Study of atmospheric visibility and its influence factors in six typical cities in China
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摘要:
采用线性相关性分析、分类变量分析等统计学方法，分析了巴彦淖尔、石家庄、廊坊、郑州、武汉、广州六市2019—2020年的PM2.5与气象观测数据，研究了PM2.5浓度与大气相对湿度对大气能见度的影响。结果表明：六市大气能见度年变化规律虽存在较大差异，但最低值均出现于每年12月—次年2月，且年变化规律基本一致；气象条件、相对湿度、PM2.5浓度对能见度的影响明显，其中相对湿度通过改变PM2.5物化性质间接影响能见度，与能见度相关程度相对较弱。PM2.5浓度与能见度的线性相关性良好，以幂函数为主；整体上，相对湿度与PM2.5浓度对大气能见度影响呈协同作用，相对湿度越大的城市对PM2.5的控制要求越高。此外，PM2.5浓度高于平台点时，大气能见度基本不随PM2.5浓度增加而继续降低，只有PM2.5浓度低于突变点时，大气能见度才会随PM2.5浓度降低而显著提升，各市能见度突变点与平台期点所对应PM2.5浓度差异较大。
Abstract:The statistical methods such as linear correlation analysis and categorical variable analysis were employed to analyze the data of particulate matter and meteorological observations in six cities (Bayannaoer, Shijiazhuang, Langfang, Zhengzhou, Wuhan, Guangzhou) from 2019 to 2020. The influences of particulate matter concentration and atmospheric relative humidity on atmospheric visibility were investigated. The results indicated that there were significant differences in the annual variation of atmospheric visibility among the six cities, but the lowest values consistently occurred from December to February of the following year, and the annual variation pattern was generally consistent. The influences of meteorological conditions, relative humidity, and particulate matter concentration on visibility were apparent, with relative humidity indirectly affecting visibility through changes in the physicochemical properties of particulate matter, but with a relatively weak correlation. There was a good linear correlation between PM2.5 concentration and visibility, mainly following a power function. Overall, relative humidity and particulate matter had a synergistic effect on visibility, and cities with higher relative humidity required stricter control of particulate matter. Additionally, the study found that when PM2.5 concentration was higher than the threshold value, visibility did not continue to decrease with increasing particulate matter, but significantly improved only when PM2.5 concentration was below the threshold value. There was a significant difference in PM2.5 concentration between the turning point and the platform period point associated with visibility in each city.
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图 1 六市国控大气城市站点与气象站点分布
Figure 1. Distribution map of state-controlled air monitoring points and weather stations in the six cities
图 2 2019—2020年六市PM2.5浓度、PM10浓度、RH与VIS月均值变化趋势
Figure 2. Variation trends of PM2.5, PM10, RH and VIS in the six cities from 2019 to 2020
图 3 2019—2020年六市PM2.5浓度、RH与VIS的月均环比变化率
Figure 3. Monthly average month on month change rate of PM2.5, RH and VIS in the six cities from 2019 to 2020
图 4 六市在不同RH区间平均VIS趋势
Figure 4. Average visibility trends of the six cities in different RH ranges
图 5 六市VIS随PM2.5浓度及RH变化规律
Figure 5. Variation trends of VIS with PM2.5 and RH in the six cities
图 6 六市不同PM2.5浓度区间对应平均VIS变化趋势
Figure 6. Average VIS tendency chart of different CPM2.5 ranges in the six cities
图 7 六市在不同PM2.5浓度区间轻微霾与轻度霾出现频率
Figure 7. Occurrence frequencies of light haze in different PM2.5 ranges in the six cities
表 1 突变点及平台期所对应PM2.5浓度
Table 1. Concentration of PM2.5 corresponding to inflection and plateau points
RH/% 巴彦淖尔/（μg/m3） 石家庄/（μg/m3） 武汉/（μg/m3） 郑州/（μg/m3） 廊坊/（μg/m3） 突变点 平台期 突变点 平台期 突变点 平台期 突变点 平台期 突变点 平台期 ≤40 63 199 68 187 60 191 60 180 40~50 63 195 69 186 66 185 61 179 50~60 59 190 71 180 71 182 65 173 62 171 60~70 60 178 70 171 66 166 53 144 57 160 70~80 56 162 63 152 63 141 46 123 52 140 80~90 56 160 52 125 51 119 37 104 47 119 90~95 44 187 42 95 22 68 47 103 ＞95 29 81 
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