Skip to main content
NCBI home page
Environmental Health Perspectives logoLink to Environmental Health Perspectives
. 2008 Jan 30;116(5):674–679. doi: 10.1289/ehp.10471

Effects of Prenatal Exposure to Coal-Burning Pollutants on Children’s Development in China

Deliang Tang 1,2,✉, Tin-yu Li 3, Jason J Liu 1, Zhi-jun Zhou 4, Tao Yuan 5, Yu-hui Chen 1, Virginia A Rauh 2, Jiang Xie 3, Frederica Perera 1,2
PMCID: PMC2367664  PMID: 18470301

Abstract

Background

Environmental pollutants such as polycyclic aromatic hydrocarbons (PAHs), lead, and mercury are released by combustion of coal and other fossil fuels.

Objectives

In the present study we evaluated the association between prenatal exposure to these pollutants and child development measured by the Gesell Developmental Schedules at 2 years of age.

Methods

The study was conducted in Tongliang, Chongqing, China, where a seasonally operated coal-fired power plant was the major source of ambient PAHs and also contributed lead and mercury to the air. In a cohort of nonsmoking women and their newborns enrolled between March 2002 and June 2002, we measured levels of PAH–DNA adducts, lead, and mercury in umbilical cord blood. PAH–DNA adducts (specifically benzo[a]pyrene adducts) provided a biologically relevant measure of PAH exposure. We also obtained developmental quotients (DQs) in motor, adaptive, language, and social areas.

Results

Decrements in one or more DQs were significantly associated with cord blood levels of PAH–DNA adducts and lead, but not mercury. Increased adduct levels were associated with decreased motor area DQ (p = 0.043), language area DQ (p = 0.059), and average DQ (p = 0.047) after adjusting for cord lead level, environmental tobacco smoke, sex, gestational age, and maternal education. In the same model, high cord blood lead level was significantly associated with decreased social area DQ (p = 0.009) and average DQ (p = 0.038).

Conclusion

The findings indicate that exposure to pollutants from the power plant adversely affected the development of children living in Tongliang; these findings have implications for environmental health policy.

Keywords: China, coal burning, lead, mercury, neurodevelopment, PAH–DNA adducts, prenatal

Molecular and epidemiologic research has shown that fetuses and infants are more susceptible than adults to the harmful effects of a variety of environmental contaminants, including polycyclic aromatic hydrocarbons (PAHs) (Perera et al. 2005), mercury [Agency for Toxic Substances and Disease Registry (ATSDR) 1999; Chang 1977; Clarkson 2002; Goldman and Shannon 2001; Harada 1977; Koos and Longo 1976; Marsh 1987; Marsh et al. 1987; National Research Council 2000; Ratcliffe et al. 1996; Satoh 2000], lead (ATSDR 2005; Cory-Schlecta and Schaumburg 2000; Dietrich et al. 1991; Emory et al. 2003; Leggett 1993; Wasserman et al. 1997, 2000), and environmental tobacco smoke (ETS), pesticides, and polychlorinated biphenyls [National Research Council 1993; Neri et al. 2006; Whyatt and Perera 1995; World Health Organization (WHO) 1986]. There is growing evidence that prenatal exposures to air pollutants from combustion of coal and other fossil fuels have adverse effects on fetal growth and early child neurodevelopment. PAHs are toxic, mutagenic, and carcinogenic air pollutants that are generated by the incomplete combustion of fossil fuels such as coal, diesel, and gasoline (Bostrom et al. 2002). PAHs are present in tobacco smoke and grilled or broiled foods, as well as in ambient air. Some PAHs are transplacental carcinogens (Bulay and Wattenberg 1971; Rice and Ward 1982; Vesselinovitch et al. 1975). Adduct concentrations in both maternal and cord blood have been shown to increase with a rise in estimated ambient exposure to PAHs, although there is substantial interindividual variation in adduct formation (Perera et al. 2005; Whyatt et al. 1998). Because PAH–DNA adducts reflect individual variations in exposure, absorption, metabolic activation, and DNA repair, they provide an informative biologic dosimeter that has been associated with risk of cancer (Bartsch et al. 1983; Pelkonen et al. 1980; Tang et al. 2001) and reproductive/developmental effects (Perera et al. 1998, 2007; Tang et al. 2006).

Experimental animal studies have demonstrated that benzo[a]pyrene (BaP), a representative PAH, is a reproductive toxicant (Archibong et al. 2002) and produces a variety of neurodevelopmental effects as a result of nervous system damage, including decreased motor activity; neuromuscular, physiologic, and autonomic abnormalities, and decreased responsiveness to sensory stimuli (Saunders et al. 2002, 2003; Wormley et al. 2004b). In epidemiologic studies, prenatal exposure to PAHs has been shown to be associated with reduced birth weight, length, and head circumference (Choi et al. 2006; Perera et al. 1998, 2003; Šrám et al. 2005; Tang et al. 2006). In the present cohort, reduction of head circumference was associated with PAH–DNA adducts in cord blood (Tang et al. 2006). Reduction of weight or head circumference at birth has been correlated with lower IQ as well as poorer cognitive functioning and school performance in childhood (Chaikind and Corman 1991; Chasnoff et al. 1992; Desch et al. 1990; Hack et al. 1991; Lucas et al. 1992; Matte et al. 2001). Several studies have associated PAH exposure with decrements in cognitive development. For example, children born during the years of maximal air pollution in the Czech Republic had learning disorders that were attributed in part to elevated levels of PAHs in the atmosphere from the mining and combustion of coal (Otto et al. 1997). Children in New York City who were more highly exposed to PAHs in utero had significantly decreased Bayley Mental Development Index scores and were more likely to be developmentally delayed at 3 years of age (Perera et al. 2006).

Lead and mercury have been shown in multiple studies to be developmental neurotoxins. Lead is produced by the combustion of coal and has been widely used as an additive in gasoline and in paint. Numerous studies have reported an association between lead exposure and decreased IQ scores on various intelligence scales even at low levels of exposure (ATSDR 2005; Canfield et al. 2003; Lanphear et al. 2005; Surkan et al. 2007). Lead has also been associated with antisocial and delinquent behavior (Needleman et al. 1996). Several studies have determined that prenatal exposure to mercury is related to adverse neurodevelopmental outcomes (ATSDR 1999; Chang 1977; Clarkson 2002; Goldman and Shannon 2001; Grandjean et al. 1997; Koos and Longo 1976; National Research Council 2000; Ratcliffe et al. 1996; Satoh 2000).

The present cohort study was conducted in Tongliang, Chongqing, which has a population of around 100,000 and is situated in a basin approximately 3 km in diameter. Before its permanent shutdown in December 2004, a coal-fired power plant located south of the town center operated every year from 1 December to 31 May to compensate for the insufficient hydraulic power during the dry season (Chow et al. 2006; Tang et al. 2006). The plant was not equipped with modern pollution reduction technology and combusted about 25,000 tons of coal during each 6-month period of operation. This report concerns the cohort of children whose gestational period included the months of power plant operation from 1 December 2001 to 31 May 2002.

