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Relative humidity (RH) has an important effect on precipitation, especially in arid and semiarid regions. Prediction of RH has been the focus of attention of climate change researchers as well. In this investigation, the accuracy of six intelligent models, including an artificial neural network (ANN), a co-active neuro-fuzzy inference system (CANFIS), principal component analysis (PCA) combined with ANN (PCA–ANN) and three hybrid wavelet-artificial intelligence models, including WANN, WCANFIS and WPCA–ANN, was evaluated in daily RH prediction. Thirty weather stations located in different climates in Iran for the period 2000–2010 were selected for the evaluation and comparison of these models. The performance of each model was evaluated using correlation coefficient (r) and normal root mean square error (NRMSE). Based on the statistical evaluation criteria, the accuracy ranks of the six models were: WPCA–ANN, WCANFIS, WANN, PCA–ANN, ANN and CANFIS. The results indicated that the WPCA–ANN model was the optimal model for estimation of RH, and the range of NRMSE and r values were from 0.009 to 0.080 and from 0.996 to 0.999, respectively. Overall, WPCA–ANN is a new approach that can be successfully applied to predict RH with a high accuracy. [ABSTRACT FROM AUTHOR]

In numerous applications of the solar energy, diffuse solar radiation plays crucial role while their availability in many countries is scarce. Many models have been developed to estimate the diffuse solar radiation that each of them is a function of different metrological parameters. The objective of this study is obtaining a model to estimate the diffuse solar radiation in warm and humid climate in Iran and specify the importance of metrological parameters especially relative humidity. Many empirical models in three categories are selected. By using the long-term data for horizontal global solar radiation and sunshine duration and by employing the regression analysis, nine new models have been created. Then, by entering two parameters, the monthly mean daily ambient temperature and relative humidity in the empirical models, nine other new models have been developed. Performances of all the models are evaluated by means of different statistical indicators. Obtained results showed adding of the ambient temperature and especially relative humidity parameters cause increasing a few accuracy even though in warm and humid climate of Iran. The result shown in the two-degree model, in terms of the clearness index and relative sunshine duration, is the best model. [ABSTRACT FROM AUTHOR]

The present study investigates the effect of climatic parameters, such as air relative humidity and wind speed, on fire spread propagation indexes in the Malekroud Forest, Iran using the FARSITE simulator based on Rothermel's original fire spread equation. Standard fuel models are used to calibrate the vegetation cover. Sorensen (SC) and kappa ( κ ) coefficients, as well as the Overestimation Index (OI), are used to estimate the simulation's accuracy. The results confirm that using both ambient condition data and appropriate fuel models is crucial to reaching reasonable results in fire propagation simulations. The values of the Rate of Fire Spread (ROS), Flame Length (FML), and Fire Line Intensity (FLI) are reported for each particular scenario. The simulation results show that the Sorensen and Kappa coefficient for situations most similar to the real fire reached 0.82 and 0.80, respectively. The investigated fire's severity is categorized as low-condition fire behavior. The simulation shows that fire propagation falls harshly in the case of air relative humidity by more than 72%, and we will not witness natural fire propagation on a large scale. [ABSTRACT FROM AUTHOR]

The present study tends to describe the survey of climatic changes in the case of the eastern and central areas of Iran and, to some extent, the northern parts. The monthly and yearly change trends in the minimum, maximum and mean temperatures, relative humidity, and the precipitation were surveyed for 26 synoptic stations in Iran during a 55-year period. The study was carried out by using the τ-Kendall test. The results showed the same temperature changes for the centrally located stations as the eastern and northern ones. Most of the stations in Zagros showed no significant temperature changes. A significant decrease in the precipitation was seen in summer in different stations. Most of the eastern and centrally located stations showed a decrease in relative humidity trend, while this condition was not recorded in Zagros and northern part of Iran. The present results also showed that the upward trend of minimum air temperature had an effect in increasing the mean air temperature in the stations with temperature ascending trend. This effect of minimum temperature was significantly more than that of the maximum temperature, which could be the result of increasing the amount of greenhouse gases and the reflection of received thermal energies, from land through the night. This increase in the temperature and a decrease in relative humidity would cause an increase in the evaporation of the received precipitation. [ABSTRACT FROM AUTHOR]

The analysis of trends in hydroclimatic parameters and assessment of their statistical significance have recently received a great concern to clarify whether or not there is an obvious climate change. In the current study, parametric linear regression and nonparametric Mann-Kendall tests were applied for detecting annual and seasonal trends in the relative humidity (RH) and dew point temperature ( T) time series at ten coastal weather stations in Iran during 1966-2005. The serial structure of the data was considered, and the significant serial correlations were eliminated using the trend-free pre-whitening method. The results showed that annual RH increased by 1.03 and 0.28 %/decade at the northern and southern coastal regions of the country, respectively, while annual T increased by 0.29 and 0.15°C per decade at the northern and southern regions, respectively. The significant trends were frequent in the T series, but they were observed only at 2 out of the 50 RH series. The results showed that the difference between the results of the parametric and nonparametric tests was small, although the parametric test detected larger significant trends in the RH and T time series. Furthermore, the differences between the results of the trend tests were not related to the normality of the statistical distribution. [ABSTRACT FROM AUTHOR]

During 1992--2003, frequency of Verticillium dahliae propagules, disease incidence and severity of verticillium wilt of cotton were determined in several cotton growing fields in Golestan province, northeastern Iran. Inoculum density varied among fields and different years ranging between 2-47 propagules/g of air-dried soil with an average of 18.96+/-0.73. In addition, the pattern of diseased plants varied with type of field and year. Simple regression analysis showed a linear relationship between inoculum density of V. dahliae at planting time on one hand, disease incidence and severity for all years on the other. The straight line model described the increase in disease intensity index over the accumulated physiological time from sowing. The number of days above 28 degrees C (T) and the area under relative humidity (RH) had significant effects on inoculum density in soil (MS) and final disease development (Y) and fitted the Y = 65.840 - 0.0034 RH + 0.57 MS - 1.7T model with R2 = 0.859 and significant F-function (p<0.0001).

Mazandaran province in northern Iran is one of the fire-prone areas in the country in which a wide area of its natural resources have been destroyed by fire in recent years. This research aimed to detect the spatiotemporal relationships between climatic variables and fire regimes in Mazandaran province in recent decades. The fire variables (dependent variables) were the number and area of fires. The climatic variables (independent variables) were seasonal temperature mean, seasonal maximum temperature mean, seasonal absolute maximum temperature, seasonal precipitation mean, seasonal relative humidity mean, seasonal wind speed mean, and seasonal maximum wind speed mean for 26 years (1996–2021). Pearson's correlation coefficient and regression models were used to investigate the temporal relationship between fire and climatic variables during study period. Data mining models were used to detect the spatial relationship between fire ignition and climatic parameters and to produce the fire danger maps. The fire occurrence map was obtained from Mazandaran Natural Resources and Watershed Administration and Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor. The climatic maps were obtained by interpolation methods in GIS. The weight of climatic parameters in fire ignition was determined using MDG and MDA statistics from random forest (RF) algorithm. Then different data mining models (logistic regression, random forest, support vector machine, and SVM-RF ensemble model) and 70% of actual fires were used for modeling fire danger in R software. The area under the curve and 30% of actual fires were applied for accuracy assessment of the models. Results of temporal relationships indicated that there are significant relationships among the number of fires and seasonal absolute maximum temperature, seasonal precipitation mean, and seasonal relative humidity mean. On the other hand, a significant relationship was observed between the area of fires and seasonal temperature mean. The results of spatial relationship demonstrated that seasonal temperature mean, seasonal precipitation mean, and seasonal relative humidity mean had the greatest spatial importance in fire ignition. The results of accuracy assessment of fire danger models indicated that SVM-RF and RF models were the best models for fire danger mapping. Therefore, using the maps obtained from these models, it is possible to predict the climate-caused fires in natural ecosystems of Mazandaran province. [ABSTRACT FROM AUTHOR]

Large lakes face considerable challenges due to human activities and climate change, impacting local weather conditions and ecosystem sustainability. Lake Urmia, Iran's largest lake and the world's second-largest saltwater lake, has undergone a substantial reduction in water levels, primarily due to drought, climate change, and excessive irrigation. This study focuses on the potential repercussions on local climate conditions, particularly investigating the impact of moisture sources, evaporation from lake surfaces, and evapotranspiration from agricultural activities, on local convection rainfall. The prevailing westerly winds in the basin suggest a hypothesis that this moisture is transported eastward within the basin, potentially leading to local precipitation as it ascends to higher altitudes near the eastern basin border. To validate this hypothesis, climate data from 1986 to 2017 from the Sarab meteorological station (east of the lake basin, influenced by local precipitation) and Saqez meteorological station (south of the basin, unaffected by local precipitation) were analyzed. The impact of lake water level reduction was assessed by categorizing data into periods of normal lake conditions (1986–1995) and water level reduction (1996–2017). Additionally, the MSWEP global precipitation product was used to examine the precipitation distribution in the entire basin over the entire period and sub-periods. The findings indicate Lake Urmia's significant influence on convective rainfall in the eastern basin, especially during the summer. Despite decreasing lake levels from 1996 to 2017, convective rainfall in the eastern basin increased during the summer, suggesting intensified agricultural irrigation, particularly in hot seasons. [ABSTRACT FROM AUTHOR]

