Remote Sensing (RS)
Nazanin Hassanzadeh; Reza Hassanzadeh; Mahdieh Hosseinjanizadeh; Mehdi Honarmand
Abstract
Extended AbstractIntroductionAir pollution is one of the most crucial environmental problem in the glob and its impact on human live and ecosystem is undeniable. The International Agency for Research on Cancer introduced air pollution as one of main causes of cancer. Therefore, by monitoring air pollution ...
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Extended AbstractIntroductionAir pollution is one of the most crucial environmental problem in the glob and its impact on human live and ecosystem is undeniable. The International Agency for Research on Cancer introduced air pollution as one of main causes of cancer. Therefore, by monitoring air pollution would be a necessity in industrialized cities. Air quality index include evaluation of the amount of NO2, SO2, O3, CO and Aerosol in the air. As, ground station has limited ability to assess the amount and distribution of these harmful gases in the urban and rural areas, therefore, remote sensing technology become a popular tool in assisting research to shed light on this subject. The current study evaluates air pollution caused by Khatoonabad Copper Smelting Factory using Sentinel P5 satellite images.Materials & MethodsThis research investigates the air pollution created by Khatoonabad Copper Smelting Factory and determines its impact radius, using Google Earth Engine system and Sentinel P5 satellite images. Khatoonabad Copper Smelting Factory is located in the northwest of Kerman province at the latitude of 29 Degree 59 Minute to 30 Degree 32 Minute and longitude of 54 Degree 52 Minute to 55 Degree 55 Minute. By performing the coding operation in the Google Earth Engine system, the images related to the average air pollution for So2 and No2 in the area of 50 km from the factory and in a period of 30 months from 07/04/2018 to 12/30/2021 were obtained. The amount and distribution of pollutants were examined based on one-day, seven-days, fourteen-days, one-month, two-months, three-months, six-months and twelve-months’ time periods from December 2020 to assess the concentration of pollution in the cold months of the year, also for the same time periods from June 2021 to assess the concentration of pollution in the warm months of the year.In order to map distribution of each pollutants, Natural Break Classification and Hot Spot Analysis methods were performed on the images obtained from Google Earth Engine in GIS. Natural Break Classification method is based on Jenk optimization and classify spatial data based on statistical properties of each input where variances between classes maximize. Hot Spot Analysis methods is a spatial and statistical method that consider spatial autocorrelation among the spatial data to classify the data according to statistical significance of each class. Points that surrounded by high values and they are statistically significant called hot spot and areas that are surrounded by low values and have high negative Z score and low P values ( P value < 0.05) are called cold spot.Results & DiscussionThe results based on an averaged image for the period of 30 months indicated that the amount of So2 from 0.0000987 to 0.000698 (mol/m2) and the amount of No2 from 0.00005854 to 0.00006932 (mol/m2) in the study area that by increasing the distance from the factory, the amount of So2 and No2 decreased. Furthermore, analyzing the average amount of So2 and No2 in different period of daily, weekly, two weeks, and monthly have showed dispersed spatial distributions in warm and cold season of the year. Therefore, Sentinel 5P data in short-term periods such as daily, weekly, two-week and even one-month cannot provide accurate information on the spatial distribution of No2 and So2 in the study area.In the data obtained from the two-month, three-month, six-month and one-year intervals, the amount of sulfur dioxide concentration has less dispersion than the short-term intervals, and as the time interval increases, the images show less dispersion of sulfur dioxide gas in polluted areas. Therefore, the obtained results indicate that Sentinel 5P images with longer time intervals of two months are able to provide more accurate and logical information about the concentration of sulfur dioxide gas in the area. However, in case of nitrogen dioxide, the imaged longer than two weeks can provide accurate information regarding the spatial distribution of this pollutant in the area.Hot spot analysis was also performed on the images obtained in one-day, seven-day, fourteen-day, one-month, two-month, and three-month intervals