Heavy metal contamination in Sungai Petani, Malaysia: a wastewater-based epidemiology study

Heavy metals are a class of noxious elements with a dense and metallic appearance, characterized by a significant atomic mass (Ghadban 2021). These metals, such as lead (Pb), mercury (Hg), cadmium (Cd), and arsenic (Ar), are often present in the environment and are known for their toxic effects on human health (Tchounwou et al. 2012; Ali et al. 2019; Balali-Mood et al. 2021). In recent years, there has been growing concern regarding the exposure of humans to heavy metals in our daily lives, as these compounds can be accumulated in the body and cause major health issues (Jaishankar et al. 2014; Engwa et al. 2019; Balali-Mood et al. 2021). Air pollution, water contamination, food contamination, and consumer products are among the numerous sources of exposure to heavy metals in our daily lives (Jaishankar et al. 2014).

Lead is a commonly occurring pollutant in the atmosphere due to vehicular emissions and industrial activities, and it can also be detected in older residential and commercial structures where lead-based paint was employed (ATSDR 2020). Similarly, mercury is frequently present in specific varieties of seafood and shellfish, dental amalgams, and light bulbs (USEPA 2015; Nutrition 2022; Poison Control 2023). Cadmium is commonly found in cigarette smoke and certain types of batteries (Richter et al. 2017; Hayat et al. 2019), while arsenic is frequently found in water supplies and in several insecticides and herbicides (Shankar et al. 2014; Bencko & Yan Li Foong 2017). The impact of metals on one's health varies depending on the type of metal, the quantity of exposure, and the duration of exposure. Short-term exposure to high levels of heavy metals can result in symptoms such as nausea, vomiting, diarrhea, stomach pain, and trembling. Conversely, long-term exposure to low levels of heavy metals can lead to severe health issues, including but not limited to kidney damage, hypertension, anemia, and neurological disorders (Jaishankar et al. 2014; Engwa et al. 2019; Rajkumar et al. 2022). Children and pregnant women are more susceptible to the harmful consequences of heavy metal exposure, since they are more vulnerable to these metals and may be at a greater risk of developmental issues (Al-Saleh et al. 2017; Wai et al. 2017; Chandravanshi et al. 2021). Given the detrimental effects of these hazardous materials on human health, exposure to heavy metals is a grave concern. Thus, it is of utmost importance to take preventive measures to reduce exposure and ensure the well-being of the community, thereby fostering a healthy and secure living environment for the populace.

Wastewater-based epidemiology (WBE) is a discipline of research that employs samples of wastewater to investigate the health condition of communities (Salvatore et al. 2015). WBE has gained significant attention in recent years owing to its potential for detecting and monitoring the prevalence and spread of diseases, including infectious diseases, as well as tracking drug use and exposure to chemicals, among other applications (Castiglioni et al. 2013; Rousis et al. 2017; Hart & Halden 2020). The principle behind WBE is that the wastewater produced by a population contains various biomarkers that reflect the health status of that population (Hart & Halden 2020) where these biomarkers may comprise substances that the body excretes, such as drugs, hormones, and pollutants from the environment (Castiglioni et al. 2013). The analysis of wastewater samples has the potential to provide valuable insights into public health by enabling us to track the spread of diseases, identify patterns of drug use, and determine the levels of environmental exposure. By examining biomarkers present in the wastewater, it is possible to assess the prevalence of these factors within the surrounding population. With ongoing advancements in this field, it is likely that wastewater analysis will continue to play a significant role in promoting global health. Several examples for WBE applications in public health surveillance had been applied in the past few years such as the early detection of the COVID-19 outbreak, where WBE was used to detect SARS-CoV-2 in wastewater samples, providing an early warning of the virus's presence in a population (Hart & Halden 2020). It has also been utilized for the purpose of monitoring the trends in drug use across various cities worldwide, thereby furnishing crucial insights for devising public health interventions (Castiglioni et al. 2013; Salvatore et al. 2015; Bishop et al. 2020).

