Hydrophilic Fraction of Dissolved Organic Matter Largely Facilitated Microplastics Photoaging: Insights from Redox Properties and Reactive Oxygen Species.

Dissolved organic matter (DOM) exists widely in natural water, which inevitably influences microplastic (MP) photoaging. Nevertheless, the impacts of DOM fractions with diverse molecular structures on MP photoaging remain to be elucidated. This study explored the photoaging mechanisms of polylactic acid (PLA)-MPs and polystyrene (PS)-MPs in the presence of DOM and its subfractions (hydrophobic acid (HPOA), hydrophobic neutral (HPON), and hydrophilic (HPI)). Across DOM fractions, HPI exhibited the highest electron accepting capacity (23 µmol e- (mg C)-1) due to its abundant tannin-like species (36.8%) with carboxylic groups, which facilitated more reactive oxygen species generation (particularly hydroxyl radical), leading to the strongest photoaging rate of two MPs by HPI. However, the sequences of bond cleavage during photoaging of each MPs were not clearly shifted as revealed by two-dimensional infrared correlation spectra. Inconspicuous effects on the extent of PS- and PLA-MPs photoaging were observed for HPOA and HPON, respectively. This was mainly ascribed to the occurrence of inhibitory mechanisms (e.g., light-shielding and quenching effect) counteracting the reactive oxygen species-promoting effects. The findings identified the HPI fraction of DOM for promoting PS- and PLA-MPs photoaging rate and first constructed a link among DOM molecular structures, redox properties, and effects on MP photoaging.

Different policies constrained agricultural non-point pollutants emission trading management for water system under interval, fuzzy, and stochastic information.

Formulating suitable policies is essential for resources and environmental management. In this study, an agricultural pollutants emission trading management model driven by water resources and pollutants control is developed to search reasonable policies for agricultural water resources allocation under multiple uncertainties. Random-fuzzy and interval information in water resources system that have directly impact on the effectiveness of management schemes is reflected through interval two-stage stochastic fuzzy-probability programming. The model was root from regional agricultural water resources system in Jining City, China under considering the relationship among effective precipitation, crop water demand, and pollutants emission. Two types policies (water consumption-control and pollutants emission-control) are designed for searching the related interaction on water resources management and water quality improvement. The results indicated that water resources policies would be of water and environmental double benefits, and a large rainfall would reduce irrigation amount from water sources and lead to a larger pollutants emission trading. The results will help for defining scientific and effective water resources protection and management policies and analyzing the related interacted effects on water consumption, pollutants control and system benefit.

A unified deep learning framework for water quality prediction based on time-frequency feature extraction and data feature enhancement.

Deep learning methods exhibited significant advantages in mapping highly nonlinear relationships with acceptable computational speed, and have been widely used to predict water quality. However, various model selection and construction methods resulted in differences in prediction accuracy and performance. Hence, a unified deep learning framework for water quality prediction was established in the paper, including data processing module, feature enhancement module, and data prediction module. In the established model, the data processing module based on wavelet transform method was applied to decomposing complex nonlinear meteorology, hydrology, and water quality data into multiple frequency domain signals for extracting self characteristics of data cyclic and fluctuations. The feature enhancement module based on Informer Encoder was used to enhance feature encoding of time series data in different frequency domains to discover global time dependent features of variables. Finally, the data prediction module based on the stacked bidirectional long and short term memory network (SBiLSTM) method was employed to strengthen the local correlation of feature sequences and predict the water quality. The established model framework was applied in Lijiang River in Guilin, China. The maximum relative errors between the predicted and observed values for dissolved oxygen (DO), chemical oxygen demand (CODMn) were 12.4% and 20.7%, suggesting a satisfactory prediction performance of the established model. The validation results showed that the established model was superior to all other models in terms of prediction accuracy with RMSE values 0.329, 0.121, MAE values 0.217, 0.057, SMAPE values 0.022, 0.063 for DO and CODMn, respectively. Ablation tests confirmed the necessity and rationality of each module for the established model framework. The established method provided a unified deep learning framework for water quality prediction.

