Agriculture plays a dual role, both contributing to and being impacted by water pollution. This study evaluates the impact of tobacco and non-tobacco farming on irrigation suitability, water quality index (WQI), water health index (WHI), heavy metal (HM) pollution, and ecological risk (ER). Twenty-four water samples from six tobacco fields (TFs) and six non-tobacco fields (NTFs), collected before cropping season and after cropping season, were analyzed for major physico-chemical properties and HMs. The results suggest that TF improves sodium and alkalinity hazards but worsens nutrient and HM pollution (Pb: 0.119 mg/L, Cd: 0.021 mg/L, Ni: 0.242 mg/L), and threatens salinity, permeability, osmotic potential (OPπ), dry residue (RS), and miscellaneous hazards. Conversely, NTF has minimal impact, except for nutrient and miscellaneous hazards. WQI shifts from good to very poor, primarily driven by turbidity, PO4-P, K+, and NO3-N. Similarly, WHI declines from excellent to very poor in TF and from good to poor in NTF. HM pollution index has risen slightly above irrigation safety limits, mainly due to Cd, Ni, and Pb, resulting in a slightly elevated ER. This study demonstrates that TF, driven by excessive agrochemical use, renders water unsuitable for irrigation, underscoring the imperative for sustainable practices.

  • Both tobacco field (TF) and non-tobacco field (NTF) waters often exceed safe limits, with TF contributing higher pollution levels.

  • TF increases sodium and alkalinity hazards, while NTF shows no significant effect.

  • TF worsens nutrient pollution, salinity, osmosis, permeability, and miscellaneous hazards.

  • Water quality, water health, heavy metal pollution, and ecological risks decline more in TF water.

Water, especially freshwater, is an essential and abundant natural resource for all living organisms, including plants, animals, humans, and other life forms (FAO & AWC 2023; Rai et al. 2024). Water has played an important role in maintaining the human race, socio-economic advancement, and ecosystem resilience since the beginning of life on Earth (Islam et al. 2016; Sharma et al. 2025). The agricultural sector is the largest consumer of water for irrigation, using 70% of the world's freshwater; in developing countries like Bangladesh, this percentage is much higher, reaching 95% (Roy et al. 2015). As an irrigated agricultural economy country, Bangladesh depends on an adequate water supply, of which 80.60% comes from groundwater and 19.40% from surface water. In Bangladesh, approximately 5,049,785 ha of land is irrigated, requiring quality water to support crop growth and yield (Vyas & Jethoo 2015). Surface water is typically the main source of irrigation for farmers because it is readily available and cost-effective. However, it is threatened by factors such as quality degradation, pollution, and overuse during dry periods (Islam et al. 2016). Agriculture plays a major role in water pollution, and its pollution surpasses urban and industrial sources (Evans et al. 2018; FAO 2018). Since the beginning of the 20th century, agriculture has become both a cause and a victim of water pollution. Agricultural pollution is caused by excessive use of agrochemicals (fertilizers and pesticides), poor post-harvest management, and inefficient irrigation systems (Roy et al. 2024b; Zia et al. 2013). These traditional agrarian practices release large amounts of nutrients, salts, organic matter, pesticides, sediments, heavy metals, pathogens, and other emerging pollutants into nearby waters through leaching, runoff, erosion, and atmospheric deposition (FAO 2018), leading to significant degradation of surface water quality for irrigation (FAO 2013; Monira et al. 2024). Two separate studies demonstrated that agriculture accounts for 37% of surface water pollution in the Philippines and 38% in the European Union (FAO 2017). FAO (2013) revealed that agriculture is North America's single largest source of surface water pollution. About 30–50% of crop production depends on commercial fertilizers, and to feed a growing population, about 200 MT of chemical fertilizers (NPK) are used worldwide annually in agriculture. In Bangladesh, excessive chemical fertilizers are commonly used to increase commercial yields, patronized by the government, with an average use of 320.9 kg/ha, which is significantly higher than India (121.4), China (301.5), Britain (287.5), and the USA (160.8) (Savci 2012). Excessive chemical fertilizers are often not fully utilized by crops, thereby entering water bodies (Corradini et al. 2015), which degrade surface water quality, pollute water sources, harming aquatic ecosystems, endangering wildlife, and threatening human health (Liu et al. 2020; Roy & Mostafa 2024). FAO (2013) reported that this pollution is responsible for almost half of the world's surface water quality, and about 66% of all Chinese lakes have undergone eutrophication to hyper-eutrophication. Several studies showed that agriculture is the main source of pollution in surface waters, contributing 81% of N, 93% of N, and 50% of chemical oxygen demand (COD) in China, compared to only 10–16% from industry (Cui et al. 2020; Zhang et al. 2020); it also contributes to 60–65% of N, 60% of P, and 60% of K pollution in Vietnam and 7.6% of pesticide pollution in Thailand (FAO 2018). The total area under tobacco cultivation in Bangladesh was 40,634 ha, and the total production was 92,327 tons in FY 2021–22. It is the 6th major cash earner but the second top export crop of Bangladesh, ranking 14th in acreage and 12th for production in the world (Roy et al. 2024a). Tobacco cultivation uses a large amount of chemical fertilizers (1,594 kg/ha), which is 2.02, 2.48, and 1.48 times more than boro rice, wheat, and winter maize, respectively (Roy et al. 2024b). Therefore, tobacco cultivation pollutes water more than other crops. Despite this situation, the issue of water pollution from agriculture has not received much attention in the world, including in Bangladesh (Evans et al. 2018).

Water quality is crucial for establishing a successful irrigation system in agriculture, and it should be regularly monitored and evaluated for the suitability of irrigation water (Shill et al. 2019). Poor irrigation water quality directly affects the quality of soil and the crops that are cultivated there, as well as significantly contributing to surface water pollution (Sappa et al. 2015). All irrigation water contains mineral salts, but their concentration and composition vary from location to location (Ayers & Westcot 1985). The suitability of irrigation water depends on its mineral content. Various water quality index (WQI) techniques have been established worldwide to assess IWQ for improved crop growth and production, as no single model can fully assess water quality (Islam & Mostafa 2022). Plants and agricultural soil are physically and chemically impacted by excessive dissolved ions in irrigation water, which lowers their productivity (Liu et al. 2020). Physical stress reduces the osmotic stress in plant cells and chemical stress disrupts plant metabolism (Bai et al. 2019). Therefore, pH, alkalinity, sodium adsorption ratio (SAR), cation ratio of soil structural stability (CROSSOpt), soluble sodium percentage (SSP), residual sodium bicarbonate (RSBC), permeability index (PI), Kelly's ratio (KR), magnesium adsorption ratio (MAR), salt index (SI), dry residue (RS), osmotic potential (OPπ), electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), HMs, and some specific ions are commonly used to assess the suitability of drinking and irrigation water (Islam et al. 2017; Wagh et al. 2018; Elsayed et al. 2020; Ali Rahmani et al. 2024; Thapa et al. 2024). The USA Salinity Laboratory diagram (USSL 1954) and the Wilcox diagram (1955) are also commonly used to interpret irrigation water suitability (Roy et al. 2015; Sharma et al. 2025). However, the impact of irrigation water on soil and plants depends on various factors, including climate, soil texture, soil organic matter, crop type, management practices, and irrigation systems. Recently, many studies have been conducted worldwide, including in Bangladesh, to evaluate the suitability of ground and river water for irrigation; a comprehensive assessment of salinity risk, permeability risk, specific ion toxicity, miscellaneous effects, and HM contamination is also found in the literature but their numbers are very few (Goher et al. 2014; Roy et al. 2015; Islam et al. 2016, 2017; Wagh et al. 2018; Elsayed et al. 2020; Zakir et al. 2020; Yousif & Chabuk 2023). However, no researcher in the world, including Bangladesh, has evaluated the suitability of pond water near crop farms for irrigation by considering both major physico-chemical parameters and HM. Additionally, no one has assessed the impact of tobacco cultivation on surface water suitability for irrigation compared to non-tobacco crops. A comprehensive study was conducted on surface water within the tobacco and non-tobacco crop-growing areas using multiple indexing tools of irrigation water suitability, water health, and HM pollution. The findings of this study will offer farmers valuable insights into the effects of tobacco and non-tobacco crop cultivation on the quality of water bodies used for irrigation. This information can assist them in implementing risk-reduction strategies to support the sustainable management of surface water resources. In addition, by understanding the devastating implications of tobacco cultivation on surface water, policymakers of the country may be encouraged to shift tobacco farmers to more profitable crops in the future.

Research location

The Kushtia district, located within the Khulna administrative division in western Bangladesh, covers an area of 1,621.15 km2. It is situated between latitudes 23°42′ and 24°12′ north and longitudes 88°42′ and 89°22′ east, falling under the Agro-Ecological Zone 11 (AEZ 11, High Ganges River Floodplain). Kazihata village (Dharmapur union, Bheramara upazila) and Kamalpur village (Pairpur union, Daulatpur upazila) of Kushtia district were selected as study sites (Figure 1) due to the high prevalence of tobacco cultivation (54.63%) (Roy et al. 2024a).
Figure 1

Map illustrating the locations within the study area. Used ArcGIS 10.7 software. T and NT are tobacco and non-tobacco; S is field soil; W is a water body.

Figure 1

Map illustrating the locations within the study area. Used ArcGIS 10.7 software. T and NT are tobacco and non-tobacco; S is field soil; W is a water body.

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Six ponds were selected across six individual TFs from two villages so that rainwater from the fields was channeled into the ponds, which were also used for irrigation (Figure 2). To compare the impacts of tobacco cultivation on surface water suitability for irrigation, another six ponds around six NTFs (two rice, two wheat, and two winter maize) were also randomly selected from the two villages. Rice, wheat, and maize are the main crops covering 65.47% of Bangladesh's cultivated land during the rabi season and compete directly with tobacco, making them the focus of this study as non-tobacco crops.
Figure 2

Image showing sampling sites and analytical instruments.

