Organics
COD of the influent ranged from 94.4–361.8 mg/L, which is according to NEQS for inland waters (400 mg/L) but are above the threshold for sewage treatment plants (i.e.,150 mg/L), whereas COD of effluent ranged from 87.8–113.5 mg/L which is below the permissible limit set by IARS (Fig. 2). COD from NUST medical center (NMC), rumi square (R. Sq), and residential area (RA) showed highest concentrations. Results obtained for influent in the present investigation are comparable with values of influent sewage water (114–394.4 mg/L) reported in a study (Agoro et al., 2018). Gray & Becker, (2002) reported the house load (60%) as major source of COD generated from toilet wastewater followed by greywater sources (40%). High COD in sewage water can be due to usage of toilet paper (Almeida et al., 1999). Table.1 shows the concentration of organics produced from sewage water samples.
Physical characteristics
The range of pH was found between 7.1–8.5 which is within the limit prescribed by NEQS (for discharge into inland waters and sewage treatment plant) whereas for effluent the values between 7.1–7.5, which was within the limits prescribed by IARS. The high pH values may be attributed to high usage of surfactants and detergents which are alkaline in nature. It also depends upon raw sewage water characteristics (Noutsopoulos et al., 2018). A similar trend (7.0–7.5) was observed in earlier study during pre-monsoon, monsoon, and post-monsoon season from wastewater coming from college campus outlet (Jain, 2018). Temp of the influent was found within a range of 16–32.4 ℃ whereas for the effluent values ranged from 15–31.8℃ which were above the limit set by NEQS (< 3℃).
Similarly, EC was found to be high within a range of 934.8–1770.5 µS/cm for influent, and 856.3–1618 µS/cm for effluent. According to the recorded date, EC near saddle club (SC) showed higher values may be due to presence of salts in horse feed and due to the wastewater generating from cafeteria. A study reported elevated levels of EC due to the presence of salts in feed of cattle (Sangwan et al., 2010). It can also be attributed to dissolved salts generating due to various reasons such as table salt in food content, natural salt present in water and other discharges of minerals (Ma et al., 2020). Similar trend was observed from laundry (1352.5 ± 857.9 µS/cm), washroom (632.5 ± 92.4 µS/cm) and kitchen wastewater (617.3 ± 200 µS/cm) (Delhiraja & Philip, 2020).
TUR of the influent ranged from 16.5–334.5 NTU, whereas for effluent it ranged from 16.1–27.5 NTU. Highest values were recorded in zone 2 near R. Sq and in zone 3 near NMC due to decay of plant biomass and contribution of organic waste generated from patient’s room, kitchen, and laundry (Tampang & Riani, 2020). High turbidity was recorded in bathroom, kitchen, and laundry wastewater due to detergents, fibers, hair, and floating food particles (Edwin et al., 2014).
TSS originates from waste produced from shaving, kitchen, laundry, hair and fibers, body fat and bacteria (Ghaitidak & Yadav, 2013) and was found to be 80–406 mg/L which was above the limit set by NEQS for discharge into inland water (200mg/L) and sewage treatment plant (400mg/L) (Fig. 3). The effluent values ranged from 40–180 mg/L, which were within the limits set by IARS. High TSS values were recorded in zone 2 and 3 from R. Sq and RA probably due to bathroom and kitchen waste and plant biomass. A similar range of TSS (272–912 mg/L) was recorded from kitchen wastewater coming from Cranfield university café (Nasaruddin & Mohamed, 2020). High TSS and turbidity values may arise due to decay of plant biomass which ultimately resulted in discharge of solids and organic compounds (Sgroi et al., 2018).
TDS of influent ranged from 700–1500 mg/L whereas the effluent values of TDS ranged from 300–1200 mg/L. Both the values were within the permissible limit set by NEQS.
The range of TS in influent ranged between 700–2500 mg/L, whereas for effluent the values ranged between 500–800 mg/L. Highest concentrations were recorded from R. Sq due to soil particles produced from rinsing vegetables, other food items, debris, and decay of plants (Bodnar et al., 2014).
Concentrations of physical characteristics are mentioned in Table. 2.
