Characterizing the physical and demographic variables associated with heavy metal distribution along urban-rural gradient

Identifying critical source areas of non-point source pollution to enhance water quality: Integrated SWAT modeling and multi-variable statistical analysis to reveal key variables and thresholds

By integrating soil and water assessment tool (SWAT) modeling and land use and land cover (LULC) based multi-variable statistical analysis, this study aimed to identify driving factors, potential thresholds, and critical source areas (CSAs) to enhance water quality in southern Alabama and northwest Florida's Choctawhatchee Watershed. The results revealed the significance of forest cover and of the lumped developed areas and cultivated crops ("Source Areas") in influencing water quality. The stepwise linear regression analysis based on self-organizing maps (SOMs) showed that a negative correlation between forest percent cover and total nitrogen (TN), organic nitrogen (ORGN), and organic phosphorus (ORGP), highlighting the importance of forests in reducing nutrient loads. Conversely, Source Area percentage was positively correlated with total phosphorus (TP) loads, indicating the influence of human activities on TP levels. The receiver operating characteristic (ROC) curve analysis determined thresholds for forest percentage and Source Area percentage as 37.47 % and 20.26 %, respectively. These thresholds serve as important reference points for identifying CSAs. The CSAs identified based on these thresholds covered a relatively small portion (28 %) but contributed 47 % of TN and 50 % of TP of the whole watershed. The study underscores the importance of considering both physical process-based modeling and multi-variable statistical analysis for a comprehensive understanding of watershed management, i.e., the identification of CSAs and the associated variables and their tipping points to maintain water quality.

Unveiling the spatial differentiation drivers of major soil element behavior along traffic network accessibility

Advancements in transportation networks have induced a spatial-temporal convergence effect, accelerating socio-economic elements flow and dismantling the conventional "core-periphery" urbanization gradient. Accessibility of transportation networks emerges as a reliable indicator of urbanization. There has been a growing global and Chinese focus on the various forms of metal pollution in urban soil. This study aims to investigate the driving forces and effects of urbanization factors (Gross Domestic Product (GDP), value added of secondary industries (VA), night light (NL), population density (PD), and road density (Distance)), soil property factors (pH, electrical conductivity (EC), and total organic carbon (TOC)), and topographic factors (elevation (DEM), aspect, and slope) on toxic heavy metal elements (Cd, As, and Hg) and trace elements (Mn, Ti, V) in surface soil (0-20 cm) across varying accessibility levels in the Beijing-Tianjin-Hebei urban agglomeration. Results reveal significant influence of accessibility on Cd and Hg levels (p < 0.05), with higher accessibility areas displaying elevated element concentrations. According to the evaluation results of the single-factor pollution index, Cd and V have the highest pollution exceedance rates (93.18% and 75.76%, respectively). Moran's Index results highlight typical spatial clustering of elements, with hotspots in areas of high accessibility. Urbanization has led to distinct spatial agglomeration patterns in element concentrations and environmental factors. Geographic detector analysis reveal that in low accessibility areas, metal element pollution and distribution are influenced by a combination of complex factors, including soil properties (pH), terrain conditions (DEM), and the urbanization process (VA). In high accessibility areas, toxic heavy metal elements are primarily driven by urbanization factors, largely influenced by transportation activities, industrial development, and population density, while elements Mn, Ti, and V are still influenced by both natural processes and urbanization activities. These findings suggest that urbanization intensifies the impact on potential toxic elements in soil, and that trace elements are increasingly affected by urbanization, warranting further attention.

Effects of urbanization on Cd accumulation in agricultural soils: From the perspective of accessibility gradient

Road accessibility clearly reflects the spatial heterogeneity of urbanization. This study therefore adopted accessibility gradient to analyze the effects of urbanization on Cadmium (Cd) accumulation in agricultural soils. In total, 212 soil samples were collected along the accessibility gradient from agricultural soils in the Guangzhou-Foshan metropolitan region. Cd concentration showed a clearly decreasing pattern in agricultural soils with a decrease in accessibility level. The decreasing patterns varied in different accessibility ranges. The urban-rural ecotone (accessibility range 10-15) was the region with the most drastic changes in Cd accumulation. The influencing factors of Cd accumulation in agricultural soils mainly include industrial pollutants, agriculture chemicals, mining activities, domestic wastes, and soil properties. The importance of these factors varies across different accessibility ranges. Our findings imply that the characteristic variation of Cd accumulation with the road accessibility gradient must be considered in the formulation of targeted policies for controlling Cd contamination in agricultural soils.

