The main pollutants emitted to the atmosphere as a result of anthropogenic human activity include: sulfur dioxide, nitrogen oxides, dust, volatile organic compounds (VOCs), persistent organic pollutants (POPs), heavy metals, greenhouse gases and odours. The enormous impact of air quality on the environment, but above all on human health and life, is the reason why it is so important to constantly monitor air quality and strive to minimize pollution and improve air quality. Epidemiological studies show that negative health effects in humans occur not only in the case of exposure to increased mass concentrations of various fractions of atmospheric dust, but also result from its chemical, physical and biological properties [1-2]. The incidence of asthma and chronic obstructive pulmonary disease (COPD) as well as increased exacerbation of these diseases is correlated with an increased concentration of harmful substances in the atmosphere, which implies a significant share of air pollution in the course of obstructive pulmonary diseases [3]. It is believed that children's bodies are more exposed to pollution than adults. This is because organisms in the growing phase are more susceptible to damage than organisms that are already developed. Moreover, children inhale more air in relation to their body weight than adults, and they also display greater physical activity, which in turn translates into greater exposure to air pollutants [4].
Research results prove the huge impact of broadly understood air pollution on the biological condition, the regularity of growth and development processes, and the general psychophysical condition of people at all stages of ontogenesis, up to the quality and length of life. Environmental factors in each phase of ontogenetic development modify all aspects of the psychophysical condition. Organisms in the prenatal period of progressive development are particularly sensitive to environmental factors, when air pollution causes: 1/ fetal hypoxia (hypoxia), reducing birth weight, and 2/ through epiegenetic activities, it will change genetically programmed developmental pathways. For example, the diversity of the living environment in Poland determines up to 8 cm difference in the final body height of young men [5].
Exposure to air pollutants is highly dependent on their concentration. According to a report by the World Health Organization, nine out of ten people breathe polluted air, and this causes around seven million deaths worldwide each year. The current state of air quality in Poland is mainly caused by the so-called low emissions, mainly from the household and municipal sectors. The factors that negatively affect air quality, especially in the case of low-emission sources, include unfavourable meteorological conditions, such as: weak wind, low air temperature or fog. However, high wind speeds and heavy rainfall result in a significant reduction in the level of pollutant concentrations. The problem contributing to poor air quality in Poland is the use of outdated motor vehicles, especially those with diesel engines, which are important sources, e.g. of dust emissions. The exceedance of air quality standards for dusts PM
Air protection is one of the priority directions of Poland's policy. Considering the improvement in air quality, the need to replace coal with other energy carriers becomes more and more clearly visible. This problem affects all countries where energy policy is based on coal, such as China, India and North America. The key activities in the field of air protection include the reduction of air pollutant emissions, allowing for the improvement of its quality and compliance with the air quality standards in force in Polish law.
At the end of 2018, the Silesian University of Technology in Poland acquired a mobile laboratory built on the Ford Transit chassis, and the obtained measurement results are used in the area of low emissions forecasting and serve as a tool to fight smog. The mobile air pollution emission laboratory is equipped with: SO2 – T100/Teledyne API analyzer, NOX – T200/Teledyne API analyzer, suspended dust meter PM SO2 – continuous automatic measurement using the fluorescence method in accordance with PN-EN 14212: 2013-02/AC:2014-06E (PN-EN 14212:2013-02/AC:2014-06E Ambient air quality – Standard method for the measurement of the concentration of sulfur dioxide by ultraviolet fluorescence [6]); NOx – continuous automatic measurement using the chemiluminescence method in accordance with PNEN 14211:2013-02 (PN-EN 14211:2013-02 Ambient air quality-Standard method for the measurement of the concentration of nitrogen dioxide and nitrogen monoxide by chemiluminescence [7]); PM PM
Owing to the co-financing granted by the Voivodship Fund for Environmental Protection and Water Management in Katowice and by the Silesian University of Technology in Poland, the launched mobile laboratory allows to carry out measurements of air pollution concentrations in the vicinity of selected emission sources, e.g. energy facilities, municipal sources, or sources of fugitive emissions. It allows to supplement and expand the spectrum of information on air quality in places not covered by systematic monitoring. Therefore, the use of the simple additive weighting method in the assessment process of air pollution may be of particular importance in interdisciplinary research on public health and its determinants.
Monitoring the phenomenon of smog, which generates high concentrations of substances in the air, hazardous to human life and health, is one of the platforms of broadly understood pro-ecological activities. In recent years, in the processing of large amounts of data and information describing the extent of air pollution, the so-called synthetic measures have become more and more important. They are determined on the basis of multivariate statistical methods, and although these methods differ in the approach to the criteria which are taken into account (setting correlation thresholds, unifying the field of compared criteria), by their application, we can replace the entire set of features describing the object with one aggregated variable.
