Spatial-Temporal Event Detection Method with Multivariate Water Quality Data

With the rapid development of the society,water contamination events cause great loss if the accidents happen in the water supply system.A large number of sensor nodes of water quality are deployed in the water supply network to detect and warn the contamination events to prevent pollution from speading.If all of sensor nodes detect and transmit the water quality data when the contamination occurs,it results in the heavy communication overhead.To reduce the communication overhead,the Connected Dominated Set construction algorithm-Rule K,is adopted to select a part fo sensor nodes.Moreover,in order to improve the detection accuracy,a Spatial-Temporal Abnormal Event Detection Algorithm with Multivariate water quality data(M-STAEDA)was proposed.In M-STAEDA,first,Back Propagation neural network models are adopted to analyze the multiple water quality parameters and calculate the possible outliers.Then,M-STAEDA algorithm determines the potential contamination events through Bayesian sequential analysis to estimate the probability of a contamination event.Third,it can make decision based on the multiple event probabilities fusion.Finally,a spatial correlation model is applied to determine the spatial-temporal contamination event in the water supply networks.The experimental results indicate that the proposed M-STAEDA algorithm can obtain more accuracy with BP neural network model and improve the rate of detection and the false alarm rate,compared with the temporal event detection of Single Variate Temporal Abnormal Event Detection Algorithm(M-STAEDA).

Long-Term Application of Remote Sensing Chlorophyll Detection Models: Jordanelle Reservoir Case Study

Algae blooms pose a threat to water quality by depleting oxygen during decomposition and also cause other issues with water quality and water use. Algae biomass is traditional monitored through field samples analyzed for chlorophyll-a, a pigment present in all algae. Field sampling can be time- and cost-intensive, especially in areas that are difficult to access and provides only limited spatial coverage. Estimations of algal biomass based on remote sensing data have been explored over the past two decades as a supplement to information obtained from limited field samples. We use Landsat data to develop and demonstrate seasonal remote sensing models, a relatively recent method, to evaluate spatial and temporal algae distributions for the Jordanelle Reservoir, located in north-central Utah. Remote sensing of chlorophyll as a monitoring and analysis method can provide a more spatially complete representation of algae distribution and biomass;information that is difficult to obtain using point samples.

Advances in Water Quality Monitoring of Inorganics: Current Trends

New methods of analysis for water quality monitoring to detect inorganic substances are required to meet the demands of determining concentration, particularly at low detection limits, analysing speciation and even identifying the pollution source. Such information is essential to inform public health decisions and to comply with more stringent legislation. This paper concentrates on two case studies, reviewing the development in monitoring methods, and predicting future trends. Arsenic and nitrates detection was selected as these pollutants are particularly problematic from a human health perspective. Additionally, the challenges faced in developing monitoring methods for these chemicals are relevant to a wide range of other inorganics. The current state of the art in detection approaches for these chemicals are discussed along with recommendations for future research to further improve the methods.

Protecting Water Quality and Public Health Using a Smart Grid

After the attacks on September 11, 2001 and the follow-up risk assessments by utilities across the United States, securing the water distribution system against malevolent attack became a strategic goal for the U.S. Environmental Protection Agency. Following 3 years of development work on a Contamination Warning System (CWS) at the Greater Cincinnati Water Works, four major cities across the United States were selected to enhance the CWS development conducted by the USEPA. One of the major efforts undertaken was to develop a process to seamlessly process “Big Data” sets in real time from different sources (online water quality monitoring, consumer complaints, enhanced security, public health surveillance, and sampling and analysis) and graphically display actionable information for operators to evaluate and respond to appropriately. The most significant finding that arose from the development and implementation of the “dashboard” were the dual benefits observed by all four utilities: the ability to enhance their operations and improve the regulatory compliance of their water distribution systems. Challenge: While most of the utilities had systems in place for SCADA, Work Order Management, Laboratory Management, 311 Call Center Management, Hydraulic Models, Public Health Monitoring, and GIS, these systems were not integrated, resulting in duplicate data entry, which made it difficult to trace back to a “single source of truth.” Each one of these data sources can produce a wealth of raw data. For most utilities, very little of this data is being translated into actionable information as utilities cannot overwhelm their staffs with manually processing the mountains of data generated. Instead, utilities prefer to provide their staffs with actionable information that is easily understood and provides the basis for rapid decision-making. Smart grid systems were developed so utilities can essentially find the actionable needle in the haystack of data. Utilities can then focus on rapidly evaluating the new information, compare it known activities occurring in the system, and identify the correct level of response required. Solution: CH2M HILL was engaged to design, implement, integrate, and deploy a unified spatial dashboard/smart grid system. This system included the processes, technology, automation, and governance necessary to link together the disparate systems in real time and fuse these data streams to the GIS. The overall solution mapped the business process involved with the data collection, the information flow requirements, and the system and application requirements. With these fundamentals defined, system integration was implemented to ensure that the individual systems worked together, eliminating need for duplicate data entry and manual processing. The spatial dashboard was developed on top of the integration platform, allowing the underlying component data streams to be visualized in a spatial setting. Result: With the smart grid system in place, the utilities had a straightforward method to determine the true operating conditions of their systems in real time, quickly identify a potential non-compliance problem in the early stages, and improve system security. The smart grid system has freed staff to focus on improving water quality through the automation of many mundane daily tasks. The system also plays an integral role in monitoring and optimizing the utilities’ daily operations and has been relied on during recovery operations, such as those in response to recent Superstorm Sandy. CH2M HILL is starting to identify the processes needed to expand the application of the smart grid system to include real-time water demands using AMI/AMR and real-time energy loads from pumping facilities. Once the smart grid system has been expanded to include Quality-Quantity-Energy, CH2M HILL can apply optimization engines to provide utility operations staffs with a true optimization tool for their water systems.

