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|Year : 2023
: 25 | Issue : 117 | Page
|The Impact of Aircraft Noise on the Cognitive Function of Elementary School Students in Korea
Kiook Baek1, Chulyong Park2, Joon Sakong3
1 Department of Occupational and Environmental Medicine, Yeungnam University Hospital, Daegu, Republic of Korea
2 Department of Occupational and Environmental Medicine, Yeungnam University Hospital, Daegu; Department of Preventive Medicine and Public Health, Collage of Medicine, Yeungnam University, Daegu, Republic of Korea
3 Department of Occupational and Environmental Medicine, Yeungnam University Hospital, Daegu; 2Department of Preventive Medicine and Public Health, Collage of Medicine, Yeungnam University, Daegu, Republic of Korea
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|Date of Submission||02-Dec-2022|
|Date of Decision||06-Feb-2023|
|Date of Acceptance||21-Feb-2023|
|Date of Web Publication||10-May-2023|
Background: This study evaluated the effects of chronic exposure to aircraft noise on the cognitive functions of Korean elementary school students attending an elementary school around a military airfield and clarified the relationship between noise exposure and cognitive functions. Methods: Five schools with average weight equivalent continuous perceived noise levels (WECPNL) of ≥75 dB were selected from four regions in Korea. Each of these schools was matched with a non-exposed school. The Korean Intelligence Test Primary (KIT-P) was used to measure the scores of four subcategories and the intelligence quotient (IQ). The noise exposure groups were divided into high-exposure (WECPNL ≥ 80 dB) and medium-exposure (75 ≤ WECPNL < 80) groups. The period of exposure during the school year was collected. A linear mixed model, with matched pairs of schools, was used for statistical analysis. Results: In the multivariable linear mixed model adjusted by possible confounders, the reasoning score was significantly lower in the high-exposure group of students than in the no-exposure group. Other scores and IQ were lower in the noise exposure groups, although these were not statistically significant. The duration of exposure did not show a significant relationship with cognitive functions. Conclusion: Chronic exposure to noise from military airfields may influence the cognitive functions, thereby reducing the learning performance of Korean children.
Keywords: airplane, children, environmental noise, cognition
|How to cite this article:|
Baek K, Park C, Sakong J. The Impact of Aircraft Noise on the Cognitive Function of Elementary School Students in Korea. Noise Health 2023;25:83-91
| Introduction|| |
Several studies have shown that exposure to noise can negatively impact children’s cognitive performance.,,,, Children, in their formative years for physical and psychological development, are particularly at risk from excess noise exposure due to their lack of ability to adjust to external factors. Previously, exposures to noise were reported to have an effect on various cognitive functions, such as attention, memory, reading performance,, and concentration. Children affected by noise exposure have been found to have weakened problem-solving abilities, increased distractibility and behavioral problems, as well as decreased reading ability., The mechanisms by which noise affects cognition are still unclear, but hypotheses include direct biological effects, indirect functional effects such as teacher and student frustration, learned helplessness, and increased arousal.
In Korea, numerous military airfields and shooting ranges located near many schools and also residential areas pose a unique threat to public health. Aircraft noise is known to cause more disturbance than traffic noise, even at equivalent decibel levels. As a result, the impacts of aircraft noise, specifically on children’s cognitive functions, have received more attention in research than the effects of road traffic noise. In addition, military aircraft noise exhibits characteristics of high-intensity activity during specific periods, particularly during working hours, as compared to the more evenly distributed noise generated by civil aircraft. Generally, the military aircraft produce higher noise levels compared to civil aircraft, and its intensity increases when multiple aircraft depart and arrive in close succession. Therefore, it is widely acknowledged that military aircraft generate a higher level of annoyance compared to private airports. Given these attributes, it can be inferred that students who primarily attend school during working hours are likely to experience annoyance as well. Previous studies have reported that because of different noise generation patterns the aircraft noise and road traffic noise affect different aspects of children’s memory. A previous report also indicated that the effect of aircraft noise is moderated by sociocultural factors and affects cognitive ability. To date, the effects of aircraft noise exposure on cognitive functions have been observed relatively consistently in several studies. However, the specific cognitive functions affected, and the exact mechanisms, are unclear. Thus, research studies that consider the noise and social characteristics of each region are needed.
