Multivariate Analysis of the Hong Kong Victoria Harbour Water Quality Data

Two multivariate techniques (principal components analysis and cluster analysis) are used to analyse Hong Kong Victoria Harbour water quality data. Results show that the sampling stations can be divided into four main groups, which is consistent with the tripartite structure obtained from an environmental approach. Moreover, it is found that the groups produced can be interpreted based on two main features of the data: nutrient level and the algae-faecal pollution.     

Quantifying urban heat island intensity in Hong Kong SAR, China

This paper addresses the methodological concerns in quantifying urban heat island (UHI) intensity in Hong Kong SAR, China. Although the urban heat island in Hong Kong has been widely investigated, there is no consensus on the most appropriate fixed point meteorological sites to be used to calculate heat island intensity. This study utilized the Local Climate Zones landscape classification system to classify 17 weather stations from the Hong Kong Observatory’s extensive fixed point meteorological observation network. According to the classification results, the meteorological site located at the Hong Kong Observatory Headquarters is the representative urban weather station in Hong Kong, whereas sites located at Tsak Yue Wu and Ta Kwu Ling are appropriate rural or nonurbanized counterparts. These choices were validated and supported quantitatively through comparison of long-term annual and diurnal UHI intensities with rural stations used in previous studies. Results indicate that the rural stations used in previous studies are not representative, and thus, the past UHI intensities calculated for Hong Kong may have been underestimated.     

Receptor modeling for analyzing PCDD/F and dioxin-like PCB sources in Hong Kong

A total of 96 ambient air samples were collected from January 2004 to December 2004 for the investigation of dioxins (polychlorinated dibenzo-para-dioxins and polychlorinated dibenzofurans [PCDD/Fs]) and dioxin-like profile for polychlorinated biphenyls (PCBs) in the atmosphere of Hong Kong. Two-day back-trajectories were obtained using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model from National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory. Receptor models – potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) – were built based on the measured PCDD/Fs and dioxin-like PCBs total toxic equivalent (TEQ) concentrations. Both models were modified in this study so as to integrate the information obtained from three sampling sites in Hong Kong. The PSCF modeling resolved two main PCDD/F and dioxin-like PCB polluting paths from Mainland China. In line with previous studies, the northerly path around the Pearl River Delta might be one of the pollution sources of Hong Kong air. On the other hand, the receptor models also indicated another potential northeasterly source from Yangtze River Delta, which was seldom mentioned as the source region of Hong Kong air. Results from the CWT modeling suggested that the most toxic air mass affecting Hong Kong is probably attributable to the northeast path rather than the north path.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

Mathematical models for accurate prediction of atmospheric visibility with particular reference to the seasonal and environmental patterns in Hong Kong

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 (NO(2)), nitrogen monoxide (NO), respirable suspended particulates (PM(10)), sulfur dioxide (SO(2)), 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 > or = 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 SO(2), CO, PM(10), NO, and NO(2). The results showed that atmospheric visibility was season dependent and would have significant correlations with temperature, the mass concentrations of PM(10) and NO(2), and the air pollution index API (correlation coefficients mid R: R mid R: > or = 0.7, p < or = 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.     

Application of multivariate statistical techniques in the assessment of water quality in the Southwest New Territories and Kowloon, Hong Kong

The application of different multivariate statistical techniques for the interpretation of a complex data matrix obtained during 2000?C2007 from the watercourses in the Southwest New Territories and Kowloon, Hong Kong was presented in this study. The data set consisted of the analytical results of 23 parameters measured monthly at 16 different sampling sites. Hierarchical cluster analysis grouped the 12 months into two periods and the 16 sampling sites into three groups based on similarity in water quality characteristics. Discriminant analysis (DA) provided better results both temporally and spatially. DA also offered an important data reduction as it only used four parameters for temporal analysis, affording 84.2% correct assignations, and eight parameters for spatial analysis, affording 96.1% correct assignations. Principal component analysis/factor analysis identified four latent factors standing for organic pollution, industrial pollution, nonpoint pollution, and fecal pollution, respectively. KN1, KN4, KN5, and KN7 were greatly affected by organic pollution, industrial pollution, and nonpoint pollution. The main pollution sources of TN1 and TN2 were organic pollution and nonpoint pollution, respectively. Industrial pollution had high effect on TN3, TN4, TN5, and TN6.     

