Displaying publications 21 - 40 of 57 in total

Abstract:
Sort:
  1. Spatial assessment and source identification of heavy metals pollution in surface water using several chemometric techniques
    Ismail A, Toriman ME, Juahir H, Zain SM, Habir NL, Retnam A, et al.
    Mar Pollut Bull, 2016 May 15;106(1-2):292-300.
    PMID: 27001716 DOI: 10.1016/j.marpolbul.2015.10.019
    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.

    Matched MeSH terms: Spatial Analysis
  2. Fulltext Risk factors and spatial distribution of extended spectrum β-lactamase-producing- Escherichia coli at retail poultry meat markets in Malaysia: a cross-sectional study
    Aliyu AB, Saleha AA, Jalila A, Zunita Z
    BMC Public Health, 2016 08 02;16:699.
    PMID: 27484086 DOI: 10.1186/s12889-016-3377-2
    BACKGROUND: The significant role of retail poultry meat as an important exposure pathway for the acquisition and transmission of extended spectrum β-lactamase-producing Escherichia coli (ESBL-EC) into the human population warrants understanding concerning those operational practices associated with dissemination of ESBL-EC in poultry meat retailing. Hence, the objective of this study was to determine the prevalence, spatial distribution and potential risk factors associated with the dissemination of ESBL-EC in poultry meat retail at wet-markets in Selangor, Malaysia.

    METHODS: Poultry meat (breast, wing, thigh, and keel) as well as the contact surfaces of weighing scales and cutting boards were sampled to detect ESBL-EC by using culture and disk combination methods and polymerase chain reaction assays. Besides, questionnaire was used to obtain data and information pertaining to those operational practices that may possibly explain the occurrence of ESBL-EC. The data were analysed using logistic regression analysis at 95 % CI.

    RESULTS: The overall prevalence of ESBL-EC was 48.8 % (95 % CI, 42 - 55 %). Among the risk factors that were explored, type of countertop, sanitation of the stall environment, source of cleaning water, and type of cutting board were found to be significantly associated with the presence of ESBL-EC.

    CONCLUSIONS: Thus, in order to prevent or reduce the presence of ESBL-EC and other contaminants at the retail-outlet, there is a need to design a process control system based on the current prevailing practices in order to reduce cross contamination, as well as to improve food safety and consumer health.

    Matched MeSH terms: Spatial Analysis
  3. Fulltext D-Shaped Polarization Maintaining Fiber Sensor for Strain and Temperature Monitoring
    Qazi HH, Mohammad AB, Ahmad H, Zulkifli MZ
    Sensors (Basel), 2016 Sep 15;16(9).
    PMID: 27649195 DOI: 10.3390/s16091505
    A D-shaped polarization-maintaining fiber (PMF) as fiber optic sensor for the simultaneous monitoring of strain and the surrounding temperature is presented. A mechanical end and edge polishing system with aluminum oxide polishing film is utilized to perform sequential polishing on one side (lengthwise) of the PMF in order to fabricate a D-shaped cross-section. Experimental results show that the proposed sensor has high sensitivity of 46 pm/µε and 130 pm/°C for strain and temperature, respectively, which is significantly higher than other recently reported work (mainly from 2013) related to fiber optic sensors. The easy fabrication method, high sensitivity, and good linearity make this sensing device applicable in various applications such as health monitoring and spatial analysis of engineering structures.
    Matched MeSH terms: Spatial Analysis
  4. Fulltext Future malaria spatial pattern based on the potential global warming impact in South and Southeast Asia
    Khormi HM, Kumar L
    Geospat Health, 2016 11 21;11(3):416.
