a multidimensional approach to the …a multidimensional approach to the influence of wind on the...
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A MULTIDIMENSIONAL APPROACH TO THE INFLUENCE
OF WIND ON THE VARIATIONS OF PARTICULATE MATTER
AND ASSOCIATED HEAVY METALS IN PLOIESTI CITY, ROMANIA
D. DUNEA1, S. IORDACHE1, C. RADULESCU1, A. POHOATA1, I.D. DULAMA2
1Valahia University of Targoviste, Aleea Sinaia no. 13, RO-130004, Targoviste, Romania,
E-mails: [email protected]; [email protected]; [email protected];
[email protected] 2Valahia University of Targoviste, Multidisciplinary Research Institute for Sciences and
Technologies, 130082, Targoviste, Romania. E-mail: [email protected]
Received March 13, 2016
The paper presents a complex analysis of airborne particulate matter (PM) levels
influenced by temperature and wind in Ploiesti city, involving multi-source data
processing, cross-spectrum analysis, kriging interpolation of in situ measurements,
and backward air trajectory modeling. The analysis pointed out the spatiotemporal
variability of PM and associated heavy metals.
Key words: PM10, PM2.5, meteorological time series, cross-spectrum analysis, air
mass backward trajectory model, heavy metals, ICP-MS.
1. INTRODUCTION
The knowledge acquired from reliable information substantiates any rational
decision. Most environmental disasters are primarily occurring due to the
“surprise” factor owing to the lack of information sources that are able to describe
timely the real situation. Any management information system that monitors air
quality must meet the requirements of a triangle of information i.e. optimal data
inputs, best available techniques for data processing/modeling, and substantiated
output information. Such advanced systems must provide finally key information,
namely comprehensive environmental indicators, warnings and alarm signals, and
optimal operative plans adapted to the identified pollution episode or
environmental hazard. Air pollution episodes are characterized by abnormally high
concentrations of air pollutants during prolonged periods resulted often due to low
winds, absence of rain, and temperature inversion [1]. Many recent evidences
pointed out that the respiratory fractions of particulate matter (PM) shorten the
lifespan of citizens and contribute to serious illnesses including cardiovascular
diseases, respiratory issues and cancer [2].
Rom. Journ. Phys., Vol. 61, No. 7–8, P. 1354–1368, 2016
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2 The influence of wind on the variations of PM and associated heavy metals in Ploiesti city 1355
Monitoring of airborne fine (PM2.5) and ultrafine (PM0.1) particles with an aerodynamic diameter below 2.5 µm, respectively 0.1 µm, requires operational and technical improvements in conjunction with new reliable methods of modeling, forecasting and early warning regarding air pollution episodes with high risk of contamination. Numerous research projects have been developed or are ongoing in Europe, involving public participation and the use of advanced technologies to monitor and forecast air quality [3, 4]. The results of such projects complement existing official monitoring networks/infrastructures for air pollution control [5].
The concentrations of air pollutants have a random evolution, being the result of a complex causal chain. Consequently, their levels are difficult to be predicted with accuracy. Understanding the spatial dispersion and in situ concentrations of air pollutants using combined techniques such as geostatistical analysis, geospatial models and/or hybrid models using artificial intelligence algorithms represent modern approaches to assess the efficiency of air quality monitoring programs in view to protect the health of population.
The need to monitor, control, and model atmospheric concentrations of airborne particles in urban areas derived from their adverse effects on human health including asthma attacks, premature mortality, allergic reactions, pulmonary dysfunctions and cardiovascular diseases [6].
Epidemiological studies identified and quantified such diagnostics preponderantly in densely populated urban areas. The PM concentrations are not influenced only by local emissions of pollutants from anthropogenic and biogenic sources, but also by the topography, season, meteorology, and predominant air mass trajectories in the site of interest. Many studies pointed out strong correlations between PM concentrations and meteorological parameters [1, 7, 8]. The possibility to forecast the exceeding of PM concentrations limit values established by national air quality standards for issuing warnings of population is very important in developing a functional air pollution forecasting system [9, 10].
