Towards the comprehensive water quality control in Lake Taihu: Correlating chlorphyll a and water quality parameters with generalized additive model

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.

Short-term variations and correlations in water quality after dam removal in the Chishui river basin

Dam damming has an adverse effect on river connectivity, leading to downstream nutrient transport and ecosystem fragmentation. Dam demolition has already been used as an effective measurement to promote the ecological restoration of rivers. Few studies have analyzed the short-term variations of water quality following dam removal. This study investigated the response of multi-element and multi-form water quality parameters, such as water temperature (TEM), dissolved oxygen (DO), pH, biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), total nitrogen (TN) and total phosphorus (TP), to the demolition of 4 dams in Chishui River Basin in short term. The study employed Spearman correlation analysis and Generalized Additive Models to identify the critical variables and examine the inter-relationship between these water quality parameters. Our results show that COD, BOD_5, and TP increased after two weeks of dam removal, while NH₃-N and TN decreased. Dams with larger volumes and higher heights led to more obvious deterioration for DO, COD, and BOD₅. We also found that denitrification and resuspension dominantly affect the water quality indicators following dam removal. Denitrification is responsible for downstream TN increase, and resuspension and related sediment transport contribute to downstream TP increase. Our study provides an opportunity to explore the transformation and migration of N and P in reservoirs following dam removal in the short term and presents a scientific basis and new thought for the short-term protection and management following the clean-up and rectification of multiple small hydropower plants.

Machine learning approach towards explaining water quality dynamics in an urbanised river

Human activities alter river water quality and quantity, with consequences for the ecosystems of urbanised rivers. Quantifying the role of human-induced drivers in controlling spatio-temporal patterns in water quality is critical to develop successful strategies for improving the ecological health of urban rivers. Here, we analyse high-frequency electrical conductivity and temperature data collected from the River Chess in South-East England during a Citizen Science project. Utilizing machine learning, we find that boosted trees outperform GAM and accurately describe water quality dynamics with less than 1% error. SHapley Additive exPlanations reveal the importance of and the (inter)dependencies between the individual variables, such as river level and Wastewater Treatment Works (WWTW) outflow. WWTW outflows give rise to diurnal variations in electrical conductivity, which are detectable throughout the year, and to an increase in average water temperature of 1 [Formula: see text] in a 2 km reach downstream of the wastewater treatment works during low flows. Overall, we showcase how high-frequency water quality measurements initiated by a Citizen Science project, together with machine learning techniques, can help untangle key drivers of water quality dynamics in an urbanised chalk stream.

Altitudinal Gradient Characteristics of Spatial and Temporal Variations of Snowpack in the Changbai Mountain and Their Response to Climate Change

The variations in the snowpack in water towers of the world due to climate change have threatened the amount and timing of freshwater supplied downstream. However, it remains to be further investigated whether snowpack variation in water towers exhibits elevational heterogeneity at different altitude gradients and which climatic factors mainly influence these differences. Therefore, Changbai Mountain, a high-latitude water tower, was selected to analyze the changes in the snowpack by the methods of modified Mann–Kendall based on the daily meteorological data from the China Meteorological Data Service Centre. Meanwhile, the responses of snowpack change to climatic factors over recent decades were assessed and generalized using additive models. The results showed that the snow depth was greater in the higher altitude areas than in the lower elevation areas at different times. Areas with a snow depth of over 70 mm increased significantly in the 2010s. Increasing trends were shown at different altitudes from December to March of the next year during 1960~2018. However, a significant decreasing trend was shown in April, except for altitudes of 600–2378 m. The snow cover time at different altitudes showed a trend of first increasing and then decreasing during 1960~2018. The date of maximum snow depth appears to be more lagged as the altitude increases. In addition, the spring snowpack melted significantly faster in the 2010s than that in the 1960s. The snowpack variation in low-altitude regions is mainly influenced by ET and relative humidity. However, the mean temperature gradually became an important factor, affecting the snow depth variation with the increase in altitude. Therefore, the results of this study will be beneficial to the ecological protection and sustainable development of water towers.

