Hamid Reza Rahmani; Zahra Khanmohammadi
Abstract
Introduction: Vegetables are the main food of the world's population, especially in developing countries. Currently, many vegetables, especially leafy vegetables, have a high percentage of nitrate due to the overuse of nitrogen fertilizers to accelerate vegetative growth. Many researches have been done ...
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Introduction: Vegetables are the main food of the world's population, especially in developing countries. Currently, many vegetables, especially leafy vegetables, have a high percentage of nitrate due to the overuse of nitrogen fertilizers to accelerate vegetative growth. Many researches have been done on nitrate accumulation in crops worldwide, because of the harmful effects of excess nitrate in edible plants for humans and live stocks. Due to the necessity of such studies in country as well as the control of nitrate concentration in leafy vegetable and cucurbits, this study was done to investigate and monitor the nitrate concentration in some leafy vegetables and cucurbits (cucumber, tomato, potato, eggplant, pepper, onion, scallion and leafy vegetables) produced in some greenhouse and farms of Isfahan province. Materials and Methods: In this study 142 different leafy vegetables (coriander, basil, chives, parsley, garden cress, dill, tarragon, mint, fenugreek) and cucurbits (fruit vegetables) including (cucumber, tomato, eggplant, pepper, onion, scallion and potato in coarse and fine sizes) collected from greenhouses and agricultural fields of Isfahan, Dorcheh, Falavarjan, Tiran, Dastgerd, Dehaghan and their surroundings. Nitrate content of plant samples was measured by spectrophotometer based on colorimetric method after reduction and production of aminoazo color complex. Then the nitrate concentration in the samples was compared with the maximum allowable nitrate concentration in agricultural products provided by the National Standard Organization of Iran.Results and discussion: The results showed that the mean concentration of nitrate in cucumber samples was 1.48 times more than the Iranian National Standardization Organization (90 mg kg-1 of fresh weight); whereas the mean concentration of nitrate in potato, tomato and pepper samples was lower than the standard limits provided by National Iranian Standards Organization (170., 150 and 200 mg kg-1 of fresh weight respectively). The mean concentration of nitrate in fine sizes of potato, tomato and pepper was more than nitrate concentration in coarse sizes. Between the studied cucurbits, the highest and lowest average nitrate concentration were observed in cucumber and tomato respectively. Also the average nitrate concentration in cucumber skin was 16.7 times more than the mean nitrate concentration of its fruit. In general, the mean concentration of nitrate in leafy vegetables was more than the Iranian National Standardization Organization (1000 mg kg-1 of fresh weight). Between the studied leafy vegetables, the average concentration of nitrate in mint (457 mg kg-1 of fresh weight), fenugreek (262 mg kg-1 of fresh weight) and tarragon (695 mg kg-1 of fresh weight) was just lower than the Iranian National Standardization Organization. The others leafy vegetables probably have nitrate limitation for consumption. Conclusion: According to the results, it seems that fruit size is not a suitable criterion for plant nitrate content and the management aspects of farms play a major role in plant nitrate content. On the other hand, the changes of nitrate content in the farms are very high, so it cannot be clearly stated that the consumption of vegetables is restricted in terms of nitrate content. However, it seems that the consumption of vegetables produced in the studied areas may be restricted for consumers’ health in some cases.
Soolmaz Shamsaie; Mozhgan Ahmadi Nadoushan; Ahmad Jalalian
Abstract
Introduction: Industrialization, urbanization, and population growth are considered as the main causes of urban air pollution that is responsible for millions of deaths per year worldwide. Besides, the impact of urban air pollution on health is considerable. Respiratory and lung diseases, and heart attacks ...
