• Gundeti Srinivas Reddy
• Mirthipati Kesavarao
• Gundeti Chinnesh
• Prakash Singh Tanwar

There has been a growing global concern about air pollution in the past decade, especially in major urban areas. Quality-measuring and estimating air quality research has recently grown in importance. Due to the Internet of Things (IoT) being applied in various sectors, monitoring pollution levels becomes simpler. Fixed sensors are insufficient to provide a complete view of air pollution around people, as the sensors must be situated far away from the focus of the pollution source. We're using both stationary and mobile sensors to study the air quality. With our methodology, we can collect and measure air quality in all parts of the spectrum. We show that our approach is viable by successfully measuring and predicting air quality with out-of-of-of-the-the-the-box creativity After conducting an investigation, we believe we've found In a smart city, this may be a promising technology for air quality monitoring. A "time series prediction" combined with "air quality measurement".

spatial pollution, air pollution, real time monitoring, pm 2.5, pm 10, data analysis, machine learning, iot, python, algorithms, predictive analysis,

"Spatial Temporal Air Pollution Prediction Through Fixed Devices", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 5, page no.b610-b622, May-2021, Available :http://www.jetir.org/papers/JETIR2105210.pdf

"Spatial Temporal Air Pollution Prediction Through Fixed Devices", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 5, page no. ppb610-b622, May-2021, Available at : http://www.jetir.org/papers/JETIR2105210.pdf