Forest fires are a major contributor of atmospheric gaseous and particulate pollutants. With respect to forest fires, Greece faces one of Europe's most severe problems during summer. To create a forest fire emissions inventory, a database which holds data for forest fires in Greece during the period 1997-2003 was established in this study and a methodology for the quantification of both gaseous and particulate matter emissions from forest fires was developed. The contribution of forest fire pollutant emissions to the total anthropogenic and natural emissions in Greece has been estimated in detail for a specific period during July 2000 when widespread forest fires occurred in the Greek mainland. The mesoscale air quality modeling system UAM-AERO was used to quantify the contribution of forest fire emissions to the air pollution levels in Greece, and it was calculated that the forest fire emissions were the largest contributors to the air pollution problem in regions tens of kilometers away from the fire source during this period. The wildfire emissions were calculated to cause an increase in the average PM 10 concentration, organic aerosol mass, and gaseous concentration of several pollutants, among them CO, NO x , and NH 3 . An average contribution of 50% to the PM 10 concentration over the region around the burnt area and downwind of the fire source (approximately 500 km) is calculated with a maximum of 80%, whereas, for CO, the average contribution was 50% during this period. The theoretical calculations were compared with in situ observations of smoke aerosols captured by a backscatter lidar system over the Greater Athens Basin as well as with surface observations of NO 2 and O 3 and the calculated concentrations were in better agreement with observations when forest fire emissions were included in the model calculations.
Municipal wastewater treatment plants (WTP) emit odorous compounds that produce nuisance to the workers and nearby residents. Several chemical compounds contribute to odour problems, among them, sulphurous organic compounds, hydrogen sulphide, phenols and indoles, ammonia, volatile amines and volatile fatty acids. In the current study, hydrogen sulphide (H 2 S) concentrations were measured during the summer period of 2007 by a portable handheld device at the WTP of Chania City (Greece). Measurements were taken in several places within the facility. The highest hydrogen sulphide levels were measured close to the primary sedimentation tanks and the tanks where the recycled activated sludge is mixed, the sludge from the primary sedimentation tanks reaching 30 ppm. In conjunction with the measurements, the Gaussian dispersion model AERMOD code was modified in order to estimate the maximum odour concentration for very short time steps using peak-tomean ratios. The probability of detection of H 2 S exceeds 50% at 400 m distance from the main emission sources (time interval of 5 s) with a relative high degree of annoyance (3.2 AU) under typical summer period conditions. Furthermore, relations between odour annoyance and odour exposure concentrations have been embedded in the model, in order to express the odour impacts in terms of probability of detection and degree of annoyance of the population near the WTP of Chania.
The purpose of this paper to present a case study on how to address the odor problem from secondary sources within a municipal wastewater treatment plant (WWTP) by first identifying the locations of the problem and second by evaluating alternative treatment technologies. The WWTP of Chania is a typical 100,000 equivalent inhabitants-facility in a warm semi-arid environment which is located close to residential areas. The installation of a chemical scrubber to control major odor sources within the plant did not succeed in eliminating complaints by nearby residents, and additional measures were required. In this case study we identify all major secondary sources of odor within the plant and evaluate the effectiveness of the different technologies that were employed to address this problem (cover installation, gas and liquid phase oxidation, activated carbon/permanganate absorption, FeCl(3) addition). In particular, we found that installation of covers and reduction of turbulence at two key locations within the WWTP was the best strategy to combat unpleasant odors. Furthermore, when the central chemical scrubber was near capacity the installation of an auxiliary system of activated carbon absorption coupled to permanganate oxidation was deemed to be a safe approach. However, despite the very high removal efficiency (>99.5%) of the unit, the addition of FeCl(3) in the liquid phase was required in order to achieve complete deodorization (below the human odor threshold level).
Piggeries are known for their nuisance odors, creating problems for workers and nearby residents. Chemical substances that contribute to these odors include sulfurous organic compounds, hydrogen sulfide, phenols and indoles, ammonia, volatile amines, and volatile fatty acids. In this work, daily mean concentrations of ammonia (NH3) and hydrogen sulfide (H2S) were measured by hand-held devices. Measurements were taken in several places within the facility (farrowing to finishing rooms). Hydrogen sulfide concentration was found to be 40 to 50 times higher than the human odor threshold value in the nursery and fattening room, resulting in strong nuisance odors. Ammonia concentrations ranged from 2 to 18 mL m(-3) and also contributed to the total odor nuisance. Emission data from various chambers of the pig farm were used with the dispersion model AERMOD to determine the odor nuisance caused due to the presence of H2S and NH3 to receptors at various distances from the facility. Because just a few seconds of exposure can cause an odor nuisance, a "peak-to-mean" ratio was used to predict the maximum odor concentrations. Several scenarios were examined using the modified AERMOD program, taking into account the complex terrain around the pig farm.
One of the main environmental impacts of pig farms are the swine odours emitted from the various stages of the process. The main cause of odour emissions from pig farms are the anaerobic processes in manure. Numerous factors affect odour emissions such as diet, manure management and manure age. The majority of the odorous compounds emitted from pig farms are sulfurous organic compounds, hydrogen sulfide, phenols and indoles, ammonia, volatile amines and volatile fatty acids (VFA’s) whose presence in the atmosphere causes annoyance at relatively low concentrations. However, the detection and quantification of these compounds at a daily basis is difficult because of their chemical instability and the fact that they can be tracked only using techniques of gas chromatography. For the needs of the present study many instantaneous measurements performed during the day in order to estimate the daily variation of their emissions. This is the reason why the compounds studied were hydrogen sulfide and ammonia. Both compounds have low odour threshold (0.47 ppb for hydrogen sulfide and 130 ppb for ammonia). In the present study, the results of odour concentration measurements sampled from a pig production unit placed close to the city of Rethymno (Crete, Greece) are presented. These measurements are used to estimate the emissions of hydrogen sulfide and ammonia from the various chambers of the pig farm. The emission data were used as input data for the dispersion model AERMOD for an area of 10 km2 surrounding the odour source in order to determine the maximum allowed emissions in order not to cause complaints from nearby residents. Modifications were performed in the model based on the “peak to mean” ratio in order to predict the maximum odour concentrations with few seconds time-scale. Also, relations between odour annoyance and odour exposure concentrations have been used in order to express the odour impacts in terms of probability of detection, probability of discrimination and degree of annoyance. These parameters were embedded into the AERMOD model in order to be able to use this program as an odour dispersion model. The results are provided as probability of detection and probability of annoyance instead of hourly mean concentrations. Several scenarios were examined using the modified AERMOD program taking into account the complex terrain around the pig farm. Finally, the effect of raising the height of the stacks to the concentrations around the facility was examined as a possible solution to the situation.
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