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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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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TL;DR: There are relatively stronger inter-pollutant correlations at the stations of Gombak and Shah Alam, and the results suggest that heavy traffic flow induces high concentrations of PM10, CO, NO2 and SO2 at the three sampling stations.
Abstract: Over the last decades, the development of the Klang Valley (Malaysia), as an urban commercial and industrial area, has elevated the risk of atmospheric pollutions. There are several significant sources of air pollutants which vary depending on the background of the location they originate from. The aim of this study is to determine the trend and status of air quality and their correlation with the meteorological factors at different air quality monitoring stations in the Klang Valley. The data of five major air pollutants (PM10, CO, SO2, O3, NO2) were recorded at the Alam Sekitar Sdn Bhd (ASMA) monitoring stations in the Klang Valley, namely Petaling Jaya (S1), Shah Alam (S2) and Gombak (S3). The data from these three stations were compared with the data recorded at Jerantut, Pahang (B), a background station established by the Malaysian Department of Environment. Results show that the concentrations of CO, NO2 and SO2 are higher at Petaling Jaya (S1) which is due to influence of heavy traffic. The concentrations of PM10 and O3, however, are predominantly related to regional tropical factors, such as the influence of biomass burning and of ultra violet radiation from sunlight. They can, though, also be influenced by local sources. There are relatively stronger inter-pollutant correlations at the stations of Gombak and Shah Alam, and the results also suggest that heavy traffic flow induces high concentrations of PM10, CO, NO2 and SO2 at the three sampling stations. Additionally, meteorological factors, particularly the ambient temperature and wind speed, may influence the concentration of PM10 in the atmosphere.
212 citations
"Identification source of variation ..." refers background 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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TL;DR: A completely automated method to improve single linkage hierarchical cluster analysis (HCA), based on a self-consistent outlier reduction approach, followed by the building-up of a descriptive function, which allows to define natural clusters.
181 citations
"Identification source of variation ..." refers methods in this paper
...HACA is an unsupervised pattern identification method to split a large group into smaller ones (Almeida et al., 2007)....
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TL;DR: In this article, the relationship between meteorological actors and air quality is studied on a local scale based on observations from four rural sites and are determined by a comprehensive correlation analysis and a multiple regression (MLR) analysis in 2 modes, with and without air quality variables as predictors.
Abstract: . In spite of the strict EU regulations, concentrations of surface ozone and PM10 often exceed the pollution standards for the Netherlands and Europe. Their concentrations are controlled by (precursor) emissions, social and economic developments and a complex combination of meteorological actors. This study tackles the latter, and provides insight in the meteorological processes that play a role in O3 and PM10 levels in rural mid-latitudes sites in the Netherlands. The relations between meteorological actors and air quality are studied on a local scale based on observations from four rural sites and are determined by a comprehensive correlation analysis and a multiple regression (MLR) analysis in 2 modes, with and without air quality variables as predictors. Furthermore, the objective Lamb Weather Type approach is used to assess the influence of the large-scale circulation on air quality. Keeping in mind its future use in downscaling future climate scenarios for air quality purposes, special emphasis is given to an appropriate selection of the regressor variables readily available from operational meteorological forecasts or AOGCMs (Atmosphere-Ocean coupled General Circulation Models). The regression models perform satisfactory, especially for O3, with an (R2 of 57.0% and 25.0% for PM10. Including previous day air quality information increases significantly the models performance by 15% (O3) and 18% (PM10). The Lamb weather types show a seasonal distinct pattern for high (low) episodes of average O3 and PM10 concentrations, and these are clear related with the meteorology-air quality correlation analysis. Although using a circulation type approach can provide important additional physical relations forward, our analysis reveals the circulation method is limited in terms of short-term air quality forecast for both O3 and PM10 (R2 between 0.12 and 23%). In summary, it is concluded that the use of a regression model is more promising for short-term downscaling from climate scenarios than the use of a weather type classification approach.
159 citations
"Identification source of variation ..." refers background in this paper
...Despite of emission sources, ambient air quality can be strongly influenced by meteorological factors through the complex relations between diverse processes - emissions, transport, chemical transformation and wet and dry deposition (Demuzere et al., 2009)....
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TL;DR: It is shown how suitable clustering criteria or grouping methods may be derived from probabilistic models for partition-type, hierarchical and tree-like clustering structures in the case of vector-valued data, dissimilarity matrices and similarity relations.
159 citations
"Identification source of variation ..." refers background or methods in this paper
...In this study, HACA is used to identify unseen 'clusters' which are illustrated by numerical, symbolical or structural data, such that the members of a significant cluster share the similarities among them and the clusters are confidently well parted (Bock, 1996)....
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...structural data, such that the members of a significant cluster share the similarities among them and the clusters are confidently well parted (Bock, 1996)....
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