Wastewater treatment plants have been identified as an important gathering spot for nanoplastics, possibly having unintended impacts on important biological nutrient removal processes. The underlying effects of long-term exposure of activated sludge to nanoplastics on nutrient removal and the mechanisms involved remain unclear. This study investigated the effect of polystyrene nanoplastics (Nano-PS) on the treatment performance and microbial community structure, and network in activated sludge. The results indicate that 1000 μg/L Nano-PS had chronic negative effects on the treatment performance in a continuous test over 140 days. Nano-PS had no significant impact in the earlier stages (0–50 days). However, as exposure time increased, the removal efficiencies of chemical oxygen demand, total phosphorous, and total nitrogen (TN) decreased by 2.7, 33.2, and 23.5%, respectively, in the later stages (87–132 days). These adverse impacts further manifested as a change in the topological characteristics, forming a smaller scale, lower complexity, and weaker transfer efficiency of the microbial network. Moreover, the scale and complexity of subnetwork-nitrogen removal bacteria and subnetwork-nitrifier were inhibited, leading to an increase in the effluent TN and NH₄⁺-N. The decreased modules and connectors (keystone taxa) likely caused the deterioration of treatment performance and functional diversity, which was consistent with the change in PICRUSt results. Less competition, denser nodes, and more complex module structures were induced as a strategy to mediate the long-term stress of nano-PS. To our knowledge, this is the first attempt to explore the long-term effects of nano-PS on the microbial interaction network of activated sludge, laying an experimental foundation for reducing the risks associated with nanoplastics.

In this study, a metal organic framework MIL-100(Fe) was synthesized for rhodamine B (RB) removal from aqueous solutions. An experimental design was conducted using a central composite design (CCD) method to obtain the RB adsorption data (n = 30) from batch experiments. In the CCD approach, solution pH, adsorbent dose, and initial RB concentration were included as input variables, whereas RB removal rate was employed as an output variable. Response surface methodology (RSM) and artificial neural network (ANN) modeling were performed using the adsorption data. In RSM modeling, the cubic regression model was developed, which was adequate to describe the RB adsorption according to analysis of variance. Meanwhile, the ANN model with the topology of 3:8:1 (three input variables, eight neurons in one hidden layer, and one output variable) was developed. In order to further compare the performance between the RSM and ANN models, additional adsorption data (n = 8) were produced under experimental conditions, which were randomly selected in the range of the input variables employed in the CCD matrix. The analysis showed that the ANN model (R² = 0.821) had better predictability than the RSM model (R² = 0.733) for the RB removal rate. Based on the ANN model, the optimum RB removal rate (>99.9%) was predicted at pH 5.3, adsorbent dose 2.0 g L−1, and initial RB concentration 73 mg L−1. In addition, pH was determined to be the most important input variable affecting the RB removal rate. This study demonstrated that the ANN model could be successfully employed to model and optimize RB adsorption to the MIL-100(Fe).

The widely detected pyrene (PYR) is prone to accumulate and pose risks to the soil ecosystem. In this study, an aerobic closed microcosm was constructed to assess the effects of PYR at the environmental concentration (12.09 mg kg⁻¹) on the structure, interactions, and metabolism of carbon sources of soil microbial communities. The results found that half-life of PYR was 37 d and its aerobic biodegradation was mainly implemented by both Gram-negative and Gram-positive bacteria as revealed by the quantitative results. High-throughput sequencing based on 16 S rRNA and ITS genes showed that PYR exposure interfered more significantly with the diversity and abundance of the bacterial community than that of the fungal community. For bacteria, rare species were sensitive to PYR, while Gemmatimonadota, Gaiellales, and Planococcaceae involved in organic pollutants detoxification and degradation were tolerant of PYR stress. Co-occurrence network analysis demonstrated that PYR enhanced the intraspecific cooperation within the bacterial community and altered the patterns of trophic interaction in the fungal community. Furthermore, the keystone taxa and their topological roles were altered, potentially inducing functionality changes. Function annotation suggested PYR inhibited the nitrogen fixation and ammonia oxidation processes but stimulated methylotrophy and methanol oxidation, especially on day 7. For the metabolism, microbial communities accelerated the metabolism of nitrogenous carbon sources (e.g. amine) to meet the physiological needs under PYR stress. This study clarifies the impacts of PYR on the structure, metabolism, and potential N and C cycling functions of soil microbial communities, deepening the knowledge of the environmental risks of PYR.

