Statistical and diagnostic evaluation of a new-generation urban dispersion modelling system against an extensive dataset in the Helsinki area

We have developed a modelling system for predicting the traffic volumes, emissions from stationary and vehicular sources, and atmospheric dispersion of pollution in an urban area. This paper describes a comparison of the NO_x and NO₂ concentrations predicted using this modelling system with the results of an urban air quality monitoring network. We performed a statistical analysis to determine the agreement between predicted and measured hourly time series of concentrations at four permanently located and three mobile monitoring stations in the Helsinki Metropolitan Area in 1996–1997 (at a total of ten urban and suburban measurement locations). At the stations considered, the so-called index of agreement values of the predicted and measured time series of the NO₂ concentrations vary between 0.65 and 0.82, while the fractional bias values range from −0.29 to +0.26. In comparison with corresponding results presented in the literature, the agreement between the measured and predicted datasets is good, as indicated by these statistical parameters. The seasonal variations of the NO₂ concentrations were analysed in terms of the relevant meteorological parameters. We also analysed the difference between model predictions and measured data diagnostically, in terms of meteorological parameters, including wind speed and direction (the latter separately for two wind speed classes), atmospheric stability and ambient temperature, at two monitoring stations in central Helsinki. The modelling system tends to overpredict the measured NO₂ concentrations both at the highest (u⩾6 m s⁻¹) and at the lowest wind speeds (u<2 m s⁻¹). For higher wind speeds, the modelling system overpredicts the measured NO₂ concentrations in certain wind direction intervals; specific ranges were found for both monitoring stations considered. The modelling system tends to underpredict the measured concentrations in convective atmospheric conditions, and overpredict in stable conditions. The possible physico-chemical reasons for these differences are discussed.     

Receptor modeling of source apportionment of Hong Kong aerosols and the implication of urban and regional contribution

Understanding the spatial–temporal variations of source apportionment of PM_2.5 is critical to the effective control of particulate pollution. In this study, two one-year studies of PM_2.5 composition were conducted at three contrasting sites in Hong Kong from November 2000 to October 2001, and from November 2004 to October 2005, respectively. A receptor model, principal component analysis (PCA) with absolute principal component scores (APCS) technique, was applied to the PM_2.5 data for the identification and quantification of pollution sources at the rural, urban and roadside sites. The receptor modeling results identified that the major sources of PM_2.5 in Hong Kong were vehicular emissions/road erosion, secondary sulfate, residual oil combustion, soil suspension and sea salt regardless of sampling sites and sampling periods. The secondary sulfate aerosols made the most significant contribution to the PM_2.5 composition at the rural (HT) (44 ± 3%, mean ± 1σ standard error) and urban (TW) (28 ± 2%) sites, followed by vehicular emission (20 ± 3% for HT and 23 ± 4% for TW) and residual oil combustion (17 ± 2% for HT and 19 ± 1% for TW). However, at the roadside site (MK), vehicular emissions especially diesel vehicle emissions were the major source of PM_2.5 composition (33 ± 1% for diesel vehicle plus 18 ± 2% for other vehicles), followed by secondary sulfate aerosols (24 ± 1%). We found that the contribution of residual oil combustion at both urban and rural sites was much higher than that at the roadside site (2 ± 0.4%), perhaps due to the marine vessel activities of the container terminal near the urban site and close distance of pathway for the marine vessels to the rural site. The large contribution of secondary sulfate aerosols at all the three sites reflected the wide influence of regional pollution. With regard to the temporal trend, the contributions of vehicular emission and secondary sulfate to PM_2.5 showed higher autumn and winter values and lower summer levels at all the sites, particularly for the background site, suggesting that the seasonal variation of source apportionment in Hong Kong was mainly affected by the synoptic meteorological conditions and the long-range transport. Analysis of annual patterns indicated that the contribution of vehicular emission at the roadside was significantly reduced from 2000/01 to 2004/05 (p < 0.05, two-tail), especially the diesel vehicular emission (p < 0.001, two-tail). This is likely attributed to the implementation of the vehicular emission control programs with the tightening of diesel fuel contents and vehicular emission standards over these years by the Hong Kong government. In contrast, the contribution of secondary sulfate was remarkably increased from 2001 to 2005 (p < 0.001, two-tail), indicating a significant growth in regional sulfate pollution over the years.     

