. and Janssens-Maenhout, G. (2019), ‘High resolution temporal profiles in the Emissions Database for Global Atmospheric Research (EDGAR)’. Nature Scientific Data, manuscript submitted. Degórska, A., Ilyin, I., Travnikov, O. and Rozovskaya, O. (2017), ‘Country-specific study of cadmium pollution in Poland: data collection, pilot results and future work’. 18 th TFMM, Prague, May 2017. Available at: https://projects.nilu.no/ccc/tfmm/prague_2017/index.html . Denier van der Gon, H., Hendriks, C., Kuenen, J., Segers, A. and Visschedijk, A. (2011), ‘Description of current
Performance improvement is taken as the primary goal in the asset management. Advanced data analysis is needed to efficiently integrate condition monitoring data into the operation and maintenance. Intelligent stress and condition indices have been developed for control and condition monitoring by combining generalized norms with efficient nonlinear scaling. These nonlinear scaling methodologies can also be used to handle performance measures used for management since management oriented indicators can be presented in the same scale as intelligent condition and stress indices. Performance indicators are responses of the process, machine or system to the stress contributions analyzed from process and condition monitoring data. Scaled values are directly used in intelligent temporal analysis to calculate fluctuations and trends. All these methodologies can be used in prognostics and fatigue prediction. The meanings of the variables are beneficial in extracting expert knowledge and representing information in natural language. The idea of dividing the problems into the variable specific meanings and the directions of interactions provides various improvements for performance monitoring and decision making. The integrated temporal analysis and uncertainty processing facilitates the efficient use of domain expertise. Measurements can be monitored with generalized statistical process control (GSPC) based on the same scaling functions.
Post-industrial and post-mining areas have often been under strong anthropogenic pressure for a long time. As a result, such areas, after the ending of industrial activity require taking steps to revitalize them. It may cover many elements of the natural or urban environment, such as water, soil, vegetated areas, urban development etc. To carry out revitalization, it is necessary to determine the initial state of such areas, often using selected chemical, geophysical or ecological. After that it is also important to properly monitor the state of such areas to assess the progress of the revitalization process. For this purpose a variety of change detection technics were developed. Post-industrial areas are very often characterized by a large extent, are difficult to access, have complicated land cover. For this reason, it is particularly important to choose appropriate methods to assess the degree of pollution of such areas. Such methods should be as economical as possible and time-effective. A very desirable feature of such methods is that they should allow a quick assessment of the entire area. Geostatistics supplemented by modern remote sensing can be effective for this purpose. Nowadays, using remote sensing, it is possible to gather information simultaneously from the entire, even vast area, with high spatial, spectral and temporal resolution. Geostatistics in turn provides many tools that are able to enable rapid analysis and inference based on even very complicated often scarce spatial data sets obtained from ground measurement and satellite observations. The goal of the article was to present selected results obtained using geostatistical methods also related to remote sensing, which may be helpful for decision makers in revitalizing post-industrial and post-mining areas. The results described in this paper were based mostly on the previous studies, carried out by authors.
). Impact of the emissions of international sea traffic on airborne deposition to the Baltic Sea and concentrations at the coastline, Oceanologia, Volume 56 Issue 2, pp. 349-372. IMO. (2015). Guidance for the development of a Ship Efficiency, London. Johansson L., Jalkanen J.P. and Kiukkonen J. (2017). Global assessment of shipping emissions in 2015 on a high spatial and temporal resolution, Atmospheric Environment, vol. 167, pp 403-415. Rajewski P., Behrendt C. and Klyus O. (2013). Clean Shipping for Small Fishing Boat on Baltic Sea. Conference DIAGO® 2013, Ostrava
Zarządzanie przedsiębiorstwem wodociągowym. Uwarunkowania funkcjonowania i współczesne koncepcje zarządzania . P. Chudziński, Ed. Warszawa: PWE, 2018, pp.185-202.  R. Brouwer, C.M. Ordens, R. Pinto, M.T. Condesso de Melo. (2018, May). „Economic valuation of groundwater protection using a groundwater quality ladder based on chemical threshold levels”. Ecological Indicators. Vol. 88, pp. 292-304. DOI: 10.1016/j.ecolind.2018.01.041.  D. Carstens, R. Armer. (2019, Feb.). „Spatio-temporal analysis of urban changes and surface water quality”. Journal of Hydrology. Vol
., Perego, A., Salvadori, G., & Tumino, A. (2017). A comprehensive view of intelligent transport systems for urban smart mobility. International Journal of Logistics Research and Applications A Leading Journal of Supply Chain Management , 20 , 39-52. van Mead, N. (2017). Uber for bikes: how “dockless” cycles flooded China – and are heading overseas. The Guardian . Retrieved from https://www.theguardian.com/cities/2017/mar/22/bike-wars-docklesschina-millions-bicycles-hangzhou Min, W., & Wynter, L. (2011). Real-time road traffic prediction with spatio-temporal