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Top-down Assessment of Air Pollution and GHGs for Dhaka , Bangladesh: Analysis of GAINS Derived Model Data

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Scott Randall

 

Executive Summary

 

A  combination  of  numerous  local  emissions  sources  in  addition  to  special  local  and regional    meteorological    conditions    gives    Dhaka    exceedingly    high    air    pollution concentrations  throughout  the  year,  and  especially  during  the  winter  season. The exposure  of  the  cities  estimated  12-15  million  residents  to  this  alarmingly  poor  air quality demands attention including immediate research and corresponding mitigation. Establishing emission inventories and conducting air pollution assessments are the first steps on the path to mitigating air quality problems.

 

The  city  of  Dhaka  was  chosen  for  this  assessment  due  to  the  current  ongoing  project Bangladesh  Air  Pollution  Management  (BAPMAN),  which  concentrates  mostly  on  the capital city Dhaka.  Through the BAPMAN project, a total bottom-up emissions inventory is  currently  being  performed,  and  it  is  useful  to  the  project  to  compare  top-down emissions data results.  The Greenhouse Gas and Air Pollution Interactions and Synergies model  (GAINS)  was  used  to  perform  this  top-down  assessment  due  to  the  model’s integrated assessment approach of capturing interactions between air pollution control and  economic  development,  as well as  its  focus on presenting  cost  effective  pollution control strategies.

 

Results from the GAINS model assessment for Dhaka shows that for 2010 the total PM2.5 emissions  were 35000  tons/year, and the total PM10  emissions were 45000  tons/year. The top sectors making up the PM emissions included Industry and Residential sectors, where  the  specific  sub-sectors  were  brick/cement  production  and  residential  cooking respectively. The top fuels making up the emissions were “no fuel use” and “fuelwood direct”.   GAINS estimates that the top 3 technical control measures available for PM can eliminate approximately 1/3 of the PM emissions at a cost of .65 MEuro/year.

 

GAINS  results  also  show  that  for  Dhaka  in  2010  the  total  SO2   emissions  were  34000 tons/year, dominated by the Industrial sector, made up of the sub-sectors of new power plants and industrial combustion, using hard coal and natural gas fuels. NOX  emissions for    Dhaka     in     2010     were    30000      tons/year,     dominated    by     the     Industrial (combustion/power plants) and Transport sectors. The fuels used by these two sectors include  natural  gas,  gasoline,  and  medium  distillates  (eg.  diesel).   GHG  emissions  for Dhaka   in  2010   exceeded  23   million  tons/year   CO2    equivalent,   dominated  by   the Industrial (combustion/power plants) and Agricultural sectors. The fuels used by these two  sectors  include  natural  gas  and  hard  coal.    No  mitigation  controls/costs  were available for SO2, NOX, and GHGs in the model.

 

GAINS  can  be  seen  a  useful  “screening-type  study”  tool  for  emissions,  especially  for developing countries such as Bangladesh due to a lack of available compiled data.   The GAINS approach can be seen as a simplified generalization tool to be used to pin point pollutants  and  related  sources  which  deem  closer  research  and  analysis  using  more specific tools or ground-based monitoring.   This report has provided this valuable data, and will be helpful in completing the ongoing bottom-up emission inventory for Dhaka within  the  BAPMAN  project. Unfortunately  though  without  the  inclusion  of  GHG controls/costs  in  the  model,  it  is  not  possible  to  begin  co-benefit/co-control  analysis. However,  it  should  be  considered  to  perform  an  assessment  using  GAINS  at  the beginning of each international emission inventory related project in order to establish a general baseline and screening analysis of top-down emissions data for the city/area of interest.

 

1    Introduction

 

Dhaka can be considered the mega-city with the world’s worst urban air quality (Gurjar  et  al.,  2008). A  combination  of  numerous  local  emissions  sources  in addition to special local and regional winter meteorological conditions gives the city  exceedingly  high  air  pollution  concentrations  throughout  the  year,  and especially during the winter season (Randall et al., 2011).   The exposure of the cities   estimated   12-15  million   residents  to  this   alarmingly  poor air quality demands attention including immediate research and corresponding mitigation. The  World  Health Organization  (WHO) estimates that up to 10,000 pre-mature deaths are associated with outdoor air pollution annually in Bangladesh (WHO, 2009).

