Stream Health Evaluation Using a Combined Approach of Multi-Metric Chemical Pollution and Biological Integrity Models

: Bouchung Stream is a large tributary of the Geum River watershed that is simultaneously affected by wastewater treatment plant efﬂuents and agricultural activities in the watershed area. The focal subject was to diagnose the chemical and biological health of the temperate stream by using a combined approach of the multi-metric water pollution index (WPI) and the index of biological integrity (IBI KR ), using datasets from 2008–2014. Water chemistry analyses indicated seasonal and inter-annual variations mainly linked to the intensity of monsoon rainfall in the watershed, potentially causing the availability of agricultural runoff water. The main events of phosphorus inﬂow and nitrogen dilutions occurred during July–August. Temporal and spatial heterogeneities were observed and were largely recognizable due to nutrient enrichment and organic matter intensiﬁcation. Chlorophyll showed weak linear relation to total phosphorus ( R 2 = 0.17) but no relation to total nitrogen ( p > 0.05). Fish compositions analyzed as trophic/tolerance guilds in relation to water chemistry showed visible decline and modiﬁcations. Average WPI site scores ranged from 33–23, indicating an excellent upstream to fair downstream water quality status. Correspondingly, IBI KR scores ranged between 38–28 approximating with WPI site classiﬁcation, as well as both indices showed higher regression relation ( R 2 = 0.90). Fish guild analyses revealed tolerant and omnivore species dominating the downstream, while sensitive and insectivores depleting in approximation with changing water chemistry and was conﬁrmed by the principal component analysis. In addition, the ﬁsh guilds meticulously responded to phosphorus inﬂows. In conclusion, overall stream health and water chemistry analyses indicated continuous chemical and biological degradation inﬂuencing the trophic and tolerance ﬁsh guilds. Moreover, the combined application approach of WPI and IBI KR could help in better understanding the chemical and biological mechanisms in rivers and streams.


Introduction
Stream health evaluation has been one of the hottest research areas over the recent decades in developed countries such as U.S.A. [1], Canada [2], France [3], Germany [4], and England [5]. Human interaction with the environment was inevitable and resulted in processes leading to several ecological perturbations in lotic ecosystems. Under such circumstances, simple chemical parametric studies, biological diversity investigations, and composition analyses were frequently carried out by considering those key factors for ecological health evaluations. However, ecological health evaluation in streams and rivers is not so simple, rather it depends on a variety of combined factors. Such factors include water chemistry [6], physical habitat condition in terms of structural components [7], climatic The principal goals were to establish the ecological health status of Bouchung Stream, South Korea, by using a dataset from 2008-2014 on the basis of seasonal, inter-annual variations of physicochemical water quality factors, modified multi-metric water pollution index (WPI), and modified multi-metric index of biotic integrity (IBIKR). Wastewater treatment plant (WWTP) effluents in combination with agricultural runoff mainly affected this stream, leading to water quality deterioration and biological degradation. A simultaneous analysis expected to yield assessments of seasonal and spatial patterns of environmental degradation, as well as changes in fish assemblages viewed though tolerance and trophic guilds.

Site Description and Study Duration
This study was performed in a temperate stream (Bouchung Stream), near Daejeon, South Korea ( Figure 1). Bouchung Stream is one of the major streams in the Geum River watershed (3rd largest river watershed in country) stretching to a total length of 72.11 km, with a water basin area 553.38 km 2 and a mean slope of 32.1% rendering a steep gradient for water flow from headwater towards downstream. The four sampling sites (S) are described as follows. S1: 2nd order stream, N: 36°32′22.88″ E: 127°40′50.4″; S2: 3rd order stream, N: 36°29′01.55″ E: 127°43′27.59″; S3: 4th order stream, N: 36°22′26.02″ E: 127°49′12.20″; and S4: 4th order stream, N: 36°17′26.27″ E: 127°41′33.47″. Forest cover constitutes 67% of the upstream area and the rest is cropland (26%) or urban areas (04%). Water quality monitoring data was collected on a monthly basis, whereas fish sampling was executed twice a year (i.e., first sampling in the pre-monsoon period from April to June and the second in the post-monsoon period from September to October, 2008-2014). S1 is a headwater stream site and did not receive any non-point source (NPS) pollutants. Sites 2 and 4 did not receive any treated effluents from any WWTP, but agricultural runoff was believed to enter the mainstream originating from the surrounding cropland areas. S3 is near urban areas where it received effluents from WWTPs, as well as industrial effluents. The decision to designating a year as wet or dry was based on the intensity of the annual rainfall in the watershed. If the annual rainfall exceeded 1200 mm, the year was designated as a wet year, otherwise as a dry year. Forest cover constitutes 67% of the upstream area and the rest is cropland (26%) or urban areas (04%). Water quality monitoring data was collected on a monthly basis, whereas fish sampling was executed twice a year (i.e., first sampling in the pre-monsoon period from April to June and the second in the post-monsoon period from September to October, 2008-2014). S1 is a headwater stream site and did not receive any non-point source (NPS) pollutants. Sites 2 and 4 did not receive any treated effluents from any WWTP, but agricultural runoff was believed to enter the mainstream originating from the surrounding cropland areas. S3 is near urban areas where it received effluents from WWTPs, as well as industrial effluents. The decision to designating a year as wet or dry was based on the intensity of the annual rainfall in the watershed. If the annual rainfall exceeded 1200 mm, the year was designated as a wet year, otherwise as a dry year.

