Biodegradability index (BDI) as an indicator for effluents quality measurement: A case study based on different industry sectors in Matara District, Sri Lanka

Wastewater, containing a variety of pollutants, is generated on a large scale from residential, commercial, agricultural, and industrial activities (Sharma et al. 2022). However, accurately quantifying its amount and composition is challenging due to its diverse nature (Jones et al. 2021). Wastewater originates from both human settlements and industrial activities, with many industries in least developed countries discharging it untreated into the environment (Gbekley et al. 2023). Effective management and pollution control of wastewater is crucial for sustainable development, especially as water demand grows at twice the rate of population growth. To conserve water resources and reduce reliance on scarce freshwater, it is essential to implement strategies for wastewater production, treatment, and reuse (Abd-Elaty et al. 2021). Thus, there is a significant demand for water quality improvement in many industrial applications.

Reducing wastewater production can create jobs in a green economy, reduce operational costs, and reduce greenhouse gas emissions (Water Quality Standards & Wastewater 2015). Therefore, the monitoring of effluents offers the practical understanding required to make informed decisions regarding the management of water quality in both the present and the future. The practice of water quality monitoring serves the purpose of providing timely notification on prevailing, continuous, and emergent issues, as well as assessing adherence to established drinking water regulations and safeguarding the integrity of other advantageous water applications (Miller et al. 2023). The utilization of monitoring data in assessments aids legislators, water management professionals, and both government and non-government professionals in evaluating the efficacy of water policies, ascertaining trends in water quality improvement or deterioration, and devising novel policies to enhance the safeguarding of human health and the environment (Wolters & Steel 2021).

The process of controlling and reducing the release of pollutants into the environment is known as environmental pollution control and thus, the characterization of wastewater structures and the description of their sources hold significant importance in this context (Zhong & Peng 2021). The expansion of industrial pollution is constrained and controlled globally by effective environmental legislation. As a result of that, industries are required to establish pollution control strategies (Chapagain et al. 2022). The main concept of established pollution control strategies should be the methods that can be applied to combat pollution; however, these strategies should be profoundly evolved with the changing nature of pollutants.

Pollution prevention is preferred over treating pollution at its origin. Many scientists and institutions have developed wastewater quality indicators, methods used in laboratories to determine if wastewater is suitable for disposal, treatment, or reuse. Traditional physicochemical indicators are commonly recognized by the public as measures of water quality, including parameters like dissolved oxygen (DO), pH, temperature, salinity, and nutrients (specifically nitrogen and phosphorus) (Retnaningsih et al. 2023). Additionally, water quality assessment also includes metrics for harmful substances such as pesticides, herbicides, and metals (Renu Nayar 2020). These indicators provide insights into factors influencing the sustainability of water systems. For instance, they help determine whether organic waste is affecting DO levels or if a specific toxic substance is responsible. However, while physicochemical indicators can identify the source of pollution, they offer limited information on the impact of pollution on fauna and flora. Additionally, obtaining these measurements is time-consuming, limiting their potential for immediate online monitoring (Yang et al. 2014).

As a solution, the Environmental Protection License (EPL) in Sri Lanka serves as a regulatory mechanism for managing the discharge of effluents, waste deposition, and emission of smoke, gases, fumes, vapor, excessive noise, and vibration into the environment resulting from specific activities, thus the mechanisms used to deal with pollution require changing (CEA 2022). Prevention of water pollution is preferred over the end of the pipe treatment of pollution (CEA 2022). The use of a rapid, highly sensitive, and real-time monitoring tool can effectively ensure the reliability of wastewater treatment performance (Cassidy et al. 2023). For example, monitoring of organic matter in the effluents from wastewater treatment plants is substantially important to evaluate the influence of effluent discharge on the water quality, the biogeochemical processes, and the ecosystem functions of the receiving water (Park et al. 2022).

