Table 1
Main concept and alternative terms of PICO search strategy.
| Main concept | Alternative terms | |
|---|---|---|
| Population | Thai | Thailand |
| Child?? | Kid?, Pregnan*, “in utero”, newborn, neonat*, infan*, toddler, preschool, young, teenage*, pupils, student? | |
| Exposure | Environment | Chemical, Pollution?, “Air quality”, PM, “particulate matter”, particle, VOCs, “volatile organic compounds”, benzene, “polyaromatic hydrocarbon”, PAH, allergen, mold, mould, fungi, cockroach, smok???, metal?, arsenic, lead, cadmium, mercury, plasticizer, POPs, “persistent organic pollutants”, “Polychlorinated biphenyl”, Dioxins, Furans, “Polybrominated compounds”, “Polyfluorinated compounds”, “Halogenated hydrocarbons”, pesticide?, herbicide?, fungicide?, organophosphate, organochlorine, carbamate, glyphosate, chlorpyrifos, Dichlorodiphenyltrichloroethane, phthalate, BPA, “bisphenol A”, Industr*, agricultur*, waste, Hazard???, Toxic |
| Health Outcomes | Perinatal | “Birth weight”, prematurity, “birth defects” |
| Growth | Weight, height, BMI, “body mass index”, obes* | |
| “Neurodevelopmental disorders” | Development*, autistic?, autism, attention, hyperactive, ADHD, learning, cognitive, intelligen*, IQ, aggressive, pervasi*, Behavior*, Behaviour* | |
| Allergy | atop??, inflammation, sensitization, IgE, hypersensitivity, “food allergy”, “atopic dermatitis”, “allergic dermatitis”, wheezing bronchitis, bronchiolitis, hyperresponsive????, hyper-responsive????, asthma, “allergic rhinitis”, “perennial rhinitis”, hayfever, “hay fever” | |
| Respiratory | “pulmonary function”, “Lung function”, “respiratory symptom?”, “respiratory infection” | |
| Endocrinology | Thyroid, diabetes, DM, metabolic? | |
| “Cardiovascular diseases” | Coronary, hypertension, “blood pressure”, Atheroscler* | |
| “Kidney diseases” | Renal, “kidney function”, “renal function”, creatinine | |
| “Liver diseases” | “Liver diseases”, cirrhosis, jaundice | |
| Poisoning | “acute poisoning”, “chronic poisoning” | |
| Cancer | Malignancy, Leukaemia, Leukemia, Lymphoma, Carcinoma | |
| Subclinical | “DNA damage”, epigenetic, miRNA, Histone, Methylation, Adduct |
[i] Population (P) = Thai children, Indicator (I) = Exposure (environmental chemical, metal, allergen, or air pollution), Comparison (C) = no comparison, Outcome (O) = Health outcomes. Both * and/or ? were applied to widen the search result by including various word endings and spellings (* = unlimited character number, ? = limited character number (to number of ?s)).
Figure 1
The article selection process.
Table 2
Epidemiological studies investigating pesticide exposure in Thai children.
| Pesticides | Authors (published year) | Type of epidemiological study | Population (age) | Sample size | Exposure assessment | Level measured | Comments |
|---|---|---|---|---|---|---|---|
| Exposure studies | |||||||
| Chlorpyrifos | Panuwat et al. (2009) [15] | Cross-sectional | Student (12–13 years) | 207 | Urinary 18 specific pesticide metabolites | 14 metabolites of chlorpyrifos, permethrin, pyrethroids were found |
|
| Glyphosate | Kongtip et al. (2017) [18] | Birth cohort | in utero 19–35 years of 28 weeks pregnancy and neonate | 82 pairs |
| Median glyphosate levels in the pregnant women’s serum were 17.5 ng/mL (0.2–189.1 ng/mL), significantly higher than those in umbilical cord serum (median 0.2 (0.2–94.9 ng/mL) | Factors for glyphosate exposure were working in agriculture or living in families that work in agriculture. |
| Organophosphate | Hanchenlaksh et al. (2011) [20] | Cross-sectional | Preschool and school child (2–12 years) | 16 Part of larger study that included adults |
| Metabolites
| Exposure for farmers’ families seems to be through transfer from the farmer to family members or contamination of the home environment |
| Liu et al. (2015) [14] | Cross-sectional | Preschool and school child (2–12 years) | 72 Part of larger study that included adults |
| Metabolites
