Assessment of air pollution caused by illegal e-waste burning to evaluate the human health risk

Environment International 125 (2019) 191–199

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Assessment of air pollution caused by illegal e-waste burning to evaluate the human health risk

T

Charu Gangwara,b, , Ranjana Choudharic, , Anju Chauhana,b, Atul Kumara,b, Aprajita Singha,b, Anamika Tripathia ⁎

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a

Pollution Ecology Research Lab, Department of Botany, Hindu College, Moradabad, India School of Science, IFTM University, Moradabad, India c National Institute of Occupational Health (NIOH), Ahmedabad, India b

ARTICLE INFO

ABSTRACT

Handling Editor: Xavier Querol

The onset of the 21st century has started a corresponding change in consumer lifestyles, resulting in the generation of a huge amount of the end-of-life electronics, known as e-waste. The e-waste recycling activities can pose a high risk to the environment and human health. We monitored air pollution levels (PM10) and heavy metal concentrations (Pb, Cu, Zn, Ni and Cr) in the air for three consecutive months in an area where illegal ewaste recycling was in operation and compared the results with other two residential sites. In addition, we measured the concentrations of the same heavy metals in human blood to find out if there exists any correlation between environmental and biological exposure. Hypoxemia and hypertension were also determined for the comparison of health status amongst the study population. The study design comprised of three sites, which were selected on the basis of different major activities in the respective areas. Air samples were collected with the help of RDS and subjected to heavy metals analysis by ICP-OES, whereas blood samples were analyzed by ICP-MS. Results showed that amongst all study sites significant highest mean concentration of PM10 (243.310 ± 22.729 μg/m3) and its heavy metal was found at e-waste burning site (SIII). High levels of heavy metal in the air were responsible for the higher exposure to the residents of SIII. Therefore, the study concluded that e-waste burning by the informal sector has significantly contributed to the high levels of the air pollution, which in turn was responsible for the highest level of heavy metal exposure to the residents. This was also associated with the occurrence of cardiovascular morbidity namely hypertension amongst the inhabitants of SIII may indicate the effect of chronic exposure to the air pollution due to e-waste processing activities, which needs to be studied further.

Keywords: E-waste burning activity PM10 Blood Heavy metal Human health Air pollution Hypertension

1. Introduction Electrical and electronic equipment (EEE) are meant to make our lives happier and simpler. They have penetrated every aspect of our lives. Most of us do not think about as to what happens to these gadgets when they are discarded or upgraded. As EEE industries are one of the fastest growing streams, they are also an emerging problem because they generate larger volumes of e-waste (electronic waste). E-waste consists of, not only toxic heavy metals such as Pb (lead), Cr (chromium), Ni (nickel), Cd (cadmium) and Hg (mercury) etc. but also valuable metals such as Ag (silver), Au (gold) and Pt (platinum) (Awasthi et al., 2016). Heavy metals are most persistent pollutants in the environment

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because of their resistance to decomposition in natural condition (Ra et al., 2013). Intensive anthropogenic activities can drastically change the natural biogeochemical cycling and balances of some heavy metals. The major anthropogenic sources of heavy metals include informal ewaste recycling, industrial point sources such as mines, foundries, and smelters, as well as diffuse sources, e.g. vehicular emissions and fossil fuel burning (Tripathi et al., 2010). When heavy metal levels exceed from their permissible limits, they become toxic. Under certain environmental conditions, heavy metals might accumulate up to toxic concentration levels, and pose negative impacts not only in an environment, but also on human health (He et al., 2017). In India, there are numerous sites known for the informal recycling or recovery of valuable materials from e-waste. The main center where

Correspondence to: C. Gangwar, Pollution Ecology Research Lab, Department of Botany, Hindu College, Moradabad, India. Corresponding author. E-mail addresses: [email protected] (C. Gangwar), [email protected] (R. Choudhari).

