Abstract. We performed MAX-DOAS measurements during the PRIDE-PRD2006 campaign in the Pearl River Delta region (PRD), China, for 4 weeks in July 2006 at a site located 60 km north of Guangzhou. The vertical distributions of NO2, HCHO, and CHOCHO were independently retrieved by an automated iteration method. The NO2 mixing ratios measured by MAX-DOAS showed reasonable agreement with the simultaneous, ground based in-situ data. The tropospheric NO2 vertical column densities (VCDs) observed by OMI on board EOS-Aura satellite were higher than with those by MAX-DOAS. The 3-D chemical transport model CMAQ overestimated the NO2 VCDs as well as the surface concentrations by about 65%. From this observation, a reduction of NOx emission strength in CMAQ seems to be necessary in order to well reproduce the NO2 observations. The average mixing ratios of HCHO and CHOCHO were 7 ppb and 0.4 ppb, respectively, higher than in other rural or semirural environments. The high ratio of 0.062 between CHOCHO and HCHO corresponds to the high VOCs reactivity and high HOx turnover rate consistent with other observations during the campaign.

Abstract. In order to characterize the features of particulate pollution in the Pearl River Delta (PRD) in the summer, continuous measurements of particle number size distributions and chemical compositions were simultaneously performed at Guangzhou urban site (GZ) and Backgarden downwind regional site (BG) in July 2006. Particle number concentration from 20 nm to 10 µm at BG was (1.7 ± 0.8)×10 4 cm −3 , about 40% lower than that at GZ, (2.9 ± 1.1)×10 4 cm −3 . The total particle volume concentration at BG was 94 ± 34 µm 3 cm −3 , similar to that at GZ, 96 ± 43 µm 3 cm −3 . More 20-100 nm particles, significantly affected by the traffic emissions, were observed at GZ, while 100-660 nm particle number concentrations were similar at both sites as they are more regional. PM 2.5 values were similar at GZ (69 ± 43 µg m −3 ) and BG (69 ± 58 µg m −3 ) with R 2 of 0.71 for the daily average PM 2.5 at these two sites, indicating the fine particulate pollution in the PRD region to be regional. Two kinds of pollution episodes, the accumulation pollution episode and the regional transport pollution episode, were observed. Fine particles over 100 nm dominated both number and volume concentrations of total particles during the late periods of these pollution episodes. Accumulation and secondary transformation are the main reasons for the nighttime accumulation pollution episode. Correspondence to: M. Hu (minhu@pku.edu.cn) 660 nm particle mass and PM 2.5 increase. When south or southeast wind prevailed in the PRD region, regional transport of pollutants took place. Regional transport contributed about 30% to fine particulate pollution at BG during a regional transport case. Secondary transformation played an important role during regional transport, causing higher increase rates of secondary ions in PM 1.0 than other species and shifting the peaks of sulfate and ammonium mass size distributions to larger sizes. SO 2− 4 , NO − 3 , and NH + 4 accounted for about 70% and 40% of PM 1.0 and PM 2.5 , respectively.

No abstract

No abstract

The formation of Secondary organic aerosol (SOA) was simulated with the Secondary ORGanic Aerosol Model (SORGAM) by a classical gas-particle partitioning concept, using the two-product model approach, which is widely used in chemical transport models. In this study, we extensively updated SORGAM including three major modifications: firstly, we derived temperature dependence functions of the SOA yields for aromatics and biogenic VOCs (volatile organic compounds), based on recent chamber studies within a sophisticated mathematic optimization framework; secondly, we implemented the SOA formation pathways from photo oxidation (OH initiated) of isoprene; thirdly, we implemented the SOA formation channel from NO₃-initiated oxidation of reactive biogenic hydrocarbons (isoprene and monoterpenes). The temperature dependence functions of the SOA yields were validated against available chamber experiments, and the updated SORGAM with temperature dependence functions was evaluated with the chamber data. Good performance was found with the normalized mean error of less than 30%. Moreover, the whole updated SORGAM module was validated against ambient SOA observations represented by the summed oxygenated organic aerosol (OOA) concentrations abstracted from aerosol mass spectrometer (AMS) measurements at a rural site near Rotterdam, the Netherlands, performed during the IMPACT campaign in May 2008. In this case, we embedded both the original and the updated SORGAM module into the EURopean Air pollution and Dispersion-Inverse Model (EURAD-IM), which showed general good agreements with the observed meteorological parameters and several secondary products such as O₃, sulfate and nitrate. With the updated SORGAM module, the EURAD-IM model also captured the observed SOA concentrations reasonably well especially those during nighttime. In contrast, the EURAD-IM model before update underestimated the observations by a factor of up to 5. The large improvements of the modeled SOA concentrations by updated SORGAM were attributed to the mentioned three modifications. Embedding the temperature dependence functions of the SOA yields, including the new pathways from isoprene photo oxidations, and switching on the SOA formation from NO₃ initiated biogenic VOC oxidations, contributed to this enhancement by 10, 22 and 47%, respectively. However, the EURAD-IM model with updated SORGAM still clearly underestimated the afternoon SOA observations up to a factor of two

ABSTRACT. Traffic allocation planning is commonly required for mass evacuation management. It primarily relies on efficient coordination and appropriate utilization of roadway capacity and available traffic resources. However, traffic and evacuee information are usually difficult to be obtained and consequently of various uncertainties in data. Especially, stochastic information may often exist in evacuation management systems. In this study, a two-stage interval-stochastic evacuation management (TISEM) model was developed for supporting the evacuation planning under uncertainty, by which stochastic and interval evacuation information could be well reflected and communicated in the system. In addition, by adopting the proposed model, a case study abstracted from the City of Wuhan was introduced and solved through an interactive method. Results indicated that useful solutions for planning evacuation routes could be generated based on results of the model. As well, through the model, complex relationships between evacuation time, environmental influences and economic factors could be systematically analyzed. It demonstrated that the proposed TISP model is practical and applicable in real world, and is helpful for authorities to make decisions allocating vehicles before evacuation starts.

No abstract

No abstract