中国药物与临床
中國藥物與臨床
중국약물여림상
CHINESE REMEDIES & CLINICS
2015年
5期
603-606
,共4页
高宇钊%房瑞玲%李少琼%申宁宁%邓亚敏%马彩云%刘桂芬
高宇釗%房瑞玲%李少瓊%申寧寧%鄧亞敏%馬綵雲%劉桂芬
고우쇠%방서령%리소경%신저저%산아민%마채운%류계분
空气污染物%预测%时间序列
空氣汙染物%預測%時間序列
공기오염물%예측%시간서렬
Air pollutants%Forecasting%Time series
目的:掌握和预警太原市大气中细颗粒物(PM2.5)日浓度的变化趋势,构建更为准确的预警模型,为改进环境质量,促进居民健康提供监测资料评价的方法学依据。方法收集2013年6月至2014年11月太原市逐日PM2.5网报数据,分析太原市18个月以来日报PM2.5的逐月逐日变动趋势,进一步揭示太原市大气中PM2.5日浓度现状;构建时间序列广义回归条件异方差模型[GARCH(1,1)],对太原市未来大气PM2.5浓度及等级进行短期预警。结果太原市大气中PM2.5日浓度变化易受季节的影响,夏秋季浓度低,冬春季浓度高;构建的GARCH(1,1)时序模型,可较好地预测未来短期内太原市大气PM2.5浓度;2014年实测数据与模型预测值分析趋势一致,2014年大气PM2.5浓度预测结果与去年同期相比基本一致。结论太原市大气PM2.5浓度季节波动明显,10月以后逐渐升高,以冬季为甚;太原市大气PM2.5浓度GARCH(1,1)时间序列模型,不仅有助于了解该地大气PM2.5浓度变化规律,且可作为环境大气质量预警的重要指标,预测效果良好,是环境质量监测数据评价的重要方法之一。
目的:掌握和預警太原市大氣中細顆粒物(PM2.5)日濃度的變化趨勢,構建更為準確的預警模型,為改進環境質量,促進居民健康提供鑑測資料評價的方法學依據。方法收集2013年6月至2014年11月太原市逐日PM2.5網報數據,分析太原市18箇月以來日報PM2.5的逐月逐日變動趨勢,進一步揭示太原市大氣中PM2.5日濃度現狀;構建時間序列廣義迴歸條件異方差模型[GARCH(1,1)],對太原市未來大氣PM2.5濃度及等級進行短期預警。結果太原市大氣中PM2.5日濃度變化易受季節的影響,夏鞦季濃度低,鼕春季濃度高;構建的GARCH(1,1)時序模型,可較好地預測未來短期內太原市大氣PM2.5濃度;2014年實測數據與模型預測值分析趨勢一緻,2014年大氣PM2.5濃度預測結果與去年同期相比基本一緻。結論太原市大氣PM2.5濃度季節波動明顯,10月以後逐漸升高,以鼕季為甚;太原市大氣PM2.5濃度GARCH(1,1)時間序列模型,不僅有助于瞭解該地大氣PM2.5濃度變化規律,且可作為環境大氣質量預警的重要指標,預測效果良好,是環境質量鑑測數據評價的重要方法之一。
목적:장악화예경태원시대기중세과립물(PM2.5)일농도적변화추세,구건경위준학적예경모형,위개진배경질량,촉진거민건강제공감측자료평개적방법학의거。방법수집2013년6월지2014년11월태원시축일PM2.5망보수거,분석태원시18개월이래일보PM2.5적축월축일변동추세,진일보게시태원시대기중PM2.5일농도현상;구건시간서렬엄의회귀조건이방차모형[GARCH(1,1)],대태원시미래대기PM2.5농도급등급진행단기예경。결과태원시대기중PM2.5일농도변화역수계절적영향,하추계농도저,동춘계농도고;구건적GARCH(1,1)시서모형,가교호지예측미래단기내태원시대기PM2.5농도;2014년실측수거여모형예측치분석추세일치,2014년대기PM2.5농도예측결과여거년동기상비기본일치。결론태원시대기PM2.5농도계절파동명현,10월이후축점승고,이동계위심;태원시대기PM2.5농도GARCH(1,1)시간서렬모형,불부유조우료해해지대기PM2.5농도변화규률,차가작위배경대기질량예경적중요지표,예측효과량호,시배경질량감측수거평개적중요방법지일。
Objective To learn and forecast the variation trend of the atmospheric (particulate matter 2.5, PM2.5) in Taiyuan, and to establish a more accurate forecasting model, so as to provide methodological evidence for the evaluation of monitoring data that may help improve environmental quality and promote public health. Methods The daily Internet data of PM2.5 in Taiyuan between June 2013 and November 2014 were collected. Then, the day-to-day and month-to-month variation trends of PM2.5 in Taiyuan over the recent 18 months were analyzed, and the cur-rent situation of daily atmospheric PM2.5 concentration in Taiyuan was further investigated. The time-series model us-ing general autoregressive conditional heteroskedasticity [GARCH (1,1)] was established for short-term forecasting of the concentration and level of atmospheric PM2.5 in Taiyuan in the future. Results The concentration change of dai-ly atmospheric PM2.5 in Taiyuan was readily influenced by the seasons, appearing low in summer and autumn, and high in winter and spring. The established GARCH (1,1) time-series model could well forecast the short-term concen-tration of atmospheric PM2.5 in the future. The trend of measured data was consistent with the value of model predic-tion in 2014. The predicted results of atmospheric PM2.5 concentration in 2014 were basically consistent with those in the same period last year. Conclusion The atmospheric PM2.5 concentration in Taiyuan fluctuated dramatically with the seasons, which gradually increases after October, especially in winter. The time-series GARCH (1,1) model of at-mospheric PM2.5 concentration in Taiyuan not only contributes to understanding the changes of atmospheric PM2.5 concentration in Taiyuan, but also can be an important indicator of forecasting environmental air quality. The model analysis can be used as one of the important methods for the data evaluation of environmental quality monitoring.