Background: Meteorological parameters and seasonal changes can play an important role in the occurrence of acute coronary syndrome (ACS). However, there is almost no evidence on a national level to suggest the associations between these variables and ACS in Iran. We aim to identify the meteorological parameters and seasonal changes in relationship to ACS. Methods: This retrospective cross-sectional study was conducted between 03/19/2015 to 03/18/2016 and used documents and records of patients with ACS in Mazandaran ProvinceHeart Center, Iran. The following definitive diagnostic criteria for ACS were used: (1) existence of cardiac enzymes (CK or CK-MB) above the normal range; (2) Greater than 1 mm ST-segment elevation or depression; (3) abnormal Q waves; and (4) manifestation of troponin enzyme in the blood. Data were collected daily, such as temperature (Celsius) changes, wind speed and its direction, rainfall, daily evaporation rate; number of sunny days, and relative humidity were provided by the Meteorological Organization of Iran. Results: A sample of 2,054 patients with ACS were recruited. The results indicated the highest ACS events from March to May. Generally, wind speed (18 PM) [IRR = 1.051 (95% CI: 1.019 to1.083), P=0.001], daily evaporation [IRR = 1.039 (95% CI: 1.003 to 1.077), P=0.032], daily maximum (P<0.001) and minimum (P=0.003) relative humidity was positively correlated withACS events. Also, negatively correlated variables were daily relative humidity (18 PM) [IRR =0.985 (95% CI: 0.978 to 0.992), P<0.001], and daily minimum temperature [IRR = 0.942 (95%CI: 0.927 to 0.958), P<0.001]. Conclusion: Climate changes were found to be significantly associated with ACS; especially from cold weather to hot weather in March, April and May. Further research is needed to fully understand the specific conditions and cold exposures.
Wind turbine noise is one of the major obstacles for the widespread use of wind energy. Noise tone can greatly increase the annoyance factor and the negative impact on human health. Noise annoyance caused by wind turbines has become an emerging problem in recent years, due to the rapid increase in number of wind turbines, triggered by sustainable energy goals set forward at the national and international level. Up to now, not all aspects of the generation, propagation and perception of wind turbine noise are well understood. For a modern large wind turbine, aerodynamic noise from the blades is generally considered to be the dominant noise source, provided that mechanical noise is adequately eliminated. The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise. Many analytical and experimental acoustical studies performed the wind turbines. Since the wind turbine noise level analyzing by numerical methods or computational fluid dynamics (CFD) could be very challenging and time consuming, soft computing techniques are preferred. To estimate noise level of wind turbine, this paper constructed a process which simulates the wind turbine noise levels in regard to wind speed and sound frequency with adaptive neuro-fuzzy inference system (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.
Water scarcity is a global concern, as the demand for water is increasing tremendously and poor management of water resources will accelerates dramatically the depletion of available water. The precise prediction of evapotranspiration (ET), that consumes almost 100% of the supplied irrigation water, is one of the goals that should be adopted in order to avoid more squandering of water especially in arid and semiarid regions. The capabilities of feedforward backpropagation neural networks (FFBP) in predicting reference evapotranspiration (ET0) are evaluated in this paper in comparison with the empirical FAO Penman-Monteith (P-M) equation, later a model of FFBP+Genetic Algorithm (GA) is implemented for the same evaluation purpose. The study location is the main station in Iraq, namely Baghdad Station. Records of weather variables from the related meteorological station, including monthly mean records of maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine hours (Rn), relative humidity (Rh) and wind speed (U2), from the related meteorological station are used in the prediction of ET0 values. The performance of both simulation models were evaluated using statistical coefficients such as the root of mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The results of both models are promising, however the hybrid model shows higher efficiency in predicting ET0 and could be recommended for modeling of ET0 in arid and semiarid regions.
Estimation and forecast of groundwater recharge and capacity of aquifer are essential issues in water resources investigation. In the current research, groundwater recharge, recharge coefficient and effective rainfall were determined through a case study using empirical methods applicable to the tropical zones. The related climatological data between January 2000 and December 2010 were collected in Selangor, Malaysia. The results showed that groundwater recharge was326.39 mm per year, effective precipitation was 1807.97 mm per year and recharge coefficient was 18% for the study area. In summary, the precipitation converted to recharge, surface runoff and evapotranspiration are 12, 32 and 56% of rainfall, respectively. Correlation between climatic parameters and groundwater recharge showed positive and negative relationships. The highest correlation was found between precipitation and recharge. Linear multiple regressions between
recharge and measured climatologic data proved significant relationship between recharge and rainfall and wind speed. It was also proven that the proposed model provided an accurate estimation for similar projects.
