Displaying publications 1 - 20 of 116 in total

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  1. Dinesh, S., Faudzi, M.M., Rafidah, M., Shakhira, B.N.I., Robiah, A.S., Shalini, S.S., et al.
    ASM Science Journal, 2014;8(1):11-20.
    MyJurnal
    In this study, Global Positioning System (GPS) simulation was employed to study the effect of radio frequency interference (RFI) on two hand-held GPS receivers; Garmin GPSmap 60CSx (evaluated GPS receiver) and Garmin GPSmap 60CS (reference GPS receiver). Both GPS receivers employed the GPS L1 coarse acquisition (C/A) signal. It was observed that the interference signal power levels required to affect the location fixes of the GPS receivers were significantly high compared to the corresponding GPS signal power levels. The noiselike C/A code structure, which modulated the L1 signal over a 2 MHz bandwidth, allowed for the signal to be received at low levels of interferences. The evaluated GPS receiver had better RFI operability as compared to the reference GPS receiver. This is because the evaluated GPS receiver had higher receiver sensitivity, allowing it to have increased carrier-to-noise density (C/N0) levels for GPS satellites tracked by the receiver. The absence of other error parameters, including ionospheric and tropospheric delays, satellite clock, ephemeris and multipath errors, and unintentional signal interferences and obstructions, resulted in the required minimum jamming power levels in this study to be significantly higher as compared to field evaluations. These minimum jamming power levels vary with location and time. This was due to the GPS satellite constellation being dynamic, causing varying GPS satellite geometry over location and time, resulting in the minimum required GPS jamming power levels being location / time dependent. In general, the lowest minimum jamming power levels were observed for readings with the highest position dilution of precision (PDOP) values, and vice versa.
    Matched MeSH terms: Geographic Information Systems
  2. Fauzi, R., Salazar, D.M., Kadzim, R.M., Hussin, A., Burbano, L.
    ASM Science Journal, 2009;3(2):161-167.
    MyJurnal
    In this project, a Geographic Information System (GIS) was used to collect and compile various field data in the Pedro Vicente Maldonado Ecuadorian Scientific Station Antarctica Base area. The main source of data was obtained from a global positioning system (GPS) survey using kinematic GPS (GPS-RTK) which allowed continuous point mapping in the terrain. GPS units were utilized in the collection of spatial data for all field work. The co-ordinates obtained were used to produce a point map which was then exported into GIS software where the proximity of cartographic phenomena and boundaries were mapped. All the collected data were subsequently gathered to develop the GIS database which was then used to generate and compile different maps to test for spatial and temporal relationships. The output of the project comprises a GIS database, spatial maps and 3D terrain model of the area. The developed GIS database can be used with other ecological datasets to provide biogeographical information, potential range distribution and sampling adequacy. The database is also applicable to geographical management and multi-disciplinary research projects.
    Matched MeSH terms: Geographic Information Systems
  3. Md Bohari NF, Sabri NF, Wan Rasdi WND, Mohd Radzi NA, Bakri NN
    Asia Pac J Public Health, 2020 12 24;33(2-3):227-233.
    PMID: 33356376 DOI: 10.1177/1010539520982718
    Although geographic information system-based studies are particularly increasing in other sectors, few have embraced their full potential in health services allocation in Malaysia. This study aimed to produce a visual map on the distribution of smoking cessation clinics (SCCs) in Malaysia and analyze its pattern against the national population of smokers. SCC addresses were obtained from the government website and mapped using geographic information system tools. A total of 199 and 449 private and public SCCs was mapped throughout the country, respectively. The lowest SCC to smoker population ratio was in the state of Negeri Sembilan with 1:3000. The highest SCC to smoker population ratio was in Sabah with 1 SCC for 15 000 smokers. Almost 70% of SCCs were primary health clinics. Smoking cessation clinics were distributed throughout all the states in Malaysia except the state of Sabah.
    Matched MeSH terms: Geographic Information Systems
  4. 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: Geographic Information Systems
  5. Husin MH, Lim YK
    Disabil Rehabil Assist Technol, 2020 08;15(6):701-707.
