<p><strong>Abstract.</strong> The real-time measurements of NH<sub>3</sub> and trace gases were conducted, in conjunction with semi-continuous measurements of water-soluble ions in PM<sub>2.5</sub> at a rural site in the North China Plain (NCP) from May to September 2013 in order to better understand of chemical characteristics for ammonia, and of the impact on formation of secondary ammonium aerosols in the NCP. Extremely high NH<sub>3</sub> and NH<sub>4</sub><sup>+</sup> concentrations were observed after a precipitation event within 7–10 days following urea application. Elevated NH<sub>3</sub> levels coincided with elevated NH<sub>4</sub><sup>+</sup>, suggesting that NH<sub>3</sub> plays a vital role in enhancing particulate ammonium. For the sampling period, the average oxidation/conversion ratios for SO<sub>4</sub><sup>2−</sup> (SOR), NO<sub>3</sub><sup>−</sup> (NOR) and NH<sub>4</sub><sup>+</sup> (NHR) were estimated to be 64 %, 24 % and 30 %, respectively. The increased NH<sub>3</sub> concentrations mainly from agricultural activities, coincided with the prevailing meteorological conditions could promote the secondary transformation, resulting in higher hourly SOR, NOR and NHR. The concentrations of NH<sub>3</sub>, NH<sub>4</sub><sup>+</sup>, and NHR had clear diurnal variations, which could be attributed to their sources, meteorological conditions, and formation mechanisms. The back trajectory analysis indicates that the transport of air masses from the North China Plain region contributed to the atmospheric NH<sub>3</sub> variations, and both regional sources and long-distance transport from southeast played important roles in the observed ammonium aerosol at rural site in the NCP. The findings of this study are expected to facilitate developing future NH<sub>3</sub> emission control policies for the North China Plain.</p>

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The purpose of this paper is to show how the results of an optimisation model that can be integrated with the decisions made within a simulation model to schedule back-end operations in a semiconductor assembly and test facility. The problem is defined by a set of resources that includes machines and tooling, process plans for each product and the following four hierarchical objectives: minimise the weighted sum of key device shortages, maximise weighted throughput, minimise the number of machines used and minimise the makespan for a given set of lots in queue. A mixed integer programming model is purposed and first solved with a greedy randomised adaptive search procedure (GRASP). The results associated with the prescribed facility configuration are then fed to the simulation model written in AutoSched AP. However, due to the inadequacy of the options built into AutoSched, three new rules were created: the first two are designed to capture the machine set-up profiles provided by the GRASP and the third to prioritise the processing of hot lots containing key devices. The computational analysis showed that incorporating the set-up from the GRASP in dynamic operations of the simulation greatly improved its performance with respect to the four objectives.

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