A 25-Year Assessment of Aerosol Dynamics and Environmental Drivers in Iran’s Lakes and Wetlands

A 25-Year Assessment of Aerosol Dynamics and Environmental Drivers in Iran’s Lakes and Wetlands | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A 25-Year Assessment of Aerosol Dynamics and Environmental Drivers in Iran’s Lakes and Wetlands Zohre Ebrahimi-Khusfi, Seyed Arman Samadi-Todar, Narjes Okati, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6778141/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract High levels of aerosols in aquatic systems are realized as indicators and agents of environmental degradation. It is imperative that the mechanisms of aerosol contamination in such sensitive habitats be understood for efficient water resource management and conservation of the ecosystem. This paper fills the gap by examining the spatiotemporal features of aerosol optical depth (AOD) over 27 wetlands and lakes in Iran for a 25-year (2000–2024) period. Monthly AOD values were combined with climatic and environmental variables, including wind speed, rain, evaporation, Palmer Drought Severity Index, enhanced vegetation index, normalized difference water index, soil salinity index, and water body coverage. Trend analysis was conducted using the Mann-Kendall test and Sen's slope estimator. The results demonstrated that aerosol concentrations increased by 51.9% over Iran's water bodies in winter, 55.6% in spring, 74.1% in summer, and 66.7% in autumn. On an annual scale, 55.6% experienced an increasing trend, with a significant increase in AODs over Parishan, Miankaleh, Sheedvar, and Gomishan wetlands, as well as Lake Urmia (Z > 1.96). The primary causes of aerosol pollution were identified through machine learning models as changes in: evaporation and rainfall in Parishan; water level and salinity in Gomishan; salinity and rainfall in Miankaleh; vegetation cover and decreased water level in Sheedvar. Based on the total Gini reduction, climatic factors contributed more significantly to air quality degradation in Parishan, Miankaleh, and Sheedvar wetlands (averaging 58%) compared to land-based drivers. Conversely, land-based factors were the primary contributors to air quality decline over Gomishan and Lake Urmia (averaging 68%). These findings are especially beneficial for comprehending the synergy between natural and anthropogenic drivers governing air quality over aquatic ecosystems. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Natural hazards Aerosol pollution Wetland degradation Remote sensing Climatic elements Human activities Machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Wetland ecosystems consist of estuaries, lakes, floodplains, marshes, rivers, fens, peatlands, mangroves, and coral reefs 1 , and are also referred to as transitional lands between aquatic and terrestrial ecosystems 2 . Wetlands provide 40% of the world's ecosystem services, but cover only around 6% of the Earth's surface 3 . Due to the well-known ecosystem services of wetlands, including greenhouse gas reduction, water purification, carbon sequestration, shoreline protection, regulation of the hydrological cycle, flood mitigation, drought mitigation, and protection of habitats from nutrient erosion, they are referred to as the kidneys of nature 4 . Wetlands are equally important in the mitigation of dust storms in arid and semi-arid environments, through trapping of dust particles and increasing the soil moisture and cover of the surrounding regions 5 , 6 . n the Ramsar Convention's foundation, one of the first modern multilateral environmental conventions to promote worldwide cooperation and effective wetlands management for wetland conservation, 27 Iranian wetlands covering around 1.5 million hectares are of worldwide importance ( https://rsis.ramsar.org/ ). The total average adjusted ecosystem service value per hectare of Iran's inland wetland ecosystems and coastal mangrove wetland ecosystems is estimated to be 67,665 and 42,171 USD, respectively 7 . Currently, due to climate change and human intervention, wetlands in most parts of the world are at high risk of destruction or conversion to different uses 8 . Among the threatening factors that affect the life of wetlands are population growth and urban development, industrialization, agriculture, waste incineration, global warming, drought, and dam construction 9 – 11 . Iran is one of the most susceptible countries in the world in terms of climate change and its consequences (Zittis et al ., 2022; Neira et al ., 2022). This is due to the reason that approximately 76.4% and 19.6% of its land surface lie in arid and semi-arid regions, respectively 2 , 12 . On the other hand, wetlands in arid regions have become a source of dust due to their dry beds and increased susceptibility to wind erosion 13 . Most wetlands of arid regions of the world, including Iran, are currently transitioning from a wet to a completely dry condition under climatic and anthropogenic conditions such as increasing temperature, decreasing rain, and prolonged dryness 14 – 17 . These changes cause wetland soil to be more exposed to direct sunlight and wind, thus getting dry and susceptible to dust emissions. Organic matter also decomposes faster, and the dispersion of their particles increases, making the soil susceptible to erosion (Gholami et al ., 2024b). During strong winds and gales, these areas become active sources of dust storms, since alluvial silt and fine dust particles are easily lifted into the atmosphere and transported at long distances 18 . Drought and water shortage in Iran during the recent years have resulted in devastating effects on various ecosystems, including wetlands, and have caused serious environmental problems such as dust storms 19 . Despite many efforts to restore and protect wetlands, all of Iran's inland wetlands are in the final stages of their life and are drying up rapidly during the last decades 20 . Atmospheric aerosols and dust particles directly and indirectly affect global climate, through absorbing and scattering of solar and terrestrial radiations 21 . Furthermore, aerosols carrying toxic elements may become important atmospheric pollutants through physical and chemical interactions in the presence of sunlight and negatively affect public health 22 , as well as economic activities (Malamiri et al., 2025). For this reason, the World Health Organization (WHO) set strict limits on the concentration of particulate matter (PM) in atmospheric air. Aerosols are widely used as an uncertain but important indicator in research on climate change and atmospheric radiative balance 23 . Aerosol optical depth (AOD) is one of the most important optical parameters that considers the amount of aerosols and the level of local air pollution to some extent. Therefore, AOD has a great impact on regional and even global climate, atmospheric radiation budget, and also atmospheric circulation 24 . Today, Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily satellite AOD dataset with high spatial resolution 22 , 25 . Wang, et al. 23 studied the spatiotemporal characteristics of dust content in Central Asia. Their findings indicated an increasing trend of dust in Kazakhstan, Uzbekistan, and Turkmenistan. In this regard, Dadashi-Roudbari and Ahmadi 26 showed that the southern parts of Iran are among the aerosol hotspots in Southwest Asia and have witnessed an increase in MODIS AOD values in recent years. Sharma, et al. 27 explored the effect of land use land cover (LULC) on AOD across India and identified that LULC characteristics are a major determining factor on aerosol concentration in the air, while similar findings were obtained over Iran through exploration of the LULC change versus aerosols and air pollution policy (Yousefi et al., 2025). Solanki and Pathak 28 investigated spatial and temporal variability of AOD over major Indian urban agglomerations from satellite data. In addition, sadat Afzalizadeh, et al. 14 investigated the relationship between MODIS AOD and land surface properties in a dried watershed in Iran as a source of dust dispersion. In recent decades, Machine Learning (ML) algorithms have gained significant popularity for predicting AOD and PM 2.5 in various regions of the world, including west Asia 29 , Middle East and North Africa Regions 30 , and Iran 31 . AOD variations are influenced by numerous atmospheric, topographic and meteorological factors, which, especially over water bodies, may have complex relationships with AOD. Therefore, the use of ML algorithms is a valuable approach to understanding the factors affecting AOD changes. Given that this powerful tool has not yet been used to identify the factors affecting AOD changes over wetlands and inland lakes in Iran, this study uses the Random Forest (RF) algorithm and the Mean Decrease Gini (MDG) criterion for this purpose. Although several studies have analyzed the trend of AOD in various regions of Iran (Rashki et al., 2014; Shaheen et al., 2023; Yousefi et al., 2023), a comprehensive study focusing on the trend analysis of AOD over Iran's inland wetlands and lakes, and the identification of effective factors using RF algorithms, has not yet been performed. Therefore, the present study seeks to fill these research gaps, aiming to analyze the trend of temporal changes in AOD over Iran's inland wetlands and lakes using the Mann-Kendall test and Sen's slope estimator. Another important goal is to identify the main driving factors influencing AOD over the water bodies that have experienced the greatest decline and adverse effects on air quality during the past 25 years (2000–2024). Present findings may assist managers and decision-makers in implementing effective measures to reduce aerosol pollution and improve air quality in residential areas surrounding these valuable ecosystems. 2. Study area Iran is a country with a population of about 87 million and an area of 1.648 million km 2 located in Southwest Asia and the Middle East. Most of its land is located in the arid region of the world 12 . There are 84 important wetlands in Iran, covering a total area of over 20 million hectares 32 , of which 32%, with an area of about 1.5 million hectares, are registered in the Ramsar Convention ( https://rsis.ramsar.org/ ). To investigate the objectives of the present study, 20 inland lakes and wetlands of international importance and 7 other significant water bodies were selected. The geographical distribution of the studied wetlands, which are mostly spread in the northern and western regions of Iran, is shown in Fig. 1 . Based on the Shuttle Radar Topography Mission (SRTM) imagery, the elevation above sea level in the studied wetlands ranges from − 31 m in Anzali Wetland to 2271 m asl in Choghakhor Wetland. The long-term average rainfall over the past 25 years (2000–2024) has varied from approximately 80 mm in the Hamoun Wetlands located in southeastern Iran to over 1200 mm in the Bujagh and Anzali Wetlands in the north. During this period, the variation of the land surface temperature (LST) of the wetlands and lakes was from about 15°C in Lake Urmia in northwestern Iran to over 40°C in the Hamun Wetland, Delta-Rud-e-Shur-Shirin, and Khuran Wetland in the southern half of Iran (Table 1 ). Table 1 Climatic and topographical characteristics of the studied wetlands and lakes in Iran. Lake and Wetland (LW) Elevation(m) Precipitation(mm) LST(°C) Aghgol 2036 374.7 23.9 Alagol, Ulmagol and Ajigol Lakes -6 243.6 24.7 Amirkelayeh Lake -28 1052.1 20.8 Anzali Wetland -31 1222.6 19.8 Bakhtegan 1561 160.3 29.2 Bujagh National Park -27 1240.2 22.4 Choghakhor Wetland 2271 449.3 18.3 Deltas of Rud-e-Gaz and Rud-e-Hara 341 128.8 42.8 Deltas of Rud-e-Shur, Rud-e-Shirin and Rud-e-Minab 5 137.0 22.2 Fereydoon Kenar, Ezbaran & Sorkh Ruds Ab-Bandans -25 946.9 22.2 Gavkhouni 1450 116.2 28.6 Gomishan Lagoon -29 274.8 21.6 Hamun 481 79.0 35.9 Hoorolazim 7 182.2 26.3 Jazmourian 368 121.5 42.6 Kanibarazan Wetland 1271 374.3 22.2 Khuran Straits 10 119.4 40.7 Lake Urmia 1286 277.9 15.4 Maharloo 1461 282.6 22.6 Mighan 1657 288.8 27.0 Miankaleh Peninsula, Gorgan Bay -29 574.4 18.3 Parishan 822 267.9 34.8 Shadegan 7 143.4 34.9 Sheedvar Island 9 117.1 36.2 Shurgol, Yadegarlu & Dorgeh Sangi Lakes 1289 395.4 23.2 Tashk 1559 158.6 23.8 Zarivar 1286 687.9 18.8 3. Material and methods Figure 2 shows the key steps followed in the present research methodology, which are as follows: Daily Aerosol Optical Depth (AOD) data acquisition. Trend analysis of AOD variations at monthly, seasonal, and annual scales over the LWs in Iran. Identification of the key aerosol driving factors and their relationship with AOD in the LWs with rising trends. Detailed description of each methodology step is provided below. 3.1 Data acquisition 3.1.1 Target factor (AOD data sets) In the present study, AOD has been considered as the target variable. The MCD19A2 product, provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) at a daily temporal scale and with a spatial resolution of one kilometer, was used to extract AOD over 27 studied lakes and wetlands (LWs). This product, which extracts the concentration of atmospheric aerosols in two (blue and green) bands (0.47 and 0.55 µm) based on the Multi-Angle Implementation of Atmospheric Correction (MAIAC), was downloaded for 27 Iranian LWs from 2000 to 2024 through the Google Earth Engine system ( https://earthengine.google.com ). In Iran, there is one Aerosol Robotic Network (AERONET) station established in the northwest of Iran (IASBS: 48.5°E; 36.7°N) and several stations (Kuwait University, Kandahar, and Solar Village) around Iran with continuous dataset, which were used for comparison with MODIS-AODs. Given that the data used are at a wavelength of 550 nm and the corresponding values are not recorded in the AERONET sits, the following equation was used to calculate the AOD AERONET 550 nm 33 , 34 : $$\:Log\left(AOD550\right)={a}_{1}+{a}_{2}\text{log}\left(550\right)+{a}_{3}{\text{l}\text{o}\text{g}\left(550\right)}^{2}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ Here, \(\:{a}_{1}\) , \(\:{a}_{2}\) , and \(\:{a}_{3}\) indicate the fitting coefficients, are the fitting coefficients that are calculated based on ground-based observations of aerosol optical depth at various wavelengths (440, 500, 675,875). Mean absolute error (MAE), root mean square error (RMSE), and root mean bias (RMB) were used to evaluate the performance of MODIS data in this study, as has been done in many previous studies 35 . The comparison referred to collocated data over the mentioned stations, during the MODIS overpass time (± 30 min), revealing a strong correlation between satellite and ground-based AOD datasets (r = 57 to 0.70; p-value < 0.05) (Fig. 3 ). Therefore, MODIS observations with high spatial resolution are consistent with AERONET measurements, and therefore, daily MCD19A2 dataset were used to analyze the trend of changes in AOD over the LWs mentioned in Table 1 . 3.1.2 Aerosol Optical Depth Driving Factors Several climatic, meteorological and terrestrial factors may affect the variations in aerosol concentration and their optical properties (Yousefi et al ., 2025). Based on research background, literature overview and data availability, the following 8 driving factors were considered here to identify the most significant factors affecting AOD changes over Iranian wetlands and lakes. These factors include precipitation (Pre), actual evaporation (ET), normalized difference water index (NDWI), normalized difference salinity index (NDSI), enhanced vegetation index (EVI), precipitation, Palmer Drought Severity Index (PDSI), lakes-wetlands area (LWA), and wind speed (WS). The Climatology Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily precipitation product, which exhibits a high correlation with ground station data 36 , 37 , was used in this study to extract precipitation for the selected wetlands and lakes. The spatial resolution of this product is 5566 meters. The MOD13Q1, with spatial resolutions of 250 m, was used to extract EVI values for selected wetlands and lakes during the study period. ET and WS values were extracted over Iran from the TerraClimate dataset with a spatial resolution of 4638 m 38 . The normalized difference of salinity index (NDSI) and the normalized difference of water index (NDWI) were calculated using Eq. (2) 39 , and Eq. (3) 40 , respectively, based on MODIS sensor imagery with a spatial resolution of 500 m. $$\:NDSI=\frac{Red-NIR}{Red+NIR}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:(Eq.2)$$ $$\:NDWI=\frac{Green-NIR}{Green+NIR}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:(Eq.3)$$ Here, NIR, Red, Green, and MIR are near-infrared, red, green, and middle infrared bands, respectively. Furthermore, water bodies were identified based on positive NDWI values, and their covered areas were calculated on a monthly scale for each wetland. It is noteworthy that all required datasets were acquired through coding within the Google Earth Engine platform for a monthly scale from 2000 to 2024. After analyzing the trends of AOD variations and identifying wetlands with significant increasing trends, other acquired data were used to identify the most important controlling factors of AOD, which will be further explained in detail in the subsequent research methodology section. 3.2 Trend analysis Various statistical methods have been proposed for time series analysis. Among these, non-parametric methods are widely used in time series of qualitative meteorological and hydrological variables. These methods are suitable for time series that exhibit skewness or kurtosis and are independent of the statistical distribution of the time series. The purpose of trend testing is to investigate the presence or absence of an upward or downward trend in the data series 41 . The Mann-Kendall test is one of the most widely used tests in the non-parametric method, presented by Mann 42 and developed by Kendall 43 . In this study, the test was used to analyze the trend of AOD changes at different time scales over the examined LWs in Iran. This test is based on two hypotheses, null and alternative. The null hypothesis states that the data series is random and has no trend, while the alternative hypothesis indicates the presence of a trend. The S statistic of the Mann-Kendall test represents the difference between each observation and all subsequent observations, calculated based on Eq. (4): $$\:S=\sum\:_{\text{k}=1}^{\text{n}-1}\sum\:_{\text{j}=\text{k}+1}^{\text{n}}\text{s}\text{g}\text{n}({\text{x}}_{\text{j}}-{\text{x}}_{\text{k}})\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:(Eq.4)\:\:\:$$ In this equation, n is the number of observations in the time series, and xj and xk are the j-th and k-th data points of the series, respectively. Then, the variance of S is calculated and the standardized Z statistics are calculated using following equations: $$\:\text{V}\text{A}\text{R}\left(\text{S}\right)=\frac{1}{18}\left[\text{n}\left(\text{n}-1\right)\left(2\text{n}+5\right)\right]\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:(Eq.5)$$ $$\:Z=\left\{\begin{array}{c}\begin{array}{cc}\frac{\text{S}-1}{\sqrt{\text{V}\text{A}\text{R}\left(\text{S}\right)}}&\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\text{i}\text{f}\:\text{S}>0\end{array}\\\:\begin{array}{cc}0&\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\text{i}\text{f}\:\text{S}=0\end{array}\\\:\begin{array}{cc}\frac{\text{S}+1}{\sqrt{\text{V}\text{A}\text{R}\left(\text{S}\right)}}&\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\text{i}\text{f}\text{S}<0\end{array}\end{array}\right.\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:(Eq.6)$$ Where S is the Mann-Kendall test statistic, xi and xj are yearly values in years i and j (j > i), n is the length of data series, and sgn is sign (+ or -) of (xj - xi). Also, Z is the standard test statistic. Z statistic positive values show increasing trend and Z negative values show decreasing trend in time series. The null hypothesis of no trend is rejected if |Z| >Zα, α. Where, |Z| and α are the absolute value of Mann-Kendall coefficient and level of statistical significance, respectively. To estimate the trend slope in a time series, the Sen's Slope estimator is one of the most suitable methods. This method was first introduced by Theil 44 and then expanded by Sen 45 . Like many other non-parametric methods such as Mann-Kendall, this method is based on analyzing the difference between observations in the time series. This method can be used when the trend in the time series is a linear trend. This means that Ft is equal to: $$\:\text{f}\left(\text{t}\right)=\text{Q}\text{t}+\text{B}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:(Eq.7)$$ Where Q is the slope of the trend line and B is the constant value (intercept). 3.3 Data standardization Standardization is a preprocessing step that should be performed before collinearity analysis and modeling, especially when the data is multidimensional. This method improves the performance of ML models by removing the effect of variable scales 46 . In this study, all variables were standardized using the Eq. (8) as follows: $$\:{Z}_{i}=\frac{{X}_{i-}\mu\:}{\sigma\:}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:(Eq.8)$$ In the formula above, Z i is the standard score for data X i , µ is the mean and σ is the standard deviation of the data. By doing this, the Z i 's will have a mean of 0 and a variance of 1. 3.4 Multicollinearity Analysis Collinearity refers to a situation where an explanatory variable in multiple regression has a linear relationship with one or more other variables, such that it can be considered a linear combination of the other variables 47 . Similarly, multicollinearity indicates a situation where there is a linear relationship between several explanatory variables, and they can be written as a linear combination of each other 48 . When collinearity or multicollinearity exists in a multiple regression model, the resulting model coefficients are not valid, as the effect of each explanatory variable on the response variable includes the effect of other variables in the model as well. Therefore, the variance of the regression coefficient estimators increases, and in practice, prediction by the regression model will be associated with a large bias. Thus, with a small change in the data used in the model, the regression coefficients will change drastically. The Variance Inflation Factor (VIF) serves as a metric indicating the degree to which prediction coefficient variance is inflated, and is calculated via the Eq. (9) 49 . \(\:VIF=\frac{1}{TC}=\frac{1}{\left(1-{R}_{i}^{2}\right)}\) (Eq. 9) Here, Ri 2 denotes the unadjusted coefficient of determination for the ith independent variable regressed against the remaining variables. The tolerance coefficient (TC) is the inverse of the VIF, with a low TC (TC < 0.2) indicating a strong correlation between independent variables. In other words, TC values greater than 0.2 indicate a low effect of multicollinearity between the independent variables under consideration. One approach to address multicollinearity is to eliminate a variable strongly linked to other variable(s), a method employed in this study. 