Mechanisms controlling the major ion chemistry
Rainwater runoff and ground water discharge are the major sources of base flow in the Mayur River. Therefore, cyclic sea salts in the dissolved load of the river is expected to carry the signatures of rainfall and groundwater in the region. Generally, the contribution of cyclic salts to riverine dissolved salt loads is expected to decrease with increasing distance from the sea. It has been long established that, Cl− in river waters with no terrestrial sources of the element declines systematically as a function of increasing distance from the sea (Stallard and Edmond 1981). The reverse has been found in the Mayur River. Although the basin of Mayur is small (~ 11 km long river with 52 km2 basin area) increasing amount of Cl− towards the upper reaches is noticeable. While Cl− was found as 217 mg/L in the lower reaches of the river (average value of four most downstream stations), upper reaches show 326.8 mg/L of Cl− (average value of four most upstream stations). This indicates the high concentration of Cl− in the Mayur River likely to be originated from either weathering of evaporites or anthropogenic inputs. However, The 22 drains discharging urban effluents into the river cancels out the possibility of evaporites being the major contributing source of Cl− in the river and indicates that the source of water in river Mayur is mostly urban waste rather than rainfall and groundwater discharge.
The Piper trilinear plot (Fig. 4b) demonstrates significant variation in water types over the year. From winter towards pre-monsoon data points cluster more towards the salinity apex (Na-SO4 type), while Na+ and Cl− remain more or less same for the both cases but SO42− increases in the pre-monsoon compared to the winter samples. In the cations trilinear plot, data points move more towards the Na apex from winter to pre-monsoon suggesting additional Na+ discharge to the dissolved load. In the anion plot, while data points cluster around high HCO3− and low SO42− in winter, pre-monsoon is marked with SO42− enrichment replacing HCO3−. This exhibits the effect of lack of rainfall in winter followed by pre-monsoon, consequently increasing the pollution effect, as HCO3− concentration does not change much (med. value 394 mg/L in winter and 312 mg/L in pre-monsoon) but SO42− concentration increases from 20.22 mg/L in winter (median value) to 287.78 mg/L in pre-monsoon (median value). In monsoon and post-monsoon, in contrast to that of winter and pre-monsoon, data points float widely in the middle of the diamond plot. However, in monsoon, a reduction of Na+ and enrichment of Ca2+ is noticed, indicating a dilution effect. Post-monsoon data points show wide and variant combination of Na+, Ca2+ and SO42− suggesting infrequent freshwater mixing with pollution caused by irregular and but intense rainfall.
Wide distribution of data points in the trilinear plot indicates the influence of anthropogenic activities in the dissolved load rather than weathering effects. The concentration ratio of HCO3:Cl + SO4:SiO2 has been found as 31:36:1 in the dissolved load and the correlation between SiO2 and Ca2+, K+, HCO3− and SO42− are negative and very weak (< |0.3|) but significant (Table 2). This suggests that silicate weathering plays a less important role in determining major ions for the whole basin. Again, most of the data point fall below the equiline of HCO3/Cl + SO4 (Fig. 5a) and HCO3/Na + K (Fig. 5b) and cluster around the equiline of HCO3/Ca + Mg (Fig. 5c) indicating that the weathering of carbonates and/or evaporites cannot explain the whole composition of Ca2+ and Mg2+ in the water. While most of the data points fall below the isometric line of Ca + Mg/Na + K (Fig. 5d) and Cl + SO4/Na + K (Fig. 5e), almost all data points clustering above the Cl + SO4 = Ca + Mg line (Fig. 5f) indicate that neither evaporate weathering nor the carbonate weathering is the dominant process in the basin.
High concentration of Na+ and Cl− in natural waters usually indicates halite dissolution which may not be case in this river basin. In majority of the cases (~ 66%) Na+ has been found comparatively lot higher than Cl− as the Na+/Cl− ratio shows an average of 2.71. This cancels out the dominance of halite dissolution and suggests possible ion-exchange process, if not the presence of silicate weathering, which in this case has been proved as less dominant earlier. This cation exchange process involves excess Na+ input by the city wastewater lines.
