Examining the Experiences and Challenges of Haze-Fog Governance in China

With the rapid development of industrialisation and urbanisation, China is facing the challenge of severe HF (Haze-Fog) pollution. This essay compares the advantages and disadvantages of China’s HF management and summarizes the important lessons China can teach the rest of the world about applying this tactic. China’s capabilities in the digital economy, National Innovation Demonstration Zones, and urban innovation systems are examined in this article, along with its shortcomings in information mechanisms and pollution sources. This essay also summarizes China’s achievements, particularly regarding local autonomy. The essay goes on to say, however, that China is probably going to be under more pressure to manage HF in the future, both in terms of long-term solutions and the economy.

Using Hourly Measurements to Explore the Role of Secondary Inorganic Aerosol in PM_(2.5)during Haze and Fog in Hangzhou, China

This paper explores the role of the secondary inorganic aerosol （SIA） species ammonium,NH4＋,nitrate,NO3-,and sulfate,SO24-,during haze and fog events using hourly mass concentrations of PM2.5 measured at a suburban site in Hangzhou,China.A total of 546 samples were collected between 1 April and 8 May 2012.The samples were analyzed and classified as clear,haze or fog depending on visibility and relative humidity （RH）.The contribution of SIA species to PM2.5 mass increased to ～50％ during haze and fog.The mass contribution of nitrate to PM2.5 increased from 11％ during clear to 20％ during haze episodes.Nitrate mass exceeded sulfate mass during haze,while near equal concentrations were observed during fog episodes.The role of RH on the correlation between concentrations of SIA and visibility was examined,with optimal correlation at 60％-70％ RH.The total acidity during clear,haze and fog periods was 42.38,48.38 and 45.51 nmol m-3,respectively,indicating that sulfate,nitrate and chloride were not neutralized by ammonium during any period.The nitrate to sulfate molar ratio,as a function of the ammonium to sulfate molar ratio,indicated that nitrate formation during fog started at a higher ammonium to sulfate molar ratio compared to clear and haze periods.During haze and fog,the nitrate oxidation ratio increased by a factor of 1.6-1.7,while the sulfur oxidation ratio increased by a factor of 1.2-1.5,indicating that both gaseous NO2 and SO2 were involved in the reduced visibility.

HAZE-TO-FOG TRANSFORMATION DURING A LONG LASTING,LOW VISIBILITY EPISODE IN NANJING

Haze-to-fog transformation during a long lasting,low visibility episode was examined using the observations from a comprehensive field campaign conducted in Nanjing,China during 4-9 December 2013.In this episode,haze was transformed into fog and the fog lasted for dozens of hours.The impacts of meteorological factors such as wind,temperature(T) and relative humidity(RH) on haze,transition and fog during this episode were investigated.Results revealed significant differences between haze and fog days,due to their different formation mechanisms.Comparison was made for boundary-layer conditions during hazy days,haze-to-fog days and foggy days.Distributions of wind speed and wind direction as well as synoptic weather conditions around Nanjing had determinative impacts on the occurrences and characteristics of haze and fog.Weakened southerly wind in southern Nanjing resulted in high concentration of pollutants,and haze events occurred frequently during the study period.The wind speed was less than 1 m s^(-1) in the haze event,which resulted in a stable atmospheric condition and weak dispersion of the pollutants.The height of the temperature inversion was about 400 m during the period.The inversion intensity was weak and the temperature-difference was 4℃ km^(-1) or less in haze,while the inversion was stronger,and temperature-difference was about 6℃ km^(-1),approaching the inversion layer intensity in the fog event.Haze event is strongly influenced by ambient RH.RH values increased,which resulted in haze days evidently increased,suggesting that an increasing fraction of haze events be caused by hygroscopic growth of aerosols,rather than simply by high aerosol loading.When RH was above 90%,haze aerosols started to be transformed from haze to fog.This study calls for more efforts to control emissions to prevent haze events in the region.

