Large Modeling Uncertainty in Projecting Decadal Surface Ozone Changes Over City Clusters of China

Weng, Xiang; Li, Jiawei; Forster, Grant L.; Nowack, Peer

doi:10.1029/2023gl103241

Cited by 4 publications

(2 citation statements)

References 64 publications

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“…The discrepancy between observed and simulated results may result from model resolutions, chemical mechanisms, meteorology and emissions scenarios. Chemical mechanisms can lead to large uncertainties in the prediction of ozone concentrations (Knote et al., 2015; Mar et al., 2016; Weng et al., 2023). For example, the model studies by WRF‐Chem show that ozone concentrations under MOZART mechanism are higher than those under CBMZ mechanism in China in summer 2030 (Weng et al., 2023) and those under RADM2 mechanism over Europe in summer 2007 (Mar et al., 2016).…”

Section: Model Evaluationmentioning

confidence: 99%

Future Ozone Changes and Their Impacts on Vegetation and Human Health in China Under the Shared Socio‐Economic Pathways

Xu,

Mao,

Liao

2024

JGR Atmospheres

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Ozone concentrations in China are increasing in recent years and future changes of ozone and their impacts have attracted much attention. We use global chemical transport model (GEOS‐Chem) to simulate the surface ozone concentrations in China in 2020 and 2050 under four Shared Socio‐economic Pathways and evaluate the impacts of future ozone pollution on vegetation and premature mortality in four polluted regions (Beijing–Tianjin–Hebei, BTH; Yangtze River Delta, YRD; Pearl River Delta, PRD; Sichuan Basin, SCB) and three major crop growing areas (Huang–Huai–Hai, HHH; Northeast Plain, NEP; middle and lower reaches of Yangtze River, MLRY) in China. The changes of simulated seasonal maximum daily 8‐hr average (MDA8) ozone from 2020 to 2050 (−15.5 to +11.9 ppbv) are significant under SSP126 (low forcing pathway) and SSP245 (medium forcing pathway) scenarios in all regions due to large changes of emissions. MDA8 ozone in summer 2050 will be above the WHO guidelines (100 μg/m3) in BTH, YRD, HHH and MLRY under four scenarios. By 2050, W126 (vegetative ozone exposure index) in summer will be much above the maximum of US secondary standard (21 ppm‐h) in HHH under SSP245, SSP370 (medium to high forcing pathway) and SSP585 (high forcing pathway) scenarios, and in MLRY under SSP370 and SSP585 scenarios. Annual ozone‐related deaths for people over 30 years old will mainly decrease in four polluted areas from 2020 to 2050 under SSPs scenarios, but only increase much under SSP245 scenario in BTH (+3.1 to +4.2 thousand) and YRD (+1.1 to +1.6 thousand).

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Section: Model Evaluationmentioning

confidence: 99%

Future Ozone Changes and Their Impacts on Vegetation and Human Health in China Under the Shared Socio‐Economic Pathways

Xu,

Mao,

Liao

2024

JGR Atmospheres

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“…Traditionally, numerical chemical transport models (CTMs) have been used for air pollution forecasting. However, these models are typically highly computationally expensive, and resolution is often an issue requiring parameterizations, which can introduce inconsistencies in model predictions (Weng et al, 2023). Machine learning (ML) may provide a complement to existing numerical CTMs and simple statistical approaches to modeling air pollution, and climate phenomena in general, as ML allows automatic learning of the behavior of a complex system from data.…”

Section: Air Pollution Forecastingmentioning

confidence: 99%

Short-term forecasting of ozone air pollution across Europe with transformers

Hickman,

Griffiths,

Nowack

et al. 2023

Environ. Data Science

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Surface ozone is an air pollutant that contributes to hundreds of thousands of premature deaths annually. Accurate short-term ozone forecasts may allow improved policy actions to reduce the risk to human health. However, forecasting surface ozone is a difficult problem as its concentrations are controlled by a number of physical and chemical processes that act on varying timescales. We implement a state-of-the-art transformer-based model, the temporal fusion transformer, trained on observational data from three European countries. In four-day forecasts of daily maximum 8-hour ozone (DMA8), our novel approach is highly skillful (MAE = 4.9 ppb, coefficient of determination $ {\mathrm{R}}^2=0.81 $ ) and generalizes well to data from 13 other European countries unseen during training (MAE = 5.0 ppb, $ {\mathrm{R}}^2=0.78 $ ). The model outperforms other machine learning models on our data (ridge regression, random forests, and long short-term memory networks) and compares favorably to the performance of other published deep learning architectures tested on different data. Furthermore, we illustrate that the model pays attention to physical variables known to control ozone concentrations and that the attention mechanism allows the model to use the most relevant days of past ozone concentrations to make accurate forecasts on test data. The skillful performance of the model, particularly in generalizing to unseen European countries, suggests that machine learning methods may provide a computationally cheap approach for accurate air quality forecasting across Europe.

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Identifying the geospatial relationship of surface ozone pollution in China: Implications for key pollution control regions

Cheng,

Peng,

Cao

et al. 2024

Science of The Total Environment

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Large Modeling Uncertainty in Projecting Decadal Surface Ozone Changes Over City Clusters of China

Cited by 4 publications

References 64 publications

Future Ozone Changes and Their Impacts on Vegetation and Human Health in China Under the Shared Socio‐Economic Pathways

Future Ozone Changes and Their Impacts on Vegetation and Human Health in China Under the Shared Socio‐Economic Pathways

Short-term forecasting of ozone air pollution across Europe with transformers

Identifying the geospatial relationship of surface ozone pollution in China: Implications for key pollution control regions

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