ABSTRACT
This study aims to evaluate the current water quality status of the Dhaleshwari River, considering seasonal variations, and identify potential pollution sources using physicochemical parameters, multivariate statistical analysis, and the water quality index (WQI) method. Sampling was conducted at six locations along the Dhaleshwari River from January 2021 to April 2022, encompassing both dry and wet seasons. Eleven physicochemical parameters were tested following standard procedures. The calculated WQI values demonstrated that the overall water quality of the Dhaleshwari River is very poor (264.7 ± 64.5). Seasonal variation in water quality was significant, with the worst quality (305.7 ± 54.3) observed during the dry season; however, water quality during the wet season was also unsatisfactory (223.6 ± 46.3). Geospatial analysis shows the distribution of water quality in the study locations. Principal components analysis was performed for source appointment, which indicated that direct wastewater discharge from surrounding industries, particularly tanneries, as well as municipal wastewater, were the major pollution sources. In conclusion, this study sheds light on the deteriorating water quality of the Dhaleshwari River due to industrialization and human activities. The findings underscore the urgent need for interventions to mitigate pollution sources and improve the overall health of the river ecosystem.
HIGHLIGHTS
Assessed Dhaleshwari River water quality seasonally.
Water quality is consistently poor, worst during the dry season.
Most physicochemical parameters exceeded standards.
Multivariate analysis indicates tanneries and surface runoff as key pollution sources.
INTRODUCTION
Water stands as a fundamental necessity supporting all life forms, encompassing humans, animals, and plants. Beyond sustenance, it serves as an indispensable resource, driving industrial and economic advancement. Its pivotal roles span ecological significance, industrial utility, and urban development, making it an essential consideration in sustainable global progress (Razo et al. 2004; Uddin et al. 2015).
Rivers function as the central water supply for domestic, industrial, and agricultural needs. However, they face pollution challenges, carrying significant municipal, industrial, and agricultural contaminants due to direct sewage discharge and surface runoff from rainfall (Hu et al. 2012; Mustapha et al. 2013; Wu et al. 2018). Geological and hydrological features, coupled with seasonal fluctuations, play a vital role in shaping pollutant concentrations in rivers. These factors control precipitation patterns, river flow rates, surface runoff, and groundwater movement, often facilitating the dispersal of pollutants (Vega et al. 1998; Shrestha & Kazama 2007). Due to the complex interplay among primary and secondary pollution sources and climatic components, it becomes challenging to assess the water quality of a river and identify possible pollution sources. Consequently, this poses a threat to meeting the water demand of an ever-growing population, particularly for drinking purposes, as freshwater resources become increasingly scarce (Cheng et al. 2009; Vörösmarty et al. 2010; Wu et al. 2018) in developing countries like Bangladesh.
Bangladesh is a riverine country. It has a rich historical past where waterways played an integral role in fostering human settlements and augmenting regional development. In recent years, the country has witnessed unprecedented industrialization, thereby achieving one of the highest economic growth rates in South Asia. With a predominant number of industries established along the river banks, primarily due to the convenience of water transportation and usage, it is imperative to note that the accelerating industrialization, unregulated urbanization, and poorly executed development plans have resulted in severe environmental degradation of the rivers. Consequently, the once revered and cherished lifeline of the nation has now transformed into a grave national concern, primarily due to the detrimental impact of water pollution (Akter 2014; Şener et al. 2017; Aminul Ahsan 2018).
In Bangladesh, industries are obligated to establish an effluent treatment plant (ETP) to treat wastewater before discharge into surface waters, and adherence to the regulations governing effluent discharge is essential (Government of the People's Republic of Bangladesh Ministry of Environment & Forest 1997). However, only a few have implemented such facilities. Furthermore, it is important to note that the intermittent operation of certain ETPs primarily serves the purpose of placating foreign buyers or expediting the acquisition of an environmental clearance certificate from the Department of Environment (Mortula & Rahaman 2002; Akter 2014). Considering the fact that the majority of the country's economic and industrial sectors are situated in the central region, the adverse effects on the rivers in that vicinity are formidable.
The Dhaleshwari River, traversing the central region of Bangladesh, holds paramount economic significance and plays a pivotal role in the country's socio-economic aspects (Hasan et al. 2020). Nearly one-third of the nation's manufacturing facilities are located in Dhaka and its surrounding regions, including Keraniganj, Savar, and Narayanganj, which are situated near the Dhaleshwari River (Hassan et al. 2020). Owing to its geographical location, the river endures a substantial influx of pollutants originating from domestic sewage, municipal discharge, agricultural runoff, and the region's robust urban and industrial activities (Ahmed et al. 2009; Wahiduzzaman et al. 2022a, b). Moreover, the river's contamination is augmented by the discharge of effluent from the Hemayetpur tannery industries and Keraniganj BSCIC (Bangladesh Small and Cottage Industries Corporation) industrial zone, both of which are situated proximate to the river (Mohanta et al. 2019). In previous years, untreated effluents originating from the Hazaribagh tannery industrial area were directly discharged into the Buriganga River. This resulted in severe pollution, leading to the river being recognized as the most polluted in the country (Alam 2003; Mohanta et al. 2019). However, upon realizing the detrimental impact of tannery waste on both human health and the environment, the Bangladesh Government acted to relocate the Hazaribagh Tannery Complex to Horindhora, Savar, adjacent to the Dhaleshwari River in 2017. This decision aimed to safeguard the Buriganga River (Akter et al. 2019; Wahiduzzaman et al. 2022a, b). From that point onward, the tanneries, in addition to numerous other anthropogenic activities, have been causing a significant decline in the water quality of the Dhaleshwari River.
