Monitoring of traffic noise pollution in urban Patna,Bihar,India

Abstract

Road traffic noise is a major environmental issue that has been faced by many people around the world, including in Patna city. There is a need to investigate noise regularly because of the rapidly increasing traffic volume. This study focuses on the estimation of the urban traffic noise level in Patna, Bihar, India. Based on a preliminary survey of traffic volume, 12 stations were selected. A sound meter was used to measure the “A”-weighted noise level. Noise parameters like L₁₀, L_50, and L₉₀ were evaluated from the collected noise data. And these noise parameters were used for the calculation of noise pollution indices like Noise Climate (NC), Equivalent Continuous Noise Level (Leq), Noise Pollution Level (NPL), and Traffic Noise Index (TNI). These noise indices compare with the standard limit of the Central Pollution Control Board (CPCB). The equivalent continuous noise level (Leq) measured at different locations in the commercial cum residential zones varies from 77.7 dBA to 92.5 dBA, which is higher than the prescribed noise level limit of the CPCB. Other noise indices, such as the NPL and TNI, are found to range from 88.2 dBA to 111.5 dBA and 77.9 dBA to 125.9 dBA, respectively, at various sampling locations and are also higher than the prescribed limit of the CPCB. The noise level of all stations was found to exceed the prescribed limit of the CPCB. There is a strong correlation exists between NPL with TNI and NC (r > 0.5). Also, there is a strong correlation exists between Leq with NPL (r > 0.5). Using NPL as a dependent variable and TNI, NC, and Leq as independent variables, the regression model was completely validated.

Keywords

Noise pollution noise climate traffic noise index Patna Bihar

Introduction

The word “Noise” refers to a sound that produces unwanted psychological effects in a person, interfering with their social activities such as work, rest, recreation, and sleep.¹ Any unwanted sound produced by humans or machines is described as “Noise”.² Especially in the low frequency range, noise pollution poses a health risk.³ The main sources of noise are industrial noise, traffic noise, and community noise, of which traffic noise affects more than other sources.⁴ In urban areas, traffic-related noise pollution accounts for two-thirds of total noise pollution.⁵ Almost 80% of traffic-related noise is generated by road traffic noise from vehicles.⁶ There are many factors involved in vehicle noise, such as the engine and exhaust system, aerodynamic friction, and interaction between the vehicle and road system.⁷ Road traffic noise has become one of the biggest challenges faced by urban planners and environmental engineers in cities today. Several studies have been conducted to investigate the impact of noise pollution on several cities throughout the world^8,9 and have shown the level of discomfort caused by noise.¹⁰ Continuously high levels of noise may cause serious damage to auditory and non-auditory systems. Various studies have been carried out on noise pollution, which has adverse effects on severe health problems such as physical and psychological problems, irritation, human performance and actions, hypertension, heart problems, fatigue, headache, and sore throat, respectively.^11–14 The louder the sound, the shorter the amount of time it takes for hearing loss to occur. A prolonged and continuous exposure to loud noise can cause some cancers.¹⁵ As a result, traffic noise control has become a major concern for communities attempting to maintain a healthy environment in which they live and work. Noise effects on human health can be categorized into four different categories depending on volume and duration.¹⁶

a. A physical problem such as a hearing problem.

b. Physiological effects include elevated blood pressure and heart problems.

c. A psychological effect that causes sleeplessness and stress.

d. Work performance is also affected.

Road traffic noise is a major environmental issue that has been faced by many people around the world including Patna city. Patna, the capital city of Bihar, is one of the ancient cities of India. Due to a lack of proper zoning, the rapid increase in urban population, individual transportation, use of loudspeakers, etc., all these factors have contributed to noise pollution in Patna city. The study has been conducted to investigate the noise pollution levels in Patna city due to a rapid increase in traffic volume, and attempt to generate a correlation between them and develop a regression model.

Materials and methods

Study area

In this study, noise pollution was assessed in Patna during the autumn season (October to November 2021). Table 1 shows the geographical location and weather characteristics of Patna city during noise data collection. Table 2 and Figure 1 shows the 12 sampling stations that were selected for the study of noise pollution within Patna city. All the selected monitoring stations have come under the Commercial cum Residential Zone.

Table 1.

Geographic locations and weather characteristics of Patna city during noise data collection.

S. No	Parameters	Documented values
1	Population (2011 census)	1,683,200
2	Latitude	25.6°N
3	Longitude	85.1°E
4	Length	35 Km
5	Annual rainfall in mm	1100
6	Avg. elevation	53 m (174 feet)
7	Width	16–18 km
8	Max temperature during Autumn season (°C)	30 ± 2
9	Min temperature during Autumn season (°C)	20 ± 2

Table 2.

