Pollution Processes Research Articles

Targeted migration mechanisms of nitrogen-containing pollutants during chemical looping co-gasification of coal and microalgae.

The chemical looping co-gasification of nitrogen-containing algal biomass and coal could effectively realize the high-value utilization of gasification products, but the mechanism of conversion of nitrogen-containing pollutants is not clear. In this work, the effects of the different ratios of microalgae on the co-gasification process were first explored, and the results showed that the 40 % coal + 60 % microalgae blending had the best synergistic effect, with a comprehensive synergistic index (CSI) of 1.35 as the maximum value. The effects of temperature, the ratio of OCs to feed (O/C) and steam flow rate on the evolution of nitrogen-containing species in the co-gasification products were explored based on the optimal mixing ratio, and the results showed that elevating the reaction temperature promotes the generation of nitrogen oxide precursors, and an appropriate amount of steam could inhibit the generation of NOx. The nitrogen in coke mainly came from the Maillard reaction between carbonyl (-CO) compounds and amino (-NH2) compounds and its main forms are pyridine nitrogen (N-5) and pyrrole nitrogen (N-6). Based on the nitrogen accumulation forms in the products of each phase, the mechanism of nitrogen transport and conversion in the chemical looping co-gasification of coal and microalgae (CM-CLCG) process was proposed. This study provides a reference for the synergistic utilization of coal and biomass and the control process of nitrogen-containing pollutants.

Analysis of COVID-19 Lockdown to Understand Air Pollution Processes and Their Impacts on Health: A Case Study in the Western Balkans

The effect of COVID-19 lockdown (LD) on many ambient air pollutants (NO, NO2, PM2.5, PM10, O3 and SO2) was assessed for the first time in the Western Balkans with an innovative approach that evaluates a variety of factors including the stringency of the LD measures, the type of location, the pollution sources, the correlation with traffic fluxes and the meteorology. To that end, observations from 10 urban sites were compared with historical time series. The time window 1 February–30 May 2020 was classified in sub-periods on the basis of the stringency of the circulation restrictions. NO2 and O3 are the pollutants most affected by restrictions to population circulation due to lockdown during the first phase of the COVID-19 pandemic, and are well correlated with traffic fluxes. A reduction in fine particulate matter (PM2.5 and PM10) concentrations is observed in all sites only during the full LD periods, while the relation between SO2 average and maximum hourly concentrations and LD periods in industrial and traffic sites vary from site to site. The reduction in NO2 concentrations during the LD resulted in a reduction in mortality associated with air pollution in the largest cities, while the interpretation of the changes in O3 and particulate matter is less clear.

A Pore-Scale Investigation of Oil Contaminant Remediation in Soil: A Comparative Study of Surfactant- and Polymer-Enhanced Flushing Agents

Pore-scale remediation investigation of oil-contaminated soil is important in several environmental and industrial applications, such as quick responses to sudden accidents. This work aims to investigate the oil pollutant removal process and optimize the oil-contaminated soil remediation performance at the pore scale to find the underlying mechanisms for oil removal from soil. The conservative forms of the phase-field model and the non-Newtonian power-law fluid model are employed to track the moving interface between two immiscible phases, and oil pollutant flushing removal process from soil pores is investigated. The effects of viscosity, interfacial tension, wettability, and flushing velocity on pore-scale oil pollutant removal regularity are explored. Then, the oil pollutant removal effects of two flushing agents (surfactant system and surfactant–polymer system) are compared using an oil content prediction curve based on UV-Visible transmittance. The results show that the optimal removal efficiency is obtained for a weak water-wetting system with a contact angle of 60° due to the stronger two-phase fluid interaction, deeper penetration, and more effective entrainment flow. On the basis of the dimensionless analysis, a relatively larger flushing velocity, resulting in a higher capillary number (Ca) in a certain range, can achieve rapid and efficient oil removal. In addition, an appropriately low interfacial tension, rather than ultra-low interfacial intension, contributes to strengthening the oil removal behavior. A reasonably high viscosity ratio (M) with a weak water-wetting state plays synergetic roles in the process of oil removal from the contaminated soil. In addition, the flushing agent combined with a surfactant and polymer can remarkably enhance the oil removal efficiency compared to the sole use of the surfactant, achieving a 2.5-fold increase in oil removal efficiency. This work provides new insights into the often-overlooked roles of the pore scale in fluid dynamics behind the remediation of oil-contaminated soil via flushing agent injection, which is of fundamental importance to the development of effective response strategies for soil contamination.

