The relations among ecological footprints, biocapacity per capita, gross domestic product per capita, natural resources, urbanization, human capital, and hydroelectric consumption are analyzed from 1971Q1 to 2017Q4 for Brazil and China. The novel quantile autoregressive distributive lag method was employed to analyze the long-run and short-run dynamics of environmental degradation. The findings revealed that economic growth has a positive role in the environmental degradation of both countries. However, human capital, natural resources, and hydropower have heterogeneous effects across quantile distribution and between the two countries. Based on the quantile dynamics of environmental degradation, the present study mentions policy implications for sustainable development.

The accumulation of total Pb, Cd, Cu, and Zn in soils (0–5 cm) and windowsill dust fractions (45–125, 10–45, and < 10 μm), and soil pollution indices (PI), were investigated in a long-term (~ 70 years) Pb smelter area and in the nearby urban city of Jiyuan, China. Principal component analysis (PCA) was utilized to identify metal contamination sources. Results showed that mean soil Pb, Cd, Cu, and Zn concentrations in the smelter area were 803, 13.8, 118, and 323 mg kg⁻¹, while those of the urban area were 270, 7.95, 51.6, and 244 mg kg⁻¹, respectively. Lead and Cd had greater soil PI than Cu and Zn. Lead concentrations in the 45–125, 10–45, and < 10-μm urban dust fractions ranged from 197.1 to 1953 (mean 1020), 202–3962 (2407), and 51.1–1258 (310.7) mg kg⁻¹, while Cd concentrations ranged from 11.1 to 111 (49.2), 10.4–159 (64.3) and 21.5–131 (60.0) mg kg⁻¹, respectively. Excessive Zn concentrations (5000–22,000 mg kg⁻¹) in some urban dust samples were found at two sampling sites, while Zn concentrations were < 2600 mg kg⁻¹ in all other samples. Based on PCA results, metal accumulation near the Pb smelter was dominated by smelting activities. The PCA results further suggested that mass vehicular transportation modes may be an important source of metals such as Cu and Zn in the urban area. Certain samples in both sub-areas had unsafe potential non-carcinogenic risks of Pb for children. These findings suggest that reducing environmentally relevant metal concentrations in this, and similar areas, will likely require a multi-faceted approach.

Climate change intensifies, so does the need to reduce carbon emissions to achieve the goal of being “carbon neutral” for China. This paper focuses on carbon emission prediction and constructs a comprehensive model integrating least absolute shrinkage and selection operator (LASSO), principal component analysis (PCA), support vector regression (SVR), and differential evolution-gray wolf optimization (DE-GWO). Firstly, LASSO is used for feature selection, and important information is extracted from various influencing factors to find out what have a great impact on carbon emission. Principal component analysis is used to extract the features of the remaining variables to avoid missing information caused by feature selection. Secondly, DE-GWO algorithm is used to optimize the parameters of SVR to improve the prediction accuracy. The scenario analysis and prediction algorithm are combined to predict China’s carbon emissions. The results show that (1) coal and oil consumption, plate glass, pig iron, and crude steel production are important factors affecting carbon emissions; (2) the use of PCA to comprehensively consider the impact of remaining factors on carbon emissions has a positive influence on carbon emissions prediction; and (3) DE-GWO optimized SVR has higher prediction accuracy than other algorithms.

Environmental phenols, as endocrine disruptors, are used widely in personal care and consumer products. However, few studies have examined the association between phenol exposure, including bisphenol A (BPA), benzophenone-3 (BP-3), and triclosan, and the prevalence of cardiovascular diseases (CVD). This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (2003–2012). Urinary BPA, BP-3, and triclosan were measured with liquid chromatography-tandem mass spectrometry (HPLC–MS/MS). The CVD was defined as a composite of 5 self-reported cardiovascular outcomes, including congestive heart failure, coronary heart disease, angina pectoris, heart attack or stroke. Multivariable logistic regression models were used to examine the association between phenols and the prevalence of CVD. A total of 8164 participants were enrolled, and 740 (9.1%) were diagnosed of CVD. The average levels of BPA, BP-3, and triclosan concentrations were 3.38, 202.63, and 99.27 ng/mL respectively. Per 1-unit increasement in log-transformed urinary BPA was associated with increased risk of CVD after adjusting all covariates (odds ratio [OR] 1.09, 95% confidence interval [CI] 1.01 to 1.18, P < 0.05). Compared with the lowest quartile (< 0.9), the multivariable-adjusted OR was 1.30 (1.03 to 1.65, P < 0.05) in the highest quartile (> 3.8). Restricted spline models confirmed that the association between BPA and the risk of CVD was non-linear (P = 0.045). Only BPA was associated with the risk of CVD, following a J-curve shaped relationship.

