The aim of this study was to investigate the use of wastewater-based epidemiology (WBE) to estimate heavy metal exposure in Sungai Petani, Malaysia. Atomic absorption spectroscopy was used to detect copper (Cu), nickel (Ni), zinc (Zn), iron (Fe), and cadmium (Cd) in wastewater from eight sewage treatment facilities in Sungai Petani in January 2022. The heavy metal concentrations were measured in both influent and effluent, and the mean concentrations in the wastewater were found to be in the following order: Fe > Ni > Zn > Cd > Cu, with a 100% detection frequency. The results of WBE estimation showed that Fe, Ni, and Zn had the highest estimated per population exposure levels, while Cd had the lowest. Compared to a similar study conducted in Penang, Malaysia, all metals except Cu were found to have higher concentrations in Sungai Petani, even though it is a non-industrial district. These findings highlight the importance of addressing heavy metal contamination in Sungai Petani and implementing effective risk management and prevention strategies.

  • A new study has found that Sungai Petani, Malaysia, has high levels of heavy metals in its wastewater.

  • The study's findings are concerning, as heavy metals can have serious health consequences.

  • Researchers urge the government to take steps to reduce heavy metal pollution in the area.

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.

Study site

The study site, as depicted in Figure 1, is located in the Sungai Petani district of Kedah, which is a state situated in the northern part of Malaysia (5.6436° N, 100.4894° E). To qualify for selection, each STP had to cater to a minimum of 10,000 individuals. Consequently, eight STPs were selected for the sampling process, namely, STP1 (Bandar Puteri Jaya), STP2 (Bandar Amanjaya – Northbound), STP3 (Amanjaya), STP4 (Bandar Laguna Merbok), STP5 (Bandar Perdana), STP6 (Bandar Seri Astana), STP7 (Bandar Utama), and STP8 (Bandar Baru, Bukit Banyan). The estimated 200,000 individuals in the studied STPs correspond to about 10% of the Kedah population.
Figure 1

Location of studied STPs in Sungai Petani, Kedah, Malaysia. The blue indicator represents eight STPs from which samples were collected.

Figure 1

Location of studied STPs in Sungai Petani, Kedah, Malaysia. The blue indicator represents eight STPs from which samples were collected.

Close modal

Sample collection

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.

Sample preparation and analysis

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).

Heavy metal exposure from the population

The WBE approach was used to evaluate the daily expected exposure of specific heavy metals among the population served by each STP. Equation (1) was used to calculate the daily load of each heavy metal per 1,000 population (mg/1,000p/day) and Equation (2) was used to estimate the exposure of the chosen heavy metal per 1,000 population per day (mg/1,000p/day). The literature was consulted to obtain excretion factor for each studied metal, where the excretion rate was approximately 72.4% for Cu (ATSDR 2021), 84% for Cd (Rafati-Rahimzadeh et al. 2017), 85% for Ni (World Health Organization 2000), 70% for Zn (Roohani et al. 2013), and 90% for Fe (Nutrition Reviews 2009).
formula
(1)
Ci represents the influent concentration of the heavy metal (ng/L), FIn is the STP's influent volume flow per day (L/d), and PS is the population the STP serves.
formula
(2)

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.

Statistical analysis

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.

Physicochemical properties of the effluent discharge

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.

Table 1

Physicochemical properties of effluent discharge from selected STPs in Sungai Petani, Kedah, Malaysia (mean ± SD; n = 3)

ParameterSTP1STP2STP3STP4STP5STP6STP7STP8Effluent standarda
Flow rate (m3/day) 4,378.9 3,198.9 13,973 2,541.3 6,443 6,443 5,584.6 2,367.9 – 
PE 64,435 26,615 23,600 20,808 13,129 12,984 10,555 10,306 – 
BOD (mg/L) 5.6 ± 2.06 15.3 ± 7.80 12.3 ± 3.77 42.0 ± 11.40 15.6 ± 3.86 47.8 ± 11.93 10.3 ± 4.50 12.8 ± 2.50 20–50 
COD (mg/L) 25.0 ± 3.83 55 ± 29.64 49.0 ± 3.83 109.0 ± 14.38 101.0 ± 79.96 99.0 ± 20.23 45.0 ± 7.57 54.0 ± 17.44 120–200 
NH3N (mg/L) 6.3 ± 3.06 11.75 ± 5.38 9.5 ± 2.38 22.0 ± 2.16 11.5 ± 5.69 21.0 ± 2.16 12.0 ± 4.08 26.0 ± 1.83 20–50 
NO3N (mg/L) <1 <1 2.8 ± 2.04 <1 <1 <1 2.9 ± 2.40 <1 10–20 
TSS (mg/L) 7.8 ± 2.36 31.5 ± 14.18 15.8 ± 2.06 44.0 ± 18.78 21.8 ± 4.27 36.0 ± 10.68 10.0 ± 3.74 17.3 ± 13.84 50–100 
pH 7.7 ± 0.20 7.1 ± 0.08 7.0 ± 0.15 7.1 ± 0.19 7.2 ± 0.05 7.1 ± 0.15 7.3 ± 0.05 7.3 ± 0.19 5.5–9.0 
OG 2.0 ± 1.00 3.0 ± 1.83 2.3 ± 0.50 4.5 ± 1.91 2.3 ± 0.96 4.3 ± 0.96 1.5 ± 1.00 1.8 ± 0.50 5–10 
TEMP (°C) 30.5 ± 1.00 30.3 ± 0.50 30.8 ± 0.50 – 29.3 ± 0.50 29.5 ± 0.58 29.0 ± 0.00 30.3 ± 0.50 <40 
ParameterSTP1STP2STP3STP4STP5STP6STP7STP8Effluent standarda
Flow rate (m3/day) 4,378.9 3,198.9 13,973 2,541.3 6,443 6,443 5,584.6 2,367.9 – 
PE 64,435 26,615 23,600 20,808 13,129 12,984 10,555 10,306 – 
BOD (mg/L) 5.6 ± 2.06 15.3 ± 7.80 12.3 ± 3.77 42.0 ± 11.40 15.6 ± 3.86 47.8 ± 11.93 10.3 ± 4.50 12.8 ± 2.50 20–50 
COD (mg/L) 25.0 ± 3.83 55 ± 29.64 49.0 ± 3.83 109.0 ± 14.38 101.0 ± 79.96 99.0 ± 20.23 45.0 ± 7.57 54.0 ± 17.44 120–200 
NH3N (mg/L) 6.3 ± 3.06 11.75 ± 5.38 9.5 ± 2.38 22.0 ± 2.16 11.5 ± 5.69 21.0 ± 2.16 12.0 ± 4.08 26.0 ± 1.83 20–50 
NO3N (mg/L) <1 <1 2.8 ± 2.04 <1 <1 <1 2.9 ± 2.40 <1 10–20 
TSS (mg/L) 7.8 ± 2.36 31.5 ± 14.18 15.8 ± 2.06 44.0 ± 18.78 21.8 ± 4.27 36.0 ± 10.68 10.0 ± 3.74 17.3 ± 13.84 50–100 
pH 7.7 ± 0.20 7.1 ± 0.08 7.0 ± 0.15 7.1 ± 0.19 7.2 ± 0.05 7.1 ± 0.15 7.3 ± 0.05 7.3 ± 0.19 5.5–9.0 
OG 2.0 ± 1.00 3.0 ± 1.83 2.3 ± 0.50 4.5 ± 1.91 2.3 ± 0.96 4.3 ± 0.96 1.5 ± 1.00 1.8 ± 0.50 5–10 
TEMP (°C) 30.5 ± 1.00 30.3 ± 0.50 30.8 ± 0.50 – 29.3 ± 0.50 29.5 ± 0.58 29.0 ± 0.00 30.3 ± 0.50 <40 

