Air quality and forecast in Changji City from 2018 to 2022

Abstract

Abstract: Objective To analyze the air quality in Changji City from 2018 to 2022,and provide important data support and decision-making basis for environmental protection,public health,urban planning and international cooperation. Methods The data of atmospheric pollutants in Changji City from 2018 to 2022 were collected,the atmospheric environment in the region was analyzed,and the seasonal differential auto-regressive integrated moving average model was used to predict the concentration trend of major atmospheric pollutants in 2023. Results PM₁₀ and PM_2.5 were the main air pollutants in Changji City from 2018 to 2022,and the pollution of other pollutants was not obvious. The six conventional pollutants had seasonality,with PM₁₀,PM_2.5,SO₂,NO₂,and CO concentrations reaching their highest in winter,while O₃ had the opposite trend,with the highest concentration in summer. The air quality index（AQI） had the highest value in 2022 and the lowest value in 2018,and predicted data showed that PM₁₀ and PM_2.5 concentrations would continue to decrease in 2023. Conclusions The air quality in Changji City is gradually improving. The predicted data shows that,compared to 2022,the concentrations of PM₁₀ and PM_2.5 in Changji City will decrease in 2023. However,the pollution situation is more pronounced in winter and continuous prevention and control measures need to be taken.

Key words: Atmospheric environmental pollution, Changji City, Air quality index

CLC Number:

R122.7

Mulati Ainiwaer, XU Tao, Xiaokaiti Yibulayin. Air quality and forecast in Changji City from 2018 to 2022. [J]OCCUPATION AND HEALTH, 2024, 40(10): 1370-1375.

References

[1] 张璟,邓豪,段雯华,等.大气污染物对呼吸系统疾病入院影响的病例交叉研究[J].中国热带医学,2020,20(6):519-522.
[2] BOOGAARD H,VAN ERP A M,WALKER K D,et al.Accountability studies on air pollution and health:The HEI experience[J].CurrEnvironHealthRep,2017,4(4):514-522.
[3] HYSTAD P,DEMERSP A,JOHNSON K C,et al.Long-term residential exposure to air pollution and lung cancer risk[J].Epidemiology,2013, 24(5):762-772.
[4] TIAN L,SUN S.Comparison of health impact of air pollution between china and other countries[J].Adv Exp MedBiol,2017,1017:215-232.
[5] LANRDIGAN P J.Air pollution and health[J].Lancet Public Health,2017,2(1):e4-e5.
[6] LANRDIGAN P J.The Lancet Commission on pollution and health[J].Lancet,2018,391(10119):462-512.
[7] 武文娟,何素艳.空气污染对居民健康影响的研究述评[J].武术研究,2021,6(3):150-153.
[8] 阿依古丽·托来干,叶尔纳尔·胡马尔汗,郭宇宏.2015—2020年昌吉市PM_2.5污染时空分布特征[J].新疆环境保护,2021,43(3):26-32.
[9] 中华人民共和国环境保护部.环境空气质量标准:GB 3095—2012[S].北京:中国环境科学出版社,2012:1-3.
[10] 李颖若,韩婷婷,汪君霞,等.Arima时间序列分析模型在臭氧浓度中长期预报中的应用[J].环境科学,2021,42(7):3118-3126.
[11] 牟敬锋,赵星,樊静洁,等.基于arima模型的深圳市空气质量指数时间序列预测研究[J].环境卫生学杂志,2017(2):102-107,117.
[12] 曾江毅,李志生,欧耀春,等.季节指数改进的PM_2.5质量浓度组合预测模型研究[J].广东工业大学学报,2022,39(3):89-94.
[13] 黎可馨. 基于季节性arima模型的中国货物周转量短期预测[J].现代信息科技,2022,6(3):141-144,148.
[14] 田文婷,宇世航.缺失数据下空气质量预测的arima乘法季节模型[J].齐齐哈尔大学学报(自然科学版),2022,38(4):83-89.
[15] 王靖思. 2010—2019年气象因素对沈阳市居民流感发病的影响及sarima时间序列模型分析[D].沈阳:中国医科大学,2021.
[16] BAS M,ORTIZ J,BALLESTEROS L,et al.Evaluation of amultiple line arregression model and sarima model in forecasting be air concentrations[J].Chemosphere,2017,177:326-333.
[17] 张亚博,谢劭峰,张继洪,等.基于miv-arima算法的PM_2.5浓度预测[C]//第十三届中国卫星导航年会,北京,2022:53-57.
[18] 姜军委,孟德强.昌吉州主要大气污染物时空变化特征[J].干旱环境监测,2020,34(3):117-122.
[19] 许晓敏,董开元.昌吉市大气污染成因及治理措施[J].干旱环境监测,2015,29(4):169-174.
[20] 李培,王新,柴发合,等.我国城市大气污染控制综合管理对策[J].环境与可持续发展,2011,36(5):8-14.
[21] 张翔. 我国大气污染防治现状与对策[J].科海故事博览,2022(30):49-51.
[22] 孙冠中,付正辉,包哲,等.基于能源消耗的乌鲁木齐工业污染排放核算及能源供应结构调整分析[J].电力科技与环保,2014(5):54-56.
[23] 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.
[24] 沈禾子,姜昊,李杰.京津冀地区污染物排放对空气质量的影响[J].合作经济与科技,2015(18):12-14.
[25] 昌敦虎,周继,李曼,等.与我国环境管理对接的大气污染源排放清单分析[J].中国环境监测,2022,38(5):18-26.