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https://doi.org/10.1016/j.envint.2018.11.051 Received 15 June 2018; Received in revised form 13 November 2018; Accepted 21 November 2018 0160-4120/ © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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the majority of the informal recycling goes on mostly via open burning of printed circuit boards (PCBs) of e-waste is Moradabad (Gangwar et al., 2017; Rajya Sabha Secretariat, 2011). These recycling activities also pose high risk to human health. These activities can affect people who are exposed to such hazardous substances, including acute lung damage stemming from the inhalation of fumes of heavy metals such as Pb and Cd. Combustion from burning of e-waste creates fine particulate matter, which is linked to pulmonary and cardiovascular disease (Jin et al., 2015; McAllister, 2013). Particulate matter (PM) is also known as particle pollution. It is the key indicator of air pollution. Now a day's ewaste recycling also contributes a significant role in deterioration of air quality (Awasthi et al., 2016). Measurement of the levels of atmospheric particulate matter is a key parameter in air quality monitoring throughout the world, leading to the cause-effect relationship between exposure PM levels and health impacts (Katheeri et al., 2012). However, ambient concentration of PM10 was investigated by many researchers. Only recently, scientists have focused on fine particles such as PM10 and PM2.5 because of the strong correlation of these fractions of PM to adverse health effects. Particulate pollution is a serious environmental issue mainly due to the presence of toxic substances and trace metals in the atmosphere and its negative impact on human health (Suvarapu and Baek, 2016). Moradabad is famous for its brass work worldwide and known as Peetal Nagri or the Brass City of India. Now a days' it has become notorious for e-waste burning therefore known as illegal e-waste recycling or burning hub (CSE, 2015). It is estimated that about 50% PCBs used in the EEE in India end up in Moradabad (Down to Earth, 2015). These circuit boards are sourced from computer monitors, CPUs, keyboards, remote control sets, cell phones and other electrical appliances. Recyclers in Moradabad buy PCBs from Delhi, Kolkata, Chennai, Bangalore and other parts of India (Singh et al., 2016). There is no clear evidence regarding any international shipment of e-waste directly reaching in Moradabad but there are strong evidences that e-waste is illegally transported via developed countries such as the United States of America to developing countries especially in China and India (Rajya Sabha Secretariat, 2011). Therefore, e-waste from different parts of India is illegally transported to Moradabad. Global recession and decreasing demand for brass products in the West forced the manufacturers and the handicraft workers to adopt the hazardous e-waste recycling to earn their livelihood (Pal et al., 2014a). However, it must be emphasized that informal recycling in Moradabad is operated in a highly stratified manner. This comprises buying, collecting, recycling and burning of the dismantled obsolete EEE. Informal recycling activities of e-waste such as open burning, dismantling, incinerating, ash washing and acid bath etc. release number of toxic or hazardous substances such as heavy metals (Pb, Cd. Ni, Cu and Zn), brominated flame retardants (BFRs), neurotoxins and volatile compound of nitrogen and chlorine etc. (Kiddee et al., 2013) causing severe health issues to the workers engaged at the processing sites apart from the environmental pollution. Therefore, present study was conducted over a period of three months to measure particulate pollution levels (PM10) and heavy metal concentration (Pb, Cu, Zn, Ni and Cr) in an area where illegal e-waste recycling is in operation. In addition, we measured heavy metal concentrations in the blood of our study population to assess the correlation with heavy metal levels in the air. For the comparison of health status amongst study population hypoxemia and hypertension were studied as end points and recorded as outcome variables.

sites are P.T.C. (Police Training Centre-SI, control and purely residential area), Banglagaon (SII, residential, commercial and rapid vehicular activity area) and Lalbagh (SIII, residential cum e-waste burning area). 2.2. PM10 sampling method Taking the predominant land-use pattern as the selection criteria, the samples of PM10 were simultaneously collected from three monitoring sites during winter season, i.e. from January 2018–March 2018. PM10 samples were collected by gravimetric method with the help of RDS (Respirable Dust Sampler) APM 460-NL machine at the rate of two samples of PM10 per week with an air flow rate of 1 to 1.5 m3/min. RDS machine was operated in three shifts (each shift of 8 h) per day. Fresh filter paper was used for the collection of PM10 particles in each shift. For calculation of 24 h average of PM10 particles, average of three shift samples was taken. After that, difference between initial and final weight of the glass fiber filter paper was obtained and divided by the total volume of air sample, which ultimately yields the mass concentration for PM10. Priority was given to the guidelines prescribed by the Central Pollution Control Board of India (CPCB Ministry of Environment and Forests, 2013) along with machine safety and availability of electricity. In the present study, 78 observations were made at each site during study period. Concentration of PM10 was calculated as follows:

PM (µg/m3) =

(W

W ) × 10 V

where W1 = Initial weight of the filter paper W2 = Final weight of the filter paper V = Volume of the air (h ⁎ × Min.⁎⁎ × Avg. reading) (⁎ = Hours, ⁎⁎ = Minute, ⁎⁎⁎ = Average)

