prediction of dust dispersion during drilling operation in · pdf fileprediction of dust...

INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCES Volume 6, No 5, 2016

© Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0

Research article ISSN 0976 – 4402

Received on December 2015 Published on March 2016 681

Prediction of dust dispersion during drilling operation in open cast coal

mines: A multi regression model Nagesha K.V 1, Sastry V.R. 2, Ram Chanda K. r3

1- Ph.D Scholar, Department of Mining Engineering, National Institute of Technology Karnataka- Surathkal, Mangalore- 575025, INDIA

2- Professor, Department of Mining Engineering, National Institute of Technology

Karnataka- Surathkal, Mangalore- 575025, INDIA 3- Assistant Professor, Department of Mining Engineering, National Institute of

Technology Karnataka- Surathkal, Mangalore- 575025, INDIA doi: 10.6088/ijes.6064

ABSTRACT

Dust pollution is one of the major concerns in mining operations. The workers and nearby

human habitats prone to various respiratory diseases due to dust pollution. Prediction of dust dispersion is required to determine the pollution level of the ambient air and also to implement various control measures to reduce their concentration. Though there are various

tools available for dust prediction, mathematical models are commonly used to predict the dust concentration, for its easy use. In the absence of specific mathematical models to predict

the dust produced from drilling operations for Indian meteorological and geo-mining conditions, dust dispersion models were developed using multiple regression analysis method. Field investigations were carried out in two large opencast coal mines in India. First mine

data was used to develop the models and the second mine data was used for validation of the models. It was found that the predicted dust concentration values of the developed models are

more close to the field monitored values compared to the USEPA model predicted values. These models can be used for predicting the dust concentration level of PM10 in atmosphere in coal mines.

Keywords Dust pollution, Dust prediction models, Multiple regression method, USEPA

model, Drilling operation, PM10.

1. Introduction

Dust is defined as small particles which are suspended in the atmosp heric; these particles further cannot be divided in to smaller particles. The dispersion of dust carried out because of

turbulent action of air in the atmospheric and mechanical disturbance of finer material. The dust formation occurs in each stage of the mining operation (Mrinal K. Ghoseand S. R. Majee 2007).

The dust produced in mining is classified into three categories, namely point source, line

source and area source. The point sources are drilling, loading, over burden (OB) dumping and coal dumping yards. Similarly, line sources are haul roads and unpaved roads and area sources are OB dump yard and coal dump yard. The dust produced from various activities

cannot be completely eliminated but can be reduced to a great extent. The haul road produces

Prediction of dust dispersion during drilling operation in open cast coal mines: A multi regression model

Nagesha1 K.V,. Sastry V.R ,. Ram Chandar K

International Journal of Environmental Sciences Volume 6 No.5 2016 682

more fugitive dust compared to other operations and after tha t drilling is the major source of fugitive dust (Nair and Sinha., 1987, Cole, C.F., and Zapert J.G., 1995).

The dust produced from drilling operation is usually in fugitive form and it discharges into

the environment in a defined flow stream. The dust emanating from drilling sources will have different sized particles and are more harmful in nature. The particulate matters are one of the major pollutants in mining activities, it comprises of PM2.5, PM10 and Total Suspended

Particulate (TSP), among which PM2.5 and PM10 are more harmful to human health (Chakraborty et al., 2002).

Generally the metrological parameters influence the dust dispersions and dust dispersion was found to be more in winter season (Lokman H. T. et al., 2012).The dust dispersion usually

more in downwind distance and dust concentration was found more up to 500m from the source (Ghose and Majee, 2000).

The dust concentration will be more in mine compared to outside of the mine, the workers working near to operation exposing to dust concentration and causes diseases like asthma,

heart attack, skin diseases etc.

