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A comprehensive assessment of fine particulate matter (PM2.5) compositions during the Southeast Asia dry season is presented. Samples of PM2.5 were collected between 24 June and 14 September 2014 using a high‐volume sampler. Water‐soluble ions, trace species, rare earth elements, and a range of elemental carbon (EC) and organic carbon were analyzed. The characterization and source apportionment of PM2.5 were investigated. The results showed that the 24 h PM2.5 concentration ranged from 6.64 to 68.2 µg m−3. Meteorological driving factors strongly governed the diurnal concentration of aerosol, while the traffic in the morning and evening rush hours coincided with higher levels of CO and NO2. The correlation analysis for non sea‐salt K+‐EC showed that EC is potentially associated with biomass burning events, while the formation of secondary organic carbon had a moderate association with motor vehicle emissions. Positive matrix factorization (PMF) version 5.0 identified the sources of PM2.5: (i) biomass burning coupled with sea salt [I] (7%), (ii) aged sea salt and mixed industrial emissions (5%), (iii) road dust and fuel oil combustion (7%), (iv) coal‐fired combustion (25%), (v) mineral dust (8%), (vi) secondary inorganic aerosol (SIA) coupled with F− (15%), and (vii) motor vehicle emissions coupled with sea salt [II] (24%). Motor vehicle emissions, SIA, and coal‐fired power plant are the predominant sources contributing to PM2.5. The response of the potential source contribution function and Hybrid Single‐Particle Lagrangian Integrated Trajectory backward trajectory model suggest that the outline of source regions were consistent to the sources by PMF 5.0.

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Efficacious operation for dam and reservoir system could guarantee not only a defenselessness policy against natural hazard but also identify rule to meet the water demand. Successful operation of dam and reservoir systems to ensure optimal use of water resources could be unattainable without accurate and reliable simulation models. According to the highly stochastic nature of hydrologic parameters, developing accurate predictive model that efficiently mimic such a complex pattern is an increasing domain of research. During the last two decades, artificial intelligence (AI) techniques have been significantly utilized for attaining a robust modeling to handle different stochastic hydrological parameters. AI techniques have also shown considerable progress in finding optimal rules for reservoir operation. This review research explores the history of developing AI in reservoir inflow forecasting and prediction of evaporation from a reservoir as the major components of the reservoir simulation. In addition, critical assessment of the advantages and disadvantages of integrated AI simulation methods with optimization methods has been reported. Future research on the potential of utilizing new innovative methods based AI techniques for reservoir simulation and optimization models have also been discussed. Finally, proposal for the new mathematical procedure to accomplish the realistic evaluation of the whole optimization model performance (reliability, resilience, and vulnerability indices) has been recommended.

This article aims to measure the dynamic impact of household consumption (final household consumption expenditure, LHC) on CO 2 emission from household's energy consumption in Malaysia from 1971 to 2010. The estimation of autoregressive distributed lag (ARDL) bounds test confirms a non-monotonic relationship between LHC and residential CO 2 emission. In the long run, there is a positive relationship between LHC and CO 2 emission as well as a negative relationship between quadratic forms of LHC and CO 2 emission which indicates the existence of an inverted U-shaped relationship between these two variables. The analysis also found a similar relationship in both the short and long run. To confirm the non-monotonous relationship, the U test of Sasabuchi-LindMehlum (2010) approach has followed to obtain the sufficient conditions for the existence of inverted U relationship. Moreover, the U test of Sasabuchi-Lind-Mehlum (2010) found that CO 2 emission increases with increasing LHC up to 6.5 units, but it declines with an additional increase of LHC which is also found by the ARDL model. However, the existence of environmental Kuznets curve implies that in the long run, household CO 2 emission declines with the additional increase of household consumption in the Malaysian economy.

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