Screening of human health risk to infants associated with the polychlorinated biphenyl (PCB) levels in human milk from Punjab Province, Pakistan

Anber Naqvi1 & Abdul Qadir1 & Adeel Mahmood2 & Mujtaba Baqar3 & Iqra Aslam1 & Nadia Jamil1 & Mehvish Mumtaz4 & Salman Saeed5 & Gan Zhang6

Abstract

This study assessed the polychlorinated biphenyl (PCB) levels in human milk and its associated health risk to infants from rural and urban settingsoffive districtsofPunjab Province, Pakistan.The∑34PCB concentrationsrangedfrom30.9to68.3ngg−1 onlipid weight (l.w.) basis. The ∑8DL-PCB concentrations were ranged from 0.29 to 1.35 ng g−1 l.w., (mean 6.2 ± 8.7 ng g−1 l.w.), with toxicity equivalenttopolychlorinateddibenzodioxins(PCDDs)rangingfrom8.58×10−6 to0.05ngTEQg−1 l.w.ThespatialtrendofPCBlevels inhumanmilkrevealedhigherbioaccumulativelevelsforurbanmothersascomparedwithruralcounterparts.Theestimateddailyintake (EDI) values of DL-PCBs to infants through trans-mammary transfer were considerably higher than tolerable daily intake limits established by WHO (i.e., 1–4 pg TEQ kg−1 bw) and other globally recognized organizations. Similarly, the hazard quotient values for TEQ ∑8DL-PCBs (range 1.21 to 79.87) were far above the benchmark value of 1 at all the sampling sites, indicating the high levels of adverse health risks to infants in the region through breast milk consumption. The ∑34PCB levels were found to be negatively correlated with mother’ age (r = −0.31; p = 0.02), parity (r = − 0.85; p = 0.001), and infant’ birth weight (r = − 0.73; p = 0.01). The present study suggests undertaking comprehensive public health risk assessment studies and firm regulatory measures to safeguard human health risks.

Keywords Polychlorinatedbiphenyls . Human milk . Human healthrisk . Infantrisk

Introduction

Polychlorinated biphenyls (PCBs) are environmental pollutants having the basic structural unit with biphenyl ring and pose persistent, bioaccumulative, and toxic characteristics that pose serious risks to environmental and human health(Orta-García et al. 2014). Due to their insulating and inflammable properties, the use of PCBs was expanded in the past for about 50 year, i.e., 1929–1977 (Erickson and Kaley 2011). PCBs can be released into the environment, attributed to the volatilization, accidental release, and leaks from facilities containing PCBs including disposal activities, open burning of waste materials, and incomplete burning of chlorine containing sewage sludge, landfills, and waste incinerations facilities (Kim and Yoon 2014). Substantial municipal waste open dumping in suburban areas around the cities of developing countries in Asia has garnered ample attention as probable sources of POPs (Someya et al. 2010).
Pakistan is among those developing countries which are in the transitional phase of their industrial and agricultural development. During the last three decades, rapid boost in industrial development and urban sprawl has raised several environmental issues pertaining to PCB pollution and put the ecological integrities and human health at risk (Ahmed 2003). The country is a party to the Stockholm Convention which emerged as major milestone to eliminate the production and unintentional environmental release of PCBs (Elabbas et al. 2013). Punjab Province is a densely populated province of Pakistan, where people residing in urban cities mainly engage in trade and industrial activities, and the rural population is principally involved in agricultural activities. The indiscriminate use of consumer goods, industrial growth, and use of agrochemical has led the PCB contamination in environmental media of the Punjab Province and highlighted a need for their monitoring in human being. At the same time, the reconditioned/ second-hand products are very popular in the electronic market of the region that sooner ends up as e-waste (Naqvi et al. 2018). These e-waste turns to illegal recycling facilities to recover metals through hazardous open burning techniques are also more common practice in rural settings in the study area (Iqbal et al. 2015; Baqar et al. 2017) that consequently releases the PCBs into environment (Li et al. 2007; Eqani et al. 2012a,b).
Human milk is the primary food for nursing infants due to its high nutritional and lipid contents (Delplanque et al. 2015). It has also been used as biomonitoring tool to examine the accumulation of contaminants in nursing mothers and human health risks to the breastfed offspring. Researchers have reported PCB levels in human milk, exposure and trans-mammary transfer, and associated health risks in infants from various parts of the world (Lu et al. 2015; Mamontova et al. 2017; Muller et al. 2017; Rawn et al. 2017). Concentrations of organochlorine pesticides (OCPs) have also been reported in human milk from only cotton crop–influenced region of Southern Punjab, Pakistan (Yasmeen et al. 2017). Despite environmental legislations in the country, a gap exists in between the legislation and its implementation (Jaspal and Haider 2014). Various studies in the country have reported high levels of PCB in different environmental matrices (Eqani et al. 2011, 2012a, 2013; Syed et al. 2013; Baqar et al. 2017; Naqvi et al. 2018).But unfortunately, PCBs in milk were not well characterized in human studies in study area and this may be due to the paucity of data and lack of interest on institutional level. Therefore, the current study was designed to (i) screen the PCB bioaccumulation levels in human milk; (ii) monitor the spatial variation pattern across the various agricultural, industrial, and urban dominated areas, and (iii) assess potential health risks to the breastfeeding infants.

