Principal component analysis (PCA) is capable of handling large sets of data. However, lack of consistent method in data pre-treatment and its importance are the limitations in PCA applications. This study examined pre-treatments methods (log (x + 1) transformation, outlier removal, and granulometric and geochemical normalization) on dataset of Mengkabong Lagoon, Sabah, mangrove surface sediment at high and low tides. The study revealed that geochemical normalization using Al with outliers removal resulted in a better classification of the mangrove surface sediment than that outliers removal, granulometric normalization using clay and log (x + 1) transformation. PCA output using geochemical normalization with outliers removal demonstrated associations between environmental variables and tides of mangrove surface sediment, Mengkabong Lagoon, Sabah. The PCA outputs at high and low tides also provided to better interpret information about the sediment and its controlling factors in the intertidal zone. The study showed data pre-treatment method to be a useful procedure to standardize the datasets and reducing the influence of outliers.
This study aims to determine the status of potentially toxic element concentrations of road dust in a medium-sized city (Rawang, Malaysia). This study adopts source identification via enrichment factor, Pearson correlation analysis, and Fourier spectral analysis to identify sources of potentially toxic element concentrations in road dust in Rawang City, Malaysia. Health risk assessment was conducted to determine potential health risks (carcinogenic and non-carcinogenic risks) among adults and children via multiple pathways (i.e., ingestion, dermal contact, and inhalation). Mean of potentially toxic element concentrations were found in the order of Pb > Zn > Cr(IV) > Cu > Ni > Cd > As > Co. Source identification revealed that Cu, Cd, Pb, Zn, Ni, and Cr(IV) are associated with anthropogenic sources in industrial and highly populated areas in northern and southern Rawang, cement factories in southern Rawang, as well as the rapid development and population growth in northwestern Rawang, which have resulted in high traffic congestion. Cobalt, Fe, and As are related to geological background and lithologies in Rawang. Pathway orders for both carcinogenic and non-carcinogenic risks are ingestion, dermal contact, and inhalation, involving adults and children. Non-carcinogenic health risks in adults were attributed to Cr(IV), Pb, and Cd, whereas Cu, Cd, Cr(IV), Pb, and Zn were found to have non-carcinogenic health risks for children. Cd, Cr(IV), Pb, and As may induce carcinogenic risks in adults and children, and the total lifetime cancer risk values exceeded incremental lifetime.
Lead contamination in topsoil of the mining and smelting area of Mitrovica, Kosovo, was investigated for total concentrations and chemical fractions by sequential extraction analysis, mineralogical fractions by X-ray diffraction (XRD) and scanning electron microscopy with energy-dispersive X-ray spectrometer (SEM-EDX). The study revealed that all samples contained Pb exceeding USEPA standard of 400 mg kg-1. The highest total concentration of Pb (125,000 mg kg-1) was the soil from the former smelter. Sequential extraction results showed that the predominant form of Pb was associated with Fe-Mn oxide-bound fraction which ranged from 45.37 to 71.61% of total concentrations, while carbonate and silicate Pb-binding fractions were dominant when physical measurements (XRD and SEM-EDX) were applied. Application of Pb isotope ratios (206Pb/207Pb and 208Pb/206Pb), measured by inductively coupled plasma mass spectrometry, identified that Pb contamination is originated from similar anthropogenic source. The results reflected that the Pb contamination in the soil of this area is serious. In order to provide proper approaches on remediation and prevention of health impacts to the people in this area, a continuous monitoring and health risk assessment are recommended.
Gadilam river basin has gained its importance due to the presence of Neyveli Lignite open cast mines and other industrial complexes. It is also due to extensive depressurization of Cuddalore aquifer, and bore wells for New Veeranam Scheme are constructed downstream of the basin. Geochemical indicators of groundwater were used to identify the chemical processes that control hydrogeochemistry. Chemical parameters of groundwater such as pH, electrical conductivity, total dissolved solids, sodium (Na(+)), potassium (K(+)), calcium (Ca(+)), magnesium (Mg(+)), bicarbonate (HCO(-)(3)), sulfate (SO(-)(4)), phosphate (PO(-)(4)), and silica (H(4)SiO(4)) were determined. Interpretation of hydrogeochemical data suggests that leaching of ions followed by weathering and anthropogenic impact controls the chemistry of the groundwater. Isotopic study reveals that recharge from meteoric source in sedimentary terrain and rock-water interaction with significant evaporation prevails in hard rock region.
