Atmospheric Pollution Research, Год журнала: 2024, Номер unknown, С. 102351 - 102351
Опубликована: Окт. 1, 2024
Язык: Английский
Atmospheric Pollution Research, Год журнала: 2024, Номер unknown, С. 102351 - 102351
Опубликована: Окт. 1, 2024
Язык: Английский
Atmospheric Pollution Research, Год журнала: 2024, Номер 15(7), С. 102148 - 102148
Опубликована: Апрель 16, 2024
Язык: Английский
Процитировано
8The Science of The Total Environment, Год журнала: 2024, Номер 934, С. 173193 - 173193
Опубликована: Май 12, 2024
Язык: Английский
Процитировано
8The Science of The Total Environment, Год журнала: 2024, Номер 945, С. 173778 - 173778
Опубликована: Июнь 7, 2024
Язык: Английский
Процитировано
4Atmosphere, Год журнала: 2025, Номер 16(3), С. 255 - 255
Опубликована: Фев. 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.
Язык: Английский
Процитировано
0International Journal of Information Technology, Год журнала: 2025, Номер unknown
Опубликована: Апрель 8, 2025
Язык: Английский
Процитировано
0Atmospheric Pollution Research, Год журнала: 2025, Номер unknown, С. 102552 - 102552
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0Environmental Pollution, Год журнала: 2025, Номер unknown, С. 126315 - 126315
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0Sustainability, Год журнала: 2024, Номер 16(6), С. 2475 - 2475
Опубликована: Март 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,
Язык: Английский
Процитировано
1Atmospheric Pollution Research, Год журнала: 2024, Номер 15(7), С. 102145 - 102145
Опубликована: Апрель 4, 2024
Язык: Английский
Процитировано
1Applied Sciences, Год журнала: 2024, Номер 14(12), С. 5026 - 5026
Опубликована: Июнь 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
Язык: Английский
Процитировано
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