Temporal Variability and Relationship between Surface Concentration of PM2.5 and Aerosol Optical Depth According to Measurements in the Middle Urals

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Temporal Variability and Relationship between Surface Concentration of PM2.5 and Aerosol Optical Depth According to Measurements in the Middle Urals. / Luzhetskaya, A. P.; Nagovitsyna, E. S.; Omelkova, E. V. и др.
в: Atmospheric and Oceanic Optics, Том 35, № S1, 01.12.2022, стр. S133-S142.

Результаты исследований: Вклад в журнал › Статья › Рецензирование

BibTeX

@article{7103a42d403e46ae99d598347ab6dd7f,

title = "Temporal Variability and Relationship between Surface Concentration of PM2.5 and Aerosol Optical Depth According to Measurements in the Middle Urals",

abstract = "We analyzed the measurements of aerosol parameters in the surface air layer with sets of Panasonic PM2.5 optical sensors and in throughout the atmospheric column by the photometric method at urban and background observation sites in the Middle Urals for 2016–2019. The features of the intra-annual and daily variations in the aerosol parameters in the surface air layer and in the atmospheric column are compared; also, the relationships between the PM2.5 concentration, AOD, and meteorological parameters in two regions are studied. For the first time for the Middle Urals, we constructed the statistical models for estimating the PM2.5 concentration. Multivariate regression models for estimating the logarithm of PM2.5 concentration are far superior to the single-factor models. The significant predictors are found to be: boundary layer height (blh, m), ln AOD, normalized difference vegetation index (NDVI), relative air humidity (Hu, %), and pressure (P, Pa).",

author = "Luzhetskaya, {A. P.} and Nagovitsyna, {E. S.} and Omelkova, {E. V.} and Poddubny, {V. A.}",

note = "This study was supported by the Russian Foundation for Basic Research (project no. 19-05-50 138).",

year = "2022",

month = dec,

day = "1",

doi = "10.1134/S1024856023010098",

language = "English",

volume = "35",

pages = "S133--S142",

journal = "Atmospheric and Oceanic Optics",

issn = "1024-8560",

publisher = "Pleiades Publishing",

number = "S1",

}

RIS

TY - JOUR

T1 - Temporal Variability and Relationship between Surface Concentration of PM2.5 and Aerosol Optical Depth According to Measurements in the Middle Urals

AU - Luzhetskaya, A. P.

AU - Nagovitsyna, E. S.

AU - Omelkova, E. V.

AU - Poddubny, V. A.

N1 - This study was supported by the Russian Foundation for Basic Research (project no. 19-05-50 138).

PY - 2022/12/1

Y1 - 2022/12/1

N2 - We analyzed the measurements of aerosol parameters in the surface air layer with sets of Panasonic PM2.5 optical sensors and in throughout the atmospheric column by the photometric method at urban and background observation sites in the Middle Urals for 2016–2019. The features of the intra-annual and daily variations in the aerosol parameters in the surface air layer and in the atmospheric column are compared; also, the relationships between the PM2.5 concentration, AOD, and meteorological parameters in two regions are studied. For the first time for the Middle Urals, we constructed the statistical models for estimating the PM2.5 concentration. Multivariate regression models for estimating the logarithm of PM2.5 concentration are far superior to the single-factor models. The significant predictors are found to be: boundary layer height (blh, m), ln AOD, normalized difference vegetation index (NDVI), relative air humidity (Hu, %), and pressure (P, Pa).

AB - We analyzed the measurements of aerosol parameters in the surface air layer with sets of Panasonic PM2.5 optical sensors and in throughout the atmospheric column by the photometric method at urban and background observation sites in the Middle Urals for 2016–2019. The features of the intra-annual and daily variations in the aerosol parameters in the surface air layer and in the atmospheric column are compared; also, the relationships between the PM2.5 concentration, AOD, and meteorological parameters in two regions are studied. For the first time for the Middle Urals, we constructed the statistical models for estimating the PM2.5 concentration. Multivariate regression models for estimating the logarithm of PM2.5 concentration are far superior to the single-factor models. The significant predictors are found to be: boundary layer height (blh, m), ln AOD, normalized difference vegetation index (NDVI), relative air humidity (Hu, %), and pressure (P, Pa).

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85150967113

UR - https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=tsmetrics&SrcApp=tsm_test&DestApp=WOS_CPL&DestLinkType=FullRecord&KeyUT=000959483300015

UR - https://elibrary.ru/item.asp?id=59580939

U2 - 10.1134/S1024856023010098

DO - 10.1134/S1024856023010098

M3 - Article

VL - 35

SP - S133-S142

JO - Atmospheric and Oceanic Optics

JF - Atmospheric and Oceanic Optics

SN - 1024-8560

IS - S1

ER -

ID: 37145470