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

Category: Urban Air Quality

Accepted Manuscripts
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


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.


Fine suspended particulate matter having aerodynamic diameters of less than 10 (РМ10) and 2.5 µm (РМ2.5) can be dangerous air pollutants, which necessitates operational monitoring. Of the 1113 major Russian cities. only a few monitor industrial emissions of PM10 and PM2.5. Here, we set a goal to develop an approach for using mobile multiwave (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 carbonate composition, were registered. We have developed algorithms with seven stages of assessing the spatial distribution and monitoring of РМ10 and РМ2.5. This algorithm implementation made it possible to establish that, in the sum of emissions of all particulate matter, the fine-mode particle fraction from tall sources of cement and construction material production (pipes with a height of ≥ 50 m) was 39%. Using GIS–mapping of fields of the total suspended particulate matter content (TSP) and ratios РМ10/TSP and РМ2.5/TSP installed, excesses in РМ10 and РМ2.5 up to 2.5 and to 2.8 times greater than the maximum single the threshold limit value were observed. Tall sources share of cement plants increased in proportion to the increase in distance from the source and equaled for РМ10 – 40–85% and for PM2.5 – 43–91%. We demonstrate the value of lidar as the basis for an optimal emissions monitoring network of particulate matters for environmental policy making.


Particulate matters Urban air pollution Tall sources Lidar measurements

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