Undergraduate/Graduate Category: Postdoc Degree Level: Ph.D ...
Undergraduate/Graduate Category: Postdoc Degree Level: Ph.D. Abstract ID# 687
PROTECT: Puerto Rico Testsite for Exploring Contamination Threats PROTECT: Puerto RicoPROTECT Testsite for Exploring Contamination Threats SIGNIFICANCE AND APPROACH The preterm birth rate in Puerto Rico is 17.7% of live births. At 50% above the U.S. average, it is the highest rate of any U.S. jurisdiction, below only Malawi (18.1%) globally, a discrepancy that is not explained by other socio-cultural factors. Our investigations suggest that the higher preterm birth rates in Puerto Rico cannot be explained by changes in obstetric practices, and that there is compelling preliminary evidence that exposure to hazardous chemicals contributes to preterm birth. Puerto Rico has 16 active Superfund sites and 200+ hazardous waste sites. Risk of exposure to contamination is high as many of these sites are unlined landfills that overlie karst aquifers which present highly susceptible pathways for exposure to contamination.
This program is supported by Award Number P42ES017198 from the National Institute of Environmental Health Sciences.
PROTECT is testing the hypothesis that exposure to hazardous chemicals contributes to the high rate of preterm birth in Puerto Rico. The Center brings together multidisciplinary researchers to study the transport, exposure, health impact and remediation of contaminants, with particular attention to chlorinated solvents and phthalates commonly found at Superfund sites, as both suspect and model agents in the high preterm birth rates in Puerto Rico.
Patterns of temporal scaling of groundwater level fluctuation *Xue Yu1, Reza Ghasemizadeh1, Ingrid Padilla2, David Kaeli3, Akram Alshawabkeh1 1Department of Civil and Environmental Engineering, Northeastern University; 2Department of Civil Engineering and Surveying, University of Puerto Rico, Mayaguez 3Department of Electrical and Computer Engineering, Northeastern University
*Xue Yu: [email protected]
Results and discussion
Motivations • Groundwater levels fluctuate continuously to achieve potentiometric balance
Mono-fractality
• The fluctuations are often not stationary
Multi-fractality
Analysis confirmed the existence of fractals in the fluctuations of groundwater level
• The fluctuations are often long range correlated, i.e. exhibiting fractals • Fractal analysis of the fluctuations is fundamental to more realistic hydrology modeling studies • Fractal analysis is useful in revealing intrinsic hydrogeological conditions and outer climatic and anthropogenic perturbations
Background • Aquifers are widely distributed in Puerto Rico • Various types of aquifers: karst aquifer, alluvial aquifer, south coast alluvial aquifer • Groundwater in karst aquifers is essentially stochastic due to the high heterogeneous hydrogeological formations • Human impacts such as significant water pumping have considerable effects on groundwater levels
Alluvial Aquifer Distribution of Fractal coefficients
Distribution in different aquifers
Wide spatial distribution of the fractal patterns even for the same type of wells
Objective Analyze the fractality (mono- and multi-) of groundwater level fluctuations, examine the origin of the fractality, and investigate the spatial patterns of the fractal scaling behaviors
South coast Aquifer
•
Singularity spectrum of groundwater level fluctuations were rather site specific, even for wells of the same types of aquifers
•
Fluctuation processes were smoother and less multifractal in the alluvial aquifer than other types of aquifers
•
Fluctuation processes were more likely to be multifractal and hetergeneous in the south coast aquifers
•
The multifractal behavior of the fluctuations in the karst aquifer is in the middle, which may be due to more turbulent groundwater flow as well as the presence of multi-porosity in the karst aquifer
Methods •
•
Data preparation
•
• Shallow aquifers generally have greater power of fractal scaling correlations
Multi-fractal analysis
o Fill missing data using line join method
o Wavelet transform modulus maxima (WTMM)
o Remove seasonality and periodicity
o Based on basic functions known as wavelets, which uses varying moments to investigate the recurrences of fluctuations
Mono-fractal analysis o Detrended fluctuation analysis (DFA)
Power-law relationship of fluctuations vs. step size 𝑨𝒍𝒐𝒈𝟏𝟎 𝑭 𝒍 = β𝒍𝒐𝒈𝟏𝟎 𝑭 𝒍 + α β : 1~1.5 anti-persistent correlation β : 1.5
Brownian noise (fBm)
β : 1.5~2 positive-persistent correlation
• Fractal behaviors in the karst aquifers are more related to the local hydrogeological conditions
Crossovers occurred at approximately 25 days • 25 days: anti-persistent correlation
• The higher monofractal and less multifractal behavior of the fluctuations in the alluvial aquifers is due to the higher storage capacity
Calculation of fractal dimension D(h) and Hurst exponent h(q) based on moments q:
• The karst aquifer exhibits more monofractal and less multifractal behaviors, which is caused by the combined effect of the lower storage capacity and the perturbations of pumping wells
ατ(𝒒) 𝒉=𝒉 𝒒 = α𝒒 𝑫(𝒉) = 𝒊𝒏𝒇𝒒 [𝒒. 𝒉 − τ 𝒒 ] To quantify the differences of the singularities due to different underlying processes, D(h) is modeled as: 𝟐
𝑫(𝒉) =] 𝑨𝑨[𝒉 𝒒 − 𝒉𝒎𝒂𝒙 ] + 𝑩[𝒉 𝒒 − 𝒉
𝒎𝒂𝒙
]+𝑪
Where B denote the measurement of the asymmetry for the curve: B >0 : left-skewed, smoother structure B
PROTECT: Puerto Rico Testsite for Exploring Contamination Threats PROTECT: Puerto RicoPROTECT Testsite for Exploring Contamination Threats SIGNIFICANCE AND APPROACH The preterm birth rate in Puerto Rico is 17.7% of live births. At 50% above the U.S. average, it is the highest rate of any U.S. jurisdiction, below only Malawi (18.1%) globally, a discrepancy that is not explained by other socio-cultural factors. Our investigations suggest that the higher preterm birth rates in Puerto Rico cannot be explained by changes in obstetric practices, and that there is compelling preliminary evidence that exposure to hazardous chemicals contributes to preterm birth. Puerto Rico has 16 active Superfund sites and 200+ hazardous waste sites. Risk of exposure to contamination is high as many of these sites are unlined landfills that overlie karst aquifers which present highly susceptible pathways for exposure to contamination.