from June in order to investigate the concentration and dispersion of pollution in the hot days of the year. Then the maps obtained from the hot months were compared with the maps of the same period from the cold months of the year. This comparison showed that in the maps obtained from the short-term intervals related to the hot months of the year, the density of hot spots was more observed in areas prone to the presence of sulfur dioxide gas. For example, the one-day image from December showed a lot of dispersion, while the one-day image from June indicated less dispersion and more density of gases in polluted areas. In addition, in the one-week, two-week and one-month maps from December hot spots and cold spots show much greater dispersion compared to similar maps in the same periods from June. However, by comparing the two-months and three-months hot spot maps of the cold months to the same maps of the hot months of the year, almost similar results were obtained, even more density were observed in the hot spot map of longer periods (more than two months) in winter time. The same trend happened by analyzing nitrogen dioxide in the studied area. ConclusionThe results obtained from the classification of images related to sulfur dioxide gas showed that the concentration of sulfur dioxide gas in the area around the desired factory has the highest concentration value and as the distance from the factory increases, the concentration of sulfur dioxide gas decreases. Also, according to the minimum and maximum concentration of sulfur dioxide in the studied area, it is concluded that more sulfur dioxide is observed in the cold months of the year than in the warm months of the year. However, in the cold months the concentration of sulfur dioxide has a greater range of changes than the hot months of the year.According to the results, the dispersion of sulfur dioxide concentration in short time intervals such as daily, weekly, fortnightly and even one month was very high in these time intervals. As a result, Sentinel 5P images are not able to provide logical and accurate information about the distribution of atmospheric sulfur dioxide concentration in daily, weekly, two-week and one-month intervals. In order to obtain accurate and logical information, images with time intervals longer than one month should be used, and the longer the time interval is, the more reliable the results will be.The results of the hot spot analysis of the images related to sulfur dioxide concentration also indicated a high concentration of sulfur dioxide gas in the area around the factory. According to the obtained results, the activity of the studied factory can be a reason for the increase in the concentration of sulfur dioxide gas in this area, which has affected a radius of about 4 to 6 kilometers and an area of about 10,700 hectares around the factory.The results obtained from the classification of images related to nitrogen dioxide gas show that the concentration of nitrogen dioxide in the area around the factory has a higher limit. According to the minimum and maximum concentration of this gas in the study area, it can be concluded that in the hot months of the year, the concentration of nitrogen dioxide gas is higher than in the cold months of the year. Considering the rapid spread of nitrogen dioxide gas in the atmosphere by the wind due to the high dynamics of this gas (Vîrghileanu et al., 2020), it can be concluded that the images obtained from the time intervals of two weeks of more can provide more information about the concentration of nitrogen dioxide in the atmosphere.The results of the hot spot analysis of the images related to nitrogen dioxide gas showed that in the time intervals of two weeks to two months in the cold months of the year, there are hot spots that indicated the presence of nitrogen dioxide gas in the atmosphere located above the factory. However, in long-term intervals such as three months, six months, one year and thirty months, in the cold and hot months, hot spots are observed towards the northwest and at a distance from the factory.The result of this research can assist environmentalist and researchers in using and interpreting Sentinel 5P data by considering different periods in cold and warm seasons for making informed decisions.
Mohammad Amin Ghannadi; Matin Shahri
Abstract
Extended AbstractIntroductionAir pollution is now considered to be one of the most important challenges Iran faces and plays a major role in changes of its climate. Factors such as population growth and the consequent increase in the number of cars, as well as the presence of various (and often old) ...
