Comparison of current worldwide air quality limit or target values/guidelines.
The first section of this chapter provides an up‐to‐date general view of air pollution/air quality topic. It indicates main pollutants and their sources and impacts and presents and discusses current air quality standards and air quality indexes worldwide; how datasets are acquired, gathered and analyzed and how the measurements are then interpreted are also presented. Recent works containing updated and detailed technical discussions for each issue addressed and additional web resources are mentioned. The great importance of air pollution monitoring is emphasized. Second, in the international context of incomplete information on air pollution in East Europe, the chapter includes a section presenting an assessment of air pollution at some sites in Romania together with its evolution from the beginning of the monitoring up to present. Availability of PM10, PM2.5, NOx, SO2 and CO concentrations is site and pollutant dependent and varies from 3 to 9 years. Investigation of temporal and spatial variation of pollutant levels, as well as of PM10 and PM2.5 relationships with the measured gaseous air pollutants and with meteorological variables, includes correlation and linear regression analysis and temporal‐trend analysis; coefficient of divergence was calculated to check up on the air pollution inter‐sites’ differences and pollutant seasonal variation intra‐site.
- air pollution
- air quality standards
- air quality index
- particulate matter
- gaseous pollutants
- temporal trends
- East Europe
1. An introduction to air pollution monitoring
The challenge of modern society to take air pollution abatement measures based on scientific knowledge has encouraged the scientists to study the atmospheric composition changes, the short‐ and long‐term pollutant effects and impacts and to simulate air pollution scenarios all over the world. The advances achieved in the field of air pollution during the past decades are due to numerous detailed investigations, the application of a large number of techniques and the acquisition of abundant monitoring data.
First, the aim of this chapter is to provide an up‐to‐date general view of air pollution/air quality topic. Second, in the international context of incomplete information on air pollution in East Europe, the chapter includes a section presenting an updated image of air pollution at some sites in Romania together with its evolution from the beginning of the monitoring up to present.
The substances that accumulate in atmosphere in such a concentration and for enough long time that they may harm the living organisms or produce damage to building materials are called
Air pollution comes from many different
However, the air pollution refers not only to ambient,
Below is an introduction to the most widespread air pollutants together with their main sources, and impacts they can have, pollutants that are frequently monitored in most of the networks (Figure 1).
Other pollutants of interest are ammonia (NH3) and methane (CH4), coming mainly from agriculture, waste management and energy production; benzo[a]pyrene (BaP), resulting from incomplete combustion of various fuels for domestic home‐heating, in particular wood and coal burning, waste burning, coke and steel production and road traffic; toxic metals: arsenic (As), cadmium (Cd), lead (Pb) and nickel (Ni), emitted mainly from the combustion of fossil fuels, metal production and waste incineration; and black carbon (BC), which is a product of incomplete combustion of fossil fuels; BC results mostly from traffic and industry.
Air qusformed in
Air pollution is mostly regulated by
|Pollutant||Time period||European Union||US EPA NAAQS||WHO||Australia||British Columbia||South Africa||Mexico||China*||India*|
|Value||Observations||Air quality limit value/guideline|
|PM10||1 year||40, 20||For protection of human health
35/year, since 2010
|24 h||50||For protection of human health
|1 year||20||For protection of ecosystems||50||20||50||50||0.03 ppm||20||20|
|SO2||1 h||350||24/year||75 ppb||200||900||350
|3 h||0.5 ppm 1/year|
|1 year||30||For protection of ecosystems|
|NO2||1 year||40||For protection of human health||53 ppb||40||30||60||40||40||40|
|1 h||200||For protection of human health
|1 year||120||Long‐term goal for protection of human health: AOT40 from 1 h values within period May–July|
|O3||1 h||6000||Long‐term goal for protection of ecosystems: AOT40 from 1 h values within period May–July||120 ppb|
|8 h||0.07 ppm||100|
|CO||8 h||10°||9 ppm
|1 h||35 ppm
The data from monitoring stations are also used to calculate
The AQI is generally based on a number of subindices for individual pollutants. The classification of air quality is based on the subindex with the highest value. Currently, there are numerous AQIs, but we do not have a methodology internationally accepted to construct these indexes. Most of them are defined using the main common gaseous pollutants: CO, NO2, O3, SO2 and particulate matter (PM10 and PM2.5). Sometimes, other pollutants, such as C6H6, NH3 or Pb, are added. Table 2 presents a compilation of some current existing AQI, the health risk category and implications for the population. At the end of Table 2, the AQ classification, the color code and how the AQI is computed, as provided by Rhenish Institute for Environmental Research at the University of Cologne (EURAD), are shown . For the rest of the regions included in Table 2, the appropriate references for AQI calculation are provided.