Materials and methods

Study area and sampling strategy

Five districts of the Punjab Province which is the most populated province of Pakistan were selected as sampling sties from northern region (Chakwal), southern region (Khanewal), the central region (Okara), eastern region (Lahore), and northeast region (Sialkot) of the province. Site selection criterion was based on the industrial and agricultural activities and spatial diversity and variation in these districts. Lahore and Sialkot are districts with major industrial clusters of the country. Steel manufacturing units, furnaces and paint industries, and electronic waste dumping sites in/near Lahore, and leather and tanning industry and steel and surgical tools manufacturing units in Sialkot are potential sources of PCB contamination in these areas (Syed et al. 2013; Mahmood et al. 2014). Whereas, District Chakwal with distinct topography is experiencing the PCB pollution mainly from cement industries located therein. Khanewal and Okara are mainly agricultural areas with extensive application history of POPs. These areas may experience the PCB contamination due to dispersion from urban areas, use of PCBs based pesticides, and the general practice of wood and solid waste burning (Ali et al. 2013).
In total, 41 milk samples from volunteer mothers (12 primiparae and 29 multiparae) were collected. The selection criteria of the subject volunteers included those who were either native to or residing in sites concerned for at least 5 years, with age ranging between 18 and 45 years with no chronic illness, substance abuse, and third trimester complications. The selection criteria in present study were as followed in previous similar studies (Thomsen et al. 2010; Dewan et al. 2013; Mamontova et al. 2017). A self-administered questionnaire was also employed to acquire information about subjects (age, parity, body mass index (BMI), eating habits, number of children carried as well as breastfed, and infant’s birth weight. Questions regarding demographic characteristics and feeding habits were modified from US
Environmental Protection Agency (USEPA) studies on food and total diet (USFDA 2004).The present study was approved by the Advanced Studies and Research Board (ASRB) of University of the Punjab (approval number: D/661-ACAD). Milk samples (20 ml each) were obtained within 3 to 4 days of delivery using breast pumps. The collected milk samples were transferred to dichloromethane (DCM)-rinsed glass bottles and stored in ice boxes for transportation to Environmental Toxicology Laboratory at the University of the Punjab, Pakistan, where they were preserved at − 20 °C until further analysis.

