Diurnal emission variation of ozone precursors: Impacts on ozone formation during Sep. 2019.

With the issue of ozone (O3) pollution having increasingly gained visibility and prominence in China, the Chinese government explored various policies to mitigate O3 pollution. In some provinces and cities, diurnal regulations of O3 precursor were implemented, such as shifting O3 precursor emission processes to nighttime and offering preferential refueling at night. However, the effectiveness of these policies remains unverified, and their impact on the O3 generation process requires further elucidation. In this study, we utilized a regional climate and air quality model (WRF-Chem, v4.5) to test three scenarios aimed at exploring the impact of diurnal industry emission variation of O3 precursors on O3 formation. Significant O3 variations were observed mainly in urban areas. Shifting volatile organic compounds (VOCs) to nighttime have slight decreased daytime O3 levels while moving nitrogen oxides (NOx) to nighttime elevates O3 levels. Simultaneously moving both to nighttime showed combined effects. Process analysis indicates that the diurnal variation in O3 was mainly attributed to chemical process and vertical mixing in urban areas, while advection becomes more important in non-urban areas, contributing to the changes in O3 and O3 precursors levels through regional transportation. Further photochemical analysis reveals that the O3 photochemical production in urban areas was affected by reduced daytime O3 precursors emissions. Specifically, decreasing VOCs lowered the daytime O3 production by reducing the ROx radicals (ROx = HO + HO˙2 + RO˙2), whereas decreasing NOx promoted the daytime O3 production by weakening ROx radical loss. Our results demonstrate that diurnal regulation of O3 precursors will disrupt the ROx radical and O3 formation in local areas, resulting in a change in O3 concentration and atmospheric oxidation capacity, which should be considered in formulating new relevant policies.

Recent H9N2 avian influenza virus lost hemagglutination activity due to a K141N substitution in hemagglutinin.

The H9N2 avian influenza virus (AIV) represents a significant risk to both the poultry industry and public health. Our surveillance efforts in China have revealed a growing trend of recent H9N2 AIV strains exhibiting a loss of hemagglutination activity at 37°C, posing challenges to detection and monitoring protocols. This study identified a single K141N substitution in the hemagglutinin (HA) glycoprotein as the culprit behind this diminished hemagglutination activity. The study evaluated the evolutionary dynamics of residue HA141 and studied the impact of the N141K substitution on aspects such as virus growth, thermostability, receptor-binding properties, and antigenic properties. Our findings indicate a polymorphism at residue 141, with the N variant becoming increasingly prevalent in recent Chinese H9N2 isolates. Although both wild-type and N141K mutant strains exclusively target α,2-6 sialic acid receptors, the N141K mutation notably impedes the virus's ability to bind to these receptors. Despite the mutation exerting minimal influence on viral titers, antigenicity, and pathogenicity in chicken embryos, it significantly enhances viral thermostability and reduces plaque size on Madin-Darby canine kidney (MDCK) cells. Additionally, the N141K mutation leads to decreased expression levels of HA protein in both MDCK cells and eggs. These findings highlight the critical role of the K141N substitution in altering the hemagglutination characteristics of recent H9N2 AIV strains under elevated temperatures. This emphasizes the need for ongoing surveillance and genetic analysis of circulating H9N2 AIV strains to develop effective control and prevention measures.IMPORTANCEThe H9N2 subtype of avian influenza virus (AIV) is currently the most prevalent low-pathogenicity AIV circulating in domestic poultry globally. Recently, there has been an emerging trend of H9N2 AIV strains acquiring increased affinity for human-type receptors and even losing their ability to bind to avian-type receptors, which raises concerns about their pandemic potential. In China, there has been a growing number of H9N2 AIV strains that have lost their ability to agglutinate chicken red blood cells, leading to false-negative results during surveillance efforts. In this study, we identified a K141N mutation in the HA protein of H9N2 AIV to be responsible for the loss of hemagglutination activity. This finding provides insight into the development of effective surveillance, prevention, and control strategies to mitigate the threat posed by H9N2 AIV to both animal and human health.