Figure 2

Image showing sampling sites and analytical instruments.

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Water sample collection and laboratory analysis

A total of 24 water samples were collected from representative ponds at two points in time: before the cropping season (BCS) (post-monsoon time) and after the cropping season (ACS) (pre-monsoon time) in 2023–2024, for the analysis of major physico-chemical parameters and heavy metals. A YSI Pro1030 pH and conductivity meter (Xylem, USA) and a portable microprocessor-based bench meter (Model: HI 9813-6, Hanna Instruments, Portugal) were used to measure the water's temperature, pH, turbidity, TDS, EC, and dissolved oxygen (DO) in situ (Islam & Mostafa 2024). Surface water samples for anion and cation analysis were collected in two cleaned 500 mL plastic bottles (washed with diluted HCl and rinsed with distilled water), corked immediately, and stored in an ice box to prevent oxidation. Samples for heavy metal (HM) and major cation (except Ca2+ and Mg2+) analysis were acidified with HNO3 (Fluka Analytical, Sigma-Aldrich, Germany) to a pH <2, transported to the laboratory within 8 h, and digested with a 1:3 mixture of HCl and HNO3 (Islam & Mostafa 2024). All heavy metals (Fe, Mn, Cu, Zn, As, Pb, Cd, Cr, and Ni) were analyzed using an atomic absorption spectrophotometer (Shimadzu-7000), while Na+ and K+ were measured directly with a flame photometer (Figure 2). TH, Ca2+, and Mg2+ were determined by titration with an ethylenediaminetetraacetic acid (EDTA) solution (Goher et al. 2014). Total suspended solids (TSS) were measured by the Whatman 42 filter paper method. The potassium permanganate method assessed COD (Goher et al. 2014). and Cl were measured by titration following the American Public Health Association's standard procedure (Islam & Mostafa 2024) A double-beam ultraviolet (UV)–visible spectrophotometer (UV mini-1240, Shimadzu) was used to quantify , NO3-N, and PO4-P. All evaluations were conducted at the Water Research Laboratory and the Central Laboratory of the University of Rajshahi. Methods were recalibrated after every 10 sample runs, with all analyses performed in triplicate to ensure accuracy. All statistical analysis, including Charge Balance Error (CBE), Irrigation Water Suitability (IWS), WQI for Irrigation, WHI, HPI, HEI, and Potential Ecological Risk Index (IR), are provided in the Supplementary Information. The cations and anion charge balance was calculated to verify data reliability, using the subsequent equation.
(1)
where and represent the molar concentration and charge of the cation; similarly, and were the same for the anion. All calculated values of CBE were within ±5% (Islam & Mostafa 2022).

Data processing and analysis

Laboratory data were compiled into a master sheet and analyzed using Statistical Package for the Social Sciences (SPSS) software (version 20) and Microsoft Excel 2016 for statistical evaluation of various water parameters.

Surface water assessment tool for irrigation suitability

No single method can effectively assess the surface water suitability for irrigation. The following methods were considered for the evaluation of IWQ, where all input values of SAR, CROSSOpt, SSP, KR, MAR, RSBC, and PI were considered in mEq/L. Pearson's bivariate correlation matrix (t0.05 = 2.571 at 5 df) was also performed to compare the differences between BCS and ACS for both fields.

  • a) Richards in 1954 proposed that the SAR can be expressed using Equation (2) and stated that SAR values <9 mEq/L are generally suitable for irrigation (Ali Rahmani et al. 2024).
    (2)
  • b) SAR is the standard for predicting the potential impact of irrigation water on soil permeability and structural hazard (USSL 1954; Ayers & Westcot 1985). Ca2+ and Mg2+ are known to support good soil structure, while K+, though less dispersive than Na+, has a stronger dispersive effect than Ca2+ and Mg2+. However, K+ is excluded from the SAR equation. To address this, Smith et al. (2014) introduced CROSSOpt, a modified indexing technique to more accurately predict infiltration hazards as Equation (3), and applicable to all irrigation waters (FAO & AWC 2023; Thapa et al. 2024).
    (3)
  • c) Wilcox in 1948 defined the SSP using Equation (4) and indicated that SSP values <20% signify excellent for irrigation water (Roy et al. 2015).
    (4)
  • d) KR, introduced in 1963 and calculated using Equation (5), indicates that values >1 are unsuitable for irrigation (Wagh et al. 2018).
    (5)
  • e) The MAR, or magnesium hazard, was calculated by Todd in 1980 using Equation (6), with MAR values >50% deemed unsuitable for irrigation (Wagh et al. 2018).
    (6)
  • f) The RSBC, defined by USSL (1954) in Equation (7), considers values <1.25 mEq/L as low alkalinity, typically suitable for irrigation (Roy et al. 2015).
    (7)
  • g) Doneen in 1964 defined the PI in Equation (8), with classifications as follows: >90 = excellent, 90–75 = good, 75–30 = fair, and <30 = unsuitable for irrigation (Islam & Mostafa 2022).
    (8)
  • h) Dry residue (RS), expressed in Equation (9) by Bai et al. (2019), is measured in μS/cm. RS values >525 mg/L are considered incompatible for irrigation.
    (9)
  • i) OPπ, calculated using Equation (10) with values in μS/cm, is considered unsuitable for irrigation when OPπ > 0.27 atm (Bai et al. 2019).
    (10)
  • j) The SI, calculated using Equation (11), predicts salt and sodium hazards to soil and plants, with all ions measured in mg/L. A positive SI value is unsuitable for irrigation (El-Defan et al. 2016).
    (11)

Assessment of WQI for irrigation suitability

The WQI is a tool for rating the combined effect of individual water quality parameters on overall water quality (Sappa et al. 2015; USSL 1954). The WQI calculation method, as defined by (Equition (11)–(13)), is extensively utilized in research due to its robust framework and consistent applicability accross diverse studies (Matta et al. 2018; Zakir et al. 2020; Ali Rahmani et al. 2024).
(12)
where Qi is the quality rating for the ith parameter, Wi is its weight, and n is the total parameter number.
(13)
where Va, Vs, and Vi represent the measured, standard, and ideal values for the ith parameter, respectively. Except for pH (7) and DO (14.6 mg/L), all other parameters of water are assumed to have an ideal value of zero (Goher et al. 2014). All standard values were based on FAO (FAO 2023) IWQ guidelines and SWQ of Bangladesh Environmental Protection Rules-2023 (BEPR 2023) (Table 1).
(14)
where Si represents the standard value for the ith parameter, and k is the proportionality constant. The WQI values are classified into five categories: <50 (excellent), 50–100 (good), 100–200 (poor), 200–300 (very poor), and >300 (unsuitable for irrigation) (Ali Rahmani et al. 2024; Islam & Mostafa 2024).
Table 1