 

Urban air pollutant emissions plus GHG emissions will be analyzed for Dhaka to determine  past  and  projected  trends in  emissions  rates,  for  the  main  activities within  the  main  sectors  responsible  for  the  bulk  of  the  emissions  for  each pollutant investigated.  This information is necessary to examine the various co- controls available in order for the maximum co-benefit to society and the global climate.     Such   a   complex   problem   requires   an   integrated   approach;   and integrated  assessment  modelling  is  an  excellent  tool  to  provide  data  for  this purpose.

 

The Greenhouse Gas and Air Pollution Interactions and Synergies model (GAINS) developed at the International Institute for Applied Systems Analysis (IIASA) was selected  as  the  most  appropriate  tool  to  perform  this  analysis  of  integrated assessment data for Dhaka.  As stated by IIASA, the purpose of the GAINS model is to:

  • To capture interactions between pollution control and economic velopment; and
  • To  identify  cost-effective  pollution-control  strategies  that  put the least burden on economic development. It is this special focus on   the   implications   for   economic   policies   of   controlling  air pollution   and   mitigating   greenhouse   gases,   and   vice   versa, advanced   methodologies   to   systematically   identify   pollution control  strategies  that  put  least  cost  to  the  economy  while maximizing  a  wide  range  of  environmental  benefits.   (IIASA, 2008)

 

The  GAINS  model targets common air pollutants as well  as GHGs, where these are based on underlying air quality policy targets (Figure 1).   GAINS is based on the  premise  that  there  distinct  and  important  linkages  between  air  pollution emissions and GHGs due to common sources and co-benefits through co-control measures.

 

For each examined component the following information will be analyzed:

  • Emissions (2010) and decadal trends
  • Activity (fuel) distributions of emissions (2010) and decadal trends
  • Sector distributions of emissions (2010) and decadal trends
  • Sub-sector distributions of emissions (2010)
  • Sub-sectors  contributing  to  the  activities  with  the  greatest  emissions (2010)
  • Mitigation control options and related removal efficiency (only available for PM) Mitigation control costs (only available for PM)

The top-down approach employed by GAINS can be seen as a valuable screening- type  tool for  cities  like  Dhaka  which  have  little  available  air  pollution  data  and related source information compiled. The approach can be seen as a simplified generalization tool to be used to pin point pollutants and related sources which deem  closer  research  and  analysis  using  more  specific  tools  or  ground-based monitoring.   The following schematic (Figure 2) illustrates the information to be examined for Dhaka and the related data flow:

 

 

 

 

2    Methods

The city of Dhaka was selected for this  integrated assessment due to the need for   relevant   data/analysis   (and   data   comparisons)   for   the   Bangladesh   Air Pollution Management (BAPMAN) project in which Dhaka is of primary focus.1

 

Part  I  of  the  GAINS  user  handbook  guidelines  (IIASA,  2009)  were  followed  in order to properly access the online data and navigate the interface.  Registration was necessary to obtain access to all of the GAINS models.

 

The  GAINS  South  Asia  model2   was  used  to  collect  emissions  data  and  related control  information  for  Dhaka,  Bangladesh.  The  default  scenario  “Final  Report: Baseline08”  (last  updated  September  2008)  was  used  in  this  analysis,  which  is the baseline scenario developed on the basis of the results from the EU funded GAINS-Asia  project  (IIASA,  2008),  which  also  includes  implementation  of  all current legislation through 2008.

 

An offline excel database was created from the exported GAINS data collected in order to complete the thorough analysis.  Data was copy and pasted from GAINS outputs into Excel, where analysis was conducted there.  It should be noted that no  additional  resources other  than  the  GAINS  South  Asia  model  were  used  for raw data collection in this report.

 

3     Results

 

The air pollution emissions components used for this analysis of Dhaka includes: PM2.5, PM10, SO2, NOX, and GHGs (which includes CO2, CH4, N2O, and FGAS3).4    A general  picture  of  the  total  top-down  emissions  data  (non-GHGs)  for  Dhaka (Figure  3)  shows  the  expected  total  emissions  increase  of  all  components  over the  decades  through  2030,  where  SO2   is  increasing  at  a  greater  rate  than  the other  components  and  is  expected  to  exceed  110000  tons/year  in  2030. A similar  picture  can be  seen  for  GHG emissions (Figure  4), where  CO2  emissions are  increasing  at  a  high  rate  and  CO2   alone  is  projected  to  exceed  30  million tons/year in 2030.

 

 

 

 

 

Each  individual  component presented above  will be specifically analyzed  in the following sections to identify the sources of the emissions (sectors and activities), as well as the effect of controls and the related costs (for PM only) – primarily for the nearest year available in the model, 2010.

