Pinus sylvestris and Picea abies canopy effects on deposition of air pollutants

Tree canopies influence atmospheric pollutant depositions depending on type,ecosystem characteristics,and local climatic conditions.This study investigated the impact of Pinus sylvestris L.and Picea abies(L.)H.Karst.,and a mixture of both,on the chemical composition of pre-cipitation.Three permanent plots within the ICP forest level Ⅱ monitoring network in Lithuania were selected to illustrate typical hemiboreal coniferous forests.The study analysed(1)the concentrations of NO_(2),NH_(3) and SO_(2) in the ambi-ent air;(2)the concentrations of SO_(4)^(2−),NO_(3)^(−),NH_(4)^(+),Na^(+),K^(+),Ca^(2+) and Cl^(-) in throughfall beneath canopies and in precipitation collected in an adjacent field,and(3)S and total N,Na^(+),K^(+),Ca^(2+)and Cl−depositions in throughfall and precipitation over 2006-2022.Results show a signifi-cant decrease in SO_(2) emissions in the ambient air;NO_(2) and NH_(3) emissions also decreased.The canopies reduced the acidity of throughfall,although they led to notably higher concentrations of SO_(4)^(2−),NO_(3)^(−),Na^(+),and particularly K^(+).During the study,low variability in NO_(3)^(-)deposition and a decrease in NH_(4)^(+)deposition occurred.Deposition loads increased by 20-30%when precipitation passed through the canopy.The cumulative deposition of S,Cl,Na,K,Ca,and N was greater under P.abies than under P.sylvestris.How-ever,K deposition in throughfall was considerably lower under P.sylvestris compared to the P.abies or mixed stand.Throughfall S depositions declined across all three coniferous plots.Overall,there was no specific effect of tree species on throughfall chemistry.

Heatwave exacerbates air pollution in China through intertwined climate-energy-environment interactions

Climate change is increasing the frequency and intensity of heatwaves, raising concerns about their detrimental effects on air quality. However, a role for heatwave-human-environment interactions in air pollution exacerbation has not been established. In the summer of 2022, record-breaking heatwaves struck China and Europe. In this study, we use integrated observational data and machine learning to elucidate the formation mechanism underlying one of the most severe ozone pollution seasons on record in central eastern China, an area that encompasses approximately half of China's total population and sown land.Our findings reveal that the worsened ozone and nitrogen dioxide pollution resulted from a mismatch between energy demand and supply, which was driven by both heatwaves and energy policy-related factors. The observed adverse heatwave-energy-environment feedback loop highlights the need for the diversification of clean energy sources, more resilient energy structures and power policies, and further emission control to confront the escalating climate challenge in the future.

Pollution source identification methods and remediation technologies of groundwater: A review

Groundwater is an important source of drinking water.Groundwater pollution severely endangers drinking water safety and sustainable social development.In the case of groundwater pollution,the top priority is to identify pollution sources,and accurate information on pollution sources is the premise of efficient remediation.Then,an appropriate pollution remediation scheme should be developed according to information on pollution sources,site conditions,and economic costs.The methods for identifying pollution sources mainly include geophysical exploration,geochemistry,isotopic tracing,and numerical modeling.Among these identification methods,only the numerical modeling can recognize various information on pollution sources,while other methods can only identify a certain aspect of pollution sources.The remediation technologies of groundwater can be divided into in-situ and ex-situ remediation technologies according to the remediation location.The in-situ remediation technologies enjoy low costs and a wide remediation range,but their remediation performance is prone to be affected by environmental conditions and cause secondary pollution.The ex-situ remediation technologies boast high remediation efficiency,high processing capacity,and high treatment concentration but suffer high costs.Different methods for pollution source identification and remediation technologies are applicable to different conditions.To achieve the expected identification and remediation results,it is feasible to combine several methods and technologies according to the actual hydrogeological conditions of contaminated sites and the nature of pollutants.Additionally,detailed knowledge about the hydrogeological conditions and stratigraphic structure of the contaminated site is the basis of all work regardless of the adopted identification methods or remediation technologies.

