Analysis of the Impact of Big Data Technology on Environmental Pollution Control Audit

As China strives towards the second centenary goal,increasing attention is being paid to environmental pollution and other related issues.Concurrently,with the rapid development of big data technology,many big data solutions have been applied to environmental pollution control audits,exerting a significant impact.This paper presents the current situation of environmental pollution audits,summarizing the application of big data from the perspectives of both domestic and international research.In terms of data collection and data analysis for environmental pollution audits,cloud platform technology,and visualization technology are selected based on multiple data sources.The impact in the field of environmental pollution control audits is further analyzed.It is found that the environmental pollution audit cloud platform is not yet perfect,the technical skills of audit personnel are insufficient,and some technologies are not mature.Relevant suggestions are put forward to provide a reference for the future development of big data technology and its integration with environmental pollution control audits.

The heterogeneous influence of economic growth on environmental pollution: evidence from municipal data of China

This study aims to investigate the influence of rapid economic development on pollution at the municipal level in China.It constructs a Stochastic Impacts by Regression on Population,Affluence and Technology model(STIRPAT model) and uses comprehensive municipal data on industrial pollution and economic performance.The dataset contains 290 cities from2003 to 2016 as a sample for the panel data analysis.The study further separates the cities into two groups by their levels of economic development for heterogeneity analysis.It reveals that a low level of economic development would aggravate environmental pollution,and when the economy reaches a high level,this economic development will improve environmental quality.We also find that the relationships between foreign direct investment and industrial dust and sulfur dioxide(SO_2) discharge are significant,while the relationship between economic growth and effluent emission is not.The more developed subsample cities present an inverted U-shaped curve between industrial pollutant emission,GDP per capita,and foreign direct investment,while the less developed subsamples show no such relationship.Since the shape of these curves differs among regions,their turning points vary accordingly.Based on this finding,this study suggests that the governments of more developed cities should balance environmental pollution and economic development by enhancing environmental regulations and adjusting industrial structure.

Fair-News: Digital Journalism Model to Prevent Information Pollution and Manipulation

As digital data circulation increases,information pollution and manipulation in journalism have become more prevalent.In this study,a new digital journalism model is designed to contribute to the solution of the main current problems,such as information pollution,manipulation,and account-ability in digital journalism.The model uses blockchain technology due to its transparency,immutability,and traceability.However,it is tough to provide the mechanisms necessary for journalism,such as updating one piece of information,instantly updating all other information affected by the updated information,establishing logical relationships between news,making quick comparisons,sorting and indexing news,and keeping the changing informa-tion about the news in the system,with the blockchain data structure.For this reason,in our study,we have developed a new data structure that provides both the immutability,transparency and traceability properties of the blockchain and can support the communication mechanisms necessary for journalism.The functionality of our proposed data structure is demonstrated in terms of communication mechanisms such as mutability,context,consistency,and reliability through example scenarios.Additionally,our data structure is compared with the data structure of blockchain technology in terms of time,space,and maintenance costs.Accordingly,while the model size increases linearly in blockchain,the model’s size remains approximately constant since the structure we developed is data-independent.In this way,maintenance costs are reduced.Since our model also has an indexing mechanism,it reduces the linear time search complexity to logarithmic time.As a result,the data structure we developed is found to have higher performance than blockchain in the journalism concept.In future studies,it is planned to test all aspects of the model with a pilot application,eliminate its shortcomings,and develop a holistic approach to the root causes of the problems in the journalism focus.

Illustrating the multi-stakeholder perceptions of environmental pollution based on big data:Lessons from China