Descriptive statistics of parameters of surface water surrounding TF and NTF

Name of parametersSurface water around TF
Surface water around NTF
MPL, FAO for IWQ (2023)MPL, BEPR for SWQ (2023)
BCS (Mean ± SD)ACS (Mean ± SD)BCS (Mean ± SD)ACS (Mean ± SD)
Temper. (°C) 26.32 ± 1.06 30.18 ± 1.44 25.30 ± 1.72 29.27 ± 1.16 – – 
Turbid. (NTU) 18.14 ± 13.25 69.12 ± 33.71 17.88 ± 18.72 57.89 ± 52.77 ≤5 – 
TSS (mg/L) 91.99 ± 13.31 121.83 ± 9.60 89.27 ± 17.14 114.17 ± 18.27 100 100 
pH 7.67 ± 0.63 7.77 ± 0.34 7.81 ± 0.37 7.72 ± 0.43 6.0–8.5 6.5–8.5 
DO (mg/L) 4.43 ± 0.47 3.03 ± 0.52 5.48 ± 0.90 3.95 ± 1.05 – ≥5 
COD (mg/L) 144.67 ± 29.00 218.33 ± 50.33 146.67 ± 45.30 206.11 ± 66.00 – 100 
EC (μS/cm) 571.17 ± 119.65 815.17 ± 49.20 861.17 ± 424.02 911.50 ± 360.18 3,000 2,250 
TDS (mg/L) 360.00 ± 74.19 526.25 ± 27.85 597.00 ± 350.03 590.75 ± 238.21 2,000 1,000 
TH (mg/L) 164.00 ± 28.37 242.25 ± 27.75 240.92 ± 124.35 280.23 ± 112.39 400 – 
Ca2+ (mg/L) 49.00 ± 7.24 70.67 ± 8.26 59.83 ± 27.41 76.33 ± 22.99 400 – 
Mg2+ (mg/L) 10.13 ± 4.00 15.19 ± 4.02 20.66 ± 14.12 21.08 ± 13.11 120 – 
Na+ (mg/L) 15.72 ± 1.25 16.68 ± 0.99 17.99 ± 2.46 21.94 ± 6.94 200 – 
K+ (mg/L) 0.40 ± 0.63 0.97 ± 0.67 0.62 ± 0.53 0.97 ± 0.57 – 
NO3-N (mg/L) 4.39 ± 1.45 10.19 ± 2.80 3.14 ± 1.89 7.57 ± 4.99 10 
PO4-P (mg/L) 0.89 ± 0.90 2.26 ± 0.85 0.72 ± 0.63 1.65 ± 0.56 
(mg/L) 46.96 ± 5.78 68.00 ± 30.46 54.31 ± 16.08 78.51 ± 50.93 960 – 
(mg/L) 149.46 ± 26.08 193.08 ± 43.43 207.86 ± 95.69 234.25 ± 96.19 610 – 
Cl (mg/L) 17.86 ± 4.87 21.76 ± 5.78 35.70 ± 31.11 38.90 ± 25.40 1,065 600 
Fe (mg/L) 1.223 ± 1.207 1.568 ± 1.141 1.007 ± 0.328 1.229 ± 0.609 
Mn (mg/L) 0.078 ± 0.039 0.093 ± 0.025 0.117 ± 0.041 0.129 ± 0.040 0.2 
Cu (mg/L) 0.066 ± 0.047 0.073 ± 0.045 0.058 ± 0.031 0.068 ± 0.048 0.2 
Zn (mg/L) 0.033 ± 0.007 0.047 ± 0.012 0.030 ± 0.008 0.038 ± 0.005 
As (mg/L) 0.010 ± 0.004 0.025 ± 0.013 0.011 ± 0.004 0.020 ± 0.009 0.2 0.1 
Pb (mg/L) 0.056 ± 0.108 0.119 ± 0.162 0.040 ± 0.015 0.079 ± 0.059 0.1 
Cd (mg/L) 0.010 ± 0.008 0.021 ± 0.009 0.011 ± 0.004 0.015 ± 0.005 0.01 
Cr (mg/L) 0.067 ± 0.008 0.098 ± 0.026 0.073 ± 0.005 0.098 ± 0.010 0.1 0.1 
Ni (mg/L) 0.081 ± 0.016 0.242 ± 0.111 0.088 ± 0.016 0.164 ± 0.042 0.2 
Name of parametersSurface water around TF
Surface water around NTF
MPL, FAO for IWQ (2023)MPL, BEPR for SWQ (2023)
BCS (Mean ± SD)ACS (Mean ± SD)BCS (Mean ± SD)ACS (Mean ± SD)
Temper. (°C) 26.32 ± 1.06 30.18 ± 1.44 25.30 ± 1.72 29.27 ± 1.16 – – 
Turbid. (NTU) 18.14 ± 13.25 69.12 ± 33.71 17.88 ± 18.72 57.89 ± 52.77 ≤5 – 
TSS (mg/L) 91.99 ± 13.31 121.83 ± 9.60 89.27 ± 17.14 114.17 ± 18.27 100 100 
pH 7.67 ± 0.63 7.77 ± 0.34 7.81 ± 0.37 7.72 ± 0.43 6.0–8.5 6.5–8.5 
DO (mg/L) 4.43 ± 0.47 3.03 ± 0.52 5.48 ± 0.90 3.95 ± 1.05 – ≥5 
COD (mg/L) 144.67 ± 29.00 218.33 ± 50.33 146.67 ± 45.30 206.11 ± 66.00 – 100 
EC (μS/cm) 571.17 ± 119.65 815.17 ± 49.20 861.17 ± 424.02 911.50 ± 360.18 3,000 2,250 
TDS (mg/L) 360.00 ± 74.19 526.25 ± 27.85 597.00 ± 350.03 590.75 ± 238.21 2,000 1,000 
TH (mg/L) 164.00 ± 28.37 242.25 ± 27.75 240.92 ± 124.35 280.23 ± 112.39 400 – 
Ca2+ (mg/L) 49.00 ± 7.24 70.67 ± 8.26 59.83 ± 27.41 76.33 ± 22.99 400 – 
Mg2+ (mg/L) 10.13 ± 4.00 15.19 ± 4.02 20.66 ± 14.12 21.08 ± 13.11 120 – 
Na+ (mg/L) 15.72 ± 1.25 16.68 ± 0.99 17.99 ± 2.46 21.94 ± 6.94 200 – 
K+ (mg/L) 0.40 ± 0.63 0.97 ± 0.67 0.62 ± 0.53 0.97 ± 0.57 – 
NO3-N (mg/L) 4.39 ± 1.45 10.19 ± 2.80 3.14 ± 1.89 7.57 ± 4.99 10 
PO4-P (mg/L) 0.89 ± 0.90 2.26 ± 0.85 0.72 ± 0.63 1.65 ± 0.56 
(mg/L) 46.96 ± 5.78 68.00 ± 30.46 54.31 ± 16.08 78.51 ± 50.93 960 – 
(mg/L) 149.46 ± 26.08 193.08 ± 43.43 207.86 ± 95.69 234.25 ± 96.19 610 – 
Cl (mg/L) 17.86 ± 4.87 21.76 ± 5.78 35.70 ± 31.11 38.90 ± 25.40 1,065 600 
Fe (mg/L) 1.223 ± 1.207 1.568 ± 1.141 1.007 ± 0.328 1.229 ± 0.609 
Mn (mg/L) 0.078 ± 0.039 0.093 ± 0.025 0.117 ± 0.041 0.129 ± 0.040 0.2 
Cu (mg/L) 0.066 ± 0.047 0.073 ± 0.045 0.058 ± 0.031 0.068 ± 0.048 0.2 
Zn (mg/L) 0.033 ± 0.007 0.047 ± 0.012 0.030 ± 0.008 0.038 ± 0.005 
As (mg/L) 0.010 ± 0.004 0.025 ± 0.013 0.011 ± 0.004 0.020 ± 0.009 0.2 0.1 
Pb (mg/L) 0.056 ± 0.108 0.119 ± 0.162 0.040 ± 0.015 0.079 ± 0.059 0.1 
Cd (mg/L) 0.010 ± 0.008 0.021 ± 0.009 0.011 ± 0.004 0.015 ± 0.005 0.01 
Cr (mg/L) 0.067 ± 0.008 0.098 ± 0.026 0.073 ± 0.005 0.098 ± 0.010 0.1 0.1 
Ni (mg/L) 0.081 ± 0.016 0.242 ± 0.111 0.088 ± 0.016 0.164 ± 0.042 0.2 

Note. SD, standard deviation; TF, tobacco field; NTF, non-tobacco field; BCS, before cropping season; ACS, after cropping season; IWQ, irrigation water quality; SWQ, inland surface water quality; and BEPR, Bangladesh Environment Protection Rules.Bold values indicate levels exceeding Maximum Permissible Limits (MPL).

Assessment of water health index (WHI) based on principal component analysis (PCA) and Pearson's correlation matrix

As with human health, no single process can assess the soil or water health index (WHI) (Kusum et al. 2023). Moreover, no indexing tools for evaluating WHI are currently available in the literature. The term ‘soil or water quality’ is sometimes used interchangeably with ‘soil/water health.’ However, many scientists, such as Kusum et al. (2023), have clearly explained and differentiated those worlds in their research. According to them, soil quality refers to the non-living mixture of stable physical and chemical properties of soil that determine its productivity for specific purposes over time, unaffected by human activities. In contrast, soil health emphasizes its dynamic physical, chemical, and biological properties as a limited, living, and non-renewable resource that supports improved performance for a particular time. According to Doran (2002), Soil health is the soil's ability to sustain living communities, influenced by its biological, chemical, and physical properties while supporting crop productivity, environmental protection, and overall ecosystem health. Assessment of soil health is crucial for sustainable agriculture, surpassing the importance of soil quality (Lehmann et al. 2020). Tejasvini (2022) identified physical, chemical, and biological properties as key indicators for monitoring soil health changes. Soil health involves a holistic understanding of soil ecosystems, and its assessment is a complex process that requires multifaceted statistical methods and the integration of a selected minimum dataset (MDS) (El-Ramady et al. 2014); but the physical, chemical, and biological properties of the soil must be taken into account (Kusum et al. 2023). Moreover, the PCA technique is an objective approach that uses various statistical tools (multiple correlation, factor analysis) to minimize bias and redundancy by selecting an MDS from the original observations for each parameter (Kowalska et al. 2018; Roy & Mostafa 2024; Uthappa et al. 2024). Researchers like Kusum et al. (2023) and Roy & Mostafa (2024) have successfully applied this technique to assess the soil health index. As soil health assessment is much more important than soil quality for sustainable agriculture, this study successfully employed the proposed new indexing tool, the WHI, to assess and predict the surface water suitability for irrigation as well as aquatic life. In calculating the WHI value, major physical (e.g., temperature, DO, TSS), chemical (e.g., anions, cations, HMs), and biological (e.g., COD) parameters were used in PCA, with a correlation matrix to reduce redundancy. There are many models for assessing the WHI; among them, the non-linear weighted approach, although more difficult to determine, is the best assessment technique (El-Ramady et al. 2014; Uthappa et al. 2024) and was applied in this study. Three steps are followed to evaluate WHI under this model (Uthappa et al. 2024): (i) choose the minimal dataset (MDS) by using PCA and correlation; (ii) transfer indicators into scores; and (iii) combine the scores into an index and compare with WHI classes. In this approach, each indicator is classified as ‘minimum is better’ or ‘optimum is better,’ and the non-linear score is calculated using the following equation (Uthappa et al. 2024):
(15)
where represents the non-linear scores, with a being the maximum value for a sigmoidal curve (a = 1 in this study), and x is the MDS indicator value. is the average value of the indicator across samples. The coefficient b is set to 10.5 for ‘minimum is better’ indicators and 1 for ‘optimum is better’ indicators (Martin-Sanz et al. 2022). Parameters such as pH, temperature, and DO are categorized as ‘optimum is better’, while the remaining parameters are classified as ‘minimum is better’ or ‘the less the better’ (Goher et al. 2014). The WHI is calculated using the following equation:
(16)
where Si is the indicator score, Wi is the principal component (PC) weight, and n is the number of parameters. The WHI values are categorized into five: <0.2 (very poor health), 0.2–0.4 (poor health), 0.4–0.6 (moderate health), 0.6–0.8 (good health), and 0.8–1.0 (excellent health) (Roy & Mostafa 2024; Uthappa et al. 2024).

