Sequential extraction and risk assessment of pollutants from one major tributary of the Ganga

The geochemical fractionation of toxic heavy metals Cd, Pb, Cr, Co, Mn, Ni, Cu, Fe and Zn was investigated in 10 different sites of river bed sediments (up, mid and downstream) of Gomti River at Lucknow city. Sequential extraction technique was used to identify the distribution of trace elements binding in different fractions i.e., exchangeable, carbonate, Fe and Mn oxide, organic matter and residual. Heavy metal concentrations were least at upstream and signi ﬁ cantly higher in mid and downstream. Fractionation indicated that dominant metals were bound in residual fraction to the bed sediments except for Cd and Pb which were bound in an equivalent fraction. Geo-accumulation index factor reveals that the enrichment of heavy metals in the bio-available fraction is contributed anthropogenically. Hierarchical cluster analysis also shows the metal pollution load in the river. Risk assessment code of Cd and Ni showed very high risk (ranged from 54.41 to 85.56 and 20.57 to 44.92 respectively) followed by Pb (high risk), Zn, Co (medium risk), Cr, Mn, Cu, Fe (low risk) in Gomti River water. Further, concentrations of Cd and Pb at mid Lucknow were 31 and 75%, high enough to pose a substantial risk to the environment. Sequential extraction technique was used to of trace metals in different Geo-accumulation index factor reveals that the enrichment of heavy metals in the bio-available fraction is contributed anthropogenically. Hierarchical cluster analysis also shows the metal pollution load in the river.


INTRODUCTION
Water is an essential life supporting component. When it gets contaminated or infected, it adversely affects to the human beings and other organism Pandey et al. 2021). Heavy metals are one of the major contaminants that pose serious problem not only in a river body but in any medium due their persistent nature and non-biodegradable properties Ostad-Ali-Askar et al. 2018). The contaminants such as metal and others are introduced into the river eco-system as a result of volcanic eruptions, weathering of rocks and from a variety of human activities such as disposal of domestic sewage, mining, dredging, processing and use of vehicles, metals and substances containing metals (Bashir et al. 2020). The maximum average of risk was due to lead and copper with the respective values of 60.10 Â 10 À7 and 33.99 Â 10 À7 from some targeted wells (Saleh et al. 2019). River sediments are not only the sink of heavy metals but also reflect the history of river pollution by deposition of various metals in river sediments (Cui et al. 2019;Jafarzadeh et al. 2022). Sometimes, partitioning of heavy metals is used to provide the information of binding sites, their source and pathways by which metals are transported to the aquatic environment (Masindi & Muedi 2018;Mohammadi et al. 2020;Nawrot et al. 2020).
In sediments, heavy metals are present in various chemical forms and exhibit different chemical and physical behaviors in terms of chemical interaction, biological availability, mobility and potential toxicity (Palansooriya et al. 2020;Pant et al. 2021). The non-residual, including exchangeable, oxidizable and reducible heavy metals are considered to be the mobile or environmentally reactive fraction with respect to geological and chemical processes (Kumar et al. 2017a(Kumar et al. , 2017bWijaya et al. 2019). It is unlikely for residual metals to be released into a solution phase through dissolution and remain fixed in sediments within the matrix of silicates and other detrital minerals (Amor et al. 2019). It is also supposed that more soluble metals are more bioavailable and vice versa. Further, sediment is an important segment and chief source of heavy metals in floating or riverine ecosystem (Tiwari & Kisku 2016;Huang et al. 2019).
When contaminated area is dredged and placed on land, contaminated sediments pose the following two types of risk to the environment; first, ecological risk to aquatic life and second toxic risks to terrestrial habitat (Kulbat & Sokołowsk 2019). More unstable conditions of the aquatic ecosystem occur, when there is seasonal flooding or storms, or bioturbation due to movement and feeding of benthic organisms. Also, activities such as dredging result in major sediment disturbances, leading to changes in the chemical properties of the sediment (Massolo et al. 2012). Environmental and health effects of heavy metals in sediments depend on mobility and bioavailability of metals, which are the function of their partitioning with sediments (Devi & Bhattacharyya 2018). Further, these contaminants are considered to be one of the greatest threats to human health and aquatic life, due to their toxic effects on the environment (Tchounwou et al. 2012;Markandeya et al. 2017). Quantification of total metal concentration alone, however, is not an enough evidence of a potential toxic effect of heavy metals and the relationship between metal concentration and their bioavailability is not clearly established (Egorova & Ananikov 2017). It is need of the hour to identify and quantify the mode of occurrence of contaminants in which a metal is present in the sediment, in order to gain a more accurate understanding of the actual and potential impacts of the prominent level of metals in sediments and to appraise the processes of downstream transport, deposition and release under changing conditions of the environment (Ali et al. 2019). Organic matter plays a major role in the accumulation of metals in sediment, their availability to the receptors, toxicity to the plants and sediment organisms and transmission to the groundwater. The knowledge of the path of metal binding and their long-term behavior under the conditions of impact of different natural and anthropogenic factors are important for understanding the processes affecting metal mobility and bioavailability in soils with organic matter (Palansooriya et al. 2020).
In the present study, an attempt has been made to quantify the level of trace metals in the water and bed sediments both in the mobile and bound phases of Gomti River. Further, the eco-toxicological potential of the metals (Cr, Cu, Fe, Cd, Pb, Ni, Mn, Co and Zn) have also been determined. Moreover, fractionable percentage of bioavailability of heavy metals has assessed the dynamics and mobility factor in different geochemical fractions as well as risk assessment. To assess anthropogenic contamination in water bodies, geo-accumulation index has also been calculated.

