direction, interactions between urban form and
wind speed, building design, green spaces, and
traffic control schemes. All of these aspects of
urban design require further research to determine
their impact on air pollution, while data from
monitoring will help to substantiate the understanding thereby derived.
Designing urban areas for walkability and public transit will also impact emissions of air pollution. PODs (Pedestrian-Oriented Development)
and TODs (Transit-Oriented Development) such
as those discussed in Newman et al. [126], can
lower transportation energy demand and thereby
reduce emissions from vehicles. A study of
walkability in Vancouver showed that urban
design had a substantial impact on walkability
and air pollution exposure, and proved an effective method for connecting air pollution monitoring to urban design and possible future
development [127]. Designing for less dependence on automobiles is a very effective strategy
for using urban form to mitigate air pollution, and
such design has other sustainability benefits such
as improved health from physical activity
[127]. Of course, designing for urban density
will result in more instances of exposure to
urban air pollution, providing additional incentive
to minimize emissions.
The air pollution monitoring community can
also contribute to improved urban form by uniting
air pollution models with urban design models.
Involving the air pollution monitoring community
in an integrated design process for cities could
lead to reductions in emissions and exposure in
existing areas by designing new developments to
decrease the negative impacts of stagnant air
within urban canyons. A stronger air quality perspective on urban design could result in more
sustainable places to live.
Acceleration of Learning and Research The
majority of investment in air pollution monitoring
is directed not toward research or improvement of
research capabilities but toward assessing compliance with existing air pollution concentrations
standards [92]. The focus on compliance to the
exclusion of other objectives reduces the value of
data acquired in that the data could also be used
for source apportionment, chemical transport
modeling, assessment of background levels, and
relationships between pollutants and other effects
[92]. The link between air pollution and urban
form could also be better established if available
data were used for that purpose. If priority continues to be given to compliance over other uses of
data, few of the other goals discussed herein will
be easily pursued. Chow and Watson [92] give
specific practices that can be implemented to shift
emphasis toward research, including calibrating
instruments to detect lower concentrations,
decreasing time resolution of sampling, and
reporting data to the level of detection rather
than rounding to convenient orders of magnitude.
Policy recommendations are also made, including
periodic reviews of instrument performance.
Acceleration of learning, of which research is a
fundamental part, should be more of a focus in
routine monitoring. Thus resources need to be
allocated to ongoing analysis and interpretation
of monitoring data.
Folke et al. [116] discuss the relationship
between adaptive capacity and sustainability.
To lay claim to sustainability, a society must be
able to manage adaptively to socio-ecological
changes. This capacity, Folke et al. [116] argue,
requires mechanisms that allow governments to
learn, meaning that the institutional capacity to
learn must be optimized. It is not optimized in
the current situation as described by Chow and
Watson [92], wherein data are primarily oriented
toward fulfilling routine functions. Adaptive
capacity also requires spaces for institutional
collaboration [116]. Data that are conventionally
less informative than they could be, as air pollution monitoring data commonly are [92], are not
optimally suited for collaborative purposes. The
case of the need for fine spatial resolution in the
MacKerron and Mourato [120] study of socioeconomic distribution of air pollution is an
example where routine data would not have
served the study’s interdisciplinary purpose.
The capacity to adapt requires not only strong
data, but the ability to extract maximum information from that data. Adaptive capacity, and
therefore sustainability, would be served by the
improvements suggested by Chow and Watson
Air Pollution Monitoring and Sustainability
411
wind speed, building design, green spaces, and
traffic control schemes. All of these aspects of
urban design require further research to determine
their impact on air pollution, while data from
monitoring will help to substantiate the understanding thereby derived.
Designing urban areas for walkability and public transit will also impact emissions of air pollution. PODs (Pedestrian-Oriented Development)
and TODs (Transit-Oriented Development) such
as those discussed in Newman et al. [126], can
lower transportation energy demand and thereby
reduce emissions from vehicles. A study of
walkability in Vancouver showed that urban
design had a substantial impact on walkability
and air pollution exposure, and proved an effective method for connecting air pollution monitoring to urban design and possible future
development [127]. Designing for less dependence on automobiles is a very effective strategy
for using urban form to mitigate air pollution, and
such design has other sustainability benefits such
as improved health from physical activity
[127]. Of course, designing for urban density
will result in more instances of exposure to
urban air pollution, providing additional incentive
to minimize emissions.
The air pollution monitoring community can
also contribute to improved urban form by uniting
air pollution models with urban design models.
Involving the air pollution monitoring community
in an integrated design process for cities could
lead to reductions in emissions and exposure in
existing areas by designing new developments to
decrease the negative impacts of stagnant air
within urban canyons. A stronger air quality perspective on urban design could result in more
sustainable places to live.
Acceleration of Learning and Research The
majority of investment in air pollution monitoring
is directed not toward research or improvement of
research capabilities but toward assessing compliance with existing air pollution concentrations
standards [92]. The focus on compliance to the
exclusion of other objectives reduces the value of
data acquired in that the data could also be used
for source apportionment, chemical transport
modeling, assessment of background levels, and
relationships between pollutants and other effects
[92]. The link between air pollution and urban
form could also be better established if available
data were used for that purpose. If priority continues to be given to compliance over other uses of
data, few of the other goals discussed herein will
be easily pursued. Chow and Watson [92] give
specific practices that can be implemented to shift
emphasis toward research, including calibrating
instruments to detect lower concentrations,
decreasing time resolution of sampling, and
reporting data to the level of detection rather
than rounding to convenient orders of magnitude.
Policy recommendations are also made, including
periodic reviews of instrument performance.
Acceleration of learning, of which research is a
fundamental part, should be more of a focus in
routine monitoring. Thus resources need to be
allocated to ongoing analysis and interpretation
of monitoring data.
Folke et al. [116] discuss the relationship
between adaptive capacity and sustainability.
To lay claim to sustainability, a society must be
able to manage adaptively to socio-ecological
changes. This capacity, Folke et al. [116] argue,
requires mechanisms that allow governments to
learn, meaning that the institutional capacity to
learn must be optimized. It is not optimized in
the current situation as described by Chow and
Watson [92], wherein data are primarily oriented
toward fulfilling routine functions. Adaptive
capacity also requires spaces for institutional
collaboration [116]. Data that are conventionally
less informative than they could be, as air pollution monitoring data commonly are [92], are not
optimally suited for collaborative purposes. The
case of the need for fine spatial resolution in the
MacKerron and Mourato [120] study of socioeconomic distribution of air pollution is an
example where routine data would not have
served the study’s interdisciplinary purpose.
The capacity to adapt requires not only strong
data, but the ability to extract maximum information from that data. Adaptive capacity, and
therefore sustainability, would be served by the
improvements suggested by Chow and Watson
Air Pollution Monitoring and Sustainability
411