Atmospheric Environment (v.41, #14)

Sources and characteristics of carbonaceous aerosol in two largest cities in Pearl River Delta Region, China by Jingchun Duan; Jihua Tan; Dingxi Cheng; Xinhui Bi; Wenjing Deng; Guoying Sheng; Jiamo Fu; M.H. Wong (2895-2903).
PM2.5 samples were collected at five sites in Guangzhou and Hong Kong, Pearl River Delta Region (PRDR), China in both summer and winter during 2004–2005. Elemental carbon (EC) and organic carbon (OC) in these samples were measured. The OC and EC concentrations ranked in the order of urban Guangzhou > urban Hong Kong > background Hong Kong. Total carbonaceous aerosol (TCA) contributed less to PM2.5 in urban Guangzhou (32–35%) than that in urban Hong Kong (43–57%). The reason may be that, as an major industrial city in South China, Guangzhou would receive large amount of inorganic aerosol from all kinds of industries, however, as a trade center and seaport, urban Hong Kong would mainly receive organic aerosol and EC from container vessels and heavy-duty diesel trucks. At Hong Kong background site Hok Tsui, relatively lower contribution of TCA to PM2.5 may result from contributions of marine inorganic aerosol and inland China pollutant. Strong correlation (R 2=0.76–0.83) between OC and EC indicates minor fluctuation of emission and the secondary organic aerosol (SOA) formation in urban Guangzhou. Weak correlation between OC and EC in Hong Kong can be related to the impact of the long-range transported aerosol from inland China. Averagely, secondary OC (SOC) concentrations were 3.8–5.9 and 10.2–12.8 μg m−3, respectively, accounting for 21–32% and 36–42% of OC in summer and winter in Guangzhou. The average values of 4.2–6.8% for SOA/ PM2.5 indicate that SOA was minor component in PM2.5 in Guangzhou.
Keywords: PM2.5; Organic carbon; Elemental carbon; Urban aerosol;

Influence of dust composition on cloud droplet formation by James T. Kelly; Catherine C. Chuang; Anthony S. Wexler (2904-2916).
Previous studies suggest that interactions between dust particles and clouds are significant; yet the conditions where dust particles can serve as cloud condensation nuclei (CCN) are uncertain. Since major dust components are insoluble, the CCN activity of dust strongly depends on the presence of minor components. However, many minor components measured in dust particles are overlooked in cloud modeling studies. Some of these compounds are believed to be products of heterogeneous reactions involving carbonates. In this study, we calculate Kohler curves (modified for slightly soluble substances) for dust particles containing small amounts of K+, Mg2+, or Ca2+ compounds to estimate the conditions where reacted and unreacted dust can activate. We also use an adiabatic parcel model to evaluate the influence of dust particles on cloud properties via water competition. Based on their bulk solubilities, K+ compounds, MgSO4·7H2O, Mg(NO3)2·6H2O, and Ca(NO3)2·4H2O are classified as highly soluble substances, which enable activation of fine dust. Slightly soluble gypsum and MgSO3·6H2O, which may form via heterogeneous reactions involving carbonates, enable activation of particles with diameters between about 0.6 and 2 μm under some conditions. Dust particles>2 μm often activate regardless of their composition. Only under very specialized conditions does the addition of a dust distribution into a rising parcel containing fine (NH4)2SO4 particles significantly reduce the total number of activated particles via water competition. Effects of dust on cloud saturation and droplet number via water competition are generally smaller than those reported previously for sea salt. Large numbers of fine dust CCN can significantly enhance the number of activated particles under certain conditions. Improved representations of dust mineralogy and reactions in global aerosol models could improve predictions of the effects of aerosol on climate.
Keywords: Long-range transport; Mineral; Aerosol; Water uptake; Desert;