A Distinguishing Method and Distribution Characteristics of Fog and Haze Days in Fuzhou

For data of atmospheric composition missing, fog and haze days were distinguished based on the standard of meteorological industry Observation and Forecasting Levels of Haze （QX/T113-2010） and four user-defined standards, and using data of surface meteorological factors in Fuzhou City, China from 2005 to 2011, temporal distributions of fog and haze days were analyzed respectively to provide methods for fog and haze forecast. The results showed that there were 28.9 fog days and 89.7 haze days per year in Fuzhou. Both fog and haze days were variable, and there might be a day difference of twice to thrice among the years. They were the most in 2007, and then decreased in recent years. Both fog and haze days were more in winter and spring, accounted for 94.1% and 70.1% respectively, while in summer and autumn, they only accounted for 5.9% and 29.9% respectively. In a day, fog mainly occurred from night to early morning, while haze occurred mainly at about noon, which demonstrated that fog and haze are different synoptic phenomena. The mass concentration of PM2.5 in fog days was 34 μg/m^3, while it was 61 μg/m^3 in haze days, and in 22% of haze days it was larger than 75 μg/m3, which was above the national second-grade ambient air quality standard.

Cause Analysis on the Extreme Fog-haze Event in East China in January 2013

Characteristics and cause of the fog-haze event in East China in January 2013 were analyzed with the meteorological conventional observation data and atmospheric composition observation data. Results showed that fog or haze days in east area of Southwest China and most areas of central east China were more than 20 days in this month, especially in east of North China, Huanghuai and Jianghuai regions,that were 10 -15 days more than in the same period of normal years. 500 hPa circulation presented zonal type, cold air wasn＇t active, south-branch trough was weaker, and precipitation amounts was small, which was the weather background of the extreme fog and haze. Northwest airflow at upper layer of East Asia mid-high latitude,less cloud,ground temperature reduction at night, and warm dry air coverd at 850 hPa that led to temperature inversion near surface layer,which was the key thermal factor of fog and haze formation. High humidity near the ground ,weak horizontal wind speed, and weak ascending motion ,which were not favorable for horizontal diffusion and vertical exchange of water vapor and pollutant but were the dynamic factors of maintaining fog and haze. Whether there was a water vapor saturated layer performance in relative humidity /〉90% below 925 hPa or not can be looked as a basis of distinguishing between fog and haze.

Changing Trend and Mutation Analysis of Number of Fog and Haze Days in Baoji City during 1981-2013

Based on data of number of fog and haze days in Baoji City from 1981 to 2013,the changing trends and mutation of number of fog and haze days in Baoji over the past 33 years were analyzed by using trend coefficient,tendency rate,linear regression analysis,anomaly percentage,Mann-Kendall mutation test and sliding t test.The results showed that during the 33 years,the number of fog and haze days in Baoji declined by 16.253d/10 a,and there was a cyclical turbulence every 6,15 or 28years.The frequency of fog and haze weather was the highest in winter,followed by spring and autumn,while it was the lowest in summer.According to the anomaly percentage of the number of fog and haze days in 12 months during 1981-2013,the anomaly percentage changed most greatly in July,followed by September,October,April,May,June,August,February and March,but it fluctuated less greatly in January.The number of fog and haze days from 1981 to 2013had obvious mutation trends in a single year and a single season,and mutation types are different.

Economy, Environment and Government: Study on the Path of Supply-Side Reform Forced by the Fog-Haze

Unbalanced development in term as industrial structure and the efficiency use of energy have aggravated environmental pollution to different degrees resulting in the increase of range, time and degree of fog-haze. This, in turn, forced the government to carry out supply-side reforms, to improve energy efficiency and optimize the industrial structure to weaken the environmental pollution. To tackle these problems, this work provides an index system for the issues related to fog-haze, uses a non-linear ST-SVAR model to reflect the effects of industrial structure and energy use efficiency on fog-haze. Results indicated that: First, current industrial structure and energy use efficiency have greater impact on the comprehensive equation of fog-haze risk than itself. With the passage of time, this influence is still gradually expanding. Second, the equations of industrial structure and energy use efficiency are strongly influenced by themselves, and other variables as the current period have less impact on them. Finally, the non-linear or asymmetric relationship is shown among industrial structure, energy use efficiency, and the fog-haze comprehensive risk equation.

