Displaying publications 1 - 20 of 119 in total

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  1. Fulltext Science and technology for disaster prevention and climate resilience in Asia
    Pereira, J.J., Hunt, J.C.R., Chan, J.C.L.
    ASM Science Journal, 2014;8(1):1-10.
    MyJurnal
    The role of science and technology (S&T) in preventing disasters and building resilience to climate change is featured in this paper, drawing primarily on the presentations and discussion of researchers, practitioners and policy makers from 31 institutions in 17 countries during the Workshop on Natural Disasters and Climate Change in Asia, held on 5–7 November 2012 in Bangi, Malaysia. Issues highlighted include advances in climate modelling and weather forecasts, with emphasis on information gaps; hazards and its cascading effects, focusing on current research and approaches; and the potential for land-based mitigation-adaptation strategies. Progress in mobilizing S&T to support disaster prevention and climate resilience is hindered by factors such as absence or lack of research, incomplete and non-existent scientific records, restricted access to data and capacity to innovate and transmit S&T, among others. The establishment of an Asian Network for Climate Science and Technology is proposed to provide and facilitate exchange of information and aid development of research co-ordination projects led by Asian researchers and possibly to act as a one-stop repository of global climate change related research too. The scope of the network would cover climate research with particular relevance to disaster resilience, including scientific capacity, which is all very distinct in Asia.
    Matched MeSH terms: Weather
  2. Applying a random parameters Negative Binomial Lindley model to examine multi-vehicle crashes along rural mountainous highways in Malaysia
    Rusli R, Haque MM, Afghari AP, King M
    Accid Anal Prev, 2018 Oct;119:80-90.
    PMID: 30007211 DOI: 10.1016/j.aap.2018.07.006
    Road safety in rural mountainous areas is a major concern as mountainous highways represent a complex road traffic environment due to complex topology and extreme weather conditions and are associated with more severe crashes compared to crashes along roads in flatter areas. The use of crash modelling to identify crash contributing factors along rural mountainous highways suffers from limitations in data availability, particularly in developing countries like Malaysia, and related challenges due to the presence of excess zero observations. To address these challenges, the objective of this study was to develop a safety performance function for multi-vehicle crashes along rural mountainous highways in Malaysia. To overcome the data limitations, an in-depth field survey, in addition to utilization of secondary data sources, was carried out to collect relevant information including roadway geometric factors, traffic characteristics, real-time weather conditions, cross-sectional elements, roadside features, and spatial characteristics. To address heterogeneity resulting from excess zeros, three specialized modelling techniques for excess zeros including Random Parameters Negative Binomial (RPNB), Random Parameters Negative Binomial - Lindley (RPNB-L) and Random Parameters Negative Binomial - Generalized Exponential (RPNB-GE) were employed. Results showed that the RPNB-L model outperformed the other two models in terms of prediction ability and model fit. It was found that heavy rainfall at the time of crash and the presence of minor junctions along mountainous highways increase the likelihood of multi-vehicle crashes, while the presence of horizontal curves along a steep gradient, the presence of a passing lane and presence of road delineation decrease the likelihood of multi-vehicle crashes. Findings of this study have significant implications for road safety along rural mountainous highways, particularly in the context of developing countries.
    Matched MeSH terms: Weather
  3. Investigation of factors affecting the injury severity of single-vehicle rollover crashes: A random-effects generalized ordered probit model
    Anarkooli AJ, Hosseinpour M, Kardar A
    Accid Anal Prev, 2017 Sep;106:399-410.