Near-road air pollutant concentrations of CO and PM2.5: A comparison of MOBILE6.2/CALINE4 and generalized additive models

The contribution of vehicular traffic to air pollutant concentrations is often difficult to establish. This paper utilizes both time-series and simulation models to estimate vehicle contributions to pollutant levels near roadways. The time-series model used generalized additive models (GAMs) and fitted pollutant observations to traffic counts and meteorological variables. A one year period (2004) was analyzed on a seasonal basis using hourly measurements of carbon monoxide (CO) and particulate matter less than 2.5 μm in diameter (PM_2.5) monitored near a major highway in Detroit, Michigan, along with hourly traffic counts and local meteorological data. Traffic counts showed statistically significant and approximately linear relationships with CO concentrations in fall, and piecewise linear relationships in spring, summer and winter. The same period was simulated using emission and dispersion models (Motor Vehicle Emissions Factor Model/MOBILE6.2; California Line Source Dispersion Model/CALINE4). CO emissions derived from the GAM were similar, on average, to those estimated by MOBILE6.2. The same analyses for PM_2.5 showed that GAM emission estimates were much higher (by 4–5 times) than the dispersion model results, and that the traffic-PM_2.5 relationship varied seasonally. This analysis suggests that the simulation model performed reasonably well for CO, but it significantly underestimated PM_2.5 concentrations, a likely result of underestimating PM_2.5 emission factors. Comparisons between statistical and simulation models can help identify model deficiencies and improve estimates of vehicle emissions and near-road air quality.     

Predictive NOx emission monitoring on board a passenger ferry

NO_x emissions from a medium speed diesel engine on board a servicing passenger ferry have been indirectly measured using a predictive emission monitoring system (PEMS) over a 1-yr period. Conventional NO_x measurements were carried out with a continuous emission monitoring system (CEMS) at the start of the study to provide historical data for the empirical PEMS function. On three other occasions during the year the CEMS was also used to verify the PEMS and follow any changes in emission signature of the engine. The PEMS consisted of monitoring exhaust O₂ concentrations (in situ electrochemical probe), engine load, combustion air temperature and humidity, and barometric pressure. Practical experiences with the PEMS equipment were positive and measurement data were transferred to a land-based office by using a modem data communication system. The initial PEMS function (PEMS1) gave systematic differences of 1.1–6.9% of the calibration domain (0–1725 ppm) and a relative accuracy of 6.7% when compared with CEMS for whole journeys and varying load situations. Further improvements on the performance could be obtained by updating this function. The calculated yearly emission for a total engine running time of 4618 h was 316 t NO_x±38 t and the average NO_x emission corrected for ambient conditions 14.3 g kWh_corr⁻¹. The exhaust profile of the engine in terms of NO_x, CO and CO₂ emissions as determined by CEMS was similar for most of the year. Towards the end of the study period, a significantly lower NO_x emission was detected which was probably caused by replacement of fuel injector nozzles. The study suggests that PEMS can be a viable option for continuous, long-term NO_x measurements on board ships.     

Assessment of contribution of SO2 and NO2 from different sources in Jamshedpur region, India

Contribution of pollution from different types of sources in Jamshedpur, the steel city of India, has been estimated in winter 1993 using two approaches in order to delineate and prioritize air quality management strategies for the development of region in an environmental friendly manner. The first approach mainly aims at preparation of a comprehensive emission inventory and estimation of spatial distribution of pollution loads in terms of SO₂ and NO₂ from different types of industrial, domestic and vehicular sources in the region. The results indicate that industrial sources account for 77% and 68% of the total emissions of SO₂ and NO₂, respectively, in the region, whereas vehicular emissions contributed to about 28% of the total NO₂ emissions. In the second approach, contribution of these sources to ambient air quality levels to which the people are exposed to, was assessed through air pollution dispersion modelling. Ambient concentration levels of SO₂ and NO₂ have been predicted in winter season using the ISCST3 model. The analysis indicates that emissions from industrial sources are responsible for more than 50% of the total SO₂ and NO₂ concentration levels. Vehicular activities contributed to about 40% of NO₂ pollution and domestic fuel combustion contributed to about 38% of SO₂ pollution. Predicted 24-h concentrations were compared with measured concentrations at 11 ambient air monitoring stations and good agreement was noted between the two values. In-depth zone-wise analysis of the above indicates that for effective air quality management, industrial source emissions should be given highest priority, followed by vehicular and domestic sources in Jamshedpur region.     

