Air Quality Modeling for the Urban Jackson, Mississippi Region Using a High Resolution WRF/Chem Model

International Journal of Environmental Research and Public Health

Anjaneyulu Yerramilli¹  Venkata B. Dodla¹  Srinivas Desamsetti¹  Srinivas V. Challa¹  John H. Young¹  Chuck Patrick¹  Julius M. Baham¹  Robert L. Hughes¹  Sudha Yerramilli³  Francis Tuluri²  Mark G. Hardy² 

[1] Trent Lott Geospatial Visualization Research Centre, Jackson State University, 1230 Raymond Road, Jackson, MS 39204, USA; E-Mails:;National Center for Biodefense Communications, Jackson State University, 1230 Raymond Road, Jackson, MS 39204, USA; E-Mail:;College of Science, Engineering and Technology, Jackson State University, Jackson, MS 39217, USA; E-Mails:

关键词: air quality;    modeling;    WRF/Chem;    urban pollution;   

DOI  :  10.3390/ijerph8062470

来源: mdpi

【 摘 要 】

In this study, an attempt was made to simulate the air quality with reference to ozone over the Jackson (Mississippi) region using an online WRF/Chem (Weather Research and Forecasting–Chemistry) model. The WRF/Chem model has the advantages of the integration of the meteorological and chemistry modules with the same computational grid and same physical parameterizations and includes the feedback between the atmospheric chemistry and physical processes. The model was designed to have three nested domains with the inner-most domain covering the study region with a resolution of 1 km. The model was integrated for 48 hours continuously starting from 0000 UTC of 6 June 2006 and the evolution of surface ozone and other precursor pollutants were analyzed. The model simulated atmospheric flow fields and distributions of NO₂ and O₃ were evaluated for each of the three different time periods. The GIS based spatial distribution maps for ozone, its precursors NO, NO₂, CO and HONO and the back trajectories indicate that all the mobile sources in Jackson, Ridgeland and Madison contributing significantly for their formation. The present study demonstrates the applicability of WRF/Chem model to generate quantitative information at high spatial and temporal resolution for the development of decision support systems for air quality regulatory agencies and health administrators.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.