Evaluation of geostatistical techniques and their hybrid in modelling of groundwater quality index in the Marand Plain in Iran

In many parts of the world, groundwater is considered as one of the main sources of urban and rural drinking water. Over the past three decades, the qualitative and quantitative characteristics of aquifers have been negatively affected by different factors such as excessive use of chemical fertilizers in agriculture, indiscreet, and over-exploitation use of groundwater. Therefore, finding the effective method for mapping the water quality index (WQI) is important for locating suitable and non-suitable areas for urban and rural drinking waters. In the present paper, the best method to estimate the spatial distribution of WQI was assessed using the inverse distance weighted, kriging, cokriging, geographically weighted regression (GWR), and hybrid models. Creating hybrid models can increase modeling capabilities. Hybrid methods make use of a combination of estimated model capabilities. In addition, to improve the results of cokriging, GWR, and hybrid methods, the auxiliary parameters of land slope, groundwater table, and groundwater transmissibility were used. In order to assess the proposed methodology, 11 qualitative parameters obtained from 63 observation wells in Marand Plain (Iran) were utilized. Four statistical measures, namely the root mean square error (RMSE), the mean absolute error (MAE), the Akaike coefficient (AIC), and the correlation coefficient (R²) along with the Taylor diagram, have been done. Classification of the WQI index showed that the quality of a number of 1, 27, 18, and 17 wells was, respectively, in excellent, good, moderate, and poor grades. The results of modeling the WQI index based on IDW, kriging, cokriging, GWR, and hybrid methods showed that the best estimate of WQI was obtained by using hybrid GWR-kriging method with three input parameters of land slope, groundwater table, and groundwater transmissibility. Therefore, hybrid kriging and GWR methods have been fairly well able to simulate the WQI index.

The Composition and Diversity of Soil Bacterial and Fungal Communities Along an Urban-To-Rural Gradient in South China

Soil microbes are of great significance to driving the biogeochemical cycles and are affected by multiple factors, including urbanization. However, the response of soil microbes to urbanization remains unclear. Therefore, we designed an urban-to-rural gradient experiment to investigate the response of soil microbial composition and diversity to urbanization. Here, we used a high-throughput sequencing method to analyze the biotic and abiotic effects on soil microbial composition and diversity along the urban-to-rural gradient. Our results showed that soil bacterial diversity was the highest in urban areas, followed by suburban areas, and was the lowest in exurbs; however, fungal diversity did not vary significantly among the three areas. Plant traits, i.e., tree richness, shrub richness, the number of tree stems, diameter at breast height of trees, and soil properties, i.e., pH, soil organic carbon, soil exchangeable calcium and magnesium, and soil water content, were only significantly influenced bacterial diversity, but not fungal diversity. The effect of trees and shrubs was higher than that of herbs on microbial composition. Soil organic carbon, pH, soil available nitrogen, soil exchangeable calcium, and magnesium were the major soil factors influencing the soil bacterial and fungal composition. Soil properties had a greater influence on bacterial than on fungal composition at genus level, while plant traits contributed more to fungal than to bacterial composition at genus level. Our study suggests that the urban-to-rural gradient affect the composition and diversity of bacterial community as well as the fungal composition, but not the fungal diversity.

Polychlorinated biphenyl concentrations, accumulation rates in soil from atmospheric deposition and analysis of their affecting landscape variables along an urban-rural gradient in Shanghai, China

This study initiated an in-situ soil experimental system to quantify the annual dynamics of polychlorinated biphenyl (PCB) congener's concentrations and accumulation rates in soil from atmosphere deposition in a rural-urban fringe, and correlated them by landscape physical and demographic variables in the area. The results showed that the concentrations of all PCB congeners significantly increased with the sampling time (p < 0.05); nearly all the PCB congener concentrations decreased while moving outwards from the urban center. The moderate average concentrations along the gradient for PCB 8, 18, and 28 were 31.003, 18.825, and 19.505 ng g-1, respectively. Tetra-CBs including PCB 44, 52, 66, and 77 were 10.243, 31.214, 8.330 and 9.530 ng g-1, respectively. Penta-CBs including PCB 101, 105, 118, and 126 were 9.465, 7.896, 17.703, and 6.363 ng g-1, respectively. Hexa-CBs including PCB 128, 138, 153, 170, 180, and 187 were 6.798, 11.522, 4.969, 6.722, 6.317, and 8.243 ng g-1 respectively. PCB 195, 206, and 209 were 8.259, 9.506, and 14.169 ng g-1, respectively. Most of the PCB congeners had a higher accumulation rate approximately 28 km from the urban center. The computed variables were found to affect the soil PCB concentrations with a threshold effect (p < 0.05). Regression analysis showed that the thresholds were 10-20 km, 1 km/km², 30%, and 20% for distance, road density, population change index, and built-up area percentage, respectively. It was concluded that factors related to industrial development, traffic, and urban sprawling (i.e. built-up areas expanding) were the sources of PCBs.

