Environmentally sound urban water management in developing countries: A case study of Hanoi

Impact of informal regulation of pollution on water quality in rivers in India

Transboundary spillovers may degrade environmental quality if countries free ride. This paper examines the extent of such degradation in water quality in international rivers. Using data from river monitoring stations in the UN's Global Emissions Monitoring System (GEMS), it compares pollution levels in international and domestic rivers. The results suggest that free riding may substantially increase pollution in international rivers, but the estimates are sensitive to the inclusion of country effects.

Water quality in rivers is deteriorating in urban and rural areas due to natural and anthropogenic factors. Understanding how changes and factors affect river water quality is crucial for managing water quality in river basins. This review focuses on analyzing key factors affecting water quality, and the temporal and spatial variations of water quality in rivers flowing in rural and urban areas. Natural processes such as weathering of rocks, evapotranspiration, atmospheric deposition, climate change, and natural disasters cause changes in the quality of river water. Anthropogenic factors could stem from industrial effluents, domestic activities, and agricultural activities such as the application of fertilizers, manures, pesticides, animal husbandry activities, irrigation practices, deforestation, and aquaculture. The seasonal variations in river water quality are discussed, and land use or cover could affect water quality parameters in a negative or positive way. In addition to traditional contaminants such as biodegradable organic matter, heavy metals, and pathogens, emerging and persistent pollutants such as organochlorine pesticides (OCPs), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), perfluoroalkyl and polyfluoroalkyl substances (PFASs), and phamaceutic active compounds (PhACs) has been found in many rivers, which could pose a threat to human and animal health. The comparison of key factors and parameters in urban and rural areas is also clarified, which provides authorities and policymakers with a deep understanding and supports decision-making in sustainable water management.

The relative importance of nonpoint-source (NPS) pollution in the degradation of water quality has increased in the last two decades due to the control of pollutant releases from point sources (Sharpley and Meyer 1994). The most important source of NPS pollutants are agriculture and urban areas, which impact water quality in rivers, lakes, estuaries and groundwaters through the release of eroded sediments, fertilizers, pesticides, and municipal sewage sludge. Because of this, NPS pollution is an important environmental concern at state and national levels. Several transport processes control the dispersal of NPS pollutants, including leaching to groundwater, surface runoff (Pereira and Rostad 1990), and aerial transport and deposition (Glotfelty et al. 1984). Once in groundwater, these contaminants can impact surface water during stream recharge. While the losses of NPS pollutants from agricultural fields or urban areas can be small as a percentage of the total amount released, the cumulative additions to river systems from large drainage areas can be significant. The development of a reliable contaminant transport model for tracing the dispersal of NPS pollutants through a heterogeneous aquifer requires knowledge of the spatial distribution of hydrologic parameters such as hydraulic conductivity or permeability (Zheng and Bennett 1995). In the commonly used stochastic approach (Dagan 1989), these distributions are treated as spatial random functions whose variance and correlation length scale are determined from hydrological information collected by head measurements, pump and tracer tests, and soil core analyses. Point estimates of the hydraulic conductivity or permeability are determined by kriging, a geostatistical interpolation procedure which estimates unknown random functions based on spatial correlations between point observations. Noninvasive geophysical techniques are becoming an increasingly popular component of hydrogeological studies since many geophysical data are sensitive to spatial variations in hydraulic conductivity and permeability. In addition, the cost of surface exploration is only a fraction of the cost of drilling and a wide area1 coverage is readily obtained. Controlled source electromagnetic (CSEM) methods provide maps of electrical conductivity and are an appropriate choice if the depth scale of investigation is on the order of 10 m-l km. We are investigating the possibility of placing electrical constraints on the subsurface permeability as part of a larger, integrated study to determine the fate of agricultural chemicals introduced at a research site near the Brazos River. The research involves a joint analysis of the electrical conductivity structure, the available soil cores, and other hydrologic data. A Bayesian approach is taken, following Copty et al. (1993), in which geophysical data are used to update the variance and correlation length scale of a hydrologicallyderived, random permeability field. In this paper we will describe the underlying theory and demonstrate an example using synthetic CSEM data that have been generated from a simulated geoelectrical section of our study site. The incorporation of CSEM data into a determination of subsurface permeability is made more difficult by the presence of man-made electrical conductors at the site. These include an aluminum pipeline and an overhead power line. Such artifacts are characteristic of the human impact at many environmental sites. We are presently applying the integral equation code of Qian and Boerner (1995) to account for the effect of the aluminum pipeline on the CSEM data.