In this context, the current study presents a multidimensional approach to the influence of wind characteristics on PM and associated heavy metals variations in the urban areas of Ploiesti city, Romania, by analyzing hourly-recorded time series of PM2.5 and PM10, temperature and wind variations between July and December 2014 using descriptive statistics, cross-spectrum analysis and geospatial modeling. The time interval corresponds to the first stage of the ROkidAIR research project (http://www.rokidair.ro/en).
2. MATERIAL AND METHODS
2.1. DESCRIPTION OF STUDY AREA
Ploiesti city is an important urban agglomeration from south-east of
Romania (44°56′24″ N Lat.; 26°01′00″ E Long.; 150 m a.s.l.), with more than
220,000 permanent residents. The climate of Ploiesti is influenced by the north-east
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1356 D. Dunea et al. 3
(40%) and south-east (23%) winds, characterized by an average speed of 3.1 m/s.
The annual average of temperature in Ploiesti city is 10.5 °C with an absolute
minimum (–30 °C) recorded in 25 January 1952, respectively a maximum of 43 °C
recorded in 19 July 2007. The multiannual average of precipitations is around
600 mm.
Ploiesti city has shown a rapid economic development in the past ten years
on various domains, but its main activity remained the oil processing and refining
industry. A distinct characteristic of the city is its close neighboring to four large
oil refineries. Consequently, many stationary emission sources are contributing to
the global emissions of air pollutants in the residential areas of the urban
agglomeration i.e. oil refining, oil extraction equipment and machinery,
metalworking facilities, chemicals and manufactured fibers, manufacturing of
rubber and plastic products, detergents etc. Mobile sources have also a significant
contribution because Ploiesti city is an important road and railway node, the
existing infrastructure is not adapted to the increased traffic, and the public
transport system is currently undersized [11].
2.2. DATA COLLECTION AND TIME SERIES ANALYSIS
Two types of datasets were considered for this study i.e. PM time series and
meteorological time series. PM10 data were recorded between July and December
2014 from three automated stations of the National Air Quality Monitoring
Network located in Ploiesti urban agglomeration i.e. PH-1, PH-3 and PH-5 (Fig. 1)
using the ROkidAIR e-platform features [5].
Temperature and wind characteristics (speed and direction) recorded hourly
at Ploiesti meteorological station (WMO ID 153770: 44° 57'19.7"N, 25° 59'17.8"E)
were obtained from Romanian Meteorological Administration (Fig. 2). Recent
studies [7, 12] pointed out that PM10 levels have negative relationships with wind
gust, yesterday precipitation, and convective boundary layer depth; positive
relationships were found between PM10 and sunshine duration, and temperature,
respectively; each meteorological variable has its importance in different seasons,
with large inter-seasonal differences, excepting wind gust [13].
A reliable technique to assess the periodic signal in the presence of noise in
the time series of various air pollutants at different sites is the spectral analysis
[14]. The in-sync periodicity of weather – air pollution variations may be explored
using the cross-spectrum analysis (CSA), which is an extension of the single
spectrum (Fourier) analysis that allows the simultaneous analysis of two series in
the same time interval. CSA tests the correlations between two series at various
common frequencies [15].
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4 The influence of wind on the variations of PM and associated heavy metals in Ploiesti city 1357
Fig. 1 – Time series of PM10 hourly measurements at three monitoring stations in the urban
agglomeration of Ploiesti, Romania, between July and December 2014 (7 days ticks).
Fig. 2 – Time series of hourly-recorded wind speed measurements at Ploiesti WMO station, Romania,
between July and December 2014 (7 days ticks).