The relationship between stakeholders and the environment influences sustainable development and human wellbeing.To illustrate the multi-stakeholder perceptions of environmental pollution in China,we interpreted a feedback loop in the perception-behavior-environment nexus from the perspective of the coupled human-environment system,measured the differences of environmental perceptions among five stakeholders(the public,government,media,companies,and scientists)and regions(including 31 provinces,autonomous regions,and municipalities in China,with exceptions of Taiwan of China,Hong Kong of China,and Macao of China due to a lack of data)using big data,and made a comparison between the perceptions and the actual pollution situation.The results showed that the five stakeholders exhibited similar perceptions of environmental pollution at the national scale,with air pollution being of most concern,followed by water pollution and soil pollution.There were significant spatial differences in environmental perceptions.All stakeholders in the developed regions in eastern China paid relatively high attention to environmental issues,while those in the northwestern regions paid much less attention.There existed a mutual influence and interaction among the different stakeholders.More attention should be paid to air pollution in Xinjiang Uygur Autonomous Region and Ningxia Hui Autonomous Region,water pollution in Hainan Province,Inner Mongolia Autonomous Region,Heilongjiang Province,and Jilin Province,and soil pollution in Hainan Province,Fujian Province,and Jilin Province.This paper provides a research paradigm on multi-stakeholder environmental perceptions based on big data,and the results provide a background reference for regional environmental governance.

Effect of FDI on China's environmental pollution: Evidence based on spatial panel data

It is clearly stated in the 19th people's congress that we should make the environmental protection as our national policy. Therefore, it is of great importance to study this issue. This article is going to consider 30 provinces of China as the cross-section, and utilize the data sample from 2006 to 2015 of these cross-sections to formulate a Spatial Panel Data Durbin Model to analyze the effect of FDI. By using these data, this article creates a comprehensive environmental pollution index with the help of entropy. The result indicates that the effect of FDI on environment has a non-linear and spatial spillover characteristic. Before reaching the critical value, FDI has a negative effect on environment; however, with the accumulation of FDI, it will create a significant positive effect on the environment.

Machine Learning-based Identification of Contaminated Images in Light Curve Data Preprocessing

Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal.Analyzing light curves to determine attitude is the most commonly used method.In photometric observations,outliers may exist in the obtained light curves due to various reasons.Therefore,preprocessing is required to remove these outliers to obtain high quality light curves.Through statistical analysis,the reasons leading to outliers can be categorized into two main types:first,the brightness of the object significantly increases due to the passage of a star nearby,referred to as“stellar contamination,”and second,the brightness markedly decreases due to cloudy cover,referred to as“cloudy contamination.”The traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive.However,we propose the utilization of machine learning methods as a substitute.Convolutional Neural Networks and SVMs are employed to identify cases of stellar contamination and cloudy contamination,achieving F1 scores of 1.00 and 0.98 on a test set,respectively.We also explore other machine learning methods such as ResNet-18 and Light Gradient Boosting Machine,then conduct comparative analyses of the results.

The Weathering Effects of Acidithiobacillus Ferrooxidans to Cu(Ⅱ) Pollution of Fine Grained Copper Mine Tailings

China has accumulated massive fine grained copper mine tailings stocks because of the past mining activities in this area. The tailings contain a variety of heavy metals, and the mass percent of Cu, which is one of the main contaminants in tailings, is up to 0.2601% (analysis by XRF). The Cu can pollute soil and groundwater by rain leaching in the form of Cu(Ⅱ), furthermore ,the fine grained copper-ore-tailings can contaminant larger area by wind for its small granularity ( < 74 μm). The main cause of weathering of mine tailings is due to oxidative dissolution of sulfides. Microorganisms, such as Acidithiobacillus ferrooxidans, play an important role in weathering. These bacteria attach to exposed to mineral surfaces by excreting extracellular polymers and oxidize the sulfide mineral. Some of these bacteria also oxidize Fe2+ to Fe3+ which can chemically oxidize sulfide minerals. These reactions produce voluminous quantities of acid mine drainage and heavy metals which are harmful to the environment and human healthy. This study aims at finding the weathering effects of A. ferrooxidans to Cu(II) pollution of fine grained copper mine tailings, and our experiment applied indigenous A. ferrooxidans FJ-01 to leach the tailings. The optimum test parameters were obtained using shaking flask experiment and SEM observation under the following experimental conditions: 39 days residence time, pulp density 1%-15% (1%, 5% and 15%), 30℃, 120 rpm, pH between 1-3 and redox potential between 400-650 mV. The test results show that the leaching rate of Cu reached 43.1% when the pulp density was 1% after 33 days and kept invariant till the end of the test. In addition, the leaching rate of Cu will decrease as the increase of pulp density, and the maximum rate of 15% pulp density was only 12.5%. From the SEM, it can be seen that the fine grain of tailings flocculated to conglobation under the action of bacterial leaching.