Sample preparation and analysis

The thawed and homogenized milk samples (1 ml each) were spiked with surrogate standards (5 ppb of 2,4,5,6-tetrachlorom-xylene (TCmX)), followed by 20-min sonication and overnight incubation at 4 °C, proceeded by PCB extraction through liquid-liquid extraction method. The extracted samples were then cleaned using solid phase extraction assembly. The clean-up columns used were assembled, from bottom to top, with 2 g acidic silica, 200 mg silica (activated at 120 °C for 2 h), and 500 mg anhydrous sodium sulphate, and preeluted with 5 mL DCM. The analytes were eluted with 4 mL n-hexane and 2 mL DCM. The sample extracts were concentrated by nitrogen purge to achieve final volume of 1 ml, followed by the addition of 50 μl iso-octane and10 μL of 13C-PCB 141 to each sample extract as a solvent keeper and internal standard, respectively.
The PCBs were determined via gas chromatograph (Agilent 7890A) coupled with a triple quad mass spectrometer (Agilent 7000A) at the State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China. The gas chromatograph-mass spectrometer (GC/MS) was run at operational mode of electron-capture negative ionization, at electron multiplier voltage of 2947 V. For PCB analysis, a CP-8 capillary column (CP7481, CP-Sil 8, 50 m × 0.25 mm × 0.12 μm) purchased from Agilent, Netherland, and helium gas as a carrier gas (flow rate 1.2 ml/min, initial pressure 20.9 psi) were used. The ion source temperature was set at 230 °C and samples were injected by employing pulsed splitless mode (total time was 1.5 min) at an initial temperature of 100 °C, with pulse pressure at 25 psi. CP-8 column’s initial temperature was raised from 100 to 160 °C at the rate of 20 °C/min, then to 240 °C at the rate of 4 °C/min, and finally to 296 °C at the rate of 8 °C/min with holdup time of 5.5 min. The analytes were identified on the basis of their particular ion chromatogram and retention time, compared with the standard. While the multi-level calibration curves were used to quantify the analyte, the detected concentrations linearity of r2 > 0.99 was achieved. In total, 34 PCB congeners, including 8 dioxin-like-PCBs (DL-PCBs) (PCB-70, PCB-126, PCB169, PCB-105, PCB-114, PCB-118, PCB-156, and PCB189), 6 indicator PCBs (PCB-52, PCB-101, PCB-118, PCB138, PCB-153, and PCB-180), and 20 other PCBs (PCB-30, PCB-37, PCB-44, PCB-49, PCB-54, PCB-60, PCB-66, PCB70, PCB-74, PCB-82, PCB-87, PCB-99, PCB-128, PCB-158, PCB-166, PCB-170, PCB-179, PCB-183, PCB-187, PCB198, and PCB-209) were analyzed.

Determination of milk fat (%)

Fat contents (%) of each milk sample were determined using separate aliquots of samples by a referenced method of the Association of Official Analytical Chemists (AOAC, International) 2000.18. Briefly, all the homogenized milk samples were hardened in a water bath at 40 °C, followed by cooling to room temperature and 10.94 ml of each milk sample was pipetted out in butyrometer, containing 10 ml of sulfuric acid and 1 mL of iso-amyl alcohol. The butyrometer was stoppered, shaken well, and kept in a water bath (at 15–21 °C), followed by placement in Gerber centrifuge for 4 min at 1100 rpm. The butyrometer was tempered again in water bath at 65 °C for 5 min and the fat column appeared at the top was measured.

Toxicity equivalence of dioxin-like PCBs

The toxicological similarities of the DL-PCBs to polychlorinated dibenzodioxins (PCDDs) were evaluated through assessment of toxicity equivalence (TEQs) for dioxins using Eq. (1), where C is the concentration of DLPCB congener and TEF is the toxic equivalency factor established by the World Health Organization (WHO)International Programme on Chemical Safety (WHO-IPCS) in 2005 (Van den Berg et al. 2006).

Association of PCBs with maternal and infant characteristics

In order to assess the association between levels of PCBs in mothers and their relationship with maternal health, their personal attributes, viz, body weight, height, BMI, age, parity, number of children carried and breast fed, location, and eating habits, were compared with PCB residues in the body. Moreover, to assess the possible health risks to infants, their anthropometric characteristics including head and chest circumference, mid arm circumference, crown to heel length, and birth weight were measured as described by Dewan et al. (2013).

