1. Introduction
Air pollution is currently a major environmental challenge for both developed and developing countries worldwide, with increasing industrialization, growing urbanization and energy consumption posing a serious threat to public health [
1,
2]. According to a report from the World Health Organization, PM
2.5 is unanimously considered to be an important indicator of air quality [
3,
4]. The rainfall processes are typically regarded as strong drivers to remove PM
2.5 so as to improve the air quality.
However, in the existing available research on an observation data analysis, there are obvious inconsistency between rainfall and the PM
2.5 removal effect, especially for sustained rainfall, a kind of typical but complex rainfall process in southern China with an uncertain duration and intervals. For example, Kao et al. [
5] found that in a rainforest environment, summers with high precipitation are negatively correlated with PM
2.5 levels; Preethi et al. [
6] proposed that the effect of simulated Indian monsoon rainfall removal depends to a large extent on climatic wind speeds; Neal et al. [
7] pointed out that there has been no systematic improvement in air quality in mid Wales for 17 years in the face of increased rainfall. In addition to the complexity of the sustained rainfall itself, the variations in regional environmental conditions are possibly another important reason. Therefore, studying the relationship between sustained rainfall and the PM
2.5 removal effect becomes a multi-factor related scientific issue.
When expressing the effect of rainfall on the removal of PM
2.5 concentrations in the air, the observed time series is usually the most intuitive expression [
8,
9]. The observed value will be considered as a sign of the presence of the rainfall process [
10]. However, sustained rainfall, which is characterized by two or more sustained rainfall events within a given period of time, is intermittent, slow to change and uncertain in its length of formation, and its complexity needs to be fully considered [
11,
12]. During sustained rainfall, the variation of PM
2.5 concentrations in hour-level time series is diverse and complex. However, existing analytical methods mainly examine overall removals at the annual/monthly time scale and mainly use the correlation analysis (CA) between rainfall amount and PM
2.5 concentration based on macroscopic monitoring data, which refers to the rainfall time series and PM
2.5 time series sampled on an annual or monthly temporal scale [
13,
14]. For example, using the data of a macro-temporal scale, Shaibu et al. [
15] confirmed that monthly PM
2.5 concentrations in the Niger Delta region of Nigeria show a significant positive correlation with monthly rainfall. Similarly, through a study of annual datasets from five air quality monitoring stations in Bahrain from 2006 to 2012, Jassim et al. [
16] indicated little correlation between rainfall and PM
2.5, leading to a year-on-year increase in PM
2.5 concentrations. They are all missing a quantitative analysis mode that applies micro-scale time data to describe the removal phenomenon. In order to further achieve air quality prediction and prevention in the short term, it is necessary to analyze its micro-temporal removal effect for atmospheric environment quality forecasting.
In addition, due to the sustained rainfall and the atmospheric pollution particulate matter itself being complex, the large-scale temporal analysis lacks guidance for specific sustained rainfall-PM
2.5 removal processes [
17]. On this basis, a part of the study proposes to extract the historical single rainfall process using hourly observation data and adopt a predetermined calculation model to quantify the removal effect of the rainfall process: Chhavi et al. [
18] analyzed the wet removal effect by calculating the PM
2.5 concentration difference before and after rainfall, including the positive and negative removal; Kapwata et al. [
19], based on the intensity of rainfall, delineate the rainfall classes, counting the percentage of positive removal to summarize the removal effect according to the influence factors, such as rainfall duration and rainfall volume, which have certain guiding significance. The above methods do not take into account the changes in the effects produced by complex sustained rainfall processes at different stages, including effects such as hygroscopic growth and the secondary transformation of gaseous pollutants which cause PM
2.5 concentrations to rise or rebound [
20,
21,
22]; at the same time, they lack universal law exploration, having difficulties in serving the scientific prediction and early warning of air quality systems and active prevention [
23,
24].
In this paper, we propose a quantitative modeling and prediction method for sustained rainfall-PM2.5 removal modes on a micro-temporal scale. The detailed contributions are as follows:
A novel micro-scale analytical framework for quantitatively elucidating the mechanism of PM2.5 removal by sustained rainfall was proposed. Compared with the yearly, monthly and daily time scales, the hourly scale is a more suitable form of information for decision making; therefore, the framework would more clearly express the complex characteristics of sustained rainfall than the analysis methods of large-scale data. The innovative hourly scale data analysis in this paper is more useful for practical applications in predicting and assessing air quality.
A set of quantitative PM2.5 removal modes based on a micro-analysis are proposed. The modes would highlight the specific and high-level patterns of the removal effect of sustained rainfall at the micro-scale than the traditional micro-scale data analysis methods. During sustained rainfall, the variation of PM2.5 concentrations in an hourly time series is diverse and complex. The analysis of hourly scales reveals new characteristic modes that are different from the traditional large scale. These "declining, rebounding, or rising" modes not only allow the analysis of historical data from different regions, but also allow the prediction of PM2.5 removal at hourly intervals using future hourly rainfall, which can help the relevant systems and departments to make timely decisions on air pollution control.
