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Applied Water Science

, 8:196 | Cite as

Study of physico-chemical properties, detection and toxicity study of organic compounds from effluent of MIDC Thane and GIDC Ankleshwar industrial zone

  • Prashant Bhimrao Koli
  • Kailas Haribhau Kapadnis
  • Uday Gangadhar Deshpande
Open Access
Original Article
  • 117 Downloads

Abstract

The anthropological activities and huge industrialization to fulfil needs of mankind are making the remarkable and disastrous effect on aquatic life and responsible for severe pollution. The research deals with the identification and detection of organic pollutants present in industrial effluent by FTIR and GC–MS techniques. The samples were collected from paint, textile and dyes industries of MIDC Thane and GIDC Ankleshwar situated in western zone of India and recognized to be the most polluted cities in Asia. The samples were collected by standard operating procedure and then operated for extraction in ether so as to dissolve maximum organic compounds. These samples after extraction sealed in airtight glass vessel and used for FTIR and GC–MS analysis. The large number of organic compounds was detected by GC–MS analysis, whereas the presence of different functional groups of organic pollutants confirmed by FTIR analysis. The physico-chemical analysis was performed for effluent samples to know the different chemical factors associated with aqua samples. The statistical analysis of collected data was carried out; it comprises the mean, standard deviation, standard errors, Pearson correlation constants and regression analysis. The calculated results compared with WHO standards and water quality index were calculated. Large number of organic and aromatic compounds identified from GC–MS data and their toxicity is discussed.

Keywords

Chemical toxicity Effluent Extraction FTIR GCMS Physico-chemical WQI 

Introduction

The rapid urbanization and immense industrialization is making a mark influence on global environment, especially water, air and land pollution. The major pollution augmenting unit is a water pollution. In general, the water is considered to be polluted when its quality impaired by several anthropological activities (Birjandi et al. 2016). Some of the major sources responsible for water pollution are excess discharge of untreated sewage from various industries, waste water production due to anthropological activities released in the rivers, costal area (Sharma et al. 2017; Kumar and Krishna 2017). The major contribution for water pollution reported due to untreated effluent of drugs, dyes, chemicals, pharmaceutical industries and nuclear waste, which are found to be huge pestilent. As most of the major pathogenic diseases, hazards and dermatological problems, are recognized due to consumption of polluted water, it is whole onus of industries that the effluent must be amend before subjecting to the discharge in various water sources (Wang et al. 2017; Nirgude et al. 2013).

In undeveloped country like India, water pollution is a major issue because of polluted waste discharged in water streams may effect on economical budget if processed for sewage treatment. Hence, majority of industries released their sewage without further process and contributing to make the water contamination. However, most of the chemical industries take enough care while releasing the waste to the water streams, but this number is less and thus enhancing the pollution (Kumar et al. 2012). The pollutants present in water contain a large number of organic and inorganic contaminants which are unnecessary, producing pestilent and fatal effects on human health as well as aquatic life. These organic and inorganic pollutants as well as different pathogenic organism make the quality of water squalor. Most of the researchers are coalesce to defeat this problem of water pollution to serve the mankind better (Hladchenko et al. 2017).

The cities like Ankleshwar and Thane industrial found to be the most polluted according to the global pollution survey. Both the cities covered large industrial area and becoming a native place for most of the pharmaceutical, drugs, dyes, paint, textiles and API manufacturing industries (Mishra and Soni 2016; Mostafa 2015). These various drugs, dyes and paint industries synthesizes huge quantity of organic products every year. Most of the products manufactured by industries are valuable to the society and domestic use, but at the same time waste by-products and waste water which is left after synthesizing these products is a major environmental threat. This waste water indiscriminately flowed in the water streams lead to an inception of water pollution imperceptibly (Ouasif et al. 2013). Most of the researchers worked on this type of serious issue and published their view and facts related to waste water from these industrial zones (Patil and Shrivastava 2016).

The effluent samples were collected from paint, Textile and dye industries. The effluent discharged by them contains astonishing contaminants which ruffles quality of water. It is previously anticipated that these industries utilize large number of chemicals that are organic in nature. Even though the finished products synthesized by these industries are probably organic in nature. The inception of water pollution is started from these organic compounds; nevertheless, these industries discharged their waste after synthesis of their products, dying, processing, etc., in the river stream or costal region (Ong et al. 2011). Textile and paint industries also utilize different colours, dyes and pigments which are organic in nature (CPCB 2016; Chandran 2016).

In the present research, an attempt has made to identify and detect the different organics that are probably present in effluent sources. Also, the physico-chemical analysis of collected water samples was examined. Finally, the results obtained from physico-chemical analysis subjected for water quality index comparison. The toxicity of organic compound detected by GC–MS has been discussed.

