Numerical study of an algorithm for air pollution sources identification with in situ and remote sensing measurement data

被引：1

作者：

Penenko, A. V. ^{[1
,2
]}

Gochakov, A. V. ^{[3
]}

Antokhin, P. N. ^{[4
]}

机构：

[1] RAS, SB, ICM&MG, Inst Computat Math & Math Geophys, Prospekt Akad Lavrentjeva 6, Novosibirsk 630090, Russia

[2] NSU, Pirogova Str 2, Novosibirsk 630090, Russia

[3] Siberian Reg Sci Res Hydrometeorol Inst, Sovetskaya St 30, Novosibirsk 630099, Russia

[4] Russian Acad Sci, Siberian Branch, VE Zuev Inst Atmospher Opt, 1 Acad Zuev Sq, Tomsk 634021, Russia

来源：

25TH INTERNATIONAL SYMPOSIUM ON ATMOSPHERIC AND OCEAN OPTICS: ATMOSPHERIC PHYSICS | 2019年 / 11208卷

基金：

俄罗斯基础研究基金会;

关键词：

inverse source problem; atmospheric chemistry; remote sensing data; in situ measurements; Novosibirsk city; adjoint ensemble;

D O I：

10.1117/12.2540901

中图分类号：

P4 [大气科学（气象学）];

学科分类号：

0706 ; 070601 ;

摘要：

The results of the inverse source problem solution for an atmospheric chemistry transport and transformation model for in situ and remote sensing measurement data are compared. The algorithm based on the ensembles of the adjoint problem solutions is applied to solve the inverse problem. The solutions are compared in the Novosibirsk city inverse modeling scenario.

引用

页数：6

共 50 条

[21] Air pollution inputs to the Mojave Desert by fusing surface mobile and airborne in situ and airborne and satellite remote sensing: A case study of interbasin transport with numerical model validation
Leifer, Ira
Melton, Christopher
Chatfield, Robert
Cui, Xinguang
Fischer, Marc L.
Fladeland, Matthew
Gore, Warren
Hlavka, Dennis L.
Iraci, Laura T.
Marrero, Josette
Ryoo, Ju-Mee
Tanaka, Tomoaki
Yates, Emma
Yorks, John E.
ATMOSPHERIC ENVIRONMENT, 2020, 224
[22] Segmentation on remote sensing imagery for atmospheric air pollution using divergent differential evolution algorithm
Meera Ramadas
Ajith Abraham
Neural Computing and Applications, 2023, 35 : 3977 - 3990
[23] Segmentation on remote sensing imagery for atmospheric air pollution using divergent differential evolution algorithm
Ramadas, Meera
Abraham, Ajith
NEURAL COMPUTING & APPLICATIONS, 2023, 35 (05): : 3977 - 3990
[24] Remote sensing identification of urban water pollution source types using hyperspectral data
Cai, Xiaolan
Wu, Luyao
Li, Yunmei
Lei, Shaohua
Xu, Jie
Lyu, Heng
Li, Junda
Wang, Huaijing
Dong, Xianzhang
Zhu, Yuxing
Wang, Gaolun
JOURNAL OF HAZARDOUS MATERIALS, 2023, 459
[25] OIL POLLUTION OF THE SOUTHEASTERN BALTIC SEA BY SATELLITE REMOTE SENSING DATA AND IN-SITU MEASUREMENTS
Bulycheva, Elena V.
Krek, Aleksander V.
Kostianoy, Andrey G.
Semenov, Aleksander V.
Joksimovich, Aleksandar
TRANSPORT AND TELECOMMUNICATION JOURNAL, 2015, 16 (04) : 296 - 304
[26] Using ENN-1 to inspect the air pollution of automobile exhaust by remote sensing data
Wang, Meng-Hui
Chao, Kuei-Hsiang
Lin, Keng-Hsien
PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2006, : 3000 - +
[27] Estimation of Air Pollution using Remote Sensing Technique in Coimbatore-A Case Study
Geetha, P.
Kokila, M.
2015 INTERNATIONAL CONFERENCE ON COMMUNICATIONS AND SIGNAL PROCESSING (ICCSP), 2015, : 794 - 798
[28] Measurement report: An assessment of the impact of a nationwide lockdown on air pollution - a remote sensing perspective over India
Pathakoti, Mahesh
Muppalla, Aarathi
Hazra, Sayan
Venkata, Mahalakshmi D.
Lakshmi, Kanchana A.
Sagar, Vijay K.
Shekhar, Raja
Jella, Srinivasulu
Rama, Sesha Sai M., V
Vijayasundaram, Uma
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2021, 21 (11) : 9047 - 9088
[29] OPTICAL-CORRELATION SPECTROMETRY FOR REMOTE SENSING OF AIR POLLUTION
BARRINGER, AR
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA, 1970, 60 (05) : 729 - +
[30] LASER REMOTE-SENSING FOR AIR-POLLUTION MEASUREMENTS
WERNER, C
APPLIED OPTICS, 1975, 14 (04): : 796 - 769

← 1 2 3 4 5 →