Metro Manila Emissions Inventory and Air Quality Management

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Metro Manila Emissions Inventory and Air Quality
Management

• Dr. Emmanuel G. Anglo
• Manila Observatory/ Ateneo de Manila University
• Previous Emissions Inventories
• Estimation Methods and Data Requirements
• Possible Indices for the Air Quality Action Plan
• Recommendations

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Background

• Air Quality Action Plan calls for targets that
• Are measurable (by sector in time and space)
• Indicate the success of actions
• Allow an objective means of selecting among
  actions and policies
• Typical Indicators
• Ambient concentrations
• Health effects or public exposure
• Economic costs
• Pollutant emission rates

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Comparison of AQ Indicators
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Estimating Vehicular Emissions

• Basic Formula
• Qv S (N x D x E)
• Where
• Qv total emissions in tons per year
• N number of units
• D distance traveled per year (km)
• E tons of pollutant emitted per km traveled

Q, N, D and E must be defined for each type of
vehicle engine size, private or public
transport, fuel
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Estimating Qv S (N x D x E)
Data come from many sources, often conflicting
and varying in quality
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Estimating Stationary Source Emissions

• Preferred Qs c x f
• where c is emission concentration (g/Ncm), f is
  volumetric flow rate (Ncm/s), Qs is emission
  rate (g/s) of a source in a facility
• Each term should be defined for each stack
• Emission concentration should be based on CEMS
• Alternative Qs Fc x EF
• Fc is the total fuel consumption and EF is a
  source-specific emission factor
• For non-energy sources, Fc may also be plant
  capacity or raw material consumption rate

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Estimating Area Source Emissions

• Landfill burning QL T x b x EF
• T is the daily volume of waste received, b is
  fraction burned
• Domestic cooking QH Fh x EF
• Fh is the total cooking fuel consumption

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Some Emissions Inventories in Metro Manila

• Manins (1991) - Dispersion modeling study funded
  by WHO
• Ayala (1993) produced EIs in 1987 and 1990 (ADB
  funded)
• URBAIR (1997) First major AQ study in Metro
  Manila funded by WB
• DOH Health Assessment (2003) and IES (2004)
  EIs by the Manila Observatory
• DENR AQ Status Report (2004)

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Comparison of Annual NCR Emissions Thousands of
Tons (KT) per Year
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Data used in MO Inventory Mobile Sources

• Data from published sources
• LTO registration, TEC AADTs (2000)
• GIS map of Metro Manila roads, including
  classification of each road (DOH)
• Total retail sales of gasoline and diesel in NCR
  (DOE)
• ADB emission factors
• Data from interviews
• Percentage of colorum vehicles
• Fuel consumption, distance traveled per vehicle
  type
• Hours per day, days per year on the road

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MM Roads
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Summary of Mobile Emissions
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Data used in MO Inventory Stationary Point
Sources

• Data from published sources
• Total sales of fuel oil, LPG in NCR (DOE)
• USEPA stationary source emission factors (AP-42)
• DENR list of industries (from SMRs)
• Name and address of industry
• Number of fuel-burning units in each industry
• Fuel type and consumption rate (total or by unit)

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Data used in MO Inventory Stationary Point
Sources

• Data from published sources
• MManila waste disposal rate per dumpsite (web
  sources)
• USEPA emission factors
• Limitations
• Population at barangay level was available but
  household emissions (cooking, garbage burning)
  were not included due to lack of EFs
• Road resuspension not included

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Estimating Emissions through Fuel Sales Data

• Works for both stationary and vehicular emissions
• Advantages
• Data readily available from oil companies and DOE
• Data divided into region, sector, year
• Disadvantages
• Bulk of fuel sold to industry cannot be accounted
  for
• Works well to estimate total consumption by motor
  vehicles, but difficult to subdivide into
  geographical or sub-sectoral emissions
• Difficult to convert into emissions without
  information on fuel efficiency or emission factors

• Fuel sales data are the most complete, most
  reliable but cannot be used as indicators nor
  targets
• Alternative or supplemental indicators are needed

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Conventional vs Alternative AQ Indicators

• Typical Conventional Indicators
• Ambient concentrations - Mean 98 percentile no.
  of exceedances
• Health Number of people and percentage of
  population exposed to high levels
• Emissions Total emissions by sector

• Most conventional indicators are hard to measure
  or have no historical records
• Estimates of these indicators are often based on
  other data anyway

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Supplemental/Alternative AQ Indices Stationary
Sources

• Number of sources in inventory
• Percentage of total industrial fuel sales
  accounted for in inventory
• Number and percentage of CEMS installed
• Energy use per industrial output
• Indicates fuel efficiency by industry type
• Fossil fuel use per energy produced
• Can be translated into emissions per MW

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Supplemental/Alternative AQ Indices Vehicular
Sources

• Number and percentage of vehicles that require
  emissions re-testing after initial failure
• Average age of registered vehicles

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For the Public MM AQ Health Index

• Part of the DOH Health Surveillance of Air
  Pollution-Related Diseases Project
• Main features
• Translate 24-hr mean PM10 and NOx concentrations
  into an index expressing the number of additional
  health cases
• AQ station data supplemented by modeling will be
  used to generate mean concentrations

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For the Public MM AQ Health Index

• Key issue Should AQH Index be specific to each
  LGU or uniform for MM?
• LGU-specific Recognizes differences in
  conditions among LGUs, but may be politically
  sensitive
• Uniform Single AQHI value for MM, but an LGU
  should know what the number means for itself

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Recommendations

• Stationary Sources
• Account for missing 90 of fuel used by
  industries in NCR
• Set a schedule for compliance with CEMS
  requirement
• Include map or coordinates of facilities and
  sources in inventory

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Recommendations

• Mobile Sources
• Review emission factors borrow from other
  similarly situated countries
• Include odometer readings in registration
• Map public tricycle routes
• Conduct a motor vehicle usage survey (per vehicle
  type)
• How many days per week is a vehicle used?
• How many hours on the road?
• How much is spent on fuel?

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Recommendations

• Gather Data on meteorology of Metro Manila
• Required if emissions are to be translated into
  ambient concentrations
• PAGASA data gathering NOT responsive to needs of
  AQ modeling and other environmental studies
• Top-level coordination is required No mention of
  its role in Clean Air Act in PAGASA modernization
  plan