Noise Mapping, Identification of Hot Spots, and Mitigation Plan for Control of Noise Pollution for Ahmedabad

doi:10.21203/rs.3.rs-4976650/v1

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Noise Mapping, Identification of Hot Spots, and Mitigation Plan for Control of Noise Pollution for Ahmedabad

https://doi.org/10.21203/rs.3.rs-4976650/v1

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Worldwide, urban centres are facing challenges due to road traffic, leading to increased noise pollution that impact residents' quality of life and the environment. This study presents a comprehensive analysis of noise pollution levels in Ahmedabad, India, across different urban zones (residential, commercial, and industrial) and times of day, utilizing QGIS software to generate noise heat maps. The noise level was measured at 133 locations in different areas of the city. The monitoring was performed using a digital sound level meter and a noise map was produced using the recorded equivalent energy noise level values. The mean noise levels were recorded consistently high, with daytime levels averaging 79.88 dB, nighttime levels at 74.76 dB, and late-night levels at 61.47 dB, respectively. Industrial areas recorded the highest noise levels, followed by commercial and residential zones. A comparative analysis with similar studies across India and globally indicates that Ahmedabad’s noise pollution exceeded the levels reported in other major urban centers. The correlation analysis calculated a strong correlation between noise levels in different zones, particularly between residential and industrial areas was observed, suggesting the widespread impact of noise pollution across the city. The findings underscore the urgent need for targeted noise management strategies, including stricter enforcement of noise regulations, urban planning that minimizes noise exposure in residential areas, and the implementation of noise barriers and soundproofing measures in industrial and commercial zones. The study highlights the critical role of spatial analysis tools like QGIS in visualizing noise distribution and informing public policy.

Noise level

QGIS

Noise heat mapping

Environment management

The rapid population growth in recent decades has led to significant expansion in urban and suburban areas, resulting in increased population density. This densification is a major contributing factor to noise pollution. Noise pollution refers to unwanted or excessive sound that negatively impacts human health and environmental quality. In urban settings, it is a significant environmental concern. The World Health Organization (WHO) Regional Office for Europe identifies noise pollution from road traffic as the second most important environmental threat to public health, following air pollution (WHO, 2011). This type of noise pollution has far-reaching effects on the health and well-being of individuals, both directly and indirectly. In response, the WHO has established an acceptable noise intensity threshold of 53 dB(A) for daytime exposure. However, certain populations, particularly children and the elderly, are considered vulnerable to noise levels even lower than this threshold, with adverse effects potentially occurring at intensities as low as 45 dB(A) (WHO, 2018a). Excessive traffic noise pollution, while not immediately quantifiable in its health impacts, poses serious long-term risks. Chronic exposure has been associated with sleep disturbances, cognitive decline, hearing loss, cardiovascular diseases, and heightened stress and irritability (Raman & Chhipa, 2014; Ouis, 2001; Begou & Kassomenos, 2020; Tangermann et al., 2022). In Western Europe alone, environmental noise from traffic accounts for the loss of approximately one million healthy life years annually (WHO, 2011). Consequently, urban planners, acoustic designers, researchers, and policymakers increasingly recognize high-intensity road noise as a significant public health concern (Cai et al., 2020; Hegewald et al., 2021). The major sources of noise pollution are industrial noise, traffic noise, and community noise. Out of these, road traffic noise is the prevalent source that affects the most in urban centers. The total of transportation noise, 70% is due to road vehicular traffic. Vehicle noise arises from mainly two parameters, i.e. engine and friction of the tyre (Akhtar et al., 2012). According to WHO, 2011 report claimed that approximately 40% of people living in European Union countries are exposed to traffic noise levels exceeding 55 dB(A) (WHO, 2011). Similarly, various studies conducted in different countries, including the U.S. (Lee et al., 2014; McAlexander et al., 2015; Seong et al., 2011), Canada (King et al., 2012; Carrier et al., 2016), Colombia (Quiñones-Bolaños et al., 2016), Chile (Sommerhoff et al., 2004), Brazil (Calixto et al., 2003), Vietnam (ky et al., 2021), Jordan (Obaidat, 2011; Abo-Qudais, & Alhiary, 2007), India (Banerjee et al., 2008; CPCB, 2011), Pakistan (Mehdi et al., 2011), Bangladesh (Arif & Ali, 2014), and the U.A.E (Hamad et al., 2017; Elmehdi, 2014), also experience traffic noise levels that surpass government-set limits.

