decision No. 926 of the Government of the Republic of Lithuania.

Dissertation was performed at Vytautas Magnus University in 2005-2011.

Scientific Supervisor: Prof. habil. dr. Regina Graţulevičienė (Vytautas Magnus University, Biomedical Sciences, Ecology and Environmental Sciences 03 B)

Council of defence of the doctoral dissertation:

Chairman Prof. habil. dr. Romaldas Juknys (Vytautas Magnus University, Biomedical Sciences, Ecology and Environmental Sciences 03 B)

Members Prof. habil. dr. Pranas Baltrėnas (Vilnius Gediminas Technical University, Technological Sciences, Environmental Engineering and Landscape Management 04T) Doc. habil. dr. Regina Rėklaitienė (Lithuanian University oh Health Sciences, Biomedical Sciences, Public Health 09B) Prof. habil. dr. Vida Stravinskienė (Vytautas Magnus University, Biomedical Sciences, Ecology and Environmental Sciences 03 B) Prof. dr. Jonė Venclovienė (Vytautas Magnus University, Biomedical Sciences, Ecology and Environmental Sciences 03 B)

Oponents: Prof. habil. dr. Remigijus Ozolinčius (Lithuanian Research Centre for Agriculture and Forestry, Biomedical Sciences, Ecology and Environmental Sciences 03B) Prof. habil.dr. Abdonas Tamošiūnas (Lithuanian University oh Health Sciences, Biomedical Sciences, Public Health 09B)

The official defence of the dissertation will be held at 2 p.m. on December 16, 2011 at a V. Čepinskis lecture the hall No 605 in 2nd House of Vytautas Magnus University. Address: Vileikos st. 8, LT-44404, Kaunas, Lithuania. Phone: +370 37 327904

Summary of doctoral dissertation was sent out on November 16, 2011

The dissertation is available at M. Maţvydas National Library of Lithuania and the libraries of Vytautas Magnus University and Institute of Forestry, Lithuanian Research Centre for Agriculture and Forestry.

INTRODUCTION Environmental noise management is an important part of the European Union policy. The current European Union environmental policy is focused on the noise level reduction. This is reflected in the adopted Directive 2002/49/EC on the environmental noise assessment and management. The directive obliges Member States to draw up strategic noise maps describing the existing noise levels in major cities and providing information on the measures necessary to improve the state. In the areas where the noise level exceeds the allowable limits it is recommended to develop plans to reduce noise and to take appropriate measures for noise level management. For the majority of the urban population the transport-related and industrial noise is one of the biggest local environmental problems. Road transport noise in urban areas is growing significantly. Deterioration quality of the living environment leads to the increase of public health problems (Clark et al., 2006). People who live in a noisy environment suffer from annoyance, sleeplessness and later on for them might arise circulatory and nervous system disorders and progression of chronic disease (Berglund et al., 2000; Stansfeld et al., 2005; Muzet, 2007). Urban environmental noise sources can be different – road motor vehicles, industries, or the domestic noise emitted by householders. Nevertheless, the transport contributions to the overall increase in ambient noise levels are highest (European ..., 1995; de Kluizenaar et al., 2007; Kropp et al., 2007). The growing traffic flow requires investigation in noise reduction as it is very important to the people who live near busy main streets (Baublys et al., 2003). Environmental noise, caused by traffic, industrial and recreational activities, is one of the main environmental problems and the source of an increasing number of complaints from the local population. The European Commission has recognized the importance of monitoring of noise exposure, and also acknowledged the necessity of substantive investigations for appropriate policies to control noise through legislation. The main goal of the local action is to reduce noise to the level which is safe to the most susceptible resident’s health. (Kaminskas, 2001). In recent years, to improve the environmental exposure assessment and noise modeling are used Geographic Information System (GIS). This tool allows an accurate assessment of noise levels in different areas of the city and to predict the noise level changes in the future. Using GIS to determine the population exposure, it is possible to simulate the data geographically and therefore an individual noise exposure to large groups of people can be evaluated without time-consuming and expensive measurements (Jarup, 2004). In recent decades, usage GIS in epidemiological studies has began for the evaluation of the environmental impact on the population health (Nuckols et al., 2004). GIS is used for the connection of the individual exposure with its consequences - damage to health (Elliott et al., 2001; Nieuwenhuijsen, 2004). Scientific studies have shown that noise has a significant impact on human health, promoting chronic disease progression (Job, 1999; Guski, 1999; Stallen, 1999; Babisch, 2000, 2002; Schwela, 2000; Van Kempen et al., 2002). Noise, identified as sound waves that can cause health problems, is seen as an environmental stressor. Noise level, life and other characteristics of the noise can act differently on the human health. Effects can be anything from annoyance, sleeplessness or stress to serious health problems - hearing impairment, cardiovascular disease, even death. Living near a main

3 road, where dominated permanent exposure to noise, people complain by disturb sleep, increased irritability (Pearson et al., 1995; Miedema et al., 2001). The long-term exposure to noise and other environmental factors in susceptible individuals can lead to irreversible health problems - develop coronary heart disease or arterial hypertension (Lundberg, 1999; Babisch, 2000; Belojevic et al., 2008). Therefore, recently was paid a high politicians and public health professionals focus on the harmful environmental factors, including noise, and to the identification and exploration of the opportunities to control them (WHO, 2000). Epidemiological studies have shown that residence more than 10 years in a noisy environment, increases the risk for hypertension approximately by 2-fold (Bluhm et al., 2007; Barregard et al., 2009). Residential environmental noise impact is greater when a person works in the noisy work environment (Passehier- Vermmer et al., 2000). Noise at work is recognized as a one of the hypertension risk factors (Hirai et al., 1991; Fogar et al., 1994; Hessel, 1994; Powazki et al., 2002; Davis et al., 2005). Age of the population is also associated with the prevalence of hypertension. Most cases of hypertension occur in older than 50 years ages (Barregard et al., 2009), but hypertension occurs at a younger age also. There is only few publicized research to determine the effect of residential noise on the health of young people. In order to create a program for the prevention of hypertension and to propose reasonable measures, it is important to assess the prevalence of hypertension among young and middle-aged population, to identify potential risk factors and their control methods. In the investigation of noise impact on arterial hypertension, it is necessary accurately determine individual noise exposure, to link the exposure with health status by using of modern data collection system GIS, and to assess the strength of the relationship between exposure levels and the risk of hypertension. The author of this work by using GIS, geocoded the study participants’ addresses linked them to the residential vehicle traffic noise level, created noise maps and conducted data analysis. To assess the noise exposure of individual women of reproductive age and the relationship with hypertension, the author carried out an epidemiological study. By using GIS and noise measurement and modeling data, individual noise exposure in Kaunas was evaluated, and traffic flow analysis and composition was estimated. Women's Health data of this environmental epidemiological study were collected by using the European Commission's 6th Framework Programme (FP6) Hiwate methodology.

