Spatial prediction of soil contamination based on machine learning: a review

● A review of machine learning (ML) for spatial prediction of soil contamination. ● ML have achieved significant breakthroughs for soil contamination prediction. ● A structured guideline for using ML in soil contamination is proposed. ● The guideline includes variable selection, model evaluation, and interpretation. Soil pollution levels can be quantified via sampling and experimental analysis; however, sampling is performed at discrete points with long distances owing to limited funding and human resources, and is insufficient to characterize the entire study area. Spatial prediction is required to comprehensively investigate potentially contaminated areas. Consequently, machine learning models that can simulate complex nonlinear relationships between a variety of environmental conditions and soil contamination have recently become popular tools for predicting soil pollution. The characteristics, advantages, and applications of machine learning models used to predict soil pollution are reviewed in this study. Satisfactory model performance generally requires the following: 1) selection of the most appropriate model with the required structure; 2) selection of appropriate independent variables related to pollutant sources and pathways to improve model interpretability; 3) improvement of model reliability through comprehensive model evaluation; and 4) integration of geostatistics with the machine learning model. With the enrichment of environmental data and development of algorithms, machine learning will become a powerful tool for predicting the spatial distribution and identifying sources of soil contamination in the future.     

Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique

● A machine learning model was used to identify lake nutrient pollution sources. ● XGBoost model showed the best performance for lake water quality prediction. ● Model feature size was reduced by screening the key features with the MIC method. ● TN and TP concentrations of Lake Taihu are mainly affected by endogenous sources. ● Next-month lake TN and TP concentrations were predicted accurately. Effective control of lake eutrophication necessitates a full understanding of the complicated nitrogen and phosphorus pollution sources, for which mathematical modeling is commonly adopted. In contrast to the conventional knowledge-based models that usually perform poorly due to insufficient knowledge of pollutant geochemical cycling, we employed an ensemble machine learning (ML) model to identify the key nitrogen and phosphorus sources of lakes. Six ML models were developed based on 13 years of historical data of Lake Taihu’s water quality, environmental input, and meteorological conditions, among which the XGBoost model stood out as the best model for total nitrogen (TN) and total phosphorus (TP) prediction. The results suggest that the lake TN is mainly affected by the endogenous load and inflow river water quality, while the lake TP is predominantly from endogenous sources. The prediction of the lake TN and TP concentration changes in response to these key feature variations suggests that endogenous source control is a highly desirable option for lake eutrophication control. Finally, one-month-ahead prediction of lake TN and TP concentrations (R² of 0.85 and 0.95, respectively) was achieved based on this model with sliding time window lengths of 9 and 6 months, respectively. Our work demonstrates the great potential of using ensemble ML models for lake pollution source tracking and prediction, which may provide valuable references for early warning and rational control of lake eutrophication.     

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured Parzen Estimator

● Hybrid deep-learning model is proposed for water quality prediction. ● Tree-structured Parzen Estimator is employed to optimize the neural network. ● Developed model performs well in accuracy and uncertainty. ● Usage of the proposed model can reduce carbon emission and energy consumption. Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants (WWTPs). However, some water quality metrics are not measurable in real time, thus influencing the judgment of the operators and may increase energy consumption and carbon emission. One of the solutions is using a soft-sensor prediction technique. This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit (BiGRU) combined with Gaussian Progress Regression (GPR) optimized by Tree-structured Parzen Estimator (TPE). TPE automatically optimizes the hyperparameters of BiGRU, and BiGRU is trained to obtain the point prediction with GPR for the interval prediction. Then, a case study applying this prediction method for an actual anaerobic process (2500 m³/d) is carried out. Results show that TPE effectively optimizes the hyperparameters of BiGRU. For point prediction of COD_eff and biogas yield, R² values of BiGRU, which are 0.973 and 0.939, respectively, are increased by 1.03%–7.61% and 1.28%–10.33%, compared with those of other models, and the valid prediction interval can be obtained. Besides, the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation. It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.     

