###### Abstract  
Accurate air quality forecasting is important in pollution prevention and risk reduction. Effective short-term and long-term forecasting models are needed. This study investigated the need for a new model to forecast air pollution concentrations in three cities with distinct characteristics: a city with high industrial activity, a city with a high population density and urbanization, and an agricultural city. The air pollution data were collected using the Sentinel-5P satellite and Google Earth Engine to apply descriptive analysis and comparison of two years, 2022 and 2023. The studied cities were Al Riyadh (high population), Al Juhail (industrial), and Najran (agricultural) in Saudi Arabia. The selected pollutants were SO\({}_{2}\), NO\({}_{2}\), CO, O\({}_{3}\), and HCHO. In addition, this study investigated the variations observed in all the pollutants during the months of the year, the correlations between the contaminants, and the correlation between NO\({}_{2}\) and the meteorological data. Based on our findings, Al Juhail had the highest level of all the pollutants during the two years, except for NO\({}_{2}\), for which the highest level was observed in Al Riyadh, which has witnessed notable urbanization and development recently. Moreover, this study developed a forecasting model for the concentration of NO\({}_{2}\) based on weather data and the previous values of NO\({}_{2}\) using Long Short-Term Memory (LSTM) and Time2 Vec. The modeling proved that any model that is trained on data collected from a specific city is not suitable for predicting the pollution level in another city and the level of another pollutant, as the three cities have different correlations with the pollutants and the weather data. The proposed model demonstrated a superior accuracy in predicting NO\({}_{2}\) concentrations compared to traditional LSTM models, effectively capturing temporal patterns and achieving minimal prediction errors, which contributes to ongoing efforts to understand the dynamics of air pollution based on cities' characteristics and the period of the year. air pollution; deep learning; time-series; LSTM; prediction; Time2 Vector 20251621103390/4/03/16/2021110390/1/2025162110390/1/2025162110390/1/2 The WHO reports that approximately 4.2 million premature deaths worldwide are caused by exposure to these pollutants [3]. The air pollutants enter the respiratory tract and deposit in the lung area, causing serious diseases such as lung cancer, heart disease, and respiratory infections such as pneumonia [4]. Other factors that affect poor air quality include high temperature, humidity, population growth, and wind speed. Air pollution is a major problem in Saudi Arabia's cities, as it is one of the largest energy-producing countries. The Saudi Green Initiative has contributed to promoting clean energy and reducing emissions in line with Vision 2030 for a better quality of life. Because air pollution is an important indicator of the quality of life in any city, this study aims to study the air pollution levels in three cities with different industrial, agricultural, and population density characteristics, as these characteristics affect the air pollution level [5]. Moreover, this study aims to build a deep learning model to predict air pollution levels based on observed data for the three cities considered in this study. These three cities were Al Riyadh, Al Juhail, and Najran. Al Riyadh was chosen because it is densely populated in the center of Saudi Arabia and is the country's capital and largest city. Rapid urban growth has brought challenges, such as high levels of air pollution due to emissions from vehicles, industries, and construction projects, which are exacerbated by the city's population density [6]. It has a hot desert climate with long, very hot summers, mild winters, and little yearly rainfall. Al Juhail is an industrial city on the eastern coast of Saudi Arabia along the Arabian Gulf. It is a major industrial center with petrochemical plants and manufacturing facilities [7].