Remote sensing of air pollution incorporating integrated-path differential-absorption and coherent-Doppler lidar

An innovative complex lidar system deployed on an airborne rotorcraft platform for remote sensing of atmospheric pollution is proposed and demonstrated.The system incorporates integrated-path differential absorption lidar(DIAL) and coherent-doppler lidar(CDL) techniques using a dual tunable TEA CO_(2)laser in the 9—11 μm band and a 1.55 μm fiber laser.By combining the principles of differential absorption detection and pulsed coherent detection,the system enables agile and remote sensing of atmospheric pollution.Extensive static tests validate the system’s real-time detection capabilities,including the measurement of concentration-path-length product(CL),front distance,and path wind speed of air pollution plumes over long distances exceeding 4 km.Flight experiments is conducted with the helicopter.Scanning of the pollutant concentration and the wind field is carried out in an approximately 1 km slant range over scanning angle ranges from 45°to 65°,with a radial resolution of 30 m and10 s.The test results demonstrate the system’s ability to spatially map atmospheric pollution plumes and predict their motion and dispersion patterns,thereby ensuring the protection of public safety.

Tb^(3+)-nucleic acid probe-based label-free and rapid detection of mercury pollution in food

Mercury is a threatening pollutant in food,herein,we developed a Tb^(3+)-nucleic acid probe-based label-free assay for mix-and-read,rapid detection of mercury pollution.The assay utilized the feature of light-up fluorescence of terbium ions(Tb^(3+))via binding with single-strand DNA.Mercury ion,Hg^(2+)induced thymine(T)-rich DNA strand to form a double-strand structure(T-Hg^(2+)-T),thus leading to fluorescence reduction.Based on the principle,Hg^(2+)can be quantified based on the fluorescence of Tb^(3+),the limit of detection was 0.0689μmol/L and the linear range was 0.1-6.0μmol/L.Due to the specificity of T-Hg^(2+)-T artificial base pair,the assay could distinguish Hg^(2+)from other metal ions.The recovery rate was ranged in 98.71%-101.34%for detecting mercury pollution in three food samples.The assay is low-cost,separation-free and mix-to-read,thus was a competitive tool for detection of mercury pollution to ensure food safety.

Hydrodynamic Cavitation Enhanced SR-Aops Degradation of Organic Pollutants in Water:A Review

SR-AOP(sulfate radical advanced oxidation process)is a novel water treatment method able to eliminate refractory organic pollutants.Hydrodynamic cavitation(HC)is a novel green technology,that can effectively produce strong oxidizing sulfate radicals.This paper presents a comprehensive review of the research advancements in these fields and a critical discussion of the principal factors influencing HC-enhanced SR-AOP and the mechanisms of synergistic degradation.Furthermore,some insights into the industrial application of HC/PS are also provided.Current research shows that this technology is feasible at the laboratory stage,but its application on larger scales requires further understanding and exploration.In this review,some attention is also paid to the design of the hydrodynamic cavitation reactor and the related operating parameters.

Distribution and Formation Causes of PM_(2.5) and O_(3) Double High Pollution Events in China during 2013–20

Fine particulate matter(PM_(2.5))and ozone(O_(3))double high pollution(DHP)events have occurred frequently over China in recent years,but their causes are not completely clear.In this study,the spatiotemporal distribution of DHP events in China during 2013–20 is analyzed.The synoptic types affecting DHP events are identified with the Lamb–Jenkinson circulation classification method.The meteorological and chemical causes of DHP events controlled by the main synoptic types are further investigated.Results show that DHP events(1655 in total for China during 2013–20)mainly occur over the North China Plain,Yangtze River Delta,Pearl River Delta,Sichuan Basin,and Central China.The occurrence frequency increases by 5.1%during 2013–15,and then decreases by 56.1%during 2015–20.The main circulation types of DHP events are“cyclone”and“anticyclone”,accounting for over 40%of all DHP events over five main polluted regions in China,followed by southerly or easterly flat airflow types,like“southeast”,“southwest”,and“east”.Compared with non-DHP events,DHP events are characterized by static or weak wind,high temperature(20.9℃ versus 23.1℃)and low humidity(70.0%versus 64.9%).The diurnal cycles of meteorological conditions cause PM_(2.5)(0300–1200 LST,Local Standard Time=UTC+8 hours)and O_(3)(1500–2100 LST)to exceed the national standards at different periods of the DHP day.Three pollutant conversion indices further indicate the rapid secondary conversions during DHP events,and thus the concentrations of NO_(2),SO_(2) and volatile organic compounds decrease by 13.1%,4.7%and 4.4%,respectively.The results of this study can be informative for future decisions on the management of DHP events.

