A study on 30 asthmatic children was conducted in Kuala Lumpur. The objective of this study was to study the relationship between respirable particulate (PM10), sulphur dioxide, ozone and various meteorological factors such as humidity, level ofrainfall and temperature with asthma attacks. This study was conducted from 1st September to 31 December 1994. Patients were selected from the Paediatric Unit, Kuala Lumpur Hospital. Questionnaires were used to obtain information from their parents on the history and severity ofasthmatic attacks ofthese patients. Questionnaires were also used to determine if the indoor sources contributed to the attack. Diary cards were used to collect information on the frequency of asthmatic attacks. Each patient's progress was followed through every week during the study period and the attacks were recorded. The data on air quality on the PM10, sulphur dioxide, nitrogen dioxide, carbon monoxide and ozone were collected hourly using the microcomputer system of air monitoring unit from the Universiti Pertanian Malaysia air quality monitoring station located at the City Hall, Kuala Lumpur. The meteorological parameters such as temperature, relative humidity and rain-fall levels were also monitored daily. The asthmatic attack percentage was obtained by dividing the number of attacks in a day with the total number of sample and multiplying by a hundred. Statistical tests indicated that there was a significant correlation between asthmatic attacks and the PM10 concentrations (r=0.73), nitrogen dioxide (r=0.57) and.carbon monoxide (r=0.53) throughout the study period. During the haze episode, more significant correlations between asthmatic attacks, PM10 concentra-tions (0.86), carbon monoxide (0.79) and nitrogen oxide (0.53) were found. Multiple regression statistical test showed that PM10 had the greatest influence on the asthmatic attack rate. The minute respirable particulate which entered the respiratory system of the asthmatics triggered attacks on these patients.
Limited data regarding methemoglobinemia in pregnancy, particularly secondary to dapsone is available up to date. We report a case of dapsone-induced methemoglobinemia in a pregnant mother with multibacillary leprosy who presented with fever, productive cough and cyanosis of 2 days duration 2 weeks after multidrug therapy was commenced. On examination, she had central cyanosis with low oxygen saturation (SpO2 = 84-88%). Arterial blood gas analysis showed PO2 of 111 mmHg and SO2 of 98 mmHg. Patient was administered 100% oxygen inhalation, but there was no improvement in cyanosis. Vitamin C (1000 mg/day) was prescribed. Dapsone was replaced by ofloxacin 200 mg twice daily. There was a gradual increase in SpO2 level. She delivered a healthy baby. In conclusion, clinicians should be aware of the side effects of dapsone and know how to promptly manage any undesirable events. Ofloxacin is a safe and feasible alternative in replacement of dapsone in pregnancy.
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.
Numerous mitigation techniques have been incorporated to capture or remove SO2 with flue gas desulfurization (FGD) being the most common method. Regenerative FGD method is advantageous over other methods due to high desulfurization efficiency, sorbent regenerability, and reduction in waste handling. The capital costs of regenerative methods are higher than those of commonly used once-through methods simply due to the inclusion of sorbent regeneration while operational and management costs depend on the operating hours and fuel composition. Regenerable sorbents like ionic liquids, deep eutectic solvents, ammonium halide solutions, alkyl-aniline solutions, amino acid solutions, activated carbons, mesoporous silica, zeolite, and metal-organic frameworks have been reported to successfully achieve high SO2 removal. The presence of other gases in flue gas, e.g., O2, CO2, NOx, and water vapor, and the reaction temperature critically affect the sorption capacity and sorbent regenerability. To obtain optimal SO2 removal performance, other parameters such as pH, inlet SO2 concentration, and additives need to be adequately governed. Due to its high removal capacity, easy preparation, non-toxicity, and low regeneration temperature, the use of deep eutectic solvents is highly feasible for upscale utilization. Metal-organic frameworks demonstrated highest reported SO2 removal capacity; however, it is not yet applicable at industrial level due to its high price, weak stability, and robust formulation.
