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