The data on ground concentrations of aerosols and small gas components (particulate matter PM2.5 and sulfur dioxide NO2) were compared with remote sensing data obtained over the territory of Krasnoyarsk from June to August 2020. We use the air monitoring system of the Krasnoyarsk Scientific Center of the Siberian Branch of the Russian Academy of Sciences (KSC SB RAS) to determine the concentration of PM2.5. NO2 concentrations were taken according to the data of the State departmental information and analytical system of the Ministry of Ecology of the region. It is shown that the remote sensing data of the MODIS MAIAC algorithm with a spatial resolution of 1 km can be used to determine the concentration of PM2.5 as an addition to the data obtained by the ground-based air monitoring system of the KSC SB RAS. The MAIAC data were calculated using two diferent models and are given to the measurement system used in the KSC SB RAS monitoring network. A high coeficient of determination between satellite and ground monitoring data was obtained. Determination coeficients were also obtained for NO 2, showing how applicable the remote sensing data are for assessing the environmental situation in Krasnoyarsk.
The issue of air quality assessment is particularly acute in large industrial and developing cities.
The low quality of atmospheric air afects the health of the population and the state of the
environment in general. Air consists of a mixture of diferent gases. Air quality is determined by a
combination of various physical, chemical, and biological properties. One of the main parameters
for assessing atmospheric air quality is the concentration of small gas components (SGC). The
small gas components of the atmosphere include methane, carbon monoxide, nitrogen dioxide,
etc. The concentration of SGC has a significant impact on the absorption of optical radiation,
and increased concentrations of SGC harm the population’s health. In addition to SGC, aerosols
or particulate matter (PM) are present in the air. Particulate matter of natural or anthropogenic
origin significantly impacts the climate and environment and, like SGC, harms human health [
After twenty years of epidemiological studies, scientists have found a significant correlation
between fine pollutants and respiratory morbidity [
Krasnoyarsk is an actively developing city. Currently, in the city, monitoring stations using the optical registration method and the weight method are mainly used to determine PM concentration. Ground-based observations from monitoring stations show important spatial and temporal information about the concentration of PM in the atmosphere. Although the network of monitoring stations in the city has dozens of observation posts, point measurements do not provide information about PM’s spatial characteristics and distribution of PM in urban areas of interest, nor do they show the influence of the city’s atmosphere on the surrounding areas.
The influence of external factors contributing to pollution in the city (the private sector, industrial facilities located outside the city) is also of interest. The time coverage of on-site PM measurements also varies greatly depending on the device’s operation period and its functionality. These reasons have led to ongoing eforts to assess PM using satellite remote sensing techniques.
Nitrogen dioxide NO2, like PM, harms the body. The main sources of NO2 are car exhaust gases, CHP emissions, solid waste incineration, gas combustion. If a small concentration of nitrogen dioxide enters the respiratory organs, a person experiences respiratory disorders, coughing, an increase in concentration can lead to oxygen starvation and other negative consequences. Nitrogen dioxide also negatively afects the environment, increasing the soil’s acidity, adversely afecting vegetation, and plays an important role in the formation of urban smog.
The city of Krasnoyarsk is the regional center of the Krasnoyarsk Territory, with a population
of more than 1 million people. It is actively developing and has an area of about 350 km2. The
coordinates of the city center are 56°00’ north latitude and 92∘ 52’ east longitude. The city and the
surrounding territories have a unique relief. From the south and west of the city, there are forests
and hilly terrain. From the north and east, the terrain is mostly flat. The Yenisei River, which
does not freeze in winter due to the nearby Krasnoyarsk hydroelectric power station, divides
the city approximately in half. Like all major cities, Krasnoyarsk is subject to a negative impact
on the environmental situation. Motor transport, the private sector, thermal power plants, large
enterprises of the metallurgical, machine-building, and chemical industries contribute many
emissions into the atmosphere and negatively afect the quality of the surrounding air. The
concentration of PM2.5 in Krasnoyarsk is 64% higher than the average in Russia [
The aerosol optical depth (AOD) parameter is usually used [
In our work, we used the data of the MAIAC product. This algorithm was developed for
processing MODIS data. MAIAC extracts aerosol parameters above the ground with a resolution
of 1 km [
The increased accuracy of MAIAC results from using the explicit surface characterization
method instead of the empirical approach to surface parameterization, which is used in the
MOD04 and MYD04 algorithms. In addition, MAIAC includes a cloud mask algorithm based on
spatial-temporal analysis, which complements traditional methods of detecting clouds at the
pixel level [
To determine how much AOD correlates with ground-based PM measurements, numerous
linear, chemical [
The method of obtaining data is based on the reflection of various wavelengths from the Earth’s surface and the registration of reflected radiation on the device’s sensor. Because of this, incorrect values are obtained on a highly reflective surface (snow, water, cloud cover). Therefore, data on the territory of Krasnoyarsk becomes available after the snow cover disappears and before it appears.
We used the OMNO2d product as satellite data on nitrogen dioxide. This product provides data
on NO2 in a vertical column of the atmosphere with a spatial resolution of 0.25× 0.25 degrees. The
OMNO2d product contains daily NO2 values for the entire territory of the Earth from October
1, 2004, to the present [
The data on PM25 of the air monitoring system of the Krasnoyarsk Scientific Center, consisting of 26 monitoring posts, were used in work. A certified CityAir air monitoring station developed by a group of companies from the Novosibirsk Technopark and the Skolkovo Innovation Center is installed at each monitoring post. These stations provide information about the state of the surrounding air, its temperature, pressure, and humidity. Additionally, an optical sensor is installed inside the station to measure the concentrations of PM25 and PM10.