Data assimilation in meteorological pre-processors: Effects on atmospheric dispersion simulations by E. Davakis; S. Andronopoulos; I. Kovalets; N. Gounaris; J.G. Bartzis; S.G. Nychas (2917-2932).
In previous work [Kovalets, I., Andronopoulos, S., Bartzis, J.G., Gounaris, N., Kushchan, A., 2004. Introduction of data assimilation procedures in the meteorological pre-processor of atmospheric dispersion models used in emergency response systems. Atmospheric Environment 38, 457–467.] the authors have developed data assimilation (DA) procedures and implemented them in the frames of a diagnostic meteorological pre-processor (MPP) to enable simultaneous use of meteorological measurements with numerical weather prediction (NWP) data. The DA techniques were directly validated showing a clear improvement of the MPP output quality in comparison with meteorological measurement data. In the current paper it is demonstrated that the application of DA procedures in the MPP, to combine meteorological measurements with NWP data, has a noticeable positive effect on the performance of an atmospheric dispersion model (ADM) driven by the MPP output. This result is particularly important for emergency response systems used for accidental releases of pollutants, because it provides the possibility to combine meteorological measurements with NWP data in order to achieve more reliable dispersion predictions. This is also an indirect way to validate the DA procedures applied in the MPP. The above goal is achieved by applying the Lagrangian ADM DIPCOT driven by meteorological data calculated by the MPP code both with and without the use of DA procedures to simulate the first European tracer experiment (ETEX I). The performance of the ADM in each case was evaluated by comparing the predicted and the experimental concentrations with the use of statistical indices and concentration plots. The comparison of resulting concentrations using the different sets of meteorological data showed that the activation of DA in the MPP code clearly improves the performance of dispersion calculations in terms of plume shape and dimensions, location of maximum concentrations, statistical indices and time variation of concentration at the detectors locations.
Keywords: Data assimilation; Atmospheric dispersion models; Meteorological pre-processors; Model evaluation; ETEX;

As part of ABC-EAREX2005 experiment, numerous trace gases were measured at Gosan on Jeju Island, South Korea in March 2005 to characterize the impact of recent outflow from the Asian continent and to inter-compare measurement techniques used by participating groups. Here we present measurements of O 3 , CO, and whole air samples of methane, C 2 – C 8 non-methane hydrocarbons (NMHCs) and C 1 – C 2 halocarbons obtained during the study. The large temporal variations in the measured trace gas concentrations at Gosan were due to the transport of background marine air and of regional pollution mainly from the Chinese subcontinent. Average mixing ratios ( ± s.d.) were 54.6 ( ± 9.0 ) ppbv and 283 ( ± 100 ) ppbv for O 3 and CO, respectively. CO showed good correlations ( r 2 = 0.62 –0.81) with combustion tracers such as ethyne and benzene but poorly correlated ( r 2 = 0.11 –0.29) with light alkanes, suggesting that the latter were contributed by non-combustion source(s). Back trajectory analysis showed that air masses mainly originated from the North China Plains and northeastern China, which together accounted for 64% of the total trajectories. The highest mean mixing ratios of O 3 and combustion-derived species were found in air masses from eastern China and Korea, indicating the significant impact of emissions from these regions. Interestingly, air masses from northeast China contained elevated levels of light alkanes and the smallest ratios of ethyne/propane and benzene/propane among the air-mass groups, suggesting contribution from natural gas leakage in the upwind region, possibly from Siberia.
Keywords: Ozone; Carbon monoxide; NMHCs; Continental outflow; Back trajectory; Nature gas leakage;

Atmospheric loss of pesticides above an artificial vineyard during air-assisted spraying by Yvan Gil; Carole Sinfort; Yves Brunet; Vincent Polveche; Bernard Bonicelli (2945-2957).
A procedure to assess pesticide emission to the air and characterise possible air pollution sources was carried out using a tracer dye and 2 mm PVC lines during air-assisted spraying of an artificial vineyard. Three experiments were performed to evaluate the method feasibility, quantify upward movements of sprayed droplets and investigate the influence of microclimatic variables on pesticide emission. During each experiment two test series were carried out with two droplet size distributions (very fine and fine spray, according to the BCPC classification). The amount of sprayed liquid collected at 2.5 m above ground varied between 9.0% and 10.7% of the total dose applied for very fine spray and between 5.6% and 7.3% for fine spray. In stable atmospheric conditions the spray drifted along the mean wind direction over the crop whereas in unstable conditions the sprayed liquid plume was larger, with a greater amount of material sent to higher levels. A statistical model based on a simple multiple regression featuring droplet characteristics and microclimatic variables (wind speed, temperature, stability parameter and relative humidity) provided a robust estimate of spray loss just above the crop, with an acceptable determination coefficient (R 2=0.84). This method is therefore suitable for quantifying spray drift and provides a way to study the influence of several variables on the amount of pesticide released into the atmosphere by air-assisted spraying, with suitable accuracy.
Keywords: Air drift; Air pollution; Fluorescent tracer dye; Microclimatic conditions; Passive collectors;