Residual heavy metals and antibiotic pollution in abandoned breeding areas along the northeast coast of Hainan Island, China.

To assess the environmental status of an abandoned aquaculture and breeding area in the northeast coast of the Hainan Island, surface and well water, sediment and surface soils were sampled and analyzed for conventional physicochemical properties, heavy metals and antibiotics. Metagenome tests were also conducted to determine the composition and diversity of the microbial community in typical habitats. Affected by the discharge of wastewater from higher-place pond aquaculture, coastal freshwater rivers have undergone significant salinization, Cl- and Na+ were as high as 4.51×103 and 1.42×103mg/L. The 3 hand-pumped wells surveyed were also suffered from varying degrees of salinization and heavy metal pollution, especially the threat of arsenic pollution. Compared with the local background values, significantly higher valves of Cu, Zn, As and Cd were observed in the surface soil and sediment, and the average concentrations for Cu, Zn, As and Cd are 5.71, 17.6, 15.4 and 0.09mg/kg respectively. For As，the Nemerow index ranges from 7 to 16 and the geoaccumulation index is between 2 and 4, indicating moderate to severe pollution levels in surface soil. 14 antibiotics were detected in the soil and sediment samples, and the highest total amount was 73μg/kg, with tetracycline being the dominant antibiotic. Sediment and forest soil showed different microbial community and the genetic diversity index of sediment was lower than that of the forest soils. For typical vegetation soil, the genetic diversity followed the order as P. elliottii × P. caribaea>Eucalyptus > C. equisetifolia. Among the soil and sediment samples, the highest abundances of antibiotic resistance genes (ARGs) were associated with elfamycin, peptide, rifamycin, and the most common antibiotic resistance mechanisms were antibiotic target alteration (54.5%), antibiotic efflux (27.6%) and antibiotic target replacement (12.1%). The metal resistance genes (MRGs) for Cu, Fe, and Zn resistance were the main MRGs in the samples. This study identified the potential ecological environment risk factors in the abandoned coastal breeding areas, and suggested continuous monitoring and assessment of the residual pollutant abatement processes in the future.

Visualizing Electron Transfer through Silver Nanoparticle Formation and Photothermal Imaging: A Case Study of Nanoscale Zerovalent Iron.

Electron release and transfer are pivotal to the efficiency of multiple biogeochemical and pollutant processes. Despite substantial efforts to develop electron-transfer characterization techniques, in situ visualization of electron transfer remains challenging. This study introduces an innovative strategy for mapping electron-transfer distance using nanoscale zerovalent iron (nZVI) as a case study. In this method, silver ions (Ag+) are employed as electron traps, forming silver nanoparticles. These nanoparticles can then be immobilized in the agarose-solidified gel used as a heterogeneous porous medium to simulate the underground aquifer and visualized through photothermal imaging. In situ detection has unveiled remarkably extensive electron transfer, reaching distances of up to centimeters from the nZVI source. Nationwide groundwater investigations have further confirmed this long electron-transfer distance, ranging from 1.7 ± 0.4 to 9.6 ± 0.1 mm (agarose gel may not fully replicate the complex and heterogeneous conditions of groundwater environments). Importantly, the efficiency of electron transfer from nZVI was primarily influenced by factors such as nZVI dosage and groundwater matrix conditions, particularly the abundance of electron-shuttling materials like natural organic matter. Surprisingly, the inherent properties of nZVI had a lesser impact on electron-transfer distance. Our work highlights the long-distance electron transfer from nZVI, revealing a previously overlooked but ubiquitous nZVI remediation process.

Modeling urban pollutant wash-off processes with ecological memory.

