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

Abstract

• The modified GWR models had higher R 2 and reflected the actual spatial features.• A manual variable excluding-selecting method is explored to avoid multicollinearity.• Influences of the dominant indicator on water quality varied with space and seasons.• Protection policies need consider sitespecific conditions and seasonal variations. G R A P H I C A L A B S T R A C Ta b s t r a c t a r t i c l e i n f o As an important regulator of pollutants in overland flow and interflow, land use has become an essential research component for determining the relationships between surface water quality and pollution sources. This study investigated the use of ordinary least squares (OLS) and geographically weighted regression (GWR) models to identify the impact of land use and population density on surface water quality in the Wen-Rui Tang River watershed of eastern China. A manual variable excluding-selecting method was explored to resolve multicollinearity issues. Standard regression coefficient analysis coupled with cluster analysis was introduced to determine which variable had the greatest influence on water quality. Results showed that: (1) Impact of land use on water quality varied with spatial and seasonal scales. Both positive and negative effects for certain land-use indicators were found in different subcatchments. (2) Urban land was the dominant factor influencing N, P and chemical oxygen demand (COD) in highly urbanized regions, but the relationship was weak as the pollutants were mainly from point sources. Agricultural land was the primary factor influencing N and P in suburban and rural areas; the relationship was strong as the pollutants were mainly from agricultural surface runoff. Subcatchments located in suburban areas were identified with urban land as the primary influencing factor during the wet season while agricultural land was identified as a more prevalent influencing factor during the dry season. (3) Adjusted R 2 values in OLS models using the manual variable excluding-selecting method averaged 14.3% higher than using stepwise multiple linear regressions. However, the corresponding GWR models had adjusted R 2~5 9.2% higher than the optimal OLS models, confirming that GWR models demonstrated better prediction accuracy. Based on our findings, water resource protection policies should consider site-specific land-use Contents lists available at ScienceDirectScience of the Total Environment j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / s c i t o t e n v conditions within each watershed to optimize mitigation strategies for contrasting land-use characteristics and seasonal variations.

Distribution and source analysis of heavy metal pollutants in sediments of a rapid developing urban river system

“…1). The river originates from Lishui Mountain streams and flows~34 km to the East China Sea, covering a drainage area of 740 km 2 with 370 km 2 within the urban center (Chen et al, 2016). The basin has an average annual temperature of 18 C and average annual rainfall of 1695 mm with~70% of precipitation falling between April to September.…”

“…This industrial structure contributed large volumes of untreated wastewaters and waste residues directly to the river system due to the absence of effective management and legislative regulations over the development period. A previous study examining non-metal water quality conditions in this watershed used data from 52 sampling sites that were divided into corresponding sub-catchments (Chen et al, 2016). In this study, land use was divided into four categories: i) urban lands including residential, commercial and transportation lands, ii) industrial lands representing industrial and mining lands, iii) agriculture lands, and iv) non-managed ecological lands including barren land and water areas (Fig.…”

Linking Forest Cover to Water Quality: A Multivariate Analysis of Large Monitoring Datasets

“…This entails that the link between LULC and water quality reflects a background "multi-pollutants" load that can be considered as temporally stable. A potential seasonal effect as observed in other studies on particular relationships between particular variables and LULC [7,8] might have been mitigated as the developed method treats several water quality variables together and as seasonal values are averaged values. For studies aiming to clearly focus on seasonal effects, we recommend specific and regular spatio-temporal sampling while focusing on homogeneous groups of pollutants regarding their seasonal variability (see e.g., Johnson et al [46]).…”

Risk analysis of heavy metal concentration in surface waters across the rural-urban interface of the Wen-Rui Tang River, China

“…The watershed has suffered severe environmental deterioration due to rapid economic development coupled with lagging infrastructure to protect the environment. Research to date in the Wen-Rui Tang River watershed has primarily focused on the effects of nitrogen and phosphorus (Mei et al, 2014;Chen et al, 2016), polycyclic aromatic hydrocarbons and organic carbon (Li et al, 2016) in the hypoxic/ anoxic waterways. In addition, Gu et al (2012) and Song et al (2012) examined the ecological risk of sediments in the Wen-Rui Tang River network.…”