Note: STP; Sewage Treatment Plant, PE; Population Equivalence, BOD; Biochemical Oxygen Demand, COD; Chemical Oxygen Demand, NH3N; Ammoniacal Nitrogen, NO3N; Nitrate Nitrogen, TSS; Total Suspended Solids, OG; Oil and Grease, TEMP; Temperature.

aEffluent standard, adopted from Malaysia's Environmental Law for effluent wastewater (Department of Environmental 2021).

Table 2

Level of heavy metals in influent and effluent discharge from selected STPs in Sungai Petani, Kedah, Malaysia (mean ± SD; n = 3)

Elements/STPsCu (mg/L)
Cd (mg/L)
Ni (mg/L)
Zn (mg/L)
Fe (mg/L)
InEfInEfInEfInEfInEf
STP1 0.10 ± 0.003 0.05 ± 0.002 0.16 ± 0.008 0.16 ± 0.003 1.03 ± 0.062 0.84 ± 0.091 0.30 ± 0.001 0.25 ± 0.003 5.90 ± 0.035 2.26 ± 0.005 
STP2 0.14 ± 0.005 0.05 ± 0.002 0.18 ± 0.005 0.13 ± 0.005 0.93 ± 0.067 0.86 ± 0.124 0.36 ± 0.004 0.20 ± 0.003 8.31 ± 0.038 7.05 ± 0.026 
STP3 0.11 ± 0.003 0.05 ± 0.003 0.15 ± 0.004 0.13 ± 0.003 1.09 ± 0.110 0.76 ± 0.084 0.39 ± 0.001 0.27 ± 0.003 7.17 ± 0.037 6.36 ± 0.022 
STP4 0.11 ± 0.006 0.09 ± 0.002 0.16 ± 0.004 0.12 ± 0.003 0.76 ± 0.090 0.68 ± 0.100 0.38 ± 0.002 0.18 ± 0.011 4.66 ± 0.080 3.60 ± 0.031 
STP5 0.16 ± 0.005 0.08 ± 0.002 0.15 ± 0.003 0.13 ± 0.004 0.81 ± 0.103 0.73 ± 0.105 0.21 ± 0.004 0.14 ± 0.006 5.91 ± 0.015 5.72 ± 0.040 
STP6 0.22 ± 0.003 0.16 ± 0.002 0.15 ± 0.004 0.11 ± 0.002 0.79 ± 0.046 0.58 ± 0.083 0.42 ± 0.004 0.17 ± 0.003 2.99 ± 0.029 1.89 ± 0.044 
STP7 0.16 ± 0.004 0.05 ± 0.002 0.15 ± 0.005 0.15 ± 0.001 1.07 ± 0.067 0.88 ± 0.047 1.16 ± 0.005 0.21 ± 0.009 9.92 ± 0.090 2.99 ± 0.019 
STP8 0.21 ± 0.010 0.04 ± 0.002 0.16 ± 0.004 0.15 ± 0.003 0.88 ± 0.067 0.84 ± 0.102 0.28 ± 0.009 0.23 ± 0.043 6.13 ± 0.026 2.86 ± 0.010 
Mean ± SD 0.15 ± 0.045 0.07 ± 0.039 0.16 ± 0.010 0.14 ± 0.017 0.92 ± 0.131 0.77 ± 0.104 0.44 ± 0.300 0.21 ± 0.043 6.37 ± 2.133 4.09 ± 1.990 
Elements/STPsCu (mg/L)
Cd (mg/L)
Ni (mg/L)
Zn (mg/L)
Fe (mg/L)
InEfInEfInEfInEfInEf
STP1 0.10 ± 0.003 0.05 ± 0.002 0.16 ± 0.008 0.16 ± 0.003 1.03 ± 0.062 0.84 ± 0.091 0.30 ± 0.001 0.25 ± 0.003 5.90 ± 0.035 2.26 ± 0.005 
STP2 0.14 ± 0.005 0.05 ± 0.002 0.18 ± 0.005 0.13 ± 0.005 0.93 ± 0.067 0.86 ± 0.124 0.36 ± 0.004 0.20 ± 0.003 8.31 ± 0.038 7.05 ± 0.026 
STP3 0.11 ± 0.003 0.05 ± 0.003 0.15 ± 0.004 0.13 ± 0.003 1.09 ± 0.110 0.76 ± 0.084 0.39 ± 0.001 0.27 ± 0.003 7.17 ± 0.037 6.36 ± 0.022 
STP4 0.11 ± 0.006 0.09 ± 0.002 0.16 ± 0.004 0.12 ± 0.003 0.76 ± 0.090 0.68 ± 0.100 0.38 ± 0.002 0.18 ± 0.011 4.66 ± 0.080 3.60 ± 0.031 
STP5 0.16 ± 0.005 0.08 ± 0.002 0.15 ± 0.003 0.13 ± 0.004 0.81 ± 0.103 0.73 ± 0.105 0.21 ± 0.004 0.14 ± 0.006 5.91 ± 0.015 5.72 ± 0.040 
STP6 0.22 ± 0.003 0.16 ± 0.002 0.15 ± 0.004 0.11 ± 0.002 0.79 ± 0.046 0.58 ± 0.083 0.42 ± 0.004 0.17 ± 0.003 2.99 ± 0.029 1.89 ± 0.044 
STP7 0.16 ± 0.004 0.05 ± 0.002 0.15 ± 0.005 0.15 ± 0.001 1.07 ± 0.067 0.88 ± 0.047 1.16 ± 0.005 0.21 ± 0.009 9.92 ± 0.090 2.99 ± 0.019 
STP8 0.21 ± 0.010 0.04 ± 0.002 0.16 ± 0.004 0.15 ± 0.003 0.88 ± 0.067 0.84 ± 0.102 0.28 ± 0.009 0.23 ± 0.043 6.13 ± 0.026 2.86 ± 0.010 
Mean ± SD 0.15 ± 0.045 0.07 ± 0.039 0.16 ± 0.010 0.14 ± 0.017 0.92 ± 0.131 0.77 ± 0.104 0.44 ± 0.300 0.21 ± 0.043 6.37 ± 2.133 4.09 ± 1.990 

