Remote sensing identification of urban water pollution source types using hyperspectral data

Enhancement of Partial Nitrification-Anaerobic Ammonia Oxidation in SBR Reactors via Surface-Modified Polyurethane Sponge Biofilm Carrier

The partial nitrification-anammox process offers a cost-effective, energy-efficient, and environmentally sustainable approach for nitrogen removal in wastewater treatment. However, its application under low ammonia nitrogen conditions faces operational challenges including prolonged start-up periods and excessive nitrite oxidation. This study employed a strategy combining polyurethane surface positive charge enhancement and zeolite loading to develop a carrier capable of microbial enrichment and inhibition of nitrate generation, aiming to initiate the partial nitrification-anammox process in a sequencing batch reactor. Operational results demonstrate that the modified carrier enabled the reactor to achieve a total nitrogen removal efficiency of 78%, with the effluent nitrate nitrogen reduced to 6.03 mg-N/L, successfully initiating the partial nitrification-anammox process. The modified carrier also exhibited accelerated biofilm proliferation (both suspended and attached biomass increased). Additionally, 16S rRNA revealed a higher relative abundance of typical anammox bacteria Candidatus Brocadia in the biofilm of the modified carrier compared to the original carrier, alongside a decline in nitrifying genera, such as Nitrolancea. These microbial shifts effectively suppressed excessive nitrite oxidation, limited nitrate accumulation, and sustained efficient nitrogen removal throughout the reactor's operation.

Advancing Urban Development: Applications of Hyperspectral Imaging in Smart City Innovations and Sustainable Solutions

Smart cities are urban areas that use advanced technologies to make urban living better through efficient resource management, sustainable development, and improved quality of life. Hyperspectral imaging (HSI) is a noninvasive and nondestructive imaging technique that is revolutionizing smart cities by offering improved real-time monitoring and analysis capabilities across multiple urban sectors. In contrast with conventional imaging technologies, HSI is capable of capturing data across a wider range of wavelengths, obtaining more detailed spectral information, and in turn, higher detection and classification accuracies. This review explores the diverse applications of HSI in smart cities, including air and water quality monitoring, effective waste management, urban planning, transportation, and energy management. This study also examines advancements in HSI sensor technologies, data-processing techniques, integration with Internet of things, and emerging trends, such as combining artificial intelligence and machine learning with HSI for various smart city applications, providing smart cities with real-time, data-driven insights that enhance public health and infrastructure. Although HSI may generate complex data and tends to cost much, its potential to transform cities into smarter and more sustainable environments is vast, as discussed in this review.

Advancing Urban Development: Applications of Hyperspectral Imaging in Smart City Innovations and Sustainable Solutions

Smart cities are urban areas that use advanced technologies to make urban living better through efficient resource management, sustainable development, and improved quality of life. Hyperspectral imaging (HSI) is a noninvasive and nondestructive imaging technique that is revolutionizing smart cities by offering improved real-time monitoring and analysis capabilities across multiple urban sectors. In contrast with conventional imaging technologies, HSI is capable of capturing data across a wider range of wavelengths, obtaining more detailed spectral information, and in turn, higher detection and classification accuracies. This review explores the diverse applications of HSI in smart cities, including air and water quality monitoring, effective waste management, urban planning, transportation, and energy management. This study also examines advancements in HSI sensor technologies, data-processing techniques, integration with Internet of things, and emerging trends, such as combining artificial intelligence and machine learning with HSI for various smart city applications, providing smart cities with real-time, data-driven insights that enhance public health and infrastructure. Although HSI may generate complex data and tends to cost much, its potential to transform cities into smarter and more sustainable environments is vast, as discussed in this review.

IR Sensors, Related Materials, and Applications

Infrared (IR) sensors are widely used in various applications due to their ability to detect infrared radiation. Currently, infrared detector technology is in its third generation and faces enormous challenges. IR radiation propagation is categorized into distinct transmission windows with the most intriguing aspects of thermal imaging being mid-wave infrared (MWIR) and long-wave infrared (LWIR). Infrared detectors for thermal imaging have many uses in industrial applications, security, search and rescue, surveillance, medical, research, meteorology, climatology, and astronomy. Presently, high-performance infrared imaging technology mostly relies on epitaxially grown structures of the small-bandgap bulk alloy mercury-cadmium-telluride (MCT), indium antimonide (InSb), and GaAs-based quantum well infrared photodetectors (QWIPs), contingent upon the application and wavelength range. Nanostructures and nanomaterials exhibiting appropriate electrical and mechanical properties including two-dimensional materials, graphene, quantum dots (QDs), quantum dot in well (DWELL), and colloidal quantum dot (CQD) will significantly enhance the electronic characteristics of infrared photodetectors, transition metal dichalcogenides, and metal oxides, which are garnering heightened interest. The present manuscript gives an overview of IR sensors, their types, materials commonly used in them, and examples of related applications. Finally, a summary of the manuscript and an outlook on prospects are given.

