Mercury emission into the atmosphere is a global concern due to its detrimental effects on human health in general. The two main sources of mercury emission are natural sources and anthropogenic sources. Mercury emission from natural sources include volcanic activity, weathering of rocks, water movement and biological processes which are obviously inevitable. The anthropogenic sources of mercury emission are from coal combustion, cement production and waste incineration. Thus, in order to reduce mercury emission it is appropriate to investigate how mercury is released from the anthropogenic sources and consequently the mercury removal technology that can be implemented in order to reduce mercury emission into the atmosphere. Many alternatives have been developed to reduce mercury emission and the recent application of activated carbon showed high potential in the adsorption of elemental mercury. This paper discusses the ability of activated carbon and variable parameters that influence mercury removal efficiency in flue gas.
The application of solar disinfection for treating stored rainwater was investigated by the authors using indicator organisms. The multiple tube fermentation technique and pour plate method were used for the detection of microbial quality indicators like total and fecal coliforms, E. coli and heterotrophic plate count. These techniques have disadvantages mainly that these are laborious and time consuming. The correlation of total coliform with that of exposure time is proposed under different factors of weather, pH and turbidity. Statistical tools like root mean square error and coefficient of determination were used to validate these proposed equations. The correlation equations of fecal coliform, E. coli and heterotrophic plate count with total coliform are suggested by using four regression analysis including Reciprocal Quadratic, Polynomial Regression (2 degree), Gaussian Model and Linear Regression in order to reduce the tedious experimental work in similar types of experiments and treatment systems.
Time of weed control and fertilizer application usually decide the profitability of crop production. The effects of weed control and macronutrients on maize crop were investigated. The study was undertaken in March 2009, using a RCBD design with split plot arrangements. The experimental set up was established at the Agricultural University Peshawar and seedbeds were prepared with the proper moisture regime. Maize was planted with one plot left weed free for first six weeks while another infested with weed. The combinations of macronutrients used were nitrogen, phosphorus, potassium, nitrogen-phosphorus, nitrogen-potassium, phosphorus-potassium and nitrogen-phosphorus-potassium. Control (no fertilizer) was included for comparison. The observations revealed that when a comparison was made between the application of fertilizers and weed control, the latter proved more important because weed infested plots had no harvestable maize plants. The role of main nutrients in crop production is well known and cannot be left aside, however weed infestation does not provide us a fair choice of fertilizers application. The maximum maize grain yield was recorded under nitrogen-phosphorus combination and promising results were obtained. The weeds and maize benefited equally in terms of fresh and dry weed biomass with an application of fertilizer in particular N singly or together with P. In view of this, application of fertilizer should be changed from broadcast to band and/or placement. In general, a positive interaction was seen between N and P promoting the growth of maize and weeds. It can be said that herbicide application for weed control is important because of the fact that hand weeding is not economical, difficult, time consuming because of perennial weeds and hot weather conditions in the month of June.
Within recorded history, most Southeast Asian peoples have been of "southern Mongoloid" physical type, whether they speak Austroasiatic, Tibeto-Burman, Austronesian, Tai-Kadai, or Hmong-Mien languages. However, population distributions suggest that this is a post-Pleistocene phenomenon and that for tens of millennia before the last glaciation ended Greater Mainland Southeast Asia, which included the currently insular world that rests on the Sunda Shelf, was peopled by short, dark-skinned, frizzy-haired foragers whose descendants in the Philippines came to be labeled by the sixteenth-century Spanish colonizers as "negritos," a term that has since been extended to similar groups throughout the region. There are three areas in which these populations survived into the present so as to become part of written history: the Philippines, the Malay Peninsula, and the Andaman Islands. All Philippine negritos speak Austronesian languages, and all Malayan negritos speak languages in the nuclear Mon-Khmer branch of Austroasiatic, but the linguistic situation in the Andamans is a world apart. Given prehistoric language shifts among both Philippine and Malayan negritos, the prospects of determining whether disparate negrito populations were once a linguistically or culturally unified community would appear hopeless. Surprisingly, however, some clues to a common negrito past do survive in a most unexpected way.
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.
Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques.
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.
