Assessment of groundwater quality: a fusion of geochemical and geophysical information via Bayesian neural networks

Deplorable quality of groundwater arising from saltwater intrusion, natural leaching and anthropogenic activities is one of the major concerns for the society. Assessment of groundwater quality is, therefore, a primary objective of scientific research. Here, we propose an artificial neural network-based method set in a Bayesian neural network (BNN) framework and employ it to assess groundwater quality. The approach is based on analyzing 36 water samples and inverting up to 85 Schlumberger vertical electrical sounding data. We constructed a priori model by suitably parameterizing geochemical and geophysical data collected from the western part of India. The posterior model (post-inversion) was estimated using the BNN learning procedure and global hybrid Monte Carlo/Markov Chain Monte Carlo optimization scheme. By suitable parameterization of geochemical and geophysical parameters, we simulated 1,500 training samples, out of which 50 % samples were used for training and remaining 50 % were used for validation and testing. We show that the trained model is able to classify validation and test samples with 85 % and 80 % accuracy respectively. Based on cross-correlation analysis and Gibb’s diagram of geochemical attributes, the groundwater qualities of the study area were classified into following three categories: “Very good”, “Good”, and “Unsuitable”. The BNN model-based results suggest that groundwater quality falls mostly in the range of “Good” to “Very good” except for some places near the Arabian Sea. The new modeling results powered by uncertainty and statistical analyses would provide useful constrain, which could be utilized in monitoring and assessment of the groundwater quality.     

Environmental and sustainability indicators and the concept of situational indicators. A systems approach

Through a systems approach indicators are shown to have the nature of variables, adopting different values or states, while other associated terms allude to special meanings assigned to specific values of the indicators. Practical implications are discussed. The concept of situational indicators of sustainability is introduced, representing a non-numerical function of both “pressures” or “driving forces” and “state” variables. A cost-effective multi-tiered approach to situational indicators is proposed. The concept of situational indicators is shown to be a particular case of indicators of systems behavior. A general systems formulation of the problem of sustainability highlights the importance of indicators of systems behavior and the central role of models in their definition.     

Holistic approach for quantification and identification of pollutant sources of a river basin by analyzing the open drains using an advanced multivariate clustering

Global scarcity of freshwater has been gearing towards an unsustainable river basin management and corresponding services to the humans. It needs a holistic approach, which exclusively focuses on effective river water quality monitoring and quantification and identification of pollutant sources, in order to address the issue of sustainability. These days, rivers are heavily contaminated due to the presence of organic and metallic pollutants released from several anthropogenic sources, such as industrial effluents, domestic sewage, and agricultural runoff. It is astonishing to note that even in many developing countries, most of these contaminants are carried through open drains, which enter river premises without proper treatment. Such practice not only devastates riverine ecosystem but also gives rise to deadly diseases, such as minimata and cancer in humans. Considering these issues, the present study develops a novel approach towards simultaneous identification of major sources of pollution in the rivers, along with critical pollutants and locations using an advanced hierarchical cluster and multivariate statistical analysis. A systematic approach has been developed by agglomerating both R-mode and Q-mode analysis, which develops monoplots, two-dimensional biplots, rotated component matrices, and dendrograms (using “SPSS” and “Analyse It” software) to reveal relationships among various quality parameters to identify the pollutant sources along with clustering of critical sampling sites and pollutants. A case study of the Ganges River Basin of India has been considered to demonstrate the efficacy and usefulness of the model by analyzing 85 open drains. Both organic and metallic pollutants are analyzed simultaneously as well as separately to get a holistic understanding of all the relationships and to broaden the perspective of water characterization. Results provide a comprehensive guidance to the policy makers and water managers to optimize corrective efforts, minimize further damage, and improve the water quality condition to ensure sustainable development of the river basin.     

Rethinking vulnerability in city-systems: A methodological proposal to assess “urban entropy”

This paper aims at proposing a possible alternative point of view to investigate the vulnerability of urban systems. The basic ideal refers to the possibility of thinking about vulnerability as deriving by the interactions of several risks that can affect the urban system and by the interactions among them. In this sense, it is possible to refer to an “integrated territorial risk”. Considering the city as a complex and dynamic system that while evolving produce entropy is the main theoretical reference supporting this study. The loss of energy during the evolution of the system corresponds to some conditions of inefficiency that involve the whole system and, as such, this lost energy can be assumed as a “systemic entropy”. Is it possible to measure the levels of this vulnerability of the urban system when it stays in ordinary conditions, namely not during stress states that modify the state of equilibrium of the system itself? It is possible to assess the production of this “internal entropy”? In order to answer to these questions in mind, this study aims at analyzing dyscrasias that can occur within the main components of the urban system in order to individuate possible strategies able both to mitigate the fragility of the urban system and to improve its resilience.     

