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Fulltext Spatial Analysis of Groundwater Hydrochemistry through Integrated Multivariate Analysis: A Case Study in the Urbanized Langat Basin, Malaysia

Zainol NFM, Zainuddin AH, Looi LJ, Aris AZ, Isa NM, Sefie A, et al.

Int J Environ Res Public Health, 2021 05 27;18(11).
PMID: 34071804 DOI: 10.3390/ijerph18115733

Rapid urbanization and industrial development in the Langat Basin has disturbed the groundwater quality. The populations' reliance on groundwater sources may induce possible risks to human health such as cancer and endocrine dysfunction. This study aims to determine the groundwater quality of an urbanized basin through 24 studied hydrochemical parameters from 45 groundwater samples obtained from 15 different sampling stations by employing integrated multivariate analysis. The abundance of the major ions was in the following order: bicarbonate (HCO3-) > chloride (Cl-) > sodium (Na+) > sulphate (SO42-) > calcium (Ca2+) > potassium (K+) > magnesium (Mg2+). Heavy metal dominance was in the following order: Fe > Mn > Zn > As > Hg > Pb > Ni > Cu > Cd > Se > Sr. Classification of the groundwater facies indicated that the studied groundwater belongs to the Na-Cl with saline water type and Na-HCO3 with mix water type characteristics. The saline water type characteristics are derived from agricultural activities, while the mixed water types occur from water-rock interaction. Multivariate analysis performance suggests that industrial, agricultural, and weathering activities have contributed to groundwater contamination. The study will help in the understanding of the groundwater quality issue and serve as a reference for other basins with similar characteristics.

Matched MeSH terms: Spatial Analysis
Fulltext Apropos: Plasmodium knowlesi malaria an emerging public health problem in Hulu Selangor, Selangor, Malaysia (2009-2013): epidemiologic and entomologic analysis

Waugh S

Parasit Vectors, 2015;8:79.
PMID: 25651916 DOI: 10.1186/s13071-015-0694-8

The use of detailed methodologies and legitimate settings justifications in spatial analysis is imperative to locating areas of significance. Studies missing this action may enact interventions in improper areas.

Matched MeSH terms: Spatial Analysis
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
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
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
Fulltext Environmental and socioeconomic impacts of pipeline transport interdiction in Niger Delta, Nigeria

Umar HA, Abdul Khanan MF, Ogbonnaya C, Shiru MS, Ahmad A, Baba AI

Heliyon, 2021 May;7(5):e06999.
PMID: 34027190 DOI: 10.1016/j.heliyon.2021.e06999

Over the years, pipelines have been the most economic medium for transporting crude oil to production and distribution facilities in the Niger Delta area of Nigeria. However, damages to the pipelines in this area by interdiction have hampered the continuous flow of crude oil to the facilities. Consequently, the revenue of the government dwindles, and the environment is severely degraded. This study assesses the economic and environmental impacts of pipeline interdiction in the Niger Delta region. Data from National oil spills detection and response agency, Nigeria is used to map spatial distribution of oil spills using Kernel Density Estimation with Geographic Information System. Literature was assessed to synthesize the historical, socioeconomic, and environmental impacts of oil spills and pipeline interdiction. Soil samples were collected from study area to determine the types of hydrocarbon pollutants and their concentrations in comparison with uncontaminated sites in the area. Results show that the range of concentrations of total petroleum hydrocarbon (TPH) for the impacted soil (IMP) was 17.27-58.36 mg/kg; remediated soil (RS) was 11.73-50.78 mg/kg which were higher than the concentrations of 0.68 mg/kg in the control samples (CS). Polycyclic aromatic hydrocarbons (PAH) concentrations were in the range of 0.43-77.54 mg/kg for IMP, 0.42-10.65 mg/kg for RS, against CS value of 0.49 mg/kg while BTEX ranged between 0.02 - 0.38 mg/kg for IMP, 0.01-2.7 for RS against CS value of 0.01. The values of the PAH were higher than the limits of the Department of Petroleum Resources, Nigeria. This study also revealed that pipeline interdiction has affected the livelihood of the inhabitants of the study area and the revenue of the Nigerian government. The major hotspots for oil spills in the Niger Delta region are Bayelsa, Rivers and Delta states.