    PMID: 31729282 DOI: 10.1080/17483107.2019.1615999
    Background/Purpose: Visual impairment is a disability more commonly caused by diseases that lead to several disadvantages to the daily activities amongst those blind. For almost a century since the white cane was first introduced, the cane has remained as the most reliable tool for those affected by blindness.Methods: By using a combination of the capabilities of Internet of Things (IoT) and existing devices, such as mobile phones, an InWalker system is proposed to expand the functionality of the typical white cane, so as to introduce several new features that enhance the safety and confidence amongst people who are blind. As such, this paper explores the existing works and projects to comprehend the motivation and the standard practices for each proposed feature. Each of the strength and drawback has been assessed thoroughly to refine the scope of this project.Results: The proposed project, InWalker, is an intelligent system that has an embedded board system with various sensors to enhance the usability of white cane. The inputs from the sensor are processed on a microcontroller, which then pass the data to a smartphone via Bluetooth for additional features, such as global positioning system (GPS) tracking and SMS services.Conclusions: Based on the initial user testing, the proposed system has successfully fulfilled most of the users' need.Implication for RehabilitationVisual impairment is a disability more commonly caused by diseases that lead to several disadvantages amongst those blind.The white cane has been seen as the most reliable tool for the visual impaired.This tool could be further improved with the integration of additional sensors that works with today's mobile devices.The proposed system, InWalker, is able to improve the overall quality of life among people who are blind through several features: obstacle detection, GPS tracking and a light illumination in dark environments for increased safety.
    Matched MeSH terms: Geographic Information Systems/instrumentation*
  6. Pradhan B, Chaudhari A, Adinarayana J, Buchroithner MF
    Environ Monit Assess, 2012 Jan;184(2):715-27.
    PMID: 21509515 DOI: 10.1007/s10661-011-1996-8
    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.
    Matched MeSH terms: Geographic Information Systems*
  7. Arebey M, Hannan MA, Basri H, Begum RA, Abdullah H
    Environ Monit Assess, 2011 Jun;177(1-4):399-408.
    PMID: 20703798 DOI: 10.1007/s10661-010-1642-x
    The integration of communication technologies such as radio frequency identification (RFID), global positioning system (GPS), general packet radio system (GPRS), and geographic information system (GIS) with a camera are constructed for solid waste monitoring system. The aim is to improve the way of responding to customer's inquiry and emergency cases and estimate the solid waste amount without any involvement of the truck driver. The proposed system consists of RFID tag mounted on the bin, RFID reader as in truck, GPRS/GSM as web server, and GIS as map server, database server, and control server. The tracking devices mounted in the trucks collect location information in real time via the GPS. This information is transferred continuously through GPRS to a central database. The users are able to view the current location of each truck in the collection stage via a web-based application and thereby manage the fleet. The trucks positions and trash bin information are displayed on a digital map, which is made available by a map server. Thus, the solid waste of the bin and the truck are being monitored using the developed system.
    Matched MeSH terms: Geographic Information Systems
  8. Al-Abadi AM, Pradhan B, Shahid S
    Environ Monit Assess, 2015 Oct;188(10):549.
    PMID: 27600115 DOI: 10.1007/s10661-016-5564-0
    The objective of this study is to delineate groundwater flowing well zone potential in An-Najif Province of Iraq in a data-driven evidential belief function model developed in a geographical information system (GIS) environment. An inventory map of 68 groundwater flowing wells was prepared through field survey. Seventy percent or 43 wells were used for training the evidential belief functions model and the reset 30 % or 19 wells were used for validation of the model. Seven groundwater conditioning factors mostly derived from RS were used, namely elevation, slope angle, curvature, topographic wetness index, stream power index, lithological units, and distance to the Euphrates River in this study. The relationship between training flowing well locations and the conditioning factors were investigated using evidential belief functions technique in a GIS environment. The integrated belief values were classified into five categories using natural break classification scheme to predict spatial zoning of groundwater flowing well, namely very low (0.17-0.34), low (0.34-0.46), moderate (0.46-0.58), high (0.58-0.80), and very high (0.80-0.99). The results show that very low and low zones cover 72 % (19,282 km(2)) of the study area mostly clustered in the central part, the moderate zone concentrated in the west part covers 13 % (3481 km(2)), and the high and very high zones extended over the northern part cover 15 % (3977 km(2)) of the study area. The vast spatial extension of very low and low zones indicates that groundwater flowing wells potential in the study area is low. The performance of the evidential belief functions spatial model was validated using the receiver operating characteristic curve. A success rate of 0.95 and a prediction rate of 0.94 were estimated from the area under relative operating characteristics curves, which indicate that the developed model has excellent capability to predict groundwater flowing well zones. The produced map of groundwater flowing well zones could be used to identify new wells and manage groundwater storage in a sustainable manner.