In this study parameters of daily and monthly rainfall, minimum, average and maximum temperatures, relative humidity, number of sunny hours and number of freezing days in the towns of Kashmar and Ghaen were analyzed for a statistical period of 20 agricultural years (1992-2011). Several traditional methods of climate classification were used to compare climate conditions of Ghaen and Kashmar towns by climate determining software. Then effective meteorological indicators on saffron cultivation were selected. Results of climate classification by traditional methods showed that these towns are located in the same climate class. Although the climate was similar, but studies showed that saffron quality in color, odor and taste in Ghaen was higher than Kashmar. The Maximum temperature and relative humidity were the same in both towns. Relative humidity in both towns was the same, especially in October and November which coincided with the beginning of saffron flowering time. Minimum temperature and sunny hours made climate conditions of Ghaen at more favorable for saffron because of mountainous nature. Sunny hours in Ghaen during flowering season was more than Kashmar. Finally, a slight difference in three climate parameters, i.e. rainfall during summer, temperature and sunny hours cannot be the only reason for lower quality of saffron in Kashmar in comparison to Ghaen and saffron quality in Kashmar could be highly raised by other factors such as better planning and management. [ABSTRACT FROM AUTHOR]

This study analyzed the multifractal characteristics of daily reference evapotranspiration (ETo) time series of the Tabriz and Urmia stations of northwestern Iran and its cross-correlation with five other meteorological variables. The results of multifractal detrended fluctuation analysis (MFDFA) of ETo, temperature, pressure, relative humidity solar radiation, and wind velocity showed that all the time series of both stations exhibited multifractality and long memory persistence with higher persistence and complexity in the datasets of Urmia station. Then, a multivariate empirical mode decomposition (MEMD)-(MFDFA) coupled framework was proposed to identify the dominant modes suitable for the forecasting of the different variables. The examination of reconstructed time series consistently displayed an increase in persistence and multifractality. The cross-correlation between different candidate variables and ETo was examined using a recently proposed multifractal cross-correlation analysis (MFCCA) method. The results showed that in each pair-wise cross-correlation analysis, the joint persistence is approximately half of the persistence of an individual time series, reinforcing the universality in the fractal cross-correlation analysis. The cross-correlation properties displayed diverse patterns in different pair-wise combinations of cross-correlation analysis despite the similarity of patterns among the data of the two stations. [ABSTRACT FROM AUTHOR]

The aim of the present study was to determine the applicability of air enthalpy for heat stress assessment of outdoor environments in different climates of Iran. The relationships between the enthalpy index and other heat stress indices such as temperature-humidity index (THI), Humidex, and Wet Bulb Globe Temperature (WBGT) were examined. Summertime atmospheric parameters including temperature (°C), relative humidity (%), and atmospheric pressure (hPa) were obtained from the Iranian Meteorological Organization for the months of June, July, and August during a 30-year period from 1987 to 2017. In this study, the De Martonne climate classification system was used, which has identified six different climate classes. Statistical analyzes were performed using the SPSS software v.23 and the Pearson correlation coefficient and linear regression tests. The highest mean air enthalpy and heat stress indices (THI, Humidex, and WBGT) were found in the arid climate during August. The air enthalpy had a direct and strong relationship with air temperature in all climates, and the strongest correlation between was observed in the semi-humid climate (R = 0.899). A direct correlation was found between the air enthalpy index and relative humidity in all climates except the semi-humid climate, and the highest association was observed in the arid climate (R = 0.747). In addition, direct and strong relationships were found between the air enthalpy and the heat stress indices, the highest correlation belonging to the semi-humid climate. With respect to the direct and strong relationships between air enthalpy and the heat stress indices, the simplicity of air enthalpy calculation, and also the possibility of estimating it using meteorological parameters, the enthalpy index can be used as a suitable alternative screening index for heat stress assessment in outdoors, especially in warm and dry regions. [ABSTRACT FROM AUTHOR]

Background: Cystic echinococcosis (CE) is one of the most important parasitic infections in subgroup seven common neglected diseases of humans and animals. It is in the list of 18 neglected tropical diseases of the WHO. We aimed to analyze the situation of the disease in Iran using Geographical Information System (GIS) and satellite data analysis. Methods: The data obtained from the Ministry of Health and Medical Education, Tehran, Iran and other related centers from 2009 to 2018 were analyzed using GIS. Then, the spatial distribution maps of the disease were generated, and the hot spots of the disease in Iran were determined using spatial analysis of ArcGIS10.5 software. Geographically weighted regression (GWR) analysis in ArcGIS10.5 was used to correlate the variables affecting the disease including temperature, relative humidity, normalized different vegetation index (NDVI) and incidence of hydatidosis. Data analysis was performed by Linear regression analysis and SPSS 21 software using descriptive statistics and chi-square test. Results: Zanjan, Khorasan Razavi, North Khorasan, Chaharmahal Bakhtiari, Hamedan, Semnan, and Ardabil provinces were the hot spots of CE. The results of geographical weighted regression analysis showed that in Khorasan Razavi, North Khorasan, Chaharmahal Bakhtiari, Hamedan, Semnan, Ardabil, Zanjan, Qazvin, and Ilam provinces, the highest correlation between temperature, humidity, vegetation density and the incidence of hydatidosis was observed (P<0.001). Conclusion: The use of maps could provide reliable estimates of at-risk populations. Climatic factors of temperature, humidity, NDVI had a greater impact on the probability of hydatidosis. These factors can be an indicator used to predict the presence of disease. Environmental and climatic factors were associated with echinococcosis. [ABSTRACT FROM AUTHOR]

The trend analysis of the meteorological parameters has an important role in climate change studies. One of the most important factors which affects the trend analysis is the existence of the autocorrelation in meteorological parameters' time series. Therefore, in this research, the effect of autocorrelation on trend of some meteorological parameters such as precipitation, relative humidity, solar radiation, wind speed and mean temperature was analyzed. For this purpose, the annual values of mentioned parameters were calculated using daily recorded data in 30 synoptic stations of Iran during 1960–2014. Then the trend of calculated annual time series was analyzed using the Mann Kendall (M–K) and modified Mann–Kendall (MM-K) tests (with considering all significant autocorrelation coefficients). The comparison of two mentioned tests showed that the autocorrelation affects the trend of the studied parameters. On the basis of the Root Mean Square Error (RMSE) results, in the trend analysis of temperature and precipitation, there were maximum and minimum differences between the statistic (Z) of the M–K and MM-K tests, respectively. The most linear relationship between results of two used trend tests was observed for precipitation and other parameters including wind speed, solar radiation, temperature and relative humidity were placed in the next steps, respectively. The trend results of the MM-K test showed that the precipitation, relative humidity and wind speed parameters had decreasing trend, while the trend of the solar radiation and temperature parameters was positive in more than 50% of studied stations. Also, the precipitation, relative humidity and wind speed parameters had negative trend slope in 63.3%, 80% and 60%, of stations, while, the solar radiation and temperature parameters had positive slope in 63.3% and 90% of studied stations, respectively. [ABSTRACT FROM AUTHOR]

Wind speed forecasting has found economic significance as it can increase operational efficiency. In this regard, an accurate forecast of wind speed is crucial in the application of wind resources. This study is intended to incorporate independent and output variables as the input of support vector regression (SVR) to forecast wind speed of Zanjan and Ahvaz stations in Iran. The independent variables were minimum, maximum, and mean temperatures, relative humidity, precipitation, average visibility, and dew point temperature. The incorporation of independent and output variables were conducted with principal component analysis (PCA) and differential weighting scheme (DWS), respectively. DWS combined the forecasts of linear regression, SVR, and group method of data handling (GMDH) in which the SVR showed the best The forecast of DWS outperformed the other three mentioned models. The incorporation of DWS and PCA (DWS-PCA) improved the forecasts and the capability of DWS-PCA as a novel method was significant in terms of forecast stability. The novel method can be a robust approach for wind speed forecasting in some subjects such as renewable energy, and meteorological decisions. [ABSTRACT FROM AUTHOR]

Background: Allergens like endotoxin and mite allergen Der p 1 are associated with early wheezing and asthma morbidity. Day-care centers can be an important source of exposure to allergens. The aim of this study was to evaluate children's exposure to endotoxin and mite allergen (Der p 1) associated with total suspended particulate matter (TSP) and settled dust in day-care centers in two phases in years of 2015 and 2016 in Tehran city, Iran. Methods: Endotoxin and mite allergen Der p 1 in TSP and settled dust were measured in 23 day-care centers in Tehran. After collecting dust samples and weighting them, and then their extraction, Endotoxin and Der p 1 allergen were determined using QCL-1000 Endpoint chromogenic LAL Assay and ELISA, respectively. Results: The mean concentrations of endotoxin and mite allergen Der p 1 in settled dust were 0.3 EU/mg and 0.2 ng/mg, respectively. The mean concentration of endotoxin and mite allergen Der p 1 in indoor air TSP were 0.8 EU/m3 and 0.4 ng/m3, respectively. A significant negative correlation was found between endotoxin both in settled dust and in TSP with measured relative humidity in winter. Also, moderate correlation was observed between Der p 1 in settled dust and relative humidity in winter; however, the correlation between allergen in TSP and relative humidity was not significant. Conclusion: Day-care centers can be an important source of endotoxin and Der p 1 allergen, so, implementation of proper interventions in these places can reduce exposure to them. [ABSTRACT FROM AUTHOR]

