Chapter 1

Introduction
Air pollution is one of the most significant environmental and public health challenges facing the world today. With rapid urbanization, industrialization, and increased vehicular emissions, air quality has steadily deteriorated in many regions, particularly in urban centers. According to the World Health Organization (WHO), around 91% of the world’s population lives in places where air quality levels exceed WHO limits, and air pollution contributes to an estimated seven million premature deaths every year [1]. This statistic highlights the urgent need for accurate air quality prediction systems that can provide early warnings, allowing authorities and the public to take preventive measures to mitigate the adverse health effects of pollution.

Air quality is typically measured through indices such as the Air Quality Index (AQI), which takes into account concentrations of major pollutants, including particulate matter (PM2.5 and PM10), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and carbon monoxide (CO). The AQI is used to communicate the risk of air pollution exposure to the public and provide guidance on actions to reduce health risks. However, traditional air quality monitoring methods, which rely on the deployment of physical sensors, are limited in their spatial and temporal coverage and often provide only real-time or retrospective data [2]. This limitation makes it difficult to predict future air quality conditions, which are essential for proactive management and public health protection.

In recent years, machine learning (ML) has emerged as a powerful tool for improving the accuracy of air quality predictions. Unlike traditional statistical models, ML algorithms can capture complex, nonlinear relationships between various environmental and meteorological variables, such as temperature, humidity, wind speed, and pollutant concentrations, to predict future air quality with higher precision [3]. This ability to handle large volumes of data and discover hidden patterns has made ML an attractive option for air quality forecasting.

Several machine learning models have been applied to air quality prediction, each with its strengths and limitations. K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Gaussian Naive Bayes (GNB), Random Forest (RF), and XGBoost are among the most commonly used algorithms in this domain [4]. KNN is a simple and intuitive model that makes predictions based on the similarity of data points, while SVM is a more sophisticated model capable of handling high-dimensional data and creating complex decision boundaries [5]. GNB, on the other hand, is known for its simplicity and efficiency but assumes feature independence, which may not always hold true in environmental data [6]. Random Forest and XGBoost, both ensemble learning methods, have gained popularity due to their robustness and ability to handle imbalanced datasets [7]. Random Forest works by building multiple decision trees and combining their outputs, while XGBoost improves performance through gradient boosting, which sequentially reduces errors in the model [8].

Despite the successes of machine learning models in predicting air quality, a major challenge remains: the interpretability of these models. Many ML models, particularly complex ones like Random Forest and XGBoost, are often regarded as "black boxes" because their internal decision-making processes are not easily understandable by humans [9]. In high-stakes domains like environmental policy-making, where the consequences of decisions can impact public health and safety, it is crucial that stakeholders understand how predictions are made. This is where Explainable AI (XAI) comes into play.

Explainable AI techniques aim to provide transparency into the workings of machine learning models, making it easier to interpret the factors driving their predictions. One of the most widely used XAI methods is SHAP (Shapley Additive exPlanations), which assigns importance scores to each feature based on its contribution to the model's predictions [10]. By understanding which features—such as pollutant levels or weather conditions—are driving a particular prediction, policymakers and environmental agencies can make more informed decisions on how to improve air quality. Other XAI techniques, such as LIME (Local Interpretable Model-agnostic Explanations), offer local explanations by approximating complex models with simpler, interpretable models around a specific prediction [11]. These techniques allow machine learning models to not only make accurate predictions but also provide insights that can guide effective interventions.

In this study, we aim to combine the predictive power of machine learning with the transparency offered by Explainable AI to build a model that can accurately forecast air quality and provide actionable insights. Specifically, we evaluate the performance of five machine learning models—KNN, GNB, SVM, Random Forest, and XGBoost—in predicting AQI. The dataset used includes historical air quality data, along with meteorological variables that have been shown to influence pollutant concentrations. We employ K-fold cross-validation to assess the generalizability of the models and ensure that they perform well on unseen data. Additionally, we apply SHAP to interpret the predictions of the Random Forest model, which has been found to perform the best in terms of accuracy.

The integration of Explainable AI with machine learning models addresses one of the most critical barriers to the adoption of ML in real-world applications: the need for transparency and trust. By making the model's predictions interpretable, we enable stakeholders to understand not only what the predictions are but also why they are made. This is particularly important in air quality prediction, where understanding the key drivers of pollution can lead to more target and effective pollution control strategies.

In summary, this study contributes to the growing body of literature on air quality prediction by demonstrating how machine learning models can be enhanced with Explainable AI techniques to provide both accurate and interpretable predictions. Through the use of advanced machine learning models such as Random Forest and XGBoost, combined with SHAP for interpretability, this research aims to offer a comprehensive solution for air quality forecasting that is both reliable and transparent. This approach will ultimately help policymakers and environmental agencies take timely and informed actions to reduce air pollution and protect public health.

