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Monitoring of Emission of Particulate Matter and Air Pollution using Lidar in Belgorod, Russia

Category: Urban Air Quality

Volume: 19 | Issue: 3 | Pages: 504-515
DOI: 10.4209/aaqr.2017.12.0593
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Fedor Lisetskii , Аndrei Borovlev

  • Federal-Regional Center of Aerospace and Surface Monitoring of the Objects and Natural Resources, Belgorod State National Research University, Belgorod 308015, Russia

Highlights

We studied emissions from all sources of air pollution in the industrial center.
Particles PM2.5 were detected beyond 3.5 km from high emission sources.
The ratios of PM10/TSP and PM2.5/TSP were obtained.
Monitoring emissions from high sources is promising when using multiwave lidars.
A new software for estimating PM10 and PM2.5 share in the industrial emissions flare.


Abstract

Fine suspended particulate matter with an aerodynamic diameter smaller than 10 (РМ10) or 2.5 µm (РМ2.5) can be a dangerous air pollutant necessitating operational monitoring. Of the 1113 major Russian cities, however, only a few monitor industrial emissions of PM10 and PM2.5. Here, we develop an approach to using mobile multi-wave (1064, 532, and 355 nm) lidar to estimate the concentration of PM10 and PM2.5. This approach was implemented for Belgorod, where 1378 sources of air pollution with anthropogenic dust, primarily of carbonate composition, were registered. We have developed algorithms with seven stages of assessing the spatial distribution and monitoring of РМ10 and РМ2.5, which made it possible to establish that fine-mode particles from tall sources of cement and construction material production (pipes with a height of ≥ 50 m) contributed 39% of the total particulate matter emissions. Using GIS to map the fields of the total suspended particulate matter (TSP) and determining the ratios of РМ10/TSP and РМ2.5/TSP, excesses in РМ10 and РМ2.5 up to 2.5 and 2.8 times greater, respectively, than the maximum threshold limit were observed. Tall sources’ contribution to emissions increased in proportion to the distance from the source, resulting in 40–85% of the РМ10 and 43–91% of the PM2.5. We demonstrate how lidar can be applied to optimize a particulate matter emissions monitoring network for environmental policy making.

Keywords

Particulate matters Urban air pollution Tall sources Lidar measurements


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