The present study was conducted to assess the short-term effects of the meteorological factors on the COVID-19 mortality in Qom, Iran. The GAM with a quasi-Poisson link function was used to evaluate the impact of temperature, DTR, relative humidity, and absolute humidity on the COVID-19 mortality, controlling potential confounders such as time trend, air pollutants, and day of the week. The results showed that the risk of COVID-19 mortality was reduced, in single-day lag/multiple-day average lag, per one-unit increase in absolute humidity (percentage change in lag 0=−33.64% (95% CI (−42.44, −23.49)), and relative humidity (percentage change in lag 0=−1.87% (95% CI (−2.52, −1.22)). Also, per one-unit increase in DTR value, COVID death risk increased in single-day and multiple-day average lag. This study demonstrated a significant relationship between the four meteorological variables and the COVID-19 mortality. [ABSTRACT FROM AUTHOR]

Since one of the important steps in the management of natural areas is to know when and where the wildfire is more likely to occur or where it has a more negative effect, examining and preparing a fire risk map of sensitive and high-risk areas is crusial. In terms of wildfires, this is one of the main necessities in wildfires control and management. Wildfire risk indicators can be very useful for predicting areas with wildfire risk potential. One of the most widely used wildfire risk indices is the Forest service fire Weather Index (FWI), which has been based on the Canadian forest service fire risk rating system since 1971. In order to evaluate the effectiveness of FWI indicators in identifying wildfire-prone areas in the vegetation areas of Iran from the climate variables of temperature, relative humidity, wind speed, and precipitation of 53 meteorological stations were monitored on a daily basis for the period of 1981-2020. In order to analyze the climate of the vegetation areas under investigation, combined satellite product of burned areas and FWI indices during the period (1981-2020) were used. After downloading and an initial processing, the examined indicators were coilculated for the border of Iran. Then, their time series was analyzed, using zonal statistics. Than the area-averaged of each of Iran's forest regions was calculated, and their seasonal maps were prepared by the kriging interpolation method. Finally, the Pearson correlation coefficient method was used to determine the relationship and correlation between FWI indices and burned areas. The results of the analysis of the combined satellite product of the burned areas showed that the maximum extent of wildfire in the forest regions of Iran is related to the summer season. In this season, a large part of the country's forests, especially in the forest regions of Arsbaran, that has the occurrence of wildfires, and the minimum amount of wildfire was related to the spring season, which was observed in a limited area in the southwest of Iran. The values of atmospheric fire indicators vary according to the different characteristics of forest regions such as vegetation, topography, and climate. The drier parts of the south, east, and southeast of Iran mainly have the highest values of FWI indices, which shows that the increase in temperature and drought affects the moisture content and, as a result, increases the occurrence and spread of fire. The FWI indices in spring for all forest regions of Iran have a high efficiency for identifying areas with wildfire occurrences. Also, in the hot season of the year, when most wildfires occur in the vegetation areas of Iran, FFMC and FWI indices had the best performances. According to the climate variability and topography of Iran's forests, it is difficult to control the occurrence of fire. Hence identifying wildfire critical areas, and determining and preparing a map of fire-risk areas is an effective step in helping forest managers to plan and manage high-risk areas in terms of fire. In addition to this, wildfire atmospheric indices obtained with high horizontal resolution can help better understand the spatiotemporal variability of fire risk, especially in Iran with its complex and diverse topography. [ABSTRACT FROM AUTHOR]

Climate change is the most important problem of the earth in the current century. In this study, the effects of climate change on precipitation, temperature, wind speed, relative humidity and surface runoff in Saghez watershed in Iran investigated. The main methods were using the Coupled Model Intercomparison Project phase 6 (CMIP6), the Soil and Water Assessment Tool (SWAT) and the Artificial Neural Network (ANN) model under the Shared Socio-economic Pathway scenarios (SSPs) using the Linear Scaling Bias Correction (LSBC) for the future period (2021–2050) compared to the base period (1985–2014). Additionally, MAE, MSE, RMSE and R2 indices used for model calibration and validation. The average projected precipitation was forecasted to decrease by 6.1%. In terms of the temperature, 1.4 Cº, and 1.6 Cº increases were predicted for minimum and maximum temperatures, respectively. Prediction of surface runoff using the SWAT model also illustrated that based on SSP1-2.6, SSP3-7.0 and SSP5-8.5 scenarios, runoff will decrease in the future period, which based on three mentioned scenarios is equals to 17.5%, 23.7% and 26.3% decrease, respectively. Furthermore, the assessment using the artificial neural network (ANN) also showed that the parameters of precipitation in the previous two days, wind speed and maximum relative humidity have the greatest effect on the watershed runoff. These findings may be helpful to reduce the impacts of climate change, and make the suitable long-term plans for management of the watersheds and water resources in the region. [ABSTRACT FROM AUTHOR]

Evaporation is one of the main components of the hydrological cycle, and its estimation is crucial and important for water resources management issues. Access to a reliable estimator tool for evaporation simulation is important in arid and semi-arid areas such as Iran, which lose more than 70% of their received precipitation by evaporation. Current research employs the Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms for training the Multilayer Perceptron (MLP) model (as MLP-BR and MLP-SCG) and comparing their performance with the Levenberg–Marquardt (LM) algorithm (as MLP-LM). For this purpose, 16 meteorological variables were used on a daily scale; including temperature (5 variables), air pressure (4 variables), and relative humidity (6 variables) as input data sets, and pan evaporation as the target variable of the MLP model. The surveys were conducted during the period of 2006–2021 in Fars Province in Iran, which is a semi-arid region and has many natural lakes. Various combinations of input-target pairs were tested by several learning algorithms, resulting in seven input scenarios: (1) temperature-based (T), (2) pressure-based (F), (3) humidity-based (RH), (4) temperature–pressure-based (T-F), (5) temperature–humidity-based (T-RH), (6) pressure–humidity-based (F-RH) and (7) temperature–pressure–humidity-based (T-F-RH). The results indicated the relative superiority of the three-component scenario of T-F-RH, and a considerable weakness in the single-component scenario of RH compared with others. The best performance with a root mean square error (RMSE) equal to 1.629 and 1.742 mm per day and a Wilmott Index (WI) equal to 0.957 and 0.949 (respectively for validation and test periods) belonged to the MLP-BR model. Additionally, the amount of R2 (greater than 84%), Nash-Sutcliff efficiency (greater than 0.8) and normalized RMSE (less than 0.1) all indicate the reliability of the estimates provided for the daily pan evaporation. In the comparison between the studied training algorithms, two algorithms, BR and SCG, in most cases, showed better performance than the powerful and common LM algorithm. The obtained results suggest that future researchers in this field consider BR and SCG training algorithms for the supervised training of MLP for the numerical estimation of pan evaporation by the MLP model. [ABSTRACT FROM AUTHOR]

Evapotranspiration is one of the most important hydro-climatological components which directly affects agricultural productions. Therefore, its forecasting is critical for water managers and irrigation planners. In this study, adaptive neuro-fuzzy inference system (ANFIS) model has been hybridized by differential evolution (DE) optimization algorithm as a novel approach to forecast monthly reference evapotranspiration (ET0). Furthermore, this model has been compared with the classic stochastic time series model. For this, the ET0 rates were calculated on a monthly scale during 1995–2018, based on FAO-56 Penman–Monteith equation and meteorological data including minimum air temperature, maximum air temperature, mean air temperature, minimum relative humidity, maximum relative humidity & sunshine duration. The investigation was performed on 6 stations in different climates of Iran, including Bandar Anzali & Ramsar (per-humid), Gharakhil (sub-humid), Shiraz (semi-arid), Ahwaz (arid), and Yazd (extra-arid). The models' performances were evaluated by the criteria percent bias (PB), root mean squared error (RMSE), normalized RMSE (NRMSE), and Nash-Sutcliff (NS) coefficient. Surveys confirm the high capability of the hybrid ANFIS-DE model in monthly ET0 forecasting; so that the DE algorithm was able to improve the accuracy of ANFIS, by 16% on average. Seasonal autoregressive integrated moving average (SARIMA) was the most suitable pattern among the time series stochastic models and superior to its competitors, ANFIS and ANFIS-DE. Consequently, the SARIMA was suggested more appropriate for monthly ET0 forecasting in all the climates, due to its simplicity and parsimony. Comparison between the different climates confirmed that the climate type significantly affects the forecasting accuracies: it's revealed that all the models work better in extra-arid, arid and semi-arid climates, than the humid and per-humid areas. [ABSTRACT FROM AUTHOR]