Materials and methods

Diethyl ether [(C2H5)2O] AR grade purchased from Merck, India. All the effluent samples (7 samples) were collected from MIDC Thane, India and GIDC, Ankleshwar, India. Solvent extraction technique was used to separate the organic contaminants present in the collected effluent samples.

Sampling of effluents

Three samples were collected from dyes and textile industries, from waste water effluent outlet Ankleshwar GIDC (Gujarat Industrial Development Co-operation, Ankleshwar), and four samples were collected from various paint and dye industries of Thane MIDC (Maharashtra Industrial Development Co-operation, Thane). Collected samples were stored in glass bottles previously washed with acetone to remove all the organics and further with nitric acid as well as by using double-distilled water, to remove all the metallic and other inorganic content previously present. All the containers then dried and kept in hot air oven for three hours at 120 °C. All the extracted samples were preserved in refrigerator for further analysis (Patil and Shrivastava 2013; Mahajan and Shrivastava 2013). The labelling of the collected samples is shown in Table 1.
Table 1

Sampling locations and source of sampling

S. no.

Sample name

Sampling point

Sampling source

01

AG1

Ankleshwar GIDC

Dyes and textiles

02

AG2

Ankleshwar GIDC

Dyes and textiles

03

AG3

Ankleshwar GIDC

Dyes and textiles

04

TM4

Thane MIDC

Paint industry

05

TM5

Thane MIDC

Paint industry

06

TM6

Thane MIDC

Dyes

07

TM7

Thane MIDC

Dyes

Sample preparation for FTIR analysis

All the samples were extracted in diethyl ether; the aqueous and organic layer was separated by use of solvent extraction technique in separating funnel. After extraction, organic layer was stored in airtight glass vessel and used for FTIR characterization (Ladwani et al. 2016).

Sample preparation for GC–MS analysis

Similar extraction procedure was followed for GC–MS analysis, i.e. solvent extraction technique (Helaleh et al. 2001). After extraction, the samples AG2, TM5 and TM6 were used for GC–MS analysis (Thermo Scientific TSQ 8000).

Detection of organics by FTIR

The main purpose of FTIR study is to investigate the possible organics present in the collected effluents. All the samples were extracted in diethyl ether. The labelled samples (Table 1) were sent for FTIR studies to detect the possible functional groups that are present in collected effluent samples (Table 2).
Table 2

Detected organic functional groups in collected wastewater sample

S. no.

Frequency in cm−1

Interpretation (functional group)

S. no.

Frequency in cm−1

Interpretation (functional group)

Sample AG1

Sample TM4

01

2974.23

Alkane C–H

01

2924 (2850–2975)

Alkane C–H

02

2862.36

C–H stretching freq in –CH3

02

2854.65

C–H stretching freq in –CH3

03

1975.11

C=O stretch in acid/aldehyde

03

1712.79

C=O stretch in acid/aldehyde

04

1450.47

C–H bending in –CH2

04

1458.18

C–H bending in –CH2

05

1381.03

C–O stretching in benzoate

05

1265.30

 

06

1288.45

C–O–C stretch in ether (broad)

06

740.67

Monosubstituted aromatics

07

1122.57

C–N stretching in sec. amines

08

929.69

Ethylene

09

840.96

Tetra-substituted benzene

10

752.24

Monosubstituted aromatic ring

Sample AG2

Sample TM5

01

2970.38

Alkane C–H

01

2924.09

Alkane C–H

02

2924.09

 

02

2862.63

C–H stretching freq in –CH3

03

2862.36

C–H stretching freq in –CH3

03

1728.22

C=O stretch in acid/aldehyde

04

1728.22

C=O stretch in acid/aldehyde

04

1597.06

C=C frequency for alkene

05

1450.47

C–H bending in –CH2

05

1458.18

C–H bending in CH2

06

1373.32

 

06

1381.03

 

07

1126.43

C–N stretching in sec. amines

07

1273.02

C–O–C stretch in ether (broad)

08

840

Tetra-substituted benzene

08

1126.43

C–N stretching in sec.amines

 

09

1064.71

C–O stretching freq in alcohols/ethers

 

10

740.67

Monosubstituted aromatics

Sample AG3

Sample TM6

01

3425.58

O–H stretch (broad)

01

2970.38

Alkane C–H

02

3001.24

Chelate H bridge (O–H)

02

2862.36

C–H stretching freq in –CH3

03

2877.81

C–H bending in –CHO

03

1450.47

C–H bending in –CH2

04

1458.18

O=S=O stretch

04

1373.32

 

05

1350.17

 

05

1126.43

C–N stretching in sec.amines

06

1296.16

C–O–C stretch in ether (broad)