方法标准验证实验数据中离群值的识别

对方法标准验证实验中测量数据进行合格性审核,对于后续方法精密度计算是一个重要环节。文献中识别离群值的Grubbs法、Dixon法等经典方法有时不能满足要求。探讨了用稳健统计法识别离群值的可行性。基于2套文献数据和XRF方法标准验证实验精密度测量数据,对Grubbs法、Dixon法、Mandel h检验法、质控指标法和稳健统计法(四分位法、迭代法、合格数据范围判定法)进行了比较。结果表明:稳健统计法可有效识别离群值。但四分位法存在过度"检出"现象。综合考虑多种方法识别结果有利于提高离群值判定结论的可靠性。对于个别难以判断的情形,可借助质控指标、技术要求以及数据是否剔除对实验室间标准偏差的影响进行取舍。     

Tailoring groundwater quality monitoring to vulnerability: a GIS procedure for network design

Monitoring networks aiming to assess the state of groundwater quality and detect or predict changes could increase in efficiency when fitted to vulnerability and pollution risk assessment. The main purpose of this paper is to describe a methodology aiming at integrating aquifers vulnerability and actual levels of groundwater pollution in the monitoring network design. In this study carried out in a pilot area in central Italy, several factors such as hydrogeological setting, groundwater vulnerability, and natural and anthropogenic contamination levels were analyzed and used in designing a network tailored to the monitoring objectives, namely, surveying the evolution of groundwater quality relating to natural conditions as well as to polluting processes active in the area. Due to the absence of an aquifer vulnerability map for the whole area, a proxi evaluation of it was performed through a geographic information system (GIS) methodology, leading to the so called “susceptibility to groundwater quality degradation”. The latter was used as a basis for the network density assessment, while water points were ranked by several factors including discharge, actual contamination levels, maintenance conditions, and accessibility for periodical sampling in order to select the most appropriate to the network. Two different GIS procedures were implemented which combine vulnerability conditions and water points suitability, producing two slightly different networks of 50 monitoring points selected out of the 121 candidate wells and springs. The results are compared with a “manual” selection of the points. The applied GIS procedures resulted capable to select the requested number of water points from the initial set, evaluating the most confident ones and an appropriate density. Moreover, it is worth underlining that the second procedure (point distance analysis [PDA]) is technically faster and simpler to be performed than the first one (GRID?+?PDA).     

On a Holistic Modeling Approach for Managing Carbon Emission Ecosystems

Effective use of historical volumes of heterogeneous and multidimensional data is a major challenge, especially projects associated with potential applications of carbon emission ecosystems. Data science in these applications becomes tedious when such varied data are accumulated and or distributed in multiple domains. Design, development, and implementation of sustainable geological storages are crucial for managing carbon dioxide (CO₂) emissions and its modeling process. The purpose of the research is to address major challenges and how best a robust “ontology-based multidimensional data warehousing and mining” approach can resolve issues associated with carbon ecosystems. The conceptualized relationships deduced among multiple domains, integration of domain ontologies, data mining, visualization, and interpretation artefacts are highlights of the study. Several data, plot, and map views are extracted from metadata storage for interpreting new knowledge on carbon emissions. Statistical mining models describe data attributes’ correlations, patterns, and trends that can help in predicting future forecast of CO₂ emissions worldwide.     

Use of a GIS-based hybrid artificial neural network to prioritize the order of pipe replacement in a water distribution network

A methodology based on the integration of a seismic-based artificial neural network (ANN) model and a geographic information system (GIS) to assess water leakage and to prioritize pipeline replacement is developed in this work. Qualified pipeline break-event data derived from the Taiwan Water Corporation Pipeline Leakage Repair Management System were analyzed. “Pipe diameter,” “pipe material,” and “the number of magnitude-3^?+? earthquakes” were employed as the input factors of ANN, while “the number of monthly breaks” was used for the prediction output. This study is the first attempt to manipulate earthquake data in the break-event ANN prediction model. Spatial distribution of the pipeline break-event data was analyzed and visualized by GIS. Through this, the users can swiftly figure out the hotspots of the leakage areas. A northeastern township in Taiwan, frequently affected by earthquakes, is chosen as the case study. Compared to the traditional processes for determining the priorities of pipeline replacement, the methodology developed is more effective and efficient. Likewise, the methodology can overcome the difficulty of prioritizing pipeline replacement even in situations where the break-event records are unavailable.     