The following algorithms are applied for Xt and Yt time series:
𝑋𝑡 = 𝑎0𝑥 + �(𝑎𝑘
𝑥cos2𝜋𝑓𝑘𝑡 +
𝑞
𝑘=1
𝑏𝑘𝑥sin2𝜋𝑓𝑘𝑡) 𝑡 = 1,… . ,𝑁
(1)
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1358 D. Dunea et al. 5
𝑌𝑡 = 𝑎0𝑦
+ �(𝑎𝑘𝑦
cos2𝜋𝑓𝑘𝑡 +
𝑞
𝑘=1
𝑏𝑘𝑦
sin2𝜋𝑓𝑘𝑡).
(2)
Both parts of the complex numbers i.e. real and imaginary parts were
smoothed to obtain the cross and quadrature densities. Squared coherency, gain,
and phase shift allowed the characterization of the crossed periodicities between
PM10 and meteorological time series. Smoothing was performed using the Parzen
window of width 5, which was found to meet the variability of air pollutants time
series [15, 16]. The resulted cross-amplitude values were interpreted as a measure
of covariance between the respective frequency components in the two tested
series.
2.3. PM2.5 MONITORING CAMPAIGNS AND ELEMENTAL ANALYSIS OF SAMPLES
Monitoring campaigns were performed in 12 sampling points of Ploiesti city
between July and December 2014 particularly during the “rush” hours (7.00–
9.00 a.m.; 12.00–2.00 p.m; and 3.00–6.00 p.m.) to assess the potential exposure of
population to elevated PM levels. Two monitoring campaigns were performed in
each month depending on the rainfall regime i.e. after a minimum of three days
following a rainy day, because the precipitations and increased relative humidity
significantly reduce the PM concentrations. The selection of 12 monitoring points
took into account their proximity to the pediatric hospital of Ploiesti, schools, and
kindergartens, resulting a quasi-radial positioning. This setup allowed an optimal
application of the kriging interpolation of in situ measurements.
The PM2.5 measurements were performed with an optical portable
monitoring system, which is measuring the fine PM fraction with an infrared beam
(Casella
Microdust Pro). The instrument was placed on a tripod at 1.50 m height,
away from obstacles that may modify the wind currents. The sampling time was
one hour at each point to ensure a sufficient PM2.5 mass for heavy metal detection,
and the log interval was 10 seconds. The instrument was moved to the next point in
a random sequence to determine the PM2.5 levels on a city scale for various hours
of the day with potential high exposure. The flow rate of the external pump
connected to the PM sensor was 3 l min-1
, which is close to the human respiratory
physiological characteristics. The 37 mm quartz fiberglass filters (QM-A
Whatman, Maidstone, Kent, UK) mounted in specific cassettes were used to collect
the PM2.5 samples. The fiberglass discs were stored at –20 °C before analysis.
Filters were stored in a room with controlled temperature (20 ± 2 °C) and
relative humidity (45 ± 5%) for 48 hours in desiccators. A thermo-analytical
electronic balance with a precision of ± 1 μg was used to weigh the filters before
and after sampling to insure a proper mass detection. The samples were digested
with 9 mL HNO3 (67% Merck, high purity) in PTFE-TFM vessels according to US
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6 The influence of wind on the variations of PM and associated heavy metals in Ploiesti city 1359
EPA method 3052 [17] with modifications. A TOPwave Microwave-assisted
pressure digester was used to heat progressively the mixtures to 200 °C in three
steps. The vessels containing digested samples were cooled for one hour, and later
on, the solutions were transferred with ultrapure water in 25 mL volumetric flasks.
Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) using iCAP™ Q
ICP-MS device allowed the quantification of trace elements in the liquid
mineralized samples using seven-level calibration and an internal standard.