As depicted in Fig. 6a, the Na+ concentration of most the samples (66.66% of total) scattered above the seawater line, which suggests excess Na+ enrichment in the Mayur waters except in monsoon (Na+/Cl− = 0.96). This excess Na+ might be influenced by cation exchange process. To further investigate the occurrence of cation exchange reactions in the studied waters, a Na–Cl versus Ca + Mg – (HCO3 + SO4) plot was constructed (Fig. 6b). Apart from cation exchange the most likely additional sources of Ca2+ and Mg2+ in natural waters are Calcite, dolomite, gypsum and anhydrite weathering. While plotting the diagram, the lithogenic Na+ available for exchange is calculated from Na+ to Cl− as it is assumed that the meteoric Na+ should be balanced by equivalent concentration of Cl− (Nkotagu 1996). Again, possible contribution of Ca2+ and Mg2+ from Calcite, dolomite, gypsum and anhydrite dissolution lo lithogenic Ca2+ and Mg2+ are accounted for by subtracting the equivalent concentrations of HCO3− and SO42− (Nkotagu 1996). Figure 6b suggests the excess Na+ in pre-monsoon and winter season may be due to cation exchange of Na+ replacing Ca2+ and/or Mg2+.
The TDS–TH ratios indicate water quality changes over time from hard brackish water in pre-monsoon to mostly hard fresh water in all other seasons confirming dilution effect in wet period. If only TDS is accounted for 58.33% of the samples fall in permissible limit for drinking water, all of which are found in monsoon and post-monsoon season (Table 3). According to the irrigation water classification based on the Sodium Absorption Ratio (SAR), most of the samples (64.58%) fall in poor category indicating poor water quality along the river. However, ~ 66% of the samples in monsoon fall in excellent to good category suggesting improvement in water quality in the monsoon. The EC-based classification further show that 93.75% samples fall in good to permissible category during monsoon. No sample was found as excellent quality.
Land use signatures on water
Multivariate techniques were applied to investigate the major land use sources of ion chemistry in the river. For this analysis, only river water samples were considered, since the adjoining canals bring area specific discharges to the river. The results of Factor Analysis (FA) are presented in Table 4. Four factors were extracted using Principal Component Analysis (PCA) based on the condition “eigenvalue > 1” explaining 79.99% of the total variation. Factor 1 explains 38.10% of the variation and has strong loading (≥ 0.75) on Salinity, TDS, TSS, Na+, K+, Mg2+ and SO42−. Since weathering effects are not strong in the river basin except land runoff and the river is dead at its source, this factor can be explained as the anthropogenic factor, most likely the urban influence. Figure 6 presents a spatial distribution of the factor coefficients along the river. This distribution suggests head waters of the river show high factor 1 coefficients which is most populous part of basin. The second factor explains 20.06% variation in the data and exhibits strong positive loading on HCO3− and PO43− and strong negative loading on DO and moderate (≥ 0.50 ~ ≤ 0.75) positive loading on NO3− and moderate negative loading on pH. This may represent the agricultural/soil runoff signature. Agricultural activities are known to have a major impact on hydrochemistry of streams. The use of N and P fertilizer is common in the Mayur river basin as more than 60% of the total basin area falls under agricultural land use. NO3− was found as high as 5.97 and 5.05 mg/L (median value) in pre-monsoon and winter, respectively. PO43− was found as 5.02 and 4.94 mg/L (median value) in the same seasons reported earlier compared to 0.35 and 1.04 mg/L in monsoon and post-monsoon, respectively. This indicates the seasonality of the agricultural practices in the basin, as the winter rice called Boro is cultivated around these seasons. Figure 7 reveals a peak of factor 2 in the middle reaches of the river where agricultural activities are concentrated. A third factor explains 12.70% of the data variation and has a strong positive loading on EC, Ca2+ and Cl− with moderate positive loading on Mg2+. This is likely to be the effect of tidal influence in river during the wet season (Fig. 7). The fourth factor has a strong negative loading on water temperature and strong positive loading on SiO2 with moderate positive loading on pH explaining 9.10% of the total variation explained. Naturally pH is inversely related with SiO2 which has been found reverse in this case. Therefore, it can be assumed that, this factor explains the massive construction works involving the use of silica that is taking place alongside the river mostly during winter (or dry season). The values of SiO2 found in the river over the seasons support this theory as in winter SiO2 in the water samples are found as 23.55 mg/L (median value) while in pre-monsoon, monsoon and post-monsoon corresponding avg. values are 4.18, 7.3 and 4.32 mg/L, respectively (Fig. 7).
