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.
An exophilic population of the vector mosquito Anopheles balabacensis Baisas was investigated in two mark-recapture studies (16.ix-13.x.1986 and 6-26.i.1987) at an inland, foothill village in Sabah, Malaysia. Wild female mosquitoes were intercepted as they came to feed on man or buffalo, given a bloodmeal, marked with fluorescent dust and released. The recapture rate was about 12%. A new method of analysis is proposed which uses cross-correlation and a time series model. The estimated survival per oviposition cycle was 0.48-0.54 and the oviposition cycle interval 2-3 days.
Amplatzer Vascular Plug IV (Abbott, USA) is usually used for the occlusion of abnormal tortuous vessels and has not been tried for the transcatheter closure of perimembranous ventricular septal defects with wind-sock morphology. Here, we report on three successful cases of perimembranous ventricular septal defect transcatheter closure using Amplatzer Vascular Plug IV. We did not observe residual shunting or new onset of complications during follow up. These preliminary positive results advocate the application and suitability of Amplatzer Vascular Plug IV for closing wind-sock-like perimembranous ventricular septal defects.
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.
Gunshot residues, produced after shooting activity, have acquired their importance in analysis due to the notoriety of firearms-related crimes. In this study, solid-phase microextraction was performed to extract the headspace composition of spent cartridges using 85-μm polyacrylate fiber at 66°C for 21 min. Organic compounds, that is, naphthalene, 2,6-dinitrotoluene, 2,4-dinitrotoluene, diphenylamine, and dibutyl phthalate were detected and analyzed by gas chromatography-flame ionization detection technique. Evaluation of chromatograms for diphenylamine, dibutyl phthalate, and naphthalene indicates the period after a gunshot was discharged, whether it was 1 days, 2-4 days, <5 days, 10 days, 20 days, or more than 30 days ago. This study revealed the potential effects of environmental factors such as occasional wind blow and direct sunlight on the estimation of time after spent cartridges were discharged. In conclusion, we proposed reliable alternative in analyzing the headspace composition of spent cartridges in a simulated crime scene.
Coral spawning times have been linked to multiple environmental factors; however, to what extent these factors act as generalized cues across multiple species and large spatial scales is unknown. We used a unique dataset of coral spawning from 34 reefs in the Indian and Pacific Oceans to test if month of spawning and peak spawning month in assemblages of Acropora spp. can be predicted by sea surface temperature (SST), photosynthetically available radiation, wind speed, current speed, rainfall or sunset time. Contrary to the classic view that high mean SST initiates coral spawning, we found rapid increases in SST to be the best predictor in both cases (month of spawning: R(2) = 0.73, peak: R(2) = 0.62). Our findings suggest that a rapid increase in SST provides the dominant proximate cue for coral mass spawning over large geographical scales. We hypothesize that coral spawning is ultimately timed to ensure optimal fertilization success.
Intensive land expansion of commercial oil palm agricultural lands results in reducing the size of peat swamp forests, particularly in Southeast Asia. The effect of this land conversion on macrofungal biodiversity is, however, understudied. We quantified macrofungal biodiversity by identifying mushroom sporocarps throughout four different habitats; logged peat swamp forest, large-scale oil palm plantation, monoculture, and polyculture smallholdings. We recorded a total of 757 clusters of macrofungi belonging to 127 morphospecies and found that substrates for growing macrofungi were abundant in peat swamp forest; hence, morphospecies richness and macrofungal clusters were significantly greater in logged peat swamp forest than converted oil palm agriculture lands. Environmental factors that influence macrofungi in logged peat swamp forests such as air temperature, humidity, wind speed, soil pH, and soil moisture were different from those in oil palm plantations and smallholdings. We conclude that peat swamp forests are irreplaceable with respect to macrofungal biodiversity. They host much greater macrofungal biodiversity than any of the oil palm agricultural lands. It is imperative that further expansion of oil palm plantation into remaining peat swamp forests should be prohibited in palm oil producing countries. These results imply that macrofungal distribution reflects changes in microclimate between habitats and reduced macrofungal biodiversity may adversely affect decomposition in human-modified landscapes.
Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS.