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of manometer

2.3. Detection of heavy metals in air samples For analysis of heavy metals concentration, total 72 square of 1 × 1 in diameter (3 monitoring samples + 1 blank) of the GF-A filter paper covered by particulate matter digested with nitric acid and perchloric acid in a ratio 1:3 on a 140 °C hot plate till white fumes arose. Residues were then re-dissolved by 0.1 M hydrochloric acid and the content was filtered through Whatman filter paper number 42, which finally madeup to 25 mL by double distilled water. The filtrate of each sample was examined for the concentrations of heavy metals i.e. Pb, Cr, Zn (Zinc), Ni and Cu by using Inductively Coupled Plasma-Optical Emission Spectrometer (ICPOES; Spectro Analytical Instruments, West Midlands, UK) collected for each site. To get the final concentration results of the blank samples were subtracted from the exposed samples. For each of the metals, the concentration of metal in the sample was then multiplied by the sample volume (i.e. 25 mL) to get the mass of that metal. These values were subsequently divided by corresponding total volume of sampled air to get the concentration of metal in the sampled air. 2.4. Ethical approval Center of Nutrition Research, Halberg Hospital and Research Institute, Moradabad (U.P.) approved the study design and protocol.

2. Materials and methods

2.5. Methodology of health study

2.1. Selection of study sites

2.5.1. Study design and sample selection Subjects were selected by the simple random sampling method. Prior to the study, health survey camps were organized in those areas to gain the co-operation and support from the residents. A total of 132 subjects who agreed to give the blood for the study were selected for the

In the present study, three monitoring sites of Moradabad City were selected based on the predominance of residence, e-waste burning and vehicular activities existing in the local areas (Fig. 1). These three study 192

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Fig. 1. Map of study sites.

main study, i.e. 50 subjects from SI, 54 from SII and 28 subjects from SIII. The participants provided informed consent form and were made familiar with the objectives of the study. Those subjects, who gave valid written consent for complete examination, were included. The inclusion criteria were the subjects who had attained the age of 18 years and above, non-smoker, without any chronic illness such as cancer, chronic diarrhea and dysentery that can alter health indicators and those residing in the houses located in a particular study area. Exclusion criterias were the persons, who were engaged in e-waste recycling activities as well as industrial activities such as metal processing and smelting etc., self-reported pregnant women and subjects consuming Ayurveda medicines (Ayurveda medicines contains various heavy metals). Those subjects who refused to give blood were also not included in the study.

blood pressure (DBP) number was noted. Blood pressure was measured in millimeters of mercury (mm Hg) (Rotter et al., 2015). However, usually the “mm Hg” was not used when reporting the blood pressure. According to WHO guidelines, if a blood pressure was recorded more than ≥140/90 mm Hg, a repeat measurement was obtained after 5-min rest, with the subject in a supine position (Rose et al., 1992). BMI was calculated as weight (kg)/height (m2) (Chinedu et al., 2015). The Asian population BMI categories are as follows: < 17.50 kg/ m2 underweight; 17.50–22.90 kg/m2 normal weight; 23–27.99 kg/m2 overweight; ≥28.00 kg/m2 obese. Blood Oxygen level (SpO2) and Pulse Rate (PR) were measured by Finger Pulse Oximeter (Arden et al., 1997) which, has LED display. For SpO2, this oximeter has a measurement range of 70%–90%, accuracy of ± 2%, resolution of ± 1% and low perfusion of < 0.4%, while for PR it has a measurement range of 30 BPM–240 BPM with accuracy of ± 1 BPM or ± 1%.

2.5.2. Study questionnaire The study protocol followed the Helsinki declaration. We interviewed all the subjects using a predesigned questionnaire to obtain information on their general, personal data, clinical data, food habits (vegetarian or non-vegetarian) as well as socioeconomic status (such as educational status, occupational status, per capita monthly income).

2.6. Human blood sampling method Collection of a blood sample was done by using the usual venipuncture technique. Samples of blood were taken from 50 subjects at SI, 54 subjects at SII and 28 subjects at SIII. 5 mL of the venous blood from the antecubital vein was collected from each subject in the EDTA vacutainer tube using disposable pyrogen-free needles and syringes. The samples were then stored at −70 °C until analysis.

2.5.3. Physical measurements Physical examination included measurements for height (cm) and weight (Kg). Height was measured with the help of measuring tape (to the minimum of 0.5 cm) while the weight was measured by electronic weighing machine. Blood pressure was measured with a simple noninvasive method using an electronic blood pressure monitor (Omron make). This monitor consists of an inflatable cuff that was wrapped around the upper arm and was attached to an electronic monitor that gave a digital readout of the blood pressure and pulse. The systolic blood pressure (SBP) number was written first, and then the diastolic

2.6.1. Detection of heavy metals in human blood Microwave-assisted acid digestion method was used to detect the heavy metals. For the analysis of heavy metal concentration in the blood, 1 mL of liquid sample was pipetted and transferred in a digestive vessel and adjusted the volume accordingly. After this, 5 mL of HNO3 and 2 mL of 30% H2O2 were added into the vessel and closed the vessel. Finally, the vessel was firmly fixed according to the microwave digester 193

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Table 1 Mean concentration of PM10 (μg/m3) and their heavy metal concentrations (μg/m3) at various sites. Sites

SI (n = 78) SII (n = 78) SIII (n = 78)

Statistical analysis

Mean SD Range Mean SD Range Mean SD Range

PM10 conc.

