Air pollution has increasingly become a crucial global issue due to its severe impacts on public health and climate. The substantial increase in air pollution data, gathered by various environmental monitoring systems worldwide, necessitates effective and efficient analytical techniques to understand and interpret such large and complex datasets. In this context, multivariate statistical analysis has stepped in as an indispensable tool to decipher these comprehensive air pollution datasets.
Multivariate analysis involves statistical interpretation of data collected simultaneously on several variables for a set of observational units. Assisted by state-of-the-art computational techniques, it serves to analyze large and complex datasets, visualize multidimensional data, reduce dimensionality, and identify hidden patterns or structures, providing a unique lens to comprehend air pollution data. Multivariate statistical methods have shown their immense potency in elucidating the dynamics of pollutant emissions, identifying the correlations among different pollutants, identifying pollution sources, and decoding temporal and spatial distribution patterns [1]. The specific ways these statistical methods influence air pollution data include:
1. Analyzing the correlation between different types of air pollutants.
2. Gauging the influence of weather patterns on pollution concentrations.
3. Evaluating the potency of air pollution control strategies.
4. Modeling future trends of air pollution based on historical data.
5. Quantifying the health risks stemming from exposure to various pollutants.
Various multivariate analytical techniques are used in air pollution data analysis, including Principal Component Analysis (PCA), Cluster Analysis (CA), and a host of other advanced methods. PCA simplifies information from multiple variables into a reduced set of 'principal components' without losing significant data, while CA groups data objects based on their inherent characteristics. Both help identify dominant patterns, linkages among air pollutants, and clusters reflecting the distribution and sources of air pollution [2].
Fig -1: Visualize the two principal components of PCA
The dispersion of the dots in the plot (Fig-1) provides an overview of how complex, multi-dimensional air pollution data has been reduced and can be interpreted in two primary components. Data points that cluster closely together share similar characteristics in their original multi-dimensional space, while those farther away are more different. Additional analytical tools like Multiple Linear Regression (MLR), Concentration-Weighted Trajectory (CWT) analysis, Positive Matrix Factorization (PMF), Data Envelopment Analysis (DEA), and Artificial Neural Network (ANN) are also instrumental in examining air pollution data. Multiple Linear Regression (MLR) is a statistical technique used to analyze the relationship between two or more independent variables and a dependent variable. The primary goal of MLR is to model the relationship between the predictors (explanatory variables) and response (dependent variable). In the context of air pollution data analysis, let's say we want to know how different factors like PM2.5 concentration, CO concentration, temperature etc., (independent variables) impact the O3 concentration (dependent variable). This relationship could be modelled using MLR. They help create relationships between pollutants and meteorological factors, identify potential pollution sources, quantify contributing pollution sources, evaluate control measure effectiveness, and model and predict pollution dispersion patterns. Despite their utility, the application of multivariate analysis to air pollution data presents several challenges, including handling missing data and outliers, verifying the assumptions of the methods used, and interpreting results, especially when numerous variables are involved [3].
However, advancements in statistical computing and algorithmic models, coupled with the emergence of machine learning and artificial intelligence technologies, offer exciting opportunities to navigate these challenges and enhance the analysis process, opening new avenues for air pollution research. Given the inherent complexities of air pollution data, multivariate statistical methods offer an effective and robust approach to deciphering intricate relationships and patterns. Although challenges still exist in the analysis and interpretation, advancements in data science and high-tech computing are progressively overcoming these, enabling researchers to delve deeper into the dynamics and impacts of air pollution. Such analytical prowess promises to aid the creation of solid, scientifically backed air pollution mitigation policies and strategies.
References:
1. Pedregosa, et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
2. Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9, 90-95.
3. Waskom, M. et al. (2021). seaborn: statistical data visualization. Journal of Open Source Software, 6(60), 3021. https://doi.org/10.21105/joss.03021
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Науковий керівник: Монастирський Любомир Степанович, доктор фізико-математичних наук, професор
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