The performance, mechanism and greenhouse gas emission potential of nitrogen removal technology for low carbon source wastewater.

Excessive nitrogen can lead to eutrophication of water bodies. However, the removal of nitrogen from low carbon source wastewater has always been challenging due to the limited availability of carbon sources as electron donors. Biological nitrogen removal technology can be classified into three categories: heterotrophic biological technology (HBT) that utilizes organic matter as electron donors, autotrophic biological technology (ABT) that relies on inorganic electrons as electron donors, and heterotrophic-autotrophic coupling technology (CBT) that combines multiple electron donors. This work reviews the research progress, microbial mechanism, greenhouse gas emission potential, and challenges of the three technologies. In summary, compared to HBT and ABT, CBT shows greater application potential, although pilot-scale implementation is yet to be achieved. The composition of nitrogen removal microorganisms is different, mainly driven by electron donors. ABT and CBT exhibit the lowest potential for greenhouse gas emissions compared to HBT. N2O, CH4, and CO2 emissions can be controlled by optimizing conditions and adding constructed wetlands. Furthermore, these technologies need further improvement to meet increasingly stringent emission standards and address emerging pollutants. Common measures include bioaugmentation in HBT, the development of novel materials to promote mass transfer efficiency of ABT, and the construction of BES-enhanced multi-electron donor systems to achieve pollutant prevention and removal. This work serves as a valuable reference for the development of clean and sustainable low carbon source wastewater treatment technology, as well as for addressing the challenges posed by global warming.

The potential and sustainable strategy for swine wastewater treatment: Resource recovery.

Swine wastewater is highly polluted with complex and harmful substances that require effective treatment to minimize environmental damage. There are three commonly used biological technologies for treating swine wastewater: conventional biological technology (CBT), microbial electrochemical technology (MET), and microalgae technology (MT). However, there is a lack of comparison among these technologies and a lack of understanding of their unique advantages and efficient operation strategies. This review aims to compare and contrast the characteristics, influencing factors, improvement methods, and microbial mechanisms of each technology. CBT is cost-effective but has low resource recovery efficiency, while MET and MT have the highest potential for resource recovery. However, all three technologies are affected by various factors and toxic substances such as heavy metals and antibiotics. Improved methods include exogenous/endogenous enhancement, series reactor operation, algal-bacterial symbiosis system construction, etc. Though MET is limited by construction costs, CBT and MT have practical applications. While swine wastewater treatment processes have developed automatic control systems, the application need further promotion. Furthermore, key functional microorganisms involved in CBT's pollutant removal or transformation have been detected, as have related genes. The unique electroactive microbial cooperation mode and symbiotic mode of MET and MT were also revealed, respectively. Importantly, the future research should focus on broadening the scope and scale of engineering applications, preventing and controlling emerging pollutants, improving automated management level, focusing on microbial synergistic metabolism, enhancing resource recovery performance, and building a circular economy based on low-cost and resource utilization.

A river basin spatial model to quantitively advance understanding of riverine tree response dynamics to water availability and hydrological management.

Ecological condition continues to decline in arid and semi-arid river basins globally due to hydrological over-abstraction combined with changing climatic conditions. Whilst provision of water for the environment has been a primary approach to alleviate ecological decline, how to accurately monitor changes in riverine trees at fine spatial and temporal scales, remains a substantial challenge. This is further complicated by constantly changing water availability across expansive river basins with varying climatic zones. Within, we combine rare, fine-scale, high frequency temporal in-situ field collected data with machine learning and remote sensing, to provide a robust model that enables broadscale monitoring of physiological tree water stress response to environmental changes via actual evapotranspiration (ET). Physiological variation of Eucalyptus camaldulensis (River Red Gum) and E. largiflorens (Black Box) trees across 10 study locations in the southern Murray-Darling Basin, Australia, was captured instantaneously using sap flow sensors, substantially reducing tree response lags encountered by monitoring visual canopy changes. Actual ET measurement of both species was used to bias correct a national spatial ET product where a Random Forest model was trained using continuous timeseries of in-situ data of up to four years. Precise monthly AMLETT (Australia-wide Machine Learning ET for Trees) ET outputs in 30 m pixel resolution from 2012 to 2021, were derived by incorporating additional remote sensing layers such as soil moisture, land surface temperature, radiation and EVI and NDVI in the Random Forest model. Landsat and Sentinal-2 correlation results between in-situ ET and AMLETT ET returned R2 of 0.94 (RMSE 6.63 mm period-1) and 0.92 (RMSE 6.89 mm period-1), respectively. In comparison, correlation between in-situ ET and a national ET product returned R2 of 0.44 (RMSE 34.08 mm period-1) highlighting the need for bias correction to generate accurate absolute ET values. The AMLETT method presented here, enhances environmental management in river basins worldwide. Such robust broadscale monitoring can inform water accounting and importantly, assist decisions on where to prioritize water for the environment to restore and protect key ecological assets and preserve floodplain and riparian ecological function.