Toward Low-Carbon Rice Production in China: Historical Changes, Driving Factors, and Mitigation Potential.

Under the "Double Carbon" target, the development of low-carbon agriculture requires a holistic comprehension of spatially and temporally explicit greenhouse gas (GHG) emissions associated with agricultural products. However, the lack of systematic evaluation at a fine scale presents considerable challenges in guiding localized strategies for mitigating GHG emissions from crop production. Here, we analyzed the county-level carbon footprint (CF) of China's rice production from 2007 to 2018 by coupling life cycle assessment and the DNDC model. Results revealed a significant annual increase of 74.3 kg CO2-eq ha-1 in the average farm-based CF (FCF), while it remained stable for the product-based CF (PCF). The CF exhibited considerable variations among counties, ranging from 2324 to 20,768 kg CO2-eq ha-1 for FCF and from 0.36 to 3.81 kg CO2-eq kg-1 for PCF in 2018. The spatiotemporal heterogeneities of FCF were predominantly influenced by field CH4 emissions, followed by diesel consumption and soil organic carbon sequestration. Scenario analysis elucidates that the national total GHG emissions from rice production could be significantly reduced through optimized irrigation (48.5%) and straw-based biogas production (18.0%). Moreover, integrating additional strategies (e.g., advanced crop management, optimized fertilization, and biodiesel application) could amplify the overall emission reduction to 76.7% while concurrently boosting the rice yield by 11.8%. Our county-level research provides valuable insights for the formulation of targeted GHG mitigation policies in rice production, thereby advancing the pursuit of carbon-neutral agricultural practices.

Systematic tracking of nitrogen sources in complex river catchments: Machine learning approach based on microbial metagenomics.

Tracking nitrogen pollution sources is crucial for the effective management of water quality; however, it is a challenging task due to the complex contaminative scenarios in the freshwater systems. The contaminative pattern variations can induce quick responses of aquatic microorganisms, making them sensitive indicators of pollution origins. In this study, the soil and water assessment tool, accompanied by a detailed pollution source database, was used to detect the main nitrogen pollution sources in each sub-basin of the Liuyang River watershed. Thus, each sub-basin was assigned to a known class according to SWAT outputs, including point source pollution-dominated area, crop cultivation pollution-dominated area, and the septic tank pollution-dominated area. Based on these outputs, the random forest (RF) model was developed to predict the main pollution sources from different river ecosystems using a series of input variable groups (e.g., natural macroscopic characteristics, river physicochemical properties, 16S rRNA microbial taxonomic composition, microbial metagenomic data containing taxonomic and functional information, and their combination). The accuracy and the Kappa coefficient were used as the performance metrics for the RF model. Compared with the prediction performance among all the input variable groups, the prediction performance of the RF model was significantly improved using metagenomic indices as inputs. Among the metagenomic data-based models, the combination of the taxonomic information with functional information of all the species achieved the highest accuracy (0.84) and increased median Kappa coefficient (0.70). Feature importance analysis was used to identify key features that could serve as indicators for sudden pollution accidents and contribute to the overall function of the river system. The bacteria Rhabdochromatium marinum, Frankia, Actinomycetia, and Competibacteraceae were the most important species, whose mean decrease Gini indices were 0.0023, 0.0021, 0.0019, and 0.0018, respectively, although their relative abundances ranged only from 0.0004 to 0.1 %. Among the top 30 important variables, functional variables constituted more than half, demonstrating the remarkable variation in the microbial functions among sites with distinct pollution sources and the key role of functionality in predicting pollution sources. Many functional indicators related to the metabolism of Mycobacterium tuberculosis, such as K24693, K25621, K16048, and K14952, emerged as significant important factors in distinguishing nitrogen pollution origins. With the shortage of pollution source data in developing regions, this suggested approach offers an economical, quick, and accurate solution to locate the origins of water nitrogen pollution using the metagenomic data of microbial communities.

