Poplar plantation has the potential to alter the water balance in semiarid Inner Mongolia

Poplar plantation is the most dominant broadleaf forest type in northern China. Since the mid-1990s plantation was intensified to combat desertification along China's northwestern border, i.e., within Inner Mongolia (IM). This evoked much concern regarding the ecological and environmental effects on areas that naturally grow grass or shrub vegetation. To highlight potential consequences of large-scale poplar plantations on the water budget within semiarid IM, we compared the growing season water balance (evapotranspiration (ET) and precipitation (PPT)) of a 3-yr old poplar plantation (Kp₃) and a natural shrubland (Ks) in the Kubuqi Desert in western IM, and a 6-yr old poplar plantation (Bp₆) growing under sub-humid climate near Beijing. The results showed that, despite 33% lower PPT at Kp₃, ET was 2% higher at Kp₃ (228 mm) as compared with Ks (223 mm) in May–September 2006. The difference derived mainly from higher ET at the plantation during drier periods of the growing season, which also indicated that the poplars must have partly transpired groundwater. Estimated growing season ET at Bp₆ was about 550 mm and more than 100% higher than at Kp₃. It is estimated that increases in leaf area index and net radiation at Kp₃ provide future potential for the poplars in Kubuqi to exceed the present ET and ET of the natural shrubland by 100–200%. These increases in ET are only possible through the permanent use of groundwater either directly by the trees or through increased irrigation. This may significantly change the water balance in the area (e.g., high ET at the cost of a reduction in the water table), which renders large-scale plantations a questionable tool in sustainable arid-land management.     

Atmospheric Volatile Organic Compound Monitoring. Ozone Induced Artefact Formation

Assessment of population exposure to VOC in ambient atmospheres is receiving heightened interest as the adverse health effects of chronic exposure to certain of these compounds are identified. Active (pumped) and passive samplers are the most commonly used devices for this type of monitoring. It has been shown, however, that these devices, along with all other preconcentration techniques, are susceptible to ozone interference. It is demonstrated that this interference occurs even at low ozone concentrations and that it may result in the under-estimation of population exposure. A convenient and effective ozone scrubbing method is identified and successfully applied and validated for both active and passive samplers for a range of VOC.     

Improvements in Air Quality Modelling by Using Surface Boundary Layer Parameters derived from Satellite Land Cover Data

Many atmospheric dispersion models include only simpletreatment of surface features to estimate the wind profilesand stability parameters. Detailed characterisation of theland cover, particularly in large and complex urbanconurbations, is especially important, as the surfacefeatures can vary significantly over the area. This paperdiscusses the use of satellite land cover data to derivespatially resolved surface boundary layer (SBL) parameters.These parameters have been used in an air quality model,PEARL (Prediction Air Quality in Urban and RegionalLocations) for estimating monthly and annual COconcentrations. Land cover data, derived from LANDSATThematic Mapper Imagery, has been used to estimate SBLparameters (surface roughness length, albeedo, Bowen ratioand anthropogenic heat flux) for a study area of 10000km² encompassing Greater London and the surroundingcounties. The SBL parameters have been assigned according tomajor land cover types for the whole area at a spatialresolution of 1 × 1 km. Predictions from two versions of the PEARL model (one with land cover data and one without)have been compared with each other and with measured data forannual and monthly CO concentrations from seven London airquality monitoring sites. This comparison shows thatdifferences between predicted and observed values can bereduced by up to a factor of three. The use of SBLparameters derived from land cover data also yields moredetailed predicted annual CO spatial patterns especially inand around suburban areas. The performance of both versionsof the model for monthly CO concentrations has been comparedwith a range of statistical measures. This comparisonconfirms that improved agreement is observed betweenmodelled and measured monthly CO concentrations when use ismade of spatially resolved SBL parameters.     

PM2.5 concentrations in London for 2008–A modeling analysis of contributions from road traffic