A composite receptor and dispersion model approach for estimation of effective emission factors for vehicles

It is difficult to estimate vehicular emission factors at traffic junctions for use in dispersion modelling studies. Firstly, because the vehicles are in various modes of operation and secondly, it is difficult to delineate the effects of other contributing sources, mainly the effects of road dust and deposited constituents, which are very prominent at traffic junctions in India. Factor analysis-multiple regression (FA-MR), a receptor modelling technique has been used in this study for apportioning the contributing sources. The measurement data consist of one year's temporal variation of suspended particulate matter (SPM), analysed for its trace metal constituents, and two gaseous components NO₂ and SO₂ at two traffic junctions in Mumbai (India). FA-MR apportioned 40% of the observed SPM to road dust and 15% to vehicular sources. Of the total Pb observed in the SPM, FA-MR apportioned 60% to vehicular sources and 20% to road dust. The field-observed vehicular counts, meteorological parameters and road geometry were used in California line source dispersion model to estimate the effective vehicular emission factor for Pb at one traffic junction. This derived emission factor was used to predict the Pb concentration at second (independent observation) traffic junction. The result was found to be more satisfactory than using default emission factors obtained from literature. Similarly, effective vehicular emission factor for NO₂ was also evaluated for one site and tested for predicting concentrations at the other site.     

Examination of the impact of photoexcited NO2 chemistry on regional air quality

Impact of the excited nitrogen dioxide (NO₂¹) chemistry on air quality in the U.S. is examined using the Community Multiscale Air Quality (CMAQ) model for a summer month. Model simulations were conducted with and without the NO₂¹ chemistry. The largest impact of the NO₂¹ chemistry in the eastern U.S. occurred in the northeast and in the western U.S. occurred in Los Angeles. While the single largest daily maximum 8-h ozone (O₃) increased by 9 ppbv in eastern U.S. and 6 ppbv in western U.S., increases on most days were much lower. No appreciable change in model performance statistics for surface-level O₃ predictions relative to measurements is noted between simulations with and without the NO₂¹ chemistry. Based on model calculations using current estimates of tropospheric emission burden, the NO₂¹ chemistry can increase the monthly mean daytime hydroxyl radicals (OH) and nitrous acid (HONO) by a maximum of 28% and 100 pptv, respectively.     

Modeling traffic air pollution in street canyons in New York City for intra-urban exposure assessment in the US Multi-Ethnic Study of atherosclerosis and air pollution

We evaluated the Danish AirGIS air quality and exposure model system using air quality measurement data from New York City in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Measurements were used from three US EPA Air Quality System (AQS) monitoring stations and a comprehensive MESA Air measurement campaign including about 150 different locations and about 650 samples of about 2 week measurements of NO_x, NO₂ and PM_2.5. AirGIS is a deterministic exposure model system based on the dispersion models Operational Street Pollution Model (OSPM) and the Urban Background Model (UBM). The UBM model reproduced the annual levels within 1–26% depending on station and pollutant at the three urban background EPA monitor stations, and generally reproduced well the seasonal and diurnal variation. The full model with OSPM and UBM reproduced the MESA Air measurements with a correlation coefficient of r² = 0.51 for NO_x, r² = 0.28 for NO₂ and r² = 0.73 for PM_2.5.     

Trends,temporal and spatial characteristics,and uncertainties in biomass burning emissions in the Pearl River Delta,China

Multi-year inventories of biomass burning emissions were established in the Pearl River Delta (PRD) region for the period 2003–2007 based on the collected activity data and emission factors. The results indicated that emissions of sulfur dioxide (SO₂), nitrogen oxide (NO_x), ammonia (NH₃), methane (CH₄), organic carbon (OC), non-methane volatile organic compounds (NMVOC), carbon monoxide (CO), and fine particulate matter (PM_2.5) presented clear declining trends. Domestic biofuel burning was the major contributor, accounting for more than 60% of the total emissions. The preliminary temporal profiles were established with MODIS fire count information, showing that higher emissions were observed in winter (from November to March) than other seasons. The emissions were spatially allocated into grid cells with a resolution of 3 km × 3  km, using GIS-based land use data as spatial surrogates. Large amount of emissions were observed mostly in the less developed areas in the PRD region. The uncertainties in biomass burning emission estimates were quantified using Monte Carlo simulation; the results showed that there were higher uncertainties in organic carbon (OC) and elemental carbon (EC) emission estimates, ranging from ?71% to 133% and ?70% to 128%, and relatively lower uncertainties in SO₂, NO_x and CO emission estimates. The key uncertainty sources of the developed inventory included emission factors and parameters used for estimating biomass burning amounts.     

