Electrochemical aptamer sensor based on AgNPs@PDANSs and "sandwich" structure guidance for the detection of tobramycin in water samples.

In this study, an ultrasensitive detection platform for tobramycin (TOB) was developed, featuring a "sandwich" structure guided by AgNCs@PDANSs and Thi-AuNCs@ZnONSs. To address the issue of large background current peak signals in tagless sensors, Thi-AuNCs@ZnONSs composites were synthesized as signal tags. Zinc oxide nanosheets (ZnONSs) served as the loading agent, and AuNCs with the electroactive molecule Thi acted as carriers. Furthermore, AgNPs@PDANSs nanocomposites, possessing excellent electrical conductivity and large specific surface areas, were prepared as substrate materials for the modified electrodes. A "sandwich" structure strategy was also introduced to enhance the accuracy of the electrochemical aptasensor. This strategy, utilizing a dual sequence for target labeling and capture, yielded higher sensitivity and simplified the sensor construction compared to methods employing a single sequence. Under optimal conditions, the detection limit for TOB was established at 1.41 pM, with a detection range of 0.05-5000 nM. The aptasensor was effectively applied in the detection of TOB in tap and lake water, demonstrating outstanding reproducibility, selectivity, and stability. These results may serve as a reference for environmental TOB detection.

Waterborne metal levels in four freshwater lakes from Harmony Point, Nelson Island, Antarctica.

The aim of this study is to analyze the waterborne metal levels in four lakes (one endorheic and three exorheic) of Harmony Point, Nelson Island, Antarctica. Water samples were analyzed by using a quadrupole type inductively coupled plasma mass spectrometer and an inductively coupled plasma optical emission spectrometer. The levels of As, Cu, Mn, Mo, and V were significantly lower and those of Cr, Mg, Na, and Sr were significantly higher in the endorheic lake than in the other lakes. Most water samples presented levels of Ag, Be, Cd, Pb, Se, Tl, and U below the limit of quantification, while for Ba, Co, and Ni around half of the samples were below this limit. The waterborne metal levels were not significantly different between the exorheic lakes. Waterborne metal levels in the freshwater lakes from Harmony Point did not show any clear relationship with their levels in the soil of the region or with bird guano, and overall, their levels indicate an environment without anthropogenic influence. Apparently, the Na levels are influenced by salt spray from the ocean, as they are related to the distance of the lakes from the ocean.

Long-term monitoring chlorophyll-a concentration using HJ-1 A/B imagery and machine learning algorithms in typical lakes, a cold semi-arid region.

Chlorophyll a (Chl-a) in lakes serves as an effective marker for assessing algal biomass and the nutritional level of lakes, and its observation is feasible through remote sensing methods. HJ-1 (Huanjing-1) satellite, deployed in 2008, incorporates a CCD capable of a 30 m resolution and has a revisit interval of 2 days, rendering it a superb choice or supplemental sensor for monitoring trophic state of lakes. For effective long-term and regional-scale mapping, both the imagery and the evaluation of machine learning algorithms are essential. The several typical machine learning algorithms, i.e., Support Vector Regression (SVR), Gradient Boosting Decision Trees (GBDT), XGBoost (XGB), Random Forest (RF), K-Nearest Neighbor (KNN), Kernel Ridge Regression (KRR), and Multi-Layer Perception Network (MLP), were developed using our in-situ measured Chl-a. A cross-validation grid to identify the most effective hyperparameter combinations for each algorithm was used, as well as the selected optimal superparameter combinations. In Chl-a mapping of three typical lakes, the R2 of GBDT, XGB, RF, and KRR all reached 0.90, while XGB algorithm also exhibited stable performance with the smallest error (RMSE = 3.11 µg/L). Adjustments were made to align the Chl-a spatial-temporal patterns with past data, utilizing HJ1-A/B CCD images mapping through XGB algorithm, which demonstrates its stability. Our results highlight the considerable effectiveness and utility of HJ-1 A/B CCD imagery for evaluation and monitoring trophic state of lakes in a cold arid region, providing the application cases contribute to the ongoing efforts to monitor water qualities.

