The decay of animal corpses may release various nutrients into the surrounding environment and participate in the biogeochemical cycle. However, few studies have involved the sulphur cycle in animal corpse decay. Here, we conducted an indoor control experiment at five different temperature gradients (23, 26, 29, 32 and 35 °C) to explore the change of sulphur cycling microbes and genes under corpse decay by metagenomic sequencing, metagenomic binning, and 16S rRNA sequencing. Our results showed that total sulphur content decreased by 41.82% due to decomposition. The dominant phyla of the S-cycle microbes were Proteobacteria, followed by Bacteroidetes and Acidobacteria. Corpse decay decreased the pathways involved in assimilatory sulphate reduction, sulfur oxidizing (Sox) system, organic sulphur transformation, and sulphur oxidation pathway, but increased the links between inorganic and organic sulphur transformation. The alpha diversity of sulphur cycling genes decreased with increasing temperature and the deterministic process was dominant in most of temperature gradients. The diversity of microbial community was the main driving factors of sulphur cycling genes, followed by corpse decay and temperature. We obtained 257 high-quality metagenome assembled genomes (MAGs) containing sulphur cycling genes, and the results of metagenome-assembled genomes provide evidence for the covariation of sulphur cycling genes, such as sir, cysD, cysH, and ssuC, which shared the function of assimilatory sulphate reduction. Our research reveals the pathways of microbial sulphur cycling and contributes to the understanding of the elemental cycle associated with animal corpse decay.

Emissions from urban transportation significantly impact the environment and public health, including air pollution, increased carbon emissions, and various health issues. Public transportation is vital in alleviating traffic congestion and reducing overall emissions. However, due to the complex urban environment, bus emissions exhibit spatial heterogeneity at fine scales, and the influencing variables have nonlinearity and complexity, making it difficult to predict real-time emissions accurately. To address this challenge, we used real-time emission data from hybrid electric vehicle buses (HEBs), which are widely used in China, to train four models: linear regression (LR), random forest (RF), XGBOOST, and graph embedding-based generative adversarial network (GE-GAN). These models predicted real-time emissions at fine scales and analyzed the interactions between emission variables. Using graph embedding and GAN algorithms, the GE-GAN model effectively captures semantic and spatial information between variables, understanding complex interactions. Among all the models, GE-GAN achieved the best results in validation tests. For CO2 prediction, the root mean square error (RMSE) was 0.98, and the mean absolute error (MAE) was 1.31. For CO prediction, the RMSE was 0.03, and the MAE was 0.08. For NOx prediction, the RMSE was 0.43, and the MAE was 0.57. The experimental results demonstrated that GE-GAN can accurately predict real-time bus emissions at a fine scale in mixed urban areas. The accurate predictions can assist planning departments in determining pollutant distribution in urban areas, optimizing public transportation routes, and contributing to reducing city carbon emissions.

To enhance the crop production and soil fertility, the application of biochar (B) and nitrogen (N) has been practiced for many years. However, the effects of combined biochar (B) and nitrogen (N) treatments on the soil methanogenic community, methane (CH4) emissions, and nitrous oxide (N2O) emissions in paddy fields are not well known. Herein, we investigated the effects of four different B rates (0, 10, 20, and 30 ton ha -1) combined with two N levels (low: 135 kg ha−1; high: 180 kg ha−1) on CH4 and N2O emissions from rice fields, as well as the composition of the soil methanogenic community. The study was conducted at two locations - Guangxi University in Nanning (GXU) and Yulin City - across different years. Results showed that in both locations, B (30 ton ha-1) combined with both N (135 and 80 kg ha-1) improved soil pH (23 ~ 29%), soil organic carbon (25 ~ 32%), total N (24 ~ 28%), available phosphorus (16 ~ 18%), available potassium (78 ~ 85%), soil microbial biomass carbon (122 ~ 129%), microbial biomass N (110 ~ 133%), and the grain yield of rice (20 ~ 30%) compared with sole N-treated plots. Furthermore, increases in the B rate (> 20 ton ha-1) under both low and high N reduced N2O emissions in both locations and all seasons. However, CH4 emissions and the abundance of methanogenic archaea were increased in high B rate (30 ton ha−1) treatments under both N levels. CH4 emissions were 15 ~ 17% higher in GXU and Yulin City in the 20 ~ 30 ton B ha−1 treatments compared with the sole N treatments. Rice grain yields were highest, CH4 and N2O emissions were lowest, and soil characteristics were enhanced in the 10 ton B ha−1 with low N fertilizer treatment. The results of our study suggested that the use of 10 ton ha−1 combined with 135 kg N ha−1 is the most efficient method for enhancing soil fertility and rice yields while maintaining environmental sustainability.

