Azman Azid

Spatial and temporal air quality pattern recognition using environmetric techniques: a case study in Malaysia

The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.

Spatial assessment and source identification of heavy metals pollution in surface water using several chemometric techniques.

UNLABELLED This study presents the determination of the spatial variation and source identification of heavy metal pollution in surface water along the Straits of Malacca using several chemometric techniques. Clustering and discrimination of heavy metal compounds in surface water into two groups (northern and southern regions) are observed according to level of concentrations via the application of chemometric techniques. Principal component analysis (PCA) demonstrates that Cu and Cr dominate the source apportionment in northern region with a total variance of 57.62% and is identified with mining and shipping activities. These are the major contamination contributors in the Straits. Land-based pollution originating from vehicular emission with a total variance of 59.43% is attributed to the high level of Pb concentration in the southern region. The results revealed that one state representing each cluster (northern and southern regions) is significant as the main location for investigating heavy metal concentration in the Straits of Malacca which would save monitoring cost and time. CAPSULE The monitoring of spatial variation and source of heavy metals pollution at the northern and southern regions of the Straits of Malacca, Malaysia, using chemometric analysis.

Spatial analysis of the air pollutant index in the southern region of Peninsular Malaysia using environmetric techniques

Air pollution is becoming a major environmental issue in the southern region of Peninsular Malaysia. Environmetric techniques (HACA, DA, and PCA/FA) were used to evaluate the spatial variations in the southern region of Peninsular Malaysia, followed by API prediction comparison using ANN and MLR models. The datasets of air pollutant parameters for 3 years (2005–2007) were applied in this study. HACA clustered three different groups of similarity based on the characteristics of air quality parameters. DA shows all seven parameters (CO, O3, PM10, SO2, NOx, NO, and NO2) gave the most significant variables after stepwise backward mode. PCA/FA identify that the major source of air pollution is due to combustion of fossil fuels in motor vehicles and industrial activities. The ANN model shows a better prediction compared to the MLR model with R2 values equal to 0.819 and 0.773 respectively. This study concluded that the environmetric techniques and modelling become an excellent tool in API assessment, air pollution source identification, apportionment, and interpretation of complex dataset with a view to get better information about the air quality, and can be setbacks in designing an API monitoring network for effective air pollution resources management.

Selection of the Most Significant Variables of Air Pollutants Using Sensitivity Analysis

This study was conducted to determine the most significant parameters for the air-pollutant index (API) prediction in Malaysia using data covering a 7-year period (2006–2012) obtained from the Malaysian Department of Environment (DOE). The sensitivity analysis method coupled with the artificial neural network (ANN) was applied. Nine models (ANN-API-AP, ANN-API-LCO, ANN-API-LO3, ANN-API-LPM10, ANN-API-LSO2, ANN-API-LNO2, ANN-API-LCH4, ANN-API-LNmHC and ANN-API-LTHC) were carried out in the sensitivity analysis test. From the findings, PM10 and CO were identified as the most significant parameters in Malaysia. Three artificial neural network models (ANN-API-AP, ANN-API-LO, and ANN-API-DOE) were compared based on the performance criterion [R2, root-mean-square error (RMSE), and squared sum of all errors (SSE)] for the best prediction model selection. The ANN-API-AP, ANN-API-LO, and ANN-API-DOE models have R2 values of 0.733, 0.578, and 0.742, respectively; RMSE values of 8.689, 10.858, and 8.357, respectively; SSE values of 762,767.22, 1,191,280.60, and 705,600.05, respectively. The findings exhibit the ANN-API-LO model has a lower value in R2 and higher values in RMSE and SSE than others. ANN-API-LO model was considered as the best model of prediction because of fewer variables was utilized as input and far less complex than others. Hence, the use of fewer parameters of the API prediction has been highly practicable for air resource management because of its time and cost efficiency.

