Spatial-Temporal Distribution Characteristics of PM<sub>2.5</sub> and PM<sub>10</sub> in the Northeast Tibetan Plateau and the Influence of Meteorological Factors

LIANG Mingwu; LI Huiting; LI Weilong; JIA Xiaodan; WANG Mingyuan; FENG Zhaohui

doi:10.12401/j.nwg.2023184

2024 Vol. 57, No. 4

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Article navigation > Northwestern Geology > 2024 > 57(4): 285-296

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LIANG Mingwu, LI Huiting, LI Weilong, JIA Xiaodan, WANG Mingyuan, FENG Zhaohui. 2024. Spatial-Temporal Distribution Characteristics of PM2.5 and PM10 in the Northeast Tibetan Plateau and the Influence of Meteorological Factors. Northwestern Geology, 57(4): 285-296. doi: 10.12401/j.nwg.2023184

Citation:

LIANG Mingwu, LI Huiting, LI Weilong, JIA Xiaodan, WANG Mingyuan, FENG Zhaohui. 2024. Spatial-Temporal Distribution Characteristics of PM_2.5 and PM₁₀ in the Northeast Tibetan Plateau and the Influence of Meteorological Factors. Northwestern Geology, 57(4): 285-296. doi: 10.12401/j.nwg.2023184

Spatial-Temporal Distribution Characteristics of PM_2.5 and PM₁₀ in the Northeast Tibetan Plateau and the Influence of Meteorological Factors

1.
China Chengtong Ecological Co., Ltd., Beijing 100070
2.
China Yangtze Power Co., Ltd., Wuhan 430071, Hubei, China
3.
Xi’an Center of China Geological Survey/Northwest China Center for Geoscience Innovation, Xi’an 710119, Shaanxi, China
4.
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

More Information

Corresponding author: JIA Xiaodan, jxiaodan@mail.cgs.gov.cn

Received Date: 25 July 2023

Revised Date: 27 September 2023

Accepted Date: 07 October 2023

Publish Date: 20 August 2024

Abstract

Abstract

With the rapid development of industrialization, the problem of air pollution can not be ignored. As an important ecological security strategic area in China, the Northeast Tibetan Plateau had prominent particulate pollution caused by soil desertification. Based on the monthly concentrations of inhalable particulate matter (PM₁₀) and fine particulate matter (PM_2.5) in 8 cities (prefectures) from 2015 to 2019 and the meteorological data, this study analyzed the spatial-temporal distribution characteristics of PM_2.5 and PM₁₀, the relationships between them and meteorological factors (precipitation, temperature and wind speed) and the degree of influence of meteorological factors. The results showed that: ①Xining city, Haidong city and Huangnan prefecture, which were densely populated and economically developed in the east of Qinghai province, had higher PM_2.5 and PM₁₀ concentrations, with an average level of 44.2 μg/m³ (99.1 μg/m³)、44.7 μg/m³ (99.7 μg/m³) and 36.5 μg/m³ (72.2 μg/m³) respectively. In terms of time distribution, the concentration of particulate matter in each region showed a downward trend year by year. The spatial distribution showed that PM_2.5 gradually increased from the west to the east, and PM₁₀ was high in the east and low in the west. ② The peak values of temperature and precipitation appeared in summer, showing a "Λ" distribution law. While the monthly concentration changes of PM_2.5 and PM₁₀ in various regions showed a "V" distribution law. The particle concentration in non-heating season was the lowest and that in heating season was the highest. ③ Among the effects of various meteorological factors, the concentrations of PM_2.5 and PM₁₀ were strongly or moderately negatively correlated with precipitation and temperature, and were negatively affected by temperature. The concentration of particulate matter was negatively correlated with wind speed. The concentration of PM_2.5 was negatively affected by wind speed, while the concentration of PM₁₀ was significantly positively affected by wind speed, indicating that wind-induced dust had a prominent contribution to air pollution, but the action mechanism between wind speed and pollutant concentration was complex. The results of this study could provide reference and theoretical basis for the improvement and prediction of air quality in typical regions.
- PM_2.5 /
- PM₁₀ /
- air pollution /
- meteorological factors /
- cause analysis /
- Northeast Tibetan Plateau