Notes: STP; Sewage Treatment Plant Permissible limit: Cu (0.2–1 mg/L), Cd (0.01–0.02 mg/L), Ni (0.2–1 mg/L), Zn (1.0 mg/L), and Fe (1.0–5.0 mg/L) (adopted from Malaysia's Environmental Law for effluent wastewater (Department of Environmental 2021).

Table 3

Comparison of heavy metal levels in STP effluent discharge from multiple countries

Country/elementsCu (mg/L)Cd (mg/L)Ni (mg/L)Zn (mg/L)Fe (mg/L)References
Chinab 0.010 ± 0.017 0.0001 ± 0.0001 0.043 ± 0.105 0.072 ± 0.213 – Feng et al. (2018)  
Egypta 0.035–0.042 0.017–0.026 0.021–0.027 0.053–0.060 – Ahmed & Hanafy (2017)  
Indonesiaa 0.020–0.472 ND–0.020 0.005–0.083 – – Juliani (2021)  
Iraqa 0.031–0.151 ND–0.119 0.019–0.334 0.382–1.460 ND–0.092 Omran et al. (2019)  
Moroccob 0.102 ± 0.047 0.076 ± 0.006 – 1.590 ± 0.195 – Chaoua et al. (2019)  
Pakistana 0.050–1.180 0.020–0.030 0.030–0.080 0.760–1.220 0.780–4.620 Sarwar et al. (2020)  
South Africaa ND–0.050 ND–0.130 – – ND–0.636 Agoro et al. (2020)  
Taiwana ND–0.721 ND–0.001 ND–0.295 ND–0.255 – Hsu et al. (2016)  
Thailanda ND–0.104 ND–0.003 ND–0.179 0.050–0.140 0.700–0.950 Sriuttha et al. (2017)  
Vietnama 1.963–3.859 0.203–0.406 – 1.989–3.891 – Huynh et al. (2021)  
Penang, Malaysiaa 0.031–0.322 0.109–0.143 0.671–0.997 0.168–0.341 1.529–9.543 Ruzi et al. (2023)  
Sungai Petani, Kedah, Malaysiaa 0.040–0.160 0.110–0.160 0.580–0.880 0.140–0.270 2.260–7.050 This study 
Country/elementsCu (mg/L)Cd (mg/L)Ni (mg/L)Zn (mg/L)Fe (mg/L)References
Chinab 0.010 ± 0.017 0.0001 ± 0.0001 0.043 ± 0.105 0.072 ± 0.213 – Feng et al. (2018)  
Egypta 0.035–0.042 0.017–0.026 0.021–0.027 0.053–0.060 – Ahmed & Hanafy (2017)  
Indonesiaa 0.020–0.472 ND–0.020 0.005–0.083 – – Juliani (2021)  
Iraqa 0.031–0.151 ND–0.119 0.019–0.334 0.382–1.460 ND–0.092 Omran et al. (2019)  
Moroccob 0.102 ± 0.047 0.076 ± 0.006 – 1.590 ± 0.195 – Chaoua et al. (2019)  
Pakistana 0.050–1.180 0.020–0.030 0.030–0.080 0.760–1.220 0.780–4.620 Sarwar et al. (2020)  
South Africaa ND–0.050 ND–0.130 – – ND–0.636 Agoro et al. (2020)  
Taiwana ND–0.721 ND–0.001 ND–0.295 ND–0.255 – Hsu et al. (2016)  
Thailanda ND–0.104 ND–0.003 ND–0.179 0.050–0.140 0.700–0.950 Sriuttha et al. (2017)  
Vietnama 1.963–3.859 0.203–0.406 – 1.989–3.891 – Huynh et al. (2021)  
Penang, Malaysiaa 0.031–0.322 0.109–0.143 0.671–0.997 0.168–0.341 1.529–9.543 Ruzi et al. (2023)  
Sungai Petani, Kedah, Malaysiaa 0.040–0.160 0.110–0.160 0.580–0.880 0.140–0.270 2.260–7.050 This study 

Note: STP; Sewage Treatment Plant, ND; Not detectable. ND, not detectable

aMean ± SD.

bRange value.