Objective and goals of the research. The aim of this research is to estimate and evaluate the motor noise level in the Kaunas city, and by controlling influence of confounding factors, to determine the residential noise impact on the risk of hypertension among 20-45 age women. To achieve this objective the following tasks were set up: 1. To assess the impact of traffic composition on the noise level in Kaunas city. 2. To determine the daily noise level variation in the different traffic intensity streets using a linear regression equation. 3. To identify the factors that may influence the relationship between noise and hypertension risk in women. 4. To assess relationship between noise exposure and the risk of hypertension controlling for influence of confounding factors.

The research is based on the hypothesis that road traffic noise can influence hypertension risk among women 20-45 years age.

Statements for defence. 1. Noise level and its variation depend on the composition of traffic flow and intensity. 2. GIS using for assessment of individual noise exposure of living place provides valuable data for predicting noise influence on the spread of hypertension. 3. Road traffic noise in living place increase hypertension risk among women in the age group of 20-45. 4. Risk of hypertension is increasing, when women living in a noisy environment are exposed to noise in work. Scientific novelty and significance. In this research, applying GIS and using Kaunas municipality noise measurement and modeling data, were created traffic flow and noise level maps and first time was evaluated influence of different truck type on prevailing environmental noise. Individual residential exposition to 24 hours and night time noise for hypertension risk was assessed for all study subjects. For the first time, by modeling traffic flow composition variations was predicted noise level changes in the main streets of Kaunas. For the first time in Lithuania we used GIS for individual exposure estimation seeking to evaluate individual residential exposure to noise amongst reproductive aged women (20-45 years) and controlling for confounding variables to assess the association with hypertension risk. The results of this study provide data on urban ambient noise level changes caused by vehicle traffic volume and composition. The data obtained have practical significance on the decision-making for reduce noise level, improve the quality of life and reduce the risk of hypertensive disease. Improved understanding of etiological mechanisms of hypertension caused by long-term noise should suggest public health specialists to design appropriate preventive measures so that the incidence of hypertension morbidity will be reduced. Approval of the research work. Research findings were presented in 7 publications, whereof 2 are in the journals included in Institute for Scientific Information database ISI Web of Science, 1 – in the journal approved by the Lithuanian scientific publications list of the Department of Sciences and Higher Education, 2 in the periodic journals and 2 publications in conference proceedings. Volume and structure of the work. The dissertation is written in Lithuanian. It consists of Introduction, Literature review, Material and methods, Results, Discussion and References. The dissertation comprises of 91 pages, including 26 tables, 21 figures and 149 references.

THE OBJECTIVE, MATERIALS AND METHODS Assessment of noise exposure. No measurements of noise levels were conducted. Instead, we used a GIS and strategic noise map to assess the outdoor noise exposure from traffic. The Kaunas municipality local noise measurement data base and strategic noise map from years 2007-2008 were used to estimate the noise exposure for the residential locations of the participants. The road traffic noise exposure of the subjects was calculated at the most exposed facade of the dwelling applying the Finnish and Swiss methodology. The methodology was based on an EMPA StL-86 model, using an acoustic algorithm. Strategic Kaunas noise map were created in accordance with

5 requirements of the European Environmental Noise Directive (END) and European Commission Working Group Assessment of Exposure to Noise (WG-AEN). For the analyses, we used EU standard noise metric LAeq 24 hr. To evaluate LAeq 24 hr noise level, we used these input data: road network, traffic flow, road surface type, speed fluctuations at road, building heights, land-use data, industrial source data, barriers (building, wall, obstacle outlines), meteorological data, population data. Traffic flow data were prepared for noise modeling on the basis of GIS database of strategic noise map of Kaunas municipality. The measurement of traffic flow intensity and noise level in the Kaunas city were carried out close to the main streets (282 measurement points), where traffic flow was more than 1200 vehicles/24 h. Using the cluster analysis methods we attributed the same noise level to the streets, where traffic flow intensity fluctuated less than 20% and the number of street lanes was the same. For road segments without traffic data, noise level mean values were calculated by using GIS. The data were recalculated into a factual level of noise. The noise was calculated at all points and a level of noise is attached to each residence in the area. For a limited area calculation points were placed in a grid, typically with a cell size of 10 × 10 m. We estimated the noise level on the ground floor for all of the residences.

Characteristics of the study participants and hypertension risk factors. An epidemiological study of pregnant women was conducted in Kaunas city, Lithuania, as a part of the European Commission FP6 HiWATE project. To recruit women who were in the early stages of pregnancy, the prenatal care practices were asked to inform their newly enrolled patients about the study. Next, the blood pressure of the patients was measured. On their first visit to a general practitioner, all pregnant women living in Kaunas city between 2007 and 2008 were invited to join the study. These women were enrolled at 23–35 weeks of gestation (97% till 25 weeks) at the four prenatal care clinics affiliated to the hospitals of the Kaunas University of Medicine. Participation was on a voluntary basis and the women were enrolled in the study only if they consented to participate in the cohort. The study ethics complied with the Declaration of Helsinki. The research protocol was approved by the Lithuanian Bioethics Committee. Further, verbal informed consent was also obtained from all subjects. In total 5202 women were approached; 79% of them agreed to participate in the study. Women with multiple pregnancies (81) or having inconsistent data for estimating noise exposure (mostly students moved out of the city during pregnancy, 907) were excluded. The study population included 3121 women, in the age group of 20-45 years at the time of interview, with a minimum length of one year of current residence. The study subject was defined hypertensive if two or more of the physician’s blood pressure measurements were: a systolic blood pressure ≥140 mmHg or a diastolic blood pressure ≥90 mmHg, regardless of antihypertensive medication or controlling of blood pressure. Women were asked to complete questionnaires provided at the clinic. The interview queried women regarding demographics, job characteristics, self-reported occupational noise exposure, chronic diseases (cardiovascular, respiratory, renal, and diabetes), and residence duration. We also asked women to report their age at inclusion (less than 30 years, 30 years and more), educational level (primary, secondary, university), social status (worker, student, unemployed – low; housekeeper, officer – medium; manager, company owner – high), marital status (married not married),

6 smoking during pregnancy (non-smoker, smoker <5 cigarette per day, and smoker 5 cigarette per day), alcohol consumption (0 drinks per week, mostly one drink per week, 2 and more drinks per week), blood pressure (<140/90 mm/Hg, ≥140 or/and ≥90 mm/Hg), body mass index (BMI) (<25 kg/m2, 25–30 kg/m2, >30 kg/m2), and other potential risk factors for hypertension. To assess the potential confounding variables we used chi square test. Predictor variables whose univariate test showed a P value of <0.25 in relation to the outcome were included in the regression models.