Estimated daily intake of PCBs in infants

Several studies reported trans-mammary transfer of PCBs from mother to infant (Needham et al. 2011). The potential health risks of DL-PCB exposure to infants through breast milk intake was assessed by determining hazard quotient (HQ) using Eq. (2), as given below: where the EDI is the estimated daily intake (EDI), and PTDI is the provisional tolerable daily intake, which is the maximum daily amount of a chemical that is considered safe during the entire human lifetime; here, PTDI value used for PCBs was 10 pg kg−1 body weight (bw) day−1 (Van Leeuwen et al. 2000). The calculated HQ value less than 1 indicates no adverse health effect is expected, whereas an HQ greater than 1 indicates the possibility of adverse health effects. However, the use of data obtained for milk sampled 3–4 days after delivery might lead to an overestimation of both the daily intake and the hazard quotient. The reduction in the PCB levels in the later weeks after delivery may moderate the EDI content of PCBs.
The EDI (ng kg−1 bw day−1) was calculated using Eq. (3), employing the concentration of PCBs (CPCB), mean lipid content (Clipid), and daily infants’ milk intake (i.e., 700 ml/ day), and infant body weight was assumed 5 kg, as assumed by Van Oostdam et al. (2005) and followed by others (EL-Saeid et al. 2017; Cok et al. 2012; Lu et al. 2015; Muller et al. 2017). Above this PTDI value, PCB exposure is considered not safe for humans during their entire life (FAO/WHO 1995).

Statistical analysis

The quantitative analysis of PCB concentrations was carried out using statistical software SPSS version 20.0 and Microsoft Excel (2010) to represent basic descriptive statistics. A Pearson’s correlation analysis was performed to assess the relationship between PCB concentrations in women body matrices and data on physiological parameters of mothers and infants. The confidence levels of p = 0.01 and p = 0.05 were used in this study. The concentrations of PCBs (ng/g lipid weight) were represented as descriptive data using mean, standard deviation, and range. The Originpro 2017 software, Arc GIS version 10.2.2, and soft stat. software 12.5 were employed to exhibit spatial patterns of PCBs.

Quality assurance and quality control (QA/QC)

The surrogate standard (2,4,5,6-tetrachloro-m-xylene (TCmX)) and internal standard (13C PCB 141) were purchased from CPA Chem Ltd. (Bulgaria) and Cambridge Isotope Laboratories, Inc. (USA), respectively. All the chemicals and solvents used during experimentation were of HPLC grade and purchased from Merck KGaA (Germany). All the glassware used in the present study were washed in a sonicator with detergent for 30 min at 40 °C, followed by rinsing with distilled water for 6–7 times, and the aluminum foil–wrapped glassware was oven-dried at 115 °C overnight and baked at450 °Cfor 6 h. A blank ofn-hexanewas run prior to every batch of five samples. All the solvent and procedural blanks were run in the same way as original samples to monitor influence of any possible contamination in the course of sample processing and the instrumental analysis. Six calibration standards of PCBs (i.e., of 2 ppb, 10 ppb, 20 ppb, 50 ppb, 100 ppb, and 200 ppb concentrations), purchased from Aqua Standard Company (USA), were used to evaluate accuracy and precision of the method and instrument. A follow-up standard (of 50 ppb) was run daily to check whether calibration is required or not. A deviation of the ion intensity ratios was considered as acceptable when it was within 20% of the mean values of calibration standards. The calibration was made when mean value exceeded 20%. Data processing was done using Agilent Mass Hunter workstation software. Mean recovery of surrogate standards of TCmX was 72 ± 7% and the results were corrected accordingly.