Sungai Petani is a district located in the northern region of Malaysia, namely, in the state of Kedah (5.6436° N, 100.4894° E). This district is renowned for its quick industrial development, making it one of the most prominent areas in northern Malaysia. The town has seen a significant transformation, evolving from a quiet place into a vibrant center for several businesses, including electronics, pharmaceuticals, and manufacturing (Pihak Berkuasa Tempatan Negeri Kedah Darul Aman n.d.). The swift economic progress in this region unfortunately exposes its inhabitants to a heightened vulnerability of heavy metal exposure from the industries, perhaps posing health risks (Cao et al. 2010; Hu et al. 2016; Anyanwu et al. 2018; Proshad et al. 2018). Industrial heavy metals can potentially reach the population through various pathways, including air and water pollution, contamination of the food chain, and occupational exposure (Mohammed et al. 2011; Ali & Khan 2019; Li et al. 2019). Prolonged exposure to heavy metals can result in significant health implications for the population, even at minimal concentrations. These repercussions include neurological impairment, organ dysfunction, reproductive complications, and the potential for cancer formation (Jaishankar et al. 2014; Yuan et al. 2016; Cabral Pinto & Ferreira da Silva 2019a, 2019b). Hence, regular monitoring of heavy metals exposure is necessary to facilitate the development of effective mitigation methods.

The aim of this investigation was to apply the WBE methodology to gain insights into the degree of heavy metal exposure in the population of Sungai Petani. Samples of wastewater were obtained from urban and densely populated residential areas and tested for the presence of specific heavy metals. Subsequently, the levels of estimated heavy metal exposure were compared to information found in published literature, to evaluate the overall heavy metal exposure situation in Malaysia. In addition, this study also monitored the incidence and elimination of heavy metals in selected sewage treatment plants (STPs) located in Sungai Petani.

Polypropylene bottles that had been cleaned with acid were used to collect unfiltered wastewater samples, including influent (In) and effluent (Ef), each totaling approximately 500 mL from each STP. The samples were collected between 9:00 am and 11:00 am on the day of sampling and pretreated with the addition of 3 mL of formic acid. To filter the samples, 47 mm glass fiber GF/F filters with a nominal cut-off size of 0.7 m (Whatman, Fontenay-sous-Bois, France) were employed. The filtered samples were then stored in a freezer at 4 °C until further analysis. Physicochemical data such as levels of chemical oxygen demand (COD), biochemical oxygen demand (BOD), nitrate nitrogen (NO₃N), ammoniacal nitrogen (NH₃N), temperature, pH, total suspended solids (TSS), and oil and grease (OG) were obtained for the effluent samples collected for this study. The sampling process was conducted once per week for three consecutive weeks in January 2022 from the same sampling point. The weather conditions were also recorded during the sampling process, specifically noting whether it was rainy or dry to account for any impact from runoff.

The analysis of heavy metals (cadmium (Cd), copper (Cu), nickel (Ni), iron (Fe), and zinc (Zn)) present in wastewater was conducted through atomic absorption spectroscopy (AAS) AA800 (Perkin Elmer, Foster City, CA, USA), following the guidelines established by the American Public Health Association (APHA). The procedure involved mixing 100 mL of wastewater with 5 mL of concentrated HNO₃, reducing it to a volume of 15–20 mL using a hot plate, and adding 10 mL of concentrated HNO₃ and HClO₄ to the mixture with cooling between each addition. The mixture was evaporated until thick white vapors emerged, and after cooling for a few minutes, it was diluted with dH₂O to 50 mL and heated to remove any chlorine or oxides of nitrogen. Cadmium-specific (wavelength 228.8 nm), copper-specific (wavelength 324.8 nm), iron-specific (wavelength 248.3 nm), nickel-specific (wavelength 232.0 nm), and zinc-specific (wavelength 213.9 nm) hollow cathode lamps were used to analyze the samples. A dilution of the standard samples (containing 1.0–0.1 mg/L for Cd, 2.0–0.2 mg/L for Cu, 2.0–0.5 mg/L for Ni, 10.0–1.0 mg/L for Fe, and 2.0–0.5 mg/L for Zn) were prepared for calibration purposes. Calibration blank and independent calibration and verification standards for each heavy metal were used to assess the AAS's calibration state, with calibration curves having r² values greater than 0.999 being accepted for quantitative analysis. The data presented were the average of three measurements, and the limit of quantification was set at 0.01 mg/L (Cd), 0.02 mg/L (Cu), 0.05 mg/L (Ni), 0.1 mg/L (Fe), and 0.5 mg/L (Zn).