PM2.5 concentration declining saves health expenditure in China

● Monthly hospitalization expenses are sensitive to increases in PM_2.5 exposure. ● The increased PM_2.5 causes patients with CHD and LRI to stay longer in the hospital. ● The impact of PM_2.5 on total expenses for stroke is greater in southern China. ● Males may be more sensitive to air pollution than females. Air pollution has been a severe issue in China. Exposure to PM_2.5 has adverse health effects and causes economic losses. This study investigated the economic impact of exposure to PM_2.5 pollution using monthly city-level data covering 88.5 million urban employees in 2016 and 2017. This study mainly focused on three expenditure indicators to measure the economic impact considering lower respiratory infections (LRIs), coronary heart disease (CHD), and stroke. The results show that a 10 µg/m³ increase in PM_2.5 would cause total monthly expenses of LRIs, CHD, and stroke to increase by 0.226%, 0.237%, and 0.374%, respectively. We also found that LRI, CHD, and stroke hospital admissions increased significantly by 10%, 8.42%, and 5.64%, respectively. Furthermore, the total hospital stays of LRIs, CHDs, and strokes increased by 2.49%, 2. 51%, and 1.64%, respectively. Our findings also suggest heterogeneous impacts of PM_2.5 exposures by sex and across regions, but no statistical evidence shows significant differences between the older and younger adult subgroups. Our results provide several policy implications for reducing unequal public health expenditures in overpolluted countries.     

Property-performance relationship of core-shell structured black TiO2 photocatalyst for environmental remediation

● Properties and performance relationship of CSBT photocatalyst were investigated. ● Properties of CSBT were controlled by simply manipulating glycerol content. ● Performance was linked to semiconducting and physicochemical properties. ● CSBT (W:G ratio 9:1) had better performance with lower energy consumption. ● Phenols were reduced by 48.30% at a cost of $2.4127 per unit volume of effluent. Understanding the relationship between the properties and performance of black titanium dioxide with core-shell structure (CSBT) for environmental remediation is crucial for improving its prospects in practical applications. In this study, CSBT was synthesized using a glycerol-assisted sol-gel approach. The effect of different water-to-glycerol ratios (W:G = 1:0, 9:1, 2:1, and 1:1) on the semiconducting and physicochemical properties of CSBT was investigated. The effectiveness of CSBT in removing phenolic compounds (PHCs) from real agro-industrial wastewater was studied. The CSBT synthesized with a W:G ratio of 9:1 has optimized properties for enhanced removal of PHCs. It has a distinct core-shell structure and an appropriate amount of Ti³⁺ cations (11.18%), which play a crucial role in enhancing the performance of CSBT. When exposed to visible light, the CSBT performed better: 48.30% of PHCs were removed after 180 min, compared to only 21.95% for TiO₂ without core-shell structure. The CSBT consumed only 45.5235 kWh/m³ of electrical energy per order of magnitude and cost $2.4127 per unit volume of treated agro-industrial wastewater. Under the conditions tested, the CSBT demonstrated exceptional stability and reusability. The CSBT showed promising results in the treatment of phenols-containing agro-industrial wastewater.     

Salinity exchange between seawater/brackish water and domestic wastewater through electrodialysis for potable water

● Present a general concept called “salinity exchange”. ● Salts transferred from seawater to treated wastewater until completely switch. ● Process demonstrated using a laboratory-scale electrodialysis system. ● High-quality desalinated water obtained at ~1 mL/min consuming < 1 kWh/m ³ energy. Two-thirds of the world’s population has limited access to potable water. As we continue to use up our freshwater resources, new and improved techniques for potable water production are warranted. Here, we present a general concept called “salinity exchange” that transfers salts from seawater or brackish water to treated wastewater until their salinity values approximately switch, thus producing wastewater with an increased salinity for discharge and desalinated seawater as the potable water source. We have demonstrated this process using electrodialysis. Salinity exchange has been successfully achieved between influents of different salinities under various operating conditions. Laboratory-scale salinity exchange electrodialysis (SEE) systems can produce high-quality desalinated water at ~1 mL/min with an energy consumption less than 1 kWh/m³. SEE has also been operated using real water, and the challenges of its implementation at a larger scale are evaluated.     