Sampling sites
Gomti River originates from Gomat Taal situated in Pilibheet district of Uttar Pradesh, India. The river travels nearly 730 km passing through the different cities of Uttar Pradesh and merges into river Ganga at Kaithi in Ghazipur district. Lucknow city is situated (26.8467°N and 80.9462°E) on the bank of river Gomti and it serves as one of the major sources of drinking water (about 800 million liter per day) for Lucknowites and people of the surrounding areas. According to the census 2011, the population of about more than 3.5 million is served by the river in different ways. A total of 10 locations [viz. upstream-Ghaila bridge (site-1), Gau ghat (site-2), Kudiya ghat (site-3), mid-Lucknow-Daliganj bridge (site-4), Hanuman setu (site-5), Nisatganj bridge (site-6), Khatu Shyam asharam (site-7), Gomti bairaj (site-8), Dilkusha garden (site-9) and downstream-Saheed path (site-10)] were selected on the basis of the load of pollutants sources in the stream (Figure 1).

Sample collection and pretreatment
The grab samples of water and bed sediments (20 cm below the surface) were collected in polyethylene bags from each of the selected locations during March to May 2019. The sediment samples were collected from three points (left, right and middle) across the river width at each of the 10 locations and background samples (control) were collected from 10 km before entry point at Lucknow city. The collected sediment samples were air-dried, large unwanted particles were hand-picked and the rest were ground with mortar and pestle for the original sample in powdery form. To prevent further microbial degradation, pH of the river water samples was adjusted up to 2. For further analyses, water and sediment samples were transported to the laboratory in ice box under low temperature (4°C) conditions and analyzed according to APHA/AWWA WEF (2012).

Quality control and assurance
AR grade chemicals and regents were used without any further purification or disturbance (Merck and Sigma-Aldrich, Germany: 99.9% purity). Glass wares used were washed with 1.15 N nitric acid followed by distilled water for several times. The quality assurance measures included meticulous infectivity control (stringent washing/ cleaning procedures), monitoring of blank levels of solvents, equipment and other materials. All the analyses were carried out in triplicate form and the recoveries of mix metal standards were determined 75-95% through the spiked sample method and standard solutions were stored at 4°C before further use.

Samples preparation and analysis
pH of Gomti river water and sediment samples were analyzed by using APHA/AWWA (2012). The Total organic matter of the sediment was determined using back titration method (Walkley & Black 1934). The sample for the metal speciation study was dried at 100°C and passed through 230 mesh number as per ASTM. The air dried samples were sieved to obtain 75 mm sediment fraction. The analysis of metal concentration was done using Atomic Absorption Spectroscopy (AAS, Model GBC Avanta-Sigma, Australia). Hollow cathode lamps (Varian) for respective metals were used at a working current ranging from 5-30 mA with 213.9-357.9 nm spectral line. The procedure of Tessier et al. (1979) was used for the speciation study except that water soluble and plant available fractions were included in this process. Thus, the sediment samples were subjected to a five stage process to extract and separate Cd, Pb, Cr, Co, Mn, Ni, Cu, Fe and Zn through exchangeable, bound to carbonate, bound to Mn/Fe oxides, bound to organic matter and residual fractions.

