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The present paper addresses a novel two-echelon multi-product Location-Allocation-Routing problem (LARP). It also considers the integration of issues such as disruption, environmental pollution, and energy-efficient vehicles as currently critical issues in a Supply Chain Network (SCN) that includes production plants, central warehouses, and retailers. The aim of this study is to minimize the total cost, which involves costs related to the establishment, shipment processes, environmental pollution, travelling, vehicle usage, and fuel consumption, in a way to cover the total demand of retailers. The problem is NP-hard; thus, to solve it approximately, we developed Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms. The numerical analysis showed that the proposed algorithms yielded high-quality results in a short computational time where the average gaps of GWO and PSO against CPLEX are 0.78% and 0.9%, respectively. Then, a case study of a dairy factory in Iran is conducted to evaluate the applicability of the proposed methodology and find the optimal policy. Finally, a set of sensitivity analyses is carried out to suggest managerial insights and decision aids. [ABSTRACT FROM AUTHOR]

Owing to the rise in population, lifestyle changes, high traffic rates in urban areas and environmental pollution, respiratory diseases have become much more prevalent on both regional and urban scales. Respiratory diseases affect over 300 million people worldwide and are thus among the major threats to humans' general well-being. The identification of underlying factors and the specification of accompanying risk areas for the temporal exacerbation of respiratory diseases are effective steps in managing the damage caused by such disorders. Here, we demonstrate a strategy for modelling the risk zone of respiratory diseases temporally, using a location-based social network (LBSN) and an artificial neural network (ANN). The main contribution of this paper is to consider the environmental and infrastructural factors and identify their relationships with the geographical locations of respiratory attacks. The study also utilizes Telegram, which is the most popular and conventional social media platform, in order to observe temporal changes in the location of respiratory attacks in Iran, in the form of a developed Telegram bot known as @respiratoryassociation. The relations between the factors behind and the location of respiratory attacks are determined using a multilayer perceptron (MLP) ANN. All the required data have been collected on a daily basis over a 5-year period from December 2013 to December 2018 in Tehran, Iran. The results indicated air pollution, especially pollution from carbon monoxide (CO) and suspended particulate matter (PM) as the most decisive factors. Following air pollution, the amount of exposure to the polluted area was determined as the second most decisive factor, which in turn increased as a result of escalations in traffic jams. Land use was determined as the third most decisive factor. Furthermore, the results revealed that the ANN performed satisfactorily, implying that the model can be used to examine the spatio-temporal behaviour of the time series of respiratory diseases with respect to environmental and infrastructural factors. [ABSTRACT FROM AUTHOR]

Current economic policy planning places much emphasis on balancing development and environmental protection. Hence, it is important to determine the drivers of environment pollution from the theoretical, scientific, and policymaking aspects in the context of continuous economic growth. This paper investigates the factors affecting per capita CO 2 emissions in 30 provinces in Iran from 2009 to 2014 with emphasis on spatial spillover effects using the Spatial Durbin Model. The findings show that per capita CO 2 emissions are positively and significantly affected by per capita GDP, industrialization, and urbanization but negatively affected by changes in population. The results of the spatial section of the model indicate that both the more and the less-polluted provinces tend to cluster together, indicating positive spatial dependence for CO 2 emissions in the provinces. Also, the spatial spillover of per capita GDP and the growth of urbanization have a negative and significant effect on per capita CO 2 emissions in the provinces, while the spatial effect of changes in population is significant but positive. In other words, the economic development and rise in urbanization in one province are the results of changes in pollution levels in neighboring provinces.