Relationship between groundwater cadmium and vicinity resident urine cadmium levels in the non-ferrous metal smelting area,China

● This study systematically examined the relationship between groundwater Cd and UCL. ● The study covered 211 UCL and sociological characteristic from nine groundwater samples. ● We found a significant positive correlation between groundwater Cd and UCL. ● Smoking status and education level also significantly affected UCL. Cadmium (Cd) has received widespread attention owing to its persistent toxicity and non-degradability. Cd in the human body is mainly absorbed from the external environment and is usually assessed using urinary Cd. Hunan Province is the heartland of the Chinese non-ferrous mining area, where several serious Cd pollution events have occurred, including high levels of Cd in the urine of residents. However, the environmental factors influencing high urinary Cd levels (UCLs) in nearby residents remain unclear. Therefore, 211 nearby residents’ UCLs and the corresponding sociological characteristics from nine groundwater samples in this area were analyzed using statistical analysis models. Groundwater Cd concentration ranged from 0.02 to 1.15 μg/L, aligning with class III of the national standard; the range of UCL of nearby residents was 0.37–36.60 μg/L, exceeding the national guideline of 0–2.5 μg/L. Groundwater Cd levels were positively correlated with the UCL (P < 0.001, correlation coefficient 95 % CI = 9.68, R² = 0.06). In addition, sociological characteristics, such as smoking status and education level, also affect UCL. All results indicate that local governments should strengthen the prevention and abatement of groundwater Cd pollution. This study is the first to systematically evaluate the relationship between groundwater Cd and UCL using internal and external environmental exposure data. These findings provide essential bases for relevant departments to reduce Cd exposure in regions where the heavy metal industry is globally prevalent.     

Development of machine learning multi-city model for municipal solid waste generation prediction

● A database of municipal solid waste (MSW) generation in China was established. ● An accurate MSW generation prediction model (WGMod) was constructed. ● Key factors affecting MSW generation were identified. ● MSW trends generation in Beijing and Shenzhen in the near future are projected. Integrated management of municipal solid waste (MSW) is a major environmental challenge encountered by many countries. To support waste treatment/management and national macroeconomic policy development, it is essential to develop a prediction model. With this motivation, a database of MSW generation and feature variables covering 130 cities across China is constructed. Based on the database, advanced machine learning (gradient boost regression tree) algorithm is adopted to build the waste generation prediction model, i.e., WGMod. In the model development process, the main influencing factors on MSW generation are identified by weight analysis. The selected key influencing factors are annual precipitation, population density and annual mean temperature with the weights of 13%, 11% and 10%, respectively. The WGMod shows good performance with R² = 0.939. Model prediction on MSW generation in Beijing and Shenzhen indicates that waste generation in Beijing would increase gradually in the next 3–5 years, while that in Shenzhen would grow rapidly in the next 3 years. The difference between the two is predominately driven by the different trends of population growth.     

A hybrid fuel cell for water purification and simultaneously electricity generation

● A novel hybrid fuel cell (F-HFC) was fabricated. ● Pollutant degradation and synchronous electricity generation occurred in F-HFC. ● BiOCl-NH₄PTA photocatalyst greatly improved electron transfer and charge separation. ● Pollutant could act as substrate directly in ambient conditions without pretreatment. ● The mechanism of the F-HFC was proposed and elucidated. The development of highly efficient energy conversion technologies to extract energy from wastewater is urgently needed, especially in facing of increasing energy and environment burdens. Here, we successfully fabricated a novel hybrid fuel cell with BiOCl-NH₄PTA as photocatalyst. The polyoxometalate (NH₄PTA) act as the acceptor of photoelectrons and could retard the recombination of photogenerated electrons and holes, which lead to superior photocatalytic degradation. By utilizing BiOCl-NH₄PTA as photocatalysts and Pt/C air-cathode, we successfully constructed an electron and mass transfer enhanced photocatalytic hybrid fuel cell with flow-through field (F-HFC). In this novel fuel cell, dyes and biomass could be directly degraded and stable power output could be obtained. About 87 % of dyes could be degraded in 30 min irradiation and nearly 100 % removed within 90 min. The current density could reach up to ~267.1 μA/cm²; with maximum power density (P_max) of ~16.2 μW/cm² with Rhodamine B as organic pollutant in F-HFC. The power densities were 9.0 μW/cm², 12.2 μW/cm², and 13.9 μW/cm² when using methyl orange (MO), glucose and starch as substrates, respectively. This hybrid fuel cell with BiOCl-NH₄PTA composite fulfills the purpose of decontamination of aqueous organic pollutants and synchronous electricity generation. Moreover, the novel design cell with separated photodegradation unit and the electricity generation unit could bring potential practical application in water purification and energy recovery from wastewater.     

