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Correlations of Agrochemical Residues in Drinking Water and Birth Defects in IL. Manoj K. Mohanty and Baojie Zhang Department of Mining and Mineral Resources Engineering Southern Illinois University at Carbondale. Outline. Introduction Objective Project Methodology Data Compilation
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Correlations of Agrochemical Residues in Drinking Water and Birth Defects in IL Manoj K. Mohanty and Baojie Zhang Department of Mining and Mineral Resources Engineering Southern Illinois University at Carbondale
Outline • Introduction • Objective • Project Methodology • Data Compilation • Data Analysis • Individual correlation coefficients (r) • Hypothesis testing • Multiple regression analysis • Monthly Average Concentrations • Conclusions • Recommendations • Acknowledgements
Introduction (cont.) Causes of Birth Defects Illustration by Electronic Illustrators Group. http://medical-dictionary.thefreedictionary.com/Birth+Defects
Introduction (cont.) US Corn-belt • Past studies indicate: • Illinois- among the highly nitrate contaminated states. • Atrazine was detected in146 streams out of 149 sampled in the Midwestern states. • Iowa study- Higher rates of intrauterine growth retardation (IUGR) with higher level of atrazine in drinking water • Indiana study- Detrimental effects of disinfectant byproducts. Source: http://en.wikipedia.org/wiki/Grain_Belt
Objective To investigate the correlation of incidence rates of various negative reproductive outcomes with the concentration of key agrochemical based contaminants and disinfectant byproducts in drinking water used in Illinois. Negative reproductive outcomes: • Birth defects • Adverse pregnancy outcomes • Preterm births Drinking Water Contaminants: • Nitrate • Nitrite • Atrazine • Total trihalomethanes (TTHM) • Five haloacetic acids (HAA5)
Project Methodology • Negative reproductive outcome (NRO) data for each county in Illinois for the five year period: 1998-2002. • Drinking-water contaminant data from community water supplies (CWS) for the same time period. • Correlation coefficients (r) between individual NRO and drinking water contaminants based on sample data and hypothesis testing. • Multiple regression analysis to investigate the correlations and their statistical significance by considering all five water contaminants simultaneously.
Birth Defect Data Example County: ADAMS
Birth Defect Data Summary • For all 102 counties in Illinois over the period of 1998-2002:
Water Contaminant Data County: ADAMS Nitrate Data
Water Contaminant Data (cont.) County: ADAMS Nitrite Data
Nitrite Data for Each County
Data Analysis • Correlation Analysis where, SSxy= SSxx= SSyy= xi: contaminant concentration for each county yi: rate of negative reproductive outcome for each county • Hypothesis testing where n represents the number of county water contaminant concentration values and r is the sample correlation coefficient
Data Analysis (cont.) Sample correlation coefficient and hypothesis testing results BD: Birth defect; APO: Adverse Pregnancy Outcome; PB: Preterm Birth
Data Analysis (cont.) Statistically Significant Correlations BD: Birth defect; APO: Adverse Pregnancy Outcome; PB: Preterm Birth
Regression Analysis: Summary Results BD: Birth defect; APO: Adverse Pregnancy Outcome; PB: Preterm Birth
Conclusions • As much as 16.3%, 35.8% and 20.6% of the variability in the rates of birth defects, adverse pregnancy outcomes and preterm births is explained by five contaminants (nitrate, nitrite, atrazine, TTHM and HAA5) in public drinking water supplies in IL. • TTHM, HAA5 and Nitrate- statistically significant for all three categories of negative reproductive outcomes. • Nitrite is significant for APO and PB only. • Atrazine is significant for all three categories of negative reproductive outcomes except the BD model based on censored data and PB model based on observed data.
Conclusions (cont.) • The monthly average concentrations of all three agro-chemical based contaminants are much higher in surface water based CWS. • Concentration of disinfectant byproducts are more in the GW based water supplies. • Atrazine concentration peaks in the months of May/June-agrees well with past studies. • The peak monthly average concentrations (118 μg/L in May and 98 μg/L in November) for TTHM are well above the corresponding MCL of 80 μg/L . • The peak concentrations of HAA5 of 75 μg/L in May and 100 μg/L in November for HAA5 are well above the corresponding MCL of 60μg/L .
Recommendations • Surface water based CWS and Ground water based CWS may be separately examined. • For developing meaningful correlations for some of the individual BD and APO, a data set covering a much longer time period (maybe 10 to 20 years) will be required. • A much more comprehensive study using controlled experiments in future should include all known factors contributing to various negative reproductive outcomes to develop predictive models for each or at least some of them.
Acknowledgements • Illinois Sustainable Technology Center • United States Geological Survey • Illinois Environmental Protection Agency • Illinois Department of Public Health • Indiana University Medical Research Center
Results (cont.) Correlation among the exploratory variables
Occurrence of a specific adverse outcome is assumed to be a rare event, therefore such occurrences are assumed to follow a Poisson distribution. Where there are a large number of birth defect cases, the confidence interval is narrow, indicating that the rate is stable. Where there are few birth defect cases, the confidence interval becomes very wide, indicating that the rate is not very stable. - where Y is the observed number of events, Yl and Yu are lower and upper confidence limits for Y respectively, c²n,a is the chi-square quantile for upper tail probability a on n degrees of freedom.