Background: Skin cancer, as one of the most common cancers, is partly associated with environmental conditions. Objectives: The main purpose of this study was to investigate the effect of environmental factors on the incidence of skin cancer. Methods: The dataset contains the age-standardized incidence rate (ASR) of skin cancer in all provinces of Iran during a six-year period of time. Also, there were meteorological variables for a 29-year period for the provinces. We used the mean of meteorological variables for the 29-year period for each province as predictors. The ASR of skin cancer in each province in six years was considered as the response variable. Also, gender, latitude, altitude, wind speed, relative humidity, cloudy hours duration, and maximum air temperature in each province were considered independent variables. A mixed effects polynomial regression model was applied to data analysis. Results: Based on the applied model, the trend of ASR of skin cancer in different provinces was statistically significant (P < 0.001). The ASR of skin cancer for males was more than the females (P < 0.001). Furthermore, the positive linear effect of latitude was significant (P = 0.002). However, the quadratic term showed a deceleration in the growth rate of skin cancer incidence with increasing latitude. Furthermore, there was a positive significant impact of altitude on skin cancer incidence (P = 0.045). Conclusions: Health policymakers in their prevention planning should consider geographical conditions as influencing factors on skin cancer incidence. Moreover, men should be given more attention as the main group at risk for this cancer. [ABSTRACT FROM AUTHOR]

This study aims to design an early warning system based on machine learning for short-term prediction of nocturnal frosts in Kurdistan Province in the west of Iran. Four models of artificial neural network (ANN), support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression were used to achieve this main goal. Hourly data of six variables of dry-bulb temperature, wet-bulb temperature, cloud cover, relative humidity, and wind speed and direction were selected as the inputs of these four models at 18:30 local time, and according to them, nocturnal temperatures were predicted for 21:30, 00:30, 03:30, and 06:30 local time. Comparing the outputs of these four models with observational data, all models predicted nocturnal temperatures both in the early hours (21:30 and 00:30 local time) and in the late hours of the night (03:30 and 06:30 local time) the same or less than the observational temperatures. Considering the different performance criteria of the models, such as mean absolute errors (MAE), mean squares errors (MSE), and root-mean-squares errors (RMSE), ANN with Posline transfer function, and Trainlm training function, has less error and better performance in predicting nocturnal temperatures compared to other models. When the main goal is predicting the temperature extremes, especially frost, it is concluded that ANN did not perform very well compared to other models. In addition, ANFIS and SVM models have a better performance in this area than other models. Finally, an early warning system for nocturnal frost was designed for Kurdistan Province in the west of Iran using these four models, and its ability was tested to make short-term nocturnal frost predictions. The results show that this system is suitable for the short-term prediction of nocturnal frosts. [ABSTRACT FROM AUTHOR]

Simple Summary: Livestock performance is influenced by different elements due to the complex interactions between the individual animals and environmental conditions such as temperature and humidity. Hence, the influence of environmental temperature and humidity on milk production quality parameters needs to be investigated. The objective of this study is to elucidate the effect of environmental temperature and humidity on milk composition, microbial load, and somatic cells in the milk of Holstein dairy cows. Our findings reveal that, when temperature increased from 6.2 °C to 31.3 °C, the milk protein, fat, solids-not-fat (SNF), and somatic cell count (SCC) significantly decreased. In contrast, the microbial count in milk significantly increased, by approximately 13.7%. When humidity increased from 54% to 82%, the milk protein, fat, SNF, and SCC significantly increased. However, under the same increase in humidity, the microbial count in milk significantly decreased, by approximately 16.3%. The results indicate that there exists a correlation between different months of the year, temperature, and humidity of the environment, in terms of milk components and somatic cells. Our findings reveal that the optimum performance, in terms of milk composition, occurred in the first quarter of the year, while milk quality decreased as temperature increased and humidity decreased. The present study aims to examine the relationships between temperature and humidity and milk composition, microbial load, and somatic cells in the milk of Holstein dairy cows. For this purpose, the temperature–humidity index, ambient temperature, and relative humidity data were obtained from the nearest weather stations. Production data were obtained from four dairy farms in Golestan province, Iran, collected from 2016 to 2021. The traits investigated were protein, fat, solids-not-fat (SNF), microbial load, and somatic cell count (SCC) in milk. The effects of the environmental temperature, humidity, month, and season on the milk composition, microbial load, and somatic cells were analyzed through analysis of variance. The effects of environmental temperature, humidity, month, and season on the milk composition, microbial load, and somatic cell composition were analyzed using a mixed procedure with a restricted maximum likelihood model. Although our findings revealed that there were significant differences in fat, protein, SNF, and SCC among the different months of the year (p < 0.01), no significant difference was observed in the total microbial count in milk. Environmental temperature presented significant impacts on fat, protein, SNF, SCC, and total microbial count within various temperature ranges (p < 0.01). When the temperature increased from 6.2 °C to 31.3 °C, the milk protein, fat, SNF, and somatic cell count significantly decreased, by approximately 4.09%, 5.75%, 1.31%, and 16.8%, respectively; meanwhile, the microbial count in milk significantly increased, by approximately 13.7%. Humidity showed an influence on fat, protein, non-fat solids, somatic cells, and total microbial count within different temperature ranges (p < 0.01). When the humidity increased from 54% to 82%, the milk protein, fat, SNF, and SCC significantly increased, by approximately 3.61%, 4.84%, 1.06%, and 10.2%, respectively; meanwhile, the microbial count in milk significantly decreased, by approximately 16.3%. The results demonstrate that there is a negative correlation between different months of the year, temperature, and the humidity of the environment, in terms of milk components and SCC. Our findings demonstrate that the optimum performance, in terms of milk composition, occurred in the first quarter of the year. As temperature increases and humidity decreases, milk quality decreases. Therefore, the adverse effects of environmental conditions on agricultural profits are not negligible, and strategies to better deal with the negative environmental effects are needed in order to improve milk quality in dairy cows. [ABSTRACT FROM AUTHOR]

Background: Although the effect of temperature on the incidence of acute myocardial infarction (AMI) has been extensively documented, no study has previously investigated the association between temperature and AMI in the Middle Eastern countries. Objective: To examine the association between daily mean temperature and AMI admission rates in Tehran, Iran. Methods: Data on daily number of admissions of patients with AMI to Tehran hospitals between March 21, 2013 and March 19, 2016, were retrieved from the Iranian Myocardial Infarction Registry (IMIR). Over the same period, data on daily mean temperature and relative humidity were measured at Mehrabad International Airport weather station of the Islamic Republic of Iran Meteorological Organization. Time-stratified case-crossover design was employed to investigate the short-term association between the daily mean temperature and the number of daily admissions of patients with AMI, after adjusting for public holidays, relative humidity, and air pollution. Results: An interquartile range (IQR) increase in daily mean temperature was associated with 15.1% (95% CI 1.3 to 30.8%) and 13% (95% CI: 1.9% to 25.4%) increase in the risk of AMI hospitalizations during the entire year, and in the warm months of the year, respectively. There were no significant associations between IQR increase in the two-day cumulative average up to the six-day cumulative average of the daily mean temperature and AMI during the entire year, and warm or cold months of the year. Conclusion: An increase in temperature would increase the rate of AMI hospitalization. [ABSTRACT FROM AUTHOR]

Pre-harvest sprouting is a concern in malting barley (Hordeum vulgare L.) in areas with a high amount of rainfall, as it decreases grain yield and malt quality. Thus, we investigated the changes in initial germination and the rate of dormancy loss in seeds of 20 promising barley genotypes at seven locations for two years (2013–2015) in Iran. Initial germination ranged between 5.23% (genotype "Teran 78/1-BC-80,411") and 38.45% (genotype "CIRU/M111") at the maturity. In several genotypes, there were negative associations between initial germination and the average temperature in March, April and May. The rate of dormancy loss and precipitation were negatively correlated in 14 genotypes in March. Thus, a hot and dry condition during seed development can decrease initial germination but can increase the rate of dormancy loss in barley. A genotype with high initial germination is suitable for cultivation in regions with a dry climate during grain development, and a genotype with a high rate of dormancy loss is appropriate in a climate with high rainfall and relative humidity. In conclusion, breeding programs must link dormancy intensity of target barley and environmental conditions (air temperature and humidity) that the crop will experience. [ABSTRACT FROM AUTHOR]

Wheat (Triticum aestivum L.) is the most imperative crop for man feeding and is planted in numerous countries under rainfed conditions in semiarid zones. It is necessary for decision-makers and governments to predict the yield of rainfed wheat before harvest and to determine the effect of the major factors on it. Different methods have been suggested for forecasting yield with various levels of accuracy. One of these approaches is the statistical regression model, which is simple and applicable for regions with scarce data available. Since the weather is the most important factor affecting the production of wheat, particularly in rainfed cultivation, regression models using weather parameters are very common. However, the coefficients of these models are location based and should be determined locally. Therefore, in this research, backward multiple linear regression (BMLR) technique based on relative importance metrics was used to determine the most important effective weather parameters (11 parameters) on rainfed wheat productions in Fars Province, south of Iran, during 2006–2013. The influence of each parameter in the final model was analyzed using the values of LMG relative importance metric. The result indicated that sunshine hours had the biggest LMG (34.73%) and, therefore, was the most effective parameter. Also, among the other considered parameters, rainy days, minimum relative humidity, and average relative humidity with LMG values of 21.97%, 21.69%, and 21.62%, respectively, had the most effects on rainfed wheat yield in the studied area. All parameters except for the sunshine hours positively affected rainfed wheat yield. The most important reason for the significance of these parameters can be the prevailing dry and semidry climate in the southern areas of Iran. The proposed model for determination of weather parameters effects on rainfed wheat could be a great guidance and aid for different stakeholders such as farmers, decision-makers, and governments. [ABSTRACT FROM AUTHOR]