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Introduction: Industrialization, urbanization, and population growth are considered as the main causes of urban air pollution that is responsible for millions of deaths per year worldwide. Besides, the impact of urban air pollution on health is considerable. Respiratory and lung diseases, and heart attacks are largely due to urban air pollution. However, there is a lack of air pollution monitoring stations (hereafter stations) in most cities worldwide because of their high expenses, and, thus, access to high spatial and temporal coverage of air pollutants and their distribution is limited. To address this issue, the main purpose of this study was to estimate CO concentration in Isfahan, Iran, based on air pollution monitoring stations and Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2018 to 2019. Material and methods: In the present work, we used adaptive neuro-fuzzy inference system )ANFIS( and Random Forest (RF) algorithms to estimate CO concentrations. To implement the ANFIS algorithm, based on collected air pollution data from the stations and Aerosol Optical Depth (AOD) data from MODIS imagery, the basic fuzzy rules were extracted. Further, with the integration of fuzzy rules and artificial neural network algorithm, ANFIS algorithm was implemented to model the dispersion of CO level in Isfahan city. To model the dispersion of CO using the RF algorithm, air pollution data and AOD data were used. Since the number of trees and the number of variables in each node are two basic parameters in the success of the RF algorithm, a 10-fold cross-validation method was used to identify value for these two variables.Results and discussion: Our findings indicated that the RF algorithm was more efficient and accurate in spatial modeling the dispersion of CO because it achieved better RMSE and MAE results than the ANFIS algorithm. The RMSE error value of the RF and ANFIS algorithms were 0.724 and 0.809 ppm, respectively. Furthermore, the MAE error value of the RF and ANFIS algorithms were 0.636 and 0.792 ppm, respectively. In the case of spatial dispersion of CO pollutants, the ANFIS algorithm showed that the amount of this pollutant varies in the city. For example, the central and northern regions of Isfahan had the most pollution and the eastern and western regions of Isfahan had the least pollution based on the ANFIS algorithm. Regarding the RF algorithm, it was observed that by moving from the southeast to the northwest of Isfahan, the amount of CO pollutant increases, and the northwestern regions of Isfahan had the highest CO pollution. The examination of numerical values obtained from the ANFIS algorithm showed that the lowest amount of CO pollution in Isfahan city was equal to 1.43 ppm and the highest amount was 2.13 ppm. In contrast, obtained results from the RF algorithm showed that the lowest amount of CO pollution in the city was equal to 0.57 ppm and the highest amount was 2.27 ppm.Conclusion: Overall, it can be concluded that since ANFIS and RF algorithms are appropriate and accurate methods in modeling environmental problems due to their nonlinear modeling, the ability to reduce the negative effects of outgoing data, and less sensitivity to the local minimum problem. It should be noted that a significant part of the error observed in the results of ANFIS and RF methods was related to the intrinsic properties of MODIS imagery (i.e., cloud cover and mixed pixel problem due to the coarse resolution of MODIS imagery), point measurements of air pollution data collected from the stations, and recorded data error at the stations.
Sona Kebriaeezadeh; Jamal Ghodduosi; Ali Asghar Alesheikh; Reza Arjmandi; Seyed Alireza Mirzahosseini
Abstract
Introduction: Rapid and uncontrolled expansion of cities, increased traffic, industrial enterprises and low-quality fuels, as well as urban morphology parameters and climatic conditions are among the factors affecting air pollution in urban areas. In Iran, the metropolis of Isfahan, which is the third ...
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Introduction: Rapid and uncontrolled expansion of cities, increased traffic, industrial enterprises and low-quality fuels, as well as urban morphology parameters and climatic conditions are among the factors affecting air pollution in urban areas. In Iran, the metropolis of Isfahan, which is the third largest urban area in the country, has an increased air pollution due to the extensive development of industrial enterprises, and population and urban growth. Therefore, in order to find the factors affecting the trend of air quality changes, trend analysis and evaluation of the relationship between land use parameters, industrial development and traffic situation with air pollution indicators were studied.Material and methods: In order to evaluate the trend using measured periodic data and simple correlation and regression methods of seven air pollutants including PM2.5, PM10, CO, SO2, NO, NO2 and NOX as dependent variables and meteorological parameters, type of land use, industry development and vehicles were analyzed as independent variables. Also, SPSS software was used to test the normal distribution of data sets including the concentration of air pollutants and meteorology from 1387 to 1394, in 10 air pollution measuring stations and three meteorological stations in Isfahan metropolis.Results and discussion: The results of the study show that the average annual concentration of PM (PM10 / PM2.5), NO and CO decreased and the average annual concentration of SO2, NO2 and NOXincreased. In addition, the average annual rainfall, temperature and wind speed increased while the trend of relative humidity in the study area did not change significantly. It was also found that the trend of residential, educational, commercial, public services, transportation and the number of industrial units and vehicles has increased significantly. However, the amount of agricultural land, green space and industrial areas has significantly decreased in the study area. The results of stepwise regression analysis showed that changing the use of agricultural land to residential areas and increasing wind speed may have caused the decreasing trend of NO, CO, and suspended particles in the study area. In addition, the increasing trend of transportation can be the most important reason for the increase in NO2 concentration. On the other hand, due to the increasing trend of NOX emissions and significant negative correlation with green space and positive correlation with transportation and industrial areas and the result of stepwise regression model, it can be concluded that reducing green space and an increase of 99.5% in the area of transportation use increases the NOX concentration in the study area. In addition, the size of utility centers increases the concentration of SO2 and there is a negative relationship between the concentration of PM2.5 and SO2 (as the concentration of SO2 increases, the concentration of PM2.5 increases).Conclusion: It is noteworthy that the relationship between air quality indicators as dependent variables with independent variables in urban areas is complex and it is not clear which specific factor or parameter is the most important scenario of air pollution in an urban context. Therefore, more detailed research is needed.
Marzieh Niliyeh Brojeni; Mozhgan Ahmadi Nadoushan
Abstract
Introduction: During the past decades, population growth, rapid industrialization, increased air pollution at low levels of the atmosphere, and the impact of heat island have caused dramatic changes in the local climate of the big cities. The release of heat energy increased greenhouse gas emissions, ...