Evaluating the role of recalcitrant dissolved organic matter in bacterial community dynamics in urbanized freshwater ecosystems

Dissolved organic matter (DOM) and recalcitrant dissolved organic matter (RDOM) play distinct roles in shaping microbial communities. However, characterizing these roles is difficult, especially in ecosystems subjected to varying degrees of anthropogenic influence. This study investigated the molecular compositions and ecological impacts of DOM and RDOM in the Fen River, Shanxi Taiyuan, comparing pristine upstream regions with highly urbanized downstream areas. Using 16S rRNA gene sequencing and LC-MS-based metabolomics, we observed significant shifts in microbial community composition, diversity, and metabolic functions. Upstream communities, characterized by higher diversity, were dominated by Bacteroidota, Proteobacteria, and Cyanobacteria, while downstream communities, influenced by pollution, exhibited increased expression of genes related to amino acid metabolism. Fluorescence spectroscopy and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) revealed that upstream DOM contained higher proportions of complex, high molecular weight compounds, including significant proportions of carboxyl-rich alicyclic molecules (CRAM) and island of stability (IOS) compounds, which play key roles in long-term carbon storage and microbial carbon sequestration. In contrast, downstream DOM was characterized as having lower aromaticity and more saturated compounds, with reduced proportions of CRAM and IOS, reflecting the impact of anthropogenic activities. These findings underscored the critical roles of CRAM and IOS in regulating DOM stability and microbial communities, further highlighting the need for targeted pollution control strategies to preserve ecosystem function in urbanized water bodies.

Molecular level decontamination of trace quinolones and Serratia marcescens in wastewater via in situ Cu(III) complexes mediated Fenton-like oxidation

Co-pollution caused by antibiotics and antibiotic-resistant bacteria (ARB) in wastewater has led to widespread concerns. Hence, their targeted and synergistic decontamination is urgently required. A homogeneous Fenton-like oxidation system comprising cupric complexes-activated peroxymonosulfate (PMS) was demonstrated to synergistically decontaminate trace quinolones (QNs) and QNs-resistant Serratia marcescens (QRSM) in wastewater. More than 99 % of QNs were degraded within 60 min under alkaline condition, and the degradation efficiency was only slightly influenced by humic acid (up to 1 %) and various anions (up to 20 %), furthermore, the degraded pathway was proposed and the environmental risk after QNs degradation were also reduced. The activation of PMS via cupric complexes coupling in situ Cu(III) complexes generation promoted intramolecular electron transfer (IET) featuring the targeted oxidation of QNs. The produced Cu(III) and •OH played primary and secondary roles in the synergistic inactivation of QRSM by destroying the cell membranes and walls, DNA bases (T, A, C, and G), antibiotic resistance genes (ARGs, including intracellular ARGs and extracellular ARGs), and total DNA (including intracellular DNA and extracellular DNA). This study demonstrates a successful strategy and provides an innovative perspective for the molecular level decontamination of trace antibiotics and ARB using a homogeneous cupric complexes-activated Fenton-like oxidation system from metal ions inherent in breeding wastewater under alkaline condition.

Effects of season and water quality on community structure of planktonic eukaryotes in the Chaohu Lake Basin

Introduction

Analyzing the correlation between planktonic eukaryotic communities (PECs) and aquatic physicochemical parameters (APPs) provides important references for predicting the impact of climate change and human activities on aquatic ecosystems.

Methods

To assess the influence of seasons and APPs on PEC structures in lakes and rivers, we utilized high-throughput sequencing of the 18S rRNA gene to analyze PEC structures in a lake and seven rivers in the Chaohu Lake Basin and analyzed their correlations with APPs.

Results

Our results revealed that PEC structure was significantly affected by season, with the highest α-diversity observed in summer. Furthermore, we identified several APPs, including water temperature, conductivity, dissolved oxygen, pH, phosphate, total phosphorus, trophic level index (TLI), nitrate, ammonia nitrogen, and total nitrogen, that significantly influenced PEC structures. Specifically, we found that Stephanodiscus hantzschii, Simocephalus serrulatus, Cryptomonas sp. CCAC_0109, Pedospumella encystans, Actinochloris sphaerica, Chlamydomonas angulosa, Gonyostomum semen, Skeletonema potamos, Chlamydomonas klinobasis, Pedospumella sp., and Neochlorosarcina negevensis were significantly correlated to TLI, while Limnoithona tetraspina, Theileria sp., and Pseudophyllomitus vesiculosus were significantly correlated to the water quality index (WQI). However, our random forest regression analysis using the top 100 species was unable to accurately predict the WQI and TLI.

Discussion

These results provide valuable data for evaluating the impact of APPs on PEC and for protecting water resource in the Chaohu Lake Basin.