scholarly journals Decentralization and Transboundary Pollution: Evidence from the Change of Water Pollution Levels in China

2017 ◽  
Author(s):  
Yongliang Yang ◽  
Manhong Shen
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Pollution Control ◽  
Water Environment ◽  
Pollution Reduction ◽  
Quality Data ◽  
Monitoring Site ◽  
Water Quality Data ◽  
Pollution Levels ◽  
Difference Test

Pollution spillover is an important issue to improve the water environment of transboundary rivers, which has been aggravated by the decentralization of China's pollution control and promotion system.This paper analyzes the evolution of the pollution reduction mandates and the possible change of water environment in China which are tested with the water quality data of state key monitoring sections in 2004-2014 .In terms of research methods, this paper mainly uses Propensity Score Matching reference with group difference test and OLS. Empirical findings support the association between decentralization and pollution levels. The pollution levels of the monitoring points located at the boundary are significantly higher than that of interior counties. The pollution of tributary is more serious than trunk stream，which quickly reversed after the system changed. Water pollution levels rapidly changes when we compare the monitoring site in front of jurisdictional boundaries with that after the jurisdictional boundaries. We draw the following conclusions that local goverments may manipulate pollution within their jurisdictions and total pollutant control system will exacerbate border pollution, while water quality inspection can reduce marginal pollution.

Calculation and Analysis of the Water Environment Capacity for the Yuanhe River in Jiangxi Province, China

2011 ◽  
Vol 281 ◽  
pp. 137-140 ◽  
Author(s):  
Mao Lan Wang ◽  
Bin Luo ◽  
Wen Bin Zhou
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Water Environment ◽  
Quality Data ◽  
Jiangxi Province ◽  
Residual Water ◽  
Water Quality Data ◽  
Control Units ◽  
Environment Capacity ◽  
Water Environment Capacity

Yuanhe River is a major source of drinking, irrigation, industrial, hydropower generation, and recreational water for the circumjacent city. It has more serious water pollution problems because it flows through some heavy industry cities. So basis of the river water environment functional zones combined the various water quality data and the monitoring hydrological data, the water environment capacity of the Yuanhe River was calculated by using the one-dimensional water quality model. The results show that the water environment capacity is 112650 t/yr for chemical oxygen demand (COD) and 3265 t/yr for ammonium nitrogen (NH3-N). Most of the control units have residual water environment capacity, only individual control units have the serious water pollution and its residual capacity of COD and NH3-N is below 0, so it is necessary to strengthen the pollution control of these control units.

scholarly journals Response of Water Quality to Landscape Patterns in an Urbanized Watershed in Hangzhou, China

2020 ◽  
Vol 12 (14) ◽  
pp. 5500 ◽  
Author(s):  
Yu Song ◽  
Xiaodong Song ◽  
Guofan Shao
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Land Use ◽  
Stream Water ◽  
Land Use Changes ◽  
Landscape Patterns ◽  
Quality Data ◽  
Stream Water Quality ◽  
Water Quality Data ◽  
Algae Biomass

Intense human activities and drastic land use changes in rapidly urbanized areas may cause serious water quality degradation. In this study, we explored the effects of land use on water quality from a landscape perspective. We took a rapidly urbanized area in Hangzhou City, China, as a case study, and collected stream water quality data and algae biomass in a field campaign. The results showed that built-up lands had negative effects on water quality and were the primary cause of stream water pollution. The concentration of total phosphorus significantly correlated with the areas of residential, industrial, road, and urban greenspace, and the concentration of chlorophyll a also significantly correlated with the areas of these land uses, except residential land. At a landscape level, the correlation analysis showed that the landscape indices, e.g., dominance, shape complexity, fragmentation, aggregation, and diversity, all had significant correlations with water quality parameters. From the perspective of land use, the redundancy analysis results showed that the percentages of variation in water quality explained by the built-up, forest and wetland, cropland, and bareland decreased in turn. The spatial composition of the built-up lands was the main factor causing stream water pollution, while the shape complexities of the forest and wetland patches were negatively correlated with stream water pollution.

