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Malaria is a vector-borne disease, likely to be affected by climate change. In this study, general circulation model (GCM)-based scenarios were used for projecting future climate patterns and malaria incidence by artificial neural networks (ANN) in Zahedan district, Iran. Daily malaria incidence data of Zahedan district from 2000 to 2019 were inquired. The gamma test was used to select the appropriate combination of parameters for nonlinear modeling. The future climate pattern projections were obtained from HadGEM2-ES. The output was downscaled using LARS-WG stochastic weather generator under two Representative Concentration Pathway (RCP2.6 and RCP8.5) scenarios. The effect of climate change on malaria transmission for 2021-2060 was simulated by ANN. The designed model indicated that the future climate in Zahedan district will be warmer, more humid, and with more precipitation. Assessment of the potential impact of climate change on the incidence of malaria by ANN showed the number of malaria cases in Zahedan under both scenarios (RCP2.6 and RCP 8.5). It should be noted that due to the lack of daily malaria data before 2013, monthly data from 2000 were used only for initial analysis; and in preprocessing and simulation analyses, the daily malaria data from 2013 to 2019 were used. Therefore, if proper interventions are not implemented, malaria will continue to be a health issue in this region.

PURPOSE: Salmonella is one of the main causes of gastroenteritis, and its incidence may be affected by meteorological variables. This is the first study about the effect of climatic factors on salmonella incidence in Kermanshah, Iran. METHODS: Data about salmonellosis cases in Kermanshah were inquired from Center for Communicable Disease Control, at the Ministry of Health and Medical Education of Iran, for the 2008 to 2018 time-frame. Meteorological variables including maximum, minimum and mean of temperature and humidity, sunshine hours and rainfall were inquired for the same time frame. Negative binomial generalized linear models (GLM) were used to assess the effect of meteorological variables on the weekly incidence of salmonellosis. RESULTS: During the years under study, 569 confirmed cases were registered in Kermanshah province. Study results showed a 3 % increase in salmonellosis incidence, after 1 % increase in minimum humidity in the week before (incidence rate ratio (IRR): 1.03; 95 % confidence interval (CI):1.02–1.05) and also a 4 % increase in incidence for 1 °C increase in mean temperature in the same week (IRR: 1.04; 95 % CI:1.02–1.06). CONCLUSIONS: Increase in minimum humidity and mean temperature may have a role in increasing the incidence of salmonellosis in Iran.

Diurnal temperature range (DTR) is one of the climate indicators likely to be related to human health. The purpose of this study was to investigate the relation between mortality and DTR in Kerman, Iran. The DLNM (Distributed Lag Non-linear Model)with a quas/i-Poisson regression model was used to evaluate the effect of DTR on mortality in age and sex groups by controlling the confounding variables (long-term trend of daily mortality, effect of day of the week, holidays, mean temperature, humidity, and air pollution). Since DTR effects on mortality may vary in cold and warm seasons, separate analyses were conducted for cold and warm seasons. DTR showed a non-linear relation with mortality. Mortality increased at the 90th percentile of DTR (CRR = 1.16, 95% CI 1.00–1.34), in lag 0–21 and at the 10th percentile of DTR in lag 0–13 (1.27, 95% CI 1.06–1.52), and lag 0–21 1.35 (95% CI 1.06–1.71). Increased mortality was more observed in the > 60 age group and in men. High levels of DTR after long lags (13 days) in the cold season were associated with increasing mortality. High and low diurnal temperature range may be a risk factor for mortality, especially in the men and elderly.

