Spatial assessment of air quality patterns in Malaysia using multivariate analysis

Dominick, Doreena; Juahir, Hafizan; Latif, Mohd Talib; Zain, Sharifuddin M.; Aris, Ahmad Zaharin

doi:10.1016/j.atmosenv.2012.06.021

Cited by 258 publications

(206 citation statements)

References 54 publications

Supporting

Mentioning

186

Contrasting

Unclassified

Order By: Relevance

“…It has strong positive loadings on PM 10 (0.758), NO 2 (0.712) and SO 2 (0.683). Cluster 1 is a combination of urban and industrial cities; thus the burning of fuels in automobiles and industrial facilities are suspected to be the source of air pollution (SO 2 , NO 2 ) as indicated in previous studies (Janssen et al, 2001;Dominick et al, 2012). …”

Section: A) Clustermentioning

confidence: 99%

“…Kuching monitoring station is located at northeast Borneo in Sarawak state which is surrounded by industrial activities. Besides that, this station is also affected by power plants such as PPLS Power Generation Plant (coal-fired) and Sejingkat power Corporations Plant (coal-fired) (Chung et al, 2012;Dominick et al, 2012). Kota Kinabalu monitoring station is in Kota Kinabalu, the capital city of Sabah state (North Borneo), the station is surrounded by high-populated residential areas and major roads.…”

Section: Study Areas Descriptionmentioning

confidence: 99%

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

“…In the present study, this technique was applied to reduce variables and to identify the most relevant variables in O 3 variations (Dominick et al, 2012). PCA is widely known due to its capability to detect the most significant variables in dataset with minimum loss of the original information (Dominick et al, 2012;Elbayoumi et al, 2014). Principal components (PCs) were extracted, such that the first PC (PC1) accounted for the largest amount of total variation in the data set, whereas the following components accounted for the remaining variations that were not considered in PC1 (Kovac-Andric et al, 2009).…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

See 3 more Smart Citations

The Influence of Spatial Variability of Critical Conversion Point (CCP) in Production of Ground Level Ozone in the Context of Tropical Climate

Awang

Elbayoumi

Ramli

et al. 2016

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

Critical conversion point (CCP) is a very crucial step in production of the ground level O 3 chemistry. Thus, a multivariate analysis was applied on the dataset of nine selected locations in Malaysia from 1999 to 2010. It incorporated hierarchical agglomerative cluster analysis (HACA) to explore the spatial variability of CCP and principal component analysis (PCA) to determine the major sources of the air pollutants that influence ozone CCP. High variability in CCP was observed between the monitoring stations that occurred during critical conversion time (CCT) from 8:00 a.m. to 11:00 a.m. The HACA results grouped the nine monitoring stations into three different clusters, based on the characteristics of ozone concentrations during CCT period. Results of PCA for the three clusters showed that the contributions to O 3 level variation during CCT by meteorological variables (UVB, temperature, relative humidity, and wind speed) are higher at 51.6%, 48.5%, and 33.3% than that of primary air pollutants (NO 2 , SO 2 , PM 10 ) at 19.2%, 21.4%, and 15.2% for cluster 1, cluster 2, and cluster 3, respectively. Therefore, applying a targeted spatial control strategy for ground level O 3 precursors during the CCT period is a crucial step.

show abstract

Section: A) Clustermentioning

confidence: 99%

Section: Study Areas Descriptionmentioning

confidence: 99%

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

See 2 more Smart Citations

The Influence of Spatial Variability of Critical Conversion Point (CCP) in Production of Ground Level Ozone in the Context of Tropical Climate

Awang

Elbayoumi

Ramli

et al. 2016

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

show abstract

“…CO and NO 2 had a strong relationship as both pollutants were from the emission of motor vehicles [7][8]. Moreover, the concentration of air pollutants was determined by differences in topography, climatic conditions and economic activities [9]. The relationship between API and PM 10 shown that as the concentration of PM 10 increased, the API level was increased as indicated in the previous [6].…”

mentioning

confidence: 83%

Spatial assessment on ambient air quality status: a case study in Klang, Selangor