    PMID: 27903054 DOI: 10.4081/gh.2016.416
    We used the Model for Interdisciplinary Research on Climate-H climate model with the A2 Special Report on Emissions Scenarios for the years 2050 and 2100 and CLIMEX software for projections to illustrate the potential impact of climate change on the spatial distributions of malaria in China, India, Indochina, Indonesia, and The Philippines based on climate variables such as temperature, moisture, heat, cold and dryness. The model was calibrated using data from several knowledge domains, including geographical distribution records. The areas in which malaria has currently been detected are consistent with those showing high values of the ecoclimatic index in the CLIMEX model. The match between prediction and reality was found to be high. More than 90% of the observed malaria distribution points were associated with the currently known suitable climate conditions. Climate suitability for malaria is projected to decrease in India, southern Myanmar, southern Thailand, eastern Borneo, and the region bordering Cambodia, Malaysia and the Indonesian islands, while it is expected to increase in southern and south-eastern China and Taiwan. The climatic models for Anopheles mosquitoes presented here should be useful for malaria control, monitoring, and management, particularly considering these future climate scenarios.
    Matched MeSH terms: Spatial Analysis
  5. The genetic characteristics and evolution of Tembusu virus
    Lei W, Guo X, Fu S, Feng Y, Tao X, Gao X, et al.
    Vet Microbiol, 2017 Mar;201:32-41.
    PMID: 28284620 DOI: 10.1016/j.vetmic.2017.01.003
    BACKGROUND: Since the turn of the 21st century, there have been several epidemic outbreaks of poultry diseases caused by Tembusu virus (TMUV). Although multiple mosquito and poultry-derived strains of TMUV have been isolated, no data exist about their comparative study, origin, evolution, and dissemination.

    METHODOLOGY: Parallel virology was used to investigate the phenotypes of duck and mosquito-derived isolates of TMUV. Molecular biology and bioinformatics methods were employed to investigate the genetic characteristics and evolution of TMUV.

    PRINCIPAL FINDINGS: The plaque diameter of duck-derived isolates of TMUV was larger than that of mosquito-derived isolates. The cytopathic effect (CPE) in mammalian cells occurred more rapidly induced by duck-derived isolates than by mosquito-derived isolates. Furthermore, duck-derived isolates required less time to reach maximum titer, and exhibited higher viral titer. These findings suggested that poultry-derived TMUV isolates were more invasive and had greater expansion capability than the mosquito-derived isolates in mammalian cells. Variations in amino acid loci in TMUV E gene sequence revealed two mutated amino acid loci in strains isolated from Malaysia, Thailand, and Chinese mainland compared with the prototypical strain of the virus (MM1775). Furthermore, TMUV isolates from the Chinese mainland had six common variations in the E gene loci that differed from the Southeast Asian strains. Phylogenetic analysis indicated that TMUV did not exhibit a species barrier in avian species and consisted of two lineages: the Southeast Asian and the Chinese mainland lineages. Molecular traceability studies revealed that the recent common evolutionary ancestor of TMUV might have appeared before 1934 and that Malaysia, Thailand and Shandong Province of China represent the three main sources related to TMUV spread.

    CONCLUSIONS: The current broad distribution of TMUV strains in Southeast Asia and Chinese mainland exhibited longer-range diffusion and larger-scale propagation. Therefore, in addition to China, other Asian and European countries linked to Asia have used improved measures to detect and monitor TMUV related diseases to prevent epidemics in poultry.

    Matched MeSH terms: Spatial Analysis
  6. Occurrence, fate and environmental risk of linear alkylbenzene sulfonate in the Langat and Selangor River basins, Malaysia
    Sakai N, Shirasaka J, Matsui Y, Ramli MR, Yoshida K, Ali Mohd M, et al.
    Chemosphere, 2017 Apr;172:234-241.