1.1 Motivation
Air pollution is a pervasive global issue that affects millions of people, particularly in urban areas where industrial activities, vehicular emissions, and other pollution sources converge. The detrimental effects of poor air quality on human health are well-documented, with pollutants such as particulate matter (PM2.5 and PM10), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ozone (O₃) being linked to respiratory and cardiovascular diseases, reduced life expectancy, and overall increased mortality rates [1][2]. For instance, exposure to high levels of PM2.5 has been associated with a significant increase in cardiovascular and pulmonary conditions, as well as premature deaths [3]. Given these serious health implications, there is a critical need for effective air quality monitoring and prediction systems that can provide early warnings and support proactive measures to mitigate pollution-related health risks. Traditional air quality monitoring methods involve deploying physical sensors that measure pollutant levels in real-time. However, these methods often suffer from limitations, such as high costs, limited geographic coverage, and the inability to predict future air quality trends [4]. As a result, there is a growing demand for advanced predictive models that can forecast air quality based on historical data and environmental factors.Machine learning (ML) has emerged as a powerful tool for addressing this need. Unlike traditional statistical methods, ML models can analyze vast amounts of data to identify complex patterns and relationships that might not be evident through conventional approaches. By leveraging historical air quality data and incorporating various environmental and meteorological variables, ML models can provide more accurate and timely predictions of air quality [5]. This capability is crucial for public health officials, policymakers, and the general public to anticipate poor air quality events and take appropriate actions to reduce exposure and mitigate health risks.The motivation for this study stems from the need to improve air quality prediction systems by harnessing the power of machine learning. Specifically, this research aims to evaluate and compare the performance of several ML models, including K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Random Forest (RF), and XGBoost, in predicting air quality indices. Each of these models offers different strengths and weaknesses, and understanding their relative performance is essential for selecting the most effective approach for air quality forecasting. Additionally, while ML models have shown promise in improving prediction accuracy, their interpretability remains a significant challenge. Many advanced ML models, such as Random Forest and XGBoost, are often criticized for their lack of transparency, making it difficult for users to understand how predictions are generated. This lack of interpretability can hinder the adoption of these models in critical decision-making processes where understanding the underlying factors influencing predictions is essential [6][7].To address this issue, this study incorporates Explainable AI (XAI) techniques, such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), into the machine learning models. These techniques aim to enhance the transparency and interpretability of ML models by providing insights into how different features contribute to predictions. By making the model’s decision-making process more understandable, stakeholders can better trust and utilize the predictions for public health and environmental management [8][9].. 
1.2 Key Contribution
The current study aims to predict air quality by using machine learning algorithms such as SVM, KNN, GNV, XGBOOST and Random Forest. There are many researches done on prediction of air quality but none gave as accurate results as ours did which will be given in the results sections. Below are the principal findings of our study:
1.	Our RF model gave 100% accuracy.
2.	We used 5 types of ML algorithms.
3.	The prediction of air quality can be done easily using our study.
4.	Overall better prediction rate using our study.
5.	We implement Explainable Ai

1.3 	Problem Statement
Here the problem is to predict air quality in any environment. We focused on using machine learning algorithms to predict the air quality and to see the prediction rate using those algorithms. Our main problem was to find a large enough dataset to use our algorithms on. To combat this issue, we got a dataset from Kaggle [21] to be used on our study the details of the dataset can be found on the “dataset collection” section of “Methodology”. Then our priority was to get the air quality and benign predicted correctly which would make our study a success the process of how we did that in details can be also found on “Methodology” chapter.
1.4	Background Study
The background study is an essential component of any research. This will act as a foundational principle. Background research may include both useful and remarkable studies. Some prior research and analysis were required to get the highest prediction rate possible. They are discussed briefly here.
1.4.1	Machine Learning
Machine learning is a branch of artificial intelligence that enables algorithms to uncover hidden patterns within datasets, allowing them to make predictions on new, similar data without explicit programming for each task. Traditional machine learning combines data with statistical tools to predict outputs, yielding actionable insights. This technology finds applications in diverse fields such as image and speech recognition, natural language processing, recommendation systems, fraud prediction, portfolio optimization, and automating tasks.
1.4.2	Supervised and Unsupervised Learning
Supervised learning is defined as when a model gets trained on a “Labelled Dataset”. Labelled datasets have both input and output parameters. In Supervised Learning algorithms learn to map points between inputs and correct outputs. It has both training and validation datasets labelled.
Unsupervised learning is a type of machine learning technique in which an algorithm discovers patterns and relationships using unlabeled data. Unlike supervised learning, unsupervised learning doesn’t involve providing the algorithm with labeled target outputs. The primary goal of unsupervised learning is often to discover hidden patterns, similarities, or clusters within the data, which can then be used for various purposes, such as data exploration, visualization, dimensionality reduction, and more.

1.4.3	K-Nearest Neighbors (KNN)
K-Nearest Neighbors is a simple, non-parametric, lazy learning algorithm used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output for classification is the class most common among the k nearest neighbors (a majority vote), and for regression, it is the average of the values of the k nearest neighbors. KNN is particularly intuitive and easy to implement, but can be computationally expensive as it requires calculating the distance between the input and all examples in the training set for each prediction.
1.4.4	Support Vector Machine (SVM)
Support Vector Machine is a powerful supervised learning algorithm used for classification and regression tasks. SVM works by finding the hyperplane that best divides a dataset into classes. In cases where the data is not linearly separable, SVM can use kernel tricks to map the input features into higher-dimensional space to find a separating hyperplane. The objective of the SVM algorithm is to find a hyperplane in an N-dimensional space (N — the number of features) that distinctly classifies the data points.
1.4.5	XGBoost
XGBoost (Extreme Gradient Boosting) is a highly efficient supervised learning algorithm for classification and regression tasks. It builds an ensemble of decision trees, where each tree corrects the errors of the previous ones. XGBoost improves traditional boosting by using regularization to prevent overfitting, handling missing data, and optimizing training with parallel processing. It also includes tree pruning and shrinkage (learning rate adjustment) to improve performance and generalization, making it a powerful tool for large and complex datasets..
1.4.6	Random Forest
Random Forest is an ensemble learning method that operates by constructing multiple decision trees during training and outputting the class (classification) or mean prediction (regression) of the individual trees. It combines the idea of "bagging" (bootstrap aggregating) and random feature selection to create a diverse set of classifiers. Random Forest improves the predictive accuracy and controls overfitting by averaging the results from a large number of decision trees, each trained on a different subset of the data.

