Scholarly article on topic 'Prediction and Analysis of Dust Dispersion from Drilling Operation in Opencast Coal Mines'

Prediction and Analysis of Dust Dispersion from Drilling Operation in Opencast Coal Mines Academic research paper on "Earth and related environmental sciences"

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Abstract of research paper on Earth and related environmental sciences, author of scientific article — V.R. Sastry, K. Ram Chandar, K.V. Nagesha, E. Muralidhar, Md. Shoeb Mohiuddin

Abstract Dust is one of the most important air pollutants and is the major area of concern for coal mining industry, because opencast coal mining operations lead to the generation of large quantity of dust, which deteriorates the surrounding environment and the public health. Dust generation from several mining activities consists of PM2.5, PM10 and RespirableSuspended Particulates(RSP) concentrations. Any new project to be proposed should perform Environmental Management Plan (EMP) studies to predict the possible dust generation from that project. Presently USEPA models are used to predict dust concentration. An attempt is made to develop two mathematical models to predict the respirable dust particle concentration in the ambient air at various locations around the dust generating source, especially from drilling operation in Open Cast coal mines. The models were developed using different statistical tools. The required data has been generated by field investigations carried out at one of the opencast coal mines in southern India. The models were developed by providing meteorological data like wind speed, wind direction, relative humidity and temperature, the geographical data like distance from the dust source and emission rate of drilling operation as the input parameters. Results from these models were compared with USEPA model and the field measured values. Predicted values were found to be in close agreement with the Field Measured Values than USEPA model predicted values.

Academic research paper on topic "Prediction and Analysis of Dust Dispersion from Drilling Operation in Opencast Coal Mines"

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Procedía Earth and Planetary Science 11 (2015) 303 - 311

Global Challenges, Policy Framework & Sustainable Development for Mining of Mineral and Fossil Energy Resources (GCPF2015)

Prediction and Analysis of Dust Dispersion from Drilling Operation in Opencast Coal Mines

V. R. Sastry*, K. Ram Chandar, K.V. Nagesha, E. Muralidhar, Md. Shoeb

Mohiuddin

Research Scholars, Dept. of Mining Engg. NITK- Surathkal, Mangalore 575025, India

Abstract

Dust is one of the most important air pollutants and is the major area of concern for coal mining industry, because opencast coal mining operations lead to the generation of large quantity of dust, which deteriorates the surrounding environment and the public health. Dust generation from several mining activities consists of PM2.5, PM10 and Respirable Suspended Particulates (RSP) concentrations. Any new project to be proposed should perform Environmental Management Plan (EMP) studies to predict the possible dust generation from that project. Presently USEPA models are used to predict dust concentration. An attempt is made to develop two mathematical models to predict the respirable dust particle concentration in the ambient air at various locations around the dust generating source, especially from drilling operation in Open Cast coal mines. The models were developed using different statistical tools. The required data has been generated by field investigations carried out at one of the opencast coal mines in southern India. The models were developed by providing meteorological data like wind speed, wind direction, relative humidity and temperature, the geographical data like distance from the dust source and emission rate of drilling operation as the input parameters. Results from these models were compared with USEPA model and the field measured values. Predicted values were found to be in close agreement with the Field Measured Values than USEPA model predicted values.

©2015PublishedbyElsevierB.V.Thisisanopenaccess article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-reviewunderresponsibilityoforganizingcommitteeof theGlobalChallenges,Policy Framework & Sustainable Development for Mining of Mineral and Fossil Energy Resources.

Keywords: Dust Pollution; Dust Dispersion; Particulate Matter; USEPA Model; Statistical Modeling; Drilling.

* Tel.: 0824 2474 000; fax: 0824 2474 061. E-mail address: vedala_sastry@yahoo.com

1. Introduction

1.1. Dust emissions

Dust is one of the major air pollutants which affects the ambient air and is hazardous or environmental nuisance and causes many respiratory disorders when dust is inhaled / exposed to it. Dust generated from different mining activities consists of different concentrations of PM2.5, PM10, and RSP. Dust pollution is one the major area of concern in coal mines, as opencast coal mining

1878-5220 © 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of organizing committee of the Global Challenges, Policy Framework & Sustainable Development for Mining of Mineral and Fossil Energy Resources. doi:10.1016/j.proeps.2015.06.065

operations/activities result in emission of large quantities of dust, which disperses too far off distances and deteriorates the surrounding environment. Dust will have effect on human health and also on the nearby flora and fauna.

Dust deposited on the ground can be a nuisance and can also influence the ecology and agriculture of a region. Nuisance from surface soiling is determined by the color contrast between the deposited dust and the surface, 'the cleanness" of the surface prior to settlement, public opinion and any other special characteristics of the area (Arup, 1995).The effects of dust on the agriculture and ecology of an area are determined by the concentration of dust particles in the ambient air, their size distribution, the deposition rate and their chemical properties. These factors can influence the chemistry of the soil and health of surrounding plants, the meteorological and local microclimate conditions, as well as the penetration rate of dust into vegetation. Apart from vegetation, dust deposition can affect animal communities and woodlands as well (Balkau, 1993).

