international cooperation in studies and provide a
standard set of data for the comparison of global
cities’ air pollution footprints. Satellite observations also allow for the tracking of these city
footprints over time, allowing for a record of the
trend toward sustainability. These advantages
make satellite observation an important evolving
tool in global cooperation in the pursuit of sustainability. Developing methods for using
satellite-derived data to provide information on
sustainability should be a priority in future.
Emissions Monitoring
Emissions monitoring provides data on the rates
of emissions from pollution sources. These data
make for strong inputs into air quality models
[78], can ensure that emissions are not exceeding
permitted values (either legally permitted or permitted with respect to sustainability), and lead to
confirmation that emissions are decreasing over
time in compliance with regulations. Emissions
trading schemes are dependent on emissions monitoring. Figure 3 shows a Google Earth image of
point sources of air pollution in North America.
Continuous Emissions Monitoring Systems
(CEMS) quantify gas or particulate emissions by
taking measurements as pollution is released.
CEMS are common for some major stationary
sources in the USA and Europe, where their use
is mandated by legislation [85, 86]. PEMS
(Predictive Emissions Monitoring Systems) use
emissions factors and activity indicators (e.g.,
fuel use, engine types, duty cycles) in calculations
that estimate emissions without measuring them.
CEMS and PEMS are commonly applied to stationary sources or point sources, but emissions
monitoring can also be applied to mobile sources
such as locomotives [87] and ships [88]. Sources
too numerous to be monitored as individual
sources are often treated as “area sources”
[89]. To treat sources such as residential furnaces
or urban fuel stations as single sources would not
be practical, so emissions from these sources are
often calculated and treated as emissions not from
the individual sources but from the bulk of those
sources of a given type over an area. Agricultural
sources such as livestock operations and emissions of N 2 O from fertilizers would be considered
area sources, and it is sometimes useful to think of
vehicle emissions as arising from area (or line)
sources. Monitoring emissions from any of these
sources requires cooperation from industry and
government [78]. Efforts made toward achieving
sustainability will rely on accurate and timely
emissions monitoring data both to inform modelbased decision making and to ensure that emission
reduction goals are achieved.
Numerical Modeling
Air quality models use input from meteorological
models and emissions from point and area sources
to estimate air pollutant levels and deposition
amounts based upon current understanding of relevant chemical and physical processes. Models
can predict chemical changes in air pollutants
over time [14], clarifying links between secondary
pollutants and sources. Models can estimate
changes in air pollution concentrations that
would result from changes in practice (i.e., emissions), making modeling a valuable tool in policy
design and analysis of proposed policy
[78]. While originally developed mainly for this
purpose, models are increasingly being used to
provide optimal estimates of air pollutant concentrations over areas where data do not exist.
Approaches for using models in conjunction
with in situ monitoring data and satellite data are
rapidly evolving [83, 90]. This practice can lead to
the most complete, consistent spatial-temporal
picture of air pollutant levels on the regional to
global scale and is also valuable for identifying
weaknesses in current emission inventories.
These “fused” data (data derived from multiple
types of observations) can extend estimates of the
impact of air pollution has on health and the
environment, helping to better document problems and improve research. Increasing interest in
providing the public with future air quality information, which as described above can help reduce
impacts, has led to models that are optimized for
forecasting on a daily basis.
Regardless of their application, air quality
models require evaluation to assess the reliability
of their results. Any model being used to inform
policy should have each of its aspects verified,
including its design, methods for representing
Air Pollution Monitoring and Sustainability
401
standard set of data for the comparison of global
cities’ air pollution footprints. Satellite observations also allow for the tracking of these city
footprints over time, allowing for a record of the
trend toward sustainability. These advantages
make satellite observation an important evolving
tool in global cooperation in the pursuit of sustainability. Developing methods for using
satellite-derived data to provide information on
sustainability should be a priority in future.
Emissions Monitoring
Emissions monitoring provides data on the rates
of emissions from pollution sources. These data
make for strong inputs into air quality models
[78], can ensure that emissions are not exceeding
permitted values (either legally permitted or permitted with respect to sustainability), and lead to
confirmation that emissions are decreasing over
time in compliance with regulations. Emissions
trading schemes are dependent on emissions monitoring. Figure 3 shows a Google Earth image of
point sources of air pollution in North America.
Continuous Emissions Monitoring Systems
(CEMS) quantify gas or particulate emissions by
taking measurements as pollution is released.
CEMS are common for some major stationary
sources in the USA and Europe, where their use
is mandated by legislation [85, 86]. PEMS
(Predictive Emissions Monitoring Systems) use
emissions factors and activity indicators (e.g.,
fuel use, engine types, duty cycles) in calculations
that estimate emissions without measuring them.
CEMS and PEMS are commonly applied to stationary sources or point sources, but emissions
monitoring can also be applied to mobile sources
such as locomotives [87] and ships [88]. Sources
too numerous to be monitored as individual
sources are often treated as “area sources”
[89]. To treat sources such as residential furnaces
or urban fuel stations as single sources would not
be practical, so emissions from these sources are
often calculated and treated as emissions not from
the individual sources but from the bulk of those
sources of a given type over an area. Agricultural
sources such as livestock operations and emissions of N 2 O from fertilizers would be considered
area sources, and it is sometimes useful to think of
vehicle emissions as arising from area (or line)
sources. Monitoring emissions from any of these
sources requires cooperation from industry and
government [78]. Efforts made toward achieving
sustainability will rely on accurate and timely
emissions monitoring data both to inform modelbased decision making and to ensure that emission
reduction goals are achieved.
Numerical Modeling
Air quality models use input from meteorological
models and emissions from point and area sources
to estimate air pollutant levels and deposition
amounts based upon current understanding of relevant chemical and physical processes. Models
can predict chemical changes in air pollutants
over time [14], clarifying links between secondary
pollutants and sources. Models can estimate
changes in air pollution concentrations that
would result from changes in practice (i.e., emissions), making modeling a valuable tool in policy
design and analysis of proposed policy
[78]. While originally developed mainly for this
purpose, models are increasingly being used to
provide optimal estimates of air pollutant concentrations over areas where data do not exist.
Approaches for using models in conjunction
with in situ monitoring data and satellite data are
rapidly evolving [83, 90]. This practice can lead to
the most complete, consistent spatial-temporal
picture of air pollutant levels on the regional to
global scale and is also valuable for identifying
weaknesses in current emission inventories.
These “fused” data (data derived from multiple
types of observations) can extend estimates of the
impact of air pollution has on health and the
environment, helping to better document problems and improve research. Increasing interest in
providing the public with future air quality information, which as described above can help reduce
impacts, has led to models that are optimized for
forecasting on a daily basis.
Regardless of their application, air quality
models require evaluation to assess the reliability
of their results. Any model being used to inform
policy should have each of its aspects verified,
including its design, methods for representing
Air Pollution Monitoring and Sustainability
401