Pollution Roses Research Articles

Aerosol pollution of the Moscow megacity by polyaromatic hydrocarbons: Seasonal variability and toxicological risks

Research on air pollution in large cities by polycyclic aromatic hydrocarbons (PAHs) is one of the priority tasks for assessing air quality and environmental risks to public health. The chemical composition of aerosols sampled in spring (2018), 2019, and winter (2019–2020) at the Aerosol Complex of Moscow State University, located on the background territory of the Moscow Megacity, is analyzed. Sixteen priority PAH compounds were identified using gas chromatography, mass spectrometry, and high-performance liquid chromatography. The median value of the total concentration of the 16 PAHs (Σ16PAH) increases from the spring season (1.43 ng/m3) to the fall season (1.68 ng/m3) and then to the winter season (2.47 ng/m3). Based on the diagnostic relationships of PAHs, the dominant contribution of transport, industrial enterprises, and the heating system to the total emissions was determined. Pollution roses indicate the location of sources of maximum concentrations of low-, medium-, and high-molecular PAHs. Pollution episodes are distinguished: in the spring of 2018 under the influence of the transport of smoke plumes of agricultural fires and in the fall of 2019 as a result of petrogenic emissions and an increase in biomass burning in the residential sector around Moscow. In the winter and fall seasons, the highest values of carcinogenic (0.45 and 0.42) and mutagenic (0.58 and 0.55) equivalents for benzo(a)pyrene were recorded in comparison with the spring season (0.26 and 0.38). The lifetime risk of developing lung cancer, calculated from the data for three seasons, is 0.5 cases per one million people.

Seasonal, Weekly, and Diurnal Black Carbon in Moscow Megacity Background under Impact of Urban and Regional Sources

Moscow megacity has a big gap in assessment of air quality, resulting in severe aerosol pollution. Black carbon (BC) concentrations over different timescales, including weekly and diurnal, are studied during four seasons of 2019–2020 at urban background site. Seasonal BC varies from 0.9 to 25.5 μg/m3 with a mean of 1.7 ± 1.4 μg/m3. Maximum mean BC equal to 2.2 ± 1.8 μg/m3 was observed in spring. Diurnal trends of black carbon concentrations differ in spring/summer and autumn/winter periods, they exhibit morning and evening peaks corresponding to traffic combined with the boundary layer height effect. The weekly cycle of BC characterizes the highest amount of combustion-related pollution on working days and the characteristics of population migration from a city for weekend. Seasonal pollution roses show the direction of the highest BC contamination. For identification of BC sources relating to traffic, heat and power plants, and industry around the site, polar plots are used. The spectral dependence of the aerosol light attenuation provides the estimate for Absorption Angstrom Exponent (AAE). We use the AAE above 1.3 and high frequency of AAE observation above 1 in order to support the assessment for a contribution of biomass burning in the region around Moscow in autumn and winter as well as of agriculture fires and wildfires in warm seasons. Air masses arriving to a city from fire-affected regions in spring and summer impact urban air pollution.

Comparing different approaches for assessing the impact of COVID-19 lockdown on urban air quality in Reading, UK

Many studies investigated the impact of COVID-19 lockdown on urban air quality, but their adopted approaches have varied and there is no consensus as to which approach should be used. In this paper we compare three of the main approaches and assess their performance using both estimated and measured data from several air quality monitoring stations (AQMS) in Reading, Berkshire UK. The approaches are: (1) Sequential approach – comparing pre-lockdown and lockdown periods 2020; (2) Parallel approach – comparing 2019 and 2020 for the equivalent time of the lockdown period; and (3) Machine learning modelling approach – predicting pollution levels for the lockdown period using business as usual (BAU) scenario and comparing with the observations. The parallel and machine learning approaches resulted in relative higher reductions and both showed strong correlation (0.97) and less error with each other. The sequential approach showed less reduction in NO and NOx, showed positive gain in PM10 and NO2 at most of the sites and demonstrated weak correlation with the other two approaches, and is not recommended for such analysis. Overall, the sequential approach showed −14, +4, −32, and + 56% change, the parallel approach showed −46, −43, −43 and + 7% change, and the machine learning approach showed −47, −44, −38 and + 5% change in NOx, NO2, NO and PM10 concentrations, respectively. The pollution roses demonstrated that the UK received easterly polluted winds from the central and eastern Europe, promoting secondary particulates and O3 formation during the lockdown. Changes in pollutant concentrations vary both in space and time according to the approach used, environment type of the monitoring site and the data type (e.g., deweathered vs. raw data). Therefore, the reported results (here or elsewhere) should be viewed in light of these factors before making any conclusion.

