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Urban heat island (UHI) is one of the most distinctive characteristics of urban climate. The objective of this study is to apply a statistical modeling of the nocturnal atmospheric UHI based on the relationship between observed air temperature from ground stations and remotely sensed temperature of the urban surface. The goal of the approach is to limit input data for the developed modeling method in order to assure transferability of the methodology in different cities. Time series of surface temperature and normalized difference vegetation index are obtained from the MODIS instrument for a 10-year period (2008-2017). The air temperature is collected from the in-situ observational network of 21 stations. The studies are conducted for different locations with gradual changes in urbanization in order to assess the impact of urbanization on the relationship between simultaneous air and surface UHI. The urbanization is described by commonly available land cover metrics. Results showed that the proposed approach provides satisfactory AUHI modeling results for the locations with the least degree of urbanization. The best results are obtained with a simple linear regression model with the iterative procedure to minimize the mean absolute gross error (MAGE). The lowest MAGE for modeled UHI is 1.18°C with 69% of the variance explained. The strongest linear relationship between simultaneous SUHI and AUHI is noted for those station pairs whose surroundings have the highest differences in urbanization, and the highest UHI intensities are observed. The strength of the SUHI/AUHI linear relationship decreases gradually with the increasing urbanization of the stations' surroundings. Index Terms-Air temperature (AT), prediction, regression, statistical modeling, surface temperature, urban climate, urban heat island (UHI).

For many years, the Polish air quality modelling system was decentralized, which significantly hampered the appropriate development of methodologies, evaluations, and comparisons of modelling results. The major contributor to air pollution in Poland is the residential combustion sector. This paper demonstrates a novel methodology for residential emission estimation utilized for national air quality modelling and assessment. Our data were compared with EMEP and CAMS inventories, and despite some inequalities in country totals, spatial patterns were similar. We discuss the shortcomings of the presented method and draw conclusions for future improvements.

One of the most important minor species in the atmosphere is nitrogen dioxide (NO2). The primary objective of the presented research was to propose a method to adjust emission inventories (emission fluxes) using tropospheric NO2 columns observed by OMI and SCIAMACHY instruments. Modified emission fluxes were used in a chemical weather model GEM-AQ. The GEM-AQ model results were compared with the monthly averaged satellite-derived column amount of NO2 over Europe for the 2008–2010 observing period. It was shown that the observed and modelled spatial distribution of high values of the NO2 column is highly correlated with the distribution of major anthropogenic sources in the modelling domain. The presented findings highlight the importance of the anthropogenic sources in the overall budget of NO2 in the polluted troposphere. Regions for which modelling results showed underestimation or overestimation compared with observations were constant for the whole analysis period. Thus, the NO2 column observations could be used for correcting emission estimates. The proposed emission correction method is based on the differences in modelled and satellite-derived NO2 columns. Modelling was done for 2011 using the original and adjusted emission inventories and compared with observed NO2 columns. The analysis was extended to compare modelling results with surface NO2 observations from selected air quality stations in Poland. A significant improvement in modelling results was obtained over regions with large overestimations in the control run for which the original emission fluxes were used.

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