Substantial advances in PM2.5 monitoring and modeling have improved our understanding of air pollution, however, the absence of long-term, spatially continuous datasets has limited insights into historical pollution dynamics across diverse environments in China. This study reconstructed monthly PM2.5 concentrations from 1981 to 2023 by integrating MERRA-2 aerosols, ERA5 meteorology, and surface observations within a unified machine learning (ML) framework. Extreme Gradient Boosting (XGB) outperformed other algorithms and was used for national-scale reconstruction. Sparse monitoring coverage, particularly in northern and northwestern China, was mitigated through a virtual observation network constructed from MERRA-2 grid cells strongly correlated with ground measurements. Emission influences were incorporated through surface gaseous pollutants (SO2, NO2, CO), improving representation of chemically driven variability. The reconstructed 43-year dataset captures seasonal cycles, spatial gradients, and multi-decadal trends consistent with independent observations. SHAP interpretation reveals spatio-temporal contrasts in PM2.5 drivers, meteorology dominates in dust-prone northwestern China and densely populated eastern regions, whereas emissions exert stronger control in cleaner, high-elevation areas such as Tibet. Nationally, temperature emerges as the dominant predictor. In eastern and southern China, winter temperature inversions lead to pollution accumulation, while concentrations drop substantially when temperatures exceed 20°C. This reconstruction fills observational gaps and extends records over four decades, providing a robust basis for understanding emission-meteorology interactions and supporting targeted air quality management and health policy planning at regional and national scales. This visual summary serves as a pivotal entry point into the research, offering a concise overview of the study methodologies and principal findings on PM2.5 estimation and its drivers across China. The graphical abstract integrates reanalysis products (MERRA-2 aerosols, ERA5 meteorology), ground observations, and machine learning (XGB, RF, SVM, LR) to generate monthly PM2.5 estimates from 1981 to 2023. Among the tested models, XGB demonstrated the best performance (R2 = 0.96-0.98 against independent datasets for 1981-2023 and 2015-2023), with minimal bias across regions. The central map illustrates regional variations in dominant feature-drivers, highlighting temperature and SO2 as the most influential predictors of PM2.5. Time-series comparisons show consistency between modelled anomalies, satellite reanalysis (1981-2023), and ground observations (2015-2023), capturing both long-term increases before 2010 and subsequent declines driven by emission control policies. SHAP feature importance analysis reveals that in eastern and southern China, temperature interacts with anthropogenic emissions (SO2, NO2) to amplify wintertime PM2.5 pollution under inversion-prone conditions. Collectively, the graphical abstract underscores the strength of machine learning in resolving spatiotemporal PM2.5 patterns, identifying region-specific drivers, and informing targeted air-quality management strategies in China.

Fine particulate matter (PM2.5) pollution is a major environmental challenge across the Middle East, including Iran. However, a substantial lack of knowledge exists regarding the linkage between aerosol trends, specific compounds, and their interrelation with emissions, mitigation strategies, and land changes. This research comprehensively evaluates the spatiotemporal trends of PM2.5 and its main precursors (SO2 and BC) concentrations in relation to LULC (Land-Use and Land-Cover) changes and mitigation policies in Iran during 1980-2023. Surface PM2.5 concentrations were estimated using five monthly MERRA-2 simulation datasets, including sea salt2.5, dust2.5, BC, OC, and SO4. The Evaluation of MERRA-2 PM2.5 against ground-based measurements confirmed that the MERRA-2 reanalysis data is ideal for monitoring PM2.5 patterns in Iran. Our trend analysis showed that dust dominates high PM2.5 concentrations in southwestern and southeastern Iran during summer, while anthropogenic aerosols (SO2 and BC) are the most significant contributors to PM2.5 in urban areas like Tehran in winter. Overall, a significant rise in aerosol occurred over Iran during 1980-2023, which reversed to a decreasing trend in PM2.5, BC and SO2 around 2006-2010. At the regional scale, aerosols variations were influenced by land-use changes, while urban and agricultural LULC changes being the primary contributors in dust-dominant regions, accounting for 38.1% and 26.4% of the variation, respectively. Our findings indicate that, although land-use changes initially influenced air pollution trends, recent clean-air policies have been essential in reducing emissions across major urban centers. Additionally, these trends in Iran align with or diverge from global patterns, reflecting the rise in industrial emissions across South Asia and contrasting with policy-driven decreases in developed regions such as Europe and North America, highlighting the urgent need for effective policies and land management to mitigate urban air pollution from diverse aerosol sources.