Various studies were published concerning the water quality of the Dhaleshwari River. However, a significant portion of the literature dates back considerably, rendering it inadequate in capturing the current state of the river. This deficiency is particularly pronounced due to the alarming and rapid degradation of the river's water quality after the establishment of tanneries in the vicinity. Islam et al. (2012) studied the water quality in the Dhaleshwari River during the period from June 2011 to May 2012 in the monsoon, post-monsoon, and pre-monsoon seasons. Real et al. (2017) conducted a study on the Dhaleshwari River from April to June 2015 with physiochemical parameters and biological indicators. The study did not investigate the seasonal variation of water quality. Aminul Ahsan (2018) conducted at the three selected locations of the Dhaleshwari River from March to May 2016 for physicochemical parameters and heavy metals. Hasan et al. (2020) determined the water quality of the Dhaleshwari River at the effluent discharge point from the central ETP of the tannery industrial area. Islam et al. (2021) investigated the water quality of the Dhaleshwari River within the close distance point of the Saver tannery industry area and made a comparison with the other similar regional rivers, but the study did not show any seasonal variation. Given the limited existing data and potential for seasonal fluctuations, a comprehensive investigation is warranted to assess the water quality status of the Dhaleshwari River. This assessment should encompass seasonal variations and employ robust methods to identify potential sources of contamination.
Considering this, the primary objectives of this study encompass an evaluation of the contemporary water quality status of the Dhaleshwari River across a vast catchment area and an identification of prospective sources of pollution. The study was predicated on 11 physicochemical parameters procured from 6 sampling locations, which included both dry and wet seasons, thereby enabling an investigation of seasonal variations. The water quality status was simplified and categorized using the water quality index (WQI) model. Additionally, the present study endeavors to determine potential sources of pollution through the implementation of multivariate statistical analyses, such as correlation matrix, principal component analysis (PCA), and cluster analysis (CA) along with geospatial analysis. The ramifications of this study are far-reaching, as they stand to be instrumental in informing policymakers, researchers, and stakeholders about the development of effective strategies aimed at enhancing the water quality of the Dhaleshwari River, thereby safeguarding the health and well-being of the surrounding communities.
MATERIALS AND METHOD
Study area
Water samples were collected from six sampling sites within the range of about 50 km of the river consisting of the main industrial settlement, including BSCIC Tannery industrial state, BSCIC Industrial State, Keraniganj, as well as the converging point of the Buriganga River.
Sampling methods
Water sampling has been carried out both during the wet and dry seasons from January 2021 to April 2022 at the six sampling stations (Table 1). In each station, samples were collected six times in each season. Water samples were collected from midstream by dipping sample bottles at 15–30 cm below the water surface in acid-pre-washed plastic 500 mL sample bottles which were then rinsed with distilled water and dried in the oven. During sample collection, bottles were washed several times with the sample. Samples were preserved at 4 °C until analysis. All the parameters were tested in the laboratory within 48 h, except biological oxygen demand (BOD5) which was analyzed with a period of 5 days. Water sample collection, preservation, and analysis were conducted following standard procedures (APHA 2005).
Sampling station . | Sampling area name . | Latitude . | Longitude . |
---|---|---|---|
1 | BSCIC Tannery Industrial State, Dhaleshwari River, Hemayetpur, Savar | 23°47′16.7″ N | 90°14′30.3″ E |
2 | Near Hazratpur Bridge, Dhaleshwari River | 23°45′17.0″ N | 90°15′19.0″ E |
3 | Alinagar, Hazratpur, Dhaleshwari River | 23°43′47.5″ N | 90°15′24.0″ E |
4 | Rohitpur, Dhaleshwari River (near BSCIC Industrial State, Keraniganj) | 23°39′47.4″ N | 90°18′22.0″ E |
5 | Pathorghata, Dhaleshwari River | 23°38′07.1″ N | 90°22′02.3″ E |
6 | Mukterpur, Munshigonj | 23°34′20.0″ N | 90°30′45.3″ E |
Sampling station . | Sampling area name . | Latitude . | Longitude . |
---|---|---|---|
1 | BSCIC Tannery Industrial State, Dhaleshwari River, Hemayetpur, Savar | 23°47′16.7″ N | 90°14′30.3″ E |
2 | Near Hazratpur Bridge, Dhaleshwari River | 23°45′17.0″ N | 90°15′19.0″ E |
3 | Alinagar, Hazratpur, Dhaleshwari River | 23°43′47.5″ N | 90°15′24.0″ E |
4 | Rohitpur, Dhaleshwari River (near BSCIC Industrial State, Keraniganj) | 23°39′47.4″ N | 90°18′22.0″ E |
5 | Pathorghata, Dhaleshwari River | 23°38′07.1″ N | 90°22′02.3″ E |
6 | Mukterpur, Munshigonj | 23°34′20.0″ N | 90°30′45.3″ E |
Sample analysis
Dissolved oxygen (DO), conductivity, total dissolved solids (TDS), and pH were measured instantly on the spot with a HACH 40d multi-parameter. Analysis of chemical oxygen demand (COD), total alkalinity, total suspended solids (TSS), and chloride was carried out using a Shimadzu 1900 UV–visible spectrophotometer according to the standard procedure (APHA 2005). Measurement of BOD5 was carried out with HACH BOD TRAK II placed within an incubator operated at 20 °C. Turbidity was measured by a HACH 2100Q turbidity meter.