Riparian areas hold vast number of flora and fauna with exceptional contributions to the ecosystem. A study was conducted in Sungai Sepetang, Sungai Rembau and Sungai Chukai to identify the insect community in a riparian zone of Peninsular Malaysia. Sampling was conducted in six consecutive months from December 2017 to May 2018 during both day and night using sweep nets. Twenty sampling stations (S1-S20) had been assembled along the riverbanks with an average distance of 200 m between each station. The 17,530 collected insects were from 11 orders and consisted of Diptera, Coleoptera, Hemiptera, Hymenoptera, Lepidoptera, Neuroptera, Orthoptera, Blattodea, Thysanoptera, Mantodea and Odonata. The three most abundant orders were Diptera (33.84%; 5933 individuals), Coleoptera (28.82%; 5053 individuals) and Hemiptera (25.62%: 4491 individuals). The collected insect community consisted of different guilds such as the scavenger, predator, herbivore, pollinator and parasitoid. Sungai Sepetang and Sungai Rembau were dominated by mangrove flora, Sonneratia caseolaris (Myrtales: Lythraceae), while Sungai Chukai was dominated by Barringtonia racemosa. There was a significant difference (p < 0.05) in the composition of insects between the three rivers though clustering analysis showed that the insect communities in Sungai Sepetang and Sungai Rembau were 100% similar compared to Sungai Chukai which consisted of a totally different community. There is a significant negative correlation between abundance of insects with salinity and wind speed at Sungai Chukai and Sungai Sepetang.
Over the last decades, the development of the Klang Valley (Malaysia), as an urban commercial and industrial area, has elevated the risk of atmospheric pollutions. There are several significant sources of air pollutants which vary depending on the background of the location they originate from. The aim of this study is to determine the trend and status of air quality and their correlation with the meteorological factors at different air quality monitoring stations in the Klang Valley. The data of five major air pollutants (PM(10), CO, SO(2), O(3), NO(2)) were recorded at the Alam Sekitar Sdn Bhd (ASMA) monitoring stations in the Klang Valley, namely Petaling Jaya (S1), Shah Alam (S2) and Gombak (S3). The data from these three stations were compared with the data recorded at Jerantut, Pahang (B), a background station established by the Malaysian Department of Environment. Results show that the concentrations of CO, NO(2) and SO(2) are higher at Petaling Jaya (S1) which is due to influence of heavy traffic. The concentrations of PM(10) and O(3,) however, are predominantly related to regional tropical factors, such as the influence of biomass burning and of ultra violet radiation from sunlight. They can, though, also be influenced by local sources. There are relatively stronger inter-pollutant correlations at the stations of Gombak and Shah Alam, and the results also suggest that heavy traffic flow induces high concentrations of PM(10), CO, NO(2) and SO(2) at the three sampling stations. Additionally, meteorological factors, particularly the ambient temperature and wind speed, may influence the concentration of PM(10) in the atmosphere.
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
It has come to attention that Malaysia have been aiming to build its own
nuclear power plant (NPP) for electricity generation in 2030 to diversify the national
energy supply and resources. As part of the regulation to build a NPP, environmental
risk assessment analysis which includes the atmospheric dispersion assessment has to
be performed as required by the Malaysian Atomic Energy Licensing Board (AELB)
prior to the commissioning process. The assessment is to investigate the dispersion of
radioactive effluent from the NPP in the event of nuclear accident. This article will focus
on current development of locally developed atmospheric dispersion modeling code
based on Gaussian Plume model. The code is written in Fortran computer language
and has been benchmarked to a readily available HotSpot software. The radionuclide
release rate entering the Gaussian equation is approximated to the value found in the
Fukushima NPP accident in 2011. Meteorological data of Mersing District, Johor of
year 2013 is utilized for the calculations. The results show that the dispersion of radionuclide
effluent can potentially affect areas around Johor Bahru district, Singapore
and some parts of Riau when the wind direction blows from the North-northeast direction.
The results from our code was found to be in good agreement with the one
obtained from HotSpot, with less than 1% discrepancy between the two.
ir quality has deteriorated in urban areas as a result of increased anthropogenic activities. Quantitative information on the influence of meteorological conditions on several pollutants in a tropical climate is still lacking. Real-time ozone (O3) and nitrogen dioxide (NO2) levels were measured nearby selected schools in Malaysia to examine the impact of meteorological factors on monitoring pollutants. The results showed the overall 10 min average concentrations of the main parameters during school holiday were 24 ppb (O3) and 33 ppb (NO2) while during school day the overall 10 min average concentrations were 26 ppb (O3) and 51 ppb (NO2). Although there are no minimum requirements for short-term exposure by MAAQG, if compared to 1 h average requirements, all concentrations were still below the suggested values. Regarding spatial distribution, a different trend in pollutant concentration among the schools was observed because of the influence of temperature (AT) and wind speed (WS). The results were verified by Pearson correlation, where significant correlations (p<0.01) were determined between air pollutants and meteorological factors, which were temperature, wind speed and relative humidity. Meanwhile, the distribution of O3 was moderately correlated with NO2. However, the results of multivariate analysis indicate that temperature and relative humidity had the most significant influence on the formation of O3. In summary, the results of this study showed that all precursors and meteorological parameters contribute to the production of O3. Hence, reducing O3 precursors, which are emitted by vehicles, is essential to lessening the exposure to O3
The Southeast Asian (SEA) region is no stranger to forest fires - the region has been suffering from severe air pollution (known locally as 'haze') as a result of these fires, for decades. The fires in SEA region are caused by a combination of natural (the El Niño weather pattern) and manmade (slash-and-burn and land clearing for plantations) factors. These fires cause the emissions of toxic aerosols and pollutants that can affect millions of people in the region. Thus, this study aims to identify the impact of the SEA haze on the Southern region of the Malaysian Peninsula and Borneo region of East Malaysia using the entire air quality observation data at surface level in 2015. Overall, the concentration of PM10 was about two-fold higher during the haze period compared to non-haze period. The concentrations of CO, flux of CO and flux of BC were aligned with PM10 during the entire observation period. The wind field and cluster of trajectory indicated that the Southern Malaysian Peninsula and Borneo were influenced mainly from the wildfires and the combustion of peat soil in the Indonesian Borneo. This study finds that wildfires from Borneo impacted the Southern Malaysian Borneo more seriously than that from Sumatra region.
