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  1. Jusoh ML, Manaf LA, Latiff PA
    Iranian J Environ Health Sci Eng, 2013 Feb 07;10(1):17.
    PMID: 23390930 DOI: 10.1186/1735-2746-10-17
    This study aims to assess the effect of EM application on the composting process of rice straw with goat manure and green waste and to evaluate the quality of both compost treatments. There are two treatment piles in this study, in which one pile was applied with EM and another pile without EM. Each treatment was replicated three times with 90 days of composting duration. The parameters for the temperature, pH, TOC and C/N ratio, show that decomposition of organic matter occurs during the 90-day period. The t-test conducted shows that there is a significant difference between compost with EM and compost without EM. The application of EM in compost increases the macro and micronutrient content. The following parameters support this conclusion: compost applied with EM has more N, P and K content (P 
  2. Nasir MF, Zali MA, Juahir H, Hussain H, Zain SM, Ramli N
    Iranian J Environ Health Sci Eng, 2012 Dec 10;9(1):18.
    PMID: 23369363 DOI: 10.1186/1735-2746-9-18
    Recent techniques in the management of surface river water have been expanding the demand on the method that can provide more representative of multivariate data set. A proper technique of the architecture of artificial neural network (ANN) model and multiple linear regression (MLR) provides an advance tool for surface water modeling and forecasting. The development of receptor model was applied in order to determine the major sources of pollutants at Kuantan River Basin, Malaysia. Thirteen water quality parameters were used in principal component analysis (PCA) and new variables of fertilizer waste, surface runoff, anthropogenic input, chemical and mineral changes and erosion are successfully developed for modeling purposes. Two models were compared in terms of efficiency and goodness-of-fit for water quality index (WQI) prediction. The results show that APCS-ANN model gives better performance with high R2 value (0.9680) and small root mean square error (RMSE) value (2.6409) compared to APCS-MLR model. Meanwhile from the sensitivity analysis, fertilizer waste acts as the dominant pollutant contributor (59.82%) to the basin studied followed by anthropogenic input (22.48%), surface runoff (13.42%), erosion (2.33%) and lastly chemical and mineral changes (1.95%). Thus, this study concluded that receptor modeling of APCS-ANN can be used to solve various constraints in environmental problem that exist between water distribution variables toward appropriate water quality management.
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