The health problems caused due to the dust are categorized into two ways like short term exposures and long term exposure. In short term exposure, the people are exposed to dust for a short duration; such people are likely to get diseases like asthma attacks, acute bronchitis

and may also increase susceptibility to respiratory infections, while long term exposure is commonly observed in people who are exposed to dust for many years, they may face health

problems such as reduced lung functioning, chronic bronchitis and also diseases like increased respiratory symptoms, such as irritation of the airways, coughing or difficulty in breathing and even premature death. Pneumoconiosis is characterized by the formation of

fibrous tissues in lungs due to dust deposition (Anon, 2001)

Presence of dust particles in the surroundings of surface mines not only causes health problems to the workers but also results in poor visibility that may lead to Heavy Earth Moving Machinery (HEMM) accidents. The HEMM accidents may occur frequently due to

the continuous deposition of dust produced from mining operations. So, in order to avoid such problems, it is necessary to predict the dust concentration from sources and to mitigate

them. There are various tools / methods available to predict the dust concentration from different

sources. Statistical models are more useful tools to predict dust concentration. They attempt to determine the underlying relationship between sets of input data and targets. They have

been used to establish an empirical relationship between air pollutant concentrations and meteorological parameters. They are quite useful in real time short-term forecasting. Examples of statistical models are regression analysis (Abdul-Wahab et al., 2005).

2. Investigations

Field investigations were carried out in two large opencast coal mines, one in south India and another opencast coal mine from north India. Figure 1 shows typical broad views of mine-1,

and mine-2. The dust produced by drilling operation was monitored by three personal dust samplers and two ambient point samplers. Figure-2 shows dust monitoring equipment near

drilling activity. The personal dust samplers were fixed to ranging rods at a height of 2m




above the ground level. These were placed at different distances with respect to downwind direction from the drilling operation. Initially to know the background concentration of

source, one instrument was kept in up wind direction. Before placing the dust monitoring instruments, metrological station was installed in mine premises and set on hourly basis. The

various metrological parameters like temperature, humidity etc., were taken from metrological station. This procedure was followed for each day during field studies at both the coal and sandstone benches. The wagon drills are of 150mm and 250mm diameters were

used for drilling on both coal and sandstone benches. These drills were drilled at a penetration rate of 0.33m/min to 0.28m/min. The dust was monitored for an average depth of

blasthole of 15m.

Figure 1: Typical broad view of Mine-1 and Mine-2

Figure 2: Personal dust monitor and ambient point samplers are placed nearer to drilling activity

2.1 Determination of rock properties

As rock properties plays a major role in emanating the dust during drilling operation, some sandstone and coal samples were collected during the field investigations from different

locations of the mine. The samples were brought to the laboratory and the required tests were carried out. Moisture content and density were determined as per International Society for

Rock Mechanics (ISRM) suggested methods.




2.2 Compressive strength

Compressive strength of coal and sandstone was determined indirectly using Protodyakanov’s strength index and Point load strength index. Protodyakonov’s Strength

Index (PSI) is a way of characterizing rock strength and it has immense possibility for practical implementation in coal cutting and drilling. It also gives an idea about the compressive strength of the rock. The PSI test was performed as per the standards. It consists

of a vertical cylinder apparatus which is 640mm in height and has as plunger of weight 2.4kg, which has to be dropped number of times (n) onto 50gm sample. 5 such 50gm samples

together will be sieved through 600micron sieve. The minus size sieve particles are poured into volume meter to determine the height (h). The cylinder is having of internal diameter 75mm and external diameter 85mm.

Protodyakonov’s Strength Index is found using the following formula.

Protodyakonov’s Strength Index (PSI) = (20 x n) / h -----------eqn(1) Where, PSI = Protodyakonov’s strength index

n =Number of drops h = Height of powder in the volume meter (mm)

Generally compressive strength is 100times of Protodyakanov’s strength index.

Point load strength index is determined on irregular samples by keeping them between two conical platens of Point load strength index apparatus. The following formula gives the Point

load strength index. PLI= P/d**2 ----------- eqn(2) Where,

PLI= Point load strength index P=Load at failure

D= Distance between conical platens. Generally compressive strength is 24 to 26 times of Point load strength index.

2.3 Schmidt rebound hardness number

Rebound hardness value was determined using Schmidt hammer. The procedure involves schimdth hammer released by means of a spring that indirectly impacts against the rock surface through a plunger; the rebound distance of the hammer is read directly from the

numerical scale that ranges from 10-100. Twenty rebound values obtained from a single impact separated by at least a plunger diameter was recorded and the average of upper ten

values was taken as rebound hardness value.

Apart from rock properties, the dust dispersion parameters and silt content were determined.