포항산업단지 주변 지역의 미세먼지 중 중금속 성분 농도 분포와 배출원 추정

The purpose of this study was to investigate the heavy metal pollution level in Pohang Industrial Complex and nearby residential areas where various industrial activities are related. The heavy metal sampling sites were selected as Cheongnimdong, Ho-dong, Sangdae-dong, Heunghae-eup, and Gigye-myeon. Sampling was carried out for 7 days in each season by sampling for 24 hours from 09 AM to 09 AM the next day at all sampling sites during the two seasons. The analysis of this study showed that the highest concentration was shown in Cheongnim-dong and most heavy metals are within the permissible levels. The concentration of heavy metals by survey period was high in November, the fall season. As a result of analysis of heavy metal emission sources and pollution roses, many emission sources were located around Cheongnimdong and Ho-dong, and high concentration of heavy metals was shown in Cheongnim-dong. The wind direction in Cheongnim-dong was high when the southwest and west winds were blowing. Therefore, the high concentration of heavy metals in Cheongnim-dong is expected to be influenced by heavy metal emission sources located in the southwest and west.

VARIATION OF PM10 CONCENTRATION DEPENDING ON THE METEOROLOGICAL PARAMETERS IN TWO BUCHAREST MONITORING STATIONS (IN GREEN AREAS)

This study presents the assessment of the PM10 emissions in areas from the south of Bucharest and Ilfov county, for a period of 11 years, during 2008-2018, taking into consideration the most important meteorological parameters with influence to air pollution, such as relative humidity, temperature, wind direction, and wind speed. During the study period, the mean annual concentrations of PM10 decreased from 43.92 μg/m3 to 29.67 μg/m3, expressed as a percentage change the decrease was 35.45% and 29.03% at the B-4 and B-7 stations, respectively. The Pearson correlation coefficient was significantly higher in the cold season (November-April). In order to further investigate the relations of air quality and wind direction pollution roses were calculated and the results indicate that the highest concentrations of PM10 occur in B-4 and B-7 at the southwestern and west direction of wind.

Assessing the Effect of Traffic Density and Cold Airflows on the Urban Air Quality of a City with Complex Topography Using Continuous Measurements

Abstract: The air quality in cities is affected by many factors such as topography, weather, emission sources, etc. In order to assess the influence of these factors on a certain location, continuous measurements of air quality as well as meteorological parameters are essential. The city of Stuttgart in Germany is known for its unique topography and air quality problems making it a very interesting place to investigate the impact of topography and thermal induced wind systems on the air quality. Stuttgart city center is surrounded by hills, elevated plains where cold air masses are produced during nights with autochthonous weather situations. Due to the varying topography, these cold air masses stream through valleys and gullies into the city centre. In this study, a stationary monitoring station was installed at Marienplatz, Stuttgart where the cold airflows from the valleys and gullies are collected together and enter the city, hence making it the optimum location for this investigation. The air pollutants such as nitrogen oxides (NOX, NO, NO2), ozone (O3), carbon monoxide (CO), Particulate Matter (PM) and Black Carbon (BC) were monitored continuously along with the meteorological parameters such as air temperature, relative humidity, air pressure, wind speed, wind direction, precipitation intensity and global radiation from March 2017 till now. In this paper, the results from March 2017 until December 2019 are presented. The influence of wind speed and wind direction on the concentration of pollutants is interpreted using wind and pollution roses. Temperature roses are plotted to understand the effect of cold airflows that bring fresh and cold air into the city. The traffic data for the federal highway passing by the monitoring station are obtained and the pollutant concentrations are related to the traffic, as it is the dominant source of emission in the city. Key words: air quality monitoring, cold airflows, urban air quality, traffic and air quality, air pollution in Stuttgart, continuous measurements, air pollutants