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Extended AbstractIntroductionAir pollution is now considered to be one of the most important challenges Iran faces and plays a major role in changes of its climate. Factors such as population growth and the consequent increase in the number of cars, as well as the presence of various (and often old) industries and the energy demand they satisfy have led to an increase in pollution in many Iranian metropolises. As one of the four Iranian industrial hubs, Arak has one of the worst air quality in this country. In addition to the presence of industries, having a relatively high population density (and consequently high traffic congestion level) and various climatic conditions affect the quality of air in Arak. It is essential to accurately measure air pollutants with a high spatial and temporal resolution, determine their distribution pattern and level of effectiveness, and provide provincial and national managers with applicable solutions. Unfortunately, air quality monitoring stations are not sufficiently and properly distributed in Iran. Many Iranian cities do not have even a single air monitoring station and many others have only one station. As the capital city of Markazi province and an industrial city, Arak has only four monitoring stations which are not simultaneously active in many cases. Failing to conduct proper site selection before the installation of ground-based monitoring stations results in local irregularities in the recorded concentration of pollutants. Furthermore, the stations are not usually calibrated on time and thus air quality monitoring observations are disrupted. In these cases, either this data is deleted from the final results or the station will be inactivated (for example, for a week or a month) by authorities. However, it seems that the observations made by these stations still include inaccurate data. Materials and MethodsThe present study has introduced a method based on composition and voting to validate the observations made by air quality monitoring stations using Sentinel-5 satellite images. Arak city was used as the study area. Level three images (L3) of the Sentinel-5 TROPOMI sensor received from the Google Earth Engine were used to monitor the concentration of pollutants in the present study. Sentinel-5 is a powerful atmospheric monitoring tool. Equipped with a spectrometer called TROPOMI, the satellite measures ultraviolet radiation reaching the Earth's surface in a high range. TROPOMI sensor is highly capable of imaging and monitoring a large number of pollutants. The present study has compared the concentration of NO2, SO2, CO and ozone pollutants monitored by ground-based stations in Arak city with Sentinel-5 images. Since the time resolution of ground-based observations is higher than satellite observations, a monthly average of pollutants' concentrations was calculated to increase the reliability of observations. In other words, the concentrations of pollutants were compared on a monthly basis. The proposed method has assumed that more accurate sets of ground observations show a higher linear correlation with satellite observations.In order to select the appropriate set, the number of observations with an acceptable accuracy must be determined. To do so, a method based on a mixture of composition and voting has been used. As previously mentioned, each observation showed average pollutant concentration in a specific month of the study period. The process started with at least four monthly observations. As a result, assuming that all 19 monthly observations were available, 16 subsets were obtained with a maximum linear correlation between ground-based observations and their satellite correspondence which showed the accuracy of the observations. The second step was the proposed voting method which showed that the monthly ground-based observations (for example October 1398) were repeated several times. The high frequency of a monthly observation indicated its higher accuracy. The presence of this particular observation in different permutations has increased the linear correlation coefficient of the observations. Therefore, for an instance a frequency of 15 or 16 for the observation made by the ground-based station in October 2017 indicated high accuracy of the observation. Results and DiscussionThe present study has compared the concentration of NO2, SO2, CO and ozone pollutants Using the proposed method, some observations have been identified as outliers or errors. RMSE criterion was used to evaluate the accuracy of the proposed method. Some observations made by the ground-based station were not consistent with other ground-based and satellite observations, and removing them increased the correlation coefficient. Removing outliers from the observations, the RMSE (originally 2%) was improved and reached 47%. ConclusionFindings indicated that some observations made by ground-based monitoring stations were incorrect, or at least the stations had sometimes failed to exhibit the real general trend of environmental pollution correctly due to local irregularities caused by various reasons, such as improper location or lack of proper calibration.
Sara Haghbayan; Behnam Tashayo
Abstract
Extended Abstract Introduction Air pollution has become a life-threatening hazard with severe consequences. Previous studies have indicated that long-term exposure to air pollution can pose a significant threat to human health or even cause death. Usually, air quality is monitored by ground-based ...