Silt content in the drill cuttings, is the ratio of fines present in the drill cuttings to the total weight of drill cuttings. It is expressed in percentage. The dust dispersion parameters are like

vertical dispersion coefficient (σz) and horizontal dispersion coefficient (σy) were determined based on downwind distance from the Pasquill-Gifford graphs (Chaulya et al., 1998, Peavyet al., 1985).

2.2 Dust monitoring




Initially the first field investigations were carried out in opencast coal mine-1. Total 30 emission samples were collected from coal benches, 20 from sandstone benches. Detailed

dust emission values obtained are given in the Table-1 and Table-2 along with respective rock properties. Similarly, about 22 concentration samples from coal and 20 dust

concentration samples were collected from sandstone benches. Detailed dust concentrations values along with other parameters are given in Table-3 and Table-4.

Similarly the second field investigations were carried out in open cast coal mine-2. Total 21 samples were collected from coal benches, 19 from sandstone benches for emission, detailed

dust emission values and dust concentrations values along with other parameters are given in Table-5 and Table-6. Similarly, same number of samples were collected for concentration for coal and sandstone and the values are given in Table-7 & 8 respectively.

Table 1:Dust emission values in coal benches along with other parameters for Mine-1

Diameter Penetration

Rate

Mo

istu

re

Co

nte

nt

Silt

Content Density

Compressive

Strength

Reb

ou

nd

Ha

rd

ness

Nu

mb

er

Field

Emission

Rate

d (mm) P (m/min) m (%) S (%) ρ

(gm/cm3) σc (MPa) R

E(gm/sec

)

250 0.33 02.8 32.0 1.25 15 23 0.758

250 0.33 08.5 30.0 1.25 16 23 0.520

150 0.28 10.4 28.5 1.24 15 23 0.539

250 0.33 16.0 25.0 1.24 17 20 0.227

250 0.33 18.0 22.2 1.26 17 19 0.170

150 0.28 15.0 24.5 1.25 17 22 0.345

250 0.33 07.9 32.0 1.25 20 21 0.782

250 0.33 08.3 30.0 1.26 17 21 0.794

150 0.28 10.2 29.0 1.22 18 22 0.525

250 0.33 07.9 33.0 1.25 16 23 0.679

250 0.33 08.5 30.0 1.25 17 23 0.621

150 0.28 10.4 28.5 1.24 16 23 0.539

250 0.33 16.0 25.0 1.24 18 20 0.223

250 0.33 18.0 22.2 1.26 18 19 0.216

250 0.33 16.0 30.0 1.26 17 23 0.217

150 0.28 15.0 24.5 1.25 16 22 0.345

250 0.33 07.9 32.0 1.25 17 21 0.782

250 0.33 08.3 30.0 1.26 18 21 0.678

150 0.28 10.2 29.0 1.22 17 22 0.525

250 0.33 07.9 33.0 1.25 20 23 0.679

250 0.33 08.5 30.0 1.25 20 23 0.520

150 0.28 10.4 28.5 1.24 17 23 0.539

250 0.33 16.0 25.0 1.24 17 20 0.227

250 0.33 18.0 22.2 1.26 17 19 0.217

150 0.28 15.0 24.5 1.25 17 22 0.432




250 0.33 07.9 32.0 1.25 18 21 0.782

250 0.33 08.3 30.0 1.26 17 21 0.794

150 0.28 10.2 29.0 1.22 17 22 0.592

250 0.33 07.9 33.0 1.25 18 23 0.679

250 0.33 08.5 30.0 1.25 20 23 0.621

Table 2: Dust emission values in sandstone benches along with other parameters in Mine-1

Diameter Penetration

Rate

Moisture

Content

Silt

Content Density

Co

mp

ress

ive

Str

en

gth

Reb

ou

nd

Ha

rd

ness

Nu

mb

er

Field

Emission

Rate

d (mm) P (m/min) m (% ) s (% ) ρ

(gm/cm3) σc(MPa) R E(gm/sec)

