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Faryad Shayesteh, , ,
Volume 5, Issue 1 (6-2018)
Volume 5, Issue 1 (6-2018)
Abstract
The role of tropospheric vertical anomalies in rainfall solid Case study: the hazard of hail in Kermanshah
Climate risks is one of the Types of hazards that damages human communities such as the phenomenon of hail, in the micro-scale, it causes financial losses and casualties. Hail is associated to the atmospheric elements and geo-location factors. Whenever weather conditions and appropriate physical processes are combined with geo-location creates and intensifies this phenomenon.
Losses resulted from hail has been more effective in the agricultural sector and in the effect of damaging the crops When growth and budding. However, it disorders in other sectors such as, blemishing residential buildings, Losing large and small animals also, damaging to the aircraft flight and its components. Hail considerable damage in Kermanshah province every year so that Farmers insure their crops against this Phenomenon and the government will incur heavy costs for
damage that is inflicted on the sector of activity.
Research methodology
The current weather data has been used with 3-hour intervals in the statistical period of 65 years (1951 to 2016) from synoptic stations of Kermanshah Province that includes the stations of Kermanshah, West Islamabad, Ravansar, Kangavar, West Gilan, and Sar-e-Pole-Zahab.
Among the 100 present weather code, Codes 99, 96, 91, 90, 89, 87 and 27 have been considered that including hail phenomenon by varying intensities and includes any appearance of this phenomenon in Hours scout and three hours earlier. Then, based on the above code, Were coded in Excel to identifies Codes 96, 91, 90, 89, 87 and 27 When entering from the Meteorological Data To the desired program among Group VII of the data, And when the written code, were identified, Hail days were marked.
Given that in this study Hail is studied regarding the synoptic conditions and temperature anomalies. Therefore, for the synoptic situation, Pressure data, vorticity, Special moisture, Components U and V, Omega transverse profile And outgoing longwave radiation, And for the temperature anomaly, Temperature and isothermal anomalies components Were getting from esrl.noaa.gov/psd site And using the software Grads were drawn maps for a selected day To determine the formation of hail.
Commentaries Results
The frequency of occurrence of hail has reached 187 in the period 65 years in Kermanshah province. This phenomenon generally occurs from mid-September to mid-June. The most number has been in Kermanshah station and the Least in Sar-Pol-Zahab station.
April has had the highest number of hail frequencies in Kermanshah province and the greatest losses in the month related to the agricultural sector. Therefore, Select System hail seems essential to examine how the temperature anomalies and the formation of hail in the month.
On the day of the event, trough hail has been formed in the East Mediterranean.Wrying the trough axis From North East to South West resulted in cold air from high latitudes to the East of the Mediterranean.
The establishment of trough in the middle and low pressure level in sea level and its following Convergence in the balance has created positive omega until balance of 200 hPa and most serious it is at the level of 400 hPa. Negative omega has maintained its association from ground surface until High levels in the study area.
The airflow of vorticity balance 1000 and 500 Hpa Suggests vorticity positive settlement area on the case study. Establishment of short wave in the vicinity of the study area and intensifying ascending conditions also Prolong Positive trough conditions from surface of Earth until 500hpa balance have been The necessary dynamic conditions for Hail in this day.
Special moisture and wind Vector with 700hpa balance of Moisture transfer has been done by two opposite vorticity system. Trough rotary motion Based on the Mediterranean and along the Red Sea on the one hand and Moving anticyclone over the Arabian Sea And the Persian Gulf and Oman Sea on the other, have conveyed Moisture of all moisture sources from The seas around to The study area.
Also OLR anomalies for the hail event day indicates being Negative in the study area and the sharp decline of Outgoing longwave in this day Compared to its long-term average And hence the conditions of cloudiness and the formation and intensification of convection has been provided.
1000 hpa positive anomaly 2 ° is representative the Higher than the average temperature conditions and in the 500hpa anomaly balance Minus 2 degrees Celsius is representative Lower than normal temperatures in the balance. These factors aggravate the vertical temperature gradient in the study area these days. 20+ degrees Celsius the Isothermal curve and -20 ° C. Respectively, the levels of 1000 and 500 Drawn to the area of study And has created a large temperature difference Between the upper and lower levels.
Keywords: Synoptic analysis, Hail hazard, Tropospheric anomalies, Vorticity, Kermanshah Province
Climate risks is one of the Types of hazards that damages human communities such as the phenomenon of hail, in the micro-scale, it causes financial losses and casualties. Hail is associated to the atmospheric elements and geo-location factors. Whenever weather conditions and appropriate physical processes are combined with geo-location creates and intensifies this phenomenon.
