Soil temperature is one of the main characteristics of soil that its changes have a great impact on many processes such as growth, plants flourishing and soil formation. Nevertheless, temperatures throughout the soils profile are not measured continuously. As a result, we encounter the lack of statistics in soils temperature data, while Meteorological parameters are being measured regularly. Since presented relationships in the previous investigations do not provide the appropriate accuracy to predict soils temperature, the objective of this paper is to introduce a high accuracy relationship based on comparison of regression methods and Artificial Neural Network (ANN) by using daily Meteorological data of three stations located in Mashhad, Sabzevar and Shiraz. Solidarity coefficients indicated that ambient temperature, evapotranspiration and evaporation have the most solidarity with the soil temperature at a depth of 5 cm, respectively. According to solidarity coefficient and results of the 2 models, air temperature, evapotranspiration, humidity and effective precipitation with daytime lag of one day were regarded as the best input parameters, respectively. The results showed that second order regression with single variable had the lowest accuracy while the highest accuracy was observed in the ANN method. In the meantime, multiple regressions had a reasonable accuracy. In calculation of freezing depth we concluded that Finish equation has an acceptable accuracy, whereas by considering an added parameter related to the precipitation depth in the cold days to the equations order, the results of the Finish equations will improve dramatically. Maximum error of 15% was observed for the recommended equation in Shiraz station and count of error in Mashhad and Sabzevar 0.6% and 2% observed respectively.

The purpose of this research was to investigate the trend of annual changes in Yazd station's Meteorological parameters including minimum and maximum average daily temperature and average daily precipitation (1961-2005), as well as the predicted annual mean of these parameters in the three upcoming thirty years of the 2040s, 2070s and 2100s, by the SDSM model, under RCP2. 6, RCP4. 5, RCP8. 5, A2, and B2 scenarios. Accordingly, by using the coefficient of determination and the MAE, R2, RMSE indicators, we evaluated the data generated by the SDSM model in comparison with the observed data in the base period. The lowest value of R2 based on the calibration and validation of the mean values of observed and simulated SRES was obtained for precipitation (86 and 80%). In terms of the R2 evaluation index, the accuracy of the small-scaled results of the minimum and maximum average temperature values was more than that of the average precipitation; however, in terms of the MAE and RMSE evaluation indicators, the accuracy of the small-scaled results of the average precipitation was higher than that of the minimum and maximum average temperature values. Subsequently, HadCM3 large-scale climatological data was used to predict the future periods (2010-2100). The results indicated that the temperature was raised in all months and seasons and the precipitation was decreasing in most of them, thereby confirming that the climate was changing in the studied region...

Background and Objective): In this research the effectiveness of weather and climate parameters in incidence of accidents in the petrochemical industry was studied and management strategies to prevent these events have been presented.Method: Two of the petrochemical companies, one of them in Assaluyeh (named Zagros, located in warm climates) and the other one in Tabriz (in cold climates) were selected for pilot study. The required data were collected by questionnaire, interview and walking through under study fields. The analyses of data have been done by Excel, SPSS software and Correlation statistical test.Findings: Climate parameters don’t have a directly impact on the petrochemical occupational accidents and there is no significant relationship between them.Discussion and Conclusion: The role of climatic parameters in the incidence of accidents in the petrochemical industry is indirect. In fact, the thermal stress in the first stage caused unsafe conditions and then unsafe behavior, and finally cause human error and occupational accidents. In this study, appropriate solutions for instance engineering or managerial measures are also suggested in order to prevent accidents and injuries.

Introduction: By crossing the borders of cities and countries, air pollution is now a global problem so that it can be claimed that there is no sound and clean air. This study aimed to investigate the effect of Meteorological parameters on the concentration of particulate matter (PM2. 5 and PM10 ) in the air of Arak city. Materials and methods: In this study, Arak city was divided into three regions using geographic information system (GIS). Based on air pollution monitoring stations in Arak city, it was tried to select one station from each region for analysis. Meteorological parameters including temperature (° C), relative humidity (٪ ), precipitation (mm), and wind speed (m/s), were obtained from Arak Meteorological Organization. Finally, the association between the concentration of PM (PM2. 5 and PM10 ) and Meteorological parameters were evaluated by SPSS. Results: Annual changes in PM (PM2. 5 and PM10 ) showed that the average particle concentrations were 25. 34 and 46. 81 µ g/m 3 in the study periods, respectively. It was also found that the citizens of Arak were 2. 5 times more exposed to PM (PM2. 5 and PM10 ) pollutants than the standard recommended by the World Health Organization. Our findings also showed strong positive linear correlations of wind velocity and temperature with PM2. 5 and PM10, as well as relative humidity with PM10, and negative correlations of precipitation with PM2. 5 and PM10, as well as relative humidity with PM2. 5. Conclusion: The distribution map of Arak city indicated that the citizens of Shariati station and the governor's office were facing high concentrations of pollutants, posing them to a serious threat. Moreover, more pollution was recorded toward the north and northwest of the city. To protect the health of citizens in Arak, therefore, it is necessary to adopt appropriate policies and rules to reduce the concentrations of PM and other pollutants in the air of this city.