Integration of environmental and health databases. To attribute the noise exposure to every subject, the health data base and the environmental noise data base were joined. Every subject’s full street address and residential noise level measurement data, and the current residence history data were combined to assess the individual noise exposure. A GIS assigning noise level was used for every woman by applying different GIS functions and possibilities. First, the study subjects data were converted to a database file structure for use in GIS software (ArcInfo version 9.3, ESRI). Geocoding was performed to obtain latitude and longitude coordinates for each patient’s home address. Initially, 63% records were matched and 37% were left unmatched. All unmatched records were reviewed and corrected, leading to another 37% matched addresses (total of 3212). Then, a spatial join was perform that allows the GIS user to append the attributes of one data layer (patient address points) to the attributes of another layer (noise level) assessed with MapNoise for ArcGIS. The non-weighted average road noise level at the geocoded current residential addresses of the participants was assessed (1 figure).

Statistical analysis. Logistic regression was used to assess the relation between the average road noise exposure during 24 hours (LAeq 24 hr) as the categorical variable and the physician-diagnosed hypertension as the outcome variable. Noise measurements data were sorted in an ascending order. The average outdoor A-weighted sound pressure level (LAeq 24 hr) from the day-evening-night time was calculated and classified into three dB(A)-categories: 1. Low noise level (≤ 50 dB(A)) 2. Moderate noise level (51-60 dB(A)) 3. High noise levels (≥ 61 dB(A))

Night-time period dB(A)-categories (Lnight): 1. Low noise level (≤ 40 dB(A)) 2. Moderate noise level (40-50 dB(A)) 3. High noise levels (≥ 51 dB(A))

We compared the risk of hypertension for three exposure categories using the reference category the subject group with an average 24 hours noise exposure below 50 dB(A) and below 40 dB(A) night noise. We used OR as a measure of association, and we applied logistic regression analysis to estimate the crude and adjusted ORs, and the 95% CIs for hypertension across three exposure categories. Odds ratio was calculated using the formula: GS = a/c / b/d = ad / bc a - a factor exposed to ill persons, b - a factor influenced by healthy individuals

7 c - a factor influenced by ill persons d - a factor influenced by healthy persons.

Figure 1. Modeled noise levels dB(A) (LAeq, 24 h) and hypertension cases in Kaunas city

Two different types of logistic regression models were analyzed. The first model was unadjusted; the second fully adjusted model included maternal age, BMI, social status, education, parity, chronicle disease, self-reported occupational noise and exposure duration at the current address. We run the stratified analyses regarding the noise exposure duration (<10 years, 10 years) of different age groups (total sample 20- 45, <30, 30 years), and self-reported occupational noise exposure (present, absent). We estimated the exposure effect by a multivariable analysis controlling for influence of major covariates included in the model that changed the adjusted ORs for noise by 10% or more. Two-tailed statistical significance was evaluated by using a P value of <0.05. Statistical analyses were carried out using the SPSS software for Windows version 12.0.1 and Microsoft Office Excel 2003. Mapping was used in ArcGIS ArcMap standard GIS software and MapNoise software. Many of the traffic noise level prediction models are to evaluate the noise level of development (Sheng et al., 2011). A number of researchers using integrated GIS with noise prediction models, using the following parameters - intensity of traffic flow, speed, road surface, street layout and so on. (Moragues et al., 1996; Li et al., 1999, 2000, 2002). In the study was calculated the noise level variation with the increase of heavy transport flow during the day and evening, using the linear regression equation: Y = a + bX a and b - constants are not known; X - independent variable (variable according to the predicted values of the dependent variable); Y - the dependent variable (predicted values) Prediction of the average noise level of 10 dB(A) increase impact on the risk of hypertension was used in SPSS version 13 package of analysis, performed regression analysis. We used binary logistic regression analysis method (Leonavičienė, 2007).

RESULTS AND DISCUSSION Environmental noise levels and hypertension incidence in Kaunas districts The study results show that 24 hours noise levels in different district of Kaunas city ranged between 65,8 dB(A) and 70,7 dB(A) (Table 1). The mean and the standard deviation of the individual 24-hour noise exposure level (dB(A) LAeq 24 hr) in Kaunas during the study period were 67,3 and 4,9, respectively. The highest noise level was in districts of Eiguliai, Dainava and Ţaliakalnis (about 70 dB(A)), the lowest noise level was in Panemunė district (65,8 dB(A)). The highest hypertension incidence was registered in Centre, Aleksotas and Dainava districts (Table 2). These areas, according to the average daily noise levels were among the modest or highly exposed. The fewer incidents per 1000 study subjects were registered in Panemunė district. A small number of pregnant women and low spread out the disease may be the result of this district peculiarity because of more proportion of middle and senior citizens reside in this green space area. The study results show that the intensity of traffic flow is the main source of noise closely related to the noise level dimensions. According to the statistical noise models, traffic flow has been seen as a linear noise source that emits a noise inconsistent.

Table 1. Average noise levels (LAeq 24 hr) in the Kaunas districts

District Average Standard 95 % CI for Min value, Max noise in 24 deviation mean dB(A) value, hours period, dB(A) dB(A) Aleksotas 67,3 5,09 65,0-69,5 57,6 73,6 Centras 67,2 6,0 65,2-69,6 56,4 75,3 Dainava 70,4 1,9 69,1-72,0 67,0 72,8 Eiguliai 70,7 2,7 68,6-72,8 66,8 70,7 Panemunė 65,8 3,9 64,2-67,6 57,3 73,3 Petrašiūnai 69,7 1,1 68,7-70,7 67,0 71,4 Šančiai 66,4 4,1 63,9-68,8 56,7 71,3 Šilainiai 67,0 5,8 63,8-70,2 55,5 71,3 Vilijampolė 67,0 5,6 64,0-70,0 56,5 74,7 Ţaliakalnis 69,8 2,9 68,5-71,1 66,3 75,0 Kaunas city 67,3 4,9 66,6-68,0 52,4 75,6

Table 2. Distribution of 20-45-year-old women population and cases of hypertension across Kaunas city districts

District All women Hypertension Hypertension cases incidence/1000 Aleksotas 266 41 154 Centras 126 24 190 Dainava 785 117 149 Eiguliai 588 80 136 Panemunė 80 6 75 Petrašiūnai 75 11 147 Šančiai 208 19 91 Šilainiai 516 71 138 Vilijampolė 316 39 123 Ţaliakalnis 161 15 93 Kaunas city 3121 423 136

The noise distribution analysis showed that the maximum noise level was found at the main streets, where the transport intensity is large (> 500 veh./h). In these streets the daytime noise level (LAeq 24 hr) exceeded the daytime limits by 21%, evening – by 27%, night – by 26%. Maximum noise levels on the day were up to 74,7 dB(A), and at night - an average of 65,3 dB(A) and above the maximum daytime limit exceeded by 6%, evening - by 10%, and night – by 8%. Most living houses in Kaunas city districts are located about 50 meters away from the main streets. In such distance noise level exceeded the limits of about 10% during the 24-hours period. Although the noise level in the housing forecourts didn’t exceed the allowable noise level. In order to determine whether there is a correlation between traffic intensity and the prevailing ambient noise, the correlation coefficient was calculated in Kaunas districts in day, evening and night (Table 3). It was found the strongest statistically

significant correlation between traffic intensity and the average daily noise level in Eiguliai (r=0,89), Petrašiūnai (r=0,88), Vilijampolė (r=0,89), and Šilainiai (r=0,87) districts. The weakest correlation coefficient was in Šančiai district (r=0,65). During the evening the strongest statistically significant correlation between traffic intensity and the average noise level was in Vilijampolė and Šilainiai districts (correlation coefficient 0,90).