To promote airport noise-related policies in the Republic of Korea, an independent study is needed that reflects the local characteristics of the impact of aircraft noise on children’s cognitive function. Therefore, in this study, we assessed the relationship between noise exposure and the cognitive functions of elementary school students near a Korean military airport.
| Materials and methods|| |
The classification of exposure and control groups
Among all military airfields in Republic of Korea four were selected for this study having a weighted equivalent continuous perceived noise level (WECPNL) greater than 75 dB . The WECPNL was a unit introduced by the International Civil Aviation Organization (ICAO) as a measure of long-term exposure to multiple aircraft noise. This unit is utilized in Korea and China and is determined by weighting the number of flights per hour to the average peak noise level of aircraft. The calculation further accounts for the temporal distribution of flights by weighting the number of flights by the time of occurrence. Further details on the calculation methodology can be found in cited literature. The target schools were selected based on the noise regulation data of the regions surrounding the military aircraft airport, using pre-existing information for classification of the areas. Our target location with military aircraft airport was located in Daegu, Gwangju, Suwon, and Hoengseong [Figure 1]. This study compared cognitive functions between the noised-exposed and control groups cross-sectionally. Of note, cognitive functions may also be affected by external factors other than noise. Therefore, a non-exposed control group was selected to match an exposed group school. To control for confounding factors, school selection, region, size, and socioeconomic status were considered when the noise-unexposed schools were matched with the noise-exposed schools. The noise level measurements and methods for each school are presented in Supplementary Material 1, which includes the WECPNL and Leq levels.
|Figure 1 Locations of study region with aircraft base on the Korean peninsula.|
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To evaluate the average noise level, noise from military aircraft was measured at each nearby elementary school for 1 week. Schools that were situated in areas previously classified as ≥80 WECPNL were marked as the high-exposure group, and those in areas corresponding to 75 ≤ WECPNL <80 were as medium-exposure group. The unexposed schools were selected schools without aircraft noise and designated as the non-exposure group.
Sample size calculation
The aim of the initial study was to ascertain the impact of noise on the schools affected by such noise. The sample size was calculated to secure a test power of 80% and a significance level of 0.05 when comparing the two groups. This calculation was based on the requirement to identify a statistical difference of 5 points in Intelligence Quotient (IQ) score, with a standard deviation of 10 points. Assuming that four confounding variables were included, 10% of the sample per confounding variable was added. Furthermore, assuming a dropout rate of 20%, 100/(100 − dropout rate) was multiplied for the target sample. Based on our calculation, the target sample size was set to 109.8. Further, to ensure the sample size has adequate power to identify the IQ 5 differences between each exposed school and the control school, a target sample size of 109.8 students per school was designated. In this study, we acquired raw data and integrated it, and then reanalyzed it using a mixed model approach to determine the effect of noise on each school. As a result, the sample size calculations for the mixed model analysis were not performed beforehand.
Data collection took place from June 2004 to February 2005. Before the field study commenced, cooperation from the students was sought from the schools selected based on pre-existing noise information (WECPNL) and regional data. The study was conducted in schools that consented to participate. The purpose and details of the study were explained to these schools, and participants were openly recruited. The study was conducted on voluntary participants who provided written consent from both themselves and their parents. The selected subjects of this study were all fifth graders who are 10–11 years old, depending on their birthday. In Republic of Korea, 5th-grade students in elementary schools typically have classes from 9:00 AM to 3:00 PM.
