Assessing Cadmium and Chromium Concentrations in Drinking Water to Predict Health Risk in Malaysia.

Although toxic Cd (cadmium) and Cr (chromium) in the aquatic environment are mainly from natural sources, human activities have increased their concentrations. Several studies have reported higher concentrations of Cd and Cr in the aquatic environment of Malaysia; however, the association between metal ingestion via drinking water and human health risk has not been established. This study collected water samples from four stages of the drinking water supply chain at Langat River Basin, Malaysia in 2015 to analyze the samples by inductivity coupled plasma mass spectrometry. Mean concentrations of Cd and Cr and the time-series river data (2004–2014) of these metals were significantly within the safe limit of drinking water quality standard proposed by the Ministry of Health Malaysia and the World Health Organization. Hazard quotient (HQ) and lifetime cancer risk (LCR) values of Cd and Cr in 2015 and 2020 also indicate no significant human health risk of its ingestion via drinking water. Additionally, management of pollution sources in the Langat Basin from 2004 to 2015 decreased Cr concentration in 2020 on the basis of autoregression moving average. Although Cd and Cr concentrations were found to be within the safe limits at Langat Basin, high concentrations of these metals have been found in household tap water, especially due to the contamination in the water distribution pipeline. Therefore, a two-layer water filtration system should be introduced in the basin to achieve the United Nations Sustainable Development Goals (SDGs) 2030 agenda of a better and more sustainable future for all, especially via SDG 6 of supplying safe drinking water at the household level.


Introduction
Cadmium (Cd) and Chromium (Cr) in the aquatic environment are mainly from the erosion of natural deposits [1,2], but can also be a result of discharge from metal refineries and runoff from waste batteries and paints [3][4][5][6]. The detrimental impact of toxic Cd and Cr on living organisms in the aquatic environment is due to their prolonged persistence and non-biodegradable characteristics [7,8]. Therefore, these metals have been listed as the toxic trace metals by the United States Environmental Protection Agency (USEPA) if ingested via drinking water. The natural weathering of mineral rocks is considered as the main source of Cd and Cr in the Langat River, Malaysia [9,10]. Low concentration of dissolved Cd (1 × 10 −3 mg/L) was reported by Mamun et al. [11] in the Langat River; however, Sarmani [12] and Yusuf [13] observed very high concentration of Cd in Langat River (35.56 × 10 −3 mg/L and 24 × 10 −3 mg/L, respectively). The high concentration of Cd in Langat River might be because of sampling locations near ex-mining sites and runoff from infrastructure development activities within the Langat Basin. Similarly, Wang et al. [14] found high dissolved Cd concentration (61.74 × 10 −2 ± 90.12 × 10 −2 mg/L) in the Huaihe River, China. Aris et al. [9] reported a low Cr concentration (6.7 × 10 −4 ± 9 × 10 −4 mg/L) in the Langat River; however, higher Cr concentration was recorded (7 × 10 −2 mg/L)

