Long-term Aerosol Characteristics over Eastern,Southeastern, and South Coalfield Regions in India

Kirti Soni & Sangeeta Kapoor &

Kulwinder Singh Parmar

Received: 26 August 2013 /Accepted: 28 November 2013 /Published online: 19 December 2013# Springer Science+Business Media Dordrecht 2013

Abstract Natural and anthropogenic aerosols over coalmines regions in India play a significant role in influenc-ing the regional radiation budget, causing climate impli-cations to the overall hydrological cycle of India. In thereference of regional climate change and air quality, wediscuss aerosol optical depth (AOD) variability andlong-term trends (from Mar 2000–Dec 2012) over east-ern, southeast, and south coalfield regions in India. Thepresent work analyses the variations and trends in aero-sol loading using Terra-MODIS (Moderate-ResolutionImaging Spectroradiometer) AOD550 data in the period2000–2012. Overall, an increasing trend in AOD550 hasbeen observed over all regions namely Raniganj(7.31 %) in eastern and Korba (5.0 %) in southeast,and Godavari Valley (32 %) in the south coalfield regionin India. This increasing trend predominantly owes to aconstant increase in the seasonal/monthly averagedAOD during the winter (Dec–Feb) and post-monsoon(Oct–Nov) seasons dominated by anthropogenic emis-sions. In contrast, a decreasing trend is observed duringpre-monsoon (Mar–May) season over eastern coalfield

region (−13 %), while at south coalfield region (44 %)and southeastern coalfield region (0.8 %), increasingtrends are observed. Similarly, increasing trends isobserved over all regions in monsoon (Jun–Sep)months. Furthermore, the values of Hurst exponent,fractal dimension, and predictability index for AODs are0.5, 1.5, and 0, respectively suggesting that the AODsin all sites follow the Brownian time series motion(true random walk). High AOD values (0.59±0.21) areobserved over eastern region Raniganj.

Keywords Trends . Aerosols . Coal mines . India

1 Introduction

Atmospheric aerosols are important from a perspectiveof ambient air pollution and health to humans and otherbiological receptors as well as for potential effects onlocal weather and global climate (Krishna, 2012).Atmospheric aerosols mainly scatter, reflect, and absorbsolar radiation (as a direct effect) and modify cloudproperties (as an indirect effect), thus perturbing theradiative balance of the atmosphere (IPCC, 2007).Atmospheric aerosols have been recognized to establisha vital parameter in climate change studies at South Asiathat detect fluctuations in atmospheric temperature (Lauet al. 2006; Gautam et al. 2009). India is the secondlargest energy consumer and contributor of variousemissions in Asia (Akimoto, 2003). The main sourcesof energy in India contain coal, biomass, petroleum,hydropower, and nuclear power. India is the world’sthird largest coal consuming nation after China and theUSA. Coal mining is one of the core industries in India

Water Air Soil Pollut (2014) 225:1832DOI 10.1007/s11270-013-1832-6

K. Soni (*)AUV Standard and EIC Section, Apex level and IndustrialMetrology Division, CSIR-National Physical Laboratory,Delhi, Indiae-mail: 2006.kirti@gmail.com

S. KapoorLaxmi Narayan College of Technology and Science(LNCTS), Bhopal, MP, India

K. S. ParmarDepartment of Mathematics, University School of Basicand Applied Sciences, G. G. Singh Indraprastha University,Dwarka 110075 Delhi, India

that plays a significant role in the economic developmentof the country (Chaulya and Chakraborty, 1995; Kumar,1996); however, it degrades the environment. Air pollu-tion in mines is mainly owing to the fugitive emissions ofparticulate matter and gases such as methane, sulfurdioxide, oxides of nitrogen, and carbon monoxide. Mostof the mining operations produce dust. The main sourcesof air pollution in the coal mining regions mostly includedrilling, blasting, overburden loading and unloading, coalloading and unloading, haul roads, transport roads, stockyards, exposed overburden dumps, coal handling plant,exposed pit faces, and workshop (CMRI, 1998).

Open-cast mining is more severe in the air pollutionproblem in comparison to underground mining. Highlevels of suspended particulate matter (SPM) increaserespiratory diseases such as chronic bronchitis and asthmacases while gaseous emissions contribute towards globalwarming as well causing health risks to the exposed pop-ulation. The uncontrolled dust not only creates a serioushealth hazard but also affects the productivity through poorvisibility, breakdown of equipment, increasedmaintenancecost, and ultimately deteriorates the ambient air quality inand around the mining site. The dust can also polluteadjacent surface waters and plants (Singh, 2006).

