Three most industrial cities of Chhattisgarh state namely: Raipur (21˚23'N, 81˚63'E), Bhilai (21˚18'N, 81˚28'E) and Korba (22˚21'N, 82˚40'E) were selected for the proposed investigation. Raipur is the capital city of the Chhattisgarh state, India with population of ≈2.0 million. The Raipur city and its neighborhood are now becoming an important regional commercial and industrial destination for the coal, power, steel and aluminum industries. Raipur is one of biggest iron and cement market in the country. Bhilai is the second-largest city inclusive of Durg city in Chhattisgarh with population of »1 million, and is located in the west of Raipur ≈ 22 km away. The town is famous for the operation of one of the largest steel plant in the World (capacity: 3.15 MT/Yr). Korba is another city in Chhattisgarh with ≈0.5 million population, famous for power supply and aluminum plant.

The samples were collected using a stainless-steel scoop in the summer, 2012 from 20 locations of industrial area of Chhattisgarh, Figure 1. The sediment samples were collected from 18 ponds of cities: Bhilai, Raipur and Korba. The sludge and sewage were collected from the industrial site (i.e. Siltara) of Raipur city. They were kept in glass bottle (250 mL) and dried at 30˚C in an oven for overnight. The samples were crushed into fine particles by mortar and sieved out the particles of mesh size < 0.1 mm. The samples were stored in aluminum foil.

A 5.0 g of the sample was extracted with 25 mL distilled water for 12 hr. The extract was decanted out for the pH value measurement. The CHNSO-IRMS Analyzer by SV Instruments Analytica Pvt. Ltd. was used for quantification of the total carbon (TC). The total carbon (TC = BC + OC + CC) in the soil sample was oxidized at 1020˚C with O₂ in constant helium flow stream by detecting the resulting CO₂ gas with a thermal conductivity detector_. The H₃PO₄ (10 drops) treated soil sample was oxidized with O₂ at 1020˚C in a similar way for determination of BC and OC contents. The OC was analyzed by titration method using K₂Cr₂O₇ as oxidant [31] . The CC content in the soil was evaluated by subtracting the BC and OC content to the TC.

The Bruker S2-Picofox TXRF portable spectrometer equipped with poly capillary lens and the X-ray beam was used for the characterization of elements in the sediment samples. A suspended solution was prepared by mixing sample (10 mg) with solution (10 mL) containing triton 1% (w/v) and Ga 10 µg/mL in ultrasonic bath for 15 min. A 10 μL of sample solution was sprayed on the quartz filter by subsequent drying. The X-ray source was focused on the filter for quantification of the elements. The peak area of the signal was computed. Three replicate measurements for each sample were carried out. The content of 18 elements (i.e. Cl, S, P, K, Al, Ca, Sr, Ba, As, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn and Pb) in each sample was analyzed.

The enrichment factor (E_f) was used to determine metal contamination in the geowaste materials with respect to the base line concentration. The concentration ratio of an element, X, to a crustal element (e.g. Al) in the soil sample to the ratio in the earth’s crust was normalised by using following indices [32] [33] .

where, symbols: X_s, X_e, Al_s and Al_e denote concentration of metal and Al in the soil and earth crust, respectively.

The color of sediments was varied from reddish (R) to black (B) due to deposition of the BC, metal oxides and organic resides. The pH value of the extract (n = 20) was ranged from 6.2 - 8.2 with mean value of 7.4 ± 0.3. The lower pH values of the SeK, SlR and SwR was observed due to presence of higher content of chlorine and sulfur, Figure 2.

The carbon content of the geowaste materials is presented in Table 1. The concentration of BC, OC and CC was ranged from 4.87 - 9.62, 0.07 - 0.32 and 0.05% - 0.17% with mean value of 8.12 ± 0.57, 0.15 ± 0.02 and 0.11% ± 0.01%, respectively. They were found to correlate well (r = 0.72), indicating origin from the similar sources i.e. burning processes. The comparable content of the BC and CC in all types of waste materials were marked, Figure 3. The significantly higher content of OC in the sewage was seen due to input by the organic waste, Figure 3. The BC content in the sediment of the studied area was observed to be higher than in of other regions, probably due to huge coal burning in this region [34] - [36] .

Se = Sediment, Sl = Sludge, Sw = Sewage, B = Black, DB = Deep back, LB = Light black, R = Reddish.

The elevated concentration of metals i.e. Al, K, Ca, Ti, Fe and Mn in the geowaste materials was observed, ranging (n = 20) from 4864 - 18806, 593 - 11179, 579 - 72184, 2135 - 8121, 9737 - 319848 and 127 - 25614 mg/kg with mean value of 11088 ± 1851, 6874 ± 1304, 18268 ± 9468, 4116 ± 671, 73262 ± 34589 and 2782 ± 2484 mg/kg, respectively, Table 2. Among them, the highest Fe content was observed in all geowaste materials due to roasting of iron ores and huge coal burning in the studied areas. The concentration of P, S, Cl, As, Sr, Ba, V, Cr, Ni, Cu, Zn and Pb was marked in the sediments at traces, ranging from 95 - 584, 91 - 1822, 31 - 1117, 12 - 105, 40 - 135, 478 - 1078, 35 - 175, 88 - 392, 14 - 77, 32 - 185, 38 - 626 and 18 - 228 mg/kg with mean value of 232 ± 63, 604 ± 240, 231 ± 119, 39 ± 14, 79 ± 12, 644 ± 78, 83 ± 15, 182 ± 41, 44 ± 7, 68 ± 18, 199 ± 71 and 85 ± 25 mg/kg, respectively, Table 2.

