A recursive estimation approach to the spatio-temporal analysis and modelling of air quality data

2006 ◽  
Vol 21 (6) ◽  
pp. 759-769 ◽  
Author(s):  
R. Romanowicz ◽  
P. Young ◽  
P. Brown ◽  
P. Diggle
Keyword(s):  
Air Quality ◽  
Temporal Analysis ◽  
Recursive Estimation ◽  
Quality Data ◽  
Spatio Temporal ◽  
Air Quality Data

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 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 Visual analytics for spatio-temporal air quality data

2020 ◽  
Author(s):  
Chiara Bachechi ◽  
Federico Desimoni ◽  
Laura Po ◽  
David Martinez Casas
Keyword(s):  
Air Quality ◽  
Visual Analytics ◽  
Quality Data ◽  
Spatio Temporal ◽  
Air Quality Data

A hierarchical Bayesian approach to the spatio-temporal modeling of air quality data

2006 ◽  
Vol 40 (3) ◽  
pp. 554-566 ◽  
Author(s):  
A. Riccio ◽  
G. Barone ◽  
E. Chianese ◽  
G. Giunta
Keyword(s):  
Air Quality ◽  
Bayesian Approach ◽  
Quality Data ◽  
Hierarchical Bayesian ◽  
Temporal Modeling ◽  
Spatio Temporal ◽  
Air Quality Data

scholarly journals Making Air Quality Data Meaningful

2020 ◽  
Author(s):  
Szu-Yu (Cyn) Liu ◽  
Justin Cranshaw ◽  
Asta Roseway
Keyword(s):  
Air Quality ◽  
Quality Data ◽  
Air Quality Data

Feature Selection and Analysis in Air Quality Data

2021 ◽  
Author(s):  
Manish Mahajan ◽  
Santosh Kumar ◽  
Bhasker Pant ◽  
Umesh Kumar Tiwari ◽  
Rijwan Khan
Keyword(s):  
Feature Selection ◽  
Air Quality ◽  
Quality Data ◽  
Air Quality Data

scholarly journals Spatio-temporal analysis of air quality and its relationship with major COVID-19 hotspot places in India

2021 ◽  
Vol 22 ◽  
pp. 100473
Author(s):  
Hasan Raja Naqvi ◽  
Guneet Mutreja ◽  
Adnan Shakeel ◽  
Masood Ahsan Siddiqui
Keyword(s):  
Air Quality ◽  
Temporal Analysis ◽  
Spatio Temporal
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