3.5 Identification of the main aerosol driving factors After excluding the problematic variables (TC < 0.2), the factors with the least collinearity effect were selected and standardized. The data related to the standardized AOD values, climatic parameters and land characteristics for selected wetland and lakes, were loaded separately in the R 4.4.1 software environment. For determining the importance of each factor affecting the AOD changes in the studied wetlands, the RF algorithm was used. Reduction of overfitting, identification of the importance of influential variables on the target variable, high accuracy, high flexibility and resistance to outliers are among the most important advantages of this algorithm 50 , 51 . Therefore, RF model was used to explore the relationship between AOD and their driving factors over selected wetlands and lakes with a significant rising trend in AOD during 2000–2024. The Mean Decrease Gini (MDG) algorithm was lastly used to determine the contribution of the influential factors, since it’s one of the common methods to evaluate the importance of features 52 . This algorithm shows how effective a particular feature is in reducing MDG at each node of the decision tree. In general, the larger decrease of MDG indicates the greater importance of that factor on changes in the target variable 53 . 3.6 Interpreting the Relationship Between Key Driving Factors and AOD While identifying and prioritizing aerosol driving factors over water bodies is of great importance, uncovering the nature of their relationship enhances our understanding of how these factors influence the increased AODs over water bodies. To this end, the Pearson correlation coefficient 54 (Eq. 10), a simple and common technique for this purpose, was employed, following previous studies 55 . \(\:r=\frac{n{\sum\:}_{i}^{n}{Aod}_{i}{*Df}_{i}-{\sum\:}_{i}^{n}{Aod}_{i}{\sum\:}_{i}^{n}{Df}_{i}}{\sqrt{n{\sum\:}_{i}^{n}{Aod}_{i}^{2}-({\sum\:}^{n}{Aod}_{i}{)}^{2}}\sqrt{n{\sum\:}_{i}^{n}{Df}_{i}^{2}-({\sum\:}^{n}{Df}_{i}{)}^{2}}}\) (Eq. 10) where n is the total number of variables in a given dataset. The AOD i and Df i are AOD and driving factors ​​of AOD, respectively in the ith month. 4. Results 4.1 Monthly variation trend of AOD The results of the Mann-Kendall test performed on the monthly average AOD values over 27 Iranian wetlands are summarized in Fig. 4 . Based on the results presented in this statistical matrix and Table 2 , regardless of significance level, the trend of changes in AOD over Iranian water bodies presented the largest increase in the months of March, May, August, October and November, with increases of 74.1%, 77.8%, 88.9%.88%, 85.2%, and 74.1%, respectively during the period 2000–2024. Furthermore, the majority of the lakes and wetlands exhibited increasing trends in AOD in January (59.3%), June (63%), July (66.7%), September (59.3%) and December (55.6%), while in February and April the fraction of Iranian’s LWs that exhibited increasing AOD trends dropped to 48.1% and 37%, respectively, indicating that in these two months, most of the studied LWs faced a decreasing trend in atmospheric aerosols. Throughout the study period, the most significant increasing trends in AOD occurred in Lake Urmia and in Gomishan and Sheedvar wetlands. In Lake Urmia, statistically significant increasing trends were observed in all months (Z > 1.96), indicating a large increase in AOD and dust activity during the last two decades, associated with the desiccation of the lake (Alizadeh et al ., 2020; Harati et al ., 2021; Abadi et al ., 2022; Hamzeh et al ., 2023; Ghasempour et al ., 2024). In the Gomishan wetland, significant increasing changes in AOD were in the months of March and May through November. The Sheedvar wetland experienced a significant increase in aerosol concentrations in January and February, as well as from August to November. Furthermore, a significant increasing trend of AOD occurred in the Tashk, Parishan, Amirkelayeh, Miankaleh, and Shurgol -Yadegarlu-Dorgeh wetlands for 3 to 4 months of the year (mostly in summer), and in the Hoor al-Azim, Delta-Rud-e-Shur-Shirin, Fereydoon Kenar, Hamun, and Jazmurian wetlands for 1 to 2 months. Conversely, the results showed significant decreasing trends (confidence level 0.05) in aerosol loading over the Gavkhouni wetland in February (Z= -2.5), March (Z=-3.5) and November (Z=-1.99). Statistically significant decreasing trends also occurred in Bujagh, Delta-Rud-e-Shur-Shirin, and Hamun wetlands in the months of January (Z=-2.2), February (Z= -2.5), and September (Z=-3.01), respectively. In synopsis, despite the large variability of the AOD trend values over the studied wetlands, attributed to different land use characteristics, contrasting meteorological patterns, prevailing wind regimes, etc, most of the wetlands exhibited the largest increases of AOD during the period with enhanced dust activity over Iran and the Middle East (May to September). This indicates that the increasing AODs are associated with enhanced dust emissions over the wetlands, attributed to their desiccation and the transformance of significant dust sources due to alluvial silt material left in the topsoil after their part or complete dryness (Rashki et al ., 2013; Behrooz et al ., 2016; Kharazmi et al ., 2018; Ebrahimi-Khusfi et al ., 2010, 2021; Khashi et al ., 2022). The main finding is a general increasing tendency in AOD over the Iranian lakes and wetlands, which is attributed to enhanced dust concentrations after the lake’s desiccation due to climate change and human intervention (increasing needs for irrigation, shrinkage of the water bodies, etc). Table 2 Percentage of lakes and wetlands with increasing and decreasing trends in AOD on monthly basis. Month Positive trend (%) Negative trend (%) Jan 59.3 40.7 Feb 48.1 51.9 Mar 74.1 25.9 Apr 37.0 63.0 May 77.8 22.2 Jun 63.0 37.0 Jul 66.7 33.3 Aug 88.9 11.1 Sep 66.7 33.3 Oct 85.2 14.8 Nov 74.1 25.9 Dec 59.3 40.7 4.2 Seasonal variation and trend of AOD in Iranian lakes and wetlands The seasonal trends in AOD over Iranian lakes and wetlands are shown in Fig. 5 . The results show that during the past 25 winter seasons, the AOD over 14 water bodies (51.9%) exhibited an increasing trend (Z > 0), which was found to be statistically significant over Parishan, Gomishan, and Sheedvar wetlands, and Lake Urmia, as well (Z > 1.96). In other water bodies, the trend of AOD changes presented a decreasing tendency in winter, among which, only in Gavkhouni (Z = -3.4) and Bujagh (Z = -2.1) wetlands, it was statistically significant (Fig. 5 ; Table 3 ). In spring, the trend of AOD changes has been increasing in 15 water bodies (55.6%) and decreasing in 12 water bodies (44.4%). In this season, similar to winter, the decreasing trend of AOD was insignificant in all areas (Z >-1.96). Conversely, statistically significant increasing trends of aerosol concentration were observed over Lake Urmia, Miankaleh, and Gomishan wetlands (Fig. 5 ; Table 3 ). In summer, when the dust activity over Iran and the Middle East maximizes (Rashki et al ., 2014; Abadi et al ., 2025), the concentrations of aerosols showed an increasing trend over 20 water bodies (74.1%), which, like the spring season, was significant in Parishan (Z = 2.3), Gomishan (Z = 4.3), Sheedvar (Z = 1.99), and Lake Urmia (Z = 4.09), while it was insignificant in the other water bodies. Nevertheless, the summer dusty season exhibits the highest fraction of increased AOD over the examined wetlands, implying larger desiccation rates of the wetlands and enhanced dust emissions. This increase in AOD, which practically corresponds to increased dust activity in areas surrounding wetlands and lakes in Iran, is especially important for regional atmospheric composition, degradation of air quality and negative effects in the aquatic ecosystems (Hamzeh et al ., 2023; Ahrari et al ., 2024; Zadifar et al ., 2024). In the 7 wetlands where declining AOD trends were observed, these trends were not significant (Fig. 5 ; Table 3 ). Out of the 18 water bodies (66.7%), where an increase in aerosol concentration occurred in autumn, 7 bodies—Shurgol-Yadegarlu-Dorgeh (Z = 2.31), Sheedvar, Gomishan (Z = 2.36), Lake Urmia (Z = 4.69), Amirkelayeh (Z = 3.06), Jazmourian (Z = 2.3), and Parishan (Z = 3.4)—have experienced a statistically significant increase. This suggests positive feedback between shrinkage of water bodies and dust-aerosol emissions that affect the nearby areas. Over other wetlands, aerosol changes have been increasing or decreasing, but without significant trends (Fig. 5 ; Table 3 ). Table 3 Percentage of the lakes and wetlands presenting increasing and decreasing trends in AOD on seasonal and annual basis. Time scale Positive trend (%) Negative trend (%) Winter 51.9 48.1 Spring 55.6 44.4 Summer 74.1 25.9 Autumn 66.7 33.3 Annual 55.6 44.4 4.3 Mutli-decadal Trends of AOD over Iranian Lakes and Wetlands The annual-averaged AODs and their trends over the 27 studied lakes and wetlands are shown in Fig. 6 , while the AOD values during the study period are displayed using box plots in Fig. 7 . Based on the current analysis, during the past 25 years, high median AOD values (exceeding 0.5) were observed in Gavkhouni, Tashk, Gomishan, Maharloo, Bakhtegan, and Hamun wetlands. The first quartiles of the AOD values for these wetlands are 0.95, 0.37, 0.42, 0.35, 0.36, and 0.47, respectively, while the third ones are 1.34, 0.73, 0.69, 0.66, 0.59, and 0.54, respectively. The first quartile, median, and third quartile AOD values for Lake Urmia, Delta-Rud-e-Shur-Shirin, Delta-Rud-e-Gaz, Miankaleh, Jazmourian, Shadegan, Sheedvar, Khuran, Hourolazim, Alagol, and Parishan range from 0.19 to 0.27, 0.3 to 0.41, and 0.36 to 0.58, respectively, indicating presence of high aerosol loading over the studied lakes. The Mann-Kendall statistic and Sen's slope estimator were respectively used to investigate the trend and slope of changes in the annual AOD time series, while the outcomes are shown in Fig. 8 . The results indicated that over the past two decades, AOD has increased over 15 (55.6%) of the studied wetlands, while decreased in 12 (44.4%). The increasing trends in Lake Urmia (Z = 3.9) and the Parishan (Z = 2.08), Gomishan (Z = 4.1), Miankaleh (Z = 2.7), and Sheedvar (Z = 2.9) wetlands were significantly higher than those in other water bodies and were statistically significant at 95% confidence level. Based on Sen's slope, the average annual changes in AOD over Lake Urmia and the mentioned wetlands were 0.024, 0.004, 0.017, 0.003, and 0.002, respectively. Conversely, the decreasing changes in Shadegan (Z = -2.03), Hamoun (Z = -2.2), and Khuran (Z = -2.03) wetlands were more pronounced than those in other wetlands and were significant at the 95% level. Based on Sen's slope, the average AOD decreased by 0.002 in Shadegan and Khuran and by approximately 0.005 per year in Hamoun wetland. The contrasting AOD trends over the various wetlands and lakes in Iran are attributed to different topographic and climatic characteristics that prevailed during the 25 years study period. Recently, Abadi et al . (2025) indicated very different trends in suspended and blowing dust events across the various regions in Iran, that control the AOD values over the wetlands. Especially for Hamun (Sistan Basin, east Iran), the large decreasing trend in AOD during the last decades has been attributed to the extreme drought and abnormal dust activity during 2000–2003 (at the beginning of the studied period) (Rashki et al. , 2014; Shaheen et al ., 2023; Yousefi et al ., 2023). After this period, high but mostly normal for this site dusty conditions prevailed, thus contributed to a declining dust-aerosol trend during 2000–2025. Conversely, over SW and western part of Iran, including Urmia Lake, the dust activity has increased significantly during the 2000s due to drought regime shift in the Mesopotamian plains that enhanced dust activity over Iran and the Middle East (Notaro et al., 2015; Hamzeh et al., 2021). More specifically, the large increase in AOD over Urmia, is associated not only with local factors (i.e. desiccation of the lake, construction of dams, etc), but also with an increase in transported dust events over the region from distant dust sources in Iraq or SW Iran (Abadi et al ., 2022; Hamzeh et al ., 2023). Therefore, apart from local topographic conditions, LULC changes and climatic parameters related with wetlands and dust emissions from the dried lakebeds, changes in synoptic meteorology may also play an important role in this trend analysis over the specific wetlands. 4.4 Identification of the Main Aerosol Driving Factors across Iran’s Lakes and Wetlands The results of the multicollinearity analysis between the factors influencing AOD showed that the tolerance coefficient (TC) values for NDWI in Parishan and Sheedvar Wetlands, and for WLA and NDWI in Gomishan Wetland, were less than 0.2 (Table 4 ), indicating problematic multicollinearity among these driving factors. After removing NDWI, the TC values of the other parameters increased to above 0.2. Furthermore, the results of the multicollinearity analysis between the factors influencing AOD in Miankaleh Wetland and Lake Urmia showed that there was no problematic multicollinearity effect among these factors. Therefore, the factors shown in Fig. 9 were ultimately used for modeling. The monthly-mean values of the selected factors for a 25-year study period are shown in the Supplementary Material (Figure S1 ). Table 4 Tolerance coefficient values for driving factors of AOD change in selected wetlands and lakes. Driving Factors Parishan Gomishan Miankaled Sheedvar Urmia lake Evaporation (ET) 0.219 0.336 0.501 0.278 0.246 Enhanced Vegetation Ιndex (EVI) 0.051 0.679 0.707 0.324 0.579 Normalized difference salinity index (NDSI) 0.185 0.337 0.324 0.255 0.463 Precipitation (Pre) 0.222 0.283 0.431 0.285 0.240 Palmer Drought Severe Index (PDSI) 0.592 0.602 0.729 0.686 0.887 Lake and wetland area (LWA) 0.193 0.096 0.291 0.113 0.212 Normalized difference water index (NDWI) 0.073 0.098 0.288 0.107 0.204 Wind speed (WS) 0.645 0.485 0.483 0.586 0.586 The importance of driving factors affecting the monthly changes in AOD on the wetlands of Parishan, Gomishan, Miankaleh, Sheedvar, and Lake Urmia is shown in Fig. 10 (a-e) . The results showed that over the past 25 years, ET (Mean Decrease Gini (MDG): 22%) has been the most important driver affecting the changes in aerosol concentration over the Parishan wetland. Following that, Pre (MDG of 19.8%), NDSI (16.5%), and WS (MDG = 15.4%) had a significant effect on the AOD variations, while EVI, PDSI, and LWA were identified as the less important drivers (MDG < 10.5%) (Fig. 10 a). The results of the MDG analysis regarding the driving factors affecting the increasing concentration of aerosols over the Gomishan International Wetland showed that the variable LWA (MDG = 29.4%) was the most significant factor affecting AOD changes. Following this, NDSI with an MDG of 25.3% ranked second. These two drivers alone account for more than 50% of the changes in aerosol concentration, indicating their key role in the increased emissions of aerosols over Gomishan wetland. Precipitation, with an MDG of 16.7%, ranked third and plays a significant role in AOD changes in this region. In contrast, the variables WS, ET, EVI, and PDSI, with MDGs of 10.6%, 9.2%, 4.4%, and 4.4%, respectively, exhibited lesser impact on the target variable (Fig. 10 b). Analysis of the results from the MDG reduction shows that the NDSI has been the strongest effective driver of increased aerosol concentrations over the Miankaleh Wetland during the past 25 years. This highlights the particular importance of soil salinity in increasing aerosol emissions in the study area. Following this, precipitation (MDG: 19.9%) has played a significant role in AOD variations. These two climatic and terrestrial drivers presented the largest contribution to the increased aerosol emissions over the Miankaleh International Wetland, demonstrating their significant impact on air pollution in this region. Wind speed (MDG: 16.7%) ranked third in importance and is recognized as an effective factor in increasing aerosol concentration. Other variables, including ET, NDWI, LWA, EVI, and PDSI, with MDG values below 12%, have played minor roles in increasing AOD over this region (Fig. 10 c). The analysis of the significance of the driving forces affecting the increase in aerosol concentration over the Sheedvar Wetland revealed that the EVI, with an average Gini reduction of 16%, had the highest impact among the main driving forces in the AOD increase over this region. Subsequently, the driving factors LWA with 14.5%, NDSI with 14.3%, and ET with 14.1% are ranked next. These three variables also played a significant role in increasing aerosol concentrations and exhibited a relatively small difference in their impact. The driving factors WS, Pre, and PDSI also presented notable importance, with 13.9%, 13.6%, and 13.5%, respectively. Although the impact of these factors is less than that of the higher-ranked factors, they still play a significant role in increasing aerosol concentration (Fig. 10 d). The findings of this research also revealed that the contribution of climatic and terrestrial factors to the increase in aerosol concentration over Lake Urmia varied significantly. Among these, the NDWI driving factor, with an MDG of 35.7%, held the greatest importance among the studied factors. Following this, the LWA factor, with an MDG of 22.6%, ranked second in importance, while the NDSI (MDG: 11.1%) was also identified as one of the main influential drivers. On the contrary, EVI, WS, Pre, PDSI, and ET drivers, with MDGs of 7.9%, 6.8%, 6.5%, 5.2%, and 4.2%, respectively, were of lesser importance compared to the first three factors (Fig. 10 e). The results presented in Fig. 11 demonstrate the percentage contributions (5) between climatic and land-based (topographic) driving factors to the increase in aerosol concentration over the examined lakes and wetlands in Iran. During the study period (2000–2024), the contribution of climatic drivers (65.5%) to the increase in aerosol concentration over Parishan Wetland was almost twice than that of terrestrial factors (34.5%). Conversely, in Gomishan Wetland, the contribution of land-based drivers (59.1%) was higher than climatic elements (40.9%). In Miankaleh Wetland, the contributions of climatic (53.2%) and terrestrial (46.8%) factors were nearly equal, indicating that both categories of drivers have played an important role in increasing aerosol concentration, while control policies and management strategies should be considered for both. The results of the analysis of the effect of terrestrial and climatic drivers on changes in AOD over the Sheedvar Wetland showed that climatic factors, with a share of 55.2%, have played the dominant role in this region. This is while the share of terrestrial factors has been estimated at 44.8%. These findings indicate that although terrestrial drivers have had a significant impact on increasing AOD and reducing air quality, climatic factors have still played a decisive role in the air quality of the area surrounding this wetland. In Lake Urmia, the role of terrestrial drivers (77.3%) in increasing aerosol emissions was significantly greater than that of climatic drivers (22.7%) and similar results are documented in previous works (Ghale et al ., 2019; Hamzeh et al ., 2023). Overall, the results show that over the past 25 years, the role of terrestrial and climatic drivers in increasing arosol loading over the studied water bodies in Iran has varied. In some wetlands, climatic drivers have played a more prominent role, while in others, terrestrial factors have been more significant. This highlights the need to develop and implement strategic management and control programs tailored to the specific conditions of each region. 4.5 Relationship Between Key Driving Factors and AOD In this study, the correlation between key driving factors and AOD was also investigated to determine the type of relationship and how they influence changes in aerosol thickness over each water body. Our findings exhibited that in Parishan Wetland, all factors except WS showed a strong negative correlation with AOD. The strongest linkage was observed with NDSI (r= -0.512; P-value < 0.01), while the weakest was with EVI (r=-0.218; P-value < 0.01). The WS showed a significant positive relation with AOD variations (r = 0.419; P-value < 0.01), likely indicating the positive effect of wind on dust emissions from the dried beds or transport of aerosols from nearby arid sources under stronger winds (Fig. 12 a).In the Gomishan, there was also a very strong indirect relationship with AOD in the majority of drivers. The most negative relationship was with NDSI (r=-0.652; P-value 0.05). WS had a positive significant relationship with AOD (r = 0.583; P-value < 0.01), maybe because of the same reasons mentioned above(Fig. 12 b). In Miankaleh Wetland, NDSI and Precipitation factors showed the strongest negative correlations with AOD (r= -0.676and − 0.546, respectively), while WS again exhibited a significant positive correlation with AOD (r = 0.628; P-value 0.01) (Fig. 12 c). In Sheedvar Wetland, the EVI and NDSI factors presented significant positive correlations with AOD (r = 0.271 and r = 0.219, respectively (P-value < 0.01)). Conversely, the WS factor exhibited a significant negative correlation with AOD (r= -0.209; P-value < 0.01). Other driving factors did not have a significant impact on the changes in AOD in this region (Fig. 12 d). In Lake Urmia, NDSI and NDWI showed the strongest negative correlations with AOD (r= -0.350 and − 0.670, respectively), while WS and EVI showed significant positive correlations with AOD variations (r = 0.34 and 0.17, respectively) (Fig. 12 e). 