PM2.5 and O3 concentration estimation based on interpretable machine learning

Wang, Siyuan; Ren, Ying; Xia, Bisheng

doi:10.1016/j.apr.2023.101866

Cited by 11 publications

(3 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of factors, the main focus is on natural factors-ranging from precipitation [35], vegetation [36], wind speed [37], and per capita GDP [30] to urbanization [38], digital economy [39,40], and other socio-economic factors-to analyze their effects on air pollutant concentrations. The specific factor research methods include GWR [41], machine learning [23,42], GeoDetector [43], and spatial metrics [44].…”

Section: Introductionmentioning

confidence: 99%

Synergistic Evolution of PM2.5 and O3 Concentrations: Evidence from Environmental Kuznets Curve Tests in the Yellow River Basin

Qi,

Miao,

Xie

et al. 2024

Sustainability

View full text Add to dashboard Cite

Air pollution, especially the synergistic pollution of PM2.5 and O3, poses a severe threat to human life and production. The synergistic formation mechanism of PM2.5 and O3 pollution is relatively confirmed, while research on their spatiotemporal synergy is urgent. Based on remotely sensed interpretation data from 90 cities in the Yellow River Basin, we analyzed the synergistic evolution of PM2.5 and O3 concentrations during 2013–2020. Combined with the environmental Kuznets curve, we performed factor analysis using a panel regression model. The synergistic evolution pattern shows a gradual decrease in PM2.5 concentration and a gradual increase in O3 concentration. There is a strong spatial auto-correlation in the two pollutants’ concentrations. The relationship between economy and PM2.5 concentration shows an “N-shaped” curve, while that between O3 concentration and economic development presents an inverse “N-shaped” curve. The environmental Kuznets curve shows that the deterioration of O3 pollution takes place later than the mitigation of PM2.5 pollution. Various factors have obvious heterogeneous effects on PM2.5 and O3 concentrations. Meanwhile, the sensitivity effect of per capita GDP on PM2.5 concentration in the midstream region is stronger than that in the upstream region, while the sensitivity effect of per capita GDP on O3 concentration is strongest in the midstream region than that in upstream and downstream region.

show abstract

Section: Introductionmentioning

confidence: 99%

Synergistic Evolution of PM2.5 and O3 Concentrations: Evidence from Environmental Kuznets Curve Tests in the Yellow River Basin

Qi,

Miao,

Xie

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…However, such models have required extensive a priori knowledge and intricate parameterization. Furthermore, these models need more generalizability, making them difficult to extend [28].…”

Section: Introductionmentioning

confidence: 99%

Industrial Heat Source-Related PM2.5 Concentration Estimates and Analysis Using New Three-Stage Model in the Beijing–Tianjin–Hebei Region

Zeng,

Sui,

et al. 2024

Atmosphere

View full text Add to dashboard Cite

The prevalent high-energy, high-pollution and high-emission economic model has led to significant air pollution challenges in recent years. The industrial sector in the Beijing–Tianjin–Hebei (BTH) region is a notable source of atmospheric pollutants, with industrial heat sources (IHSs) being primary contributors to this pollution. Effectively managing emissions from these sources is pivotal for achieving air pollution control goals in the region. A new three-stage model using multi-source long-term data was proposed to estimate atmospheric, delicate particulate matter (PM2.5) concentrations caused by IHS. In the first stage, a region-growing algorithm was used to identify the IHS radiation areas. In the second and third stages, based on a seasonal trend decomposition procedure based on Loess (STL), multiple linear regression, and U-convLSTM models, IHS-related PM2.5 concentrations caused by meteorological and anthropogenic conditions were removed using long-term data from 2012 to 2021. Finally, this study analyzed the spatial and temporal variations in IHS-related PM2.5 concentrations in the BTH region. The findings reveal that PM2.5 concentrations in IHS radiation areas were higher than in background areas, with approximately 33.16% attributable to IHS activities. A decreasing trend in IHS-related PM2.5 concentrations was observed. Seasonal and spatial analyses indicated higher concentrations in the industrially dense southern region, particularly during autumn and winter. Moreover, a case study in Handan’s She County demonstrated dynamic fluctuations in IHS-related PM2.5 concentrations, with notable reductions during periods of industrial inactivity. Our results aligned closely with previous studies and actual IHS operations, showing strong positive correlations with related industrial indices. This study’s outcomes are theoretically and practically significant for understanding and addressing the regional air quality caused by IHSs, contributing positively to regional environmental quality improvement and sustainable industrial development.