Comparative analysis of the urban impact on water quality in the main rivers of Central Russia has been performed based on hydrochemical observations dataset for the period 2005-2019. It was obtained from monitoring results at the gauging stations located upstream and downstream of 11 cities in the studied region. The pollution level was estimated by comparison of the annually-averaged concentration values for 43 water quality indicators with their defined maximum permissible concentrations. The most substantial growth in concentrations downstream of the cities compared to their upstream values was revealed for heavy metals, nitrogen compounds, phosphates and oil products. A much less significant negative impact was imposed by cities on transparency, oxygen and major ions content. The cities were ranked by the total number of hydrochemical indicators which concentrations 1) increase downstream of cities, 2) exceed the maximum permissible concentrations before and after entering cities, 3) exceed the maximum permissible concentration before and after entering cities while sufficiently increasing (> 10%) at the downstream station. Additionally, the indicators were identified that experienced the most considerable impact from the urban areas. Among the studied cities, three groups were identified differing from each other in the degree of impact on the river water quality.

Sustainable irrigation is necessary and achievable, but direct costs and environmental impacts can be substantial

The efficacy of a water quality management strategy highly depends on the analysis of water quality data, which must be intensively analyzed from both spatial and temporal perspectives. This study aims to analyze spatial and temporal trends in water quality in Code River in Indonesia and correlate these with land use and land cover changes over a particular period. Water quality data consisting of 15 parameters and Landsat image data taken from 2011 to 2017 were collected and analyzed. We found that the concentrations of total dissolved solid, nitrite, nitrate, and zinc had increasing trends from upstream to downstream over time, whereas concentrations of parameter biological oxygen demand, cuprum, and fecal coliform consistently undermined water quality standards. This study also found that the proportion of natural vegetation land cover had a positive correlation with the quality of Code River's water, whereas agricultural land and built-up areas were the most sensitive to water pollution in the river. Moreover, the principal component analysis of water quality data suggested that organic matter, metals, and domestic wastewater were the most important factors for explaining the total variability of water quality in Code River. This study demonstrates the application of a GIS-based multivariate analysis to the interpretation of water quality monitoring data, which could aid watershed stakeholders in developing data-driven intervention strategies for improving the water quality in rivers and streams.

Water pollution is caused in rural areas, especially by uncontrolled waste deposits located in river bed and in urban areas is discharged, irregular, untreated sewage. The Cuejdiu River is a tributary of the Bitriţa River in the left side, with a total length of 24 km. Evaluation of water quality of the Cuejdiu River was achieved by processing the results obtained from tests analyzed for 30 samples, taken on March 17, 2012, between the mouth of the River Bestriţa and up to the exit of the village Cuiejdi. Analysis of water samples were performed in the laboratory of the Department of Geography, at the Faculty of Geography and Geology and were taken into account several parameters such as conductivity, pH, total acidity, nitrates, chlorine. The values obtained were cartographically represented to highlight differences between the sector that cross of the rural area and the sector located on urban area. Water Management System Neamţ realizes assessments of ecological and chemical status of the river Cuejdiu only in urban areas, classifying water quality in relation to general indicators. Thus, data obtained over a period of 10 years, from 2000 to 2010, and performing through charts, helped us to observe the time evolution of water quality in the area. The impact of human activities is evident in both urban and rural areas. In urban area the connections made by citizens, particularly those who live on the ground blocks, to the rainwater system, instead of sewerage systems, lead to the degradation of water quality. As regards the rural area, the situation is just as difficult, meaning that the waste discharged into the riverbed changes the chemical composition of water.

