key: cord-0332814-ca74vhq3
authors: Pařil, Vilém; Tóthová, Dominika
title: Assessment of the burden on population due to transport-related air pollution: The Czech core motorway network
date: 2020-07-15
journal: nan
DOI: 10.1016/j.jclepro.2020.123111
sha: 3955cdd4f9268b7160e3515022f2baedb7214b44
doc_id: 332814
cord_uid: ca74vhq3

Abstract Negative externalities of transport are a crucial issue in environmental discussion and policy. The mobility of our society is responsible for many negative environmental impacts, both on our planet and on human health. This paper aims to assess one aspect of a negative externality of transport related to air pollution from particulate matter up to 10 micrometers in diameter (PM10) and its impact on human health, using the example of the key Czech Republic highway D1. The assessment method precisely follows the geographical routing of this motorway through varying elevations in which different buffer zones are identified to assess PM10 concentration changes according to distance from the road in the 2007–2016 period. The resulting relation between PM10 and highway proximity is then discussed in an econometric analysis of the entire road transport network of the Czech Republic. In the final step, we assess the size and demographic structure of the population affected by the highway PM10 pollution problem, and we compare several methods to assess economically related morbidity. The results show falling levels of PM10 pollution, not only with increasing distance but also in intertemporal comparison, with concentrations lower by 2μg.m3 in the 2012–2016 period than in 2007–2011 despite increasing road traffic on the highway. This means a very significant reduction in the number of cases and economic value for all analysed endpoints between EUR 485,056 and 842,891.

In response to global environmental threats, the European Union (EU) has long endeavored to be a global environmental leader and to improve the environment through a range of instruments, such as environmental action programs and institutional and legislative frameworks. Many environmental issues are related to human mobility and transport, which cause some of the most severe air pollution problems, especially in residential areas. The context of the assessment of external costs in cost-benefit analysis is primarily related to construction and maintenance.

Significant benefits concern the time saved and improvements in the overall level of accessibility (Martens and Di Commo, 2017; Forslund and Johansson, 1995) . This approach is used for the analysis of specific problems linked to road transport, such as reducing air pollution (Cavallaro et al., 2018; Rotaris et al., 2010) . The research objective is to more accurately assess the impact of road transport on air pollution and consequently on population morbidity in monetized values.

The research methodology is applied to the Czech core highway D1 and its traffic-related air pollution, as represented by PM 10 , over a 10-year period with an emphasis on long-term trends.

Cost-benefit analysis (CBA) is considered to be one of best possible methods for transport project assessment, and it generally provides very important results for comparing different transport solutions. CBA is used differently according to country and to policy-making decision processes (Hansson and Nerhagen, 2019) . Recently, the emphasis on long-term strategic planning in the EU was enriched by territorial impact assessment (TIA; 2020), which makes it possible to measure the regional impacts of strategic planning. However, this instrument has not yet been introduced in the Czech Republic as an integral part of the general planning process. It is instead used only for the purposes of separate assessments of regional development, regardless of whether it involves environmental or transport policy. The application of CBA methodology in our study is a key factor that can provide better results in long-term transport project assessment in the Czech Republic and beyond, because it provides a way to integrate CBA with a TIA approach, with an emphasis on dynamic and long-term data (Schade and Rothengatter, 2003) . As shown in the research considering air pollution with regard to its geographical routing and demographical grouping, it is achievable and feasible, and there are already available data and methods to launch this important step, both in CBA related to each transport project separately, as well as in the strategy and planning phases, when it is necessary to compare more variants of potential solutions.

Air pollution from transport is a growing problem, especially in developed countries, due mainly to the increasing demands for freight and passenger transport. The Czech Republic has a relatively dense network of expressways and motorways, often near residential areas. In addition, high volumes of traffic generate negative external costs in the form of noise (e.g. Serrano-Hernández et al., 2017 , Hammer et al., 2014 and air pollution (e.g. Watkiss et al., 2000; Le Boennec, 2017) . Transport also has significant overall impacts on sustainability (de Campos et al., 2019) . However, the highest risk to human health is air pollution from transport, which this article addresses.