5. Discussion 5.1 Trend of AOD Changes Over Iranian Lakes and Wetlands Monitoring and analyzing trends in AOD is a key step in understanding air pollution status over various ecosystems, particularly wetlands and lakes, some of which have experienced significant changes in recent years due to human intervention and climate change 14 . Over the past 25 years (2000–2024), there has been a significant decreasing trend in AOD over three important Iranian international wetlands: Hamun, Shadegan, and Khuran, while a substantial increase has been observed over Lake Urmia and the Gomishan, Sheedvar, Miankaleh, and Parishan wetlands. AOD data from 2000 to 2020 was used to validate a predicted dust source map in Shadegan wetland, reporting a non-significant increasing trend in aerosols 56 . These findings did not align with current results. Given that these researchers did not specify on the temporal scale or extent of the wetland area examined for the trend analysis, it appears that one reason for the discrepancy is that the AOD trend in their study was solely based on barren lands within the wetland area. In another study, dust event frequency around this wetland over a period overlapping with the present study (2000–2017) showed no significant trend 57 . Recently, another study reported an increase in Shadegan wetland's water level in 2020 compared to 2000 3 . Therefore, considering these findings, the primary cause for the decreasing aerosol concentration trend in recent decades can be attributed to the increased water level in this international wetland. In the Hamun wetland, located in the Sistan Basin, the aerosol trend showed an increase from 2001 to 2009 and a decrease from 2010 to 2019 58 , consistent with the findings of the present study in that time frame, while the general decrease in AOD during the 2010s in east Iran was mostly attributed to changes in meteorological factors (Yousefi et al ., 2023). The water area of Khuran wetland decreased by approximately 2% in 2020 compared to 2000 3 , while according to the present study, the AOD over this region was 0.35 in 2000 and about 0.28 in 2020, indicating a 20% decrease. Comparing the findings of these two studies does not show an inverse relationship between AOD changes and water coverage in Khuran wetland. In other words, despite the increase in the dried-up bed of this wetland, the AOD has decreased, likely indicating less dynamic of local dust emissions from the dried lake beds. On the other hand, this AOD increase could be attributed to changes in wind patterns and the transport of dust plumes from other regions or to the deposition of regional emitted particles due to high humidity in this area. In the period 2000–2010, the trend of winter AODs and warm-season aerosol concentrations in Iran showed a significant increase, followed by a decrease during 2010–2018, which largely aligns with the aerosol concentration trend over many water bodies in Iran during those periods 59 , 60 . In general, the decreasing changes in aerosols in dust-prone regions could be due to severe precipitation anomalies in recent years, which have particularly affected the frequency of dust events in high-rainfall years 61 . Given that the primary focus of this study is on lakes and wetlands that have shown an increasing trend in aerosols during the past 25 years, the main drivers of these changes in the Parishan, Gomishan, Miankaleh, Sheedvar wetlands, and Lake Urmia have been analyzed in detail and discussed below. 5.2 Main Driving Factors and their relationships with AOD in Iran’s Lakes and wetlands In recent years, several climatic, terrestrial factors and human interventions have influenced changes in aerosol concentration trends in Iran 14 , 61 , 62 . According to the findings of this study, significant increasing trends in aerosols, which are related with intensified wind erosion and dust emission phenomena, were observed over Parishan (southwestern Iran), Gomishan and Miankaleh (northern Iran), and Sheedvar (southern Iran) wetlands, as well as over Lake Urmia (northwestern Iran). Changes in various climatic factors, including rainfall, temperature, wind speed, and drought, as well as terrestrial factors such as soil moisture content, vegetation cover, and soil salinity, affect the multi-decadal variations and trends of atmospheric aerosols 63 – 65 . However, the impact of each factor varies among the different ecosystems 66 , especially on the wetlands. The analysis of long-term monthly trend changes of the driving forces affecting AOD showed a weak positive correlation between ET and AOD in the Sheedvar International Wetland, while negative correlations between these two parameters were observed in the Parishan, Gomishan, Miankaleh, and Lake Urmia. Current findings showed an increasing trend of AOD in these areas, which may indicate the transfer of dust particles from adjacent wind erosion-sensitive areas, and not so local dust emissions (Hamzeh et al ., 2023), an issue that requires further information and more comprehensive analysis, which is beyond the scope of this study. During the study period, the relationship between vegetation cover and AOD in Parishan, Gomishan, and Miankaleh was inverse, which is consistent with results of previous studies 58 . However, a direct relationship between these two parameters was observed in the Sheedvar wetland and Lake Urmia. This result is consistent with the findings of 67 , who observed both positive and negative correlations between vegetation and AOD in parts of East Africa. Over the past 25 years, the relationship between AOD and NDSI has been weakly positive in Sheedvar and weakly negative in other water bodies. This result indicates the dual role of the salinity of the bed of Iran's inland aquatic ecosystems on AOD. This could be related to the difference in size and chemical composition of salt aerosols and dust aerosols, and their respective effects on aerosol thickness 68 , 69 . While a very weak positive correlation was observed between rainfall and AOD in the Sheedvar wetland, decreased rainfall has led to an increase in AOD in other water bodies in Iran. A dual relationship was also observed regarding this driving factor in different wetlands. This is because under decreased rainfall, the processes of aerosol deposition and removal from the atmosphere are reduced 70 , 71 . On the other hand, decreased rainfall leads to a reduction in soil moisture and an increase in dust emissions 72 . The inverse relationship between AOD-PDSI in these four international water bodies also highlighted the intensifying effect of droughts on increasing AOD, which is consistent with the findings of previous studies on the effect of drought on increasing dust aerosol emissions 73 , 74 . Analysis of the relationship between LWA and AOD showed that the decrease in the water level of the Miankaleh wetland did not have significant impact on AOD changes, while the average decrease in NDWI, which indicates a decrease in water content and soil moisture content, had a significant impact on increasing AOD in this region. In the three wetlands of Sheedvar, Gomishan, and Parishan, changes in LWA - and in Lake Urmia, simultaneous changes in water level and NDWI - have led to an increase in aerosols, which is consistent with many findings of other researchers 57 , 75 . The relationship between WS and AOD in the Sheedvar wetland was negative. One of the possible reasons for this result can be the time scale of the analysis between these two parameters, because this relationship was established on a monthly scale, while the effect of wind on dust emissions usually occurs at shorter scales, i.e. hourly or daily 58 . On the other hand, the Sheedvar wetland is located near the open waters of the Persian Gulf and the Oman Sea, and the likely dominant aerosol type in this region is marine mixed with dust, composed by a significant salt spray caused by sea winds. These types of aerosols are produced more with increasing wind speed, but due to their size and specific chemical composition, they may have less impact on increasing AOD compared to dust aerosols. On the contrary, the relationship of monthly fluctuations in wind speed and AOD in the Parishan, Gomishan, Miankaleh wetlands, and Lake Urmia have been positive. This indicates that in the past 25 years, the sensitivity of the dried beds of these areas to dust storms has increased more compared to the Sheedvar wetland. This is consistent with the findings of previous studies, which had pointed to the key effect of wind speed on increasing aerosol concentrations in arid lands 76 . In general, changes in the water level of all three water bodies, Gomishan, Sheedvar, and Lake Urmia, were identified as one of the main factors affecting the increase in aerosol concentrations. Past studies have also reported that the water levels of these three bodies, as well as Parishan and Miankaleh wetlands, decreased in 2020 compared to 2000 3,77 , which is consistent with current findings showing a decreasing trend in wetland levels. The main causes of increased aerosol emissions over northern China's wetlands in the period 2001–2023 were reported to be population density and humidity changes 66 , which is partly consistent with the results of the present study that refer to the role of humidity changes in increasing aerosol emissions, but not with the population density parameter. The reasons for the discrepancy in other findings are the difference in the selection of factors affecting AOD, and the type of model used to identify the factors influencing its changes, while local topography and regional meteorology play an important role in AOD variations, which may highly affect the correlations. 6. Conclusions The increase in aerosols over aquatic ecosystems is a warning sign of disruption in the functioning of these valuable ecosystems and intensification of sand and dust storms. Understanding the trend of changes in Aerosol Optical Depth (AOD) in these areas and identifying their main drivers is of great importance. This issue has not received much attention, and therefore, this study focused on AOD changes over Iranian wetlands and lakes, aiming to assess their interrelations. According to current findings, in winter, spring, summer and autumn, as well as on an annual scale, 51.9%, 55.6%, 74.1%, 66.7%, and 55.6% of Iran's inland wetlands and lakes have experienced an increasing trend in AOD, respectively. A significant increasing trend was observed in five international wetlands: Parishan, Gomishan, Miankaleh, Sheedvar, and Lake Urmia. Evaporation and rainfall were identified as the main drivers of increased aerosol concentrations in the Parishan International Wetland. Changes in water area and soil salinity in the Gomishan Wetland and Lake Urmia played a key role in increasing aerosol concentrations over these valuable ecosystems. In addition, precipitation and soil salinity were identified as the main drivers of air quality reduction over the Miankaleh Wetland. The main cause of air quality degradation in Sheedvar wetland was the reduction of vegetation cover and the increase in the dried area of the wetland. The increasing trend of aerosols over the three international wetlands of Parishan, Miankaleh, and Sheedvar was mainly influenced by changes in climatic elements, while in the Gomishan Wetland and Lake Urmia, it was due to changes in land-based factors. In general, these findings highlighted the variety of factors affecting Iran's wetlands, and as a result, any action to reduce air pollution should be tailored to each wetland at risk, considering its specific environmental conditions. The results of this research can be useful for controlling air pollution and reducing its adverse environmental effects in Iran. Considering that the driving forces affecting AOD, especially climatic factors, have significant fluctuations, accurate prediction of their future trend is associated with uncertainty. Therefore, continuous monitoring of air quality over aquatic ecosystems, especially wetlands that have experienced an increasing trend of aerosols, is essential. Also, considering that the mentioned water bodies are of international importance, cooperation with international organizations is necessary to prevent further degradation of these ecosystems. Declarations Acknowledgments The authors thank the efforts of the MODIS and Terr Climate product teams in producing and distributing these worthwhile datasets. The authors also thank the AERONET network and the diligent people working towards gathering and processing the aerosol data used in this research. Funding This is not applicable. Authors' Contributions: Zohre Ebrahimi-Khusfi : writing – original draft, visualization, resources, project administration, methodology, investigation, formal analysis, data curation, conceptualization, writing – review & editing. Seyed Arman Samadi-Todar : software, methodology, formal analysis, data curation, conceptualization, writing – review & editing. Narjes Okati : writing – original draft, investigation, formal analysis. Dimitris Kaskaoutis: methodology, writing – review & editing. Ethical approval This is not applicable. Consent to participate This is not applicable. Consent to publish All authors have read and agreed to the published version of the manuscript. Competing interests The authors declare no competing interests. Data availability Data available on request from the authors. References Imdad, K. et al. Wetland health, water quality, and resident perceptions of declining ecosystem services: a case study of Mount Abu, Rajasthan, India. Environmental science and pollution research 30 , 116617-116643 (2023). Amindin, A. et al. Long term analysis of international wetlands in Iran: Monitoring surface water area and water balance. International Journal of Applied Earth Observation and Geoinformation 126 , 103637 (2024). Rahimi, E., Jahandideh, M., Dong, P. & Ahmadzadeh, F. Potential anthropogenic and climatic factors affecting Iran’s international wetlands. Journal of Environmental Studies and Sciences 13 , 557-574 (2023). Kool, J., Lhermitte, S., Hrachowitz, M., Bregoli, F. & McClain, M. E. Seasonal inundation dynamics and water balance of the Mara Wetland, Tanzania based on multi-temporal Sentinel-2 image classification. International Journal of Applied Earth Observation and Geoinformation 109 , 102766 (2022). Belyaev, A. I., Pugacheva, A. M. & Korneeva, E. A. Assessment of ecosystem services of wetlands of the volga–akhtuba floodplain. Sustainability 14 , 11240 (2022). Nasab, S. E. et al. Waterless wetlands, as a new source of wind erosion: Developing remediation strategies for the Hamoon Wetland in Iran. Ecological Informatics 78 , 102368 (2023). Badamfirooz, J., Mousazadeh, R. & Sarkheil, H. A proposed framework for economic valuation and assessment of damages cost to national wetlands ecosystem services using the benefit-transfer approach. Environmental Challenges 5 , 100303 (2021). Zekarias, T., Govindu, V., Kebede, Y. & Gelaw, A. Geospatial analysis of wetland dynamics on Lake Abaya-Chamo, the main rift valley of Ethiopia. Heliyon 7 (2021). Li, T. et al. Contributions of various driving factors to air pollution events: Interpretability analysis from Machine learning perspective. Environment International 173 , 107861 (2023). Mumuni, A. S., Mensah, H., Asamoah, S. & Simpeh, E. K. A systematic review on the causes and effects of urbanization on wetlands in Sub-Saharan Africa. Urbanization, Sustainability and Society 2 , 153-177 (2025). Elegbede, I. O. et al. Wetland Ecosystems in Nigeria. Wetlands of Tropical and Subtropical Asia and Africa: Biodiversity, Livelihoods and Conservation , 317 (2025). Sadeghi, S. H. & Hazbavi, Z. in Global Degradation of Soil and Water Resources: Regional Assessment and Strategies 287-314 (Springer, 2022). Khashi, K., Azhdary Moghaddam, M. & Hashemi Monfared, S. Effects of wetlands and wind velocity on dust propagation in arid areas: a critical case study in south-east of Iran. International Journal of Environmental Science and Technology 19 , 12169-12180 (2022). sadat Afzalizadeh, M., Nadoushan, M. A., Jalalian, A. & Chamani, A. Dust source dynamics in arid Iran: Examining the relationship between MODIS AOD and land surface characteristics in a dried catchment. Advances in Space Research 75 , 3326-3334 (2025). Stirling, E., Fitzpatrick, R. & Mosley, L. Drought effects on wet soils in inland wetlands and peatlands. Earth-Science Reviews 210 , 103387 (2020). Khelifa, R., Mahdjoub, H. & Samways, M. J. Combined climatic and anthropogenic stress threaten resilience of important wetland sites in an arid region. Science of the Total Environment 806 , 150806 (2022). Wang, M. et al. Interannual changes of urban wetlands in China’s major cities from 1985 to 2022. ISPRS Journal of Photogrammetry and Remote Sensing 209 , 383-397 (2024). Zarrinabadi, E. Soil erosion and fluxes of sediment within landscapes of the Canadian Prairies. (2023). Rahdari, M. R., Kharazmi, R. & Caballero-Calvo, A. in Google Earth Engine and Artificial Intelligence for Earth Observation 195-212 (Elsevier, 2025). Abou Zaki, N. et al. Evaluating impacts of irrigation and drought on river, groundwater and a terminal wetland in the Zayanderud Basin, Iran. Water 12 , 1302 (2020). Mehta, M., Singh, R., Singh, A. & Singh, N. Recent global aerosol optical depth variations and trends—A comparative study using MODIS and MISR level 3 datasets. Remote sensing of environment 181 , 137-150 (2016). Filonchyk, M., Yan, H. & Zhang, Z. Analysis of spatial and temporal variability of aerosol optical depth over China using MODIS combined Dark Target and Deep Blue product. Theoretical and Applied Climatology 137 , 2271-2288 (2019). Wang, D., Zhang, F., Yang, S., Xia, N. & Ariken, M. Exploring the spatial-temporal characteristics of the aerosol optical depth (AOD) in Central Asia based on the moderate resolution imaging spectroradiometer (MODIS). Environmental monitoring and assessment 192 , 1-15 (2020). Lin, C., Cohen, J. B., Wang, S. & Lan, R. Application of a combined standard deviation and mean based approach to MOPITT CO column data, and resulting improved representation of biomass burning and urban air pollution sources. Remote Sensing of Environment 241 , 111720 (2020). Yousefi, R., Wang, F., Ge, Q. & Shaheen, A. Long-term aerosol optical depth trend over Iran and identification of dominant aerosol types. Science of the Total Environment 722 , 137906 (2020). Dadashi-Roudbari, A. & Ahmadi, M. Evaluating temporal and spatial variability and trend of aerosol optical depth (550 nm) over Iran using data from MODIS on board the Terra and Aqua satellites. Arabian Journal of Geosciences 13 , 1-23 (2020). Sharma, V. et al. Spatial variation and relation of aerosol optical depth with LULC and spectral indices. Atmosphere 13 , 1992 (2022). Solanki, R. & Pathak, K. Aerosol optical depth spatial and temporal variability using satellite data over Indian major cities. Journal of Environmental Health and Sustainable Development (2022). Nabavi, S. O., Haimberger, L., Abbasi, R. & Samimi, C. Prediction of aerosol optical depth in West Asia using deterministic models and machine learning algorithms. Aeolian research 35, 69-84 (2018). Berhane, S. A., Althaf, P., Kumar, K. R., Bu, L. & Yao, M. A Comprehensive Analysis of AOD and its Species from Reanalysis Data over the Middle East and North Africa Regions: Evaluation of Model Performance Using Machine Learning Techniques. Earth Systems and Environment , 1-26 (2024). Pashayi, M., Satari, M., Shahraki, M. M. & Amini, S. MAIAC AOD profiling over the Persian Gulf: A seasonal-independent machine learning approach. Atmospheric Pollution Research 15 , 102128 (2024). Aazami, J. & Joorabian Shooshtari, S. Update data of the Iranian wetlands (2023). Journal of Applied Research in Water and Wastewater 10 , 156-160 (2023). Eck, T. F. et al. Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols. Journal of Geophysical Research: Atmospheres 104 , 31333-31349 (1999). Li, W. et al. Comprehensive validation and comparison of three VIIRS aerosol products over the ocean on a global scale. Remote Sensing 14 , 2544 (2022). Li, X., Zhang, C., Li, W., Anyah, R. O. & Tian, J. Exploring the trend, prediction and driving forces of aerosols using satellite and ground data, and implications for climate change mitigation. Journal of Cleaner Production 223 , 238-251 (2019). Aksu, H. et al. Spatiotemporal analysis of drought by CHIRPS precipitation estimates. Theoretical and Applied Climatology 148 , 517-529 (2022). López-Bermeo, C., Montoya, R. D., Caro-Lopera, F. J. & Díaz-García, J. A. Validation of the accuracy of the CHIRPS precipitation dataset at representing climate variability in a tropical mountainous region of South America. Physics and Chemistry of the Earth, Parts A/B/C 127 , 103184 (2022). Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific data 5 , 1-12 (2018). Haq, Y. U., Shahbaz, M., Asif, S., Ouahada, K. & Hamam, H. Identification of soil types and salinity using MODIS terra data and machine learning techniques in multiple regions of Pakistan. Sensors 23 , 8121 (2023). McFeeters, S. K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International journal of remote sensing 17 , 1425-1432 (1996). Azhar, S. C., Aris, A. Z., Yusoff, M. K., Ramli, M. F. & Juahir, H. Classification of river water quality using multivariate analysis. Procedia Environmental Sciences 30 , 79-84 (2015). Mann, H. B. Nonparametric tests against trend. Econometrica: Journal of the econometric society , 245-259 (1945). Kendall, M. G. Rank correlation methods . 1st Edn edn, (Charles Griffin & Co Ltd 1948). Theil, H. A rank-invariant method of linear and polynomial regression analysis. Indagationes mathematicae 12 , 173 (1950). Sen, P. K. Asymptotically efficient tests by the method of n rankings. Journal of the Royal Statistical Society Series B: Statistical Methodology 30 , 312-317 (1968). Gal, M. S. & Rubinfeld, D. L. Data standardization. NYUL Rev . 94, 737 (2019). Chen, Q., Shao, K. & Zhang, S. Enhanced PM2. 5 estimation across China: An AOD-independent two-stage approach incorporating improved spatiotemporal heterogeneity representations. Journal of Environmental Management 368 , 122107 (2024). Ebrahimi-Khusfi, Z., Nafarzadegan, A., Ebrahimi-Khusfi, M. & Mosavai, A. Machine Learning approaches for identifying factors influencing dust sensitivity in the Gavkhouni Basin, Central Iran. International Journal of Environmental Science and Technology , 1-20 (2024). Daoud, J. I. in Journal of Physics: Conference Series. 