Water Quality Evaluation and Pollution Source Apportionment of Surface Water in a Major City in Southeast China Using Multi-Statistical Analyses and Machine Learning Models.

The comprehensive evaluation of water quality and identification of potential pollution sources has become a hot research topic. In this study, 14 water quality parameters at 4 water quality monitoring stations on the M River of a city in southeast China were measured monthly for 10 years (2011-2020). Multiple statistical methods, the water quality index (WQI) model, machine learning (ML), and positive matrix factorisation (PMF) models were used to assess the overall condition of the river, select crucial water quality parameters, and identify potential pollution sources. The average WQI values of the four sites ranged from 68.31 to 77.16, with a clear trend of deterioration from upstream to downstream. A random forest-based WQI model (WQIRF model) was developed, and the results showed that Mn, Fe, faecal coliform, dissolved oxygen, and total nitrogen were selected as the top five important water quality parameters. Based on the results of the WQIRF and PMF models, the contributions of potential pollution sources to the variation in the WQI values were quantitatively assessed and ranked. These findings prove the effectiveness of ML in evaluating water quality, and improve our understanding of surface water quality, thus providing support for the formulation of water quality management strategies.

Effect of Annealing Temperature on the Mechanical and Corrosion Behavior of a Newly Developed Novel Lean Duplex Stainless Steel.

The effect of annealing temperature (1000-1150 °C) on the microstructure evolution, mechanical properties, and pitting corrosion behavior of a newly developed novel lean duplex stainless steel with 20.53Cr-3.45Mn-2.08Ni-0.17N-0.31Mo was studied by means of optical metallographic microscopy (OMM), scanning electron microscopy (SEM), magnetic force microscopy (MFM), scanning Kelvin probe force microscopy (SKPFM), energy dispersive X-ray spectroscopy (EDS), uniaxial tensile tests (UTT), and potentiostatic critical pitting temperature (CPT). The results showed that tensile and yield strength, as well as the pitting corrosion resistance, could be degraded with annealing temperature increasing from 1000 up to 1150 °C. Meanwhile, the elongation at break reached the maximum of 52.7% after annealing at 1050 °C due to the effect of martensite transformation induced plasticity (TRIP). The localized pitting attack preferentially occurred at ferrite phase, indicating that the ferrite phase had inferior pitting corrosion resistance as compared to the austenite phase. With increasing annealing temperature, the pitting resistance equivalent number (PREN) of ferrite phase dropped, while that of the austenite phase rose. Additionally, it was found that ferrite possessed a lower Volta potential than austenite phase. Moreover, the Volta potential difference between ferrite and austenite increased with the annealing temperature, which was well consistent with the difference of PREN.