Results and discussion

PCB congener and homolog profile

The descriptive statistical values for PCB homologs in human milk samples on milk lipid basis are given in Table 1. The mean ∑34PCB concentration in human milk was calculated as 44.1 ± 27.3 ng g-1 l.w. and ranged between 30.9 and 68.3 ng g-1 l.w. The bioaccumulative trend of PCB homologs in human milk were in order: Tetra-PCBs (52%) > Penta-PCBs (21%) > Hexa-PCBs (14%) > Tri-PCBs (7%) > Hepta-PCBs (5%) > Octa-PCB (0.8%) > Deca-PCB (0.7%). The dominance of lighter PCBs, i.e., Tri-PCBs, Tetra-PCBs and Penta-PCBs, are in line to their environmental prevalence in the study area (Syed et al. 2014; Baqar et al. 2017). The maternal dietary exposure accounts approximately 80% of the overall PCB exposure (Çok et al. 2012), and 25% of this body burden is transferred to infants through breastfeeding (Quinn et al. 2011).
The most dominant congener was PCB-60 (mean 3.9 ng g−1 l.w.), followed by PCB-66 (3.8 ng g−1 l.w.) and PCB-70 (3.5 ng g−1 l.w.). Whereas, the lowest concentrations was exhibited by PCB-166 with mean value of 0.11 ng g−1 l.w. Predominant occurrence of non-persistent congeners (PCB66 and PCB-70) in the present study might be associated with the recent and continuous exposure of sampling population with these PCB commercial mixtures (Covaci et al. 2001). Similarly, high levels of these non-persistent PCB congeners were also reported from the regions (Bawa et al. 2018; Naqvi et al. 2018). Some of the persistent PCB congeners, including PCB-153 (mean 1.9 ng g−1 l.w.), PCB-138 (1.6 ng g−1 l.w.), and PCB-180 (0.75 ng g−1 l.w.) were also determined, with relative lower contribution, approximately 12% to total PCB concentrations. However, in contrast, the persistent PCB congeners were most dominating congeners in human milk in developed countries (Polder et al. 2008; Guerranti et al. 2011; Mannetje et al. 2013).
The mean ∑34PCB concentrations in present study as compared with the literature (Table 2) were found to be lower than those previously reported from Russia, i.e., 240 ng g−1 l.w. (Tsydenova et al. 2007), and Eastern Siberia, i.e., 395 ng g−1 l.w. (Mamontova et al. 2017), and higher than Turkish study, i.e., 9.9 ng g−1 l.w. (Çok et al. 2012). However, the current concentrations are comparable with PCB levels reported from urban cities from neighboring country, Indiaviz; New Delhi (23 ng g−1 l.w.), Mumbai (30 ng g−1 l.w.), Kolkata (40 ng g−1 l.w.), Chennai (34 ng g−1 l.w.) (Devanathan et al. 2009), and Canada (50 ng g−1 l.w.) (Rawn et al. 2017).

Indicator PCBs and their comparison with other countries

Among the 34 PCB congeners, six indicator (or marker) PCB congeners (i.e., PCB-52, PCB-101, PCB-118, PCB138, PCB-153, and PCB-180) were also evaluated (Table 3). The mean concentration of these indicators PCBs in milk samples was found to be 10.3 ± 11.7 ng g−1 l.w. Among these indicator PCBs, the persistent PCB congeners (i.e., PCB-153, PCB-138, and PCB-180) contributed 12% of the total PCBs, and were in order as follows: PCB-153 (mean 1.9 ng g−1 l.w.) > PCB-138 (1.6 ng g−1 l.w.) > PCB-180 (0.75 ng g−1 l.w.). The concentrations of these persistent congeners in present study were slightly comparable with those found in the milk l.w.) (Cok et al. 2012), and lower than previous study from Pakistan which reported the concentrations of PCB-153, PCB-138, and PCB-180 as 6 ng g−1 l.w., 3 ng g−1 l.w., and 204 ng g−1 l.w., respectively (Khawaja et al. 2010). Whereas, the concentrations of these congeners in the present study were much lower than those reported from Eastern Siberia (Mamontova et al. 2017), Russia (Tsydenova et al. 2007), and Central Taiwan (55.4 ng g-1 l.w.) (Wang et al. 2004).