012009 (IOP Publishing). Ali, J., Khan, R., Ahmad, N. & Maqsood, I. Random forests and decision trees. International Journal of Computer Science Issues (IJCSI) 9 , 272 (2012). Ao, Y., Li, H., Zhu, L., Ali, S. & Yang, Z. The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling. Journal of Petroleum Science and Engineering 174 , 776-789 (2019). Miao, S. et al. Random forest algorithm for the relationship between negative air ions and environmental factors in an urban park. Atmosphere 9 , 463 (2018). Yu, M. Short-term wind speed forecasting based on random forest model combining ensemble empirical mode decomposition and improved harmony search algorithm. International Journal of Green Energy 17 , 332-348 (2020). Pearson, K. Determination of the coefficient of correlation. Science 30 , 23-25 (1909). Li, J., Ge, X., He, Q. & Abbas, A. Aerosol optical depth (AOD): spatial and temporal variations and association with meteorological covariates in Taklimakan desert, China. PeerJ 9 , e10542 (2021). Khanfari, V., Asgari, H. M. & Dadollahi-Sohrab, A. Forecasting Wetland Transformation to Dust Source by Employing CA-Markov Model and Remote Sensing: A Case Study of Shadgan International Wetland. Wetlands 44 , 96 (2024). Ebrahimi-Khusfi, Z., Dargahian, F. & Nafarzadegan, A. R. Predicting the dust events frequency around a degraded ecosystem and determining the contribution of their controlling factors using gradient boosting-based approaches and game theory. Environmental Science and Pollution Research 29 , 36655-36673 (2022). Namdari, S., Valizadeh Kamran, K. & Sorooshian, A. Analysis of some factors related to dust storms occurrence in the Sistan region. Environmental Science and Pollution Research 28 , 45450-45458 (2021). Yousefi, R., Wang, F., Ge, Q., Lelieveld, J. & Shaheen, A. Aerosol trends during the dusty season over Iran. Remote Sensing 13 , 1045 (2021). Yousefi, R., Wang, F., Ge, Q., Shaheen, A. & Kaskaoutis, D. G. Analysis of the winter AOD trends over Iran from 2000 to 2020 and associated meteorological effects. Remote Sensing 15 , 905 (2023). Abadi, A. R. S., Hamzeh, N. H., Kaskaoutis, D. G., Opp, C. & Kazemi, A. F. Long-Term Spatio-Temporal Analysis, Distribution, and Trends of Dust Events over Iran. Atmosphere 16 , 334 (2025). Bashirian, F., Rahimi, D. & Movahedi, S. Effects of land use changes on local dust event in Urmia Lake basin. Land Use Policy 148, 107416 (2025). Mousavi, H., Panahi, D. M. & Kalantari, Z. Dust and climate interactions in the Middle East: Spatio-temporal analysis of aerosol optical depth and climatic variables. Science of the Total Environment 927 , 172176 (2024). Khachak, S. H. et al. Application of Remote Sensing and Spatial Fuzzy Multi-criteria Decision Analysis to Identify Potential Dust Sources in Lake Urmia Basin, Northwest Iran. Journal of the Indian Society of Remote Sensing 52 , 2057-2071 (2024). Ghasempour, R., Aalami, M. T., Saghebian, S. M. & Kirca, V. O. Analysis of spatiotemporal variations of drought and soil salinity via integrated multiscale and remote sensing-based techniques (Case study: Urmia Lake basin). Ecological Informatics 81 , 102560 (2024). Xiang, Z. et al. Spatiotemporal Variations and Driving Factor Analysis of Aerosol Optical Depth in Terrestrial Ecosystems in Northern Xinjiang from 2001 to 2023. Atmosphere 15 (2024). Khamala, G. W., Makokha, J. W. & Boiyo, R. The spatiotemporal and dependency analysis of selected meteorological parameters and normalized difference vegetation index with aerosol optical depth over east Africa. Heliyon 10 (2024). Fei, K. c., Wu, L. & Zeng, Q. c. Aerosol optical depth and burden from large sea salt particles. Journal of Geophysical Research: Atmospheres 124 , 1680-1696 (2019). Madry, W. L., Toon, O. B. & O'Dowd, C. Modeled optical thickness of sea‐salt aerosol. Journal of Geophysical Research: Atmospheres 116 (2011). Cugerone, K., De Michele, C., Ghezzi, A. & Gianelle, V. Aerosol removal due to precipitation and wind forcings in Milan urban area. Journal of Hydrology 556 , 1256-1262 (2018). Chen, M. et al. A parameterized study on rainfall removal of aerosols. Aerosol Science and Engineering 7 , 355-367 (2023). Liu, J., Ding, J., Li, X., Zhang, J. & Liu, B. Identification of dust aerosols, their sources, and the effect of soil moisture in Central Asia. Science of the total environment 868 , 161575 (2023). Al-Taei, A. I., Alesheikh, A. A. & Boloorani, A. D. Evaluating the effects of land use/land cover change on the emergence of hazardous dust sources in the Tigris-Euphrates Basin. Spatial Information Research 32 , 569-582 (2024). Boloorani, A. D. et al. Assessing the role of drought in dust storm formation in the Tigris and Euphrates basin. Science of The Total Environment 921 , 171193 (2024). Adnan, M. S. G. et al. Heatwave vulnerability of large metropolitans in Bangladesh: An evaluation. Geomatica 76 , 100020 (2024). Meng, H., Bai, G. & Wang, L. Analysis of the spatial and temporal distribution characteristics of AOD in typical industrial cities in northwest China and the influence of meteorological factors. Atmospheric Pollution Research 15 , 101957 (2024). Ebrahimi-Khusfi, Z., Nafarzadegan, A. R., Ebrahimi-Khusfi, M. & Zandifar, S. Monitoring the water surface of wetlands in Iran and their relationship with air pollution in nearby cities. Environmental Monitoring and Assessment 194 , 488 (2022). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6778141","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":484734040,"identity":"e6b29b68-b9b8-4b22-a131-c17062eeb8d3","order_by":0,"name":"Zohre Ebrahimi-Khusfi","email":"data:image/png;base64,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","orcid":"","institution":"University of Jiroft","correspondingAuthor":true,"prefix":"","firstName":"Zohre","middleName":"","lastName":"Ebrahimi-Khusfi","suffix":""},{"id":484734041,"identity":"534c9a56-4155-47f1-a080-b8e6e4e05e68","order_by":1,"name":"Seyed Arman Samadi-Todar","email":"","orcid":"","institution":"University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Seyed","middleName":"Arman","lastName":"Samadi-Todar","suffix":""},{"id":484734042,"identity":"91957db4-e361-4fa7-b2b4-515c9f1807d8","order_by":2,"name":"Narjes Okati","email":"","orcid":"","institution":"University of Zabol","correspondingAuthor":false,"prefix":"","firstName":"Narjes","middleName":"","lastName":"Okati","suffix":""},{"id":484734043,"identity":"c1ffc7bc-f46c-43a1-b5a3-0ac444d1aee1","order_by":3,"name":"Dimitris Kaskaoutis","email":"","orcid":"","institution":"University of Western Macedonia","correspondingAuthor":false,"prefix":"","firstName":"Dimitris","middleName":"","lastName":"Kaskaoutis","suffix":""}],"badges":[],"createdAt":"2025-05-29 16:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6778141/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6778141/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-27607-4","type":"published","date":"2025-12-16T15:57:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86706092,"identity":"b838f0b6-c421-4c94-b967-bdc5f070daa2","added_by":"auto","created_at":"2025-07-14 17:20:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":665428,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical location of the country and areas of the studied lakes and wetlands in Iran.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6778141/v1/acd8e403e96e544d8e1457ea.png"},{"id":86706091,"identity":"650193eb-bb0e-4525-abd8-671783fbd254","added_by":"auto","created_at":"2025-07-14 17:20:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":83979,"visible":true,"origin":"","legend":"\u003cp\u003eResearch methodology flowchart.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6778141/v1/70b48fc17e2acd4ea44c5691.png"},{"id":86705752,"identity":"08954fc8-261b-4034-bb9f-499ad001ba29","added_by":"auto","created_at":"2025-07-14 17:12:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":75995,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between AOD-MODIS and AOD-AERONET in (a)IASBS, (b) Doshanbeh, (c) Kuwait University, and (d) Solar Village. The statistical indicators of the linear regression are shown in the figure.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6778141/v1/d06e2b726e8aae961a19aa4a.png"},{"id":86706093,"identity":"d322ba90-4e4a-4258-b051-70bf24c4f681","added_by":"auto","created_at":"2025-07-14 17:20:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":67317,"visible":true,"origin":"","legend":"\u003cp\u003eMann-Kendall statistic values for monthly changes in AOD over lakes and wetlands in Iran (2000-2024).\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6778141/v1/89afaaa47d5fdff1deb6c3c0.png"},{"id":86705754,"identity":"36bb2ee2-1224-415d-ad09-010f3f2796d3","added_by":"auto","created_at":"2025-07-14 17:12:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":120761,"visible":true,"origin":"","legend":"\u003cp\u003eMann-Kendall statistical values of the seasonally changes in AOD over Iranian lakes and wetlands (2000-2024).\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6778141/v1/2678b7bfbead8ce4bb41639d.png"},{"id":86706771,"identity":"1f68696e-6dfe-43d8-b1e8-c55afefff875","added_by":"auto","created_at":"2025-07-14 17:28:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":118227,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual mean aerosol optical depth values in Iranian lakes and wetlands from 2000 to 2024.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6778141/v1/bcb71ba31129af7f275f328f.png"},{"id":86705760,"identity":"b40d3c93-9097-4833-9bd4-659c4f2d268d","added_by":"auto","created_at":"2025-07-14 17:12:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":24398,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical Characteristics of AOD over Iranian lakes and wetlands during 2000-2024.\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6778141/v1/9039b411a60bcc924b05692a.png"},{"id":86705768,"identity":"6ff552d3-8d10-4698-b962-e105e805d080","added_by":"auto","created_at":"2025-07-14 17:12:32","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":33352,"visible":true,"origin":"","legend":"\u003cp\u003eMann-Kendall and Sen's slope values for annual changes in AOD over lakes and wetlands in Iran (2000-2024).\u003c/p\u003e","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6778141/v1/1d05cd0140ce6cab9fe1936d.png"},{"id":86707091,"identity":"3e186e4b-ae93-4201-8d46-e52e1e8760b3","added_by":"auto","created_at":"2025-07-14 17:36:32","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":34580,"visible":true,"origin":"","legend":"\u003cp\u003eTolerance coefficient values for AOD driving factors after removing problematic factors. (a) Parishan wetland, (b) Gomishan wetland, (c) Miankaleh wetland, (d) Sheedvar, and (e) Urmia Lake.\u003c/p\u003e","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6778141/v1/d9bef917632ceba84071cb9f.png"},{"id":86706768,"identity":"e1d151f9-d31a-4ebc-9f1a-78c0b9f42c4f","added_by":"auto","created_at":"2025-07-14 17:28:32","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":87444,"visible":true,"origin":"","legend":"\u003cp\u003eRelative importance of AOD drivers over international wetlands: (a) Parishan, (b) Gomishan, (c) Miankaleh, (d) Sheedvar, and (e) Urmia Lake, based on mean decrease Gini (MDG) value.\u003c/p\u003e","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-6778141/v1/b7eb3ff193eb7ab0d8a8b062.png"},{"id":86705774,"identity":"778b7930-ef82-490d-8d99-cc640d4cd001","added_by":"auto","created_at":"2025-07-14 17:12:32","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":44508,"visible":true,"origin":"","legend":"\u003cp\u003eThe contribution of climatic and terrestrial drivers to changes in AOD over water bodies with an increasing AOD trend in Iran.\u003c/p\u003e","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-6778141/v1/866eb60a606fb722643226b0.png"},{"id":86705764,"identity":"327dca3e-02f0-4849-9b91-92ec6c403d6f","added_by":"auto","created_at":"2025-07-14 17:12:32","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":130054,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation coefficients between driving factors and aerosol optical depth (AOD) in(a) Parishan wetland, (b) Gomishan wetland, (c) Miankaleh wetland, (d) Sheedvar, wetland and (e) Urmia Lake.\u003c/p\u003e","description":"","filename":"Onlinefloatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-6778141/v1/f51cb9bb5504423061950384.png"},{"id":98813954,"identity":"b83ea0ad-9d3c-4635-80ed-cd93996d95ec","added_by":"auto","created_at":"2025-12-22 16:08:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3084020,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6778141/v1/a55d9492-3b95-40bc-a104-b3ab15d3ca58.pdf"},{"id":86706096,"identity":"088d794a-1a5b-4b9f-89e0-4a628f6f8c74","added_by":"auto","created_at":"2025-07-14 17:20:32","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1692380,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6778141/v1/a67436f0b461c1776bd58b5b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A 25-Year Assessment of Aerosol Dynamics and Environmental Drivers in Iran’s Lakes and Wetlands","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWetland ecosystems consist of estuaries, lakes, floodplains, marshes, rivers, fens, peatlands, mangroves, and coral reefs \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, and are also referred to as transitional lands between aquatic and terrestrial ecosystems \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Wetlands provide 40% of the world's ecosystem services, but cover only around 6% of the Earth's surface \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Due to the well-known ecosystem services of wetlands, including greenhouse gas reduction, water purification, carbon sequestration, shoreline protection, regulation of the hydrological cycle, flood mitigation, drought mitigation, and protection of habitats from nutrient erosion, they are referred to as the kidneys of nature \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Wetlands are equally important in the mitigation of dust storms in arid and semi-arid environments, through trapping of dust particles and increasing the soil moisture and cover of the surrounding regions \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. n the Ramsar Convention's foundation, one of the first modern multilateral environmental conventions to promote worldwide cooperation and effective wetlands management for wetland conservation, 27 Iranian wetlands covering around 1.5\u0026nbsp;million hectares are of worldwide importance (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rsis.ramsar.org/\u003c/span\u003e\u003cspan address=\"https://rsis.ramsar.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The total average adjusted ecosystem service value per hectare of Iran's inland wetland ecosystems and coastal mangrove wetland ecosystems is estimated to be 67,665 and 42,171 USD, respectively \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCurrently, due to climate change and human intervention, wetlands in most parts of the world are at high risk of destruction or conversion to different uses \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Among the threatening factors that affect the life of wetlands are population growth and urban development, industrialization, agriculture, waste incineration, global warming, drought, and dam construction \u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Iran is one of the most susceptible countries in the world in terms of climate change and its consequences (Zittis \u003cem\u003eet al\u003c/em\u003e., 2022; Neira \u003cem\u003eet al\u003c/em\u003e., 2022). This is due to the reason that approximately 76.4% and 19.6% of its land surface lie in arid and semi-arid regions, respectively \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. On the other hand, wetlands in arid regions have become a source of dust due to their dry beds and increased susceptibility to wind erosion \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Most wetlands of arid regions of the world, including Iran, are currently transitioning from a wet to a completely dry condition under climatic and anthropogenic conditions such as increasing temperature, decreasing rain, and prolonged dryness \u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. These changes cause wetland soil to be more exposed to direct sunlight and wind, thus getting dry and susceptible to dust emissions. Organic matter also decomposes faster, and the dispersion of their particles increases, making the soil susceptible to erosion (Gholami \u003cem\u003eet al\u003c/em\u003e., 2024b). During strong winds and gales, these areas become active sources of dust storms, since alluvial silt and fine dust particles are easily lifted into the atmosphere and transported at long distances \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Drought and water shortage in Iran during the recent years have resulted in devastating effects on various ecosystems, including wetlands, and have caused serious environmental problems such as dust storms \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Despite many efforts to restore and protect wetlands, all of Iran's inland wetlands are in the final stages of their life and are drying up rapidly during the last decades \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAtmospheric aerosols and dust particles directly and indirectly affect global climate, through absorbing and scattering of solar and terrestrial radiations \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Furthermore, aerosols carrying toxic elements may become important atmospheric pollutants through physical and chemical interactions in the presence of sunlight and negatively affect public health \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, as well as economic activities (Malamiri et al., 2025). For this reason, the World Health Organization (WHO) set strict limits on the concentration of particulate matter (PM) in atmospheric air. Aerosols are widely used as an uncertain but important indicator in research on climate change and atmospheric radiative balance \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAerosol optical depth (AOD) is one of the most important optical parameters that considers the amount of aerosols and the level of local air pollution to some extent. Therefore, AOD has a great impact on regional and even global climate, atmospheric radiation budget, and also atmospheric circulation \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Today, Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily satellite AOD dataset with high spatial resolution \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Wang, et al. \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e studied the spatiotemporal characteristics of dust content in Central Asia. Their findings indicated an increasing trend of dust in Kazakhstan, Uzbekistan, and Turkmenistan. In this regard, Dadashi-Roudbari and Ahmadi \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e showed that the southern parts of Iran are among the aerosol hotspots in Southwest Asia and have witnessed an increase in MODIS AOD values in recent years. Sharma, et al. \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e explored the effect of land use land cover (LULC) on AOD across India and identified that LULC characteristics are a major determining factor on aerosol concentration in the air, while similar findings were obtained over Iran through exploration of the LULC change versus aerosols and air pollution policy (Yousefi et al., 2025). Solanki and Pathak \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e investigated spatial and temporal variability of AOD over major Indian urban agglomerations from satellite data. In addition, sadat Afzalizadeh, et al. \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e investigated the relationship between MODIS AOD and land surface properties in a dried watershed in Iran as a source of dust dispersion.\u003c/p\u003e\u003cp\u003eIn recent decades, Machine Learning (ML) algorithms have gained significant popularity for predicting AOD and PM\u003csub\u003e2.5\u003c/sub\u003e in various regions of the world, including west Asia \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, Middle East and North Africa Regions \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, and Iran \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. AOD variations are influenced by numerous atmospheric, topographic and meteorological factors, which, especially over water bodies, may have complex relationships with AOD. Therefore, the use of ML algorithms is a valuable approach to understanding the factors affecting AOD changes. Given that this powerful tool has not yet been used to identify the factors affecting AOD changes over wetlands and inland lakes in Iran, this study uses the Random Forest (RF) algorithm and the Mean Decrease Gini (MDG) criterion for this purpose.\u003c/p\u003e\u003cp\u003eAlthough several studies have analyzed the trend of AOD in various regions of Iran (Rashki et al., 2014; Shaheen et al., 2023; Yousefi et al., 2023), a comprehensive study focusing on the trend analysis of AOD over Iran's inland wetlands and lakes, and the identification of effective factors using RF algorithms, has not yet been performed. Therefore, the present study seeks to fill these research gaps, aiming to analyze the trend of temporal changes in AOD over Iran's inland wetlands and lakes using the Mann-Kendall test and Sen's slope estimator. Another important goal is to identify the main driving factors influencing AOD over the water bodies that have experienced the greatest decline and adverse effects on air quality during the past 25 years (2000\u0026ndash;2024). Present findings may assist managers and decision-makers in implementing effective measures to reduce aerosol pollution and improve air quality in residential areas surrounding these valuable ecosystems.\u003c/p\u003e"},{"header":"2. Study area","content":"\u003cp\u003eIran is a country with a population of about 87\u0026nbsp;million and an area of 1.648\u0026nbsp;million km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e located in Southwest Asia and the Middle East. Most of its land is located in the arid region of the world \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. There are 84 important wetlands in Iran, covering a total area of over 20\u0026nbsp;million hectares \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, of which 32%, with an area of about 1.5\u0026nbsp;million hectares, are registered in the Ramsar Convention (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rsis.ramsar.org/\u003c/span\u003e\u003cspan address=\"https://rsis.ramsar.