An integrated simulation-optimization approach for combined allocation of water quantity and quality under multiple uncertainties.

Multiple uncertainties such as water quality processes, streamflow randomness affected by climate change, indicators' interrelation, and socio-economic development have brought significant risks in managing water quantity and quality (WQQ) for river basins. This research developed an integrated simulation-optimization modeling approach (ISMA) to tackle multiple uncertainties simultaneously. This approach combined water quality analysis simulation programming, Markov-Chain, generalized likelihood uncertainty estimation, and interval two-stage left-hand-side chance-constrained joint-probabilistic programming into an integration nonlinear modeling framework. A case study of multiple water intake projects in the Downstream and Delta of Dongjiang River Basin was used to demonstrate the proposed model. Results reveal that ISMA helps predict the trend of water quality changes and quantitatively analyze the interaction between WQQ. As the joint probability level increases, under strict water quality scenario system benefits would increase [3.23, 5.90] × 109 Yuan, comprehensive water scarcity based on quantity and quality would decrease [782.24, 945.82] × 106 m3, with an increase in water allocation and a decrease in pollutant generation. Compared to the deterministic and water quantity model, it allocates water efficiently and quantifies more economic losses and water scarcity. Therefore, this research has significant implications for improving water quality in basins, balancing the benefits and risks of water quality violations, and stabilizing socio-economic development.

EISA-EXPOSOME: One Highly Sensitive and Autonomous Exposomic Platform with Enhanced in-Source Fragmentation/Annotation.

Lacking a highly sensitive exposome screening technique is one of the biggest challenges in moving exposomic research forward. Enhanced in-source fragmentation/annotation (EISA) has been developed to facilitate molecular identification in untargeted metabolomics and proteomics. In this work, with a mixture of 50 pesticides at three concentration levels (20, 4, and 0.8 ppb), we investigated the analytical performance of the EISA technique over the well-accepted targeted MS/MS mode (TMM) in the detection and identification of chemicals at low levels using a quadrupole time-of-flight (qTOF) instrument. Compared with the TMM method, the EISA technique can recognize additional 1, 20, and 23 chemicals, respectively, at the three concentration levels (20, 4, and 0.8 ppb, respectively) investigated. At the 0.8 ppb level, intensities of precursor ions and fragments observed using the EISA technique are 30-1,154 and 3-80 times higher, respectively, than those observed at the TMM mode. A higher matched fragment ratio (MFR) between the EISA technique and the TMM method was recognized for most chemicals. We further developed a chemical annotation informatics algorithm, EISA-EXPOSOME, which can automatically search each precursor ion (m/z) in the MS/MS library against the EISA MS1 spectra. This algorithm then calculated a weighted score to rank the candidate features by comparing the experimental fragment spectra to those in the library. The peak intensity, zigzag index, and retention time prediction model as well as the peak correlation coefficient were further adopted in the algorithm to filter false positives. The performance of EISA-EXPOSOME was demonstrated using a pooled dust extract with a pesticide mixture (n = 200) spiked at 5 ppb. One urine sample spiked with a contaminant mixture (n = 50) at the 5 ppb level was also used for the validation of the pipeline. Proof-of-principal application of EISA-EXPOSOME in the real sample was further evaluated on the pooled dust sample with a modified T3DB database (n = 1650). Our results show that the EISA-EXPOSOME algorithm can remarkably improve the detection and annotation coverage at trace levels beyond the traditional approach as well as facilitate the high throughput screening of suspected chemicals.

Eight novel brominated flame retardants in indoor and outdoor dust samples from the E-waste recycling industrial park: Implications for human exposure.

