In this paper we calculate AQI for PM2.5 and PM10 for measurements obtained over several months, from December 2021 to April 2022. There were several measurement locations divided into urban and rural areas. The results have found that AQI for PM2.5 pollutant consistently had a higher value than AQI for PM10 pollutant, which reflects the fact that negative impact of PM2.5 on human health is stronger than that of PM10. The measurements show that higher AQIs and worse air quality were found in the rural areas. A more pronounced difference between urban and rural areas is with PM2.5 pollutant, which may be caused by heating with solid fuel in the villages. The current rising of electricity and gas prices is also an important factor on the worsening of air quality, since more people are looking into alternative sources of heating, such as solid fuel combustion. Air quality index, measurement, particulate matter, SPS30 Particulate matter (PM) are small solid or liquid particles of varying size and composition.
• •
PM10, or particles with aerodynamic diameter less than 10 µm,
PM2.5-10, or coarse particles, which are smaller than 10 µm but larger than 2.5 µm,
PM2.5, also known as fine particles, which are smaller than 2.5 µm
and PM0.5, also known as ultrafine particles, which are smaller than 0.5 µm [
PM can be described by its mass concentration, number concentration, surface area and size
distributions. Different metrics are suitable for describing different sizes. For example, ultrafine
particles are more suited for measuring the number concentration, while larger particles are often
described by their mass concentration [
PM10 and PM2.5 are classified as air pollutants [
2022 Copyright for this paper by its authors. is evidence that exposure to PM concentration increases the morbidity and mortality, including premature death of individuals with lung or heart problems [11] – [14].
The sources of PM can be natural or anthropogenic. One of the major anthropogenic sources of PM
is the residential wood combustion, which contributes to higher concentrations of PM during the heating
season in fall, winter, and spring, especially in rural areas [
In our previous research, we have focused on measuring the mass concentration of PM [18] – [21] due to the negative health effects of PM. The short-term analysis of PM mass concentrations has found that the air in rural areas is often more polluted and has worse quality than in urban areas, which was also supported by calculating hourly averages of PM mass concentrations. These were our first measurements in November and December 2021, during the peak of the heating season [18]. Other articles dealt with finding out, how much the distance from the source of PM affects the air quality [19] as well as analyzing how often we should measure PM [20]. The correlation between PM and meteorological factors (temperature, humidity, pressure) was a subject of [18], [21]. In our last published paper on PM [21], it was found that looking into long-term analysis of PM is necessary, as the air quality across months in different locations can vary. The metric with most telling value in this case is AQI (air quality index), calculated from daily averages of air pollutants (PM10 and PM2.5).
This paper will deal with the long-term analysis of PM2.5 and PM10 (specifically, the AQIs calculated from the daily averages of mass concentrations) for measurements which took place over several months (December 2021 to April 2022) at several locations, both urban and rural. The purpose of this comparison is to confirm, whether the conclusion from [18] (namely that air quality in rural areas is worse than in urban areas due to the impact of wood combustion being a more prominent source of heating) still stands with more measurements over longer periods of time.
This chapter will deal with the description of the measurement system built for the purpose of measuring PM, the locations of measurement, data processing, calculating AQI. 2.1.
To collect data, a measurement system based on Arduino Nano board was built. The sensory part of the measurement system was consisting of PM sensor SPS30 (the accuracy is shown in Table 1, temperature and humidity sensor SHT30, and temperature and atmospheric pressure sensor MS5611.
Conditions <100 µg/m3 >100 µg/m3 <100 µg/m3 >100 µg/m3 <1000 particles/cm3 >1000 particles/cm3 <1000 particles/cm3
SHT30 and MS5611 sensors were included due to the calculations of correlation between PM and meteorological factors (temperature, humidity, pressure), which is not a subject of this paper. The correlation between particulate matter and meteorological factors was a focus of our previous papers [18], [21]. Data was logged in a *.csv file on a microSD card (which communicated with Arduino Nano via microSD module) every 5 seconds. Each measurement was timestamped by RTC module DS3231. 2.2.
