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成都地区空气湿度演变规律及其对空气质量的影响研究
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Abstract:
为研究成都地区空气湿度的演变特征及对空气质量的影响情况，本文利用2014年成都地区温江站湿度、温度和PM10、PM2.5质量浓度的地面观测资料，分析研究了成都地区空气湿度和颗粒物浓度的不同时间尺度下的演变规律，并讨论了湿度对颗粒物浓度的影响作用，结果表明：成都地区总体上空气湿度较大，相对湿度主要集中于90%~100%，其标准差为16.3%；绝对湿度主要集中于5~10 g/m3，其标准差为5.07 g/m3，两类湿度参数在不同时间尺度的演变特征存在一定差异；利用冬季典型高、低PM2.5事件，分别对两类湿度进行分析，两类湿度对空气质量的影响并不一致，在雨季发生降水时，相对、绝对湿度越大，这时颗粒物浓度越小；而在降水偏少的冬季，相对、绝对湿度越大，越有利于颗粒物的吸湿增长，对空气质量反而不利。利用近年来更高时空分辨率的数值天气预报，结合本地不同季节空气湿度的特征，可作为空气污染潜势预报的参考依据。
In order to study the characteristics of air relative humidity, absolute humidity and its effects on air quality in Chengdu, based on the ground observation data of relative humidity and temperature of Wenjiang station in Chengdu in 2014 and the mass concentration of PM10 and PM2.5 two kinds of particulate matter, the characteristics of air humidity and particulate matter concentration in Chengdu under different time scales were analyzed and studied, the effect of humidity on the concentration of particulate matter was discussed, and the results show that: The air humidity in Chengdu is relatively high, the relative humidity is mainly concentrated in 90% - 100%, and its standard deviation is 16.3%, with a large range of change, while the absolute humidity is mainly concentrated in 5 - 10 g/m3, the standard deviation is 5.07 g/m3, there are some differences in the evolution characteristics of the two kinds of humidity parameters under different time scales. By using the typical high and low PM2.5 events in winter, the effect of the two kinds of humidity on air quality is not consistent. When precipitation occurs during the rainy season, the greater the relative and absolute humidity is, the smaller the particle concentration will be; in the winter with less precipitation, the greater the relative and absolute humidity is, the more favorable the moisture absorption growth of particulate matter is, and the worse the air quality is. The numerical weather forecast with higher spatial and temporal resolution in recent years, combined with the characteristics of local air humidity in different seasons, can be used as a reference for the forecast of air pollution potential.
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