Water Chemistry Analyses
In total, we measured 16 water chemistry parameters during this study. The parameters included pH, dissolved oxygen (DO), water temperature (Temp.), electrical conductivity (EC), total suspended solids (TSS), total number of coliform bacteria, biological oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN), total dissolved nitrogen (TDN), ammonia nitrogen (NH 4 -N), nitrate nitrogen (NO 3 -N), total phosphorus (TP), total dissolved phosphorus (TDP), ortho-phosphorus (PO 4 -P), and chlorophyll-a (CHL-a). The pH, DO, Temp, EC, and CHL-a was measured with a multi-parameter water quality sensor (YSI Sonde 6600, Environmental monitoring system, Yellow Springs, OH, USA). Total number of coliform bacteria (TNCB) was estimated using the standard method [42]. Total nitrogen and TDN were analyzed by method of second derivative after digestion in persulfate solution [43,44]. The NH 4 -N and NH 3 -N were estimated by phenate method and ion chromatography, respectively, followed by extract filtration of the source water sample through GF/C filters. The TP and allied species (i.e., TDP and PO 4 -P) were estimated using ascorbic acid method followed by persulfate oxidation [44,45]. TSS, BOD, and COD were measured by following standard methods [42,44]. Nutrient analyses were repeated thrice to ensure validity, while the estimation of BOD, COD, and TSS were performed twice [42,46]. The data was collected on a monthly basis throughout the study duration under all hydrological conditions prevailing the stream watershed.

Fish Sampling
Fish sampling was performed as per modified wading method [47,48] for evaluation of stream health which was derived from the Ohio EPA method [49]. Fifty min of fish sampling was based on the catch per unit effort (CPUE) method [49], exploiting all habitat grounds like riffle, run, and pools, with direction from both upstream to downstream, and covering a stretch of at least 200 m. The casting nets had mesh sizes of 5 × 5 mm and dimensions of 1.5 m × 1.5 m × 3.14 m, and kick nets had a mesh size of 4 × 4 mm and dimensions 1.8 m × 0.9 m, which were used as sampling gear. In situ identification of each fish followed by immediate release to its habitat was performed. All fish specimens were identified observing the salient body features as described by Kim and Park [50], while Nelson's [51] system was followed for systematic classifications. However, if any ambiguity was faced in proper identification, samples were preserved in 10% formalin solution for further research and identification in laboratory. Based on the concept of Sanders et al. [52], all sampled fishes were carefully examined if they bore any external anomalies such as deformities (D), erosion(s) (E), lesions (L), and/or tumors (T) (DELT) as insight into the status of individual fish health. The trophic and tolerance guild analyses were executed following previously conducted regional studies in South Korea [53].

Modified Multi-Metric Index of Biotic Integrity (IBI)
Development of IBI for regional applications based on identification of ecological perturbations across the study area were followed by biological data collection. Next, was the identification of reckonable characteristics (metrics) of fish assemblages that were considered to be changing or shifting systematically in space and time. Composed of eight metrics, constituting three main categories viz. species richness and composition, trophic and tolerance guild compositions, and the fish abundance, along with an insight to physical health condition of fish. The details of metrics mentioned in Table 2. Half of the eight metrics (i.e., M 1 , M 2 , M 3 and M 7 ) were evaluated by maximum species richness line (MSRL) [54], keeping in view the stream order at each site. The scoring criteria for each metric was 5, 3, and 1 followed by the criteria of Reference [1], which reflected that the biotic integrity either approximated, deviated, or greatly deviated from the pristine conditions. The final scores obtained after summing up all metric values led to evaluate as five categories of biological health from excellent (36)(37)(38)(39)(40), good (28)(29)(30)(31)(32)(33)(34), fair (20)(21)(22)(23)(24)(25)(26), poor  and very poor (08-13). Detailed account of specified metrics and their characteristics as well as the respective scoring standards could be consulted from An et al. [40].