The measurements were carried out during several months (from November 2021 to April 2022) at several locations, in both urban and rural areas. Urban areas were situated in Košice, which is the 2nd largest city in Slovakia. The first Košice location was Department of Theoretical and Industrial Electrical Engineering at Technical University of Košice (DTIEE). The department building is located at university campus near a small road, which is not very busy. The distance between DTIEE and the nearest busy road (four-lane road) is approx. 250 m. The highway is separated from DTIEE by a small park. The measurements at DTIEE were carried out outside the window on the 1st floor of the building. The window faces the aforementioned park. Most urban measurements took place in this location. Next Košice location was the balcony on the 11th floor of MEI Hostel, which is located 1.64 km by air to the south-west of DTIEE. The hostel is situated between a busy road on one side and the park on the other side of the building. The balcony was facing towards the park. The final Košice location was the neigborhood “Dargovských hrdinov” (which translates to “The Dargov Heroes”) also known as Furča. The neighborhood is located approx. 2.5 km by air to the east of DTIEE. The measurements were carried out on the balcony of the11th floor. The balcony faces a four-lane road.
Rural areas were situated in two small villages. The first village was located 80 km by air to the north-east of Košice. The measurement took place outside the window on the first floor of a family house, which uses wood combustion as the primary source of energy. Many other houses are located close-by, which also heat with wood. The second and final rural location was village located 28 km to the south-east of Košice. The measuring set-up was placed on the balcony of a family house. Similarly to the previous rural location, at the same street there are 5 family houses close-by. All of them use wood combustion as a primary heat source. Most rural measurements took place in this location. 2.3.
AQI indicates the levels of air pollutants from a public health perspective. Several air pollutants are
used to calculate AQI; namely PM2.5, PM10, CO, SO2, NO2, O3. Because each air pollutant impacts
human health differently at different levels of concentration, calculating AQI can tell us more about the
air quality than just the measured concentrations of air pollutants, or their averages. PM2.5 have more
pronounced negative effects on human health [
PM10* (µg/m3) be measured [24]. Specifically, this means that to calculate AQI sub-indices of PM2.5 and PM10, the measurements each day must last for at least 18 hours.
Sub-indices are calculated using the following equation [24]: = − −
( − ) + , where AQIp = index for pollutant p, Cp = truncated concentration of pollutant p, BPHi = concentration breakpoint greater than or equal to Cp, BPLo = concentration breakpoint less than or equal to Cp, IHi = AQI value corresponding to BPHi, ILo = AQI value corresponding to BPLo.
From then, total AQI is determined as:
= max( 2.5, 10, 3, , 2, 2) ,
Only PM2.5 and PM10 were measured, therefore we can only calculate sub-indices AQIPM2.5 and AQIPM10. Although we cannot calculate AQI for all air pollutants that are typically considered, even AQIPM2.5 and AQIPM10 have still an informational value for us regarding the impact of particulate matter on air quality. 2.4.
The measured data was processed with MATLAB scripts and functions. First, raw data from *.csv files are imported. Then we check for validity of measurements for each day – if at least 18 hours of measurements per day are recorded, we can calculate daily averages of particulate matter. If the measurement was shorter than 18 hours per day, the measured data is unusable for calculating AQI. Using MATLAB script, we get rid of invalid measurements. The next step is to calculate daily averages of PM2.5 and PM10 from the valid measurements and truncate the result appropriately (one decimal for PM2.5, no decimals for PM10). From daily averages of PM2.5 and PM10, it is possible to calculate their AQI sub-indices. We have created MATLAB function called aqicalc to process large amounts of data at once using equation ( 1 ). The function calculates AQI for a given category of particulate matter (PM2.5 or PM10) and it also returns the distribution of air quality categories (i.e., how many AQI subindices fell under the good, moderate, unhealthy for sensitive groups, unhealthy, very unhealthy, or hazardous category). The last step is to plot the calculated AQI sub-indices in a bar graph and visualize the development of air quality in time.
We will consider AQI sub-indices for PM2.5 and PM10 for urban as well as rural areas (locations of measurement were described in detail in chapter 2.2 Locations of Measurement) and the impact of each air pollutant on air quality (via the category of AQI they fall under). 3.1.
As mentioned in chapter 2.2 Locations of Measurement, urban measurements were carried out in several locations in Košice, namely DTIEE, MEI Hostel and Košice-Furča. Most of these measurements took place at DTIEE. Only one measurement took place at MEI Hostel (15 December 2021) and two measurements took place in Košice-Furča (24 January and 14 February 2022). All urban measurements are shown in Figure 1, where a total of 39 AQIs for valid measurements are plotted (valid measurements lasting 18 hours per day or more). In reality, more measurements were carried out. However, during many days, the measurements lasted less than 18 hours, thus rendering them invalid and unusable for the purposes of this paper. The red bars in Figure 1 represent AQIPM2.5, the black bars represent AQIPM10 and the horizontal dashed lines are the limit values of AQI categories: G - good, M - moderate, U (SG) – unhealthy for sensitive groups, U- unhealthy, which correspond to Table 3. No measurements fell in the very unhealthy or hazardous category; therefore, we did not visualize those categories in Figure 1. Table 4 and Table 5 show the distribution of AQI (for PM2.5 and PM10 respectively) by month, measured in urban areas.
