The role of meteorology on predicting SO2 concentrations around a refinery:: A case study from Oman

被引:16
作者
Abdul-Wahab, Sabah A. [1 ]
机构
[1] Sultan Qaboos Univ, Coll Engn, Dept Mech & Ind Engn, Al Khoud 123, Oman
关键词
atmospheric pollution; sulphur dioxide; correlations; meteorological parameters; Oman refinery;
D O I
10.1016/j.ecolmodel.2006.02.021
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
In this work, SO2 correlations were developed for the prediction of maximum SO2 values and their locations around the vicinity of a refinery. The proposed correlations are capable of estimating the hourly maximum SO2 concentrations from meteorological conditions. Correlation parameters were calculated by multiple regression analysis, using maximum SO2 concentration as dependent variable and the meteorological parameters as independent variables. The SO2 data used for the development of these correlations were generated from the industrial source complex short-term (ISCST) model. It was found that wind speed and atmospheric stability class had the most effect on the predicted SO2 concentration whereas neither mixing height, nor wind direction, nor temperature had an influence on the maximum SO2 concentration. Therefore, the suggested correlations require only knowledge of the wind speed and stability class parameters. On the other hand, the developed correlations for estimating the locations of these maximum values of SO2 concentrations contained only one term that describes the dependence of the locations on wind direction. The derived correlations were shown to be statistically significant. They are much simpler to use than the ISCST model. Further, they are invaluable for determining locations at risk of exceeding the SO2 standard. (c) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码:13 / 20
页数:8
相关论文
共 38 条
[1]   Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations [J].
Abdul-Wahab, SA ;
Bakheit, CS ;
Al-Alawi, SM .
ENVIRONMENTAL MODELLING & SOFTWARE, 2005, 20 (10) :1263-1271
[2]   Evaluation of the industrial source complex short-term model: Dispersion over terrain [J].
Abdul-Wahab, SA .
JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION, 2004, 54 (04) :396-408
[3]   A statistical model for predicting carbon monoxide levels [J].
Abdul-Wahab, SA ;
Al-Rubiei, R ;
Al-Shamsi, A .
INTERNATIONAL JOURNAL OF ENVIRONMENT AND POLLUTION, 2003, 19 (03) :209-224
[4]   SO2 dispersion and monthly evaluation of the industrial source complex short-term (ISCST32) model at Mina Al-Fahal refinery, Sultanate of Oman [J].
Abdul-Wahab, SA .
ENVIRONMENTAL MANAGEMENT, 2003, 31 (02) :276-291
[5]   Patterns of SO2 emissions:: a refinery case study [J].
Abdul-Wahab, SA ;
Al-Alawi, SM ;
El-Zawahry, A .
ENVIRONMENTAL MODELLING & SOFTWARE, 2002, 17 (06) :563-570
[6]   Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks [J].
Abdul-Wahab, SA ;
Al-Alawi, SM .
ENVIRONMENTAL MODELLING & SOFTWARE, 2002, 17 (03) :219-228
[7]   IER photochemical smog evaluation and forecasting of short-term ozone pollution levels with artificial neural networks [J].
Abdul-Wahab, SA .
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION, 2001, 79 (B2) :117-128
[8]   Predicting ozone levels - A statistical model for predicting ozone levels in the Shuaiba Industrial Area, Kuwait [J].
AbdulWahab, S ;
Bouhamra, W ;
Ettouney, H ;
Sowerby, B ;
Crittenden, BD .
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 1996, 3 (04) :195-204
[9]  
ABDULWAHAB SA, 1999, ICHEME ENV PROTECTIO, V58, P3
[10]   Evaluating the performance of the industrial source complex - (short term) model [J].
Al-Sudairawi, M. ;
MacKay, K.P. .
Environmental Software, 1988, 3 (04) :180-185