PM10 airborne particles and soot deposit collected after a fire incident at a chemical store were analyzed in order to determine the concentrations of polycyclic aromatic hydrocarbons (PAHs). The samples were extracted with 1:1 hexane-dichloromethane by ultrasonic agitation. The extracts were then subjected to gas chromatography-mass spectrometric (GC-MS) analysis. The total PAHs concentrations in airborne particles and soot deposit were found to be 3.27 +/- 1.55 ng/m3 and 12.81 +/- 24.37 microg/g, respectively. Based on the molecular distributions of PAHs and the interpretation of their diagnostic ratios such as PHEN/(PHEN + ANTH), FLT/(FLT + PYR) and BeP/(BeP + BaP), PAHs in both airborne particles and soot deposit may be inferred to be from the same source. The difference in the value of IP/(IP + BgP) for these samples indicated that benzo[g, h, i] perylene and coronene tend to be attached to finer particles and reside in the air for longer periods. Comparison between the molecular distributions of PAHs and their diagnostic ratios observed in the current study with those reported for urban atmospheric and roadside soil particles revealed that they are of different sources.
Double perovskite-type catalysts including La2CoMnO6 and La2CuMnO6 are first evaluated for the effectiveness in removing volatile organic compounds (VOCs), and single perovskites (LaCoO3, LaMnO3, and LaCuO3) are also tested for comparison. All perovskites are tested with the gas hourly space velocity (GHSV) of 30,000hr-1, and the temperature range of 100-600°C for C7H8 removal. Experimental results indicate that double perovskites have better activity if compared with single perovskites. Especially, toluene (C7H8) can be completely oxidized to CO2 at 300°C as La2CoMnO6 is applied. Characterization of catalysts indicates that double perovskites own unique surface properties and are of higher amounts of lattice oxygen, leading to higher activity. Additionally, apparent activation energy of 68kJ/mol is calculated using Mars-van Krevelen model for C7H8 oxidation with La2CoMnO6 as catalyst. For durability test, both La2CoMnO6 and La2CuMnO6 maintain high C7H8 removal efficiencies of 100% and 98%, respectively, at 300°C and 30,000hr-1, and they also show good resistance to CO2 (5%) and H2O(g) (5%) of the gas streams tested. For various VOCs including isopropyl alcohol (C3H8O), ethanal (C2H4O), and ethylene (C2H4) tested, as high as 100% efficiency could be achieved with double perovskite-type catalysts operated at 300-350°C, indicating that double perovskites are promising catalysts for VOCs removal.
This study was carried out to determine the concentration of lead (Pb), anions, and cations at six primary schools located around Kuala Lumpur. Low volume sampler (MiniVol PM10) was used to collect the suspended particulates in indoor and outdoor air. Results showed that the concentration of Pb in indoor air was in the range of 5.18 ± 1.08 μg/g-7.01 ± 0.08 μg/g. All the concentrations of Pb in indoor air were higher than in outdoor air at all sampling stations. The concentrations of cations and anions were higher in outdoor air than in indoor air. The concentration of Ca(2+) (39.51 ± 5.01 mg/g-65.13 ± 9.42 mg/g) was the highest because the cation existed naturally in soil dusts, while the concentrations of NO3 (-) and SO4 (2-) were higher in outdoor air because there were more sources of exposure for anions in outdoor air, such as highly congested traffic and motor vehicles emissions. In comparison, the concentration of NO3 (-) (29.72 ± 0.31 μg/g-32.00 ± 0.75 μg/g) was slightly higher than SO4 (2-). The concentrations of most of the parameters in this study, such as Mg(2+), Ca(2+), NO3 (-), SO4 (2-), and Pb(2+), were higher in outdoor air than in indoor air at all sampling stations.
