GHG Global Emission Prediction of Synthetic N Fertilizers Using Expectile Regression Techniques
Agriculture accounts for a large percentage of nitrous oxide (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>N</mi><mn>2</mn></msub><mi>O</mi><...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/14/2/283 |
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| author | Kaoutar Benghzial Hind Raki Sami Bamansour Mouad Elhamdi Yahya Aalaila Diego H. Peluffo-Ordóñez |
| author_facet | Kaoutar Benghzial Hind Raki Sami Bamansour Mouad Elhamdi Yahya Aalaila Diego H. Peluffo-Ordóñez |
| author_sort | Kaoutar Benghzial |
| collection | DOAJ |
| description | Agriculture accounts for a large percentage of nitrous oxide (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>N</mi><mn>2</mn></msub><mi>O</mi></mrow></semantics></math></inline-formula>) emissions, mainly due to the misapplication of nitrogen-based fertilizers, leading to an increase in the greenhouse gas (GHG) footprint. These emissions are of a direct nature, released straight into the atmosphere through nitrification and denitrification, or of an indirect nature, mainly through nitrate leaching, runoff, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>N</mi><mn>2</mn></msub><mi>O</mi></mrow></semantics></math></inline-formula> volatilization processes. <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>N</mi><mn>2</mn></msub><mi>O</mi></mrow></semantics></math></inline-formula> emissions are largely ascribed to the agricultural sector, which represents a threat to sustainability and food production, subsequent to the radical contribution to climate change. In this connection, it is crucial to unveil the relationship between synthetic N fertilizer global use and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>N</mi><mn>2</mn></msub><mi>O</mi></mrow></semantics></math></inline-formula> emissions. To this end, we worked on a dataset drawn from a recent study, which estimates direct and indirect <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>N</mi><mn>2</mn></msub><mi>O</mi></mrow></semantics></math></inline-formula> emissions according to each country, by the Intergovernmental Panel on Climate Change (IPCC) guidelines. Machine learning tools are considered great explainable techniques when dealing with air quality problems. Hence, our work focuses on expectile regression (ER) based-approaches to predict <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>N</mi><mn>2</mn></msub><mi>O</mi></mrow></semantics></math></inline-formula> emissions based on N fertilizer use. In contrast to classical linear regression (LR), this method allows for heteroscedasticity and omits a parametric specification of the underlying distribution. ER provides a complete picture of the target variable’s distribution, especially when the tails are of interest, or in dealing with heavy-tailed distributions. In this work, we applied expectile regression and the kernel expectile regression estimator (KERE) to predict direct and indirect <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>N</mi><mn>2</mn></msub><mi>O</mi></mrow></semantics></math></inline-formula> emissions. The results outline both the flexibility and competitiveness of ER-based techniques in regard to the state-of-the-art regression approaches. |
| first_indexed | 2024-03-11T09:10:41Z |
| format | Article |
| id | doaj.art-14c7ae753c014ee9bffeec61d279d1f6 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-11T09:10:41Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
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| series | Atmosphere |
| spelling | doaj.art-14c7ae753c014ee9bffeec61d279d1f62023-11-16T19:02:40ZengMDPI AGAtmosphere2073-44332023-01-0114228310.3390/atmos14020283GHG Global Emission Prediction of Synthetic N Fertilizers Using Expectile Regression TechniquesKaoutar Benghzial0Hind Raki1Sami Bamansour2Mouad Elhamdi3Yahya Aalaila4Diego H. Peluffo-Ordóñez5SDAS Research Group, Ben Guerir 43150, MoroccoSDAS Research Group, Ben Guerir 43150, MoroccoSDAS Research Group, Ben Guerir 43150, MoroccoSDAS Research Group, Ben Guerir 43150, MoroccoSDAS Research Group, Ben Guerir 43150, MoroccoSDAS Research Group, Ben Guerir 43150, MoroccoAgriculture accounts for a large percentage of nitrous oxide (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>N</mi><mn>2</mn></msub><mi>O</mi></mrow></semantics></math></inline-formula>) emissions, mainly due to the misapplication of nitrogen-based fertilizers, leading to an increase in the greenhouse gas (GHG) footprint. These emissions are of a direct nature, released straight into the atmosphere through nitrification and denitrification, or of an indirect nature, mainly through nitrate leaching, runoff, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>N</mi><mn>2</mn></msub><mi>O</mi></mrow></semantics></math></inline-formula> volatilization processes. <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>N</mi><mn>2</mn></msub><mi>O</mi></mrow></semantics></math></inline-formula> emissions are largely ascribed to the agricultural sector, which represents a threat to sustainability and food production, subsequent to the radical contribution to climate change. In this connection, it is crucial to unveil the relationship between synthetic N fertilizer global use and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>N</mi><mn>2</mn></msub><mi>O</mi></mrow></semantics></math></inline-formula> emissions. To this end, we worked on a dataset drawn from a recent study, which estimates direct and indirect <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>N</mi><mn>2</mn></msub><mi>O</mi></mrow></semantics></math></inline-formula> emissions according to each country, by the Intergovernmental Panel on Climate Change (IPCC) guidelines. Machine learning tools are considered great explainable techniques when dealing with air quality problems. Hence, our work focuses on expectile regression (ER) based-approaches to predict <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>N</mi><mn>2</mn></msub><mi>O</mi></mrow></semantics></math></inline-formula> emissions based on N fertilizer use. In contrast to classical linear regression (LR), this method allows for heteroscedasticity and omits a parametric specification of the underlying distribution. ER provides a complete picture of the target variable’s distribution, especially when the tails are of interest, or in dealing with heavy-tailed distributions. In this work, we applied expectile regression and the kernel expectile regression estimator (KERE) to predict direct and indirect <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>N</mi><mn>2</mn></msub><mi>O</mi></mrow></semantics></math></inline-formula> emissions. The results outline both the flexibility and competitiveness of ER-based techniques in regard to the state-of-the-art regression approaches.https://www.mdpi.com/2073-4433/14/2/283air qualitybio-meteorologyexpectile regressiongreenhouse gas emissionsnitrogen-based fertilizersnitrous oxide |
| spellingShingle | Kaoutar Benghzial Hind Raki Sami Bamansour Mouad Elhamdi Yahya Aalaila Diego H. Peluffo-Ordóñez GHG Global Emission Prediction of Synthetic N Fertilizers Using Expectile Regression Techniques Atmosphere air quality bio-meteorology expectile regression greenhouse gas emissions nitrogen-based fertilizers nitrous oxide |
| title | GHG Global Emission Prediction of Synthetic N Fertilizers Using Expectile Regression Techniques |
| title_full | GHG Global Emission Prediction of Synthetic N Fertilizers Using Expectile Regression Techniques |
| title_fullStr | GHG Global Emission Prediction of Synthetic N Fertilizers Using Expectile Regression Techniques |
| title_full_unstemmed | GHG Global Emission Prediction of Synthetic N Fertilizers Using Expectile Regression Techniques |
| title_short | GHG Global Emission Prediction of Synthetic N Fertilizers Using Expectile Regression Techniques |
| title_sort | ghg global emission prediction of synthetic n fertilizers using expectile regression techniques |
| topic | air quality bio-meteorology expectile regression greenhouse gas emissions nitrogen-based fertilizers nitrous oxide |
| url | https://www.mdpi.com/2073-4433/14/2/283 |
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