Generative adversarial networks for detecting contamination events in water distribution systems using multi-parameter,multi-site water quality monitoring

Contamination events in water distribution networks(WDNs)can have a huge impact on water supply and public health;increasingly,online water quality sensors are deployed for real-time detection of contamination events.Machine learning has been used to integrate multivariate time series water quality data at multiple stations for contamination detection;however,accurate extraction of spatial features in water quality signals remains challenging.This study proposed a contamination detection method based on generative adversarial networks(GANs).The GAN model was constructed to simultaneously consider the spatial correlation between sensor locations and temporal information of water quality indicators.The model consists of two networksda generator and a discriminatordthe outputs of which are used to measure the degree of abnormality of water quality data at each time step,referred to as the anomaly score.Bayesian sequential analysis is used to update the likelihood of event occurrence based on the anomaly scores.Alarms are then generated from the fusion of single-site and multi-site models.The proposed method was tested on a WDN for various contamination events with different characteristics.Results showed high detection performance by the proposed GAN method compared with the minimum volume ellipsoid benchmark method for various contamination amplitudes.Additionally,the GAN method achieved high accuracy for various contamination events with different amplitudes and numbers of anomalous water quality parameters,and water quality data from different sensor stations,highlighting its robustness and potential for practical application to real-time contamination events.

基于视觉感知与V-detector的水质异常检测方法

为了快速准确地对水质进行评价分析,提出了一种新的水质异常检测方法.该方法将视觉感知技术与人工免疫系统中的否定选择原理相结合.首先采用视觉驱动的生物监测技术对水质指标进行采集,然后利用基于否定选择原理的可变V-detector算法对水质指标进行评价分析.由于视觉感知技术具有敏感性、综合性以及人工免疫系统具有自组织、自学习、自识别等特点,故该方法能够快速准确地对水质进行实时检测评价.实验结果表明该方法是一种实用的水质检测评价方法.

Ultrastable and ultrasensitive pH-switchable carbon dots with high quantum yield for water quality identification,glucose detection,and two starch-based solid-state fluorescence materials

It is attractive and encouraging to develop new fluorescent carbon dots(CDs)with excellent optical properties and promising applications prospects.Herein,highly-efficient green emissive CDs(m-CDs)with a high quantum yield(QY)of 71.7%in water are prepared through a facile solvothermal method.Interestingly,the m-CDs exhibit excellent fluorescence stability in the pH range of 1–9.However,the fluorescence intensity of the m-CDs is almost completely quenched as the pH is increased from 9 to 10.The mechanism of the unique pH-responsive behavior is discussed in detail and a plausible mechanism is proposed.Owing to the unique pH-responsive behavior,the m-CDs are used as a on-off fluorescent probe for water quality identification.By combining the reversible pH-ultrasensitive optical properties of the m-CDs in the pH range of 9–10 with the glucose oxidase-mimicking(GOx-mimicking)ability of Au nanoparticles(AuNPs),glucose can be quantitatively detected.Finally,two environment-friendly starch-based solid-state fluorescence materials(powder and film)are developed through green preparation routes.

Research on modeling method of continuous spectrum water quality online detection based on random forest

It's common to use the method of continuous spectroscopy in water quality testing. But there're some problems with it. For example, the scanning results have a large number of nonlinear signals, and the covariance between variables is serious, which can lead to a decrease in the model prediction accuracy. In this paper, the standard solutions of nitrate nitrogen(NO_(3)-N) and nitrite nitrogen(NO_(2)-N) were used as the subject to be tested, and the data of the scanned waves and absorbance were obtained by use of spectral detector. The data were processed by noise reduction first and then the random forest(RF) algorithm was adopted to establish the regression relationship between concentration and absorbance. For comparison, partial least squares(PLS) and support vector machine(SVM) algorithm models were also established. For the same given data, the three reverse models can make the projection of the concentration respectively. The experimental results show that the RF algorithm predicts NO_(2)-N concentrations significantly better than the SVM algorithm and PLS algorithm. This proves that the RF algorithm has good prediction ability in spectral water quality detection because of its high model accuracy and better adaptability, which could be a reference for similar research on continuous spectral water quality online detection.