Air pollution is a complex process and is affected by meteorological conditions and other chemical components. Numerous studies have demonstrated that data-driven spatio-temporal prediction models of PM₂.₅ concentration are comparable with the model-driven model. However, data-driven models are usually depending on the statistical correlation between PM₂.₅ and other factors and have challenges in dealing with causality in complex systems. In this paper, we argue that domain knowledge should be incorporated into data-driven models to enhance prediction accuracy and make the model more physically realistic. We focus on the influence of dynamic wind-field on PM₂.₅ concentration distribution and fuse the pollution diffusion distance with the deep learning model based on a wind-field surface. In order to model spatial dependence between monitoring stations, which is dynamic and anisotropic because of the wind-field, we proposed a hybrid deep learning framework, dynamic directed spatio-temporal graph convolution networks (DD-STGCN). It expanded the ability to deal with space-time prediction in the continuous and dynamic wind-field. We used a directed graph time-series to describe the vertex state and topological relationship between vertices and replaced traditional Euclidean distance with wind-field diffusion distance to describe the proximity relationship between vertices. Our experiment results demonstrated that the DD-STGCN model achieved a better prediction ability than LSTM, GC-LSTM, and STGCN models. Compared to the best comparison model, MAPE, MAE, and RMSE were improved by 10.2%, 9.7%, and 9.6% in 12 h on an average, respectively. The performance of our model was further tested during a haze period. In the case that two models both considered the effect of wind, compared with the pure data-driven model, our model performed better in prediction distribution and showed the benefit of spatial interpretability provided by domain knowledge.

In recent decades, the 1,2,4-triazole fungicides are widely used for crop diseases control, and their toxicity to wild lives and pollution to ecosystem have attracted more and more attention. However, how to quickly and efficiently evaluate the toxicity of these compounds to environmental organisms is still a challenge. In silico method, such like Quantitative Structure-Activity Relationship (QSAR), provides a good alternative to evaluate the environmental toxicity of a large number of chemicals. At the present study, the acute toxicity of 23 1,2,4-triazole fungicides to zebrafish (Danio rerio) embryos was firstly tested, and the LC₅₀ (median lethal concentration) values were used as the bio-activity endpoint to conduct QSAR modelling for these triazoles. After the comparative study of several QSAR models, the 2D-QSAR model was finally constructed using the stepwise multiple linear regression algorithm combining with two physicochemical parameters (logD and μ), an electronic parameter (QN₁) and a topological parameter (XᵛPC₄). The optimal model could be mathematically described as following: pLC₅₀ = −7.24–0.30XᵛPC₄ + 0.76logD - 26.15QN₁ - 0.08μ. The internal validation by leave-one-out (LOO) cross-validation showed that the R²ₐdⱼ (adjusted noncross-validation squared correlation coefficient), Q² (cross-validation correlation coefficient) and RMSD (root-mean-square error) was 0.88, 0.84 and 0.17, respectively. The external validation indicated the model had a robust predictability with the q² (predictive squared correlation coefficient) of 0.90 when eliminated tricyclazole. The present study provided a potential tool for predicting the acute toxicity of new 1,2,4-triazole fungicides which contained an independent triazole ring group in their molecules to zebrafish embryos, and also provided a reference for the development of more environmentally-friendly 1,2,4-triazole pesticides in the future.

In order to accurately monitor the changes in a freshwater ecosystem in response to anthropogenic stressors, microbe–environment correlations and microbe–microbe interactions were combined to determine crucial indicator taxa in contaminated sediments. The diversity, composition, and co–occurrence pattern of bacterial communities in 23 sediment samples collected from Lake Taihu were explored using 16S rRNA amplicon sequencing analysis. Fisher's exact test showed that the cluster analyses of samples could show a direct correlation between the relative abundance of bacterial communities and the physicochemical properties of the sediment (P < 0.0001), suggesting that bacterial communities can be used to monitor contamination gradients in freshwater sediments. According to the microbe–environment correlation, 24 orders and 60 families were initially identified via indicator species analysis as indicator taxa of different pollution levels. The co–occurrence network further showed that topological features of bacterial communities were clearly different at different pollution levels, although the diversity and composition of bacterial communities displayed similarities between minimally and moderately polluted sites. Indicator taxa were then screened for keystone species, which co–occurrence relationships showed the high degree and low betweenness centrality values (i.e. degree >5, betweenness centrality <1000) of the network. Nine orders and 13 families were finally extracted as crucial indicator taxa of the different pollution levels in eutrophic Lake Taihu. Obtaining crucial indicator taxa from environmental sequences allows to trace increasing levels of pollution in aquatic ecosystems and provides a novel mean to monitor watersheds sensitive to anthropic influences.