Therefore, it is of the utmost importance to carry out noise measurement research, particularly for the loud field, intending to assess noise levels and develop appropriate remediation strategies for managing the impact of noise. There are different types of procedures, which can help to analyze the impact of noise pollution. This analysis with an initial evaluation of decisions may help manage and mitigate noise pollution. According to certain studies, GPS, GIS, and different kinds of techniques are used to create noise visualization maps. Noise mapping may help to evaluate equivalent noise pollution because it provides an assessable representation of the spatial pattern of traffic noise. GIS noise mapping, which is a visual depiction of the variation of noise levels in a particular region, is a helpful visualization technique. These noise maps may help to find out the hotspots of noise pollution that can be identified to tackle the issue of higher noise levels in the particular study region. The research conducted by Chauhan et al. highlights the urgent requirement for systematic noise surveillance in urban regions of the National Capital Territory (NCT) of Delhi. This study centers on fundamental approaches including site selection, temporal analysis, and noise mapping based on GIS. A further investigation conducted in Delhi revealed that the noise levels beyond the recommended thresholds by more than 25 dB. Specifically, industrial sectors recorded noise levels as high as 81.5 dB, while residential areas reached 67 dB. These measurements significantly exceed the limitations established by the Central Pollution Control Board (Khan & Lone, 2017). Similarly, in Surat City, located in Western India, the noise levels in a specifically designated silent zone were recorded at 79 dB, which is considerably higher than the officially recommended standard (Ranpise & Tandel, 2021). One significant aspect that contributes to the increasing noise levels in India is the presence of vehicular traffic in urban areas. This traffic leads to road congestion and excessive honking, as stated by Goswami and Swain (2017) and Kalawapudi et al. (2020).

This study aims to assess the data collected from several sites in the neighborhood of Ahmedabad city in order to determine the trend of noise pollution in the area. The aim of this study was to quantify the range of noise intensity during the day, night, and midnight hours (1 AM-6 AM) at various locations in the urban area of Ahmedabad. Additionally, the study analyzed the effects of noise pollution and compared them to the permissible standards set by the Central Pollution Control Board (CPCB) in India.

2.1. Description of Study Area

The study area selected for analysis of noise pollution is a district of Indian state of Gujarat i.e. Ahmedabad, which is located in the central part of the state. It holds significant importance in the state, sharing boundaries with several other districts. To the east, it is bordered by Kheda district, while the northern boundary is shared with Mehsana district. The southern part of Ahmedabad district is bounded by Anand district, and to the west, it shares its boundary with Surendranagar district. Ahmedabad district is situated between longitudes 71°30’ East and latitudes 22°30’ North. The geographical area of Ahmedabad district is 8,087 km² as per record. The present study is done over a busy traffic corridor of Ahmedabad city of India as many transportation facilities have been instated in the city, with an international airport and a bus rapid transit system (BRTS) for the fast and comfortable movement of people. Along with development of the city, many environment-related problems have occurred and one of these is noise pollution (Vora & Vasani, 2023). Ahmedabad city is the 7th largest metropolis of India covering a geographical area of approximately 464 km² having a road network of around 2600 km (Lakhtaria et al., 2021). The population which was around 5.58 million in the year 2011 has risen estimated at 8,854,444 in 2024 and grown by 203,839 in the last year, which represents a 2.36% annual change.

2.2. SLM 109

The ST-109 Class 1 Sound Level Meter is a reliable and versatile instrument for noise measurement and monitoring. The ST-109 provides accurate and precise noise data across a wide measurement range of 30–130 dB. With its ability to simultaneously measure various noise parameters, including SPL, Leq, SEL, Max, and Min, and support for different time and frequency weightings, is well-suited for a variety of environmental and occupational noise assessment applications.

2.3. GIS Mapping Tool (QGIS)

QGIS (Quantum Geographic Information System) is an essential tool for noise mapping, enabling the collection, analysis, and visualization of noise pollution data. Field measurements are imported and georeferenced in QGIS, where interpolation techniques and contour mapping create continuous noise-level surfaces. Heat maps and thematic maps overlay noise data with other relevant factors, aiding in identifying noise pollution hotspots and informing policymakers (Vilas et al., 2018b) Network analysis tools study traffic patterns, helping design effective noise control measures and improve urban environments. Road graph plugin must be configured in this connection and OSM can provide the necessary road network data.

Data was collected by a sound level meter (SLM) at 133 different locations from 1 AM to 6 AM.

2.4. Noise Data Collection

The noise measurement was done using a Sound level meter at various identified geographical locations, considering various land uses. For the present study purpose, data was collected in three different periods: morning Hours (6 AM—10 PM), Night hours (10 PM—1 AM), and Night Hours (1 AM—6 AM) in November 2023. In total, noise data was collected at 133 locations in Ahmedabad City (Fig. 1).