The research focus is usually on air transport emissions (Monks et al., 2009 ) and road transportrelated air pollutants. In the Czech Republic, only 35.21% (25 out of 71) of CBA projects conducted from 2015 to 2018 included air pollution assessments (Ministry of Transport, 2018) . In all these cases, air pollution is automatically assessed in CBA in calculations of passenger kilometers or ton-kilometers. This article aims to precisely identify the effect of road transport on health in the long term using the example of Czech core highway D1. The average daily intensity of vehicles on the D1 motorway is about 38,000 vehicles per day, and there are sections where the intensity reaches almost 100,000 vehicles per day (RSD, 2016) .

According to the World Health Organization (WHO) (2000), common pollutants from transport include nitrogen dioxide, ozone, and other photochemical oxidants, particulate matter, and sulfur dioxide. In our paper, we focus on the assessment of air quality using PM 10 (defined as particles below 10 μm in aerodynamic diameter), which are considered one of the main pollutants (Maibach et al., 2008) , usually bounding polycyclic aromatic hydrocarbons (PAHs) (Yin and Xu, pollutants, PM 10 is measured at all measuring stations in the Czech Republic. According to the WHO (2006) , PM 10 is strongly correlated to other pollutants. Thus it is an appropriate indicator with which to assess the impact of air pollution. The individual studies that we used to determine the exposure-response function (ERF) often only follow the impact of PM 10 concentration for this reason. Based on the WHO (2000) recommendation, we did not set any lower limit for PM 10 to indicate the level of pollution that is detrimental to health. Adverse effects on health have been observed at levels not far from natural background concentration values of about 6 μg/m 3 (Correia et al., 2013) . Many epidemiological studies have demonstrated negative health consequences from excessive PM 10 in the child and adult populations (Kirshnan et al., 2019; Sánchez et al., 2019; Gouveia et al., 2018; Künzli et al., 2001; Abbey et al., 1999; Englert, 1999) .

The most frequent impacts of PM 10 are related to cardiovascular, respiratory, cancer, and cerebrovascular effects, which are manifested in increased morbidity and mortality. A long-term concentration of particulate matter is associated with natural-cause mortality , especially for cardiovascular disease mortality or morbidity (Kirrane et al., 2019; Dabass, 2018; Haikerwal et al., 2015; Mills et al., 2009; Metzger et al., 2004) . PM 10 also has respiratory health effects that can lead to increased mortality and morbidity (Kim et al., 2018; Mathew et al., 2015; Gehring, 2013; Hoek et al., 2012; Zanobetti et al., 2009; Ostro et al., 2005) . A relationship has also been shown between exposure to PM 10 and cancer, primarily lung cancer (Dimitriou and Kassomenos, 2018; Raaschou-Nielsen et al., 2013; Pope et al., 2002; Nyberg et al., 2000) and cerebrovascular disease (Wettstein et al., 2018; Staffogia et al., 2014; Zhang et al., 2011; Torén et al., 2007) .

Many authors have addressed the quantification of the health impacts of air pollution, but using different methods. A similar approach was used by Seethaler et al. (2003) . They calculated air pollution-related health costs using a tri-national study of Austria, France, and Switzerland on health costs due to transport-related air pollution. Meisner et al. (2015) assessed the magnitude of health impacts and economic costs of fine particulate matter pollution in the Republic of Macedonia. Health impacts were converted to Disability-Adjusted Life Years (DALYs) and then translated into economic terms. Martinez et al. (2018) obtained particulate matter concentration data from air quality monitoring stations in the Skopje metropolitan area, applied relevant concentration-response functions from the literature, and calculated the burden of disease and societal cost of mortality attributable to particulate matters. Künzli et al. (2000) estimated the impact of outdoor traffic-related air pollution on public health in Austria, France, and Switzerland, using attributable cases of morbidity and mortality. Health impact assessments in the area of traffic air pollution have been conducted, e.g. Khreis et al. (2018) , Tobollik et al. (2016), Chart-asa and Gibson (2015) , and Boldo et al. (2006) .