193.187 ± 17.020 135–265 232.880 ± 19.367 172–276 243.310 ± 22.729 176–371

Heavy metal concentration in PM10 (n = 78) Zn

Ni

Pb

Cu

Cr

15.358 ± 4.234 4.330–20.210 42.515 ± 12.827 22.870–87.670 66.814 ± 19.023 23.670–93.880

0.985 ± 0.524 0.450–2.450 2.586 ± 0.969 1.230–5.920 5.129 ± 1.755 3.560–13.230

3.341 ± 1.192 2.390–7.890 9.278 ± 2.908 4.560–16.450 9.572 ± 3.109 6.230–18.340

2.742 ± 1.194 1.450–6.980 23.312 ± 6.590 13.780–39.540 75.560 ± 10.497 54.860–97.650

0.884 ± 0.387 0.450–2.340 2.124 ± 0.994 1.670–7.680 3.641 ± 1.480 1.500–6.760

Conc. = concentration, SD = standard deviation, Min. = minimum, Max. = maximum, n = number.

Ambient Air Quality Standard) value i.e. 100 μg/m3 given by CPCB (CPCB, 2009). Similar results were also reported by Pandey et al. (Pandey et al., 2014). However the concentration of PM10 was found 1.93, 2.32 and 2.43 times higher at SI, SII and SIII respectively than the standard value. The higher concentration of PM10 was found in January (i.e. 371 μg/m3, which was 3.71 times higher than the NAAQS standard (CPCB, 2009) because a drop of temperature and wind levels in winter season follows the pattern of high pollution levels (EPCA. and CSE, 2018). Besides this, increase in PM10 concentration was also reported in winter season might be attributed to the anthropogenic activities such as the burning of coal and wood, high traffic density and combustion of fossil fuel for heating etc. Particulate matter persists in the atmosphere for longer period of time as calm conditions prevail throughout the winter season (Yadav et al., 2014). The trends of variation in PM10 concentrations were more or less similar during the three months. Higher concentrations of PM10 at SIII may be due to the emissions from crude and rudimentary practices of ewaste processing such as dismantling, combustion, incineration and open burning. Consistence with this results, Fang et al. (2013) and Awasthi et al. (2016) reported that during the mechanical separation and dismantling of PCBs, fine particulate matter will be released into the air which poses negative impacts on the environment. Also, dry atmospheric conditions in February and March in comparison to January would have produced more particulate matter from surfaces of roads and soils (Suvarapu and Baek, 2016). Besides these practices, rapid vehicular activities are a major cause of particulate pollution at SII. Particulate emission from vehicles and re-suspension of soil contributes significantly to the concentration of PM10 at SII (Mahima et al., 2013). The lowest concentration of particulate matter amongst all sites

instruction manual. Once the microwave reaction system was completed the vessels were taken out, one by one carefully and decanted the sample into the 10 mL volumetric flasks. Vessels and vessel caps were washed with de-ionized water and contents were then poured into flasks. Final volume was made up to 10 mL with de-ionized water. The samples were filtered with 0.45 μ membrane filter papers and the filtrate was introduced into ICP-MS (Inductively Coupled Plasma-Mass Spectrophotometer) for the analysis of heavy metals i.e. Pb, Zn, Ni, Cr and Cu. 2.7. Statistical analysis Data analysis was performed using SPSS version 16.0 statistical software. The basic statistical parameters such as mean, standard deviation, minimum and maximum were computed along with chi square and one-way analysis of variance (ANOVA) to assess the influence of different variables on the concentrations of heavy metals in the blood samples. 3. Results and discussion 3.1. Mass concentration of PM10 The mean concentrations of PM10 are being presented in Table 1 while the variations in monthly average are depicted in Fig. 2. The highest monthly mean concentration of PM10 was recorded in the month of January while the lowest was in the month of March at all study sites confirming with foggy and dry months' experiences in this winter season. Mean PM10 concentration was recorded 193.187 ± 17.020 μg/m3 at SI, 232.880 ± 19.367 at SII and 243.310 ± 22.729 μg/m3 at SIII which exceeded the NAAQS (National

Fig. 2. Concentration of PM10 at various study sites during different months. 194