The UN World Water Development Report 2016, <i>Water and Jobs</i>: A Critical Review

The water quality of urban inland rivers is an important index of urban environmental health, which can reflect a city’s development level and its social and economic development. The water quality of these rivers strongly impacts the health and quality of life of the residents of urban and surrounding areas. Therefore, it is necessary to accurately assess the quality of water in urban inland rivers, which can also aid environmental protection departments in providing river governance. Generally, the water quality status of a city’s inland rivers is assessed and released by environmental monitoring stations in various regions that deploy the corresponding water quality detection equipment at certain major locations of the river. However, these detection devices can only detect water quality at fixed locations, and often, the water quality of an urban inland river changes owing to the impact of the surrounding environment and residents it serves. Therefore, the water quality around a detection point does not always reflect the water quality of the entire river section. To better express the water quality status of a city’s inland river, we propose a method based on a mobile crowd-sensing system that obtains the water quality data of the river during an entire period of time and then fuzes these sensing data to obtain the best truth-value estimate of the water quality of the river. We can use this water quality truth value to conduct an objective evaluation of the water quality of a city’s inland rivers. The water quality parameters obtained by the method can better represent the water quality status of the river, and the data are more accurate compared to the data collected and released by an environmental monitoring station. Through simulation and comparative analysis, we found that the water quality data obtained by the proposed method were more accurate, indicating that our method has more practical value than the detection device method.

Excess nutrients from agriculture in the Mississippi River drainage, USA have degraded water quality in freshwaters and contributed to anoxic conditions in downstream estuaries. Consequently, water quality is a significant concern associated with conversion of lands to bioenergy production. This study focused on the Arkansas‐White‐Red river basin (AWR), one of five major river basins draining to the Mississippi River. The AWR has a strong precipitation gradient from east to west, and advanced cellulosic feedstocks are projected to become economically feasible within normal‐to‐wet areas of the region. In this study, we used large‐scale watershed modeling to identify areas along this precipitation gradient with potential for improving water quality. We compared simulated water quality in rivers draining projected future landscapes with and without cellulosic bioenergy for two future years, 2022 and 2030 with an assumed farmgate price of $50 per dry ton. Changes in simulated water quantity and quality under future bioenergy scenarios varied among subbasins and years. Median water yield, nutrient loadings, and sediment yield decreased by 2030. Median concentrations of nutrients also decreased, but suspended sediment, which is influenced by decreased flow and in‐stream processes, increased. Spatially, decreased loadings prevailed in the transitional ecotone between 97° and 100° longitude, where switchgrass, Panicum virgatum L., is projected to compete against alternative crops and land uses at $50 per dry ton. We conclude that this region contains areas that hold promise for sustainable bioenergy production in terms of both economic feasibility and water quality protection.

Water resource is most essential basic resource for human being. Today water resource management has become an important issue (Kharake, Pathare, Deshmukh, Arebian J Geosci 14(10):1–10, 2021) for all developing countries. Rapid growth of population and its repetitive activities along the river pose a concerned impact on the river system. The water quality and quantity are under constant pressure by the presence of different human activities like removal of vegetation, industrial activities, and encroachment, domestic and religious activities. These all activities resulted in degradation of water quality. These all problems are largely concentrated in and around urban areas. Keeping this view in the account systematic study has been carried out the water quality of Godavari river in Nashik city. Water samples from 10 sampling stations have been collected during 1st week of June 2019. Physico-chemical parameters have been analyzed by standard method. The Karl Pearson correlation matrix has been established for examining relationship between the water quality parameters, and the study is conducted to analyze the water quality status of Godavari river in terms of water quality index (WQI). The overall values showed good water quality status (WQI 133.44) at upper stream in the study area, but as it enters in urban area water quality becomes deteriorate (WQI 35.01). The field observations reveal that water quality is declining due to many human activities mainly industrial, domestic and religious waste. To analyze the water quality index (WQI) is the main aim of the research with remedial measures to mitigate the deterioration and related consequences in future.