Statistical Analysis
Seasonal and inter-annual deviations of the water quality factors as well as regression analyses were performed on the log-transformed datasets in Sigma Plot version 10 [55]. Means and standard deviations were calculated by using SPPS v.22. PAST software [56] used for principal component analysis (PCA) to assess which factors have changed during the research time in the streams under study, as well as for correlation analysis.

Seasonal Variations in Water Chemistry
Seasonal precipitation pattern was one of the key factors influencing the stream water quality ( Figure 2). Seasonally, one third of total precipitation in Korea occurred during the fleeting time of summer monsoon (July-August) and showed conspicuous hydrological variations. The summary statistics of water chemistry factors at the four sites are mentioned in Table 3. The seasonal water quality changes showed identical patterns during the dry season, but summer monsoon rainfall was the critical factor during the study. pH in downstream ranged higher as compared to the rest of three sites and showed a minimal seasonal change brought about by precipitation. Krishnaram et al. [57] quantified pH of 6.7-8.4 as safe for aquatic biota for sustainable productivity, while pH below 4.0 and above 9.6 could be hazardous to fish. All sites showed a reasonable drop in the average values of EC during summer monsoon. Due to agricultural and rainwater inflow, S3 and S4 showed higher levels of TSS and increased with the increasing precipitation and vice versa. Water temperature and salinity level significantly changed the dissolution of oxygen in water [58]. The amount of TSS indicated aquatic pollution caused by extraneous origins and affected running water quality in an adverse manner. Higher contents of TSS increased the density of water, influencing osmoregulation, decreasing gas solubility, as well as other uses of water [59]. Total number of coliform bacteria (TNCB) showed a huge variation in upstream and lesser change in downstream sites and remained unresponsive to the seasonal downpour. Total number of coliform bacteria indicated if the effluents contained fecal matter [60]. The higher the number of bacteria, the higher are the chances of fecal contamination, hence a greater risk water borne diseases would prevail [61,62]. Biological oxygen demand change at S1 was nominal while at S2, S3, and S4, it showed the highest average values from April to June, and then decreasing trend was observed during the summer monsoon and onwards. An almost similar pattern was exhibited by COD, but was less affected by the seasonal rainfall. The higher the values of BOD and COD, the higher were the chances of organic and inorganic pollutants in the water bodies.     The seasonal values of nutrient contributing factors, mainly comprising of nitrogen, phosphorus, and their allied chemical species, were significantly affected by the summer monsoon precipitation pattern ( Figure 3). The summer monsoon rainfall showed almost no effect on the seasonal variations of nitrogen regime, except adding it up in the downstream region. Total Nitrogen, TDN, and NO3-N showed reciprocal seasonal changes during summer monsoon. On the other hand, TP showed spatial variations. In headwater zones, average seasonal change appeared nominal and less affected by seasonal rainfall due to none-existing agricultural pastures. However, as the stream stretched downwards, it showed huge deviations in TP levels, with pronounced changes approximating with the increased rainfall during the summer monsoon. It showed that agricultural runoff currents have the potential to increase phosphorus levels downstream. Total dissolved phosphorus showed less visible seasonal changes, displaying a shadowed peak after the summer monsoon rainfall that denoted the agricultural runoff effect. PO4-P was parallel throughout the years in headwater sites, but showed conspicuous variations at downstream waters. The highest average PO4-P values (43.83 ± 44.02 µg/L) were recorded during the summer monsoon rainfall. The outcome of average sestonic CHL-a concentration showed almost no seasonal change in headwater zones, but a gradual increase The seasonal values of nutrient contributing factors, mainly comprising of nitrogen, phosphorus, and their allied chemical species, were significantly affected by the summer monsoon precipitation pattern ( Figure 3). The summer monsoon rainfall showed almost no effect on the seasonal variations of nitrogen regime, except adding it up in the downstream region. Total Nitrogen, TDN, and NO 3 -N showed reciprocal seasonal changes during summer monsoon. On the other hand, TP showed spatial variations. In headwater zones, average seasonal change appeared nominal and less affected by seasonal rainfall due to none-existing agricultural pastures. However, as the stream stretched downwards, it showed huge deviations in TP levels, with pronounced changes approximating with the increased rainfall during the summer monsoon. It showed that agricultural runoff currents have the potential to increase phosphorus levels downstream. Total dissolved phosphorus showed less visible seasonal changes, displaying a shadowed peak after the summer monsoon rainfall that denoted the agricultural runoff effect. PO 4 -P was parallel throughout the years in headwater sites, but showed conspicuous variations at downstream waters. The highest average PO 4 -P values (43.83 ± 44.02 µg/L) were recorded during the summer monsoon rainfall. The outcome of average sestonic CHL-a concentration showed almost no seasonal change in headwater zones, but a gradual increase in downstream reciprocating with TP. The CHL-a concentration appeared diluted with summer monsoon, and showed a slight possibility of eutrophication in proceeding months. Large change in nutrient contributing factors, starting at S3 and downstream, reflected the effect of urban land-use, as well as intensive agricultural pasture runoff that constituted approximately 58% of the surrounding area. Overall, it could conclude that headwater zones were minimally influenced, while downstream zones seemed susceptible to the effluents originating from the WWTP, industrial effluents, and intensive agricultural farming in the area [38].
Water 2018, 10, x FOR PEER REVIEW 9 of 27 in downstream reciprocating with TP. The CHL-a concentration appeared diluted with summer monsoon, and showed a slight possibility of eutrophication in proceeding months. Large change in nutrient contributing factors, starting at S3 and downstream, reflected the effect of urban land-use, as well as intensive agricultural pasture runoff that constituted approximately 58% of the surrounding area. Overall, it could conclude that headwater zones were minimally influenced, while downstream zones seemed susceptible to the effluents originating from the WWTP, industrial effluents, and intensive agricultural farming in the area [38].