    Matched MeSH terms: Geographic Information Systems
  9. Mogaji KA, Lim HS
    Environ Monit Assess, 2017 Jul;189(7):321.
    PMID: 28593561 DOI: 10.1007/s10661-017-5990-7
    This study integrates the application of Dempster-Shafer-driven evidential belief function (DS-EBF) methodology with remote sensing and geographic information system techniques to analyze surface and subsurface data sets for the spatial prediction of groundwater potential in Perak Province, Malaysia. The study used additional data obtained from the records of the groundwater yield rate of approximately 28 bore well locations. The processed surface and subsurface data produced sets of groundwater potential conditioning factors (GPCFs) from which multiple surface hydrologic and subsurface hydrogeologic parameter thematic maps were generated. The bore well location inventories were partitioned randomly into a ratio of 70% (19 wells) for model training to 30% (9 wells) for model testing. Application results of the DS-EBF relationship model algorithms of the surface- and subsurface-based GPCF thematic maps and the bore well locations produced two groundwater potential prediction (GPP) maps based on surface hydrologic and subsurface hydrogeologic characteristics which established that more than 60% of the study area falling within the moderate-high groundwater potential zones and less than 35% falling within the low potential zones. The estimated uncertainty values within the range of 0 to 17% for the predicted potential zones were quantified using the uncertainty algorithm of the model. The validation results of the GPP maps using relative operating characteristic curve method yielded 80 and 68% success rates and 89 and 53% prediction rates for the subsurface hydrogeologic factor (SUHF)- and surface hydrologic factor (SHF)-based GPP maps, respectively. The study results revealed that the SUHF-based GPP map accurately delineated groundwater potential zones better than the SHF-based GPP map. However, significant information on the low degree of uncertainty of the predicted potential zones established the suitability of the two GPP maps for future development of groundwater resources in the area. The overall results proved the efficacy of the data mining model and the geospatial technology in groundwater potential mapping.
    Matched MeSH terms: Geographic Information Systems*
  10. Rizeei HM, Azeez OS, Pradhan B, Khamees HH
    Environ Monit Assess, 2018 Oct 04;190(11):633.
    PMID: 30288624 DOI: 10.1007/s10661-018-7013-8
    Groundwater hazard assessments involve many activities dealing with the impacts of pollution on groundwater, such as human health studies and environment modelling. Nitrate contamination is considered a hazard to human health, environment and ecosystem. In groundwater management, the hazard should be assessed before any action can be taken, particularly for groundwater pollution and water quality. Thus, pollution due to the presence of nitrate poses considerable hazard to drinking water, and excessive nutrient loads deteriorate the ecosystem. The parametric IPNOA model is one of the well-known methods used for evaluating nitrate content. However, it cannot predict the effect of soil and land use/land cover (LULC) types on calculations relying on parametric well samples. Therefore, in this study, the parametric model was trained and integrated with the multivariate data-driven model with different levels of information to assess groundwater nitrate contamination in Saladin, Iraq. The IPNOA model was developed with 185 different well samples and contributing parameters. Then, the IPNOA model was integrated with the logistic regression (LR) model to predict the nitrate contamination levels. Geographic information system techniques were also used to assess the spatial prediction of nitrate contamination. High-resolution SPOT-5 satellite images with 5 m spatial resolution were processed by object-based image analysis and support vector machine algorithm to extract LULC. Mapping of potential areas of nitrate contamination was examined using receiver operating characteristic assessment. Results indicated that the optimised LR-IPNOA model was more accurate in determining and analysing the nitrate hazard concentration than the standalone IPNOA model. This method can be easily replicated in other areas that have similar climatic condition. Therefore, stakeholders in planning and environmental decision makers could benefit immensely from the proposed method of this research, which can be potentially used for a sustainable management of urban, industrialised and agricultural sectors.
