Heavy metal pollution in soils and sediments is becoming a matter of wide concern, this study was carried out in Dawa County of the Liaohe River Delta, with the aim of exploring the impacts of land use levels on heavy metal contamination of soil and sediment. A total of 129 soil samples were collected in different land use intensities (LUI). Soil metals (Fe, Mn, Cd, Cr, Cu, Ni, Pb and Zn) and soil salinity, pH, soil organic carbon (SOC), nitrate nitrogen (NO₃⁻-N), available phosphorus (AP) and grain sizes were analyzed. Correlation analysis indicated that SOC and grain size played important roles in affecting the heavy metal distribution. The factor analysis results indicated that heavy metal contamination was most probably caused by industrial and agricultural wastewater discharges, domestic sewage discharge and atmospheric deposition. Using ANOVA, it found that human activities significantly changed soil physic-chemical properties through soil erosion, leaching and fertilizer application, further affecting the behaviors of heavy metals in the soil and sediments. The anthropogenic factors could lead to potential environmental risk, as indicated by the Geo-accumulation index (I_geo) results of heavy metals. Overall, the heavy metals generally had approached or even exceeded moderately polluted (0 < I_geo < 1, 1 < I_geo < 2), but the Pb and Cu pollution level was low (I_geo < 0), and the Cd pollution level was moderately or strongly polluted (2 < I_geo < 3, 3 < I_geo < 4) in the five land use levels. This study will provide valuable information for appropriately determining how land should be used in future reclamation areas, as well as for the sustainable management of estuarine areas around the world.

Urbanization is one of the most widespread anthropogenic activities, which brings a range of physical and biochemical changes to hydrological system and processes. Increasing direct runoff caused by land use change has become a major challenge for urban ecological security. Reliable prediction of the quantity and rate of surface runoff is an inherently difficult and time-consuming task for large ungauged urban areas. In this study, we combined Geographic Information System and remote sensing technology with an improved Soil Conservation Service curve number model to evaluate the effects of land use change on direct runoff volume of the four-ring area in Shenyang, China, and analyzed trends of direct runoff at different scales. Through analyzing trends of direct runoff from 1984 to 2015 at different scales, we explored how urbanization and other potential factors affect direct runoff changes. Total direct runoff volume increased over time, and trends varied from the inner urban area to suburban area. Zones 1 and 2 had a tendency toward decreasing direct runoff volume and risks, while Zones 3 and 4 showed gradual increases at both regional and pixel scales. The most important influence on direct runoff change was urban surface change caused by urbanization. This study presents a framework for identifying hotspots of runoff increase, which can provide important guidance to urban managers in future green infrastructure planning, in the hopes of improving the security of urban water ecological patterns.

In the context of urban warming associated with rapid urbanization, the relationship between urban landscape patterns and land surface temperature (LST) has been paid much attention. However, few studies have comprehensively explored the effects of two/three-dimensional (2D/3D) building patterns on LST, particularly by comparing their relative contribution to the spatial variety of LST. This study adopted the ordinary least squares regression, spatial autoregression and variance partitioning methods to investigate the relationship between 2D/3D building patterns and summertime LST across 2016–2017 in Shanghai. The 2D and 3D building patterns in this study were quantified by four 2D and six 3D metrics. The results showed that: (1) During the daytime, 2D/3D building metrics had significant correlation with LST. However, 3D building patterns played a significant role in predicting LST. They explained 51.0% and 10.2% of the variance in LST, respectively. (2) The building coverage ratio, building density, mean building projection area, the standard deviation of building height, and mean building height highly correlated with LST. Specifically, the building coverage ratio was the main predictor, which was obviously positively correlated with LST. The correlation of building density and average projected area with LST was positive and significant, while the correlation of building height standard deviation and average building height with LST was negative. The increase in average height and standard deviation of buildings and the decrease in building coverage ratio, average projected area, and density of buildings, can effectively improve the urban thermal environment at the census tract level. (3) Spatial autocorrelation analysis can elaborate the spatial relationship between building patterns and LST. The findings from our research will provide important insights for urban planners and decision makers to mitigate urban heat island problems through urban planning and building design.

The Hun-Taizi River watershed includes the main part of the Liaoning central urban agglomeration, which contains six cities with an 80-year industrial history. A total of 272 samples were collected from different land use areas within the study area to estimate the concentration levels, spatial distributions and potential sources of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb) and zinc (Zn) with a geographic information system (GIS), principal component analysis (PCA) and canonical correspondence analysis (CCA). Only the concentration of Cd was over the national standard value (GB 15618–2018). However, the heavy metal concentrations at 24.54%, 71.43%, 63.37%, 85.71, 70.33%, 53.11%, and 72.16% of the sampling points were higher than the local soil background values for As, Cd, Cr, Cu, Hg, Ni, Pb and Zn, respectively, which were used as standard values in this study. The maximal values of Cd (16.61 times higher than the background value) and Hg (12.18 times higher than the background value) had high concentrations, while Cd was present in the study area at higher values than in some other basins in China. Cd was the primary pollutant in the study area due to its concentration and potential ecological risk contribution. The results of the potential ecological risk index (RI) calculation showed that the overall heavy metal pollution level of the soil was considerably high. Three groups of heavy metals with similar distributions and sources were identified through PCA. The results of the CCA showed that the distribution of mines was the strongest factor affecting the distributions of Ni, As, Zn, Pb, and Cd. However, Cu was strongly influenced by the distance to the nearest river. These findings can provide scientific support for critical planning and strategies for soil pollution control and removal to support the sustainable development of the study area.