scholarly journals Characterizing water quality monitoring visualization with Hadoop and Google Maps

2017 ◽  
Vol 12 (4) ◽  
pp. 882-893 ◽  
Author(s):  
Weijian Huang ◽  
Xinfei Zhao ◽  
Yuanbin Han ◽  
Wei Du ◽  
Yao Cheng
Keyword(s):  
Water Quality ◽  
Water Environment ◽  
Large Data ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Data Sets ◽  
Google Maps ◽  
Water Quality Data ◽  
Computer Clusters

Abstract In water quality monitoring, the complexity and abstraction of water environment data make it difficult for staff to monitor the data efficiently and intuitively. Visualization of water quality data is an important part of the monitoring and analysis of water quality. Because water quality data have geographic features, their visualization can be realized using maps, which not only provide intuitive visualization, but also reflect the relationship between water quality and geographical position. For this study, the heat map provided by Google Maps was used for water quality data visualization. However, as the amount of data increases, the computational efficiency of traditional development models cannot meet the computing task needs quickly. Effective storage, extraction and analysis of large water data sets becomes a problem that needs urgent solution. Hadoop is an open source software framework running on computer clusters that can store and process large data sets efficiently, and it was used in this study to store and process water quality data. Through reasonable analysis and experiment, an efficient and convenient information platform can be provided for water quality monitoring.

Widespread global concern over harm from drinking water

2021 ◽  
Author(s):  
Sera Young ◽  
Joshua Miller ◽  
Chad Staddon ◽  
Aaron Salzberg ◽  
Julius Lucks ◽  
...  
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Risk Perceptions ◽  
Water Environment ◽  
Quality Data ◽  
Safe Drinking Water ◽  
Future Expectations ◽  
Water Quality Data ◽  
Current Water ◽  
Nationally Representative

Abstract Poor drinking water quality is a global crisis that affects billions of individuals. Understanding who is most impacted is necessary to develop programs that ensure sustainable, reliable, and resilient access to safe water. But current water indicators do not capture people’s experienced and anticipated harm from drinking water, which means we have had limited understanding of how individuals conceptualize, navigate, and are affected by their water environment. Here, we analyzed data from nationally representative surveys undertaken in 142 countries in which people reported their recent experiences and future expectations of harm from drinking water. Prevalence of reported harm from drinking water in the prior two years was 14.5% (range: 0.8%–54.3%). More than half of the world’s population (54.4%) anticipated that they would experience serious harm from their drinking water in the next two years. Greater public sector corruption was associated with greater anticipated harm from drinking water, even when adjusting for indicators of water infrastructure and economic development. Disparities in anticipated harm across countries and by gender and household location indicate that targeted policies are required to address risk perceptions, equitably improve access to safe drinking water, and increase trust in institutions that supply and regulate water services. The addition of experiential survey data to global data collection efforts will complement objective water quality data and provide novel insights about which strategies will most effectively advance progress toward safe drinking water for all.

scholarly journals Temporal Variation of Surface Water Quality in the Swan River Catchment in Himachal Pradesh India

2017 ◽  
Vol 20 ◽  
pp. 55-61
Author(s):  
Avinash Kumar Sharda ◽  
Harish Chander Sharma ◽  
Brij Bhushan
Keyword(s):  
Water Quality ◽  
Regression Analysis ◽  
Pollution Control ◽  
Rapid Development ◽  
Himachal Pradesh ◽  
Quality Data ◽  
Water Quality Data ◽  
River Catchment ◽  
Swan River