Meteorological impacts on the unexpected ozone pollution in coastal cities of China during the unprecedented hot summer of 2022.

Surface ozone pollution under climate warming has become a serious environmental issue. In the summer of 2022, abnormal warming spread over most of the Northern Hemisphere and resulted in the abnormal increase in O3 concentrations. In this study, we focused on the coastal cities in China and investigated the O3 trends in July during 2015 to 2022. Four regions with different locations and emission levels were selected for comparison. A significant increase of O3 concentration in July 2022 were observed in the southern coastal cities (16.7-22.8 µg m-3) while the opposite characteristics were found in the northern coastal cities (decrease of 0.26-2.18 µg m-3). The results indicated various distribution patterns of the O3 concentrations responded to heat wave across China. The weakening of East Asian summer monsoon, extension of the western Pacific subtropical high, significant warming, stronger solar radiation, lower relative humidity, less rainfall and sinking motion of atmosphere in 2022 were beneficial for O3 generation and accumulation in the southern coastal areas. Meteorological changes in July 2022 could lead to an increase of 15.6 % in O3 concentrations in southern coastal cities compared to that in 2015-2021, based on the analysis of machine learning. Air temperature was the main contributor to high O3 concentrations in the coast of Fujian province, while other coastal cities depended on relative humidity. This study indicated the challenge of O3 pollution control in coastal areas under global warming, especially in extreme heat wave events.

Nanoreactor based on single-atom nanoenzymes promotes ferroptosis for cancer immunotherapy.

Immunotherapy is a promising mainstream approach in anti-tumor therapy. It boasts advantages such as durable responses and lower side effects. However, there are still some limitations to be addressed. Current cancer immunotherapy has shown low response rates due to inadequate immunogenicity of certain tumor cells. To address these challenges, an acid-specific nanoreactor was developed, designed to induce immunogenicity by triggering ferroptosis in tumor cells. The nanoreactor integrates glucose oxidase (GOx) with a single-atom nanoenzyme (SAE), which exhibits high peroxidase (POD)-like activity in the acidic tumor microenvironment (TME). This specific acid-sensitivity transforms endogenous hydrogen peroxide (H2O2) into cytotoxic hydroxyl radicals (•OH). GOx enhances the POD-like SAE activity in the nanoreactor by metabolizing glucose in tumor cells, producing gluconic acid and H2O2. This nanoreactor induces high levels of oxidative stress within tumor cells through the synergistic action of SAE and GOx, leading to depletion of GSH and subsequently triggering ferroptosis. The resulting nanoreactor-induced ferroptosis leads to immunogenic cell death (ICD) and significantly recruits T lymphocyte infiltration in tumor tissues. This study was designed with the concept of triggering ferroptosis-dependent ICD mechanism in bladder cancer cells, and developed an acid-specific nanoreactor to enhance the immunotherapy efficacy for bladder cancer, which introduces a novel approach for immunotherapy of bladder cancer.

Identification, pathological, and genomic characterization of novel goose reovirus associated with liver necrosis in geese, China.

Since 2021, a novel strain of goose reovirus (GRV) has emerged within the goose farming industry in Guangdong province, China. This particular viral variant is distinguished by the presence of white necrotic foci primarily localized in the liver and spleen, leading to substantial economic losses for the poultry industry. However, the etiology, prevalence and genomic characteristics of the causative agent have not been thoroughly investigated. In this study, we conducted an epidemiological inquiry employing suspected GRV samples collected from May 2021 to September 2022. The macroscopic pathological and histopathological lesions associated with GRV-infected clinical specimens were examined. Moreover, we successfully isolated the GRV strain and elucidated the complete genome sequence of the isolate GD21/88. Through phylogenetic and recombination analysis, we unveiled that the GRV strains represent a novel variant resulting from multiple reassortment events. Specifically, the µNS, λC, and σNS genes of GRV were found to have originated from chicken reovirus, while the σA gene of GRV exhibited a higher degree of similarity with a novel duck reovirus. The remaining genes of GRV were traced back to Muscovy duck reovirus. Collectively, our findings underscore the significance of GRV as a pathogenic agent impacting the goose farming industry. The insights gleaned from this study contribute to a more comprehensive understanding of the epidemiology of GRV in Southern China and shed light on the genetic reassortment events exhibited by the virus.

