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: dan.dunea@valahia.ro; stefania.iordache@yahoo.com; radulescucristiana@yahoo.com;

alinpohoata@yahoo.com 2Valahia University of Targoviste, Multidisciplinary Research Institute for Sciences and

Technologies, 130082, Targoviste, Romania. E-mail: dulama_id@yahoo.com

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

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

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].

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)

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

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

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;

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

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,

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

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.

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.

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.

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.

REFERENCES

1. D. Dunea, St. Iordache, Time Series Analysis of Air Pollutants Recorded from Romanian EMEP

Stations at Mountain Sites, Environ. Eng. Manag. J., 14, 11, 2725-2735 (2015).

2. Y. Olsen, D.G. Karottki, D.M. Jensen, G. Bekö, B.U. Kjeldsen, G. Clausen, et al., Vascular and

lung function related to ultrafine and fine particles exposure assessed by personal and indoor

monitoring: a cross-sectional study. Environ. Health 13, 1,112 (2014).

3. WHO Regional Office for Europe, Review of evidence on health aspects of air pollution –

REVIHAAP Project Technical Report, on http://www.euro.who.int/_data/assets/

pdf_file/0004/193108/REVIHAAP-Final-technical-report-final-version.pdf?ua=1 (2013).

4. H.Y. Liu, E. Skjetne, M. Kobernus, Mobile phone tracking: in support of modelling traffic-

related air pollution contribution to individual exposure and its implications for public health

impact assessment. Environ. Health 12:93, doi:10.1186/1476-069X-12-93 (2013).

5. St. Iordache, D. Dunea, E. Lungu, L. Predescu, D. Dumitru, C. Ianache, R. Ianache, A

cyberinfrastructure for air quality monitoring and early warnings to protect children with

1368 D. Dunea et al. 15

respiratory disorders – Proceedings of Control Systems and Computer Science (CSCS), 20th

International Conference on Control Systems and Computer Science, pp. 789-796, doi:

10.1109/CSCS.2015.39 (2015).

6. M. Brauer, M. Amann, R.T. Burnett, A. Cohen, F. Dentener, et al, Exposure assessment for

estimation of the global burden of disease attributable to outdoor air pollution. Environ. Sci.

Technol. 46, 652-660 (2012).

7. S. Hormann, B. Pfeiler, E. Stadlober, Analysis and prediction of particulate matter PM10 for the

winter season in Graz, Austrian J. Stat. 34, 4, 307-326 (2005).

8. J. Hooyberghs, C. Mensink , G. Dumont, F. Fierens, O. Brasseur, A neural network forecast for

daily average PM10 concentrations in Belgium, Atmos. Environ. 39, 3279-3289 (2005).

9. X. Feng, Q. Li, Y. Zhu, J. Hou, L. Jin, J. Wang, Artificial neural networks forecasting of

PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation,

Atmos. Environ. 107, 118-128 (2015).

10. D. Dunea, A. Pohoata, St. Iordache, Using wavelet – feedforward neural networks to improve

air pollution forecasting in urban environments, Environ. Monit. Ass., 187, 477, 1-16 (2015).

11. St. Iordache, D. Dunea, Current Trends of Major Air Pollutants in an Urban-Industrial Area

from Ploiesti City, Proc.15th International Multidisciplinary Scientific GeoConference,

SGEM2015, Book4, pp. 913-920. doi: 10.5593/SGEM2015/B41/S19.118 (2015).

12. D. Dunea, An exploratory analysis of PM10 particulate matter relationships with weather data

and spatial variation, Proc.14th International Multidisciplinary Scientific GeoConference,

SGEM2014, Book 4 ,Vol. 2, pp 273-280 (2014).

13. I. Barmpadimos , C. Hueglin, J. Keller, S. Henne, A.S.H. Prevot, Influence of meteorology on

PM10 trends and variability in Switzerland from 1991 to 2008, Atmos. Chem. Phys., 11, 1813-

1835 (2011).

14. P. Bloomfield, Fourier Analysis of Time Series: An Introduction. John Wiley and Sons, Inc.,

1976.

15. St. Iordache, D. Dunea, Cross-spectrum analysis applied to air pollution time series from

several urban areas of Romania, Environ. Eng. Manag. J. 12, 4, 677-684 (2013).

16. T. Hiesa, R. Treffeisen, L. Sebald, E. Reimer, Spectral analysis of air pollutants. Part 1:

elemental carbon time series, Atmos. Environ., 34, 21, 3495-3502 (2000).

17. USEPA method 3052 – Microwave assisted acid digestion of siliceous and organically based

matrices (1996).

18. Compendium of Methods for the Determination of Inorganic Compounds in Ambient Air.

Compendium Method IO-3.5 – Determination of Metals in Ambient Particulate Matter using

Inductively Coupled Plasma/Mass Spectrometry (ICP/MS), U.S. Environmental Protection

Agency, (1999).

19. A.F. Stein, R.R. Draxler, G.D. Rolph, B.J.B. Stunder, M.D. Cohen, F. Ngan, NOAA's HYSPLIT

atmospheric transport and dispersion modeling system, Bull. Amer. Meteor. Soc., 96, 2059-

2077 (2015).

20. L. Labzovskii, F. Toanca, D. Nicolae, Determination of Saharan Dust Properties over

Bucharest, Romania, Part 2: Study Cases Analysis, Rom. J. Phys. 59, 1097-1108 (2014).

21. S. Dilmaghani, Spectral analysis of air quality data, Ph.D. Dissertation, University of Southern

California, Los Angeles, CA (2007).

22. M. Oprea, C. Ianache, S.F. Mihalache, E.G. Dragomir, D. Dunea, St. Iordache, T. Savu, On the

development of an intelligent system for particulate matter air pollution monitoring, analysis

and forecasting in urban regions. In: Proc. 19th International Conference on System Theory,

Control and Computing (ICSTCC), pp: 711-716, doi: 10.1109/ICSTCC.2015.7321377, (2015).

23. C. Radulescu, St. Iordache, D. Dunea, C. Stihi, I.D. Dulamă, Risks assessment of heavy metals

on public health associated with atmospheric exposure to PM2.5 in urban area, Rom. J. Phys.,

60, 7-8, 1171-1182 (2015).