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References

[1]	蔡春霞, 贾晓丹, 鲍国臣, 等. 典型燃煤电厂大气污染物沉降对周边水源地的影响及贡献研究[J]. 西北地质, 2024, 57(1):64-72. Google Scholar CAI Chunxia, JIA Xiaodan, BAO Guochen, et al. Impact and contribution of atmospheric pollutant deposition from a typical power plant on surrounding water sources[J]. Northwestern Geology, 2024, 57(1):64–72. Google Scholar
[2]	蔡振媛, 覃雯, 高红梅, 等. 三江源国家公园兽类物种多样性及区系分析[J]. 兽类学报, 2019, 39(4): 410-420 Google Scholar CAI Zhenyuan, QIN Wen, GAO Hongmei, et al. Species diversity and fauna of mammals in Sanjiangyuan National Park[J]. Acta Theriologica Sinica, 2019, 39(4): 410-420. Google Scholar
[3]	郭立平, 乔林, 石茗化, 等. 河北廊坊市连续重污染天气的气象条件分析[J]. 干旱气象, 2015, 33(3): 497-504 Google Scholar GUO Liping, QIAO Lin, SHI Minghua, et al. Analysis about meteorological conditions of continuous heavy pollution episodes in Langfang of Hebei province[J]. Journal of Arid Meteorology, 2015, 33(3): 497-504. Google Scholar
[4]	韩福财. 西宁市空气质量指数AQI现状分析[J]. 青海环境, 2017, 27(1): 36-38 Google Scholar HAN Fucai. Analysis on current situation of air quality index AQI in Xining. Journal of Qinghai Environment[J], 2017, 27(1): 36-38. Google Scholar
[5]	韩婧, 代志光, 李文韬. 西安市灰霾天气下PM_2.5浓度与气象条件分析[J]. 环境污染与防治, 2014, 36(2): 52-56 Google Scholar HAN Jing, DAI Zhiguang, LI Wentao. Analysis of PM_2.5 concentration and meteorological conditions in haze weather of Xi’an city[J]. Environmental Pollution & Control, 2014, 36(2): 52-56. Google Scholar
[6]	贺博文, 聂赛赛, 王帅, 等. 承德市PM_2.5中碳质组分的季节分布特征及来源解析[J]. 环境科学, 2021, 42(11): 5152-5161 Google Scholar HE Bowen, NIE Saisai, WANG Shuai, et al. Seasonal Variation and Source Apportionment of Carbonaceous Species in PM_2.5 in Chengde[J]. Environmental Science, 2021, 42(11): 5152-5161. Google Scholar
[7]	李江苏, 段良荣, 张天娇. 中国城市PM_2.5和PM₁₀时空分布特征和影响因素分析[J/OL]. 环境科学, 2023, 1-18 Google Scholar LI Jiangsu, DUAN Liangrong, ZHANG Tianjiao. Analysis of Spatio-temporal Distribution Characteristics and Influencing Factors of PM_2.5 and PM₁₀ in Chinese Cities[J/OL]. Environmental Science, 2023, 1-18. doi:10.13227/j.hjkx.202305037 Google Scholar
[8]	李同昇, 陈谢扬, 芮旸, 等. 西北地区生态保护与高质量发展协同推进的策略和路径[J]. 经济地理, 2021, 41(10): 154-164 Google Scholar LI Tongsheng, CHEN Xieyang, RUI Yang, et al. Strategy and path of cooperative promotion of ecological protection and high-quality development in northwest China. Economic Geography[J], 2021, 41(10): 154-164. Google Scholar
[9]	李轶冰. 江河源区生态环境演变与时空格局[D]. 咸阳: 西北农林科技大学, 2007 Google Scholar LI Yibing. Environment evolvement and spatio-temporal pattern in the source regions of Yangtze, Yellow and Lancang River[D]. Xianyang: Northwest A&F University, 2007. Google Scholar
[10]	刘娜, 余晔, 张莉燕, 等. 2016—2018年西宁市颗粒物来源及输送差异分析[J]. 环境科学学报, 2021, 41(10): 4212-4227 Google Scholar LIU Na, YU Ye, ZHANG Liyan, et al. Difference analysis of source and transportation in particulate matter in Xining during 2016-2018[J]. Acta Scientiae Circumstantiae, 2021, 41(10): 4212-4227. Google Scholar
