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Tehran, the most crowded city in Iran, suffers from severe air pollution, particularly during the cold months. This research endeavored to examine the statistical relationships between criteria air pollutants (CO, NO 2 , SO 2 , O 3 , PM 10 , and PM 2.5 ) and meteorological elements (temperature, rainfall, wind speed, relative humidity, air pressure, sunshine hours, solar radiation, and cloudiness), as well as assess and compare the efficacy of six different algorithms (multiple linear regression (MLR), generalized additive model (GAM), classification and regression trees (CART), random forest (RF), gradient boosting machine (GBM), and deep learning (DL)) in modeling pollutants and climatic factors responsible for variations in Tehran's air pollution levels from 2001 to 2021 using R 4.3.2 software. The results of this study showed that O 3 was strongly affected by weather conditions, while other pollutants were mainly influenced by each other than by meteorological parameters and more extensive research is required to pinpoint the precise impact of human activity on these pollutant levels in Tehran. Also based on the predictive model performance evaluation and concerning the principle of parsimony, in half of the cases, the MLR outperformed other models, despite its seeming simplicity and principal assumptions dependence. In other situations, the GAM was a good substitute., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The monitoring and modeling of changes, based on a time-series LULC approach, is fundamental for planning and managing regional environments. The current study analyzed the LULC changes as well as estimated future scenarios for 2027 and 2037. To achieve accuracy in predicting LULC changes, the Land Change Modeler (LCM) was used for the Latian Dam Watershed, which is located approximately in the northeast of Tehran. The LULC time-series technique was specified utilizing four atmospherically endorsed surface reflectance Landsat images for the years t 1 (1987), t 2 (1998), t 3 (2007), and t 4 (2017) to authenticate the LULC predictions, so to obtain estimates for t 5 (2027) and t 6 (2037). The LULC classes identified in the watershed were water bodies, build-up areas, vegetated areas, and bare lands. The dynamic modeling of the LULC was based on a multi-layer perceptron (MLP), the neural network in LCM, which presented good results with an average accuracy rate equivalent to 84.89 percent. The results of the LULC change analysis showed an increase in the build-up area and a decrease in bare lands and vegetated areas within the duration of the study period. The results of this research could help in the formulation of public policies designed to conserve environmental resources in the Latian Dam Watershed and, consequently, minimize the risks of the fragmentation of orchards and vegetated areas. Also, careful regional planning ensuring the preservation of natural landscapes and open spaces is critical to creating a resilient regional environment and sustainable development., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

In the south of Fars Province in Iran, there are several closed basins where the salinity of water and soil resources is one of the main problems. Therefore, to manage the water and soil resources of these basins, identifying the source of salinity and its expansion in playa is necessary. Thus, the Izad-Khast pit in the south of Fars Province of Iran was selected as one of these basins for research. To achieve the goal, 16 soil samples were randomly taken from the basin, and their EC was determined. Landsat 8 image was selected according to sampling day, and ten salinity indices were extracted from it. Then, the best index was determined by the relationship between salinity indices and ground EC using linear regression. Using the determined index and linear equation derived from linear regression and Landsat 7 and 8 images, salinity maps were obtained in three periods 2010, 2015, and 2020. Then, using maps of the three mentioned intervals and the CA-Markov method, soil salinity prediction maps were extracted for 2025 and 2030. Based on the research results, salinity index S2 = (Blue - R)/(Blue + R) provided the best results. Salinity maps derived from this index show that the highest level is related to the area with no salinity or low salinity, and high degrees of salinity are concentrated in some parts of the hills and in some areas of the plain, respectively, which determines the origin of salts. The results also showed that the kappa coefficient of CA-Markov is 0.7282, which shows the high ability of the model to predict soil salinity, in which the distance factor from gypsum and salt minerals is the most critical predictor factor. According to the forecasts, from 2020 to 2025, about 1 km 2 and then from 2025 to 2030, about 1.6 km 2 will be added to the saline lands., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)