Nor Azam Ramli

Transformation of nitrogen dioxide into ozone and prediction of ozone concentrations using multiple linear regression techniques

Analysis and forecasting of air quality parameters are important topics of atmospheric and environmental research today due to the health impact caused by air pollution. This study examines transformation of nitrogen dioxide (NO2) into ozone (O3) at urban environment using time series plot. Data on the concentration of environmental pollutants and meteorological variables were employed to predict the concentration of O3 in the atmosphere. Possibility of employing multiple linear regression models as a tool for prediction of O3 concentration was tested. Results indicated that the presence of NO2 and sunshine influence the concentration of O3 in Malaysia. The influence of the previous hour ozone on the next hour concentrations was also demonstrated.

Monsoonal differences and probability distribution of PM10 concentration

There are many factors that influence PM10 concentration in the atmosphere. This paper will look at the PM10 concentration in relation with the wet season (north east monsoon) and dry season (south west monsoon) in Seberang Perai, Malaysia from the year 2000 to 2004. It is expected that PM10 will reach the peak during south west monsoon as the weather during this season becomes dry and this study has proved that the highest PM10 concentrations in 2000 to 2004 were recorded in this monsoon. Two probability distributions using Weibull and lognormal were used to model the PM10 concentration. The best model used for prediction was selected based on performance indicators. Lognormal distribution represents the data better than Weibull distribution model for 2000, 2001, and 2002. However, for 2003 and 2004, Weibull distribution represents better than the lognormal distribution. The proposed distributions were successfully used for estimation of exceedences and predicting the return periods of the sequence year.

Surfactants in South East Asian Aerosols

Environmental Context.Surfactants are present in all atmospheric aerosols with potential effects on surface tension, cloud droplets and even human health. They appear to be anionic and associated with yellow aqueous extracts, suggesting a humic-like character. These surfactants are probably derived from the oxidation of combustion-generated soot (from vehicles and forest fires). Abstract.The concentration of surfactants in aerosols was determined at several sites in South East Asia, Bangi, Penang and Kota Kinabalu in Malaysia and Bangkok, Thailand, as methylene blue active substances (MBAS) and ethyl violet active substances (EVAS) for anionic surfactants and disulphine blue active substances (DBAS) for cationic surfactants. The methodology used is based on the formation of extractable ion-association complexes of surfactants and dye in organic solvents followed by spectrometric measurement of the intensity of the extracted coloured complex. Results showed surfactants in aerosols are mostly in the anionic form as MBAS and EVAS, and higher in aerosols collected in congested areas, especially in times of forest fires. Concentrations are in the range of 34.6 to 285.0 pmol m−3 for MBAS and 129.9 to 932.2 pmol m−3 for EVAS. Several different types of soot and humic acid seem possible sources of surfactants in atmospheric aerosols. Laboratory experiments suggested that combustion products, especially from motor vehicles, are important primary sources of surfactants in aerosols. There is also some laboratory evidence that there are secondary sources for these surfactants in aerosols, possibly humic-like substances (HULIS) from the oxidation and photochemical reaction of soots and humic acid.

Multivariate methods to predict ground level ozone during daytime, nighttime, and critical conversion time in urban areas

Ground–level ozone (O3) is known to exhibit strong daily variations that lead to complexity of the pollutants’ analysis and predictions. This study aimed to introduce and explore the variations in O3 concentrations during daytime (DT), nighttime (NT), and critical conversion time (CCT) using multiple linear regression (MLR) and principal component regression (PCR) analyses. The original variables and principal component analysis (PCA) results were used as the input for MLR analysis. Hourly averages of six air pollutants and four meteorological parameters at Shah Alam during 1999–2009 were selected for this study. The monitoring records in 2010 were used to assess the developed models using several performance indicators. Results showed that the MLR model during DT exhibited optimal performance in terms of normalized absolute error, index of agreement, prediction accuracy, and coefficient of determination (R2) with values of 0.2762, 0.9211, 0.8581, and 0.7354, respectively. PCR during CCT also showed significantly higher performance than that during DT and NT. This result was evidenced by higher percentage of total variances, which could be explained by the selected variables in PCA during CCT.

Comparison of Linear Interpolation Method and Mean Method to Replace the Missing Values in Environmental Data Set

Data collected in air pollution monitoring such as PM10, sulphur dioxide, ozone and carbon monoxide are obtained from automated monitoring stations. These data usually contained missing values due to machine failure, routine maintenance, changes in the siting of monitors and human error. Incomplete datasets can cause bias due to systematic differences between observed and unobserved data. Therefore, it is important to find the best way to estimate these missing values to ensure the quality of data analysed are of high quality. Incomplete data matrices are problematic: incomplete datasets may lead to results that are different from those that would have been obtained from a complete dataset (Hawthorne and Elliott, 2004). There are three major problems that may arise when dealing with incomplete data. First, there is a loss of information and, as a consequence, a loss of efficiency. Second, there are several complications related to data handling, computation and analysis, due to the irregulaties in data structure and the impossibility of using standard software. Third, and more important, there maybe bias due to systematic differences between observed and unobserved data. One approach to solve incomplete data problems is the adoption of imputation techniques (Junninen et al., 2004). Thus, this study compared the performance between linear interpolation method (imputation technique) and substitution of mean value for replacement of missing values in environmental data set.