Water Quality Determination
Water samples were collected one time in 2015 from the four stages of drinking water supply chain (i.e., river water, water treatment plant (WTP), household (HH) tap water, and post-filtration water) at Langat River Basin, Malaysia. Three replicates of water samples were collected from the eight points of Langat River where the WTPs collect water for drinking water treatment purposes. Three replicates of water samples were also collected from the outlets of the eight WTPs. Three replicates of household tap water and post-filtration filtered water samples were also collected on the basis of the five types of water filtration systems in the same households ( Figure 1). A Chelex 100 resin column ion-exchange method was applied to analyze the dissolved Cd and Cr concentrations in the water samples [61,62] by the inductive coupled plasma mass spectrometry (ICP-MS). Standards of several concentrations were prepared to calibrate the analysis of these metals by ICP-MS. Blanks were also prepared to avoid the error in the results of metal concentrations. Multi-element calibration standard III (PerkinElmer, Lot #CL7-173YPY1, PE #N9300233) was used for the recoveries of the standard reference material (SRM); it was calculated for Cd at 94.966% ± 0.295% and Cr at 99.803% ± 0.005%. ANOVA was performed using SPSS software (IBM Corp., Armonk, NY, USA, Version 21.0) to compare Cd and Cr concentrations among the four stages of drinking water supply chain and among the sampling locations in the Langat River Basin. water) at Langat River Basin, Malaysia. Three replicates of water samples were collected from the eight points of Langat River where the WTPs collect water for drinking water treatment purposes. Three replicates of water samples were also collected from the outlets of the eight WTPs. Three replicates of household tap water and post-filtration filtered water samples were also collected on the basis of the five types of water filtration systems in the same households ( Figure 1). A Chelex 100 resin column ion-exchange method was applied to analyze the dissolved Cd and Cr concentrations in the water samples [61,62] by the inductive coupled plasma mass spectrometry (ICP-MS). Standards of several concentrations were prepared to calibrate the analysis of these metals by ICP-MS. Blanks were also prepared to avoid the error in the results of metal concentrations. Multi-element calibration standard III (PerkinElmer, Lot #CL7-173YPY1, PE #N9300233) was used for the recoveries of the standard reference material (SRM); it was calculated for Cd at 94.966% ± 0.295% and Cr at 99.803% ± 0.005%. ANOVA was performed using SPSS software (IBM Corp., Armonk, NY, USA, Version 21.0) to compare Cd and Cr concentrations among the four stages of drinking water supply chain and among the sampling locations in the Langat River Basin.

Human Health Risk Assessment
The USEPA has listed Cd and Cr as highly toxic contaminants that can have cancer risks if ingested for a long period of time [32,47,63]. Therefore, to assess the human health risk, the USEPA established a model of chronic daily intake (CDI) of chemicals [64], non-carcinogenic hazard quotient (HQ), and carcinogenic lifetime cancer risk (LCR) [3] based on Cd and Cr ingestion via drinking water [63,65].

Household Questionnaire Survey
According to the latest population census by the Department of Statistic Malaysia, the total number of households in the Langat River Basin is 1,494,865 [68]. A 402-household questionnaire survey was conducted at the basin using Equation (IV) [69,70] to obtain the average daily drinking water intake by the population in the basin. Additionally, the body weight of household members was used to calculate the CDI of Cd and Cr ingestion through drinking water.
Here: n = sample size; N = population size; e = level of precision (0.05 at 95% confidence level).

Prediction Model of Metal Concentration in Water
Time series (2005-2014) monthly Langat River water quality data for Cd and Cr were provided by the Department of Environment (DOE) Malaysia. Therefore, the time series auto regression moving average statistical analysis was applied to estimate Cd and Cr concentration models in January 2020 on the basis of DOE (2005-2014) and laboratory data (2015-2016) [71][72][73]. Moreover, the assumptions of time series data analysis were fulfilled with a significant augmented Dickey-Fuller (ADF) unit root test for these metals at 0.01 level. Assumptions were also confirmed through autocorrelation (PACF) and partial autocorrelation (PACF) plots at 95% confidence level.