A sectorial approach for assessing heavy metal pollution in rivers neglects the inter-relationship between its environmental compartments and thus fails to report realistic pollution status and associated ecological and human health risks. Therefore, a systems approach was adopted to assess heavy metal pollution and associated risks in the Yamuna River (Delhi, India), one of the world’s most polluted and populated river–city pairs. Sampling sites selected along the river with distinct land use were uncultivated natural floodplain vegetation, marshy area, invasive community, arable land, and human settlements. The multivariate analysis identified sources of pollutions (Pb, Cd, Cr, and Ni [anthropogenic]; Fe and Zn [geogenic]). Across the land use, a high log Kₚ value of Zn and Pb in water–soil phase than in water–sediment phase indicates their long-range transfer, whereas low log Kₚ (water–soil) of Cd suggests river sediments as its reservoirs. Comparison of pollution indices of Cd, Cr, and Pb in water, sediment, and soil across the land use suggested the role of vegetation in reducing pollution in the environment. Ecological risk also gets reduced progressively from water to sediment to the soil in naturally vegetated sites. Similarly, in river water, Cr, Cd, Ni, and Pb pose carcinogenic and non-carcinogenic risks to adults and children, which are also reduced in sediments and soil of different vegetation regimes. This study showed the eco-remediation services rendered by natural vegetation in reducing pollution and associated ecological and human health risks. To conclude, using a systems approach has significance in assessing pollution at the ecosystem level, and focusing on riverbank land use remains significant in developing methods to reduce pollution and ecological and human health risks for sustainable riverbank management.

The Mexican maquiladora industry is applying Lean Manufacturing Tools (LMT) in its production lines; however, few studies have investigated its relationship with sustainability (social, economic, and environmental). This paper presents a second-order structural equation model (SEM) relating 8 LMT integrated into three independent latent variables: continuous improvement (Kaizen and Gemba), supporting tools (Andon, visual management, and Poka-yoke), and machinery and equipment (total productive maintenance, overall equipment effectiveness, and Jidoka) that are related to social, economic, and environmental sustainability as dependent variables. The model is validated with information obtained from 249 companies using partial least squares. Findings show that the application of LMT in the Mexican maquiladora industry avoids the generation of waste and reprocessing. Likewise, the improvement of production processes reduces the waste emitted into the environment and reduces energy consumption. Also, when companies have environmental programs, the work environment is safe, and labor relations are improved, increasing morale and the commitment to work for the company, gaining economic and ecological benefits.

Urban resilience efficiency is an important indicator to explore the relationship between resource consumption and urban resilience, shedding new light on the study of urban sustainable development. Based on the panel data of 2008, 2012, and 2017, this paper makes a spatiotemporal assessment on the urban resilience efficiency of 126 cities in the Yangtze River Economic Belt (YREB) in China by applying an entropy weight-TOPSIS method and a slack-based measure (SBM) model. Combined with the analysis of a geographically weighted regression model (GWR), the influencing factors on resilience efficiency are also investigated. The results show that both the resource consumption index (RC, inputs) and the urban resilience index (UR, outputs) presented a steady upward trend, and their spatial distribution characteristics were similar, showing a gradual decrease from the eastern coastal cities to the central and western inland cities. Derived from inputs and outputs, the mean values of resilience efficiency index (RE) in three periods were 0.3149, 0.2906, and 0.1625, respectively, revealing that there had been a noticeable decline. Spatially, its spatial distribution has evolved from a relatively balanced pattern to an unbalanced one, showing a gradual decrease from west to east. The results of the GWR model analysis indicate that the total electricity consumption and area of construction land had a considerable correlation with the overall urban resilience of the YREB. Furthermore, total quantity of water supply and science and technology (S&T) expenditure continued to be the main driving factors on urban resilience of the upstream cities. The midstream regions mainly depended on the scale of construction land, and the influencing factors are relatively single. The influencing factors in the downstream areas have changed from dominance of resources and capital factors to the single dominance of resource factors, and total electricity consumption had a strong explanatory power. Based on these findings, we had put forward the overall and local regional policy implications.

