Evaluation of heavy metal contamination and their distribution in waters around Oil and Natural gas drilling sites

doi:10.21203/rs.3.rs-1550148/v1

PDF

Research Article

Evaluation of heavy metal contamination and their distribution in waters around Oil and Natural gas drilling sites

https://doi.org/10.21203/rs.3.rs-1550148/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Oil and natural gas production has been an important economic booster in the recent past. However, unconventional methods have risen environmental health concerns. This study presents preliminary results of heavy metal concentrations in water (surface and groundwater) samples around oil and natural gas drilling sites in East-West Godavari districts of A.P, India. A total of 36 samples, 24 surface water (SW) and 12 groundwater (GW) were collected to evaluate the distribution of pH, EC, TDS, As, Cd, Cr, Cu, Mo, Ni, Pb, Zn, and radiogenic elements (U, Th). Results acquired were treated with principal component analysis (PCA)/factor analysis (FA), hierarchical cluster analysis (HCA), and regression coefficient analysis to identify a collective contamination source.

Mean concentrations obtained for surface and groundwater were 7.60 and 7.34 for pH; 4048 and 2964 mg/L for TDS; 8.50 and 5.91 µs/cm for EC; 11.5 and 10.7 μg/L for As; 14.6 and 10.8 μg/L for Cr; 0.60 and 0.70 μg/L for Cd; 18.6 and 29.1 μg/L for Cu; 3.00 and 4.20 μg/L for Mo; 19.9 and 24.8 for Ni; 15.2 and 13.4 μg/L for Pb; 4.60 and 3.10 μg/L for Th; 1.00 and 8.30 μg/L for U; 187 and 348 μg/L for Zn respectively. FA recognized four factors accountable for data structure elucidating 86.23 % of the total variance in SW, four factors in GW explaining 91.6%, and permitted to assemble particular parameters based on collective features. As, Cd, Cu, Mo, Pb, and U were linked and well-ordered by diverse origin with related influence from anthropogenic and geogenic sources. This study describes the importance and efficacy of multivariate statistical methods for evaluation and understanding the data to get better evidence about the water quality and project some mitigation methods to avert the contamination affected by harmful heavy metals in the future.

Heavy metals

surface water

groundwater

contamination

multivariate analysis

Having endured a constant decrease in the use of non-renewable resources and the relinquishment of cleaner technologies, the oil industry to maximize production has invested in new technologies, such as hydraulic fracturing to overcome the financial costs. During oil and natural gas production using hydraulic fracturing, so-called backwater or produced water covers as the major byproduct stream evolved after injecting the fluids into the formation. This flow back fluids that remain underneath pose a severe threat to the environment as 5–60% of the fracturing fluid flows back to the surface and the rest residues remain underground (Carter, 2013). These fluids contain different chemicals including inorganic and organic compounds which are known to be detrimental to human health (John., 2016; Govil etal., 2018). Heavy metals are among them in drilling waste discharges that pose a risk of contaminating the environment (Muhammad et al., 2019; Govil et al. 2012; Krishna and Mohan 2014). Natural and anthropogenic sources producing heavy metals are considered the major environmental pollution source affecting the surface and groundwater that disturb plants and human being chain. One of the sources is the waste fluid generated during hydraulic fracturing that is left in open-air pits to evaporate, liberating harmful VOCs (volatile organic compounds) into the atmosphere, generating unclean air and acid rain. 75% of the total 600 identified chemicals during HF are known to affect the skin, eyes, and other vital sense organs including the respiratory and gastrointestinal system (40%) rest of the chemicals toxic in nature affect the nervous, urinary, and endocrine systems (37%) and in turn found to cause cancer and mutations (25%) in human bodies (Colborn et al., 2011).

Multivariate statistical methods like principal component analysis (PCA), factor analysis (FA), cluster analysis (CA), and correlation analysis were applied to surface and groundwater data to understand the status of the water quality around the sampling points nearby the drilling sites. A large dataset has also been applied to better understand the water quality in different monitoring programs carried out by Renato et al. 2018; Igibah CE et al. 2019; Mrazovac and Miloradov-Vojinovi 2011, with the perspective. These studies characterize the evidence that influences the water structure and related contamination factors affecting the human health and risk characterization in the contaminated areas (Shrestha and Kazama 2007; Simeonov et al. 2003; Reghunath et al. 2002; Ammar et al. 2014; Carlon et al. 2001; Howladar et al. 2017).