Different hydrodynamic processes regulated on water quality (nutrients,dissolved oxygen,and phytoplankton biomass) in three contrasting waters of Hong Kong

The subtropical Hong Kong (HK) waters are located at the eastern side of the Pearl River Estuary. Monthly changes of water quality, including nutrients, dissolved oxygen (DO), and phytoplankton biomass (Chl-a) were routinely investigated in 2003 by the Hong Kong Environmental Protection Department in three contrasting waters of HK with different prevailing hydrodynamic processes. The western, eastern, and southern waters were mainly dominated by nutrient-replete Pearl River discharge, the nutrient-poor coastal/shelf oceanic waters, and mixtures of estuarine and coastal seawater and sewage effluent of Hong Kong, respectively. Acting in response, the water quality in these three contrasting areas showed apparently spatial–temporal variation pattern. Nutrients usually decreased along western waters to eastern waters. In the dry season, the water column was strongly mixed by monsoon winds and tidal currents, which resulted in relatively low Chl-a (<5 μg l^?1) and high bottom DO (>4 mg l^?1), suggesting that mixing enhanced the buffering capacity of eutrophication in HK waters. However, in the wet season, surface Chl-a was generally >10 μg l^?1 in southern waters in summer due to halocline and thermohaline stratification, adequate nutrients, and light availability. Although summer hypoxia (DO <2 mg l^?1) was episodically observed near sewage effluent site and in southern waters induced by vertical stratification, the eutrophication impacts in HK waters were not as severe as expected owing to P limitation and short water residence time in the wet season.     

A distributed modelling system for simulation of monthly runoff and nitrogen sources, loads and sinks for ungauged catchments in Denmark

We validated an existing physically based 3D MIKE SHE groundwater resource model (DK-model) at 175 Danish gauging stations covering different catchment sizes in order to calculate monthly water runoff in the 50% ungauged part of Denmark. Model performance was in most cases good (61% of gauging stations had a Nash-Sutcliffe (NS) coefficient >0.60) but nevertheless showed a large seasonal and georegion specific bias. Therefore, bias correction factors had to be developed before applying the DK-model simulations of runoff in the ungauged areas. Simulated monthly runoff from ungauged areas and the measured monthly runoff from 178 gauging stations were distributed to 2663 smaller Hydrological Units (ca. 15 km(2)) and linked with a new empirical model for flow-weighted monthly total nitrogen (TN) concentrations (R(2) = 0.43; P < 0.0001) developed based on 20 years of observations (1990-2009) in 83 small catchments for calculation of monthly gross diffuse TN-loads from HU's. Nitrogen retention was calculated in streams, lakes and wetlands utilising both lake specific models and rate coefficients to calculate N retention in surface water bodies. The whole model complex was linked in the DK-QN concept for simulation of monthly TN losses from point sources and diffuse sources, TN retention and resulting loadings to Danish coastal waters. The DK-QN model was validated in 118 gauged catchments and the model simulations had for >25% of the observations of monthly discharge weighted TN concentrations a NS larger than 0.26. Catchment specific monthly TN-loadings were modelled with a higher performance as 50% of the catchments had a NS greater than 0.75. The model concept allows calculation of N retention in streams, lakes and wetlands and the average annual model calculated N retention amounted to 21% of the modelled gross riverine TN loadings. The average annual gross TN loading to surface freshwater in Denmark derived from diffuse sources amounted to 97 000 tonnes N (91% of gross TN loadings) which is 54% of the total estimated N-leaching from the root zone on the Danish land area (212 000 tonnes N) during the period 1990-2009.     

Comprehensive two-dimensional gas chromatography, a valuable technique for screening and semiquantitation of different chemical compounds in ultrafine 30 nm and 50 nm aerosol particles

Comprehensive two dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOF-MS) was used for screening and semiquantitation of semivolatile organic compounds in aerosol particles. As the volatility was a prerequisite parameter for the analysis, some compounds were transformed via derivatization such as silylation into more volatile ones. The identification of the analytes was made by comparing the GC retention indices and the TOF mass spectra with the NIST and the Golm metabolome database reference libraries. The data treatment was simplified by exploiting an additional classification of the identified compounds, namely the main functional group or specific element present in the molecule leading to different groups of compounds. This methodology was applied to identify compounds in 30 ± 4 nm, 50 ± 5 nm and total suspended particles (TSP) collected during spring and autumn of 2009 and summer of 2010 at the Station for Measuring Forest Ecosystem Atmosphere Relations (SMEAR II) at Hyyti?l? (Finland). The number of identified compounds was higher than 400, which were the most relevant compounds present in the samples, in terms of concentrations. The analysis of aerosol particles of different sizes, collected simultaneously, revealed that the number of compounds increased with the particle size whereas the normalized response factor decreased in most of the cases, aldehydes being an exception. This decrease could be associated with the formation or aggregation of new compounds onto the particles when they grow in the atmosphere.