[11]	刘彦春, 靳聪丽, 王雨薇, 等. 校园3种植被类型对春季空气PM_2.5含量的影响[J]. 河南科技大学学报(自然科学版), 2020, 41(3): 88-92 Google Scholar LIU Yanchun, JIN Congli, WANG Yuwei, et al. Effects of campus 3 vegetation types on air PM_2.5 content in spring[J]. Journal of Henan University of Science and Technology (Natural Science), 2020, 41(3): 88-92. Google Scholar
[12]	刘妍妍. 湖南省2021年春节期间PM_2.5重污染特征及来源分析[J]. 江西科学, 2021, 39(05): 893-900 Google Scholar LIU Yanyan. Analysis on the characteristics and source of heavy pollution process of PM_2.5 during the Spring Festival in 2021 in Hunan province[J]. Jiangxi science, 2021, 39(05): 893-900. Google Scholar
[13]	刘震. 黄河流域9市空气质量与社会经济及人群健康效应研究[D]. 兰州: 兰州大学, 2021 Google Scholar LIU Zhen. Study on the air quality, social economic development and population health effect in 9 cities of the Yellow River Basin[D]. Lanzhou: Lanzhou University, 2021. Google Scholar
[14]	毛旭, 张涛. 2018年4月大气环流和天气分析[J]. 气象, 2018, 44(07): 977-984 Google Scholar MAO Xu, ZHANG Tao. Analysis of the April 2018 atmosphere circulation and weather[J]. Meteorological Monthly, 2018, 44(07): 977-984. Google Scholar
[15]	聂赛赛, 王帅, 崔建升, 等. 石家庄市大气污染物的季节性时空特征及潜在源区[J]. 环境科学, 2021, 42(11): 5131-5142 Google Scholar NIE Saisai, WANG Shuai, CUI Jiansheng, et al. Spatial-Temporal Characteristics and Potential Source Areas of Seasonal Atmospheric Pollution in Shijiazhuang[J]. Environmental Science, 2021, 42(11): 5131-5142. Google Scholar
[16]	彭丽思, 孙涵, 聂飞飞. 中国大气污染时空格局演变及影响因素研究[J]. 环境经济研究, 2017, (1): 48-56. Google Scholar
[17]	裘阅, 汪家权, 胡淑恒. 2015-2016年安徽省PM_2.5和PM₁₀时空分布特征[J]. 合肥工业大学学报(自然科学版), 2020, 43(1): 113-118 Google Scholar QIU Yue, WANG Jiaquan, HU Shuheng. Spatial-temporal distribution of PM_2.5 and PM_10-2.5 in Anhui Province, 2015-2016[J]. Journal of Hefei University of Technology (Natural Science), 2020, 43(1): 113-118. Google Scholar
[18]	石春娥, 邓学良, 吴必文, 等. 降水和风对大气PM_2.5、PM₁₀的清除作用分析[J]. 环境科学学报, 2018, 38(12), 4620-4629 Google Scholar SHI Chune, DENG Xueliang, WU Biwen, et al. The scavenging effect of precipitation and wind on PM_2.5 and PM₁₀[J]. Acta Scientiae Circumstantiae, 2018, 38(12), 4620-4629. Google Scholar
[19]	孙蓉花, 陈学刚, 魏疆, 等. 乌鲁木齐市PM_2.5浓度与气象条件耦合分析[J]. 环境污染与防治, 2017, 39(12): 1353-1357 Google Scholar SUN Ronghua, CHEN Xuegang, WEI Jiang, et al. Coupling analysis of PM_2.5 concentrations and meteorological conditions in Urumqi[J]. Environmental Pollution & Control, 2017, 39(12): 1353-1357. Google Scholar
[20]	唐家翔, 符传博, 杨仁勇, 等. 海口市PM_2.5演变特征及其与气象因素的关系[J]. 环境污染与防治, 2018, 40(4): 445-449 Google Scholar TANG Jiaxiang, FU Chuanbo, YANG Renyong, et al. Evolution of PM_2.5 in Haikou and its relationship with weather factors[J]. Environmental Pollution & Control, 2018, 40(4): 445-449. Google Scholar
[21]	王冰玉, 蔡颖, 郑凯, 等. PM_2.5对HBE细胞致癌致突变相关基因表达的影响[J]. 癌变·畸变·突变, 2020, 32(1): 33-38 Google Scholar WANG Bingyu, CAI Ying, ZHENG Kai, et al. Effect of PM_2.5 on expression of genes related to carcinogenesis and mutagenesis in HBE cells[J]. Carcinogenesis, Teratogenesis & Mutagenesis, 2020, 32(1): 33-38. Google Scholar
[22]	王涛, 邵田田. 2017年上海市PM_2.5和PM₁₀变化特征及来源分析[J]. 河南科学, 2018, 36(11): 1752-1758 Google Scholar WANG Tao, SHAO Tiantian. Characteristics and sources of PM_2.5 and PM₁₀ in Shanghai in 2017[J]. Henan science, 2018, 36(11): 1752-1758. Google Scholar