The present study was carried out keeping in view the growing demand for non-renewable energy sources using advanced technologies, thereby to sustain and protect the environment, with objectives (i) a preliminary assessment of ten toxic heavy metals, that are associated with other chemicals in water (surface and groundwater) samples around oil and natural gas drilling sites. (ii) evaluating the contribution of heavy metals to the proximity of drilling sites using multivariate statistical methods like the PCA/FA, cluster analysis. Baseline information regarding metal distribution and accumulation in the surface water and groundwater would also be generated. Further, these studies will also be beneficial in identifying the major contaminants and help in combating and designing an approach to control the emission and spread of pollutants in the study environment.

Description of the Study area

The proposed study area (Fig. 1) covers the East and West Godavari districts of Andhra Pradesh. The West Godavari District lies in between 16° 15' to 17° 30' N Latitude and 80° 50' to 80° 55' E Longitude and East Godavari District lie in between 16° 30' to 17° 00' N Latitude and 81° 30' to 82° 30' E Longitude. The main soils in the districts are alluvial clay, sandy clay, and black clay. There are 10 drilling sites spread over the two districts. It is seen that in most of the drilling sites the backwater waste in the form of sediment waste is deposited on the surface and the backwater is stored in the storage tanks surrounding the drilling site.

The study area is characterized by tropical rainy climate with aggressive summer with a temperature range of 17.4°C in winter and 43.5°C in summer (Climatologically normal 1981–2010) and the average annual rainfall in West Godavari district is 1078mm and in East Godavari district is 1100mm (Groundwater brochure – West Godavari, East Godavari, (CGWB, 2013; Nagiredddi, 2007). The significant rainfall occurs during the southeast monsoon from June to October. The districts are underlain by Archaean crystallines, Gondwanas, Deccan Traps, Tertiaries, and alluvial sediments. About 45% of the district is underlain by Gondwana formations, 40% is underlain by Alluvium and the rest is by Archaean crystalline rocks.

Water Sampling, Sample Preparation, And Analysis

A total of thirty- six (36) representative surface water (24) and groundwater (12) were sampled in one-liter containers with air-tight screws from different surface backwaters and bore wells/hand pumps. These containers were altogether washed and rinsed with dilute hydrochloric acid and distilled water in the lab to keep away from any possible contaminations. During groundwater sample collection, the borewell/hand pumps were first pumped for 10 minutes to pump out the stagnant water present in the pipes. Next, fresh groundwater was collected in a 1-liter clean plastic container. While surface water sample collection was collected from 30 cm below the water surface using a bailer and a clean and well-dried 1-liter container.

Experimental

Physico-chemical parameters pH, TDS, EC, and Temperature were measured directly in sampled water immediately after sampling in the field using the Combo instrument. Collected samples were filtered into 100ml PET bottles using 0.40 µm Millipore filter paper and then shifted and acidified (2ml of HNO₃) to each 100ml of the sample and were measured for heavy metals by HR ICP-MS Instrument.

Analytical Procedures And Instrumentation

This study highlights the combined methodology of various statistical indices to evaluate the heavy metal contamination status on surface and groundwater around drilling sites in and around the study area. Given this, 36 representative surface water (24) and groundwater (12) samples were collected from bore wells, canals, and hand pumps. The heavy metals (As, Cd, Cu, Cr, Mo, Ni, Pb, and Zn) and radiogenic elements (Th and U) were analyzed using HR-ICP-MS and compared with the WHO standards for drinking appropriateness. Physico-chemical parameters like pH, electrical conductivity (EC) expressed in micro Siemens/cm, total dissolved solids (TDS) in mg/kg were determined using pen-type models, whereas, heavy metal concentrations in µg/L. Plasma Quad (VG Elemental Ltd., Winsford, Cheshire, UK) High-resolution inductively coupled plasma mass spectrometer was used to measure the heavy metal concentrations. Multielement reference water standards 1640A and 1643E from the National Institute of Standards and Technology (NIST, USA) were used to prepare calibration curves. The accuracy and precision of the results were compared with the certified values of the reference materials.