250 0.33 14.2 24.1 2.25 38 34 0.357

250 0.33 17.0 26.3 2.28 42 29 0.190

150 0.28 12.5 29.6 2.25 49 31 0.324

250 0.33 08.1 32.6 2.28 38 33 0.865

250 0.33 09.4 37.8 2.35 39 32 0.705

150 0.28 10.2 29.3 2.27 42 28 0.900

250 0.33 08.2 29.2 2.39 41 27 0.679

250 0.33 08.3 31.9 2.25 41 34 0.638

150 0.28 11.2 35.3 2.38 39 34 0.830

150 0.28 12.5 29.2 2.25 42 26 0.324

250 0.33 08.1 32.2 2.38 44 31 0.865

250 0.33 09.4 37.3 2.25 49 34 0.705

150 0.28 10.2 29.3 2.37 47 31 0.900

250 0.33 09.4 37.3 2.25 44 35 0.705

150 0.28 10.2 29 2.37 49 34 0.912

250 0.33 08.2 29 2.29 47 32 0.679

250 0.33 08.3 31 2.25 48 29 0.638

150 0.28 11.2 35 2.28 49 37 0.831

150 0.28 12.5 29 2.35 47 27 0.324

250 0.33 09.4 37 2.35 49 29 0.705

Table 3: Dust concentration values in coal benches along with other parameters in Mine-1

Distanc

e Temperature

Rela

tiv

e

Hu

mid

ity

Win

d

Sp

eed

Sigma

(z)

Sigma

(y)

Field

Emission

Rate

Field

Measured

Concentration

d (m) T (oC) RH (% ) u

(m/s) σz(m) σy(m) E(gm/sec) C (µg/m3)

15 35.5 41.5 2.3 11.0 18 0.380 340

25 35.5 36.3 1.9 07.5 14 0.200 290




20 37.5 40.2 2.1 07.5 14 0.278 338

18 37.5 38.9 1.5 11.0 18 0.307 330

30 37.5 38.9 2.3 07.5 14 0.235 310

26 36.8 39.3 2.4 07.5 14 0.278 322

27 33.2 40.2 1.8 11.0 18 0.358 320

50 33.6 52.1 2.8 07.5 14 0.087 126

32 33.2 52.4 2.5 07.5 14 0.074 180

45 30.3 42.2 1.5 11.0 18 0.261 280

55 30.3 31 2.3 07.5 14 0.216 285

135 30.3 51.3 2.4 07.5 14 0.174 220

18 30.3 60.4 2.9 11.0 18 0.167 150

30 30.3 60.4 2.8 07.5 14 0.083 120

26 30.3 60.4 2.5 07.5 14 0.111 135

27 37.5 38.9 1.5 11.0 18 0.307 330

50 37.5 38.9 2.3 07.5 14 0.235 310

32 36.8 38.7 2.4 07.5 14 0.278 352

21 33.2 50.5 1.8 11.0 18 0.358 320

42 33.6 52 2.9 07.5 14 0.087 126

29 33.2 52.4 2.5 07.5 14 0.074 190

60 30.3 60.4 1.5 11.0 18 0.261 280

Table 4: Dust concentration values in sandstone benches along with other parameters in Mine-1

Distance Temperature Relative

Humidity

Wind

Speed

Sigma

(z)

Sigma

(y)

Field

Emission

Rate

Field

Measured

Concentration

d (m) T (oC) RH (% ) u (m/s) σz(m) σy(m) E

(gm/sec) C (µg/m3)

56 24.0 31.2 1.5 7.0 14 0.077 090

10 24.0 45.3 2.4 7.5 14 0.065 090

70 29.0 46.3 1.5 7.5 14 0.106 120

20 30.0 44.0 2.9 7.0 14 0.100 126

25 30.0 59.2 2.8 7.5 14 0.207 252

30 30.0 46.4 2.5 7.5 14 0.105 110

56 30.0 46.0 2.9 7.0 14 0.081 095

10 30.0 50.1 2.8 7.5 14 0.228 315

35 29.0 49.0 2.2 7.5 14 0.293 330

40 25.0 49.3 2.0 7.0 14 0.100 126

75 25.0 49.1 2.9 7.0 14 0.089 126

80 25.0 42.3 2.9 7.0 14 0.081 120

20 21.0 41.2 2.9 7.5 14 0.259 315

25 21.0 52.0 2.5 7.5 14 0.214 310

30 28.9 46.0 2.1 7.0 14 0.161 250

56 30.0 46.5 2.7 7.0 14 0.081 095




10 30.0 31.2 2.8 7.5 14 0.228 315

15 29.0 52.8 2.2 7.5 14 0.293 380

35 28.9 44.6 2.3 7.5 14 0.170 215

40 28.8 31.2 2.9 7.5 14 0.152 210

Table 5: Dust emission values in coal benches along with other parameters for Mine-2

