Chronic exposure to heavy metals from informal e-waste recycling plants and children's attention, executive function and academic performance

Journal Pre-proof Chronic exposure to heavy metals from informal e-waste recycling plants and children's attention, executive function and academic performance

Fitria Nurbaidah Soetrisno, Juana Maria Delgado-Saborit PII:

S0048-9697(20)30609-4

DOI:

https://doi.org/10.1016/j.scitotenv.2020.137099

Reference:

STOTEN 137099

To appear in:

Science of the Total Environment

Received date:

28 October 2019

Revised date:

28 January 2020

Accepted date:

2 February 2020

Please cite this article as: F.N. Soetrisno and J.M. Delgado-Saborit, Chronic exposure to heavy metals from informal e-waste recycling plants and children's attention, executive function and academic performance, Science of the Total Environment (2018), https://doi.org/10.1016/j.scitotenv.2020.137099

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© 2018 Published by Elsevier.

Journal Pre-proof Chronic Exposure to Heavy Metals from Informal E-waste Recycling Plants and Children’s Attention, Executive Function and Academic Performance Fitria Nurbaidah Soetrisno1,2 and Juana Maria Delgado-Saborit1,3* 1

School of Geography, Earth and Environmental Sciences, University of Birmingham,

Edgbaston, Birmingham, B15 2TT, UK. BP Berau Ltd, Tangguh LNG, West Papua, Indonesia

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ISGlobal Barcelona Institute for Global Health, Barcelona Biomedical Research Park,

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Barcelona, Spain

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* Corresponding author [email protected]

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Keywords: e-waste; heavy metals; cognitive performance; children; TMT Test; soil; water

Journal Pre-proof ABSTRACT E-waste contains valuable metals that require appropriate waste management plans. However, rudimentary e-waste processing methods are a source of heavy metals environmental pollution. This study has characterised concentrations of heavy metals in soil (n=10), water (n=10) and hair (n=44) of children in areas surrounding Jakarta (Indonesia), where e-waste is being or has been conducted in the past, and in a reference unexposed site. Chronic exposure

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to Mn, Pb, Hg, As and Cd and its associations with attention and executive function,

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characterised with the Trail Making Test (TMT), along with academic performance scores was conducted using multivariate regression analysis. Models were adjusted for age, gender,

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parental education, environmental tobacco smoke and residential traffic. Lead (3653± 3355

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mg/kg), cadmium (3.4± 0.9 mg/kg) and mercury (15.2± 28.5 mg/kg) concentrations from soil

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and manganese concentrations in water (1.43± 0.64 mg/L) in the exposed sites were higher than current regulations. Heavy metal concentrations in hair of children living near e-waste

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facilities was higher than for children living in non-exposed areas (Pb: 0.155 ± 0.187 vs

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0.0729 ± 0.08 mg/g; Mn: 0.130 ± 0.212 vs 0.018 ± 0.045 mg/g; Hg: 0.008 ± 0.0042 vs 0.002 ± 0.0011 mg/g) suggesting chronic exposure to heavy metals. Manganese exposure was associated with worse cognitive performance in the domains of attention (TMT-A score: 66 s, 95% CI 0.09, 132), executive function (TMT-B score: 105 s, 95% CI 11.5, 198) and social sciences (-29%, 95% CI -54, -4.7) (per unit of Mn in hair mg/g). These results suggest that informal e-waste activities contribute to local heavy metal soil contamination, and could be an important source of metal exposure to children living in the vicinity of these facilities with putative impacts on their cognitive performance. E-waste management regulation and remediation programmes should be implemented to reduce environmental pollution and associated health effects.

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Journal Pre-proof 1. INTRODUCTION The generation of electronic waste (e-waste) is growing massively at a global scale (Pradhan and Kumar, 2014). Approximately 65.4 million tonnes of e-waste are generated annually due to the high international demand for electrical and electronic products (Venugopal et al., 2016). Wealthy countries tend to landfill or export their e-waste to developing countries instead of

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recycling their e-waste locally because of lack of facilities, high labour cost and strong

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environmental regulations (Robinson, 2009). Consequently, some developing countries in

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Asia and Africa are becoming popular destinations for the illegal export of e-waste due to

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cheap labour wages and inadequate governmental regulations (Awasthi et al., 2016). In addition, e-waste is illegally recycled in those countries to recover economic valuable

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resources such as gold, silica, plastic, iron, and aluminium from electronic parts and base

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materials (Awasthi et al., 2016). Therefore, the inadequate legislation combined with the economic value of the recovered materials is propitiating an emerging market of illegal

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recycling of discarded e-waste products by informal recyclers in developing countries (Rochman et al., 2017; Venugopal et al., 2016). Rudimentary processing methods of e-waste, like manual disassembly, strong acid digestion, and open burning, are commonly applied at home or in small industries in developing countries (Awasthi et al., 2016) leading to heavy metal environmental pollution in the local area. Thus, environmental exposure to heavy metals from e-waste becomes a major public health concern in the affected areas (Matlock et al., 2002). Children are vulnerable receptors of e-waste heavy metal contamination since they have higher intake of air, water, and food by body mass; have additional routes of exposure such as breastfeeding and placental exposures; are characterised by high-risk behaviours (e.g. hand-

Journal Pre-proof to-mouth activities) (Grant et al., 2013). Children are also still at the stage of physiology and nervous system development, have faster metabolic rates and low rates of toxin elimination, and have haematological and neurological effects at lower thresholds than in adults (Grant et al., 2013; Koller et al., 2004). The effects of environmental exposure to metals on children’s health have been reported extensively, with the majority focusing on the adverse effects on the central nervous system

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where the main consequences were reduction in intelligence quotients (IQ) and attention

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deficits (Sanders et al., 2015; Wang et al., 2012). Neurodevelopmental outcomes in children have been reported for exposures to lead (Haefliger et al., 2009; Koller et al., 2004), cadmium

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(Alam and Carandang, 2016; Xu et al., 2015), arsenic (Ilmiawati et al., 2015), manganese and

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mercury (Xu et al., 2015). Therefore, children living close to e-waste recycling facilities and

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landfills are at risk of cognitive and neurodevelopmental problems (Chen et al., 2011) due to

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their higher risk of exposure to heavy metals.