Particulate matter with an aerodynamic diameter of 10 μm or less (PM10) pollution poses a considerable threat to human health, and the first step in quantifying health impacts of human exposure to PM10 pollution is exposure assessment. Population-weighted exposure level (PWEL) estimation is one of the methods that provide a more refined exposure assessment as it includes the spatiotemporal distribution of the population into the pollution concentration estimation. This study assessed the population weighting effects on the estimated PM10 concentrations in Malaysia for years 2000, 2008, and 2013. Estimated PM10 annual mean concentrations with a spatial resolution of 5 kilometres retrieved from satellite data and population count obtained from the Gridded Population of the World version 4 (GPWv4) from the Centre for International Earth Science Information Network (CIESIN) were overlaid to generate the PWEL of PM10 for each state. The calculated PWEL of PM10 concentrations were then classified based on the World Health Organization (WHO) and the national Air Quality Guidelines (AQG) and interim targets (IT) for comparison. Results revealed that the annual mean PM10 concentrations in Malaysia ranged from 31 to 73 µg/m3 but became generally lower, ranging from 20 to 72 µg/m3 after population weighting, suggesting that the PM10 population exposure in Malaysia might have been overestimated. PWEL of PM10 distribution showed that the majority of the population lived in areas that complied with the national AQG, but were vulnerable to exposure level 3 according to the WHO AQG and IT, indicating that the population was nevertheless potentially exposed to significant health effects from long-term exposure to PM10 pollution.
Asthma is a chronic condition of great public health concern globally. The associated morbidity, mortality and healthcare utilisation place an enormous burden on healthcare infrastructure and services. This study demonstrates a multistage quantile regression approach to predicting excess demand for health care services in the form of asthma daily admissions in London, using retrospective data from the Hospital Episode Statistics, weather and air quality. Trivariate quantile regression models (QRM) of asthma daily admissions were fitted to a 14-day range of lags of environmental factors, accounting for seasonality in a hold-in sample of the data. Representative lags were pooled to form multivariate predictive models, selected through a systematic backward stepwise reduction approach. Models were cross-validated using a hold-out sample of the data, and their respective root mean square error measures, sensitivity, specificity and predictive values compared. Two of the predictive models were able to detect extreme number of daily asthma admissions at sensitivity levels of 76 % and 62 %, as well as specificities of 66 % and 76 %. Their positive predictive values were slightly higher for the hold-out sample (29 % and 28 %) than for the hold-in model development sample (16 % and 18 %). QRMs can be used in multistage to select suitable variables to forecast extreme asthma events. The associations between asthma and environmental factors, including temperature, ozone and carbon monoxide can be exploited in predicting future events using QRMs.
This study investigated the potential relationship between dengue cases and air quality - as measured by the Air Pollution Index (API) for five zones in the state of Selangor, Malaysia. Dengue case patterns can be learned using prediction models based on feedback (lagged terms). However, the question whether air quality affects dengue cases is still not thoroughly investigated based on such feedback models. This work developed dengue prediction models using the autoregressive integrated moving average (ARIMA) and ARIMA with an exogeneous variable (ARIMAX) time series methodologies with API as the exogeneous variable. The Box Jenkins approach based on maximum likelihood was used for analysis as it gives effective model estimates and prediction. Three stages of model comparison were carried out for each zone: first with ARIMA models without API, then ARIMAX models with API data from the API station for that zone and finally, ARIMAX models with API data from the zone and spatially neighbouring zones. Bayesian Information Criterion (BIC) gives goodness-of-fit versus parsimony comparisons between all elicited models. Our study found that ARIMA models, with the lowest BIC value, outperformed the rest in all five zones. The BIC values for the zone of Kuala Selangor were -800.66, -796.22, and -790.5229, respectively, for ARIMA only, ARIMAX with single API component and ARIMAX with API components from its zone and spatially neighbouring zones. Therefore, we concluded that API levels, either temporally for each zone or spatio- temporally based on neighbouring zones, do not have a significant effect on dengue cases.
The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.