Evaluation of the HPI

The HPI assesses water quality by evaluating the effect of heavy metals using a weighted arithmetic mean, as defined in the following equations (Islam & Mostafa 2024):
(17)
(18)
(19)
where Mi is the measured value, and Ii is the ideal value (mg/L), which is 0 for Cr, Cd, As, and Pb; 0.1 for Mn and Cu; 0.3 for Fe; 0.07 for Ni; and 0.5 for Zn (Islam & Mostafa 2024). Si represents the standard value, based on FAO (2023) recommendations for IWQ and BEPR (2023) for SWQ (Table 1). The HPI threshold value for irrigation suitability is 100, as suggested by Matta et al. (2018).

Assessment of the heavy metal evaluation index

The heavy metal evaluation index (HEI) assesses surface water quality concerning HM contamination and is calculated using the following equation (Islam & Mostafa 2024; Rai et al. 2024):
(20)
where Hc represents the measured value, and HMAC is the maximum admissible concentration based on FAO (2023) IWQ guidelines (Table 1). HEI is categorized as low (<10), medium (10–20), or high (>20) (Rai et al. 2024; Saleh et al. 2018).

Evaluation of the ecological risk of surface water based on the potential ecological risk index

The potential ecological risk index (IR) is a widely used tool for assessing ecological risks (ER). This approach is popularly used worldwide to comprehensively assess the ecological sensitivities and synergistic effects of all HMs. In this study, IR was used to evaluate the ER and calculated using the following equation (Kowalska et al. 2018):
(21)
where represents the toxicity response coefficient for each HM: Cd = 30, As = 10, Cu = 5, Ni = 5, Pb = 5, Cr = 2, and Zn = 2 (Kowalska et al. 2018), PI is the single pollution, and is the ER factor. IR values classify risk into five categories: <90 (low), 90–180 (moderate), 180–360 (strong), 360–720 (very strong), and ≥720 (extremely high) (Kowalska et al. 2018).

Impact of tobacco cultivation on irrigation water suitability compared to non-tobacco

The excessive use of pesticides, fertilizers, and other agrochemicals in agriculture often results in toxic runoff with rain or irrigation water, contaminating nearby water bodies.

Salinity hazards

Water salinity, measured by TDS and ECW, is a key factor affecting crop productivity. Dissolved salts reduce the soil's osmotic potential, impacting both crop yield and soil physical properties (USSL 1954; El-Defan et al. 2016; Islam & Mostafa 2022; FAO 2023). TDS measures dissolved ions directly, while ECW measures them indirectly using an electrode. Since plants only absorb fresh water, higher ECW significantly reduces the water available to plants. For example, water with an ECW of 1 μS/cm contains about 1.74 pounds of salt per acre-foot (Ayers & Westcot 1985). The results showed that during BCS, the ECW and TDS values were (571.17 ± 119.65 μS/cm) and (360.00 ± 74.19 mg/L) for TF water, and (861.17 ± 424.02 μS/cm) and (597.00 ± 350.03 mg/L) for NTF water. During ACR, ECW increased by 42.72%, reaching (815.17 ± 49.20 μS/cm) for TF water, and by 5.84%, reaching (911.50 ± 360.18 μS/cm) for NTF water. TDS increased by 46.18%, reaching (526.25 ± 27.85 mg/L) in TF water, while it remained almost unchanged in NTF water at (590.75 ± 238.21 mg/L). Islam & Mostafa (2022) found similar values for Bangladeshi pond water: 685.31 μS/cm for ECW and 402.54 mg/L for TDS. However, all values were well below the maximum permissible limit (MPL) for IWQ set by FAO (2023) and SWQ by BEPR (2023) (Table 1). According to irrigation water criteria, ECW (μS/cm) < 700 and TDS (mg/L) < 450 represent excellent or freshwater; ECW = (700–1,500) and TDS = (450–900) represent good water; ECW = (1,500–3,000) and TDS = (900–2,000) refers to doubtful water; and ECW > 3,000 and TDS > 2,000 indicate water unsuitable for irrigation (Roy et al. 2015; El-Defan et al. 2016; Islam & Mostafa 2022). The US salinity hazard diagram (1954) in Figure 4(a) shows that all values fall within the S1 (very low salinity) category. Statistical analysis of salinity hazards shows that TF water was classified as Grade A (non-saline) during BCS, but it significantly deteriorated to Grade B (slightly saline) during ACS (as 5.39 > t0.05). In contrast, the water quality of NTF remained unchanged at Grade B (slightly saline) with no significant difference (as 0.81 < t0.05). High ECW and TDS reduce crop productivity by causing physical drought, as plants struggle to compete with ions in the soil solution, leading to wilting (osmotic effect) even when the soil is wet. Soluble salts in irrigation water can also burn leaf tissue (toxic effect) when applied to foliage. Increased salinity can corrode machinery, cause nutrient imbalances (especially Ca2+, , and K+), reduce chlorophyll, disrupt metabolic processes such as respiration and photosynthesis, delay germination, and stunt growth by affecting cell division and elongation (FAO & AWC 2023). Conversely, increasing soil salt levels to a threshold level improves soil aeration, root growth, and root penetration. Halophytes thrive in saline conditions and can enhance the quality of some crops, such as higher sugar content in carrots, higher soluble solids in tomatoes and melons, better grain quality in wheat, and improved freezing tolerance in citrus. However, most crop plants are glycophytes and cannot tolerate the stress caused by high salinity hazards (FAO & AWC 2023). Though the impact of salt stress depends not only on its intensity but also on the crop species, age, and other factors like drought, extreme temperatures, flooding, poor soil conditions, nutrient deficiencies, and pests or diseases (FAO & AWC 2023). Results show tobacco cultivation raises the salinity risk for nearby surface water, potentially stressing sensitive plants and causing moderate soil leaching. This may be due to rainwater runoff carrying extra minerals from tobacco fields (TFs) into nearby water bodies, along with seasonal variations, increasing ECw and TDS values. In contrast, non-tobacco crops have no statistically significant impact on surface water salinity risk, as they use less chemical fertilizers and pesticides compared to tobacco farming (Roy et al. 2024b).

Sodium hazards

Salty irrigation water poses two key issues in crop production: salinity and sodium hazards. Salinity relates to salt concentration, while sodicity refers to salt composition (FAO & AWC 2023). Na+ with creates alkaline soil, and Na+ with Cl forms saline soil, both of which are harmful to plant growth. Sodicity in irrigation water deteriorates soil structure and indirectly causes nutrient imbalances in plants. Although Na+ is not essential for plants, excessive uptake can lead to toxicity (Roy & Mostafa 2024). Divalent cations such as Ca2+ and Mg2+ bind soil particles into aggregates, improving structure but increasing tillage costs. Conversely, Na+ causes de-flocculation, breaking down soil aggregates, sealing pores, and reducing infiltration, percolation, and aeration, leading to anoxic or hypoxic conditions for roots (Suarez & Jurinak 2012). Poor soil structure increases waterlogging, root diseases, and soil stickiness when wet and crusting when dry (FAO & AWC 2023). Additionally, high Na+ displaces essential cations (K+, Ca2+, and Mg2+), reducing nutrient availability and soil microbial activity, ultimately harming crop growth and yield (FAO 2013). Clay-rich soils are mostly vulnerable, as their hydraulic conductivity and infiltration rates drop with lower salinity and higher exchangeable Na+ levels (Suarez & Jurinak 2012). However, some factors like soil texture, dissolved organic carbon, clay composition, pH, calcite, and Al- and Fe-oxide content influence soil response to sodicity (FAO & AWC 2023). The sodium hazard standard values of key indicators for irrigation water suitability are SAR (<9 mEq/L), CROSSOpt (<9 mEq/L), SSP (<20%), KR (<1), and SI (<0 mg/L) (Roy et al. 2015; El-Defan et al. 2016; Wagh et al. 2018). Figures 3(a)–3(c), 3(e), and 3(j) show the SAR, CROSSOpt, SSP, KR, and SI values. Results showed that during BCS, the SAR (mEq/L), CROSSOpt (mEq/L), SSP (%), KR, and SI (mg/L) were 0.54, 1.12, 17.76, 0.21, and −221.93 for TF, and 0.53, 0.98, 15.87, 0.19, and −272.50 for NTF, indicating that all water was excellent and suitable for irrigation.
Figure 3

Box plot diagram showing suitability evaluation of surface water for irrigation. SAR, sodium adsorption ratio, SSP, soluble sodium percentage; KR, Kelley's ratio; MAR, magnesium adsorption ratio; RSBC, residual sodium bicarbonate; PI, permeability index; and RS, dry residue index.

Figure 3

Box plot diagram showing suitability evaluation of surface water for irrigation. SAR, sodium adsorption ratio, SSP, soluble sodium percentage; KR, Kelley's ratio; MAR, magnesium adsorption ratio; RSBC, residual sodium bicarbonate; PI, permeability index; and RS, dry residue index.

Close modal
Figure 4

Plot for irrigation water suitability analysis: (a) USSL (1954); (b) Wilcox (1955).

Figure 4

Plot for irrigation water suitability analysis: (a) USSL (1954); (b) Wilcox (1955).