We report on the analysis of contributions from road traffic emissions to fine particulate matter (PM_2.5) concentrations within London for 2008 with the OSCAR Air Quality Assessment System. A spatiotemporal evaluation of the OSCAR system has been conducted with measurements from the London air quality network (LAQN). For the predicted and measured hourly time series of concentrations at 18 sites in London, the medians of correlation, mean absolute error, index of agreement, and factor of two (FAC2) of all stations were 0.80, 4.1 μg/m³, 0.86, and 74%, respectively. Spatial evaluation of modeled and observed annual mean concentrations also showed a fairly good agreement, with all the values falling within the FAC2 range. According to model predictions, the urban increment (including the contributions from urban traffic and other urban sources) was evaluated to be on the average 18%, 33%, 39%, and 43% of the total PM_2.5 in suburban environments, in the urban background, near roads, and near busy roads, respectively. However, the highest values of the urban traffic increment can be around 50% of the total PM_2.5 concentrations near motorways and major roads. The total concentrations (including regional background, and the contributions from urban traffic and other urban sources) can therefore be almost three times the regional background. The total urban increment close to busy roads was around 7–8 μg/m³, in which the estimated traffic contribution is more than 2 μg/m³. On the average, urban traffic contributes approximately 1 μg/m³ of PM_2.5 to the urban background across London. According to modeling, approximately two-thirds of the traffic increment originated from exhaust emissions and most of the rest was due to brake and tire wear.

Implications: The urban increment and traffic contribution to the total PM_2.5 are significant and spatially heterogeneous across London. The highly heterogeneous distribution of PM_2.5 hence requires detailed modeling studies to be carried out at high spatial resolution, which can be particularly important for exposure and health impact assessment. This type of information can be used to quantify health impacts resulting from specific sources of PM_2.5 such as traffic emissions, to aid city and national decision makers when formulating pollution control strategies.     

Evaluation of a CMAQ simulation at high resolution over the UK for the calendar year 2003

A comprehensive ‘operational’ evaluation of the performance of the Community Multiscale Air Quality (CMAQ) modelling system version 4.6 was conducted in support of pollution assessment in the UK for the calendar year 2003. The model was run on multiple grids using one-way nests down to a horizontal resolution as fine as 5 km over the whole of the UK. The model performance was evaluated for pollutants with standards and limit values (e.g. O₃, PM₁₀) and species contributing to acidic and nitrogenous deposition (e.g. NH₃, SO₄^2–, NO₃^–, NH₄⁺) against data from operational national monitoring networks. The key performance characteristics of the modelling system were found to be variable according to acceptance criteria and to depend on the type (e.g. urban, rural) and location of the sites, as well as on the time of the year. As regards the techniques that were used for ‘operational’ evaluation, performance generally complied with expected levels and ranged from good (e.g. O₃, SO₄^2–) to moderate (e.g. PM₁₀, NO₃^–). At a few sites low correlations and large standard deviations for some species (e.g. SO₂) suggest that these sites are subject to local factors (e.g. topography, emission sources) that are not well described in the model. Overall, the model tends to over predict O₃ and under predict aerosol species (except SO₄^2–). Discrepancies between predicted and observed concentrations may be due to a variety of intertwined factors, which include inaccuracies in meteorological predictions, chemical boundary conditions, temporal variability in emissions, and uncertainties in the treatment of gas and aerosol chemistry. Further work is thus required to investigate the respective contributions of such factors on the predicted concentrations.     

Predictions of U.K. regulated power station contributions to regional air pollution and deposition: a model comparison exercise

Contributions of the emissions from a U.K. regulated fossil-fuel power station to regional air pollution and deposition are estimated using four air quality modeling systems for the year 2003. The modeling systems vary in complexity and emphasis in the way they treat atmospheric and chemical processes, and include the Community Multiscale Air Quality (CMAQ) modeling system in its versions 4.6 and 4.7, a nested modeling system that combines long- and short-range impacts (referred to as TRACK-ADMS [Trajectory Model with Atmospheric Chemical Kinetics-Atmospheric Dispersion Modelling System]), and the Fine Resolution Atmospheric Multi-pollutant Exchange (FRAME) model. An evaluation of the baseline calculations against U.K. monitoring network data is performed. The CMAQ modeling system version 4.6 data set is selected as the reference data set for the model footprint comparison. The annual mean air concentration and total deposition footprints are summarized for each modeling system. The footprints of the power station emissions can account for a significant fraction of the local impacts for some species (e.g., more than 50% for SO2 air concentration and non-sea-salt sulfur deposition close to the source) for 2003. The spatial correlation and the coefficient of variation of the root mean square error (CVRMSE) are calculated between each model footprint and that calculated by the CMAQ modeling system version 4.6. The correlation coefficient quantifies model agreement in terms of spatial patterns, and the CVRMSE measures the magnitude of the difference between model footprints. Possible reasons for the differences between model results are discussed. Finally, implications and recommendations for the regulatory assessment of the impact of major industrial sources using regional air quality modeling systems are discussed in the light of results from this case study.