Validation of ERA5 Boundary Layer Meteorological Variables by Remote-Sensing Measurements in the Southeast China Mountains

Remote Sensing, Год журнала: 2024, Номер 16(3), С. 548 - 548

Опубликована: Янв. 31, 2024

Mountainous terrains are typical over southeast China, with complex and diverse topography, large terrain undulations, rich geographic features, meteorological variations. Previous studies show that ERA5 variables generally accurate respect to plains or urban agglomerations, while their applicability mountainous areas remains inconclusive. In this paper, using high-precision measurements probed by ground-based remote sensing instruments in May–July 2023 at a Shanghuang site the vertical accuracy of reanalysis datasets were comparatively evaluated. Our findings depict horizontal wind speeds data good performance compared Doppler lidar observations. quantitative terms, about 8% higher than observed values below height 400 m, above an increasing negative bias is along as altitude increases. Differing from speeds, there discrepancy between observations, deviation −150% 40%. terms thermal variables, temperature extracted consistent low troposphere. Nevertheless, systematic errors occur 2000–3000 overall presentation shows gradually increase altitude. Concerning relative humidity, general trend similar microwave radiometer, but 500 2500 m range 40% 100%. This study also reveals poorly representative requires further improvements during extreme weather events such rainstorms typhoons. particular, middle lower levels deviate strongly Given importance atmospheric thermodynamic stratifications both environmental climatic issues, results expand application China. More importantly, it provides credible reference for predictions climate modelings China region.

Язык: Английский

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Unexpectedly High Levels of H₂O₂ Drive Sulfate Formation over the Residual Layer in Beijing

Environmental Science & Technology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 2, 2025

Hydrogen peroxide (H2O2) plays a key role in atmospheric chemistry, but knowledge of its variation, sources, and impact on sulfate formation remains incomplete, especially the urban boundary layer aloft. Here, we conducted field campaign with measurements H2O2 related species at tower-based site (∼528 m above ground surface) Beijing spring 2022. The observed hourly concentration reached up to 21.2 ppbv an average value 3.4 ± 3.7 during entire observation period, which was higher than values from previous observations throughout world. budget revealed that two known sources (self-reaction HO2 radicals ozonolysis alkenes) could not account for significant H2O2, leading considerable unknown source strength (∼0.14–0.53 h–1) noon after sunset. Based levoglucosan signal, distribution fire points, backward trajectories, biomass burning emissions southwest (e.g., North China Plain) were found contribute greatly formation. Besides, photochemical aging PM2.5 might also have potential production noon. unexpectedly high concentrations aloft made vital contribution (0.2–1.1 μg m–3 h–1), be transported surface turbulent mixing. Our findings provide improved understanding chemistry megacity, as well

Язык: Английский

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Effects of nocturnal boundary layer subsidence and long-distance transports on PM2.5 vertical profiles in the Yangtze River Delta of China measured by PM sensor on unmanned aerial vehicle and PM Lidar

Environmental Pollution, Год журнала: 2025, Номер 371, С. 125935 - 125935

Опубликована: Фев. 26, 2025

Язык: Английский

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The effect of cross-regional transport on ozone and particulate matter pollution in China: A review of methodology and current knowledge

The Science of The Total Environment, Год журнала: 2024, Номер 947, С. 174196 - 174196

Опубликована: Июнь 26, 2024

China is currently one of the countries impacted by severe atmospheric ozone (O

Язык: Английский

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Machine Learning Reveals the Parameters Affecting the Gaseous Sulfuric Acid Distribution in a Coastal City: Model Construction and Interpretation

Environmental Science & Technology Letters, Год журнала: 2023, Номер 10(11), С. 1045 - 1051

Опубликована: Апрель 12, 2023

Although the fundamental mechanisms of atmospheric new particle formation events are largely associated with gaseous sulfuric acid monomer (SA), parameters affecting SA generation and elimination remain unclear, especially in coastal areas where certain sulfur-containing precursors abundant. In this study, we utilized machine learning (ML) combination field observations to map link between influencing parameters. The developed random forest (RF) model performed well creating simulations an R2 0.90, significant factors were ultraviolet, methanesulfonic (MSA), SO2, condensation sink, relative humidity descending order. Among five factors, MSA served as indicator for species from marine emissions. black box ML was broken determine marginal contribution these output using partial dependence plots centered-individual conditional expectation plots. These results indicated that had a positive impact on performance RF model, co-occurring relationship observed during nocturnal period. Our findings reveal emitted environment have should be considered areas.

