Microplastics contamination in water supply system and treatment processes.

Due to global demand, millions of tons of plastics have been widely consumed, resulting in the widespread entry of vast amounts of microplastic particles into the environment. The presence of microplastics (MPs) in water supplies, including bottled water, has undergone systematic review, assessing the potential impacts of MPs on humans through exposure assessment. The main challenges associated with current technologies lie in their ability to effectively treat and completely remove MPs from drinking and supply water. While the risks posed by MPs upon entering the human body have not yet been fully revealed, there is a predicted certainty of negative impacts. This review encompasses a range of current technologies, spanning from basic to advanced treatments and varying in scale. However, given the frequent detection of MPs in drinking and bottled water, it becomes imperative to implement comprehensive management strategies to address this issue effectively. Consequently, integrating current technologies with management options such as life-cycle assessment, circular economy principles, and machine learning is crucial to eliminating this pervasive problem.

Thermodynamic sensitivity of ammonia oxidizers-driven N2O fluxes under oxic-suboxic realms.

In terrestrial ecosystems, the nitrogen dynamics, including N2O production, are majorly regulated by a complex consortium of microbes favored by different substrates and environmental conditions. To better predict the daily, seasonal and annual variation in N2O fluxes, it is critical to estimate the temperature sensitivity of different microbial groups for N2O fluxes under oxic and suboxic conditions prevalent in soil and wetlands. Here, we studied the temperature sensitivity of two groups of ammonia oxidizers, archaea (AOA) and bacteria (AOB), in relation to N2O fluxes through both nitrification and nitrifier-denitrification pathways across a wide temperature gradient (10-55 °C). Using square root theory (SQRT) and macromolecular rate theory (MMRT) models, we estimated thermodynamic parameters and cardinal temperatures, including maximum temperature sensitivity (TSmax). The distinction between N2O pathways was facilitated by microbial-specific inhibitors (PTIO and C2H2) and controlled oxygen supply environments (oxic: ambient level; and suboxic: ∼4%). We found that nitrification supported by AOA (NtA) and AOB (NtB) dominated N2O production in an oxic climate, while only AOB-supported nitrifier-denitrification (NDB) majorly contributed (>90%) to suboxic N2O budget. The models predicted significantly higher optimum temperature (Topt) and TSmax for NtA and NDB compared to NtB. Intriguingly, both NtB and NDB exhibited significantly wider temperature ranges than NtA. Altogether, our results suggest that temperature and oxygen supply control the dominance of specific AOA- and AOB-supported N2O pathways in soil and sediments. This emergent understanding can potentially contribute toward novel targeted N2O inhibitors for GHG mitigation under global warming.

Soil microbiome feedback to climate change and options for mitigation.

Microbes are a critical component of soil ecosystems, performing crucial functions in biogeochemical cycling, carbon sequestration, and plant health. However, it remains uncertain how their community structure, functioning, and resultant nutrient cycling, including net GHG fluxes, would respond to climate change at different scales. Here, we review global and regional climate change effects on soil microbial community structure and functioning, as well as the climate-microbe feedback and plant-microbe interactions. We also synthesize recent studies on climate change impacts on terrestrial nutrient cycles and GHG fluxes across different climate-sensitive ecosystems. It is generally assumed that climate change factors (e.g., elevated CO2 and temperature) will have varying impacts on the microbial community structure (e.g., fungi-to-bacteria ratio) and their contribution toward nutrient turnover, with potential interactions that may either enhance or mitigate each other's effects. Such climate change responses, however, are difficult to generalize, even within an ecosystem, since they are subjected to not only a strong regional influence of current ambient environmental and edaphic conditions, historical exposure to fluctuations, and time horizon but also to methodological choices (e.g., network construction). Finally, the potential of chemical intrusions and emerging tools, such as genetically engineered plants and microbes, as mitigation strategies against global change impacts, particularly for agroecosystems, is presented. In a rapidly evolving field, this review identifies the knowledge gaps complicating assessments and predictions of microbial climate responses and hindering the development of effective mitigation strategies.

Roles of microalgae-based biofertilizer in sustainability of green agriculture and food-water-energy security nexus.