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To investigate the objectives of the present study, 20 inland lakes and wetlands of international importance and 7 other significant water bodies were selected. The geographical distribution of the studied wetlands, which are mostly spread in the northern and western regions of Iran, is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Based on the Shuttle Radar Topography Mission (SRTM) imagery, the elevation above sea level in the studied wetlands ranges from \u0026minus;\u0026thinsp;31 m in Anzali Wetland to 2271 m asl in Choghakhor Wetland. The long-term average rainfall over the past 25 years (2000\u0026ndash;2024) has varied from approximately 80 mm in the Hamoun Wetlands located in southeastern Iran to over 1200 mm in the Bujagh and Anzali Wetlands in the north. During this period, the variation of the land surface temperature (LST) of the wetlands and lakes was from about 15\u0026deg;C in Lake Urmia in northwestern Iran to over 40\u0026deg;C in the Hamun Wetland, Delta-Rud-e-Shur-Shirin, and Khuran Wetland in the southern half of Iran (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClimatic and topographical characteristics of the studied wetlands and lakes in Iran.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLake and Wetland (LW)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElevation(m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrecipitation(mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLST(\u0026deg;C)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAghgol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e374.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlagol, Ulmagol and Ajigol Lakes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e243.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmirkelayeh Lake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1052.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnzali Wetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1222.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBakhtegan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e160.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBujagh National Park\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1240.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChoghakhor Wetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e449.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeltas of Rud-e-Gaz and Rud-e-Hara\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e128.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeltas of Rud-e-Shur, Rud-e-Shirin and Rud-e-Minab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e137.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFereydoon Kenar, Ezbaran \u0026amp; Sorkh Ruds Ab-Bandans\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e946.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGavkhouni\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGomishan Lagoon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e274.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHamun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHoorolazim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e182.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJazmourian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e121.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKanibarazan Wetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e374.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKhuran Straits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e119.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLake Urmia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e277.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaharloo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e282.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMighan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e288.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiankaleh Peninsula, Gorgan Bay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e574.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParishan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e267.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShadegan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e143.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSheedvar Island\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e117.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShurgol, Yadegarlu \u0026amp; Dorgeh Sangi Lakes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e395.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTashk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e158.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZarivar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e687.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"3. Material and methods","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the key steps followed in the present research methodology, which are as follows:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eDaily Aerosol Optical Depth (AOD) data acquisition.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTrend analysis of AOD variations at monthly, seasonal, and annual scales over the LWs in Iran.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIdentification of the key aerosol driving factors and their relationship with AOD in the LWs with rising trends.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eDetailed description of each methodology step is provided below.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data acquisition\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1 Target factor (AOD data sets)\u003c/h2\u003e\u003cp\u003eIn the present study, AOD has been considered as the target variable. The MCD19A2 product, provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) at a daily temporal scale and with a spatial resolution of one kilometer, was used to extract AOD over 27 studied lakes and wetlands (LWs). This product, which extracts the concentration of atmospheric aerosols in two (blue and green) bands (0.47 and 0.55 \u0026micro;m) based on the Multi-Angle Implementation of Atmospheric Correction (MAIAC), was downloaded for 27 Iranian LWs from 2000 to 2024 through the Google Earth Engine system (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthengine.google.com\u003c/span\u003e\u003cspan address=\"https://earthengine.google.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Iran, there is one Aerosol Robotic Network (AERONET) station established in the northwest of Iran (IASBS: 48.5\u0026deg;E; 36.7\u0026deg;N) and several stations (Kuwait University, Kandahar, and Solar Village) around Iran with continuous dataset, which were used for comparison with MODIS-AODs.\u003c/p\u003e\u003cp\u003eGiven that the data used are at a wavelength of 550 nm and the corresponding values are not recorded in the AERONET sits, the following equation was used to calculate the AOD AERONET 550 nm \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Log\\left(AOD550\\right)={a}_{1}+{a}_{2}\\text{log}\\left(550\\right)+{a}_{3}{\\text{l}\\text{o}\\text{g}\\left(550\\right)}^{2}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{2}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{3}\\)\u003c/span\u003e\u003c/span\u003e indicate the fitting coefficients, are the fitting coefficients that are calculated based on ground-based observations of aerosol optical depth at various wavelengths (440, 500, 675,875). Mean absolute error (MAE), root mean square error (RMSE), and root mean bias (RMB) were used to evaluate the performance of MODIS data in this study, as has been done in many previous studies \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe comparison referred to collocated data over the mentioned stations, during the MODIS overpass time (\u0026plusmn;\u0026thinsp;30 min), revealing a strong correlation between satellite and ground-based AOD datasets (r\u0026thinsp;=\u0026thinsp;57 to 0.70; p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Therefore, MODIS observations with high spatial resolution are consistent with AERONET measurements, and therefore, daily MCD19A2 dataset were used to analyze the trend of changes in AOD over the LWs mentioned in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2 Aerosol Optical Depth Driving Factors\u003c/h2\u003e\u003cp\u003eSeveral climatic, meteorological and terrestrial factors may affect the variations in aerosol concentration and their optical properties (Yousefi \u003cem\u003eet al\u003c/em\u003e., 2025). Based on research background, literature overview and data availability, the following 8 driving factors were considered here to identify the most significant factors affecting AOD changes over Iranian wetlands and lakes. These factors include precipitation (Pre), actual evaporation (ET), normalized difference water index (NDWI), normalized difference salinity index (NDSI), enhanced vegetation index (EVI), precipitation, Palmer Drought Severity Index (PDSI), lakes-wetlands area (LWA), and wind speed (WS).\u003c/p\u003e\u003cp\u003eThe Climatology Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily precipitation product, which exhibits a high correlation with ground station data \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, was used in this study to extract precipitation for the selected wetlands and lakes. The spatial resolution of this product is 5566 meters.\u003c/p\u003e\u003cp\u003eThe MOD13Q1, with spatial resolutions of 250 m, was used to extract EVI values for selected wetlands and lakes during the study period. ET and WS values were extracted over Iran from the TerraClimate dataset with a spatial resolution of 4638 m \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe normalized difference of salinity index (NDSI) and the normalized difference of water index (NDWI) were calculated using Eq.\u0026nbsp;(2) \u003csup\u003e39\u003c/sup\u003e, and Eq.\u0026nbsp;(3) \u003csup\u003e40\u003c/sup\u003e, respectively, based on MODIS sensor imagery with a spatial resolution of 500 m.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:NDSI=\\frac{Red-NIR}{Red+NIR}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:(Eq.2)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:NDWI=\\frac{Green-NIR}{Green+NIR}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:(Eq.3)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eHere, NIR, Red, Green, and MIR are near-infrared, red, green, and middle infrared bands, respectively. Furthermore, water bodies were identified based on positive NDWI values, and their covered areas were calculated on a monthly scale for each wetland.\u003c/p\u003e\u003cp\u003eIt is noteworthy that all required datasets were acquired through coding within the Google Earth Engine platform for a monthly scale from 2000 to 2024. After analyzing the trends of AOD variations and identifying wetlands with significant increasing trends, other acquired data were used to identify the most important controlling factors of AOD, which will be further explained in detail in the subsequent research methodology section.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Trend analysis\u003c/h2\u003e\u003cp\u003eVarious statistical methods have been proposed for time series analysis. Among these, non-parametric methods are widely used in time series of qualitative meteorological and hydrological variables. These methods are suitable for time series that exhibit skewness or kurtosis and are independent of the statistical distribution of the time series. The purpose of trend testing is to investigate the presence or absence of an upward or downward trend in the data series \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The Mann-Kendall test is one of the most widely used tests in the non-parametric method, presented by Mann \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e and developed by Kendall \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. In this study, the test was used to analyze the trend of AOD changes at different time scales over the examined LWs in Iran.\u003c/p\u003e\u003cp\u003eThis test is based on two hypotheses, null and alternative. The null hypothesis states that the data series is random and has no trend, while the alternative hypothesis indicates the presence of a trend. The S statistic of the Mann-Kendall test represents the difference between each observation and all subsequent observations, calculated based on Eq.\u0026nbsp;(4):\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:S=\\sum\\:_{\\text{k}=1}^{\\text{n}-1}\\sum\\:_{\\text{j}=\\text{k}+1}^{\\text{n}}\\text{s}\\text{g}\\text{n}({\\text{x}}_{\\text{j}}-{\\text{x}}_{\\text{k}})\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:(Eq.4)\\:\\:\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn this equation, n is the number of observations in the time series, and xj and xk are the j-th and k-th data points of the series, respectively. Then, the variance of S is calculated and the standardized Z statistics are calculated using following equations:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:\\text{V}\\text{A}\\text{R}\\left(\\text{S}\\right)=\\frac{1}{18}\\left[\\text{n}\\left(\\text{n}-1\\right)\\left(2\\text{n}+5\\right)\\right]\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:(Eq.5)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:Z=\\left\\{\\begin{array}{c}\\begin{array}{cc}\\frac{\\text{S}-1}{\\sqrt{\\text{V}\\text{A}\\text{R}\\left(\\text{S}\\right)}}\u0026amp;\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\text{i}\\text{f}\\:\\text{S}\u0026gt;0\\end{array}\\\\\\:\\begin{array}{cc}0\u0026amp;\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\text{i}\\text{f}\\:\\text{S}=0\\end{array}\\\\\\:\\begin{array}{cc}\\frac{\\text{S}+1}{\\sqrt{\\text{V}\\text{A}\\text{R}\\left(\\text{S}\\right)}}\u0026amp;\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\text{i}\\text{f}\\text{S}\u0026lt;0\\end{array}\\end{array}\\right.\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:(Eq.6)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere S is the Mann-Kendall test statistic, xi and xj are yearly values in years i and j (j\u0026thinsp;\u0026gt;\u0026thinsp;i), n is the length of data series, and sgn is sign (+\u0026thinsp;or -) of (xj - xi). Also, Z is the standard test statistic. Z statistic positive values show increasing trend and Z negative values show decreasing trend in time series. The null hypothesis of no trend is rejected if |Z| \u0026gt;Zα, α. Where, |Z| and α are the absolute value of Mann-Kendall coefficient and level of statistical significance, respectively.\u003c/p\u003e\u003cp\u003eTo estimate the trend slope in a time series, the Sen's Slope estimator is one of the most suitable methods. This method was first introduced by Theil \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e and then expanded by Sen \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Like many other non-parametric methods such as Mann-Kendall, this method is based on analyzing the difference between observations in the time series. This method can be used when the trend in the time series is a linear trend. This means that Ft is equal to:\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:\\text{f}\\left(\\text{t}\\right)=\\text{Q}\\text{t}+\\text{B}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:(Eq.7)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere Q is the slope of the trend line and B is the constant value (intercept).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Data standardization\u003c/h2\u003e\u003cp\u003eStandardization is a preprocessing step that should be performed before collinearity analysis and modeling, especially when the data is multidimensional. This method improves the performance of ML models by removing the effect of variable scales \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. In this study, all variables were standardized using the Eq.\u0026nbsp;(8) as follows:\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$$\\:{Z}_{i}=\\frac{{X}_{i-}\\mu\\:}{\\sigma\\:}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:(Eq.8)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the formula above, Z\u003csub\u003ei\u003c/sub\u003e is the standard score for data X\u003csub\u003ei\u003c/sub\u003e, \u0026micro; is the mean and σ is the standard deviation of the data. By doing this, the Z\u003csub\u003ei\u003c/sub\u003e's will have a mean of 0 and a variance of 1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Multicollinearity Analysis\u003c/h2\u003e\u003cp\u003eCollinearity refers to a situation where an explanatory variable in multiple regression has a linear relationship with one or more other variables, such that it can be considered a linear combination of the other variables \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Similarly, multicollinearity indicates a situation where there is a linear relationship between several explanatory variables, and they can be written as a linear combination of each other \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhen collinearity or multicollinearity exists in a multiple regression model, the resulting model coefficients are not valid, as the effect of each explanatory variable on the response variable includes the effect of other variables in the model as well. Therefore, the variance of the regression coefficient estimators increases, and in practice, prediction by the regression model will be associated with a large bias. Thus, with a small change in the data used in the model, the regression coefficients will change drastically. The Variance Inflation Factor (VIF) serves as a metric indicating the degree to which prediction coefficient variance is inflated, and is calculated via the Eq.\u0026nbsp;(9) \u003csup\u003e49\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:VIF=\\frac{1}{TC}=\\frac{1}{\\left(1-{R}_{i}^{2}\\right)}\\)\u003c/span\u003e\u003c/span\u003e (Eq.\u0026nbsp;9)\u003c/p\u003e\u003cp\u003eHere, Ri\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e denotes the unadjusted coefficient of determination for the ith independent variable regressed against the remaining variables. The tolerance coefficient (TC) is the inverse of the VIF, with a low TC (TC\u0026thinsp;\u0026lt;\u0026thinsp;0.2) indicating a strong correlation between independent variables. In other words, TC values greater than 0.2 indicate a low effect of multicollinearity between the independent variables under consideration. One approach to address multicollinearity is to eliminate a variable strongly linked to other variable(s), a method employed in this study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Identification of the main aerosol driving factors\u003c/h2\u003e\u003cp\u003eAfter excluding the problematic variables (TC\u0026thinsp;\u0026lt;\u0026thinsp;0.2), the factors with the least collinearity effect were selected and standardized. The data related to the standardized AOD values, climatic parameters and land characteristics for selected wetland and lakes, were loaded separately in the R 4.4.1 software environment. For determining the importance of each factor affecting the AOD changes in the studied wetlands, the RF algorithm was used. Reduction of overfitting, identification of the importance of influential variables on the target variable, high accuracy, high flexibility and resistance to outliers are among the most important advantages of this algorithm \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Therefore, RF model was used to explore the relationship between AOD and their driving factors over selected wetlands and lakes with a significant rising trend in AOD during 2000\u0026ndash;2024. The Mean Decrease Gini (MDG) algorithm was lastly used to determine the contribution of the influential factors, since it\u0026rsquo;s one of the common methods to evaluate the importance of features \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. This algorithm shows how effective a particular feature is in reducing MDG at each node of the decision tree. In general, the larger decrease of MDG indicates the greater importance of that factor on changes in the target variable \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Interpreting the Relationship Between Key Driving Factors and AOD\u003c/h2\u003e\u003cp\u003eWhile identifying and prioritizing aerosol driving factors over water bodies is of great importance, uncovering the nature of their relationship enhances our understanding of how these factors influence the increased AODs over water bodies. To this end, the Pearson correlation coefficient \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e (Eq.\u0026nbsp;10), a simple and common technique for this purpose, was employed, following previous studies \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r=\\frac{n{\\sum\\:}_{i}^{n}{Aod}_{i}{*Df}_{i}-{\\sum\\:}_{i}^{n}{Aod}_{i}{\\sum\\:}_{i}^{n}{Df}_{i}}{\\sqrt{n{\\sum\\:}_{i}^{n}{Aod}_{i}^{2}-({\\sum\\:}^{n}{Aod}_{i}{)}^{2}}\\sqrt{n{\\sum\\:}_{i}^{n}{Df}_{i}^{2}-({\\sum\\:}^{n}{Df}_{i}{)}^{2}}}\\)\u003c/span\u003e\u003c/span\u003e (Eq.\u0026nbsp;10)\u003c/p\u003e\u003cp\u003ewhere n is the total number of variables in a given dataset. The AOD\u003csub\u003ei\u003c/sub\u003e and Df\u003csub\u003ei\u003c/sub\u003e are AOD and driving factors ​​of AOD, respectively in the ith month.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Monthly variation trend of AOD\u003c/h2\u003e\u003cp\u003eThe results of the Mann-Kendall test performed on the monthly average AOD values over 27 Iranian wetlands are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Based on the results presented in this statistical matrix and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, regardless of significance level, the trend of changes in AOD over Iranian water bodies presented the largest increase in the months of March, May, August, October and November, with increases of 74.1%, 77.8%, 88.9%.88%, 85.2%, and 74.1%, respectively during the period 2000\u0026ndash;2024. Furthermore, the majority of the lakes and wetlands exhibited increasing trends in AOD in January (59.3%), June (63%), July (66.7%), September (59.3%) and December (55.6%), while in February and April the fraction of Iranian\u0026rsquo;s LWs that exhibited increasing AOD trends dropped to 48.1% and 37%, respectively, indicating that in these two months, most of the studied LWs faced a decreasing trend in atmospheric aerosols.