https://escholarship.org/content/qt7001s8mq/qt7001s8mq.pdf?t=ojx5jo

Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression

Response of water quality to land use and sewage outfalls in different seasons

Decadal and seasonal trends of nutrient concentration and export from highly managed coastal catchments.

Effects of land use change, wetland fragmentation, and best management practices on total suspended sediment concentrations in an urbanizing Oregon watershed, USA.

The present study was designed to determine the spatiotemporal patterns in water quality of River Soan using multivariate statistics. A total of 26 sites were surveyed along River Soan and its associated tributaries during pre- and post-monsoon seasons in 2008. Hierarchical agglomerative cluster analysis (HACA) classified sampling sites into three groups according to their degree of pollution, which ranged from least to high degradation of water quality. Discriminant function analysis (DFA) revealed that alkalinity, orthophosphates, nitrates, ammonia, salinity, and Cd were variables that significantly discriminate among three groups identified by HACA. Temporal trends as identified through DFA revealed that COD, DO, pH, Cu, Cd, and Cr could be attributed for major seasonal variations in water quality. PCA/FA identified six factors as potential sources of pollution of River Soan. Absolute principal component scores using multiple regression method (APCS-MLR) further explained the percent contribution from each source. Heavy metals were largely added through industrial activities (28 %) and sewage waste (28 %), nutrients through agriculture runoff (35 %) and sewage waste (28 %), organic pollution through sewage waste (27 %) and urban runoff (17 %) and macroelements through urban runoff (39 %), and mineralization and sewage waste (30 %). The present study showed that anthropogenic activities are the major source of variations in River Soan. In order to address the water quality issues, implementation of effective waste management measures are needed.

Water Quality Assessment of River Soan (Pakistan) and Source Apportionment of Pollution Sources Through Receptor Modeling

Most studies using multivariate techniques for pollution source evaluation are conducted in free-flowing rivers with distinct point and nonpoint sources This study expanded on previous research to a managed “canal” system discharging into the Indian River Lagoon, Florida, where water and land management is the single most important anthropogenic factor influencing water quality Hydrometric and land use data of four drainage basins were uniquely integrated into the analysis of 25 yr of monthly water quality data collected at seven stations to determine the impact of water and land management on the spatial variability of water quality Cluster analysis (CA) classified seven monitoring stations into four groups (CA groups) All water quality parameters identified by discriminant analysis showed distinct spatial patterns among the four CA groups Two-step principal component analysis/factor analysis (PCA/FA) was conducted with (i) water quality data alone and (ii) water quality data in conjunction with rainfall, flow, and land use data The results indicated that PCA/FA of water quality data alone was unable to identify factors associated with management activities The addition of hydrometric and land use data into PCA/FA revealed close associations of nutrients and color with land management and storm-water retention in pasture and citrus lands; total suspended solids, turbidity, and NO 3 + NO 2 with flow and Lake Okeechobee releases; specific conductivity with supplemental irrigation supply; and dissolved O 2 with wetland preservation The practical implication emphasizes the importance of basin-specific land and water management for ongoing pollutant loading reduction and ecosystem restoration programs

Cecilia Conrad

Papers

Linking Spatial Variations in Water Quality with Water and Land Management using Multivariate Techniques.