The Simple Additive Weighting (SAW) method belongs to the group of single criteria synthesis methods, which rank the examined objects on the basis of a linear combination of the weight vector W [k×1] and the decision matrix D [m×k] (m – object, k – criterion) [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]. The weight vector W can be filled arbitrarily (subjective weights) or using mathematical methods (objective weights), and regardless of the determination method of the weights of the values of the coefficients defining the degree of influence of the k-th criterion on the final decision, it should be within the range 〈0;1 〉. The SAW method requires the determination of the nature of each of the criteria: cost criterion or qualitative criterion (in the case of the first one, it is desirable to minimize the obtained values, and in the case of the second one – to maximize the obtained values). In turn, the decision matrix D consists of real numbers dij corresponding to the numerical value adopted by a given criterion for the selected object. In order to ensure the comparability of the values obtained by the objects under criterion k, the decision matrix D should be linearly re-scaled [27]:
in the case of the so-called cost criterion:
in the case of the so-called qualitative criterion:
which gives a new decision matrix V [m×k]. By multiplying this matrix and the weight vector W [k×1], we can determine the ranking vector R [m×1]: the best object is the one that reaches the highest value of the ranking coefficient.
As part of the article, the measurements results of the concentration of PM
Location of the measurement point (50.292934N, 18.682164E) [28]
The distance of the sampling point from the nearest building development was about 12 m, and it was greater than the distance required for the place of air sampling [29]. The following communication routes are located in the vicinity of PP:
from the north-east, at a distance of approximately 500 m – road DW902, in the north-west direction at a distance of about 450 m – road DW901, towards the west, at a distance of approximately 600 m – road DK78.
In addition, to the north, at a distance of about 600 m, there is a well-developed railway infrastructure (Gliwice railway station).
In the immediate vicinity of the PP, there is a typical urban development including public utility buildings, a shopping centre and multi-family residential buildings.
The compilation of the measured values of the above-mentioned substances is presented in Table 1. The ranges of variability of the measured parameters are presented in Table 2.
Compilation of the concentration of PM10 [μg/m3] and PM2.5 [μg/m3] and the content of SO2 [μg/m3], NO [μg/m3], NO2 [μg/m3] and NOx [μg/m3] in the “winter period” of the year 2020
Measurement date | PM10 [μg/m3] | PM2.5 [μg/m3] | SO2 [μg/m3] | NO [μg/m3] | NO2 [μg/m3] | NOX [μg/m3] |
---|---|---|---|---|---|---|
1/01/2020 | 40.462 | 28.323 | 25.478 | 3.275 | 8.706 | 13.715 |
2/01/2020 | 124.902 | 87.432 | 41.503 | 38.821 | 27.195 | 86.583 |
3/01/2020 | 107.254 | 75.078 | 32.006 | 26.421 | 14.956 | 55.371 |
… | … | … | … | … | … | … |
29/03/2020 | 55.676 | 38.973 | 7.273 | 2.754 | 14.338 | 18.552 |
30/03/2020 | 20.938 | 14.656 | 6.421 | 3.986 | 15.239 | 21.336 |
31/03/2020 | 39.466 | 27.626 | 8.923 | 5.641 | 20.317 | 28.951 |
1/10/2020 | 18.258 | 12.781 | 22.049 | 14.453 | 19.972 | 42.070 |
2/10/2020 | 26.515 | 18.560 | 21.959 | 9.113 | 21.565 | 35.493 |
3/10/2020 | 24.387 | 17.071 | 22.209 | 1.164 | 7.612 | 9.392 |
… | … | … | … | … | … | … |
29/12/2020 | 43.747 | 30.623 | 19.279 | 13.790 | 24.309 | 45.409 |
30/12/2020 | 39.855 | 27.899 | 18.920 | 41.474 | 45.820 | 109.232 |
31/12/2020 | 54.991 | 38.493 | 37.079 | 13.620 | 34.108 | 54.943 |
The ranges of variability of the measured parameters
Measurement date | PM10 [μg/m3] | PM2.5 [μg/m3] | SO2 [μg/m3] | NO [μg/m3] | NO2 [μg/m3] | NOX [μg/m3] |
---|---|---|---|---|---|---|
max | 174.146 | 121.902 | 41.503 | 112.511 | 45.820 | 216.371 |
min | 7.403 | 5.182 | 5.552 | 0.959 | 5.935 | 7.406 |
Additionally, the following were also measured: air temperature, air humidity, air pressure, as well as wind speed and direction (Table 3).
