In situ experimental study of carbon monoxide generation by gasoline-powered electric generator in an enclosed space

On the basis of currently available data, approximately 97% of generator-related carbon monoxide (CO) fatalities are caused by operating currently marketed, carbureted spark-ignited gasoline-powered generators (not equipped with emission controls) in enclosed spaces. To better understand and to reduce the occurrence of these fatalities, research is needed to quantify CO generation rates, develop and test CO emission control devices, and evaluate CO transport and exposure when operating a generator in an enclosed space. As a first step in these efforts, this paper presents measured CO generation rates from a generator without any emission control devices operating in an enclosed space under real weather conditions. This study expands on previously published information from the U.S. Consumer Product Safety Commission. Thirteen separate tests were conducted under different weather conditions at half and full generator load settings. It was found that the CO level in the shed reached a maximum value of 29,300 +/- 580 mg/m3, whereas the oxygen (O2) was depleted to a minimum level of 16.2 +/- 0.02% by volume. For the test conditions of real weather and generator operation, the CO generation and the O2 consumption could be expressed as time-averaged generation/consumption rates. It was also found that the CO generation and O2 consumption rates can be correlated to the O2 levels in the space and the actual load output from the generator. These correlations are shown to agree well with the measurements.     

Improvements to lawn and garden equipment emissions estimates for Baltimore, Maryland

Lawn and garden equipment are a significant source of emissions of volatile organic compounds (VOCs) and other pollutants in suburban and urban areas. Emission estimates for this source category are typically prepared using default equipment populations and activity data contained in emissions models such as the U.S. Environmental Protection Agency's (EPA) NONROAD model or the California Air Resources Board's (CARB) OFFROAD model. Although such default data may represent national or state averages, these data are unlikely to reflect regional or local differences in equipment usage patterns because of variations in climate, lot sizes, and other variables. To assess potential errors in lawn and garden equipment emission estimates produced by the NONROAD model and to demonstrate methods that can be used by local planning agencies to improve those emission estimates, this study used bottom-up data collection techniques in the Baltimore metropolitan area to develop local equipment population, activity, and temporal data for lawn and garden equipment in the area. Results of this study show that emission estimates of VOCs, particulate matter (PM), carbon monoxide (CO), carbon dioxide (CO2), and nitrogen oxides (NO(x)) for the Baltimore area that are based on local data collected through surveys of residential and commercial lawn and garden equipment users are 24-56% lower than estimates produced using NONROAD default data, largely because of a difference in equipment populations for high-usage commercial applications. Survey-derived emission estimates of PM and VOCs are 24 and 26% lower than NONROAD default estimates, respectively, whereas survey-derived emission estimates for CO, CO2, and NO(x) are more than 40% lower than NONROAD default estimates. In addition, study results show that the temporal allocation factors applied to residential lawn and garden equipment in the NONROAD model underestimated weekend activity levels by 30% compared with survey-derived temporal profiles.     

Variation in biogenic volatile organic compound emission pattern of Fagus sylvatica L. due to aphid infection

Volatile organic compounds (VOCs) have been the focus of interest to understand atmospheric processes and their consequences in formation of ozone or aerosol particles; therefore, VOCs contribute to climate change. In this study, biogenic VOCs (BVOCs) emitted from Fagus sylvatica L. trees were measured in a dynamic enclosure system. In total 18 compounds were identified: 11 monoterpenes (MT), an oxygenated MT, a homoterpene (C₁₄H₁₈), 3 sesquiterpenes (SQT), isoprene and methyl salicylate. The frequency distribution of the compounds was tested to determine a relation with the presence of the aphid Phyllaphis fagi L. It was found that linalool, (E)-β-ocimene, α-farnesene and a homoterpene identified as (E)-4,8-dimethyl-1,3,7-nonatriene (DMNT), were present in significantly more samples when infection was present on the trees. The observed emission spectrum from F. sylvatica L. shifted from MT to linalool, α-farnesene, (E)-β-ocimene and DMNT due to the aphid infection. Sabinene was quantitatively the most prevalent compound in both, non-infected and infected samples. In the presence of aphids α-farnesene and linalool became the second and third most important BVOC emitted. According to our investigation, the emission fingerprint is expected to be more complex than commonly presumed.     

