Assessment of textile industry effluents and their impact on local water bodies in an urban setting of Bangladesh

Abstract

Industrial pollution is a significant challenge for developing countries, particularly in Bangladesh, where the textile industry severely impacts water quality by discharging effluents containing toxic chemicals. This study aims to evaluate the pollutant load of effluents released by six textile industries in Chattogram City. Effluent samples were analyzed for various physicochemical, biological, and heavy metal parameters. The average physicochemical and biological parameters, including color, temperature, resistivity, total dissolved solids (TDS), total suspended solids (TSS), electrical conductivity (EC), salinity, turbidity, pH, dissolved oxygen (DO), chemical oxygen demand (COD), 5-day biochemical oxygen demand (BOD₅), total coliform (TC), and fecal coliform (FC), were found to be 33.14 PCU, 27.52 °C, 416.85 Ὡ-m, 282.37 mg/L, 0.94 mg/L, 7.80 mS/cm, 2.16 ppt, 135.73 NTU, 7.72, 2.00 mg/L, 2107.54 mg/L, 176.44 mg/L, 108.33 counts/100 mL, and 26.17 counts/100 mL, respectively. The average concentration of heavy metals followed the order of Fe>Cr>Mn>Cd>Pb. Except for temperature, color, resistivity, TDS, TSS, turbidity, Cd, and Mn, all other parameters exceeded the permissible limits set by the Bangladesh Environmental Conservation Rules (BECR. Bangladesh Environmental Conservation Rules (BECR). Dhaka: BECR, 2023), indicating that the effluents are unsuitable for direct discharge into surface water bodies. This study highlights the pressing challenges of textile industry pollution and underscores the need for sustainable practices to protect aquatic ecosystems and human health. Moreover, the findings provide critical insights for policymakers to develop effective pollution control strategies.

Full text

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Discover Environment
Research
Assessment oftextile industry effluents andtheir impact onlocal
water bodies inanurban setting ofBangladesh
Md.MehediHassanMasum1· Md.BashirulIslam1· Md.RezviAnowar2· KandilaArsh3· SadiaAlam4
Received: 10 August 2024 / Accepted: 17 April 2025
© The Author(s) 2025 OPEN
Abstract
Industrial pollution is a signicant challenge for developing countries, particularly in Bangladesh, where the textile indus-
try severely impacts water quality by discharging euents containing toxic chemicals. This study aims to evaluate the
pollutant load of euents released by six textile industries in Chattogram City. Euent samples were analyzed for vari-
ous physicochemical, biological, and heavy metal parameters. The average physicochemical and biological parameters,
including color, temperature, resistivity, total dissolved solids (TDS), total suspended solids (TSS), electrical conductivity
(EC), salinity, turbidity, pH, dissolved oxygen (DO), chemical oxygen demand (COD), 5-day biochemical oxygen demand
(BOD₅), total coliform (TC), and fecal coliform (FC), were found to be 33.14 PCU, 27.52°C, 416.85Ὡ-m, 282.37mg/L,
0.94mg/L, 7.80mS/cm, 2.16ppt, 135.73 NTU, 7.72, 2.00mg/L, 2107.54mg/L, 176.44mg/L, 108.33 counts/100mL, and
26.17 counts/100mL, respectively. The average concentration of heavy metals followed the order of Fe>Cr>Mn>Cd>Pb.
Except for temperature, color, resistivity, TDS, TSS, turbidity, Cd, and Mn, all other parameters exceeded the permissible
limits set by the Bangladesh Environmental Conservation Rules (BECR. Bangladesh Environmental Conservation Rules
(BECR). Dhaka: BECR, 2023), indicating that the euents are unsuitable for direct discharge into surface water bodies. This
study highlights the pressing challenges of textile industry pollution and underscores the need for sustainable practices
to protect aquatic ecosystems and human health. Moreover, the ndings provide critical insights for policymakers to
develop eective pollution control strategies.
Keywords Heavy metals· Industrial euent· Textile industry· Wastewater· ETP
1 Introduction
Water pollution from the textile industry is one of the most critical environmental challenges facing developing nations,
particularly Bangladesh [1, 2]. This issue not only threatens aquatic ecosystems but also poses serious risks to public health
and economic stability. The textile industry is the primary source of export revenue for Bangladesh, generating $42.613
billion in exports during the 2021–22 scal year and playing a substantial role in driving the country’s economic growth
Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s44274- 025-
00245-3.
* Md. Mehedi Hassan Masum, mehedi.ce.cuet@gmail.com | 1Institute ofRiver, Harbor andEnvironmental Science (IRHES), Chittagong
University ofEngineering & Technology (CUET), Chattogram4349, Bangladesh. 2Department ofFashion Design andTechnology, Port City
International University, Chattogram, Bangladesh. 3Department ofCivil Engineering, Chittagong University ofEngineering & Technology,
Chattogram, Bangladesh. 4Department ofFashion Design (FD), Chattogram BGMEA University ofFashion andTechnology (CBUFT),
Chattogram, Bangladesh.

