Air Quality Research Based on B-Spline Functional Linear Model: A Case Study of Fujian Province, China

Abstract

It is generally accepted that air quality is closely related to human health. In this study, to investigate the dynamic characteristics of air quality and explore the driving factors of air pollution, the Air Quality Index (AQI) and concentration data of six air pollutants (CO, NO2, O3, PM2.5, PM10, and SO2) were fitted to functional curves using the B-spline basis function. Compared with discrete data, functional data can better express the dynamic characteristics of data and reduce information loss. Additionally, functional clustering based on the principal component coefficient was established to analyze the spatiotemporal dynamic characteristics of air quality, and a functional linear model was established to analyze the relationship between pollutants and anthropogenic factors. The results showed that air pollutants in Fujian Province were found to have certain temporal and spatial heterogeneity, among which the seasonal characteristics of NO2 and O3 (high in summer, low in winter) were opposite to those of the other pollutants considered. The spatial distribution of air pollution was low (high) pollution in inland (coastal) areas, and the primary air pollutants in Fujian Province were PM10 and PM2.5. The functional linear model indicated that anthropogenic factors (e.g., vehicle numbers and emissions of industrial NOX emissions) were found to have a notable impact on air pollutants. The findings of this study could act as a reference in support of air pollution control.

Advancing sustainable air quality through calibration of miniature air quality monitors with SRA-SVR combined model

“…Exploratory analysis is the first step in data analysis and aims to understand the characteristics, trends and relationships of the data (Liu et al, 2014;Xu et al, 2023). Since the six pollutants are studied in the same way, we randomly select PM 2.5 for the study and the process for the rest of the pollutants can be given similarly.…”

Spatio-Temporal Dynamics and Haze Agglomeration Analysis in the Beijing-Tianjin-Hebei Region: A WOA-LSTM Approach

“…This model employs meteorological factors as inputs, processing them through a multilayer neural network optimized via backpropagation algorithms. Xu et al [10] conducted an air quality study in Fujian Province, China, employing a B-spline functional linear model. This model elucidated the relationship between pollutant concentration, temporal factors, and meteorological conditions, providing a scientific basis for targeted air quality improvement strategies.…”