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Spatial and temporal air quality pattern recognition using environmetric techniques: a case study in Malaysia

Overview of attention for article published in Environmental Science: Processes & Impacts, January 2013
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Title
Spatial and temporal air quality pattern recognition using environmetric techniques: a case study in Malaysia
Published in
Environmental Science: Processes & Impacts, January 2013
DOI 10.1039/c3em00161j
Pubmed ID
Authors

Sharifah Norsukhairin Syed Abdul Mutalib, Hafizan Juahir, Azman Azid, Sharifah Mohd Sharif, Mohd Talib Latif, Ahmad Zaharin Aris, Sharifuddin M. Zain, Doreena Dominick

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.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 2 2%
Unknown 115 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 18%
Student > Master 20 17%
Researcher 14 12%
Student > Bachelor 13 11%
Unspecified 5 4%
Other 17 15%
Unknown 27 23%
Readers by discipline Count As %
Environmental Science 41 35%
Engineering 12 10%
Computer Science 6 5%
Mathematics 5 4%
Unspecified 5 4%
Other 16 14%
Unknown 32 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 January 2014.
All research outputs
#22,758,309
of 25,373,627 outputs
Outputs from Environmental Science: Processes & Impacts
#1,688
of 1,851 outputs
Outputs of similar age
#258,406
of 288,986 outputs
Outputs of similar age from Environmental Science: Processes & Impacts
#79
of 91 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,851 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 91 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.