The noise mapping process for Ahmedabad involved georeferencing collected noise data, creating a boundary polygon, and adding noise level measurements to the attribute table. A Triangulated Irregular Network (TIN) interpolation generated a continuous noise level surface, which was clipped to the Ahmedabad boundary. Noise-level contour lines were then created, resulting in a comprehensive horizontal noise map. This detailed map aids urban planning, environmental management, and decision-making in Ahmedabad.

2.5. Noise Heat Maps

QGIS software was considered for noise mapping. The dB values recorded in each period (Morning hours, Night hours (10PM-1AM) and Night hours (1AM-6AM) of the day have been considered for the development of noise contours. The noise contours were developed after triangulation of the noise data (Triangular interpolation network). For representing the data more precisely, contour lines have been incorporated at the interval of 2dB. This was considered as base for the development of heat maps. The noise heat maps were developed for morning hours, Night hours and Night hours and the same are represented in Fig. 5.

The maximum and minimum readings recorded in the morning hours (6AM-10PM) is 84.96 dB and 75 dB at Kamod Circle and Sardar Patel Seva Sankul - Bus stop Haridarshan Park, Jashoda Nagar respectively.

The maximum and minimum readings recorded in Night hours (10PM-1AM) is 77.9 dB and 72.03 dB at Manekbaug Cross Road and G.C.S. Medical College - Bus stop respectively.

The maximum and minimum readings recorded in Night hours(1AM-6AM) is 64.92 dB and 58.03 dB at APMC Market - Bus stop and Surdhara Circle respectively.

In the present study, the noise level data from Ahmedabad observed consistently high levels across different times of the day. The mean noise levels recorded were 79.88 ± 2.84 dB during the day, 74.76 ± 1.75 dB at night, and 61.47 ± 1.97 dB during the late-night hours (1 AM-6 AM). The maximum noise levels reached 84.96 dB at during the day, 77.9 dB at night, and 64.92 dB during late-night hours (Fig. 2). The minimum noise levels recorded were 75 dB during the day, 72.03 dB at night, and 58.03 dB during late-night hours. These findings show that Ahmedabad's noise levels were about the same as, or marginally greater than, those seen in other major Indian cities as compared to other research studies. A study conducted in Delhi by Chauhan et al. (2010) recorded that the average noise levels during the day in commercial and industrial sectors ranged from 75 to 80 dB, which was similar to the values recorded in Ahmedabad. Goswami & Swain (2017) found that the average noise levels in commercial areas of Bhubaneswar throughout the day were approximately 75 dB, which was slightly lower than the levels seen in Ahmedabad. A study carried out in Mumbai by Vijay et al. (2013) likewise showed high noise levels, with daytime averages above 80 dB in heavily traveled areas, which was in line with the values observed in Ahmedabad. This indicated that Ahmedabad's noise pollution exceeded other large Indian cities noted for their high levels of urban noise pollution. Comparing these findings with research conducted in other countries, the noise levels in Ahmedabad were also consistent with or higher than those recorded in other urban regions around the world. For instance, research in São Paulo, Brazil, revealed typical daytime noise levels in commercial districts to be about 75 to 82 dB, close to the values documented in Ahmedabad (de Paiva Vianna et al., 2015). In contrast, research in London revealed lower average noise levels, around 65 to 70 dB during the day in commercial zones (Gulliver & Briggs, 2011), emphasizing that Ahmedabad’s noise pollution is substantially greater than in several European cities.

Residential Areas

The present study revealed significant variations in noise levels in residential areas during different times of the day. The mean noise levels recorded were 79.83 ± 2.66 dB during the day, 74.85 ± 1.70 dB at night, and 61.68 ± 1.90 dB during the late-night hours (1 AM-6 AM). The maximum noise level recorded during the day was 84.9 dB at the Bhairavnath bus stand, while the minimum was 75 dB at the Sardar Patel bus stand, respectivily. At night, the noise levels ranged between 72.03 dB (Medical college) and 77.9 dB (Manekbaug Cross Road). During the late-night hours, the noise levels were notably lower, with a maximum of 64.85 dB at the Ajeshwari Society - Bus stop and a minimum of 58.14 dB at Zundal Circle. The higher noise level during the daytime witnessed to more traffic and bus stands near the residential area (Fig. 3). These noise levels were considerably higher than those reported in other studies focusing on residential areas. For instance, Khan & Lone (2017) observed noise levels in residential areas of Delhi that exceeded prescribed standards by about 25 dB, reaching up to 67 dB. This is still lower than the average day and night noise levels in the current study. Similarly, Ranpise & Tandel (2021) recorded noise levels in a silent zone of Surat city, Western India, at 79 dB, which aligns closely with the nighttime average in the current study but is still lower than the daytime average.

