Assessment of the impact of motor transport on the ecological state of urbanized areas using the pollution index

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Introduction Motor vehicle has a significant impact on the environment and, as a consequence, on the health of the population of urbanised areas [1]. Thus, high levels of noise and air pollution are created in residential areas, degradation of soils and vegetation of roadside strips occurs. In this regard, it is important to objectively assess the ecological state of the urban environment in terms of pollution from road transport. Currently, there are indicators for assessing the state of individual environmental components, such as the air pollution index, total soil pollution index, noise pollution index, but there is no index that would combine these three indicators, although its existence would allow solving the following tasks: to assess the pollution of the territory by motor transport in an integrated manner and to develop priority directions of environmental protection measures based on the assessment of the most vulnerable components of the environment. The difficulty in developing such indices is the lack of the necessary data set to check the objectivity of priorities of some parameters over others. Earlier attempts were made to calculate an integral ecological indicator of the state of urban environment. Thus, in [2] the pollution index was defined as the sum of ratios of the obtained results to the established maximum permissible concentrations (MPC) or background values of the studied environmental parameters: atmospheric air, soil and vegetation. It should be noted that in this approach quality standards are set only for a limited list of pollutants, and background values are not legislated. Scientists [3] have proposed an index of technosphere compatibility that takes into account socio-economic indicators and allows the use of artificial neural networks for risk forecasting. The authors [4] developed a methodology for assessing the level of environmental safety of natural water from different water supply sources and ranked them based on the integral index of anthropogenic pollution. All the scientific works described above do not solve the problems set earlier. The authors of this study consider the use of the analytical hierarchy process (AHP) (T.L. Saaty method) [5], which, despite the subjectivity of the approach, allows successfully to solve such problems. Thus, the use of AHP helped the author [6] to identify the location of zones of possible groundwater occurrence, which served as a prerequisite for the development of a set of measures for the rational use of water resources. Scientists [7] performed an analysis of the biopositivity of different types of road surfaces and made a choice in favour of asphalt concrete. In [8], using the T.L. Saaty method, priority scenarios for the development of emergency situations at the facilities of the fuel and energy complex were identified. The authors [9] determined the indicators and evaluated the environmental component of locomotives using different energy sources, and identified the prospects for solving environmental problems in the process of designing and creating innovative locomotives. In the study [10], the most and least effective ways of waste utilisation were established according to the complex criteria of AHP, taking into account both economic and environmental components. Thus, the chosen method, has shown its validity in studies close to the subject of this article. The purpose of this study is to develop and validate the approach to assessing environmental pollution of urbanised territories by road transport. Research methods The development of an index of pollution of urbanised areas by motor vehicles included two main stages: 1) to determine priorities between indicators using the analytical hierarchy process; 2) to search for a mathematical solution allowing to objectively rank the combinations of adopted classes of atmospheric air pollution by emissions, noise level and soil pollution by increasing danger. Analytical hierarchy process consists of decomposing the problem into simpler constituent parts and further processing the sequence of judgements by pairwise comparisons. This method allows to justify decision making in the presence of several criteria. It involves comparing each parameter with others and assigning a relative importance value. The decomposition principle involves structuring the problem in the form of a hierarchy. In order to prioritise the criteria and obtain scores for alternative solutions, the AHP constructs matrices of pairwise comparisons. For each matrix, a vector of local priorities is determined [5]. Analytical hierarchy process is based on the basic principles. 