Label-free liquid crystal-based optical detection of norfloxacin using an aptamer recognition probe in soil and lake water.

Norfloxacin (NOX), a broad spectrum fluoroquinolone (FQ) antibiotic, is commonly detected in environmental residues, potentially contributing to biological drug resistance. In this paper, an aptamer recognition probe has been used to develop a label-free liquid crystal-based biosensor for simple and robust optical detection of NOX in aqueous solutions. Stimuli-receptive liquid crystals (LCs) have been employed to report aptamer-target binding events at the LC-aqueous interface. The homeotropic alignment of LCs at the aqueous-LC interface is due to the self-assembly of the cationic surfactant cetyltrimethylammonium bromide (CTAB). In the presence of the negatively charged NOX aptamer, the ordering changes to planar/tilted. On addition of NOX, the aptamer-NOX binding causes redistribution of CTAB at the LC-aqueous interface and the homeotropic orientation is restored. This results in a bright-to-dark optical transition under a polarized optical microscope (POM). This optical transition serves as a visual indicator to mark the presence of NOX. The devised aptasensor demonstrates high specificity with a minimum detection limit of 5 nM (1.596 ppb). Moreover, the application of the developed aptasensor for the detection of NOX in freshwater and soil samples underscores its practical utility in environmental monitoring. This proposed LC-based method offers several advantages over conventional detection techniques for a rapid, feasible and convenient way to detect norfloxacin.

Size-resolved aerosol at a Coastal Great Lakes Site: Impacts of new particle formation and lake spray.

The quantification of aerosol size distributions is crucial for understanding the climate and health impacts of aerosols, validating models, and identifying aerosol sources. This work provides one of the first continuous measurements of aerosol size distribution from 1.02 to 8671 nm near the shore of Lake Michigan. The data were collected during the Lake Michigan Ozone Study (LMOS 2017), a comprehensive air quality measurement campaign in May and June 2017. The time-resolved (2-min) size distribution are reported herein alongside meteorology, remotely sensed data, gravimetric filters, and gas-phase variables. Mean concentrations of key aerosol parameters include PM2.5 (6.4 µg m-3), number from 1 to 3 nm (1.80x104 cm-3) and number greater than 3 nm (8x103 cm-3). During the field campaign, approximately half of days showed daytime ultrafine burst events, characterized by particle growth from sub 10 nm to 25-100 nm. A specific investigation of ultrafine lake spray aerosol was conducted due to enhanced ultrafine particles in onshore flows coupled with sustained wave breaking conditions during the campaign. Upon closer examination, the relationships between the size distribution, wind direction, wind speed, and wave height did not qualitatively support ultrafine particle production from lake spray aerosol; statistical analysis of particle number and wind speed also failed to show a relationship. The alternative hypothesis of enhanced ultrafine particles in onshore flow originating mainly from new particle formation activity is supported by multiple lines of evidence.

Ultrasensitive detection of chloramphenicol in water using functionalized polymers with an aluminium organic framework.

Metal-Organic Frameworks (MOFs) are extensively used as electrode material in various sensing applications due to their efficacious porous nature and tunable properties. However, pristine MOFs lack conductive attributes that hinder their wide usage in electrochemical applications. Electropolymerization of several aromatic monomers has been a widely used strategy for preparing conducting electrode materials for various sensing applications in the past decades. Herein, we report a similar approach by employing the electropolymerization method to create a functional polymer layer to enhance the sensitivity of an Aluminium Organic Framework (DUT-4) for the selective detection of Chloramphenicol (CAP) antibiotic in aqueous environment. The combined strategy using the conducting polymer layer with the porous Al MOF provides surpassing electrochemical performance for sensing CAP with regard to the very low detection limit (LOD = 39 nM) and exceptionally high sensitivity (11943 µA mM-1 cm-2). In addition, the fabricated sensor exhibited good selectivity, reproducibility and stability. The developed method was successfully evaluated in various real samples including lake water and river water for CAP detection with good recovery percentages even at lower concentrations.

