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

scholarly journals Spatio-Temporal Correlation Analysis of Air Quality in China: Evidence from Provincial Capitals Data

2020 ◽  
Vol 12 (6) ◽  
pp. 2486
Author(s):  
Qingchen Liu ◽  
Xinyi Li ◽  
Tao Liu ◽  
Xiaojun Zhao
Keyword(s):  
Air Quality ◽  
Temporal Correlation ◽  
Temporal Analysis ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Capital Cities ◽  
Temporal Correlations ◽  
Agglomeration Effects ◽  
Provincial Capital ◽  
Spatio Temporal

In China, public health awareness is growing as people get more concerned about the air quality. Based on the air quality index (AQI) of 31 provincial capital cities (2015–2018) in China, we studied the spatio-temporal correlations of air quality between cities. With spatial, temporal and spatio-temporal analysis, we systematically obtained many interesting results where the traditional analyses may be lacking. Firstly, the air quality of cities has spatial spillover and agglomeration effects and further the spatial correlation becomes higher with time. Secondly, there exists temporal correlation between the current AQI and its past values on multiple time scales, which shows certain periodicity. Thirdly, due to the changing characteristics of time, social activities and other factors affect the air quality positively. However, with the panel data model, the coefficients of spatio-temporal correlation vary for different cities.

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. 

Spatio-Temporal Analysis of Frontal Midline Theta Rhythm in EEG during 3-D Maze Task

2009 ◽  
Vol 129 (10) ◽  
pp. 1778-1784
Author(s):  
Yasuaki Uehara ◽  
Keita Tanaka ◽  
Yoshinori Uchikawa ◽  
Bong-Soo Kim
Keyword(s):  
Theta Rhythm ◽  
Temporal Analysis ◽  
Maze Task ◽  
Frontal Midline Theta ◽  
Spatio Temporal

Spatio-temporal analysis and prediction of grain security in Shaanxi Province

2010 ◽  
Vol 17 (4) ◽  
pp. 770-775
Author(s):  
Ren YANG ◽  
Zhi-Yuan REN ◽  
Qian XU ◽  
Mei-Xia WANG
Keyword(s):  
Temporal Analysis ◽  
Shaanxi Province ◽  
Spatio Temporal ◽  
Grain Security
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