| Children in Thai farming families can be exposed to pesticides indirectly through transfer of pesticide residues from the farmer to the spouse and subsequently to the child, or through contamination of the home environment. | |
| Panuwat et al. (2009) [16] | Cross-sectional | School child (10–15 years) | 306 Part of larger study that included adults | Urinary parathion metabolites (PNP, DMP, DMAP, DMTP) | Metabolites
| 25–60% of the PNP (metabolites of #parathion) detected among farmers and children | |
| Petchuay et al. (2006) [13] | Case-controlled | Preschool (2–5 years) | 54 (farm: 37, rubber plantation: 17) | Urinary DAP metabolites (DMP, DEP, DMTP, DETP, and DEDTP) | Metabolites | Farm children tended to have significantly higher DAP concentrations compared with children living outside the farming area, particularly during the dry vegetable-planting season. | |
| Rohitrattana et al. (2014) [19] | Cohort | in utero (20–35 years of 28 weeks pregnancy until 2 months post-partum) | 86, 87, 51 (28 weeks pregnancy, delivery, 2 months post- partum) |
| Metabolites: 50th percentile of DMP, DEP, DETP, DEDTP, total DEP, DAPs (nmol/L: nmole/g creatinine)
| The levels of urinary OP metabolites at 28 weeks, delivery, and 2 months postpartum fluctuated depending on their pesticide exposures both at home and agricultural activities. | |
| Paraquat | Kongtip et al. (2017) [18] | Birth cohort | in utero 19–35 years of 28 weeks pregnancy and neonate | 82 pairs |
| Median paraquat levels in pregnant women’s serum were <0.4 ng/mL (0.2–58.3 ng/mL) and similar to those in umbilical cord serum (median 0.2 (0.2–47.6 ng/mL) | Factors for paraquat exposure were work in agriculture or live in families that work in agriculture. |
| Permethrin Pyrethroids | Panuwat et al. (2009) [15] | Cross-sectional | Student (12–13 years) | 207 | Urinary 18 specific pesticide metabolites | 14 metabolites of chlorpyrifos, permethrin, pyrethroids were found. |
|
| Pyrethroid | Rohitrattana et al. (2014) [17] | Case-controlled | School child (6–8 years) | 53 (Case 24, controlled 29) |
| Median urinary PYR metabolites were not significantly between both group, neither wet nor dry seasons. | Positive correlation (r = 0.40–0.46) between PYR residues collected from the hands and urinary PYR metabolites PYR use in rice farms and households may be significant sources of PYR exposure among children living. |
| Exposure and health outcome assessment studies | |||||||
| $Organochlorine | Asawasinsopon et al. (2006) [8] | Cross-sectional | in utero | 39 pairs | Maternal and cord blood serum DDT metabolites | p,p’-DDE) was the highest level in both maternal and cord serum (geometric mean of 1,191 ng/g lipids in maternal serum and 742 ng/g lipids in cord serum) | Negative association between cord serum total T4 levels and DDT metabolites |
| Organophosphate | Fielder et al. (2015) [11] | Case-controlled | School child (6–8 years) | 53 (case 24, controlled 29) | Urinary metabolites of OPs (6 DAP%, TCPy) | Rice farm had significantly higher total DAP and TCPy | No significant adverse neurobehavioral effects were observed between participant groups during either the high or low pesticide use season |
| Kongtip et al. (2017) [12] | Birth cohort | in utero | 50 pairs (20–35 years of pregnant women and 5 months old of their child) | Maternal urinary DAP metabolites: DMP, DEP, DETP, DEDTP | Median adjusted urinary DAP levels of 28 weeks gestational age of pregnant women were 36.83, 15.15, 0.07, and 0.15 nmol/L for DMP, DEP, DETP, and DEDTP, respectively. | Higher total DEP and total DAP metabolite level from the 28 weeks GA of pregnant women were significantly associated with reduced cognitive and motor composite scores on the Bayley-III at five months old of their child. | |
| Pyrethroid | Fielder et al. (2015) [11] | Case-controlled | School child (6–8 years) | 53 (case 24, controlled 29) | Urinary metabolites of PYR (DCCA) | Rice farm had significantly higher DCCA | No significant adverse neurobehavioral effects were observed between participant groups during either the high or low pesticide use season |
[i] #Parathion was banned in 2007; data was collected in 2006.