Metal Concentrations in Drinking Water Supply Chain
Concentrations of Cd and Cr in the drinking water supply chain (Table 1) at the Langat River basin, Malaysia, were within the drinking water quality standards of Ministry of Health Malaysia (MOH), World Health Organization (WHO), USEPA, and European Commission (EC). The skewness (<2) and kurtosis (<2) analyses of Cd and Cr concentrations in the river, treated, and tap water indicated normal distribution of the data, except in the household (HH) filtered water data of Cr because the kurtosis value was >4. The maximum high concentration of Cd (34.3 × 10 −4 mg/L) in the Langat River might be due to the natural weathering of Cd from the zinc ores such as sphalerite (ZnS) or Cd minerals such as greenockite [79] in the Titiwangsa Granite Hill Range of the basin. The point sources of pollution from sewage treatment plant effluent also attributed high concentration of Cd. Similarly, waste dumping in the river, runoff from landfills, and industrial waste from the metal finishing process at Bukit Tempoi might have contributed to high Cd concentration in the Langat River. Accordingly, the maximum concentration of Cr (12.2 × 10 −4 mg/L) in the Langat River indicated pollution in the mid-stream of the river basin from metal finishing industries such as electroplating, etching, and preparation of metal components for various industries [6,80]. Similarly, corrosion inhibitors, pigments from industrial effluents, and lithogenic sources contributed to high concentrations of Cr in Langat River [10,81].
The one-way ANOVA of Cd (F = 27.6; p = 5.99 × 10 −14 ) and Cr (F = 13.1; p = 1.56 × 10 −7 ) in the Langat River Basin found significant differences at 0.05 confidence level among the four stages of drinking water supply chain (Table A1). The least significant difference (LSD) of the post hoc test also found significant mean differences of Cd concentration between river water and water treatment plants (p = 4.3 × 10 −9 ), tap water (p = 3.5 × 10 −11 ), and HH filtered water (p = 6 × 10 −13 ) at 95% confidence interval ( Figure 2). Similarly, significant differences were found in the concentration of Cr between river water and treatment plants (p = 9 × 10 −5 ) and HH filtered water (p = 2 × 10 −6 ) ( Figure 3). Moreover, significant differences of Cd and Cr concentrations were also observed among the river water sampling points, as well as among the WTPs, tap water, and HH filtered water at a 95% confidence level ( Figure 4).
The mean dissolved concentration of Cd in the supply water of the basin was estimated as being 0.42 × 10 −3 ± 0.19 × 10 −3 mg/L ( Table 2) and was within the drinking water quality standard proposed by MOH and WHO (0.003 mg/L). The highest concentrations of dissolved Cd was observed at the location Hentian Kajang II (0.75 × 10 −3 ± 0.02 × 10 −3 mg/L), followed by the location Universiti Kebangsaan Malaysia (UKM) III 0.73 × 10 −3 ± 0.04 × 10 −3 mg/L. The high concentration of dissolved Cd in the water distribution system might have been due to corrosion in galvanized (i.e., zinc-coated) pipelines or cadmium-containing solders in fittings and taps. Hence, the leaching of Cd from galvanized pipes occurred because of the presence of Cd and lead (Pb) impurities in the zinc [82] of galvanized pipe along with the residence time of low pH water from the use of lime in water treatment [17].  (Table A2).

Figure 2.
Difference in means of Cd concentrations in the drinking water supply chain at the Langat River Basin, Malaysia. Note: * significant at a 95% confidence level (Table A2).   (Table A2).  (Table A2).   Similarly, the mean concentration of dissolved Cr in the supply water of the basin (0.37 × 10 −3 ± 0.21 × 10 −3 mg/L) was lower than the maximum limit of drinking water quality standard proposed by the MOH, WHO, and EC (0.5 mg/L). The highest concentrations of dissolved Cr were recorded at Hentian Kajang VI (0.71 × 10 −3 ± 0.41 × 10 −3 mg/L) and Universiti Kebangsaan Malaysia (UKM) III (0.63 × 10 −3 ± 0.02 × 10 −3 mg/L). The high concentration of dissolved Cr at Hentian Kajang and UKM might have been due to corrosion of Cr in the steel pipes (steel alloy and chromium) of the drinking water distribution system [83][84][85][86]. Moreover, the stagnant water period in the water distribution system was also an important factor to increase the concentration of dissolved Cr in supply water [87].
The high dissolved concentrations of Cr in the Distilled II (0.66 × 10 −3 ± 0.003 × 10 −3 mg/L) and Alkaline III (0.35 × 10 −3 ± 0.13 × 10 −3 mg/L) filtered waters might have been due to corrosion of galvanized iron pipes linked to steel pipes at the end of the reticulation system along with stagnant water time within the filter. Moreover, rust inside distilled filters and a lack of cleaning activities also contributed to high concentrations of Cr in the drinking water. However, the mean concentration of dissolved Cr (0.2 × 10 −3 ± 0.15 × 10 −3 mg/L) in the HH filtered water at the basin was below the maximum limit of the drinking water quality standard of Cr (0.50 mg/L) proposed by the MOH, WHO, and EC.