Numerical Simulation of the Settling Flux of Biodeposition in a Bay with Cage Culture Through Similarity Theory and a Simplified Pollution Source

The settling flux of biodeposition affects the environmental quality of cage culture areas and determines their environmental carrying capacity.Simple and effective simulation of the settling flux of biodeposition is extremely important for determining the spatial distribution of biodeposition.Theoretically,biodeposition in cage culture areas without specific emission rules can be simplified as point source pollution.Fluent is a fluid simulation software that can simulate the dispersion of particulate matter simply and efficiently.Based on the simplification of pollution sources and bays,the settling flux of biodeposition can be easily and effectively simulated by Fluent fluid software.In the present work,the feasibility of this method was evaluated by simulation of the settling flux of biodeposition in Maniao Bay,Hainan Province,China,and 20 sampling sites were selected for determining the settling fluxes.At sampling sites P1,P2,P3,P4,P5,Z1,Z2,Z3,Z4,A1,A2,A3,A4,B1,B2,C1,C2,C3 and C4,the measured settling fluxes of biodeposition were 26.02,15.78,10.77,58.16,6.57,72.17,12.37,12.11,106.64,150.96,22.59,11.41,18.03,7.90,19.23,7.06,11.84,5.19 and 2.57 g d^(−1)m^(−2),respectively.The simulated settling fluxes of biodeposition at the corresponding sites were 16.03,23.98,8.87,46.90,4.52,104.77,16.03,8.35,180.83,213.06,39.10,17.47,20.98,9.78,23.25,7.84,15.90,6.06 and 1.65 g d^(−1)m^(−2),respectively.There was a positive correlation between the simulated settling fluxes and measured ones(R=0.94,P=2.22×10^(−9)<0.05),which implies that the spatial differentiation of biodeposition flux was well simulated.Moreover,the posterior difference ratio of the simulation was 0.38,and the small error probability was 0.94,which means that the simulated results reached an acceptable level from the perspective of relative error.Thus,if nonpoint source pollution is simplified to point source pollution and open waters are simplified based on similarity theory,the setting flux of biodeposition in the open waters can be simply and effectively simulated by the fluid simulation software Fluent.

Impact of air pollution on urbanization:evidence at China’s city level

This paper investigates the effect and transmission mechanism of air pollution on urbanization based on data from China’s 107 cities during 2005–2018.In order to identify the impact of air pollution on China’s urbanization,we utilized night light data to represent the level of urbanization and used temperature inversion as an instrumental variable to mitigate endogeneity within the two-stage least squares framework.The results suggest that air pollution significantly slowed China’s urbanization process with economic growth acting as the transmission mechanism.The heterogeneity analyses revealed that air pollution had a greater negative impact on urbanization in northern regions than that in southern regions,and a greater negative impact in resource-oriented cities than that in non-resource-based cities.We also find that air pollution was to the detriment of urbanization in larger cities,which have more than 3 million residents,while it did not have a significant impact on Type II large cities,which have fewer than 3 million residents.