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was found at SI (P.T.C., residential area) which has the highest green coverage amongst all study sites. 3.2. Heavy metals in PM10 Concentrations of all heavy metals in PM10 are given in Table 1. Maximum mean concentration of Pb (i.e. 9.572 ± 3.109 μg/m3) in PM10 samples was found at SIII while minimum (3.341 ± 1.192 μg/ m3) was found at SI. The higher concentration of Pb at SIII is mainly due to the burning of PCBs because Pb is the main component of ewaste. During the burning process, Pb is released in the environment. Besides this emission from vehicles were also responsible for high concentration of this toxic metal at SII and SIII. Vehicle constitutes over 20% of the total mass of fine particles emitted from cars releasing leaded gasoline (Pal et al., 2014a). Approximately 75% of Pb contained in leaded gasoline and is emitted directly into the atmosphere (Dai et al., 2015). Concentration of Pb in PM10 was exceeding the permissible limit (1.5 μg/m3) given by CPCB (CPCB, 2009) at all study sites during the entire study period. The highest mean concentration of Cu (75.560 ± 10.497 μg/m3) and Zn (66.814 ± 19.023 μg/m3) was recorded at SIII while the lowest at SI. Cu and Zn not only alloyed easily with each other like in brass (copper/zinc) but also with other metals most widely in bronze. Besides this, these metals are one of the important constituents of PCBs. According to E-waste Guide Information (E-waste Guide Information, 2013), Cu and Zn constitutes about 6.95 and 2.2% respectively in typical composition of PCBs. Cu is also main constituent of copper cables. Informal recyclers generally use crude processes like burning and acid baths, in order to recover valuable metals from PCBs (Gangwar et al., 2016). During these processes toxic fumes are released in the atmosphere leading to increase in significant atmospheric concentration of Cu and Zn. Similar results were also investigated by Eguchi et al. (2012), Song and Li (2014) and He et al. (2017). They concluded that during the formal and informal e-waste recycling heavy metals were released into the environment which in turn contaminates the quality of environment. Rao (2014) also concluded that uncontrolled e-waste disposal and recycling activities generate and release highly toxic metals such as Hg, Pb, Cd, Cr, Cr(IV), Co, Cu, Ni, and Zn. The highest mean concentration of Ni (5.129 ± 1.755 μg/m3) and Cr (3.641 ± 1.480 μg/m3) was found at SIII followed by SII whereas the lowest concentration was found at SI. In addition to the e-waste processing, Ni in the atmosphere originates from the smelting and combustion of fossil fuel, particularly oil (Kaushik et al., 2006). Also, uses of oil lubricants at service centers and tire abrasions/vehicle exhausts are the other possible sources of Cr in the study areas (Pal et al., 2014b).

Fig. 3. Prevalence of hypertension in subjects of various study sites.

3.3. Physical measurements of human health All physical characteristics of subjects from different study sites are presented in Table 2. Mean SBP varied between 100 and 188 mm Hg in subjects of SI, 99 to 188 mm Hg in subjects of SII and 105–198 mm Hg in subjects of SIII while the mean DBP was ranging from 50 to 126 mm Hg, 65–126 mm Hg and 65 to 131 mm Hg in subjects of SI, SII and SIII respectively. Results revealed that although subjects of all study sites were having mean systolic and diastolic blood pressure, in the range of prehypertension (Williams et al., 2004), SIII population had a significant higher cardiovascular risk as a result of significant higher prevalence of hypertension (68%) as compared to other two sites (Fig. 3). Similar finding was also reported by Chan An et al. (2017). Mancia et al. (2013) reported that hypertension was defined as an average SBP ≥ 140 mm Hg or DBP ≥ 90 mm Hg and/or current use of antihypertensive medication prescribed by a physician. Hypertension is a most prevailing disease that poses a negative impact on major organs, such as the brain, heart, and kidneys. The risk factors for blood pressure elevation generally include increasing age, high BMI, smoking, frequently drinking alcohol, stress, diabetes, lack of physical activity and dyslipidemia etc. (Yoo, 2015). Analysis of χ2 (Chi square test) detected a highly significant statistical difference in the occurrence of hypertension between SI-SIII with p value of 0.0006 followed by SII and SIII with p value of 0.0310. Besides this a non-significant statistical difference was found between SI and SII with p value of 0.1204. These results consistently indicated that the pollution caused by e-waste recycling activities at SIII was incomparable to that brought by residential activities at SI and residential cum rapid vehicular activities at SII. Associations of ambient PM10 concentration with mean SBP and DBP appeared in strong moderate correlation with r values of 0.628 and 0.678 (Table 3A) which indicated that air particulate matter concentration is a modifiable risk factor for development of hypertension in subjects of all study sites (Baccarelli et al., 2011). Consistent with our result, Kelishadi et al.