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.
A comprehensive study of the chemical composition of rainwater was carried out from October 2016 to September 2017 in the equatorial tropical rainforest region of northwestern Borneo. Monthly cumulative rainwater samples were collected from different locations in the Limbang River Basin (LRB) and were later categorized into seasonal samples representing northeast monsoon (NEM), southwest monsoon (SWM), and inter-monsoon (IM) periods. Physical parameters (pH, EC, TDS, DO, and turbidity), major ions (HCO3-, Cl-, Ca2+, Mg2+, Na+, and K+) and trace metals (Co, Ni, Cd, Fe, Mn, Pb, Zn, and Cu) were analyzed from collected rainwater samples. Rainwater is slightly alkaline with mean pH higher than 5.8. Chloride and bicarbonate are the most abundant ions, and the concentration of major ions in seasonal rainwater has shown slight variation which follows a descending order of HCO3-> Cl-> Na+ > Ca2+ > Mg2+ > K+ in NEM and Cl- > HCO3- > Na+ > Ca2+ > K+ > Mg2+ in SWM and Cl- > HCO3- > Na+ > Ca2+ > Mg2+ > K+ in IM period. Trace metals such as Fe and Ni have shown dominance in seasonal rainwater samples, and all the metals have shown variation in concentration in different seasons. Variation in chemical characteristic of seasonal rainwater samples identified through piper diagram indicates dominance of Ca2+-Mg2+-HCO3- and mixed Ca2+-Mg2+-Cl- facies during NEM, SWM, and IM periods. Statistical analysis of the results through two-way ANOVA and Pearson's correlation also indicates significant variation in physico-chemical characteristics. This suggests a variation in contributing sources during the monsoon seasons. Factor analysis confirmed the source variation by explaining the total variance of 79.80%, 90.72%, and 90.52% with three factor components in NEM, SWM, and IM rainwater samples with different loading of parameters. Enrichment factor analysis revealed a combined contribution of marine and crustal sources except K+ which was solely from crustal sources. Sample analysis of backward air mass trajectory supports all these findings by explaining seasonal variation in the source of pollutants reaching the study area. Overall, the results show that the chemical composition of seasonal rainwater samples in LRB was significantly influenced by natural as well as anthropogenic processes. These include (long-range and local) industrial activities, fossil fuel combustion, forest burning, transportation activities including road transport and shipping activities, and land-derived soil dust along with chemical constituents carried by seasonal wind.
Principal component analysis (PCA) and correlation have been used to study the variability of particle mass and particle number concentrations (PNC) in a tropical semi-urban environment. PNC and mass concentration (diameter in the range of 0.25->32.0 μm) have been measured from 1 February to 26 February 2013 using an in situ Grimm aerosol sampler. We found that the 24-h average total suspended particulates (TSP), particulate matter ≤10 μm (PM10), particulate matter ≤2.5 μm (PM2.5) and particulate matter ≤1 μm (PM1) were 14.37 ± 4.43, 14.11 ± 4.39, 12.53 ± 4.13 and 10.53 ± 3.98 μg m(-3), respectively. PNC in the accumulation mode (<500 nm) was the most abundant (at about 99 %). Five principal components (PCs) resulted from the PCA analysis where PC1 (43.8 % variance) predominates with PNC in the fine and sub-microme tre range. PC2, PC3, PC4 and PC5 explain 16.5, 12.4, 6.0 and 5.6 % of the variance to address the coarse, coarser, accumulation and giant fraction of PNC, respectively. Our particle distribution results show good agreement with the moderate resolution imaging spectroradiometer (MODIS) distribution.
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.
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.
Frequent haze occurrences in Malaysia have made the management of PM10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM10 variation and good forecast of PM10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.