Trends and geographic variation in adverse impacts of nitrogen use in Europe on human health, climate, and ecosystems: A review

de Vries, Wim; Posch, Maximillian; Simpson, Dave; de Leeuw, Frank A.A.M.; van Grinsven, Hans J.M.; Schulte-Uebbing, Lena F.; Sutton, Mark A.; Ros, Gerard H.

doi:10.1016/j.earscirev.2024.104789

Cited by 2 publications

References 474 publications

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A Comparative Analysis of Advanced Modeling Techniques for Global Methane Emission Forecasting Using SARIMA, LSTM, and GRU Models

ÖNDER

2024

Preprint

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Forecast methods are an important aid to the early detection of future levels of pollutant amounts released from global pollutants. This research predicts changes in future global methane gas emissions using SARIMA, LSTM, and GRU models, and also compares the accuracy of these three prediction methods. In the study, a time series analysis was conducted by focusing on the monthly methane (CH4) gas emission amounts recorded between 1984 and 2024. Methane emission data measured between 1984 and 2024 were used as input in the development of the models. By comparing the prediction results and actual values, they were evaluated with performance criteria such as R², RMSE, MAE, and MAPE%. The results revealed that all three methods performed well in estimating global methane gas emissions. The SARIMA model shows the best performance, followed by the LSTM and GRU models. It was determined that the SARIMA model had the lowest error rate with 0.0020 MAPE, 0.0335 MAE, 0.0335 RMSE, and 0.9998 R² values. It has been revealed that estimated global methane emission values may be approximately 1.5 times higher than today's level by 2050.

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A Comparative Analysis of Advanced Modeling Techniques for Global Methane Emission Forecasting Using SARIMA, LSTM, and GRU Models

ÖNDER

2024

Preprint

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CHIMBO Air Quality Modeling System: Verification and Processes Analysis

Landi,

Paglione,

Morichetti

et al. 2024

Atmosphere

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This study presents an evaluation of the CHIMBO modeling chain applied to the Italian domain, specifically focusing on the Po Valley subdomain over the one-year period of 2019. The comparison between simulated and observed data indicates that the performance of the CHIMBO model aligns well with existing literature on other state-of-the-art models. The results demonstrate that the CHIMBO chain is particularly effective for regional-scale quantitative assessments of pollutant distribution, comparable to that of CAMS ensemble models. The analysis of key chemical species in particulate matter reveals that the CHIMBO model accurately represents the average concentrations of organic and elemental carbon, as well as secondary inorganic compounds (sulfate, nitrate, and ammonium), particularly at background monitoring stations in the flat terrain of the Po Valley, with the exception of Aosta, a city located at about 500 m asl. However, seasonal discrepancies were identified, especially during winter months, when significant underestimations were observed for several species, including elemental and organic carbon, predominantly at background sites. These underestimations are likely attributed to various factors: (i) inadequate estimations of primary emissions, particularly from domestic heating; (ii) the limited effectiveness of secondary formation processes under winter conditions characterized by low photochemical activity and high humidity; and (iii) excessive dilution of pollutants during calm wind conditions due to overestimation of wind intensity. In conclusion, while the CHIMBO modeling chain serves as a robust tool for mesoscale atmospheric composition investigations, limitations persist related to emissions inventories and meteorological parameters, which remain critical drivers of atmospheric processes.

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Trends and geographic variation in adverse impacts of nitrogen use in Europe on human health, climate, and ecosystems: A review

Cited by 2 publications

References 474 publications

A Comparative Analysis of Advanced Modeling Techniques for Global Methane Emission Forecasting Using SARIMA, LSTM, and GRU Models

A Comparative Analysis of Advanced Modeling Techniques for Global Methane Emission Forecasting Using SARIMA, LSTM, and GRU Models

CHIMBO Air Quality Modeling System: Verification and Processes Analysis

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