Methane Dynamics in Inner Mongolia: Unveiling Spatial and Temporal Variations and Driving Factors DOI Creative Commons

Sirui Yan,

Yichun Xie, Ge Han

et al.

Published: Dec. 23, 2024

Methane (CH4), the second-largest greenhouse gas contributing to global warming, has a high warming potential despite its short atmospheric lifespan. Inner Mongolia, due carbon and energy consumption industries, faces significant methane emission challenges. This study uses TROPOMI satellite data (February 2019 December 2022) analyze long-term trends spatial distribution of in Mongolia. The results indicate heterogeneity concentration China. Higher concentrations are observed southeastern regions, whereas central regions exhibit relatively lower concentrations. Temporally, show an increasing trend with seasonal peaks from late August early September. Using multiple stepwise regression geographically weighted (GWR) methods, identifies key factors influencing Increased precipitation soil temperature, along intensified human activity, contribute higher levels, while rising surface temperatures increased vegetation suppress GWR model provides better fit compared traditional especially levels. research offers insights for developing strategies mitigate emissions supports China's control targets.

Language: Английский

Short-term trend and temporal variations in atmospheric methane at an Atlantic coastal site in Southwestern Europe DOI
R. Padilla, J.A. Adame, Pablo J. Hidalgo

et al.

Atmospheric Environment, Journal Year: 2024, Volume and Issue: 333, P. 120665 - 120665

Published: June 19, 2024

Language: Английский

Citations

2
Tibetan lake change linked to large-scale atmospheric oscillations via hydroclimatic trajectory DOI
Rong Wang, Yuanbo Liu,

Liping Zhu

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 951, P. 175465 - 175465

Published: Aug. 14, 2024

Language: Английский

Citations

1
Seasonal and trend variation of methane concentration over two provinces of South Africa using Sentinel-5p data DOI Creative Commons

Swelihle Sinothile Sibiya,

Paidamwoyo Mhangara, Lerato Shikwambana

et al.

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(8)

Published: July 8, 2024

Abstract South Africa faces the urgency to comprehensively understand and manage its methane (CH 4 ) emissions. The primary aim of this study is compare CH concentrations between Eastern Cape Mpumalanga regions dominated by cattle farming coal mining industries, respectively. concentration trends were analyzed for period 2019 2023 using satellite data. Trend analysis revealed significant increasing in both provinces, supported Mann–Kendall tests that rejected null hypothesis no trend (Eastern Cape: p -value = 8.9018e −08 Mpumalanga: 2.4650e −10 ). Cape, a leading province, exhibited cyclical patterns concentrations, while Mpumalanga, major displayed similar with sharper points. results show seasonal variations provinces. High are observed northwestern region during December-January–February (DJF) season, lower March–April-May (MAM) June-July–August (JJA) seasons province. In there dominance high southwestern moderately low northeastern regions, consistently across all seasons. also showed an from highlights urgent need address emissions activities mitigate environmental impacts promote sustainable development. Utilizing geographic information system (GIS) remote sensing technologies, policymakers stakeholders can identify sources more effectively, thereby contributing conservation resource management.

Language: Английский

Citations

0
Mapping seamless monthly XCO2 in East Asia: Utilizing OCO-2 data and machine learning DOI Creative Commons

Terigelehu Te,

Chunling Bao, Hasi Bagan

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 133, P. 104117 - 104117

Published: Aug. 31, 2024

High spatial resolution XCO2 data is key to investigating the mechanisms of carbon sources and sinks. However, current satellites have a narrow swath uneven observation points, making it difficult obtain seamless full-coverage data. We propose novel method combining extreme gradient boosting (XGBoost) with particle swarm optimization (PSO) construct relationship between OCO-2 auxiliary (i.e., vegetation, meteorological, anthropogenic emissions, LST data), map monthly concentration in East Asia from 2015 2020. Validation results based on TCCON ground station demonstrate high accuracy model an average R2 0.93, Root Mean Square Error (RMSE) 1.33 Absolute Percentage (MAPE) 0.24 % five sites. The show that atmospheric shows continuous increasing trend 2020, annual growth rate 2.21 ppm/yr. This accompanied by clear seasonal variations, highest winter lowest summer. Additionally, activities contributed significantly concentrations, which were higher urban areas. These findings highlight dynamics regional concentrations over time their association human activities. study provides detailed examination distribution trends Asia, enhancing our comprehension CO2 dynamics.

Language: Английский

Citations

0
Study of atmospheric CH4, CO2 and N2O at Waliguan WMO/GAW global station: Time series trend, seasonal variation, and attribution analysis association with meteorological factors DOI
Yuanyuan Wei,

Xiaojing Yang,

Yifan Jia

et al.

Atmospheric Environment, Journal Year: 2024, Volume and Issue: unknown, P. 120994 - 120994

Published: Dec. 1, 2024

Language: Английский

Citations

0
Methane Dynamics in Inner Mongolia: Unveiling Spatial and Temporal Variations and Driving Factors DOI Creative Commons

Sirui Yan,

Yichun Xie, Ge Han

et al.

Published: Dec. 23, 2024

Methane (CH4), the second-largest greenhouse gas contributing to global warming, has a high warming potential despite its short atmospheric lifespan. Inner Mongolia, due carbon and energy consumption industries, faces significant methane emission challenges. This study uses TROPOMI satellite data (February 2019 December 2022) analyze long-term trends spatial distribution of in Mongolia. The results indicate heterogeneity concentration China. Higher concentrations are observed southeastern regions, whereas central regions exhibit relatively lower concentrations. Temporally, show an increasing trend with seasonal peaks from late August early September. Using multiple stepwise regression geographically weighted (GWR) methods, identifies key factors influencing Increased precipitation soil temperature, along intensified human activity, contribute higher levels, while rising surface temperatures increased vegetation suppress GWR model provides better fit compared traditional especially levels. research offers insights for developing strategies mitigate emissions supports China's control targets.

Language: Английский

Citations

0