Sampling locations for assessment of noise pollution within Patna city.

S. No	Station code	Stations
1	S1	Kargil chowk near Gandhi Maidan
2	S2	Kurji More
3	S3	Boring Road Chauraha
4	S4	Saguna More
5	S5	Lal Chowk near Danapur railway station
6	S6	Malahipakdi Chowk
7	S7	Dakbanglow Chauraha
8	S8	Patliputra Golambar
9	S9	Mahendru Post Office
10	S10	New Bus stand, Pahari
11	S11	Anishabaad Golambar
12	S12	Dhanki more, Kumhrar

Figure 1.

Study area and sampling stations.

Measurement of noise

All measurements of noise followed the noise pollution (regulation and control) rules 2000, as prescribed by the Central Pollution Control Board.¹⁷ A sound level meter (SLM) (IEC 61672 Class II ANSI S1.4 Type 2) was used to measure ‘A’-weighted noise levels for noise pollution analysis. The SLM was held in a tripod stand at a height of 1–1.5 m above the ground and 5–7 m away from the centre of the carriage way (Central Pollution Control Board (CPCB) protocols). For each sampling location, the noise level was recorded per minute (i.e., 60 readings were recorded every hour). SLM was performed in the fast response mode with an evaluation of the A-weighting curve. All measurements were taken when weather conditions were favourable (no rain or strong winds). The speed of the wind can significantly impact the accuracy of a measurement. The use of wind screens on the top of the SLM can reduce this noise. The sound level meter was calibrated up to 94 dB for each location data measurement that followed.¹⁸ These measurements were taken simultaneously from both sides of the road. The day time schedule selected for noise data collection was; morning time 9:00 a.m. to 11:00 a.m.; afternoon time 1:00 p.m. to 3:00 p.m. and evening time 5:00 p.m. to 7:00 p.m. This time was selected by a preliminary survey of taken stations due to the high volume of traffic. After noise data collected from all the selected locations; statistical analysis was done.

Noise pollution indices

Different noise parameters like L_10, L_50, and L₉₀ were evaluated from collected noise data with the help of Microsoft Excel. And these noise parameters were used for the calculation of noise pollution indices like (1) Noise Climate (NC), (2) Equivalent Continuous Noise Level (L_eq), (3) Noise Pollution Level (NPL), & (4) Traffic Noise Index (TNI).^19,20 The following formula was used to obtain the noise pollution indices

L_{e q} = 10 \times \log \sum_{i = 1}^{i = n} 10^{\frac{L i}{10}} \times t i

(1)

T N I = L_{e q} + 4 (L_{10} - L_{90}) - 30

(2)

N P L = L e q + (L_{10} - L_{90})

(3)

N C = L_{10} - L_{90}

(4)

Where, n is the number of sound samples, L_i is the noise level of the i^th sample, t_i is the time duration of the i^th sample, NC is Noise Climate represents Sound level Fluctuate, L₁₀ denotes peak noise level, L₅₀ denotes average noise level, L₉₀ denotes background noise level, L_eq is an Equivalent continuous noise level, NPL denotes noise pollution level, TNI indicates the traffic noise index.

As a result of the adverse effects of noise on human health, noise control measures are implemented. India has also introduced, that categories our area into four categories and sets limits for the allowable noise limits for daytime and night time in different zones.²¹ Table 3 provides ambient noise levels for different zones.

Table 3.

Ambient noise standards.

Area code	Category of area/zone	Limits in dB
Area code	Category of area/zone	Day time	Night time
A	Industrial area	75	70
B	Commercial area	65	55
C	Residential area	55	45
D	Silence zone	50	40

The limit in Decibels (dBA) refers to the time-weighted average of the decibel level on scale A, which is comparable to human hearing.²²

Determination of statistical and regression analysis

The descriptive statistical analysis was conducted using Microsoft Excel 2019. Statistical correlations and regression analyses were performed using IBM SPSS Statistics 28.