Matched MeSH terms: Spatial Analysis
Fulltext Spatial cluster analysis of COVID-19 in Malaysia (Mar-Sep, 2020)

Ullah S, Mohd Nor NH, Daud H, Zainuddin N, Gandapur MSJ, Ali I, et al.

Geospat Health, 2021 May 05;16(1).
PMID: 33969966 DOI: 10.4081/gh.2021.961

Coronavirus disease 2019 (COVID-19) is the current worldwide pandemic as declared by the World Health Organization (WHO) in March 2020. Being part of the ongoing global pandemic, Malaysia has recorded a total of 8639 COVID-19 cases and 121 deaths as of 30th June 2020. This study aims to detect spatial clusters of COVID-19 in Malaysia using the Spatial Scan Statistic (SaTScan™) to guide control authorities on prioritizing locations for targeted interventions. The spatial analyses were conducted on a monthly basis at the state-level from March to September 2020. The results show that the most likely cluster of COVID-19 occurred in West Malaysia repeatedly from March to June, covering three counties (two federal territories and one neighbouring state) and moved to East Malaysia in July covering two other counties. The most likely cluster shows a tendency of having moved from the western part to the eastern part of the country. These results provide information that can be used for the evidence- based interventions to control the spread of COVID-19 in Malaysia. A Correction has been published: https://doi.org/10.4081/gh.2023.1233

Matched MeSH terms: Spatial Analysis
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
Spatial scaling effects on soil bacterial communities in Malaysian tropical forests

Tripathi BM, Lee-Cruz L, Kim M, Singh D, Go R, Shukor NA, et al.

Microb Ecol, 2014 Aug;68(2):247-58.
PMID: 24658414

Spatial scaling to some extent determines biodiversity patterns in larger organisms, but its role in microbial diversity patterns is much less understood. Some studies have shown that bacterial community similarity decreases with distance, whereas others do not support this. Here, we studied soil bacterial communities of tropical rainforest in Malaysia at two spatial scales: a local scale with samples spaced every 5 mover a 150-m transect, and a regional scale with samples 1 to 1,800 km apart. PCR-amplified soil DNA for the bacterial 16S rRNA gene targeting the V1–V3 region was pyrosequenced using Roche/454 GS FLX Titanium platform. A ranked partial Mantel test showed a weak correlation between spatial distance and whole bacterial community dissimilarity, but only at the local scale. In contrast, environmental distance was highly correlated with community dissimilarity at both spatial scales,stressing the greater role of environmental variables rather than spatial distance in determining bacterial community variation at different spatial scales. Soil pH was the only environmental parameter that significantly explained the variance in bacterial community at the local scale, whereas total nitrogen and elevation were additional important factors at the regional scale.We obtained similar results at both scales when only the most abundant OTUs were analyzed. A variance partitioning analysis showed that environmental variables contributed more to bacterial community variation than spatial distance at both scales. In total, our results support a strong influence of the environment in determining bacterial community composition in the rainforests of Malaysia. However, it is possible that the remaining spatial distance effect is due to some of the myriad of other environmental factors which were not considered here, rather than dispersal limitation.

Matched MeSH terms: Spatial Analysis
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
Fulltext Polycyclic aromatic hydrocarbons in coastal sediment of klang strait, Malaysia: distribution pattern, risk assessment and sources

Tavakoly Sany SB, Hashim R, Salleh A, Rezayi M, Mehdinia A, Safari O

PLoS One, 2014;9(4):e94907.
PMID: 24747349 DOI: 10.1371/journal.pone.0094907

Concentration, source, and ecological risk of polycyclic aromatic hydrocarbons (PAHs) were investigated in 22 stations from surface sediments in the areas of anthropogenic pollution in the Klang Strait (Malaysia). The total PAH level in the Klang Strait sediment was 994.02±918.1 µg/kg dw. The highest concentration was observed in stations near the coastline and mouth of the Klang River. These locations were dominated by high molecular weight PAHs. The results showed both pyrogenic and petrogenic sources are main sources of PAHs. Further analyses indicated that PAHs primarily originated from pyrogenic sources (coal combustion and vehicular emissions), with significant contribution from petroleum inputs. Regarding ecological risk estimation, only station 13 was moderately polluted, the rest of the stations suffered rare or slight adverse biological effects with PAH exposure in surface sediment, suggesting that PAHs are not considered as contaminants of concern in the Klang Strait.