Inter-Annual Variations in Water Chemistry
The annual rainfall that mainly concentrated during the monsoon periods resulted in the interannual water quality fluctuations ( Figure 4). The annual rainfall data helped in designating the years as either wet or dry; 2010-2012 were the wet years in the stream watershed area, while 2011 was wettest year with maximum rainfall. The water quality parameters varied in relation to wet and dry years. The annual mean values of pH were lower in dry years, but increased during wet years.

Inter-Annual Variations in Water Chemistry
The annual rainfall that mainly concentrated during the monsoon periods resulted in the inter-annual water quality fluctuations ( Figure 4). The annual rainfall data helped in designating the years as either wet or dry; 2010-2012 were the wet years in the stream watershed area, while 2011 was wettest year with maximum rainfall. The water quality parameters varied in relation to wet and dry years. The annual mean values of pH were lower in dry years, but increased during wet years. Dissolved oxygen, Temp, EC, and TSS annual mean variations did not change during wet and dry years. However, TNCB showed a great variation during wet and dry years. During dry years, bacterial count was higher, whereas it increasingly dropped in the proceeding dry years. It confirmed that there were more chances of water borne diseases during the dry years [62]. Chemical oxygen demand showed no major changes during these years. On the other hand, BOD showed a very fine, but steep decline in annual mean values during 2008-2014, except in 2012, which showed a slight increase after the wet year. Similarly, the annual rainfall mainly influenced the inter-annual trends of nutrient contributing parameters ( Figure 5). The annual mean value of TN was the lowest during 2009 (dry year), but the highest in 2012, which was post-wet year, and again showed a decline in the proceeding dry years. Similarly, TDN showed a sturdy increase from 2009 to 2012 that was due to higher annual rainfall. Except NH 4 -N, all nitrogen species were affected by flood years. Like TN, TP also showed a decrease during 2008-2009, which was a dry span, but was affected by wet years by carrying more P from the agricultural pastures. The mean annual rate of TDP was dispersed around 20 µg/L, but appeared influenced by wet years. The PO 4 -P showed many outlying values, but the annual mean values showed a slight decrease in the first two dry years with higher annual means recorded during the wet years, and then was similar during the ensuing dry years. Sestonic CHL-a annual mean values started dropping during 2008-2010 which were higher during the dry years, and then showed approximation in the wet years. This type of variation in the inter-annual means of nutrient contributing factors and sestonic CHL-a reflected the intensive agricultural activities downstream, as well as urban land-use [7,38,63]. Dissolved oxygen, Temp, EC, and TSS annual mean variations did not change during wet and dry years. However, TNCB showed a great variation during wet and dry years. During dry years, bacterial count was higher, whereas it increasingly dropped in the proceeding dry years. It confirmed that there were more chances of water borne diseases during the dry years [62]. Chemical oxygen demand showed no major changes during these years. On the other hand, BOD showed a very fine, but steep decline in annual mean values during 2008-2014, except in 2012, which showed a slight increase after the wet year. Similarly, the annual rainfall mainly influenced the inter-annual trends of nutrient contributing parameters ( Figure 5). The annual mean value of TN was the lowest during 2009 (dry year), but the highest in 2012, which was post-wet year, and again showed a decline in the proceeding dry years. Similarly, TDN showed a sturdy increase from 2009 to 2012 that was due to higher annual rainfall. Except NH4-N, all nitrogen species were affected by flood years. Like TN, TP also showed a decrease during 2008-2009, which was a dry span, but was affected by wet years by carrying more P from the agricultural pastures. The mean annual rate of TDP was dispersed around 20 µg/L, but appeared influenced by wet years. The PO4-P showed many outlying values, but the annual mean values showed a slight decrease in the first two dry years with higher annual means recorded during the wet years, and then was similar during the ensuing dry years. Sestonic CHL-a annual mean values started dropping during 2008-2010 which were higher during the dry years, and then showed approximation in the wet years. This type of variation in the inter-annual means of nutrient contributing factors and sestonic CHL-a reflected the intensive agricultural activities downstream, as well as urban land-use [7,38,63].