Bacteria inactivation by sulfate radical: progress and non-negligible disinfection by-products

● Status of inactivation of pathogenic microorganisms by SO₄^•− is reviewed. ● Mechanism of SO₄^•− disinfection is outlined. ● Possible generation of DBPs during disinfection using SO₄^•− is discussed. ● Possible problems and challenges of using SO₄^•− for disinfection are presented. Sulfate radicals have been increasingly used for the pathogen inactivation due to their strong redox ability and high selectivity for electron-rich species in the last decade. The application of sulfate radicals in water disinfection has become a very promising technology. However, there is currently a lack of reviews of sulfate radicals inactivated pathogenic microorganisms. At the same time, less attention has been paid to disinfection by-products produced by the use of sulfate radicals to inactivate microorganisms. This paper begins with a brief overview of sulfate radicals’ properties. Then, the progress in water disinfection by sulfate radicals is summarized. The mechanism and inactivation kinetics of inactivating microorganisms are briefly described. After that, the disinfection by-products produced by reactions of sulfate radicals with chlorine, bromine, iodide ions and organic halogens in water are also discussed. In response to these possible challenges, this article concludes with some specific solutions and future research directions.     

Factors affecting the distribution of microplastics in soils of China

● Microplastic (MP) abundance in soil of China was highly heterogeneous. ● MP abundance was higher near large rivers and central land affected by monsoons. ● MP abundance was correlated with longitude, mulching film, and average temperature. ● Factors suitable for predicting MP pollution using models were discussed. Microplastics (MPs) are found worldwide in high abundance, posing a potential threat to ecosystems. Despite the ubiquity of MPs in the environment, very little is known about the regional distribution of MPs and underlying factors affecting this distribution in the field, which likely include human activity, but also features of the environment itself. Here, out of a total of 1157 datapoints investigated in 53 Chinese studies, 9.68% datapoints were removed as outliers in the heterogeneity analysis. This review revealed that the abundance of MPs was highly heterogeneous. In addition, microplastic (MP) distribution maps based on China demonstrated that the highest abundance of MPs tended to occur near large rivers and central land affected by the intersection of two monsoons. The model-fitting and previous studies showed that MP abundance in China was correlated with longitude, agricultural mulching film usage per capita, temperature, and precipitation. However, due to the heterogeneity of MPs and the low matching degree between the current environmental data and the sampling points, this pattern was not as evident as reported in any single study. Factors affecting the distribution of MPs can not be captured by linear relationships alone, and systematic selection of suitable environmental factors and further model optimization are needed to explore the cause of MP pollution in soil. Overall, this review revealed an uneven distribution of MPs and serves as a reference for model prediction to assess and control plastic pollution in natural soil environments.     

A new prediction method of industrial atmospheric pollutant emission intensity based on pollutant emission standard quantification

● Established a quantification method of pollutant emission standard. ● Predicted the SO₂ emission intensity of single coking enterprises in China. ● Evaluated the influence of pollutant discharge standard on prediction accuracy. ● Analyzed the SO₂ emissions of Chinese provincial and municipal coking enterprises. Industrial emissions are the main source of atmospheric pollutants in China. Accurate and reasonable prediction of the emission of atmospheric pollutants from single enterprise can determine the exact source of atmospheric pollutants and control atmospheric pollution precisely. Based on China’s coking enterprises in 2020, we proposed a quantitative method for pollutant emission standards and introduced the quantification results of pollutant emission standards (QRPES) into the construction of support vector regression (SVR) and random forest regression (RFR) prediction methods for SO₂ emission of coking enterprises in China. The results show that, affected by the types of coke ovens and regions, China’s current coking enterprises have implemented a total of 21 emission standards, with marked differences. After adding QRPES, it was found that the root mean squared error (RMSE) of SVR and RFR decreased from 0.055 kt/a and 0.059 kt/a to 0.045 kt/a and 0.039 kt/a, and theR² increased from 0.890 and 0.881 to 0.926 and 0.945, respectively. This shows that the QRPES can greatly improve the prediction accuracy, and the SO₂ emissions of each enterprise are highly correlated with the strictness of standards. The predicted result shows that 45% of SO₂ emissions from Chinese coking enterprises are concentrated in Shanxi, Shaanxi and Hebei provinces in central China. The method created in this paper fills in the blank of forecasting method of air pollutant emission intensity of single enterprise and is of great help to the accurate control of air pollutants.     