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[3]. In 2023, the country had over ve thousand active textile industries [4], reecting a decline from 2013, when Bangla-
desh had approximately 5.88 thousand garment factories [5]. Among various industries, the textile industry stands out as
the most polluting, signicantly contributing to environmental degradation due to high levels of industrial waste [6–8].
The textile production process involves preparing raw materials, dyeing, and printing fabrics for color and design,
and nishing to achieve the desired characteristics of the nal product. This process generates euent primarily due
to the large volumes of water and chemicals used, especially during dyeing and washing stages [9–11]. The euents
are characterized by intense color, high temperature (> 40°C), high pH (> 7), high salinity, elevated concentrations of
biochemical oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC), suspended solids (SS),
and lower dissolved oxygen (DO) levels. They also contain toxicants, surfactants, bers, heavy metals [Iron (Fe), Copper
(Cu), Zinc (Zn), Chromium (Cr), Cadmium (Cd), Manganese (Mn), Lead (Pb), Arsenic (As)], and ions [Potassium (K⁺), Mag-
nesium (Mg2⁺), Chloride (Cl⁻), Bicarbonate (HCO₃⁻), Sulfate (SO₄2⁻), Nitrate (NO₃⁻), Phosphate (PO₄3⁻), etc.] [2, 12, 13]. The
indiscriminate discharge of untreated euents poses signicant health risks and environmental impacts [14–16]. The
euents adversely aect soil and water resources, contributing to their degradation and threatening food security by
contaminating agricultural land and crops [7, 14, 15]. Surface water bodies and groundwater sources [7, 9] are also heav-
ily impacted, endangering aquatic biodiversity, including sheries [17]. Moreover, textile euents pose risks to aquatic
organisms due to their carcinogenic, genotoxic, cytotoxic, and allergenic properties [9, 18, 19].
Globally, several studies investigated textile euents, particularly in India [20, 21], Nigeria [2, 22], Pakistan [23], and
Ethiopia [24]. Nergis etal. [23] studied euents from nine textile industries in Karachi, Pakistan, and found that BOD,
COD, TSS, TDS, and sulde concentrations were 2–5 times higher than permissible limits. Heavy metals such as Cr, Fe,
Mn, Zn, Hg, and Cu were also detected in the study. Similarly, in Kaduna, Nigeria, euent samples from ve large textile
industries exceeded permissible limits for Color, COD, TSS, NH3, BOD5, and S2− by around 350, 24, 13, 8, and 7 times,
respectively [22]. Similar ndings also reported in India for various textile industrial euents [20, 21]. In Ethiopia, textile
euent from the Hawassa Textile Factory exhibited pH levels of 8–11, temperatures of 18–26°C, and varying concentra-
tions of EC, TDS, TSS, BOD, and COD [12].
In Bangladesh, studies on textile euents were conducted in Dhaka, Gazipur, EPZ, Savar, and Narayanganj [25–27].
Kamal etal. [25] assessed euents from the Dhaka Export Processing Zone (DEPZ) and reported values for temperature,
color, pH, DO, EC, BOD, COD, TS, total alkalinity, and total hardness that exceeded Department of Environment (DoE)
standards [28]. Heavy metals such as Ca, Mg, Zn, Ni, and Cu also exceeded allowable limits. Rahman etal. [27] investi-
gated euents from the Chattogram Export Processing Zone (CEPZ) and found that COD, BOD, and DO concentrations
were signicantly higher than acceptable limits, although pH and TDS values were within permissible ranges. Islam and
Mostafa [18] examined euents from three districts, reporting varying concentrations of Cr, Mn, Fe, Cu, Zn, and Pb, with
only Fe exceeding permissible limits. The variation in textile euent characteristics was due to dierences in chemicals
and additives used during production processes [29].
Despite the textile industry’s signicant contribution to the economy of Bangladesh, its adverse eects on natural
resources and ecosystems are undeniable. Therefore, it is crucial to assess the extent of pollution from textile euents
and develop eective management strategies to mitigate their impact on the environment and public health. This
study aims to investigate the physicochemical, biological, and heavy metal contents in euents from six textile indus-
tries in Chattogram City and assess their impact on surrounding surface water bodies. Additionally, it seeks to identify
correlations and clustering patterns among pollution parameters to enhance understanding of their interactions and
contributions to environmental contamination. By providing foundational data, this study supports the development of
eective pollution mitigation strategies, ensuring the protection of public health and local ecosystems. The novelty of
this study lies in its focused assessment of pollutants specically from textile industry euents in Chattogram City, the
business capital of Bangladesh, a region that has not been thoroughly investigated despite its high industrial activity.
2 Materials andmethods
2.1 Sampling area
Chattogram City, depicted in Fig.1a, is the second-largest port city in Bangladesh, with a population of 60 million and
an annual growth rate of 1.5% [30]. The city experiences a tropical monsoon climate (according to the Köppen classi-
cation), characterized by relatively warm temperatures and high humidity [31]. Temperatures in the city range from 17
to 39°C (average 25°C), while annual rainfall varies between 2400 and 3000mm [31, 32], with peak rainfall of 720mm

