Monitoring subsurface coal fires in Jharia coalfield using observations of land subsidence from differential interferometric synthetic aperture radar (DInSAR) Nishant Gupta, Tajdarul H Syed∗ and Ashiihrii Athiphro Department of Applied Geology, Indian School of Mines, Dhanbad, India. ∗ Corresponding author. e-mail:
[email protected]
Coal fires in the Jharia coalfield pose a serious threat to India’s vital resource of primary coking coal and the regional environment. In order to undertake effective preventative measures, it is critical to detect the occurrence of subsurface coal fires and to monitor the extent of the existing ones. In this study, Differential Interferometric Synthetic Aperature Radar (DInSAR) technique has been utilized to monitor subsurface coal fires in the Jharia coalfield. Results showed that majority of the coal fire-related subsidence were concentrated on the eastern and western boundaries of the coalfield. The magnitude of subsidence observed was classified into high (10–27.8 mm), low (0–10 mm) and upliftment (−10–0 mm). The results were strongly supported by in situ observations and satellite-based thermal imagery analysis. Major subsidence was observed in the areas with repeated sightings of coal fire. Further, the study highlighted on the capability of the methodology for predicting potential coal fire zones on the basis of land surface subsidence only. The results from this study have major implications for demarcating the hazardous coal fire areas as well as effective implementation of public safety measures.
1. Introduction Accounting for nearly 70% of India’s energy production, coal remains a key contributor to the economic development of the country. The Jharia coalfield of eastern India is the primary producer of high grade coking coal, which is a critical component of India’s major steel industry. However, in spite of its economic importance, Jharia coalfield has been burning underground for nearly a century and hosts the maximum number of uncontrollable surface and subsurface coal fires in India (Chatterjee et al. 2006). Jharia coal fires lead to considerable loss of valuable, non-renewable, reserve of prime coking coal in addition to having adverse effects on the regional environment (Chatterjee 2006). Land subsidence associated
with coal fires add to the growing concern of hazardous impact on the masses residing in the study area. Thus the monitoring of coal fires, because of its socio-economic importance and environmental impacts, present an interesting challenge to scientists and policy-makers alike. Concerns over the degrading health and safety of the local population justify a detailed assessment of the extent and direction of propagation of coal fires. Such an assessment will also contribute towards the identification and implementation of the essential preventive measures. Previously, Bhattacharya et al. (1991) and Mukherjee et al. (1991) utilized airborne thermal data for the identification of coal fires and their depth of occurrence, in the Jharia coalfield, using linear heat flow equation. The efficacy of thermal
Keywords. Coal fire; Jharia coalfield; DInSAR; thermal remote sensing. J. Earth Syst. Sci. 122, No. 5, October 2013, pp. 1249–1258 c Indian Academy of Sciences
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data from spaceborne Landsat-5 Thematic Mapper (TM) for the detection of coal fires in the Jharia coalfield was first established by Reddy et al. (1993) and Saraf et al. (1995). Then on, Thermal Infra-Red (TIR) and Short Wavelength Infra-Red (SWIR) bands of Landsat-5 TM have been effectively used to identify surface and subsurface coal fires (Prakash et al. 1997) and to calculate the subpixel area and temperature (Prakash and Gupta 1999). In order to differentiate between surface and subsurface coal fires and to study its lateral propagation, Chatterjee (2006) implemented the technique of pixel integrated temperature modelling on Landsat TM thermal IR images. Additional coal fire studies utilizing thermal bands were carried out by Chatterjee et al. (2007) (using images from Landsat-5 TM and Landsat-7 ETM+), Gautam et al. (2008) (using NOAA/AVHRR) and Martha et al. (2010) (using images from ASTER). Most recently, Mishra et al. (2011), empirically, estimated a scaled variation between the temperature of surface and subsurface coal fires obtained from the thermal band of Landsat ETM+ data and in situ temperature observations using thermal imaging camera. In the past, Synthetic Aperture Radar (SAR) interferometry (InSAR) has been effectively utilized to monitor subsidence caused by various natural and anthropogenic factors (such as earthquake (e.g., Pathier et al. 2006), mining activities (e.g., Herrera