Theories of tropical tree diversity emphasize dispersal limitation as a potential mechanism for separating species in space and reducing competitive exclusion. We compared the dispersal morphologies, fruit sizes, and spatial distributions of 561 tree species within a fully mapped, 50-hectare plot of primary tropical forest in peninsular Malaysia. We demonstrate here that the extent and scale of conspecific spatial aggregation is correlated with the mode of seed dispersal. This relationship holds for saplings as well as for mature trees. Phylogenetically independent contrasts confirm that the relationship between dispersal and spatial pattern is significant even after controlling for common ancestry among species. We found the same qualitative results for a 50-hectare tropical forest plot in Panama. Our results provide broad empirical evidence for the importance of dispersal mode in establishing the long-term community structure of tropical forests.
Energy is one of the most important factors in the socioeconomic development of a country. In a developing country like Malaysia, the development of islands is mostly related to the availability of electric power. Power generated by renewable energy sources has recently become one of the most promising solutions for the electrification of islands and remote rural areas. But high dependency on weather conditions and the unpredictable nature of these renewable energy sources are the main drawbacks. To overcome this weakness, different green energy sources and power electronic converters need to be integrated with each other. This study presents a battery storage hybrid standalone photovoltaic-wind energy power supply system. In the proposed standalone hybrid system, a DC-DC buck-boost bidirectional converter controller is used to accumulates the surplus hybrid power in the battery bank and supplies this power to the load during the hybrid power shortage by maintaining the constant dc-link voltage. A three-phase voltage source inverter complex vector control scheme is used to control the load side voltage in terms of the voltage amplitude and frequency. Based on the simulation results obtained from MATLAB/Simulink, it has been found that the overall hybrid framework is capable of working under variable weather and load conditions.
The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system's modeling equations based on the Bode plot equations and the vector fitting (VF) algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model.
The concentrations of airborne particulate matter (PM) is often measured as a mass concentration. However, the other way to express particulate matter is by using the Particle Number Count ([PNC]) concentrations. This study aims to analyse the seasonal variation of airborne particulate matter in terms of [PNC] by using R packages and the Boosted Regression Trees (BRTs) technique. The study was conducted at IOES, Universiti of Malaya in Bachok, Kelantan. The monitoring was important to understand the variability of seasonal effects due to different seasons. In this work, only the datasets for three seasons (Inter Monsoon, North East Monsoon and South-West Monsoon) were analysed involving 25,958 data. The air quality monitoring equipment involved was the particle counter Environment Dust Monitor GRIMM Model 180 and a weather station for recording the meteorological parameters. The data analysis was completed by using R software and its package for evaluating seasonal variability and providing the statistical analysis. The relationship between variables was studied by using the Boosted Regression Tree (BRT) technique. The interaction between independent variables towards the [PNC] in different seasons was discussed. The best setting result of BRT model evaluation R² is 0.22 (North-East Monsoon), 0.87 (Intern monsoon 1), and 0.59 for South West Monsoon which indicated that the model developed is acceptable except for NEM and intern monsoon seasons. Temperature (57 %) and wind direction (67%) were found to be the highest factor influenced by the formation of [PNC] concentrations in this area. Finally, good results indicated that BRT technique is an acceptable way to analysed air pollution data.
The statistical characteristics of wind direction that was recorded at maximum wind speed in Peninsular Malaysia for two monsoons from 1999 to 2008 for seven stations were analyzed in this study. Modeled by von Mises distribution, the change in parameters values namely mean direction and concentration parameter was measured. Statistical summary, graphical representations, Watson-William Test and linear-circular correlation are used in the analysis. It is found that there is a significant change in the mean direction of wind over the period of ten years for most stations in Peninsular Malaysia. However, there is a weak relationship between wind direction and wind speed. This study suggested the presence of prominent direction of wind that blows in Peninsular Malaysia by monsoon. This finding may provide useful information on giving a better understanding of the behavior of the wind in Peninsular Malaysia and the potential use of wind as an alternative source of energy.
The Malacca Straits is located between Peninsula Malaysia and Sumatra Island. This investigation used equation of motion (Navier-Stokes equation) with the following driving forces: tides, wind of National Centers for Environmental Prediction (NCEP) for year of 2007, salinity and temperature. The equation of motion was solved by means of Hamburg Shelf Ocean Model (HAMSOM). The results for both southwest and northeast monsoon were explained and discussed. The simulation results both for February and August 2007 were relatively similar. Current surface simulation in the Malacca Straits agrees well with the current pattern of previous works. The magnitude of current was between 10-70 cm/s to the northwest. While at the layer 30-50 m in the Malacca Straits, the currents have the magnitude of 10-30 cm/s towards northwest. For the bottom current, the current speed was 0-20 cm/s towards northwest. For the surface and 30-50 m layer, generally the current magnitudes were greater in February compared to those in August. While for the bottom layer, the current magnitudes between February and August were relatively the same.