Table 3. Correlation between transport traffic flow and noise level in Kaunas city districts

Day Evening Night District 6:00-18:00 h 18:00-22:00 h 22:00-6:00 h

Veh/h Lday, r Veh/h Levening r Veh/h Lnight, r dB(A) dB(A) dB(A) Aleksotas 354 66,4 0,68* 388 61,5 0,70* 118 56,9 0,62* Centras 675 66,3 0,79* 507 61,6 0,80* 81 56,4 0,75* Dainava 737 69,6 0,75* 518 67,3 0,67* 116 60,0 0,56 Eiguliai 818 70,0 0,89* 598 67,6 0,89* 98 60,5 0,90* Panemunė 391 64,9 0,86* 279 62,7 0,77* 52 55,5 0,63* Petrašiūnai 614 69,3 0,88* 429 67,1 0,78* 74 59,2 0,60 Vilijampolė 792 66,1 0,89* 525 64,0 0,90* 77 56,0 0,87* Šančiai 350 65,5 0,65* 247 64,0 0,66* 82 56,6 0,62* Šilainiai 755 66,1 0,87* 459 64,3 0,90* 85 56,4 0,84* Ţaliakalnis 1061 69,7 0,86* 821 65,1 0,81* 154 59,3 0,88* Kaunas city 647 67,3 0,74* 481 63,8 0,68* 94 57,9 0,71* *p<0,001; r-correlation coefficient

Analysis of traffic flows and noise emissions from highways and main city roads show that with the increase of traffic intensity increases the noise level near residential homes. Majority of the population dwellings exposed to unacceptable noise were closer to the main street. Because the traffic flows and noise levels are closely linked, the continued growth of traffic in urban area can further increase the noise level. Noise level depends not only on the total volume of the vehicle flow, but also on the condition of the vehicles, vehicle weight and speed. A combined total of Kaunas city traffic intensity analysis showed that exist statistically significant dependence between the cars and the ambient noise level. Day and night time correlation coefficient was 0,70; evening – 0,66. Dependence of ambient noise from other vehicles is similar. Bus and heavy transport impute in the average daily flow rate and noise level is a moderate, the correlation coefficient is 0.50 (Table 4).

Table 4. Dependence of vehicle type on 24 hours noise level in Kaunas city

Transport Day Evening Night 6:00-18:00 h 18:00-22:00 h 22:00-6:00 h Veh/h r Veh/h r Veh/h r Cars 628 0,72** 491 0,66** 97 0,70** Buses 17 0,50** 9 0,49** 2 0,41** Trolleybuses 7 0,39** 4 0,29** 1 0,13 Trucks 43 0,48** 18 0,32** 1 0,29**

The impact heavy transport in the noise level variation Our data show, that noise level depends on the average equivalent traffic flow. Highest traffic intensity was found in the main streets of Kaunas districts and national highways, where 20,000–30,000 vehicles passed during 24-hours. The highest noise level was also stated there. Road transport noise level is highly dependent on the flow structure of the street (heavy transport, cars, motorcycles and other). Noise level depends not only on the total volume of the vehicle flow, but also on the condition of the vehicles, vehicle weight and speed. The largest impact has the increase of heavy vehicle traffic flows. Increase the speed of the tire and pavement interaction also affects the noise level. In Kaunas city's industrial areas and in areas crossed the main roads, the largest share of traffic have the heavy vehicles, about 63-89%. Heavy transport generates biggest noise (about 80 dB(A)) level in the streets. Mode noise in urban areas is very important for the city bus types, pavement condition. Usually noise level in the Kaunas city streets is 65-70 dB(A). One truck, which goes 90 kilometers per hours, makes the same noise like 28 cars, which goes at the same speed (U.S. Department..., 1980). This shows heavy transport contribution to the overall ambient noise level. In Kaunas city there are three streets which belong to the Republican road network. It is predicted that in the main streets of the city-highway heavy vehicle movements will increase, therefore it is expected the noise level in the city will increase. In this study we analyzed the trucks contribution to the general environmental noise in several areas and modeled changes of noise levels in different Kaunas city districts. The contribution of trucks to noise in the general environment decrease with increasing traffic flow. There was predicted how heavy transport will change day noise levels if the load will double the number of vehicles (Table 5). It was found that heavy vehicles input to noise level is higher when traffic is low. The contribution of trucks to noise in the general environment decrease with increasing traffic flow.

Table 5. The average day noise level variation, when double the number of vehicles

Traffic flow Heavy transport X (veh/h) a constant b constant (p) Increasing noise level <300 veh/h 13 61,33 0,12 (p<0,05) 1,56 300-500 veh/h 32 66,79 0,01 (p=0,308) 0,32 >500 veh/h 74 69,79 0,013 (p<0,05) 0,96

Using logistical regression equation variation of noise level we can forecast, that with the growth of the traffic flow, increase of truck proportion double will result the rise of traffic noise by 1 dB(A) on the streets with traffic flow of 500 veh/h or more, while increase in noise by 1,56 dB(A) on the streets with traffic flow of 300 veh/h or less. So, heavy transport’s influence on acoustic pollution is higher in the districts with lower traffic flows. Traffic intensity and the noise level variation estimates In Kaunas areas near the highway traffic noise is continuously sustained high 15-18 hours a day, and reduced noise levels, the movement subsides for a short between 2 and 4 hours only in the night. The intensity of the traffic in the city is one of the most important factors that determine the noise level (Maciunas, 1999).