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Extended Abstract Introduction Air pollution has become a life-threatening hazard with severe consequences. Previous studies have indicated that long-term exposure to air pollution can pose a significant threat to human health or even cause death. Usually, air quality is monitored by ground-based stations that can collect data regarding temperature, humidity, pressure, and several pollutants such as Ozone (O3), Carbon Monoxide (CO), Carbon Dioxide (CO2), Sulfur dioxide (SO2), Nitrogen dioxide (NO2), and nanoparticles (e.g. PM1, PM2.5, and PM10). However, ground-based stations are costly, scattered, and often cannot cover large areas. These stations collect the concentration ofparticulate matter with a diameter of less than 2.5 µm (PM2.5) over a year.Collected data may be lost due to an unexpected shutdown of the device. Datacollected in ground-based stations are not sufficient by their own and as a result they are modeled. The resulting models also have flaws, so new resources are needed to solve this problem. One of these resources is the use of mobile sensors to produce high-resolution temporal and spatial air quality data. As opposed to traditional air quality monitoring stations, the use of dynamic and mobile sensors is quickly developing. These mobile sensors measure the concentration of the same air pollutants as those measured by ground stations. Land-use regression (LUR) models are increasingly used to estimate the level of PM2.5exposure in urban areas. Land-use regression models often use data received fromground-based stations. Therefore, modeling the concentrations of particulate matter in a city leads to a significant increase in modeling error. Data from mobile sensors can increase the accuracy of this contaminant modeling process. The present study aims to improve modeling accuracy by integrating ground-based stations with mobile sensors. Therefore, using the proposed framework, we can accurately estimate air quality at any time and place and provide higher resolution estimations for heterogeneous urban environments. Materials & Methods The study area covers Isfahan city. With a population of more than two million and an area of 200 square kilometers, Isfahan is located in central Iran. 13% of the total pollutants entering Isfahan belong to urban industries, 11% to domestic sources, and 76% of all pollutants belong to traffic related sources in Isfahan. Therefore, most of the PM2.5concentrations are generated by the transportation system in Isfahan. The effective solution to the air pollution problem needs to have a comprehensive understanding of the air pollution process. Such an understanding primarily depends on reliable records that can depict the temporal and spatial variations in air pollution which is not possible due to the limited number of ground-based stations. The proposed method of the present study is to combine ground-based stations with mobile sensors to increase the accuracy of PM2.5concentration estimation and modeling. One of the existing methods used to estimate PM2.5levels is land use regression. Previous studies used only ground-based stations to create this model, which was not sufficiently accurate. The present study sought to increase the accuracy of PM2.5concentration modelling in contamination values of near or beyond the threshold. Using the LUR model, a prediction map was generated usinga combination of ground-based stations and mobile sensor which helps us to reach a more accurateestimation and prediction of PM2.5concentrations in a heterogeneous region such as this city. Results & Discussion Reliable and accurate estimate of temporal/spatial distribution of air pollutant concentration cannot be achieved using a limited number of ground-based stations. The present study took advantage of 14 mobile sensors along with 7 ground-based stations. Results indicated that the root mean square error of the seven ground-based stationsequaled 1.80 while the RMSE of the combination of these stations equaled 0.59. The skewness index shows asymmetry of data as compared to the standard normal distribution.This index is used to determine whether the data distribution is normal or not. Skewnessvalue of standard normal curvesequals zero. In the histogram obtained from a combination of all stations, this value is 0.11, while in the histogram obtained from the ground-based stations, skewness value equals 0.8803. In general, the results indicated that integrating ground-based stations with mobile sensors results in a PM2.5concentration distribution which looks more like a normal distribution. The normality of data distribution implies that the histogram of data frequency is approximately a normal curve, and thus T-test is used to examine whether or not the results were significant. Conclusion In this study, a new framework was proposed to integrateground-basedstations and mobile sensors with the aim of improving the accuracy of PM2.5 pollutant concentration estimation. The results of the t-test show that with only ground-based stations, the actual pattern and its distribution over the city will fail. In fact, data received from mobilesensors provide additional data necessary for air pollution profiling.
Hadi Fadaei
Abstract
Extended Abstract Introduction One of the major environmental issues and requirementsof the contemporary worldis the acquisition of knowledge and related technologies. Urban Heat Island (UHI) refers to the occurrence of higher surface temperature in urban areas compared to the surrounding rural areas ...