Accurate identification of distant, large, and frequent sources of emission in cities is a complex procedure due to the presence of large-sized pollutants and the existence of many land use types. This study aims to simplify and optimize the visualization mechanism of long time-series of air pollution data, particularly for urban areas, which is naturally correlated in time and spatially complicated to analyze. Also, we elaborate different sources of pollution that were hitherto undetectable using ordinary plot models by leveraging recent advances in ensemble statistical approaches. The high performing conditional bivariate probability function (CBPF) and time-series signature were integrated within the R programming environment to facilitate the study's analysis. Hourly air pollution data for the period between 2007 to 2016 is collected using four air quality stations, (ca0016, ca0058, ca0054, and ca0025), situated in highly urbanized locations that are characterized by complex land use and high pollution emitting activities. A conditional bivariate probability function (CBPF) was used to analyze the data, utilizing pollutant concentration values such as Sulfur dioxide (SO2), Nitrogen oxides (NO2), Carbon monoxide (CO) and Particulate Matter (PM10) as a third variable plotted on the radial axis, with wind direction and wind speed variables. Generalized linear model (GLM) and sensitivity analysis are applied to verify and visualize the relationship between Air Pollution Index (API) of PM10 and other significant pollutants of GML outputs based on quantile values. To address potential future challenges, we forecast 3 months PM10 values using a Time Series Signature statistical algorithm with time functions and validated the outcome in the 4 stations. Analysis of results reveals that sources emitting PM10 have similar activities producing other pollutants (SO2, CO, and NO2). Therefore, these pollutants can be detected by cross selection between the pollution sources in the affected city. The directional results of CBPF plot indicate that ca0058 and ca0054 enable easier detection of pollutants' sources in comparison to ca0016 and ca0025 due to being located on the edge of industrial areas. This study's CBPF technique and time series signature analysis' outcomes are promising, successfully elaborating different sources of pollution that were hitherto undetectable using ordinary plot models and thus contribute to existing air quality assessment and enhancement mechanisms.
In this study, the kinetic parameters of rice husk ash (RHA)/CaO/CeO(2) sorbent for SO(2) and NO sorptions were investigated in a laboratory-scale stainless steel fixed-bed reactor. Data experiments were obtained from our previous results and additional independent experiments were carried out at different conditions. The initial sorption rate constant (k(0)) and deactivation rate constant (k(d)) for SO(2)/NO sorptions were obtained from the nonlinear regression analysis of the experimental breakthrough data using deactivation kinetic model. Both the initial sorption rate constants and deactivation rate constants increased with increasing temperature, except at operating temperature of 170 °C. The activation energy and frequency factor for the SO(2) sorption were found to be 18.0 kJ/mol and 7.37 × 10(5)cm(3)/(g min), respectively. Whereas the activation energy and frequency factor for the NO sorption, were estimated to be 5.64 kJ/mol and 2.19 × 10(4)cm(3)/(g min), respectively. The deactivation kinetic model was found to give a very good agreement with the experimental data of the SO(2)/NO sorptions.
In this work, the removal of SO(2) and NO from simulated flue gas from combustion process was investigated in a fixed-bed reactor using rice husk ash (RHA)/CaO-based sorbent. Various metal precursors were used in order to select the best metal impregnated over RHA/CaO sorbents. The results showed that RHA/CaO sorbents impregnated with CeO(2) had the highest sorption capacity among other impregnated metal oxides for the simultaneous removal of SO(2) and NO. Infrared spectroscopic results indicated the formation of both sulfate (SO(4)(2-)) and nitrate (NO(3)(-)) species due to the catalytic role played by CeO(2). Apart from that, the catalytic activity of the RHA/CaO/CeO(2) sorbent was found to be closely related to its physical properties (specific surface area, total pore volume and average pore diameter).