Empirical Modelling on Chlorophyll and Nutrient Contributing Factors
The regression relationship between log-transformed CHL-a, TN, TP, TN:TP, and empirical linear models of CHL-a showed non-significant relation with TN (R 2 = 0.0001). On the contrary, variation of CHL-a linked up to 17% with TP (R 2 = 0.17) and showed a positive relation with TP ( Figure 6). Also, the change in CHL-a accounted for 15% with TN:TP (R 2 = 0.159) and showed a negative relation with TN:TP ambient ratios in the present study. It showed an equal contribution (7%) of N and P in TN:TP ambient ratios in the ambient water as well.

Empirical Modelling on Chlorophyll and Nutrient Contributing Factors
The regression relationship between log-transformed CHL-a, TN, TP, TN:TP, and empirical linear models of CHL-a showed non-significant relation with TN (R 2 = 0.0001). On the contrary, variation of CHL-a linked up to 17% with TP (R 2 = 0.17) and showed a positive relation with TP ( Figure 6). Also, the change in CHL-a accounted for 15% with TN:TP (R 2 = 0.159) and showed a negative relation with TN:TP ambient ratios in the present study. It showed an equal contribution (7%) of N and P in TN:TP ambient ratios in the ambient water as well. Therefore, it could be argued that the primary nutrient which was subsidizing the bulk share in the primary productivity (sestonic CHL-a) was phosphorus and originated from the agricultural paddocks in the area, while the nitrogen might be ample to fuel sustainable algal growth. Hitherto conducted numerous studies on streams [7,[64][65][66] suggested that other primary factors instrumental as limiting factors in the stream ecosystem were turbidity and short water residence time leading to light limitation. Similar results were pointed out in the Yeongsan River (South Korea) by Song et al. [67]. However, such generalized phenomena was only evident during July-August, due to higher floods caused by intensive rainfall. Therefore, the TN:TP ratio could be designated as the vital regulatory factor in primary productivity. Further, it was supported by the earlier findings of Downing and MeCauly [68] who related TN:TP ratio more depended upon P than N under P limitation circumstances. But the non-significant relation of TN:TP ratios with N was due to lower variation as well as higher ambient nitrogen concentration compared to phosphorus [63].

Correlation of Water Chemistry Parameters
The WPI showed a very strong positive correlation (r < 0.70) with IBI, whereas TSS showed strong positive correlation with TP and COD (Figure 7). Sestonic CHL-a indicated strong and moderately strong positive relationships with COD, BOD, and TSS, respectively. TN:TP ambient ratios showed a moderately negative relationship with TP, and very weak negative relationship with TN. However, TN and other allied chemical species showed moderately strong positive relationships with each other, indicating industrial effluents were the leading factors in the nitrogen inflow to the watershed. Similar was the case with TP and other allied chemical species. However, both the indices (i.e., WPI and IBI) showed very low relationships with water chemistry factors, except TP which showed a slightly higher, yet still very weak relationship with both indices. Therefore, it could be argued that the primary nutrient which was subsidizing the bulk share in the primary productivity (sestonic CHL-a) was phosphorus and originated from the agricultural paddocks in the area, while the nitrogen might be ample to fuel sustainable algal growth. Hitherto conducted numerous studies on streams [7,[64][65][66] suggested that other primary factors instrumental as limiting factors in the stream ecosystem were turbidity and short water residence time leading to light limitation. Similar results were pointed out in the Yeongsan River (South Korea) by Song et al. [67]. However, such generalized phenomena was only evident during July-August, due to higher floods caused by intensive rainfall. Therefore, the TN:TP ratio could be designated as the vital regulatory factor in primary productivity. Further, it was supported by the earlier findings of Downing and MeCauly [68] who related TN:TP ratio more depended upon P than N under P limitation circumstances. But the non-significant relation of TN:TP ratios with N was due to lower variation as well as higher ambient nitrogen concentration compared to phosphorus [63].