Accuracy and reproducibility were ensured by using blanks for each step of the
digestion and dilution procedures. US EPA compendium method [18] was applied
to determine the concentrations of the metals in the airborne PM2.5 fraction. NIST
SRM 1648a – Urban Particulate Matter reference was used to check the precision
of the analysis results. An acceptable threshold for certification of the recovery was
established for all detected metals i.e. between 83% and 99%. The isotopic
measurements using the ICP-MS technique for the analyzed elements achieved a
precision of 1–2% RSD. The detection limits for the tested elements were as
follows: Cr (μg/L) < 0.2%; Mn (μg/L) < 0.2%; Fe (μg/L) < 1.0%; Ni (μg/L) <
< 0.1%; Cd (μg/L) < 0.1%; and Pb (μg/L) < 0.1%.
2.4. STRUCTURE OF THE GEO-INFORMATION SYSTEM
Positions of each sampling point was determined using Garmin GPS devices,
which facilitated the development of the corresponding map layers in QGIS
software (www.qgis.org) based on WGS-84 reference system.
Kriging interpolation was applied to obtain the specific isolines of PM2.5
concentration. The geospatial analysis capabilities of ROkidAIR e-platform were
used to establish the results of overlapping the distribution of particulate matter and
the wind characteristics, as well as the backward air mass trajectories.
NOOA HYSPLIT trajectory based geographic model [19] was applied to
explain the PM variations using long-range transport of the pollutants in and
around of Romania towards Ploiesti city location during high PM episodes. The
backward trajectory model is an efficient tool providing accurate results regarding
the potential trajectories of emissions transport from the originating source regions
of air pollution [1, 9, 20].
3. RESULTS AND DISCUSSION
The aim of the experiments was to examine the influence of wind
characteristics and temperature on the PM variations in Ploiesti city, Romania. The
most evident observation was that the increasing of wind speed diminishes the
ambient PM concentrations. A negative linear relationship between wind speed and
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1360 D. Dunea et al. 7
PM10 concentrations was reported also in [13]. However, the predominant air mass
trajectory, its spatiotemporal fluctuation, as well as the urban topography/street
“canyons”, have a major influence on PM distribution in the city. Recent studies
pointed out the importance of the local environment where a monitoring station is
located [9, 15].
Consequently, Table 1 presents the statistical description of the PM10 time series
recorded at three automated stations in Ploiesti during the experiments. The PM10
averages calculated for each station were close, the highest value belonging to PH-1, an
urban-traffic station located near a heavy-circulated street. The maximum value
recorded at this station was 212.2 µg·m-3 in December. An aggregated time series was
obtained by computing pairwise the average of the value recorded at each station in the
same hour. The resulted series has maintained the distribution and dispersion of the
original time series, and improved the S/N Ratio.
Table 1
The descriptive statistics of PM10 (µg m-3), wind speed (m · s-1), and temperature (C) time series
recorded hourly between July and December 2014 in Ploiesti city.
Statistical descriptor PH-1
station
PH-3
station
PH-5
station
Aggregated
time series
Wind
speed
Temperature
Count 3325 1794 3685 3882 4174 4174
Minimum 1.46 0.69 0.13 2.70 0.0 –24.2
Maximum 212.19 145.45 145.07 142.88 7.0 34.2
Average 33.4 29.8 32.8 32.7 1.6 12.9
Median 30.0 23.5 27.0 28.8 1.0 13.2
Coeff. of Var.(%) 60.6 65.7 64.8 55.1 65.4 76.4
Skewness 1.9 2.2 1.7 1.5 0.9 –0.1
Kurtosis 7.2 6.5 3.8 3.5 1.2 –0.7
S/N ratio 4.3 3.6 3.8 5.2 3.7 2.3
CSA was applied using the aggregated PM10 and wind speed, aggregated
PM10 and ambient temperature, and wind speed and temperature (Table 2). Missing
data replacement was solved by interpolation from adjacent points having 4174
observations for analysis.
