|Year : 2022 | Volume
| Issue : 3 | Page : 165-171
Geographical information system–Aided noise pollution mapping of urban Puducherry, South India
James T Devasia1, Mahalakshmy Thulasingam1, Subitha Lakshminarayanan1, Bijaya N Naik1, Sabesan Shanmugavelu2, Hari K Raju2, KC Premarajan1
1 Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Department of Preventive and Social Medicine, Dhanvantri Nagar, Gorimedu, JIPMER Campus, Puducherry, India
2 Division of GIS & VBD Stratification/ Mapping, Vector Control Research Centre, Medical Complex, Indra Nagar, Priyadarshini Nagar, Puducherry, India
|Date of Submission||02-Aug-2021|
|Date of Decision||07-Feb-2022|
|Date of Acceptance||24-Mar-2022|
|Date of Web Publication||26-Sep-2022|
Dr. Mahalakshmy Thulasingam
Additional Professor, Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Department of Preventive and Social Medicine, Dhanvantri Nagar, Gorimedu, JIPMER Campus, Puducherry - 605 006
Source of Support: None, Conflict of Interest: None
Context: Noise pollution and its influence on environmental and quality of human life are a major concern and hot topic of scientific research in the twenty-first century. Aims: Spatial analysis of noise pollution in urban Puducherry, South India. Settings and Design: Cross-sectional study conducted in 36 locations of urban Puducherry. Methods and Material: Noise measurements were taken using a calibrated NOR 132 digital sound level meter using the prescribed parameters set by the Central Pollution Control Board. Geo coordinates were taken using Garmin Oregon 550 GPS. Noise measurements were classified according to the Bureau of Indian Standards for town planning into five zones. Statistical Analysis Used: Noise pollution map of urban Puducherry for three time points of the day was generated using ArcGIS Desktop v10.3 with Geo-statistical module and Inverse Distance method. Results: Seventeen percent of the sites are high noise sources (80–90 dB), two thirds (65%) of the study sites fall into concentrated average noise zones (70–80 dB), and less than one fifth (18%) of the study sites are in relatively quiet zones across different measurement time slots. Conclusions: Long-term strategy for noise control should be incorporated in the development of new townships and other infrastructures in accordance with the noise control norms. Implications for future research include monitoring noise pollution levels in rural areas and health effects of noise pollution in bystanders and drivers.
Keywords: ArcGIS, geographical information system, mapping, noise pollution
|How to cite this article:|
Devasia JT, Thulasingam M, Lakshminarayanan S, Naik BN, Shanmugavelu S, Raju HK, Premarajan K C. Geographical information system–Aided noise pollution mapping of urban Puducherry, South India. Indian J Occup Environ Med 2022;26:165-71
|How to cite this URL:|
Devasia JT, Thulasingam M, Lakshminarayanan S, Naik BN, Shanmugavelu S, Raju HK, Premarajan K C. Geographical information system–Aided noise pollution mapping of urban Puducherry, South India. Indian J Occup Environ Med [serial online] 2022 [cited 2022 Dec 3];26:165-71. Available from: https://www.ijoem.com/text.asp?2022/26/3/165/357020
| Introduction|| |
In 1999, World Health Organization (WHO) had reported several adverse effects of noise pollution on humans and estimates that ten percent of the world's population is exposed to noise pollution that could eventually result in permanent hearing loss. Evidence shows an increase in mortality and prevalence of cardiovascular disease in the noise-exposed groups, and a loss of 45,000 disability-adjusted life-years (DALY) every year for children aged 7–19 years in the high-income countries.
Geographical Information Systems (GIS) software can detect noise hot spots, spatial relationship with road network and building structure, which helps organize the noise risk zones, development of cities and towns, and propose control measures. Evidence from Karachi, Pakistan, Seoul, Republic of Korea, and the City of El-Mina, North Lebanon, use GIS technology to visualize and analyze the extent of the problem., Several studies performed in Indian cities found higher sound level than the standards prescribed by Central Pollution Control Board (CPCB), and Ministry of Environment and Forest, Govt. of India (MoEF),[7–10] but there is paucity of literature on noise pollution mapping in major cities, including Puducherry. There is a crucial need to assess the burden due to noise pollution in order to understand the public health hazards posed by it. This study aimed to develop a noise pollution spatial map of Urban Puducherry among different zones of urban Puducherry.
| Subjects and Methods|| |
Study design and setting
A cross-sectional study was conducted in February 2015 in urban Puducherry. Urban Puducherry municipality lies between 11°55′52.44″N, 79°50′6.95″E, about 170 km from Chennai with a population of 244,377 persons in 60,638 households covering an area of 20 sq. km. According to census 2011, 69.16% of inhabitants are in urban area with a density of 3232 persons per square km. Puducherry is well connected with road networks of National Highways, State highways, and rural roads.