06

925.83

≡CH bending frequency

07

1249.87

C–H out of plane bending

07

840.96

Tetra-substituted benzene

08

1111.10

Anhydride C=O stretch

09

941.26

≡CH bending frequency

10

756.10

Monosubstituted aromatics

Detection and identification of organics by GC–MS

The samples AG-2, TM-5 and TM-6 were extracted in diethyl ether. These extracted samples were analysed by GC–MS (Thermo Scientific TSQ 8000). The GC–MS data from which speculated organics obtain their corresponding spectrum are depicted in Table 3. The imperceptibly concentration is detected by GC–MS. The toxic, fatal and environmental hazards of selected compounds are discussed in results and discussion section (Srebrenkoska et al. 2014; Kotowska et al. 2012).
Table 3

Probable organic compounds detected from collected effluent samples AG2, TM5 and TM6 by GC–MS technique

S. no.

Name of compound

RT

Molecular formula

Molecular weight in g

CAS number

Sample AG2

01

4-Methyl-3-hexanol

4.83

C7H16O

116

615-29-2

02

2,3-Dimethyl-2-butanol

4.83

C6H14O

102

594-60-5

03

3-Heptanol

4.83

C7H16O

116

589-82-2

04

Hexadecane

11.49

C16H34

226

544-76-3

05

2,6,10-Trimethyl tetradecane

11.49

C17H36

240

14905-56-7

06

Heptadecane

11.49

C17H36

240

629-78-7

07

2,6-Dihexadecanoate-1-(+)-ascorbic acid

14.13

C38H68O8

652

28474-90-0

08

n-Hexadecanoic acid

14.33

C16H32O2

256

57-10-3

09

Palmitic acid anhydride

14.33

C32H62O3

494

623-65-4

10

3-Ethyl-5-(2-ethylbutyl)-octadecane

14.38

C26H54

366

55282-12-7

11

3,5-Dehydro-6-methoxy-pivalate-cholest-22-ene-21-ol

14.38

C33H54O3

498

NA

12

2,6,10-Trimethyl tetradecane

14.38

C17H36

240

14905-56-7

13

1-Monolinoleoylglycerol trimethylsilyl ether

19.50

C27H54O4Si2

470

54284-45-6

14

2,3-Bis(trimethylsilyl) oxylporpyl ester,(z,z,z)-9,12,15-octadecatrienoic acid

19.50

C27H52O4Si2

440

55521-22-7

15

25-(Trimethylsilyl) oxy-(3a, 5Z, 7E)-9, 10-secocholesta-5, 7, 10 (19)-tiene-1-3-diol

