Based on its measurements of the MODIS spectrometer installed on the TERRA and AQUA satellites, data on aerosol optical depth (AOD) with different spatial resolution are formed: 10, 3, 1 km. The relationship between AOT values measured using remote sensing and PM2.5 measured at automated observation posts (APS) was investigated. It is shown that the data with a spatial resolution of 1 km make it possible to see dusty zones inside the city. Aerosol Index was used to take into account the contribution of external factors, such as smoke from fires, to the ecological situation of the city. This information can be used as an objective assessment of the environmental situation.
Aerosol index of the atmosphere is a qualitative indicator that indicates the presence in the air of particles that absorb radiation in the ultraviolet range. Aerosol index can take values from 0 to 5. An AI value of 5 corresponds to a very high concentration of aerosols that can reduce visibility or affect human health, and values less than 0.2 correspond to clean, clear air. 2
In July 2018, there were three automated monitoring stations (AMS) of the regional ecological system in Krasnoyarsk, which measured PM2.5 concentrations. Figure 1 shows the location of the AMS from which data were used in our study.
To measure PM2.5, the AMS equipment uses radioisotope principle of operation, which is generally accepted all over the world. It is based on the absorption of β-radiation by dust particles deposited on the filter belt. The isotope C14 is used as a source of β-radiation. Dust is deposited on the filter belt as a result of pumping the air sample by the pump. Measurement of the radiation absorption value is carried out using the built-in Geiger-Muller detector counter. We used average daily concentrations to estimate the amount of air pollution.
We also used the MAIAC algorithm [9], which was developed for processing MODIS data. MAIAC extracts aerosol parameters above ground with a resolution of 1 km. The MCD19A2 (MAIAC) MODIS product contains spectrophotometer data from Terra and Aqua satellites. This product was published on May, 2018, and contains AOT data from February, 2000 [10]. Aerosol parameters include optical depth at wavelengths from 0.47 to 0.67 µm and aerosol type, including background, smoke and dust models [11].
In our study, we used AOT data at a wavelength of 0.47 µm.
In [
Improved accuracy of MAIAC results from use of the method of the apparent surface characteristics in contrast to
the empirical approach to parameterize the surface, which is used in the MOD04/MYD04 algorithms. Moreover,
MAIAC incorporates a cloud mask algorithm, based on spatiotemporal analysis, which complements traditional
methods for the detection of clouds at the pixel level [
In addition to MAIAC data, we used daily aerosol data from MODIS Level 2, Collection 6.1 from Aqua and Terra
satellites, which were obtained with a spatial resolution of 10×10 km2 (in nadir). MYD04/MOD04 aerosol products
were obtained on the basis of spectral radiation measured by MODIS using seven spectral channels in the wavelength
range from 470 to 2130 nm [
In our study we used measurements of PM2.5 from AMS ground-based posts and satellite measurements of AOT for July 2018. We studied the relationship between measurements of AOT and PM2.5 on the scale of Krasnoyarsk. The frequency of AMS measurements is 1 measurement in 20 minutes. We used average PM2.5 values per day. For the correlation at city level between the data of AOT and PM2.5 was available 10 days, only 30 pairs. The days were chosen taking into account the absence of clouds.
The study used AI calculated from satellite data of the OMPS device (Ozone Mapping Profiler Suite), installed on
the American meteorological satellite Suomi NPP. The spatial resolution of one pixel is 50×50 km2 (in nadir). The
Aerosol Index is calculated using backscattered UV radiation in the range 300-380 nm [
Different approaches to air quality assessment allow to see pollution at different scales. In particular, using the MAIAC product it is possible to track pollution on an intra-urban scale. However, for a larger area, the use of this algorithm is hampered by the lack of data for the cloud-covered area. Aerosol index well detects smoke from fires. Aerosol index of the atmosphere – a qualitative indicator that indicates the presence in the air of particles that absorb radiation in the ultraviolet range.
In Krasnoyarsk in the period from 14 July to early August established a dense haze of forest fires. Using the AOD parameter gives large data gaps, making it difficult to track pollution dynamics. The use of AI, on the contrary, makes it possible to trace the dynamics of the spread of smoke plumes from fires over the city.
Figure 4 shows the spread of smoke plume from fires for the period from 14 to 24 July 2019.
In this paper, we used a new MAIAC algorithm to estimate AOT from MODIS data with a spatial resolution of 1 km, comparing it with a classical algorithm with a coarser spatial resolution of 10 km. Our analysis shows that the correlation between PM2.5 and AOT with a spatial resolution of 1 and 10 km is approximately similar. However, using a higher spatial resolution, it is possible to identify areas of dust pollution in the city on the block level of details. This will make it possible to determine more qualitatively environmentally unfavorable areas of the city. Using the ground-based AMS data, in addition to satellite data with high spatial resolution (MAIAC), it is possible to create an information basis for a modern system of environmental monitoring on a regional scale and contribute to the improvement of the environmental situation in the city.
At a time when monitoring of AOD data is not possible, the environmental situation can be monitored using AI, but it has a rather low spatial resolution, which makes it inapplicable to the identification of dust pollution zones on an intra-urban scale. However, the use of this index helps to identify external factors affecting the environmental situation in the city, regardless of the time of year and the presence of clouds. [3] Van de Kassteele J. et al. Statistical mapping of PM10 concentrations over Western Europe using secondary information from dispersion modeling and MODIS satellite observations //Stochastic environmental research and risk assessment. 2006. Vol. 21. No. 2. P. 183194. [4] Yap X. Q., Hashim M. A robust calibration approach for PM 10 prediction from MODIS aerosol optical depth //Atmospheric Chemistry & Physics Discussions. 2012. Vol. 12. No. 12. P. 3148331505. [5] Kloog I. et al. Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the MidAtlantic states //Environmental science & technology. 2012. Vol. 46.
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[11] Lyapustin A. et al. Corrigendum to" Discrimination of biomass burning smoke and clouds in MAIAC algorithm" published in Atmos. Chem. Phys., 12, 9679–9686, 2012 //Atmospheric Chemistry and Physics. 2012.
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