Rapid detection of furanyl fentanyl in complex matrices using Leidenfrost desorption-assisted low-temperature arc plasma ionization mass spectrometry.

The abuse of illicit drugs poses serious threats to the physical and mental health of users, as well as to the overall safety and welfare of society. In this work, we present a newly developed technique for drug detection based on mass spectrometry. This technique combines Leidenfrost desorption with low-temperature arc plasma ionization mass spectrometry. This method is applicable for detecting furanyl fentanyl in complex matrices. Key advantages of this technique include minimal sample fragmentation and high sensitivity for detection. The Leidenfrost desorption plays a pivotal role in this methodology, as it spontaneously concentrates analyte molecules during the gradual evaporation of the solvent. Eventually, these concentrated molecules are redistributed at their highest concentrations, resulting in exceptionally high sensitivity. In the course of our investigation, we achieved a remarkable detection limit of 10 pg mL-1 for furanyl fentanyl in pure water. Moreover, the characteristic ion peaks of furanyl fentanyl can be distinctly identified within complex matrices such as wine, beverages, urine, and lake water. This innovative drug detection technology offers several advantages, including a simple setup, cost-effectiveness, rapid detection, high sensitivity, and minimal sample pretreatment.

Chelativorans salis sp. nov., a slightly halophilic bacterium isolated from an enrichment system with saline lake sediment.

A Gram-stain-negative, non-motile, and slightly halophilic alphaproteobacterium, designated strain EGI FJ00035T, was isolated from enrichment sediment samples of a saline lake in Xinjiang Uygur Autonomous Region, PR China. The taxonomic position of the isolate was determined using the polyphasic taxonomic and phylogenomic analyses. Phylogenetic analysis based on the 16S rRNA gene sequences indicated that strain EGI FJ00035T formed a distinct clade with 'Chelativorans alearense' UJN715 and 'Chelativorans xinjiangense' lm93 with sequence similarities of 98.44 and 98.22 %, respectively, while sharing less than 96.7 % with other valid type strains. The novel isolate could be distinguished from other species of the genus Chelativorans by its distinct phenotypic, physiological, and genotypic characteristics. Optimal growth of strain EGI FJ00035T occurred on marine agar 2216 at pH 7.0 and 30 °C. The major respiratory quinone was Q-10, while the major fatty acids (>5 %) were C19 : 0 cyclo ω8c, summed feature 8 (C17 : 1 ω6c and/or C17 : 1 ω7c), C16 : 0, C18 : 0, and iso-C17 : 0. The detected polar lipids included diphosphatidylglycerol, phosphatidylcholine, phosphatidylethanolamine, phosphatidylglycerol, unidentified aminophospholipids, unidentified glycolipids, and an unidentified lipid. Based on its genome sequence, the G+C content of strain EGI FJ00035T was 63.2 mol%. The average nucleotide identity, average amino acid identity, and digital DNA-DNA hybridization values of strain EGI FJ00035T against related members of the genus Chelativorans were below the thresholds for delineation of a novel species. According our polyphasic taxonomic data, strain EGI FJ00035T represents a new species of the genus Chelativorans, for which the name Chelativorans salis sp. nov. is proposed. The type strain of the proposed novel isolate is EGI FJ00035T (=KCTC 92251T=CGMCC 1.19480T).

An indispensable role of overlying water in nitrogen removal in shallow lakes.

The nitrogen (N) removal characteristics in water columns and sediments of shallow lakes, influenced by various factors, may exhibit spatial variations in lakes with algal-macrophyte dominance. The N removal rates in water columns and sediments of Lake Taihu were investigated. Our findings indicated that the total N removal rates in Lake Taihu followed the order of algae-dominance > macrophyte-dominance > pelagic lake (without the presence of algae and macrophytes). Correlation analysis revealed that the key environmental factors affecting denitrification and anammox in sediments of algae/macrophyte-type lakes were nitrate nitrogen (NO3--N), nitrite nitrogen (NO2--N), ammonia nitrogen (NH4+-N), and chlorophyll a (Chl-a). The linear regression demonstrated that a significant correlation between the denitrification and the anammox in sediments, with a correlation coefficient of 0.81 (p < 0.01). The contributions to N removal from the water columns and sediments in Lake Taihu were 53 % and 47 %, respectively. Denitrification predominantly drove N removal from sediments, whereas anammox dominated the N removal in water columns. Thus, N removal from the water columns is nonnegligible in shallow eutrophic lakes. This study enhances our understanding of N biogeochemical cycling dynamics in sediment-water and algae/macrophyte ecosystems across various shallow eutrophic lake regions.