Matched MeSH terms: Spatial Analysis
Statistical and spatial analysis for soil heavy metals over the Murray-Darling river basin in Australia

Tao H, Al-Hilali AA, Ahmed AM, Mussa ZH, Falah MW, Abed SA, et al.

Chemosphere, 2023 Mar;317:137914.
PMID: 36682637 DOI: 10.1016/j.chemosphere.2023.137914

Heavy metals (HMs) are a vital elements for investigating the pollutant level of sediments and water bodies. The Murray-Darling river basin area located in Australia is experiencing severe damage to increased crop productivity, loss of soil fertility, and pollution levels within the vicinity of the river system. This basin is the most effective primary production area in Australia where agricultural productivity is increased the gross domastic product in the entire mainland. In this study, HMs contaminations are examined for eight study sites selected for the Murray-Darling river basin where the inverse Distance Weighting interpolation method is used to identify the distribution of HMs. To pursue this, four different pollution indices namely the Geo-accumulation index (Igeo), Contamination factor (CF), Pollution load index (PLI), single-factor pollution index (SPLI), and the heavy metal pollution index (HPI) are computed. Following this, the Pearson correlation matrix is used to identify the relationships among the two HM parameters. The results indicate that the conductivity and N (%) are relatively high in respect to using Igeo and PLI indexes for study sites 4, 6, and 7 with 2.93, 3.20, and 1.38, respectively. The average HPI is 216.9071 that also indicates higher level pollution in the Murray-Darling river basin and the highest HPI value is noted in sample site 1 (353.5817). The study also shows that the levels of Co, P, Conductivity, Al, and Mn are mostly affected by HMs and that these indices indicate the maximum HM pollution level in the Murray-Darling river basin. Finally, the results show that the high HM contamination level appears to influence human health and local environmental conditions.

Matched MeSH terms: Spatial Analysis
Fulltext The mapping of spatial patterns of violent crime in peninsular Malaysia: normal mixture model approach

Syerrina Zakaria, Nuzlinda Abdul Rahman

Pertanika Journal of Science & Technology, 2015;23(2):207-222.
MyJurnal

The objective of this study was to explore the geographic distribution and temporal patterns of violent crime cases in Peninsular Malaysia by using the tools and techniques for spatial analysis. This study will also provide a general picture of violent crime patterns in Malaysia. The unit of analysis is district and the violent crime data from the year 2000 until 2009 were used in this study. In order to obtain the optimum number of components of crime in the space-time period, the space-time Normal Mixture Models were used. Based on the results of this model, the mapping of the crime occurrences was made. This map displays the spatial distribution of crime occurrence in 82 districts of Peninsular Malaysia. From this analysis, more violent crimes were shown to have occurred in developed states such as Selangor, Wilayah Persekutuan Kuala Lumpur and Johor. The findings of this study could be used by policy makers or responsible agencies to take any relevant actions in terms of crime prevention, human resource allocation and law enforcement so as to overcome this important issue in future.

Matched MeSH terms: Spatial Analysis
Fulltext Spatial clustering of colorectal cancer in Malaysia

Syed Soffian SS, Mohammed Nawi A, Hod R, Abdul Maulud KN, Mohd Azmi AT, Hasim Hashim MH, et al.

Geospat Health, 2023 May 25;18(1).
PMID: 37246545 DOI: 10.4081/gh.2023.1158

INTRODUCTION: The rise in colorectal cancer (CRC) incidence becomes a global concern. As geographical variations in the CRC incidence suggests the role of area-level determinants, the current study was designed to identify the spatial distribution pattern of CRC at the neighbourhood level in Malaysia.
METHOD: Newly diagnosed CRC cases between 2010 and 2016 in Malaysia were identified from the National Cancer Registry. Residential addresses were geocoded. Clustering analysis was subsequently performed to examine the spatial dependence between CRC cases. Differences in socio-demographic characteristics of individuals between the clusters were also compared. Identified clusters were categorized into urban and semi-rural areas based on the population background.
RESULT: Most of the 18 405 individuals included in the study were male (56%), aged between 60 and 69 years (30.3%) and only presented for care at stages 3 or 4 of the disease (71.3%). The states shown to have CRC clusters were Kedah, Penang, Perak, Selangor, Kuala Lumpur, Melaka, Johor, Kelantan, and Sarawak. The spatial autocorrelation detected a significant clustering pattern (Moran's Index 0.244, p< 0.01, Z score >2.58). CRC clusters in Penang, Selangor, Kuala Lumpur, Melaka, Johor, and Sarawak were in urbanized areas, while those in Kedah, Perak and Kelantan were in semi-rural areas.
CONCLUSION: The presence of several clusters in urbanized and semi-rural areas implied the role of ecological determinants at the neighbourhood level in Malaysia. Such findings could be used to guide the policymakers in resource allocation and cancer control.