Heavy metal concentrations in domestic wastewater

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.

Correlation between effluent physicochemical with heavy metal

The results in Figure 2 indicate that there are significant correlations between several wastewater sample parameters. BOD demonstrated a significant positive correlation with OG (r = 0.935, p ≤ 0.001) and Cu (r = 0.882, p ≤ 0.01) and a negative correlation with Cd (r = −0.840, p ≤ 0.01) and Ni (r = −0.879, p ≤ 0.01). These results may indicate that these metals may be contributing to the elevated BOD levels in the effluent. COD exhibited significant positive correlation with TSS (r = 0.808, p ≤ 0.05), OG (r = 0.745, p ≤ 0.05), and Cu (r = 0.727, p ≤ 0.05), and significant negative correlations with Cd (r = −0.819, p ≤ 0.05), Ni (r = −0.808, p ≤ 0.05), and Zn (r = −0.830, p ≤ 0.05). TSS also showed a strong positive correlation with OG (r = 0.938, p ≤ 0.001) and a negative correlation with Cd (r = −0.851, p ≤ 0.01). A positive correlation was found between pH and Cd (r = 0.811, p ≤ 0.05), suggesting that higher pH levels may result in higher Cd contents in wastewater.
Figure 2

Pearson's correlation coefficients (r) for the physicochemical parameters and heavy metal levels from selected STPs effluent in Sungai Petani, Kedah, Malaysia. Correlations are defined as weak (0 < |r| < 0.3), moderate (0.3 < |r| < 0.7), or strong (|r| > 0.7). PE, population equivalence; Temp, temperature.

Figure 2

Pearson's correlation coefficients (r) for the physicochemical parameters and heavy metal levels from selected STPs effluent in Sungai Petani, Kedah, Malaysia. Correlations are defined as weak (0 < |r| < 0.3), moderate (0.3 < |r| < 0.7), or strong (|r| > 0.7). PE, population equivalence; Temp, temperature.

Close modal

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.

Removal efficiency of heavy metals

Figure 3 illustrates the average removal efficiency of Cd, Cu, Ni, Fe, and Zn from all the STPs. Although the removal of heavy metals in domestic STPs occurs quite frequently to some heavy metals, it is somewhat surprising that none of these STPs applied any specialized wastewater treatment processes that aim to remove heavy metals from the wastewater (only biological). The average removal efficiency of heavy metals from all STPs was as follows: Cu > Zn > Fe > Cd > Ni. Copper, Zn, and Fe removal were relatively high, by the average of 49.97% ± 17.807, 39.01% ± 17.891, and 34.12% ± 24.909, respectively, compared to Cd and Ni. This result indicated a similarity to a study conducted in Penang, Malaysia (Ruzi et al. 2023), where STPs demonstrated a great capability of removing high quantities of Cu, Zn, and Fe from influent wastewater. This finding is also consistent with recent studies suggesting that the presence of biological adsorption in the STP may help in their removal during the treatment process (Zwain et al. 2014; Li et al. 2021). In contrast, both Cd and Ni had less than a 50% removal rate from wastewater, prompting this study's strong recommendation of a novel approach involving nano-adsorbents, photocatalysts, electrocatalysts, and nano-membranes, which have been shown to significantly remove heavy metals from wastewater (Anjum et al. 2019; Kaur & Roy 2021).
Figure 3

The average removal of heavy metals from the selected STPs in Sungai Petani, Kedah, Malaysia (n = 8).

Figure 3

The average removal of heavy metals from the selected STPs in Sungai Petani, Kedah, Malaysia (n = 8).

Close modal

Estimation of population heavy metals exposure

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.

Table 4

Weekly estimated exposure rate of heavy metal within the STP served population in Sungai Petani Kedah, Malaysia