Responses of phytoplankton community structure and association to variability in environmental drivers in a tropical coastal lagoon

Spatial and seasonal heterogeneity in phytoplankton communities are governed by many biotic and abiotic drivers. However, the identification of long-term spatial and temporal trends in abiotic drivers, and their interdependencies with the phytoplankton communities' structure is understudied in tropical brackish coastal lagoons. We examined phytoplankton communities' spatiotemporal dynamics from a 5-year dataset (n = 780) collected from 13 sampling stations in Chilika Lagoon, India, where the salinity gradient defined the spatial patterns in environmental variables. Generalized additive models showed a declining trend in phytoplankton biomass, pH, and dissolved PO₄ in the lagoon. Hierarchical modelling of species communities revealed that salinity (44.48 ± 28.19%), water temperature (4.37 ± 5.65%), and season (4.27 ± 0.96%) accounted for maximum variation in the phytoplankton composition. Bacillariophyta (Indicator Value (IV): 0.74) and Dinophyta (IV: 0.72) emerged as top indicators for polyhaline regime whereas, Cyanophyta (IV: 0.81), Euglenophyta (IV: 0.79), and Chlorophyta (IV: 0.75) were strong indicators for oligohaline regime. The responses of Dinophyta and Chrysophyta to environmental drivers were much more complex as random effects accounted for ~70-75% variation in their abundances. Prorocentrum minimum (IV: 0.52), Gonyaulax sp. (IV: 0.52), and Alexandrium sp. (IV: 0.51) were potential indicators of P-limitation. Diploneis weissflogii (IV: 0.43), a marine diatom, emerged as a potential indicator of N-limitation. Hierarchical modelling revealed the positive association between Cyanophyta, Chlorophyta, and Euglenophyta whereas, Dinophyta and Chrysophyta showed a negative association with Cyanophyta, Chlorophyta, and Euglenophyta. Landsat 8-Operational Land Imager satellite models predicted the highest and lowest Cyanophyta abundances in northern and southern sectors, respectively, which were in accordance with the near-coincident field-based measurements from the lagoon. This study highlighted the dynamics of phytoplankton communities and their relationships with environmental drivers by separating the signals of habitat filtering and biotic interactions in a monsoon-regulated tropical coastal lagoon.

Compositional changes of sedimentary microbes in the Yangtze River Estuary and their roles in the biochemical cycle

Due to the geographical circumstances, the Yangtze River Estuary (YRE) and the adjacent East China Sea are extensively influenced by both anthropogenic activities and environmental factors. To reveal the responses of microbes in surface sediment to environmental factors and their contributions to the biogeochemical cycle in this area, surface sediment and overlying water samples were collected at 21 stations from the estuary to the coastal region. Water and sediment parameters were determined, and 16S rRNA genes of microbes in sediment samples were sequenced using high throughput sequencing technology. The results indicated that ocean currents, sediment density (SD), nutrients, sulfate (SO₄^2-), and salinity were the key factors shaping the microbial communities. Coastal microbes were affected mainly by SD, whereas anthropogenic discharge might have been responsible for a decrease in indigenous microbial diversity in the ocean. Due to the anthropogenic discharge, the most representative bacteria in the nearshore were aerobic and chemoheterotrophic bacteria, including ammonia-oxidizing bacteria, nitrite-oxidizing bacteria, denitrifying bacteria, and polyphosphate accumulating organisms. In the offshore, anaerobic bacteria, thermophilic bacteria, halophilic bacteria, sulfate-reducing bacteria, and sulfide oxidizing bacteria were the dominant bacteria, and these were characterized by strong solidarity and cooperative properties within the malnourished environment. In summary, these results provide a new perspective for revealing the biogeochemical significance of the bacterial lineages in the YRE, as well as constructive guidance for the management of the marginal sea ecosystems in distinct regions.

Water-Sediment Physicochemical Dynamics in a Large Reservoir in the Mediterranean Region under Multiple Stressors

Nowadays, the Mediterranean freshwater systems face the threat of water scarcity, along with multiple other stressors (e.g., organic and inorganic contamination, geomorphological alterations, invasive species), leading to the impairment of their ecosystem services. All these stressors have been speeding up, due to climate variability and land cover/land use changes, turning them into a big challenge for the water management plans. The present study analyses the physicochemical and phytoplankton biomass (chlorophyll-a) dynamics of a large reservoir, in the Mediterranean region (Alqueva reservoir, Southern Portugal), under diverse meteorological conditions and land cover/land use real scenarios (2017 and 2018). The most important stressors were identified and the necessary tools and information for a more effective management plan were provided. Changes in these parameters were further related to the observed variations in the meteorological conditions and in the land cover/land use. The increase in nutrients and ions in the water column, and of potentially toxic metals in the sediment, were more obvious in periods of severe drought. Further, the enhancement of nutrients concentrations, potentially caused by the intensification of agricultural activities, may indicate an increased risk of water eutrophication. The results highlight that a holistic approach is essential for a better water resources management strategy.

