Pollutant Roses Research Articles

Уровни выбросов и идентификация источников NOX и SO2 в юго-восточной части Балтики по результатам наблюдений на станции фонового мониторинга Дьябла-Гура (Польша)

The aim of this study was to investigate the levels of emissions and identify the sources of NOx and SO2 pollution in a rural station in the south-eastern Baltic (Diabla Gora). This analysis was conducted using hourly NOx and SO2 concentration data and meteorological variables (wind speed and direction), which were analyzed over a period of four years (2018—2021). Time series plots, wind and pollutant roses, application of a conditional probability function, and inverse trajectories were considered to analyze the data and identify emission sources. The average concentration of NOx and SO2 for the study period was 2.12 µg/m3 and 0.27 µg/m3, respectively. The results showed a definite pattern for daily and monthly varia­tions, with peaks in the morning hours and at the beginning and end of the year (January and December, respectively). The main identified sources of NOx and SO2 emissions were located south of the monitoring station, particularly during cold periods when the highest concentra­tions were recorded.

Immission levels and identification of sulfur dioxide sources in La Oroya city, Peruvian Andes.

La Oroya is a city in the Peruvian Andes that has suffered a serious deterioration in its air quality, especially due to the high rate of sulfur dioxide (SO2) emissions, which underlines the importance of knowing its sources of contamination and variation over the years. In this sense, this study aimed to evaluate the immission levels and determine the sources of SO2 contamination in La Oroya. This analysis was performed using the hourly concentration data of SO2, and meteorological variables (wind speed and direction), which were analyzed for a period of three years (2018–2020). Graphs of time series, wind and pollutant roses, bivariate polar graphs, clustering k-means, nonparametric statistical tests, and the application of the conditional bivariate probability function were performed to analyze the data and identify the emission sources. The mean concentration of SO2 was 264.2 μg m−3 for the study period, where 55.66 and 2.37% of the evaluated days exceeded the guideline values recommended by the World Health Organization and the Peruvian Environmental Quality Standard for air for 24 h, respectively. The results showed a defined pattern for the daily and monthly variations, with peaks in the morning hours (0900–1000 h LT) and at the end of the year (December), respectively. The main sources of SO2 emissions identified were light and heavy vehicles that travel through the Central Highway, the La Oroya Metallurgical Complex, the transit of vehicles within the city, and the diesel–electric locomotives that provide cargo transportation services and tourism passenger transportation. The article attempts to contribute to the development of adequate air quality management policies.

Development of a noise-assisted multivariate empirical mode decomposition framework for characterizing PM 2.5 air pollution in Taiwan and its relation to hydro-meteorological factors

To better understand air pollution problems, the relationships between PM2.5 and hydro-meteorological variables are studied using a state-of-the-art multivariate nonlinear and non-stationary filtering method, noise-assisted multivariate empirical mode decomposition (NAMEMD), and the time-dependent intrinsic correlation (TDIC) algorithm. Three characteristic scales (annual, diurnal and semi-diurnal) are shown to be significant to PM2.5 characterization, based on using NAMEMD filtering. Temporal fluctuations of local correlations among PM2.5 and hydro-meteorological variables are presented. On diurnal and semi-diurnal scales, seasonal variation of the local correlation between temperature and humidity is observed. A combined wind speed and direction analysis can be conducted using the NAMEMD-based algorithm. The pollutant roses that are generated from the reconstructed wind directions reveal the sources of PM2.5 on different scales. PM2.5 is found to be related to land breeze at the diurnal scale and to winter monsoons at the annual scale. The scale-dependent wind direction that contributes to the increase of PM2.5 can be identified.

Use of inorganic and organic markers associated with their directionality for the apportionment of highly correlated sources of particulate matter

Particulate matter source identification using receptor models is one of the tools applied in air quality management. These models have limitations such as the collinearity effects, hindering their application and interpretation. Positive Matrix Factorization (PMF) models use chemical markers for the definition of likely sources, leaving to users the factors interpretation. This can lead to biased interpretations, as chemical species can be markers for several sources, particularly when there is source similarity. The Region of Greater Vitória, located southeast of Brazil, is a complex site in which similar industrial activities are installed, such as a pelletizing plant and a steel plant, that produce iron pellets and sinter, both iron-agglomerates with similar chemical profiles. To minimize the effects of collinearity between those sources, a new PMF approach is proposed by using inorganic and organic chemical species and the directionality of pollutant using wind roses. The proposed methodology determines the following consolidated markers: elemental carbon (EC) and organic carbon (OC) for vehicular sources; chloride (Cl) and sodium (Na) for sea salt; iron (Fe) for industrial sources. This association was possible by identifying the directionality of the chemical species. Cl a typical sea salt marker also attributed to industrial sintering activities. Some PMF factors showed high OC loadings, a typical marker for both vehicular exhaust and coal burning. The definition of the most appropriate sources for those factors was only possible due to the assessment of the pollutant roses. Pollutant roses generally showed that higher concentrations of potassium (K), a marker of biomass burning, was predominantly associated with winds from an industrial park, and are most likely associated with sintering emissions. Results showed that combining both organic and inorganic markers with the pollutant roses for identification of the directionality of predominant sources improved the interpretation of PMF factor numbers in source apportionment studies.