The significantly elevated noise levels during the day and night in this study may be attributed to a high volume of vehicular traffic, urban congestion, and possibly industrial or construction activities in close proximity to residential areas. Even during late-night hours, when noise levels typically decrease, the recorded levels are still relatively high compared to the prescribed standards by regulatory bodies such as the Central Pollution Control Board (CPCB) in India.

The implications of these findings are concerning, as chronic exposure to elevated noise levels, particularly in residential areas, can lead to various health issues, including sleep disturbances, cardiovascular diseases, and increased stress levels. The comparison with other studies underscores the need for more effective noise control strategies, particularly in densely populated urban areas where noise pollution remains a pervasive issue.

Commercial Areas

The results of this study showed that noise levels in commercial areas vary significantly throughout the day. The mean noise levels reported were 79.67 ± 2.97 dB during the day, 74.86 ± 1.75 dB at night, and 61.33 ± 2.13 dB during the late-night hours (1 AM-6 AM). The maximum recorded noise levels reached 84.96 dB at Kamod Circle during the day, 77.78 dB at Narayanpura Char Rasta during the night, and 64.92 dB at APMC Market - Bus stop in the late-night period, with minimum levels of 75.08 dB (Shivranjani Junction), 72.06 dB (Pushpkunj - Bus stop), and 58.03 dB (Surdhara Circle), respectively. This shows that commercial areas have larger swings in noise levels, presumably due to late-night activities such as restaurants, pubs, or late-night transportation. During the late-night period, the average noise level declined to 61.33 dB, with a range from 58.03 dB to 64.92 dB. The prevalence of late-night noise in business districts, albeit lower than daytime levels, still illustrates the continuous activity that can potentially harm adjoining residential zones (Fig. 4).

When compared to earlier research focused on business areas, the noise levels in this study appear significantly elevated. For instance, a study by Chauhan et al. (2010) revealed average noise levels in commercial areas of Delhi ranging from 68 to 74 dB during peak hours, which is lower than the average daytime levels recorded in the current study. Additionally, Goswami & Swain (2017) recorded commercial area noise levels in Bhubaneswar, Odisha, averaging around 75 dB, which is still below the day and night averages observed here. The greater noise levels during the day in commercial districts can likely be ascribed to heavier traffic, increased economic activity, and the presence of several commercial establishments working simultaneously. The noise levels at night remain relatively high as well, indicating extended business hours and overnight motor activity. Even during the late-night hours, when commercial activity often diminishes, the reported noise levels are still above what is typically deemed appropriate for calm areas.

These elevated noise levels are a matter for concern, since they can contribute to significant health effects, including stress, hearing impairment, and cardiovascular concerns for persons who work or spend extended periods in these commercial zones. The comparison with other studies underscores the necessity for tighter enforcement of noise laws in commercial areas, particularly during peak hours, to defend public health.

Industrial Areas

The data collected from the industrial regions demonstrated that noise levels were notably high across all hours of the day. The mean noise levels measured were 80.62 ± 2.83 dB during the day, 74.29 ± 1.82 dB at night, and 61.47 ± 1.66 dB during the late-night hours (1 AM-6 AM). The greatest noise levels reached 84.75 dB at Sanand Chowki during the day, 77.86 dB at Swastik Industries during night, and 64.67 dB at Sitaram Bapa Chowk in the late-night hours. The minimum noise levels measured were 75.5 dB during the day, 72.12 dB at night, and 58.66 dB during late-night hours. When comparing these findings with previous research conducted in industrial settings, the results are consistent with or somewhat above the noise levels reported in similar contexts. For instance, Khan & Lone (2017) investigated noise levels in industrial regions of Delhi, where they recorded daytime noise levels ranging from 78 dB to 82 dB, which agrees with the current study’s findings. Similarly, another study by Singh & Davar (2004) in Kanpur's industrial zones found daytime noise levels between 76 dB and 81 dB, which are equivalent to the average levels recorded here.

The constant high noise levels in industrial regions can be attributed to the continuous operation of heavy machinery, increased vehicular mobility connected with industrial activities, and other noise-generating procedures such as loading and unloading of products. These operations contribute to persistent high noise levels throughout the day and night, providing considerable health concerns to workers and neighboring communities. Prolonged exposure to such elevated noise levels has been connected with poor health outcomes, including hearing loss, hypertension, cardiovascular disorders, and stress-related conditions.