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Introduction: During the past decades, population growth, rapid industrialization, increased air pollution at low levels of the atmosphere, and the impact of heat island have caused dramatic changes in the local climate of the big cities. The release of heat energy increased greenhouse gas emissions, and land use change are among the main causes of local climate change in cities. The effects of urban environments on the atmosphere appear more often as thermal islands. Green space would be effective in reducing the temperature and increasing the humidity, and finally reducing the thermal island phenomenon as well as reducing runoff, improving the comfort of the citizens and, ultimately, the sustainability of the urban environment. The objectives of this study were to prepare land use maps and NDVI vegetation index, as well as land surface temperature maps, and to study the distribution of thermal patterns of land surface and temporal and spatial variations of vegetation and their relation in Isfahan from 1985 to 2016. Material and methods: For this purpose, satellite imagery was downloaded from the US Geological Survey site. Using the three Landsat satellite TM images of August 1985, 2010, and 2016, the NDVI index was quantitated using Terrset software, and their maps were prepared. Then, by generating land use maps using the maximum likelihood supervised classification method, the analysis of the changes in land uses (such as city, road, agricultural fields, barren lands, river, mountains, and green spaces) was done. Finally, Land Surface Temperature (LST) index was used to estimate the land surface temperature (LST) and its relationship with the vegetation maps. Result and discussion: The trend of land use/cover changes in the study area showed that during the study period, severe degradation occurred in the green space of the area and the main part of these changes was the conversion of green spaces to urban areas. Also, the results indicated an inverse relationship between LST and NDVI index. The results showed that the growth of urban heat islands was toward areas that had encountered poor vegetation and developed constructional uses (residential, industrial, etc.). The results also indicate an accelerated increase in temperature in recent years compared to previous years, as the average annual temperature increase in the period from 2010 to 2016 was 0.61 °C, while the average temperature increase of 0.05 °C was observed from1985 to 2010. Conclusion: The analysis of the changes in thermal islands of Isfahan was indicative of the increase of thermal islands and spatial reduction in urban cool areas. It can be concluded that the changes occurred in this 30-year period (1985-2016) in various aspects, such as population increase, urban area increase, and land use change eventually increased the area of hot spots. Because of the correlation between surface temperature and NDVI vegetation index, the necessity of protecting vegetation and green space, especially in urban areas, is a critical variable for climate change modification for responsible institutions in urban management. The results of this study could provide critical insights on precise and effective urban vegetation management with the purpose of Urban Heat Island mitigation for urban planners and managers.
Seyed Hamidreza Rozati; Abdolhamid Ghanbaran
Volume 12, Issue 4 , January 2015
Abstract
Wind is one of the climate parameters that architecture and environmental designer have paid attention to itfrom past time till now. In some cases, wind flow in the urban open space is useful and it help to the natural ventilation and it causes improve human comfort and reduce energy consumption. On ...
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Wind is one of the climate parameters that architecture and environmental designer have paid attention to itfrom past time till now. In some cases, wind flow in the urban open space is useful and it help to the natural ventilation and it causes improve human comfort and reduce energy consumption. On other hands in some cases wind flow causes reduce human comfort in urban open spaces. So in each climate architectures and other designer can decide how wind flow should control in the urban open spaces according to the wind comfort. In this article we want according to the Isfahan climate dates and statistical calculations and compared with wind comfort criteria, find the mount of the wind comfort criteria in the urban open spaces in Isfahan. So in this study three criteria consist of “Wind Chill Factor”, “Beaufort Factor” and “Penwarden Factor” was used. The resultsof this study shown that wind flow just in the January cans reduce human comfort in open spaces and in the design of these spaces have to reduce the amount of the wind flow in this month. In other hand, in the June, July, August months, if the wind flow increase till 6m/s, the human comfort can improve. Wind flow in other month of year has favorable position and it can make human comfort so it is better that the wind speed keeps in the open space and street in this months.
Farid Moore; Saman Khabazi; Behnam Keshavarzi; Mohammad Saraji
Volume 11, Issue 2 , July 2013
Abstract
In order to examine polycyclic aromatic hydrocarbon (PAH) contamination in surface water and wastewater of Isfahan metropolis, 18 samples were collected sixty kilometers from the center of Isfahan City. The highest level of Total PAH (Σ PAH) occurred in treated wastewater, discharged by wastewater ...
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In order to examine polycyclic aromatic hydrocarbon (PAH) contamination in surface water and wastewater of Isfahan metropolis, 18 samples were collected sixty kilometers from the center of Isfahan City. The highest level of Total PAH (Σ PAH) occurred in treated wastewater, discharged by wastewater treatment plant of Mobarake steel plant (3.04μg/l). Based on different isomer ratio in most of the samples, pyrolysis was considered to be the possible source of PAH compositions. Profiles of the total carcinogenic and non-carcinogenic PAHs in sampling stations showed that in most samples the concentration of carcinogenic compounds was higher than that of non-carcinogenic ones. PAHs composition, according to the number of rings, displayed the following trend: 4 rings >3 rings> 5, 6 rings > 2 rings. The potentially toxic PAH compositions, in water and wastewater samples, were assessed using TEQ. Pierson correlation coefficient of PAHs in water and wastewater samples indicates that compositions, having the same number of rings, displayed good correlation. Cluster Analysis of water and wastewater samples indicated that the samples belong to three main clusters.