The calibration curves were prepared using multielement standard solution after dilution to micrograms per liter levels. The accuracy and precision (QC/QA) of the analysis were compared with certified values of reference water samples which were used to check the reliability of the calibration curve. The values achieved by HR ICP-MS were in good agreement with that of certified values and the precision is better than 2% and shows comparable accuracy.

Data Handling And Statistical Techniques

Multivariate statistical methods concerning distribution and correlation among the studied parameters were applied to surface and groundwater data using SPSS 10.0 programming platform. The sample locations were noted using Atrax global positioning system (GPS). Descriptive statistical parameters, for example, minimum, maximum, mean, standard deviation, kurtosis, skewness, regression analysis were reported, while multivariate measurements such as PCA (the principal component of analysis) and CA (cluster analysis) were also carried out. Varimax standardized rotation on the data was utilized to perform PCA, whereas, the dendrogram strategy was utilized to link with the sample concentrations (Liu et al. 2003; McKenna 2003; Omo-Irabor et al. 2008). Principal components obtained provide us the most important parameters which represent the information of the total deducted data with the minimum loss of the unique data. Further, the PCA method is an effective method that explains the difference in the spacious arrangement between associated factors and altering into smaller ones based on pattern recognition of uncorrelated variables. Ward’s hierarchical agglomerative cluster analysis method with square euclidean distances was executed on the normalized dataset.

Descriptive statistics of 13 parameters of surface and groundwater in the study area are summarized in Tables (1a, 1b). The groundwater pH values varied between 6.69 to 7.68 with mean pH of 7.34 and surface water pH values varied between 6.40 to 8.69 with a mean of pH 7.60. A maximum number of the water samples shows a neutral to basic and alkaline nature. TDS values of groundwater display 110 to 10,000 mg/L and surface water shows 80 to 10,000 mg/L. The prescribed limit of TDS in drinking water is 500mg/L. Observed elevated values of TDS though not injurious to humans but have health effects on individuals suffering from kidney and heart-related ailments. High TDS values indicated health-related issues like laxative or constipation effects (Sasikaran et al. 2012).

The ionic concentrations, pH, TDS in surface water are indicated as electrical conductivity, total dissolved solids. pH value (6.67–8.69) moderately acidic to alkaline, TDS (Total Dissolved Solids) vary from 80 to 10000 mg/L and EC (Electro conductivity) between 0.16 to 20 µs/cm. In groundwater samples concentration, pH value (6.69–7.68 ppm) moderately acidic to alkaline, TDS (Total Dissolved Solids) vary from 400 to 20000 mg/L and EC (Electro conductivity) between 0.80 to 20 µs/cm. Distribution of heavy metals and radiogenic elements in surface and groundwater in the study area occur in the order of Cd < U < Mo < Th < As < Pb < Cr < Cu < Ni < Zn and Cd < Th < Mo < U < As < Cr < Pb < Cu < Ni < Zn respectively.

The correlation coefficient analysis was carried out for ten heavy metals for surface and groundwater as shown in tables (2a, 2b). There was important linearity observed between these metals. The significance of the correlation is based on the ‘r-value which indicates how one metal behaves with the other and the best linearity between the variables is determined by coefficients (-1,1) interval. Higher and positive values of the ‘r-value, indicates the good correlation between the variables and r = 0 indicates a poor correlation, whereas negative values of ‘r’ indicate an inverse relationship. The correlation (Tables 2a, 2b) as noticed in surface water samples As-Cu; As-Pb; Cr-Mo; Cr-Ni; Cd-Cu; Cu-Pb; Cu-Zn; Th-U; and groundwater samples As-Cd; As-Cu; As-Mo; As-U; Cr-Ni; Cd-Mo; Cu-Mo; Cu-U; Mo-U; Pb-Th.

