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Considered one of the most versatile and widely used classical thermodynamic models to correlate phase equilibria behavior of nonideal systems, the nonrandom two‐liquid (NRTL) theory does not explicitly account for specific chemical associations such as hydrogen bonding. This deficiency has been recognized as the cause for unsatisfactory representation of association systems like methanol–alkanes binaries. This work presents a practical approach to integrate Wertheim's perturbation theory for association contribution with the classical NRTL model. Specifically, the association contribution is calculated from pre‐determined molecule‐specific association strengths while the physical interaction contribution is captured with NRTL binary interaction parameters. The resulting association NRTL model correlates fluid phase equilibria of association systems with few adjustable parameters and offers improved predictive capability for higher order systems.

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Association theory has been extensively applied to equations of state such as statistical associating fluid theory and cubic plus association. This work presents an integration of association theory with the nonrandom two-liquid segment activity coefficient (NRTL-SAC) model. Replacing the four conceptual segments (hydrophobic, hydrophilic, polar attractive, and polar repulsive) in the original NRTL-SAC model, the association NRTL-SAC model represents interacting molecules with two conceptual segments (nonpolar and polar) and two association site types (hydrogen bond acceptor and donor). Given the molecule-specific parameters, the model shows much improved predictive capability than the original NRTL-SAC model for phase equilibria of chemical mixtures in which associations take place.

Wild fish caught by anglers (WFAs) were confirmed to be usually contaminated with metals, and the contamination status is radically affected by the growth and length of the fish. To determine the contamination levels of metals and health risks in WFAs with different length ranges of fish, this study ascertained the concentration of eight metals, including Cu, Pb, Zn, Cr, Cd, As, Hg and Se, in 171 wild fishes collected from the watershed of Liujiang River. The assessment of metal pollution and health risks from the consumption of these fishes with seven length ranges were accomplished. The obtained results implied a relatively high concentration of Zn, Cr, and Cd up to 109.294 mg/kg, 4.226 mg/kg, and 0.196 mg/kg (wet weight), respectively, which exceed the corresponding Maximum Residue Limit (MRL). The negative correlation between Cu, Zn, Cr, and Cd was observed to be significant with fish length, signifying a possible occurrence of biological dilution on these metals. The WFAs were mostly contaminated with Cr and Cd irrespective of the length ranges of fish, which were denoted by the average pollution index (Pi) of Cr and Cd and were commonly found to be beyond 0.2. Based on the results of health risk assessment analysis, most of the target hazard quotient (THQ) values of Cr were below 1, implying that the consumption of wild fish for adults has insignificant health risk. For children, the total target hazard quotient (TTHQ) values of beyond 1 were found in fishes with the length range of <25 cm, particularly a TTHQ value 1.627 in the range of 10–15 cm, indicating that children are being prone to serious health risks owing to the consumption of WFAs. The weekly recommended consumption of WFAs with the length range of 10–15 cm for adults and children was 0.298 kg/week and 0.149 kg/week, respectively. These are substantially lower than the current rate of fish consumption (0.42 kg/week), and therefore, the wild fish with the length range of 10–15 cm should be avoided for consumption.

It is difficult to accurately identify and extract bodies of water and underwater vegetation from satellite images using conventional vegetation indices, as the strong absorption of water weakens the spectral feature of high near-infrared (NIR) reflected by underwater vegetation in shallow lakes. This study used the shallow Lake Ulansuhai in the semi-arid region of China as a research site, and proposes a new concave-convex decision function to detect submerged aquatic vegetation (SAV) and identify bodies of water using Gao Fen 1 (GF-1) multi-spectral satellite images with a resolution of 16 meters acquired in July and August 2015. At the same time, emergent vegetation, "Huangtai algae bloom", and SAV were classified simultaneously by a decision tree method. Through investigation and verification by field samples, classification accuracy in July and August was 92.17% and 91.79%, respectively, demonstrating that GF-1 data with four-day short revisit period and high spatial resolution can meet the standards of accuracy required by aquatic vegetation extraction. The results indicated that the concave-convex decision function is superior to traditional classification methods in distinguishing water and SAV, thus significantly improving SAV classification accuracy. The concave-convex decision function can be applied to waters with SAV coverage greater than 40% above 0.3 m and SAV coverage 40% above 0.1 m under 1.5 m transparency, which can provide new methods for the accurate extraction of SAV in other regions.

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