    PMID: 28728062 DOI: 10.1016/j.aap.2017.07.008
    Rollover crashes are responsible for a notable number of serious injuries and fatalities; hence, they are of great concern to transportation officials and safety researchers. However, only few published studies have analyzed the factors associated with severity outcomes of rollover crashes. This research has two objectives. The first objective is to investigate the effects of various factors, of which some have been rarely reported in the existing studies, on the injury severities of single-vehicle (SV) rollover crashes based on six-year crash data collected on the Malaysian federal roads. A random-effects generalized ordered probit (REGOP) model is employed in this study to analyze injury severity patterns caused by rollover crashes. The second objective is to examine the performance of the proposed approach, REGOP, for modeling rollover injury severity outcomes. To this end, a mixed logit (MXL) model is also fitted in this study because of its popularity in injury severity modeling. Regarding the effects of the explanatory variables on the injury severity of rollover crashes, the results reveal that factors including dark without supplemental lighting, rainy weather condition, light truck vehicles (e.g., sport utility vehicles, vans), heavy vehicles (e.g., bus, truck), improper overtaking, vehicle age, traffic volume and composition, number of travel lanes, speed limit, undulating terrain, presence of central median, and unsafe roadside conditions are positively associated with more severe SV rollover crashes. On the other hand, unpaved shoulder width, area type, driver occupation, and number of access points are found as the significant variables decreasing the probability of being killed or severely injured (i.e., KSI) in rollover crashes. Land use and side friction are significant and positively associated only with slight injury category. These findings provide valuable insights into the causes and factors affecting the injury severity patterns of rollover crashes, and thus can help develop effective countermeasures to reduce the severity of rollover crashes. The model comparison results show that the REGOP model is found to outperform the MXL model in terms of goodness-of-fit measures, and also is significantly superior to other extensions of ordered probit models, including generalized ordered probit and random-effects ordered probit (REOP) models. As a result, this research introduces REGOP as a promising tool for future research focusing on crash injury severity.
    Matched MeSH terms: Weather
  4. Developing a dengue prediction model based on climate in Tawau, Malaysia
    Jayaraj VJ, Avoi R, Gopalakrishnan N, Raja DB, Umasa Y
    Acta Trop, 2019 Sep;197:105055.
    PMID: 31185224 DOI: 10.1016/j.actatropica.2019.105055
    Dengue is fast becoming the most urgent health issue in Malaysia, recording close to a 10-fold increase in cases over the last decade. With much uncertainty hovering over the recently introduced tetravalent vaccine and no effective antiviral drugs, vector control remains the most important strategy in combating dengue. This study analyses the relationship between weather predictors including its lagged terms, and dengue incidence in the District of Tawau over a period of 12 years, from 2006 to 2017. A forecasting model purposed to predict future outbreaks in Tawau was then developed using this data. Monthly dengue incidence data, mean temperature, maximum temperature, minimum temperature, mean relative humidity and mean rainfall over a period of 12 years from 2006 to 2017 in Tawau were retrieved from Tawau District Health Office and the Malaysian Meteorological Department. Cross-correlation analysis between weather predictors, lagged terms of weather predictors and dengue incidences established statistically significant cross-correlation between lagged periods of weather predictors-namely maximum temperature, mean relative humidity and mean rainfall with dengue incidence at time lags of 4-6 months. These variables were then employed into 3 different methods: a multivariate Poisson regression model, a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and a SARIMA with external regressors for selection. Three models were selected but the SARIMA with external regressors model utilising maximum temperature at a lag of 6 months (p-value:0.001), minimum temperature at a lag of 4 months (p-value:0.01), mean relative humidity at a lag of 2 months (p-value:0.001), and mean rainfall at a lag of 6 months (p-value:0.001) produced an AIC of 841.94, and a log-likelihood score of -413.97 establishing it as the best fitting model of the methodologies utilised. In validating the models, they were utilised to develop forecasts with the model selected with the highest accuracy of predictions being the SARIMA model predicting 1 month in advance (MAE: 7.032, MSE: 83.977). This study establishes the effect of weather on the intensity and magnitude of dengue incidence as has been previously studied. A prediction model remains a novel method of evidence-based forecasting in Tawau, Sabah. The model developed in this study, demonstrated an ability to forecast potential dengue outbreaks 1 to 4 months in advance. These findings are not dissimilar to what has been previously studied in many different countries- with temperature and humidity consistently being established as powerful predictors of dengue incidence magnitude. When used in prognostication, it can enhance- decision making and allow judicious use of resources in public health setting. Nevertheless, the model remains a work in progress- requiring larger and more diverse data.
    Matched MeSH terms: Weather
  5. Modern analytical and bioanalytical technologies and concepts for smart and precision farming
    Tsong JL, Khor SM
    Anal Methods, 2023 Jul 06;15(26):3125-3148.