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occurring from May to September [33]. Chattogram plays a vital role in the national economy, handling 75% of the coun-
try’s exports and 80% of its imports, contributing 12% ($25.5 billion) to the GDP. The city is home to 1057 industries, the
second-highest in Bangladesh after Dhaka (1351) [34]. It also hosts 510 large garment manufacturers, 33 textile mills,
and other manufacturing units [35]. The textile industry in Chattogram encompasses apparel manufacturing, knitting,
dyeing, weaving, spinning, and printing that operate fully or partially and produce a diverse range of textile products,
such as ready-made garments, knitted and woven fabrics, dyed textiles, and nished apparel [36, 37]. Waste generated
from the industries is typically discharged into the Karnaphuli River, which serves as the primary drainage system for
the Chattogram region, through the existing canal network [38].
2.2 Sample collection
Euent samples were collected from six major textile industries in Chattogram City (Fig.1a), selected for their high produc-
tion capacity and signicant contribution to local pollution. Three samples were taken from each industry at 3-h intervals
during working hours (10a.m.–6p.m.) at discharge points before mixing with nearby water bodies. This approach allowed
for the assessment of daily uctuations in euent quality. Using the grab sampling method [39, 40], samples were collected
in labelled standard black glass bottles. Immediately after collection, the samples were placed in ice-lled insulated contain-
ers to preserve their integrity during transportation. The samples were transported to the laboratory as quickly as possible
(within 2h in this study) via the shortest route, as recommended by Maity etal. [41]. Upon arrival, the samples were stored
in a refrigerator at 4°C until analysis commenced.
2.3 Analytical protocols andinstrumental analysis
Collected euent samples underwent various analyses (Fig.1b) at the Environmental Engineering Laboratory of Port City
International University and the Institute of River, Harbor, and Environmental Science Laboratory at Chittagong University
of Engineering and Technology, Chattogram. Each sample was analyzed to measure key physical parameters, including
color, temperature, resistivity, total dissolved solids (TDS), electrical conductivity (EC), total suspended solids (TSS), salinity,
Fig. 1 a Study area map showing the geographic locations of sampling points in dierent industrial locations, b experimental analysis of
sample collected

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and turbidity; chemical-biological parameters, including pH, dissolved oxygen (DO), biochemical oxygen demand (BOD5),
chemical oxygen demand (COD), total coliform (TC), and fecal coliform (FC), and heavy metals, including iron (Fe), cadmium
(Cd), chromium (Cr), lead (Pb), and manganese (Mn). All tested euent quality parameters were mainly compared with the
surface water quality and industrial discharge standards outlined in the Bangladesh Environmental Conservation Rules (BECR)
[28]. The technical specications of the devices and methods used in the laboratory tests are provided in Table1.
2.4 Quality assurance andquality control
Safety measures during water sample collection, transportation, and testing included personal protective equipment
(PPE), sterilized equipment, rinsed containers, proper labeling, ice storage, ventilation, spill kits, rst aid, and proper waste
disposal. Before collecting euent samples, the collection bottles were pre-washed, acid-cleaned with diluted HNO₃,
and dried to prevent contamination [46]. Furthermore, sample testing followed quality assurance and quality control
(QA/QC) protocols outlined in the Standard Methods for the Examination of Water and Wastewater [43] and Protocol
for Faecal Sludge Testing [47]. Time-sensitive water quality parameters were analyzed within 24h of collection, while
other parameters were tested within 72h in a controlled environment. If experimental values exceeded the limits of the
equipment or reagents, sample dilution was performed, particularly for DO, COD, BOD₅, TC, and FC. To ensure accuracy,
all equipment was calibrated using a manual 3-point calibration with standard solutions before the experimental analysis
of the physical and chemical parameters listed in Table1. Additionally, high-precision compatible reagents with accuracy
ranging from ± 0.5 to ± 2% were used to enhance measurement reliability across a wide concentration range. The Hach
DR 3900 VIS Spectrophotometer, used in this study, features automatic wavelength calibration (resolution: 0.1nm, range:
190–1100nm), which enables the creation of calibration curves for specic heavy metals (Fe, Cd, Cr, Pb, and Mn) [45]. The
Standard Adjust function renes the calibration curve using known standard solutions, where the reagent blank value
shifts the curve along the y-axis [48]. The corrected concentration is determined by multiplying absorbance (Abs) by a
concentration factor and subtracting the reagent blank value. Ranges, accuracy, and other specications mentioned in
Table1 were also veried to ensure reliable experimental results.
2.5 Statistical analysis methods
Descriptive statistics, including mean, median, coecient of variation (CV), standard deviation (SD), minimum, maximum,
range, interquartile range (IQR), skewness, and kurtosis, were used to analyze the euent quality parameters. Statistical
analysis was performed using SPSS (version 23), and whisker box plots were generated in Origin Pro 2021b. Spearman’s
rank correlation was applied to assess relationships among parameters at a 95% condence level (p < 0.05), following
methodologies from previous studies [49, 50]. Additionally, Principal Component Analysis (PCA) was conducted to iden-
tify clusters among parameters with similar characteristics. PCA transforms original variables into principal components
(PCs), which are linear combinations of the variables and capture variance in the data [51]. Loadings near ± 1 indicate a
strong impact on the variables, while values near 0 suggest a weak impact [31].
3 Results anddiscussion
3.1 Variation ofphysical parameters
The euent physical parameters (color, temperature, resistivity, TDS, EC, salinity, TSS, and turbidity) of the six textile indus-
tries are summarized statistically in Table2. The maximum and minimum values for these parameters were as follows:
color (57–18 PCU), temperature (38–22°C), resistivity (902–161.50 Ω-m), TDS (842–2.10mg/L), TSS (2.69–0.05mg/L), EC
(13.29–4.01 mS/cm), salinity (3.58–0.58 ppt), and turbidity (288–21.40 NTU). The mean (± SD) values were 33.14 ± 12.04,
27.52 ± 4.41, 416.85 ± 319.89, 282.37 ± 320.28, 0.94 ± 0.78, 7.80 ± 3.42, 2.16 ± 1.13, and 135.73 ± 90.53, respectively. Accord-
ing to BECR Standards (Table1), most physical parameters (except EC) were within allowable limits, indicating that the
euents are suitable for discharge into inland surface water. These results are comparable to studies conducted in Egypt
by Kamal etal. [52], Pakistan by Nergis etal. [23], and Nigeria by Yousu and Sonibare [22]. Nergis etal. [23] reported color,
temperature, TDS, and TSS values ranging from 103 to 4673 PCU, 34.7 to 47.8°C, 1056 to 7130mg/L, and 49 to 462mg/L,
respectively, in euents from nine textile mills. Similarly, Kamal etal. [52] found that euents from ve textile factories

