[1] Studies were conducted as part of Asian Pacific Regional Aerosol Characterization Experiment (ACE-Asia) to characterize the major ion and elemental composition of aerosol particle samples collected at Gosan, an ACE-Asia supersite (GOS, Korea, total suspended particle or TSP samples) and at Zhenbeitai (ZBT, China, TSP and particles < 2.5 mm diameter or PM 2.5 samples), a site closer to the sources for Asia dust. The concentrations of 24 elements in the ZBT PM 2.5 samples were correlated with Al (an indicator of mineral dust), and the ratios of these elements to Al were similar to those in a loess certified reference material, but a second group of elements was enriched over crustal proportions most likely as a result of pollution emissions. ) also were generally well correlated with Al in both the ZBT and GOS samples, with the exception being WS K + at ZBT, where biomass burning may have had an effect. The percentage of calcium that was soluble approached 100% at ZBT versus $60% at GOS, and the ratio WS Ca 2+ /Al also was higher at ZBT. The molar ratio of sulfate to WS Ca 2+ was $0.1 at ZBT but increased to near unity at GOS, where the aerosol nitrate/WS Ca 2+ ratio was tenfold to hundredfold higher compared with ZBT, presumably because of anthropogenic influences. The observed differences in aerosol characteristics between sites can only be explained as the end product of different source contributions combined with complex processes involving gas-particle conversion, size-dependent fractionation, and aerosol mixing.

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A method of predicting the particle removal efficiency of gravitational wet scrubbers and the particle size distribution properties, that considers diffusion, interception, and impaction, is presented to study the particle removal mechanisms of gravitational wet scrubbers. This method assumes a lognormal size distribution of aerosol particles as well as three additive collection efficiencies. Thus, the overall collection efficiency is described as the sum of all three. It is represented as a U-shaped curve with a minimum in the region of around 1.0 mm in particle diameter. This allows aerosols in the diffusion-and in the impactiondominant regions to be removed at a higher rate compared with aerosol in the intermediate region. As aerosols pass through the gravitational wet scrubber, the geometric standard deviations of the size distribution of polydispersed aerosols decrease. The geometric mean diameter of aerosol in the diffusion-dominant region increases, whereas it decreases in the impaction-dominant region. The present study also shows that in optimum operation conditions such as low droplet falling velocity, small droplet size, and high liquid-to-gas flow ratio, the gravitational wet scrubber has sufficient ability to remove particles whose diameters are much smaller than 1.0 mm.

Since 2007, the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) has monthly issued multimodel ensemble (MME) seasonal predictions for 3 months, with 1 month lead time, and disseminated it to APEC member economies. This paper gives a comprehensive documentation of the current status of the APCC operational multimodel performance, with a large set of retrospective and real-time (2008-2013) predictions of temperature and precipitation. In order to investigate the enhancement in seasonal predictability that can be achieved by empirically weighted MME (using multiple regression) and calibrated MME (by correcting single-model prediction using a stepwise pattern projection method) schemes, operationally implemented at the APCC, we compare them with a simple averaged MME (with equal weightings), for predicting seasonal mean temperature and precipitation 1 month ahead. The results indicate that the simple averaged MME consistently outperforms the multiple regression-based MMEs, when considering all aspects of the predictions from operational prediction systems (i.e., in different variables, regions, and seasons) whereas the calibrated MME shows the capability to reduce errors and improve forecast skills in a large proportion of cases. The possible causes of the failure and success of the different MME methods implemented in the APCC operations are discussed.

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This paper presents a practical and objective procedure for a Bayesian inversion of geophysical data. We have applied geostatistical techniques such as kriging and simulation algorithms to acquire a prior model information. Then the Markov chain Monte Carlo (MCMC) method is adopted to infer the characteristics of the marginal distributions of model parameters. Geostatistics which is based upon a variogram model provides a means to analyze and interpret the spatially distributed data. For Bayesian inversion of dipole-dipole resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger and well logging data for obtaining a prior information by cokriging and simulations from covariogram models. Indicator approaches make it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the Markov chain Monte Carlo approach, based on Gibbs sampling, to examine the characteristics of a posterior probability density function and marginal distributions of each parameter. The MCMC technique provides a robust result from which information given by the indicator method, that is fundamentally non-parametric, is fully extracted. We have used the a prior information proposed by the geostatistical method as the full conditional distribution for Gibbs sampling. And to implement Gibbs sampler, we have applied the modified Simulated Annealing (SA) algorithm which effectively searched for global model space. This scheme provides a more effective and robust global sampling algorithm as compared to the previous study.