    PMID: 28081507 DOI: 10.1016/j.chemosphere.2016.12.139
    Five homologs (C10-C14) of linear alkylbenzene sulfonate (LAS) were quantitated in surface water collected in the Langat and Selangor River basins using liquid chromatography-tandem mass spectrometry (LC-MS/MS). A geographic information system (GIS) was used to spatially analyze the occurrence of LAS in both river basins, and the LAS contamination associated with the population was elucidated by spatial analysis at a sub-basin level. The LAS concentrations in the dissolved phase (<0.45 μm) and 4 fractions separated by particle size (<0.1 μm, 0.1-1 μm, 1-11 μm and >11 μm) were analyzed to elucidate the environmental fate of LAS in the study area. The environmental risks of the observed LAS concentration were assessed based on predicted no effect concentration (PNEC) normalized by a quantitative structure-activity relationship model. The LAS contamination mainly occurred from a few populated sub-basins, and it was correlated with the population density and ammonia nitrogen. The dissolved phase was less than 20% in high contamination sites (>1000 μg/L), whereas it was more than 60% in less contaminated sites (<100 μg/L). The environmental fate of LAS in the study area was primarily subject to the adsorption to suspended solids rather than biodegradation because the LAS homologs, particularly in longer alkyl chain lengths, were considerably absorbed to the large size fraction (>11 μm) that settled in a few hours. The observed LAS concentrations exceeded the normalized PNEC at 3 sites, and environmental risk areas and susceptible areas to the LAS contamination were spatially identified based on their catchment areas.
    Matched MeSH terms: Spatial Analysis
  7. Fulltext Geographical distribution and spatio-temporal patterns of hospitalization due to dengue infection at a leading specialist hospital in Malaysia
    Low GKK, Papapreponis P, Isa RM, Gan SC, Chee HY, Te KK, et al.
    Geospat Health, 2018 05 07;13(1):642.
    PMID: 29772885 DOI: 10.4081/gh.2018.642
    Increasing numbers of dengue infection worldwide have led to a rise in deaths due to complications caused by this disease. We present here a cross-sectional study of dengue patients who attended the Emergency and Trauma Department of Ampang Hospital, one of Malaysia's leading specialist hospitals. The objective was to search for potential clustering of severe dengue, in space and/or time, among the annual admissions with the secondary objective to describe the spatio-temporal pattern of all dengue cases admitted to this hospital. The dengue status of the patients was confirmed serologically with the geographic location of the patients determined by residency, but not more specific than the street level. A total of 1165 dengue patients were included in the analysis using SaTScan software. The mean age of these patients was 27.8 years, with a standard deviation of 14.2 years and an age range from 1 to 77 years, among whom 54 (4.6%) were cases of severe dengue. A cluster of general dengue cases was identified occurring from October to December in the study year of 2015 but the inclusion of severe dengue in that cluster was not statistically significant (P=0.862). The standardized incidence ratio was 1.51. General presence of dengue cases was, however, detected to be concentrated at the end of the year, which should be useful for hospital planning and management if this pattern holds.
    Matched MeSH terms: Spatial Analysis
  8. Fulltext Correlation analysis of air pollutant index levels and dengue cases across five different zones in Selangor, Malaysia
    Thiruchelvam L, Dass SC, Zaki R, Yahya A, Asirvadam VS
    Geospat Health, 2018 05 07;13(1):613.
    PMID: 29772882 DOI: 10.4081/gh.2018.613
    This study investigated the potential relationship between dengue cases and air quality - as measured by the Air Pollution Index (API) for five zones in the state of Selangor, Malaysia. Dengue case patterns can be learned using prediction models based on feedback (lagged terms). However, the question whether air quality affects dengue cases is still not thoroughly investigated based on such feedback models. This work developed dengue prediction models using the autoregressive integrated moving average (ARIMA) and ARIMA with an exogeneous variable (ARIMAX) time series methodologies with API as the exogeneous variable. The Box Jenkins approach based on maximum likelihood was used for analysis as it gives effective model estimates and prediction. Three stages of model comparison were carried out for each zone: first with ARIMA models without API, then ARIMAX models with API data from the API station for that zone and finally, ARIMAX models with API data from the zone and spatially neighbouring zones. Bayesian Information Criterion (BIC) gives goodness-of-fit versus parsimony comparisons between all elicited models. Our study found that ARIMA models, with the lowest BIC value, outperformed the rest in all five zones. The BIC values for the zone of Kuala Selangor were -800.66, -796.22, and -790.5229, respectively, for ARIMA only, ARIMAX with single API component and ARIMAX with API components from its zone and spatially neighbouring zones. Therefore, we concluded that API levels, either temporally for each zone or spatio- temporally based on neighbouring zones, do not have a significant effect on dengue cases.