1.2. Dust dispersion models

Dust Dispersion models are important predictive tools that are used to simulate the way the atmosphere transports and diffuses contaminants from industrial sources of pollution. Much of the research has focused on regional dispersion models. Other models have been created for industry specific purposes. Furthermore, some of the past research has focused on dust dispersion modeling in the mining industry. However, the air dispersion models which are widely used for air pollution may include, Industrial Source Complex Short Term Model (ISCST3), Fugitive Dust Model (FDM) and American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD).

The trend in recent years has been to use more statistical models instead of traditional deterministic models (Kolehmainen et al., 2001).The statistical models are based on semi-empirical relations among available data and measurements (Gokhale et al., 2004). They depend on the statistical analysis of previous air quality data and do not necessarily reveal any relation between cause and effect. 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., 1996, 2003, 2005), time series analysis (Hsu, 1992) and artificial neural networks (Gardner et al., 1998; Abdul-Wahab, 2001; Elkamel et al., 2001).

The ISCST3 Model is a steady-state Gaussian Plume Model, which can be used to assess pollutant concentration from a wide variety of sources associated with an industrial source complex. Emission sources are categorized into four basic types of sources namely, Point, Area, Volume and Open Pit sources. The volume source option and the area source option may also be used to simulate line sources. The ISCST3 model estimates the concentration or deposition value for each source and receptor combination for each hour of input meteorology and calculates user selected short-term averages. The input data for the model are user source dimension, emission rates, wind speed, wind direction, ambient air temperature, mixing height, stability class and receptor coordinates (Anon, 1995).

Cole et al., (1995) have studied the ISC3 model to test three Georgia stone quarries. It was stated that the ISC3 model had a history of over predicting particulate concentrations based on the data obtained by the U.S. Department of Energy's Hanford, WA site (Cole et al., 1995). This study determined emission rates for operations, modeled dispersion of the emitted particulates and compared modeled with measured particulate concentrations for each of the three stone quarries. The model's testing methodology was similar to that used in the EPA study (Anon, 1994 a, b; 1995a). The number and type of PMio sampling stations were unknown. However, it was determined that there were at least two sampling stations at each site because there was a primary downwind site and a site located upwind of the prevailing winds to allow for subtraction of ambient PMio concentrations. Once the comparison of modeled versus measured results are completed, it was determined that the model over predicted the actual PMio concentrations in a range of a factor of less than 1 (87% over prediction) to a factor of 5 (Cole et al., 1995). The study concluded that there could be two reasons for the over prediction. One was that the ISC3 model failed at that time to account for any deposition of the particulates. The other reason was that the emission factor for unpaved roads over-predicts the amount of emissions from haul trucks. The emissions factor was cited as the primary possible cause of over-prediction during the study. It was noted that the hauling operations contributed 79%-96% of the PMio emissions from the entire quarrying operation (Cole et al., 1995). EPA has been modifying a deposition routine for the ISC3model. They used an initial deposition routine created by EPA and found that it reduced the

modeled results by 5%. However, even with this reduction in modeled PMio concentrations, there is still a significant over-prediction. This has led the National Stone, Sand and Gravel Association (NSSGA) to embark on a series of studies published during 1991-2001, that attempts to better quantify the PMio emissions from haul trucks (Richards et al., 2001).

Reed et al., (2001) carried out a study on the ISC-3 model using a theoretical rock quarry. The study also concluded that hauling operations contributed the majority of PMio concentrations and that the haul truck emissions factors may be part of the cause of the over prediction of PMio concentrations by the ISC-3 model. Reed described a model called the Dynamic Component Program (DCP) that can be used for predicting dust dispersion from haul trucks. The model is based on a Gaussian equation similar to that used by the ISC3 model.

Singh et al., (2006) carried out comparison and performance evaluation of dispersion models FDM and ISCST3. Various statistical approaches were used to compare and evaluate the models under study and it was found that FDM is more accurate than ISCST3.

Chaulya et al., (2003) carried out a study for the determination of emission rate for SPM to calculate emission rate of various opencast mining activities and validation of commonly used two air quality models for Indian mining conditions The average index of agreement values for PAL2 and FDM was found to be 0.665 and 0.752, respectively, which showed that the prediction by PAL2 and FDM models are accurate by 66.5 and 75.2%, respectively.

By considering all these different reviews, it can be concluded that none of the existing models can predict the PMio dust concentration more accurately for Indian mining conditions.