The value of radiation energy is considered to be the main component of the design of clean energy devices. Due to the fact that this amount is not measured or in some cases the radiation station may not be available in Iran, so it is necessary to estimate this component. In this study, Kerman and Yazd were selected as the regions with the maximum potential of solar energy. Then Artificial Neural Network (ANN) and Support Vector Machine (SVM) capabilities were evaluated in solar energy estimation. For this purpose, the daily data of 25 years (1992-2017) including maximum temperature, mean temperature, relative humidity, sunshine hours and solar radiation were collected at the synoptic stations of these regions. To evaluate the performance of models, common evaluation statistics were used. The results showed that at the Yazd station, in the ANN method, the lowest values of RMSE, MAE and the highest values of IA and R2 were 2.381, 1.760, 0.869, and 0.962 , respectively. These values at Kerman Station are equal to 2.708, 2.050, 0.945 and 0.810 , respectively.In the SVM method, the lowest values of RMSE, MAE and the highest values of IA and R2 at the Yazd station were 2.028, 1.540, 0.901 and 0.973, respectively, and at Kerman station were 2.407 , 1.896, 0.956 and 0.846 , resp ectively. Overall, the efficiency of the SVM method in both regions was more accurate than the ANN method.

Drought is one of the most significant environmental disasters, especially in arid and semi-arid regions. Drought indices as a tool for management practices seeking to deal with the drought phenomenon are widely used around the world. One of these indicators is the Palmer drought severity index (PDSI), which is used in many parts of the world to assess the drought situation and continuation. In this study, the drought state of Fars Province in Iran was evaluated by using the PDSI over 1995–2014 according to meteorological data from six weather stations in the province. A statistical downscaling model (SDSM) was used to apply the output results of the general circulation model in Fars Province. To implement data processing and prediction of climate data, a statistical period 1995–2014 was considered as the monitoring period, and a statistical period 2019–2048 was for the prediction period. The results revealed that there is a good agreement between the simulated precipitation (R2>0.63; R2, determination coefficient; MAE 0.95, MAE

Due to the impacts of climate change on Probable Maximum Precipitation (PMP), and its importance in designing hydraulic structures, PMP estimation is crucial. In this study, the effect of climate change on 24-h probable maximum precipitation (PMP24) was investigated in a part of the Qareh-Su basin located in the Southeast of Caspian Sea. So far, there are no studies emphasizing on climate change impact on hydrological (physical) PMP values have been conducted in the study area. For this purpose, the climatic data were applied during the years 1988–2017. To generate future data, the outputs of the CanESM2 (Second Generation Canadian Earth System Model) model as a general circulation model (GCM) under optimistic (RCP2.6), middle (RCP4.5), and pessimistic (RCP8.5) emission scenarios, and statistical downscaling model (SDSM) were used in the near (2019-2048) and the far (2049-2078) future periods. The PMP24 values were estimated using a physical method in the baseline and future periods under the three scenarios. The PMP24 value was estimated at 143 mm for the baseline-period, using a physical approach. These values were 98, 105, and 109 for the near-future and 129, 122, and 126mm for the far-future period. The results showed that the physical approach's PMP24 values tend to fall at 14-38%. Overall, the PMP24 values decrease in the future, and the rate of decrease in the near-future was more than the rate of the far-future. The spatial distribution maps of PMP24 in the baseline and future-periods showed that the PMP24 values decreased from west to east.

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.

Accurate prediction of dew point temperature is very important in decision making in many fields of water resources planning and management, agricultural engineering and climatology. This study investigates the ability of some data-driven models (DDMs) in predicting daily dew point temperature. These models include traditional group method of data handling (GMDH), improved GMDH models (GMDH1, GMDH2), and two hybrid GMDH-based models (GMDH-HS and GMDH-SCE) which were developed by combination of GMDH with two optimization algorithms, harmony search (HS) and shuffled complex evolution (SCE). 11 years of daily recorded weather variables at Kerman synoptic station including mean temperature (Ta), sunshine hours (S), soil temperature (Ts), mean relative humidity (Rh), and wind speed (Ws) were used to evaluate the proficiency of developed models. Sensitivity analysis revealed that Rh is the most influential input variable in predicting dew point temperature. Seven quantitative standard statistical indices including coefficient of efficiency (CE), correlation coefficient (CC), root mean square error (RMSE), mean square relative error (MSRE), mean absolute percentage error (MAPE), relative bias (RB) and threshold statistic (TSx) were employed to examine the performance of applied models. The results indicated the superiority of combinatorial models (GMDH-HS and GMDH-SCE) to the other developed models in predicting the dew point temperature (Tdp). In terms of threshold statistic, GMDH2-HS had the highest values of TSx (the best model) and GMDH2-SCE, GMDH1-HS, GMDH1-SCE, GMDH2 and GMDH1 got the next ranks, respectively. It was observed that GMDH2-HS could predict the Tdp (with CE = 0.979 and RMSE = 0.745) better than the other models (with CE = 0.958 and RMSE = 0.932, in average), indicating its high efficiency.

