As emerging pollutants, microplastics have attracted the attention of scholars from all over the world. However, there is a lack of research on freshwater areas, even in densely populated urban areas. This study investigated eight urban lakes in Changsha, China. It was found that microplastic concentrations ranged from 2425 ± 247.5 items/m³ to 7050 ± 1060.66 items/m³ in the surface water of research areas and the maximum concentration was found in Yuejin Lake, a tourist spot in the center of the city. Anthropogenic factors are an important reason for microplastic abundance in urban lakes. The major shape of microplastics was linear and most of the microplastics were transparent. More than 89.5% of the microplastics had a size of less than 2 mm. Polypropylene was the dominant type in the studied waters. This study can provide a valuable reference for a better understanding of microplastic pollution in urban areas of China.

Constructed wetlands (CWs) have been proven as a reliable alternative to traditional wastewater treatment technologies. Microorganisms in CWs, as an important component, play a key role in processes such as pollutant degradation and nutrient transformation. Therefore, an in-depth analysis of the community structure and diversity of microorganisms, especially for functional microorganisms, in CWs is important to understand its performance patterns and explore optimized strategies. With advances in molecular biotechnology, it is now possible to analyze and study microbial communities and species composition in complex environments. This review performed bibliometric analysis of microbial studies in CWs to evaluate research trends and identify the most studied pollutants. On this basis, the main functional microorganisms of CWs involved in the removal of these pollutants are summarized, and the effects of these pollutants on microbial diversity are investigated. The result showed that the main phylum involved in functional microorganisms in CWs include Proteobacteria, Bacteroidetes, Actinobacteria and Firmicutes. These functional microorganisms can remove pollutants from CWs by catalyzing chemical reactions, biodegradation, biosorption, and supporting plant growth, etc. Regarding microbial alpha diversity, heavy metals and high concentrations of nitrogen and phosphorus significantly reduce microbial richness and diversity, whereas antibiotics can cause large fluctuations in alpha diversity. Overall, this review can provide new ideas and directions for the research of microorganisms in CWs.

Organophosphate esters (OPEs) are widely used as flame retardants and plasticizers. OPEs have been released into various environments (e.g., water, sediments, dust and air, and soil). To investigate the occurrence and distribution of OPEs in various environments in China, this review collects and discusses the published scientific studies in this field. Chlorinated OPEs, as flame retardants, are the predominant OPEs found in the environment. The analysis of data revealed large concentration variations among microenvironments, including inflowing river water (range: 0.69–10.62 µgL⁻¹), sediments (range: 0.0197–0.234 µg/g), dust (range: 8.706–34.872 µg/g), and open recycling sites’ soil (range: 0.122–2.1 µg/g). Moreover, OPEs can be detected in the air and biota. We highlight the overall view regarding environmental levels of OPEs in different matrices as a starting point to monitor trends for China. The levels of OPEs in the water, sediment, dust, and air of China are still low. However, dust samples from electronic waste workshop sites were more contaminated. Human activities, pesticides, electronics, furniture, paint, plastics and textiles, and wastewater plants are the dominant sources of OPEs. Human exposure routes to OPEs mainly include dermal contact, dust ingestion, inhalation, and dietary intake. The low level of ecological risk and risk to human health indicated a limited threat from OPEs. Furthermore, current challenges and perspectives for future studies are prospected. A criteria inventory of OPEs reflecting the levels of OPEs contamination association among different microenvironments, emerging OPEs, and potential impact of OPEs on human health, particularly for children are needed in China for better investigation.

Dongting Lake, the second largest freshwater lake in China, is an important water source for the Yangtze River Basin. The water area of Dongting Lake fluctuates significantly daily, which may cause flooding and other relevant disasters. Although remote sensing techniques may provide lake area estimates with reasonable accuracy, they are not available in real-time and may be susceptible to weather conditions. To address this issue, this paper attempted to examine the relationship between lake area and the water levels at the hydrological stations. Multi-temporal water area data were derived through analyzing Moderate Resolution Imaging Spectroradiometer (MODIS) imagery using the Automatic Water Extraction Index (AWEI). Then we analyzed the inter- and intra-annual variations in the water area of the Dongting Lake. Corresponding water level information at hydrological stations of the Dongting Lake were obtained. Simple linear regression (SLR) models and stepwise multiple linear regression (SMLR) models were constructed using water levels and water level differences from the upstream and downstream hydrological stations. We used the data from 2004 to 2012 and 2012, respectively, to build the model, and applied the data from 2013 to 2015 to evaluate the models. Results suggest that the maximum water area of the Dongting Lake during 2000–2015 has a clear decreasing trend. The variations in the water area were characterized by hydrological seasons, with the annual minimum and maximum water areas occurring in January and September, respectively. The water level at the Chengjingji station, and water level differences between upstream stations and the Chengjingji station, play a major role in estimating the water area. Further, results also show that the SMLR established in 2012 performs the best in estimating water area of the Dongting Lake, especially with high water levels.

Monthly precipitation modeling is important in various applications, e.g. streamflow forecasts and water resources management. This paper develops an operational precipitation forecasting scheme, using Bayesian Non-homogeneous Hidden Markov Chain (NHMM) model and teleconnection index. Although the Hidden Markov Chain model has been investigated before in similar studies, the NHMM algorithm employed in this study allows modeling both non-stationary transition probabilities and emission matrix. Climatic teleconnection that affect precipitation is used to drive changes in transition probabilities of different states in the Markov model. The proposed framework is illustrated for multiple-station precipitation analysis in NingXiang County, a southern inland area in China with a high population density. A simulation model is constructed to examine the model's capacity in capturing variabilities and temporal-spatial characteristics exhibiting in monthly precipitation data during 1961–2013. Results indicate that the proposed NHMM model captures the precipitation characteristics at different stations well. Spearman correlation between conditional mean of simulated ensembles and observed data is 0.87–0.9, with few variations at distinct stations. The proposed framework has general applications and can be applied to simulate and generate stochastic monthly precipitation. Further application of the method is also described in the paper.