Several studies have been conducted in the field of assessing damages from air pollution from transport, including the human health impacts on the population at the European level (EU Projects and Programs), such as ESCAPE, EXIOPOL, HEIMTSA, NEED and other European studies (e.g. projects of the European Environment Agency or projects of the European Topic Centre on Air and Climate Change) and at the national level. Korzhenevych et al. (2014) consider these two studies on the assessment of the external costs of transport to be essential at the European level: HEATCO -Developing Harmonised European Approaches for Transport Costing and Project Assessment (Bickel et al., 2006) , and CAFÉ CBA -Clean Air for Europe Cost-Benefit Analysis (Hurley et al., 2005) , which were evaluated in the HEIMTSA project -Health and Environment Integrated Methodology and Toolbox for Scenario Assessment (Friedrich et al., 2011) . Both studies use the impact pathway approach, which was developed under the External Cost of Energy project (ExternE), with its own "ExternE Methodology" calculating external environmental costs (for more on this methodology, see Bickel and Friedrich, 2005) . The impact pathway analysis identified the most significant impacts of emissions, their quantifiability and the monetary valuation of costs. Other international organizations provide similar data. Like the European studies mentioned above, the studies are based on epidemiological research. Recommendations with relevant functions and economic values can be found, for example, in publications by the US EPA (2020), and WHO (2013). This paper is structured as follows: the first chapter provides a concise description of the material and the scientific methods used in our research, focusing on data and sources. The second part of the paper contains the evaluation of economic consequences and achieved results. The last part contains a discussion, the limits of this study, and recommendations for practice.

The method used in the research is based on the following data sources and methodological steps: -Health impact assessment.

These steps are described in detail in the following paragraphs.

The first methodological point defines detailed geographical routing of the most important motorway in the Czech Republic, the D1, which connects the three largest agglomerations in the country: Prague, Brno, and Ostrava. We used this routing based on a publication on parts of the D1 motorway from the Road and Motorway Directorate of the Czech Republic, from which we took detailed information on the route and type of motorway communication of all motorway sections (RSD, 2010) . The D1 motorway is an unfinished road project, and one section is still under construction from Říkovice near Hulín to Lipník nad Bečvou; a distance of close to 25 km is routed on first-class road number 47. We included this unfinished section in the analysis. The D1 motorway intersects with several regions: Prague, Central Bohemia, Vysočina, South Moravia, Olomouc, Zlín, and the Moravian-Silesian region.

We designated four distance zones in our research to compare changes in PM 10 concentrations depending on proximity to the D1 motorway. This classification is based on the assumption that the closer an area is to motorway traffic, the higher the concentration of PM 10 . We defined the first zone as the 100 meter distance zone (intersected areas). It is based on the key decrease of PM 10 exposure at a distance of 100 m synthesized by Karner et al. (2010) . We defined the second distance zone of 250 m (neighboring areas) according to a more accurate study on distance from highway exposure depending on wind speed and rainy conditions, as reported by Yazdi et al. (2015) . They showed that with high-speed wind (velocity >10 m/s), the PM 10 concentration can increase to a distance of around 250 m, after which it starts to decrease. The third distance zone of 500 m (closer burdened areas) is used under the European Commission approach in the development of a methodology to assess populations exposed to high levels of noise and air pollution close to major transport infrastructure (Ritchie et al., 2006) ; this methodology defines high exposure zones for road infrastructure as those of less than 500 m. The last category, with a 2,500 meter distance zone (broader areas), defines the average pollution level in the relevant geographical area (corresponding with the basic unit of settlement in the Czech Republic).

The next methodological step is to identify the relevant affected population. We used this static approach in accordance with Mommens et al. (2019) . We conducted this identification based on the 2011 Population and Housing Census carried out by the Czech Statistical Office (CSO, 2011).

We used the most detailed dataset based on population in basic units of settlement (22,505 units in the Czech Republic) according to its cadastral areas from the census registry (CSO, 2018) . We used the Czech National Geoportal INSPIRE (CNG, 2018) to identify affected residential areas.