For years, agrochemical fertilizers have been used in agriculture for crop production. However, intensive utilization of chemical fertilizers is not an ecological and environmental choice since they are destroying soil health and causing an emerging threat to agricultural production on a global scale. Under the circumstances of the increasing utilization of chemical fertilizers, cultivating microalgae to produce biofertilizers would be a wise solution since desired environmental targets will be obtained including (1) replacing chemical fertilizer while improving crop yields and soil health; (2) reducing the harvest of non-renewable elements from limited natural resources for chemical fertilizers production, and (3) mitigating negative influences of climate change through CO2 capture through microalgae cultivation. Recent improvements in microalgae-derived-biofertilizer-applied agriculture will be summarized in this review article. At last, the recent challenges of applying biofertilizers will be discussed as well as the perspective regarding the concept of circular bio-economy and sustainable development goals (SDGs).

Photo-assisted microbial fuel cell systems: critical review of scientific rationale and recent advances in system development.

Bioelectrochemical systems such as microbial fuel cells (MFCs) have gained extensive attention due to their abilities to simultaneously treat wastewater and generate renewable energy resources. Recently, to boost the system performance, the photoelectrode has been incorporated into MFCs for effectively exploiting the synergistic interaction between light and microorganisms, and the resultant device is known as photo-assisted microbial fuel cells (photo-MFCs). Combined with the metabolic reaction of organic compounds by microorganisms, photo-MFCs are capable of simultaneously converting both chemical energy and light energy into electricity. This article aims to systematically review the recent advances in photo-MFCs, including the introduction of specific photosynthetic microorganisms used in photo-MFCs followed by the discussion of the fundamentals and configurations of photo-MFCs. Moreover, the materials used for photoelectrodes and their fabrication approaches are also explored. This review has shown that the innovative strategy of utilizing photoelectrodes in photo-MFCs is promising and further studies are warranted to strengthen the system stability under long-term operation for advancing practical application.

Employment of osmotic pump as a novel feeding system to operate the laminar-flow microfluidic microbial fuel cell.

Laminar-flow microfluidic microbial fuel cell (LMMFC) has attracted attention due to the advantage of the liquid-liquid interface between anolyte and catholyte without the use of membrane as a separator resulting in less fabrication cost. Unlike previous studies of LMMFC using syringe pumps, this study proposes the use of osmotic pumps to feed anolyte and catholyte in the microchannel without any additional power supply. The osmotic pump was constructed with two cylindrical chambers separated by a forward osmosis membrane, with the initial draw solution concentration of 90 g l-1 NaCl. We have, for the first time, demonstrated using the osmotic pumps to deliver both anolyte and catholyte and create co-laminar flow in LMMFC. Under the catholyte and anolyte flow rates of 18 ml/h and 40 ml/h respectively, LMMFC cultivated with Shewanella oneidensis produced the maximum power density of 87 mW m-2 and current density of 747 mA m-2 with the internal resistance of 1660 Ω. Further studies are warranted to develop osmotic pumps-fed LMMFC into a potential platform for portable biosensors.

An overview of deploying membrane bioreactors in saline wastewater treatment from perspectives of microbial and treatment performance.

The discharged saline wastewater has severely influenced the aquatic environment as the treatment performance of many wastewater treatment techniques is limited. In addition, the sources of saline wastewater are also plentiful from agricultural and various industrial fields such as food processing, tannery, pharmaceutical, etc. Although high salinity levels negatively impact the performance of both physicochemical and biological processes, membrane bioreactor (MBR) processes are considered as a potential technology to treat saline wastewater under different salinity levels depending on the adaption of the microbial community. Therefore, this study aims to systematically review the application of MBR widely used in the saline wastewater treatment from the perspectives of microbial structure and treatment efficiencies. At last, the concept of carbon dioxide capture and storage will be proposed for the MBR-treating saline wastewater technologies and considered toward the circular economy with the target of zero emission.

Phthalates in the environment: characteristics, fate and transport, and advanced wastewater treatment technologies.

Phthalates are well-known emerging contaminants that harm human health and the environment. Therefore, this review aims to discuss about the occurrence, fate, and phthalates concentration in the various environmental matrices (e.g., aquatic, sediment, soil, and sewage sludge). Hence, it is necessary to treat sources containing phthalates before discharging them to aqueous environment. Various advanced wastewater treatments including adsorption process (e.g., biochar, activated carbon), advanced oxidation processes (e.g., photo-fenton, ozonation, photocatalysis), and biological treatment (membrane bioreactor) have been successfully to address this issue with high removal efficiencies (70-95%). Also, the degradation mechanism was discussed to provide a comprehensive understanding of the phthalate removal for the reader. Additionally, key factors that influenced the phthalates removal efficiency of these technologies were identified and summarized with a view towards pilot-scale and industrial applications.

