A Clustering Framework to Reveal the Structural Effect Mechanisms of Natural and Social Factors on PM2.5 Concentrations in China

Abstract

Understanding the mechanisms of various factors that affect PM2.5 can assist in the development of scientific measures to improve air quality. Nevertheless, existing research has concentrated on exploring local effect mechanisms, while structural effect mechanisms at regional or national scales have scarcely been analysed. Consequently, this study presents an analytical framework for elucidating the structural effect mechanisms of associated factors on PM2.5. Geographically and temporally weighted regression was used to explore the local effect mechanisms. This was followed by spatial clustering analysis to reveal these mechanisms by detecting their aggregation patterns. In the analysis, datasets for annual mean PM2.5 and socio-economic factors in China from 1999 to 2016 were employed. Urban population, gross industrial output, and sulphur dioxide emissions were identified as factors affecting changes in PM2.5 concentrations. These three factors had both negative and positive effects, while the gross industrial output had the largest coefficient variation degree. Three geographically related factors exhibited different impacts on PM2.5 concentrations in most of mainland China. These factors were the urban population roughly west of the Heihe-Tengchong line, gross industrial output primarily in southwestern China, and sulphur dioxide emissions primarily in southern China.

Spatiotemporal Distributions of PM2.5 Concentrations in the Beijing–Tianjin–Hebei Region From 2013 to 2020

“…In addition, some machine learning methods were employed to estimate [PM 2.5 ], such as random forest (RF) (Stafoggia et al, 2019;Zhao et al, 2020), artificial neural network (ANN) (Polezer et al, 2018), adaptive deep neural network (SADNN) (Chen et al, 2021), and support vector machine (SVM) (Moazami et al, 2016). However, the parameters in the machine learning models cannot explain the spatiotemporal relationship between PM 2.5 and AOD, owing to an unknown mechanism, causing the model to lack reasoning capability (Yang et al, 2021). The LME + GWR model is weak in dealing with nonlinear relationships between various predictors, but it can accurately capture the spatiotemporal variability of PM 2.5 -AOD, which is better than the LME model and LME + GAM model (Zhang K. et al, 2019;Guo et al, 2021).…”

Influencing factors and trend prediction of PM2.5 concentration based on STRIPAT-Scenario analysis in Zhejiang Province, China

“…China did not include the PM 2.5 index into the scope of routine air quality monitoring before 2012, resulting in the lack of measured value of PM 2.5 . To maintain the continuity of data, the average annual PM 2.5 concentration value in each city from 2006 to 2012 is obtained from the aerosol estimates data of the atmospheric composition analysis group of Dalhousie University, Canada (Weagle et al, 2018 ; Yang et al, 2021 ). The average annual PM 2.5 concentration in each city from 2013 to 2019 comes from the measured values of urban environmental monitoring stations, specifically from the Statistical Yearbook of Zhejiang Province.…”

A Quantitative Modeling and Prediction Method for Sustained Rainfall-PM2.5 Removal Modes on a Micro-Temporal Scale

“…Sustained rainfall processes are often concomitant by changes in the accompanying meteorological factors such as temperature, humidity, wind speed and direction, which will affect the removal effect to varying degrees [33]. Therefore, in this paper, we conside the effect and intensity of the existing influence factors [34][35][36][37], and balance the availabil ity of their own observational data and the predictability of future trends to establish a se of accompanying influence factors F, including the (i) direct factor (FD) to describe rainfal characteristics, and the (ii) indirect factor (FI) to describe environmental characteristics The factors and impact effects are shown in Table 1. When the temperature near the ground is high, atmospheric convection is intensified, which tends to reduce PM2.5 concentrations, and conversely PM2.5 is not easily dispersed Humidity 𝐻 Changes in PM2.5 are closely related to the moisture content of the air, with "hygroscopic increase" occurring due to the adsorption of particulate matter concentrations The quantitative measurement steps are:…”

“…Sustained rainfall processes are often concomitant by changes in the accompanying meteorological factors such as temperature, humidity, wind speed and direction, which will affect the removal effect to varying degrees [33]. Therefore, in this paper, we consider the effect and intensity of the existing influence factors [34][35][36][37], and balance the availability of their own observational data and the predictability of future trends to establish a set of accompanying influence factors F, including the (i) direct factor (F D ) to describe rainfall characteristics, and the (ii) indirect factor (F I ) to describe environmental characteristics. The factors and impact effects are shown in Table 1.…”