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

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.

Spatial and Temporal Variations in Water Quality and Land Use in a Semi-Arid Catchment in Bolivia

Increasing pressures caused by human activities pose a major threat to water availability and quality worldwide. Water resources have been declining in many catchments during recent decades. This study investigated patterns of river water quality status in a peri-urban/rural catchment in Bolivia in relation to land use during a 26 year period. Satellite images were used to determine changes in land use. To assess water quality, data in the dry season from former studies (1991–2014), complemented with newly collected data (2017), were analysed using the National Sanitation Foundation-Water Quality Index method and the Implicit Pollution Index method. The highest rates of relative increase in land use area were observed for forest, urban, and peri-urban areas, whereas relative decreases were observed for water infiltration zones, bare soil, shrubland, and grassland areas. The water quality indices revealed clear water quality deterioration over time, and from catchment headwaters to outlet. Statistical analyses revealed a significant relationship between decreasing water quality and urban expansion. These results demonstrate the need for an effective control programme, preferably based on water quality index approaches as in the present study and including continuous monitoring of runoff water, mitigation of pollution, and water quality restoration, in order to achieve proper water management and quality.

Linking Land Use Metrics Measured in Aquatic–Terrestrial Interfaces to Water Quality of Reservoir-Based Water Sources in Eastern China

The effects of anthropogenic land use on the water quality of reservoir-based water sources are understudied. We trained a self-organizing map (SOM) to measure the spatial pattern of nutrients over the course of a year in 61 reservoirs located in eastern China. In the linear regression model and one-way analyses of variance, we found that water quality was influenced by period of the year (flood, low, and normal periods based on rainfall conditions) and reservoir altitude (plains vs. mountains). Our results indicated that land use metrics measured in aquatic–terrestrial interfaces significantly influenced the water quality of reservoirs. The land use intensity (LUI) and the proportion of construction land had a positive correlation with ammonia nitrogen (NH3-N) and chemical oxygen demand (CODMn) concentrations, and redundancy analysis indicated that the percent of landscape (PLAND) represented by construction land was positively correlated with CODMn, NH3-N, total phosphorus (TP), and total nitrogen (TN) concentrations. The proportion of cropland was not correlated with any water quality property except for CODMn concentration. The total explained variance for water quality was highest when the scale was large (the area defined by a 1500 m radius around the reservoir), indicating that management which ensures water safety should be carried out at this scale.

Exploring the Spatial Variation Characteristics and Influencing Factors of PM2.5 Pollution in China: Evidence from 289 Chinese Cities

Haze pollution has become an urgent environmental problem due to its impact on the environment as well as human health. PM2.5 is one of the core pollutants which cause haze pollution in China. Existing studies have rarely taken a comprehensive view of natural environmental conditions and socio-economic factors to figure out the cause and diffusion mechanism of PM2.5 pollution. This paper selected both natural environmental conditions (precipitation (PRE), wind speed (WIN), and terrain relief (TR)) and socio-economic factors (human activity intensity of land surface (HAILS), the secondary industry's proportion (SEC), and the total particulate matter emissions of motor vehicles (VE)) to analyze the effects on the spatial variation of PM2.5 concentrations. Based on the spatial panel data of 289 cities in China in 2015, we used spatial statistical methods to visually describe the spatial distribution characteristics of PM2.5 pollution; secondly, the spatial agglomeration state of PM2.5 pollution was characterized by Moran’s I; finally, several regression models were used to quantitatively analyze the correlation between PM2.5 pollution and the selected explanatory variables. Results from this paper confirm that in 2015, most cities in China suffered from severe PM2.5 pollution, and only 17.6% of the sample cities were up to standard. The spatial agglomeration characteristics of PM2.5 pollution in China were particularly significant in the Beijing–Tianjin–Hebei region. Results from the global regression models suggest that WIN exerts the most significant effects on decreasing PM2.5 concentration (p < 0.01), while VE is the most critical driver of increasing PM2.5 concentration (p < 0.01). Results from the local regression model show reliable evidence that the relation between PM2.5 concentrations and the explanatory variables varied differently over space. VE is the most critical factor that influences PM2.5 concentrations, which means controlling motor vehicle pollutant emissions is an effective measure to reduce PM2.5 pollution in Chinese cities.