Figure 3 Temporal variations of TSS in sewage water
Table.1Concentration of organics produced from sewage water
Locations
|
COD (mg/L)
|
Avg
|
Range
|
Influent
|
R.Sq
|
195.19
|
128.11–330.61
|
BM
|
242.69
|
135.68–361.83
|
BHC
|
204.30
|
126.40–268.82
|
SNS
|
181.07
|
126.40–239.95
|
SMME
|
205.71
|
94.41–261.75
|
SC
|
234.17
|
155.22–282.00
|
NSC
|
234.21
|
185.76–332.16
|
RA
|
258.36
|
136.00–315.95
|
NMC
|
169.56
|
120.01–212.35
|
Gate-14
|
199.00
|
129.62–249.85
|
Loc-3
|
206.12
|
119.26–270.71
|
Inlet
|
208.44
|
160.11–247.04
|
GHC
|
185.20
|
86.43–235.20
|
C1
|
247.73
|
171.21–326.43
|
IGIS
|
211.26
|
105.64–310.12
|
Effluent
|
CT
|
99.13
|
87.82–113.45
|
R. Sq = Rumi Square; BM = Bhittai Mess; BHC = Boys Hostel Complex; SNS = Opposite SNS; SMME = Gate-7 (SMME); SC = Opposite Saddle Club; NSC = NUST Sports Complex; RA = Residential Area; NMC = NUST Medical Complex; Gate-14; Loc-3 = Location – 3; Inlet = inlet of ICWs; GHC = Girls Hostel Complex; C1 = Opposite Concordia 1; IGIS = Opposite IGIS; CT = Collection tank.
Table.2Physical characteristics of sewage water produced from manholes
Locations
|
pH
|
Temp
|
TUR
|
EC
|
TS
|
TSS
|
TDS
|
Avg
|
Range
|
Avg
|
Range
|
Avg
|
Range
|
Avg
|
Range
|
Avg
|
Range
|
Avg
|
Range
|
Avg
|
Range
|
|
(℃)
|
(NTU)
|
(µS/cm)
|
(mg/L)
|
(mg/L)
|
(mg/L)
|
Influent
|
R. Sq
|
7.52
|
7.21–7.81
|
24.89
|
16.35–32.1
|
83.08
|
45.2–169.2
|
1350.25
|
980.0–1541.50
|
1550.00
|
1000–2600
|
193.50
|
95–406
|
1233.33
|
1100–1500
|
BM
|
7.43
|
7.16–7.81
|
24.98
|
16.3–32.35
|
58.89
|
48.45–78.1
|
1395.52
|
1133.14–1584.50
|
1250.00
|
700–1900
|
152.83
|
80–247
|
1200.00
|
900–1500
|
BHC
|
7.54
|
7.33–7.95
|
24.97
|
16.25–32.1
|
90.86
|
49.5–139.2
|
1427.86
|
1106.13–1648.50
|
1433.33
|
1000–1800
|
202.33
|
90–403
|
1200.00
|
1000–1400
|
SNS
|
7.35
|
7.11–7.67
|
24.84
|
16.0–31.8
|
86.03
|
65.25–126
|
1556.18
|
1234.59–1750.0
|
1333.33
|
900–2100
|
220.17
|
90–330
|
1083.33
|
900–1300
|
SMME
|
7.88
|
7.68–8.2
|
25.00
|
16.25–32.1
|
54.88
|
26.65–135.15
|
1377.08
|
1124.47–1587.50
|
1400.00
|
1200–1700
|
120.67
|
90–170
|
1166.67
|
1000–1500
|
SC
|
7.54
|
7.41–7.76
|
24.95
|
16.35–31.85
|
72.03
|
59.2–95.55
|
1557.38
|
1258.29–1914.51
|
1250.00
|
800–2000
|
179.33
|
90–320
|
1150.00
|
900–1300
|
NSC
|
7.40
|
7.04–7.81
|
25.03
|
16.5–31.55
|
104.03
|
81.85–143.5
|
1444.79
|
1104.22–1770.52
|
1300.00
|
1000–1800
|
185.17
|
90–271
|
1150.00
|
700–1500
|
RA
|
7.38
|
7.04–7.77
|
24.85
|
16.35–31.45
|
98.99
|
68.75–142.5
|
1446.79
|
1065.72–1706.55
|
1216.67
|
900–1700
|
200.00
|
110–420
|
1100.00
|
900–1500
|
NMC
|
7.64
|
7.22–8.00
|
25.08
|
16.25–32.1
|
63.08
|
23.45–167.5
|
1119.55
|
856.31–1360.52
|
1516.67
