گرد و غبار

Air pollution is one of the most important environmental problems. Asphalt plants are the sources of air pollutants, especially particulate matters. The purpose of this study is to investigate the emission rate of PM2.5 and PM10 emitted from asphalt plants in the southwest of city of Tehran, also the present study was conducted to estimate the dispersion of particulate matters concentrations on surranding areas of a mining complex and city of Tehran.

This descriptive-cross sectional study was performed on the asphalt plants in a mining complex in the southwest of Tehran. In this study, at first the concentrations of PM2.5 and PM10 emitted from asphalt plants located in the southwest of Tehran were calculated using emission factors suggested by the US Environmental Protection Agency. Then, the dispersion of particulate matters concentrations was estimated on the surrounding areas of the mining complex with a radius of 50 km using the AERMOD model for the average times of 24 hours and annually using emission rate, meteorology and topography data. At last, the simulated concentrations were compared to EPA and WHO standards.

The maximum particulate matter concentrations are in the mining complex, and is gradually reduced by moving away from it. Dispersion of particles in the directions of dominant winds has been uniform. The maximum PM2.5 and PM10 concentrations have been estimated about 1 µg/m3 and 2 µg/m3 in Qods, 1 µg/m3 and 2 µg/m3 in Shahriyar, 0.6 µg/m3 and 1 µg/m3 in Andisheh and 0.2 µg/m3 and 0.4 µg/m3 in Tehran respectively in a 24-hour period. The amount of PMs that reached the west of Tehran continues to the eastern regions of the city. PMs concentrations were not significant in the average annual period.

Although dust emission rate from asphalt plants in the southwest of Tehran is acceptable, but it affects on the air quality in the surrounding areas of the mining complex and to some extent in Tehran. The cumulative effects of different industries can increase the potential health risks in the communities vicinity of this mining complex.

Background & objectives:

The phenomenon of dust is very critical and catastrophic in our country, especially in Kerman region. Part of the damage caused by this phenomenon is due to lack of sufficient recognition of the impact of dust on the environment. Therefore, the present study conducted to evaluate and prioritize the effects of dust on water, air, soil and animal and plant ecosystems in Kerman city in 2019 in order to inform citizens and authorities about the damage caused by this phenomenon and to plan for controlling them.

The research method was applied descriptive-analytical study. Five criteria including aquatic, soil, air, plant and animal ecosystems, each with 4 sub-criteria, were considered for measuring the environmental effects of dust. Library and field studies (in the form of questionnaires, observations and interviews) were used to collect the information. The statistical population of the study was all environmental experts in Kerman. VIKOR model was used to rank the measurement criteria.

Among the criteria studied using VIKOR model, air, water and soil criteria with quality level of 0, 0.105 and 0.351 were in the first, second and third ranks of negative dust effects, respectively. Animal and plant ecosystems with a quality level of 0.656 were ranked fourth. Among the research indices, the index of endangering the public health of citizens by increasing heavy metals, threatening the citychr('39')s drinking water resources and increasing respiratory diseases with an average of 4.07, 4.04 and 4.01were in the highest average of harmful effects of dust, respectively.

Based on the findings, among the 5 criteria studied, dust had the most negative effect on the air of Kerman and then on the water and soil of the region, respectively. Therefore, based on the priority obtained in this study and coherent planning, Kerman urban crisis management can take successful steps to reduce and deal with the dust phenomenon.

Trace elements are the common pollutants of soils, sediments and street dust in the urban environment, which pose a potential threat to public health. Therefore, this study was conducted to potential ecological risk assessment of As, Cd, Cr, Cu, Pb, Ni and Zn in street dust collected from industrial, commercial and residential areas of city of Hamedan in 2019 using ecotoxicological criteria.

In this descriptive study, a total of 378 street dust specimens were collected from 18 sampling sites. After acid digestion of dust specimens, the element contents were determined using ICP-OES. Also, potential ecological risk factor (Er), risk index (RI) and the ecotoxicological criteria were calculated. All statistical analyses were done by SPSS software.

Based on the results obtained, the mean contents of the examined elements in dust specimens (mg/kg) were 2.31, 0.225, 41.3, 48.8, 65.2, 79.2 and 211 for As, Cd, Cr, Cu, Pb, Ni and Zn, respectively. The computed values of Er showed that, factor values were decreased in the order Cd > As > Pb > Ni > Cu > Zn > Cr. Also, the mean values of RI with 77 represented that, all the examined elements have the "low potential ecological risk". Moreover, the computed values of the ecotoxicological criteria showed that the street dust specimens of the study area were non-polluted with trace elements.

Although the examined elements have been shown the low potential ecological risk, Cd with 38.2% of the mean values of RI showed significant potential to occurrence the ecological risk, therefore, source identification and sources control of this element and also periodic monitoring of contaminated soils are recommended for maintenance of public health.