Intraday and interday variations of 69 volatile organic compounds (BVOCs and AVOCs) and their source profiles at a semi-urban site

To study the intraday and interday patterns and possible sources of volatile organic compounds (VOCs), 6-h active sampling was performed in April, May, June, July, and August 2017 and in January 2018 in a semi-urban site in Bolu, Turkey. Totally 69 VOCs having biogenic (BVOCs, i.e., isoprene, monoterpenes and oxygenated VOCs) and anthropogenic origins (AVOCs) were examined. Fifty-four of sixty-nine analyzed VOCs could be detected. Decanal followed by benzaldehyde, benzene, phenol, and toluene were detected as the leading anthropogenic VOCs whereas alpha-pinene and hexanal were the dominant biogenic VOCs. There was a decrease in concentrations of most of the VOCs in January and April when light intensity and temperature were relatively low. Atmospheric levels of total biogenic VOCs exceeded that of anthropogenic VOCs in all months except for January and April. Dependence of biogenic VOC emissions on the light intensity, temperature and the increase in leaves were considered to be effective in their higher levels in summer and daytime periods. The daytime anthropogenic VOCs concentrations were higher than the nighttime anthropogenic VOCs probably due to intense vehicle traffic during working hours and/or increased volatilization from their sources at elevated temperatures. The VOCs that significantly and negatively correlated with ozone were evaluated as effective BVOCs in the ozone formation while the maximum incremental reactivity (MIR) method gave the contribution of AVOCs. Positive Matrix Factorization (PMF) was applied for the source apportionment. G score graphs and G score pollution roses were also used to identify possible sources of investigated VOCs. Solvent evaporation, gasoline-powered vehicle emissions, fossil fuel (residential heating), biogenic (hornbeam, grass, oak, beech) emissions, diesel/domestic activities and forested city atmosphere were identified as the possible VOC sources in the study area.

Verification of the EDMS model adapted to Budapest Liszt Ferenc Airport

This paper quantifies the ability of the emission and dispersion modelling system to reproduce observed pollutant concentrations in the vicinity of a European airport. For the validation, one receptor point was selected at Liszt Ferenc International Airport, terminal building 2, where an air quality monitoring station operates. Modelling results superimposed upon background concentrations generated from a suburban monitoring station in the vicinity were compared with hourly measured concentrations using statistical indicators for three compounds (CO, NOX and PM10). Acceptable correlation coefficients (0.53-0.76) were obtained, although in the case of PM10 modelled values show slight but significant underestimation for the entire studied period. Also, an obvious overestimate of NOX concentration was found for certain days. Pollution roses were generated that highlighted the spatial distribution of the pollution sources that affect the air quality at the receptor point. Sensitivity of model to input data, especially to the emission inventory is discussed. The contribution of aircraft ground movement and apron area emission was found to be well characterised, but in the case of CO and NOX a small (15 ± 5%) deficiency was found for ground vehicles emission of parking area, and a much higher (65 ± 5%) difference was obtained for PM10.