The exclusion criteria in both the exposure and control groups were as follows: (1) students with any diseases associated with clinically diagnosed conductive hearing impairment or mental retardation. (2) Students with perforated tympanic membrane or otitis media on otoscopic examination. (3) Students with markedly different hearing between the left and right ears. (4) Students whose residence was located near an airfield for control groups.
General characteristics survey
Sex, maternal education level, noise exposure period after entering elementary school, and subjective socioeconomic status (SES) were investigated. The noise exposure period was calculated in units of months by checking the question “Since when did the student attend this school?” The SES item was categorized as “very poor,” “poor,” “average,” and “good.”
Evaluation of cognitive functions
The Korean Intelligence Test-Primary (KIT-P) was used to evaluate cognitive functions. The KIT-P is widely used as a standardized and objective intelligence test similar to the intelligence quotient (IQ) test, for evaluating the overall learning performance, problem-solving skills, and adaptability of elementary school students in Korea, and it has been used in various epidemiologic studies.,
Based on the multifactorial theory of intelligence, the KIT-P comprises four subcategories: vocabulary, reasoning, numeracy, and perception. The scores of the subcategories are summed and represented by a single IQ score. The vocabulary section of the test measures the ability to understand the meaning of words by matching synonyms and completing phrases. Reasoning measures logical thinking, which consists of reasoning of numbers and logical relationships of sequences. The numeracy section tests the ability to perform arithmetic operations and compare quantitative differences. Lastly, perceptual space skills (plane, three-dimensional structure, and direction) are tested in the perception section. In detail, the KIT-P’s vocabulary test consists of finding synonyms and also applying the most appropriate word in the blank space of a given sentence. Regarding reasoning, it is divided into language reasoning and numeric sequence reasoning. Language reasoning is tested by identifying four words with common properties among five given words. The numeric reasoning test involves inferring the next number in a sequence. Regarding numeracy, it consists of four basic arithmetic operations and application problems, such as the ability to handle numbers, simple quantitative problems, and recognition of quantitative differences. Perceptual ability is the ability to grasp the spatial position or spatial relationship, such as plane, solid, direction, etc., and involves finding a figure that can make a foursquare when matched with the presented figure. The KIT-P consists of 100 questions, with 30 questions to test vocabulary, 30 questions to test reasoning (15 each for language and numeric sequence, respectively), 20 questions to test numeracy, and 20 questions to test perception. The KIT-P requires approximately 40 minutes to complete. For our study, its administration was supervised by a licensed clinical psychologist. The examiner entered the examination room prepared by each school to encourage students to take the test, creating an environment where students could write their answers comfortably and without excessive anxiety. At the start of the test, the examiner provided necessary instructions and demonstrated how to record answers on the answer sheet through examples. During the example solving process, any questions from the students were answered, but no questions were answered once the test had started and the students had begun writing their answers. The score of each subcategory was expressed as a percentile rank of the general population. Scores from all subcategories were summed up to a single IQ score that was normalized with a mean of 100 and a standard deviation of 15, from the general population data.
The general characteristics, including gender, region, subjective SES, and maternal education level were collected for the exposure and control groups. The P value was calculated using the chi-square test for categorical variables. The average scores of each subcategory and IQ were also calculated according to the level and duration of aircraft noise. The t-test was used to compare the two groups, and analysis of variance was performed to compare more than two groups. To assess the association of exposure period and each scores of KIT-P, Spearman analysis was performed.
A linear mixed model was used for multivariate analysis of the exposure period and exposure status of the school, considering the matched pairs and confounders. In the mixed linear models, five school pairs were assigned as random effects. Since the level of exposure to noise, which is the most important fixed effect being investigated, was considered equal for all students by each school, when considering each school as a random effect, the effect of the fixed effect could be diluted by collinearity, so it was not considered a random effect or second-level cluster. As dependent variables, the scores of each KIT-P and IQ were used. First, the association between exposure period and each score of KIT-P was analyzed. Then, the variable exposure status was added. In Model 1, only the variable(s) with interests were set as fixed effects, whereas in Model 2, theoretical confounders including maternal education level, subjective SES, and sex were additionally adjusted. Any students with a missing confounder were excluded only from the relevant model. Subgroup analysis was done for sex and SES.