A Brief Analysis of the Causes and Countermeasures of Fog-Haze in Beijing-Tianjin-Hebei Region

Since China broke the blockade and opened the country to the outside world, many township enterprises develop quickly. Together with the pollution caused by China’s coal-burning as the main source of national energy, the pollution caused by small and medium-sized enterprises in towns and villages due to their small investment, low technology level and weak environmental awareness, and the pollution caused by a sharp increase in motor vehicle emissions lead to the fact that fog-haze has been rampant in China’s cities and urban agglomerations for nearly two decades, especially in the Beijing-Tianjin-Hebei region. This paper sorted out the current situation of fog-haze and analyzed the causes of fog-haze from the two aspects of natural and man-made causes, discussed the harms of fog-haze to human body, environment and life, and put forward the concrete measures to solve the fog-haze problem.

Analysis of Climatic Characteristics of Fog and Haze Weather in Shaoyang in the Past 60 Years and Its Forecast

Based on conventional observation data and NCEP reanalysis data at 10 national basic stations and reference stations of Shaoyang City during 1951-2014,300 cases of typical regional dense fog process appeared in the history were selected. From meteorological factors and weather situation,temporal-spatial distribution characteristics and trend change characteristics of dense fog in Shaoyang were analyzed. The results showed that（ 1） temporal-spatial distribution of dense fog in Shaoyang region was uneven,and interannual variability of fog days had large volatility and bad periodicity; dense fog days in Shaoyang region was obviously more in winter half year and less in summer half year. Dense fog was the most in November and the least in July. Dense fog mostly concentrated during 03： 00-09： 00; appearance time mostly concentrated during 05： 00-07： 30,and dissipation time mostly appeared after 08： 30. Dense fog appeared early and dissipated late in winter half year,and vice verse in summer half year.（ 2） Seen from meteorological factors,ground and 850 h Pa of wind velocity was generally 0-3 m/s,which was all small. Moreover,there existed temperature inversion from ground to 850 h Pa. Relative humidity on dense fog day was larger,and precipitation or cloudy day mostly appeared in prior day.（ 3） There were four kinds of ground weather situation forming dense fog： uniform pressure field type,cold and high pressure bottom type,cold and high pressure rear type,frontal type. Based on grasping change characteristics,rule and formation reason of dense fog,some forecast focus was found.

Analysis of Meteorological Conditions for Fog and Haze in the Eastern and Central Regions of China during January, 2013

Based on the data of conventional meteorological observation, NCEP reanalysis data and atmospheric composition observation, a comprehensive analysis of the three kinds of persistent fog and haze in eastern China in January 2013 was carried out. The results show that the process of persistent fog and haze is in the background of static weather, and the zonal circulation in the middle and high latitudes is not conducive to the south of the cold air. In the eastern part of China, near-surface wind speed is low under the controlled of pressure field, which is conducive to the formation and maintenance of haze. The formation of inversion layer, the height of the mixed layer, the stratified structure of the upper dry layer, the ground wind speed and so on can represent the static stability of the atmosphere. In the actual forecast, fog and haze can be distinguished from the angle of relative humidity, PM2.5 concentration, diurnal variation characteristics, mixed layer height and energy structure, industrial structure and local and surrounding economic development level.

角膜光密度评价复性近视散光Trans-PRK术后haze特点1例

1例复性近视散光女性患者在经上皮准分子激光角膜表面切削(Trans-PRK)术后早期因发现怀孕停用糖皮质激素滴眼液,随访过程中出现角膜上皮下混浊(haze),并使用Pentacam眼前节分析仪测量角膜光密度进行haze程度和特点的评价。