Airborne heavy metals in two cities of North Rhine Westphalia – Performing inhalation cancer risk assessment in terms of atmospheric circulation

The main objective of this study was to examine the levels of four heavy metals (As, Cd, Pb and Ni) in PM10 samples collected in two urban background stations in Dortmund and Bielefeld, in relation to atmospheric circulation. Pollution roses, Conditional Probability Function (CPF) roses and backward air mass trajectory clusters were used to identify air currents associated with the importation of PM10 and of the included metal constituents. In addition, PM10, NO2, SO2, O3, As, Cd, Ni and Pb concentrations were analyzed by a Principal Component Analysis (PCA) to reveal major local emission sources of PM10 metal content. Traffic was the main emitter of PM10, As, Cd, and Pb in both cities, highlighting the existence of non-negligible lead quantities in unleaded gasoline, whilst nickel emissions were associated with heavy fuel oil combustion in industries and primarily for domestic heating. The created CPF roses and trajectory clusters were in good agreement, clearly revealing that eastern air currents enriched the locally produced PM10 load with additional aerosols from Eastern Europe. The concentrations of arsenic and cadmium were also enhanced by the arrival of air parcels from the East, indicating the anthropogenic origin of the exogenous aerosols due to combustion. The induced cancer risk (CRinh) for adults, due to inhalation of individual metal constituents, was also estimated in terms of atmospheric circulation, indicating higher risk in Dortmund than in Bielefeld. CRinh values for arsenic exceeded the limit of 1 × 10−6 in both cities, primarily during the influence of eastern circulation.

Atmospheric trace metals measured at a regional background site (Welgegund) in South Africa

Abstract. Atmospheric trace metals can cause a variety of health-related and environmental problems. Only a few studies on atmospheric trace metal concentrations have been conducted in South Africa. Therefore the aim of this study was to determine trace metal concentrations in aerosols collected at a regional background site, i.e. Welgegund, South Africa. PM1, PM1–2. 5 and PM2. 5–10 samples were collected for 13 months, and 31 atmospheric trace metal species were detected. Atmospheric iron (Fe) had the highest concentrations in all three size fractions, while calcium (Ca) was the second-most-abundant species. Chromium (Cr) and sodium (Na) concentrations were the third- and fourth-most-abundant species, respectively. The concentrations of the trace metal species in all three size ranges were similar, with the exception of Fe, which had higher concentrations in the PM1 size fraction. With the exception of titanium (Ti), aluminium (Al) and manganese (Mg), 70 % or more of the trace metal species detected were in the smaller size fractions, which indicated the influence of industrial activities. However, the large influence of wind-blown dust was reflected by 30 % or more of trace metals being present in the PM2. 5–10 size fraction. Comparison of trace metals determined at Welgegund to those in the western Bushveld Igneous Complex indicated that at both locations similar species were observed, with Fe being the most abundant. However, concentrations of these trace metal species were significantly higher in the western Bushveld Igneous Complex. Fe concentrations at the Vaal Triangle were similar to levels thereof at Welgegund, while concentrations of species associated with pyrometallurgical smelting were lower. Annual average Ni was 4 times higher, and annual average As was marginally higher than their respective European standard values, which could be attributed to regional influence of pyrometallurgical industries in the western Bushveld Igneous Complex. All three size fractions indicated elevated trace metal concentrations coinciding with the end of the dry season, which could partially be attributed to decreased wet removal and increases in wind generation of particulates. Principal component factor analysis (PCFA) revealed four meaningful factors in the PM1 size fraction, i.e. crustal, pyrometallurgical-related and Au slimes dams. No meaningful factors were determined for the PM1–2. 5 and PM2. 5–10 size fractions, which was attributed to the large influence of wind-blown dust on atmospheric trace metals determined at Welgegund. Pollution roses confirmed the influence of wind-blown dust on trace metal concentrations measured at Welgegund, while the impact of industrial activities was also substantiated.