This study was conducted in order to explore the effect of thermal comfort on all-cause mortality using three indices in different lag times, in a semi-arid to dry region of Iran. Three thermal comfort indices based on the energy balance of the human body including physiologically equivalent temperature (PET), predicted mean vote (PMV), and standard effective temperature (SET) were used to assess the effects of thermal comfort on mortality. Distributed lag non-linear models were used to assess the relation. The natural cubic spline was chosen as the basis function for the space of predictors and lags, with 4 degrees of freedom. All three indices showed the same pattern in general, but the relative risk for PMV values were more than the other indices in different lags. For all three indices, lag 0 had the highest relative risk of mortality in warm and hot indices. The relative risk for warm and hot values was more than cool and cold values in lag 0, and for the PMV index, it was larger than the two other indices. These results were different in lags 5 to 8, and the relative risks for cool and cold values were more than warm and hot values. This study showed that heat stress has a stronger and more immediate adverse effect on mortality than cold stress. Also, the elderly and females are more vulnerable than others. The most apparent effect was seen in lags 0–12.

The association between heat or heat waves and mortality should often be reported in a way that makes it sensible by health policymakers. In this study we aimed to assess the effect of heat and heat waves on mortality using attributable risks during 2005-2017. Nine heat waves were defined using a combination of severity and duration of mean daily temperature. Heat wave effects were assessed using added and main effects. Added effects were assessed as a binary variable and main effects were assessed by comparing the median temperature (in heat wave days) to Minimum Mortality Temperature (MMT). The effects of heat, mild heat and extreme heat on mortality were also assessed. Distributed Lag Non-linear Models were used to assess the relations in a bi-dimensional perspective in which the quadratic b-spline was chosen as the basis function for the dimension of the exposure and the natural cubic b-spline was chosen for lag dimension. The backward perspective was used to estimate the attributable risks. The total mortality attributed to non-optimal temperatures for all days was 1.91% (CI 95%: -6.36, 8.47). The attributable risks (AR) were 2.23%, 2.02% and 0.25% for heat, mild heat and extreme heat days, respectively. AR was more for females and the above 65 years old groups than other groups in heat, mild heat and extreme heat days. While the stronger heat waves defined based on temperature above the 95 and 98th percentile had a significant attributable risk for total mortality in the added effects; the weaker heat waves (defined based on temperature above of the 90th percentile (HW1, HW2, HW3) had higher attributable risks, significant for HW1 and HW2, in the main effects. Apparently weaker heat waves show more immediate effects, while stronger heat waves increase mortality over several days.

Recently there has been a lot of interest in global warming and temperature variation's effect on health. This study evaluates the impact of Diurnal Temperature Range (DTR) on mortality in Tabriz, northwest of Iran. Distributed Lag Non-linear Models combined with a quasi-Poisson regression were used to assess the impact of DTR on cause, age and gender specific mortality, controlled for potential confounders such as long-term trend of daily mortality, day of week effect, holidays, mean temperature, humidity, wind speed and air pollutants. As the effect of DTR may vary between the hot season (from May to October) and cold season (from November to April of the next year), we conducted analyses separately for these two seasons. In high DTR values (all percentiles), the Cumulative Relative Risk (CRR) of Non-Accidental Death, Respiratory Death and Cardiovascular Death increased in the cold season. In full year and high DTR value (percentile 90), the CRR of respiratory deaths increased as well (CRR, Lag 0-6:1.32(1.006, 1.75). Although there was no clear significant effect in low DTR values, high values of DTR increase the risk of mortality in the cold season, in Tabriz, Iran.

