Characterizing nitrogen deposited on urban road surfaces: Implication for stormwater runoff pollution control.

Nitrogen (N) is one of the most important pollutants on urban road surfaces. Understanding the N deposition forms, load characteristics, and influential factors can help to provide management and control strategies for road stormwater runoff pollution. This study focuses on a highly urbanized area in Guangzhou, China, and presents the characteristics of both dissolved and particulate N deposition forms as well as their correlations with land-use types and traffic factors. In addition, an artificial neural network (ANN) based classification model is utilized to estimate N pollution hotspot area and total nitrogen (TN) flux from road to receiving water bodies. The results showed that N on urban road surfaces mainly existed in the form of particulate organic nitrogen. Land use types dominated by residential area (RA) and urban village (UV) have higher TN build-up loads. Geodetector analysis indicated that land use has a greater impact on nitrogen build-up loads than traffic factors. Through classification and estimation using the ANN model, RA, and UV were classified as hotspot areas, and the TN flux from roads in the study area was calculated to be 3.35 × 105 g. Furthermore, it was estimated that the annual TN flux from roads in Guangzhou accounts for 19 % of the city's total urban domestic discharge. These findings are expected to contribute to the pollution control of stormwater runoff from urban road surfaces and provide valuable guidance for enhancing the ecological health of urban water environments.

Multi-scenario prediction and attribution analysis of carbon storage of ecological system in the Huaihe River Basin, China.

Evaluating the impact of large-scale human activities on carbon storage through land use changes is of growing interest in terrestrial ecosystem assessments. The Huaihe River Basin, a vital Chinese grain production area, has undergone marked land use changes amid socio-economic acceleration. Evaluating the impacts of land use change on carbon storage and future carbon sequestration is imperative for regional ecosystem sustainability and Chinese food security, simultaneously, furnishing data support to regional land use planning and decision-making processes. Nonetheless, the mechanisms linking land use changes to carbon storage and the future carbon reservoir responses remain unclear. We utilized a multi-source dataset and representative scenarios, integrating PLUS, InVEST models, and Geodetector to assess land use change impacts on carbon storage in the Huaihe River Basin (2000-2030). The data indicates the following: (1) from 2000 to 2020, cultivated land decreased by 28,344.69 km2, construction land increased by 26,914.56 km2, and other land types changed little. (2) Land use change resulted a carbon loss of 1.17 × 108 t, primarily due to the expansion of construction land. (3) All four simulation scenarios exhibited diminished carbon storage relative to 2020, with the economic development scenario recording the lowest at 4.98 × 109 t and the ecological protection scenario the highest at 5.06 × 109 t. (4) Elevation predominantly drives carbon storage changes, with its interaction with NPP having the greatest impact. The factors synergistically enhance their explanatory power. The research provides a scientific basis for strategies aimed at augmenting regional carbon sequestration and refining low-carbon land management, safeguarding ecosystem stability.

Analysis of driving factors for potential toxic metals in major urban soils of China: a geodetetor-based quantitative study.

Potential toxic metal (PTM) is hazardous to human health, but the mechanism of spatial heterogeneity of PTM at a macro-scale remains unclear. This study conducts a meta-analysis on the data of PTM concentrations in the soil of 164 major cities in China from 2006 to 2021. It utilizes spatial analysis methods and geodetector to investigate the spatial distribution characteristics of PTMs. The geographic information systems (GIS) and geodetector were used to investigate the spatial distribution characteristics of PTMs, assess the influence of natural factors (NFs) and anthropogenic factors (AFs) on the spatial heterogeneity of PTMs in urban soils, and identified the potential pollution areas of PTMs. The results indicated that the pollution levels of PTMs in urban soils varied significantly across China, with higher pollution levels in the south than in the north. Cd and Hg were the most severely contaminated elements. The geodetector analysis showed that temperature and precipitation in NFs and land use type in AFs were considered as the main influencing factors, and that both AF and NF together led to the PTM variation. All these factors showed a mutually enhancing pattern which has important implications for urban soil management. PTM high-risk areas were identified to provide early warning of pollution risk under the condition of climate change.

Spatial and temporal characteristic of PM2.5 and influence factors in the Yellow River Basin.