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

● A novel deep learning framework for short-term water demand forecasting. ● Model prediction accuracy outperforms other traditional deep learning models. ● Wavelet multi-resolution analysis automatically extracts key water demand features. ● An analysis is performed to explain the improved mechanism of the proposed method. Short-term water demand forecasting provides guidance on real-time water allocation in the water supply network, which help water utilities reduce energy cost and avoid potential accidents. Although a variety of methods have been proposed to improve forecast accuracy, it is still difficult for statistical models to learn the periodic patterns due to the chaotic nature of the water demand data with high temporal resolution. To overcome this issue from the perspective of improving data predictability, we proposed a hybrid Wavelet-CNN-LSTM model, that combines time-frequency decomposition characteristics of Wavelet Multi-Resolution Analysis (MRA) and implement it into an advanced deep learning model, CNN-LSTM. Four models - ANN, Conv1D, LSTM, GRUN - are used to compare with Wavelet-CNN-LSTM, and the results show that Wavelet-CNN-LSTM outperforms the other models both in single-step and multi-steps prediction. Besides, further mechanistic analysis revealed that MRA produce significant effect on improving model accuracy.     

Target the neglected VOCs emission from iron and steel industry in China for air quality improvement

● Haze formation in China is highly correlated with iron and steel industry. ● VOCs generated in sinter process were neglected under current emission standard. ● Co-elimination removal of sinter flue gas complex pollutants are timely needed. Recent years have witnessed significant improvement in China’s air quality. Strict environmental protection measures have led to significant decreases in sulfur dioxide (SO₂), nitrogen oxides (NO_x), and particulate matter (PM) emissions since 2013. But there is no denying that the air quality in 135 cities is inferior to reaching the Ambient Air Quality Standards (GB 3095–2012) in 2020. In terms of temporal, geographic, and historical aspects, we have analyzed the potential connections between China’s air quality and the iron and steel industry. The non-target volatile organic compounds (VOCs) emissions from iron and steel industry, especially from the iron ore sinter process, may be an underappreciated index imposing a negative effect on the surrounding areas of China. Therefore, we appeal the authorities to pay more attention on VOCs emission from the iron and steel industry and establish new environmental standards. And different iron steel flue gas pollutants will be eliminated concurrently with the promotion and application of new technology.     

A critical review of the emerging research on the detection and assessment of microplastics pollution in the coastal,marine, and urban Bangladesh

● Coastal and marine regions are the most studied for microplastic pollution. ● Tourism is a major cause of microplastic pollution in coastal regions. ● Sediments contain larger microplastics while fish ingest smaller microplastics. ● Inland lakes, rivers, and freshwater fish are impacted by microplastic pollution. ● Microplastics are found in edible salts, however, presence is less in refined salt. The research on the extent and effects of microplastics pollution in the Global South is only getting started. Bangladesh is a South Asian country with one of the fastest growing economies in the world, however, such exponential economic growth has also increased the pollution threats to its natural and urban environment. In this paper, we reviewed the recent primary research on the assessment of the extent of microplastics pollution in Bangladesh. From the online databases, we developed a compilation of emerging research articles that detected and quantified microplastics in different coastal, marine, and urban environments in Bangladesh. Most of the studies focused on the coastal environment (e.g., beach sediment) and marine fish, while limited data were available for the urban environment. We also discussed the relationship of the type of anthropogenic activities with the observed microplastic pollution. The Cox’s Bazar sea beach in south-east Bangladesh experienced microplastics pollution due to tourism activities, while fishing and other anthropogenic activities led to microplastics pollution in the Bay of Bengal. While microplastics larger than 1 mm were prevalent in the beach sediments, smaller microplastics with size below 0.5 mm were prevalent in marine fish samples. Moreover, the differences in microplastic abundance, size, shape, color, and polymer type found were depended on the sampling sites and relevant anthropogenic activities. It is imperative to identify major sources of microplastics pollution in both natural and urban environment, determine potential environmental and human health effects, and develop mitigating and prevention strategies for reducing microplastics pollution.     