Hourly composition of gas and particle phase pollutants at a central urban background site in Milan, Italy

A comprehensive range of gas and particle phase pollutants were sampled at 1-hour time resolution in urban background Milan during summer 2012. Measurements include several soluble inorganic aerosols (Cl−,NO2−,NO3−,SO42−,Ca2+,K+,Mg2+,Na+,NH4+) and gases (HCl, HNO2,HNO3, NH3, NO, NO2,O3, SO2), organic, elemental and black carbon and meteorological parameters. Analysis methods used include mean diurnal pattern on weekdays and Sundays, pollution roses, bivariate polar plots and statistical models using backtrajectories. Results show how nitrous acid (HONO) was mainly formed heterogeneously at nighttime, with a dependence of its formation rate on NO2 consistent with observations during the last HONO campaign in Milan in summer 1998, although since 1998 a drop in HONO levels occurred following to the decrease of its precursors. Nitrate showed two main formation mechanisms: one occurring through N2O5 at nighttime and leading to nitrate formation onto existing particles; another occurring both daytime and nighttime following the homogeneous reaction of ammonia gas with nitric acid gas. Air masses reaching Milan influenced nitrate formation depending on their content in ammonia and the timing of arrival. Notwithstanding the low level of SO2 in Milan, its peaks were associated to point source emissions in the Po valley or shipping and power plant emissions SW of Milan, beyond the Apennines. A distinctive pattern for HCl was observed, featured by an afternoon peak and a morning minimum, and best correlated to atmospheric temperature, although it was not possible to identify any specific source. The ratio of primary-dominated organic carbon and elemental carbon on hourly PM2.5 resulted 1.7. Black carbon was highly correlated to elemental carbon and the average mass absorption coefficient resulted MAC=13.8 ± 0.2 m2 g−1. It is noteworthy how air quality for a large metropolitan area, in a confined valley and under enduring atmospheric stability, is nonetheless influenced by sources within and outside the valley.

Decomposition of the wind and nonwind effects on observed year‐to‐year air quality variation

AbstractChanges in emissions and wind are often identified as the two dominant factors contributing to year‐to‐year variations in the concentration of primary pollutants. However, because changes in wind and emissions are intertwined, it has been difficult to quantitatively differentiate their effects on air quality directly from observed data. In particular, if the annual mean concentration of pollutants is higher than the previous year, it is difficult to identify whether the deterioration in air quality is caused by wind blowing from more polluted regions or an increase in contributing emissions. In this paper, based on wind and pollution roses, we propose a method to differentiate the effects of wind and nonwind (e.g., emissions) changes using direct observation. An index (L) is first defined to quantify the validity of the linear decomposition. The method is then validated by idealized experiments, numerical experiments, and a 2 year observation data set from an actual emissions control program. Finally, we demonstrate the proposed method by studying long‐term particulate matter (PM10) variations in Hong Kong during 2000–2011. We find that for most of the period, the linear decomposition of the changes in annual PM10 is valid (up to 90% confidence) and is dominated by the change in nonwind effects (e.g., emissions), whereas the average absolute effect from the wind variability is about 20%. Sensitivity analyses also suggest that our method should work in any location as long as the observed wind and pollution data have sufficient duration and resolution to resolve the corresponding wind and pollution roses.

Using pollution roses to assess sulfur dioxide impacts in a township downwind of a petrochemical complex

This study used pollution roses to assess sulfur dioxide (SO2) pollution in a township downwind of a large petrochemical complex based on data collected from a single air quality monitoring station. The pollution roses summarized hourly SO2 concentrations at the Taishi air quality monitoring station, located approximately 7.8–13.0 km south of the No. 6 Naphtha Cracking Complex in Taiwan, according to 36 sectors of wind direction during the preoperational period (1995–1999) and two postoperational periods (2000–2004 and 2005–2009). The 99th percentile of hourly SO2 concentrations 350˚ downwind from the complex increased from 28.9 ppb in the preoperational period to 86.2–324.2 ppb in the two postoperational periods. Downwind SO2 concentrations were particularly high during 2005–2009 at wind speeds of 6–8 m/sec. Hourly SO2 levels exceeded the U.S. Environmental Protection Agency (EPA) health-based standard of 75 ppb only in the postoperational periods, with 65 exceedances from 0–10˚ and 330–350˚ downwind directions during 2001–2009. This study concluded that pollution roses based on a single monitoring station can be used to investigate source contributions to air pollution surrounding industrial complexes, and that it is useful to combine such directional methods with analyses of how pollution varies between different wind speeds, times of day, and periods of industrial development. Implications: The pollution roses summarize SO2 concentrations by wind direction and to investigate source contribution to air quality. Percentile statistics can catch pollution episodes occurring in a very short time at specific wind directions and speeds. The downwind areas have already exceeded regulated 1-hr SO2 standard since the operation of the complex.