Management of lakes for the protection of water quality, aquatic life and other uses must be approached somewhat differently in the tropics from how it is approached at temperate latitudes. More than half of all tropical lakes are accounted for by natural river lakes or reservoirs. Therefore, degradation of water quality in rivers will have direct negative effects on the majority of lakes in the tropics. Also, regulation of rivers, which is one result of river impoundment, is a potential cause of damage to river lakes. Tropical lakes are more sensitive than temperate lakes to increases in nutrient supply and show higher proportionate changes in water quality and biotic communities in response to eutrophication. Tropical lakes are especially prone to loss of deep‐water oxygen, and in order to maintain ecological stasis therefore require more stringent regulation of organic and nutrient loading than temperate lakes. Nutrient containment must be more strongly oriented toward nitrogen, the most probable limiting nutrient in tropical lakes, than has been the case at temperate latitudes. However, phosphorus control is also important. Nitrogen management may be more feasible in the tropics because of high temperature, which is one of the critical conditions for efficient denitrification. Planktonic and benthic communities of the tropics bear a close resemblance, both in composition and diversity, to those of temperate latitudes; there is no parallel to the latitudinal gradient in biodiversity that is characteristic of terrestrial ecosystems. Foci of biodiversity, which require special attention, include the endemic species of ancient lakes and the diverse fish communities of very large rivers. The latter are an especially valuable untapped economic resource, but face severe impairment due to hydrological regulation and pollution of rivers. Effective management programs for tropical lakes will focus on interception of nutrients, protection of aquatic habitats from invasive species, and minimization of hydrological changes in rivers to which lakes are connected. In the absence of protective management, tropical lakes will decline greatly in their utility for water supply, production of commercially useful species, and recreation.

This research has the aim of establishing the amount of land that the Conservation Area National System (SINAC) needs to buy and specifying its use inside the Golfo Dulce Forest Reserve in order to maintain the good quality of the rivers. The study was done inside Rincon sub-basin in the Osa Peninsula, a land dedicated to primary and secondary forest (Melina tree plantations), mangrove forest, urbanism, chaparral, pasture for grazing livestock and crop cultivation, mainly rice and African palm; uses that may change in time affecting the actual water quality (WQ) in rivers and the bay. Changes in land uses modify the WQ and it can be predicted using WQ models based on environmental variables through the evaluation of different scenarios. It was found through modeling that pasture or chaparral land use has no negative impact on WQ; all the contrary happens with increments in crop cultivation or human population. At the present, the coastal area, adjacent to the reserve, has low agricultural activity but that could increase over time, affecting the WQ. Therefore, SINAC should continue with its acquisition land activity to counteract the negative environmental effect of agricultural activity to preserve the actual good quality of the rivers.

One: Introduction.- Uncertainty, system identification, and the prediction of water quality.- The validity and credibility of models for badly defined systems.- Two: Uncertainty and Model Identification.- An approach to the analysis of behavior and sensitivity in environmental systems.- Distribution and transformation of fenitrothion sprayed on a pond: modeling under uncertainty.- Input data uncertainty and parameter sensitivity in a lake hydrodynamic model.- Maximum likelihood estimation of parameters and uncertainty in phytoplankton models.- Model identification methods applied to two Danish lakes.- Analysis of prediction uncertainty: Monte Carlo simulation and nonlinear least-squares estimation of a vertical transport submodel for Lake Nantua.- Multidimensional scaling approach to clustering multivariate data for water-quality modeling.- Nonlinear steady-state modeling of river quality by a revised group method of data handling.- Three: Uncertainty, Forecasting, and Control.- Parameter uncertainty and model predictions: a review of Monte Carlo results.- A Monte Carlo approach to estimation and prediction.- The need for simple approaches for the estimation of lake model prediction uncertainty.- Statistical analysis of uncertainty propagation and model accuracy.- Modeling and forecasting water quality in nontidal rivers: the Bedford Ouse study.- Adaptive prediction of water quality in the River Cam.- Uncertainty and dynamic policies for the control of nutrient inputs to lakes.- Four: Commentary.- Uncertainty and forecasting of water quality: reflections of an ignorant Bayesian.