Effects of land use/cover on surface water pollution based on remote sensing and 3D-EEM fluorescence data in the Jinghe Oasis

Multi-scale response relationship between water quality of rivers entering lakes from different pollution source areas and land use intensity: a case study of the three lakes in central Yunnan

As the main supply source of lakes, the water quality of the rivers entering the lakes directly determines the water safety and sustainable development of the lakes. Human activities are the direct cause of changes in the water quality of rivers entering lakes, and land use intensity is the direct manifestation of human activities on the land surface. Although significant progress has been made in studying the relationship between land use changes and water quality in lakes, there is still a lack of research on exploring the relationship between land use intensity and water quality at multiple scales, especially in comparative studies of different pollution source areas. To address this problem, this study used Pearson's correlation analysis and land use intensity index method to explore the response relationship between river water quality and land use intensity at different spatial and temporal scales and different pollution source areas using three lakes in central Yunnan as examples. The results showed that land use intensity was generally positively correlated with water quality, but the response relationship between land use intensity and different water quality indicators was significantly different at different scales and for different pollution source areas. Compared to non-urban areas, the impact of land use intensity on water quality is more significant in urban areas. Compared to the rainy season, the correlation between CODNa, TP, and NH3-N values and land use intensity is stronger during the dry season, while the correlation between COD, TN, and land use intensity is weaker during the dry season. When viewed at different scales, different water quality indicators have different scale effects, but overall, the larger the scale, the stronger the correlation. Therefore, in the work of lake water environmental governance, it is necessary to consider comprehensively from multiple scales and perspectives and adopt measures that are more suitable for regional water pollution prevention and control.

Water quality assessment for organic matter load in urban rivers considering land cover dynamics

The strategy of considering a model that is comparable to the Soil Conservation Service Curve-Number (SCS-CN) method that employs land use maps to estimate the effects of land use on the water quality has considerable potential for application. This paper presents the LUPC (Land Use Pollutant Contribution) Model to estimate water pollution from the watershed land use obtained by satellite image classification (Sentinel-2). It defines that each land use produces a specific pollutant load per unit area, called Pollutant Standard Index (PSI), which undergoes degradation and/or retention until it reaches the river. This decay estimate is based on a Kernel Function. Organic matter (OM) was the pollutant chosen for the definition of the LUPC model and fractions of labile and refractory organic matter (LOM, ROM). The model was applied to the Barigüi River basin, and five samples were collected at 12 points along the river. Water quality parameters such as dissolved organic carbon (DOC) and UV-Visible absorbance in addition to chemical and biological oxygen demand (COD and BOD), dissolved oxygen (DO), and nitrogen and phosphorus fractions were the reference for modeling purposes. The results indicate that organic loads can be estimated from watershed characteristics, despite influence from seasonal influences captured by the PSI values and the basin shape parameter. Considering its versatile response, the LUPC model can be used for integrated water resources and land use planning and management and be indicator of the potential pollution of rivers by OM.

Assessing the impact of land use and land cover on river water quality using water quality index and remote sensing techniques

The impact of land use on water quality is becoming a global concern due to the increasing demand for freshwater. This study aimed to assess the effects of land use and land cover (LULC) on the surface water quality of the Buriganga, Dhaleshwari, Meghna, and Padma river system in Bangladesh. To determine the state of water, water samples were collected from twelve locations in the Buriganga, Dhaleshwari, Meghna, and Padma rivers during the winter season of 2015 and collected samples were analysed for seven water quality indicators: pH, temperature (Temp.), conductivity (Cond.), dissolved oxygen (DO), biological oxygen demand (BOD), nitrate nitrogen (NO₃-N), and soluble reactive phosphorus (SRP) for assessing water quality (WQ). Additionally, same-period satellite imagery (Landsat-8) was utilised to classify the LULC using the object-based image analysis (OBIA) technique. The overall accuracy assessment and kappa co-efficient value of post-classified images were 92% and 0.89, respectively. In this research, the root mean squared water quality index (RMS-WQI) model was used to determine the WQ status, and satellite imagery was utilised to classify LULC types. Most of the WQs were found within the ECR guideline level for surface water. The RMS-WQI result showed that the "fair" status of water quality found in all sampling sites ranges from 66.50 to 79.08, and the water quality is satisfactory. Four types of LULC were categorised in the study area mainly comprised of agricultural land (37.33%), followed by built-up area (24.76%), vegetation (9.5%), and water bodies (28.41%). Finally, the Principal component analysis (PCA) techniques were used to find out significant WQ indicators and the correlation matrix revealed that WQ had a substantial positive correlation with agricultural land (r = 0.68, P < 0.01) and a significant negative association with the built-up area (r =  - 0.94, P < 0.01). To the best of the authors' knowledge, this is the first attempt in Bangladesh to assess the impact of LULC on the water quality along the longitudinal gradient of a vast river system. Hence, we believe that the findings of this study can support planners and environmentalists to plan and design landscapes and protect the river environment.