Effect of Cu-ZSM-5 catalysts with different CuO particle size on selective catalytic oxidation of N,N-Dimethylformamide

● A series of Cu-ZSM-5 catalysts were tested for DMF selective catalytic oxidation. ● Cu-6 nm samples showed the best catalytic activity and N₂ selectivity. ● Redox properties and chemisorbed oxygen impact on DMF catalytic oxidation. ● Isolated Cu²⁺ species and weak acidity have effects on the generation of N₂. N, N-Dimethylformamide (DMF), a nitrogen-containing volatile organic compound (NVOC) with high emissions from the spray industry, has attracted increasing attention. In this study, Cu-ZSM-5 catalysts with different CuO particle sizes of 3, 6, 9 and 12 nm were synthesized and tested for DMF selective catalytic oxidation. The crystal structure and physicochemical properties of the catalyst were studied by various characterization methods. The catalytic activity increases with increasing CuO particle size, and complete conversion can be achieved at 300–350 °C. The Cu-12 nm catalyst has the highest catalytic activity and can achieve complete conversion at 300 °C. The Cu-6 nm sample has the highest N₂ selectivity at lower temperatures, reaching 95% at 300 °C. The activity of the catalysts is determined by the surface CuO cluster species, the bulk CuO species and the chemisorbed surface oxygen species. The high N₂ selectivity of the catalyst is attributed to the ratio of isolated Cu²⁺ and bulk CuO species, and weak acidity is beneficial to the formation of N₂. The results in this work will provide a new design of NVOC catalytic oxidation catalysts.     

Simple fabrication of carboxymethyl cellulose and κ-carrageenan composite aerogel with efficient performance in removal of fluoroquinolone antibiotics from water

● A composite aerogel was simply obtained to remove various fluoroquinolones (FQs). ● The structural and textural properties of this composite aerogel are improved. ● Its adsorption capacity was improved at a low content of coexisting Cu²⁺ or Fe³⁺ ion. ● Two substructural analogs of FQs are compared to explore the adsorption mechanisms. ● This aerogel after saturated adsorption can be reused directly for Cu²⁺ adsorption. 3D composite aerogels (CMC-CG) composed of carboxymethyl cellulose and κ-carrageenan were designed and fabricated using the one-pot synthesis technique. The optimized CMC-CG showed a good mechanical property and a high swelling ratio due to its superior textural properties with a proper chemically cross-linked interpenetrating network structure. CMC-CG was utilized for the removal of various fluoroquinolones (FQs) from water and exhibited high adsorption performance because of effective electrostatic attraction and hydrogen bonding interactions. Ciprofloxacin (CIP), a popular FQ, was used as the representative. The optimized CMC-CG had a theoretically maximal CIP uptake of approximately 1.271 mmol/g at the pH of 5.0. The adsorption capacity of CMC-CG was improved in the presence of some cations, Cu²⁺ and Fe³⁺ ions, at a low concentration through the bridging effect but was reduced at a high concentration. The investigation of adsorption mechanisms, based on the adsorption kinetics, isotherms and thermodynamic study, Fourier transform infrared spectrometry and X-ray photoelectron spectroscopy analyses before and after adsorption, and changes in the adsorption performance of CMC-CG toward two molecular probes, further indicated that electrostatic attraction was the dominant interaction rather than hydrogen bonding in this adsorption. CMC-CG after saturated adsorption of CIP could be easily regenerated using a dilute NaCl aqueous solution and reused efficiently. Moreover, the disused aerogel could still be reused as a new adsorbent for effective adsorption of Cu²⁺ ion. Overall, this study suggested the promising applications of this composite aerogel as an eco-friendly, cost-effective, and recyclable adsorbent for the efficient removal of FQs from water.     