Determination of the water quality monitoring network (WQMN) is a vital stage for surveying ecosystem health. Studies have been done in determining the optimal number and location of sampling points, but seasonality of water quality, especially for heavy metals, has been rarely studied. For the first time, this study proposes a framework to determine the optimal location of sampling points to monitor lead (Pb). This study was conducted for the Karoun River, located in southwestern Iran. First, hydraulic characteristics of the river were simulated by implementing of MIKE11 software as well as water quality(variation of Pb concentration). Nash‑Sutcliffe coefficient were 0.91 and 0.91 for discharge calibration and validation, respectively. Second, 16 potential sampling points were proposed using modified Sanders’ approach considering seasonality. For a better accuracy in the WQMN layout and a more efficient site selection of sampling points, a 1-km buffer is stretched along the river for determining non-point source pollution sources and prioritizing candidate points. This leads to considering different land uses in the study area, while GIS software has been employed. Seasonal changes and land use have a significant impact on the location of optimal sampling points. The presented framework can be used to improve water quality and support watershed protection efforts.

Background The Crimean-Congo Hemorrhagic fever (CCHF) is endemic in Iran and has a high fatality rate. The aim of this study was to investigate the association between CCHF incidence and meteorological variables in Zahedan district, which has a high incidence of this disease. Methods Data about meteorological variables and CCHF incidence was inquired from 2010 to 2017 for Zahedan district. The analysis was performed using univariate and multivariate Seasonal Autoregressive Integrated Moving Average (SARIMA) models and Generalized Additive Models (GAM) using R software. AIC, BIC and residual tests were used to test the goodness of fit of SARIMA models, and R2 was used to select the best model in GAM/GAMM. Results During the years under study, 190 confirmed cases of CCHF were identified in Zahedan district. The fatality rate of the disease was 8.42%. The disease trend followed a seasonal pattern. The results of multivariate SARIMA showed the (0,1,1) (0,1,1)12 model with maximum monthly temperature lagged 5 months, forecasted the disease better than other models. In the GAM, monthly average temperature lagged 5 months, and the monthly minimum of relative humidity and total monthly rainfall without lag, had a nonlinear relation with the incidence of CCHF. Conclusions Meteorological variables can affect CCHF occurrence.

Few studies have investigated the different extreme temperature effects (heat-cold) of one geographical location at the same time in Iran. This study was conducted to assess the impact of heat and cold waves on mortality in Urmia city, which has a cold and mountainous climate. Distributed Lag Non-linear Models combined with a quasi-Poisson regression were used to assess the impact of heat (HW) and cold waves (CW) on mortality in subgroups, controlled for potential confounders such as long-term trend of daily mortality, day of week effect, holidays, mean temperature, humidity, wind speed and air pollutants. The heat/cold effect was divided into two general categories A-main effect (the effect caused by temperature), B-added effect (the effect caused by persistence of extreme temperature). Results show that there was no relation between HW and respiratory and cardiovascular death, but in main effects, HW(H1) significantly increased, the risk of Non-Accidental Death (NAD) in lag 0 (Cumulative Excess Risk (CER) NAD = 31(CI; 4–65)). Also in added effects, HW had a significant effect on NAD (CER H1; NAD; lag;0-2 = 31(CI; 5, 51), CER H2; NAD; lag;0-2 = 26(CI; 6, 48)). There was no relation between CW and respiratory death and cardiovascular death, but in added effects, CW(C1) significantly decreased, the risk of non-accidental death in initial lags (CER C1; NAD; lag;0-2 = −19 (CI; −35, −2)). It seems that high temperatures and heat waves increase the risk of non-accidental mortality in northwest of Iran.

One of the most important problems in flood manegment is the damages induced by this phenomenon. Expected annual damage (EAD) is an important index for basin vulnerability against flood. Prediction of flood damages requires the analysis of spatial and temporal risk and must be calculated by the combination of hydrologic, hydraulic and economic models. In this research, the uncertainty was considered in the flood risk analysis. The probability of flood occurrence was calculated by the parabolistic model. By using the river analysis systems software (HEC-RAS) and the geographic information system (GIS) and utilizing the Google-Earth software, the floodplains of Zayande Roud river in Esfahan province were investigated with the return period of 25, 50, 100, 200 and 500 years. The Monte Carlo method was also sed to perform the uncertainty analysis in the proposed method. The logarithmic persion type III was selected as the best distribution of flood. The damage-stage relationship was calculated as well. Based on the uncertinity analysis, the river discharge could be regarded as the major parameter in the uncertainty of EAD.

