The aim of this study is to analyze the impact of financial development, trade openness, renewable energy, and nonrenewable energy consumption on CO2 emissions in India by analyzing the quarterly data from 1980 to 2020. Quantile ARDL and Wavelet Coherence methods are employed to examine the nexus. In the long and short run, nonrenewable energy consumption, financial development, and trade openness have a positive impact on CO2 emissions. When emissions are already high, it suggests that financial development may also lead to increased CO2 emissions. Moreover, Renewable energy consumption has a negative impact on CO2 emissions irrespective of the emission level that whether it is high or low in the nations, which shows that if financial enhancement increases, carbon emissions decrease. Finally, we test the EKC hypothesis, and the QARDL findings support the EKC in India. Additionally, the wavelet coherence study found a causal relationship between the CO2 emissions and independent variables, and the findings under the Wald test reject the parameter constancy for all variables. To create effective policies for environmental deterioration, the empirical findings of the current analysis can be used as guidelines for policy implications.

The ecological sensitivity of rural landscapes exhibits complexity and diversity. Traditional evaluation methods, which merely take into account a single factor or a limited number of factors, struggle to effectively manage uncertain information. This leads to inaccurate classification of ecological units in rural landscape ecological images, thereby undermining the precise assessment of the distribution of ecological sensitivity in rural landscapes. Therefore, a deep learning based algorithm for dividing rural landscape ecological sensitive areas is proposed. By selecting six major factors that affect the ecological sensitivity of rural landscape, such as geological environment, ecological and hydrological conditions, an ecological sensitivity evaluation index system is constructed, which is used as an input vector, and fuzzy neural network is used to output the ecological sensitivity of rural landscape; In addition, support vector machine is used to divide the ecological units of the collected rural landscape ecological images, and the division results and the sensitivity evaluation results of each unit of rural landscape are used as the input data of ArcGIS software to realize the visual presentation of the unit division results of rural landscape ecological sensitive areas. The results showed that with the increase of slope, the ecological sensitivity of rural landscape showed a trend of first increasing and then decreasing, the vegetation coverage rate decreased, and the ecological sensitivity of rural landscape showed a trend of gradually increasing; This algorithm can effectively evaluate the sensitivity of each unit of rural landscape, and visually present the unit division results of ecological sensitive areas of rural landscape. This algorithm can compare and analyze the changes of ecological sensitivity under different time dimensions.

The rupture of Fundão Dam spilled contaminated tailings across the Doce river basin, severely damaging municipalities such as the urban Barra Longa and the rural Gesteira. The wave of tailings led to the sediment deposition in rivers margins, causing the loss of riparian forests and cropping areas. Sediment analyses confirmed the presence of toxic compounds (sodium and ether amine) and a very low fertility. In consequence, there was a sharp decline in agro-pastoral production in Gesteira, leading to land abandonment and rural exodus. In the urban area of Barra Longa, the wave of tailings damaged the urban floodplain and the square, which were rehabilitated using grey infrastructure. Alternatively, we proposed a new landscape recovery plan for both Barra Longa and Gesteira based on Nature and Community-based solutions that contemplate the inclusion of green infrastructure, the remediation of toxic compounds, the restoration of soil fertility, permeability and stabilization, riparian forest rehabilitation and the recovery of agro-pastoral productivity, ultimately aiming at reducing the flood risk and land abandonment.

This study employs the AHP-entropy weight methodology and a spatial econometric regression model to evaluate the ecological sustainability and its changes between the current situation and the planning scenario at the 2024 Chengdu International Horticultural Exposition in China. The results indicate a notable shift: a reduction in areas of low and highest sustainability and significant expansion in medium levels, which spans 34.04 hm2. The transformation of village settlements, wastelands, and farmland into exhibition gardens and water bodies is shown to bolster medium-level ecological sustainability by enhancing rain and flood security and mitigating the risk of flood disasters. The development of Integrated Service areas will lead to an increase in impervious surfaces. The anticipated forest loss, along with declines in vegetation coverage, three-dimensional green volume, and vegetation carbon stock will adversely affect the highest sustainability. The study identifies a robust correlation between ecological sustainability level and quantitative indicators, with regression coefficients ranging from 0.5875 to 0.7148. This analysis provides policymakers with valuable insights and directions for the sustainable planning and development of mega-events.

