Page 34 - Undergraduate Research Opportunities Program Proceedings 2017-18

School of Science
Department of Mathematics

Below-cloud washout (BCW) is one of the most efficient removal mechanisms for PM2.5. This study investigates the
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correlation between the BCW coefficients of PM2.5 species (NO3 , SO4 and NH4 ) as well as PM2.5 and rain intensity in
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heavy rainfall events, using the power regression model ( = ). Hourly data on ambient PM2.5 and meteorological

factors were collected in HKUST super-site, a clean rural area, from 2011 to 2017. This study is presented on the basis of
39 selected precipitation cases for PM2.5 species and 47 selected precipitation cases for PM2.5. The collected results can
be applied in 3-D air quality model to improve the PM2.5 simulation during the rainy season.

Supervisor: FUNG Jimmy Chi Hung / MATH Co-Supervisor: LU Xingcheng / ENVR
Student: JEONG Eall Hyuk / SSCI Course: UROP1000, Summer

Due to the possible hazardous effects of PM2.5, it is crucial to monitor the deposition of PM2.5 by precipitation. Through
analyzing the 2011-2012 observation data (Na, K, Mg, and Ca composition data), the Below-cloud washout (BCW)
coefficients of Na, K, Mg, and Ca are calculated. Further, the uncertainty assessment of scavenging coefficient is
recognized. Na, K, Mg, and Ca are not fully studied in the current period; hence, the calculated BCW coefficients of Na, K,
Mg, and Ca could further assist in understanding about the BCW of aerosol particles by precipitation in Hong Kong.

Retrieval of Aerosol Optical Depth Using Machine Learning Algorithm

Supervisor: FUNG Jimmy Chi Hung / MATH Co-Supervisor: LU Xingcheng / ENVR
Student: GUPTA Abhishek / SENG Course: UROP1000, Summer

Atmospheric Aerosol Optical Depth is a major factor that causes random variation of solar radiation intensity and affects
atmospheric pollution. Most of the methods of obtaining AOD suffer from resolution or scarce observation points. By
observing the various correlations between AOD and PM concentrations, Surface Temperature, Relative Humidity and
Boundary Layer Height, an estimation model using the above physical quantities is proposed to obtain AOD conveniently
and accurately. A multi-layered back-propagated neural network is used to map the non-linear relationships between
the variables and predict AOD values. On testing with unbiased data, the network shows a high accuracy, leading to the
conclusion that such a model can effectively predict the Aerosol Optical Depth.

Use Kolmogorov–Zurbenko Filter to Detrend the Air Quality Data

Supervisor: FUNG Jimmy Chi Hung / MATH Co-Supervisor: LU Xingcheng / ENVR
Student: HU Mingyun / MATH-AM Course: UROP1000, Summer

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Ozone (O ) pollution has become one of the most severe environmental issues in Hong Kong. To investigate the long-
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term trend of surface O and correlations between O and meteorological variables, O data is detrended into various
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spectral components based on the Kolmogorov–Zurbenko filter (KZ filter), using measurement data at four
meteorological sites (Central, Tsuen Wan, Tung Chung, Yuen Long) for the period of 2003-2017. In general, the long-
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term components of O have undergone a fluctuating increase during the period, while temperature plays a leading role
in the formation of O among the meteorological variables. The seasonal components of O have moderate correlations
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with meteorological variables in Hong Kong.

Supervisor: FUNG Jimmy Chi Hung / MATH Co-Supervisor: LU Xingcheng / ENVR
Student: HWANG Min Ho / MAEC Course: UROP1000, Summer

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