Data on NO2 were obtained from the State regional environmental departmental information and analytical data system of the Krasnoyarsk Territory. There are 7 automated observation posts (AOP) installed on the territory of Krasnoyarsk, providing information on the concentrations of carbon monoxide, sulfur dioxide, nitrogen dioxide, hydrogen sulfide, and other pollutants. For our study, we used data from two AOPs since data from other AOP had gaps in the study period. In Figure 1, a red circle is circled the monitoring stations used to determine the concentrations of PM25 and NO2.
To assess the compliance of the satellite sensing data with the ground monitoring data, the satellite sensing data were adjusted according to meteorological parameters. We used data on temperature, humidity, and atmospheric air pressure. All these parameters were obtained using a network of monitoring posts of the KSC SB RAS. In addition to the parameters given above, the planetary boundary layer height (PBLH) obtained from the atmospheric model of the Global Forecasting System (GFS) was used.
To assess the extent to which satellite monitoring data can be used to analyze the environmental situation in the city according to the PM25 parameter, we calculated the correlation coeficient between the PM25 data obtained from ground-based observations and the data on the AOD parameter obtained from satellite monitoring data. Since the AOD data is available only in the absence of snow cover, the period from June to August 2019 and 2020 was taken, a total of 184 days. However, the AOD data is also limited by the presence of cloud cover. As a result, the ifnal number of days used in the calculations was 80. For comparison, the AOD data located above the ground monitoring station was taken, then they were averaged, getting an average value over the city. The ground monitoring data was taken as the PM25 concentration value measured for 12 hours of the day. The data between the posts was averaged, obtaining the average PM2.5 value in the city.
To compare the AOD and PM25 data, the AOD data were recalculated to PM25 units of measurement (mg/m3). We used two models.
The first model has a linear form, as shown in formula (1)
PM = · AOD + , where PM is the calculated PM values in mg/m3, and are linear regression coeficients and are equal to 7.2 · 10− 3 and 5.3 · 10− 3, respectively. The result obtained using this formula is shown in Figure 2.
The next model considered has the form shown in formula (2). It includes meteorological parameters and takes into account aerosol characteristics. This formula is widely used for comparing AOD and PM data:
PM = PBLH
AOD ⧸︃ (︃
︂[ 1 − RH ]︂ − )︃ · 1 − RH0
To obtain the calculated values according to the formula (2), it is necessary to take into
account, in addition to the AOD values, such parameters as PBLH — the height of the boundary
layer of the atmosphere, RH — air humidity, RH0 — the average value of air humidity for the
selected territory, the variables and are aerosol characteristics and are obtained from [
To assess the extent to which satellite monitoring data can be used to assess the environmental situation in the city according to the NO2 parameter, we calculated the correlation coeficient between NO2 data obtained from ground-based observations and NO2 data obtained from satellite monitoring data. Since the data has a spatial resolution of 0.25 degrees, the territory of Krasnoyarsk is covered by two pixels, which were averaged. The time period from June 1 to August 31, 2020, was chosen to compare satellite and ground data. NO2 satellite monitoring data is provided in moles/cm3 units in the vertical column of the atmosphere, data from ground monitoring posts are provided in mg/m3 units. To calculate the correlation coeficient between the two data series, we converted the satellite monitoring data series using formula (3).
NO2 calc = · NO2 sat + · + · + · + · PBLH (3) where NO2 sat is the NO2 data obtained using the OMNO2d product, is the air humidity, is the air temperature, is the air pressure, PBLH is the height of the atmospheric boundary layer. The results obtained using formula (3) are shown in Figure 4.
Satellite AOD measurements, ground-based PM25 data, and meteorological parameters were used to compare the PM25 data. Applying formula (1) for the satellite data series and comparing the calculated data series with ground measurements, we obtain the graph shown in Figure 2.
Satellite monitoring data were converted to the units of ground monitoring stations data and then compared with them. The coeficient of determination between these data sets was calculated (2 = 0.71). Such a high value of the coeficient of determination suggests that the data for PM, calculated using satellite monitoring, have high compliance with the data measured by ground monitoring stations.
The next stage of the work is to obtain the calculated values of PM using the formula (2). The result is shown in Figure 3.
When adding meteorological parameters and aerosol characteristics of PM25 to the model, it was possible to increase the correlation between the two data series significantly. Therefore, satellite monitoring data can be used in addition to data from ground observation posts to improve the assessment of the environmental situation in Krasnoyarsk. However, the data from the first model can also be used to assess air quality since they have a strong correlation with ground data. Data from the linear model can be used in places where weather parameters are unknown.
To compare the NO2 data, we also used data obtained using satellite measurements and ground measurements at monitoring posts. Weather parameters were also used to increase the correlation between these two data series.
Applying formula (3) to a series of data obtained from satellite measurements and comparing the calculated data series with ground monitoring data, we obtain the graph shown in Figure 4.
The value of the determination coeficient for NO 2 is worse than the value for PM25. Most likely, this is due to the coarser spatial resolution of satellite data. However, this accuracy is suficient for assessing the impact of the urban environmental situation on the surrounding suburban territory; this accuracy is suficient.
The joint processing of data from ground-based monitoring networks with remote sensing data will improve the assessment of Krasnoyarsk’s environmental situation. It will be possible to obtain data on the spatial distribution of pollution in the city, which will strengthen the understanding of the influence of the city’s atmosphere on the surrounding areas and the influence of the surrounding areas on the environmental situation in the city.
Using satellite monitoring data, it is possible to significantly supplement the data obtained from ground monitoring posts, strengthening the understanding of atmospheric processes occurring in the city and beyond.