Assessing the influence of environmental niche segregation in ammonia oxidizers on N2O fluxes from soil and sediments.

Understanding the environmental niche segregation of ammonia-oxidizing archaea (AOA) and bacteria (AOB) and its impact on their relative contributions to nitrification and nitrous oxide (N2O) production is essential for predicting N2O dynamics within an ecosystem. Here, we used ammonia oxidizer-specific inhibitors to measure the differential contributions of AOA and AOB to potential ammonia oxidization (PAO) and N2O fluxes over pH (4.0-9.0) and temperature (10-45 °C) gradients in five soils and three wetland sediments. AOA and AOB activities were differentiated using PTIO (2-phenyl-4,4,5,5-tetramethylimidazoline-1-oxyl 3-oxide), 1-octyne, and acetylene. We used square root growth (SQRT) and macromolecular rate theory (MMRT) models to estimate cardinal temperatures and thermodynamic characteristics for AOA- and AOB-dominated PAO and N2O fluxes. We found that AOA and AOB occupied different niches for PAO, and soil temperature was the major determinant of niche specialization. SQRT and MMRT models predicted a higher optimum temperature for AOA-dominated PAO and N2O fluxes compared with those of AOB. Additionally, PAO was dominated by AOA in acidic conditions, whereas both AOA- and AOB-dominated N2O fluxes decreased with increasing pH. Consequently, net N2O fluxes (AOA and AOB) under acidic conditions were approximately one to three-fold higher than those observed in alkaline conditions. Moreover, structural equation and linear regression modeling confirmed a significant positive correlation (R2 = 0.45, p < 0.01) between PAO and N2O fluxes. Collectively, these results show the influence of ammonia oxidizer responses to temperature and pH on nitrification-driven N2O fluxes, highlighting the potential for mitigating N2O emissions via pH manipulation.

Measuring Responses of Dicyandiamide-, 3,4-Dimethylpyrazole Phosphate-, and Allylthiourea-Induced Nitrification Inhibition to Soil Abiotic and Biotic Factors.

Nitrification inhibitors (NIs) such as dicyandiamide (DCD), 3,4-dimethylpyrazole phosphate (DMPP), and allylthiourea (AT) are commonly used to suppress ammonia oxidization at different time scales varying from a few hours to several months. Although the responses of NIs to edaphic and temperature conditions have been studied, the influence of the aforementioned factors on their inhibitory effect remains unknown. In this study, laboratory-scale experiments were conducted to assess the short-term (24 h) influence of eight abiotic and biotic factors on the inhibitory effects of DCD, DMPP, and AT across six cropped and non-cropped soils at two temperature conditions with three covariates of soil texture. Simultaneously, the dominant contributions of ammonia-oxidizing archaea (AOA) and bacteria (AOB) to potential ammonia oxidization (PAO) were distinguished using the specific inhibitor 2 phenyl-4,4,5,5-tetramethylimidazoline-1-oxyl 3-oxide (PTIO). Our results revealed that AT demonstrated a considerably greater inhibitory effect (up to 94.9% for an application rate of 75 mg of NI/kg of dry soil) than DCD and DMPP. The inhibitory effect of AT was considerably affected by the relative proportions of silt, sand, and clay in the soil and total PAO. In contrast to previous studies, the inhibitory effects of all three NIs remained largely unaffected by the landcover type and temperature conditions for the incubation period of 24 h. Furthermore, the efficacy of all three tested NIs was not affected by the differential contributions of AOA and AOB to PAO. Collectively, our results suggested a limited influence of temperature on the inhibitory effects of all three NIs but a moderate dependence of AT on the soil texture and PAO. Our findings can enhance the estimation of the inhibitory effect in soil, and pure cultures targeting the AOA and AOB supported ammonia oxidization and, hence, nitrogen dynamics under NI applications.

A spatial prioritization method for identifying potential eco-risk distributions of heavy metals in soil and birds.