Urbanization increases the extent of impervious surfaces, runoff, sediment, and nutrient loadings downstream, leading to the deterioration of urban surface waters. During pollutant wash-off from urban surfaces, the peak concentration of pollutants typically occurs after the rainfall peak. However, current urban wash-off models do not consider this time delay, assuming that the effect of rainfall on the wash-off process is immediate. Ecological memory (EM) is defined as the capacity of past states or experiences to influence the present or future ecological responses of a community ecosystem. In this study, ecological memory was calculated to reflect the lagging impact of rainfall on the wash-off process. Incorporating ecological memory into the original wash-off model improved its performance across all pollutant types and rainfall conditions, with the adjusted R-squared value increasing from -0.01-0.61 to -0.08-0.83. Among the factors impacting ecological memory, rainfall intensity significantly affected the distributions of ecological memory (p<0.05 according to the Kolmogorov-Smirnov test). The first flush phenomenon, which involves a larger concentration or mass of pollutants in the initial portion of a storm event compared to the rest, showed no significant difference in the distributions of ecological memory between rain events with and without this phenomenon. The improved urban wash-off model can be applied for real-time simulation of urban pollutants and help develop site-specific measures for reducing urban nonpoint source pollution.

New modeling framework for describing the effects of landscape pattern changes on nutrient pollution transport.

Landscape pattern plays a crucial role in regulating hydrological and pollutant migration processes. However, there is a lack of quantitative tools to describe the nutrient pollution transport process under the influence of different landscape patterns. To fill this gap, this study presents a new modeling framework, namely the Landscape Pattern-Source Flow Sink model (LP-SFS). The model consists of three modules: nutrient pollution emission, land transport, and river transport. Each module is implemented using a separate calculation program. It characterizes the transport path of pollutants on landscape units conceptually and operationally and focuses on quantifying the blocking effect of grid-scale landscape units on nutrients. The framework takes the Luanhe River Basin in the North China Plain as an exemplary case. The simulation results of the new framework indicated that in regions predominantly occupied by forest and urban (FU), the intensity of terrestrial pollutant migration attained the highest level. In the slope zone ranging from 25 to 35°, owing to the relatively strong responsiveness of water flow and gravity to the slope, soil erosion is inclined to occur, thereby causing the intensity of terrestrial nutrient pollution transport in this slope zone to reach the maximum value of 4.55kg/km2. Furthermore, in the elevation zone <700m, urban and cultivated land was concentratedly distributed, which leads to more pollutants entering surface water bodies and increases the intensity of terrestrial migration of pollutants. The complex boundary shape of forestland and grassland in the watershed weakened the transport capacity of nutrients, resulting in pollutants remaining in the soil and being difficult to be transported to surface water bodies. This new method is applicable to large-scale watersheds with strong spatial heterogeneity and severe landscape fragmentation and can provide technical support for nutrient pollution control and optimization of land resource allocation in large-scale watersheds.

Exploration of Quantum Chemistry Methods to Explain Mechanism of Mechanochemical Degradation of Typical Organic Pollutants.

The high-efficiency ball milling treatment technology primarily combines the excitation of oxidation processes with high-speed physical collisions, thereby promoting the reaction processes and enhancing the degradation effectiveness of materials. This technology has gained widespread attention in recent years for its application in the degradation of organic solid chemical pollutants. In this study, quantum chemical density functional theory (DFT) was employed to first analyze the impact of electron addition and subtraction on molecular chemical bonds. The molecular energies of the target pollutants and their possible intermediates were then calculated, and the theoretical energies required for the degradation pathways of the target organic compounds under oxidative-enhanced ball milling were computed. This further validated the accuracy of the ball milling experimental results. The theoretical energy required for the complete mineralization of solid organic chemicals through ball milling degradation was calculated, with values of 16,730.74 kJ/mol for lindane, 20,162.46 kJ/mol for tetrabromobisphenol A, 10,628.04 kJ/mol for sulfamethoxazole, and 4867.99 kJ/mol for trimethoprim. By combining different ball milling experimental conditions, the theoretical reaction time required for the complete mineralization of the target organic chemicals can be calculated. The comparison of theoretical calculations with the experimental results provides new insights into the ball milling degradation process and degradation pathways of the target pollutants.