Table 1

a: Descriptive Statistical data (mg/L) of surface water analysis
	Minimum	Maximum	Mean	Std. deviation	Skewness	Kurtosis	WHO
pH	6.40	8.69	7.60	0.53	0.29	-0.13
TDS	80.0	10000	4048	3913	0.48	-1.54
EC(µS /cm)	0.16	20.0	8.50	7.78	0.34	-1.60
As	0.90	61.7	11.5	14.5	2.20	5.50	10
Cr	7.40	125.3	14.6	8.40	1.70	2.20	50
Cd	0.10	1.70	0.60	0.40	1.10	0.70	5
Cu	1.30	57.2	18.6	155	1.10	0.90	2000
Mo	0.20	13.3	3.00	2.80	0.70	-0.60
Ni	4.00	38.5	19.9	10.2	0.08	-0.90	70
Pb	1.10	88.6	15.2	18.7	2.80	10.0	10
Th	0.50	25.1	4.60	5.80	2.20	5.50
U	0.01	4.10	1.00	1.10	1.20	1.10	30
Zn	0.50	1288	186.8	271	3.10	12.1	3000

Table 1b: Descriptive Statistical data (mg/L) of groundwater analysis

	Minimum	Maximum	Mean	Std. deviation	Skewness	Kurtosis	WHO
pH	6.69	7.68	7.34	0.28	-0.98	1.276
TDS	110	10000	2964	3863	1.23	-0.134
EC(μS /cm)	0.80	20.0	5.91	7.72	1.23	-0.125
As	1.00	59.6	10.7	17.5	2.30	2.3	10
Cr	8.60	19.8	10.8	2.90	2.80	2.8	50
Cd	0.20	3.50	0.70	0.90	2.70	2.7	5
Cu	3.40	92.8	29.1	30.9	1.20	1.2	2000
Mo	0.30	21.4	4.20	6.60	2.00	2.0
Ni	9.90	105	24.8	26.0	3.10	3.1	70
Pb	2.10	44.7	13.4	11.7	1.80	1.8	10
Th	0.50	10.6	3.10	3.20	1.40	1.4
U	0.10	29.8	8.30	10.3	1.60	1	30
Zn	6.80	2505	348	692	3.20	3.2	3000

Table 2

a: Correlation coefficient matrix of heavy metals in surface water
	As	Cr	Cd	Cu	Mo	Ni	Pb	Th	U	Zn
As	1.00	0.34	0.54	0.70**	0.46	0.21	0.64*	0.03	0.22	0.03
Cr	0.42	1.00	0.11	0.48	0.67*	0.93**	0.09	0.16	-0.08	0.04
Cd	0.54	0.11	1.00	0.76**	0.11	-0.01	0.57	0.49	0.32	0.46
Cu	0.70**	0.48	0.76**	1.00	0.48	0.40	0.75**	0.46	0.42	0.52*
Mo	0.46	0.67*	0.11	0.48	1.00	0.59	0.12	0.19	0.15	0.04
Ni	0.21	0.93**	-0.01	0.40	0.59	1.00	0.12	0.11	-0.06	0.08
Pb	0.64*	0.09	0.57	0.75**	0.12	0.12	1.00	0.21	0.33	0.37
Th	0.03	0.16	0.49	0.46	0.19	0.11	0.21	1.00	0.59*	0.33
U	0.22	-0.08	0.32	0.42	0.15	-0.06	0.33	0.59*	1.00	0.13
Zn	0.03	0.04	0.46	0.52*	0.04	0.08	0.37	0.33	0.13	1.00
Level of significance = 0.05; *level of significance = 0.01

Table 2

b: Correlation coefficient matrix of heavy metals in groundwater
	As	Cr	Cd	Cu	Mo	Ni	Pb	Th	U	Zn
As	1.00	0.10	0.79*	0.78**	0.94**	-0.04	0.27	-0.16	0.82**	-0.20
Cr	0.10	1.00	-0.12	-0.04	0.15	0.94**	-0.20	-0.15	-0.03	-0.17
Cd	0.79**	-0.12	1.00	0.58	0.74**	-0.15	0.17	-0.07	0.58	-0.06
Cu	0.78*	-0.04	0.58	1.00	0.76**	-0.13	0.12	0.12	0.81**	0.34
Mo	0.94*	0.15	0.74**	0.76**	1.00	-0.00	0.01	-0.28	0.89**	-0.21
Ni	-0.04	0.94**	-0.15	-0.13	-0.00	1.00	-0.22	-0.03	-0.23	-0.02
Pb	0.27	-0.20	0.17	0.12	0.01	-0.22	1.00	0.52*	-0.04	-0.05
Th	-0.16	-0.15	-0.07	0.12	-0.28	-0.03	0.52*	1.00	-0.28	0.43
U	0.82**	-0.03	0.58	0.81*	0.89**	-0.23	-0.04	-0.28	1.00	-0.16
Zn	-0.20	-0.17	-0.06	0.34	-0.21	-0.02	-0.05	0.43	-0.16	1.00
Level of significance = 0.05; *level of significance = 0.01

