Identification source of variation on regional impact of air quality pattern using chemometric
03 Aug 2015-Aerosol and Air Quality Research (Taiwan Association for Aerosol Research)-Vol. 15, Iss: 4, pp 1545-1558
TL;DR: In this paper, the effectiveness of hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), factor analysis (FA), and multiple linear regressions (MLR) for assessing the air quality data and air pollution sources pattern recognition were applied.
Abstract: This study intends to show the effectiveness of hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), factor analysis (FA) and multiple linear regressions (MLR) for assessing the air quality data and air pollution sources pattern recognition. The data sets of air quality for 12 months (January–December) in 2007, consisting of 14 stations around Peninsular Malaysia with 14 parameters (168 datasets) were applied. Three significant clusters - low pollution source (LPS) region, moderate pollution source (MPS) region, and slightly high pollution source (SHPS) region were generated via HACA. Forward stepwise of DA managed to discriminate 8 variables, whereas backward stepwise of DA managed to discriminate 9 out of 14 variables. The method of PCA and FA has identified 8 pollutants in LPS and SHPS respectively, as well as 11 pollutants in MPS region, where most of the pollutants are expected derived from industrial activities, transportation and agriculture systems. Four MLR models show that PM10 categorize as the primary pollutant in Malaysia. From the study, it can be stipulated that the application of chemometric techniques can disclose meaningful information on the spatial variability of a large and complex air quality data. A clearer review about the air quality and a novel design of air quality monitoring network for better management of air pollution can be achieved.
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TL;DR: In this paper, the authors present an organized review of the broad aspects related to urban air quality modeling such as urban microclimate, geospatial data, chemical transport models, computational fluid dynamics (CFD) models and integration of CFD and mesoscale models.
Abstract: According to World Health Organization, 9 out of 10 people breathe polluted air and the ambient air pollution accounts for nearly 4.2 million early deaths worldwide. There is an urgent need for scientific management of urban air systems. Mathematical modeling of air quality helps the researchers and urban authorities in devising scientific management plans for mitigation of the associated impacts. We present an organized review of the broad aspects related to urban air quality modeling such as – urban microclimate, geospatial data, chemical transport models, computational fluid dynamics (CFD) models and integration of CFD and mesoscale models. The paper also discusses about the influence of urban land scape features on air quality, accuracy of emission inventory and model validation methods. The present review provides a vantage point to the researchers in the emerging field of high resolution urban air quality modeling for devising the location specific mitigation plans for the scientific management of the clean air.
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TL;DR: In this paper, the water quality status as stated in NWQS is categorized as Class I on dry season and Class II on wet sea-son, the major pollutants in Kenyir Lake are Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), Dissolve Oxygen and pH which are contributed largely by untreated or partially treated sewage from tourism development and construction activities around the basin.
Abstract: Water ecosystem deterioration can be affected by various factors of either natural environment or physical changes in the river basin. Data observation were made during dry season (April 2017) and wet season (December 2017). 21 sampling stations were selected along Kenyir Lake Basin. Overall, the water quality status as stated in NWQS is categorized as Class I on dry season and Class II on wet sea-son. The major pollutants in Kenyir Lake are Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), Dissolve Oxygen and pH which are contributed largely by untreated or partially treated sewage from tourism development and construction activities around the basin. The sedimentation problem level in the Kenyir Lake Basin is not in critically stage but the flow rate of water and land use ac-tivities (development around basin) will be contributed to the increasing levels of sedimentation. The good site management such as the implementation of proper site practice measures to control and treat run-off prior to discharge will ensure that the construction works will not affect the quality and quantity of the receiving waters or have significant impact upon the receiving waters.
19 citations
Cites background from "Identification source of variation ..."
...The main sources of pollutants were possibly waste product and effluent which from development and activities in the construction, tourism, agricultural areas and inorganic wastes which ultimately contaminated the river basin [31]....
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TL;DR: In this paper, Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) coupled with sensitivity analysis (SA) were used to recognize the pollutant relationship status over particulate matter (PM10) in eastern region.
Abstract: The comprehensives of particulate matter studies are needed in predicting future haze occurrences in Malaysia. This paper presents the application of Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) coupled with sensitivity analysis (SA) in order to recognize the pollutant relationship status over particulate matter (PM10) in eastern region. Eight monitoring studies were used, involving 14 input parameters as independent variables including meteorological factors. In order to investigate the efficiency of ANN and MLR performance, two different weather circumstances were selected; haze and non-haze. The performance evaluation was characterized into two steps. Firstly, two models were developed based on ANN and MLR which denoted as full model, with all parameters (14 variables) were used as the input. SA was used as additional feature to rank the most contributed parameter to PM10 variations in both situations. Next, the model development was evaluated based on selected model, where only significant variables were selected as input. Three mathematical indices were introduced (R2, RMSE and SSE) to compare on both techniques. From the findings, ANN performed better in full and selected model, with both models were completely showed a significant result during hazy and non-hazy. On top of that, UVb and carbon monoxide were both variables that mutually predicted by ANN and MLR during hazy and non-hazy days, respectively. The precise predictions were required in helping any related agency to emphasize on pollutant that essentially contributed to PM10 variations, especially during haze period.
14 citations
Cites background or methods from "Identification source of variation ..."
...Thus, a serious attention is needed by all parties, not only by government sector, but also more to individual responsibility (Azid et al. 2015a)....
