云南边境五城市空气污染物分布特征

利用2015~2021年云南省5个边境城市6种常规大气污染物的质量浓度数据,探究其污染特征、时空变化及空间异质性。结果表明,研究区域污染物年际浓度变化起伏较大,PM₁₀、PM_2.5年均浓度分别为(42.6±8.2),(25.4±4.2)µg/m³,均低于中国《环境空气质量标准》(GB 3095-2012)二级浓度限值。PM、NO₂和O_3-8h月均浓度呈U型变化趋势,其中3月份浓度最高。5个城市PM和NO₂浓度季节变化均表现为:春季>冬季>秋季>夏季,O_3-8h表现为:春季>夏季>秋季>冬季,而CO冬季污染程度最小,SO₂无明显的季节变化规律。根据Sen-MK的逐日浓度趋势分析,污染物总体呈现下降趋势,其中PM₁₀下降速率最高达11×10^-3µg/(m³×d),而O_3-8h呈现上升趋势。变异系数(COD)表明,污染物的空间分布极不均匀,特别是SO₂的COD均大于0.2,春季O_3-8h空间分布更加均匀。Person相关分析表明,PM与NO₂、CO、O_3-8h表现出较强的相关性,且西双版纳(BN)PM与其他大气污染物相关性较其他城市强。

Spatiotemporal characteristics of ambient air pollutants in five border cities of Yunnan province: variations

The pollution characteristics, spatiotemporal variation and spatial heterogeneity was explored based on ambient particulate matter (PM with diameter £2.5µm PM_2.5], £10µm PM₁₀]) and gaseous pollutants (Ozone O₃], sulfur dioxide SO₂], nitrogen dioxide NO₂], carbon monoxide CO]) data collected from 2015 to 2021 in five border cities of Yunnan province. Annual average concentrations of ambient air pollutants fluctuate enormously and that of PM₁₀ (42.6±8.2)µg/m³ and PM_2.5 (25.4±4.2)µg/m³ exceed the Grade Ⅱ limit value recommended the Chinese Ambient Air Quality Standards (GB 3095-2012). The monthly average concentrations showed a U-shaped trend for PM, NO₂ and O_3-8h, and their concentrations peaked in March. PM and NO₂ concentrations followed a comparable seasonal pattern: spring>winter>autumn>summer. By contrast, seasonal average concentration of O_3-8h decreased as: spring>summer>autumn>winter and that of CO was the lowest in winter. In addition, SO₂ showed no obvious seasonal variation. Sen-MK results indicated a general trend of descending in daily average concentrations of ambient air pollutants and PM₁₀ had the highest decline rate at 11×10^-3µg/m³ per day, while an inverse trend of O_3-8h. The coefficient of variation (COD) revealed that the spatial distribution of ambient air pollutants is extremely uneven, especially for SO₂ with COD>0.2 but that of O_3-8h was more uniform in spring. Person correlation analysis supported a strong correlation between PM and NO₂, CO as well as O_3-8h, and correlations between PM and other pollutants are stronger in Xishuangbanna (BN) than other studied cities.