The Yellow River Basin has been instrumental in advancing ecological preservation and fostering national high-quality development. However, since the advent of China's reform and opening-up policies, the basin has faced severe environmental pollution issues. This study leverages remote sensing data from 1998 to 2019. As per the "Basin Scope and Its Historical Changes" published by the Yellow River Conservancy Commission of the Ministry of Water Resources, the Yellow River Basin is categorized into upstream, midstream, and downstream regions for analysis of their spatial and temporal distribution traits using spatial autocorrelation methods. Additionally, we employed probes to study the effects of 10 factors, including mean surface temperature and air pressure, on PM2.5. The study findings reveal that (1) the annual average concentration of PM2.5 in the Yellow River Basin exhibited a fluctuating trend from 1998 to 2019, initially increasing, then decreasing, followed by another increase before ultimately declining. (2) The air quality in the Yellow River Basin is relatively poor, making it challenging for large-scale areas with low PM2.5 levels to occur. (3) The PM2.5 concentration in the Yellow River Basin exhibits distinct high and low-value concentration areas indicative of air pollution. Low-value areas are predominantly found in the sparsely populated central and southwestern plateau regions of Inner Mongolia, characterized by a better ecological environment. In contrast, high-value areas are prevalent in the inland areas of Northwest China, with poorer natural conditions, as well as densely populated zones with high energy demand and a relatively developed economy. (4) The overall population density in the Yellow River Basin, as well as in the upstream, midstream, and downstream regions, serves as a primary driving factor. (5) The primary drivers in the middle reaches and the entire Yellow River Basin remain consistent, whereas those in the upper and lower reaches have shifted. In the upstream, air pressure emerges as a primary driver of PM2.5, while in the downstream, NDVI and precipitation become the main influencing factors.

[Spatial Differentiation Characteristics and Influencing Factors of Surface Soil Cd in Karst Plateau Area of Guiyang City].

In order to explore the spatial differentiation characteristics and variation law of soil Cd content in a high geological background area, 14 421 topsoil samples were collected from topsoil in the karst area of Guiyang City. Global Moran's I index, cold hot spot analysis, semi-variance function, and Kriging interpolation were used to reveal the spatial structure and distribution law of soil Cd content. The influence of environmental factors on soil Cd content and its main controlling factors were analyzed through analysis of variance and geographic detector. The results showed that： ① The Cd content of karst surface soil in Guiyang varied from 0.03 to 1.36 mg·kg-1, with an average of 0.440 mg·kg-1, which was 1.77 times and 5.95 times the Guizhou soil Cd background value and Chinese soil Cd background value, respectively. The over-standard rate of soil Cd was 30%, which was 4.29 times that of 7% of soil Cd in China. ② There was a significant spatial positive correlation of soil Cd content, showing an aggregation trend in the global space, whereas in the local region, the northeast and southwest were hot spots, and the north was a cold spot. The nugget coefficient of soil Cd content was 10.37%, indicating that soil Cd was mainly affected by structural factors. ③ In terms of spatial distribution, soil Cd showed different accumulation trends. In some massive soils, such as Xifeng County, Xiuwen County, Qingzhen City, Huaxi District, and Nanming District, the soil ω（Cd）was less than 0.3 mg·kg-1. The soil ω（Cd）was between 0.3 and 0.6 mg·kg-1,and soil Cd in Baiyun District, Wudang District, Guanshan Lake area, and Yunyan area as a whole lied within this range. The soil ω（Cd）between 0.6 and 0.9 mg·kg-1 was concentrated in the southwest of Qingzhen City, the south of Huaxi District, and the north of Kaiyang County, whereas soil ω（Cd） between 0.9 and 1.2 mg·kg-1 was mainly concentrated in the southwest of Qingzhen City. The extreme value of soil Cd content （ > 1.2 mg·kg-1） was mostly distributed in Kaiyang County, Xiuwen County, Qingzhen City, and Huaxi District. ④ The results of analysis of variance and geo-detector showed that different environmental factors had significant effects on the spatial differentiation of soil Cd, but their explanatory power on soil Cd content varied： stratum （0.176 5） > soil type （0.026 0） > organic matter （0.025 1） > altitude （0.010 5） > parent rock （0.007 3） > land use （0.006 4） > pH （0.001 3）, and the interaction between stratum and arbitrary environmental factors was the greatest. Therefore, stratum was the main factor affecting the spatial differentiation of soil Cd content.