Squared coherency and phase spectrum allowed the approximation of the
periodicities caused by PM10 emissions and meteorological influences. Computed
cross-amplitudes estimated the covariance between the in-sync frequency
components in the bivariate analysis. The five highest peaks of cross-amplitude
were filtered out being ranked decreasingly. The associated corresponding periods
were as follows:
PM10 – Wind speed (independent variable): cycles of 20, 21 days, 1 day, 28
and 38 days;
PM10 – Temperature (independent variable): cycles of 31, 34 days, 1 day,
38 and 43 days;
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8 The influence of wind on the variations of PM and associated heavy metals in Ploiesti city 1361
Wind speed – Temperature (independent variable): cycles of 1 day, 114
and 34 days.
Table 2
Cross-spectrum analysis results of aggregated PM10 (µg · m-3), wind speed (m · s-1)
and temperature (C) time series recorded hourly between July and December 2014 in Ploiesti City,
Romania – cross amplitude values were computed by subtracting means and detrending
(periods were ranked by highest cross-amplitude values)
Row Frequency Period
(hours)
Period
(days)
Cross
amplitude
Squared
coherency
Phase
spectrum
PM10 – Wind speed
16 0.001953 512 21 2195.62 0.95 3.03
17 0.002075 481.9 20 1823.10 0.97 –2.98
342 0.041748 24 1 836.46 0.76 2.49
12 0.001465 682.7 28 837.25 0.82 2.39
9 0.001099 910.2 38 697.67 0.53 2.78
PM10 – Temperature
11 0.001343 744.7 31 13507.66 0.89 –0.95
10 0.001221 819.2 34 13221.87 0.80 –1.23
342 0.041748 24 1 7625.51 0.79 2.40
9 0.001099 910.2 38 4520.29 0.36 –1.97
8 0.000977 1024 43 2303.78 0.31 –0.39
Wind speed – Temperature
341 0.041626 24 1 1575.29 0.99 0.07
342 0.041748 23.9 1 1246.40 0.99 0.10
340 0.041504 24 1 734.40 0.98 0.12
3 0.000366 2730.6 114 259.16 0.84 0.29
10 0.001221 819.2 34 165.29 0.33 –0.21
Daily, weekly and monthly cycles are mostly associated with anthropogenic
emission sources and can provide information regarding the pattern of emission
cycles and their variation over time [15, 21]. The average of the first two cycles
extracted by CSA for PM10 and both meteorological parameters was 26.5 days.
The daily cycle ranked in the third position. Consequently, CSA technique may
characterize an air pollutant species based on its periodic behavior, classify its
health impact in urban areas, and relate the species with similar periodicities to a
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1362 D. Dunea et al. 9
specific emission source in the area. Furthermore, CSA provides support in
designing experimental plans for air quality monitoring in urban environments.
Based on a previous study [15] that suggested a main cycle of 20–25 days
regarding the PM10-meteorological factors synchronization, the monitoring plan
developed for Ploiesti city was adjusted to perform a monitoring campaign at each
20–25 days. A complementary monitoring campaign was performed randomly
in-between these cycles to acquire supplemental data.
It is well documented that fine PM fraction, with an aerodynamic diameter
below 2.5 μm, causes health issues to population mainly in three ways i.e.
ingestion, inhalation and dermal contact. The monitoring campaigns performed
between July and December 2014 in Ploiesti City, Romania in 12 locations, aimed
to evaluate the PM2.5 levels and associated heavy metals resulted from industrial
areas, heavy traffic and traffic jams, construction activities and commercial
sources. The average concentration of PM2.5 in Ploiesti (measured at PH-2
automatic station, which has the only available PM2.5 monitor in Ploiesti) was
18.6 µg m-3
, based on the average concentrations of the 2009–2012 interval, which
ranged from 16.9 to 20.7 µg m-3
[22]. In the last reported year by the Romanian
Ministry of Environment i.e. 2013, the average of PM2.5 concentrations was
17.3 µg m-3
calculated using the reported data in EEA Airbase (Min = 3.27;
Max = = 53.79; Coeff. of Var. = 54.2%; S/N ratio = 5.3).
Table 3
Overall results and statistics of PM2.5 measurements (µg · m-3), performed in 12 monitoring points
between July and December 2014 in Ploiesti City, Romania
Statistical
descriptor
All period
average
All period
Max.