Comparative Analysis and Implications

The comparison analysis revealed that noise levels are consistently higher in industrial regions, followed by residential and commercial sectors. The decreasing trend in noise levels from day to late night across all zones showed a natural decline in activity. Nonetheless, the continued presence of relatively high noise levels in commercial and industrial districts suggests the necessity of strict noise management measures, especially at night and late at night. For residential areas, there is a clear need for legislation and enforcement to limit noise levels, especially at night, to ensure the health and well-being of residents. To reduce health hazards and adhere to occupational safety regulations, industrial locations must adopt noise control measures and provide workers with appropriate hearing protection.

The comparative examination of noise level studies across several cities in India, as well as in Bangladesh and Qatar, demonstrates a major and widespread issue of noise pollution, particularly in urban contexts. For instance, in Delhi, studies repeatedly reveal that noise levels in commercial, industrial, and residential zones frequently exceed authorized limits, with levels ranging from 66.5 to 84.7 dB(A) in most places (Chauhan, et al., 2023). Similar findings are reported in Moradabad, where noise levels in residential zones ranged between 72.86 to 109.70 dB(A), and in Haridwar, where noise levels reached up to 102.4 dB(A) in specific places (Chauhan, et al., 2010; Sharma, et al., 2016). These high noise levels are mostly related to transportation and industrial operations, which are prominent in densely populated urban regions.

In Qatar, traffic noise at major intersections remained continuously high, with values between 67.6 dB(A) and 77.5 dB(A), even during weekends, showing a chronic concern with noise pollution (Khadija & Khaled, 2022). Similarly, in Chattogram, Bangladesh, noise levels were reported at worrisome heights, sometimes reaching up to 90 dB(A), indicating serious noise pollution issues in urban areas (Masum et al., 2021). The impact of road traffic is particularly evident in Indian cities like New Delhi and Surat, where noise levels near sensitive locations such as schools and hospitals often exceed acceptable limits, creating major health hazards (Ranpise, et al., 2022). For example, in South Delhi, noise levels were reported to be as high as 81.1 dB(A) near main roadways surrounded by high-rise structures (Alam, et al., 2021).

The COVID-19 pandemic provided a new perspective, as shown in Kanpur, where there was a considerable drop in noise levels during lockdowns, revealing the deep influence of human activities on urban noise pollution (Mishra, et al., 2023). Health implications, such as increasing incidences of hearing damage found in Odisha, underline the chronic effects of continuous noise exposure (Sahu, et al., 2020). Additionally, the geographical analysis of noise pollution in Puducherry underscores the need to understand noise pollution patterns to inform urban design and mitigation methods, with 17% of the sites being classified as high noise sources (Devasia, et al., 2022).

Noise Heat Maps

The use of QGIS software for generating noise heat maps provided a comprehensive way for displaying and analyzing spatial fluctuations in noise levels across different times of the day. By utilizing dB values obtained during morning hours, night hours (10 PM-1 AM), and night hours (1 AM-6 AM), the noise data is efficiently transformed into a visual format that enables for easy interpretation and comparison.

The construction of noise contours by the triangulation of noise data, specifically utilizing a Triangular Interpolation Network (TIN), permits the creation of a continuous surface that reflects noise distribution with a high level of accuracy. The introduction of contour lines at 2 dB intervals significantly enhances the precision of these maps, offering unambiguous demarcations of noise intensity levels across the research region (Fig. 5).

The resulting heat maps demonstrate considerable regional and temporal fluctuations in noise levels. For instance, the maximum noise level measured during the morning hours was 84.96 dB at Kamod Circle, while the minimum was 75 dB at Sardar Patel Seva Sankul - Bus Stop Haridarshan Park, Jashoda Nagar. During the late-night hours (10 PM-1 AM), the maximum noise level was measured at Manekbaug Cross Road (77.9 dB), with the lowest at G.C.S. Medical College - Bus Stop (72.03 dB). In the early morning hours (1 AM-6 AM), the noise levels declined considerably, with the maximum being 64.92 dB at APMC Market - Bus Stop and the lowest at Surdhara Circle (58.03 dB).

These findings were consistent with the expected diurnal variation in noise levels, where higher noise levels frequently occur during peak traffic hours in the morning and late evening, and lower levels during the quieter early morning hours. Urban planners and legislators can identify problem regions and create focused noise reduction measures by using heat maps, which provide a visual representation of noise pollution. The spatial representation of noise levels through these maps can also improve public awareness and engagement, as the visual format is more accessible than raw data.

The usage of QGIS in this context highlights the software's adaptability in environmental research, particularly in the analysis and management of urban noise pollution. These heat maps add to our understanding of the spatial dynamics of noise pollution in urban environments by offering a thorough and rigorously scientific representation of noise data.