In both natural aquatic environments and industrial discharge, the process of oxidation occurs wherein any substance that is susceptible to oxidation undergoes chemical and biological (specifically bacterial) reactions. This leads to a consequential reduction in the oxygen content of the water (Bagheri et al. 2017). Within this particular framework, biological oxygen demand (BOD) denotes the quantification of oxygen utilized by bacteria during the process of organic matter decomposition, thereby serving as an indicator of the matter's capacity for biodegradation in aquatic environments. In contrast, chemical oxygen demand (COD) serves as a measure of the amount of DO necessary for the oxidation of chemical organic substances. The assessment of water pollution levels is important, as shown by numerous studies (Geerdink et al. 2017; Young & Lipták 2018), which highlight the significance of these measurements. In recent studies, the BOD/COD ratio has emerged as a prominent indicator for evaluating the biodegradability of total organic carbon (Boruah & Deka 2023). It functions as an indicator of the capacity of a waste contaminant to diminish the presence of oxygen. Moreover, previous studies have demonstrated the usefulness of the BOD:COD ratio as a predictive indicator for the correlation between BOD and COD, as well as for assessing the presence of organic substances in wastewater and industrial water (Andrio et al. 2019a, 2019b; Bader et al. 2022). Modifying this proportion, as exemplified in research pertaining to tofu wastewater and cow dung (Andrio et al. 2019a, 2019b), can contribute to informed decision-making regarding treatment technology and discharge regulations. A higher ratio of BOD to COD typically indicates reduced biodegradability, which has implications for the development of wastewater treatment strategies aimed at attaining acceptable levels of organic matter in the surrounding ecosystem (Lee & Nikraz 2014; Dhanke & Wagh 2020; Al-Rosyid et al. 2022).

The determination of BOD₅ (BOD₅ refers to the biological oxygen demand after 5 days of incubations) (Medvedev et al. 2023) and COD is often performed in analytical procedures in the assessment of wastewater (Aguilar-Torrejón et al. 2023). These measurements quantify the amount of organic matter that can undergo oxidation through chemical or biological processes (Nidheesh et al. 2021). They are strongly interrelated, making their combined use advisable. COD estimates are generally higher than BOD₅ and depend on the specific water sample being analyzed. The BOD₅ to COD ratio should not exceed 1.0 (Gutiérrez-Mosquera et al. 2022), indicating the fraction of biodegradable organic matter in wastewater. However, this ratio provides empirical, not absolute, results, and is useful for comparing different samples rather than quantifying specific pollutants.

Furthermore, BOD₅ and COD were designed to evaluate the impact of localized organic effluents on water sources, limiting their applicability for broader environmental monitoring. For instance, BOD can increase due to elevated nutrient levels (nitrogen and phosphorus) causing eutrophication, even without significant external organic carbon input. A key limitation of BOD₅ is the 5-day incubation period required for data collection (Yan et al. 2021). Furthermore, these methods do not accurately reflect in situ oxygen consumption rates due to artificial incubation conditions, such as lack of airflow, currents, and light, which differ from natural environmental factors.

However, despite the extensive breadth of this investigation, there are still certain areas of research that need to be further examined. The current body of research pertaining to the utilization of the BOD₅:COD ratio as an indicator for effluents originating from the hospitality and vehicle service sectors in the Matara District is notably scarce (Kumarathilaka et al. 2015). Hotel effluent contains various organic pollutants, including nutrients, pathogens, and chemicals, while vehicle washing center wastewater is characterized by dirt, oils, and other contaminants (Sharma & Sanghi 2012). Additionally, there is a lack of comprehensive research on the correlation between the BOD₅:COD ratio and other significant physicochemical parameters in these industries (Amarasinghe et al. 2017; Young et al. 2021). Therefore, a more detailed biodegradability index (BDI) is necessary to better understand water quality in terms of biodegradability. However, comprehensive comparative analyses of these industries within Sri Lanka's specific environmental conditions and regulatory frameworks are limited (Dissanayake et al. 2007; Kumara et al. 2015). Addressing these research gaps can enhance our understanding of effluent biodegradability in various industries and their impact on local water bodies, contributing to more effective environmental management strategies.

The present study focuses on the utilization of BDI as a matrix for assessing the effluents discharged by hotels and tourism establishments, as well as vehicle service stations, within the Matara District of Sri Lanka. In this light, we conducted this study with the objectives (1) to conduct a comparative analysis of BDI values among different industrial effluents and a reference site such as the Nilwala River, (2) to investigate the correlation between the aforementioned ratio and a range of physicochemical parameters, such as pH, temperature, electrical conductivity (EC), total suspended solids (TSS), alkalinity, and BDI of wastewater, within distinct industry categories, and (3) to conduct an analysis and identification of any notable disparities in the BDI between the effluents of the reference sites, specifically focusing on the pristine sites of Nilwala River as reference sites, and those originating from other industries within the surrounding area.