This study emphasizes on near surface observation of chemically active trace gases such as nitrogen dioxide (NO2) over Islamabad on a regular basis. Absorption spectroscopy using backscattered extraterrestrial light source technique was used to retrieve NO2 differential slant column densities (dSCDs). Mini multi-axis-differential optical absorption spectroscopy (MAX-DOAS) instrument was used to perform ground-based measurements at Institute of Environmental Sciences and Engineering (IESE), National University of Sciences and Technology (NUST) Islamabad, Pakistan. Tropospheric vertical column densities (VCDs) of NO2 were derived from measured dSCDs by using geometric air mass factor approach. A case study was conducted to identify the impact of different materials (glass, tinted glass, and acrylic sheet of various thicknesses used to cover the instrument) on the retrieval of dSCDs. Acrylic sheet of thickness 5 mm was found most viable option for casing material as it exhibited negligible impact in the visible wavelength range. Tropospheric NO2 VCD derived from ground-based mini MAX-DOAS measurements exceeded two times the Pak-NEQS levels and showed a reasonable comparison (r (2) = 0.65, r = 0.81) with satellite observations (root mean square bias of 39 %) over Islamabad, Pakistan.
This study aimed to determine bioavailable heavy metal concentrations (As, Cd, Co, Cu, Cr, Ni, Pb, Zn) and their potential sources in classroom dust collected from children's hand palms in Rawang (Malaysia). This study also aimed to determine the association between bioavailable heavy metal concentration in classroom dust and children's respiratory symptoms. Health risk assessment (HRA) was applied to evaluate health risks (non-carcinogenic and carcinogenic) due to heavy metals in classroom dust. The mean of bioavailable heavy metal concentrations in classroom dust found on children's hand palms was shown in the following order: Zn (1.25E + 01 μg/g) > Cu (9.59E-01 μg/g) > Ni (5.34E-01 μg/g) > Cr (4.72E-02 μg/g) > Co (2.34E-02 μg/g) > As (1.77E-02 μg/g) > Cd (9.60E-03 μg/g) > Pb (5.00E-03 μg/g). Hierarchical cluster analysis has clustered 17 sampling locations into three clusters, whereby cluster 1 (S3, S4, S6, S15) located in residential areas and near to roads exposed to vehicle emissions, cluster 2 (S10, S12, S9, S7) located near Rawang town and cluster 3 (S13, S16, S1, S2, S8, S14, S11, S17, S5) located near industrial, residential and plantation areas. Emissions from vehicles, plantations and industrial activities were found as the main sources of heavy metals in classroom dust in Rawang. There is no association found between bioavailable heavy metal concentrations and respiratory symptoms, except for Cu (OR = 0.03). Health risks (non-carcinogenic and carcinogenic risks) indicated that there are no potential non-carcinogenic and carcinogenic risks of heavy metals in classroom dust toward children health.
This study aims to determine the concentrations of surfactants in the surface microlayer (SML), subsurface water (SSW) and fine mode aerosol (diameter size
The focus of this work is to investigate the emission characteristics of a stationary diesel engine while utilizing an emulsion fuel from a novel preparation process. The emulsion preparation was performed in real time without using any surfactant. Instead of mechanically breaking the water down into droplets, the water is delivered thermally, by changing its phase from gas to liquid. Steam is used in this proposed process, where it will be converted into suspended water droplets once it meets colder diesel. The product is called steam-generated water-in-diesel emulsion fuel (S/D). The method is expected to reduce the moving components of a previous surfactant-less system; therefore, reducing costs and increasing the system reliability. The emission characteristics of S/D were compared with EURO 2 diesel (D2), and a conventional emulsion denoted as E10. E10 was prepared using 10% water (volumetric) and SPAN80 as a surfactant. The emission characterizations were carried out based on the exhaust gas of a single cylinder naturally aspirated CI engine fueled with D2, S/D, and E10. Compared to D2, both emulsions significantly reduced the emissions of nitrogen oxides (NOx) (E10 max ↓58.0%, S/D max ↓40.0%) and particulate matter (PM) (E10 max ↓20.0%, S/D max ↓57.0%).