This paper is organized as follows. The study area and data on analytical framework are viewed in
Section 2.
Section 3 introduces the quantitative definition of the sustained rainfall-PM
2.5 removal mode on a micro-temporal scale, then presents the rainfall-PM
2.5 removal phenomenon predicting algorithm based on the quantitative model and
Section 4 discusses the experimental results. Finally, the discussions are presented in
Section 5.
5. Discussion
In this paper, we mainly analyze the effect of sustained rainfall on PM2.5 removal from the perspective of a microscopic temporal scale with historical observation data at the hourly scale. By combining previous research results, we propose a model analysis framework to quantitatively describe the removal effect from a more refined perspective for combining the mechanism of sustained rainfall effect on PM2.5. The primary conclusions are summarized as follows.
In this paper, we use hourly scale observations for proposing models to quantitatively express sustained rainfall processes with intermittent duration and relative complexity. It is able to provide data boundaries for studying the role of rainfall removal. Moreover, we consider a large number of environmental influences and construct a concomitant factor model F, which can improve the accuracy and information dimension of the analysis. Based on the above considerations, we conclusively propose the removal modes for a quantitative description of the removal phenomenon.
, the PM2.5 concentration change has a continuous decreasing trend during the rainfall process, which has good improvement of the air quality for a period of time after the precipitation.
, the PM
2.5 concentration change is due to the fact that when the removal of particulate pollutants by prolonged precipitation reaches its limit [
29], a small portion of the particulate matter does not completely settle to the ground and floats into the air again, thus showing a slight rebound of the concentration values.
, PM2.5 concentrations continue to rise during rainfall, but drop sharply after the end and are lower than the average concentration values before it.
, PM2.5 concentration changes in a continuous upward trend when the rainfall duration is too short or small; the humid air will make the suspended pollutants expand, which is more likely to cause the accumulation of pollutants and make the PM2.5 concentration rise.
, due to the longer duration of the process, there is often a short gap or the secondary precipitation is weak precipitation and other phenomena, which will cause a serious concentration rebound, making the concentration of particulate matter higher than before the precipitation.
ML, PM2.5 concentrations continue to rise without rebound during rainfall, and the rise tends to scale off after the end, eventually making the PM2.5 concentrations rise.
The method in this paper is able to classify the proposed model by historical observation data. The results show that of the sustained rainfall processes occurring in Nanjing from 2016 to 2020, only 85 were able to provide complete removal of PM2.5, 63.4% of the precipitation processes resulted in PM2.5 rebound and up to 177 sustained rainfall processes ultimately led to elevated PM2.5 concentrations.
Based on the above quantitative modeling framework, we construct a classifier in combination with the model identification method, considering it for the accurate forecasting of future air quality in short periods. The accuracy evaluation results of the model show that the ROC of our constructed classifier performs well, and the AUC refers to more than 0.85, showing the reasonableness and effectiveness of the method in this paper.
Due to the limited data acquisition, more years of hourly and environmental data for PM2.5 are lacking in this paper. Therefore, it lacks the samples of the lasting ascent mode and the delayed removal mode. Future studies are expected to obtain more hourly temporal observations for the purpose of removal modes construction and acquisition, ultimately to improve the accuracy of the prediction classifiers.
6. Conclusions
Rainfall is an effective way to remove major air pollutants such as PM2.5. However, most studies on the relationship between rainfall data and PM2.5 concentrations have only focused on the changes in the air quality under the influence of long-time span rainfall, ignoring the effects of single rainfall processes that lead to increases or rebound changes in the PM2.5 concentrations, in addition to the effect of wet deposition.
Therefore, based on the definition and generalization of the sustained rainfall process and its effects, this paper uses a time-series statistical method to extract and calculate the single sustained rainfall process and its removal effect factors based on microscopic time-scale observation data; in this process, the effect evaluation index is specifically proposed to quantitatively describe the degree of the removal effect, so as to establish a time-series effect model of PM2.5 concentration removal by rainfall. The potential, deep-seated effect of rainfall processes on PM2.5 concentrations is explored. The model is further combined with pattern recognition theory to design an effect pattern classifier for the sample characteristics of the rainfall process, and finally realize the micro-temporal prediction of air quality after a single rainfall. Using the hourly observation data of Nanjing from 2016 to 2020, a total of 427 sustained rainfall processes were collected using this micro-temporal time-series effects model, and the rate of process, rate of rebound and rate of final were calculated and classified into six types of modes: 85 totally removal mode, 191 partly removal mode, 14 delayed removal mode, 85 rebounding sscend mode and 12 lasting ascent mode. The classifier was constructed based on the factors, indicators and model categories, and the ROC evaluation index showed that the classifier has good performance and is capable of quantitatively predicting future PM2.5 concentration decreases, increases and rebound effects using easily accessible rainfall and PM2.5 concentration forecast information, with a view to providing decision-making information for future regional ambient air quality forecasting and refined control. For the acquisition of hour-by-hour PM2.5 concentration forecasts, further investigation of the finer variation characteristics within rainfall periods is required on the basis of this study.