Statistical analysis
Statistical analysis and data visualization were conducted using R programming, Graph Pad Prism-8, and Microsoft Excel-16. Analysis of variance (one-way ANOVA) was used to determine the statistically significant differences among sampling stations. t-test was conducted to evaluate the significant seasonal variation. Pearson product-moment correlation was performed to evaluate the interaction between the tested parameters with statistical significance. A map of the study area and sampling locations was generated in ESRI ArcMap 10.5 software.
Multivariate statistical analysis
In this study, various multivariate statistical techniques, including correlation coefficient analysis (CCA), CA, and PCA, are used to facilitate the interpretation of complex data matrices to identify the possible sources of pollutants and their influence on water quality (Lee et al. 2001; Wunderlin et al. 2001; Reghunath et al. 2002; Simeonova et al. 2006; Nagaraju et al. 2014; Dossou et al. 2021). All the multivariate analyses were carried out using R Studio desktop (V-2022.12.0-353). PCA is a multivariate statistical tool that is used to combine complex input variables with large amounts of information into new, uncorrelated variables that are called principal components. CA, represented by a dendrogram, was conducted to differentiate the sampling locations into groups depending on the similarities of parameters in samples and the similar sources of origin.
Water quality index
The WQI is a method that combines multiple physical, chemical, and biological water quality parameters into a single dimensionless number by normalizing values (Miller et al. 1986), which was developed by Fed (1965). It helps the general public and policymakers easily understand the water's quality. The WQI calculation has been carried out by the ‘weighted arithmetic index’ method, which is similar to Horton's index and developed by Brown et al. (1970). The WQI model and Equations (1)–(5) are based on the relative weight of each water quality parameter and have been widely applied in European, African, and Asian countries for the assessment of water quality of the ground and surface water (Yisa & Jimoh 2010; Tyagi et al. 2020; Ram et al. 2021; Rahman & Habiba 2023).
Table 2 shows the classification of water quality based on the WQI value.
WQI value . | Water quality . |
---|---|
<50 | Excellent |
50–100 | Good |
100–200 | Poor |
200–300 | Very poor |
>300 | Unsuitable for use without proper treatment |
WQI value . | Water quality . |
---|---|
<50 | Excellent |
50–100 | Good |
100–200 | Poor |
200–300 | Very poor |
>300 | Unsuitable for use without proper treatment |
RESULTS AND DISCUSSION
Distribution of the physical parameters and seasonal fluctuations
Parameters . | Sampling points . | Dry season . | Wet season . | DoE standard for drinking . | DoE standard for agriculture . | DoE standard for recreational activities . | ||||
---|---|---|---|---|---|---|---|---|---|---|
Mean . | Maximum . | Minimum . | Mean . | Maximum . | Minimum . | |||||
pH | S1 | 7.80.5 | 8.15 | 6.97 | 7.50.6 | 7.9 | 6.55 | 6.5–8.5 | 6.5–8.5 | 6.5–8.5 |
S2 | 7.50.4 | 8.2 | 7 | 7.30.3 | 7.5 | 6.8 | ||||
S3 | 7.90.5 | 8.8 | 7.2 | 7.60.3 | 8 | 7.26 | ||||
S4 | 7.90.5 | 8.7 | 7.5 | 7.60.3 | 8 | 7.2 | ||||
S5 | 7.40.2 | 7.8 | 7.3 | 7.20.5 | 7.7 | 6.5 | ||||
S6 | 7.10.4 | 7.5 | 6.12 | 7.20.4 | 7.72 | 6.55 | ||||
EC | S1 | 1,199.4 | 1,550 | 945.5 | 198.535.1 | 241 | 158 | 1,200 | – | – |
S2 | 494.677.3 | 550 | 326 | 156.553.2 | 236 | 126 | ||||
S3 | 555.779.6 | 648 | 439 | 286.9181.3 | 520 | 124.1 | ||||
S4 | 528.799.39 | 698 | 440 | 246.4150 | 482 | 130 | ||||
S5 | 591.5167.9 | 808 | 398 | 143.513.3 | 152 | 124 | ||||
S6 | 467.8111.9 | 644 | 312 | 196.595.5 | 401 | 103 | ||||