|
700–2200
|
208.67
|
80–522
|
1050.00
|
900–1200
|
Gate-14
|
7.52
|
7.08–7.95
|
24.83
|
16.4–31.3
|
47.71
|
40.15–64.75
|
1288.38
|
913.75–1685.00
|
1500.00
|
1000–1900
|
136.33
|
80–250
|
1133.33
|
900–1300
|
Loc-3
|
7.44
|
7.27–7.68
|
25.29
|
16.1–31.6
|
91.50
|
35.95–123.5
|
1309.77
|
1081.60–1450.0
|
1433.33
|
900–2000
|
228.17
|
120–400
|
1183.33
|
900–1500
|
Inlet
|
7.59
|
7.33–7.98
|
25.12
|
16.2–31.8
|
86.31
|
69.65–130
|
1317.99
|
1037.93–1643.50
|
1416.67
|
1100–1800
|
159.33
|
70–310
|
1166.67
|
1100–1300
|
GHC
|
7.56
|
7.32–7.76
|
25.53
|
16.85–32.25
|
74.78
|
51.2–119
|
1270.26
|
944.08–1498.50
|
1500.00
|
1100–2000
|
141.17
|
107–210
|
1083.33
|
900–1200
|
C1
|
7.98
|
7.53–8.40
|
24.80
|
16.2–31.6
|
39.66
|
16.515–93.5
|
1312.50
|
1080.47–1439.0
|
1374.92
|
700–2100
|
150.67
|
80–280
|
1200.00
|
1100–1300
|
IGIS
|
8.10
|
7.73–8.46
|
25.14
|
16.35–32.15
|
83.37
|
35.2–144.4
|
1571.52
|
1210.12–1785.50
|
1516.67
|
900–2500
|
192.67
|
150–270
|
1183.33
|
900–1500
|
Effluent
|
CT
|
7.24
|
7.05–7.52
|
24.99
|
15.3–31.8
|
20.50
|
16.1–22.85
|
1217.96
|
934.78–1618.0
|
650.00
|
500–800
|
99.83
|
40–180
|
750
|
300–1200
|
Nutrients
Nutrients monitored during present investigation include TKN, NO2−- N, NO3−- N and PO43−P and their concentrations are given in Table. 3. Concentration of TKN in influent ranged from 43.4–140.7 mg/L, whereas for effluent the values ranged between 40.1–59.5mg/L. Maximum TKN concentration was recorded near Institute of Geographic Information System (IGIS) in zone 1, and near R. Sq in zone 2, whereas minimum was recorded in effluent.
The range of NO2−- N in influent was found between 0.02–0.57 mg/L, whereas for effluent the values ranged between 0.04–0.2 mg/L. The max nitrite concentration in zone 1 was found near Concordia-1 Cafe (C1), in zone 2 it was found near R. Sq and in zone 3 near NMC and collection tank (CT) due to usage of fertilizers for horticulture purposes. Due to high DO concentrations, nitrification process takes place in which ammonia is converted to nitrate and nitrite (EPA, 2000).
The range of NO3−- N in influent was recorded between 0.65–13.3 mg/L, whereas for effluent the values ranged between 1.21–6.78 mg/L. Maximum nitrate concentration was recorded near the School of Natural Sciences (SNS) in zone 2 and near Girls Hostel Complex (GHC) and CT in zone 3. Nitrate is the most oxidized form of nitric acid present in aquatic habitat (Ma et al., 2020). The application of natural fertilizers to lands acts as a source of nitrate in wastewater (Akan et al., 2007). Nitrate and nitrite are present in chemical fertilizers, human sewage, and animal waste and fertilizers (USEPA, 2023).