STPsWeekEstimated exposure rate (mg/1,000p/day)
CuCdNiZnFe
STP1 9.01 11.97 76.75 25.82 507.42 
 8.82 13.27 83.95 35.34 402.62 
 9.76 14.24 86.67 26.99 426.17 
STP2 23.24 22.61 117.93 75.89 1,137.28 
 24.24 26.04 155.54 74.52 1,282.58 
 20.92 26.61 121.32 32.97 910.52 
STP3 96.50 108.55 1,305.35 306.19 4,730.03 
 67.06 109.96 462.52 404.30 4,777.40 
 103.04 90.22 500.13 284.20 4,641.88 
STP4 14.84 21.52 95.98 59.67 639.97 
 18.89 22.97 105.75 73.63 711.34 
 23.95 26.17 127.02 67.00 545.79 
STP5 107.10 84.13 385.67 112.17 3,140.77 
 65.07 85.30 583.12 137.41 3,369.79 
 161.32 92.31 437.63 182.28 3,151.68 
STP6 127.48 86.25 519.58 336.02 2,033.42 
 228.92 90.97 418.00 266.54 1,508.53 
 101.44 90.97 447.19 297.74 1,402.67 
STP7 194.39 95.74 693.43 1,218.43 6,571.36 
 51.16 89.44 741.98 1,235.06 5,382.66 
 99.39 94.48 565.20 182.92 5,542.57 
STP8 36.81 42.12 217.33 81.40 1,533.77 
 21.58 43.76 227.06 83.37 1,475.06 
 137.09 43.22 271.93 106.35 1,683.88 
Min  8.82 11.97 76.75 25.82 402.62 
Max  228.92 109.96 1,305.35 1,235.06 6,571.36 
Average  73.00 59.70 364.46 237.76 2,396.21 
STPsWeekEstimated exposure rate (mg/1,000p/day)
CuCdNiZnFe
STP1 9.01 11.97 76.75 25.82 507.42 
 8.82 13.27 83.95 35.34 402.62 
 9.76 14.24 86.67 26.99 426.17 
STP2 23.24 22.61 117.93 75.89 1,137.28 
 24.24 26.04 155.54 74.52 1,282.58 
 20.92 26.61 121.32 32.97 910.52 
STP3 96.50 108.55 1,305.35 306.19 4,730.03 
 67.06 109.96 462.52 404.30 4,777.40 
 103.04 90.22 500.13 284.20 4,641.88 
STP4 14.84 21.52 95.98 59.67 639.97 
 18.89 22.97 105.75 73.63 711.34 
 23.95 26.17 127.02 67.00 545.79 
STP5 107.10 84.13 385.67 112.17 3,140.77 
 65.07 85.30 583.12 137.41 3,369.79 
 161.32 92.31 437.63 182.28 3,151.68 
STP6 127.48 86.25 519.58 336.02 2,033.42 
 228.92 90.97 418.00 266.54 1,508.53 
 101.44 90.97 447.19 297.74 1,402.67 
STP7 194.39 95.74 693.43 1,218.43 6,571.36 
 51.16 89.44 741.98 1,235.06 5,382.66 
 99.39 94.48 565.20 182.92 5,542.57 
STP8 36.81 42.12 217.33 81.40 1,533.77 
 21.58 43.76 227.06 83.37 1,475.06 
 137.09 43.22 271.93 106.35 1,683.88 
Min  8.82 11.97 76.75 25.82 402.62 
Max  228.92 109.96 1,305.35 1,235.06 6,571.36 
Average  73.00 59.70 364.46 237.76 2,396.21 

STP; Sewage Treatment Plant.

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.

Study limitation

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.