Understanding the patterns and processes underlying water quality and pollution risk in West-Africa River using self-organizing maps and multivariate analyses

Rivers are dynamic systems in complex interactions with their surrounding environments. Reliable and fast interpretation of water quality is therefore needed for sustainable river management. Unfortunately, water quality and environmental status interactions have not yet been documented sufficiently in West-Africa. This study explored the spatial-latitudinal and seasonal features of water quality along the Sô River Basin (SRB, West Africa) using self-organizing map (SOM) and principal component analysis. Twenty-two water quality variables were measured in the surface layer at 12 different sampling sites during a twenty-four-month period from July 2016 to June 2018. The results revealed three water quality groups, following an upstream-downstream pollution gradient: (1) upstream and middle reach sites with high dissolved oxygen and Secchi disk depth values, which are more suitable for the aquatic biota; (2) downstream sites with high concentrations of ammonium, biochemical oxygen demand, and heavy metals especially in flood period, reflecting both high organic and heavy metal pollution; and (3) brackish downstream sites characterized by less heavy metal and organic pollutions. No significant variation was observed between seasons. However, the SRB relatively suffered from higher risks of heavy metal contamination and organic pollution in wet seasons. Although hydroclimatic processes affect the water quality, anthropogenic inputs of point and non-point sources were identified and discussed as a more prominent factor contributing to variation in the water quality condition. These results offer insights into the water quality dynamics in river-estuary system as well as potential pollution sources, crucial for defining sanitation, and management measures.

Multicriterial approach to the determination of buffer zones for the Moravian Karst protected landscape area in the Czech Republic

Allogenic recharge is an important source of pollution affecting karst protected terrains. In order to prevent the introduction of contaminants and the deterioration of karst phenomena, it is necessary to determine buffer zones that require protection, which is sometimes difficult, especially in densely populated areas. A multicriterial approach for the determination of buffer zones was proposed and verified. The method combines two criteria to identify those watercourses whose catchments lying outside the protected area need to be protected, namely the water quality index and the mass flow of pollutants. The case study was performed in the Moravian Karst (Czech Republic), which is fed by more than 10 small allogenic watercourses. The study proved that not only water quality, but also mass flow of pollutants conditioned by watercourse discharge must be considered. This approach can be adapted for all types of protected landscape areas with water pollution originating in surrounding areas.

The Encounter of Biomolecules in Metal-Organic Framework Micro/Nano Reactors

In nature, biochemical reactions often take place in confined spaces, as typically exemplified by cells. As numerous cellular reactors can be integrated to maintain the living system, researchers have made constant efforts to construct cell-like structures for achieving similar transformations in vitro. Micro/nano reactors engineered by polymers and colloids are becoming popular and being applied in many fields, especially there has been an increasing trend toward constructing metal-organic framework (MOF) micro/nano reactors with the thriving of MOF nanotechnologies. Because of the uniform pores of MOFs, the transmission of substances can be regulated more accurately. Along with properties of large specific surface area, functional diversity and precise control of the particle size, MOFs are also ideal platforms for building distinct microenvironments for biological substances. Compared with traditional polymersomes and colloidosomes, the unique characteristics of MOFs render them potent micro/nano reactor shell materials, mimicking cells for applications in enzymatic catalysis, sensing, nanotherapy, vaccine, biodegradation, etc. This review highlights recent signs of progress on the design of MOF micro/nano reactors and their applications in biology, discusses the existing problems, and prospects their promising properties for smarter multifunctional applications.