This program is supported by Award Number P42ES017198 from the National Institute of Environmental Health Sciences.
PROTECT is testing the hypothesis that exposure to hazardous chemicals contributes to the high rate of preterm birth in Puerto Rico. The Center brings together multidisciplinary researchers to study the transport, exposure, health impact and remediation of contaminants, with particular attention to chlorinated solvents and phthalates commonly found at Superfund sites, as both suspect and model agents in the high preterm birth rates in Puerto Rico.
Patterns of temporal scaling of groundwater level fluctuation *Xue Yu1, Reza Ghasemizadeh1, Ingrid Padilla2, David Kaeli3, Akram Alshawabkeh1 1Department of Civil and Environmental Engineering, Northeastern University; 2Department of Civil Engineering and Surveying, University of Puerto Rico, Mayaguez 3Department of Electrical and Computer Engineering, Northeastern University
*Xue Yu: [email protected]
Results and discussion
Motivations • Groundwater levels fluctuate continuously to achieve potentiometric balance
Mono-fractality
• The fluctuations are often not stationary
Multi-fractality
Analysis confirmed the existence of fractals in the fluctuations of groundwater level
• The fluctuations are often long range correlated, i.e. exhibiting fractals • Fractal analysis of the fluctuations is fundamental to more realistic hydrology modeling studies • Fractal analysis is useful in revealing intrinsic hydrogeological conditions and outer climatic and anthropogenic perturbations
Background • Aquifers are widely distributed in Puerto Rico • Various types of aquifers: karst aquifer, alluvial aquifer, south coast alluvial aquifer • Groundwater in karst aquifers is essentially stochastic due to the high heterogeneous hydrogeological formations • Human impacts such as significant water pumping have considerable effects on groundwater levels
Alluvial Aquifer Distribution of Fractal coefficients
Distribution in different aquifers
Wide spatial distribution of the fractal patterns even for the same type of wells
Objective Analyze the fractality (mono- and multi-) of groundwater level fluctuations, examine the origin of the fractality, and investigate the spatial patterns of the fractal scaling behaviors
South coast Aquifer
•
Singularity spectrum of groundwater level fluctuations were rather site specific, even for wells of the same types of aquifers
•
Fluctuation processes were smoother and less multifractal in the alluvial aquifer than other types of aquifers
•
Fluctuation processes were more likely to be multifractal and hetergeneous in the south coast aquifers
•
The multifractal behavior of the fluctuations in the karst aquifer is in the middle, which may be due to more turbulent groundwater flow as well as the presence of multi-porosity in the karst aquifer
Methods •
•
Data preparation
•
• Shallow aquifers generally have greater power of fractal scaling correlations
Multi-fractal analysis
o Fill missing data using line join method
o Wavelet transform modulus maxima (WTMM)
o Remove seasonality and periodicity
o Based on basic functions known as wavelets, which uses varying moments to investigate the recurrences of fluctuations
Mono-fractal analysis o Detrended fluctuation analysis (DFA)
Power-law relationship of fluctuations vs. step size 𝑨𝒍𝒐𝒈𝟏𝟎 𝑭 𝒍 = β𝒍𝒐𝒈𝟏𝟎 𝑭 𝒍 + α β : 1~1.5 anti-persistent correlation β : 1.5
Brownian noise (fBm)
β : 1.5~2 positive-persistent correlation
• Fractal behaviors in the karst aquifers are more related to the local hydrogeological conditions
Crossovers occurred at approximately 25 days • 25 days: anti-persistent correlation
• The higher monofractal and less multifractal behavior of the fluctuations in the alluvial aquifers is due to the higher storage capacity
Calculation of fractal dimension D(h) and Hurst exponent h(q) based on moments q:
• The karst aquifer exhibits more monofractal and less multifractal behaviors, which is caused by the combined effect of the lower storage capacity and the perturbations of pumping wells
ατ(𝒒) 𝒉=𝒉 𝒒 = α𝒒 𝑫(𝒉) = 𝒊𝒏𝒇𝒒 [𝒒. 𝒉 − τ 𝒒 ] To quantify the differences of the singularities due to different underlying processes, D(h) is modeled as: 𝟐
𝑫(𝒉) =] 𝑨𝑨[𝒉 𝒒 − 𝒉𝒎𝒂𝒙 ] + 𝑩[𝒉 𝒒 − 𝒉
𝒎𝒂𝒙
]+𝑪
Where B denote the measurement of the asymmetry for the curve: B >0 : left-skewed, smoother structure B