In order to test the hypothesis of connection between the changes in climatic parameters and declining northern Zagros forests in the west of Iran, long-term trends of Meteorological parameters and reference evapotranspiration (ET0) on seasonal and annual time scales using three synoptic Meteorological stations of Piranshahr, Saghez, and Khoram-Abad were examined. The Penman-Monteith combination equation was applied to calculate the ET0. To examine the long-term trends of Meteorological parameters and ET0, the non-parametric tests of Mann-Kendal and Spearman were used and the magnitude of the slope of trends lines was tested using the Sen-slope estimator. Results showed that the increasing trends of air temperature in Piranshahr (1986-2009) were statistically significant (P<0.01). Wind speed exhibited a significant positive trend in Saghez (1970-2009) and Khoram-Abad (1970-2010) by Spearman test (Saghez at 0.05% level, Khoram-Abad at 0.01% level). On seasonal time scale, the trends of climatic parameters and ET0 were similar by Mann-Kendal and Spearman tests and ET0 trends were positive in most stations. This preliminary research indicated that long-term trends of some climatic parameters and ET0 in the northern Zagros region were statistically significant so that the hypothesis of the connection between decline of northern Zagros forests and changes in Meteorological parameters was confirmed. Greater number of Meteorological stations throughout the Zagros region, however, should be included in future research.

Meteorological parameters play a significant role in affecting ambient air quality of an urban environment. As Dhaka, the capital city of Bangladesh, is one of the air pollution hotspot among the megacities in the world, however the potential Meteorological influences on criteria air pollutants for this megacity are remained less studied. The objectives of this research were to examine the relationships between Meteorological parameters such as daily mean temperature (o C), relative humidity (%) and rainfall (mm) and, the concentration of criteria air pollutants (SO2, CO, NOx, O3, PM2. 5 and PM10) from January, 2013 to December, 2017. This study also focused on the trend analysis of the air pollutants concentration over the period. Spearman correlation was applied to illustrate the relationships between air pollutants concentration and temperature, relative humidity and rainfall. Multiple linear and non-linear regressions were compared to explore potential role of Meteorological parameters on air pollutants’ concentrations. Trend analysis resulted that concentration of SO2 is increasing in the air of Dhaka while others are decreasing. Most of the pollutants resulted negative correlation with atmospheric temperature and relative humidity, however, they showed variable response to seasonal variation of Meteorological parameters. Regression analysis resulted that both the multiple non-linear and linear model performed similar for predicting concentrations of particulate matters but for gaseous pollutants both model performances were poor. This research is expected to contribute in improving the forecast accuracy of air pollution under variable Meteorological parameters considering seasonal fluctuations.

Projection of climate change in basins is very important for determining water capacity, water resources management as well as watershed management studies and environmental hazards. Therefore, in this study, temperature and precipitation changes were projected in Atrak basin, in the northern Khorasan province. For this purpose, the output projection of canESM2 global model were used considering three scenarios of Representative Concentration Pathway (RCP) 2. 6, RCP 4. 5 and RCP 8. 5 using SDSM as downscaling model and temperature and precipitation changes in the period (2021-2050) were compared with the base period (1995-2019). For calibration and validation of SDSM model, station observational data and NCEP/NCAR data were evaluated. MAE, MSE, RMSE and R2 correlation indices were determined to clarify the performance of the model. The results showed that the SDSM model has a high ability to simulate temperature and precipitation changes in the study area. According to the results of the CanESM2 model, in the future periods, temperature and precipitation would be increased compared to the base period, which might be from 0. 9 and 1. 0 degree Celsius for minimum and maximum temperatures, respectively. Most of the temperature changes would be related to the eastern parts of the study area while, the amount of precipitation in the basin would be increased from 1. 5 up to 11. 7 percent, the largest increase of which would be related to the central and northern regions of the basin. The highest and lowest temperature and precipitation changes in the basin are predicted based on RCP 8. 5 and RCP 2. 6 scenarios.

Prediction of water consumption and its effective factors is an important step in water crisis management. Studies showed that Meteorological parameters are considered as the most important factor for short-term prediction of water consumption. In this research, cluster-based sliced inverse regression method was used to identify the Meteorological variables affecting the household water consumption in Qom. In addition to dimension reduction, this method can be used to remove collinearity. The data consisted of seven Meteorological parameters and monthly household water consumption from 2001 to 2013. Data analysis indicated that instead of seven primary variables, only two new components which are linear combinations of independent variables can be used. The negatively charged maximum wind speed and relative humidity (0. 757 and 0. 4) of the first component, and the negatively charged average minimum temperature (0. 753) and positively charged average air temperature (0. 634) of the second component had the greatest impact on the components. The regression analysis indicated that the average minimum temperature coefficient 0. 018, the maximum wind speed coefficient-0. 004, and determination coefficient 0. 92% are significant. Comparing the method proposed in this paper with the usual method of principal component analysis (PCA) for multivariate data analysis indicated that cluster-based sliced inverse regression has fewer errors. Moreover, noticing the impact of collinearity on the outputs of neural networks, the method proposed in this paper had better performance than the usual methods and consequently predicts water consumption.