Akpor
O. B.
,
Ohiobor
G. O.
&
Olaolu
T. D.
2014
Heavy metal pollutants in wastewater effluents: Sources, effects and remediation
.
Advances in Bioscience and Bioengineering
2
(
4
),
37
.
Ali
H.
,
Khan
E.
&
Ilahi
I.
2019
Environmental chemistry and ecotoxicology of hazardous heavy metals: Environmental persistence, toxicity, and bioaccumulation
.
Journal of Chemistry
2019
,
e6730305
.
Alrawiq
N.
,
Jusoh
K.
,
Latif
M. T.
&
Ismail
B.
2015
The distribution and accumulation status of heavy metals (Pb, Cd, Cu and Zn) in soils from the rice fields of Mada in Kedah, Malaysia
.
International Journal of Chemical Sciences
131
,
265
283
.
Al-Saleh
I.
,
Al-Rouqi
R.
,
Elkhatib
R.
,
Abduljabbar
M.
&
Al-Rajudi
T.
2017
Risk assessment of environmental exposure to heavy metals in mothers and their respective infants
.
International Journal of Hygiene and Environmental Health
220
(
8
),
1252
1278
.
Anjum
M.
,
Miandad
R.
,
Waqas
M.
,
Gehany
F.
&
Barakat
M. A.
2019
Remediation of wastewater using various nano-materials
.
Arabian Journal of Chemistry
12
(
8
),
4897
4919
.
Anyanwu
B. O.
,
Ezejiofor
A. N.
,
Igweze
Z. N.
&
Orisakwe
O. E.
2018
Heavy metal mixture exposure and effects in developing nations: An update
.
Toxics
6
(
4
),
65
.
Arshad
H.
,
Mehmood
M. Z.
,
Shah
M. H.
&
Abbasi
A. M.
2020
Evaluation of heavy metals in cosmetic products and their health risk assessment
.
Saudi Pharmaceutical Journal: SPJ
28
(
7
),
779
790
.
ATSDR
2020
Toxicological Profiles for Lead | ATSDR. Available from: https://www.atsdr.cdc.gov/toxprofiles/tp13.pdf.
ATSDR
2021
Cadmium Toxicity: What Are the U.S. Standards for Cadmium Exposure? | Environmental Medicine | ATSDR [Online]. Available from: https://www.atsdr.cdc.gov/csem/cadmium/Safety-Standards.html
.
Balali-Mood
M.
,
Naseri
K.
,
Tahergorabi
Z.
,
Khazdair
M. R.
&
Sadeghi
M.
2021
Toxic mechanisms of five heavy metals: Mercury, lead, chromium, cadmium, and arsenic
.
Frontiers in Pharmacology
12
,
643972
.
Bencko
V.
&
Yan Li Foong
F.
2017
The history of arsenical pesticides and health risks related to the use of Agent Blue
.
Annals of Agricultural and Environmental Medicine: AAEM
24
(
2
),
312
316
.
Benson
N.
2014
Trace metals levels in inorganic fertilizers commercially available in Nigeria
.
Journal of Scientific Research and Reports
3
,
610
620
.
Bishop
N.
,
Jones-Lepp
T.
,
Margetts
M.
,
Sykes
J.
,
Alvarez
D.
&
Keil
D. E.
2020
Wastewater-based epidemiology pilot study to examine drug use in the Western United States
.
Science of The Total Environment
745
,
140697
.
Boogaerts
T.
,
Ahmed
F.
,
Choi
Phil.M.
,
Tscharke
B.
,
O'Brien
J.
,
De Loof
H.
,
Gao
J.
,
Thai
P.
,
Thomas
K.
,
Mueller
J. F.
,
Hall
W.
,
Covaci
A.
&
van Nuijs
A. L. N.
2021
Current and future perspectives for wastewater-based epidemiology as a monitoring tool for pharmaceutical use
.
Science of The Total Environment
789
,
148047
.
Cabral Pinto
M. M. S.
&
Ferreira da Silva
E. A.
2019a
Heavy metals of Santiago island (Cape Verde) alluvial deposits: Baseline value maps and human health risk assessment
.
International Journal of Environmental Research and Public Health
16
(
1
),
2
.
Cao
H.
,
Cao
H.
,
Chen
J.
,
Zhang
J.
,
Zhang
H.
,
Qiao
L.
&
Men
Y.
2010
Heavy metals in rice and garden vegetables and their potential health risks to inhabitants in the vicinity of an industrial zone in Jiangsu, China
.
Journal of Environmental Sciences
22
(
11
),
1792
1799
.
Castiglioni
S.
,
Bijlsma
L.
,
Covaci
A.
,
Emke
E.
,
Hernández
F.
,
Reid
M.
,
Ort
C.
,
Thomas
K.V.
,
van Nuijs
A.L.N.
,
de Voogt
P.
&
Zuccato
E.
2013
Evaluation of uncertainties associated with the determination of community drug use through the measurement of sewage drug biomarkers
.
Environmental Science & Technology
47
(
3
),
1452
1460
.
Chandravanshi
L.
,
Shiv
K.
&
Kumar
S.
2021
Developmental toxicity of cadmium in infants and children: A review
.
Environmental Analysis, Health and Toxicology
36
(
1
),
e2021003
.
Chaoua
S.
,
Boussaa
S.
,
El Gharmali
A.
&
Boumezzough
A.
2019
Impact of irrigation with wastewater on accumulation of heavy metals in soil and crops in the region of Marrakech in Morocco
.
Journal of the Saudi Society of Agricultural Sciences
18
(
4
),
429
436
.
Chen
C.
,
Han
X.
,
Wang
G.
,
Liu
D.
,
Bao
L.
,
Jiao
C.
,
Luan
J.
,
Hou
Y.
,
Xu
Y.
,
Wang
H.
,
Zhang
Q.
,
Zhou
H.
,
Fu
J.
&
Pi
J.
2021
Nrf2 deficiency aggravates the kidney injury induced by subacute cadmium exposure in mice
.
Archives of Toxicology
95
(
3
),
883
893
.
Cheng
X.
,
Wei, Cong, Ke
X.
,
Pan
J.
,
Wei
G.
,
Chen
Y.
,
Wei, Chaohai, Li
F.
&
Preis
S.
2022
Nationwide review of heavy metals in municipal sludge wastewater treatment plants in China: Sources, composition, accumulation and risk assessment
.
Journal of Hazardous Materials
437
,
129267
.
Cox
P. A.
1989
The Elements. Their Origin, Abundance, and Distribution. Oxford University Press, Oxford, UK
.
Department of Environmental
2021
Jabatan Alam Sekitar – Kementerian Alam Sekitar dan Air [Online]. Available from: https://www.doe.gov.my/
.
Engwa