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Extended Abstract Introduction One of the major environmental issues and requirementsof the contemporary worldis the acquisition of knowledge and related technologies. Urban Heat Island (UHI) refers to the occurrence of higher surface temperature in urban areas compared to the surrounding rural areas due to high urbanization. Urban Heat Island (UHI) is an important ecological effect of rapid urbanization. While the temporal and spatial importance of UHIs and their causes have been discussed in previous studies, precise identification of the morphology and shape of the earth and its relation with UHIs have not been studied. Urban heat islands occur primarily due tourban developmentand changes in land surface. This has created unfavorable conditions and many problemsfor citizens. Vegetation cover can reduce the effect of heat island. Satellite data can be used to determine the distribution of urban heat islands, but new methods of measurement are still needed to get better results.Ground data can also help in validation of remote sensing analysis. The present study has investigatedurban heat islands occurring in the city of Tehran and its suburbs due to urbanization and traffic. Method The present study has been carried out in Tehran, the capital city of Iran, located in the northern part of the country,on the southern slopes of the Alborz Mountain Range, along 51⁰ to 51⁰ 40′ easternlongitudeand 35 ⁰ 30′ to 35 ⁰ 51′ northernlatitude. According to the latest population and housing census in 2011 performed by the Statistical Center of Iran, Tehran has a population of 8,154,051 and still is the most densely populated city of Iran with a clear demographic difference with other cities of the country. The study area borders with mountainous areas of the north and desertsof the south, thus the southern and northern regions of the study area have different climates. The northern regions have cold and dry climates, while the southern parts suffer from hot and dry climates. The elevation varies from 900 to 1800 meters. This huge difference inelevationis due to the vast area of the city. In Tehran metropolis, the average annual temperature varies between 18 and 15 ° C, and different parts of the city have an average temperature difference of 3 ° Cdue to the elevation difference in the city. Average monthly relative humidity including minimum and maximum relative humidity recorded at Mehrabad station shows that in in the morningof July to January, humidity changes from at least 38% to a maximum of 79%. Midnight relative humidity varies from 15% to 18% in June to 47% in February. The annual rainfall in Tehran is mainly influenced by the difference in elevation and varies between 422 mm in the north and at least 145 mm in the southeast. The number of rainy days also follows the same pattern and varies between 89 days in the north and 33 days in the south. Also in this urban area, 205 to 213 days of each yearhave a clear sky with some cloud. In this exploratory study, Landsat 8 satellite images for Tehran were obtained and processed (geometrical, radiometric and atmospheric corrections). The Operation Land Imager(OLI)with its three new bands: a deep blue band for coastal / aerosols studies (band 1), a short-wave infrared band for cirrus cloudsdetection and Band Quality Assessment (Band 9), and an Infrared Thermal Sensor (TIRS) which offers two high resolution thermal bands (approx. 30 m) (band 10, 11) were used. In addition, two of the valuable thermal bands at 10.9 µm and 12.0 µm have Landsat 8 images. In this study, spectral reflections of all terrestrial members of spectral phenomena were obtained based on the total wavelengths of Landsat 8 (wavelengths of 430-2290 nm). For UHI estimation,surface temperature can be obtained from the two thermal bandsand improved using split-window methods.The relation between thermal islands can be calculated using air pollution ground data. The present study tries to select suitable indices such as Normalized Difference Vegetation Index (NDVI). The vegetation index (NDVI) of land surface was calculated using spectral bands. Results The LST map was produced using Landsat OLI 8 satellite images. Temperature in this map was obtained using standard deviation from the classified values,and areas affected by the UHI were identified subsequently. According to the LST map, the surface temperature varies between 21.5 ° C and 57.9 ° C. On the day of imaging, the lowest average temperature of water was 35 ° C and the maximum average temperature of bare lands was 48 ° C in the study area. Recommendations It is recommended to use spectral reflectance measurements such as field spectroradiometer in natural conditions to evaluate the spectral reflectance accuracy. At a later stage, spectral reflection of different phenomena can be used to classify satellite images and examine their relationship with the urban heat islands
BiBi Mariam SajadianJaghargh; Alireza Vafaei Nezhad; Ali Asghar Alesheikh
Abstract
Extended Abstract
The ubiquity of mobile devices, such as smart phones and tablets, has contributed to the development of pervasive systems, including navigation and health systems. The main characteristicsof pervasive systems are the necessity of dynamic reconfiguration and proper adaptation to the ...