ArcGIS 10.3 used to create regularly spaced grid with size of 500 m x 500 m. The grid size was chosen based on the law of physics that upper noise level of heavy road traffic noise 90 dB will diminish to acceptable 60 dB in 500 m. Twenty-four sites were chosen systematically for every 15 intersection points in the grid. In addition, 12 study sites with high noise levels such as industrial, commercial, and traffic junctions were selected randomly, as shown in [Figure 1]. Areas that are within 500 m proximity of each other were excluded. However, due to the fact that Puducherry is a highly urbanized district with closely linked commercial and residential zones, it was impossible to exclude a couple of study sites. The sample size was calculated based on the feasibility of the study.
Study procedure and tools
Nor132 Type 2 Digital Sound Level Meter (SLM) with a frequency range of 8 Hz to 16K Hz, dB range 0 to 120 dB (Norsonic AS, Norway) was used to measure the sound level. SLM was placed in a tripod at a distance of one meter away from the observer and one and a half meters from the ground level and free from any visible obstruction. SLM was calibrated for each measurement and equipped with a windscreen to reduce the wind-generated noise for outdoor measurements. The noise level was measured at 36 sites during the specified time slots T1, T2, and T3 (8:00 am to 9:00 am, 12:00 pm to 1:00 pm, and 6:00 pm to 7:00 pm). Measurement parameters were set as per Central Pollution Control Board (CPCB) guidelines. A-weighted frequency filter is used to measure noise indices during one hour; they are Leq (integrated average of sound pressure level during one-hour measurement), Lmax (maximum sound pressure level during one-hour measurement), and Lmin (minimum sound pressure level during one-hour measurement).
Garmin Oregon 550 differential Global Positioning System (DGPS) with an accuracy of 10 ft to 16 ft (3 m- 5 m) with 95% typical (Garmin Ltd, Kansas, United States) was used to collect the latitude and longitude of the site at T1 time slot. The same location was used in the subsequent time slot spatial analysis.
ArcGIS Desktop v10.3 with Geo-statistical module (Environmental Systems Research Institute, California, United States) was used in this study. Inverse distance-weighted (IDW) interpolation was used to generate noise maps. IDW assumes that the variable of interest has higher local influence that diminishes with distance from its sampled location, which is factual with noise level. Bureau of Indian Standards (BIS) for town planning was used to classify the Noise Risk Zones.
Ethical approval for this study was obtained from the Scientific Advisory Committee and Institute Ethics Committee of Jawaharlal Institute of Postgraduate Medical Education and Research, India (JIP/IEC/SC/2014/10/678).
Industrial Zone—Isolated or clustered building structures operate light manufacturing units, small scale, or service industries.
Commercial Zone—Isolated or clustered building structures that operate retail shops, restaurants, other businesses, and professional offices.
Residential Zone—Building structures used for residential purposes.
Silence Zone—Areas up to 100 meters around the building structures used for hospitals, educational institutions, and courts.
Traffic Junctions—Areas where moving vehicles stop and go by automatic or semi-automatic control.
Methods of analysis
Data retrieval from the SLM was done using NorXfer 5.0 (Norsonic AS, Norway), and the GPS coordinates were exported using DNR Garmin (Department of Natural Resources, Minnesota USA). IDW interpolation was used for noise analysis and parameters for IDW interpolation were set to defaults, and the extent of spatial analysis was limited to the boundary of urban Puducherry.
| Results|| |
WHO noise standards for industrial, commercial, residential, and silent areas are given in [Table 1] and CPCB equivalent in [Table 2]. CPCB standard compared with developed countries: noise level in industrial is 5-15 dB higher, 5-10 dB higher in commercial and residential, and 5 dB higher in silent zone. The sound levels at T1, T2, and T3 time slots across 36 study sites were classified according to BIS standards to understand the Noise Risk Zones.