Most state or local agencies report the AQI on their public web sites. Real‐time monitoring data and forecasts of air quality that are color coded in terms of the air quality index are available from US Environmental Protection Agency's AirNow web site www.airnow.gov. Real‐time AQI visual map for more than 60 countries over the world is available at https://waqi.info. To convert an air pollutant concentration to an AQI or conversely, EPA has also developed a calculator . As one observes, the AQI is country or city specific, and even the interpretation of an AQI varies considerably from one region to other; this makes the comparison of calculated values in various regions difficult. To minimize these difficulties within its boundaries and to facilitate the international comparison of near real time of AQ, European Union introduced in 2006 the Common AQI in the framework of CITEAIR Project . Moreover, the AQIs do not take into account the coexistence of all the air pollutants. Reference  shows how a multi‐pollutant and multi‐site AQI could be designed in order to get an aggregate measure of air pollution. However, the AQI has the advantage to concentrate multiple and multi‐scale measurements in a unique indicator and allows to follow the evolution of air quality in a given region or city providing timely and understandable information for population and supporting local authorities governments in decisions to prevent and avoid adverse health effects. Critical and comparative reviews of the existing AQIs and proposal of alternatives are provided by references [25–27].
Apart from evaluation of air quality at various spatial scales, air pollution monitoring provides essential information to validate the predictive methods and dispersion models, which represent an important set of tools for simulating air pollution scenarios.
One concern that must be mentioned here is the future changes in air quality that will result from climate changes. Many studies indicated a warmer and a more humid climate, with a higher frequency of occurrence of heat waves, of stronger local storms and a higher probability of decrease in frequency of mid‐latitude cyclones. Shortly, a warmer and more humid climate will increase the CO2 and VOC levels, will determine (region‐specific) increases or decreases of O3, a greater conversion of SO2 to sulfate will take place, and patterns of NOx will be affected. Due to an increased presence of reactive gaseous species even PM2.5 speciation might be changed, and this will, in turn, affect the Earth’s radiative balance. Simulations of future changes in air quality that will result from changes in both meteorological forcing and air pollutant emissions are presented by Glotfelty et al.  up to 2050 following the IPCC AR4 SRES A1B scenario. It shows that global air quality is projected to degrade by the mid‐21st century on global average, but the changes are regional in nature: for example, PM2.5 level will reduce in Europe and Africa, whereas it will increase in South and Southeast Asia, Indonesia, Australia and South America.
Moreover, thinking about the future long‐term air pollution, we must also consider that changes in future air quality will have economic consequences whose projections must be also analyzed. With respect to this, the very recent report “The Economic Consequences of Outdoor Air Pollution”  supplies us with a comprehensive assessment of the regional and global economic consequences of outdoor air pollution for the period 2015–2060. Linking the pollutant emissions to labor productivity, healthcare expenditures and changes in crop yields (market costs) and to mortality and morbidity/illness (non‐market costs), the projections are indeed of great concern, even if they are subject to uncertainties. The results indicate, among other consequences, that “by 2060, a large number of deaths are projected to take place in densely populated regions with high concentrations of PM2.5 and O3 (especially China and India) and in regions with aging populations, such as China and Eastern Europe. The projected mortality effects of PM2.5 exposure are much larger than those of O3. The market costs of air pollution, flowing from reduced labor productivity, additional health expenditures and crop yield losses, are projected to lead to global annual economic costs of 1% of global gross domestic product (GDP) by 2060. The projected GDP losses are especially large in China (–2.6%), the Caspian region (–3.1%) and Eastern Europe (Non‐OECD EU –2.7% and Other Europe –2.0%), where air pollution impacts lead to a reduction in capital accumulation and a slowdown in economic growth. In per capita terms, the average global welfare costs from mortality and morbidity are projected to increase from less than USD 500 per person in 2015 to around USD 2 100‐2 800 in 2060” .
