Sayyed Mohammad Hosseini,
Volume 26, Issue 80 (3-2026)
Abstract
for the spatial analysis of precipitation in the Middle East, have been used gridded precipitation data from the World Precipitation Climatology Center (GPCC) with a monthly temporal resolution and a spatial resolution of 0.5×0.5 arc degrees. Therefore, a matrix of 80 x 160 dimensions was obtained for the Middle East region (160 longitudinal cells and 80 transverse cells). The reason for choosing network data is their proper spatial and temporal separation and their up-to-date compared to station data. The period under investigation is from 1970 to 2020 AD. Finally, the long-term maps of the Middle East precipitation were drawn on an annual and monthly basis. The results indicate that precipitation in the Middle East tends to concentrate and cluster in the spatial and temporal dimension. In other words, due to the special geographical location of the Middle East region, such as uneven topography, distance and proximity to moisture-feeding sources (Caspian Sea, Black Sea, Mediterranean Sea, Atlantic Ocean, and Indian Ocean) and the direction of unevenness, Precipitation in high altitude areas, It is concentrated in the neighborhood of seas and oceans and also in the windy slopes of the mountain range of the region. The uneven distribution of geographical conditions has caused uneven distribution of Precipitation in the Middle East. So that; The center and gravity of the Middle Eastern Precipitation is concentrated in the eastern end of the Black Sea, southern Turkey in the neighborhood of Syria and Iraq, the Ararat-Zagors belt in the west of Iran, the southern shore of the Caspian Sea, the Pamir highlands and the Bay of Bengal in India, and the Hindu Kush highlands in Pakistan. Is. However, the many parts of the Middle East, due to their proximity to large deserts (African Sahara, Lut Desert, Dasht-Kavir, Arabia's Rab-al-Khali and Afghan deserts), have less than 100 mm of Precipitation. The results showed that the maximum Precipitation of this region has been transferred to the winter season, and the summer season is still the driest period in the Middle East, and only the coasts of the Indian Ocean and the Bay of Bengal have monsoon rains
Dr. Vahab Amiri, Dr. Nassim Sohrabi, Dr. Seyed Mohammadali Moosavizadeh,
Volume 26, Issue 80 (3-2026)
Abstract
This study investigates the impact of natural and anthropogenic factors on the physicochemical composition of groundwater in the Qazvin aquifer. Based on the optimized Gibbs diagram, the concentration of samples at the end of the freshwater interaction path with silicate units results from geochemical evolution due to the dissolution of these geological units and an increase in the Na/(Na+Ca) ratio. The ion exchange mechanism was assessed using bivariate diagrams of Ca+Mg vs. SO4+HCO3 and Schoeller's chloro-alkaline indices CAI-1 and CAI-2. The results indicate that in 68% of the samples, direct ion exchange, and in 32%, reverse ion exchange control the groundwater chemistry. The changes in Ca vs. SO4 indicate that gypsum dissolution alone is not the source of these ions. These changes could be due to ion mobility and transport during pedogenic processes (sulfur biogeochemical cycle) and anthropogenic factors. The study also examined the role of factors such as agricultural input, atmospheric input, soil nitrogen, sewage input, manure input, chemical fertilizers, and the denitrification process in groundwater pollution using NO3/Na vs. Cl/Na and the NO3/Cl vs. Cl diagrams. The results reveal that agricultural and sewage inputs significantly impact the NO3 and Cl content. Furthermore, in some locations, especially in the southeast of the aquifer, the denitrification process causes a decrease in NO3 concentration. These findings can contribute to effective water resource management in this strategic aquifer by understanding the controlling mechanisms of physicochemical composition and identifying potential groundwater pollution sources.
Zahra Hedjazizadeh, Al Karbalaee, Mokhtar Fatahian,
Volume 26, Issue 80 (3-2026)
Abstract
This study investigates the spatial dynamics of the subtropical anticyclone over Iran during boreal summer, using daily ERA5 reanalysis data (1980–2020) and the Getis-Ord Gi* statistic to identify statistically significant hotspots (p < 0.01) in 500-hPa geopotential height (Z500) anomalies for June–August. Results reveal that the peak statistical hotspot occurs in July: a prominent warm cluster with Z-scores up to +4.1 (99% confidence level) forms over southwestern Iran (27°–32°N, 48°–60°E), reflecting the strongest positive departure from the long-term Z500 climatology. Conversely, a cold cluster with Z-scores reaching −10.2 emerges over the northwest (West Azerbaijan and Kurdistan provinces) the lowest value recorded over the entire period indicating pronounced geopotential depression driven by the orographic influence of the Alborz–Zagros ranges and incursions of mid-latitude systems. Histogram analysis of Z-scores confirms a distinctly bimodal distribution in July, with high frequencies in the [+2.5, +4.1] and [−10.2, −2.5] ranges and a pronounced trough near Z ≈ 0, underscoring strong spatial segregation between warm and cold clusters. Notably, the eastern half of Iran (central and eastern regions) consistently lacks significant hotspots across all three months, suggesting the presence of a dynamic transition zone shaped by the competition between subtropical and mid-latitude circulations. In August, although absolute Z500 exceeds 5890 m, the Z-score diminishes (+4.0), indicating that cumulative surface heating elevates the mean geopotential height but its anomalous intensity relative to climatology weakens compared to July. Collectively, these findings suggest that the dynamical peak of the Iranian subtropical high lags the peak of surface heating by approximately one month.
