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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.
Mohammed Hashim Ameen; Huda Jamal Jumaah; Bahareh Kalantar; Naonori Ueda; Alfian Abdul Halin; Abdullah Saeb Tais; Sarah Jamal Jumaah. Evaluation of PM2.5 Particulate Matter and Noise Pollution in Tikrit University Based on GIS and Statistical Modeling. Sustainability 2021, 13, 9571 .
AMA StyleMohammed Hashim Ameen, Huda Jamal Jumaah, Bahareh Kalantar, Naonori Ueda, Alfian Abdul Halin, Abdullah Saeb Tais, Sarah Jamal Jumaah. Evaluation of PM2.5 Particulate Matter and Noise Pollution in Tikrit University Based on GIS and Statistical Modeling. Sustainability. 2021; 13 (17):9571.
Chicago/Turabian StyleMohammed Hashim Ameen; Huda Jamal Jumaah; Bahareh Kalantar; Naonori Ueda; Alfian Abdul Halin; Abdullah Saeb Tais; Sarah Jamal Jumaah. 2021. "Evaluation of PM2.5 Particulate Matter and Noise Pollution in Tikrit University Based on GIS and Statistical Modeling." Sustainability 13, no. 17: 9571.
This paper proposes a UAV-based PM2.5 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, PM2.5 concentration data and environmental data).
Huda Jumaah; Bahareh Kalantar; Alfian Halin; Shattri Mansor; Naonori Ueda; Sarah Jumaah. Development of UAV-Based PM2.5 Monitoring System. Drones 2021, 5, 60 .
AMA StyleHuda Jumaah, Bahareh Kalantar, Alfian Halin, Shattri Mansor, Naonori Ueda, Sarah Jumaah. Development of UAV-Based PM2.5 Monitoring System. Drones. 2021; 5 (3):60.
Chicago/Turabian StyleHuda Jumaah; Bahareh Kalantar; Alfian Halin; Shattri Mansor; Naonori Ueda; Sarah Jumaah. 2021. "Development of UAV-Based PM2.5 Monitoring System." Drones 5, no. 3: 60.
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.
Huda Jumaah; Mohammed Hashim Ameen; Bahareh Kalantar; Hossein Mojaddadi Rizeei; Sarah Jamal Jumaah. Air quality index prediction using IDW geostatistical technique and OLS-based GIS technique in Kuala Lumpur, Malaysia. Geomatics, Natural Hazards and Risk 2019, 10, 2185 -2199.
AMA StyleHuda Jumaah, Mohammed Hashim Ameen, Bahareh Kalantar, Hossein Mojaddadi Rizeei, Sarah Jamal Jumaah. Air quality index prediction using IDW geostatistical technique and OLS-based GIS technique in Kuala Lumpur, Malaysia. Geomatics, Natural Hazards and Risk. 2019; 10 (1):2185-2199.
Chicago/Turabian StyleHuda Jumaah; Mohammed Hashim Ameen; Bahareh Kalantar; Hossein Mojaddadi Rizeei; Sarah Jamal Jumaah. 2019. "Air quality index prediction using IDW geostatistical technique and OLS-based GIS technique in Kuala Lumpur, Malaysia." Geomatics, Natural Hazards and Risk 10, no. 1: 2185-2199.