Soils provide the service of attenuating and detoxifying pollutants. Such ability, natural attenuation capacity (NAC), is one of the most important ecosystem services for urban soils. We improved the ecosystem-service performance index (EPX) model by integrating with entropy weight determination method to evaluate the NAC of residential soils in Beijing. Eleven parameters related to the soil process of pollutants fate and transport were selected and 115 residential soil samples were collected. The results showed that bulk density, microbial functional diversity and soil organic matter had high weights in the NAC evaluation. Urban socio-economic indicators of residential communities such as construction age, population density and property & management fee could be employed in kinetic fittings of NAC. It could be concluded urbanization had significant impacts on NAC in residential soils. The improved method revealed reasonable and practical results, and it could be served as a potential measure for application to other quantitative assessment.

The current debate on climate change is over whether global warming can be limited in order to lessen its impacts. In this sense, evidence of a decrease in the statistical polarization in greenhouse gas (GHG) emissions could encourage countries to establish a stronger multilateral climate change agreement. Based on the interregional and intraregional components of the multivariate generalised entropy measures (Maasoumi, 1986), Gigliarano and Mosler (2009) proposed to study the statistical polarization concept from a multivariate view. In this paper, we apply this approach to study the evolution of such phenomenon in the global distribution of the main GHGs. The empirical analysis has been carried out for the time period 1990–2011, considering an endogenous grouping of countries (Aghevli and Mehran, 1981; Davies and Shorrocks, 1989). Most of the statistical polarization indices showed a slightly increasing pattern that was similar regardless of the number of groups considered. Finally, some policy implications are commented.

This study separates marine carrying capacity into four key dimensions, i.e., social, economic, resource, and ecological, and uses the entropy method to evaluate the carrying capacity of China's 11 coastal regions during the period 2007–2016. We then predict the values of marine carrying capacity in the subsequent five years (2017–2021) using the grey Verhulst model. Results reveal a significant disparity in marine carrying capacity among the 11 coastal regions of China, and social and ecological carrying capacities illustrate among the four subcategories. Pearl River Delta in the south has the highest marine carrying capacity value and shows an increasing trend, while Yangtze River Delta and Bohai Rim Region in the north are stable. With regard to the predicted values for 2017–2021, forecasting results illustrate that the industrial structure of China's coastal areas is gradually turning towards the mode of diversified and comprehensive utilization of marine resources.

In this study, two ocean oil spill detection approaches based on four scattering matrices measured by fully polarimetric synthetic aperture radar (SAR) are presented and compared. The first algorithm is based on the co-polar correlation coefficient, ρ, and the scattering matrix decomposition parameters, Cloud entropy (H), mean scattering angle (α) and anisotropy (A). While each of these parameters has oil spill signature in it, we find that combining these parameters into a new parameter, F, is a more effective way for oil slick detection. The second algorithm uses the total power of four polarimetric channels image (SPAN) to find the optimal representation of the oil spill signature. Otsu image segmentation method can then be applied to the F and SPAN images to extract the oil slick features. Using the L-band fully polarimetric Uninhabited Aerial Vehicle – synthetic aperture radar (UAVSAR) data acquired during the 2010 Deepwater Horizon oil spill disaster event in the Gulf of Mexico, we are able to successfully extract the oil slick information in the contaminated ocean area. Our result shows that both algorithms perform well in identifying oil slicks in this case.

Understanding the effects of habitat heterogeneity on the functioning of macrobenthic communities is essential to the conservation of biodiversity in coastal ecosystems. However, the effects of habitat heterogeneity on the functional trait composition and diversity of seagrass bed macrobenthos are as scarce. In the present study, functional diversity indices (i.e., functional dispersion, functional richness, and Rao's quadratic entropy), RLQ analysis, and fourth-corner analysis indicated that macrobenthic functional trait composition and diversity differ among seagrass bed microhabitats (interior, edge, and bare sediment). More specifically, functional traits were more evenly distributed in the seagrass bed interior and edge habitats, when compared to bare sediment, and functional diversity was significantly higher (p < 0.01). Functional trait distributions were influenced by environmental parameters (e.g., total organic carbon, organic matter, and grain size). Suspension-feeding and burrowing bivalves preferentially inhabited bare sediment with high sand content and low TOC, whereas herbivorous, small, and sensitive species mainly inhabited muddy sediments with higher organic supply.