    Matched MeSH terms: Geographic Information Systems
  11. Ahmed AA, Pradhan B
    Environ Monit Assess, 2019 Feb 26;191(3):190.
    PMID: 30809746 DOI: 10.1007/s10661-019-7333-3
    This study proposes a neural network (NN) model to predict and simulate the propagation of vehicular traffic noise in a dense residential area at the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia. The proposed model comprises of two main simulation steps: that is, the prediction of vehicular traffic noise using NN and the simulation of the propagation of traffic noise emission using a mathematical model. First, the NN model was developed with the following selected noise predictors: the number of motorbikes, the sum of vehicles, car ratio, heavy vehicle ratio (e.g. truck, lorry and bus), highway density and a light detection and ranging (LiDAR)-derived digital surface model (DSM). Subsequently, NN and its hyperparameters were optimised by a systematic optimisation procedure based on a grid search approach. The noise propagation model was then developed in a geographic information system (GIS) using five variables, namely road geometry, barriers, distance, interaction of air particles and weather parameters. The noise measurement was conducted continuously at 15-min intervals and the data were analysed by taking the minimum, maximum and average values recorded during the day. The measurement was performed four times a day (i.e. morning, afternoon, evening, and midnight) over two days of the week (i.e. Sunday and Monday). An optimal radial basis function NN was used with 17 hidden layers. The learning rate and momentum values were 0.05 and 0.9, respectively. Finally, the accuracy of the proposed method achieved 78.4% with less than 4.02 dB (A) error in noise prediction. Overall, the proposed models were found to be promising tools for traffic noise assessment in dense urban areas.
    Matched MeSH terms: Geographic Information Systems
  12. Aburas MM, Ahamad MSS, Omar NQ
    Environ Monit Assess, 2019 Mar 05;191(4):205.
    PMID: 30834982 DOI: 10.1007/s10661-019-7330-6
    Spatio-temporal land-use change modeling, simulation, and prediction have become one of the critical issues in the last three decades due to uncertainty, structure, flexibility, accuracy, the ability for improvement, and the capability for integration of available models. Therefore, many types of models such as dynamic, statistical, and machine learning (ML) models have been used in the geographic information system (GIS) environment to fulfill the high-performance requirements of land-use modeling. This paper provides a literature review on models for modeling, simulating, and predicting land-use change to determine the best approach that can realistically simulate land-use changes. Therefore, the general characteristics of conventional and ML models for land-use change are described, and the different techniques used in the design of these models are classified. The strengths and weaknesses of the various dynamic, statistical, and ML models are determined according to the analysis and discussion of the characteristics of these models. The results of the review confirm that ML models are the most powerful models for simulating land-use change because they can include all driving forces of land-use change in the simulation process and simulate linear and non-linear phenomena, which dynamic models and statistical models are unable to do. However, ML models also have limitations. For instance, some ML models are complex, the simulation rules cannot be changed, and it is difficult to understand how ML models work in a system. However, this can be solved via the use of programming languages such as Python, which in turn improve the simulation capabilities of the ML models.
    Matched MeSH terms: Geographic Information Systems
  13. Golkarian A, Naghibi SA, Kalantar B, Pradhan B
    Environ Monit Assess, 2018 Feb 17;190(3):149.