A methodology for treating missing data applied to daily rainfall data in the Candelaro River Basin (Italy)

Environmental time series are often affected by the “presence” of missing data, but when dealing statistically with data, the need to fill in the gaps estimating the missing values must be considered. At present, a large number of statistical techniques are available to achieve this objective; they range from very simple methods, such as using the sample mean, to very sophisticated ones, such as multiple imputation. A brand new methodology for missing data estimation is proposed, which tries to merge the obvious advantages of the simplest techniques (e.g. their vocation to be easily implemented) with the strength of the newest techniques. The proposed method consists in the application of two consecutive stages: once it has been ascertained that a specific monitoring station is affected by missing data, the “most similar” monitoring stations are identified among neighbouring stations on the basis of a suitable similarity coefficient; in the second stage, a regressive method is applied in order to estimate the missing data. In this paper, four different regressive methods are applied and compared, in order to determine which is the most reliable for filling in the gaps, using rainfall data series measured in the Candelaro River Basin located in South Italy.     

新发展阶段生态环境监测面临的若干问题和解决思路

“十四五”时期是我国全面建成小康社会、实现第一个百年奋斗目标之后,乘势而上开启全面建设社会主义现代化国家新征程、向第二个百年奋斗目标进军的第一个五年,我国将进入新发展阶段。通过研究新发展阶段需求,分析生态环境监测存在的问题和短板,指出在职能转变、“减污降碳”、“真、准、全”等方面距离深入治污攻坚要求存在的差距,并以新发展理念为指导,研究提出生态环境监测体系与监测能力现代化建设的内涵实质、重要基础以及关键突破口,为加快构建生态环境监测新发展格局提供参考。     

用稳健统计法处理方法标准验证实验中方法检出限数据

方法检出限是分析方法基本性能参数之一。根据《环境监测分析方法标准制修订技术导则》(HJ 168—2010)的要求,此参数需要6家以上实验室基于特定样品测试数据的标准偏差得到。目前,标准文本大多采用各家实验室方法检出限中最大值。研究提出用稳健统计法处理方法验证数据,无需识别和删去离群值,可将离群值的影响降到最小。基于4组公开发表数据和2组实测数据,比较了不同方法计算结果。结果表明:参与统计的3组数据29个检测项目中,常规方法确认的方法检出限被判为离群值占27.6%。取最大值存在数值偏高的风险。提出了用曼德尔k检验法识别1组方法检出限数据中的离群值,该方法同样可以用于实验室数据与标准文本中方法检出限符合性判断。     

Biochemical profile of non-enzymatic stress markers in the plant species “<Emphasis Type="Italic">Urginea maritima</Emphasis>” in a Mediterranean natural reserve exposed to oxidative stress

Protected areas decrease degrading natural ecosystems due to pollution such as air pollution. In 1981, the inhabitants founded Bentael natural reserve in Byblos, Lebanon, to secure their region against urbanization projects, like the recently constructed road that threatens the biodiversity of the reserve. This study was conducted to determine the oxidative stress resulting from this pollution and that menaces 360 floral species among them a rare species “Urginea maritima.” In this research, the biomonitoring approach was experienced to assess the oxidative stress. Biomonitoring possesses has the advantage to be low cost and a constructive method to generate valuable data for further examinations. The studied parameters were air pollutants, ascorbic acid, photosynthetic pigments, leave’s pH, relative water content, proline, carbohydrates, and hydrogen peroxide, in three chosen spots, near the pollution source (P1), opposite the latter spot (P2), and in an area relatively far from the source of contamination and which was chosen as the control site (Ctrl). The results showed in P1 detection of air pollutants higher of about 80% than in Ctrl, modifications in stress markers: increased concentration of the reactive oxygen species “hydrogen peroxide,” rise in the concentration of the osmoregulator amino acid “proline,” and depletion in chlorophyll content, in contrast to an increase in pheophytin. All these findings can be exploited as early diagnosis of air pollution and confirmed the ability to use such biomonitor (“Urginea maritima”) as a way to assess the environmental pollution levels and consequently affirm the danger of such landscape activities on natural reserves.     

