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Background: This work conducted quantitative and qualitative analyses on the heavy metals in 32 surface sediment samples collected from eight sampling sites across four seasons in the Yutan reservoir, a typical mountainous hilly riverine reservoir in Southwest China. Results: Nine heavy metals were identified: (Cr, Cd, Pb, Cu, Zn, Hg, As, Ni, and Co). The concentrations varied within the ranges of 23.16 ~ 34.62, 0.53 ~ 1.09, 31.88 ~ 59.04, 51.94 ~ 85.84, 106.76 ~ 227.69, 0.08 ~ 0.15, 12.57 ~ 25.60, 12.06 ~ 27.64 and 11.04 ~ 14.56 mg/kg, respectively, following a decreased concentration order of Zn > Cu > Pb > Cr > Ni > As > Co > Cd > Hg. Except for Cd, which accumulated in winter, and Hg, which reduced in spring. The concentration of heavy metals showed no significant seasonal variation and generally had higher contents at the river mouth and lower concentrations in open water areas and branches. The geoaccumulation index (Igeo) assessment also indicated that the reservoir was uncontaminated by Cr and Ni (Igeo < 0), slightly polluted by Pb, Cu, Zn, and Co (0 < Igeo < 1), moderately polluted by Hg and As (1 < Igeo < 2), and heavily polluted by Cd (1.44 < Igeo < 2.48). Notably, the most polluted sites were at the river mouth, followed by the sediments in branches with slight pollution. Source tracing analysis revealed that Cr, Ni, Cd, Pb, Hg, As, organic matter, total nitrogen, and total phosphorus were primarily attributed to non-point sources. In contrast, Fe was linked to the hardware industry. Moreover, Al originated from sewage and drinking water treatment processes. Cu and Zn were discharged from three components, indicating complex sources. Conclusions: The findings underscored that non-point sources were the primary contributors to the increased risk of heavy metal contamination in the reservior's sediment. In addition, to effectively manage the risk and enhance the aquatic environment, greater focus should be placed on the inner load of heavy metals in the sediment of the mountainous hilly riverine reservoirs, particularly after controlling external pollution sources. [ABSTRACT FROM AUTHOR]

Rural green development is a concrete practice of rural revitalization. Currently, research on quantitative evaluation methods for rural green development levels are not well developed. In this study, an evaluation model of the rural green development level in Chongqing City, China was developed based on the parameters of ecology, living, and production. An entropy weight method, Theil index, optimal scale regression model, and GIS were used to analyze the spatio-temporal characteristics, trends, and influencing factors of the rural green development level from 2018 to 2020 in Chongqing City. The results showed that: (1) the overall "ecology, living, and production" dimensions and the comprehensive index of the development level in the city were generally increasing, and the proportion of counties at a high-level increased from 23.68% in 2018 to 81.58% in 2020; (2) the Theil index of the city in was 0.0185, 0.0121, and 0.0114 in 2018, 2019, and 2020 respectively, indicating that the differences in development level among regions decreased as the development level increased; (3) the level of rural green development showed a clear upwards trend, and the proportion of counties with low-speed growth, medium-speed growth, and high-speed growth from 2018 to 2020 was 5.26%, 81.58%, and 13.16%, respectively; and (4) the optimal scale regression analysis showed that the factors with greater impacts on the rural green development level are social security and employment expenditure level of government finance, health expenditure level of government finance, with their contributions is 40.3% and 26%, respectively. The results from this study demonstrate the significance of exploring research methods for rural green development and ways to improve the level of rural green development. [ABSTRACT FROM AUTHOR]