The higher content of elements i.e. P, S, Ca, Ba, Cr, Fe, Cu and Pb was observed in the sediment of Bhilai city, due to input by the Steel plant effluents, Figure 4. Similarly, higher content of other eight elements i.e. Al, K, As, Sr, Ti, V, Ni and Zn was marked in Korba city due to huge coal burning by the Thermal power plants, Figure 4.

The content of elements i.e. Al, As, Ni, Cu, V, Ti and K was increased as the depth profile of the sediment was increased from 0 to 30 cm, may be due to their less binding with the organic materials, Figure 5. The reverse trend was marked with other elements i.e. Cl, S, Ca, Ba, Sr, Cr, Mn, Fe, Zn and Pb, may be due to higher adsorption with the organic materials, Figure 5.

The content of HMs and nutrient in the sediment, sludge and sewage wastes of Raipur city is presented in

Figure 6. Sludge is a solid waste material produced by the iron and thermal power plants in the studied area. Sewage is also a solid waste contributed by domestic households, industrial and agricultural wastes. The highest content of Fe and Ca was marked in the sludge waste. The sewage waste was found to be very rich (1.2%) in nutrient (i.e. P + K + Cl + S) levels. However, the higher content of HMs (i.e. As + Ti + V + Cr + Mn + Ni + Cu + Zn + Pb) was seen in the sediment. The concentration of HMs i.e. Fe, Mn, As and Pb in the sediment of the study area was found to be higher than sediments of other regions due to a huge mineral and coal roasting [11] - [30] .

The E_f values of the elements are summarized in Table 3. The background concentration of Al, Fe, K, Ca, Ti, Mn, P, S, Cl, V, Cr, Zn, Ni, Cu, Pb and As reported was 82000, 39000, 23000, 22000, 4650, 775, 62, 655, 370, 97, 92, 67, 47, 28, 17 and 4.8 mg/kg, respectively [37] . Two metals i.e. As and Pb was found to be highly enriched (E_f > 27) in all samples. The highest E_f value for elements i.e. S, Cl, Cu and Zn was seen in the sewage waste, may be due to contribution by vehicle and cooking effluents. The extremely high E_f value of Fe, Mn, Cr and Ca was observed in the sludge waste due to input of the sponge iron and thermal power plant effluents.

The elemental and carbon fractions in the sediment, sludge and sewage are shown in Figure 7. The fraction of 18 elements (i.e. Al, K, P, S, Cl, As, Ca, Sr, Ba, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn and Pb), BC, OC and CC in all sediment and sewage wastes was found comparable. However, two metals i.e. Ca and Fe were enriched several folds higher in the sludge due to sponge iron and their effluents.

Cluster analysis was performed on the dataset by Ward’s method using Euclidean distance as similarity measure. The variables were interrelated to each other according their maximum similarities. First, the interrelation takes place between two variables which have the most similarity and the next repetition other similar pair clusters were related together. Four clusters or class of sample sites were identified with distinct cluster centers (Figure 8).

Class-I (n = 1) and Class-II (n = 1) comprise each one 5.56% of the total samples. Class I has the highest concentration of P, Ca and heavy metals (Cu, Cr, Mn, Fe, Zn and Pb). Class-II has the highest concentrations of OC, CC, S, Cl, Ni, As, Sr and Ba. Class-III (n = 6) comprises 33.33% of the total samples and has the highest concentration of Al, Ti and V. Class-IV (n = 10) has the highest concentration of K and lower or intermediate concentration of other elements between the class. On the whole, the concentrations of elements exceed highly their respective limit concentrations. The results of k-means clustering of the sediments in all the samples are shown in Table 4.

Using factor analysis (FA), linear correlation between heavy metal concentrations in the sediments was determined. After varimax rotation, heavy metals belonging to a given factor were defined by factor matrix. Metals having strong correlation were grouped into factors and the identification of factors is based on dominant influence [38] . Six factors with eigenvalues higher than 1 [39] were extracted by principal component method. These factors explained 86.56% of the total variance. The variable loadings, eigenvalues, explained variance and cumulative variance are presented in Table 5.

Factor-1 exhibit 34.31% of the total variance with high positive loadings on Mn (0.86), Cu (0.86), Zn (0.80) and Sr (0.81). This factor indicates strong association of Mn, Cu, Zn and Sr in the sediments. Factor-2 exhibit 19% of the total variance with high negative loadings on BC (−0.94), OC (−0.94) and CC (−0.89). This suggests that

BC, OC and CC proceed from the same source. Factor-3 accounts for 12.14% of the total variance with high positive loadings on Ca (0.85), Cr (0.82) and Fe (0.91). Taking account of some high concentrations in Fe, Ca this factor can be attributed to mixed sources of Fe in the sediments from geogenic and anthropogenic source.

Factor-4 accounts for 9.04% of the total variance with high positive loading on Cl (0.73). High concentrations of Cl in the sediments could come from runoff, wastewater. Factor 5 accounts for 6.53% of the total variance with high negative loadings on Al (−0.73), V (−0.82) and Ti (−0.80). This factor shows the relationship of anthropogenic Ti and V with reference element Al. Factor 6 accounts for 5.55% of the total variance with high negative loading on pH (−0.87).

The geowaste materials i.e. sediment, sludge and sewage are rich with heavy metal and nutrient contents. The remarkably higher nutrient level is observed in the sewage, found useful as manure for crop productions. Iron is seen to be extremely enriched up to 27% in the sludge waste, and could be a cheaper resource for Fe recovery. The high BC levels in geomaterials may stabilize the heavy metal and nutrient contents. The toxic metals i.e. As and Pb were highly enriched (E_f > 27) in all geowaste materials. The vertical distribution of As was increased remarkably with increasing depth profile of the sediment.

Significant loadings > 0.7 (in bold) at p < 0.05.