Mapping of noise pollution by different interpolation methods in recovery section of Ghandi telecommunication Cables Company

Taghizadeh-Mehrjardi R, PhD ^1*, Zare M, PhD ², Zare S, MSc ³

1- Assistant Prof., Faculty of Agriculture and Natural Resources, University of Ardakan, Ardakan, Iran. 2- PhD student, LUNAM Université, Universitéd’Angers, Laboratoired’ergonomieetd’épidémiologie en santé au travail (LEEST), Angers, France. 3- Expert in Dept of Electrical Engineering, National Iranian Oil Company (NIOC), Yazd, Iran.

Abstract Received: September 2013, Accepted: January 2013

Background: Noise pollution and workers' noise exposure are common in industrial factories in Iran. In order to reduce this noise pollution, evaluation and investigation of noise emission are both necessary. In this study, different noise mapping methodsare used for determining the distribution of noise.

Materials and Methods: In the present study, for preparing a noise map in a hall of an industrial factory, sampling was carried out in 6×6 m grid. After data normalization the variogram was developed. For interpolation of mentioned parameter, kriging and Inverse Distance Weighting methods were used. The best model for interpolation was selected by cross validation and error evaluation methods, such as Route Mean Square Error(RMSE).

Results: The results showed that kriging method is better than other methods for prediction of noise property. The noise map was prepared, using the best interpolation method in Geographical Information System environment.

Conclusion: Workers in this industrial hall were exposed to noise which is mainly induced by noisy machines. Noise maps which were produced in this study showed the distribution of noise and, also revealed that workers suffer from serious noise pollution.

Key words: Noise Pollution, Interpolation, Industry.

Introduction

Noise pollution is a major problem and a complaint among workers within industrial environments (1). It is estimated that most of industries in Iran suffer from problems which are induced by noise (2). Exposure to noise levels, especially higher than threshold limits, will lead to hearing loss and other biological disorders such as cardio vascular effects, sleep disorders and nervous complaints (3). Most of workers within industries in Iran are suffering from noise exposure, and Complaints regarding noise level from workers are high, showing that noise problems are among the important problems in Iran.

In order* to manage noise problems in industries, noise distribution must be evaluated and illustrated in work places before proposing any noise control program. Noise map is the best way to represent the sound level distribution existing in a given district.

In general, noise maps have two main purposes. First, they can be used for proposing a noise action plans for management and reduction of noise level. Secondly a noise map is to provide information on noise levels to estimate how many people are affected.

Two main stages can be distinguished in mapping process, 1) the sampling stage, during which measurements are taken from environmental variable at selected locations; and 2) the prediction stage, during which the observations are interpolated to a fine grid. The quality of the resulting map is determined by both stages. Geostatisticians have concentrated most on the second stage, by applying different types of interpolation methods (4).

Geostatistical methods were developed to create mathematical models of spatial correlation structures with a variogram as the quantitative measure of spatial correlation. The variogram is commonly used in geostatistics and the interpolation technique of kriging, providing the ‘‘best’’, unbiased, linear estimate of a regionalized variable in alocation where sampling isn't done, where ‘‘best’’ is defined in a least-squares sense. The emphasis is set on local accuracy, i.e. closeness of the estimate to the actual, but unknown value without any regard for the global statistical properties of the estimates. Kriging estimation variances are independent from the estimated values and are related to the spatial arrangement of the sample data and variogrammodel (5).

The accuracy of different spatial interpolation methods for pollution parameters mapping in soil, air and water have been evaluated by some research recently (6). Kriging method was used to estimate heavy metals concentration in groundwater and concluded that it is the best estimator for spatial prediction of Lead (7). Tsai et al used geographic information systems (GIS) and concluded that noise maps are a useful way of evaluating noise levels (8). The present study aimed to evaluate accuracy of two interpolation methods (kriging and IDW (Inverse Distance Weighting)) for prediction of noise values in Recovery section of Ghandi Telecommunication Cable Co. Noise map of this department also was provided to be compared for the noise level distribution with noise regulation standards.

Materials and Methods

This is a case study which was done in the recovery section of Ghandi Telecommunication Cable Co. in summer 2011. This section has 66 meter length and 66 meter wide with 7 meter height. The floor and walls were made of concrete and break. 25 people worked in this department and the most important noise resources were Prickly Machine, Caterpillar, Halter Table, Peeler Wiring and Mill. The location of study area and distribution of 106 sampling points (data was collected based on systematic method) is shown in Figure 1.

