An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou, China

Jian, Le; Zhao, Yun; Zhu, Yan‐Rong; Zhang, Meibian; Bertolatti, Dean

doi:10.1016/j.scitotenv.2012.03.025

Cited by 147 publications

(55 citation statements)

References 60 publications

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“…Correlations between individual meteorological factors and PM 2.5 concentrations have been analyzed in such mega-cities as Nanjing (T. Shen and Li, 2016), Beijing (Huang et al, 2015;Yin et al, 2016), Wuhan , Hangzhou (Jian et al, 2012), Chengdu (Zeng and Zhang et al, 2017) and Hong Kong (Fung et al, 2014). These studies suggested that meteorological influences on PM 2.5 concentrations varied significantly across regions.…”

Section: Discussionmentioning

confidence: 99%

“…Meanwhile, the washing-off effect from the same amount of precipitation on PM 2.5 concentrations in Xi'an, a city with higher PM 2.5 concentrations, was lower than that in Guangzhou (Guo et al, 2016), indicating local PM 2.5 concentrations also exerted a key role in the negative effects of precipitation. Meanwhile, temperature can either be negatively correlated with PM 2.5 concentrations by accelerating the flow circulation and promoting the dispersion of PM 2.5 (Li et al, 2015b), or positively correlated with PM 2.5 concentrations through inversion events (Jian et al, 2012). Given the complexity of interactions between meteorological factors and PM 2.5 , characteristics and variations of meteorological influences on PM 2.5 concentrations should be further investigated for specific regions across China, respectively, based on long-term observation data.…”

Section: Discussionmentioning

confidence: 99%

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Understanding meteorological influences on PM<sub>2.5</sub> concentrations across China: a temporal and spatial perspective

et al. 2018

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Abstract. With frequent air pollution episodes in China, growing research emphasis has been put on quantifying meteorological influences on PM 2.5 concentrations. However, these studies mainly focus on isolated cities, whilst meteorological influences on PM 2.5 concentrations at the national scale have not yet been examined comprehensively. This research employs the CCM (convergent cross-mapping) method to understand the influence of individual meteorological factors on local PM 2.5 concentrations in 188 monitoring cities across China. Results indicate that meteorological influences on PM 2.5 concentrations have notable seasonal and regional variations. For the heavily polluted North China region, when PM 2.5 concentrations are high, meteorological influences on PM 2.5 concentrations are strong. The dominant meteorological influence for PM 2.5 concentrations varies across locations and demonstrates regional similarities. For the most polluted winter, the dominant meteorological driver for local PM 2.5 concentrations is mainly the wind within the North China region, whilst precipitation is the dominant meteorological influence for most coastal regions. At the national scale, the influence of temperature, humidity and wind on PM 2.5 concentrations is much larger than that of other meteorological factors. Amongst eight factors, temperature exerts the strongest and most stable influence on national PM 2.5 concentrations in all seasons. Due to notable temporal and spatial differences in meteorological influences on local PM 2.5 concentrations, this research suggests pertinent environmental projects for air quality improvement should be designed accordingly for specific regions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Understanding meteorological influences on PM<sub>2.5</sub> concentrations across China: a temporal and spatial perspective

et al. 2018

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“…One of the main objectives of time-series analysis is to identify the nature of the phenomenon represented by the sequence of observations. The literature review shows that time-series analysis has been largely applied for modelling and analysis in various fields such as electrical energy [30], the environment [31], and health care establishments [32].…”

