Physico-chemical assessment of water quality and primary productivity of the River Kaldiya, Assam (India)

Introduction

Rivers are large, natural streams of flowing water. They help not only in facilitating nutrient cycling but also support a vast array of global biodiversity (Lim et al., 2024) [1] . Besides, rivers are also useful for human civilization, providing essential ecosystem services such as fresh water for irrigation, industrial processes and domestic consumption, while also acting as a natural buffer for climate regulation (Lim and Do, 2023) [2] . In recent years, the pollution in river systems has increased significantly and has become a major environmental concern. The discharges of untreated sewage, solid waste, agricultural run-off etc. are the major contributors to water pollution (Dwivedi, 2017) [3] . Therefore, assessment of quality and primary productivity of the river water is a must for early detection of pollutants that will provide the empirical data necessary for legislative action and resource protection. The Kaldiya is an important river of Assam. It originates from Bhutan, where it is locally known as 'Baga Noi'. It flows approximately 72 km through a mix of hilly terrain and plains. Upon entering India in the state of Assam, the river traverses several villages, Gondhaligaon, Jalaghat, Golagaon, Bhaluki, Kawaimari, Balapara etc. This river serves as a vital resource for local agriculture, irrigation, household needs and fishing-based livelihoods. Thus, it is considered to be the lifeline of rural households of a major part of lower Assam and hence, its physico-chemical assessment has become imperative (Kusre et al., 2013) [4] . Thus, it is considered to be the lifeline of rural household of major part of lower Assam. However, no study has been carried out till date to evaluate the suitability of its water for various agricultural and house hold activities. In view of the above, the present study was designed to determine the quality and gross primary productivity of water of the river Kaldiya in the state of Assam.

Materials and Methods

Location of the study

The study was conducted in three different sites of Kaldiya River spanning approximately 5.9 km. Site-I is located at 26°25'28"N and 91°08'59"E near the Balapara region. Site-II is at 26°26'10"N and 91°11'12"E, which is situated near Kawaimari Village. Site-III is located at 26°26'56"N and 91°11'47"E near the Bangaon region. The Site-II is around 3.89 km upstream of Site-I, whereas Site-III is approximately 1.9 km upstream from Site-II.

Period of the study: The collection of water samples was conducted during the period from October to December, 2025. This specific time frame was selected as this period represents the post monsoon transition. In this period, the river flow decreases and actual pollutant level becomes clearer after monsoon dilution, which makes the monitoring essential.

Parameters studied: A total of twelve water quality parameters were selected for the analysis. The parameters were temperature, odour, TDS, conductivity, dissolved oxygen, pH, alkalinity, and hardness, Ca2 + and Mg 2+ , Nitrate (NO3 -) and Phosphate (PO4 3-). Temperature was measured using a standard laboratory thermometer, for odour olfactory sense was used. TDS, pH and conductivity were determined using calibrated digital TDS meter, pH meter and Conductivity meter, respectively. Alkalinity, hardness, Ca 2+ and Mg 2+ were estimated using titrimetric methods. Nitrate and Phosphate concentration were determined using specific commercially available detection kits. The Dissolved Oxygen levels were determined using the modified Winkler's titrimetric method. Chemical parameters that fell below the detection limit (BDL) were treated as half the limit of detection (LOD/2) for statistical analysis following standard environmental data processing protocols (Hornung and Reed, 1990) [5] . For measurement of primary productivity, light bottle and dark bottle techniques were carried out as described by Gaarder and Gran (1927) [6] . Water samples were filled in three sets of bottles and designated as Initial, Light and Dark. The light and dark bottles were incubated for a designated period. Then the Dissolved Oxygen concentrations were determined using modified Winkler's method to calculate Gross Primary Productivity (GPP), Net Primary Productivity (NPP) and Net Community Production (NCP). The Primary productivity was calculated using the following equations:

Calculation of Water Quality Index (WQI)

Water Quality Index (WQI) was calculated using the Weighted Arithmetic Index Method, as proposed by Brown et al. (1970) [7] . Out of the twelve parameters analyzed, only ten, viz. TDS, Conductivity, DO, pH, Alkalinity, Hardness, Ca 2+ , Mg 2+ , NO3 -and PO4 3-were selected for the WQI calculation. Parameters such as odor, though informative, are subjective and not quantitatively suitable for inclusion in the index. The Weighted Arithmetic Water Quality Index (WQI) was calculated using the following formula:

Where, Qn was the quality rating of nth water quality parameter, Wn is the unit weight of nth water quality parameter. The quality of Qn is calculated using the equation: The WAWQI across three sites were predominantly "Poor" water quality during the study period. For Site-I the water quality status remained stable at 59.9 across all three months. However Site-II consistently showed higher indices, peaking at 69.46 in the month of November. Site-III showed a "Good" water quality status of 49.99 in October before deteriorating to 59.78 and 59.76 in the subsequent months as illustrated in Fig. 1. Although individually the parameters like pH, TDS, Conductivity, Alkalinity, Hardness, etc remained within permissible limit, their cumulative weight impacted the result in an overall index that necessitates conventional treatment of the water before domestic consumption.

Discussion

Data generated during the study demonstrates a clear picture of environmental impact and natural recovery driven by human activity and seasonal transitions. Site-III is located upstream with very less human interference, which resulted in the best water quality among the all three sites. In contrast, Site-II showed high pollution due to high population density and domestic activities. These activities introduced organic waste and nutrients into the water, which is reflected in the high WQI values and the peak in GPP and NCP at this site. Moving downstream to Site-I, the water quality begins to show a signs of recovery. It might be due to the distance of 3.89 km from Site-II, reduced input of waste along the path and also the presence of vegetation which allowed for natural dilution and biological filtration, demonstrating the river's unique capacity for selfpurification over distance (Fisenko, 2006) [8] . Across all the sites, a notable spike in both WQI and Productivity occurs during November. It might be ascribed to the seasonal change. As monsoon rain ceases, water level drops and flow velocity slows down (Jeba and Baskarasanjeevi, 2025) [9] . This causes the nutrients and pollutants to become more concentrated. However, by December these values begin to decline because the onset of winter brings lower temperature and shorter daylight hours, which results in slowing down of the metabolic rates of aquatic plants and thereby reducing photosynthesis (Rabaey et al., 2021) [11] . The seasonal shift is also validated by correlation between WAWQI and NCP (0.765) and WAWQI and GPP (0.721), which signifies that biological activity is directly associated with the level of pollution. These high values prove that the pollution recorded at the sites selected for our study is primarily nutrient-based and triggers rapid organic growth. The correlation with NPP (0.305) is lower, which suggests that even though total production is high, the net energy stored is limited by environmental stress or increased plant respiration during these polluted phases (Kamali et al., 2020) [10] .

It may be concluded that the ecosystem of the river Kaldiya has undergone tremendous alteration in recent years due to intensification of activities with growing human population. Nonetheless, a comprehensive study is warranted to evaluate how water quality and productivity of the river has changed over the years. Further, regular monitoring of the river across multiple sites and periods is the need of the hour to understand the driving factors that significantly affects the water quality and primary productivity of the water of Kaldiya river.

Acknowledgement

AcknowledgementsThe authors are highly grateful to Bhattadev University, Bajali, Pathsala for providing the necessary support and institutional facilities to carry out this research work.

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