In this study for spatial prediction of noise exposure, 106 temporary noise monitoring stations in the Recovery Section were selected to determine noise exposure status during eight hour working period. Moreover, during noise measurement (eight hour working period), other factors (i.e. Temperature and air condition) were mostly stable. To identify noisy areas, noise survey was conducted within Recovery Section, and it was divided to 6 ×6 m stations. The sample sizes were selected based on the recovery section area. Noise measurement was done in centre of each station at 2 meter height. The measurement was done with a sound level meter model TES 52A on slow response and A-filter. The sound level meter was held at arm's length. The sound level meter had also been calibrated before and after each use. The calibrator model was TES-1356, and calibration was done in 1000 Hz frequency and 94 dB sound level.

After collection and normalization of data (Logarithmic method), kriging and IDW methods of interpolation were used. Finally, the best method of interpolation was selected using cross-validation. The study was proceeded to prepare the map based on this interpolation and the Geographical Information System. Figure 2 shows the flowchart of this study.

Kriging: The presence of a spatial structure where observations are close to each other are more alike than those that are far apart (spatial autocorrelation) is a prerequisite to the application of geostatistics (4;9). The experimental variogram measures the average degree of dissimilarity between un-sampled values and a nearby data value (10; 11), and thus can depict autocorrelation at various distances. The value of the experimental variogram for a separation distance of h (referred to as the lag) is half the average squared difference between the value at z(xi) and the value at z(xi + h) (9;11).

(1)

Where n (h) is the number of data pairs within a given class of distance and direction. If the values at Z (xi) and Z (xi + h) are auto correlated the result of Eq. (1) will be small, relative to an uncorrelated pair of points. From analysis of the experimental variogram, a suitable model (e.g. spherical, exponential) is then fitted, usually by weighted least squares, and the parameters (e.g. range, nugget and sill) are then used in the kriging procedure.

IDW: In interpolation with IDW method, a weight is attributed to the point to be measured. The amount of this weight is depended to the distance of the point to another unknown point. These weights are controlled on the bases of power of ten. With increase of power of ten, the effect of the points that are farther diminishes. Lesser power distributes the weights more uniformly between neighbouring points.

In this method the weight factor is calculated with the use of the following formula:

=the weight of point, = The distance between point i and the unknown point, = The power ten of weight

Finally, RMSE was used to evaluate model performances in cross-validation mode (13). The smallest RMSE indicate the most accurate predictions. The RMSE was derived according to Eqs. (4).

is observed value at point , is predicted value at point , is number of samples.

Results

The variable exhibits a non-normal distribution of measured values and therefore does not initially satisfy the basic assumption of geostatistics of statistical normality. This restriction is eliminated, by applying a data amenable transform to the sample values that make them more to analysis and estimation. The most useful data transform is the log-transform.

Since natural log values can be back transformed to real values, we can use a semi-variogram model derived from the transformed sample values to predict the spatial variation of logarithmic values of the noise. A statistical summary of the measuring noise in various locations near machines is presented in Table 1. As shown in this table, the parameter had high skeness, therefore itwas normalized using logarithmic method (5).

Our task now is to fit models to the experimental or sample values choosing models and fitting them to data remain among the most controversial topics in geostatistic. There are still controversial who fit models by eye and who defined their practice with vigor. They may justify their attitude on the grounds that when kriging the resulting estimates are much the same for all reasonable models of the variogram. There are others who fit models numerically and automatically using “black box” software, often without any choice, judgment or control.

Table 1: statistical analysis of noise value in various locations at Recovery Section

Measuring location	LP Min	LP Max	LP Mean	SD	Kurtosis	skeness	Exposure Time
Near Mill Machine	89.6	91.2	90.36	0.67	- 0.5	0.4	141 minutes
Prickly Machine	79.9	81.7	80.8	0.69	- 0.3	0.3	232 minutes
Caterpillar	79.8	81.7	80.98	0.69	- 0.3	0.4	21 minutes
Halter Table	79.6	81.4	80.66	0.69	- 0.5	0.2	95 minutes
Peeler Wiring	81.5	84.1	82.86	1.05	- 0.3	0.4	220 minutes
Noise in all of Recovery section	77.9	92.1	84.54	3.28	- 0.4	0.3	293 minutes