Dioxin-like PCB profile and toxicity equivalence

Monitoring of DL-PCB congeners is of key importance due to their dietary uptake by adults and potential of toxic trans-mammary transfer into breast feeding infants (WHO 2003). In present study, total eight dioxin-like PCBs (DL-PCBs), including three non-ortho and five mono-ortho DL-PCBs, were monitored (Table 3). The ∑8DL-PCB concentrations were ranged from 0.29 to 1.4 ng g−1 l.w., (mean 6.2 ± 8.7 ng g−1 l.w.) exhibiting 2.70% contribution of the total PCB concentrations in human milk.
Toxic equivalents (TEQ) of DL-PCBs based on the WHO2005-TEF values were calculated as previously explained in the “Determination of milk fat (%)” section (Van den Berg et al. 2006) and are given in Table 3. The mean TEQ concentration of DL-PCBs was 63 pg TEQ g−1 l.w. (range 8.6 × 10−3 to 50 pg TEQ g−1 l.w.) which was found to be higher than those in milk samples from Canadian mothers (1.5 pg g−1 l.w.) (Rawn et al. 2017) and Chinese mothers (2.9 pg g−1 l.w.) (Lu et al. 2015). However, the current findings were comparable with human milk samples from Hong Kong (n = 65) with mean TEQ concentrations ranging between 58 and 97 pg g−1 l.w. (Srogi 2008).

Spatial patterns of PCB homologs and potential sources

Spatial distributionpattern ofPCB homologsamong sampling sites depicts the abundance of Σ34PCB concentration distribution trend in human milk samples as follows: Chakwal (mean 68.3 ng g−1 l.w.) > Sialkot (57.6 ng g−1 l.w.) > Khanewal (43.7 ng g−1 l.w.) > Lahore (36 ng g−1 l.w.) > Okara
Among all the sampling sites, relative higher levels of PCB contamination in Chakwal district might be attributed to the four mega-scale cement industries, with production capacity of 7700 thousand metric tons (Punjab Directorate of Industries 2012) and utilizing the waste derived fuels (i.e., TDFs and RDFs) as source of energy generation (Cheema and Badshah 2013). In recent times, the cement industries exhaust have been identified as a vital pollution sources of the higher chlorinated biphenyls (Rodriguez 2016; Jin et al. 2017; Naqvi et al. 2018). Likewise, studies have highlighted vehicular diesel emissions in Chakwal district due to heavy traffic load and thousands of trucks passing nearby may lead to PCB atmospheric emissions (Aziz et al. 2014; Naqvi et al. 2018). Moreover, a co-incineration facility to destroy 300 tons of PCBs has been established in a rotary kiln of one of the cement industry in Chakwal District (GEF 2014), where the PCB-contaminated oil are being concentrated from different parts of the country for destruction, although the co-incineration at extreme temperatures with appropriate turbulence and residence time may destroy the POPs (Lundin and Jansson 2017). However, in the absence of optimal conditions in transportation and on-site storage at the destruction facility, the PCB residues may enter surrounding environment, posing serious human and ecological risk. Therefore, higher concentrations in Chakwal could be associated with synergistic effects of these possible multiple sources existing in the area.
Moreover, the spatial PCB homolog trends in urban mothers (n = 24) and rural mothers (n = 17) were performed that revealed higher levels of PCBs in human milk’s lipid fraction of urban mothers (Fig. 2). Similar trends were observed in women populations in different environmental settings from Italy and India, where the highest levels of PCBs were observed in the milk samples of women living in urban settings (Turci et al. 2006; Devanathan et al. 2009).This might be attributed to higher exposure levels to air-borne PCBs in urban environment as compared with rural settings (Ampleman et al. 2015). However, considerable levels of PCBs, particularly lighter PCB homologs in rural population, are associated with burning of agricultural waste, cow dung, and wood in indoor environment of rural areas (Weber et al. 2018). Previously, studies have found high levels of low molecular PCBs possibly associated to their-long-range atmospheric transport (Yeo et al. 2004; Fang et al. 2017).
Whereas, conventional PCB sources, including open burning of municipal solid waste, thermal power plants, paint additives, transformers repair and maintenance, and emissions from dumping sites, may attribute to high exposures to PCBs in urban areas (Syed et al. 2014; Baqar et al. 2017). Among all classes of PCBs, levels of TetraPCBs were predominant in both urban and rural areas in the present study. These lower chlorinated biphenyls are highly volatile, capable of long-range transference and distribution in the environment, which may be the reason of their abundance in sampling mothers from both urban and rural settings ((Mahmood et al. 2014; Syed et al. 2013).