Analysis of the spatial differentiation and driving force of arable land abandonment and non-grain in the hilly mountainous areas of Gannan.

Food security has a bearing on national development and people's livelihoods and is an important guarantee of social stability for national development. The problems of arable land abandonment and non-grain are becoming more and more serious, and national food security is difficult to guarantee, which will seriously hinder the forward development of China's society and economy. Taking Ruijin City of Jiangxi Province as an example, this study calculated the abandonment level and non-grain level of arable land in Ruijin City respectively from two aspects, and explored the spatial differentiation law of farmland abandonment and non-grain level in the hilly and mountainous areas of southern Jiangxi Province by using spatial autocorrelation and cold and hot spot analysis methods, and the causes of arable land abandonment and non-grain spatial differentiation in the hilly mountainous areas of Gannan were revealed by the methods of Geodetector factor detection and interaction detection. Conclusions of the study: (1) Ruijin City, the abandoned area was 1216.73 hm2, the abandonment rate of each village ranged from 0.01 % to 50.62 %, and the comprehensive abandonment rate was 4.90 %; the area of non-grain was 2937.27 hm2, and the rate of non-grain of each village ranged from 0.01 % to 100.00 %, and the comprehensive non-grain rate was 11.83 %. The area of non-grain was 2937.27 hm2, and the rate of non-grain in each village ranged from 0.01 % to 100.00 %, and the comprehensive rate of non-grain was 11.83 %. (2) The phenomenon of abandonment of arable land and non-grain in Gannan hilly and mountainous areas has a certain clustering and driving effect in space. Globally, the phenomena of arable land abandonment and non-grain in Ruijin City are positively correlated, with the global Moran's I of arable land abandonment rate being 0.05, and the global Moran's I of arable land non-grain being 0.73. (3) Whether or not arable land in the hilly mountainous areas of Gannan is abandoned is affected by the combination of socioeconomics, natural resources, farming conditions, and economic location, with elevation, the degree of arable land contiguity, and population density being the dominant factors. The interaction of elevation, degree of concentration and contiguity, field regularity, and per capita arable land area increased the spatial variability of arable land abandonment in the hilly mountainous areas of Gannan. Whether the phenomenon of non-grain occurs or not is affected by socio-economic conditions, farming conditions and economic location, of which the proportion of paddy fields, land transfer price, arable land area, and urban-rural gradient are the dominant factors. The proportion of paddy land, the price of land transfer, the area of arable land, and the urban-rural gradient interact with each other, and the tendency of arable land to be planted with non-grain crops is more serious.

Quantitative Effects of Anthropogenic and Natural Factors on Heavy Metals Pollution and Spatial Distribution in Surface Drinking Water Sources in the Upper Huaihe River Basin in China.

The water quality of sources in the Huaihe River Basin significantly affects the lives and health of approximately 16.7% of China's population. Identifying and quantifying pollution sources and risks is essential for effective water resource management. This study utilized Monte Carlo simulations and Geodetector to assess water quality and eutrophication, as well as to evaluate the sources of heavy metals and the associated health risks for both adults and children. The results showed that eutrophication of water sources in Huaihe River was severe, with an overall EI value of 37.92; 67.8% of the water sources were classified as mesotrophic and 32.2% classified as eutrophic. Water quality and eutrophication levels in the southern mountainous regions were better than those in the densely populated northern areas. Adults were found to have a higher carcinogenic risk than children, whereas children faced a higher noncarcinogenic risk than adults. Cr presented the highest carcinogenic risk, affecting more than 99.8% of both adults and children at levels above 1 × 10-6 but not exceeding 1 × 10-4. The noncarcinogenic risk from metals did not surpass a level of 1, except for Pb. As was primarily influenced by agricultural activities and transportation, whereas Cd, Cr, and Pb were mainly affected by industrial activities, particularly in local textile industries such as knitting and clothing manufacturing. The analysis demonstrated that the influence of anthropogenic factors on heavy metal distribution was significantly enhanced by indirect natural factors. For example, the explanatory power of Precipitation and Road Network Density on As was 0.362 and 0.189, respectively, whereas their interaction had an explanatory power as high as 0.673. This study indicates that the geodetector method is effective in elucidating the factors influencing heavy metal distribution in water, thereby providing valuable insights into pollution sources in global drinking water.