    Matched MeSH terms: Spatial Analysis
  9. Predicting crash frequency for multi-vehicle collision types using multivariate Poisson-lognormal spatial model: A comparative analysis
    Hosseinpour M, Sahebi S, Zamzuri ZH, Yahaya AS, Ismail N
    Accid Anal Prev, 2018 Sep;118:277-288.
    PMID: 29861069 DOI: 10.1016/j.aap.2018.05.003
    According to crash configuration and pre-crash conditions, traffic crashes are classified into different collision types. Based on the literature, multi-vehicle crashes, such as head-on, rear-end, and angle crashes, are more frequent than single-vehicle crashes, and most often result in serious consequences. From a methodological point of view, the majority of prior studies focused on multivehicle collisions have employed univariate count models to estimate crash counts separately by collision type. However, univariate models fail to account for correlations which may exist between different collision types. Among others, multivariate Poisson lognormal (MVPLN) model with spatial correlation is a promising multivariate specification because it not only allows for unobserved heterogeneity (extra-Poisson variation) and dependencies between collision types, but also spatial correlation between adjacent sites. However, the MVPLN spatial model has rarely been applied in previous research for simultaneously modelling crash counts by collision type. Therefore, this study aims at utilizing a MVPLN spatial model to estimate crash counts for four different multi-vehicle collision types, including head-on, rear-end, angle, and sideswipe collisions. To investigate the performance of the MVPLN spatial model, a two-stage model and a univariate Poisson lognormal model (UNPLN) spatial model were also developed in this study. Detailed information on roadway characteristics, traffic volume, and crash history were collected on 407 homogeneous segments from Malaysian federal roads. The results indicate that the MVPLN spatial model outperforms the other comparing models in terms of goodness-of-fit measures. The results also show that the inclusion of spatial heterogeneity in the multivariate model significantly improves the model fit, as indicated by the Deviance Information Criterion (DIC). The correlation between crash types is high and positive, implying that the occurrence of a specific collision type is highly associated with the occurrence of other crash types on the same road segment. These results support the utilization of the MVPLN spatial model when predicting crash counts by collision manner. In terms of contributing factors, the results show that distinct crash types are attributed to different subsets of explanatory variables.
    Matched MeSH terms: Spatial Analysis
  10. Fulltext Geographical information system (GIS) application in tuberculosis spatial clustering studies: a systematic review
    Norazman Mohd Rosli, Shamsul Azhar Shah, Mohd Ihsani Mahmood
    Malaysian Journal of Public Health Medicine, 2018;18(1):0-0.
    MyJurnal
    Tuberculosis (TB) is known as a disease that prone to spatial clustering. Recent development has seen a sharp rise in the number of epidemiologic studies employing Geographical Information System (GIS), particularly in identifying TB clusters and evidences of etiologic factors. The aim of this systematic review is to determine evidence of TB clustering, type of spatial analysis commonly used and the application of GIS in TB surveillance and control. A literature search of articles published in English language between 2000 and November 2015 was performed using MEDLINE and Science Direct using relevant search terms related to spatial analysis in studies of TB cluster. The search strategy was adapted and developed for each database using appropriate subject headings and keywords. The literature reviewed showed strong evidence of TB clustering occurred in high risk areas in both developed and developing countries. Spatial scan statistics were the most commonly used analysis and proved useful in TB surveillance through detection of outbreak, early warning and identifying area of increased TB transmission. Among others are targeted screening and assessment of TB program using GIS technology. However there were limitations on suitability of utilizing aggregated data such as national cencus that were pre-collected in explaining the present spatial distribution among population at risk. Spatial boundaries determined by zip code may be too large for metropolitan area or too small for country. Nevertheless, GIS is a powerful tool in aiding TB control and prevention in developing countries and should be used for real-time surveillance and decision making.
    Matched MeSH terms: Spatial Analysis
  11. Fulltext Predictive analysis across spatial scales links zoonotic malaria to deforestation
    Brock PM, Fornace KM, Grigg MJ, Anstey NM, William T, Cox J, et al.
    Proc Biol Sci, 2019 Jan 16;286(1894):20182351.