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Extended Abstract
The ubiquity of mobile devices, such as smart phones and tablets, has contributed to the development of pervasive systems, including navigation and health systems. The main characteristicsof pervasive systems are the necessity of dynamic reconfiguration and proper adaptation to the continuous changes in different contexts. The existence of dynamic capabilities has been considered in the design and implementation of a context aware system, including context acquisition, context understanding and computing, decision making, and context presentation.Context acquisition: This domain of research focuses on using personal sensing devices which measure various parameters by means of portable devices and save them on the external/internal database for further processing. The aim of researches is collecting, sharing, and/or reusing data in other applications or through a web interface.Context understanding and computing: The most works are in the field of context monitoring, data management, understanding or computing. The ability to automate context reasoning about various types of contexts and their properties are considered using various context models and algorithms. Most applications are customized for a specific case such as air pollution, tourist, navigation, and health care. Context presentation: This category of research has commonly focused on context-aware application adaptation. The adaptation happens between the real world, the map and user’s location and orientation. A number of studies have been carried out in the field of tourist guides or navigation adapting the presentation style to the changing requirements of the user.Most studies in ubiquitous health care have only been carried out in a small number of areas and using external portable sensors and developing applications on mobile phones. A major problem with these kinds of applications is collecting and sharing data, monitoring, or reasoning without having an active role in decision making in different environmental conditions. Using external tools such as portable devices is costly and limits using the systems.
This paper has focused on the design and implementation of a context aware ubiquitous system which has been customized for severe environmental conditions (in particular, air pollution). Air pollution is a spatial-temporal phenomenon and it causes changes in health conditions and it increases mortality. Eclipse Kepler software, java, PHP programming language and MySQL and SQLit database and also Google Maps API was used in this research. The proposed system design approach is based on distributed architecture in the portion of data collection and processing. Data collecting is done by means of software and hardware sensors. The context aware system is able to automatically identify the user’s context and represent required data and information after computing and reasoning. Contexts based on their impact on the decision-making process can be divided into two categories: passive and active contents.We used an active context in the research such as time, location, traffic, direction, air pollution. Collecting required data is done automatically with high speed and accuracy, and data plays an active role in decision making. In the system architecture, servers were embedded to enter data automatically and only data relating to health conditions is entered manually. Processing environment was divided into two parts, in case of abounding calculations, processing is transferred to the server so that only light processing is performed on the client. At every stage of the process, the user interface provided outputs in the form of recommendations and notifications. The system represents user-friendly environment. Context information can be posted on the process server and retrieved from the history. The proposed system can become an important tool to enable patients to be aware of air pollution conditions, not only to be applied in managing and monitoring their health information, but also in decision making, finding the best solution in severe environment, sharing data and communicating with family and doctor. The application represents suitable solution for solving the shortest path problem according to spatial-temporal and traffic condition. In fact, the path with the lowest level of air pollution is chosen as the best path.The system indirectly encourages greater use of the ubiquitous health system and motivates patients to acquire an active role in their health management and helps them to improve their health condition. The information collected and posted on the server can be reused in professional station and it presents useful information to health experts. We are broadly concerned about patients’ privacy in the design of the system.
Nahid Sajadian
Abstract
To date, a number of plans have been implemented to reduce air pollution in the city of Tehran.But the problem is that, along with other shortcomings,these planshave often been a passive and temporaryreaction to the increase of air pollution with adherence to crisis management rather than risk management, ...