一种分光光度法实现的多参数水质在线检测仪

针对传统分光光度法实现的水质在线检测仪存在的检测参数单一、效率低下、光源受环境温度干扰等问题,设计了一种高效快速的多参数水质在线检测仪。该仪器以总磷、CODCr、氨氮为检测对象,通过优化检测流程提高了检测效率。同时采用双光束测量法补偿光源受环境温度的影响。经性能测试显示,该仪器在示值误差、重复性、零点漂移、量程漂移等关键指标上均优于国家标准。

紫外可见光谱在水质COD检测中的应用研究

加强水质监测是严控水污染和确保水质健康的关键性工作,为加强对水质污染问题的处理,文章提出紫外可见光谱技术在水质COD检测中的应用,从检测原理入手,提出了一种预测模型,以达到检测水质COD的目的。

地下水水质分析的检测方法及质量控制

地下水是重要的自然资源之一,是我国城市居民主要的用水来源。但是地下水非常容易受到周边环境的干扰,导致发生污染,而一旦地下水遭到污染,如果继续饮用,将会给人们的身体健康造成严重的伤害,同时,对于周边自然生态环境来说,也会产生严重的影响,导致生态环境遭到破坏。本文通过对地下水常见的污染物进行分析,对地下水水质分析检测方法进行了探讨,并就如何提升地下水水质分析质量进行了探究,以供参考。

化学沉淀法在水质处理中的应用与优化

文章简要介绍了化学沉淀法在水质处理中的应用与优化。首先探讨了化学沉淀法的原理和常用沉淀剂,以及反应条件对沉淀效率的影响;然后列举了该方法在重金属污染处理、悬浮物去除和有机物清除等方面的应用,重点研究了pH值控制、沉淀剂选择和搅拌条件优化等策略;最后简要评述了化学沉淀法的优势与局限性,并展望了未来发展方向。

养殖工船作业型水下机器人结构设计与研究

水质检测是深远海集约化养殖的重要环节,水质好坏直接影响着鱼类的生长。目前,水质检测的主要方式是构建水质监测网络系统和人力采集水样检测,这些检测方式存在操作复杂且效率低、作业区域受限、监测周期长等问题。为此,研发设计了一款用于养殖工船水质检测的作业型水下机器人。首先提出水质检测系统和水下机器人系统的总体设计方案。然后利用Solidworks建模软件对水下机器人机械结构进行整体设计,外壳采用流线型结构,其上搭载抽水管口,采用水平2个和垂直4个推进器布局设计,保证水下机器人能够自由运动;基于Ansys软件对机器人的外壳、固定结构和耐压舱等进行应力数值模拟,并结合理论计算对机器人的设计进行完善。最后完成机器人样机软件调试以及性能、自由度和水质检测等水下试验测试。结果表明:水下机器人的密封性、自由度、抽水检测等性能试验均满足工作要求,可以达到对指定位置进行分层和定点水质采样,所获得的水质检测数据可为水产养殖提供数据参考。

水质检测实验室质量管理措施分析

水质检测与分析是保障生活饮用水水质安全的关键措施之一,水质检测的过程、结果与质量决定了人们的用水安全,因此至关重要。本文以水质检测实验室为切入点,总结实验室分析全过程质量控制和质量监督措施,为实验室质量管理提供有效的措施。

饮用水中离子色谱法检测的应用进展

在人民群众生活方式不断改变、生活品质逐渐提升的过程中,对饮用水也具有更高的要求。饮用水质量及卫生问题作为一个重大问题,现阶段已受到整个社会的广泛关注,进而也在一定程度上促进了相关检测技术的快速发展。作为高效液相色谱法中的一种,离子色谱法不仅科学,而且高效,在水质、土壤以及大气检测中得到了广泛应用,离子色谱法是检测饮用水的最佳方法,能对多种阴离子进行检测,在饮用水检测工作中具有非常重要的作用。文章主要分析了离子色谱法在饮用水检测中的运用进展,希望能保证饮用水质量;本研究结果也为饮用水质量检测后期标准制定提供了借鉴内容。