 

3.1    PM2.5

 

PM2.5  emissions for Dhaka are modeled to reach 48000 tons/year in 2030, where the  current  level  for  2010  is  modeled  at  approximately  35000  tons  (Figure  5). These  PM2.5   emission  will  be  analyzed  for  the  particular  activity  levels,  sector levels,   and   sector   emissions   from   specific   activities   making   up   these   total emission values.  Control options and associated costs will also be presented.

 

 

3.1.1    Activity

 

The  PM2.5   emissions  (as  shown  in  Figure  5)  can  be  broken  down  into  specific activities (source fuel types) as presented in Figure 6.  The “no fuel use”5  activity represents   the   greatest   contribution   of   PM2.5    emissions   which   is   greatly increasing over time, exceeding 35000 tons/year PM in 2030.  The second largest contribution of PM2.5 emissions comes from the “fuelwood direct” activity, which is  gradually  decreasing  over  time,  estimated  to  be  closer  to  5000  tons/year  in 2030.     The   other   activities   associated   with   PM2.5    emissions   have   minimal emission contributions in comparison to the previous two activities mentioned; for   example   the   “Gasoline   and   other   light   fractions   of   oil”   and   “Medium distillates  (diesel, light  fuel  oil)”  activities  never contribute for  more  than  1000 tons PM2.5 emissions for a given year.

 

 

The modeled situation for the associated activities for PM2.5  emissions for 2010 (Figure 7) show that the “no fuel use” activity represents approximately twice as much of the PM emissions as the “fuelwood direct” activity for that year.

 

 

3.1.2    Sector

 

The  PM2.5  emissions can be  broken  down  in to  general  sectors  as presented  in Figure 8, which shows a gradual decrease over time of PM2.5  emissions coming from the residential sector and a steady increase from the industrial sector.  Here the transport sector displays a low  contribution  of  PM2.5  emissions, emitting under 2000 tons/year. A  graph displaying the specific  sector  distributions of PM2.5 emissions from 1990-2030 is in Appendix A.

 

The specific sector distribution for 2010 (Figure 9) shows that a majority of PM2.5 emissions comes from the brick production industry, approximately 15000 tons. Residential cooking stoves also show a large contribution for 2010 at 11000 tons, making  up  almost  10  times  the  PM2.5 contribution  compared  to  the  transport related specific sectors (1300 tons).

 

 

 

 

 

3.1.3    Sector-Activity

 

 

 

Figure  7  shows  that  the  activity  “no  fuel  use”  (emissions  not  due  to  fuel combustion)  has  the  greatest  contribution  to PM2.5   emissions  in  2010,  over 20000 tons PM2.5.   The specific sectors which make up this activity contribution can  be  seen  in  Figure  10,  where  brick  production  is  the  major  source  for  this activity type (15000 tons), followed by cement production (3000 tons).

 

 

Figure 7 also shows that the activity “fuelwood direct” has a large contribution of PM2.5   emissions  in  2010,  approximately  9000  tons  PM2.5.   The  specific  sectors which  make  up  this  activity  contribution  can  be  seen  in  Figure  11,  where residential   cooking   stoves   is   the   major   source   for   this   activity   type   at approximately 8500 tons.

 

 

 

 

3.1.4    Controls

 

 

21 specific mitigation/controls for PM2.5  were available and the results of these control  options  in  regards  to  no-control  options  are  shown  in  Figure  12  an explanation of the abbreviations of the controls can be found in Appendix B.

 

 

A  list  of  the  top  10  controls  and  their  PM2.5  removal  efficiency  can  be  seen  in Table  1.  The  chosen  control  options  are  applicable  to  the  Industry  (industrial process and industry) and Residential sectors. No controls were available for the Transport  or  Agricultural  sector  for  this  particular  analysis.   Implementation  of the top three controls listed in Table 1 (control #1, 6, and 7 - one control for each sector)6    can   reduce   the   PM2.5    emissions   for   year   2010   by   approximately 12000 tons/year. This is more than 1/3 of the total PM2.5 emissions for that year.

 

 

 

3.1.5    Costs

 

Costs were determined for each of the 21 control options for PM emissions and are presented in Figure 13.

 

Table  2  presents  the  costs  of  the  top  PM2.5   control  options  listed  in  Table  1. Costs range from less than 1 Euro up to over 16000 Euros/ton of reduced PM2.5 emission.  The implementation of top control measures #1, 6, and 7 would cost a total of 650000 Euros/year to save 12000 tons PM2.5  emissions/year (1/3 of the total annual PM2.5 emissions for 2010).