Background: Distinguishing dynamic variations of the climate from the physical urban indicators is a challenge to assess the factors affecting weather severity. Hence, the time-series of the severe weather threat index (SWEAT) were considered in the four urban areas of Turkey and Iran to identify its affecting factors among the climatic variables and urban indicators in 2018. The SWEAT data were obtained from the upper-air sounding database of the University of Wyoming. The climatic variables were extracted from the Asia Pacific data research center (APDRC). The spatial statistics for urban expansion were collected from global human built-up and settlement extent (HBASE) data sets. A quantitative measuring of the Pearson correlation test was used to expose the relationships between dependent index (SWEAT) and independent variables (climatic and anthropogenic). Results: Results revealed that the high and extreme severity classes of the weather condition in the Ankara, Istanbul, Mashhad, and Tehran are estimated as 7.7% (28 days), 15.3% (56 days), 1.1% (4 days), and 4.4% (16 days), respectively. The strongest values of the annual SWEAT index, exposing the unstable and severe weather conditions, were observed for Istanbul and Ankara urban regions. This result may be corresponding to the highest values of mean annual precipitation and relative humidity in addition to the largest values of urban expansion and sprawl index. The statistical correlation tests in annual scale confirmed the effective role of climatic elements of precipitation, relative humidity, and cloudiness (R from 0.94 to 0.99) and the urban expansion indicators (R from 0.86 to 0.91) in increasing annual severe weather index of SWEAT at above 85–95% of confidence level. Conclusions: The correlations between the urban expansion indicators and outcome SWEAT index can be strengthened by some climatic elements (e.g., precipitation, humidity, and cloudiness), revealing the mediator and magnifier task. However, the mentioned correlations can be weakened by another climatic variable (i.e., air temperature), revealing a moderator and modifier task. Ultimately, investigation of the weather severity indices (e.g., SWEAT index) could be applied to identify the local and regional evidence of climate change in the urban areas. [ABSTRACT FROM AUTHOR]

Background: It is unclear whether seasonal changes, school closures or other public health interventions will result in a slowdown of the current coronavirus disease 2019 (COVID-19) pandemic. We aimed to determine whether epidemic growth is globally associated with climate or public health interventions intended to reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Methods: We performed a prospective cohort study of all 144 geopolitical areas worldwide (375 609 cases) with at least 10 COVID-19 cases and local transmission by Mar. 20, 2020, excluding China, South Korea, Iran and Italy. Using weighted random-effects regression, we determined the association between epidemic growth (expressed as ratios of rate ratios [RRR] comparing cumulative counts of COVID-19 cases on Mar. 27, 2020, with cumulative counts on Mar. 20, 2020) and latitude, temperature, humidity, school closures, restrictions of mass gatherings, and measures of social distancing during an exposure period 14 days previously (Mar. 7 to 13, 2020).Results: In univariate analyses, there were no associations of epidemic growth with latitude and temperature, but weak negative associations with relative humidity (RRR per 10% 0.91, 95% confidence interval [CI] 0.85-0.96) and absolute humidity (RRR per 5 g/m3 0.92, 95% CI 0.85-0.99). Strong associations were found for restrictions of mass gatherings (RRR 0.65, 95% CI 0.53-0.79), school closures (RRR 0.63, 95% CI 0.52-0.78) and measures of social distancing (RRR 0.62, 95% CI 0.45-0.85). In a multivariable model, there was a strong association with the number of implemented public health interventions (p for trend = 0.001), whereas the association with absolute humidity was no longer significant.Interpretation: Epidemic growth of COVID-19 was not associated with latitude and temperature, but may be associated weakly with relative or absolute humidity. Conversely, public health interventions were strongly associated with reduced epidemic growth. [ABSTRACT FROM AUTHOR]

According to the impact of climate change on the thermal stratification which results in a quality change of dams outflow, this study aims to present a framework to assess the impacts of climate change on altering thermal regimes of dam reservoirs. Therefore, five Atmosphere–Ocean Global Circulation Models (AOGCM-AR5 models) under two emission scenarios (RCP2.6 and RCP8.5) are used to investigate the change of air temperature, precipitation, wind speed, relative humidity, dam inflow and water temperature in 2020–2039. To quantify these impacts, three models are used, namely rainfall–runoff IHACRES, hydrodynamic model CE-QUAL-W2 and a downscaling model of LARS-WG. The efficiency of the proposed framework was evaluated in Latian Dam in Iran. Results show an increment of long-term air temperature average by 0.7 °C and, consequently, water surface temperature by 0.59 °C in 2020–2039 relative to the baseline period (1990–2009). Precipitation, wind speed and relative humidity decrease due to climate change in the future by 57%, 17% and 11%, respectively. The simulated dam inflows show that the maximum of 89% of the dam inflow decreases in the future period relative to the baseline period. The results indicate that the water temperature will increase in the coming period as the air temperature increases and this will lead to the extension of thermal stratification period up to 52 days. [ABSTRACT FROM AUTHOR]

Microbial species such as bacteria and fungi can be transported by dust storms over long distances, and may change the mycobiota in downwind. This study aimed to evaluate phenotypes and genotypes of airborne fungi during the Middle Eastern dust (MED) events and normal days in Khorramabad, Iran. The samples were collected regularly every six days at three locations during April 2018–March 2019, with additional samplings during MED days. For phenotypic analyses, the Petri dishes were incubated at 25 °C for 72–120 h. Molecular identification of fungi was carried out using polymerase chain reaction (PCR). The average (±SD) of total fungal concentration was 460.9 (±493.2) CFU/m3. The fungi with the highest average concentrations included Cladosporium cladosporioides, Penicillium brevicompactum, and Cladosporium iridis, respectively. The average concentration of fungi during dust days (967.65 CFU/m3) was 3.6 times higher than those in normal days (267.10 CFU/m3). During normal and dust days, 61 and 45 species were detected, respectively. Aspergillus and Cladosporium spp. were relatively more dominant during normal and dust days, respectively. Eight fungal species were only observed during MED days, including Talaromyces albobiverticillius that was detected for the first time in Iran. Despite air temperature, relative humidity and wind speed were associated to the fungal concentrations. Dust events lead to the changes in the air pollutants composition and mycobiota, identification of new fungi, and elevated fungal concentrations that may extremely affect the public health. [ABSTRACT FROM AUTHOR]

Given the role of different land uses in climate change, the present study seeks to identify the effect of land use change on the building bioclimatic design pattern in a dry climatic type. For this purpose, Yazd station in central regions of Iran has been analysed as a representative of dry climate type. The Air Pollution Modelling (TAPM) which is a regional climate software was used to identify the effect of forest cover on climate change in regional dimensions and finally its impact on heating and cooling solutions for buildings. For this study, two scenarios were considered. One scenario was the real situation in the area and the other one was the simulation of the effect of forest cover on climate change in the studied area. Finally, the results of this study revealed that if there is a forest cover, due to the temperature increase in all months of the year, the average annual temperature will increase by 9.20 °C compared to the real conditions. On the other hand, in the forest cover simulation scenario, the relative humidity will increase by 13.4% per year. The findings also showed that, despite forest cover, the annual temperature difference and temperature fluctuations are reduced by 4 °C. Furthermore, the results of this study indicated that if there is a forest cover, the heating requirements will be reduced in total and the demand for the bioclimatic design of cooling and dehumidification will be increased. [ABSTRACT FROM AUTHOR]

The present study aims to assess seasonal trends in thermal comfort and heat stress indices for outdoor occupations over a 15‐year period using the Iranian Meteorological Organization's database for Sari, Iran. Hourly and daily meteorological data including temperature, wind speed, cloudiness, relative humidity and vapor pressure measurements for Sari city from 2000 to 2014 are used. The wet‐bulb globe temperature (WBGT) and universal thermal climate index (UTCI) are calculated, along with thermal comfort indices including physiological equivalent temperature (PET), standard effective temperature (SET) and predicted mean vote (PMV). The highest and lowest mean monthly air temperature values for the 15‐year period were recorded for August (27.55 ± 0.57°C) and January (7.9 ± 0.68°C), respectively. The highest and lowest mean monthly relative humidity values were observed for December (81.2 ± 2.6%) and August (73.7 ± 1.94%), respectively. The strongest correlations were found between all of the thermal comfort and heat stress indices under study and air temperature (r = 0.97–0.99; P < 0.001), as well as all indices and mean radiant temperature (r = 0.96–0.98; P < 0.001). Strong significant correlations were observed between all heat stress and thermal comfort indices (r > 0.93; P < 0.001). The use of meteorological data can enable the prediction of physiologically stressful conditions via knowledge of the strong positive correlation between heat stress and certain physiological indices, as highlighted in this study. [ABSTRACT FROM AUTHOR]