Construction of ecological security pattern of arid area based on landscape ecological risk assessment: a case study of the Wu-Chang-Shi urban agglomeration.

The construction of ecological security pattern (ESP) holds paramount importance in ensuring regional environment sustainability. This study introduces an innovative approach to ESP construction grounded in landscape ecological risk (LER) assessment, with Wu-Chang-Shi urban agglomeration in Xinjiang, China, serving as a case study. Initially, LER within the area was evaluated using the LER Index (LERI) method. Subsequently, the Geodetector model was employed to discern the relationship between multi-source data and LER. Furthermore, ecological resistance and corridors were delineated utilizing the minimum cumulative resistance (MCR) model. Lastly, the corridors were optimized using the gravity model, finalizing the ESP construction. Study results reveal that LER was always fluctuating from 1990 to 2010, and tended to stabilize from 2010 to 2020. Factor detection underscores the predominant influence of land use on LER, followed by elevation and vegetation distribution. The ESP shows the imperative for improving connectivity of the natural areas that are fragmented by urban land, highlighting the great significance of the woodland-originating corridors. Finally, strategies are proposed to enhance woodland and water coverage, boost landscape diversity in nature reserves, and prioritize ecological conservation in corridor regions. In summation, the study furnishes a framework for analyzing arid regions in Eurasia. Furthermore, the research idea of evaluation-analysis-remodeling also offers insights into environmental management in developing areas with more diverse climate types.

Improvements in the InVEST water purification model for detecting spatio-temporal changesintotal nitrogen and total phosphorus discharges in the Huai river watershed, China.

Improving water quality to provide freshwater is an urgent requirement for regional and even global social development. More accurate simulation of non-point sources pollution, monitored mainly by total nitrogen (TN) and total phosphorus (TP), has always been a challenge for InVEST water purification model, particularly in agricultural areas. This can be attributed to the fact that there is no reference data for TN and TP to rectify the outcomes modelled by this model. This paper provided these data to rectify simulation results of TN and TP to ensure their accuracy. The Huai River watershed (HRW) is an important grain production area with slow economic development, and non-point source pollution has exceeded point-source pollution. There is an urgent need for water management authorities to obtain complete spatio-temporal data on TN and TP loads and their exports to improve water quality. The reference data onloads and exports of TN and TP were estimated for the entire watershed and its sub-watersheds through an investigation-evaluation technique during 1980-2018. TN and TP loads generated from the agricultural sector were the major pollution sources in the HRW and had similar time trends during the same period. The spatial distribution of TN and TP exports was modelled byusingthe InVEST water purification model, and it was found that the temporal trends for the final exports of TN and TP into river systems were similar to those for TN and TP loads in the HRW for 1980-2018. Key driving factors were detected using the Geo-detector method to quantify the contribution rates of factors to the spatiotemporal exports of TN and TP. Our results showed that individual factors, such as precipitation and land use/cover, were the most important factors driving spatio-temporal variations in TN and TP exports in the HRW from 1980 to 2018. Meanwhile, the contribution rates of interactions between land use/cover and other factors were consistently highest in this watershed during the same period. In this study, we estimated the loads and exports of TN and TP, and modelled their spatial patterns in this watershed from 1980 to 2018, providing important information on TN and TP for water-related management authorities. We also provide a method for other river systems to calibrate the parameters in the biophysical table of InVEST water purification model based on final exports of TN and TP.

Investigating spatial patterns and determinants of tourist attractions utilizing POI data: A case study of Hubei Province, China.

Exploring the spatial distribution characteristics of tourist attractions and the influencing factors is of significant importance for destination development, yet little relevant research has been conducted. This study explores the spatial patterns and determinants of tourist attractions using Hubei Province of China as a case based on the POI (Points of Interest) data, combined with standard deviation ellipse, GeoDetector method and so on. The results show that: (1) The distribution of tourist attractions in Hubei Province is concentrated in Wuhan and Huanggang. (2) The overall spatial patterns of tourist attractions in Hubei Province show a trend of "overall dispersion, partial concentration", with the direction of northwest-southeast. (3) The permanent population, passenger traffic volume, per capita GDP, and the added value of the tertiary industry are the primary factors influencing the spatial distribution of tourist attractions in Hubei Province. Additionally, topography and river systems factors also impact their distribution. This study provides critical information for theory and practice in terms of tourism resources optimization.

