**2.1 Influential parameters**

#### *2.1.1 Slope*

Road slope greatly influences commuting, traffic arrangement, speed, and driving patterns. Noise pollution varies between roads with different slopes. The mean slope of the study areas was 0–3%.

**135**

*A Mathematical Model of Noise Pollution in Streets of Tehran near IKIA Airport*

The National Cartographic Center's 1:2000 maps, field studies, and Google Maps™ were used to calculate land use (residential, commercial, administrative,

Considering the direct influence of urban traffic on the noise level, the authors measured raw data for the number of vehicles, survey time, and other data at each point in order to calculate the traffic of survey points via extra processing. The correlation between traffic level and noise level made this data essential. A reasonable high-traffic hour at the peak mounting traffic (7–8 am) was selected to measure peak traffic. The vehicle data, converted to their equivalent according to saloon

where Va is traffic volume in a complete period, V is the calculated traffic during the measurement time in a period, Cf is the correction factor of the

> *Cf* = \_ *Tc Tc* − *Ts*

where *Tc* is the complete measurement period (min), Ts is the short stoppage

Due to high traffic and impossibility of direct measurements, road widths were estimated base on the number of lanes (every 3–3.65 m) and matching them with

The sound data were collected using the device TES Sound Level Meter 1353H (calibrated by a qualified company) measurements were performed in the mornings at 15–20 m intervals. The longitude and latitude of each measurement point were

If during measurements, a vehicle with very high noise levels (bus, heavy truck,

Data analysis was performed via ArcGIS 10.4.1. Raster polygon layers of sound

After testing the relationship between noise level variable and different combi-

*Leq* (*m*) = 70.554 + 0.002 × *Traffic* − 0.078 × *Residential* (3)

nations of independent variables, the best model was selected using Eq. (3).

etc.) passed nearby the measuring device at low speeds or stopped, the authors attempted to remove its effect from measurements as it would introduce abnormal

Google Earth maps and the mean value for multiple sections of each road.

*Va* = *V* × *Cf* (1)

(2)

*DOI: http://dx.doi.org/10.5772/intechopen.89794*

vehicles, were calculated using Eq. (1) [10].

*2.1.2 Land use*

*2.1.3 Traffic*

measurement.

time (min).

*2.1.4 Road width*

Cf is calculated via Eq. (2)

**2.2 Generating the noise level map**

recorded via a Garmin GPS device.

The data model:

variations in measurements leading to statistical errors.

data for the four studied areas are presented in **Figure 2**.

and natural ground).

*A Mathematical Model of Noise Pollution in Streets of Tehran near IKIA Airport DOI: http://dx.doi.org/10.5772/intechopen.89794*

#### *2.1.2 Land use*

*Environmental Impact of Aviation and Sustainable Solutions*

Standard suggestion of the environmental organization of Iran for noise is shown in **Table 1**. From the results of contour map and this table, divide four the

**Day (7 am–10 pm) Unit in dB**

 55 Residential region 60 Residential-commercial region 65 Commercial region 70 Residential-industry region 75 Industry region

*Contours of the noise around the airport. Invert the output of software to decibels: Black contour: 80 dB; Brown* 

**Type of region**

With comparison the noise map from the noise exposure forecast modeling with the ICAO land use recommendations in **Table 1**, and knowing that in the airport, we also have noise pollution from numerous vehicles and factories that may develop in the near future, should have a master plan for decreasing the noise of the airport, should do it first at the origin of it and then by barriers with a suitable plan for building near IKIA. The next section suggests some recommendations that may be

Road slope greatly influences commuting, traffic arrangement, speed, and driving patterns. Noise pollution varies between roads with different slopes. The mean

regions and in **Figure 1** the result is shown.

*contour: 75 dB; red contour: 70 dB; and at last contour 65 dB.*

**134**

used for the airport.

**Night (10 pm–7 am) Unit in dB**

*Standard for noise values [9].*

*2.1.1 Slope*

**Table 1.**

**Figure 1.**

**2.1 Influential parameters**

slope of the study areas was 0–3%.