DO | S1 | 2.4 | 4.3 | 1.3 | 5.40.9 | 6.61 | 4.54 | 6 | – | 5 |
S2 | 6.71.8 | 10 | 4.8 | 6.41.1 | 8 | 5.5 | ||||
S3 | 4.82.4 | 7.2 | 1.2 | 5.81.7 | 7.8 | 2.8 | ||||
S4 | 42.66 | 9 | 1.61 | 6.21.8 | 7.9 | 2.4 | ||||
S5 | 5.11.8 | 6 | 2.5 | 4.80.5 | 5.2 | 4 | ||||
S6 | 2.42.0 | 5.3 | 0.4 | 3.81.5 | 5.3 | 2.1 | ||||
TDS | S1 | 588150.2 | 845 | 459.33 | 88.711.8 | 102 | 79.5 | 1,000 | 1,000 | 1,000 |
S2 | 281.317.9 | 299 | 253 | 113.356.2 | 191 | 70 | ||||
S3 | 291.637.5 | 332 | 219 | 156.6103.6 | 295 | 65.1 | ||||
S4 | 292.550.5 | 356 | 220 | 130.186.4 | 284 | 68.2 | ||||
S5 | 292.587.7 | 411 | 199 | 69.93.4 | 72.3 | 65 | ||||
S6 | 246.665.4 | 350 | 165 | 137.686.1 | 308 | 78 | ||||
TSS | S1 | 28.814.6 | 47 | 16 | 57.519.9 | 78 | 39 | 10 | – | – |
S2 | 13.68 | 28 | 4 | 178.6 | 25 | 4.8 | ||||
S3 | 21.115 | 47 | 10 | 34.726.6 | 77 | 4 | ||||
S4 | 22.715.7 | 45 | 5 | 63.255.9 | 151 | 5 | ||||
S5 | 246.5 | 32 | 16 | 27.317.7 | 48 | 5 | ||||
S6 | 34.920.9 | 80 | 10 | 56.939.3 | 121 | 20 | ||||
TS | S1 | 607.2153 | 870 | 469 | 139.527.8 | 178 | 114 | – | – | – |
S2 | 280.443.6 | 316 | 192 | 96.634.1 | 143.1 | 61 | ||||
S3 | 325.659 | 440 | 257 | 197.5101.9 | 309 | 69.1 | ||||
S4 | 309.760.30 | 401 | 253 | 216.8128.6 | 470 | 80.2 | ||||
S5 | 316.581.3 | 427 | 231 | 91.912.6 | 100 | 77.3 | ||||
S6 | 279.373.6 | 392 | 195 | 163.756 | 241 | 106 | ||||
Turbidity | S1 | 8.42.8 | 11 | 4.6 | 52.823.7 | 78 | 31.9 | 5 | – | – |
S2 | 10.85.9 | 23 | 4.2 | 30.631 | 72 | 11.2 | ||||
S3 | 34.427.3 | 77.5 | 8.2 | 27.527.7 | 76 | 9.81 | ||||
S4 | 41.239.11 | 117 | 11.4 | 131.2224.5 | 530 | 11.4 | ||||
S5 | 16.72.9 | 19.2 | 13.9 | 35.217.3 | 47.4 | 23 | ||||
S6 | 69.456.5 | 170 | 13.8 | 76.058.8 | 144 | 18 | ||||
BOD5 | S1 | 15 | 23.3 | 10.73 | 3.30.8 | 4.2 | 2.2 | ≤ 2 | ≤ 12 | ≤ 3 |
S2 | 9.15.2 | 16 | 2 | 8.318.5 | 46 | 8 | ||||
S3 | 124.9 | 22 | 8 | 9.73.1 | 14 | 6 | ||||
S4 | 10.51.76 | 13 | 8 | 8.93 | 14 | 4 | ||||
S5 | 10.51.9 | 12 | 8 | 73.5 | 12 | 4 | ||||
S6 | 124.3 | 18 | 8 | 10.46.3 | 22 | 3 | ||||
COD | S1 | 40.919.4 | 65 | 21 | 10.81.8 | 12.5 | 9 | 10 | 100 | 10 |
S2 | 23.913.6 | 39 | 6 | 22.55.3 | 30 | 18 | ||||
S3 | 3914.7 | 70 | 22 | 3215.2 | 56 | 15 | ||||
S4 | 33.58.62 | 46 | 24 | 27.615.8 | 58 | 6.3 | ||||
S5 | 29.57.5 | 36 | 22 | 19.813.3 | 39 | 10 | ||||
S6 | 4222.8 | 90 | 20 | 40.426.9 | 21.5 | 10 | ||||
Chloride | S1 | 82.218 | 110 | 68 | 5.41.8 | 8 | 4 | 250 | – | – |
S2 | 24.511 | 45 | 14 | 7.92.3 | 11 | 6 | ||||
S3 | 43.428.1 | 98 | 18 | 23.621.9 | 70 | 8 | ||||
S4 | 46.528.4 | 101 | 21 | 14.410.8 | 32 | 5 | ||||
S5 | 37.59.1 | 50 | 28 | 15.411.2 | 32 | 7.5 | ||||
S6 | 39.713.4 | 54 | 12 | 11.88.1 | 30 | 8 | ||||
Total alkalinity | S1 | 280.455.8 | 340.66 | 230 | 142.751.8 | 220 | 112 | 150 | – | – |
S2 | 19690.5 | 324 | 66 | 7241.1 | 129 | 31 | ||||
S3 | 204.971.1 | 281 | 101 | 13094.8 | 281 | 21 | ||||
S4 | 218.761.5 | 280 | 102 | 121.7103 | 281 | 24 | ||||
S5 | 13550 | 210 | 110 | 101.5104.6 | 256 | 24 | ||||
S6 | 138.853.1 | 254 | 88 | 77.723.3 | 106 | 29 |
Parameters . | Sampling points . | Dry season . | Wet season . | DoE standard for drinking . | DoE standard for agriculture . | DoE standard for recreational activities . | ||||
---|---|---|---|---|---|---|---|---|---|---|
Mean . | Maximum . | Minimum . | Mean . | Maximum . | Minimum . | |||||
pH | S1 | 7.80.5 | 8.15 | 6.97 | 7.50.6 | 7.9 | 6.55 | 6.5–8.5 | 6.5–8.5 | 6.5–8.5 |
S2 | 7.50.4 | 8.2 | 7 | 7.30.3 | 7.5 | 6.8 | ||||
S3 | 7.90.5 | 8.8 | 7.2 | 7.60.3 | 8 | 7.26 | ||||
S4 | 7.90.5 | 8.7 | 7.5 | 7.60.3 | 8 | 7.2 | ||||
S5 | 7.40.2 | 7.8 | 7.3 | 7.20.5 | 7.7 | 6.5 | ||||
S6 | 7.10.4 | 7.5 | 6.12 | 7.20.4 | 7.72 | 6.55 | ||||
EC | S1 | 1,199.4 | 1,550 | 945.5 | 198.535.1 | 241 | 158 | 1,200 | – | – |
S2 | 494.677.3 | 550 | 326 | 156.553.2 | 236 | 126 | ||||