Inventory of Atmospheric Pollutant Emissions from Burning of Crop Residues in China Based on Satellite-retrieved Farmland Data

The burning of crop residues emits large quantities of atmospheric aerosols.Published studies have developed inventories of emissions from crop residue burning based on statistical data.In contrast,this study used satellite-retrieved land-cover data(1 km×1 km)as activity data to compile an inventory of atmospheric pollutants emitted from the burning of crop residues in China in 2015.The emissions of PM10,PM2.5,VOCs,NOx,SO2,CO,and NH3 from burning crop straw on nonirrigated farmland in China in 2015 were 610.5,598.4,584.4,230.6,35.4,3329.3,and 36.1 Gg(1 Gg=109 g),respectively;the corresponding emissions from burning paddy rice residues were 234.1,229.7,342.3,57.5,57.5,1122.1,and 21.5 Gg,respectively.The emissions from crop residue burning showed large spatial and temporal variations.The emissions of particulate matter and gaseous pollutants from crop residue burning in nonirrigated farmland were highest in east China,particularly in Shandong,Henan,Anhui,and Sichuan provinces.Emissions from burning paddy rice residue were highest in east and central China,with particularly high levels in Shandong,Jiangsu,Zhejiang,and Hunan provinces.The monthly variations in atmospheric pollutant emissions were similar among different regions,with the highest levels observed in October in north,northeast,northwest,east,and southwest China and in June and July in central and south China.The developed inventory of emissions from crop residue burning is expected to help improve air quality models by providing high-resolution spatial and temporal data.

基于SerialLite Ⅱ协议的磁共振成像数据传输系统设计

为了提高磁共振成像信号的信噪比、减少信号传输中的干扰,一种主流的技术方案是将信号采集装置置于屏蔽室,靠近接收线圈.针对该方案,本文提出了基于SerialLite Ⅱ协议的光纤数据传输方案,以解决信号采集的参数配置和回波数据的高速传输问题.电路以现场可编程门阵列(Field Programmable Gate Array,FPGA)器件和小型可插拔(Small Form Pluggable,SFP)光纤模块为核心,基于SerialLite Ⅱ协议实现信号采集的配置参数的下传与回波数据的上传.为了便于编程,在FPGA中构建NIOS Ⅱ软核处理器,以完成信号采集模块参数的发送、接收和配置.本文还研制了实验样机,进行了大量的数据传输测试,结果表明本方案能够实现高速的数据传输,64 K字节的数据通过30 m光纤传输所需的时间仅约为364.2 μs,并且具有可靠、延时低的特点.

Climate Change Impacts on Water Resource and Air Pollution in Ka­bul Sub-basins,Afghanistan

This Climate Change Impacts on Water Resources and Air Pollution,research is carried out to analysis Hydro-meteorological and groundwater data in Kabul Sub-basins,Afghanistan.The main objective of this research is to find out natural causes of climate change effects on surface and to,groundwater resources and air pollutions,these data are collected from diferent Hydrometeoroiogical stations and observations in Kabul Sub-basins for different years(1957 to 2017).For completion this research they used two categories of data analysis;one is hydro meteorological analysis,and the other is groundwater level analysis.In hydro meteorological analysis air temperature,rainfall and discharge have been recovered by this research in Kabul Sub-basins,a number of air temperature,rainfall,discharge of surface water and groundwater are changes due to climate changes from 1957 to 2017.For climate changes effects this article used air pollution data of national,international development bank of Asia,WHO standards and parameters;PM_(2.5),PM_(10),TSP,NO_(2),SO_(2),O_(3),CO and Pb.From comparing PM_(10) are very higher in the air of Afghanistan.The discharge of Panjsher river due to glacier melting and climate changes increasing.The challenges during this research are lack of equipment.

Development of Cloud Based Air Pollution Information System Using Visualization

Air pollution caused by fine dust is a big problem all over the world and fine dust has a fatal impact on human health.But there are too few fine dust measuring stations and the installation cost of fine dust measuring station is very expensive.In this paper,we propose Cloud-based air pollution information system using R.To measure fine dust,we have developed an inexpensive measuring device and studied the technique to accurately measure the concentration of fine dust at the user’s location.And we have developed the smartphone application to provide air pollution information.In our system,we provide collected data based analytical results through effective data modeling.Our system provides information on fine dust value and action tips through the air pollution information application.And it supports visualization on the map using the statistical program R.The user can check the fine dust statistics map and cope with fine dust accordingly.

