The geospatial distribution and mapping of urban noise pollution in Astana city

Abstract

Over the past three decades, noise pollution has become a noticeable phenomenon in cities. Noise is an environmental stressor that adversely affects interpersonal communication, diminishes quality of life, impairs attention and cognitive function, and triggers emotional responses, resulting in noise-related discomfort. According to WHO (World Health Organization) guidelines, any noise above a threshold value of 55 dB is considered to be harmful to human health. Excessive noise can cause stress, sleep disturbances, hearing loss, and other health problems, impacting both psychological and physical well-being. Astana, the capital of Kazakhstan, experiences varying noise levels influenced by urban development, traffic, construction, and industrial activity. In central areas and along major roads, noise levels often exceed recommended limits, especially during peak traffic hours. This study provides quantitative evaluation of geospatial distributions of noise levels in the rapidly growing city of Astana, Kazakhstan. Over the entire measurement period, from October 22 to November 22, 2024 the average noise level in Astana was recorded at 73 dB. In this study 840 noise pollution measurements were conducted at 20 different noise measurement stations (NM1-NM20) in Astana. Data were collected at various times of day—morning (8:00–9:30), midday (13:00–14:30), and evening (17:30–19:00)—using the Sound Level Meter (SLM) UT352 and SLM NIOSH mobile app. The average discrepancy between measurements taken with the NIOSH SLM mobile app and the UT 352 SLM device ranges from 0.13 to 6.86 dB, depending on location. Noise measurements were taken in different geographic locations, including central business districts with heavy traffic, as well as residential neighborhoods where background noise is primarily influenced by vehicle traffic and the activities of residents in Astana. In essence, the city’s rapid urbanization is complicated by air and noise pollution, while its extreme climate exacerbates the environmental challenges. The objective of the study is to evaluate noise effects and analyze the characteristics of background noise in the urban environment, with the aim of developing strategies to enhance the urban environmental conditions in Astana city, Kazakhstan.

Keywords

Noise pollution SLM GIS geospatial distributions meteorological parameters urbanization Astana

Introduction

Noise pollution refers to any alteration in the physical characteristics of the environment caused by sounds, whether pleasant or unpleasant, that can harm the health, and well-being of populations.^1,2 The World Health Organization (WHO 2011) classifies noise levels above 55 decibels (dB) as noise pollution. For comparison, a normal conversation usually measures around 60 dB, while bus engines can produce noise levels ranging from 80 to 95 dB.³ Noise is recognized as an environmental stressor that negatively affects interpersonal communication, well-being, quality of life, attention, cognitive function, and triggers emotional responses, leading to noise discomfort.^4–6 A very prolonged exposure to strong sound has the long, spiral-shaped basilar membrane in the cochlea of the inner ear vibrate. These types of cells are not able to renew themselves in humans, which leads to a severe hearing loss that can be partial or turn into deafness. The degree of hearing loss is influenced by the length and the intense noise of a sound.⁷ Environmental noise pollution is considered a widespread stressor that increases the risk of hearing loss, cardiovascular diseases, mental stress, and dementia.^8–10 Prolonged or excessive noise exposure can lead to aggression, difficulty in communication, low mood, confusion, headaches, elevated blood pressure, sleep disturbances, anxiety, and various other physical and mental health issues.^11–14

In 2018, it was estimated that more than 100 million people in Europe were exposed to road traffic noise levels exceeding the WHO’s recommended thresholds of L_den¹ 53 dB(A) and L_night² 45 dB(A).^15,16 Noise pollution has various social and economic impacts which are usually not taken seriously. There is a decrease in property prices in noisy areas, e.g. those next to highways or in industrial zones, making a loss of money for the owners of the property and investors. Adding to the above, noise can have far-reaching implications on social interactions especially among the sensitive populations, such as the elderly, and as a result, it leads to social isolation and more burdens on the healthcare and social service community. Besides, noise pollution hampers overall economic productivity by lowering employee satisfaction and performance, especially in office environments and industries, the goal of which is to be extremely precise and require high concentration.^17,18 Urban areas implement smart pollution control management and monitoring systems to prevent noise pollution, collecting data from these systems to take the necessary actions to address and reduce noise pollution.¹⁹ For noise measurement, many studies have used both Class 1 and Class 2 SLMs^20,21 and currently, many studies use mobile SLM apps, which are available on mobile phones.^22–24 Noise mapping by GIS applications is considered one of the most effective methods for understanding environmental noise and helps in assessing noise exposure and supports the development and implementation of sound control policies.^25,26

Noise regulations differ from state to state and vary between cities, with some cities not having any noise laws in place at all. In Kazakhstan, Order No. 15 of the Ministry of Health of the Republic of Kazakhstan, dated February 16, 2022, titled ‘On Approval of Hygienic Standards for Physical Factors Affecting Humans,’ sets the regulatory requirements for noise and other environmental physical factors (daytime ∼55 dB, nighttime ∼45 dB).