Assessment of Impacts of Climate Change on Tile Discharge and Nitrogen Yield Using the DRAINMOD Model

The detrimental impacts of agricultural subsurface tile flows and their associated pollutants on water quality is a major environmental issue in the Great Lakes region and many other places globally. A strong understanding of water quality indicators along with the contribution of tile-drained agriculture to water contamination is necessary to assess and reduce a significant source of non-point source pollution. In this study, DRAINMOD, a field-scale hydrology and water quality model, was applied to assess the impact of future climatic change on depth to water table, tile flow and associated nitrate loss from an 8.66 ha agricultural field near Londesborough, in Southwestern Ontario, Canada. The closest available climate data from a weather station approximately 10 km from the field site was used by the Ontario Ministry of Natural Resources and Forestry (MNRF) to generate future predictions of daily precipitation and maximum and minimum air temperatures required to create the weather files for DRAINMOD. Of the 28 models applied by MNRF, three models (CGCM3T47-Run5, GFDLCM2.0, and MIROC3.2hires) were selected based on the frequency of the models recommended for use in Ontario with SRA1B emission scenario. Results suggested that simulated tile flows and evapotranspiration (ET) in the 2071–2100 period are expected to increase by 7% and 14% compared to 1960–1990 period. Results also suggest that under future climates, significant increases in nitrate losses (about 50%) will occur along with the elevated tile flows. This work suggests that climate change will have a significant effect on field hydrology and water quality in tile-drained agricultural regions.

Water Use Efficiency and Sensitivity Assessment for Agricultural Production System from the Water Footprint Perspective

The increasing shortage of water resources and the growing demand for crops make water use efficiency a decisive factor for the sustainable and healthy development of the agricultural system. In order to evaluate agricultural water use efficiency from the water footprint perspective, the current study constructed the comprehensive water efficiency (CWE) index based on eight single agricultural water use efficiency performance parameters. The water resources utilization and efficiency in the wheat production system of China from 2006 to 2015 were analyzed and the sensitivity of single indices for CWE was identified. The results show that the national crop water footprint (CWF) for wheat production was estimated to be, including 46.3% blue, 36.6% green and 17.0% blue components, respectively. The spatial distribution patterns of water use efficiency performance indices were different. CWE of the country was 0.387, showing an upward trend over time and decreased from the southeast to the northwest geographically. Crop water productivity (CWP), productive water ratio (PWR) and rainwater consumption ratio (RCR) turned out to be the first three sensitive parameters for CWE in China. The improvement of China’s overall CWE relied on reducing inefficient blue-green water use and increasing the output capacity for per unit water. Advanced agricultural water-saving technologies were in high need for goal achievement, especially for the Huang-Huai-Hai plain, which held more than 70% of Chinese wheat production and CWF. The results provide support for efficient utilization and sustainable development of water resources in the agricultural system.

Assessing Potable Water Quality and Identifying Areas of Waterborne Diarrheal and Fluorosis Health Risks Using Spatial Interpolation in Peshawar, Pakistan

Waterborne diseases have become one of the major public health concerns worldwide. This study is aimed to investigate and develop spatial distribution mapping of the potable water quality parameters in the city of Peshawar, Pakistan. A total of 108 water samples collected across the entire study area were subjected to physio-chemical and biological analyses. Tested parameters included pH, turbidity, temperature, fluoride concentration levels, and bacterial counts (faecal coliforms). Inverse distance weighting (IDW) interpolation in geographic information systems (GIS) was used for spatial analysis. Test results revealed that 48% of water samples had faecal coliforms count (per 100 mL) greater than World Health Organization (WHO) minimum limits, while 31% of samples had fluoride concentrations in excess of the WHO maximum guide values. Spatial distribution mapping was developed for faecal coliforms count and fluoride ion concentration using ArcGIS to highlight the high-risk settlements in the study area. Results showed that around 20% area under faecal coliforms and approximately 33% area based on fluoride concentrations fall under the need for treatment category. The pH and turbidity were found in compliance with WHO desirable limits. The sanitary inspection score significantly depicted that ineffective multi-barrier approaches consequently deteriorated the water quality at the consumer’s end. Findings from the present study shall be useful to policymakers for adopting necessary remedial measures before it severely affects public health.