30 Sept.
Average
30 Sept.
Max.
02 Oct.
Average
02 Oct.
Max.
Average 9.7 44.9 20.4 68.5 8.2 59.8
Min 1.2 1.5 0.8 1.2 1.7 3.0
Max 61.5 297.8 208.7 693.0 22.9 334.0
Coeff. of Var.(%) 133.4 156.1 206.5 300.9 102.5 171.5
In this study, the average of PM2.5 calculated using data of all the
monitoring campaigns was 9.7 µg · m-3
(Table 3). Compared to official data reports
from previous years monitored in just one location in the city center, this lower
value is due to the multi-locational assessment and discontinuous measurements.
However, the results pointed out the differences between the concentrations
recorded in 12 sampling points. For example, large amplitudes were noticed
between the minimum and maximum of the averages (1.2–61.5 µg m-3
), as well as
for the maximums recorded in various points (1.5–297.8 µg m-3
). This suggests
that, at urban scale, some areas are more impacted than others are. Consequently,
the screening campaigns in conjunction with complex dispersion modeling can
provide a ranking of the critical areas. The results can substantiate the prioritization
of measures to reduce the PM emissions and to plan the air quality. Furthermore,
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10 The influence of wind on the variations of PM and associated heavy metals in Ploiesti city 1363
these actions can establish where to deploy supplemental continuous PM2.5
monitoring instruments for protecting the health of residents.
The next results of the present study are related to the heavy metals content
from PM2.5 samples collected between July and December 2014 in Ploiesti City.
The carcinogenic risk and the harmful health effects via inhalation and ingestion of
Ni (nickel), Cr (chromium) and Cd (cadmium) were revealed by many
epidemiological studies. Since the concentrations of these metals in collected PM2.5
samples were above the safe range in some of the monitored areas, a complex
health risk assessment should be considered in the near future. The average
concentrations of elements i.e. Pb (lead), Cd, Ni, Cr, Fe (iron), and Mn
(manganese) contained in PM2.5 are shown in Table 4. All these metals were
detected in all of PM2.5 samples collected from the 12 locations of Ploiesti city. The
concentration of each metal varied largely between locations and sampling dates.
The ranking of metals was Fe > Pb > Mn > Ni > Cr > Cd. Fe was the most enriched
metal in PM2.5 originating from metalworking emission sources, vehicle and brake
wear, and re-suspension of road dust (101.3 ng · m-3
). The most toxic metal i.e. Pb
had an overall average of 20.61 ng · m-3
due to intense oil processing activities and
heavy traffic in the area. The average concentration of Mn was 17.6 ng · m-3
,
originating from industrial emissions, combustion of fossil fuels, and reentry of
manganese-containing soils. Ni species associated with combustion, incineration,
and metals smelting and refining are often salts, including nickel oxides, nickel
sulfate, nickel silicate, nickel sulfide, and nickel chloride [23]. In Ploiesti city, Ni
concentrations were elevated (11.3 ng · m-3
). Cr reached also high average levels
(6.9 ng · m-3
) mainly in the cold months due to residential heating and industrial
point sources. Cadmium had the lowest concentration (0.98 ng · m-3
) but its
presence due to emissions from car and railway traffic, domestic heating and
industrial activities, could determine adverse health effects at prolonged exposure
periods.
Table 4
The average concentration of metals (ng · m-3) in PM2.5 samples collected from 12 monitoring points
between July and December 2014 in Ploiesti City, Romania
Metal All period
average
All period
Max.