Infectious diseases are one of the most important health and economic problems in the world, especially in developing countries. Hence, the present study was aimed to investigate the epidemiological prevalence and predict the fungal infections' effective factors among operating personnel at a petroleum refinery complex located in the south of Iran on warm and humid conditions. A group of 200 individuals was selected to collect fungal infections samples from five parts of their body including the auricle, ear canal, foot toes, foot palm, and foot nail. After sampling, the collected samples were transported into a laboratory and cultured in a culture medium for three weeks in thermal conditions of 25 centigrade on an incubator. In the next step, the grown fungi were detected using the colony examination and fungal characteristics of staining with lactophenol cotton blue, and the slide culture method. As well as, air temperature and relative humidity were measured using a WBGT. Data were analyzed finally using SPPSS software version 22.The mean and standard deviation of the air temperature and relative humidity in the operating units were 39.2 ± 7.34 Celsius degrees and 75.8 ± 15.31 percent, respectively. Of 200 samples cultured in the laboratory, 188 showed negative culture results and 12 samples had positive culture results. The fungal infections were founded in measured body parts of some people including the auricle of four subjects, ear canal of nine subjects, foot toes of five subjects, foot palm of six subjects, and foot nail of twelve subjects. Of 1000 isolated samples, 15 cases had been infected with trichophyton, eight cases with onychomycosis, six cases with aspergillus, four subjects with Candiais, and three cases with microsporum harborum. Based on the results, trichophyton were the most common fungal infections. As well as, foot nail and ear canal areas were founded as the most prevalence of places with the infections. [ABSTRACT FROM AUTHOR]

Sustainable and renewable energy resources will play a significant role in the future of the 21st century. One of the most significant resources of sustainable energy is wind energy. Therefore, detection of the potential locations and selection of the optimal place to find a wind farm are a primary step toward using sustainable energy resources. In this research, the feasibility of establishing wind farms in three provinces in the east and north-east parts of Iran is studied using multiple criteria decision-making methods. Fifty proposed options to build a wind farm are ranked based on 13 effective criteria, including area, distance to power lines, distance to the substation, distance to a road, estimated capacity, distance to the city, altitude, population, population density, annual rainfall, average temperature, average wind speed and relative humidity by applying Shannon entropy weighting technique and using three methods of the simple additive weighting (SAW), technique for order of preference by similarity to ideal solution (TOPSIS) and elimination and choice translating reality (ELECTRE). Shannon entropy weighting technique reveals that the criteria of distance to power transmission lines and relative humidity will have the most and least weights, respectively. The ranking results show that, based on the TOPSIS and ELECTRE methods, Gezi Bojnourd option is the best site and Neishabour Hesar Yazdan case is the worst site. Furthermore, Bashirabad Torbatjam case is the most desirable site in the SAW method. The sensitivity analysis results show that the minimum sensitivity to the criteria weights is recorded in the SAW method. On the contrary, ELECTRE method offers the most sensitivity and variation in ranking. [ABSTRACT FROM AUTHOR]

AbstractThe box tree moth (BTM), Cydalima perspectalis, and the fall webworm (FWW), Hyphantria cunea are two invasive forest pests in northern Iran. Entomopathogenic nematodes (EPNs) are usually compatible with integrated pest management programs. In this study, the efficacy of five isolates of EPNs was evaluated against the two pests in laboratory and field conditions. LC50 values of Steinernema carpocapsae (IRTabriz), S. feltiae (IRTabriz), S. feltiae (Entonem), Oscheius rugaoensis and Heterorhabditis bacteriophora (IRTabriz) were as follow: 44.8, 66.5, 113.8, 190.7 and 515.9 infective juveniles (IJs) larva−1 on the fifth instar larvae of BTM; and 75.2, 96.6, 131.0, 258.0 and 292.5 IJs larva−1 on fifth instar larvae of FWW, respectively in the laboratory tests 48 h post-treatment at 25 °C and 60% relative humidity. Under field conditions, the foliar applications of S. carpocapsae (IRTabriz), S. feltiae (IRTabriz), and S. feltiae (Entonem) at the rate of 106 IJs L−1 resulted in 36–58% mortality after 48 h against BTM and 15–52% mortality against FWW, respectively. The results demonstrate that EPNs are promising candidates for biocontrol of BTW and FWW. [ABSTRACT FROM AUTHOR]

Due to depletion of fossil fuels and environmental issues, renewable energy consumption is increasingly growing. Solar energy as the most abundant renewable energy source available is becoming more popular around the world. In the current study, the optimal sites for solar photovoltaic power plants in East Azerbaijan province, Northwest Iran, were investigated. A total of 17 variables were categorized into four groups: climatic, geomorphological, environmental, and access-economic. In order to integrate the variables, a model based on catastrophe theory in the context of GIS was applied. The relative importance and weight of the criteria are computed based on the internal mechanism of the catastrophic system, thus greatly reducing subjectivism and uncertainties of the decision-making process. Five optimal sites located in the western part of the province within the counties of Malekan, Bonab, Ajabshir, Shabestar, and Tabriz were identified as suitable sites for the construction of solar photovoltaic power plants, where there are ideal conditions in terms of many environmental-human variables such as high potential of solar energy, high sunshine hours, low relative humidity, suitable slope, poor vegetation, distance to protected areas, proximity to the population centers, excellent access to the roads and to the main power lines. [ABSTRACT FROM AUTHOR]

Climate change can have significant impacts on crop growth, yield, water requirement and, consequently, crop water productivity. In this study, the effect of climate change under RCP2.6, RCP4.5, and RCP8.5 projection scenarios of the CanESM2 model on soybean yield and water requirement was investigated in Kermanshah, west of Iran. Crop growth was simulated using crop growth simulation models (DSSAT and AquaCrop) based on historical (1985-2015) and projected (2025-2064) weather data. Using the AquaCrop model in RCP2.6, RCP4.5, and RCP8.5 scenarios, the average increase in seasonal crop evapotranspiration (ETc) was estimated to be 9.4, 11, and 14.9%, respectively. The results of the DSSAT model showed 4.1, 8.5, and 12.1% increase in seasonal ETc under the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. Based on the AquaCrop and DSSAT models, soybean yield decreases by 5.3, 3.7, and 2% and by 5.7, 4.8, and 1.6% for the RCP8.5, RCP4.5, and RCP2.6 scenarios, respectively. The results also show a decrease in crop water productivity under climate change scenarios as a result of increased ETc and reduced grain yield. According to AquaCrop and DSSAT models, the maximum daily ETc that should be used for the design of irrigation systems will increase by 11.5 and 10.2%, respectively. [ABSTRACT FROM AUTHOR]

The main functions of climate normals are twofold. They offer a reference point for evaluating recent or ongoing observations and form the basis for various climate datasets that rely on anomalies. Additionally, they are frequently employed to predict the probable conditions that one might encounter in a specific area. The World Meteorological Organization (WMO) advises regularly reviewing climate normals every decade to keep up with the evolving climate. Atmospheric Science and Meteorological Research Center (ASMERC) is proud to release "Iran Climate Normals" for the periods of 1981–2010 and 1991–2020 including a suite of monthly and annual statistics that are based on temperature, precipitation, sea-level pressure, vapor pressure, station-level pressure, snow-depth, wind speed, visibility, soil temperature, relative humidity, dew point, and cloud amount measurements. This study documents the procedures used for quality control, homogenization of daily observations, and calculation of normal values. For each station and each parameter, the results of the outliers due to the error and the homogeneity assessment are reported. Out of all the parameters, the soil temperature has the highest error percentage. However, this does not necessarily imply that it has the most measurement errors; it could be due to the ease of detecting errors for this specific parameter. Of the 143 stations, 56 had a breakpoint recorded in two parameters or more at a specific point in time. According to the analysis of the temperature and precipitation parameters, (a) the new normal of mean, maximum, and minimum temperatures are 0.47, 0.5, and 0.6 °C above the 1981–2010 period; (b) the normal annual precipitation has increased by an average of 5.4 mm in 1991–2020 compared to 1981–2010; (c) comparing the two periods, the changes in precipitation normals vary in different parts of Iran and different months, while the temperature normals increase in all stations across Iran except for four stations (Gorgan, Kerman, Shiraz, Bandar-e Lengeh); (d) changes in the fourth quintile of monthly precipitation are more than average, and minimum temperature changes are higher than maximum and mean temperatures; and (e) generally, the latter period is characterized by a warmer climate almost across Iran, wetter conditions over the Zagros mountain range and the western part of the Caspian Sea coasts, and drier conditions over the east, center, and west of Iran. [ABSTRACT FROM AUTHOR]

In the restoration or rehabilitation of traditional buildings, compatible materials with known characteristics must be used. However, the existing literature lacks comprehensive studies on the characterisation of Persian mud–straw plaster, focusing primarily on Persian adobe. Moreover, previous research on Persian adobe has primarily employed XRF and XRD tests, neglecting ion chromatography, moisture sorption isotherm determination, and thermogravimetric analysis with differential scanning calorimetry. Consequently, there is a shortage of information regarding the elemental composition, mineralogical characteristics, moisture sorption behaviour, and thermal properties of Persian mud–straw plaster, as well as Persian adobe bricks. This paper aims to address this research gap by examining historical and new adobe bricks and mud–straw plaster used in Iran, utilising a comprehensive array of analytical techniques. The results from XRF analysis reveal relatively similar chemical compositions across all samples, while XRD analysis indicates predominantly similar mineral phases. Ion chromatography results demonstrate higher conductivity and chloride concentrations in the mud–straw samples than the adobe samples, with higher values for new samples than historical ones. Freshly used straw, clay, or soil may have higher chloride concentrations caused by the arid climate and soil salinisation in the area. Additionally, moisture sorption isotherm determination results show that adobe and mud–straw plaster with a higher salt load of chlorides have significantly higher moisture absorption. The increased straw quantity in the samples increases the moisture content. Furthermore, thermogravimetric analysis and differential scanning calorimetry indicate that, at low heating, adobe and mud–straw plaster lose water due to dehydration, and at high heating, they lose carbon dioxide due to decarboxylation. The comprehensive characterisation of Persian adobe and mud–straw plaster in this study fills a significant gap in the literature and offers invaluable insights for informing restoration and rehabilitation processes, ensuring the compatibility of the materials used. [ABSTRACT FROM AUTHOR]

