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Muntaseer Billah, Satoru Chatani and Kengo Sudo Department of Earth and Environmental Science Graduate School of Environmental Studies Nagoya University,

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Presentation on theme: "Muntaseer Billah, Satoru Chatani and Kengo Sudo Department of Earth and Environmental Science Graduate School of Environmental Studies Nagoya University,"— Presentation transcript:

1 Muntaseer Billah, Satoru Chatani and Kengo Sudo Department of Earth and Environmental Science Graduate School of Environmental Studies Nagoya University, Nagoya, Japan Presented at the 8th Annual CMAS Conference, Chapel Hill, NC, October 19-21, 2009

2 Bangladesh  Location: 20°34´ and 26°38´ N 88°01´ and 92°41´ E  Area: 147, 570 sq km  Population: 158.6 millions  Population density: 1045/ sq km  Population growth: 1.8%  Urban population: 27%  Major cities: Dhaka (12 millions), Chittagong (7 millions), Khulna (3.5millions) Rajshahi (3 millions)  Climate: Tropical monsoon climate, with a hot and rainy summer and a dry winter Average Winter temp. (Max. 26°c Min. 11°c) Average Summer temp. (Max. 36°c Min. 21°c) Bangladesh: at a glance Source: World Bank

3 Background  Air pollution is the major environmental threat in Bangladesh, particularly big cities e.g., Dhaka, Chittagong, Khulna, Rajshahi…  Air pollution cause annually  ~15000 deaths (~5000 in Dhaka)  ~million cases of sickness requiring medical treatment  ~850 million of minor illness  Economic cost of air pollution in four major cities around US$200-$800 million per year  Equivalent to 0.7%-3% of country’s GDP per year Construction work Vehicle emission Brick kiln emission

4 Air Quality Status in Dhaka Monthly average of PM 10 and PM 2.5 Dhaka experiences winter peak ozone Real time gas monitors USEPA certified PM samplers

5 Objective  Surrounded by India which is a significant air pollutants emitter in Asia  During high pollution episodes, Bangladesh receives most air masses from India.  During low pollution episode, Bangladesh receives air masses from Bay of Bengal  Regional sources of air pollution may be significant for Bangladesh  Both local and regional contribution of air pollution need to be identified Main Objective To identify and quantify the local and regional source contribution of air pollution in Bangladesh Average wind field generated by MCIP for January 2004

6 Modeling Tools  Meteorological Model: Weather Research and Forecasting (WRF) version 3.1  Met Data: NCAR/NCEP reanalysis data (1˚× 1˚)  Air Quality Model: Community Multiscale Air Quality Model (CMAQ) version 4.7  Emission Data: REAS emission inventory developed by Frontier Research Center for Global Change. Physics optionScheme MicrophysicsWRF Single-Moment 3- class scheme Long wave radiation RRTM scheme Short wave radiation Dudhia scheme Surface layerMM5 similarity Land surfaceNoah Land Surface Model Planetary Boundary Layer Yonsei University Scheme Cumulus Parameterization Grell 3d ensemble cumulus scheme MechanismOption Chemical mechanism Statewide Air Pollution Research Center mechanism (SAPRC99) Aerosol moduleaero4 WRF CMAQ

7 Domain Setup Domain-1Domain-2 Area3600 km 2 1200 km 2 WRF Grids81 ×81×2779 ×79×27 CMAQ Grids69 ×69×2767 ×67×27 Grid Size45 km15 km Horizontal Co-ordinate Lambert conformal Geographical Co-ordinate 6°N to 40° N 70°E to 110°E 18°N to 28°N 84°E to 96°E Dhaka City Model ConfigurationStudy Area

8  January 2004 Episode Selection Month-long episodes have been chosen for this sensitivity study to represent typical peak pollution episode in Bangladesh Monthly average PM10 and PM2.5  Air pollution in Bangladesh has distinct seasonal variation  High pollution episode observed during dry winter season  Relatively cleaner atmosphere during wet summer season

9 Emission Database and Sensitivity Cases CaseEmission sensitivity Case-1 (Base case) Original REAS emission Case-2Shut-off emission in Region-1 (Inside Bangladesh) Case-35-times increase of emission in Region-1 (Inside Bangladesh) Case-4Shut-off emission in Region-2 (West Bengal) Case-5Shut-off emission in Region-3 (North India) Case-6Shut-off emission in Region-2 (West Bengal) and Region-3 (North India) Potential emission source region Region-1 Region-2 Region-3 Sensitivity Cases

10 With Original REAS emission CASE-1  CMAQ can capture 24-hour average PM2.5 trends but underestimate.  CMAQ can not capture hourly variation of gaseous pollutants and largely underestimate. Possible Reasons:  Same emission input was used for both domain.  Seasonal variation of emission is not considered in REAS inventory.  Biomass burning is not included in REAS inventory.

11 Comparison of NO2 with satellite NO2 column data CASE-1 Original REAS emission SCIAMACHY CMAQ

12 Shut-off emission in Region-1 (Inside Bangladesh) CASE-2 0.2 ppm to 0.5 ppm CO 20 to 40 µg/m 3 PM2.5 40 to 45 ppb O3 CMAQ Result – Monthly Average for January 2004 O3 PM2.5 CO

13 5-times increase of emission in Region-1 (Inside Bangladesh) CASE-3 Domain-1: Monthly Average Domain-2: Comparison with hourly observation COO3 PM2.5

14 Shut-off emission in Region-2 (West Bengal) CASE-4 Difference between Case1 and Case4 CO O3 PM2.5 Avg: 3 ppb Max: 9 ppb Avg: 0.04 ppm Max: 0.2 ppm Avg: 7 µg/m 3 Max: 23 µg/m 3

15 CASE-5 Shut-off emission in Region-3 (North India) Difference between Case1 and Case5 COO3 PM2.5 Avg: 0.04 ppm Max: 0.1 ppm Avg: 4 ppb Max: 8 ppb Avg: 7 µg/m 3 Max: 13 µg/m 3

16 Contribution of West Bengal (Region-2) and North India (Region-3) in % CASE-4 vs CASE-5 West Bengal North India CO O3PM2.5 CO O3PM2.5

17 Shut-off emission in Region-2 (West Bengal) and Region-3 (North India) CASE-6 Contribution in % PM2.5 O3CO

18 Estimated Transboundary Contribution

19  WRF was able to generate required meteorological inputs for CMAQ model for this region.  CMAQ captured the PM2.5 trends well  Concentrations of gaseous pollutant were largely underestimated by CMAQ. These discrepancies were heavily depended on emission input of CMAQ model.  CMAQ was highly sensitive to emission input which revealed the underestimation of REAS emission in this region by factor of 3 ~5.  Significant contributions of transboundary transport of pollution were found inside Bangladesh. Conclusions

20  Performance evaluation for Kolkata City (24-h average air quality data is available for 2007-2008).  Use of another emission inventory for this region e.g., Streets et al. (2003)  Development and use of own emission inventory. Future Direction of Study

21 Thank You

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