To date, no comprehensive analysis of noise pollution has been conducted in Astana. The aim of the study are: (1) collecting noise pollution data during morning, afternoon, and evening across various locations in Astana city; Investigating the geographic distribution of noise pollution to identify areas with the highest and lowest levels; (2) comparing the gathered data from different locations and measurement points, and evaluating the noise levels in accordance with international standards. Our measurements are not intended for compliance or regulatory purposes, we followed certain guidelines or standards set by WHO,⁴ and our results will be compared to assessing the impact of noise pollution in certain parts of Astana city. The findings can contribute to a better understanding of the environmental issues and support the development of targeted strategies for noise mitigation and urban noise management in Astana city.

Methodology

Study area

Astana, located at 51° 10′ N, 71° 26′ E, is the capital of Kazakhstan and lies in the center of the Eurasian continent, has experienced rapid growth over the past 25 years. As a uniquely centralized city built on flat terrain, Astana experiences extreme temperature fluctuations—from −40°C in winter to +40°C in summer. Serving as an administrative hub, it is both vibrant with activity and constantly evolving through rapid development and ongoing construction.^27,28 Given the city’s young age, fast-paced growth, and significant urban expansion, this study addresses a critical gap in existing research on noise pollution. Noise pollution in such an environment can have substantial effects on public health, quality of life, and the broader environment, making it an important area for investigation. From October 22 to November 22, 2024 we analyzed the impact of geographic location type on environmental conditions across 20 sites (NM1–NM20) in Astana (Figure 1). The sites were categorized as residential areas (NM4, NM7, NM8, NM10, NM11), parks (NM5, NM12, NM18, NM20), road junctions with high traffic congestion (NM1, NM3, NM13, NM14, NM17), social activity sites near public facilities (NM15, NM16), and control sites with minimal direct emissions (NM2, NM5, NM9). These locations represent areas influenced by diverse noise pollution sources, including high-traffic roadways, public transport hubs, construction zones, residential neighborhoods, commercial centers, and green spaces.

Figure 1.

Map of the study site. (This figure was created with ArcGIS 10.3; background map adapted from basemaps).

Data collection and noise measurements

Noise pollution levels were measured in decibels (dB) using a professional grade UT352 Sound Level Meter (SLM) and the NIOSH SLM mobile app (Figure 2). The 840 noise pollution measurements were conducted at 20 different locations (NM1-NM20) in Astana. Spot measurements of the instantaneous noise levels at 20 sites were carried out during the morning (8:00 to 09:30), midday (13:00 to 14:30) and evening (17:30 to 19:00) periods between 22nd October to 22nd November 2024 using the UT352 Sound Level Meter (SLM) and the NIOSH SLM mobile app. These instruments allowed for precise recording of noise levels, with measurements linked to specific geographic coordinates and times in Astana city.

Figure 2.

SLM UT352 and SLM NIOSH mobile app during simultaneous measurement.

The SLM UT352 is a Class 2 digital sound level meter compliant with IEC 61672-1 standards, featuring an A-weighting frequency filter, a measurement range of 30–130 dB, and both fast and slow response modes. A foam windscreen was used to protect the microphone from wind-induced distortions. The NIOSH SLM mobile, developed by the National Institute for Occupational Safety and Health (NIOSH), was used on a smartphone with a calibrated external microphone where possible. The app is designed to comply with ANSI S1.4-2014/Part 1 standards when used with proper external microphones. It applies A-weighting and calculates equivalent continuous sound level (Leq) in real time. The smartphone was placed on a stable surface or mounted next to the UT352 at the same height and orientation to ensure consistent exposure to ambient noise conditions.

