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.
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.
