Spatial-temporal models for ambient hourly PM10 in Vancouver

Li, Kathy H.; Le, Nhu D.; Sun, Li; Zidek, James V.

doi:10.1002/(sici)1099-095x(199905/06)10:3<321::aid-env355>3.0.co;2-d

Cited by 27 publications

(7 citation statements)

References 17 publications

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“…In addition, the model was adjusted for deterministic seasonal components, similar to [ 37 ]. The effect of the measures (M) was analyzed separately for summer (S) and winter (W) to allow for seasonal variability.…”

Section: Methodsmentioning

confidence: 99%

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Evaluation of the Impact of Low Emission Zone and Heavy Traffic Ban in Munich (Germany) on the Reduction of PM10 in Ambient Air

Fensterer¹,

Küchenhoff²,

Maier³

et al. 2014

IJERPH

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Concentrations of ambient fine particles (PM10: particles with an aerodynamic diameter ≤ 10 µm) are still exceeding current air quality standards in many European cities. In Munich (Germany), low emission zone and transit bans for heavy-duty vehicles were introduced in 2008 aiming at reduction of traffic emissions contribution to PM10. The effects of those measures on PM10 mass concentrations in Munich were investigated with a semiparametric regression model for modeling PM10 levels adjusted for time, background pollution, public holidays and wind direction. The reduction of PM10 concentration after the introduction of the measures was larger at a traffic monitoring site (13.0 %, 19.6 % in summer, and 6.8 % in winter) and smaller in urban background (4.5 %, 5.7 % in summer, and 3.2 % in winter). The effect was most pronounced on Fridays and on the weekends in summer.

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Section: Methodsmentioning

confidence: 99%

“…Percentage changes of PM 10 levels were modeled through logarithmic concentration levels [ 37 ]. In particular, it was suggested that the measures, the public holidays and the seasons yielded to percentage effects on the PM 10 concentration.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the Impact of Low Emission Zone and Heavy Traffic Ban in Munich (Germany) on the Reduction of PM10 in Ambient Air

Fensterer¹,

Küchenhoff²,

Maier³

et al. 2014

IJERPH

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show abstract

“…For missing data gaps less than or equal to 8 h, the average value of the succeeding and preceding six intervals of 1 h was used [17]. Otherwise, a slight modification of the hour mean method [18] was implemented, i.e. to replace the missing hourly value with the mean of all known hourly observations of the same season.…”

Section: Data Collectedmentioning

confidence: 99%

Short- and long-term forecasting of ambient air pollution levels using wavelet-based non-linear autoregressive artificial neural networks with exogenous inputs

Cabaneros¹,

Calautit²,

Hughes³

2020

Int. J. EI

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Roadside air pollution is a major issue due to its adverse effects on human health and the environment. This highlights the need for parsimonious and robust forecasting tools that help vulnerable members of the public reduce their exposure to harmful air pollutants. Recent results in air pollution forecasting applications include the use of hybrid models based on non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable inputs (NARX) and wavelet decomposition techniques. However, attempts employing both methods into one hybrid modelling system have not been widely made. Hence, this work further investigates the utilisation of wavelet-based NARX-ANN models in the shortand long-term prediction of hourly NO 2 concentration levels. The models were trained using emissions and meteorological data collected from a busy roadside site in Central London, United Kingdom from January to December 2015. A discrete wavelet transformation technique was then implemented to address the highly variable characteristic of the collected NO 2 concentration data. Overall results exhibit the superiority of the wavelet-based NARX-ANN models improving the accuracy of the benchmark NARX-ANN model results by up to 6% in terms of explained variance. The proposed models also provide fairly accurate long-term forecasts, explaining 68-76% of the variance of actual NO 2 data. In conclusion, the findings of this study demonstrate the high potential of wavelet-based NARX-ANN models as alternative tools in short-and long-term forecasting of air pollutants in urban environments.

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“…Studies have shown that the spatial distribution of pollution depends on the geographical distribution of various landuse patterns and emission sources. , The temporal heterogeneity of ambient concentrations can appear in various scales, including seasonal and short-term changes that are associated with the temporal variations of meteorological variables in the corresponding scales. The nonstationary characteristics of averaged ambient pollutant concentration across space and time has been the major focus of ambient pollution modeling − (i.e., nonstationarity in the mean trend). The nonstationarity of variances across space and time is also important to the understanding of the spatiotemporal distribution of ambient air quality concentrations. , …”

Section: Introductionmentioning

confidence: 99%

Quantile-Based Bayesian Maximum Entropy Approach for Spatiotemporal Modeling of Ambient Air Quality Levels

Yu¹,

Wang²

2013

Environ. Sci. Technol.

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Understanding the daily changes in ambient air quality concentrations is important to the assessing human exposure and environmental health. However, the fine temporal scales (e.g., hourly) involved in this assessment often lead to high variability in air quality concentrations. This is because of the complex short-term physical and chemical mechanisms among the pollutants. Consequently, high heterogeneity is usually present in not only the averaged pollution levels, but also the intraday variance levels of the daily observations of ambient concentration across space and time. This characteristic decreases the estimation performance of common techniques. This study proposes a novel quantile-based Bayesian maximum entropy (QBME) method to account for the nonstationary and nonhomogeneous characteristics of ambient air pollution dynamics. The QBME method characterizes the spatiotemporal dependence among the ambient air quality levels based on their location-specific quantiles and accounts for spatiotemporal variations using a local weighted smoothing technique. The epistemic framework of the QBME method can allow researchers to further consider the uncertainty of space-time observations. This study presents the spatiotemporal modeling of daily CO and PM10 concentrations across Taiwan from 1998 to 2009 using the QBME method. Results show that the QBME method can effectively improve estimation accuracy in terms of lower mean absolute errors and standard deviations over space and time, especially for pollutants with strong nonhomogeneous variances across space. In addition, the epistemic framework can allow researchers to assimilate the site-specific secondary information where the observations are absent because of the common preferential sampling issues of environmental data. The proposed QBME method provides a practical and powerful framework for the spatiotemporal modeling of ambient pollutants.

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Spatial-temporal models for ambient hourly PM10 in Vancouver

Cited by 27 publications

References 17 publications

Evaluation of the Impact of Low Emission Zone and Heavy Traffic Ban in Munich (Germany) on the Reduction of PM10 in Ambient Air

Evaluation of the Impact of Low Emission Zone and Heavy Traffic Ban in Munich (Germany) on the Reduction of PM10 in Ambient Air

Short- and long-term forecasting of ambient air pollution levels using wavelet-based non-linear autoregressive artificial neural networks with exogenous inputs

Quantile-Based Bayesian Maximum Entropy Approach for Spatiotemporal Modeling of Ambient Air Quality Levels

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