Risk assessment
The possible environmental and ecological risks of the heavy metals in the sediments were assessed by calculating risk factors, i.e., geo-accumulation index (I -geo ), mobility factor (MF) of metals and risk assessment code (RAC) to find out the status of pollutants risk in bed sediment. Geo-accumulation index (I -geo ), which is widely used for evaluating the quantity of metal contamination or pollution in soil/ sediment samples of terrestrial or aquatic environments, was applied to assess the pollution status of the studied area. It is a quantitative measure of metal pollution (Muller 1979). This assessment index has been calculated in soils and sediments by Equation (1) (Abrahim & Parker 2008;Shi et al. 2010): where Bn is the background value of clear water sample, 1.5 is the background matrix correction factor and C n is the total heavy metal concentration in the sediment sample.

Mobility factor of metals in bed-sediment
Mobility factor provides an indication of the bio-availability or non-bioavailability of the metal. This process may be assessed as the ratio of the concentration of metals in re-mobilizable fractions to the combined concentration in all geochemical fractions. In this, 5-stage sequential extraction procedure was used. F-1 is the exchangeable fraction, F-2 is the carbonate fraction, F-3 is the Fe-Mn oxide fraction, F-4 is the organic fraction and F-5 is the residual fraction. Based on their proposal, the mobility factor of metals may be obtained using Equation (2). The mobility of metals in sediments may be evaluated by the absolute and relative content of fractions weakly bound to the sediment components (Jokinen et al. 2020). The mobility factor (MF) was calculated using relative index as per the following Equation (2): where F-1 is the exchangeable fraction; F-2 is the carbonate fraction; F-3 is the Fe-Mn oxide fraction; F-4 is the organic fraction and F-5 is the residual fraction.

Risk assessment code (RAC)
Risk assessment code (RAC) which was originally developed by Perin et al. (1985), is widely used in ecological risk assessment of heavy metals in sediments. RAC classification is based on the bonding between metals and different geochemical fractions present in sediments which may be released and enter into the food chain. In the present study, RAC is being used to assess the risk of heavy metals in surface sediments of the Gomti River. It further, assesses the possible release of heavy metals in solution based on the percentage of exchangeable and carbonate fractions in sediments and the model value to calculate RAC is given in following Equation (3) (Tang et al. 2010).

Statistical analysis
The level of significance was calculated by one-way ANOVA in the analysis of variance. Sigma state 3.5 was applied for calculation of least standard deviation (LSD) and pair-wise comparison was assessed by Paliwal comparison method.

Factor analysis/principal component analysis
It is a multivariate statistical technique, which attempts to find out gravity of particular factor lower dimensional linear structure from the data set. These factors can be interpreted in terms of new variables. It is also used to simplify the dataset (Cattel 1965). 1 st principal component is oriented such that it explains about 75% of the variance. Then 2nd principal component is oriented to explain remaining 10% variance. In factor analysis, one chooses the number of components up front and then seeks to orient them together so that in sum they explain as much of the total variance as possible.

Hierarchical cluster analysis
Hierarchical cluster analysis (HCA) is a multivariate statistical technique, which classifies the parameters into clusters based on their similarities representing in dendrogram. HCA classifies water quality parameters into different groups so that variables within a cluster start with the most similar pair of variables and form higher clusters step by step. To yield different clusters of the data set, dendrogram can be fragmented at different levels. Each fragmented level provides a visual summary of the cluster through a picture of the groups and their proximity with a dramatic reduction in dimensionality of the original data (Leal et al. 2016). In this study, the Ward's method is used evaluate distance between clusters (Bhardwaj & Parmar 2020).

RESULTS AND DISCUSSION
3.1. Organic matter (OM) and pH in bed-sediment The results indicate that organic matter in bed sediment ranges from 0.95 to 7.98%. The organic matter content in background sediment was 0.795%, while the ordinary organic carbon of city area was 1.9%, which was nearly two and a half times greater than the normal that showed an organic load of the river in the city area. In the background (control), pH of water sample was 7.53, whereas river water pH was found to range from 7.56 to 8.20 while bed sediment pH ranged from 7.98 to 8.51. The average pH of river water was 7.89 in city area while background pH of river water and bed sediments was found within the acceptable limit. pH is an important factor for determination of water quality and the extent of pollution in the river system. A pH range of 6.5-8.5 is typically acceptable as per the guidelines suggested by WHO (2011). Kumar et al. (2020) also found that pH of the river water was found to be higher than normal value.