Several researchers studied the reduction of air qualityas well as effects on vegetation and wildlife in and nearbymining areas (Chaudhari and Gajghate, 2000; Crabbeet al. 2000; Wheeler et al. 2000; Nanda and Tiwary,2001). Zeller et al. (1979) reported that the normal oper-ations in open-cast mining like removal of top soil, exca-vation, size reduction, water removal, transportation,loading, stockpiling etc., generate and emit particulatematter. Sinha and Banerjee (1997) observed that majorair pollutants produced by open-cast mining are SPM andRSPM. Ghosh (2002) and Ghosh and Majee (2007)carried out studies at Jharia (Dhanbad, Jharkhand) coal-fields; Reddy and Ruj (2003) reported ambient air qualitystatus in Raniganj; Singh and Puri (2004) and Singh(2006) have carried out studies at six stations in Korba;Mukhopadhyay et al. (2010) reported in Bankola region(Raniganj); Jaiprakash et al. (2010) carried outmonitoringand simulation of PM in Dhanbad (Jharkhand) coal mine,and Trivedi et al. (2009; 2010a; b) carried out an airpollution study for the five mines. Chaulya (2004, 2005)has carried out AQM studies from September 1998 toAugust 1999 in IB Valley Orissa, Chaulya et al. (2012)also reported analytical results of physico-chemical pa-rameters and proximate analysis of coal dust collectedfrom the road surface of four opencast coal mines located

in different coalfields in India. Besides ambient air qual-ity, a study of radio-elemental characteristics of fly ashfrom a thermal power plant of Chandrapur using coalfrom Padmapur mines is also reported by Menon et al.(2011). Using satellite imagery, Mishra et al. (2012) stud-ied air pollution concentration over Jharia. More recently,George et al. (2013) carried out studies at Chandrapurcoal mine and reported PM10 in the ambient air and itscomparison with other environments. But no such workhas been done in eastern (Raniganj), southeastern(Korba), and south coalfield region (Godavari valley).Therefore, the present study was carried out for long-term period of 13 years from Mar 2000 to Dec 2012 toanalyze the ambient air quality in the mining areas.

2 Study Area, Data, and Methodology

2.1 Study Area

Coalfield region has been identified as hot spots and themost awful polluted area in India. There is widespreadconcern of air pollution due to emission of particulatesfrom various mining and associated activities. Raniganjis one of the India’s oldest coalfields; it is the birth placeof coal mining in India; mining started in this coalfieldin 1774. In west Bengal Raniganj coalfield is the impor-tant coalfield. This coalfield is the easternmost of theDamodar Valley coalfields. Ranigunj coalfields coversan area of 1,530 km2 spreading over Burdwan,Birbhum, and Bankuaand Purulia Districts in WestBengal and Dhanbad District in Jharkhand. Coal iscurrently being produced by underground as well asopencast mining methods by the Eastern Coalfield Ltd,a subsidiary of Coal India Ltd. In addition, a smallportion of the coalfield is operated by BCCL (BharatCoking Coal Ltd), SAIL (Steel Authority of India), andother private companies (Singh et al. 2010).

KorbaCoalfield is located inKorba district in the Indianstate of Chhattisgarh in the basin of the Hasdeo River, atributary of the Mahanadi. “Korba” Coalfield in southeast-ern region covers an area of about 530 km2. There areseveral coal-based thermal power stations in the area con-suming coal from Korba Coalfield. Korba Super ThermalPower Plant of NTPC has installed capacity of 2,600MW.It gets coal from Gevra and Kusmunda mines.

Godavari Valley Coalfield is located in the districts ofAdilabad, Karimnagar, Khammam, and Warangal in theIndian state of Andhra Pradesh. It is the only coalfield in

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South India. It lies in the basin of the Godavari River.The cumulative basin area of Godavari Valley Coalfieldis 17,400 km2. The coal bearing area is 11,000 km2.However, the area considered potential for regionalexploration is 1,700 km2.