[23]	王遵娅, 柳艳菊, 丁婷, 等. 2018年春季气候异常及可能成因分析[J]. 气象, 2018, 44(10): 1360-1369 Google Scholar WANG Zunya, LIU Yanju, DING Ting, et al. Features and possible causes for climate anomalies in spring 2018[J]. Meteorological Monthly, 2018, 44(10): 1360-1369. Google Scholar
[24]	魏玉香, 童尧青, 银燕, 等. 南京SO₂, NO₂和PM₁₀变化特征及其与气象条件的关系[J]. 大气科学学报, 2009, 32(3): 451-457 Google Scholar WEI Yuxiang, TONG Yaoqing, YIN Yan, et al. The variety of main air pollutants concentration and its relationship with meteorological condition in Nanjing city[J]. Transactions of Atmospheric Science, 2009, 32(3): 451-457. Google Scholar
[25]	魏旖梦. 西北五省(区)大气污染物特征及其影响因素研究[D]. 乌鲁木齐: 新疆大学, 2021 Google Scholar WEI Yimeng. Study on the characteristics of air pollutants and its influencing factors in northwest of China[D]. Urumqi: Xinjiang University, 2021. Google Scholar
[26]	许文轩, 田永中, 肖悦, 等. 华北地区空气质量空间分布特征及成因研究[J]. 环境科学学报, 2017, 37(8), 3085-3096 Google Scholar XU Wenxuan, TIAN Yongzhong, XIAO Yue, et al. Study on the spatial distribution characteristics and the drivers of AQI in North China[J]. Acta Scientiae Circumstantiae, 2017, 37(8), 3085-3096. Google Scholar
[27]	薛澜, 翁凌飞. 中国实现联合国2030年可持续发展目标的政策机遇和挑战[J]. 中国软科学, 2017, (1): 1-12 Google Scholar XUE Lan, WENG Lingfei. The policy opportunities and challenges in China's implementation of 2030 sustainable development goals[J]. China Soft Science, 2017, (1): 1-12. Google Scholar
[28]	杨洪斌, 李元宜, 邹旭东等. 辽宁空气中度污染和重污染天气类型分析[J]. 气象与环境学报, 2009, 25(6): 15-17. Google Scholar
[29]	杨淼. 以幸福的名义建设生态西宁[J]. 青海科技, 2014, (04): 12-14. Google Scholar
[30]	杨稳强. 西北地区大气污染特征及其影响因素分析[D]. 兰州: 西北师范大学, 2023 Google Scholar YANG Wenqiang. Characteristics of air pollution in Northwest China and its influencing factors[D]. Lanzhou: Northwest Normal University, 2023. Google Scholar
[31]	杨文涛, 谯鹏, 刘贤赵, 等. 2011~2017年中国PM_2.5多尺度时空分异特征分析[J]. 环境科学, 2020, 41(12): 5236-5244 Google Scholar YANG Wentao, QIAO Peng, LIU Xianzhao, et al. Analysis of Multi-scale Spatio-temporal Differentiation Characteristics of PM_2.5 in China from 2011 to 2017[J]. Environmental Science, 2020, 41(12): 5236-5244. Google Scholar
[32]	姚振, 田兴元, 姬丙艳, 等. 西宁—乐都地区土地质量地球化学评估[J]. 西北地质, 2012, 45(1): 317-323. Google Scholar
[33]	中国气象局. 2019年全国生态气象公报[EB/OL]. 2020, [2020-06-30]. Google Scholar
[34]	张红. 典型沿江城市空气污染物特征及与气象条件的耦合关系研究[D]. 合肥: 中国科学技术大学, 2018 Google Scholar ZHANG Hong. Study on Relationship & Characteristics of Atmospheric Pollution and Meteorological Conditions of Cities along the Yangtz River[D]. Hefei: University of Science and Technology of China, 2018. Google Scholar
[35]	张天先, 陈长彬, 姚洪发. 天津市大气环境中PM_2.5与PM₁₀时空分布特征研究[J]. 洛阳理工学院学报(自然科学版), 2020, 30(04): 8-13. Google Scholar
[36]	章异平, 徐军亮, 赵西平, 等. 基于灰色关联的洛阳市空气质量影响因素分析[J]. 河南科技大学学报(自然科学版), 2012, 33(1): 100-104. Google Scholar
[37]	周安琪, 刘建伟, 周旭, 等. 北京大气PM_2.5载带金属浓度、来源及健康风险的城郊差异[J]. 环境科学, 2021, 42(06): 2595-2603 Google Scholar ZHOU Anqi, LIU Jianwei, ZHOU Xu, et al. Concentrations, sources, and health risks of PM_2.5 carrier metals in the Beijing urban area and suburbs[J]. Environmental Science, 2021, 42(06): 2595-2603. Google Scholar