show abstract

“…With the implementation of the "Air Pollution Prevention and Control Action Plan" in 2013 and "Blue Sky Protection Campaign" in 2017, notable reductions in PM 2.5 have been achieved in economically developed areas of China such as the Beijing-Tianjin-Hebei region (39.6% reduction from 2013 to 2017, and 20.3% reduction from 2017 to 2020), the Yangtze River Delta area (34.3% reduction from 2013 to 2017, and 20.5% reduction from 2017 to 2020), and the Pearl River Delta (27.7% reduction from 2013 to 2017) [11]. Despite the decreasing trend in the annual average PM 2.5 , extremely high PM 2.5 cases during winter were still frequently observed in cities around northwestern China, particularly in the Guanzhong Plain [12][13][14]. In order to formulate efficient control strategies and substantially reduce the frequency of severe haze events, a comprehensive understanding and thorough investigation of the regional sources and dominant contributing factors are still required.…”

Section: Introductionmentioning

confidence: 99%

Chemical Composition and Source of PM2.5 during Winter Heating Period in Guanzhong Basin

Cao,

Tao,

Zheng

et al. 2023

Atmosphere

View full text Add to dashboard Cite

An intensive field campaign was carried out from December 2022 to March 2023 at six different sites across five major cities (Xi’an, Baoji, Xianyang, Weinan, and Hancheng) in the Guanzhong Basin, China, covering most of the heating period there, which is characterized by high PM2.5 pollution levels. During the campaign, the mean PM2.5 concentrations at these sites exceeded the 24 h PM2.5 standard (75 μg m−3), except the site at Hancheng, with mean PM2.5 concentrations of 57.8 ± 32.3 μg m−3. The source apportionment of PM2.5 varied significantly across sites, with vehicle exhaust being the dominant source at urban sites located in Xi’an and Baoji, coal combustion at suburban sites in Hancheng, and comparable contribution from coal combustion and industrial emissions at suburban sites in Xianyang and Weinan. Compared with clean condition, the contribution of vehicle exhaust and secondary inorganic sources (SIs) were largely enhanced during heavy PM2.5 pollution periods, while the contribution from biomass burning (BB) and dust decreased significantly at all sites. Combined with an analysis of meteorological parameters, the study further found that higher contributions of SIs and heavy PM2.5 pollution were generally associated with higher relative humidity (RH). In addition, higher PM2.5 concentrations at suburban sites were related to lower wind speeds, which could be explained by the stagnant condition favoring the accumulation of local emissions as well as the formation of secondary pollutants. In contrast, at urban sites (e.g., Xianyang), higher PM2.5 concentrations were more associated with the strong influence of vehicle exhaust at slightly higher wind speeds.

show abstract

PM2.5 and O3 concentration estimation based on interpretable machine learning

Cited by 11 publications

References 74 publications

Synergistic Evolution of PM2.5 and O3 Concentrations: Evidence from Environmental Kuznets Curve Tests in the Yellow River Basin

Synergistic Evolution of PM2.5 and O3 Concentrations: Evidence from Environmental Kuznets Curve Tests in the Yellow River Basin

Industrial Heat Source-Related PM2.5 Concentration Estimates and Analysis Using New Three-Stage Model in the Beijing–Tianjin–Hebei Region

Chemical Composition and Source of PM2.5 during Winter Heating Period in Guanzhong Basin

Contact Info

Product

Resources

About