The National Cartographic Center's 1:2000 maps, field studies, and Google Maps™ were used to calculate land use (residential, commercial, administrative, and natural ground).

### *2.1.3 Traffic*

Considering the direct influence of urban traffic on the noise level, the authors measured raw data for the number of vehicles, survey time, and other data at each point in order to calculate the traffic of survey points via extra processing. The correlation between traffic level and noise level made this data essential. A reasonable high-traffic hour at the peak mounting traffic (7–8 am) was selected to measure peak traffic. The vehicle data, converted to their equivalent according to saloon vehicles, were calculated using Eq. (1) [10].

$$V\_a = V \times C\_f \tag{1}$$

where Va is traffic volume in a complete period, V is the calculated traffic during the measurement time in a period, Cf is the correction factor of the measurement.

Cf is calculated via Eq. (2)

$$\mathbf{C}\_f = \frac{T\_c}{T\_c - T\_s} \tag{2}$$

where *Tc* is the complete measurement period (min), Ts is the short stoppage time (min).

#### *2.1.4 Road width*

Due to high traffic and impossibility of direct measurements, road widths were estimated base on the number of lanes (every 3–3.65 m) and matching them with Google Earth maps and the mean value for multiple sections of each road.

#### **2.2 Generating the noise level map**

The sound data were collected using the device TES Sound Level Meter 1353H (calibrated by a qualified company) measurements were performed in the mornings at 15–20 m intervals. The longitude and latitude of each measurement point were recorded via a Garmin GPS device.

If during measurements, a vehicle with very high noise levels (bus, heavy truck, etc.) passed nearby the measuring device at low speeds or stopped, the authors attempted to remove its effect from measurements as it would introduce abnormal variations in measurements leading to statistical errors.

Data analysis was performed via ArcGIS 10.4.1. Raster polygon layers of sound data for the four studied areas are presented in **Figure 2**.

The data model:

After testing the relationship between noise level variable and different combinations of independent variables, the best model was selected using Eq. (3).

$$L\_{eq\\_(m)} = 70.5\,\text{S} 4 + 0.002 \times Traffic - 0.078 \times Residential \tag{3}$$

where *Leq*(*m*) is the noise level (dB), *Trafficm* is the vehicle traffic, and *Residential* denotes the percentage of residential land use.

As shown, noise level is related to the traffic volume and residential land use independent variables. There is a positive linear relationship between noise level and vehicle volume, indicating that higher vehicle volumes resulted in increased noise. The relationship predicts that one vehicle per hour increase in vehicle traffic volume will increase noise level by 0.002 dB. There is a negative, linear relationship between noise level and percentage of residential land use, indicating that 1% increase in residential land use will increase noise level by 0.078 dB. T**ables 2** and **3** show the data for observation times, goodness of fit indices (*R<sup>2</sup>* , *RAdj* 2 ), mean square error (MSE), results of t and F tests, and their respective significance, the estimated parameters (coefficients of independent variables) and their confidence interval, and the variance analysis table and F statistical value. The following section provides an analysis for each table.

#### *2.2.1 Normality of the residuals*

**Figure 3** shows residuals according to their frequencies, representing a relatively normal distribution.

### *2.2.2 Linearly or nonlinearity of the relationships between dependent and independent variables*

The linearity analysis was performed using a graph separating dependent and independent variables. According to **Figures 4** and **5**, the maximum value of goodness of fit index for the traffic and noise level relationship was 0.64, followed by 0.489 for the percentage of residential land use. Road width ranked third however it could simultaneously be used in the regression model due to very high correlation with the traffic variable.

