The rapid urbanization and industrialization over the past seven decades have led to significant air pollution in large cities. Consequently, the air quality in urban centers has severely declined, posing risks to both human health and the environment [1,2]. Unfortunately, there is a lack of spatiotemporal air quality data for populated areas, hindering data-driven interventions to address environmental deterioration [1]. Regular air quality monitoring is essential to devise suitable strategies to prevent its negative effects on human health and the ecosystem of the area of interest [3,4]. Moreover, these monitoring methods can help track the immediate effects associated with the shift toward sustainable energy transportation systems. The detection and monitoring of trace gases using remote sensing data from satellites offer numerous advantages, such as global coverage for extended periods, enabling researchers to examine the concentration of trace gases over a wider area and map their distribution [5]. Additionally, precise measurements of trace gases at multiple locations help identify sources and sinks, allowing for reasonable budgets to be generated. However, ongoing urbanization and industrialization have complicated the monitoring and control of air quality, particularly in rapidly developing nations, such as China and India. Despite suffering from poor air quality, these nations continue to produce synthetic gases to meet industrial growth without fully understanding the adverse environmental effects [1,6]. The high levels of air pollution in certain regions of Asia, such as South Asia and East Asia, have been associated with higher incidences of respiratory, mental, and other health issues [7,8,9,10]. It is estimated that Asia alone accounts for nearly 6.7 million premature deaths annually to poor air quality [11].

Besides India and China, Pakistan is also suffering from high air pollution levels owing to significant population and economic growth. The largest and fastest-growing sources of air pollution in Pakistan over the past decade have been the automotive and industrial sectors. During the period 2001–2013, the number of vehicles in Pakistan increased by 130% [12,13]. The city of Lahore alone accounts for 23–26% of extra carbon monoxide (CO) emissions due to an inadequate and inefficient mass-transit system [14].

In Lahore and Islamabad, emissions from vehicles significantly contribute to the deteriorating air quality, highlighting the urgent need for interventions. The air pollution crisis in Lahore is worsened by the involvement of 40% of the city’s 7 million registered vehicles, which emit higher than permissible levels of hazardous air pollutants and contribute to smog-related issues. The situation is exacerbated by traffic congestion and the operation of heavy transport vehicles without road-worthiness certification [15]. It underscores the critical need for a transition to green energy in the transportation sector. Leveraging green transportation systems could substantially reduce air pollution and improve public health. Green transportation, which includes electric vehicles, hybrid cars, biofuels, and effective public transit systems, could substantially reduce air pollution and improve public health. It also helps combat climate change by reducing emissions, conserving energy, and promoting efficient resource use [16,17,18]. Pakistan was among the top 10 nations most hit by extreme weather events from 1991 to 2010 [19]. Since 2010, Pakistan has experienced numerous instances of intense heatwaves, torrential rains, and widespread floods. Hence, it is important to explore the chemical composition of the atmosphere over Pakistan by monitoring chemically active trace gases for understanding their impact on surface air temperature, heat waves, and climate change.

Atmospheric pollution is mainly caused by higher concentrations of various trace gas species including CO and oxides of nitrogen (NO𝑥) and sulfur (SO𝑥). The primary emissions from anthropogenic sources are the trace gases such as CO, nitrogen dioxide (NO₂), and sulfur dioxide (SO₂). CO is a hazardous air pollutant that negatively impacts air quality and poses risks to all forms of life. While present in trace amounts, it can severely impair oxygen supply in the body, leading to severe health problems which include drowsiness and irritation in the eyes [20]. The main sources of CO include vehicular emissions, fossil fuel combustion, industry, home heating, and vegetation burning, as well as natural sources like forest fires and volcanoes [2]. NO₂, generated from burning fossil fuels in transportation, industry, and power generation, is another hazardous gas contributing to air pollution. Exposure to NO₂ can cause respiratory symptoms, reduced lung function, and increased cardiovascular risks, and has led to millions of premature deaths globally [21,22]. Similarly, SO₂, generated by natural and human activities such as volcanic eruptions, fossil fuel burning, and industrial operations, directly affects air quality and poses risks to human health, ecosystems, and the environment. Pakistan’s heavy reliance on coal and industrial activities has resulted in high SO₂ emissions, exceeding WHO standards [23]. During the COVID-19 pandemic, many cities experienced lockdown measures, resulting in reduced travel, cutting pollution, fuel consumption, and emissions. Post-pandemic, promoting sustainable options like cycling, electric vehicles, and public transport is crucial for climate mitigation. Cities adopt low emissions zones, shared mobility, and innovative transport for efficient, eco-friendly systems [24,25]. This situation closely resembled a green energy transportation scenario, providing valuable insights into the potential improvements in air quality. Analyzing data from different time periods, including before, during, and after the pandemic, is essential to assess the potential impact of adopting green transportation systems [26]. The Pakistan Environmental Protection Agency (Pak-EPA) is attempting to analyze the concentration of NO₂ in a few Pakistani cities, along with other air quality examinations, but frequent updates are needed to investigate its influence on climate change. By leveraging data from the Sentinel-5P satellite, which measures air pollutants such as NO₂, CO, and SO₂, researchers can obtain frequent, accurate, and comprehensive information on the levels and distribution of these pollutants in urban areas [27].