Comparison of land-use regression models between Great Britain and the Netherlands

Land-use regression models have increasingly been applied for air pollution mapping at typically the city level. Though models generally predict spatial variability well, the structure of models differs widely between studies. The observed differences in the models may be due to artefacts of data and methodology or underlying differences in source or dispersion characteristics. If the former, more standardised methods using common data sets could be beneficial. We compared land-use regression models for NO₂ and PM_10, developed with a consistent protocol in Great Britain (GB) and the Netherlands (NL).Models were constructed on the basis of 2001 annual mean concentrations from the national air quality networks. Predictor variables used for modelling related to traffic, population, land use and topography. Four sets of models were developed for each country. First, predictor variables derived from data sets common to both countries were used in a pooled analysis, including an indicator for country and interaction terms between country and the identified predictor variables. Second, the common data sets were used to develop individual baseline models for each country. Third, the country-specific baseline models were applied after calibration in the other country to explore transferability. The fourth model was developed using the best possible predictor variables for each country.A common model for GB and NL explained NO₂ concentrations well (adjusted R² 0.64), with no significant differences in intercept and slopes between the two countries. The country-specific model developed on common variables for NL but not GB improved the prediction.The performance of models based upon common data was only slightly worse than models optimised with local data. Models transferred to the other country performed substantially worse than the country-specific models. In conclusion, care is needed both in transferring models across different study areas, and in developing large inter-regional LUR models.     

Ground-level ozone in the Pearl River Delta region: Analysis of data from a recently established regional air quality monitoring network

The ambient air quality monitoring data of 2006 and 2007 from a recently established Pearl River Delta (PRD) regional air quality monitoring network are analyzed to investigate the characteristics of ground-level ozone in the region. Four sites covering urban, suburban, rural and coastal areas are selected as representatives for detailed analysis in this paper. The results show that there are distinct seasonal and diurnal cycles in ground-level ozone across the PRD region. Low ozone concentrations are generally observed in summer, while high O₃ levels are typically found in autumn. The O₃ diurnal variations in the urban areas are larger than those at the rural sites. The O₃ concentrations showed no statistically significant difference between weekend and weekdays in contrast to the findings in many other urban areas in the world. The average ozone concentrations are lower in urban areas compared to the sites outside urban centers. Back trajectories are used to show the major air-mass transport patterns and to examine the changes in ozone from the respective upwind sites to a site in the center of the PRD (Wanqingsha). The results show higher average ozone concentrations at the upwind sites in the continental and coastal air masses, but higher 1 h-max O₃ concentrations (by 8–16 ppbv) at the center PRD site under each of air-mass category, suggesting that the ozone pollution in the PRD region exhibits both regional and super-regional characteristics.     

Improved land cover and emission factors for modeling biogenic volatile organic compounds emissions from Hong Kong

This paper describes a study of local biogenic volatile organic compounds (BVOC) emissions from the Hong Kong Special Administrative Region (HKSAR). An improved land cover and emission factor database was developed to estimate Hong Kong emissions using MEGAN, a BVOC emission model developed by Guenther et al. (2006). Field surveys of plant species composition and laboratory measurements of emission factors were combined with other data to improve existing land cover and emission factor data. The BVOC emissions from Hong Kong were calculated for 12 consecutive years from 1995 to 2006. For the year 2006, the total annual BVOC emissions were determined to be 12,400 metric tons or 9.82 × 10⁹ g C (BVOC carbon). Isoprene emission accounts for 72%, monoterpene emissions account for 8%, and other VOCs emissions account for the remaining 20%. As expected, seasonal variation results in a higher emission in the summer and a lower emission in the winter, with emission predominantly in day time. A high emission of isoprene occurs for regions, such as Lowest Forest-NT North, dominated by broadleaf trees. The spatial variation of total BVOC is similar to the isoprene spatial variation due to its high contribution. The year to year variability in emissions due to weather was small over the twelve-year period (?1.4%, 2006 to 1995 trendline), but an increasing trend in the annual variation due to an increase in forest land cover can be observed (+7%, 2006 to 1995 trendline). The results of this study demonstrate the importance of accurate land cover inputs for biogenic emission models and indicate that land cover change should be considered for these models.     