19.50

C30H52O3Si

488

55759-94-9

16

Ethyl iso-allocholate

19.69

C26H44O5

536

NA

17

(3a,5Z,7E)-9, 10-Secocholesta-5, 7, 10 (19)-tiene-3, 24,25-triol

19.69

C27H44O3

416

40013-87-4

18

3-Acetoxy-7,8-epoxylanostan-11-ol

19.69

C32H54O4

502

NA

19

4-en-3-one-stigmast

21.11

C29H48O

412

1058-61-3

20

Cypionate testosterone

21.11

C27H40O3

412

58-20-8

21

4-en-3-one-cholest

21.11

C27H44O

384

601-57-0

Sample TM5

22

Nonane

4.69

C9H20

128

111-84-2

23

Decane

4.69

C10H22

142

124-18-5

24

2,4-Dimethyl hexane

4.69

C8H18

114

589-43-5

25

1-Ethyl-3-methyl benzene

5.69

C9H12

120

620-14-4

26

1-Ethyl-2-methyl benzene

5.69

C9H12

120

611-14-3

27

1,2,4-Trimethyl benzene

5.69

C9H12

120

95-63-6

28

1,2,3-Trimethyl benzene

5.82

C9H12

120

526-73-8

29

3,5-Dimethyl octane

6.29

C10H22

142

15869-93-9

30

4,6-Dimethyl undecane

6.29

C13H28

184

17312-82-2

31

4-Methyl-decane

6.64

C11H24

156

2847-72-5

32

2-Ethylhexyl ester, trichloroacetic acid

6.64

C10H17Cl3O2

275

16397-79-8

33

5-Ethyl-2-methyl heptane

6.64

C10H22

142

13475-78-0

34

Undecane

7.89

C11H24

156

1120-21-4

35

Dodecane

7.89

C12H26

170

112-40-3

36

4,5-Dimethyl nonane

7.89

C11H24

156

17302-23-7

37

(Z)-9,17-Octadecadienal

20.34

C18H32O

264

56554-35-9

38

7,11-Hexadecadienal

20.34

C16H28O

236

NA

39

12-Methyl-E,E-2,13-octadecadienal-1-ol

20.34

C19H36O

280

NA

Sample TM6

40

5-Methyl-3-hexanol

4.84

C7H15O

115

615-29-2

41

Phenol

5.43

C6H6O

94

108-95-2

42

p-Hydroxy phenyl phosphonic acid

5.43

C6H7O4P

174

33795-18-5

43

Carbamic acid, phenyl ester

5.43

C7H7NO2

137

622-46-8

44

2,6-Dimethyl quinoline

10.17

C11H11N

157

877-43-0

45

2,8-Dimethyl quinoline

10.17

C11H11N

157

1463-17-8

46

4,8-Dimethyl quinoline

10.17

C11H11N

157

13362-80-6

47

7-Methoxy-2,2,4,8-tetramethyl tricyclo[5.3.1.0(4,11)] undecane

10.37

C16H28O

236

NA

48

2-Acetyl-3,3-dimethyl-2-(3-oxo-1-butenyl)-(E)-cyclopentanone

10.37

C13H18O3

222

70412-49-6

49

3-(1,1-Dimethylethyl)-4-methoxy, phenol

10.37

C11H16O2

180

88-32-4

50

2,6-bis(1,1-dimethylethyl)—2,5-cyclohexadiene-1,4-dione

10.44

C14H20O2

220

719-22-2

51

2,5-di-tert-butyl-1,4-benzoquinone

10.44

C14H20O2

220

2460-77-7

52

Furan-5-yl-1(5,6,7,8-tetrahydro-2,8,8-trimethyl-4H-cyclohepta, ethanone

10.44

C14H20O2

220

71596-88-8

53

Butylated hydroxytoluene

10.76

C15H24O

220

128-37-0

54

4,6-di(1,1-dimethylethyl)-2-methyl-phenol

10.76

C15H24O

 

616-55-7

55

2,4,6-tris(1-methylethyl)-Phenol

10.76

C15H24O

220

2934-07-8

56

Heptadecane

11.48

C17H36

240

629-78-7

NA not applicable; CAS chemical abstracts service

Statistical analysis of data obtained from physico-chemical results

Table 4.
Table 4

Physico-chemical properties of collected industrial effluent samples

Parameter

PH

Electrical conductivity

Sulphate

Chloride

COD

BOD

TDS

Unit

(µmho/cm)

(µmho/cm)

(mg/L)

(mg/L)

(mg/L O2)

(mg/L O2)

(mg/L)

AG1

8.2

398

91.60

262

1610

536

1590

AG2

7.8

402

82

300

1460

486

1620

AG3

7.7

500

76

387

1570

523

1480

TM4

8.5

398

93.50

281.50

1760

586

1805

TM5

7.5

402

86.42

310

1350

450

1745

TM6

7.4

500

72.83

418

1465

488

1654

TM7

7.2

530

80.00

497

1580

526

1970

Mean

7.26

447.14

83.19

350.78

1542.14

513.57

1694.85

S.D

0.95

40.1246

7.7233

85.76

131.58

43.68

160.61

S.E

0.36

15.16

2.92

32.42

49.73

16.51

60.70

WHO

6.5–9.5

1400

500

250

1000

US-EPA

6.5–8.5

250

250

250

500

SD standard deviation; SE standard error

Pearson correlation coefficient (r) data of different parameters

Table 5.
Table 5

Pearson correlation coefficient (r) of different physico-chemical parameters for waste water effluent samples

Parameters

Correlation coefficient (r) of different parameters

pH

EC

Sulphates

Chlorides

COD

BOD

TDS

PH

1

      

Electrical conductivity

− 0.9514

1

     

Sulphates

0.81

− 0.797

1

    

Chlorides

− 0.9758

0.9603

− 0.7435

1

   

COD

0.2916

− 0.239

0.4545

− 0.1145

1

  

BOD

0.2917

− 0.0237

0.4546

− 0.1146

1

1

 

TDS

− 0.2684

0.1763

0.2143

0.4009

0.1654

0.1649

1

Water quality index

Table 6.
Table 6

Water quality index data for collected industrial effluents

Parameter

Tested value (Vn)

AG1

AG2

AG3

TM4

TM5

TM6

TM7

(Wn)

Sn

V10

pH

8.2

7.8

7.7

8.5

7.5

7.4

7.2

0.219

7.5

7.0

EC

398

402

500

398

402

500

530

0.371

300

0

Sulphates

91.60

82

76

93.50

86.42

72.83

80

0.01236

500

0

Chlorides

262

300

387

281.5

310

418

497

0.0074

250

0

TDS

1590

1620

1480

1805

1745

1654

1970

0.0037

1000

0

∑Qn

695.86

740

624

743.80

549.79

593.82

628.46

∑Wn.Qn

103.65

88.26

93.52

116.40

73.38

81.37

76.69

WQI

168

143

152.45

189.75

119.62

132.65

125.26

Sn, std. value; V10, ideal value; Qn, quality rating; Wn, unit weight; WQI, water quality index

Results and discussions

Physico-chemical properties

  1. (a)

    pH The negative proton ions are universal species that decide the acidic and basic condition of aqua solution. The species strongly affect different properties, characteristics of water. The major reliable contribution made by the authentic institutes such as WHO, US-EPA to control important water parameters (Elango et al. 2017). The present investigation was compared against the standard data of these institutes. Here, the maximum value of protonic ions was shown by sample AG1, i.e. 8.2 (slightly alkaline, MIDC Thane), while the least value was shown by sample TM7, i.e. 5.76 (Acidic, GIDC Ankleshwar). All the samples comprise the given range prescribed by the standard data.