We investigate the role of military expenditure on emission in USA during the period 1960-2015. To achieve the objectives of this study, two measures of military expenditure are utilised, while several timeseries models are constructed with the gross domestic product (GDP) per capita, population, energy consumption per capita, non-renewable energy consumption per capita, renewable energy consumption per capita, urbanisation, trade openness and financial development serving as additional determinants of air pollution. We also use ecological indicator as an alternative measure of pollution. Moreover, different timeseries methods are utilised including a likelihood-based approach with two structural breaks. The output of this research concluded that all the variables are cointegrated. It is found that military expenditure has mixed impact on CO2 emissions. Real GDP per capita, energy consumption per capita, non-renewable energy consumption per capita, population and urbanisation increase CO2 emissions per capita in the long-run, while renewable energy consumption, financial development and trade openness reduce it. There is also evidence for the mixed role of military expenditure, when ecological footprint is utilised as the environmental degradation index. From the output of this research, few policy recommendations are offered for the examined country.
Economic losses due to health-related implications of air pollution were huge and incurred significant burdens towards healthcare providers. The objective of this study is to systematically review published literature on the financial implications of air pollution on health in Asia. Four databases: PubMed, Scopus, NHS Economic Evaluation Database (NHS EED), and Web of Science (WoS) were used to identify all the relevant articles. It was limited to all articles that had been published in the respected databases from January 2007 until March 2017. Twenty-four articles were included in this review. Five of the 24 studies (20.8%) reported financial implications of air pollution-related disease through value of statistical life (VOSL) which ranged from USD180 million to USD2.2 billion, six (25%) studies used cost of illness (COI) to evaluate air pollution-related morbidity and found that the cost ranged from USD5.4 million to USD9.1 billion. Another six studies (25%) used a combination of VOSL and COI for both mortality and morbidity valuation and found that the financial implications ranging from USD253 million to USD2.9 billion. Thirteen (54.2%) studies reported healthcare cost associated with both hospital admission and outpatient visit, five (20.1%) on hospital admission only, and one (4.2%) on outpatient visit only. Economic impacts of air pollution can be huge with significant deterioration of health among the Asians.
The main aim of this article is to examine empirically the impact of urbanization on carbon dioxide emissions in Singapore from 1970 to 2015. The autoregressive distributed lags (ARDL) approach is applied within the analysis. The main finding reveals a negative and significant impact of urbanization on carbon emissions in Singapore, which means that urban development in Singapore is not a barrier to the improvement of environmental quality. Thus, urbanization enhances environmental quality by reducing carbon emissions in the sample country. The result also highlighted that economic growth has a positive and significant impact on carbon emissions, which suggests that economic growth reduces environmental quality through its direct effect of increasing carbon emissions in the country. Despite the high level of urbanization in Singapore, which shows that 100 % of the populace is living in the urban center, it does not lead to more environmental degradation. Hence, urbanization will not be considered an obstacle when initiating policies that will be used to reduce environmental degradation in the country. Policy makers should consider the country's level of economic growth instead of urbanization when formulating policies to reduce environmental degradation, due to its direct impact on increasing carbon dioxide emissions.
Like other developing countries, Pakistan is also facing changes in temperature per decade and other climatic abnormalities like droughts and torrential rains. In order to assess and identify the extent of temperature change over Pakistan, the whole Pakistan was divided into five climatic zones ranging from very cold to hot and dry climates. Similarly, seasons in Pakistan are defined on the basis of monsoon variability as winter, pre-monsoon, monsoon, and post-monsoon. This study primarily focuses on the comparison of surface temperature observations from Pakistan Meteorological Department (PMD) network with PRECIS (Providing Regional Climates for Impacts Studies) model simulations. Results indicate that PRECIS underestimates the temperature in Northern Pakistan and during the winter season. However, there exists a fair agreement between PRECIS output and observed datasets in the lower plain and hot areas of the country. An absolute increase of 0.07 °C is observed in the mean temperature over Pakistan during the time period of 1951-2010. Especially, the increase is more significant (0.7 °C) during the last 14 years (1997-2010). Moreover, SCIAMACHY observations were used to explore the evolution of atmospheric CO2 levels in comparison to temperature over Pakistan. CO2 levels have shown an increasing trend during the first decade of the twenty-first century.