Exploring the Relevance of Green Space and Epidemic Diseases Based on Panel Data in China from 2007 to 2016

Urban green space has been proven effective in improving public health in the contemporary background of planetary urbanization. There is a growing body of literature investigating the relationship between non-communicable diseases (NCDs) and green space, whereas seldom has the correlation been explored between green space and epidemics, such as dysentery, tuberculosis, and malaria, which still threaten the worldwide situation of public health. Meanwhile, most studies explored healthy issues with the general green space, public green space, and green space coverage, respectively, among which the different relevance has been rarely explored. This study aimed to examine and compare the relevance between these three kinds of green space and incidences of the three types of epidemic diseases based on the Panel Data Model (PDM) with the time series data of 31 Chinese provinces from 2007 to 2016. The results indicated that there exists different, or even opposite, relevance between various kinds of green space and epidemic diseases, which might be associated with the process of urban sprawl in rapid urbanization in China. This paper provides a reference for re-thinking the indices of green space in building healthier and greener cities.

Attributing the hydrological impact of different land use types and their long-term dynamics through combining parsimonious hydrological modelling, alteration analysis and PLSR analysis

Understanding the relationship between hydrological processes and environmental changes is important for improved water management. The Geba catchment in Ethiopia, forming the headwaters of Tekeze-Atbara basin, was known for its severe land degradation before the recent success in integrated watershed management. This study analyses the hydrological response attributed to land management change using an integrated approach composed of (i) simulating the hydrological response of Land Use/Cover (LULC) changes; (ii) assessing the alteration of streamflow using Alteration of Hydrological Indicators (IHA); and (iii) quantifying the contribution of individual LULC types to the hydrology using Partial Least Square Regression model (PLSR). The results show that the expansion of agricultural and grazing land at the expense of natural vegetation has increased the surface runoff 77% and decreased dry season flow by 30% in the 1990s compared to 1970s. However, natural vegetation started to recover from the late 1990s and dry season flows increased by 16%, while surface runoff declined by 19%. More pronounced changes of the streamflow were noticed at sub-catchment level, mainly associated with the uneven spatial distribution of land degradation and rehabilitation. However, the rate of increase of low-flow halted in the 2010s, most probably due to an increase of water withdrawals for irrigation. Fluctuations in hydrological alteration parameters are in agreement with the observed LULC change. The PLSR analysis demonstrates that most LULC types showed a strong association with all hydrological components. These findings demonstrate that changing water conditions are attributed to the observed LULC change dynamics. The combined analysis of rainfall-runoff modelling, alteration indicators and PLSR is able to assess the impact of environmental change on the hydrology of complex catchments. The IHA tool is robust to assess the magnitude of streamflow alterations obtained from the hydrological model while the PLSR method is useful to zoom into which LULC is responsible for this alteration.

Assessment of the Geographical Detector Method for investigating heavy metal source apportionment in an urban watershed of Eastern China

Assessing heavy metal pollution in river sediments and identifying the key factors contributing to metal pollution are critical components for devising river environmental protection and remediation strategies to protect human and ecological health. This is especially important in urban areas where metals from a wide range of sources contribute to sediment pollution. In this study, the metal enrichment factor (EF) was used to measure the watershed distribution of Cu, Zn, Pb and Cd in sediments in the Wen-Rui Tang urban river system in Wenzhou, Eastern China. The Geographical Detector Method (GDM) was specifically evaluated for its ability to analyze spatial relationships between metal EFs and their anthropogenic and natural control factors, including densities of industry (DI), livestock (DL), service industries (DS), population (DP), and roads (DR), along with agricultural area (AG), sediment total organic carbon (TOC), and soil types (ST). Results showed that the watershed was highly contaminated by all metals with an EF trend of Cd ≫ Zn > Cu > Pb. The spatial distribution of EFs demonstrated high contamination of all metals in the southwestern region of the watershed where industrial activities were concentrated, and higher Cu and Zn concentrations in the northeastern region having a high density of livestock production. GDM results identified DI as the dominant determinant for all metals, while TOC and ST were determined to have a moderate secondary influence for Zn, Pb and Cd. Additionally, GDM revealed several additive and nonlinear interactions between anthropogenic and natural factors influencing metal concentrations. Compared to other correlation, multiple linear regression and geographically weighted regression, GDM demonstrated distinct advantages of being able to assess both categorical and continuous variables and determine both single and multiple factor interactions. These attributes provide a more comprehensive understanding of metal spatial distributions while avoiding multicollinearity issues when identifying significant contributing factors at the watershed scale.