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*A Mathematical Model of Noise Pollution in Streets of Tehran near IKIA Airport*

*DOI: http://dx.doi.org/10.5772/intechopen.89794*

**Traffic**

1 0.6931 −0.5235 −0.6140

0.1575 −0.6536

0.5788 0.8285

0.6242

−0.5469

−0.5731

−0.0680

−0.6015

0.7075

1

0.5876

−0.2807

−0.7782

−0.5012

−0.3898

1

−0.3095

0.0749

0.0175

0.0557

1

−0.1258

−0.1251

0.0715

1

−0.6486

0.6459

1

−0.4349

1

1

Traffic Sound Level

Slope Residential Commercial Administrative

Natural Ground

Road Width

**Table 2.**

*Variable correlations.*

**sound Level**

**Slope**

**Residential**

**Commercial**

**Administrative**

**Natural Ground**

**Road Width**


#### *A Mathematical Model of Noise Pollution in Streets of Tehran near IKIA Airport DOI: http://dx.doi.org/10.5772/intechopen.89794*

**Table 2.** *Variable correlations.*

*Environmental Impact of Aviation and Sustainable Solutions*

denotes the percentage of residential land use.

vides an analysis for each table.

*2.2.1 Normality of the residuals*

*independent variables*

normal distribution.

**Figure 2.**

*Noise level map of Tajrish.*

with the traffic variable.

show the data for observation times, goodness of fit indices (*R<sup>2</sup>*

*2.2.2 Linearly or nonlinearity of the relationships between dependent and* 

where *Leq*(*m*) is the noise level (dB), *Trafficm* is the vehicle traffic, and *Residential*

, *RAdj* 2

), mean square

As shown, noise level is related to the traffic volume and residential land use independent variables. There is a positive linear relationship between noise level and vehicle volume, indicating that higher vehicle volumes resulted in increased noise. The relationship predicts that one vehicle per hour increase in vehicle traffic volume will increase noise level by 0.002 dB. There is a negative, linear relationship between noise level and percentage of residential land use, indicating that 1% increase in residential land use will increase noise level by 0.078 dB. T**ables 2** and **3**

error (MSE), results of t and F tests, and their respective significance, the estimated parameters (coefficients of independent variables) and their confidence interval, and the variance analysis table and F statistical value. The following section pro-

**Figure 3** shows residuals according to their frequencies, representing a relatively

The linearity analysis was performed using a graph separating dependent and independent variables. According to **Figures 4** and **5**, the maximum value of goodness of fit index for the traffic and noise level relationship was 0.64, followed by 0.489 for the percentage of residential land use. Road width ranked third however it could simultaneously be used in the regression model due to very high correlation

**136**


**Table 3.**

*Model summary.*

**Figure 3.** *The frequency of the regression model residuals.*

**139**

**3. Conclusion**

*Noise level and road slope relationship.*

**Figure 5.**

*A Mathematical Model of Noise Pollution in Streets of Tehran near IKIA Airport*

The negative impact of aircraft noise, in particular around airports, is increasing. More and more people suffer not only from annoyance, but recent studies indicate that intermediate and high noise levels also contribute to physiological and psychological effects that in extreme cases can cause severe health problems. The aircraft industry has launched an ambitious plan for the next 15 years to reduce the noise emission levels from aircraft by as much as 20 dB. Even if this goal can be reached, reduced noise emission levels for new aircraft will have little or no influence on the total noise situation around airports in future. This is due to a slow

In order to stay competitive and to cope with an increasing number of neighborhood complaints and noise-impact related constraints, airport owners will have to

The International Civil Aviation Organization (ICAO) has defined a four-point

For improving this method airports authorities should develop and buy new aircrafts that have less noise such as boeing 757 instead of boeing 727 and etc.

space, administrative, and commercial land use; road width, and road slope.

The results indicate the critical significance of urban traffic in noise pollution, as by a large difference it had the highest contribution to noise level, followed by green

Commercial and business land uses generated the highest noise pollutions. With their high commuting levels and passenger traffic, malls and commercial centers produce high noise levels, especially at certain hours in the morning, resulting in higher noise and environmental pollutions compared to natural ground or residen-

For reduced noise pollution in Tehran and generally all urban areas, it is recommended to promote good driving behaviors and vehicle technical control for their

renewal rate for aircraft combined with an increase in passenger volume.

look for novel solutions to reduce noise emission levels.

tial areas. Sound levels above 70 dB irritate humans.