Physiological and Biochemical Responses of Perennial Ryegrass Mixed Planting with Legumes under Heavy Metal Pollution

In artificially controlled pot experiments,perennial ryegrass was mixed with other leguminous plants(white clo-ver and alfalfa)and treated with lead,zinc and cadmium(337 mg·kg^(-1),648 mg·kg^(-1),and 9 mg·kg^(-1),respectively)to simulate compound pollution conditions.The results showed that the concentrations of heavy metals,trans-port factors,and bioconcentration factors in mixed planting of ryegrass decreased compared with those in mono-culture.Regardless of whether heavy metal pollution was introduced,mixed planting increased the aboveground and underground biomasses of ryegrass.The different mixed planting treatments had no significant impact on the chlorophyll concentration of ryegrass.The mowing time,mixed planting treatment,and heavy metal treatment had impacts on antioxidant and osmotic adjustment substances,and there were some interactions.The mixed planting treatment did not significantly affect glutathione concentration,cysteine concentration,or nonprotein thiol.Mixed planting generally increased the nitrogen and phosphorus concentrations of ryegrass while reducing the stoichiometric ratio of carbon,nitrogen,and phosphorus.These results suggest that the mixed planting of ryegrass with legumes promotes the growth of ryegrass in the presence of high concentrations of heavy metal pollution.However,it does not enhance the ability of ryegrass to remediate heavy metal pollution in the soil.

Research on the impact of manufacturing servitization on environmental pollution:spatial econometric analysis of sulfur dioxide based on 284 prefecture-level cities

Using China’s regional input–output table,the paper constructs indicators of manufacturing servitization,matches manufacturing servitization at the regional level with city data,and uses spatial econometrics to empirically analyze the impact of manufacturing servitization on urban sulfur dioxide(SO_(2))emissions within the classical Environmental Kuznets Curve(EKC)framework.The results show that manufacturing servitization can reduce SO_(2) emissions.Producer servitization and consumptive services can both significantly reduce industrial SO_(2) emissions.Transportation and warehousing servitization,information servitization,leasing,and commercial servitization,technology research and development servitization significantly reduce SO_(2) emissions;technology research and development servitization,in particular,have the largest influence coefficient,while the reduction effect of servitization in the wholesale and retail and finance sectors is not significant.The study also found that servitization reduced the SO_(2) emissions through technological innovation and industrial structure upgrading.

Impact of Air Pollutants on Lung Function and Inflammatory Response in Asthma in Shanghai

Objective Air pollution is a leading public health issue.This study investigated the effect of air quality and pollutants on pulmonary function and inflammation in patients with asthma in Shanghai.Methods The study monitored 27 asthma outpatients for a year,collecting data on weather,patient self-management[daily asthma diary,peak expiratory flow(PEF)monitoring,medication usage],spirometry and serum markers.To explore the potential mechanisms of any effects,asthmatic mice induced by ovalbumin(OVA)were exposed to PM_(2.5).Results Statistical and correlational analyses revealed that air pollutants have both acute and chronic effects on asthma.Acute exposure showed a correlation between PEF and levels of ozone(O_(3))and nitrogen dioxide(NO_(2)).Chronic exposure indicated that interleukin-5(IL-5)and interleukin-13(IL-13)levels correlated with PM_(2.5)and PM_(10)concentrations.In asthmatic mouse models,exposure to PM_(2.5)increased cytokine levels and worsened lung function.Additionally,PM_(2.5)exposure inhibited cell proliferation by blocking the NF-κB and ERK phosphorylation pathways.Conclusion Ambient air pollutants exacerbate asthma by worsening lung function and enhancing Th2-mediated inflammation.Specifically,PM_(2.5)significantly contributes to these adverse effects.Further research is needed to elucidate the mechanisms by which PM_(2.5)impacts asthma.