Then we combined residential areas with the ESRI ArcGIS program module, namely ArcČR 3.3 (ArcDATA, 2018), through which we intersected the D1 motorway according to distance zones (100/250/500/2,500 m) with the residential cadastral areas of individual basic units of settlement.

With this approach, we identified an affected number of inhabitants in concerned basic units of settlement, and also identified the demographics and age structure of the relevant population.

Our research does not reflect the daytime variation of exposure because we can divide the population examined by the research into three basic categories: seniors, children, and the working-age population. We assume children pass half their daytime in the closest possible school and half at home; the 2011 Census does not reflect children's daytime mobility (CSO, 2011) . The second group, seniors, is expected to pass most of their time very close to their residence. The last population age category, working-age population, is the most time-space flexible class. The 2011 Census (CSO, 2011) provides data on the commuting of working people.

The commuting datasets show that movements during the daytime vary but are largely directed towards larger cities in their vicinity. From this point of view, our final estimation of long-term exposure could be slightly underestimated. We can consider it as a lower limit of the burdened population.

The key methodological step lies in the pollutant concentration analysis based on the statistics of the Czech Hydrometeorological Institute, from which we took historical data on long-term Kuenen et al. (2014) . These three dispersion models use Czech national REZZO pollutant classification (including REZZO 1-3 categories for static emission sources and REZZO 4 for mobile emission sources). The output results provide a nationwide grid network for PM 10 , PM 2.5 , benzopyrene, nitrogen oxides, ground-level ozone, benzene, heavy metals, and sulfur dioxide.

We separated PM 10 concentrations and intersected the Czech grid network with relevant distance zones. We analyzed three long-term periods in terms of PM 10 concentration: 2007-2011, 2010-2014, and 2012-2016 . For distance zones, we achieved the following numbers of concerned basic units of settlement: the 100-meter zone included 820 residential areas, the 250-meter zone included 1,011 areas, the 500-meter zone includes 1,293 areas, and the 2,500-meter zone included 2,640 areas.

This article discusses two interrelated issues that we address in the next two sub-chapters.

First, an air pollution analysis in the Czech Republic was carried out near the motorway network, including a motorway network already in operation and a planned motorway network, as there are currently significantly higher traffic intensities on these routes than on surrounding routes.

We based this section on data on long-term air quality monitoring (CHMI, 2018) and the occurrence of harmful particles between 2007 and 2016 (last available dataset, with annual averages of concentrations). The traffic intensity was monitored by the Transport Censuses in the Czech Republic in 1995 Republic in , 2000 Republic in , 2005 Republic in , 2010 Republic in , and 2016 . We consider the change between 2010 and 2016 to be the most comparable and relevant period.

In the next step we examined the impact of the motorways on the concentration of air pollution.

We checked whether the existence of roads really has an effect on the PM 10 air pollution concentration, primarily through the indicator of transport intensity. We included other variables such as population living in the vicinity of road infrastructure (as a high exposure zone) and population density. Because air pollution is affected by changes in altitude (US EPA, 1978; Hoek et al., 2008) Transport intensity of motor vehicles, population size in the area to 500 m from the road, population density in the area to 500 m from the road, and the average altitude in meters were selected as explanatory variables.

The power of influence of the non-standardized regression coefficients is estimated by controlling the effect of other independent variables in the model. Multiple regression through standardized regression coefficients also helps to determine the relative strength of the influence of independent variables on a dependent variable. Here we find which variables have the most significant impact on the variance of a dependent variable and vice versa (Mareš et al., 2015) .

We can formally write the multiple regression model as (formula 1):

where is a dependent variable, is a constant, , , are regression coefficients, , , are independent variables, and is random error.

Parameters are estimated by the ordinary least squares method, formally written as (formula 2):

The aim of this method is to find those parameters (estimates for ) for which the error term is minimized (formula 3):

The last methodological step is to identify relevant PM 10 health impacts according to the concerned population categories and distance zones (100/250/500/2,500 m). We compared the results from several international studies on the long-term health impact of PM 10 and defined the relevant impacts. In order to determine the health impacts on the population of the Czech Republic living near the D1 motorway with a focus on acute and chronic morbidity and the resulting monetary valuation, we selected three European studies to which we assigned the exposure-response function (ERF cases / (year · person · μg / m 3 )], and used their monetary valuation per case or per day based on individual health effects and risk groups (see Table A -HEATCO -Developing harmonized European approaches for transport costing and project assessment, 2006 (Bickel et al., 2006) .