New Insights on the Formation of Nucleation Mode Particles in a Coastal City Based on a Machine Learning Approach.

Atmospheric particles have profound implications for the global climate and human health. Among them, ultrafine particles dominate in terms of the number concentration and exhibit enhanced toxic effects as a result of their large total surface area. Therefore, understanding the driving factors behind ultrafine particle behavior is crucial. Machine learning (ML) provides a promising approach for handling complex relationships. In this study, three ML models were constructed on the basis of field observations to simulate the particle number concentration of nucleation mode (PNCN). All three models exhibited robust PNCN reproduction (R2 > 0.80), with the random forest (RF) model excelling on the test data (R2 = 0.89). Multiple methods of feature importance analysis revealed that ultraviolet (UV), H2SO4, low-volatility oxygenated organic molecules (LOOMs), temperature, and O3 were the primary factors influencing PNCN. Bivariate partial dependency plots (PDPs) indicated that during nighttime and overcast conditions, the presence of H2SO4 and LOOMs may play a crucial role in influencing PNCN. Additionally, integrating additional detailed information related to emissions or meteorology would further enhance the model performance. This pilot study shows that ML can be a novel approach for simulating atmospheric pollutants and contributes to a better understanding of the formation and growth mechanisms of nucleation mode particles.

Sources appointment and health risks of PM2.5-bound trace elements in a coastal city of southeastern China.

To gain a comprehensive understanding of sources and health risks of trace elements in an area of China with high population densities and low PM2.5 concentrations, 15 trace elements (Al, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Sn, Ba, Pb) in PM2.5 were monitored from December 2020 to November 2021 in a representative city, Xiamen. The concentrations of trace elements in Xiamen displayed an obvious seasonal variation and were dominated by K, Fe, Al, Ca and Zn. Based on Positive Matrix Factorization analysis, source appointment revealed that the major sources of trace elements in Xiamen were traffic, dust, biomass and firework combustion, industrial manufacture and shipping emission. According to health risk assessment combined with the source appointment results, it indicated that the average noncarcinogenic risk was below the threshold and cancer risk of four hazardous metals (Cr, Ni, As, Pb) exceeded the threshold (10-6). Traffic-related source had almost half amount of contribution to the health risk induced by PM2.5-bound trace elements. During the dust transport period or Spring Festival period, the health risks exceeded an acceptable threshold even an order of magnitude higher, suggesting that the serious health risks still existed in low PM2.5 environment at certain times. Health risk assessment reminded that the health risk reduction in PM2.5 at southeastern China should prioritize traffic-related hazardous trace elements and highlighted the importance of controlling vehicles emissions in the future.

Recent Advances in Machine Learning for Network Automation in the O-RAN.

The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation usingML in O-RAN.We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support forML techniques. The survey then explores challenges in network automation usingML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects whereML techniques can benefit.

Remote teaching system for robotic surgery and its validation: results of a randomized controlled study.

BACKGROUND: Currently, only a limited number of remote assistance modalities are utilized in the basic phase of robotic surgery training to facilitate the rapid acquisition of robotic surgery skills by surgeons. This study aimed to investigate the benefits of real-time remote surgical robotic skill training based on a multi-channel video recording and playback system. METHODS: We randomly divided 40 medical students without prior expertise in the use of surgical robots into two groups to assess the performance of trainees on a robotic simulator (Mimic dV-Trainer). The remote group received remote training, while the control group received live one-on-one guidance. We compared the learning curves of the two groups based on simulator scores. Furthermore, the NASA task load index (NASA-TLX) scale was used to measure the fatigue load of the trainers. RESULTS: We observed no significant differences in the demographics or initial baseline skill levels between the two groups. Participants in the remote group achieved higher total scores in the Match Board 2 and Thread the Rings 1 exercises compared to the control group. In addition, trainers in the remote group reported lower subjective fatigue load than in the control group. CONCLUSIONS: The remote approach to surgical robotics skills training based on the Remote Teaching System for Robotic Surgery (ReTeRoS) is both feasible and has the potential for large-scale training.