|Table 2: CPCB standards (Ambient air quality standards in respect of noise, The Noise Pollution (Regulation and Control) Rules, 2000)|
Click here to view
[Table 3] depicts the distribution of areas under the BIS classification of risk zones in each time slot. Sound level Leq ranges from 63.5 to 83.3 dB in T1, and 62.2 to 81.7 dB in T2 and T3 time slots. Lmin and Lmax varies from 50 to 65 dB and 90 to 100 dB, respectively, in all time slots. Our result confirms an increase of 60% sites from T1 to T3 into zone 2 and 30% decrease in zone 3. In contrast, in zone4, the escalation is over 150% of study sites from morning to evening measurements. Across T1, T2, and T3 time slots, 17% of the study sites are in zones of high noise sources (80-90 dB) and two-thirds (65%) sites fall into concentrated average noise zones (70- 80 dB), which is well above the CPCB limit. Less than one-fifth (18%) of the study sites are in relatively quiet zones.
The noise mapping at T1, T2, and T3 time slots was generated using Leq, Lmax, and Lmin noise indices [[Figure 2], [Figure 3], and [Figure 4], respectively]. It is evident that from T1 to T3, the extent of the noise level is increased in residential and commercial zones; the same can be perceived in Lmin, and Lmax interpolated surface seen in insets.
|Figure 2: Inverse Distance Weighted interpolation (IDW) of Noise level during 8:00 am – 9:00 am. Average noise level (Leq) map. (a) IDW mapping of minimum noise level (Lmin). (b) IDW mapping of maximum noise. All maps were generated by the authors using ArcGIS Desktop v 10.3 software (https://desktop.arcgis.com/en/)|
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|Figure 3: Inverse Distance Weighted interpolation (IDW) of Noise level during 12:00 pm – 1:00 pm. Average noise level (Leq) map. (a) IDW mapping of minimum noise level (Lmin). (b) IDW mapping of maximum noise. All maps were generated by the authors using ArcGIS Desktop v 10.3 software (https://desktop.arcgis.com/en/)|
Click here to view
|Figure 4: Inverse Distance Weighted interpolation (IDW) of Noise level during 6:00 pm – 7:00 pm. Average noise level (Leq) map. (a) IDW mapping of minimum noise level (Lmin). (b) IDW mapping of maximum noise. All maps were generated by the authors using ArcGIS Desktop v 10.3 software (https://desktop.arcgis.com/en/)|
Click here to view
| Discussion|| |
Appling BIS criteria, it is found that in T1, T2, and T3 time slots, the heart of the city has high noise pollution compared to the periphery of city limits. The urbanization, Commercial, government, and private office establishments and highly populated vicinity increase the noise level, and the study done in England also reported the same. Data from Agartala study also depict that traffic noise can hinder the efficiency of workers in government and other commercial buildings that are close to main road network.
The day time noise level ranges from 65 to 76 dB (A); out of three locations observed, two of them were below the CPCB standard. The locations above the CPCB standard were near the bus stand where the traffic volume was high. Traffic noise reported was the main cause for the noise pollution in industrial zones. Kolhapur city study also pointed out that there is less vehicular traffic in the industrial zone, and thus, the noise limit is lower compared with other zones. Another study done in Kolhapur shows that in industrial zone and industrial cum residential zones, they were marginally below the daytime ambient noise level prescribed by CPCB. The daytime highest recorded noise level Leq was 72.25 dB (A), and when observed during 4:00 pm to 5:00 pm, it was below the CPCB standard of 75 dB (A)
In T1 time slot noise mapping, Reddiyarpalayam PHC, two Bus Stops Murugampakkam and Moolakulam were above 80 dB (A). These locations are on National and State Highways with a cluster of commercial and local business establishments. The amount of traffic volume on this highway and frequent traffic congestion causes high noise pollution. Five sites were in concentrated noise zones (quite at times 60-70 dB), despite being industrial zones or commercial zones. Gandhi Square and Aurobindo Ashram Junction are located in the “White town” of Puducherry primarily because the inhabitants in this neighborhood are foreigners. This may be due to strict adherence to noise regulations and green belts in this part of the town, despite the high flow of traffic and people.