Research characterising metal exposure from e-waste facilities in different environmental

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matrices is emerging (Alam et al., 2019; Li et al., 2011; Pradhan and Kumar, 2014; Zhao et al., 2019). Likewise, metal concentrations have been measured in blood (Huo et al., 2007; Liu et al., 2018; Liu et al., 2014; Wang et al., 2009; Wang et al., 2012; Yu et al., 2019; Zheng et al., 2011) and urine (Wang et al., 2011; Zeng et al., 2016) to characterize short-term exposures of children in contact with e-waste; and in hair for chronic exposures (Ni et al., 2014; Rehman et al., 2018; Wang et al., 2009; Wittsiepe et al., 2017; Zheng et al., 2011). Research about the association between low heavy metals exposure and children’s cognitive performance has been mostly performed in the general population. In contrast, despite ewaste facilities being identified as a source of heavy metal exposure, and their exposure potentially affecting children cognitive abilities, research investigating the effects of metal

Journal Pre-proof exposure on cognitive performance in children is very scarce (Liu et al., 2018; Liu et al., 2014; Wang et al., 2012; Xu et al., 2015). The aim of this study is to assess the environmental pollution near an e-waste recycling facility; to characterise chronic exposure to heavy metals and their association with cognitive performance and attention in children living in the vicinity of an e-waste recycling area. In particular, this research has characterised the concentration of heavy metals in soil, water and

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children´s hair from samples collected in an area in the vicinity of e-waste recycling

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workshops and at a distant reference area. It has collected information on academic performance, attention and executive function of the biomonitored children alongside other

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relevant demographic information from questionnaires to assess the association between

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heavy metal chronic exposure from e-waste recycling facilities and cognitive abilities using

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multivariate regression analysis.

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2. METHODS 2.1 Site Description

Three urban (kota) locations in West Java were selected as the case sites for this research: Depok, Bogor and Bekasi. A fourth location was selected as a reference site, Sukatani Village (Figure S1). The recyclers in Bogor focus on collecting and processing used lead acid batteries. This activity was the main occupation for almost all residents in a village in Bogor from 1978 until 2003. Based on a 2010 assessment, the level of lead contamination in the village was very high, so an encapsulation programme was undertaken in 2014.

Journal Pre-proof The informal e-waste recycling carried out in Depok and Bekasi is still active and focuses on the collection and processing of used computers and televisions (Figures S2-S8). The recycling areas are sporadic in these cities. No crops, vegetables, and fruits grow in the contaminated area since government declared that the soil in that area was contaminated and launched an encapsulation program. Sukatani Village, the reference site, is a residential village in Depok located far from any ewaste recycling area, and at a distance of 83 km from Bogor. Sukatani village is a middle up

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to low economic level family residential area. No manufacturing or industrial activity takes

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place in this village.

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2.2 Population Description

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The selection criteria was children aged 6-9 from low to middle socioeconomic level. One school in the exposed site (Bogor) within 1 km from the informal e-waste recycling sites, and

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one in the referece site (Sukatani village) whose students could meet the selection criteria

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were contacted. The schools in both locations (Bogor and Sukatami Village) have similar socioeconomic conditions. Also, both schools are away from the main street, so traffic is not very busy. Potential participants were identified with the support of the teachers and headmasters. The study was then advertised among these families of children aged 6-9 going to one school in Bogor and another school in Sukatani Village. The population sample was selected from families interested in their children participating in the study to provide samples of hair for metal biomonitoring, and to conduct attention and executive function testing. Those families who met the study criteria enrol their children in the study. A total of 22 children in each location were recruited, with 22 children from Bogor (the exposed group) and 22 children from Sukatani (the non-exposed or reference group).

Journal Pre-proof Consent from the parents to participate in the study was obtained prior to starting sample collection. In addition, the governing body of both schools gave permision to access anonymised pupils academic records age 6-9. Parents of children participating in hair biomonitoring and attention testing gave also permission to access their academic records (not anonymised). Participants in each location were equally distributed in first (N=11) and second grade

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(N=11). There were 3 participants in the exposed site with missing data because they did not

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return the questionnaire. Therefore, the effective sample size for multivariate analysis was 41

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(22 reference and 19 exposed).

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The study protocols were approved by the Ethics Board at the University of Birmingham

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Birmingham Ethics Regulation.

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School of Geography, Earth and Environmental Science, according to University of

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2.3 Environmental Monitoring

Five soil and five water samples were collected from five housing areas near the active ewaste recycling areas in the Depok and Bekasi. The same number of soil and water samples was also collected from five housing areas in the Sukatani Village. Soil samples were taken from top soil (0-15 cm) and stored in pre-cleaned plastic bags. The soil was loose dirt not covered by rocks, grass, or ground cover (Teichman et al., 1993). Water samples were collected through tap water that comes from a borehole in the household around the e-waste recycling plant and the reference site. Water was collected from the tap early in the morning, prior to any domestic use. After the tap is turned on, a proper flow rate

Journal Pre-proof was maintained to ensure collection of sufficient water in a suitable container (Cole et al., 2010). Secondary data was obtained from non-governmental organisations in Indonesia that have been conducting research in collaboration with the WHO on lead contamination levels in the environment and in children from informal e-waste recycling activities in Bogor. The

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environmental data were available for lead levels in 33 soil samples collected in 2014.

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2.4 Biological Monitoring

Hair samples were collected from 22 children living in Bogor (exposed group) and from 22

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children living in Sukatani Village (reference group). In order to reduce the influence of

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potential exogenous contamination, the scalp hair samples weighing approximately 1.0 g

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were taken from the occipital region of the head by using round tip scissors (Mohamed et al., 2015; Rafiee et al., 2020). A hair length of around 2 to 5 cm is cut from the children hair and

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stored in plastic bags.

In addition, secondary data on lead concentrations from 36 blood samples collected from children in Bogor in 2010 children living near informal e-waste was obtained. Lead blood levels had been analysed through a collaboration framework research between an Indonesian non-governmental organisation and the World Health Organization (WHO).

2.5 Academic Performance, Attention and Executive Function Test Anonymised academic performance on reading, mathematics, written expression and oral language, arts, science, social sciences, and sports was collected through the school official

Journal Pre-proof alumni report for all the students in first and second grade at the participating schools (Nexposed=101, Nreference=86). Academic performance was linked to each children participating in the biomonitoring and cognitive testing. Participants completed the trail making test (TMT), which consists of two parts. In part A, the participant has to connect 25 scattered dots numbered from 1 to 25 in sequential order. In part B, the participant is instructed to connect sequentially the dots alternating between numbers

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and letters (e.g. 1-A-2-B, etc). In both cases, the subject is instructed to complete the

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sequence as soon as possible without errors. The score of each part is the time (in seconds) required to complete each sequence. If the time employed to complete Part B is longer than

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300 seconds, the score is capped at 300. Part A measures visual attention, whilst part B

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measures executive function. The higher the scores, the worse the cognitive performance in

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2.6 Questionnaire survey

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those domains as longer is required to complete the test.