The range of PO43−-P in influent was found between 43.4–140 mg/L, whereas for effluent the values ranged between 40.1–59.5 mg/L. Maximum mean phosphorus concentration was recorded near C1 in zone1, near Boys Hostel Complex (BHC) in zone 2, and RA in zone 3 (Fig. 4d) due to detergents used in kitchen and washrooms, whereas the values were minimum in integrated constructed wetland due to adsorption and uptake by plants. Delhiraja & Philip, (2020) reported the concentration of total phosphate in laundry wastewater (25.68 ± 22.8 mg/L) due to the presence of phosphate-rich detergents.
Table.3Nutrients load produced from sewage water
Locations
|
NO2−-N
|
NO3−-N
|
PO43-P
|
TKN
|
Avg
|
Range
|
Avg
|
Range
|
Avg
|
Range
|
Avg
|
Range
|
mg/L
|
mg/L
|
mg/L
|
mg/L
|
Influent
|
R. Sq
|
0.06
|
0.03–0.13
|
2.82
|
1.15–4.11
|
28.62
|
18.20–39.07
|
88.64
|
66.5–140.7
|
BM
|
0.04
|
0.02–0.07
|
2.29
|
0.90–4.01
|
20.52
|
11.84–33.28
|
78.06
|
64.40–90.0
|
BHC
|
0.05
|
0.02–0.06
|
2.45
|
0.64–3.67
|
30.21
|
17.94–48.74
|
75.95
|
63.7.0–90.3
|
SNS
|
0.06
|
0.05–0.07
|
3.39
|
1.64–7.44
|
27.13
|
16.95–37.67
|
81.43
|
62.3–109.2
|
SMME
|
0.04
|
0.02–0.06
|
2.71
|
2.07–4.08
|
19.85
|
9.80–32.72
|
79.86
|
71.4–85.40
|
SC
|
0.06
|
0.04–0.07
|
3.24
|
1.99–4.50
|
24.76
|
20.32–32.7
|
78.75
|
69.3–103.6
|
NSC
|
0.06
|
0.02–0.08
|
3.60
|
2.59–4.90
|
24.43
|
15.68–43.58
|
77.01
|
67.0–84.0
|
RA
|
0.06
|
0.02–0.08
|
2.81
|
1.88–3.95
|
29.85
|
16.73–43.26
|
75.48
|
67.2–88.90
|
NMC
|
0.07
|
0.02–0.26
|
2.16
|
1.51–3.13
|
14.24
|
7.50–19.47
|
68.83
|
49.0–76.30
|
Gate-14
|
0.05
|
0.03–0.07
|
2.00
|
1.67–2.37
|
13.34
|
8.67–18.07
|
66.09
|
56.7–79.80
|
Loc-3
|
0.06
|
0.04–0.08
|
3.71
|
1.83–6.21
|
20.35
|
11.98–34.70
|
69.77
|
49.0–84.70
|
Inlet
|
0.05
|
0.03–0.07
|
2.21
|
1.68–2.66
|
19.34
|
12.07–31.66
|
69.77
|
57.4–81.90
|
GHC
|
0.06
|
0.04–0.08
|
4.63
|
1.27–13.33
|
22.12
|
13.07–31.82
|
64.52
|
43.4–77.70
|
C1
|
0.25
|
0.03–0.57
|
2.86
|
2.22–3.42
|
24.99
|
9.29–47.37
|
89.48
|
70.0–119.01
|
IGIS
|
0.12
|
0.03–0.26
|
2.99
|
0.04–0.08
|
22.15
|
8.29–35.89
|
89.98
|
63.0–134.4
|
Effluent
|
CT
|
0.16
|
1.20–6.77
|
2.97
|
0.03–0.46
|
10.64
|
6.50–15.28
|
49.91
|
40.1–59.51
|
R. Sq = Rumi Square; BM = Bhittai Mess; BHC = Boys Hostel Complex; SNS = Opposite SNS; SMME = Gate-7 (SMME); SC = Opposite Saddle Club; NSC = NUST Sports Complex; RA = Residential Area; NMC = NUST Medical Complex; Gate-14; Loc-3 = Location – 3; Inlet = inlet of ICWs; GHC = Girls Hostel Complex; C1 = Opposite Concordia-1; IGIS = Opposite IGIS; CT = Collection tank.