Heavy metals concentration in river water and its bed-sediment
The average concentration of heavy metals i.e., Cd, Pb, Cr, Co, Mn, Ni, Cu, Fe and Zn of background sample in river water were below detection limit ( (Tables 1 and 2). The minimum concentration of heavy metals was observed in upstream (site 1 and 2), while maximum concentration was observed in midstream (site 7 and 8). In the present study, heavy metal (i.e., Cd, Pb, Cr, Co, Mn, Ni, Cu, Fe and Zn) concentration in river water and bed sediments in city area were found to be many folds higher than the background sample. This may be due to direct drainage of both sewage and industrial effluents to the river without proper treatment. Kumar et al. (2020) also found the load of heavy metals in river water and sediment samples in concentrated amount which is supporting the present study. The river receives high volume of mixed domestic as well as industrial wastewater into the midstream. Moreover, a barrage is constructed in the midstream of site-8 which inhibits the flow of river water causing the deposition of suspended matter in that area. This contaminated stretch poses the highest risk to the aquatic environment as a source of pollution. Kisku et al. (2016) have also reported similar findings. High concentrations of heavy metals have been found in the sediments. Ali et al. (2019) have also found the load of heavy metals in river sediment sample which supports the present study. The major accumulation mechanism of heavy metals in sediments lead to the existence of five categories; exchangeable, bound to reducible phases (Fe and Mn oxide), carbonate, organic matter and residual. These categories have different remobilization behavior under varying environmental conditions. The adsorptive and exchangeable fractions are a consequence of human activity. The carbonate bound fractions are considered to have a weak link and may equilibrate with aqueous phase, thus becoming more readily bioavailable. The combination of Fe/Mn oxide and the organic matter have a scavenging effect and may provide a sink for heavy metals (Guan et al. 2018).

Sequential fraction percentage of heavy metals in bed-sediments
Sequential fraction showed that maximum fraction of Cd (60.32%) in sediments was in the exchangeable bound form which significantly decreased in carbonate phase (12.7%), Fe/Mn oxide phase (9.63%), organic matter phase (10.88%) and residual phase (6.47%), respectively. Thus, the sequential fraction of Cd in the bed sediments followed the sequence exchangeable . carbonate . organic . Fe/Mn oxide . residual. Bioavailable forms of Cd in bed sediment ranged from 54.41% to 85.56% while non-available ranged from 14.44% to 45.59% (Table 3).
Maximum amount of Pb was available in exchangeable bound form (45.96%) which significantly decreased in different fractions like Fe/Mn oxide (23.42%), residual (19.53%), carbonate phase (7.68%) and organic matter phase (3.41%), respectively. Thus, Pb in the bed sediments was bound to a different phase in the sequence as exchangeable . Fe/Mn oxide .   (Figure 2(a)-2(e)). Cr was available predominantly in residual bound from (67.39%), which significantly decreased in the different fraction like Fe/Mn oxide (20.79%), carbonate (5.29%), exchangeable (3.38%) and organic matter phase (3.15%), respectively. Thus, the sequential fraction of Cr in the bed sediments was bound to a different phase in the sequence as residual . Fe-Mn oxide . carbonate . exchangeable . organic. Bioavailable forms of Cr in bed sediment ranged from 5.44 to 13.11% while non-available forms ranged from 23.52 to 67.86% (Figure 2(a)-2(e)).
Co was found in residual bound from (31.65%), which further decreased as organic matter bound (25.04%), Fe/Mn oxide (22.21%), carbonate (11.94%) and exchangeable bound (9.17%), respectively. Thus, the sequential fraction of Co in the bed sediments was bound to a different phase in the sequence as residual . organic . Fe/Mn oxide . carbonate . exchangeable. Bioavailable forms of Co in bed sediments ranged from 15.35 to 24.74% (21.1%) while non-available forms ranged from 75.26 to 84.65%.
Fe was found mostly in residual bound form (63.61%), which significantly decreased in the pattern as Fe/Mn oxide (26.5%), organic matter (8.66%), carbonate (1.15%) and exchangeable (0.08%) bound, respectively. The sequential fraction of Fe in the bed sediments was bound to a different phase in the sequence as residual . Fe/Mn oxide . organic . carbonate . exchangeable. Bioavailable form of Fe in bed sediment ranged from 0.81 to 2.10%, while non-available form ranged from 97.90 to 99.19%. The total Fe concentration in sediment was 989.02 μg/g, while background concentration of Fe was observed as 763.0 μg/g.
Zn showed the maximum in residual bound (44.15%) which was significantly decreased in the different fraction like Fe/Mn oxide (33.95%), carbonate (10.09%), exchangeable (6.63%) and organic matter (5.19%) bound, respectively (Figure 2(a)-2(e)). The sequential fraction of Zn in the bed sediments was bound to a different phase in the sequence of residual . Fe/Mn oxide . carbonate . exchangeable . organic. Bioavailable form of Zn in bed sediment ranged 9.87-21.85% while non-available forms 78.15 to 90.13%.