Prediction Model of Metal Concentrations in Drinking Water Supply Chain
The time series data of Cd and Cr concentrations in Langat River complied with the time series data analysis at 99% confidence interval. The compliance of time series data analysis was based on the significant augmented Dickey-Fuller (ADF) unit root test for Cd and Cr at 0.05 level (Table A3). The ADF unit root test of Cr with constant was not significant (p = 0.65) at the 0.05 level; however, the ADF unit root test of Cr with constant (i.e., considering Cr trend) was significant (p = 7.17 × 10 −2 ) at the 0.05 level. Similarly, the autocorrelation (ACF) plots based on the differences in Cd and Cr concentrations showed significant autocorrelation only at Lag 1, although the ADF unit root test of Cd and Cr data remained static at a 95% confidence level. Similarly, the partial autocorrelation (PACF) plots based on the differences Cd and Cr concentrations with a 95% confidence band showed that the autocorrelation was only significant at Lag 1 and Lag 2 ( Figures A1 and A2). Therefore, this study used a monthly (2005-2020) auto regression model to estimate the Lag effects on Cd (Table A4) and Cr (Table A5) concentrations in the drinking water supply chain of the Langat River Basin. The impact of the prior three months (i.e., identified Lags in Table A6) had significant influence on the Cd concentration trend in Langat River after 2016. The predicted Cd concentration considering the influences of environmental parameters (i.e., water flow, rainfall, and temperature) was also similar to the determined Cd concentration in 2015. Similarly, the impact of prior month (i.e. identified Lag in Table A7) had significant influence on the Cr concentration trend in Langat River after 2016.
Lag 1 to Lag 7 effects in the auto-regressive Cd model (Table A4) suggested that the consecutive prior seven months had a significant impact on the Cd concentration of the current month in Langat River, where Lag 7 (t = 2.32; p = 0.02) was significant at the 0.05 level. In addition, the sixth-order auto-generative coefficient weight 0.21 (t = 2.23; p = 0.03) was significant at the 0.05 level to have an autocorrelation-free Cd concentration forecast model, because Lag 7 in both ACF and PACF crossed the 95% interval line, indicating the existence of autocorrelation. Therefore, the auto-regressive moving average of Cd concentration based on the data from January 2005 to August 2015 forecasted 9.7 × 10 −4 mg/L in January 2020 ( Figure 5) and a mean Cd concentration of 9.75 × 10 −4 ± 1.33 × 10 −4 mg/L during 2005-2020 ( Figure 6). Moreover, the predicted Cd concentration (9.7 × 10 −4 mg/L) in 2020 was a little bit higher than the mean Cd concentration (9.69 × 10 −4 ± 1.57 × 10 −4 ) mg/L in Langat River during 2005-2015 ( Figure 6). The predicted Cd concentration (9.7 × 10 −4 mg/L) at January 2020 in Langat river was significant (R 2 = 0.08; F = 2.4; p = 0.03) at a 95% confidence interval. Similarly, the forecast of Cd concentration (9.8 × 10 −4 mg/L) in August 2015 was similar to the real concentration of Cr 9.7 × 10 −4 mg/L in August 2015 in Langat River considering the influence of significant environmental parameters ( Figure A3). Moreover, the concentration of Cd in the Langat River was influenced by the Cd concentration of the prior 3 months (t = −2.37; p = 0.02; Table A6) and the model was significant (R 2 = 0.05; F = 9.4; p = 2.2 × 10 −8 ; Figure A3) at the 0.05 level.
in Table A7) had significant influence on the Cr concentration trend in Langat River after 2016.
Lag 1 to Lag 7 effects in the auto-regressive Cd model (Table A4) suggested that the consecutive prior seven months had a significant impact on the Cd concentration of the current month in Langat River, where Lag 7 (t = 2.32; p = 0.02) was significant at the 0.05 level. In addition, the sixth-order autogenerative coefficient weight 0.21 (t = 2.23; p = 0.03) was significant at the 0.05 level to have an autocorrelation-free Cd concentration forecast model, because Lag 7 in both ACF and PACF crossed the 95% interval line, indicating the existence of autocorrelation. Therefore, the auto-regressive moving average of Cd concentration based on the data from January 2005 to August 2015 forecasted 9.7 × 10 mg/L in January 2020 ( Figure 5) and a mean Cd concentration of 9.75 × 10 ± 1.33 × 10 mg/L during 2005-2020 ( Figure 6). Moreover, the predicted Cd concentration (9.7 × 10 mg/L) in 2020 was a little bit higher than the mean Cd concentration (9.69 × 10 ± 1.57 × 10 ) mg/L in Langat River during 2005-2015 ( Figure 6). The predicted Cd concentration (9.7 × 10 mg/L) at January 2020 in Langat river was significant (R² = 0.08; F = 2.4; p = 0.03) at a 95% confidence interval. Similarly, the forecast of Cd concentration (9.8 × 10 mg/L) in August 2015 was similar to the real concentration of Cr 9.7 × 10 mg/L in August 2015 in Langat River considering the influence of significant environmental parameters ( Figure A3). Moreover, the concentration of Cd in the Langat River was influenced by the Cd concentration of the prior 3 months (t = 2.37; p = 0.02; Table A6) and the model was significant (R² = 0.05; F = 9.4; p = 2.2 × 10 ; Figure A3) at the 0.05 level.    (Table A5) suggested that the impact of the prior 2 months had significant effects on the Cr concentration in Langat River. Similarly, the second order autogenerative coefficient weight of 0.47 (t = 5.998; p = 2.13 × 10 ; Table A5) was significant at the 0.05 level to have an autocorrelation-free Cr concentration forecast model; the ACF and PACF correlogram of the initial few Lags crossed the 95% interval line, indicating the existence of autocorrelations. Therefore, the auto-generative moving average of Cr concentration based on the data from January 2005 to August 2015 forecasted 1.32 × 10 ³ mg/L in January 2020 (Figure 7) as well as a mean Cr concentration of 1.48 × 10 ³ ± 8.84 × 10 mg/L during 2005-2020. Moreover, the predicted Cr concentration (1.32 × 10 ³ mg/L) in January 2020 was lower than the mean Cr concentration (1.56 × 10 ³ ± 1.05 × 10 ³ mg/L) in Langat River during 2005-2015. However, the predicted Cr concentration (1.32 × 10 ³ mg/L) at January 2020 in Langat river was significant (R² = 0.44; F = 130.28; p = 6.7 × 10 ³¹; Figure 7) at a 95% confidence level. Similarly, considering the control variables such as water flow, rainfall, and temperature, the Cr concentration in Langat River at 2015 was significantly influenced by the concentration of prior two months (Lag 2, t = 3.6744; p = 0.0004; Table A7). The model was significant (R² = 0.36; F = 5.37; p = 5.1 × 10 ; Figure A4) at the 0.05 level and the forecasted and real concentrations of Cr in the Langat River were almost similar.  (Table A5) suggested that the impact of the prior 2 months had significant effects on the Cr concentration in Langat River. Similarly, the second order auto-generative coefficient weight of −0.47 (t = −5.998; p = 2.13 × 10 −8 ; Table A5) was significant at the 0.05 level to have an autocorrelation-free Cr concentration forecast model; the ACF and PACF correlogram of the initial few Lags crossed the 95% interval line, indicating the existence of autocorrelations. Therefore, the auto-generative moving average of Cr concentration based on the data from January 2005 to August 2015 forecasted 1.32 × 10 −3 mg/L in January 2020 (Figure 7) as well as a mean Cr concentration of 1.48 × 10 −3 ± 8.84 × 10 −4 mg/L during 2005-2020. Moreover, the predicted Cr concentration (1.32 × 10 −3 mg/L) in January 2020 was lower than the mean Cr concentration (1.56 × 10 −3 ± 1.05 × 10 −3 mg/L) in Langat River during 2005-2015. However, the predicted Cr concentration (1.32 × 10 −3 mg/L) at January 2020 in Langat river was significant (R 2 = 0.44; F = 130.28; p = 6.7 × 10 −31 ; Figure 7) at a 95% confidence level. Similarly, considering the control variables such as water flow, rainfall, and temperature, the Cr concentration in Langat River at 2015 was significantly influenced by the concentration of prior two months (Lag 2, t = 3.6744; p = 0.0004; Table A7). The model was significant (R 2 = 0.36; F = 5.37; p = 5.1 × 10 −4 ; Figure A4) at the 0.05 level and the forecasted and real concentrations of Cr in the Langat River were almost similar.

