College of Engineering
Department of Civil and Environmental Engineering
Department of Atmospheric, Oceanic and Space Sciences
|
Prof.
ANNA
M.
MICHALAK |
University
of
Michigan College of Engineering Department of Civil and Environmental Engineering Department of Atmospheric, Oceanic and Space Sciences |
 |
 |
 |
 |
 |
 |
 |
||||||
RESEARCH INTERESTS
|
||
| My research interests focus on characterizing complexity and quantifying uncertainty in environmental systems with the goal of improving our understanding of these systems and our ability to forecast their variability. My current research interests focus on water quality monitoring and contaminant source identification, use of remote sensing data for earth system characterization, and atmospheric greenhouse gas emission and sequestration estimation. The common theme of my research is the development and application of statistical and geostatistical data fusion methods for optimizing the use of limited in situ and remote sensing environmental data. I am also interested in the environmental policy, economic and legal impact and applicability of environmental research. | ||
|
Data Fusion for Water
Quality Monitoring and Forecasting
|
||||||||||||||||||
| The broad
availability of clean water is one of
the biggest achievements of environmental engineering as a profession. Despite the great technological and
regulatory achievements that aim to protect potable and recreational
water
uses, however, water quality is still primarily addressed in a
“real-time” framework. The development of
water quality forecasting
systems is essential to long-term sustainable water resource management. In anticipation of this goal, new tools are
needed to merge water quality data in statistically rigorous manner
while
making optimal use of the information provided by the available
measurements. Unlike weather monitoring
and forecasting, water quality assessment will always suffer from a
relative
sparsity of data due to the difficulty and expense associated with data
collection. As a result, a probabilistic
framework is essential to the success of any water quality monitoring
framework. Our group’s work in this area
focuses on inverse modeling methods for contaminant source
identification, geostatistical
data assimilation methods for surface and ground water quality
monitoring, and
statistical models for estimating the spatial and temporal distribution
of
groundwater contaminant plumes. The
long-term goal of this work is the development of water quality
forecasting
tools analogous to the existing weather forecasting products. Such a data assimilation framework for water
quality forecasting would have a great impact on environmental
management and
protection of human health. Recent and ongoing projects:
For additional information, please refer to publications. |
  |
|||||||||||||||||
Assessing Anthropogenic Impacts on Biogeochemical Cycles |
||||||||||||||||||
| Human
activity has had a significant impact on the cycling of trace gases,
nutrients,
water, and energy in the Earth system. Understanding
the
Earth’s current climate and predicting
its future
variability requires knowledge about the relationships controlling the
feedback
among the various components of the Earth system. Our
group’s
work in this area focuses on constraining
the global and regional budgets of carbon dioxide through the
development of
new geostatistical inverse modeling tools, attributing observed
variability to
biospheric, oceanic, and anthropogenic activities, and developing tools
for
merging data from diverse types of measurements taken across various
spatial
and temporal scales. In the long term,
as restrictions on carbon emissions become more commonplace, the tools
that we
develop could be used to inform systems such as carbon markets and
national/international treaties that will require scientific validation
of
carbon emission reduction and sequestration goals. Recent and ongoing projects:
For additional information, please refer to publications. |
   |
|||||||||||||||||
Use of Remote Sensing Data for Earth System Characterization |
||||||||||||||||||
| Existing
and upcoming remote sensing data products offer a unique opportunity
for
improving our understanding of the earth system, but they require new
and
rigorous statistical methods for merging information from data
collected at
different spatial and temporal resolutions. Several
of
our ongoing research projects focus on the use
of remote
sensing data for characterizing environmental systems.
This work involves both existing instruments
that provide data that can be used in conjunction with in situ
measurements,
and upcoming instruments aimed specifically at characterizing the
global carbon
cycle. Through collaboration with the
Orbiting Carbon Observatory (OCO) satellite team, we are developing
sampling and
gap-filling strategies for this satellite, which will allow it to
characterize
the spatial and temporal variability of atmospheric CO2. This satellite will be launched in 2008,
resulting in a unique atmospheric dataset that will make it possible to
constrain the carbon budget with greater precision than is currently
possible. In several related new
projects, we are using the current CO2 measurement network
to
understand the influence of processes and parameters that can be
measured by
satellite on carbon fluxes at global and regional scales. Recent and ongoing projects:
For additional information, please refer to publications. |
  |
|||||||||||||||||
Other Ongoing Projects
|
||||||||||||||||||
| In addition
to the main research
areas described above, our research group also collaborates with the
University of Michigan School of Public Health on projects related to
exploring the links between air quality and human health. |
||||||||||||||||||
|
||||||||||||||||||
Last modified: 05/21/10 by Anna M. Michalak |
||||||||||||||||||