LCD面板C/FOG工艺制造虚拟计量方法研究

针对液晶显示器(LCD)面板的“Chip/FPC on Glass”(C/FOG)工艺生产制造过程中存在的计量延迟大、生产异常无法提前预测的问题,本文提出一种基于神经网络的C/FOG工艺生产制造虚拟计量方法。该方法利用生产机台上的传感器采集生产过程中的过程状态数据,构建基于多尺度一维卷积及通道注意力模型(MS1DC-CA)的虚拟计量模型。通过多个尺度的卷积核提取不同尺度范围内的状态数据特征。在对含有缺失值的原始数据预处理中,提出了基于粒子群算法改进的K近邻填补方法(PSO-KNN Imputation)进行缺失值填充,保留特征的同时,减少因填充值引入的干扰。最后在实际生产采集的数据上进行实验对比分析,实际不良率主要集中在0.1%~0.5%,该虚拟计量模型的拟合均方误差为0.397 7‱,低于其他现有拟合模型,在平均绝对误差、对称平均绝对百分比误差和拟合优度3种评价指标下也均优于其他现有的拟合模型,具有良好的预测性能。

Influences of tree characters on throughfall and stemflow from rainfall and fog in Popa Mountain Park, Myanmar

Available water for communities is insufficient in the central part of Myanmar due to limited rainfall and surface water resources. Over the last two decades, afforestation and reforestation projects have been implemented in this region to provide sufficient water to local communities, expecting forested areas to store more rainwater than other land uses. However, there has been no research and very limited information on rainfall partitioning into throughfall(TF) and stemflow(SF), particularly concerning tree characters. Gross rainfall, TF under different canopy types, and SF of different tree types were measured in 2019. TF and SF were frequently observed even without rain but under foggy conditions. Therefore, both were partitioned into TF and SF from rainfall and fog individually. Sparser canopies resulted in larger TF from rainfall than denser canopies. However, a denser canopy delivered larger TF from fog than a sparser one. TF rates from rainfall in sparser and denser canopies were 54.5% and 51.5%, respectively, while those from fog were 15.2% and 27.2%, respectively. As a result, total TF rate in the denser canopy(70.7%) was significantly larger than that from the sparser one(64.3%). Short trees with small crown projection area and smooth bark(Type Ⅰ) resulted in larger SF from rainfall than taller trees with large crown projection area and rough bark(Type Ⅱ). However, Type Ⅱ trees resulted in larger SF from fog. SF rates by rainfall from Type Ⅰ and Ⅱ trees were 17.5% and 12.2%, respectively, while those by fog were 22.2% and 39.5%, respectively. No significant total SF rates were found for Type Ⅰ(22.5%) and Ⅱ trees(20.1%). A denser canopy results in larger TF, and Type Ⅰ trees result in larger SF. In an area where foggy conditions occur frequently and for a lengthy period, however, Type Ⅱ trees will result in larger SF. These three tree characters(dense canopies, short trees with small crown projection area and smooth bark, and tall trees with large crown projection area and rough bark) should be considered for afforestation and reforestation projects in the Popa Mountain Park to enhance net water input by forests.

Privacy-preserved learning from non-i.i.d data in fog-assisted IoT:A federated learning approach

With the prevalence of the Internet of Things(IoT)systems,smart cities comprise complex networks,including sensors,actuators,appliances,and cyber services.The complexity and heterogeneity of smart cities have become vulnerable to sophisticated cyber-attacks,especially privacy-related attacks such as inference and data poisoning ones.Federated Learning(FL)has been regarded as a hopeful method to enable distributed learning with privacypreserved intelligence in IoT applications.Even though the significance of developing privacy-preserving FL has drawn as a great research interest,the current research only concentrates on FL with independent identically distributed(i.i.d)data and few studies have addressed the non-i.i.d setting.FL is known to be vulnerable to Generative Adversarial Network(GAN)attacks,where an adversary can presume to act as a contributor participating in the training process to acquire the private data of other contributors.This paper proposes an innovative Privacy Protection-based Federated Deep Learning(PP-FDL)framework,which accomplishes data protection against privacy-related GAN attacks,along with high classification rates from non-i.i.d data.PP-FDL is designed to enable fog nodes to cooperate to train the FDL model in a way that ensures contributors have no access to the data of each other,where class probabilities are protected utilizing a private identifier generated for each class.The PP-FDL framework is evaluated for image classification using simple convolutional networks which are trained using MNIST and CIFAR-10 datasets.The empirical results have revealed that PF-DFL can achieve data protection and the framework outperforms the other three state-of-the-art models with 3%–8%as accuracy improvements.

