جستجوی مقالات مرتبط با کلیدواژه « آلاینده ها » در نشریات گروه « محیط زیست »

Despite its great impact on economic progress, the cement industry produces a variety of environmental pollutants, including NOx, carbon dioxide, and carbon monoxide, the release of which can cause serious damage to the environment. In the meantime, the biggest impact of this industry on the environment is related to its burners, and the present research was written with the aim of investigating the effect of the location of calciner burners on reducing the emission of environmental pollutants in Qaen Cement Factory. The research method in the current research is quantitative, and in order to numerically investigate the effect of two positions for calciner burners in the lower part (main position) and in the upper part on the emission of pollutants, was used Ansys Fluent 2022 software. The findings of the research show that the Qaen cement factory in the calciner section is facing the problem of incomplete combustion, which has led to the production of carbon monoxide instead of carbon dioxide. In addition to reducing temperature and increasing fuel consumption, this incomplete combustion causes the production of significant carbon monoxide, which destroys the environment. Also, the results indicate that in the upper burner mode, due to the reduction in the chance of fuel and air mixing, the combustion becomes incomplete and the emission of carbon monoxide increases, but the production of Nox decreases due to the decrease in the combustion temperature.

The increase of various industries and the ever-increasing growth of the population of the planet have caused all kinds of contamination in the environment. One of the most important contaminants in water, which has many risks for human health and living organisms and important environmental risks, is anionic compounds in high concentration. Chromium is one of these contaminants that causes contamination in the environment and brings many risks to human health and other living organisms. Various methods have been evaluated to remove heavy metals from water, which often include chemical or energy-intensive processes. Therefore, it is important to modify these sources with the help of affordable adsorbents and with low energy consumption. Previous studies showed that biochar, metal-coated biochars and carbon-metal composites are highly effective in removing chromium contaminants with a concentration of 20 mg/liter from water, but so far the effect of these adsorbents on removing this anionic contaminant in high concentrations has not been investigated.

In this research, the efficiency of different adsorbents (biochar, copper-coated biochar, aluminum-coated biochar, iron-coated biochar, biochar-copper composite, biochar-aluminum composite and biochar-iron composite) on the removal of chromium, with a concentration of 300 mg per liter, from water was investigated. Metal-coated biochars were prepared from the combination of metals (copper, iron and aluminum) with a concentration of 10000 mg/kg with biochar produced at 600 degrees Celsius and various biochar-metal composites were prepared from the combination of these metals with rice straw and then the samples were pyrolyzed at 600 degrees Celsius. In order to determine the efficiency of the adsorbents, 0.5 grams of each adsorbent was mixed with 40 ml of chromium solution with a concentration of 300 mg/l and pH= 6 and shaken for three hours until they reached equilibrium. Then the samples were centrifuged for 5 minutes at 6000 rpm and after the filtration, the final concentration of the contaminant was obtained and the percentage of chromium removal was calculated.

The results of the present study showed that all of the applied adsorbents were effective in removing high concentrations of chromium from water. The lowest removal rate of this contaminant was related to the biochar sample, which only removed 15.28% of this contaminant from water; And the highest amount of removal was achieved using iron composite, which removed 44.45% of the contaminant from the aqueous solution. According to the results of this research, it can be observed that coating biochar and rice straw with metals has been able to increase the efficiency of this adsorbent in removing chromium from water. For example, iron composite and iron-coated biochar were able to remove 44.45 and 30.86% of chromium contaminant from the aqueous solution, respectively, while rice straw biochar was only able to remove 15.28% of this contaminant from water.

The results of present study showed that iron coated biochar and biochar-iron composite had more ability to remove chromium contaminant from aqueous solution than other metal coated biochars and biochar-metal composites and were able to perform more successfully. Therefore, the use of these adsorbents can be effective in the treatment of chromium-contaminated water.