Quantitative assessment and driving factors analysis of surface urban heat island of urban agglomerations in China based on GEE.

The urban heat island (UHI) effect generated by the development of high-speed urbanization has become one of the major problems affecting the urban ecological environment. As the main body of urbanization in China, China's urban agglomerations are the core areas of urban heat island effect. The purpose of this study is to study the spatial-temporal characteristics and driving factors of surface urban heat island in 19 urban agglomerations in China, with a view to providing theoretical references for the prevention of urban thermal environmental risks. Based on Google Earth Engine (GEE), this paper estimated the surface urban heat island intensity (SUHII) of 19 urban agglomerations in China from 2003 to 2019 using MODIS land surface temperature (LST) data. Correlation analysis and regression analysis were used to explore the correlation between the change of SUHII and driving factors. Finally, the driving factors of SUHII were detected by the geo-detector model. Results showed that (1) the SUHII of 19 urban agglomerations in arid and semi-arid areas of northwestern China is higher than that in humid areas of eastern and southeastern China. (2) The SUHII of 19 urban agglomerations in China generally shows a decreasing trend, and the spatial variation of the change trend is significant. (3) There are positive correlations between SUHII and reference evapotranspiration (ET0), population density (POP), gross domestic product (GDP), nitrogen dioxide (NO2), ozone (O3), and ultraviolet aerosol index (UVAI); negative correlations with normalized difference vegetation index (NDVI), DEM, sulfur dioxide (SO2), carbon monoxide (CO), and formaldehyde (HCHO); the correlations all pass the significance test of P < 0.05 and are statistically significant. (4) The factor detection results showed that NDVI, land cover type (LC), and UVAI were the main driving factors of SUHII. The interaction detection results showed that the interaction between O3 and UVAI had the most significant impact on SUHII.

Promoting the sustainable development of traditional villages: Exploring the comprehensive assessment, spatial and temporal evolution, and internal and external impacts of traditional village human settlements in hunan province.

The protection and development of traditional villages are crucial for improving the human settlement suitability (HSS). The paper takes 703 traditional villages in Hunan Province as the research object and establishes the HSS evaluation system by using the pressure-state-response model. Then this paper introduces the vector autoregressive model to explore the interactions and contributions within the three major subsystems. Finally, this paper adopts Geodetector model and GTWR model to study the external driving effects and temporal-spatial influence mechanisms. The main findings of this paper are as follows. First, the overall trend of the composite index of traditional villages is upward. Its spatial pattern transitions from a low index in the northwest to a medium index in the central region and a high index in the southeast. Second, the state system becomes the main driver of the response system change and it is highly influenced by the pressure system. Distance from medical facilities, Distance from educational institutions, Distance from the intangible cultural heritage sites, and Degree of relief are the four most important driving factors affecting the HSS in Hunan Province. At the same time, Distance to medical facilities and Distance to intangible cultural heritage sites have a positive impact, while Distance to educational institutions and Degree of relief have a negative impact. Fourly, these four factors have a significant spatiotemporal impact on the HSS in the Xiangxi region. This paper provides a scientific basis for the sustainable development and conservation of traditional villages in Hunan from multiple perspectives.

Interprovincial spatial distribution patterns and socioeconomic factors on traditional Chinese medicine (TCM) service utilization in China.

The traditional Chinese medicine (TCM) industry in China exhibits significant regional disparities in health service utilization, the underlying reasons for which are yet to be fully explored. This study employs Geodetector models to analyze the factors affecting TCM service utilization, providing the first examination of spatial distribution patterns and influencing factors for both TCM outpatient (TCMOSU) and inpatient services (TCMISU). The findings of this study reveal spatial disparities across China's provinces, showing a prevalence of TCMOSU in the east and TCMISU decreasing from southwest to northeast. Global Moran's I autocorrelation analysis revealed a positive spatial correlation between TCMOSU and TCMISU across Chinese provinces, suggesting spatial clustering and the potential for interregional collaboration in the development of TCM services. Local Moran's I autocorrelation analysis revealed clusters of TCMOSU in wealthier eastern provinces, such as Jiangsu and Tianjin, and clusters of TCMISU in the southwest. Factor detector analysis revealed that disposable income per capita was the most significant factor linking higher incomes with increased TCMOSU. In contrast, TCMISU was primarily influenced by demographic factors, such as the illiteracy rate and population urbanization rate, emphasizing traditional practices in lower education regions. Interaction detector analysis revealed the joint effects of these factors, demonstrating how regional economic status, health status, and healthcare resource indicators interact with other factors for TCMOSU and how demographic factors significantly influence the prevalence of TCMISU. This study highlights the importance of considering health status together with regional economic, demographic, and healthcare resources when formulating TCM healthcare policies and allocating such resources in China. Promoting the balanced and coordinated regional development of TCM services across the country requires the development of strategies that account for these varied regional characteristics.