Table 3

a: loading for varimax rotated factor matrix explaining 86.29% of the total variance for surface water
Parameters	VF1	VF2	VF3	VF4
As	0.719	0.036	-0.567	-0.300
Cr	0.575	0.769	0.125	0.098
Cd	0.731	-0.456	-0.114	0.126
Cu	0.971	-0.098	-0.085	0.080
Mo	0.590	0.568	0.100	-0.234
Ni	0.505	0.769	0.168	0.156
Pb	0.716	-0.334	-0.397	0.024
Th	0.535	-0.309	0.691	-0.100
U	0.458	-0.437	0.406	-0.537
Zn	0.466	-0.343	0.187	0.710
Eigenvalue	4.154	2.240	1.231	1.004
Loading%	41.544	22.398	12.308	10.043
Cumulative%	41.544	63.942	76.250	86.293

Table 3b: loading for varimax rotated factor matrix explaining 91.65 % of the total variance for groundwater.

Parameters	VF1	VF2	VF3	VF4
As	0.967	0.066	0.098	0.184
Cr	-0.012	0.865	0.471	0.110
Cd	0.814	-0.107	0.021	0.093
Cu	0.844	-0.196	0.337	-0.306
Mo	0.963	0.200	-0.003	-0.001
Ni	-0.164	0.799	0.562	0.052
Pb	0.133	-0.520	0.331	0.720
Th	-0.201	-0.572	0.684	0.164
U	0.920	0.045	-0.145	-0.144
Zn	-0.126	-0.410	0.548	-0.674
Eigenvalue	4.185	2.248	1.559	1.172
Loading%	41.852	22.480	15.594	11.723
Cumulative%	41.852	64.333	79.927	91.650

Principle Component Analysis

The varimax method with kaiser normalization was utilized for obtaining principal components. The related effect of the PCA established on factor analysis of the correlated chemical components for surface and groundwater are shown in tables (3a, 3b). Four constituents of PCA analysis showed 86.29% of the variance in the surface water data set of the study area. The eigenvectors classified the 10 heavy metals into four groups. The first constituent (VF1) is loaded with arsenic, cadmium, copper, and lead; the second constituent (VF2) is loaded with chromium and nickel, while the third constituent (VF3) thorium and fourth constituent (VF4) were not significant. Whereas, for groundwater data set the four constituents of PCA analysis showed 91.65% of the variance for 10 heavy metals into four groups. The first constituent (VF1) is loaded with major elements As, Cd, Cu, Mo, and U; the second constituent (VF2) is loaded with As, Cr, and Ni; the third component is loaded with As and Th; while the fourth constituent is loaded with Pb only.

Cluster Analysis

Cluster Analysis (CA) is one of the cluster-based statistical methods to classify different clusters based on their characteristic similarity to evaluate the surface water quality. Hierarchical agglomerative clustering is the most collective approach that delivers spontaneous comparison associations between anyone sample and the entire data set and delivers visual outlines of the clustering process and it is usually shown by a dendrogram (Vega et al., 1998; Singh et al., 2005; Tabachnick and Fidell, 1996; Shrestha and Kazama, 2007; Tokatlı et al., 2013; Tokatlı, 2014b).

Dendrogram obtained for surface water (Fig. 2) displayed five clusters with Cd, Cu, Ni (Cluster I), Cu, Ni, As (Cluster II), Ni, As, Pb (Cluster III), As, Pb, Zn (Cluster IV), Pb, Zn, Th, U, Cr (Cluster V). Cluster I and II specify the related activity of factor 1; Cluster IV and V represent the same activity of factors 3, 4 obtained by factor analysis. The subsequent multielement factors were separated into factors with strong anthropogenic influence. Whereas, Cluster III signifies the collective activity of factor 1 and factor 2. Correspondingly, for groundwater, the dendrogram obtained (Fig. 3) also showed three clusters with As, Mo, Cd (Cluster I), Mo, Cd, Cu, U (Cluster II), Cd, Cu, U, Pb, Th, Cr, Ni, Zn (Cluster III). This clustering points to collective causes of the natural method of degeneration of soil parts primarily carbonates. Cluster I and Cluster III resemble similar activity when compared to factor analysis components wherein Cr, N, Th, Pb played the dominating heavy metals. Therefore, the dominant source may be attributed to the drilling activities with backwater waste and partially from surrounding agriculture activity.