et al. 2007) and groundwater withdrawal (e.g., Motagh et al. 2008)). This technique presents a method to obtain highly precise measurements of surface displacement, most prominently in the vertical direction. InSAR involves the computation of an interferogram using SAR image pairs of the same area acquired at different times. In this study an advanced InSAR technique based on small perpendicular and temporal baseline, twopass Differential Interferometric SAR (DInSAR) was used. DInSAR provides information on twodimensional deformation in the line of sight (LOS) of radar by calculating a differential interferogram for the image pair acquired during repeat pass of the radar over the same area at different times (Massonnet and Feigl 1998). DInSAR provides a cost effective way of obtaining information on two-dimensional deformation, with wide and continuous spatial coverage, when compared to Differential Global Positioning System (DGPS) and other instrumental methods, which are only able to measure ground deformation at discrete points. While the spatial coverage of levelling techniques is comparable to that of DInSAR, the benchmark density is significantly lower than that of DInSAR. Apart from being cost intensive, levelling surveys are significantly constrained in both space and time when compared to DInSAR
(Wegmuller et al. 1999; Tom´as et al. 2005). However, the DInSAR technique is limited by the fact that it does not provide statistics of deformation rate at individual points within the interferogram as is the case with the recently developed Persistent Scatterer InSAR (PSInSAR) technique. The PSInSAR technique allows highly accurate measurements of deformation that occurs at individual, phase-dependent radar targets called permanent or persistent scatterers (PS) (Jiang et al. 2011). Previously, DInSAR technique has been extensively used for analysing temporal and spatial characteristics of land subsidence due to various geophysical phenomena (e.g., Carnec et al. 1996; Galloway et al. 1998; Amelung et al. 1999; Chatterjee et al. 2006). In the recent past, DInSAR technique has been used for the assessment of land subsidence caused by mining activities (e.g., Colesanti et al. 2005; Herrera et al. 2007; Jung et al. 2007). While Prakash et al. (2001) and Voigt et al. (2004) devised an integrated approach with the combination of optical, thermal and microwave data, Jiang et al. (2011) utilized advanced InSAR techniques to study coal fires. The current study presents the most recent and comprehensive assessment of land surface subsidence caused by coal fire in the Jharia coalfield of India. The methodology involved the implementation of repeat pass DInSAR to make precise measurements of vertical land subsidence primarily attributed to underlying coal fires. Here we validated the DInSAR-based subsidence measurements with independent results from thermal imagery and in situ observations. The study, further hypothesized that areas with discernible vertical subsidence but without any thermal signature or in situ sighting of coal fire are potentially hazardous zones with high probability of subsurface coal fire occurrence. 2. Study area Jharia coalfield, one of the leading coal producing belts in India, is bounded by the latitudes 23◦ 39 – 23◦ 50 N and longitudes 86◦ 05 –86◦ 30 E and located within the state of Jharkhand (figure 1a). The sickle shaped coalfield (figure 1b) contains the only remaining reserve of prime coking coal in India and occupies an area of approximately 450 km2 . Major portions of this coalfield is operated by Bharat Coking Coal Limited (BCCL 2008). Geologically the rocks of the study area, belonging to the Gondwana Supergroup, range in age from Upper Carboniferous to Lower Cretaceous age, lie unconformably over the older Archaean rocks. In Jharia coalfield, the major coal bearing formation is the Barakar Formation consisting of bands of coarseto-medium grey and white sandstones, shales and
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Figure 1. (a) Location map of Jharia coalfield in the overall Indian perspective. (b) Magnified view of the study area represented by a false colour composite (FCC) illustrating the Jharia coalfield with some of the major collieries affected by coal fires.
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coal seams. Tectonically, this area consists of NE– SW-trending faults with exposures of lamprophyre and dolerite dykes in a criss-cross manner (Saraf et al. 1995). Figure 1(b) shows a false colour composite (FCC) image of the study area prepared using Landsat ETM+ bands 7, 5 and 3 (RGB). The FCC (figure 1b) prepared shows coal bands and coal dumps in the shades of brown to brownish black colour.
