Climate, Environment and Biodiversity
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Browsing Climate, Environment and Biodiversity by Subject "Air quality—Research"
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- ItemAssessment of urban air quality in Makassar South Sulawesi Indonesia(Universiti Teknologi Malaysia, 2013) SattarAn assessment of the urban air quality in Makassar area, which covers SO2, CO, NO2, O3, Pb, and TSP sampled over a period of eleven years (i.e 2001 to 2011), with PM10 monitored for six years (2006-2011) are discussed and presented in this thesis. The air quality data were obtained from secondary measurements made by the Office of Ministry of Environment Sulawesi, Maluku and Papua, the Environment Board of the Province of South Sulawesi, and the Environmental Agency of Makassar City. In addition, the primary data of airborne PM10 concentration sampled on a weekly basis for a period of one year (i.e February 2012 to January 2013) at one Makassar site are also reported. PM10 was sampled using a standard size selective high volume air sampler and analyzed for its elemental, black carbon and ionic species constituents. Results showed that the overall average concentrations of SO2, CO, NO2, O3, Pb, TSP and PM10 measured at eight monitoring sites of Makassar was 74.9 µg/m3, 1007 µg/m3, 42.5 µg/m3, 53.7 µg/m3, 0.70 µg/m3, 179 µg/m3, 53.9 µg/m3, respectively. The concentration of the particulate matter found in the study area was typically influenced by the dry and wet season experienced in the region. A total of nineteen elemental components (i.e Ag, Al, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Si, Ti and Zn), four ionic species (i.e Cl-, NO3 -, SO4 2-, NH4 +) and black carbon, together constituted a mere 28.8% of the PM10 mass concentration, while the remaining 71.2% are yet to be explained. However, the use of a more rigorous source apportionment model based on positive matrix factorization (PMF) successfully identified six major sources of air pollution in the area, which include marine, motor vehicle, road dust, soil dust, industry and biomass burning, each contributing 25%, 24%, 16%, 13%, 12%, and 10% of the particulate mass concentration, respectively
- ItemAtmospheric PM2.5 and particle number concentration in semi-urban industrial-residential airshed(Universiti Teknologi Malaysia, 2020) Dahari, NadhiraAir pollution is one of the crucial factors that cause premature death and health problems. Fine particulate matter (PM2.5) has a high association with adverse health effects due to its capability to penetrate deep into the human respiratory system. The deterioration of air quality in Malaysia, especially Johor Bahru city, is worrying due to the swift industrial, transportation as well as housing expansion. Air pollution has a closer relationship with the particle number concentration (PNC) rather than the particle mass concentration. However, measurement of the PM2.5 is normally reported in particle mass concentration. Due to the light-weighted small particle sizes that dominate the PNC, they are accounted for only a few percent of the total particle mass concentration. Thus, these small particles could be neglected if the toxicological effects are determined primarily by the mass concentration rather than the PNC. This study aims to investigate the 24 h mean PM2.5 mass concentrations, meteorological parameters and PNC, besides determining the concentrations of the trace metals and water-soluble inorganic ions of the PM2.5 pollutant collected at the industrial-residential airshed of Skudai, Johor Bahru. This research analysed the source apportionment of the PM2.5 composition and the relationship of the PM2.5 mass concentrations with PNC. The meteorological variables, PNC data and PM2.5 samples were collected from August 2017 until January 2018. The source apportionment of the PM2.5 composition were determined using Positive Matrix Factorisation (PMF). This study found that the highest 24 h PM2.5 mass concentration is 44.6 µgm-3, with a mean value of 21.85 µgm-3 throughout the SW through the NE monsoon. 