Conference Agenda

Satellite remote sensing measurements of atmospheric greenhouse gas represent independent constraints to help verify carbon emission inventories. To improve understanding of the global carbon cycle, China is preparing to launch the next generation carbon monitoring satellite, TanSat-2, which will collect column measurements of carbon dioxide (CO₂) and methane (CH₄). In this paper, we investigate the potential of TanSat-2 observations for assessing anthropogenic carbon emissions and ecosystem carbon sinks. To achieve this objective, we introduce a new carbon flux estimation method that combines atmospheric measurements of CO₂ and solar induced fluorescence (SIF) to jointly constrain net primary production (NPP) and fossil fuel combustion (FF). We apply empirical orthogonal functions (EOF) analysis to the NPP and FF inventories to identify dominant spatial–temporal patterns to reduce the size of state vector and to help disaggregate the natural and fossil fuel combustion sources of CO₂. Using closed-loop numerical experiments, Observing System Simulation Experiment (OSSE), we find that TanSat-2 CO₂ and SIF observations lead to an NPP error reduction of up to 95% over Siberia and Amazon, and to error reductions of about 80% for FF emissions over Siberia, North Aisa, US and South Africa if measurement bias could be eliminated. However Our OSSE experiments show that presence of even a small XCO₂ bias can cause serious of distortion or mis-allocation of CO2 sources and sinks. Therefore systematic observation bias needs to be properly addressed n retrieval and data application. We also explore the importance of adopting a large across-track swath in delivering robust CO₂ flux estimates and develop an error matrix tool that could be used in future mission assessment.

GIS-based data-driven analysis for urban GHG emissions in Northern Ireland

Ming Jun Huanga* ^a, Neil Hewitt ^a, Leila Darvishvand ^a, Xingying Zhang ^b, Lu Zhang ^b

^a Belfast school of architecture and built environment, Ulster University, N. Ireland, UK

^b National Satellite Meteorological Center, China Meteorological Administration, China

* m.huang@ulster.ac.uk

In Northern Ireland, green house gas (GHG) emission from buildings takes one third of the total GHG emission. Understanding the distribution and characteristics of the residential building stock is essential for informing housing policy, urban regeneration, and sustainable planning. Northern Ireland, with its diverse urban and rural environments, offers a unique context for studying building typologies and development patterns. This study aims to perform a GIS-based, data-driven analysis of residential buildings across Northern Ireland, focusing on the spatial distribution of property types, construction age bands, property sizes, energy efficiency levels, central heating fuel types, and demographic patterns.

The study integrates multiple datasets, including energy performance data, census records, and building stock data, within a geographic information system (GIS) framework. Datasets were pre-processed to ensure consistency and spatial integration. Data-driven methods, including statistical aggregation and exploratory spatial analysis, were employed to map and quantify the proportions of terraced, semi-detached, detached, and apartment-type properties, as well as the distributions of construction periods, property sizes, energy efficiency levels, and central heating fuel types across different districts. Thematic maps reveal distinct regional patterns, highlighting variations in housing types and historical development trends. Preliminary findings suggest strong spatial clustering of certain property types, construction age bands, and central heating fuel types, offering insights into regional housing characteristics and potential areas for targeted policy interventions, retrofitting initiatives, and sustainable development efforts.