The main exposure of interest (PAHs) resulted primarily from the burning of coal fuel by the local power plant. In 1995 nearly all domestic heating and cooking units were converted to natural gas, and in 2002 motor vehicles were limited in number. Air monitoring analyses carried out as part of the study showed that PAHs of medium molecular weight (168–266 Da) increased by 1.5–3.5 times during the Tongliang power plant’s operational period (Chow et al. 2006). The plant also emitted lead and mercury. Emissions from coal combustion in China are a major source of lead contamination in China (Guo et al. 1994, 2002; Zajusz-Zubek and Konieczynski 2003; Zhang et al. 2003). The average lead content of Chinese coal is about 1,000 times higher than in U.S. coal (Kunli et al. 2005; Mastalerz and Drobniak 2005). The Chinese government banned lead in gasoline in the year 2000; however, some resuspension of lead-containing dust from the roadside soil may have contributed to lead levels in cord blood. Coal combustion is responsible for an estimated 46% of Chongqing’s total mercury emissions (Wang et al. 2006). The major route of mercury exposure is via ingestion of fish and shellfish contaminated by methylmercury derived from deposition of mercury emitted to the air.

The present study evaluated the association between levels of PAH–DNA adducts, lead, and mercury and 2-year cognitive development as measured by the Gesell Developmental Schedules (GDS) (Gessel and Amatruda 1941). We tested the hypothesis that after adjusting for potential confounders, prenatal exposure to PAHs, lead, or mercury would be associated with lower DQs in motor, adaptive, language, and social areas at 2 years of age.

Materials and Methods

Study subjects

Subjects were children born to nonsmoking Chinese women who gave birth at any one of three Tongliang county hospitals between 4 March 2002 and 19 June 2002. The women were selected using a screening questionnaire when they checked in for delivery. Eligibility criteria included non-smoking status, age ≥ 20 years, and residence within 2.5 km of the Tongliang power plant. Of 150 eligible consenting women, 149 completed the interview and contributed a cord blood sample at the time of delivery. The retention rate for the full cohort was 88.7% at the 2-year follow-up. One hundred thirty-three mother–child pairs remained in the study 2 years after the study had begun. All of the 133 children were tested with the GDS. One hundred ten subjects had complete data on all measures and variables required for the analysis. Table 1 presents the demographic characteristics of the 110 children who are the subjects of the present report. As shown in Table 1, this subset did not differ significantly with respect to demographic characteristics from the group not included in the analysis (e.g., maternal age, education, gestational age, birth weight, birth length, and birth head circumference). All subjects signed the consent form approved by the Columbia University Institutional Review Board and Chongqing University of Medical Sciences.

Table 1.

Demographic characteristics of the study sample (n = 110).a

Demographic characteristic Mean ± SD (range)
Maternal age (years) 25.2 ± 3.2 (20.34–34.28)
Maternal education (%)
 < High school 43.6
 ≥ High school 56.4
Cord adducts (adducts/108 nucleotides) 0.32 ± 0.14 (0.125–0.812)
Cord lead (μg/dL) 3.60 ± 1.59 (0.82–12.93)
Cord mercury (ppb) 7.0 ± 4.43 (2.28–39.72)
Prenatal ETS exposure (hr/day) 0.293 ± 0.586 (0–5)
Sex of newborn (% female) 50.9
Gestational age (days) 277.3 ± 11.3 (224–294)
Open in a new tab
a

This subset did not differ significantly with respect to demographic characteristics from the group not included in the analysis [e.g., maternal age (p = 0.579), gestational age (p = 0.232), birth weight (p = 0.614), birth length (p = 0.564), or birth head circumference (p = 0.563), by t-test]. However, maternal education differed between the groups (p = 0.056).

Personal interview

A trained interviewer administered a 45-min questionnaire after delivery that included demographic information, lifetime residential history (location of birth and duration of each residency), history of active and passive smoking, occupational exposure during pregnancy (chemical exposure, including coal products from hot asphalt or tar roofing material, agriculture herbicides, and insecticides), medication information, alcohol use during each trimester of pregnancy, consumption of PAH-containing meat (frequency of eating fried, broiled, or barbecued meat), and fish consumption (as a potential source of mercury). ETS exposure was measured by a set of questions about timing, frequency, and the amount of exposure to cigarette, cigar, and pipe smoke in the home. Socioeconomic information related to income and education was also collected. As reported in previous studies, the Chinese are reluctant to reveal their true income (Xu et al. 2006); hence, we considered education level a more reliable indicator of socioeconomic status.

Biological sample collection and analysis

Umbilical cord blood was collected at delivery. The blood samples for PAH–DNA adducts were collected in heparinized BD Vacutainer tubes (10 mL, green) (Becton Dickinson, Franklin Lakes, NJ, USA). The blood samples for mercury and lead analysis were collected in EDTA Vacutainer tubes (4 mL, lavender). Samples were transported to the field laboratory at the Tongliang County Hospital immediately after collection. The buffy coat, packed red blood cells, and plasma were separated and stored at −70°C.

PAH–DNA adducts

We analyzed BaP–DNA adducts in extracted white blood cell DNA using a high-performance liquid chromatography (HPLC)/fluorescence method that detects BaP tetraols (Alexandrov et al. 1992; Rojas et al. 1994), modified as described previously (Perera et al. 2007). Briefly, 100 μg of DNA were used for each analysis. Many precautions were taken to avoid the presence of fluorescent contaminants. DNA samples were dissolved in 0.1 N HCl and hydrolyzed at 90°C for 6 hr. The resulting solution was analyzed in a Shimadzu HPLC system with an RF-10Axl spectrofluorometric detector (Shimadzu, Kyoto, Japan). Samples were injected using the Shimadzu S IL-10A automatic sample injector. We calculated tetraol concentrations by comparing the samples analyzed with an external calibration curve generated from the fluorescence peak of a known amount of authentic benzo[a]pyrene diol epoxide (BPDE) tetraol standard each time a set of samples was analyzed. Calibration was carried out with DNA from calf thymus alone (background) and spiked with 2, 4, and 8 pg anti-BPDE tetraol. These standard solutions were then treated in the same way as the tested samples. The correlation coefficient was 0.98, and the mean coefficient of variation for analyses repeated on different days was 12%. The detection threshold of BPDE tetraols [r-7,c-10,t-8,t-9-tetrahydroxy-7,8,9,10-tetrahydrobenzo[a]pyrene (BaP tetraol I-1) and r-7,t-9,t-10,t-8-tetrahydroxy-7,8,9,10-tetrahydrobenzo[a]pyrene (BaP tetraol I-2)] was 0.25 pg (signal-to-noise ratio > 3) so that, in the present study, with 100 μg DNA, this assay could detect 0.25 adducts per 108 nucleotides. Assays were performed on all samples that were of adequate quantity and quality for analysis. All samples were run coded.