This work examines the impregnated carbon-based sorbents for simultaneous removal of SO(2) and NOx from simulated flue gas. The carbon-based sorbents were prepared using palm shell activated carbon (PSAC) impregnated with several metal oxides (Ni, V, Fe and Ce). The removal of SO(2) and NOx from the simulated flue gas was investigated in a fixed-bed reactor. The results showed that PSAC impregnated with CeO(2) (PSAC-Ce) reported the highest sorption capacity among other impregnated metal oxides for the simultaneous removal of SO(2) and NOx. PSAC-Ce showed the longest breakthrough time of 165 and 115 min for SO(2) and NOx, respectively. The properties of the pure and impregnated PSAC were analyzed by BET, FTIR and XRF. The physical-chemical features of the PSAC-Ce sorbent indicated a catalytic activity in both the sorption of SO(2) and NOx. The formation of both sulfate (SO(4)(2-)) and nitrate (NO(3-)) species on spent PSAC-Ce further prove the catalytic role played by CeO(2).
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.
In order to reduce the negative impact of coal utilization for energy generation, the pollutants present in the flue gas of coal combustion such as sulfur dioxide (SO(2)) and nitrogen oxide (NO) must be effectively removed before releasing to the atmosphere. Thus in this study, sorbent prepared from rice husk ash that is impregnated with copper is tested for simultaneous removal of SO(2) and NO from simulated flue gas. The effect of various sorbent preparation parameters; copper loading, RHA/CaO ratio, hydration period and NaOH concentration on the sorbent desulfurization/denitrification capacity was studied using Design-Expert Version 6.0.6 software. Specifically, Central Composite Design (CCD) coupled with Response Surface Method (RSM) was used. Significant individual parameters that affect the sorbent capacity are copper loading and NaOH concentration. Apart from that, interaction between the following parameters was also found to have significant effect; copper loading, RHA/CaO ratio and NaOH concentration. The optimum sorbent preparation condition for this study was found to be 3.06% CuO loading, RHA/CaO ratio of 1.41, 8.05 h of hydration period and NaOH concentration of 0.80 M. Sorbent characterization using SEM, XRD and surface area analysis were used to describe the effect of sorbent preparation parameters on the desulfurization/denitrification activity.
In the early days of abundant resources and minimal development pressures, little attention was paid to growing environmental concerns in Malaysia. The haze episodes in Southeast Asia in 1983, 1984, 1991, 1994, and 1997 imposed threats to the environmental management of Malaysia and increased awareness of the environment. As a consequence, the government established Malaysian Air Quality Guidelines, the Air Pollution Index, and the Haze Action Plan to improve air quality. Air quality monitoring is part of the initial strategy in the pollution prevention program in Malaysia. Review of air pollution in Malaysia is based on the reports of the air quality monitoring in several large cities in Malaysia, which cover air pollutants such as Carbon monoxide (CO), Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), Ozone (O3), and Suspended Particulate Matter (SPM). The results of the monitoring indicate that Suspended Particulate Matter (SPM) and Nitrogen Dioxide (NO2) are the predominant pollutants. Other pollutants such as CO, O(x), SO2, and Pb are also observed in several big cities in Malaysia. The air pollution comes mainly from land transportation, industrial emissions, and open burning sources. Among them, land transportation contributes the most to air pollution. This paper reviews the results of the ambient air quality monitoring and studies related to air pollution and health impacts.
PM2.5 mass concentration and associated water-soluble ionic species in a sub-urban coastal area of Kuala Terengganu, Malaysia were investigated intermittently from year 2006 to 2009. A total of 78 weekly PM2.5 samples were analyzed. The mass concentration of PM2.5 exhibited annual, seasonal and diurnal variations. Temporal distributions of rainfall, sporadic haze episodes and local air flow (sea breeze circulation) were factors controlling PM2.5 mass variations in the study area. Although the PM2.5 concentrations were increased during haze episodes in 2006 (August and October) and2007 (October), their concentrations however, were still within the international guidelines. The average concentration of individual ions was in decreasing trend; SO42-> NH4+> K+> Na+> NO3-> Cl-> Ca2+. The concentrations of SO42- and NH4+ accounted for > 70% of the water-soluble aerosol mass. More than 80% of ionic species associated with PM2.5 are from non-marine sources. Major processes affecting the ionic composition of PM2.5 are biomass burning, crustal loading and sea spray. Air quality mitigation strategies should focus on anthropogenic activities emitting SO2, which promotes aerosol SO42- formation.