    PMID: 37376849 DOI: 10.1039/d3ay00647f
    Unpredictable natural disasters, disease outbreaks, climate change, pollution, and war constantly threaten food crop production. Smart and precision farming encourages using information or data obtained by using advanced technology (sensors, AI, and IoT) to improve decision-making in agriculture and achieve high productivity. For instance, weather prediction, nutrient information, pollutant assessment, and pathogen determination can be made with the help of new analytical and bioanalytical methods, demonstrating the potential for societal impact such as environmental, agricultural, and food science. As a rising technology, biosensors can be a potential tool to promote smart and precision farming in developing and underdeveloped countries. This review emphasizes the role of on-field, in vivo, and wearable biosensors in smart and precision farming, especially those biosensing systems that have proven with suitably complex and analytically challenging samples. The development of various agricultural biosensors in the past five years that fulfill market requirements such as portability, low cost, long-term stability, user-friendliness, rapidity, and on-site monitoring will be reviewed. The challenges and prospects for developing IoT and AI-integrated biosensors to increase crop yield and advance sustainable agriculture will be discussed. Using biosensors in smart and precision farming would ensure food security and revenue for farming communities.
    Matched MeSH terms: Weather
  6. Climate Change, Extreme Weather Events, and Human Health Implications in the Asia Pacific Region
    Hashim JH, Hashim Z
    Asia Pac J Public Health, 2016 Mar;28(2 Suppl):8S-14S.
    PMID: 26377857 DOI: 10.1177/1010539515599030
    The Asia Pacific region is regarded as the most disaster-prone area of the world. Since 2000, 1.2 billion people have been exposed to hydrometeorological hazards alone through 1215 disaster events. The impacts of climate change on meteorological phenomena and environmental consequences are well documented. However, the impacts on health are more elusive. Nevertheless, climate change is believed to alter weather patterns on the regional scale, giving rise to extreme weather events. The impacts from extreme weather events are definitely more acute and traumatic in nature, leading to deaths and injuries, as well as debilitating and fatal communicable diseases. Extreme weather events include heat waves, cold waves, floods, droughts, hurricanes, tropical cyclones, heavy rain, and snowfalls. Globally, within the 20-year period from 1993 to 2012, more than 530 000 people died as a direct result of almost 15 000 extreme weather events, with losses of more than US$2.5 trillion in purchasing power parity.
    Matched MeSH terms: Weather*
  7. Urbanization, Extreme Events, and Health: The Case for Systems Approaches in Mitigation, Management, and Response
    Siri JG, Newell B, Proust K, Capon A
    Asia Pac J Public Health, 2016 Mar;28(2 Suppl):15S-27S.
    PMID: 26219559 DOI: 10.1177/1010539515595694
    Extreme events, both natural and anthropogenic, increasingly affect cities in terms of economic losses and impacts on health and well-being. Most people now live in cities, and Asian cities, in particular, are experiencing growth on unprecedented scales. Meanwhile, the economic and health consequences of climate-related events are worsening, a trend projected to continue. Urbanization, climate change and other geophysical and social forces interact with urban systems in ways that give rise to complex and in many cases synergistic relationships. Such effects may be mediated by location, scale, density, or connectivity, and also involve feedbacks and cascading outcomes. In this context, traditional, siloed, reductionist approaches to understanding and dealing with extreme events are unlikely to be adequate. Systems approaches to mitigation, management and response for extreme events offer a more effective way forward. Well-managed urban systems can decrease risk and increase resilience in the face of such events.
    Matched MeSH terms: Weather
  8. Fulltext Household survey of container-breeding mosquitoes and climatic factors influencing the prevalence of Aedes aegypti (Diptera: Culicidae) in Makkah City, Saudi Arabia
    Aziz AT, Dieng H, Ahmad AH, Mahyoub JA, Turkistani AM, Mesed H, et al.
    Asian Pac J Trop Biomed, 2012 Nov;2(11):849-57.
    PMID: 23569860 DOI: 10.1016/S2221-1691(12)60242-1
    To investigate the prevalence of container breeding mosquitoes with emphasis on the seasonality and larval habitats of Aedes aegypti (Ae. aegypti) in Makkah City, adjoining an environmental monitoring and dengue incidence.
    Matched MeSH terms: Weather*
  9. Fulltext A time-series prediction model of acute myocardial infarction in northern of Iran: the risk of climate change and religious mourning
    Sharif Nia H, Gorgulu O, Naghavi N, Froelicher ES, Fomani FK, Goudarzian AH, et al.
    BMC Cardiovasc Disord, 2021 11 23;21(1):563.
    PMID: 34814834 DOI: 10.1186/s12872-021-02372-0
    BACKGROUND: Although various studies have been conducted on the effects of seasonal climate changes or emotional variables on the risk of AMI, many of them have limitations to determine the predictable model. The currents study is conducted to assess the effects of meteorological and emotional variables on the incidence and epidemiological occurrence of acute myocardial infarction (AMI) in Sari (capital of Mazandaran, Iran) during 2011-2018.

