Microplastics pollution in the Asian water tower: Source, environmental distribution and proposed mitigation strategy.

Microplastics generated from fragmentation of leftover plastics and industrial waste has reached in the remotely located Asian water tower (AWT) region, the 3rd pole of earth and origin site of several freshwater rivers. The accumulation of microplastics in AWT ecosystem has potential to alter the climatic condition contributing in global warming and disturbing the biodiversity structural dynamics. The present paper provides a comprehensive critical discussion over quantitative assessment of microplastics in different ecosystems (i.e. river, lakes, sediment and snow or glacier) of AWT. The hydrodynamic fate and transport of microplastics and their ecological impact on hydromorphology and biodiversity of AWT has been exemplified. Furthermore, key challenges, perspectives and research directions are identified to mitigate microplastics associated problems. During survey, the coloured polyethylene and polyurethane fibers are the predominant microplastics found in most areas of AWT. These bio-accumulated MPs alter the rhizospheric community structure and deteriorate nitrogen fixation process in plants. Significance in climate change, MPs pollution is enhancing the emissions of greenhouse gases (NH3 by ∼34% and CH4 by ∼9%), contributing in global warming. Considering the seriousness of MPs pollution, this review study can enlighten the pathways to investigate the effect of MPs and to develop monitoring tools and sustainable remediation technologies with feasible regulatory strategies maintaining the natural significance of AWT region.

Response of biofilm-based systems for antibiotics removal from wastewater: Resource efficiency and process resiliency.

Biofilm-based systems have efficient stability to cope-up influent shock loading with protective and abundant microbial assemblage, which are extensively exploited for biodegradation of recalcitrant antibiotics from wastewater. The system performance is subject to biofilm types, chemical composition, growth and thickness maintenance. The present study elaborates discussion on different type of biofilms and their formation mechanism involving extracellular polymeric substances secreted by microbes when exposed to antibiotics-laden wastewater. The biofilm models applied for estimation/prediction of biofilm-based systems performance are explored to classify the application feasibility. Further, the critical review of antibiotics removal efficiency, design and operation of different biofilm-based systems (e.g. rotating biological contactor, membrane biofilm bioreactor etc.) is performed. Extending the information on effect of various process parameters (e.g. hydraulic retention time, pH, biocarrier filling ratio etc.), the microbial community dynamics responsible of antibiotics biodegradation in biofilms, the technological problems, related prospective and key future research directions are demonstrated. The biofilm-based system with biocarriers filling ratio of ∼50-70% and predominantly enriched with bacterial species of phylum Proteobacteria protected under biofilm thickness of ∼1600 µm is effectively utilized for antibiotic biodegradation (>90%) when operated at DO concentration ≥3 mg/L. The C/N ratio ≥1 is best suitable condition to eliminate antibiotic pollution from biofilm-based systems. Considering the significance of biofilm-based systems, this review study could be beneficial for the researchers targeting to develop sustainable biofilm-based technologies with feasible regulatory strategies for treatment of mixed antibiotics-laden real wastewater.

Microbial biomarkers as indication of dynamic and heterogeneous urban water environments.

Water samples for the 16S rRNA gene and water quality analyses were collected from around 155 km of river segments surrounding the urban areas in Xi'an, China. Multiple statistical analyses showed that the dynamic shifts of microbial communities in the Chan, Ba, and Feng Rivers from the spring to the summer seasons were apparent but little in the Zao River. The heterogeneity of microbial distributions was more due to the influence of hydrologic conditions and various sources of inflows in the rivers. The LEfSe analysis showed that the Chan and Zao Rivers, both more impacted by the sewage effluents, were more differentially abundant with bacteria related to polluted water, but the Ba and Feng Rivers, both on the outer side of the city, were more abundant with microbial communities in soil and freshwater environments in the summer. Multiple statistical analyses indicated that environmental variables had significant impacts on microbial communities. The geographical information system-based spatial analysis showed heterogeneity of microbial community distributions along the rivers. This study showed that the high-throughput sequencing analysis could identify some pathogenic bacteria that would significantly threaten public health and eco-environments in urban rivers.

Monitoring hydrological drought using long-term satellite-based precipitation data.