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Table 1 Technical specication of the devices and methods used in the laboratory tests
a BECR standards for surface water quality (Inland Surface Water) and industrial discharge limits
b U.S. EPA standards include National Recommended Water Quality Criteria, Secondary Drinking Water Standards, and Euent Guidelines and Standards
Parameters Unit of the parameters Name of the Instrument/methods used for experimental
investigation Range Accuracy (%) Surface water quality
standardsa [28]USEPA
standardsb
[42]
Physical Parameters
Temperature °C HI9814 (GroLine) − 5.0 to 105.0 ± 0.5 40 –
TDS mg/L HI9814 (GroLine) 0 to 3000 ± 2 2100 500
TSS mg/L Standard methods provided by APHA [43] 100 –
Turbidity NTU TU-2016 (Lutron) 0 to 1000 ± 5.0 – 5–50
Color PCU HI97727 (Hanna. CAL) 0–500 ± 5.0 150 –
EC mS/cm HI9814 (GroLine) 0.00 to 6.00 ± 2.0 1.2 0.3–1.5
Resistivity Ὡ-m ASTM D1125-23 [44] 0.00 to 10,000 ± 2.0 833.3 –
Salinity ppt HI98319 (Hanna) 0–70 ± 1.0 – –
Chemical-biological parameters
pH – HI9814 (GroLine) − 2.00 to 16.00 ± 2.0 6–9 6.5–9
DO mg/L HI98198 (Hanna. opdo) 0.00 to 50 ± 1.5 4.5–8 ≥ 5
 BOD5mg/L Titrimetric method (SM 5210B) [43] n/a ± 2.0 30 20–30
COD mg/L Reactor digestion method (SM 5220D) [43] 200 to 15,000 ± 0.5 200 100–250
TC Count/100 mL MF method (USEPA 9132, SM 9221E, SM 992G) [43] n/a ± 3.0 50 –
FC Count/100 mL n/a ± 3.0 – –
Heavy metals
Fe mg/L Hach DR3900 VIS Spectrophotometer [45]1.0 to 30.0 ± 2 3.0 0.3
Cd mg/L 0.7 to 80 ± 0.7 2.0 0.00025
Cr mg/L 0.01 to 0.7 ± 0.5 0.5 0.011
Pb mg/L 0.1 to 2.0 ± 1.0 0.1 0.065
Mn mg/L 0.1 to 20.0 ± 1.0 2.0 0.05

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had average values of color, turbidity, TDS, and TSS ranging from 222–1208 PCU, 56.6–158.5 NTU, 1637–6170mg/L, and
55.3–254.7mg/L, respectively.
The bar charts (Fig.2a–h) show variations in the physical parameters of the euent samples across dierent textile
industries. These variations can be attributed to dierences in production processes, chemicals used, and treatment
methods/practices, as noted in previous studies. For example, Jamaluddin and Nizamuddin [53] reported that euent
quality depends on the consistency of treatment practices, such as the regular or irregular use of euent treatment
plants (ETPs). Jerič etal. [54] mentioned that euents from the same machinery varied depending on processes such as
desizing, scouring, bleaching, mercerizing, dyeing, and nishing.
Color, TSS, and turbidity exhibited similar variation patterns across industries, as shown in Fig.2a, e, and h, suggesting
a strong correlation among these parameters. Industry 3 (S3) recorded the highest average values for color (55 PCU),
TSS (224mg/L), and turbidity (285 NTU). The color in textile euents, primarily from dyes, colorants, and metals, can
signicantly aect aquatic ecosystems by inhibiting photosynthesis in photoautotrophic organisms [9]. Similarly, TSS,
originating from unreacted dyes, ber particles, sizing agents, and chemical residues, increases water turbidity and
reduces photosynthesis by aecting oxygen demand [20, 55].
Electrical conductivity (EC) measures the ability of water to conduct electricity, reecting the concentration of dis-
solved ions such as salts and minerals, while resistivity is its reciprocal [44]. In textile euents, these ions often originate
from chemical additives, unreacted dyes, and detergents used in washing, dyeing, and nishing processes [56]. The low
resistivity value of 171 Ω-m for Industry 1 (S1) (Fig.2c) suggests a high concentration of ionic substances (e.g., sodium,
potassium, and iron) in the euents. This elevated ion concentration can negatively impact surface water quality and
harm aquatic life [57]. EC, being the inverse of resistivity, varies with temperature-higher temperatures lead to increased
EC. Furthermore, EC is directly correlated with Total Dissolved Solids (TDS), as a greater concentration of dissolved solids
enhances conductivity. This direct correlation is shown in Fig.2d and f, where TDS and EC values follow the same order:
S2>S6>S1>S3>S5>S4. The EC values for all industrial euents exceeded the BECR standard limit, indicating a high con-
centration of dissolved ions that could pose risks to aquatic organisms.
Similarly, salinity is inuenced by dissolved ions, particularly salts, and plays a critical role in water quality. The maxi-
mum salinity (3.58 ppt) found in the euent from Industry 1 (S1) (Fig.2g) was below the standard limit; however, high
salinity can inhibit aquatic vegetation growth by decreasing osmotic pressure [57].
3.2 Variation ofchemical‑biological parameters
Table3 provides descriptive statistics for the chemical-biological parameters (pH, DO, COD, BOD5, TC, and FC) in euents
from six textile industries. The mean (± SD) values were 7.72 ± 1.47 for pH, 2.00 ± 2.05mg/L for DO, 2107.54 ± 615.89mg/L
for COD, 176.44 ± 205.51mg/L for BOD5, 108.33 ± 77.93 count/100mL for TC, and 26.17 ± 13.97 count/100mL for FC. The
maximum and minimum values ranged as follows: pH (4.01–13.29), DO (0.14–6.22mg/L), COD (1077.44–3232.10mg/L),
BOD5 (52.70–693.90mg/L), TC (12.00–300.00 count/100mL), and FC (6.00–47.00 count/100mL). All chemical-biological
parameters exceeded the standard limits for inland surface water set by BECR (Table1), indicating that the euents are
Table 2 Descriptive statistics
for physical euent
parameters of six textile
industries in Chattogram City
Parameters Color Temp Resistivity TDS TSS EC Salinity Turbidity
Minimum 18.00 22.00 161.50 2.10 5.00 4.01 0.58 21.40
Maximum 57.00 38.00 902.00 842.00 269.00 13.29 3.58 288.00
Mean 33.14 27.52 416.85 282.37 93.56 7.80 2.16 135.73
Median 30.00 26.50 221.00 152.91 75.50 6.06 2.37 109.72
Mode 27.00 24.00 161.50 627.00 129.00 4.01 0.58 21.40
SD 12.04 4.41 319.89 320.28 78.24 3.42 1.13 90.53
CV (%) 36.34 16.02 76.74 113.42 60.65 43.86 52.55 66.70
Skewness 0.85 0.72 0.77 0.57 0.842 0.62 − 0.30 0.55
Kurtosis − 0.06 0.08 − 1.52 − 1.32 − 0.191 − 1.36 − 1.58 − 0.95
Percentiles
25% 25.00 24.00 182.18 2.97 33.00 5.34 0.81 65.28
50% 30.00 26.50 221.00 152.91 75.50 6.06 2.37 109.72
75% 38.25 30.48 810.75 608.25 144.00 11.73 3.21 208.00

