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.

Prediction of the Level of Air Pollution Using Principal Component Analysis and Artificial Neural Network Techniques: a Case Study in Malaysia

Air pollution is strongly tied to climate change. Industrialization and fossil fuel combustion are the main contributors leading to climate change, also being significant sources of air pollution. Malaysia is a developing country with a focus on industrialization. The preference of using private cars is a common practice in Malaysia, resulting in the after-effects of haze and transboundary air pollution. Hence, air pollution has become a severe issue in Malaysia in recent times. Exposure to air pollutants such as ozone and airborne particles is associated with increases in hospital admissions and mortality. For the past few years, the focus of the research is moving towards air quality and the impacts of air pollution on health in Malaysia. In this study, we establish the definition of air pollution, the motivation to study it, and its impacts and sources of air pollution and climate change. We discuss the air quality monitoring system in Malaysia and compare Malaysian ambient air quality standards with global standards. We also look comprehensively on the health impacts of air pollution globally and in the Malaysian context. We discuss where the health impact studies in Malaysia are lacking and what are the gaps in the research. The role of the Malaysian government concerning air pollution and its impacts is discussed. Lastly, we look into the future work and research opportunities with a focus on engineering, estimation, predictive models and lack of research projects.

Air pollution and its health impacts in Malaysia: a review

The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML) Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI). Principal component analysis (PCA) identified CH4, CO, SO2, and H2S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R2 and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality.

An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning.

This paper describes the application of
principal component analysis (PCA) and artificial neural network (ANN) to predict
the air pollutant index (API) within the seven selected Malaysian air monitoring
stations in the southern region of Peninsular Malaysia based on seven years database
(2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward
ANN analysis demonstrated that the rotated principal component scores (RPCs) were
the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O3,
PM10, NO2, CH4, NmHC, THC, wind direction, humidity
and ambient temp) out of 12 prediction variables were the most significant parameters
to predict API. The results proved that the ANN method can be applied successfully
as tools for decision making and problem solving for better atmospheric management.

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Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia

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

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.

/pdf/spatial-and-temporal-air-quality-pattern-recognition-using-2nu7qt6e8o.pdf

Sharifah Norsukhairin Syed Abdul Mutalib

Papers

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