Today, dust phenomena are among the most important environmental hazards and pose a serious threat to human health and the environment. Dust in barley as one of the pollutants has various adverse effects and negative consequences, among which can be reduced growth and yield of agricultural products, intensification of damage caused by pests and plant diseases, increased road accidents due to reduced visibility, The cancellation of flights and the resulting financial losses, increased treatment costs, closure of industrial units, pollution of water resources, increased erosion of buildings, decreased efficiency of solar photovoltaic systems due to turbidity.

Therefore, due to the importance of dust and in order to predict how dust is spread, the artificial neural network model was used. This model can be useful and cost-effective information for future implementation of air pollution control strategies and cost reduction.

To model the dust distribution using artificial neural network model, statistics and meteorological information of Kashan synoptic station, which were recorded daily by the Environment Department in 1996, were used. The proposed neural network model has four input layers that include humidity, temperature, wind speed, wind direction and an output layer, the daily concentration of suspended particles is 2.5 micrometers per cubic meter. The model training process was performed using multilayer perceptron neural network and post-diffusion rule and using sigmoid membership function in Matleb software environment. In the neural network model, the number of neurons in the hidden layer and the appropriate number of rounds or IPAC to achieve the best neural network structure, with the least error for each model, were determined using trial and error. The number of neurons and apex for the model in 2017 is 15 and 37,000, respectively.

The correlation coefficient of the model for predicting PM2.5 concentration is equal to 0.80 which is obtained by comparing real data with simulated data. The validation results of the model with real data are close to 80%, so the neural network model can be used to predict PM2.5 concentration. According to the average regression diagram, the predicted values obtained from the model are closer to the diagonal axis and have no dispersion. Also, based on the results of the step-by-step regression method, it was determined that among the four variables used for relative humidity modeling, it has the most impact and importance in dust emission modeling.

According to the accuracy and the results, this method can be used to predict the air pollution of Kashan caused by suspended particles. Due to the high capability of the perceptron neural network in predicting the concentration and distribution of dust, the application of this model can be a suitable and fast solution for predicting the amount and spread of dust.

Considering the importance of cement industries in the country's economic growth and its effect on emissions of air pollutants (particulate matter); in this research, the effects of Tehran cement factory on the environmental of the region have been investigated. The main purpose of this study is an assessment of environmental pollutant particles with air quality index in 2016 in Tehran Industrial Cement Complex. 

In this study, the particulate matter PM10 and PM2.5, was measured during a one-year period (2016), in four different seasons. To measure dust particles in the ambient air of the device is used gauge 531 MET ONE GT TSI Manufacturing Co and it has the ability to measure particles with PM10, PM2.5, PM7, and PM1. The next 8530 TSI Dust Trak II counterparty bombing device is a desktop dust-removing device that has the capability to record data. The samples were taken at 6 measuring stations in the Tehran Cement Industrial Complex, and finally, the results were evaluated as a seasonal AQI. In AQI method, which is one of the most widely used methods for assessing the air quality, at first, the pollution index is measured at a station and then determined at all stations as the highest index of contamination quality.

The first finding from the present study was that the pollutant responsible for the whole spring station was a refinery, which is classified as clean. The other pollutant responsible for the entire summer stations related to the road toll (Healthy classification), Responsible pollutant for the entire autumn season station area (unhealthy classification for sensitive groups), and Responsible pollutant for the whole winter season is related to residential buildings (Healthy Classification). So the most polluting plants or four different seasons of the year is autumn PM2.5.

So the state of air quality with an average concentration of 106.2 mg/m3 in the Tehran cement industry classified as unhealthy for sensitive groups and were characterized by the orange color. Finally, suggestions are made to reduce particulate emissions in the industrial area.