Язык: Английский

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A new implementation of FLEXPART with Enviro-HIRLAM meteorological input, and a case study during a heavy air pollution event

Big Earth Data, Год журнала: 2024, Номер 8(2), С. 397 - 434

Опубликована: Фев. 23, 2024

We integrated Enviro-HIRLAM (Environment-High Resolution Limited Area Model) meteorological output into FLEXPART (FLEXible PARTicle dispersion model). A simulation requires input from a numerical weather prediction (NWP) model. The publicly available version of can utilize either ECMWF (European Centre for Medium-range Weather Forecasts) Integrated Forecast System (IFS) forecast or reanalysis NWP data, NCEP (U.S. National Center Environmental Prediction) Global (GFS) data. primary benefits using are that it runs at higher resolution and accounts aerosol effects in fields. compared backward trajectories generated with (both without effects) to GFS IFS inputs, case study heavy haze event which occurred Beijing, China November 2018. found results were considerably different when inputs. When included the NWP, there was small but noticeable difference calculated trajectories. Moreover, looking potential emission sensitivity instead simply expressing as lines, additional information, may have been missed only be inferred.

Язык: Английский

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New Insights on the Formation of Nucleation Mode Particles in a Coastal City Based on a Machine Learning Approach

Environmental Science & Technology, Год журнала: 2023, Номер 58(2), С. 1187 - 1198

Опубликована: Дек. 20, 2023

Atmospheric particles have profound implications for the global climate and human health. Among them, ultrafine dominate in terms of number concentration exhibit enhanced toxic effects as a result their large total surface area. Therefore, understanding driving factors behind particle behavior is crucial. Machine learning (ML) provides promising approach handling complex relationships. In this study, three ML models were constructed on basis field observations to simulate nucleation mode (PNCN). All exhibited robust PNCN reproduction (R2 > 0.80), with random forest (RF) model excelling test data = 0.89). Multiple methods feature importance analysis revealed that ultraviolet (UV), H2SO4, low-volatility oxygenated organic molecules (LOOMs), temperature, O3 primary influencing PNCN. Bivariate partial dependency plots (PDPs) indicated during nighttime overcast conditions, presence H2SO4 LOOMs may play crucial role Additionally, integrating additional detailed information related emissions or meteorology would further enhance performance. This pilot study shows can be novel simulating atmospheric pollutants contributes better formation growth mechanisms particles.

Язык: Английский

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Research on the influencing factors of PM2.5 in China at different spatial scales based on machine learning algorithm

Environment International, Год журнала: 2025, Номер 200, С. 109536 - 109536

Опубликована: Май 15, 2025

PM2.5 pollution is one of the prominent environmental issues currently faced in China, influenced by various factors and showed significant spatial differences. In this study, Light Gradient Boosting Machine (LightGBM) model was employed combination with SHapley Additive exPlanation (SHAP) methods to explore key impact (precursor emissions, meteorological conditions, geographical features socioeconomic factors) on average annual levels from 2015 2022 at both city grid China. The results show that incorporating pollutant concentration into enhances its performance, R2 improving significantly 0.79 0.93, which underscores importance outstanding predictive performance LightGBM algorithm. Further, after increasing resolution applying a grid-level model, further improves 0.96 ∼ 0.99. SHAP analysis revealed urban areas are such as NO2, CO, SO2, accounting for 49.3 % total impact. contrast, grid-based highlights dominant role temperature precipitation influencing non-urban areas. Moreover, also suggested Yangtze River Delta (YRD) Pearl (PRD) mainly controlled primary while Beijing-Tianjin-Hebei (BTH), Fenwei Plain (FWP) Sichuan Basin (SCB), atmospheric oxidation capacity limiting factor. This study potential machine learning control offers insights developing more effective region-specific policies.

Язык: Английский

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The interactions of aerosol and planetary boundary layer over a large city in the Mongolian Plateau

The Science of The Total Environment, Год журнала: 2023, Номер 907, С. 167985 - 167985

Опубликована: Окт. 20, 2023

Язык: Английский

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AI model to improve the mountain boundary layer height of ERA5

Atmospheric Research, Год журнала: 2024, Номер 304, С. 107352 - 107352

Опубликована: Март 19, 2024

Язык: Английский

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