Foamed mortar with a density of 1300 kg/m³ was prepared. In the initial laboratory trials, water-to-cement (w/c) ratios ranging from 0.54 to 0.64 were tested to determine the optimal value for foamed mortar corresponding to the highest compressive strength without compromising its fresh state properties. With the obtained optimal w/c ratio of 0.56, two types of foamed mortar were prepared, namely cement-foamed mortar (CFM) and slag-foamed mortar (SFM, 50% cement was replaced by slag weight). Four different curing conditions were adopted for both types of foamed mortar to assess their compressive strength, ultrasonic pulse velocity (UPV) and thermal insulation performance. The test results indicated that utilizing 50% of slag as cement replacement in the production of foamed mortar improved the compressive strength, UPV and thermal insulation properties. Additionally, the initial water curing of seven days gained higher compressive strength and increased UPV values as compared to the air cured and natural weather curing samples. However, this positive effect was more pronounced in the case of compressive strength than in the UPV and thermal conductivity of foamed mortar.
Surveillance of mosquitoes and their distribution in association with rainfall, relative humidity, and temperature were conducted in selected suburban and forested areas, namely, Sungai Penchala (Kuala Lumpur) and Taman Alam (Selangor) for 12 months. Armigeres kesseli was the most abundant species in Sungai Penchala while Aedes butleri was the most dominant species in Taman Alam. A positive correlation between mosquito distribution and rainfall was observed in selected mosquito species in Sungai Penchala (Armigeres kesseli, r = 0.75; Armigeres subalbatus, r = 0.62; and Aedes albopictus, r = 0.65) and Taman Alam (Armigeres sp, r = 0.59; Ae. butleri, r = 0.85; and Ae. albopictus, r = 0.62). However, no significant cor- relation was found either between selected mosquito species in both study areas and relative humidity or temperature. Results obtained suggested that vector control programs to be conducted based on temporal distribution of vectors in order to achieve beneficial outcomes with effective costing.
Recent statistics from the Social Security Organization (SOCSO) of Malaysia shows Commuting Accident (CA)
increased consistently by 1500 cases per year from 2008 to 2012. This has resulted in the rising of fatality rate,
extensive claims of compensation payment and not to mention loss of valuable talents. However, little is known
about the contributing factors to the occurrences of CA in Malaysia because CA is never considered to be work
related before. This study aims to explore work related factors with occurrence of CA using 5-year statistical data
from SOCSO, Malaysia. Moreover, we also reviewed studies published between 1990s and 2014 to support the
statistical findings. Motorbike is found as the most common vehicle used which involved in CA. Individual factors
concerning workers behaviour is a main risk factor of CA. Family related factors (parenting responsibility), work
burden, workplace support as well as environmental factors such as bad weather and bad road conditions are also
significant contributions of CA. It is very important to develop behavioural intervention strategies and provide proper
training. Hence, more attention should be directed to young individual workers in balancing them with capabilities and
organization performance demand. This may lead to the elimination of the other causes of CA. In conclusion, solutions
to this problem involve not only a particular party to ensure the wellbeing of workers in Malaysia, but all authorities
should play roles in enhancing safety and health matter of workers especially on the occurrence of accident.
The determination of variance of surface air temperature is very essential since it has a direct impact on vegetation, environment and human livelihood. Forecast of surface air temperature is difficult because of the complex physical phenomenon and the random-like behavior of atmospheric system which influences the temperature event on the earth surface. In this study, forecast models based on artificial neural network (ANN) and genetic programming (GP) approaches were proposed to predict lead seven days minimum and maximum surface air temperature using the weather parameters observed at the station Chennai, India. The outcome of this study stated that models formulated using ANN approach are more accurate than genetic programming for all seven days with the highest coefficient of determination (R2), least mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) on deployment with independent test dataset. ANN models give statistically acceptable mean absolute error of 0.59oC for lead day one in minimum temperature forecast and 0.86oC variance for lead day one in maximum temperature forecast. The study also clarified that the level of accuracy of the proposed prediction models were found to be better for smaller lead days when compared with higher lead days with both approaches.
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.
Wind turbines are massive electrical structures. They produce large returns when illuminated by radar waves. These
scatterings have a great impact on the operation of surveillance, air traffic control and weather radars. This paper presents
two geometric modelling methods for reshaping wind turbine towers so that the Radar Cross Section (RCS) of wind turbines
is reduced. In the proposed reshaping methods, bump structures are created on the surface of the conventional cylinder
wind turbine tower. When a reshaped tower is illuminated by radar waves, the bump structures scatter incident radar
waves into insignificant directions so that the strength of back-scattering is declined and the RCS of the wind turbine is
decreased. The test results confirmed that the proposed methodssignificantly reduce bi-static RCS values of wind turbines.