2.2 MODIS Dataset

The main dataset consists of Terra MODIS (version 5.1,level 3, 1 deg×1 deg) monthly AOD550 observationscentered over the mining area in India. More specifically,MODIS data sets during the period March 2000 toDecember 2012 have been obtained at three sites in threestates in eastern, southeastern, and south coalfield in Indianamely Raniganj in West Bengal, Korba in Chhattisgarh,and Godavari valley in Andhra Pradesh (see Fig. 1). Theaerosol optical depth (AOD) uncertainty over land isΔAOD=± (0.05+15 %) (Levy et al. 2010). A criticalin using the level 3 dataset for the current analysis is theavailability of level 2 data (10×10 km) around the studylocations (Gupta et al. 2013). The total data set has beenclassified as per the India Meteorological Department(IMD) [Ministry of Environment and Forests, 2004] intofour seasons, namely winter (December–January–February), pre-monsoon (March–April–May), monsoon(June–July–August–September), and post-monsoon(October–November).

2.3 Statistical Analysis

The percentage (%) variation in AOD during a studyperiod has been calculated from the following formula(Kaskautis et al. 2012b).

x %ð Þ ¼ aN

x

!� 100 ð1Þ

Where x is the variable, a is the slope value of thelinear regression analysis, and N is the whole number ofrecords in the given period. Using the SPSS version 17, alinear regression model of first order was used to deter-mine the best fit line. The confidence intervals at the 95%level were calculated to test the statistical significance ofthe trends using the compared t test and p value (<0.05).

2.4 Hurst Exponent

In general, Hurst exponent (H) denotes the index ofdependence. It measures the relative tendency of a time

series either to regress strongly to the mean or to clusterin a direction. Hurst exponent value ranges between 0and 1 and a value of 0.5 indicates a true random walk(a Brownian time series). Random walk suggests thatthere is no correlation between any element and a futureelement. A Hurst exponent value of 0.5<H<1 indicates“persistent behavior” or a positive autocorrelation, while avalue in the range of 0<H<0.5 points out “anti-persistentbehavior” or a negative autocorrelation (Rangarajan andSant 2004). H is defined as follows:

H ¼ a−12

�������� ð2Þ

Where, a is a slope value from the linear regressionanalysis.

2.5 Fractal Dimension

The Hurst exponent is related to the fractal dimensionDof the time series curve by the formula

D ¼ 2−H ð3Þ

Fractal dimensional analysis is mainly well suited toanalyze the variability of a given time series. IfD is 1.5,there is no correlation among amplitude changes con-sistent to two successive time intervals. Thus, no trendin amplitude can be distinguished from the time seriesand, therefore, the process is unpredictable. However, asD decreases to 1, the process becomes more and morepredictable and it shows “persistence”, while for valuesbetween 1.5 and 2 the process exhibits “anti-persis-tence”. Specifically, a decrease in the amplitude of theprocess is more likely to lead to an increase in the future;hence, the predictability again increases (Rangarajanand Sant 2004; Parmar and Bhardwaj, 2013).

2.6 Predictability Index

The predictability index (PI) describes the behavior of thetime series and is defined as follows (Rangarajan 1997):

PI ¼ 2 D − 1:5j j ð4Þ

If the PI is close to zero, then the correspondingprocess approximates the usual Brownian motion, andis thus unpredictable. If the PI is nearly one, the processis very predictable.

Water Air Soil Pollut (2014) 225:1832 Page 3 of 12, 1832

3 Results and Discussion

3.1 Annual and Seasonal AOD Variability

Mining has a great effect on the air quality. Air pollutionin mines is mostly owing to the fugitive emissions ofparticulate matter and gases including methane, sulfurdioxide, oxides of nitrogen, and carbon monoxide.

Maximum mining operations produce dust. The majorprocesses producing dust are drilling, blasting, hauling,loading, transporting, and crushing. According to thepresent study (Fig. 2), the long-term (2000–2012) low-ermost monthly average AOD550 values are detectedover the southeastern coalfield region (0.35±0.16),south coalfield region (0.44±0.14), and eastern coalfieldregion (0.59±0.21). The results show that the eastern

Fig. 1 Map showing (in dark black circle) the mining regions (the study locations) in India