Diameter

Penetration

Rate

Moisture

Content

Silt

Content

Density

Co

mp

ress

ive

Str

en

gth

Reb

ou

nd

Ha

rd

ness

Nu

mb

er

Field

Emission

Rate

d (mm) P (m/min) m (% ) s (% ) ρ

(gm/cm3)

σc

(MPa) R

E

(gm/sec)

250 0.33 2.84 32.0 1.25 15 23 0.912

250 0.33 08.5 30.0 1.25 18 23 0.712

150 0.28 10.4 28.5 1.24 21 23 0.562

250 0.33 16.0 25.0 1.24 20 20 0.412

250 0.33 18.0 22.2 1.26 19 19 0.222

150 0.28 15.3 24.5 1.25 17 19 0.412

250 0.33 07.9 32.0 1.25 15 19 0.622

250 0.33 08.3 30.0 1.26 16 19 0.572

150 0.28 10.2 29.0 1.22 13 18 0.678

250 0.33 07.9 33.0 1.25 19 23 0.789

250 0.33 08.3 31.0 1.24 15 23 0.612

150 0.28 10.2 30.0 1.25 16 23 0.782

150 0.28 07.1 39.0 1.25 17 20 0.672

160 0.28 07.6 39.8 1.24 14 19 1.002

150 0.28 08.9 34.0 1.24 17 19 0.926

150 0.33 07.4 38.0 1.26 18 19 0.462

150 0.28 07.9 38.8 1.25 19 19 0.673

150 0.28 07.8 36.2 1.25 20 18 0.622

250 0.28 02.4 36.0 1.26 19 18 1.210

250 0.33 07.9 33.0 1.25 19 23 0.617

250 0.33 08.3 30.0 1.26 16 19 0.323




able 6: Dust emission values in sandstone benches along with other parameters in Mine-2

Diameter

Penetration

Rate

Moisture

Content

Silt

Content

Density

Co

mp

ress

ive

Str

en

gth

Reb

ou

nd

Ha

rd

ness

Nu

mb

er

Field

Emission

Rate

d (mm) P (m/min) m (% ) s (% ) ρ

(gm/cm3)

σc (MPa) R E (gm/sec)

150 0.28

12.5 29.0 2.35 49 37

0.667

250 0.33

08.1 32.0 2.38 48 32

0.712

250 0.33

09.4 37.0 2.35 49 31

0.634

150 0.28

10.2 29.0 2.37 42 34

0.923

250 0.33

08.2 29.0 2.39 41 32

0.623

250 0.33

08.3 31.0 2.35 40 34

0.823

150 0.28

11.2 35.0 2.38 41 32

0.611

250 0.33

07.9 32.0 2.35 42 35

0.923

250 0.33

08.5 30.0 2.37 50 31

0.588

150 0.28

10.4 28.5 2.39 46 34

0.603

250 0.33

16.1 25.0 2.38 44 34

0.603

250 0.33

18.2 22.2 2.37 47 35

0.411

150 0.28

10.2 29.0 2.37 42 34

0.473

250 0.33

08.2 29.0 2.39 41 32

0.612

250 0.33

08.3 31.0 2.35 40 34

0.912

150 0.28

11.2 35.0 2.38 41 32

0.588

250 0.33

07.9 32.0 2.35 42 35

0.823

250 0.33

10.2 29.0 2.37 42 34

0.603

150 0.33 08.2 29.0 2.39 41 32

0.411

Table 7: Dust concentration values in coal benches along with other parameters in Mine-2


Humidity

Wind

Speed

Sigma

(z)

Sigma

(y)

Fie

ld E

mis

sio

n

Ra

te

Fie

ld

Co

ncen

tra

tio

n

Ra

te

d (m) T (Oc) RH (% ) u (m/s) σz(m) σy(m) E

(gm/sec) C (µg/m3)

