Random forest model based fine scale spatiotemporal O3 trends in the Beijing-Tianjin-Hebei region in China, 2010 to 2017

Ma, Runmei; Ban, Jie; Wang, Qing; Zhang, Yayi; Yang, Yang; He, Mike Z.; Li, Shenshen; Shi, Wenjiao; Li, Tiantian

doi:10.1016/j.envpol.2021.116635

Cited by 70 publications

(46 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ozone is the main component of smog in Los Angeles; its formation is directly related to automobile exhaust and gas particle conversion due to photochemical reactions. It is also one of the most prominent air pollution problems in the Beijing–Tianjin–Hebei urban agglomeration [ 45 ]. Among the six pollutants in the BTHUA, only the levels of O 3 pollution are increasing, and the increase is more significant in the summer in the southern region with Hengshui as the general center.…”

Section: Discussion and Proposalsmentioning

confidence: 99%

Control Models and Spatiotemporal Characteristics of Air Pollution in the Rapidly Developing Urban Agglomerations

Liang

Wang

2021

IJERPH

View full text Add to dashboard Cite

This paper systematically summarizes the hierarchical cross-regional multi-directional linkage in terms of air pollution control models implemented in the Beijing-Tianjin-Hebei urban agglomeration, including the hierarchical linkage structure of national-urban agglomeration-city, the cross-regional linkage governance of multiple provinces and municipalities, the multi-directional linkage mechanism mainly involving industry access, energy structure, green transportation, cross-regional assistance, monitoring and warning, consultation, and accountability. The concentration data of six air pollutants were used to analyze spatiotemporal characteristics. The concentrations of SO2, NO2, PM10, PM2.5, CO decreased, and the concentration of O3 increased from 2014 to 2017; the air pollution control has achieved good effect. The concentration of O3 was the highest in summer and lowest in winter, while those of other pollutants were the highest in winter and lowest in summer. The high pollution ranges of O3 diffused from south to north, and those of other pollutants decreased significantly from north to south. Finally, we suggest strengthening the traceability and process research of heavy pollution, increasing the traceability and process research of O3 pollution, promoting the joint legislation of different regions in urban agglomeration, create innovative pollution discharge supervision mechanisms, in order to provide significant reference for the joint prevention and control of air pollution in urban agglomerations.

show abstract

Section: Discussion and Proposalsmentioning

confidence: 99%

Control Models and Spatiotemporal Characteristics of Air Pollution in the Rapidly Developing Urban Agglomerations

Liang

Wang

2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…Thus, our model has good prediction performance (R 2 = 0.87) when combined with the initial VOC species. In previous studies using TVOCs, the influence of VOC composition was neglected (Liu et al, 2021;Ma et al, 2021a).…”

Section: Prediction Performance Of the Modelmentioning

confidence: 99%

“…Thus, they are mostly applied to sampling cases with a short time span (days or weeks) (Xue et al, 2014;Ou et al, 2016), and identifying O3 formation sensitivity in a timely manner is difficult. Compared to traditional methods, machine learning (ML) is able to capture the main factors affecting atmospheric O3 formation in a timely manner with great flexibility (without the constraints of time and space) and high computational efficiency (Wang et al, 2020c;Grange et al, 2021;Yang et al, 2021a). Recently, ML based on convolutional neural network (CNN), random forest (RF) and artificial neural network (ANN) models has been applied in simulating atmospheric O3 and shown good performance in O3 prediction (Ma et al, 2020;Xing et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, ML based on convolutional neural network (CNN), random forest (RF) and artificial neural network (ANN) models has been applied in simulating atmospheric O3 and shown good performance in O3 prediction (Ma et al, 2020;Xing et al, 2020). For example, Ma et al (Ma et al, 2021a) simulated O3 concentrations in the Beijing-Tianjin-Hebei (BTH) region from 2010-2017 using an RF model that considered meteorological variables and output variables from chemical transport models, and the correlation coefficient (R 2 ) between the observed and modelled O3 concentrations was greater than 0.8. Liu et al (Liu et al, 2021) also reported a high accuracy (80.4%) for classifying pollution levels of O3 and PM2.5 at 1464 monitoring sites in China using an RF model.…”

Section: Introductionmentioning

confidence: 99%

“…According to these previous studies, the RF model has shown good performance in terms of prediction accuracy and computational efficiency (Wang et al, 2016;Wang et al, 2017b). However, many ML studies have used total VOCs (TVOCs) to simulate O3 formation and rarely considered the effect of VOC species on O3 formation sensitivity (Feng et al, 2019;Liu et al, 2021;Ma et al, 2021a). Thus, they were unable to identify the chemical reactivity of a single species to O3 formation, which may lead to underestimations or even misunderstandings of the role of VOCs in O3 formation because the same concentration of TVOCs with different compositions may lead to different OPEs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Ozone formation sensitivity study using machine learning coupled with reactivity of VOC species

Zhan

Zhang

et al. 2021

Preprint

View full text Add to dashboard Cite

Abstract. The formation of ground-level ozone (O3) is dependent on both atmospheric chemical processes and meteorological factors. Traditional models have difficulty assessing O3 formation sensitivity in a timely manner due to the limitations of flexibility and computational efficiency. In this study, a random forest (RF) model coupled with the reactivity of volatile organic compound (VOC) species was used to investigate the O3 formation sensitivity in Beijing from 2014 to 2016, and evaluate the relative importance (RI) of chemical and meteorological factors to O3 formation. The results showed that the O3 prediction performance using initial concentrations of VOC species (R2 = 0.87) was better than that using total VOCs (TVOCs) concentrations (R2 = 0.77). Meanwhile, the RIs of VOC species correlated well with their O3 formation potentials (OFPs). O3 formation presented a negative response to NOx, PM2.5 and relative humidity, and a positive response to temperature, solar radiation and VOCs. The O3 isopleth curves calculated by the RF model were generally comparable with those calculated by the box model. O3 formation shifted from a VOC-limited regime to a transition regime from 2014 to 2016. This study demonstrates that the RF model coupled with the initial concentrations of VOC species could provide an accurate, flexible, and computationally efficient approach for O3 sensitivity analysis.

show abstract

Measuring the Impact of Industrial Pollution on Population in Beijing, China: An Empirical Analysis Based on the GWR Model

Miao,

2024

Environmental Science and Engineering

View full text Add to dashboard Cite

Random forest model based fine scale spatiotemporal O3 trends in the Beijing-Tianjin-Hebei region in China, 2010 to 2017

Cited by 70 publications

References 28 publications

Control Models and Spatiotemporal Characteristics of Air Pollution in the Rapidly Developing Urban Agglomerations

Control Models and Spatiotemporal Characteristics of Air Pollution in the Rapidly Developing Urban Agglomerations

Ozone formation sensitivity study using machine learning coupled with reactivity of VOC species

Measuring the Impact of Industrial Pollution on Population in Beijing, China: An Empirical Analysis Based on the GWR Model

Contact Info

Product

Resources

About