As alternatives for legacy brominated flame retardants, novel brominated flame retardants (NBFRs) have a wide array of applications in the electronic and electrical fields. The shift of recycling modes of electronic and electrical waste (e-waste) from informal recycling family workshop to formal recycling facilities might come with the change the chemical landscape emitted including NBFRs, however, little information is known about this topic. This study investigated the occurrence characteristics, distribution, and exposure profiles of eight common NBFRs and their derivatives in an e-waste recycling industrial park in central China and illustrated the differences in various functional zones in the recycling park. The highest level of ΣNBFRs in dust samples was found in e-waste storage area at median concentration of 27,400 ng/g, followed by e-waste dismantling workshops (23,300 ng/g), workshop outdoor area (7770 ng/g), and residential area outdoor (536 ng/g). In the e-waste dismantling associated dust samples, tetrabromobisphenol A bis(2,3-dibromopropyl ether) (TBBPA-BDBPE), tetrabromobisphenol A (TBBPA) and 2,4,6-tris(2,4,6-tribromophenoxy)-1,3,5-triazine (TTBP-TAZ) were the predominant components. This paper presented the first evidence regarding the occurrence characteristic and distribution of tetrabromobisphenol S (TBBPS), tetrabromobisphenol A bismethyl ether (TBBPA-BME) and tetrabromobisphenol S bis(2,3-dibromopropyl ether) (TBBPS-BDBPE) in the e-waste associated dust samples. By comparing with previous studies performed in China, this paper also noticed the significant decrease of TBBPA concentrations in the dust probably due to the shift of e-wastes sources and recycling modes. We further assessed the risk of occupational workers exposure to NBFRs. The median EDI (estimated daily intake) value of ΣNBFRs among e-waste dismantling workers was 9.71 ng/kg BW/d with the maximum EDI value being 19.6 ng/kg BW/d, hundreds of times higher than those exposed by general population. The study raises great concern for the health risk of occupational exposure to NBFRs in the e-waste recycling industrial park.

Source or sink role of an urban lake for microplastics from Guangdong-Hong Kong-Macao greater bay area, China.

Plastic production and consumption in China are larger than others in the world, and the challenge of microplastic pollution is widespread. With the development of urbanization in the Guangdong-Hong Kong-Macao Greater Bay Area, China, the environmental pollution of microplastics is becoming an increasingly prominent issue. Here, the spatial and temporal distribution characteristics, sources, and ecological risks of microplastics were analyzed in water from an urban lake, Xinghu Lake, as well as the contribution of rivers. Importantly, the roles of urban lakes for microplastics were demonstrated through the investigations of contributions and fluxes for microplastic in rivers. The results showed that the average abundances of microplastics in water of Xinghu Lake were 4.8 ± 2.2 and 10.1 ± 7.6 particles/m3 in wet and dry seasons, and the average contribution degree of the inflow rivers was 75%. The size of microplastics in water from Xinghu Lake and its tributaries was concentrated in the range of 200-1000 µm. In general, the average comprehensive potential ecological risk indexes of microplastics in water were 247 ± 120.6 and 273.1 ± 353.7 in wet and dry seasons, which the high ecological risks of them were found through the adjusted evaluation method. There were also mutual effects among microplastic abundance, the concentrations of total nitrogen and organic carbon. Finally, Xinghu Lake has been a sink for microplastics both in wet and dry seasons, and it would be a source of microplastics under the influence of extreme weather and anthropogenic factors.

On the accuracy of official Chinese crop production data: Evidence from biophysical indexes of net primary production.

With rapid economic growth and urbanization, self-sufficiency in crop production has become central to China's agriculture policy. Accurate crop production statistics are essential for research, monitoring, and planning. Although researchers agree that China's statistical authority has considerably modernized over time, China's economic statistics have still been viewed as unreliable and often overstated to meet growth targets at different administrative levels. Recent increases in crop production reported by national statistics have also come under increasing scrutiny. This paper investigates crop production data quality from a planetary boundary perspective-comparing net primary production (NPP) harvested obtained from national statistics with satellite-driven NPP estimates that are supported by detailed observation of land cover, combined with observations on physical factors that limit plant growth. This approach provides a powerful means to check the plausibility of China's grain production statistics at different administrative levels that can generate insights about their discrepancies and can contribute to improved crop production measurements. We find some evidence of potential misreporting problems from the lower administration level where the risk of manipulation of statistics is higher. We also find problems from provincial-level major grain producers. These values can also affect the national totals. Although the numbers are affected by large uncertainties, we find that improving the spatial resolution of key agricultural parameters can greatly improve the reliability of the indicator that in turn can help improve data quality. More reliable production data will be vital for relevant research and provide better insights into food security problems, the carbon cycle, and sustainable development.