The phytotoxic risk of ambient air pollution to local vegetation was assessed in Selangor State, Malaysia. The AOT40 value was calculated by means of the continuously monitored daily maximum concentration and the local diurnal pattern of O3. Together with minor risks associated with the levels of NO2 and SO2, the study found that the monthly AOT40 values in these peri-urban sites were consistently over 1.0 ppm.h, which is well in exceedance of the given European critical level. Linking the O3 level to actual agricultural crop production in Selangor State also indicated that the extent of yield losses could have ranged from 1.6 to 5.0% (by weight) in 2000. Despite a number of uncertainties, the study showed a simple but useful methodological framework for phytotoxic risk assessment with a limited data set, which could contribute to appropriate policy discussion and countermeasures in countries under similar conditions.
In this work, the application of response surface and neural network models in predicting and optimizing the preparation variables of RHA/CaO/CeO(2) sorbent towards SO(2)/NO sorption capacity was investigated. The sorbents were prepared according to central composite design (CCD) with four independent variables (i.e. hydration period, RHA/CaO ratio, CeO(2) loading and the use of RHA(raw) or pretreated RHA(600 degrees C) as the starting material). Among all the variables studied, the amount of CeO(2) loading had the largest effect. The response surface models developed from CCD was effective in providing a highly accurate prediction for SO(2) and NO sorption capacities within the range of the sorbent preparation variables studied. The prediction of CCD experiment was verified by neural network models which gave almost similar results to those determined by response surface models. The response surface models together with neural network models were then successfully used to locate and validate the optimum hydration process variables for maximizing the SO(2)/NO sorption capacities. Through this optimization process, it was found that maximum SO(2) and NO sorption capacities of 44.34 and 3.51 mg/g, respectively could be obtained by using RHA/CaO/CeO(2) sorbents prepared from RHA(raw) with hydration period of 12h, RHA/CaO ratio of 2.33 and CeO(2) loading of 8.95%.
High performance sorbents for flue gas desulfurization can be synthesized by hydration of coal fly ash, calcium sulfate, and calcium oxide. In general, higher desulfurization activity correlates with higher sorbent surface area. Consequently, a major aim in sorbent synthesis is to maximize the sorbent surface area by optimizing the hydration conditions. This work presents an integrated modeling and optimization approach to sorbent synthesis based on statistical experimental design and two artificial intelligence techniques: neural network and genetic algorithm. In the first step of the approach, the main and interactive effects of three hydration variables on sorbent surface area were evaluated using a full factorial design. The hydration variables of interest to this study were hydration time, amount of coal fly ash, and amount of calcium sulfate and the levels investigated were 4-32 h, 5-15 g, and 0-12 g, respectively. In the second step, a neural network was used to model the relationship between the three hydration variables and the sorbent surface area. A genetic algorithm was used in the last step to optimize the input space of the resulting neural network model. According to this integrated modeling and optimization approach, an optimum sorbent surface area of 62.2m(2)g(-1) could be obtained by mixing 13.1g of coal fly ash and 5.5 g of calcium sulfate in a hydration process containing 100ml of water and 5 g of calcium oxide for a fixed hydration time of 10 h.