Correlation of Water Chemistry Parameters
The WPI showed a very strong positive correlation (r < 0.70) with IBI, whereas TSS showed strong positive correlation with TP and COD (Figure 7). Sestonic CHL-a indicated strong and moderately strong positive relationships with COD, BOD, and TSS, respectively. TN:TP ambient ratios showed a moderately negative relationship with TP, and very weak negative relationship with TN. However, TN and other allied chemical species showed moderately strong positive relationships with each other, indicating industrial effluents were the leading factors in the nitrogen inflow to the watershed. Similar was the case with TP and other allied chemical species. However, both the indices (i.e., WPI and IBI) showed very low relationships with water chemistry factors, except TP which showed a slightly higher, yet still very weak relationship with both indices.

Chemical Health Evaluation Based on Modified Multi-Metric Water Pollution Index (WPI)
A modified multi-metric index for water pollution assessment was applied to diagnose the chemical health status of the stream and to obtain scores, which led to the categorization of the stream according to chemical health (Table 1). It is composed of seven metrics (M1-M7) and constituting four major categories viz. nutrient regime (TN, TP and TN:TP), organic matter (BOD), ionic contents and solids (TSS and EC), and primary production indicator (CHL-a). Major nutrient contributing factors (i.e., TN, TP and TN:TP mass ratios) were key determinants of the water quality and eutrophication [7,69,70].
The chemical criteria of ambient TN metric were classified as oligotrophic (3 mg/L), mesotrophic (1.5-3 mg/L), and eutrophic (>3 mg/L) respectively. All sites categorized as mesotrophic had a TN level ranging from 1.5-3 mg/L. S3 showed the highest mean TN level (2.26 mg/L). Similarly, according to TP level criteria (<30, 30-100 and >100 µg/L), S1 was oligotrophic (23.57) and the rest of the sites were mesotrophic, while S3 showed the highest mean TP (43.83). TN:TP ambient ratios showed oligotrophic states at all sites showing higher values. Previous studies have shown that TN:TP ratio in ambient water was an indirect indicator of nutrient limitation for primary productivity [71,72], and if lower, showed higher nutrient pollutants leading to eutrophication [21]. Mean concentration of sestonic CHL-a confirmed the assessment of S1 and S2 as oligotrophic, having CHL-a levels lower than 3 µg/L. However, at S3 and S4, they showed a five times increase under the mesotrophic conditions approximating the levels of TP in downstream, but less primary productivity at S2. Similarly, it was also in line with the values obtained from TN:TP ratios in the stream that again depressed the notion of eutrophication in downstream sites. Meanwhile, biological oxygen demand levels were maximized at S3 (1.15 mg/L).
In the case of TSS and EC, S1 was in an oligotrophic state, but S2 showed an increase indicating it as a recipient of agricultural runoff. Total suspended solids were the highest at S4 (12.86 mg/L), approximating with the values obtained for EC (204.92 µS/cm), which was an indication of increasing nutrient pollution in imminent years in the downstream water of Bouchung Stream. The final scores indicated that S1 could be chemically categorized as excellent, with a score of 33; S2 as good with a score of 29; and S3 and S4 as fair, each having a score of 23 ( Figure 8). These results showed a starting point of chemical health deterioration in the downstream waters that was mainly due to agricultural runoff waters, especially during floods caused by summer monsoons. It also confirms that this results in sedimentation in the downstream regions by causing higher turbidity. A major share was contributed by the WWTP at S3, which was also confirmed by the results of the WPI. Earlier studies opulently supported the degradation of downstream water chemical health [41,73].

Chemical Health Evaluation Based on Modified Multi-Metric Water Pollution Index (WPI)
A modified multi-metric index for water pollution assessment was applied to diagnose the chemical health status of the stream and to obtain scores, which led to the categorization of the stream according to chemical health (Table 1). It is composed of seven metrics (M 1 -M 7 ) and constituting four major categories viz. nutrient regime (TN, TP and TN:TP), organic matter (BOD), ionic contents and solids (TSS and EC), and primary production indicator (CHL-a). Major nutrient contributing factors (i.e., TN, TP and TN:TP mass ratios) were key determinants of the water quality and eutrophication [7,69,70].
The chemical criteria of ambient TN metric were classified as oligotrophic (3 mg/L), mesotrophic (1.5-3 mg/L), and eutrophic (>3 mg/L) respectively. All sites categorized as mesotrophic had a TN level ranging from 1.5-3 mg/L. S3 showed the highest mean TN level (2.26 mg/L). Similarly, according to TP level criteria (<30, 30-100 and >100 µg/L), S1 was oligotrophic (23.57) and the rest of the sites were mesotrophic, while S3 showed the highest mean TP (43.83). TN:TP ambient ratios showed oligotrophic states at all sites showing higher values. Previous studies have shown that TN:TP ratio in ambient water was an indirect indicator of nutrient limitation for primary productivity [71,72], and if lower, showed higher nutrient pollutants leading to eutrophication [21]. Mean concentration of sestonic CHL-a confirmed the assessment of S1 and S2 as oligotrophic, having CHL-a levels lower than 3 µg/L. However, at S3 and S4, they showed a five times increase under the mesotrophic conditions approximating the levels of TP in downstream, but less primary productivity at S2. Similarly, it was also in line with the values obtained from TN:TP ratios in the stream that again depressed the notion of eutrophication in downstream sites. Meanwhile, biological oxygen demand levels were maximized at S3 (1.15 mg/L).
In the case of TSS and EC, S1 was in an oligotrophic state, but S2 showed an increase indicating it as a recipient of agricultural runoff. Total suspended solids were the highest at S4 (12.86 mg/L), approximating with the values obtained for EC (204.92 µS/cm), which was an indication of increasing nutrient pollution in imminent years in the downstream water of Bouchung Stream. The final scores indicated that S1 could be chemically categorized as excellent, with a score of 33; S2 as good with a score of 29; and S3 and S4 as fair, each having a score of 23 ( Figure 8). These results showed a starting point of chemical health deterioration in the downstream waters that was mainly due to agricultural runoff waters, especially during floods caused by summer monsoons. It also confirms that this results in sedimentation in the downstream regions by causing higher turbidity. A major share was contributed by the WWTP at S3, which was also confirmed by the results of the WPI. Earlier studies opulently supported the degradation of downstream water chemical health [41,73].

