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.     

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.     

Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural network

● Used a double-stage attention mechanism model to predict ozone. ● The model can autonomously select the appropriate time series for forecasting. ● The model outperforms other machine learning models and WRF-CMAQ. ● We used the model to analyze the driving factors of VOCs that cause ozone pollution. Ozone is becoming a significant air pollutant in some regions, and VOCs are essential for ozone prediction as necessary ozone precursors. In this study, we proposed a recurrent neural network based on a double-stage attention mechanism model to predict ozone, selected an appropriate time series for prediction through the input attention and temporal attention mechanisms, and analyzed the cause of ozone generation according to the contribution of feature parameters. The experimental data show that our model had an RMSE of 7.71 μg/m³ and a mean absolute error of 5.97 μg/m³ for 1-h predictions. The DA-RNN model predicted ozone closer to observations than the other models. Based on the importance of the characteristics, we found that the ozone pollution in the Jinshan Industrial Zone mainly comes from the emissions of petrochemical enterprises, and the good generalization performance of the model is proved through testing multiple stations. Our experimental results demonstrate the validity and promising application of the DA-RNN model in predicting atmospheric pollutants and investigating their causes.     

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.     

Characterization and variation of dissolved organic matter in composting: a critical review

● Effect of composting approaches on dissolved organic matter (DOM). ● Effect of composting conditions on the properties of DOM. ● Character indexes of DOM varied in composting. ● The size, hydrophobicity, humification, and electron transfer capacity increased. ● The hydrophilicity, protein-like materials, and aliphatic components reduced. As the most motive organic fraction in composting, dissolved organic matter (DOM) can contribute to the transfer and dispersal of pollutants and facilitate the global carbon cycle in aquatic ecosystems. However, it is still unclear how composting approaches and conditions influence the properties of compost-derived DOM. Further details on the shift of DOM character indexes are required. In this study, the change in properties of compost-derived DOM at different composting approaches and the effect of composting conditions on the DOM characteristics are summarized. Thereafter, the change in DOM character indexes’ in composting was comprehensively reviewed. Along with composting, the elements and spectral properties (chromophoric DOM (CDOM) and fluorescent DOM (FDOM)) were altered, size and hydrophobicity increased, and aromatic-C and electron transfer capacity were promoted. Finally, some prospects to improve this study were put forward. This paper should facilitate the people who have an interest in tracing the fate of DOM in composting.     

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.     

Twenty years of achievements in China’s implementation of the Stockholm Convention

● China’s implementation of the SC was systematically studied. ● Implementation process of the SC can be roughly divided into three stages. ● DDT and HCH concentrations in the air have been steadily decreasing. ● China has safely disposed of 6352.1 tons of pesticide POPs. Persistent organic pollutants (POPs) are extremely harmful to the environment and human health; the Stockholm Convention on Persistent Organic Pollutants was therefore adopted by the international community in 2001 to eliminate or reduce the production, use, and emissions of POPs. China is the largest developing country that has signed the Stockholm Convention, and thus plays an important role in its implementation. This paper systematically studies the practice and achievements of China since it signed the Stockholm Convention 20 years ago. China has established an implementation guarantee system including institutions, implementation mechanisms, policies, law enforcement, and scientific and technological support. During the 20 years since the implementation of the Stockholm Convention, dichlorodiphenyltrichloroethane (DDT) and hexachlorocyclohexane (HCH) concentrations in the air have been steadily decreasing, and Perfluorooctane sulfonic acid/Perfluorooctane sulfonyl fluoride (PFOS/PFOSF) concentrations in water bodies have decreased. In the past 20 years, China has safely disposed of 6352.1 tons of pesticide persistent organic pollutants and 36998 sets of electrical equipment containing polychlorinated biphenyls (PCBs), with a disposal rate of 100%. In the future, China will further strengthen the construction of persistent organic pollutant monitoring networks, scientific research, publicity, education, and international cooperation to improve environmental quality, providing a reference for other countries to implement the Stockholm Convention.     