[38]	邹佳乐, 林尧林, 杨薇. 中国近年PM_2.5污染研究进展[J]. 环境污染与防治, 2019, 41(3): 357-361 Google Scholar ZOU Jiale, LIN Yaolin, YANG Wei. Advances on PM_2.5 pollution research in China in recent years[J]. Environmental Pollution & Control, 2019, 41(3): 357-361. Google Scholar
[39]	Cao Q, Rui G, Liang Y. Study on PM_2.5 pollution and the mortality due to lung cancer in China based on geographic weighted regression model[J]. BMC Public Health. 2018, 18(1): 925. Google Scholar
[40]	Gaio V, Roquette R, Dias CM, et al. Ambient air pollution and lipid profile: Systematic review and meta-analysis. Environmental Pollution, 2019, 254(Pt B): 113036. Google Scholar
[41]	Han X, Xiao J, Wang L, et al. Identification of areas vulnerable to soil erosion and risk assessment of phosphorus transport in a typical watershed in the Loess Plateau[J]. Science of The Total Environment, 2020, 758. Google Scholar
[42]	Hsu CY, Chiang HC, Lin SL, et al. Elemental characterization and source apportionment of PM₁₀ and PM_2.5 in the western coastal area of central Taiwan[J]. Science of the Total Environment, 2016, 541: 1139-1150. doi: 10.1016/j.scitotenv.2015.09.122 CrossRef Google Scholar
[43]	Hwang SH, Park WM. Radon and PM₁₀ concentrations in underground parking lots and subway stations with health risks in South Korea[J]. Environmental Science and Pollution Research, 2018, 25(35): 35242-35248. doi: 10.1007/s11356-018-3428-6 CrossRef Google Scholar
[44]	Li R, Zhou R, Zhang J. Function of PM_2.5 in the pathogenesis of lung cancer and chronic airway inflammatory diseases[J]. Oncology Letters, 2018, 15(5): 7506-7514. Google Scholar
[45]	Menard, S, Standards for standardized logistic regression coefficients[J]. The Journal of Social Forces, 2011, 89 (4), 1409-1428. Google Scholar
[46]	Pham, TG, Degener, J, Kappas, M. Integrated universal soil loss equation (USLE) and geographical information system (GIS) for soil erosion estimation in a Sap basin: central Vietnam[J]. International Soil and Water Conservation Research, 2018, 6: 99-110. doi: 10.1016/j.iswcr.2018.01.001 CrossRef Google Scholar
[47]	Qiu X, Duan L, Gao J, et al. Chemical composition and source apportionment of PM₁₀ and PM_2.5 in different functional areas of Lanzhou, China[J]. Science of the Total Environment, 2016, 40: 75-83. Google Scholar
[48]	Raaschou-Nielsen O, Beelen R, Wang M, et al. Particulate matter air pollution components and risk for lung cancer[J]. Environment International, 2016, 87: 66-73. doi: 10.1016/j.envint.2015.11.007 CrossRef Google Scholar
[49]	Shen F, Ge X, Hu J, et al. Air pollution characteristics and health risks in Henan Province, China[J]. . Environmental Research, 2017, 156: 625-634. doi: 10.1016/j.envres.2017.04.026 CrossRef Google Scholar
[50]	Wu F, Wang W, Man YB, et al. Levels of PM_2.5/ PM₁₀ and associated metal(loid)s in rural households of Henan Province, China[J]. Science of the Total Environment, 2015, 512-513: 194-200. doi: 10.1016/j.scitotenv.2015.01.041 CrossRef Google Scholar