Prediction Model of Metal Concentrations in Drinking Water Supply Chain
Cd concentration in the drinking water supply chain at Langat Basin both in 2015 and 2020 were within the maximum limit of the drinking water quality standard of the MOH (0.003 mg/L), WHO (0.003 mg/L), and USEPA (0.0022 mg/L). Cadmium concentration (3.11 × 10 mg/L) in HH filtration water in 2020 ( Figure 6) was also well below the maximum tolerable daily intake of Cd through drinking water (8.3 × 10 mg/L) [37]. Therefore, Cd ingestion through HH filtration water in the Langat Basin posed no health risk because the HQ (2.67 × 10 ² ± 1.23 × 10 ² mg/L and 2.69 × 10 ² mg/L in 2015 and 2020, respectively; Figure 8) were significantly within the safe limit (i.e., HQ < 1 at 95% confidence level).
Accordingly, Cr concentration in the drinking water supply chain in Langat Basin in 2015 and 2020 were within the safe limit of the drinking water quality standard of the MOH (0.05 mg/L), WHO (0.05 mg/L), and USEPA (0.011 mg/L). The concentration of Cr (2.13 × 10 mg/L) in HH filtration water in 2020 were predicted in this study (Figure 6), however, the maximum tolerable daily intake of Cr through drinking water for humans has yet to be fixed [94]. Hence, Cr ingestion through HH filtration water in Langat Basin showed no potential non-carcinogenic human health risk (2.13 × 10 ³ ± 1.55 × 10 ³ mg/L and 2.24 × 10 ³ mg/L in 2015 and 2020, respectively; Figure 8) because the values were within the safe limit (i.e., HQ < 1). Accordingly, the LCR values of Cr ingestion through HH filtration water (1.28 × 10 ± 9.29 × 10 mg/L and 1.35 × 10 mg/L in 2015 and 2020, respectively; Figure 9) were within the safe limit because the LCR values were not greater than 1 × 10 mg/L at a 95% confidence level.