$Organochlorine was banned in early 1983; data was collected in 2003–2004.
%Common 6 DAP metabolites: DMP, DEP, DMT, DMDTP, DETP, and DEDTP.
Abbreviations: DAP: dialkyl phosphate, DCCA: cis/trans-2,2-(dichloro)-2-dimethylvinylcyclopropane carboxylic acid, DDT: dichlrodiphenyltrichloroethane, DEDTP: diethyl dithiophosphate, DEP: diethyl phosphate, DETP: diethyl thiophosphate, DMAP: dimethylalkylphosphate, DMDTP: dimethyldithiophosphate, DMP: dimethylphosphate, DMTP: dimethylthiophosphate, OPs: organophosphates, PNP: paranitrophenol, p,p’, DDE: 1,1-dichloro-2,2-di(4-chlorophenyl)ethylene, PYR: pyrethroid, TCPy: 3,5,6-trichloropyridinol, 3-PBA: 3-phenoxybenzoic acid.
Table 3
Epidemiological studies investigating heavy metal exposure in Thai children.
| Heavy metals | Authors (published year) | Type of epidemiological study | Population (age)* | Sample size | Exposure assessment | Level measured | Comments |
|---|---|---|---|---|---|---|---|
| Exposure studies | |||||||
| Cadmium | Chaiwonga et al. (2013) [39] | Cross-sectional | School child (9–12 years and 13–15 years) | 748 |
|
| The likely exposure sources was dietary (approximately 20–50% of them consume home-grown rice) |
| Lead | Chomchai et al. (2005) [30] | Case-controlled | Infant and preschool child (6 months–4 years) | 296 (exposure = 114, controlled 149) |
|
|
|
| Maharachpong et al. (2006) [31] | Cross-sectional | Preschool child to adolescent (4–14 years) | 319 (from 242 households) |
|
| The lead content level in soil was exponentialy declined with distance from boat-repair yards (point source contamination) | |
| Mitchell et al. (2012) [43] | Cross-sectional | Child (6 months–14 years) | 642 |
| 5.1% had EBLL (cBLL ≥ 10 μg/dL) with highest prevalence in children younger than 2 years. | The risk factors of EBLL were anemia (Hb < 10 g/dL), exposure to car batteries, and taking traditional medicines | |
| Neesanan et al. (2011) [21] | Retrospective study | Preschool child (3–7 years) | 213 |
|
| Risk factors of EBLL were male gender and and source of drinking water from either tap or canal | |
| Ruangkanchanasetr et al. (1999) [32] | Cross-sectional | Infant to school child | 511 (infant 84, preschool child 60, school child 377) |
|
|
| |
| Swaddiwudhipong et al. (2013) [22] | Cross-sectional | Child (1–14 years) | 254 |
|
|
| |
| Swaddiwudhipong et al. (2014) [44] | Cross-sectional | Child (1–14 years) | 695 |
|
| The metal pots were safe for cooking rice but might be unsafe for acidic food preparation. | |
| Thaweboon et al. (2005) [24] | Cross-sectional | Preschool child (3–6 years) | 8 Part of larger study that included adult | Salivary and blood lead |
| Saliva was not correlated with BLL (γ =–0.025). | |
| Untimanon et al. (2011) [42] | Case-controlled | Child (<15 years) | 24 (case 22, controlled 2) Part of larger study that included adult |
| Mean lead contamination in case vs. controlled
|
| |
| Exposure and health outcome assessment studies | |||||||
| Arsenic | Hinhumpatc et al. (2013) [35] | Case-controlled (follow-up study) | School child (5–8 years) | 60 (case 40, controlled 20) |
| The DNA damage (salivary and urinary 8-OHdG) was increase but DNA repair capacity (hOGG1) was decreased in exposed group, |
|
| Intarasunanont et al. (2012) [45] | Case-controlled | In utero and in vitro (newborn) | 71 (case 55, controlled 16) |
| Arsenic levels in case vs. controlled were
| In utero arsenic exposure affects DNA methylation, particularly at the p53 promoter region, which may be linked to the mechanism of arsenic carcinogenesis. | |
| Phookphan et al. (2017) [36] | Case-controlled (follow-up study) | School child (6–9 years) | 81 (case 40, controlled 41) |
| Arsenic in toenails in exposed and unexposed children was 8.08 ± 1.47 μg/g and 0.76 ± 0.14 μg/g, respectively. | A follow-up study from arsenic exposure in utero with continued exposure throughout early life [45] | |
| Vitayavirasak et al. (2005) [34] | Case-controlled | School child (10 years) | 130 (high exposure area 50, low exposure area 50, controlled 30) |
|
|
| |
| Cadmium | Swaddiwudhipong et al. (2015) [37] | Case-controlled | School child (7–12 years) | 594 (case 301, controlled 293) |