A Lightweight Intrusion Detection System Using Convolutional Neural Network and Long Short-Term Memory in Fog Computing

The Internet of Things(IoT)links various devices to digital services and significantly improves the quality of our lives.However,as IoT connectivity is growing rapidly,so do the risks of network vulnerabilities and threats.Many interesting Intrusion Detection Systems(IDSs)are presented based on machine learning(ML)techniques to overcome this problem.Given the resource limitations of fog computing environments,a lightweight IDS is essential.This paper introduces a hybrid deep learning(DL)method that combines convolutional neural networks(CNN)and long short-term memory(LSTM)to build an energy-aware,anomaly-based IDS.We test this system on a recent dataset,focusing on reducing overhead while maintaining high accuracy and a low false alarm rate.We compare CICIoT2023,KDD-99 and NSL-KDD datasets to evaluate the performance of the proposed IDS model based on key metrics,including latency,energy consumption,false alarm rate and detection rate metrics.Our findings show an accuracy rate over 92%and a false alarm rate below 0.38%.These results demonstrate that our system provides strong security without excessive resource use.The practicality of deploying IDS with limited resources is demonstrated by the successful implementation of IDS functionality on a Raspberry Pi acting as a Fog node.The proposed lightweight model,with a maximum power consumption of 6.12 W,demonstrates its potential to operate effectively on energy-limited devices such as low-power fog nodes or edge devices.We prioritize energy efficiency whilemaintaining high accuracy,distinguishing our scheme fromexisting approaches.Extensive experiments demonstrate a significant reduction in false positives,ensuring accurate identification of genuine security threats while minimizing unnecessary alerts.

Joint Optimization of Energy Consumption and Network Latency in Blockchain-Enabled Fog Computing Networks

Fog computing is considered as a solution to accommodate the emergence of booming requirements from a large variety of resource-limited Internet of Things(IoT)devices.To ensure the security of private data,in this paper,we introduce a blockchain-enabled three-layer device-fog-cloud heterogeneous network.A reputation model is proposed to update the credibility of the fog nodes(FN),which is used to select blockchain nodes(BN)from FNs to participate in the consensus process.According to the Rivest-Shamir-Adleman(RSA)encryption algorithm applied to the blockchain system,FNs could verify the identity of the node through its public key to avoid malicious attacks.Additionally,to reduce the computation complexity of the consensus algorithms and the network overhead,we propose a dynamic offloading and resource allocation(DORA)algorithm and a reputation-based democratic byzantine fault tolerant(R-DBFT)algorithm to optimize the offloading decisions and decrease the number of BNs in the consensus algorithm while ensuring the network security.Simulation results demonstrate that the proposed algorithm could efficiently reduce the network overhead,and obtain a considerable performance improvement compared to the related algorithms in the previous literature.

A novel hybrid authentication protocol utilizing lattice-based cryptography for IoT devices in fog networks

The Internet of Things(IoT)has taken the interconnected world by storm.Due to their immense applicability,IoT devices are being scaled at exponential proportions worldwide.But,very little focus has been given to securing such devices.As these devices are constrained in numerous aspects,it leaves network designers and administrators with no choice but to deploy them with minimal or no security at all.We have seen distributed denial-ofservice attacks being raised using such devices during the infamous Mirai botnet attack in 2016.Therefore we propose a lightweight authentication protocol to provide proper access to such devices.We have considered several aspects while designing our authentication protocol,such as scalability,movement,user registration,device registration,etc.To define the architecture we used a three-layered model consisting of cloud,fog,and edge devices.We have also proposed several pre-existing cipher suites based on post-quantum cryptography for evaluation and usage.We also provide a fail-safe mechanism for a situation where an authenticating server might fail,and the deployed IoT devices can self-organize to keep providing services with no human intervention.We find that our protocol works the fastest when using ring learning with errors.We prove the safety of our authentication protocol using the automated validation of Internet security protocols and applications tool.In conclusion,we propose a safe,hybrid,and fast authentication protocol for authenticating IoT devices in a fog computing environment.