The proposed reshaping methods are practical, flexible and effective in alleviating the scatterings of wind turbines.
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.
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.
A grid-connected photovoltaic (PV) system operates under fluctuated weather condition has been modeled and characterized based on specific test bed. A mathematical model of a small-scale PV system has been developed mainly for residential usage, and the potential results have been simulated. The proposed PV model based on three PV parameters, which are the photocurrent, IL, the reverse diode saturation current, Io, the ideality factor of diode, n. Accuracy of the proposed model and its parameters evaluated based on different benchmarks. The results showed that the proposed model fitting the experimental results with high accuracy compare to the other models, as well as the I-V characteristic curve. The results of this study can be considered valuable in terms of the installation of a grid-connected PV system in fluctuated climatic conditions.
Air pollution has been widely known to have an influence on health of the general population.
Air pollution can result from natural causes, human activities and transboundary air pollution.
Weather and climate play crucial role in determining the pattern of air quality. In recent years,
air pollution and recurrent episodes of haze has become a major concern in Malaysia.
Surveillance data on concentrations of main air pollutants such as carbon dioxide, (CO2),
Nitrogen Dioxide (NO2), Ozone (O3), sulphur dioxide (SO2) and particulate matter (PM10)
were found to be higher during the haze days and this may have an impact on health of the
community as reflected by an increase in hospital admissions particularly the respiratory and
cardiovascular diseases.
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.
Keratosis obturans appears to be an obscure and relatively uncommon entity, even in literature search of journals and reference texts, so much so that there is not even any prevalence or incidence statistics available. However, the condition did not appear to be as uncommon based on our clinical observations. We have managed to obtain 64 patients representing 67 ears with keratosis obturans in our study period of about 18 months with a pattern of occurrence during this period. Humid weather seemed to play a role in the frequency of its appearance during certain period in our observation. There also appears to be a correlation between the severity of symptoms (predominantly pain and hearing loss) and the presenting appearance of the condition, i.e., presence or absence of granulation tissue, as well as that the degree of difficulty in exenteration of the keratosis obturans (matrix and content) depending on the expansion of the bony canal. Our figures showed the majority of the patients are females and young individuals, the majority of them occur unilaterally. The condition also appear to stop short of involving the tympanic membrane with only the bony canal being expanded with the surrounding oedema creating an apparent "canal stenosis".
A warning system for the sooty blotch and flyspeck (SBFS) fungal disease complex of apple, developed originally for use in the southeastern United States, was modified to provide more reliable assessment of SBFS risk in Iowa. Modeling results based on previous research in Iowa and Wisconsin had suggested replacing leaf wetness duration with cumulative hours of relative humidity (RH) ≥97% as the weather input to the SBFS warning system. The purpose of the present study was to evaluate the performance of a RH-based SBFS warning system, and to assess the potential economic benefits for its use in Iowa. The warning system was evaluated in two separate sets of trials-trial 1 during 2010 and 2011, and trial 2 during 2013-2015-using action thresholds based on cumulative hours of RH ≥97% and ≥90%, respectively, in conjunction with two different fungicide regimes. The warning system was compared with a traditional calendar-based system that specified spraying at predetermined intervals of 10 to 14 days. In trial 1, use of the RH ≥97% threshold caused substantial differences between two RH sensors in recording number of hours exceeding the threshold. When both RH thresholds were compared for 2013-2015, on average, RH ≥90% resulted in a 53% reduction in variation of cumulative hours between two identical RH sensors placed adjacent to each other in an apple tree canopy. Although both the SBFS warning system and the calendar-based system resulted in equivalent control of SBFS, the warning system required fewer fungicide sprays than the calendar-based system, with an average of 3.8 sprays per season (min = 2; max = 5) vs. 6.4 sprays per season (min = 5; max = 8), respectively. The two fungicide regimes provided equivalent SBFS control when used in conjunction with the warning system. A partial budget analysis showed that using the SBFS warning system with a threshold of RH ≥90% was cost effective for orchard sizes of >1 ha. The revised warning system has potential to become a valuable decision support tool for Midwest apple growers because it reduces fungicide costs while protecting apples as effectively as a calendar-based spray schedule. The next step toward implementation of the SBFS warning system in the North Central U.S. should be multiyear field testing in commercial orchards throughout the region.