High atmospheric black carbon (BC) levels due to human activities pose a severe air pollution issue in China, especially in urban agglomerations. In this talk, we analyzed the trends of black carbon-aerosol optical depth (BCAOD) from Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS) from 2005 to 2022. Four densely populated and highly polluted urban regions (Beijing-Tianjin-Hebei [BTH], Sichuan Basin [SCB], Yangtze River Delta [YRD], and Pearl River Delta [PRD]) were selected for the analysis. BCAOD from MERRA-2 data showed significant negative trends over the four urban regions during the years 2005-2022, with rates of -0.0007, -0.0008, -0.0007, and -0.0006 yr-1 in BTH, YRD, SCB, and PRD, respectively. Similar significant BCAOD trends from CAMS were also observed in the urban selected regions with rates of -0.0005, -0.0006, -0.0009, -0.0006 yr-1 in BTH, YRD, SCB, and PRD, respectively. The decreasing trend in BCAOD could be mainly attributed to the air pollution policies implemented by the Chinese government.

Considering the uncertainty of the reanalysis data is essential, the uncertainty of Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) SO2 concentration still is lacking. In this talk, we evaluated the MERRA-2 SO2 concentrations in China. Monthly SO2 concentrations from 100 ground observation during the years 2015-2021 were used to evaluate the monthly MERRA-2 SO2 data. Our results showed that the MERRA-2 SO2 concentrations exhibit a moderate Pearson correlation coefficient (R) with ground-based SO2 measurements (R = 0.62). In terms of root-mean square error (RMSE) and mean absolute error (MAE), biases (≤ 10 μg m-3) were found mainly for extensive regions (about 70%) of the Chinese sites. According to the results, the relative mean bias (RMB) and fractional gross error (FGE) showed values greater than 1 and 0, respectively, in the eastern China, which indicates that MERRA-2 overestimates the SO2 measurements in the urban regions, while underestimation of MERRA-2 SO2 was found in rural regions of China. A parameterized method could be suggested to improve the quality of MERRA-2 SO2

High black carbon (BC) levels due to combustion processes pose a severe air pollution issue in China, especially in urban agglomerations. Although the BC levels, source apportionment, and radiative impacts have been extensively analyzed, limited research has investigated the effect of ongoing human activities and mitigation strategies on the changes in BC levels in urban regions of East China. In this study, the trends of BC in China are analyzed from 1980 to 2022, using BC concentrations and BC-aerosol optical depth (BC AOD ) data from Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS) from 2005 to 2022. Eight representative areas, including six densely populated and highly polluted urban regions (Beijing–Tianjin–Hebei [BTH], Central China [CC], Sichuan Basin [SCB], Yangtze River Delta [YRD], North East China [NEC], and Pearl River Delta [PRD]), were selected for the analysis. A significant, but variable increase in BC concentrations was observed during 1980–2021. There was an increase in BC between 1980–1990 and 1999–2007, but it remained stable from 1990 to 2000 and dropped sharply after 2010. The average increase in BC concentrations was approximately ten times higher in East China than in West China, signaling the increase in population, traffic, and demand for energy consumption for a society with progressive increasing gross domestic product. BC dynamics in urban regions were highly associated with population density, gross domestic product, and land use land cover (LULC) changes. The declining trend in BC concentrations during the last decade was mainly attributed to the air pollution mitigation policies implemented by the Chinese government, having as a result the decrease in combustion emissions especially in urban areas. Our findings highlight the need for further studies on the impact of BC on human health and regional climate change in China.