This study was undertaken to assess the levels of trace metals (As, Cd, Cu, Pb, and Zn) in two common species of cockles (Anadara granosa and Anadara inaequivalvis) from two coastal areas in Thailand (Pattani Bay) and Malaysia (the Setiu Wetlands). A total of 350 cockles were collected in February and September 2014. Trace metals were determined by Inductively Coupled Plasma Mass Spectrometry. We observed that cockles in both areas had a higher accumulation of metals in September. Notably, the biota-sediment accumulation (BSAF) of Cd was highest in both areas. A strong positive correlation of Cd with the length of the cockles at Pattani Bay (r(2) = 0.597) and the Setiu Wetlands (r(2) = 0.675) was noted. It was suggested that As could be a limiting element (BSAF < 1) of cockles obtained from Pattani Bay. In comparison with the permissible limits set by the Thailand Ministry of Public Health and the Malaysia Food Regulations, mean values of As, Cd, Cu, Pb, and Zn were within acceptable limits, but the maximum values of Cd and Pb exceeded the limits for both areas. Regular monitoring of trace metals in cockles from both areas is suggested for more definitive contamination determination.
In this paper, an attempt has been made to assess, prognosis and observe dynamism of soil erosion by universal soil loss equation (USLE) method at Penang Island, Malaysia. Multi-source (map-, space- and ground-based) datasets were used to obtain both static and dynamic factors of USLE, and an integrated analysis was carried out in raster format of GIS. A landslide location map was generated on the basis of image elements interpretation from aerial photos, satellite data and field observations and was used to validate soil erosion intensity in the study area. Further, a statistical-based frequency ratio analysis was carried out in the study area for correlation purposes. The results of the statistical correlation showed a satisfactory agreement between the prepared USLE-based soil erosion map and landslide events/locations, and are directly proportional to each other. Prognosis analysis on soil erosion helps the user agencies/decision makers to design proper conservation planning program to reduce soil erosion. Temporal statistics on soil erosion in these dynamic and rapid developments in Penang Island indicate the co-existence and balance of ecosystem.
The geochemistry and distribution of major, trace and rare earth elements (REE's) was studied in the surface sediments of the Lower Baram River during two seasons: the Monsoon (MON) and Post - monsoon (POM). The major geochemical processes controlling the distribution and mobility of major, trace and REE's in the Lower Baram River surface sediments was revealed through factor analysis. The risk assessment of major and trace element levels was studied at three specific levels; i.e. the enrichment level [Contamination Factor (Cf), with the geo-accumulation index (Igeo)], the availability level [metals bound to different fractions, risk assessment code (RAC)], and the biological toxicity level [effect range low (ERL) and effect range medium (ERM)]. The results of all the indices indicate that Cu is the element of concern in the Lower Baram River sediments. The geochemical fractionation of major and trace elements were studied through sequential extraction and the results indicated a higher concentration of Mn in the exchangeable fraction. The element of concern, Cu, was found to be highly associated in the organic bound (F4) fraction during both seasons and a change in the redox, possibly due to storms or dredging activities may stimulate the release of Cu into the overlying waters of the Lower Baram River.
The objective of this study was to identify the spatial and temporal variabilities of selected nutrients in the Setiu Wetlands Lagoon (SWL), Malaysia. Water samples were collected quarterly at ten monitoring sites. This study presents results from a 10-year field investigation (2003 to 2010 and 2014 to 2015) of water quality in the SWL. For the spatial pattern, four clusters were identified with hierarchical cluster analysis. Analysis of the temporal trend shows that the high total suspended solid loading in 2010 was due to large-scale land clearing upstream of the SWL. The enrichment of ammonium after 2010 could plausibly be due to land-based aquaculture diffuse discharges. In 2005-2007, expansion of oil palm plantations within the Setiu catchment had doubled the phosphorus concentration in the SWL. The natural and anthropogenic alterations of the lagoon inlets profoundly influenced the spatial distribution patterns of nutrients in the SWL. These results suggest that intense anthropogenic disturbances close to the SWL accounted for the water quality deterioration.
A set of methodological tools was tested to assess the sensitivity of several ecological and biological indices to eutrophication while at the same time attempting to explore a linkage among pressures, classification assessment and drivers. Industrial discharges, harbor activities, natural interactions and river discharges are the pressures most related to the eutrophication process in tropical coastal water bodies. Among the eutrophication indices used, TRIX and operational indicators overestimated the eutrophication status in the study area, but EI and chl-a seems to be a rather responsive index to reflect the first stage of eutrophication. It is noteworthy that EI and chl-a showed better overall agreement with the ecological quality status (EcoQ) showing that probably it reflects the indirect relation of macrobenthic with water eutrophication in a better way. An ecological boundary of EI and chl-a from moderate to poor may be needed in order to explain the poor status of relatively eutrophic Klang Strait coastal sites.