Seasonal pulse effect of microplastics in the river catchment-From tributary catchment to mainstream.

Rivers have received extensive attention as a major pathway for microplastics (<5000 µm) from land to ocean. This study investigated the seasonal variation of microplastic contamination in surface water of the Liangfeng River catchment, a tributary of the Li River in China, based on a fluorescence-based protocol, and further explored the migration process of microplastic in the river catchment. The abundance of microplastics (50-5000 µm) was (6.20 ± 0.57)-(41.93 ± 8.13) items/L, of which 57.89-95.12% were small-sized microplastics (<330 µm). The microplastic fluxes in the upper Liangfeng River, lower Liangfeng River, and upper Li River were (14.89 ± 1.24) × 1012, (5.71 ± 1.15) × 1012, and (1.54 ± 0.55) × 1014 items/year, respectively. The 3.70% of microplastic load in the mainstream came from the tributary input. Fluvial processes can effectively retain 61.68% of microplastics in the surface water of river catchments, especially for small-sized microplastics. The rainy season is the main period of microplastic retention (91.87%) in the tributary catchment by fluvial processes, while exporting 77.42% of one-year microplastic emissions from the tributary catchment into the mainstream. This study is the first to reveal the transport characteristics of small-sized microplastics in river catchments based on flux variation, which not only can partly explain the "missing small-sized microplastic fraction" in the ocean, but also contribute to improving microplastic model.

An improved approach for evaluating landscape ecological risks and exploring its coupling coordination with ecosystem services.

The rapid urbanization has accelerated the destruction of regional ecosystems, triggering ecological risks and threatening sustainable development. Landscape ecological risk (LER) evaluation is an effective tool to mitigate such negative impacts. However, the existing evaluation systems exhibit certain subjectivity. Therefore, an improved LER evaluation method was proposed, which incorporates ecosystem services (ESs) to characterize landscape vulnerability. The method was validated using the Pearl River Delta urban agglomeration (PRDUA) as the study area. The results showed that the optimal grain size and extent for landscape pattern analysis in the PRDUA were determined to be 150 m and 6km × 6 km, respectively. The comparison results with the traditional LER evaluation method demonstrated the improved method's superior rationality and reliability. The hotspot analysis based on the Getis-Ord Gi* method revealed that the hotspots of LER were mainly concentrated in the densely populated areas of the south-central region of the PRDUA. The coupling coordination degree (CCD) between LERs and ESs showed four different levels of development in both temporal and spatial dimensions, generally dominated by moderately balanced development and lagging ESs, reflecting the unbalanced ecological environment and socio-economic development of the PRDUA. It is recommended that the ecosystems in the PRDUA be managed and protected separately according to the delineated Ecological Protection Area (EPA), Urban Built-up Area (UBA), and Urban Ecological Boundary Area (UEBA). This study can provide an important reference for regional ecosystem conservation and management.

Sulfone-Modified Covalent Organic Frameworks Enabling Efficient Photocatalytic Hydrogen Peroxide Generation via One-Step Two-Electron O2 Reduction.

Photocatalytic oxygen reduction reaction (ORR) offers a promising hydrogen peroxide (H2 O2 ) synthetic strategy, especially the one-step two-electron (2e- ) ORR route holds great potential in achieving highly efficient and selectivity. However, efficient one-step 2e- ORR is rarely harvested and the underlying mechanism for regulating the ORR pathways remains greatly obscure. Here, by loading sulfone units into covalent organic frameworks (FS-COFs), we present an efficient photocatalyst for H2 O2 generation via one-step 2e- ORR from pure water and air. Under visible light irradiation, FS-COFs exert a superb H2 O2 yield of 3904.2 µmol h-1 g-1 , outperforming most reported metal-free catalysts under similar conditions. Experimental and theoretical investigation reveals that the sulfone units accelerate the separation of photoinduced electron-hole (e- -h+ ) pairs, enhance the protonation of COFs, and promote O2 adsorption in the Yeager-type, which jointly alters the reaction process from two-step 2e- ORR to the one-step one, thereby achieving efficient H2 O2 generation with high selectivity.