| 19% of children in this study had urinary cadmium ≥ 1 μg/g creatinine, which was higher in girls and in those consuming rice grown in cadmium-contaminated areas. | Significantly higher geometric mean levels of urinary excretion of β2-MG and calcium (early renal effects) were found among children in contaminated areas compared to those in comparison (non-contaminated) areas |
| Lead | Pusapukdepob et al. (2007) [29] | Cross-sectional | Child (0–15 years) | 126 (case 89, controlled 37) Part of larger study that included adult |
|
|
|
| Youravong et al. (2006) [26] | Cross-sectional | Preschool to school child (6–10 years) | 292 | Blood lead | Children with BLL ≥ 10 μg/dL was 21%. | Lead exposure was associated with carries in deciduous teeth but not in permanent teeth (cariogenicity) in dose-response relationship. | |
| Youravong et al. (2013) [28] | Cross-sectional | Preschool to school child (6–10 years) (same population as [26]) | 120 | Salivary and blood lead | The salivary lead level low correlated with blood lead level (R2 = 0.18, p = 0.05) | There was no association between salivary lead and dental caries. | |
| Mercury | Umbangtalad et al. (2007) [46] | Case-controlled | School child (elementary student) | 59 Part of larger study included adult |
|
| The hazard quotient (HQ) based on the inorganic mercury exposure was < 1 (no risk). |
[i] *Age group reference: newborn: birth–1 month, infant: 1–23 months, preschool child: 2–5 years, school child: 6–12 years, adolescent: 13–18 years, child: birth–18 years.
Abbreviations: BLL: blood lead level, cBLL: DMA: dimethylarsinic acid, EBLL: elevated blood lead level, Hb: haemoglobin, iAs: inorganic arsenic, MG: macroglobulin, MMA: monomethylarsonic acid.
Table 4
Epidemiological studies investigating air pollution exposure in Thai children.
| Air pollution | Authors (published year) | Type of epidemiological study | Population (age)* | Sample size | Exposure assessment | Level measured | Comments |
|---|---|---|---|---|---|---|---|
| Exposure studies | |||||||
| Aeroallergens | Lee et al. (2006) [61] | Cross-sectional | Dust from young child’s home | 50 | Endotoxin level and dust mite in dust from mattress of young child’s home |
|
|
| Pumhirun et al. (1997) [60] | Cross-sectional | School child and adolescent (10–18 years) | Total 100 Part of larger study that included adult | Skin prick test (30 aeroallergens) | Allergic rhinitis patients were mostly sensitised to house dust mite (D. farina, D. pteronyssinus) and American cockroach. | 85% of patients sensitive to house dust mite were positive to both D. pteronyssinus and D. farina (substantial cross-reactivity) | |
| Benzene | Ruchirawat et al. (2005) [47] | Case-controlled | School child (10–12 years, male) | 69 |
|
| School children in Bangkok were exposed in total Benzene more than control group (living outside the city) |
| ETS | Anuntaseree et al. (2008) [57] | Cross-sectional | Parent of infants | 3,256 | Questionnaires | Prevalence of at least one smoker in household was 47.2%, paternal smoking in the present of their infant was 35.1%, maternal smoking 0.3% |
|
| Thongthai et al. (2008) [58] | Cross-sectional | Adolescent (15–19 years) | 2,596 Part of larger study that included adult (≥15 years, total n = 28,248) | Questionnaires | Tobacco smoke affected 60% of population | Smokers were more likely to be male and older, but those exposed to secondhand smoke tend to be female and younger. | |
| PAHs | Ruchirawat et al. (2005) [47] | Case-controlled | School child (10–12 years, male) | 69 |
|
| School children in Bangkok were exposed in total PAH more than control group (living outside the city) |
| Exposure and health outcome assessment studies | |||||||
| Benzene | Buthbumrung et al. (2008) [51] | Case-controlled | School child (9–13 years, male) | 171 (exposure 109, controlled 62) |
|
|
|
| Navasumrit et al. (2005) [48] | Case-controlled | School child (12–14 years) | 71 (exposure 41, controlled 30) |
| |||
| Ruchirawat et al. (2007) [49] | Case-controlled | School child (9–13 years, male) | 184 (case 115, controlled 69) |
| Benzene levels on children studying in Bangkok were significantly higher than (3.5 times) students studying in rural school (control group) |