Long-term (over 30a) satellite-based quantitative rainfall estimate (SRE) products provide an ideal data source for hydrological drought monitoring. This study mainly explores the suitability of the two long-term SREs, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and the Climate Hazards Group (CHG) Infrared Precipitation with Stations (CHIRPS), for hydrological drought monitoring. A hydrological drought index called the standardized streamflow index (SSI) was used as an example and the Grid-based Xinanjiang (GXAJ) hydrological model was used for streamflow generation of the SREs. A middle size basin in the humid region of south China was selected as case study. The obtained results show that both SREs present acceptable performances for hydrological modeling, and CHIRPS outperformed PERSIANN-CDR. SSIs calculated by the SRE simulations generally fit well with the trend of observation-based on SSI but apparent deviations in drought intensity were also found. In contrast to hydrological modeling, performance of the SRE-based SSI showed almost no change after model recalibration. Both SREs generally present acceptable classification accuracy but tended to underestimate the levels of drought types. Both SREs accurately captured the beginning, end, and duration of this drought event; however, several deviations were found in severity and intensity estimation of the drought event. In general, both SREs are suitable for hydrological drought monitoring. Although the CHIRPS generally presented better performance, the PERSIANN-CDR is still adequate for hydrological drought monitoring.

A spatial assessment of urban waterlogging risk based on a Weighted Naïve Bayes classifier.

Urban waterlogging occurs frequently and often causes considerable damage that seriously affects the natural environment, human life, and the social economy. The spatial evaluation of urban waterlogging risk represents an essential analytic step that can be used to prevent urban waterlogging and minimize related losses. The Weighted Naïve Bayes (WNB) classifier is a powerful method for knowledge discovery and probability inference under conditions of uncertainty; a WNB classifier can be applied to estimate the likelihood of hazards. Six spatial factors were considered to be added to the WNB, which may improve the efficiency in predicting urban waterlogging risk during analysis. As such, a spatial framework integrating WNB with GIS was developed to assess the risk of urban waterlogging using the primary urban area of Guangzhou in China as an example. The results show that 1) the rationality of six spatial factors was determined according to the Conditional Probability Tables and weights; 2) the Most Accurate Sampling Table has objectivity; and 3) the areas with a high likelihood of waterlogging risk were mainly located in the southwestern part of the study area. The northeastern zones are relatively free of waterlogging risk. The results reveal a more accurate spatial pattern of urban waterlogging risk that can be used to identify risk "hot spots". The resulting gridded estimates provide a realistic reference for decision making related to urban waterlogging.

Response of net primary production to land use and land cover change in mainland China since the late 1980s.

Land use and land cover patterns in mainland China have substantially changed in the recent decades under the economic reform policies of the government. The terrestrial carbon cycle, particularly the net primary productivity (NPP), has been substantially changed on both local and national scales. With the growing concern over the effects of the terrestrial carbon cycle on global climate changes, the impacts of land use and cover change (LUCC) on NPP need to be understood. In this study, variations in NPP caused by LUCC (e.g., urbanization and conversion of other land use to forest and grassland) in mainland China from the late 1980s to 2015 were evaluated based on land cover datasets and NPPs simulated from the Carnegie-Ames-Stanford Approach model. The results indicate that the national total losses in NPP attributed to urbanization reached 1.695 TgC between the late 1980s and 2015. A large proportion (63.02%) of the total losses was due to the transformation from cropland to urban land. Urban expansion decreased the monthly and total NPPs over southern China, which includes the South China Region, Southwest China Region, and the middle and lower regions of the Yangtze River. However, the total NPP increased in the majority of urbanized areas in Northern China, including the Huang-Huai-Hai Region, Inner Mongolia Region (MGR), Gan-Xin Region (GXR), and Northeast China Region; monthly NPP in GXR and MGR increased throughout the year. By contrast, the conversion to grassland or forestland increased the monthly and total NPPs of Northern China, suggesting that returning to forestland and grassland could increase the carbon sequestration capacity of terrestrial ecosystems in mainland China. Among the sub-regions, the Loess Plateau Region contributed the largest increase in NPP, which was prompted by the conversion to grassland and forestland.