A comprehensive risk assessment of metals in riverine surface sediments across the rural-urban interface of a rapidly developing watershed

Metal contamination in aquatic environments is a severe global concern to human health and aquatic ecosystems. This study used several risk assessment indices, to evaluate metal (Cu, Zn, Pb, Cd and Cr) environmental risk of riverine surface sediments across the rural-urban interface of the rapidly developing Wen-Rui Tang River watershed in eastern China. Risk assessments were determined for 38 sites based on the potential ecological risk index (RI), consensus-based sediment quality guidelines (SQGs) and risk assessment code (RAC). Land-use cluster analysis showed that sediments were severely contaminated, especially for Cd, whose concentrations were ∼100 times higher than background levels and had a high proportion in the bioaccessible fraction. According to RI, E_r^Cd was identified with extremely high risk potential, resulting in the highest ecological risk of Cluster 4 (industrial). Similarly, risk within Cluster 4 (industrial) was also ranked highest by SQGs assessment due to the high proportion of industrial land use. Zinc was determined with high risk due to its high concentration compared to its effect range medium (ERM) value. Discrepancies in predicting environmental risks from metals among the three indices were mainly attributed to the contrasting definitions of these metrics. Environmental risk uncertainty derived from spatial variation was further estimated by Monte Carlo simulation and ranked as: Zn > Cd > Cr > Pb > Cu. This comprehensive environmental risk assessment provides important information to guide remediation strategies for management of metal contamination at the watershed scale.

Analysis of Spatial Pattern Evolution and Influencing Factors of Regional Land Use Efficiency in China Based on ESDA-GWR

In order to give an in-depth understanding of the contradictions arising from the land resource supply and demand, this study selected 30 provinces (some are autonomous regions or municipalities) in China to be the research unit, used the carbon emission as an undesirable output, and adopted the Super-SBM DEA model and ESDA-GWR method to research the evolution characteristics and influencing factors of land use efficiency in China in 2003–2013. The results indicated that: (1) The land use efficiency in China overall was moderately ineffective and the overall utilization level was low; (2) The Global Spatial Autocorrelation was instable and had maintained a high level; (3) The “hot spots” mainly being distributed in the southeast coastal regions and “cold spots” being found in the central and western regions, so that as time goes on, the pattern of “high in the east and low in the west” has been gradually formed and stabilized. (4) The GWR model analysis showed that the natural factors such as NDVI, DMSP/OLS and DEM have a significant impact on land use efficiency, thereby providing an important contribution to this study. For the eastern coastal areas, the emphasis should be improving their OT, PF and PGDP, for the western region, should focus on improving its comprehensive economic development level to improve the DMSP/OLS, while strengthening the ecological environment to improve the level of NDVI.

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

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 (NH3-N), nitrogen (NO3−), and total phosphate (TP), as well as oxygen-consuming organic matter indicators COD (chemical oxygen demand), biochemical oxygen demand (BOD5), dissolved oxygen (DO), and potassium permanganate index (CODMn). 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.

Relationship between water quality and macro-scale parameters (land use, erosion, geology, and population density) in the Siminehrood River Basin

To date, few studies have investigated the simultaneous effects of macro-scale parameters (MSPs) such as land use, population density, geology, and erosion layers on micro-scale water quality variables (MSWQVs). This research focused on an evaluation of the relationship between MSPs and MSWQVs in the Siminehrood River Basin, Iran. In addition, we investigated the importance of water particle travel time (hydrological distance) on this relationship. The MSWQVs included 13 physicochemical and biochemical parameters observed at 15 stations during three seasons. Primary screening was performed by utilizing three multivariate statistical analyses (Pearson's correlation, cluster and discriminant analyses) in seven series of observed data. These series included three separate seasonal data, three two-season data, and aggregated three-season data for investigation of relationships between MSPs and MSWQVs. Coupled data (pairs of MSWQVs and MSPs) repeated in at least two out of three statistical analyses were selected for final screening. The primary screening results demonstrated significant relationships between land use and phosphorus, total solids and turbidity, erosion levels and electrical conductivity, and erosion and total solids. Furthermore, water particle travel time effects were considered through three geographical pattern definitions of distance for each MSP by using two weighting methods. To find effective MSP factors on MSWQVs, a multivariate linear regression analysis was employed. Then, preliminary equations that estimated MSWQVs were developed. The preliminary equations were modified to adaptive equations to obtain the final models. The final models indicated that a new metric, referred to as hydrological distance, provided better MSWQV estimation and water quality prediction compared to the National Sanitation Foundation Water Quality Index.