    PMID: 29455381 DOI: 10.1007/s10661-018-6507-8
    Ever increasing demand for water resources for different purposes makes it essential to have better understanding and knowledge about water resources. As known, groundwater resources are one of the main water resources especially in countries with arid climatic condition. Thus, this study seeks to provide groundwater potential maps (GPMs) employing new algorithms. Accordingly, this study aims to validate the performance of C5.0, random forest (RF), and multivariate adaptive regression splines (MARS) algorithms for generating GPMs in the eastern part of Mashhad Plain, Iran. For this purpose, a dataset was produced consisting of spring locations as indicator and groundwater-conditioning factors (GCFs) as input. In this research, 13 GCFs were selected including altitude, slope aspect, slope angle, plan curvature, profile curvature, topographic wetness index (TWI), slope length, distance from rivers and faults, rivers and faults density, land use, and lithology. The mentioned dataset was divided into two classes of training and validation with 70 and 30% of the springs, respectively. Then, C5.0, RF, and MARS algorithms were employed using R statistical software, and the final values were transformed into GPMs. Finally, two evaluation criteria including Kappa and area under receiver operating characteristics curve (AUC-ROC) were calculated. According to the findings of this research, MARS had the best performance with AUC-ROC of 84.2%, followed by RF and C5.0 algorithms with AUC-ROC values of 79.7 and 77.3%, respectively. The results indicated that AUC-ROC values for the employed models are more than 70% which shows their acceptable performance. As a conclusion, the produced methodology could be used in other geographical areas. GPMs could be used by water resource managers and related organizations to accelerate and facilitate water resource exploitation.
    Matched MeSH terms: Geographic Information Systems
  14. Aburas MM, Ho YM, Ramli MF, Ash'aari ZH
    Environ Monit Assess, 2018 Feb 20;190(3):156.
    PMID: 29464400 DOI: 10.1007/s10661-018-6522-9
    The identification of spatio-temporal patterns of the urban growth phenomenon has become one of the most significant challenges in monitoring and assessing current and future trends of the urban growth issue. Therefore, spatio-temporal and quantitative techniques should be used hand in hand for a deeper understanding of various aspects of urban growth. The main purpose of this study is to monitor and assess the significant patterns of urban growth in Seremban using a spatio-temporal built-up area analysis. The concentric circles approach was used to measure the compactness and dispersion of built-up area by employing Shannon's Entropy method. The spatial directions approach was also utilised to measure the sustainability and speed of development, while the gradient approach was used to measure urban dynamics by employing landscape matrices. The overall results confirm that urban growth in Seremban is dispersed, unbalanced and unsustainable with a rapid speed of regional development. The main contribution of using existing methods with other methods is to provide several spatial and statistical dimensions that can help researchers, decision makers and local authorities understand the trend of growth and its patterns in order to take the appropriate decisions for future urban planning. For example, Shannon's Entropy findings indicate a high value of dispersion between the years 1990 and 2000 and from 2010 to 2016 with a growth rate of approximately 94 and 14%, respectively. Therefore, these results can help and support decision makers to implement alternative urban forms such as the compactness form to achieve an urban form that is more suitable and sustainable. The results of this study confirm the importance of using spatio-temporal built-up area and quantitative analysis to protect the sustainability of land use, as well as to improve the urban planning system via the effective monitoring and assessment of urban growth trends and patterns.
    Matched MeSH terms: Geographic Information Systems
  15. Balogun AL, Yekeen ST, Pradhan B, Wan Yusof KB
    Environ Pollut, 2021 Jan 01;268(Pt A):115812.
    PMID: 33143984 DOI: 10.1016/j.envpol.2020.115812
    This study develops an oil spill environmental vulnerability model for predicting and mapping the oil slick trajectory pattern in Kota Tinggi, Malaysia. The impact of seasonal variations on the vulnerability of the coastal resources to oil spill was modelled by estimating the quantity of coastal resources affected across three climatic seasons (northeast monsoon, southwest monsoon and pre-monsoon). Twelve 100 m3 (10,000 splots) medium oil spill scenarios were simulated using General National Oceanic and Atmospheric Administration Operational Oil Modeling Environment (GNOME) model. The output was integrated with coastal resources, comprising biological, socio-economic and physical shoreline features. Results revealed that the speed of an oil slick (40.8 m per minute) is higher during the pre-monsoon period in a southwestern direction and lower during the northeast monsoon (36.9 m per minute). Evaporation, floating and spreading are the major weathering processes identified in this study, with approximately 70% of the slick reaching the shoreline or remaining in the water column during the first 24 h (h) of the spill. Oil spill impacts were most severe during the southwest monsoon, and physical shoreline resources are the most vulnerable to oil spill in the study area. The study concluded that variation in climatic seasons significantly influence the vulnerability of coastal resources to marine oil spill.
