Maciej Kryza

Local regression models for spatial interpolation of urban heat island—an example from Wrocław, SW Poland

Geographically weighted regression algorithm (GWR) has been applied to derive the spatial structure of urban heat island (UHI) in the city of Wrocław, SW Poland. Seven UHI cases, measured during various meteorological conditions and characteristic of different seasons, were selected for analysis. GWR results were compared with global regression models (MLR), using various statistical procedures including corrected Akaike Information Criterion, determination coefficient, analysis of variance, and Moran’s I index. It was found that GWR is better suited for spatial modeling of UHI than MLR models, as it takes into account non-stationarity of the spatial process. However, Monte Carlo and F3 tests for spatial stationarity of the independent variables suggest that for several spatial predictors a mixed GWR–MLR approach is recommended. Both local and global models were extended by the interpolation of regression residuals and used for spatial interpolation of the UHI structure. The interpolation results were evaluated with the cross-validation approach. It was found that the incorporation of the spatially interpolated residuals leads to significant improvement of the interpolation results for both GWR and MLR approaches. Because GWR is better justified in terms of statistical specification, the combined GWR + interpolated regression residuals (GWR residual kriging; GWRK) approach is recommended for spatial modeling of UHI, instead of widely applied MLR models.

Modelling future impacts of air pollution using the multi-scale UK Integrated Assessment Model (UKIAM).

Integrated assessment modelling has evolved to support policy development in relation to air pollutants and greenhouse gases by providing integrated simulation tools able to produce quick and realistic representations of emission scenarios and their environmental impacts without the need to re-run complex atmospheric dispersion models. The UK Integrated Assessment Model (UKIAM) has been developed to investigate strategies for reducing UK emissions by bringing together information on projected UK emissions of SO2, NOx, NH3, PM10 and PM2.5, atmospheric dispersion, criteria for protection of ecosystems, urban air quality and human health, and data on potential abatement measures to reduce emissions, which may subsequently be linked to associated analyses of costs and benefits. We describe the multi-scale model structure ranging from continental to roadside, UK emission sources, atmospheric dispersion of emissions, implementation of abatement measures, integration with European-scale modelling, and environmental impacts. The model generates outputs from a national perspective which are used to evaluate alternative strategies in relation to emissions, deposition patterns, air quality metrics and ecosystem critical load exceedance. We present a selection of scenarios in relation to the 2020 Business-As-Usual projections and identify potential further reductions beyond those currently being planned.

The Effect of Emission from Coal Combustion in Nonindustrial Sources on Deposition of Sulfur and Oxidized Nitrogen in Poland

Abstract Poland has one of the largest sulfur and nitrogen emissions in Europe. This is mainly because coal is a main fuel in industrial and nonindustrial combustion. The aim of this paper is to assess the amount of sulfur and nitrogen deposited from SNAP sector 02 (nonindustrial sources) coal combustion. To assess this issue, the Fine Resolution Atmospheric Multipollutant Exchange (FRAME) model was used. The results suggest that industrial combustion has the largest impact on deposition of oxidized sulfur, whereas the oxidized nitrogen national deposition budget is dominated by transboundary transport. The total mass of pollutants deposited in Poland, originating from nonindustrial coal combustion, is 45 Gg of sulfur and 2.5 Gg of nitrogen, which is over 18% of oxidized sulfur and nearly 2% of oxidized nitrogen deposited. SNAP 02 is responsible for up to 80% of dry-deposited sulfur and 11% of nitrogen. The contribution to wet deposition is largest in central Poland in the case of sulfur and in some areas can exceed 11%. For oxidized nitrogen, nonindustrial emissions contribute less than 1% over the whole area of Poland. The switch from coal to gas fuel in this sector will result in benefits in sulfur and nitrogen deposition reduction.

The role of annual circulation and precipitation on national scale deposition of atmospheric sulphur and nitrogen compounds