| |
| Ruchirawat et al. (2010) [52] | Case-controlled | School child (9–13 years, male) | 276 (city 165, rural 111) Part from larger study that includes adult |
| School children in city and rural areas were exposed to 19.38 and 8.4 μg/m3 benzene respectively |
| |
| DEPs: urban air pollution | Sriyaraj et al. (2008) [67] | Cross-sectional | School child (6–12 years) | 511 | Standard ISAAC questionnaires | Prevalence of cigarette smoking for mother was 4.5%, father was 49.3% and mother during the 1st year of child’s life was 5.3% |
|
| ETS | Sritippayawan et al. (2006) [59] | Case-controlled | Preschool child (0–5 years) | 71 |
|
| ETS exposure increased the risk of desaturation (SpO2 < 9%) in RSV-LRI but was not associated with RSV-LSI itself |
| PAHs | Pongpiachan et al. (2015) [56] | Cross-sectional | Preschool child (risk of PM2.5 intake in preschool children of age 0–5 years old) | Not applicable (risk assessment from environmental monitoring results) | PM2.5-bound PAHs | The average values of Σ3,4-ring PAHs and B[a]P equivalent concentrations in world urban cities were significantly much higher than those in samples collected from northern provinces during both sampling periods. | The cancer risk related to exposure through inhalation appears to be minor, while direct ingestion could potentially be a significant pathway for children due to their hand-to-mouth activities. |
| Ruchirawat et al. (2006) [65] | Case-controlled | School child (10–12 years, male) | 69 (case 44, controlled 25) |
| Ambient levels of PAHs are relatively high in Bangkok. | PAH-DNA adduct levels in lymphocytes were 5-fold higher in Bangkok | |
| Ruchirawat et al. (2007) [49] | Case-controlled | School child (9–13 years, male) | 184 (case 115, controlled 69) | Ambient PAHs monitoring | PAHs of children studying in Bangkok were significantly higher than students studying in rural school (control group) |
| |
| Ruchirawat et al (2010) [52] | Cross-sectional | School child (9–13 years, male) | 276 (city 165, rural 111) Part from larger study that include adult |
| School children in city and rural, respectively were exposed to:
| Low level of benzene exposure, alone or concurrently with other carcinogens, resulted in early biological effect in the study populations. | |
| Tuntawiroon et al. (2007) [50] | Case-controlled | School child (8–13 years) | 184 (case 115, controlled 69) |
| Concentration of urinary 1-HOP was significantly higher in Bangkok schoolchildren. |
| |
| PM | Aekplakorn et al. (2003) [62] | Cohort | School child (6–14 years, asthma and non-asthma) | 175 |
| A 10 µg/m3 increment was associated with changes in the highest FVC (–6.3 ml, 95% CI: –9.8, –2.8), FEV1 (–6.0 ml, 95% CI: –9.2, 2.7), PEFR (–18.9 ml.sec–1, 95% CI: –28.5, –9.3) and forced expiratory flow 25 to 75% of the FVC (FEF25–75%) (–3.7 ml.sec–1, 95% CI: –10.9, 3.5) in asthmatic children. | Declines in pulmonary function among asthmatic children were associated with increased in PM10 |
| Langkulsen et al. (2006) [64] | Cross-sectional | School child (10–15 years) | 878 (completed PFT 722) |
|
| Increased chronic respiratory symptom and impaired lung function in high-pollution area | |
| Preutthipan et al. (2004) [63] | Cohort | School child | 133 (asthma 93, non-asthma 40) | PM10 air monitoring |
|
| |
| SO2 | Aekplakorn et al. (2003) [62] | Cohort | School child (6–14 years, asthma and non-asthma) | 175 |
|
| No association of pulmonary function decline with increased in SO2 |
| VOCs | Aungudornpukdee et al. (2009) [54] | Cross-sectional | School child (6–13 years) | 2,956 |
|
| The associated factors of visual-motor coordination deficit were gender, monthly parental income, children’s age, residential period, and household ETS |
| Singkaew et al. (2013) [66] | Case-controlled | School child (4–11 years) | 6 Part of larger study that included adult |
|
| The lifetime cancer and non-cancer risk in all high-risk group including children were in acceptable range based on the US EPA health risk assessment. | |
[i] *Age group reference: newborn: birth–1 month, infant: 1–23 months, preschool child: 2–5 years, school child: 6–12 years, adolescent: 13–18 years, child: birth–18 years.