"balanced approach" that includes: Reduction of noise at source;

Land-use planning;

*DOI: http://dx.doi.org/10.5772/intechopen.89794*

**Figure 4.** *Noise level and vehicle volume relationship.*

*A Mathematical Model of Noise Pollution in Streets of Tehran near IKIA Airport DOI: http://dx.doi.org/10.5772/intechopen.89794*

**Figure 5.** *Noise level and road slope relationship.*

#### **3. Conclusion**

*Environmental Impact of Aviation and Sustainable Solutions*

**R2**

**Std. error of the estimate**

1 0.800 0.640 0.638 2.8233 0.641 497.107 1 280 0.000

**R2 change**

2 0.826 0.682 0.680 2.6577 0.042 36.980 1 279 0.000 1.936

**Change statistics Durbin-**

**Change**

**F change df1 df2 Sig. F** 

**Watson**

**Model R R2 Adjusted** 

**138**

**Figure 4.**

*Noise level and vehicle volume relationship.*

**Figure 3.**

**Table 3.** *Model summary.*

*The frequency of the regression model residuals.*

The negative impact of aircraft noise, in particular around airports, is increasing. More and more people suffer not only from annoyance, but recent studies indicate that intermediate and high noise levels also contribute to physiological and psychological effects that in extreme cases can cause severe health problems. The aircraft industry has launched an ambitious plan for the next 15 years to reduce the noise emission levels from aircraft by as much as 20 dB. Even if this goal can be reached, reduced noise emission levels for new aircraft will have little or no influence on the total noise situation around airports in future. This is due to a slow renewal rate for aircraft combined with an increase in passenger volume.

In order to stay competitive and to cope with an increasing number of neighborhood complaints and noise-impact related constraints, airport owners will have to look for novel solutions to reduce noise emission levels.

The International Civil Aviation Organization (ICAO) has defined a four-point "balanced approach" that includes:

Reduction of noise at source;

For improving this method airports authorities should develop and buy new aircrafts that have less noise such as boeing 757 instead of boeing 727 and etc.

Land-use planning;

The results indicate the critical significance of urban traffic in noise pollution, as by a large difference it had the highest contribution to noise level, followed by green space, administrative, and commercial land use; road width, and road slope.

Commercial and business land uses generated the highest noise pollutions. With their high commuting levels and passenger traffic, malls and commercial centers produce high noise levels, especially at certain hours in the morning, resulting in higher noise and environmental pollutions compared to natural ground or residential areas. Sound levels above 70 dB irritate humans.

For reduced noise pollution in Tehran and generally all urban areas, it is recommended to promote good driving behaviors and vehicle technical control for their

sound level as well as implementing sound barriers for preventing the sound leaking into residential areas. Further, it is recommended that for future roads or revamping the existing ones, more lanes be implemented to produce wider roads, prevent the construction of tall buildings on the sided of main roads, and maintaining a standard distance between buildings and main roads, freeways, and other motorways.

The negative impact of aircraft noise, in particular around airports, is increasing. More and more people suffer not only from annoyance, but recent studies indicate that intermediate and high noise levels also contribute to physiological and psychological effects that in extreme cases can cause severe health problems. The aircraft industry has launched an ambitious plan for the coming 15 years to reduce the noise emission levels from aircraft by as much as 20 dB [1].

Other strategies for reducing noise pollution in urban areas include designating suitable locations for land uses in comprehensive and development plans, use of standard, low-noise vetches, imposing limitations on the passage of automobiles and motorcycles, imposing speed limits, improving traffic behaviors and extending public transport. Sound barriers around motorways and the use of sound-absorbent materials in commercial and residential buildings or natural ground near residential areas or roads will greatly reduce noise pollution levels. In addition, proper city-wide planning requires establishing sufficient noise-pollution measurement stations and sound level maps for different urban regions and land uses.