Cord lead

Portions of umbilical cord blood were sent to the Laboratory of the Department of Occupational Health, School of Public Health, Fudan University in Shanghai, China. Whole blood samples were analyzed per the standard U.S. Environmental Protection Agency (EPA) method, the PE-800 Zeeman atomic absorption spectrometer with Zeeman background correction system (U.S. EPA 2007). The lead standard (1.0 g/L) was purchased from the Shanghai Institute of Testing Technology. Nitric acid of ultrapure reagent grade was purchased from the Shanghai Reagent Factory. All vessels were soaked in 0.3% nitric acid for 24 hr and then washed with double-distilled water. Preparation of standards and samples was carried out under lead-free conditions. One hundred microliters of the sample was diluted 1:1 with 0.1% HNO3. We used a PE-800 Zeeman atomic absorption spectrometer with Zeeman background correction system. The wavelength was 283.3 nm, lamp current was 10 mA, slit width was 0.7 nm, and sample volume was 10 μL. For quality control, we used NYCOMED whole blood (batch 905; 400 ± 24 μg/L; Zurich, Switzerland) certified reference material. The determination limit of this method was 0.09 μg/L. The recovery rate of this method in the laboratory is > 92%; the precision is 1.7–3.8%.

Cord mercury

Researchers at the Laboratory of the Department of Occupational Health, School of Public Health, Fudan University, also performed the analysis of mercury using the Automatic Mercury Analyzer AMA-254 (Milestone Inc., Monroe, CT, USA) that can directly test total mercury in the samples without any pretreatment. The method complies with U.S. EPA method 7473. A reference standard, BCR 40 (mercury concentration, 0.35 ± 0.06 μg/g), which was developed by the Commission of the European Community (2008), was used to ensure the validity of the method. As required by the method, the first working curve range set by the instrument was used for calibration. Our calibration curve provided parameters within the acceptable range set by the analyzer.

Measures of child neurodevelopment and covariates

The GDS was designed to provide a neurologic and intellectual evaluation of the infant or child at the time of testing and for making decisions regarding services needed by the infant (Gesell and Amatruda 1941). Unlike tools that assess IQ or academic skills, the GDS was designed to measure physical, emotional, and behavioral development. Later versions of the scales provided more objective observational procedures and acceptable reliability (Bernheimer and Keogh 1988; Knobloch and Pasamanick 1974). In the present study, the GDS was selected for comparability with other studies done in the Chinese population because it has been adopted by the Chinese Pediatric Association, has been validated against a Chinese reference population (Song and Zhu 1987), and is widely used for assessing child development in China and in other countries (Cui et al. 2001; Ke et al. 2004; Zhang and Li 1994; Zhu et al. 2005). Of relevance to the present study, the GDS has been adopted by the WHO for use in resource-poor settings to assess the effectiveness of a program to support health professionals in encouraging parents to stimulate their child’s development in the first 2 years of life (WHO 1999, 2004). This program was introduced to China’s health system in 2001 by WHO and UNICEF. It is considered a high priority for China, particularly for the benefit of those children living in rural areas characterized by difficult access to services and relatively poor economic resources. Recently, the GDS has been used successfully to evaluate developmental gains as a result of the WHO program (Jin et al. 2007), placing importance on the GDS as a complementary tool in health-related research and intervention, as in the present study. Within a Chinese population, there was a significant correlation between developmental assessment at 6–12 months on the GDS and mental development at 6–7 years on the Chinese version of the Wechsler Intelligence Scales for Children (p < 0.01) (Zhou et al. 2004).

Children in the cohort who were 2 years of age were administered the version of the GDS for 0- to 3-year-old children, revised by the Beijing Mental Development Cooperative Group (1985) and adapted to the Chinese population. This version of the GDS accurately assesses development of children 4 weeks to 6 years of age (Beijing Mental Development Cooperative Group 1985). The items are grouped into four main categories of functioning: motor behavior, including locomotion, reaching, balance, comprehension, drawing, and hand control; language behavior assessed by means of vocabulary, word comprehension, conversation, and word production; adaptive behavior, including eye–hand coordination, imitation, object recovery, comprehension, discriminative performance, perception, completion, and number conception; and personal and social behavior, including reactions to persons, personal habits, initiative and independence, play responses, and acquired information. Each child is assigned a developmental quotient (DQ) in each of the four specific domains: motor, adaptive, language, and social. The standardized mean (± SD) of the DQ is 100 ± 15. A child with a DQ lower than 85 is considered to have a high probability of some organic impairment (Knobloch and Pasamanick 1974). Scores of 70–84 indicate moderate delay; scores of < 70 indicate severe delay. A score of 84 is the cutoff point for determining normal and developmental delay (Hudon et al. 1998). A study by Jin et al. (2007), which also used the GDS, showed means similar to those in our study.

Testing was conducted by two trained physicians to maximize reliable assessment and valid interpretation. Testers completed a 1-year course at Shanghai Jiaotong University and passed standardized exams to become certified. Therefore, both interexaminer and intraexaminer variability were minimal. Further, the two examiners split the testing by domains, not by subjects. As a result, for any one domain, all subjects were tested by the same examiner.

Research workers abstracted relevant information on covariates from maternal and infant medical records after delivery, such as date of delivery, gestational age, and sex of newborn. Other covariates were derived from questionnaire data on socioeconomic status and environmental exposures.

Statistical analysis

Age-adjusted DQs in the motor, adaptive, language, and social areas, and the average of these four DQs served as the outcome variables. Initially, in separate linear regression models, associations between each prenatal exposure (PAH–DNA adducts, lead, or mercury in cord blood) and neurodevelopmental DQs were assessed, with adjustment for known potential confounders. The final model included those prenatal exposures (PAH–DNA adducts, lead, or mercury) that were associated with outcomes at the level of p < 0.1, along with known potential confounders. Residual plots did not suggest nonlinearities or the need for a transformation of cord adducts. Recent studies have called into question the linearity of the dose–response curve for lead (Lanphear et al. 2005). Moreover, there were influential outliers in the data that nonetheless were within the range seen in other studies. Therefore, we dichotomized cord lead values, as well as mercury values, at the median to minimize the influence of outliers. ETS was assessed as a continuous variable (hours of exposure/day). There were no significant correlations between mercury and adducts (r = 0.079, p = 0.410); lead and adducts (r = −0.052, p = 0.586); lead and mercury (r = −0.120, p = 0.210), or PAH–DNA adducts and ETS (r = 0.074, p = 0.440) (Spearman’s rho). Thus, collinearity was not an issue.