Prediction Model of Metal Concentrations in Drinking Water Supply Chain
Cd concentration in the drinking water supply chain at Langat Basin both in 2015 and 2020 were within the maximum limit of the drinking water quality standard of the MOH (0.003 mg/L), WHO (0.003 mg/L), and USEPA (0.0022 mg/L). Cadmium concentration (3.11 × 10 −4 mg/L) in HH filtration water in 2020 ( Figure 6) was also well below the maximum tolerable daily intake of Cd through drinking water (8.3 × 10 −4 mg/L) [37]. Therefore, Cd ingestion through HH filtration water in the Langat Basin posed no health risk because the HQ (2.67 × 10 −2 ± 1.23 × 10 −2 mg/L and 2.69 × 10 −2 mg/L in 2015 and 2020, respectively; Figure 8) were significantly within the safe limit (i.e., HQ < 1 at 95% confidence level).
Accordingly, Cr concentration in the drinking water supply chain in Langat Basin in 2015 and 2020 were within the safe limit of the drinking water quality standard of the MOH (0.05 mg/L), WHO (0.05 mg/L), and USEPA (0.011 mg/L). The concentration of Cr (2.13 × 10 −4 mg/L) in HH filtration water in 2020 were predicted in this study (Figure 6), however, the maximum tolerable daily intake of Cr through drinking water for humans has yet to be fixed [94]. Hence, Cr ingestion through HH filtration water in Langat Basin showed no potential non-carcinogenic human health risk (2.13 × 10 −3 ± 1.55 × 10 −3 mg/L and 2.24 × 10 −3 mg/L in 2015 and 2020, respectively; Figure 8) because the values were within the safe limit (i.e., HQ < 1). Accordingly, the LCR values of Cr ingestion through HH filtration water (1.28 × 10 −5 ± 9.29 × 10 −6 mg/L and 1.35 × 10 −5 mg/L in 2015 and 2020, respectively; Figure 9) were within the safe limit because the LCR values were not greater than ≥1 × 10 −5 mg/L at a 95% confidence level.  All eight water treatment plants (WTPs) in Langat Basin follow the conventional water treatment method. However, this conventional method was unable to fully remove trace metals from the  All eight water treatment plants (WTPs) in Langat Basin follow the conventional water treatment method. However, this conventional method was unable to fully remove trace metals from the  All eight water treatment plants (WTPs) in Langat Basin follow the conventional water treatment method. However, this conventional method was unable to fully remove trace metals from the treated water mainly because of frequent changes in turbidity in Langat River [3]. For instance, Frey [95] reported that a conventional coagulation method can remove Cr (III) from water; however, it cannot remove Cr (VI). Similarly, Brandhuber [96] reported that the total Cr removal varied between 40% and 100% with the conventional method because Cr (VI) cannot be removed by alum or ferric coagulation or by lime softening. Therefore, a two-layer water filtration system should be introduced in the Langat Basin because treated water contamination in the long pipeline was evident between WTPs and households; additionally, the conventional method was unable to fully remove metals from treated water. Hence, the reverse osmosis membrane technology can be appropriate to install in a kitchen tap of the household managed by the water billing agency because it can remove more than 90% of trace metals [97].