The Influence of Air Pollution Concentrations on Solar Irradiance Forecasting Using CNN-LSTM-mRMR Feature Extraction

Maintaining a steady power supply requires accurate forecasting of solar irradiance,since clean energy resources do not provide steady power.The existing forecasting studies have examined the limited effects of weather conditions on solar radiation such as temperature and precipitation utilizing convolutional neural network(CNN),but no comprehensive study has been conducted on concentrations of air pollutants along with weather conditions.This paper proposes a hybrid approach based on deep learning,expanding the feature set by adding new air pollution concentrations,and ranking these features to select and reduce their size to improve efficiency.In order to improve the accuracy of feature selection,a maximum-dependency and minimum-redundancy(mRMR)criterion is applied to the constructed feature space to identify and rank the features.The combination of air pollution data with weather conditions data has enabled the prediction of solar irradiance with a higher accuracy.An evaluation of the proposed approach is conducted in Istanbul over 12 months for 43791 discrete times,with the main purpose of analyzing air data,including particular matter(PM10 and PM25),carbon monoxide(CO),nitric oxide(NOX),nitrogen dioxide(NO_(2)),ozone(O₃),sulfur dioxide(SO_(2))using a CNN,a long short-term memory network(LSTM),and MRMR feature extraction.Compared with the benchmark models with root mean square error(RMSE)results of 76.2,60.3,41.3,32.4,there is a significant improvement with the RMSE result of 5.536.This hybrid model presented here offers high prediction accuracy,a wider feature set,and a novel approach based on air concentrations combined with weather conditions for solar irradiance prediction.

Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

Big data and information and communication technologies can be important to the effectiveness of smart cities.Based on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems.Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions.Relating to air pollution occurs a main environmental problem in smart city environments.The effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution forecast.Therefore,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart Cities.The projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city environment.To achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input data.For air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL models.The simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.

Research Progress of Microplastics Pollution in Environment

Microplastics pollution has become one of the focuses of global environmental science research.Microplastics include micro plastic particles and nano-plastic particles,which come from the decomposition of plastic products,the release of microfibers and the industrial process of plastic particles.The distribution of microplastics in water,soil and atmosphere is summarized,and the widespread existence of microplastics in different environmental media is emphasized.This paper also summarizes the potential impact of microplastics on ecosystems and organisms,and pays attention to the transmission and accumulation of microplastics in the food chain,as well as its potential threat to human health.Finally,the paper discusses the methods and technologies of microplastics treatment and monitoring at present,and puts forward the direction of further research on microplastics pollution in order to formulate more effective management and mitigation strategies.

Climate change,ambient air pollution,and students'mental health

The impact of global climate change and air pollution on mental health has become a crucial public health issue.Increased public awareness of health,advancements in medical diagnosis and treatment,the way media outlets report environmental changes and the variation in social resources affect psychological responses and adaptation methods to climate change and air pollution.In the context of climate change,extreme weather events seriously disrupt people's living environments,and unstable educational environments lead to an increase in mental health issues for students.Air pollution affects students'mental health by increasing the incidence of diseases while decreasing contact with nature,leading to problems such as anxiety,depression,and decreased cognitive function.We call for joint efforts to reduce pollutant emissions at the source,improve energy structures,strengthen environmental monitoring and governance,increase attention to the mental health issues of students,and help student groups build resilience;by establishing public policies,enhancing social support and adjusting lifestyles and habits,we can help students cope with the constantly changing environment and maintain a good level of mental health.Through these comprehensive measures,we can more effectively address the challenges of global climate change and air pollution and promote the achievement of the United Nations Sustainable Development Goals.

Bivariate Analysis of Pollutants Monthly Maxima in Mexico City Using Extreme Value Distributions and Copula

In the present work, we are interested in studying the joint distributions of pairs of the monthly maxima of the pollutants used by the environmental authorities in Mexico City to classify the air quality in the metropolitan area. In order to obtain the joint distributions a copula will be considered. Since we are analyzing the monthly maxima, the extreme value distributions of Weibull and Fréchet are taken into account. Using these two distributions as marginal distributions in the copula a Bayesian inference was made in order to estimate the parameters of both distributions and also the association parameters appearing in the copula model. The pollutants taken into account are ozone, nitrogen dioxide, sulphur dioxide, carbon monoxide, and particulate matter with diameters smaller than 10 and 2.5 microns obtained from the Mexico City monitoring network. The estimation was performed by taking samples of the parameters generated through a Markov chain Monte Carlo algorithm implemented using the software OpenBugs. Once the algorithm is implemented it is applied to the pairs of pollutants where one of the coordinates of the pair is ozone and the other varies on the set of the remaining pollutants. Depending on the pollutant and the region where they were collected, different results were obtained. Hence, in some cases we have that the best model is that where we have a Fréchet distribution as the marginal distribution for the measurements of both pollutants and in others the most suitable model is the one assuming a Fréchet for ozone and a Weibull for the other pollutant. Results show that, in the present case, the estimated association parameter is a good representation to the correlation parameters between the pair of pollutants analyzed. Additionally, it is a straightforward task to obtain these correlation parameters from the corresponding association parameters.