Study on the Monitoring Malfunction of Water Pollution during Drought or Flood Period and Low-carbon and High-value Methodology--A Case Study of the Correlation Test of Water,Soil and Gas Pollution in Xiangxiang County

Based on the low-carbon and high-value methodology of chemical ecology and chemical informatics,combining theory and methods,taking saving,environmental protection,low carbon,high production,high value and circulation as values and aims,the relationship between human and land as a basis,ecosystem as a center,overall control as a goal and agricultural ecological engineering as a mean,environmental pollution detection,as one of bottlenecks for agricultural products and food security,should be solved firstly;through the field survey in dry years from 2009 to 2010 when drought and flood were frequent and the frequency of drought was higher than that of flood,plus the determination of surface water flow and water quantity in a small typical river basin,the correlation of local water,soil and gas in the county could be found,and the transfer of monitoring focus from water environment to atmospheric environment was possible and necessary.The study would promote the quantitative research on the correlation among water,soil and gas,and the results were in accordance with the conclusions of related studies.

Air Pollution Prediction Via Graph Attention Network and Gated Recurrent Unit

PM2.5 concentration prediction is of great significance to environmental protection and human health.Achieving accurate prediction of PM2.5 concentration has become an important research task.However,PM2.5 pollutants can spread in the earth’s atmosphere,causing mutual influence between different cities.To effectively capture the air pollution relationship between cities,this paper proposes a novel spatiotemporal model combining graph attention neural network(GAT)and gated recurrent unit(GRU),named GAT-GRU for PM2.5 concentration prediction.Specifically,GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities,and GRU is to extract the temporal dependence of the long-term data series.The proposed model integrates the learned spatio-temporal dependencies to capture long-term complex spatio-temporal features.Considering that air pollution is related to the meteorological conditions of the city,the knowledge acquired from meteorological data is used in the model to enhance PM2.5 prediction performance.The input of the GAT-GRU model consists of PM2.5 concentration data and meteorological data.In order to verify the effectiveness of the proposed GAT-GRU prediction model,this paper designs experiments on real-world datasets compared with other baselines.Experimental results prove that our model achieves excellent performance in PM2.5 concentration prediction.

Traffic Maps and Smartphone Trajectories to Model Air Pollution, Exposure and Health Impact

In this study, we explored to combine traffic maps and smartphone trajectories to model traffic air pollution, exposure and health impact. The approach was step-by-step modeling through the causal chain: engine emission, traffic density versus traffic velocity, traffic pollution concentration, exposure along individual trajectories, and health risk. A generic street with 100 km/h speed limit was used as an example to test the model. A single fixed-time trajectory had maximum exposure at velocity of 45 km/h at maximum pollution concentration. The street population had maximum exposure shifted to a velocity of 15 km/h due to the congestion density of vehicles. The shift is a universal effect of exposure. In this approach, nearly every modeling step of traffic pollution depended on traffic velocity. A traffic map is a super-efficient pre-processor for calculating real-time traffic pollution exposure at global scale using big data analytics.

Investigation on Inorganic Pollution Level in Surface Sediments of Naples and Salerno Bay

In this study, superficial marine sediments collected from 96 sampling sites were analyzed for 53 inorganic elements. Each sample was digested in aqua regia and analyzed by ICP-MS. A developed multifractal inverse distance weighted (IDW) interpolation method was applied for the compilation of interpolated maps for both single element and factor scores distributions. R-mode factor analysis have been performed on 23 of 53 analyzed elements. The 3 factor model, accounting 84.9% of data variability, were chosen, The three elemental associations obtained have been very helpful to distinguish anthropogenic from geogenic contribution. The aim of this study is to distinguish distribution patterns of pollutants on the sea floor of NaplesandSalernobays. In general, local lithologies, water dynamic and anthropogenic activities determine the distribution of the analyzed elements. To estimate pollution level in the area, Italian guidance, Canadian sediment quality guidance and Long’s criteria are chosen to set the comparability. As the results shows, arsenic and lead may present highly adverse effect to living creatures.

