阿克达拉站PM10和PM2.5质量浓度长期变化趋势及短期升高事件研究

doi:10.12118/j.issn.1000-6060.2025.045

Abstract

Abstract:

Based on continuous observations of PM₁₀ and PM_2.5 at the Akedala regional atmospheric background station from 2011 to 2023, we employed statistical and meteorological correlation analyses, the HYSPLIT-4 model, the potential source contribution function (PSCF), and the concentration-weighted trajectory (CWT) method to investigate the variation trends and source characteristics of PM₁₀ and PM_2.5 mass concentrations. The results showed that: (1) From 2011 to 2023, the annual average mass concentration of PM₁₀ increased from 12.1 μg·m^-3 to 23.2 μg·m^-3, while PM_2.5 increased from 7.3 μg·m^-3to 10.8 μg·m^-3, with annual growth rates of 0.81 μg·m^-3·a^-1 and 0.31 μg·m^-3·a^-1, respectively, indicating that PM₁₀ increased faster than PM_2.5. (2) PM₁₀ and PM_2.5 exhibited clear seasonal variations, with higher values in spring and winter and lower values in summer and autumn. The PM_2.5/PM₁₀ ratio displayed a bimodal distribution (0.4-0.5 and 0.8-0.9), where lower ratios indicated natural contributions and higher ratios reflected anthropogenic sources. (3) Air mass trajectory analysis suggested that PM₁₀ primarily originated from eastern arid regions of Kazakhstan (PSCF values 0.4-0.7), whereas PM_2.5 mainly came from anthropogenic sources in northern Xinjiang (PSCF values 0.5-0.8). (4) In 2023, four high-concentration PM₁₀ events (peaks 681.1-822.6 μg·m^-3) and five high-concentration PM_2.5 events (peaks 294.2-551.4 μg·m^-3) were observed, with durations typically less than one hour, exhibiting “short-term high-intensity” characteristics. (5) Compared with Lin’an station, PM₁₀ and PM_2.5 at Akedala showed “low baseline, high variability, and short peaks”, with baseline values lower than Lin’an but higher peak concentrations. This study provides a scientific basis for understanding atmospheric background particulate matter in arid regions and analyzing transboundary transport patterns, supporting air quality assessment and regional collaborative governance in northwest China.

Key words: PM_2.5, PM₁₀, pollution characteristics, temporal variation, regional transport

LI Jialin, ZHONG Yuting, DONG Yinxi, CAI Haiyang, TAO Rui. Long-term variation trends and short-term elevation events of PM₁₀ and PM_2.5 mass concentrations at Akedala station[J].Arid Land Geography, 2026, 49(1): 47-55.

Figures/Tables 8

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References 16

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季节	气团主要来源路径	100 m高度24 h			1000 m高度48 h
季节	气团主要来源路径	轨迹类型	轨迹占比/%	总占比/%	轨迹类型	轨迹占比/%	总占比/%
冬季	北部路径	1	19.6	19.6	1 3 4	15.2 12.4 8.1	35.7
	西北路径	2 3 4	27.3 5.4 3.9	36.6	2	18.3	18.3
	东部路径	5 6	15.8 8.0	23.8	5 6	22.5 13.5	36.0
春季	东部路径	1	33.2	33.2	1 2 4	24.5 11.3 18.6	54.4
	西部路径	3 5	14.9 4.2	19.1	3 5	9.7 2.1	11.8
	西北路径	2 4	27.3 10.4	37.7	6	23.8	23.8
夏季	东部路径	1 4 5	33.2 8.4 10.2	51.8	1 2 4	28.4 16.9 7.5	52.8
	局地循环路径	2	5.1	5.1	3 5 6	5.1 4.3 7.8	17.2
	西北路径	3 6	27.3 15.8	43.1	4	30.0	30.0
秋季	东部路径	1 3 4	33.2 7.5 12.1	52.8	1 3	25.6 18.2	43.8
	西北路径	2 5	27.3 4.3	31.6	2 5	19.2 7.4	26.6
	北部路径	6	15.6	15.6	4 6	19.6 10.0	29.6

传输路径	轨迹占比/%	平均传输距离/km	平均高度/m	主要季节分布
东部路径	33.2	16.2	910.3	春季、秋季
西北路径	27.3	11.3	1105.8	四季
北部路径	19.6	18.3	973.4	冬季
西部路径	14.9	27.0	1083.1	春季
局地循环路径	5.1	43.8	1548.8	夏季

颗粒物类型	源区等级	区域名称	经度/°E	纬度/°N	PSCF值	CWT贡献浓度/μg·m^-3
PM₁₀	主要源区	巴尔喀什湖以东至阿克达拉站	75~88	45~48	0.4~0.7	60~80
PM₁₀	次要源区	哈萨克斯坦中部（阿斯塔纳市）	70~73	50~52	0.3~0.5	40~60
PM₁₀	次要源区	乌鲁木齐市	87~89	43~45	0.2~0.4	30~50
PM_2.5	主要源区	阿克达拉站东南部（乌鲁木齐市至昌吉回族自治州）	87~89	43~45	0.5~0.8	30~40
PM_2.5	次要源区	巴尔喀什湖区域	73~76	45~48	0.3~0.5	15~25

Long-term variation trends and short-term elevation events of PM₁₀ and PM_2.5 mass concentrations at Akedala station

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Long-term variation trends and short-term elevation events of PM10 and PM2.5 mass concentrations at Akedala station

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Long-term variation trends and short-term elevation events of PM₁₀ and PM_2.5 mass concentrations at Akedala station