A self-designed questionnaire (Supporting Information) was used to survey parents and children to gather information on factors associated with heavy metals exposure on children’s body, on other sources of exposure (e.g. parental smoking habit, location of home and school related to traffic, food sourced locally), socioeconomic factors (e.g. parents’ level of education, parents’ occupation, house ownership, private medical insurance), age and gender of children, and existence of learning disabilities (self-reported). Children were asked to report information about their engagement with learning (e.g. school attendance frequency, attendance to support classes after school) and frequency of contact with e-waste facilities (e.g. playing near or in the e-waste facilities, working in the e-waste facilities).

Journal Pre-proof 2.7 Chemical Analysis Soil, water and hair samples were analysed with an Atomic Absorption Spectroscopy (AAS) to characterise concentrations of Pb, Mn, Cd, As, and Hg following standard operational procedures. Five-point calibration curves were prepared covering the range 0.1-1 mg/L. 20 mL of water samples were acidified with concentrated HNO3 until pH 2 prior to AAS analysis. For soil analysis, 12 mL of concentrated HNO3 was added to 0.25 g of soil sample

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prior to heating with medium heat reaching a temperature of 150 – 179 ºC for 3 hours. After

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cooling the sample, it is filtered with a polypropylene filter and made up to 50 ml with distilled water prior to AAS analysis. Hair analysis is conducted in the same way, but using

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0.1 g of hair sample instead of 0.25 g used in soil. The AAS is operated according to

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2.8.1 Univariate Analysis

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2.8 Data Analysis

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manufacturing specifications.

The analyses were performed using SPSS software version 21. All study variables were tested using the Shapiro–Wilk test to determine whether the data distribution was normal. The Mann–Whitney test was used for ordinal and categorical data to analyse the differences between the exposed and reference groups with non-normal distribution. Statistical analyses were performed using an independent sample t-test to analyse the differences between the exposed and the reference groups with normal distribution. Chi square test was used for categorical data to analyse differences between the exposed and the reference groups and the association between two variables.

Journal Pre-proof 2.8.2 Multivariate Regression Analysis Multivariate regression analysis using STATA 14 was used to illustrate the effects of heavy metals on the children’s cognitive performance, adjusted for age, parental education, environmental tobacco smoke at home, and residential traffic exposure.

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3. RESULTS

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3.1 Participants Characteristic

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From an original sample population of 386 children, 44 children joined the study, 22 from the

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exposed location (Bogor) and 22 from the reference site (Sukatami Village). Table 1 presents

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a summary of socio-demographic conditions extracted from the questionnaire. The age of the participants ranged 6 to 9 years old with a predominance of 7 year olds. There

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were no statistically significant differences between both groups for age or gender (p > 0.05),

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neither for traffic levels at school, school attendance, parental involvement in education, home ownership, number of people living at home, number of family members smoking, and the source of food consumption (Table 1). There were statistically significant differences in parents occupation (p < 0.01), level of parent’s education (p < 0.01), and level of residential traffic exposure (p < 0.01) between exposed and reference groups.

3.2 Level of Heavy Metals in Soil In Depok and Bekasi, soil samples were taken from two e-waste recycling activities: from recyclers who only perform disassembly and mechanical shredding, and recyclers who only

Journal Pre-proof perform open burning and chemical dissolution. Heavy metals (Pb, Cd, Mn, Hg, As) were detected in soil samples from the reference (Sukatani) and exposed sites (Depok, Bekasi) as shown in Table 2. Lead and manganese in soil were significantly higher in the exposed than in the reference site (p < 0.05). Very high concentrations of lead and mercury were detected in the locations where open burning and acid digestion activities were performed (Table S1) compared with those locations were only dismantling took place. Assessment of soil mercury levels was performed only in the exposed site, with concentrations ranging 0.21 to 66 mg/kg.

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No statistically significant differences were observed for cadmium or arsenic (p > 0.10)

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between both sites.

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Lead concentration from soil samples collected at Bogor (exposed site) representing levels

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before and after the encapsulation programme were available from the Ministry of

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Environment. Soil in Bogor was highly polluted with lead associated with activities focused on collecting and processing used lead acid batteries, which had been active from 1978 up

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until 2003. This was demonstrated in the initial assessment in 2010 were soil lead levels

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ranged 14- 82,400 mg/kg. After the encapsulation programme, lead concentrations in soil samples collected and analysed in 2014 significantly reduced to levels ranging 12-811 mg/kg.

3.2 Level of Heavy Metals in Water Concentrations of lead, cadmium and arsenic in water samples were below the limit of detection (Table 3) in both the exposed and reference sites. The mean concentration of manganese in water at the exposed site was slightly higher compared with the reference site, but it was not statistically significantly different (0.05< p < 0.10). Assessment of mercury in water was only performed in the exposed site with a mean level of 0.12 µg/L.

Journal Pre-proof Mercury was only detected in the locations where open burning and acid digestion activities are performed (Table S2), but water samples collected from locations where only dismantling took place were below the detection limit. Similarly, Mn concentrations were higher in water collected near location were smelting and acid digestion took place compared with locations that only dismantled e-waste.

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3.3 Level of Heavy Metals in Hair

Concentrations of lead, manganese and mercury in hair samples collected from children at

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Bogor (exposed site) and at Sukatani Village (reference site) were above the limit of

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detection (Table 4). In contrast, cadmium and arsenic could not be detected in hair samples.

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The mean hair lead and mercury concentrations of the exposed participants were higher

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compared to that of the participants in the reference area, and the differences were statistically significant (p < 0.05). Hair manganese levels of the exposed participants ranged

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from not detected to 0.693 mg/g, whereas Mn concentrations in the reference participants ranged from not detected to 0.11 mg/g, but differences were not statistically significantly different (p > 0.10).

3.4 Level of Heavy Metals in Blood Lead concentrations measured in blood samples collected from 36 children at Bogor (exposed site) in 2010 before the encapsulation programme (secondary data) show blood lead concentrations of 35.5±11.1 µg/dL, with concentrations ranging 4.5 to 379 µg/dL. Average blood lead levels (BLL) exceeded the safe limit defined by the WHO (BLLs limit = 10 μg/dL).

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3.5 Attention, Executive Function and Academic Performance scores Results of the trail making test part A and part B as well as scores from the school reports of the participants are summarised in Table 5. All participants from the reference site (Sukatani Village) could finish the TMT-A and TMT-B tests faster than participants living in the

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exposed (Depok-Bekasi), and these differences were statistically significant (p < 0.01).

