The air quality dynamic prediction system in Yinchuan city is given out simply，and the prediction result and error about the system are analyzed in detail. Results show that the system can forecast pollution meteorological condition in 24 hours ，and the prediction result corresponds to monitoring result well，the prediction accuracy for PM10 and SO2 is 61% and 92%，respectively；the average absolute error of predicted value for PM10 is relatively less in winter，summer and autumn，but it is contrary to that of SO2；Considering the influence of blow sand condition on predicting accuracy of the system，the blowing sand coefficient in the model can be adjusted progressively according to different time and seasons，thus it can improve prediction accuracy of the model for air quality in windy and dusty season effectively，so as to be adapted to the special climate and environmental condition in Yinchuan city.

The dates of the first soaking rainfalls in the south and middle parts of Ningxia were forecasted statistically by using the optimal subset regression method, the forecast equation was set up and it king rainfall in the regions in recent 30 years was discussed in detail effect was analyzed, and the(hange of the dates of the first soarainfall is 23.3% in April, 23.3% in May, and 33 Results show 3 %  in .Tune that the occurrence in Yinc;huan region
probability of the first soaking ，during the last 30 years the dates of the first dates of the first Apri1,20% and tinn fnr the dates   in March, 20% soaking rainfalls were later in 1970x, earlier in 1980x, and medium in 1990s between that in 1970s and 1980x.  The soaking rainfall(onc;entrated relatively in southern mountain areas of Ningxia, the probability of o<(urrenc;e is 53% in 17% in May and Marc;h,and only 10% in June, and it is found that there is a slight trend of postponing with fluc;tuaof the first soaking rainfalls in this region in recent 30 years. The effect of forecast using the optimal subset regression method for the date of the first soaking rainfall is good in  Ningxia