Statistical analysis was performed using SPSS 26.00 (IBM, USA) and R project (https://r-project.org). The R packages “lme4” and “performance” were utilized for mixed model calculation. A P value <0.05 was considered statistically significant. To avoid missing variables that might be significant in multivariate analysis, a relevance level of P value <0.25 was set for univariate analysis when determining the inclusion of variables in multivariate analysis.
| Results|| |
Five nearby elementary schools were selected as the noise-exposed group, and another five schools were selected as the control group. There were 290 and 190 students in the high-exposure and mid-exposure groups, respectively, and 509 unexposed students in the control group. In total, 989 students participated in this study. Detailed participant characteristics, including region, sex, maternal education level, and subjective SES are presented in [Table 1]. There were significant differences in the proportions of region, maternal education level, and subjective socioeconomic status among the four groups. Three missing values exist in the mother’s education level.
When the high-exposure group and the high-exposure control group were compared, reasoning, vocabulary, numeracy, perception, and IQ scores were all lower in the exposed group, but these were not statistically significant. When comparing the medium-exposure group and the medium-exposure control group, all values of reasoning ability, vocabulary, numeracy, perception, and IQ were lower in the exposure group, but these were not statistically significant. When comparing the high-exposure group, the medium-exposure group, and the non-exposure group, the vocabulary and IQ scores of the high-exposure group were significantly lower than those of the control group [Table 2].
|Table 2 The results of cognitive test among participants by exposure group|
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The exposure period did not show a statistically significant relationship with the vocabulary, reasoning, numeracy, perception, and IQ scores [Table 3]. However, the exposure period, and IQ (P = 0.183) and vocabulary (P = 0.166) scores showed potentially significant correlation (P < 0.25) in Spearman analysis. As a result, they were included in the final models with exposure status of school as independent variable.
|Table 3 Correlation matrix of exposure period and cognitive function test|
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In analysis with both exposure period and exposure status, Model 1, in which exposure period and exposure group were used as a fixed effect and five pairs of schools were included as a random effect, the exposure period did not show a significant relationship with reasoning, vocabulary, IQ, numeracy, and perception scores. As for the difference according to the exposure group, the reasoning score (−3.86 points, 95% CI −6.81 to −0.91) and IQ (−5.51 points, 95% CI −10.48 to −0.53) was found to be significantly lower in the high-exposure group than in the control group. In Model 2, which was adjusted for sex, maternal education level, and SES, only the reasoning score was significantly lower in the high-exposure group compared with that in the control score (−3.41 points, 95% CI −6.32 to −0.51). In the other KIT-P parameters including the total score, the medium-exposure group and the high-exposure group showed consistently lower results than the control group, but these were not statistically significant [Table 4]. The diagnostic statistics of each model were presented in Supplementary material 2. In subgroup analysis, high noise level exposure group shows significantly lower reasoning level in female and high SES group (Supplementary material 3).
|Table 4 Multivariable linear mixed model of aircraft noise exposure and cognitive function test adjusted for possible confounders|
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| Discussion|| |
To evaluate the association of aircraft noise with children’s cognitive function, the KIT-P subcategory, and IQ test scores were compared between the exposed and control groups. Initially, comparisons between each exposed and control school were performed, but no significant difference was observed in the univariate analysis. However, when all control schools were combined into one group, significant differences were observed in some variables. After adjusting for potential confounders and taking the exposure period into account, the mixed model approach, which can use all of the participants’ data in a single model while adjusting for potential confounders, revealed a significant negative association between reasoning score and noise exposure. The results indicated that the high-noise exposed group had significantly lower reasoning scores compared to the control group. Overall, the findings indicate that cognitive functions were associated with aircraft noise.
