International audienceThe CAMx photochemical grid model was used to model ozone (O3) and particulate matter (PM) over a European modeling domain for calendar year 2006 as part of the Air Quality Model Evaluation International Initiative (AQMEII). The CAMx base case utilized input data provided by AQMEII for emissions, meteorology and boundary conditions. Sensitivity of model outputs to input data was investigated by using alternate input data and changing other important modeling assumptions including the schemes to represent photochemistry, dry deposition and vertical mixing. Impacts on model performance were evaluated by comparisons with ambient monitoring data. Base case model performance for January and July 2006 exhibited under-estimation trends for all pollutants both in winter and summer, except for SO2. SO2 generally had little bias although some over-estimation occurred at coastal locations and this was attributed to incorrect vertical distribution of emissions from marine vessels. Performance for NOx and NO2 was better in winter than summer. The tendency to under-predict daytime NOx and O3 in summer may result from insufficient NOx emissions or overstated daytime dilution (e.g., too deep planetary boundary layer) or monitors that are located near sources (e.g., roadside monitors). Winter O3 was biased low and this was attributed to a low bias in the O3 boundary conditions. PM10 was widely under-predicted in both winter and summer. The poor PM10 was influenced by underestimation of coarse PM emissions. Sensitivities of O3 concentrations to precursor emissions are quantified using the decoupled direct method in CAMx. The results suggest that O3 production over the central and southern Europe during summer is mostly NOx-limited but for a more northerly city, London, O3 production can be limited either by NOx or VOC depending upon daily meteorological conditions
The NDMA formation potential (NDMA FP) of four commonly used amine-based cationic water treatment polymers was assessed in reactions with chlorine-based oxidants (free chlorine, monochloramine and chlorine dioxide) and nitrosifying agents (nitrite and nitrate). Relatively high dosages of polymers were directly exposed to oxidants for long reaction times in the FP tests to assess the potential to form NDMA and obtain mechanistic insight. Results show that the NDMA FP of the polymers generally follows the trend of aminomethylated polyacrylamide (Mannich polymer)>>poly(epichlorohydrin-dimethylamine) (polyamine) > poly(diallyldimethylammonium chloride) (polyDADMAC) > cationic polyacrylamide copolymer (cationic PAM). The high NDMA FP of Mannich polymer was largely due to the high amount of dimethylamine (DMA) residue in the polymer solution. For the other three polymers, the DMA concentration was increased after oxidation, indicating polymer degradation, and the trend of DMA increase agreed with that of NDMA FP. Among the oxidants, NDMA formation followed the order of monochloramine > free chlorine > chlorine dioxide, despite that the DMA release from the polymers caused by the oxidant followed the opposite order. At equal dosages, nitrite and nitrate generated NDMA from the polymers at levels comparable to those by free chlorine and chlorine dioxide; even so, the nitrosifying agents are unlikely to contribute significantly to NDMA formation due to expected lower concentrations in drinking water treatment systems. Jar tests followed by monochloramination of real water samples using conditions in line with those at potable water treatment plants generally showed relatively small contributions from polyamines and polyDADMACs to the overall NDMA formation.
Air quality models are increasingly used to develop estimates of dry and wet deposition of sulfate and nitrate in watersheds (because of lack of measurements) in an effort to determine the acidifying deposition load into the aquatic systems. These models need to be rigorously evaluated to ensure that one can rely on the modeled quantities instead of the measured quantities. In the United State (U.S.), these models have been proposed for use in establishing national standards based on modeled quantities. The U.S. Environmental Protection Agency (EPA) is considering aquatic acidification as the main ecological endpoint of concern in determining the secondary national ambient air quality standards for nitrogen oxides and sulfur oxides. Acidification is tied to depositions of sulfur and nitrogen, which are linked to ambient concentrations of the elements. As EPA proposes to use a chemical transport model in linking deposition to ambient concentration, it is important to investigate how the currently used chemical transport models perform in predicting depositions and ambient concentrations of relevant chemical species and quantify the variability in their estimates. In this study, several annual simulations by multiple chemical transport models for the entire continental U.S. domain are evaluated against available measurement data for depositions and ambient concentrations of sulfur oxides and reactive nitrogen species. The model performance results vary by evaluation time-scale and geographical region. Evaluation of annualized quantities (annual average ambient concentrations and annual total depositions) suppresses the large variances shown in the evaluation using the observation's native shorter-term time-scales (e.g., weekly). In OPEN ACCESSAtmosphere 2012, 3 401 addition, there is a large degree of bias and error (especially for deposition fluxes) in the modeling results that brings to question the suitability of using air quality models to provide estimates of deposition loads. Variability in the ratio of deposition to ambient concentration, so-called the Transference Ratio that EPA has proposed to use in linking deposition to ambient concentration, is also examined. Our study shows that the Transference Ratios as well as total reduced nitrogen deposition, another modeled parameter EPA proposed to use in the process of determining the new secondary standard, vary considerably by geographical region and by model simulation.
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