Nomenclature

PM Particulate Matter

RSP Respirable Suspended Particulates

SPM Suspended Particulate Matter

ISCST3 Industrial Source Complex Short Term Model

FDM Fugitive Dust Model

PAL2 Point, Area and Line sources model_

2. Field investigations

To predict the dust generation and dispersion from drilling operation in terms of different particulate matter at various places and horizons, field investigations were carried out in three phases. Phase-1 studies were carried out during pre-summer season, Phase-2 studies were carried out during summer and rainy season and Phase-3 studies were carried out during winter season in one of the opencast coal mines in Southern India. A broad view of OC mine where field investigations were carried out is shown in Figure 1. Benching method is adopted to remove overburden as well as coal in this mine. Overburden is fragmented using drilling and blasting. Blastholes of 250mm diameter are drilled with wagon drills (Figure 2). After blasting, the fragmented material is loaded with the help of shovels into dumpers and transported to dump yard (Figure 3).

Fig. 1. Broad view of the opencast coal mine

Fig. 2. Drilling ofblastholes in progress

Fig. 3. Loading and transporting in progress

The ambient air monitoring was carried out using various instruments during drilling operation as shown in Fig.4. The air quality monitoring instruments (Table-1) were placed around the drilling operation site and sampling of ambient air was carried out. These instruments were placed in different directions w.r.t wind directions (Down Wind & Upwind) at different distances from the drilling activity site. Five units of Personal Dust Monitors and two units of Point Samplers were placed at 5m to 80m from the source. The input parameter in context to drilling operation and meteorological conditions were taken into consideration in order to develop a new dust propagation model.

Table 1.Ambient Air Monitoring Instruments

Name ofthe Instrument Type of Particulate

Personal Dust Monitors/Dust Dosi Meter Respirable dust

(5 units) (PMio)

Point Samplers (2 units) Respirable dust (PMio

and PM2.5)

Meteorological Monitoring Station

Meteorological parameters

(a) (b) (c)

Fig. 4 Different Instruments placed around the Drilling Activity (a) Respirable Dust Sampler Unit placed towards Down Wind Distance from the Dust Source (b) Respirable Dust Sampler Unit placed at Upwind Distance from the Dust Source (c) Point Sampler Units placed near the Drilling Source From field investigations; the data required were collected for drilling activity. More than 200 samples were collected by monitoring the ambient air near the Drilling source at various distances to determine the particulate concentration. The dust monitoring was done to collect about 15 to 18 samples at one particular distance on hourly basis (from 5m to 80m) and average of all those readings and the micrometeorological data recorded at the monitoring region are shown in Tables 2.

Table 2 (a). Measured Dust Concentrations around Drilling Activity.

Wind Direction Wind Measured

Sample No. Distance (m) Temperature (°C) RH (%) Speed (Km/hr) Cone. (^g/m3)

1 10 DW 29 50 4 693

2 5 DW 27 72 4 550

3 9 DW 27 72 4 351

4 12 DW 27 72 4 252

5 13 DW 25 50 5 252

6 16 DW 25 50 5 63

7 15 DW 25 50 5 126

Table 2 (b). Measured Dust Concentrations around Drilling Activity (Continued...)

Wind Direction Wind Measured

Sample No Distance (m) Temperature (°C) RH (%) Speed (Km/hr) Cone. (^g/m3)

8 21 DW 24 54 6 252

9 25 DW 29 50 4 252

10 42 DW 30 46 4 126

11 29 DW 30 46 4 126

12 60 DW 30 46 4 126

13 56 DW 30 46 4 126

14 70 DW 25 49 7 126

15 75 DW 25 49 7 63

16 80 DW 25 49 7 63

17 6 UP 27 72 4 441

18 10 UP 27 72 4 63

19 13 UP 24 54 6 63

20 20 UP 21 64 6 63

21 25 UP 21 64 6 55

22 31 UP 21 64 6 50

23 35 UP 25 49 7 63

24 38 UP 21 64 6 63

3. Results and analysis

From the field studies it was observed that the dust concentration was high in core zones and also at nearby buffer zone areas around the mine. The different activity sites in open cast mines like drilling site, site office area etc. comes under core zone and the nearby housing colony and villages are included under buffer zone. The RSP concentration is more towards downwind direction and it was slightly decreasing with increase in distance from source. Towards upwind, there was no significant dust concentration.

The models were developed using the input parameters like geographical parameters, meteorological parameters and the emission values obtained from the field investigations are given as input to statistical tools/software like MINITAB and MATLAB. Here the Concentration was chosen as the response which varies/ depends on other predictors like distance, wind speed etc.. Then the regression equations are obtained and the equations are analyzed. The MINITAB tool gave better equation in terms of better prediction of RSP concentrations for Drilling operation. This MIN Model equation was further validated and the results were compared with USEPA Model values (Fig.5) and with the field measured values. The regression equation developed for Drilling activity is as follows,

MINITAB MODEL Equation. (MIN MODEL)

Cd = -1457 - 3.98 D + 0.827 WD + 38.4 T+6.81 RH + 58.3 WS

Where,

Cd - Dust Concentration near Drilling Activity Source (p.g/m3). WD = Wind Direction wrt Drilling activity.

