Samples of fog water collected in the area of Guangzhou during February, March and April of 2005 are used in this work to study the chemical composition of fog water in polluting fog there. Three typical episodes of polluting fog are analyzed in terms of ionic concentration and their possible sources. It is found that the concentration of various ions in fog water is much higher than those in rainwater. Fog not only blocks visual range but contains liquid particles that result in high degree of pollution and are very harmful to human health. SO4= is the anion with the highest concentration in fog water, followed by NO3-. For the cation, Ca++ and NH4+ are the highest in concentration. It is then known that rainwater is more acidic than fog water, indicating that ionic concentration of fog water is much higher than that of rainwater, but there are much more buffering materials in fog water, like NH4+ and Ca++. There is significant enrichment of Ca++, SO4=, and Mg++ in fog water. In the Guangzhou area, fog water from polluting fog is mainly influenced continental environment and human activity. The episodes of serious fog pollution during the time have immediate relationships with the presence of abundant water vapor and large amount of polluting aerosol particles.
Using the non-hydrostatic meso-scale model MM5v3, dense fog that occurred from March 7 to March 8, 2001 over the Mts. Nanling area was studied. With integrated field experiments and observations, the occurrence, development and lift mechanism of fog were analyzed. The results indicate that before the coming of stratiform clouds, southerly warm and wet air ascended along mountainside and cooling condensation formed mountain fog. Then fog was formed by the stratiform on cloud-contacting mountaintop. A front inversion layer accelerated the development and extended the duration of the lower cloud and fog. The intensity, occurrence time, mass content and the variation of temperature and relative humidity of the fog agreed with those of the observation. It showed that the meso-scale model has the potential to forecast mountain fog.