    PMID: 30963872 DOI: 10.1098/rspb.2018.2351
    The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria ( Plasmodium knowlesi) is associated with deforestation although mechanisms are unknown. Here, a novel application of a method for predicting disease occurrence that combines machine learning and statistics is used to identify the key spatial scales that define the relationship between zoonotic malaria cases and environmental change. Using data from satellite imagery, a case-control study, and a cross-sectional survey, predictive models of household-level occurrence of P. knowlesi were fitted with 16 variables summarized at 11 spatial scales simultaneously. The method identified a strong and well-defined peak of predictive influence of the proportion of cleared land within 1 km of households on P. knowlesi occurrence. Aspect (1 and 2 km), slope (0.5 km) and canopy regrowth (0.5 km) were important at small scales. By contrast, fragmentation of deforested areas influenced P. knowlesi occurrence probability most strongly at large scales (4 and 5 km). The identification of these spatial scales narrows the field of plausible mechanisms that connect land use change and P. knowlesi, allowing for the refinement of disease occurrence predictions and the design of spatially-targeted interventions.
    Matched MeSH terms: Spatial Analysis
  12. Fulltext Socio-epidemiological and land cover risk factors for melioidosis in Kedah, Northern Malaysia
    Abu Hassan MR, Aziz N, Ismail N, Shafie Z, Mayala B, Donohue RE, et al.
    PLoS Negl Trop Dis, 2019 03;13(3):e0007243.
    PMID: 30883550 DOI: 10.1371/journal.pntd.0007243
    BACKGROUND: Melioidosis, a fatal infectious disease caused by Burkholderia pseudomallei, is increasingly diagnosed in tropical regions. However, data on risk factors and the geographic epidemiology of the disease are still limited. Previous studies have also largely been based on the analysis of case series data. Here, we undertook a more definitive hospital-based matched case-control study coupled with spatial analysis to identify demographic, socioeconomic and landscape risk factors for bacteremic melioidosis in the Kedah region of northern Malaysia.

    METHODOLOGY/PRINCIPAL FINDINGS: We obtained patient demographic and residential information and clinical presentation and medical history data from 254 confirmed melioidosis cases and 384 matched controls attending Hospital Sultanah Bahiyah (HSB), the main tertiary hospital of Alor Setar, the capital city of Kedah, during the period between 2005 and 2011. Crude and adjusted odds ratios employing conditional logistic regression analysis were used to assess if melioidosis in this region is related to risk factors connected with socio-demographics, various behavioural characteristics, and co-occurring diseases. Spatial clusters of cases were determined using a continuous Poisson model as deployed in SaTScan. A land cover map in conjunction with mapped case data was used to determine disease-land type associations using the Fisher's exact test deploying simulated p-values. Crude and adjusted odds ratios indicate that melioidosis in this region is related to gender (males), race, occupation (farming) and co-occurring chronic diseases, particularly diabetes. Spatial analyses of disease incidence, however, showed that disease risk and geographic clustering of cases are related strongly to land cover types, with risk of disease increasing non-linearly with the degree of human modification of the natural ecosystem.

    CONCLUSIONS/SIGNIFICANCE: These findings indicate that melioidosis represents a complex socio-ecological public health problem in Kedah, and that its control requires an understanding and modification of the coupled human and natural variables that govern disease transmission in endemic communities.

    Matched MeSH terms: Spatial Analysis
  13. Spatial and temporal risk quotient based river assessment for water resources management
    Wan Mohtar WHM, Abdul Maulud KN, Muhammad NS, Sharil S, Yaseen ZM
    Environ Pollut, 2019 May;248:133-144.
