Virtual flows of aquatic heavy metal emissions and associated risk in China

Predicting Cd(II) adsorption capacity of biochar materials using typical machine learning models for effective remediation of aquatic environments

The screening and design of "green" biochar materials with high adsorption capacity play a pivotal role in promoting the sustainable treatment of Cd(II)-containing wastewater. In this study, six typical machine learning (ML) models, namely Linear Regression, Random Forest, Gradient Boosting Decision Tree, CatBoost, K-Nearest Neighbors, and Backpropagation Neural Network, were employed to accurately predict the adsorption capacity of Cd(II) onto biochars. A large dataset with 1051 data points was generated using 21 input variables obtained from batch adsorption experiments, including preparation conditions for biochar (2 features), physical properties of biochar (4 features), chemical composition of biochar (9 features), and adsorption experiment conditions (6 features). The rigorous evaluation and comparison of the ML models revealed that the CatBoost model exhibited the highest test R2 value (0.971) and the lowest RMSE (20.54 mg/g), significantly outperforming all other models. The feature importance analysis using Shapley Additive Explanations (SHAP) indicated that biochar chemical compositions had the greatest impact on model predictions of adsorption capacity (42.2 %), followed by adsorption conditions (37.57 %), biochar physical characteristics (12.38 %), and preparation conditions (7.85 %). The optimal experimental conditions optimized by partial dependence plots (PDP) are as follows: as high Cd(II) concentration as possible, C(%) of 33 %, N(%) of 0.3 %, adsorption time of 600 min, pyrolysis time of 50 min, biochar dosage of less than 2 g/L, O(%) of 42 %, biochar pH value of 11.2, and DBE of 1.15. This study unveils novel insights into the adsorption of Cd(II) and provides a comprehensive reference for the sustainable engineering of biochars in Cd(II) wastewater treatment.

Modifying national industrial structure for reducing heavy metals in China: A nexus-based multi-objective optimization approach

Heavy metals (HMs) exhibit significant toxicity and can lead to a range of health issues. Certain HMs share common emission sources, necessitating an exploration of the nexus among various HMs for achieving collaborative reductions. Considering the efficacy and feasibility of industrial modification to environmental pressures, this paper proposes a novel nexus-based optimization approach based on nexus analysis, multi-region input-output (MRIO) table, and multi-objective optimization to mitigate atmospheric HMs. The atmospheric HM emission inventory in 2017 is first compiled. Subsequently, the Integrated Nexus Strength of HMs Risk (HMR-INS) is proposed and employed to determine the range of sectoral output variations. Finally, a multi-objective optimization approach is employed based on the MRIO table in 2017. Compared with the traditional optimization method, the proposed approach performs better regarding HM-related risks and total output, leading to a 1.9 million tons increase in reduction on HM-related risks and a 1.37 trillion yuan increment in total output. Some further analyses are also given to provide feasible solutions for industrial modification, which considers both the economic efficiency and the stability of the industrial structure.

Revealing transmissions of atmospheric heavy metals hidden in the Chinese supply chain

Heavy metals (HMs) pose significant risks to human health and the environment. Identifying the sectors that play a significant role in the transmission of HMs has rarely been considered and represents an efficient method to control and manage HMs. By combining atmospheric HM emission inventories, the multi-regional input-output approach, and a betweenness-based method, this study revealed the transmission of HMs (comprehensively evaluated by the Heavy Metal Pollution Load, HMPL) in 2017. In 2017, 119.86 million tons of HMPL were transmitted through China's supply chain, and Cr was the main contributor to HMPL transmission. The results suggest that metal smelting is the primary contributor to HMPL transmission, and metal smelting in Jiangsu, Hebei, Henan, Shandong, and Anhui are the top five critical nodes. These results suggest that the sector's role changes dramatically with respect to HM control under this perspective. The role of HM emission-intensive sectors changed the most, as their production-based HMPLs accounted for 84% of the total HMPL; however, the HMPL transmitted by these sectors accounted for only 45% of the total. The critical HMPL transmission sectors identified in this study provide a basis for policy-making from a transmission perspective.

