During the past years, air quality has become an important global issue, due to its impact on people's lives and the environment, and has caused severe problems for humans. As a prevention to effectively control air pollution, forecasting models have been developed as a base for decision-makers and urban managers during the past decades. In general, these methods can be divided into three classes: statistical methods, machine learning methods and hybrid methods. This study's primary intent is to supply an overview of air pollution prediction techniques in urban areas and their advantages and disadvantages. A comparison has also been made between the methods in terms of error assessment and the use of geospatial information systems (GIS). In addition, several approaches were applied to actual data, and the findings were compared to those acquired from previous published literatures. The results showed that forecasting using machine learning and hybrid methods has provided better results. It has also been demonstrated that GIS can improve the results of the forecasting methods.

In the global COVID-19 epidemic, humans are faced with a new challenge. The concept of quarantine as a preventive measure has changed human activities in all aspects of life. This challenge has led to changes in the environment as well. The air quality index is one of the immediate concrete parameters. In this study, the actual potential of quarantine effects on the air quality index and related variables in Tehran, the capital of Iran, is assessed, where, first, the data on the pollutant reference concentration for all measuring stations in Tehran, from February 19 to April 19, from 2017 to 2020, are monitored and evaluated. This study investigated the hourly concentrations of six particulate matters (PM), including PM2.5, PM10, and air contaminants such as nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO). Changes in pollution rate during the study period can be due to reduced urban traffic, small industrial activities, and dust mites of urban and industrial origins. Although pollution has declined in most regions during the COVID-19 quarantine period, the PM2.5 rate has not decreased significantly, which might be of natural origins such as dust. Next, the air quality index for the stations is calculated, and then, the interpolation is made by evaluating the root mean square (RMS) of different models. The local and global Moran index indicates that the changes and the air quality index in the study area are clustered and have a high spatial autocorrelation. The results indicate that although the bad air quality is reduced due to quarantine, major changes are needed in urban management to provide favorable conditions. Contaminants can play a role in transmitting COVID-19 as a carrier of the virus. It is suggested that due to the rise in COVID-19 and temperature in Iran, in future studies, the effect of increased temperature on COVID-19 can be assessed. | peer reviewed

For groundwater evaluation, delineation, discovery, and resource management in drought and flood zones, the geographical information system (GIS) has a wide range of uses. For the study area, various thematic layers were prepared, such as a digital elevation map (DEM), geomorphology, LULC, soil, drainage density, precipitation, and slope. The thematic layers were combined using the WIOA technique. The possible areas for groundwater have been demarcated into four zones: 1-poor, 2-moderate, 3-good, and 4-very good. In the eastern parts of the district, very strong (GWPZs) were found, while in the west and mid regions, moderate and bad categories were found. Drought and flood potential danger areas were divided into four zones: 1-no risk, 2-low risk, 3-moderate risk, and 4-high risk. In the middle part of the region, there was a higher risk of drought and a reduced risk of flooding in the eastern part of the area, an elevated risk of flooding in the eastern part of the area, and a lower to no risk of flooding in the western and central regions. The groundwater, drought, and flood potential zonation map built in the present study will be useful for scholars, and implementers in exploring appropriate water exploration locations and implementing resource utilization.

The adverse health effects associated with the inhalation and ingestion of naturally occurring radon gas produced during the uranium decay chain mean that there is a need to identify high-risk areas. This study detected radon-prone areas using a geographic information system (GIS)-based probabilistic and machine learning methods, including the frequency ratio (FR) model and a convolutional neural network (CNN). Ten influencing factors, namely elevation, slope, the topographic wetness index (TWI), valley depth, fault density, lithology, and the average soil copper (Cu), calcium oxide (Cao), ferric oxide (Fe₂O₃), and lead (Pb) concentrations, were analyzed. In total, 27 rock samples with high activity concentration index values were divided randomly into training and validation datasets (70:30 ratio) to train the models. Areas were categorized as very high, high, moderate, low, and very low radon areas. According to the models, approximately 40% of the study area was classified as very high or high risk. Finally, the radon potential maps were validated using the area under the receiver operating characteristic curve (AUC) analysis. This showed that the CNN algorithm was superior to the FR method; for the former, AUC values of 0.844 and 0.840 were obtained using the training and validation datasets, respectively. However, both algorithms had high predictive power. Slope, lithology, and TWI were the best predictors of radon-affected areas. These results provide new information regarding the spatial distribution of radon, and could inform the development of new residential areas. Radon screening is important to reduce public exposure to high levels of naturally occurring radiation.