Humans can be directly exposed to potentially toxic metals in the urban environment via inhalation, ingestion, or dermal contact of dust particles. This paper focuses on human health risk assessment of urban dust contaminated with potentially toxic metals. The levels, sources, and human health risks of nine potentially toxic metals (i.e., As, Cd, Cu, Cr, Ni, Pb, Co, Mn, and Zn) in 200 road dust samples from Rafsanjan area were investigated. Pollution level was assessed using the pollution index (PI) and geoaccumulation index (I geo ), and the health risk assessment was performed following the methodology described by the US Environmental Protection Agency. The mean concentrations of As, Cu, Pb, Cd, Cr, Ni, Zn, Co, and Mn in road dust were 105.3 ± 5.7, 791.4 ± 29.8, 123.1 ± 9.7, 28.4 ± 3.3, 3.1 ± 0.6, 18.4 ± 1.6, 252.6 ± 8.3, 16.5 ± 1.4, and 525.9 ± 21.0 mg kg -1 , respectively. Thus, the concentrations of potentially toxic metals in road dust were higher than their corresponding natural background values, indicating that all studied potentially toxic metals were impacted by anthropogenic activities. The results of the current study are comparable to other studies conducted on road dust in other cities worldwide. Both of I geo and PI decreased following order Cu > Mn > Pb > As > Zn > Cd > Ni > Cr > Co. Health risk assessment indicated that both of children and adults could be exposed to a potential increased risk of developing cancer over a lifetime from exposure to arsenic through ingestion of the dust samples. However, Pb ingestion can increase cancer risk in children.

In this study, an artificial neural network (ANN) model was developed for predicting the yield and life cycle environmental impacts based on energy inputs required in processing of black tea, green tea, and oolong tea in Guilan province of Iran. A life cycle assessment (LCA) approach was used to investigate the environmental impact categories of processed tea based on the cradle to gate approach, i.e., from production of input materials using raw materials to the gate of tea processing units, i.e., packaged tea. Thus, all the tea processing operations such as withering, rolling, fermentation, drying, and packaging were considered in the analysis. The initial data were obtained from tea processing units while the required data about the background system was extracted from the EcoInvent 2.2 database. LCA results indicated that diesel fuel and corrugated paper box used in drying and packaging operations, respectively, were the main hotspots. Black tea processing unit caused the highest pollution among the three processing units. Three feed-forward back-propagation ANN models based on Levenberg-Marquardt training algorithm with two hidden layers accompanied by sigmoid activation functions and a linear transfer function in output layer, were applied for three types of processed tea. The neural networks were developed based on energy equivalents of eight different input parameters (energy equivalents of fresh tea leaves, human labor, diesel fuel, electricity, adhesive, carton, corrugated paper box, and transportation) and 11 output parameters (yield, global warming, abiotic depletion, acidification, eutrophication, ozone layer depletion, human toxicity, freshwater aquatic ecotoxicity, marine aquatic ecotoxicity, terrestrial ecotoxicity, and photochemical oxidation). The results showed that the developed ANN models with R 2 values in the range of 0.878 to 0.990 had excellent performance in predicting all the output variables based on inputs. Energy consumption for processing of green tea, oolong tea, and black tea were calculated as 58,182, 60,947, and 66,301 MJ per ton of dry tea, respectively.

Due to the scarcity of fossil fuels in the future, the optimal use of these products can not only increase the efficiency of power plants, but it can also be effective in reducing the production of pollutants. To deal with these situations, optimal resource allocation and reallocation was studied using the data envelopment analysis (DEA) models. The current study adopted a resource allocation model in DEA framework when undesired outputs are produced in production process. This alternative resource allocation model is, however, sensitive to uncertainty of the data. In this contribution, we, therefore, introduce a stochastic resource allocation model when there are random data and undesirable products. An applied illustrative study to the power industry consisting 21 electricity production & distribution companies for eight years (2011–2019) is performed to compare the resource reallocations and their efficiencies. The important findings are: First, if we decide to deactivate two companies, the fuel consumption, employees and net electricity generation must be reduced. These reductions will lead to a reduction in pollutants. Second, the low price of electricity in Iran leads to excessive consumption of this product, which in turn leads to the inefficiency of many companies. In order to improve the performances of the companies, the amount of sold-out electricity must significantly be increased. • This paper adopted a resource allocation model in DEA with undesired outputs. • Our resource allocation model is sensitive to uncertainty of the data. • A stochastic resource allocation model is given when we have random data and undesirable products. • An application on 21 electricity production & distribution companies for eight years is given. [ABSTRACT FROM AUTHOR]