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coalfield region exhibit the highest AODs values due toopen cast mines and extensive mining activities as wellas a large number of coal fires. Reddy and Ruj (2003)reported that industrial activities, indiscriminate open airburning of coal by the local inhabitants for cooking aswell as coking purposes, vehicular traffic, etc. are re-sponsible for the high concentration of pollutants overthe eastern coalfield region. Moreover, there are largepoint sources heavily clustered along the eastern coalbelt, and homogeneously spread around the rest of theIG region (Garg et al. 2002; Dalvi et al. 2006). Includedamong the emission sources scattered over the entireeastern region are coal-based thermal power plants,steel, sugar, and other small and medium industries anumber of which uses coal as fuel, coal mining, crudeoil production, natural gas production, etc. and openburning of litter and biofuels used for domestic cooking(Garg et al. 2001, 2002; Dalvi et al. 2006). Singh andSharma (2004) also reported that mining and associatedactivities have higher the background levels of particu-late pollution in the eastern region (Raniganj). The coalhandling plant and poorly maintained roads, causing inthe transport of coal dust by means of wind, are identi-fied as the sources of particulate pollution in a coalmining area. Coal burning and transportation activitiesappear to be most important causes of SO2 and NOx incoal mining areas. In addition, Tiwary and Dhar (2006)reported high suspended particulate matter and the

presence of noxious gases over the eastern coalfieldregion (Raniganj) due to land subsidence and mine fire,a large portion of land is under fire and thereby enhanc-ing noxious gases like CO and H2S in the environment.Moreover, CO level also found high which is partly dueto vehicular exhaust from heavy mining equipment.Open-cast mining operations contribute the major airpollutants to the atmosphere and are responsible forenvironmental degradation by deteriorating the air qual-ity in respect to dust and other gaseous pollutants. Themain sources of pollution in the southeastern region(Korba) are coal-based power plants, smelters, and opencast mines at Gevra, Dipka, and Kusmunda. Large-scaletransportation of coal raises a pollution problem.

The long-term seasonal mean values of MODISAOD550 are summarized in Table 1 and plotted inFig. 3 (a–d) and Fig. 4. The highest seasonal AODsare observed during the monsoon season, over all themining sites eastern coalfield region (0.72±0.29), south-eastern coalfield region (0.50±0.19), and south coalfieldregion (0.47±0.17), Moreover, eastern coalfield regionexhibit highest seasonal AOD during monsoon andamong all sites and all seasons. During the pre-monsoon and early monsoon seasons (April–June)IGP is strongly affected by the locally produced andregionally transported aerosols along with dust, mainlyoriginating from adjacent arid agriculture lands, Thardesert and west Asia (Dey et al. 2004; Prasad and Singh

Fig. 2 Monthly variation ofAOD550 for the measurementperiod during Mar 2000 toDec 2012, over coal mines inIndia

Water Air Soil Pollut (2014) 225:1832 Page 5 of 12, 1832

2007; Remer et al. 2008; Gautam et al. 2009; Giles et al.2011). The transportation of the dust-laden air masses isdriven by the pre-monsoon westerlies and, owing toenhanced convection, the dust aerosols are verticallyadvected to the higher altitudes up to the Himalayas(Guleria et al. 2012; Gautam et al. 2009; 2013) creatinga gradient of decreasing values further to the east(Kaskaoutis et al. 2011). In this respect, Eastern coal-field region exhibits higher AODs during this period,followed by southeastern coalfield region, south coal-field region, farthest to the east. In contrast, the lowestseasonal AOD values are observed during post-monsoon season over the south coalfield region andeastern coalfield region. Similarly, southeastern coal-field region lowest AOD values are found in winterseason. A decline in AOD from summer to winter isobserved and this is attributed to rain washout processes.The amount and changeability of AOD detected over allthe sites are mainly linked to the wind pattern andregional topography of the region as well as local emis-sions, dust transport, boundary-layer dynamics, mon-soon rainfall, and seasonal forest and agricultural fires(Di Girolamo et al. 2004; Singh et al. 2004; Gangulyet al. 2006; Ramachandran et al. 2012).

3.2 Annual Trend Variability

Figure 2 represents the long-term (Mar 2000 to Dec2012) MODIS AOD variation and trends as estimatedfrom the corresponding monthly mean values. The trendvalues on yearly or monthly basis (slope/year or month)as well as the % variation in the AOD values are sum-marized in Table 2 increasing trends in AOD550 havebeen observed over all sites viz., Raniganj (eastern

coalfield region; 7.31 %) in West Bengal and Korba(southeastern region; 5.0 %) in Chhattisgarh, Godavarivalley (south coalfield region; 32%) in Andhra Pradesh.Highest and statistically significant trend value is ob-served only in the south coalfield region.

