scispace - formally typeset
Search or ask a question
Journal ArticleDOI

Identification source of variation on regional impact of air quality pattern using chemometric

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.

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI
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.

41 citations

Journal ArticleDOI
TL;DR: It is suggested that ANN was an effective tool to compute the MWQ in mangrove estuarine zone and a powerful alternative prediction model as compared to the other modelling methods.

27 citations

Journal ArticleDOI
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]....

    [...]

Journal ArticleDOI
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)....

    [...]

  • ...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)....

    [...]

Journal ArticleDOI
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)....

    [...]

  • ...…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)....

    [...]

References
More filters
DOI
01 Jan 2012

13 citations


"Identification source of variation ..." refers background in this paper

  • ...The combustion of these fuels in industries and vehicles has been a main source of air pollution (Romieu and Hernandez, 1999; Mutalib et al., 2013)....

    [...]

Book ChapterDOI
01 Jan 2014
TL;DR: In this paper, environmetric techniques (HACA, DA, and PCA/FA) were used to evaluate the spatial variations in the southern region of Peninsular Malaysia, followed by API prediction comparison using ANN and MLR models.
Abstract: Air pollution is becoming a major environmental issue in the southern region of Peninsular Malaysia. Environmetric techniques (HACA, DA, and PCA/FA) were used to evaluate the spatial variations in the southern region of Peninsular Malaysia, followed by API prediction comparison using ANN and MLR models. The datasets of air pollutant parameters for 3 years (2005–2007) were applied in this study. HACA clustered three different groups of similarity based on the characteristics of air quality parameters. DA shows all seven parameters (CO, O3, PM10, SO2, NOx, NO, and NO2) gave the most significant variables after stepwise backward mode. PCA/FA identify that the major source of air pollution is due to combustion of fossil fuels in motor vehicles and industrial activities. The ANN model shows a better prediction compared to the MLR model with R2 values equal to 0.819 and 0.773 respectively. This study concluded that the environmetric techniques and modelling become an excellent tool in API assessment, air pollution source identification, apportionment, and interpretation of complex dataset with a view to get better information about the air quality, and can be setbacks in designing an API monitoring network for effective air pollution resources management.

13 citations


"Identification source of variation ..." refers background or methods in this paper

  • ...Most of the pollutants in the MPS region are originated from burning of biomass and fossil fuels, particularly from industrial, residential and vegetation areas, motor vehicles, and natural emission sources (Mutalib et al., 2013; Azid et al., 2014b)....

    [...]

  • ...The index is important in evaluating the air quality of different sources (Azid et al., 2014a)....

    [...]

  • ...The status of air quality in Malaysia is monitored by the establishment of Recommended Malaysian Air Quality Guideline (RMAQG) issued by the Malaysian Department of Environment (DOE) since 1989 (Dominick et al., 2012; Mutalib et al., 2013; Azid et al., 2014a)....

    [...]

  • ...…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)....

    [...]

  • ...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)....

    [...]

Journal ArticleDOI
TL;DR: The primary aim is to replace truncated Gaussian distributions by appropriate generalized Pareto distributions and to explore properties and the relationship of discriminant functions in both models.

10 citations


"Identification source of variation ..." refers background in this paper

  • ...With the rapid economic growth, air pollution is the main subject that has been adversely affecting human health, agricultural crops, animals and ecosystems (Moustris et al., 2010; Azid et al., 2015)....

    [...]

  • ...Air pollution control is needed to prevent the situation from worsening in the long run (Moustris et al., 2010; Azid et al., 2015)....

    [...]

  • ...: +609-666 3410 E-mail address: hafizanjuahir@unisza.edu.my especially to human health (Moustris et al., 2010)....

    [...]

Journal ArticleDOI
TL;DR: In this article, multiple linear regression (MLR) method was used to establish the relation between the O3 at time t + 1 and other indices including hourly air pollutant concentra- tions and meteorological conditions.
Abstract: In this study, multiple linear regression (MLR) method was used to establish the rela- tionship between the O3 at time t + 1 and other indices including hourly air pollutant concentra- tions and meteorological conditions at time t. Then O3 was predicted using the obtained best-fitting MLR. The results indicated that the relationship between the O3 at time t + 1 and other indices including hourly air pollutant concentrations and meteorological conditions at time t agreed with MLR well, The values of mean absolute percentage error (MAPE), correlation co- efficient (R), coefficient of determination (R 2 ), mean square error (MSE), and root mean square error (RMSE) were 29.09 %, 0.95, 0.90, 45.33, and 6.73, respectively when determining the best-fitting equation. In addition, MLR could predict hourly ozone concentrations successfully. The values of MAPE, R, R 2 , MSE, and RMSE were 10.37 %, 0.93, 0.86, 0.33, and 0.57, respec- tively when predicting. It also indicated that the hourly air pollutant concentrations and mete- orological conditions at time t could be applied on the prediction of ozone of time t +1.

7 citations


"Identification source of variation ..." refers methods in this paper

  • ...…has been used for investigating the relationship among various independent and dependent variables by fitting a linear equation to observed data (Pai et al., 2009; Ul-Saufi et al., 2011) and gives the percentage of the contribution of each parameter to the atmospheric pollution (Aertsen et al.,…...

    [...]

Journal ArticleDOI
TL;DR: A case study in scale of health at intensive care unit (ICU) using the two methods of modelling, it was found that the FCRM models appeared to be the better model, having a lower MSE.
Abstract: The multiple linear regression (MLR) model is well-known in analyzing linear model. Whereas, the new technique in clustering data, the fuzzy c-regression models (FCRM) are being widely used in analyzing the nonlinear model. The FCRM models are tested on simulated data and the FCRM models can approximate the given nonlinear system with a higher accuracy. A case study in scale of health at intensive care unit (ICU) using the two methods of modelling as mentioned above was carried out. The comparison between the MLR and FCRM models were done to find the better model by using the mean square error (MSE). After comparing the two models, it was found that the FCRM models appeared to be the better model, having a lower MSE. The MSE for MLR model is 498.29 whereas the MSE for FCRM models is 97.366. Keywords: Multiple linear regression (MLR) model; fuzzy c-regression models (FCRM); mean square error (MSE). 2010 Mathematics Subject Classification 62H12; 62M20.

2 citations

Related Papers (5)