1. Partitioning: breaking down the problem into smaller, manageable components, which are then organised into a hierarchical structure, allowing the key elements of the problem and their relationships to be identified. 2. Pairwise comparisons: assessing the relative importance of each parameter by comparing them in pairs in a hierarchy at the same level. 3. Hierarchical synthesis: synthesising pairwise comparisons to obtain an overall ranking of parameter priorities at each level of the hierarchy. 4. Consistency analysis: assessing the mutual consistency of pairwise comparisons to ensure logical judgements using a consistency coefficient. 5. Sensitivity analysis: assessing the susceptibility of the results to changes in pairwise comparisons to identify the most critical parameters and reveal the stability of the solution. Results and discussion Based on the previously conducted long-term studies of the state of the environment [11-13] affected by motor vehicles, three main parameters were identified using the analytical hierarchy process: air pollution by emissions, noise level and soil pollution. To determine the weight of each parameter in the pollution of urbanised areas by motor vehicles, a pairwise comparison and priority matrix (Table 1) was drawn up on the basis of a scale of relative importance, in which each element reflects the ratio of the importance of one criterion with respect to the other. In this case, the element in the first row and second column is equal to two, which means that atmospheric pollution is considered twice as important as noise level and three times as important as soil pollution. Table 1. Pairwise comparison and priority matrix Parameter Air pollution (AP) Noise level (NL) Soil pollution (SP) Air pollution (AP) 1 2 3 Noise level (NL) 1/2 1 2 Soil pollution (SP) 1/3 1/2 1 Sum 1.83 3.5 6.0 Source: compiled by S.A. Novikova. Emissions from road transport are emitted directly into the atmospheric air, directly affecting this component of the environment and, as a consequence, the health of people living in the pollution zone. Noise pollution ranks second in the rating of environmental problems (after air pollution) affecting the health of the population of urbanised areas[2]. Soil is an accumulator of harmful substances, including those coming from atmospheric air. The assessment of the danger of contaminated soil in settlements is determined by its role as a source of secondary pollution of the air basin[3]. Next, the priority matrix was calculated (see Table 1), where each matrix element is equal to the sum of row elements divided by the sum of column elements. Thus, the normalised matrix was obtained, which was used to calculate the weights of parameters (Table 2). Table 2. Normalized matrix Parameter (а1) Air pollution (AP) (а2) Noise level (NL) (а3) Soil pollution (SP) (А1) Air pollution (AP) 0.55 0.57 0.50 (А2) Noise level (NL) 0.27 0.29 0.33 (А3) Soil pollution (SP) 0.18 0.14 0.17 Source: compiled by S.A. Novikova. The weights (w) of the parameters were calculated using the following formulas: ; (1) ; (2) . (3) 1. Atmospheric Pollution (AP): w1 = wAP = ~ 0.5. 2. Noise Level (NL): w2 = wNL = ~ 0.3. 3. Soil contamination (SC): w3 = wSC = ~ 0.2. The weights of parameters are normalised so that their sum is equal to 1. Thus, the weights of parameters (w) correspond to: AP - 0.5, NL - 0.3, SC - 0.2. In fact, the criteria weights are equal to the arithmetic mean of the elements of each row of the normalised matrix. The arithmetic mean gives an idea of the importance of each criterion as a whole. According to the obtained criteria weights, atmospheric pollution has the highest contribution to the total pollution of urbanised areas (50%), followed by noise (30%) and soil pollution (20%). Further in the work the degree of data consistency was determined. Consistency means that in the presence of the main (basic) array of raw data, all other data can be logically derived from them, i.e. the relations of the elements of the whole matrix should not be contradictory. The degree of consistency was determined by the formula: . (4) A consistency index (CI) was used to test the consistency of the judgement matrices: (5) where λmax is the maximum eigenvalue of the matrix, n is the number of parameters; (n - 1) is the number of all possible pairwise comparisons of a given element in a fixed row for an nth order square matrix. Consequently, the CI has the meaning of the deviation from absolute consistency attributable to one pairwise comparison. A criterion called the consistency ratio (CR) is introduced: (6) where RCI is the random consistency index. The RCI values in the AHP are pre-calculated. An acceptable value of no more than 20% is acceptable. Specifically, = (3.165 - 3) / (3 - 1) = 0.083, = 0.083/0.58 = 0.14 = 14%, hence the data is agreed. As a basis for classification of the proposed complex pollution index the authors used gradations given in normative