Establishment of an integrated decision-making method for planning the ecological restoration of terrestrial ecosystems

Ecological restoration of terrestrial ecosystems facilitates environmental protection and enhances sustainable development of land resources. With increasingly severe land degradation, new and effective methods must be developed for the restoration of ecological functions. In this study, we developed a regional risk assessment approach to support the planning of ecological restoration of a terrestrial ecosystem located in the Daye area in central China. The study area was divided into six sub-regions where ecological risks were characterized by building a non-linear model to represent ecological interactions among the risk components there. Socio-economic conditions in the areas were evaluated and presented using an analytic hierarchy process. Assessment of different stakeholders there was conducted based on multiple-criteria decision analysis. Then, integrated assessment was performed using the technique of order preference for an ideal solution. We divided the degraded land in Daye into areas with different priorities for restoration or rectification and presented corresponding sequential time intervals for the action. The results are as follows: (i) the top priority rectification areas (totaling 358 km²) are mainly distributed in northeast and northwest regions; (ii) the high priority rectification areas are concentrated in the central region spanning 226 km²; (iii) the medium priority rectification areas comprised a large amount of arable and forest land spanning 605 km²; and (iv) the low priority rectification areas cover the rest part of the Daye area spanning 195 km². The assessment tool was proven to be useful in planning regional ecological restoration in terrestrial ecosystems.

Development of optimal steam explosion pretreatment and highly effective cell factory for bioconversion of grain vinegar residue to butanol

BACKGROUND

The industrial vinegar residue (VR) from solid-state fermentation, mainly cereals and their bran, will be a potential feedstock for future biofuels because of their low cost and easy availability. However, utilization of VR for butanol production has not been as much optimized as other sources of lignocellulose, which mainly stem from two key elements: (i) high biomass recalcitrance to enzymatic sugar release; (ii) lacking of suitable industrial biobutanol production strain. Though steam explosion has been proved effective for bio-refinery, few studies report SE for VR pretreatment. Much of the relevant knowledge remains unknown. Meanwhile, recent efforts on rational metabolic engineering approaches to increase butanol production in Clostridium strain are quite limited. In this study, we assessed the impact of SE pretreatment, enzymatic hydrolysis kinetics, overall sugar recovery and applied atmospheric and room temperature plasma (ARTP) mutant method for the Clostridium strain development to solve the long-standing problem.

RESULTS

SE pretreatment was first performed. At the optimal condition, 29.47% of glucan, 71.62% of xylan and 22.21% of arabinan were depolymerized and obtained in the water extraction. In the sequential enzymatic hydrolysis process, enzymatic hydrolysis rate was increased by 13-fold compared to the VR without pretreatment and 19.60 g glucose, 15.21 g xylose and 5.63 g arabinose can be obtained after the two-step treatment from 100 g VR. Porous properties analysis indicated that steam explosion can effectively generate holes with diameter within 10-20 nm. Statistical analysis proved that enzymatic hydrolysis rate of VR followed the Pseudop-second-order kinetics equation and the relationship between SE severity and enzymatic hydrolysis rate can be well revealed by Boltzmann model. Finally, a superior inhibitor-tolerant strain, Clostridium acetobutylicum Tust-001, was generated with ARTP treatment. The water extraction and enzymolysis liquid gathered were successfully fermented, resulting in butanol titer of 7.98 g/L and 12.59 g/L of ABE.

CONCLUSIONS

SE proved to be quite effective for VR due to high fermentable sugar recovery and enzymatic hydrolysate fermentability. Inverse strategy employing ARTP and repetitive domestication for strain breeding is quite feasible, providing us with a new tool for solving the problem in the biofuel fields.