Landsat Land Use Classification for Assessing Health Risk from Industrial Air Pollution

Balter, B.M.; Balter, D.B.; Egorov, V.V.; Stalnaya, M.V.; Faminskaya, M.V.

doi:10.1134/s0001433818090062

Cited by 1 publication

(2 citation statements)

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“…Another global land use classification based on MODIS data is MCD12Q1 (Garcia-Mora et al, 2012); it has a lower resolution (500 m) but for AERMOD, this may be sufficient. Using GLC30, GLC10 and MCD12Q1 with AERMOD was considered in (Balter et al, 2018). Rather than using a global classification, one can produce a classification of Landsat data based on locally chosen training sites, as in this paper.…”

Section: Review Of Space Data Usable In Pollutant Dispersion Modelsmentioning

confidence: 99%

“…(here, only GLC10 is listed) with concentrations obtained by applying them to supervised classification of multi-year Landsat data (weighted maximum likelihood ML) with locally selected training sites, as described in (Balter et al, 2018). The classification procedure uses unsupervised clustering of the full mul- (Balter et al, 2018).…”

Section: Impact Of Classificationmentioning

confidence: 99%

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Space Observations and Global Climatic Data Reanalysis in AERMOD Modeling Package to Enhance the Industrial Air Pollution and Health Risk Assessment

Faminskaya¹

2020

GEP

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We try to enhance the AERMOD industrial pollution dispersion model with remote sensing observations and climatic models based on them. In this paper, we focus on surface parameters (albedo, roughness, Bowen ratio) and land use classification on which they depend. We model maximum hourly concentrations and the resulting acute health risk and assess the effect on them produced by using remote sensing data for local areas around industrial plants instead of global standard AERMOD parameters. We consider five real multi-source plants for the effect of classification and two of them for the effect of surface parameters. The effect on the critical pollutant is measured in three ways: a) as difference between the yearly maxima of hourly concentrations of a critical pollutant ("absolute"); b) the same limited to daytime workhours and 95% quantile instead of absolute maximum ("regulatory"); c) as maximum hourly difference over a year ("instant"). The measure of effect is divided either by the reference concentration of the pollutant, which yields the impact on health risk, or by the concentration obtained with AERMOD standards, which yields relative measure of impact. For a), the impact of roughness dominates, that of albedo is small and that of the Bowen ratio is almost zero. For b), the impact of roughness is less prominent, and that of albedo and Bowen ratio is noticeable. For c), the impact is considerable for all three parameters. The effect of land use classification is considerable in all three cases a)c). We provide the figures for different measures of remote sensing data effect and discuss the perspective of using remote sensing data in regulatory context.

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Section: Review Of Space Data Usable In Pollutant Dispersion Modelsmentioning

confidence: 99%

Section: Impact Of Classificationmentioning

confidence: 99%

Space Observations and Global Climatic Data Reanalysis in AERMOD Modeling Package to Enhance the Industrial Air Pollution and Health Risk Assessment

Faminskaya¹

2020

GEP

View full text Add to dashboard Cite

show abstract

Landsat Land Use Classification for Assessing Health Risk from Industrial Air Pollution

Cited by 1 publication

References 1 publication

Space Observations and Global Climatic Data Reanalysis in AERMOD Modeling Package to Enhance the Industrial Air Pollution and Health Risk Assessment

Space Observations and Global Climatic Data Reanalysis in AERMOD Modeling Package to Enhance the Industrial Air Pollution and Health Risk Assessment

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