 

3.2    PM10

PM10  emissions for Dhaka are modeled to reach 64000 tons/year in 2030, where the current level for 2010 is modeled at 45000 tons/year (Figure 14).  These PM10 emissions  will  be  analyzed  for  the  particular  activity  levels,  sector  levels,  and sector  emissions from  specific activities  making up these total  emission values. Control options and associated costs will also be presented.

 

 

3.2.1    Activity

 

The  PM10  emissions  (as  shown  in  Figure  14)  can  be  broken  down  into  specific activities (source fuel types) as presented in Figure 15.  The “no fuel use” activity represents   the   greatest   contribution   of   PM10     emissions   which   is   greatly increasing  over  time,  reaching  50000  tons/year  in  2030. The  second  largest contribution of PM10  emissions comes from the “fuelwood direct” activity, which is gradually decreasing over time, estimated to be closer to 6000 tons in 2030. The  other  activities  associated  with  PM10    emissions  have  minimal  emission contributions   in   comparison   to   the   previous   two   activities   mentioned;   for example the “Gasoline  and  other  light fractions of  oil”  and  “Medium distillates (diesel,  light  fuel  oil)”  activities  never  contribute  for  more  than  1000  tons  PM emissions for a given year.

 

 

The modeled situation for the associated activities for PM10 emissions for 2010 (Figure  16)  show  that  the  “no  fuel  use”  activity  (emissions  not  due to fuel combustion) represents approximately twice as much of the  PM10 emissions as the “fuelwood  direct” activity for that year,  with  other  activities  at  minimal levels.

 

 

The  PM10  emissions  can  be  broken  down  in  to  general  sectors  as  presented  in Figure 17, which similarly to PM2.5  shows a gradual decrease over time of PM10 emissions  coming  from  the  residential  sector  and  a  steady  increase  from  the industrial  sector.   Here  the  transport  sector  also  displays a  low contribution  of PM10  emissions, emitting under 2000 tons/year.   A graph displaying the specific sector distributions of PM10 emissions from 1990-2030 is in Appendix B.

 

The specific sector distribution for 2010 (Figure 18) shows that a majority of PM10 emissions comes from the brick production industry, approximately 17000 tons. Residential cooking stoves also show a large contribution for 2010 at 11000 tons, making  up  almost  10  times  the  PM10   contribution  compared  to  the  transport related specific sectors (1500 tons).

 

 

 

3.2.3    Sector-Activity

 

Figure  16  shows  that  the  activity  “no  fuel  use”  (emissions  not  due  to  fuel combustion) has the greatest contribution of PM10  emissions in 2010, over 25000 tons PM/year.   The specific sectors which make up this activity contribution can be seen in Figure 19, where brick production is the major source for this activity type (16000 tons/year), followed by cement production (7000 tons/year).

 

 

 

Figure 16 shows that the activity “fuelwood direct” also has a large contribution of PM10  emissions in 2010, approximately 10000 tons PM10.  The specific sectors which  make  up  this  activity contribution can be seen in Figure  20,  where residential cooking stoves is  the major source for this activity type at approximately 9000 tons.

 

 

 

3.2.4    Controls

 

 

 

21  specific  mitigation/controls for  PM10  were  available  in  the  model  (the  same controls as indicated for PM2.5) and the results of these control options in regards to no-control options are shown in Figure 21; an explanation of the abbreviations of the controls can be found in Appendix B.

 

 

A list of the top 10 controls and their  PM10  removal efficiency can be seen in Table  3. The chosen  controls option are applicable  to  the  Industry  (industrial process and industry) and Residential sectors; no controls were available for the Transport  or  Agricultural  sector  for  this  particular  analysis. Implementation of the top three controls listed in Table 3 (control #1, 6, and 7 - one control for each sector)7  can  reduce PM10 emissions for year  2010  by approximately 17600 tons/year. This is more than 1/3 of the total PM10 emissions for that year.

 

 

3.2.5    Costs

 

Costs were determined for each of the 21 controls options and are presented in Figure 13.  Table 4 presents the costs of the top PM10 control options listed in Table 3. Costs range from less than 1 Euro up to over 5400 Euros/ton of reduced PM10 emission, where the  most  cost  efficient measure is Control  #7. The implementation of top control  measures  #1,  6,  and  7 would  cost  a total of 650000  Euros/year  to  save  17600 tons PM10 emissions/year (1/3 of the total annual PM10 emissions for 2010).