Close modal

The high Ca2+ and Mg2+ levels, along with low Na+ concentrations, ensured excellent water quality with minimal sodium hazards during BCS for both TF and NTF waters. The average Ca2+ levels were 32 mg/L in Bangladeshi surface water, but 124 mg/L in Kushtia water (Islam & Mostafa 2022). Surprisingly, during ACS, sodium hazards decreased compared to BCS for both fields, with SAR, CROSSOpt, SSP, KR, and SI values improving by 12.39, 0.18, 23.05, 28.01, and 46.91 for TF water, and by −7.39, 21.50%, 4.12, 6.20, and 27.95% for NTF water, respectively (Figure 3). The reduction in sodium hazards was due to a greater increase in Ca2+ (mg/L) levels (49.00 ± 7.24 to 70.67 ± 8.26 for TF and 59.83 ± 27.41 to 76.33 ± 22.99 for NTF) compared to Na+ (15.72 ± 1.25 to 16.68 ± 0.99 for TF and 17.99 ± 2.46 to 21.94 ± 6.94 for NTF) (Table 1). This rise in Ca2+ may result from gypsum fertilizer (CaSO4.2H2O, 23% Ca2+) leaching into nearby surface waters. However, statistical analysis revealed that the reduction in sodium hazards was significant for TF water (SAR = 2.59, SSP = 3.05, KR = 3.43, and SI = 5.32, all >t0.05 = 2.571). For NTF water, improvements were not significant (SAR = 0.72, CROSSOpt = 2.14, SSP = 0.41, and KR = 0.53, all <t0.05), except for SI (3.72 > t0.05). Thus, it can be inferred that tobacco cultivation reduces the risk of sodium in the surrounding surface water for irrigation, while non-tobacco crops have no statistically significant effect.

Water pH and alkalinity hazards

Irrigation water pH within an acceptable range is generally not harmful to plant growth. However, extreme pH levels can cause nutrient imbalances, increase equipment corrosion, and enhance potential sodium hazards due to high concentrations of major anions such as , Cl, and (Ayers & Westcot 1985; Roy et al. 2015). High pH of irrigation water can significantly affect crop production, cause salt precipitation, affect the efficiency of coagulation and flocculation processes, and reduce the effectiveness of pesticides (FAO & AWC 2023). The study revealed that the pH value of the area remained within the normal range (6.0–8.5) but was slightly alkaline (FAO 2023). It increased slightly from (7.67 ± 0.63) to (7.77 ± 0.34) in TF but decreased slightly from (7.81 ± 0.37) to (7.72 ± 0.43) in NTF (Table 1). Statistical analysis showed that this slight increase in pH for TF water and decrease for NTF water were not significant (0.76 for TF and 0.73 for NTF, all <t0.05), suggesting that cultivation of tobacco or non-tobacco crops does not significantly affect the pH of surrounding irrigation water.

The water's pH below 8.5 suggests a lack of carbonate ions (FAO 2023), so only RSBC, not residual sodium carbonate, was used to assess alkalinity hazards, with an ideal RSBC value of <1.15 mEq/L for irrigation suitability (El-Defan et al. 2016). Irrigation water with RSBC >2.5 mEq/L can clog soil pores with salts, restricting water and air movement (Sudhakar & Narsimha 2013). Alkaline water raises soil pH, causing , Ca2+, Mg2+, and Fe2/3+ deficiencies and creating an imbalanced system by precipitating Ca2+ and Mg2+ that reduces salinity hazards but raises sodium hazards to levels higher than those indicated by the SAR (FAO & AWC 2023). Figure 3(f) shows RSBC values (mEq/L) during BCS as −0.83 for TF and −1.28 for NTF, which decreased to −1.62 (94.20%) for TF and −1.71 (33.26%) for NTF during ACS, i.e., all water samples were slightly alkaline for both fields during BCS and further improved after the harvest season. This was due to the higher increase of Ca2+ (44.22% for TF, 27.58% for NTF) compared to Na+ (6.11% for TF, 21.96% for NTF). This may be due to fertilizers, particularly urea, TSP, and DAP, runoff from fields during rainfall or irrigation, which may acidify soil, promote limestone dissolution, enhance algae growth, and contribute to organic matter decomposition, all of which increase bicarbonate levels in nearby surface water by altering pH and facilitating reactions with atmospheric CO2. Statistical analysis revealed that the improvement in alkalinity was significant for TF water (2.70 > t0.05) but not for NTF water (1.60 < t0.05). Therefore, tobacco farming significantly improves water alkalinity, while non-tobacco crops have no significant effect on nearby surface water alkalinity.

Magnesium hazards

Magnesium content is crucial for irrigation water suitability, as a high MAR >50% can increase soil alkalinity, degrade physical properties, and reduce crop yield (El-Defan et al. 2016; Islam et al. 2017; Wagh et al. 2018; Islam & Mostafa 2022). Figure 3(d) shows that during BCS, MAR (%) values in TF ranged from 16.72 to 36.84, averaging 24.76, and increased by 5.08% during ACS. In NTF during BCS, MAR values ranged from 18.38 to 32.70, averaging 26.02, but decreased by 14.31% during ACS. However, all MAR values remained within the ideal range (<50%), indicating suitability for irrigation. This slight fluctuation in MAR is likely due to natural processes, as no Mg-containing fertilizers were used in the fields. Statistical analysis shows that MAR fluctuations in both fields were insignificant (0.32 for TF and 1.09 for NTF, both <t0.05). Therefore, it suggests that tobacco and non-tobacco cultivation have no significant impacts on magnesium hazards.

Permeability hazards

The PI evaluates irrigation water suitability, influenced by high Na+, Ca2+, Mg2+, and alkalinity levels. Excess Na+ causes soil de-flocculation, sealing pores, and reducing air and water flow, especially in clay-rich soils (FAO & AWC 2023). Figure 3(g) shows a decrease in PI values from 57.21 to 45.59% for TF and from 51.35 to 46.50% for NTF. According to the PI classification, all values fall within the ‘poor’ irrigation suitability range (30–75%). Wilcox's (1955) irrigation suitability diagrams showed water quality in both fields during BCS ranged from excellent to acceptable, with no classification changes during ACS (Figure 4(b)). This may be due to increased ion from fertilizer runoff such as urea, triple super phosphate (TSP), and di-ammonium phosphate (DAP), soil acidification, limestone dissolution, organic matter decomposition, and atmospheric CO2. Statistical analysis showed a significant deterioration in PI for TF water (4.83 > t0.05), but not for NTF water (2.37 < t0.05). Therefore, tobacco farming likely has a significant negative impact on surface water permeability, while non-tobacco crops do not.

Osmotic potential and dry residual hazards

Dry residue index (RS) and osmotic potential (OPπ) are key indicators of irrigation water suitability, with higher RS and OPπ reflecting greater mineralization and less compatibility with irrigation. The higher the salinity of the soil solution, the greater the osmotic pressure, making it harder for roots to absorb water. For proper plant growth, RS should be <525 mg/L and OPπ < 0.27 atm (Bai et al. 2019; FAO & AWC 2023). Figure 3(h) shows that RS increased beyond the threshold (525 mg/L), from 399.82 to 570.62 for TF (42.72%) and from 602.82 to 638.05 for NTF (5.84%), making the water unsuitable for irrigation. Similarly, the OPπ (atm) values became unsuitable for irrigation (OPπ > 0.27) in ACS, rising from 0.21 to 0.29 for TF (38.10%) and from 0.31 to 0.33 for NTF (6.45%). This may be due to mineral runoff from farms, which raises EC and TDS levels in surface water during ACS. Statistical analysis of RS and OPπ reveals that tobacco cultivation has a significant negative impact on surface water around farms because RS (5.39) > t0.05 and OPπ (5.32) > t0.05. In contrast, non-tobacco crop cultivation has no significant effect, as RS (0.81) and OPπ (0.71) are both <t0.05.

Specific ion toxicity or nutrient pollution

High ion concentrations, particularly Na+, can cause nutrient deficiencies (e.g., Ca2+, , and K+), leading to crop injuries and disorders like blossom-end rot in tomato, melon, and pepper, soft-nose in mango, and cracking in apple, ultimately reducing crop yield and quality (FAO & AWC 2023). Although K+ is an essential plant macro-nutrient, high concentrations of K+ in irrigation water can negatively affect soil permeability, physical properties, and plant nutrition, and can cause plant toxicity (Islam & Mostafa 2022). The concentration of K+ increased from 0.40 ± 0.63 to 0.62 ± 0.53 for TF (142.50%) and 0.97 ± 0.67 to 0.97 ± 0.57 for NTF (56.45%) (Table 1) but remained below the IWQ standard (2 mg/L, FAO 2023), likely due to runoff from muriate of potash (MOP, 50% K) and sulfate of potash (SOP, 28% K) fertilizers. However, statistical analysis shows that tobacco cultivation significantly impacts surface water around farms (t = 4.03 > t0.05), while non-tobacco crops have no significant effect (t = 2.09 < t0.05). Chloride (Cl) is essential in low concentrations but can be toxic to crops like onion, bean, strawberry, avocado, and citrus at levels >140 mg/L (Roy et al. 2015; Islam et al. 2016, 2017; Elsayed et al. 2020), causing leaf burn and reducing uptake (FAO & AWC 2023). It does not affect the soil's physical properties, as the soil particles do not absorb it (FAO 2023). Sulfate is less harmful than chloride (Cl) since only half contributes to salinity as Na2SO4 or MgSO4, with the rest precipitating as CaSO4 (El-Defan et al. 2016). However, high concentrations (>192 mg/L) can interfere with other nutrient uptake (Roy et al. 2015). Table 1 shows that and Cl levels in both BCS and ACS remain within the MPL of IWQ (FAO 2023) and SWQ standards (BEPR 2023). Statistical analysis revealed that non-tobacco crops had no significant effect on (1.70 < t0.05) or Cl (1.20 < t0.05), while tobacco cultivation significantly affected Cl (3.41 > t0.05) but not (1.97 < t0.05), likely due to increased EC in TF during ACS (El-Defan et al. 2016). Water suitability for irrigation can also be assessed by potential salinity (PS), calculated as (Cl + ½ mE/L). El-Defan et al. (2016) state that PS values of 3–7 mE/L are suitable for low permeability soils, while 3–15 mE/L values are appropriate for medium permeability soils. PS analysis shows the values increased (decreased suitably for irrigation) from 1.48 to 2.03 for TF (37.01%) and 2.14 to 2.73 for NTF (27.79%), but the change was statistically insignificant, as 2.27 (TF) and 2.09 (NTF) are both <t0.05. Thus, the water of both fields remains suitable for irrigation on all types of soil.

