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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.

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Citations
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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]....

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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)....

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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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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)....

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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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Journal ArticleDOI
TL;DR: This environmetric study deals with modeling and interpretation of river water monitoring data from the basin of the Saale river and its tributaries the Ilm and the Unstrut to reveal important information about the ecological status of the region of interest.
Abstract: This environmetric study deals with modeling and interpretation of river water monitoring data from the basin of the Saale river and its tributaries the Ilm and the Unstrut. For a period of one year of observation between September 1993 and August 1994 a data set from twelve campaigns at twenty-nine sampling sites from the Saale river and six campaigns from the river Ilm at seven sampling sites and from river Unstrut at ten sampling sites was collected. Twenty-seven chemical and physicochemical properties were measured to estimate the water quality. The application of cluster analysis, principal components analysis, and apportioning modeling on absolute principal components scores revealed important information about the ecological status of the region of interest:identification of two separate patterns of pollution (upper and lower stream of the rivers);identification of six latent factors responsible for the data structure with different content for the two identified pollution patterns; anddetermination of the contribution of each latent factor (source of emission) to the formation of the total concentration of the chemical burden of the river water. As a result more objective ecological policy and decision making is possible.

148 citations


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

  • ...Chemometric in the environmental field is verified to be a functional tool to identify the sources of pollution (Simeonov et al., 2002; Mutalib et al., 2013; Azid et al., 2015)....

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Journal ArticleDOI
TL;DR: In this article, a combination of principal component analysis (PCA) and artificial neural networks (ANN) was developed to determine its predictive ability for the air pollutant index (API).
Abstract: This study focused on the pattern recognition of Malaysian air quality based on the data obtained from the Malaysian Department of Environment (DOE). Eight air quality parameters in ten monitoring stations in Malaysia for 7 years (2005–2011) were gathered. Principal component analysis (PCA) in the environmetric approach was used to identify the sources of pollution in the study locations. The combination of PCA and artificial neural networks (ANN) was developed to determine its predictive ability for the air pollutant index (API). The PCA has identified that CH4, NmHC, THC, O3, and PM10 are the most significant parameters. The PCA-ANN showed better predictive ability in the determination of API with fewer variables, with R 2 and root mean square error (RMSE) values of 0.618 and 10.017, respectively. The work has demonstrated the importance of historical data in sampling plan strategies to achieve desired research objectives, as well as to highlight the possibility of determining the optimum number of sampling parameters, which in turn will reduce costs and time of sampling.

146 citations

Journal ArticleDOI
TL;DR: In this paper, Artificial Neural Networks are used in order to forecast the maximum daily value of the European Regional Pollution Index as well as the number of consecutive hours, during the day, with at least one of the pollutants above a threshold concentration, 24 to 72 h ahead.
Abstract: The difficulty in forecasting concentration trends with a reasonable error is still an open problem. In this paper, an effort has been made to this purpose. Artificial Neural Networks are used in order to forecast the maximum daily value of the European Regional Pollution Index as well as the number of consecutive hours, during the day, with at least one of the pollutants above a threshold concentration, 24 to 72 h ahead. The prediction concerns seven different places within the Greater Athens Area, Greece. The meteorological and air pollution data used in this study have been recorded by the network of the Greek Ministry of the Environment, Physical Planning, and Public Works over a 5-year period, 2001–2005. The hourly values of air pressure and global solar irradiance for the same period have been recorded by the National Observatory of Athens. The results are in a very good agreement with the real-monitored data at a statistical significance level of p < 0.01.

118 citations


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

  • ...The presence of CO, NO2, NO and NOx are related to the fossil fuel combustion of agricultural systems (Mukhopadhyay and Forssell, 2005), while the presence of non-methane hydrocarbons are related to the fossil fuel combustion of transportation (Kopmann 2007)....

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Journal ArticleDOI
TL;DR: In this article, in situ measurements of carbon dioxide (CO2) and the principal gases linked to biomass burning at the Mace Head Observatory, Ireland, reveal a strong correlation in 1998-99 and 2002-03, both periods with intense global fires.

117 citations


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

  • ...The processes that had led to the accumulation of CH4 appeared to have led to the depletion of O3, to be precise, accumulation and depletion under a shallow night-time inversion (Simmonds et al., 2005)....

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Journal ArticleDOI
TL;DR: In this paper, the authors evaluate air pollution (CO2, SO2, and NOx) from fossil fuel combustion in India using an Input-Output Structural Decomposition Analysis approach to find out their sources of changes.

103 citations


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

  • ...The concentrations of CO, NO2, SO2, CH4, Humidity, NMHC, NO, and wind speed 10 m show a negative influence compared to O3, PM10, NOx, Ambient Temperature, THC, and UVB....

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  • ...(6i)–(6iv): a) Original air quality parameters (14 variables) Total API = –0.15(CO) – 501.09(NO2) – 210.13(SO2) + 0.70(PM10) + 58.94(O3) + 0.19(Temp) – 0.24(CH4) – 0.14(Humidity) – 7.94(NMHC) – 576.91(NO) + 597.93(NOx) + 1.26(THC) + 6.43e–-03(UVB) – 0.88(Wind Speed 10m) + 18.30 [R2 = 0.873, adjusted R2 = 0.861, and RMSE=3.108] (6i) b) LPS (9 variables) Total API = –524.57(NO2) + 59.48(SO2) + 0.82(PM10) + 2.71(CH4) – 6.99e–02(Humidity) + 6.70(NMHC) + 87.74(NOx) + 1.47e–02(UVB) – 0.36(Wind Speed 10 m) + 8.72 [R2 = 0.865, adjusted R2 = 0.846, and RMSE = 2.187] (6ii) c) MPS (9 variables) Total API = –8216.13(NO2) + 1803.87(SO2) + 1.61(PM10) – 49.44(CH4) – 2.18(Humidity) – 13.64(NMHC) + 5478.53(NOx) – 3.95e–-02(UVB) – 1.28(Wind Speed 10 m) + 229.68 [R 2= 0.999, adjusted R 2= 0.993, and RMSE = 1.430] (6iii) T ab le 3 ....

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  • ...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)....

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  • ...The analysis is considered to be the most suitable tool for the reduction and interpretation of meaningful data (Kannel et al., 2007; Satheeshkumar and Khan, 2011; Mutalib et al., 2013)....

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  • ...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)....

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