The Gulf and Red Sea possess diverse coastal and marine environments that support rapidly expanding mass tourism. Despite the associated environmental risks, there is no analysis of the tourism-related literature or recent analysis of impacts. Environmental issues reported in 101 publications (25 from the Gulf, 76 from the Red Sea) include 61 purported impacts (27 from the Gulf, 45 from the Red Sea). Gulf literature includes quantitative studies (68% publications) and reviews (32%), and addresses mostly land reclamation and artificial habitats. Most Gulf studies come from Iran and UAE (64%). Red Sea literature includes quantitative studies (81%) and reviews (11%), with most studies occurring in Egypt (70%). The most published topics relate to coral breakage and its management. A full account of tourism’s environmental impacts is constrained by limited tourism data, confounding of impacts with other coastal developments, lack of baseline information, shifting baselines, and fragmentation of research across disciplines.

Petroleum products spill and leakage have become two major environmental challenges in Iran. Sampling was performed in the petroleum reservoir waste water of Tehran and Kerman Provinces of Iran. Alkane degrading bacteria were isolated by enrichment in a Bushnel–Hass medium, with hexadecane as sole source of carbon and energy. The isolated strains were identified by amplification of 16S rDNA gene and sequencing. Specific primers were used for identification of alkane hydroxylase gene. Fifteen alkane degrading bacteria were isolated and 8 strains were selected as powerful degradative bacteria. These 8 strains relate to Rhodococcus jostii, Stenotrophomonas maltophilia, Achromobacter piechaudii, Tsukamurella tyrosinosolvens, Pseudomonas fluorescens, Rhodococcus erythropolis, Stenotrophomonas maltophilia, Pseudomonas aeruginosa genera. The optimum concentration of hexadecane that allowed high growth was 2.5%. Gas chromatography results show that all strains can degrade approximately half of hexadecane in one week of incubation. All of the strains have alkane hydroxylase gene which are important for biodegradation. As a result, this study indicates that there is a high diversity of degradative bacteria in petroleum reservoir waste water in Iran.

Radionuclides which present in different beach sands are sources of external exposure that contribute to the total radiation exposure of human. 226Ra, 235U, 232Th, 40K and 137Cs analysis has been carried out in sand samples collected at six depth levels, from eight locations of the northern coast of Iran, Ramsar, using high-resolution gamma-ray spectroscopy. The average Specific activities of natural radionuclides viz., 226Ra, 235U, 232Th, 40K and 137Cs, in the 0–36cm depth sand were found as: 19.2±0.04, 2.67±0.17, 17.9±0.06, 337.5±0.61 and 3.35±0.12 Bqkg−1, respectively. The effects of organic matter content and pH value of sand samples on the natural radionuclide levels were also investigated. Finally, the measured radionuclide concentrations in the Ramsar beach were compared with the world average values, as reported by UNSCEAR (2000). None of the studied beaches were considered as a radiological risk.

Imam Khomeini Port (IKP) is the largest Iranian commercial port. Because of many petrochemical industries and urban areas are located around this port and also having heavy ship traffic, concentrations of PAHs, mercury and other heavy metals were measured as the first time in surface sediment of the jetties. The highest concentrations of PAHs, Hg, Cu, Pb and Fe were recorded at Site 1, located in the vicinity of the petrochemical industrial zone, where ships are repaired. The highest concentration of Zn was found at Site 4, which is the jetty for loading mineral materials. The comparison between measured values in this study and some sediment quality guidelines indicated that the concentrations of mercury and PAHs are much higher than other studies. Also, the ratios of PAHs in the stations showed a mixture of both of pyrolytic and petrogenic sources with a dominance of pyrolytic sources.

Caviar (fish roe of sturgeon) may contain high levels of contaminants. An inductively coupled plasma-optical emission spectrometer and a direct mercury analyzer were used to assess the contents of four heavy metals (Hg, Se, Sn, and Ba) in caviar of wild Persian sturgeon sea foods. The levels of Hg ranged from 1.39 to 1.50 μg g(-1), Se from 0.90 to 1.10 μg g(-1), Sn from 0.23 to 0.33, and Ba from 0.71 to 1.17 μg g(-1). Evaluation of these levels showed that except for Hg, the average concentrations of other metals are significantly lower than adverse level for the human consumption when compared with Food and Agricultural Organization of the United Nations and World Health Organization permissible limits. Therefore, their contribution to the total body burden of these heavy metals can be considered as negligibly small given that caviar is a luxury product.

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NOx), nitrogen dioxide (NO2), nitrogen monoxide (NO), ozone (O3), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 μm (PM10) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R (2) of more than 0.82 between modeled and observed hourly pollutant levels for CO, NOx, NO2, NO, and PM10. However, predicted O3 levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.