3. Data used Several key-independent satellite and field-based datasets have been utilized in this study. For example, the Envisat Advanced Synthetic Aperture (ASAR) data for the study area was obtained from the European Space Agency (ESA). Two images acquired on 13 October 2007 and 22 December 2007 were processed to generate a deformation map using the DInSAR technique. These images have a temporal baseline of 70 days and a perpendicular baseline of 154 m. Landsat Enhanced Thematic Mapper Plus daytime images over the study area (Path/Row: 140/44) were also acquired for 19 November 2007 at 04:33:17.4738125 GMT and 5 December 2007 at 04:33:23.8403750 GMT from Glovis (http://glovis.usgs.gov). Due to the unavailability of night time ETM+ imagery over the study area, scenes acquired during local winter were selected for this study. While the Digital Elevation Model (DEM) used in this study was obtained from Shuttle Radar Topography Mission (SRTM) (Rodriguez et al. 2006), the required orbit data was acquired from ESA Doris orbits (DOR). Specific sightings of coal fire and polygonal boundaries of surface and subsurface coal fire areas in the Jharia coalfield were produced from a digitized version of the field survey map published by Bharat Coking Coal Limited in their master plan (BCCL 2008).
4. Methodology 4.1 Processing of microwave data In general, C band Envisat ASAR data have been proven to be very effective in some of the key missions outlined by the ESA. DInSAR technique precisely quantifies the line-of-sight (LOS) displacement, thus becoming an important tool for the measurement of surface displacement from space. This displacement gets encoded in the phase difference between two SAR images, which can be measured at each point (pixel) of a phase difference image, which is termed as an interferogram. An interferogram is generally created from
two SAR images with similar imaging geometries, acquired at different times, for the same area. These two images are precisely coregistered before the phase difference is computed for each pixel. Interferograms used in this study were produced using the Repeat Orbit Interferometry Package (ROI PAC), which was developed at Jet Propulsion Laboratory and the California Institute of Technology (JPL/Caltech). The interferogram thus generated, from two SAR images, consists of a phase difference (Φ) (equation 1) which is an aggregate of phase difference related to topographic information of the terrain (Φtopo ), the line-of-sight surface displacement between two acquisitions (Φdisp ), delay in radar signal due to variations in the tropospheric water content in atmosphere (Φatm ), noise in the interferometric signal (Φnoise ) and due to the difference in the relative positions of the satellite at the time of acquisition (Φorbital ) (Chang et al. 2004). Φ = Φtopo + Φdisp + Φatm + Φnoise + Φorbital .
(1)
While using the DInSAR technique, the Φdisp , was estimated by removing the phase differences contributed by other sources from the total on the LHS of equation (1). The Φtopo , was removed using a 3 arc-second (approx 90 m) photogammetric SRTMDEM in two pass DInSAR technique (Massonnet and Feigl 1998). The Φatm , can either be accounted for by using independent observations such as GPS (Ge et al. 2003), or neglected if the tropospheric delay is considered to be homogeneous at the time of the radar image acquisition. In this study, the Φatm term was neglected following the assumption that the magnitude and spatial extent of phase delay produced by atmospheric fluctuations will be negligible in arid and semi-arid areas (Zhou et al. 2013), which is also the regional setting applicable for the Jharia coalfield. Precise orbit data was conjunctively used to remove orbital phase differences (Φorbital ) and to estimate the spatial baseline in order to register the DEM to SAR image coordinates. The phase difference Φnoise , is an artifact of spatial and temporal decorrelation and the thermal noise of the radar instrument (Mora et al. 2003). Errors in the removal of these phase differences account for the total error in the DInSAR-based estimates of subsidence. However, the magnitude of uncertainty depends on the terrain characteristics as well as on the quality of the datasets used to remove these phase differences. The displacement phase Φdisp , due to the differences in ground elevation in the two SAR images is represented by equation (2). 4π δR (2) Φdisp = − λ
Monitoring subsurface coal fires in Jharia coalfield where δR is the height displacement along the slant-range direction and radar wavelength is represented by λ. The measured phase in an interferogram is a multiple of 2π. Thus a C-band microwave signal (λ = 5.6 cm), a complete cycle of 2π phase change will represent a displacement of λ/2, i.e., 2.8 cm between the radar and each target on the surface. In this study, phase unwrapping of interferograms, required to generate a displacement map, was performed, using Statistical-Cost, Network-Flow Algorithm for Phase Unwrapping (SNAPHU) (Chen and Zebker 2002).