Miss Sorayya Derikvand, Dr Behrooz Nasiri, Dr Hooshang Ghaemi, Dr Mostafa Karampoor, Dr Mohammad Moradi,
Volume 26, Issue 81 (6-2026)
Abstract
sudden stratospheric warming has an obvious effect on the Earth's surface climate. In this research, the changes in precipitation during the occurrence of this phenomenon have been investigated. For this purpose, after revealing the warmings that occurred during the studied period (1986-2020), 18 warmings were identified. The 5th decile and 9th decile of precipitation were calculated for the precipitation data of 117 stations. And the size of the difference from the normal rainfall was checked in two ways. First, the precipitation at the time of warming was compared with the long-term average, and then the trend of changes in precipitation at three times before thewarming, at the same time as the warming, and after the warming was finished. Finally, these results were obtained. Warmings according to the month in which they occur; They have a different effect on the amount of precipitation. In the sudden stratospheric warming that occurred in December, January and February, the northwest experiences the most rainfall changes and is above normal, and the probability of rainfall above the 9th decile increases up to 65%. Western and southwestern regions also have higher than average rainfall and the probability of heavy rainfall is high. Precipitation on the shores of the Caspian Sea shows an inverse relationship with sudden stratospheric warming, so in all the investigations of this research, the lack of precipitation at the time of warming in these areas is significant. Southern regions have less than normal rainfall in all sudden stratospheric warming events. The center of Iran has higher than average rainfall in the sudden stratospheric warming months of March. Eastern Iran also has heavy rains compared to normal during the sudden stratospheric warming months of March.
Hossein Asakereh, Mansureh Taheri,
Volume 26, Issue 81 (6-2026)
Abstract
One of the climatic characteristics of temperature is the occurrence of extreme temperature. In the present study, the trend of hot days with extreme temperature associated with the coastal plains of the Persian Gulf was investigated. Two environmental and atmospheric databases were used. Environmental data include the average of daily maximum temperature reported from 12 synoptic stations in Persian Gulf coastline (Khuzestan, Bushehr, and Bandar Abbas Provinces) from 1961 to the end of 2018. The extreme temperature for each day temperature was defined to be higher than the average of 75th percentile of the observations at each station and on the same day. Also, the ‘day with extreme temperature’ was applied to a day when the extreme temperature occurred in at least 50% of the stations. The number of hot days with extreme temperature in the study is 554 days, of which 291 days occurred in the warm season and 263 days in the cold season. These days were classified into six groups by performing cluster analysis on sea-level pressure in hot days. Then, for each group, the trend of hot days was examined. In general, it can be concluded that the slope of the line in all groups except the fourth and sixth groups were positive and, as a result, hot days with extreme temperature were increasing.
Ms Atefeh Bosak, Dr Zahra Hejazizadeh, Dr Akbar Heydari Tashekaboud,
Volume 26, Issue 81 (6-2026)
Abstract
Air pollution has significant impacts on human health, environmental quality, and the sustainable development of cities. This study aimed to evaluate PM10 using meteorological data from the city of Ahvaz through statistical methods and artificial neural networks. Daily meteorological data and air quality control station data for 4485 days (from 2011 to 2023) were obtained from the National Meteorological Organization and the Khuzestan Department of Environment. Initially, the data were processed and refined, and their normality was assessed using the Kolmogorov-Smirnov test. Given the non-normality of the data, Spearman's and Kendall's Tau-b methods were employed to examine their correlations. The time series and statistical information of the data were obtained using Python programming language. Furthermore, to predict future PM10 levels, the Multilayer Perceptron (MLP) neural network method was utilized. The results of these analyses indicated a significant correlation between meteorological variables and PM10. The Spearman and Kendall Tau-b correlations showed that PM10 had a positive and significant correlation with wind speed (0.094 and 0.061) and temperature (0.284 and 0.187) at a 99% confidence level. Conversely, PM10 exhibited a negative and significant correlation with visibility (-0.408 and -0.300), wind direction (-0.048 and -0.034), precipitation (-0.159 and -0.125), and relative humidity (-0.259 and -0.173) at the 99% confidence level. For future PM10 predictions, the MLP neural network was used. The model was of the Sequential type with an input layer consisting of 6 neurons, three hidden layers of Dense type with 16, 32, and 64 neurons, and an output layer with a linear activation function. The mean squared error (MSE) for the training set was 0.0034, and for the validation data, it was 0.0012. For the test set, the obtained validation accuracy was mse_mlp=0.0048 and val_loss=0.0012. The results indicate a significant direct or inverse correlation between meteorological data and PM10. Additionally, the outcomes of the MLP neural network demonstrated that the network provided satisfactory performance and acceptable predictions for PM10 data in Ahvaz.