The World Health Organization (WHO) has been compiling and presenting evidence of the link between environmental noise and cognitive impairment in children. In 2018, after reviewing previous evidence on aircraft noise, WHO found that it can negatively impact children’s reading and oral comprehension skills and strongly recommended regulating noise levels near airports.
Since the associated mechanisms remain unclear, Evans and Maxwell suggested that decreased auditory discrimination and language perception, which are parameters of reading comprehension, are the main causes for decreased reading ability. Previous studies have reported that chronic exposure to aircraft noise influences central information processing and cognitive functions including reading, memory, language skills, and concentration.,,,, As one of the representative large-scale studies, the RANCH project (road traffic and aircraft noise exposure and children’s cognition and health: exposure–effect relationships and combined effects) conducted in the United Kingdom reported that exposures to chronic aircraft noise affect reading comprehension and recognition memory., In a meta-analysis of epidemiological studies on children’s reading, reading comprehension was statistically significantly lower in the chronic aircraft noise-exposed group., Notably, a decrease in reading skills has been found in the groups exposed to aircraft noise. The results of the different studies are relatively consistent but the mechanisms are unclear. In the NORAH study (noise-related annoyance, cognition, and health study) conducted in Germany, a decrease in reading skills with increased noise exposure was observed, but a decrease in verbal precursors, such as phonological processing and listening comprehension, was not identified. In the same study, a decrease in word and text reading skills was identified along with a decrease in reading ability in the group exposed to aircraft noise, but a decrease in sentence reading and verbal tasks (epidosic memory, short-term memory, rapid naming, phonological awareness, speech perception) was not observed. In another study conducted among children living near to the old and new Munich International Airport, noise affected speech perception, but this did not mediate a decline in reading skills. Reading is a skill that requires the coordination of various abilities in an integrated manner. The mechanism by which aircraft noises reduce reading abilities, and affect the pathways relating to the different abilities that contribute to reading, are still only proposed at the hypothetical level. Although reading skills were not measured directly in this study, the KIT-P was used to assess cognitive functions included items on language skill; vocabulary and reasoning skills (15 out of 30 questions were language reasoning questions); as index scores related to language. From the KIT-P, there was no difference between the exposed group and the non-exposed group in vocabulary skills, whereas a decrease in the reasoning score was evident. Of the different cognitive functions, only certain abilities were affected. The identification of a deficit in a specific area can be a clue to determining the mechanism by how chronic noise exposures were associated with reading skills and cognitive functions.
The subgroup analysis found significant associations between noise and reasoning score only in female and high SES participants. This suggests that certain groups may be more sensitive to noise or that factors such as sex and SES may moderate its effect. The relationship between noise and cognitive function remains unclear, with some studies showing that women perform worse in simple arithmetic in noisy environments, while others show no effect of sex. Further research is needed to determine if sex and SES act as moderating factors.
In this study, there was no significant difference between the exposed group and the non-exposed group in numeracy ability. This finding is consistent with the results of previous studies., For perception, no significant differences according to different aircraft noise exposure levels were observed in previous studies using embedded figures task and block design test. In this study, we did not detect a significant difference in perception either. Moreover, there was no significant difference in the total IQ score between the exposed and unexposed groups. Furthermore, in this study, students exposed to the ≥80 WECPNL level of noise did not show a significant score difference compared with students in the control group. This finding suggests that chronic aircraft noise exposure should be at a fairly high level to have a statistically identifiable effect on cognitive functions. Additionally, high-level aircraft noise exposure seems to associated with only a specific area of cognition, rather than the overall cognitive functions.