One of the most serious pests of walnut trees in recent years in Kermanshah Province is the pear lace bug, Stephanitispyri (F.) (Hemiptera: Tingidae). Damage of lace bug is not usually economic, but serious invasions can cause considerable damage or even death to the host. A field study was carried out to evaluate the fluctuations of S. pyri population on walnut trees in Kermanshah from January to December 2015. Data were analyzed using Taylor's power law and Iwao's patchiness regression methods to determine spatial distribution. In order to estimate the biological parameters of S. pyri in two different laboratory and semi-field conditions, the theory of age-stage two-sex life table was used. None of the life table parameters (r intrinsic rate of natural increase; R: net reproductive rate; A: finite rate of increase; T: mean generation time; and, GRR: gross reproductive rate) in the laboratory conditions had a significant difference from the field conditions, and more than 70% of individuals successfully passed the pre-imaginal developmental stages. The pear lace bug overwintered as adult within weeds until late March; after that, they migrated to the walnut trees and distributed on the leaves. As a result, there was no significant relationship between the fluctuations of the pear lace bug population and changes in temperature or relative humidity. Spatial distribution of the pear lace bug population was determined based on both Taylor and Iwao regression methods of aggregated type. [ABSTRACT FROM AUTHOR]

Cydalima perspectalis (Walker), box tree moth (BTM) is a major invasive pest of Buxus hyrcana Pojark in northern Iran that has spread across Asia and Europe. To reduce the use of insecticides in the management of forest pests, developing integrated pest management (IPM) programs using biocontrol agents can be an effective strategy. During our studies on native entomopathogenic fungi (EPF) in northern Iran, Beauveria bassiana (B2, F4, D2, B1) were naturally isolated from BTM larvae in Hyrcanian forests. Under laboratory conditions, the potential of the naturally occurring EPFs as a biological control method against the third-instar larvae of BTM was evaluated. The mean lethal concentration values (LC50) of isolates B2, F4, D2 and B1 were found to be 1.2×107, 2.0×107, 2.8×107 and 1.0×108 conidia ml-1, respectively on third-instar BTM larvae in the laboratory tests 96 hours after the treatment at 26 ± 1 °C, 75 ± 5% relative humidity. Under field conditions, the foliar applications of isolate B2 formulations containing linseed oil, corn oil, soybean oil, and wettable powder resulted in 13.2-82.2% mortality against BTM in Maskupa, Mazandaran and 8.8-70.4% mortality in Sisangan Forest Park, Mazandaran (after 14 days at a concentration of 108 conidia ml-1), respectively. This investigation suggests that BTM larvae in Hyrcanian forests contain pathogenic B. bassiana isolates and highlights for the first time their potential as a biological control agent against BTM. [ABSTRACT FROM AUTHOR]

Mosquitoes are the most important vectors of serious infectious diseases in the world. Dengue, Zika, chikungunya and yellow fever are emerging and re-emerging infectious diseases, associated with the distribution of two key vectors i.e. Aedes aegypti and Aedes albopictus throughout the world including countries neighbouring Iran. Entomological surveillance was planned and performed monthly from May to December during 2014–2020 in selected counties of Mazandaran Province, North of Iran, by ovitrap, larval collection, hand catch and human baited trap. Overall, 4410 Aedes specimens including 2376 larvae (53.9%) and 2034 (46.1%) adults belonging to six species, namely Aedes vexans, Aedes geniculatus, Aedes caspius, Aedes echinus, Aedes pulcritarsis and Aedes flavescence were collected and morphologically identified. Over the seven years of surveillance, Ae. aegypti and Ae. albopictus were not found by any sampling method. Aedes vexans and Ae. geniculatus were the most abundant species, their populations peaked in October and November and was positively correlated with precipitation and relative humidity. Aedes flavescence was a new species record for the province. A flowchart for planning and implementation of invasive mosquito surveillance for provincial health authorities in the country is proposed. These surveillance efforts provide basic and timely information for the health system to act promptly on integrated and intensified surveillance and control programs should Ae. aegypti and Ae. albopictus detected in the province. [ABSTRACT FROM AUTHOR]

Evaporation is one of the major components of the hydrological cycle and its accurate estimation affects water resource management, irrigation planning, and environmental studies. In this paper, an explicit mathematical function was proposed based on the response surface function (RSF) to predict pan evaporation. The second-order RSFs were found to be more efficient and accurate than the linear RSF. The ability of the proposed RSF-based method was investigated with a wavelet-based adaptive neuro-fuzzy inference system (WANFIS) and wavelet-based support vector regression (WSVR) to estimate the daily evaporation of the pan. Daily climatic variables such as wind speed, sunshine hour, air temperature, and relative humidity from two climate stations (Chahnimeh and Pishien reservoirs) in Iran were used as inputs in all models. The modelling results were evaluated using RMSE, d, NSE, CI, and R2 in both regions. RSF, WANFIS, and WSVR all could be successfully employed in modelling the evaporation estimation. WANFIS model shows the best results and its estimated statistics factors in Chahnimeh are RMSE = 3.05, NSE = 0.90, d = 0.98, CI = 0.88, and R2 = 0.98 and for Pishien are RMSE = 1.51, NSE = 0.84, d = 0.96, CI = 0.81, and R2 = 0.92. Also, the RSF model provides satisfactory results similar to the WSVR model. [ABSTRACT FROM AUTHOR]