逐步Ⅱ型删失数据下逆威布尔分布的参数推断

逐步Ⅱ型删失方案在可靠性工程和生物医学中应用最为广泛,为此考虑在该删失方案下对寿命数据中常见的逆威布尔分布两参数进行推断。为解决对数似然函数过于复杂、求解较为困难的问题,利用EM算法进行参数估计,利用Bootstrap法进行区间估计。通过Monte Carlo模拟比较了不同删失模式下两参数的估计效果并通过实例分析展示了该估计方法的有效性。

基于高通量监测数据的PMF源解析数据输入量研究

为探究数据输入量的变化对源解析结果的影响,以上海某工业区的大气重金属高通量监测数据为例,按不同数据量将监测数据分别输入至正定矩阵因子分解(Positive Matrix Factorization, PMF)模型中,通过考察模型中Q理论值(Qtheo)与Q计算值(Qtrue)的接近程度、源分类以及源贡献与研究区污染源分布特点的吻合情况,分析数据输入量对源解析结果的影响。结果显示:该区域大气重金属污染受工业生产主导(64.44%),其次是扬尘(19.60%)和交通运输(15.96%)。通过对数据量的考察,发现输入量为60~120时能够得出研究区域的污染源数量与贡献率,但考虑到测试成本、获取数据的时间,认为输入量为60~80时,也能得出合理的源解析结果。短期高通量的分钟级数据集,有益于PMF模型输出高精密度、高时效性的源解析结果,是解决应急污染监控的最佳手段。

基于数据流的K-S变化检测的动态多目标规划算法

为了更加准确地判断环境是否发生变化并快速追踪动态多目标规划问题(dynamicmulti-objectiveoptimization problem,DMOP)当前时刻的Pareto前沿,提出了一种基于数据流的Kolmogorov-Smirnov(K-S)变化检测的动态多目标规划(DSK-SDMOP)算法。该算法以NSGA-Ⅱ为基础,通过数据流建立2个时刻的检验窗口,再利用K-S检验基于数据流的Pareto最优前沿是否发生变化,检测2个窗口的数据是否服从同一分布来判断环境是否发生变化,并就环境变化的剧烈程度实行相应的应答机制,以提高对环境的适应程度。利用基于数据流的K-S检测方法,对环境变化不会过于敏感,而且不用提前假设对应目标值的分布,易于操作。通过5个动态多目标规划标准测试函数对该算法进行测试,并和现有的2种算法进行对比分析,结果表明该算法处理动态多目标规划问题具有良好的性能。

基于用电数据的潍坊市工业污染源冬奥会管控效果评估

利用潍坊市安装用电监控的工业企业在冬奥会前和会中的生产用电数据及部分企业大气污染物排放在线监测数据,分析企业生产用电量和排放量的变化特征及潜在的相关性,评估管控措施落实状况及减排效果。结果表明:管控期间,潍坊市工业企业用电量相比管控前平均下降57.96%;完全停产类企业用电量下降75%以上,大气污染物(SO_(2)、NO_(x)、烟粉尘)排放量下降100%;限产类企业用电量降幅呈现随管控措施加严而增大的趋势;延迟复产类企业延迟复产率为47.83%~96.00%,用电量下降50.76%。根据管控期间各行业企业的用电量变化状况,判定各企业基本落实了减排要求。可见,企业用电数据在一定情况下,可以替代或辅助工业企业排放数据,反映企业管控措施落实状况及减排效果。

基于GPS数据的实验室制氢废液污染实时监测

为了提高制氢废液污染监测效果,文中提出基于GPS数据的实验室制氢废液污染实时监测方法。利用GPS系统采集污染数据,通过串口读取GPS数据,定位污染位置。以降低监测能耗为目的,采用在线监测机制和短信通知监测相互转换形式实现实验室制氢废液污染实时监测。结合GPS、GPRS、GIS三种技术对监测所得数据进行管理与分析,为实验室制氢废液污染治理提供技术支持。通过实际应用结果表明,文中设计方法监测能耗较低,安全性强,监测数据传输效率较高,证明所提方法具有较好的应用性能,可用于实验室制氢废液污染的实时监测。