However, Moorthy et al. (2013) found the lowestincreasing trend over northern India compared to therest of the country corresponding to 0.0071/year or1.52 % per year, which is not considered as statisticallysignificant. Furthermore, the sites that they used aredifferent from the current ones, along with the dataset and period of observations. In this respect,Ramachandran et al. (2012) based on MODISAOD550 during the period 2000–2009, reported pos-itive annual trends in Ranchi (11.1 %), Bhubaneshwar(16.2 %), and Kolkata (9.3 %) in eastern; Raipur(26.5 %) and Mumbai (24.5 %) in central as wellas Hyderabad (41.9 %) and Bengaluru (50.2 %) inlocated in southern India. The increasing trends overdifferent coal mines locations in India reported in thepresent study are consistent with Ramachandran et al.(2012). An overall increasing AOD trend of 7.7 % isreported to Kanpur based on AERONET data during2001–2010 (Kaskaoutis et al. 2012b). The trends arehigher in the south coalfield region (Godavari valley;32 %) in south India. The increase in AODs on anannual mean basis over the southern coalfields regioncan be directly attributed to the increase in urbaniza-tion. For each case the statistical significance of theslope was checked by applying the p value, i.e.,p<0.05 for statistically significant variations at the95 % confidence level. The statistically significanttrends at the 95 % confidence level are presented inbold in Table 2.

3.3 Seasonal Trend Variability

The trends in AOD values during the different seasonsare further examined over all three regions eastern,southeastern, and south coalfield for the period Mar2000 to Dec 2012 and the results are summarized inTable 2. Figure 3a–d show the seasonal variation ofMODIS AOD during all the seasons. The results revealan increasing and statistically significant trend for winterAOD at south coalfield region (0.0103/year), and alsoover the eastern coalfield region (0.006/year) and south-eastern coalfield region (0.0047/year), although not be-ing statistically significant. The trends are consistentaccording to the aerosol increasing scenario over India,

Table 1 AOD average values (Mar 2000–Dec 2012)

Eastern coalfieldregion

South easterncoalfield region

South easterncoalfield region

Overall 0.59±0.21 0.35±0.16 0.44±0.14

WINa 0.57±0.09 0.24±0.06 0.39±0.08

PMb 0.53±0.10 0.32±0.06 0.50±0.12

MONc 0.72±0.29 0.50±0.19 0.47±0.17

POMd 0.47±0.11 0.26±0.05 0.37±0.09

aWinterb Pre-monsooncMonsoond Post-monsoon

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especially in post-monsoon and winter (Kaskaoutis et al.2011). The increasing trend in AOD during winter sug-gests an increase in the amount of aerosols mostly ofdust and anthropogenic activities dominated over theregion in this period and also during winter the bound-ary layer is thin and holds the pollutants in a smallervolume consequently, confinement of aerosols. Highestpositive trend value (58 %) is observed over the southcoalfield region compare to eastern coalfield region(31 %) and southeastern coalfield region (27.5 %)during post-monsoon season. Similarly in winter seasonhighest trend value is found over the south coalfield

region (35 %) followed by southeastern coalfield region(26 %) and eastern coalfield region (14 %). During allseasons and at all sites trend values are higher in the southcoalfield region. Thus it is evident that the south coalfieldregion is gradually transformed into a polluted region.

During the pre-monsoon season the condition changesand decreasing and statistically significant trend isobserved only over the eastern coalfield region(−0.0052/year), while in the south coalfield region(0.0169/year) and southeastern coalfield region(0.0002/year), increasing trends are observed withoutbeing statistically significant. A decreasing trend in

Fig. 3 Seasonal variation of AOD550 for the measurement period during Mar 2000 to Dec 2012, over various measurement sites during awinter, b pre-monsoon, c monsoon, and d post-monsoon seasons