D = Distance between source ofDust (Drilling area) and Monitoring Point (m).

T = Ambient Temperature (°C).

RH = Relative Humidity (%).

WS = Wind Speed (Km/hr). The values predicted by different models (MIN model and USEPA model) and their % of error of values with the Field measured values are tabulated below in Table-3.

Table 3. Measured and Predicted Values using Different Models

% Error b/w % Error b/w

Distance Field Measured MIN Model USEPA Model field values and field values and

(m) Value (^g/m3) Value (^g/m3) Value (^g/m3) MIN Model USEPA Model

values values

5 550 432 350 21 36

9 351 416

12 252 404

13 252 232 16 63 220 15 126 224 18 63 212 21 252 247 25 252 280 42 126 223 29 126 275 60 126 152 56 126 167 70 126 115 75 63 95 80 63 75

194 19 45

148 60 41

113 8 55

93 249 48

99 78 21

83 237 32

61 2 76

75 11 70

46 77 63

65 118 48

33 20 74

35 33 72

17 9 87

16 51 75

15 19 76

(c) (d)

Fig.5 Comparison of Variation in Concentration Values at Different Distances

(a) Field Measured Values Vs. Models Predicted Values

(b) Field Measured Values Vs. MIN Model Predicted Values

(c) Field Measured Values Vs. USEPA Model Predicted Values

(d) USEPA Model Predicted Values Vs. MIN Model Predicted Values

Fig. 5 shows the comparison of field measured Concentration values with the predicted values obtained using USEPA Model and the developed MIN Model at varying distances (5m to 80m) from the dust generating source. It is observed here that the respirable dust concentration gradually decreased at distances away from the drilling source, about 54% decrease at 15m to 42m, 77% decrease in concentration from 5m to 60m and 89% decrease at the maximum distance of 80m w.r.t initial concentration value at source from 550 p.g/m3 to 63 p.g/m3.The values predicted using the developed MIN model and MAT model has better agreement with the measured values compared to USEPA model values. Among the two developed models, MIN Model has better accuracy in predicting the particulate matter concentration near the drilling source.

Fig.6 shows the variation in respirable dust concentration along down wind and up wind directions at different distances from the drilling source. It could be observed here that the dust dispersion is predominantly along the down wind direction. Wind has the major influence in dust dispersion. At upwind side the variation of dust concentration was not considerable from 25m from the source. There is sudden drop in dust concentration from 25m onwards. In fact, the upwind values are actually the background concentration. But, there was significant change in concentration values along the down wind direction due to the dust emission from drilling activity. The decrease in concentration along downwind directions at different distances (from 5m to 80m) is 89%. The concentration values varied from 550p.g/m3 to 63 p.g/m3 at 5m to 80m. Along the upwind the concentration values ranged from 441p.g/m3 to 63p,g/m3 at distances 6m to 38m._

ÍAEMK--P OS FABTinmTE MATTER IXMCTKTEATIOM ALUNUtt JNI> WKÜT1Í.HH

_rasKHCKfoQ_

Fig.6 Comparison of Variation in Concentration Values along Down Wind and Upwind Directions

4. Conclusions

The field investigations to measure the ambient airborne dust concentrations ranging from 693 p.g/m3 upto 126 p.g/m3 have indicated that the respirable particulate matter (PMio and PM2.5) are the major pollutants which disperse easily along the downwind direction, due to their less size, and affects the ambient air majorly. Also it is observed that the dust generated by drilling will disperse up to 80m to more thanlOOm from the source and then gradually the heavier dust fractions will be settled before dispersing to longer distances. This may be one of the factors that the workers/ personnel working at the site were most affected by the dust emissions in opencast coal mines. It is observed here that the dust dispersion is predominantly along the down wind direction and the wind direction has the major influence in dust dispersion. At upwind side the concentration variation was minimal so it implies that the upwind values are actually the background concentration. The developed models were compared with field measured values and USEPA predicted values. The predicted concentration values of respirable suspended particulate matter (RSP) using developed model shows 80-88% similarity with the field measured values, where as it is 51% similarity shown by USEPA model. It means that the developed MIN Model predicts 88% of the samples with correct values; whereas USEPA Model predicts 56% of samples correctly. It indicates that the developed MIN model can better predict the RSP concentration around the Drilling activity source.

Acknowledgements

We thank the management of Singareni Collieries Company Limited (SCCL) for allowing us to carry

out the field investigations and also our due thanks to the staff at the Department of Mining Engg. NITK, Surathkal, for their constant support for carrying out the research work.

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