To effectively address the issue of air pollution and assess the usage of green energy transportation in evaluating air quality, a comprehensive review of the relevant literature was conducted. Monitoring air quality is vital for a sustainable environment, achieved through various methods such as active/passive gas sampling, automatic point monitoring, photochemical/optical sensors, remote optical sensing, and imagery data. These approaches provide a holistic understanding of pollution, enabling precise assessments and targeted interventions. Combined with deep learning, these techniques offer a detailed air quality view, helping policymakers in developing effective pollution control strategies.

In traditional approaches, the active and passive sampling methods involve collecting samples of gases and vapors using pumps, sorbent tubes, or diffusion techniques [28,29]. The other approach that was utilized by the US Environmental Protection Agency was automatic point monitoring to detect and calculate the concentration of selected gases [30]. It provides continuous measurements and real-time data availability, which helps to identify pollution hotspots and develop mitigation strategies.

Traditional air quality monitoring methods have limitations. Active sampling is accurate but expensive, slow, and limited. Passive sampling is less sensitive, delayed, and prone to interference. Automatic point monitoring is costly, fixed, and has technical problems. Despite their usefulness, these methods should be combined with others for a complete understanding of air quality.

Apart from traditional methods, sensor-based systems, like photochemical and optical sensor systems, use light-sensitive sensors to detect pollutants in the air, offering mobility and simultaneous measurement of multiple pollutants [30]. This is especially useful for urban areas with diverse pollution sources. Another sensor-based approach is remote optical monitoring, which employs electromagnetic spectrum measurements to determine pollutant concentrations in real-time [31]. Space-based sensors also utilize image-based monitoring with aerosol optical thickness for assessing air pollutants, using various methods based on the application and available resources [32].

Air quality monitoring using Internet of Things (IoT) sensors allows real-time monitoring of air quality parameters [33]. The Atmospheric Air Surveil System (AASS) is a transportable prototype that uses IoT sensors to monitor parameters like CO and CO₂ in outdoor environments. The AASS system utilizes microcontrollers, gas sensors, and GPS to measure gas concentrations and transmit the processed data to a Data Acquisition unit via MQTT and cloud services. The data are then stored in a remote server, which can be accessed remotely. This cost-effective AASS system offers real-time air quality data for analysis and decision-making.

The aforementioned techniques provide precise air quality measurements at a specific site, but they are restricted by spatial and temporal constraints. To address this, remote sensing techniques have emerged for broader regional and global air quality monitoring. These methods encompass satellite-based sensing, airborne measurements, and mobile ground-based monitoring [34]. Optical, radar, and LiDAR satellites offer high spatial and temporal resolutions, and advanced satellite-based technologies have the potential to provide highly accurate and comprehensive data than traditional ground-based monitoring methods [35,36,37].

Recent improvements in satellite and aerial remote sensing technology have made it possible to collect precise data on air pollution across vast areas [38,39,40]. This aids in precise air quality mapping and trend tracking. Deep learning and machine learning analyze these data for real-time monitoring and prediction; this is crucial for public health in urban areas [38]. These techniques excel due to their capacity to efficiently manage diverse data [39].

In recent years, there has been a growing interest in using machine learning and deep learning techniques for air quality prediction and estimation. Lin et al. used a random forest regression model to forecast PM2.5 and nitrate levels based on road site data [41]. The model showed strong predictive accuracy, gauged by the R-squared value. However, precision depends on data quality and site conditions, potentially limiting applicability to diverse locations.

Shafi et al. [42] utilized K-means clustering to detect abrupt changes in air quality. The method successfully grouped data into clusters based on similarity, detecting notable changes linked to weather and human activities. This highlights the K-means clustering promise in crafting early warning systems to predict air quality shifts. These techniques provide prompt action to counter the adverse effects of pollution on health and the environment.

Choi et al. [43] employed affordable sensors and machine learning to monitor Seoul’s air quality for urban planning. Their model effectively predicted pollutants, like PM2.5 and NO₂, using sensor data. The study underscores the value of budget-friendly sensor-based monitoring and machine learning for the swift identification of pollution areas, providing proactive solutions in air quality management and urban planning.

