Simulation of the daily average PM10 concentrations at Ta-Liao with Box–Jenkins time series models and multivariate analysis

Liu, Pao-Wen Grace

doi:10.1016/j.atmosenv.2009.01.055

Cited by 45 publications

(24 citation statements)

References 24 publications

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“…Note that these methods are considered subjective (McGregor and Bamzelis 1995). We applied the latter procedure, as perhaps the most common method, for calculating the number of components to be retained (Sindosi et al 2003;Liu 2009). Factor analysis was applied to our initial data sets consisting of nine correlated variables (eight explanatory variables including four meteorological variables characterising the weather of actual days and the same four meteorological variables characterising the weather of antecedent days, as well as one resultant variable including actual daily pollen concentrations of the given taxa) in order to transform the original variables into fewer uncorrelated variables.…”

Section: Factor Analysis With Special Transformationmentioning

confidence: 99%

Plants remember past weather: a study for atmospheric pollen concentrations of Ambrosia, Poaceae and Populus

Matyasovszky

Makra

Csépe

et al. 2014

Theor Appl Climatol

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After extreme dry (wet) summers or years, pollen production of different taxa may decrease (increase) substantially. Accordingly, studying effects of current and past meteorological conditions on current pollen concentrations for different taxa have of major importance. The purpose of this study is separating the weight of current and past weather conditions influencing current pollen productions of three taxa. Two procedures, namely multiple correlations and factor analysis with special transformation are used. The 11-year (1997The 11-year ( -2007) data sets include daily pollen counts of Ambrosia (ragweed), Poaceae (grasses) and Populus (poplar), as well as daily values of four climate variables (temperature, relative humidity, global solar flux and precipitation). Multiple correlations of daily pollen counts with simultaneous values of daily meteorological variables do not show annual course for Ambrosia, but do show definite trends for Populus and Poaceae. Results received using the two methods revealed characteristic similarities. For all the three taxa, the continental rainfall peak and additional local showers in the growing season can strengthen the weight of the current meteorological elements. However, due to the precipitation, big amount of water can be stored in the soil contributing to the effect of the past climate elements during dry periods. Higher climate sensitivity (especially water sensitivity) of the herbaceous taxa (Ambrosia and Poaceae) can be definitely established compared to the arboreal Populus. Separation of the weight of the current and past weather conditions for different taxa involves practical importance both for health care and agricultural production.

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Section: Factor Analysis With Special Transformationmentioning

confidence: 99%

Plants remember past weather: a study for atmospheric pollen concentrations of Ambrosia, Poaceae and Populus

Matyasovszky

Makra

Csépe

et al. 2014

Theor Appl Climatol

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“…The optimum number of retained factors can be determined by different statistical criteria [40]. The most common and widely accepted one is to specify a least percentage (80%) of the total variance in the original variables that has to be achieved [41]. After performing the factor analysis, a special transformation of the retained factors was made to discover to what degree the above-mentioned explanatory variables affect the resultant variable, and to give a rank of their influence [42].…”