During the past decade, the scientific community has noticed the important impacts of suspended dust in the air on climate, human health, the environment, and various socio-economic sectors. The dust concentration has a negative relationship with human health so that the increase of dust concentration leads to an increase in the number of patients with heart and respiratory diseases, and human health is seriously threatened. The undesirable phenomenon of dust storms is one of the natural disasters that affect Zabol city due to its geographical location and proximity to the dried bed of Hamoon wetland. Therefore, this study aimed to investigate the temporal changes in dust concentrations over a 10-year period in Zabol city.

The concentration of dust particles (PM10) was measured using fixed air pollution measuring devices during a period of 10 years from 2009 to 2019. All data were recorded on a daily and annual basis and the variations during this period were examined in this study. The given data were measured and analyzed based on the air quality index in order to evaluate the air quality changes.  

In this study, the average annual concentration of PM10 has been 405, 425, 451, 659, 532, 152, 222, 196, 170, and 67 μg/m3, respectively, from 2009 to 2019. The highest particle concentration was determined at 4932 µg/m3 in the summer of 2016, which was the result of  120-day storms, the dried bed of Hamoon wetland, and the transfer of dust particles.

According to the standard, the concentration of PM10 should not exceed 50 µg/m3; however, this value has reached several times the standard limit and 33 times the standard limit at maximum during the 10-year period.

Dust storm typically occurs in arid and semi-arid regions. This phenomenon is more visible across the western, southwestern and central regions of Iran in the summertime. Previously conducted studies have reported that this phenomenon affects human health, agriculture, industry, infrastructure and transportation. The aim of this study is to investigate the economic effects of dust storms.

The present study is a descriptive investigation. The available articles and reports in valid Persian databases (Civilica, Magiran and SID) as well as English databases such as Web of Science, Scopus, Embase, PubMed and Google scholar were reviwed separately.

The dust storm phenomenon covers a wide range of geographical area. In fact, it occurs in all parts of the world and is considered as an international issue. During the occurrence of dust storm phenomenon, the concentration of various pollutants increases, which generally affects the atmosphere, biosphere, hydrosphere and throposphere. Dust storm have far-reaching environmental, health, and socio-economic effects that are linked in a chain, and ultimately each of these effects will have irreparable economic consequences. Therefore, the phenomenon of dust has wide economic effects such as the impact on the exports, delays in air travel, disruption of road transport, destructive effects on agriculture (agriculture and horticulture), road accidents, road damage, closure of schools, home appliances, electronic equipments, public billboards, irrigation canals, power plants and public health.

Dust storm phenomenon should not only be considered as an environmental issue, but due to the great economic consequences for the infrastructure of the affected areas and the country, it should be considered as a national and international concern. It is necessary to accompany many executive departments of the country to reduce its consequences. To reduce the man-made resources of the dust phenomenon, this study proposes integrated assessments and strategies that promote sustainable water and land management in farms, rangelands, deserts and urban areas, despite climate change.

Distribution of air pollutants in industrial units is one of the issues that always affects the environment and ecosystem of adjacent areas. Air pollution is an inevitable part of modern life, and the knowledge of air pollution existed in the past and must be continued to make policies. The cement manufacturing process produces millions of tons of byproducts each year, including dust, toxic gases and heavy metals, which will pose health and respiratory hazards and environmental pollution. Therefore, awareness of pollutant concentrations can be used as key information in pollution control programs. There are still many activities to complete effective control strategies for the prevention, continuous improvement and application of modern technologies to substantially reduce dust in the cement industry. The amount of dust produced can be significantly reduced using modern technologies such as cyclones, bag filters, and electrostatic filters. There are various types of dust in the cement industry including, raw materials such as limestone, marl, clay, iron ore, mixing dust, dryer outlet dust, furnace outlet dust, clinker dust, gypsum dust, and cement dust. In this study, the purpose of dust is the furnace outlet dust. Air pollution is a very complex process that depends on many factors. Therefore, it is very difficult to predict such data with nonlinear dynamics and to determine how the pollutants are to be dispersed and propagated in the atmosphere, which will also be extremely costly. Then, each section can be modeled using this data and solving the equations involving atmospheric processes in the form of data networks. Air quality modeling can be considered as a suitable tool for predicting future air quality and determining emission control strategies. In recent years, one of the best models that has shown good performance in pattern and control modeling is intelligent systems, which include neural networks. Multilayer perceptron neural networks are a model for information processing made by mimicking biological neural networks such as the human brain. The key element of this model is the new structure of its information processing system, consisting of a large number of elements (neurons) with strong internal communications that work together to solve specific problems. Artificial neural networks by empirical data processing transmit knowledge by the law behind the data into the network structure; this is called learning. Ability to learn is essentially the most important feature of an intelligent system. A system that can learn is more flexible and easier to program, so it is better responsive to new problems and equations. Artificial neural networks have been employed for various purposes such as simulation, pattern recognition, classification, prediction, and optimization in engineering sciences. The ability of these networks to map between input and output information with acceptable error has made this method a useful tool for modeling engineering processes. In the basic state, a neural network consists of three layers, input layer, hidden layer, and output layer. Also, each layer contains a number of neurons. In the neural network, neurons are active (on or 1) and inactive (off or 0) and each wing (synapse or communication between nodes) has a weight. Positive-weighted wings trigger or activate the next inactive node, and negative-weighted edges inactivate or inhibit the next inactive node (if it was active). To calculate the output, each of the previous layer inputs is multiplied by the network weights and summed with the corresponding bias. Sabzevar city is located on the west of Khorasan Razavi province in Mashhad-Tehran highway. This city is bounded by Esfarayen in the north, Neyshabur in the east, Kashmar and Bardeskan in the south, Shahroud city in Semnan province in the west. Based on the latest country divisions, Sabzevar has five Bakhsh including, Markazi, Roudab, Khoshab, Davarzan, and Sheshtamad. Sabzevar Cement Factory is one of the sources of dust emission in southwest of Sabzevar in Roudab Bakhsh. The plant started operating on an area of 150 hectares, with a capacity of 3400 tons per day, with Portland cement type 2, 1-325, and 1-425 from October 2007. The plant also uses Bag filter, Bag house, and electrostatic filters for dust, and allocates an area of 50 hectares to build a green belt to protect the environment. The purpose of this research was to predict the amount of dust from the main chimney (baking furnace of Sabzevar cement plant) using a perceptron neural network model.

