Cities in developing countries like Shiraz in Iran face significant challenges due to a lack of an integrated solid waste management system. Climate change, soil, and water pollution are examples of environmental issues caused by improper Municipal Solid Waste Management Systems (MSWMS) in developing countries. The aim of this study is to find solutions for these environmental problems based on the experiences of developed countries. The data was collected using several methods such as visual observations, studying accessible documents of the current situation of the MSWMS in Shiraz, and participating in an interview with engineers the 'Shiraz Municipality Waste Management Organization' (SMWMO).  Results present the current functional elements of MSWMS in Shiraz, Shiraz waste diversion rate (0.22), and its Zero Waste Index (.015). Moreover, the results present some recommendations to find a way to transform cities like Shiraz into zero-waste cities. Results indicate that establishing zero-waste policies, legal frameworks, and financial strategies as well as convincing private sector involvements in installing waste-to-energy facilities and a proper sanitary landfill to move the city toward optimum recycling and zero landfilling in addition to reducing consumption and maximizing diversion rate and finally sustainable development by the cooperation of government, NGOs and media programs would solve many problems of the MSWMS and would solve environmental issues in many cities.

Contaminant vulnerability in the critical zones like groundwater (GW)-seawater (SW) continuum along the entire Gujarat coast was investigated for the first time through an extensive water monitoring survey. The prime focus of the study was to evaluate whether or not: i) seawater intrusion induced metal load translates to toxicity; ii) in the coastal groundwater, metal distribution follows the pattern of other geogenic and anthropogenic contaminants like NO₃- and F-; and iii) what future lies ahead pertaining to metal fate in association with saturation conditions of the coastal aquifers. The spatial distribution of contaminants depicts that the Gulf of Khambhat area is highly contaminated. Ecological risk assessment (ERA) indicates that the Gujarat coast is experiencing a high ecological risk compared to the southeast coast of India. Investigation results revealed that metals, pH, NO₃, and CO₃ are more vulnerable at the SW-GW mixing interface. An increase in pH is reflected in fewer ionic species of metals in the GW. Salinity ingress due to seawater intrusion (SWI) reduces the toxicities of all trace metals except Cu, attributed to the increase of Ca in GW, leading to dissociation of CuCO₃. Reactive species are dominant for Zn and Cd; and M-CO₃ ligands are dominant for Cu and Pb owing to the undersaturation of dolomite and calcite in the aquifer system. SWI tends to increase the metal load but the toxicity of metals varies with the density of industries, anthropogenic activities, changes in the mixing-induced saturation conditions, and intensive salt production across the coast. Multivariate analysis confirmed that the hydrogeochemical processes change due to GW-SW mixing and dictates over natural weathering. The ecological risk index (ERI) for the Arabian sea is experiencing moderate (300 ≥ ERI>150) to high ecological risk (ERI >600). Children population is likely to encounter a high health risk through ingestion and dermal exposure than adults. Overall, the study emphasizes the complexity of toxicity-related health impacts on coastal communities and suggests the dire need for frequent water monitoring along the coastal areas for quick realization of sustainable development goals.

The UN Sustainable Development Goal, SDG 7.3, is to double the global rate of improvement in energy efficiency by 2030. To meet this and other energy targets, countries encourage the development and adoption of energy-efficient products. An extensively researched phenomenon in this context is the energy rebound effect, especially in transportation. However, the direct relationship between the energy rebound effect and car emission levels has barely been investigated. Understanding this relationship is important, because energy-related emissions are closely linked to mortality, morbidity, and climate change. We assess the emission consequences in the private car market in Israel of a rebound effect associated with a policy promoting energy-efficient cars. We find that the baseline rebound before introduction of the policy was 40%. In the following three periods marked by policy changes, it grew to 54%, 69%, and 88%. Using household data with specific car characteristics and usage, we calculate the added greenhouse gas (GHG) emission consequences of this rebound by the end of the studied period to be about 5% of the country's per-capita target. Notably, estimates for the emission consequences using “average car” values were almost twice as high. The reason for this gap derives from the co-dependance between car usage and car efficiency. We discuss the implications of this gap in meeting emission goals.