Seasonal Variation in Schools’ Indoor Air Environments and Health Symptoms among Students in an Eastern Mediterranean Climate

ABSTRACT The indoor air quality (IAQ) in classrooms highly affects the health and productivity of students. This article aims to clarify seasonal variation in indoor environment and sick building syndromes (SBS) symptoms in an Eastern Mediterranean climate. A series of field measurements were conducted during the fall and winter seasons from October 2011 to March 2012 in 12 naturally ventilated schools located in the Gaza Strip. Data on environmental perception and health symptoms were obtained from 724 students by using a validated questionnaire. The results showed that indoor PM10 and PM2.5 concentrations were 426.3 ± 187.6 μg/m3 and 126.6 ± 94.8 μg/m3, respectively. The CO2 concentrations and ventilation rate widely exceeded their reference values during the winter season. The prevalence rates of general symptoms were relatively high at baseline assessment in the fall season and increased significantly during follow-up in the winter season. Significant increases in disease symptoms such as mucosal irritation and pre-existing asthma symptoms among students could be related to poor indoor air quality. Five distinct groups of SBS symptoms from factor analysis of students’ related symptoms were significantly correlated with PM10 and PM2.5, CO2, ventilation rate, and indoor temperature. As vulnerable children, this situation negatively affects their school performance and health.

Odour Nuisance near Semi-Aerobic Landfill: A Distance-Based Study in Malaysia

The decomposition of biodegradable waste in landfills is known to produce odour emissions that cause discomfort to nearby residents. Therefore, the aim of this study is to investigate perception of odour as a nuisance among residents in relation to their distance from a landfill. A survey is conducted, and 507 respondents living within a 7.0 kilometre radius from the landfill participated. Questionnaires are sent out to the respondents to investigate their background, socioeconomic status, and perception on odour. The selected landfill is semi-aerobic and is used for the disposal of non-hazardous domestic and industrial wastes. Respondents for radius < 0.9 km are employees of the landfill. Based on survey results, almost 26 % of the respondents strongly agree that ‘odour is a nuisance’. The level of agreement on ‘odour is a nuisance’ decreases with distance; that is, even respondents (1 %) who live within 6.0 – 6.9 km agree to this perception. A possible reason is that odour concentration is not only high at the origin/source, but also emanates from waste collection and transportation. Respondent perception on ‘odour is a nuisance’ is investigated in terms of race, age, type of house, education, occupation, and income.

Temporal Analysis of Ozone and Nitrogen Oxides Fluctuations at Pasir Gudang, Malaysia

Ozone (O3) and nitrogen oxides (NOx) are closely related in the atmosphere. In ambient air, these pollutants always fluctuated depending on their emission sources and meteorological influences. The paper is aims to gain insight understanding of the monthly temporal variation of O3 and NOx concentrations to enable proper control strategies against these pollutants. One-year monitoring records from 1st January to 31st December 2009 of O3 and NOx at Pasir Gudang, were obtained from Department of Environmental Malaysia. Temporal analysis of O3 and NOx concentration fluctuation during annual and monthly were assessed using time series and scatter plots. The annual variations of O3 concentrations were negatively correlated with annual variation of NO and NO2 concentrations. Results suggest that NO concentration are higher than O3 and NO2 especially in May 2009. However, zero exceedences were recorded in the studied period for all pollutants against the Malaysia Ambient Air Quality Guidelines.

Filling the Missing Data of Air Pollutant Concentration Using Single Imputation Methods

Hourly measured PM10 concentration at eight monitoring stations within peninsular Malaysia in 2006 was used to conduct the simulated missing data. The gap lengths of the simulated missing values are limited to 12 hours since the actual trend of missingness is considered short. Two percentages of simulated missing gaps were generated that are 5 % and 15 %. A number of single imputation methods (linear interpolation (LI), nearest neighbour interpolation (NN), mean above below (MAB), daily mean (DM), mean 12-hour (12M), mean 6-hour (6M), row mean (RM) and previous year (PY)) were calculated to fill in the simulated missing data. In addition, multiple imputation (MI) was also conducted to compare between the single imputation methods. The performances were evaluated using four statistical criteria namely mean absolute error, root mean squared error, prediction accuracy and index of agreement. The results show that 6M perform comparably well to LI. Thus, this show that the effect of smaller averaging time gives better prediction. Other single imputation methods predict the missing data well except for PY. RM and MI performs moderately with the increasing performance in higher fraction of missing gaps whereas LR makes the worst methods for both simulated missing data percentages.

H2S Emissions within Vicinity of Swine Farming Area in Nibong Tebal, Pulau Pinang, Malaysia

Animal farming industries is important in Malaysia because its contribution to the economy. The production causes all the major environmental negative impact such as water pollution from waste water and malodours emanating from farms. The current methods of disposing of manures were no longer adequate or suitable for the new, large and intensive animal farming. Inappropriate technologies, poor maintenance and inadequate dimensions and design of the treatment systems in addition to inappropriate production method are causing serious environmental problems especially odorous emissions. Present of Hydrogen Sulphide (H2S) in the air contribute to the odour pollution. Static monitoring has been done and maximum concentration of H2S is 220.6 ppb. Temperature and relative humidity fluctuations were seem to have influence on concentration of H2S. Linear regression analysis was shown that relative humidity has higher influence on correlation gaseous pollution compared to temperature. Correlation coefficient for H2S and relative humidity was in range 0.675 to 0.881 in the morning and 0.417 to 0.729 in the evening, which are in range of strong correlations.