Partitioning urban forest evapotranspiration based on integrating eddy covariance of water vapor and carbon dioxide fluxes.

Partitioning of evapotranspiration (ET) in urban forest lands plays a vital role in mitigating ambient temperature and evaluating the effects of urbanization on the urban hydrological cycle. While ET partitioning has been extensively studied in diverse natural ecosystems, there remains a significant paucity of research on urban ecosystems. The flux variance similarity (FVS) theory is used to partition urban forest ET into soil evaporation (E) and vegetation transpiration (T). This involves measurements from eddy covariance of water vapor and carbon dioxide fluxes, along with an estimated leaf-level water use efficiency (WUE) algorithm. The study compares five WUE algorithms in partitioning the average transpiration fraction (T/ET) and validates the results using two years of oxygen isotope observations. Although all five FVS-based WUE algorithms effectively capture the dynamic changes in hourly scale T and E across the four seasons, the algorithm that assumes a constant ratio of intercellular CO2 concentration (ci) to ambient CO2 concentration (ca) provides the most accurate simulation results for the ratio of T/ET. The performance metrics for this specific algorithm include the RMSE of 0.06, R2 of 0.88, the bias of 0.02, and MAPE of 8.9 %, respectively. Comparing urban forests to natural forests, the T/ET in urban areas is approximately 2.4-25.3 % higher, possibly due to the elevated air temperature (Ta), greater leaf area index (LAI), and increased soil water availability. Correlation analysis reveals that the T/ET dynamic is primarily controlled by Ta, LAI, net radiation, ca, and soil water content at half-hourly, daily, and monthly scales. This research provides valuable insights into the performance and applicability of various WUE algorithms in urban forests, contributing significantly to understanding the impact of urbanization on energy, water, and carbon cycles within ecosystems.

Spatiotemporal variation reconstruction of total phosphorus in the Great Lakes since 2002 using remote sensing and deep neural network.

Total phosphorus (TP) is non-optically active, thus TP concentration (CTP) estimation using remote sensing still exists grand challenge. This study developed a deep neural network model (DNN) for CTP estimation with synchronous in-situ measurements and MODIS-derived remote sensing reflectance (Rrs) (N = 3916). Using DNN, the annual and intra-annual CTP spatial distributions of the Great Lakes since 2002 were reconstructed. Then, the reconstructions were correlated to nine potential factors, e.g., Chlorophyll-a, snowmelt, and cropland, to explain seasonal and long-term CTP variations. The results showed that DNN reliably estimated CTP from MODIS Rrs, with R2, mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and root mean squared logarithmic error (RMSLE) of 0.83, 1.05 µg/L, 2.95 µg/L, 9.92%, and 0.13 on the test set. The near-surface CTP in the Great Lakes decreased significantly (p < 0.05) during 2002 - 2022, primarily attributed to cropland reduction, coupled with improvements in basin natural ecosystems. The sensitivity analysis verified the model robustness when confronted with input feature changes < 35%. This result along with the marginal difference between CTP derived from two sensors (R2 = 0.76, MAE = 2.12 µg/L, RMSE = 2.51 µg/L, MAPE = 11.52%, RMSLE = 0.24) suggested the model transferability from MODIS to VIIRS. This transformation facilitated optimal usage of MODIS-related archive and enhanced the continuity of CTP estimation at moderate resolution. This study presents a practical method for spatiotemporal reconstruction of CTP using remote sensing, and contributes to better understandings of driving factors behind CTP variations in the Great Lakes.

Solid-state, reagent-free and one-step laser-induced synthesis of graphene-supported metal nanocomposites from metal leaves and application to glucose sensing.