Today, air pollution in oilfield and refinery areas and around it is one of the major challenges that unfortunately creates important problems for the residents of the region.In the present study, to determine the relationship and correlation between NO2 emissions from the flare of Syraf gas condensate refinery and heavy metals in the soil. The emission rate of this pollutant was calculated using the proposed emission factors by the US Environmental Protection Agency 1.0417g/s and the air modeling was done using AERMOD software. The most dispersion of NO2 is based on the wind direction from the west to the east. Sampling results from soil at 6 points around the site were compared with the standards of the Environmental Protection Agency for soil and it was found the lead metal content was higher than the standard. one-way ANOVA and Duncan's multiple domain test used in Spss software, the highest lead dispersion due to the distribution of more pollutant sources such as 19, 12, 14 phases, as well as the LNG site and the Aram Cement Factory located in the western part of the site, is related to this area of the site. The correlation between heavy metals and NO2 pollutants showed that there was a positive and significant correlation between this pollutant and lead metal in soil surface, Because of the Nitrite and Nitrate anionic compounds in the soil, these compounds form salt with the adsorption of lead metal.

Gorgan Bay in Golestan Province is important biological and fisheries area; while in recent years, it has been contaminated Severely; Therefore, the most important pollutant factors were studied using SWOT model in this study.For this purpose, weaknesses, strengths, threats and opportunities have been identified in the ecosystem and were evaluated by 10 ecology and hydrology scientists. The results included 2.4 for external factors and 1.89 for internal factors. Accordingly, both values are in the range of defensive strategies. Defensive strategies control existing weaknesses and threats to improve ecosystem conditions. The most important pressure factors on the ecosystem are being the main canals of the sea closed to the bay, the entry of various pollutants into the ecosystem and poor physiography of Gorgan Bay. According to the importance of the surface water resources in terms of food production for society, and in order to sustainable development, we should manage them consciously to provide suitable habitat for aquatic species in addition to protecting water resources.

Air pollution is a global threat to public and environmental health, especially in urban areas. Therefore, modeling is used to control and planing concentration of pollutants. In this paper, a model is proposed based on linear regression for short term forecasting of CO, PM10 and O3 based on meteorological parameters, and the results are presented. Data of meteorological parameters including humidity, pressure, minimum and maximum temperature, wind speed and wind direction (Birjand Meteorological Organization), and air pollution data (CO, PM10, and O3 concentrations) from the Birjand weather organization were prepared and used as daily average. SPSS16 software was used for linear regression modeling. The results showed that the highest correlation coefficient for CO pollutant with minimum temperature was 0.53 and the lowest correlation coefficient with the value of 0.166 was wtih the wind direction. The maximum correlation coefficient of PM10 contamination with wind speed was 0.33 and the lowest correlation coefficient of this pollutant with a pressure was 0.882. Finally, the highest correlation coefficient of O3 contamination with maximum temperature was 0.50 and also with regard to the regression coefficient obtained for carbon monoxide (R = 0.33) compared to the other two pollutants, has been obtained better.

During the intensive flood in 1384, the floodplains in east Azerbayjan were waterlogged for a period of 1 month. The aim of the study was to assess the effect of the flood on the level of contamination of the soils in this region. For this purpose, 20 soil samples were collected from the area immediately after the flood event from the upper (0–30 cm) soil layer together with four samples from the 30–60 cm depth layer.  The analysis included basic physico-chemical soil properties, contents of Cd, Ni, Pb, Cu, and Zn metals. The concentrations of identified metals at all the samples were below the acceptable limits for agriculture use. The results show that this flooding episode in “clean” agricultural area had no immediate negative impact on the soils properties (organic matter percent, acidity, and nitrogen content) and did not result in excessive soil contamination.

The compacted clay liners (CCLs) due to their low permeability and suitable capability for contaminant retention are widely used in engineering waste disposal sites. Generally, the change in properties of soil pore fluid has a very distinguished impact on the behaviour of clayey soils. In spite of several researches, which have been performed on the process of clay and organic contaminant interaction, there are few researches on the influence of dielectric constant of organic contaminant and initial hydration of bentonite on the geotechnical and geo-environmental properties of organic contaminated bentonite. Such a process is very common in many industrial and waste disposal projects.