 

 

 

3.3    SO2

 

 

SO2  emissions for Dhaka are modeled to reach 113000 tons/year in 2030, where the current modeled level for 2010 is 34000 tons (Figure 22). These SO2  emission will be analyzed for the particular  activity  levels, sector levels, and sector emissions from specific activities making up these total emission values.

 

 

 

 

 

3.3.1    Activity

 

The SO2 emissions (as  shown  in  Figure  22)  can  be  broken down into specific activities (source fuel types) as presented in Figure 23. The “hard coal” activity represents the greatest contribution of SO2  emissions which is greatly increasing over time, reaching 74000   tons/year SO2 in 2030. The   second   largest contributions  of  SO2 emissions comes  from  the  “heavy  fuel  oil”,  “natural  gas”, and  “medium  distillates” activities, which are gradually increasing over  time, estimated to be closer to 12000, 14000, and 8000 tons in 2030 respectively.  The other activities associated with SO2 emissions have minimal emission contributions in comparison to the previous activities mentioned.

 

 

The  modeled  situation  for  the  associated  activities  for  SO2  emissions  for  2010 (Figure   24)   show   that   the   “Hard   coal”   activity   (15000   tons)   represents approximately twice as much of the SO2  emissions as the next greatest activity “natural gas” (7000 tons) for that year, with other activities of “Biomass fuels”, “Heavy  fuel  oil”,  and  “Medium  distillates”  very  similar  at  approximately  3000 tons each.

 

 

 

 

3.3.2    Sector

 

The  SO2   emissions  can  be  broken  down  in  to  general  sectors  as  presented  in Figure  25,  which  shows  a  large  increase  over  time  from  the  industrial  sector, while  the  transport  and  residential  sectors  have  a  much  smaller  increase  in comparison.       Here  the  transport  sector  displays  a  low  contribution  of  SO2 emissions, holding under 4000 tons/year.   A graph displaying the specific sector distributions of SO2  emissions from 1990-2030 is in Appendix D.

 

 

The specific sector distribution for 2010 (Figure 26) shows that a majority of SO2 emissions comes   from  combustion  within Industry, approximately 15000 tons/year. Power plants also make up a large share of the SO2 emission at approximately 12000 tons/year.

 

 

3.3.3    Sector-Activity

 

Figure 24 shows that the activity “hard coal” has the greatest contribution to SO2 emissions in 2010, over 14000 tons SO2.  The specific sectors which make up this activity contribution can be seen in Figure 27, where power plants are the major source for this activity type (12000 tons/year), followed by industry combustion (approximately 3000 tons/year).

 

 

 

Figure 24 also shows that  the  activity “natural gas” has a large contribution of SO2 emissions in 2010, approximately 7000 tons SO2. The specific sectors which make up this activity contribution can be seen in  Figure 28, where the industry combustion sector makes up the total 7000 tons from this specific activity.

 

 

 

3.3.4    Controls and Costs

 

 

Removal efficiency for  controls listed in the model were 0%, which means that no control data is available, thus no cost data is also available.

 

3.4    NOX

 

NOX  emissions for Dhaka are modeled to reach 60000 tons/year in 2030, where the  current  modeled  level for 2010 is 30000 tons/year (Figure  29). These NOX emission will be analyzed  for the particular activity levels, sector levels, and sector emissions from specific activities making up these total emission values.

 

 

3.4.1    Activity

 

The  NOX   emissions  (as  shown  in  Figure  29)  can  be  broken  down  into  specific activities (source fuel types) as presented in Figure 30.  The “natural gas” activity represents the greatest contribution of NOX  emissions which is greatly increasing over  time,  reaching  almost  16000  tons/year NOX in 2030. The  second  largest contributions  of  NOX   emissions  comes  from  the  “Gasoline”, “hard  coals”, and “medium   distillates”  activities,  which are gradually increasing over   time, estimated to be closer to 12000, 16000, and 8000 tons/year in 2030 respectively. The “biomass fuels” activity was a significant contribution to NOX emissions prior to 2010, but since is predicted to decline as a source of NOX  emissions. The other activities associated with NOX  emissions (“heavy fuel oil” and “no fuel use”) are have minimal emission contributions in comparison  to  the  previous  activities mentioned.

 

 

The  modeled situation for the associated  activities for NOX emissions  for  2010 (Figure  31)  show  that  the “Natural gas” activity  (12500  tons/year) represents more  than  twice  as  much  of the NOX emissions as the  next  greatest  activity “Gasoline”  (5500  tons/year)  for  that  year,  with other  activities of “Biomass fuels”, “Hard coal”, and “Medium  distillates” very similar  at approximately  4-5000 tons/year each.

