Search

Signalised traffic intersections (TIs) are considered as pollution hot-spots in urban areas, but the knowledge of fundamental drivers governing emission, dispersion and exposure to vehicle-emitted nanoparticles (represented by particle number concentration, PNC) at TIs is yet to be established. A number of following key factors, which are important for developing an emission and exposure framework for nanoparticles at TIs, are critically evaluated as a part of this review article. In particular, (i) how do traffic- and wind-flow features affect emission and dispersion of nanoparticles? (ii) What levels of PNCs can be typically expected under diverse signal- and traffic-conditions? (iii) How does the traffic driving condition affect the particle number (PN) emissions and the particle number emission factors (PNEF)? (iv) What is the relative importance of particle transformation processes in affecting the PNCs? (v) What are important considerations for the dispersion modelling of nanoparticles? (vi) What is extent of exposure at TIs with respect to other locations in urban settings? (vii) What are the gaps in current knowledge on this topic where the future research should focus? We found that the accurate consideration of dynamic traffic flow features at TIs is essential for reliable estimates of PN emissions. Wind flow features at TIs are generally complex to generalise. Only a few field studies have monitored PNCs at TIs until now, reporting over an order of magnitude larger peak PNCs (0.7–5.4 × 10 5 cm −3 ) compared with average PNCs at typical roadsides (∼0.3 × 10 5 cm −3 ). The PN emission and thus the PNEFs can be up to an order of magnitude higher during acceleration compared with steady speed conditions. The time scale analysis suggests nucleation as the fastest transformation process, followed by dilution, deposition, coagulation and condensation. Consideration of appropriate flow features, PNEFs and transformation processes emerged as important parameters for reliable modelling of PNCs at TIs. Computation of respiratory deposition doses (RDD) based on the available PNC data suggest that the peak RDD at TIs can be up to 12-times higher compared with average RDD at urban roadsides. Systematic field and modelling studies are needed to develop a sound understanding of the emissions, dispersion and exposure of nanoparticles at the TIs.

It is well known that skin sea surface temperature (SSST) is different from bulk sea surface temperature (BSST) by a few tenths of a degree Celsius. However, the extent of the error associated with dry deposition (or uptake) estimation by using BSST is not well known. This study tries to conduct such an evaluation using the on-board observation data over the South China Sea in the summers of 2004 and 2006. It was found that when a warm layer occurred, the deposition velocities using BSST were underestimated within the range of 0.8–4.3%, and the absorbed sea surface heat flux was overestimated by 21 W m−2. In contrast, under cool skin only conditions, the deposition velocities using BSST were overestimated within the range of 0.5–2.0%, varying with pollutants and the absorbed sea surface heat flux was underestimated also by 21 W m−2. Scale analysis shows that for a slightly soluble gas (e.g., NO2, NO and CO), the error in the solubility estimation using BSST is the major source of the error in dry deposition estimation. For a highly soluble gas (e.g., SO2), the error in the estimation of turbulent heat fluxes and, consequently, aerodynamic resistance and gas-phase film resistance using BSST is the major source of the total error. In contrast, for a medium soluble gas (e.g., O3 and CO2) both the errors from the estimations of the solubility and aerodynamic resistance are important. In addition, deposition estimations using various assumptions are discussed. The largest uncertainty is from the parameterizations for chemical enhancement factors. Other important areas of uncertainty include: (1) various parameterizations for gas-transfer velocity; (2) neutral-atmosphere assumption; (3) using BSST as SST, and (4) constant pH value assumption.

The multilayer perceptron (MLP) model is frequently used to assess the relative importance (RI) of surface ozone influential variables when they are used to investigate ozone variation mechanisms. Previous studies, however, suffer from two limitations: 1) indentifying or searching the optimal MLP topology to avoid a biased RI assessment inevitably incurs a heavy computational burden, and 2) the model is suitable only for local-scale analysis of ozone variation mechanisms. To tackle both problems, we selected three typical air-quality monitoring sites in Hong Kong as our study targets, as the ozone variations at these sites are inhomogeneously affected by regional and local factors. An MLP model trained by automatic relevance determination (referred to as MLP-ARD), a Bayesian MLP approach, was employed to assess the RI for both regional and local ozone influential variables. The results indicated the following remarks: 1) The MLP-ARD model, due to its high degree of resistance to both the over-fitting and the under-fitting problems, is exempt from identifying or searching the optimal MLP topology when used to obtain an unbiased RI assessment and thus avoid the heavy computational burden. Furthermore, the RI assessment results obtained with the MLP-ARD method are comparable to those of the best assessment method in the literature. Based on these findings, decision-makers can scientifically promulgate a site-specific air pollution control strategy site; 2) Regional-scale analysis of ozone variation is indispensable, as taking the key regional ozone influential variables into account significantly improves the prediction accuracy of the MLP-ARD model, especially on peak ozone days.