    METHODS: In this study, a time series analysis was used to determine the variation of variables over time. All series were seasonally adjusted and Poisson regression analysis was performed. In the analysis of meteorological data and emotional distress due to religious mourning events, the best results were obtained by autoregressive moving average (ARMA) (5,5) model.

    RESULTS: It was determined that average temperature, sunshine, and rain variables had a significant effect on death. A total of 2375 AMI's were enrolled. Average temperate (°C) and sunshine hours a day (h/day) had a statistically significant relationship with the number of AMI's (β = 0.011, P = 0.014). For every extra degree of temperature increase, the risk of AMI rose [OR = 1.011 (95%CI 1.00, 1.02)]. For every extra hour of sunshine, a day a statistically significant increase [OR = 1.02 (95% CI 1.01, 1.04)] in AMI risk occurred (β = 0.025, P = 0.001). Religious mourning events increase the risk of AMI 1.05 times more. The other independent variables have no significant effects on AMI's (P > 0.05).

    CONCLUSION: Results demonstrate that sunshine hours and the average temperature had a significant effect on the risk of AMI. Moreover, emotional distress due to religious morning events increases AMI. More specific research on this topic is recommended.

    Matched MeSH terms: Weather*
  10. Fulltext Incidence, risk factors and clinical epidemiology of melioidosis: a complex socio-ecological emerging infectious disease in the Alor Setar region of Kedah, Malaysia
    Hassan MR, Pani SP, Peng NP, Voralu K, Vijayalakshmi N, Mehanderkar R, et al.
    BMC Infect Dis, 2010;10:302.
    PMID: 20964837 DOI: 10.1186/1471-2334-10-302
    Melioidosis, a severe and fatal infectious disease caused by Burkholderia pseudomallei, is believed to an emerging global threat. However, data on the natural history, risk factors, and geographic epidemiology of the disease are still limited.
    Matched MeSH terms: Weather
  11. Fulltext Seasonality of adult fragility fractures and association with weather: 12-year experience of a UK Fracture Liaison Service
    Ogliari G, Ong T, Marshall L, Sahota O
    Bone, 2021 Jun;147:115916.
    PMID: 33737194 DOI: 10.1016/j.bone.2021.115916
    PURPOSE: To investigate the monthly and seasonal variation in adult osteoporotic fragility fractures and the association with weather.

    METHODS: 12-year observational study of a UK Fracture Liaison Service (outpatient secondary care setting). Database analyses of the records of adult outpatients aged 50 years and older with fragility fractures. Weather data were obtained from the UK's national Meteorological Office. In the seasonality analyses, we tested for the association between months and seasons (determinants), respectively, and outpatient attendances, by analysis of variance (ANOVA) and Tukey's test. In the meteorological analyses, the determinants were mean temperature, mean daily maximum and minimum temperature, number of days of rain, total rainfall and number of days of frost, per month, respectively. We explored the association of each meteorological variable with outpatient attendances, by regression models.

    RESULTS: The Fracture Liaison Service recorded 25,454 fragility fractures. We found significant monthly and seasonal variation in attendances for fractures of the: radius or ulna; humerus; ankle, foot, tibia or fibula (ANOVA, all p-values <0.05). Fractures of the radius or ulna and humerus peaked in December and winter. Fractures of the ankle, foot, tibia or fibula peaked in July, August and summer. U-shaped associations were showed between each temperature parameter and fractures. Days of frost were directly associated with fractures of the radius or ulna (p-value <0.001) and humerus (p-value 0.002).

    CONCLUSION: Different types of fragility fractures present different seasonal patterns. Weather may modulate their seasonality and consequent healthcare utilisation.

    Matched MeSH terms: Weather
  12. Forecasting asthma-related hospital admissions in London using negative binomial models
    Soyiri IN, Reidpath DD, Sarran C
    Chron Respir Dis, 2013 May;10(2):85-94.