The laser-induced method to prepare three-dimensional (3D) porous graphene has been widely used in many fields owing to its low-cost, easy operation, maskless patterning and ease of mass production. Metal nanoparticles are further introduced on the surface of 3D graphene to enhance its property. The existing methods, however, such as laser irradiation and electrodeposition of metal precursor solution, suffer from many shortcomings, including complicated procedure of metal precursor solution preparation, strict experimental control, and poor adhesion of metal nanoparticles. Herein, a solid-state, reagent-free, and one-step laser-induced strategy has been developed for the fabrication of metal nanoparticle modified-3D porous graphene nanocomposites. Commercial transfer metal leaves were covered on a polyimide film followed by direct laser irradiation to produce 3D graphene nanocomposites modified with metal nanoparticles. The proposed method is versatile and applicable to incorporate various metal nanoparticles including gold silver, platinum, palladium, and copper. Furthermore, the 3D graphene nanocomposites modified with AuAg alloy nanoparticles were successfully synthesized in both 21 Karat (K) and 18K gold leaves. Its electrochemical characterization demonstrated that the synthesized 3D graphene-AuAg alloy nanocomposites exhibited excellent electrocatalytic properties. Finally, we fabricated LIG-AuAg alloy nanocomposites as enzyme-free flexible sensors for glucose detection. The LIG-18K electrodes exhibited the superior glucose sensitivity of 1194 µA mM-1 cm-2 and low detection limits of 0.21 µM. The LIG-21K nanocomposite sensors showed two linear ranges from 1 µM to 1 mM and 2 mM-20 mM with good sensitivity. Furthermore, the flexible glucose sensor showed good stability, sensitivity, and ability to sense in blood plasma samples. The proposed one-step fabrication of reagent-free and metal alloy nanoparticles on LIG with excellent electrochemical performance opens up possibilities for diversifying potential applications of sensing, water treatment and electrocatalysis.

Assessing long-term climate change impact on spatiotemporal changes of groundwater level using autoregressive-based and ensemble machine learning models.

To evaluate the long-term climate change impacts on groundwater fluctuations of the Ardabil plain, Iran, a groundwater level (GWL) modeling was proposed in this study. Accordingly, the outputs of Global Climate Models (GCMs) under the sixth report of Coupled Model Intercomparison Project (CMIP6) and future scenario of the Shared Socioeconomic Pathway 5-8.5 (SSP5-8.5), were used as climate change forcing to the Machine learning (ML) models. The GCM data were first downscaled and projected for the future via Artificial Neural Networks (ANNs). Based on the results, compared to 2014 (the last year of the base period), the mean annual temperature may increase by 0.8 °C per decade until 2100. On the other hand, the mean precipitation may decrease by about 8% compared to the base period. Then, the centroid wells of clusters were modeled by Feedforward Neural Network (FFNN), examining different input combination sets to simulate both autoregressive and non-autoregressive models. Since each of the ML models can extract different kinds of information from a dataset, after finding the dominant input set via FFNN, GWL time series were modeled via various ML methods. The modeling results indicated that the ensemble of shallow ML models could lead to a 6% more accurate outcome than the individual shallow ML models, and 4% than the deep learning models. Also, the simulation results for future GWLs illustrated that temperature can impact groundwater oscillations directly, whereas precipitation may not have uniform impacts on the GWLs. The uncertainty evolving in the modeling process was quantified and observed to be in acceptable range. Modeling results showed that the main reason for the declining GWL in the Ardabil plain could be primarily linked to the excessive exploitation of the water table, while climate change impact could be also notable.

Remote sensing retrieval of inland water quality parameters using Sentinel-2 and multiple machine learning algorithms.

Remote sensing has long been an effective method for water quality monitoring because of its advantages such as high coverage and low consumption. For non-optically active parameters, traditional empirical and analytical methods cannot achieve quantitative retrieval. Machine learning has been gradually used for water quality retrieval due to its ability to capture the potential relationship between water quality parameters and satellite images. This study is based on Sentinel-2 images and compared the ability of four machine learning algorithms (eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Network (ANN)) to retrieve chlorophyll-a (Chl-a), dissolved oxygen (DO), and ammonia-nitrogen (NH3-N) for inland reservoirs. The results indicated that XGBoost outperformed the other three algorithms. We used XGBoost to reconstruct the spatial-temporal patterns of Chl-a, DO, and NH3-N for the period of 2018-2020 and further analyzed the interannual, seasonal, and spatial variation characteristics. This study provides an efficient and practical way for optically and non-optically active parameters monitoring and management at the regional scale.

Detection, occurrence, influencing factors and environmental risks of paralytic shellfish toxins in seawater in a typical mariculture bay.