The current study focused on the monitoring of pollution loads in the Kalpakkam coastal zone of India in terms of physico-chemical characteristics of sediment. The investigation took place at 12 sampling points around the Kalpakkam coastal zone for one year beginning from 2019. The seasonal change of nutrients in the sediment, such as nitrogen, phosphorus, potassium, total organic carbon, and particles size distribution, was calculated. Throughout the study period, the pH (7.55 to 8.99), EC (0.99 to 4.98 dS/m), nitrogen (21.74 to 58.12 kg/ha), phosphorus (7.5 to 12.9 kg/ha), potassium (218 to 399 kg/ha), total organic carbon (0.11 to 0.88%), and particle size cumulative percent of sediments (from 9.01 to 9.39%) was observed. A number of multivariate statistical techniques were used to examine the changes in sediment quality. The population means were substantially different according to the three-way ANOVA test at the 0.05 level. Principal component analysis and cluster analysis showed a substantial association with all indicators throughout all seasons, implying contamination from both natural and anthropogenic causes. The ecosystem of the Kalpakkam coastal zone has been affected by nutrient contamination.
The current study aims to investigate the influence of seasonal changes on the pollution loads of the sediment of a coastal area in terms of its physicochemical features. The research will focus on analyzing the nutrients, organic carbon and particle size of the sediment samples collected from 12 different sampling stations in 3 different seasons along the coastal area. Additionally, the study discusses about the impact of anthropogenic activities such as agriculture and urbanization and natural activities such as monsoon on the sediment quality of the coastal area. The nutrient changes in the sediment were found to be: pH (7.96-9.45), EC (2.89-5.23 dS/m), nitrogen (23.98-57.23 mg/kg), phosphorus (7.75-11.36 mg/kg), potassium (217-398 mg/kg), overall organic carbon (0.35-0.99%), and sediment proportions (8.91-9.3%). Several statistical methods were used to investigate changes in sediment quality. According to the three-way ANOVA test, the mean value of the sediments differs significantly with each season. It correlates significantly with principal factor analysis and cluster analysis across seasons, implying contamination from both natural and man-made sources. This study will contribute to developing effective management strategies for the protection and restoration of degraded coastal ecosystem.
Introduction Pollution of air in urban cities across the world has been steadily increasing in recent years. An increasing trend in particulate matter, PM 2.5, is a threat because it can lead to uncontrollable consequences like worsening of asthma and cardiovascular disease. The metric used to measure air quality is the air pollutant index (API). In Malaysia, machine learning (ML) techniques for PM 2.5 have received less attention as the concentration is on predicting other air pollutants. To fill the research gap, this study focuses on correctly predicting PM 2.5 concentrations in the smart cities of Malaysia by comparing supervised ML techniques, which helps to mitigate its adverse effects. Methods In this paper, ML models for forecasting PM 2.5 concentrations were investigated on Malaysian air quality data sets from 2017 to 2018. The dataset was preprocessed by data cleaning and a normalization process. Next, it was reduced into an informative dataset with location and time factors in the feature extraction process. The dataset was fed into three supervised ML classifiers, which include random forest (RF), artificial neural network (ANN) and long short-term memory (LSTM). Finally, their output was evaluated using the confusion matrix and compared to identify the best model for the accurate prediction of PM 2.5. Results Overall, the experimental result shows an accuracy of 97.7% was obtained by the RF model in comparison with the accuracy of ANN (61.14%) and LSTM (61.77%) in predicting PM 2.5. Discussion RF performed well when compared with ANN and LSTM for the given data with minimum features. RF was able to reach good accuracy as the model learns from the random samples by using decision tree with the maximum vote on the predictions.
This comprehensive paper conducts an in-depth review of personal exposure and air pollutant levels within the microenvironments of Asian city transportation. Our methodology involved a systematic analysis of an extensive body of literature from diverse sources, encompassing a substantial quantity of studies conducted across multiple Asian cities. The investigation scrutinizes exposure to various pollutants, including particulate matters (PM10, PM2.5, and PM1), carbon dioxide (CO2), formaldehyde (CH2O), and total volatile organic compounds (TVOC), during transportation modes such as car travel, bus commuting, walking, and train rides. Notably, our review reveals a predominant focus on PM2.5, followed by PM10, PM1, CO2, and TVOC, with limited attention given to CH2O exposure. Across the spectrum of Asian cities and transportation modes, exposure concentrations exhibited considerable variability, a phenomenon attributed to a multitude of factors. Primary sources of exposure encompass motor vehicle emissions, traffic dynamics, road dust, and open bus doors. Furthermore, our findings illuminate the influence of external environments, particularly in proximity to train stations, on pollutant levels inside trains. Crucial factors affecting exposure encompass ventilation conditions, travel-specific variables, seat locations, vehicle types, and meteorological influences. The culmination of this rigorous review underscores the need for standardized measurements, enhanced ventilation systems, air filtration mechanisms, the adoption of clean energy sources, and comprehensive public education initiatives aimed at reducing pollutant exposure within city transportation microenvironments. Importantly, our study contributes to the growing body of knowledge surrounding this subject, offering valuable insights for policymakers and researchers dedicated to advancing air quality standards and safeguarding public health.