Developing an automated BIM-based life cycle assessment approach for modularly designed high-rise buildings

Modular construction has attracted increasing attention due to its energy and environmental benefits. Digital technologies such as building information modelling (BIM) have also been explored to generate and manage data through the lifecycle of buildings. Although research has been performed in the area of integrating BIM and modular construction, BIM-based automated lifecycle assessment (LCA) of prefabricated buildings remains unexplored. This study therefore aims to develop a BIM-based LCA method for prefabricated buildings incorporating different assessment levels with unique system boundaries and functional units. The developed approach can support automated assessments through all lifecycle phases of a prefabricated building. It is achieved through an automated process of creating parameters to merge LCA data into the building model, systematic zoning, model setup and impact estimation. This approach is applied to evaluate the energy and environmental performances of a case building in Hong Kong. The case study validated the efficiency of the developed BIM-based LCA method in providing a systematic and detailed assessment of modularly designed buildings. This study extends the knowledge in automated BIM-based LCA by addressing specific characteristics of prefabrication and promotes the incorporation of comprehensive and detailed LCA data into BIM models for improved design robustness and holistic performances of buildings. This validated approach will enhance the willingness of designers to apply LCA during the design stages for minimizing the energy and environmental impacts of both new and renovated buildings with prefabrication.     

Evaluation of surface water quality by using Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) method and discriminant analysis method: a case study Coruh River Basin

In this study, the water quality of the Coruh River Basin, which is located in the Eastern Black Sea Region of Turkey, was evaluated. The water quality data measurement results obtained by the State Hydraulic Works 26th Regional Directorate from four different sites over a course of 4 years between the years 2011 and 2014 in the Coruh River Basin were used as the data. In this study, the water quality was evaluated by using the Canadian Council of Ministers of the Environmental Water Quality Index (CCME WQI) method and discriminant analysis (DA). The water quality of the Coruh River Basin was calculated as 30.4 and 71.35 by using the CCME WQI and classified as “poor,” “marginal,” and “fair”. These values show that the water of the Coruh River Basin is degraded and under threat and its overall quality is not close to natural or desired levels. The monitoring sites were divided into two groups by the cluster analysis (CA). DA is a multivariate analysis technique used to divide individuals or objects into different groups and assign them into predetermined groups. As a result of DA, calcium (Ca) and sulfate (SO₄) were determined to be significant parameters in the determination of the water quality of the Coruh River Basin. The success of DA depends on the percentage of correct classification. As a result of the analysis, 23% of the parameters in the first measurement point, 69.2% of the parameters in the second and third measurement points, and 76.9% of the parameters in the fourth measurement point were classified correctly. Since the second measurement point is the discharge point of a copper mine, it can be said that the water quality parameters measured may provide accurate results in detecting pollution at this point.     

实验室能力验证中的分割水平检测样品与稳健统计技术

论述了分割水平检测样品和稳健统计技术在对实验室能力验证活动中的应用,并对导致CNAL T040水中金属元素检测能力验证计划中部分结果异常的原因进行了分析。     

Optimizing the flow adjustment of constituent concentrations via LOESS for trend analysis

Trend analysis of stream constituent concentrations requires adjustment for exogenous variables like discharge because concentrations often have variable relations with flow. To remove the influence of flow on stream water quality data, an accurate characterization of the relationship between the constituent and streamflow is needed. One popular method, locally weighted regression (LOESS), provides an effective means for flow-adjusting concentrations. The LOESS fit can be tailored to the data via the smoothing parameter (f), so that the user can avoid overfitting or oversmoothing the data. However, it is a common practice to use a single f value when flow-adjusting water quality data for trend analysis. This study provides a robust, automated method for determining the optimal f value (f_opt) for each dataset via an iterative K-fold cross-validation procedure that minimizes prediction error in LOESS. The method is developed by analyzing datasets of seven different constituents across 17 sites (119 datasets total) from a stream monitoring program in northwest Arkansas (USA). We recommend using 10 iterations of 10-fold cross-validation (10?×?10 CV) in order to select f_opt when flow-adjusting water quality data with LOESS. The use of a default f value did not produce different trend interpretations for the data used here; however, the proposed approach may be helpful in other water quality studies which employ similar statistical fitting methods. Additionally, we provide an implementation of the method in the R statistical computing environment.     