    Matched MeSH terms: Geographic Information Systems
  16. Ahmad Kamal N, Muhammad NS, Abdullah J
    Environ Pollut, 2020 Apr;259:113909.
    PMID: 31927277 DOI: 10.1016/j.envpol.2020.113909
    Malaysia is a tropical country that is highly dependent on surface water for its raw water supply. Unfortunately, surface water is vulnerable to pollution, especially in developed and dense urban catchments. Therefore, in this study, a methodology was developed for an extensive temporal water quality index (WQI) and classification analysis, simulations of various pollutant discharge scenarios using QUAL2K software, and maps with NH3-N as the core pollutant using an integrated QUAL2K-GIS. It was found that most of the water quality stations are categorized as Class III (slightly polluted to polluted). These stations are surrounded by residential areas, industries, workshops, restaurants and wet markets that contribute to the poor water quality levels. Additionally, low WQI values were reported in 2010 owing to development and agricultural activities. However, the WQI values improved during the wet season. High concentrations of NH3-N were found in the basin, especially during dry weather conditions. Three scenarios were simulated, i.e. 10%, 50% and 70% of pollution discharge into Skudai river using a calibrated and validated QUAL2K model. Model performance was evaluated using the relative percentage difference. An inclusive graph showing the current conditions and pollution reduction scenarios with respect to the distance of Skudai river and its tributaries is developed to determine the WQI classification. Comprehensive water quality maps based on NH3-N as the core pollutant are developed using integrated QUAL2K-GIS to illustrate the overall condition of the Skudai river. High NH3-N in the Skudai River affects water treatment plant operations. Pollution control of more than 90% is required to improve the water quality classification to Class II. The methodology and analysis developed in this study can assist various stakeholders and authorities in identifying problematic areas and determining the required percentage of pollution reduction to improve the Skudai River water quality.
    Matched MeSH terms: Geographic Information Systems
  17. 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: Geographic Information Systems
  18. Moharir KN, Pande CB, Gautam VK, Singh SK, Rane NL
    Environ Res, 2023 Jul 01;228:115832.
    PMID: 37054834 DOI: 10.1016/j.envres.2023.115832
    The Damoh district, which is located in the central India and characterized by limestone, shales, and sandstone compact rock. The district has been facing groundwater development challenges and problems for several decades. To facilitate groundwater management, it is crucial to monitoring and planning based on geology, slope, relief, land use, geomorphology, and the types of the basaltic aquifer in the drought-groundwater deficit area. Moreover, the majority of farmers in the area are heavily dependent on groundwater for their crops. Therefore, delineation of groundwater potential zones (GPZ) is essential, which is defined based on various thematic layers, including geology, geomorphology, slope, aspect, drainage density, lineament density, topographic wetness index (TWI), topographic ruggedness index (TRI), and land use/land cover (LULC). The processing and analysis of this information were carried out using Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) methods. The validity of the results was trained and tested using Receiver Operating Characteristic (ROC) curves, which showed training and testing accuracies of 0.713 and 0.701, respectively. The GPZ map was classified into five classes such as very high, high, moderate, low, and very low. The study revealed that approximately 45% of the area falls under the moderate GPZ, while only 30% of the region is classified as having a high GPZ. The area receives high rainfall but has very high surface runoff due to no proper developed soil and lack of water conservation structures. Every summer season show a declined groundwater level. In this context, results of study area are useful to maintain the groundwater under climate change and summer season. The GPZ map plays an important role in implementing artificial recharge structures (ARS), such as percolation ponds, tube wells, bore wells, cement nala bunds (CNBs), continuous contour trenching (CCTs), and others for development of ground level. This study is significant for developing sustainable groundwater management policies in semi-arid regions, that are experiencing climate change. Proper groundwater potential mapping and watershed development policies can help mitigate the effects of drought, climate change, and water scarcity, while preserving the ecosystem in the Limestone, Shales, and Sandstone compact rock region. The results of this study are essential for farmers, regional planners, policy-makers, climate change experts, and local governments, enabling them to understand the groundwater development possibilities in the study area.