Recommendations
A two-layer water filtration system at the basin should be introduced to achieve the SDG target 6.1 of obtaining safe drinking water supply before 2030. Because the traditional coagulation method is unable to completely remove metals from treated water, and treated water contamination in the long pipeline was evident in between WTPs and households, a reverse osmosis filtration system with the capacity to remove more than 90% of metals could be installed at the kitchen tap of household. The installed reverse osmosis filtration system at household could be managed by the water billing agency and a less-expensive pond sand filtration at the treatment plants could be maintained. Furthermore, in managing the drinking water, the proactive leadership roles of local authority would be appropriate to enable the PENTA-HELIX (i.e. consists of five types of stakeholders such as public, private, academia, non-governmental organization and community) partnership model to bring public, business, academia, NGO (non-governmental organization), and community sectors into the same multi-stakeholder platform.

Conclusions
Cadmium and chromium concentrations in the drinking water supply chain at Langat Basin were within the drinking water quality standard of the Ministry of Health Malaysia and WHO. Moreover, Cd and Cr ingestion through household filtration water in the Langat Basin poses no health risk because the hazard quotients (HQ) of Cd and Cr were significantly within the safe limit in 2015 and 2020. Similarly, the LCR (lifetime cancer risk) value of Cr ingestion through household filtration water was within the safe limit in 2015 and 2020. However, high concentrations of these metals have been found in the household tap water mainly because of contamination in the water distribution pipeline. The age-old water distribution pipelines in between water treatment plants and households as well as the old water reticulation systems at the households in the Langat Basin might have attributed to Cd and Cr concentrations in the household tap water. Similarly, the irregular cleaning activities of the household water filtration systems might have also attributed to the Cd and Cr concentrations in the drinking water. preparing the study area map through ArcGIS. The authors also acknowledge the suggestions of Che Abd Rahim Mohamed, Lubna Alam, and Goh Choo Ta.

Conflicts of Interest:
The authors declare no conflict of interest.