Nutrient pollution, mainly from nitrogen (NO3-N) and phosphorus (PO4-P), stimulates excessive plant growth, weakens stalks resulting in severe lodging, delays maturity or poor quality, and causes eutrophication, oxygen depletion, and harmful algal blooms (FAO 2023). Table 1 shows that in BCS, NO3-N and PO4-P values were below the MPL standards of IWQ (FAO 2023) and SWQ (BEPR 2023). But ACS, NO3-N (mg/L) increased from (4.39 ± 1.45) to (10.19 ± 2.80) for TF and from (3.14 ± 1.89) to (7.57 ± 4.99) for NTF; while PO4-P (mg/L) increased from (0.89 ± 0.90) to (2.26 ± 0.85) for TF and from (0.72 ± 0.63) to (1.65 ± 0.56). Tobacco cultivation caused 1.31 times more NO3-N and 1.47 times more PO4-P pollution, exceeding MPL standards only in TF during ACS. FAO (2013) estimated surface water pollution from fertilizers using delivery coefficients of 0.75% for N and 0.34% for P. Tobacco cultivation (205 kg N, 134 kg P/ha) releases 1.54 kg N and 0.46 kg P per ha, while non-tobacco crops (rice, wheat, maize; 148 kg N, 74 kg P/ha) (Roy et al. 2024a) releases 1.12 kg N and 0.25 kg P per ha to surrounding surface water. Statistical analysis indicates that both tobacco and non-tobacco cultivation negatively affect surface water suitability for irrigation, increasing NO3-N (4.81 for TF and 2.96 for NTF, >t0.05) and PO4-P (10.93 for TF and 5.54 for NTF, > t0.05).

HM toxicity

Many potentially toxic elements are usually present in low amounts in soil and water. While trace amounts of Fe, Mn, Zn, Cu, and Ni are beneficial to plants (FAO 2023), elements like As, Pb, Cd, and Cr are non-essential and highly toxic to plants, animals, and humans. These harmful metals can enter ecosystems through agrochemicals, particularly phosphate fertilizers and pesticides (Rodriguez-Eugenio et al. 2018). Cr poses less risk to humans due to its low solubility, strong soil retention, and low plant uptake. Pb is strongly adsorbed by soil colloids, and although absorbed by plant roots, its low translocation rate makes it relatively less harmful to higher organisms. In contrast, Cd and As are highly toxic due to their availability, mobility, photo-toxicity to plants like beans, beets, and turnips, and potential for bioaccumulation in edible plant parts (FAO 2023). Table 1 shows that the concentrations (mg/L) of nine analyzed HM increased in ACS compared to BCS. However, only Pb (0.119 ± 0.162), Cd (0.021 ± 0.009), and Ni (0.242 ± 0.111) in TF, and only Cd (0.015 ± 0.005) in NTF, exceeded the MPL for IWQ (FAO 2023) and SWQ (BEPR 2023). Sharma et al. (2025) reported a similar trend. Statistical analysis revealed significant increases in As (3.75), Pb (2.77), Cd (5.24), Cr (4.02), and Ni (3.34) in TF, and Zn (7.01), Cu (4.58), As (3.59), Cr (6.13), and Ni (9.24) in NTF, with values exceeding t0.05 at 5 df (2.571). This may result from surface runoff into nearby waters, driven by excessive HM-containing agrochemical application, including pesticides, DAP, TSP, and SOP fertilizers. Based on the exceeded MPL and t-test analysis, it can be inferred that tobacco cultivation significantly increases Pb, Cd, and Ni levels in the surrounding surface water, while non-tobacco crop cultivation has no significant impact on HM concentration.

Miscellaneous hazards

Turbidity and TSS are key indicators of irrigation water quality, reflecting the presence of solid particles, organic matter, plankton, and potential pathogen vectors (FAO 2013). Turbidity (NTU) and TSS (mg/L) pollution increased significantly in both field waters. From Table 1, turbidity rose from 18.14 ± 13.25 to 69.12 ± 33.71 in TF (269%) and from 17.88 ± 18.72 to 57.89 ± 52.77 in NTF (224%). Similarly, TSS increased from 91.99 ± 13.31 to 121.83 ± 9.60 in TF (132.44%) and from 89.27 ± 17.14 to 114.17 ± 18.27 in NTF (27.89%). Both turbidity and TSS levels in ACS exceeded the MPL standard for IWQ (FAO 2023) and SWQ (BEPR 2023), likely due to algal blooms from nutrient and organic matter enrichment, along with fine plant particles (<200 μm) entering surface water via runoff. Statistical analysis shows that both tobacco and non-tobacco cultivation significantly and negatively impacted turbidity (TF: 3.14, NTF: 2.62 > t0.05) and TSS (TF: 4.68, NTF: 4.22 > t0.05) in surrounding surface water. COD levels (mg/L) exceeded the MPL (100 mg/L) for SWQ (BEPR 2023) across all samples: TF BCS (144.67 ± 29.00), TF ACS (218.33 ± 50.33), NTF BCS (146.67 ± 45.30), and NTF ACS (206.11 ± 66.00) (Table 1), attributed to increased organic matter and HM in surface water. Statistical analysis revealed that tobacco and non-tobacco cultivation have a significant negative effect on COD (TF: 7.01 and NTF: 5.79, both are >t0.05). According to FAO (2013), COD in irrigation water does not directly affect plants and soil. A report displayed that agriculture contributes 24–52% N, 22–90% P, and 28% COD pollution in the Chinese river basins (FAO 2023). DO in irrigation water is crucial for crop production (Wang et al. 2023). Low DO levels create oxygen stress, affect seed germination, reduce root biomass production, impair nutrient absorption, and inhibit plant growth (Wang et al. 2023). DO levels in farm surface water decreased significantly during ACS compared to BCS for both TF (4.43 ± 0.47 to 3.03 ± 0.52) and NTF (5.48 ± 0.90 to 3.95 ± 1.05), falling below the MPL for SWQ (BEPR 2023). This decline is likely due to algal blooms driven by nutrient and organic matter enrichment. Statistical analysis shows that tobacco (6.58 > t0.05) and non-tobacco crop cultivation (3.88 > t0.05) significantly negatively affect DO levels in nearby surface water.

Hydro-chemical facies and water type

The Piper diagram in 1953 was used to classify hydro-geochemical facies by tracing water types and their dominant ions (Islam & Mostafa 2022). Analyzed data from representative samples in the study area, shown in Figure 5 (Piper-tri-angle diagram), reveal that water in both fields was predominantly Ca–Mg–HCO3 type during BCS, with no change in water class after the cultivation season. Islam & Mostafa (2022) also reported that Bangladesh's water is mainly Ca–Mg–HCO3. The major cations and anions in both fields follow the decreasing order: Ca2+ > Mg2+/Na+ > K+ and > > Cl > NO3-N > PO4-P. This trend aligns with the findings of Thapa et al. (2024). Water analysis showed all samples were classified as hard (150–300 mg/L) (Roy et al. 2015; Islam et al. 2017; Islam & Mostafa 2022), with no values exceeding the MPL of IWQ (FAO 2023). TH increased from 164.00 ± 28.37 to 242.25 ± 27.75 for TF (47.71%) and from 240.92 ± 124.35 to 280.23 ± 112.39 for NTF (16.32%) (Table 1), likely due to ion enrichment in surface water near farms. Statistical analysis showed a significant increase in TH in both fields (5.14 and 4.21, both are > t0.05). Hard irrigation water (high TH, low SAR) helps soften the soil, maintaining good soil physical structure and water movement.
Figure 5

Piper diagram showing water classification around TF and NTF.

Figure 5

Piper diagram showing water classification around TF and NTF.

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Impact of tobacco cultivation on surface water quality for irrigation, compared to non-tobacco crops

The WQI effectively communicates the combined impact of water quality parameters (Islam et al. 2017; Islam & Mostafa 2022). Figure 6(a) shows that water quality was good (B-category) during BCS for both fields, but ACS deteriorated notably, dropping to very poor (D-category) for TF (83.27 to 241.75, 190.31%) and NTF (82.05 to 202.59, 146.91%). A similar trend was reported by Matta et al. (2018). During BCS, the main contributors to surface water contamination for both fields were turbidity >PO4-P /DO > K+ > pH > NO3-N > COD > TSS. During ACS, the order shifted slightly to turbidity >PO4-P > K+ > DO > NO3-N > pH > COD > TSS (Figure 6(b)). Tobacco and non-tobacco crop cultivation significantly contribute to surface water quality deterioration, with tobacco causing 1.32 times more contamination. The declines are mainly driven by turbidity, PO4-P, K+, DO, and NO3-N in both fields. This decline is likely due to excess urea (NO3-N), TSP and DAP (PO4-P), and MOP or SOP (K+) fertilizer application. These nutrients, along with organic matter and fine plant particles (<200 μm), enter surrounding surface water through leaching, runoff, and irrigation return, causing algal blooms, increased turbidity, decreased DO, and resulting water quality degradation from good to very poor.
Figure 6

(a) Comparison of WQI values of surface water surrounding TF and NTF; (b) surface diagram showing the contributions of physico-chemical parameters to the WQI.

Figure 6

(a) Comparison of WQI values of surface water surrounding TF and NTF; (b) surface diagram showing the contributions of physico-chemical parameters to the WQI.