A qualidade do ar em Lisboa. Valores médios e situações extremas

AIR QUALITY IN LISBON: MEAN AND EXTREME VALUES - Different aspects of the air pollution in Lisbon were analised: spatial distribution, cyclical variation (yearly, weekly and daily) and dependence on athmospheric conditions. During January 1993, when air pollution attained very high levels, we found that a very strong relationship was established between air pollution and wind speed and direction as well as between air pollution and the eight of the thermal inversion over Lisbon. During the more polluted periods, wind speed never reached 2 m/s and the thermal inversion was always under 165m. As for wind direction, pollution roses confirmed that the highest pollution levels occurred with a flux favourable to the transport of pollutants from the main emission areas.

A comparative study of traffic related air pollution next to a motorway and a motorway flyover

The aim of this study was to investigate the influence of the vertical position of a motorway on the pollutant concentrations in the vicinity of the motorway. Therefore, a near-road monitoring campaign was performed along two adjoining motorway stretches: one motorway at ground level and a motorway flyover. PM2.5 and nitrogen oxides (NO, NO2 and NOx) were measured at both sides of the motorway on a 30-min resolution. In addition, EC (elemental carbon) was measured on both sides of the road on a daily basis using low volume samplers and thermal–optical transmission (TOT) analysis of filters. PNC (particle number concentration) was measured at one side. The measurement locations on each side of the road were located at 29 m and 102 m from the motorway at ground level and at 60 m and 120 m from the motorway flyover. Pollution roses, showed increased average concentrations in the direction of the road for nitrogen oxides and PM2.5. The impact of road traffic appeared to be higher at ground level compared to the flyover. The difference in concentration between both sides of the road for PM2.5, NO, NO2 and NOx was analysed using linear mixed models. The concentration difference, which can be seen as the contribution of road traffic to the pollutant concentration, was calculated taking into account prevailing wind directions. The results indicate that taking into account confounding parameters (time of day, day of the week, distance to the road and wind speed), the contribution of the motorway traffic to pollutant concentrations is significantly higher when the motorway is at ground level. Furthermore, time of day and day of the week also have a significant effect on the concentration difference. The linear mixed model estimated a reduced road increment at the motorway flyover compared to the ground level motorway of 119 ppb for NO, 29 ppb for NO2 and 3.5 μg m−3 for PM2.5. taking into account confounding parameters.The highest EC and PNC concentrations were measured downwind of the motorway at ground level.