To ensure the accuracy and reliability of noise measurements, calibration and environmental precautions were implemented for both the professional sound level meter (SLM UT352) and the NIOSH-approved mobile application. Prior to each measurement session, the SLM UT352 was calibrated using an acoustic calibrator at 94 dB to verify proper functioning. For the mobile app, a reference calibration was performed using the simultaneously recorded values from the UT352 to align the smartphone microphone response. To minimize the impact of wind noise—which can significantly affect sound pressure readings—both devices were equipped with foam windshields. Additionally, measurements were conducted with the devices positioned away from direct wind exposure, using physical barriers or orienting the microphones at a 90-degree angle to the wind direction when necessary. These precautions helped ensure consistency and comparability across the two measurement methods.

Inverse Distance Weighting (IDW) interpolation method

The Inverse Distance Weighting (IDW) method is widely used in environmental studies to interpolate spatial data, including noise pollution measurements, as it provides reliable estimates by assigning more weight to values from nearby locations. In the Inverse Distance Weighting (IDW) interpolation method, a weight is assigned to each measured point. The value of this weight depends on the distance between the measured point and an unknown point. These weights are regulated based on a power parameter. As the power value increases, the influence of distant points decreases. Conversely, a lower power value results in a more uniform distribution of weights among neighboring points.

Results and discussion

The values of SLM UTC352 and SLM NIOSH mobile app

The noise levels differ based on the location of the measurement points (Table 1). Generally, the values recorded by the SLM UT352 are higher than those from the SLM NIOSH mobile app (Table 1). The discrepancies between the two devices are most prominent at locations like NM7, NM8, NM12, and NM17. However, at certain points, such as NM11 and NM20, the data from both devices are nearly identical (Table 1).

Table 1.

Coordinates of noise measurements and average noise values in Astana city.

Measurement points	Geographic coordinates	Average values of SLM UT352 (dB)	Average values of SLM NIOSH mobile app (dB)	Difference in values between SLM UT352 and SLM NIOSH mobile app (dB)
NM1 (dB)	51°06′12″N 71°24′21″E	69.65	71.68	2.04
NM2 (dB)	51°05′19″N 71°24′43″E	68.14	70.07	1.93
NM3 (dB)	51°05′32″N 71°25′23″E	68.83	70.92	2.10
NM4 (dB)	51°05′48″N 71°25′00″E	77.68	79.26	1.58
NM5 (dB)	51°06′09″N 71°24′52″E	69.98	72.10	2.12
NM6 (dB)	51°07′15″N 71°25′42″E	66.85	68.05	1.20
NM7 (dB)	51°05′05″N 71°24′28″E	68.85	73.07	4.22
NM8 (dB)	51°06′55″N 71°24′47″E	71.62	72.03	0.42
NM9 (dB)	51°05′21″N 71°24′10″E	73.91	75.61	1.70
NM10 (dB)	51°06′46″N 71°25′59″E	78.35	79.29	0.93
NM11 (dB)	51°05′51″N 71°25′25″E	66.87	69.23	2.36
NM12 (dB)	51°08′15″N 71°26′50″E	52.85	58.47	5.61
NM13 (dB)	51°07′40″N 71°25′51″E	57.02	59.67	2.65
NM14 (dB)	51°07′18″N 71°26′19″E	77.29	78.05	0.76
NM15 (dB)	51°08′29″N 71°27′50″E	78.14	78.27	0.13
NM16 (dB)	51°05′01″N 71°25′13″E	68.22	70.85	2.63
NM17 (dB)	51°05′46″N 71°24′27″E	63.54	66.20	2.66
NM18 (dB)	51°07′56″N 71°27′26″E	79.08	78.37	0.72
NM19 (dB)	51°07′44″N 71°28′08″E	77.84	76.37	1.47
NM20 (dB)	51°07′16″N 71°27′26″E	52.01	58.87	6.86

In addition to mobile apps and fixed systems, portable sound level meters such as the SLM UT352 are crucial for measuring noise. Unlike mobile apps, which can be limited by the accuracy of a smartphone’s microphone, professional sound level meters offer higher precision and more detailed measurements. The largest difference in measurements is observed at location NM20 (6.86 dB). Also, a significant difference was recorded in NM12 (5.61 dB) and NM7 (4.22 dB). The smallest difference is observed at location NM18 (0.72 dB). Another location with a slight difference is NM15 (0.13 dB). In most locations, the difference between the two devices is within 1–3 dB, suggesting that their measurements are relatively consistent. However, at certain locations (NM7, NM12, NM20), there are notable discrepancies, which could be attributed to specific conditions or differences in the device settings (Table 1).