    PMID: 23620439 DOI: 10.1177/1479972313482847
    Health forecasting can improve health service provision and individual patient outcomes. Environmental factors are known to impact chronic respiratory conditions such as asthma, but little is known about the extent to which these factors can be used for forecasting. Using weather, air quality and hospital asthma admissions, in London (2005-2006), two related negative binomial models were developed and compared with a naive seasonal model. In the first approach, predictive forecasting models were fitted with 7-day averages of each potential predictor, and then a subsequent multivariable model is constructed. In the second strategy, an exhaustive search of the best fitting models between possible combinations of lags (0-14 days) of all the environmental effects on asthma admission was conducted. Three models were considered: a base model (seasonal effects), contrasted with a 7-day average model and a selected lags model (weather and air quality effects). Season is the best predictor of asthma admissions. The 7-day average and seasonal models were trivial to implement. The selected lags model was computationally intensive, but of no real value over much more easily implemented models. Seasonal factors can predict daily hospital asthma admissions in London, and there is a little evidence that additional weather and air quality information would add to forecast accuracy.
    Matched MeSH terms: Weather*
  13. Fulltext Air Quality Prediction Using the Fractional Gradient-Based Recurrent Neural Network
    Arora S, Sawaran Singh NS, Singh D, Rakesh Shrivastava R, Mathur T, Tiwari K, et al.
    Comput Intell Neurosci, 2022;2022:9755422.
    PMID: 36531923 DOI: 10.1155/2022/9755422
    In this study, the air quality index (AQI) of Indian cities of different tiers is predicted by using the vanilla recurrent neural network (RNN). AQI is used to measure the air quality of any region which is calculated on the basis of the concentration of ground-level ozone, particle pollution, carbon monoxide, and sulphur dioxide in air. Thus, the present air quality of an area is dependent on current weather conditions, vehicle traffic in that area, or anything that increases air pollution. Also, the current air quality is dependent on the climate conditions and industrialization in that area. Thus, the AQI is history-dependent. To capture this dependency, the memory property of fractional derivatives is exploited in this algorithm and the fractional gradient descent algorithm involving Caputo's derivative has been used in the backpropagation algorithm for training of the RNN. Due to the availability of a large amount of data and high computation support, deep neural networks are capable of giving state-of-the-art results in the time series prediction. But, in this study, the basic vanilla RNN has been chosen to check the effectiveness of fractional derivatives. The AQI and gases affecting AQI prediction results for different cities show that the proposed algorithm leads to higher accuracy. It has been observed that the results of the vanilla RNN with fractional derivatives are comparable to long short-term memory (LSTM).
    Matched MeSH terms: Weather
  14. Fulltext Habitat disturbance results in chronic stress and impaired health status in forest-dwelling paleotropical bats
    Seltmann A, Czirják GÁ, Courtiol A, Bernard H, Struebig MJ, Voigt CC
    Conserv Physiol, 2017;5(1):cox020.
    PMID: 28421138 DOI: 10.1093/conphys/cox020
    Anthropogenic habitat disturbance is a major threat to biodiversity worldwide. Yet, before population declines are detectable, individuals may suffer from chronic stress and impaired immunity in disturbed habitats, making them more susceptible to pathogens and adverse weather conditions. Here, we tested in a paleotropical forest with ongoing logging and fragmentation, whether habitat disturbance influences the body mass and immunity of bats. We measured and compared body mass, chronic stress (indicated by neutrophil to lymphocyte ratios) and the number of circulating immune cells between several bat species with different roost types living in recovering areas, actively logged forests, and fragmented forests in Sabah, Malaysia. In a cave-roosting species, chronic stress levels were higher in individuals from fragmented habitats compared with conspecifics from actively logged areas. Foliage-roosting species showed a reduced body mass and decrease in total white blood cell counts in actively logged areas and fragmented forests compared with conspecifics living in recovering habitats. Our study highlights that habitat disturbance may have species-specific effects on chronic stress and immunity in bats that are potentially related to the roost type. We identified foliage-roosting species as particularly sensitive to forest habitat deterioration. These species may face a heightened extinction risk in the near future if anthropogenic habitat alterations continue.
    Matched MeSH terms: Weather
  15. The siamang in Malaya. A field study of a primate in tropical rain forest
    Chivers DJ
    Contrib Primatol, 1974;4:1-335.
    PMID: 4474071
    Matched MeSH terms: Weather
  16. Fulltext Data Assessment on the relationship between typical weather data and electricity consumption of academic building in Melaka
    Ngah Nasaruddin A, Tee BT, Mohd Tahir M, Md Jasman MES
    Data Brief, 2021 Apr;35:106797.