The aim of this study is to investigate the effect of the polymethyl acrylate (PMA) additive on the formation of particulate matter (PM) and nitrogen oxide (NOX) emission from a diesel coconut and/or Calophyllum inophyllum biodiesel-fueled engine. The physicochemical properties of 20% of coconut and/or C. inophyllum biodiesel-diesel blend (B20), 0.03 wt% of PMA with B20 (B20P), and diesel fuel were measured and compared to ASTM D6751, D7467, and EN 14214 standard. The test results showed that the addition of PMA additive with B20 significantly improves the cold-flow properties such as pour point (PP), cloud point (CP), and cold filter plugging point (CFPP). The addition of PMA additives reduced the engine's brake-specific energy consumption of all tested fuels. Engine emission results showed that the additive-added fuel reduce PM concentration than B20 and diesel, whereas the PM size and NOX emission both increased than B20 fuel and baseline diesel fuel. Also, the effect of adding PMA into B20 reduced Carbon (C), Aluminum (Al), Potassium (K), and volatile materials in the soot, whereas it increased Oxygen (O), Fluorine (F), Zinc (Zn), Barium (Ba), Chlorine (Cl), Sodium (Na), and fixed carbon. The scanning electron microscope (SEM) results for B20P showed the lower agglomeration than B20 and diesel fuel. Therefore, B20P fuel can be used as an alternative to diesel fuel in diesel engines to lower the harmful emissions without compromising the fuel quality.
The Antarctic continent is known to be an unpopulated region due to its extreme weather and climate conditions. However, the air quality over this continent can be affected by long-lived anthropogenic pollutants from the mainland. The Argentinian region of Ushuaia is often the main source area of accumulated hazardous gases over the Antarctic Peninsula. The main objective of this study is to report the first in situ observations yet known of surface ozone (O3) over Ushuaia, the Drake Passage, and Coastal Antarctic Peninsula (CAP) on board the RV Australis during the Malaysian Antarctic Scientific Expedition Cruise 2016 (MASEC'16). Hourly O3 data was measured continuously for 23 days using an EcoTech O3 analyzer. To understand more about the distribution of surface O3 over the Antarctic, we present the spatial and temporal of surface O3 of long-term data (2009-2015) obtained online from the World Meteorology Organization of World Data Centre for greenhouse gases (WMO WDCGG). Furthermore, surface O3 satellite data from the free online NOAA-Atmospheric Infrared Sounder (AIRS) database and online data assimilation from the European Centre for Medium-Range Weather Forecasts (ECMWF)-Monitoring Atmospheric Composition and Climate (MACC) were used. The data from both online products are compared to document the data sets and to give an indication of its quality towards in situ data. Finally, we used past carbon monoxide (CO) data as a proxy of surface O3 formation over Ushuaia and the Antarctic region. Our key findings were that the surface O3 mixing ratio during MASEC'16 increased from a minimum of 5 ppb to ~ 10-13 ppb approaching the Drake Passage and the Coastal Antarctic Peninsula (CAP) region. The anthropogenic and biogenic O3 precursors from Ushuaia and the marine region influenced the mixing ratio of surface O3 over the Drake Passage and CAP region. The past data from WDCGG showed that the annual O3 cycle has a maximum during the winter of 30 to 35 ppb between June and August and a minimum during the summer (January to February) of 10 to 20 ppb. The surface O3 mixing ratio during the summer was controlled by photochemical processes in the presence of sunlight, leading to the depletion process. During the winter, the photochemical production of surface O3 was more dominant. The NOAA-AIRS and ECMWF-MACC analysis agreed well with the MASEC'16 data but twice were higher during the expedition period. Finally, the CO past data showed the surface O3 mixing ratio was influenced by the CO mixing ratio over both the Ushuaia and Antarctic regions. Peak surface O3 and CO hourly mixing ratios reached up to ~ 38 ppb (O3) and ~ 500 ppb (CO) over Ushuaia. High CO over Ushuaia led to the depletion process of surface O3 over the region. Monthly CO mixing ratio over Antarctic (South Pole) were low, leading to the production of surface O3 over the Antarctic region.