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AODs in pre-monsoon seasons over the eastern coalfieldregion, suggests a decrease in the amount of naturalaerosols aided by the variation in atmospheric parame-ters like winds and temperature. The declining aerosoltrend over northern India was firstly noted and examined

by Kaskaoutis et al. (2011) and attributed to changes innatural aerosol emissions, land use/land cover changesover Thar desert and changes in soil moisture and rain-fall. A more detailed analysis (Kaskaoutis et al. 2012a)revealed that the aerosol trends over northern India inlate pre-monsoon and monsoon seasons during the2000s are mainly controlled by the extreme aerosol yearsof 2002 and 2003, when due to deficit of monsoonrainfall and prolonged dry conditions, the dust activity,and aerosol lifetime had been significantly increased.The same trend was verified by using the KanpurAERONET data (Kaskaoutis et al. 2012b) and ground-based measurements in Delhi (Lodhi et al. 2013). It is,therefore, important to note that this neutral-to-decliningtrend of aerosols over IGP during pre-monsoon andmonsoon seasons are still apparent by adding 2.5 yearsin the trend analysis from the initial period (2000–2009)examined by Kaskaoutis et al. (2011). Increasing trendsover the south coalfield region and southeastern coal-field region are may be due to the two reasons such as anincrease in wind speed and boundary layer height.During the pre-monsoon season large quantities of soil

Fig. 4 AOD550 in different seasons of the three regions namelyeastern, southeast, and south coalfield regions in India

Table 2 Statistical parameters for AOD

Observation site Intercept±error Slope±error T test H D PI R2 Yearly mean trends

Intercept Slope Trend/year (%)

Eastern coalfieldregion

Overall 0.566±0.034 0.000±0.000 16.499 0.737 0.5 1.5 0.000 0.004 0.0033/year 7.31 %

WINa 0.522±0.03 0.001±0.001 17.297 1.73 0.501 1.498 0.004 0.79 0.006/year 14 %

PMb 0.556±0.032 −0.001±0.001 17.299 −0.98 4.995 1.501 0.001 0.025 −0.0052/year −13 %

MONc 0.711±0.090 0.000±0.003 7.94 0.081 0.5 1.5 0.000 0.709 0.0044/year 8.06 %

POMd 0.384±0.040 0.006±0.003 9.721 2.371 0.503 1.497 0.006 0.466 0.011/year 31 %

Southeastern coalfieldregion

Overall 0.337±0.026 0.000±0.000 13.007 0.416 0.5 1.5 0.000 0.001 0.0013/year 5.0 %

WINa 0.214±0.020 0.001±0.001 10.61 1.24 0.4995 1.501 0.001 0.042 0.0047/year 26 %

PMb 0.319±0.020 0.000±0.001 15.579 0.27 0.5 1.5 0.000 0.002 0.0002/year 0.8 %

MONc 0.496±0.055 0.000±0.002 9.09 0.032 0.5 1.5 0.00 0.000 0.00001/year 0.03 %

POMd 0.244±0.018 0.003±0.001 12.388 2.323 0.501 1.499 0.002 0.184 0.0055/year 27.5 %

Southeastern coalfieldregion

Overall 0.372±0.022 0.001±0.000 17.069 3.063 0.499 1.50 0.001 0.082 0.011/year 32 %

WINa 0.317±0.025 0.004±0.001 12.562 3.232 0.502 1.498 0.004 0.23 0.0103/year 35 %

PMb 0.388±0.034 0.006±0.001 11.302 3.866 0.503 1.497 0.006 0.288 0.0169/year 44 %

MONc 0.452±0.050 0.001±0.002 8.95 0.389 0.4995 1.501 0.001 0.003 0.0044/year 12.3 %

POMd 0.256±0.027 0.008±0.002 9.523 4.718 0.504 1.496 0.008 0.481 0.0164/year 58 %

The statistically significant trends at the 95 % confidence level are presented in boldaWinterb Pre-monsooncMonsoond Post-monsoon

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derived dust aerosol to get lifted into the atmospherefrom the dry lands of semiarid region. Boundary layerheight exhibited an increasing trend beginning fromwinter to pre-monsoon season. This will make availablesufficient opportunity for all types of natural and anthro-pogenic aerosols to get accommodated in it (Gangulyet al. 2006). Similarly, increasing trends are observedover all regions in monsoon (Jun–Sep) months, mainlyinfluenced by inter-annual variations of dust occur-rences (Kaskautis et al. 2012).

In contrast, during the post-monsoon season increas-ing trends have been observed over all mining areas,e.g., eastern coalfield region (0.0111/year), southeasterncoalfield region (0.0055/year), and south coalfield re-gion (0.0164/year). All trend values are statisticallysignificant during post-monsoon season except easterncoalfield region and highest positive trend value isfound over the south coalfield region (58 %).