The study was conducted in the Matara District along the Nilwala River, which is the third largest in the southern province of Sri Lanka covering a distance of 72 km (the area of the Nilwala River Basin is 922 km²) (Jayawardana et al. 2016). The river originates at Panilkanda in the Sinharaja rainforest near Deniyaya at an altitude of 1,050 m and flows largely through urban, agricultural, and other areas of land use to join the Indian Ocean at Thotamuna (Jayawardana et al. 2016; Wickramarachchi & Wijesekera 2022). Nearly 90% of the area covered by the catchment of the Nilwala River belongs to the Matara District (Panditharathne et al. 2022). The area of the river basin is about 1,073 km². Nilwala River is the primary drinking water source to cover the water supply requirement in the Matara District (Chathuranika et al. 2022).

Vehicle service centers, sawmills, and tanneries, as well as medium and small-scale industries, such as textile, batik, apparel, food processing industries, and restaurants and hotels are also located in the urban areas along the river (Abeysiriwardana & Wijesekera 2022). The lower reaches of the river run through a thriving commercial city, and its vast areas are rapidly changing land use and deforestation. The predominant type of land use in those areas is urban buildings such as cities and industries (Chathuranika et al. 2022).

Those industries are categorized into three lists based on their potential for pollution by the Central Environmental Authority (CEA), namely categories ‘A’, ‘B’ and ‘C’ (CEA 2023).

• Category ‘A’ consists of 80 significantly higher polluting industrial activities.

• Category ‘B’ consists of 33 intermediate levels of contaminant activity.

• Category ‘C’ consists of 25 less polluting industrial activities assigned to local authorities.

As a result, Matara District has 416 ‘A’ category industries, 471 ‘B’ category industries, and 1,327 ‘C’ category industries including hotels, service centers, saw mills, tanneries, textile, batik, apparel, food processing, and painting industries. Therefore, the unavailability of BOD and COD ratios in different industry sectors can lead to time consumption and reduce the overall efficiency of CEA service.

Table 1 presents the details of the sampling points, categorized by type and their specific locations within the study area. Since small-scale vehicle washing centers are prominent in the river basin, we selected six vehicle service stations (sites 11–17) as one of the selected industries and two tourist hotels (site 9 and site 10), were selected as another industrial category (Table 1).

Effluent data used in this study were obtained from performance results of the above-mentioned sites in Matara District over seven years from 2015 to 2022. Sampling was done as mentioned in the standard methods for the examination of water and wastewater by the American Public Health Association (APHA) requirements (Bridgewater et al. 2017).

To conduct the laboratory testing for COD, BOD₅, and alkalinity, composite water samples from each sampling site were collected into 1.5 L glass sampling bottles, preserved on site, and transported to the CEA's water laboratory in Koggala, Galle District, Sri Lanka where they were stored at 4 °C temperature.

BOD over 5 days (following Winkler's approach) and COD for each sample were measured in laboratory conditions.

At the sampling site, the temperature, pH, and EC characteristics were instantly determined by using a portable multi-parameter water quality analyzer (Horiba-U52) and TSS was determined for effluent samples from the industries while total alkalinity was determined for the water samples from a reference water body (Table 2).

Three replicates were used to test each parameter. Double-distilled water and chemicals of analytical quality were utilized in the analysis. All chemical and physical analyses were performed under ISO 17025 quality standards to prevent random and intentional measurement errors.

The temperature, pH, EC, TSS, and total alkalinity were determined to analyze the relationship of these parameters with BDI.

Experimental results were statistically analyzed using one-way ANOVA, Mann–Kendall trend analysis, and multiple linear regressions. One-way ANOVA (Tukey's pairwise test) at a 5% significance level was conducted to investigate the significant difference between the BDI of the natural water body (reference) and other industry sectors. Before analysis, the data were assessed for normality and homogeneity of variance using IBM SPSS 26.0 software using the Anderson Darling's test.

The time series BDI was plotted to understand the temporal variation (diurnal and seasonal variations) of the BDI with time in each industrial setting by using approximately 150 samples. Further, the Mann–Kendall trend analysis was conducted to predict whether the BDI is significant with time in each selected site category (reference water body, hotel and tourism, and vehicle service stations).

Finally, the regression analysis with some common parameters (pH, temperature, TSS, and EC) was conducted to understand the relationship of the BDI with other physicochemical parameters.

It indicates that BDI in a significant number of sites is below the ideal BDI value for ready biodegradability (0.4). However, the majority of the BDI in the Nilwala River is ranged from 0.2 to 0.5 while the BDI of hotels and tourism industries and vehicle washing centers range from 0.2 to 0.48 (Figure 2(a)).