Effect of ambient polycyclic aromatic hydrocarbons and nicotine on the structure of Aβ42 protein

● B[a]P, nicotine and phenanthrene molecules altered the secondary structure of Aβ₄₂. ● β-content of the peptide was significantly enhanced in the presence of the PAHs. ● Nicotine made stable cluster with Aβ₄₂ peptide via hydrogen bonds. ● Phenanthrene due to its small size, interfered with the Aβ₄₂ monomer more strongly. Recent studies have correlated the chronic impact of ambient environmental pollutants like polycyclic aromatic hydrocarbons (PAHs) with the progression of neurodegenerative disorders, either by using statistical data from various cities, or via tracking biomarkers during in-vivo experiments. Among different neurodegenerative disorders, PAHs are known to cause increased risk for Alzheimer’s disease, related to the development of amyloid beta (Aβ) peptide oligomers. However, the complex molecular interactions between peptide monomers and organic pollutants remains obscured. In this work, we performed an atomistic molecular dynamics study via GROMACS to investigate the structure of Aβ₄₂ peptide monomer in the presence of benzo[a]pyrene, nicotine, and phenanthrene. Interestingly the results revealed strong hydrophobic, and hydrogen-bond based interactions between Aβ peptides and these environmental pollutants that resulted in the formation of stable intermolecular clusters. The strong interactions affected the secondary structure of the Aβ₄₂ peptide in the presence of the organic pollutants, with almost 50 % decrease in the α-helix and 2 %–10 % increase in the β-sheets of the peptide. Overall, the undergoing changes in the secondary structure of the peptide monomer in the presence of the pollutants under the study indicates an enhanced formation of Aβ peptide oligomers, and consequent progression of Alzheimer’s disease.     

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.     

Surface-enhanced Raman spectroscopy for emerging contaminant analysis in drinking water

● Definition of emerging contaminants in drinking water is introduced. ● SERS and standard methods for emerging contaminant analysis are compared. ● Enhancement factor and accessibility of SERS hot spots are equally important. ● SERS sensors should be tailored according to emerging contaminant properties. ● Challenges to meet drinking water regulatory guidelines are discussed. Emerging contaminants (ECs) in drinking water pose threats to public health due to their environmental prevalence and potential toxicity. The occurrence of ECs in our drinking water supplies depends on their physicochemical properties, discharging rate, and susceptibility to removal by water treatment processes. Uncertain health effects of long-term exposure to ECs justify their regular monitoring in drinking water supplies. In this review article, we will summarize the current status and future opportunities of surface-enhanced Raman spectroscopy (SERS) for EC analysis in drinking water. Working principles of SERS are first introduced and a comparison of SERS and liquid chromatography-tandem mass spectrometry in terms of cost, time, sensitivity, and availability is made. Subsequently, we discuss the strategies for designing effective SERS sensors for EC analysis based on five categories—per- and polyfluoroalkyl substances, novel pesticides, pharmaceuticals, endocrine-disrupting chemicals, and microplastics. In addition to maximizing the intrinsic enhancement factors of SERS substrates, strategies to improve hot spot accessibilities to the targeting ECs are equally important. This is a review article focusing on SERS analysis of ECs in drinking water. The discussions are not only guided by numerous endeavors to advance SERS technology but also by the drinking water regulatory policy.     

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.     

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.     

Control strategies for disinfection byproducts by ion exchange resin,nanofiltration and their sequential combination

● Effects of AER adsorption and NF on DBP precursors, DBPs, and TOX were examined. ● A treatment approach of resin adsorption followed by nanofiltration was developed. ● Both DOC and Br⁻ could be effectively removed by the sequential approach. ● DBPs, TOX, and cytotoxicity were significantly reduced by the sequential approach. Disinfection byproducts (DBPs) are emerging pollutants in drinking water with high health risks. Precursor reduction before disinfection is an effective strategy to control the formation of DBPs. In this study, three types of anion exchange resins (AERs) and two types of nanofiltration (NF) membranes were tested for their control effects on DBP precursors, DBPs, and total organic halogen (TOX). The results showed that, for AER adsorption, the removal efficiencies of DBP precursors, DBPs, and TOX increased with the increase of resin dose, and the strong basic macroporous anion exchange resin (M500MB) had the highest removal efficiencies. For NF, the highest removal efficiencies were achieved at an operating pressure of 4 bar, and the membrane (NF90) with a smaller molecular weight cut-off, had a better control efficiency. However, AER adsorption was inefficient in removing dissolved organic carbon (DOC); NF was inefficient in removing Br⁻ resulting in insufficient control of Br-DBPs. Accordingly, a sequential approach of AER (M500MB) adsorption followed by NF (NF90) was developed to enhance the control efficiency of DBPs. Compared with single AER adsorption and single NF, the sequential approach further increased the removal efficiencies of DOC by 19.4%–101.9%, coupled with the high Br⁻ removal efficiency of 92%, and thus improved the reduction of cyclic DBPs and TOX by 3.5%–4.9%, and 2.4%–8.4%, respectively; the sequential approach also reduced the cytotoxicity of the water sample by 66.4%.     