Correlations

The correlation matrix provided shows the relationships between noise levels during three different time periods: day, night, and nighttime (1 am-6 AM). Here's the interpretation:

The strong negative correlation between day and night noise levels (r=-0.989) suggests a distinct variation in noise patterns between these periods, possibly due to differences in human activities and traffic patterns. Typically, noise levels are higher during the day due to increased activity, and lower at night when most people are resting, except for some potential nighttime industries or events that might increase noise levels. The weak correlations involving the nighttime (1 am -6 AM) and daytime (r = .044) suggest that this time might have a more independent noise pattern, possibly influenced by a different set of activities (e.g., late-night traffic or early morning industrial activities). The very low correlation values indicate that noise levels during early morning hours are not strongly linked to either daytime or nighttime noise patterns.

The provided correlation matrix describes the relationships between noise levels in three different zones: Residential, Commercial, and Industrial. Here's the interpretation:

The extremely high correlations between the noise levels in Residential, Commercial, and Industrial areas suggest that the factors affecting noise pollution are quite uniform across these zones. This could imply that the same noise sources, such as traffic, industrial activities, and general urban noise, are impacting all areas equally. The perfect correlation between Residential and Industrial areas (1.000) might indicate that these zones are geographically close or share common boundaries, where noise from industrial activities could significantly affect residential areas. It may also suggest that there is a lack of sufficient noise regulation or mitigation measures to buffer residential zones from industrial noise. The near-perfect correlation (0.998) between Residential and Commercial areas could imply that commercial activities, which might include retail stores, restaurants, and business operations, contribute significantly to noise pollution in residential neighborhoods. This close relationship might affect the quality of life for residents, particularly during peak commercial hours.

The results indicate a need for targeted noise control policies and urban planning strategies that consider the close interrelation of noise levels across different zones. Effective noise management might include establishing noise barriers, implementing stricter noise regulations in industrial and commercial areas, and designing urban layouts that minimize noise exposure to residential areas. Understanding the high correlation between these zones is crucial for mitigating the potential health impacts of noise pollution on urban populations.

The present study conducted a complete examination of noise pollution across different zones and times of day in Ahmedabad, India, using modern noise mapping techniques assisted by QGIS software. The data demonstrated that noise levels across all zones—residential, commercial, and industrial—consistently exceed the prescribed limits set by regulatory agencies such as the Central Pollution Control Board (CPCB) of India. The measured mean noise levels during the day, night, and late-night hours were 79.88 ± 2.84 dB, 74.76 ± 1.75 dB, and 61.47 ± 1.97 dB, respectively, with highest levels reaching 84.96 dB during the day. These values are comparable to or higher than those recorded in other large Indian cities and urban regions globally, showing a serious and pervasive issue of noise pollution in Ahmedabad. The study's findings emphasize significant diurnal patterns, with the maximum noise levels recorded during daytime hours, particularly in commercial and industrial zones, and a moderate reduction during late-night hours. The spatial distribution of noise, depicted through heat maps, illustrates the persistence of high noise levels in specific hotspots, even during periods normally associated with decreased activity. This persistence was particularly concerning in residential areas, where elevated noise levels during the day and night could have major health repercussions, including sleep problems, cardiovascular disorders, and increased stress. The correlation analysis further indicated robust correlations between noise levels in residential, commercial, and industrial zones, demonstrating that noise pollution sources are pervasive and not restricted to specific areas. By addressing the high levels of noise in this area, local authorities and stakeholders can improve the quality of life for residents and create a healthier, more sustainable living environment for the community. This interrelationship highlights the need for a coordinated approach to noise management across all urban zones.

Implementation Policy

Based on the findings of this study, numerous implementation options are advised to minimize noise pollution in Ahmedabad:

1. The city should establish tighter noise control measures, notably in industrial and commercial districts. Regular monitoring and enforcement should be prioritized to ensure compliance with permitted noise levels imposed by regulatory organizations like the Central Pollution Control Board (CPCB).

2. Urban planners should consider the spatial distribution of noise sources when developing residential, commercial, and industrial zones. Establishing buffer zones between residential areas and noise-generating activities, such as industry and main traffic routes, can assist limit noise exposure.

3. The placement of noise barriers along major roads and surrounding industrial areas can help to confine noise within designated zones. Additionally, the creation of green belts and urban trees can operate as natural sound absorbers, contributing to noise reduction.

4. Public awareness campaigns should be conducted to educate communities about the health dangers connected with noise pollution and the need of noise control measures. Encouraging community involvement in monitoring noise levels and reporting breaches can also boost compliance.

5. Encouraging the adoption of noise-reducing technologies in industries and vehicles, such as quieter machinery and electric vehicles, can greatly contribute to lowering total noise levels. Regular maintenance of vehicles and machines should be mandatory to decrease noise emissions.