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...The MLR is a traditional methodology to examine the impact of dependent variable by identifying the relationship of each independent variables (Azid et al. 2015b; Azid et al. 2015c)....
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TL;DR: In this article, the results of sample analyses indicate that during the beehive firework display, the ratios of metal concentrations in PM_(2.5) to the background level at leeward sampling site were 1,828 for Ba, 702 for K, 534 for Sr, 473 for Cu, 104 for Mg, 121 for Al, and 98 for Pb.
Abstract: This study investigates metals in the PM_(1.0) and PM_(2.5) collected using a micro-orifice uniform deposition impactor (MOUDI) sampler in the YanShuei area of southern Taiwan during a beehive firework display. The results of sample analyses indicate that during the beehive firework display, the ratios of metal concentrations in PM_(2.5) (D) to the background level (B) at leeward sampling site were 1,828 for Ba, 702 for K, 534 for Sr, 473 for Cu, 104 for Mg, 121 for Al, and 98 for Pb. The corresponding data for PM_(1.0) were 3036, 838, 550, 676, 594, 190, and 126, respectively. According to the results of metal composition ratio, Principal Component Analysis (PCA), and upper continental crust (UCC) analyses, the concentrations of particle-bound Al, Ba, Cu, K, Mg, Pb, and Sr increased during the beehive firework displays, suggesting that firework-display aerosols contained abundant metal elements of Al, Ba, Cu, K, Mg, Pb, and Sr. Before (background), trial, during, and after the beehive firework display, the Ba, K, Cu, Mg, Pb, and Sr (commonly regarded as firework display indicator elements) accounted for 0.520, 2.45, 26.4 and 0.849% mass of PM1, respectively, while for PM_(2.5) the corresponding data were 0.777, 2.32, 23.8, and 0.776%, respectively.
12 citations
Cites background from "Identification source of variation ..."
...0) can be classified into several groups by their sources (Allen et al., 2001; Marcazzan et al., 2001; Manoli et al., 2002; AlMomani, 2003; Azid et al., 2015; Chen et al., 2015; Fang et al., 2015; Liang et al., 2015)....
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...…characteristic values of over 1 in Principal Component Analysis (PCA) (SPSS v.12.0) can be classified into several groups by their sources (Allen et al., 2001; Marcazzan et al., 2001; Manoli et al., 2002; AlMomani, 2003; Azid et al., 2015; Chen et al., 2015; Fang et al., 2015; Liang et al., 2015)....
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References
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55 citations
"Identification source of variation ..." refers methods in this paper
...The PCA was utilized to identify the emission source (Hopke, 1985)....
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TL;DR: 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 are presented.
Abstract: 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.
50 citations
"Identification source of variation ..." refers background in this paper
...However, the highest values of R2 (which was near to 1) will be declared as the best linear model (Norusis 1990; Mutalib et al., 2013; Azid et al., 2013, 2014a)....
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TL;DR: In this article, principal factor analyses were performed on the data collected as part of the Philadelphia Regional and Local Sulfur Aerosol Concentrations Study conducted from July to October 1982, where mass concentrations of 22 trace elements analyzed by proton induced x-ray emission (PIXE) analysis and their associated analytical uncertainties were examined.
48 citations
Additional excerpts
...…0.75 (> 0.75) is considered as “strong”, the values range from 0.50–0.75 (0.50 ≥ factor loading ≥ 0.75) is considered as “moderate”, and the values range from 0.30–0.49 (0.30 ≥ factor loading ≥ 0.49) is considered as “weak” factor loadings (Liu et al., 2003; Azid et al., 2014a; Azid et al., 2015)....
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TL;DR: In this paper, the authors used principal component analysis (PCA) and ANN to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database.
Abstract: This paper describes the application of
principal component analysis (PCA) and artificial neural network (ANN) to predict
the air pollutant index (API) within the seven selected Malaysian air monitoring
stations in the southern region of Peninsular Malaysia based on seven years database
(2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward
ANN analysis demonstrated that the rotated principal component scores (RPCs) were
the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O3,
PM10, NO2, CH4, NmHC, THC, wind direction, humidity
and ambient temp) out of 12 prediction variables were the most significant parameters
to predict API. The results proved that the ANN method can be applied successfully
as tools for decision making and problem solving for better atmospheric management.
48 citations
"Identification source of variation ..." refers background or methods in this paper
...Most of the air pollution sources derived from land transportation (mobile source), industrial emissions (stationary source), and open burning sources (Afroz et al., 2003; Azmi et al., 2010; Abdullah et al., 2012; Azid et al., 2013, 2014a, b)....
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...The smallest RMSE and the closest R2 value to 1, the better model shall be performed (Dominick et al., 2012; Mutalib et al., 2013; Azid et al., 2013, 2014a)....
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...However, the highest values of R2 (which was near to 1) will be declared as the best linear model (Norusis 1990; Mutalib et al., 2013; Azid et al., 2013, 2014a)....
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TL;DR: In this article, a multivariate statistical technique, principal component analysis (PCA), was proposed to screen the ozone scenarios and Kaiser's varimax rotation method was also employed to separate the study area into several homogenous influence regimes.
47 citations
"Identification source of variation ..." refers background in this paper
...Eigenvalues over than 1 were deemed as significant and subsequently varimax factors (VFs), which are the new groups of variables are generated (Yu and Chang, 2000)....
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