Slope failure has become a major concern in Malaysia due to the rapid development and urbanisation in the country. It poses severe threats to any highway construction industry, residential areas, natural resources and tourism activities. The extent of damages that resulted from this catastrophe can be lessened if a long-term early warning system to predict landslide prone areas is implemented. Thus, this study aims to characterise the relationship between Oxisols properties and soil colour variables to be manipulated as key indicators to forecast shallow slope failure. The concentration of each soil property in slope soil was evaluated from two different localities that consist of 120 soil samples from stable and unstable slopes located along the North-South Highway (PLUS) and East-West Highway (LPT). Analysis of variance established highly significant difference (P
This study aimed to determine the spatial distribution of PM2.5 and PM10 collected in four regions (North, Central, South and East Coast) of Peninsular Malaysia during the southwest monsoon. Concurrent measurements of PM2.5 and PM10 were performed using a high volume sampler (HVS) for 24 h (August to September 2018) collecting a total of 104 samples. All samples were then analysed for water soluble inorganic ions (WSII) using ion chromatography, trace metals using inductively coupled plasma-mass spectroscopy (ICP-MS) and polycyclic aromatic hydrocarbon (PAHs) using gas chromatography-mass spectroscopy (GC-MS). The results showed that the highest average PM2.5 concentration during the sampling campaign was in the North region (33.2 ± 5.3 μg m-3) while for PM10 the highest was in the Central region (38.6 ± 7.70 μg m-3). WSII recorded contributions of 22% for PM2.5 and 20% for PM10 mass, with SO42- the most abundant species with average concentrations of 1.83 ± 0.42 μg m-3 (PM2.5) and 2.19 ± 0.27 μg m-3 (PM10). Using a Positive Matrix Factorization (PMF) model, soil fertilizer (23%) was identified as the major source of PM2.5 while industrial activity (25%) was identified as the major source of PM10. Overall, the studied metals had hazard quotients (HQ) value of <1 indicating a very low risk of non-carcinogenic elements while the highest excess lifetime cancer risk (ELCR) was recorded for Cr VI in the South region with values of 8.4E-06 (PM2.5) and 6.6E-05 (PM10). The incremental lifetime cancer risk (ILCR) calculated from the PAH concentrations was within the acceptable range for all regions.
It is important to have good indoor air quality, especially in indoor office environments, in order to enhance productivity and maintain good work performance. This study investigated the effects of indoor office activities on particulate matter of less than 2.5 μm (PM2.5) and ozone (O3) concentrations, assessing their potential impact on human health. Measurements of indoor PM2.5 and O3 concentrations were taken every 24 h during the working days in five office environments located in a semi-urban area. As a comparison, the outdoor concentrations were derived from the nearest Continuous Air Quality Monitoring Station. The results showed that the average 24 h of indoor and outdoor PM2.5 concentrations were 3.24 ± 0.82 μg m-3 and 17.4 ± 3.58 μg m-3 respectively, while for O3 they were 4.75 ± 4.52 ppb and 21.5 ± 5.22 ppb respectively. During working hours, the range of PM2.5 concentrations were 1.00 μg m-3 to 6.10 μg m-3 while for O3 they were 0.10 ppb to 38.0 ppb. The indoor to outdoor ratio (I/O) for PM2.5 and O3 was <1, thus indicating a low infiltration of outdoor sources. The value of the hazard quotient (HQ) for all sampling buildings was <1 for both chronic and acute exposures, indicating that the non-carcinogenic risks are negligible. Higher total cancer risk (CR) value for outdoors (2.67E-03) was observed compared to indoors (4.95E-04) under chronic exposure while the CR value for acute exposure exceeded 1.0E-04, thus suggesting a carcinogenic PM2.5 risk for both the indoor and outdoor environments. The results of this study suggest that office activities, such as printing and photocopying, affect indoor O3 concentrations while PM2.5 concentrations are impacted by indoor-related contributions.