To identify similarities in BOD:COD ratios between months and determine the months with the highest and lowest variation throughout the period, the coefficient of variation (CV) for each month was calculated by considering the monthly average BDI from 2015 to 2022 (Table 3). The CV considers the standard deviation relative to the mean and can give us an indication of the relative variation in the ratios.

In this study, Mann–Kendall was used for the analysis of trends in the BDI for the period from 2019 to 2022 for different industrial categories. The trend is considered statistically significant at a 0.05 confidence level. There is a downward trend in the data (Table 4).

The Mann–Kendall trend analysis concludes that there is no significant evidence of either an upward or a downward trend for the BDI values of the reference water body (Nilwala River) and ‘vehicle service station’ category at a significance level of 0.05. However, based on the Mann–Kendall trend test using normal approximation, there is evidence of an upward trend in the ‘hotel and tourism industries’ data with time at a significance level of 0.05 (Table 4).

A multiple linear regression analysis using the enter method was conducted to examine the relationship of the BDI with other physicochemical parameters (pH, temperature, TSS, conductivity, and alkalinity) in different industry categories. Temperature, F (2, 460) = 3.637, p = 0.043, alkalinity F (1, 229) = 4.653, p = 0.032, and TSS F (2, 228) = 7.663, p = 0.001, were significant by explaining 1.1% (R² = 0.011), 2% (R² = 0.02), and 6.3% (R² = 0.063) of the variance in the outcome variables, respectively (Table 4). pH (B = −0.011, t = −1.947), alkalinity (B = −0.002, t = −2.157), and TSS (B = −0.001, t = −3.027) contributed significantly to BDI (Supplementary material, Table S2).

This comprehensive analysis aims to provide a thorough examination of recent research findings pertaining to the variation of BDI in diverse industries. It explores the spatial and temporal dimensions of BDI, identifies emerging trends, and investigates the correlation between BDI and various parameters. The objective is to facilitate a comprehensive understanding of the biodegradability of waste materials in the Matara District.

In the present study, between 2015 and 2022, the BDI in the Nilwala River ranged from 0.1 to 0.7, while the BDI of hotels and tourism ranged from 0.1 to 0.75, and the BDI of vehicle washing centers ranged from 0.05 to 0.71. However, the previous studies conducted by Samudro & Mangkoedihardjo (2020) and Chandra & Singh (2012) found that BDI > 0.5 indicated high biodegradability. Furthermore, Ghauch et al. (2018) reported that BDI between 0.2 and 0.5 indicates moderate biodegradability during Congo red dye degradation under different conditions. Conversely, some other studies asserted that BDI can be as low as <0.2 (Velicu & Yamamoto 2011); BDI around 0.35 (Rudaru et al. 2022); and BDI > 0.4 (Lee & Nikraz 2014). The results of the present study correspond to the published literature, as they showed a BDI ranging from low to high biodegradability among the sampling points represented by different industries.

Moreover, the one-way ANOVA test results show significant differences between the BDI values of 17 sampling sites, with site 9 and site 10 (hotels and tourism) recording the highest mean BDI value and site 6 (reference water body) recording the lowest mean BDI value. Given the significance of natural organic matter's biodegradability, we found that natural water bodies typically contain more biodegradable natural organic matter than industrial effluents. Furthermore, studies have shown that seasonal variations and anthropogenic activities can also influence its BDI (Houghton & Quarmby 1999; Haider et al. 2018). During both the dry and rainy seasons, a past study on the Nilwala River confirmed that sampling site 6 (Bandaththara power plant) and site 8 (Matara Mahanama Bridge) have a higher weighted arithmetic water quality index (WAWQI). Moreover, the same study showed that some sampling points in the Nilwala River are suitable for drinking even during the dry season (Diwyanjalee & Premarathne 2024). Therefore, the BDI in natural water bodies represents a complex interplay of molecular structure and complexity (Houghton & Quarmby 1999), concentration, and composition (Thakur 2006). This study examined various industrial sites and discovered that they had lower-than-optimal biodegradability ratios. Higher levels of organic matter are likely to be present, which could stop biodegradation from happening properly and lead to possible toxic risks (Papadopoulos et al. 2001; Andrio et al. 2019a, 2019b).