Atmospheric circulation and rainfall are important factors controlling the deposition of atmospheric pollutants. This paper aims to quantify the role of these factors in the deposition of sulphur and nitrogen compounds, using case studies in the United Kingdom and Poland. The FRAME model has been applied to calculate deposition for the base year (2005), dry and wet years (2003 and 2000 for the UK and 2003 and 1974 for Poland, respectively), and for years with contrasting annual wind patterns (1986 and 1996 for the UK, and 1998 and 1996 for Poland). Variation in annual wind and rainfall resulted in statistically significant changes in spatial patterns of deposition and the national deposition budget of sulphur and nitrogen compounds in both countries. The deposition budgets of S and N are 5% lower than for the reference year if the dry year is considered in both countries. For the wet year, there is an increase in country total deposition by up to 17%. Years with an increased frequency of eastern winds are associated with an increase in deposition of up to 14% in Poland and 8% in the UK. The national deposition budget is below the average for the years with high frequencies of W winds, especially for the UK (up to 13%). Wet deposition varies due to meteorological factors to a larger extent than dry deposition. In Poland, the changes in national deposition budget due to meteorological factors exceed the changes resulting from emission abatements in years 2000-2009 for nitrogen compounds. In the UK, emission abatements influence the national deposition budget to a larger extent than meteorological changes (except for NH(x)). The findings are important in relation to future climate changes, especially considering the potential increase in annual precipitation. This may lead to an increase in deposition over mountainous areas with sensitive ecosystems, where annual rainfall brings significant load of S and N. Changes in annual wind speed and frequency can modify the spatial pattern of deposition. An increased frequency of W winds will benefit both countries through reduced S and N deposition. NW areas of Poland and the UK will suffer from above-average deposition during years with enhanced easterly flow, and this may result in critical loads for acid and nitrogen deposition being exceeded over the areas that are at present sufficiently protected from acidification and eutrophication, despite the ongoing emission abatements.

Quantifying missing annual emission sources of heavy metals in the United Kingdom with an atmospheric transport model.

An atmospheric chemical transport model was adapted to simulate the concentration and deposition of heavy metals (arsenic, cadmium, chromium, copper, lead, nickel, selenium, vanadium, and zinc) in the United Kingdom. The model showed that wet deposition was the most important process for the transfer of metals from the atmosphere to the land surface. The model achieved a good correlation with annually averaged measurements of metal concentrations in air. The correlation with measurements of wet deposition was less strong due to the complexity of the atmospheric processes involved in the washout of particulate matter which were not fully captured by the model. The measured wet deposition and air concentration of heavy metals were significantly underestimated by the model for all metals (except vanadium) by factors between 2 and 10. These results suggest major missing sources of annual heavy metal emissions which are currently not included in the official inventory. Primary emissions were able to account for only 9%, 21%, 29%, 21%, 36%, 7% and 23% of the measured concentrations for As, Cd, Cr, Cu, Ni, Pb and Zn. A likely additional contribution to atmospheric heavy metal concentrations is the wind driven re-suspension of surface dust still present in the environment from the legacy of much higher historic emissions. Inclusion of two independent estimates of emissions from re-suspension in the model was found to give an improved agreement with measurements. However, an accurate estimate of the magnitude of re-suspended emissions is restricted by the lack of measurements of metal concentrations in the re-suspended surface dust layer.

Influence of Selected Meteorological Variables on the Questing Activity of Ixodes ricinus Ticks in Lower Silesia, SW Poland

ABSTRACT: The relationship between climate data and tick questing activity is crucial for estimation of the spatial and temporal distribution of the risk of ticks and tick-borne diseases. This study establishes correlations between selected meteorological variables provided by the Weather Research and Forecasting model (WRF) and the questing activity of Ixodes ricinus nymphs and adults on a regional scale across Lower Silesia, Poland. Application of Generalized Linear Mixed Models (GLMM), built separately for adults and nymphs, showed that solar radiation, air temperature, and saturation deficit appeared to be the meteorological variables of prime importance, whereas the wind speed was less important. However, the effect of meteorological parameters was different for adults and nymphs. The adults are also more influenced by forest cover and the percentage of forest type if compared to nymphs. The WRF model providing meteorological variables separately for each location and day of tick sampling can be useful in studies of questing activity of ticks on a regional scale.

Modelling deposition and air concentration of reduced nitrogen in Poland and sensitivity to variability in annual meteorology.

The relative contribution of reduced nitrogen to acid and eutrophic deposition in Europe has increased recently as a result of European policies which have been successful in reducing SO(2) and NO(x) emissions but have had smaller impacts on ammonia (NH(3)) emissions. In this paper the Fine Resolution Atmospheric Multi-pollutant Exchange (FRAME) model was used to calculate the spatial patterns of annual average ammonia and ammonium (NH(4)(+)) air concentrations and reduced nitrogen (NH(x)) dry and wet deposition with a 5 km × 5 km grid for years 2002-2005. The modelled air concentrations of NH(3) and dry deposition of NH(x) show similar spatial patterns for all years considered. The largest year to year changes were found for wet deposition, which vary considerably with precipitation amount. The FRAME modelled air concentrations and wet deposition are in reasonable agreement with available measurements (Pearsons correlation coefficients above 0.6 for years 2002-2005), and with spatial patterns of concentrations and deposition of NH(x) reported with the EMEP results, but show larger spatial gradients. The error statistics show that the FRAME model results are in better agreement with measurements if compared with EMEP estimates. The differences in deposition budgets calculated with FRAME and EMEP do not exceed 17% for wet and 6% for dry deposition, with FRAME estimates higher than for EMEP wet deposition for modelled period and lower or equal for dry deposition. The FRAME estimates of wet deposition budget are lower than the measurement-based values reported by the Chief Inspectorate of Environmental Protection of Poland, with the differences by approximately 3%. Up to 93% of dry and 53% of wet deposition of NH(x) in Poland originates from national sources. Over the western part of Poland and mountainous areas in the south, transboundary transport can contribute over 80% of total (dry + wet) NH(x) deposition. The spatial pattern of the relative contribution of national sources to total deposition of NH(x) may change significantly due to the general circulation of air.