Estimated long-term outdoor air pollution concentrations in a cohort study

Several recent studies associated long-term exposure to air pollution with increased mortality. An ongoing cohort study, the Netherlands Cohort Study on Diet and Cancer (NLCS), was used to study the association between long-term exposure to traffic-related air pollution and mortality. Following on a previous exposure assessment study in the NLCS, we improved the exposure assessment methods.Long-term exposure to nitrogen dioxide (NO₂), nitrogen oxide (NO), black smoke (BS), and sulphur dioxide (SO₂) was estimated. Exposure at each home address (N=21 868) was considered as a function of a regional, an urban and a local component. The regional component was estimated using inverse distance weighed interpolation of measurement data from regional background sites in a national monitoring network. Regression models with urban concentrations as dependent variables, and number of inhabitants in different buffers and land use variables, derived with a Geographic Information System (GIS), as predictor variables were used to estimate the urban component. The local component was assessed using a GIS and a digital road network with linked traffic intensities. Traffic intensity on the nearest road and on the nearest major road, and the sum of traffic intensity in a buffer of 100 m around each home address were assessed. Further, a quantitative estimate of the local component was estimated.The regression models to estimate the urban component explained 67%, 46%, 49% and 35% of the variances of NO₂, NO, BS, and SO₂ concentrations, respectively. Overall regression models which incorporated the regional, urban and local component explained 84%, 44%, 59% and 56% of the variability in concentrations for NO₂, NO, BS and SO₂, respectively.We were able to develop an exposure assessment model using GIS methods and traffic intensities that explained a large part of the variations in outdoor air pollution concentrations.     

Personal exposures to NO2 in the EXPOLIS-study: relation to residential indoor,outdoor and workplace concentrations in Basel,Helsinki and Prague

Personal exposures, residential indoor, outdoor and workplace levels of nitrogen dioxide (NO₂) were measured for 262 urban adult (25–55 years) participants in three EXPOLIS centres (Basel; Switzerland, Helsinki; Finland, and Prague; Czech Republic) using passive samplers for 48-h sampling periods during 1996–1997. The average residential outdoor and indoor NO₂ levels were lowest in Helsinki (24±12 and 18±11 μg m⁻³, respectively), highest in Prague (61±20 and 43±23 μg m⁻³), with Basel in between (36±13 and 27±13 μg m⁻³). Average workplace NO₂ levels, however, were highest in Basel (36±24 μg m⁻³), lowest in Helsinki (27±15 μg m⁻³), with Prague in between (30±18 μg m⁻³). A time-weighted microenvironmental exposure model explained 74% of the personal NO₂ exposure variation in all centres and in average 88% of the exposures. Log-linear regression models, using residential outdoor measurements (fixed site monitoring) combined with residential and work characteristics (i.e. work location, using gas appliances and keeping windows open), explained 48% (37%) of the personal NO₂ exposure variation. Regression models based on ambient fixed site concentrations alone explained only 11–19% of personal NO₂ exposure variation. Thus, ambient fixed site monitoring alone was a poor predictor for personal NO₂ exposure variation, but adding personal questionnaire information can significantly improve the predicting power.     