     
  2. (b)

    Electrical conductivity Total dissolved salts are generally responsible for high values of electrical conductivity (Manikandan et al. 2015). Values of electrical conductivity were in the range of 398–530 μm/cm. The highest value was observed for sample TM7, probably due to the presence of dissolved inorganic salts in the collected sample. The electrical conductivity data pass the permissible limit prescribed by the WHO and US-EPA standards.

     
  3. (c)

    Sulphates Probably sulphates are present in water streams due to utilization of sulphonates and sulphuric acid for various industrial processes such as tanning. Most of the time magnesium and calcium sulphates are responsible for permanent water hardness. The sulphates data obtained for all the effluent sample are listed in table 4. This data matches within the priscribed limit of WHO and US-EPA (Periyasamy and Rajan 2009).

     
  4. (d)

    Chlorides Chloride ions originate probably from sewage, industrial effluent, several natural sources, in certain cases due to urban activities such as saline intrusion, de-icing salt process, etc. In all the collected samples, the chloride values were found in the range of 262–497 mg/L. The values of chloride ion found to be very high and beyond the decided level of WHO.

     
  5. (e)

    BOD The biological oxygen demand is found to be higher in all the samples indicating the more percentage of bacterial content utilizing the oxygen, probably due to the presence of higher organics content in the sample.

     
  6. (f)

    COD The higher values of COD are attributed to more carbon containing waste present in all the samples, because all the samples which are collected are from organic source (Brahmbhatt and Pandya 2015).

     
  7. (g)

    TDS The total dissolved solids are the organic contaminants and inorganic insoluble, suspended particles, sulphates, phosphates, sodium chloride, etc., are present due to which the water quality is ruffled. The TDS value limited by authorized institutes is said to be 1000 mg/L, while all the samples have astonishing TDS values (Rawway et al. 2016).

     

Correlation coefficient

Correlation coefficient was calculated to know the relationship between two variables. The positive relationship is assumed when the value expected is 1; the strong negative relationship is expected when R has a value − 1. The net zero value indicates no relationship between two variables. The correlation coefficient was calculated by using formula
$$ {\mathbf{r}} = \frac{{{\mathbf{n}}\left( {\sum xy} \right) - (\sum x)(\sum y)}}{{\left[ {n\sum X^{2} - (\sum x)^{2} } \right]\left[ {n\sum Y^{2} - (\sum y)^{2} } \right]}} $$
(1)

The correlation coefficient values for selected variables depicted in Table 5 show good agreement of values for present variables (Saxena and Saxena 2015; Nagaraju et al. 2017). The correlation coefficient values are depicted in Table 5.

Water quality index

Water quality index is 100-point scale of water parameters considered for physico-chemical analysis. In the present discussion, five parameters are examined for water quality index. The water quality index was found to be very high for all the collected effluent samples; all the effluent samples are unsuitable for potable and other purposes due to high WQI. The standard water quality index report is shown in Table 7. The diagrammatic representation of calculated water quality index of all the collected effluent samples is summarized in Fig. 1.
Table 7

Water quality index report status

S. no.

Water quality index range

Quality of water

Water quality index legend

01

90–100

Unsuitable for potable purpose

02

70–89

Very poor quality water

03

50–70

Ruffled water quality

04

25–50

Good quality water

05

0–25

Excellent quality water

Fig. 1

Diagrammatic representation of calculated water quality index (WQI) for all the collected effluent samples

Toxicity of selected compounds detected by GC–MS in effluents

As the samples contain dissolved organics, these samples were analysed by GC–MS. The detected organics and their data obtained are as shown in Table 3. In this section, the toxicity of some selected organic compounds detected in GC–MS is discussed.
  1. (a)

    2-ethyl, hexyl ester trichloroacetic acid The compound in association with water (drinking or effluent) produces complex by-products disinfectants which are found to raise the risk of colon, rectal and bladder cancer in human (Kumar and Pandit 2012) and adverse effects on reproduction.

     
  2. (b)

    3,5-dehydro-6-methoxy-pivalate-cholest-22-ene-21-ol The compound was investigated for microbiological studies and showed toxic effects against the S. pyogenes organism that was found to be effective. This compound serves as an effecive reagent to inhibit the growth of most of pathogenic and non-pathogenic microorganisms (Singariya et al. 2016).

