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Abstract. Scientific research studies conducted in various parts of the world confirm that PM10 concentrations in urban air depend to a great extent on the resuspension processes of the dust deposited on the road surface. The paper presents the results of the study related to the determination of the re-entrained PM10 emissions from four selected streets of Krakow (Southern Poland) together with the assessment of its impact on air quality. Examined streets are characterised by different traffic intensity (from 500 to over 20 000 vehicles per day) and individual vehicle structure. Dust material sampling and estimation of the PM10 emission were conducted according to the U.S. EPA methodology (AP 42 Fifth Edition). Two variants of sample collection were applied: from the road surface including the area at the curb (4 streets) and from the road surface alone (1 street). The estimates of resuspended road dust emission as well as the reference values derived from the U.S. EPA guidelines were used to assess the impact of this emission on the PM10 levels in the air at the location of one of the analysed streets. This assessment was conducted using the CALINE4 mathematical model. The study showed that the PM10 emissions from the re-entrained road dust can be responsible for up to 25 % in the winter and 50 % in the summer of the total PM10 concentrations in the air near the roads.

[1] An uncertainty assessment of a volatile organic compounds (VOCs) emission inventory using a Monte Carlo study according to the ''Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories'' has been performed. For the episode of 1-10 July 2000 hourly biogenic VOC (BVOC) emissions from forests in Poland were calculated in 10 km  10 km resolution with a semiempirical BVOC model (seBVOC). Driving parameters of the model were land cover, temperature, light intensity, foliar biomass, leaf area index (LAI), and plant-specific emission factors. The hourly meteorology input has been modeled with the nonhydrostatic Multiscale Climate Chemistry Model (MCCM). For each of the driving parameters, probability distribution functions (PDFs) based on the normal and log-normal distributions have been identified. Repeated runs of the seBVOC model in the Monte Carlo study with random figures drawn from the probability distribution functions yield the error distribution and the uncertainties. The results show an uncertainty in isoprene emission of the entire modeled period and modeling domain in the range from À71% to 73%, in monoterpene emissions in the range of À57% to 140%, and in other VOC (OVOC) emissions in the range of À55% to 57%. Uncertainties in daily estimates for the domain were higher ranging between À84% and 98% for isoprene, À63% and 147% for monoterpenes, and 63% and 72% for other VOCs. Largest uncertainty results from errors of the emission factors followed by errors in temperature and foliar biomass. These uncertainties cover only a subset of possible variables and are less than the total uncertainty.Citation: Smiatek, G., and M. Bogacki (2005), Uncertainty assessment of potential biogenic volatile organic compound emissions from forests with the Monte Carlo method: Case study for an episode from 1 to 10

Air pollution in urban-industrial areas is caused by simultaneous impact of many factors, including different types of emission sources. Ambient air quality in Krakow is a crucial problem regarding the regularly occurring exceedances of limit values of particulate matter and some of its chemical compounds. This paper presents quantification of urban, suburban and industrial background of dust substances concentrations that are present in the industrialized area, located in the vicinity of scattered household and road traffic emission sources. There were included the concentrations of such substances as: particulate matter (PM10), benzo(a)pyrene, arsenic, cadmium, lead and nickel. The impact of daytime and season of the year (especially heating and non-heating season) on variability of air pollutant concentrations was examined. In order to distinguish between local and inflow background of air pollutants the additional meteorological data concerning wind speed and direction was considered. The performed analyses included application of statistical methods, among others principal component analysis (PCA). Some of the results were visualized via R programming environment, providing tools for air pollution data processing (openair package). The backward trajectories modelling using HYSPLIT model, allowed the validation of wind direction analyses. The conducted research revealed the strong dependence of air pollution background type influencing the measurement results on instantaneous wind direction.