Improving grassland ecosystem services for human wellbeing in the karst desertification control area: Anthropogenic factors become more important.

Elucidating the spatial and temporal patterns of grassland ecosystem service value (ESV) changes under different karst geomorphic types (KGTs) is crucial for promoting regional sustainable development and enhancing human well-being. Karst ecosystems are characterized by high spatial heterogeneity. However, analyses of the drivers of spatial and temporal changes in ESV in karst grasslands at multiple scales are lacking. In this study, the South China Karst (SCK) region was selected as the focus area, the gross ecosystem product (GEP) accounting method was used to quantify the grassland ESV from 2000 to 2020, and the GeoDetector model was used to elucidate the spatial and temporal evolution of the GEP, the drivers, and their interactions in different KGTs. The results indicate the following: (1) Over the past 20 years, the grassland GEP of SCK has increased from ¥ 14,844.24 × 108 in 2000 to ¥ 17,174.90 × 108 in 2020. Among the various KGTs, the karst gorge exhibited the fastest GEP increase (24.93 %) and karst hilly depressions the slowest (6.22 %). (2) The karst grassland GEP showed a strong positive spatial correlation with significant clustering characteristics (p < 0.05). (3) There are significant differences in the factors influencing the GEP of grasslands with different KGT values, and although they are generally influenced by factors such as NPP, precipitation, and population density, anthropogenic factors are becoming increasingly important. In addition, the multifactor interaction explained GEP better than the single factor. Based on our findings, we propose targeted grassland ESV restoration approaches and management recommendations for various KGTs dominated by distinct factors. Our results provide a scientific basis for decision-making regarding karst ecosystem service enhancement and value realization.

[Spatiotemporal variation and driving factors of vegetation coverage in Shanxi Province, China]. / å±±è¥¿çœæ¤è¢«è¦†ç›–åº¦çš„æ—¶ç©ºå˜åŒ–åŠé©±åŠ¨å› å­.

Understanding the spatiotemporal variations and driving factors of regional vegetation coverage is crucial for developing scientific plans for ecological environment protection and maintaining regional ecological balance. Based on the Google Earth Engine (GEE) platform and using Landsat Collection 2 data, we investigated the spatiotemporal variation and driving factors of vegetation coverage in Shanxi Province, China, from 1990 to 2020, by employing methods such as pixel-based binary model, trend analysis, zonal statistics, and geodetector. The results showed that vegetation coverage in Shanxi Province showed a fluctuating upward trend from 1990 to 2020. Vegetation coverage in 44.4% of this region had been significantly improved, and the area with significant degradation accounted for 7.4%. Vegetation coverage in Shanxi Province was positively correlated with elevation, slope, and mountain terrain relief. The area proportion of vegetation coverage growth was the highest in the plateau and hilly regions. Factor detection results showed that land use type, landform type, annual average precipitation, and soil type were the main influencing factors of the spatial differentiation of vegetation coverage in Shanxi Province. Results of the interaction detection showed that the interaction between driving factors all showed enhancement. The interaction between natural factors showed a downward trend, while the interaction results of social factors showed an upward trend, reflecting that the impacts of human activities on vegetation coverage in Shanxi Province were gradually increasing.

A Modified Model for Quantitative Heavy Metal Source Apportionment and Pollution Pathway Identification.