    PMID: 33614870 DOI: 10.1016/j.dib.2021.106797
    Exposure to hot and humid weather conditions will often lead to consuming a vast amount of electricity for cooling. Heating, ventilation, and air conditioning (HVAC) systems are customarily known as the largest consumers of energy in institutions and other facilities which raises the question regarding the impact of the weather conditions to the amount energy consumed. The academic building is a perfect example where a constant fixed daily operating characteristic is measured by the hour, aside from the occasional semester break. Therefore, it can be assumed that the daily HVAC services on an academic facility will operate on a fixed schedule each day, having a similar pattern all year round. This article aims to present an analysis on the relationship between typical weather data by implying the test reference year (TRY) and academic building electricity consumption in an academic building located at Durian Tunggal, Melaka. Typical weather data were generated in representing the weather data between 2010 and 2018 using the Finkelstein-Schafer statistic (F-S statistic) in addition to a data set of electricity consumption. Descriptive analysis and correlation matrix analysis were conducted using JASP software for two sets of sample data; Set A and Set B, with data points of 12 and 108, respectively. The result showed an alternate result with a positive correlation between 1)mean temperature-electricity consumption, and 2)mean rainfall-electricity consumption for data Set A, and a negative correlation between 1)mean temperature-electricity consumption and 2)mean rainfall-electricity consumption for data Set B.
    Matched MeSH terms: Weather
  17. Fulltext Macroeconomic dataset for generating macroeconomic volatility among selected countries in the Asia Pacific region
    Chow YP, Muhammad J, Amin Noordin BA, Cheng FF
    Data Brief, 2018 Feb;16:23-28.
    PMID: 29167816 DOI: 10.1016/j.dib.2017.11.015
    This data article provides macroeconomic data that can be used to generate macroeconomic volatility. The data cover a sample of seven selected countries in the Asia Pacific region for the period 2004-2014, including both developing and developed countries. This dataset was generated to enhance our understanding of the sources of macroeconomic volatility affecting the countries in this region. Although the Asia Pacific region continues to remain as the most dynamic part of the world's economy, it is not spared from various sources of macroeconomic volatility through the decades. The reported data cover 15 types of macroeconomic data series, representing three broad categories of indicators that can be used to proxy macroeconomic volatility. They are indicators that account for macroeconomic volatility (i.e. volatility as a macroeconomic outcome), domestic sources of macroeconomic volatility and external sources of macroeconomic volatility. In particular, the selected countries are Malaysia, Thailand, Indonesia and Philippines, which are regarded as developing countries, while Singapore, Japan and Australia are developed countries. Despite the differences in level of economic development, these countries were affected by similar sources of macroeconomic volatility such as the Asian Financial Crisis and the Global Financial Crisis. These countries were also affected by other similar external turbulence arising from factors such as the global economic slowdown, geopolitical risks in the Middle East and volatile commodity prices. Nonetheless, there were also sources of macroeconomic volatility which were peculiar to certain countries only. These were generally domestic sources of volatility such as political instability (for Thailand, Indonesia and Philippines), natural disasters and anomalous weather conditions (for Thailand, Indonesia, Philippines, Japan and Australia) and over-dependence on the electronic sector (for Singapore).
    Matched MeSH terms: Weather
  18. Risk of concentrations of major air pollutants on the prevalence of cardiovascular and respiratory diseases in urbanized area of Kuala Lumpur, Malaysia
    Tajudin MABA, Khan MF, Mahiyuddin WRW, Hod R, Latif MT, Hamid AH, et al.
    Ecotoxicol Environ Saf, 2019 Apr 30;171:290-300.