Molecular Composition of Anthropogenic Oxygenated Organic Molecules and Their Contribution to Organic Aerosol in a Coastal City.

Organic aerosols (OA) have gained attention as a substantial component of atmospheric aerosols owing to their impact on atmospheric visibility, climate, and human health. Although oxygenated organic molecules (OOMs) are essential contributors to OA formation, the sources, transformations, and fates of the OOMs are not fully understood. Herein, anthropogenic OOMs (AOOMs), anthropogenic volatile organic compounds (AVOCs), and OA were concurrently measured in Xiamen, a coastal city in southeastern China. Our results show that the AOOMs exhibited a high nitrogen content (76%) and a low oxidation degree. Strong photochemical processes of aromatic VOCs were the predominant sources of AOOMs. Also, NOx concentrations and the occurrence of multigeneration OH radical oxidations were the critical factors that might influence the formation of AOOMs. Finally, the newly developed aerosol dynamic model's results show that more than 35% of the OA mass growth rate is attributed to the gas-particle partitioning of AOOMs. Further sensitivity testing demonstrates that the contribution of AOOMs to OA growth is significantly enhanced during high-particulate-concentration periods, especially under low-temperature conditions. This study emphasizes the vital role of photochemically produced AOOMs derived from AVOCs in OA growth in a coastal urban atmosphere.

Effectiveness of a vision-based handle trajectory monitoring system in studying robotic suture operation.

Data on surgical robots are not openly accessible, limiting further study of the operation trajectory of surgeons' hands. Therefore, a trajectory monitoring system should be developed to examine objective indicators reflecting the characteristic parameters of operations. 20 robotic experts and 20 first-year residents without robotic experience were included in this study. A dry-lab suture task was used to acquire relevant hand performance data. Novices completed training on the simulator and then performed the task, while the expert team completed the task after warm-up. Stitching errors were measured using a visual recognition method. Videos of operations were obtained using the camera array mounted on the robot, and the hand trajectory of the surgeons was reconstructed. The stitching accuracy, robotic control parameters, balance and dexterity parameters, and operation efficiency parameters were compared. Experts had smaller center distance (p < 0.001) and larger proximal distance between the hands (p < 0.001) compared with novices. The path and volume ratios between the left and right hands of novices were larger than those of experts (both p < 0.001) and the total volume of the operation range of experts was smaller (p < 0.001). The surgeon trajectory optical monitoring system is an effective and non-subjective method to distinguish skill differences. This demonstrates the potential of pan-platform use to evaluate task completion and help surgeons improve their robotic learning curve.

A Lightweight Trust Mechanism with Attack Detection for IoT.

In this paper, we propose a lightweight and adaptable trust mechanism for the issue of trust evaluation among Internet of Things devices, considering challenges such as limited device resources and trust attacks. Firstly, we propose a trust evaluation approach based on Bayesian statistics and Jøsang's belief model to quantify a device's trustworthiness, where evaluators can freely initialize and update trust data with feedback from multiple sources, avoiding the bias of a single message source. It balances the accuracy of estimations and algorithm complexity. Secondly, considering that a trust estimation should reflect a device's latest status, we propose a forgetting algorithm to ensure that trust estimations can sensitively perceive changes in device status. Compared with conventional methods, it can automatically set its parameters to gain good performance. Finally, to prevent trust attacks from misleading evaluators, we propose a tango algorithm to curb trust attacks and a hypothesis testing-based trust attack detection mechanism. We corroborate the proposed trust mechanism's performance with simulation, whose results indicate that even if challenged by many colluding attackers that can exploit different trust attacks in combination, it can produce relatively accurate trust estimations, gradually exclude attackers, and quickly restore trust estimations for normal devices.