6. The continuous use of modern tools like QGIS for noise mapping should be included into the city's urban planning and environmental monitoring framework. Periodic updates to noise maps can assist track changes over time and detect developing hotspots, enabling proactive noise management.

By following these measures, Ahmedabad may effectively minimize noise pollution, improve public health, and increase the general quality of life for its citizens. The findings of this study provide a solid platform for creating tailored noise management methods that address the unique issues provided by urban noise pollution in this rapidly increasing city.

Acknowledgment

The authors acknowledge and extend their appreciation to the Gujarat Pollution Control Board (GPCB) Government of Gujarat for supporting the data collection and writing a manuscript.

Compliance with ethical standards Conflict of interest

The authors declare that there are no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper

Funding: Funding is not available

Data Available: Data will be made available on a reasonable basis through the corresponding author.

Ethical Approval: Not applicable.

Consent for publication All the authors have permitted publication.

Consent to Publish: Consent to all participants.

Abo-Qudais, S.; Alhiary, A. Statistical models for traffic noise at signalized intersections. Build. Environ. 2007, 42, 2939–2948.
Ahmed, K.I.; Pandya, G.H.; Stoilova, K.; Stoilov, T.; Babisch, W.; Beule, B.; Kokowski, P. Analysis of traffic noise in a road intersection configuration. Appl. Acoust. 2016, 12, 1–8.
Apparicio, P.; Carrier, M.; Gelb, J.; Séguin, A.-M.; Kingham, S. Cyclists’ exposure to air pollution and road traffic noise in central city neighbourhoods of Montreal. J. Transp. Geogr. 2016, 57, 63–69.
Arif, A.M.; Ali, M.A. Differentiating Sources of Noise at Busy Road Intersections in Dhaka City. Int. Proc. Chem. Biol. Environ. Eng. 2014, 78, 6–10.
Begou, P.; Kassomenos, P. Exposure to the road traffic noise in an urban complex in Greece: The quantification of healthy life years lost due to noise-induced annoyance and noise-induced sleep disturbances. Environ. Sci. Pollut. Res. Int. 2020, 28, 12932–12943.
Cai, Y.; Ramakrishnan, R.; Rahimi, K. Long-term exposure to traffic noise and mortality: A systematic review and meta-analysis of epidemiological evidence between 2000 and 2020. Environ. Pollut. 2020, 269, 116222.
Calixto, A.; Diniz, F.B.; Zannin, P.H. The statistical modeling of road traffic noise in an urban setting. Cities 2003, 20, 23–29.
Carrier, M.; Apparicio, P.; Séguin, A.M. Road traffic noise in Montreal and environmental equity: What is the situation for the most vulnerable population groups? J. Transp. Geogr. 2016, 51, 1–8.
Chauhan, A., Pawar, M., Kumar, D., Kumar, N., & Verma, T. (2010). Assessment of noise level status in different areas of Moradabad city. Environmental Conservation Journal, 11(3), 159–164.
de Paiva Vianna, K. M., Cardoso, M. R., & da Silva, M. G. (2015). Noise pollution and annoyance: An urban soundscapes study. Environmental Pollution**, 206, 66–75.
Elmehdi, H.M. Using mathematical models to predict annoyance from combined noise sources in the city of Dubai. In Inter Noise; Citeseer: Melbourne, VIC, Australia, 2014.
Goswami, S., & Swain, S. (2017). Noise pollution at commercial places of Bhubaneswar City, Odisha, India: A comparative analysis. International Journal of Current Research and Academic Review, 5(9), 30–36.
Gulliver, J., & Briggs, D. J. (2011). Personal exposure to particulate air pollution in transport microenvironments. Atmospheric Environment, 45(28), 4666–4674.
Hamad, K.; Khalil, M.; Shanableh, A. Modeling roadway traffic noise in a hot climate using artificial neural networks. Transp. Res. Part D Transp. Environ. 2017, 53, 161–177.
Hegewald, J.; Schubert, M.; Lochmann, M.; Seidler, A. The Burden of Disease Due to Road Traffic Noise in Hesse, Germany. Int. J. Environ. Res. Public Health 2021, 18, 9337.
Kalawapudi, K., Singh, T., Dey, J., Vijay, R., & Kumar, R. (2020). Noise pollution in Mumbai Metropolitan Region (MMR): An emerging environmental threat. Environmental Monitoring and Assessment, 192(2). https://doi.org/10.1007/s10661-020-8121-9