Results and discussion

Noise parameters like L₁₀, L₅₀, and L₉₀ were calculated. L₁₀ is the level exceeded for 10% of time. These sound pressure levels are probably due to sporadic or intermittent events. L₁₀ is used in planning applications. L₅₀ is the level exceeded 50% of time. It represents the median of the fluctuating noise levels. L₉₀ is the level exceeded 90% of the time. It is generally considered to represent the background or ambient noise environment. Graphical representation of all noise parameters, i.e., L₁₀, L₅₀, and L_90, for all stations in the day time is shown in Figure 2. Table 4. Illustrates the descriptive statistical analysis of the noise parameters and noise indices. The maximum value of L₁₀ was 95.3dBA, which was recorded at S9 (Mahendru Post Office) in the evening, and the minimum value of 79.7 dBA was obtained at S4 (Saguna More) in the morning. The maximum value of the average noise level (L₅₀) was recorded (85 dBA) at S6 (Malahipakdi Chowk) in the morning, and the minimum value of the average noise was recorded (69.7 dBA) at S9 in the morning. The background noise was expressed as L₉₀, i.e., a noise level that exceeded 90% of the total noise level. The maximum value of L₉₀ was 79.7 dBA at S6 in the afternoon whereas the minimum value was 65.3 dBA at S9 in the morning.

Figure 2.

Noise parameters of all stations in (a) Morning, (b) Afternoon, and (c) Evening.

Table 4.

Descriptive statistical analysis of the noise parameters and noise indices.

Parameters	Morning			Afternoon			Evening
Parameters	Minimum	Maximum	Average ± std.	Minimum	Maximum	Average ± std.	Minimum	Maximum	Average ± std.
L₁₀	79.7	92.9	85.87 ± 4.05	82.6	93.1	86.89 ± 3.03	82.9	95.3	88.92 ± 3.27
L₅₀	69.7	85.0	77.67 ± 4.22	71.7	84.9	79.12 ± 3.80	75.9	84.7	81.23 ± 2.59
L₉₀	65.3	78.2	71.67 ± 3.20	65.9	79.7	73.26 ± 4.20	70.1	78.5	73.81 ± 3.11
Leq	77.7	92.5	84.49 ± 4.74	80.4	90.6	85.25 ± 3.20	80.5	91.3	86.47 ± 3.51
NPL	88.2	107.2	98.69 ± 6.25	90.6	104.1	98.84 ± 4.17	90.6	111.5	101.41 ± 5.65
TNI	77.9	115.5	99.60 ± 11.03	81.3	105.6	97.57 ± 7.14	83.2	125.9	103.75 ± 10.93
NC	9.4	18.6	14.20 ± 2.71	8.9	16.7	13.59 ± 2.41	10.1	20.2	14.94 ± 2.95

Equivalent noise levels (Leq)

Noise may consist of different types of sounds with different pressure levels and different time duration; a combined effect of rating is done by the Equivalent Noise Level Concept. Graphical representation of the equivalent noise level (L_eq) of all selected locations in the morning, afternoon, and evening with an acceptable noise level is given in Figure 3(a). The observation for the S1, which is situated in a commercial cum residential zone, showed that the Leq was 90.2 dB(A), 84.7 dB(A), and 88.3 dB(A) in the morning, afternoon, and evening, respectively. It was found above the day time noise standards of 65 dB(A) as prescribed by CPCB standards (Table 3). Leq for S2, which also comes under the commercial cum residential zone, was 88.2 dB(A), 82.3 dB(A), and 87 dB(A) in the morning, afternoon, and evening, respectively. It was also found above the day time noise standard of 65 dB(A) given by CPCB standards. S3 noise parameters (L_eq) in different time slots were also above the prescribed value of CPCB, which also comes under the commercial-residential zone. Similarly, the equivalent noise levels of S4, S5, S6, S7, S8, S9, S10, S11, and S12 all fall under the commercial cum residential zone and exceed the prescribed limits of 65 dB(A). The maximum L_eq recorded was 92.5 dB(A) at S6 between 9:00 a.m. to 11:00 a.m., While it minimum of 77.7 dB(A) during morning time 9:00 a.m. to 11:00 a.m. at S5. It was seen that at S1, S2, and S3, L_eq in the afternoon had a lower value in comparison to the morning and evening. It was found at S4, S5, S7, S9, S10, and S11 L_eq of morning time was lower than afternoon and evening time. Similarly, it was seen at S6, S8, and S12, L_eq of evening time was lower than morning and afternoon time.

Figure 3.

Comparative study of noise indices (a) Leq, (b) TNI, (c) NPL, (d) NC.