Reclamation history and development intensity determine soil and vegetation characteristics on developed coasts

The question of where and how to carry out reclamation work in coastal areas is still not well addressed in coastal research. To answer the question, it is essential to quantify the impact of reclamation and the associated ecological and/or environmental responses. In this study, ordinary least square (OLS) analysis and geographical weighted regression (GWR) analysis were performed to identify the reclamation variables that affect soil and vegetation characteristics. Reclamation related variables, including residential population (RP), years of reclamation (YR), income per capita (IP), and land use-based human impact index (HII), were used to explain nitrate, ammonium, total phosphorous, and heavy metals in soil, and the height, density, and above-ground biomass of native hydrophytic vegetation. It was found that variables IP, RP, and HII could be used to explain the height of Scirpus and Phragmites australis as well as above-ground biomass with a R² value of no >0.55, and almost all the variables could explain the hydrophytic vegetation characteristics with a higher R² value. In comparison to OLS, GWR more reliably reflected the reclamation effects on soil and vegetation characteristics. By GWR analysis, total soil phosphorous, and nitrate and ammonium nitrogen could be explained by RP, YR, and HII, with the highest Ad-R² value of 0.496, 0.631 and 0.632, respectively. Both of the GWR and OLS analysis revealed that HII and RP were the better variables for explaining the soil and vegetation characteristics. This work demonstrated that coastal reclamation was highly spatial dependent, which sheds a light on the future development of spatial explicit and process-based models to guide coastal reclamation around the world.

Heavy metal monitoring, analysis and prediction in lakes and rivers: state of the art

Several research efforts have been conducted to monitor and analyze the impact of environmental factors on the heavy metal concentrations and physicochemical properties of water bodies (lakes and rivers) in different countries worldwide. This article provides a general overview of the previous works that have been completed in monitoring and analyzing heavy metals. The intention of this review is to introduce the historical studies to distinguish and understand the previous challenges faced by researchers in analyzing heavy metal accumulation. In addition, this review introduces a survey on the importance of time increment sampling (monthly and/or seasonally) to comprehend and determine the rate of change of different parameters on a monthly and seasonal basis. Furthermore, suggestions are made for future research to achieve more understandable figures on heavy metal accumulation by considering climate conditions. Thus, the intent of the current study is the provision of reliable models for predicting future heavy metal accumulation in water bodies in different climates and pollution conditions so that water management can be achieved using intelligent proactive strategies and artificial neural network (ANN) techniques.

Impacts from Land Use Pattern on Spatial Distribution of Cultivated Soil Heavy Metal Pollution in Typical Rural-Urban Fringe of Northeast China

Under rapid urban sprawl in Northeast China, land conversions are not only encroaching on the quantity of cultivated lands, but also posing a great threat to black soil conservation and food security. This study's aim is to explore the spatial relationship between comprehensive cultivated soil heavy metal pollution and peri-urban land use patterns in the black soil region. We applied spatial lag regression to analyze the relationship between PLI (pollution load index) and influencing factors of land use by taking suburban cultivated land of Changchun Kuancheng District as an empirical case. The results indicate the following: (1) Similar spatial distribution characteristics are detected between Pb, Cu, and Zn, between Cr and Ni, and between Hg and Cd. The Yitong River catchment in the central region, and the residential community of Lanjia County in the west, are the main hotspots for eight heavy metals and PLI. Beihu Wetland Park, with a larger-area distribution of ecological land in the southeast, has low level for both heavy metal concentrations and PLI values. Spatial distribution characteristics of cultivated heavy metals are related to types of surrounding land use and industry; (2) Spatial lag regression has a better fit for PLI than the ordinary least squares regression. The regression results indicate the inverse relationship between heavy metal pollution degree and distance from long-standing residential land and surface water. Following rapid urban land expansion and a longer accumulation period, residential land sprawl is going to threaten cultivated land with heavy metal pollution in the suburban black soil region, and cultivated land irrigated with urban river water in the suburbs will have a higher tendency for heavy metal pollution.

Spatial patterns and origins of heavy metals in Sheyang River catchment in Jiangsu, China based on geographically weighted regression

Multivariate statistical analyses combined with geographically weighted regression (GWR) were used to identify spatial variations of heavy metals in sediments and to examine relationships between metal pollution and land use practices in watersheds, including urban land, agriculture land, forest and water bodies. Seven metals (Cu, Zn, Pb, Cr, Ni, Mn and Fe) of sediments were measured at 31 sampling sites in Sheyang River. Most metals were under a certain degree enrichment based on the enrichment factors. Cluster analysis grouped all sites into four statistically significant cluster, severely contaminated areas were concentrated in areas with intensive human activities. Correlation analysis and PCA indicated Cu, Zn and Pb were derived from anthropogenic activities, while the sources of Cr and Ni were complicated. However, Fe and Mn originated from natural sources. According to results of GWR, there are stronger association between metal pollution with urban land than agricultural land and forest. Moreover, the relationships between land use and metal concentration were affected by the urbanization level of watersheds. Agricultural land had a weak associated with heavy metal pollution and the relationships might be stronger in less-urbanized. This study provided useful information for the assessment and management of heavy metal hazards in studied area.