The HCA was performed based on the major cations, major anions and the physical parameters, which produced three main clusters (Fig. 8a). Interestingly, the clusters give a clear indication of changing water quality and dominant land use types running along the river. First five sampling stations fall in cluster 1 which are located in the upstream and dominated by urban land use. The last three stations at the downstream are clustered together and are dominated by agriculture. The third cluster represents four sampling stations located in the middle reach of the river organized among two sub-clusters indicating a mix of the two dominant land use types, urban and agriculture. However, interestingly, the major two land use dominated clusters (agriculture and urban) are grouped in one mother cluster while the mixed land use show a very different mother cluster. Thus it illustrates how characteristically distinct an agricultural runoff and urban wastewater might be, but the mixture of two can be far different from their original characters.
The behavior of the hydrochemical variables of each of these clusters is further investigated by developing biogeochemical fingerprints (Fig. 8b). For each cluster, values of the variables are averaged over sites that fall within the cluster and then fingerprints are developed by plotting spider diagrams with the log of the ratio of the median value for each variable in the cluster of interest to the median value for sites in the reference cluster (Wayland et al. 2003). In this spider diagram, points derived from cluster of interest that are plotted on the x-axis (where y = 0) refer to no difference from the reference cluster. The points that plotted over the x-axis (y > 0) indicate higher concentration in the ‘interest cluster’ than that of the ‘reference cluster’ and vice versa.
First, urban cluster was investigated using the agricultural cluster as reference. DO, Na+ and K+ is found as low in the urban cluster while Salinity, EC, Ca2+, Cl−, HCO3−, NO3−, PO43−, SiO2 and TSS is found as high in concentration. These findings are not consistent with available previous researches (e.g., Wayland et al. 2003; Fitzpatrick and Long 2007) as they found higher amount of Na+, K+ and SO42− in urban streams compared to the agricultural streams. The log of urban cluster versus mixed cluster suggests, most of parameters have higher concentration in the urban cluster except DO. Again, the log of the ratio of agriculture versus mixed cluster shows higher concentration (y > 0) of DO, Na+, K+, Mg2+ and TDS and lower concentration (y < 0) of salinity, EC, Cl−, NO3−, PO43−, SiO2 and TSS.
The spider diagram reveals that mixing of urban wastewater leads to higher rate of decreasing DO in streams compared to the agricultural environment. Also, it is clear that urban waste water tends to increase salinity and conductivity in stream water than that of agricultural environment. Agricultural runoff is expected to naturally produce more N and P products along with lesser amount of sodium and potassium compared to the urban streams. This study shows the other way around. Possible explanation for this reverse result can be the influence of shrimp farm end-products in the agricultural sites. There are several shrimp production ponds around the agricultural sites that discharge their waste directly into the river. Shrimp farms are well known for using NaCl and KCl to reduce crop mortality, thus enriching wastewater with Na+, K+ and Cl−. In this case, the agriculture dominated sites of the Mayur River are also rich in Na+, K+ and Cl− and show higher concentration of these ions compared to the mixed water zone (sampling station 6, 7, 8 and 9). This explanation is supported by the fact that the average concentration of Na+ and K+ at the urban, mixed and agricultural sites in the river are 278.04 and 18.96, 166.73 and 15.22, 339.17 and 38.24 mg/L, respectively. The sharp increase of average Na+ and K+ in the agricultural sites indicates the strong influence of shrimp waste in the river. Increase of Ca2+, Mg2+, Cl− and SO42− towards the tail waters of the river where the agricultural sites are found, also are indications of the presence of shrimp farm end-products in water.