A study of the total atmospheric sulfur dioxide load using ground-based measurements and the satellite derived Sulfur Dioxide Index

We present characteristics of the sulfur dioxide (SO₂) loading over Thessaloniki, Greece, and seven other selected sites around the world using SO₂ total column measurements from Brewer spectrophotometers together with satellite estimates of the Version 8 TOMS Sulfur Dioxide Index (SOI) over the same locations, retrieved from Nimbus 7 TOMS (1979–1993), Earth Probe TOMS (1996–2003) and OMI/Aura (2004–2006). Traditionally, the SOI has been used to quantify the SO₂ quantities emitted during great volcanic eruptions. Here, we investigate whether the SOI can give an indication of the total SO₂ load for areas and periods away from eruptive volcanic activity by studying its relative changes as a correlative measure to the SO₂ total column. We examined time series from Thessaloniki and another seven urban and non-urban stations, five in the European Union (Arosa, De Bilt, Hohenpeissenberg, Madrid, Rome) and two in India (Kodaikanal, New Delhi). Based on the Brewer data, Thessaloniki shows high SO₂ total columns for a European Union city but values are still low if compared to highly affected regions like those in India. For the time period 1983–2006 the SO₂ levels above Thessaloniki have generally decreased with a rate of 0.028 Dobson Units (DU) per annum, presumably due to the European Union's strict sulfur control policies. The seasonal variability of the SO₂ total column exhibits a double peak structure with two maxima, one during winter and the second during summer. The winter peak can be attributed to central heating while the summer peak is due to synoptic transport from sources west of the city and sources in the north of Greece. A moderate correlation was found between the seasonal levels of Brewer total SO₂ and SOI for Thessaloniki, Greece (R = 0.710–0.763) and Madrid, Spain (R = 0.691) which shows that under specific conditions the SOI might act as an indicator of the SO₂ total load.     

Gas-phase chemistry of benzyl alcohol: Reaction rate constants and products with OH radical and ozone

A bimolecular rate constant, k_{OH+Benzyl alcohol}, of (28 ± 7) × 10^?12 cm³ molecule^?1 s^?1 was measured using the relative rate technique for the reaction of the hydroxyl radical (OH) with benzyl alcohol, at (297 ± 3) K and 1 atm total pressure. Additionally, an upper limit of the bimolecular rate constant, k_{O3+Benzyl alcohol}, of approximately 6 × 10^?19 cm³ molecule^?1 s^?1 was determined by monitoring the decrease in benzyl alcohol concentration over time in an excess of ozone (O₃). To more clearly define part of benzyl alcohol's indoor environment degradation mechanism, the products of the benzyl alcohol + OH were also investigated. The derivatizing agents O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine (PFBHA) and N,O-bis(trimethylsilyl) trifluoroacetamide (BSTFA) were used to positively identify benzaldehyde, glyoxal and 4-oxopentanal as benzyl alcohol/OH reaction products. The elucidation of other reaction products was facilitated by mass spectrometry of the derivatized reaction products coupled with plausible benzyl alcohol/OH reaction mechanisms based on previously published volatile organic compound/OH gas-phase reaction mechanisms.     

Aerosol formation yields from the reaction of catechol with ozone

The formation of secondary organic aerosol from the gas-phase reaction of catechol (1,2-dihydroxybenzene) with ozone has been studied in two smog chambers. Aerosol production was monitored using a scanning mobility particle sizer and loss of the precursor was determined by gas chromatography and infrared spectroscopy, whilst ozone concentrations were measured using a UV photometric analyzer. The overall organic aerosol yield (Y) was determined as the ratio of the suspended aerosol mass corrected for wall losses (M_o) to the total reacted catechol concentrations, assuming a particle density of 1.4 g cm^?3. Analysis of the data clearly shows that Y is a strong function of M_o and that secondary organic aerosol formation can be expressed by a one-product gas–particle partitioning absorption model. The aerosol formation is affected by the initial catechol concentration, which leads to aerosol yields ranging from 17% to 86%. The results of this work are compared to similar studies reported in the literature.     

Influence of air mass source region on nanoparticle events and hygroscopicity in central Virginia,U.S.