Traffic noise index

This index measures traffic noise intensity by combining noise levels, which gives a better correlation to dissatisfaction.²³ Under the Noise Pollution Rules 2000 under the Environmental Protection Act of 1986, the permissible value for the TNI is 74 dBA. Graphical representation of TNI for all stations in the morning, afternoon, and evening with the standard limit is shown in Figure 3(b). The TNI was observed to be exceeding its permissible values at every location. The maximum observed value of the TNI was 125.9 dBA at S9 (Mahendru Post Office) in the evening whereas the minimum TNI was 81.3 dBA at station 12 (Dhanki More, Kumhrar) in the afternoon. It was observed that at S1, S2, S6, and S12, the TNI value in the morning was higher than that in the evening and afternoon. At S3, S4, S5, S7, S8, S9, S10, and S12, TNI values in the evening were higher than that in the morning and afternoon.

Noise pollution level

To calculate NPL, both the NC and L₅₀ indices are used. It provides information about noise pollution with fluctuations in the noise level, which is considered the best indicator of the physiological and psychological effects of noise pollution.¹⁶ Under the Noise Pollution Rules 2000 under the Environmental Protection Act of 1986, the permissible value for TNI is 88 dBA. The observed values of NPL for all stations during the day are shown in Figure 3(c). The highest NPL was observed at 111.5 dBA from 5:00 p.m. to 7:00 p.m., and the lowest NPL was observed at 88.2 dBA from 9:00 a.m. to 11:00 a.m. NPL was observed to be exceeding its permissible values at every location, every time. It was observed that at S1, S3, and S6 in the morning, NPL value was higher than in the afternoon and evening. Whereas at S3, S4, S5, S7, S9, and S10, the NPL value was higher in the evening than in the morning and afternoon. At S8 afternoon time, NPL was greater than in the morning and evening. It was also observed that at S11 and S12, the NPL value in the evening time is equal to the NPL value in the afternoon time.

Noise climate

A noise climate (NC) is a range over which sound levels fluctuate over a given period.²⁴ S9 (Mahendru Post Office) represents the highest fluctuation, with NC levels of 20.2 dBA in the evening time between 7:00 p.m. and 9:00 p.m. S12 (Dhanki more, Kumhrar) shows the lowest fluctuation of 8.9 dBA in the afternoon time between 1:00 p.m. to 3:00 p.m. A graphical representation of the noise climate of all stations in the morning, afternoon, and evening is shown in Figure 3(d). It was seen that at S2, S6, and S12 morning time sound level fluctuation was more than in the afternoon and evening time. At S1, S3, and S8, the afternoon NC value was higher than in the morning and evening. Whereas at S4, S5, S7, S9, S10, and S11, the sound level fluctuation was higher in the evening than it was in the morning and afternoon.

Spatial distributions

The spatial distribution of noise indices at 12 stations was generated by ArcMap 10.3 software and is given in Appendix Figures 4–6. Appendix Figure 4 shows the spatial distribution of noise indices in the morning. Similarly, Appendix Figures 5 and 6 show the spatial distribution of noise in the afternoon and evening, respectively. Mainly, the spatial distribution of noise establishes a relationship to other stations. It is seen that all stations exceed the prescribed limit defined by the CPCB (Table 3).

Correlations of noise indices

The Pearson correlation primarily explains relationships between two or more variables. Statistically, it shows weak or strong relationships and directions between two or more variables. The Pearson coefficient ‘r’ indicates a linear relationship between two or more variables (a positive correlation indicates a proportional relationship, while a negative correlation indicates an inverse relationship). The coefficient of correlation r less than 0.5 means weakly related and r greater than 0.5 means strongly related.^25,26 The correlation between noise indices is generated using IBM SPSS Statistics version 28 (given in Table 5). There is a strong correlation between NPL with TNI and NC (r > 0.5). Also, there is a strong correlation exists between Leq and NPL (r > 0.5) whenever a weak correlation exists between Leq and NC. It is also observed that in morning and evening cases strong correlation exists between Leq with TNI whenever, in the afternoon case it is found weak correlation.

Regression analysis

An analysis of correlation shows that NPL is well correlated with TNI, NC, and Leq. Therefore, NPL is treated as a dependent variable. TNI, NC, and Leq are treated as independent variables. A summary of the model was generated using SPSS software, as shown in Table 6–8. According to Table 6, the multiple correlation coefficient (R) for NPL is 1, which indicates that there is a significant relationship between the actual and predicted values. The predicted values are obtained as a linear combination of TNI (X₁), NC (X₂), and Leq (X₃). A correlation coefficient of 1 indicates that NPL (Y) is highly correlated with the three independent variables. In Table 7, the ANOVA results show that the F value (model) is significant. The multiple regression equation of NPL is as shown in equation (5).