30 Sept.
Average
02 Oct.
Average
Lead (Pb) 20.61 31.3 22.4 16.1
Cadmium (Cd) 0.98 1.8 1.6 1.2
Nickel (Ni) 11.3 22.5 8.7 3.8
Chromium (Cr) 6.9 16.2 5.4 4.1
Iron (Fe) 101.32 121.5 104.6 93.2
Manganese (Mn) 17.63 19.5 16.8 13.4
A case study is presented in the following lines to exemplify the influence of
wind characteristics and backward air mass trajectories on the PM variations in
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1364 D. Dunea et al. 11
Ploiesti city. The selected time interval was between September 30 and October 2,
2014.
Fig. 3 – Kriging interpolation of PM2.5 in situ measurements (µg · m-3) performed in Ploiesti city
(maximum values), Romania, on September 30 (up) and October 2, 2014 (down)
using the ROkidAIR e-platform features.
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12 The influence of wind on the variations of PM and associated heavy metals in Ploiesti city 1365
Fig. 4 – Wind speed (m · s-1) and direction (º) recorded hourly during September 25
and October 2, 2014 at Ploiesti WMO station.
Figure 3 shows the kriging interpolation of PM2.5 maximum values recorded
in Ploiesti city at two dates i.e. September 30 and October 2, 2014. The synthetic
results regarding the PM2.5 concentrations and heavy metals content of these two
monitoring campaigns were presented in Tables 3 and 4. Figure 4 presents the wind
characteristics recorded for the selected time interval.
Fig. 5 – Wind speed (m · s-1) and PM10 (µg · m-3) recorded hourly during September 25 and October
2, 2014 at Ploiesti WMO station (6 hours ticks); wind speed values were scaled by a factor of 10.
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1366 D. Dunea et al. 13
The combined results suggest that the high concentrations of PM2.5 occurring
in the west of the city in September 30 were slowly moved and dispersed towards
east, and partially to north directions up to October 2, 2014. This trajectory was
determined by the south-west and west wind directions, which presented calm to
weak winds according to Beaufort scale. The complete scenario of the PM
pollution episode is provided by Fig. 5, which presents the continuous evolution of
hourly-recorded measurements of PM10 recorded from the automated stations. The
rising of PM concentrations in the area started in the evening of September 30,
decreased during the night of October 1, and started to rise again in the evening of
the same day, but the decreasing of concentrations lasted more. This trend supports
the findings from in situ measurements. NOOA HYSPLIT model was applied to
simulate the backward trajectories in Ploiesti city between September 25 and
October 2 (Fig. 6).
Fig. 6 – NOOA HYSPLIT backward trajectory model applied for Ploiesti city location, Romania,
between September 25 and October 2, 2014 highlighting a pollution episode and the influence of air
mass trajectory on airborne particulate matter variations; GDAS meteorological data was used.
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14 The influence of wind on the variations of PM and associated heavy metals in Ploiesti city 1367
Of particular interest was the simulated backward trajectory that came along the
24 meridian and entered in the area of Ploiesti city from west-southwest direction.
The origin of this trajectory was Ukraine. Based on the simulated altitude, this
trajectory was the only one that came from a higher altitude, suggesting a transport
of PM that contributed to the local emissions.
4. CONCLUSIONS
Air quality protection requires reliable and updated information about the
polluting factors to ensure an efficient decision-making process for the protection
of inner city residents. The resulted decisions must be optimized for risk situations,
as well as for daily supervision of air pollutants levels.
The cross-spectrum analysis might be used for identifying air pollution
patterns based on the interactions between pollutants and meteorological factors.
Consequently, CSA is useful for the parameterization and calibration of air
pollution models by pointing out the synchronizations between time series.
Furthermore, the technique can provide comprehensive classifications of the
monitoring sites, supporting source apportionment and the optimization of the
monitoring operations.
The multidimensional approach involving geospatial and air mass backward
trajectory modeling, can supplement PM monitoring data from local instruments
providing a complex image of the air pollution episodes and their originating
source.
Acknowledgments. This study received funding from the European Economic Area Financial
Mechanism 2009–2014 under the project ROkidAIR Towards a better protection of children against
air pollution threats in the urban areas of Romania contract no. 20SEE/30.06.2014.
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