关于生活饮用水中金属元素检测方法分析及质量控制探讨

在生活饮用水中,不同金属元素的含量会因为自然环境和生产活动等因素变化而发生改变。因此,在检测和质量控制时需要综合考虑各种因素,选择合适的检测方法,确保测试结果的准确性和可靠性。同时,为了更好地保障人民的生命健康,需要制定更加严格的水质标准和检测方法,加强对生活饮用水中金属元素的监管,以确保人民饮用水的水质安全。基于此,本文阐述了生活饮用水中金属元素的危害及检测意义,对生活饮用水中金属元素的检测方法进行了分析,探讨了生活饮用水中金属元素检测质量的控制措施。

2023年湖南省各级疾控中心水质检测能力调查分析

目的了解湖南省各级疾控中心水质检测能力现状,为实验室能力建设提供参考。方法2023年以调查表的方式,对湖南省各级疾控中心的水质检测能力开展调查分析。共计回收调查表131份,其中省级1份,地市级14份(含省会城市1份),县级(含县级市)86份,区级30份。结果2023年省疾控中心具备《生活饮用水卫生标准》(GB 5749-2022)中95项水质指标检测能力,地市级、县级、区级水质全项指标(97项)检测能力M(P25,P75)依次为91(86,94)、44(39,51)、39(35,44),常规指标(43项)检测能力M(P_(25),P_(75))依次为41(41,43)、38(34,40)、33(31,38),不同级别全项指标、常规指标检测能力差异均有统计学意义(χ^(2)分别为46.84、37.14,P<0.001);两两比较分析结果显示,地市级、县级、区级全项指标和常规指标检测能力逐渐递减,差异有统计学意义(校正后的P<0.05)。省疾控中心暂未具备总α放射性和总β放射性指标检测能力,目前该两项指标由省职业病防治院开展检测;地市级疾控中心贾第鞭毛虫、隐孢子虫、2-甲基异莰醇、土臭素检测能力最低(不具备检测能力机构有11个),其次为总α放射性、总β放射性(不具备检测能力机构有8个);县区级疾控中心总α放射性和总β放射性指标的检测能力最低(不具备检测能力机构占87.07%),其次为三氯乙酸、二氯乙酸、臭氧、三卤甲烷、二氯一溴甲烷、三溴甲烷、一氯二溴甲烷、溴酸盐、总氯(不具备检测能力机构占比超过40%)。结论2023年湖南省各级疾控中心水质检测能力较2012年均有明显提升,但与国家能力建设要求还有较大差距。县区级疾控中心对常规指标中新增的消毒副产物指标的检测能力应重点加强。

多参数水质检测智能传感器信号处理及建模

针对多参数水质检测中智能传感器易受到环境、噪声、光源等干扰因素的影响,提出一种结合递推平均滤波算法和小波变换算法的传感器信号处理方法.该方法利用递推平均滤波算法对传感器输入信号的周期性干扰因素进行预处理,并利用小波变换算法进一步减少传感器信号的噪声.小波变换算法能够通过伸缩和平移来细化采集信号,以此实现对时频的局部化处理,最终达到对高频部分进行时间细化、对低频部分进行频率细化的目的.实验结果显示,在同样的阈值条件下,降低噪声的数量越多,对噪声的抑制作用越显著.由此验证了所提方法的有效性.

基于聚类算法的城市供排水水质检测系统

城市供排水水质检测需要使用多种仪器、步骤繁杂,设计一种基于聚类算法的城市供排水水质自动检测系统。硬件设计方面,设计多参数水质检测仪,在传感器检测模块中,应用传感器实施城市供排水水质中余氯、pH值、TDS值以及温度的检测。设计控制单片机用于系统检测控制。软件设计方面,基于自适应密度峰值聚类算法实施光谱信号数据挖掘,在水质检测融合模块中,设计水质检测融合模型,输出各项目对应的检测结果。测试结果表明,通过设计系统能够实现供水水质与排水水质的自动检测,说明该系统有一定应用前景。

川法小曲酒泡粮废水的水质和有机组分的检测分析

泡粮作为川法小曲酒生产过程中必不可少的一道工序,能使粮食吸水膨胀,使淀粉结构松弛,为蒸煮糊化创造条件。泡粮废水作为酿酒废水的主要部分之一,对其进行分析检测是开发高效处理手段和资源化利用的必要步骤。本文对泡粮废水的水质经行了检测分析,并利用傅里叶红外光谱、元素分析、紫外分光光度法等对泡粮水中的具体有机组分进行了检测分析。水质分析结果显示泡粮水是一种COD_(Cr)和BOD_5含量高、总氮和总磷含量高的“三高”工业废水,指标远高出了国家废水的排放标准。有机组分分析结果表明,泡粮废水是富氧有机物,含有较多的可溶性多糖和酸类物质,具有资源化回收利用的价值。