In the T2 noise map, high noise zones increased from three to five sites, viz., Reddiyarpalayam PHC, Women and Children Hospital, and Housing Board Junction has high traffic volume compared to the other sites at the T2 time slot. Chinna Kadai Bus Stop is located in the downtown area of Puducherry and has a cluster of business establishments. Ajantha Junction has heavy traffic with public transportation and three-wheeler tempos, which creates frequent traffic congestion, causing excess noise levels. Guwahati study reported serious noise pollution in the silence zone area. The noise level in silence zone ranges from 65 dB (A) to 75 dB (A). In Puducherry, Yogamoorthi and Beena reported that the noise level in hospital areas was above 60 dB (A) due to the large crowd attending the hospital and noise level at various educational institutions ranges from 60 dB (A) to 75 dB (A). It also indicated that traffic noise was the major source of noise pollution in the silence zone.
In the T3 time slot noise map, there is a larger number of high noise sources (> 80 dB) compared to T1 and T2 noise maps. This indicates that the mobility of people is more in evening times than morning and noon; shopping malls and other retail business establishments are more active during this time, causing an increase in the volume of public and private transportation, which is the primary source of noise pollution. Similar findings were reported in Asansol, West Bengal. Daytime noise level varies from 59 dB (A) to 89 dB (A), traffic congestion, vehicle speed, and type of vehicle reported as the main reason for the excess noise level in traffic junction. Puducherry study reported the noise level in traffic junction in the range of 70 dB (A) to 80 dB (A). In our study, the sound level, Leq ranges from 74 dB (A) to 82 dB (A)—that is a noticeable increase in sound level at the traffic junctions in 20 years. Traffic policemen are more prone for exposure to these high noise levels at traffic junctions.,
In comparison with Yogamoorthi study, Leq at various hospital areas is in the range of 76 dB (A) to 82 dB (B), i.e., a difference of 20 dB (A) increases from 1996 to 2015, four times louder in noise level. Similarly in educational institutions, Leq ranges from 69 dB (A) to 79 dB (A), i.e., an increase of 10 dB (A) in 20 years. This is more than double the loudness level.
The study conducted in an automotive industry in Tehran the capital of Iran reported that an elevated systolic and diastolic blood pressure of subjects was exposed to noise level above 85 dB. Significant relationship with annoyance was also reported with chronic exposure to noise. Report of the European Environment Agency cites that long-term exposure to noise can cause a variety of health effects including annoyance, sleep disturbance, cognitive impairment in children and negative effects on the cardiovascular and metabolic system, leading to ischaemic heart disease and premature deaths. In fact, according to some World Health Organization (WHO) findings, noise is the second largest environmental cause of health problems, just after the impact of air pollution.
As this is one of the first studies in Puducherry using GIS technologies, these data can be used as a baseline for future review. Earlier studies have suggested that implementation of a GIS model in the monitoring and modeling of sound propagation can facilitate the processing and analysis of data and the creation of scenarios with the main purpose of offering solutions for lowering the vulnerability and the risk to noise pollution. Based on the observations, it is recommended that immediate measures be taken for control of road traffic noise emission and reduce the excess noise level in select sites. Strict implementation of the existing laws in all zones, especially the silence zone, would be a practical solution to bring down the noise level. Placing signboards at the noise hot spots and strict monitoring of rules will significantly reduce the problem. A fitness certificate from the competent authority to comply with vehicular noise emission should be made mandatory for older automobiles. Community-based organizations and stakeholders' involvement to form an “action team for noise” can be the first step in community participation in this direction. Awareness programs can be organized in the community to educate students and the public on ways to bring down the noise level. Long-term strategy for noise control should be incorporated in the development of new townships and other infrastructures in accordance with the noise control norms. Implications for future research include monitoring noise pollution levels in rural areas and the health effects of noise pollution in bystanders and drivers.
| Conclusion|| |
The noise measurement presented in this study has discovered that in urban Puducherry, the road traffic noise exceeded the daytime ambient air quality standard prescribed by the CPCB in 90% of the selected sites. The land use pattern, geographic characteristics, the type of zone, and other topographical features determine the excess noise level. The public, private, and commercial vehicles are the primary sources of noise emission. Increasing number of motor vehicles, population growth, and urbanization contributes to the substantial increase in noise level.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3]