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.     

Catalytic reduction of water pollutants: knowledge gaps,lessons learned,and new opportunities

● Advances, challenges, and opportunities for catalytic water pollutant reduction. ● Cases of Pd-based catalysts for nitrate, chlorate, and perchlorate reduction. ● New functionalities developed by screening and design of catalytic metal sites. ● Facile catalyst preparation approaches for convenient catalyst optimization. ● Rational design and non-decorative effort are essential for future work. In this paper, we discuss the previous advances, current challenges, and future opportunities for the research of catalytic reduction of water pollutants. We present five case studies on the development of palladium-based catalysts for nitrate, chlorate, and perchlorate reduction with hydrogen gas under ambient conditions. We emphasize the realization of new functionalities through the screening and design of catalytic metal sites, including (i) platinum group metal (PGM) nanoparticles, (ii) the secondary metals for improving the reaction rate and product selectivity of nitrate reduction, (iii) oxygen-atom-transfer metal oxides for chlorate and perchlorate reduction, and (iv) ligand-enhanced coordination complexes for substantial activity enhancement. We also highlight the facile catalyst preparation approach that brought significant convenience to catalyst optimization. Based on our own studies, we then discuss directions of the catalyst research effort that are not immediately necessary or desirable, including (1) systematic study on the downstream aspects of under-developed catalysts, (2) random integration with hot concepts without a clear rationale, and (3) excessive and decorative experiments. We further address some general concerns regarding using H₂ and PGMs in the catalytic system. Finally, we recommend future catalyst development in both “fundamental” and “applied” aspects. The purpose of this perspective is to remove major misconceptions about reductive catalysis research and bring back significant innovations for both scientific advancements and engineering applications to benefit environmental protection.     

Approaching the upper boundary of driver-response relationships: identifying factors using a novel framework integrating quantile regression with interpretable machine learning

● A novel framework integrating quantile regression with machine learning is proposed. ● It aims to identify factors driving observations to upper boundary of relationship. ● Increasing N:P and TN concentration help fulfill the effect of TP on CHL. ● Wetter and warmer decrease potential and increase eutrophication control difficulty. ● The framework advances applications of quantile regression and machine learning. The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships, and can help inform ecosystem management, but has rarely been explored. In this study, we propose a novel framework integrating quantile regression with interpretable machine learning. In the first stage of the framework, we estimate the upper boundary of a driver-response relationship using quantile regression. Next, we calculate “potentials” of the response variable depending on the driver, which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter plot. Finally, we identify key factors impacting the potential using a machine learning model. We illustrate the necessary steps to implement the framework using the total phosphorus (TP)-Chlorophyll a (CHL) relationship in lakes across the continental US. We found that the nitrogen to phosphorus ratio (N׃P), annual average precipitation, total nitrogen (TN), and summer average air temperature were key factors impacting the potential of CHL depending on TP. We further revealed important implications of our findings for lake eutrophication management. The important role of N׃P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient criteria. Future wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication management. The novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships.     