Section: Factor Analysis and Special Transformationmentioning

confidence: 99%

Risk Factors for Asthma Hospitalization among Adults and Elderly

Makra¹

2015

Arch Otolaryngol Rhinold

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Joint effect of biological (pollen) and chemical air pollutants on asthma emergency room (ER) visits was analyzed for Szeged region of Southern Hungary. Our database of a nine-year period (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) includes daily number of asthma emergency room (ER) visits, and daily mean concentrations of CO, PM 10 , NO, NO 2 , O 3 and SO 2 , furthermore two pollen variables (Ambrosia and total pollen excluding Ambrosia), as well. The analysis was performed for ER visits of asthma bronchiale using two age groups (adults and the elderly) of males and females for three seasons. Factor analysis was performed in order to clarify the relative importance of the pollutant variables affecting asthma ER visits. Asthma ER visits denote notably stronger associations with the pollutants in adult male than in adult female patients both for the pollen season of Ambrosia and the pollen-free season. Furthermore, adults are substantially more sensitive to severe asthma attack than the elderly for the season of total pollen excluding Ambrosia pollen. The joint effect of the chemical and pollen variables is the highest for the asthma ER cases in the pollen season of Ambrosia, basically due to the extra impact of the total pollen excluding Ambrosia pollen and partly due to Ambrosia pollen. A nonparametric regression technique was applied to discriminate between events of ER visit -no ER visit using pollen and chemical pollutants as explaining variables. Based on multiple correlations, the strongest relationships between ER visits and pollutants are observed during the pollen-free season. The elderly group with asthma bronchiale is characterized by weaker relationships between ER visits and pollutants compared to adults. Ratio of the number of correct decisions on the events of ER visit -no ER visit is lowest for the season of total pollen excluding Ambrosia pollen. Otherwise, similar conclusions hold as those received by multiple correlations. on aeroallergens. These include impacts on pollen amount, pollen allergen city, pollen season, plant and pollen distribution, and other plant attributes [18][19][20][21][22]. Hence, due to the continually increasing air pollution, respiratory diseases are of major concern worldwide. Air pollution of Hungary is one of the highest in Europe. Around 16,000 annual premature deaths attributable to exposure to ambient PM 10 concentrations are estimated in the country [23,24]. Furthermore, airborne pollen levels are also high. The Carpathian basin, involving Hungary (Figure 1) is considered the most polluted region with airborne ragweed (Ambrosia) pollen in Europe. Ambrosia in Hungary discharges the most pollen of all taxa; the ratio of its pollen release compared to the total pollen release in the late summer period is around 60-71% [25]. Highest counts on peak days in Szeged, Southern Hungary, are about one order of magnitude higher than those in other cities of Europe [26]. The sensitivity of patients to ragweed in Szeged is 83.7% [27]. About 30% of the Hungar...

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“…Metode ini juga telah banyak diterapkan untuk peramalan dalam berbagai bidang. (Hikichi, Salgado, & & Beijo, 2017) misalnya, menerapkan ARIMA untuk memprediksi banyaknya sertifikasi ISO 14001 di Amerika utara, (Goyal, Chan, & Jaiswal, 2006) dan (Liu, 2009) menggunakannya untuk memprediksi polusi di lingkungan perkotaan, (Ediger & Akar, 2007) memanfaatkannya untuk memprediksi kebutuhan energi berdasarkan jenisnya, sedangkan (Mohamed & Bodger, 2005) Pada umumnya, nilai yang akan datang berkorelasi dengan beberapa nilai terbaru. Dengan demikian, untuk meramalkan y t (h) seringkali tidak semua nilai yang ada di y perlu untuk digunakan.…”

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Prediksi Harga Bahan Pokok Nasional Jangka Pendek Menggunakan ARIMA

Rasyidi

2017

JISEBI

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Abstrak-Fluktuasi harga bahan pokok yang tidak terkendali dapat menyebabkan kerugian bagi konsumen maupun produsen. Salah satu langkah untuk mengatasi permasalahan tersebut yaitu dengan membuat prediksi harga yang akurat sehingga tindakan preventif dapat dilakukan untuk meminimalkan gejolak harga. Dalam studi ini, ARIMA digunakan untuk memprediksi harga bahan pokok nasional dalam jangka pendek. Data harga harian dari dua belas bahan pokok pada empat horizon prediksi (1 hingga 30 hari ke depan) digunakan untuk menguji kinerja ARIMA dalam memprediksi harga bahan pokok. Hasil eksperimen menujukkan bahwa model ARIMA yang dihasilkan mampu memprediksi harga dengan tingkat error ratarata sebesar 2.22%.Kata Kunci-ARIMA, Bahan Pokok, Prediksi, Peramalan Abstract-Uncontrolled price fluctuation of basic commodities can harm both consumers and producers. One way to overcome the problem is by making accurate price prediction so that preventive actions can be conducted to minimize the price fluctuation. In this study, ARIMA is used to make short-term price prediction of national basic commodities. Daily pricing data of twelve commodities in four prediction horizons (1 to 30 days ahead) is used to test the performance of ARIMA in predicting the commodity prices. The experimental results showed that the ARIMA model was able to predict the price quite accurately with an average error rate of 2.22%.

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Simulation of the daily average PM10 concentrations at Ta-Liao with Box–Jenkins time series models and multivariate analysis

Cited by 45 publications

References 24 publications

Plants remember past weather: a study for atmospheric pollen concentrations of Ambrosia, Poaceae and Populus

Plants remember past weather: a study for atmospheric pollen concentrations of Ambrosia, Poaceae and Populus

Risk Factors for Asthma Hospitalization among Adults and Elderly

Prediksi Harga Bahan Pokok Nasional Jangka Pendek Menggunakan ARIMA

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