According to the Department of Statistics, the intensity of traffic flow across the streets of Kaunas in the last 7 years (2004 - 2010) grew from 1,6% to 7,2% (Lithuania Statistics ..., 2011). The biggest increase in traffic was noticed in the arterial road crossing on the border town of Kaunas (10-12 %). On this highway the maximum average noise level was 75 dB(A) in the daytime and 65 dB(A) at night (Baubonytė et al., 2007) Over the past 7 years in Kaunas registered private cars increased by 16,5%, trucks increased by 31,1%. Number of cars per capita has grown steadily and in 2010 reached 486 cars per 1000 population. Every year the number of cars increases by more than 3 %, while heavy transport - more than 5%. It is forecast that over the next few years in the city of Kaunas the vehicle daily noise level will increase by an average of 0,2 dB(A) per year. If not taken measures to reduce noise, 25% increases in traffic will increases 1dB(A) average daily noise level (Table 6).

Table 6. Day noise level variation, when traffic flow increase by 1%

Transport Number of cars Number of trucks Number of all transport traffic grow, 590 veh/h 44 veh/h 648 veh/h % a=63,45 a=65,36 a=63,16 b=0,006 (p< 0,05) b=0,037 (p< 0,05) b=0,006 (p< 0,05) Increasing noise Increasing noise Increasing noise level, level, dB(A) level, dB(A) dB(A) 1 0,035 0,016 0,039 2 0,07 0,032 0,078 3 0,11 0,048 0,12 4 0,14 0,064 0,16 5 0,18 0,08 0,20 6 0,21 0,09 0,23 7 0,25 0,11 0,27 8 0,28 0,13 0,31 9 0,32 0,14 0,35 10 0,35 0,16 0,39 ...... 100 3,5 1,6 3,9

Evaluation of the relation between noise pollution and hypertension risk based on case-control study A total of 423 hypertension cases amongst 3,121 pregnant women aged 20-45 years (13,6%) were registered. In general, it was observed that women who had an increased BMI, were 30 years or older, or had a low social status more often suffered from hypertension in comparison to women without such variables. The analysis by three different levels of road traffic noise exposure (low, moderate, and high) shows that prevalence of most characteristics of the exposure groups were similar (Table 7). There were no differences in social and demographic characteristics, education, and occupational noise exposure. However, prevalence of BMI differed between exposure groups (P < 0.05). The prevalence of hypertension amongst the 20-45 years age group women of the lowest exposure category (≤50 dB(A)) was 13,1%, of the moderate exposure category (51-60 dB(A)) was 13,6%, and of the highest exposure category (≥61 dB(A)) was 18.1%.

Table 7. Distribution of subjects for various characteristic by noise level

Risk factors Low noise level N Moderate noise level High noise level (%) N (%) N (%) Age group <30 years 897 (50,3) 636 (53,7) 71 (45,8) 30 years 885 (49,7) 548 (46,3) 84 (54,2)

Body mass index (kg/m2) Normal (<25,0) 1069 (60,0) 697 (58,9) 83 (53,5) Increased (25,1-30,0) 512 (28,7)* 320 (27,0)* 48 (31,0)* Obesity (>30,0) 201 (11,3)* 167 (14,1)* 24 (15,5)*

Marital status Married 1528 (85,7) 985 (83,2) 122 (78,7) Not married 254 (14,3) 199 (16,8) 33 (21,3)

Parity No child 406 (22,8) 253 (21,4) 44 (28,4) ≥1 child 1376 (77,2) 931 (78,6) 111 (71,6)

Socio economic status Low 477 (26,8) 347 (29,3) 48 (31,0) Medium 1020 (57,2) 658 (55,6) 80 (51,6) High 285 (16,0) 179 (15,1) 27 (17,4)

Education Primary school 42 (2,4) 56 (4,7) 13 (8,4) Secondary school 698 (39,2) 448 (37,8) 68 (43,9) University degree 1042 (58.5) 680 (57,4) 74 (47,7)

Smoking Non-smoker 1687 (94,7) 1109 (93,7) 142 (91,6) <5 cigarettes per day 52 (2,9) 44 (3,7) 8 (5,2) ≥5 cigarettes per day 43 (2,4) 31 (2,6) 5 (3,2)

Alcohol consumption 0 drinks per week 860 (48,3) 582 (49,2) 70 (45,2) 1 drink per week 892 (50,1) 582 (49,2) 79 (51,0) ≥2 drinks per week 30 (1,7) 20 (1,6) 6 (3,9)

Ethnic group Lithuanian 1728 (97) 1162 (98,1) 147 (94,8) Other 54 (3) 22 (1,9) 8 (5,2)

Noise exposure duration <10 years 1193 (67,9) 829 (70,0) 101 (64,2) ≥10 years 589 (33.1) 355 (30,0) 54 (34,8)

Occupational noise exposure No 1531 (85,9) 1008 (85,1) 138 (89,0) Yes 251 (14,1) 176 (14,9) 17 (11,0)

Chronic disease No 1333 (74,8) 897 (75,8) 113 (72,9) Yes 449 (25,2) 287 (24,2) 42 (27,1) ` Arterial hypertension No 1548 (86,9) 1023 (86,4) 127 (81,9) Yes 234 (13,1) 161 (13,6) 28 (18,1) *P < 0.05

Hypertension prevalence during the first trimester pregnancy amongst 20-45 years old women was coincident with the samples of the not pregnant 20-45 years old women in Lithuania (Andruškienė et al., 2007). A modest exposure effects were noted for the three exposure levels (≤50, 51-60 and ≥61 dB(A)) demonstrating slight increasing hypertension odds ratios at increasing road noise levels amongst the 20-45 years age women (Table 8). However, only 5,0% of all the study subjects lived in an environment where the average noise level (LAeq 24 hr) was higher than 61 dB(A). In this environment, 6,6% of all hypertension cases were registered. Therefore, the results were not statistically significant. The crude hypertension odds ratios of women exposed to moderate noise level was 1,04 (95% CI 0,84-1,29), while the OR of the highest exposure was 1,44 (95% CI 0,93-2,23) in comparison to the lowest exposure (≤50 dB(A)). The data showed that the unadjusted analyses were likely to be confounded by BMI and possibly also by other variables. Therefore, in the study concerning the noise effect on the different age groups, two models of adjusting were used: unadjusted (crude results) and adjusted. The increasing road noise exposure effects on hypertension were seen in the second and the third traffic noise exposure categories in two models. However, a consistent statistically significant effect was only found in women 30 years in the third noise category (≥61 dB(A)) (Table 8). The data showed that an exposure effect of road traffic noise was stronger in the 30 years age group. A modest exposure effect of the road traffic noise was indicated for 20-45 years old women who lived at exposure levels ≥61 dB(A). Here, after full adjustment the odds ratio was 1,36 (95% CI 0,86-2,15) in comparison to the lowest category (≤50 dB(A)). No obvious effect was found between the women <30 years, whereas in the ≥30 years age group an exposure-response relationship was indicated. After full adjustment for potential confounding factors, we observed a statistically significant increased hypertension risk with exposure to moderate and high noise levels; adjusted OR 1,08; 95% CI 0,79-1,49 in 51-60 dB(A) and OR 1,81; 95% CI 1.02-3.22 in highest exposure category (≥64 dB(A)). Seeking to study whether subjects that were exposed for longer times have a higher hypertension risk, we ran stratified analysis by residence duration (Table 9). After full adjustment for potential confounding factors, we observed a slightly increasing risk with <10 years exposure for 20-45 years old women (OR 1,03, 95% CI 0,78-1,36 and OR 1,47; 95% CI 0,83-2,59), respectively, for 51-60 and ≥64 dB(A). However, there was no evident difference between road traffic noise exposure below 10 years and above 10 years and effect on hypertension in the studied age groups. The effect estimate for the exposure ≥10 years was also statistically non-significant (OR 0,95; 95% CI 0,64-1,42 and OR 1,34, 95% CI 0,61-2,91; respectively, for 51-60 and ≥64 dB(A). Similar results were found for ≥30 years age women, the corresponding OR was 0,93; 95% CI 0,51-1,70 and OR 2,18; 95% CI 0,74-6,38.