Khan, A., Lone, H. G., & Alam, P. (2017). Study and Assessment of Road Traffic Noise at Some Selected Locations of New Delhi India. IJIRST-International Journal for Innovative Research in Science & Technology|, 3(10).
Khan, M. A., & Lone, I. A. (2017). Noise pollution assessment in the industrial and residential areas of Delhi. Environmental Science and Pollution Research, 24(5), 4622–4631.
King, G.; Roland-Mieszkowski, M.; Jason, T.; Rainham, D.G. Noise Levels Associated with Urban Land Use. J. Hered. 2012, 89,1017–1030.
Ky, N.M.; Lap, B.Q.; Hung, N.T.Q.; Thanh, L.M.; Linh, P.G. Investigation and Assessment of Road Traffic Noise: A Case Study in Ho Chi Minh City, Vietnam. Water Air Soil Pollut. 2021, 232, 259.
Lee, E.Y.; Jerrett, M.; Ross, Z.; Coogan, P.F.; Seto, E.Y. Assessment of traffic-related noise in three cities in the United States. Environ. Res. 2014, 132, 182–189.
McAlexander, T.P.; Gershon, R.R.M.; Neitzel, R.L. Street-level noise in an urban setting: Assessment and contribution to personal exposure. Environ. Health 2015, 14, 18.
Mehdi, M.R.; Kim, M.; Seong, J.C.; Arsalan, M.H. Spatio-temporal patterns of road traffic noise pollution in Karachi, Pakistan. Environ. Int. 2011, 37, 97–104.
Obaidat, M.T. Spatial Mapping of Traffic Noise Levels in Urban Areas. J. Transp. Res. Forum 2011, 47, 1711.
Ouis, D. Annoyance from Road Traffic Noise: A Review. J. Environ. Psychol. 2001, 21, 101–120.
Raman, M.; Chhipa, R.C. Study of Noise Pollution at Major Intersections in Jaipur City. Int. J. Eng. Sci. Technol. Res. 2014, 3, 74–80.
Ranpise, R. B., & Tandel, B. N. (2022). Noise Monitoring and Perception Survey of Urban Road Traffic Noise in Silence Zones of a Tier II City—Surat, India. Journal of The Institution of Engineers (India): Series A, 103(1), 155–167. https://doi.org/10.1007/s40030-021-00598-x
Seong, J.C.; Park, T.H.; Ko, J.H.; Chang, S.; Kim, M.; Holt, J.B.; Mehdi, M.R. Modeling of road traffic noise and estimated human exposure in Fulton County, Georgia, USA. Environ. Int. 2011, 37, 1336–1341.
Singh, N., & Davar, S. C. (2004). Noise pollution—Sources, effects, and control. Journal of Human Ecology, 16(3), 181–187.
Sommerhoff, J.; Recuero, M.; Suárez, E. Community noise survey of the city of Valdivia, Chile. Appl. Acoust. 2004, 65, 643–656.
Tandel, B. M., Ranpise, A. S., & Tandel, R. (2020). Noise pollution assessment in industrial zones: A study of Surat city, Gujarat, India. International Journal of Environmental Research and Public Health, 17(6), 1978.
Tangermann, L.; Vienneau, D.; Hattendorf, J.; Saucy, A.; Künzli, N.; Schäffer, B.; Wunderli, J.M.; Röösli, M. The association of road traffic noise with problem behaviour in adolescents: A cohort study. Environ. Res. 2022, 207, 112645.
Vijay, R., Mulani, N., & Tare, V. (2013). Urban noise pollution scenario: Case study of Mumbai City. Environmental Monitoring and Assessment, 185, 8235–8248.
Vilas, P.; Prashant, N.P. Measurement and Analysis of Noise at Signalised Intersections. J. Environ. Res. Dev. 2015, 9, 662.
WHO. Environmental Noise Guidelines for the European Region; WHO: Copenhagen, Denmark, 2018.
WHO; Regional Office for Europe. Burden of Disease from Environmental Noise; WHO: Copenhagen, Denmark, 2011.

Table 1

Correlation between the different periods for all monitoring stations in Ahmadabad.
Period	Day	Night	Night (1 am-5 am)
Day	1
Night	-0.989	1
Night (1am-5am)	0.044	0.104	1

Table 2

Correlation between the different zones for all monitoring stations in Ahmadabad.
Zone	Residential	Commercial	Industrial
Residential	1
Commercial	.998^*	1
Industrial	1.000^**	.997^*	1

No competing interests reported.

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Noise Mapping, Identification of Hot Spots, and Mitigation Plan for Control of Noise Pollution for Ahmedabad

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Abstract

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1. Introduction

2. Methodology

2.1. Description of Study Area

2.2. SLM 109

2.3. GIS Mapping Tool (QGIS)

2.4. Noise Data Collection

2.5. Noise Heat Maps

3. Results and Discussion

Conclusion

Declarations

References

Tables

Additional Declarations

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