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

Evaluating the Spatiotemporal Variations in Atmospheric CO2 Concentrations in China and Identifying Factors Contributing to Its Increase DOI

Weixin Zhu,

Hong Zhang, Xiaoyu Zhang

et al.

Atmospheric Pollution Research, Journal Year: 2025, Volume and Issue: unknown, P. 102458 - 102458

Published: Feb. 1, 2025

Language: Английский

Citations

0
Dust Intensity Across Vegetation Types in Mongolia: Drivers and Trends DOI Creative Commons
Chunling Bao, Yonghui Yang, Hasi Bagan

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 410 - 410

Published: Jan. 25, 2025

Dust storms, characterized by their rapid movement and high intensity, present significant challenges across atmospheric, human health, ecological domains. This study investigates the spatiotemporal variations in dust intensity (DI) its driving factors Mongolia from 2001 to 2022, using data ground observations, reanalysis, remote sensing satellites, statistical analyses. Our findings show an increasing DI trend at approximately two-thirds of monitoring stations, with rising average rate 0.8 per year during period. Anthropogenic dominate as primary drivers regions such Forest, Meadow Steppe, Typical Desert Gobi Desert. For example, GDP significantly impacts Forest Steppe areas, contributing 25.89% 14.11% influencing DI, respectively. Population emerges key driver Grasslands (20.77%), (26.65%), (37.66%). Conversely, climate change is dominant factor Alpine southern–central Hangay Uul, temperature (20.69%) relative humidity (20.67%) playing critical roles. These insights are vital for Mongolian authorities: promoting green economic initiatives could mitigate economically active regions, while adaptation strategies essential climate-sensitive Meadows. The also provide valuable guidance addressing environmental issues other arid semi-arid worldwide.

Language: Английский

Citations

0
Spatiotemporal Characteristic of XCO2 and Its Changing Contribution Rate from Different Influencing Indicators in Mongolian Plateau of Central Asia DOI Creative Commons

A Yunga,

Zhengyi Bao, Siqin Tong

et al.

Atmosphere, Journal Year: 2025, Volume and Issue: 16(5), P. 560 - 560

Published: May 8, 2025

The Mongolian Plateau plays a crucial role in global carbon cycling, but the spatiotemporal characteristics of XCO2 concentration and its driving mechanism remain insufficiently explored. To solve this scientific issue, synergistic methodology mathematical statistics—the Pearson correlation random forest model—was established using main source Orbiting Carbon Observatory 2 (OCO-2) satellite data. Results indicate following: (1) Average was 412 ppm, with an annual growth rate 2.29 ppm/a from 2018 to 2022, along higher values south lower north. seasonal change displayed clear temporal feature, order spring (414.83 ppm) > winter (413.4 autumn (411.3 summer (409.12 ppm). spatial distributions spring, autumn, were relatively consistent, all showing concentrations east west, whereas exhibited opposite pattern. (2) From perspective natural environment, negatively correlated normalized difference vegetation index (NDVI), precipitation (PRE), temperature (TEMP). Temporal analysis further revealed that negative most pronounced eastern region, which these three elements high. (3) According model, influence both single interactive factors on plateau’s varied significantly. A comparison NDVI had highest contribution (0.35), followed by fossil fuel combustion emissions (ODIAC), wind direction (WD), speed (WS). As for interaction effects, combination ODIAC showed (over 0.25), indicating strong joint XCO2. Other important interactions included WS WD, WS, (all above 0.05). These findings provide valuable insights into mechanisms Plateau, offering reference regional emission reduction policies.

Language: Английский

Citations

0
Atmospheric CO2 in the megacity Hangzhou, China: Urban-suburban differences, sources and impact factors DOI
Yuanyuan Chen,

Yanran Lu,

Bing Qi

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 926, P. 171635 - 171635

Published: March 13, 2024

Language: Английский

Citations

3
Convergent control of soil temperature on seasonal carbon flux in Tibetan alpine meadows: An in-situ monitoring study DOI Creative Commons

Yuhua Xing,

Pei Wang, Dapeng Zhang

et al.

Ecological Indicators, Journal Year: 2023, Volume and Issue: 156, P. 111116 - 111116

Published: Oct. 20, 2023

The Tibet Plateau, with its extensive carbon pools, plays a pivotal role in the global budget. Nevertheless, driving factors of dioxide budget remain disputed, and impact freeze–thaw process on release is still unclear due to harsh climate lack monitoring data. To clarify primary affecting alpine meadow ecosystems examine release, we employed LI-8150 automated continuous measurement system. This system, conjunction eddy covariance meteorological data, Boosted Regression Tree (BRT) model, multiple stepwise regression analysis, were used analyze seasonal variations flux (e.g., net ecosystem exchange [NEE], gross productivity [GPP], respiration [Reco]). We also investigate sources sinks ecosystem, as well predominant factor flux. Our findings include: (1) shift seasonally monthly daily scales. On scale, functions moderate sink June, July, August, September weak source from October through May. (2) Overall, located northeastern Qinghai Lake basin, serves (-58.53 g C m−2 year−1). (3) Soil temperature most observed NEE, Reco, GPP, contributing 48.05 %, 78.61 65.05 respectively. temperature, soil water dynamics influenced by freeze thaw processes, their interaction plant growth collectively play crucial regulating ecosystems. provide first-hand observational data for offer future guidance studying Plateau.

Language: Английский

Citations

6
Analysis of Spatiotemporal Distribution Characteristics of Carbon Dioxide Column Concentration in Inner Mongolia Region DOI

云嘎 阿

Geographical Science Research, Journal Year: 2024, Volume and Issue: 13(02), P. 389 - 398

Published: Jan. 1, 2024

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