Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/29717
Title: Mathematical models for accurate prediction of atmospheric visibility with particular reference to the seasonal and environmental patterns in Hong Kong
Authors: Mui, KW 
Wong, LT 
Chung, LY
Keywords: Air pollution index
Atmospheric visibility
Nitrogen dioxide
Respirable suspended particulates
Issue Date: 2009
Publisher: Springer
Source: Environmental monitoring and assessment, 2009, v. 158, no. 1-4, p. 333-341 How to cite?
Journal: Environmental monitoring and assessment 
Abstract: Atmospheric visibility impairment has gained increasing concern as it is associated with the existence of a number of aerosols as well as common air pollutants and produces unfavorable conditions for observation, dispersion, and transportation. This study analyzed the atmospheric visibility data measured in urban and suburban Hong Kong (two selected stations) with respect to time-matched mass concentrations of common air pollutants including nitrogen dioxide (NO2), nitrogen monoxide (NO), respirable suspended particulates (PM10), sulfur dioxide (SO2), carbon monoxide (CO), and meteorological parameters including air temperature, relative humidity, and wind speed. No significant difference in atmospheric visibility was reported between the two measurement locations (p≥0.6, t test); and good atmospheric visibility was observed more frequently in summer and autumn than in winter and spring (p<0.01, t test). It was also found that atmospheric visibility increased with temperature but decreased with the concentrations of SO2, CO, PM10, NO, and NO2. The results showed that atmospheric visibility was season dependent and would have significant correlations with temperature, the mass concentrations of PM10 and NO2, and the air pollution index API (correlation coefficients |Râ|≥0.7, p≥0.0001, t test). Mathematical expressions catering to the seasonal variations of atmospheric visibility were thus proposed. By comparison, the proposed visibility prediction models were more accurate than some existing regional models. In addition to improving visibility prediction accuracy, this study would be useful for understanding the context of low atmospheric visibility, exploring possible remedial measures, and evaluating the impact of air pollution and atmospheric visibility impairment in this region.
URI: http://hdl.handle.net/10397/29717
ISSN: 0167-6369
EISSN: 1573-2959
DOI: 10.1007/s10661-008-0587-9
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