Table 8. Odds ratios (OR) and their 95 % confidence intervals (CI) of hypertension associated with three levels of road traffic noise exposure and exposure duration using two models of adjusting

Age groups Noise level Noise level Noise level ≤50 dB(A) 51-60 dB(A) ≥61 dB(A)

20-45 years age group Crude OR (95% CI) 1 1,04 (0,84-1,29) 1,44 (0,93-2,23) OR* (95% CI) 1 1,00 (0,80-1,25) 1,36 0,86-2,15

<30 years age group Crude OR (95% CI) 1 0,99 (0,73-1,35) 0,99 (0,48-2,04) OR* (95% CI) 1 0,91 (0,66-1,25) 0,82 (0,38-1,78)

≥30 years age group Crude OR (95% CI) 1 1,10 (0,81-1,49) 1.88 (1.09-3.25) OR* (95% CI) 1 1,08 (0,79-1,49) 1,81 (1,02-3,22)

OR* adjusted by age, BMI, social status, education, chronicle disease, parity, exposure duration at the current address, and self-reported occupational noise

Table 9. Odds ratios (OR) and their 95 % confidence intervals (CI) of hypertension associated with three levels of road traffic noise exposure and exposure duration using two models of adjusting

Age groups Noise level Noise level Noise level ≤50 dB(A) 51-60 dB(A) ≥61 dB(A)

Noise exposure <10 years 20-45 years age group Crude OR (95% CI) 1 1,08 (0,83-1,42) 1,50 (0,86-2,60) OR* (95% CI) 1 1,03 (0,78-1,36) 1,47 (0,83-2,59)

<30 years age group Crude OR (95% CI) 1 1,03 (0,74-1,44) 1,19 (0,54-2,59) OR* (95% CI) 1 0,97 (0,69-1,37) 1,11 (0,50-2,48)

≥30 years age group Crude OR (95% CI) 1 1,18 (0,75-1,84) 1,77 (0,67-4,70) OR* (95% CI) 1 1,10 (0.69-175) 2,12 (0,93-4.83) Noise exposure ≥10 years 20-45 years age group Crude OR (95% CI) 1 0,97 (0,66-1,41) 1,33 (0,64-2,74) OR* (95% CI) 1 0,95 (0,64-1,42) 1,34 (0,61-2,91)

<30 years age group Crude OR (95% CI) 1 1,05 (0,63-1,75) 0,97 (0,32-2,92) OR* (95% CI) 1 1,05 (0,61-1,81) 0,77 (0,23-2,58)

≥30 years age group Crude OR (95% CI) 1 0,88 (0,50-1,54) 1,97 (0,90-4,34) OR* (95% CI) 1 0,93 (0,51-1,70) 2,18 (0,74-6,38) OR* adjusted by age, BMI, social status, education, chronicle disease, parity, exposure duration at the current address, and self-reported occupational noise

Some women participating in the study not only lived in a noisy environment, but also was exposed to noise at work. Most women working in noisy environment are teacher in schools and kindergartens, shopping centers counters, textile enterprise workers. Table 10 shows how occupational noise could influence the hypertension risk to 20-45 years old women who lived in three different noise level categories. The results showed a positive relation between the road traffic noise, noise at the work place, and an increased risk of hypertension amongst the 20-45 years old women. The association was noted when important potential confounders were included in the model. The fully adjusted OR for women residing in the highest road noise category (≥61 dB(A)) not exposed to noise at work was 1,12 (95% CI 0,60-2,09), while OR for women exposed to noise at work was 1,70 (95% CI 0,88-3,28).

Table 10. Crude and adjusted OR and 95% CI of hypertension associated with different levels of road traffic noise exposure to women exposed to occupational noise

Age groups Noise level Noise level Noise level ≤50 dB(A) 51-60 dB(A) ≥61 dB(A) Occupational Occupational noise Occupational noise noise No Yes No Yes No Yes 20-45 years age group

Crude OR 1 1 1,10 0,99 1,22 1,76 (95% CI) (0,81-1,51) (0,74-1,34) (0,67-2,19) (0,93-3,31)

OR* 1 1 1,02 0,96 1,12 1,70 (95% CI) (0,73-1,42) (0,70-1,30) (0,60-2,09) (0,88-3,28)

<30 years age group

Crude OR 1 1 1,01 0,98 1,23 1,12 (95% CI) (0,66-1,56) (0,64-1,51) (0,77-1,95) (0,38-3,32)

OR* 1 1 0,94 0,92 0,60 1,24 (95% CI) (0,59-1,50) (0,58-1,44) (0,21-1,75) (0,41-3,80)

≥30 years age group

Crude OR 1 1 1,23 1,01 1,52 2,36 (95% CI) (0,77-1,95) (0,67-1,52) (0,72-3,24) (1,06-5,24)

OR* 1 1 1,01 1,02 1,52 2,03 (95% CI) (0,67-1,78) (0,66-1,56) (0,67-3,44) (0,87-4,74)

OR* adjusted by age, BMI, social status, education, chronicle disease, parity, exposure duration at the current address, self-reported occupational noise

Occupational noise and noisy living place tended to increase hypertension risk twice for ≥30 year’s age women (OR 2,03; 95% CI 0,87-4,74). OR for ≥ 30 years women residing in the highest road noise category (≥61 dB(A)) not exposed to noise at work was 1,52 (95% CI (0,67-3,44)). However, these results are not statistically significant.

Subjective assessment of noise in the working environment should be considered as an important factor, which together with the noisy living environment might affect the risk of arterial hypertension. Residents often complain about the noise annoyance at noise level even lower than the threshold noise levels harmful to health. Even the 42 dB(A) noise level for daytime and night-time identifies as frustrating (Miedema et al., 1998), and according World health organization, many people 50 dB(A) noise level consider as troublesome (Berglung et al., 2000).