Spatial distribution of wet deposition of nitrogen in South Korea

A method is developed to estimate wet deposition of nitrogen in a 11×14 km (0.125°Lon.×0.125°Lat.) grid scale using the precipitation chemistry monitored data at 10 sites scattered over South Korea supplemented by the routinely available precipitation rate data at 65 sites and the estimated emissions of NO₂ and NH₃ at each precipitation monitoring site. This approach takes into account the contributions of local NO₂ and NH₃ emissions and precipitation rates on wet deposition of nitrogen. Wet deposition of nitrogen estimated by optimum regression equations for NO₃⁻ and NH₄⁺ derived from annual total monitored wet deposition and that of emissions of NO₂ and NH₃ is incorporated to normalize wet deposition of nitrogen at each precipitation rate class, which is divided into 6 classes. The optimum regression equations for the estimation of wet deposition of nitrogen at precipitation monitoring sites are developed using the normalized wet deposition of nitrogen and the precipitation rate at 10 precipitation chemistry monitoring sites. The estimated average annual total wet depositions of NO₃⁻ and NH₄⁺ are found to be 260 and 500 eq ha⁻¹ yr⁻¹ with the maximum values of 400 and 930 eq ha⁻¹ yr⁻¹, respectively. The annual mean total wet deposition of nitrogen is found to be about 760 eq ha⁻¹ yr⁻¹, of which more than 65% is contributed by wet deposition of ammonium while, the emission of NH₃ is about half of that of NO₂, suggesting the importance of NH₃ emission for wet deposition of nitrogen in South Korea.     

Emission profile of 18 carbonyl compounds,CO, CO2, and NOx emitted by a diesel engine fuelled with diesel and ternary blends containing diesel,ethanol and biodiesel or vegetable oils

The impact of vehicular emissions on air depends, among other factors, on the composition of fuel and the technology used to build the engines. The reduction of vehicular emissions requires changes in the fuel composition, and improving the technologies used in the manufacturing of engines and for the after-treatment of gases. In general, improvements to diesel engines have targeted not only emission reductions, but also reductions in fuel consumption. However, changes in the fuel composition have been shown to be a more rapid and effective alternative to reduce pollution. Some factors should been taken into consideration when searching for an alternative fuel to be used in diesel engines, such as emissions, fuel stability, availability and its distribution, as well as its effects on the engine durability. In this work, 45 fuel blends were prepared and their stability was evaluated. The following mixtures (v/v/v) were stable for the 90-day period and were used in the emission study: diesel/ethanol – 90/10%, diesel/ethanol/soybean biodiesel – 80/15/5%, diesel/ethanol/castor biodiesel – 80/15/5%, diesel/ethanol/residual biodiesel – 80/15/5%, diesel/ethanol/soybean oil – 90/7/3%, and diesel/ethanol/castor oil – 90/7/3%. The diesel/ethanol fuel showed higher reduction of NO_x emission at a lower load (2 kW) when compared with pure diesel. The other fuels showed a decrease of NO_x emissions in the ranges of 6.9–75% and 4–85% at 1800 rpm and 2000 rpm, respectively. The combustion efficiencies of the diesel can be enhanced by the addition of the oxygenate fuels, like ethanol and biodiesel/vegetable oil, resulting in a more complete combustion in terms of NO_x emission. In the case of CO₂ the decreases were in the ranges of 5–24% and 4–6% at 1800 rpm and 2000 rpm, respectively. Meanwhile, no differences were observed in CO emission. The carbonyl compounds (CC) studied were formaldehyde, acetaldehyde, propionaldehyde, acrolein, acetone, crotonaldehyde, butyraldehyde, butanone, benzaldehyde, isovaleraldehyde, valeraldehyde, o-toluenaldehyde, m-toluenaldehyde, p-toluenaldehyde, hexaldehyde, octaldehyde, 2,5-dimethylbenzaldehyde, and decaldehyde. Among them, formaldehyde, acetaldehyde, acetone, and propionaldehyde showed the highest emission concentrations. When ternary blend contains vegetable oil, there is a strong tendency to increase the emissions of the high weight CC and decrease the emissions of the low weight CC. The highest concentration of acrolein was observed when the fuel contains diesel, ethanol and biodiesel. With the exception of NO_x, the use of ternary blended fuels resulted on the increase in the emission rates of the studied compounds.     

