scholarly journals Effect of COVID-19 pandemic induced lockdown (general holiday) on air quality of Dhaka City

2020 ◽  
Author(s):  
Md. Saiful Islam ◽  
Tahmid Anam Chowdhury
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Environmental Protection Agency ◽  
Time Series Data ◽  
Urban Air Pollution ◽  
Motor Vehicles ◽  
Series Data ◽  
Dhaka City ◽  
Criteria Pollutants ◽  
Us Epa

Abstract A worldwide pandemic of COVID-19 has forced to implement a lockdown during April-May 2020 by restricting people's movement, the shutdown of industries and motor vehicles in Dhaka, Bangladesh, to contain the virus. This type of strict measures returned an outcome of the reduction of urban air pollution around the world. The present study aims to investigate the reduction of the concentration of pollutants in the air of Dhaka City and the reduction of the Air Quality Index (AQI). Necessary time-series data of the concentration of PM2.5, NO2, SO2, and CO have been collected from the archive of the U.S. Environmental Protection Agency (US EPA) and Sentinel-5P. The time-series data have been analyzed by descriptive statistics, and AQI is calculated following an appropriate formula suggested by US EPA based on the criteria pollutants. The study found that the concentrations of PM2.5, NO2, SO2, and CO have been reduced by 23, 30, 07, and 07% during April-May 2020, respectively, compared with the preceding year's concentration. Moreover, the AQI has also been reduced by up to 35% than the previous year in April-May 2020. However, the magnitude of pollution reduction in Dhaka is lower than other cities and countries globally, including Delhi, Sao Paulo, Wuhan, Spain, Italy, USA, etc. The main reason includes the poor implementation of lockdown, especially in the first week of April and the second fortnight of May. The findings will be useful for policymakers to find a way to control the pollution sources to enhance Dhaka's air quality.

scholarly journals PREDICTION OF OZONE (O3) VALUES USING SUPPORT VECTOR REGRESSION METHOD

2021 ◽  
Vol 7 (4) ◽  
pp. 81-88
Author(s):  
Chasandra Puspitasari ◽  
Nur Rokhman ◽  
Wahyono
Keyword(s):  
Air Quality ◽  
Support Vector Regression ◽  
Time Series Data ◽  
Motor Vehicles ◽  
Primary Data ◽  
Series Data ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Linear Kernel ◽  
Air Quality Prediction

A large number of motor vehicles that cause congestion is a major factor in the poor air quality in big cities. Ozone (O3) is one of the main indicators in measuring the level of air pollution in the city of Surabaya to find out how air quality. Prediction of Ozone (O3) value is important as a support for the community and government in efforts to improve the air quality. This study aims to predict the value of Ozone (O3) in the form of time series data using the Support Vector Regression (SVR) method with the Linear, Polynomial, RBF, and ANOVA kernels. The data used in this study are 549 primary data from the daily average of ozone (O3) value of Surabaya in the period 1 July 2017 - 31 December 2018. The data will be used in the training and testing process until prediction results are obtained. The results obtained from this study are the Linear kernel produces the best prediction model with a MAPE value of 21.78% with a parameter value 𝜆 = 0.3; 𝜀 = 0.00001; cLR = 0.005; and C = 0.5. The results of the Polynomial kernel are not much different from the Linear kernel which has a MAPE value of 21.83%. While the RBF and ANOVA kernels each produce a model with MAPE value of 24.49% and 22.0%. These results indicate that the SVR method with the kernels used can predict Ozone values quite well.

scholarly journals Missing Value Imputation of Time-Series Air-Quality Data via Deep Neural Networks

2021 ◽  
Vol 18 (22) ◽  
pp. 12213
Author(s):  
Taesung Kim ◽  
Jinhee Kim ◽  
Wonho Yang ◽  
Hunjoo Lee ◽  
Jaegul Choo
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Air Quality ◽  
Time Series Data ◽  
Quality Data ◽  
Series Data ◽  
Missing Value ◽  
Missing Value Imputation ◽  
Spatio Temporal ◽  
Air Quality Data

To prevent severe air pollution, it is important to analyze time-series air quality data, but this is often challenging as the time-series data is usually partially missing, especially when it is collected from multiple locations simultaneously. To solve this problem, various deep-learning-based missing value imputation models have been proposed. However, often they are barely interpretable, which makes it difficult to analyze the imputed data. Thus, we propose a novel deep learning-based imputation model that achieves high interpretability as well as shows great performance in missing value imputation for spatio-temporal data. We verify the effectiveness of our method through quantitative and qualitative results on a publicly available air-quality dataset.

scholarly journals Pengaruh Harga Bensin terhadap Kecelakaan Lalu Lintas di Indonesia

2016 ◽  
Vol 16 (2) ◽  
pp. 173-186
Author(s):  
Eny Kusdarwati ◽  
Djoni Hartono
Keyword(s):  
Time Series ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Time Series Data ◽  
Motor Vehicles ◽  
Series Data ◽  
Gasoline Prices ◽  
Real Price ◽  
Crude Oil Prices ◽  
The Impact

The Impact of Gasoline Price on Trac Accident in IndonesiaTraffic accident ranks the ninth largest of the cause of death in Indonesia. The most of researches studying Indonesia on traffic accidents were only blaming on human, motor vehicles, and environment as main culprits, not incorporating economic factors into the models. This study aims to analyze the impact of real gasoline prices on trac accident in Indonesia and the factors of influence them. This research employs time series data from 1970 to 2013 with OLS analysis world crude oil prices as instrument variable. The estimator results show that real price of gasoline and the policy of usage of motorcycle light insignificant on traffic accident. Meanwhile, real GDP and asphalt roads significantly decrease the traffic accident. However, motorcycles significantly increase the traffic accident.Keywords: Real Price of Gasoline; Trac Accident; Externality AbstrakKecelakaan lalu lintas menempati urutan kesembilan penyebab kematian di Indonesia. Kebanyakan penelitian kecelakaan di Indoneia menitikberatkan pada faktor manusia, kendaraan, dan lingkungan, tetapi belum ada yang memasukkan faktor-faktor ekonomi ke dalam modelnya. Tujuan penelitian ini adalah ingin mengetahui pengaruh harga riil bensin terhadap kecelakaan lalu lintas di Indonesia serta faktor-faktor yang memengaruhinya. Penelitian ini menggunakan data time series Indonesia dari tahun 1970 hingga 2013 dan menggunakan OLS dengan variabel instrumen harga minyak mentah dunia. Hasil estimasi menunjukkan bahwa harga riil bensin dan kebijakan penggunaan lampu utama sepeda motor tidak signifikan terhadap kecelakaan lalu lintas. Sedangkan PDB riil dan jalan aspal signifikan berpengaruh menurunkan kecelakaan. Namun, sepeda motor berdampak signifikan meningkatkan kecelakaan lalu lintas.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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