Local source identification of trace metals in urban/industrial mixed land-use areas with daily PM10 limit value exceedances

This study presents the analysis of the concentration levels, inter-site variation and source identification of trace metals at three urban/industrial mixed land-use sites of the Cantabria region (northern Spain), where local air quality plans were recently approved because the number of exceedances of the daily PM10 limit value according to the Directive 2008/50/EC had been relatively high in the last decade (more than 35 instances per year). PM10 samples were collected for over three years at the Torrelavega (TORR) and Los Corrales (CORR) sites and for over two years at the Camargo (GUAR) site and analysed for the presence of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), lead (Pb), nickel (Ni), titanium (Ti), vanadium (V), molybdenum (Mo), manganese (Mn), iron (Fe), antimony (Sb) and zinc (Zn). Analysis of enrichment factors revealed an anthropogenic origin of most of the studied elements; Zn, Cd, Mo, Pb and Cu were the most enriched elements at the three sites, with Fe and V as the least enriched elements. Positive Matrix Factorisation (PMF) and pollutant roses (Cu at TORR, Zn at CORR and Mn at GUAR) were used to identify the local sources of the studied metals. Analysis of PMF results revealed the main sources of trace metals at each site as road traffic at the TORR site, iron foundry and casting industry at the CORR site and a ferro-manganese alloy industry at the GUAR site. Other sources were also identified at these sites, but with much lower contributions, such as minor industrial sources, combustion and traffic mixed with the previous sources.

Comparison of the IFDM building downwash model predictions with field data

The building downwash parameterization for plume models proposed in Cosemans et al. (2012) is integrated in a plume model (IFDM, Immission Frequency Distribution Model) and the predictions of this model are compared with real-life field data. The field data are time series for four monitoring sites of daily arsenic (As) concentrations measured over a two year period (Jan. 2010–Dec. 2011) near a plant that emits some As. In-stack emission measurements as well as measured As concentrations in ambient air show considerable time-dependent variations which prohibited a straightforward comparison of measured and computed concentrations using average emissions. Therefore, two model evaluations have been done. First, the time-variability was considered small enough during twenty months (out of the 24 months in the two-year period) to compare the central moving averages of the measured concentrations with those computed using constant emissions. Time series compare well, and none of the measured annual average concentrations has a bias larger than 50% while 7 out of 8 year averages (4 monitoring sites, two years) have a bias lower than 30%. During the four months not used in this evaluation, measured daily concentrations are about four times higher than during the other twenty months, resulting in yearly average increase up to 80%. Using a time-varying emission scenario, using information from daily As-measurements in the recirculation zone on top of the building with the sources, the model is able to reproduce the annual mean concentrations at the different locations including all the months. Finally, not only the yearly averages, but also the time series of measured and computed concentrations, pollutant roses and Q–Q plots are evaluated.

Contribution of point sources to trace metal levels in urban areas surrounded by industrial activities in the Cantabria Region (Northern Spain)

The present work deals with the characterization of selected heavy metals (Ti, V, Cr, Mn, Cu, Mo, Pb, Ni, Cd and As) in PM10 collected in 2008 in three urban areas of Cantabria (Northern Spain) which are influenced by industrial activities. Ternary plots were used to identify the main tracers at the studied sites. Pollutant roses of these tracers were used as tools which give information about the location of the most important emission sources due to the characteristic wind patterns in Cantabria. Pollutant roses showed a significant contribution of the local industry to trace metal levels at the selected urban sites.

Reverse modelling for the determination of fugitive sources of PM10

Time-series of half-hourly PM10 concentrations and meteorological data measured near industrial sites where dusty materials are processed can be used to estimate PM10 source strengths for these activities using reverse modelling. Reverse modelling starts with building an over-determined system of linear equations: Observed_Cmj = Computed_Cmj(known sources)+ Σ i=1, NComputed_Cmj(Qi) where the N values of Qi are determined by least squares regression. The paper focuses on regression solutions tainted by noise fitting that are physically meaningless, on rules to reduce the risk for noise fitting, and on how to use cumulative frequency distributions, time series for different averaging times and pollutant roses to obtain a physically sound solution.