\u003c/p\u003e\u003cp\u003eThroughout the study period, the most significant increasing trends in AOD occurred in Lake Urmia and in Gomishan and Sheedvar wetlands. In Lake Urmia, statistically significant increasing trends were observed in all months (Z\u0026thinsp;\u0026gt;\u0026thinsp;1.96), indicating a large increase in AOD and dust activity during the last two decades, associated with the desiccation of the lake (Alizadeh \u003cem\u003eet al\u003c/em\u003e., 2020; Harati \u003cem\u003eet al\u003c/em\u003e., 2021; Abadi \u003cem\u003eet al\u003c/em\u003e., 2022; Hamzeh \u003cem\u003eet al\u003c/em\u003e., 2023; Ghasempour \u003cem\u003eet al\u003c/em\u003e., 2024). In the Gomishan wetland, significant increasing changes in AOD were in the months of March and May through November. The Sheedvar wetland experienced a significant increase in aerosol concentrations in January and February, as well as from August to November. Furthermore, a significant increasing trend of AOD occurred in the Tashk, Parishan, Amirkelayeh, Miankaleh, and Shurgol -Yadegarlu-Dorgeh wetlands for 3 to 4 months of the year (mostly in summer), and in the Hoor al-Azim, Delta-Rud-e-Shur-Shirin, Fereydoon Kenar, Hamun, and Jazmurian wetlands for 1 to 2 months. Conversely, the results showed significant decreasing trends (confidence level 0.05) in aerosol loading over the Gavkhouni wetland in February (Z= -2.5), March (Z=-3.5) and November (Z=-1.99). Statistically significant decreasing trends also occurred in Bujagh, Delta-Rud-e-Shur-Shirin, and Hamun wetlands in the months of January (Z=-2.2), February (Z= -2.5), and September (Z=-3.01), respectively. In synopsis, despite the large variability of the AOD trend values over the studied wetlands, attributed to different land use characteristics, contrasting meteorological patterns, prevailing wind regimes, etc, most of the wetlands exhibited the largest increases of AOD during the period with enhanced dust activity over Iran and the Middle East (May to September). This indicates that the increasing AODs are associated with enhanced dust emissions over the wetlands, attributed to their desiccation and the transformance of significant dust sources due to alluvial silt material left in the topsoil after their part or complete dryness (Rashki \u003cem\u003eet al\u003c/em\u003e., 2013; Behrooz \u003cem\u003eet al\u003c/em\u003e., 2016; Kharazmi \u003cem\u003eet al\u003c/em\u003e., 2018; Ebrahimi-Khusfi \u003cem\u003eet al\u003c/em\u003e., 2010, 2021; Khashi \u003cem\u003eet al\u003c/em\u003e., 2022). The main finding is a general increasing tendency in AOD over the Iranian lakes and wetlands, which is attributed to enhanced dust concentrations after the lake\u0026rsquo;s desiccation due to climate change and human intervention (increasing needs for irrigation, shrinkage of the water bodies, etc).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePercentage of lakes and wetlands with increasing and decreasing trends in AOD on monthly basis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive trend (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative trend (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e59.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e77.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e63.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJul\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAug\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e88.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOct\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNov\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDec\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e59.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Seasonal variation and trend of AOD in Iranian lakes and wetlands\u003c/h2\u003e\u003cp\u003eThe seasonal trends in AOD over Iranian lakes and wetlands are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The results show that during the past 25 winter seasons, the AOD over 14 water bodies (51.9%) exhibited an increasing trend (Z\u0026thinsp;\u0026gt;\u0026thinsp;0), which was found to be statistically significant over Parishan, Gomishan, and Sheedvar wetlands, and Lake Urmia, as well (Z\u0026thinsp;\u0026gt;\u0026thinsp;1.96). In other water bodies, the trend of AOD changes presented a decreasing tendency in winter, among which, only in Gavkhouni (Z = -3.4) and Bujagh (Z = -2.1) wetlands, it was statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn spring, the trend of AOD changes has been increasing in 15 water bodies (55.6%) and decreasing in 12 water bodies (44.4%). In this season, similar to winter, the decreasing trend of AOD was insignificant in all areas (Z \u0026gt;-1.96). Conversely, statistically significant increasing trends of aerosol concentration were observed over Lake Urmia, Miankaleh, and Gomishan wetlands (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn summer, when the dust activity over Iran and the Middle East maximizes (Rashki \u003cem\u003eet al\u003c/em\u003e., 2014; Abadi \u003cem\u003eet al\u003c/em\u003e., 2025), the concentrations of aerosols showed an increasing trend over 20 water bodies (74.1%), which, like the spring season, was significant in Parishan (Z\u0026thinsp;=\u0026thinsp;2.3), Gomishan (Z\u0026thinsp;=\u0026thinsp;4.3), Sheedvar (Z\u0026thinsp;=\u0026thinsp;1.99), and Lake Urmia (Z\u0026thinsp;=\u0026thinsp;4.09), while it was insignificant in the other water bodies. Nevertheless, the summer dusty season exhibits the highest fraction of increased AOD over the examined wetlands, implying larger desiccation rates of the wetlands and enhanced dust emissions. This increase in AOD, which practically corresponds to increased dust activity in areas surrounding wetlands and lakes in Iran, is especially important for regional atmospheric composition, degradation of air quality and negative effects in the aquatic ecosystems (Hamzeh \u003cem\u003eet al\u003c/em\u003e., 2023; Ahrari \u003cem\u003eet al\u003c/em\u003e., 2024; Zadifar \u003cem\u003eet al\u003c/em\u003e., 2024). In the 7 wetlands where declining AOD trends were observed, these trends were not significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOut of the 18 water bodies (66.7%), where an increase in aerosol concentration occurred in autumn, 7 bodies\u0026mdash;Shurgol-Yadegarlu-Dorgeh (Z\u0026thinsp;=\u0026thinsp;2.31), Sheedvar, Gomishan (Z\u0026thinsp;=\u0026thinsp;2.36), Lake Urmia (Z\u0026thinsp;=\u0026thinsp;4.69), Amirkelayeh (Z\u0026thinsp;=\u0026thinsp;3.06), Jazmourian (Z\u0026thinsp;=\u0026thinsp;2.3), and Parishan (Z\u0026thinsp;=\u0026thinsp;3.4)\u0026mdash;have experienced a statistically significant increase. This suggests positive feedback between shrinkage of water bodies and dust-aerosol emissions that affect the nearby areas. Over other wetlands, aerosol changes have been increasing or decreasing, but without significant trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePercentage of the lakes and wetlands presenting increasing and decreasing trends in AOD on seasonal and annual basis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime scale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive trend (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative trend (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWinter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSummer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutumn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Mutli-decadal Trends of AOD over Iranian Lakes and Wetlands\u003c/h2\u003e\u003cp\u003eThe annual-averaged AODs and their trends over the 27 studied lakes and wetlands are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, while the AOD values during the study period are displayed using box plots in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Based on the current analysis, during the past 25 years, high median AOD values (exceeding 0.5) were observed in Gavkhouni, Tashk, Gomishan, Maharloo, Bakhtegan, and Hamun wetlands. The first quartiles of the AOD values for these wetlands are 0.95, 0.37, 0.42, 0.35, 0.36, and 0.47, respectively, while the third ones are 1.34, 0.73, 0.69, 0.66, 0.59, and 0.54, respectively. The first quartile, median, and third quartile AOD values for Lake Urmia, Delta-Rud-e-Shur-Shirin, Delta-Rud-e-Gaz, Miankaleh, Jazmourian, Shadegan, Sheedvar, Khuran, Hourolazim, Alagol, and Parishan range from 0.19 to 0.27, 0.3 to 0.41, and 0.36 to 0.58, respectively, indicating presence of high aerosol loading over the studied lakes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Mann-Kendall statistic and Sen's slope estimator were respectively used to investigate the trend and slope of changes in the annual AOD time series, while the outcomes are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The results indicated that over the past two decades, AOD has increased over 15 (55.6%) of the studied wetlands, while decreased in 12 (44.4%). The increasing trends in Lake Urmia (Z\u0026thinsp;=\u0026thinsp;3.9) and the Parishan (Z\u0026thinsp;=\u0026thinsp;2.08), Gomishan (Z\u0026thinsp;=\u0026thinsp;4.1), Miankaleh (Z\u0026thinsp;=\u0026thinsp;2.7), and Sheedvar (Z\u0026thinsp;=\u0026thinsp;2.9) wetlands were significantly higher than those in other water bodies and were statistically significant at 95% confidence level. Based on Sen's slope, the average annual changes in AOD over Lake Urmia and the mentioned wetlands were 0.024, 0.004, 0.017, 0.003, and 0.002, respectively. Conversely, the decreasing changes in Shadegan (Z = -2.03), Hamoun (Z = -2.2), and Khuran (Z = -2.03) wetlands were more pronounced than those in other wetlands and were significant at the 95% level. Based on Sen's slope, the average AOD decreased by 0.002 in Shadegan and Khuran and by approximately 0.005 per year in Hamoun wetland. The contrasting AOD trends over the various wetlands and lakes in Iran are attributed to different topographic and climatic characteristics that prevailed during the 25 years study period. Recently, Abadi \u003cem\u003eet al\u003c/em\u003e. (2025) indicated very different trends in suspended and blowing dust events across the various regions in Iran, that control the AOD values over the wetlands. Especially for Hamun (Sistan Basin, east Iran), the large decreasing trend in AOD during the last decades has been attributed to the extreme drought and abnormal dust activity during 2000\u0026ndash;2003 (at the beginning of the studied period) (Rashki \u003cem\u003eet al.\u003c/em\u003e, 2014; Shaheen \u003cem\u003eet al\u003c/em\u003e., 2023; Yousefi \u003cem\u003eet al\u003c/em\u003e., 2023). After this period, high but mostly normal for this site dusty conditions prevailed, thus contributed to a declining dust-aerosol trend during 2000\u0026ndash;2025. Conversely, over SW and western part of Iran, including Urmia Lake, the dust activity has increased significantly during the 2000s due to drought regime shift in the Mesopotamian plains that enhanced dust activity over Iran and the Middle East (Notaro et al., 2015; Hamzeh et al., 2021). More specifically, the large increase in AOD over Urmia, is associated not only with local factors (i.e. desiccation of the lake, construction of dams, etc), but also with an increase in transported dust events over the region from distant dust sources in Iraq or SW Iran (Abadi \u003cem\u003eet al\u003c/em\u003e., 2022; Hamzeh \u003cem\u003eet al\u003c/em\u003e., 2023). Therefore, apart from local topographic conditions, LULC changes and climatic parameters related with wetlands and dust emissions from the dried lakebeds, changes in synoptic meteorology may also play an important role in this trend analysis over the specific wetlands.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Identification of the Main Aerosol Driving Factors across Iran\u0026rsquo;s Lakes and Wetlands\u003c/h2\u003e\u003cp\u003eThe results of the multicollinearity analysis between the factors influencing AOD showed that the tolerance coefficient (TC) values for NDWI in Parishan and Sheedvar Wetlands, and for WLA and NDWI in Gomishan Wetland, were less than 0.2 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), indicating problematic multicollinearity among these driving factors. After removing NDWI, the TC values of the other parameters increased to above 0.2. Furthermore, the results of the multicollinearity analysis between the factors influencing AOD in Miankaleh Wetland and Lake Urmia showed that there was no problematic multicollinearity effect among these factors. Therefore, the factors shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e were ultimately used for modeling. The monthly-mean values of the selected factors for a 25-year study period are shown in the Supplementary Material (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTolerance coefficient values for driving factors of AOD change in selected wetlands and lakes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDriving Factors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParishan\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGomishan\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMiankaled\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSheedvar\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUrmia lake\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEvaporation (ET)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.246\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnhanced Vegetation Ιndex (EVI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.579\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormalized difference salinity index (NDSI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrecipitation (Pre)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.240\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePalmer Drought Severe Index (PDSI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.887\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLake and wetland area (LWA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormalized difference water index (NDWI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWind speed (WS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.586\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe importance of driving factors affecting the monthly changes in AOD on the wetlands of Parishan, Gomishan, Miankaleh, Sheedvar, and Lake Urmia is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e\u003cb\u003e(a-e)\u003c/b\u003e. The results showed that over the past 25 years, ET (Mean Decrease Gini (MDG): 22%) has been the most important driver affecting the changes in aerosol concentration over the Parishan wetland. Following that, Pre (MDG of 19.8%), NDSI (16.5%), and WS (MDG\u0026thinsp;=\u0026thinsp;15.4%) had a significant effect on the AOD variations, while EVI, PDSI, and LWA were identified as the less important drivers (MDG\u0026thinsp;\u0026lt;\u0026thinsp;10.5%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eThe results of the MDG analysis regarding the driving factors affecting the increasing concentration of aerosols over the Gomishan International Wetland showed that the variable LWA (MDG\u0026thinsp;=\u0026thinsp;29.4%) was the most significant factor affecting AOD changes. Following this, NDSI with an MDG of 25.3% ranked second. These two drivers alone account for more than 50% of the changes in aerosol concentration, indicating their key role in the increased emissions of aerosols over Gomishan wetland. Precipitation, with an MDG of 16.7%, ranked third and plays a significant role in AOD changes in this region. In contrast, the variables WS, ET, EVI, and PDSI, with MDGs of 10.6%, 9.2%, 4.4%, and 4.4%, respectively, exhibited lesser impact on the target variable (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eAnalysis of the results from the MDG reduction shows that the NDSI has been the strongest effective driver of increased aerosol concentrations over the Miankaleh Wetland during the past 25 years. This highlights the particular importance of soil salinity in increasing aerosol emissions in the study area. Following this, precipitation (MDG: 19.9%) has played a significant role in AOD variations. These two climatic and terrestrial drivers presented the largest contribution to the increased aerosol emissions over the Miankaleh International Wetland, demonstrating their significant impact on air pollution in this region. Wind speed (MDG: 16.7%) ranked third in importance and is recognized as an effective factor in increasing aerosol concentration. Other variables, including ET, NDWI, LWA, EVI, and PDSI, with MDG values below 12%, have played minor roles in increasing AOD over this region (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003eThe analysis of the significance of the driving forces affecting the increase in aerosol concentration over the Sheedvar Wetland revealed that the EVI, with an average Gini reduction of 16%, had the highest impact among the main driving forces in the AOD increase over this region. Subsequently, the driving factors LWA with 14.5%, NDSI with 14.3%, and ET with 14.1% are ranked next. These three variables also played a significant role in increasing aerosol concentrations and exhibited a relatively small difference in their impact. The driving factors WS, Pre, and PDSI also presented notable importance, with 13.9%, 13.6%, and 13.5%, respectively. Although the impact of these factors is less than that of the higher-ranked factors, they still play a significant role in increasing aerosol concentration (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003eThe findings of this research also revealed that the contribution of climatic and terrestrial factors to the increase in aerosol concentration over Lake Urmia varied significantly. Among these, the NDWI driving factor, with an MDG of 35.7%, held the greatest importance among the studied factors. Following this, the LWA factor, with an MDG of 22.6%, ranked second in importance, while the NDSI (MDG: 11.1%) was also identified as one of the main influential drivers. On the contrary, EVI, WS, Pre, PDSI, and ET drivers, with MDGs of 7.9%, 6.8%, 6.5%, 5.2%, and 4.2%, respectively, were of lesser importance compared to the first three factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ee).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe results presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e demonstrate the percentage contributions (5) between climatic and land-based (topographic) driving factors to the increase in aerosol concentration over the examined lakes and wetlands in Iran. During the study period (2000\u0026ndash;2024), the contribution of climatic drivers (65.5%) to the increase in aerosol concentration over Parishan Wetland was almost twice than that of terrestrial factors (34.5%). Conversely, in Gomishan Wetland, the contribution of land-based drivers (59.1%) was higher than climatic elements (40.9%). In Miankaleh Wetland, the contributions of climatic (53.2%) and terrestrial (46.8%) factors were nearly equal, indicating that both categories of drivers have played an important role in increasing aerosol concentration, while control policies and management strategies should be considered for both. The results of the analysis of the effect of terrestrial and climatic drivers on changes in AOD over the Sheedvar Wetland showed that climatic factors, with a share of 55.2%, have played the dominant role in this region. This is while the share of terrestrial factors has been estimated at 44.8%. These findings indicate that although terrestrial drivers have had a significant impact on increasing AOD and reducing air quality, climatic factors have still played a decisive role in the air quality of the area surrounding this wetland. In Lake Urmia, the role of terrestrial drivers (77.3%) in increasing aerosol emissions was significantly greater than that of climatic drivers (22.7%) and similar results are documented in previous works (Ghale \u003cem\u003eet al\u003c/em\u003e., 2019; Hamzeh \u003cem\u003eet al\u003c/em\u003e., 2023). Overall, the results show that over the past 25 years, the role of terrestrial and climatic drivers in increasing arosol loading over the studied water bodies in Iran has varied. In some wetlands, climatic drivers have played a more prominent role, while in others, terrestrial factors have been more significant. This highlights the need to develop and implement strategic management and control programs tailored to the specific conditions of each region.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Relationship Between Key Driving Factors and AOD\u003c/h2\u003e\u003cp\u003eIn this study, the correlation between key driving factors and AOD was also investigated to determine the type of relationship and how they influence changes in aerosol thickness over each water body. Our findings exhibited that in Parishan Wetland, all factors except WS showed a strong negative correlation with AOD. The strongest linkage was observed with NDSI (r= -0.512; P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while the weakest was with EVI (r=-0.218; P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The WS showed a significant positive relation with AOD variations (r\u0026thinsp;=\u0026thinsp;0.419; P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01), likely indicating the positive effect of wind on dust emissions from the dried beds or transport of aerosols from nearby arid sources under stronger winds (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ea).In the Gomishan, there was also a very strong indirect relationship with AOD in the majority of drivers. The most negative relationship was with NDSI (r=-0.652; P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and the least with EVI (r= -0.084; P-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05). WS had a positive significant relationship with AOD (r\u0026thinsp;=\u0026thinsp;0.583; P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01), maybe because of the same reasons mentioned above(Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eb). In Miankaleh Wetland, NDSI and Precipitation factors showed the strongest negative correlations with AOD (r= -0.676and \u0026minus;\u0026thinsp;0.546, respectively), while WS again exhibited a significant positive correlation with AOD (r\u0026thinsp;=\u0026thinsp;0.628; P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01). On the contrary, the WLA factor showed a positive but non-significant relation with AOD (r\u0026thinsp;=\u0026thinsp;0.084; P-value\u0026thinsp;\u0026gt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ec). In Sheedvar Wetland, the EVI and NDSI factors presented significant positive correlations with AOD (r\u0026thinsp;=\u0026thinsp;0.271 and r\u0026thinsp;=\u0026thinsp;0.219, respectively (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01)). Conversely, the WS factor exhibited a significant negative correlation with AOD (r= -0.209; P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Other driving factors did not have a significant impact on the changes in AOD in this region (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ed). In Lake Urmia, NDSI and NDWI showed the strongest negative correlations with AOD (r= -0.350 and \u0026minus;\u0026thinsp;0.670, respectively), while WS and EVI showed significant positive correlations with AOD variations (r\u0026thinsp;=\u0026thinsp;0.34 and 0.17, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ee).