Synergistic effects of pollution reduction and carbon mitigation from socioeconomic factors, land use and urban innovation: a case study of Wuhan metropolitan area

Achieving synergistic effects in pollution reduction and carbon mitigation is a major national strategy for China. Given the common origins and processes of air pollutants and greenhouse gases, this study constructs a theoretical framework for the study of the synergistic effects of air pollution and carbon emissions. Based on the coupling coordination degree model and the geographically and temporally weighted regression model, it identifies significant factors influencing the synergistic effects of air pollution and carbon emissions and their varying mechanisms of action. Results are as follows: 1) The spatial and temporal trends of PM2.5 pollution and carbon emissions in the Wuhan metropolitan area exhibit homogeneity. The coupling coordination degree between air pollution and carbon emissions shows an initial increase followed by a decrease over time and a spatial pattern of “local clustering of areas with medium–high-level coupling coordination”. 2) Twelve factors significantly impact the synergistic effects of air pollution and carbon emissions at the county level in the Wuhan metropolitan area: number of inversion days, precipitation, temperature, vegetation coverage, number of green patents, total population, regional GDP, per capita regional GDP, proportion of secondary industry, total nighttime light, energy consumption efficiency and built-up area. 3) The impact intensity of these factors on the synergistic effects of air pollution and carbon emissions varies not only over time but also across different regions within the same year. Regions with strong impact forces shift over time. This manuscript provides a solid foundation for theoretical research on and practical strategies for advancing differentiated pollution reduction and carbon mitigation coordination.

Quick Computing CFD Model to Predict Chemical Pollution in Room

Purpose. The problem of accidental contamination of workspaces attracts special attention, since in the event of such extreme situations, intense chemical contamination of the air in work areas occurs. This poses a threat of toxic exposure to workers. When assessing the consequences of such situations, it is necessary to take into account the time factor, in particular, to quickly determine the creation of concentrations of chemically hazardous substances. In this regard, an urgent task is to develop effective mathematical models for rapid assessment of the consequences of extreme situations in the working areas of chemically hazardous facilities. The paper considers a CFD model for analyzing the process of chemical air pollution in a workspace during an accidental release of a chemically hazardous substance. The solution of the problem is based on the numerical integration of the fundamental equations of continuum mechanics. Methodology. To calculate the air velocity field in the working room during the operation of supply and exhaust ventilation, a mathematical model of the motion of an inviscid fluid was used. The equation of convective diffusion motion was used to calculate the concentration of a chemically hazardous substance in the workspace. The integration of the modeling equations was carried out using finite difference schemes. Findings. A dynamic model has been created to calculate the spread of a chemically hazardous substance in a workspace. On the basis of the built CFD model, a computer program was created to conduct a computational experiment. Originality. A CFD model has been created to predict the level of air pollution in a workspace in the event of toxic gas emissions. The model is based on the fundamental equations of aerodynamic mechanics and mass transfer. The model makes it possible to determine the effect of the ventilation mode, the intensity of emission of a chemically hazardous substance, the location of equipment in the workspace, and the dynamics of the formation of concentration fields. Practical value. The developed CFD model can be used to quickly analyze the consequences of accidental emissions of a chemically hazardous substance in a workplace and assess the risk of toxic exposure of workers.

Hedonic Test of Instant Powdered Ginger Drink with Sugar and Sugar-Palm Sugar Combination

Stress, environmental pollution, radiation, and excessive food processing contribute to the production of free radicals, which are harmful to the body. Antioxidants play a role in preventing free radicals from damaging nucleotides. Ginger contains high levels of antioxidants, making it beneficial for overall health. However, ginger has a short shelf life, and its traditional preparation methods are less preferred by many people. Therefore, ginger is now available in the form of instant drink powders, which can be prepared like tea or coffee. This study is a descriptive study using the hedonic test method. Thirty students were selected as panelists. The panelists evaluated two types of ginger drinks made with different sweeteners: sugar and a combination of sugar and palm sugar (sugar-palm sugar). The parameters assessed in this study were color, aroma, taste, and sweetness level. The results of the hedonic test showed that the ginger drink sweetened with the sugar-palm sugar combination had the highest preference percentages across all parameters: color, aroma, taste, and sweetness level. For color, the "like" preference level achieved a percentage of 40%. For aroma, the "really like" preference level reached 43.33%. Taste received a "really like" preference level of 53.33%, and sweetness achieved a "like" preference level of 36.67%. In conclusion, the panelists preferred the ginger drink sweetened with the sugar-palm sugar combination over the drink sweetened with just sugar. This preference is attributed to the addition of palm sugar, which balances the ginger's strong flavor while retaining its distinct taste. Furthermore, the combination of ginger and palm sugar produces a harmonious blend of color, aroma, taste, and sweetness that complements both ingredients.