Urban green space can improve the air quality of urban human settlements. This study aimed to investigate the spatial differences of air quality among the different plant community structures and types of urban park green spaces. We select 17 sample sites in Beijing Olympic Forest Park, and they are located in different areas of plant community structures and types. The study entailed an analysis of the interrelationships between the plant community structures, types, and PM2.5, O3, and PM2.5–O3 compound data. The results showed that PM2.5 was lower in tree-shrub-grass, tree-shrub, and tree-grass than in shrub-grass and grass plant community areas; PM2.5 was lower in evergreen coniferous, mixed coniferous and broadleaved, and deciduous broadleaved plant communities than that in grass or shrub ones. In different plant community structures, types areas, O3 was higher than 100 μg·m–3, and there were no significant differences among the plant community areas. The air quality index with PM2.5–O3 composite pollution value as the main parameter reached the level of “moderate pollution”, and the result that deserves further attention. The research results provide a basic scientific basis for the planning, design, and updating optimization of functional urban green spaces based on evidence-based design.

In urban areas, surface water bodies play an important role in mitigating thermal discomfort, which is mainly caused by increasing air temperatures. Based on daily temperature and relative humidity data recorded by the Baghdad weather station for the two years 2018 and 2021, the monthly human discomfort index was calculated and then combined with monthly surface water areas extracted by a modified normalized difference water index using Sentinel-2A satellite imagery for the same period. The results show that the winter and most spring months of these years have no discomfort, and the summer months (July and August) in 2021 have the highest discomfort with severe thermal stress due to the large deficit in rainfall events. The monthly relationship between urban water surfaces and the level of the discomfort index was also studied, which was non-linear and followed the exponential decay function. This means that as the amount of surface water increased, the levels of the discomfort index decreased exponentially until no discomfort conditions existed.

With the continuous development of urban construction, the contradiction between human settlement and the natural environment is becoming increasingly prominent. Increasing the green area and improving the local environment and microclimate through roof planting can effectively alleviate this contradiction. On the basis of the research on the current situation and challenging problems of planting roofs, a comprehensive study of construction, stab-resistance, waterproofing, irrigation and plant landscape is combined with a designed roof case. Research results show that the planting roof is simple in construction, has high resistance to plant root thorns, can take into account the function and aesthetics of plant landscape design, and saves irrigation water by more than 60%, which will help the popularization and promotion of urban planting roofs.

The study assessed traffic noise exposure levels of citizens walking on the sidewalks at important road crossings in commercial land zone regions of Chennai’s suburbs. The noise data from signalized and unsignalized intersections are collected with a class I sound level meter for peak and non-peak hours from 6 AM to 10 PM. Different noise indices like A-weighted equivalent continuous noise level (Leq), Traffic Noise Index (TNI), Noise Pollution Level (Lnp) and Noise Climate (NC) are calculated and compared with Central Pollution Control Board (CPCB, 2017) and WHO standards. The inhabitants’ noise exposure levels are at an average value of 80–107.1 dB(A) which is alarmingly higher than the threshold levels of 70 dB(A) by WHO. A Risk Exposure Assessment (REA) questionnaire survey conducted on the area revealed that unsafe health situations persist for the public in the study regions.

The aim of the research is to determine the effect of heat treatment and microwave irradiation on the sorption properties of a natural clinoptilolite and glauconite to Pb2+ ions. To improve the sorption capacity the samples were heat treated at 550 °C for 3 hours or microwaved at 790 W for 30 minutes. The XRD and XRF analysis present the content of investigated samples and prove the increase in the sorption capacity after treatment. After contact with Pb, its content in the natural clinoptilolite increased to 2.66%, and in the thermally treated – to 6.035%. The PbO content in natural glauconite increased to 3.9%, but after microwaving it reached 5.2% of the total sample weight. Heat treatment is useful for improving the sorption capacity of clinoptilolite, and microwave irradiation can significantly increase the adsorption capacity of glauconite.

A common technique for assessing the overall water quality state of surface water and groundwater systems globally is the water quality index (WQI) method. The aim of the research is to use four machine learning classifier algorithms: Gradient boosting, Naive Bayes, Random Forest, and K-Nearest Neighbour to determine which model was most effective at forecasting the various water quality index and classes of the Albanian Shkumbini River. The analysis was performed on the data collected during a 4-year period, in six monitoring points, for nine parameters. The predictive accuracy of the models, XGBoost, Random Forest, K-Nearest Neighbour, and Naive Bayes, was determined to be 98.61%, 94.44%, 91.22%, and 94.45%, respectively. Notably, the XGBoost algorithm demonstrated superior performance in terms of F1 score, sensitivity, and prediction accuracy, the lowest errors during both learning (RMSE = 2.1, MSE = 9.8, MAE = 1.13) and evaluating (RMSE = 0.0, MSE = 0.01, MAE = 0.01) stages. The findings highlighted that Biochemical oxygen demand (BOD), Bicarbonate (HCO3), and Total Phosphor had the most positive impact on the Shkumbini River’s water quality. Additionally, a statistically significant, strong positive correlation (r = 0.85) was identified between BOD and WQI, emphasizing its crucial role in influencing water quality in the Shkumbini River.