 

 

3.4.2    Sector

 

The  NOX emissions can be broken down in to general  sectors  as  presented  in Figure  32,  which  shows  a  large  increase  over  time  from  the  industrial  sector, while  the  transport sector  is  also  increasing  as  well. The  industrial  sector  is estimated to contribute with over 35000  tons/year NOX in  the year  2030. A graph  displaying  the  specific  sector distributions  of SO2 emissions  from 1990-2030 is in Appendix E.

 

 

 

The specific sector distribution for 2010 (Figure 33) shows that a majority of NOX emissions comes   from combustion within industry, approximately  12000 tons/year. Power plants and light/heavy duty vehicles also make up a large share of the NOX emission at approximately 7000 tons/year each for 2010.

 

 

 

3.4.3    Sector-Activity

 

 

 

Figure 31 shows that the activity “natural gas” has the greatest contribution of NOX emissions  in  2010,  at  approximately  12000  tons/year  NOX. The  specific sectors which make up this activity contribution can be seen in Figure 34, where industrial combustion is the major source for this activity type (7000 tons/year), followed  by  power  heat  plants  (3000  tons/year), and new power plants (1500 tons/year).

 

 

Figure 31 shows that the activity “Gasoline and other light fractions of oil” also has a fair contribution of NOX  emissions in 2010, approximately 5500 tons/year NOX.  The specific sectors which make up this activity contribution can be seen in Figure 35, where the light duty vehicle sector makes up a majority of the total 7000 tons/year from this specific activity.

 

 

Figure 31 also shows that the activity “Medium distillates” has a fair contribution of NOX emissions  in  2010,  approximately  4500  tons/year  NOX. The  specific sectors which make up this activity contribution can be seen in Figure 36, where the  heavy duty vehicle sector makes up a almost  2000  tons/year,  followed  by maritime transport medium-vessels (1000 tons/year).

 

 

3.4.4    Controls and Costs

 

Removal efficiency for controls listed in the model where 0%, which means that no control data is available, thus no cost data is also available.

 

3.5    GHG’s

 

GHG  emissions  for  Dhaka  are  modeled  to  reach  42  million  tons/year  (CO2 equivalent)  in  2030,  where  the  current  modeled  level  for  2010  is  23  million tons/year (CO2  equivalent) (Figure 37).  Approximately 2/3 of the GHGs is CO2  in 2010, and in 2020 it is 3/4 CO2  (Figure 4).  These GHG emissions will be analyzed for the particular activity levels, sector levels, and sector emissions from specific activities making up these total emission values.

 

 

 

3.5.1    Activity

 

 

 

The  GHG  emissions  (as  shown  in  Figure  37)  can  be  broken  down  into  specific activities  (source  fuel  types)  as  presented  in  Appendix  F. The  “natural  gas” activity  represents the  greatest  contribution  of GHG  emissions  which  is greatly increasing  over  time,  reaching  almost  19  million  tons/year  (CO2  equivalent) in 2030.  The second largest contributions of GHG emissions comes from the “hard coal” activity which is gradually increasing over time, estimated to be closer to 10 million tons/year (CO2  equivalent) in 2030.   The other activities associated with GHG  emissions  (“heavy  fuel  oil”,  “cattle”,  and  “area  of  activity  –  agriculture”) have  minimal  emission  contributions  in  comparison  to  the  previous  activities mentioned.

 

The modeled  situation  for  the  associated activities  for GHG emissions for  2010 (Figure  38)  shows  that  the  “Natural  gas”  activity  (11  million  tons/year  CO2 equivalent) represents by far the greatest GHG emissions in comparison to the next  greatest  activities  of  “Area  of  activity  -  agriculture”  (2.6  million  tons/year CO2  equivalent), “Hard  coal”  (2.6  million tons/year CO2 equivalent), and “other cattle – not included cows” (1.6 million tons/year CO2  equivalent).

 

 

 

3.5.2    Sector

 

 

 

The GHG emissions can be broken down in to general sectors as presented  in Figure  39,  which  shows a large  increase  over  time  from  the  industrial  sector, while   the   transport,   residential, and   agricultural   sectors   are   just   slightly increasing over  time.   The  agricultural  sector  is estimated  to  have  the  greatest contribution of GHGs in 1990, but by 2005 the industrial sector had almost twice as much emissions as the agricultural sector.   The industrial sector is estimated to contribute over 30 million tons/year GHG (CO2  equivalent) in the year 2030, which  is  5  times  as much  emissions  as  the  next  highest  sector  for  that  year (agriculture). A graph   displaying the specific sector distributions of GHG emissions from 1990-2030 is in Appendix G.