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Impact of tobacco cultivation on water health for irrigation and aquatic life, compared to non-tobacco crops

Significantly varying water properties were identified through PCA. PCs with high initial eigenvalues (≥1), representing at least 5% of data variance, accounted for the greatest variation in the dataset, reaching 93.84% for TF and 92.61% for NTF (Table 2). Absolute loading values within 10% of the maximum under the same PC (Roy & Mostafa 2024; Uthappa et al. 2024) and parameter correlations significant at ≤t0.05 (Martin-Sanz et al. 2022) were used for MDS to minimize redundancy. Factor analysis extracted seven PCs for TF with weights (Wi) of 0.310, 0.208, 0.139, 0.135, 0.072, 0.071, and 0.064, and five PCs for NTF with Wi of 0.474, 0.205, 0.127, 0.118, and 0.076 (eigenvalues ≥1). For TF,, Ca2+, As, and Cr showed the highest variation in PC1, but only was selected due to its positive correlation with the others at t0.01 (Table 2). In PC2, Mg2+ and had the highest variation, but only Mg2+ was chosen for similar reasons. Fe, Ni, Zn, K, and pH were selected from PCs 3–7, respectively, as no alternatives met the 10% maximum factor loading criterion. For NTF, TH, temperature, , Mn, and Zn were extracted similarly from PCs1–5 (Tables 2 and 3). Figure 7(a) shows that the WHI values during BCS were 0.856 (excellent, grade-A) for TF and 0.661 (good, grade-B) for NTF, attributed to higher TH, TDS, ECW, and major ions in NTF compared to TF (Table 1).
Table 2

PCA of surface water parameters around TF and NTF

PCA (rotated component matrix)
FactorSurface water around TF
Surface water around NTF
PC1PC2PC3PC4PC5PC6PC7PC1PC2PC3PC4PC5
Eigenvalues 7.87 5.26 3.52 3.43 1.84 1.81 1.62 11.78 5.10 3.17 2.93 1.89 
% of Variance 29.13 19.49 13.04 12.69 6.80 6.71 5.99 43.65 18.88 11.74 10.84 7.02 
Cumulative % 29.13 48.62 61.66 74.35 81.14 87.85 93.84 43.65 62.52 74.26 85.11 92.12 
PC Weight (Wi0.310 0.208 0.139 0.135 0.072 0.071 0.064 0.474 0.205 0.127 0.118 0.076 
Temperature 0.368 0.614 −0.075 0.433 −0.344 0.341 −0.011 −0.069 0.916 0.101 −0.323 −0.030 
Turbidity 0.634 0.205 0.199 0.575 0.262 −0.022 0.180 0.574 0.371 0.571 0.368 0.102 
pH 0.054 −0.112 0.026 0.114 −0.114 0.075 0.925 −0.178 0.039 −0.134 −0.934 0.100 
EC 0.614 0.675 −0.053 0.146 −0.128 0.074 −0.325 0.919 −0.100 0.355 −0.047 0.047 
DO −0.812 −0.094 0.220 −0.434 0.140 −0.246 0.071 −0.134 −0.620 −0.533 −0.458 0.148 
COD 0.716 0.160 0.447 0.476 0.160 −0.034 −0.015 0.826 0.383 0.338 0.034 0.123 
TDS 0.605 0.713 −0.119 0.185 −0.039 0.069 −0.257 0.910 −0.188 0.307 −0.117 0.017 
TSS 0.479 0.486 0.148 0.500 0.155 0.042 0.406 0.590 0.507 0.533 0.021 −0.012 
TH 0.658 0.721 0.074 0.090 −0.062 0.041 0.007 0.987 −0.011 0.134 −0.029 −0.010 
NO3-N 0.581 0.379 0.164 0.535 0.194 0.169 −0.158 0.744 0.390 0.327 0.328 −0.147 
PO4-P 0.559 0.201 −0.154 0.266 −0.658 −0.136 0.256 0.732 0.465 −0.081 0.259 −0.192 
 0.933 −0.016 0.244 −0.131 0.025 0.107 0.048 0.199 0.322 0.882 0.169 0.022 
 −0.099 0.869 −0.297 0.212 −0.120 −0.099 0.005 0.914 −0.046 −0.372 0.022 0.016 
Cl 0.621 0.301 0.526 −0.196 −0.097 −0.147 0.119 0.958 −0.136 0.169 −0.008 −0.014 
Na+ 0.138 0.077 0.052 0.728 −0.098 −0.542 0.225 0.749 0.263 0.384 0.306 0.179 
K+ 0.119 0.187 −0.178 0.048 −0.085 0.926 0.115 0.884 0.121 0.044 0.243 −0.056 
Ca2+ 0.837 0.492 −0.049 0.166 −0.044 0.004 −0.023 0.970 0.140 0.115 0.003 −0.085 
Mg2+ 0.066 0.914 0.246 0.023 −0.067 0.060 0.032 0.936 −0.173 0.071 0.021 0.017 
As 0.907 0.072 0.264 0.197 0.014 −0.089 0.112 0.770 0.495 −0.156 0.287 0.034 
Zn 0.228 −0.071 −0.067 0.141 0.885 −0.124 −0.032 −0.285 0.399 0.202 0.119 0.758 
Fe 0.312 −0.105 0.849 0.148 −0.202 0.162 0.032 −0.052 0.722 0.211 0.091 0.302 
Mn −0.031 −0.272 −0.816 0.179 −0.256 0.376 0.031 −0.175 0.037 0.127 0.844 0.427 
Cu 0.359 −0.378 0.676 0.339 −0.023 −0.162 −0.242 0.736 0.100 0.541 0.268 0.179 
Pb −0.274 0.148 0.708 0.396 0.084 −0.216 0.372 0.539 0.491 0.400 0.506 0.124 
Cd 0.125 0.741 0.415 0.209 0.334 0.207 0.026 −0.198 0.193 0.092 −0.044 −0.856 
Cr 0.913 0.005 0.034 0.219 0.129 0.083 −0.005 −0.096 0.912 0.057 0.029 −0.250 
Ni 0.256 0.342 0.134 0.820 0.017 0.232 0.049 0.311 0.783 0.284 0.287 0.198 
PCA (rotated component matrix)
FactorSurface water around TF
Surface water around NTF
PC1PC2PC3PC4PC5PC6PC7PC1PC2PC3PC4PC5
Eigenvalues 7.87 5.26 3.52 3.43 1.84 1.81 1.62 11.78 5.10 3.17 2.93 1.89 
% of Variance 29.13 19.49 13.04 12.69 6.80 6.71 5.99 43.65 18.88 11.74 10.84 7.02 
Cumulative % 29.13 48.62 61.66 74.35 81.14 87.85 93.84 43.65 62.52 74.26 85.11 92.12 
PC Weight (Wi0.310 0.208 0.139 0.135 0.072 0.071 0.064 0.474 0.205 0.127 0.118 0.076 
Temperature 0.368 0.614 −0.075 0.433 −0.344 0.341 −0.011 −0.069 0.916 0.101 −0.323 −0.030 
Turbidity 0.634 0.205 0.199 0.575 0.262 −0.022 0.180 0.574 0.371 0.571 0.368 0.102 
pH 0.054 −0.112 0.026 0.114 −0.114 0.075 0.925 −0.178 0.039 −0.134 −0.934 0.100 
EC 0.614 0.675 −0.053 0.146 −0.128 0.074 −0.325 0.919 −0.100 0.355 −0.047 0.047 
DO −0.812 −0.094 0.220 −0.434 0.140 −0.246 0.071 −0.134 −0.620 −0.533 −0.458 0.148 
COD 0.716 0.160 0.447 0.476 0.160 −0.034 −0.015 0.826 0.383 0.338 0.034 0.123 
TDS 0.605 0.713 −0.119 0.185 −0.039 0.069 −0.257 0.910 −0.188 0.307 −0.117 0.017 
TSS 0.479 0.486 0.148 0.500 0.155 0.042 0.406 0.590 0.507 0.533 0.021 −0.012 
TH 0.658 0.721 0.074 0.090 −0.062 0.041 0.007 0.987 −0.011 0.134 −0.029 −0.010 
NO3-N 0.581 0.379 0.164 0.535 0.194 0.169 −0.158 0.744 0.390 0.327 0.328 −0.147 
PO4-P 0.559 0.201 −0.154 0.266 −0.658 −0.136 0.256 0.732 0.465 −0.081 0.259 −0.192 
 0.933 −0.016 0.244 −0.131 0.025 0.107 0.048 0.199 0.322 0.882 0.169 0.022 
 −0.099 0.869 −0.297 0.212 −0.120 −0.099 0.005 0.914 −0.046 −0.372 0.022 0.016 
Cl 0.621 0.301 0.526 −0.196 −0.097 −0.147 0.119 0.958 −0.136 0.169 −0.008 −0.014 
Na+ 0.138 0.077 0.052 0.728 −0.098 −0.542 0.225 0.749 0.263 0.384 0.306 0.179 
K+ 0.119 0.187 −0.178 0.048 −0.085 0.926 0.115 0.884 0.121 0.044 0.243 −0.056 
Ca2+ 0.837 0.492 −0.049 0.166 −0.044 0.004 −0.023 0.970 0.140 0.115 0.003 −0.085 
Mg2+ 0.066 0.914 0.246 0.023 −0.067 0.060 0.032 0.936 −0.173 0.071 0.021 0.017 
As 0.907 0.072 0.264 0.197 0.014 −0.089 0.112 0.770 0.495 −0.156 0.287 0.034 
Zn 0.228 −0.071 −0.067 0.141 0.885 −0.124 −0.032 −0.285 0.399 0.202 0.119 0.758 
Fe 0.312 −0.105 0.849 0.148 −0.202 0.162 0.032 −0.052 0.722 0.211 0.091 0.302 
Mn −0.031 −0.272 −0.816 0.179 −0.256 0.376 0.031 −0.175 0.037 0.127 0.844 0.427 
Cu 0.359 −0.378 0.676 0.339 −0.023 −0.162 −0.242 0.736 0.100 0.541 0.268 0.179 
Pb −0.274 0.148 0.708 0.396 0.084 −0.216 0.372 0.539 0.491 0.400 0.506 0.124 
Cd 0.125 0.741 0.415 0.209 0.334 0.207 0.026 −0.198 0.193 0.092 −0.044 −0.856 
Cr 0.913 0.005 0.034 0.219 0.129 0.083 −0.005 −0.096 0.912 0.057 0.029 −0.250 
Ni 0.256 0.342 0.134 0.820 0.017 0.232 0.049 0.311 0.783 0.284 0.287 0.198 

Note. Rotation method: Varimax with Kaiser Normalization, TF, tobacco field; NTF, non-tobacco field.Bold underlined values indicate the highest factor loading, while only bold-only values represents those within 10% of the maximum for each PC.

