Issue 1

The aim of the present work was to characterize particulate matter (PM) and elemental
carbon (EC) indoor and outdoor concentration levels in the Athens area and to examine the
contribution of ambient air to the observed indoor levels.
24-hr simultaneous indoor and outdoor PM10 and PM2.5 measurements were conducted at a
central (CR) and a suburban (SR) residence, and at an office in the commercial centre of
Athens (CO), during cold and warm period of 2006. The absorption coefficient (α) was
measured on the collected filters, as a surrogate for EC concentration levels.
Ambient PM levels were very high at both central sites and significant at SR (mean 24-hr
PM10: 87.4, 50.3 and 87.3 μg m-3 and PM2.5: 50.7, 20.2 and 42.8 μg m-3 at CR, SR and CO).
The measured absorption coefficient values were very high at CR and CO for both size
fractions.
Indoor PM concentration and absorption coefficient values were lower than the respective
outdoor ones, but still significant at the two central sites.
Very good correlations were observed between indoor and outdoor data (especially for
absorption coefficient values), indicating a large contribution of the ambient atmosphere to the
indoor levels, more pronounced in finer particles.

In this study, the Bayesian approach is proposed to estimate the noise variances of Kalman
filter based statistical models for predicting the daily averaged PM10 concentrations of a typical
coastal city, Macau, with Latitude 22°10’N and Longitude 113°34’E. By using the
measurements in 2001 and 2002, the Bayesian approach is capable to estimate the most
probable values of the noise variances in the Kalman filter based prediction models. It turns
out that the estimated process noise variance of the time-varying autoregressive model with
exogenous inputs, TVAREX, is significantly (~76%) less than that of the time-varying
autoregressive model of order 1, TVAR(1), since the TVAREX model incorporates important
mechanisms which govern the daily averaged PM10 concentrations in Macau. By further using
data between 2003 and 2005, the choice of the noise variances is shown to affect the model
performance, measured by the root-mean-squared error, of the TVAR(p) model and the
TVAREX model. In addition, the optimal estimates of noise variances obtained by Bayesian
approach for both models are located in the region where the model performance is
insensitive to the choice of noise variances. Furthermore, the Bayesian approach will be
demonstrated to provide more reasonable estimates of noise variances compared to the
noise variances found by simply minimizing the root-mean-squared prediction error of the
model. By comparing the optimized TVAREX model and the TVAR(p) models in predicting the
daily averaged PM10 concentrations between 2003 and 2005, it is found that the TVAREX
model outperforms the TVAR(p) models in terms of the general performance and the episode
capturing capability.

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.