High levels of sulfur dioxide (SO2) due to human activities pose a serious air pollution issue in China, especially in urban agglomerations. However, limited research has investigated the impact of anthropogenic emissions on higher SO2 concentrations in urban regions compared to rural areas in China. Here, we analyzed the trends in SO2 concentrations from 1980 to 2021 in China using the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) dataset. SO2 column concentrations from the Copernicus Atmosphere Monitoring Service (CAMS) and the Ozone Monitoring Instrument (OMI) during the years 2007–2021 were also examined for validation and comparison purposes. Eight representative areas, including four urban regions (Pearl River Delta [PRD], Beijing-Tianjin-Hebei [BTH], Yangtze River Delta [YRD], and Sichuan Basin [SCB]) and four rural regions (Northeast Region [NER], Mongolian Region [MR], West Region [WR], and Tibetan Plateau Region [TR]) were selected for the analysis. Overall, a significant but fluctuating increase in SO2 concentrations over China was observed during 1980–2021. During 1980–1997 and 2000–2010, there was an increase in SO2 concentration, while during 1997–2000 and 2010–2021, a decreasing trend was observed. The average increase in SO2 concentration was approximately 16 times higher in urban regions than in the rural background. We also found that SO2 dynamics were highly associated with expansion of urban areas, population density, and gross domestic product. Nonetheless, since 2007, SO2 concentrations have exhibited a downward trend, which is mainly attributed to the air pollution policies implemented by the Chinese government. Our findings highlight the need for further studies on the impact of SO2 on regional climate change in China.

<p>Sulfur dioxide (SO<sub>2</sub>) plays a key role in the formation of atmospheric sulfate that can adversely affect urban environment, human health, air quality, and the Earth’s climate system. In this talk, we present results of SO<sub>2</sub> trends over the 4 urban regions (YRD, BTH, PRD, and SCB) of China during 2007-2020, using the SO<sub>2 </sub>mass<sub> </sub>concentrations of the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and the SO<sub>2 </sub>total column of Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA). The SO<sub>2</sub> concentration decreased significantly during 2007-2020 over the 4 regions: YRD, BTH, PRD, and SCB, with the rate of -0.04 µg.m<sup>-3 </sup>per year, -0.05 µg.m<sup>-3 </sup>per year, -0.01 µg.m<sup>-3</sup><sup> </sup>per year, and -0.03 µg.m<sup>-3</sup> per year, receptively. Using CAMSRA data, total column of SO<sub>2</sub> also experienced significant decreasing trends during 2007-2020 over YRD, PRD, and SCB, with the rate of -0.15 mg.m<sup>-2</sup> per year, -0.05 mg.m<sup>-2</sup> per year, and -0.06 mg.m<sup>-2</sup> per year, receptively. The decreasing SO<sub>2</sub> levels after 2007 were mainly attributed to Chinese air pollution control policies. This work contributes to a better understanding of the impact of Chinese policies on the SO<sub>2</sub> level over the urban regions of China.</p>

High levels of fine particulate matter (PM2.5) pose a severe air pollution challenge in China. Both land use changes and anthropogenic emissions can affect PM2.5 concentrations. Only a few studies have addressed the long-term impact of land surface changes on PM2.5 in China. We conducted a comprehensive analysis of PM2.5 trends over China using the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) during 1980-2020. The monthly mean PM2.5 concentrations of MERRA-2 were evaluated across mainland China against independent surface measurements from 2013 to 2020, showing a good agreement. For the trend analysis, China was subdivided into six regions based on land use and ambient aerosols types. Our results indicate an overall significant PM2.5 increase over China during 1980-2020 with major changes in-between. Notwithstanding continued urbanization and associated anthropogenic activities, the PM2.5 reversed to a downward trend around 2007 over most regions except for the part of China that is most affected by desert dust. Statistical analysis suggests that PM2.5 trends during 1980-2010 were associated with urban expansion and deforestation over eastern and southern China. The trend reversal around 2007 is mainly attributed to Chinese air pollution control measures. A multiple linear regression analysis reveals that PM2.5 variability is linked to soil moisture and vegetation. Our results suggest that land use and land cover changes as well as pollution controls strongly influenced PM2.5 trends and that drought conditions affect PM2.5 particularly over desert and forest regions of China. This work contributes to a better understanding of the changes in PM2.5 over China.

