A5058874663- Atmospheric and Environmental Gas Dynamics
- Atmospheric chemistry and aerosols
- Air Quality and Health Impacts
- Environmental Impact and Sustainability
- Spatial and Panel Data Analysis
- Economic and Environmental Valuation
- Vehicle emissions and performance
- Energy, Environment, and Transportation Policies
- Soil Geostatistics and Mapping
- Climate Change Policy and Economics
- Air Quality Monitoring and Forecasting
Polish Academy of Sciences
2016-2019
Polish Academy of Learning
2019
Systems Research Institute
2016-2018
Emission inventories (EIs) are the fundamental tool to monitor compliance with greenhouse gas (GHG) emissions and emission reduction commitments. Inventory accounting guidelines provide best practices help EI compilers across different countries regions make comparable, national estimates regardless of differences in data availability. However, there a variety sources error uncertainty that originate beyond what inventory can define. Spatially explicit EIs, which key product for atmospheric...
Greenhouse gas (GHG) inventories at national or provincial levels include the total emissions as well for many categories of human activity, but there is a need spatially explicit GHG emission inventories. Hence, aim this research was to outline methodology producing high-resolution inventory, demonstrated Poland. sources were classified into point, line, and area types then combined calculate emissions. We created vector maps all economic activity covered by IPCC guidelines, using official...
Agricultural activity plays a significant role in the atmospheric carbon balance as source and sink of greenhouse gases (GHGs) has high mitigation potential. The agricultural emissions display evident geographical differences regional, national, even local levels, not only due to spatially differentiated activity, but also very geographically different emission coefficients. Thus, resolved inventories are important for obtaining better estimates content design GHG processes adapt global rise...
Consider the problem of allocation spatially correlated gridded data to finer spatial scale, conditionally on covariate information observable in a fine grid.Spatial dependence process can be captured with conditional autoregressive structure, suitable for (areal level) data.Also geostatistical methods, particularly empirical universal kriging, used this purpose.In study, we compare prediction results as well standard errors two disaggregation procedures, based inventory agricultural ammonia...