Losses resulted from hail has been more effective in the agricultural sector and in the effect of damaging the crops When growth and budding. However, it disorders in other sectors such as, blemishing residential buildings, Losing large and small animals also, damaging to the aircraft flight and its components. Hail considerable damage in Kermanshah province every year so that Farmers insure their crops against this Phenomenon and the government will incur heavy costs for
damage that is inflicted on the sector of activity.
Research methodology
The current weather data has been used with 3-hour intervals in the statistical period of 65 years (1951 to 2016) from synoptic stations of Kermanshah Province that includes the stations of Kermanshah, West Islamabad, Ravansar, Kangavar, West Gilan, and Sar-e-Pole-Zahab.
Among the 100 present weather code, Codes 99, 96, 91, 90, 89, 87 and 27 have been considered that including hail phenomenon by varying intensities and includes any appearance of this phenomenon in Hours scout and three hours earlier. Then, based on the above code, Were coded in Excel to identifies Codes 96, 91, 90, 89, 87 and 27 When entering from the Meteorological Data To the desired program among Group VII of the data, And when the written code, were identified, Hail days were marked.
Given that in this study Hail is studied regarding the synoptic conditions and temperature anomalies. Therefore, for the synoptic situation, Pressure data, vorticity, Special moisture, Components U and V, Omega transverse profile And outgoing longwave radiation, And for the temperature anomaly, Temperature and isothermal anomalies components Were getting from esrl.noaa.gov/psd site And using the software Grads were drawn maps for a selected day To determine the formation of hail.
Commentaries Results
The frequency of occurrence of hail has reached 187 in the period 65 years in Kermanshah province. This phenomenon generally occurs from mid-September to mid-June. The most number has been in Kermanshah station and the Least in Sar-Pol-Zahab station.
April has had the highest number of hail frequencies in Kermanshah province and the greatest losses in the month related to the agricultural sector. Therefore, Select System hail seems essential to examine how the temperature anomalies and the formation of hail in the month.
On the day of the event, trough hail has been formed in the East Mediterranean.Wrying the trough axis From North East to South West resulted in cold air from high latitudes to the East of the Mediterranean.
The establishment of trough in the middle and low pressure level in sea level and its following Convergence in the balance has created positive omega until balance of 200 hPa and most serious it is at the level of 400 hPa. Negative omega has maintained its association from ground surface until High levels in the study area.
The airflow of vorticity balance 1000 and 500 Hpa Suggests vorticity positive settlement area on the case study. Establishment of short wave in the vicinity of the study area and intensifying ascending conditions also Prolong Positive trough conditions from surface of Earth until 500hpa balance have been The necessary dynamic conditions for Hail in this day.
Special moisture and wind Vector with 700hpa balance of Moisture transfer has been done by two opposite vorticity system. Trough rotary motion Based on the Mediterranean and along the Red Sea on the one hand and Moving anticyclone over the Arabian Sea And the Persian Gulf and Oman Sea on the other, have conveyed Moisture of all moisture sources from The seas around to The study area.
Also OLR anomalies for the hail event day indicates being Negative in the study area and the sharp decline of Outgoing longwave in this day Compared to its long-term average And hence the conditions of cloudiness and the formation and intensification of convection has been provided.
1000 hpa positive anomaly 2 ° is representative the Higher than the average temperature conditions and in the 500hpa anomaly balance Minus 2 degrees Celsius is representative Lower than normal temperatures in the balance. These factors aggravate the vertical temperature gradient in the study area these days. 20+ degrees Celsius the Isothermal curve and -20 ° C. Respectively, the levels of 1000 and 500 Drawn to the area of study And has created a large temperature difference Between the upper and lower levels.
Keywords: Synoptic analysis, Hail hazard, Tropospheric anomalies, Vorticity, Kermanshah Province
- Shiva Gharibi, Dr Kamran Shayesteh,
Volume 8, Issue 3 (12-2021)
Volume 8, Issue 3 (12-2021)
Abstract
Application of Sentinel 5 satellite imagery in identifying air pollutants Hotspots in Iran
Shiva Gharibi1, Kamran Shayesteh2
1- PhD Student of Environmental Science, Malayer University, Malayer, Iran.
2-Assistant professor, Department of Environmental Sciences, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran
k.shayesteh@malayeru.ac.ir
Extended abstract
1- Introduction
Today, poor air quality is one of the most important environmental problems in many cities around the world. Air pollution can have a devastating effect on humans, plants, organisms, and human assets, and efforts are being made to anticipate and analyze the amount of distribution and transmission of air pollutants in order to minimize the adverse effects on air quality and climate. Among the most important air pollutants are (CO), (SO2), (NO2), (O3) and aerosols (AI). Numerous studies have been conducted on the monitoring of these pollutants based on information and statistics from pollution monitoring devices, but the use of satellite images in the field of monitoring and measuring pollutants has been limited. Due to the increasing growth of these pollutants, in this study, an attempt has been made to identify the average spatial concentration of the most important air pollutants as the actual sources of pollution on the scale of Iran from October 2018 to December 2019. Also, identifying the most polluted centers in Iran based on the average of 5 pollutants is another goal of this study. Therefore, the aim of this study is to demonstrate the ability of Sentinel satellite to monitor air pollutants, and the ability of GPW images to produce a population density map for the first time on an Iranian scale.