<p>High levels of fine particulate matter (PM2.5) pose a severe air pollution challenge in China. In this talk, we present results of a comprehensive analysis of PM2.5 trends over China during 1980–2020 using the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The monthly mean PM2.5 concentrations of MERRA-2 show a good agreement with independent surface measurements across mainland China from 2013 to 2020. For the trend analysis, China was subdivided into six regions based on land use and ambient aerosols types. PM2.5 levels significantly increased over all regions during 1980-2020. Over the forest regions of China, significant increasing trends were observed in the northern (0.073 µg.m<sup>-3</sup> per year) and northeastern (0.34 µg.m<sup>-3</sup> per year) regions. In northwest China, PM2.5 experienced a significant increasing trend during 1980-2020 with a rate of 0.2 µg.m<sup>-3</sup> per year. The Tibet region in southwest China also experienced a significant increase in PM2.5. Comparatively, the upward trends of PM2.5 were most pronounced in the eastern and southern regions with significant rates of 0.6 and 0.25 µg.m<sup>-3</sup> per year, respectively. However, PM2.5 concentration trends changed from upward to downward around 2007 over the eastern and southern regions. The trend reversal around 2007 is mainly attributed to Chinese air pollution control measures.</p>

High aerosol levels pose severe air pollution and climate change challenges in Iran. Although regional aerosol optical depth (AOD) trends have been analyzed during the dusty season over Iran, the specific factors that are driving the spatio-temporal variations in winter AOD and the influence of meteorological dynamics on winter AOD trends remain unclear. This study analyzes the long-term AOD trends over Iran in winter during the period 2000–2020 using the updated Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) and the Moderate Resolution Imaging Spectroradiometer (MODIS) datasets. Our results showed that the winter AOD exhibited a significant upward trend during the period 2000–2010 followed by a significant decrease during the period 2010–2018. We found that the winter AOD trends are important over this arid region due to multiple meteorological mechanisms which also affect the following spring/summer dusty period. Ground-based observations from Aerosol Robotic Network data (AERONET) in the Middle East region display trends comparable to those of both MERRA-2 and MODIS and indicated that aeolian dust and the meteorological dynamics associated with it play a central role in winter AOD changes. Furthermore, this study indicated that a significant downward trend in winter sea level pressure (SLP) during the early period (2000–2010) induced hot and dry winds which originated in the desert regions in Iraq and Arabia and blew toward Iran, reducing relative humidity (RH) and raising the temperature and thus promoting soil drying and dust AOD accumulation. In contrast, a significant increase in winter SLP during the late period (2010–2018) induced cold and wet winds from northwestern regions which increased RH and lowered the temperature, thus reducing dust AOD. This suggests that the changes in AOD over Iran are highly influenced by seasonal meteorological variabilities. These results also highlight the importance of examining wintertime climatic variations and their effects on the dust aerosol changes over the Middle East.