Table 2 Physical health characteristics of subjects from different sites. Physical measurements

Age Height (meter) Weight (kg) SBP (mm Hg) DBP (mm Hg) SpO2 (%) PR (bpm) BMI (kg/m2)

SI (P.T.C.)

SII (Banglagaon)

SIII (Lalbagh)

(n = 50)

(n = 54)

(n = 28)

Mean ± SD

Range

Mean ± SD

Range

Mean ± SD

Range

37 ± 15.326 1.637 ± 0.093 59.316 ± 6.204 128.860 ± 16.300 82.460 ± 13.942 98.420 ± 5.842 85.000 ± 9.794 22.229 ± 2.617

18–70 1.50–1.84 52–72 100–188 50–126 82–111 65–104 17.3–32

41 ± 14.213 1.610 ± 0.100 61.407 ± 10.789 139.963 ± 20.337 90.556 ± 15.925 96.370 ± 6.883 89.037 ± 12.501 23.859 ± 4.636

19–70 1.43–1.98 40–95 99–188 65–126 55–105 68–118 14.34–38.05

43 ± 16.321 1.639 ± 0.048 57.439 ± 6.317 133.000 ± 21.635 85.607 ± 11.711 86.929 ± 12.887 94.714 ± 6.463 21.362 ± 1.945

19–70 1.57–1.70 45–77 105–198 65–131 64–115 71–100 17.80–27.94

SBP = systolic blood pressure, DBP = diastolic blood pressure, SpO2 = blood oxygen level, PR = pulse rate, BMI = body mass index, n = number, bpm = beats per minute, ± = standard deviation. 195

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Table 3 Correlation between various physical health characteristics and air quality parameters. A. Correlation between particulate matter concentration and various physical health parameters (2 tailed significant correlation at the 0.05 level) Physical measurements

Correlation value (r value) with particulate matter

Sig. (2-tailed)

a

SBP (mm Hg) DBP (mm Hg) SpO2 (%) PR (bpm)

0.628 0.678a −0.781a 0.918a

0.568 0.526 0.429 0.260

B. Correlation between heavy metal concentration of human blood and PM10 (2 tailed significant at the 0.05 level) Heavy metals in blood Cr Ni Cu Zn Pb

Heavy metals in air

Correlation value (r value) a

Cr Ni Cu Zn Pb

0.985 0.954a 0.910a 0.896a 0.581a

Sig. (2-tailed) 0.109 0.193 0.272 0.292 0.606

Sig. = significant, SBP = systolic blood pressure, DBP = diastolic blood pressure, SpO2 = blood oxygen level, PR = pulse rate. a Significant.

(2011) found the effect of air pollutants notably particulate matter on pre-hypertension and concluded that association between particulate matter and prehypertension was independent of aerologic factors like weather, temperature or humidity and of major cardiovascular risk factors such as age, diabetes, dyslipidemia and obesity. All subjects of different sites had a normal mean pulse rate (60–100 bpm) (Table 2). The mean blood oxygen level (SpO2%) was recorded as 98.420 ± 5.842% and 96.370 ± 6.883% in subjects of SI and SII respectively were found within the normal range (i.e. ≥96%, (Vold et al., 2015)). Mean SpO2 (86.929 ± 12.887%) in subjects of SIII was found significantly lower than that of the residents of other study sites. A strong and negative correlation was found between SpO2 and PM10 concentration which revealed that higher particulate matter concentrations were responsible for low level of SpO2 in study subjects (Table 3A). Consistent with results, Gibson et al. (2014) also found the correlation between air pollution and low SpO2. Saturations of SpO2 < 90% represented hypoxemia (Majumdar et al., 2011). Hypoxemia refers to low oxygen in the arterial blood, and can cause tissue hypoxia as the blood is not supplying enough oxygen to the body (Samuel and Franklin, 2008). Wheezing, choking sensation, frequent cough, waking up out of breath, shortness of breath in resting stage, whereas bluish discoloration of the skin and severe shortness of breath after physical activity etc. are the most common symptoms of health which related to low blood oxygen levels (Khirfan et al., 2018). Mean BMI of subjects of SII was found under the category, overweight (23–27.99 kg/m2) while the subjects of SI and SIII had normal mean BMI (17.50–22.90 kg/m2) (Table 2). At SI, out of the 50 subjects, 2% subject had underweight, 58% subjects had normal weight, 34% subjects showed overweight and 6% subjects had a BMI in the obese category. At SII out of the 54 subjects, underweight, normal weight, overweight and obese BMI was found in 6%, 35% and 39% and 20% subjects respectively. At SIII, out of the 28 subjects, 7% subjects were overweight while 93% subjects had normal weight. BMI and hypertension are positively correlated, i.e. people having a high BMI also have a higher risk of hypertension. In spite of having the mean BMI of the study subjects in normal range, and highest percentage (93%) of the subjects in the category of normal weight as compared to other two sites (SI with 58% and SII had 35% of normal weight subjects), SIII had highest prevalence of hypertension amongst the subjects of all study sites, which might indicate probably to some toxic environmental exposure that played significant causal role in hypertension rather than any lifestyle or dietary factor. Besides this, SIII reported highest biological levels of Pb amongst all sites (Fig. 4ii), approximately 21 times as that of SI, which might be a significant causative factor for