Dhaka and its neighboring areas suffer from severe air pollution, especially during dry season (November-April). We investigated temporal and directional variations in particulate matter (PM) concentrations in Dhaka, Gazipur, and Narayanganj from October 2012 to March 2015 to understand different aspects of PM concentrations and possible sources of high pollution in this region. Ninety-six-hour backward trajectories for the whole dry season were also computed to investigate incursion of long-range pollution into this area. We found yearly PM10 concentrations in this area about three times and yearly PM2.5 concentrations about six times greater than the national standards of Bangladesh. Dhaka and its vicinity experienced several air pollution episodes in dry season when PM2.5 concentrations were 8-13 times greater than the World Health Organization (WHO) guideline value. Higher pollution and great contribution of PM2.5 most of the time were associated with the north-westerly wind. Winter (November to January) was found as the most polluted season in this area, when average PM10 concentrations in Dhaka, Gazipur, and Narayanganj were 257.1, 240.3, and 327.4 μg m(-3), respectively. Pollution levels during wet season (May-October) were, although found legitimate as per the national standards of Bangladesh, exceeded WHO guideline value in 50 % of the days of that season. Trans-boundary source identifications using concentration-weighted trajectory method revealed that the sources in the eastern Indian region bordering Bangladesh, in the north-eastern Indian region bordering Nepal and in Nepal and its neighboring areas had high probability of contributing to the PM pollutions at Gazipur station.
Meteorology over coastal region is a driving factor to the concentration of air particles and reactive gases. This study aims to conduct a research to determine the level of year-round air particles and the interaction of the meteorological driving factors with the particle number and mass in 2018, which is moderately influenced by Southeast Asian haze. We obtained the measurement data for particle number count (PNC), mass, reactive gases, and meteorological factors from a Global Atmospheric Watch (GAW) station located at Bachok Marine Research Center, Bachok, Kelantan, Malaysia. For various timeseries and correlation analyses, a 60-second resolution of the data has been averaged hourly and daily and visualized further. Our results showed the slight difference in particle behavior that is either measured by unit mass or number count at the study area. Diurnal variations showed that particles were generally high during morning and night periods. Spike was observed in August for PM2.5/PNC2.5 and PM10/PNC10 and in November for PMCoarse/PNCCoarse. From a polar plot, the particles came from two distinct sources (e.g., seaside and roadside) at the local scale. Regional wind vector shows two distinct wind-blown directions from northeast and southwest. The air mases were transported from northeast (e.g., Philippines, mainland China, and Taiwan) or southwest (e.g., Sumatra) region. Correlation analysis shows that relative humidity, wind direction, and pressure influence the increase in particles, whereas negative correlation with temperature is observed, and wind speed may have a potential role on the decline of particle concentration. The particles at the study area was highly influenced by the changes in regional wind direction and speed.
Automatic recording of birdsong is becoming the preferred way to monitor and quantify bird populations worldwide. Programmable recorders allow recordings to be obtained at all times of day and year for extended periods of time. Consequently, there is a critical need for robust automated birdsong recognition. One prominent obstacle to achieving this is low signal to noise ratio in unattended recordings. Field recordings are often very noisy: birdsong is only one component in a recording, which also includes noise from the environment (such as wind and rain), other animals (including insects), and human-related activities, as well as noise from the recorder itself. We describe a method of denoising using a combination of the wavelet packet decomposition and band-pass or low-pass filtering, and present experiments that demonstrate an order of magnitude improvement in noise reduction over natural noisy bird recordings.
Difficulties in controlling the effects of outdoor thermal environment on the human body are attracting considerable research attention. This study investigated the outdoor thermal comfort of urban pedestrians by assessing their perceptions of the tropical, micrometeorological, and physical conditions via a questionnaire survey. Evaluation of the outdoor thermal comfort involved pedestrians performing various physical activities (sitting, walking, and standing) in outdoor and semi-outdoor spaces where the data collection of air temperature, globe temperature, relative humidity, wind speed, solar radiation, metabolic activity, and clothing insulation data was done simultaneously. A total of 1011 participants were interviewed, and the micrometeorological data were recorded under outdoor and semi-outdoor conditions at two Malaysian university campuses. The neutral temperatures obtained which were 28.1 °C and 30.8 °C were within the biothermal acceptable ranges of 24-34 °C and 26-33 °C of the PET thermal sensation ranges for the outdoor and semi-outdoor conditions, respectively. Additionally, the participants' thermal sensation and preference votes were highly correlated with the PET and strongly related to air and mean radiant temperatures. The findings demonstrated the influence of individuals' thermal adaptation on the outdoor thermal comfort levels. This knowledge could be useful in the planning and designing of outdoor environments in hot and humid regions to create better thermal environments.
