Recognizing the Relationship between Spatial Patterns in Water Quality and Land-Use/Cover Types: A Case Study of the Jinghe Oasis in Xinjiang, China

In order to protect the water environment in seriously polluted basins, the impacts of anthropogenic activities (sewage outfalls and land use) on water quality should be assessed. The Bayesian network (BN) provides a convenient way to model these complex processes. In this study, anthropogenic impacts on chemical oxygen demand (COD) and biochemical oxygen demand (BOD) were evaluated in the Huaihe River basin (HRB) considering dry and wet seasons and different spatial scales. The results showed that anthropogenic activities had the most significant impacts on COD and BOD at the catchment scale. In dry seasons, sewage outfalls played an important role in organic pollution. Farmland became the most important source in wet seasons although it had a “sink” process in dry seasons. Intensive human activities in urban made significant contributions to increased COD levels. Grassland had a negative relationship with organic pollution, especially in dry seasons. Therefore, governments should implement strategies to control organic matters transported from urban and farmland regions. Increasing the efficiency of wastewater treatments and the percentage of grassland in the riparian zone could improve water quality. These results can enhance understanding of anthropogenic impacts on water quality and contribute to efficient management for river basins.

Section: Discussionsupporting

confidence: 92%

Assessing Anthropogenic Impacts on Chemical and Biochemical Oxygen Demand in Different Spatial Scales with Bayesian Networks

Jin

et al. 2020

“…The relationship between water quality of rivers and riparian land use are subject to spatiotemporal variation [14][15][16]. The development of GIS has helped advanced both quantitative and qualitative analysis tools for understanding the relationship between land structure and water quality, and have contributed greatly to watershed planning and management fields [17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Influence of Landscape Structures on Water Quality at Multiple Temporal and Spatial Scales: A Case Study of Wujiang River Watershed in Guizhou

Ren

Zhen-hua

et al. 2019

Water quality is highly influenced by the composition and configuration of landscape structure, and regulated by various spatiotemporal factors. Using the Wujiang river watershed as a case study, this research assesses the influence of landscape metrics-including composition and spatial configuration-on river water quality. An understanding of the relationship between landscape metrics and water quality can be used to improve water contamination predictability and provide restoration and management strategies. For this study, eight water quality variables were collected from 32 sampling sites from 2014 through 2017. Water quality variables included nutrient pollutant indicators ammoniacal nitrogen (NH 3 -N), nitrogen (NO 3 − ), and total phosphate (TP), as well as oxygen-consuming organic matter indicators COD (chemical oxygen demand), biochemical oxygen demand (BOD 5 ), dissolved oxygen (DO), and potassium permanganate index (COD Mn ). Partial least squares (PLS) regression was used to quantitatively analyze the influence of landscape metrics on water quality at five buffer zone scales (extending 3, 6, 9, 12, and 15 km from the sample site) in the Wujiang river watershed. Results revealed that water quality is affected by landscape composition, landscape configuration, and precipitation. During the dry season, landscape metrics at both landscape and class levels predicted organic matter at the five buffer zone scales. During the wet season, only class-level landscape metrics predicted water contaminants, including organic matter and nutrients, at the middle three of five buffer scales. We identified the following important indicators of water quality degradation: percent of landscape, edge density, and aggregation index for built-up land; aggregation index for water; CONTAGION; COHESION; and landscape shape index. These results suggest that pollution can be mitigated by reducing natural landscape composition fragmentation, increasing the connectedness of region rivers, and minimizing human disturbance of landscape structures in the watershed area.

“…If the quality of the whole is not good, then the quality of the part will be impacted [32]. Given that a wetland is a part of the watershed, e.g., the wetland condition depends critically on human activities that affect land use throughout the watershed [33], neither measurement of human activities in the watershed area nor the monitoring of wetland ecological factors can comprehensively and accurately assess the status of the wetland-watershed ecosystem. we developed the wetland condition index (WCI) to evaluate wetland condition combining the landscape development intensity index (LDI) and the water environment index (WEI).…”

Section: Basic Ideamentioning

confidence: 99%

Development of the Wetland Condition Index (WCI) by Combining the Landscape Development Intensity Index (LDI) and the Water Environment Index (WEI) for Humid Regions of China

Wang

Liu

et al. 2019

Human use and management have a marked effect on wetland from different scales; it is necessary to develop a multi-scale integrated method to assess wetland conditions. So, this research aids the development of the wetland condition index (WCI) for humid regions of China by combining two main sub-indices: (i) the landscape development intensity index (LDI), which assesses human-dominated impacts; and (ii) the water environment index (WEI), which assesses changes in water quality and phytoplankton. We measured terrain and land use in the watersheds of wetlands using remote imaging data with geographic information systems (GIS) software. Also, we monitored the physical and chemical variables of the water bodies of 27 wetlands in urbanized and moderately urbanized areas in Nanjing City of China for this study. There were significant inconsistencies between the city’s level of development and the values of the WCI and its sub-indices. The WCI of urbanized areas was better than that for moderately urbanized areas, and the sub-indices LDI and WEI were only slightly correlated. In other words, wetlands with a low LDI value did not necessarily have a low water environment index value. Due to wetland restoration and human management activities, integrating the LDI and WEI is increasingly necessary for wetlands in urbanized areas than for moderately urbanized areas. This method could guide the design of wetlands to optimize their qualities and benefits to residents and reinforce wetland conservation.