    PMID: 30784832 DOI: 10.1016/j.envpol.2019.02.011
    Malaysia depends heavily on rivers as a source for water supply, irrigation, and sustaining the livelihood of local communities. The evolution of land use in urban areas due to rapid development and the continuous problem of illegal discharge have had a serious adverse impact on the health of the country's waterways. Klang River requires extensive rehabilitation and remediation before its water could be utilised for a variety of purposes. A reliable and rigorous remediation work plan is needed to identify the sources and locations of streams that are constantly polluted. This study attempts to investigate the feasibility of utilising a temporal and spatial risk quotient (RQ) based analysis to make an accurate assessment of the current condition of the tributaries in the Klang River catchment area. The study relies on existing data sets on Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Suspended Solids (TSS), and Ammonia (NH3) to evaluate the water quality at thirty strategic locations. Analysis of ammonia pollution is not only based on the limit established for river health but was expanded to include the feasibility of using the water for water intake, recreational activities, and sustaining fish population. The temporal health of Klang River was evaluated using the Risk Matrix Approach (RMA) based on the frequency of RQ > 1 and associated colour-coded hazard impacts. By using the developed RMA, the hazard level for each parameter at each location was assessed and individually mapped using Geographic Information System (GIS). The developed risk hazard mapping has high potential as one of the essential tools in making decisions for a cost-effective river restoration and rehabilitation.
    Matched MeSH terms: Spatial Analysis
  14. Spatial and statistical trend characteristics of rainfall erosivity (R) in upper catchment of Baram River, Borneo
    Vijith H, Dodge-Wan D
    Environ Monit Assess, 2019 Jul 13;191(8):494.
    PMID: 31302794 DOI: 10.1007/s10661-019-7604-z
    The upper catchment region of the Baram River in Sarawak (Malaysian Borneo) is undergoing severe land degradation due to soil erosion. Heavy rainfall with high erosive power has led to a number of soil erosion hotspots. The goal of the present study is to generate an understanding about the spatial characteristics of seasonal and annual rainfall erosivity (R), which not only control sediment delivery from the region but also determine the quantity of material potentially eroded. Mean annual rainfall and rainfall erosivity range from 2170 to 5167 mm and 1632 to 5319 MJ mm ha-1 h-1 year-1, respectively. Seasonal rainfall and rainfall erosivity range from 848 to 1872 mm and 558 to 1883 MJ mm ha-1 h-1 year-1 for the southwest (SW) monsoon, 902 to 2200 mm and 664 to 2793 MJ mm ha-1h-1year-1 for the northeast (NE) monsoon and 400 to 933 mm and 331 to 1075 MJ mm ha-1 h-1 year-1 during the inter-monsoon (IM) period. Linear regression, Spearman's Rho and Mann Kendall tests were applied. Considering the regional mean rainfall erosivity in the study area, all the methods show an overall non-significant decreasing trend (- 9.34, - 0.25 and - 0.30 MJ mm ha-1 h-1 year-1, respectively for linear regression, Spearman's Rho and Mann Kendall tests). However, during SW monsoon and IM periods, rainfall erosivity showed a non-significant decreasing trend (- 25.45, - 0.52, - 0.40, and - 8.86, - 1.07, - 0.77 MJ mm ha-1 h-1 year-1, respectively) whereas in NE, monsoon season erosivity showed a non-significant increasing trend (14.90, 1.59 and 1.60 MJ mm ha-1 h-1 year-1, respectively). The mean erosivity density ranges from 0.77 to 1.38 MJ ha-1 h-1 year-1 and shows decreasing trend. Spatial distribution pattern of erosivity density indicates significantly higher occurrence of erosive rainfall in the lower elevation portion of the study area. The spatial pattern of mean rainfall erosivity trends (linear, Spearman's Rho and Mann Kendall) suggests that the study area can be divided into two zones with increasing rainfall erosivity trends in the northern zone and decreasing trends in the southern zone. These results can be used to plan conservation measures to reduce sediment delivery from localized soil erosion hotspots.
    Matched MeSH terms: Spatial Analysis
  15. Projection the long-term ungauged rainfall using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model
    Tukimat NNA, Ahmad Syukri NA, Malek MA
    Heliyon, 2019 Sep;5(9):e02456.