In this study, first, the amount of dust emitted from the main chimney of Sabzevar cement plant was studied through field surveying in three seasons in 2017-18 by the Experts of Mashhad Environmental Trust Company (Pak Afarinan Avizheh). All sampling and measurement procedures were in accordance with the requirements of the Iranian Environmental Protection Agency. 180 dust samples were collected and then, compared with Iranian Environmental Protection Agency standards and the US Environmental Protection Agency (EPA). The parameters of output gas velocity parameters, temperature, voltage, fuel, and furnace feed were used as perceptron neural network input. Perceptron neural network with 5 neurons as input in the first layer, 10 neurons in the hidden layer, and 1 neurons in the hidden layer. The Levenberg-Marquardt algorithm was used to train the network, which is one of the most used and fastest optimization algorithms. Perceptron neural network is one of the supervised learning methods. To evaluate this method, the data were divided into two training and test groups. The model was trained on the training data. The model error was estimated on the test data. In order to include all data in both training and test groups, k-fold cross-validation method with k = 5 was used. In this way, the data were divided into 5 sections and at each run, 4 groups were considered as training and 1 groups as testing. The network with the least error over the test data was selected. The amount of dust concentration was estimated based on input parameters with the above mentioned neural network (input, hidden and output three layers) and above method. In order to observe the effect of each of the input parameters on the estimation of dust concentration with the perceptron neural network, all possible scenarios should be considered. Since five input parameters are selected, so 25, there are 32 subsets, which results in 31 cases with the removal of the null set. The perceptron neural network was run with 10 neurons in the hidden layer and one neuron in the output layer on each mode (subset). Thus, all subsets of one-member, two-member, three-member, four-member and five-member problem parameters were investigated. In each case, the number of input layer neurons is equal to the number of selected parameters. For each subset of input parameters a neural network was constructed and the amount of dust was estimated. The subset that had the least error in predicting dust concentration was selected as the influencing parameter. For each subset, 5 models were trained, validated each time, and the model with the least error mean squared on the test data was selected. Correlation coefficients and mean squared error were used to evaluate the accuracy of the model in predicting dust concentration. All implementations were performed in MATLAB software.

The results showed that the perceptron model has a good accuracy in predicting the dust concentration of Sabzevar cement plant. Perceptron neural network model was able to Show the correlation coefficient as much as 0.98168 and 0.98249, and Mean square error as much as 0.709 and 280, respectively in two modes of using all parameters (output gas speed, temperature, voltage, fuel, and furnace feed) and the influential parameter (temperature). This indicates a higher correlation and lower error of the perceptron neural network model in using only the temperature parameter than in the prediction of dust concentration in all parameters.

Achieving environmental pollution standards in the cement industry requires proper design and increased efficiency of dust collecting equipment to reduce dust particles. Nowadays, due to the increasing development of cement factories to produce cement needed in the country as well as the growing importance of environmental considerations in various industries, especially the cement industry, the need for better and more efficient technologies to control pollutants and wastes from cement plants has become more and more prevalent. The cement industry is one of the industries that is always referred to as the pollutant of the environment. Although in recent years the industry has tried to meet the environmental standards required by modernizing its equipment and machinery, it is still one of the most important contributors to air pollution. The results of this study showed that the, this model can be a suitable and fast way to better manage industrial dust concentration and ensure quality monitoring results and reduce costs because of the high capability of perceptron neural network in predicting dust concentration.