This research was performed on sodium-bentonite soil samples which were exposed to two different organic materials (Ethanol and Acetic Acid) which have different dielectric constant. Furthermore, two different pre-hydration and post-hydration conditions were studied in this research. After achieving equilibrium condition, the influence of initial hydration and change on the dielectric constant of pore fluid upon interaction of organic material and bentonite was investigated. The investigation focuses attention on the plasticity properties of bentonite by the use of Atterberg limit tests.

The achieved results indicate that in Casagrande's plasticity chart, two pre-hydrated sodium-bentonite soil samples which were exposed further to ethanol and acetic acid are classified as CH and shifted from CH to MH, respectively.

 Discussion and Conclusion:

The change on the dielectric constant of pore fluid and different hydration conditions cause a change on the thickness of the double layer of clay fraction of the soil. This causes a noticeable change on the structure and behaviour of clay fraction of the soil sample. This variation on bentonite behaviour has been discussed based on the current available theory of double layer. Furthermore, the theoretical limitation for interpretation of results has been addressed.

The ability of Pinch Technology is related to design and analysis of processes; however, it can’t be used individually for heat and power system. In the other words, exergy analysis can apply for evaluation of heat and power systems. It can determine unavoidable parts of energy systems and specify the maximum potential of optimization.

The main limitation of exergy analysis is the lake of general and clear method for design of system. The combined pinch and exergy analysis that using the advantages of both methods satisfy the limitations. In this paper, the RAMIN Ahvaz thermal power plant as a case study is defined. Furthermore, using CPEA the potential of optimization has been determined and evaluated.

Three proposed modification scenarios have been used for optimization of Power Plant as follows: 1. approaching pinch temperatures 2. Increasing steam in boiler and 3.Decreasing pressure of condenser.

With employing both methods the efficiency of power plant has been increased by 1.7%. In addition, the fuel consumption is about 1569 m3/h and flue gases have been reduced by 1.7% (20t/h). So, the emissions have been reduced by 1.7%. The Thermoflow (Steam Pro) software has been applied for simulation of case study. In addition, Aspen Pinch Software has been employed for CPEA.

Envirnmental factors such as seasonal winds, together with pollutants, dust and dune sand, reduce the drivers’ visibility, and when settled over the road surfaceproduce a negative impact on the skid resistance of asphalt pavements. In this study, the effect of dust and dune sand on the surface macro texture of road asphalt pavements in several sites, located in the Sistan and Balouchestan Province in Iran, was investigated. For this purpose, five road sections located in the areas with long periods of intense dusty and sandy winds , were monitored over a period of one and half months. In each site, the road surface under consideration was initially cleaned. Then, throughout the monitoring process, the images from these road surfaces were digitally taken and processed using an image processing software. Thereby, the road surface brightness parameter was calculated. Moreover, the macro texture depth of road surface in each site was also measured. The results indicated that there is an adverse relationship with strong correlation between the surface contaminations or brightness with the mean macro texture depth of road surface. The results also indicated that the rate of reduction in the mean road surface texture depth and brightness would reduce with time.

Air pollution is a global threat to public and environmental health, especially in urban areas. Therefore, modeling is used to control and planing concentration of pollutants. In this paper, a model is proposed based on linear regression for short term forecasting of CO, PM10 and O3 based on meteorological parameters, and the results are presented. Data of meteorological parameters including humidity, pressure, minimum and maximum temperature, wind speed and wind direction (Birjand Meteorological Organization), and air pollution data (CO, PM10, and O3 concentrations) from the Birjand weather organization were prepared and used as daily average. SPSS16 software was used for linear regression modeling. The results showed that the highest correlation coefficient for CO pollutant with minimum temperature was 0.53 and the lowest correlation coefficient with the value of 0.166 was wtih the wind direction. The maximum correlation coefficient of PM10 contamination with wind speed was 0.33 and the lowest correlation coefficient of this pollutant with a pressure was 0.882. Finally, the highest correlation coefficient of O3 contamination with maximum temperature was 0.50 and also with regard to the regression coefficient obtained for carbon monoxide (R = 0.33) compared to the other two pollutants, has been obtained better.