2- Methodology
Using the Python programming language in the Google Earth Engine program environment, various products related to CO, SO2, NO2, O3 and AI pollutant images, obtained from Sentinel-5 satellite images during the study period and in the scale of Iran, were obtained for monitoring of air pollutants and determination of pollutants focuses. The output variable is defined as a set of images based on the time filter (2019) and the spatial filter (Iran borders). The output of the average concentration of pollutants for each month is calculated separately and annually in these filters. Then, the spatial map of the average concentration of pollutants in the Arc map software was analyzed and statistical information related to the average concentration of these pollutants was processed by SPSS statistical software. To determine the hotspots in terms of all pollutants, the raster location map of each pollutant was classified using the Jenks algorithm. In order to identify the share of provinces and counties, the amount of pollutants was also analyzed by spatial statistics in GIS environment and using the Zonal Statistics command based on the defined administrative boundaries. The G statistic was used for Cluster analysis, and in order to identify Hot Spots and Cold Spots, Getis-Ord Gi statistic (Gi) was used in GIS environment.To determine the population of each province, the latest census information of Iran as well as satellite images related to the fourth version of Gridded Population of World (GPW) product were used. Finally, The Moran index was used to determine the pattern of pollutants distribution and the spatial autocorrelation.
3- Results
Spatial output from the processing of Sentinel-5 satellite images during the study period for identifying air pollution centers in Iran showed that the highest levels of nitrogen dioxide were recorded in the majority of cities in Tehran and Alborz provinces and then in the centers of other provinces. In the case of carbon monoxide, the highest rate is in Tehran and the coasts of the Caspian Sea and Khuzestan, and coastal areas of Bushehr and Hormozgan provinces. The highest amount of ozone is in the northern parts of the provinces of West and East Azerbaijan, Ardabil, Gilan, Mazandaran, Golestan and Northern Khorasan. Most of the dust was in the southern, eastern, southeastern and central provinces of Iran. The highest amount of sulfur dioxide pollutants is recorded in Tehran and then in the provinces of Khuzestan, Kerman, Hormozgan, Bushehr, Markazi, Qom, Isfahan and Khorasan Razavi. Provincially, the highest share of nitrogen dioxide is in the provinces of Tehran, Alborz, Qazvin and Qom. The highest provincial share of carbon monoxide is in Khuzestan, Gilan and Mazandaran provinces. The highest share of dust is in the southeastern provinces, including Sistan and Baluchestan, the highest share of sulfur dioxide is in Khuzestan province, and the highest share of ozone pollution is in the coastal provinces of Caspian Sea. Compliance of the average 5 pollutants with Google Earth images showed that the contaminated areas are located in the cities of Abadan, Imam Khomeini Port, Mahshahr Port and Ahvaz (Khuzestan Province), Tehran, Pakdasht (Tehran Province) and Assaluyeh Port (Bushehr Province). The results of comparing the average concentrations of pollutants in different seasons showed that there was no significant difference between CO, NO2 and O3 pollutants in different seasons, but suspended particles and aerosols in winter and autumn seasons have a significant difference with the amount of this pollutant in spring and autumn. Also, SO2 pollutant in autumn had lower concentrations than other seasons. The results of clustering analysis to determine the status of significant spatial clusters showed that the data are in the confidence range and have spatial auto-correlation and cluster distribution pattern.
4- Discussion & Conclusions
According to Sentinel-5 satellite images, most of the pollution centers in Iran are related to petrochemical industries and refineries, which are located in the cities of Abadan, Imam Khomeini port, Mahshahr port and Ahvaz (Khuzestan province), Assaluyeh port (Bushehr province) and common pollutants. By these centers are NOX, SO2, CO, suspended particles and aerosols. Also, other centers (Tehran, Pakdasht in Tehran province) are located in the most populous urban areas of, which have been identified as hotspots in high production of NO2 and CO, due to high population and urban traffic. Due to the higher population density of Tehran and Pakdasht than other cities in Iran, air pollution can be more important in these cities. Therefore, the use of satellite imagery to monitor Iran's air pollutants and the location of hotspots can be very cost-effective and time-consuming.
Keywords: Air Pollution Monitoring, Sentinel, Satellite Imagery, Polluted Hotspot, Moran’s Index.
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