4.2 Processing of Landsat ETM+ image Emitted thermal radiations from the Earth’s surface are precisely recorded by thermal sensors and the information is stored as digital numbers (0–255). Thermal anomaly of the Earth’s surface created by surface and/or subsurface coal fires can be detected and mapped by converting these digital numbers to radiant temperature and subsequently to kinetic temperature. Here, scene specific radiance values were calculated from raw digital values using equation (3) (Markham and Barker 1986), Lλ =
Lmax(λ) − Lmin(λ) Qcal Qcal max
(3)
where Lmin(λ) = minimum detected spectral radiance of the scene, Lmax(λ) = maximum detected spectral radiance of the scene, Qcal = grey level of the analysed pixel, Qcal max = maximum grey level (255). Once the digital numbers for the thermal band have been converted to radiance values, the inverse of Planck’s function was applied to derive its corresponding temperature values. The radiant temperature was determined using the following equation:
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coal fire. The selection of threshold temperature is dependent on the geology of the area since radiant energy has to pass through these less conductive subsurface materials (Saraf et al. 1995). Here, the threshold temperature was refined in the context of threshold temperature estimates established in previous studies (Martha et al. 2010; Mishra et al. 2011). The final thermal anomaly map, referred to as coal fire map, was produced on the basis of the consideration that pixels with temperature 40◦ C would represent surface coal fires. Actual land surface temperature was not taken into consideration as our primary aim was to qualitatively determine the location and extent of surface and subsurface fire areas. The Scan Line Corrector (SLC) of Landsat ETM+ instrument failed on 31 March 2003, since then all Landsat ETM+ images have wedge-shaped gaps on both sides of each scene. The net effect is that approximately 22% of the pixels in a Landsat7 scene are lacking any kind of information. A technique developed by Scaramuzza et al. (2004) which fills the gaps in one scene with data from another Landsat scene is used to correct for the missing data pixels. In this technique, a local linear histogram matching is carried out to find a linear transformation from one image to another based on the standard deviation and mean values of each band, of each scene. To obtain quality results, cloud free images, with minimal time gap were used for the purpose of gap filling.
5. Results 5.1 Identification of areas undergoing subsidence using DInSAR technique
The displacement map produced using a pair (2007/10/13–2007/12/22) of SAR acquisitions for monitoring vertical ground movement in the study where K1 = calibration constant (666.09 W/m2/sr/μm) area is shown in figure 2. Results, in general, and K2 = calibration constant (1281.71 K) (Landsat-7 show that the study area has experienced varyScience Data Users Handbook 2008). The radiant ing degrees of subsidence as well as upliftment. temperature was then converted to kinetic tem- Since the focus of the present work is on analysing perature with an emissivity (ε) value of 0.96 using subsidence related to coal fires, we have followed equation (5). the convention of assigning positive and negative values, in the displacement map, as respective 1 Tkin = Trad × 1/4 . (5) measures of subsidence and upliftment in the ε study area. The displacement map (figure 2) shows that Computation of thermal anomaly maps reflecting the presence of surface and subsurface coal both the eastern flank and western flank of the fires, using Landsat-7 ETM+ thermal band 6, also Jharia coalfield have witnessed major subsidence, involved the selection of a threshold temperature primarily due to coal fires. Areas such as Kujama, to discriminate between pixels with and without Kusunda, Tetulmari, North Tisra, Bararee and T =
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Figure 2. Displacement map corresponding to the SAR image pair (2007/10/13–2007/12/22). Illustrated is vertical displacement measured in millimetres. A positive value of vertical displacement indicates subsidence whereas negative vertical displacement values indicate upliftment.
Lodna in the eastern flank and Phularitand, Damuda and Mahuda in the western flank are some of the major areas showing high magnitudes of subsidence. On an average, displacement values ranged between −25 mm (upliftment) and 28 mm (subsidence) with a mean value of 10 mm measured over a period of 70 days in Jharia coalfield. The primary cause of subsidence observed here is the occurrence of subsurface coal fires. Since mining activities are limited both in terms of spatial extent and magnitude, use of small temporal baseline between the SAR image pairs limit the impact of mining-related subsidence in the displacement map (figure 2) thus produced. In addition, the magnitude of subsidence induced due to mining activities is generally assumed to be negligible since areas with coal fires normally have insignificant mining activities because of the associated hazards. Variations in the magnitude of subsidence (high to low) are caused by the heterogeneity in the intensity of coal fires. This varying intensity of coal fires can be attributed to diversity in the coal seam layer and its proximity to land surface, via cracks and/or fissures, that act as pathways for supplying oxygen required for the propagation of coal fires. Also observed in displacement map are minor areas of upliftment such as in Tisra and Bararee.