Li et al. [44] used a machine learning model to assess the impact of clean air actions in improving air quality in Beijing on the basis of data from 2008 to 2017. The findings revealed substantial decreases in pollutants including PM2.5, SO₂, and NO₂ due to these actions. The study underscores the actions’ efficacy while underscoring the necessity for ongoing endeavors to sustain and enhance air quality. Moreover, it showcases machine learning’s utility in gauging the impact of environmental policies on air pollution.

Huang et al. [45] developed an accurate PM2.5 concentration prediction model using remote sensing data and machine learning algorithms. The random forest algorithm performed the best with an R-squared value of 0.80, RMSE of 6.62, and MAE of 4.58. In another study, Banerjee et al. [46] investigated the potential relationship between air pollution, economic growth, and COVID-19 mortality rates in India using machine learning techniques. The study concluded that air pollution levels and economic growth were significant predictors of COVID-19 mortality rates in India. Specifically, a 10 μg/m³ increase in PM2.5 concentrations was associated with a 9.4% rise in COVID-19 deaths, while a 1% increase in gross domestic product (GDP) was linked to a 5.5% decrease in COVID-19 deaths.

Cosemans et al. [47] compared the performance of three machine learning algorithms in predicting air pollutant concentrations at different locations across Europe. Random forest and support vector regression outperformed both linear regression and regularization. Researchers have also proposed a deep learning-based model based on air quality and meteorological data to accurately identify the major sources of air pollution [45,48], which can help policymakers take targeted actions to reduce emissions. Zhang et al. and Zhou et al. [49,50] have developed deep learning-based approaches that utilize satellite remote sensing data to identify the sources of particulate matter pollution with high accuracy.

Besides monitoring air quality, researchers have also attempted to estimate the concentration of pollutants and predict air quality based on measured data. Kow et al. [51] proposed a new approach for air quality estimation using image data and deep learning neural networks, achieving high accuracy in predicting AQI values in real time. Similarly, Sharma et al. [52] reported a novel technique for forecasting PM10 concentrations in the most polluted hotspots in Australia using satellite data and deep learning methods, achieving high accuracy with a mean absolute error of less than 10. Another study by Kurnaz et al. [53] predicted the concentrations of two air pollutants, SO₂ and PM10, in the city of Sakarya in Turkey, with high accuracy. Similarly, Mao et al. [54] have reported a deep learning method for predicting air quality. In another study, the researchers proposed an effective convolutional neural network (CNN) for visual understanding of transboundary air pollution based on Himawari-8 satellite images [55]. The CNN-based model was shown to accurately identify and classify different types of pollutants.

This [56] study presents a novel deep predictive model for accurately predicting spatiotemporal PM2.5 in Los Angeles County using meteorological data, wildfire data, remote-sensing satellite imagery, and ground-based sensor data. The model employs a graph convolutional network (GCN) and a convolutional long short-term memory (ConvLSTM) to learn and predict spatiotemporal correlations in air pollution data. The model achieves state-of-the-art accuracy in predicting hourly PM2.5 at seven sensor locations in Los Angeles County. The root mean square error (RMSE) and normalized root mean square error (NRMSE) decrease over time with later frames, but this is expected as the nature of PM2.5 results in concentrations 24 h in the future being more correlated with 24 h in the past as compared to concentrations 48 h in the future.

Das et al. [57] compared the performance of MLP, RNN, and LSTM models in predicting air pollutants such as PM10 and SO₂. The evaluation metrics used were MSE, RMSE, MAE, and R2. The LSTM model outperformed the MLP and RNN models in terms of accuracy. The study also compared the performance of the proposed model with existing studies in the literature and found that the LSTM model predicted PM10 and SO₂ pollutants with high accuracy. The study provides valuable insights into the use of deep learning models for air pollutant prediction.

In [58], multiple techniques for forecasting air pollution levels using statistical and deep learning methods were used. The data were used from government-built air pollution monitoring stations in Kolkata and evaluated the performance of different models based on two performance indicators, RMSE and MAE. It is observed that Holt–Winter-based forecasting models outperform for PM2.5, PM10, and SO₂ time series, while deep learning-based models, such as ConvLSTM and Bi-LSTM dominate for NO₂ time series data.

Shin et al. [59] present a study on the use of an FCN-based deep learning regression model for real-time indoor air quality monitoring. The dataset is preprocessed to reduce skewness and convert the raw 1D dataset into 2D image input/output datasets, after which the model is trained with various hyperparameters. The results show a decrease in the average prediction error for the MAE and RMSE compared with a deep neural network model.

LSTM and BiLSTM networks excel in air quality forecasting by capturing sequential dependencies, handling missing data, and modeling complex temporal relationships. They retain crucial information from past observations, considering weather and pollution factors, and enhance prediction by incorporating future insights. Optimizing these models requires experimentation, considering data quality, features, and architecture [60].