3.4 Statistical Analysis

More than 13 years Terra-MODIS (Moderate-ResolutionImaging Spectroradiometer) monthly average AOD550

data in the period 2000–2012 have been considered forthe present study. Statistical analysis consists of mean,median, mode, minimum, maximum, range, standarddeviation, kurtosis, skewness, and coefficient of varia-tion. Mean explains average value. Median gives amiddle value of an ordered sequence also it is namedas positional average. The mode is defined as a valuewhich occurs in the maximum number of time that ishaving the maximum frequency. Minimum and maxi-mum explains a minimum and maximum value ofsample data. The range is used for studying a variationof data. Standard deviation gives a measure of“spread” or “variability” of a sample. Kurtosis refersto a degree of flatness or peakedness in a region aboutthe mode of a frequency curve. Skewness discussessymmetry of data. Coefficient of variation gives arelative measure of sample (Parmar and Bhardwaj2013). Over eastern and southeastern region average,median, and mode values are equal thus data exhibitsnormal behavior while over southern region only meanand median is approximately equal but the mode isdifferent. Standard deviation values suggest that AODdata values are close to each other. Skewness valuesuggests that the curve is symmetrical and leptokurtic.The graphical representations of statistical parametersof AOD550 are represented in Fig. 5.

Furthermore, the statistical parameters HE, FD, andPI are also summarized in Table 2. It is shown that themostly of the values of HE are nearly equal to 0.5 for allsites, which indicates a true random walk (Browniantime series). Similarly, the value of PI is nearly equal to0 and the FD equal to 1.5, meaning that there is nocorrelation among amplitude changes consistent to twosuccessive time intervals. Thus, no trend in amplitudecan be distinguished from the time series and, therefore,the process is unpredictable at all sites.

4 Conclusion

The present work examined the aerosol optical depthvariability and long-term trends (from Mar 2000–Dec2012) over eastern, southeast, and south coalfield regionsin India. Linear regression analysis along with statisticalsignificance tests were applied for the trends in themonthly averaged aerosol data set. Substantial deviationsin the AOD trends were detected depending on themonth and season. In general, the high AOD500 values(0.59±0.21) are observed over the eastern coalfieldregion followed by south coalfield region (0.44±0.14)and southeastern coalfield region (0.35±0.16) inIndia. Overall, an increasing trend in AOD550 hasbeen observed over all regions namely eastern coal-field region (7.31 %), southeastern coalfield region

Fig. 5 Graphical representation of statistical analysis of AOD550

over eastern, southeast, and south coalfield regions in India

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(5.0 %), and south coalfield region (32 %) in India.This increasing trend predominantly owes to a constantincrease in the seasonal/monthly averaged AOD duringthe winter (Dec–Feb) and post-monsoon (Oct–Nov)seasons dominated by anthropogenic emissions. Winterseason displaysmuch lower levels of AODsmainly overthe south coalfield region and the southeast coalfieldregion because of the monsoon rainfall and the post-monsoon rainfall washes out most of the aerosol parti-cles in the atmosphere. An important finding from thepresent analysis was that during pre-monsoon season adecreasing trend is observed (Mar–May) over an easterncoalfield region (−13 %), while in the south coalfieldregion (44 %) and southeastern coalfield region (0.8 %),increasing trends are observed. Similarly, increasingtrends are also observed over all regions in monsoon(Jun–Sep) months, mainly influenced by inter-annualvariations of dust occurrences. During all seasonsand at all sites trend values are higher in the southcoalfield region. Thus it is evident that the southcoalfield region is gradually transformed into a pol-luted region. A low value of standard deviation forAOD550 over all eastern, southeastern, and southcoalfield region indicates that the data points tendto be very close to the mean and skewness value sug-gests that curve is symmetric and exhibits leptokurticbehavior. Furthermore, the values of Hurst exponent,fractal dimension, and predictability index for AODsare 0.5, 1.5, and 0, respectively suggesting that theAODs in all sites follow the Brownian time seriesmotion (true random walk).

Acknowledgment Authors are grateful to the Director, CSIR-NPL. The authors would like to thank MODIS science data teamsfor processing the data via Giovanni website (http://gsfc.nasa.gov/).We are also thankful to the two anonymous reviewers for theirvaluable suggestions and comments in improving the manuscript.

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