Semi-statistical model for evaluating the effects of source emissions and meteorological effects on daily average NOx concentrations in South Taiwan

被引:10
作者
Lin, CH
Wu, YL
机构
[1] Fooyin Univ, Dept Environm Engn & Sanitat, Kaohsiung 831, Taiwan
[2] Natl Cheng Kung Univ, Dept Environm Engn, Tainan 70101, Taiwan
关键词
statistical model; emission; regression model; back trajectories; NOx;
D O I
10.1016/S1352-2310(03)00085-2
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study developed a new semi-statistical model based on a Lagrangian approach. The overall effects on the observed pollutant levels at a receptor site were divided into two groups, one including the effects of emissions from various upwind sources and the other including all other effects (including the overall effects of atmospheric dilution, chemical transformation, and wet and dry depositions). The former effects were directly accounted for by a new parameter, an emission factor, defined as the accumulated emission uptake along the air trajectory toward the analyzed receptor site. All other effects were represented by a pollutant transfer coefficient. Meteorological parameters, excluding wind direction, were suggested to simulate this coefficient. The model was used to simulate variations in daily average NOx concentrations at a receptor site in south Taiwan during 1995-1999. Four meteorological factors, temperature, humidity, wind speed and pressure, were used to simulate the pollutant transfer coefficient. The full model successfully explained 61% of the analyzed concentration variations. The emission factor was the single most important factor in the model. When this factor was omitted, the determination coefficient of the model decreased from 61% to 48%. However, the pollutant transfer coefficient still dominated the analyzed variations of concentration. (C) 2003 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:2051 / 2059
页数:9
相关论文
共 34 条
[1]   A STATISTICAL TRAJECTORY TECHNIQUE FOR DETERMINING AIR-POLLUTION SOURCE REGIONS [J].
ASHBAUGH, LL .
JOURNAL OF THE AIR POLLUTION CONTROL ASSOCIATION, 1983, 33 (11) :1096-1098
[2]   Accounting for meteorological effects in measuring urban ozone levels and trends [J].
Bloomfield, P ;
Royle, JA ;
Steinberg, LJ ;
Yang, Q .
ATMOSPHERIC ENVIRONMENT, 1996, 30 (17) :3067-3077
[3]   QUALITATIVE DETERMINATION OF SOURCE REGIONS OF AEROSOL IN CANADIAN HIGH ARCTIC [J].
CHENG, MD ;
HOPKE, PK ;
BARRIE, L ;
RIPPE, A ;
OLSON, M ;
LANDSBERGER, S .
ENVIRONMENTAL SCIENCE & TECHNOLOGY, 1993, 27 (10) :2063-2071
[4]   An enhanced ozone forecasting model using air mass trajectory analysis [J].
Cobourn, WG ;
Hubbard, MC .
ATMOSPHERIC ENVIRONMENT, 1999, 33 (28) :4663-4674
[5]   Comparing neural networks and regression models for ozone forecasting [J].
Comrie, AC .
JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION, 1997, 47 (06) :653-663
[6]   Meteorological controls on ozone at an elevated eastern United States regional background monitoring site [J].
Cooper, OR ;
Moody, JL .
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2000, 105 (D5) :6855-6869
[7]   Assessment of interannual ozone variation in urban areas from a climatological perspective [J].
Cox, WM ;
Chu, SH .
ATMOSPHERIC ENVIRONMENT, 1996, 30 (14) :2615-2625
[8]   Modeling the effects of meteorology on ozone in Houston using cluster analysis and generalized additive models [J].
Davis, JM ;
Eder, BK ;
Nychka, D ;
Yang, Q .
ATMOSPHERIC ENVIRONMENT, 1998, 32 (14-15) :2505-2520
[9]   A model for predicting maximum and 8 h average ozone in Houston [J].
Davis, JM ;
Speckman, P .
ATMOSPHERIC ENVIRONMENT, 1999, 33 (16) :2487-2500
[10]   Long-term trends in ground level ozone over the contiguous United States, 1980-1995 [J].
Fiore, AM ;
Jacob, DJ ;
Logan, JA ;
Yin, JH .
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 1998, 103 (D1) :1471-1480