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Fig. 2 Bar chart depicting
variations across industries for
a Color, b Temp, c Resistivity, d
TDS, e TSS, f EC, g Salinity, and
h Turbidity
(a
)(
b)
(c
)(
d)
S1 S2 S3 S4 S5 S6
0
10
20
30
40
50
60
Color (PCU)
S1 S2 S3 S4 S5 S6
15.0
17.5
20.0
22.5
25.0
27.5
30.0
32.5
35.0
37.5
40.0
Temp (˚C)
S1 S2 S3 S4 S5 S6
0
200
400
600
800
1000
Resistivity (Ω-m)
S1 S2 S3 S4 S5 S6
0
200
400
600
800
1000
TDS (mg/L)
(e
)(
f)
(g)(h)
S1 S2 S3 S4 S5 S6
0
50
100
150
200
250
300
TSS (mg/L)
S1 S2 S3 S4 S5 S6
0
2
4
6
8
10
12
14
EC (mS/cm)
S1 S2 S3 S4 S5 S6
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Salinity (ppt)
S1 S2 S3 S4 S5 S6
0
50
100
150
200
250
300
Turbidity (NTU)

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unsuitable for discharge into surface water bodies. These ndings align with previous studies conducted by Roy etal.
[57] in Bangladesh, Joshi and Santani [58] in India, and Panhwar etal. [59] in Pakistan. Joshi and Santani [58] reported pH
values of 9.17–12.53, COD levels of 449.5–2,078.55mg/L, and BOD levels of 71.35–852.63mg/L in various textile eu-
ent samples. Similarly, Panhwar etal. [59] found pH values of 6.2–9.8, COD levels of 1002–4056mg/L, and BOD levels of
177–1910mg/L in euents from ten textile mills.
The bar charts (Fig.3a–f) illustrate variations in the chemical and biological parameters of euent from selected
industries. The high pH (avg. 10.6) observed in Industry 1 (S1) (Fig.3a) indicates alkaline euent, which is detrimental to
aquatic life and biological processes [25]. Similar ndings were reported in HR Textiles Mills, where pH reached 11.9 due
to alkaline substances (NaOCl, NaOH, sodium phosphate, surfactants, etc.) used in bleaching processes [60]. Dissolved
oxygen levels in all euents (Fig.3b) were below the acceptable limit, with Industry 5 (S5) showing the lowest average
DO (0.17mg/L). This suggests a high organic load, as microbial decomposition of biodegradable organic matter (e.g.,
dyes, chemicals, and bers) consumes oxygen, producing stable compounds like CO2, SO4, PO4, and NO3 [9, 61]. Both
BOD5 and COD values (Fig.3c, d) exceeded permissible limits, with COD values signicantly higher than BOD5. A high
COD/BOD5 ratio (12–46) across all industries indicates a substantial presence of non-biodegradable organic matter and
potentially toxic heavy metals, which could pose severe risks to aquatic ecosystems by creating anaerobic conditions [25].
The biological parameters (TC, FC) (Fig.3e, f) indicate the presence of waterborne pathogens. The high TC count in
Industry 3 (S3) (300 counts/100mL) is likely due to elevated TSS levels, contributing to increased pathogen and con-
taminant loads. TC levels exceeding the standard limit contribute to water pollution and pose signicant health risks to
exposed individuals [20].
3.3 Variation ofheavy metals
Table4 presents descriptive statistics for heavy metal concentrations (Fe, Cd, Cr, Pb, and Mn) in euents from six textile
industries. The concentrations of Fe ranged from 0.14 to 6.85mg/L, with a mean (± SD) of 2.75 ± 1.75mg/L. Similarly, Cr,
Mn, Cd, and Pb ranged from 0.5 to 4.57mg/L, 0.46 to 2.99mg/L, 0.09 to 3.17mg/L, and 0.07 to 1.12mg/L, respectively,
with mean values in the order of Cr (2.12mg/L)>Mn (1.45mg/L)>Cd (1.25mg/L)>Pb (0.71mg/L). All maximum concen-
trations exceeded the allowable limits set by BECR standards (Table1). These ndings are consistent with previous stud-
ies [58, 62, 63], which reported elevated levels of heavy metals in textile euents. For instance, Joshi and Santani [58]
observed Fe concentrations ranging from 0.30 to 111.38, Cd from 0.02 to 0.74mg/L, Cr from 1.16 to 2.20mg/L, Pb from
