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The purpose of this study is to determine the amount of air pollution in Tehran, Isfahan, Semnan, Mashhad, Golestan, and Shiraz during the Corona era and before. For this purpose, Sentinel satellite images were used to investigate the concentration of Methane (CH4), Carbon Monoxide (CO), Carbon Dioxide (CO2), Nitrogen Dioxide (NO2), Ozone (O3), Sulfur Dioxide (SO2), aerosol pollutants in In the era before and during Corona. Furthermore, greenhouse effect-prone areas were determined in this study. In the following, the state of air inversion in the studied area was determined by taking the temperature on the surface of the earth and in the upper atmosphere, as well as the wind speed into account. In this research, the prediction of air temperature for the year 2040 was conducted using the Markov and Cellular Automaton (CA)-Markov methods, considering the impact of air pollution on the air temperature of metropolises. Additionally, the Radial Basis Function (RBF) and Multilayer Perceptron (MLP) methods have been developed to determine the relationship between pollutants, areas prone to air inversions, and temperature values. According to the results, pollution caused by pollutants has decreased in the Corona era. According to the results, there is more pollution in Tehran and Isfahan metropolises. In addition, the results showed that air inversions in Tehran is the highest. Additionally, the results showed a high correlation between temperature and pollution levels (R2=0.87). Thermal indices in the studied area indicate that Isfahan and Tehran, with high values of Surface Urban Heat Island (SUHI) and being in the 6th class of thermal comfort (Urban Thermal Field Variance Index (UTFVI)), are affected by thermal pollution. The results showed that parts of southern Tehran province, southern Semnan and northeastern Isfahan will have higher temperatures in 2040 (class 5 and 6). Finally, the results of the neural network method showed that the MLP method with R2=0.90 is more accurate than the RBF method in predicting pollution amounts. This study significantly contributes by introducing innovative advancements through the application of RBF and MLP methods to assess air pollution levels during the COVID-19 and pre-pandemic periods, while also investigating the intricate relationships among greenhouse gases, air inversion, air temperature, and pollutant indices within the atmosphere. The utilization of these methods notably enhances the accuracy and reliability of pollution predictions, amplifying the originality and significance of this research.
RESUMO
BACKGROUND: In general, chronic pain is categorized into two mechanism-based groups: nociceptive and neuropathic pain. This dichotomous approach is questioned and a dimensional perspective is suggested. The present study investigated neuropathic characteristics in different syndromes of chronic pain. We also examined the association of neuropathic characteristics with various pain related and psychological variables. METHODS: From April 2010 to January 2012, 400 patients suffering from a chronic pain condition enrolled for multidisciplinary pain treatment were considered for inclusion in the study. Criteria for inclusion were age over 18 years and having chronic pain according to ICD-10 (F45.41) criteria. The pain DETECT questionnaire was used to assess neuropathic characteristics of pain. RESULTS: Thirty-seven percent of patients with different pain diagnoses demonstrated distinct neuropathic characteristics. The diagnostic groups for neuropathic pain, musculoskeletal pain and post traumatic or surgical pain showed the most neuropathic features. The level of depression, pain chronicity and intensity, disability and length of hospital stay were significantly higher in patients suffering from neuropathic symptoms. A high level of depression and pain chronicity as well as high intensity of pain explained most of the variance in the neuropathic scores. Disability and length of hospital stay significantly predicted neuropathic characteristics only when examined separately, but not if included in a common regression model. CONCLUSIONS: Any type of chronic pain may have more or less neuropathic characteristics. The pain-related parameters of high intensity and chronicity as well as negative affectivity and functional disability strongly correlate with neuropathic characteristics of pain.