The accumulation process of pollutants in deposited particles of different sizes on different roads in Beijing, China.

Road-Deposited Sediments (RDS) samples were collected from four different roads in Beijing, and the distribution of pollutants in RDS with various particle sizes was compared. In this study, the cumulative mass of RDS exhibited a positive correlation with the number of dry days, and the RDS load below 75μm was also influenced by road traffic volume. As traffic volume escalated, there was a corresponding increase in the load of these smaller RDS. Most pollutants accumulated within RDS with sizes below 150μm, rendering them the primary contributors to the pollution. In terms of the antecedent dry-weather days, fifteen days emerged as a potentially pivotal point, with both the rate of pollutant accumulation and the contribution of pollution sources to RDS having stabilized after this duration. The origins of pollutants in roads of different functional areas exhibited certain disparities. The pollutants on major roads with high traffic volume were attributed to frequent vehicular activities. The pollutants on residential roads stemmed from soil particles and fallen leaves in the roadside green belts as well as from human activities. And the pollutants on urban-rural crossroad might be attributed to the industrial contamination with factories located on one side.

USE OF GEO-INFORMATION SYSTEMS FOR GROUNDWATER MONITORING

Abstract . The article is devoted to the study of the possibilities of using geographic information systems (GIS) for monitoring the state of groundwater, which is important for ensuring environmental safety and sustainable management of water resources. Groundwater is an important source of fresh water for the population, industry and agriculture, but it is vulnerable to various pollutions caused by both natural and anthropogenic factors. Therefore, the implementation of reliable monitoring systems that allow timely detection, assessment and forecasting of changes in water quality is necessary to prevent the deterioration of the ecological situation and prevent the spread of pollution. Geographic information systems (GIS ) offer significant opportunities for the collection and integration of large volumes of spatial data, including satellite images, remote sensing results, automated sensor data, and field observations. GIS allow creating multi-layered maps reflecting the ecological state of underground aquifers, modeling pollution processes and predicting their possible impact on nearby ecosystems and water sources. Thanks to the use of GIS, it is possible to combine data from different sources in a single interactive system, which simplifies analysis and provides the ability to quickly respond to any detected deviations. The article examines in detail the key methods used within GIS for groundwater monitoring, including satellite sounding to analyze changes in the earth's surface, spectral indices to assess the state of vegetation and soil salinity, and radar methods to determine soil electrical conductivity. Particular attention is paid to the application of three-dimensional modeling, which allows visualization of the spread of pollution in underground aquifers, which facilitates decision-making regarding the optimal location of monitoring stations and the design of protective structures. In combination with automated sensors recording water parameters in real time, GIS provides continuous monitoring of the physico-chemical indicators of groundwater and allows for prompt assessment of their condition. The article also analyzes the possibilities of using predictive modeling in GIS to assess the further spread of pollution and develop environmental risk management measures. It is described how mathematical models in GIS can help predict the impact of anthropogenic and natural factors on groundwater, which ensures the development of scientifically based solutions to reduce pollution and maintain water quality at a safe level. The implementation of GIS in groundwater monitoring is an important step towards increasing the efficiency of water resources management, ensuring their protection from pollution and preserving the ecological stability of the regions.

Chemical characterization and source identification of PM2.5 in the Huaxi urban area of Guiyang

In 2020, 123 PM2.5 samples were collected across different seasons in Huaxi District, Guiyang. The primary chemical components of PM2.5, including water-soluble ions (WSIIs), metallic elements, organic carbon (OC), and elemental carbon (EC), were analyzed. During the sampling period, the average PM2.5 concentration was 39.7 ± 22.3 µg/m2. Chemical mass closure (CMC) was used to reconstruct PM2.5 mass, yielding a reconstructed concentration of 29.1 ± 16.5 µg/m2. The major components were organic matter (OM), sulfate + nitrate + ammonium (SNA), and mineral dust (MD), with mean concentrations of 12.2 ± 6.3 µg/m2, 8.2 ± 4.0 µg/m2, and 6.3 ± 4.6 µg/m2, respectively. From clean days (CD) to lightly-moderately polluted days (LMPD), nitrate oxidation ratio (NOR) increased from 0.09 to 0.16, while sulfate oxidation ratio (SOR) and OC/EC ratio rose by 21.7% and 13.5%, indicating stronger secondary reactions on polluted days. The study also examined changes in chemical components under different atmospheric oxidizing and humidity conditions, revealing that sulfate and nitrate concentrations increased with relative humidity (RH) between 60 and 80%, while other components, especially MD, showed a declining trend due to hygroscopic growth and subsequent gravitational settling and precipitation. The average NO3−/SO42− ratio was 0.67, indicating that fixed sources such as industrial and coal emissions were the main contributors to PM2.5. This study provides insights into the chemical composition, pollution processes, and formation mechanisms of PM2.5, which are crucial for developing effective air pollution control strategies. Furthermore, source apportionment was conducted with the positive matrix factorization (PMF) model. The Coal combustion, secondary, traffic, Industrial and dust source contributions to the PM2.5 mass were approximately 30.5%, 20.0%, 18.3%,16.7% and 14.5%, respectively.