Y = - 0.519 + 0.053 X_{1} + 0.846 X_{2} + 0.97 X_{3}

(5)

Here, the coefficient of X₁ is 0.053, which represents the partial effects of TNI on NPL while holding the other variables constant. The estimated positive sign implies that such effects are positive; that is, NPL would increase by 0.053 for every unit increase in TNI, and this coefficient value is significant at the 1 percent level in Table 8. Similarly, the coefficients of X₂ and X₃ are 0.846 and 0.97, respectively, representing the partial effects of NC and Leq on NPL, holding the other variables constant. Table 8 shows that the estimated positive sign implies that for every unit increase in NC and Leq, the NPL will increase by 0.846 and 0.97, respectively. This coefficient value is significant at the 1 percent level. The model is generated using afternoon data and validated using morning and evening data. The validation found that it was 100% accurate.

Conclusions

An assessment of road traffic noise in Patna city leads to the following conclusions: Patna is the fastest-growing city. Patna city suffers from high noise levels because of its narrow roads, dense population, and many individual vehicles. Also, due to high construction work in Patna region such as flyover, road construction, sewage pipeline work etc., rise the noise level. As a result of the lack of proper zoning, many areas are mixed with other zones, i.e., silence areas are mixed with commercial zones, and also commercial zones are mixed with residential zones, causing an increase in noise pollution. The equivalent continuous noise level (L_eq) measured at different locations in the commercial cum residential zones varies from 77.7 dBA to 92.5 dBA, which is higher than the prescribed noise level limit of the CPCB. Other noise indices, such as the NPL and TNI, are found to range from 88.2 dBA to 111.5 dBA and 77.9 dBA to 125.9 dBA, respectively, at various sampling locations and are also higher than the prescribed limit of the CPCB. The noise levels of all the selected locations exceed the prescribed limit of CPCB. Higher noise levels were found at S9 (Mahendru Post Office) in the evening due to higher traffic volumes. Now a days Patna is facing an enormous problem of exceeding the noise level. So, noise has a direct and indirect effect on people living in Patna. This type of work may also be done in rapidly developing cities. So, looking at the results from the study, it is concluded that proper zoning, less honking, proper management, promoting e-vehicles is required to be done to give a peaceful environment.

There is a strong correlation exists between NPL with TNI and NC (r > 0.5). Also, there is a strong correlation exists between Leq with NPL (r > 0.5). Validation of the regression model using NPL as a dependent variable and TNI, NC, and Leq as independent variables was 100% accurate. It indicates there is a strong relationship between these noise indices.

Footnotes

Acknowledgements

We would like to thank National Institute of Technology Patna for its financial support.

Declaration of conflicting interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Data availability

All data generated or analysed during this study are included in this article using directly or in the form of figures [and also its supplementary files].

ORCID iDs

Saurabh Kumar

Reena Singh

Appendix

Table 5.

Correlations between noise indices.

	Leq	NPL	TNI	NC
Morning
Leq	1
NPL	0.914″	1
TNI	0.737″	0.924″	1
NC	0.357	0.705″	0.839″	1
Afternoon
Leq	1
NPL	0.818″	1
TNI	0.414	0.844″	1
NC	0.085	0.643″	0.911″	1
Evening
Leq	1
NPL	0.896^″	1
TNI	0.695″	0.936″	1
NC	0.528	0.85″	0.967″	1

″Correlation is significant at the 0.01 level (2-tailed).

Table 6.

Statistics for the Model.

Model	R	R square	Adjusted R square	Std. Error of the estimate
1	1.00	1.00	1.00	0.11

Table 7.

Statistics of ANOVA for the Model.

Model	Sum of squares	df	Mean square	F	Significant
Regression	462.87	3	154.29	12339.12	<0.001
Residual	0.11	9	0.01
Total	462.98	12

Table 8.

Model coefficients.

Model	Coefficients
	Unstandardized coefficients		Standardized coefficients	t	Significant
	B	Std. Error	Beta	t	Significant
(Constant)	−0.519	0.888	—	−0.585	0.573
TNI	0.053	0.009	0.061	5.799	<0.001
NC	0.846	0.017	2.577	50.832	<0.001
Leq	0.970	0.015	3.341	63.126	<0.001

Figure 4.

Spatial distribution of noise indices in the morning. (a) Leq, (b) TNI, (c) NPL, (d) NC.

Figure 5.

Spatial distribution of noise indices in the afternoon. (a) Leq, (b) TNI, (c) NPL, (d) NC.

Figure 6.

Spatial distribution of noise indices in the evening. (a) Leq, (b) TNI, (c) NPL, (d) NC.

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