One can, therefore, have an idea about the severe global economic consequences of air pollution and the need of stronger policies to improve the air quality results to be of huge importance for all of us. Within this context, to monitor air pollutants is of great necessity of two‐fold importance: in order to take informed decisions, to develop and strengthen the political strategies when the societal and economic challenges are addressed and also to respond to the scientific questions of atmospheric sciences.
2. Case study: assessment of air pollution over Northern Romania
2.1. The air pollution monitoring in Romania
A systematic air pollution monitoring in Romania started in early 2000s, beginning with Bucharest, the capital of Romania, and has been gradually developed to the rest of the country. Before 2000s, air pollution was investigated in some fixed points of interest (next to industrial sources, traffic hot spots, parks…) only by manual sampling. The number of fixed sampling points was city dependent and variable in time (for example, Bucharest had between 14 and 5 sampling points); decreasing trend was due to technical issues; 30 min and sometimes 24 h were used as sampling periods for total suspended particles (TSP) and gases NO2, SO2, CH2O, NH3 and O3; and TSP were sometimes selectively analyzed for their content of Pb, Cd, Zn and Cu, experimental methods used not being reported. Measured data indicated frequent exceedances of the maximum admitted concentrations (CMA) at that time. For example, between 1996 and 2000 in Bucharest, TSP levels ranged from 150 to 350 µg m-3 (annual average), CMA being of 500 µg m-3. I do not analyze the air pollution before 2000, as measurements were done following local protocols, and the imposed thresholds varied in time, were country specific and were not correlated with the regulations worldwide. All these make the comparison of registered pollutant concentrations in those times with data from other cities very difficult and of very limited usefulness.
Nowadays, a number of 143 monitoring stations of all types, traffic, industrial, urban background, rural and remote background, operate at the country scale. Within the context of air quality monitoring in Europe, reports of the National Environmental Protection Agency (owner of the National Air Quality automatic Monitoring Network) are focused only on compliances with the European Union regulations counting exceedances of the limit values. The very few addressed topics regarding air pollution using some monitoring data in few cities are presented in references [30–35]. Most extensive review image of the air pollution problem in Bucharest metropolitan area was published in 2015 by Iorga et al. [16, 36].
The following part of the chapter focuses on the assessment and analysis of daily concentrations of major pollutants using the longest monitoring datasets available at present.
2.2. Description of selected stations, data and methods
I selected two urban sites in cities (medium‐size) of national importance (Iasi, Cluj‐Napoca), with regional role and potential influence at European scale, a regional background site in mountains (Miercurea Ciuc) and the single remote rural background site (Poiana Stampei) for which Romania reports data within EMEP, the
The air pollution in above sites is compared here with the urban background air pollution in Bucharest, the capital of Romania. Bucharest (approx. 44° 26’ N, 26° 06’ E) represents the most developed city of the country and is located at a relatively equal distance from the Danube River and Carpathian Mountains. The Air Quality Network of Bucharest consists of eight stations that are distributed at different spatial levels (inner core city, larger urban zone and sub‐city area) covering the main types of anthropogenic activities. Detailed information about Bucharest can be found in reference .