Matched MeSH terms: Spatial Analysis
Fulltext Area-Level Determinants in Colorectal Cancer Spatial Clustering Studies: A Systematic Review

Soffian SSS, Nawi AM, Hod R, Chan HK, Hassan MRA

Int J Environ Res Public Health, 2021 10 06;18(19).
PMID: 34639786 DOI: 10.3390/ijerph181910486

The increasing pattern of colorectal cancer (CRC) in specific geographic region, compounded by interaction of multifactorial determinants, showed the tendency to cluster. The review aimed to identify and synthesize available evidence on clustering patterns of CRC incidence, specifically related to the associated determinants. Articles were systematically searched from four databases, Scopus, Web of Science, PubMed, and EBSCOHost. The approach for identification of the final articles follows PRISMA guidelines. Selected full-text articles were published between 2016 and 2021 of English language and spatial studies focusing on CRC cluster identification. Articles of systematic reviews, conference proceedings, book chapters, and reports were excluded. Of the final 12 articles, data on the spatial statistics used and associated factors were extracted. Identified factors linked with CRC cluster were further classified into ecology (health care accessibility, urbanicity, dirty streets, tree coverage), biology (age, sex, ethnicity, overweight and obesity, daily consumption of milk and fruit), and social determinants (median income level, smoking status, health cost, employment status, housing violations, and domestic violence). Future spatial studies that incorporate physical environment related to CRC cluster and the potential interaction between the ecology, biology and social determinants are warranted to provide more insights to the complex mechanism of CRC cluster pattern.

Matched MeSH terms: Spatial Analysis
Spatial analysis of environmental factors influencing typhoid endemicity in Kelantan, Malaysia

Shamsul Azhar Shah, Suzuki H, Mohd Rohaizat Hassan, Saito R, Nazarudin Safian, Shaharudin Idrus

Sains Malaysiana, 2012;41:911-919.

The determination of the high-risk area and clusters of typhoid cases is critical in typhoid control. The purpose of this study was to identify and describe the epidemiology and spatial distribution of typhoid in four selected districts in Kelantan using GIS (geographical information system). A total of 1215 (99%) of the cases were coordinated with GPS (global positioning system) and mapping was done using ArcGIS 9.2. Spatial analysis was performed to determine the cluster and high-risk area of typhoid. Results showed that typhoid incidence was not associated with race and sex. Most affected were from the age group of 5-14 followed by 15-24 year olds. Nine sub-districts were categorized as highly endemic. In addition typhoid has shown a significant tendency to cluster and a total of 22 hotspots were found in Kota Bharu, Bachok and Tumpat with a few sub districts identified as high risk for typhoid. No significant relationships between the treated water ratio and flood risk area were found with the cluster of cases. The cluster of typhoid cases in the endemic area did not appear to be related to environmental risk factors. Understanding the characteristics of these clusters would enable the prevention of typhoid disease in the future.

Matched MeSH terms: Spatial Analysis
Spatial analysis and source profiling of beta-agonists and sulfonamides in Langat River basin, Malaysia

Sakai N, Mohd Yusof R, Sapar M, Yoneda M, Ali Mohd M

Sci Total Environ, 2016 Apr 01;548-549:43-50.
PMID: 26799806 DOI: 10.1016/j.scitotenv.2016.01.040