S3 | 555.779.6 | 648 | 439 | 286.9181.3 | 520 | 124.1 | ||||
S4 | 528.799.39 | 698 | 440 | 246.4150 | 482 | 130 | ||||
S5 | 591.5167.9 | 808 | 398 | 143.513.3 | 152 | 124 | ||||
S6 | 467.8111.9 | 644 | 312 | 196.595.5 | 401 | 103 | ||||
DO | S1 | 2.4 | 4.3 | 1.3 | 5.40.9 | 6.61 | 4.54 | 6 | – | 5 |
S2 | 6.71.8 | 10 | 4.8 | 6.41.1 | 8 | 5.5 | ||||
S3 | 4.82.4 | 7.2 | 1.2 | 5.81.7 | 7.8 | 2.8 | ||||
S4 | 42.66 | 9 | 1.61 | 6.21.8 | 7.9 | 2.4 | ||||
S5 | 5.11.8 | 6 | 2.5 | 4.80.5 | 5.2 | 4 | ||||
S6 | 2.42.0 | 5.3 | 0.4 | 3.81.5 | 5.3 | 2.1 | ||||
TDS | S1 | 588150.2 | 845 | 459.33 | 88.711.8 | 102 | 79.5 | 1,000 | 1,000 | 1,000 |
S2 | 281.317.9 | 299 | 253 | 113.356.2 | 191 | 70 | ||||
S3 | 291.637.5 | 332 | 219 | 156.6103.6 | 295 | 65.1 | ||||
S4 | 292.550.5 | 356 | 220 | 130.186.4 | 284 | 68.2 | ||||
S5 | 292.587.7 | 411 | 199 | 69.93.4 | 72.3 | 65 | ||||
S6 | 246.665.4 | 350 | 165 | 137.686.1 | 308 | 78 | ||||
TSS | S1 | 28.814.6 | 47 | 16 | 57.519.9 | 78 | 39 | 10 | – | – |
S2 | 13.68 | 28 | 4 | 178.6 | 25 | 4.8 | ||||
S3 | 21.115 | 47 | 10 | 34.726.6 | 77 | 4 | ||||
S4 | 22.715.7 | 45 | 5 | 63.255.9 | 151 | 5 | ||||
S5 | 246.5 | 32 | 16 | 27.317.7 | 48 | 5 | ||||
S6 | 34.920.9 | 80 | 10 | 56.939.3 | 121 | 20 | ||||
TS | S1 | 607.2153 | 870 | 469 | 139.527.8 | 178 | 114 | – | – | – |
S2 | 280.443.6 | 316 | 192 | 96.634.1 | 143.1 | 61 | ||||
S3 | 325.659 | 440 | 257 | 197.5101.9 | 309 | 69.1 | ||||
S4 | 309.760.30 | 401 | 253 | 216.8128.6 | 470 | 80.2 | ||||
S5 | 316.581.3 | 427 | 231 | 91.912.6 | 100 | 77.3 | ||||
S6 | 279.373.6 | 392 | 195 | 163.756 | 241 | 106 | ||||
Turbidity | S1 | 8.42.8 | 11 | 4.6 | 52.823.7 | 78 | 31.9 | 5 | – | – |
S2 | 10.85.9 | 23 | 4.2 | 30.631 | 72 | 11.2 | ||||
S3 | 34.427.3 | 77.5 | 8.2 | 27.527.7 | 76 | 9.81 | ||||
S4 | 41.239.11 | 117 | 11.4 | 131.2224.5 | 530 | 11.4 | ||||
S5 | 16.72.9 | 19.2 | 13.9 | 35.217.3 | 47.4 | 23 | ||||
S6 | 69.456.5 | 170 | 13.8 | 76.058.8 | 144 | 18 | ||||
BOD5 | S1 | 15 | 23.3 | 10.73 | 3.30.8 | 4.2 | 2.2 | ≤ 2 | ≤ 12 | ≤ 3 |
S2 | 9.15.2 | 16 | 2 | 8.318.5 | 46 | 8 | ||||
S3 | 124.9 | 22 | 8 | 9.73.1 | 14 | 6 | ||||
S4 | 10.51.76 | 13 | 8 | 8.93 | 14 | 4 | ||||
S5 | 10.51.9 | 12 | 8 | 73.5 | 12 | 4 | ||||
S6 | 124.3 | 18 | 8 | 10.46.3 | 22 | 3 | ||||
COD | S1 | 40.919.4 | 65 | 21 | 10.81.8 | 12.5 | 9 | 10 | 100 | 10 |
S2 | 23.913.6 | 39 | 6 | 22.55.3 | 30 | 18 | ||||
S3 | 3914.7 | 70 | 22 | 3215.2 | 56 | 15 | ||||
S4 | 33.58.62 | 46 | 24 | 27.615.8 | 58 | 6.3 | ||||
S5 | 29.57.5 | 36 | 22 | 19.813.3 | 39 | 10 | ||||
S6 | 4222.8 | 90 | 20 | 40.426.9 | 21.5 | 10 | ||||
Chloride | S1 | 82.218 | 110 | 68 | 5.41.8 | 8 | 4 | 250 | – | – |
S2 | 24.511 | 45 | 14 | 7.92.3 | 11 | 6 | ||||
S3 | 43.428.1 | 98 | 18 | 23.621.9 | 70 | 8 | ||||
S4 | 46.528.4 | 101 | 21 | 14.410.8 | 32 | 5 | ||||
S5 | 37.59.1 | 50 | 28 | 15.411.2 | 32 | 7.5 | ||||
S6 | 39.713.4 | 54 | 12 | 11.88.1 | 30 | 8 | ||||
Total alkalinity | S1 | 280.455.8 | 340.66 | 230 | 142.751.8 | 220 | 112 | 150 | – | – |
S2 | 19690.5 | 324 | 66 | 7241.1 | 129 | 31 | ||||
S3 | 204.971.1 | 281 | 101 | 13094.8 | 281 | 21 | ||||
S4 | 218.761.5 | 280 | 102 | 121.7103 | 281 | 24 | ||||
S5 | 13550 | 210 | 110 | 101.5104.6 | 256 | 24 | ||||
S6 | 138.853.1 | 254 | 88 | 77.723.3 | 106 | 29 |
Note: DoE standard – 1997 for drinking, agriculture, and recreational activities (ECR 1997).