Nowadays, air pollution is one of the most important problems almost all over the world. There are many strategies to control and reduce air pollution, one of which is prediction of this event and getting ready to deal with the negative effects of it. The aim of this study is to provide a multi-layer structure of artificial neural networks (ANN) for predicting of carbon monoxide pollution at subsequent 24 hours in Tehran metropolis.
To predict the amount of CO emissions in near future (subsequent 24 hours), wind speed and direction, temperature, relative humidity, and barometric pressure characteristics are used as meteorological data, and concentration of carbon monoxide is considered as a pollution parameter. To eliminate the noise of data, wavelets transform method and determining the threshold with normal distribution are used before training the ANN. Finally, two neural networks as two general models are proposed and used for modelling.
Findings: The results show that the correlation coefficient, index of agreement, accuracy of prediction, and root mean square error for model no. 1 with duplicate data are 0.9012, 0.915, 0.848, and 0.1012 and for model no. 2 with duplicate data are 0.9572, 0.978, 0.963, and 0.0385 respectively. Moreover, the results of listed parameters for model no. 1 with new data are 0.9086, 0.89, 0.885, and 0.0825 and for model No. 2 with new data are 0.8678, 0.928, 0.932, and 0.1163 respectively.
Results showed that there is a good agreement between predicted and observed values, hence the proposed models have a high potential for air pollution prediction.

1. According to the studies report about the rates of diseases in the world in 2015, air pollution is responsible for 1.5 million deaths in China and 4.2 million people around the world. Despite statistics that compare the amount of Tehran’s pollutants with international standards, the claim is not far from reality that the air pollution is the main environmental problem in Tehran. Millions of people breathed in the most polluted air of Tehran throughout the day and if the growth of pollution sources continued to produce, so this issue can become a humanitarian disaster. The result of fuel consumption in various sectors is to produce the particles and gases that constant exposure against them involves problems and dangerous complications for human health and other living things and this is only part of the adverse effects of air pollution.
2. AIRREX device, Model HG-550 or analyzer of pollutants for the vehicles made of Korea that is one of standard devices by capability to measure the output gases from exhaust of vehicles; is used in the study to measure the pollutants emitted from exhaust of commercial vehicles in an industrial unit. The analyzer displays also the air ratio to fuel and excess air ratio. The instruction for usage the device is that probe of device puts inside the exhaust of vehicle, the device is turned on and after warming, it starts to analyze the output gas. The amount of pollution of output materials is determined based on standards developed by Iranians national standards organization to number ISIR 9181 and standard set by the environmental protection agency under standard of pollution limit for all vehicles according to the standard table of pollution limit for domestic and imported vehicles as follows.
3. As it has been shown, the major emitted pollutants include carbon monoxide and unburned hydrocarbons that have adverse effects to the air pollution and human health and the environment. Most of these gases are emitted from exhaust because of technical fault of vehicles; and the first step to reduce the production of pollutant gases is their timely service and adjustment because the results of most researches reveal the importance of motor adjustment and circular services of vehicles for reduction of amount of air pollution. One of other tools and techniques to reduce the pollutants in the exhaust of vehicles is the use of catalytic converters on the route of vehicles’ exhaust. Catalyst converter is a device that installs in the engine exhaust system and then converts three harmful exhaust compounds from engine (hydrocarbons, carbon monoxide and nitrogen oxides) into harmless compounds by the catalyzers.