In separate models, in contrast to adducts and lead, mercury was not associated (p < 0.1) with DQ in any domain and was not included in the final multiple regression model: motor area β = −2.258 [95% confidence interval (CI), −6.60 to 2.08], p = 0.310; adaptive area, β = −1.202 (95% CI, −6.86 to 4.45), p = 0.678; language area, β = −1.254 (95% CI, −6.11 to 3.60), p = 0.614; social area, β = −2.366 (95% CI, −6.93 to 2.20), p = 0.312; average, β = −1.862 (95% CI, −5.92 to 2.19), p = 0.370. Self-reported occupational exposure to chemicals was not associated with DQs. Gestational age, sex, maternal education, and ETS were included in the final model for consistency with other studies. The educational level variable was dichotomized (< high school, ≥ high school). Maternal education level and maternal age were significantly intercorrelated (r = 0.217, p = 0.008), but maternal education level was the more significant contributor to neurodevelopmental outcomes. We did not adjust for fish intake, a potential source of methyl mercury exposure, because only nine mothers reported consuming any fish during pregnancy. We used logistic regression to estimate the association between prenatal exposures (adducts and lead) and developmental delay, adjusting for the same covariates as in the final multiple regression model.

As noted above, in the present cohort high PAH–DNA adduct levels were associated with decreased birth head circumference (p = 0.057) after controlling for potential confounders (Tang et al. 2006). We therefore assessed whether the observed effects of PAH–DNA adducts on DQs were mediated by reduction in head circumference by including birth head circumference in the final regression model.

Results

The demographic and exposure characteristics of the study subjects are shown in Table 1. The mean PAH–DNA adduct level was 0.32 ± 0.14 adducts/108 nucleotides; 80% of the newborns had detectable adduct levels. Mean lead and mercury concentrations were 3.6 ± 1.59 μg/dL and 7.0 ± 4.43 μg/L, respectively. Table 2 shows the distribution of DQs. All DQ domains were significantly intercorrelated (p < 0.01), with r-values ranging from 0.56 to 0.86.

Table 2.

Distribution of GDS DQ scores (n = 110).a

Mean ± SD (range) Normal [n (%)] Developmental delay [n (%)]
Motor area 97.53 ± 11.47 (65–135) 95 (86.4) 15 (13.6)
Adaptive area 98.71 ± 14.90 (50–124) 96 (87.3) 14 (12.7)
Language area 102.10 ± 12.83 (56–122) 99 (90.0) 11 (10.0)
Social area 99.40 ± 11.79 (57–121) 100 (90.9) 10 (9.1)
Average 99.42 ± 10.74 (57–120) 103 (93.6) 7 (6.4)
Open in a new tab
a

Normal, > 84; developmental delay, ≤ 84.

The results of multiple regression analysis are shown in Table 3. Increased cord adduct level was inversely associated with decreases in the motor area DQ (β = −16.01; 95% CI, −31.30 to −0.72; p = 0.043), language area DQ (β = −16.63; 95% CI, −33.73 to 0.46; p = 0.059), and average DQ (β = −14.57; 95% CI, −28.77 to −0.38; p = 0.047) after adjusting for cord lead level, ETS, sex, gestational age, and maternal education level. In the same model, high cord blood lead level was significantly associated with decreased social area DQ (β = −6.08; 95% CI, −10.53 to −1.63; p = 0.009) and average DQ (β = −4.24; 95% CI, −8.20 to −0.29; p = 0.038). There was no indication that the effect of PAH on DQs was mediated by the reduction in head circumference. Birth head circumference did not have a significant effect on the DQs, and its inclusion in the final model did not alter the effect of PAH–DNA adducts on DQs.

Table 3.

Results of multiple regression analysesa of GDS DQ scores at 2 years of age and PAH–DNA adducts and cord lead in newborn cord blood (n = 110).

Motor
Adaptive
Language
Social
Average
β (95% CI) p-Value β (95% CI) p-Value β (95% CI) p-Value β (95% CI) p-Value β (95% CI) p-Value
Cord adducts (adducts/108 nucleotides) −16.01 (−31.30 to −0.72) 0.043 −15.51 (−35.63 to 4.61) 0.134 −16.64 (−33.73 to 0.46) 0.059 −9.29 (−25.28 to 6.70) 0.258 −14.58 (−28.77 to −0.37) 0.047
Cord lead (H/L)b −3.72 (−7.98 to 0.53) 0.089 −3.59 (−9.20 to 2.01) 0.212 −4.34 (−9.10 to 0.43) 0.077 −6.08 (−10.53 to −1.63) 0.009 −4.24 (−8.20 to −0.29) 0.038
Open in a new tab
a

Model included sex, gestational age, maternal education, and ETS as covariates.

b

Using median of cord lead (3.5 μg/dL) as cutoff for H/L (high/low).

The frequency of developmental delay as shown in Table 2 ranged from 9.1% (social) to 13.6% (motor), with 6.4% for the average score. By logistic regression analysis, a 0.1 unit (0.1 adduct per 108 nucleotides) in cord adducts was associated with increased odds of being developmentally delayed in the motor area [odds ratio (OR) = 1.91; 95% CI, 1.22 to 2.97; p = 0.004]. An elevated cord lead level was significantly associated with increased probability of being developmentally delayed in the motor area (OR = 3.85; 95% CI, 1.04 to 14.25; p = 0.043) and the social area (OR = 7.29; 95% CI, 1.35 to 39.45; p = 0.021) (Table 4).

Table 4.

Results of logistic regression analyses of GDS scores at age 2 and adducts and cord lead in newborn cord blood (n = 110).a,b

Motor
Adaptive
Language
Social
Average
OR (95% CI) p-Value OR (95% CI) p-Value OR (95% CI) p-Value OR (95% CI) p-Value OR (95% CI) p-Value
Cord adducts (adducts/108 nucleotides) 1.91 (1.22 to 2.97) 0.004 1.16 (0.76 to 1.76) 0.500 1.31 (0.84 to 2.05) 0.234 1.52 (0.93 to 2.50) 0.095 1.67 (0.93 to 3.00) 0.088
Cord lead (H/L)c 3.85 (1.04 to 14.25) 0.043 1.43 (0.43 to 4.68) 0.559 2.70 (0.70 to 10.50) 0.150 7.29 (1.35 to 39.45) 0.021 4.25 (0.70 to 25.89) 0.116
Open in a new tab
a

Model included sex, gestational age, maternal education, and ETS as covariates.

b

The odds ratios for cord adducts represent the effect of a 0.1-unit increment in cord adducts.

c

Using median of cord lead (3.5 μg/dL) as cutoff [H/L (high/low)].

