SOME PROBLEMS AND TREATMENT IN THE REGRESSION ANALYSIS OF METEOROLOGICAL DATA

In this paper,some problems of regression analysis in the meteorological application are discussed and main reasons for statistical inference failures are analysed.We may find the failure problems with diagnos- tic method and solve them by different treatment.It has been proved that the treatment make the accuracy and stability of forecasting improved greatly.

PROJECTION PURSUIT PRINCIPAL COMPONENT ANALYSIS AND ITS APPLICATION TO METEOROLOGY

Projection Pursuit (PP) Principal Component Analysis (PCA) method is herein introduced and applied to the field of meteorology for the first time. Some problems relevant to meteorological application are dis- cussed in detail and comparisons with EOF method are made with the emphasis on robustness.

Investigation of PM_(2.5) pollution during COVID-19 pandemic in Guangzhou,China

The COVID-19 pandemic has raised awareness about various environmental issues,includ-ing PM_(2.5) pollution.Here,PM_(2.5) pollution during the COVID-19 lockdown was traced and an-alyzed to clarify the sources and factors influencing PM_(2.5) in Guangzhou,with an emphasis on heavy pollution.The lockdown led to large reductions in industrial and traffic emissions,which significantly reduced PM_(2.5) concentrations in Guangzhou.Interestingly,the trend of PM_(2.5) concentrations was not consistent with traffic and industrial emissions,as minimum concentrations were observed in the fourth period(3/01-3/31,22.45 μg/m^(3))of the lockdown.However,the concentrations of other gaseous pollutants,e.g.,SO_(2),NO_(2) and CO,were corre-lated with industrial and traffic emissions,and the lowest values were noticed in the sec-ond period(1/24-2/0_(3))of the lockdown.Meteorological correlation analysis revealed that the decreased PM_(2.5) concentrations during COVID-19 can be mainly attributed to decreased in-dustrial and traffic emissions rather than meteorological conditions.When meteorological factors were included in the PM_(2.5) composition and backward trajectory analyses,we found that long-distance transportation and secondary pollution offset the reduction of primary emissions in the second and third stages of the pandemic.Notably,industrial PM_(2.5) emis-sions from western,southern and southeastern Guangzhou play an important role in the formation of heavy pollution events.Our results not only verify the importance of control-ling traffic and industrial emissions,but also provide targets for further improvements in PM_(2.5) pollution.

Chemical characteristics and source apportionment of PM_(10) during a brown haze episode in Harbin, China

This study investigates the correlation between PM10 and meteorological factors such as wind speed, atmospheric visibility, dew point, relative humidity, and ambient temperature during a brown haze episode. In order to identify the potential sources of PMlo during brown haze episode, respirable par- ticulate matter （PM10） was collected during both non-haze days and haze days and further analyzed for metallic elements, ionic species, and carbonaceous contents. Among them, ionic species contributed 45-64% to PM10, while metallic elements contributed 7-21% to PM10 which was smaller than the other chemical constituents. The average OC/EC ratio （42） in haze days was about three times of the average OC/EC ratio （14） in non-haze days. By using chemical mass balance （CMB） receptor model, the major sources were apportioned, including traffics, incinerators, coal combustion, steel industry, petrochemical industry, and secondary aerosols, etc. The contribution to PM10 concentration of each source was calcu- lated for all the samples collected. The results showed that coal combustion was the major source of PM10 in non-haze days and secondary aerosols were the major source in haze days, followed by petrochemical industry, incinerators, and traffics, while other sources had negligible effect.