Snapshot of cyanobacterial toxins in Pakistani freshwater bodies.

Cyanobacteria are known to produce diverse secondary metabolites that are toxic to aquatic ecosystems and human health. However, data about the cyanotoxins occurrence and cyanobacterial diversity in Pakistan's drinking water reservoirs is scarce. In this study, we first investigated the presence of microcystin, saxitoxin, and anatoxin in 12 water bodies using an enzyme-linked immunosorbent assay (ELISA). The observed cyanotoxin values for the risk quotient (RQ) determined by ELISA indicated a potential risk for aquatic life and human health. Based on this result, we made a more in-depth investigation with a subset of water bodies (served as major public water sources) to analyze the cyanotoxins dynamics and identify potential producers. We therefore quantified the distribution of 17 cyanotoxins, including 12 microcystin congeners using a high-performance liquid chromatography-high-resolution tandem mass spectrometry/mass spectrometry (HPLC-HRMS/MS). Our results revealed for the first time the co-occurrence of multiple cyanotoxins and the presence of cylindrospermopsin in an artificial reservoir (Rawal Lake) and a semi-saline lake (Kallar Kahar). We also quantified several microcystin congeners in a river (Panjnad) with MC-LR and MC-RR being the most prevalent and abundant. To identify potential cyanotoxin producers, the composition of the cyanobacterial community was characterized by shotgun metagenomics sequencing. Despite the noticeable presence of cyanotoxins, Cyanobacteria were not abundant. Synechococcus was the most abundant cyanobacterial genus found followed by a small amount of Anabaena, Cyanobium, Microcystis, and Dolichospermum. Moreover, when we looked at the cyanotoxins genes coverage, we never found a complete microcystin mcy operon. To our knowledge, this is the first snapshot sampling of water bodies in Pakistan. Our results would not only help to understand the geographical spread of cyanotoxin in Pakistan but would also help to improve cyanotoxin risk assessment strategies by screening a variety of cyanobacterial toxins and confirming that cyanotoxin quantification is not necessarily related to producer abundance.

Centurial deposition records of polychlorinated biphenyls and organochlorine pesticides in sediment cores from a plateau deep-water lake of China: Significance of anthropogenic impacts, transformation signals and ecological risks revealed by full congener analysis.

Lake Fuxian, the largest deep freshwater lake in China, has been suffering from increasing ecological and environmental issues along with the rapid urbanization and industrialization in the past 40 years. To better understand the historical pollution of persistent organic pollutants (POPs) in Lake Fuxian, comprehensive analyses of 209 polychlorinated biphenyl (PCB) congeners and 20 organochlorine pesticides (OCPs) were conducted in two intact sediment cores (Core V1 and Core V2). The total mass concentrations of PCBs ranged from 7.60 to 31.47 ng/g (dry weight basis) and 5.55 to 28.90 ng/g during the period of 1908-2019 in Core V1 and 1924-2019 in Core V2, respectively. PCBs exhibited a consecutive increasing trend from 1940s to 2019 in Core V1. The temporal trend of PCBs in Core V2 basically matched to the history of PCB usage and prohibition in China (increasing from 1940s to mid-1960s, a remarkable drop in mid-1970s, and then increasing until 2019). Moreover, low-chlorinated PCBs were dominant among PCB homologues. Mono-CBs, di-CBs, tri-CBs and tetra-CBs accounted for 86.71 %-98.57 % in sediment segments. The PCB sources included unintentional emission and atmospheric deposition, as well as biological transformation. The total mass concentrations of OCPs ranged from 0.74 to 3.82 ng/g in Core V1 and 0.35 to 2.23 ng/g in Core V2, respectively. Similar trend was observed in the two sediment cores with peaks in the early 1990s. The predominant OCPs were γ-hexachlorohexane (γ-HCHs), dieldrin and p,p'-DDD. The ecological risks posed by PCBs and p-p'-DDD in Lake Fuxian were relatively low. In contrast, dieldrin might pose a potential threat to exposed organisms and apparently adverse ecological effects were caused by γ-HCH. This study will provide important baseline information on historical POPs contamination of Lake Fuxian.