Measurement of Total Nitrogen Concentration in Surface Water Using Hyperspectral Band Observation Method

Nitrogen overload is one of the main reasons for the deterioration of surface water quality. Hence, monitoring nitrogen loadings is vital in maintaining good surface water quality. Increasingly, the use of spectral reflectance to monitor nitrogen concentration in water has shown potentials, but it poses some problems. Therefore, it is necessary to explore new methods of quantitative monitoring of nitrogen concentration in surface water. In this paper, hyperspectral data from surface water in the Ebinur Lake watershed are used to select sensitive bands using spectral transformation, the spectral index, and a coupling of these two methods. The particle swarm optimization support vector machine (PSO-SVM) model, constructed on the basis of sensitive bands, is used quantitatively to estimate the total nitrogen concentration in surface water and subsequently to verify its accuracy. The results show that the bands near 680, 850, and 940 nm can be used as sensitive bands for estimation of the total nitrogen concentration of surface water in arid regions. Compared with the best estimation models constructed by sensitive bands selected using the spectral transformation or the spectral index alone, the best model based on the coupling of these two measures is more accurate (R2 = 0.604, Root Mean Square Error (RMSE) = 1.61 mg/L, Residual Prediction Deviation (RPD) = 2.002). This coupling method leads to a robust, accurate, and strong predictability model, and can contribute to improved quantitative estimation of water quality indexes of rivers in arid regions.

Influence of Land Use/Land Cover on Surface-Water Quality of Santa Lucía River, Uruguay

Land use/land cover is one of the critical factors that affects surface-water quality at catchment scale. Effective mitigation strategies require an in-depth understanding of the leading causes of water pollution to improve community well-being and ecosystem health. The main aim of this study is to assess the relationship between land use/land cover and biophysical and chemical water-quality parameters in the Santa Lucía catchment (Uruguay, South America). The Santa Lucía river is the primary potable source of the country and, in the last few years, has had eutrophication issues. Several multivariate statistical analyses were adopted to accomplish the specific objectives of this study. The principal component analysis (PCA), coupled with k-means cluster analysis (CA), helped to identify a seasonal variation (fall/winter and spring/summer) of the water quality. The hierarchical cluster analysis (HCA) allowed one to classify the water-quality monitoring stations in three groups in the fall/winter season. The factor analysis (FA) with a rotation of the axis (varimax) was adopted to identify the most significant water-quality variables of the system (turbidity and flow). Finally, another PCA was run to link water-quality variables to the dominant land uses of the watershed. Strong correlations between TP and agriculture-land use, TP and livestock farming, NT and urban areas arose. It was found that these multivariate exploratory tools can provide a proper overview of the water-quality behavior in space and time and the correlations between water-quality variables and land use.

Spatially Varying and Scale-Dependent Relationships of Land Use Types with Stream Water Quality

Understanding the complex relationships between land use and stream water quality is critical for water pollution control and watershed management. This study aimed to investigate the relationship between land use types and water quality indicators at multiple spatial scales, namely, the watershed and riparian scales, using the ordinary least squares (OLS) and geographically weighted regression (GWR) models. GWR extended traditional regression models, such as OLS to address the spatial variations among variables. Our results indicated that the water quality indicators were significantly affected by agricultural and forested areas at both scales. We found that extensive agricultural land use had negative effects on water quality indicators, whereas, forested areas had positive effects on these indicators. The results also indicated that the watershed scale is effective for management and regulation of watershed land use, as the predictive power of the models is much greater at the watershed scale. The maps of estimated local parameters and local R² in GWR models showcased the spatially varying relationships and indicated that the effects of land use on water quality varied over space. The results of this study reinforced the importance of watershed management in the planning, restoration, and management of stream water quality. It is also suggested that planners and managers may need to adopt different strategies, considering watershed characteristics—such as topographic features and meteorological conditions—and the source of pollutants, in managing stream water quality.