Uncorrected Proof
The average percentages of exchangeable metals were observed in exchangeable phase. Thus, Cd and Pb were maximum in exchangeable bound while Cr, Ni, Co, Mn Cu and Fe in residual bound. Heavy metals in both exchangeable (F-1) and carbonate (F-2) fractions of sediment were found to be relatively mobile and readily available for biological uptake, a process facilitated by the low pH. Jokinen et al. (2020) have also found that the similar observation for the uptake of heavy metals. In the exchangeable phase, the level of trace metals increased through ion exchange process. This proportion is considered to be one, which constitutes the immediate nutrient reservoir for the aquatic organism. The fractions of these metals are considered to be bioavailable for aquatic as well as other life forms.
Ni, Cd, Cr and Pb have a special affinity with carbonate and may co-precipitate with carbonate minerals at high pH (Ugwu & Igbokwe 2019). The residual fraction of trace metals in this form are not soluble as tightly bound under experimental conditions. The greater the percentage contribution of the heavy metal, the smaller the pollution zone (Masindi & Muedi 2018). In this, Cu and Zn are considered as a toxic element and found very rare but they are insoluble. The metals in the residual fraction are usually retained within the crystal lattice of minerals and in well crystallized oxide and are thus considered to be immobile (Namur & Humphreys 2018).
The lowest percentage of bioavailable forms was found in case of Fe and highest for Cd in sediment samples. Cd (31.3-81.2%), Cr (3.84-51.5%) and Co (18.2-65%) were also found in the non-residual fraction. In the sediment samples, the lowest percentage of non-bioavailable form was found for Cd and highest percentage for Fe. Kumar et al. (2020) have also found that the non-bioavailable form was found highest for Fe which is the similar to the present study.

Results of factor analysis/ principal component analysis
Eigen values are normally used to define the number of principal components or Factors that can be taken for further study. First two principal components have Eigen values greater than or close to unity and explain 80.487%, of the total variances of evidence contained in the original dataset for trace metals. According to Hakanson (1980), the Cd values range from 40.53 to 1598.44 which indicate a very high contamination degree of sediments. Table 4 shows the rotated component matrix and component score for different metals in factors analysis of bed sediment in the river.
Factor 1: Having Eigen value with 5.51 and 49.58% of the variance, has high loadings on Mn, Ni, Fe, Co and Cr, low loadings with Cu, Zn, Pb and Cd. Factor 2: It explain 49.58% of the variance, Eigen value with 5.51 and has high loadings on Zn, and Cu, moderate loadings with Cu and low loadings on Zn, Pb and Cd (Table 4). Principal component analysis shows the total variance for metals of bed sediment of river as shown in Table 5.

Hierarchical cluster analysis
Hierarchical cluster analysis was performed using Ward's method with squared Euclidean distance as similarity measure provided visually meaningful dendrogram. In this study, trace metals were divided into two major clusters, Fe in cluster 1 and Zn,

Uncorrected Proof
Mn, Pb, Ni, Cd, Cr, Co and Cu in cluster 2, further cluster 2 was subdivided into three sub clusters made with Zn in cluster 2 (1) and Mn in cluster 2 (2) and Pb, Ni, Cd, Cr, Co and Cu in cluster 2 (3). Figure 3 shows the Scree plot for total metals in bed sediment between metal components and Eigen value. The dendrograms of hierarchical cluster analysis showed the cluster of variables for total metal in bed sediment (Figure 4). Results revealed the presence of high concentration of Cd, Pb and Zn due to anthropogenic source and the water was classified as category V, whereas the presence of heavy metals at other sites was due to mine waste, which eroded from the river banks and was classified as II to III categories water (Ferati et al. 2015).