EFi represents the excretion factor of the selected heavy metal excreted as an unchanged parent or metabolite during elimination from the body, MWpi stands for the parent's molecular weight, and MWmi is the metabolite's molecular weight.

The studies were conducted in triplicate, and the average was taken as the final result. The data were compiled and analyzed by using Microsoft Excel 2016 and GraphPad Prism version 9.1.0 for Windows, which was created by GraphPad Software, San Diego, CA, USA. Depending on the type of information obtained for each parameter, the results were presented as the mean and standard deviation (SD), and they were analyzed appropriately.

The results of physicochemical testing on the effluent discharge during the study period, including BOD, COD, NH₃N, NO₃N, TSS, pH, OG, and temperature, are presented in Table 1. All data obtained were found to be within the permissible range according to the Malaysia Sewage and Industrial Effluent Discharge Standards as stipulated by Malaysia's Environmental Law, Environmental Quality Act, 1974, and the Malaysia Environmental Quality (Sewage and Industrial Effluents) Regulations, 1979, 1999, 2000, and 2009. This physicochemical testing of effluent discharge from wastewater is critical for maintaining water quality standards and protecting the environment.

The primary cause of heavy metal presence in domestic wastewater is attributed to industrial activities, which have increased the likelihood of heavy metal exposure to the local population through various means such as air and water pollution, contamination of the food chain, and occupational exposure (Akpor et al. 2014; Cheng et al. 2022; Rafique et al. 2022). In addition, there are numerous other factors that can also contribute to increased levels of heavy metals in domestic wastewater. One contributing aspect is household sources, where items such as personal care products, daily products, and cooking utensils can introduce heavy metals into domestic wastewater due to their composition containing a mixture of heavy metals (Odukudu et al. 2014; Omenka & Adeyi 2016; Ran et al. 2019; Arshad et al. 2020; Nutrition 2022). Another element that might contribute to the presence of heavy metals in domestic wastewater is the plumbing system in the specific area. If the pipe system is badly maintained, it can corrode and release metals such as lead and copper into the water supply, which eventually ends up in the residential wastewater (Godwin et al. 2015).

Table 2 shows the details on the presence of Cd, Cu, Ni, Fe, and Zn in all wastewater samples. The selected STPs showed the detection of these five metals in both influent and effluent, with varying concentrations. The order of mean heavy metal concentrations in wastewater samples was Fe > Ni > Zn > Cd > Cu in both influent and effluent with 100% detection frequency. It is probable that these heavy metals found in the wastewater could originate from the domestic discharge of the population assuming that the studied STPs were from residential areas, However, their presence can also be influenced by other sources, such as soil erosion, urban runoff, or aerosol particles (Taiwo et al. 2011; Kamran et al. 2013 and Kaizer & Osakwe 2010).