In this research, the impact of meteorological parameters on SO2 accumulation between 2009 to 2015 years over Urmia City was investigated. Due to the polluted days and no remarkable effects relating to increasing industrial activities and transport services as well as the number of lorries and cars around the Urmia City, the parameter of meteorology plays a very crucial role in this issue. Therefore, some meteorological parameters considered were wind speed, wind direction, nebulosity, and temperature and SO2 pollutant as air pollution index. This study revealed that SO2 accumulation over Urmia City happens when the wind speed is 2-3 m/s, prevailing wind direction is Eastern, South-Eastern, nebulosity is none or low and the temperature is between -5 to °C. February month is the most polluted month in year and the highest pollution occurs at 15-20 h.

In this research measuring ozone concentration in air quality control Company at Aqdasieh station، and meteorological parameters at Resalat station، was compared and investigated. This investigation was performed for 7 months (from Oct. 2007 to Apr. 2008). The average daily Ozone concentration showed the least quantity of Ozone in early morning and late night air. Comparing the sunshine radiation with variation of Ozone concentration indicated that these two items have direct relations. Increasing the sunshine radiation concludes the increasing of photochemical activity and consequently increasing the Ozone concentration. Investigating the monthly average Ozone concentration showed that the least registered amount of Ozone was during these 7 months of measurement in Azar (November) and the most registered amount of Ozone occurred in Farvardin (April). The most important factor of main increasing happened in April and in the solar radiation and photochemical activities.

Arsenic is a naturally occurring element which pose a potential risk to groundwater quality, as well as food safety through As transportation in soil-water-plant systems. Therefore, plants such as cereal are the main way of As transportation to the food chain. In this study, to evaluate the arsenic transfer rate in agricultural products and the risk of its poisoning, surface water, soil and wheat samples were collected from the Bijar County, which has a history of naturally occurring arsenic pollution. The As concentration of surface water and soil samples are in ranged of 0.52-484.64 μg/L and 138.2 to 1775.2 mg/kg respectively. Also some physico-chemical parameters of water and soil which had direct effects on As pollution were estimated. The As concentration of wheat shoot samples were between 1.79-30.58 mg/kg and As concentration of wheat seed samples were 0.003-0.08 mg/kg. Based on these results, arsenic levels in waters and soils were significantly correlated, but arsenic accumulation in wheat samples were not correlated significantly with soil arsenic concentration.

Groundwater is the water that is said to have gathered in the basement of saturated aqueous layers.One of the main sources of water supply in all countries of the world is considered and its use inagricultural irrigation and municipal and industrial uses is growing. Excessive use of groundwater andcontaminants entering the municipal solid waste from industrial sources, petroleum hydrocarbons,Pollution from heavy metals such as arsenic, radioactive waste and nitrates has caused many problemsfor these sources. So there are many ways to restore contaminated groundwater among the ways thatwe can deal with the leachate to groundwater, using iron nanoparticles for groundwater treatment, biocleanup of groundwater contaminated with petroleum hydrocarbons, including simulation methods tosimulate pumping methods - filtration and aeration of contaminated groundwater in place recoverynoted. The study concludes that nanotechnology methods in addition to a good performance that canbe the cost of drinking water from groundwater sources also minimized. And the best place for wastedisposal to prevent the infiltration of leachate into groundwater in arid and semi-arid areas is located.

During the intensive flood in 1384, the floodplains in east Azerbayjan were waterlogged for a periodof 1 month. The aim of the study was to assess the effect of the flood on the level of contamination ofthe soils in this region. For this purpose, 20 soil samples were collected from the area immediatelyafter the flood event from the upper (0–30 cm) soil layer together with four samples from the 30–60-cm depth layer. The analysis included basic physicochemical soil properties, contents of Cd, Ni, Pb,Cu, and Zn metals. The concentrations of identified metals at all the samples were below theacceptable limits for agriculture use. The results show that this flooding episode in “clean” agriculturalarea had no immediate negative impact on the soils properties (organic matter percent, acidity, andnitrogen content) and did not result in excessive soil contamination.