Properties of coarse particles in the atmosphere of the United Kingdom

Abstract The recent rapid expansion of PM2.5 monitoring alongside PM10 has allowed estimation of concentrations of coarse particles (PM2.5–10) by difference. This has been carried out for sites using comparable measurement techniques for both metrics, and the properties of the hourly average concentration data have been evaluated. In the UK atmosphere, coarse particles generally represent well under half of PM10 mass. The ratio of PMcoarse to PM10 averages 0.29 ± 0.14 (n = 43) for FDMS measurements and 0.38 ± 0.30 (n = 12) for TEOM measurements and does not show a very consistent pattern according to site type. The values are mostly higher for TEOM measurements than for FDMS which is explicable in terms of semi-volatile losses predominantly from fine particles in the TEOM. Sites where PMcoarse/PM10 exceeds 0.5 generally have a marine or industrial influence. Correlations between hourly PM2.5 and PM10 are generally very high (mean R = 0.80 for FDMS data and mean R = 0.85 for TEOM data) whilst those between PMcoarse and PM2.5 are very much lower (mean R = 0.09 for FDMS measurements and mean R = 0.41 for TEOM measurement sites). When inter-site correlations are examined between sites of similar characteristics within 30 km of one another, the inter-site correlation of PM2.5 (R = 0.56–0.87) is very much higher than that of PMcoarse (R = 0.25–0.60). Coefficients of Divergence indicate lower spatial homogeneity for PMcoarse than for PM2.5. When hourly data are used to construct pollution roses, it is typical for PM2.5 and PM10 to show a similar pattern dominated either by long-range transport or local traffic sources while PMcoarse can show quite a different pattern indicative of a different range of contributory sources. Plots of particle concentration versus wind speed show a monotonic decline of concentration with increasing wind speed for PM2.5, but an initial decline and an increase at higher wind speeds for PMcoarse. There are relatively few datasets allowing an examination of temporal trend but those available for 4.5–10 years generally show no significant trend with time for fine or coarse particles, or for PM10. Weekday/weekend and diurnal concentration patterns for coarse and fine particles show a strong anthropogenic influence on PMcoarse and a larger weekday/weekend difference for PMcoarse than PM2.5. It is concluded that coarse and fine particles show very different behaviour as a result of their different sources and properties. The lower spatial homogeneity of coarse particles may be a contributory factor in why they appear to be less toxic than fine particles in epidemiological studies.

Quantification of diffuse and fugitive PM10 sources by integrated “hot-spot” method

Between 2002 and 2006, various exceedances of air quality directive 1999/30/EC were reported in a number of PM10 monitoring stations in Flanders. A study was carried out with the objectives: i) to identify and quantify the sources that have caused or have contributed to the exceedances over this period; and ii) to propose suitable emission reduction measures to comply with the current air quality standards (2010). We followed an integrated multi-disciplinary approach consisting of a detailed analysis of the PM10 data series, specific air quality measuring campaigns, air quality modelling and expertise on emission reduction measures (including current BAT and BREF studies). The data analysis was based on 5 steps: 1) The pollution roses of the individual monitoring stations were analyzed. 2) The variation of the PM10 concentration in function of wind speed was assessed to determine whether we were dealing with a fugitive source of primary particles (e.g. mineral dust) or a source of smaller secondary particles taken up and dispersed by the ambient air flow (e.g. industrial combustion sources or traffic sources). 3) The temporal patterns of the potential sources were analyzed. 4) Specific measurement campaigns were organized in the neighbourhood of the potential sources. This included mapping of PM10 concentrations and a detailed analysis of the chemical composition. 5) The (fugitive) PM10 sources were quantified by means of reversed modelling. After deduction of a high background concentration, observed in all monitoring stations in Flanders, results for the individual monitoring stations show that in almost all cases the peak concentrations can be attributed to local fugitive PM10 emissions. Thus for most of the analysed cases it could be concluded that the cause of the exceedances or nearly exceedances is the combination of high background concentrations and local contributions from diffuse or fugitive sources. The total contribution of these non-registered diffuse sources was estimated to be 9–16% of the measured annual averaged PM10 concentrations.

PM 10 metal concentrations and source identification using positive matrix factorization and wind sectoring in a French industrial zone