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No statistically significant differences were observed in mean scores for school subjects across the participants in the two groups, except for sports and arts (p<0.01), reporting higher

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scores in the exposed group. A similar trend was observed when the full cohort of

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anonymised students was analysed (Nexposed=101, Nreference=86). Sports and arts scores were

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statistically significant lower in the reference group, and no differences were observed for the other academic subjects. However, scores in the full cohort were generally lower than in the

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group that included only the research participants, and these differences were significant for

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language, natural and social sciences in the e-waste exposed site, and for natural sciences in the reference site (Table S3).

3.6 Association between Heavy metal exposure with Children Cognitive Function Figure 1 and Table 6 presents the results of the multiple linear regression models assessing the associations between location, Pb and Mn concentrations in hair and scores of the TMT test and academic performance marks. The analysis could not be completed with regards of Cd and As, as concentrations in hair of children were below the limit of detection in all the samples. It could neither be completed for Hg hair concentrations, as only 6 cases had

Journal Pre-proof concentrations above the limit of detection, but one case did not return the questionnaire information, and another case was missing the academic performance scores. Mn hair concentrations were statistically significant associated with a detriment in attention (TMT-A scores), executive function (TMT-B scores) and academic scores in social sciences. Associations with a p-value < 0.10 were also observed for maths and language scores. Chronic Pb exposure was suggestive (0.05 < p <0.10) of lower scores in the executive No statistically significant associations were observed

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function test of the participants.

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between lead hair concentrations and attention (TMT-A) or any of the academic performance

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scores.

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No interaction (p>0.10) was observed between Mn and Pb exposure with cognitive function

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or academic achievement in the participating children group (Table 6).

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Children living in the area near the e-waste recycling facilities had higher scores (i.e. worse performance) in the executive performance (TMT-B) test, but the difference was not

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statistically significant in the fully adjusted model (0.05
Journal Pre-proof 4. DISCUSSION 4.1 Environmental Heavy Metals from E-waste Recycling Activity The environmental sampling was performed in three areas related with e-waste recycling activities and one reference site in West Java Island (Indonesia), namely Bogor, Depok and

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Bekasi representing high-exposure to e-waste, and Sukatani Village as a reference site.

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4.1.1. Soil

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Recycling of lead acid batteries in Bogor started in 1978 and was the main activity of the

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region until 2003, when it stopped. Based on a soil assessment performed in 2010, the

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informal e-waste recycling activities in Bogor had caused very significant soil contamination in the village area of almost 350 ha (KPBB, 2014), despite the e-waste recycling activity

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ending in 2003 (7 years prior to analysis). The lead concentrations in soil were extremely

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high compared with European limit values (Table 2). The two other exposed locations (Depok and Bekasi) are still actively conducting informal ewaste recycling focused on computer equipment. Concentrations of heavy metals from soil samples collected in Depok-Bekasi also showed heavy metal contamination. High concentrations of Pb, Mn and Hg were measured; with lower concentrations of Cd, and As. The concentrations of Pb and Hg in soil from these sites exceeded 15 and 12 times the European limit values, with levels of Cd also above the EU limit value. On the contrary, concentrations of arsenic in the exposed sites and all heavy metals in the reference site (Sukatani Village) were below the European limit values.

Journal Pre-proof Concentrations of Pb and Mn in the reference site were 40 and 3 times lower (p<0.05) than in the e-waste exposed sites (Depok and Bekasi), which strongly suggest that informal recycling of e-waste related to computer equipment is a source of heavy metals soil contamination. The fact that the concentration of Hg in soil are 15 times the EU limit value suggests that e-waste recycling of computer equipment is also a source of Hg contributing to soil pollution. Comparison of the soil lead concentrations between Depok and Bekasi and Bogor showed

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that Depok and Bekasi had lower concentrations of lead in soil than in Bogor before

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encapsulation. This might be related to factors such as quantity of e-waste recycling, type of e-waste recycling process and location of the recycling area (Grant et al., 2013; Sepulveda et

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al., 2010). The e-waste recycling locations in Depok and Bekasi are more sporadic than in

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Bogor, who was very concentrated spatially. The volume of e-waste recycling activities in

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Bogor was also higher than in Depok and Bekasi. Hence, e-waste pollution generated in Bogor site could accumulate in much higher concentrations in the environment than in Depok

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and Bekasi. The type of e-waste recycling process would also determine which element is

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released in higher amounts into the environment. Whereas recyclers in Bogor focused only on lead acid battery disposal and recycling, e-waste recyclers in Depok and Bekasi focus on recycling computer equipment. Therefore, the recycling process and related pollution sources of heavy metals are different in both cases (Grant et al., 2013; Sepulveda et al., 2010). Nonetheless, the elevated lead concentrations measured in Depok and Bekasi, which are higher than the concentrations measured after the encapsulation programme implementation in Bogor, are suggestive that there is widespread contamination of soil with lead in these two areas where e-waste is currently being recycled. Our results are consistent with prior studies conducted in e-waste recycling centres that show heavy metal contamination of soils. Studies in Guiyu (China) and Delhi (India) have shown

Journal Pre-proof that e-waste recycling activities cause high contamination of heavy metals in the soil, plants, and ground water (Li et al., 2011; Luo et al., 2011; Pradhan and Kumar, 2014). The concentrations of lead in soil in Bogor prior to the encapsulation program and current lead soil concentrations at Depok-Bekasi sites were of similar order of magnitude than those measured in several e-waste recycling areas in Delhi (India) (Awasthi et al., 2016; Bridgen et al., 2005; Labunska et al., 2005; Rajkumar et al., 2012), Phillipines (Fujimori et al., 2012), and China (Xu et al., 2015). In contrast, lower Pb concentrations were measured in a formal

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recycling facility in Bangalore (India) (Ha et al., 2009), in simple household e-waste

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recycling workshops in Vietnam (Oguri et al., 2018) and Wenling (China) (Tang et al., 2010),

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in acid leaching areas in Guiyu (China) (Quan et al, 2015 ; Yekeen et al 2016), in an

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abandoned e-waste recycling site in Longtang in Guangdong province (China) (Wu et al.,

2012).

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2015), and in formal and informal e-waste recycling sites in Phillipines (Fujimori et al.,

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Manganese concentrations analysed from soils collected in Depok-Bekasi site are three to

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five fold higher than those reported in informal e-waste recycling workshops in Vietnam (Oguri et al., 2018), Phillipines (Fujimori et al., 2012), or China (Wu et al., 2015; Xu et al., 2015).

Cadmium concentrations in soil were higher than those measured in an abandoned e-waste recycling site in Longtang in Guangdong province (China) (Wu et al., 2015), but similar than those reported in Wenling (China) (Luo et al., 2011), Phillipines (Fujimori et al., 2012) and Vietnam (Oguri et al., 2018). In contrast, 6 to 20 fold larger concentrations were analysed from soils in an incinerator site in Guandong (China) (Luo et al., 2011) and a large scale recycling plant in Qingyuan (China) (Zheng et al., 2013).