Figure 3 presents a comparison of average noise levels recorded using a SLM UT352 and estimated by the SLM NIOSH mobile app across different monitoring sites. The results indicate a general agreement between the two methods, with notable differences emerging at higher noise levels. At moderate noise levels (below 80 dB), both methods exhibit strong correlation, suggesting that stable environmental conditions contribute to consistent readings. However, at higher noise levels (>85 dB), SLM measurements often exceed NIOSH SLM Mobile app estimates, particularly in areas with intermittent noise sources such as machinery and construction activity. This suggests that the NIOSH model, which averages exposure over time, may not fully capture short-duration, high-intensity noise events. Conversely, location with continuous background noise, such as industrial zones with steady equipment operation, show minimal differences between the two methods. In these cases, the NIOSH model closely approximates real-time SLM readings, reinforcing its reliability for steady-state noise assessment.

Figure 3.

Comparison of average noise levels measured by SLM UT352 and SLM NIOSH mobile app at various monitoring locations.

At all locations, the noise levels are relatively high, frequently surpassing 70 dB. The minimum values range from 40 to 70 dB, suggesting a relatively high baseline noise level across most locations. The results illustrate the trend and correlation between the two methods, with both lines showing similar general patterns but revealing distinct peaks and troughs at specific locations. The chart emphasizes that, despite differences in the absolute values between the two methods, the overall trend remains consistent across location. SLM UT352 measurements tend to fluctuate more due to their real-time, instantaneous nature, whereas the SLM NIOSH mobile app model smooths the data over time, often leading to less variability. This dual-axis chart is useful for understanding how noise levels are captured by the two different methods, highlighting their respective strengths and weaknesses in tracking noise fluctuations and trends across locations.

The scatter plot in Figure 4 illustrates the relationship between the average noise levels measured by SLM UT352 and SLM NIOSH mobile app across various locations. The red dashed line represents the line of perfect correlation, where UT352 SLM and NIOSH SLM mobile app values are equal. Most of the data points fall close to this line, indicating a strong positive correlation between the two measurement methods. However, some locations show noticeable deviations, with the SLM UT352 values being either higher or lower than the corresponding SLM NIOSH mobile app values. This suggests slight variations in the way these two methods capture noise levels, likely due to differences in measurement techniques or environmental factors influencing the readings. The plot provides valuable insight into the consistency of noise level assessments across locations, helping to identify potential outliers or areas where further investigation may be needed to reconcile differences between the two methods.

Figure 4.

Correlation between average noise levels recorded by SLM UT352 and SLM NIOSH mobile app across different locations.

Geographical distribution of noise effects and the influence of time of day on noise levels

Figure 5 presents the average noise levels along with their respective minimum and maximum values for both instruments at various monitoring locations. The blue markers indicate SLM UT352 readings, while the orange markers represent SLM NIOSH mobile app estimates, with error bars capturing the full range of fluctuations. In most locations, the NIOSH model shows a broader range of values, suggesting it accounts for more variations over time, whereas SLM UT352 provides more stable but occasionally lower estimates. Several locations, such as NM6, NM12, NM13 and NM20, exhibit substantial differences between minimum and maximum readings, particularly in the SLM NIOSH mobile app estimates, which may be due to the model’s sensitivity to long-term exposure trends. Conversely, sites like NM5 and NM17 display relatively narrow error bars, indicating consistent noise levels with minimal fluctuation. This figure highlights the importance of considering measurement variability when comparing noise assessment methods, as differences in data collection techniques can influence overall exposure interpretations.

Figure 5.

Measurement ranges of noise levels recorded by UT352 SLM and NIOSH SLM mobile app across different locations.

The heatmap in Figure 6 provides a visual representation of key noise metrics across different locations. It includes the average noise levels (SLM UT352 and SLM NIOSH mobile app) and the variance of those levels for both measurement methods. The color gradient represents the values, with lighter shades indicating higher values and darker shades representing lower ones. The annotations within the heatmap show the exact values for each location, allowing for quick comparison. From the heatmap, we observe that some locations, such as NM10 and NM4, display high average noise levels across both methods, while locations like NM12 and NM20 show lower values. Similarly, locations like NM6 and NM18 have high variance, suggesting more fluctuation in noise levels, whereas other areas, like NM9, exhibit relatively consistent noise measurements. This heatmap serves as a useful tool for identifying patterns and variations in noise levels, highlighting areas that might require further investigation or intervention. Noise measurements were taken in different geographic locations, including central business districts with heavy traffic, as well as residential neighborhoods where background noise is primarily influenced by vehicle traffic and the activities of local residents in Astana. Different points in the city demonstrated variability in noise levels depending on their location and functional purpose. Points located in the city’s central areas showed higher noise levels compared to points in residential areas or the hospital area, confirming the influence of population density and traffic load on noise levels. Noise level interpolation maps were created for 20 noise measurements in Astana city using the IDW method in ArcGIS 10.3 (Figure 7).