Table 3
 
 

Note. Black for tobacco, and violate for non-tobacco water. Correlation is significant at the 0.01 level (**) and 0.05 level (*) (2-tailed). Tem. represents temperature and Tur. is the turbidity.

Figure 7

(a) Comparison of the WHI of surface water during BCS and ACS around the farms; (b) radar diagram showing the MDS parameters with their contributions to WHI.

Figure 7

(a) Comparison of the WHI of surface water during BCS and ACS around the farms; (b) radar diagram showing the MDS parameters with their contributions to WHI.

Close modal

Interestingly, WHI values declined drastically during ACS to 0.176 (very poor, grade-E) for TF (79.44%) and 0.368 (poor, grade-D) for NTF (44.32%), indicating significant water health degradation due to crop cultivation. However, the deterioration rate for TF was 2.32 times higher than NTF, as the erosion rates for most water parameters were greater in TF than in NTF (Table 1). Among MDS, caused the most degradation in TF, followed by Mg, Ni, Fe, K, Zn, and pH, while TH led degradation in NTF, followed by , Zn, temperature, and Mn (Figure 7(b)). Surface water in both fields was suitable for irrigation use and aquatic life during BCS but ACS became inconsistent, with tobacco cultivation contributing more significantly to water health deterioration than NTF.

Impact of tobacco cultivation on heavy metal pollution and evaluation index for irrigation, compared to non-tobacco crops

HM contamination in surface water was assessed using the HPI and HEI for both TF and NTF, as these widely-used indexing techniques help identify and quantify pollution trends (Goher et al. 2014; Saleh et al. 2018; Islam & Mostafa 2024; Rai et al. 2024). The HPI values for both fields were similar, indicating good water quality during BCS. However, it deteriorated in ACS: 88.88 (suitable) to 170.89 (unsuitable) for TF (108.92%) and 85.80 (suitable for irrigation) to 120.05 (unsuitable for irrigation) for NTF (39.92%) (Table 4). During BCS, 16.67% of samples were excellent, 50% good, and 33.33% poor in both fields. However, during ACS, this changed to 16.67% good, 50% poor, and 33.33% very poor for TF water, while 33.33% were good and 66.67% poor for NTF. The order of HM contributing to HPI degradation for both waters was Cd > Pb/Cr > Ni > As > Cu > Mn > Zn > Fe. The HEI values were similar and less contaminated (A-grade) during BCS for both fields. However, during ACS, the HEI value increased by 86.30% for TF (from 3.74 to 6.97, low pollution) and 43.63% for NTF (from 3.85 to 5.53, low pollution). The order of heavy metals contributing to HEI degradation was Cd > Ni > Pb > Cr > Mn > Cu > Fe > As > Zn for TF, and Cd > Cr > Ni > Pb > Mn > Cu > Fe > As > Zn for NTF. Both HPI and HEI analyses showed that Mn, Cu, Fe, As, and Zn had minimal (<5%) impact on surface water quality. The main pollutants driving HPI and HEI degradation in both fields were Cd, Ni, Pb, and Cr, likely entering surface water through rainwater runoff from excessive use of chemical fertilizers like TSP, DAP, SOP, and HM-containing pesticides (FAO 2013). Sharma et al. (2025) previously identified heavy metals, such as Cd, Cr, Ni, and Pb, as the key factors influencing the WQI. This section infers that both tobacco and non-tobacco crop cultivation significantly impact HM pollution in the surrounding surface water, although the intensity remains minimal in both fields.

Table 4

Comparison of tobacco and non-tobacco crop impacts on heavy metal pollution and ERs in the surrounding surface water

Indexing methodWater across the TF
Water across the NTF
BCSACSCommentsBCSACSComments
HPI 81.88 (suitable) 170.89 (Unsuitable) HPI increased by 108.72% 85.80 (suitable) 120.05 (Unsuitable) HPI increased by 39.92% 
HEI 3.74 (Low) 6.97 (Low) HEI increased by 86.30% 3.85 (Low) 5.53 (Low) HEI increased by 43.63% 
Potential ecological risk (IR) 39.61 (Low Risk) 83.24 (Moderate Risk) IR increased by 110.12% 40.85 (Low Risk) 58.25 (Low Risk) IR increased by 42.58% 
Indexing methodWater across the TF
Water across the NTF
BCSACSCommentsBCSACSComments
HPI 81.88 (suitable) 170.89 (Unsuitable) HPI increased by 108.72% 85.80 (suitable) 120.05 (Unsuitable) HPI increased by 39.92% 
HEI 3.74 (Low) 6.97 (Low) HEI increased by 86.30% 3.85 (Low) 5.53 (Low) HEI increased by 43.63% 
Potential ecological risk (IR) 39.61 (Low Risk) 83.24 (Moderate Risk) IR increased by 110.12% 40.85 (Low Risk) 58.25 (Low Risk) IR increased by 42.58% 

Impact of tobacco cultivation on potential ecological risk, compared to NTF

The IR tool assessed the ER of TF and NTF, showing low and similar risks during BCS (39.61 for TF, 40.85 for NTF). However, risks increased by 110.12 and 42.58% during ACS, reaching moderate levels for TF (83.24) and low levels for NTF (58.25), indicating tobacco cultivation poses 2.51 times higher potential environmental risk than NTF (Table 4). The decreasing order of parameters contributing to ER generation for both fields was Cd > Ni > Pb > As > Cr > Cu > Zn. However, the main contributors were Cd, Ni, and Pb, while As, Cr, Cu, and Zn had negligible impacts (<5%). These risks likely arise from the overuse of chemical fertilizers (TSP, DAP, and SOP) and HM-containing pesticides (FAO 2013). This section concludes that both tobacco and non-tobacco crop cultivation pose potential environmental risks in surrounding surface water, with a medium risk for TF and a low risk for NTF.

Balanced agrochemical use in TF and NTF, raising pond banks to prevent leaching, runoff, and irrigation return, as well as adopting good management practices can reduce risks when using pond water for irrigation purposes such as (i) selecting suitable crops for irrigation (e.g., amaranth and halophytes; with avoiding rice, wheat, vegetables, vines, and trees), (ii) using appropriate irrigation methods (avoiding sprinkler), (iii) timing of irrigation (e.g., avoiding vegetative and early reproductive stages but encouraging germination and flowering to harvesting stages), (iv) improving soil structure with lime and organic matter, and (v) supplementing Ca2+ deficiency with gypsum (FAO & AWC 2023). Since TF requires more fertilizers and pesticides than NTF (Roy et al. 2024b), its contribution to surface water pollution is higher.

This study offers valuable insights into the differing impacts of tobacco and non-tobacco crop farming on surrounding aquatic ecosystems. The results exposed that out of 27 assessed water quality parameters, turbidity, TSS, DO, COD, NO3-N, PO4-P, Pb, Cd, and Ni in TF water exceeded safe limits, especially during the ACS. Irrigation water suitability indices confirmed that TF improves sodium-related hazards (SAR, SSP, KR, and SI) and alkalinity (RSBC) hazards, while considerably worsening salinity (ECw, TDS), PI, OPπ, RS, nutrient pollution, and miscellaneous hazards, with insignificant impact on water pH, MAR, CROSSOpt, PS, and . Conversely, NTF showed minimal influence on most indices, except for SI, nutrient pollution (NO3-N, PO4-P), and miscellaneous hazards. TF released higher nitrogen (1.54 kg N/ha) and phosphorus (0.46 kg P/ha) into adjacent water bodies than NTF crops like rice, wheat, and maize (1.12 kg N and 0.25 kg P per ha). Diagrams (USSL, Wilcox, and Piper) demonstrated consistent water types (Ca–Mg–HCO3) and permeability levels between BCS and ACS, indicating water type remained stable even as pollution increased. The WQI declined from good to very poor, while the WHI dropped from excellent to very poor for TF and from good to poor for NTF. Heavy metal indices (HPI and HEI) showed minimal pollution during BCS, but significant increases during ACS, by 108.92% for TF and 39.92% for NTF, while the IR followed a similar trend, rising 110.12% in TF and 42.58% in NTF, with Cd, Ni, and Pb as the main contributors. Excessive agrochemical use in tobacco farming accelerates the degradation of nearby aquatic systems, making irrigation water increasingly unsuitable and threatening long-term water sustainability. The findings emphasize the need for sustainable practices, such as balanced agrochemical use, improved field management, and conversion to less polluting crops, to mitigate water risks. These insights can support both farmers and policymakers in promoting environmentally sustainable agriculture. Future research should extend to evaluating the broader ecological impacts of these farming systems on aquatic biodiversity and ecosystem functioning.

The authors gratefully acknowledge the farmers of Bheramara and Daulatpur upazilas for allowing water sample collection from their fields, and thank the Department of Agricultural Extension (DAE), Kushtia, for their valuable information, support, and cooperation.

A.R. contributed in research design, sample collection and analysis, data analysis, table and graph creation, drafting the initial manuscript, and editing. M.G.M. assisted with research design, manuscript revisions and editing, cover page preparation, and final manuscript submission.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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