Journal Pre-proof Concentrations of Hg in soil measured in the dismantling areas (Table S1) are similar than those reported in e-waste recycling sites in India from places that have ash from burning and areas where CRT have been handled (Bridgen et al., 2005), from China recyling parts using acid processing and open-burning (Li et al., 2011) and from paddy soil in an e-waste recycling area in China (Fu et al., 2008). Higher concentrations were measured in locations where smelting was conducted, consistent with concentrations measured in soil from slum sites in Bangalore

(Ha et al., 2009), ash from burning site in Mandoli industrial area

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(Bridgen et al., 2005) and topsoil from a household e-waste reclycling workshop in Wenling

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(Tang et al., 2010).

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Arsenic concentrations were l4-fold lower than those measured in an e-waste area in Qinyuan

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(China) and similar than those reported in farmland soil in the same Chinese region (Wang et

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al., 2015).

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Overall, the current results suggest widespread contamination of soils with heavy metals in areas where lead acid batteries (Bogor) and computer and television equipment (Depok-

4.1.2.Water

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Bekasi) e-waste recycling is conducted.

Mn levels in water samples collected from exposed and reference sites were 14 times and 7 times higher than typical Mn concentrations in drinking water, respectively (ATSDR, 2012) and 4.7 and 2.5 times higher the WHO recommended value. Mn concentrations were lower than those analysed from wells and the tap of houses in the vicinity of an informal e-waste recycling area in Longtang (China) (Wang et al., 2015) and lower than concentrations

Journal Pre-proof measured in wells, tap water and ponds near an abandoned e-waste facility in the same Chinese area (Wu et al., 2015). One of the reasons why waters in the e-waste recycling areas might not contaminated with Hg, Pb, Cd and As could be because recyclers in Depok-Bekasi tend not to discharge their waste water into the environment. Instead, the recyclers usually collect the waste water into a container, which is subsequently sold to a third party. In this way, water pollution associated

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with cyanide leaching or mercury amalgamation is prevented.

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4.1.3. Sources of e-waste environmental pollution in the area of study

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The contamination from e-waste recycling can be attributed to metals in the equipment itself,

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to emissions produced during the recycling process, and from the disposal of substances used

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during the recycling. In particular, the sources of lead, mercury and manganese could be from the printed circuit boards and cathode ray tubes used in old PC monitors (Alam and

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Carandang, 2016; Xu et al., 2015). Very high concentrations of lead and mercury were detected in soil samples where open burning and acid digestion are performed, strongly suggesting that these activities are the main source of soil pollution. This is also consistent with mercury being a reagent in chemical digestions to dissolve solid material (Tsydenova and Bengtsson, 2011).

4.2 Children’s exposure to e-waste heavy metal contamination A cross-sectional study involving 22 children from Bogor, a historical e-waste recycling site and 22 children from a reference site not exposed to e-waste, i.e. (Sukatani Village) was

Journal Pre-proof conducted. Children from the exposed site had heavy metals concentrations (lead, manganese, mercury) in hair higher than children from the reference site. In addition, blood samples collected in 2010 from 36 children aged 6 to 8 years in Bogor reported blood lead levels (BLL) greatly exceeding the safe limit defined by the WHO. Our results are consistent with results reported in previous studies in China that involved the biomonitoring of blood samples from children in e-waste recycling areas. These studies showed that children who lived in e-waste recycling areas had BLLs > 10 ng/dL and significantly higher hair

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et al., 2007; Wang et al., 2012; Zheng et al., 2011).

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concentrations of heavy metals (cadmium, lead, copper) than children in reference areas (Huo

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The BLL measured in Bogor in children prior to the encapsulation program were more than 6

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and 7 times higher than recent concentrations reported in Guiyu (6.24 µg/dL) and Haojiang

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(4.75 µg/dL) for children aged 3 to 8 years old (Yang et al., 2013; Zeng et al., 2016), and 2 times higher than older concentrations reported in Guiyu (Huo et al., 2007). Likewise, the

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BLL of workers of an e-waste recycling unit in Southeast China were 3 times lower than

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those reported for children in Bogor (Wang et al., 2012). Hair was selected as a matrix in the present study because it is not an invasive procedure, it is easy to collect and transport and can represent chronic exposure as compared to blood or urine samples (Batista et al., 2009; Liang et al., 2017). Children selected in the exposed group lived near e-waste recycling sites in Bogor, where very high levels of lead contamination had been observed. There are no reference values for human hair from Indonesian children population prior to this research. Hence, for benchmarking purposes, the concentrations in this study were compared with concentrations reported by Miekeley et al (1998) who analysed 1091 hair samples in Rio de Janeiro exposed to traffic and industrial pollution (Miekeley et al., 1998).

Journal Pre-proof The mean hair concentrations of lead and manganese in both the exposed and reference participants exceeded the concentrations reported by Miekeley et al (1998). Lead concentrations in the present study for both children living near the e-waste facilities in Depok-Bekasi (exposed site) and Sukatani village (reference site) are considerably higher than those measured in hair in Longtang town (Qingy-uan City) for e-waste workers, residents in the e-waste recycling area and areas away from e-waste recycling (Zheng et al.,

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2011). Similarly, lead and Mn concentrations measured in another e-waste area in China

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(Taizhou) were 2 and 20 , respectively fold lower than those reported in the present study

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(Wang et al., 2009).

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In contrast, Cd and arsenic were detected in samples collected from participants in Longtang town (Zheng et al., 2011), the e-waste area of Taizhou, and the industrial areas of Ningbo and

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Shaoxing (Wang et al., 2009), but were undetected in our samples.

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Heavy metal concentration in hair is associated with age, sex, anatomic location, hair colour,

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geographic origin, food intake habits and exposure (Miekeley et al., 1998; Zheng et al., 2011). These factors should be taken into consideration when comparing hair concentrations from different populations as a possible source of variability observed in the results. The more likely route of exposure for children to heavy metals from e-waste recycling activity in the exposed sites is through absorption from the soil during dermal contact and ingestion (e.g. pica behaviour), from drinking contaminated water, and from the consumption of vegetables grown and meat reared locally. Another consideration is that a high concentration of heavy metals in the environment does not directly translate into a high dose of heavy metals in the body. The mechanism of human exposure to environmental contaminations from e-waste recycling activities is a very

Journal Pre-proof complex process affected by several factors such as the exposure time, synergistic or additive effects of other chemicals present in the environment with concurrent exposures, parental involving in recycling (i.e. children in the presence of their parents, whilst parents are recycling e-waste), children daily activities, metabolic rate of xenobiotics, or individual susceptibilities (Grant et al., 2013; Sepulveda et al., 2010).