Upon analyzing the effluent samples, it was discovered that Fe (4.09 ± 1.990 mg/L) and Ni (0.77 ± 0.104 mg/L) had the highest concentration among the other metals, with the highest concentrations being recorded in STP2 (Fe: 7.05 ± 0.026 mg/L) and STP7 (Ni: 0.88 ± 0.047 mg/L) compared to the others, and yet their concentration values were still within the permissible limits. When comparison was done to the earlier study conducted in Penang, Malaysia (Table 3), the average concentrations of Fe from the STPs of this study was significantly higher, where the previous study only recorded the average Fe concentration at 3.43 ± 2.408 mg/L (Ruzi et al. 2023), although Penang is a more industrialized area than Sungai Petani. The high value of iron (Fe) in comparison to other heavy metals is expected due to its status as the most abundant element in the world and also known as the fourth most prevalent element found in the Earth's crust, making up around 5% of its composition (Cox 1989; Hans Wedepohl 1995; Sánchez et al. 2017). It is believed that there are multiple possible factors for Fe to enter the natural water or wastewater system such as industrial discharges, stormwater runoff, and pipe corrosion, and landfills can result in an increase in the level of iron concentration in the wastewater system due to contamination from various sources (Godwin et al. 2015).

Out of the five heavy metals detected in the wastewater sample, only Cd (0.14 ± 0.017 mg/L) was found to exceed the maximum permissible limit (0.02 mg/L) imposed by Malaysia's Environmental Law, with the highest concentration found in STP1 (0.16 ± 0.003 mg/L). Compared to a prior study conducted in Penang, Malaysia, the average concentration of Cd in these two areas was not significantly different, with 0.13 ± 0.011 against 0.14 ± 0.017 mg/L (Ruzi et al. 2023). The findings of this study are deeply concerning, as they demonstrated a significant discharge of Cd into the environment. This is particularly alarming given the well-documented negative effects of Cd on ecosystems, including the disruption of the food chain and other aspects of the living ecosystem (Godwin et al. 2015; Murtaza et al. 2015). Cadmium, for example, can accumulate in the tissues of aquatic organisms including fish, shellfish, and algae. When polluted organisms are consumed by larger predators, the concentration of cadmium in the food chain increases, potentially causing harm to higher trophic levels such as humans. The degree of the toxic effect on humans is determined by exposure level and duration, as well as individual susceptibility factors such as age and health state. In general, the most common effect of Cd toxicity is kidney damage, where this metal will be accumulated in the kidneys (Satarug et al. 2006; Chen et al. 2021) and lead to renal tubular dysfunction and damage (Johri et al. 2010; Vervaet et al. 2017). However, other metals such as Cu and Zn were present at low concentrations in the effluent at the involved STPs and were deemed safe.

The correlations between the metals Cu, Cd, and Ni are also noteworthy. Copper showed a negative correlation with Cd (r = −0.775, p ≤ 0.05) and Ni (r = −0.917, p ≤ 0.001), suggesting that the presence of Cu may reduce the levels of Cd and Ni in the wastewater. Copper and Ni showed a positive correlation (r = 0.840, p ≤ 0.01), indicating that their sources or behaviors in the wastewater may be similar. Overall, these results may highlight the complex interrelationships between different parameters in wastewater and suggest that the presence of certain heavy metals may have a significant influence on the overall wastewater quality. These findings also may be useful in developing appropriate measurements to improve the wastewater treatment and reduce the levels of dangerous pollutants in the environment.

WBE offers a unique and powerful approach for predicting and managing heavy metal exposure within populations. Its advantages in terms of early warning, cost-effectiveness, non-invasiveness, and detailed data collection make it a crucial tool for public health agencies and environmental monitoring bodies (Boogaerts et al. 2021; Mao et al. 2021; Shrestha et al. 2021). Analyzing pooled wastewater samples from entire communities provided a real-time snapshot of heavy metal exposure. This allows for early detection of potential outbreaks or elevated exposure levels before clinical symptoms manifest (Boogaerts et al. 2021; Mao et al. 2021). Integrating WBE with heavy metals data can strengthen exposure predictions and guide public health interventions for the future healthcare (Mao et al. 2021). Overall, by embracing WBE, the world can move toward a future where heavy metal exposure is effectively monitored and managed, safeguarding the health of communities and ecosystems.