Geochemical approaches are popular for evaluations based on heavy metal concentrations in sediments or soils for eco-risk assessment. This study proposes a systematic geochemical approach (SymGeo) to explore six heavy metals in topsoils and bird tissues and organs of the target birds. We assume that the proposed approach based on field-collected heavy metals in topsoils and feathers can predict the areas with the potential risk of the heavy metals in birds. Finite mixture distribution modeling (FMDM) was used to identify background values of the heavy metal concentrations in topsoil. A spatial enrichment factor (EF), potential contamination index (PCI), contamination degree (Cod), and potential ecological risk index (PRI) based on FMDM results for topsoil, and a potential risk index (PRIbird) of heavy metals in the birds, were utilized for systematic prioritization of high eco-risk areas. Using multiple EF, PRI, and Cod results and multiple PRI-based maps of the heavy metals in feathers, we systematically prioritized risk areas where there is a high potential for heavy metal contamination in the birds. Our results indicate that heavy metal concentrations in the feather, liver, and kidney are not spatially cross-autocorrelated but are statistically significantly correlated with some heavy metals in topsoil due to external and internal depositions. Further, multiple EF, Cod, and RI distributions for topsoil, along with the PRI of the feather, showed that adequate coverages for potential risk for birds were greater than 71.05% in the top 30% and 84.69% in the top 20% potential eco-risk priority area of heavy metals in bird liver and kidney. Hence, our proposed approach suggests that assessments of heavy metals in bird feathers and topsoils without bird organs can be utilized to identify spatially high-risk areas. The proposed approach could be improved by incorporating water and sediment samples to enhance the crowdsourcing and the species-specific data.

Exploring hybrid consensus models to assess roadkill.

Accurate information provided by reliable models is essential for identifying hotspots and mitigating roadkill. However, existing methods, such as kernel density estimation (KDE) and maximum entropy modeling (ME) may individually identify only a subset of the suitable locations for mitigation, because KDE cannot detect hotspots once local abundances are depressed, and ME may only partially identify current hotspots due to imperfect discrimination skill. Here, we propose a hybrid consensus modeling (HCM) approach that leverages the strengths of both KDE and ME by using their consensus to identify the core subset of hotspots. We collected herpetofauna (amphibians and reptiles) roadkill data (N = 839) along four roads in Taiwan (R.O.C.) to evaluate the statistical performance and theoretical mitigation efficiency of HCM, KDE and ME, and to compare the allocation among roads, spatial clustering, and environmental conditions in the identified hotspots. HCM was applied on the herpetofauna dataset as well as separately on amphibians and reptiles. Although the discrimination skill of KDE and ME models for both target clades together was good to excellent (AUCKDE = 0.944, AUCME = 0.822), the highest theoretical mitigation efficiency, was displayed by HCM Consensus (2.89), followed by KDE (2.58), and ME (1.91). Furthermore, we show that theoretical mitigation efficiency increases with decreasing spatial clustering (Moran's I). Given pervasive budget constraints, we recommend to limit permanent mitigation measures such as fenced culverts to HCM Consensus hotspots, temporary measures to KDE hotspots, and to target additional monitoring at ME hotspots.

Topsoil microbial community structure responds to land cover type and environmental zone in the Western Pacific region.

Soil encompasses diverse microbial communities that are essential for fundamental ecosystem functions such as biogeochemical cycling. To better understand underlying biogeochemical processes, it is necessary to know the structure of soil archaeal and bacterial communities and their responses to edaphic and climate variables within and across various land cover types (LCTs) and environmental zones (ENZs). Here we sampled eighty-nine sites across five ENZs and four LCTs within the Western Pacific region. Through leveraging the second-generation sequencing of topsoil samples, we showed that α-diversity (taxonomic diversity) of archaea strongly varied within LCTs, whereas bacterial α-diversity was significantly controlled by both LCT and ENZ. Soil archaea and bacteria showed global niche differentiation associated with contrasting diversity responses to latitude and differential responses of microbial diversity patterns to edaphic and climate variables within LCTs and ENZs. In contrast to α-diversity, microbial ß-diversity (the compositional dissimilarity between sites) was majorly governed by ENZs, particularly for archaea (P < 0.01). Our results highlight the importance of LCTs and ENZs for understanding soil microbial contributions to nutrient dynamics and ecosystem resilience under land-use intensification and climate change.