 

The specific sector distribution for 2010 (Figure 40) shows that a majority of GHG emissions  comes  from  the  “other  combustion” and “non-IGGC8  plants“ within industry, over 10 million   tons/year  (CO2 equivalent). Smaller individual agricultural activities are the next greatest specific sectors contributing to GHG emissions, together equaling over 5 million tons/year (CO2  equivalent).

 

 

 

3.5.3    Sector-Activity

 

Figure 38 shows that the activity “natural gas” has the greatest contribution of GHG  emissions  in  2010,  at  approximately  11  million  tons/year. The  specific sector which makes up this activity contribution is industrial combustion, and the increase of these emissions over time can be seen in Figure 41.  Hard coals is the activity with the second greatest contribution to GHG, where the specific sector of  new  power  plants  makes  up  most  of  the  emissions  for  this  activity;  the increase of this specific sector over time is seen in Figure 42.

 

 

 

3.5.4    Controls and Costs

 

No control strategies are available for GHGs in the model.

 

4     Conclusion and Discussion

 

The total top-down emissions for PM, SO2, NOX, and GHGs for 2010, as well as the  decadal  trends,  have been presented for Dhaka. The  top  sectors,  sub-sectors, and activities making up the emissions for each pollutant have also been analyzed. The overall results for this analysis for 2010 in Dhaka is summarized in Table 5.

 

 

 

Initially it was thought that GAINS was producing a major underestimation of PM emissions for the transportation sector, as first stated in Sivertsen (2010). After field visits to Dhaka as well as through additional research, it was discovered that up to 73% of the traffic sources (not counting motor bikes) runs on CNG (Wadud, 2011), which would explain the low PM estimations from GAINS for this sector.9 On the contrary, as initially expected, the majority of PM emissions   are originating  from  the  brick production  industry.  However,  it  was  surprising  that residential cooking stoves also show a large contribution, making up almost 10 times the PM contribution compared to the transport related specific sectors. So it  can be generally  concluded  that  PM  emissions  come  from  the  industry  and residential  sector (industry is slowly taking  over  as   the   dominant   source sector),primarily   from brick kiln production, and some cement production (industrial sector) and from residential cooking stoves (residential sector).

 

As  expected,  a  majority of  the  SO2  and  NOX  emissions  are  generated  from  the Industry  sector,  originating  from  combustion  of  natural  gas  and  hard  coal  in power plants and industry. In addition some NOX  emissions are generated by the Transport sector, due to combustion of gasoline and other medium distillates in light and heavy duty vehicles.

 

Also as expected, a majority of  the  GHG  emissions are coming  from  Industry (including power generation) and Agriculture, following the typical global pattern of  approximately  25%  of  GHGs  from the  Agricultural sector, and 50% from Industry (including power) (WRI, 2005). 

 

Overall, emissions in Dhaka are greatly increasing for the selected pollutants over time, where these emission rates vary for each sector.  Comparing the emissions for   2010   and   2030   broken   down into each sector  (Table  6), shows that Residential and Transport related emissions of PM2.5  and PM10 will decrease by 2030,  and  Industrial  emission  will  greatly  increase,  while  Agricultural  based emissions  will  increase  for  PM10   but  decrease  for  PM2.5.    SO2,  NOX,  and  GHG emissions are increasing for all sectors by 2030, while as expected, SO2  and GHG emissions are increasing at a greater rate within the Industrial sector, and NOX emissions are increasing at a greater rate in the Transport sector.

 

 

It can be valuable to research  these  sector  emission  results  further  between 2010 and 2030 using the GAINS model to determine which activities (fuels) and sub-sectors  that  are  contributing  to the increases (and decreases) in emissions over time in Dhaka.

 

 

As a basis to achieve a more integrated  management  of air pollution in Dhaka, control measures and their costs were also evaluated.  The overall results for the mitigation  measures  and  associated  costs  for  Dhaka  in  2010  is  summarized  in Table 7 for PM2.5  and PM10.

 

 

 

The  high  reduction  potential  for  these  top  three  measures  for  PM  is  fairly impressive, as they are able to reduce up to 1/3 of the total PM emissions at a relatively low costs/ton.   It  is unfortunate  that  mitigation  controls (and related costs) were not available for SO2, NOX and  GHGs. Without this information for GHGs, co-benefits for co-controls were not able to be realized – thus ultimately undermining the purpose and goals of the GAINS model.