High dust concentrations in the Eastern Mediterranean - Middle East (EMME) region have serious effects on air quality, human health and climate. This study used long-term aerosol datasets during the main dusty season (April–July: AMJJ) over the EMME from 2000 to 2020, based on Moderate-Resolution Imaging Spectroradiometer (MODIS)/Terra-C6.1, Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), and Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA) retrievals and analyzed the spatio-temporal variations and trends of dust, as well as the influencing factors. The dust aerosol optical depth (DAOD) experienced a significant upward trend during 2000–2010, followed by a significant decrease during 2010–2017. After 2017 and till 2020, the DAOD presented rather a stable trend. Aerosol Robotic Network (AERONET) data in the EMME region display trends compatible to those of both MERRA-2 and CAMSRA DAOD. The DAOD trends were linked to changes in regional meteorological parameters in the EMME. A significant downward trend in AMJJ sea-level pressure (SLP) during the early period (2000−2010) induced hot and dry winds from desert regions towards the EMME, which reduced relative humidity (RH) and raised temperature, thus favored soil drying and dust outbreaks through enhancing evaporation. In contrast, a significant increase in winter SLP during the late period (2010–2017), accompanying an increase in North Atlantic Oscillation index, induced cold, wet winds from northwest regions, which increased RH and lowered temperature, thus reducing dust loading in EMME. Positive anomalies in winter soil moisture persisted in the following AMJJ, and consequently suppressed dust activity. DAOD variability over the dust-prone regions was linked to various meteorological parameters via a multiple linear regression (MLR) model. The results show that climatic variability strongly affects the dust trends and contribute to better understanding of meteorological – dust dynamics in the EMME region.

Atmospheric aerosols considered as one of the main concerns in the ongoing climate change. Meteorological changes have a significant role in the inter-decadal Aerosols variation. In this talk, long term (2000-2019) aerosol optical depth (AOD) and metrological factors data from the reanalysis-based Modern Era Retrospective Analysis for Research and Applications (MERRA-2) were used provide deep insight into the relationship between meteorological factors and AOD variability over Iran during the dusty season (MJJA: May, June, July, and August). Prior to regression analyses, Iran was divided into three parts based on the climatological conditions (west part: dusty area, north part: wetter, and center: dry area). Using a multiple linear regression model, AOD variability over Iran was significantly linked to sea level pressure and soil moisture. Winter surface temperatures and relative humidity are the main contributors to MJJA AOD variability over the western and northern parts. AOD was not affected by precipitation. Our results suggested that climatic variations strongly affect the dust cycle, with a strong dependence on wintertime conditions in the region.

Dust Aerosol Optical Depth (DAOD) is considered as one of the main sources of uncertainty in the assessment of climate change. In this talk, we present results of DAOD trend over the Eastern Mediterranean (EM) region in the dusty season (April- May- June- and July: AMJJ) during the years 2003-2019 using long-term DAOD from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and the Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA). MERRA-2 and CAMSRA DAOD displayed significant positive trends during the years 2003-2010 over the region at the rates of 0.007 year−1 and 0.005 year−1, respectively. In contrast, significant negative MERRA-2 and CAMSRA DAOD trends occurred during the years 2010 -2017 with the rates of -0.009 year−1 and -0.004 year−1, respectively. Moreover, trend analysis was also attempted for the Angstrom Exponent (AE440-870) and Fine Mode Fraction (FMF500) from 3 AERONET sits in the region. AERONET data are compatible with the trend of MERRA-2 and CAMSRA DAOD. This suggests that the aerosols trend on the EM region is influenced by aeolian dust level.

The present study documents the winter aerosol optical depth (AOD) trends over the Eastern Mediterranean and Middle East (EMME) region using MERRA‐2 and moderate‐resolution imaging spectroradiometer (MODIS) collection 6.1 data. A significant upward AOD trend was identified during the years 2000–2010, whereas the AOD followed a significant downward trend during the years 2010–2017. Our analysis indicates that aeolian dust is the main contributor to AOD changes. The winter AOD changes are related to meteorological factors over the EMME region. During the early period (2000–2010), a significant decrease in sea level pressure induced dry and hot southeasterly winds blowing from the desert regions in the Middle East toward the EMME, which increased the temperature and reduced the relative humidity, thus enhancing evaporation and promoting soil drying. In contrast, during the late period (2010–2017), a significant increase in sea level pressure, accompanied by an increase in the North Atlantic Oscillation (NAO) index, induced northwesterly winds from higher latitudes, which decreased the temperature and increased the relative humidity, reducing dust mobilization in the EMME, in particular, in Iraq and Egypt. This shows to what extent AOD trends in the EMME region are controlled by changing meteorological weather conditions.