Samples of fine particulate organic matter were collected outside Durham, NC in the Duke Research Forest as part of the CELTIC study in July 2003. Particulate samples were collected on quartz filters using high volume air sampling equipment, and samples were analyzed for polar and non-polar organic species. Among compounds analyzed, oxidation products of α-pinene, namely pinic acid and pinonic acid, were identified in all samples. Pinic acid, being a dicarboxylic acid, has a low vapor pressure of the order of 10−8  Torr and is expected to contribute significantly to secondary organic aerosol (SOA) formation from the oxidation of α-pinene. Source contribution estimates from primary organic aerosol emissions were computed using the organic species as molecular markers with the chemical mass balance (CMB) model. The unapportioned organic carbon (OC) was determined as the difference between measured OC and OC apportioned to primary sources. This unapportioned OC was then correlated with pinic and pinonic acid to get a better understanding of the role of monoterpene oxidation products to form SOA. A reasonably good fit between pinic acid concentrations and unapportioned OC levels is indicative of the contribution of α-pinene oxidation products to SOA formation in ambient atmosphere. The results are significant considering the role of monoterpene emissions to global atmospheric chemistry.
Keywords: α-pinene; Source attribution; PM2.5; Molecular markers; Organic speciation;

Artificial neural networks are functional alternative techniques in modelling the intricate vehicular exhaust emission dispersion phenomenon. Pollutant predictions are notoriously complex when using either deterministic or stochastic models, which explains why this model was developed using a neural network. Neural networks have the ability to learn about non-linear relationships between the used variables. In this paper a recurrent neural network (Elman model) based forecaster for the prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the city of Palermo is proposed. The effectiveness of the presented forecaster was tested using a time series recorded between 1 January 2003 to 31 December 2004 in eight monitoring stations in urban area of Palermo (Italy). Experimental trials show that the developed and tuned model is appropriate, giving small values of root mean square error (RMSE) , mean absolute error (MAE) and mean square error (MSE). In addition, the related correlation coefficient ranges from 0.72 to 0.97 for each forecasted pollutant, underlying a small difference between the forecasted and the measured values. The above results make the proposed forecaster a powerful tool for pollution management systems.
Keywords: Recurrent neural networks; Neural based forecasting models; Automatic pollutant management systems;

Estimation of vehicular emissions by capturing traffic variations by K.S. Nesamani; Lianyu Chu; Michael G. McNally; R. Jayakrishnan (2996-3008).
Increase in traffic volumes and changes in travel-related characteristics increase vehicular emissions significantly. It is difficult, however, to accurately estimate emissions with current practice because of the reliance on travel forecasting models that are based on steady state hourly averages and, thus, are incapable of capturing the effects of traffic variations in the transportation network. This paper proposes an intermediate model component that can provide better estimates of link speeds by considering a set of Emission Specific Characteristics (ESC) for each link. The intermediate model is developed using multiple linear regression; it is then calibrated, validated, and evaluated using a microscopic traffic simulation model. The improved link speed data can then be used to provide better estimates of emissions. The evaluation results show that the proposed emission estimation method performs better than current practice and is capable of estimating time-dependent emissions if traffic sensor data are available as model input.
Keywords: Traffic congestion; Link speed; Vehicle emissions; Microscopic traffic simulation; Travel forecasting models;

Use of a vehicle-modelling tool for predicting CO2 emissions in the framework of European regulations for light goods vehicles by Georgios Fontaras; Hariton Kouridis; Zissis Samaras; Daniel Elst; Raymond Gense (3009-3021).
The reduction of CO2 emissions and fuel consumption from road transportation constitutes an important pillar of the EU commitment for implementing the Kyoto Protocol. Efforts to monitor and limit CO2 emissions from vehicles can effectively be supported by the use of vehicle modelling tools. This paper presents the application of such a tool for predicting CO2 emissions of vehicles under different operating conditions and shows how the results from simulations can be used for supporting policy analysis and design aiming at further reductions of the CO2 emissions. For this purpose, the case of light duty goods (N1 category) vehicle CO2 emissions control measures adopted by the EU is analysed. In order to understand how certain design and operating aspects affect fuel consumption, a number of N1 vehicles were simulated with ADVISOR for various operating conditions and the numerical results were validated against chassis dynamometer tests. The model was then employed for analysing and evaluating the new EU legislative framework that addresses CO2 emissions from this vehicle class. The results of this analysis have shown the weaknesses of the current regulations and revealed new potential in CO2 emissions control. Finally the TREMOVE model was used for simulating a possible scenario for reducing CO2 emissions at fleet level.
Keywords: Fuel consumption; Road transport CO2 emissions; Advisor;