Assessment of occurrence, partitioning and ecological risk for 144 steroid hormones in Taihu Lake using UPLC-MS/MS with machine learning model.

Steroid hormones (SHs) have attracted mounting attention due to their endocrine-disrupting effects on humans and aquatic organisms. However, the lack of analytical methods and toxicity data for a large number of SHs has limited the effective management of SH contamination in the water-sediment systems. In this study, we developed a highly sensitive analytical method for the simultaneous quantification of 144 SHs to investigate their occurrence, spatial distribution and partitioning in the water and sediment in Taihu Lake. The results showed that the total concentrations of SHs in water and sediment were 366.88-998.23 ng/L (mean: 612.84 ng/L) and 17.46-150.20 ng/g (mean: 63.41 ng/g), respectively. The spatial distribution of SHs in Taihu Lake might be simultaneously influenced by the pollution sources, lake hydrodynamics, and sediment properties. The sediment-water partitioning result implied that 28 SHs were in dynamic equilibrium at the water-water interface. In addition, 22 and 12 SHs tended to spread to water and settle into sediment, respectively. To assess the ecological risk of all SHs, a robust random forest model (R2 = 0.801) was developed to predict the acute toxicity of SHs for which toxicity data were not available from publications. Risk assessment showed that SHs posed a high ecological risk throughout Taihu Lake, with the highest risk in the northwestern areas. Estrone, 17ß-estradiol and 17α-ethynylestradiol were the dominant risk contributors and were therefore recommended as the priority SHs in Taihu Lake. This work provided a valuable dataset for Taihu Lake, which would help to provide guidance and suggestions for future studies and be useful for the government to develop the mitigation and management measures.

[Spatial and Temporal Distribution and Risk Assessment of Heavy Metals in Surface Water of Changshou Lake Reservoir, Chongqing].

To understand the water pollution status and environmental risks of Changshou Lake, the concentrations of heavy metals (Cr, Cu, Zn, As, Cd, and Pb) in the water were collected and analyzed during different seasons. The study investigated temporal and spatial variations, distribution characteristics, pollution levels, and health risks associated with heavy metals in Changshou Lake. The results showed that all six heavy metals were below than the Class Ⅰ standard of the Surface Water Environmental Quality Standard (GB 3838-2002), but recent years have witnessed an increasing trend, with Cu, As, and Pb showing a significant increase (P<0.05). The temporal and spatial distributions of these heavy metals were different. Temporally, Cr and Cd concentrations in surface water were higher in summer, As and Zn were higher in spring, and Pb and Cu were higher in autumn and winter. Spatially, the concentrations of Cr, As, Cu, Zn, and Pb showed higher concentrations in the southern outlet of the reservoir, the northwestern Longxi River inlet, and the central part of the reservoir, whereas Cd was higher in the northern stagnant area. The overall levels of heavy metals in the water body of Changshou Lake were low, with Cr and Cu slightly polluted, while other heavy metals were identified as having an insignificant pollution level. Drinking water was the primary exposure pathway to carcinogenic and non-carcinogenic heavy metals in surface water bodies. The health risk values of Cr and As in water bodies were high, ranging from 6.2×10-10 to 3.0×10-4 and 5.1×10-8 to 3.9×10-5, respectively. The corresponding contribution rates for children and adults to the total health risk were high, with Cr accounting for 87.18% and 87.20%, respectively, while As accounted for 12.73% and 12.71%, respectively. Therefore, it is crucial to prioritize environmental risks associated with Cr and Cu, as well as the health risks associated with Cr and As in Changshou Lake These findings provide a scientific foundation for water pollution control and environmental quality improvement in Changshou Lake, and rational development and utilization of water resources.