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Trend of AOD Changes Over Iranian Lakes and Wetlands\u003c/h2\u003e\u003cp\u003eMonitoring and analyzing trends in AOD is a key step in understanding air pollution status over various ecosystems, particularly wetlands and lakes, some of which have experienced significant changes in recent years due to human intervention and climate change \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Over the past 25 years (2000\u0026ndash;2024), there has been a significant decreasing trend in AOD over three important Iranian international wetlands: Hamun, Shadegan, and Khuran, while a substantial increase has been observed over Lake Urmia and the Gomishan, Sheedvar, Miankaleh, and Parishan wetlands.\u003c/p\u003e\u003cp\u003eAOD data from 2000 to 2020 was used to validate a predicted dust source map in Shadegan wetland, reporting a non-significant increasing trend in aerosols \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. These findings did not align with current results. Given that these researchers did not specify on the temporal scale or extent of the wetland area examined for the trend analysis, it appears that one reason for the discrepancy is that the AOD trend in their study was solely based on barren lands within the wetland area. In another study, dust event frequency around this wetland over a period overlapping with the present study (2000\u0026ndash;2017) showed no significant trend \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Recently, another study reported an increase in Shadegan wetland's water level in 2020 compared to 2000 \u003csup\u003e3\u003c/sup\u003e. Therefore, considering these findings, the primary cause for the decreasing aerosol concentration trend in recent decades can be attributed to the increased water level in this international wetland.\u003c/p\u003e\u003cp\u003eIn the Hamun wetland, located in the Sistan Basin, the aerosol trend showed an increase from 2001 to 2009 and a decrease from 2010 to 2019 \u003csup\u003e58\u003c/sup\u003e, consistent with the findings of the present study in that time frame, while the general decrease in AOD during the 2010s in east Iran was mostly attributed to changes in meteorological factors (Yousefi \u003cem\u003eet al\u003c/em\u003e., 2023).\u003c/p\u003e\u003cp\u003eThe water area of Khuran wetland decreased by approximately 2% in 2020 compared to 2000 \u003csup\u003e3\u003c/sup\u003e, while according to the present study, the AOD over this region was 0.35 in 2000 and about 0.28 in 2020, indicating a 20% decrease. Comparing the findings of these two studies does not show an inverse relationship between AOD changes and water coverage in Khuran wetland. In other words, despite the increase in the dried-up bed of this wetland, the AOD has decreased, likely indicating less dynamic of local dust emissions from the dried lake beds. On the other hand, this AOD increase could be attributed to changes in wind patterns and the transport of dust plumes from other regions or to the deposition of regional emitted particles due to high humidity in this area.\u003c/p\u003e\u003cp\u003eIn the period 2000\u0026ndash;2010, the trend of winter AODs and warm-season aerosol concentrations in Iran showed a significant increase, followed by a decrease during 2010\u0026ndash;2018, which largely aligns with the aerosol concentration trend over many water bodies in Iran during those periods \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. In general, the decreasing changes in aerosols in dust-prone regions could be due to severe precipitation anomalies in recent years, which have particularly affected the frequency of dust events in high-rainfall years \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Given that the primary focus of this study is on lakes and wetlands that have shown an increasing trend in aerosols during the past 25 years, the main drivers of these changes in the Parishan, Gomishan, Miankaleh, Sheedvar wetlands, and Lake Urmia have been analyzed in detail and discussed below.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Main Driving Factors and their relationships with AOD in Iran\u0026rsquo;s Lakes and wetlands\u003c/h2\u003e\u003cp\u003eIn recent years, several climatic, terrestrial factors and human interventions have influenced changes in aerosol concentration trends in Iran \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. According to the findings of this study, significant increasing trends in aerosols, which are related with intensified wind erosion and dust emission phenomena, were observed over Parishan (southwestern Iran), Gomishan and Miankaleh (northern Iran), and Sheedvar (southern Iran) wetlands, as well as over Lake Urmia (northwestern Iran). Changes in various climatic factors, including rainfall, temperature, wind speed, and drought, as well as terrestrial factors such as soil moisture content, vegetation cover, and soil salinity, affect the multi-decadal variations and trends of atmospheric aerosols \u003csup\u003e\u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. However, the impact of each factor varies among the different ecosystems \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, especially on the wetlands.\u003c/p\u003e\u003cp\u003eThe analysis of long-term monthly trend changes of the driving forces affecting AOD showed a weak positive correlation between ET and AOD in the Sheedvar International Wetland, while negative correlations between these two parameters were observed in the Parishan, Gomishan, Miankaleh, and Lake Urmia. Current findings showed an increasing trend of AOD in these areas, which may indicate the transfer of dust particles from adjacent wind erosion-sensitive areas, and not so local dust emissions (Hamzeh \u003cem\u003eet al\u003c/em\u003e., 2023), an issue that requires further information and more comprehensive analysis, which is beyond the scope of this study.\u003c/p\u003e\u003cp\u003eDuring the study period, the relationship between vegetation cover and AOD in Parishan, Gomishan, and Miankaleh was inverse, which is consistent with results of previous studies \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. However, a direct relationship between these two parameters was observed in the Sheedvar wetland and Lake Urmia. This result is consistent with the findings of \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, who observed both positive and negative correlations between vegetation and AOD in parts of East Africa.\u003c/p\u003e\u003cp\u003eOver the past 25 years, the relationship between AOD and NDSI has been weakly positive in Sheedvar and weakly negative in other water bodies. This result indicates the dual role of the salinity of the bed of Iran's inland aquatic ecosystems on AOD. This could be related to the difference in size and chemical composition of salt aerosols and dust aerosols, and their respective effects on aerosol thickness \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhile a very weak positive correlation was observed between rainfall and AOD in the Sheedvar wetland, decreased rainfall has led to an increase in AOD in other water bodies in Iran. A dual relationship was also observed regarding this driving factor in different wetlands. This is because under decreased rainfall, the processes of aerosol deposition and removal from the atmosphere are reduced \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. On the other hand, decreased rainfall leads to a reduction in soil moisture and an increase in dust emissions \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. The inverse relationship between AOD-PDSI in these four international water bodies also highlighted the intensifying effect of droughts on increasing AOD, which is consistent with the findings of previous studies on the effect of drought on increasing dust aerosol emissions \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAnalysis of the relationship between LWA and AOD showed that the decrease in the water level of the Miankaleh wetland did not have significant impact on AOD changes, while the average decrease in NDWI, which indicates a decrease in water content and soil moisture content, had a significant impact on increasing AOD in this region. In the three wetlands of Sheedvar, Gomishan, and Parishan, changes in LWA - and in Lake Urmia, simultaneous changes in water level and NDWI - have led to an increase in aerosols, which is consistent with many findings of other researchers \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe relationship between WS and AOD in the Sheedvar wetland was negative. One of the possible reasons for this result can be the time scale of the analysis between these two parameters, because this relationship was established on a monthly scale, while the effect of wind on dust emissions usually occurs at shorter scales, i.e. hourly or daily \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. On the other hand, the Sheedvar wetland is located near the open waters of the Persian Gulf and the Oman Sea, and the likely dominant aerosol type in this region is marine mixed with dust, composed by a significant salt spray caused by sea winds. These types of aerosols are produced more with increasing wind speed, but due to their size and specific chemical composition, they may have less impact on increasing AOD compared to dust aerosols. On the contrary, the relationship of monthly fluctuations in wind speed and AOD in the Parishan, Gomishan, Miankaleh wetlands, and Lake Urmia have been positive. This indicates that in the past 25 years, the sensitivity of the dried beds of these areas to dust storms has increased more compared to the Sheedvar wetland. This is consistent with the findings of previous studies, which had pointed to the key effect of wind speed on increasing aerosol concentrations in arid lands \u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn general, changes in the water level of all three water bodies, Gomishan, Sheedvar, and Lake Urmia, were identified as one of the main factors affecting the increase in aerosol concentrations. Past studies have also reported that the water levels of these three bodies, as well as Parishan and Miankaleh wetlands, decreased in 2020 compared to 2000 \u003csup\u003e3,77\u003c/sup\u003e, which is consistent with current findings showing a decreasing trend in wetland levels. The main causes of increased aerosol emissions over northern China's wetlands in the period 2001\u0026ndash;2023 were reported to be population density and humidity changes \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, which is partly consistent with the results of the present study that refer to the role of humidity changes in increasing aerosol emissions, but not with the population density parameter. The reasons for the discrepancy in other findings are the difference in the selection of factors affecting AOD, and the type of model used to identify the factors influencing its changes, while local topography and regional meteorology play an important role in AOD variations, which may highly affect the correlations.\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThe increase in aerosols over aquatic ecosystems is a warning sign of disruption in the functioning of these valuable ecosystems and intensification of sand and dust storms. Understanding the trend of changes in Aerosol Optical Depth (AOD) in these areas and identifying their main drivers is of great importance. This issue has not received much attention, and therefore, this study focused on AOD changes over Iranian wetlands and lakes, aiming to assess their interrelations. According to current findings, in winter, spring, summer and autumn, as well as on an annual scale, 51.9%, 55.6%, 74.1%, 66.7%, and 55.6% of Iran's inland wetlands and lakes have experienced an increasing trend in AOD, respectively. A significant increasing trend was observed in five international wetlands: Parishan, Gomishan, Miankaleh, Sheedvar, and Lake Urmia. Evaporation and rainfall were identified as the main drivers of increased aerosol concentrations in the Parishan International Wetland. Changes in water area and soil salinity in the Gomishan Wetland and Lake Urmia played a key role in increasing aerosol concentrations over these valuable ecosystems. In addition, precipitation and soil salinity were identified as the main drivers of air quality reduction over the Miankaleh Wetland. The main cause of air quality degradation in Sheedvar wetland was the reduction of vegetation cover and the increase in the dried area of the wetland. The increasing trend of aerosols over the three international wetlands of Parishan, Miankaleh, and Sheedvar was mainly influenced by changes in climatic elements, while in the Gomishan Wetland and Lake Urmia, it was due to changes in land-based factors.\u003c/p\u003e\u003cp\u003eIn general, these findings highlighted the variety of factors affecting Iran's wetlands, and as a result, any action to reduce air pollution should be tailored to each wetland at risk, considering its specific environmental conditions. The results of this research can be useful for controlling air pollution and reducing its adverse environmental effects in Iran. Considering that the driving forces affecting AOD, especially climatic factors, have significant fluctuations, accurate prediction of their future trend is associated with uncertainty. Therefore, continuous monitoring of air quality over aquatic ecosystems, especially wetlands that have experienced an increasing trend of aerosols, is essential. Also, considering that the mentioned water bodies are of international importance, cooperation with international organizations is necessary to prevent further degradation of these ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors\u0026nbsp;thank\u0026nbsp;the efforts of the\u0026nbsp;MODIS and Terr Climate\u0026nbsp;product\u0026nbsp;teams\u0026nbsp;in\u0026nbsp;producing\u0026nbsp;and distributing these\u0026nbsp;worthwhile\u0026nbsp;datasets. The authors also\u0026nbsp;thank\u0026nbsp;the AERONET network and the\u0026nbsp;diligent\u0026nbsp;people\u0026nbsp;working\u0026nbsp;towards\u0026nbsp;gathering\u0026nbsp;and processing\u0026nbsp;the aerosol data used in this\u0026nbsp;research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' Contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZohre Ebrahimi-Khusfi\u003c/strong\u003e: writing – original draft, visualization, resources, project administration, methodology, investigation, formal analysis, data curation, conceptualization, writing – review \u0026amp; editing. \u003cstrong\u003eSeyed Arman Samadi-Todar\u003c/strong\u003e: software, methodology, formal analysis, data curation, conceptualization, writing – review \u0026amp; editing. \u003cstrong\u003eNarjes Okati\u003c/strong\u003e: writing – original draft, investigation, formal analysis. \u003cstrong\u003eDimitris Kaskaoutis:\u003c/strong\u003e methodology, writing – review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData available on request from the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eImdad, K.\u003cem\u003e et al.\u003c/em\u003e Wetland health, water quality, and resident perceptions of declining ecosystem services: a case study of Mount Abu, Rajasthan, India. \u003cem\u003eEnvironmental science and pollution research\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 116617-116643 (2023).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eAmindin, A.\u003cem\u003e et al.\u003c/em\u003e Long term \u003c/span\u003eanalysis of international wetlands in Iran: Monitoring surface water area and water balance. \u003cem\u003eInternational Journal of Applied Earth Observation and Geoinformation\u003c/em\u003e \u003cstrong\u003e126\u003c/strong\u003e, 103637 (2024).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eRahimi, E., Jahandideh, M., Dong, P. \u0026amp; Ahmadzadeh, F. Potential anthropogenic and climatic factors affecting Iran\u0026rsquo;s international wetlands. \u003cem\u003eJournal of Environmental Studies and Sciences\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 557-574 (2023).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eKool, J., Lhermitte, S., Hrachowitz, M., Bregoli, F. \u0026amp; McClain, M. E. Seasonal inundation dynamics and water balance \u003c/span\u003eof the Mara Wetland, Tanzania based on multi-temporal Sentinel-2 image classification. \u003cem\u003eInternational Journal of Applied Earth Observation and Geoinformation\u003c/em\u003e \u003cstrong\u003e109\u003c/strong\u003e, 102766 (2022).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eBelyaev, A. I., Pugacheva, A. M. \u0026amp; Korneeva, E. A. Assessment of ecosystem services of wetlands of the volga\u0026ndash;akhtuba floodplain. \u003cem\u003eSustainability\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 11240 (2022).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eNasab, S. E.\u003cem\u003e et al.\u003c/em\u003e Waterless wetlands, as a new source of wind erosion: Developing remediation strategies for the Hamoon Wetland in Iran. \u003cem\u003eEcological Informatics\u003c/em\u003e \u003cstrong\u003e78\u003c/strong\u003e, 102368 (2023).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eBadamfirooz, J., Mousazadeh, R. \u0026amp; Sarkheil, H. A proposed framework for economic valuation and assessment of damages cost to national wetlands ecosystem services using the benefit-transfer approach. \u003cem\u003eEnvironmental Challenges\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 100303 (2021).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eZekarias, T., Govindu, V., Kebede, Y. \u0026amp; Gelaw, A. Geospatial analysis of wetland dynamics on Lake Abaya-Chamo, the main rift valley of Ethiopia. \u003cem\u003eHeliyon\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e (2021).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eLi, T.\u003cem\u003e et al.\u003c/em\u003e Contributions of various driving factors to air pollution events: Interpretability analysis from Machine learning perspective. \u003cem\u003eEnvironment International\u003c/em\u003e \u003cstrong\u003e173\u003c/strong\u003e, 107861 (2023).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eMumuni, A. S., Mensah, H., Asamoah, S. \u0026amp; Simpeh, E. K. A systematic review on the causes and effects of urbanization on wetlands in Sub-Saharan Africa. \u003c/span\u003e\u003cem\u003eUrbanization, Sustainability and Society\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 153-177 (2025).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eElegbede, I. O.\u003cem\u003e et al.\u003c/em\u003e Wetland Ecosystems in Nigeria. \u003cem\u003eWetlands of Tropical and Subtropical Asia and Africa: Biodiversity, Livelihoods and Conservation\u003c/em\u003e, 317 (2025).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eSadeghi, S. H. \u0026amp; Hazbavi, Z. in \u003cem\u003eGlobal Degradation of Soil and Water Resources: Regional Assessment and Strategies\u003c/em\u003e 287-314 (Springer, 2022).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eKhashi, K., Azhdary Moghaddam, M. \u0026amp; Hashemi Monfared, S. Effects of wetlands and wind velocity on dust propagation in arid areas: \u003c/span\u003ea critical case study in south-east of Iran. \u003cem\u003eInternational Journal of Environmental Science and Technology\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 12169-12180 (2022).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003esadat Afzalizadeh, M., Nadoushan, M. A., Jalalian, A. \u0026amp; Chamani, A. Dust source dynamics in arid Iran: Examining the relationship between MODIS AOD and land surface characteristics in a dried catchment. \u003cem\u003eAdvances in Space Research\u003c/em\u003e \u003cstrong\u003e75\u003c/strong\u003e, 3326-3334 (2025).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eStirling, E., Fitzpatrick, R. \u0026amp; Mosley, L. Drought effects on wet soils in inland wetlands and peatlands. \u003cem\u003eEarth-Science Reviews\u003c/em\u003e \u003cstrong\u003e210\u003c/strong\u003e, 103387 (2020).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eKhelifa, R., Mahdjoub, H. \u0026amp; Samways, M. J. Combined climatic and anthropogenic stress threaten resilience of important wetland sites in an arid region. \u003cem\u003eScience of the Total Environment\u003c/em\u003e \u003cstrong\u003e806\u003c/strong\u003e, 150806 (2022).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eWang, M.\u003cem\u003e et al.\u003c/em\u003e Interannual changes of urban wetlands in China\u0026rsquo;s major cities from 1985 to 2022. \u003cem\u003eISPRS Journal of Photogrammetry and Remote Sensing\u003c/em\u003e \u003cstrong\u003e209\u003c/strong\u003e, 383-397 (2024).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eZarrinabadi, E. Soil erosion and fluxes of sediment within landscapes of the Canadian Prairies. (2023).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eRahdari, M. R., Kharazmi, R. \u0026amp; Caballero-Calvo, A. in \u003cem\u003eGoogle Earth Engine and Artificial Intelligence for Earth Observation\u003c/em\u003e 195-212 (Elsevier, 2025).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eAbou Zaki, N.\u003cem\u003e et al.