    Matched MeSH terms: Geographic Information Systems*
  19. Wong HL, Garthwaite DG, Ramwell CT, Brown CD
    Environ Sci Pollut Res Int, 2017 Dec;24(34):26444-26461.
    PMID: 28948535 DOI: 10.1007/s11356-017-0064-5
    This study investigated changes over 25 years (1987-2012) in pesticide usage in orchards in England and Wales and associated changes to exposure and risk for resident pregnant women living 100 and 1000 m downwind of treated areas. A model was developed to estimate aggregated daily exposure to pesticides via inhaled vapour and indirect dermal contact with contaminated ground, whilst risk was expressed as a hazard quotient (HQ) based on estimated exposure and the no observed (adverse) effect level for reproductive and developmental effects. Results show the largest changes occurred between 1987 and 1996 with total pesticide usage reduced by ca. 25%, exposure per unit of pesticide applied slightly increased, and a reduction in risk per unit exposure by factors of 1.3 to 3. Thereafter, there were no consistent changes in use between 1996 and 2012, with an increase in number of applications to each crop balanced by a decrease in average application rate. Exposure per unit of pesticide applied decreased consistently over this period such that values in 2012 for this metric were 48-65% of those in 1987, and there were further smaller decreases in risk per unit exposure. All aggregated hazard quotients were two to three orders of magnitude smaller than one, despite the inherent simplifications of assuming co-occurrence of exposure to all pesticides and additivity of effects. Hazard quotients at 1000 m were 5 to 16 times smaller than those at 100 m. There were clear signals of the impact of regulatory intervention in improving the fate and hazard profiles of pesticides used in orchards in England and Wales over the period investigated.
    Matched MeSH terms: Geographic Information Systems
  20. Kura NU, Ramli MF, Ibrahim S, Sulaiman WN, Aris AZ
    Environ Sci Pollut Res Int, 2014;21(11):7047-64.
    PMID: 24532282 DOI: 10.1007/s11356-014-2598-0
    In this study, geophysics, geochemistry, and geostatistical techniques were integrated to assess seawater intrusion in Kapas Island due to its geological complexity and multiple contamination sources. Five resistivity profiles were measured using an electric resistivity technique. The results reveal very low resistivity <1 Ωm, suggesting either marine clay deposit or seawater intrusion or both along the majority of the resistivity images. As a result, geochemistry was further employed to verify the resistivity evidence. The Chadha and Stiff diagrams classify the island groundwater into Ca-HCO3, Ca-Na-HCO3, Na-HCO3, and Na-Cl water types, with Ca-HCO3 as the dominant. The Mg(2+)/Mg(2+)+Ca(2+), HCO3 (-)/anion, Cl(-)/HCO3 (-), Na(+)/Cl(-), and SO4 (2-)/Cl(-) ratios show that some sampling sites are affected by seawater intrusion; these sampling sites fall within the same areas that show low-resistivity values. The resulting ratios and resistivity values were then used in the geographical information system (GIS) environment to create the geostatistical map of individual indicators. These maps were then overlaid to create the final map showing seawater-affected areas. The final map successfully delineates the area that is actually undergoing seawater intrusion. The proposed technique is not area specific, and hence, it can work in any place with similar completed characteristics or under the influence of multiple contaminants so as to distinguish the area that is truly affected by any targeted pollutants from the rest. This information would provide managers and policy makers with the knowledge of the current situation and will serve as a guide and standard in water research for sustainable management plan.
    Matched MeSH terms: Geographic Information Systems
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