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To date, a number of plans have been implemented to reduce air pollution in the city of Tehran.But the problem is that, along with other shortcomings,these planshave often been a passive and temporaryreaction to the increase of air pollution with adherence to crisis management rather than risk management, and no decision-making support system has been used in management decisions based on these plans.Therefore, due to the importance of the subject, this research was carried out by an analytical-applied method using hourly data, carbon monoxide density of 12 stations from a collection of air pollution measurement stations belonging to the air quality company, as well as meteorological dataof wind speed, wind direction and the temperature at the Mehrabad station, all related to the year 1389, and the number of the cars on the highways and streets of city of Tehran with the aim of predicting the temporal-spatial air pollution caused by the urban transport of Tehran Metropolis in line with the application of the spatial decision- making of the air quality management and with the ultimate goal of optimal management of urban transport of Tehran Metropolis. In this regard, since the ultimate goal of the present study is to use its results in controlling the optimal urban transportation as an important source of air pollutants, the LUR method was used to measure carbon monoxide index in the transportationof Tehran metropolis along with other pollutants. An artificial neural network was then used to predict the time of the possible occurrence of air pollution with emphasis on using risk management, and then, based on time predictions resulted from the artificial neural network, the regions with high possibility of air pollution occurrence were identified using the Kriging index.According to the findings of this research,the results were appropriate, so that this model could be used in the air quality management support system to reach the ultimate goal of optimal urban transport management in Tehran Metropolis.
Hosseyn Najafi; Rasul Afzali; Hosseyn Hataminejad; Roghayyeh Shams
Volume 22, Issue 85 , May 2013, , Pages 59-78
Abstract
Nowadays the air Pollution is one of the must serious problems in urban areas. More than 90 percent of total pullotion in urban environments consist five kinds of air pollutants. Comparation of static and daynamic resources of air pollution shows that share of dynamics resources is highly more ...
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Nowadays the air Pollution is one of the must serious problems in urban areas. More than 90 percent of total pullotion in urban environments consist five kinds of air pollutants. Comparation of static and daynamic resources of air pollution shows that share of dynamics resources is highly more than section.
Different Organizations and institute have role in field of environmental management and specially in air pullation management investigation about municipalities as local and administrative managers roll in laws shows that local management role in neglected in laws and many duties of municipalities are remove to other urban organizations.
In This paper the main gole is studing the role and legal situation of municipalities in field of air pullotion management. This research is apply with describtive analytical method. Data gathering is documentary by referring to laws and regulations of municipalities and national environment organization. The result show that the role of municipality is participatory like other organizations such as police. The role of local management is neglected in law collection some studies instead of municipality are remove of macro level organization like environmental organization. So lack of attention to role of municipalities for reduction of air pollution is one of reasons for failure of air pollution control programs.
Nahid Sajjadian; Mahyar Sajjadian
Volume 19, Issue 75 , November 2010, , Pages 78-83
Abstract
Tehran is one of the most polluted cities in the world in terms of air pollution, and according to surveys, about 70% of this contamination is due to transportation and heavy traffic. Traffic monitoring is now being conducted using traffic lights and related equipment, as well as air pollution sensing ...
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Tehran is one of the most polluted cities in the world in terms of air pollution, and according to surveys, about 70% of this contamination is due to transportation and heavy traffic. Traffic monitoring is now being conducted using traffic lights and related equipment, as well as air pollution sensing stations. But the problem is that these systems lack the necessary ability of immediate reactions and traffic management in terms of time and location and according to air quality index. It seems that the use of an expert system based on GPS, dynamic GIS, and timed relationship databases is capable of providing intelligence and immediate operation to the traffic control system. The research method is analytical-practical. According to the findings of the research, the expert system, based on the correct use of GIS, GPS and timed relationship databases, is capable of providing intelligence and immediate reactions to a traffic control system based on air quality management. Finally, based on the findings of the research, a conceptual design of such an expert system was proposed.