|Station name, site designation (population)||Station type||Latitude||Longitude||Altitude
|Pollutants included in analysis
and the start year of monitoring
|Poiana Stampei, PS
|Remote rural background||47°19′30″ N||25°08′04″ E||908||PM10 (2010), NOx (2010), O3 (2010),
SO2 (2010), CO (2010)
|Miercurea Ciuc, MC
|Regional rural||46°21′34″ N||25°48′06″ E||710||PM10 (2009), NOx (2009), O3 (2008),
SO2 (2009), CO (2009)
|Urban background||46°46′26″ N||23°35′49″ E||333||PM10 (2007), PM2.5 (2009), NOx
(2006), O3 (2006), SO2 (2006), CO (2006)
|Urban background||47°09′25″ N||27°35′25″ E||44||PM10 (2006), PM2.5 (2009), NOx
(2006), O3 (2006), SO2 (2006), CO (2006)
Area (2 272 163),
Lacul Morii, LM
|Urban background||44°26′33″ N||26°03′36″ E||90||PM10, PM2.5, NOx, SO2, CO, O3|
Data used in the present study are extracted from AirBase v.8 database  of European Environment Agency (EEA) for background stations in above locations. However, in order to have completeness of data series for Iasi, some PM2.5 data were added from a traffic station. Availability of the concentrations is site and pollutant dependent and varies from 3 to 9 years. Most data cover the period from January 1, 2006 to December 31, 2013. I focus here on PM10 and PM2.5, and NOx, SO2 and CO (Table 3), as primary gaseous pollutants that accumulate in urban atmosphere and significantly contribute to the photochemical formation of ozone and other oxidants and to a fraction of the particulate matter . O3 daily averages were added in order to seek if they could help to better understand the correlations between particulates and primary gaseous pollutants.
A synthetic database of daily averaged datasets of pollutants from AirBase and local meteorology series (air temperatures, relative humidity, atmospheric pressure, wind speed and direction) was prepared in order to have completeness for all sites for common time periods per site, as Table 3 specifies. When it was necessary, conversion of hourly gaseous pollutants and local meteorology data to daily averages was done by averaging over 24 h periods from midnight to midnight.
Last monitoring year is 2013 for all sites and pollutants. The sampling periods and detailed analysis of pollution corresponding to the selected station in Bucharest used here for comparison are presented in references [36, 39].
Statistical examination of temporal and spatial variation of PM10 and PM2.5 concentrations, as well as their relationships with the measured gaseous air pollutants and meteorological variables, includes:
Correlation analysis, expressed by Pearson coefficients (COR), statistically significant at 95% confidence interval.
Single and multiple linear regression analysis, between daily PM as dependent variable and meteorological factors and gaseous pollutants as independent variables, respectively.
Temporal trend analysis for detecting and estimating a monotonic annual and seasonal trend of ambient pollutant concentrations was performed using the non‐parametric Mann‐Kendall’s test and Sen’s method using MAKESENS software .
Coefficient of divergence (COD), a self‐normalizing parameter, was applied to evaluate the differences in the average concentrations of pollutants at each site for paired seasons and to compare monitoring sites. COD provides information on the degree of uniformity between monitoring stations and seasons. For example, a low COD and a high COR are expected for sites impacted by similar pollution sources. A COD value between 0 and 0.2 will indicate uniformity, and a COD between 0.4 and 1 will indicate heterogeneity. The coefficient of divergence is calculated as:
Inventories of emitted air pollutants have been substantially improved during the past few years, in particular for main pollutants, including fine particulates and ozone. WebDab contains all emission data officially submitted to the secretariat of the Convention on Long‐range Transboundary Air Pollution (LRTAP Convention) by Parties to the Convention . Romania updated its reports to the emission database WebDab of EMEP in 2015. Pollutant emission trends per site (Figure 3) were evaluated using the gridded data from WebDab for the corresponding time periods of ambient mass concentrations of pollutants, considering the national total economic sectors.
As shown in Figure 3, the total emissions of gaseous pollutants decreased for all sites, especially starting with 2006, whereas the PM10 and PM2.5 emissions show a different pattern: positive trends for IS and MC and stable emissions for CN and PS sites. Even if the particulate emissions in Bucharest are 10 times higher than in all other sites, due to implementation of the environmental development plan, Bucharest has decreased its particulate emissions from about 5970 Mg in 2000 to 3060 Mg in 2013. Emissions of PM seem to be of major concern among the pollutants in Romania. The decreasing trend of gaseous emissions follows the general decreasing trend of emissions (SO2 decreased by 58%, NOx and CO by about 25%) at EU scale , the strongest decrease being for SO2 (range: 34% for MC–66% for IS), followed by CO (range: 1% for MC–42% for CN).