Optimal operation of reservoirs is one of the most important issues in water resources management. In this study, a novel metaheuristic optimisation algorithm, called the water cycle algorithm (WCA), was used to derive operating policy for a multi-reservoir system. In the first step, the performance of the model was successfully assessed through several benchmark functions. The WCA was then used to derive the optimal operation of four- and ten-reservoir systems. It was then applied to the monthly operation of Golestan and Voshmgir consecutive dams located in Gorganrood basin in the north of Iran. In this way, the objective function was defined as minimising the total deficit for the study period. The WCA results were compared with the results of other developed evolutionary algorithms, including a genetic algorithm, harmony search algorithm, particle swarm optimisation and imperialist competitive algorithm. The results showed that, for cases of four- and ten-reservoir systems, the best solutions achieved by the WCA were 306·3918 and 1172·4197, which had differences of 0·5% and 1% compared with the global optimum solutions, respectively. In addition, it was found that the WCA was superior to other algorithms in calculating the annual deficit of the Golestan–Voshmgir multi-reservoir system.

The investigation of local scour below hydraulic structures is so complex that makes it difficult to establish a general model to provide an accurate estimation for the local scour dimension. During the last decades, Data Driven Methods (DDM) have been used extensively in the modeling and prediction of unknown or complex behaviors of systems One of these methods is Group Method of Data Handling (GMDH), that is a self-organization approach and increasingly produces a complex model during the performance evaluation of the input and output data sets. So, the objective of this study was to investigate the potential of the GMDH method in the accurate estimation of local scouring geometry (maximum scour depth, the distance of maximum local scour depth till Ski-jump bucket and length of local scour) below the Siphon spillway with Ski-jump bucket energy dissipaters for a set of experimental data. 80% of data set was used for the training period and the remaining data set was used for the test period. The average values of MSRE, MPRE, CE and RB for the nonlinear second order transfer function (FUNC1) were calculated to be 0.92, 0.02, 8.74, -0.01; also, for the nonlinear first order transfer function (FUNC2), they were 0.85, 0.02, 10.43 and -0.02, respectively. The results indicated that the performance of FUNC1 was better than FUNC2. Also, the value of the coefficient of determination (R2) for the estimation of local scour dimension using different methods such as s linear regression, nonlinear regression and ANN indicated the high performance of the developed model of GMDH in the accurate estimation for local scour dimensions.

The present study was an effort to study body image, sexual satisfaction, and marital adjustment in middle-aged married women. Three hundred and thirty middle-aged women were selected out of the women attending consultation centers in Gorgan, Iran, and they were asked to complete Dyadic Adjustment Scale (DAS), the Body Self-image Questionnaire, and the Persian language modified version of SQOL. Data analyses were conducted via descriptive statistics, t tests, one-way analysis of variance, Pearson’s correlation coefficients, and stepwise multiple regression. The results revealed that sexual satisfaction, body image, and marital adjustment were significantly different in terms of demographic (i.e., age, educational level, duration of marriage, living arrangement, occupation, and monthly income) and health factors (i.e., disease, exercise, frequency of sexual intercourse with spouse, and sleeping problem) excluding duration of marriage and sleeping problem for marital adjustment. It was also shown that sexual satisfaction, body image, and marital adjustment were positively and significantly correlated. Stepwise multiple regression analysis results also indicated the influencing factors on each of the variables.