Climate and land cover shape the fungal community structure in topsoil.

Globally, soils are subject to radical changes in their biogeochemistry as rampant deforestation and other forms of land use and climate change continue to transform planet Earth. To better understand soil ecosystem functioning, it is necessary to understand the responses of soil microbial diversity and community structure to changing climate, land cover, and associated environmental variables. With next-generation sequencing, we investigated changes in topsoil fungi community structure among different land cover types (from Forest to Cropland) and climate zones (from Hot to Cold zones) in the Western Pacific Region. We demonstrated that climate zones substantially (P = 0.001) altered the soil fungal beta-diversity (change in community composition), but not alpha-diversity (taxonomical diversity). In particular, precipitation, temperature, and also latitude were the best predictors of beta-diversity. Individual fungal classes displayed divergent but strong responses to climate variables and latitude, suggesting niche differentiation at lower taxonomic levels. We also demonstrated that fungal taxonomic diversity differentially responded to latitude across land covers: fungal diversity increased towards lower latitudes in the Forest and Cropland (R2 = 0.19) but increased towards both lower and higher latitudes in Fallow land (R2 = 0.45). Further, alpha-diversity was significantly influenced by soil pH in Forest (P = 0.02), and by diurnal temperature range in Fallow land and mean annual precipitation in Cropland. Collectively, various land cover types had differential influence on the latitude diversity gradient, while climate, and to some extent, edaphic variables, were crucial in shaping soil fungal community structure. Our results can also serve as a baseline for estimating global change impacts on fungal community structure in the Western Pacific Region.

Real-Time Identification of Irrigation Water Pollution Sources and Pathways with a Wireless Sensor Network and Blockchain Framework.

Real-time identification of irrigation water pollution sources and pathways (PSP) is crucial to ensure both environmental and food safety. This study uses an integrated framework based on the Internet of Things (IoT) and the blockchain technology that incorporates a directed acyclic graph (DAG)-configured wireless sensor network (WSN), and GIS tools for real-time water pollution source tracing. Water quality sensors were installed at monitoring stations in irrigation channel systems within the study area. Irrigation water quality data were delivered to databases via the WSN and IoT technologies. Blockchain and GIS tools were used to trace pollution at mapped irrigation units and to spatially identify upstream polluted units at irrigation intakes. A Water Quality Analysis Simulation Program (WASP) model was then used to simulate water quality by using backward propagation and identify potential pollution sources. We applied a "backward pollution source tracing" (BPST) process to successfully and rapidly identify electrical conductivity (EC) and copper (Cu2+) polluted sources and pathways in upstream irrigation water. With the BPST process, the WASP model effectively simulated EC and Cu2+ concentration data to identify likely EC and Cu2+ pollution sources. The study framework is the first application of blockchain technology for effective real-time water quality monitoring and rapid multiple PSPs identification. The pollution event data associated with the PSP are immutable.

Assessing the association and predictability of heavy metals in avian organs, feathers, and bones using crowdsourced samples.

Birds are bioindicators for research on the relationship between environmental heavy metal concentration levels and accumulation levels in bird tissues. We use roadkill samples, collected by citizen science participants, to investigate the accumulation levels and associations of seven heavy metals in internal organs (heart, liver, and kidney), feathers (primary and breast), and bones (sternum and femur) of two focal species, Amaurornis phoenicurus and Gallinula chloropus. We found that heavy metal accumulation varied by target tissue, and that variables are associated with bird species and heavy metal type. Although Zn and Cu were highest by concentration among both species, Cu was mostly accumulated in internal organs, As in feathers, and Pb in bones. Concentrations of As, Ni, and Pb in feathers of both focal species were lower than those reported in literature, whereas Cd and Cr were above toxic levels. The results also showed that spatial correlation for heavy metal concentration among bird tissues were weaker than non-spatial correlation, suggesting low spatial autocorrelations and variability. In addition, multiple regression analysis revealed significant correlation for Cr, As, and Pb estimations in A. phoenicurus heart, sternum, and kidney, respectively; and potentially Cr in G. chloropus femur by using feathers. These results support the feasibility of using feathers as indicators of As, Cr, and Pb heavy metal contamination to enhance our understanding of heavy metal accumulation in birds, although caution is required for feather-based estimations of Cd, Cu, and Ni concentration.