Distribution of Atmospheric Pollution in Southern Benin

This study focuses on air quality in southern Benin in order to show public authorities what the Beninese populations are exposed to for efficient decision-making. Two sampling campaigns were carried out, one in the wet period and the other in the dry season. The measurements were taken using a monitor called an “Air Quality Monitor”. For data processing, the multiple comparison methods of Dun (1961) and the Wilcoxon test were used. To maintain legitimacy, all spatial data were included in the official cartographic repository of Benin: WGS 1984, Transverse Mercator Universe Projection (UTM), Zone 31 North. The Moran statistic was used to measure the levels of spatial autocorrelation of the variables studied and to test the significance. In order to locate the spatial subsets, the local spatial association indices of Anselin Local Moran and Getis-Ord, Gi* were used. In terms of results, on the 13 monitoring sites and the 8 parameters chosen to determine air quality, we do not note any significant inter-seasonal difference. Of the eight parameters, only three parameters present spatial autocorrelation leading to predictions of ambient air quality over the entire study area based on the distance separating the points, namely, PM2.5, PM10 and ambient air quality index (AQI). The localities affected by atmospheric pollution in South Benin are located in the south-western part of Benin, headed by Cotonou, which is heavily polluted by CO2, TCOV, PM10 and PM2.5.

Progress in the Treatment of Heavy Metal Pollution of Water Sediment

In recent years,as China's industrialization process and urban-rural integration strategy have continued to deepen,some industrial and domestic wastewater has been discharged directly into rivers without effective treatment.This has resulted in the continuous accumulation and enrichment of pollutants in water bodies.This phenomenon results in a significant accumulation of heavy metals in the sediment of water bodies,which not only represents a significant threat to the ecological environment but also ultimately poses a risk to human health.The objective of this study is to provide a comprehensive review of the current status of heavy metal pollution in water sediment in China.In addition,this paper analyzes the advantages and limitations of existing techniques for the harmless treatment of heavy metal pollution and forecasts the development direction of this field.

Ecological Remediation Measures for Non-point Source Pollution Based on Source-Sink Landscape Theory: A Case Study of Huanghou Basin

The growth of society and population has led to a range of water pollution issues.Among these,non-point source pollution assessment and treatment pose a particular challenge due to its formation mechanism.This has become a focal point and challenge in water pollution treatment research.The study area for this research was the Huanghou basin in Guizhou Province,southwest China.The land use type of the basin was analyzed using remote sensing technology,and water quality data was collected by distributing points throughout the basin,based on source-sink landscape theory.The distribution map of the basin’s source-sink landscape and the results of water quality data accurately and efficiently identified the areas with high risk of non-point source pollution in the western and southwestern residential and agricultural areas of the upper basin.Hence,a strategy of“increasing sinks and decreasing sources”was proposed.The strategy was implemented at both macro and micro levels to address non-point source pollution in the basin using ecological remediation techniques.The work to control karst rocky desertification should continue at a macro level.The rocky desertification area in the basin should gradually transform into grassland and forested land,while increasing the overall area of the sink landscape.Ecological restoration techniques,such as slope planting,riparian zone vegetation restoration,increasing plant abundance,and restoring aquatic plants,can effectively control non-point source pollution at the micro level.Compared to traditional control methods,this remediation strategy focuses on source and process control.It is more effective and does not require large-scale water pollution control projects,which can save a lot of environmental control funds and management costs.Therefore,it has greater research significance and application value.