Photochemistry in the urban agglomeration along the coastline of southeastern China: Pollution mechanism and control implication.

The concentrations of ground-level ozone (O3) in China have undergone a rapid increase in recent years, resulting in adverse impacts on the air quality and climate change. However, limited research has been conducted on the coastal urban agglomerations with increasingly serious O3 pollution. Therefore, in order to better understand in situ photochemistry, comprehensive field observations of O3 and its precursors, coupled with the model simulation, were conducted in autumn of 2019 at six sites in an urban agglomeration along the coastline of southeastern China. Results indicated that O3 pollution in the southern part of the urban agglomeration was more severe than that in the northern part, due to higher levels of O3 precursors and stronger atmospheric oxidation capacity (AOC) in the southern regions. Oxygenated volatile organic compounds (OVOCs), NO2, and CO dominated the total OH reactivity, and the site-average daytime Ox (O3 + NO2) increments correlated well (R2 = 0.94) with the total OH reactivity of CO and VOCs at these sites except for Quanzhou, where industrial emissions (35.1 %) and solvent usages (33.7 %) dominated the VOC sources. However, vehicle exhausts (31.1 %) were the most predominant contributors to the VOC sources at other sites. The results of model simulations showed that net O3 formation rates were larger at the southern sites. Furthermore, O3 production was mainly controlled by VOCs at most sites, but co-limited by VOCs and NOx at Quanzhou. The most significant VOC groups contributing to O3 formation were aromatics and alkenes, with m/p-xylene, toluene, propene, and ethene being the main contributors at these sites. This study offers a more comprehensive understanding of the characteristics and formation of photochemical pollutions on the scale of the urban areas, indicating the critical need to reduce VOC emissions as a means of mitigating their photochemical effects.

Receptor Binding Properties of Neuraminidase for influenza A virus: An Overview of Recent Research Advances.

Influenza A viruses (IAVs) pose a serious risk to both human and animal health. IAVs' receptor binding characteristics account for a major portion of their host range and tissue tropism. While the function of neuraminidase (NA) in promoting the release of progeny virus is well-known, its role in the virus entry process remains poorly understood. Studies have suggested that certain subtypes of NA can act as receptor-binding proteins, either alone or in conjunction with haemagglutinin (HA). An important distinction is that NA from the avian influenza virus have a second sialic acid-binding site (2SBS) that is preserved in avian strains but missing in human or swine strains. Those observations suggest that the 2SBS may play a key role in the adaptation of the avian influenza virus to mammalian hosts. In this review, we provide an update of the recent research advances in the receptor-binding role of NA and highlight its underestimated importance during the early stages of the IAV life cycle. By doing so, we aim to provide new insights into the mechanisms underlying IAV host adaptation and pathogenesis.

The effect of nitrous acid (HONO) on ozone formation during pollution episodes in southeastern China: Results from model improvement and mechanism insights.

Two ozone (O3) processes, summer episode dominated by local production and autumn episode dominated by regional transport, were chosen to investigate the role of HONO in different pollution processes. Meteorological conditions, diurnal variation of O3, potential source contribution factor (PSCF) analysis, concentration weighted trajectory (CWT) models, and the distribution of the eight-hour maximum values of O3 on mainland China all prove that summer O3 was mainly locally generated while autumn O3 episode was more susceptible to regional transport. The gaps between observations and simulation results with the default HONO chemistry in Master Chemical Mechanism (MCM) of Observation Based Model (OBM) were higher in summer episode (0.58 ppb) than autumn episode (0.37 ppb). Although we implemented nine additional sources in the model to revise the HONO chemistry, the simulated values were still lower than the observed values. HONO promoted O3 production by accelerating the reaction of HO2 + NO and RO2 + NO, and promoted loss of O3 by the reaction of OH + NO2 and RO2 + NO2. The net production rate of O3 with HONO constraint increased by 28.50 % in summer and 22.43 % in autumn, which also indicated that HONO played more important role in the O3 production in summer. The difference of NOx of daily RIR between the cases with and without HONO constraint was higher in summer O3 episode (0.15 %/%) than that in autumn O3 episode (0.09 %/%), the same as to VOCs with -0.20 %/% in summer O3 episode and - 0.14 %/% in autumn O3 episode, which indicated that the presence or absence of the HONO constraint has a greater impact on the RIR value in the case of dominant local generation. In brief, the O3 sensitivity would be more favorable for VOCs without HONO constrained in the model, which would inevitably mislead policy makers to develop efficient policies to control O3 pollution.