Unveiling microplastics pollution in a subtropical rural recreational lake: A novel insight.

While global attention has been primarily focused on the occurrence and persistence of microplastics (MP) in urban lakes, relatively little attention has been paid to the problem of MP pollution in rural recreational lakes. This pioneering study aims to shed light on MP size, composition, abundance, spatial distribution, and contributing factors in a rural recreational lake, 'Nikli Lake' in Kishoreganj, Bangladesh. Using density separation, MPs were extracted from 30 water and 30 sediment samples taken from ten different locations in the lake. Subsequent characterization was carried out using a combination of techniques, including a stereomicroscope, Fourier transform infrared spectroscopy (FTIR) and field emission scanning electron microscopy (FE-SEM). The results showed a significant prevalence of MPs in all samples, with an average amount of 109.667 ± 10.892 pieces/kg3 (dw) in the sediment and 98.167 ± 12.849 pieces/m3 in the water. Small MPs (<0.5 mm), fragments and transparent colored particles formed the majority, accounting for 80.2%, 64.5% and 55.3% in water and 78.9%, 66.4% and 64.3% in sediment, respectively. In line with global trends, polypropylene (PP) (53%) and polyethylene (PE) (43%) emerged as the predominant polymers within the MPs. MP contents in water and sediment showed positive correlations with outflow, while they correlated negatively with inflow and lake depth (p > 0.05). Local activities such as the discharge of domestic sewage, fishing waste and agricultural runoff significantly influence the distribution of polypropylene. Assessment of pollution factor, pollution risk index and pollution load index values at the sampling sites confirmed the presence of MPs, with values above 1. This study is a baseline database that provides a comprehensive understanding of MP pollution in the freshwater ecosystem of Bangladesh, particularly in a rural recreational lake. A crucial next step is to explore ecotoxicological mechanisms, legislative measures and future research challenges triggered by MP pollution.

Variations in the concentration, inventory, source, and ecological risk of polycyclic aromatic hydrocarbons in sediments of the Lake Chaohu.

In this study, the ecological risk assessment of PAHs pollution, the existing S-T model was improved and applied to this PAHs pollution assessment in surface sediment in Lake Chaohu. The potential sources and contributions of PAHs in the surface sediment were estimated by molecular diagnostic ratio (MDR) and positive matrix factorization (PMF). The results showed that the average concentration of 16 priority PAHs in the surface sediment was 718.16 ng/g in 2009 and 334.67 ng/g in 2020. In 2020, PAHs concentration has decreased compared to 2009 and the dominant composition has changed from high- to low-molecular-weight PAHs. The estimated PAHs mass inventory of the top 2 cm surface sediment was 2712 tons in 2009 and 1263 tons in 2020. Ecosystem risk assessment by improved S-T models suggested that the overall ecosystem risk of the studied regions was acceptable.

Spatial and Temporal Variability of Saxitoxin-Producing Cyanobacteria in U.S. Urban Lakes.

Harmful cyanobacterial blooms (HCBs) are of growing global concern due to their production of toxic compounds, which threaten ecosystems and human health. Saxitoxins (STXs), commonly known as paralytic shellfish poison, are a neurotoxic alkaloid produced by some cyanobacteria. Although many field studies indicate a widespread distribution of STX, it is understudied relative to other cyanotoxins such as microcystins (MCs). In this study, we assessed eleven U.S. urban lakes using qPCR, sxtA gene-targeting sequencing, and 16S rRNA gene sequencing to understand the spatio-temporal variations in cyanobacteria and their potential role in STX production. During the blooms, qPCR analysis confirmed the presence of the STX-encoding gene sxtA at all lakes. In particular, the abundance of the sxtA gene had a strong positive correlation with STX concentrations in Big 11 Lake in Kansas City, which was also the site with the highest quantified STX concentration. Sequencing analysis revealed that potential STX producers, such as Aphanizomenon, Dolichospermum, and Raphidiopsis, were present. Further analysis targeting amplicons of the sxtA gene identified that Aphanizomenon and/or Dolichospermum are the primary STX producer, showing a significant correlation with sxtA gene abundances and STX concentrations. In addition, Aphanizomenon was associated with environmental factors, such as conductivity, sulfate, and orthophosphate, whereas Dolichospermum was correlated with temperature and pH. Overall, the results herein enhance our understanding of the STX-producing cyanobacteria and aid in developing strategies to control HCBs.