    PMID: 31687558 DOI: 10.1016/j.heliyon.2019.e02456
    An accuracy in the hydrological modelling will be affected when having limited data sources especially at ungauged areas. Due to this matter, it will not receiving any significant attention especially on the potential hydrologic extremes. Thus, the objective was to analyse the accuracy of the long-term projected rainfall at ungauged rainfall station using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model. The SDSM was used as a climate agent to predict the changes of the climate trend in Δ2030s by gauged and ungauged stations. There were five predictors set have been selected to form the local climate at the region which provided by NCEP (validated) and CanESM2-RCP4.5 (projected). According to the statistical analyses, the SDSM was controlled to produce reliable validated results with lesser %MAE (<23%) and higher R. The projected rainfall was suspected to decrease 14% in Δ2030s. All the RCPs agreed the long term rainfall pattern was consistent to the historical with lower annual rainfall intensity. The RCP8.5 shows the least rainfall changes. These findings then used to compare the accuracy of monthly rainfall at control station (Stn 2). The GIS-Kriging method being as an interpolation agent was successfully to produce similar rainfall trend with the control station. The accuracy was estimated to reach 84%. Comparing between ungauged and gauged stations, the small %MAE in the projected monthly results between gauged and ungauged stations as a proved the integrated SDSM-GIS model can producing a reliable long-term rainfall generation at ungauged station.
    Matched MeSH terms: Spatial Analysis
  16. Fulltext Self-Adaptive Filtering Approach for Improved Indoor Localization of a Mobile Node with Zigbee-Based RSSI and Odometry
    Loganathan A, Ahmad NS, Goh P
    Sensors (Basel), 2019 Nov 01;19(21).
    PMID: 31683837 DOI: 10.3390/s19214748
    This study presents a new technique to improve the indoor localization of a mobile node by utilizing a Zigbee-based received-signal-strength indicator (RSSI) and odometry. As both methods suffer from their own limitations, this work contributes to a novel methodological framework in which coordinates of the mobile node can more accurately be predicted by improving the path-loss propagation model and optimizing the weighting parameter for each localization technique via a convex search. A self-adaptive filtering approach is also proposed which autonomously optimizes the weighting parameter during the target node's translational and rotational motions, thus resulting in an efficient localization scheme with less computational effort. Several real-time experiments consisting of four different trajectories with different number of straight paths and curves were carried out to validate the proposed methods. Both temporal and spatial analyses demonstrate that when odometry data and RSSI values are available, the proposed methods provide significant improvements on localization performance over existing approaches.
    Matched MeSH terms: Spatial Analysis
  17. Economic and efficiency based optimisation of water quality monitoring network for land use impact assessment
    Camara M, Jamil NR, Abdullah AFB, Hashim RB, Aliyu AG
    Sci Total Environ, 2020 May 30;737:139800.
    PMID: 32526579 DOI: 10.1016/j.scitotenv.2020.139800
    The evaluation of the importance of having accurate and representative stations in a network for river water quality monitoring is always a matter of concern. The minimal budget and time demands of water quality monitoring programme may appear very attractive, especially when dealing with large-scale river watersheds. This article proposes an improved methodology for optimising water quality monitoring network for present and forthcoming monitoring of water quality under a case study of the Selangor River watershed in Malaysia, where different monitoring networks are being used by water management authorities. Knowing that the lack of financial resources in developing countries like Malaysia is one of the reasons for inadequate monitoring network density, to identify an optimised network for cost-efficiency benefits in this study, a geo-statistical technique coupled Kendall's W was first applied to analyse the performance of each monitoring station in the existing networks under the monitored water quality parameters. Second, the present and future changes in non-point pollution sources were simulated using the integrated Cellular Automata and Markov chain model (CA-Markov). Third, Station Potential Pollution Score (SPPS) determined based on Analytic Hierarchy Process (AHP) was used to weight each station under the changes of non-point pollution sources for 2015, 2024, and 2033 prior to prioritisation. Finally, according to the Kendall's W test on kriging results, the weights of non-point sources from the AHP evaluation and fuzzy membership functions, six most efficient sampling stations were identified to build a robust network for the present and future monitoring of water quality status in the Selangor River watershed. This study proposes a useful approach to the pertinent agencies and management authority concerned to establish appropriate methods for developing an efficient water quality monitoring network for tropical rivers.