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

Heavy metal pollution of aquatic environments in rapidly developing industrial regions is of considerable global concern due to its potential to cause serious harm to aquatic ecosystems and human health. This study assessed heavy metal contamination of sediments in a highly industrialized urban watershed of eastern China containing several historically unregulated manufacturing enterprises. Total concentrations and solid-phase fractionation of Cu, Zn, Pb, Cr and Cd were investigated for 39 river sediments using multivariate statistical analysis and geographically weighted regression (GWR) methods to quantitatively examine the relationship between land use and heavy metal pollution at the watershed scale. Results showed distinct spatial patterns of heavy metal contamination within the watershed, such as higher concentrations of Zn, Pb and Cd in the southwest and higher Cu concentration in the east, indicating links to specific pollution sources within the watershed. Correlation and PCA analyses revealed that Zn, Pb and Cd were dominantly contributed by anthropogenic activities; Cu originated from both industrial and agricultural sources; and Cr has been altered by recent pollution control strategies. The GWR model indicated that several heavy metal fractions were strongly correlated with industrial land proportion and this correlation varied with the level of industrialization as demonstrated by variations in local GWR R² values. This study provides important information for assessing heavy metal contaminated areas, identifying heavy metal pollutant sources, and developing regional-scale remediation strategies.

Effects of land use/cover on surface water pollution based on remote sensing and 3D-EEM fluorescence data in the Jinghe Oasis

The key problem in the reasonable management of water is identifying the effective radius of surface water pollution. Remote sensing and three-dimensional fluorescence technologies were used to evaluate the effects of land use/cover on surface water pollution. The PARAFAC model and self-organizing map (SOM) neural network model were selected for this study. The results showed that four fluorescence components, microbial humic-like (C1), terrestrial humic-like organic (C2, C4), and protein-like organic (C3) substances, were successfully extracted by the PARAFAC factor analysis. Thirty water sampling points were selected to build 5 buffer zones. We found that the most significant relationships between land use and fluorescence components were within a 200 m buffer, and the maximum contributions to pollution were mainly from urban and salinized land sources. The clustering of land-use types and three-dimensional fluorescence peaks by the SOM neural network method demonstrated that the three-dimensional fluorescence peaks and land-use types could be grouped into 4 clusters. Principal factor analysis was selected to extract the two main fluorescence peaks from the four clustered fluorescence peaks; this study found that the relationships between salinized land, cropland and the fluorescence peaks of C1, W2, and W7 were significant by the stepwise multiple regression method.

The Impacts of Land Use Patterns on Water Quality in a Trans-Boundary River Basin in Northeast China Based on Eco-Functional Regionalization

The relationships between land use patterns and water quality in trans-boundary watersheds remain elusive due to the heterogeneous natural environment. We assess the impact of land use patterns on water quality at different eco-functional regions in the Songhua River basin during two hydrological seasons in 2016. The partial least square regression indicated that agricultural activities associated with most water quality pollutants in the region with a relative higher runoff depth and lower altitude. Intensive grazing had negative impacts on water quality in plain areas with low runoff depth. Forest was related negatively with degraded water quality in mountainous high flow region. Patch density and edge density had major impacts on water quality contaminants especially in mountainous high flow region; Contagion was related with non-point source pollutants in mountainous normal flow region; landscape shape index was an effective indicator for anions in some eco-regions in high flow season; Shannon’s diversity index contributed to degraded water quality in each eco-region, indicating the variation of landscape heterogeneity influenced water quality regardless of natural environment. The results provide a regional based approach of identifying the impact of land use patterns on water quality in order to improve water pollution control and land use management.