Hydrogen and Oxygen Isotope Composition and Water Quality Evaluation for Different Water Bodies in the Ebinur Lake Watershed, Northwestern China

Wetlands are sensitive indicators of climate change and have a profound impact on the supply of water resources in surrounding areas. In this study, the hydrochemical, isotopic characteristics (δ18O and δ2H) of groundwater and surface water (lake, reservoir, and river) in the Ebinur Lake Watershed, northwestern China, were investigated to reveal the relationships between various water bodies. The results suggest that the groundwater is alkaline and has pH and total dissolved solids (TDS) values less than those of surface water. Ca2+ and SO42− are the major ions in the groundwater and river water, whereas lake water and reservoir water are enriched in Na+ and SO42−. With the decrease in elevation, both groundwater and river water are affected by carbonate dissolution at high elevation and by evaporitic rock dissolution at low elevation; thus, the water surrounding Ebinur Lake is subjected to runoff affected by intense evaporation–dissolution of evaporitic rocks. The stable isotope compositions suggested that the upstream part of the river is recharged by glacial meltwater from high mountains, whereas the middle–downstream parts of the river are recharged by low-elevation precipitation. Shallow groundwater and reservoir water are mainly recharged by river water and are more enriched in the downstream part of river. Water samples were also classified according to different indices, such as chemical oxygen demand (COD), NH3-N, volatile phenol, sulfate, Zn, Co, Cu, total hardness, and Cr6+, and results showed that most groundwater is suitable for drinking and irrigation purposes. Except for Cr6+, the metal concentrations are within permissible limits. However, both groundwater and reservoir water are affected to some extent by nearby rivers from anthropogenic activity.

First flush of non-point source pollution and hydrological effects of LID in a Guangzhou community

To study the first flush effect of nonpoint source pollution in the Guangzhou community unit, runoff from roads, roofs, and green spaces during three rainfall events was collected and analyzed for pollutants. Nine runoff pollution indices were considered. The dimensionless cumulative curve of pollutant mass vs. volume, the first flush coefficient (b) and the mass first flush ratio (MFF_n) were used to assess the first flush effect of different underlying surfaces. The assessment results pointed out that the roof was most prone to first flush effect. And ammonia nitrogen and phosphorus were the main pollutants in the first flush in the study area. For a quantitative analysis of the first flush, the Storm Water Management Model (SWMM) was used to simulate the hydrological effect of low impact development (LID) implementation in the community. The results showed that the first flush strength was reduced after setting LID. And LID measures, such as green roofs and sunken green spaces, contribute to flood control and rainwater purification. This research can be relevant regarding for constructing sponge cities and reducing the pollution caused by the first flush.

Rapid assessment of heavy metal pollution using ion-exchange resin sachets and micro-XRF core-scanning

Conventional pollution monitoring strategies for heavy metals are often costly and unpractical. Innovative sampling and analytical approaches are therefore needed to efficiently monitor large areas. This study presents a novel, simple, fast, and inexpensive method to monitor heavy metal pollution that uses cation-exchange resin sachets and the micro-XRF core-scanning technique (XRF-CS). The resin passive samplers act as concentrators of cationic species and can be readily deployed spatially and temporally to record pollution signals. The large number of analytical tasks are then overcome by the fast and non-destructive XRF-CS to precisely assess elemental concentrations. Quantifying element loading involves direct comparison with a set of identically prepared and scanned resin reference standards containing Ca, Ti, Cr, Mn, Ni, Cu, Zn, Pb. The results show that within the test range (from 0-1000 s mg kg^-1), the calibration lines have excellent regressions (R² ≥ 0.97), even at the shortest exposure time (1 s). A pilot field survey of a suspected polluted area in central Taiwan, where 30 resin sachets had been deployed, identified a pollution hot spot in a rapid and economical manner. Therefore, this approach has the potential to become a valuable tool in environmental monitoring and forensics.