Response of PM2.5 pollution to meteorological and anthropogenic emissions changes during COVID-19 lockdown in Hunan Province based on WRF-Chem model.

In December 2019, the New Crown Pneumonia (the COVID-19) outbroke around the globe, and China imposed a nationwide lockdown starting as early as January 23, 2020. This decision has significantly impacted China's air quality, especially the sharp decrease in PM2.5 (aerodynamic equivalent diameter of particulate matter less than or equal to 2.5 µm) pollution. Hunan Province is located in the central and eastern part of China, with a "horseshoe basin" topography. The reduction rate of PM2.5 concentrations in Hunan province during the COVID-19 (24.8%) was significantly higher than the national average (20.3%). Through the analysis of the changing character and pollution sources of haze pollution events in Hunan Province, more scientific countermeasures can be provided for the government. We use the Weather Research and Forecasting with Chemistry (WRF-Chem, V4.0) model to predict and simulate the PM2.5 concentrations under seven scenarios before the lockdown (2020.1.1-2020.1.22) and during the lockdown (2020.1.23-2020.2.14). Then, the PM2.5 concentrations under different conditions is compared to differentiate the contribution of meteorological conditions and local human activities to PM2.5 pollution. The results indicate the most important cause of PM2.5 pollution reduction is anthropogenic emissions from the residential sector, followed by the industrial sector, while the influence of meteorological factors contribute only 0.5% to PM2.5. The explanation is that emission reductions from the residential sector contribute the most to the reduction of seven primary contaminants. Finally, we trace the source and transport path of the air mass in Hunan Province through the Concentration Weight Trajectory Analysis (CWT). We found that the external input of PM2.5 in Hunan Province is mainly from the air mass transported from the northeast, accounting for 28.6%-30.0%. To improve future air quality, there is an urgent need to burn clean energy, improve the industrial structure, rationalize energy use, and strengthen cross-regional air pollution synergy control.

Land-Water Transport and Sources of Nitrogen Pollution Affecting the Structure and Function of Riverine Microbial Communities.

The characterization of variations in riverine microbiota that stem from contaminant sources and transport modes is important for understanding biogeochemical processes. However, the association between complex anthropogenic nitrogen pollution and bacteria has not been extensively investigated owing to the difficulties faced while determining the distribution of nitrogen contaminants in watersheds. Here, we employed the Soil and Water Assessment Tool alongside microbiological analysis to explore microbial characteristics and their responses to complex nitrogen pollution patterns. Significant variations in microbial communities were observed in sub-basins with distinct land-water pollution transport modes. Point source-dominated areas (PSDAs) exhibited reduced microbial diversity, high number of denitrification groups, and increased nitrogen cycling compared with others. The negative relative deviations (-3.38) between the measured and simulated nitrate concentrations in PSDAs indicated that nitrate removal was more effective in PSDAs. Pollution sources were also closely associated with microbiota. Effluents from concentrated animal feeding operations were the primary factors relating to the microbiota compositions in PSDAs and balanced areas. In nonpoint source-dominated areas, contaminants from septic tanks become the most relevant sources to microbial community structures. Overall, this study expands our knowledge regarding microbial biogeochemistry in catchments and beyond by linking specific nitrogen pollution scenarios to microorganisms.