As industrial growth in the lower Shiwalik hills has risen in past two decades, the last 10 years in the Una district has seen a rapid development in industrial and urban growth due to grant of industrial package by the central government of India. As a result, several production plants have sprung up within the Swan River catchment, threatening the water quality of this area. However, the actual effects on water quality are heretofore unknown. In this paper, we assess the water quality of the Swan River catchment by calculating the National Sanitation Foundation Water Quality Indicators (NSFWQI) and Overall Index of Population (OIP) between 2003-2012. Data on monitored cross sections were collected from State Pollution Control Board of Himachal Pradesh, India. The results indicate that there has been recent (within five years) considerable improvement in the water quality due to enforcement of proper pollution control technologies. The relationship between economic growth (GDP) and water quality was also studied.We carried out regression analysis of the water quality data to determine significant parameters as independent variables and WQI and OIP as dependent variables. The regression analysis further identified that the contribution of each variable with significant values r = 0.733, R2 = 0.695. The study further suggests that sustainable development is possible through adoption of proper treatment technologies, enforcement of formal legislation, and preparation of remedial action plans to reduce the environmental stresses.HYDRO Nepal JournalJournal of Water Energy and EnvironmentIssue: 20Page: 55-61

scholarly journals GIS-based methodologies for assessing nitrate, nitrite and ammonium distributions across a major UK basin, the Humber

2004 ◽  
Vol 8 (4) ◽  
pp. 823-833 ◽  
Author(s):  
H. Davies ◽  
C. Neal
Keyword(s):  
Water Quality ◽  
River Network ◽  
Geographical Information ◽  
Quality Data ◽  
Monitoring Site ◽  
River Channels ◽  
Mean Flow ◽  
Water Quality Data ◽  
Catchment Characteristics ◽  
Humber River

Abstract. The distributions of nitrate, nitrite and ammonium at various monitoring sites across the Humber basin (area 24 000 km2) were examined within a Geographical Information System (GIS) framework. This basin contains diverse characteristics, from areas of high population and industry to rural and arable regions. The Humber River is a major provider of and nutrient fluxes to the North Sea from the UK. Within the GIS analysis, the distributions of mean and mean flow weighted concentrations, flux and flux per unit area, were investigated. Empirical relationships between land characteristics and water quality for the whole catchment draining to each water quality monitoring site were established. Thirty-eight catchments were chosen for this analysis, with areas ranging from 46 km2 to 8225 km2. These catchments are distributed across the Humber, encompassing the different conditions across the basin, thus allowing relationships between water quality and catchment characteristics to be used to estimate the nitrogen concentrations and flux throughout the basin river network. The main water quality data source was the Land Ocean Interaction Study (LOIS) dataset. The Environment Agency of England and Wales water quality datasets were used to infill areas of sparse LOIS monitoring network density within the Humber. The work shows the feasibility of estimating nitrate and, to a lesser extent, nitrite and ammonium concentrations and fluxes across the river network based on land characteristics, using a GIS methodology. The estimations work particularly well for the main river channels. However, there are local anomalies which are more difficult to predict. Maps showing concentration variations at 500 m intervals along the Humber basin river networks are presented; these are of particular value for environmental managers and socio-economists. Keywords: GIS, nitrate, nitrite, ammonium, catchment characteristics

scholarly journals A Fusion Water Quality Soft-Sensing Method Based on WASP Model and Its Application in Water Eutrophication Evaluation

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaoyi Wang ◽  
Jie Jia ◽  
Tingli Su ◽  
Zhiyao Zhao ◽  
Jiping Xu ◽  
...  
Keyword(s):  
Water Quality ◽  
Unscented Kalman Filter ◽  
Water Environment ◽  
Quality Analysis ◽  
Quality Data ◽  
Model Parameters ◽  
Water Quality Data ◽  
Soft Sensing ◽  
Water Eutrophication ◽  
Quality Indexes