Marble industry worldwide produces large amount of non-degradable marble dust powder (MDP) waste during mining and processing stages. MDP mainly comprises of CaCO₃ with small amounts of Mg, Fe or Si in various forms. In India, mainly in Rajasthan state, marble is quarried in huge amounts and MDP thus produced is collected improperly and dumped at any abandoned land or identified disposal sites. On the other hand, the composition of sub soil/lake brines of Rajasthan is typical in nature as it does not have much Ca²⁺ and Mg²⁺ impurities but contains higher levels of SO₄²⁻. Therefore, the common salt (NaCl) produced from such brines is contaminated with Na₂SO₄ (8–30 wt%) depending upon SO₄²⁻ concentration in the brine. Such a salt produced is suitable neither for edible purpose nor for industrial usage. Herein, we have reacted MDP with HCl, and the resulting solution (CaCl₂ and MgCl₂ slurry) is used in stoichiometric ratio of Ca²⁺ to SO₄²⁻ in brines to produce high-purity NaCl and gypsum (CaSO₄·2H₂O) via fractional crystallization. Remaining magnesium-containing solution was reacted with Na₂CO₃ to prepare high purity magnesium carbonate hydrate. Purity of crystallized NaCl, CaSO₄·2H₂O and MgCO₃·6H₂O has been ascertained through analytical and spectral methods (TGA, FTIR, P-XRD). Field emission scanning electron microscopy was used to elucidate morphology of crystals. The method reported for improving purity of NaCl along with CaSO₄·2H₂O and MgCO₃·6H₂O production from sulphate-rich brines is simple and economic, and allows management of MDP generated in huge amounts, which poses problems of disposal.

Excess nitrogen (N) resulting from human activity causes environmental issues, including eutrophication in agricultural watersheds with intensive livestock farming. Among the N sources in Korea, on-site swine wastewater treatment facilities (OSWTFs) tend to be densely distributed in watersheds with intensive livestock farming. Therefore, it is critical to sustainably manage livestock excreta. This study used the Soil and Water Assessment Tool (SWAT) to investigate the effects of various pollution sources, including OSWTFs, on N loads in rivers in the Cheongmi watershed, which is an intensive livestock farming and agricultural area in Korea. The simulated hydrological and water quality outputs were calibrated and validated for 2012–2019 using Sequential Uncertainty Fitting ver. 2 in the SWAT-Calibration and Uncertainty Program. The hydrological simulations agreed with the observations, with a correlation coefficient (R²) of ≥ 0.8 and Nash–Sutcliffe coefficient of 0.67–0.86. The simulated total N (TN) was also strongly correlated with the observed monthly average loading (R², 0.36–0.73) and annual average concentration (R² ≥ 0.5), demonstrating the reliability of the model constructed herein. A simulation of management scenarios indicates that, if the permissible N concentration in effluent from OSWTFs was reduced to 60 mg N/L, the TN concentrations in rivers would decrease by up to 50%. The findings of this study indicate that more stringent effluent water quality standards are required for OSWTFs to protect water quality and aquatic ecosystems in intensive swine farming watersheds.

The aim of the study was to investigate the effects of age and sex on the content of heavy metals (Pb, Cd, Zn and Cu) in the hair, liver and the longissimus lumborum muscle of roe deer (Capreolus capreolus L.). In addition, correlations between heavy metals in individual tested matrices were studied as well as an attempt was made to explain the reasons for their accumulation in specific research matrices. The levels of Zn and Cu were measured using atomic absorption spectroscopy (F-AAS). The levels of Pb and Cd were analysed by means of electrothermal atomic absorption spectroscopy (ET-AAS). In the own research, sex-related statistically significant differences in concentrations of analysed elements were confirmed. Higher levels of the elements were observed in samples collected from does (except for Cd in the liver samples of 6–7-month-old male fawns) but only in some cases, these values were statistically significantly higher. In some cases, age-related differences in the content of individual metals were also confirmed. Age-dependent increased Pb levels in the muscle and liver were reported in 6–7-month-old, <3–4-year-old, <5–6-year-old males. Similar relationships were confirmed for Zn in the bucks’ muscles. In the group of does, the increase in heavy metal concentrations with aging was observed for Zn in the muscle and for Pb in the liver. In the other cases, the trend of age-related level changes regarding heavy metals was not clearly demonstrated. Significant positive correlations between Cu and Cd were found for all samples, and significant negative correlations of Cu with Pb were found for the hair and liver. There was a negative correlation of Zn with Cu in all matrices. The study also revealed a negative correlation between Zn and Pb in muscle and a strong significant negative correlation between Zn and Cd in hair and in the liver.