Measured values of air temperature, air humidity and air pressure, wind speed and direction
Measurement date | Air temperature [°C] | Air humidity [%] | Air pressure [hPa] | Wind speed [m/s] | Wind direction [°] |
---|---|---|---|---|---|
1/01/2020 | 2.626 | 77.801 | 1005.725 | 1.203 | 208.410 |
2/01/2020 | −0.240 | 74.948 | 1003.083 | 0.514 | 151.699 |
3/01/2020 | 0.998 | 73.279 | 996.589 | 0.954 | 128.822 |
… | … | … | … | … | … |
29/03/2020 | 4.583 | 72.266 | 988.835 | 3.349 | 340.415 |
30/03/2020 | 0.262 | 59.337 | 996.989 | 2.493 | 270.373 |
31/03/2020 | 0.566 | 65.272 | 999.125 | 1.642 | 272.971 |
1/10/2020 | 12.491 | 95.095 | 979.778 | 0.995 | 89.145 |
2/10/2020 | 14.304 | 86.464 | 979.089 | 0.946 | 173.747 |
3/10/2020 | 19.404 | 65.669 | 975.675 | 2.524 | 179.362 |
… | … | … | … | … | … |
29/12/2020 | 4.897 | 78.060 | 968.197 | 1.015 | 148.936 |
30/12/2020 | 3.773 | 92.317 | 978.256 | 0.646 | 193.396 |
31/12/2020 | −0.311 | 97.963 | 983.239 | 1.055 | 171.390 |
The scaled decision matrix V is presented in Table 4.
The weight of features (parameters) wj were determined on the basis of a survey of experts' opinions. The compiled list of weights of the features (parameters) wj is presented in Table 5.
Scaled decision matrix V
Measurement date | j = 1 | j = 2 | j = 3 | j = 4 | j = 5 | j = 6 |
---|---|---|---|---|---|---|
1/01/2020 | 0.802 | 0.802 | 0.446 | 0.979 | 0.931 | 0.970 |
2/01/2020 | 0.295 | 0.295 | 0.000 | 0.661 | 0.467 | 0.621 |
3/01/2020 | 0.401 | 0.401 | 0.264 | 0.772 | 0.774 | 0.770 |
… | … | … | … | … | … | … |
29/03/2020 | 0.710 | 0.710 | 0.952 | 0.984 | 0.789 | 0.947 |
30/03/2020 | 0.919 | 0.919 | 0.976 | 0.973 | 0.767 | 0.933 |
31/03/2020 | 0.808 | 0.808 | 0.906 | 0.958 | 0.639 | 0.897 |
1/10/2020 | 0.935 | 0.935 | 0.541 | 0.879 | 0.648 | 0.834 |
2/10/2020 | 0.885 | 0.885 | 0.544 | 0.927 | 0.608 | 0.866 |
3/10/2020 | 0.898 | 0.898 | 0.537 | 0.998 | 0.958 | 0.990 |
… | … | … | … | … | … | … |
29/12/2020 | 0.782 | 0.782 | 0.618 | 0.885 | 0.539 | 0.818 |
30/12/2020 | 0.805 | 0.805 | 0.628 | 0.637 | 0.000 | 0.513 |
31/12/2020 | 0.715 | 0.715 | 0.123 | 0.887 | 0.294 | 0.773 |
Weights of features (parameters) wj used in the example
wj | |||||
---|---|---|---|---|---|
j = 1 | j = 2 | j = 3 | j = 4 | j = 5 | j = 6 |
0.163 | 0.163 | 0.121 | 0.286 | 0.095 | 0.173 |
The ranking vector R [m×1] is presented in Table 6.