0.16 to 0.35mg/L, and Mn from 0.07 to 7.74mg/L in textile euents from six sites. Imtiazuddin etal. [63] analyzed eu-
ents from seven textile mills and found Fe concentrations of 1.08–3.11mg/L, Cd of 0.001–0.18mg/L, Cr of 1.05–1.86mg/L,
and Mn of 0.88–1.85mg/L.
The bar charts (Fig.4a–e) illustrate the variation in the heavy metal concentrations across six textile industries. Fig-
ure4a shows that S6 had the highest Fe concentration, while S3 had the lowest. Similarly, Fig.4b–e indicate that Cd,
Cr, and Pb concentrations were highest in S6, whereas Mn was highest in S2. Conversely, Cd and Cr were lowest in S3,
and Pb and Mn were lowest in S4. Heavy metals in textile euents originate from various processing steps, including
Table 3 Descriptive statistics
for euent chemical-
biological parameters of
six textile industries in
Chattogram City
Parameters pH DO COD BOD5TC FC
Minimum 5.50 0.14 1077.40 52.70 12.00 6.00
Maximum 11.34 3.22 3232.10 112.80 300.00 47.00
Mean 7.72 1.50 2274.21 84.22 108.33 26.17
Median 7.52 0.98 2515.50 83.70 88.50 31.00
Mode 5.50 0.90 1077.40 52.70 12.00 8.00
SD 1.47 1.11 629.28 20.79 77.93 13.97
CV (%) 19.07 123.25 58.41 39.45 71.94 53.39
Skewness 1.23 0.47 − 0.73 − 0.08 1.14 − 0.29
Kurtosis 1.51 − 1.49 − 0.58 − 1.49 1.13 − 1.47
Percentiles
25% 7.01 0.79 1755.63 63.15 59.50 8.75
50% 7.52 0.98 2515.50 83.70 88.50 31.00
75% 7.83 2.88 2670.23 103.68 133.25 37.50

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dyeing, printing, and nishing, where they are used as dye-stripping agents, oxidizing agents, organometallic nish-
ers, and essential components of dyes [25, 64]. Agrawal [64] reported that metals such as Pb, Cr, Cd, Fe, Zn, and Cu are
extensively used in the production of color pigments for textile dyes. The elevated concentrations of Fe, Cd, Cr, and Pb
in S6 euents may be attributed to the extensive use of metal-containing dyes, pigments, and chemicals during dyeing,
printing, and nishing processes. In contrast, the lower concentrations of Cd, Cr, Pb, and Mn in S3 and S4 euents may
result from more eective treatment practices or the use of alternative, less toxic materials.
Heavy metals pose a signicant threat to the environment. When discharged into nearby water bodies, they can
severely degrade water quality, harm aquatic life, and contaminate soil and the food chain. They also pose serious risks
to humans, plants, and animals [65, 66]. Heavy metal toxicity can damage blood cells, the liver, kidneys, lungs, and other
vital organs, as well as impair central nervous system function. Recent studies [67–69] highlighted that heavy metals such
as chromium (Cr6+), arsenic (As5+), and cadmium (Cd2+) are highly toxic even at trace levels, causing severe health issues,
including organ damage, neurodegenerative disorders, and cancers. Due to their non-biodegradable nature, these metals
bioaccumulate in the ecosystem, posing long-term risks to human health and the environment. Chronic exposure to these
toxic metals is linked to neurodegenerative diseases such as Parkinson’s, Alzheimer’s, muscular dystrophy, and multiple
sclerosis. Furthermore, heavy metals can disrupt biological treatment processes and disturb the aquatic food chain [70].
Fig. 3 Bar chart depicting
variations across industries for
a pH, b DO, c COD, d BOD5, e
TC, and f FC
(a
)(
b)
(c
)(
d)
(e)(f)
S1 S2 S3 S4 S5 S6
5
6
7
8
9
10
11
12
pH
S1 S2 S3 S4 S5 S6
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
DO (mg/L)
S1 S2 S3 S4 S5 S6
0
500
1000
1500
2000
2500
3000
3500
COD (mg/L)
S1 S2 S3 S4 S5 S6
0
20
40
60
80
100
120
BOD
5
(mg/L)
S1 S2 S3 S4 S5 S6
0
50
100
150
200
250
300
TC (count/ 100 mL)
S1 S2 S3 S4 S5 S6
0
10
20
30
40
50
FC (count/ 100 mL)

