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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.