Geo-accumulation index (I -geo ) and mobility factor of metals
The possible environmental and ecological impacts of heavy metals in the sediments were assessed through the calculated risk factors such as geo-accumulation index (I -geo ), mobility factor (MF) of metals and risk assessment code (RAC) to find out the status of pollutants risk in bed sediment. The result indicated that geo-accumulation index (I -geo ) was observed maximum for Cd (2.30) and Cu (2.27) which was more than 2, indicating moderate to strong pollution,   Uncorrected Proof bed sediments ( Figure 5(a)). Guan et al. (2018) also found that geo-accumulation index of Cd and Cu was more than 2 indicating strong pollution. The distribution of metals in different phases, using the BCR procedure, offers an indication of their availability, which in turn reflects the risk associated with the presence of metals in the aquatic environment (Guan et al. 2018). Geo-accumulation index (I -geo ), a widely used practical relationship for evaluating the quantity of metal contamination or pollution in soil or sediment samples of terrestrial or aquatic environments, was applied to evaluate the pollution status of the studied area (Muller 1979).
Mobility factors were observed for different heavy metals in bed sediments of the river which ranged from 0.012 to 3.192. The maximum mobility factor was observed for Cd (3.192) and minimum for Fe (0.012). The decreasing order of mobility factor was observed as Cd . Pb . Ni . Mn . Co . Zn . Cr . Cu . Fe (Figure 5(b)).

Risk Assessment Code (RAC)
The result showed that risk assessment code of Cd and Ni ranged from 54.41 to 85.56 and 20.57 to 44.92, respectively which were under very high risk while risk assessment code of Pb varied from 32.14 to 76.48 which was found to be at high risk. Zn (9.87-21.85) and ) were at medium risk, while Cr (5.44-13.11), Mn (13.13-29.63), Cu (3.85-8.79) and Fe (0.81-2.10) were found at low risk. The risk assessment code gives an indication of the possible risk by applying a scale to Water Supply Vol 00 No 0, 12 the percentage of metals present in the exchangeable and carbonate fractions. Accordingly, if this value is ,1% there is no risk for the aquatic system; 1-10% indicates low risk, 11-30% medium risk, 31-50% high risk, and .50% very high risk (Jain 2004;Singh et al. 2005).
The lowest mobility factor was found for Fe and highest for Cd. The lowest risk assessment code values were low risk for the Mn, Fe, Cr and Cu, while highest risk assessment code values were for the Pb and Cd. Other metals were found to be of medium risk. Zn, Mg and Co was very high risk. Risk assessment code revealed that more than 50% of Cd is in exchangeable or carbonates bound fractions as reported by Al-Mur (2020) and therefore comes under the very high risk category and can easily enter the food chain because of the toxicity, mobility and availability of Cd. It can pose serious problems to the aquatic ecosystem.

CONCLUSIONS
The geochemical variations and distribution patterns of selected metals are very instructive in the sediments of Gomti River. Among the metals; Cr, Cu, Mn, Ni, Fe and Zn were distributed mainly in residual fraction while Pb was in carbonate fractions. Risk assessment code and I -geo factors in the sediments showed medium risk for Cr, Cu, Mn, Ni, Mg, Fe; high risks for Zn and very high risks for Cd and Pb. Mobility factors were in the order of Cd . Pb, preferentially associated with the bioavailable fractions and could be used as indicators for contribution from the anthropogenic sources, while Cr, Co, Mn, Ni, Cu, Fe and Zn were associated to a greater extent with the residual fraction, indicative of natural origins. Overall Cd, Pb, Ni and Cr emerged as major pollutants in the sediments in the mid-stream of the river, which were in insecure proximity to densely populated urban/semi-urban locality, where diverse anthropogenic activities contributed to the majority of the hazardous pollutants in the study area.

CONSENT FOR PUBLICATION
Not applicable.

COMPETING INTERESTS
The authors declare that they have no competing interests in this section.

FUNDING
There was no funding support for this study.