Relative Abundance of Ammonia Oxidizing Archaea and Bacteria Influences Soil Nitrification Responses to Temperature.

Ammonia oxidizing archaea (AOA) and bacteria (AOB) are thought to contribute differently to soil nitrification, yet the extent to which their relative abundances influence the temperature response of nitrification is poorly understood. Here, we investigated the impact of different AOA to AOB ratios on soil nitrification potential (NP) across a temperature gradient from 4 °C to 40 °C in twenty different organic and inorganic fertilized soils. The temperature responses of different relative abundance of ammonia oxidizers for nitrification were modeled using square rate theory (SQRT) and macromolecular rate theory (MMRT) models. We found that the proportional nitrification rates at different temperatures varied among AOA to AOB ratios. Predicted by both models, an optimum temperature (Topt) for nitrification in AOA dominated soils was significantly higher than for soils where AOA and AOB abundances are within the same order of magnitude. Moreover, the change in heat capacity ( Δ C P ‡ ) associated with the temperature dependence of nitrification was positively correlated with Topt and significantly varied among the AOA to AOB ratios. The temperature ranges for NP decreased with increasing AOA abundance for both organic and inorganic fertilized soils. These results challenge the widely accepted approach of comparing NP rates in different soils at a fixed temperature. We conclude that a shift in AOA to AOB ratio in soils exhibits distinguished temperature-dependent characteristics that have an important impact on nitrification responses across the temperature gradient. The proposed approach benefits the accurate discernment of the true contribution of fertilized soils to nitrification for improvement of nitrogen management.

Assessing thermodynamic parameter sensitivity for simulating temperature responses of soil nitrification.

Soil nitrification responses to temperature have major implications for the global nitrogen cycle. Temperature sensitivity of soil nitrification has been modeled using several mathematical models, yet the extent to which model-generated thermodynamic parameters are accurate and sensitive in describing temperature sensitivity is unclear. In this study, we performed global sensitivity analysis to identify the key thermodynamic parameters that are most influential when simulating the temperature response of the soil nitrification potential (NP) across two different temperature gradients (4-40 °C and 20-45 °C) which are imposed upon sixteen different soils with square root growth (SQRT) and macromolecular rate theory (MMRT) models. We found that two thermodynamic parameters stand out as moderately to highly sensitive, and are uniquely identifiable in each model, regardless of the temperature range. The minimum and maximum measured temperatures seem to have no impact on the list of sensitive parameters but do influence the parameter ranges, especially for the SQRT model. However, parameters that control the minimum temperature and curvature of the NP response curve (Tmin and ΔC‡P) were found to have little to no sensitivity to SQRT and MMRT model outputs, respectively. We show that the parameter sensitivity and range of measured temperatures influence the complementary model's ability to describe the temperature sensitivity of soil nitrification. Our proposed framework enhances the accurate interpretation of existing thermodynamic parameters that explain the temperature sensitivity of soil biochemical processes, and provides methodological recommendations for future temperature sensitivity studies.

Modeling Nitrogen Dynamics in a Waste Stabilization Pond System Using Flexible Modeling Environment with MCMC.

This study presents an approach for obtaining realization sets of parameters for nitrogen removal in a pilot-scale waste stabilization pond (WSP) system. The proposed approach was designed for optimal parameterization, local sensitivity analysis, and global uncertainty analysis of a dynamic simulation model for the WSP by using the R software package Flexible Modeling Environment (R-FME) with the Markov chain Monte Carlo (MCMC) method. Additionally, generalized likelihood uncertainty estimation (GLUE) was integrated into the FME to evaluate the major parameters that affect the simulation outputs in the study WSP. Comprehensive modeling analysis was used to simulate and assess nine parameters and concentrations of ON-N, NH3-N and NO3-N. Results indicate that the integrated FME-GLUE-based model, with good Nash-Sutcliffe coefficients (0.53-0.69) and correlation coefficients (0.76-0.83), successfully simulates the concentrations of ON-N, NH3-N and NO3-N. Moreover, the Arrhenius constant was the only parameter sensitive to model performances of ON-N and NH3-N simulations. However, Nitrosomonas growth rate, the denitrification constant, and the maximum growth rate at 20 °C were sensitive to ON-N and NO3-N simulation, which was measured using global sensitivity.