Comparison of the performances of land use regression modelling and dispersion modelling in estimating small-scale variations in long-term air pollution concentrations in a Dutch urban area

The performance of a Land Use Regression (LUR) model and a dispersion model (URBIS – URBis Information System) was compared in a Dutch urban area. For the Rijnmond area, i.e. Rotterdam and surroundings, nitrogen dioxide (NO₂) concentrations for 2001 were estimated for nearly 70 000 centroids of a regular grid of 100 × 100 m.A LUR model based upon measurements carried out on 44 sites from the Dutch national monitoring network and upon Geographic Information System (GIS) predictor variables including traffic intensity, industry, population and residential land use was developed. Interpolation of regional background concentration measurements was used to obtain the regional background. The URBIS system was used to estimate NO₂ concentrations using dispersion modelling. URBIS includes the CAR model (Calculation of Air pollution from Road traffic) to calculate concentrations of air pollutants near urban roads and Gaussian plume models to calculate air pollution levels near motorways and industrial sources. Background concentrations were accounted for using 1 × 1 km maps derived from monitoring and model calculations.Moderate agreement was found between the URBIS and LUR in calculating NO₂ concentrations (R = 0.55). The predictions agreed well for the central part of the concentration distribution but differed substantially for the highest and lowest concentrations. The URBIS dispersion model performed better than the LUR model (R = 0.77 versus R = 0.47 respectively) in the comparison between measured and calculated concentrations on 18 validation sites. Differences can be understood because of the use of different regional background concentrations, inclusion of rather coarse land use category industry as a predictor variable in the LUR model and different treatment of conversion of NO to NO₂.Moderate agreement was found between a dispersion model and a land use regression model in calculating annual average NO₂ concentrations in an area with multiple sources. The dispersion model explained concentrations at validation sites better.     

Interpretation of air quality in relation to monitoring station's surroundings

This study explores the appropriateness of the locality of air monitoring stations which are meant to indicate air quality in the area. Daily variations in NO₂ and PM₁₀ concentrations at 14 monitoring stations in Hong Kong are examined. The daily variations in NO₂ at a number of background monitoring stations exhibit patterns similar to variations in traffic volume while variations in PM₁₀ concentration exhibit less discernible pattern. Principal component analysis (PCA) and cluster analysis (CA) are applied to analyse NO₂ and PM₁₀ measurements between January 2001 and December 2005. The results show that NO₂ concentrations at background stations within the urban area are highly influenced by vehicle emissions. The effect vehicle emission has on NO₂ at stations within new towns is smaller. CA results also show that variations in PM₁₀ concentrations are distinguished by the area the station is located in. PCA results show that there are two principal components (PC's) associated with variations in roadside concentration of PM₁₀. The strong influence of roadside emissions towards concentrations of NO₂ and PM₁₀ at a number of urban background stations may be due to their close proximity to busy roadways and the high density of surrounding tall buildings, which creates an enclosure that hinders dispersion of roadside emissions and results in air pollution behaviour that reflects variation in traffic.     

Application of a Lagrangian particle model to assess the impact of harbour,industrial and urban activities on air quality in the Taranto area,Italy

This paper evaluates the relative impact on air quality of harbour emissions, with respect to other emission sources located in the same area. The impact assessment study was conducted in the city of Taranto, Italy. This area was considered as representative of a typical Mediterranean harbour region, where shipping, industries and urban activities co-exist at a short distance, producing an ideal case to study the interaction among these different sources. Chemical and meteorological field campaigns were carried out to provide data to this study. An emission inventory has been developed taking into account industrial sources, traffic, domestic heating, fugitive and harbour emissions. A 3D Lagrangian particle dispersion model (SPRAY) has then been applied to the study area using reconstructed meteorological fields calculated by the diagnostic meteorological model MINERVE. 3D short term hourly concentrations have been computed for both all and specific sources. Industrial activities are found to be the main contributor to SO₂. Industry and traffic emissions are mainly responsible for NO_x simulated concentrations. CO concentrations are found to be mainly related to traffic emissions, while primary PM₁₀ simulated concentrations tend to be linked to industrial and fugitive emissions. Contributions of harbour activities to the seasonal average concentrations of SO₂ and NO_x are predicted to be up to 5 and 30 μg m⁻³, respectively to be compared to a overall peak values of 60 μg m⁻³ for SO₂ and 70 μg m⁻³ for NO_x. At selected urban monitoring stations, SO₂ and NO_x average source contributions are predicted to be both of about 9% from harbour activities, while 87% and 41% respectively of total concentrations are predicted to be of industrial origin.     