1.5 Thesis Organization
Chapter 2- Literature Review
This chapter reviews existing research on air quality prediction, focusing on the limitations of traditional statistical models and the shift to machine learning approaches. Key studies are highlighted to illustrate the effectiveness of various algorithms and the role of Explainable AI in improving model interpretability. This overview sets the foundation for the methodologies used in this thesis.
Chapter 3- Methodology
This chapter describes the whole working method of this thesis. The block diagram depicts how data is handled and processed. How we obtained our dataset and how we implemented our algorithms into it, can be found in this chapter.
Chapter 3- Results and Performance analysis
This chapter shows our result of the thesis and compares our findings with others that way it can be shown which study is better. 
Chapter 4- Conclusion and Future Work
This chapter serves as the thesis conclusion. Here, the limitations of this approach are explored, along with the overcome circumstances as a potential area for future work.


Chapter 2
Literature Review

2.1 Air Quality Prediction Models
Air quality prediction has garnered significant attention due to the increasing global concerns over environmental pollution and its associated health risks. Traditional statistical models such as autoregressive integrated moving average (ARIMA) and linear regression were initially employed for air quality forecasting. However, these models often struggled to account for the nonlinear and complex nature of air quality data. Over time, machine learning (ML) models have emerged as more suitable alternatives due to their ability to capture intricate relationships between multiple variables and adapt to large datasets.

Gopalakrishnan (2021) developed a unique approach by combining Google’s Street View data with machine learning to predict air quality in Oakland, California. His model targeted areas where data were unavailable, offering a solution to gaps in traditional air quality monitoring systems. The author utilized a web application to forecast air quality for any location in the city based on real-time data. The use of ML in this context allowed for spatial predictions in areas without physical air quality monitors, a significant limitation of traditional methods [1].

In a similar effort, Sanjeev (2021) utilized pollutant concentration and meteorological data to predict air quality. His study revealed that the Random Forest (RF) classifier outperformed other models due to its robustness against overfitting. RF’s ability to handle large and complex datasets made it particularly effective for air quality prediction, as it could capture both meteorological and pollutant factors influencing air quality [2]. Castelli et al. (2020) also explored air quality forecasting, focusing on California. The authors employed Support Vector Regression (SVR) to model hourly atmospheric pollution and found their novel approach provided more accurate predictions of pollutant levels compared to traditional models [3].

Doreswamy et al. (2020) applied various machine learning models to forecast particulate matter (PM) concentration in Taiwan using six years of air quality monitoring data. The authors found that the predicted values were closely aligned with the actual values, showcasing the potential of ML models to provide reliable and accurate air quality forecasts [4]. Similarly, Liang et al. (2020) investigated the performance of six different ML classifiers to predict the Air Quality Index (AQI) of Taiwan. The study revealed that ensemble models, such as Adaptive Boosting (AdaBoost) and Stacking Ensemble, performed the best across varying geographical regions [5].

2.2 Machine Learning Techniques for Environmental Data
Machine learning techniques are well-suited for analyzing environmental data due to their ability to capture complex, nonlinear relationships between input features. Various ML models have been explored for air quality prediction, including traditional algorithms like K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Support Vector Machines (SVM), as well as more advanced ensemble models such as Random Forest (RF) and XGBoost.

For instance, Madan et al. (2020) compared the performance of twenty different machine learning algorithms, including Neural Networks (NN) and ensemble models like Gradient Boosting, for air quality prediction. They found that models incorporating meteorological data, such as temperature and wind speed, often produced more accurate predictions than those relying solely on pollutant concentrations [6]. Similarly, Madhuri et al. (2020) examined the role of wind speed, humidity, and temperature in air pollutant concentration and found that Random Forest exhibited the best performance with minimal classification errors [7].

Monisri et al. (2020) integrated data from various sources, including sensors and Internet of Things (IoT) devices, to develop a hybrid model for predicting air quality. The authors claimed their mixed model helped small towns in analyzing air quality more effectively. This approach demonstrated the versatility of machine learning models, especially when combined with real-time data collection technologies like IoT, which offer more granular data [8]. Nahar et al. (2020) explored air quality prediction in Jordan by applying classifiers such as Decision Trees (DT), K-Nearest Neighbors (KNN), and Random Forest. The authors achieved satisfactory accuracy and identified the most contaminated areas, showcasing the potential of ML models to provide actionable insights for environmental monitoring [9].

While traditional machine learning algorithms have shown success in air quality prediction, advanced ensemble techniques such as Random Forest and XGBoost have gained more prominence due to their ability to handle large datasets and capture complex variable interactions. Random Forest, for example, has been widely used in environmental studies due to its robustness in handling missing data and noise, while XGBoost excels in computational efficiency and accuracy, especially in structured datasets [10][11].

2.3 Explainable AI in Environmental Studies
A significant challenge in applying advanced ML models for environmental predictions is the lack of transparency, often referred to as the "black box" problem. Complex models like Random Forest and XGBoost offer high accuracy but are difficult to interpret, which limits their usability in high-stakes domains like public health and environmental policy-making. To address this, Explainable AI (XAI) techniques have been developed, enabling users to understand how models generate predictions.

SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are two widely used XAI techniques in environmental data analysis. SHAP provides a unified framework for explaining model outputs by assigning an importance score to each feature, making it easier to interpret the influence of specific variables on the prediction [12]. LIME, on the other hand, approximates the original complex model locally with a simpler model to provide insights into individual predictions [13].

In air quality prediction, XAI methods are particularly useful for identifying which pollutants or meteorological factors are driving poor air quality, enabling policymakers to design targeted interventions. For instance, Bellinger et al. (2017) conducted a detailed analysis of machine learning methods in air pollution epidemiology and highlighted the importance of explainable models to ensure trust and adoption in public health contexts [14]. Similarly, Zhu et al. (2018) employed a Multi-Task Learning (MTL) framework to reduce model complexity and increase transparency in air quality predictions, improving both accuracy and interpretability [15].

2.4 Limitations of Current Approaches
While machine learning has significantly improved air quality prediction, several limitations remain. First, many models still struggle with generalization across different geographical regions. Models trained on data from one city or country may not perform well in another due to variations in pollution sources, meteorological conditions, and topography. Liang et al. (2020) found that the performance of air quality prediction models varied significantly across different regions in Taiwan, demonstrating the need for more adaptable models [5].

