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.

peer reviewed | 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.

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.

The objective of this article is to present comprehensive findings and analysis of studies performed on air pollutant dispersion in urban environments. It captures India’s rising environmental pollution due to urbanization, industrialization, and population growth. Dispersion of pollutants due to the wind in the lower Atmospheric Boundary Layer (ABL) is a major concern nowadays. The dispersion field around the buildings is a critical parameter to analyze and it primarily depends on the correct simulation of the wind flow structure. Therefore, studies performed on this in past years are being reviewed. Additionally, a brief review of different air dispersion models that are integrated with the Geographic Information System (GIS) has been studied in this article to assess the exposure. The results of these studies provide the urban air dispersion model aligning to three sub-models i.e., Emission, Weather Prediction, and Dispersion models. Various factors like wind speed, wind direction, cloud cover, traffic emission, disposal of waste, transportation, and others are considered. This study also captures the problems and risks being faced while creating a model, and its possible mitigation approaches.

Soil erosion is one of the major environmental problems facing the world. The multi-scale characteristics of soil erosion and the complexity of its influencing factors put forward higher requirements for soil erosion prevention and control. Based on GIS technology and the RUSLE model, this paper quantitatively studies the temporal and spatial variation characteristics of soil erosion intensity in Chengde City(CC) from 2003 to 2018 and analyzes the temporal and spatial characteristics of R, K, LS, C, P factors according to the model calculation results, and analyzes the formation mechanism of key units of soil erosion in CC. The results show that: The area of tolerable erosion in CC in 2018 was 35152.19 km2 (accounting for 90.22% of the total area), which was at the level of tolerable erosion on the whole. The average soil erosion modulus of CC in 2003, 2006, 2009, 2012, 2015, and 2018 were 41.38, 45.06, 46.58, 83.66, 27.67, and 73.34 t.km-2.y-1, reaching the maximum value of 83.66 t.km-2.y-1 in 2012, showing a rising trend and then declining trend in the research period. Soil erosion deteriorated in some areas of CC and regional differences increased, which caused serious environmental problems. Fitting results showed that the R factor was one of the important factors for the increase of regional differences and average erosion modulus. According to the characteristics of the problem, a precise governance model of soil erosion prevention based on the intensity and causes of soil erosion was put forward, and a “landing” scheme of soil erosion prevention and control measures was put forward. Furthermore, the control of soil and water loss in key areas should be strengthened in the future.

Noise is an underestimated threat that can cause several short- and long-term health problems. It is increasingly becoming a potential hazard to health, physically and psychologically, and affects the general well-being of an individual. The objective of the current study was to examine noise levels at ten different locations in the city of Bhubaneswar, Odisha State, India based on the land use pattern in urban and rural setup. The paper focuses on deploying geospatial techniques using ArcGIS desktop to perform better sampling and further interpolate the statistical data using the Kriging technique to generate a surface representing the distribution of noise levels in various areas. In addition, a health impact survey enabled us to understand the perspectives of the people in and around the monitoring location where health issues like stress, headache, hypertension, and sleeping disorders emerged as some of the most common issues faced. Noise levels were in the range of 43.0 to 74.5 (A) Leq. in rural areas and 61 to 96.5 dB (A) Leq in urban areas. In the current study, noise levels in rural and urban areas exceeded the recommended noise limits as per The Noise Pollution (Regulation and Control) Rules, 2000.

Flood occurs when the water inundates normally dry ground, which could happen in a variety of ways like excessive rainfall, overflowing of embankments, dams, rivers, snowmelt, and other factors. Floods are one form of a natural hazard which are difficult to contain and control. A flood susceptibility mapping using Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) techniques were carried out at Sairang village in Aizawl, Mizoram in Northeast India. The study area Sairang is situated on the banks of the Tlawng river, the longest river in Mizoram. Floods have wreaked havoc in Sairang frequently resulting in huge losses and damage to property with numerous loss of life over the years. The total study area is 131.27 sq km and the resulting flood hazard potential zonation map shows that 1/3 of the watershed area falls in Vey High and High Potential Flood Hazard Zonation areas.

The expansion of urbanization and the amplification of anthropic activities in the Rif region require the establishment of wells. However, the irrational exploitation of water and natural conditions have generated the rise of the water table and the increase in pollution. Thus, the assessment of water quality has emerged as a significant concern. This study’s goal is to assess the adequacy of groundwater quality in two aquifers in the vicinity of the Mediterranean Zone - Drouich Province and Oriental Region, Morocco, for drinking water needs by taking 62 water samples of the Kert aquifer for 2019. The Water Quality Index (WQI) classifies water quality: as excellent, good, poor, very poor, etc. That is essential for conveying information about water quality to people and decision-makers in the affected area. The WQI in the Kert aquifer varies from 62.3 to 392.3. The calculation of the water quality index (WQI) of the Kert aquifer view is based that 45.16% of groundwater samples are of poor quality, making them acceptable for drinking. The study’s analysis is established with a geographic information system (GIS) setting. The index map provides decision-makers with a complete and interpretable picture for better water resource planning and management. SVM models are shown to account for 87.71% of the varying water quality score. Different statistical and intelligence models may make the index more predictable. These forecasts assist us in better managing the aquifer’s water quality.

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 atmosphere, hydrosphere, and lithosphere are subjected to different processes, leading to natural hazards like weathering, erosion, floods, cyclones, landslides, earthquakes, tectonic movements, etc. Environmental degradation is a serious aspect of the recent past, mainly due to natural and manmade interactions. The pressure for infrastructure development due to rapid urbanization has led to the expansion of construction activities. It has catapulted the frequency of landslides to dramatic proportions in recent decades, especially along western ghats. The West Coast of India (WCI) has attracted the attention of Geo-scientists due to its neo-tectonic setup, continuing seismic activities, sea-level changes, and also due to environmental degradation. It is followed that very limited attempts have been made related to the land sliding along the west coast tract of Maharashtra. The present investigations are emphasized mainly to locate the landslide vulnerable zones of Shastri River Basin (SRB), Ratnagiri district of Maharashtra by using remote sensing data, GIS techniques along field studies. The area lies within a triple junction of Koyana-Kurduwadi Lineament (KKL), West Coast Fault (WCF), and Panvel Flexure (PF). Based on the integration of data from various thematic maps viz. lithology, lineaments, slope, geomorphology, land use-land cover along with inventory map, Landslide Vulnerable Map (LVM) of the SRB has been prepared. It follows that about 29% area of the SRB forms a highly vulnerable zone for land sliding. These zones are mainly confined to steep slopes, wasteland, highly weathered basalts, and deep valleys and in the vicinity of lineaments.