Urbanization can change the local climate of an area, one manifestation of which is a rise in the local temperature of built-up areas, a phenomenon known as an urban heat island. The thermal response of built-up areas in comparison to natural areas is quantified in terms of surface urban heat island (SUHI) intensity. The work presented here evaluates the seasonal SUHI intensities in Delhi using local climate zones (LCZs) and conventional SUHI indicators in parallel. Statistical analyses are carried out to determine the relationship between them and to delineate heat stressed zones in the Delhi city region. The present study is the first one that utilizes LCZs for seasonal SUHI analysis in Delhi. The land surface temperature (LST) is assessed using a hundred and five night-time images from MODIS. Unambiguous night-time SUHI effect is seen for all seasons. The maximum night-time SUHI intensity is 3.5° C, between “compact low-rise” (LCZ 3) and “low plants” (LCZ D) in summer and winter. The conventional indicator “Inside urban-Inside rural” gives the highest night-time SUHI intensity of 3.3° C, in autumn. Statistical analyses show that “compact low-rise” (LCZ3) and “large low-rise” (LCZ8) are the most heat-stressed LCZs. The largest number of distinct thermal zones is created in the monsoon, followed by summer and winter. The results suggest that in order to minimize the UHI effect, further urban expansion in the Delhi region should be restricted to LCZ 5 (open mid-rise) and LCZ 6 (open low-rise).

Surface Urban Heat Island (SUHI) is a phenomenon of high spatial and temporal variability. However, studies that investigate urban land surface temperature (LST) observed in different seasons frequently utilize a single satellite measurement and do not incorporate temporal composites. Temporally aggregated data increase clear sky coverage, which is important in many aspects of urban climatology. However, it is critical to account for possible errors and quality of the data that are utilized. The objective of this paper is to analyze the impact of MODIS Quality Control (QC) and the view angle on temporally aggregated urban surface temperature and long-term SUHI intensity. To achieve this, a weighted arithmetic mean was utilized whose weights were based on the view angle of satellite observation and the MODIS QC flags; namely, LST retrieval errors and emissivity errors. In order to investigate the impact of the MODIS QC on long-term LST composites, five exponential powers were applied to weights during the temporal aggregation process, resulting in five thresholds of best quality pixel promotion. It was found that there are significant differences between temporal composites that take into account the MODIS QC and the view angle and those that do not (obtained by means of a simple arithmetic mean with no weights applied), in terms of spatial distribution and density distribution of urban and rural LST. The differences were more distinctive in spring or daytime cases than in autumn or nighttime cases. The impact of the MODIS QC and the view angle on temporal composites was highest in the city center. Ten SUHI indicators were utilized. It was found that the impact on long-term SUHI intensity is weaker than on the spatial pattern of LST and that SUHI indicators are inconsistent.

Urban heat island (UHI) is one of the most distinctive characteristics of urban climate. The objective of the presented study was to apply a statistical modelling 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 was to limit input data for the developed modelling method in order to assure transferability of the methodology to different cities. Time series of surface temperature and NDVI were obtained from the MODIS instrument for a 10 year period (2008-2017). The air temperature was collected from the in-situ observational network of 21 stations. The study was 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 was described by commonly available land cover metrics. Results showed that the proposed approach provided satisfactory AUHI modelling results for the locations with the least degree of urbanization. The best results were obtained with a simple linear regression model with the iterative procedure to minimize the mean absolute gross error (MAGE). The lowest MAGE for modelled UHI was 1.18°C with 69% of the variance explained. The strongest linear relationship between simultaneous SUHI and AUHI was noted for those station pairs which their surroundings have the highest differences in urbanization, and the highest UHI intensities observed. The strength of the SUHI/AUHI linear relationship decreases gradually with the increasing urbanization of the stations' surroundings.

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.

In the scope of the AQMEII Phase 1 project the GEM-AQ model was run over Europe for the year 2006. The modelling domain was defined using a global variable resolution grid with a rotated equator and uniform resolution of 0.2° × 0.2° over the European continent. Spatial distribution and temporal variability of the GEM-AQ model results were analysed for surface ozone and PM₁₀ concentrations. Model results were compared with measurements available in the ENSEMBLE database. Statistical measures were used to evaluate performance of the GEM-AQ model. The mean bias error, the mean absolute gross error and the Pearson correlation coefficient were calculated for the maximum 8 h running average ozone concentrations and daily mean PM₁₀ concentrations. The GEM-AQ model performance was characterized for station types, European climatic regions and seasons. The best performance for ozone was obtained at suburban stations, and the worst performance was obtained for rural stations where the model tends to underestimate. The best results for PM₁₀ were calculated for urban stations, while over most of Europe concentrations at rural sites were too high. Discrepancies between modelled and observed concentrations were discussed in the context of emission data uncertainty as well as the impact of large-scale dynamics and circulation of air masses. Presented analyses suggest that interpretation of modelling results is enhanced when regional climate characteristics are taken into consideration.