Over the next several years, grid-based photochemical models such as the community multiscale air quality (CMAQ) model, the regional modeling system for aerosols and deposition (REMSAD), the comprehensive air quality model with extensions (CAMx), and other regional models will be used by regulatory agencies in the United States for designing emission control strategies to meet and maintain the National Ambient Air Quality Standards (NAAQS) for O3, PM2.5, and regional haze. In this study, temporal scale analysis is applied as a technique to evaluate an annual simulation of meteorology, O3, and PM2.5 and its chemical components over the continental US utilizing two modeling systems. The spectral decomposition of total PM2.5 mass from hourly observations and CMAQ and REMSAD model predictions revealed that days of high PM2.5 concentrations are generally characterized by positive forcing from fluctuations having periods equal to or greater than a day (i.e., the diurnal, synoptic, and longer-term components) while the magnitude of intra-day fluctuations showed only small differences between average and episodic conditions. Both modeling systems did not capture most of the variability of the high-frequency, intra-day component for all variables for which hourly measurements were available. Furthermore, it is illustrated that correlations were insignificant on the intra-day time scale for all variables, suggesting that these models in the setup used for this study were not skillful in simulating the higher-frequency variations in meteorological variables and the levels of all pollutants. The models exhibited greatest skills at capturing longer-term (seasonal) fluctuations for temperature, wind speed, O3, sulfate and nitrate. Correlations for total PM2.5, ammonium, elemental carbon (EC), organic carbon (OC) and crustal PM2.5 correlations were highest for the synoptic time scale implying problems with factors other than meteorology, such as emissions or lateral chemical boundary conditions, in capturing the baseline fluctuations.

In this paper, the concept of scale analysis is applied to evaluate ozone predictions from two regional-scale air quality models. To this end, seasonal time series of observations and predictions from the RAMS3b/UAM-V and MM5/MAQSIP (SMRAQ) modeling systems for ozone were spectrally decomposed into fluctuations operating on the intra-day, diurnal, synoptic and longer-term time scales. Traditional model evaluation statistics are also presented to illustrate how the scale analysis approach can help improve our understanding of the models’ performance. The results indicate that UAM-V underestimates the total variance (energy) of the ozone time series when compared with observations, but shows a higher mean value than the observations. On the other hand, MAQSIP is able to better reproduce the average energy and mean concentration of the observations. However, both modeling systems do not capture the amount of variability present on the intra-day time scale primarily due to the grid resolution used in the models. For both modeling systems, the correlations between the predictions and observations are insignificant for the intra-day component, high for the diurnal component because of the inherent diurnal cycle but low for the amplitude of the diurnal component, and highest for the synoptic and baseline components. This better model performance on longer time scales suggests that current regional-scale models are most skillful in characterizing average patterns over extended periods, rather than in predicting concentrations at specific locations, during 1–2 day episodic events. In addition, we discuss the implications of these results to using the model-predicted daily maximum ozone concentrations in the regulatory framework in light of the uncertainties introduced by the models’ poor performance on the intra-day and diurnal time scales.

In this study, the concept of scale analysis is applied to evaluate two state-of-science meteorological models, namely MM5 and RAMS3b, currently being used to drive regional-scale air quality models. To this end, seasonal time series of observations and predictions for temperature, water vapor, and wind speed were spectrally decomposed into fluctuations operating on the intra-day, diurnal, synoptic and longer-term time scales. Traditional model evaluation statistics are also presented to illustrate how the method of spectral decomposition can help provide additional insight into the models’ performance. The results indicate that both meteorological models under-represent the variance of fluctuations on the intra-day time scale. Correlations between model predictions and observations for temperature and wind speed are insignificant on the intra-day time scale, high for the diurnal component because of the inherent diurnal cycle but low for the amplitude of the diurnal component, and highest for the synoptic and longer-term components. This better model performance on longer time scales suggests that current regional-scale models are most skillful for characterizing average patterns over extended periods. The implications of these results to using meteorological models to drive photochemical models are discussed. r 2001 Elsevier Science Ltd. All rights reserved.