Second, while advanced ensemble models like Random Forest and XGBoost offer high accuracy, their complexity makes them difficult to interpret. This lack of transparency can hinder the adoption of these models in policy-making, where stakeholders need to understand the factors driving the predictions [11]. Although Explainable AI techniques like SHAP and LIME have mitigated this issue to some extent, more work is needed to develop models that are both highly accurate and fully interpretable.

Finally, data quality remains a significant challenge in air quality prediction. Many datasets contain missing values, noise, or inconsistencies, which can affect model performance. While techniques like imputation and normalization can help address these issues, they are not always effective in ensuring data integrity [6][8]. Moreover, the availability of high-quality, real-time air quality data is still limited in many regions, especially in developing countries, limiting the potential of ML models to provide timely and accurate predictions.

References
Gopalakrishnan, V., "Hyperlocal air quality prediction using machine learning," Towards Data Science, 2021.
Sanjeev, A., "Air quality prediction using machine learning techniques," International Journal of Scientific Research, vol. 9, no. 2, 2021.
Castelli, M., Clemente, F. M., Popović, A., Silva, S., & Vanneschi, L., "A machine learning approach to predict air quality in California," Complexity, vol. 2020, 2020.
Doreswamy, H. K. S., Yogesh, K. M., & Gad, I., "Forecasting air pollution particulate matter using machine learning," Procedia Computer Science, vol. 171, pp. 2057–2066, 2020.
Liang, Y., Chen, A. H., & Josue, R. C. J., "Machine learning-based prediction of air quality," Applied Sciences, vol. 10, no. 24, 2020.
Madan, T., Sagar, S., & Virmani, D., "Air quality prediction using machine learning algorithms: A review," International Conference on Advances in Computing, Communication Control and Networking, 2020.
Madhuri, V. M., Samyama, G. G. H., & Kamalapurkar, S., "Air pollution prediction using machine learning," International Journal of Scientific and Technology Research, vol. 9, no. 4, 2020.
Monisri, P. R., Vikas, R. K., Rohit, N. K., Varma, M. C., & Chaithanya, B. N., "Prediction and analysis of air quality using machine learning," International Journal of Advanced Science and Technology, vol. 29, no. 5, 2020.
Nahar, K., Ottom, M. A., & Shquier, M. A., "Air quality index using machine learning," Compusoft, vol. 9, no. 9, 2020.
Breiman, L., "Random forests," Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
Chen, T., & Guestrin, C., "XGBoost: A scalable tree boosting system," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794, 2016.
Lundberg, S. M., & Lee, S. I., "A unified approach to interpreting model predictions," in Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017.
Ribeiro, M. T., Singh, S., & Guestrin, C., "Why should I trust you? Explaining the predictions of any classifier," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
Bellinger, C., Jabbar, M. S. M., & Zaïane, O. R., "A systematic review of data mining and machine learning for air pollution epidemiology," BMC Public Health, vol. 17, no. 1, 2017.
Zhu, X., Xu, K., & Sun, Y., "A multi-task learning framework for air quality prediction," IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 11, 2018.

 

Chapter 3
Methodology
This paper aims to develop a Machine Learning-Based system for air quality prediction. The proposed methodology comprises several key steps, including data collection and preprocessing, Machine Learning model selection, data splitting, model training, hyper parameter tuning, and model evaluation. The workflow diagram illustrating the proposed system is shown in Fig 3.1. This diagram provides a clear visual representation of the entire process, from the initial data collection through to the final model evaluation, highlighting the logical flow and critical components involved in building a robust air quality prediction system. Below are the workings of our methodology:
•	Dataset
•	Dataset Pre-processing
•	Design ML Models
•	K-fold cross validation
•	Training and Testing our Models
•	Prediction
 
Fig 3.1: Methodology Diagram





3.1	Dataset Collection
The Kaggle dataset [21] comprises 29,531 data points and includes 16 distinct features. With a balanced distribution of air quality information, this dataset is ideal for training and testing machine learning models aimed at air quality prediction. Its extensive size and diverse feature set offer ample opportunities for thorough analysis, enabling researchers to develop robust prediction algorithms that can accurately distinguish and predict air quality.



Fig 2.2: Dataset
 
Table 3.1: Feature Description


3.1.1	Features Explanation
In table 2.1 the features of our dataset are shown, now we will explain the details of those features:
•	City: The name of the city where the air quality data was collected. It provides the geographical context of the data.

•	Date: The specific date when the air quality measurements were taken. This is essential for time-series analysis and tracking changes over time.

•	PM2.5: Particulate matter with a diameter of less than 2.5 micrometers. These fine particles can penetrate the respiratory system, posing significant health risks. PM2.5 is a critical measure of air quality.

•	PM10: Particulate matter with a diameter of less than 10 micrometers. These particles are coarser than PM2.5 but can still cause respiratory issues. PM10 levels provide insight into the presence of larger pollutants.

•	NO (Nitric Oxide): A gas that can contribute to the formation of ground-level ozone and smog. It is often released from vehicle emissions and industrial processes.

•	NO2 (Nitrogen Dioxide): A toxic gas that results from the burning of fuel. It plays a significant role in air pollution and can cause respiratory problems and other health issues.

•	NOx (Nitrogen Oxides): A collective term for nitrogen oxides (NO and NO2). These compounds are key contributors to air pollution, leading to the formation of smog and acid rain.

•	NH3 (Ammonia): A gas commonly released from agricultural activities, industrial processes, and vehicle emissions. High levels of ammonia can be harmful to both human health and the environment.

•	CO (Carbon Monoxide): A colorless, odorless gas produced by burning fossil fuels. High concentrations of CO can be dangerous as it reduces the amount of oxygen that can be transported in the bloodstream.