Electrical conductivity (EC) measurements obtained from various sampling stations exhibited diverse ranges, varying from 312 to 1,550 μs/cm in the dry season to103–520 μs/cm in the wet season. In the dry season, the highest EC value was found at S1, whereas the lowest EC value was observed at S6 in the rainy season (Table 3). In all the sampling stations, high seasonal fluctuations were observed (Figure 2(b)). Notably, a statistically significant difference in EC values was observed among the sampling stations (ANOVA, p ≤ 0.01) and seasons (t-test, p ≤ 0.01). Therefore, the dilution that occurs during the rainy season might reduce the EC magnitude in the water (Rahman et al. 2021). Overall, all the observed EC values except for S1 were found within the limit of 1,200 μs/cm set in the Environment Conservation Rules (ECR), 1997 (schedule 10), DoE, Bangladesh (ECR 1997).
DO values obtained from all the sampling stations showed high fluctuations throughout the year (0.4–10 mg/L) (Figure 2(c)). Variation between the sampling stations was also high with the lowest average value (2.4 ± 1.3) observed at S1 and the highest average value (6.7 ± 1.8) found at S2, both in the dry season. Relatively less fluctuation was observed between the sampling stations in the wet season. Statistically significant differences in DO value were found among sampling stations (ANOVA, p < 0.01). But no statistically significant seasonal variation was found (t-test, p > 0.05). Average DO values at different stations showed that all the stations in the dry season except for S2 had lower DO than the set value (≥6 mg/L) in ECR (1997) (schedule 3), while a DO value of 4–6 mg/L is recommended for aquatic animals. Comparatively better DO (4.11–7.31 mg/L) was reported in the same river in a study (Aminul Ahsan 2018) and suggested that DO is suitable for supporting the biological habitat.
TDS values derived from all the sampling stations exhibit significant seasonal fluctuations (t-test, p < 0.01). Notably, the highest maximum value (845 mg/L) and the peak average value (588150.2 mg/L) were recorded at S1 during the dry season, while the lowest average value (69.93.4 mg/L) was observed at S5 during the wet season (Figure 2(d)). ANOVA analysis revealed significant variation in TDS values among the sampling stations (p < 0.05). However, the TDS of all samples was found to be within the acceptable limit (1,000 mg/L) set in ECR (1997) (schedule 3B). Ghosh & Hossain (2019) reported similar phenomena in a study that illustrated the different values in the dry and wet seasons, 400 and 270 mg/L, respectively.
The total solids (TS) is the component that has an enormous capacity to impact the water quality and consists of organic matter, anion–cation, silts, and other foreign chemicals (Matta et al. 2020). This parameter exhibits considerable seasonal variability, with the maximum mean value of 607.2 ± 153 mg/L recorded during the dry season at S1 and the minimum average value of 91.9 ± 12.6 mg/L observed during the wet season at S5 (Figure 3(b)). Notably, ANOVA analysis revealed statistically significant differences in TS values among the various sampling stations (p ≤ 0.05), while t-test results indicated significant variations across seasons (p < 0.01).
Water turbidity is an important physical indicator that defines the purity of water. If the turbidity is higher and the water seems to be comparatively dark that potentially indicates the magnitude of organic contaminants, silts, and other suspended particles in the waterbody. Turbidity measurements obtained from all sampling stations exhibited considerable fluctuations throughout the year (11–530 mg/L) (Figure 3(c)). During the wet season, turbidity was slightly higher in most stations than in the dry season, which may be attributed to water turbulence and heavy precipitation during the monsoon season that results in soil and clay being washed up and carried along with the river flow. However, there was no statistical significance in the turbidity variations observed among the sampling stations (ANOVA, p > 0.05) and seasons (t-test, p > 0.05). None of the turbidity values obtained met the recommended value (5 mg/L) by the ECR (1997).