There are few epidemiological studies about climate change and the effect of temperature variation on health using human thermal indices such as the Physiological Equivalent Temperature (PET) Index in Iran. This study was conducted in Tabriz, the northwest of Iran and Distributed Lag Non-linear Models (DLNM) combined with quasi-Poisson regression models were used to assess the impacts of PET on mortality by using the DLNM Package in R Software. The effect of air pollutants, time trend, day of the week and holidays were controlled as confounders. There was a significant relation between high (30°C, 27°C) and low (-0.8°C, -9.2°C and -14.2°C) PET and total (non-accidental) mortality; and a significant increase in respiratory and cardiovascular deaths in high PET values. Heat stress increased Cumulative Relative Risk (CRR) for total (non-accidental), respiratory and cardiovascular mortality significantly (CRR Non Accidental Death, PET=30°C, lag 0-30=1.67, 95%CI: 1.31-2.13; CRR Respiratory Death, PET=30°C, lag 0-13=1.88, 95%CI: 1.30-2.72; CRR Cardiovascular Death, PET=30°C, lag0-30=1.67 95%CI: 1.16-2.40). Heat stress increases the risk of total (non-accidental), respiratory mortality, but cold stress decreases the risk of total (non-accidental) mortality in Tabriz which is one of the cold cities of Iran.

Diurnal Temperature Range (DTR) is a meteorological index which represents temperature variation within a day. This study assesses the impact of high and low values of DTR on mortality. Distributed Lag Non-linear Models combined with a quasi-Poisson regression model was used to assess the impact of DTR on cause, age and gender specific mortality, controlled for potential confounders such as long-term trend of daily mortality, day of week effect, holidays, mean temperature, humidity, wind speed and air pollutants. As the effect of DTR may vary between the hot season (from May to October) and cold season (from November to April of the next year), we conducted analyses separately for these two seasons. In high DTR values (all percentiles), the Cumulative Relative Risk (CRR) of Non-Accidental Death, Respiratory Death and Cardiovascular Death increased in the full year and hot season, and especially in lag (0−6) of the hot season. In the cold season and high DTR values (all percentiles), the CRR of Non-Accidental Death and Cardiovascular Death decreased, but the CRR of Respiratory Death increased. Although there was no clear significant effect in low DTR values. High values of DTR increase the risk of mortality, especially in the heat season, in Urmia, Iran.

Background: Congenital hypothyroidism (CH) is a common endocrine disease and an important cause of mental retardation. The purpose of this study was to investigate the probable role of season and climatic factors in the incidence of CH in Kerman province, Iran. Methods: Incidence data were collected from the CH screening program files from 2005 to 2011 in Kerman province, a number of 288,437 infants were included in the study. Climate data were collected from the Meteorological Office. The relations were tested by χ2-test, Pearson correlation, and negative binomial regression. Results: The overall incidence of CH in Kerman province was 2.68 per 1000 births. There was a significant difference in both the monthly and seasonal incidence of CH (p Conclusions: It seems like there is no clear relation between CH incidence and climate factors, in Kerman Province. However, CH incidence was highest in October (Autumn) and lowest in June (Summer).

Malaria is a major health problem in many tropical and subtropical countries and in the south of Iran. In this study, due to the importance of the disease in Sistan and Baluchestan province, the influence of temperature, humidity, and rainfall on malaria has been evaluated in areas with a high incidence of malaria. Malaria incidence data were inquired from the Province Health Authority, and climatic variables were inquired from the Bureau of Meteorology from 2000 to 2012 and were analyzed on a monthly basis. Changes in incidence of malaria with climatic factors were analyzed by negative binomial regression by Stata 11, and the correlations were calculated with Minitab15 for determining the potential impact of meteorological variables with and without lags on malaria transmission. The incidence of malaria had a significant positive correlation with the average, minimum, and maximum monthly temperatures and a negative correlation with rainfall and low humidity (60% had a positive impact on incidence; as in the town of Chabahar after adjusting variables such as rainfall and temperature; every one percent increase in humidity caused a 4% increase in malaria incidence in the same month and a significant 6% increase in the next month. Temperature and humidity over 60% are effective climate parameters in the incidence of malaria. These factors should be considered in planning for controlling and preventing malaria.