The hydraulic fracturing jobs performed on shale rocks are connected with atmospheric emissions of dusts and exhaust gases from high-power motors supplying pump aggregates used for fracturing operations and from other technological devices. The total power of motors driving technological systems depends on the specific character of deposit and well and may range between a dozen to tens of thousands kW. An exemplary set of technological systems used for frac jobs is presented in figure 1. The following substances are emitted to the atmosphere during engine operation, e.g. nitrogen oxides (NO x ), sulfur dioxide (SO 2 ), carbon oxide (CO), dust PM10, ammonia, benzo(a)pyrene (B(a)P), benzene, toluene, xylene, formaldehyde, acetaldehyde, acrolein. As a consequence admissible concentrations of these substances in air can be exceeded.The influence of dust and gaseous emissions accompanying shale rock fracturing jobs is addressed in this paper. Model analyses were performed. An exemplary model of a process used for simulating propagation of atmospheric emissions in a specified calculation area (1,150 m × 1,150 m) were based on the analysis of hydraulic fracturing jobs performed in wells in Poland and abroad. For making calculations more actual, the model was located in the Gdańsk area and was ascribed its typical meteorological and orographic parameters. In the center of this area a rig site 150 m x 150 m was distinguished. The emission field was generated by 12 high-power engines supplying pump aggregates, 1680 kW each. The time of work of particular engines was established for 52 hrs (13 frac jobs, each lasting 4 hrs). It was assumed that all engines will operate simultaneously and using 100% of their power.Attention was paid to the correct modelling of the real emission field. Technical parameters of motors and the applied fuels were characterized. Emission indices were worked out by, e.g. U.S. Environmental Protection Agency or European Environment Agency.The calculations of air pollutions from analyzed motors were performed with a mathematical modelling method using Gaussian plum.The results of calculations could be used for evaluating spatial distribution of maximum 1 hour concentrations (S 1 ), incidence of exceeding admissible 1 hour concentration values (P(D 1 )), percentile 99.8 or 99.726 from 1 hour concentrations and average concentrations (S a ) for selected most important for the air quality contaminants, i.e. NO x (as NO 2 ), SO 2 , CO, PM10, benzo(a)pyrene, benzene, toluene, ) or dust PM10 can be expected to exceed these values, e.g. in a situation when the total power installed in motors driving technological systems in the course of hydraulic fracking will be higher than assumed in the analyses.The results of calculations show to a significant impact of nitrogen oxides (NO x ) and dust PM10 emissions on air quality. The risk that emission standards are exceeded beyond the rig area is conditioned both by technological factors (total power of operating motors, parameters of combusted fuel, reduced emiss...

Assessment of the impact on air quality for combustion sources should be carried out using advanced modelling systems with chemical transformation modules taken into account, especially for the facilities characterized by significant emission of gaseous air pollutants (including SO2). This approach increases the reliability of the obtained evaluation results by modelling the formation of secondary inorganic aerosol (SIA) in the air which can substantially contribute to PM10. This paper assesses in this regard selected chemical transformation modules (MESOPUFF, RIVAD/ARM3, ISORROPIA/RIVAD) available in the CALPUFF model (v. 6.42) and its application in the atmospheric dispersion modelling of air emissions from a coal-fired large combustion plant (LCP) not equipped with a flue gas desulphurization (FGD) system. It has been proven that consideration an additional mechanism of secondary sulfate aerosol formation in aqueous phase in the ISORROPIA/RIVAD module (AQUA option) causes a significant increase in the annual average concentration of PM10 in the air compared to the other considered options, along with the calculation variant which excludes chemical transformation mechanisms. Type of the selected chemical transformation module has no significant effect on the results of modelled NO, NO2 and NOx concentrations in the air. However, it can lead to different SO2 results, especially for annual averaged, and in some points, for the hourly averaged concentrations.

The paper presents the impact of the cleaning of a dual carriageway located in a street canyon in Krakow (Southern Poland) on the levels of the PM10 and PM2.5 air concentrations. For this purpose, representative dust samples from the analysed street were collected corresponding to the street cleaning situation, the re-entrained road dust PM10 and PM2.5 emissions were estimated in accordance with the U.S. EPA guidelines, and the particulate matter atmospheric dispersion modelling was carried out using the CALINE4 model for a selected episode of street cleaning. The modelling results were compared with the measurement results of the PM10 and PM2.5 air concentrations obtained from the air quality monitoring station (traffic type) located in the middle of this street canyon. The results of the air dispersion modelling in the canyon zone confirmed the strong impact of street cleaning on the temporary (1-hour) PM10 concentrations in the direct vicinity of the cleaned section depending on meteorological conditions. During the cleaning episode, no significant increase in the PM2.5 concentration in the air was observed.