Comparison among chemical and biochemical contaminants and seasonality
COD is an important measurement in water quality assessment; it indicates the quantity of soluble organic matter in the water. That enables us to determine the required oxygen for the oxidation of different organic particulate matters in water (Napacho & Manyele 2010; Matta et al. 2020). The obtained COD values from various sampling stations and seasons exhibit substantial fluctuations, with values ranging from 6 to 90 mg/L throughout the year (Table 3). The seasonal variation found among the stations was insignificant, except S1 (Figure 4(b)). The ANOVA results revealed no statistically significant variation in COD value among the sampling stations (p > 0.05) and seasons (t-test, p > 0.05). In every sampling point, the COD was detected at >23 mg/L, which was reported as the maximum average value (23 mg/L) in a study (Hasan et al. 2020) in the river. All the COD values were higher than the recommended limit of 10 mg/L set by the ECR (1997) for drinking and recreational purposes. Likewise, the BOD5 and COD also exceeded all the reference values in the sampling areas, which could symbolize the ongoing impairment of the water quality in the river.
The chloride values obtained from various sampling stations depict a significant seasonal variance (t-test, p < 0.01). During the dry season, the maximum chloride value (110 mg/L) and the highest mean value (82.218 mg/L) were observed at S1. The elevated chloride concentration at S1 is likely attributed to the release of wastewater from the tannery industry, which is located adjacent to S1. Conversely, the low chloride concentration (5.4 ± 1.8 mg/L) recorded at the same station during the wet season may be a result of the high river water flow during the monsoon, leading to the transportation of discharged pollutants downstream. It is noteworthy that all the values obtained were within the limit (250 mg/L) set for drinking purposes (Figure 4(c)). However, our detected values are considerably higher than the other studies (Aminul Ahsan 2018; Hasan et al. 2020).
A marked variation in the total alkalinity value was observed between the dry and wet seasons at all of the sampling stations. The highest mean alkalinity value (280.455.8 mg/L) was found at S1 during the dry season, while the lowest value (7241.1 mg/L) was found at S2 during the wet season. There were no statistically significant differences in the total alkalinity values among the sampling stations (as determined by ANOVA, p > 0.05). However, there was a strong seasonal variation in the values (as determined by the t-test, p < 0.01). During the dry season, the total alkalinity value was found to be greater than the set value (150 mg/L) in the ECR (1997). On the other hand, most of the values were found to be within the set value during the wet months (Figure 4(d)). This scenario is probably due to the dilution effect. During the wet season, rainfall could dilute the organic contaminants and reduce the ions, which could increase the pH level. As a result, total alkalinity also decreases in the wet season with the fall in pH. Moreover, rainwater is a little bit acidic, which could also be a reason for decreasing the pH and alkalinity (Aher 2018). However, in the dry season, since no rainfall occurred, the river water pH and alkalinity were comparatively higher than in the wet season (Islam et al. 2015). Essien-Ibok et al. (2010) reported a significant negative correlation (r = −0.88, p < 0.001) between rainfall and total alkalinity in a separate study of a river.
Interaction between and among the physical and chemical components
In the wet season, significant positive correlations have been found between BOD5–COD (r = 0.94, p < 0.01), pH–EC (r = 0.84), and EC–TS (r = 0.92), p < 0.05. Moreover, a positive correlation was detected between pH–TS, pH–total alkalinity, EC–TDS, TDS–COD, TDS–TS, and SS–turbidity at a statistical significance level of p < 0.1. Comparatively less correlation among the parameters has been found in the wet season than in the dry season. In a compelling research article by Kibena et al. (2014), a striking pattern emerges as they delve into the dynamics of organic contaminants and suspended solid particles within a water body. Their findings reveal a robust and significant correlation during the dry season, underscoring the pronounced influence of environmental factors during this period. However, as the rains descend and the wet season unfolds, this correlation exhibits a notable decline, shedding light on the intricate interplay between these contaminants and the shifting hydrological conditions.
This phenomenon might be attributed to the heightened river water flow during the wet season, which results in a dilution effect and subsequently lowers the concentration of these contaminants. Additionally, the increased flow rate fosters greater water mixing, reducing stagnation and, in turn, diminishing the likelihood of chemical reactions among various pollutants. These dynamic environmental factors underscore the complexity of contaminant behavior within aquatic systems, revealing a complex relationship worthy of further exploration. An equally noteworthy understanding from this study is the absence of a surge in contaminants during the wet season. This valuable observation suggests that runoff during this period did not introduce substantial organic waste into the river water, challenging conventional assumptions about seasonal fluctuations in water quality. Furthermore, the CCA (Figure 5(b)) failed to find a significant, strong positive correlation with the organic suspended parameters in the wet season. Specify that potential pollution sources may be attributed to discrete point discharges rather than broad-scale environmental influences. The study also demonstrates that industrial effluents, domestic sewage discharges, and tannery wastes may need closer scrutiny and targeted remediation efforts to safeguard the ecological integrity of the water body. These findings will help us to reevaluate our understanding of pollutant dynamics within aquatic ecosystems, challenging us to think beyond seasonal trends and consider the localized impacts of point-source pollution.
In PC2, which consists of 29.9% variance (Figure 6(a)), moderate positive loading with COD (0.36), TSS (0.47), and turbidity (0.493) and negative loading with pH (−0.32) and DO (−0.38) can be associated with inorganic components discharged from domestic and industrial activities as well as several point and nonpoint sources (Al-badaii et al. 2013).