     
  3. (c)

    4,6-di(1, 1-dimethylethyl)-2-methyl-phenol This is one of the toxic compounds detected in cigarette mainstream smoke (CMS), and if this compound is found in water streams then its free radicals are responsible for cardiovascular disease, (Smith et al. 2002) occupational and environmental lung diseases. Substituted phenols with electron releasing molecules can frame conceivably poisonous phenoxyl free radicals, and those substituted phenols with electron pulling back impact apply their lethality for the most part through lipophilicity (Vallyathan et al. 1998).

     
  4. (d)

    Palmitic acid The major threat of suffering from cardiovascular diseases as well as enhancement of LDL level in the bloodstream may cause due to feeding of palmitic acid anhydride according to WHO reports. Oxidation of fats in the body and energy utilization rates may alter due to palmitic acid (Borsheim et al. 2006).

     
  5. (e)

    Carbamic acid, phenyl ester The assessment of accessible poisonous quality information for fish, aquatic invertebrates and amphibian plants is moderate.

     
  6. (f)

    3-(1,1-dimethylethyl)-4-methoxy, phenol Most of the substituted phenol used as an antioxidant in food and fats, viz. BHA and BHA, is for preservation of food products (Davi and Gundi 1999). The toxicity of phenolic compounds can be explained by the substitution of this surface-active agent with new products, as polyethoxylated alcohols, with the substitution of the phenolic group, because of the problems correlated with toxicity accumulation and oestrogenic effects that this surfactant induced in some animals and organisms. Regular monitoring of phenolic compounds is essential due to their toxicity and bioaccumulation effects in animal and vegetable organisms (Kumar and Pacha 2015).

     
  7. (g)

    Heptadecane However, carcinogenic reports, reproductive toxicity and genotoxicity chronic reports are not available for heptadecane, but several subchronic cases are reported for this compound.

     
  8. (h)

    3-acetoxy-7,8-epoxylanostan-11-ol The compound was investigated from plant extract of Hamelia patens using chloroform as extractor in the research work reported that 3-acetoxy-7,8-epoxylanostan-11-ol compound shows an antidepressant activity for animals (Surana and Wagh 2017).

     
  9. (i)

    4-en-3-one-stigmast This compound was extracted from bark of Anacardium occidentale (cashew), reported that the 4-en-3-one-stigmast shows hypoglycaemic effects (lowering of blood sugar level) in healthy dogs (Alexander-Lindo et al. 2004).

     
  10. (j)

    2,3-bis(trimethylsilyl)oxylporpyl ester, (z,z,z)-9,12,15-octadecatrienoic acid The extraction of Diospyros Montana (Roxb.) reported the presence of this compound which shows some phytochemical effects (Bodele and Shahare 2018).

     

Conclusions

All the samples collected from Ankleshwar GIDC and Thane MIDC from paint and dyes industries were analysed by various physico-chemical parameters. The samples were found to be heavily contaminated, and some parameters not comply with standards reported by WHO and US-EPA. All the tested parameters such as BOD, COD, chlorides, sulphates and TDS were found to be very higher for AG1, AG2, AG3, TM4, TM5, TM6 and TM7 samples. Except pH and electrical conductivity passes the limit for all effluent samples. The water quality index report for all the samples was found to be extreme and not within the range of WQI; hence, it can be suggested that effluent must be treated by concern industries before discharging them into the river stream or nearby costal region so that contaminated water may become decontaminated. The infrared spectrum of the samples showed the corresponding functional groups of possible organic compounds in the samples as depicted in Table 2. The most important parameter analysed was GC–MS studied for the detection of organic contaminants. A wide range of organics were detected by this technique as the data as reported in Table 3. The most fatal and harmful compounds that can be harmful to the humans health as well as hazardous to the aquatic life are described such as, 2-ethyl, hexyl ester trichloroacetic acid, 3, 5-dehydro-6-methoxy-pivalate-cholest-22-ene-21-ol, 4, 6-di(1, 1-dimethylethyl)-2-methyl-phenol, palmitic acid, carbamic acid, phenyl ester, 3-(1, 1-dimethylethyl)-4-methoxy, phenol, heptadecane,3-acetoxy-7,8-epoxylanostan-11-ol,4-en-3-one-stigmast,2,3-bis(trimethylsilyl)oxylporpyl ester,(z,z,z)-9,12,15-octadecatrienoic acid.

The large number of industries discharge harmful waste effluents into the water sterams that should be monitored by the concern industries. There must be some strong regulations for discharging such a toxic chemical in the form of effluent. The industries such as paint, dyes, chemical, pharmaceutical and API should take initiate to minimize the pollution by taking government help for waste management such as construction of adequate sanitary landfills sites, catalytic activities to convert harmful chemicals into less harmful chemicals or compounds, photocatalysis, recycling of waste water, conduction of epidemiological study in the polluted area and asses the health survey on the water consumers, etc.