G. A.
,
Ferdinand
P. U.
,
Nwalo
F. N.
,
Unachukwu
M. N.
,
Engwa
G. A.
,
Ferdinand
P. U.
,
Nwalo
F. N.
&
Unachukwu
M. N.
2019
Mechanism and Health Effects of Heavy Metal Toxicity in Humans
.
IntechOpen
.
Feng
J.
,
Chen, Xiaolin, Jia
L.
,
Liu
Q.
,
Chen, Xiaojia, Han
D.
&
Cheng
J.
2018
Effluent concentration and removal efficiency of nine heavy metals in secondary treatment plants in Shanghai, China
.
Environmental Science and Pollution Research
25
(
17
),
17058
17065
.
Ghadban
M.
2021
Heavy Metals Overview & Examples | Heavy Metals in Chemistry. Available from: https://study.com/academy/lesson/heavy-metals-definition-examples.html.
Godwin
A.
,
Oghenekohwiroro
E.
,
Funso
A.
&
Olaniyi
O.
2015
Using EF, PLI and Igeo for the assessment of heavy metal pollution and sediment quality of Asejire Reservoir, southwest Nigeria
.
International Journal of Environment and Pollution
3
(
4
),
77
90
.
Gong
Q.
,
Chen
P.
,
Shi
R.
,
Gao
Y.
,
Zheng
S.-A.
,
Xu
Y.
,
Shao
C.
&
Zheng
X.
2019
Health assessment of trace metal concentrations in organic fertilizer in northern China
.
International Journal of Environmental Research and Public Health
16
(
6
),
1031
.
Hans Wedepohl
K.
1995
The composition of the continental crust
.
Geochimica et Cosmochimica Acta
59
(
7
),
1217
1232
.
Hayat
M. T.
,
Nauman
M.
,
Nazir
N.
&
Ali
S.
2019
Chapter 7 – Environmental hazards of cadmium: Past, present, and future
. In:
Cadmium Toxicity and Tolerance in Plants
(
Hasanuzzaman
M.
,
Prasad
M. N. V.
&
Fujita
M.
, eds.).
Academic Press
,
Cambridge, MA
, pp.
163
183
.
Hsu
L.-C.
,
Huang
C.-Y.
,
Chuang
Y.-H.
,
Chen
H.-W.
,
Chan
Y.-T.
,
Teah
H. Y.
,
Chen
T.-Y.
,
Chang
C.-F.
,
Liu
Y.-T.
&
Tzou
Y.-M.
2016
Accumulation of heavy metals and trace elements in fluvial sediments received effluents from traditional and semiconductor industries
.
Scientific Reports
6
(
1
),
34250
.
Hu
W.
,
Huang
B.
,
He
Y.
&
Kalkhajeh
Y. K.
2016
Assessment of potential health risk of heavy metals in soils from a rapidly developing region of China
.
Human and Ecological Risk Assessment: An International Journal
22
(
1
),
211
225
.
Jaishankar
M.
,
Tseten
T.
,
Anbalagan
N.
,
Mathew
B. B.
&
Beeregowda
K. N.
2014
Toxicity, mechanism and health effects of some heavy metals
.
Interdisciplinary Toxicology
7
(
2
),
60
72
.
Johri
N.
,
Jacquillet
G.
&
Unwin
R.
2010
Heavy metal poisoning: The effects of cadmium on the kidney
.
BioMetals
23
(
5
),
783
792
.
Kaizer
A.
&
Osakwe
S.
2010
Physicochemical characteristics and heavy metal levels in water samples from five river systems in Delta State, Nigeria
.
Journal of Applied Sciences and Environmental Management
14
(
1
),
1
5
.
Kamran
S.
,
Shafaqat
A.
,
Samra
H.
,
Sana
A.
,
Samar
F.
,
Muhammad
B. S.
,
Saima
A. B.
&
Hafiz
M. T.
2013
Heavy metals contamination and what are the impacts on living organisms
.
Greener Journal of Environmental Management and Public Safety
2
(
4
),
172
179
.
Karn
R.
,
Ojha
N.
,
Abbas
S.
&
Bhugra
S.
2021
A review on heavy metal contamination at mining sites and remedial techniques
.
IOP Conference Series: Earth and Environmental Science
796
(
1
),
012013
.
Kaur
S.
&
Roy
A.
2021
Bioremediation of heavy metals from wastewater using nanomaterials
.
Environment, Development and Sustainability
23
(
7
),
9617
9640
.
Li
C.
,
Zhou
K.
,
Qin
W.
,
Tian
C.
,
Qi
M.
,
Yan
X.
&
Han
W.
2019
A review on heavy metals contamination in soil: Effects, sources, and remediation techniques
.
Soil and Sediment Contamination: An International Journal
28
(
4
),
380
394
.
Mohammed
A. S.
,
Kapri
A.
,
Goel
R.
,
2011
Heavy metal pollution: Source, impact, and remedies
. In:
Biomanagement of Metal-Contaminated Soils
(
Khan
M. S.
,
Zaidi
A.
,
Goel
R.
&
Musarrat
J.
, eds.).
Environmental Pollution
.
Springer Netherlands
,
Dordrecht
, pp.
1
28
.
Murtaza
G.
,
Javed
W.
,
Hussain
A.
,
Wahid
A.
,
Murtaza
B.
&
Owens
G.
2015
Metal uptake via phosphate fertilizer and city sewage in cereal and legume crops in Pakistan
.
Environmental Science and Pollution Research
22
(
12
),
9136
9147
.
Nutrition
C.
for F.S. and A.
2022
Mercury Levels in Commercial Fish and Shellfish (1990–2012)
.
FDA
.
Nutrition Reviews
2009
Excretion of iron
.
Nutrition Reviews
13
(
9
),
261
263
.
Odukudu
F. B.
,
Ayenimo
J. G.
,
Adekunle
A. S.
,
Yusuff
A. M.
&
Mamba
B. B.
2014
Safety evaluation of heavy metals exposure from consumer products
.
International Journal of Consumer Studies
38
(
1
),
25
34
.
Omran
I. I.
,
Al-Saati
N.
,
Hashim
K.
,
Al-Saati
Z.
,
Kot
P.
,
Al Khaddar
R. M.
,
Al-Jumeily
D.
,
Shaw
A.
,
Ruddock
F.
&
Aljefery
M.
2019
Assessment of Heavy Metal Pollution in the Great Al-Mussaib Irrigation Channel
.
Desalination and Water Treatment
.
Pihak Berkuasa Tempatan Negeri Kedah Darul Aman
n.d.
Info Sungai Petani – Portal PBT Kedah
.
Poison Control
2023
Safe Cleanup Tips for Broken CFLs
.
Proshad
R.
,
Kormoker
T.
,
Mursheed
N.
,
Monirul Islam
Md
,
Bhuyan
Md. I.
,
Sazedul Islam
Md.
&
Mithu
T. N.
2018
Heavy metal toxicity in agricultural soil due to rapid industrialization in Bangladesh: A review