Source identification and impact of landscape pattern on riverine nitrogen pollution in a typical urbanized watershed, Beijing, China

This study explored the sources of nitrate and the impact of landscape pattern on nitrogen pollution in the highly urbanized Beiyun River Watershed, China during 2016 by applying a dual stable isotope approach (δ¹⁵N-NO₃^-and δ¹⁸O-NO₃^-) combined with multiple statistical analyses. The sources of riverine nitrate principally originated from manure and sewage, nitrification of soil nitrogen, fertilizer nitrification, and atmospheric deposition. A Bayesian model was used to estimate the source contributions and results showed that manure and sewage were the major contributors to river nitrate with combined proportions of 77.59% and 89.57% in the rainy season and the dry season, respectively. Results from multiple stepwise regression indicated that the typical artificial land use types and landscape configuration metrics reflecting landscape fragmentation related well with riverine nitrogen variables for different seasons (R²>0.6). Industrial land, unused land and patch density of built-up land and road were positively correlated with riverine nitrogen over seasons, whereas the interspersion and juxtaposition index of forest land was negatively related with nitrate. Regulating built-up land and unused land, connecting forest land with other land use types and diminishing discharges of industrial and domestic wastewater would be effective ways to improve urban river water quality.

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

Heavy metal pollution is a major concern in China because of its serious effects on human health. To assess potential human health and ecological risks of heavy metal pollution, concentration data for seven heavy metals (As, Pb, Cd, Cr, Hg, Cu, Zn) from 14 sites spanning the rural-urban interface of the Wen-Rui Tang River watershed in southeast China were collected from 2000 to 2010. The heavy metal pollution index (HPI), hazard index (HI) and carcinogenic risk (CR) metrics were used to assess potential heavy metal risks. Further, we evaluated the uncertainty associated with the risk assessment indices using Monte Carlo analysis. Results indicated that all HPI values were lower than the critical level of 100 suggesting that heavy metal levels posed acceptable ecological risks; however, one site having an industrial point-source input reached levels of 80-97 on several occasions. Heavy metal concentrations fluctuated over time, and the decrease after 2007 is due to increased wastewater collection. The HI suggested low non-carcinogenic risk throughout the study period (HI < 1); however, nine sites showed CR values above the acceptable level of 10^-4 for potential cancer risk from arsenic in the early 2000s. Uncertainty analysis revealed an exposure risk for As at all sites because some CR values exceeded the 10^-4 level of concern; levels of Cd near an old industrial area also exceeded the Cd exposure standard (2.6% of CR values > 10^-4). While most metrics for human health risk did not exceed critical values for heavy metals, there is still a potential human health risk from chronic exposure to low heavy metal concentrations due to long-term exposure and potential metal interactions. Results of this study inform water pollution remediation and management efforts designed to protect public health in polluted urban area waterways common in rapidly developing regions.

Quantifying longitudinal land use change from land degradation to rehabilitation in the headwaters of Tekeze-Atbara Basin, Ethiopia

The spatiotemporal variability of the Land Use/Cover (LULC) is a strong influence on the land management and hydrological processes of a river basin. In particular in semi-arid regions like the Tekeze-Atbara (T-A) basin, accurate information about LULC change is a prerequisite for improved land and water management. The human-induced landscape transformations in the T-A basin, one of the main tributaries of the Nile River, were investigated for the last four decades (1972-2014). Separate LULC maps for the years 1972, 1989, 2001, and 2014 were developed based on satellite images, Geographic Information System (GIS) and ground information. Change detection analysis based on the transitional probability matrix was applied to identify systematic transitions among the LULC categories. The results show that >72% of the landscape has changed its category during the past 43years. LULC in the basin experienced significant shifts from one category to other categories by 61%, 47%, and 45%, in 1972-1989, 1989-2001, and 2001-2014, respectively. Although both net and swap (simultaneous gain and loss of a given LULC during a certain period) change occurred, the latter is more dominant. Natural vegetation cover, including forests, reduced drastically with the rapid expansion of crops, grazing areas and bare lands during the first two decades. However, vegetation started to recover since the 1990s, when some of the agricultural and bare lands have turned into vegetated areas. Forest land showed a continuous decreasing pattern, however, it has increased by 28% in the last period (2001-2014). In contrast, plantation trees have increased by 254% in the last three decades. The increase of vegetation cover is a result of intensive watershed management programs during the last two decades. The driving forces of changes were also discussed and rapid population growth and changing government policies were found to be the most important.