Environmental DNA Biomonitoring Reveals the Interactive Effects of Dams and Nutrient Enrichment on Aquatic Multitrophic Communities.

Dam construction and nutrient enrichment are two pervasive stressors in rivers worldwide, which trigger a sharp decline in biodiversity and ecosystem services. However, the interactive effects of both stressors on multitrophic taxonomic groups remain largely unclear. Here, we used the multitrophic datasets captured by the environmental DNA (eDNA) approach to reveal the interactions between dams and nutrient enrichment on aquatic communities from the aspects of taxonomic α diversity, ß diversity, and food webs. First, our data showed that dams and nutrient enrichment jointly shaped a unique spatial pattern of aquatic communities across the four river systems, and the dissimilarity of community structure significantly declined (i.e., structural homogenization) under both stressors. Second, dams and nutrients together explained 40-50% of the variations in aquatic communities, and dams had a stronger impact on fish, aquatic insects, and bacteria, yet nutrients had a stronger power to drive protozoa, fungi, and eukaryotic algae. Finally, we found that additive, synergistic, and antagonistic interactions of dams and nutrient enrichment were common and coexisted in river systems and led to significantly simplified aquatic food webs, with decreases in modularity (synergistic) and robustness (additive) and an increase in coherence (synergistic). Overall, our study highlights that eDNA-based datasets can provide multitrophic perspectives for fostering the understanding of the interactive effects of multiple stressors on rivers.

Potential Life-Cycle Environmental Impacts of the COVID-19 Nucleic Acid Test.

Massive diagnostic testing has been performed for appropriate screening and identification of COVID-19 cases in the ongoing global pandemic. However, the environmental impacts of COVID-19 diagnostics have been least considered. In this paper, the environmental impacts of the COVID-19 nucleic acid diagnostics were assessed by following a full cradle-to-grave life-cycle approach. The corresponding life-cycle anthology was established to provide quantitative analysis. Moreover, three alternative scenarios, i.e., material substitution, improved waste treatment, and electric vehicle (EV)-based transportation, were further proposed to discuss the potential environmental mitigation and conservation strategies. It was estimated that the life cycle of a single COVID-19 nucleic acid diagnostic test in China would lead to the emission of 612.9 g CO2 equiv global warming potential. Waste treatment, as a step of life cycle, worsen the environmental impacts such as global warming potential, eutrophication, and ecotoxicity. Meanwhile, diesel-driven transportation was considered as the major contributor to particulate air. Even though COVID-19 diagnostics are of the greatest importance to end the pandemic, their environmental impacts should not be ignored. It is suggested that improved approaches for waste treatment, low-carbon transportation, and a reliable pool sampling strategy are critical for the achievement of sustainable and green diagnostics.

A simulation-optimization approach for supporting conservative water allocation under uncertainties.

In this paper, a hybrid method integrated unbiased grey model (UGM) and artificial neural network (ANN) into an interval two-stage fuzzy credibility-constrained programming (ITFCP) framework is proposed for water resources allocation of the Yalong River research area. Through the grey correlation analysis and the eXtreme Gradient Boosting (XGboost) algorithm, the economic and social indicators are related to the water demands of different water sectors in different regions can be obtained for building water demand prediction model. According to the unbiased grey prediction of the socio-economic development data of each region in the Yalong River Basin (YRB), water demand prediction models are constructed by using neural network. The establishment of a hybrid two-stage interval fuzzy credibility-constrained programming model can analyze the uncertainties existing in the process of water resources allocation. Taking 2020, 2025, and 2030 as the planning years, the developed model studies and reveals the system benefits at different credibility levels, the water shortage of each user in sub-regions and the water resources allocation situation to provide suggestion for managers to optimize the allocation of water resources. Compared to the previous methods, this integrated model can help decision-makers set management policies more sustainably and profitably.

Prediction and optimization of regional land-use patterns considering nonpoint-source pollution control under conditions of uncertainty.

