Atmospheric Pollution Research, Journal Year: 2024, Volume and Issue: unknown, P. 102351 - 102351
Published: Oct. 1, 2024
Language: Английский
Atmospheric Pollution Research, Journal Year: 2024, Volume and Issue: unknown, P. 102351 - 102351
Published: Oct. 1, 2024
Language: Английский
Atmospheric Pollution Research, Journal Year: 2024, Volume and Issue: 15(7), P. 102148 - 102148
Published: April 16, 2024
Language: Английский
Citations
8The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 934, P. 173193 - 173193
Published: May 12, 2024
Language: Английский
Citations
8The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 945, P. 173778 - 173778
Published: June 7, 2024
Language: Английский
Citations
4Atmosphere, 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
0Atmospheric Pollution Research, Journal Year: 2025, Volume and Issue: unknown, P. 102552 - 102552
Published: April 1, 2025
Language: Английский
Citations
0Environmental Pollution, Journal Year: 2025, Volume and Issue: unknown, P. 126315 - 126315
Published: April 1, 2025
Language: Английский
Citations
0International Journal of Information Technology, Journal Year: 2025, Volume and Issue: unknown
Published: April 8, 2025
Language: Английский
Citations
0Sustainability, 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
1Atmospheric Pollution Research, Journal Year: 2024, Volume and Issue: 15(7), P. 102145 - 102145
Published: April 4, 2024
Language: Английский
Citations
1Applied 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