Multi-scale analysis of the relationship between landscape patterns and a water quality index (WQI) based on a stepwise linear regression (SLR) and geographically weighted regression (GWR) in the Ebinur Lake oasis

Water quality is highly dependent on landscape characteristics. This study explored the relationships between landscape patterns and water quality in the Ebinur Lake oasis in China. The water quality index (WQI) has been used to identify threats to water quality and contribute to better water resource management. This study established the WQI and analyzed the influence of landscapes on the WQI based on a stepwise linear regression (SLR) model and geographically weighted regression (GWR) models. The results showed that the WQI was between 56.61 and 2886.51. The map of the WQI showed poor water quality. Both positive and negative relationships between certain land use and land cover (LULC) types and the WQI were observed for different buffers. This relationship is most significant for the 400-m buffer. There is a significant relationship between the water quality index and landscape index (i.e., PLAND, DIVISION, aggregation index (AI), COHESION, landscape shape index (LSI), and largest patch index (LPI)), demonstrated by using stepwise multiple linear regressions under the 400-m scale, which resulted in an adjusted R ² between 0.63 and 0.88. The local R ² between the LPI and LSI for forest grasslands and the WQI are high in the Akeqisu River and the Kuitun rivers and low in the Bortala River, with an R ² ranging from 0.57 to 1.86. The local R ² between the LSI for croplands and the WQI is 0.44. The local R ² values between the LPI for saline lands and the WQI are high in the Jing River and low in the Bo River, Akeqisu River, and Kuitun rivers, ranging from 0.57 to 1.86.

Human health risks of polycyclic aromatic hydrocarbons in the urban soils of Nanjing, China

Polycyclic aromatic hydrocarbons (PAHs) are a major group of toxic pollutants in urban areas. We calculated the critical concentrations of PAHs in the urban soils of Nanjing, China based on a human health risk assessment. In the study area, the risk was divided into four levels and toxic equivalent values of benzo[a]pyrene (BaP_eq) corresponded to <70ngg^-1, 70-700ngg^-1, 700-7000ngg^-1, and >7000ngg^-1. By this standard, most urban areas in Nanjing fall under level II (potentially low risk), while older urban districts, commercial centers, and transportation centers exceed the critical concentration (level III) at present. Additionally, the correlations between PAH concentrations, factors associated with urbanization, and soil properties were analyzed. Population density and black carbon content were determined to be the key factors involved. Multiple linear regression models and the scenario simulation method were used to predict PAH levels in urban soils through 2030. The results indicated that the future distribution characteristics of soil BaP_eq under various scenarios were different than at present, but PAH concentrations remained stable only under the low‑carbon scenario. Therefore, the consumption of traditional fossil fuels should be controlled and replaced with alternative energy sources. In addition, the growth of traffic land use should be controlled in the southern and southwestern parts of the urban area.

Ecosystem services response to urbanization in metropolitan areas: Thresholds identification

Ecosystem service is the key comprehensive indicator for measuring the ecological effects of urbanization. Although various studies have found a causal relationship between urbanization and ecosystem services degradation, the linear or non-linear characteristics are still unclear, especially identifying the impact thresholds in this relationship. This study quantified four ecosystem services (i.e. soil conservation, carbon sequestration and oxygen production, water yield, and food production) and total ecosystem services (TES), and then identified multiple advantageous area of ecosystem services in the peri-urban area of Beijing City. Using piecewise linear regression, the response of TES to urbanization (i.e., population density, GDP density, and construction land proportion) and its thresholds were detected. The results showed that, the TES was high in the north and west and low in the southeast, and there were seven multiple advantageous areas (distributed in the new urban development zone and ecological conservation zone), one single advantageous area (distributed in the ecological conservation zone), and six disadvantageous areas (mainly distributed in the urban function extended zone). TES response to population and economic urbanization each had a threshold (229personkm^-2 and 107.15millionyuankm^-2, respectively), above which TES decreased rapidly with intensifying urbanization. However, there was a negative linear relationship between land urbanization and TES, which indicated that the impact of land urbanization on ecosystem services was more direct and effective than that of population and economic urbanization. It was also found that the negative impact of urbanization on TES was highest in the urban function extended zone, followed in descending order by that in the new urban development zone and ecological conservation zone. According to the detected relationships between urbanization and TES, the economic and population urbanization should be strengthened accompanied by slowing or even reducing land urbanization, so as to achieve urban ecological sustainability with less ecosystem services degradation.