In order to rationalize a surface water quality monitoring network (WQMN), it is critical to appropriately design surface water quality sampling locations. This is due to high installation, operational, and maintenance costs for each sampling representative of the whole water system conditions. The main objective of this study was to propose an integrated method to determine the most appropriate sampling points in the Khoy watershed northwest of Iran, where financial resources and water quality data are limited. Multi criteria evaluation method including analytic network process (ANP) and Fuzzy logic were incorporated in River Mixing Length (RML) procedure in order to identify exact locations of sampling points. Based on RML procedure, 15 candidate sampling points were identified to suitably select sampling points based on budget deficiency. Relative weights for 12 criteria and 10 sub-criteria related to non-point sources and surficial rocks as well as criteria of topography were then calculated by the ANP method. According to the obtained results, a new total potential pollution score (TPPS) was presented to prioritize 15 candidate sampling points. Then, the values of TPPS were classified and fuzzified to distinguish real differences between scores. Based on current monitoring stations and budget deficiency, the hierarchy value, and Fuzzy rank, six points are proposed as the most appropriate locations for surface water quality monitoring. Furthermore, four points are identified as the second most appropriate for enhancing a robust WQMN in the study area in order for an expansion plan in the future. The results of this study should be valuable for water quality monitoring agencies looking for an efficient approach for selecting exact sampling locations.

Background: Traffic accidents is one of the main causes of mortality, and has a high impact on overall health and development. Iran is a country with a high prevalence of traffic accidents, and this has been a big concern. In this study, we aimed to study the past decade’s trend in mortality due to traffic accident in Kerman, Iran, and its relation with ambient temperature by using Generalized Additive Mixed Models. Methods: Meteorological data, such as temperature, humidity, and wind speed were obtained from the Meteorological Organization of Kerman and traffic mortality data were obtained from the Health Deputy of Kerman University of Medical Sciences. In order to explore the relations, Generalized Additive Mixed Models (GAMMs) were used. Additive models are nonparametric forms of regression models in which the summation of regression coefficients × explanatory variables of a linear regression is replaced by a summation of arbitrary smooth functions of the explanatory variables. Results: About 2900 deaths due to traffic accident occurred from March 2005 until May 2017 in Kerman. The monthly average deaths were 20.01(±14.43) that was more for men than women (15.42 vs 4.58), and was highest in the age 31-60 group. Non-linear effect of time on mortality was significant. There was a negative effect of time on mortality until 2009, while all other confounders were adjusted. After a short-term increasing trend till early 2011, and then a decreasing trend until 2013, an increasing trend was observed again. Holding pollutants, temperature and year (trend effect) stable, mortality tended to be higher for the first half of year, with an increasing slope in the first 3 months of the year. Monthly average temperatures below 13°C was related with increased mortality, but average temperatures around 20°C had no effect on mortality. High temperatures (>23°C) were associated with less mortality. Conclusions: Most traffic accident related deaths occurred in first half of year (spring and summer), and it is probably because of increased inter-city traveling during the summer break. Our results showed that high extreme temperatures have less contribution on the mortality. Keywords: Accident, Mortality, Trend, Ambient Temperature

The present study was conducted to compare the impact of heat waves on mortality and years of life lost (YLL) in Kerman, Iran during the years 2005–2017. Daily mean temperature in a combination of intensity and duration were used in order to define heat waves (90, 95, and 98th percentile and ≥ 2, 3, and 4 consecutive days). YLL was calculated according to Iran’s life table and by considering the discount rate. In order to investigate the impact of heat waves in different lags and its cumulative effect on mortality and YLL, Poisson and linear models within distributed lag nonlinear models were used respectively. A maximum lag of 14 days was considered. The best model was selected based on AIC (Akaike Information Criteria). The model was adjusted for air pollutants, public holidays, days of the week, and humidity. The average daily mortality and YLL were 10.54 ± 4.31 deaths and 175.58 ± 91.39 years respectively. They were higher in men and in heat waves matching a definition of above the 98th temperature percentile and ≥ 3 days, than others. Except heat waves defined as the 98th percentile and ≥ 4 days, the impact of heat waves on mortality and YLL were the highest at lag 0. The cumulative relative risk of total mortality was significantly higher in heat waves above the 95 and 98th percentiles. The cumulative effect of heat waves on total YLL was significantly higher only above the 98th percentile. Men over 65 years old were the most vulnerable and had the highest mortality and YLL. Heat waves with temperatures above the 98th percentile that lasted at least 2 or 3 consecutive days had a significant effect in increasing both total YLL and mortality in Kerman, Iran.