As can be seen from Figure 1 as well as Table 4 and Table 5, AQI for PM10 takes on a consistently
lower value than AQI for PM2.5. This agrees with the current research into particulate matter, which
says that negative health effects cause by the exposure to PM2.5 are more severe than the exposure to
the same levels of concentration to PM10 [
Most rural measurements took place in a village to the south-east of Košice, with the exception of three measurements (8 December 2021, 14-15 December 2021, and 30-31 January 2022) which took place in a different village, to the north-east of Košice. Measurements carried out in December – February were short and relatively isolated. A longer measurement lasted from 12 to 31 March (except that AQIs for 21 March are missing since less than 18 hours were recorded that day). Figure 2 shows all valid AQI calculated for PM2.5 (red bars) and PM10 (black bars). Horizontal dashed lines represent AQI category thresholds, which, just like in Figure 1, correspond to Table 3.
Table 6 and Table 7 show the distribution of PM2.5 and PM10 AQIs by month, measured in rural areas. AQI was calculated for a total of 30 valid days (at least 18 hours measured per day) from December 2021 to March 2022. For PM2.5 (Table 6), most AQIPM2.5 fell under the moderate category (17), followed by the unhealthy for sensitive groups category (10), followed by the unhealthy category (2) and only one measurement (in January 2022) showed good air quality. Very unhealthy or hazardous air quality was not recorded for PM2.5. As for PM10 (Table 7), 26 out of 30 days showed good air quality and only 4 AQIPM10 were in the moderate category (February and March 2022). No AQIPM10 in rural areas was in the unhealthy for sensitive groups, unhealthy, very unhealthy or hazardous category. 4. Conclusion
Some areas only had a few measurements (MEI Hostel, Košice-Furča, the village to the north-east of Košice), which means we cannot make conclusions from only a few days’ worth of measurements. Most measurements took place at DTIEE (urban area) and the village to the south-east of Košice (rural area), however, these measurements were not carried out simultaneously.
While the shorter measurements (e.g., MEI Hostel, Košice-Furča) may be sufficient for short-term analysis, they are not well suited for the long-term analysis. There need to be more measurements carried out at more places, simultaneously. Those measurements should be continuous and last several months (ideally at least a year) to be able to draw better and more reliable conclusions about the air quality from the long-term perspective. This will be possible to achieve if the sensors are available, which are currently difficult to procure due to the semiconductor shortage.
One thing which is repeated with great consistency throughout all measurements is AQIPM2.5 having a higher value than AQIPM10, thus we conclude that PM2.5 has a more negative impact on the air quality than PM10, which is consistent with current research on the effects of particulate matter on human health. This is regardless of whether the measurements were carried out in an urban or a rural area and regardless of when the measurements took place.
It is difficult to draw conclusions about how the seasons affect measurements of PM due to insufficient amount of data. However, from the March and April measurements (which are carried out more consistently than previous months), there appears to be a decreasing trend in AQIs for both PM2.5 and PM10. This coincides with the end of the heating season, which may be a factor impacting the levels of PM mass concentrations.
Most measurements for both urban and rural areas together were carried out in March 2022. From these measurements we can see that the AQIs are higher, and the air quality is worse in the rural areas than in the urban areas. This is also the case for some of previous months with less measurements, (namely in February 2022) as well as the combined measurements in December 2021 to March 2022 (since no measurements in April 2022 were carried out in the rural areas, we will not take April measurements in urban areas into consideration when comparing the air quality in the urban and rural areas). This difference in the air quality between the urban and rural areas is more pronounced in PM2.5 pollutant, which is the more important factor on the air quality out of PM2.5 and PM10. This may also be linked to the greater usage of wood combustion as a heat source in the villages than in the large cities.
It is also important to consider that the measurements were carried out in late 2021 and early 2022. The prices of gas and electricity rose since then and are expected to keep rising in the future. It is reasonable to expect that more and more people will look for alternative sources of heating, e.g., wood combustion, which will have further negative impact the air quality. Thus, we conclude that continuing the measurement and monitoring of particulate matter in the future is of the great importance. The future research will be focused on correcting the shortcomings of this paper (such as the lack of long-term and continuous measurements in some of the measurement places) as well as further developing the measurement system into a more complex and connected device using IoT (Internet of Things) and WSN (Wireless Sensor Network) technologies with an option to visualize the measured data in real time.