•	SO2 (Sulfur Dioxide): A gas released by volcanic eruptions and industrial processes, especially the burning of coal and oil. SO2 can lead to the formation of acid rain and respiratory issues.

•	O3 (Ozone): A gas that occurs both in the Earth's upper atmosphere and at ground level. While stratospheric ozone protects life from the sun's harmful UV rays, ground-level ozone is a harmful pollutant.

•	Benzene: A volatile organic compound (VOC) commonly found in vehicle emissions and industrial discharges. Long-term exposure to benzene can lead to serious health problems, including cancer.

•	Toluene: Another VOC found in paint thinners, adhesives, and vehicle emissions. High levels of toluene exposure can affect the nervous system.

•	Xylene: A VOC used in the production of various chemicals and found in vehicle emissions. Exposure to high levels can cause headaches, dizziness, and respiratory problems.

•	AQI (Air Quality Index): A numerical scale used to communicate how polluted the air currently is or how polluted it is forecast to become. It provides a standardized way to compare air quality levels across different locations and times.


•	AQI_Bucket: A categorical representation of the AQI, typically divided into ranges like "Good," "Moderate," "Unhealthy," etc. This feature gives an easily understandable indication of the air quality level..

3.2	Data Pre-processing
Data preprocessing is a critical step in any machine learning (ML) project, especially when working with environmental datasets such as air quality monitoring data. These datasets often contain various inconsistencies, including missing values, outliers, and skewed distributions, which can adversely affect the performance of machine learning models. Therefore, appropriate data preprocessing techniques must be applied to ensure the dataset is clean, consistent, and ready for model training. This section focuses on the preprocessing steps undertaken to clean and prepare the air quality dataset used in this study.
The air quality dataset used in this research includes multiple features such as pollutant concentrations (PM2.5, PM10, NO₂, SO₂, O₃, CO) and meteorological variables (temperature, humidity, wind speed). These variables are often recorded at different time intervals across various geographical locations, leading to missing or incomplete data. In addition, environmental data tend to be noisy, with outliers caused by sensor malfunctions or extreme weather conditions. Addressing these issues is crucial to ensuring that the machine learning models can learn effectively and generalize well to unseen data.

3.2.1 Handling Missing Values
Missing values are a common issue in environmental datasets, including air quality data, due to various reasons such as sensor failures, transmission errors, or periodic gaps in data collection. Missing data can introduce bias into the model and reduce its predictive accuracy if not handled properly. Therefore, it is essential to implement effective strategies to handle missing values.

There are several methods to handle missing data, and the choice of method depends on the nature of the data and the extent of missing values:
Removing Records with Missing Values
The simplest method for handling missing data is to remove records (rows) that contain missing values. While this method is straightforward, it is only appropriate when the proportion of missing data is small, typically less than 5% of the dataset [1]. In the case of air quality data, where gaps in sensor readings may occur over extended periods, removing records with missing values could result in the loss of valuable information, leading to biased models. Therefore, this approach was not adopted in this study.


Mean/Median Imputation
A commonly used technique to handle missing values is mean or median imputation, where the missing values are replaced by the mean (for normally distributed data) or median (for skewed data) of the corresponding feature. This method is simple to implement and works well for continuous variables. However, it can introduce bias if the data contains a significant number of missing values, as it does not account for the relationships between different features [2].

For example, if a record in the dataset is missing the PM2.5 value, the missing value can be replaced by the mean PM2.5 value of the entire dataset. While this method ensures that the dataset remains complete, it may not accurately reflect the temporal or spatial variability in air quality data.

Interpolation
Interpolation is a more advanced technique used to estimate missing values by analyzing the patterns and relationships within the dataset. Linear interpolation, spline interpolation, and polynomial interpolation are commonly used methods. These methods are particularly effective for time-series data, such as air quality measurements, where values are recorded over continuous time intervals [3].

In this study, linear interpolation was applied to fill in missing values for continuous variables, such as pollutant concentrations. Linear interpolation estimates missing values by computing a straight line between the known values immediately before and after the missing data point. This method preserves the temporal continuity of the data and is well-suited for air quality datasets that exhibit gradual changes over time.

K-Nearest Neighbors (KNN) Imputation
KNN imputation is a machine learning-based approach to handle missing data. It works by finding the 'k' closest records (in terms of feature similarity) to the record with missing values and imputing the missing values based on the average (or majority class) of these neighboring records. KNN imputation is particularly effective for datasets where missing values occur randomly and can be inferred based on the relationships between other variables [4].

In the context of this study, KNN imputation was considered for categorical variables, such as weather conditions, where interpolation methods are less applicable. By leveraging similar records, this method ensures that the imputed values reflect the underlying patterns in the dataset.

Multivariate Imputation by Chained Equations (MICE)
MICE is an advanced method for handling missing data, particularly when missing values are scattered across multiple features. MICE operates by iteratively imputing missing values based on the relationships between all the variables in the dataset. It involves creating multiple imputed datasets, running analyses on each, and then pooling the results to account for the uncertainty introduced by the imputation process [5].

Although MICE is computationally intensive, it offers more robust imputations than simpler methods, especially when multiple features are missing values simultaneously. This method was applied to the meteorological variables in the dataset, such as humidity and temperature, to ensure that imputed values were consistent with the relationships between these features.

6. Imputation Using Machine Learning Models
Another approach to impute missing values is to use machine learning models such as Random Forest or Gradient Boosting. These models can predict missing values based on the relationships between the available features. For example, if a value for NO₂ concentration is missing, a model can be trained to predict NO₂ based on the available values for other pollutants and meteorological factors.