Applications of a parameterised Jarvis-type multiplicative stomatal conductance model with data collated from open-top chamber experiments on field grown wheat and potato were used to derive relationships between relative yield and stomatal ozone uptake. The relationships were based on thirteen experiments from four European countries for wheat and seven experiments from four European countries for potato. The parameterisation of the conductance model was based both on an extensive literature review and primary data. Application of the stomatal conductance models to the open-top chamber experiments resulted in improved linear regressions between relative yield and ozone uptake compared to earlier stomatal conductance models, both for wheat (r 2=0.83) and potato (r 2=0.76). The improvement was largest for potato. The relationships with the highest correlation were obtained using a stomatal ozone flux threshold. For both wheat and potato the best performing exposure index was AFst6 (accumulated stomatal flux of ozone above a flux rate threshold of 6 nmol ozone m−2 projected sunlit leaf area, based on hourly values of ozone flux). The results demonstrate that flux-based models are now sufficiently well calibrated to be used with confidence to predict the effects of ozone on yield loss of major arable crops across Europe. Further studies, using innovations in stomatal conductance modelling and plant exposure experimentation, are needed if these models are to be further improved.
Keywords: Flux; Ozone; Solanum tuberosum; Stomatal conductance; Triticum aestivum; Yield–response relationship;

Seasonal behavior of Saharan dust events at the Mediterranean island of Lampedusa in the period 1999–2005 by D. Meloni; A. di Sarra; G. Biavati; J.J. DeLuisi; F. Monteleone; G. Pace; S. Piacentino; D.M. Sferlazzo (3041-3056).
Multi-filter rotating shadowband radiometer (MFRSR) measurements have been carried out at Lampedusa (35.52°N, 12.63°E) in 1999, and continuously since 2001. This study describes the Saharan dust (SD) events at Lampedusa on the basis of daily average optical depth at 500 nm, τ, and Ångström exponent, α, derived from these observations. Back-trajectories ending at Lampedusa at 2000 and 4000 m altitude were calculated by means of the HYSPLIT model. SD events are identified as those for which the trajectories interact with the mixed layer in places where the surface wind exceeds 7 m s−1, or spend a large fraction of time over the Sahara. The SD days display values of αα⩽1, with Δα equal to the standard deviation of the daily α. Out of 911 days with cloud-free intervals, 233 (26%) are classified as SD, and correspond to 111 episodes of various duration, from 1 to 13 consecutive days. The occurrence of SD events is maximum in summer (33%), when also the largest seasonal average of τ (0.40) is measured, and minimum in winter (7%), when the smallest seasonal average of α (0.08) is found. SD days have been identified from the back-trajectories also in days lacking of observations, due to either cloudiness or measurement interruptions. The frequency of occurrence of SD days shows little change with respect to the cloud-free periods (24%). The seasonal distribution shows a peak in May (38%), followed by July (37%). Regions of SD production were derived from the HYSPLIT trajectories and NCEP-reanalysis surface winds. Finally, the MFRSR measurements at the solar zenith angle of 60° have been used to derive the single scattering albedo (SSA) for cases clearly dominated by dust (τ⩾0.40 and αα⩽0.5). The average SSA for the whole period is 0.77±0.04 at 415.6 nm and 0.94±0.04 at 868.7 nm.
Keywords: Aerosol; Desert dust; Aerosol optical depth; Ångström exponent; Single scattering albedo;

Observational data, collected during a wood smoke episode in Houston, Texas, were used to assess the extent to which acid-catalyzed reactions of carbonyls might contribute to secondary organic aerosol (SOA) formation. The wood smoke episode was chosen for this analysis because of relatively high concentrations of acidic aerosol, coupled with high concentrations of SOA precursors during the episode. Photochemical modeling, coupled with ambient measurements, indicated that acid aerosol-mediated organic aerosol formation reactions, not accounted for in most current photochemical models, may have led to SOA formation of up to a few μg m−3. In photochemical simulations, acid-mediated organic aerosol formation was modeled by calculating the rate of impingement of aldehyde molecules on acidic particles, and then assuming that a fraction of the impingements resulted in reaction. For reaction probabilities on the order of 0.005–0.0005, the model predicted SOA concentrations were consistent with estimates of SOA based on observations. In addition, observed concentrations of particulate phase ammonium during the episode were consistent with high concentrations of the types of organic acids that would be formed through acid-catalyzed reactions of carbonyls. Although there are substantial uncertainties in the estimates of heterogeneous SOA formation, collectively, these data and modeling analyses provide evidence for the importance of acid-catalyzed SOA formation reactions.
Keywords: Biomass combustion; Secondary organic aerosol; Sulfate; Acid; Heterogeneous reactions;