Table 2: RSS value of different models of variogram

Model	Gaussian	Spherical	Exponential	Linear
RSS	3.7e-7	5.08e-7	6.1e-7	4.8e-7

Table 3: Best-fitted variogram models and their parameters

Property	Model	Nugget	Sill	Range
Noise	Gaussian	1e-16	2.69e-16	32.5

Discussion

The noise map (Figure 5) indicates the noise levels for eight hours in Recovery Section, and it shows distributions of noise levels in all of locations in this Section. As shown in this Fig., high noise levels between 84-88dB can be observed near operational machines. In this area, there are a number of noise sources such as Prickly Machine, Caterpillar, Halter Table, Peeler Wiring, Mill, vacuum pumps, couch rolls, air and steam valves etc. Each makes a contribution to the overall sound pressure level (noise level) at a given position. But the highest level of noise, >88dB, is in the North of the hall where it is not near the major machine in the field, it is probably related to old fans operating in this area. Noise mapping in this study simply showed measurements at predetermined positions identified by applying a 6 m grid to the floor plan which it has high accuracy than traditional methods.

To indicate the locations with noise level higher than governmental noise regulations, measuring noise levels monitored in the map were compared with the Iranian Noise Standards which was produced by Ministry of Health (Figure6). As shown, the noise level exceeds Noise Standards in some locations where workers were working. Also, it is indicated that noise, especially near some machines, is higher or within standard regulation which is 85 dB A in Iran.

Generally, analysis showed that kriging performed better than IDW technique in characterizing the spatial variability which is in line with the work done by Rizzo and Mouser (16); Nazari-Zadeh et al (17); and Ahmad (18). They also revealed that geostatistical methods are the best model for interpolation, but we must be careful about it.

Geostatistic obviously does not offer a statistical model which is advantageous in every situation. Careful analysis of the measurement data using common sense can sometimes result in the same conclusions as those resulting from lengthily and computationally heavy calculations. In general, as spacing between samples is large compared to the dimensions of the investigated field, the potential advantageous of a geostatisticalanalysis becomes less. For spacing beyond the range of spatial auto-correlation, kriging estimates reduction of the same results as for the classical random sampling. A geostatistical analysis is not only computationally heavy, it requires also an impotent number of samples to be taken and analysed as acute as possible. At least 30 to 50 pairs of observations are necessary to calculate one point of the experimental variogram. Since the lag range over which the variogram is calculated, it should be approximately one fourth to one half of the dimension of the field studied, the experimental variogram should contain points ranging from very small to relative large lags.

As a result, geostatistical investigation is mostly based on hundred, even thousands, of observation. If one observation of the variable is costly, this requirement may jeopardize a geostatistical analysis. Summarized, the disadvantageous of geostatistical approach toward the spatial inventory of soil variables, also called are:

1- In practice, observations need to be numerical.

2- Large data sets are required.

3- Storing information processing power is needed.

The advantages are:

1- It is a reproducible procedure which is easy to verify and update.

2- No classification of data is required. Hence all problems concerning classification disappear.

3- The numerical output can serve as an input for further processing in GIS

4- It yields as conceptually much more realistic inventory than the traditional groundwater maps.

Conclusion

Workers in this industrial hall are exposed to noise which is mainly induced by noisy machines. Noise maps which produced in this study showed the distribution of noise and, it revealed that workers suffer from serious noise pollution. Unacceptable noise in which the more percentage of the workers are exposed must be managed by control action urgently. This study showed that a useful way for evaluating and illustrating noise level in industrial halls is noise mapping. Additionally, Results showed that geostatistical method had more considerable accuracy than IDW method. Using this method can identify the locations which immediately requirenoise controls.

Acknowledgements

The authors would like to acknowledge the University of Ardakan and the recovery section of Ghandi telecommunication Cables Company inYazd province.

Conflict of interests: Non declared.

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*Corresponding author: Ruhollah Taghizadeh-Mehrjardi, Faculty of Agriculture and Natural Resources, University of Ardakan, Ardakan, Iran. Email Address: rh_taghizade@yahoo.com

IDW					kriging	Property
5	4	3	2	1		Property
1.09	1.07	1.04	1.03	1.02	0.97	Noise