Paralytic shellfish toxins (PSTs) producing algae are widely distributed in the global coastal aquatic environment, posing a threat to coastal ecosystem health and mariculture safety. However, the levels and potential environmental risks of PSTs frequently detected in shellfish remain largely unexplored in seawater of mariculture zones. In this study, a new method for trace detection of 13 common PSTs (<1.0 ng/L) in seawater was established based on off-line solid phase extraction (SPE) and on-line SPE-liquid chromatography-tandem mass spectrometry (on-line SPE-LC-MS/MS), and a systematic investigation of PSTs in seawater of the Laizhou Bay, a typical aquaculture bay in China, was conducted to understand their pollution status, environmental impact factors and ecological risks for the first time. Eleven PSTs were detected in the seawater of Laizhou Bay with total concentrations ranging from 0.75 to 349.47 ng/L (mean, 176.27 ng/L), which indicates the rich diversity of PSTs in the mariculture bay and demonstrates the reliability of the proposed analytical method. C1, C2, GTX2, GTX3, dcGTX2, and dcGTX3 were found to be the predominant PSTs, which refreshed the knowledge of PST contamination in the coastal aquatic environment. PST levels in seawater exhibited the highest levels in the southeastern mouth of Laizhou Bay and decreased toward the inner bay. Correlation analyses showed that climatic factors, nutrient status and hydrological conditions had significant effects on the distribution of PST in mariculture bay. Preliminary environmental risk assessments revealed that aquatic organisms throughout the waters of Laizhou Bay are at risk of chronic PST toxicity. These findings imply that the risk of PST in seawater of mariculture bay has previously been grossly underestimated, and that the coastal aquatic environment in North China and even the world may be at more serious risk of PST pollution, which should be taken seriously.

A comparison study on the role of urbanization in altering the short-duration and long-duration intense rainfall.

Urbanization has significantly changed the regional hydrological cycle and energy balance. However, the different roles of urbanization on intense rainfall with short and long persistence need a better understanding, particularly in coastal mega cities with complex terrains. In this study, we compared the spatial and diurnal characteristics of intense rainfall across two coastal cities with different degrees of urbanization with 70 meteorological stations. The effects of anthropogenic and geographical factors on short-duration intense rainfall (SDIR) and long-duration intense rainfall (LDIR) were investigated using the statistical method. SDIR and LDIR events show different spatial patterns that SDIR events center in the highly urbanized regions while LDIR events center in the coastal mountainous regions. In terms of diurnal features, it is found that the higher occurrence frequency of SDIR in the dense urban region than that in suburbs occurs over the period with strong urban heat island intensity (UHII). It indicated that thermal contrast between urban and suburbs breeds an atmospheric environment for inspiriting the convection and SDIR events. The urban-induced increase in SDIR events depended on urbanization stages, with only dense urban regions showing a significant influence. The results of geographical detector model (GDM) also demonstrated that the synergy of build-up area and population explained 48 %-51 % of spatial heterogeneity of SDIR. Nevertheless, urbanization has little effect in modifying the diurnal features of LDIR events, while it might influence spatial rainfall patterns by enhancing rainfall peaks. The GDM results indicated that terrain positively dominates the spatial distribution for LDIR events, and the interactions of multi-factors (terrain, urbanization, or distance to the coast et al.) have enhanced explanatory power (q values up to 0.70). The results provide a fundamental understanding for the effects of anthropogenic and geographical factors on different types of rainfall events in coastal mega cities.

An ensemble machine learning model for water quality estimation in coastal area based on remote sensing imagery.

