Volume 12 - 2010

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.

About 140 samplings of fine and coarse particles were gathered during the year 2006 in Kozani that represent an urban area surrounded by opencast coal mining. A low volume dichotomus sampler has been used to trap suspended particles. The filters used were teflon, which are ideal for analysis in the determination of PAHs. The determination of Polycyclic Aromatic Hydrocarbons (PAHs) has been carried out by the use of the analytic technique of large volume injection and gas chromatography – mass spectrometry (LVI - GC/MS). The extraction of substances has been made in a two stage procedure, firstly with agitation in a magnetic shaker and secondly by the use of ultrasonic bath. This technique has given high recoveries of PAHs, in short time intervals. The mean daily concentrations of fine particles varied from 4 to 48 μg m-3 and annual mean was 16 μg m-3. The mean daily concentrations of coarse particles respectively varied from 2 to 67 μg m-3 with 23 μg m-3 annual mean concentration. The ΣPAH concentrations for fine samples were 4.80 ± 7.06 ng m-3 and for coarse samples were 1.36 ± 1.59 ng m-3. The mean B[a]Py concentration for fine particles was 0.38 ng m-3. Finally, diagnostic ratios were used to characterize and identify PAHs emission source in this study.

The relationship between the viable airborne bacterial and fungal concentrations and the
respirable particulate matter with aerodynamic diameter less than 10 μm (PM10), 2.5 μm
(PM2.5), and 1 μm (PM1) in the ambient air was studied. An Andersen six stage viable particle
sampler and a MAS 100 sampler were used for microbial measurements. Duplicates of
samples were collected at each sampling period (20 campaigns in total) at a residential site in
the city of Chania (Crete, Greece) during April, May and June 2008.
Mean concentration of the total sum of the six size fractions was 79 + 41 CFU m-3 for
mesophilic heterotrophic bacteria, whereas for mesophilic fungi it was five times higher (395 +
338 CFU m-3). Particulate matter measurements at the same time period at the same site
revealed that the mean concentrations of PM10, PM2.5, and PM1 were 46 + 14, 35 + 14, and 28
+ 12 μg m-3, respectively, whereas the mean cumulate counts of PM1 particles was 5,059 +
1,973 particles cm-3. The mean arithmetic concentration of the size distribution of the airborne
fungi had a maximum at aerodynamic diameters between 2.1 and 3.3 μm. However, a
maximum was not observed for the mean arithmetic concentration of the size distribution of
the airborne heterotrophic bacteria. It was also observed that concentrations of airborne
bacteria and fungi outdoors were highly variable and do not correlate with the particle number
(PM1) or mass concentration of PM10, PM2.5 and PM1. Thereby, the R2-values in all
correlations were less than 0.3. However, the concentrations of airborne bacteria and fungi
were decreased with increasing mass concentrations of PM10, PM2.5, or PM1 while were
increased with increasing number concentration of PM1. In addition, the concentrations of
airborne bacteria were increased with increasing concentrations of airborne fungi. Finally, the
microbial or the particulate matter data did not correlate with meteorological parameters, such
as temperature, relative humidity, wind speed and UV radiation in ambient conditions.

In this paper, a detailed spatial allocated emissions inventory for Cyprus is presented. Topdown
and bottom-up methodologies are implemented and emissions are estimated from all
anthropogenic sources for the reference year 2002 with the aid of state-of-the-art algorithms.
Emission factors representative for the situation in Cyprus, in the case of major industrial
units, and default emission factors for the rest emission sources are adopted. Main results
indicate that power generation and road transport are the major contributors to total
emissions. Power generation share was 36% in total CO2, 62% in total SΟ2, 20% in total NOx
and 55% in total Ν2Ο emissions, while the share of road transport was 29% in CO2, 22% in
SΟ2, 35% in NOx and 6% in Ν2Ο emissions. Furthermore, comparisons to other existing
emission inventories for Cyprus and official national emission data are presented and
differences are partially explained.
A GIS-based approach is applied to generate, organize and spatially allocate emissions data
in a 5x5 km² grid. GIS-aided spatial overlay techniques are used and maps of spatial
allocation factors and gridded emissions data are produced and presented, showing that
highest emission densities appear in cells, which include large industrial plants and/or cover
urban areas.