43.33% of the daily PM2.5 mass exceeded the 24 h World Health Organization Guideline, while 8.33% of the concentration exceeded the 24 h Malaysia Ambient Air Quality Standard. The ambient temperature throughout the monsoon seasons shows a significant positive correlation (p < 0.05) with PM2.5 mass (r2 = 0.43 to r2 = 0.54), while the wind speed (r2 = -0.23 to r2 = -0.01) and the relative humidity (r2 = -0.47 to r2 = -0.27) show negative correlations. The rainfall on the other hand shows weak correlation towards PM2.5 mass. The accumulation mode particles (0.27 µm < Dp < 1.0 µm) corresponded to 94~98% of the total particle number concentration, with highest hourly mean of 372.20 #cm-3 during the SW monsoon. The accumulation mode has the highest correlation value of r2 = 0.8701 among the other particle size bins. The major trace elements identified were Fe (279.2 ± 69.2 ngm-3), Ba (200.1 ± 57.2 ngm-3), Zn (133.2 ± 67.6 ngm-3), Mg (116.3 ± 43.8 ngm-3) and Al (104.1 ± 30.6 ngm-3). For inorganic ions, the secondary inorganic aerosols (SIA) were highly contributed by NO3- (639.9 ± 138.1 ngm-3), SO42- (556.9 ± 203.0 ngm-3) and NH4+ (424.1 ± 106.1 ngm-3). Despite the anthropogenic activities as the sources of particulates, a minor fraction of pollutants may also due to the regional transboundary transport. The PMF analysis shows that non-combustion traffic source is the main contributor to the ambient PM2.5 (25.4 %). The six predominant sources identified were (1) mineral dust pollution (4.2 %), (2) source of mixed road dust and biomass burning (18.1%), (3) mixed secondary inorganic aerosol and road dust emission (18.1%), (4) emission of the non-combustion traffic source (25.4%), (5) industrial emission (18.1 %) and (6) undefined (16.1 %). The comprehensive findings of this study may support the need to control the PM2.5 sources.
- ItemTrends and prediction of air pollutants in Pasir Gudang Industrial Area, Johor, Malaysia(Universiti Teknologi Malaysia, 2014) Afzali, AfsanehThe trends and prediction of the air quality of Pasir Gudang industrial area in Johor are discussed and presented in this thesis. An attempt was also made to study the pollutants concentrations recorded by the Larkin monitoring station. However, studies on the trends, meteorological influences and the predictions of atmospheric pollution were given a greater emphasis for the Pasir Gudang industrial area. The statistical analysis based on a simple correlation coefficient and regression analysis showed that although there is a relationship between each pollutant i.e ozone (O3), particulate Matter with diameter of 10 micrometers or less (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2) and carbon monoxide (CO) concentrations and a combination of meteorological parameters such as wind speed, temperature, humidity rate and solar radiation in Pasir Gudang with the correlation coefficient (r) of 0.64, 0.42, 0.71, 0.55 and 0.49, respectively, the inclusion of the previous day’s pollutants concentrations significantly presents better prediction models with the correlation coefficient of 0.73, 0.68, 0.83, 0.68 and 0.67, respectively. Subsequently, the prediction of PM10 based on its previous day’s concentrations through artificial neural network resulted in a much better model prediction with the value of r=0.69 and 0.70 compared to the statistical model with the value of r=0.64. The spatial variation of SO2, NO2 and PM10 emitted from various industrial sources in Pasir Gudang were also predicted using American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) air dispersion model. The Weather Research and Forecasting (WRF) model was applied to simulate the required meteorological variables for the selected date i.e 2-16 July, 2010. The WRF output values i.e. temperature, wind speed and wind direction were compared with the onsite measured data in Pasir Gudang, Senai, KLIA and Kluang stations. The results showed the accuracy of WRF model performance in simulating temperature and wind speed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) value of less than 2.8 and 3.5, respectively, while it has some difficulties in simulating the wind direction near a coastal area. The maximum ground level concentration of pollutants i.e SO2, NO2 and PM10 simulated through AERMOD coupled with WRF in Pasir Gudang industrial area was 36.2, 59.8 and 5.4 ug/m3, respectively, which were within the Malaysia ambient air quality guidelines over the receptor grid. The evaluation of AERMOD through the quantile-quantile (Q-Q) plots showed that most of the predicted and observed pair points are lying close to the one-to-one line. Besides, the sensitivity of AERMOD model to its input parameters i.e stack characteristics and meteorological variables showed that the model is more sensitive to stack gas temperature and stack height as well as wind speed.