Investigating trends and causes of simultaneous high pollution from PM2.5 and ozone in China, 2015–2023 DOI
Fangyuan Wang, Xiao Han,

Huan Xie

et al.

Atmospheric Pollution Research, Journal Year: 2024, Volume and Issue: unknown, P. 102351 - 102351

Published: Oct. 1, 2024

Language: Английский

How does the impact of a two-stage air pollution control policy on air quality different? Evidence from 258 cities in China DOI
Lanlan Li,

Minzheng He,

Xue Liang

et al.

Atmospheric Pollution Research, Journal Year: 2024, Volume and Issue: 15(7), P. 102148 - 102148

Published: April 16, 2024

Language: Английский

Citations

8

Determination of major drive of ozone formation and improvement of O3 prediction in typical North China Plain based on interpretable random forest model DOI

L. Yao,

Han Yan, Xin Qi

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 934, P. 173193 - 173193

Published: May 12, 2024

Language: Английский

Citations

8

Spatio-temporal differentiation characteristics and the influencing factors of PM2.5 emissions from coal consumption in Central Plains Urban Agglomeration DOI
Fujie Yang,

Jiayi Yu,

Cheng Zhang

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 945, P. 173778 - 173778

Published: June 7, 2024

Language: Английский

Citations

4

Optimizing the Architecture of a Quantum–Classical Hybrid Machine Learning Model for Forecasting Ozone Concentrations: Air Quality Management Tool for Houston, Texas DOI Creative Commons
Victor Oliveira Santos, Paulo Alexandre Costa Rocha, Jesse Van Griensven Thé

et al.

Atmosphere, Journal Year: 2025, Volume and Issue: 16(3), P. 255 - 255

Published: Feb. 23, 2025

Keeping track of air quality is paramount to issue preemptive measures mitigate adversarial effects on the population. This study introduces a new quantum–classical approach, combining graph-based deep learning structure with quantum neural network predict ozone concentration up 6 h ahead. The proposed architecture utilized historical data from Houston, Texas, major urban area that frequently fails comply regulations. Our results revealed smoother transition between classical framework and its counterpart enhances model’s results. Moreover, we observed min–max normalization increased ansatz repetitions also improved hybrid performance. was evident evaluating assessment metrics root mean square error (RMSE), coefficient determination (R2) forecast skill (FS). Values for R2 FS horizons considered were 94.12% 31.01% 1 h, 83.94% 48.01% 3 75.62% 57.46% forecasts. A comparison existing literature both QML models methodology could provide competitive results, even surpass some well-established forecasting models, proving be valuable resource forecasting, thus validating this approach.

Language: Английский

Citations

0

A comprehensive analysis of O3 variation and source contributions of VOCs-oriented to O3 pollution episodes over Jinan city, China DOI
Lianhuan Fan,

Sen Gu,

Huaizhong Yan

et al.

Atmospheric Pollution Research, Journal Year: 2025, Volume and Issue: unknown, P. 102552 - 102552

Published: April 1, 2025

Language: Английский

Citations

0

Assessing the contribution of VOCs to SOA formation and identifying their key species during ozone pollution episodes in a typical petrochemical city DOI
Xiaoyu Yan, Fuhong Gao, Yuanyuan Ji

et al.

Environmental Pollution, Journal Year: 2025, Volume and Issue: unknown, P. 126315 - 126315

Published: April 1, 2025

Language: Английский

Citations

0

Lossless and closed co-occurring pattern mining algorithm for spatio-temporal datasets (C-STCOP) DOI

S. Sharmiladevi,

S. Siva Sathya,

S. LourduMarie Sophie

et al.

International Journal of Information Technology, Journal Year: 2025, Volume and Issue: unknown

Published: April 8, 2025

Language: Английский

Citations

0

Quantitative Estimation of the Impacts of Precursor Emissions on Surface O3 and PM2.5 Collaborative Pollution in Three Typical Regions of China via Multi-Task Learning DOI Open Access
Mengnan Liu, Mingliang Ma, Mengjiao Liu

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(6), P. 2475 - 2475

Published: March 16, 2024

The coordinated control of PM2.5 and O3 pollution has become a critical factor restricting the improvement air quality in China. In this work, precursors related influencing factors were utilized to establish estimation models North China Plain (NCP), Yangzi River Delta (YRD), Pearl (PRD) using multi-task-learning (MTL) model. prediction accuracy these three MTL was high, with R2 values ranging from 0.69 0.83. Subsequently, used quantitatively reveal relative importance each collaborative simultaneously. Precursors meteorological two most for regions, their larger than 29.99% 15.89%, respectively. Furthermore, response precursor region. NCP YRD, important are SO2 HCHO, while HCHO NO2. Therefore, VOC emissions reduction is measure pollution, NO2 emission regions. terms PRD, SOX, Thus, NO2, SO2, PRD. Overall, study provides clues references NCP,

Language: Английский

Citations

1

Seasonal patterns, vertical profiles, and sensitivity analysis of long-term O3 pollution observations in Hefei City, China DOI

Xiaoqian Zhao,

Xiang Yan, Ying Pan

et al.

Atmospheric Pollution Research, Journal Year: 2024, Volume and Issue: 15(7), P. 102145 - 102145

Published: April 4, 2024

Language: Английский

Citations

1

Quantitative Analysis of Spatiotemporal Patterns and Factor Contributions of Surface Ozone in the North China Plain DOI Creative Commons
Yi Li, Mengjiao Liu,

Lingyue Lv

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(12), P. 5026 - 5026

Published: June 9, 2024

Over the past decade, surface ozone has emerged as a significant air pollutant in China, especially North China Plain (NCP). For effective management NCP, it is crucial to accurately estimate levels and identify primary influencing factors for pollution this region. This study utilized precursors such volatile organic compounds (VOCs) nitrogen oxides (NOX), meteorological data, land cover, normalized difference vegetation index (NDVI), terrain, population data build an extreme gradient boosting (XGBoost)-based estimation model NCP during 2019 2021. Four models were developed using different NO2 formaldehyde (HCHO) datasets from Sentinel-5 TROPOMI observations Copernicus Atmosphere Monitoring Service (CAMS) reanalysis data. Site-based validation results of these four showed high accuracy with R2 values above 0.86. Among models, two higher spatial coverage ratio selected, their averaged produce final products. The indicated that VOCs NOX main pollutants causing relative contributions accounted more than 23.34% 10.23%, respectively, while HCHO also played role, contributing over 5.64%. Additionally, had notable impact, 28.63% pollution, each individual factor 2.38%. distribution identified Hebei–Shandong–Henan junction hotspot, peak occurring summer, particularly June. Therefore, hotspot region promoting reduction NOx can play important role mitigation O3 improvement quality

Language: Английский

Citations

1