A high-resolution multi-scale industrial water use dataset in China

Water is crucial for achieving the UN Sustainable Development Goals, particularly SDG 6. As a major source of water use and pollution, industrial sector requires improved water management based on more systematic and refined analysis. Such analysis, however, is compromised by the accuracy, granularity, and coverage of industrial water data. Here we present an open dataset of China’s industrial water use, compiled from 1,480,265 plant-level reports. This high-resolution multi-scale dataset offers unparalleled details, supporting multi-scale analysis at the province, city, and county levels, and across 2-digit, 3-digit, and 4-digit industrial classifications. It provides comprehensive information on water use, recycling, pollution, and wastewater processing. Such data enables further macro- and micro-level analysis, including multi-regional input-output analysis, structural decomposition analysis, statistical analysis, machine learning, as well as many other advanced analytical methods. This dataset can equip researchers and policymakers with a valuable tool to advance sustainable water management, fostering alignment with global sustainability goals.

One-Pot Synthesis of Highly Dispersed VO2 on g-C3N4 Nanomeshes for Advanced Oxidation

Advanced oxidation catalyzed by metal oxides is a promising approach for degrading organic pollutants in wastewater. A critical strategy to enhance the performance of these catalysts is optimizing the dispersion of their active components through innovative synthesis methods. In this study, we report a one-pot synthesis of g-C3N4 nanomeshes supported with highly dispersed VO2 catalysts (V-g-C3N4) for the advanced oxidation of methylene blue (MB). The characterization results reveal that the involvement of VCl3 in the pyrolysis of melamine facilitates the formation of g-C3N4 nanomeshes with abundant amino groups (NH/NH2). The strong interaction between vanadia species and amino groups prevents VO2 particles from agglomerating, resulting in a significantly higher vanadia dispersion than V-g-C3N4-im synthesized via the traditional impregnation method. V-g-C3N4 exhibits a sophisticated microstructure and surface structure, which leads to a rate constant 2.3-fold higher than V-g-C3N4-im in the catalytic degradation of methylene blue using H2O2 as the oxidant. X-ray photoelectron spectroscopy, trapping experiments, and electron paramagnetic resonance measurements reveal that the rapid adsorption and fast diffusion of MB over g-C3N4 nanomeshes, together with the efficient H2O2 activation into ·OH radicals via the V4+/V5+ redox cycle, synergistically contribute to the superior MB removal efficiency of V-g-C3N4. Moreover, V-g-C3N4 demonstrates no significant decrease in activity even after the fourth cycle, indicating its excellent stability during the pollutant removal process.

Multi-enzyme simulation activity of MXene-based metal composites and the application for detection and degradation of phenolic pollutants