Uncertainty in health risks due to anthropogenic primary fine particulate matter from different source types in Finland

The emission-exposure and exposure-response (toxicity) relationships are different for different emission source categories of anthropogenic primary fine particulate matter (PM_2.5). These variations have a potentially crucial importance in the integrated assessment, when determining cost-effective abatement strategies. We studied the importance of these variations by conducting a sensitivity analysis for an integrated assessment model. The model was developed to estimate the adverse health effects to the Finnish population attributable to primary PM_2.5 emissions from the whole of Europe. The primary PM_2.5 emissions in the whole of Europe and in more detail in Finland were evaluated using the inventory of the European Monitoring and Evaluation Programme (EMEP) and the Finnish Regional Emission Scenario model (FRES), respectively. The emission-exposure relationships for different primary PM_2.5 emission source categories in Finland have been previously evaluated and these values incorporated as intake fractions into the integrated assessment model. The primary PM_2.5 exposure-response functions and toxicity differences for the pollution originating from different source categories were estimated in an expert elicitation study performed by six European experts on air pollution health effects. The primary PM_2.5 emissions from Finnish and other European sources were estimated for the population of Finland in 2000 to be responsible for 209 (mean, 95% confidence interval 6–739) and 357 (mean, 95% CI 8–1482) premature deaths, respectively. The inclusion of emission-exposure and toxicity variation into the model increased the predicted relative importance of traffic related primary PM_2.5 emissions and correspondingly, decreased the predicted relative importance of other emission source categories. We conclude that the variations of emission-exposure relationship and toxicity between various source categories had significant impacts for the assessment on premature deaths caused by primary PM_2.5.     

Assimilation of conventional and satellite wind observations in a mesoscale atmospheric model for studying atmospheric dispersion

A mesoscale atmospheric model PSU/NCAR MM5 is used to provide operational weather forecasts for a nuclear emergency response decision support system on the southeast coast of India. In this study the performance of the MM5 model with assimilation of conventional surface and upper-air observations along with satellite derived 2-d surface wind data from QuickSCAT sources is examined. Two numerical experiments with MM5 are conducted: one with static initialization using NCEP FNL data and second with dynamic initialization by assimilation of observations using four dimensional data assimilation (FDDA) analysis nudging for a pre-forecast period of 12 h. Dispersion simulations are conducted for a hypothetical source at Kalpakkam location with the HYSPLIT Lagrangian particle model using simulated wind field from the above experiments. The present paper brings out the differences in the atmospheric model predictions and the differences in dispersion model results from control and assimilation runs. An improvement is noted in the atmospheric fields from the assimilation experiment which has led to significant alteration in the trajectory positions, plume orientation and its distribution pattern. Sensitivity tests using different PBL and surface parameterizations indicated the simple first order closure schemes (Blackadar, MRF) coupled with the simple soil model have given better results for various atmospheric fields. The study illustrates the impact of the assimilation of the scatterometer wind and automated weather stations (AWS) observations on the meteorological model predictions and the dispersion results.     

Urban tracer dispersion experiments during the second DAPPLE field campaign in London 2004

As part of the DAPPLE programme two large scale urban tracer experiments using multiple simultaneous releases of cyclic perfluoroalkanes from fixed location point sources was performed. The receptor concentrations along with relevant meteorological parameters measured are compared with a three screening dispersion models in order to best predict the decay of pollution sources with respect to distance. It is shown here that the simple dispersion models tested here can provide a reasonable upper bound estimate of the maximum concentrations measured with an empirical model derived from field observations and wind tunnel studies providing the best estimate. An indoor receptor was also used to assess indoor concentrations and their pertinence to commonly used evacuation procedures.