Emission factors and exposures from ground-level pyrotechnics

Potential exposures from ground-level pyrotechnics were assessed by air monitoring and developing emission factors. Total particulate matter, copper and SO₂ exposures exceeded occupational health guidelines at two outdoor performances using consumer pyrotechnics. Al, Ba, B, Bi, Mg, Sr, Zn, and aldehyde levels were elevated, but did not pose a health hazard based on occupational standards. Emission factors for total particulate matter, metals, inorganic ions, aldehydes, and polyaromatic hydrocarbons (PAHs) were determined for seven ground-supported pyrotechnics through air sampling in an airtight room after combustion. Particle generation ranged from 5 to 13% of the combusted mass. Emission factors (g Kg^?1) for metals common to pyrotechnics were also high: K, 23–45; Mg, 1–7; Cu, 0.05–7; and Ba, 0.03–6. Pb emission rates of 1.6 and 2.7% of the combusted mass for two devices were noteworthy. A high correlation (r² ≥ 0.89) between metal concentrations in pyrotechnic compositions and emission factors were noted for Pb, Cr, Mg, Sb, and Bi, whereas low correlations (r² ≤ 0.1) were observed for Ba, Sr, Fe, and Zn. This may be due to the inherent heterogeneity of multi-effect pyrotechnics. The generation of inorganic nitrogen in both the particulate (NO₂^?, NO₃^?) and gaseous (NO, NO₂) forms varied widely (<0.1–1000 mg Kg^?1). Aldehyde emission factors varied by two orders of magnitude even though the carbon source was carbohydrates and charcoal for all devices: formaldehyde (<7.0–82 mg Kg^?1), acetaldehyde (43–210 mg Kg^?1), and acrolein (1.9–12 mg Kg^?1). Formation of lower molecular weight PAHs such as naphthalene and acenaphthylene were favored, with their emission factors being comparable to that from the combustion of household refuse and agricultural debris. Ba, Sr, Cu, and Pb had emission factors that could produce exposures exceeding occupational exposure guidelines. Sb and unalloyed Mg, which are banned from consumer fireworks in the US, were present in significant amounts.     

Measurements of primary trace gases and NOY composition in Houston,Texas

Concentrations of CO, SO₂, NO, NO₂, and NO_Y were measured atop the University of Houston's Moody Tower supersite during the 2006 TexAQS-II Radical and Aerosol Measurement Project (TRAMP). The lowest concentrations of all primary and secondary species were observed in clean marine air in southerly flow. SO₂ concentrations were usually low, but increased dramatically in sporadic midday plumes advected from sources in the Houston Ship Channel (HSC), located NE of the site. Concentrations of CO and NO_x displayed large diurnal variations in keeping with their co-emission by mobile sources in the Houston Metropolitan Area (HMA). CO/NO_x emission ratios of 5.81 ± 0.94 were observed in the morning rush hour. Nighttime concentrations of NO_x (NO_x = NO + NO₂) and NO_Y (NO_Y = NO + NO₂ + NO₃ + HNO₃ + HONO + 21N₂O₅ + HO₂NO₂ + PANs + RONO₂ + p-NO₃^? + …) were highest in winds from the NNW-NE due to emission from mobile sources. Median ratios of NO_x/NO_Y were approximately 0.9 overnight, reflecting the persistence and/or generation of NO_Z (NO_Z = NO_Y ? NO_x) species in the nighttime Houston boundary layer, and approached unity in the morning rush hour. Daytime concentrations of NO_x and NO_Y were highest in winds from the HSC. NO_x/NO_Y ratios reached their minimum values (median ca 0.63) from 1300 to 1500 CST, near local solar noon, and air masses often retained enough NO_x to sustain additional O₃ formation farther downwind. HNO₃ and PANs comprised the dominant NO_Z species in the HMA, and on a median basis represented 17–20% and 12–15% of NO_Y, respectively, at midday. Concentrations of HNO₃, PANs, and NO_Z, and fractional contributions of these species to NO_Y, were at a maximum in NE flow, reflecting the source strength and reactivity of precursor emissions in the HSC. As a result, daytime O₃ concentrations were highest in air masses with HSC influence. Overall, our findings confirm the impact of the HSC as a dominant source region within the HMA. A comparison of total NO_Y measurements with the sum of measured NO_Y species (NO_Yi = NO_x + HNO₃ + PANs + HONO + p-NO₃^?) yielded excellent overall agreement during both day ([NO_Y](ppb) = ([NO_Yi](ppb)11.03 ± 0.16) ? 0.42; r² = 0.9933) and night ([NO_Y](ppb) = ([NO_Yi](ppb)11.01 ± 0.16) + 0.18; r² = 0.9975). A similar comparison between NO_Y–NO_x concentrations and the sum of NO_Zi (NO_Zi = HNO₃ + PANs + HONO + p-NO₃^?) yielded good overall agreement during the day ([NO_Z](ppb) = ([NO_Zi](ppb)11.01 ± 0.30) + 0.044 ppb; r² = 0.8527) and at night ([NO_Z](ppb) = ([NO_Zi](ppb)11.12 ± 0.69) + 0.16 ppb; r² = 0.6899). Median ratios of NO_Z/NO_Zi were near unity during daylight hours but increased to approximately 1.2 overnight, a difference of 0.15–0.50 ppb. Differences between NO_Z and NO_Zi rarely exceeded combined measurement uncertainties, and variations in NO_Z/NO_Zi ratios may have resulted solely from errors in conversion efficiencies of NO_Y species and changes in NO_Y composition. However, nighttime NO_Z/NO_Zi ratios and the magnitude of NO_Z ? NO_Zi differences were generally consistent with recent observations of ClNO₂ in the nocturnal Houston boundary layer.     