Correlation among physicochemical parameters
Pearson correlation matrix was carried out to find the correlation among different physicochemical parameters. There was a significant positive correlation between and among Temp, TDS, EC, COD, TS, TSS, TUR, TKN, PO43− -P and NO3− -N i.e. (p < 0.05) as shown in Table.4. EC showed strong positive correlation with TDS (p = 0.02, R2 = 0.73) and Temp (p = 0.01, R2 = 0.78 ), TDS with TS (p = 0.004, R2 = 0.88), Temp (p < 0.05, R2 = 0.95 ), TUR (p = 0.03, R2 = 0.71 ), TS with TUR (p = 0.01, R2 = 0.77 ), PO43− -P (p = 0.003, R2 = 0.91), TSS with TUR (p = 0.01, R2 = 0.78) and COD showed significant positive relation with Temp (p = 0.01, R2 = 0.83 ) at p > 0.05.
Table.4 Pearson correlation matrix among physicochemical parameters
Parameters
|
pH
|
Temp
|
TUR
|
EC
|
NO2− - N
|
COD
|
NO3− - N
|
PO43− -P
|
TSS
|
TDS
|
TKN
|
TS
|
pH
|
1
|
|
|
|
|
|
|
|
|
|
|
|
Temp
|
-0.445
|
1
|
|
|
|
|
|
|
|
|
|
|
TUR
|
-0.297
|
0.776
|
1
|
|
|
|
|
|
|
|
|
|
EC
|
-0.044
|
0.888*
|
0.788
|
1
|
|
|
|
|
|
|
|
|
NO2− - N
|
0.417
|
0.199
|
-0.282
|
0.355
|
1
|
|
|
|
|
|
|
|
COD
|
-0.227
|
0.916*
|
0.540
|
0.840
|
0.524
|
1
|
|
|
|
|
|
|
NO3− - N
|
-0.390
|
0.889
|
0.442
|
0.707
|
0.403
|
0.916*
|
1
|
|
|
|
|
|
PO43− -P
|
-0.290
|
0.725
|
0.874*
|
0.653
|
-0.342
|
0.494
|
0.563
|
1
|
|
|
|
|
TSS
|
-0.541
|
0.812
|
0.887*
|
0.766
|
-0.181
|
0.557
|
0.488
|
0.671
|
1
|
|
|
|
TDS
|
-0.432
|
0.977*
|
0.848
|
0.856*
|
0.059
|
0.872
|
0.834
|
0.818
|
0.803
|
1
|
|
|
TKN
|
-0.331
|
0.484
|
0.892*
|
0.516
|
-0.585
|
0.133
|
0.096
|
0.758
|
0.828
|
0.560
|
1
|
|
TS
|
-0.536
|
0.865
|
0.883*
|
0.682
|
-0.265
|
0.675
|
0.690
|
0.910*
|
0.778
|
0.942*
|
0.686
|
1
|
*p < 0.05 |
Removal efficiency of physicochemical parameters through ICW
The wastewater collected from all sampling locations enters the ICWs for treatment. Figure shows the removal efficiency of different physicochemical parameters through ICW (Fig. 4). The percentage removal efficiency was calculated using the following Eq. (1) (Shukla et al., 2021).
Where,
Ci = mean concentration of influent
Ce = mean concentration of effluent
Removal efficiency calculated was found to be TUR 74, PO43− - P 52, COD 53, TKN 36, TDS 35, TS 57 and TSS 54% respectively. A study reported removal efficiencies of COD 98, TSS 94, and TN 96%, respectively (Dong et al., 20111). Shukla et al. (2021) reported removal efficiencies of TDS 54, TSS 53, COD 78, and PO43− - P 78% for sewage water through horizontal subsurface flow constructed wetlands (HSSFCW).
Heavy metals
They are persistent and non-biodegradable pollutants with atomic density greater than 6g/cm3. Their release in the environment may cause serious health problems in living beings. The heavy metals (HM) analyzed for the present study were cadmium, lead, chromium, zinc, and iron. They may be present in sewage water produced from different sources like batteries, car washing detergents, paints, pigments, rusting od pipes and personal care products (Tekere et al., 2016; Ogoyi et al., 2011).