    PMID: 30612017 DOI: 10.1016/j.ecoenv.2018.12.057
    Rapid urbanisation in Malaysian cities poses risks to the health of residents. This study aims to estimate the relative risk (RR) of major air pollutants on cardiovascular and respiratory hospitalisations in Kuala Lumpur. Daily hospitalisations due to cardiovascular and respiratory diseases from 2010 to 2014 were obtained from the Hospital Canselor Tuanku Muhriz (HCTM). The trace gases, PM10 and weather variables were obtained from the Department of Environment (DOE) Malaysia in consistent with the hospitalisation data. The RR was estimated using a Generalised Additive Model (GAM) based on Poisson regression. A "lag" concept was used where the analysis was segregated into risks of immediate exposure (lag 0) until exposure after 5 days (lag 5). The results showed that the gases could pose significant risks towards cardiovascular and respiratory hospitalisations. However, the RR value of PM10 was not significant in this study. Immediate effects on cardiovascular hospitalisations were observed for NO2 and O3 but no immediate effect was found on respiratory hospitalisations. Delayed effects on cardiovascular and respiratory hospitalisations were found with SO2 and NO2. The highest RR value was observed at lag 4 for respiratory admissions with SO2 (RR = 1.123, 95% CI = 1.045-1.207), followed by NO2 at lag 5 for cardiovascular admissions (RR = 1.025, 95% CI = 1.005-1.046). For the multi-pollutant model, NO2 at lag 5 showed the highest risks towards cardiovascular hospitalisations after controlling for O3 8 h mean lag 1 (RR = 1.026, 95% CI = 1.006-1.047), while SO2 at lag 4 showed highest risks towards respiratory hospitalisations after controlling for NO2 lag 3 (RR = 1.132, 95% CI = 1.053-1.216). This study indicated that exposure to trace gases in Kuala Lumpur could lead to both immediate and delayed effects on cardiovascular and respiratory hospitalisations.
    Matched MeSH terms: Weather
  19. Using satellite fire detection to calibrate components of the fire weather index system in Malaysia and Indonesia
    Dymond CC, Field RD, Roswintiarti O, Guswanto
    Environ Manage, 2005 Apr;35(4):426-40.
    PMID: 15902449
    Vegetation fires have become an increasing problem in tropical environments as a consequence of socioeconomic pressures and subsequent land-use change. In response, fire management systems are being developed. This study set out to determine the relationships between two aspects of the fire problems in western Indonesia and Malaysia, and two components of the Canadian Forest Fire Weather Index System. The study resulted in a new method for calibrating components of fire danger rating systems based on satellite fire detection (hotspot) data. Once the climate was accounted for, a problematic number of fires were related to high levels of the Fine Fuel Moisture Code. The relationship between climate, Fine Fuel Moisture Code, and hotspot occurrence was used to calibrate Fire Occurrence Potential classes where low accounted for 3% of the fires from 1994 to 2000, moderate accounted for 25%, high 26%, and extreme 38%. Further problems arise when there are large clusters of fires burning that may consume valuable land or produce local smoke pollution. Once the climate was taken into account, the hotspot load (number and size of clusters of hotspots) was related to the Fire Weather Index. The relationship between climate, Fire Weather Index, and hotspot load was used to calibrate Fire Load Potential classes. Low Fire Load Potential conditions (75% of an average year) corresponded with 24% of the hotspot clusters, which had an average size of 30% of the largest cluster. In contrast, extreme Fire Load Potential conditions (1% of an average year) corresponded with 30% of the hotspot clusters, which had an average size of 58% of the maximum. Both Fire Occurrence Potential and Fire Load Potential calibrations were successfully validated with data from 2001. This study showed that when ground measurements are not available, fire statistics derived from satellite fire detection archives can be reliably used for calibration. More importantly, as a result of this work, Malaysia and Indonesia have two new sources of information to initiate fire prevention and suppression activities.
    Matched MeSH terms: Weather
  20. Effects of northeast monsoon on trace metal distribution in the South China Sea off Peninsular Malaysia
    Adiana G, Shazili NA, Marinah MA, Bidai J
    Environ Monit Assess, 2014 Jan;186(1):421-31.
    PMID: 23974537 DOI: 10.1007/s10661-013-3387-9
    Concentrations of trace metals in the South China Sea (SCS) were determined off the coast of Terengganu during the months of May and November 2007. The concentrations of dissolved and particulate metals were in the range of 0.019-0.194 μg/L and 50-365 μg/g, respectively, for cadmium (Cd), 0.05-0.45 μg/L and 38-3,570 μg/g for chromium (Cr), 0.05-3.54 μg/L and 21-1,947 μg/g for manganese (Mn), and 0.03-0.49 μg/L and 2-56,982 μg/g for lead (Pb). The order of mean log K D found was Cd > Cr > Pb > Mn. The study suggests that the primary sources of these metals are discharges from the rivers which drain into the SCS, in particular the Dungun River, which flows in close proximity to agricultural areas and petrochemical industries. During the northeast monsoon, levels of particulate metals in the bottom water samples near the shore were found to be much higher than during the dry season, the probable result of re-suspension of the metals from the bottom sediments.
    Matched MeSH terms: Weather
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