Discussion

A main finding of the study was that the level of PAH–DNA adducts in newborn cord blood was associated with reductions in 2-year DQs. After adjusting for cord lead level, ETS, and other covariates, increased PAH–DNA adducts were significantly associated with reduced DQs in the motor and language areas and with reduced average DQ. Adducts were associated with increased odds of developmental delay in the motor area. Experimental animal studies have demonstrated that prenatal exposure to BaP, a representative PAH, produces a variety of neurodevelopmental effects, including impairment of long-term potentiation, a cellular correlate of learning and memory, in offspring (Wormley et al. 2004a). In epidemiologic studies, prenatal exposure to PAHs has been shown to be associated with adverse birth outcomes (Choi et al. 2006; Perera et al. 1998, 2006) and with a decrement in the mental development index on the Bayley Scales of Development at 3 years of age (Perera et al. 2006). As noted above, several prior studies have shown that high PAH–DNA adduct levels were associated with reduced fetal growth (Perera et al. 1998; Tang et al. 2006), including decreased birth head circumference (p = 0.057) in the present Chinese cohort, after controlling for potential confounders (Tang et al. 2006).

Because PAH–DNA adduct levels reflect individual variation in exposure, absorption, metabolic activation, and DNA repair, the level of PAH–DNA adducts provides a biologically relevant dosimeter of exposure. Although we can hypothesize that adduct formation resulting in mutations or apoptosis during critical windows of brain development might adversely affect child development, here we are considering adducts as a biologically relevant measure of exposure. We note that the cord adduct levels in the Tongliang study were significantly higher than in New York City or Polish newborns, consistent with the higher PAH exposure in Tongliang (Perera et al. 2005). Specifically, the mean adduct concentration was 45% higher in the Tongliang newborns compared with New York City newborns (Perera et al. 2005). As in several prior studies (Perera et al. 2005), adducts in cord blood were not correlated with ETS exposure.

In the present study, lead was significantly associated with reductions in social and average DQs. Lead was also associated with increased odds of developmental delay in the motor and social areas. These findings are consistent with recent studies showing low-level effects of lead: cord lead levels in the range of 0.82–12.93 μg/dL (mean, 3.6 μg/dL) found in this study were significantly associated with decreased social area DQ and decreased average DQ. As noted above, low-level lead exposure as measured by cord blood lead is associated not only with lower IQ (ATSDR 2005; Canfield et al. 2003; Lanphear et al. 2005; Surkan et al. 2007) but also with antisocial and delinquent behavior (Needleman et al. 1996).

In this study, prenatal mercury exposure had no observable effect on the GDS scores. This nonpositive finding may be attributable to the relatively low levels of mercury in cord blood in the present cohort (mean ± SD, 7.0 ± 4.42 μg/L) compared with the level of methylmercury in the Faroe Island cohort (mean, 22.9 μg/L) that reported adverse effects of prenatal mercury exposure (Grandjean et al. 1997). In the present cohort, only nine mothers consumed any fish or shellfish, which is the most important source of mercury.

A limitation of the study is that we do not have data on postnatal blood PAH–DNA, lead, and mercury levels to allow us to examine the impact of postnatal exposure on 2-year cognitive development. Because the power plant was not shut down until May 2004, the subjects continued to receive seasonal exposure to the plant emissions after birth. However, fetal development is considered to be the period of greatest susceptibility to PAHs and lead. Another limitation of the present study is the small sample size, precluding evaluation of interactions between pollutants. Moreover, all babies were exposed to PAHs from the power plant in both the second and third trimesters, and we were therefore unable to separate exposures by trimester. The deficits in development at 2 years of age may be educationally meaningful because compromised function at an early age may have a negative impact on subsequent school performance (Drillien et al. 1988). As noted above, within a Chinese population, there was a significant correlation between developmental assessment at 6–12 months on the GDS and mental development at 6–7 years on the Chinese version of the Wechsler Intelligence Scales for Children (p < 0.01) (Zhou et al. 2004). Continued follow-up of the present cohort will determine whether prenatal PAH exposure is a risk factor for subsequent cognitive effects.

Conclusion

The present results indicate that among children living in Tongliang, Chongqing, in utero exposure to PAHs from a coal-fired power plant adversely affected motor and language development. Prenatal exposure to lead adversely affected social development. Because coal-fired power plants currently produce 75% of China’s electricity and most new plants in China are being built to burn coal, the results from the Tongliang study are relevant to the development of other children living in China and have significant implications for policies concerning energy and public health.

Footnotes

We acknowledge L. Qu and C.-J. Lin, Columbia Center for Children’s Environmental Health; Y. Li, Fudan University; W. Wang, Jiaotong University.

This research was supported by the V. Kann Rasmussen Foundation, Energy Foundation, Schmidt Family Foundation, Rockefeller Brothers Fund, Porpoise Fund, and the National Institute of Environmental Health Sciences (5 P01 ES009600, 5 R01 ES008977).