the highest prevalence of hypertension reported at SIII (Fig. 3). Our results are in agreement with the earlier studies, which have confirmed a causal relationship between Pb, other heavy metals and the development of hypertension (Alissa and Ferns, 2011; Chan An et al., 2017; Nuttall, 2015). 3.4. Heavy metal concentrations in human blood The mean concentrations of Zn, Pb, Cr, Ni, and Cu in blood samples of all subjects from different study sites are shown in Fig. 4. The levels of Cr in blood samples of the subjects varied from 0 to 0.004 ppm, of Ni varied from 0 to 0.270 ppm, of Cu varied from 0.120 to 0.578 ppm, of Zn varied from 6.454 to 15.319 ppm and of Pb varied from 0 to 0.102 ppm at SI. The levels of Cr, Ni, Cu, Zn and Pb in blood samples of all subjects of SII varied between 0–1.105, 0–0.352, 0.103–1.152, 7.976–16.844 and 0–0.257 ppm respectively. The concentration of Cr ranged between 0.0003 and 1.105 ppm, of Ni ranged between 0 and 0.354 ppm, of Cu ranged between 0.509 and 1.152, of Zn ranged between 8.153 and 16.844 ppm and, of Pb ranged between 0 and 0.687 ppm in blood samples of subjects of SIII. The mean concentration of all studied heavy metals were found higher in subjects of SIII followed by SII, while lowest values were in subjects of SI. Subjects belonging to SII had 29.50, 2.83, 1.86, 1.32 and 2.20 times higher mean concentrations of Cr, Ni, Cu, Zn and Pb in their blood respectively when compared to subjects residing at SI. It was also observed that the subjects belonging to SIII had 48.50, 19.67, 2.31, 1.33 and 20.49 times higher concentration of Cr, Ni, Cu, Zn and Pb in their blood when compared to subjects residing at SI. We conclude that emission from open air burning of e-waste might have led to high exposure of heavy metals like Pb, Cr, Ni, Cu and Zn to the residents of SIII. Atiemo et al. (2012) had reported that release of heavy metals during burning of PCBs were responsible for higher exposure of Pb and Cd to people. Our study results are also supported by Pal et al. (2014b,c), who had concluded that people living in the industrial and e-waste burning areas are suffering from many diseases related to air pollution. Srigboh et al. (2016) investigated the heavy metal concentration in blood and urine amongst e-waste recycling workers at the Agbogbloshie e-waste site in Ghana. The study concluded that local population, who was residing near e-waste working site was continuously exposed to many of the same hazards as the ewaste workers, including exposure to toxic elements particularly Pb. Wittsiepe et al. (2017) also reported that uncontrolled e-waste recycling activities increases the ambient burden of heavy metals and contributes to the toxic exposure of the general population. In our study, 196

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Fig. 4. Mean concentrations of heavy metals (i) Zn, (ii) Pb, (iii) Cr, (iv) Ni and (v) Cu in blood samples of subjects from various study sites.

significant and strong positive correlations were found between the individual heavy metal concentration in the blood and respective, metal specific air level, such as Cr (r = 0.985), Ni (r = 0.954), Cu (r = 0.910), Zn (r = 0.896) whereas significant but moderate level correlation existed between Pb (r = 0.581) concentrations in blood and air. This shows that individual metal concentration in blood was significantly affected by its corresponding concentration in the air (Table 3B). The concentration of Cr in blood was higher in 12%, 72% and 93% of study subjects from SI, SII and SIII respectively exceeding the biological limit of 0.0005 ppm as set by ACGIH (2008). Similarly Ni concentration of blood was reported higher in 12% subject of SI, 67% subject of SII and 96% subject of SIII than biological limit of 0.0005 ppm as set by ACGIH (ACGIH, 2008) whereas Pb concentration in 2% subject of SII and 14% subject of SIII than biological limit of 0.2 ppm as set by ACGIH (ACGIH, 2017). Results of one-way ANOVA (Table 4) showed that study sites have

Table 4 Results of one-way ANOVA for heavy metal concentrations in blood samples collected from different study sites. Heavy metals Chromium (Cr) Nickel (Ni) Copper (Cu) Zinc (Zn) Lead (Pb)

df 2 2 2 2 2

F 2.587 25.852b 84.619b 22.110NS 13.483b

df = degree of freedom, NS = not significant. a Significant at 5%. b Significant at 1%.