In this study, palm oil mill sludge was used as a precursor to prepare biochar using conventional pyrolysis. Palm oil mill sludge biochar (POSB) was prepared at different preparation variables, i.e., heating temperature (300-800 °C), heating rate (10-20 °C/min) and holding time (60-120 min). The prepared biochars were tested for sulfur dioxide (SO2) adsorption in a fixed bed reactor using 300 ppm of SO2 gas at 300 ml/min (with N2 gas as balance). Response surface central composite experimental design was used to optimize the production of biochar versus SO2 removal. A quadratic model was developed in order to correlate the effect of variable parameters on the optimum adsorption capacity of SO2 gas. The experimental values and the predicted results of the model were found to show satisfactory agreement. The optimum conditions for biochar preparation to yield the best SO2 removal was found to be at 405 °C of heating temperature, 20 °C/min of heating rate and 88 min of holding time. At these conditions, the average yield of biochar and adsorption capacity for SO2 gas was reported as 54.25 g and 9.75 mg/g, respectively. The structure of biochar and their roles in SO2 adsorption were investigated by surface area, morphology images, infrared spectra, and proximate analysis, respectively. The characterization findings suggested that POSB adsorbs SO2 mainly by the functional groups.
Palm oil sludge (POS) is an organic waste generated from the palm oil industry. POS causes environmental pollution if it is improperly disposed. In this study, the potential of activated POS biochar, as an adsorbent for the removal of SO2 gas was tested. POS biochar was physically activated using CO2 gas. The effects of activation preparation variables i.e. activation temperature (300-700 °C), activation time (30-150 min) and CO2 flow rate (100-500 ml/min) were investigated using design expert version 8.0.7.1 software. Central Composite Design (CCD) was used to develop a quadratic model to correlate the operating variables with the activated biochar adsorption capacity. Analysis of variance (ANOVA) was performed to identify the significant factors on the experimental design response. The optimum preparation conditions of activated POS biochar were found to be at activation temperature of 442 °C, activation time of 63 min and CO2 flow rate of 397 ml/min. The maximum adsorption capacity at the optimum conditions was recorded as 16.65 mg/g. The adsorption capacity increased significantly after the activation process. Characteristics of the activated POS biochar proposed that SO2 was physically adsorbed. Furthermore, it was found that the adsorption capacity can be further enhanced by increasing the reaction temperature to 100 °C or with 15% of relative humidity in the inlet gas. The prepared adsorbents can be regenerated by thermal treatment.
The mechanistic modeling of the sulfation reaction between fly ash-based sorbent and SO2 is a challenging task due to a variety reasons including the complexity of the reaction itself and the inability to measure some of the key parameters of the reaction. In this work, the possibility of modeling the sulfation reaction kinetics using a purely data-driven neural network was investigated. Experiments on SO2 removal by a sorbent prepared from coal fly ash/CaO/CaSO4 were conducted using a fixed bed reactor to generate a database to train and validate the neural network model. Extensive SO2 removal data points were obtained by varying three process variables, namely, SO2 inlet concentration (500-2000 mg/L), reaction temperature (60-80 degreesC), and relative humidity (50-70%), as a function of reaction time (0-60 min). Modeling results show that the neural network can provide excellent fits to the SO2 removal data after considerable training and can be successfully used to predict the extent of SO2 removal as a function of time even when the process variables are outside the training domain. From a modeling standpoint, the suitably trained and validated neural network with excellent interpolation and extrapolation properties could have immediate practical benefits in the absence of a theoretical model.
The air pollution index (API) is an important figure used for measuring the quality of air in the environment. The API is determined based on the highest average value of individual indices for all the variables which include sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and suspended particulate matter (PM10) at a particular hour. API values that exceed the limit of 100 units indicate an unhealthy status for the exposed environment. This study investigates the risk of occurrences of API values greater than 100 units for eight urban areas in Peninsular Malaysia for the period of January 2004 to December 2014. An extreme value model, known as the generalized Pareto distribution (GPD), has been fitted to the API values found. Based on the fitted model, return period for describing the occurrences of API exceeding 100 in the different cities has been computed as the indicator of risk. The results obtained indicated that most of the urban areas considered have a very small risk of occurrence of the unhealthy events, except for Kuala Lumpur, Malacca, and Klang. However, among these three cities, it is found that Klang has the highest risk. Based on all the results obtained, the air quality standard in urban areas of Peninsular Malaysia falls within healthy limits to human beings.