We calculated daily back-trajectories using the NOAA-HYSPLIT model to analyze 7 years of precipitation and PM2.5 data from three National Park sites in the Western US. Using a k-means clustering algorithm, the trajectories were segregated into six main transport patterns. At each site, we calculated trajectory clusters for 1, 5, and 10 days to represent short, medium and long-range flow patterns. Most clusters show marked seasonality. Faster flow patterns are more prevalent in winter, and slower/stagnant patterns are more prevalent in summer. The analyses between the 1, 5, and 10-day clusters revealed that the clusters of different duration show very different predictive power for rainfall and PM2.5. We found that the 1-day clusters are a better predictor for precipitation and PM2.5 concentrations, followed by the 5-day clusters. The 10-day clusters did a poorer job of differentiating precipitation and PM2.5. This is because the 10-day clusters show the greatest variability during the first day or two of transport.
Keywords: Aerosols; Cluster analysis; Trajectories;

Ensemble-based data assimilation and targeted observation of a chemical tracer in a sea breeze model by Amy L. Stuart; Altug Aksoy; Fuqing Zhang; John W. Nielsen-Gammon (3082-3094).
We study the use of ensemble-based Kalman filtering of chemical observations for constraining forecast uncertainties and for selecting targeted observations. Using a coupled model of two-dimensional sea breeze dynamics and chemical tracer transport, we perform three numerical experiments. First, we investigate the chemical tracer forecast uncertainties associated with meteorological initial condition and forcing error. We find that the ensemble variance and error builds during the transition between land and sea breeze phases of the circulation. Second, we investigate the effects on the forecast variance and error of assimilating tracer concentration observations extracted from a truth simulation for a network of surface locations. We find that assimilation reduces the variance and error in both the observed variable (chemical tracer concentrations) and unobserved meteorological variables (vorticity and buoyancy). Finally, we investigate the potential value to the forecast of targeted observations. We calculate an observation impact factor that maximizes the total decrease in model uncertainty summed over all state variables. We find that locations of optimal targeted observations remain similar before and after assimilation of regular network observations.
Keywords: Air quality modeling; Data assimilation; Ensemble modeling; Adaptive observations;

Motor vehicles are one of the largest sources of air pollutants worldwide. Despite their importance, motor vehicle emissions are inadequately understood and quantified, esp. in developing countries. In this study, the real-world emissions of carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxide (NO) were measured using an on-road remote sensing system at five sites in Hangzhou, China in 2004 and 2005. Average emission factors of CO, HC and NO x for petrol vehicles of different model year, technology class and vehicle type were calculated in grams of pollutant per unit of fuel use (g l−1) from approximately 32,260 petrol vehicles. Because the availability of data used in traditional on-road mobile source estimation methodologies is limited in China, fuel-based approach was implemented to estimate motor vehicle emissions using fuel sales as a measure of vehicle activity, and exhaust emissions factors from remote sensing measurements. The fuel-based exhaust emission inventories were also compared with the results from the recent international vehicle emission (IVE) model. Results show that petrol vehicle fleet in Hangzhou has significantly high CO emissions, relatively high HC and low NO x , with the average emission factors of 193.07±15.63, 9.51±2.40 and 5.53±0.48 g l−1, respectively. For year 2005 petrol vehicles exhaust emissions contributed with 182,013±16,936, 9107±2255 and 5050±480 metric ton yr−1 of CO, HC and NO x , respectively. The inventories are 45.5% higher, 6.6% higher and 53.7% lower for CO, HC and NO x , respectively, than the estimates using IVE travel-based model. In addition, a number of insights about the emission distributions and formation mechanisms have been obtained from an in-depth analysis of these results.
Keywords: Remote sensing; Emission factor; Fuel-based emission inventory; IVE model;