One of the fundamental sustainable development goals has been recognized as having access to clean water for drinking purposes. In the Anthropocene era, rapid urbanization put further stress on water resources, and associated groundwater contamination expanded into a significant global environmental issue. Natural arsenic and related water pollution have already caused a burden issue on groundwater vulnerability and corresponding health hazard in and around the Ganges delta. A field based hydrogeochemical analysis has been carried out in the elevated arsenic prone areas of moribund Ganges delta, West Bengal, a part of western Ganga- Brahmaputra delta (GBD). New data driven heuristic algorithms are rarely used in groundwater vulnerability studies, specifically not yet used in the elevated arsenic prone areas of Ganges delta, India. Therefore, in the current study, emphasis has been given on integration of heuristic algorithms and random forest (RF) i.e., “RF-particle swarm optimization (PSO)”, “RF-grey wolf optimizer (GWO)” and “RF-grasshopper optimization algorithm (GOA)”, to identify groundwater vulnerable zones on the basis of field based hydrogeochemical parameters. In addition, correspondence health hazard of this area was assessed through human health hazard index. The spatial distribution of groundwater vulnerability revealed that middle-eastern and north-western part of the study area covered by very high and high, whereas central, western and south-western part are covered by very low and low vulnerability zones in outcomes of all the applied models. The evaluation result indicates that RF-GOA (AUC = 0.911) model performed the best considering testing dataset, and thereafter RF-GWO, RF-PSO and RF with AUC value is 0.901, 0.892 and 0.812 respectively. Findings also revealed the groundwater in this study region is quite unfavorable for drinking and irrigation purposes. The suggested models demonstrate their usefulness in foretelling sustainable groundwater resource management in various deltaic regions of the world through taking appropriate measures by policy-makers.

Plastic pollution is one of several anthropogenic stressors putting pressure on ecosystems of the Caribbean Large Marine Ecosystem (CLME). A ‘Clean Ocean’ is one of the ambitious goals of the United Nations (UN) Decade of Ocean Science for Sustainable Development. If this is to be realized, it is imperative to build upon the work of the previous decades (1980–2020). The objectives of the present study were to assess the state of knowledge about: (i) the distribution, quantification, sources, transport and fate of marine debris/litter and microplastics in the coastal/marine environment of the CLME and, (ii) the effects of plastics on biodiversity. Snapshots, i.e., peer-reviewed studies and multi-year (1991–2020) marine debris data from International Coastal Cleanup (ICC) events, indicated that plastic debris was a persistent issue in multiple ecosystems and environmental compartments of the CLME. Collectively, a suite of approaches (debris categorization, remote sensing, particle tracking) indicated that plastic debris originated from a combination of land and marine-based sources, with the former more significant than the latter. Rivers were identified as an important means of transporting mismanaged land-based waste to the marine environment. Oceanic currents were important to the transport of plastic debris into, within and out of the region. Plastic debris posed a threat to the biodiversity of the CLME, with specific biological, physical, ecological and chemical effects being identified. Existing data can be used to inform interventions to mitigate the leakage of plastic waste to the marine environment. Given the persistent and transboundary nature of the issue, further elucidation of the problem, its causes and effects must be prioritized, while simultaneously harmonizing regional and international approaches.

Anthropogenic heat emission (AHE) is an important driver of urban heat islands (UHIs). Further, both urban thermal environment research and sustainable development planning require an efficient estimation of anthropogenic heat flux (AHF). Therefore, this study proposed an improved multi-source AHF model, which was constructed using diverse data sources and small-scale samples, to better represent the spatiotemporal distribution of AHF. The performances of three machine learning algorithms (Cubist, gradient boosting decision tree, and simple linear regression) were quantitatively evaluated, and the impact of spatiotemporal heterogeneity on AHF estimation was considered for the first time. The results showed that multi-source datasets and sophisticated algorithms could more effectively reduce the estimation error and improve the accuracy of the spatiotemporal distribution of AHF than simple linear regression. In practical applications, the Cubist model performed better, with prediction errors being less than 0.9 W⋅m−2. Further, the characteristics of different heat sources from the model outputs varied widely, and the building metabolic heat exhibited significant seasonal spatiotemporal variations, which were largely determined by the regional climate. In contrast, industrial and transportation heat showed marginal monthly fluctuations. Similarly, spatiotemporal heterogeneity significantly affected the estimation of building metabolic heat (0.62 W⋅m−2), but it did not affect other heat sources. The proposed improved AHF model was verified to effectively capture the spatiotemporal variations of building heat and solve the issue of overestimation of industrial heat in urban regions. This study provides new methods and ideas for the accurate spatiotemporal quantification of AHF that can supplement future studies on climate warming, UHI, and air pollution.