Background Estimating the effects of climate change on human health can help health policy makers plan for the future. In Iran, there are few studies, about investigating the effects of climate change on mortality. This study aimed to project the effect of low (cold) and high (heat) temperature on mortality in a dry region of Iran, Kerman. Methods Mortality attributed to temperature was projected by estimating the temperature-mortality relation for the observed data, projection of future temperatures by the statistical downscaling model (SDSM), and quantifying the attributable fraction by applying the observed temperature-mortality relation on the projected temperature. Climate change projection was done by three climate scenarios base on Representative Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5). Adaptation was considered by using different minimum mortality temperatures (MMT) and risk reduction approaches. The current decade (2010–19) was considered as the reference period. Results All three climate change scenarios, showed that the mean of temperature will rise about 1 °C, by 2050 in Kerman. The number of deaths attributed to heat were obviously higher than cold in all periods. Assuming no adaptation, over 3700 deaths attributed to temperature will happen in each decade (2020s, 2030s and 2040s) in the future, in which over 3000 deaths will be due to heat and over 450 due to cold. In the predictions, as Minimum Mortality Temperature (MMT) went up, the contribution of heat to mortality slightly decreased, and cold temperature played a more important role. By considering the risk reduction due to adaptation, the contribution of heat in mortality slightly and insignificantly decreased. Conclusion The results showed that although low temperatures will contribute to temperature-related mortality in the future, but heat will be a stronger risk factor for mortality, especially if adaptation is low.

Probable maximum precipitation (PMP) estimation is one of the most important components for designing hydraulic structures. The aim of this study was the estimation of 24 h PMP (PMP24) using statistical and hydro-meteorological (physical) approaches in the humid climate of the Qareh-Su basin, which is located in the northern part of Iran. Firstly, for the statistical estimate of PMP, the equations of empirical curves of the Hershfield method were extracted and the Hershfield standard and modified methods were written in Java programming language, as a user-friendly and multi-platform application called the PMP Calculator. Secondly, a hydro-meteorological approach, which is called the convergence model, was used to calculate PMP24. The results of both approaches were evaluated based on statistical criteria, such as the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), mean absolute percentage error (MAPE), correlation coefficient (r), and coefficient of determination (R2). The maximum values of PMP24 for the Hershfield standard and modified methods were estimated to be 448 and 201 mm, respectively, while the PMP obtained by the physical approach was 143 mm. Comparison of PMP24 values with the maximum 24 h precipitation demonstrated that based on performance criteria including the MAE, MSE, RMSE, MAPE, r, and R2, the physical approach performed better than the statistical approach and it provided the most reliable estimates for PMP. Also, the accuracy of the Hershfield modified method was better than the standard method using modified Km values, and the standard method gives excessively large PMP for construction costs.

Introduction: The present study was conducted to compare the added effect of heat waves on mortality and years of life lost in Kerman, Iran during the years 2005-2016.Methods: Daily mean temperatur...

Geographic information systems (GIS) and remote sensing techniques are used as a decision support system to identify potential soil aquifer treatment (SAT) sites for groundwater recharge of Kerman aquifer, which is located in the southeast of Iran. These sites are identified using a single-objective multi-criteria analysis. To ensure technical feasibility, environmental sustainability, social acceptability and economical viability a number of criteria are considered for the site selection. The criteria selected for the different variables and acceptable ranges are based on standards published in national and international guidelines and technical documents. Multi-criteria evaluation was performed combining all produced thematic maps by means of the weighted index overlay method in order to select sites meeting all the criteria. The resulting map of this analysis shows potential sites are located in the north, southwest and southeast of the study area. Considering field observations, a potential site, which is located in the southwest of the study area, is proposed as the most suitable site for SAT. The result indicates that the study area has sufficient required suitable space for groundwater recharge with treated wastewater.