Cadmium
Cadmium (Cd) data include concentrations of some locations which were not detected (i.e. near RA, NMC, Gate-14, Loc-3, Inlet, GHC, C1, IGIS, CT). The concentration was found in a range of 0–0.02 mg/L. Maximum values were recorded in R. Sq (0.02mg/L) due to the use of detergents, washing powders, body care products, tobacco products, car washing, road runoff associated with lubricating oil, wear and tear of tyres and brake pads. Households account for about 60% of cadmium contamination, making it a major source (Drozdova et al., 2019). Comber & Gunn, (1996) reported the use of washing powders and dishwasher detergents that contain phosphates as significant source of Cd. It is present as an impurity in phosphates which are used as softeners. Other reported sources include paints, galvanized pipes, batteries, alloys, and metal fittings (Agoro et al., 2020; Ali & Ilahi 2019; Sharma et al., 2015). Cd was not detected in the final effluent. However, the concentrations in all locations were within limits set by WHO guidelines (0.03mg/L). The figure shows cadmium concentration in influent and effluent (Fig. 5a).
Lead
In present investigation the range of Pb concentration was found to be 0.04–0.34 mg/L. Maximum concentration was recorded in zone 3 near gate-14 (0.34 mg/L) possibly due to rusting of pipelines, paints, and pigments. These concentrations were beyond the limits set by WHO guidelines (0.01mg/L). Stormwater can be the largest source of lead followed by laundry and bathrooms (Gray & Becker, 2002). Other studies reported sources of lead including pesticides, pipelines, metal products, cables, and paints in wastewater samples (Douglas et al., 2022; Tyagi, 2014). The high accumulation of Pb can cause hematological changes, anaemia, hypertension cardiovascular, reproductive, kidney and joint disorders in adults, whereas in children it can affect growth and learning ability leading to brain disorders (Mahugija et al., 2018; Galadima & Garba, 2012). The Pb concentration in effluent was found to be 0.07 mg/L. The figure shows lead concentration in influent and effluent (Fig. 5b).
Chromium
The range of Cr concentration was found to be 0.08–0.43 mg/L. It was not detected in some locations (i.e near RA, NMC, Gate-14, Loc-3, Inlet, GHC, C1, IGIS, CT). Maximum concentration was recorded near R. Sq, maybe due to car washing as Cr is used in coating on shower heads and car bumpers (Sun et al., 2009), as inhibitor in water cooling systems, paints and pigments. Research laboratories and other laboratories used in chemical industries discharge ample amount of trivalent and hexavalent Cr into the environment (Das, 2004). Chino et al. (1991) reported the contribution of Cr in domestic wastewater as 28% from tap water, 37% from laundry, 25% from bathroom, 8% from kitchen and 2% from feces. The present concentrations were beyond the limits set by WHO guidelines (0.05 mg/L). Cr toxicity may lead to asthma, lungs and live inflammations, and dermal infections (Dursun et al., 2003; Bagchi et al., 2002). Cr was not detected in the final effluent. The figure shows chromium concentration in influent and effluent (Fig. 5c).
Zinc
The range of zinc (Zn) concentration was found to be 1.18–3.27 mg/L. The maximum mean concentration was recorded near the School of Mechanical and Manufacturing and Engineering (SMME) which could be linked to massive work done on heavy metal analysis and usage of material used for construction purposes. The presence of Zn may be associated with pipes, coatings, leaching from plumbing material, and fittings (Noutsopoulos et al., 2018). Zinc carbonates may be used as pesticides (Zhou et al., 2000). The concentration of Zn in final effluent was found to be 1.34 mg/L. The figure shows zinc concentration in influent and effluent (Fig. 5d).
Iron
The range of iron (Fe) concentration was found to be 0.22–33.17 mg/L. It is an essential micronutrient for plants, humans, and animals. It was not detected in some locations. Maximum concentration was recorded in R. Sq, Bhittai mess (BM) and SMME. Tjadraarmadja & Diaper, (2006) reported natural content in food, food coloring, diet supplement, steel and iron products, pipes, pigment in paints and cosmetics as the significant sources of Fe. The Fe concentration in effluent was found to be 4.14 mg/L. Its presence in all sampling locations indicated that it is available in abundance in the natural environment. However, its excess can lead to serious health problems such as liver cirrhosis, diabetes, stomach pain, cancer, and other heart and brain related diseases. It can produce bad color, odor and taste and may stain utensils and clothes (Tekere et al., 2016; Akhtar et al., 2022). The figure shows Fe concentration in influent and effluent (Fig. 5e).