Exposure and risk assessment to nursing infants (milk)

The potential risks to the health of infants through transmammary transfer via human milk were assessed in the present study. For this purpose, the estimated daily intake of DLPCBs through lactation and hazard quotient (Table 4) were determined by using the equations previously described by FAO/WHO (1995). The calculated EDI for DL-PCBs ranged from 12 to 799 pg TEQ kg−1 bw day−1 (mean 266 pg TEQ kg−1 bw day−1) was found to be significantly higher than the tolerable daily intake limits provided by WHO (1–4 pg TEQ kg−1 bw day−1), the USEPA (0.001–0.01 pg TEQ kg−1 bw day−1), the European Union Scientific Committee on Food (2 pg TEQ kg−1 bw day−1), and the Joint FAO/WHO Expert Committee on Food Additives (2.3 pg TEQ kg−1 bw day−1) (USEPA 2000; European Commission 2001; WHO/FAO 2001). Highest EDI was observed in Sialkot, with EDI = 390 pg TEQ kg−1 bw day−1 and lowest EDI was found in samples from Lahore, i.e., EDI = 225 pg TEQ kg−1 bw day−1 (Table 4). The EDI in infants from the present study area was lower than the values from India (EDI = 28-500 pg TEQ kg−1 bw day−1 for human milk collected from women residing near municipal waste dumping sites and ED = 18-35 pg TEQ kg−1 bw day−1 for women from reference site) (Kunisue et al. 2004), Croatia (EDI = 50,000–700,000 pg TEQ kg−1 bw day−1) (Klincic et al. 2016) Turkey (EDI = 13,000–201,000 pg TEQ kg−1 bw day−1) (Cok et al. 2012), and Tanzanian mothers (EDI = 200–114,000 pg TEQ kg−1 bw day−1) but higher than the concentrations in Chinese mothers (mean EDI = 32.4 pg TEQ kg−1 bw day−1) respectively. Moreover, in this study, the hazard quotient for TEQ ∑8DLPCBs ranged from 1.21 to 79.87 (mean 26.59) which is far above the benchmark value of 1 at all the sampling sites which is in agreement to the findings from Tanzania and China, in which HQ was above one in most of the infants (Lu et al. 2015; Muller et al. 2017). The hazard quotient higher than one indicates high levels of adverse health risks to infants through breast milk consumption from exposed mothers, and if the PCB exposure prevails, the infants’ health would be susceptible to high levels of risk. These findings relate to early-life exposure in infant; therefore, long-term effects of perinatal exposure to PCBs at different stages of infants’ growth and exposure to PCBs from other sources in addition to decontamination kinetics studies of PCBs in lactating mothers need to be monitored in future studies from the region. Other studies have showed adverse outcome at the age of 7 and 42 months (Koopman-Esseboom et al. 1996; Grandjean et al. 2003) and also that despite higher exposure to PCBs, breastfed infants performed better than formula-fed infants in terms of tests for cognitive development and fluency of movements (Boersma and Lanting 2002). Also, duration of nursing is restricted to a limited span of human life; hence, breastfeeding is continued to be promoted on the basis of the casting evidences regarding benefits of human milk on infant’s health (Aliyu et al. 2010; Costopoulou et al. 2013).

