High spatial resolution WRF-Chem model over Asia: Physics and chemistry evaluation

Sicard, Pierre; Crippa, Paola; Marco, Alessandra De; Castruccio, Stefano; Giani, Paolo; Cuesta, Juan; Paoletti, Elena; Feng, Zhaozhong; Anav, Alessandro

doi:10.1016/j.atmosenv.2020.118004

Cited by 56 publications

(17 citation statements)

References 133 publications

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“…The benchmark developed by Boylan and Russell et al. (2006) was especially for the model performance evaluation of PM 2.5 , which has been extensively used in a lot of previous studies (Hu et al., 2016; J. Jiang et al., 2020; Sicard et al., 2020; Zhang, Fung, Lau, et al., 2020; Zhang, Fung, Zhang, et al., 2020). This statistical matric was chosen to illustrate how effective the model prediction after the DA.…”

Section: Resultsmentioning

confidence: 99%

Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method

et al. 2021

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Section: Resultsmentioning

confidence: 99%

Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method

et al. 2021

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“…We used the accumulated stomatal O 3 uptake (POD1) as a metric of O 3 damage because it is considered as a better index than only O 3 concentrations in the air 21 . However, estimating POD1 at fine scale is challenging as it requires hourly inputs 22 . For the first time, an economic valuation of wood losses was based on POD1 at high spatial horizontal resolution (12 km 2 ).…”

Section: Discussionmentioning

confidence: 99%

Economic impacts of ambient ozone pollution on wood production in Italy

Sacchelli¹,

Carrari²,

Paoletti³

et al. 2021

Sci Rep

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Worldwide, tropospheric ozone (O3) is a potential threat to wood production, but our understanding of O3 economic impacts on forests is still limited. To overcome this issue, we developed an approach for integrating O3 risk modelling and economic estimates, by using the Italian forests as a case study. Results suggested a significant impact of O3 expressed in terms of stomatal flux with an hourly threshold of uptake (Y = 1 nmol O3 m−2 leaf area s−1 to represent the detoxification capacity of trees), i.e. POD1. In 2005, the annual POD1 averaged over Italy was 20.4 mmol m−2 and the consequent potential damage ranged from 790.90 M€ to 2.85 B€ of capital value (i.e. 255–869 € ha−1, on average) depending on the interest rate. The annual damage ranged from 31.6 to 57.1 M€ (i.e. 10–17 € ha−1 per year, on average). There was also a 1.1% reduction in the profitable forest areas, i.e. with a positive Forest Expectation Value (FEV), with significant declines of the annual national wood production of firewood (− 7.5%), timber pole (− 7.4%), roundwood (− 5.0%) and paper mill (− 4.8%). Results were significantly different in the different Italian regions. We recommend our combined approach for further studies under different economic and phytoclimatic conditions.

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“…Although pure mathematical analyses lack the in-depth understanding of chemical mechanisms, statistical models can achieve higher accuracies with less computational requirements. For example, recently some CTM studies for China O 3 estimation reported that the coefficient of determination values were 0.5~0.7 [ 12 , 13 ], while those of statistical models could be greater than 0.8 [ 14 , 15 ]. Therefore, statistical models are considered a reasonable means of providing high-quality data for air pollution control.…”

Section: Introductionmentioning

confidence: 99%

New Deep Learning Model to Estimate Ozone Concentrations Found Worrying Exposure Level over Eastern China

Wang

Jiang

et al. 2022

IJERPH

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Ozone (O3), whose concentrations have been increasing in eastern China recently, plays a key role in human health, biodiversity, and climate change. Accurate information about the spatiotemporal distribution of O3 is crucial for human exposure studies. We developed a deep learning model based on a long short-term memory (LSTM) network to estimate the daily maximum 8 h average (MDA8) O3 across eastern China in 2020. The proposed model combines LSTM with an attentional mechanism and residual connection structure. The model employed total O3 column product from the Tropospheric Monitoring Instrument, meteorological data, and other covariates as inputs. Then, the estimates from our model were compared with real observations of the China air quality monitoring network. The results indicated that our model performed better than other traditional models, such as the random forest model and deep neural network. The sample-based cross-validation R2 and RMSE of our model were 0.94 and 10.64 μg m−3, respectively. Based on the O3 distribution over eastern China derived from the model, we found that people in this region suffered from excessive O3 exposure. Approximately 81% of the population in eastern China was exposed to MDA8 O3 > 100 μg m−3 for more than 150 days in 2020.

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High spatial resolution WRF-Chem model over Asia: Physics and chemistry evaluation

Cited by 56 publications

References 133 publications

Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method

Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method

Economic impacts of ambient ozone pollution on wood production in Italy

New Deep Learning Model to Estimate Ozone Concentrations Found Worrying Exposure Level over Eastern China

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