This study assessed the aerosol climatology over Iran, based on the monthly data of aerosol optical depth (AOD) derived from the reanalysis-based Modern Era Retrospective Analysis for Research and Applications (MERRA-2) and the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS). In addition, sea level pressure, wind speed, temperature, relative humidity, precipitation, and soil moisture from the ERA5 reanalysis dataset were applied to investigate the climate-related effects on temporal AOD changes. Our analysis identified positive and negative AOD trends during 2000–2010 and 2010–2018, respectively, which are likely linked to aeolian dust changes. The dust-driven AOD trends were supported by changes in the Ångström exponent (AE) and fine mode fraction (FMF) of aerosols over Iran. During the early period (2000–2010), results of AOD-meteorology correlation analyses suggest reduced soil moisture, leading to increased dust emissions, whereas our results suggest that during the later period (2010–2018) an increase of soil moisture led to decreased AOD levels. Soil moisture appears to be a key factor in dust mobilization in the region, notably in southwestern Iran, being influenced by adjacent mineral dust sources. These phenomena were affected by large-scale sea level pressure transformations and the associated meteorology in the preceding winter seasons. Using a multiple linear regression model, AOD variability was linked to various meteorological factors in different regions. Our results suggest that climatic variations strongly affect the dust cycle, with a strong dependence on wintertime conditions in the region.

Based on the importance of the effects of aerosols on climate pattern change, our study contributes towards a better understanding of the Aerosol Optical Depth (AOD) trends from different datasets and the contribution of each dominant aerosol over Iran. A long-term AOD dataset (1980–2018) from the reanalysis-based Modern Era Retrospective Analysis for Research and Applications (MERRA-2) and the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) /Terra Collection 6.1(C6.1) and Level 2 (L2) in the years 2001-2018. The result of AOD trend showed some differences between MERRA-2 and MODIS in autumn and winter. But, generally, the increasing and slightly decreasing trends appeared over the southwest and north of the country, respectively. The upward trend was mainly observed in the southwest of Iran because of the proximity to the major source areas of natural mineral dust in spring and summer of both AOD datasets which was also obtained in the regional trend analysis and the city of Ahvaz experienced a strong positive trend compared with other selected cities. Also, an unforeseen downward trend was observed in the last decade. Finally, the classification of major aerosol types during 1980-2018 indicated that the mixed aerosols (43.28%) and clean marine (37.38%) were the dominate aerosols followed by the clean continental (9.78%) and desert dust (5.56%) with minor contributions of biomass burning/urban industrial (3.98%) aerosols. Later, the increase of desert dust around 2010 was another obvious result in spring and summer. Our study results indicate that the variation in dust aerosols has a key role in determining the AOD changes in Iran which are contributed in regional climate change and environmental evolutions.

The paper focuses on analysis of long-term changes of aerosol optical depth (AOD) over Iran. It describes contributions of dominant aerosol in the aerosol load over Iran covering the period 1980-2018. For this purpose, a long-term AOD dataset from the reanalysis-based Modern Era Retrospective Analysis for Research and Applications (MERRA-2), the satellite-based Moderate Resolution Imaging Spectroradiometer (the new version of MODIS/Terra and Aqua) as well as a new AOD product (MERRA-2 MODIS merged) were used. The ground-based AOD measurements of the five Aerosol Robotic Network (AERONET) sites used for validation demonstrated better consistency of the MERRA-2 MODIS merged (MMM) product. Analysis of these datasets demonstrated high AOD in the southwest of Iran because of the proximity to the major source areas of natural mineral dust in spring and summer. In contrast, low AOD was mostly observed along the high elevation lands in the northern and western highlands. The trend analysis of AODs revealed differences between the AOD datasets, but agree on the positive trends over southwestern Iran and negative trend in northern Iran. Classification of major aerosol types indicated that the clean marine and mixed aerosols were the dominant aerosol types during the cold and hot seasons, respectively, and the increase of desert dust around 2010 was another obvious result in spring and summer. Our results indicate that the variation in dust aerosol has a key role in determining the AOD long-term changes in Iran which has contributed in regional climate change and environmental evolutions.