Numerous numerical experiments have been performed to cloud model seeding over the last two decades. Silver iodide nucleation has been parameterized using different methods in these studies. The results of these studies indicate that cloud seeding can change the distribution of precipitation in most cases. Moreover, most of these numerical simulations have been used only in the field of convective cloud seeding and are incapable of complete simulation of atmospheric conditions. For this purpose, the governing equations should be parameterized in three dimensions for the general case and be used in the appropriate model. In this study, the effect of cloud seeding, whether increasing or decreasing in rainfall, has been studied. For this purpose, the WRF numerical model has been developed to simulate the cloud seeding. Since, it is virtually impossible to repeat experiments under similar meteorological conditions, a model that can simulate the effect of cloud seeding on microphysical processes and precipitation could avoid many speculations or inaccurate estimates. The basic hypothesis of cloud seeding is based on the physical principle that at sub-freezing temperatures the equilibrium vapor pressure relative to ice is lower than the equilibrium vapor pressure relative to liquid water. Therefore, the saturated environment with 100% relative humidity relative to water (RHW = 100%) will be supersaturated relative to ice at temperatures below zero degrees Celsius (Pruppacher and Klett, 2010). As a result, in a cloud that is saturated with liquid water and composed of supercooled cloud water droplets, ice particles grow rapidly to form larger and heavier drops which could be fall as rain drops. In that environment, tiny, supercooled cloud droplets either grow in upward motion or evaporate to provide vapour for ice to grow. Therefore, in the cloud seeding with silver iodide, ice particles are expected to be produced and grow in the cold part of the cloud, and the liquid water of the cloud will be transformed into ice phase species more quickly. The operational cloud seeding project has been carried out in the northwest area of Iran. At the time of operational project, the seeding target area was under the influence of the eastern Mediterranean low pressure center, this trough has caused the formation of divergence in its downstream in the upper levels of the atmosphere in the target area and has led to the formation of severe upward movements. Stable and thick clouds have formed in the area. Under the above mentioned environmental conditions, 44 pyropatrons of 4% silver iodide were fired at the target area by a seeding aircraft. Silver iodide particles measuring 0.1 to 1 mm are very effective in freezing nuclei. In this study, the effect of seeding is coded based on the model of Meyers et. al (1995) and Seto et. al (2011) by applying the seeding conditions into the Morrison scheme code within the WRF model and changing the number density and mixing ratio of cloud ice due to the silver iodide injected into the atmosphere. By simulating the effect of cloud seeding, meteorological quantities, including precipitation under seeding conditions, are estimated by changing the Morrison microphysical scheme in the WRF model. The WRF numerical model was also run in control mode (without applying cloud seeding relations). By comparing the output rainfall of the numerical model in seeding mode with the output rainfall of the numerical model in control mode, the amount of cloud seeding effect was determined. The results showed that the changes resulting from seeding in the studied cloud seeding operation were not favorable in all stations, and in some cases, the decrease in precipitation was seen 2 hours after seeding. This decrease in some stations, such as Maragheh, Tabriz, Sahand, and Khoy, starts from seeding time and continues until the end. But in a station like Sarab, although the rainfall decreases slightly at the beginning of cloud seeding, over time, it increases to 7% after two hours. While seeding in Parsabad, and Ahar stations resulted in precipitation enhancement by 3%, 9%, and 27% two hours after seeding, respectively. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) is a key parameter in meteorological studies and forecasting of atmospheric events such as rain and flood. Due to the spatial limitations of GPS and meteorological stations, as well as observational discontinuities in the time domain, PWV modeling is of great importance. Obtaining PWV using direct measurements and water vapor measuring devices is a difficult task. The best way to get information on PWV variations indirectly is to use GNSS measurements. The GNSS meteorological technique can provide continuous and almost instantaneous observations of the amount of PWV around a GNSS station. Research has shown that the accuracy of weather forecasts can be improved using GNSS-dependent techniques. Models based on GNSS observations for estimating PWV are known as tropospheric analytical models. The tomographic model is one of the most famous and widely used tropospheric models. There are limitations such as a large number of unknown parameters, rank deficiency of design matrix and the inevitability of using regularization methods, assuming the amount of water vapor inside each voxel is constant and also, the need for initial amounts of water vapor inside the voxels in the voxel-based tomography (VBT) method. Such limitations have led researchers to use machine learning methods to estimate the spatio-temporal variation of PWV. In this paper, the spatio-temporal modeling of PWV is suggested using the generalized regression neural network (GRNN) model. The GRNN model is a type of artificial neural network (ANN) that uses radial basis functions (RBF) as an activity function in the hidden layer. As a result, its accuracy is higher than the ANN model. Eight parameters of longitude, latitude and height of GPS station, day of year (DOY), time (min.), relative humidity (RH), temperature (T) and pressure (P) are considered as inputs of GRNN and ANN models and the PWVs corresponding to these eight parameters are the outputs. After the training step, to evaluate the GRNN and ANN models, the observations of two GPS networks are used. In the GPS network of north-west of Iran, observations of 23 GPS stations in the period of 300 to 314 (winter season) from 2011 have been used. For the central Alborz GPS network, observations of 11 stations at the period of 162 to 176 (summer season) in 2016 are also used. Results obtained from GRNN and ANN models in two interior control stations, one exterior control station (outside the GPS network territory) and also in Tabriz and Tehran radiosonde stations are compared and evaluated with the results of VBT, ECMWF, Saastamoinen and GPT3 models. The statistical parameters of root mean square error (RMSE), relative error and correlation coefficient (R) are used to evaluate the accuracy of the models. At the north-west GPS network, the averaged RMSE values of GRNN, ANN, VBT, ECMWF, Saastamoinen and GPT3 models in the two interior control stations are calculated as 2.14, 2.57, 3.32, 3.36, 6.31 and 4.35 mm, respectively. For the central Alborz GPS network, the averaged RMSE of two interior control stations are computed as 2.01, 2.42, 3.24, 3.26, 6.00 and 4.06 mm, respectively. For the exterior control station, the GRNN model has less error than the ANN, VBT and Saastamoinen models, but more than the ECMWF and GPT3 model. The results of this paper show that the GRNN model has a very high accuracy compared to other analytical and empirical models of the troposphere. This model has the ability to show the spatio-temporal variations of precipitable water vapor with high accuracy at the GPS network territory and; it can considered as an alternative for the other analytical and empirical models. [ABSTRACT FROM AUTHOR]

BACKGROUND: In this study, the quality of land suitability for vine cultivation in south‐western Iran was evaluated and a land suitability map for vine cultivation was developed using the fuzzy‐analytic hierarchy process (AHP). The best harvest time of vines was determined based on the relationship between total soluble solids (TSS) and titratable acidity (TA) with fruit colour (red, green, and blue or RGB) in ten points and also the growing degree‐days (GDD) maps from April to December. The relationship between GDD and effective parameters in vine cultivation was determined using principal component analysis (PCA) and Pearson correlation methods. RESULTS: The results illustrated that the maximum temperature and relative humidity (RH) have the greatest effect on vine cultivation and its yield (weight 0.24). The results of the land suitability map showed that central regions have better conditions for growing vines (32%). The measurements of TA and TSS depicted that vines of the northern parts have higher TA and lower TSS and there is a significant relationship between them and fruit colour. The results of GDD maps showed that the harvest time of ruby vine and Gezel‐azm is July and August, respectively. Also, the temperature (Tmean, Tmax and Tmin), wind speed, and GDD were the most important parameters to determine the best location for vine cultivation. CONCLUSION: Determining land suitability for cultivation, the best harvest time, the time of grape ripening, and following the exact time of export and import of vine has a vital role to increase its productivity and services. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Pistachio is an export product with very high economic value in different regions of the world, including Iran. Since each agricultural product requires specific climatic and environmental conditions, land management based on appropriate models, identification, and evaluation is important to determine suitable places for its cultivation to be used with maximum yield. This study aims to identify the ecological potential of pistachio cultivation areas in the south of Fars province, Iran, with an emphasis on climatic parameters. In this regard, the ecological needs of pistachio such as topographic indicators (altitude, slope), geological features, and climatic characteristics (precipitation, relative humidity, and temperature) were prepared. Then, using near and small fuzzy membership functions, a fuzzy map of each of the effective parameters in cultivating this crop was prepared. Also, using the opinions of experts and a pairwise comparison of each parameter, the importance of each parameter for the cultivation of this crop was quantified. Using the weights prepared by the AHP method, land suitability maps were prepared for the cultivation of this crop. Finally, by preparing a map of cold needs from April to October, suitable areas for growing 8 different cultivars of pistachio (Kerman, Peters, Kaleghoochi, Ouhadi, Ahmad Aghaei, Akbari, Fandoghi Ghafouri, Choruk) were identified. The results of AHP and fuzzy methods showed that the central parts of the study area (about 50%) have more potential for the cultivation of this plant. [ABSTRACT FROM AUTHOR]

In recent years, there have been considerable changes in the distribution of diseases that are potentially tied to ongoing climate variability. The aim of this study was to investigate the association between the incidence of cutaneous leishmaniasis (CL) and climatic factors in an Iranian city (Isfahan), which had the highest incidence of CL in the country. CL incidence and meteorological data were acquired from April 2010 to March 2017 (108 months) for Isfahan City. Univariate and multivariate seasonal autoregressive integrated moving average (SARIMA), generalized additive models (GAM), and generalized additive mixed models (GAMM) were used to identify the association between CL cases and meteorological variables, and forecast CL incidence. AIC, BIC, and residual tests were used to test the goodness of fit of SARIMA models; and R2 was used for GAM/GAMM. 6798 CL cases were recorded during this time. The incidence had a seasonal pattern and the highest number of cases was recorded from August to October. In univariate SARIMA, (1,0,1) (0,1,1)12 was the best fit for predicting CL incidence (AIC=8.09, BIC=8.32). Time series regression (1,0,1) (0,1,1)12 showed that monthly mean humidity after 4-month lag was inversely related to CL incidence (AIC=8.53, BIC=8.66). GAMM results showed that average temperature with 2-month lag, average relative humidity with 3-month lag, monthly cumulative rainfall with 1-month lag, and monthly sunshine hours with 1-month lag were related to CL incidence (R2=0.94). The impact of meteorological variables on the incidence of CL is not linear and GAM models that include non-linear structures are a better fit for prediction. In Isfahan, Iran, meteorological variables can greatly predict the incidence of CL, and these variables can be used for predicting outbreaks. [ABSTRACT FROM AUTHOR]

This research was conducted at the Behbahan Agriculture Research Station, Behbahan, south-west of Iran, for two years from 2016 to 2018 to study the postharvest losses of short-day onion cultivars in uncontrolled storage conditions (no heating, cooling and ventilation systems). Seeds were sown in nursery in early October and seedling transplanted at 2-3 leaf stage. Bulbs were harvested when 50-80% of foliage had dried and fallen and prepared for the experiment in both years. In the 1st year of experiment, the average of daily storage temperature and relative humidity ranged from 14.5 to 40.5°C and 27 to 60%, respectively. The average of daily storage temperature and relative humidity varied from 15 to 38 °C and 30 to 71%, respectively, in the 2nd year of experiment. This research was performed using a split plot experiment based on a randomized complete block design encompassing 80 treatments with 4 replications. Main plot consisted of 10 cultivars (‘Seba’, ‘Texas Early White’, ‘Imperateriz’, ‘Siroos’, ‘7030’, ‘X P red’, ‘Sapidan’, ‘Golden I’, ‘Texas Early Grano’ and ‘Primavera’) and bulbs evaluation in storage at eight times (one months after start of storage with one month interval) was considered as sub plot. The monthly bulb weight losses percentage decreased with time. The minimum weight loss (9.56%) was recorded in ‘Seba vultivar. The damage of black mold increased until the fourth month of storage and then decreased. ‘Sapidan’ was the most sensitive cultivar to damage of black mold (23.23%). The highest percentage of bulb rotting (29.65%) was observed in ‘Texas Early White’ cultivar. Bulb sprouting was observed from the fifth month of storage and the highest bulb sprouting (19.88%) belonged to ‘Siroos’ cultivar. Results were suggestive of difference in storability among cultivars, the maximum and minimum storability being recorded in ‘Seba’ and ‘Sapidan’, cultivars, respectively. [ABSTRACT FROM AUTHOR]