In recent years, the scale of shrimp ponds has rapidly increased adjacent to mangrove forests. Discharge of shrimp pond effluent has led to degradation of the surrounding environment and reduction of biodiversity in the estuary. But it remains poorly understood how shrimp pond effluent affects functional traits and functional diversity of mangroves. We sampled roots, stems and leaves of Kandelia obovata and other mangrove plants, as well as sediments and pore water from shrimp pond effluent polluted area (P) and clean area (control area, C) in Zhangjiang Estuary in southeast coast of China. Twenty plant functional traits and six functional diversity indices were analyzed to explore the effects of shrimp pond effluent on individual plants and mangrove communities. The results showed that the discharge of shrimp pond effluent significantly affected the nutrient content in soils and pore water, for example, sediment NH₄⁺ and NO₃⁻ concentration increased from 0.26 ± 0.06 to 0.77 ± 0.29 mg/g and from 0.05 ± 0.03 to 0.16 ± 0.05 mg/g, respectively, when comparing the C and P site. Furthermore, some mangrove plant functional traits such as plant height, diameter at breast height, canopy thickness and specific leaf area were significantly increased by the effluent discharge. Functional diversity in the polluted area reduced as a whole compared to the control area. In particular, ammonium and nitrate nitrogen input is the main reason to induce the changes of plant functional traits and functional diversity. Besides, the community structure changed from functional differentiation to functional convergence after shrimp pond effluent discharge. In addition, the long-term shrimp pond effluent discharge may lead to the ecological strategy shift of K. obovata, while different organs may adopt different ways of nutrient uptake and growth strategies in the face of effluent disturbance. In conclusion, pollution from shrimp pond does affect the functional traits of mangrove plants and functional diversity of mangrove community. These results provide strong evidence to assess the impact of effluent discharges on mangrove plants and provide theoretical basis for conservation and sustainable development of mangroves.

Asphalt-surfaced areas such as roads have been reported as major non-combustion sources of reactive organic compounds in urban areas. Emission of latter compounds from asphalt is exacerbated due to exposure to sunlight and high temperature, contributing to negative human and environmental health outcomes. Furthermore, loss of asphalt components over time is linked to bitumen's aging that reduces service life of roads. Here, we introduce a designed bio-grafted-silica nano-filler derived from wood pellet as a sink for latter volatile compounds in an asphalt mixture. Molecular modeling calculations showed the remarkable adsorptive activity of the bio-grafted silica for trapping select asphalt volatiles, especially for the sulfur-containing aromatics and the oxygen-containing aromatics. Laboratory experiment revealed that the bitumen modified with bio-grafted silica exhibited up to 23% lower signs of aging. Thermogravimetric analysis proved that the modified bitumen exhibited a 16% reduction in mass loss compared to neat bitumen. Dynamic vapor sorption analysis also showed bio-grafted silica adsorbed higher amounts of a candidate volatile than pristine silica. The study outcomes highlights the advantages of a bio-derived modifier in asphalt to address concerns associated with the loss of hazardous compounds.

The pollutants can enter water bodies at various point and non-point sources, and wastewater discharge remains a major pathway. Wastewater treatment effectively reduces contaminants, it is expensive and requires an eco-friendly and sustainable alternative approach to reduce treatment costs. Algae have recently emerged as a potentially cost-effective method to remediate toxic pollutants through the mechanism of biosorption, bioaccumulation, and intracellular degradation. Hence, before discharging the wastewater into the natural environment better solutions for environmental resource recovery and sustainable developments can be applied. More importantly, algae are a potential feedstock material for various industrial applications such as biofuel production. Currently, researchers are developing algae as a source for pharmaceuticals, biofuels, food additives, and bio-fertilizers. This review mainly focused on the potential of algae and their specific mechanisms involved in wastewater treatment and energy recovery systems leading to important industrial precursors. The review is highly beneficial for scientists, wastewater treatment plant operators, freshwater managers, and industrial communities to support the sustainable development of natural resources.

Decision Support System (DSS) is a novel approach for smart, sustainable controlling of environmental phenomena and purification processes. Toluene is one of the most widely used petroleum products, which adversely impacts on human health. In this study, Fusarium Solani fungi are utilized as the engine of the toluene bioremediation procedure for the monitoring part of DSS. Experiments are optimized by Central Composite Design (CCD) - Response Surface Methodology (RSM), and the behavior of the mentioned fungi is estimated by M5 Pruned model tree (M5P), Gaussian Processes (GP), and Sequential Minimal Optimization (SMOreg) algorithms as the prediction section of DSS. Finally, the control stage of DSS is provided by integrated Petri Net modeling and Failure Modes and Effects Analysis (FMEA). The findings showed that Aeration Intensity (AI) and Fungi load/Biological Waste (F/BW) are the most influential mechanical and biological factors, with P-value of 0.0001 and 0.0003, respectively. Likewise, the optimal values of main mechanical parameters include AI, and the space between pipes (S) are equal to 13.76 m³/h and 15.99 cm, respectively. Also, the optimum conditions of biological features containing F/BW and pH are 0.001 mg/g and 7.56. In accordance with the kinetic study, bioremediation of toluene by Fusarium Solani is done based on a first-order reaction with a 0.034 s-1 kinetic coefficient. Finally, the machine learning practices showed that the GP (R2 = 0.98) and M5P (R2 = 0.94) have the most precision for predicting Removal Percentage (RP) for mechanical and biological factors, respectively. At the end of the present research, it is found that by controlling seven possible risk factors in bioremediation operation through the FMEA- Petri Net technique, efficiency of the process can be adjusted to optimum value.