Sub-region (district) and sector level SO2 and NOx emissions for India: assessment of inventories and mitigation flexibility

Sub-regional and sector level distribution of SO₂ and NO_x emissions inventories for India have been estimated for all the 466 Indian districts using base data for years 1990 and 1995. Although, national level emissions provide general guidelines for assessing mitigation alternatives, but significant regional and sectoral variability exist in Indian emissions. Districts reasonably capture this variability to a fine grid as 80% of these districts are smaller than 1°×1° resolution with 60% being smaller than even 1/2°×1/2°. Moreover, districts in India have well-established administrative and institutional mechanisms that would be useful for implementing and monitoring measures. District level emission estimates thus offer a finer regional scale inventory covering the combined interests of the scientific community and policy makers. The inventory assessment methodology adopted is similar to that prescribed by the Intergovernmental Panel on Climate Change (IPCC) for greenhouse gas (GHG) emissions. The sectoral decomposition at district level includes emissions from fossil fuel combustion, non-energy emissions from industrial activities and agriculture. Total SO₂ and NO_x emissions from India were 3542 and 2636 Gg, respectively (1990) and 4638 and 3462 Gg (1995) growing at annual rate of around 5.5%. The sectoral composition of SO₂ emissions indicates a predominance of electric power generation sector (46%). Power and transport sector emissions equally dominate NO_x emissions contributing nearly 30% each. However, majority of power plants are situated in predominantly rural districts while the latter are concentrated in large urban centers. Mitigation efforts for transport sector NO_x emissions would therefore be higher. The district level analysis indicates diverse spatial distribution with the top 5% emitting districts contributing 46.5 and 33.3% of total national SO₂ and NO_x emissions, respectively. This skewed emission pattern, with a few districts, sectors and point sources emitting significant SO₂ and NO_x, offers mitigation flexibility to policy makers for cost-effective mitigation.     

The impact of transportation control measures on emission reductions during the 2008 Olympic Games in Beijing,China

Traffic congestion and air pollution were two major challenges for the planners of the 2008 Olympic Games in Beijing. The Beijing municipal government implemented a package of temporary transportation control measures during the event. In this paper, we report the results of a recent research project that investigated the effects of these measures on urban motor vehicle emissions in Beijing. Bottom–up methodology has been used to develop grid-based emission inventories with micro-scale vehicle activities and speed-dependent emission factors. The urban traffic emissions of volatile organic compounds (VOC), carbon monoxide (CO), nitrogen oxides (NO_x) and particulate matter with an aerodynamic diameter of 10 μm or less (PM₁₀) during the 2008 Olympics were reduced by 55.5%, 56.8%, 45.7% and 51.6%, respectively, as compared to the grid-based emission inventory before the Olympics. Emission intensity was derived from curbside air quality monitoring at the North 4th Ring Road site, located about 7 km from the National Stadium. Comparison between the emission intensity before and during the 2008 Olympics shows a reduction of 44.5% and 49.0% in daily CO and NO_x emission from motor vehicles. The results suggest that reasonable traffic system improvement strategies along with vehicle technology improvements can contribute to controlling total motor vehicle emissions in Beijing after the Olympic Games.