The accurate estimation of coastal water quality parameters (WQPs) is crucial for decision-makers to manage water resources. Although various machine learning (ML) models have been developed for coastal water quality estimation using remote sensing data, the performance of these models has significant uncertainties when applied to regional scales. To address this issue, an ensemble ML-based model was developed in this study. The ensemble ML model was applied to estimate chlorophyll-a (Chla), turbidity, and dissolved oxygen (DO) based on Sentinel-2 satellite images in Shenzhen Bay, China. The optimal input features for each WQP were selected from eight spectral bands and seven spectral indices. A local explanation strategy termed Shapley Additive Explanations (SHAP) was employed to quantify contributions of each feature to model outputs. In addition, the impacts of three climate factors on the variation of each WQP were analyzed. The results suggested that the ensemble ML models have satisfied performance for Chla (errors = 1.7%), turbidity (errors = 1.5%) and DO estimation (errors = 0.02%). Band 3 (B3) has the highest positive contribution to Chla estimation, while Band Ration Index2 (BR2) has the highest negative contribution to turbidity estimation, and Band 7 (B7) has the highest positive contribution to DO estimation. The spatial patterns of the three WQPs revealed that the water quality deterioration in Shenzhen Bay was mainly influenced by input of terrestrial pollutants from the estuary. Correlation analysis demonstrated that air temperature (Temp) and average air pressure (AAP) exhibited the closest relationship with Chla. DO showed the strongest negative correlation with Temp, while turbidity was not sensitive to Temp, average wind speed (AWS), and AAP. Overall, the ensemble ML model proposed in this study provides an accurate and practical method for long-term Chla, turbidity, and DO estimation in coastal waters.

Trace determination of multiple hydrophilic cyanotoxins in freshwater by off- and on-line solid phase extraction coupled to liquid chromatography-tandem mass spectrometry.

Hydrophilic cyanotoxins (HCTs), such as paralytic shellfish toxins (PSTs), anatoxin-a (ATX-a), and cylindrospermopsin (CYN) are highly toxic and toxin-producing algae are widely distributed worldwide. However, HCTs, especially PSTs, are rarely reported in freshwater due to analytical limitations. This may result in an underestimation of the ecological risks and health risks. This study developed a new method to detect ATX-a, CYN, and thirteen common PSTs in freshwater simultaneously by using off-line solid phase extraction (SPE) and liquid chromatography-tandem mass spectrometry (LC-MS/MS). The limits of detection (LODs) of all targets were lower than 0.05 µg/L, which could meet the regulatory requirements for monitoring of HCTs in drinking water in different countries and regions. To improve the detection sensitivities for trace PSTs, a method based on off-line SPE and on-line SPE-LC-MS/MS was established with LOD around 0.001 µg/L. GTX1&4, GTX2&3, and GTX5 were detected in freshwater in China for the first time, highlighting that overall communities are facing potential risks of exposure to various PSTs in China. High concentrations of ATX-a and CYN were also detected in freshwater from Northern China. The proposed method helps to understand the pollution status of HCT in water bodies, especially during the non-algal bloom period.

Synchronous N and P Removal in Carbon-Coated Nanoscale Zerovalent Iron Autotrophic Denitrification─The Synergy of the Carbon Shell and P Removal.

Fe0 is a promising electron donor for autotrophic denitrification in the simultaneous removal of nitrate and phosphorus in low C/N wastewater. However, P removal may inevitably inhibit bio-denitrification. It has not been well recognized and led to an overdose of iron materials. This study employed carbon-coated zerovalent iron (Fe0@C) to support autotrophic denitrification to mitigate the inhibition effects of P removal and enhance both N and P removal. The critical role of the carbon shell in Fe0@C was to block the direct contact between Fe0 and P and NO3--N, to maintain the Fe0 activity. Besides, P inhibited the chemical reduction of NO3--N by competing for Fe0 active sites. This indirectly boosted H2 generation and promoted bio-denitrification. P removal displayed negligible effects on microbial species but indirectly enhanced the nitrogen metabolic activities because of promoted H2 in Fe0@C-based autotrophic denitrification. Bio-denitrification, in turn, strengthened Fe-P co-precipitation by promoting the formation of ferric hydroxide as a secondary adsorbent for P removal. This study demonstrated an efficient method for simultaneous N and P removal in autotrophic denitrification and revealed the synergistic interactions among N and P removal processes.

Development of miniature self-powered single-chamber microbial fuel cell and its response mechanism to copper ions in high and trace concentration.

Copper ions are widely present in water environment and are involved in various biochemical reaction processes, causing irreversible damage to the human body. In this study, we design and establish a self-powered miniature single-chamber microbial fuel cell (SCMFC) reactor using xurography technology. Optimal volume of 188 µL is obtained by controlling the distance between the anode and cathode. Copper ions in two concentration gradients are tested and good linear response curves are obtained. The opposite responses to copper ions in the trace concentration range (0-0.4 mg/L) and high concentration range (1.0-8.0 mg/L) are observed. The results show that at trace concentration range, the inhibitory effect of copper ions on the biofilm activity of micro-SCMFC is dominant; while high concentration copper ions are involved in chemical reactions that produce Cu2O, which may act as a catalyst and promote electron transfer. A good linear response to trace concentration (0-0.4 mg/L) of copper ions with detection limits of 0.05 mg/L is obtained in this study. It could be used in drinking water for trace copper ion detection. The investigation of the mechanisms provides the scientific basis for the design of the efficient detection of copper ions by SCMFC.