This paper introduces a Semivariance-Transinformation (S-T) based method for designing an optimum bay water nutrients monitoring network in San Francisco bay (S.F. bay), USA. Phosphorus and nitrogen are the most important nutrients that lead to eutrophic condition. The monthly phosphate and nitrate+nitrite data recorded during September 2006 to August 2015 was obtained over 14 active stations located at S.F. bay and was used in the research. Semivariance and discrete transinformation entropy have been applied to calculate the optimum range of the monitoring distance. The study indicated the ranges of 28 to 82 and 37 to 50km for the phosphate and nitrate+nitrite respectively. Useful information can be obtained from the monitoring network, if the monitoring distance is included in the mentioned intervals. The findings of the research introduce a new approach in the field of water quality monitoring networks design.

In situ droplet-size distributions were measured using a laser in situ scattering and transmissiometry (LISST-100X) particle size analyzer during the evaluation of natural and chemical dispersion efficiency of crude oils under different wave and current conditions. An entropy grouping of the in situ dispersed oil droplet-size spectra has classified the multi-modal droplet-size distributions into different groups based on similar droplet-size spectra characteristics within groups and distinction between groups. A generalized linear logistic regression model was fitted to analyze the effects of a number of factors and their interactions on the grouping of oil droplet-size spectra. The grouped results corresponded to the oil dispersion efficiency at different levels. This new method for droplet-size distribution data analysis can have significant implication in field evaluation of natural and chemical dispersion efficiency of oil.

The stated goal of the research is to investigate the surface water quality of the Baitarani River in Odisha to ascertain its compatibility for various uses. Large, complex datasets generated during the one-year (2021-2022) monitoring program were collected from 13 locations and encompassed 22 parameters. To examine temporal and spatial fluctuations in and to interpret these datasets, MCDMs like TOPSIS and the Entropy-based Water Quality Index (EWQI) were utilized. The physical and chemical outcomes of the current experiment were compared to WHO standards. According to the analysis’s results, turbidity and total coliform (TC) are indicators that have a greater impact on water quality in all locations during both seasons and are directly linked to home and agricultural non-point source pollution. As per EWQI interpretation, 30.77 % of the observations in PRM and POM fall under the poor category. The findings showed how anthropogenic activities have harmed St. 8, 11, 12, and 13 and require effective management. A quantifiable approach was also carried out to decide the efficacy of TOPSIS. Farming attributes, including SAR, % Na, RSC, MR, KI, and PI, were estimated to delineate the agriculturally practicable zones. This work can offer a reference database for the betterment of water quality.

Air pollution affects public health and the environment, creating great concern in developed and developing countries. In India, there are numerous reasons for air pollution, and festivals like Diwali also contribute to air contamination. Determining the polluted region using several air contaminants is significant and should be analyzed carefully. This study aims to analyze the air quality in Tamil Nadu, India, during the Diwali festival from 2019 to 2021, based on multiple air pollutants. The study models the impact of air pollution as a Multi-Attribute Decision-Making (MADM) problem. It introduces a hybrid approach, namely the Analytical Hierarchy Process-Entropy-VlseKriterijumska Optimizacija I Kompromisno Resenje (AHP-Entropy-VIKOR) model, to analyze and rank the areas based on the quality of air. A combined approach of AHP and entropy is employed to determine the weights of multiple air pollutants. The VIKOR approach ranks the areas and identifies the areas with the worst air quality during the festival. The proposed model is validated by performing the Spearman’s rank correlation with two existing MADM methods: Combinative Distance Based Assessment (CODAS) and Weighted Aggregates Sum Product Assessment (WASPAS). Sensitivity analysis is carried out to assess the effects of the priority weights and the dependency of the pollutants in ranking the regions. The highest air pollution level during the festival was seen in Cellisini Colony (2019), Rayapuram (2020), T. Nagar and Triplicane (2021) in their respective year. The results demonstrate the consistency and efficiency of the proposed approach.

Understanding dynamic future changes in precipitation can provide prior information for nonpoint source pollution simulations under global warming. However, the evolution of the dependence structure and the unevenness characteristics of precipitation are rarely considered. This study applied a two-stage bias correction to daily precipitation and max/min temperature data in the Daqing River Basin (DQRB) with the HadGEM3-RA climate model. Validated from 1981 to 2015, future scenarios under two emission paths covering 2031–2065 and 2066–2100 were projected to assess variations in both the amount and unevenness of precipitation. The results suggested that, overall, the two-stage bias correction could reproduce the marginal distributions of variables and the evolution process of the dependence structure. In the future, the amount of precipitation in the plains is expected to increase more than that in the mountains, while precipitation unevenness, as measured by relative entropy, shows a slight increase in the mountains and a decrease in the plains, with enhanced seasonality. Conditioned on rising temperatures, high-/low-intensity precipitation tends to intensify/weaken precipitation unevenness. Additionally, the potential application of the bias correction method used herein and the possible impacts of uneven precipitation on nonpoint source pollution are given for further analyses. This study can provide useful information for future nonpoint source pollution simulations in the DQRB.