China’s carbon neutrality: an extensive and profound systemic reform

● China has pledged ambitious carbon peak and neutrality goals for mitigating global climate change. ● Major challenges to achieve carbon neutrality in China are summarized. ● The new opportunities along the pathway of China’s carbon neutrality are discussed from four aspects. ● Five policy suggestions for China are provided. China is the largest developing economy and carbon dioxide emitter in the world, the carbon neutrality goal of which will have a profound influence on the mitigation pathway of global climate change. The transition towards a carbon-neutral society is integrated into the construction of ecological civilization in China, and brings profound implications for China’s socioeconomic development. Here, we not only summarize the major challenges in achieving carbon neutrality in China, but also identify the four potential new opportunities: namely, the acceleration of technology innovations, narrowing regional disparity by reshaping the value of resources, transforming the industrial structure, and co-benefits of pollution and carbon mitigation. Finally, we provide five policy suggestions and highlight the importance of balancing economic growth and carbon mitigation, and the joint efforts among the government, the enterprises, and the residents.     

Discovery of emerging organic pollutants in the atmosphere through an omics approach

● We review the framework of discovering emerging pollutants through an omics approach. ● High-resolution MS can digitalize atmospheric samples to full-component data. ● Chemical features and databases can help to translate untargeted data to compounds. ● Biological effect-directed untargeted analyses consider both existence and toxicity. Ambient air pollution, containing numerous known and hitherto unknown compounds, is a major risk factor for public health. The discovery of harmful components is the prerequisite for pollution control; however, this raises a great challenge on recognizing previously unknown species. Here we systematically review the analytical techniques on air pollution in the framework of an omics approach, with a brief introduction on sample preparation and analysis, and in more detail, compounds prioritization and identification. Through high-resolution mass spectrometry (HRMS, typically coupled with chromatography), the complicated environmental matrix can be digitalized into “full-component” data. A key step to discover emerging compounds is the prioritization of compounds from massive data. Chemical fingerprints, suspect lists and biological effects are the most vital untargeted strategies for comprehensively screening critical and hazardous substances. Afterward, compressed data of compounds can be identified at various confidence levels according to exact mass and the derived molecular formula, MS libraries, and authentic standards. Such an omics approach on full-component data provides a paradigm for discovering emerging air pollutants; nonetheless, new technological advancements of instruments and databases are warranted for further tracking the environmental behaviors, hence to evaluate the health risk of key pollutants.     

The impact of different voltage application modes on biodegradation of chloramphenicol and shift of microbial community structure

● Presented coupled system enhanced biodegradation of antibiotic chloramphenicol. ● HRT and electrical stimulation modes were key influencing factors. ● Electrical stimulation had little effect on the chloramphenicol metabolic pathway. ● Microbial community structure varied with the voltage application mode. Exoelectrogenic biofilms have received considerable attention for their ability to enhance electron transfer between contaminants and electrodes in bioelectrochemical systems. In this study, we constructed anaerobic-aerobic-coupled upflow bioelectrochemical reactors (AO-UBERs) with different voltage application modes, voltages and hydraulic retention times (HRTs). In addition, we evaluated their capacity to remove chloramphenicol (CAP). AO-UBER can effectively mineralize CAP and its metabolites through electrical stimulation when an appropriate voltage is applied. The CAP removal efficiencies were ~81.1%±6.1% (intermittent voltage application mode) and 75.2%±4.6% (continuous voltage application mode) under 0.5 V supply voltage, which were ~21.5% and 15.6% greater than those in the control system without voltage applied, respectively. The removal efficiency is mainly attributed to the anaerobic chamber. High-throughput sequencing combined with catabolic pathway analysis indicated that electrical stimulation selectively enriched Megasphaera, Janthinobacterium, Pseudomonas, Emticicia, Zoogloea, Cloacibacterium and Cetobacterium, which are capable of denitrification, dechlorination and benzene ring cleavage, respectively. This study shows that under the intermittent voltage application mode, AO-UBERs are highly promising for treating antibiotic-contaminated wastewater.