Analysis on characteristics and trend of PM10 pollution in Urumqi City based on ARIMA model

Abstract

Abstract: Objective To fit the optimal prediction model and explore the temporal distribution characteristics and trend changes of inhalable particles（PM₁₀） pollution in Urumqi City,providing reference for promoting atmospheric governance. Methods Based on the monitoring data of PM₁₀ in Urumqi City from 2016 to 2022,the database of PM₁₀ monthly average concentration was constructed, and the autoregressive integrated moving average model（ARIMA） was used to fit the prediction model of PM₁₀ and analyze its distribution characteristics,and forecast the trend change of PM₁₀ concentration from 2023 to 2024. Results There was a statistically significant difference in the monthly average concentration of PM₁₀ in Urumqi City from 2016 to 2022（P<0.01）,and the optimal model was ARIMA（0.0,1）（1,1,0）₁₂. The monthly average concentration values of PM₁₀ in Urumqi City had been decreasing year by year,reaching their maximum values in January,February and December. According to the prediction,the monthly average concentration of particulate matter PM₁₀ in Urumqi City from 2023 to 2024 was consistent with that from 2016 to 2022. Conclusion The optimal model is ARIMA（0,0,1）（1,1,0）₁₂. The monthly average concentration of PM₁₀ in Urumqi City shows a trend of increasing in autumn and winter,and decreasing year by year. This model can effectively predict and analyze the monthly average concentration of PM₁₀ in Urumqi City in the short term.

Key words: Atmospheric particulate matter, Time series analysis, Prediction

CLC Number:

R181.3⁺4

CHEN Peidi, ZHOU Mingzhang, XIAO Tingting, ZHENG ShuaiYin, LIU Xiaohang. Analysis on characteristics and trend of PM₁₀ pollution in Urumqi City based on ARIMA model. [J]OCCUPATION AND HEALTH, 2024, 40(15): 2086-2090.

References

[1] 林珊珊,何慧慧,贾瑞,等.大气污染与气象因素对郑州市不同人群和急慢性呼吸系统疾病的影响[J].职业与健康,2023,39(5):577-583,588.
[2] 巢晓琴,张荣,阿迪拉·苏力旦,等.2015—2021年新疆部分地区大气污染物与肺癌发病人数的时间序列关联分析[J].职业与健康,2023, 39(17):2310-2317.
[3] 江文斌,鲁柯柯,何加芬,等.2016—2020年南昌市大气PM_2.5短期暴露对人群循环系统疾病超额死亡的风险评估[J].环境卫生学杂志,2023,13(4):280-285.
[4] 褚一凡. 大气颗粒物和植被覆盖与心血管代谢性疾病的关联性研究[D].蚌埠:蚌埠医学院,2023.
[5] 陈雨萌,曹淦翔,冯浩,等.我国中老年人大气PM_2.5长期暴露与肝脏疾病发病关系的前瞻性队列研究[J].环境卫生学杂志,2023,13(3):162-169.
[6] 宋孝光,武昊,付俊杰,等.2016—2020年江西省南昌市大气污染物与儿童呼吸疾病门诊病例数的时间序列关联研究[J].现代预防医学,2022,49(14):2539-2545,2552.
[7] 李燃,夏依章,张艳,等.2020年成渝地区大气污染所致老年人群健康风险评估及预测分析[J].中国医药导报,2023,20(14):159-162.
[8] 孙凤霞,先兆娟,韦丽,等.南京市大气PM_2.5污染控制对循环系统疾病的健康收益评估[J].现代预防医学,2023,50(11):1970-1974.
[9] 张广来,张宁.健康中国战略背景下空气污染的心理健康效应[J].中国人口·资源与环境,2022,32(2):15-25.
[10] 张佳音,郭云飞,肖一一,等.陕西区域环境空气质量时空特征[J].中国沙漠,2023,43(3):36-46.
[11] 赵艳艳,张晓平,陈明星,等.中国城市空气质量的区域差异及归因分析[J].地理学报,2021,76(11):2814-2829.
[12] 安玉琴,丁婷婷,王苏玮,等.2015—2019年石家庄市市区空气污染物监测分析[J].职业与健康,2022,38(4):521-524.
[13] 张浩玲,林海,李俊毅,等.2015—2018年中山市大气污染物与急性心脑血管疾病发病的广义相加模型分析[J].职业与健康,2022, 38(18):2541-2545.
[14] 张宇晶,刘瑶,廖岩,等.集中空调通风系统送风PM₁₀卫生问题的文献分析[J].职业与健康,2023,39(14):1999-2002,2008.
[15] 刘励坤,刘婷婷,许瑞君,等.大气污染物短期暴露对自杀死亡风险的影响[J].环境卫生学杂志,2022,12(12):862-869.
[16] 谢惠芳,陈佩弟,孙高峰.2015—2019年乌鲁木齐市大气污染物(PM_2.5和PM₁₀)对呼吸系统疾病死亡的影响[J].职业与健康,2021,37(17):2406-2411.
[17] 朱翠云,何清,赵竹君,等.乌鲁木齐市区与南郊山区颗粒物污染特征对比分析[J].中国环境科学,2022,42(9):4073-4085.
[18] PANTELIS L,VASILIS P,THEODOR P,et al.CO₂ concentration forecasting in smart cities using a hybrid ARIMA-TFT model on multivariate time series IoT data[J].Scientific Reports,2023,13(1):17266-17266.
[19] 蒋建国,孙定勇,张艳秋,等.应用ARIMA模型对河南省肺结核流行趋势预测分析[J].现代疾病预防控制,2023,34(7):495-499,563.
[20] 陈佩弟,孙高峰,谢惠芳.乌鲁木齐市大气污染时空分布特征及不同年龄段人群健康风险评价研究[J].职业与健康,2021,37(18):2531-2537.
[21] 刘子龙,代斌,崔卓彦,等.大气污染物浓度变化特征及潜在源分析——以乌鲁木齐为例[J].干旱区研究,2021,38(2):562-569.
[22] 徐冰钰,亚森·麦麦提,闫琪.2015—2019年乌鲁木齐市气象因素对大气污染物的影响及污染物变化趋势分析[J].职业与健康,2022, 38(20):2828-2833.
[23] 黄琦,杨凌霄,李岩,等.济南城区大气PM_2.5、PM_1.0的污染特征及大气传输[J].山东大学学报(工学版),2020,50(1):95-100,108.
[24] 李丽光,李晓岚,赵梓淇,等.沈阳近地层大气颗粒物浓度垂直变化特征及与气象因子的关系[J].气象与环境学报,2021,37(1):16-25.
[25] 彭旭,谌冉冉,彭欣城,等.居民对“煤改气”政策的满意度及其影响因素[J].现代商贸工业,2019,40(23):145-146.
[26] 环境保护部,国家质量监督检验检疫总局.环境空气质量标准:GB3095—2012[S/OL].(2018-07-10)[2023-10-24].http://www.doc88.com/p-2671792528398.html.

Analysis on characteristics and trend of PM₁₀ pollution in Urumqi City based on ARIMA model

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