It is known, that the polluted air influences straightforwardly on human wellbeing. Along these lines, the air quality checking surveys the nature of air and recognize defiled territories. Geographic information systems (GIS) provides appropriate tools for the purpose of creating models and describing spatial relationships. This study aims to develop an AQI prediction algorithm based on some meteorological parameters collected using an inverse distance weighted geostatistical technique analysis results, from measurements of three meteorological stations adjacent to the study area Kuala Lumpur of the period June to August 2018. A GIS spatial statistical analysis approach was used. An ordinary least squares (OLS) process was adopted for the 3 months data separately and three models have been obtained. An accuracy value of model performance has been computed were set as (97, 99, and 97%) respectively, specified thru the analysis. So as to test the model, validation applied again using predicted AQI and compared them with observed AQI data, the accuracy was set as (96, 99, and 93%), respectively. The result indicated a very good fit of the OLS model to the observed points, verified that the consequences of these analyses are able to monitor and predict AQI with high accuracy.

This paper proposes a UAV-based PM_2.5 air quality and temperature-humidity monitoring system. The system includes an air quality detector comprising four Arduino sensor modules. Specifically, it includes a dust (DSM501A) sensor and a temperature and humidity (DHT11) sensor. The NEO-6M GPS module and DS3231 real-time module are also included for input visualization. A DIY SD card logging shield and memory module is also available for data recording purposes. The Arduino-based board houses multiple sensors and all are programmable using the Arduino integrated development environment (IDE) coding tool. Measurements conducted in a vertical flight path show promise where comparisons with ground truth references data showed good similarity. Overall, the results point to the idea that a light-weight and portable system can be used for accurate and reliable remote sensing data collection (in this case, PM_2.5 concentration data and environmental data).

This research aims to calculate the rate of rainfall for two consecutive years, 2018 and 2019, to study and analyze the drought periods during these years. The research was conducted in Kirkuk province north of Iraq, by dividing the study area into five regions based on five stations of rainfall records. Two types of data used in investigations; remotely sensed daily precipitations from integrated multi-satellite retrievals and monthly precipitations average rate from NASA power application. The methodology involved the calculation of rainfall rats by three methods; Mean, Thiessen’s polygons, and the Isohyetal technique. Data were analyzed, and final maps were produced using Geographic Information Systems. For the year 2018 the average annual rainfall rate produced by Mean, Thiessen’s polygons, and Isohyetal technique were; 1.376, 1.341, and 1.343 respectively. But, for the year 2019 the average annual rainfall rate produced by Mean, Thiessen’s polygons, and Isohyetal technique were; 0.966, 0.932, and 0.882 respectively. The results showed the scarcity of water and the lack of rainfall during the study years, which has a great impact on the region's exposure to drought, represented by the drying up of rivers.

This study is an effort to spatially assess the air quality in Iraq. A statistical model was applied to predict air quality and determine the relationships of air pollutants with the Air Quality Index (AQI) in 2020. Remotely sensed and ground station data were used in this study. Three methods have been applied; spatial interpolation, Least Square (LS) statistical method, and Geographically Weighted Regression (GWR) model. Analysis revealed that air quality was unhealthy in Iraq. The northern part showed moderate air quality whereas contamination levels were high in most of the southern part of Iraq. Predictive (LS), and (GWR) models were developed. The resulting correlation coefficient R2 was equal to 0.88 and 0.85 for LS and GWR respectively. Accuracy evaluations were 95% and 94%. The results of the study have the potential to assist the related government departments and disaster management authorities in decision-making and urban air contamination risk reduction.

In this paper, we assess the extent of environmental pollution in terms of PM2.5 particulate matter and noise in Tikrit University, located in Tikrit City of Iraq. The geographic information systems (GIS) technology was used for data analysis. Moreover, we built two multiple linear regression models (based on two different data inputs) for the prediction of PM2.5 particulate matter, which were based on the explanatory variables of maximum and minimum noise, temperature, and humidity. Furthermore, the maximum prediction coefficient R² of the best models was 0.82, with a validated (via testing data) coefficient R² of 0.94. From the actual total distribution of PM2.5 particulate values ranging from 35–58 μg/m³, our best model managed to predict values between 34.9–60.6 μg/m³. At the end of the study, the overall air quality was determined between moderate and harmful. In addition, the overall detected noise ranged from 49.30–85.79 dB, which inevitably designated the study area to be categorized as a noisy zone, despite being an educational institution.

Sentinel-2 satellites, equipped with board-mounted multispectral payloads, provide images of the Earth in the wavelengths of the electromagnetic spectrum. These sensors can be used on Earth's surface to observe it with a very high spatial resolution, and there is a wide range of near-infrared and non-infrared spectral bands. These spectral ranges allow for a wide array of purposes, like assessing vegetation health land use and land cover changes, coastal and inland water quality assessment, and making disaster maps. In this case study, we mapped and analyzed different types of Sentinel-2 images that imaged Kirkuk city landscape in Iraq. The methodology is based on Artificial Intelligence (AI) analyzing patterns of Sentinel hub data and geographic information systems (GIS)-based mapping. Furthermore, minimum noise fraction (MNF) and pixel purity index (PPI) were applied on each image to enhance the spectral unmixing accuracy which provides higher precise endmember spectra. A deep neural remote sensing has been used for 10 m resolution Sentinel-2 image segmentation of Kirkuk. This produces such a result where features are exactly distinguished and identified. This is performed by automatically detecting and classifying features on the image to provide better identification and estimation. Analyzing received data through Sentinel-2 provides us with the basis to identify such patterns, trends, and insights as key facts supporting urban planning, environmental sustainability, and so much more. Received: 25 March 2024 | Revised: 31 May 2024 | Accepted: 23 August 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Huda Jamal Jumaah: Conceptualization, Methodology, Formal analysis, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Aydin Adnan Rashid: Software. Serri Abdul Razzaq Saleh: Validation. Sarah Jamal Jumaah: Investigation.