This study intends to determine the health impacts from two office life cycles (St.1 and St.2) using life cycle assessment (LCA) and health risk assessment of indoor metals in coarse particulates (particulate matter with diameters of less than 10µm). The first building (St.1) is located in the city centre and the second building (St.2) is located within a new development 7km away from the city centre. All life cycle stages are considered and was analysed using SimaPro software. The trace metal concentrations were determined by inductively couple plasma-mass spectrometry (ICP-MS). Particle deposition in the human lung was estimated using the multiple-path particle dosimetry model (MPPD). The results showed that the total human health impact for St.1 (0.027 DALY m-2) was higher than St.2 (0.005 DALY m-2) for a 50-year lifespan, with the highest contribution from the operational phase. The potential health risk to indoor workers was quantified as a hazard quotient (HQ) for non-carcinogenic elements, where the total values for ingestion contact were 4.38E-08 (St.1) and 2.59E-08 (St.2) while for dermal contact the values were 5.12E-09 (St.1) and 2.58E-09 (St.2). For the carcinogenic risk, the values for dermal and ingestion routes for both St.1 and St.2 were lower than the acceptable limit which indicated no carcinogenic risk. Particle deposition for coarse particles in indoor workers was concentrated in the head, followed by the pulmonary region and tracheobronchial tract deposition. The results from this study showed that human health can be significantly affected by all the processes in office building life cycle, thus the minimisation of energy consumption and pollutant exposures are crucially required.
Increasing urbanization and changes in land use in Langat river basin lead to adverse impacts on the environment compartment. One of the major challenges is in identifying sources of organic contaminants. This study presented the application of selected chemometric techniques: cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) to classify the pollution sources in Langat river basin based on the analysis of water and sediment samples collected from 24 stations, monitored for 14 organic contaminants from polycyclic aromatic hydrocarbons (PAHs), sterols, and pesticides groups. The CA and DA enabled to group 24 monitoring sites into three groups of pollution source (industry and urban socioeconomic, agricultural activity, and urban/domestic sewage) with five major discriminating variables: naphthalene, pyrene, benzo[a]pyrene, coprostanol, and cholesterol. PCA analysis, applied to water data sets, resulted in four latent factors explaining 79.0% of the total variance while sediment samples gave five latent factors with 77.6% explained variance. The varifactors (VFs) obtained from PCA indicated that sterols (coprostanol, cholesterol, stigmasterol, β-sitosterol, and stigmastanol) are strongly correlated to domestic and urban sewage, PAHs (naphthalene, acenaphthene, pyrene, benzo[a]anthracene, and benzo[a]pyrene) from industrial and urban activities and chlorpyrifos correlated to samples nearby agricultural sites. The results demonstrated that chemometric techniques can be used for rapid assessment of water and sediment contaminations.
Toxicity Identification Evaluation (TIE) is a useful method for the classification and identification of toxicants in a composite environment water sample. However, its extension to a larger sample size has been restrained owing to the limited throughput of toxicity bioassays. Here we reported the development of a high-throughput method of TIE Phase I. This newly developed method was assisted by the fluorescence-based cellular oxidation (CO) biosensor fabricated with roGFP2-expressing bacterial cells in 96-well microplate format. The assessment of four river water samples from Langat river basin by this new method demonstrated that the contaminant composition of the four samples can be classified into two distinct groups. The entire toxicity assay consisted of 2338 tests was completed within 12 h with a fluorescence microplate reader. Concurrently, the sample volume for each assay was reduced to 50 μL, which is 600 to 4700 times lesser to compare with conventional bioassays. These imply that the throughput of the CO biosensor-assisted TIE Phase I is now feasible for constructing a large-scale toxicity monitoring system, which would cover a whole watershed scale.
Brunei Bay is a unique ecosystem which offers a vast biodiversity. This study was carried out to define the source of metals in the surface sediment of Brunei Bay to ensure the bay's health. The secondary data were analysed using chemometrics analysis to verify the possible factors that influence metals distribution in Brunei Bay sediment. Samples were collected several times during 2013 to 2014 using Ponar grab at 16 stations within the bay. Samples were then dried, pre-treated, digested and analysed using Inductively Coupled Plasma Mass Spectrometry (ICPMS) in the laboratory. Overall, the mean concentration of metal, sediment pH and clay fraction were significantly changed during different sampling periods, as the changes were presumed affected by seasonal changes. The Pearson correlation has pointed that metals were dominantly derived by natural input; however, the total organic carbon was proven to be derived by anthropogenic sources. Moreover, the principal component analysis has verified that the distribution of metals in the bay's sediment was dominantly influenced by natural processes. However, the utilization and manipulation of marine resources are slightly affecting the bay's ecosystem which may deteriorate the ecosystem health soon.