Promotion of anammox process by different graphene-based materials: Roles of particle size and oxidation degree.

Graphene oxide (GO) and reduced graphene oxide (RGO) have been applied in the anaerobic ammonium oxidation (anammox) process for nitrogen removal as electron shuttles. However, there is still controversy about their efficacy. In this study, nine graphene-based materials with a gradient of three particle sizes (large (l), medium (m) and small (s) sizes) and oxidation degrees, were used to compare their effects on the anammox process efficiency. The graphene-based materials include GO and its reduced products (RGO250 and RGO800) obtained at temperatures of 250 °C and 800 °C respectively. It was observed that their enhancements on the anammox process were in the order of GO > RGO800 > RGO250. In detail, at the dose of 100 mg/L, specific anammox activities (SAA) were promoted by 6.7% (l-GO), 4.9% (l-RGO800), 11.5% (m-GO), 7.3% (m-RGO800), 13.2% (s-GO) and 8.3% (s-RGO800) compared to the control respectively; while RGO250 with the same dose inhibited the process. In addition, the enhancement of the anammox process was increasing with the decreasing size of GO and RGO800. The nitrite reductase (NIR) activity was greatly increased by up to 24.9% with the presence of GO, which might be attributed to organized and specific electron transport with oxygen functional groups. The finding of hydroxyl on RGO and increasing content of oxygen determined after reaction detected by Fourier transform infrared spectroscopy and energy dispersive spectrometer respectively, indicated the essential condition for RGO's function on transferring electrons for key enzymes in annamox bacteria. Most importantly, O/C (Oxygen/Carbon) ratios of graphene-based materials had greater effects on the promotion of the anammox process than the particle size and electrical conductivity.

Seasonal and short-term variations of bacteria and pathogenic bacteria on road deposited sediments.

The bacteria (including pathogenic bacteria) attached to road deposited sediments (RDS) may interrelate with the microbe in the atmosphere, soil and water through resuspension and wash-off, and is of great significance to human and ecological health. However, the characteristics of bacterial communities with different time scale on RDS were unknown to dates. Climate change prolonged the dry days between rain events in many areas, making the varied trend of bacterial communities might be more significant in short term. This study revealed the characteristics of bacterial communities on RDS in urban and suburban areas through seasonal and daily scale. The correlations between other factors (land use, particle size, and chemical components) and the bacterial communities were also analyzed. It was found that the season showed a higher association with the bacterial community diversity than land use and particle size in urban areas. The bacterial community diversity increased substantially throughout the short-term study period (41 days) and the variation of dominant bacteria could be fitted by quadratic function in suburbs. In addition, urbanization notably increased the bacterial community diversity, while the potential pathogenic bacteria were more abundant in the suburban areas, coarse RDS (>75 µm), and in spring. The chemical components on RDS showed special correlations with the relative abundance of dominant bacteria. The research findings would fill the knowledge gap on RDS bacterial communities and be helpful for the future research on the assembly process of bacterial communities.

Seasonal source identification and source-specific health risk assessment of pollutants in road dust.

Humans who are exposed to metals in road dust may have potential health risks through touching, ingesting, and inhaling the suspended road dust. There were limited studies to link seasonal emission sources to health risks from metals in road dust. In this study, metals in road dust from different functional areas were seasonally monitored. The contributions of the pollutant sources in study areas varied with seasons. By combining the source apportionment model (PMF), road dust emission model, and health risk models (HI: hazard index and ILCR: incremental lifetime carcinogenic risk), industrial and construction activity was identified as the crucial source of both the pollutants in road dust (29-47%), and the HI for adults (27-45%) and children (41-50%) in different seasons. The traffic non-exhaust emission dominated in the carcinogenic risks for children in spring (45%) and summer (36%). Factors such as seasons, particle size, metal bioavailability, human exposure time, and exposure area were all taken into consideration to avoid overestimating or underestimating health risks. The carcinogenic risks for children (1.6 E-06) and adults (2.8 E-06) exposed to Cr both exceed the minimum threshold (10-6). It means that the potential risks were acceptable but could not be completely neglected. Measured metals mainly posed hazard to human health through ingestion route. Pb and Mn, Fe and Mn were the main harmful elements that induced non-carcinogenic risks for adults and children, respectively. Effectively identifying the source-specific health risks in different seasons will help in the formulation of adaptive strategies to diminish the potential risks.