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4.3 Heavy Metals Exposure and Children Cognitive Function

There is much research on the health consequences of heavy metals exposure in children,

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which shows that the central nervous system is the main part of the body that experiences the

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adverse effects of toxic metals (Boucher et al., 2012; Calderon et al., 2001; Mason et al.,

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2014; Morgan et al., 2001; Rodrigues et al., 2016; Sun et al., 2015; Tchounwou et al., 2012; Wang et al., 2012; Xu et al., 2015). Therefore, heavy metals generated from e-waste

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recycling activities have potential health consequences on cognitive development in children,

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e.g. decreased IQ, impaired cognitive and neuropsychological function, hyperactivity, attention deficits and neurodevelopmental abnormalities (Alam and Carandang, 2016; Burgos et al., 2017; Xu et al., 2015). However, prior studies involving children in e-waste recycling areas have reported inconclusive results in terms of the relation and the effect of heavy metals contamination on cognitive function in children. On the one hand, Xu et al. (2015) showed that children from e-waste recycling areas had higher BLLs and lower IQ levels compared to children from reference areas. On the other hand, Wang et al. (2012) reported no significant differences in IQ levels among children from e-waste recycling areas and reference areas. The current study shows that children exposed to manganese had lower scores for attention, executive function and social science academic scores, consistent with Xu et al (2015) results.

Journal Pre-proof The contrast between our results with those from Wang et al. (2012) could be attributed to several factors. First, the interaction of the subjects with the contaminated e-waste recycling site will characterise the human exposure (Sepulveda et al., 2010). Second, cognitive functions in children are affected by non-environmental factors such as heredity, socioeconomic status, food source and nutrition levels, lifestyle factors, social community networks and parents’ education level. Third, differences in study design, such as sample size, sampling and lab analysis and cognitive measurement tools, could be also contributory

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factors to the contrasting results in the scarce available literature.

Counterintuitively, a small effect was observed for higher academic scores in Sports and Art

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in the exposed site. Whereas the TMT test was consistently administered to both groups in a

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systematic way, the difference in academic performance score between both groups might

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have been related to different evaluation criteria across different schools. This would introduce potential for bias on the results of the academic performance, but would not affect

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the results of the TMT test. However, it could be also that the results are confounded by other

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covariates that are not appropriately captured in the information collected from parents and children in the questionnaire (e.g. local programmes promoting arts and sport, accessibility to sporting fields, etc) and hence cannot be adjusted for in the current multivariate model analysis.

Children living near the e-waste facility enrolled in the study have higher scores in language, natural and social sciences than children in the full cohort. Similarly, children in the reference site participating in the study have higher scores in natural science than the full cohort. However, no differences between the participants and the full cohort were observed for sports and arts (Table S3). Overall, this might indicate some selection bias, where more cognitively proficient children have participated in the study. This might suggest that the effects of heavy

Journal Pre-proof metal exposure on attention, executive function and academic performance could be potentially larger in the full cohort. TMT is a well-known instrument for screening neurological disease or impairment (Reitan, 2004). Performance on the TMT is indicated to have a strong association with overall intelligence and is a specific, sensitive marker of neurological impairment (Bowie and Harvey, 2000), executive function (Ardila et al., 2000), attention (Espy and Cwik, 2004;

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Gorenstein et al., 1989; Moffitt and Silva, 1988; Perugini et al., 2000; Shue and Douglas,

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1992), reading disabilities (Narhi et al., 1997).

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The TMT results suggest that children from the exposed sites, who are chronically exposed to

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higher heavy metals concentrations, tend to have poorer cognitive performance in domains of attention and executive function. The strongest associations were observed between chronic

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manganese exposure and executive function. Our results are consistent with findings reported

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for children living near a ferro-manganese alloy plant in Brazil, for whom Mn exposure was associated with lower IQ (Carvalho et al., 2014; Menezes et al., 2011) and detrimental

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performance in tests of executive function of inhibition responses, strategic visual formation and verbal working memory (Carvalho et al., 2014). The suggested association between chronic Mn exposures and maths (0.05
Journal Pre-proof Roels et al., 2012; Schneider et al., 2009; Schneider et al., 2006). Similar effects have been reported in human occupational studies (Chang et al., 2010; Chang et al., 2009; Dydak et al., 2011; Kim et al., 2011; Stepens et al., 2010; Wasserman et al., 2011). Overall, there is mechanistic evidence suggesting that Mn exposure could affect brain structures related with the working memory network, and thus affect working memory performance and executive function (Guilarte, 2013; Wasserman et al., 2006).

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The results of the present study are also consistent with lowered IQ in children exposed to a

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waste disposal site containing heavy metals from industrial origin such as lead and arsenic in Chile (Burgos et al., 2017). Lead, cadmium, and mercury have been also associated with

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lowered IQ and impaired cognitive function, with every 10 ug/dL of blood lead level increase

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linked with a reduction of IQ level between 2 and 3 points in children (Xu et al., 2015).

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reduction in IQ (Sun et al., 2015).

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Likewise, Song et al (2015) also reported associations between blood lead levels and a

Heavy metal pollution from e-waste recycling activities is emerging as a new public health

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concern, especially in children. Their vulnerability steams from the fact that their physiological, physical, and nervous systems are still developing (Grant et al., 2013). Moreover, high-risk behaviour in children, such as hand-to-mouth activities, increases their likelihood of being exposed to contaminated soil or water as compared to adults. The number of available samples collected for environmental monitoring of soil and water, human biomonitoring (hair) and cognitive testing was a limitation of this study. However, power calculations show that the number was sufficient to detect differences in the environmental and human matrices considered, whereas it was more limited to detect effects in cognition. A larger sample size would be advisable to assess cognitive effects related to metal exposure from informal e-waste recycling. In addition, no data on nutrition,

Journal Pre-proof undernutrition, BMI or waist circumference was available from our subjects, so the effect of nutrition on our results could not be controlled for. Other environmental (e.g. noise, sleep duration and quality) and socioeconomic factors (e.g. level of deprivation) might also affect the cognitive and academic scores. However, such information was not available, and hence could not be controlled for. In spite of this, the results of the present study add to the limited body of evidence and suggests a detrimental association between chronic exposure to metals from e-waste recycling areas and cognitive performance in children in the domains of

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attention (TMT-A), executive function (TMT-B), and academic scores, consistent with the

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results on IQ scores previously reported by Xu et al (2015).