Figure 6.

Noise metrics heatmap.

Figure 7.

The noise maps of spatial distribution of noise value using of IDW (Inverse Distance Weighting) for the period from October 22 to November 22, 2024.

Figure 7 illustrates the spatial variations in noise levels throughout Astana city, with the legend indicating a range from 41.78 to 83.26 dB for the period from October 22 to November 22, 2024. The red areas mark noise hotspots where pollution may be particularly problematic. Extended exposure to levels above 70 dB can result in hearing damage, increased stress, and disturbed sleep patterns. If noise levels surpass acceptable thresholds – for example, the WHO recommends keeping residential noise below 55 dB – authorities might need to implement regulations. At noise levels of around 70 dB, profound changes occur in the nervous system, including mental illness, as well as changes in vision, hearing and blood composition. The levels of noise in the band 55–65 dB are a source for distraction and difficulties to invest attention and concentration, the two of which are crucial in an environment of work and study. Noise in excess of 65 dB over time causes the person to tire more, raises blood pressure levels, and increases the heart rate, thus posing an increased risk of cardiovascular diseases. The map shows low noise zones (41.78–53.49 dB, depicted in green) predominantly in the northern and southeastern regions, which likely correspond to residential areas, parks, or regions with lower traffic and human activity. Moderate noise zones (53.49–68.46 dB, shown in yellow and orange) cover a large part of the city and probably represent mixed-use areas characterized by moderate traffic, urban activities, and some industrial or commercial influences. Meanwhile, high noise areas (68.46–83.26 dB, indicated in red) are concentrated around locations NM4, NM10, and NM18, suggesting these areas experience heavy traffic, industrial operations, construction, or are commercial hubs.

The noise level in the central areas of the city was highest during the day, primarily due to heavy car traffic and activity in business and commercial zones. This was particularly evident in the morning and evening when the movement of people and vehicles was at its peak. In the evening, the noise level increased further not only because of greater transport activity but also due to recreational and social events. In the morning, when most people commuting or at work, noise levels were lower in residential areas. However, in central and highly commercial areas, (e.g., NM1 and NM2), noise levels were notably higher due to the dense traffic and pedestrian activity (Table 2).

Table 2.

Noise pollution characteristics based on measurements.

