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| 001 | 1045966 | ||
| 005 | 20251216202226.0 | ||
| 024 | 7 | _ | |a 10.1016/j.apr.2025.102694 |2 doi |
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| 100 | 1 | _ | |a Yuan, Yue |0 P:(DE-Juel1)166383 |b 0 |
| 245 | _ | _ | |a Evaluating the impact of anthropogenic drivers and meteorological factors on air pollutants by explainable machine learning in Shandong Province, China |
| 260 | _ | _ | |a Amsterdam |c 2025 |b Elsevier B.V. |
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| 520 | _ | _ | |a Unexpected haze in North China Plain during the COVID-19 lockdown has been regarded as a natural window to explore the meteorological impact on formatting PM2.5 pollution but with limitations in quantifying weather elements’ contributions. In this study, daily data of six air pollutants (including PM2.5, PM10, SO2, NO2, O3, and CO) and six meteorological factors (including temperature, pressure, relative humidity (RH), wind speed (WS), wind direction (WD), and precipitation) from 2015 to 2020 across 16 capital cities in Shandong province, China, was used to drive the Machine Learning and the SHapley Additive exPlanation (SHAP) models. By applying these models, contributions from anthropogenic drivers to pollutant reductions and contributions from meteorological factors to the haze event were investigated. Results show that the COVID-19 lockdown measures reduced concentrations of NO2, PM2.5, PM10, CO and SO2 by −52.1 %, −40.0 %, −45.5 %, −29.4 % and −38.7 % respectively. On average, an 18.9 % increase in O3 was observed. PM2.5 pollution was mainly driven by temperature with a SHAP value of 19.7 μg/m3, followed by RH (5.8 μg/m3), precipitation (0.9 μg/m3), WD (0.3 μg/m3), pressure (0.1 μg/m3) and WS (0.1 μg/m3) during the haze period. Relative to the post-haze period, high-pressure systems coupled with lower temperatures and weakened surface winds hindered the dispersion of PM2.5 whilst higher RH was in favour of PM2.5 production during the haze period. This study underscores the intricate interplay between emissions, meteorological conditions, and regulatory measures in air pollution, offering critical insights into future air quality management strategies by air pollution prediction. |
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| 700 | 1 | _ | |a Shen, Fuzhen |0 P:(DE-Juel1)194205 |b 1 |e Corresponding author |
| 700 | 1 | _ | |a Sheng, Chunyan |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Zhang, Zeming |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Guo, Weihua |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Zhu, Wengang |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Zhu, Hui |0 P:(DE-HGF)0 |b 6 |
| 773 | _ | _ | |a 10.1016/j.apr.2025.102694 |g Vol. 16, no. 12, p. 102694 - |0 PERI:(DE-600)2645757-X |n 12 |p 102694 - |t Atmospheric pollution research |v 16 |y 2025 |x 1309-1042 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1045966/files/1-s2.0-S130910422500296X-main.pdf |y OpenAccess |
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