Conclusions 1. Noise data dispersion analysis showed that the average daily noise level of Kaunas districts ranging from 65,8 dB(A) to 70,7 dB(A) and the noise level variation depends on the intensity of traffic flow and composition. The strongest correlation between traffic intensity and the average daily noise level was in such districts: Eiguliai (r=0,89), Petrašiūnai (r=0,88), Vilijampolė (r=0,89) and Šilainiai (r=0,87). The greatest impact on noise levels has cars: day and night correlation coefficient of 0,70 (p <0,005), and the evening – 0,66 (p <0,005). 2. Heavy vehicles input to noise level is higher when traffic intensity in the streets is low. The growth of the traffic flow, increase of truck proportion double will result in the rise of traffic noise by 1 dB(A) on the streets with traffic flow of 500 veh/h or more, and increase in noise by 1,56 dB(A) on the streets with traffic flow of 300 veh/h or less. 3. The study shows that traffic noise can increase women hypertension risk. A modest exposure effect of the road traffic noise was indicated for 20-45 years old women who lived at exposure levels ≥61 dB(A), after full adjustment the odds ratio was 1,36 (95% CI 0,86-2,15) in comparison to the lowest category (≤50 dB(A)). 4. The data showed that an exposure effect of road traffic noise was stronger in the 30 years age group. After full adjustment for potential confounding factors, a statistically significant increased hypertension risk about 80% with exposure high noise levels was revealed (OR 1,81; 95% CI 1,02-3,22). 5. Occupational noise and noisy living place has a tendency to increase hypertension risk twice for ≥30 year’s age women (OR 2,03; 95% CI 0,87-4,74) and by 70% (OR 1,70; 95% CI 0,88-3,28) among 20-45 years old women. 6. Long-term (≥ 10 years) noise exposure in the living environment has a tendency to increase the risk of hypertension. 30-45 years old women living in the largest area of the noise level may increase the risk more than twice (OR 2,18; 95 % CI 0,74-6.38). 7. To improve quality of life and to reduce hypertension risk in Kaunas population, traffic noise in 24 hours period has to be reduced.

LIST OF PUBLICATION Publications which are assessed by the Institute for Scientific Information database ISI Web of Science

1. Graţulevičienė R., Bendokienė I. Influence of truck traffic on acoustic pollution in Kaunas districts crossed by highways. Journal of Environmental Engineering and Landscape Management. 2009, 17(4):198-204 2. Bendokienė I., Graţulevičienė R., Dėdelė A. Road traffic noise and risk of hypertension amongst reproductive age women. Noise & Health, November-December 2011, Volume 13 (in press) In peer-reviewed journals, including international scientific databases 1. Baubonytė I., Graţulevičienė R. Road traffic flow and environmental noise in Kaunas city. Aplinkos tyrimai, inţinerija ir vadyba. 2007. Nr. 1(39), P. 49-54 2. Dėdelė A., Graţulevičienė R., Bendokienė I. 2011. Individual exposure to nitrogen dioxide and pretem birth risk in Kaunas. Aplinkos tyrimai, inţinerija ir vadyba. 2(56), p. 49-56. In conference proceedings and other publications 1. Bendokienė I., Graţulevičienė R. Triukšmingos gyvenamosios ir darbo aplinkos itaka 20-45 metu moteru hipertenzijos rizikai.//Tarptautinės mokslinės – praktinės konferencijos „Ţmogaus ir gamtos sauga“ leidinys. 2011, ISSN 1822-1823, Akademija, p. 79-82 2. Bendokienė I., Graţulevičienė R. Automobilių transporto keliamo triukšmo įtaka moterų arterinės hipertenzijos rizikai Kauno mieste// Tarptautinės mokslinės – praktinės konferencijos „Ţmogaus ir gamtos sauga“ medţiaga. Human and Nature Safety-2010. Proceedings of the International Scientific Conference. Part 1. 2010; Vol.1:105-108. ISSN 1822-1823. 3. Graţulevičienė R., Lekavičiūtė J., Baubonytė I. Relationship between environmental noise and myocardial infarction risk. Environmental engineering [Elektroninis išteklius]: 7th International Conference on Environmental Engineering, Vilnius, Lithuania, May 22-23, 2008: proceedings [CD]. ISBN 9789955282563 p. [1-6]. 4. Baubonytė I, Stanikūnienė M. Autotransporto srautų intensyvumas ir jo keliamas triukšmas Ţemutinėje Kauno miesto dalyje // Tarptautinės mokslinės – praktinės konferencijos „Ţmogaus ir gamtos sauga“ leidinys, ISSN 1822-1823, Akademija, 2006, 261 p.

AKNOWLEDGEMENTS I am thankful to my scientific supervisor prof., habil. dr. Regina Graţulevičienė for scientific advice and full support for scientific workflows. Also I would like to express my special for all Department of Environmental VDU workers and colleagues for advice and support. I am thankful to Kaunas City Environmental Protection Department for the opportunity to research noise measurement data and databases. I also thank colleagues for their supports sincerely thank my family and friends for their understanding and support during PhD studies.