The elemental composition data of ambient aerosols collected upon selected wind sectors in the highly industrialised harbour of Dunkirk (France) were interpreted using pollution roses, elemental ratios, Enrichment Factors (EF), Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) receptor model. The objective was to identify the possible sources of PM10 aerosols, their respective chemical tracers and to determine their relative contribution at the sampling site. PM10 particles samples were collected from June 2003 to March 2005 in order to analyse up to 35 elements (Ag, Al, As, Ba, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Eu, Fe, K, La, Mg, Mn, Mo, Na, Ni, Pb, Rb, S, Sb, Sc, Si, Sm, Sr, Th, Ti, U, V, Zn and Zr) using Inductively Coupled Plasma (ICP)-Atomic Emission Spectrometry (AES) and ICP-Mass Spectrometry (MS). A significant effort has been made on estimating the total uncertainty of each result by regularly analysing blanks, quality controls and SRM NIST standards. Based on this procedure, a selected set of 24 "robust" elements was compared to the 35-element matrix in order to evaluate the sturdiness of our PMF statistical treatment. Eight source factors were resolved by PCA for all the wind sectors explaining 90% of the total data variance. The PMF results confirmed that eight physically interpretable factors contributed to the ambient particulate pollution at the sampling site: crustal dust (11%), marine aerosols (12%), petrochemistry activities (9.2%), metallurgical sintering plant (8.6%), metallurgical coke plant (12.6%), ferromanganese plant (6.6%), road transport (15%) and a less clearly interpretable profile probably associated to dust resuspension (13%). These weighted contributions against wind direction frequencies demonstrate that industrial sources are the most important contributors to this site (37%) followed by the natural sources (detrital and marine sources) (23%) and the road transport (15%).

Spatial variability of sulfur dioxide and sulfate over complex terrain in East Tennessee, USA

In 2004 and 2005, the East Tennessee Ozone Study (ETOS) enhanced its regional measurement program with annular denuder systems to quantify sulfur dioxide (SO 2) and PM 2.5 sulfate (SO 4 2−) at five sampling sites that were representative of the complex terrain and physiographic features of East Tennessee. Intersite spatial variability was more defined for SO 2 than for SO 4 2−, which showed a fairly uniform structure in both daytime and nighttime measurements. Pollution roses indicated that two sites may have been influenced by the proximity of SO 2 emission sources. The data suggest that SO 2 is affected by nearby sources in the study area while the sources of SO 4 2− are regionally distributed.

Temporal distribution, behaviour and reactivities of BTEX compounds in a suburban Atlantic area during a year

The temporal distribution of BTEX compounds (benzene, toluene, ethylbenzene and xylene isomers) in ambient air was studied in a suburban Atlantic area in the northwest of Spain. These compounds mean serious risks for public and occupational health and for biological and physical environments. A total of 1200 samples were analysed during a year, and in most of the samples toluene was the most abundant VOC followed by benzene or m+p-xylene. The average concentrations were 1.62, 2.15, 0.60, 0.94 and 0.47 microg m(-3) for benzene, toluene, ethylbenzene, m+p-xylenes and o-xylene respectively, although they reached values as high as 50.53, 28.57, 21.33, 46.13 and 22.74 microg m(-3) for the compounds in the same order. An exhaustive study was carried out to establish how both emission sources and the climatic conditions affected the BTEX levels measured in the outskirts of a small city. Wind directions and speeds, daily and seasonal BTEX profiles, ratios among the concentrations measured in the sampling site, in the city centre and in the fuels commonly used by the cars in the city were studied. The pollution roses revealed the presence of diffuse BTEX sources affecting the sampling point, although all the compounds reached their highest levels when the wind blew from an industrial area in the SW of the city. The daily BTEX profiles were well-correlated with the traffic pattern in the accesses to the city and the seasonal profile showed that the highest concentrations were reported in summertime. The ratios T/B, m+p-X/B and o-X/B were 1.32, 0.58 and 0.29 respectively; these values were lower than the ones reported in previous studies carried out in the centre of the same city. This could be caused by the major distance from the sampling point to the city centre and by the changes in the fuels and in the kind of vehicles used in the city. The correlation coefficients among the TEX fraction were always higher than 0.72 but benzene only showed a good correlation with these compounds in winter. The correlations between BTEX concentrations and their contributions to the photochemistry in the sampling area were used in order to evaluate the distance from the road traffic source and to estimate the presence of fresh or old air masses in the sampling point. This study provides data which can be useful to establish the BTEX levels in suburban areas from the coastal Atlantic region and improves the knowledge of the BTEX presence in this kind of area.