2.4. Ambient pollutant concentrations
2.4.1. Levels of PM10, PM2.5, NOx, CO, SO2, O3
Particulate matter and gaseous pollutant variability are presented in detail in Figure 4. Figure 4a and 4b provides a box‐plot comparison of the annual levels of daily averages of PM10 and PM2.5 mass concentrations by site for the corresponding monitoring periods, including EU limit values . Measured ambient annual (mean, median and 95th percentile) PM10 concentrations have the highest values at IS urban site and the lowest at PS remote site; at all sites, observations situate below the EU limits with the exception of IS city, where in 2013 a value of 44.64 ± 20.78 µg m-3 has been reached. This value is comparable with the value of 45.10 µg m-3 representing the mean PM10 concentration during 2005–2010 in Bucharest, when a decrease from about 46 to 35 µg m-3 was registered. Concentrations higher than 100 µg m-3 appear very often at IS (and more frequent than in Bucharest in 2010 [16, 36]) and even in the alpine basin of MC, although here 95th percentile data are below 100 µg m-3. The cleanest air appears to be in PS (mean concentration of 15 µg m-3 in 2013), and the urban city CN is the second in rank. PM2.5 levels exceed frequently the EU target of 25 µg m-3 at both urban sites. Our observations fit very well within the range of European concentrations (from about 20 µg m-3 (Finland) to about 75 µg m-3 (Bulgaria)), data extracted from Ref.  based on 90.4 percentile of daily mean concentration values corresponding to the 36th highest daily mean in 2013.
The average of PM2.5/PM10 mass concentration ratios situates between 0.38 (IS) and 0.71 (MC), indicating a higher contribution to PM10 samples of coarse particles for IS and of fine fraction for MC. Together with results for CN site (0.63) and Bucharest (from 0.7 for industrial sites to 0.8 for a traffic site in the very centre of the city), our observations are consistent with the PM2.5/PM10 mass ratios from 0.5 to 0.9 at most sites across the Europe.
As shown in Figure 4c, the annual average SO2 concentration in IS was 6.92 µg m-3 in 2006 and has gradually decreased to 3.45 µg m-3 in 2013, and in CN decreased from 6.83 to 5.69 µg m-3. Lower values were observed for regional MC and remote PS sites. The decrease in ambient concentrations of SO2 and CO in IS was related to lower local emissions of SO2 and CO based on the positive correlation ambient‐emitted SO2 and CO, respectively (CORSO2=0.94; CORCO=0.96). The same conclusion stands for CO in Cluj‐Napoca, but in a lower extent for SO2 (CORSO2=0.41). For PS site, the ambient SO2 concentrations increased slightly from 4.67 to 6.91 µg m-3, especially due to intensive use of coal for residential household activities. Multi‐annual average temperature at PS is 4.3°C.
The annual averages of NOx and O3 concentrations show a lower variability at each site, their average values 2006–2013 varying between 40 (42) µg m-3 and 55 (34) µg m-3 in mid‐sized cities IS and CN, respectively.
2.4.2. Seasonal variability and site inter‐comparison
The seasonal variability (Table 4) and inter‐site comparison (Table 5) were investigated using the coefficient of divergence (COD) and coefficient of correlation (COR). As an example, Figure 5 shows the extreme differences between seasons in Iasi.
COD values for the pairs of seasons ranged from 0.07 to 0.12 at PS site, and this indicates almost no seasonality here. Seasonal changes in pollutant concentrations were modest for Spring‐Summer and Winter‐Autumn for IS and CN, and surprisingly, some season‐to‐season variability appears at MC site.
|Coefficients of divergence (COD)||Inter‐sites’ correlation coefficients (COR)|
Overall, the inter‐site calculated COR indicates a positive correlation among all sites suggesting that they all suffer from the same pollution source categories. A very similar situation was found to characterize Greater Bucharest Area (COR varies from 0.55 to 0.88) and the Greater Athens Area, where COR varies from 0.55 to 0.84 . However, COD values differentiate the sites, showing: air pollution at the remote PS site is very different from that of all the other sites; cities Iasi, Cluj‐Napoca and Bucharest are relatively similar, and air pollution at regional rural MC site is relatively different from the others. The highest contributor to COD value of the paired PS‐IS sites is PM10, and highest contributors to COD for the pair MC‐Bucharest are NOx, PM10 and PM2.5.