Disposal of about 1750tons of solid wastes per day is the result of a rapid population growth in the province of Khuzestan in the south west of Iran. Most of these wastes, especially hospital solid wastes which have contributed to the pollution of the environment in the study area, are not properly managed considering environmental standards and regulations. In this paper, the framework of a master plan for managing hospital solid wastes is proposed considering different criteria which are usually used for evaluating the pollution of hospital solid waste loads. The effectiveness of the management schemes is also evaluated. In order to rank the hospitals and determine the share of each hospital in the total hospital solid waste pollution load, a multiple criteria decision making technique, namely analytical hierarchy process (AHP), is used. A set of projects are proposed for solid waste pollution control and reduction in the proposed framework. It is partially applied for hospital solid waste management in the province of Khuzestan, Iran. The results have shown that the hospitals located near the capital city of the province, Ahvaz, produce more than 43% of the total hospital solid waste pollution load of the province. The results have also shown the importance of improving management techniques rather than building new facilities. The proposed methodology is used to formulate a master plan for hospital solid waste management.

The fast industrialization and urbanization in the world have led to increasing heavy metal pollution in the water supplies. Here, we examined the spatial and temporal distribution and contamination of heavy metals and physicochemical parameters in the drinking water sources of Mashhad in Iran. In this survey, 432 samples of drinking water were collected from 5 zones and 36 stations from August 2017 to May 2018. The results of heavy metal measurements showed that the average concentrations of arsenic (As), mercury (Hg), chrome (Cr), nickel (Ni) and Pb were 0.198±0.11, 0.018±0.04, 5.80±7.87, 1.695±2.16 and 0.574± 0.22 μg L-1 for arsenic (As), mercury (Hg), chrome (Cr), nickel (Ni) and Pb, respectively. This result showed that the concentration of heavy metals and physicochemical parameters were comparatively lower than the threshold values throughout the study period; however, some stations showed metrics values above the acceptable limit. Thus, there is still potential contamination in drinking water due to potential heavy metal interactions and long-term exposure. Results of this study showed the current pollution status of drinking water in Mashhad needs remediation efforts to protect human health in urban regions, which highlighted a basis for decision-making in the future to take the main action on contamination control. [ABSTRACT FROM AUTHOR]

The basic purpose of this study is to scrutinize the asymmetric effect of oil price changes on environmental pollution in Canada, China, India, Iran, Germany, Japan, Russia, South Korea, Saudi Arabia, and the USA. The study uses time series annual data of selected courtiers from 1981 to 2018 and applies non-linear ARDL (NARDL) model to examine the long- and short-run asymmetries. The results show that positive shocks in diesel prices in the USA, India, Japan, Germany, South Korea, Iran, and Canada, while negative shocks in China and India reduce carbon emissions in the long run. However, an increase in gasoline prices in Russia and Iran while the decrease in gasoline prices in the USA, Russia, Japan, and Canada decreases in carbon emissions in the long run. Asymmetric findings also suggest that positive and negative changes in oil prices affect carbon emissions differently in China, the USA, India, Russia, Japan, Germany, South Korea, Iran, Saudi Arabia, and Canada in the short and long run. However, sign and magnitude of positive and negative shocks of oil prices are more important in environmental economics polices. Therefore, based on sign and magnitude, more taxation of fossil fuel and clean energy subsidies are recommended for the top carbon-emitting economies. [ABSTRACT FROM AUTHOR]

Background: Solid waste is one of the main environmental pollutants. The aim of this study was to survey the knowledge, attitude and practice of students and teachers about waste management for developing educational methods in Arak city. Materials and Methods: This is a cross sectional and analytical study that was carried out on 1739 students and 149 teachers in Arak city within 2012. Cluster sampling performed and data was collected with questionnaire including knowledge, attitude, practice and appropriate resources of education for waste management. Finally data was analyzed with statistical tests. Results: The mean score of knowledge and attitude of students was 4.1±1.9 (out of 8) and 20.63±5.9 (out of 35) respectively. About educational sources, 441 students (29% ) mentioned to book and impact of internet was very low (32%). Moreover, there was significant difference between different education levels about the best educational method (p=0.001). Conclusion: Based on our results and level of knowledge and attitude of students and their access to educational resourses and huge of weaste in school educational program planned via book, teacher and peer education for student recommended. [ABSTRACT FROM AUTHOR]