This study described the preliminary surface and groundwater heavy metal and their probable source distribution around oil and natural gas drilling sites. Various multivariate statistical methods like PCA, Factor, and cluster analysis were applied for 10 heavy metal constituents of 36 surface and groundwater samples. The study area effects verified that both natural and anthropogenic practices were the two major factors for the chemical compositions of groundwater. FA recognized four factors accountable for data organization explaining 86.23% of the total variance in surface water and four factors in groundwater explaining 91.6% and allowed to group selected parameters according to common features. As, Cd, Cu, Mo, Pb, and U were accompanied and organized by mixed origin with similar influences from anthropogenic and geogenic sources.

The recommendations in the study area would be to monitor the quality of the groundwater by applying basic quality testing and to monitor the activities in the drilling sites. To further contain the backwater chemicals generated which are harmful to human health. Further, suggest suitable remedial measures to avert contamination due to heavy metals in near future.

Acknowledgments

The authors are thankful to Dr. V. M. Tiwari, Director, CSIR-NGRI for his permission to publish this paper. This work was supported by UGC-JRF fellowship and the MLP-6406-28 (DSS) project funds were utilized to conduct fieldwork. Our sincere thanks are also to Dr. D. Srinivasa Sarma for his continuous support and encouragement. Thanks are also due to Dr. M. Satyanarayanan for extending HR-ICP-MS analytical facility. This study forms a part of DBM’s doctoral thesis. This is CSIR-NGRI contribution No. NGRI/Lib/2022/Pub-30.