Prediction of chlorophyll a and risk assessment of water blooms in Poyang Lake based on a machine learning method.

Four different methods were used to identify the important factors influencing chlorophyll-a (Chl-a) content: correlation analysis (CC-NMI), principal component analysis (PCA), decision tree (DT), and random forest recursive feature elimination (RF-RFE). Considering the relationship between Chl-a and its active and passive factors, we established machine learning combination models based on multiple linear regression (MLR), multi-layer perceptron (MLP), and support vector regression (SVR) to predict Chl-a content for Poyang Lake, China. Then, the predictive effects of different combination models were compared and evaluated from multiple perspectives. Considering the actual needs for eutrophication prevention and control, the concept of risk probability was then introduced to assess the risk degree of risk associated with water blooms in Poyang Lake. The results indicated that the mean R2 for the Chl-a predictions using the MLR, MLP, and SVR models was 0.21, 0.61, and 0.75, respectively. Consequently, the SVR model demonstrated higher precision and more accurate predictions. Compared to other methods, integrating the SVR model with the RF-RFE method significantly improved the prediction accuracy, with the R2 increasing to 0.94. For Poyang Lake, 8.8% of random samples indicated a low risk level with a water bloom probability of 21.1%-36.5%; one sample indicated a medium risk level with a risk probability of 45.5%. The research results offer valuable insights for predicting eutrophication and conducting risk assessments for Poyang Lake. They also provide reliable scientific support for making decisions about eutrophication in lakes and reservoirs. Therefore, the results hold significant theoretical importance, practical value, and potential for widespread application.

Key results from the salt lake regional smoke, ozone, and aerosol study (SAMOZA).

The Northern Wasatch Front area is one of ~ 50 metropolitan regions in the U.S. that do not meet the 2015 O3 standard. To better understand the causes of high O3 days in this region we conducted the Salt Lake regional Smoke, Ozone and Aerosol Study (SAMOZA) in the summer of 2022. The primary goals of SAMOZA were: Measure a suite of VOCs, by Proton Transfer Reaction Mass Spectrometry (PTR-MS) and the 2,4-dinitrophenylhydrazine (DNPH) cartridge method.Evaluate whether the standard UV O3 measurements made in SLC show a positive bias during smoke events, as has been suggested in some recent studies.Use the observations to conduct photochemical modeling and statistical/machine learning analyses to understand photochemistry on both smoke-influenced and non-smoke days.Implications: The Northern Wasatch Front area is one of ~50 metropolitan regions in the U.S. that do not meet the 2015 O3 standard. To better understand the causes of high O3 days in this region we conducted the Salt Lake regional Smoke, Ozone and Aerosol Study (SAMOZA) in the summer of 2022. A number of policy relevant findings are identified in the manuscript including role of smoke and NOx vs VOC sensitivity.

We found no significant difference in the O₃ measurements using a "scrubber-less" UV instrument compared to the standard O₃ measurements at PM_2.5 concentrations up to 60 µg m⁻³.On days with smoke, we found that PM_2.5, CO, O₃ and nearly all VOCs were significantly enhanced. On average, NO_x was also enhanced on days with smoke, but this was complicated by day of week effects.Photochemical modeling of O₃ production rates at the Utah Tech Center demonstrates a strong sensitivity to VOC concentrations and less sensitivity to NOx. For non-smoke days, achieving the current O₃ standard would require regional reductions in VOCs of ~40% or reductions in NO_x ~ 60%.The photochemical modeling shows that formaldehyde and other OVOCs, along with alkenes, were the most important O₃ precursors.Generalized Additive Modeling (GAM) gave similar MDA8 O₃ enhancements on smoky days as the photochemical modeling. Analysis of the GAM results show that 23% of the smoke days have GAM residuals that exceed the U.S. EPA's criteria for inclusion as exceptional event documentation.