    Matched MeSH terms: Spatial Analysis
  18. Modeling the spatio-temporal dynamics of air pollution index based on spatial Markov chain model
    Alyousifi Y, Ibrahim K, Kang W, Zin WZW
    Environ Monit Assess, 2020 Oct 21;192(11):719.
    PMID: 33083907 DOI: 10.1007/s10661-020-08666-8
    An environmental problem which is of concern across the globe nowadays is air pollution. The extent of air pollution is often studied based on data on the observed level of air pollution. Although the analysis of air pollution data that is available in the literature is numerous, studies on the dynamics of air pollution with the allowance for spatial interaction effects through the use of the Markov chain model are very limited. Accordingly, this study aims to explore the potential impact of spatial dependence over time and space on the distribution of air pollution based on the spatial Markov chain (SMC) model using the longitudinal air pollution index (API) data. This SMC model is pertinent to be applied since the daily data of API from 2012 to 2014 that have been gathered from 37 different air quality stations in Peninsular Malaysia is found to exhibit the property of spatial autocorrelation. Based on the spatial transition probability matrices found from the SMC model, specific characteristics of air pollution are studied in the regional context. These characteristics are the long-run proportion and the mean first passage time for each state of air pollution. It is found that the probability for a particular station's state to remain good is 0.814 if its neighbors are in a good state of air pollution and 0.7082 if its neighbors are in a moderate state. For a particular station having neighbors in a good state of air pollution, the proportion of time for it to continue being in a good state is 0.6. This proportion reduces to 0.4, 0.01, and 0 for the cell of moderate, unhealthy, and very unhealthy states, respectively. In addition, there exists a significant spatial dependence of API, indicating that air pollution for a particular station is dependent on the states of the neighboring stations.
    Matched MeSH terms: Spatial Analysis
  19. Fulltext Using GIS and stakeholder involvement to innovate marine mammal bycatch risk assessment in data-limited fisheries
    Verutes GM, Johnson AF, Caillat M, Ponnampalam LS, Peter C, Vu L, et al.
    PLoS One, 2020;15(8):e0237835.
    PMID: 32817725 DOI: 10.1371/journal.pone.0237835
    Fisheries bycatch has been identified as the greatest threat to marine mammals worldwide. Characterizing the impacts of bycatch on marine mammals is challenging because it is difficult to both observe and quantify, particularly in small-scale fisheries where data on fishing effort and marine mammal abundance and distribution are often limited. The lack of risk frameworks that can integrate and visualize existing data have hindered the ability to describe and quantify bycatch risk. Here, we describe the design of a new geographic information systems tool built specifically for the analysis of bycatch in small-scale fisheries, called Bycatch Risk Assessment (ByRA). Using marine mammals in Malaysia and Vietnam as a test case, we applied ByRA to assess the risks posed to Irrawaddy dolphins (Orcaella brevirostris) and dugongs (Dugong dugon) by five small-scale fishing gear types (hook and line, nets, longlines, pots and traps, and trawls). ByRA leverages existing data on animal distributions, fisheries effort, and estimates of interaction rates by combining expert knowledge and spatial analyses of existing data to visualize and characterize bycatch risk. By identifying areas of bycatch concern while accounting for uncertainty using graphics, maps and summary tables, we demonstrate the importance of integrating available geospatial data in an accessible format that taps into local knowledge and can be corroborated by and communicated to stakeholders of data-limited fisheries. Our methodological approach aims to meet a critical need of fisheries managers: to identify emergent interaction patterns between fishing gears and marine mammals and support the development of management actions that can lead to sustainable fisheries and mitigate bycatch risk for species of conservation concern.
    Matched MeSH terms: Spatial Analysis
  20. Spatial distribution of fine and coarse particulate matter during a southwest monsoon in Peninsular Malaysia
    Othman M, Latif MT, Jamhari AA, Abd Hamid HH, Uning R, Khan MF, et al.
    Chemosphere, 2021 Jan;262:127767.
    PMID: 32763576 DOI: 10.1016/j.chemosphere.2020.127767
    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.
    Matched MeSH terms: Spatial Analysis
Related Terms
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links