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Probability a

0.081 0.00036 0.00033 0.000005 0.00004

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significant effects on the levels of all heavy metals (Cr, Ni, Cu, Zn and Pb) in blood of the study population. This finding understandably represented that the location of study sites influenced the biological levels of various heavy metals in the residents.

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4. Conclusion Illegal e-waste recycling was a major cause of increase in PM10 concentration, which exceeded from NAAQ standard given by CPCB at the e-waste burning site. Crude and rudimentary methods of e-waste processing by the informal sector have significantly contributed to the high levels of air particulate matter, particularly heavy metal levels, in an illegal e-waste burning area. Chronic exposure to air pollution due to e-waste processing, in turn was responsible for alarming levels of toxic heavy metal exposure to the local residents, which was associated with significant prevalence of cardiovascular morbidity, namely hypertension in the local inhabitants. Although the cause effect relationship could not be established, our study reports highest prevalence of hypertension in the e-waste area, which indicates towards the probable link between chronic exposure to e-waste and cardiovascular risk factor. The presence of high levels of Cr, Ni, Cu and Zn in residents of control and residential site is a matter of concern, because it raised the issue of possible atmospheric spread of some of the metals with serious public health concern. The study recommended that health risk to local residents of the city should not be ignored and may be addressed through continuous environmental monitoring by Government and non-government agencies, along with the monitoring of the health of the general population, residing in the e-waste processing areas for the long term health effects of heavy metal exposures, and timely intervention measures should be planned for safeguarding the health of the residents. Acknowledgement The authors cordially acknowledge Council of Science and Technology, U.P., Vigyan Bhawan, Lucknow (Project Number CST/ 1971) for the financial assistance of the study. Thanks are also due to Dr. R. B. Singh (Cardiologist) and Dr. P. K. Shukla (Cardiologist) for valuable suggestions. We are highly thankful to Dr. V. K. Sharma for providing statistical support and motivation. References ACGIH, 2008. Threshold limit values for chemical substances and physical agents and biological exposure indices. In: Ame. Conf. of Gov. Indus. Hygien. Cincinnati, OH. ACGIH, 2017. TLV and BEIs. Based on Documentation of the Threshold Limit Values for Chemical Substances and Physical Agents & Biological Exposure Limit. www.acigh. org (978-1-607 260-90-5). Alissa, M.E., Ferns, A.G., 2011. Heavy Metal Poisoning and Cardiovascular Disease. J. of Toxic. Hindawi Publishing Corporation, pp. 1–21. 870125. https://doi.org/10.1155/ 2011/870125 (21 pages). Arden, P.C., Dockery, D.W., Kanner, R.E., Villegas, G.M., Schwartz, J., 1997. Oxygen saturation, pulse rate, and particulate air pollution: a daily time-series panel study. Am. J. Respir. Crit. Care Med. 159, 365–372. Atiemo, S.M., Ofosu, F.G., Kwame Aboh, I.J., Kuranchie-Mensahravindra, H., 2012. Assessing the heavy metals contamination of surface dust from waste electrical and electronic equipment (E-WASTE) recycling site in Accra, Ghana. Res. J. Environ. Earth Sci. 4 (5), 605–611 (2041–0492). Awasthi, K.A., Zeng, X., Li, L., 2016. Environmental pollution of electronic waste recycling in India: a critical review. Environ. Pollut. 211, 259–270. Baccarelli, A., Barretta, F., Dou, C., Zhang, X., McCracken, J.P., Díaz, A., Bertazzi, P.A., Schwartz, J., Wang, S., Hou, L., 2011. Effects of particulate air pollution on blood pressure in a highly exposed population in Beijing, China: a repeated measure study. Environ. Health 10 (108), 1–10. http://www.ehjournal.net/content/10/1/108. Chan An, H., Sung, J.H., Lee, J., Sim, S.C., Kim, H.S., Kim, Y., 2017. Association between cadmium and Lead exposure and blood pressure among workers of a smelting industry: a cross-sectional study. Ann. Occup. Environ. Med. 29 (47), 1–8. https://doi. org/10.1186/s40557-017-0202-z. Chinedu, S.N., Emeka, E.J., Jweala, Olubanke, O., Ogunlana, Dominic, E., Nuttall, Azuh, Q., F., 2015. Body mass index and blood pressure in as semi-urban community in Ota, Nigeria. Food Public Health 5 (5), 157–163. CPCB (Central Pollution Control Board), 2009. National Ambient Air Quality Standards

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