Noise measurements (NM)	Noise measurement characteristics based on geolocations
NM1 (51°06′12″N, 71°24′21″E)	Situated next to the Alau Ice Palace, the noise level in the area remains moderate out of season, despite the quiet surroundings. These areas are marked by a heavy flow of cars, particularly during rush hours (morning and evening), resulting in a persistent noise level due to the high traffic volume and human activity.
NM2 (51°05′19″N 71°24′43″E)	The business area experiences higher activity during the day and evening, with offices and parking areas nearby. Noise from Mangilik El Avenue does not reach this location, as the traffic density and number of cars are moderate.
NM3 (51°05′32″N 71°25′23″E)	A residential area with a hospital nearby. The noise level was lower in the morning and afternoon but increased in the evening due to peak visitor hours and higher vehicle traffic, including ambulances.
NM4 (51°05′48″N 71°25′00″E)	The point is located at a busy intersection where heavy vehicle traffic creates high noise levels, especially during rush hours. The analysis showed that noise levels peak in the morning and evening periods due to heavy traffic on the roads. A special parameter was used to collect data to account for traffic-related noise, allowing us to record its maximum values and frequency during the day.
NM5 (51°06′09″N 71°24′52″E)	The location is within a botanical park, where noise levels remain generally moderate, particularly during midday when visitor activity is low. However, in the evening, increased foot traffic and recreational activities contribute to rising noise levels.
NM6 (51°07′15″N 71°25′42″E)	The point is positioned in front of the entrance to a large shopping mall, where noise levels fluctuate throughout the day. While pedestrian movement is constant, peak noise levels occur during rush hours due to high vehicle congestion and shopper activity in the surrounding area.
NM7 (51°05′05″N 71°24′28″E)	The location is situated across from the second city hospital, where the morning is relatively quiet, and traffic congestion is moderate. However, the noise level rises throughout the day due to nearby construction work and truck traffic.
NM8 (51°06′55″N 71°24′47″E)	The location is at the intersection of Zhanibek-Kereya and Kabanbay Batyr streets, across from the Ushkempirov Palace of Martial Arts. Traffic is consistently heavy, with no major congestion during rush hours. However, the noise from the roads consistently exceeds 70 dB throughout the day.
NM9 (51°05′21″N 71°24′10″E)	The location is situated across from Nazarbayev University, along Kabanbay Batyr Avenue. This area experiences heavy car traffic, including trucks, resulting in consistently high noise levels.
NM10 (51°06′46″N 71°25′59″E)	The location is at the heart of traffic congestion, at the intersection of Mangilik El and Zhanibek-Kerey Avenues. Even during off-peak hours, the noise level remains consistently high throughout the day.
NM11 (51°05′51″N 71°25′25″E)	The point is located in a residential area where noise levels remain moderate due to the proximity of a school and kindergarten. No significant differences in noise levels are observed in the morning, lunchtime or evening.
NM12 (51°08′15″N 71°26′50″E)	The location is in the heart of Triathlon park, far from major highways, with minimal congestion throughout the day. The park areas generally had low noise levels, typical of green spaces. However, on windy days, background noise increased due to natural factors.
NM13 (51°07′40″N 71°25′51″E)	The location is situated among the courtyards of residential complexes. While there is car congestion, it is primarily concentrated in the parking areas.
NM14 (51°07′18″N 71°26′19″E)	The location is on the Syganak-Mangiilik El bridge, where traffic is present throughout the day. Traffic flow varies due to the opening and closing of the road for the LRT construction.
NM15 (51°08′29″N 71°27′50″E)	The location is near the Astana Mall shopping center, on the busy Tauelsizdik Avenue with heavy traffic. Traffic remains intense throughout the day, and congestion occurs during rush hours. These areas are marked by heavy traffic and a significant rise in noise pollution, especially during windy weather.
NM16 (51°05′01″N 71°25′13″E)	The location is near the Expo exhibition center, on the eastern side of the entrance to the Mega Silk way shopping center. Due to the presence of offices and busy establishments, the area experiences high noise levels during the day.
NM17 (51°05′46″N 71°24′27″E)	The location is in a residential area with low noise levels, as the buildings are widely spaced and consist of few apartments. This results in a quiet environment with minimal background noise, and the noise readings remain consistently low throughout the day with little variation.
NM18 (51°07′56″N 71°27′26″E)	The location is at the intersection of Tauelsizdik and Koshkarbaev Avenues, near the U.S. Embassy, in an area with heavy traffic. Due to its proximity to the central bridge of the city, the intersection experiences high noise levels, particularly during the evening rush hour. The area is marked by intense traffic and a significant increase in noise pollution during windy weather.
NM19 (51°07′44″N 71°28′08″E)	The point is located near the monument to Baurzhan Momyshuly, along the street of the same name, the traffic flow, regardless of the time of day, is also very intense due to the logistical features of the street (the street leads to the central city bridge).
NM20 (51°07′16″N 71°27′26″E)	The location is in Presidential park, on the southern side of the Palace of Peace and Accord. Aside from occasional work equipment, no motor vehicles are present within a half-kilometer radius. The considerable distance from busy roads makes this area relatively quiet throughout the day. The park areas are characterized by low noise levels, typical of green spaces.

Conclusion

This study provides an initial analysis and quantitative evaluation of noise levels in the rapidly growing city of Astana, Kazakhstan. Over the entire measurement period, from October 22 to November 22, the average noise level in Astana was recorded at 73 dB. According to WHO, the Environmental Noise Directive (END) requires noise levels should not exceed 55 dB L_den (day-evening-night level) and 50 dB L_night (night-time level). The WHO Environmental Noise Guidelines (2018) propose stricter limits to minimize the health risks associated with road traffic noise, recommending levels below 53 dB Lden for day-evening-night exposure and below 45 dB Lnight for nighttime exposure. Our results indicated that Astana’s noise pollution threshold is exceeded almost by a factor of two, with maximum recorded values reaching 89.1 dB and minimum values at 46.3 dB.

The average discrepancy between measurements taken with the SLM NIOSH mobile app and the SLM UT352 device ranges from 0.13 to 6.86 dB, depending on location. This suggests that, on average, the mobile app records slightly higher noise levels.