The management of regional eco-environmental risks is the key to promoting regional economic sustainability from the macro level, and accurate evaluation of the evolutionary trends of regional ecological risk in the future is of high importance. In order to clearly identify the possible impact of future development scenario selection for the Chengdu-Chongqing Economic Zone (C-C E Zone) on the evolution of landscape ecological risk (LER), we introduced the Patch-generating Land Use Simulation (PLUS) model to simulate land use data for the C-C E Zone from 2030 to 2050 for two scenarios: natural development (ND) and ecological protection (EP). Based on the ecological grid and landscape ecological risk index (LERI) model, the landscape ecological risk (LER) evolutionary trends seen in the C-C E Zone from 2000 to 2050 were analyzed and identified. The results showed that: (1) The PLUS model can obtain high-precision simulation results in the C-C E Zone. In the future, the currently increasing rate of land being used for construction will be reduced, the declining rates of forest and cultivated land area will also be reduced, and the amount of land being used for various purposes will remain stable going into the future. (2) This study found that the optimal size of the ecological grid in the LERI calculation of the mountainous area was 4 × 4 km. Additionally, the mean values of the LERI in 2030, 2040, and 2050 were 0.1612, 0.1628, and 0.1636 for ND and 0.1612, 0.1618, and 0.1620 for EP. (3) The hot spot analysis results showed that an area of about 49,700 km2 in the C-C E Zone from 2000 to 2050 belongs to high agglomeration of LER. (4) Since 2010, the proportions of high and extremely high risk levels have continued to increase, but under the EP scenario, the high and extremely high risk levels in 2040 and 2050 decreased from 14.36% and 6.66% to 14.33% and 6.43%. Regional analysis showed that the high and extremely high risk levels in most regions increased over 2010–2050. (5) Under the ND scenario, the proportions of grids with decreased, unchanged, and increased risk levels were 15.13%, 81.48%, and 3.39% for 2000–2010 and 0.54%, 94.75%, and 4.71% for 2040–2050. These trends indicated that the proportion of grids with changed risk levels gradually decreased going into the future. This study analyzed the evolutionary trends of LER at the C-C E Zone for the ND and EP scenario. On the whole, the LER for the C-C E Zone showed an upward trend, and the EP scenario was conducive to reducing the risk. These research results can serve as a valuable data reference set for regional landscape optimization and risk prevention and control. [ABSTRACT FROM AUTHOR]

According to China's second national survey of pollution sources, the contribution of agricultural non-point sources (ANS) to water pollution is still high. Risk prevention and control are the main means to control costs and improve the efficiency of ANS, but most studies directly take pollution load as the risk standard, leading to a considerable misjudgment of the actual pollution risk. To objectively reflect the risk of agricultural non-point source pollution (ANSP) in Chongqing, China, we investigated the influences of initial source input, intermediate transformation, and terminal absorption of pollutants via literature research and the Delphi method and built a PTA (pressure kinetic energy, transformation kinetic energy, and absorption kinetic energy) model that covers 12 factors, with the support of geographical information system (GIS) technology. The terrain factor calculation results and the calculation results of other factors were optimized by Python tools to reduce human error and workload. Via centroid migration analysis and Kernel density analysis, the risk level, spatial aggregation degree, and key prevention and control regions could be accurately determined. There was a positive correlation between the water quality of the rivers in Chongqing and the risk assessment results of different periods, indirectly reflecting the reliability of the assessment results by the proposed model. There was an obvious tendency for the low-risk regions transforming into high-risk regions. The proportion of high-risk regions and extremely high-risk regions increased from 17.82% and 16.63% in 2000 to 18.10% and 16.76% in 2015, respectively. And the risk level in the main urban areas was significantly higher than that in the southeastern and northeastern areas of Chongqing. The centroids of all grades of risky areas presented a successive distribution from west to east, and the centroids of high-risk and extremely high-risk regions shifted eastward. From 2000 to 2015, the centroids of high-risk and extremely high-risk regions moved 4.63 km (1.68°) and 4.48 km (12.08°) east by north, respectively. The kernel density analysis results showed that the high-risk regions were mainly concentrated in the main urban areas and that the distribution of agglomeration areas overall displayed a transition trend from contiguous distribution to decentralized concentration. The risk levels of the regions with a high proportion of cultivated land and artificial surface were significantly increased, and the occupation of cultivated land in the process of urbanization promoted the movement of the centroids of high-risk and extremely high-risk regions. The identification of key areas for risk prevention and control provides data scientific basis for the development of prevention and control strategies. [ABSTRACT FROM AUTHOR]