Maternal characteristics

The concentration of POPs in women body increases with age due to bioaccumulation in lipids that could be excreted during pregnancy and lactation stages, thereby lowering POPs body burden (Noren and Merionyte 2000). In present study, the age of sampling mothers has shown negative correlation with PCB concentrations (r = − 0.31, p = 0.02). However, the observation was similar to the trends found in previous studies for POPs in Pakistan (Yasmeen et al. 2017). The ∑34PCB levels were found higher in human milk of primiparae and lower in multiparae, depicting significant strong negative relationship between parity and ∑34PCB levels (r = − 0.85, p = 0.001). The primiparae and multiparae constituted 29% and 71% of the sampling population in the present study, respectively.
Food is considered as a vital source of PCB exposure to human. The sampling population was evaluated on the basis of eating habits such as meat, dairy, and vegetable or their combination using Pearson’s correlation coefficients (Table 5). These eating habits were showed a weak but positive correlation with ∑34PCB concentration (r = 0.353, p = 0.012).Similarly, weak positive correlation was observed between PCB levels in human milk and BMI (r = 0.03, p = 0.44) and environmental settings of mothers (r = 0.23, p = 0.07). Most of the women in the present study preferred to eat dairy products (34%) and vegetables (30%). Human milk is rich in milkfat and PCBs being lipophilic tendto accumulate in lipidrich body part of humans. Perhaps, this is why eating habits showed a positive correlation with level of PCBs in human milk. The fat contents (%) in maternal milk were ranged between 0.8 and 5.2% (mean 3.01 ± 1.03). A positive association of lipids was observed with maternal age, parity, and number of children carried (Table 6).

Infant’s anthropometric measures

In utero exposure to chemicals may be deemed factor affecting the fetal growth (Tan et al. 2009). Different growth parameters of infants, including birth weight, head circumference, chest circumference, and mid arm circumference were correlated with the determined ∑34PCB concentrations (Table 5). A significant negative relationship was observed between the infant birth weight and PCB levels (r = − 0.73, p = 0.01), reflecting the decrease in the birth weight of the infants with high levels of PCB accumulations in mothers. Similarly, a significant negative correlation was found between ∑34PCBs and other neonatal growth parameters, viz, head circumference (r = − 0.59, p = 0.001), mid arm circumference (r = −0.303, p = 0.027), and chest circumference (r = − 0.29, p = 0.03). However, a negative but non-significant relationship was also observed between the PCB concentration and infant’s crown to heel length (r = − 0.2, p = 0.1). Linear regression plots were drawn to show the relationship between these parameters and concentration of human milk samples. These results were similar to studies conducted in Spain and USA (Ribas et al. 2002; Sagiv et al. 2007; Wolff et al. 2007).

Conclusions

The PCB levels in human milk of rural and urban population from five districts of Punjab, Pakistan, revealed the possible recent exposure to PCBs in the region as indicated by predominant occurrence of non-persistent PCB congeners. The bioaccumulations of PCBs were relatively higher in urban mothers than rural counterparts, depicting a comparatively higher exposure risks in urban settings. The spatial distribution pattern of PCB homologs on concentration basis was observed as follows: Chakwal > Sialkot > Khanewal >Lahore > Okara. Industrialization, urbanization, and e-waste handling were identified as key sources to PCB exposure to the population in the study area. Levels of estimated daily intake (EDI) to infants through lactation were evaluated, which were hundred folds higher than the recommended to tolerable daily intake limits established by the WHO, USEPA, and other reputable regional organizations. Similarly, the hazard quotient (HQ) values were significantly exceeding the benchmark value (i.e., HQ < 1). The parity, eating habit, and age of the mothers were found to be factors affecting the PCB levels. Breast milk is a vital component in infants’ development so the PCB exposure and body burden needs to be controlled at source through firm regulatory mechanism, and exposure-elimination information must be disseminated to the vulnerable populations to safeguard public health. However, comprehensive epidemiological health risk studies are recommended in the region to assess the risk associated to PCB exposure at all hierarchical levels of life.

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