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5. CONCLUSION

The overall results of the current study strongly suggest that informal e-waste activities are

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contributing to local contamination of soils with heavy metals, and are an important source of

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exposure to heavy metals to children living in the vicinity of these facilities with putative impacts on their attention, executive function and academic performance. Effective e-waste management regulation and remediation actions are required to reduce chronic exposure to metals from informal e-waste recycling in children and adults alike and to avoid widespread contamination of heavy metals to the environment. 6. ACKNOLEDGEMENTS The present work was financially supported by the Indonesia Endowment Fund for Education (LPDP). Dr JM Delgado-Saborit is a recipient of funds from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 750531. Authors thank Dr Maria Thompson on her assistance during chemical

Journal Pre-proof analysis. The authors would like to express their thanks to all of the subjects who agreed to participate in this study.

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Table 1. Socio demographic condition of participants. Number (%)

All

Number (%) Age 6 years old 7 years old 8 years old 9 years old

41 (100)

19 (46.34)

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-p

1 (2.4) 24 (58.5) 14 (34.1) 2 (4.9)

12 (63.2) 7 (31.8)

re

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Gender Boys Girls

19 (46.3) 22 (53.7)

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Parents occupation Employee in private company Employee in public government Entrepreneur Unemployment Others

20 (48.8) 1 (2.4) 1 (2.4) 4 (9.8) 15 (36.6)

Reference (Sukatani) p value 22 (53.66)

of

Socio demographic variables

Exposed (DepokBekasi)

6 (31.6) 13 (68.4) 4 (21.1) 1 (5.3) 3 (15.8) 11 (57.9)

House ownership Permanent house Rent house Family member smoke

9 (22) 5 (12.2) 26 (63.4) 1 (2.4))

9 (47.4) 3 (15.8) 7 (36.8)

41 (100)

19 (100)

31 (68.9) 10 (24.4)

16 (84.2) 3 (15.8)

b

0.078

a

0.01

*a

0.000

**b

0.205

a

0.524

a

1 (4.5) 12 (54.5) 7 (31.8) 2 (9.1) 13 (59.1) 9 (40.9) 16 (72.7) 1 (4.5) 1 (4.5) 4 (18.2)

Level of parent’s education Unfinished any school

Elementary school Secondary school Senior high school Bachelor or higher Medical insurance beside national insurance Yes No

0.776

2 (9.1) 19 (86.4) 1 (4.5)

22 (100)

15 (68.2) 7 (31.8)

Journal Pre-proof 28 (68.3) 13 (31.7) 22 (53.7) 16 (39.0)

11 (50) 5 (22.7)

Busy most times Busy all the time School traffic Quiet, only residential traffic Busy sometimes Busy most times Busy all the time

2 (4.9) 1 (2.4)

2 (9.1) 1 (4.5)

41 (100)

19 (100)

a b

Chi-square test Mann-Whitney U test

22 (100)

re lP

na

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Parents help for study at home Yes No

38 (92.7) 2 (4.9) 1 (2.4)

16 (84.2) 2 (10.3) 1 (5.3)

22 (100)

41 (100)

19 (100)

22 (100)

38 (92.7) 3 (7.3)

16 (84.2) 3 (15.8)

* **

**a

0.466

a

0.463

a

0.154

a

0.091

a

22 (100)

19 (100)

41 (100)

0.000 11 (50) 11 (50)

6 (27.3) 16 (72.7)

3 (15.8) 16 (84.2)

-p

9 (22) 32 (78)

Source of food consumption Sourced mainly locally Sourced from local outside Mainly comes from far away source

After school courses attendance yes No

14 (63.6) 8 (36.4)

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Number of people living in house 1 2 +3

School attendance Always - 5 days 3 days in a week 1-2 days in a week

14 (73.7) 5 (26.3)

of

Yes No Residential traffic Quiet, only residential traffic Busy sometimes

22 (100)

0.01 < p value ≤ 0.05 p value ≤ 0.001

Journal Pre-proof Table 2. Summary concentrations of heavy metals in soil at exposed and reference sites

Parameters Pb (mg/Kg)

Mean ± SD

Min-Max

Mean (SD)

Min-Max

3653± 3355

93 - 7662

93.2± 15.8

79 - 115

0.032

Directive 86/278/EEC Limit Values 300

Mn (mg/Kg)

2718± 1111

2088 - 4693

842± 480

269 - 1336

0.008

N.A b

10 (100%)

Cd (mg/Kg)

3.4± 0.9

3-5

2.8± 4.45

2-3

0.421

3

10 (100%)

Hg (µg/Kg)

15.2± 28.5

0.21 - 66

N.A

N.A

5 (50%)

22.2± 24.4

N.D – 59

9.2± 20.6

ND - 46

ro

1 50

4 (40%)

WHO desirable/tolerable rangeb

Number of samples > LOD 0

Exposed Depok-Bekasi (n = 5)

As (mg/Kg)

c

Reference (n = 5)

p e

p valuea

N.A

0.421

f o

Number of samples > LOD 10 (100%)

a) Mann – Whitney test; b) N.A Concentration not available; c) N.D Below Detection Limit

r P

Table 3. Summary concentrations of heavy metals in water at exposed and reference sites Exposed Depok-Bekasi (n = 5) Parameters Pb (mg/L)

Mean ± SD

Min-Max

N.Dc

N.D

Mn (mg/L)

1.43± 0.64

0.76 - 2.19

Cd (mg/L)

N.D

N.D

l a

Reference (n = 5)

rn

p valuea

Mean (SD)

Min-Max

N.D

N.D

N.D

J

0.81± 0.11

0.73 - 0.98

0.095

0.4

10

N.D

N.D

N.D

0.003

0

N.A

N.A

6

3

N.D

N.D

u o

Hg (µg/L)

0.12± 0.11

ND – 0.27

N.A

As (mg/L)

N.D

N.D

N.D

d

0

a) Mann – Whitney test; b) N.A WHO, 2011, c) N.D Below Detection Limit, d) Concentration not available

Page 1 of 42

Journal Pre-proof Table 4. Summary concentrations of heavy metals in hair from exposed and reference participants

Parameters Pb (mg/g)

Mean ± SD

Min-Max

Mean (SD)

Min-Max

0.155 ± 0.187

0.160 - 0.841

0.0729 ± 0.08

0.005 - 0.255

0.042

<0.0093

Number of samples > LOD 43

Mn (mg/g)

0.130 ± 0.212

ND - 0.693

0.018 ± 0.045

ND - 0.11

0.132

<0.0012

17

Cd (mg/g)

N.D

N.D

N.D

N.D

N.D

Hg (mg/g)