During the wet season, our analysis using PC1 uncovered a significant source of variability, accounting for 44.8% of the observed changes in water quality (Figure 6(b)). Notably, key parameters such as EC, TS, and turbidity displayed moderate positive loadings of 0.42, 0.44, and 0.42, respectively. This observation suggests that surface water runoff during this period may not have introduced significant organic contaminants into the water. Instead, our findings disclose the possibility of point sources contributing to the presence of suspended particles in the water. Our results align with the work of Das et al. (2011), who reported elevated levels of TDS, EC, salinity, alkalinity, and hardness in tannery effluents. In contrast, DO levels were found to be lower. These findings underscore the potential influence of point sources, such as tannery effluents, on the water quality parameters we observed. PC2 explaining 27.05% of the variance. In this case, we observed positive loadings for pH (0.35) and alkalinity (0.48), indicating a correlation between these parameters. Conversely, parameters such as BOD5 and COD displayed negative loadings of −0.47 and −0.46, respectively. These findings imply that water samples with higher pH and alkalinity levels tend to exhibit lower BOD5 and COD values. These results provide valuable insights into the dynamics of water quality during the wet season, find out the potential contributors to observed variations, and highlight the interconnection between various water quality parameters.
Spatiotemporal groupings and WQI
In addition to industrial impacts, agricultural activities along the Dhaleshwari riverbanks, spanning from S2 to S5, bring their own set of ecological challenges (Figure 8). Runoff from these farmlands carries an excess of fertilizers, pesticides, herbicides, and organic matter, exerting further stress on water quality (DoE 2016). Furthermore, soil erosion and rainwater runoff from these fields contribute to heightened turbidity levels and increased concentrations of TSS and SS in surface waters. The sediment disturbance resulting from riverbank cutting for brick fields in the Rajnagar, Baluchar, Ruhitpur, S4, and S5 regions further exacerbates TSS, SS, and turbidity levels in river waters (DoE 2016). Moreover, tidal dynamics trigger a merging of Buriganga and Dhaleshwari waters, amplifying pollution at the S6 juncture (DoE 2016). The Buriganga (declared ECA in 2009) is one of Bangladesh's most polluted rivers and serves as a secondary pollution source for the Dhaleshwari River at this juncture. The situation is compounded by the daily discharge of approximately 6,000 tons of untreated liquid waste, encompassing tannery, residential, and municipal wastewater, into the Buriganga, where it subsequently commingles with the Dhaleshwari River (Kibria et al. 2016; Uddin & Jeong 2021). Further intensifying the environmental challenge in this region are the numerous shipbuilding yards, both small and large, which discharge substantial quantities of oil, grease, and heavy metals into the river. This complex interplay of various industrial, agricultural, and natural factors underscores the multifaceted environmental challenges faced by the Buriganga and Dhaleshwari river ecosystems. Most sample locations exhibited poor water quality during the dry and wet seasons (Supplementary Table S2 and Figure S1). However, a stark contrast is evident between Figure 8(a) and 8(b) during the wet season. This suggests that the dilution effect may play a significant role in improving water quality during rainfall.
CONCLUSIONS
This study presents a comprehensive assessment of the present water quality and pollution levels in the Dhaleshwari River, encompassing both dry and wet seasons. The findings demonstrate that most of the analyzed parameters exceed the standard limits set in the rules. The calculated WQI further underscores the severity of the situation, categorizing the water quality of the Dhaleshwari River as ‘very poor’. Substantial variability in water quality was observed among the six sampling stations, with the dry season registering the most unfavorable conditions. However, it is noteworthy that the WQI indicates poor water quality, across all sampling stations in both seasons. This investigation identifies industrial waste discharge from tannery industries, alongside other multifunctional industries within the BSCIC industrial estate, shipyards, as well as municipal discharge, as the primary sources of pollution affecting the Dhaleshwari River. The results of this study highlight the necessity for urgent and concerted efforts aimed at water quality improvement and pollution control in the Dhaleshwari River basin. Strategies must prioritize the mitigation of industrial waste and the management of surface runoff and municipal discharge to safeguard the ecological integrity of this vital water resource. The findings presented herein also underscore the need for continued monitoring and environmental stewardship to preserve and restore the health of the Dhaleshwari River, recognizing its critical role in sustaining both human communities and the broader ecosystem.
ACKNOWLEDGEMENTS
The authors would also like to thank Dr Abdul Hamid, Director General, Department of Environment, Ministry of Environment, Forest and Climate Change, Government of the People's Republic of Bangladesh, for his kind support and approval for data support. The authors also appreciated Dhaka Laboratory, the Department of Environment, the Ministry of Environment, Forest and Climate Change, and the Government of the People's Republic of Bangladesh for their data collection and analysis support.
FUNDING
This study was supported by the Department of Environment (DoE), Ministry of Environment, Forest and Climate Change, Government of the People's Republic of Bangladesh.
AUTHORSHIP CONTRIBUTION STATEMENT
M.S.A. and S.B.: conceptualization, investigation, resources, and review and editing; F.M.R.: writing – original draft, data curation, formal analysis, and visualization; M.S.: investigation, formal analysis, and data curation; M.K.H.: formal analysis and data curation; M.A.R.: validation, visualization, and review and editing; M.M.R.: validation and review and editing; M.H.R.: supervision, methodology, and review and editing.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.