Evaluation of the contribution of local sources to trace metals levels in urban PM2.5 and PM10 in the Cantabria region (Northern Spain)

The aim of this study was to evaluate the contribution of local emission sources to the selected trace metals levels (As, Ni, Cd, Pb, Ti, V, Mn, Cu, Mo and Hg) in urban particulate matter, PM10 and PM2.5, in the Cantabria region (Northern Spain) during the year 2008. PM10 and PM2.5 samples were collected and characterized in an urban sampling site at Santander (Cantabria); additional PM10 samples were collected at a rural site at Los Tojos (50 Km SW) to evaluate both the urban and rural environment. The particulate matter and the metals regulated by the EC air quality directives (Pb, As, Ni and Cd) did not exceed the EC limit or target values in the Cantabria region. The metal concentrations were much lower at the rural site except for Ni. Different techniques were used to analyse the possible sources of the studied metals: enrichment factor, urban impact, wind and pollutant roses and principal component analysis (PCA). A combination of these techniques allowed us to demonstrate that the local emission sources of metals in the particulate matter at Santander city were the most important. Ti, Ni, As and V were identified as the main urban background tracers while Mn and Pb were the main local industrial tracers. The urban background was found to be the major contributor to PM10. The relationship between the emission tracers and the major air pollutants (NO(2), NO, CO, SO(2),O(3) and PM(10)) was also studied by PCA; a significant relationship between tracers and the major air pollutants was not found, showing a decoupled relationship between major pollutants and metal species in particulate matter.

Pollutant roses for daily averaged ambient air pollutant concentrations

Pollutant roses are indispensable tools to identify unknown (fugitive) sources of heavy metals at industrial sites whose current impact exceeds the target values imposed for the year 2012 by the European Air Quality Daughter Directive 2004/207/EC. As most of the measured concentrations of heavy metals in ambient air are daily averaged values, a method to obtain high quality pollutant roses from such data is of practical interest for cost-effective air quality management. A computational scheme is presented to obtain, from daily averaged concentrations, 10° angular resolution pollutant roses, called PRP roses, that are in many aspects comparable to pollutant roses made with half-hourly concentrations. The computational scheme is a ridge regression, based on three building blocks: 1. ordinary least squares regression; 2. outlier handling by weighting based on expected values of the higher percentiles in a lognormal distribution; 3. weighted averages whereby observed values, raised to a power m, and daily wind rose frequencies are used as weights. Distance measures are used to find the optimal value for m. The performance of the computational scheme is illustrated by comparing the pollutant roses, constructed with measured half-hourly SO 2 data for 10 monitoring sites in the Antwerp harbour, with the PRP roses made with the corresponding daily averaged SO 2 concentrations. A miniature dataset, made up of 7 daily concentrations and of half-hourly wind directions assigned to 4 wind sectors, is used to illustrate the formulas and their results.

Study of Meteorological Aspects and Urban Concentration of SO2 in Atmospheric Environment of La Plata, Argentina

This article presents and discusses SO(2) (ppbv) concentration measurements combined with meteorological data (mainly wind speed and direction) for a five-year campaign (1996 to 2000), in a site near an oil refinery plant close to the city of La Plata and surroundings (aprox. 740.000 inh.), considered one of the six most affected cities by air pollution in the country. Since there is no monitoring network in the area, the obtained results should be considered as medium term accumulated data that enables to determine trends by analyzing together gas concentrations and meteorological parameters. Preliminary characterization of the behaviour of the predominant winds of the region in relation with potential atmospheric gas pollutants from seasonal wind roses is possible to carry out from the data. These results are complemented with monthly averaged SO(2) measurements. In particular, for year 2000, pollutant roses were determined which enable predictions about contamination emission sources. As a general result we can state that there is a clear increase in annual SO(2) concentration and that the selected site should be considered as a key site for future survey monitoring network deployment. Annual SO(2) average concentration and prevailing seasonal winds determined in this work, together with the potential health impact of SO(2) reveals the need for a comprehensive and systematic study involving particulate matter an other basic pollutant gases.

Interpretation of Meteorological Pollutant Roses in Areas Subject to Nocturnal Drainage Winds

Interpretation of Meteorological Pollutant Roses in Areas Subject to Nocturnal Drainage Winds