SANTRAUKA Hipotezė: kelių transporto keliamas triukšmas gali turėti įtakos moterų hipertenzijos rizikai. Tyrimų tikslas. nustatyti ir įvertinti autotransporto keliamą triukšmo lygį Kauno mieste ir, kontroliuojant ryšį iškreipiančiuosius veiksnius, nustatyti gyvenamosios aplinkos triukšmo įtaką hipertenzijos rizikai 20-45 m. amţiaus moterims. Tyrimų uždaviniai. Tikslui pasiekti buvo iškelti šie uţdaviniai: 1. Įvertinti transporto srautų sudėties įtaką triukšmo lygiui Kauno mieste. 2. Nustatyti ir įvertinti paros triukšmo lygio kitimą skirtingo transporto intensyvumo gatvėse, naudojant tiesinę regresijos lygtį. 3. Nustatyti veiksnius, galinčius turėti įtakos ryšiui tarp triukšmo ir moterų hipertenzijos rizikos. 4. Kontroliuojant ryšį iškreipiančiuosius veiksnius, nustatyti ryšį tarp gyvenamosios vietos ekspozicijos triukšmu ir moterų hipertenzijos rizikos. Mokslinis naujumas. Šiame darbe naudojant GIS ir viso Kauno miesto triukšmo matavimo ir modeliavimo duomenis, buvo sukurti triukšmo lygio ir transporto srautų ţemėlapiai, įvertinta skirtingų transporto rūšių įtaka vyraujančiam aplinkos triukšmui Kauno mieste. Modeliuojant transporto srautų sudėties kitimus, prognozuotas triukšmo lygio kitimas pagrindinėse Kauno miesto gatvėse. Pirmą kartą Lietuvoje naudojant GIS, populiacijos lygmenyje nustatyta individuali triukšmo ekspozicija reprodukcinio amţiaus (20-45 m) moterims. Siekiant nustatyti transporto triukšmo įtaką hipertenzijos rizikai į daugiaveiksnę analizę įtraukti ryšį iškreipiantieji veiksniai. Įvertintas gyvenamosios vietos aplinkos ir darbo aplinkos triukšmo lygio ryšys su hipertenzijos rizika. Praktinė darbo reikšmė. Šio tyrimo rezultatai teikia duomenų apie urbanistinės aplinkos triukšmo lygio kitimus, susijusius su transporto srautų intensyvumu ir sudėtimi. Gauti triukšmo kitimo duomenys tarnauja priimant sprendimus, siekiant sumaţinti triukšmą, pagerinti gyvenimo kokybę ir sumaţinti hipertenzinės ligos riziką. Ginamieji disertacijos teiginiai: 1. Triukšmo lygis ir jo kitimas priklauso nuo transporto srautų sudėties ir intensyvumo. 2. Individualaus gyvenamosios aplinkos triukšmo lygio įvertinimas, naudojant GIS, teikia duomenų prognozuojant triukšmo sukeliamas pasekmes hipertenzijos plitimui. 3. Gyvenamosios aplinkos transporto keliamas triukšmas didina 20-45 m. amţiaus moterų hipertenzijos riziką. 4. Hipertenzijos rizika didėja, kai triukšmingoje aplinkoje gyvenančios moterys dirba triukšmingoje aplinkoje. Disertacinio darbo aprobavimas ir publikacijos. Pagrindiniai disertacijos teiginiai pristatyti ir aprobuoti 3 mokslinėse konferencijose Lietuvoje. Disertacijos tema paskelbtos 5 mokslinės publikacijos, iš kurių 2 ISI WOS leidiniuose, 1 straipsnis referuojamas TDB bazėse, 2 straipsniai testiniuose ir periodiniuose leidiniuose.

IŠVADOS

1. Naudojat triukšmo sklaidos analizę, nustatyta, kad vidutinis paros triukšmo lygis Kauno seniūnijose svyruoja nuo 65,8 dBA iki 70,7 dBA, o triukšmo lygio kitimas tiesiogiai priklauso nuo transporto srautų intensyvumo ir sudėties. Stipriausias ryšys tarp transporto intensyvumo ir vidutinio dienos triukšmo lygio buvo Eigulių (r=0,89), Petrašiūnų (r=0,88), Vilijampolės (r=0,89) ir Šilainių (r=0,87) seniūnijose. Didţiausią įtaką triukšmo lygiui turi lengvieji automobiliai: dienos ir nakties metu koreliacijos koeficientas buvo apie 0,70 (p<0,005), o vakaro metu – 0,66 (p<0,005). 2. Krovinio transporto įtaka bendram triukšmo lygio didėjimui didesnė maţo transporto intensyvumo gatvėse. Gatvėse, kuriose transporto srautas dienos metu didesnis nei 500 aut./val., krovininio transporto srautui padidėjus dvigubai, triukšmas padidėtų beveik 1 dBA (p<0,05), o esant srautui maţesniam nei 300 aut./val., triukšmo lygis padidėtų 1,56 dBA (p<0,05). 3. Nustatyta, kad didėjant individualiai ekspozicijai triukšmu, didėjo moterų hipertenzijos rizika. Gyvenančioms triukšmingiausioje aplinkoje (≥61 dBA) rizika padidėja iki 36 proc. (1,36; 95 proc. PI 0,86-2,15) lyginant su maţos (≤50 dBA) ekspozicijos veiktomis moterimis. 4. Didţiausia hipertenzijos rizika stebėta vyresnėms moterims: kontroliuojant ryšį iškreipiančiųjų veiksnių įtaką, 30-45 metų moterims, gyvenančios didţiausioje triukšmo ekspozicijos zonoje (>61 dBA), hipertenzijos rizika padidėja apie 2 kartus (SGS 1,81; 95 proc. PI 1,02-3,22). 5. Hipertenzijos rizika didėja, kai triukšmingoje aplinkoje gyvenančios moterys dirba triukšmingoje aplinkoje. 20-45 metų moterims paros triukšmo lygis ≥61 dBA ir triukšmas darbe turėjo tendenciją didinti hipertenzijos riziką iki 70 proc. (SGS 1,70, 95 proc. PI 0,88-3,28), o vyresnėms nei 30 metų moterims – 2 kartus (SGS 2,03; 95 proc. PI 0,87-4,74). 6. Ilgalaikis (≥10 metų) triukšmo poveikis gyvenamojoje aplinkoje turi tendenciją didinti hipertenzijos riziką. 30-45 metų moterims, gyvenančioms didţiausioje triukšmo lygio zonoje ši rizika gali padidėti daugiau kaip 2 kartus (SGS 2,18; 95 proc. PI 0,74-6,38). 7. Vidutinio paros triukšmo lygio padidėjimas 10 dBA 30-45 metų moterims padidintų hipertenzijos riziką 20 proc. (SGS 1,20; 95 proc. PI 0,92-1,56), o 20-45 metų moterims – 15 proc. (SGS 1,15; 95 proc. PI 0,97-1,36). 8. Gyvenamojoje aplinkoje sumaţinus transporto keliamą triukšmą, sumaţėtų hipertenzijos rizika, pagerėtų gyvenimo kokybė.

About the author Inga Bendokienė was born in Šakiai district, on 22nd of February, 1981. Graduated Šakiai “Ţiburys” gymnasium in 1999, and entered Vytautas Magnus University. Graduated Faculty of Natural Sciences of Vytautas Magnus University and gained bachelor degree in Environmental Sciences in 2003. Graduated Faculty of Natural Sciences of Vytautas Magnus University and gained master degree in Environmental management in 2005. During 2005-2011 was PhD student in the Vytautas Magnus University. From 2005 works as a specialist in the Environmental protection division of Kaunas municipality. Inga Bendokienė is married, has a four year old son.

Trumpos žinios apie autorę Inga Bendokienė gimė 1981 m. vasario 22 d. Šakių raj. 1999 m. baigė Šakių ,,Ţiburio“ gimnaziją. 2003 m. įgijo aplinkotyros bakalauro kvalifikacinį laipsnį Vytauto Didţiojo universiteto Gamtos mokslų fakultete. 2005 m. įgijo aplinkosaugos organizavimo magistro kvalifikacinį laipsnį Vytauto Didţiojo universiteto Gamtos mokslų fakultete. 2005-2011 metais studijavo Vytauto Didţiojo universiteto doktorantūroje. Nuo 2005 m. dirba Kauno miesto savivaldybės Aplinkos apsaugos skyriuje (vyriausioji specialistė). Inga Bendokienė ištekėjusi, turi ketverių metų sūnų.