Ultrasensitive photoelectrochemical microcystin-LR immunosensor using carboxyl-functionalized graphene oxide enhanced gold nanoclusters for signal amplification.

An ultrasensitive photoelectrochemical (PEC) immunosensor based on gold nanoclusters (AuNCs) with 11-mercaptoundecanoic acid (MUA) ligands was fabricated for determination of microcystin-LR (MC-LR). The PEC immunosensor was developed by loading the monoclonal MC-LR antibody (Ab) to the MUA-AuNCs modified gold electrodes. After different measurement conditions being optimized, silver nanoparticles (AgNPs), gold nanorods (AuNRs), graphene oxide (GO) and carboxyl-functionalized graphene oxide (cGO) were introduced into MUA-AuNCs to enhance the sensing properties. The experimental result revealed that the sensitivity of PEC immunosensors was enhanced by both their photoelectrochemical properties and antibody loading properties with dependent relationship, which was different from the enhancement strategy of PEC sensors based on redox reactions. Among different hybrid nanocomposites, MUA-AuNCs/cGO not only improved the photoelectrochemical properties, but also loaded more antibodies for sensing, which resulted in best sensing performance. Thus, a universal method was proposed to enhance the sensing performance of PEC immunosensors based on impedance changes. Finally, MUA-AuNCs/cGO based PEC immunosensors exhibited a wide linear range of 0.001 nM-1000 nM with low detection limit of 0.011 pM (S/N = 3) for MC-LR determination. Meanwhile, the designed PEC immunosensors showed high selectivity, reproducibility and specificity, which provided the promising applications in aquatic environment.

A generalized machine learning approach for dissolved oxygen estimation at multiple spatiotemporal scales using remote sensing.

Dissolved oxygen (DO) is an effective indicator for water pollution. However, since DO is a non-optically active parameter and has little impact on the spectrum captured by satellite sensors, research on estimating DO by remote sensing at multiple spatiotemporal scales is limited. In this study, the support vector regression (SVR) models were developed and validated using the remote sensing reflectance derived from both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and synchronous DO measurements (N = 188) and water temperature of Lake Huron and three other inland waterbodies (N = 282) covering latitude between 22-45 °N. Using the developed models, spatial distributions of the annual and monthly DO variability since 1984 and the annual monthly DO variability since 2000 in Lake Huron were reconstructed for the first time. The impacts of five climate factors on long-term DO trends were analyzed. Results showed that the developed SVR-based models had good robustness and generalization (average R2 = 0.91, root mean square percentage error = 2.65%, mean absolute percentage error = 4.21%), and performed better than random forest and multiple linear regression. The monthly DO estimates by Landsat and MODIS data were highly consistent (average R2 = 0.88). From 1984 to 2019, the oxygen loss in Lake Huron was 6.56%. Air temperature, incident shortwave radiation flux density, and precipitation were the main climate factors affecting annual DO of Lake Huron. This study demonstrated that using SVR-based models, Landsat and MODIS data could be used for long-term DO retrieval at multiple spatial and temporal scales. As data-driven models, combining spectrum and water temperature as well as extending the training set to cover more DO conditions could effectively improve model robustness and generalization.

Efficient phosphorus removal by a novel halotolerant fungus Aureobasidium sp. MSP8 and the application potential in saline industrial wastewater treatment.

