Geoscience Reference
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development in suburban areas but high-density land use in central cities associated
with urban sprawl.
After World War II, since the building of interstate highways, urban sprawl
is the dominant development pattern throughout much of the US. Over the past
several decades, populations have moved away from central cities to surrounding
areas. During the 1970s and 1980s, over 95% of US population growth happened
in suburban areas. Sprawl is converting forestland, agriculture land and wetlands
into developed area including residential, commercial, industrial, and transportation
uses. Low-density development tends to occupy far more land than higher-density
urban centers (Robinson et al.
2005
). As a result, more impact might be caused by
land development on natural environment including water quality in low-density
less-urbanized suburban areas than high-density highly-urbanized cities, which is
proved by the results of this study.
Considering the stronger adverse impact of urbanization on water quality in less-
urbanized suburban areas than highly-urbanized central cities associated with urban
sprawl, “smart growth” might be a better development pattern. Smart growth is
an urbanization pattern that urban development is concentrated in the center of a
city to avoid urban sprawl, and compact, transit-oriented, walkable, and bicycle-
friendly land use is encouraged (Miller and Hoel
2002
; Shore
2006
). The goal of
“smart growth” includes “better coordinated planning with input from the public,
providing multiple transportation and housing choices, providing green space to
make communities attractive, using mixed-use development, and infill strategies
(new construction within the urban core of cities and the inner suburbs)” (Miller
and Hoel
2002
). Under smart growth, less-density development in suburbs is lim-
ited, and high-density development is kept in central cities. Thus, the adverse impact
of urbanization on water quality in suburbs found in this study might be controlled.
Clearly, this study provides an evidence for policy makers to oppose urban sprawl
and to support smart growth.
In addition, this study extended the application of GWR to water resources
research. Conventional statistical techniques are not able to explore the spatially
varying correlations between urbanization and water quality indicators, and unable
to examine the spatially varying abilities of urbanization indicators to explain water
quality change. GWR can also unveil previously unknown information on the local
pollution sources. For instance, this study found that the impact of developed land on
dissolved nutrients is stronger in highly-urbanized areas than less-urbanized areas
using GWR. This result can lead environmental scientists to find varying sources
of dissolved nutrients across space. In highly-urbanized areas, they are mainly con-
tributed by urban sources. However, in less-urbanized areas, more sources might
be responsible for dissolved nutrients, which bring mixed effect on the relation-
ship of developed land and nutrients, causing weaker relationship, and so non-urban
sources (e.g. agricultural activities) should also get attention. If conventional statis-
tical techniques were used, no different pollution sources would be detected since
the relationship between water quality and land use was the same for the whole
study area. GWR is also able to find which specific water pollutants at certain sam-
pling sties are more affected by human activities than others, which can also help