\u003c/em\u003e Evaluating impacts of irrigation and drought on river, groundwater and a terminal wetland in the Zayanderud Basin, Iran. \u003cem\u003eWater\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 1302 (2020).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eMehta, M., Singh, R., Singh, A. \u0026amp; Singh, N. Recent global aerosol optical depth variations and trends\u0026mdash;A comparative study using MODIS and MISR level 3 datasets. \u003cem\u003eRemote sensing of environment\u003c/em\u003e \u003cstrong\u003e181\u003c/strong\u003e, 137-150 (2016).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eFilonchyk, M., Yan, H. \u0026amp; Zhang, Z. Analysis of spatial and temporal variability of aerosol optical depth over China using MODIS combined Dark Target and Deep Blue product. \u003cem\u003eTheoretical and Applied Climatology\u003c/em\u003e \u003cstrong\u003e137\u003c/strong\u003e, 2271-2288 (2019).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eWang, D., Zhang, F., Yang, S., Xia, N. \u0026amp; Ariken, M. Exploring the spatial-temporal characteristics of the aerosol optical depth (AOD) in Central Asia based on the moderate resolution imaging spectroradiometer (MODIS). \u003cem\u003eEnvironmental monitoring and assessment\u003c/em\u003e \u003cstrong\u003e192\u003c/strong\u003e, 1-15 (2020).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eLin, C., Cohen, J. B., Wang, S. \u0026amp; Lan, R. Application of a combined standard deviation and mean based approach to MOPITT CO column data, and resulting improved representation of biomass burning and urban air pollution sources. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e \u003cstrong\u003e241\u003c/strong\u003e, 111720 (2020).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eYousefi, R., Wang, F., Ge, Q. \u0026amp; Shaheen, A. Long-term aerosol optical depth trend over Iran and identification of dominant aerosol types. \u003cem\u003eScience of the Total Environment\u003c/em\u003e \u003cstrong\u003e722\u003c/strong\u003e, 137906 (2020).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eDadashi-Roudbari, A. \u0026amp; Ahmadi, M. Evaluating temporal and spatial variability and trend of aerosol optical depth (550 nm) over Iran using data from MODIS on board the Terra and Aqua satellites. \u003cem\u003eArabian Journal of Geosciences\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1-23 (2020).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eSharma, V.\u003cem\u003e et al.\u003c/em\u003e Spatial variation and relation of aerosol optical depth with LULC and spectral indices. \u003cem\u003eAtmosphere\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1992 (2022).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eSolanki, R. \u0026amp; Pathak, K. Aerosol optical depth spatial and temporal variability using satellite data over Indian major cities. \u003cem\u003eJournal of Environmental Health and Sustainable Development\u003c/em\u003e (2022).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eNabavi, S. O., Haimberger, L., Abbasi, R. \u0026amp; Samimi, C. Prediction of aerosol optical depth in West Asia using deterministic models and machine learning algorithms. \u003cem\u003eAeolian research\u003c/em\u003e 35, 69-84 (2018).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eBerhane, S. A., Althaf, P., Kumar, K. R., Bu, L. \u0026amp; Yao, M. A Comprehensive Analysis of AOD and its Species from Reanalysis Data over the Middle East and North Africa Regions: Evaluation of Model Performance Using Machine Learning Techniques. \u003cem\u003eEarth Systems and Environment\u003c/em\u003e, 1-26 (2024).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003ePashayi, M., Satari, M., Shahraki, M. M. \u0026amp; Amini, S. MAIAC AOD profiling over the Persian Gulf: A seasonal-independent machine learning approach. \u003cem\u003eAtmospheric Pollution Research\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 102128 (2024).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eAazami, J. \u0026amp; Joorabian Shooshtari, S. Update data of the Iranian wetlands (2023). \u003cem\u003eJournal of Applied Research in Water and Wastewater\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 156-160 (2023).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eEck, T. F.\u003cem\u003e et al.\u003c/em\u003e Wavelength dependence of the optical depth of biomass burning, urban, and desert \u003c/span\u003edust aerosols. \u003cem\u003eJournal of Geophysical Research: Atmospheres\u003c/em\u003e \u003cstrong\u003e104\u003c/strong\u003e, 31333-31349 (1999).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eLi, W.\u003cem\u003e et al.\u003c/em\u003e Comprehensive validation and comparison of three VIIRS aerosol products over the ocean on a global scale. \u003cem\u003eRemote Sensing\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 2544 (2022).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eLi, X., Zhang, C., Li, W., Anyah, R. O. \u0026amp; Tian, J. Exploring the trend, prediction and driving forces of aerosols using satellite and ground data, and implications for climate change mitigation. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e \u003cstrong\u003e223\u003c/strong\u003e, 238-251 (2019).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eAksu, H.\u003cem\u003e et al.\u003c/em\u003e Spatiotemporal analysis of drought by CHIRPS precipitation estimates. \u003cem\u003eTheoretical and Applied Climatology\u003c/em\u003e \u003cstrong\u003e148\u003c/strong\u003e, 517-529 (2022).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eL\u0026oacute;pez-Bermeo, C., Montoya, R. D., Caro-Lopera, F. J. \u0026amp; D\u0026iacute;az-Garc\u0026iacute;a, J. A. Validation of the accuracy of the CHIRPS precipitation dataset at representing climate variability in a tropical mountainous region of South America. \u003cem\u003ePhysics and Chemistry of the Earth, Parts A/B/C\u003c/em\u003e \u003cstrong\u003e127\u003c/strong\u003e, 103184 (2022).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eAbatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. \u0026amp; Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958\u0026ndash;2015. \u003cem\u003eScientific data\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 1-12 (2018).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eHaq, Y. U., Shahbaz, M., Asif, S., Ouahada, K. \u0026amp; Hamam, H. Identification of soil types and salinity using MODIS terra data and \u003c/span\u003emachine learning techniques in multiple regions of Pakistan. \u003cem\u003eSensors\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 8121 (2023).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eMcFeeters, S. K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. \u003cem\u003eInternational journal of remote sensing\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 1425-1432 (1996).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eAzhar, S. C., Aris, A. Z., Yusoff, M. K., Ramli, M. F. \u0026amp; Juahir, H. Classification of river water quality using multivariate analysis. \u003cem\u003eProcedia Environmental Sciences\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 79-84 (2015).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eMann, H. B. Nonparametric tests against trend. \u003cem\u003eEconometrica: Journal of the econometric society\u003c/em\u003e, 245-259 (1945).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eKendall, M. G. \u003cem\u003eRank correlation methods\u003c/em\u003e. 1st Edn edn, (Charles Griffin \u0026amp; Co Ltd 1948).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eTheil, H. A rank-invariant method of linear and polynomial regression analysis. \u003cem\u003eIndagationes mathematicae\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 173 (1950).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eSen, P. K. Asymptotically efficient tests by the method of n rankings. \u003cem\u003eJournal of the Royal Statistical Society Series B: Statistical Methodology\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 312-317 (1968).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eGal, M. S. \u0026amp; Rubinfeld, D. L. Data standardization. \u003cem\u003eNYUL Rev\u003c/em\u003e. 94, 737 (2019).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eChen, Q., Shao, K. \u0026amp; Zhang, S. Enhanced PM2. 5 estimation across China: An AOD-independent two-stage approach incorporating improved spatiotemporal heterogeneity representations. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e \u003cstrong\u003e368\u003c/strong\u003e, 122107 (2024).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eEbrahimi-Khusfi, Z., Nafarzadegan, A., Ebrahimi-Khusfi, M. \u0026amp; Mosavai, A. Machine Learning approaches for identifying factors influencing dust sensitivity in the Gavkhouni Basin, Central Iran. \u003cem\u003eInternational Journal of Environmental Science and Technology\u003c/em\u003e, 1-20 (2024).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eDaoud, J. I. in \u003cem\u003eJournal of Physics: Conference Series.\u003c/em\u003e 012009 (IOP Publishing).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eAli, J., Khan, R., Ahmad, N. \u0026amp; Maqsood, I. Random forests and decision trees. \u003cem\u003eInternational Journal of Computer Science Issues (IJCSI)\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 272 (2012).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eAo, Y., Li, H., Zhu, L., Ali, S. \u0026amp; Yang, Z. The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling. \u003cem\u003eJournal of Petroleum Science and Engineering\u003c/em\u003e \u003cstrong\u003e174\u003c/strong\u003e, 776-789 (2019).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eMiao, S.\u003cem\u003e et al.\u003c/em\u003e Random forest algorithm for the relationship between negative air ions and environmental factors in an urban park. \u003cem\u003eAtmosphere\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 463 (2018).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eYu, M. Short-term wind speed forecasting based on random forest model combining ensemble empirical mode decomposition and improved harmony search algorithm. \u003cem\u003eInternational Journal of Green Energy\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 332-348 (2020).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003ePearson, K. Determination of the coefficient of correlation. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 23-25 (1909).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eLi, J., Ge, X., He, Q. \u0026amp; Abbas, A. Aerosol optical depth (AOD): spatial and temporal variations and association with meteorological covariates in Taklimakan desert, China. \u003cem\u003ePeerJ\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, e10542 (2021).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eKhanfari, V., Asgari, H. M. \u0026amp; Dadollahi-Sohrab, A. Forecasting Wetland Transformation to Dust Source by Employing CA-Markov Model and Remote Sensing: A Case Study of Shadgan International Wetland. \u003cem\u003eWetlands\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 96 (2024).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eEbrahimi-Khusfi, Z., Dargahian, F. \u0026amp; Nafarzadegan, A. R. Predicting the dust events frequency around a degraded ecosystem and determining the contribution of \u003c/span\u003etheir controlling factors using gradient boosting-based approaches and game theory. \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 36655-36673 (2022).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eNamdari, S., Valizadeh Kamran, K. \u0026amp; Sorooshian, A. Analysis of some factors related to dust storms \u003c/span\u003eoccurrence in the Sistan region. \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 45450-45458 (2021).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eYousefi, R., Wang, F., Ge, Q., Lelieveld, J. \u0026amp; Shaheen, A. Aerosol trends during the dusty season over Iran. \u003cem\u003eRemote Sensing\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1045 (2021).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eYousefi, R., Wang, F., Ge, Q., Shaheen, A. \u0026amp; Kaskaoutis, D. G. Analysis of the winter AOD trends over Iran from 2000 to 2020 and associated meteorological effects. \u003cem\u003eRemote Sensing\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 905 (2023).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eAbadi, A. R. S., Hamzeh, N. H., Kaskaoutis, D. G., Opp, C. \u0026amp; Kazemi, A. F. Long-Term Spatio-Temporal Analysis, Distribution, and Trends of Dust Events over Iran. \u003cem\u003eAtmosphere\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 334 (2025).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eBashirian, F., Rahimi, D. \u0026amp; Movahedi, S. Effects of land use changes on local dust event in Urmia Lake basin. \u003cem\u003eLand Use Policy\u003c/em\u003e 148, 107416 (2025).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eMousavi, H., Panahi, D. M. \u0026amp; Kalantari, Z. Dust and climate interactions in the Middle East: Spatio-temporal analysis of aerosol optical depth and climatic variables. \u003cem\u003eScience of the Total Environment\u003c/em\u003e \u003cstrong\u003e927\u003c/strong\u003e, 172176 (2024).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eKhachak, \u003c/span\u003eS. H.\u003cem\u003e et al.\u003c/em\u003e Application of Remote Sensing and Spatial Fuzzy Multi-criteria Decision Analysis to Identify Potential Dust Sources in Lake Urmia Basin, Northwest Iran. \u003cem\u003eJournal of the Indian Society of Remote Sensing\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, 2057-2071 (2024).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eGhasempour, R., \u003c/span\u003eAalami, M. T., Saghebian, S. M. \u0026amp; Kirca, V. O. Analysis of spatiotemporal variations of drought and soil salinity via integrated multiscale and remote sensing-based techniques (Case study: Urmia Lake basin). \u003cem\u003eEcological Informatics\u003c/em\u003e \u003cstrong\u003e81\u003c/strong\u003e, 102560 (2024).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eXiang, Z.\u003cem\u003e et al.\u003c/em\u003e Spatiotemporal Variations and Driving Factor Analysis of Aerosol Optical Depth in Terrestrial Ecosystems in Northern Xinjiang from 2001 to 2023. \u003cem\u003eAtmosphere\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e (2024).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eKhamala, G. W., Makokha, J. W. \u0026amp; Boiyo, R. The spatiotemporal and dependency analysis of selected meteorological parameters and normalized difference vegetation index with aerosol optical depth over east Africa. \u003cem\u003eHeliyon\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e (2024).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eFei, K. c., Wu, L. \u0026amp; Zeng, Q. c. Aerosol optical depth and burden from large sea salt particles. \u003cem\u003eJournal of Geophysical Research: Atmospheres\u003c/em\u003e \u003cstrong\u003e124\u003c/strong\u003e, 1680-1696 (2019).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eMadry, W. L., Toon, O. B. \u0026amp; O\u0026apos;Dowd, C. Modeled optical thickness of sea‐salt aerosol. \u003cem\u003eJournal of Geophysical Research: Atmospheres\u003c/em\u003e \u003cstrong\u003e116\u003c/strong\u003e (2011).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eCugerone, K., De Michele, C., Ghezzi, A. \u0026amp; Gianelle, V. Aerosol removal due to precipitation and wind forcings in Milan urban area. \u003cem\u003eJournal of Hydrology\u003c/em\u003e \u003cstrong\u003e556\u003c/strong\u003e, 1256-1262 (2018).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eChen, M.\u003cem\u003e et al.\u003c/em\u003e A parameterized study on rainfall removal of aerosols. \u003cem\u003eAerosol Science and Engineering\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 355-367 (2023).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eLiu, J., Ding, J., Li, X., Zhang, J. \u0026amp; Liu, B. Identification of dust aerosols, their sources, and the effect of soil moisture in Central Asia. \u003cem\u003eScience of the total environment\u003c/em\u003e \u003cstrong\u003e868\u003c/strong\u003e, 161575 (2023).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eAl-Taei, A. I., Alesheikh, A. A. \u0026amp; Boloorani, A. D. Evaluating the effects of land use/land cover change on the emergence of hazardous dust sources in the Tigris-Euphrates Basin. \u003cem\u003eSpatial Information Research\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 569-582 (2024).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eBoloorani, A. D.\u003cem\u003e et al.\u003c/em\u003e Assessing the role of drought in dust \u003c/span\u003estorm formation in the Tigris and Euphrates basin. \u003cem\u003eScience of The Total Environment\u003c/em\u003e \u003cstrong\u003e921\u003c/strong\u003e, 171193 (2024).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eAdnan, M. S. G.\u003cem\u003e et al.\u003c/em\u003e Heatwave vulnerability of large metropolitans in Bangladesh: An evaluation. \u003cem\u003eGeomatica\u003c/em\u003e \u003cstrong\u003e76\u003c/strong\u003e, 100020 (2024).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eMeng, H., Bai, G. \u0026amp; \u003c/span\u003eWang, L. Analysis of the spatial and temporal distribution characteristics of AOD in typical industrial cities in northwest China and the influence of meteorological factors. \u003cem\u003eAtmospheric Pollution Research\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 101957 (2024).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eEbrahimi-Khusfi, Z., Nafarzadegan, A. R., Ebrahimi-Khusfi, M. \u0026amp; Zandifar, S. Monitoring the water surface of wetlands in Iran and their relationship with air pollution in nearby cities. \u003c/span\u003e\u003cem\u003eEnvironmental Monitoring and Assessment\u003c/em\u003e \u003cstrong\u003e194\u003c/strong\u003e, 488 (2022).\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Aerosol pollution, Wetland degradation, Remote sensing, Climatic elements, Human activities, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-6778141/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6778141/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh levels of aerosols in aquatic systems are realized as indicators and agents of environmental degradation. It is imperative that the mechanisms of aerosol contamination in such sensitive habitats be understood for efficient water resource management and conservation of the ecosystem. This paper fills the gap by examining the spatiotemporal features of aerosol optical depth (AOD) over 27 wetlands and lakes in Iran for a 25-year (2000\u0026ndash;2024) period. Monthly AOD values were combined with climatic and environmental variables, including wind speed, rain, evaporation, Palmer Drought Severity Index, enhanced vegetation index, normalized difference water index, soil salinity index, and water body coverage. Trend analysis was conducted using the Mann-Kendall test and Sen's slope estimator. The results demonstrated that aerosol concentrations increased by 51.9% over Iran's water bodies in winter, 55.6% in spring, 74.1% in summer, and 66.7% in autumn. On an annual scale, 55.6% experienced an increasing trend, with a significant increase in AODs over Parishan, Miankaleh, Sheedvar, and Gomishan wetlands, as well as Lake Urmia (Z\u0026thinsp;\u0026gt;\u0026thinsp;1.96). The primary causes of aerosol pollution were identified through machine learning models as changes in: evaporation and rainfall in Parishan; water level and salinity in Gomishan; salinity and rainfall in Miankaleh; vegetation cover and decreased water level in Sheedvar. Based on the total Gini reduction, climatic factors contributed more significantly to air quality degradation in Parishan, Miankaleh, and Sheedvar wetlands (averaging 58%) compared to land-based drivers. Conversely, land-based factors were the primary contributors to air quality decline over Gomishan and Lake Urmia (averaging 68%). These findings are especially beneficial for comprehending the synergy between natural and anthropogenic drivers governing air quality over aquatic ecosystems.\u003c/p\u003e","manuscriptTitle":"A 25-Year Assessment of Aerosol Dynamics and Environmental Drivers in Iran’s Lakes and Wetlands","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 17:12:27","doi":"10.21203/rs.3.rs-6778141/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-01T08:01:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-25T22:28:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-19T12:58:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205736203698186332436514801805741859113","date":"2025-08-11T05:43:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272220149899722076032343727403491642724","date":"2025-08-07T10:27:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T11:59:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189310616059519515316211663338465475671","date":"2025-07-13T17:12:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42791607324692167797802977249330119814","date":"2025-07-11T17:12:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121307654558586570897653147924405927968","date":"2025-07-11T05:57:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-11T05:16:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-03T16:34:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-30T06:55:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-30T02:59:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-29T15:53:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"440c5be2-5bb5-44a2-9dce-eceeee828d5c","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51457401,"name":"Earth and environmental sciences/Climate sciences"},{"id":51457402,"name":"Earth and environmental sciences/Environmental sciences"},{"id":51457403,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2025-12-22T16:01:15+00:00","versionOfRecord":{"articleIdentity":"rs-6778141","link":"https://doi.org/10.1038/s41598-025-27607-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-16 15:57:40","publishedOnDateReadable":"December 16th, 2025"},"versionCreatedAt":"2025-07-14 17:12:27","video":"","vorDoi":"10.1038/s41598-025-27607-4","vorDoiUrl":"https://doi.org/10.1038/s41598-025-27607-4","workflowStages":[]},"version":"v1","identity":"rs-6778141","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6778141","identity":"rs-6778141","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}