10 45 38.9 3.2 12 20 0.912 625

15 45 38.9 3.2 12 20 0.712 426

20 45 38.9 3.1 12 20 0.562 362

10 45 38.9 3.2 12 20 0.412 431

20 45 38.9 3.2 12 20 0.222 276

40 46 40.0 3.2 12 20 0.412 317

50 46 40.0 3.1 12 20 0.622 378




60 46 40.0 2.9 12 20 0.572 387

70 46 40.0 2.9 12 20 0.678 501

20 46 40.0 2.9 12 20 0.789 526

30 46 40.0 2.9 12 20 0.612 593

6 47 38.6 3.1 12 20 0.782 498

8 47 38.6 3.2 12 20 0.672 528

16 47 38.6 3.1 12 20 1.002 623

24 47 38.6 3.1 12 20 0.926 547

15 47 38.6 3.1 12 20 0.462 489

52 46 38.9 3.2 12 20 0.673 463

10 45 38.9 3.1 12 20 0.622 428

64 45 38.9 3.2 12 20 1.21 662

35 46 40.1 3.2 12 20 0.617 548

29 45 40.2 3.1 12 20 0.323 275

Table 8: Dust concentration values in sandstone benches along with other parameters in Mine-2


Humidity

Wind

Speed

Sigma

(z)

Sigma

(y)

Field

Emission

Rate

Field

Concentrati

on Rate

d (m) T (Oc) RH (% ) u (m/s) σZ(m) σy(m) E

(gm/sec) C (µg/sec)

20 47.0 38.6 3.0 12 20 0.667 612

45 47.5 40.1 3.0 12 20 0.712 459

55 47.5 38.1 3.0 12 20 0.634 359

65 47.5 40.1 3.0 12 20 0.923 536

75 47.5 40.1 3.0 12 20 0.623 412

25 47.5 38.1 3.0 12 20 0.823 526

35 47.5 40.1 3.0 12 20 0.611 501

5 48.0 40.0 3.0 12 20 0.923 578

10 48.0 40.0 3.0 12 20 0.588 511

15 48.0 30.4 3.2 12 20 0.603 399

20 48.0 40.0 3.2 12 20 0.603 429

10 48.0 40.0 3.3 12 20 0.411 421

20 48.0 40.0 3.1 12 20 0.473 452

40 48.0 37.8 2.9 12 20 0.612 365

50 48.0 40.0 2.9 12 20 0.912 467

60 48.0 40.0 2.9 12 20 0.588 411

70 48.0 40.0 2.9 12 20 0.823 561

20 48.0 40.0 2.9 12 20 0.603 642

30 48.0 40.0 3.0 12 20 0.411 322




3.0 Results and Discussion

To develop mathematical model, complete data of mine-1 was used and mine-2 data was used for validation of the model. In Mine-1, dust emission values ranged between 0.170

gm/sec to 0.912gm/sec. Similarly dust concentration values ranged between 90 to 380µg/m3. To validate the developed models the second field investigations were carried out in open

cast coal mine-2, the values are ranged between 0.222gm/sec to 1.210gm/sec for emission and for concentration the values are ranged between 275 to 662µg/m3. The monitoring distance was varied between 6m to 135m. Initially Dust prediction models were developed by

multiple regression method was used. Further to validate, the predicted values from SPSS model were compared with field data and United States of Environmental Protection Agency

(USEPA) Model predicted values.

3.1 Development of dust dispersion model by multiple regression method

Dust prediction models were developed by multiple regression method. Mathematical equations were developed for dust emission and dust concentration, using Statistical Package

for Social Sciences (SPSS) software, which is effectively being used for statistical analysis. To develop a mathematical model using multiple regression analysis, 50 sets of data was used

for emission rate equation and 42 sets of data was used for dust concentration equation, of Mine-1 data. The performance of the model was evaluated by set of statistical parameters. The various statistical parameters were correlation coefficients, regression coefficients and

Variable Influence parameter (VIF) (S.K. Chaulya, et al., 2002).