Beta-agonists and sulfonamides are widely used for treating both humans and livestock for bronchial and cardiac problems, infectious disease and even as growth promoters. There are concerns about their potential environmental impacts, such as producing drug resistance in bacteria. This study focused on their spatial distribution in surface water and the identification of pollution sources in the Langat River basin, which is one of the most urbanized watersheds in Malaysia. Fourteen beta-agonists and 12 sulfonamides were quantitatively analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). A geographic information system (GIS) was used to visualize catchment areas of the sampling points, and source profiling was conducted to identify the pollution sources based on a correlation between a daily pollutant load of the detected contaminant and an estimated density of human or livestock population in the catchment areas. As a result, 6 compounds (salbutamol, sulfadiazine, sulfapyridine, sulfamethazine, sulfadimethoxine and sulfamethoxazole) were widely detected in mid catchment areas towards estuary. The source profiling indicated that the pollution sources of salbutamol and sulfamethoxazole were from sewage, while sulfadiazine was from effluents of cattle, goat and sheep farms. Thus, this combination method of quantitative and spatial analysis clarified the spatial distribution of these drugs and assisted for identifying the pollution sources.

Matched MeSH terms: Spatial Analysis
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
Groundwater quality assessment for irrigation by adopting new suitability plot and spatial analysis based on fuzzy logic technique

S C, M V P, S V, M N, K P, Panda B, et al.

Environ Res, 2022 03;204(Pt A):111729.
PMID: 34478727 DOI: 10.1016/j.envres.2021.111729

This study was focused on identifying the region suitable for agriculture-based, using new irrigation groundwater quality plot and its spatio-temporal variation with fuzzy logic technique in a geographic information system (GIS) platform. Six hundred and eighty groundwater samples were collected during pre, southwest, northeast, and post monsoon periods. A new ternary plot was also attempted to determine the irrigation suitability of water by considering four essential parameters such as sodium adsorption ratio (SAR), permeability index (PI), Sodium percentage (Na %), and electrical conductivity (EC). The derived ternary plot was the most beneficial over other available plots, as it incorporated four parameters, and it differs from the US Salinity Laboratory (USSL) plot, such that the groundwater with higher EC could also be used for irrigation purposes, depending on the Na%. The ternary plot revealed that the groundwater predominantly manifested good to moderate category during post, northeast, and southwest monsoons. The assessment with the amount of fertilizer used during the study period showed that the NPK fertilizers were effectively used for irrigation during monsoon periods. Spatial maps on EC, Kelly's ratio, Mg hazard, Na%, PI, potential salinity (PS), SAR, residual sodium carbonate (RSC), and soluble sodium percentage (SSP) were prepared for each season using fuzzy membership values, integrated for each season. A final suitability map derived by an overlay of all the seasonal outputs has identified that the groundwater in the western and the eastern part of the study area are suitable for agriculture. The study recommends cultivation of groundwater-dependent short-term crops, along the western and northern regions of the study area during the pre-monsoon season.

Matched MeSH terms: Spatial Analysis
Fulltext Spatial distribution of COVID-19 cases, epidemic spread rate, spatial pattern, and its correlation with meteorological factors during the first to the second waves

Rendana M, Idris WMR, Abdul Rahim S

J Infect Public Health, 2021 Oct;14(10):1340-1348.
PMID: 34301503 DOI: 10.1016/j.jiph.2021.07.010

Currently, many countries all over the world are facing the second wave of COVID-19. Therefore, this study aims to analyze the spatial distribution of COVID-19 cases, epidemic spread rate, spatial pattern during the first to the second waves in the South Sumatra Province of Indonesia. This study used the geographical information system (GIS) software to map the spatial distribution of COVID-19 cases and epidemic spread rate. The spatial autocorrelation of the COVID-19 cases was carried out using Moran's I, while the Pearson correlation was used to examining the relationship between meteorological factors and the epidemic spread rate. Most infected areas and the direction of virus spread were predicted using wind rose analysis. The results revealed that the epidemic rapidly spread from August 1 to December 1, 2020. The highest epidemic spread rate was observed in the Palembang district and in its peripheral areas (dense urban areas), while the lowest spread rate was found in the eastern and southern parts of South Sumatra Province (remote areas). The spatial correlation characteristic of the epidemic distribution exhibited a negative correlation and random distribution. Air temperature, wind speed, and precipitation have contributed to a significant impact on the high epidemic spread rate in the second wave. In summary, this study offers new insight for arranging control and prevention strategies against the potential of second wave strike.

Matched MeSH terms: Spatial Analysis

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