These observed upliftments can be in-part due to the preventive measures adopted to control the propagation and remedy the immediate hazardous implications of coal fire. These measures include surface sealing and blanketing with cohesive and incombustible materials like sand and soil. It has been documented that over 22 million cubic metres of surface blanketing work has been carried out in the area (BCCL 2008). Furthermore, these areas of upliftment are also noted to be in close proximity to major opencast mines and hence it is most likely that these upliftments are caused due to the dumping of overburden materials from the nearby opencast mines. In order to validate the cause of subsidence observed in the displacement map (figure 2), the DInSAR-based results were further corroborated with a field survey map (BCCL 2008). This field survey map was prepared on the basis of demographic as well as scientific field surveys. Figure 3 shows the displacement map overlaid with georeferenced polygonal boundaries (pink colour) representing areas with surface and subsurface coal fires (BCCL 2008). This was done to emphasize the direct correlation between the spatial distribution of surface subsidence and the co-occurrence of subsurface coal fires. For the ease of visibility,
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Figure 3. Displacement map overlaid with georeferenced polygonal boundaries (pink) representing in situ observations of coal fire. The colours represent varying degrees of subsidence classified as high (red), low (blue) and negative/upliftment (white).
displacements observed in this map were further classified into three classes and colour coded accordingly. Red and blue colours represent high (10 mm–27.8 mm) and low (0 mm–10 mm) magnitude of subsidence respectively, whereas white colour represents upliftment (−10 mm–0 mm) (figure 3). Figure 3 reveals a strong positive correlation between areas showing subsidence and established coal fire sightings. Majority of the high magnitude subsidence, with a maximum value of 27.8 mm, were observed in parts of Block II, Shatabdi OCP, Katras, Mudidih, Jogta, Bassuriya, Rajapur and Lodna collieries (figure 3), which are also areas renowned for repeated instances of coal fire related hazards. 5.2 Validation using thermal IR Landsat-7 ETM+ data Figure 4 shows the coal fire map overlaid by georeferenced polygonal boundaries (purple colour) representing the spatial extent of surface and subsurface coal fire obtained from the field survey map (BCCL 2008). As mentioned earlier, the coal fire map illustrated in figure 4 was generated from Landsat-7 ETM+ thermal band 6. This thermal anomaly map represents pixel integrated temperature from surface and subsurface coal fires. The image selected for this purpose was acquired during the period between the acquisition dates of
the two Envisat ASAR images, which were processed to obtain a displacement map (figure 2). This was done so as to achieve maximum correlation between the results obtained from Landsat7 ETM+ thermal band data and Envisat ASAR data. Figure 4 reveals that the eastern flank of Jharia coalfield is more affected by surface and subsurface coal fires than the western flank. Collieries like Rajapur, Tisra, Lodna, Ena industry, Kusunda and Kujama in the eastern flank and Shatabdi OCP, Block II and Damuda show strong thermal anomaly signals due to coal fires. The effectiveness of the DInSAR technique to monitor coal fires is highlighted by the comparison of observed land subsidence and merged analysis of ancillary datasets depicting coal fire zones. Results revealed that most of the areas affected by subsidence, in the displacement map (figure 2), are also areas with large thermal anomalies and reported sightings of coal fire (figure 4). But, perhaps most importantly, land subsidence, measured as a surrogate of coal fires, was also observed in areas outside those demarcated in figure 4. Hence, these areas were classified as potential subsurface coal fire areas. These potential subsurface coal fire areas in the displacement map (figure 2) show subsidence signatures but does not show anomalous thermal values and are thus rendered undetectable in the merged analysis of in situ and remotely sensed observations (figure 4). This is also
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Figure 4. Coal fire map overlayed by georeferenced polygonal boundaries (purple) representing in situ observation of coal fire. The colours depict thermal anomaly with respect to reference temperatures of 33◦ C and 40◦ C and thus represent locations of surface (red), subsurface (yellow) coal fires within a background temperature (blue).