Biological Health Evaluation Based on Modified Multi-Metric Index of Biotic Integrity
The biological health evaluation of the stream was carried out after the application of modified multi-metric index of biotic integrity (IBI KW ) model ( Table 2). The eight metrics (M 1 -M 8 ) classified into three main categories viz. species richness and composition, trophic composition, and fish abundances and health condition. Native and benthic species indicated that the biotic integrity was near to excellent, but sensitive species were higher at the S1 and S2 that apparently would have migrated upstream due to intensifying pollutants downstream. The proportion of tolerant species was lower at S1, but higher at S2, followed by S4 and S3, which indicated the onset of nutrient pollution as confirmed by the WPI model final scores in this stream. Surprisingly, omnivore species at all sites approximated with tolerant species in the same manner, although the scoring criteria was different. The obtained values for native insectivore fish species showed that S1 and S3 had the most number of native species, whereas S3 and S4 were home to almost half of the native species. The number of native individuals showed that the S4 had the least number of native individuals that showed a major modification in fish composition from headwaters down the line. The relative abundance (RA, %), total number of individuals (TNI), as well as number of individuals sampled at each site along with their tolerance and trophic guilds, showed a prodigious disparity (Table 4). It was too hard to enlist all 64 species; therefore, some of them were dropped having a relative abundance lower than 0.10%.
Biological stream health has started dwindling downstream due to deteriorating water quality and the indicators included the decreasing sensitive species as well as increasing tolerant species downstream. The decline in total number of individuals sampled spatially, as well as temporally, was another corroboration of the rigorousness of impending decline of biological health. The lower scores obtained by metrics in the downstream sites were due to the degrading water quality attributed to the WWTP and the agricultural runoff [49,74,75]. The IBI final score values steered to categorized the S1 as excellent, S2 as fair, and S3 and S4 designated as good (Figure 8). Moreover, the community structure mainly based on the fish compositions of tolerance and trophic guilds (Figure 9) showed that the dominant trophic guilds were the native insectivores, followed by omnivores, whereas the higher relative abundance in tolerance guild was composed of intermediate, followed by tolerant species clearly indicating the tolerant species on the rise due to degrading water quality. Zacco platypus is a tolerant species and the most dominant fish species (27.35%), showing a spatial abundance followed by Zacco koreanus (11.62%), which is a sensitive species and displayed a spatial decline. The results are line with the previous studies [7].

Responses of Trophic and Tolerant Guilds to Water Chemistry
Where it revealed an undeviating relationship between the scores of modified multi-metric indices of water pollution and biotic integrity (R 2 = 0.90, F = 510.41, p < 0.0001) (Figure 10), it also showed that the relative abundance of trophic and tolerance guilds was affected by water quality factors, although some appeared to be indecisive on fish distribution ( Figure 11). The results of intermediate species (IS), tolerant species (TS), and sensitive species (SS) in relation to ambient BOD, COD, TN, TP, and sestonic CHL-a showed when BOD, COD, and TN were lower, and IS and TS showed a wide distribution exhibiting a direct functional relationship with the changing water chemistry. However, SS showed varied distribution along the gradient with respect to the BOD, COD, TN, TP, and sestonic CHL-a, which showed SS more sensitive to the changing water chemistry. TP and sestonic CHL-a level were less influential in controlling IS and TS abundance along the stream region. These results were in line with previous findings as well [38,49]. Omnivores showed no distinct effect of COD and TN but linear distribution was observed in case of BOD, TP, EC, and sestonic CHL-a, which showed a small response that water pollution affected the distribution of omnivores ( Figure 12). Instead, carnivores showed BOD, COD, and EC affected their distribution while TN, TP, and sestonic CHL-a had a direct linear relation on carnivore abundance. The native insectivores showed also direct linear relationship with BOD, COD, TN, TP, EC, and sestonic CHL-a along the stream gradient. The IBI showed an indistinct influence of COD, TN, TP, TN:TP, and sestonic CHL-a (log-transformed) which showed that these water quality parameters had an inadequate role in the fish distribution. However, the BOD, TSS, and EC had a little effect (less than 10%) on the overall fish distribution estimated IBI ( Figure 13). Earlier regional studies supported these results as well [6,22].