2.4.3. Associations between particulate matter levels and gaseous pollutants—meteorology influence
Table 6 synthesizes the relationships between daily PM10 and PM2.5 and daily averaged gaseous pollutant concentrations over the entire sampling periods up to 2013 per site. It shows good correlations between both PM10 (PM2.5)‐NOx and PM10 (PM2.5)‐CO, and a less‐defined correlation with SO2. However, the strength of these correlations varies among sites: probably a common road traffic origin in cities IS and CN but with differences in contribution percentages of NOx versus CO (IS has a higher percentage of old vehicles than CN), a lower capability of the area to disperse the pollutants at MC site, low traffic and higher coal and wood combustion at the remote site PS.
Similar correlation coefficients (0.4–0.8 for PM10‐NOx relationship, about 0.4–0.7 for PM10‐CO) were reported at different sites in UK and Greece . Bucharest data indicate correlation coefficients of 0.4–0.7 for PM10‐NOx relationship, 0.2–0.5 for PM10‐CO relationship and 0.1–0.4 for PM10‐SO2 relationship. The daily mean O3 concentrations negatively correlated with both PM10 and PM2.5 could be explained by the reaction of O3 with NO, which is a major sink for O3. At the site MC, a positive correlation PM10‐O3 appears. As in some situations in the UK atmosphere , short periods with positive correlation PM‐O3 during photochemical episodes were reported in Bucharest Greater Area during 2005–2007 . Our positive correlation might indicate such situations when both PM and O3 are generated by photochemical activity for MC in warm season, but the calculated coefficient is very low, and probably these episodes are swamped by the 4‐year analysis.
The associations between PM10 and primary gaseous pollutant levels were investigated further by multiple linear regressions performed using daily mean PM10 values and daily averaged gaseous pollutants NOx, SO2 and CO for the same periods. For each pollutant, the multiple regressions were performed only for NOx, SO2 and CO for which single correlation coefficients with PM10 were higher than 0.30 (Table 6). The multivariate linear regression model is widely recognized as a useful tool to show associations between primary pollutants [36, 46], to calculate combustion/non‐combustion fraction of PM  or to predict daily concentrations of PM . For present sites, the model was applied assuming NOx, SO2 and CO as tracers for anthropogenic activities. In this model, slopes will represent the association of anthropogenic activities with PM10 (contribution of anthropogenic activities to PM10), and intercepts are assumed to represent the non‐anthropogenic contribution (the natural contribution) to PM10. The natural contributions to PM10 thus re‐constructed are shown in Figure 6 for each site.
|Site||Temperature||Atmospheric pressure||Relative humidity||Wind speed||Wind direction|
|IS (n = 2068)||-0.18||0.15||-0.13||-0.10||-0.12|
|CN (n = 1327)||-0.22||0.16||-0.12||-0.19||-0.34|
|MC (n = 1339)||-0.55||0.08||0.22||-0.37||-0.09|
|PS (n = 1199)||-0.16||0.20||-0.20||-0.40||-0.02|
|IS (n = 1733)||-0.32||0.27||0.05||-0.14||-0.13|
|CN (n = 1327)||-0.48||0.17||0.16||-0.26||-0.30|
|MC (n = 326)||-0.51||0.05||0.29||-0.42||-0.14|
Correlation analysis of PM and daily averaged local meteorological variables (Table 7) revealed a similar behavior for PM10 and PM2.5 with all parameters with the exception of PM relationship with the relative humidity.