Target Detection Algorithm in Foggy Scenes Based on Dual Subnets

Under the influence of air humidity,dust,aerosols,etc.,in real scenes,haze presents an uneven state.In this way,the image quality and contrast will decrease.In this case,It is difficult to detect the target in the image by the universal detection network.Thus,a dual subnet based on multi-task collaborative training(DSMCT)is proposed in this paper.Firstly,in the training phase,the Gated Context Aggregation Network(GCANet)is used as the supervisory network of YOLOX to promote the extraction of clean information in foggy scenes.In the test phase,only the YOLOX branch needs to be activated to ensure the detection speed of the model.Secondly,the deformable convolution module is used to improve GCANet to enhance the model’s ability to capture details of non-homogeneous fog.Finally,the Coordinate Attention mechanism is introduced into the Vision Transformer and the backbone network of YOLOX is redesigned.In this way,the feature extraction ability of the network for deep-level information can be enhanced.The experimental results on artificial fog data set FOG_VOC and real fog data set RTTS show that the map value of DSMCT reached 86.56%and 62.39%,respectively,which was 2.27%and 4.41%higher than the current most advanced detection model.The DSMCT network has high practicality and effectiveness for target detection in real foggy scenes.

Hierarchical Privacy Protection Model in Advanced Metering Infrastructure Based on Cloud and Fog Assistance

The Advanced Metering Infrastructure(AMI),as a crucial subsystem in the smart grid,is responsible for measuring user electricity consumption and plays a vital role in communication between providers and consumers.However,with the advancement of information and communication technology,new security and privacy challenges have emerged for AMI.To address these challenges and enhance the security and privacy of user data in the smart grid,a Hierarchical Privacy Protection Model in Advanced Metering Infrastructure based on Cloud and Fog Assistance(HPPM-AMICFA)is proposed in this paper.The proposed model integrates cloud and fog computing with hierarchical threshold encryption,offering a flexible and efficient privacy protection solution that significantly enhances data security in the smart grid.The methodology involves setting user protection levels by processing missing data and utilizing fuzzy comprehensive analysis to evaluate user importance,thereby assigning appropriate protection levels.Furthermore,a hierarchical threshold encryption algorithm is developed to provide differentiated protection strategies for fog nodes based on user IDs,ensuring secure aggregation and encryption of user data.Experimental results demonstrate that HPPM-AMICFA effectively resists various attack strategies while minimizing time costs,thereby safeguarding user data in the smart grid.

Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling

More devices in the Intelligent Internet of Things(AIoT)result in an increased number of tasks that require low latency and real-time responsiveness,leading to an increased demand for computational resources.Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension.However,the effective allocation of resources for task execution within fog environments,characterized by limitations and heterogeneity in computational resources,remains a formidable challenge.To tackle this challenge,in this study,we integrate fog computing and cloud computing.We begin by establishing a fog-cloud environment framework,followed by the formulation of a mathematical model for task scheduling.Lastly,we introduce an enhanced hybrid Equilibrium Optimizer(EHEO)tailored for AIoT task scheduling.The overarching objective is to decrease both the makespan and energy consumption of the fog-cloud system while accounting for task deadlines.The proposed EHEO method undergoes a thorough evaluation against multiple benchmark algorithms,encompassing metrics likemakespan,total energy consumption,success rate,and average waiting time.Comprehensive experimental results unequivocally demonstrate the superior performance of EHEO across all assessed metrics.Notably,in the most favorable conditions,EHEO significantly diminishes both the makespan and energy consumption by approximately 50%and 35.5%,respectively,compared to the secondbest performing approach,which affirms its efficacy in advancing the efficiency of AIoT task scheduling within fog-cloud networks.