true for some of the areas within the polygonal boundaries marked in figure 3. This lack of resolve can be attributed to the coarse geometric resolution (60 m) of band 6 (thermal) of Landsat-7 due to which the thermal anomalies generated by coal fires on the surface occupy only a fraction of the pixel size of Landsat-7 ETM+ band 6 image (Voigt et al. 2004; Kuenzer et al. 2007). As a consequence of which, initiation of fresh subsurface coal fires, due to the progression of opencast or underground mining activities, having a thermal anomaly spread over a small area (Chatterjee et al. 2007) could not be detected in the coal fire map. Alternatively, subsurface coal fires present at great depths are also unable to produce surface thermal anomaly that are significant enough to be detected by the thermal band of Landsat-7 ETM+ or during field survey. Further, the accuracy of detection of thermal anomalies from daytime Landsat-7 ETM+ images is affected by many false anomalies related to topographic and solar irradiation effects (Voigt et al. 2004; Kuenzer et al. 2007). The results presented here are also in concurrence with some of the earlier studies carried out to map the surface and subsurface coal fire in the Jharia coalfield using various remote sensing data. The results obtained here are comparable with those obtained from the analysis of NOAA/ AVHRR data (Agarwal et al. 2006), Landsat TM thermal IR data (Chatterjee 2006; Chatterjee et al.
2007) and Landsat ETM+ band 6 thermal data (Mishra et al. 2011) used in association with ASTER data for mapping of coal fires in Jharia coalfield (Martha et al. 2010). However, it is important to note that the use of DInSAR technique presented a unique opportunity to explore the capability of locating potential subsurface coal fires in the vicinity of those specified in previous studies. The relevance of this study is embellished by its capability of demarcating the boundaries of hazardous areas undergoing high magnitudes of subsidence, as a consequence of coal fires, as well as those with potential hazardous consequences. This has major implications for the management, selection and application of preventive measures in order to mitigate the environmental and socio-economic impacts of coal fires.
6. Conclusion Jharia coalfield is the primary source of coking coal in India. It is, however, associated with surface and subsurface coal fires which lead to considerable wastage of this valuable non-renewable resource of fossil fuel. In addition, coal fires are almost always associated with environmental hazards and have a major impact on socio-economic structure of the region.
Monitoring subsurface coal fires in Jharia coalfield In the study we used observations of vertical subsidence from Envisat satellite to characterize and quantify the subsurface coal fire occurring in the Jharia coalfield. Results revealed that major belts of subsidence are located in the eastern and western flanks of the Jharia coalfield. The estimates thus obtained were further validated with information from in situ field surveys and satellite based thermal infrared data from Landsat ETM+ satellite. Major areas of subsidence correlated very well with the field survey map as well as the coal fire map derived from thermal remote sensing. The results demonstrated the potential of DInSAR technique for identification and mapping of coal fire zones using vertical subsidence as a surrogate of coal fire. The areas with significant subsidence outside those which have been correlated with ancillary data were classified as potential coal fire zones. These potential coal fire zones were hypothesized to show subsurface signatures of coal fire but currently undetected by in situ and thermal remote sensing techniques. The method and results presented here have major implications for classifying and marking boundaries of coal fire zones leading to improved implementation of pre-emptive methods of fire control and safety. In addition, the combination of InSAR and Thermal IR techniques provide a high potential for an improved monitoring of areas affected by coal fires. But, it is important to note that subsidence due to coal fires is most likely a discontinuous process, thus the process itself is hard to monitor. While the utilization of DInSAR technique has proved to be an important tool for monitoring coal fires in the region, the socio-economic significance of the coal fire warrants detailed GPS-based field investigations to validate the potential fire areas demarcated in this study as well as further validation of this technique in other coal fire prone zones. Acknowledgements Envisat original data is copyright European Space Agency and was provided under the Category-1 Proposal 9370. Authors would also like to thank the anonymous reviewer for the comments and suggestions, which has significantly improved the quality of the manuscript. References Agarwal R, Singh D, Chauhan D S and Singh K P 2006 Detection of coal mine fires in the Jharia coal field using NOAA/AVHRR data: Detection of coal mine fires in the Jharia coal field using NOAA/AVHRR data; J. Geophys. Eng. 3 212–218. Amelung F, Galloway D L, Bell J W, Zebker H A and Laczniak R J 1999 Sensing the ups and downs of Las
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MS received 7 December 2012; revised 9 April 2013; accepted 13 April 2013