References

  1. Alexandrov K, Rojas M, Geneste O, Castegnaro M, Camus AM, Pesruzzelli S, et al. An improved fluorometric assay for dosimetry of benzo(a)pyrene diol-epoxide-DNA adducts in smokers’ lung: comparisons with total bulky adducts and aryl hydrocarbon hydroxylase activity. Cancer Res. 1992;52:6248–6253. [PubMed] [Google Scholar]
  2. Archibong AE, Inyang F, Ramesh A, Greenwood M, Nayyar T, Kopsombut P, et al. Alteration of pregnancy related hormones and fetal survival in F-344 rats exposed by inhalation to benzo(a)pyrene. Reprod Toxicol. 2002;16:801–808. doi: 10.1016/s0890-6238(02)00058-8. [DOI] [PubMed] [Google Scholar]
  3. ATSDR. Toxicological Profile for Mercury. Atlanta, GA: Agency for Toxic Substances and Disease Registry, U.S. Department of Health and Human Services, Public Health Service; 1999. [Google Scholar]
  4. ATSDR. Toxicological Profile for Lead. Atlanta, GA: Agency for Toxic Substances and Disease Registry, U.S. Department of Health and Human Services, Public Health Service; 2005. [Google Scholar]
  5. Bartsch H, Terracini B, Malaveille C. Quantitative comparisons of carcinogenicity, mutagenicity and electrophilicity of 10 direct-acting alkylating agents and the initial 06-alkylguanine ratio in DNA with carcinogenic potency in rodents. Mutat Res. 1983;110:181–219. doi: 10.1016/0027-5107(83)90140-9. [DOI] [PubMed] [Google Scholar]
  6. Beijing Mental Development Cooperative Group. Gesell Developmental Diagnosis Scale. Beijing, China: Beijing Mental Development Cooperative Group; 1985. [Google Scholar]
  7. Bernheimer LP, Keogh BK. Stability of cognitive performance of children with developmental delays. Am J Ment Retard. 1988;92:539–542. [PubMed] [Google Scholar]
  8. Bostrom CE, Gerde P, Hanberg A, Jernstrom B, Johansson C, Kyrklund T, et al. Cancer risk assessment, indicators, and guidelines for polycyclic aromatic hydrocarbons in the ambient air. Environ Health Perspect. 2002;110:451–488. doi: 10.1289/ehp.110-1241197. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Bulay OM, Wattenberg LW. Carcinogenic effects of polycyclic hydrocarbon carcinogens administrated to mice during pregnancy on the progeny. J Natl Cancer Inst. 1971;46:397–402. [PubMed] [Google Scholar]
  10. Canfield RL, Henderson CR, Cory-Slechta DA, Cox C, Jusko TA, Lanphear BP. Intellectual impairment in children with blood lead concentrations below 10 μg per deciliter. N Engl J Med. 2003;348:1517–1521. doi: 10.1056/NEJMoa022848. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chaikind S, Corman H. The impact of low birth weight on special education costs. J Health Econ. 1991;10:291–311. doi: 10.1016/0167-6296(91)90031-h. [DOI] [PubMed] [Google Scholar]
  12. Chang LW. Neurotoxic effects of mercury—a review. Environ Res. 1977;14:329–373. doi: 10.1016/0013-9351(77)90044-5. [DOI] [PubMed] [Google Scholar]
  13. Chasnoff IJ, Griffith DR, Freier C, Murray J. Cocaine/poly-drug use in pregnancy: two-year follow-up. Pediatrics. 1992;89:284–289. [PubMed] [Google Scholar]
  14. Choi H, Jedrychowski W, Spengler J, Camann DE, Whyatt RM, Rauh V, et al. International studies of prenatal exposure to polycyclic aromatic hydrocarbons and fetal growth. Environ Health Perspect. 2006;114:1744–1750. doi: 10.1289/ehp.8982. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Chow JC, Watson JG, Chen LW, Koracin D, Zielenska B, Tang D, et al. Exposure to PM2.5 and PAHs from the Tongliang, China, epidemiological study. J Environ Sci Health A Tox Hazard Subst Environ Eng. 2006;41:517–542. doi: 10.1080/10934520600564253. [DOI] [PubMed] [Google Scholar]
  16. Clarkson TW. The three modern faces of mercury. Environ Health Perspect. 2002;110:11–23. doi: 10.1289/ehp.02110s111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Commission of European Community. Community Bureau of References. Brussels: Commission of European Community; 2008. [Google Scholar]
  18. Cory-Schlecta DA, Schaumburg HH. Lead, inorganic. In: Spencer PS, Schaumburg HH, Ludolph AC, editors. Experimental and Clinical Neurotoxicology. 2. New York: Oxford University Press; 2000. pp. 708–720. [Google Scholar]
  19. Cui H, Hou J, Ma G. Influences of rearing style on the intellectual development of infants [in Chinese] Wei Sheng Yan Jiu. 2001;30:362–364. [PubMed] [Google Scholar]
  20. Desch LW, Anderson SK, Snow JH. Relationship of head circumference to measures of school performance. Clin Pediatr (Phila) 1990;29:389–392. doi: 10.1177/000992289002900705. [DOI] [PubMed] [Google Scholar]
  21. Dietrich KN, Succop PA, Berger OG, Hammond PB, Bornschein RL. Lead exposure and the cognitive development of urban preschool children: the Cincinnati Lead Study cohort at age 4 years. Neurotoxicol Teratol. 1991;13:203–211. doi: 10.1016/0892-0362(91)90012-l. [DOI] [PubMed] [Google Scholar]
  22. Drillien CM, Pickering RM, Drummond MB. Predictive value of screening for different areas of development. Dev Med Child Neurol. 1988;30:294–305. doi: 10.1111/j.1469-8749.1988.tb14554.x. [DOI] [PubMed] [Google Scholar]
  23. Emory E, Ansari Z, Pattillo R, Archibold E, Chevalier J. Maternal blood lead effects on infant intelligence at age 7 months. Am J Obstet Gynecol. 2003;188:S26–S32. doi: 10.1067/mob.2003.244. [DOI] [PubMed] [Google Scholar]
  24. Gesell A, Amatruda C. Developmental Diagnosis: Normal and Abnormal Child Development. New York: Hoeber; 1941. [Google Scholar]
  25. Goldman LR, Shannon MW. Technical report: mercury in the environment: implications for pediatricians. Pediatrics. 2001;108:197–205. doi: 10.1542/peds.108.1.197. [DOI] [PubMed] [Google Scholar]
  26. Grandjean P, Weihe P, White R, Debes F, Arak S, Yokoyama K, et al. Cognitive deficit in 7-year-old children with prenatal exposure to methylmercury. Neurotoxicol Teratol. 1997;20:1–12. doi: 10.1016/s0892-0362(97)00097-4. [DOI] [PubMed] [Google Scholar]
  27. Guo R, Yang J, Liu D, Liu Z. Transformation behavior of trace elements during coal pyrolysis. Fuel Process Technol. 2002;77–78:137–143. [Google Scholar]
  28. Guo YT, Hao HM, Li J, Chang JL. Dissipation patterns of arsenic, fluorine, mercury, lead and cadmium in the process of coal incineration [in Chinese] China Coalfield Geol. 1994;6:54–56. [Google Scholar]
  29. Hack M, Breslau N, Weissman B, Aram D, Klein N, Borawski E. Effect of very low birth weight and subnormal head size on cognitive ability at school age. N Engl J Med. 1991;325:231–237. doi: 10.1056/NEJM199107253250403. [DOI] [PubMed] [Google Scholar]
