Identifying the factors influencing PM2.5 in southern Taiwan using dynamic factor analysis

Kuo, Yi‐Ming; Wang, Sheng Wei; Jang, Cheng‐Shin; Yeh, Naichia; Yu, Hwa‐Lung

doi:10.1016/j.atmosenv.2011.08.043

Cited by 26 publications

(16 citation statements)

References 43 publications

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“…(2) translates into Eq. (3) (Kisekka et al, 2013;Kuo et al, 2011;Ritter and Muñoz-Carpena, 2006;Zuur and Pierce, 2004;Zuur et al, 2007):…”

Section: Dynamic Factor Analysis (Dfa)mentioning

confidence: 99%

“…is a linear combination of common trends, in which α m (t) is the mth unknown common trend (with 1 ≤ m ≤ M) at time t and γ m,n is the factor loading that indicates the importance of each of the common trends to each response variable (Kisekka et al, 2013;Kuo et al, 2011). Factor loading (A cut-off point of 0.15) was applied to test which common trends are related to the macrofaunal time series (Ligas et al, 2010).…”

Section: Dynamic Factor Analysis (Dfa)mentioning

confidence: 99%

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Common trends in German Bight benthic macrofaunal communities: Assessing temporal variability and the relative importance of environmental variables

Shojaei

Gutow

Dannheim

et al. 2016

Journal of Sea Research

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We examined long-term variability in the abundance of German Bight soft bottom macro-zoobenthos together with major environmental factors (sea surface temperature, winter NAO index, salinity, phosphate, nitrate and silicate) using one of the most comprehensive ecological long-term data sets in the North Sea . Two techniques, Min/Max Autocorrelation Factor Analysis (MAFA) and Dynamic Factor Analysis (DFA) were used to identify underlying common trends in the macrofaunal time series and the relationships between this series and environmental variables. These methods are particularly suitable for relatively short (N15-25 years), non-stationary multivariate data series. Both MAFA and DFA identify a common trend in German Bight macrofaunal abundance i.e. a slight decrease (1981-mid-1990s) followed by a sharp trough in the late 1990s. Subsequently, scores increased again towards 2011. Our analysis indicates that winter temperature and North Atlantic Oscillation were the predominant environmental drivers of temporal variation in German Bight macrofaunal abundance. The techniques applied here are suitable tools to describe temporal fluctuations in complex and noisy multiple time series data and can detect distinct shifts and trends within such time series.

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“…(2) translates into Eq. (3) (Kisekka et al, 2013;Kuo et al, 2011;Ritter and Muñoz-Carpena, 2006;Zuur and Pierce, 2004;Zuur et al, 2007):…”

Section: Dynamic Factor Analysis (Dfa)mentioning

confidence: 99%

Section: Dynamic Factor Analysis (Dfa)mentioning

confidence: 99%

Common trends in German Bight benthic macrofaunal communities: Assessing temporal variability and the relative importance of environmental variables

Shojaei

Gutow

Dannheim

et al. 2016

Journal of Sea Research

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“…DFA was initially applied to economic time series [46][47][48][49] and was later extended to include explanatory variables in ecological systems. Because of its inherent advantages (efficiency, explanatory power, suitable for non-stationary data), it has since been applied successfully across a wide range of environmental systems, from the dynamics of squid populations [50] to variations in groundwater levels and quality [51][52][53][54], from soil moisture dynamics [55][56][57], to air quality [58] and, more recently, to link vegetation and climate dynamics [59][60][61].…”

Section: Introductionmentioning

confidence: 99%

Combined Spatial and Temporal Effects of Environmental Controls on Long-Term Monthly NDVI in the Southern Africa Savanna

et al. 2013

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Deconstructing the drivers of large-scale vegetation change is critical to predicting and managing projected climate and land use changes that will affect regional vegetation cover in degraded or threated ecosystems. We investigate the shared dynamics of spatially variable vegetation across three large watersheds in the southern Africa savanna. Dynamic Factor Analysis (DFA), a multivariate time-series dimension reduction technique, was used to identify the most important physical drivers of regional vegetation change. We first evaluated the Advanced Very High Resolution Radiometer (AVHRR)-vs. the Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) datasets across their overlapping period (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010). NDVI follows a general pattern of cyclic seasonal variation, with distinct spatio-temporal patterns across physio-geographic regions. Both NDVI products produced similar DFA models, although MODIS was simulated better. Soil moisture and precipitation controlled NDVI for mean annual precipitation (MAP) < 750 mm, and above this, evaporation and mean temperature dominated. A second DFA with the full AVHRR (1982-2010) data found that for OPEN ACCESSRemote Sens. 2013, 5 6514 MAP < 750 mm, soil moisture and actual evapotranspiration control NDVI dynamics, followed by mean and maximum temperatures. Above 950 mm, actual evapotranspiration and precipitation dominate. The quantification of the combined spatio-temporal environmental drivers of NDVI expands our ability to understand landscape level changes in vegetation evaluated through remote sensing and improves the basis for the management of vulnerable regions, like the southern Africa savannas.

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“…The explanatory variables can then be included in the DFA model to reduce the common trends, i.e. unexplained trends (Kuo et al, 2011b). The optimal DFA model is the one which has the lowest Akaike Information Criterion (AIC) value.…”

Section: Dynamic Factor Analysis (Dfa)mentioning

confidence: 99%

“…In recent years, studies have employed the DFA to identify the common trends of the multivariate time series for various purposes, such as subsurface water quality characterization (Muñoz-Carpena et al, 2005;Ritter and Muñoz-Carpena, 2006), extreme rainfall analysis (Kuo et al, 2011a), and temporal pattern analysis of ecological species (Zuur et al, 2003). In the specific context of studies on particulate matter, Kuo et al (2011b) have used the DFA to analyze the PM2.5 observations at multiple stations in southern Taiwan and to reveal the common PM2.5 temporal variations across space and their underlying implications.…”

Section: Introductionmentioning

confidence: 99%

A study of the temporal dynamics of ambient particulate matter using stochastic and chaotic techniques

Lin

Sivakumar

et al. 2013

Atmospheric Environment

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Identifying the factors influencing PM2.5 in southern Taiwan using dynamic factor analysis

Cited by 26 publications

References 43 publications

Common trends in German Bight benthic macrofaunal communities: Assessing temporal variability and the relative importance of environmental variables

Common trends in German Bight benthic macrofaunal communities: Assessing temporal variability and the relative importance of environmental variables

Combined Spatial and Temporal Effects of Environmental Controls on Long-Term Monthly NDVI in the Southern Africa Savanna

A study of the temporal dynamics of ambient particulate matter using stochastic and chaotic techniques

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