Regression-based air temperature spatial prediction models: an example from Poland

A Geographically Weighted Regression – Kriging (GWRK) algorithm, based on the local Geographically Weighted Regression (GWR), is applied for spatial prediction of air temperature in Poland. Hengl’s decision tree for selecting a suitable prediction model is extended for varying spatial relationships between the air temperature and environmental predictors with an assumption of existing environmental dependence of analyzed temperature variables. The procedure includes the potential choice of a local GWR instead of the global Multiple Linear Regression (MLR) method for modeling the deterministic part of spatial variation, which is usual in the standard regression (residual) kriging model (MLRK). The analysis encompassed: testing for environmental correlation, selecting an appropriate regression model, testing for spatial autocorrelation of the residual component, and validating the prediction accuracy. The proposed approach was performed for 69 air temperature cases, with time aggregation ranging from daily to annual average air temperatures. The results show that, irrespective of the level of data aggregation, the spatial distribution of temperature is better fitted by local models, and hence is the reason for choosing a GWR instead of the MLR for all variables analyzed. Additionally, in most cases (78%) there is spatial autocorrelation in the residuals of the deterministic part, which suggests that the GWR model should be extended by ordinary kriging of residuals to the GWRK form. The decision tree used in this paper can be considered as universal as it encompasses either spatially varying relationships of modeled and explanatory variables or random process that can be modeled by a stochastic extension of the regression model (residual kriging). Moreover, for all cases analyzed, the selection of a method based on the local regression model (GWRK or GWR) does not depend on the data aggregation level, showing the potential versatility of the technique.

Modelling meteorological conditions for the episode (December 2009) of measured high PM 10 air concentrations in SW Poland – application of the WRF model

The weather research and forecasting model has been applied to derive information on meteorological variables for the period with high concentrations of PM 10 (1–30 December 2009) in SW Poland. Three one-way nested domains have been used and the results for the innermost domain have been compared with surface and radiosonde meteorological measurements for pressure (PRES), air temperature (TMP), specific humidity (SPFH), wind speed (WIND) and direction (WDIR). The model results are in good agreement with the surface measurements for TMP, PRES and SPFH, with the index of agreement (IOA) above 0.9. The model underestimate the observed PRES, TMP and SPFH except for the mountainous site Śniezka. The WIND is biased high, the overall IOA is 0.62, and range from 0.41 to 0.73 for all stations. The IOA is above 0.73 for TMP and SPFH for radiosonde measurements and the errors decrease with height.

The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland

Our study examines the role of auxiliary variables in the process of spatial modelling and mapping of climatological elements, with air temperature in Poland used as an example. The multivariable algorithms are the most frequently applied for spatialization of air temperature, and their results in many studies are proved to be better in comparison to those obtained by various one-dimensional techniques. In most of the previous studies, two main strategies were used to perform multidimensional spatial interpolation of air temperature. First, it was accepted that all variables significantly correlated with air temperature should be incorporated into the model. Second, it was assumed that the more spatial variation of air temperature was deterministically explained, the better was the quality of spatial interpolation. The main goal of the paper was to examine both above-mentioned assumptions. The analysis was performed using data from 250 meteorological stations and for 69 air temperature cases aggregated on different levels: from daily means to 10-year annual mean. Two cases were considered for detailed analysis. The set of potential auxiliary variables covered 11 environmental predictors of air temperature. Another purpose of the study was to compare the results of interpolation given by various multivariable methods using the same set of explanatory variables. Two regression models: multiple linear (MLR) and geographically weighted (GWR) method, as well as their extensions to the regression-kriging form, MLRK and GWRK, respectively, were examined. Stepwise regression was used to select variables for the individual models and the cross-validation method was used to validate the results with a special attention paid to statistically significant improvement of the model using the mean absolute error (MAE) criterion. The main results of this study led to rejection of both assumptions considered. Usually, including more than two or three of the most significantly correlated auxiliary variables does not improve the quality of the spatial model. The effects of introduction of certain variables into the model were not climatologically justified and were seen on maps as unexpected and undesired artefacts. The results confirm, in accordance with previous studies, that in the case of air temperature distribution, the spatial process is non-stationary; thus, the local GWR model performs better than the global MLR if they are specified using the same set of auxiliary variables. If only GWR residuals are autocorrelated, the geographically weighted regression-kriging (GWRK) model seems to be optimal for air temperature spatial interpolation.