Shafii¹,

Saudi²,

Mahmud³

et al. 2018

J. Fundam and Appl Sci.

View full text Add to dashboard Cite

Klang as the center of economic and industrial zone in Malaysia has been exposed to poor air quality condition over the years. This study was conducted to evaluate the spatial variation pattern of air quality status in Klang, Selangor by using a four years (2010-2013) secondary database from the Malaysian Department of Environment (DOE). The finding shows that carbon monoxide (CO) had a strong correlation with nitrogen dioxide (NO 2 ) (r = 0.76, p < 0.001), while Air Pollutant Index (API) had moderate correlation with particulate matter (PM 10 ) (r = 0.64, p < 0.001). Principal Component Analysis (PCA) indicates that the most significant air pollutants were NO 2 , CO and PM 10 . Statistical Process Control (SPC) reveals that several PM 10 data beyond the limitations of SPC and the national guidelines. This study shows that active collaboration among all relevant environmental departments and agencies should be implemented for the effective management of air quality.

show abstract

Association of the Indoor Environment With Dry Eye Metrics

et al. 2020

View full text Add to dashboard Cite

ry eye is a multifactorial chronic disease of the tears and ocular surface. 1 Worldwide, the prevalence of dry eye ranges from 5% to 50% and varies owing to population characteristics, disease definition, and other risk factors. 2 In the United States, approximately 16.4 million adults (6.8%) have been diagnosed with dry eye. 3 Symptoms of dry eye are heterogeneous and include painful symptoms (ie, dryness, burning, irritation) and vision-related symptoms (ie, poor or fluctuating vision), which can negatively affect physical health, mental health, and quality of life. 2 Signs of dry eye are likewise heterogeneous and include decreased tear production, increased tear evaporation, inflammation, and high tear osmolarity. 4 Numerous risk factors for dry eye have been iden-tified, including age, sex, comorbidities such as depression and arthritis, and medications such as antihistamines. 2 Dry eye is also influenced by environmental factors, including wind, 5 high temperature, 6 low humidity, 5 high altitude, 7 and air pollution. 5 In a study of 500 hospital-based individuals in India, 6 wind and high temperature exposure, as assessed by self-report, correlated with dry eye prevalence (odds ratios, 2.15 and 1.91, respectively). In a study of 3.41 million US veterans, the risk of a diagnosis of dry eye was 6% and 7% higher in zip codes where wind speed and humidity were 1 SD less than the mean, respectively. The risk of a dry eye diagnosis was also 13% higher in zip codes where aerosol optical depth, a measure of atmospheric aerosols that include air-IMPORTANCE The ocular surface is continuously exposed to the environment. Although studies have focused on associations between outdoor environmental conditions and dry eye, information on associations between the indoor environment and dry eye is lacking.OBJECTIVE To determine associations between the indoor environment and dry eye. DESIGN, SETTING, AND PARTICIPANTSThis prospective cross-sectional study sample of 97 veterans with a wide range of dry eye metrics was recruited from the Miami Veterans Affairs Healthcare eye clinic from October 19, 2017, to August 30, 2018. Dry eye metrics were first evaluated in the clinic, followed by indoor home environmental metrics within 1 week using a handheld particle counter. Data were analyzed from October 19, 2017, to August 30, 2018.MAIN OUTCOMES AND MEASURES Symptoms of dry eye were assessed with standardized questionnaires. Dry eye signs were assessed via standard examination. Indoor environmental metrics included temperature, humidity, and particulate matter mass and count. RESULTSOf the 97 participants included in the analysis, 81 (84%) were men, with a mean (SD) age of 58.2 (11.9) years. Dry eye symptoms were in the moderate range with a mean (SD) Ocular Surface Disease Index (OSDI) score of 31.

show abstract

Spatial assessment of air quality patterns in Malaysia using multivariate analysis

Cited by 258 publications

References 54 publications

The Influence of Spatial Variability of Critical Conversion Point (CCP) in Production of Ground Level Ozone in the Context of Tropical Climate

The Influence of Spatial Variability of Critical Conversion Point (CCP) in Production of Ground Level Ozone in the Context of Tropical Climate

Spatial assessment on ambient air quality status: a case study in Klang, Selangor

Association of the Indoor Environment With Dry Eye Metrics

Contact Info

Product

Resources

About