Evaluation of water quality variation in lakes, rivers, and ex-mining ponds in Malaysia (review)

AbstractThreat posed by the heavy metals has been increasing globally rendering many water bodies unfit for human consumption. This could be due to the increase in concentrations of these metals above natural background. This article reviews the literature data on variation of water quality in rivers, lakes, and ex-mining ponds in Malaysia and other selected countries. World Health Organization (WHO), United States Environmental Protection Agency, and Malaysian water quality standards (INWQS) are used as the baseline for the pollution and health risk assessments. It illustrates that concentrations of Pb, Cd, and As in lakes and ex-mining ponds, and Mn, Cd and Pb in rivers exceed permissible limits for direct consumption. The levels of dissolved oxygen, TSS, and chemical oxygen demand (COD) are not within WHO and INWQS limits, pH of lakes and ex-mining ponds are lower than reference standards while that of rivers are high. Principal component analysis reveals that TSS, COD, BOD, Pb, and As are highly assoc...

Spatial Assessment of Water Quality Affected by the Land-Use Changes Along Kuantan River Basin

This study addresses the effects of development on water quality in the Kuantan River Basin from 2003 to 2008. Chemometrics analysis namely MLR, HACA, DA and PCA was utilised as part of the methods for this study. From the result, MLR was irrefutably proven as an efficient predicting method for missing data. HACA classified seven stations as Low Polluted Stations (LPS), six stations as Moderate Polluted Stations (MPS) and two stations as High Polluted Stations (HPS). DA result depicted the accuracy rate for all reclassified data was 83.61 % respectively, while the constituting parameters namely Dissolved Oxygen (DO), Escherichia coli (E. coli), pH, Phosphate (PO4), Chemical Oxygen Demand (COD), and Chloride (Cl), gave the biggest impacts towards water quality by means of forward and backward stepwise methods. The PCA result after varimax rotation indicated that five varimax factors have presented strong parameter coefficient exceeding 0.7 by E. coli, coliform, Dissolved Solids (DS), Total Solids (TS), Chlorine (Cl), Ammonical Nitrogen (NH3NL), nitrate and pH. The relationship between land use and water quality denoted that after applying Spearman correlation based on 90 % interval population distribution, aspects influencing the rate of DO was successfully identified.

Consumption of water from ex-mining ponds in Klang Valley and Melaka, Malaysia: A health risk study

Evaluation of health risks due to heavy metals exposure via drinking water from ex-mining ponds in Klang Valley and Melaka has been conducted. Measurements of As, Cd, Pb, Mn, Fe, Na, Mg, Ca, and dissolved oxygen, pH, electrical conductivity, total dissolved solid, ammoniacal nitrogen, total suspended solid, biological oxygen demand were collected from 12 ex-mining ponds and 9 non-ex-mining lakes. Exploratory analysis identified As, Cd, and Pb as the most representative water quality parameters in the studied areas. The metal exposures were simulated using Monte Carlo methods and the associated health risks were estimated at 95th and 99th percentile. The results revealed that As was the major risk factor which might have originated from the previous mining activity. For Klang Valley, adults that ingested water from those ponds are at both non-carcinogenic and carcinogenic risks, while children are vulnerable to non-carcinogenic risk; for Melaka, only children are vulnerable to As complications. However, dermal exposure showed no potential health consequences on both adult and children groups.

Spatial Variations of Drinking Water Quality Monitoring in Water Treatment Plant Using Environmetric Techniques

This research investigates the relationship between the physicochemical levels and the drinking water quality in Kuala Kubu Bharu, Selangor, Malaysia based on three different classes of drinking water. The environmetric techniques such as the discriminant analysis (DA), the principal component analysis (PCA) and the factor analysis (FA) were applied to analyze the spatial variation of the most significant physicochemical parameters of the drinking water quality and to determine the source of pollution. Seven physicochemical variables were analyzed. The forward and backward stepwise DA managed to discriminate six and two variables, respectively from the original seven variables. PCA and FA (varimax functionality) were to identify the origin of each water quality variable based on the three different drinking water classes. This study shows that environmetric method is the ideal way into provide meaningful information on the spatial variability of sophisticated drinking water quality data.