In the present research, we tried to investigate water-related properties, but main objects were to envisage the organic compound present in the effluent sources. We discussed the toxicity of some organic compounds from the GC-MS data (listed in Table 3). However, it is highly impossible to seperate the individual organic compounds due to presence of large number compounds present in the water streams. Also, all the compounds detected by GC–MS are organic, and hence, their quantitative and qualitative separation is very complex task. Analysis of this type of effluent is very beneficial to the society and ecosystem. The detailed toxicity of all the listed compounds has been discussed in the results and discussion section.

Notes

Acknowledgement

Authors are grateful to the Sophisticated Analytical Instrumentation facility (SAIF), Chandigarh, for providing GC–MS Facility and CIC, KTHM College, Nashik, for providing IR spectrums and Pratap College, Amalner, as well as L.V.H. College, Nashik, for providing necessary laboratory facilities.

References

  1. Alexander-Lindo RL, Morrison ES, Nair MG (2004) Hypoglycaemic effect of stigmast-4-en-3-one and its corresponding alcohol from the bark of Anacardium occidentale (cashew). Phytother Res 18(5):403–407CrossRefGoogle Scholar
  2. Birjandi N, Younesi H, Bahramifar N (2016) Treatment of wastewater effluents from paper-recycling plants by coagulation process and optimization of treatment conditions with response surface methodology. Appl Water Sci 6(4):339–348CrossRefGoogle Scholar
  3. Bodele SK, Shahare NH (2018) Phytochemical screening and GC–MS analysis of Diospyros Montana (Roxb.) root. Int J Res Pharmacol Pharmacother 7(2):100–107Google Scholar
  4. Børsheim E, Kien CL, Pearl WM (2006) Differential effects of dietary intake of palmitic acid and oleic acid on oxygen consumption during and after exercise. Metab Clin Exp 55(9):1215–1221CrossRefGoogle Scholar
  5. Brahmbhatt NH, Pandya KY (2015) Performance evaluation of effluent treatment plant and hazardous waste management of pharmaceutical industry of Ankleshwar. Adv Appl Sci Res 6(4):157–161Google Scholar
  6. Chandran D (2016) A review of the textile industries waste water treatment methodologies. Int J Sci Eng Res 7:392–403Google Scholar
  7. CPCB (2016) Report on environmental quality monitoring for assessment of comprehensive environmental pollution index (CEPI), for critically polluted area Ankleshwar in GujaratGoogle Scholar
  8. Davi ML, Gnudi F (1999) Phenolic compounds in surface water. Water Res 33(14):3213–3219CrossRefGoogle Scholar
  9. Elango G, Rathika G, Elango S (2017) Physico-chemical parameters of textile dyeing effluent and its impacts with case study. Int J Res Chem Environ 7(1):17–24Google Scholar
  10. Helaleh MI, Takabayashi Y, Fujii S, Korenaga T (2001) Gas chromatographic–mass spectrometric method for separation and detection of endocrine disruptors from environmental water samples. Anal Chim Acta 428(2):227–234CrossRefGoogle Scholar
  11. Hladchenko LN, Matvyeyeva EL, Kipnis LS (2017) Assessment of wastewater toxicity after their treatment by biosorbents Ecolan-M and Econadin. J Water Chem Technol 39(5):294–298CrossRefGoogle Scholar
  12. Kotowska U, Biegańska K, Isidorov VA (2012) Screening of trace organic compounds in municipal wastewater by gas chromatography-mass spectrometry. Pol J Environ Stud 21(1):129–138Google Scholar
  13. Kumar GV, Krishna KR (2017) Comparative study on the water quality status of Andra reservoir and Denkada anicut constructed on Champavati River, Vizianagaram, India. Appl Water Sci 7(3):1497–1504CrossRefGoogle Scholar
  14. Kumar PK, Pacha MM (2015) Assessment of phenolic compounds in the surface waters of Godavari Canal, Andhra Pradesh, India. Curr World Environ 10(1):338–342CrossRefGoogle Scholar
  15. Kumar JK, Pandit AB (2012) Drinking water disinfection techniques. CRC Press, Boca RatonCrossRefGoogle Scholar
  16. Kumar KR, Suman M, Archana S (2012) Water quality assessment of raw sewage and final treated water with special reference to waste water treatment plant Bhopal, MP, India. Res J Recent Sci 1:185–190Google Scholar
  17. Ladwani KD, Ladwani KD, Ramteke DS, Deo S (2016) Detection and identification of organic compounds in wastewater of final effluent treatment plant by FTIR and GC–MS. J Adv Chem Sci 9:246–247Google Scholar
