The increase in population has increased the need for agricultural and food products, and thus agricultural production should be increased. This goal may cause increases in emissions and environmental impacts by increasing the consumption of agricultural inputs. The prediction of environmental impacts plays an important role in evaluating pollutant emissions in crop production. This study employed two artificial intelligence (AI) methods: the adaptive neuro-fuzzy inference system–fuzzy c-means (ANFIS–FCM) algorithm as a novel computational method, and an artificial neural network (ANN) as a conventional computational method to predict the environmental impacts of soybean production in different scenarios (i.e., soybean cultivation after rapeseed (R-S), wheat (W-S), and fallow (F-S)). The life cycle of soybean production was assessed in terms of environmental impacts through the IMPACT2002+ method in SimaPro. In the present study, the production of one ton of soybeans was considered the functional unit, and the boundary of the system was considered the gate of the field. According to the results, the production of each ton of soybean in the defined scenarios resulted in 0.0009 to 0.0016 DALY, 5476.18 to 8799.80 MJ primary, 1033.68 to 1840.70 PDF × m² × yr, and 563.55 to 880.61 kg CO₂-eq damage to human health, resources, ecosystem quality, and climate change, respectively. Moreover, the weighted analysis indicated that various soybean production scenarios led to 293.87–503.73 mPt damage to the environment, in which the R-S scenario had the best environmental performance. According to the results, the ANFIS–FCM algorithm acted as the best prediction model of environmental indicators for soybean cultivation in all cases related to the ANN. The range of calculated R² for the ANFIS-FCM and ANN models were between 0.9967 to 0.9989 and 0.9269 to 0.9870, respectively. It can be concluded that the proposed ANFIS–FCM model is an efficient technique for obtaining accurate environmental prediction parameters of soybean cultivation.

In this study, we examine the relationship between short- and long-term effects of precipitation deficits, integrated in a drought index called ‘Joint Deficit Index (JDI)’, and tree-ring growth of oak trees (Quercus infectoria) in the Javan-Roud region, Kermanshah province (Iran). Precipitation data from regional climate stations cover the period 1968–2008, and the tree-ring width chronology span over the period 1818–2008. This chronology is highly correlated with the JDI (January–September) series for the instrumental period (P < 0.01). Therefore, the regional JDI was reconstructed using a simple linear regression model which accounts for 40% of JDI (January–September) variances over the instrumental period. Reconstructed drought periods persisted on average for 2 years, and some severe drought lasted for 3–4 years. However, one 7-year drought was reconstructed over the period 1923–1929. The most severe droughts occurred in the periods 1960–1963 and 1999–2001. While the reconstructed JDIs have no significant correlation with the North Atlantic Oscillation, a significant negative correlation was observed with the Southern Oscillation Index in November and December of pre growth during the common period from 1877 to 2008. An appropriate agreement was found between our reconstruction and historical famines in Iran and also reconstructed results from neighbouring countries in the east (China) and west (Turkey and other countries around the Mediterranean Sea), particularly for the most severe droughts. The highest agreement was achieved with regions in the pathway of westerly storm tracks travelling via the Mediterranean region into Iran.

Dust pollution is a major environmental issue that threatens public health and ecosystems. This study investigates the relationship between wetland area reduction and the intensification of dust pollution in southwestern Iran, utilizing satellite data from Landsat, Sentinel-5, and MODIS (2010–2022). High-resolution imagery from Google Earth Engine (GEE) was used to extract dust event frequency data, while Aerosol Optical Depth (AOD) and Absorbing Aerosol Index (AAI) were employed to monitor dust pollution levels. Normalized Difference Water Index (NDWI) analysis revealed that the Hoor Al-Azim and Shadegan wetlands experienced their driest conditions in 2010, 2011, 2021, and 2022. A strong inverse correlation was observed between wetland area and dust event frequency, with correlation coefficients of − 0.85 for Hoor Al-Azim and − 0.90 for Shadegan. When the area of Hoor Al-Azim dropped below 60 km², dust events increased to 20–35 occurrences. Similarly, in Shadegan, dust events exceeded 20 when the wetland area fell below 100 km². Wind rose analysis indicated that prevailing northeast and northwest winds, combined with dry conditions in northern parts of Shadegan, significantly contribute to dust formation. Furthermore, the study established a clear inverse relationship between wetland area reduction and the number of polluted days for both PM10 and PM2.5. For each 1 km² decrease in wetland area, PM10 polluted days increased by 0.03, explaining 56% of the variation, while PM2.5 polluted days increased by 0.05, accounting for 64%. Principal Component Analysis (PCA) further demonstrated that wetland area changes explain 87.5% and 90.1% of the variance in PM10 and PM2.5 levels, respectively. These findings highlight the critical role of wetland preservation in mitigating dust pollution and improving air quality.

The most popular metric for drought monitoring is the standardized precipitation index (SPI). It does have some drawbacks, though, such as using gamma probability distribution function as the default distribution and not taking into account seasonal variations. The effectiveness of SPI in monitoring droughts in various Iranian climates was examined in this study, and in order to address its limitations, it was contrasted with the modified SPI (mod‐SPI). Then, the performance of the mod‐SPI and SPI in drought monitoring was compared with nine meteorological stations throughout Iran during the period 1956–2010. The results showed that the gamma distribution function was not selected in any of the 12 timescales (TS) considered at the studied meteorological stations. Based on the Cohen's kappa index analysis, there is a clear difference between the SPI and mod‐SPI in monitoring drought classes, which is more evident at low TS (less than 6 months), but at much higher TS (above TS‐9), the two indices almost coincide. The results showed that in the arid and semiarid areas, the SPI and mod‐SPI are significantly different, so it seems that the SPI monitors aridity rather than drought. The results imply that using mod‐SPI instead of SPI provides more accurate drought monitoring by eliminating the seasonal effects of precipitation data. Considering the low efficiency of SPI in short‐term drought monitoring (less than 9 months) in arid and semiarid regions, it is recommended to use mod‐SPI instead of SPI in most parts of Iran, especially on the southern coasts of Iran.