Current source apportionment models have successfully identified emission sources and quantified their contributions. However, when being utilized for heavy metal source apportion in soil, their accuracy needs to be improved, regarding migration patterns. Therefore, this work intended to improve the pre-existing principal component analysis and multiple linear regression with distance (PCA-MLRD) model to effectively locate pollution pathways (traffic emissions, irrigation water, atmospheric depositions, etc.) and achieve a more precise quantification. The dataset of soil heavy metals was collected from a typical area in the Chang-Zhu-Tan region, Hunan, China in 2021. The identification of the contribution of soil parent material was accomplished through enrichment factors and crustal reference elements. Meanwhile, the anthropogenic emission was identified with principal component analysis and GeoDetector. GeoDetector was used to accurately point to the pollution source from a spatial differentiation perspective. Subsequently, the pollution pathways linked to the identified sources were determined. Non-metal manufacturing factories were found to be significant anthropogenic sources of local soil contamination, mainly through rivers and atmospheric deposition. Furthermore, the influence of irrigation water on heavy metals showed a more pronounced effect within a distance of 1000 m, became weaker after that, and then gradually disappeared. This model may offer improved technical guidance for practical production and the management of soil heavy metal contamination.

[Impacts of Urban Form on Carbon Emissions Under the Goal of Carbon Emission Peak and Carbon Neutrality: A Case Study of the Yangtze River Economic Belt].

Clarifying the mechanism of influence of urban form on carbon emissions is an important prerequisite for achieving urban carbon emission reduction. Taking the Yangtze River Economic Belt as an example, this study elaborated on the general mechanism of urban form on carbon emissions, used multi-source data to quantitatively evaluate the urban form, and explored the impacts of urban form indicators on carbon emissions from 2005 to 2020 at global and sub-regional scales with the help of spatial econometric models and geodetector, respectively. The results showed that:① The carbon emissions of the Yangtze River Economic Belt increased from 2 365.31 Mt to 4 230.67 Mt, but the growth rate gradually decreased. Its spatial distribution pattern was bipolar, with high-value areas mainly distributed in core cities such as Shanghai and Chongqing and low-value areas concentrated in the western regions of Sichuan and Yunnan. ② The area of construction land in the study area expanded over the past 15 years, but the population density of construction land had been decreasing. The degree of urban fragmentation was decreasing, and the difference between cities was also progressively narrowing. The average regularity of urban shape improved, and the compactness increased significantly. ③ All indicators of urban scale had significant positive effects on carbon emissions at the global scale, urban fragmentation had a significant negative effect in 2005, and the effective mesh size (MESH) indicator of urban compactness showed a significant negative correlation with carbon emissions in the study period. ④ Total class area, patch density, and effective mesh size had the most significant impacts on carbon emissions in upstream cities. Effective mesh size, mean perimeter-area ratio, and total class area had higher influences in midstream cities. Effective mesh size, percentage of like adjacencies, and largest patch index were the key factors to promote carbon reduction in downstream cities. Cities in different regions should comprehensively consider the impacts of various urban form indicators on carbon emissions and then optimize their urban form to promote sustainable development.

[Ecological risk assessment and influencing factors in the Wuhan Metropolitan Area based on supply and demand bundles of ecosystem services]. / åŸºäºŽç”Ÿæ€ç³»ç»ŸæœåŠ¡ä¾›éœ€ç°‡çš„æ­¦æ±‰éƒ½å¸‚åœˆç”Ÿæ€é£Žé™©è¯„ä¼°åŠå½±å“å› ç´ .

In the context of rapid urbanization, metropolitan areas are facing the risk of supply-demand mismatches among ecosystem services. Investigating the patterns, relationships, and driving factors of multiple supply-demand risks is of great significance to support the efficient management of regional ecological risks. We quantified the single/comprehensive supply-demand risk rates of six ecosystem services in Wuhan Metropolitan Area at the township scale in 2000, 2010, and 2020. By applying the self-organizing feature map network and optimal parameter geo-detector, we identified supply-demand risks bundles of ecosystem services and influencing factors of comprehensive risks. The results showed significant spatial variations in the supply-demand risks of typical ecosystem services from 2000 to 2020. The supply-demand risk associated with grain production, water yield, carbon sequestration, and green space recreation increased, while soil conservation and water purification risks decreased. The comprehensive ecosystem services supply-demand risk increased from 0.41 to 0.45, indicating a 'core area increase and periphery decrease' trend. Throughout the study period, the area exhibited bundles of comprehensive extremely high-risk bundles (B1), comprehensive high-risk bundles (B2), water purification high-risk bundles (B3), and grain production-soil conservation risk bundles (B4). The transition of risk types from B3 to B2 and from B2 to B1 suggested an increase in the combination and intensity of supply-demand risk. Vegetation cover, nighttime light index, and population density were the main driving factors for spatial variations in comprehensive supply-demand risk. Ecologi-cal risk assessment based on ecosystem services supply-demand bundles could provide an effective and reliable way to regulate multiple regional risk issues.