.
International Journal of Advanced Geosciences
6
(
1
),
83
.
Rafati-Rahimzadeh
M.
,
Rafati-Rahimzadeh
M.
,
Kazemi
S.
&
Moghadamnia
A.
2017
Cadmium toxicity and treatment: An update
.
Caspian Journal of Internal Medicine
8
(
3
),
135
145
.
Rafique
M.
,
Hajra
S.
,
Tahir
M. B.
,
Gillani
S. S. A.
&
Irshad
M.
2022
A review on sources of heavy metals, their toxicity and removal technique using physico-chemical processes from wastewater
.
Environmental Science and Pollution Research
29
(
11
),
16772
16781
.
Rajkumar
V.
,
Lee
V. R.
&
Gupta
V.
2022
Heavy metal toxicity
. In:
StatPearls
.
StatPearls Publishing
,
Treasure Island, FL
.
Ran
C.
,
Liu
Y.
,
Siddiqui
A. R.
,
Siyal
A. A.
,
Mao
X.
,
Kang
Q.
,
Fu
J.
,
Ao
W.
&
Dai
J.
2019
Pyrolysis of textile dyeing sludge in fluidized bed: Analysis of products, and migration and distribution of heavy metals
.
Journal of Cleaner Production
241
,
118308
.
Riaz
M. A.
,
Akhtar
A. B. T.
,
Riaz
A.
,
Mujtaba
G.
,
Ali
M.
&
Ijaz
B.
2017
Heavy metals identification and exposure at workplace environment its extent of accumulation in blood of iron and steel recycling foundry workers of Lahore, Pakistan
.
Pakistan Journal of Pharmaceutical Sciences
30
(
4
),
1233
1238
.
Richter
P.
,
Faroon
O.
&
Pappas
R. S.
2017
Cadmium and cadmium/zinc ratios and tobacco-related morbidities
.
International Journal of Environmental Research and Public Health
14
(
10
),
1154
.
Roohani
N.
,
Hurrell
R.
,
Kelishadi
R.
&
Schulin
R.
2013
Zinc and its importance for human health: An integrative review
.
Journal of Research in Medical Sciences
18
(
2
),
144
157
.
Rousis
N. I.
,
Gracia-Lor
E.
,
Zuccato
E.
,
Bade
R.
,
Baz-Lomba
J. A.
,
Castrignanò
E.
,
Causanilles
A.
,
Covaci
A.
,
de Voogt
P.
,
Hernàndez
F.
,
Kasprzyk-Hordern
B.
,
Kinyua
J.
,
McCall
A.-K.
,
Plósz
B. G.
,
Ramin
P.
,
Ryu
Y.
,
Thomas
K. V.
,
van Nuijs
A.
,
Yang
Z.
&
Castiglioni
S.
2017
Wastewater-based epidemiology to assess pan-European pesticide exposure
.
Water Research
121
,
270
279
.
Ruzi
I. I.
,
Ishak
A. R.
,
Abdullah
M. A.
,
Zain
N. N. M.
,
Tualeka
A. R.
&
Aziz
M. Y.
2023
Assessment of heavy metal concentrations in Penang, Malaysia's wastewater treatment plants: A wastewater-based epidemiology approach
.
Trends in Sciences
20
(
5
),
6523
.
Salvatore
S.
,
Bramness
J. G.
,
Reid
M. J.
,
Thomas
K. V.
,
Harman
C.
&
Røislien
J.
2015
Wastewater-based epidemiology of stimulant drugs: Functional data analysis compared to traditional statistical methods
.
Plos One
10
(
9
),
e0138669
.
Sánchez
M.
,
Sabio
L.
,
Gálvez
N.
,
Capdevila
M.
&
Dominguez-Vera
J. M.
2017
Iron chemistry at the service of life
.
IUBMB Life
69
(
6
),
382
388
.
Sarwar
T.
,
Shahid
M.
,
Natasha, Khalid
S.
,
Shah
A. H.
,
Ahmad
N.
,
Naeem
M. A.
,
ul Haq
Z.
,
Murtaza
B.
&
Bakhat
H. F.
2020
Quantification and risk assessment of heavy metal build-up in soil–plant system after irrigation with untreated city wastewater in Vehari, Pakistan
.
Environmental Geochemistry and Health
42
(
12
),
4281
4297
.
Satarug
S.
,
Haswell-Elkins
M. R.
&
Moore
M. R.
2006
Kidney dysfunction and hypertension: Role for cadmium, p450 and heme oxygenases?
The Tohoku Journal of Experimental Medicine
208
(
3
),
179
202
.
Sriuttha
M.
,
Khammanichanh
A.
,
Patawang
I.
,
Tanomtong
A.
,
Tengjaroenkul
B.
&
Neeratanaphan
L.
2017
Cytotoxic assessment of Nile tilapia (Oreochromis niloticus) from a domestic wastewater canal with heavy metal contamination
.
Cytologia
82
(
1
),
41
50
.
Taiwo
A. M.
,
Adeogun
A. O.
,
Olatunde
K. A.
&
Adegbite
K. I.
2011
Analysis of groundwater quality of hand-dug wells in peri-urban area of Obantoko, Abeokuta, Nigeria for selected physico-chemical parameters
.
Number
12
(
1
),
9
.
Tchounwou
P. B.
,
Yedjou
C. G.
,
Patlolla
A. K.
&
Sutton
D. J.
2012
Heavy metal toxicity and the environment
.
Experientia Supplementum (2012)
101
,
133
164
.
Ullah
N.
,
Rehman
M. U.
,
Ahmad
B.
,
Ali
I.
,
Younas
M.
,
Aslam
M. S.
,
Rahman
A.
,
Taheri
E.
,
Fatehizadeh
A.
&
Rezakazemi
M.
2022
Assessment of heavy metals accumulation in agricultural soil, vegetables and associated health risks
.
PLoS One
17
(
6
),
e0267719
.
USEPA, O
2015
Mercury in Dental Amalgam
.
Vervaet
B. A.
,
D'Haese
P. C.
&
Verhulst
A.
2017
Environmental toxin-induced acute kidney injury
.
Clinical Kidney Journal
10
(
6
),
747
758
.
Wai
K. M.
,
Mar
O.
,
Kosaka
S.
,
Umemura
M.
&
Watanabe
C.
2017
Prenatal heavy metal exposure and adverse birth outcomes in Myanmar: A birth-cohort study
.
International Journal of Environmental Research and Public Health
14
(
11
),
1339
.
World Health Organization
2000
Nickel
.
World Health Organization – Europe
.
Yuan
W.
,
Yang
N.
&
Li
X.
2016
Advances in understanding how heavy metal pollution triggers gastric cancer
.
BioMed Research International
2016
,
7825432
.
Zwain
H. M.
,
Vakili
M.
&
Dahlan
I.
2014
Waste material adsorbents for zinc removal from wastewater: A comprehensive review
.
International Journal of Chemical Engineering
2014
,
e347912
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).