In order to assess the influence of input parameters on output, stepwise regression was used. In stepwise regression, one after other parameter was used. The parameter that is not influencing the output was deleted from the model and parameter that influenced output, was

included in the model. If the coefficient value for a variable is zero or less than their standard error, that variable is considered in the model otherwise it is deleted. The best fit of the model

was assessed using the R2 value, the R2 value obtained for emission equation is 0.82 and for concentration is 0.76. P-value (probability test) is below 0.05, which indicates that the correlation at a 95% confidence level is more significant. Equation-3 is developed to predict

emission rate and equation-4 for concentration for the drilling operation

Ed= 0.499 - 0.037m+ 0.015S -------------------------------eqn.(3)

Where,

Ed =Emission from drilling (gm/sec)

m =Moisture content (%)

S =Silt content (%)

Cd= 366.89+335.791E – 2.954Rh- 0.997D ----------------eqn.(4)

Where,

Cd =Concentration from drilling activity (µg/m3)

E =Emission (gm/sec)

D =Distance form source (m)

Rh =Relative Humidity (%)




From Table-9, it can be stated that from R2and adjusted R2, model gives more than 82 per cent satisfactory results with a standard error of 28 per cent. Similarly, F test and P- test carried out using ANOVA analysis has also resulted in better validation of the model (Table-

10)

Table 9: Model Summary for Estimation of Emission rate

Model R R Square Adjusted R Square Std. Error of the

Estimate

1 0.908 0.824 0.817 0.280

Table 10: Analysis of Variance (ANOVA) for Estimation of Emission rate

Model Activity Sum of

Squares

Degree of

Freedom

Mean

Square

F- test

Value

Significance

(P-Value)

1

Regression 1.548 2 0.774 110.10 0.000

Residual 0.330 47 0.007 -- --

Total 1.878 49 -- -- --

From Table-11, coefficients of parameters determine relationship between input variable and output variable by using coefficients. The variables in Table-10 are more significant because

the ‘P’ value is less than 0.05. The regression coefficients (B) of predictors are also statistically significant. The moisture content is negatively more significant to the output. In addition to this, the model assessment has been carried out by using Variable Influence

Factor (VIF) method. If VIF factor is more than 10, then that variable is deleted because of collinearity. The collinearity is the expression of the relationship between two independent variables.

Table 11: Coefficients of Emission Model for Estimation of Emission rate

Parameters

Unstandardized

Coefficients Beta T-test Sig.(P) VIF

B Std. Error

Constant 0.499 0.174 --- 2.862 0.006 ---

Moisture content -0.037 0.005 -

0.652

-

7.199 0.000 2.193

Silt content 0.015 0.004 0.313 3.451 0.001 2.193

Similar to the Emission Rate, from R2 and adjusted R2, standard error values for Concentration are given in Table-12. Prediction resulted in around 76 per cent satisfactory




level with 36.5 per cent error. F- test and P- test also proved that the model is very effective (Table-13). Coefficients and VIF values obtained are shown in Table-14.

Table 12: Model Summary for Estimation of Concentration

Model R R

Square

Adjusted R

Square

Std. Error of the

Estimate

2 0.87 0.76 0.73 36.53

Table 13: Analysis of Variance (ANOVA) for Estimation of Concentration

Model Activity Sum of

Squares

Degree

of

Freedom

Mean

Square

F- test

Value

Significance

(P-Value)

2

Regression 119977.691 3 39992.564 29.967 0.000

Residual 37367.809 28 1334.565 --- ---

Total 157345.500 31 ---- --- ---

Table 14: Model Coefficients for Estimation of Concentration

Parameters

Standardized

Coefficients Beta T-test Sig.(P) VIF

B Std. Error

Constant 366.898 54.856 --- 6.688 0.000 ---

Emission 335.791 89.862 0.428 3.737 0.001 1.548

RH -2.954 1.110 -

0.298

-

2.662 0.013 1.479

Distance -0.997 0.378 -

0.330

-

2.637 0.014 1.842

Some parameters are not included in the developed model because the data was pertaining to only one mine. Results of SPSS model predicted values with field measured values from

mine-2 of Emission Rate and Concentration values are having percentage of error is within 30% in both cases, indicating developed models are moderately satisfactory. Plots drawn

between actual fields measured values from mine-1 data with predicted values from models in case of Emission rate and Concentration, resulted in R2 value of 0.82 and 0.86 respectively, which shows better correlation (Figure-3).