Key Ecological Factor Identification with PCA
Principal component analysis (PCA) used for the distribution of biological components and selected water quality factors from the headwaters to downstream region [76]. Results indicated that the stream regions could be divided into distinct headwater, mid-stream, and downstream regions ( Figure 14). In the PCA ordination, it accounted for 71.96% cumulative percent variance in first six principal components (PC). However, axes I and II cumulatively accounted for 37.68% variance in the data matrix of biological (IS, SS, TS, intermediate, omnivores, and carnivores) and physicochemical (BOD, COD, EC, TSS, TN, TP, TN:TP ratio, and CHL-a etc.) indicators. The first PC explained the most of water chemistry factors but the loading strength was weak (r = 0.30-0.50) ( Table  5). The second PC showed moderately strong (r = 0.50-0.70) loading strength for TN and other allied chemical species.

Key Ecological Factor Identification with PCA
Principal component analysis (PCA) used for the distribution of biological components and selected water quality factors from the headwaters to downstream region [76]. Results indicated that the stream regions could be divided into distinct headwater, mid-stream, and downstream regions ( Figure 14). In the PCA ordination, it accounted for 71.96% cumulative percent variance in first six principal components (PC). However, axes I and II cumulatively accounted for 37.68% variance in the data matrix of biological (IS, SS, TS, intermediate, omnivores, and carnivores) and physicochemical (BOD, COD, EC, TSS, TN, TP, TN:TP ratio, and CHL-a etc.) indicators. The first PC explained the most of water chemistry factors but the loading strength was weak (r = 0.30-0.50) ( Table 5). The second PC showed moderately strong (r = 0.50-0.70) loading strength for TN and other allied chemical species.  Third compartment explained a moderately strong relationship between WPI and IBI. The fifth and sixth PCs explained the negative relationship between TS and SS, as well as between carnivores and omnivores. The headwater stream populated by sensitive species with TN and DO confirmed the chemical health condition was near to excellent and least impaired by the anthropogenic activities ( Figure 14). The mid-stream area showed rapidly changing water quality like TP, TDP, PO 4 -P, NH 4 -N, and BOD also mostly populated by the intermediate species. The mid-stream region acted as transitional zone. It was mostly impaired due to the agricultural and WWTP effluents. The third compartment was the downstream region that was mainly home to the increasing tolerant species with higher loading values of water quality parameters such as pH, temperature, TSS, and CHL-a along with direct influence of chemical pollution (COD) indicating the role of industrial activities downstream. Overall, the PCA showed the downstream region having greater influence of chemical and nutrient contributing factors, because of anthropogenic activities like WWTP and agricultural runoff that played key roles in the trophic and tolerance guild distribution [7].

Conclusions
In conclusion, we observed that the headwater stream had near to pristine conditions displaying the least influence of human perturbations. However, the anthropogenic effects became clearer downstream, which may be linked to WWTP, industrial unit effluents, as well as agricultural runoff. The results obtained by WPI and IBI approximated each other spatially, and the PCA showed tolerant species increasing in downstream and escalating levels of nutrient contributing factors along with chemical pollution. Increasing nutrient contributing factor levels coupled with higher COD and

Conclusions
In conclusion, we observed that the headwater stream had near to pristine conditions displaying the least influence of human perturbations. However, the anthropogenic effects became clearer downstream, which may be linked to WWTP, industrial unit effluents, as well as agricultural runoff. The results obtained by WPI and IBI approximated each other spatially, and the PCA showed tolerant species increasing in downstream and escalating levels of nutrient contributing factors along with chemical pollution. Increasing nutrient contributing factor levels coupled with higher COD and higher abundance of tolerant species could predict increasing pollutants that may lead to severe ecological degradation. This joint approach of WPI and IBI KR as well as the water quality status showed that the monsoon was a key factor which can change the fish assemblages and nutrient inflows. Overall, these analyses successfully clarified the evaluation of chemical and biological health of the stream and could be applied in future studies.