To determine the risk state distribution, risk level, and risk evolution situation of agricultural non-point source pollution (AGNPS), we built an 'Input-Translate-Output' three-dimensional evaluation (ITO3dE) model that involved 12 factors under the support of GIS and analyzed the spatiotemporal evolution characteristics of AGNPS risks from 2005 to 2015 in Chongqing by using GIS space matrix, kernel density analysis, and Getis-Ord Gi* analysis. Land use changes during the 10 years had a certain influence on the AGNPS risk. The risk values in 2005, 2010, and 2015 were in the ranges of 0.40–2.28, 0.41–2.57, and 0.41–2.28, respectively, with the main distribution regions being the western regions of Chongqing (Dazu, Jiangjin, etc.) and other counties such as Dianjiang, Liangping, Kaizhou, Wanzhou, and Zhongxian. The spatiotemporal transition matrix could well exhibit the risk transition situation, and the risks generally showed no changes over time. The proportions of 'no-risk no-change', 'low-risk no-change', and 'medium-risk no-change' were 10.86%, 33.42%, and 17.25%, respectively, accounting for 61.53% of the coverage area of Chongqing. The proportions of risk increase, risk decline, and risk fluctuation were 13.45%, 17.66%, and 7.36%, respectively. Kernel density analysis was suitable to explore high-risk gathering areas. The peak values of kernel density in the three periods were around 1110, suggesting that the maximum gathering degree of medium-risk pattern spots basically showed no changes, but the spatial positions of high-risk gathering areas somehow changed. Getis-Ord Gi* analysis was suitable to explore the relationships between hot and cold spots. Counties with high pollution risks were Yongchuan, Shapingba, Dianjiang, Liangping, northwestern Fengdu, and Zhongxian, while counties with low risks were Chengkou, Wuxi, Wushan, Pengshui, and Rongchang. High-value hot spot zones gradually dominated in the northeast of Chongqing, while low-value cold spot zones gradually dominated in the Midwest. Our results provide a scientific base for the development of strategies to prevent and control AGNPS in Chongqing. [ABSTRACT FROM AUTHOR]

The 2017 Wulong earthquake (Ms 5.0) took place in Chongqing, China, causing serious damage to the local infrastructure. However, the mechanism of this earthquake is still unclear. We deployed a 155 km long magnetotelluric (MT) profile with 24 broadband sounding stations across the Wulong earthquake zone. The phase tensor and phase-out-of-quadrant data analysis show that the whole region is mainly characterized with 3-D electrical resistivity structure. High-quality full electrical resistivity impedances were inverted by a 3-D scheme to image the structure beneath the profile. The 3-D electrical resistivity model indicates that the dynamic environment of the central and eastern Chongqing and the Two Lakes plains is different from the neighboring areas. The different stress environment of the study area observed suggests that this area is prone to earthquakes. The Wulong earthquake (Ms 5.0) may have been generated by the local stress adjustment of the Qiyaoshan basement fault. • A profile MT survey imaged a significant conductor in the source zone of the 2017 Wulong earthquake. • The model shows the resistivity is different from the surrounding area which may explains the stress environment. • The Wulong earthquake may be a significant event due to local stress adjustment under the strong earthquakes in the area. [ABSTRACT FROM AUTHOR]

The residue of phthalate acid esters (PAEs) in plastic film greenhouses had become a global concern environmental pollution problem. However, few studies have focused on the occurrence and fate of PAEs in the soil-air interface of the greenhouses. In this study, the occurrence, health risks, and soil-air exchange of PAEs from ten soil samples and four air samples of different greenhouses were investigated by a case study. The concentrations of total PAEs and individual PAEs congeners between two seasons were significantly different in the greenhouse soil and atmosphere. Di-(2-ethylhexyl) phthalate (1.04 mg kg−1) and diisobutyl phthalate (0.16 mg kg−1) were the predominant PAE congeners of soil in spring and autumn, respectively. Di-(2-ethylhexyl) phthalate was the major PAE congeners in the greenhouse atmosphere both of spring (0.03 μg m−3) and autumn (1.32 μg m−3). Health risks assessment showed that the predominant exposure route for adults was dietary intake, and the total carcinogenic risk level of PAEs was acceptable. Besides, fugacity model analysis indicated that di-(2-ethylhexyl) phthalate tended to deposit from air to soil with mean deposition flux of 175.1 kg (h⋅km2)−1 in autumn and 11.9 kg (h⋅km2)−1 in spring. On the contrary, diethyl phthalate escaped from soil with mean volatilization flux of 0.005 kg (h⋅km2)−1 in autumn and 0.025 kg (h⋅km2)−1 in spring. Other PAE congeners have tried to establish an equilibrium status through recycling continuously between the soil and air, and the source-sink relationships depended on their concentrations and hydrophobicity. This study showed that the distribution and movement of PAEs in the soil-air interface might be principally caused by temperature and their chemical properties. • DEHP were the major PAEs in soil and air of the plastic greenhouses. • Seasonal variation had further impact on the occurrence of PAEs in soil and air. • Soil has become a sink of DEHP, whereas a source of DEP. • Seasonal variation occurred in soil - air exchange for mid molecular weight PAEs. [ABSTRACT FROM AUTHOR]