 

It is also interesting to compare Dhaka  sectoral emissions to other South Asian cities  known  for  high  pollution  levels  (Figure  43,  using  PM2.5   for  2010  as  an example).    As  previously  discussed,  it  is  evident  here  that  Dhaka  has  a  much smaller proportion of PM2.5 emissions from the transport sector in comparison to the  other  representative  cities,  and  Delhi  seems  to  have  a  more  typical  ratio between the sectors for a mega-city, in comparison to Dhaka and Bangkok.

 

 

Some recommendations are given based on this assessment for Dhaka:

  •  It  should  be  considered  to  perform  an  assessment  using  GAINS  at  the beginning  of  each  international  air  pollutant/GHG  emission  inventory related  project  in  order  to  establish  a  general  baseline  based  on  a  top- down emission analysis for the city/area of interest.

 

  • It  should  be  considered  to  use  GAINS  for  evaluating  potential  available emission control measures and related costs for the city/area of interest.

 

  • Collaboration   with   IIASA   should   be   encouraged   in   order   for   the development   of   GAINS   scenarios   to   produce   specific   analysis   of components/GHGs, controls, and associated costs for an area over time.

 

Some critical remarks about GAINS data and the user interface:

 

  • Some activities have very weak descriptions, such as “no fuel use” activity and “area of activity”.
  • Some    control    measures    have    very    confusing    and    over-specific descriptions.
  • Missing  control  measures  for  GHGs  and  other  components  is  highly unfortunate.
  • Very weak (health) impacts section; needs to be further developed.

 

At  this  level  GAINS  can  be  seen  as  a  useful  top-down  emission  estimate  tool. GAINS is a good first  emission inventory tool,   because it is difficult to compile data   in   developing   countries   such   as   Bangladesh   due   to   poor   reporting procedures,  lack  of  centralized  data  clearinghouses,  and  reduced  amount  of electronic  information.  GAINS  modelling  data  can  be  valuable  to  fill  gaps  in studies  (such  as  for  bottom-up  emissions  inventories,  source  apportionments, health  impact  assessments,  etc.),  and/or  to  use  the  modelling  data  as a  gauge where data received is of questionable quality.   However, without the inclusion of  GHG  controls/costs,  it  is  unfortunately  not  possible  to  begin  co-benefit/co- control analysis.

 

5    References

 

Gurjar, B.R., Butler, T.M., Lawrence, M.G., Lelieveld, J. (2008)  Evaluation of emissions and air quality in megacities.  Atmos. Environ., 42, 1593-1606.

 

IIASA (2008)  GAINS Asia: A scientific tool to combat air pollution and climate change simultaneously. Laxenburg, International Institute for Applied Systems Analysis.

URL: http://www.iiasa.ac.at/rains/reports/Asia/gainsbrochure_web.pdf

 

IIASA (2009)  GAINS Online: Tutorial for advanced users. Laxenburg, International Institute for Applied Systems Analysis.

URL: http://www.iiasa.ac.at/rains/reports/GAINS-tutorial.pdf

 

Randall, S., Sivertsen, B., Schneider, P., Dam, V.T., Nasiruddin, M., Biswas, W., Saroar, G., Rana, M. (2011) Bangladesh Air Pollution Management Project (BAPMAN).  Ambient air pollution screening study in Dhaka: 31 January - 15 February 2011.  Kjeller (NILU OR 28/2011). In press.

 

Sivertsen, B. (2010) Integrated approaches using local air quality assessment in GHG abatement strategies. Presented at Better Air Quality Conference, Singapore, 8-11 November 2010. Kjeller (NILU F 66/2010).

 

Wadud, Z., Kahn, T. (2011)  CNG conversion of motor vehicles in dhaka: valuation of the co-benefits.  In: TRB 90th Annual Meeting Compendium of Papers DVD. Washington DC, Transportation Research Board. Paper #11-2764.

URL:  http://trid.trb.org/view.aspx?id=1092656

 

WHO (2009) Country profiles of environmental burden of disease: Bangladesh. Geneva, World Health Organization.

URL:http://www.who.int/quantifying_ehimpacts/national/countryprofile/bangladesh.pdf

 

WRI (2005)  World greenhouse gas emissions in 2005. Washington, DC  World Resources Insititute.

URL: http://www.wri.org/chart/world-greenhouse-gas-emissions-2005

 

 

Source: bapman.nilu.no

 


 

 

 

 

 

 

 

 

 

 

 

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