  30. Harada Y. Congenital Minamata disease. In: Tsubak R, Irukayama K, editors. Minamata Disease: Methyl Mercury Poisoning in Minamata and Niigata. Tokyo: Kodansha; 1977. pp. 209–239. [Google Scholar]
  31. Hudon L, Moise KJ, Jr, Hegemier SE, Hill RM, Moise AA, Smith EO, et al. Long-term neurodevelopmental outcome after intrauterine transfusion for the treatment of fetal hemolytic disease. Am J Obstet Gynecol. 1998;179:858–863. doi: 10.1016/s0002-9378(98)70178-4. [DOI] [PubMed] [Google Scholar]
  32. Jin X, Sun Y, Jiang F, Ma J, Morgan C, Shen X. “Care for Development” intervention in rural China: a prospective follow up study”. J Dev Behav Pediatr. 2007;28:213–218. doi: 10.1097/dbp.0b013e31802d410b. [DOI] [PubMed] [Google Scholar]
  33. Ke DH, Su Q, Zhuang XY, Gao XX. Effect of active medical intervention on the physique and neuropsychological development of full-term infants: follow-up effect evaluation [in Chinese] Chin J Clin Rehab. 2004;8:6706. [Google Scholar]
  34. Knobloch H, Pasamanick B. Gesell and Amatruda’s Developmental Diagnosis. New York: Harper & Row; 1974. [Google Scholar]
  35. Koos BJ, Longo LD. Mercury toxicity in the pregnant woman, fetus, and newborn infant. A review. Am J Obstet Gynecol. 1976;126:390–409. doi: 10.1016/0002-9378(76)90557-3. [DOI] [PubMed] [Google Scholar]
  36. Kunli L, Jidong L, Lianwu C. Lead distribution in permo-carboniferous coal from the North China Plate, China. Environ Geochem Health. 2005;27:31–37. doi: 10.1007/s10653-004-5669-1. [DOI] [PubMed] [Google Scholar]
  37. Lanphear BP, Hornung R, Khoury J, Yolton K, Baghurst P, Bellinger DC, et al. Low-level environmental lead exposure and children’s intellectual function: an international pooled analysis. Environ Health Perspect. 2005;113:894–899. doi: 10.1289/ehp.7688. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Leggett RW. An age-specific kinetic model of lead metabolism in humans. Environ Health Perspect. 1993;101:598–616. doi: 10.1289/ehp.93101598. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Lucas A, Morley R, Lister G, Leeson-Payne C. Effect of very low birth weight on cognitive abilities at school age. N Engl J Med. 1992;326:202–203. doi: 10.1056/NEJM199201163260313. [DOI] [PubMed] [Google Scholar]
  40. Marsh DO. Dose-response relationships in humans: methyl mercury epidemics in Japan and Iraq. In: Eccles CU, Annau Z, editors. The Toxicity of Methyl Mercury. Baltimore, MD: Johns Hopkins University Press; 1987. pp. 45–53. [Google Scholar]
  41. Marsh DO, Clarkson TW, Cox C, Myers GJ, Amin-Zaki L, Al-Tikriti S. Fetal methylmercury poisoning. Relationship between concentration in single strands of maternal hair and child effects. Arch Neurol. 1987;44:1017–1022. doi: 10.1001/archneur.1987.00520220023010. [DOI] [PubMed] [Google Scholar]
  42. Mastalerz M, Drobniak A. Arsenic, cadmium, lead, and zinc in the Danville and Springfield coal members (Pennsylvanian) from Indiana. Int J Coal Geol. 2005;71:37–53. [Google Scholar]
  43. Matte TD, Bresnahan M, Begg MD, Susser E. Influence of variation in birth weight within normal range and within sibships on IQ at age 7 years: cohort study. BMJ. 2001;323:310–314. doi: 10.1136/bmj.323.7308.310. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. National Research Council. Pesticides in the Diets of Infants and Children. Washington, DC: National Academy Press; 1993. [PubMed] [Google Scholar]
  45. National Research Council. Toxicological Effects of Methylmercury. Washington, DC: National Academy Press; 2000. [Google Scholar]
  46. Needleman HL, Riess JA, Tobin MJ, Biesecker GE, Greenhouse JB. Bone lead levels and delinquent behavior. JAMA. 1996;275:363–369. [PubMed] [Google Scholar]
  47. Neri M, Ugolini D, Bonassi S, Fucic A, Holland N, Knudsen LE, et al. Children’s exposure to environmental pollutants and biomarkers of genetic damage. II. Results of a comprehensive literature search and meta-analysis. Mutat Res. 2006;612:14–39. doi: 10.1016/j.mrrev.2005.04.003. [DOI] [PubMed] [Google Scholar]
  48. Otto D, Skalik I, Bahboh R, Hudnell K, Šrám R. Neurobehavioral performance of Czech school children born in years of maximal air pollution [Abstract] Neurotoxicology. 1997;18:903. [Google Scholar]
  49. Pelkonen O, Karki NT, Sotaniemi BA. Determination of carcinogen-activating enzymes in the monitoring of high-risk groups. In: Davis W, Harrap KR, Stathopoulos G, editors. Human Cancer: Its Characterization and Treatment. Amsterdam: Excerpta Medica; 1980. pp. 48–57. [Google Scholar]
  50. Perera F, Tang D, Whyatt R, Lederman SA, Jedrychowski W. DNA damage from polycyclic aromatic hydrocarbons measured by benzo[a]pyrene-DNA adducts in mothers and newborns from Northern Manhattan, the World Trade Center Area, Poland, and China. Cancer Epidemiol Biomarkers Prev. 2005;14:709–714. doi: 10.1158/1055-9965.EPI-04-0457. [DOI] [PubMed] [Google Scholar]
  51. Perera FP, Rauh V, Tsai WY, Kinney P, Camann D, Barr D, et al. Effects of transplacental exposure to environmental pollutants on birth outcomes in a multi-ethnic population. Environ Health Perspect. 2003;111:201–205. doi: 10.1289/ehp.5742. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Perera FP, Rauh V, Whyatt RM, Tsai WY, Tang D, Diaz D, et al. Effect of prenatal exposure to airborne polycyclic aromatic hydrocarbons on neurodevelopment in the first 3 years of life among inner-city children. Environ Health Perspect. 2006;114:1287–1292. doi: 10.1289/ehp.9084. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Perera FP, Tang D, Rauh V, Tu YH, Tsai WY, Becker M, et al. Relationship between polycyclic aromatic hydrocarbon-DNA adducts, environmental tobacco smoke, and child development in the World Trade Center cohort. Environ Health Perspect. 2007;115:1497–1502. doi: 10.1289/ehp.10144. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Perera FP, Whyatt RM, Jedrychowski W, Rauh V, Manchester D, Santella RM, et al. Recent developments in molecular epidemiology: a study of the effects of environmental polycyclic aromatic hydrocarbons on birth outcomes in Poland. Am J Epidemiol. 1998;147:309–314. doi: 10.1093/oxfordjournals.aje.a009451. [DOI] [PubMed] [Google Scholar]
  55. Ratcliffe HE, Swanson GM, Fischer LJ. Human exposure to mercury: a critical assessment of the evidence of adverse health effects. J Toxicol Environ Health. 1996;49:221–270. doi: 10.1080/713851079. [DOI] [PubMed] [Google Scholar]
  56. Rice JM, Ward JM. Age dependence of susceptibility to carcinogenesis in the nervous system. Ann N Y Acad Sci. 1982;381:274–289. doi: 10.1111/j.1749-6632.1982.tb50392.x. [DOI] [PubMed] [Google Scholar]