Efficient halotolerant phosphorus accumulation microorganisms are of great significance for the treatment of high-salt wastewater. In this study, a halotolerant fungus strain named MSP8 was isolated and identified as Aureobasidium sp. Salinity resistance results showed that strain MSP8 can resist the salinity from 0% to 17%, and 77.2% phosphorus removal was achieved at the optimal salinity of 5%. The strain also showed wide environmental adaptability (pH of 3-7; temperature of 20-30 °C). Batch tests and scanning electron microscope equipped with an energy dispersive spectrometer (SEM-EDS) characterization results verified the key role of extracellular polymeric substance (EPS) secreted by MSP8 in phosphorus removal. The actual brewery and chemical wastewater treatments exhibited that above 53.5% of phosphorus can be removed by MSP8. The excellent adaptation of MSP8 made it a potential candidate for phosphorus removal especially in saline wastewater treatment.

Effect of steel slag in recycling waste activated sludge to produce anaerobic granular sludge.

The amount of waste activated sludge (WAS) has grown dramatically in China. WAS is considered as a problematic and hazardous waste, which should be disposed in a safe and sustainable manner. In order to recycle WAS to an anaerobic granular sludge (AnGS) process for anaerobic digestion, Fe powder and steel slags (rusty and clean slags) were used to enhance the granulation process. The results demonstrated that both rusty and clean slags encouraged the development of granular sludge. Adding 10 g/L clean slags could increase AnGS granulation rate by 37%. In the presence of clean slags, extracellular polymeric substances (EPS) concentration in granules increased noticeably to 715 mg/g mixed liquor suspended solids (MLSS). High throughput sequencing analysis exhibited more diversity and higher abundance of functional microbial communities in the batch bottle with 10 g/L clean slags. This study suggested that adding clean slags at 10 g/L dosage was a sustainable and effective method for the sludge granulation.

Electrospray-Printed Three-Tiered Composite Membranes with Enhanced Mass Transfer Coefficients for Phenol Removal in an Aqueous-Aqueous Membrane Extractive Process.

The aqueous-aqueous membrane extractive process is an ideal approach to remove recalcitrant organics from highly saline and harsh wastewater. However, it is still challenging to develop highly efficient membranes for the extractive process. In this work, three-tiered polydimethylsiloxane (PDMS)/polyvinylidene fluoride (PVDF) nanofiber/nonwoven fabric composite membranes were prepared by electrospinning and electrospray printing for the first time. An ultrathin and defect-free PDMS selective layer was fabricated on the surface of a PVDF/nonwoven fabric nanofibrous substrate by electrospray printing. Meanwhile, the thicknesses of the PDMS selective layer were able to be finely controlled by electrospray printing. The novel three-tiered composite membrane #N3-1 with the thinnest PDMS layer (3.0 ± 0.4 µm) and a thin and porous supporting layer showed an exceptionally high k0 of 37.9 ± 2.8 × 10-7 m/s and an excellent salt rejection above 99.95% over a 105 h continuous operation. Moreover, #N3-1 exhibited outstanding k0 at feed pH of 2 and 11 over 100 h without loss of salt rejection. In addition, the effects of the nonwoven fabric supporting layer on the phenol mass transfer coefficient (k0, m/s) of resultant extractive membranes were also studied symmetrically. A thin and porous nonwoven supporting layer #N3 was capable of improving the k0 of resultant composite membrane significantly.

Nitrogen removal bacterial strains, MSNA-1 and MSD4, with wide ranges of salinity and pH resistances.

Nitrogenous wastewater is difficult to treat using conventional microorganisms in high salinity and acidic/alkaline environments. Two halotolerant bacteria, heterotrophic nitrifying Stenotrophomonas sp. MSNA-1 and aerobic denitrifying Pseudomonas sp. MSD4, were isolated, and the amplification of functional genes provided the evidences of nitrogen removal performance. The results regarding salinity and pH resistance showed that strain MSNA-1 is robust at salinities of 0-15% and pH of 3-10. It can remove 51.2% of NH4+-N (180 mg/L) at salinity of 10% (pH: 7) and 49.2% of NH4+-N under pH 4 (salinity: 3%). For strain MSD4, it is robust at salinities of 0-10% and pH of 5-11. It can remove 62.4% of TN (100 mg/L) at salinity of 7% (pH: 7) and 72.2% of TN under pH 9 (salinity: 3%). Their excellent salinity and pH resistances make them promising candidates for treating nitrogenous wastewaters under extreme conditions with low operational cost.