When planning new developments or improving infrastructure, it is crucial to consider not only aesthetic and functional aspects but also the impact of noise on residents’ quality of life. Noise pollution data can be useless in developing public health protection measures, such as setting noise level regulations for residential and public spaces in Astana. Continued data collection across different districts and seasons will allow for a more accurate assessment of seasonal noise variations and their effects on ecosystems and public health in Astana. Systematic monitoring can help detect rising noise levels caused by urban expansion, increased traffic, or social and recreational activities, and such data can be useful for developing effective regulations and timely interventions.

These findings highlight the need for sustainable urban strategies aimed at reducing noise pollution and improving environmental quality. Implementing environmental standards, such as maximum permissible noise levels for different zones, as well as creating green spaces and noise protection areas, is vital for enhancing urban sustainability. Additionally, the development of advanced methods and technologies for more precise noise measurement and analysis will support a comprehensive approach to tackling noise pollution.

The analysis indicates that central areas and high-traffic zones require careful traffic management and urban planning in Astana. Noise data can assist in designing new transportation networks and residential areas to minimize the impact of noise on residents. Potential solutions include the construction of noise barriers, improvements in transport infrastructure, and the strategic placement of residential buildings in quieter locations away from major roads and transit hubs.

This study underscores the importance of systematic noise monitoring and analysis to create safer and more comfortable urban environments. The collected data can be used to optimize city planning, generate noise level maps for different areas of Astana, and enhance transportation policies as part of the Smart City initiative. Future work requires noise measurements covering all seasons to evaluate potential seasonal influences on the results.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was carried out with the financial support of the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan under Contract №107/KMU-5-24-26 dated 20.06.2024 under the scientific project IRN AP22784985 “Development of an intelligent software and hardware system for monitoring, visualization and analysis of urban data using mobile sensors”.

Ethical considerations

Clinical trial number: not applicable. This study does not involve human subjects or animals.

ORCID iD

Gulden Ormanova

Data Availability Statement

The data created and/or analyzed during this study will be available upon reasonable request to the corresponding author.*

References

D’Agosto

. Noise pollution, vibration, visual intrusion, and emission of solid and liquid waste. In: D’Agosto MA. Transportation, Energy Use and Environmental Impacts. Elsevier, 2019, pp. 259–280. https://doi.org/10.1016/B978-0-12-813454-2.00007-6

Zhang

, et al. Causal effects of noise and air pollution on multiple diseases highlight the dual role of inflammatory factors in ambient exposures. Sci Total Environ 2024; 951: 175743. https://doi.org/10.1016/j.scitotenv.2024.175743

Rios

Montezano

Camargo

, et al. Impact of environmental factors on hypertension and associated cardiovascular disease. Can J Cardiol 2023; 39(9): 1229–1243. https://doi.org/10.1016/j.cjca.2023.07.002

World Health Organization . Burden of disease from environmental noise: quantification of healthy life years lost in Europe. WHO Regional Office for Europe, 2011. https://www.who.int/publications/i/item/9789289002295

Fajersztajn

Guimaraes

Duim

, et al. Health effects of pollution on the residential population near a Brazilian airport: a perspective based on literature review. J Transport Health 2019; 14: 100565. https://doi.org/10.1016/j.jth.2019.05.004

Razavi-Termeh

Sadeghi-Niaraki

Yao

, et al. Assessment of noise pollution-prone areas using an explainable geospatial artificial intelligence approach. J Environ Manag 2024; 370: 122361. https://doi.org/10.1016/j.jenvman.2023.122361

Natarajan

Batts

Stankovic

. Noise-induced hearing loss. J Clin Med 2023; 12(6): 2347. https://doi.org/10.3390/jcm12062347

Cantuaria

Waldorff

Wermuth

, et al. Residential exposure to transportation noise in Denmark and incidence of dementia: national cohort study. BMJ 2021; 374: n1794. https://doi.org/10.1136/bmj.n1794

Welch

Shepherd

Dirks

, et al. Health effects of transport noise. Transp Rev 2023; 43(6): 1190–1210. https://doi.org/10.1080/01441647.2023.2194017

10.

Duggan

Hasdianda

Baker

, et al. The effect of noise-masking earbuds (SleepBuds) on reported sleep quality and tension in health care shift workers: prospective single-subject design study. JMIR Formativ Res 2022; 6(3): e28353. https://doi.org/10.2196/28353

11.