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3.4 Spearman’s correlation andPCA analysis
Figure5 presents a correlation plot (95% condence interval) illustrating the relationships among the physical, chemical-
biological, and heavy metal parameters of euents from selected industries. The plot reveals weak-to-strong correlations,
with positive correlations shown in red and negative correlations in blue. Strong positive correlations were observed
among TSS, turbidity, COD, TC, and salinity, while resistivity showed strong positive correlations with TDS, EC, DO, and
BOD₅. Additionally, BOD₅ was positively associated with TDS, EC, DO, and heavy metals; COD correlated with color, TSS,
and TC; turbidity was linked to color, TSS, TC, and salinity; and EC was positively related to resistivity, TDS, BOD₅, and
heavy metals.
On the other hand, heavy metals negatively correlated with color, TSS, turbidity, TC, and salinity. Similarly, color showed
negative correlations with pH, resistivity, TDS, EC, and BOD₅; TDS with color, temperature, TSS, TC, and salinity; TC with
pH, resistivity, TDS, and BOD₅; and BOD₅ with color, turbidity, TC, and salinity. No signicant relationships were detected
among heavy metals, pH, temperature, and FC, and FC showed no signicant correlation with other parameters. Addi-
tionally, BOD₅ exhibited no signicant correlation with pH, temperature, TSS, COD, and FC; color showed no correlation
with temperature, DO, and FC; turbidity had no correlation with pH, temperature, TDS, EC, DO, COD, and FC; and salinity
did not show correlation with pH, temperature, DO, COD, TC, and FC.
The correlations identied in this study (Fig.5) highlight signicant interrelationships among various physical, chemi-
cal, and biological parameters in textile industry euents. The strong positive correlations between TSS, turbidity, COD,
TC, and salinity suggest that these parameters often co-vary, potentially indicating shared sources of contamination that
could exacerbate environmental degradation. Understanding these relationships can inform targeted monitoring and
remediation strategies, improving water quality management and mitigating ecological impacts. Furthermore, the lack
of signicant correlations between heavy metals, pH, temperature, and FC underscores the need for further research
into the origins and behavior of these pollutants.
TableS1 presents the loading scores of the first three principal components (PC1, PC2, and PC3) for the different
effluent parameters. Each row corresponds to a specific parameter, and the values in the columns (PC1, PC2, PC3)
represent the weight or contribution of that parameter to each principal component. Varimax rotation (Fig.6) was
applied to the PCA to enhance the interpretability of the data. PC1 accounts for 49.0% of the total variation, PC2
contributes 18.3%, and PC3 accounts for the remaining variation. PC1 is strongly influenced by parameters such
as pH, resistivity, TDS, EC, DO, BOD₅, Fe, Cd, Cr, Pb, and Mn, indicating a positive loading. This suggests that these
parameters tend to vary together in a similar direction across the samples, likely representing common underlying
variations in the dataset.
The PCA (Fig.6) identied four distinct clusters of euent parameters based on their similarity: Cluster 1 (COD, tur-
bidity, TC, color, and TSS); Cluster 2 (BOD₅, TDS, DO, EC, resistivity, Cr, Mn, and Pb); Cluster 3 (pH, Cd, and Fe); and Cluster
4 (FC, temperature, and salinity). BOD₅, TDS, Cr, Cd, TSS, and salinity strongly inuenced PC1, while pH, FC, Pb, and COD
Table 4 Descriptive statistics
for euent heavy metal
parameters of six textile
industries in Chattogram City
Parameters Fe Cd Cr Pb Mn
Minimum 0.14 0.09 0.51 0.07 0.46
Maximum 6.85 3.17 4.57 1.12 2.99
Mean 2.75 1.25 2.12 0.71 1.45
Median 2.89 1.30 1.77 0.80 1.27
Mode 2.89 0.09 0.51 0.87 0.46
SD 1.75 0.75 1.47 0.33 0.87
CV (%) 63.47 59.88 69.33 46.66 59.94
Range 6.71 3.08 4.06 1.05 2.53
Skewness 0.47 0.69 0.64 − 0.55 0.39
Kurtosis 0.20 1.50 − 1.08 − 1.01 − 1.32
Percentiles
25% 1.01 0.88 0.78 0.44 0.69
50% 2.89 1.30 1.77 0.80 1.27
75% 4.02 1.51 3.40 1.02 2.18

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had a signicant inuence on PC2. BOD₅ and Cr, Fe and Cd, TC and turbidity, and color and TSS were positively correlated.
In contrast, BOD₅ and salinity, color and Fe, salinity and TDS, COD and pH, EC and temperature, turbidity and Fe, and
resistivity and TSS showed negative correlations. Although small correlations between pH and salinity, color and FC, COD
and EC, and TC and Pb are possible, they are less likely to be signicant.
Total Suspended Solids (TSS), color, and turbidity were grouped into a single cluster (Cluster 1) due to their positive
correlation, as demonstrated by PCA analysis. The presence of suspended particles in wastewater contributes to elevated
TSS, color, and turbidity levels. These particles often contain organic compounds and other chemicals that increase COD
levels, as these substances require oxygen for degradation [71, 72]. Contaminated particles can also increase the TC
count. Similarly, PC2 (second-order variation) was strongly inuenced by parameters such as color, TSS, turbidity, FC, and
Mn, as evidenced by their signicant positive loadings (TableS1). This indicates a shared variation pattern among these
parameters. Cluster 4, which includes FC, temperature, and salinity, suggests that temperature signicantly impacts FC
Fig. 4 Bar chart depicting
variations across industries for
a Fe, b Cd, c Cr, d Pb, and e Mn
(a
)(
b)
(c
)(
d)
S1 S2 S3 S4 S5 S6
0
1
2
3
4
5
6
7
Fe (mg/L)
S1 S2 S3 S4 S5 S6
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Cd (mg/L)
S1 S2 S3 S4 S5 S6
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Cr (mg/L)
S1 S2 S3 S4 S5 S6
0.00
0.25
0.50
0.75
1.00
1.25
Pb (mg/L)
(e)
S1 S2 S3 S4 S5 S6
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Mn (mg/L)

