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
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 683
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.
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 684
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
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 685
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
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 686
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
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 687
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
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 688
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
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 689
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
Distance Temperature Relative
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
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 690
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
Distance Temperature Relative
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
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 691
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 (%)
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 692
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
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 693
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).
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 694
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.
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 695
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
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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 696
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