The spatial convergence and drivers of environmental efficiency under haze constraints - Evidence from China

Haze has seriously threatened the ecological environment and public health, so researching environmental efficiency under haze constraints is significant in managing pollution and evaluating the balance between economic development and environmental health. Based on the panel data of China's 99 cities during 2005–2017, this paper uses α-convergence and β-convergence models to analyze the spatial convergence of environmental efficiency under haze constraints and its drivers in China,specifically in Eastern China, Central China, and Western China.The main results are as follows: (1) The trend of environmental efficiency under haze constraints in these regions evidently follows a dumbbell curve. In recent years, it has been growing slowly in Eastern China, Central China, and Western China, and the environmental efficiency in the Eastern China is the highest, the Central China is the second, and the Western China is the lowest; (2) it demonstrates both α-convergence and β-convergence in Central China, Western China, and Eastern China, and the speed of convergence is the fastest in the Central China, the second in the Western China and the slowest in the Eastern China; there is a “catch-up effect” in cities from less environmentally efficient regions to more efficient regions; and (3) Industrial structure and degree of trade dependence have a significantly negative correlation; characteristics of the region and dependence on foreign investment have significantly positive correlations with the environmental efficiency of Eastern China and Central China; environmental regulations and economic scale limit the improvement of environmental efficiency of Central China and Western China; and technological innovation significantly impacts Eastern China only. Although the paper refers to the specific evidence of China, with haze as a new indicator in the comprehensive evaluation of environmental efficiency can be extended to other developing countries as well as to other pollutants such as land pollution and waste water. The understanding of drivers of environmental efficiency under haze constraints in China provides international policy-makers with valuable reference for formulating effective measures to balance the dilemma between economic development and environmental health.     

Did the ecological engineering have a great impact on the land use change?

Overuse of land resources has increasingly contributed to environmental crises in China. To mitigate widespread land degradation, actions have been taken to maintain and restore the ecological environment through efforts such as ecological engineering. By analyzing trends in land use, the impact and effectiveness of ecological engineering can be determined. In this study, such changes in Huanjiang County in China were considered. In the early 1990s, an eco-immigration policy and “returning farmland to forest program” were implemented in the county, drastically impacting land use. Land use/land cover changes were detected and analyzed using remote sensing data recorded over 4 years (1995, 2000, 2005, and 2010). Land transfer flow and the rate of land use change elucidated the extent of changes, while nuclear density analysis indicated spatial agglomeration. The results indicate that, over a period of 15 years, farmland area increased, while forest area decreased initially before subsequently increasing. From 1995 to 2000, the highest transfer flow was observed in the grassland to farmland conversion (79.34%). From 2000 to 2005, the transfer flow of conversions was the highest for forest to farmland (56.79%). Land use changes were not prominent from 2005 to 2010. Direct drivers of land use change exert obvious impacts on land use, and indirect drivers impact direct drivers that are then channeled through direct anthropogenic drivers (e.g., land use policies). We found that ecological engineering has a very significant impact on land use change, and that impact varies from region to region.     

江苏省水环境质量监测现状与对策分析

简述了近40年来江苏省水环境质量监测工作正朝着监测对象不断完善、监测频次渐趋合理、监测指标全面覆盖的方向发展。指出,监测能力建设滞后于管理需求、重复监测影响管理决策、冗余监测增加工作负荷、监测点位设置缺乏针对性是当前江苏省水环境质量监测存在的主要问题。提出,加强监测系统能力建设、建立水质监测权威性、提高水质监测效能、提升数据研判能力的水环境质量监测建议,为环境管理、污染防治提供科学决策支撑。     

Detection of seasonal variations in surface water quality using discriminant analysis

In the study, discriminant analysis (DA) was applied to water quality data set monitored in winter and summer months along 3 years to investigate seasonal variations of Tahtali Reservoir water quality. The variables were divided into two categories as “parameters naturally found in surface waters—Al, Fe, Mn, F, Ba, Cl, and SO₄” and “pollutants—biochemical oxygen demand, chemical oxygen demand, and NO₃”. Linear discriminant functions were calculated to distinguish between winter and summer samples. Results revealed that water quality variables (naturally found in surface water and also pollutants) were distinctly different between two seasons. Seasonal changes of water quality caused by anthropogenic activities should be taken into consideration by the water managers especially in summer seasons in which higher concentrations were observed. The study showed that DA can be applied successfully to establish the seasonal variation in surface water quality and make it possible to design a future temporal sampling strategy in an optimal manner.