0.008 ± 0.0042

ND – 0.010

0.002 ± 0.0011

ND – 0.003

N.D

N.D

Exposed Depok-Bekasi (n = 22)

As (mg/g) N.D N.D *Independent-Samples T-test, **Miekeley et al. 1998

Reference (n = 22)

l a

p value*

Reference values for human hair**

p e

ro

f o

0.021 N.D

0 <0.0023

6 0

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Journal Pre-proof Table 5. Summary scores of attention and academic performance marks Research Participants Exposed (n = 22)

Full school cohort

Reference (n = 22)

Exposed (n = 101) a

Mean (SD)

Min-Max

Mean (SD)

Min-Max

p valuea

N/A

N/A

f o

N/A

N/A

N/A

N/A 68 - 90 71 - 91

N/A 74.34± 16.9 77.5 ± 16.9

N/A 26 - 100 20 - 100

N/A 0.258 0.065

77.6± 6.5

60 - 90

78.0 ± 16.0

20 - 100

0.813

79.6± 6.4

69 – 95

76.6±16.1

26 - 100

0.108

81.5± 2.4 76.4± 6.5

76 - 85 68 - 90

75.6 ± 3.1 75.4± 3.9

70 - 80 60 - 83

< 0.00001 < 0.00001

Mean (SD)

Min-Max

Mean (SD)

Min-Max

p value

TMT A

60.96± 26.09

19 - 117

38.64± 15.12

15 - 60

TMT B Math Language Natural Sciences Social Sciences Sports Arts

62.23± 30.75 79.0± 5.72 83.8± 4.46

19 - 120 69 - 90 77 - 91

44.50± 15.82 78.4± 14.6 83.4 ± 13.1

12 - 70 38 - 100 54 - 100

0.001 < 0.00001 0.99 0.731

N/A 76.4± 6.5 81.0± 4.6

80.4± 6.45

70 - 90

83.8 ± 11.3

62 - 100

0.243

82.6± 6.8

75 – 95

80.9±14.5

38 - 100

81.8± 2.46 80.0± 0.93

80 - 85 77 –835

75.3 ± 3.5 75.7±2.6

70 - 80 70-80

r P

a) Independent-Samples T-test

l a

0.932

o r p

e

<0 .00001 <0 .00001

Reference (n = 86)

n r u

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Page 3 of 42

Journal Pre-proof Table 6. Change (coefficient and 95% confidence interval, CI) in attention scores (TMT-A, seconds), executive function scores (TMT-B, seconds) and academic performance (%) for a unit (µg/g) increase in Pb and Mn hair concentration (mg/g) and for location. Location represents subjects in exposed area vs reference area (basis). Model adjusted for age, gender, parental education, environmental tobacco smoke at home, and residential traffic exposure.

Cognitive Scores

β (95% CI)

p-value

β (95% CI)

p-value

β (95% CI)

p-value

Mn x Pb Hair interaction p-value

TMT_A

9.5 (-10 , 29)

0.339

2.5 (-55, 60)

0.930

66 (0.09, 132)

0.050*

0.977

TMT_B

20 (-2.5 , 42)

0.079

#

-3.4 (-13 , 6.6)

0.494

0.033* 0.051#

0.645

Math

-22 (-46, 4)

0.086

#

0.887

-25 (-70, 20)

0.228

0.815

-29 (-54, -4.7) 12 (-10, 35)

0.027* 0.218

0.907

0.223 0.656

3.9 (-11, 19)

0.560

0.592

Location (Exposed vs Reference, n = 41)

Pb_Hair (n = 39)

Mn_Hair (n = 14)

54 (-3.8, 114)

0.066

-0.4(-30, 28)

0.977

p e

Language

-2.6 (-11, 6.2)

0.551

-2.2 (-28, 23)

0.860

Science

-3.4 (-12, 5.5)

0.440

-0.1 (-26, 25)

0.995

Social

-4.09 (-15, 6.5)

0.437

-11 (-41, 20)

Sports

7.6 (4.7, 10)

0.000*

6.9 (-4.5, 18)

Art

4.6 (2.9, 6.5)

0.000*

1.6 (-5.7, 8.9)

l a

* p-value < 0.05: # 0.05 < p-value < 0.10

r P

0.471

f o

ro

#

105 (11.5, 198) -32 (-64, 0.11)

0.558

0.537

n r u

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Page 4 of 42

Journal Pre-proof

Mn in Hair 250

*

200

Coefficient

150

*

100 50

#

#

Maths

Language

*

0 -50 -100 TMT_B

Science

Pb in Hair 200

50

-50 -100

100 50 0

Maths

Language

Science

Social

Arts

*

*

Sports

Arts

Location

Jo ur

Coefficient

150

TMT_B

na

TMT_A

lP

0

200

Sports

re

Coefficient

100

250

Arts

-p

#

150

Sports

ro

250

Social

of

TMT_A

#

-50 -100 TMT_A

TMT_B

Maths

Language

Science

Social

Figure 1. Change (coefficient and 95% confidence interval, CI) in attention scores (TMT-A, seconds), executive function scores (TMT-B, seconds) and academic performance (%) for a unit (µg/g) increase in Mn (top) and Pb hair concentration (mg/g) and for location (bottom). Location represents subjects in exposed area vs reference area (basis). Model adjusted for age, parental education, environmental tobacco smoke at home, and residential traffic exposure. * represents p-value< 0.05, # represents p-value<0.10

Page 1 of 42

Journal Pre-proof

Chronic Exposure to Heavy Metals from Informal E-waste Recycling Plants and Children’s Attention, Executive Function and Academic Performance

ro

of

Declaration of Interests

School of Geography, Earth and Environmental Sciences, University of Birmingham,

lP

1

re

-p

Fitria Nurbaidah Soetrisno1,2 and Juana Maria Delgado-Saborit1,3*

na

Edgbaston, Birmingham, B15 2TT, UK.

BP Berau Ltd, Tangguh LNG, West Papua, Indonesia

3

ISGlobal Barcelona Institute for Global Health, Barcelona Biomedical Research Park,

Barcelona, Spain

Jo ur

2

* Corresponding author [email protected]

Competing interests

There are no competing interests.

Page 2 of 42

Journal Pre-proof Highlights

of ro -p re lP na



Residential soil polluted with heavy metals near informal e-waste recycling sites. Residential drinking water in the areas of study has elevated Mn concentrations Children living near informal e-waste recycling sites have 2, 4 and 7 times higher concentrations of Pb, Hg and Mn in their hair than those living in reference sites. E-waste Mn pollution impacts children attention, executive function and academic performance

Jo ur

  

Page 3 of 42