Air pollution is a global issue and meteorological factors play an important role in its transportationand regional concentration. The current research is aimed to analyse the variations in meteorologicalparameters in a seasonal and geographical location context in the Jiangsu province of China, and itscorrelation with the six criteria air pollutants, and air quality index (AQI). The present analysis willsupplement the limited understanding on the relation between the regions prevalent climatic conditionsand atmospheric pollution. The meteorological data analysis showed Suzhou city located in thesouthern region of the Jiangsu province with high average temperature, relative humidity, and rainfall.Maximum values of temperature, UV index, sunshine, relative humidity, and rainfall occurred duringsummer, while air pressure in winter. High values of all meteorological parameters occurred in thenorthern and southern region of the province. The data correlation study revealed AQI to havenegative correlation with most meteorological parameters, and positive correlation with air pressure inall cities.

The present study attempts to investigate some blood parameters of Otolithes ruber during different seasons in terms of both temperature and pollution. For so doing it uses 10 specimens, for each station and season, collected from 5 polluted stations, including Petrochemical, Ghanam, Zangi, Douragh, Patil, and Sajafi as the control group, away from pollution in Musa Creek. The fish are anesthetized with 1ml of clove extract per liter. Their blood samples are taken immediately from the caudal vein, using a heparinized syringe. Afterwards, the serum is separated in a centrifuge with a speed of 6000 rpm for 2 minutes. The desired factors are measured by the Mindray BS200 auto-analyzer and the total protein level, by Bradford's usual laboratory methods. Results show that AST, ALT, ALP, Glucose, and Triglycerides have increased in more polluted stations (P≤0.05). In sheer contrast, total protein and Albumin have decreased as pollution grows (P≥0.05). According to this study, environmental water pollution of the fish has a large impact on the concentration of measured blood parameters, whereas the influence of seasonal changes on most of them is low.

Four regions of the Farahzad River Valley with different topography were selected to fully survey it and study the effects of morphology on local climate. then one of the hot days of the month of June 2021 (June 6th) was selected because the wind speeds increase in spring. According to the comparison of the simulation results with the existing site plans, the temperature in area 3 was the highest, 39.60 degrees, and the wind speed was 3.57 m/s. On the other hand, the study and analysis of the maps showed that the temperature of the roads in regions 3 and 4 were higher than the other two regions with a temperature range of 37.69-38.40, so the presence of impervious asphalt surfaces on the roads is very effective in increasing the air temperature in these areas.  Comparisons also showed that tall buildings and vegetation create shaded areas and increase wind speed. Based on this, two scenarios were designed. In the first scenario, doubling the height of buildings increased wind speed in Region 3 by 3.42 m/s and decreased temperatures by 1.59 degrees. In the second scenario, when tall trees were planted at certain distances around the streets, the temperature in Region 3 decreased by 1.68 degrees and the wind speed increased by 1.68 m/s. The results show that the differences in the topography of urban valleys cause ventilation of the environment and that the effect of this feature in other environments is more effective through planting than through buildings.

Global fire cases in recent years and their vast damages are vivid reasons to study the wildfires more deeply. A 25-year period natural wildfire database and a wide array of environmental variables are used in this study to develop an artificial neural network model with the aim of predicting potential fire spots. This study focuses on non-human reasons of wildfires (natural) to compute global warming effects on wildfires. Among the environmental variables, this study shows the significance of temperature for predicting wildfire cases while other parameters are presented in a next study. The study area of this study includes all natural forest fire cases in United States from 1992 to 2015. The data of eight days including the day fire occurred and 7 previous days are used as input to the model to forecast fire occurrence probability of that day. The climatic inputs are extracted from ECMWF. The inputs of the model are temperature at 2 meter above surface, relative humidity, total pressure, evaporation, volumetric soil water layer, snow melt, Keetch–Byram drought index, total precipitation, wind speed, and NDVI. The results show there is a transient temperature span for each forest type which acts like a threshold to predict fire occurrence. In temperate forests, a 0.1-degree Celsius increase in temperature relative to 7-day average temperature before a fire occurrence results in prediction model output of greater than 0.8 for 4.75% of fire forest cases. In Boreal forests, the model output for temperature increase of less than 1 degree relative to past 7-day average temperature represents no chance of wildfire. But the non-zero fire forest starts at 2 degrees increase of temperature which ends to 2.62% of fire forest cases with model output of larger than 0.8. It is concluded that other variables except temperature are more determinant to predict wildfires in temperate forests rather than in boreal forests.