Phenolic pollutants are commonly employed in industries such as petrochemicals, dyeing, textiles, and pesticides, which cause great harm to our living environment and food safety. In this paper, the MXenes-based metal composites (M/MXenes, M = Cu, Co, Ni, Zn, Fe, Mn) with multi-enzyme mimic activity were prepared by a facile molten salt method. The M/MXenes have mimic catalytic activity of peroxidase (POD), ascorbic acid oxidase (AAO), catalase (CAT), superoxide dismutase (SOD) and laccase. The catalytic properties of M/MXenes were analyzed and discussed in detail. In addition, M/MXenes still have excellent catalytic performance and stability in harsh pH and high temperature environments, making them very suitable for practical applications. Due to the excellent laccase mimic activity of M/MXenes, a simple colorimetric sensor was constructed for the detection and degradation of phenolic pollutants. Because obvious color changes can be observed during the catalytic process of phenolic pollutants by M/MXenes, M/MXenes were fixed into sodium alginate (SA) to form a simple hydrogel-based sensing platform for 2, 4-chlorophenol, methoxyphenol, 4-bromophenol, 3-bromo-4-methoxyphenol, 3-bromo-4-methylphenol, hydroquinone and 1,8-naphthol. By introducing different concentrations of phenolic compounds, a series of color changes on the gel carrier can be observed by naked eyes. Then, the red (R), green (G) and blue (B) values matching with the color change of the hydrogel were read out by the color recognition application in the smartphone, and the RGB value was converted into the concentration of phenolic compounds, which offered a convenient and reliable method for the rapid detection of phenolic compounds. Thus, the strategy offers the potential in rational design of nanozymes with the advantages of multi-enzyme mimic activity, simple preparation, low cost and good stability.

Analysis of Meteorological Causes of a Severe Pollution Process in Beijing in 2016

Analysis of Meteorological Causes of a Severe Pollution Process in Beijing in 2016

Improving PM2.5 and PM10 predictions in China from WRF_Chem through a deep learning method: Multiscale depth-separable UNet

Accurate predictions of atmospheric particulate matter can be applied in providing services for air pollution prevention and control. However, the forecasting accuracy of traditional air quality models is limited owing to model uncertainties. In this study, we developed a deep learning model, named multiscale depth-separable UNet (MDS-UNet), to improve PM2.5 and PM10 concentration forecasts from WRF_Chem over China. Results showed that MDS-UNet was able to capture the complex nonlinear errors between model predictions and observations, which was helpful in correcting the biases and spatiotemporal distribution patterns of PM2.5 and PM10 concentrations predicted by WRF_Chem. MDS-UNet made a better performance in the improvement of both PM2.5 and PM10 prediction accuracy than UNet and CNN during the 0–24 forecasts. Using MDS-UNet, the reductions in the root-mean-square error (RMSE) of the regionally averaged PM2.5 and PM10 concentration forecasts were 35.08% and 17.74%, respectively. During the 0–24-h forecast period, MDS-UNet performed well in terms of PM2.5 and PM10 over six key urban agglomerations in China. Taking a pollution process as a case study, results demonstrated that, compared with WRF_Chem, MDS-UNet was able to make the best improvement in YRD, the Sichuan Basin, and central China, with reductions in the RMSE of the PM2.5 forecasts of 55.22%, 55.53%, and 52.17%, respectively; and for PM10 forecasts these reductions were 44.90%, 40.97%, and 46.79%, respectively. Through this analysis, it was apparent that MDS-UNet demonstrated a better effect in terms of improving both PM2.5 and PM10 predictions in these key urban agglomerations during an important pollution process.

The performance of a 100 m3 outdoor atmospheric simulation chamber in China

A new outdoor atmospheric simulation chamber with a volume of ∼100 m3, the largest one in Asia, was recently constructed in the Research Center for Eco-Environmental Sciences of the Chinese Academy of Sciences (Beijing, China). This Atmospheric Environment Simulation System (AESS-RCEES) is a hemispherical-shaped photoreactor constructed using sixteen stainless-steel brackets covered by double-layer Teflon film, offering two advantages over other outdoor chambers: 1) The AESS-RCEES effectively minimizes the “greenhouse effect” by continuously circulating cooling air through the double layer, resulting in mean temperature differences between the inside and outside of around 12 °C in winter and 9 °C in summer. 2) The chamber can quickly introduce ambient air through a special inlet system, completely replacing the air inside within 45 min. Using AESS-RCEES, the wall loss rates of various gaseous species and ambient particles have been investigated, it was observed that the loss rates of ambient particles were significantly slower than those of artificial (NH4)2SO4 aerosol and secondary organic aerosol (SOA) formed by α-pinene ozonolysis. This suggests that the mixture of primary and SOA in the ambient air may have a longer lifetime than the aerosols generated individually. Investigation performed on propene/NOx photo-oxidation and α-pinene ozonolysis have shown that the AESS-RCEES is suitable for simulating atmospheric conditions and analyzing gas and particle chemical composition under controlled environmental conditions. This makes it a valuable platform for studying complex pollution and key chemical processes in the real atmosphere.