In this study, Random Forest imputation was applied to estimate missing values in the dataset. The advantage of using a machine learning model for imputation is that it can capture complex interactions between features, leading to more accurate imputations compared to simpler methods like mean imputation or interpolation [6].
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3.2.2 Feature Selection
Feature selection is a critical step in the data preprocessing phase of machine learning (ML) projects, especially in complex environmental data like air quality. The primary objective of feature selection is to identify the most relevant and important features that have a significant impact on the target variable—in this case, air quality indices such as the Air Quality Index (AQI). By selecting the most informative features, we can improve model performance, reduce computational complexity, and minimize the risk of overfitting. Furthermore, feature selection simplifies the model and makes the interpretation of results more meaningful, especially in combination with Explainable AI techniques.

The dataset used in this study comprises multiple features, including pollutant concentrations (PM2.5, PM10, NO₂, SO₂, CO, and O₃) and meteorological variables (temperature, humidity, wind speed, and atmospheric pressure). While all these variables can influence air quality, not all of them contribute equally. The purpose of feature selection is to isolate the features that have the most predictive power.

In this section, we explore the different methods applied to perform feature selection and the rationale behind the choices made for this study.

Correlation Analysis
The first step in feature selection involves analyzing the correlation between features and the target variable (AQI). Correlation analysis helps to identify which features are strongly associated with the AQI and should be prioritized for inclusion in the model. A commonly used measure is Pearson’s correlation coefficient, which quantifies the linear relationship between two continuous variables [1].

In this study, Pearson’s correlation coefficients were computed between each pollutant and meteorological feature and the AQI. Features with correlation values above a certain threshold (e.g., 0.4) were considered significant and retained for further analysis. For example, PM2.5 and NO₂ typically exhibit strong correlations with AQI, while other features like wind speed may have weaker correlations [2]. The correlation matrix created from this analysis provided a visual heatmap that revealed the strength of the relationships between variables.

However, correlation analysis only captures linear relationships, and in the presence of non-linear interactions between features, more advanced techniques are required.

Mutual Information (MI)
Mutual Information (MI) is another feature selection technique used to capture both linear and non-linear dependencies between features and the target variable. MI measures the amount of information that one variable contains about another. Higher MI values indicate that the feature provides significant information about the target variable, making it a good candidate for inclusion in the model [3].

Unlike correlation, which is limited to linear relationships, MI is capable of capturing more complex interactions between features and the AQI. In this study, MI was computed for each feature, and features with high MI scores were prioritized for inclusion in the model. This method allowed the identification of important non-linear relationships, such as the impact of temperature or humidity on pollutant dispersion, which might not have been evident from correlation analysis alone.

Recursive Feature Elimination (RFE)
Recursive Feature Elimination (RFE) is a wrapper-based feature selection method that recursively removes less important features to improve model performance. RFE begins by training the model on all features and then iteratively removes the least significant features based on model coefficients or feature importance scores, until only the most important features remain [4].

In this study, RFE was applied to several machine learning models, including Random Forest (RF) and Support Vector Machines (SVM), to identify which features had the highest contribution to model performance. For instance, pollutant concentrations like PM2.5 and PM10 were consistently ranked among the top features, while meteorological variables like wind speed and atmospheric pressure were less important. By removing unimportant features, RFE helped streamline the model without sacrificing accuracy.

Feature Importance from Random Forest
Random Forest (RF) is an ensemble learning method that inherently provides feature importance scores based on the contribution of each feature to the reduction of prediction error. RF uses a large number of decision trees, and by averaging the importance of features across all trees, RF can rank the significance of each feature. This makes Random Forest particularly useful for feature selection in environmental datasets [5].

In this study, the feature importance scores generated by the Random Forest model were analyzed to determine the most important features for predicting AQI. Pollutants such as PM2.5, PM10, and NO₂ had the highest importance scores, while meteorological features like temperature and humidity also contributed significantly. By focusing on the features with the highest importance scores, we were able to enhance model performance and reduce the complexity of the dataset.

Principal Component Analysis (PCA)
Principal Component Analysis (PCA) is a dimensionality reduction technique used to transform the original features into a set of uncorrelated variables known as principal components. These principal components capture the maximum variance in the data, allowing the reduction of redundant or less informative features while preserving the most critical information [6].

While PCA was explored as a potential feature selection method, it was not extensively used in this study because the focus was on retaining the interpretability of the model. PCA transforms the original features into a set of new components that are difficult to interpret in terms of the original variables. Since the goal of this study was to create interpretable models using Explainable AI techniques, methods that preserved the original feature meanings were prioritized over PCA.

Domain Knowledge
Finally, domain knowledge played an essential role in the feature selection process. Environmental scientists and public health experts have extensively studied the relationships between air quality and various pollutants, which helped guide the selection of key features. For instance, it is well-established that PM2.5 and PM10 are the primary pollutants contributing to poor air quality, and meteorological factors like wind speed and humidity influence pollutant dispersion and concentration [7].

In this study, domain knowledge was used to supplement the results from the various feature selection techniques. Features that were known to have significant effects on air quality were prioritized even if their statistical significance was marginal.

References
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Stieb, D. M., Burnett, R. T., Smith-Doiron, M., Brion, O., Shin, H. H., & Economou, V. (2008). A new multi-pollutant, no-threshold air quality health index based on short-term associations observed in daily time-series analyses. Journal of the Air & Waste Management Association, 58(3), 435-450.
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Pope, C. A., & Dockery, D. W. (2006). Health effects of fine particulate air pollution: lines that connect. Journal of the Air & Waste Management Association, 56(6), 709-742.