Moreover, it is worth noting that the hotel and tourism industries exhibited the highest BDI values, as illustrated in Figure 3. The elevated levels of organic matter in sewage waste contribute to a greater BOD due to the proliferation of a more extensive microorganism population (Mtui 2001). A combination of organic matter characteristics, environmental conditions, microbial community dynamics, nutrient availability, inhibitory substances, operational practices, and seasonal variations affect the BDI in wastewater from hotel and tourism effluents. Nies (1999) conducted a previous study which revealed that certain cleaning agents, disinfectants, and personal care products may contain compounds that inhibit microbial activity, thereby reducing biodegradability. Henze et al. (2008) found that hotels with advanced pretreatment systems, including pretreatment, can significantly affect the higher BDI as they reduce toxic loads and produce more balanced organic matter. Furthermore, peak tourist seasons can result in higher volumes of wastewater with varying organic loads, affecting the BDI. During off-peak times, wastewater composition and volume may change, influencing biodegradability (Gössling 2002). The selected hotels in the present study maintain a relatively constant tourist attraction, as they cater to both local and foreign tourists annually. As a result, local celebrations, gatherings, and check-ins are common during off-season foreign tourist seasons.

The outcomes from the ANOVA indicated that there was no statistically significant temporal variation in the BDI scores among the various industries. This insignificant temporal variation can be related to several factors driven by the nature of industrial operations and wastewater management practices. Many industries function with highly standardized and controlled production processes, resulting in consistent wastewater composition over time (Metcalf & Eddy 2014). This uniformity is further proved by regular supply chains, which lead to consistency in the types and concentrations of organic compounds in the wastewater, thereby reducing variability in the BDI (Henze et al. 2008).

Pretreatment systems, designed to stabilize wastewater composition, also play a crucial role in minimizing temporal variations in BDI (Tchobanoglous et al. 2003). Industrial operations are frequently required to comply with stringent environmental standards that dictate the maintenance of a consistent standard of wastewater quality. Adherence to these rules requires consistent monitoring and management of wastewater parameters, such as BDI. In their study, Davis & Masten (2013) found that numerous industries maintain stable production cycles, resulting in predictable patterns of wastewater creation. Continuous operations in industries result in reduced temporal variability in effluent properties, unlike businesses that are seasonal in nature.

The employing of equalization tanks in industrial wastewater treatment systems supports homogenizing wastewater by mixing it, hence averaging out any fluctuations in the concentration of pollutants and organic matter. This is responsible for more consistent BDI (Tchobanoglous et al. 2003). Additionally, the consistent use of chemicals with standardized formulations and dosages leads to predictable and stable effluent characteristics (Thakur 2006). In contrast, the Nilwala River, a natural tropical water body, does not show statistically significant temporal variation in the BDI. This stability can be related to several environmental factors. The region's stable climatic conditions (Horne & Goldman 1994), continuous organic matter input from evergreen vegetation (Wetzel 2001), and consistent hydrological patterns (Ogden & Thorpe 2002) contribute to this stability. Furthermore, stable pH and DO levels (Kalff 2002), diverse and active microbial communities (Rittmann & McCarty 2001), and adequate nutrient availability (Thakur 2006) support consistent biodegradation rates. Limited seasonal variability in human activity (GESAMP 2001) and the natural buffering capacity of tropical ecosystems also play significant roles (Wetzel 2001).

The Mann–Kendall trend analysis depicted extremely low R² values, indicating that factors other than time are likely more important in determining the BDI. The Mann–Kendall trend test further shows that the BDI for most industrial categories remains stable over time. Nonetheless, an increasing trend in the hotel and tourism industries suggests an improvement in effluent quality, possibly due to enhanced wastewater management practices.

The regression coefficients indicate significant negative contributions of pH (B = −0.011, t = −1.947), alkalinity (B = −0.002, t = −2.157), and TSS (B = −0.001, t = −3.027) to the BDI. This suggests that as pH, alkalinity, and TSS increase, the BDI decreases. These findings align with previous studies and contribute to a broader understanding of the factors influencing biodegradability in aquatic environments. Previous research by Thakur (2006) demonstrated that BDI tends to increase with higher levels of DO because adequate oxygen levels support aerobic microbial activity, enhancing the biodegradation of organic matter. This is crucial as aerobic conditions generally favor a higher rate of organic matter breakdown, leading to an increase in BDI. Similarly, Lim et al. (2001) found that higher temperatures generally boost microbial metabolism and enzymatic activity, which can also lead to higher BDI values. However, it is important to note that extremely high temperatures can inhibit microbial activity, as highlighted by Wetzel (2001).