Ranking vector R [m×1]
“Object” | Rank (synthetic value) | Ranking place |
---|---|---|
22/11/2020 | 0.976 | 1 |
25/12/2020 | 0.964 | 2 |
22/03/2020 | 0.956 | 3 |
11/02/2020 | 0.952 | 4 |
12/02/2020 | 0.945 | 5 |
02/02/2020 | 0.944 | 6 |
21/03/2020 | 0.941 | 7 |
12/03/2020 | 0.940 | 8 |
11/03/2020 | 0.938 | 9 |
18/10/2020 | 0.934 | 10 |
… | … | … |
03/12/2020 | 0.586 | 159 |
18/03/2020 | 0.578 | 160 |
01/12/2020 | 0.573 | 161 |
11/12/2020 | 0.509 | 162 |
02/12/2020 | 0.455 | 163 |
02/01/2020 | 0.437 | 164 |
05/03/2020 | 0.433 | 165 |
09/11/2020 | 0.409 | 166 |
16/01/2020 | 0.300 | 167 |
17/01/2020 | 0.064 | 168 |
Partial rankings of “objects” according to concentration of dusts: PM
Partial rankings of “objects” in terms of measurement results of the concentrations of dusts: PM10 and PM2.5
Place in ranking | „Object” | Concentration of dust PM10 | Place in ranking | „Object” | Concentration of dust PM2.5 |
---|---|---|---|---|---|
1 | 14/10/2020 | 7.403 | 1 | 14/10/2020 | 5.182 |
2 | 23/02/2020 | 10.552 | 2 | 23/02/2020 | 7.386 |
3 | 11/03/2020 | 11.191 | 3 | 11/03/2020 | 7.834 |
4 | 12/03/2020 | 11.752 | 4 | 12/03/2020 | 8.226 |
… | … | … | … | … | … |
166 | 16/01/2020 | 113.446 | 166 | 16/01/2020 | 79.413 |
167 | 02/01/2020 | 124.902 | 167 | 02/01/2020 | 87.432 |
168 | 17/01/2020 | 174.146 | 168 | 17/01/2020 | 121.902 |
Partial rankings of “objects” in terms of measurement results of the concentrations of chemical compounds: SO2, NO, NO2 and NOx
Place in ranking | “Object” | Concentration of dust PM10 | Place in ranking | „Object” | Concentration of dust PM2.5 |
---|---|---|---|---|---|
1 | 21/03/2020 | 5.552 | 1 | 22/11/2020 | 0.959 |
2 | 22/03/2020 | 5.876 | 2 | 26/12/2020 | 1.051 |
3 | 18/02/2020 | 5.926 | 3 | 03/10/2020 | 1.164 |
4 | 12/03/2020 | 6.141 | 4 | 25/12/2020 | 1.351 |
… | … | … | … | … | … |
166 | 31/12/2020 | 37.079 | 166 | 09/11/2020 | 64.873 |
167 | 14/01/2020 | 38.031 | 167 | 16/01/2020 | 96.890 |
168 | 02/01/2020 | 41.503 | 168 | 17/01/2020 | 112.511 |
Place in ranking | “Object” | Concentration of NO2 | Place in ranking | “Object” | Concentration of NOx |
1 | 22/11/2020 | 5.935 | 1 | 22/11/2020 | 7.406 |
2 | 18/10/2020 | 6.790 | 2 | 25/12/2020 | 9.094 |
3 | 25/12/2020 | 7.028 | 3 | 18/10/2020 | 9.249 |
4 | 03/10/2020 | 7.612 | 4 | 03/10/2020 | 9.392 |
… | … | … | … | … | … |
166 | 27/03/2020 | 42.210 | 166 | 05/03/2020 | 129.471 |
167 | 17/01/2020 | 44.248 | 167 | 16/01/2020 | 183.417 |
168 | 30/12/2020 | 45.820 | 168 | 17/01/2020 | 216.371 |
The method of Simple Additive Weighting (SAW) used in the article belongs to the group of single criteria synthesis methods which creates a ranking of the examined objects on the basis of a linear combination of the weight vector W [k×1] and the decision matrix D [m×k] (m – object, k – criterion). Since the adopted criteria/measurement results of the concentration of dusts PM
In terms of the aggregated values of R, the most favourable air quality conditions were reported successively as follows:
22/11/2020 (R = 0.976): the “object” was in the first places three times in the partial rankings (in terms of the concentration level of NO (0.959 [μg/m3]), NO2 (5.935 [μg/m3]) and NOX (7.406 [μg/m3]), but it also took the 13th place in terms of dust concentration level (the concentration of PM 25/12/2020 (R = 0.964): the “object” did not ever come first in the partial rankings: in terms of dust concentration level (PM 22/03/2020 and even despite the fact that this “object” was never placed first in the partial rankings. Due to the high level of dust concentration (the concentration of the content of dust PM
With respect to the aggregated values R, the least favourable air quality conditions were reported accordingly on 17/01/2020 (R = 0.064), 16/01/2020 (R = 0.300) and 9/11/2020 (R = 0.409). On 17/01/2020, the highest concentration of dust PM
By comparing the measurement results of the concentrations of harmful substances in the atmosphere with the observation results of atmospheric conditions (direction and speed of wind, temperature, pressure and humidity of air, etc.), it is possible to determine the level of correlation relationships between the above-mentioned elements. For example, using the Pearson linear correlation coefficient (rxy), we can state that in the “winter period” of 2020, the correlation relationship (a measure of the strength of the linear relationship) between the concentration level of dusts PM