e. zallaghi

The main subject of this study was health impact assessment (HIA) of all-cause mortality, ALRI, LC, COPD, and IHD attributed to PM2.5 exposure using the AirQ+ model in Ahvaz as one of the most polluted cities of the world from 2008 through 2017.

In this descriptive-ecological study, the data related to mortality and PM2.5 was taken from the health deputy of Ahvaz University of Medical Sciences and Environmental and Meteorological Organizations of Ahvaz. After validating the data according to WHO guidelines, they were entered into the AirQ+ software. Also, BI values in 100 thousand individuals and RR with 95% confidence intervals were used.

The highest concentration of PM2.5 was in 2010 (70.72 µg/m3) and the lowest in 2014 (41.97 µg/m3), which is higher than the WHO standard (10 µg/m3) in all years of measurement. There was a significant relationship between PM2.5 concentrations, ratio, and attributed cases in all health outcomes. So, with the increase of air pollution in Ahvaz city due to PM2.5 pollutants, mortality and risk of contracting these diseases increased.

Appropriate measures and policies should be adopted to reduce air pollution by controlling PM2.5 pollutant sources and reducing the health effects caused by this pollutant in urban residents.

Among environmental changes, water plays a very vital role in the political, social and economic issues of countries, which can be used as one of the most practical sources of water supply available to humans and animals. Investigating surface water fluctuations in terms of importance, location and nature has gained special importance in recent years. Miankale International Wetland with an area of 68,000 hectares in the north of Iran is of special importance. First, by performing radiometric and atmospheric corrections on Landsat-8 satellite images for the years 2013 to 2023 and using the Gram Schmidt integrator to increase spatial resolution, to extract the NDWI, MNDWI, AWEI and WI2015 indices in order to differentiate the water level. Wetland was treated from non-water. In order to classify images, supervised classifiers such as maximum likelihood, support vector machine (SVM) and artificial neural network (ANN) were used. Also, in order to improve the results, the output of the classifiers was merged using the majority voting method. The results of the research showed that the majority voting method was chosen as the most suitable classification method with the highest level of accuracy. In 2023, compared to 2013, water level of the wetland has decreased from 452.351 km2 to 298.059 km2 (34.11%), and the wetland drought has increased from 6.209 km2 to 160.19 km2 (more than 2000%). The obtained results can be useful for management decisions to preserve more natural resources of our country.