Socioeconomic development, leading to significant changes in land-use patterns, has further influenced the output of regional nonpoint-source (NPS) pollution. Multiple uncertainties exist in the processes of land-use changes and NPS pollution export. These uncertainties can deeply affect the management of regional land-use patterns and control of NPS pollution. In this research, an integrated land-use prediction and optimization (ILUPO) model based on system dynamics, export coefficient, interval linear programming, and fuzzy parameter programming models was proposed. The ILUPO model can provide future land-use patterns and NPS pollution loads, and also help optimize the patterns under multiple pollution reduction scenarios. Interval and fuzzy uncertainties in the processes of land-use changes and NPS pollution output can be effectively addressed. The developed model was applied to a water source area in the central part of northern Guangdong Province in South China. For the prediction period 2020-2030 under the high-speed development scenario, results show that cropland area would decrease, while grassland and waterbody areas would increase. In contrast, these three types of land-use would show opposite variation trends under the low-speed development scenario. Construction land area would decrease, while forestland area would increase under both the low-speed and high-speed development scenarios. Variation of the predicted land-use patterns would lead to an increase of total nitrogen loads under each of the scenario, while the total phosphorus loads would show relatively complex variation trends. Regional land-use patterns should be further optimized to mitigate NPS pollution. However, the pollution loads in the study area cannot be reduced by >5% through land-use adjustment. Because cropland would still be the critical source of NPS pollution after optimization, strictly controlling the areas of cropland would be important for the management of such pollution in the research area. In addition, certain areas of grassland and waterbody would need to be converted into cropland and construction land to balance the economic benefit of the system and NPS pollution control. Multiple results obtained from the model under different scenarios of pollution reduction targets and α-cut levels can provide decision-making supports for the local policy makers. The developed ILUPO model can yield insights useful for the planning and adjustment of regional land-use patterns while considering NPS pollution control under conditions of uncertainty.

Water-carbon nexus relationship and interaction mechanism analysis within Beijing-Tianjin-Hebei urban agglomeration.

As the total water resources consumption control and carbon mitigation continuous improvement, the weak water-carbon incorporate management is increasingly exposed. In this study, a water-carbon nexus assessment framework is proposed to analyze the nexus relationship between water consumption and carbon emission, and distinguishes the coupled water-carbon transmission intensity and the transfer paths under regional and industrial scales. According to the practical input-output table, water consumption, and carbon emission information, the framework is applied to Beijing-Tianjin-Hebei urban agglomeration (BTHUA), a population, resource, and trade intensive area of China. Inter-regional/intra-regional water consumption and carbon emission transfer fluxes between sectors, the pairwise ecological relationship, and the water-carbon nexus were analyzed. Results indicated that the water-carbon transfer indexes from Hebei to Beijing and Tianjin were 161.85 kg/m3 and 113.88 kg/m3 in the study period, along with the most water consumption and carbon emission, and the worst water-carbon nexus. From the industrial perspective, electricity and gas supplying industry provided 7.8% and 29.1% of the total carbon transfer in Tianjin and Hebei, as the most key node sectors on the water-carbon nexus in the BTHUA. The research provides valuably supporting the adjustment of the existing urban agglomeration water-carbon nexus management schemes.

Changes of vegetational cover and the induced impacts on hydrological processes under climate change for a high-diversity watershed of south China.

Understanding the changes in hydrological process is a key subject for water resource management of a high-diversity watershed. In this paper, through an establishment of a SWAT-based model, the effects of climate change and its induced vegetation change on hydrological process were analyzed in the East River Basin. The model could well simulate the hydrological processes of the basin including surface runoff (SURQ), groundwater (GWQ), lateral flow (LATQ), total water yield (WYLD), actual evapotranspiration (ET), and groundwater recharge (PERC). Under the vegetation change induced by temperature increase, the effects of the vegetation change on hydrological process were larger than that of the temperature change. Under the vegetation change caused by the increase of temperature and precipitation, the vegetation change enhanced the effects of climate change on annual SURQ, LATQ, GWQ, WYLD, and PERC of the basin. Under spatial scale, when the temperature and precipitation changed simultaneously, the increase of precipitation could promote the increase of annual ET in sub-watersheds. Also, the annual SURQ, WYLD, GWQ and ET in western sub-watersheds were more sensitive to the cumulative changes of vegetation and climate. This work can provide useful information to decision makers in water resource management of watersheds.