Drought has threatened sustainability in terrestrial ecosystems. Although a series of indices have been proposed, they have largely overlooked the performance in characterizing regional or local drought, which is significant in acknowledging the impacts of drought on regional carbon cycling. To extract site-specific optimal drought index for vegetation in China, vegetation net primary production (NPP) estimated by Terrestrial Ecosystem Carbon Flux model, was compared with 11 worldwide-used indices, through sensitivity analysis and Generalizes Estimation Equation method. It was found that NPP in northern areas were significantly sensitive and correlated to 7 ~ 11 drought indices, whereas southern areas were sensitive to 1 ~ 4 indices. Among the drought indices, moisture index was highlighted as the best one, followed by standardized precipitation index, effective drought index, drought reconnaissance index, and standardized precipitation evapotranspiration index. Using optimal drought indices for local applications, the dynamics of drought and its impact on vegetation were quantified from 2000 to 2022. A general increase in vegetation NPP and decrease in drought were demonstrated in major northern and eastern areas. Moderately-extremely drought led to > 20% and even > 40% loss in NPP across northern and northwestern areas, much larger than the loss in southern areas. Moreover, forests were exposed to a mean loss of −20.1% and standard deviation of 13.2% in NPP, smaller than croplands and grasslands. These results contribute to a better understanding of drought dynamic and coupling of vegetation production, and guide for planning mitigations in drought-prone hotpots to ensure region's resilience.

This study proposes an ecological reservoir operation by linking ecological model and optimization model of reservoir. A remote sensing based ecological model was developed in which water temperature and total dissolved solids were simulated in the surface layer of reservoir. Moreover, ecological suitability maps were generated by a fuzzy inference system in which water temperature and total dissolved solids were inputs and normalized suitability was output. An ecological reservoir operation model was developed consistent with the ecological model to minimize ecological suitability loss of the reservoir as well as water supply loss. Moreover, a non-ecological operation was developed to minimize water supply loss only. Reliability and vulnerability indices and average habitat suitability in the reservoir were applied to measure the performance of the optimization model. Results indicated that reliability of water supply is reduced 6% due to using ecological operation model of the reservoir. Furthermore, vulnerability of water supply is increased more than 14% using the ecological operation. In contrast, the average habitat suitability of the reservoir is increased more than 0.35 by ecological operation which means it is remarkably effective on mitigating ecological impacts. Based on case study results, applying ecological operation of the reservoir is necessary to mitigate the ecological impacts of the reservoir on the ecological sustainability of reservoirs as well as downstream. The proposed method is able to balance the habitat requirements and humans’ needs.

Reservoirs are essential sources to ensure water supply, especially in regions with a semiarid climate, prone to prolonged drought periods and water scarcity. An exemplary case is the semiarid region of Paraíba State, Brazil. Monitoring of water surface reflectance (WSR) and total suspended solids (TSS) is indispensable to address issues such as erosion, sediment transport, depo sition, reservoir siltation, water pollution, and associated human health risks, ultimately aiding in water resources management. This study introduced a novel approach to estimating the spatiotemporal dynamics of WSR and TSS, utilizing automated methods and MODIS images over 17 years (2000 ~ 2016) for the Epitácio Pessoa Reservoir. Three models, (a) model 1 (M1), (b) model 2 (M2), and (c) model 3 (M3), were employed to estimate WSR and TSS, chosen for their replicability, mathematical simplicity, and ecosystem similarity. A total of 775 images from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite, along with observed data, were used for the estimation. Model M3 yielded the best performance for estimating TSS, with values ranging from 270 ~ 961 mg/L (r = 0.57). Furthermore, a strong correlation (r > 0.93) was observed between the mean reflectance values for pixels sampled using two distinct extraction procedures (longitudinal profile and specific sampling points). This strong correlation underscores the potential utility of MODIS images for effective monitoring of WSR and TSS in comparatively smaller bodies of water in the Brazilian semiarid region. Importantly, this study represents one of the first applications for estimating WSR and TSS for a reservoir situated in a semiarid climate region. The estimates of WSR and TSS could hold significant implications for environmental monitoring and water management of such reservoirs. In conclusion, automated estimation of WSR and TSS could serve as a valuable tool for decision-makers in predicting silting, sediment yield, and reservoir water quality.

A microbubble scrubber is a hybrid type scrubber that combines the advantages of a general scrubber with the advantages of the microbubble. Microbubble which has generally under 50 μm diameter is one of the effective ways to remove air pollutants, like PM, NOx, and SOx. The low-pressure microbubble (LPMB) scrubber is a low-power, high-efficiency method that uses a blower to draw flue gas into the solution and generate microbubbles in the water by using low-pressure or negative pressure. The objective of this study was to enhance the removal efficiency of air pollutants in an LPMB scrubber by determining its optimal operating conditions for generating a large number of microbubbles. To achieve this, we developed a CFD model based on a pilot-scale LPMB scrubber and conducted case studies under different operating conditions using fluid flow analysis. The case study consisted of 12 cases according to the pressure difference (1,000, 3,000, 5,000, and 7,000 Pa) between the scrubber inlet and outlet and the initial water level (–0.2, 0, and +0.2 m). The simulation results showed that the optimal operating conditions were a pressure difference of 5,000 Pa and an initial water level of –0.2 m. The removal rates of PM, NOx, and SOx were 99.9, 92.6, and 99.0%, respectively when operating under the optimal operating conditions of the LPMB scrubber. The results suggest that the proposed optimal operating conditions can effectively enhance the removal efficiency of the LPMB scrubber.

