A review of the CAMx, CMAQ, WRF-Chem and NAQPMS models: Application, evaluation and uncertainty factors.

With the gradual deepening of the research and governance of air pollution, chemical transport models (CTMs), especially the third-generation CTMs based on the "1 atm" theory, have been recognized as important tools for atmospheric environment research and air quality management. In this review article, we screened 2396 peer-reviewed manuscripts on the application of four pre-selected regional CTMs in the past five years. CAMx, CMAQ, WRF-Chem and NAQPMS models are well used in the simulation of atmospheric pollutants. In the simulation study of secondary pollutants such as O3, secondary organic aerosol (SOA), sulfates, nitrates, and ammonium (SNA), the CMAQ model has been widely applied. Secondly, model evaluation indicators are diverse, and the establishment of evaluation criteria has gone through the long-term efforts of predecessors. However, the model performance evaluation system still needs further specification. Furthermore, temporal-spatial resolution, emission inventory, meteorological field and atmospheric chemical mechanism are the main sources of uncertainty, and have certain interference with the simulation results. Among them, the inventory and mechanism are particularly important, and are also the top priorities in future simulation research.

Solid-phase microextraction combined with gas chromatography/triple quadrupole tandem mass spectrometry for determination of nitrated polycyclic aromatic hydrocarbons in sediments.

Nitro-polycyclic aromatic hydrocarbons have been detected in various environmental media. However, determination in sediment matrix is challenging due to the lack of a suitable method. In this study, a reliable method for determining 15 nitro-polycyclic aromatic hydrocarbons in sediments was developed based on accelerated solvent extraction and solid-phase microextraction coupled with gas chromatography-tandem mass spectrometry. The accelerated solvent extraction and solid-phase microextraction are sample pre-treatment techniques that have advantages, such as rapid operation and minimal sample volume. Initially, the solid-phase microextraction was optimized using five commercial fibers and from that 65 µm polydimethylsiloxane/divinylbenzene fiber was selected as the best fiber. Further, the accelerated solvent extraction conditions were optimized by Taguchi experimental design, such as extraction temperature (120℃), extraction solvent (dichloromethane), number of cycles (two), static extraction period (4 min), and rinse volume (90%). The method parameters, such as limits of quantitation, and intraday and interday accuracy and precision, were in the range of 0.067-1.57 ng/g, 75.2-115.2%, 69.9-115.4%, and 1.0-16.5%, respectively. Upon meeting all the quality criteria, the method was applied successfully to analyze real sediment samples. Therefore, our study creates a new prospect for the future application of direct immersion solid-phase microextraction in sediment analysis.

Correlation-driven direct sampling method for geostatistical simulation and training image evaluation.

Multiple-point geostatistics (MPS) is a competitive algorithm that produces a set of geologically realistic models. Viewing a training image (TI) as a prior model, MPS extracts patterns from the TI and reproduces patterns which are compatible with the hard data (HD). However, two challenges within the MPS framework are the geologically complex simulation and the TI evaluation. With the objective to achieve a high-quality simulation, we explore a way to address these two issues. First, correlation-driven direct sampling (CDS) is proposed to realize geostatistical simulation. We introduce the correlation-driven distance as a measure of similarity between two patterns. The weights in our distance measurement are derived by the concepts of the ellipse, correlation coefficient, Gaussian function, and affine transformation. Second, we fulfill TI evaluation on the basis of the consistency between TI and HD. Inspired by CDS, the minimum correlation-driven distance (MCD) is proposed to improve the evaluation accuracy. We suggest a conditioning pattern extraction history strategy to speed up the evaluation program. Third, the local consistency is presented to address nonstationarity. The program automatically divides the simulation domain into several subareas. A two-dimensional (2D) channelized reservoir image and a three-dimensional (3D) rock image are used to validate our proposed method. In comparison with previous methods, CDS yields better simulation quality. The further applications include a set of 2D TI evaluations and a 3D simulation domain segmentation. MCD exhibits sensible evaluation accuracy, excellent computational efficiency, and the ability to deal with nonstationarity.