Poulsen

Sørensen

Hvidtfeldt

, et al. Concomitant exposure to air pollution, green space, and noise and risk of stroke: a cohort study from Denmark. Lancet Reg Health Eur 2023; 31: 100705. https://doi.org/10.1016/j.lanepe.2023.100705

12.

Rao

Kwan

, et al. Examining the effects of mobility-based air and noise pollution on activity satisfaction. Transp Res, Part D Transp Environ 2020; 89: 102633. https://doi.org/10.1016/j.trd.2020.102633

13.

Terry

Rothendler

Zipf

, et al. Effects of the COVID-19 pandemic on noise pollution in three protected areas in metropolitan Boston (USA). Biol Conserv 2021; 256: 109039. https://doi.org/10.1016/j.biocon.2021.109039

14.

Klompmaker

Hoek

Bloemsma

, et al. Associations of combined exposures to surrounding green, air pollution and traffic noise on mental health. Environ Int 2019; 129: 525–537. https://doi.org/10.1016/j.envint.2019.05.058

15.

Flanagan

Mattisson

Oudin

, et al. Health impact assessment of exposure to road traffic noise and air pollution according to pre- and post-densification scenarios in Helsingborg, Sweden. City Environ Interact 2024; 24: 100176. https://doi.org/10.1016/j.cacint.2024.100176

16.

Jarosińska

Héroux

MÈ

Wilkhu

, et al. Development of the WHO environmental noise guidelines for the European region: an introduction. Int J Environ Res Publ Health 2018; 15(4): 813. https://doi.org/10.3390/ijerph15040813

17.

Stansfeld

Matheson

. Noise pollution: non-auditory effects on health. Br Med Bull 2003; 68(1): 243–257. https://doi.org/10.1093/bmb/ldg033

18.

Basner

Babisch

Davis

, et al. Auditory and non-auditory effects of noise on health. Lancet 2014; 383(9925): 1325–1332. https://doi.org/10.1016/S0140-6736(13)61613-X

19.

Fakhimi

Khani

Sardroud

. Smart-city infrastructure components. In: Solving urban infrastructure problems using smart city technologies. Elsevier, 2021, pp. 17–54. https://doi.org/10.1016/B978-0-12-823537-3.00002-4

20.

Wang

, et al. Application of land use regression to map environmental noise in Shanghai, China. Environ Int 2022; 161: 107111. https://doi.org/10.1016/j.envint.2022.107111

21.

Chung

. Alternative audiometric calibration methods: evaluation of sound level measuring apps for audiometric calibration. J Commun Disord 2023; 105: 106351. https://doi.org/10.1016/j.jcomdis.2023.106351

22.

Celestina

Kardous

Trost

. Smartphone-based sound level measurement apps: evaluation of directional response. Appl Acoust 2021; 171: 107673. https://doi.org/10.1016/j.apacoust.2020.107673

23.

Huyan

Ramkissoon

Laka

, et al. Assessing the usefulness of mobile apps for noise management in occupational health and safety: quantitative measurement and expert elicitation study. JMIR mHealth uHealth 2023; 11(1): e46846. https://doi.org/10.2196/46846

24.

Song

Liu

Kwan

, et al. Machine-based understanding of noise perception in urban environments using mobility-based sensing data. Comput Environ Urban Syst 2024; 114: 102204. https://doi.org/10.1016/j.compenvurbsys.2023.102204

25.

Zheng

Chen

Huang

, et al. Mapping environmental noise of Guangzhou based on land use regression models. J Environ Manag 2025; 373: 123931. https://doi.org/10.1016/j.jenvman.2023.123931

26.

Ramseier

Schalcher

Wunderli

, et al. Impact of buildings, forests and cliffs on aircraft noise mapping: case study. Transp Res, Part D Transp Environ 2024; 133: 104279. https://doi.org/10.1016/j.trd.2024.104279

27.

Ormanova

Hopke

Dhammapala

, et al. Chemical characterization and source apportionment of atmospheric fine particulate matter (PM2.5) at an urban site in Astana, Kazakhstan. Atmos Pollut Res 2025; 16(1): 102324. https://doi.org/10.1016/j.apr.2024.102324

28.

Rakhimberdina

Ormanova

Yedilkhan

. Development of a prediction model for the assessment of air quality in the city of Astana, Kazakhstan. In: 2025 IEEE European Technology and Engineering Management Summit (E-TEMS), Bruges, Belgium, 2025, pp. 303–308. https://doi.org/10.1109/E-TEMS64751.2025.11239309