  57. Rojas M, Alexandrov K, van Schooten FJ, Hillebrand M, Kriek E, Bartsch H. Validation of a new fluorometric assay for benzo[a]pyrene diolepoxide-DNA adducts in human white blood cells: comparisons with 32P-postlabeling and ELISA. Carcinogenesis. 1994;15:557–560. doi: 10.1093/carcin/15.3.557. [DOI] [PubMed] [Google Scholar]
  58. Satoh H. Occupational and environmental toxicology of mercury and its compounds. Ind Health. 2000;38:153–164. doi: 10.2486/indhealth.38.153. [DOI] [PubMed] [Google Scholar]
  59. Saunders CR, Ramesh A, Shockley DC. Modulation of neurotoxic behavior in F-344 rats by temporal disposition of benzo(a)pyrene. Toxicol Lett. 2002;129:33–45. doi: 10.1016/s0378-4274(01)00467-2. [DOI] [PubMed] [Google Scholar]
  60. Saunders CR, Shockley DC, Knuckles ME. Fluoranthene-induced neurobehavioral toxicity in F-344 rats. Int J Toxicol. 2003;22:263–276. doi: 10.1080/10915810305114. [DOI] [PubMed] [Google Scholar]
  61. Song J, Zhu YM. Children’s Neuropsychological Tests. 2. Shanghai, China: Shanghai Scientific and Technological Publishing Company; 1987. [Google Scholar]
  62. Šrám RJ, Binková B, Dejmek J, Bobak M. Ambient air pollution and pregnancy outcomes: a review of the literature. Environ Health Perspect. 2005;113:375–382. doi: 10.1289/ehp.6362. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Surkan PJ, Zhang A, Trachtenberg F, Daniel DB, McKinlay S, Bellinger DC. Neuropsychological function in children with blood lead levels < 10 microg/dL. Neurotoxicology. 2007;28:1170–1177. doi: 10.1016/j.neuro.2007.07.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Tang D, Li TY, Liu JJ, Chen YH, Qu L, Perera FP. PAH-DNA Adducts in cord blood and fetal and child development in a Chinese cohort. Environ Health Perspect. 2006;114:1297–1300. doi: 10.1289/ehp.8939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Tang D, Phillips DH, Stampfer M, Mooney LA, Hsu Y, Cho S, et al. Association between carcinogen-DNA adducts in white blood cells and lung cancer risk in the physicians health study. Can Res. 2001;61:6708–6712. [PubMed] [Google Scholar]
  66. U.S. EPA. Graphite Furnace Atomic Absorption Spectrophotometry, Method No. 7010. Washington, DC: U.S. Environmental Protection Agency; 2007. [Google Scholar]
  67. Vesselinovitch SC, Kandala DR, Mihailovich N. Neoplastic response of mouse tissues during perinatal age periods and its significance in chemical carcinogenesis. NCI Monogr. 1975;51:230–250. [PubMed] [Google Scholar]
  68. Wang D, He L, Wei S, Feng X. Estimation of mercury emission from different sources to atmosphere in Chongqing, China. Sci Total Environ. 2006;366:722–728. doi: 10.1016/j.scitotenv.2005.09.054. [DOI] [PubMed] [Google Scholar]
  69. Wasserman GA, Liu X, Lolacono NJ, Factor-Litvak P, Kline J, Popovac D, et al. Lead exposure and intelligence in 7-year-old children: the Yugoslavia Prospective Study. Environ Health Perspect. 1997;105:956–962. doi: 10.1289/ehp.97105956. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Wasserman GA, Liu X, Popovac D, Factor-Litvak P, Kline J, Waternaux C, et al. The Yugoslavia Prospective Lead Study: contributions of prenatal and postnatal lead exposure to early intelligence. Neurotoxicol Teratol. 2000;22:811–818. doi: 10.1016/s0892-0362(00)00106-9. [DOI] [PubMed] [Google Scholar]
  71. WHO. Geneva: World Health Organization; 1986. Principles for Evaluating Health Risks from Chemicals during Infancy and Early Childhood: The Need for a Special Approach. Environmental Health Criteria Series, No. 59. [Google Scholar]
  72. WHO. A Critical Link, Interventions for Physical Growth and Psychological Development. Geneva: World Health Organization, Department of Child and Adolescent Health and Development; 1999. [Google Scholar]
  73. WHO. The Importance of Caregiver-Child Interactions for the Survival and Healthy Development of Young Children. Geneva: World Health Organization, Department of Child and Adolescent Health and Development; 2004. [Google Scholar]
  74. Whyatt RM, Perera FP. Application of biologic markers to studies of environmental risks in children and the developing fetus. Environ Health Perspect. 1995;103:105–110. doi: 10.1289/ehp.95103s6105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Whyatt RM, Santella RM, Jedrychowski W, Garte SJ, Bell DA, Ottman R, et al. Relationship between ambient air pollution and procarcinogenic DNA damage in Polish mothers and newborns. Environ Health Perspect. 1998;106:821–826. doi: 10.1289/ehp.98106821. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Wormley DD, Chirwa S, Nayyar T, Wu J, Johnson S, Brown LA, et al. Inhaled benzo(a)pyrene impairs long-term potentiation in the F1 generation rat dentate gyrus. Cell Mol Biol (Noisy-le-Grand) 2004a;50:715–721. [PubMed] [Google Scholar]
  77. Wormley DD, Ramesh A, Hood DB. Environmental contaminant-mixture effects on CNS development, plasticity, and behavior. Toxicol Appl Pharmacol. 2004b;197:49–65. doi: 10.1016/j.taap.2004.01.016. [DOI] [PubMed] [Google Scholar]
  78. Xu F, Yin XM, Zhang M, Leslie E, Ware R, Owen N. Family average income and diagnosed type 2 diabetes in urban and rural residents in regional mainland China. Diabet Med. 2006;23:1239–1246. doi: 10.1111/j.1464-5491.2006.01965.x. [DOI] [PubMed] [Google Scholar]
  79. Zajusz-Zubek E, Konieczynski J. Dynamics of trace elements release in a coal pyrolysis process. Fuel. 2003;82:1281–1290. [Google Scholar]
  80. Zhang J, Han C-L, Xu Y-Q. The release of the hazardous elements from coal in the initial stage of combustion process. Fuel Process Technol. 2003;84:121–133. [Google Scholar]
  81. Zhang X, Li J. The revise of Gesell Developmental Scale on 3½–6 years of age in Beijing [in Chinese] Zhongguo Dang Dai Er Ke Za Zhi. 1994;2:148–150. [Google Scholar]
  82. Zhou X, Luo Y, Liang J, Chen T, Zhuang N, Zheng S, et al. Follow-up study of mental developments in high-risk children [in Chinese] Zhejiang Da Xue Xue Bao Yi Xue Ban. 2004;33:449–451. doi: 10.3785/j.issn.1008-9292.2004.05.016. [DOI] [PubMed] [Google Scholar]
  83. Zhu H, Zhao ZY, Jiang YJ, Liang L, Wang JY, Mao HQ, et al. Multifactorial analysis of effects of mothers’ auto-immune thyroid disease on their infants’ intellectual development [in Chinese] Zhonghua Er Ke Za Zhi. 2005;43:340–344. [PubMed] [Google Scholar]

Articles from Environmental Health Perspectives are provided here courtesy of National Institute of Environmental Health Sciences

RESOURCES