Environmental Engineering
M.I. Rumaling; F.P. Chee; H.W.J. Chang; C.M. Payus; S.K. Kong; J. Dayou; J. Sentian
Abstract
BACKGROUND AND OBJECTIVES: Air quality in some developing countries is dominated by particulate matter, especially those with size 10 micrometers and smaller or PM10. They can be inhaled and sometimes can get deep into lungs; some may even get into bloodstream and cause serious health problems. Therefore, ...
Read More
BACKGROUND AND OBJECTIVES: Air quality in some developing countries is dominated by particulate matter, especially those with size 10 micrometers and smaller or PM10. They can be inhaled and sometimes can get deep into lungs; some may even get into bloodstream and cause serious health problems. Therefore, future PM10 concentration forecasting is important for early prevention and in urban development planning, which is crucial for developing cities. This paper presents the development of PM10 forecasting model using nonlinear autoregressive with exogenous input model.METHODS: To improve performance of nonlinear autoregressive with exogenous input model, principal component analysis is used prior to the model for variable selection. The first stage of principal component analysis involves Scree plot, which determines the number of principal components based on explained variance. This is then followed by selecting variables using a rotated component matrix, based on their strength of contribution towards variation of PM10 concentration. To test the model, PM10 data in Kota Kinabalu from 2003 – 2010 was used. Neural network models are developed using this data by varying number of input variables with the inclusion of temporal variables. The developed forecasting models are evaluated using data PM10 in the city from 2011 to 2012. Four performance indicators, namely root mean square error, mean absolute error, index of agreement and fractional bias are reported.FINDINGS: Results from principal component analysis show that five variables including wind direction index, relative humidity, ambient temperature, concentration of nitrogen dioxide and concentration of ozone strongly contribute to the variation of PM10 concentration. By using these variables together with temporal variables as input in the nonlinear autoregressive with exogenous input models, the resultant model shows good forecasting performance, with root mean square error of 7.086±0.873 µg/m3. The selection of significant variables helps in reducing input variables inside the forecast model without degrading its forecast performance.CONCLUSION: This model shows very promising performance in forecasting PM10 concentration in Kota Kinabalu as it requires fewer input variables and does not require variable transformation.
Environmental Management
U.G. Abhijna
Abstract
Multivariate statistical techniques such as cluster analysis, multidimensional scaling and principal component analysis were applied to evaluate the temporal and spatial variations in water quality data set generated for two years (2008-2010) from six monitoring stations of Veli-Akkulam Lake and compared ...
Read More
Multivariate statistical techniques such as cluster analysis, multidimensional scaling and principal component analysis were applied to evaluate the temporal and spatial variations in water quality data set generated for two years (2008-2010) from six monitoring stations of Veli-Akkulam Lake and compared with a regional reference lake Vellayani of south India. Seasonal variations of 14 different physicochemical parameters analyzed were as follows: pH (6.42-7.48), water temperature (26.0-31.28°C), salinity (0.50-26.81 ppt), electrical conductivity (47-20656.31 µs/cm), dissolved oxygen (0.078-7.65 mg/L), free carbon-dioxide (3.8-51.8 mg/L), total hardness (27.20-2166.6 mg/L), total dissolved solids (84.66-4195 mg/L), biochemical oxygen demand (1.57-25.78 mg/L), chemical oxygen demand (5.35-71.14 mg/L), nitrate (0.012-0.321 µg/ml), nitrite (0.24-0.79 µg/ml), phosphate (0.04-5.88 mg/L), and sulfate (0.27-27.8 mg/L). Cluster analysis showed four clusters based on the similarity of water quality characteristics among sampling stations during three different seasons (pre-monsoon, monsoon and post-monsoon). Multidimensional scaling in conjunction with cluster analysis identified four distinct groups of sites with varied water quality conditions such as upstream, transitional and downstream conditions in Veli-Akkulam Lake and a reference condition at Vellayani Lake. Principal Component Analysis showed that Veli-Akkulam Lake was seriously deteriorated in water quality while acceptable water quality conditions were observed at reference lake Vellayani. Thus the present study could estimate the effectiveness of multivariate statistical approaches for assessing water quality conditions in lakes.
EndNote