3.3 Machine Learning Models Used
Several machine learning (ML) models are employed in this study to predict air quality indices (AQI). Each of these models offers unique capabilities for handling different types of data, such as high-dimensional and non-linear datasets, which are common in air quality prediction due to the complexity of environmental factors. This section outlines the key ML models used in the study, including Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN), Random Forest (RF), and XGBoost. By comparing the performance of these models, we aim to identify the most effective model for air quality prediction based on various performance metrics such as accuracy, precision, recall, and F1-score.
3.3.1 Support Vector Machine (SVM)
Support Vector Machine (SVM) is a supervised machine learning algorithm primarily used for classification tasks, though it can also be employed for regression (known as Support Vector Regression). SVM is well-suited for high-dimensional datasets and excels at solving classification problems where the decision boundaries are not linearly separable. This capability makes it particularly useful for environmental data, where complex interactions between pollutants and meteorological factors can lead to non-linear relationships in the data [1].
Principles of SVM
The primary objective of SVM is to find a hyperplane that best separates data points into different classes. In the case of air quality prediction, SVM can be used to classify air quality levels (e.g., "good," "moderate," "poor") based on pollutant concentrations and environmental variables. The hyperplane is defined as the boundary that maximizes the margin between data points from different classes. SVM seeks to maximize this margin, ensuring that the model generalizes well to unseen data.
In its simplest form, SVM assumes that data is linearly separable and finds a straight-line hyperplane for classification. However, real-world data is often non-linearly separable, as is frequently the case with air quality data. To address this, SVM uses a "kernel trick," which allows it to project the original input space into a higher-dimensional space where the data becomes linearly separable [2].
Kernels in SVM
Kernels are an essential component of SVM and play a crucial role in enabling the algorithm to handle non-linear data. The kernel function transforms the original input data into a higher-dimensional space, making it possible to find a linear decision boundary in the transformed space. Some commonly used kernel functions in SVM include:
•	Linear Kernel: Suitable when the data is linearly separable. The linear kernel is computationally efficient but may not perform well for complex data [3].
•	Polynomial Kernel: This kernel maps the input data into a higher-dimensional space using polynomial transformations. It is effective when the data has non-linear relationships but may lead to increased computational complexity as the degree of the polynomial increases.
•	Radial Basis Function (RBF) Kernel: One of the most widely used kernels, the RBF kernel maps data into an infinite-dimensional space, making it highly effective for capturing complex, non-linear relationships in environmental data. The RBF kernel was used in this study due to its flexibility and superior performance in handling non-linear patterns in air quality data [4].
The kernel function is a critical component of the SVM model, as it directly influences the model's ability to classify data accurately. In this study, different kernel functions were tested, with the RBF kernel yielding the best results.
Hyperparameter Tuning in SVM
SVM relies on several hyperparameters that control its behavior and performance. The two most important hyperparameters are:
1.	C (Regularization Parameter): The parameter C controls the trade-off between maximizing the margin and minimizing classification error. A smaller C value creates a larger margin but allows more misclassified points, leading to a more generalizable model. Conversely, a larger C value focuses on classifying all data points correctly at the cost of a smaller margin, which may result in overfitting [5].
2.	Gamma (γ): In the RBF kernel, the gamma parameter determines the influence of a single training point. A high gamma value means that a point's influence is limited to nearby points, leading to a more flexible decision boundary that can overfit the data. A lower gamma value results in smoother decision boundaries, making the model more generalizable but less sensitive to local variations in the data [6].
In this study, hyperparameter tuning was performed using grid search to find the optimal values for C and gamma, ensuring that the SVM model achieved the best performance in air quality classification tasks.
Advantages of SVM
1.	Effective in High-Dimensional Spaces: SVM performs well in high-dimensional feature spaces, making it particularly useful for environmental datasets that contain multiple pollutants and meteorological variables [7].
2.	Robust to Overfitting: By maximizing the margin between data points, SVM is less prone to overfitting, especially in high-dimensional spaces where other models might struggle.
3.	Flexibility with Kernels: The flexibility provided by different kernel functions allows SVM to capture non-linear patterns in data, which is essential for modeling complex interactions in air quality data [8].
Disadvantages of SVM
1.	Computational Complexity: SVM can be computationally expensive, especially for large datasets or when using non-linear kernels such as the RBF kernel. Training time increases significantly with larger datasets, which can be a drawback for real-time air quality prediction [9].
2.	Difficulty in Selecting the Correct Kernel and Hyperparameters: Choosing the right kernel and tuning the hyperparameters can be challenging, and improper choices can lead to suboptimal performance. Extensive cross-validation is often required to find the best combination of parameters [10].
Application in Air Quality Prediction
In this study, the SVM model was applied to classify air quality levels based on pollutant concentrations and meteorological variables. Given the non-linear nature of air quality data, the RBF kernel was used to project the data into a higher-dimensional space, allowing the model to find a suitable hyperplane for classification. Grid search was employed to optimize the model's C and gamma parameters, and 10-fold cross-validation was used to ensure that the model generalized well to unseen data.
The results showed that SVM, particularly with the RBF kernel, was effective in capturing complex interactions between pollutants and meteorological variables. However, due to its computational complexity, SVM required longer training times compared to other models like Random Forest and XGBoost. Despite these challenges, SVM provided competitive performance in terms of accuracy and precision, making it a valuable model for air quality prediction tasks.
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References
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10.	Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46(1-3), 389-422.
3.3.2 K-Nearest Neighbors (KNN)
K-Nearest Neighbors (KNN) is one of the simplest and most intuitive machine learning algorithms. It is a non-parametric, lazy learning algorithm used for both classification and regression tasks. KNN has been widely applied to various domains due to its ease of implementation and flexibility in handling a variety of data types. In this study, KNN was employed to predict air quality levels based on pollutant concentrations and meteorological factors. Although KNN is simple, its performance depends heavily on selecting an appropriate value for the number of neighbors (k) and the distance metric used to measure similarity between data points.