During autumn, 2006, variation in the frequency of aerosol nucleation events, as inferred from nanoparticle growth events, and associated hygroscopicity were investigated as a function of air mass transport history at a mixed deciduous forest in central Virginia, U.S. Above-canopy size distributions of aerosols between 0.012 and 0.700 μm diameter, size-resolved particle hygroscopicity at eight dry diameters between 0.012 and 0.400 μm, and cloud condensation nuclei (CCN) activity were characterized. Air mass back trajectories were clustered to identify source regions. Growth events were most frequent in fast-moving air masses (mean = 9 m s^?1) that originated over the north central U.S. Under these flow regimes, mean values for preexisting sub-μm aerosol number concentrations (4700 cm^?3), corresponding surface area (142 μm² cm^?3), air temperature (6.2 °C), and relative humidity (RH, 49.4%) were relatively low compared to other regimes. Under stagnant flow conditions (mean = 3 m s^?1), mean number concentrations were higher (>6000 cm^?3) and size fractions <0.1 μm diameter exhibited enhanced hygroscopicity compared to other source regions. These results indicate that precursors emitted into relatively clean, cold, and dry air transported over the southeastern U.S. reacted to form condensable intermediates that subsequently produced new aerosols via nucleation and growth. This pathway was an important source for CCN. During events in October, nanoparticles were produced in greater numbers and grew more rapidly compared to November and December.     

Simulation of global warming potential (GWP) from rice fields in the Tai-Lake region,China by coupling 1:50,000 soil database with DNDC model

Quantifying greenhouse gas (GHG) emissions from wetland ecosystems is a relatively new issue in global climate change studies. China has approximately 22% of the world's rice paddies and 38% of the world's rice production, which are crucial to accurately estimate the global warming potential (GWP) at regional scale. This paper reports an application of a biogeochemical model (DeNitrification and DeComposition or DNDC) for quantifying GWP from rice fields in the Tai-Lake region of China. For this application, DNDC is linked to a 1:50,000 soil database, which was derived from 1107 paddy soil profiles compiled during the Second National Soil Survey of China in the 1980–1990s. The simulated results show that the 2.34 Mha of paddy soil cultivated in rice–wheat rotation in the Tai-Lake region emitted about ?1.48 Tg C, 0.84 Tg N and 5.67 Tg C as CO₂, N₂O, and CH₄ respectively, with a cumulative GWP of 565 Tg CO₂ equivalent from 1982 to 2000. As for soil subgroups, the highest GWP (26,900 kg CO₂ equivalent ha^?1 yr^?1) was linked to gleyed paddy soils accounting for about 4.4% of the total area of paddy soils. The lowest GWP (5370 kg CO₂ equivalent ha^?1 yr^?1) was associated with submergenic paddy soils accounting for about 0.32% of the total area of paddy soils. The most common soil in the area was hydromorphic paddy soils, which accounted for about 53% of the total area of paddy soils with a GWP of 12,300 kg CO₂ equivalent ha^?1 yr^?1. On a regional basis, the annual averaged GWP in the polder, Tai-Lake plain, and alluvial plain soil regions was distinctly higher than that in the low mountainous and Hilly soil regions. As for administrative areas, the average annual GWP of counties in Shanghai city was high. Conversely, the average annual GWP of counties in Jiangsu province was low. The high variability in soil properties throughout the Tai-Lake region is important and affects the net greenhouse gas emissions. Therefore, the use of detailed soil data sets with high-resolution digital soil maps is essential to improve the accuracy of GWP estimates with process-based models at regional and national scales.     

Analysis of air pollution data at a mixed source location using boosted regression trees

This paper explores the use of boosted regression trees to draw inferences concerning the source characteristics at a location of high source complexity. Models are developed for hourly concentrations of nitrogen oxides (NO_X) close to a large international airport. Model development is discussed and methods to quantify model uncertainties developed. It is shown that good explanatory models can be developed and further, allowing for interactions between model variables significantly improves the model fits compared with non-interacting models. Methods are used to determine which variables exert most influence over predicted concentrations and to explore the NO_X dependency for each. Model predictions are used to estimate aircraft take-off contributions to total concentrations of NO_X and determine how these predictions are affected by annual variations in meteorological conditions and runway use patterns. Furthermore, the results relating to the aircraft contributions to total NO_X concentration are compared with those from a more detailed independent field campaign. Finally, we find empirical evidence that plumes from larger aircraft disperse more rapidly from the point of release compared with smaller aircraft. The reasons for this behaviour and the implications are discussed.     