Technologies for pollutant removal and resource recovery from blackwater: a review

● Blackwater is the main source of organics and nutrients in domestic wastewater. ● Various treatment methods can be applied for resource recovery from blackwater. ● Blackwater treatment systems of high integration and efficiency are the future trend. ● More research is needed for the practical use of blackwater treatment systems. Blackwater (BW), consisting of feces, urine, flushing water and toilet paper, makes up an important portion of domestic wastewater. The improper disposal of BW may lead to environmental pollution and disease transmission, threatening the sustainable development of the world. Rich in nutrients and organic matter, BW could be treated for resource recovery and reuse through various approaches. Aimed at providing guidance for the future development of BW treatment and resource recovery, this paper presented a literature review of BWs produced in different countries and types of toilets, including their physiochemical characteristics, and current treatment and resource recovery strategies. The degradation and utilization of carbon (C), nitrogen (N) and phosphorus (P) within BW are underlined. The performance of different systems was classified and summarized. Among all the treating systems, biological and ecological systems have been long and widely applied for BW treatment, showing their universality and operability in nutrients and energy recovery, but they are either slow or ineffective in removal of some refractory pollutants. Novel processes, especially advanced oxidation processes (AOPs), are becoming increasingly extensively studied in BW treatment because of their high efficiency, especially for the removal of micropollutants and pathogens. This review could serve as an instructive guidance for the design and optimization of BW treatment technologies, aiming to help in the fulfilment of sustainable human excreta management.     

Numerical simulation of benzene transport in shoreline groundwater affected by tides under different conditions

● An approach for assessing the transport of benzene on the beach was proposed. ● The behavior of benzene in the subsurface of the beach was impacted by tide. ● Tidal amplitude influenced the travel speed and the benzene biodegradation. ● Hydraulic conductivity had the impact on plume residence time and biodegradation. ● Plume dispersed and concentration decreased due to high longitudinal dispersivity. The release and transport of benzene in coastal aquifers were investigated in the present study. Numerical simulations were implemented using the SEAM3D, coupled with GMS, to study the behavior of benzene in the subsurface of tidally influenced beaches. The transport and fate of the benzene plume were simulated, considering advection, dispersion, sorption, biodegradation, and dissolution on the beach. Different tide amplitudes, aquifer characteristics, and pollutant release locations were studied. It was found that the tide amplitude, hydraulic conductivity, and longitudinal dispersivity were the primary factors affecting the fate and transport of benzene. The tidal amplitude influenced the transport speed and percentage of biodegradation of benzene plume in the beach. A high tidal range reduced the spreading area and enhanced the rate of benzene biodegradation. Hydraulic conductivity had an impact on plume residence time and the percentage of contaminant biodegradation. Lower hydraulic conductivity induced longer residence time in each beach portion and a higher percentage of biodegradation on the beach. The plume dispersed and the concentration decreased due to high longitudinal dispersivity. The results can be used to support future risk assessment and management for the shorelines impacted by spill and leaking accidents. Modeling the heterogeneous beach aquifer subjected to tides can also be further explored in the future study.     

Determination of growth kinetics of microorganisms linked with 1,4-dioxane degradation in a consortium based on two improved methods

● Evaluated three methods for determining the consortia’s growth kinetics. ● Conventional method is flawed since it relies on the total biomass concentration. ● Considering only selected bacterial taxa improved the accuracy. ● Considering oligotrophs and copiotrophs further improved the accuracy. The conventional method for determining growth kinetics of microbial consortia relies on the total biomass concentration. This may be inaccurate for substrates that are uncommon in nature and can only be degraded by a small portion of the microbial community. 1,4-dioxane, an emerging contaminant, is an example of such substrates. In this work, we evaluated an improved method for determining the growth kinetics of a 1,4-dioxane-degrading microbial consortium. In the improved method, we considered only bacterial taxa whose concentration increase correlated to 1,4-dioxane concentration decrease in duplicate microcosm tests. Using PEST (Parameter Estimation), a model-independent parameter estimator, the kinetic constants were estimated by fitting the Monod kinetics-based simulation results to the experimental data that consisted of the concentrations of 1,4-dioxane and the considered bacterial taxa. The estimated kinetic constants were evaluated by comparing the simulation results with experimental results from another set of microcosm tests. The evaluation was quantified by the sum of squared relative residual, which was four orders of magnitude lower for the improved method than the conventional method. By further dividing the considered bacterial taxa into oligotrophs and copiotrophs, the sum of squared relative residual further decreased.