Response of dryland vegetation under extreme wet events with satellite measures of greenness and fluorescence.

Extreme wet events in central Australia triggered large vegetation responses that contributed greatly to large global land carbon sink anomalies. There remain significant uncertainties on the extent to which these events over dryland vegetation can be monitored and assessed with satellite data. In this study, we investigated the vegetation responses of the major Australian semiarid biomes to two extreme wet events utilizing multi-satellite observations of (1) solar-induced chlorophyll fluorescence (SIF), as a proxy for photosynthetic activity and (2) the enhanced vegetation index (EVI), as a measure of canopy chlorophyll or greenness. We related these satellite observations with gross primary productivity (GPP) estimated from eddy covariance tower sites, as a performance benchmark. The C3-dominated Mulga woodland was the most responsive biome to both wet pulses and exhibited the highest sensitivity to soil moisture. The C4-dominated Hummock grassland was more responsive to the 2011 "big wet" event, relative to the later 2016-2017 wet pulse. EVI swiftly responded to the extreme wet events and showed markedly amplified seasonal amplitude, however, there was a time lag as compared with SIF during the post-wet period, presumably due to the relatively slower chlorophyll degradation in contrast with declines in photosynthetic activity. Despite a robust linear SIF-GPP relationship (r2 ranging from 0.59 to 0.85), the spatially coarse SIF derived from the Global Ozone Monitoring Experiment-2 (GOME-2) yielded high retrieval noise over the xeric biomes, hindering its capacity to capture thoroughly the dryland vegetation dynamics in central Australia. Our study highlights that synchronous satellite observations of greenness and fluorescence can potentially offer an improved understanding of dryland vegetation dynamics and can advance our ability to detect ecosystem alterations under future changing climates.

A decreasing carbon allocation to belowground autotrophic respiration in global forest ecosystems.

Belowground autotrophic respiration (RAsoil) depends on carbohydrates from photosynthesis flowing to roots and rhizospheres, and is one of the most important but least understood components in forest carbon cycling. Carbon allocation plays an important role in forest carbon cycling and reflects forest adaptation to changing environmental conditions. However, carbon allocation to RAsoil has not been fully examined at the global scale. To fill this knowledge gap, we first used a Random Forest algorithm to predict the spatio-temporal patterns of RAsoil from 1981 to 2017 based on the most updated Global Soil Respiration Database (v5) with global environmental variables; calculated carbon allocation from photosynthesis to RAsoil (CAB) as a fraction of gross primary production; and assessed its temporal and spatial patterns in global forest ecosystems. Globally, mean RAsoil from forests was 8.9 ± 0.08 Pg C yr-1 (mean ± standard deviation) from 1981 to 2017 and increased significantly at a rate of 0.006 Pg C yr-2, paralleling broader soil respiration changes and suggesting increasing carbon respired by roots. Mean CAB was 0.243 ± 0.016 and decreased over time. The temporal trend of CAB varied greatly in space, reflecting uneven responses of CAB to environmental changes. Combined with carbon use efficiency, our CAB results offer a completely independent approach to quantify global aboveground autotropic respiration spatially and temporally, and could provide crucial insights into carbon flux partitioning and global carbon cycling under climate change.

Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest.

Satellite observations of Amazon forests show seasonal and interannual variations, but the underlying biological processes remain debated. Here we combined radiative transfer models (RTMs) with field observations of Amazon forest leaf and canopy characteristics to test three hypotheses for satellite-observed canopy reflectance seasonality: seasonal changes in leaf area index, in canopy-surface leafless crown fraction and/or in leaf demography. Canopy RTMs (PROSAIL and FLiES), driven by these three factors combined, simulated satellite-observed seasonal patterns well, explaining c. 70% of the variability in a key reflectance-based vegetation index (MAIAC EVI, which removes artifacts that would otherwise arise from clouds/aerosols and sun-sensor geometry). Leaf area index, leafless crown fraction and leaf demography independently accounted for 1, 33 and 66% of FLiES-simulated EVI seasonality, respectively. These factors also strongly influenced modeled near-infrared (NIR) reflectance, explaining why both modeled and observed EVI, which is especially sensitive to NIR, captures canopy seasonal dynamics well. Our improved analysis of canopy-scale biophysics rules out satellite artifacts as significant causes of satellite-observed seasonal patterns at this site, implying that aggregated phenology explains the larger scale remotely observed patterns. This work significantly reconciles current controversies about satellite-detected Amazon phenology, and improves our use of satellite observations to study climate-phenology relationships in the tropics.