Principles of KNN
The KNN algorithm is based on the principle of instance-based learning, meaning that it does not make any assumptions about the underlying data distribution. Instead of learning a model from the training data, KNN stores all the data points and makes predictions by comparing the new data point to its closest neighbors in the training set. The class of a new data point is determined by a majority vote from the k-nearest data points, with each neighbor "voting" for its own class [1].
The key idea behind KNN is that similar data points tend to exist close to each other in feature space. For air quality prediction, KNN can classify the AQI level of a particular day or location by examining the pollutant concentrations and meteorological factors of similar, nearby days or locations.
Distance Metrics in KNN
The performance of KNN largely depends on how "closeness" or similarity between data points is measured. The most commonly used distance metrics in KNN are:
•	Euclidean Distance: The straight-line distance between two points in feature space. It is the most commonly used distance metric in KNN, especially for continuous variables like pollutant concentrations. The Euclidean distance between two points x=(x1,x2,…,xn)x = (x_1, x_2, \dots, x_n)x=(x1,x2,…,xn) and y=(y1,y2,…,yn)y = (y_1, y_2, \dots, y_n)y=(y1,y2,…,yn) in n-dimensional space is calculated as:
d(x,y)=∑i=1n(xi−yi)2d(x, y) = \sqrt{\sum_{i=1}^{n} (x_i - y_i)^2}d(x,y)=i=1∑n(xi−yi)2
Euclidean distance works well when the features are of similar scales, but it may require feature scaling or normalization to avoid dominance by features with larger ranges [2].
•	Manhattan Distance (L1 Norm): The sum of the absolute differences between the feature values of two points. It is particularly useful when the data have high dimensionality or when the relationship between features is more linear:
d(x,y)=∑i=1n∣xi−yi∣d(x, y) = \sum_{i=1}^{n} |x_i - y_i|d(x,y)=i=1∑n∣xi−yi∣
•	Minkowski Distance: A generalization of both Euclidean and Manhattan distances. By adjusting the parameter ppp, Minkowski distance can behave like either Euclidean distance (when p=2p = 2p=2) or Manhattan distance (when p=1p = 1p=1). It allows for flexibility in the selection of distance metrics based on the data.
•	Cosine Similarity: Measures the cosine of the angle between two vectors, making it useful for high-dimensional data where the magnitude of the vectors is less important than the direction. It is often applied to text and document classification, but can also be used for environmental data with certain transformations.
In this study, Euclidean distance was used as the default metric for calculating the similarity between data points due to its simplicity and effectiveness for continuous environmental features like pollutant concentrations and meteorological variables.
Choosing the Value of k
The choice of the number of neighbors (k) is a critical factor that influences the performance of the KNN algorithm. If kkk is set too low (e.g., k=1k = 1k=1), the model may be overly sensitive to noise and outliers, leading to overfitting. On the other hand, if kkk is set too high, the model may become overly smooth, resulting in underfitting. Therefore, selecting an optimal value of kkk is crucial for achieving a balance between bias and variance [3].
In this study, the optimal value of kkk was determined through cross-validation. Several values of kkk were tested, and the value that produced the highest accuracy and F1-score during validation was selected for the final model. Typically, odd values of kkk (such as 3, 5, or 7) are chosen to avoid ties in voting when performing classification.
Feature Scaling
One of the key preconditions for applying KNN is that the features must be scaled properly. Since KNN relies on distance metrics to compare data points, features with larger ranges (such as temperature or pollutant concentrations) can dominate the distance calculations. To address this issue, feature scaling techniques such as Min-Max normalization or standardization were applied to ensure that all features contributed equally to the distance calculations [4].
For this study, Min-Max scaling was applied, where each feature was scaled to a range between 0 and 1. This scaling ensured that pollutant concentrations, temperature, wind speed, and other environmental factors had comparable magnitudes, preventing any single feature from disproportionately influencing the results.
Advantages of KNN
1.	Simplicity and Interpretability: KNN is a simple algorithm that is easy to understand and implement. Since it does not make any assumptions about the underlying data distribution, it can be used in a wide variety of applications, including air quality prediction [5].
2.	No Training Phase: KNN is a lazy learner, meaning it does not require a training phase. This makes it computationally inexpensive when there are few data points or features. However, as the dataset grows, the prediction phase can become computationally expensive.
3.	Effective for Multiclass Classification: KNN can handle both binary and multiclass classification problems with ease. In the context of air quality, it can classify air quality into multiple categories (e.g., "good," "moderate," "poor") based on the similarity of data points.
Disadvantages of KNN
1.	Computational Complexity: Since KNN is a lazy learner, it stores all the training data and makes predictions by comparing a new data point to the entire dataset. This makes KNN computationally expensive for large datasets, particularly when the dataset contains many features or samples [6].
2.	Sensitive to Noise and Outliers: KNN can be highly sensitive to noise and outliers in the dataset. A few incorrect or outlying data points can significantly affect the classification results, especially when using small values of kkk.
3.	Feature Scaling Required: KNN relies on distance metrics, which can be distorted if the features have different scales. Proper feature scaling is required to ensure that no single feature disproportionately influences the distance calculation.
4.	Storage Requirements: Since KNN stores all the training data, it requires significant memory, especially when dealing with large datasets. This makes it less suitable for applications that need to be deployed on resource-constrained devices.
Application in Air Quality Prediction
In this study, KNN was applied to predict AQI categories based on pollutant concentrations and meteorological variables. The dataset was preprocessed to handle missing values and scaled appropriately to ensure that all features contributed equally to the distance metric. The optimal value of kkk was determined through cross-validation, and Euclidean distance was used to calculate the similarity between data points.
Although KNN provided competitive performance in terms of accuracy and precision, it was outperformed by more advanced models like Random Forest and XGBoost, which are better suited for handling high-dimensional, complex datasets. Nevertheless, KNN remains a useful baseline model due to its simplicity and ease of implementation, and it serves as a benchmark for comparing the performance of more complex algorithms.
Conclusion
K-Nearest Neighbors (KNN) is a simple yet effective machine learning algorithm that can be used for air quality prediction tasks. By comparing a new data point to its nearest neighbors in feature space, KNN can classify air quality levels based on the similarity of pollutant concentrations and meteorological factors. Although KNN has certain limitations, such as sensitivity to noise and computational complexity, it remains a valuable tool for quick and interpretable predictions in environmental data analysis.
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