Figure 5 (a-e) Concentrations (mg/L) of heavy metals in sewage system (a) Cd (b) Pb (c) Cr (d) Zn and (e) Fe
Inverse Distance Weighted (IDW) interpolation of heavy metals using Arc GIS
IDW was performed using ArcGIS 10.7. It is a mapping variable technique in which the concentrations at known sampling points are used to estimate the concentrations of unknown locations (Khouni et al., 2021). The concentration range of each metal was based on spatial distribution of HM with map legend that reflected the data range for each metal. Spatial map of each heavy metal clearly depicts the increasing and decreasing trends/concentrations of a metal at each location. The color coding used in interpolation of spatial distribution of each HM exhibited red color for high concentrations and blue color for lowest concentration in each case. The interpolation of Cd, Cr, Zn and Fe showed that zone-2 is highly contaminated, whereas on Pb it showed that zone-2 and zone-3 both are highly contaminated as shown (Fig. 6a-e).
Figure 6 (a-e) Spatial distribution of (a) Cd, (b) Cr, (c) Pb, (d) Zn and (e) Fe using IDW interpolation
Principal Component Analysis
Principal Component Analysis (PCA) is a mathematical technique of data reduction designed to convert larger set of correlated data into smaller set of uncorrelated data (Hao et al., 2013; Deb et al., 2008). In the present study PCA was performed on five heavy metals in sixteen sampling locations using XLSTAT. Scree plot in (Fig. 7) shows the relationship of each variable with its eigenvalue. It indicates the first two major components responsible for producing 89.61% variation in entire data set in which first components account for 72.38% and second one accounts for 17.22% and third accounts for 7.07% of the total variation. Eigenvalues greater than 1 show that the first component (F1) accounts for the maximum variation (3.6) in data set as shown in (Table.5). Eigen vectors show that Fe is the most dominant variable responsible for producing variation in F1, Cr in F2 and Zn in F3. Moreover, there exists strong positive correlation between Fe and Zn, Cr and Cd, Cr and Zn, Cr and Fe as shown in biplot (Fig. 8).
Table 5
Cumulative variance and eigenvalues shown for each of the five heavy metals in sixteen sampling points
Heavy metals (mg/L)
|
F1
|
F2
|
F3
|
Pb
|
0.486
|
-0.864
|
-0.118
|
Cd
|
0.895
|
0.195
|
-0.378
|
Zn
|
0.899
|
0.028
|
0.394
|
Fe
|
0.945
|
-0.036
|
0.164
|
Cr
|
0.938
|
0.272
|
-0.121
|
Eigenvalue
|
3.619
|
0.861
|
0.354
|
Variability (%)
|
72.384
|
17.226
|
7.074
|
Cumulative %
|
72.384
|
89.611
|
96.685
|
Statistical analysis on heavy metals
Pearson correlation and regression was performed on heavy metals in sewage samples (Table.6). It showed that Fe exhibited positive correlation (R2 = 0.73, p = 0.00002) with Zn, and Cr exhibited strong positive correlation with Cd (R2 = 0.84, p = 0.0000005), Fe (R2 = 0.69, p = 0.00005), and Zn (R2 = 0.63, p = 0.0002) at p < 0.05. The positive correlation among these metals indicates that these originate from the same source (Agoro et al., 2020; Tytla, 2019; Titilawo et al., 2018).
Table.6 Pearson correlation among heavy metals in sewage water
Heavy metals
mg/L
|
Pb
|
Cd
|
Zn
|
Fe
|
Cr
|
Pb
|
1
|
|
|
|
|
Cd
|
0.309681895
|
1
|
|
|
|
Zn
|
0.372845662
|
0.67539429
|
1
|
|
|
Fe
|
0.459737193
|
0.76748097
|
0.862824746*
|
1
|
|
Cr
|
0.241908637
|
0.919464273*
|
0.802198809*
|
0.841137011*
|
1
|
*p < 0.05 |