  18. Mahajan SV, Shrivastava VS (2013) Identification of organic compounds in ground water samples by FTIR and GC–MS. Int J Chem Sci 11(3):1582–1588Google Scholar
  19. Manikandan P, Palanisamy PN, Baskar R, Sivakumar P, Sakthisharmila P (2015) Physico chemical analysis of textile industrial effluents from Tirupur city, TN, India. Int J Adv Res Sci Eng 4(2):93–104Google Scholar
  20. Mishra P, Soni R (2016) Analysis of dyeing and printing waste water of Balotara textile industries. Int J Chem Sci 14(4):1929–1938Google Scholar
  21. Mostafa M (2015) Waste water treatment in textile Industries-the concept and current removal technologies. J Biodivers Environ Sci 7(1):501–525Google Scholar
  22. Nagaraju A, Sreedhar Y, Thejaswi A, Sayadi MH (2017) Water quality analysis of the Rapur area, Andhra Pradesh, South India using multivariate techniques. Appl Water Sci 7(6):2767–2777CrossRefGoogle Scholar
  23. Nirgude NT, Shukla S, Venkatachalam A (2013) Physico-chemical analysis of some industrial effluents from Vapi industrial area, Gujarat, India. Rasayan J Chem 6:68–72Google Scholar
  24. Ong ST, Keng PS, Lee WN, Ha ST, Hung YT (2011) Dye waste treatment. Water 3(1):157–176CrossRefGoogle Scholar
  25. Ouasif H, Yousfi S, Bouamrani ML, El Kouali M, Benmokhtar S, Talbi M (2013) Removal of a cationic dye from wastewater by adsorption onto natural adsorbents. J Mater Environ Sci 4(1):1–10Google Scholar
  26. Patil MR, Shrivastava VS (2013) Identification of organics by FTIR and GC–MS. Asian J Chem Environ Res 6:22–26Google Scholar
  27. Patil MR, Shrivastava VS (2016) Adsorptive removal of methylene blue from aqueous solution by polyaniline-nickel ferrite nanocomposite: a kinetic approach. Desalination Water Treat 57(13):5879–5887CrossRefGoogle Scholar
  28. Periyasamy M, Rajan MR (2009) Physico-chemical characteristics and water quality index of electroplating industry effluent. J Ind Pollut Control 25(1):1–8Google Scholar
  29. Rawway M, Kamel MS, Abdul-Raouf UM (2016) Microbial and physico-chemical assessment of water quality of the River Nile at Assiut Governorate (Upper Egypt). J Ecol Health Environ 14(1):7–14Google Scholar
  30. Saxena U, Saxena S (2015) Correlation study on physico-chemical parameters and quality Assessment of ground water of Bassi Tehsil of district Jaipur, Rajasthan, India, SGVU. Int J Environ Sci Technol 1(1):78–91Google Scholar
  31. Sharma D, Kansal A, Pelletier G (2017) Water quality modeling for urban reach of Yamuna river, India (1999–2009), using QUAL2Kw. Appl Water Sci 7(3):1535–1559CrossRefGoogle Scholar
  32. Singariya P, Mourya KK, Gadi BR (2016) Evaluation of Microcidal and Nitrogen assimilatory enzymes activity and identification of β-sitosterol in C. Environ Impact Biodivers 113–131Google Scholar
  33. Smith CJ, Perfetti TA, Morton MJ, Rodgman A, Garg R, Selassie CD, Hansch C (2002) The relative toxicity of substituted phenols reported in cigarette mainstream smoke. Toxicol Sci 69(1):265–278CrossRefGoogle Scholar
  34. Srebrenkoska V, Zezova S, Spasova S, Golomeova S (2014) Methods for waste waters treatment in textile industry. In: International scientific conference “UNITECH 2014”, Gabrovo, pp 248–252Google Scholar
  35. Surana AR, Wagh RD (2017) GC–MS profiling and antidepressant-like effect of the extracts of Hamelia patens in animal model. Bangladesh J Pharmacol 12(4):410–416CrossRefGoogle Scholar
  36. Vallyathan V, Shi X, Castranova V (1998) Reactive oxygen species: their relation to pneumoconiosis and carcinogenesis. Environ Health Perspect 106(Suppl 5):1151CrossRefGoogle Scholar
  37. Wang G, Zhang J, Li X, Bao Z, Liu Y, Liu C, He R, Luo J (2017) Investigating causes of changes in runoff using hydrological simulation approach. Appl Water Sci 7:2245–2253.  https://doi.org/10.1007/s13201-016-0396-1 CrossRefGoogle Scholar

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© The Author(s) 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Research Centre in Chemistry and PG Department of ChemistryPratap College of Arts, Science and Commerce, Affiliated to North Maharashtra UniversityAmalner, Taluka -Amalner, JalgaonIndia
  2. 2.Research Centre in Chemistry and PG Department of ChemistryLoknete Vyankatrao Hiray Arts, Science and Commerce, College, Affiliated to SPPU, Pune (MH)Panchavati, NashikIndia

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