Post on 16-Mar-2020
Practices and Challenges of
Applying CALPUFF in China as
a Regulatory Air Dispersion
Model
Tao Yang
May 11, 2010
trinityconsultants.com
Introduction
� CALPUFF is a non-steady-state
Lagrangian Gaussian puff model
� complex terrain effects
� overwater transport
� coastal interaction effects
� simple chemical transformation
� …
Introduction
� Three main components of CALPUFF
modeling:
� CALMET: generate hourly 3-D gridded
meteorological data
� CALPUFF: dispersion modeling
� CALPOST and other processing tools:
result processing and graphical display
Applying CALPUFF in China
� Adopted by the Ministry of China Environmental
Protection (MEP) for long-range transport
dispersion modeling analyses
� Recommended for EIA projects with study
domain larger than 50 km
� Recommended for complex terrain and complex
wind field
� Intensive effort in data preparation:
� Terrain data
� Land Use and Land Cover data
� Surface observational data
� Sounding data
� Meso-scale meteorological data
� Overwater data
� …
Challenges of Applying CALPUFF
in China
Case Study - Purpose and
Methodology
� Reveal influences caused by land use
change with rapid urbanization to the
pollutant dispersion
� USGS LULC data vs. updated LULC data
according to satellite image
� Reveal influences of observation data
� Cases with and without observational data
Case Study – Modeling Domain
Settings
� In case study, Shanghai area is chosen as the
modeling domain
� The central point located at 121.365°E, 31.19°N� MM5 meso-scale data is used with FDDA option
turned on, 1 km resolution, 40 layers
� CALMET grid spacing and CALPUFF grid
receptor spacing is set to 500 m
Case Study – Modeling Parameter
Settings
� Emission Sources:
� Four stacks (100 m height, NOx emission 20 g/s)
� Modeling Period:
� 10-20 of January, April, July, and October in 2009
� Modeling Cases:
� Case 1: Without observational data & original LULC
� Case 2: With observational data & original LULC
� Case 3: With observational data & updated LULC
Results and Discussion
Stack1
Stack3 Stack4
Stack2
ug/m3
ug/m3 ug/m3
ug/m3
Results and Discussion
� The charts of average concentration of top 50
records indicate:
� For all of the stacks and seasons, the updated LULC
data results in higher ground level concentrations
� The influence of updated LULC data is less for
January and July
Results and Discussion
� Similar patterns of concentration distribution is found
between Stack1 and Stack2 and between Stack 3 and
Stack4.
� Results of April for both stack3 and stack4 show
significant difference in the study cases with
observational data and case without observational data
� Using representative meteorological stations is a critical
factor for CALMET/CALPUFF applications in China
Thank you!Questions?
Contact Information:
Tao Yang
tyang@trinityconsultants.com