The negative correlations of PM10 and PM2.5 with temperature, relative humidity and wind speed indicate dilution of ambient concentrations of PM due to an increased atmospheric boundary layer, scavenging by fog or cloud droplets and deposition onto ground surfaces (precipitation data were not available) and dispersion of particles, especially of fine fraction, by winds. The negative correlation with temperature could be due also to increased emissions (Figure 3) or a reduced dispersion (highest coefficients were obtained at MC site) and stable atmospheric conditions (atmospheric pressure) during cold seasons. In cold seasons, low speed wind conditions and lower temperature could result in a lower boundary layer that traps pollution to the ground. In warm seasons, more intense winds, higher temperature (that could reflect positive correlations with solar radiation) and higher boundary layer could result in pollution transport. The multi‐annual averages of relative humidity for the corresponding monitored periods have high values for all sites: 72% (IS), 77% (CN), 81% (MC) and 82% (PS). Relative humidity values in the range 70–90% for MC and PS sites appeared frequently, and they were found to be associated with low winds; temperature inversion episodes in MC and PS areas are frequently mentioned in climatology, as well. These combined factors might explain the positive correlation PM10‐relative humidity.
The PM10 and PM2.5 dependence of wind direction (Figure 7 indicates this dependence for PM10, but PM2.5 presents the same distribution) gives certain insights into the distribution of emission sources around the selected monitoring sites. Particulate matter concentrations are associated with southwesterly winds for MC, while in larger cities IS and CN the PM10 and PM2.5 are distributed relatively equal in all sectors with the exception of NW‐NE sector. Highest PM10 concentrations (range: 60–80 µg m-3) appear to come from S‐SE directions in Cluj‐Napoca, and highest PM10 (from 100 to 180 µg m-3) come from all directions between NE and NNW in Iasi. At the remote site PS, the highest PM10 levels (of about 60 µg m-3) appeared on the direction NE‐SW, whereas intermediate and low values are associated to all directions from NE to NW.
If one compares meteorological factors that influence the concentration of particulates for the above sites, it results that the most important are temperature, wind speed, humidity and, on the last position, the atmospheric pressure. For Bucharest, the order is changed: wind speed, temperature, atmospheric pressure and humidity. A literature survey revealed that wind speed, relative humidity and temperature seem to compete for the first position, but also their squared terms and interactions between them play some role. In any case, the order of importance of meteorological variable influences on ambient PM levels is regional in nature, and no general conclusion might be drawn.
2.5. Pollutant trends: annual and seasonal
Pollutant annual and seasonal average concentrations at all sites were further investigated in order to determine if
In southeastern United States, decreasing trends from -5.1 to -9.7% yr-1 for SO2 and decreases of annual mean CO and NOx concentrations at rates ranging from -1.2 to -7.2% yr-1 (-6.0 to -9.0% yr-1) were reported , which are also higher than the corresponding decreasing rates determined for all selected locations in Romania. However, calculated temporal trends of main pollutants during 1997–2012 in Makkah, Saudi Arabia, indicate both increases (3.4% yr-1 for PM10, 6.1% yr-1 for SO2, 4.7% yr-1 for O3) and decreases (-2.6% yr-1 for CO, -3.5% yr-1 for NO) . Among potential factors responsible for the observed trends all over the world are emissions for traffic, changing weather patterns, construction activities, windblown re‐suspensions, emissions of O3 anthropogenic precursors, whose predominance is of regional nature, but large‐scale meteorological phenomena (North Atlantic Oscillation for example), implementation of pollution abatement strategies or the economic crisis influences are also important .
This study contributes to the knowledge on air pollution in East Europe, presenting an updated assessment of the ground‐level concentrations of major air pollutants in different environments, from highest to background values, and using data covering the longest available time period. Ambient air pollution levels, their variability and trends are discussed in the context of air quality status and trends in Bucharest, Europe and worldwide. Specific‐air pollutant trends are analyzed in order to show if they follow the trend of pollutant emissions.
The air pollution data were extracted from European AQ database Airbase v.8 (accessed in July 2015), and meteorological data from http://rp5.ru for WMO_ID=15069, 15090, 15120, 15170 stations. The author acknowledges the team of Google Earth.
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