Hysteresis analysis reveals how phytoplankton assemblage shifts with the nutrient dynamics during and between precipitation patterns.

The escalation of global eutrophication has significantly increased due to the impact of climate change, particularly the increased frequency of extreme rainfall events. Predicting and managing eutrophication requires understanding the consequences of precipitation events on algal dynamics. Here, we assessed the influence of precipitation events throughout the year on nutrient and phytoplankton dynamics in a drinking water reservoir from January 2020 to January 2022. Four distinct precipitation patterns, namely early spring flood rain (THX), Plum rain (MY), Typhoon rain (TF), and Dry season (DS), were identified based on rainfall intensity, duration time, and cumulative rainfall. The study findings indicate that rainfall is the primary driver of algal dynamics by altering nutrient levels and TN:TP ratios during wet seasons, while water temperature becomes more critical during the Dry season. Combining precipitation characteristics with the lag periods between algal proliferation and rainfall occurrence is essential for accurately assessing the impact of rainfall on algal blooms. The highest algae proliferation occurred approximately 20 and 30 days after the peak rainfall during the MY and DS periods, respectively. This was influenced by the intensity and cumulative precipitation. The reservoir exhibited two distinct TN/TP ratio stages, with average values of 52 and 19, respectively. These stages were determined by various forms of nitrogen and phosphorus in rainfall-driven inflows and were associated with shifts from Bacillariophyta-dominated to Cyanophyta-dominated blooms during the MY and DS seasons. Our findings underscore the interconnected effects of nutrients, temperature, and hydrological conditions driven by diverse rainfall patterns in shaping algal dynamics. This study provides valuable insights into forecasting algal bloom risks in the context of climate change and developing sustainable strategies for lake or reservoir restoration.

Remote sensing retrieval and driving analysis of phytoplankton density in the large storage freshwater lake: A study based on random forest and Landsat-8 OLI.

Remote sensing monitoring of seasonal changes in phytoplankton density and analyses of the driving factors of phytoplankton densities are necessary for assessing the health of aquatic ecosystems, controlling lake eutrophication, and formulating ecological restoration policies. Building upon the satellite-ground synchronization experiment that involves the in situ aquatic ecological monitoring conducted in Nansi Lake, which is the largest storage lake situated along the eastern route of the South-to-North Water Diversion Project, we developed a phytoplankton density retrieval model utilizing the random forest (RF) method and Landsat-8 OLI data. On this basis, we mapped the seasonal fluctuations and spatial disparities in the phytoplankton densities from 2013 to 2023. Subsequently, we conducted a detailed analysis of the driving factors and considered both the natural and anthropogenic aspects. The results indicate that (1) the RF model, when utilizing three band combinations, yielded favorable results with R2, RMSE and MAE values of 0.67, 1.31 × 106 cells/L and 1.18 × 106 cells/L, respectively. (2) The phytoplankton densities exhibited both seasonal and spatial variations, with higher concentrations in summer and autumn than in spring and winter. Significantly, the northwestern region of Zhaoyang Lake and the southeastern region of Weishan Lake had substantially greater phytoplankton densities than did the other areas. Furthermore, overarching upward trends were observed from 2013 to 2023, reflecting an annual rate of increase of 3.32%. (3) An analysis of the causal factors indicated that temperatures and gross agricultural production levels are the primary drivers influencing the seasonal variations and distributions of phytoplankton densities. In the future, we will delve into the potential of deep learning and utilize various satellite sensors to explore the intricacies of phytoplankton monitoring, as well as the complex mechanisms that influence aquatic ecological health.