Abbreviations: Cr: creatinine, CYP450: cytochrome P450, DEP: diesel exhaust particle, ETS: environmental tobacco smoke, FEV1: forced expiratory volume at 1 second, FVC: forced vital capacity, GIS: Geographical Information System, GSTs: glutathione-S-transferases, MA: muconic acid, OR: odds ratios, PAHs: Polyaromatic hydrocarbons, PEFR: peak expiratory flow rate, PFT: pulmonary function test, PM: Particulate matter, SO2: Sulphur dioxide, t,t-MA: t,t-muconic acid, 1-HOP: 1-hydroxypyrene, VOCs: volatile organic compounds, 8-OHdG: 8-oxo-7, 8-dihydro-2’-deoxyguanosine, 95% CI: 95% confidence interval.
Table 5
Health effects related to environmental exposure of particular geographical areas in Thailand.
| Region | District, province | Hazards | Health effects association/health risk | References | |
|---|---|---|---|---|---|
| Yes | No | ||||
| Northern | Umpang, Tak | Lead | NA | NA | [22, 43, 44] |
| Mae Sot, Tak | Cadmium | Early renal effect | NA | [37, 39] | |
| Phonom Pha, Phichit | Mercury (inorganic) | NA | Hazard quotient | [46] | |
| Mae Moh, Lampang | Air pollution (SO2, PM) | Decline of pulmonary function (PM) | Decline of pulmonary function (SO2) | [62] | |
| Chiang Mai | Air pollution (DEPS) | Allergic diseases (rhinitis, atopic dermatitis) | NA | [67] | |
| Mae rim, Chiang Mai | Organochlorine | Negative association of cord serum total T4 levels with DDT metabolites | NA | [8] | |
| Northeastern | Amnatchareon | Organophosphate | Cognitive and motor development in infant | NA | [12] |
| Central | Bangkok | Lead (some areas) | NA | NA | [30, 32] |
| Air pollution (PM, methane, traffic related PAHs, benzene aeroallergen, ETS) | Respiratory symptom Impaired pulmonary function Increased severity of RSV infection (ETS) Increased PAH-DNA adduct levels Increased DNA damage and decreased DNA repair capacity Increased bulky carcinogen-DNA adduct levels Cancer risk Allergic rhinitis (aeroallergen sensitization) | Upper respiratory infection | [47, 48, 49, 50, 51, 52, 59, 60, 63, 64, 65, 68] | ||
| Greater Bangkok (rice farming) | Organophosphates, chlorpyrifos, and pyrethroid | NA | Neurobehavioral in school age children | [11] | |
| Klity village, Kanchanaburi | Lead | Dental caries, IQ deterioration | NA | [29] | |
| Kanchanaburi | Organophosphate | Cognitive and motor development in infant | NA | [12] | |
| Map Ta Phut, Rayong | VOCs (petrochemical industrial area) | NA | Visual-motor coordination deficits Health risk (cancer and non-cancer) | [54, 66] | |
| Nakhon Sawan | Organophosphate | Cognitive and motor development in infant | NA | [12] | |
| Southern | Singhanakorn district, Songkhla | Lead | Dental caries | Dental morphologic change | [25, 26] |
| Ron Phibul district, Nakhon Sri Thammarat | Arsenic | Increased DNA damage and decreased DNA capacity Cancer risk Hypomethylation of inflammatory genes (COX2, EGR1, and SOC3) positively correlated with levels of 8-nitroguanine. | NA | [34, 35, 36] | |
[i] Abbreviations: DDT: Dichlorodiphenyltrichloroethane, DEPs: diesel exhaust particles, ETS: environmental tobacco smoke, IQ: intelligent quotient, NA: not applicable, PAH: polyaromatic hydrocarbons, PM: particulate matter, SO2: Sulphur dioxide, VOCs: volatile organic compounds.