Unintended mitigation effect of air pollutant regulation on the aquatic cadmium: Evidence from the 11-FYPEP in China

This paper evaluates the unintended mitigation effect of air pollutant regulation on aquatic cadmium (Cd) emissions in the China's Eleventh Five-Year Plan for Environmental Protection (11-FYPEP), by employing a continuous Difference-in-Difference-in-Difference (DDD) estimator. We find that: (1) Although the 11-FYPEP did not target to reduce Cd emission, the implementation of 11-FYPEP reduced the emissions by 2.8 %. (2) The Cd emission is closely related to the industrial level, because the reduction of Cd is 6.1 % higher in areas with lower industrial output, and the mediating effect of the number of industrial enterprises accounts for 6.8 % of the Cd reduction. Based on our findings, implications like improving production efficiency and modifying industrial structure are proposed, as the 11-FYPEP achieves Cd reduction in an unsustainable way.

Interprovincial Metal and GHG Transfers Embodied in Electricity Transmission across China: Trends and Driving Factors

With the increasing proportion of low-carbon power in electricity generation mix, power generation will be transformed from carbon-intensive to metal-intensive. In this context, metal and GHG transfers embodied in electricity transmission of China from 2015 to 2019 are quantified by the Quasi-Input-Output model. Combined with complex network theory, we have distinguished whether metal and GHG transfers show different trends as electricity trade changes. Driving factors contributing to forming the metal and GHG transfers are also explored based on the Quadratic Assignment Procedure. The results show that the electricity trade change has strengthened the metal transfer network significantly, while several key links in the GHG transfer network have weakened. Moreover, we find provincial differences in low-carbon electricity investment contributing to the metal transfer while affecting the GHG transfer little. The above facts imply an expanding embodied metal transfer in the future and shed light on policy making for power system decarbonization.

A stepwise emission clustering analysis method for analyzing the effects of heavy metal emissions from multiple income groups

Emissions of various heavy metals (i.e. Hg, As, Cd, Cr and Pb) have emerged as one of the most tremendous pressures on the socio-economic and environmental systems. The pressures caused by such heavy metals would be getting intensified if no adequate and timely solution is implemented, especially because their emissions are associated with economic activities (i.e. regional trade and household consumption). In this study, emissions of various heavy metals derived from regional trade as well as rural and urban household consumption are quantified to identify the critical regions and significant household consumption. In order to detail the impacts of household consumption on such emissions, rural and urban household are divided respectively into five and seven groups with hierarchic income from poorest to richest. The Hg, As, Cd, Cr and Pb emissions caused by intermediate commodity consumption are 1172.86, 2607.57, 23.28, 57.49 and 85.16 tons, respectively. Among them, such emissions induced by self-consumption are 662.95, 1539.34, 12.28, 33.10 and 43.49 tons. Shandong, Guangdong and Jiangsu are identified for high self-emissions due to their advanced economy and rare resources. On the contrast, Hebei and Shanxi with abundant resources are the critical regions for the high transfer-out emissions. Moreover, emissions of multiple heavy metals are inequal due to the variations of rural and urban income groups. Emissions caused by R5 are identified through stepwise cluster analysis for its significant difference in compare with other income groups. It is verified that the main difference of emissions in economically developed regions are caused by rural income groups, while urban income groups are the critical reason for the disaggregation of emissions in less developed regions. Policies should be further implemented based on the regional similarity and income-group inequality.