In this study, the correlation between exposure duration and cognitive function was statistically insignificant. There is no general consensus on the minimum period of noise exposure that may influence cognitive functions. However, according to the Munich airport studies, which were generally viewed with high validity in this field, suggested that 2 years of chronic noise exposure may be enough to have cumulative effects on cognitive functions. They reported that the reading ability of students from nearby schools slightly improved after shutting down the airport, which remained the same until 1 year after the construction of the new airport, and the reading ability declined significantly 2 years after that. It is difficult to estimate the minimum period of noise exposure that influences cognitive functions since pre-school noise exposure was not considered in this study. Although not reported, in this study, there was no significant difference in the cognitive function scores based on a 2-year exposure period, as categorical variable instead of continuous variable. Since this study did not investigate the effect of stopping noise exposure on cognition, a separate study is needed to determine whether the effect is of an acute or subacute nature and reversible following the cessation of noise exposure, or whether the effect is of a chronic nature, and the adverse effect on cognition persists even after noise exposure is stopped.
This study has several limitations, including being a cross-sectional study that cannot fully explain the mechanisms or establish a causal relationship. At the research design stage schools were selected based on previously designated noise information for compensation, and the control group was selected accordingly. Only areas with airport noise exposure above WECPNL 75 were considered, and other factors such as school building structure or road traffic effects were not taken into account. For individual assessment of participants, we did not assess individual aircraft noise exposure before participants started elementary school or other potential sources of noise. Participants’ subjective perception or sensitivity to noise, which can affect the relationship between noise and cognitive function, was not evaluated. This study has not assessed individuals’ subjective perception or sensitivity to noise, which could play a role in moderating the relationship between noise and cognitive function. Another limitation in individual level is that only the index scores and total score results of the KIT-P test suggest only four area of cognitive function. It would be important to measure more detailed part of cognition to determine which areas are associated. This study had a limitation in its noise exposure classification as it relied only on WECPNL and did not consider event-based or statistical indicators. WECPNL, which considers the number of noise occurrences and duration, is an energy-based indicator, but does not fully represent the dynamic and temporal nature of noise and may underestimate its health effects. Further studies should include a larger number of students and consider the effects of noise from various sources with a longitudinal study design, and should also evaluate the minimum level and duration of noise exposure for influencing cognitive functions.
The strength of the study methodology lies in the attempt to minimize differences in baseline cognitive function between the exposed and control groups by matching schools within the same city. The final multivariable mixed model considered the impact of confounders such as maternal educational background and SES, though the number of confounders considered is limited.
This study, which reanalyzes previous results with updated statistical methods, shows that chronic aircraft noise exposure has a significant impact on specific cognitive areas. The findings, which show a negative association between noise and reasoning skills, suggest that noise may also have a negative effect on learning abilities. These results, along with others, may provide evidence for discussions about the suitability of having schools near military airfields. Currently, damage from military airfield noise is compensated for by the Military Airfields And Military Firing Ranges Noise Prevention And Compensation Act in South Korea. However, compensation for schools was omitted in the military noise compensation-related legislation enacted in November 2020. There is a lack of strategies to address the learning disruptions caused by noise, with current efforts primarily focused on noise reduction measures such as installing noise-reduction windows. Therefore, not only noise prevention measures be implemented in schools near military airports. Therefore, not only should schools near military airfields implement noise prevention measures, but measures to substantially improve the study environment for students, such as additional teachers, reduced class sizes, adjusted learning times, and supplementary resources, should also be considered.
K.B.: Conceptualization, Methodology, Resources, Writing − Original Draft.
C.B.: Formal analysis, Writing − Review & Editing, Methodology.
J.S.: Data Curation, Investigation, Writing − Review & Editing, Supervision, Funding acquisition, Project administration.
Financial support and sponsorship
This work was supported by the Daegu Metropolitan Office of Education and 2022 Yeungnam University Research Grant (Grand No. 222A480008).Conflicts of interest
There are no conflicts of interest.
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Department of Preventive Medicine and Public Health, College of Medicine, Yeungnam University, 170 Hyeonchung-ro, Nam-gu, Daegu 42415
Republic of Korea
Source of Support: None, Conflict of Interest: None
[Table 1], [Table 2], [Table 3], [Table 4]