Exploring the environmental risks and seasonal variations of potentially toxic elements (PTEs) in fine road dust in resource-based cities based on Monte Carlo simulation, geo-detector and random forest model.

The environmental pollution caused by mineral exploitation and energy consumption poses a serious threat to ecological security and human health, particularly in resource-based cities. To address this issue, a comprehensive investigation was conducted on potentially toxic elements (PTEs) in road dust from different seasons to assess the environmental risks and influencing factors faced by Datong City. Multivariate statistical analysis and absolute principal component score were employed for source identification and quantitative allocation. The geo-accumulation index and improved Nemerow index were utilized to evaluate the pollution levels of PTEs. Monte Carlo simulation was employed to assess the ecological-health risks associated with PTEs content and source orientation. Furthermore, geo-detector and random forest analysis were conducted to examine the key environmental variables and driving factors contributing to the spatiotemporal variation in PTEs content. In all PTEs, Cd, Hg, and Zn exhibited higher levels of content, with an average content/background value of 3.65 to 4.91, 2.53 to 3.34, and 2.15 to 2.89 times, respectively. Seasonal disparities were evident in PTEs contents, with average levels generally showing a pattern of spring (winter) > summer (autumn). PTEs in fine road dust (FRD) were primarily influenced by traffic, natural factors, coal-related industrial activities, and metallurgical activities, contributing 14.9-33.9 %, 41.4-47.5 %, 4.4-8.3 %, and 14.2-29.4 % to the total contents, respectively. The overall pollution and ecological risk of PTEs were categorized as moderate and high, respectively, with the winter season exhibiting the most severe conditions, primarily driven by Hg emissions from coal-related industries. Non-carcinogenic risk of PTEs for adults was within the safe limit, yet children still faced a probability of 4.1 %-16.4 % of unacceptable risks, particularly in summer. Carcinogenic risks were evident across all demographics, with children at the highest risk, mainly due to Cr and smelting industrial sources. Geo-detector and random forest model indicated that spatial disparities in prioritized control elements (Cr and Hg) were primarily influenced by particulate matter (PM10) and anthropogenic activities (industrial and socio-economic factors); variations in particulate matter (PM10 and PM2.5) and meteorological factors (wind speed and precipitation) were the primary controllers of seasonal disparities of Cr and Hg.

Identifying interactive effects of spatial drivers in soil heavy metal pollutants using interpretable machine learning models.

The accurate identification of spatial drivers is crucial for effectively managing soil heavy metals (SHM). However, understanding the complex and diverse spatial drivers of SHM and their interactive effects remains a significant challenge. In this study, we present a comprehensive analysis framework that integrates Geodetector, CatBoost, and SHapley Additive exPlanations (SHAP) techniques to identify and elucidate the interactive effects of spatial drivers in SHM within the Pearl River Delta (PRD) region of China. Our investigation incorporated fourteen environmental factors and focused on the pollution levels of three prominent heavy metals: Hg, Cd, and Zn. These findings provide several key insights: (1) The distribution of SHM is influenced by the combined effects of various individual factors and interactions within the source-flow-sink process. (2) Compared with the spatial interpretation of individual factors, the interaction between Hg and Cd exhibited enhanced spatial explanatory power. Similarly, interactions involving Zn mainly demonstrated increased spatial explanatory power, but there was one exception in which a weakening was observed. (3) Spatial heterogeneity plays a crucial role in determining the contributions of environmental factors to soil heavy metal concentrations. Although individual factors generally promote metal accumulation, their effects fluctuate when interactions are considered. (4) The SHAP interpretable method effectively addresses the limitations associated with machine-learning models by providing understandable insights into heavy metal pollution. This enables a comparison of the importance of environmental factors and elucidates their directional impacts, thereby aiding in the understanding of interaction mechanisms. The methods and findings presented in this study offer valuable insights into the spatial heterogeneity of heavy metal pollution in soil. By focusing on the effects of interactive factors, we aimed to develop more accurate strategies for managing SHM pollution.