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 Air Quality Index is a numerical tool used to quantify air pollution levels and classify pollution severity. It plays a vital role in ensuring healthcare system stability by understanding air pollution's dynamic behavior and shifts in pollution intensity. To analyze the probabilistic transition between pollution severity levels, a Markov Chain model was utilized. This study examined six air pollution states (Clean, Healthy, Unhealthy for Sensitive Groups, Unhealthy, Very Unhealthy, Hazardous) across 12 stations in Tehran's northern, southern, eastern, western, and central regions from 2018 to 2022. Results revealed that the western and southern areas exhibited the highest pollution levels, with over 44% and 34% of instances corresponding to unhealthy indices, respectively. In contrast, northern Tehran consistently maintained cleaner air quality. Probability transition matrices highlighted the greatest stability continuity in healthy air quality across all regions. Transitioning between clean/healthy air to very unhealthy/hazardous air was minimal, with hazardous air quality almost absent in most stations, except for the west and south (25% stability). The probability of continued unhealthy air quality in these areas reached 60%, indicating heightened pollution. The findings of transition probability matrices indicated that the western and southern regions had the highest likelihood of sustained pollution, while the northern region consistently maintained cleaner air. The probability of continuous clean air in the west was below 30%, while transitioning from very unhealthy/hazardous air to unhealthy air reached 50%. Conversely, the northern Tehran region exhibited over 40% stability for unhealthy air quality and over 50% for clean and healthy air.

The study investigated the water quality, wetland area, and dust storm impacts on the Shadegan wetland in Iran from 2018-2023 using satellite data. The results highlight the severe challenges the wetland has faced in recent years. Key water quality indices, including water temperature, turbidity, and CDOM levels, indicate increasing pollution, particularly in 2022-2023. Water temperatures were highest in 2018 and 2021, peaking at 52°C, while turbidity and CDOM levels reached their maximum in 2022, affecting the northern, western, and southern parts of the wetland. The study also revealed a significant reduction in the wetland area, with considerable drying observed in the northern, eastern, and western sections during 2018, 2021, and 2022, with critical implications for the wetland's ecological health. Dust storm frequency and intensity have escalated, peaking in 2022-2023, correlating with periods of poor water quality and reduced wetland area. The highest dust pollution levels were recorded in 2018, 2021, 2022, and 2023, with over 214 dust days in the northern parts and over 146 dust days in the southern and southeastern parts of the wetland in 2022. Wind analysis and HYSPLIT show the pollution from the drying wetland affects a broad regional area, including Khuzestan province and the western, northwestern, and southwestern regions of Iran. The findings emphasize the urgent need for wetland conservation and restoration, as well as regional cooperation to rehabilitate the ecosystem and implement effective water resource management strategies.

Mazandaran province, due to its strategic geographical location and high influx of tourists during holidays, coupled with the presence of power plants and industrial complexes, is facing pollution challenges. This research, conducted in 2022, focuses on monitoring nitrogen dioxide and ozone pollutants in the province and investigating the role of tourists during certain multi-day holidays. Utilizing Sentinel 5 satellite imagery, including daily images from holidays and non-holidays, the study compensates for the lack of regular pollution data in Mazandaran's monitoring stations by validating the satellite data against Tehran stations. Linear regression equations determine the levels of nitrogen dioxide and ozone pollutants, leading to the creation of an Air Quality Index (AQI) map for Mazandaran province during holidays. The results highlight a strong correlation (0.3-0.7) between satellite and ground data, with ozone pollutants exhibiting a higher correlation. The skew error is consistently zero, and the mean square error varies between 1.9-6.2 ppb for nitrogen dioxide and 1.1-5.1 ppb for ozone. Pollution estimates for different points in Mazandaran province during holidays and non-holidays indicate higher ozone pollution compared to nitrogen dioxide. Ozone pollution is particularly unhealthy and very unhealthy during holiday periods, especially along the coastal areas, while non-holiday days show cleaner and healthier air quality. The pollution index map illustrates higher ozone pollution in coastal cities during holidays compared to other regions, emphasizing the impact of tourists. This research demonstrates the effectiveness of Sentinel 5 satellite in monitoring air pollution and underscores the significant influence of tourists and transportation on Mazandaran province's air quality. The findings advocate for sustainable tourism practices to mitigate the adverse effects on Mazandaran's air quality during holidays.

Accurate spatial estimation of temperature is very important in meteorological research. This study investigated the efficiency of temperature products of the Tropical Rainfall Measuring Mission (TRMM) satellite in estimating temperature in Mazandaran Province, and its accuracy were compared with inverse distance weighting and Co-Kriging interpolation methods. Finally, a new method was proposed to improve the accuracy of temperature estimation by combining the TRMM temperature products and terrains. Data recorded at 25 meteorological stations and 26 monthly and annual TRMM satellite images in 2012 and 2013 were used. The results showed a significant correlation between temperature data and satellite products, latitude, and altitude in significance level of 95%. Analyzing error indices showed that TRMM products have underestimation error that this bias error contributed to about 60% of error in these satellite images. Despite the larger error of TRMM products than interpolation methods, the regression analysis results demonstrated the superiority of satellite temperature products over interpolation methods. Furthermore, higher correlation of observed and estimated data showing that satellite products give a better understanding of cold and hot points of the study area despite its underestimation error. Combining satellite temperature products with influential covariates of altitude and latitude in the regression equation reduced the temperature estimation error of the TRMM products by 80%. The estimation precision increased over 70% compared to other temperature interpolation methods. Analyzing isotherm maps indicate the higher temperature of eastern coasts than western coasts. Moreover, evaluating different temperature estimation methods showed the higher precision of the methods that involved covariates than other methods.