Figure 3: Field Measured Values Vs. SPSS Predicted values

Further to validate the developed models, the results of SPSS model predicted values with

field measured values from mine-2 of Emission Rate and Concentration values are having percentage of error is within 30% in both cases, indicating developed models are satisfactory.

Out of 40 cases, in 13 cases results less than 10 percent of error, in 14 cases the error is between 11-20 percent and in 13 results percentage of error between 21-30 percent. Similarly, percentage of error for concentration is within 30%, indicating the developed models are

satisfactory. Out of 40 cases, about in 16 cases the percentage of error is less than 10 per cent and in 12 cases the percentage of error is between 10 to 20 per cent, 12 cases about in 20 to

30 percent.

3.2. Comparison of Developed models with United States of Environmental Protection

Agency (USEPA) Model

To validate the developed models in predicting the PM10 concentrations due to drilling activity in sandstone and coal benches, the models developed were compared with USEPA Model. The results show that the USEPA model predictions has high error of 93%, whereas,

SPSS models predicts with error of less than 30%, From comparison results Figure-4, it can be observed that “SPSS” model predicted values are very close to the field measured values,

implying that SPSS model has better predictions with more accuracy. It could be concluded that the multi regression models using “SPSS” may be used to predict PM10 Dust Concentrations from drilling activity in opencast coal mines.




Figure 4: Field Concentration Vs. Predicted concentrations using different models

for drilling operation

4. Conclusions

In the present study, a detailed experimental and theoretical investigation were carried out to develop dust prediction models based on the investigations carried out in opencast mines and

the following conclusions are drawn.

1. Field data of mine-1was used to develop the dust prediction models and the mine-2

data was used for validation of the models. 2. Multiple regression correlation coefficients for emission and concentration model is

0.82 and 0.76 respectively, for 5% level of significance. 3. Variable Influence factors (VIF) of the input variable is lower than 10 that indicated

there is no collinearity.

4. Based on stepwise regression analysis, moisture content is negatively influencing to dust emission rate. Silt content is positively influencing to dust emission rate.

5. Based on multiple regression analysis, silt content was found to be more influencing to produce emission rate.

6. Results shown that the multi regression models predicted values are within 30 per

cent compared to field measured values and USEPA model. 7. The developed models can be effectively used in predicting dust emission and

concentration due to drilling operation in opencast coal miens

5. References

1. Abdul-Wahab, S.A., Bakheit, C.S., and Al-Alawi, S.M, 2005. Principal component

and multiple regression analysis in modeling of ground- level ozone and factors affecting its concentrations. Environmental Modelling Software. 20, pp 1263–1271.

2. Chakraborty, M.K., Ahmad, M., Singh, R.S., Pal, D., Bandopadhyay, C., and Chaulya, S.K, 2002. Determination of the emission rate from various opencast mining operations. Environmental Modelling and Software. 17 pp 467–480.




3. Cole, C.F., and Zapert J.G., 1995. Air quality dispersion model validation at Three Stone Quarries. National Stone Association, 14884 pages.

4. Chaulya, S.K., Chakraborty, M.K., Singh, R.S., 1998. Air pollution monitoring and modelling technique for opencast mine. Environmental Geochemistry. 2, pp 35–42.

5. Ghose, M. K., and Majee, S. R. 2000a. Status of air pollution in Indian opencast coalmines. Jl. Minetech. 21(2), pp 48–52.

6. Mrinal K. Ghoseand S. R. Majee. 2007. Characteristics of hazardous airborne dust

around an indian surface coal mining area. Environmental Monitoring and Assessment. 130, pp 17–25.

7. Nair, P. K., and Sinha, J. K. 1987. Dust control at deep hole drilling for open pit mines and development of a dust arrestor. Journal of Mines, Metals and Fuels’ 35(8), pp 360–364.

8. Lokman H.T., Pinar Suren , Omar Alagha, FerhatKaraca and Gurdal Tuncel. 2012. Effect of meteorological parameters on fine and coarse particulate matter mass

concentration in a Coal-Mining Area in Zonguldak, Turkey. Journal of the Air and Waste Management Association. 58, pp 543–552.

9. Annon (2003). www.epa.gov/air September 2003 EPA-452/F-03-001. Accessed on

18 June 2015.

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