documents[4] for three indicators: air pollution, noise level, soil pollution, each of which can be characterised by four classes, which corresponds to 64 variants of combinations of these classes. We assume that the same order classes of different indicators are equivalent to each other. Consequently, we can calculate the index of pollution of urbanised territories by motor vehicles (IPUTMV), which is equal to the sum of products of parameter weights (w) by the corresponding parameter values: , (7) where 0.5, 0.3, 0.2 - mathematical weights of chemical pollution of atmosphere, noise level and chemical pollution of soil, respectively; , и - indexes of indicator classes in order; m = 0, if , , = 1; m = 3, if , , > 1. Exponentiation to the degree of 1.3 allows to correct ranking of classes of the proposed pollution index taking into account combinations of parameter classes (atmospheric emissions, noise level, soil pollution) and their mathematical weights. Ranking the index values, we obtain a classification for assessing pollution of urbanised territories by motor vehicles (Figure). Visualization of the classification of the complex index of pollution of urbanized areas by motor vehicles Source: compiled by S.A. Novikova. As a test of the proposed index based on the results of earlier studies [11-13], the authors carried out calculations for the cities of the Irkutsk agglomeration, which showed that the territory under consideration is characterised by a high level of pollution (Table 3). It should be noted that with the same qualitative indicators, the quantitative values of the index differ. Thus, in the cities of Irkutsk and Angarsk the values of IPUTMV are 3.54, which corresponds to the upper boundary of the proposed gradation (see Figure), while in Usolye-Sibirsk and Shelekhov IPUTMV are equal to 4.48, i.e. practically on the border with an extremely high level of pollution, which indicates the need for increased attention to the problem of urban pollution. Based on the ranking of parameters, the priority measures should be aimed at reducing emissions into the atmospheric air (AP = 4). Table 3. Index of pollution of urbanized areas by motor vehicles in the cities of the Irkutsk agglomeration Cities of the Irkutsk agglomeration Air pollution Noise level Soil pollution Index of urbanized areas pollution by motor vehicles Pollution level Irkutsk 3 3 1 3.54 High Angarsk 3 3 1 3.54 High Usolye-Sibirskoye 4 3 1 4.48 High Shelekhov 4 3 1 4.48 High Source: compiled by S.A. Novikova, S.M. Ugay. Thus, the paper proposes a method of determining the index of pollution of urbanised areas by motor vehicles, which makes it possible to identify the level of technogenic load on the state of the environment, and the analytical hierarchy process provides a systematic approach in assessing complex environmental problems. Conclusion The interest in using the results of summary calculations for managing the quality of environmental components in cities of the Russian Federation is constantly growing. Moreover, one of the strategic objectives of the country's development is to radically reduce the level of atmospheric air pollution in large industrial centres[5]. The method described in this paper allows us to assess in a first approximation the overall pollution of urbanised areas based on three indicators: 1) air pollution; 2) noise level; 3) soil pollution. In the presence of approbation, technically this approach allows to obtain the necessary information on the level of pollution of urbanised territories, is convenient and easy to use. The complex index can be considered universal, as it is based on generally accepted classifications of the indicators included in it and can potentially be applied in any locality subject to regulatory and legal acts[6].

About the authors

Svetlana A. Novikova

Irkutsk State Transport University; Siberian Federal Scientific Center for Agrobiotechnology of the Russian Academy of Sciences

Author for correspondence.
Email: eco-science@mail.ru
ORCID iD: 0000-0003-2534-3379
SPIN-code: 8593-1545

Senior Lecturer, Department of Technosphere Safety, Irkutsk State Transport University; postgraduate student of the Siberian Federal Scientific Center of Agrobiotechnology of the Russian Academy of Sciences

15 Chernyshevsky St, Irkutsk, 664074, Russian Federation; 2b Centralnaya St, Novosibirsk Region, Krasnoobsk urban settlement, 630501, Russian Federation

Sergey M. Ugay

Siberian Federal Scientific Center for Agrobiotechnology of the Russian Academy of Sciences

Email: usm64@mail.ru
ORCID iD: 0000-0003-1615-5544
SPIN-code: 6524-6765

Doctor of Technical Sciences, Associate Professor, Head of the Special Experimental Design and Technology Bureau

2b Centralnaya St, Novosibirsk Region, Krasnoobsk urban settlement, 630501, Russian Federation

References

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