Moreover, the BDI is optimal within a neutral pH range (6.5–7.5). Henze et al. (2008) noted that extreme pH levels, whether acidic or alkaline, can inhibit microbial activity, thus reducing the BDI. This aligns with the present study's findings that increased pH negatively impacts BDI. Furthermore, Metcalf & Eddy (2014) emphasized the necessity of adequate nutrient levels, such as nitrogen and phosphorus, for microbial growth and activity, leading to higher BDI values. However, excessive nutrients can cause eutrophication, altering microbial dynamics and potentially reducing biodegradability.

High levels of suspended solids and turbidity can have dual effects on BDI. On one hand, they can block light and reduce oxygen levels, inhibiting microbial activity and lowering BDI. On the other hand, some suspended solids can provide a substrate for microbial attachment, aiding biodegradation, as noted by Tchobanoglous et al. (2003). Additionally, Davis & Masten (2013) highlighted that turbidity potentially leads to a higher BDI if the organic material is biodegradable.

Lee & Nikraz (2014) discovered that pH, hardness, TSS, and conductivity show strong positive relationships with biodegradability, whereas chloride and alkalinity show strong negative relationships. This reinforces the findings of the present study, where increased alkalinity was associated with decreased BDI. The multiple linear regression analysis further highlighted the significant influence of pH, alkalinity, and TSS on the BDI, corroborating the findings of Agarwal & Saxena (2011), who found that high alkalinity is associated with increased COD and decreased biochemical oxygen demand (BOD):COD ratios. Lim et al. (2001) also identified temperature as a factor enhancing both BOD and biodegradability.

Environmental conditions such as temperature (Rittmann & McCarty 2001), pH (Tchobanoglous et al. 2003), and DO (Metcalf & Eddy 2014) are critical factors influencing biodegradability. Additionally, microbial dynamics (Rittmann & McCarty 2001), nutrient availability (Thakur 2006), physical factors including exposure to sunlight (Velicu & Yamamoto 2011), water flow and mixing (Tchobanoglous et al. 2003), and the presence of inhibitory substances (Nies 1999) also play significant roles. Understanding these factors is crucial for assessing the biodegradability of pollutants and the overall health of aquatic ecosystems.

A significant spatial variation of BDI can be observed among different sampling sites including natural water bodies, vehicle service stations and hotel and tourism industries. Site 6 (Bandaththara of reference water body) showed the lowest level of BDI which is significantly different from site 9 (hotel 1), it could be difficult to biodegrade as well. The highest BDI is recorded at site 9. A BOD:COD ratio greater than 0.4 signifies the presence of water that is readily biodegradable. The study found that these sites exhibited lower than optimal biodegradability ratios, indicating the presence of higher concentrations of organic matter, which could hinder effective biodegradation and consequently give rise to potential toxic risks. The hotel and tourism industry exhibited the highest BDI values, attributed to elevated levels of organic matter present in sewage waste.

The absence of significant temporal variation in BDI indicates stability in water quality over time, underscoring the importance of consistent wastewater treatment efforts. The Mann–Kendall test's findings further validate these observations, suggesting that temporal changes in BDI values across the industry categories have not shown significant trends over the studied period. The low R² values indicate that the linear relationship between time and the BOD:COD ratio is very weak. This implies that factors other than time are likely playing a significant role in determining the BOD:COD ratio.

The Mann–Kendall trend test confirms that the BDI for most industrial categories remains stable over time. However, an increasing trend in the hotel and tourism industries suggests an improvement in effluent quality, possibly due to improved wastewater management practices.

The significant correlation between BDI and parameters such as temperature, alkalinity, and TSS indicates that these factors influence the biodegradability of effluents. This highlights the need to consider a range of physicochemical parameters in effluent quality assessments. However, decompositions are taking place in the water and can be taken as an indicator of the degradation of organic matter in wastewater. It can be provided as useful information for the design and management of effluent discharge that makes prediction more realistic for future trends.

Currently, there is a lack of regular monthly effluent analysis conducted in vehicle service centers, hotels, and the tourism industries, with the exception of the Nilwala River, which serves as a reference water body. The presence of more frequent data could have led to more promising results. Based on the findings derived from this study, the subsequent recommendations are put forth for careful consideration. The discharges in the reference river undergo changes as a result of the expansion of industrial and commercial activities, as well as the subsequent increase in population. These changes primarily stem from the non-point sources of pollutants being emitted. Hence, it is imperative to implement ongoing monitoring protocols in order to evaluate the quality of water. Furthermore, it is imperative to augment the frequency of industrial-level inspections regularly and frequently to ascertain the methods employed for waste water disposal and evaluate their suitability.

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

The authors declare there is no conflict.