Classification analysis of blue and green water quantities for a large-scale watershed of southwest China.

Traditional blue water resources assessment and management may not meet the needs of sustainable water resource utilization; ignoring the number of green water resources will underestimate the availability of water resources. To rationally allocate and scientifically manage the limited water resources, it is necessary to divide the rich and poor flow situation of blue water and green water. The MIKE SHE-MIKE HYDRO integrated coupled model was selected and used in the Yalong River basin to ascertain the blue and green water in the hydrological cycle. The model was calibrated by matching simulated discharge against observed streamflow discharge at the Tongziling Station. At the same time, the research analyzed the component of green water and the total amount of blue water or green water on a temporal scale. The set pair analysis (SPA) was introduced to classify blue water and green water, which can not only understand the amount and distribution characteristics of water resources in the Yalong River Basin but also rationally allocate the total of water resources in the basin from the perspective of the regional water cycle. Furthermore, according to the situation of blue water and green water in the basin, the related policies are formulated to realize the efficient utilization of water resources in the Yalong River basin.

Design, Synthesis and Biological Evaluation of N-phenylindole Derivatives as Pks13 Inhibitors againstMycobacterium tuberculosis.

Polyketide synthase 13 (Pks13), an essential enzyme for the survival of Mycobacterium tuberculosis (Mtb), is an attractive target for new anti-TB agents. In our previous work, we have identified 2-phenylindole derivatives against Mtb. The crystallography studies demonstrated that the two-position phenol was solvent-exposed in the Pks13-TE crystal structure and a crucial hydrogen bond was lost while introducing bulkier hydrophobic groups at indole N moieties. Thirty-six N-phenylindole derivatives were synthesized and evaluated for antitubercular activity using a structure-guided approach. The structure-activity relationship (SAR) studies resulted in the discovery of the potent Compounds 45 and 58 against Mtb H37Rv, with an MIC value of 0.0625 µg/mL and 0.125 µg/mL, respectively. The thermal stability analysis showed that they bind with high affinity to the Pks13-TE domain. Preliminary ADME evaluation showed that Compound 58 displayed modest human microsomal stability. This report further validates that targeting Pks13 is a valid strategy for the inhibition of Mtb and provides a novel scaffold for developing leading anti-TB compounds.

Copula-based exposure risk dynamic simulation of dual heavy metal mixed pollution accidents at the watershed scale.

Most heavy metal exposure and pollution results from multiple industrial activities, including metal processing in refineries, and microelectronics. These issues pose a great threat to human health, ecological balance, and even societal stability. During 2012-2017, China, in particular, faced the challenge of 23 heavy metals accidents, six of which were extraordinarily serious accidents. Accidental environmental pollution is rarely caused by a single heavy metal, but rather by heavy metal mixtures. To address the need for a joint exposure risk assessment for heavy metal mixed pollution accidents at the watershed scale, a Copula-based exposure risk dynamic simulation model was proposed. A coupled hydrodynamic and accidental heavy metal exposure model is constructed for an hourly simulation of the exposure fate of heavy metals from each risk source once accidental leakage has occurred. The Copula analysis was introduced to calculate the dual heavy metal joint exposure probability in real time. This method was applied to an acute Cr6+-Hg2+ joint exposure risk assessment for 43 electroplating plants in nine sub-watersheds within the Dongjiang River downstream basin. The results indicated seven risk sources (i.e., S1, S4, H18, H23, H27-H28, and H34) that presented relatively high exposure risk to their surrounding sub-watersheds. Spatially, the acute exposure risk level was highest in the tributary basin (sub-watershed XW) than in the mainstream (sub-watershed DW2) and the river network (sub-watershed RW) of the lower reaches of the Dongjiang River. This research highlights an effective probabilistic approach for performing a joint exposure risk analysis of heavy metal mixed pollution accidents at the watershed scale.