As a preliminary and major step for land use planning of the coming years, the study of variability of the past decades’ climatic conditions with comprehensive indicators is of high importance. Given the fact that one of the affected areas by climatic change includes variability of thermal comfort, this study uses the physiologically equivalent temperature (PET) to identify and evaluate bioclimatic conditions of 40 meteorological stations in Iran. In this study, PET changes for the period of 1960 to 2010 are analyzed, with the use of Mann-Kendall non-parametric test and Pearson parametric method. The study focuses particularly on the diversity in spatio-temporal distribution of Iran’s bioclimatic conditions. The findings show that the mean frequency percentage of days with comfort is 12.9 % according to the total number of selected stations. The maximum and minimum frequency percentage with values of 17.4 and 10.3 belong to Kerman and Chabahar stations, respectively. The findings of long-term trend analysis for the period of 1960–2010 show that 55 % of the stations have significant increasing trend in terms of thermal comfort class based on the Pearson method, while it is 40 % based on Mann-Kendall test. The results indicate that the highest frequency of days with thermal comfort in the southern coasts of Iran relates to the end of autumn and winter, nevertheless, such ideal conditions for the coastal cities of Caspian Sea and even central stations of Iran relate to mid-spring and mid-autumn. Late summer and early autumn along with late spring can be identified as the most ideal times in the west and northwest part of Iran. In addition, the most important inhibiting factors of thermal comfort prove to be different across the regions of Iran. For instance, in the southern coasts, warm to very hot bioclimatic events and in the west and northwest regions, cold to very cold conditions turn out to be the most important inhibiting factors. When considering the variations across the studied period, an increase in the frequency of thermal comfort condition is observed in almost half of the stations. Moreover, based on Pearson and Mann-Kendall methods, the trend of changes in monthly averages of PET has decreased in most stations and months, which can lead to different consequences in each month and station. Thus, it is expected that due to PET changes in recent decades and to the intensified global warming conditions, Iran’s bioclimatic conditions change in a way that transfers the days with comfort to early spring and late autumn.

This study presents a spatiotemporal analysis of bioclimatic comfort conditions for Iran using mean daily meteorological data from 1995 to 2014, analyzed through Physiological Equivalent Temperature (PET) index and Universal Thermal Climate Index (UTCI) indices, and bioclimatic clustering. The results of this study demonstrate that due to the climate variability across Iran during the year, there is at any point in time a location with climatic condition suitable for tourism. Mean values demonstrate maxima in bioclimatic comfort indices for the country in late winter and spring and minima for summer. Seven statistically significant clusters in bioclimatic indices were identified. Comparing these with clustering performed on PET and UTCI, the maximum overlaps between the two indices. In the following, the outputs of this research showed that most appropriate bioclimatic clustering for Iran includes seven clusters. These clustering locations according to climatic suitability for tourism provide a valuable contribution to tourism management in the country, particularly through marketing destinations to maximize tourist flow.

Background and purpose: Human health is affected by a variety of human and natural phenomena that surround the environment. Atmospheric pollutants and thermal comfort conditions concern the quality of surrounding air. Given the influential role of lakes on the climatic conditions of their surrounding environment, the effect of different scenarios of Maharlu Lake in the southeastern part of Shiraz on the changes of thermal comfort conditions was modeled. Materials and Methods: In this study, cooling power index and temperature humidity index were used to explore climate comfort conditions according to the long-term observational data from 1960 to 2010. Results: It was found that temperature humidity index has a declining trend in most months of the year. Maximum decreasing changes were observed in November and May with means of −0.31 and −0.29, respectively. However, the maximum of decade and significant changes of cooling power index belonged to April and November with means of 1.36 and 1.22, respectively. Conclusion: Low relative humidity was seen in all the seasons; maximum decrease was observed during summer and in August with 11% decrease. Also, the dried lake outputs showed that the temperature during hot seasons increased, and the temperature during cold seasons decreased.