Section: Time Series Analysismentioning

confidence: 99%

Seasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems

et al. 2016

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Monitoring complex production systems is primordial to ensure management, reliability and safety as well as maintaining the desired product quality. Early detection of emergent abnormal behaviour in monitored systems allows pre-emptive action to prevent more serious consequences, to improve system operations and to reduce manufacturing and/or service costs. This study reports the design of a new methodology for the detection of abnormal situations based on the integration of time-series analysis models and statistical process control (SPC) tools for the joint development of a monitoring system to help supervising of the behaviour of emergency department services (EDs). The monitoring system developed is able to provide early alerts in the event of abnormal situations. The seasonal autoregressive moving average (SARMA)-based exponentially weighted moving average (EWMA) anomaly detection scheme proposed was successfully applied to the practical data collected from the database of the paediatric emergency department (PED) at Lille regional hospital centre, France. The method developed utilizes SARMA as a modelling framework and EWMA for anomaly detection. The EWMA control chart is applied to the uncorrelated residuals obtained from the SARMA model. The detection results of the EWMA chart are compared with two other commonly applied residual-based tests: a Shewhart individuals chart and a Cumulative Sum (CUSUM) control chart.

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“…For example, Li, Qian, Ou, Zhou, Guo, and Guo [24], Zhang et al [28] and Tian, Qiao, and Xu [25] have analyzed the impacts of meteorological conditions on PM pollution in different season. Jian et al [29], Li et al [30], and Qin et al [31] have predicted PM concentrations using meteorological data. However, most studies have some limitations, due to their analyses focusing on a certain city or region, which is hard to reflect back to the whole state of PM pollution [24,25,28].…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Particulate Pollution (PM2.5 and PM10) and Their Spacescale-Dependent Relationships with Meteorological Elements in China

Chen

Zeng

et al. 2017

Sustainability

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Particulate matter (PM) pollution in China has an obvious characteristic of spatial distribution. It is well known that intensive anthropogenic activities, such as fossil fuel combustion and biomass burning, have great influence on the spatial distribution of PM pollution. However, the spacescale-dependent relationships between PM concentrations and weather conditions remain unclear. Here, we investigated the characteristics of two types of particulate pollution, including PM 2.5 and PM 10 , and their spatial relationships with meteorological elements in 173 cities throughout China from March 2014 to February 2015. Results: (1) High PM 2.5 concentrations were distinctly located southeast of the Hu Line, and high PM 10 concentrations were distinctly situated north of the Yangtze River; (2) Spacescale-dependent relationships were found between PM pollution and meteorological elements. The influence of temperature had similar inverted V-shaped characteristics, namely, there was serious PM pollution when temperature was about 15 • C, and there was slight PM pollution when temperature was less or more than 15 • C. Annual precipitation, wind speed, and relative humidity were negatively correlated with PM, while annual atmospheric pressure was positively correlated with PM; (3) The ideal meteorological regions were identified according to the quantified spatial relationships between PM and meteorological elements, which could be defined by a combination of the following conditions: (a) temperature <10 • C or >21 • C; (b) precipitation >1500 mm; (c) atmospheric pressure <900 hPa; (d) wind speed >3 m/s; and (e) relative humidity >65%, where air pollutants can easily be scavenged. The success of this research provides a meteorological explanation to the spatial distribution characteristics of PM pollution in China.

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An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou, China

Cited by 147 publications

References 60 publications

Understanding meteorological influences on PM<sub>2.5</sub> concentrations across China: a temporal and spatial perspective

Understanding meteorological influences on PM<sub>2.5</sub> concentrations across China: a temporal and spatial perspective

Seasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems

Characteristics of Particulate Pollution (PM2.5 and PM10) and Their Spacescale-Dependent Relationships with Meteorological Elements in China

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An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou, China

Cited by 147 publications

References 60 publications

Understanding meteorological influences on PM&lt;sub&gt;2.5&lt;/sub&gt; concentrations across China: a temporal and spatial perspective

Understanding meteorological influences on PM&lt;sub&gt;2.5&lt;/sub&gt; concentrations across China: a temporal and spatial perspective

Seasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems

Characteristics of Particulate Pollution (PM2.5 and PM10) and Their Spacescale-Dependent Relationships with Meteorological Elements in China

Contact Info

Product

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Understanding meteorological influences on PM<sub>2.5</sub> concentrations across China: a temporal and spatial perspective

Understanding meteorological influences on PM<sub>2.5</sub> concentrations across China: a temporal and spatial perspective