Jalil Badamfirooz; Alireza Rahmati; Nooshin Daneshpajooh; Roya Mousazadeh; Reza Mirzaei
Introduction: Arak is one of the eight most polluted cities in Iran, whose pollution is mainly due to the activities of various industries located in the city or its suburbs. Using air pollution modeling it would be possible to estimate the effect of emissions of suspended particles and gases from the ...
Introduction: Arak is one of the eight most polluted cities in Iran, whose pollution is mainly due to the activities of various industries located in the city or its suburbs. Using air pollution modeling it would be possible to estimate the effect of emissions of suspended particles and gases from the activities of various industries on the local environment. This study is an attempt to investigate the impact of existing and under construction industries in Arak on the air quality of the city using the ADMS model as a widely used and trusted model of the Department of Environment (DoE).Material and methods: Because the main sources of air pollution in Arak are of focal type, in modeling air pollution, 17 large industries (including 98 chimneys) located in the city were considered as pollution points. In addition to the emission data, the geometrical data of the chimneys including the height and diameter of their opening and the temperature of the exhaust air were also included in the model. To validate the model outputs, the measurement values of the environmental stations were compared with the values estimated by the modeling using Pearson linear correlation coefficient.Results and discussion: The results showed that the concentration of CO, SO2, and NO2 in all stations was within the permissible level announced by the DoE. The dispersion of suspended particles (contour lines) in the city was to the west and southwest and up to a radius of 3 km in the prevailing and semi-prevailing wind direction. This for Shazand was to the west up to a radius of 1 km in the prevailing wind direction and to the southwest up to 5 km in the direction of semi- prevailing wind until reaching the background concentration of 19.1 μg. Accumulation of SO2 contour lines in Shazand pollution center was observed up to a radius of 7 km in the west direction and up to a radius of 10 km in the southwest direction. The accumulation of NO2 contour lines was the same as NO2. The radius of impact of CO gas was extended from Arak to Shazand. Accumulation of CO contour lines in Arak was up to a radius of 5 km in the direction of west and southwest. The accumulation of H2S contour lines in Shazand was up to a radius of 5 km towards the directions of west, southeast, and southwest. In general, the difference between the sampling and modeling results indicated the pollution sources that were not observed in the model and were beyond those emitted from the factories. In most stations, the modeled and directly monitored SO2 concentrations were not much different and the correlation coefficient of the data was high, indicating the accuracy of the calculations and the prominent role of industries in the emission of this gaseous pollutant. Also, in most stations, the results of environmental measurement of NO2 were less than the modeled values, revealing that the industries had a greater share in the emission of this gas. The overestimate of this emission may probably be due to the inclusion of the under construction industries in the model. The environmental concentration of CO in all stations was higher than the modeled values. Industries have a small share in the pollution load of this pollutant while in urban areas, the concentration of CO depends on the mobile sources and traffic load.Conclusion: In general, the pollution levels of the city showed that the center of pollution was in the southeast of Arak and in the complex of the refinery, petrochemical company, and thermal power plant. According to the overlaid contours of emissions, a number of points (14 points) that were closest to the pollution centers were proposed as critical stations, two points as control stations, and 4 points as the stations exposed to pollution in each period.
َArdavan Zarandian; Roya Mousazadeh; Jalil Badamfirooz; Alireza Rahmati
Volume 16, Issue 2 , July 2018, , Pages 111-132
Changes in land use and/or land cover (LULC) are associated with many socio-economic and physical environmental factors. Due to the multiplicity and diversity of variables involved and drivers that cause changes, the prediction of future conditions of LULC patterns is complex and faces ...
Changes in land use and/or land cover (LULC) are associated with many socio-economic and physical environmental factors. Due to the multiplicity and diversity of variables involved and drivers that cause changes, the prediction of future conditions of LULC patterns is complex and faces many uncertainties. Meanwhile, environmental and development planners need to consider clearly how their current decisions may shape the future structure of the landscape. Therefore, in the policy-making and planning process, there is always the question of how to predict future LULC changes. In recent years, thanks to advances in remote sensing knowledge and spatial data generated from satellite imagery as well as evolving modelling tools, it has been possible to simulate complex natural systems and simplify them with a specific emphasis on more important variables depending on the issues being investigated.
Materials and methods:
With this in mind, the present study was conducted in a pilot forested landscape of the Hycanian vegetative region located in Mazandaran Province in northern Iran to detect the changing trends of LULC over the period of 1984-2016 as well as to project and analyze the plausible future shape of the landscape by the year 2040 using InVest scenario-generator software model (Sharp, 2014). To conduct this modelling process, two plausible future scenarios were defined entitled Business As Usual (BAU), which reflected the continuation of current LULC changes with no management intervention, and Balanced Development (BD) involving governmental intervention to prevent current changes through conservation and restoring forest cover along with an adjusted developmental policy for human settlements. Then, the input data required to run the model was provided and the future landscapes under both scenarios were simulated and compared.
Results and discussion:
The results showed that, under the BAU scenario, there will be dramatic changes in the landscape structure which will lead to a significant loss in the natural forest cover, destruction of farmlands and its replacement with human settlements. Conversely, the BD scenario showed how land management through forest conservation and restoration policies, simultaneously with adjusted land conversion for settlement construction, can be transformed into a win-win strategy for a balanced development strategy. Also, in this study, the InVEST scenario generator model was compared with some other models (Azinmehr et al., 2013; Blainski et al., 2017; Eskandari, 2014; Han et al., 2015; Samie et al., 2017) used to simulate LULC, and its advantages and limitations were discussed.
Finally, the scenario simulation with the method introduced here can be used in different studies (including various environmental assessments, economic valuations, etc.) to make the predictions more accurate. Moreover, this kind of modelling can make insight for planners and decision makers in the fields of development, conservation and land use planning, so that future plans are based on logical assumptions with less uncertainty.