Spatio-temporal modeling and forecasting of air quality data

2014 ◽  
Author(s):  
Tsz-leung Yan
Keyword(s):  
Air Quality ◽  
Quality Data ◽  
Temporal Modeling ◽  
Modeling And Forecasting ◽  
Spatio Temporal ◽  
Air Quality Data

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 PENYEBARAN POLUTAN DALAM KASUS KEBAKARAN HUTAN DAN LAHAN DI SUMATERA SELATAN TAHUN 2015

2017 ◽  
Vol 18 (2) ◽  
pp. 61
Author(s):  
Erwin Mulyana
Keyword(s):  
Air Quality ◽  
Black Carbon ◽  
Temporal Analysis ◽  
Quality Data ◽  
Display System ◽  
Rain Intensity ◽  
Grid Analysis ◽  
Spatio Temporal ◽  
Air Quality Data ◽  
Area Data

IntisariKebakaran hutan dan lahan di Sumatera Selatan tahun 2015 menimbulkan bencana kabut asap yang sangat masif sehingga kualitas udara dalam beberapa hari mencapai kategori berbahaya. Dalam tulisan ini dibahas penyebaran polutan di wilayah Sumatera Selatan akibat kebakaran hutan dan lahan yang terjadi di wilayah tersebut. Data yang digunakan dalam penelitian ini adalah data hotspot dari satelit MODIS dengan tingkat kepercayaan 70 %, curah hujan TRMM serta curah hujan dari penakar yg ada di Sumatera Selatan,data kualitas udara (ISPU), data black carbon dari MERRA-2 Model M2T1NXAER v5.12.4. dengan resolusi 0.5o x 0.625o, serta arah dan kecepatan angin lapisan 925 mb. Analisis spasio temporal penyebaran black carbon yang dipadukan dengan arah dan kecepatan angin menggunakan perangkat lunak Grid Analysis and Display System (GrADS). Intensitas hujan dari 16 penakar hujan sejak minggu kedua bulan Agusus 2015 hingga akhir Oktober 2015 sebesar 36 mm. Selama bulan Juni-November 2015, Jumlah hotspot terbanyak terjadi pada bulan September (6.839 titik) dan Oktober (7.709 titik). Lokasi hotspot sebagian besar berada di Kabupaten OKI dengan jumlah mencapai 10.581 titik. Kualitas udara pada bulan September 2015 dominan masuk kategori tidak sehat sedangkan bulan Oktober 2015 dominan masuk kategori sangat tidak sehat – berbahaya. Angin pada lapisan 925 mb umumnya bertiup dari arah tenggara hingga timur sehingga black carbon dari kebakaran di daerah OKI menyebar ke arah wilayah Kabupaten Musi Banyuasin serta Kabupaten Banyuasin.  AbstractIn 2015, Forest and Land fires inflict serious and massive smoke disaster so that air quality in several days laid in dangerous category. This paper discussed pollutant dispersed in South Sumatera due to forest and land fire in this area. Data that used in this paper were MODIS satellite hotspot data with 70 % confidence level, rainfall from TRMM satellite and from ground observation at South Sumatera, Air quality data (ISPU), MERRA-2 Model M2T1NXAER v5.12.4 black carbon data, also wind direction and speed at 925 mb height. Spatio temporal analysis of black carbon dispersion combined with wind speed and direction using Grid Analysis and Display System (GrADS) software. Rain intensity from 16 rainfall gauge since week two of August 2015 until end of October 2015 was 36 mm. During June-November 2015, the number on highest hotspot observed was in September (6.839) and October (7.709). Hotspot location mainly in OKI district as much as 10.581. Air quality in September 2015 mainly laid in unhealthy category, meanwhile in October 2015 laid mainly stated as unhealthy to dangerous. Wind at 925 mb height generally came from South East and East so black carbon came from fires at OKI district dispersed to Musi Banyuasin and Banyuasin district. 

scholarly journals Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)

2021 ◽  
Vol 18 (3) ◽  
pp. 1333
Author(s):  
Ahmad R. Alsaber ◽  
Jiazhu Pan ◽  
Adeeba Al-Hurban 
Keyword(s):  
Air Quality ◽  
Missing Data ◽  
Random Forest ◽  
Missing Values ◽  
Imputation Method ◽  
Environmental Data ◽  
Environmental Research ◽  
Quality Data ◽  
Data Set ◽  
Air Quality Data

In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for NO2 (18.4%), CO (18.5%), PM10 (57.4%), SO2 (19.0%), and O3 (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data.

aiRe - A web-based R application for simple, accessible and repeatable analysis of urban air quality data

2021 ◽  
Vol 138 ◽  
pp. 104976
Author(s):  
Juan José Díaz ◽  
Ivan Mura ◽  
Juan Felipe Franco ◽  
Raha Akhavan-Tabatabaei
Keyword(s):  
Air Quality ◽  
Urban Air Quality ◽  
Quality Data ◽  
Urban Air ◽  
Web Based ◽  
Air Quality Data
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