不同时间尺度气象要素与空气污染关系的KZ滤波研究

空气污染状况受气象要素和污染源排放的共同影响，为了评估大气污染控制措施的效果，需将由污染源排放的浓度数据分离出来.本文利用KZ滤波方法将天津市6个监测站点2015~2017年逐日的O₃、PM_2.5和PM₁₀浓度资料和6个同期气象数据分解为长期分量、短期分量和季节分量，计算各分量对原始时间序列方差的贡献.采用逐步回归法建立O₃及颗粒物3种分量与相应尺度气象要素的线性模型.结果表明，上述3种污染物浓度数据经分解后，季节分量对总方差贡献最大，其次为短期分量；气温和相对湿度是影响O₃季节和短期分量的主要气象因素，其中温度占主导地位，且呈现正相关，与相对湿度呈负相关；风速、气压、降水与颗粒物的短期及季节浓度变化呈负相关，相对湿度与之呈正相关，温度与短期分量呈正相关、与季节分量的变化呈负相关；经逐步回归消除气象影响的PM₁₀的长期分量有波动下降的趋势，PM_2.5浓度在2017年年初有所上升，其余部分有下降趋势，O₃长期分量浓度有所上升；这几年间颗粒物污染控制措施的效果较为显著，O₃污染有所加重.

Study on the relationship between meteorological elements and air pollution at different time scales based on KZ filtering

Air pollution condition is affected by meteorological elements and emissions from air pollution sources. In order to evaluate the effect of air pollution control measures, we need to separate the contributions from air pollution sources. In this study, the KZ filter was used to decompose the time series of three pollutants including O₃, PM_2.5 and PM₁₀ as well as time series of several meteorological factors into long-term, short-term and seasonal components. Data of air pollutants were collected from six air quality monitoring sites in Tianjin during 2015~2017. The contribution of each component to the total variance of the original air quality data was calculated. Stepwise regression was used to establish the relationship between air pollutants (O₃, PM_2.5 and PM₁₀) and meteorological variables for each time scale. Our results showed that seasonal component contributed most to the total variance, followed by short-term component. Temperature and relative humidity were the major factors affecting seasonal and short-term changes of O₃. Temperature was positively correlated with short-term component. Relative humidity was negatively correlated with the seasonal component of O₃; Wind speed, air pressure and precipitation were negatively correlated with particle concentrations at short-term and seasonal time scales. Relative humidity was positively correlated with them. Temperature was positively correlated with short-term component of particles, and was negatively correlated with seasonal component. Long-term concentration of PM₁₀ showed a downward trend after removing the effects of meteorological factors. The concentration of PM_2.5 increased in early 2017, and for the rest of the study time periods, its concentration showed a downward trend. The long-term concentration of O₃ was increased during the studied years. We can conclude that the effect of particulate pollution control measures was significant. However, the pollution of O₃ was aggravated.