Effects of land use types on dissolved trace metal concentrations in the Le'an River Basin, China

Using land use types in multiple spatial scales (entire basin, buffer zones, and slopes) as well as statistical and spatial analysis, relationships between land use types and concentrations of dissolved trace metals were determined in the Le'an River Basin, China. The result showed that farmland and urban land were determined as the source of the pollutants, while forestland and grassland were identified as the sink of the pollutants. The temporal differences of relationships between land use types and concentrations of dissolved trace metals mainly due to the discrepancy of rainfall characteristics. Land use type close to river was a better indicator for the effectiveness of concentrations of trace metals, especially at scale of 0-200 m. Forestland and grassland on lower slopes greatly affected the water quality, and the former had no significant or weak influences on higher slopes. Urban land had the greater positive correlations with concentrations of dissolved trace metals on higher slopes, which are mainly due to frequent mining activity. Further analysis suggested that the buffer zones with low slope needed to be seriously taken into consideration for effective land use management in similar basin.

Evaluation of water quality based on a machine learning algorithm and water quality index for the Ebinur Lake Watershed, China

The water quality index (WQI) has been used to identify threats to water quality and to support better water resource management. This study combines a machine learning algorithm, WQI, and remote sensing spectral indices (difference index, DI; ratio index, RI; and normalized difference index, NDI) through fractional derivatives methods and in turn establishes a model for estimating and assessing the WQI. The results show that the calculated WQI values range between 56.61 and 2,886.51. We also explore the relationship between reflectance data and the WQI. The number of bands with correlation coefficients passing a significance test at 0.01 first increases and then decreases with a peak appearing after 1.6 orders. WQI and DI as well as RI and NDI correlation coefficients between optimal band combinations of the peak also appear after 1.6 orders with R² values of 0.92, 0.58 and 0.92. Finally, 22 WQI estimation models were established by POS-SVR to compare the predictive effects of these models. The models based on a spectral index of 1.6 were found to perform much better than the others, with an R² of 0.92, an RMSE of 58.4, and an RPD of 2.81 and a slope of curve fitting of 0.97.

Spatial Regression and Prediction of Water Quality in a Watershed with Complex Pollution Sources

Fast economic development, burgeoning population growth, and rapid urbanization have led to complex pollution sources contributing to water quality deterioration simultaneously in many developing countries including China. This paper explored the use of spatial regression to evaluate the impacts of watershed characteristics on ambient total nitrogen (TN) concentration in a heavily polluted watershed and make predictions across the region. Regression results have confirmed the substantial impact on TN concentration by a variety of point and non-point pollution sources. In addition, spatial regression has yielded better performance than ordinary regression in predicting TN concentrations. Due to its best performance in cross-validation, the river distance based spatial regression model was used to predict TN concentrations across the watershed. The prediction results have revealed a distinct pattern in the spatial distribution of TN concentrations and identified three critical sub-regions in priority for reducing TN loads. Our study results have indicated that spatial regression could potentially serve as an effective tool to facilitate water pollution control in watersheds under diverse physical and socio-economical conditions.

The impact of anthropogenic emissions and meteorological conditions on the spatial variation of ambient SO2 concentrations: A panel study of 113 Chinese cities

China has received increased international criticism in recent years in relation to its air pollution levels, both in terms of the transmission of pollutants across international borders and the attendant adverse health effects being witnessed. Whilst existing research has examined the factors influencing ambient air pollutant concentrations, previous studies have failed to adequately explore the determinants of such concentrations from either a source or diffusion perspective. This study addressed both source (specifically, anthropogenic emissions) and diffusion (namely, meteorological conditions) indicators, in order to detect their respective impacts on the spatial variations seen in the distribution of air pollution. Spatial panel data for 113 major cities in China was processed using a range of global regression models-the ordinary least square model, the spatial lag model, and the spatial error model-as well as a local, geographic weighted regression (GWR) model. Results from the study suggest that in 2014, average SO₂ concentrations exceeded China's first-level target. The most polluted cities were found to be predominantly located in northern China, while less polluted cities were located in southern China. Global regression results indicated that precipitation exerts a significant effect on SO₂ reduction (p<0.001) and that a regional increase of 1mm in precipitation can reduce SO₂ concentrations by 0.026μg/m³. Both emission and temperature factors were found to aggravate SO₂ concentrations, although no such significant correlation was found in relation to wind speed. GWR results suggest that the association between SO₂ and its factors varied over space. Increased emissions were found to be able to produce more pollution in the northwest than in other parts of the country. Higher wind speeds and temperatures in northwestern areas were shown to reinforce SO₂ pollution, while in southern regions, they had the opposite effect. Further, increased precipitation was found to exert a greater inhibitory effect on SO₂ pollution in the country's northeast than that in other areas. Our findings could provide a detailed reference for formulating regionally specific emission reduction policies in China.