NemA Catalyzes Trivalent Organoarsenical Oxidation and Is Regulated by the Trivalent Organoarsenical-Selective Transcriptional Repressor NemR

Synthetic aromatic arsenicals such as roxarsone (Rox(V)) and nitarsone (Nit(V)) have been used as animal growth enhancers and herbicides. Microbes contribute to redox cycling between the relatively less toxic pentavalent and highly toxic trivalent arsenicals. In this study, we report the identification of nemRA operon from Enterobacter sp. Z1 and show that it is involved in trivalent organoarsenical oxidation. Expression of nemA is induced by chromate (Cr(VI)), Rox(III), and Nit(III). Heterologous expression of NemA in Escherichia coli confers resistance to Cr(VI), methylarsenite (MAs(III)), Rox(III), and Nit(III). Purified NemA catalyzes simultaneous Cr(VI) reduction and MAs(III)/Rox(III)/Nit(III) oxidation, and oxidation was enhanced in the presence of Cr(VI). The results of electrophoretic mobility shift assays and fluorescence assays demonstrate that the transcriptional repressor, NemR, binds to either Rox(III) or Nit(III). NemR has three conserved cysteine residues, Cys21, Cys106, and Cys116. Mutation of any of the three resulted in loss of response to Rox(III)/Nit(III), indicating that they form an Rox(III)/Nit(III) binding site. These results show that NemA is a novel trivalent organoarsenical oxidase that is regulated by the trivalent organoarsenical-selective repressor NemR. This discovery expands our knowledge of the molecular mechanisms of organoarsenical oxidation and provides a basis for studying the redox coupling of environmental toxic compounds.

How does soil pollution risk perception affect farmers' pro-environmental behavior? The role of income level

Soil pollution is a serious environmental issue in China. As a key subject of agricultural practices, promoting Chinese farmers' Pro-Environmental Behavior (PEB) through increasing their soil pollution risk perception is an important means for soil protection, agricultural transformation and ecological development. In this study, we distinguish four dimensions of soil pollution risk perception: fact perception (FP), loss perception (LP), cause perception (CP) and response behavior ability perception (RBAP). We conceptualize a model that depicts the relationships between the four dimensions of Chinese farmers' soil pollution risk perception and their PEB and the moderating effect of farms' household income level on these relationships. Using a questionnaire survey to collect empirical data, we find: first, the four dimensions of Chinese farmers' soil pollution risk perception have positive effects on their PEB; second, Chinese farmers' household income level positively moderates the relationships between their FP, LP and CP and their PEB but its moderating effect on the relationship between their RBAP and their PEB is not significant. Relevant theory and policy implications for environmental management are discussed in the paper.

Spatial-temporal characteristics and driving factors of the human health impacts of five industrial aquatic toxic metals in China

With the rapid advancement of industrialization without effective supervision, industrial aquatic toxic metal (TM) emissions pose threats to human health in China. Due to differences in socioeconomic development, the regional disparity of industrial aquatic TM emissions is obvious nationwide. In this study, the human health impacts (HHIs) of industrial aquatic TM emissions (i.e., mercury (Hg), cadmium (Cd), hexavalent chromium (Cr(VI)), lead (Pb), and arsenic (As)) in the 31 provinces of China were evaluated based on the ReCiPe method, and the driving factors affecting HHIs from 2000 to 2015 were decomposed using the logarithmic mean Divisia index (LMDI) method. The results showed that the HHIs gradually decreased, with more than an 80% decrease from 2000 to 2015. The order of the TMs contributing to the national HHIs in 2015 was as follows: As (79.5%) > Cr(VI) (19.6%) > Hg (0.4%) > Pb (0.2%) = Cd (0.2%), and 21 (68%) provinces were dominated by industrial aquatic As emissions. Economic development is the major driving factor of the increase in HHIs, while the HHI strength and wastewater discharge intensity are the key driving factors causing reductions in the HHIs. Hunan, Inner Mongolia, Hubei, and Jiangxi accounted for approximately 55% of the total HHIs in 2015. Some suggestions for reducing HHIs based on the local realities of different provinces were put proposed considering two aspects: economic strategy and technical capability.