Examining intra-urban variation in fine particle mass constituents using GIS and constrained factor analysis

Recent studies have used land use regression (LUR) techniques to explain spatial variability in exposures to PM_2.5 and traffic-related pollutants. Factor analysis has been used to determine source contributions to measured concentrations. Few studies have combined these methods, however, to construct and explain latent source effects. In this study, we derive latent source factors using confirmatory factor analysis constrained to non-negative loadings, and develop LUR models to predict the influence of outdoor sources on latent source factors using GIS-based measures of traffic and other local sources, central site monitoring data, and meteorology. We collected 3–4 day samples of nitrogen dioxide (NO₂) and PM_2.5 outside of 44 homes in summer and winter, from 2003 to 2005 in and around Boston, Massachusetts. Reflectance analysis, X-ray fluorescence spectroscopy (XRF), and high-resolution inductively-coupled plasma mass spectrometry (ICP-MS) were performed on particle filters to estimate elemental carbon (EC), trace element, and water-soluble metals concentrations. Within our constrained factor analysis, a five-factor model was optimal, balancing statistical robustness and physical interpretability. This model produced loadings indicating long-range transport, brake wear/traffic exhaust, diesel exhaust, fuel oil combustion, and resuspended road dust. LUR models largely corroborated factor interpretations through covariate significance. For example, ‘long-range transport’ was predicted by central site PM_2.5 and season; ‘brake wear/traffic exhaust’ and ‘resuspended road dust’ by traffic and residential density; ‘diesel exhaust’ by percent diesel traffic on nearest major road; and ‘fuel oil combustion’ by population density. Results suggest that outdoor residential PM_2.5 source contributions can be partially predicted using GIS-based terms, and that LUR techniques can support factor interpretation for source apportionment. Together, LUR and factor analysis facilitate source identification, assessment of spatial and temporal variability, and more refined source exposure assignment for evaluation of source contributions to health outcomes in epidemiological studies.     

Assessing spatial variability of SO2 field as detected by an air quality network using Self-Organizing Maps,cluster, and Principal Component Analysis

In Bilbao (Spain), an air quality network measures sulphur dioxide levels at 4 locations. The objective of this paper is to develop a practical methodology to identify redundant sensors and evaluate a network's capability to correctly follow and represent SO₂ fields in Bilbao, in the frame of a continuous network optimization process.The methodology is developed and tested at this particular location, but it is general enough to be useable at other places as well, since it is not tied neither to the particular geographical characteristics of the place nor to the phenomenology of the air quality over the area.To assess the spatial variability of SO₂ measured at 4 locations in the area, three different techniques have been used: Self-Organizing Maps (SOMs), cluster analysis (CA) and Principal Component Analysis (PCA). The results show that the three techniques yield the same results, but the information obtained via PCA can be helpful not only for that purpose but also to throw light on the major mechanisms involved. This might be used in future network optimization stages. The main advantage of cluster analysis and SOMs is that they provide readily interpretable results. All the calculations have been carried out using the freely available software R.     

Further studies on the uptake of persistent organic pollutants (POPs) by polyurethane foam disk passive air samplers

Passive air samplers (PAS) can be used to monitor semi-volatile organic compounds in the atmosphere. Polyurethane foam (PUF) disks are a popular sampling medium because they have a high retention capacity for such compounds. This paper reports a highly time-resolved uptake study, to derive uptake rate data under field conditions, and investigate the effects of using different foam densities on the uptake rate. PUF disks were deployed alongside an active sampler, for periods of up to 12 weeks. The uptake rates were measured for a range of gas- and particle-bound persistent organic pollutants (polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs)), of different properties, to explore whether gas–particle partitioning affected uptake rate. Uptake rates for two different densities of foam (0.021 and 0.035 g cm^?3) were not statistically significantly different from each other. Uptake rates of light PCBs averaged ～6.5 m³ day^?1, somewhat higher than in previous studies; higher wind speeds and lower temperatures in this study are the likely reason for this difference. The study showed: i) the uptake rate of the compound with lowest K_OA considered in this study (PCB-28/31) declined in the later weeks, indicating an approach to equilibrium; ii) uptake rates of lighter BDEs and heavier PCBs (compounds of intermediate K_OA in this study) remain similar throughout the study period, indicating that they are not approaching equilibrium during the 12-week-study; iii) uptake rates were typically: ～8 m³ day^?1 for PCB-52; ～9.5 m³ day^?1 for PCB-95; ～11 m³ day^?1 for BDE-28 and ～2 m³ day^?1 BDE-99. The latter compound has an important particle-bound component and this lowers the sampling rate compared to predicted uptake rates for compounds which are in the gas phase only. It is shown that knowledge of gas–particle partitioning is needed to correct for this effect, and to improve predicted uptake rates.