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growth and salinity levels. Higher temperatures can accelerate the growth of faecal coliform bacteria while simultane-
ously reducing their lifespan. Additionally, increased temperatures can enhance evaporation rates, potentially raising
salinity levels in wastewater [73].
Most euent parameters (BOD₅, TDS, DO, EC, resistivity, Cr, Mn, and Pb) were grouped into Cluster 2. The inclusion of
TDS, EC, and resistivity in the same cluster is scientically justied, as TDS and EC are inversely correlated with resistivity.
TDS measures the concentration of dissolved ions and compounds, which increase electrical conductivity and reduce
resistivity. Thus, higher TDS levels result in lower resistivity [74]. BOD and DO are also part of Cluster 2, as BOD reects the
oxygen demand caused by microbial decomposition of organic matter, which inuences oxygen dynamics in water [75].
Fig. 5 Correlation plot among
physical, chemical-biological,
and heavy metal parameters
of the euents discharged
from six textile industries in
Chattogram City
Fig. 6 Plot of loadings (PC1
versus PC2) of the 19 variables
pH
Color
Temp
Resistivity
TDS
TSS
TurbidityEC
DO
COD
BOD5
TC
FC
Salinity Fe Cd
Cr
pd
Mn
-10 -5 0510
-6
-4
-2
0
2
4
6
PC2 (18.3%)
PC1 (49.0%)
Scores
95% Confidence Ellipse
Loadings
-0.4 -0.20.0 0.20.4
-0.4
-0.2
0.0
0.2
0.4

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Interestingly, most heavy metals were also present in Cluster 2 (Fig.6). The distinct angle between heavy metals and
other physicochemical factors in the PCA plot suggests that heavy metals exhibit dierent characteristics compared to
other parameters. A similar pattern was observed in Cluster 3. Overall, the PCA results (TableS1 and Fig.6) reveal signi-
cant interactions among euent parameters from textile industries. The clustering of related parameters, such as TSS,
turbidity, and COD, indicates that they likely originate from similar pollution sources, emphasizing the need for targeted
management strategies. The presence of BOD₅, TDS, DO, and heavy metals in the same cluster highlights the complex
interplay between organic pollution and toxic elements, necessitating integrated water quality monitoring and stricter
discharge regulations for textile industries.
4 Conclusion
This study assessed the pollution contributions from euents discharged by six prominent textile industries in Chatto-
gram City by analyzing various physical, chemical-biological, and heavy metal parameters. Among the nineteen param-
eters analyzed, EC, pH, DO, COD, BOD₅, TC, Fe, Cd, Cr, Mn, and Pb exceeded the allowable BECR or USEPA standard limits.
Correlation and PCA analyses revealed strong positive correlations among several parameters, indicating interdepend-
encies that could exacerbate local surface water pollution. The ndings highlight the potential risks posed by these
euents to the environment, public health, and aquatic ecosystems. To address these issues, the study emphasizes the
need for hybrid or integrated euent treatment technologies that combine advanced ltration systems (membrane
bioreactors and nanoltration) with bioremediation (microbial consortia and phytoremediation) and chemical treatments
(advanced oxidation processes), oering a more sustainable and ecient solution compared to conventional methods
(coagulation-occulation and activated sludge process). Implementing real-time monitoring systems and data-driven
decision-making AI tools could further optimize treatment processes and ensure compliance with environmental quality
standards. Additionally, authorities must enforce stricter regulations on pollution from textile industries while promoting
public awareness and education through community engagement and outreach programs. However, the study is lim-
ited by its geographical focus on Chattogram City and the lack of consideration for seasonal variations. Future research
should expand to include comparisons across various regions of Bangladesh, investigate seasonal variations in euent
characteristics, and assess the specic impacts on receiving water bodies. Furthermore, exploring the role of socioeco-
nomic factors in promoting cleaner textile production practices could provide valuable insights for decision-makers and
practitioners to develop sustainable euent management strategies.
Acknowledgements The authors are grateful acknowledged to the Department of Civil Engineering of Port City International University (PCIU)
for their cooperation during the experimental investigation.
Author contributions Md. Mehedi Hassan Masum (Research Assistant Professor): conceptualization, methodology, resources, investigation,
data collection, formal analysis, visualization, software, writing—original draft. Md. Bashirul Islam (Research Lecturer): conceptualization,
methodology, resources, investigation, data curation, formal analysis, software, writing—original draft. Md. Rezvi Anowar (Senior Lecturer):
Conceptualization, Methodology, Data Collection, Formal Analysis, Visualization, Writing—review & editing. Kandila Arsh (PG Student): con-
ceptualization, methodology, data collection, investigation formal analysis, writing—review & editing. Sadia Alam (Assistant Professor): con-
ceptualization, methodology, data collection, writing—review & editing.
Funding There is no funding information available for the work.
Data availability Data will be available on request.
Declarations
Ethics approval and consent to participate Not applicable.
Consent for publication All authors consent to the publication of the manuscript and supplementary material.
Competing interests The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which
permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to
the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modied the licensed material. You
do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party
material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If

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material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds
the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco
mmons. org/ licen ses/ by- nc- nd/4. 0/.
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