Agriculture sector accounts significant numbers of injuries and fatalities in the workplace particularly related to pesticide management. Among three main pathways of pesticide exposure, dermal contact is the most common route, which exposure usually occurs during pesticide mixing/loading, application, harvesting and other farming activities. This review aims to present and discuss several vital components of pesticide dermal exposure among agriculture workers, as well as pesticide application in agriculture sector in Malaysia involving different commodity agriculture sub-sectors. Pesticide exposure was discussed from perspective of three pesticide management activities (i.e. preparation, application and cleaning) that contribute to the risk of exposure through three routes (i.e. emission, deposition, transfer). Moreover, this paper also discussed pesticide dermal exposure risk assessment methods which can be defined into exposure assessment and effect assessment. The exposure rate was affected by various factors such as application equipment, application rate and duration, type of pesticide formulation, pesticide management stage, usage of personal protective equipment, training and aptitude of the applicator as well as environmental factors (i.e. temperature, humidity, wind speed and direction). The factors mention earlier have been used to explain the exposure distribution over different parts of the body and support the fact that pesticide type was not a major factor in total exposure.
A cylindrical data set consists of circular and linear variables. We focus on developing an outlier detection procedure
for cylindrical regression model proposed by Johnson and Wehrly (1978) based on the k-nearest neighbour approach.
The procedure is applied based on the residuals where the distance between two residuals is measured by the Euclidean
distance. This procedure can be used to detect single or multiple outliers. Cut-off points of the test statistic are generated
and its performance is then evaluated via simulation. For illustration, we apply the test on the wind data set obtained
from the Malaysian Meteorological Department.
On March 11, 2011, a serious accident occurred in Daiichi nuclear reactor plant, Fukushima,
Japan which caused radioactive materials been released into the atmosphere in the form of
aerosols and dust particles. Sea water around the plant was also found contaminated with high
radioactivity readings. These radioactive materials could be transported by the winds and ocean
current across international borders and cannot be controlled by human. Thus, a continuous
monitoring activity of radionuclide content in the air and sea water needs to be conducted by the
authorities. In addition to radioactivity monitoring, Malaysia should also control the entry of
contaminated food in order to prevent radionuclide ingestion by human. The radionuclide 131I,
134Cs and 137Cs were used as a measure of pollution levels and counted with gamma spectrometry
using standard analysis method suggested by AOAC International. In this paper, details description
of the role of Radiochemical and Environment Group, Nuclear Malaysia who’s responsible in
analyzing the radioactivity in the food samples due to Fukushima Daiichi, Japan accident was
included. The radioactivity limit adopted and analysis results from this monitoring were discussed
Oceanographic cruises in Pahang water in October 2003 and April 2004, monsoon transition months, produce data on water characteristics. The temperature in both months showed higher values in nearshore compared to the offshore stations. The nearshore salinity in both months is lower than offshore stations. Comparatively, there were smaller differences in temperature and salinity in October than in April, with very little variation between nearshore and offshore stations. T-S diagram showed significant differences between October and April water characteristics. According to the water characteristic observations, the temperature and salinity in October was lower than in April, while dissolved oxygen was higher than in April. The lower temperature and salinity taken during the sampling time in October suggested that during this time, the study area already received the influences of strong winds due to upcoming monsoon. The warmer and saltier water obtained in April showed that during this time, the study area was influenced by southwest monsoon. Winds related to rainfall were observed to have impact to the dynamics of water characteristics during both months.
Annual air temperature data obtained from twenty-two meteorological stations across Malaysia are modeled using multiple regression. A correlation test was conducted to find statistical relationship between each of the dependent variables: annual maximum and annual average air temperature and predictor variables: longitude, latitude, elevation and wind speed. Regression models using least square estimation method were developed relating the dependent variables to independent variables and the adequacy of the models is determined by the coefficient of determination. The result shows that the longitude and wind speed factors have a significant influence on the annual air temperature in Malaysia.
In this paper, four types of circular probability distribution were used to evaluate which circular probability distribution gives the best fitting for southwesterly Malaysian wind direction data, namely circular uniform distribution, von Mises distribution, wrapped-normal distribution and wrapped-Cauchy distribution. The four locations chosen were Alor Setar, Langkawi, Melaka and Senai. Two performance indicators or goodness of fit tests which are mean circular distance and chord length were used to test which distribution give the best fitting.