Seasonal haze episodes and the associated inimical health impacts have become a regular crisis among the ASEAN countries. Even though many emerging experimental and epidemiological studies have documented the plausible health effects of the predominating toxic pollutants of haze, the consistency among the reported findings by these studies is poorly understood. By addressing such gap, this review aimed to critically highlight the evidence of physical and psychological health impacts of haze from the available literature in ASEAN countries. Systematic literature survey from six electronic databases across the environmental and medical disciplines was performed, and 20 peer-reviewed studies out of 384 retrieved articles were selected. The evidence pertaining to the health impacts of haze based on field survey, laboratory tests, modelling and time-series analysis were extracted for expert judgement. In specific, no generalization can be made on the reported physical symptoms as no specific symptoms recorded in all the reviewed studies except for throat discomfort. Consistent evidence was found for the increase in respiratory morbidity, especially for asthma, whilst the children and the elderly are deemed to be the vulnerable groups of the haze-induced respiratory ailments. A consensual conclusion on the association between the cardiovascular morbidity and haze is unfeasible as the available studies are scanty and geographically limited albeit of some reported increased cases. A number of modelling and simulation studies demonstrated elevating respiratory mortality rates due to seasonal haze exposures over the years. Besides, evidence on cancer risk is inconsistent where industrial and vehicular emissions are also expected to play more notable roles than mere haze exposure. There are insufficient regional studies to examine the association between the mental health and haze. Limited toxicological studies in ASEAN countries often impede a comprehensive understanding of the biological mechanism of haze-induced toxic pollutants on human physiology. Therefore, the lack of consistent evidence among the reported haze-induced health effects as highlighted in this review calls for more intensive longitudinal and toxicological studies with greater statistical power to disseminate more reliable and congruent findings to empower the institutional health planning among the ASEAN countries.
The devastating health effects of particulate matter (PM10) exposure by susceptible populace has made it necessary to evaluate PM10 pollution. Meteorological parameters and seasonal variation increases PM10 concentration levels, especially in areas that have multiple anthropogenic activities. Hence, stepwise regression (SR), multiple linear regression (MLR) and principal component regression (PCR) analyses were used to analyse daily average PM10 concentration levels. The analyses were carried out using daily average PM10 concentration, temperature, humidity, wind speed and wind direction data from 2006 to 2010. The data was from an industrial air quality monitoring station in Malaysia. The SR analysis established that meteorological parameters had less influence on PM10 concentration levels having coefficient of determination (R 2) result from 23 to 29% based on seasoned and unseasoned analysis. While, the result of the prediction analysis showed that PCR models had a better R 2 result than MLR methods. The results for the analyses based on both seasoned and unseasoned data established that MLR models had R 2 result from 0.50 to 0.60. While, PCR models had R 2 result from 0.66 to 0.89. In addition, the validation analysis using 2016 data also recognised that the PCR model outperformed the MLR model, with the PCR model for the seasoned analysis having the best result. These analyses will aid in achieving sustainable air quality management strategies.
The main purpose of this work is to analyze the impact of environmental degradation proxied by CO2 emissions per capita along with some other explanatory variables namely energy use, trade, and human capital on economic growth in selected higher CO2 emissions economies namely China, the USA, India, and Japan. For empirical analysis, annual data over the period spanning between 1971 and 2013 are used. After using relevant and suitable tests for checking data properties, the panel fully modified ordinary least squares (FMOLS) method is employed as an analytical technique for parameter estimation. The panel group FMOLS results reveal that almost all variables are statistically significant, whereby test rejects the null hypotheses of non cointegration, demonstrating that all variables play an important role in affecting the economic growth role across countries. Where two regressors namely CO2 emissions and energy use show significantly negative impacts on economic growth, for trade and human capital, they tend to show the significantly positive impact on economic growth. However, for the individual analysis across countries, the panel estimate suggests that CO2 emissions have a significant positive relationship with economic growth for China, Japan, and the USA, while it is found significantly negative in case of India. The empirical findings of the study suggest that appropriate and prudent policies are required in order to control pollution emerging from areas other than liquefied fuel consumption. The ultimate impact of shrinking pollution will help in supporting sustainable economic growth and maturation as well as largely improve society welfare.