Investigate the growth adaptation law of the Tamarix chinensis root system in response to the groundwater level in a muddy coastal zone. The high groundwater level (0.7–0.9 m), medium groundwater level (1.1–1.3 m) and low groundwater level (1.5–1.7 m) T. chinensis forests on the beaches of the Yellow River Delta were used as the research objects. Full excavation methods were used to excavate root systems with different groundwater levels; then, the aboveground biomass, root biomass, root spatial distribution, root topological structure and fractal characteristics of T. chinensis response characteristics to groundwater level were measured and analysed. The results showed that with the decrease in the groundwater level, the soil water content and soil salt content showed upward trends. At high groundwater levels, T. chinensis reduced root biomass allocation to reduce the damage to roots caused by salinity. At low groundwater levels, T. chinensis strengthened the development of root systems, which greatly enhanced the ability of T. chinensis to balance its water intake. The root biomass at the high groundwater level was 43.06% lower than that at the low groundwater level. The relationship between root and shoot growth of T. chinensis at high groundwater levels and medium groundwater levels indicated allometric growth, and at low groundwater levels, roots and shoots grew uniformly. The root distribution of T. chinensis tended to be shallow at the different groundwater levels, showing the characteristics of a horizontal root type. At high groundwater levels, the root topological structure tended to be dichotomous, and the fractal dimension and fractal abundance values were both large, at 1.31 and 2.77, respectively. The branch complexity increased to achieve spatial expansion and increase plant stability. However, the topological structure of the medium and low groundwater level T. chinensis tended to be herringbone-like, the fractal dimension and fractal abundance values were small, the second branch was limited, and the structure was simple. The topological structure and fractal characteristics of the T. chinensis root system responded to different groundwater levels in a coordinated manner. Based on the differences in the growth and architecture of the T. chinensis root system, the T. chinensis root system has strong phenotypic plasticity to the heterogeneous water-salt habitat of the groundwater-soil system, and the T. chinensis root system shows strong root adaptability to water and salt stress.

Silver nanoparticles (AgNP) have been extensively applied in different industrial areas, mainly due to their antibiotic properties. One of the environmental concerns with AgNP is its incorrect disposal, which might lead to severe environmental pollution. The interplay between AgNP and plants is receiving increasing attention. However, little is known regarding the phytotoxic effects of biogenic AgNP on terrestrial plants. This study aimed to compare the effects of a biogenic AgNP and AgNO₃ in Sorghum bicolor seedlings. Seeds were germinated in increasing concentrations of a biogenic AgNP and AgNO₃ (0, 10, 100, 500, and 1000 μM) in a growth chamber with controlled conditions. The establishment and development of the seedlings were evaluated for 15 days. Physiological and morpho-anatomical indicators of stress, enzymatic, and non-enzymatic antioxidants and photosynthetic yields were assessed. The results showed that both AgNP and AgNO₃ disturbed germination and the establishment of sorghum seedlings. AgNO₃ released more free Ag⁺ spontaneously compared to AgNP, promoting increased Ag⁺ toxicity. Furthermore, plants exposed to AgNP triggered more efficient protective mechanisms compared with plants exposed to AgNO₃. Also, the topology and connectivity of the correlation-based networks were more impacted by the exposure of AgNO₃ than AgNP. In conclusion, it is plausible to say that the biogenic AgNP is less toxic to sorghum than its matrix AgNO₃.

The positioning of quality detection points as well as the frequency of sampling is a crucial aspect for the implementation of Water Safety Plans (WSPs), which have been proposed worldwide to ensure water quality and to minimize the risk from contamination in water distribution networks (WDNs). In this regard, some international legislations and best practices about quality of drinking water suggest very fine sampling frequencies, but they do not specify where the detection points should be located in a WDN. In this paper, three different approaches, based on empiricism, optimization and topology, respectively, were applied to locate detection quality points in a WDN. The comparison highlighted that empirical approach commonly adopted by water utility practitioners is unsatisfactory. The optimization-based approach, although performing significantly better, is difficult to apply, since it requires a calibrated hydraulic model. The topological approach, based on the use of the betweenness centrality and not requiring any hydraulic information and simulation, proves to be effective, and it can be easily adopted by water utilities to identify the location for quality detection points, due to its simplicity compared with the optimization-based approach.

Logistics network is one of the most important parts of supply chains with significant share in achieving sustainability across them. In this paper, we investigate a new multi-objective mixed integer linear programming model for the design of multimodal logistics network. A bi-objective mathematical model is introduced and two conflicting objectives including the minimization of total cost and the total environmental impact are taken into account. Effective environmental life cycle assessment–based method is incorporated in the model to estimate the relevant environmental impacts. Due to budget constraints, financing decisions for facility construction are considered in the proposed model. To cope with the model objective functions, the augmented ε-constraint method is applied. Computational analysis is also provided by using a cement multimodal rail-road logistics network case study to present the significance of the proposed model. Results show that utilizing the proposed multi-period optimization model influences the location of multimodal terminals and their construction time. Also, the results show that the use of the proposed model enhances the efficiency of terminals. On the other hand, computational results indicate that preferences of decision-makers and the importance of environmental objective have significant impacts on the topology of transportation network.