Water environment protection is of great significance for both economic development and improvement of people’s livelihood, where modeling of water environment evolution is indispensable in water quality analysis. However, many water quality indexes related to water quality model cannot be measured online, and some model parameters always vary among different water areas. Thus, this paper proposes a water quality soft-sensing method based on the water quality mechanism model to simulate evolution of water quality indexes online, where unscented Kalman filter is utilized to estimate model parameters. Furthermore, a modified fuzzy comprehensive evaluation method is presented to evaluate the level of water eutrophication condition. Finally, the water quality data collected from Taihu Lake and Beihai Lake are used to validate the effectiveness and generality of the proposed method. The results show that the proposed soft-sensing method is able to describe the variation of related water quality indexes, with better accuracy compared to nonlinear least squares based method and traditional trial-and-error based method. On this basis, the water eutrophication condition can be also accurately evaluated.

A Software Platform for Water Environment Monitoring

2014 ◽  
Vol 898 ◽  
pp. 743-746
Author(s):  
Chun Long Li ◽  
Xian Xiang Chen ◽  
Zhen Fang ◽  
Jian Hua Tong ◽  
Hong Zhang ◽  
...  
Keyword(s):  
Water Quality ◽  
Low Cost ◽  
Water Environment ◽  
Smart Phone ◽  
Oxygen Demand ◽  
Quality Data ◽  
Environment Monitoring ◽  
Software Platform ◽  
Water Quality Data ◽  
Lightweight Framework

This paper describes a software platform for water environment monitoring. The main monitored parameters are temperature, turbidity, PH, dissolved oxygen, chemical oxygen demand (COD), total phosphorus, total nitrogen, nitrogen ammonia (NH) and heavy metal such as Pb, Zn and Cu etc. This platform was designed using java language and java web technology, which are widely used in many software platforms including water environment monitoring. Low cost and lightweight framework are the major aspects of the software platform because free software (Tomcat and MySQL) and SSH framework are adopted in this software platform. People can view water quality data in a computer or a smart phone browser in the form of table and chart. The water quality data transmitted from General Packet Radio Service (GPRS) wireless network are stored into the MySQL database automatically once the software platform is started. Data collected by this platform is real-time, once a record is out of limits, a message will be sent to mobile phone. Through data collected, environment protection administrators can predict and get the conclusion whether the water is polluted or not.

scholarly journals Identification of the Relative Importance of Factors Affecting the Spatial Variability of River Water Quality

2021 ◽  
Vol 13 (20) ◽  
pp. 11319
Author(s):  
Jiyu Seo ◽  
Jeongeun Won ◽  
Jeonghyeon Choi ◽  
Sangdan Kim
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Spatial Variability ◽  
River Water ◽  
River Water Quality ◽  
Quality Data ◽  
Future Climate Change ◽  
Relative Importance ◽  
Water Quality Data ◽  
Factors Affecting

Understanding the temporal and spatial variability of water quality is important in order to establish effective customized management strategies for polluted aquatic ecosystems. Although various water quality management methods have been proposed based on insights into river water pollution factors through physically based modeling or statistical techniques, it is difficult to find studies that analyze the relative importance of these water pollution factors in a relatively large watershed using a step-by-step methodology. In this study, the spatial variability of river water quality is analyzed using time-averaged river water quality data collected from 40 sites in the Nakdong river basin, located on the Korean Peninsula. We focused on biological oxygen demand, total suspended solids, total nitrogen, and total organic carbon. A two-step exhaustive search approach was used to find a linear model that best links the various factors of the watershed with the average river water quality. The optimal model was selected by applying cross-correlation analysis and Bayesian inference. Through the process of finding the optimal statistical model, the major factors that have the most influence on river water quality were identified by analyzing the factors affecting river water quality, their levels of influence, and their levels of uncertainty. Identifying a set of processes provides insight into the key factors influencing spatial variability in average stream water quality conditions. We were able to identify the relative influences and uncertainties of the hydrological, climatic, topographical, and geological characteristics of the watershed on the spatial variability of river water quality. The proposed spatial variability model of average river water quality can be used to predict river water quality responses to future climate change, land use pattern change, and soil management strategy change.

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