All calculations were based on the assumption that all the heavy metals present in the influent wastewater originated from the populations rather than from any other sources. Each aspect of the heavy metal exposure is documented in Table 4 and expressed as mg/1,000p/day. Iron, Ni, and Zn were among the highest estimated exposure levels in the population, with average exposure levels of 2,396.22 ± 1,914.386, 364.46 ± 290.305, and 237.76 ± 324.062 mg/1,000p/day, respectively. Meanwhile, Cu and Cd had recorded the lowest exposure level, with an average exposure level of 73.00 ± 62.925 and 59.70 ± 35.559 mg/1,000p/day.

When the data were compared to the most recent study conducted in Penang, Malaysia, it was extremely surprising to find that the estimated exposure levels for these metals (aside from Cu) were much higher in Sungai Petani, with the average estimated exposure level of Fe at 1,924.77 ± 2,451.772 mg/1,000p/day, Ni at 270.70 ± 122.752 mg/1,000p/day, Zn at 110.17 ± 50.678 mg/1,000p/day, and Cd at 46.91 ± 20.936 mg/1,000p/day (Ruzi et al. 2023). The higher concentration of metals observed in the Penang region, compared to Sungai Petani, may be attributed to the region's significant industrialization and densely populated areas. This research suggests that there are multiple factors contributing to the increased levels of metals, which may include the possible exposure of individuals living and working in heavy industrial areas such as mining, iron, and steel production (Zhuang et al. 2014; Riaz et al. 2017; Wang et al. 2020; Karn et al. 2021); agricultural practices that utilize heavy metal-contaminated sources, such as polluted soil and water supplies; or heavy metal-based products, such as metal-containing pesticides and fertilizers, that can lead to elevated levels of these metals in the soil and crops (Gimeno-García et al. 1996; Benson 2014; Alrawiq et al. 2015; Gong et al. 2019; Iyama et al. 2022; Ullah et al. 2022), which are then consumed by humans. It is possible that the population of Sungai Petani is facing higher exposure to heavy metals compared to those living in Penang. This suggests that necessary measures need to be taken to decrease the exposure levels and safeguard the health of the public.

Although the estimation of heavy metal exposure can provide a more concrete interpretation of the WBE data, it is also susceptible to interpretation from a variety of sources.

These will cause inaccuracies, which include sample collection and preparation variations, analyte loss, population fluctuation, and analyte contamination by other substances. To improve the precision of WBE estimations, multiple methodologies must be considered, especially when evaluating the population's exposure to heavy metals, stability, and metabolism rate of the biomarker in humans and sewage are key elements to consider. In addition, this study recommends computing corrective factors for the biomarker to eliminate any inaccuracies caused by contamination from external sources or biomarker deposition in the sludge. In addition, this study also was limited to a single small region of Malaysia and a short brief duration, which may not represent the population's long-term exposure. While the tropical environment may mitigate the seasonal effects of heavy metal intake, extrapolating from a single study may lead to erroneous conclusions. For a fuller exposure evaluation of the study area and for a reliable comparison with other regions, future research should include more states and investigation periods.

The study conducted in the Sungai Petani district found that Fe, Ni, and Zn had the highest estimated levels of per 1,000 population exposure when utilizing the WBE approach. This study provides valuable insights into heavy metal exposure rates and patterns in the region, covering a significant portion of the Sungai Petani population. The study can also contribute to enhancing the understanding and representativeness of future studies and providing more accurate estimations of heavy metal exposure in the general population. However, WBE is susceptible to various forms of error that must be addressed to improve estimation precision. Future studies should address the limitations of this research, such as extending the study period and evaluating the population's exposure to heavy metals in additional states across the country.

The authors express deep gratitude to the personnel of Indah Water Konsortium Sdn Bhd who helped in obtaining wastewater samples and providing essential data at the selected wastewater treatment plants.

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

The authors declare there is no conflict.