Ammar FH, Chkir N, Zouari K, Hamelin B, Deschamps P, Aigoun A (2014) Hydro-geochemical processes in the Complexe Terminal aquifer of southern Tunisia: an integrated investigation based on geochemical and multivariate statistical methods. J Afr Earth Sci 100:81–95
Carter KE (2013) Hydraulic Fracturing and Organic Compounds-Uses, Disposal and Challenges, SPE 165692
Carlon C, Critto A, Marcomini A, Nathanail P (2001) Risk-based characterization of the contaminated industrial site using multivariate and geostatistical tools. Environ Pollut 111:417–427
Colborn ThKwiatkowskiwski Carol, Schultz Kim and Bachran Mary (2011) Natural Gas Operation from a public health perspective. Hum Ecol Risk Assessment: Int J 17(5):1039–1056
Igibah CE, Tanko JA (2019) Assessment of urban groundwater quality using Piper trilinear and multivariate techniques: a case study in the Abuja, North-central, Nigeria. Environ Syst Res 8:14
Govil PK, Sorlie JE, Sujatha D, Krishna AK, Murthy NN, Mohan KR (2012) Assessment of heavy metal pollution in lake sediments of Katedan Industrial Development Area, Hyderabad, India. Environ Earth Sci 66:121–128
Govil PK, Aradhi K, Krishna (2018) "Soil and Water Contamination by Potentially Hazardous Elements: A Case History From India". Elsevier BV
Ground water brochure (2013) Krishna district, West Godavari district and East Godavari district. Central Ground Water Board, Ministry of Water Resources, Southern region, Hyderabad
Howladar MF, Al Numanbakth MA, Faruque MO (2017) An application of Water Quality Index (WQI) and multivariate statistics to evaluate the water quality around Maddhapara Granite Mining Industrial Area, Dinajpur, Bangladesh. Environ Syst Res 6:13
Igibah CE, Tanko JA (2019) Assessment of urban groundwater quality using Piper trilinear and multivariate techniques: a case study in the Abuja, North-central, Nigeria. Environ Syst Res 8:14
John Picthel (2016) Oil and Gas Production Wastewater: Soil Contamination and Pollution Prevention, Applied and Environmental Soil Science. Article ID 2707989:24. http://dx.doi.org/10.1155/2016/2707989
Jaiswal RK, Mukherjee S, Krishnamurthy J, Saxena R (2003) Role of remote sensing and GIS techniques for generation of groundwater prospect zones towards rural development–an approach. Int J Remote Sens 24(5):993–1008
Katalakute G, Wagh V, Panaskar D, Mukate S (2016) Impact of drought on environmental, agricultural and socio-economic status in Maharashtra State, India. Nat Resour Conserv 4(3):35–41
Krishna AK, Mohan KR (2014) Risk assessment of heavy metals and their source distribution in waters of a contaminated industrial site. Environ Sci Pollut Res 21:3653–3669
Liu CW, Lin KH, Kuo YM (2003) Application of factor analysis in the assessmentgroundwaterater quality in a blackfoot disease area in Taiwan. Sci Total Environ 313:77–89
McKenna JE Jr (2003) An enhanced cluster analysis program with bootstrap Significance testing for ecological community analysis. Environ Model Softw 18:205–220
Mohammad Mosaferi M, Pourakbar M, Shakerkhatibi E, Fatehifar M, Belvasi (2014) "Quality modeling of drinking groundwater using GIS in rural communities, northwest of Iran", Journal of Environmental Health Science and Engineering, 2014
Mrazovac S, Miloradov-Vojinovi M (2011) Correlation of main physicochemical parameters of some groundwater in northern Serbia. J Geochem Explor 108:176–118
Nagireddi Srinivasa Rao (2007) Distribution of iron in the surface and groundwaters of East Godavari district. Andhra Pradesh, India
Omo-Irabor OO, Olobaniyi SB, Oduyemi K, Akunna J (2008) Surface,e, and groundwater water quality assessment using multivariate analytical methods: a case study of the Western Niger Delta, Nigeria. Phys Chem Earth 33:666–673
Renato ISA, Carolina SM, Cassio FB, Brisa MF, Nadal Martí S, Jordi, Josep LD, Segura-Muñoz Susana I (2018) Water quality assessment of the Pardo River Basin, Brazil: a multivariate approach using limnological parameters, metal concentrations and indicator bacteria. Arch Environ Contam Toxicol
Reghunath R, Murthy TRS, Raghavan BR (2002) The utility of multivariate statistical techniques in hydrogeochemical studies: an example from Karnataka, India. Water Res 36:2437–2442
Shrestha S, Kazama F (2007) Assessment of surface water quality using multivariate statistical techniques: a case study of the Fuji river basin, Japan. Environ Model Softw 22:464–475
Sasikaran S, Sritharan K, Balakumar S, V Arasaratnam (2012) Physical, chemical and microbial analysis of bottled drinking water. Ceylon Med J 57:111–116
Simeonov V, Stratis JA, Samara C, Zachariadis G, Voutsa D, Anthemidis A, Sofoniou M, Kouimtzis TH (2003) Assessment of the surface water quality in Northern Greece. Water Res 37:4119–4124
SPSS® (Statistical Package for Social Studies) version 6.1, USA. (1995). Professional Statistics 6.1, 385, Marija J.Norusis/SPSS Inc., Ch
Wagh VM, Panaskar DB, Varade AM, Mukate SV, Gaikwad SK, Pawar RS, Muley AA, Aamalawar ML (2016a) Major ion chemistry and quality assessment of the groundwater resources of Nanded tehsil, a part of southeast Deccan Volcanic Province, Maharashtra, India. Environ Earth Sci 75(21):1418

PDF

Reviews received at journal
08 May, 2022
Reviewers invited by journal
07 May, 2022
Editor assigned by journal
12 Apr, 2022
First submitted to journal
12 Apr, 2022

You are reading this latest preprint version

Evaluation of heavy metal contamination and their distribution in waters around Oil and Natural gas drilling sites

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Description of the Study area

Water Sampling, Sample Preparation, And Analysis

Experimental

Analytical Procedures And Instrumentation

Data Handling And Statistical Techniques

Results And Discussion

Principle Component Analysis

Cluster Analysis

Conclusions

Recommendations

Declarations

References

Status:

Version 1