The mesoporous graphene oxide-supported ferroferric oxide (Fe 3 O 4 /GO) nanocomposites (the average size of 30.08 nm) were controllably synthesized in the present study. The successful in situ growth of Fe 3 O 4 nanoparticles on GO surface was ascribed to the oxygen-containing groups on GO. The magnetic separation was employed for Sb(III) removal from aqueous solutions and artificial intelligence techniques were adopted to reduce the number and cost of experiments, in order to render these nanocomposites of a practical value. The three methods, including response surface methodology (RSM), artificial neural network-genetic algorithm (ANN-GA) and artificial neural network-particle swarm optimization (ANN-PSO), were used to model and optimize the removal of Sb(III) from aqueous solutions. These three models were evaluated based on correlation coefficient (R 2 ) and mean squared error (MSE). The higher R 2 value and lower MSE of ANN-GA demonstrated the superiority of ANN-GA model over ANN-PSO and RSM models. Analysis of variance, gradient boosted regression trees (GBRT) and Garson method exhibited that contact time was the most influential variable for the Sb(III) removal. Fitting of isotherm data showed that the removal process was controlled by the monolayer adsorption on a homogeneous surface based on the values of R 2 , x 2 , sum of absolute errors (SAE) and average percentage errors (APE). The adsorption process followed the pseudo-second-order model, which was spontaneous and entropy-driven. It was observed that the adsorption process was accompanied with the redox reaction based on the XPS analysis. The regeneration experiments showed that the mesoporous Fe 3 O 4 /GO nanocomposites are an effective and reusable adsorbent within four regeneration cycles.

In case of an unplanned emission event from a nuclear power plant, the local population can be protected more efficiently when valid atmospheric dispersion model results are available. Atmospheric dispersion models use local meteorological variables as inputs. When atmospheric dispersion in the future is being predicted, a forecast of the local meteorological variables is needed. The most important variable in atmospheric dispersion modelling is wind, and accurately predicting ground level winds presents a challenge to numerical weather prediction models. We therefore develop hybrid models for forecasting local ground level wind at a single location where the terrain is complex and the average wind is weak and fluctuating. Wind speed and direction are modelled as west-east and south-north wind components. Each model is composed of a numerical weather prediction model and a Gaussian process statistical model that uses numerical weather predictions as some of its inputs and is trained on historical data to predict the output component. The most advanced Gaussian process models studied are of Gaussian process nonlinear autoregressive model with exogenous input (GPNARX) type. In addition to numerical weather predictions, they also use local meteorological variables, including the output variable, as their inputs. Numerical weather prediction results based on large scale information and fundamental knowledge of the system are thus supplemented by local measurements that better reflect the effects of local topography and land use. The models are tested by prediction and simulation. The wind components predicted by more advanced models are more accurate than raw or post-processed numerical weather prediction results. As an example, a model predicting 2D wind vector 1.5 h in advance achieves a NRMSE of 0.214 if it uses all the immediately available information. This is better than both the persistence model with NRMSE of 0.188 and post-processed NWP with NRMSE of 0.164. This demonstrates that hybrid modelling provides the best weather information for short-term and medium-term atmospheric dispersion forecasting. While the method is motivated by nuclear emission sources, it could also be applied to other pollution.

Floods are the most common and among the most severe natural disasters in many countries around the world. As global warming continues to exacerbate sea level rise and extreme weather, governmental authorities and environmental agencies are facing the pressing need of timely and accurate evaluations and predictions of flood risks. Current flood forecasts are generally based on historical measurements of environmental variables at monitoring stations. In recent years, in addition to traditional data sources, large amounts of information related to floods have been made available via social media. Members of the public are constantly and promptly posting information and updates on local environmental phenomena on social media platforms. Despite the growing interest of scholars towards the usage of online data during natural disasters, the majority of studies focus exclusively on social media as a stand-alone data source, while its joint use with other type of information is still unexplored. In this paper we propose to fill this gap by integrating traditional historical information on floods with data extracted by Twitter and Google Trends. Our methodology is based on vine copulas, that allow us to capture the dependence structure among the marginals, which are modelled via appropriate time series methods, in a very flexible way. We apply our methodology to data related to three different coastal locations on the South coast of the United Kingdom (UK). The results show that our approach, based on the integration of social media data, outperforms traditional methods in terms of evaluation and prediction of flood events.