All these studies calculate the economic valuation of health effects. An overview and description are given in Table 1 (Korzhenevych et al., 2014) : Bronchodilator use change in the probability of daily bronchodilator usage per 1 μg/m 3 PM 10

The following formula (Bickel and Friedrich, 2005, modified) is used to calculate the increase in the impact of air pollution from traffic on the population (formula 4):

I is case per year per average person. The c i is PM 10 concentration, and s i is the slope of ERF.

We multiplied the calculated cases of individual health effects by the monetary values presented in the particular studies (EEA, HEIMTSA, HEATCO). We converted this result into prices for the Czech Republic for 2017 expressed in euro (based on the ExternE methodology). We took inflation into account with the EU harmonized consumer price index (Eurostat, 2018) and the exchange rate between countries based on the PPP-adjusted exchange rate (OECD, 2018)

In the following sections, the results of two sub-studies are presented: the identification of the In Table A .2 in the Appendix, we identify municipalities with cadastral territory affected by the D1 motorway, and the affected residential zones with the basic settlement units affected at a distance of 100/250/500 and 2,500 m. The affected resident population is included in the relevant age categories (which is important for assessing the health impacts caused by PM 10 ; see below).

entirely or partially affected in residential areas. In individual risk areas, there are residential zones of about 6,000 (100 m zone), 14,000 (250 m zone), and 31,000 (500 m zone) residents affected by increased pollution of the air due to the motorway. Figure 

The maps above show gradual, slight improvement in air quality on most of the D1 motorway route. The improvement is quantified in Table 2 , which shows the difference in the degree of pollution both between the individual distance zones (100/250/500/2,500 m) and between the different periods of comparison. we cannot attribute this improvement only to lower pollutant concentration from transport. We ran a multivariate linear regression analysis to determine the impact of some explanatory variables on the level of PM 10 air pollution. The initial model entered these variables: transport intensity (trans_int), population in the vicinity of road infrastructure (popul_num), population density in the area to 500 m (popul_dens), and average altitude (alt_avg). To fulfil the assumption of normality, trans_int, popul_num, and popul_dens were logarithmized. The analysis finally includes four independent variables (see Table 3 ). All statistical analysis was conducted with the IBM SPSS software package. The multivariate regression analysis was carried out to find the best model for explaining the variability of the average concentration of PM 10 in the vicinity of roads in the Czech Republic through the transport intensity, average altitude, and population. Table 4 clearly shows the main summary of the model. All of the entered variables were significant for the model. The results show that a set of estimated independent variables explains 53.1% of the variance of the dependent variables.

According to the adjusted R Square that takes into account the number of regressors included in the model, the proportion of overall variability is 53.1%.

The analysis of variance in Table 5 indicates the results of the test if the explained variable is dependent on the explanatory variables. Also, according to the F Test in one-way analysis of variance, we can reject the null hypothesis about the insignificance of the model. In other words, the model including these variables is useful. Coefficient B represents the influence of the independent variable on the dependent variable. We see that there is a positive relationship between the PM 10 concentration and the variables transport intensity and population density, and a negative relationship between the PM 10 concentration and the variables average altitude and population. Thus, as transport intensity and population density increase, the PM 10 concentration also increases, and as average altitude and population decrease, the PM 10 concentration also decreases. According to the standardized beta coefficient, average altitude is the most important indicator influencing PM 10 concentration.

53.1% of the variance in the average PM 10 concentration in the vicinity of road infrastructure in the Czech Republic data is explained by the variables transport intensity, population, population density, and average altitude. During our time periods (2007-2011, 2010-2014, 2012-2016) we see a moderate reduction in air pollution. When we try to evaluate the impact of the reduction of pollution on the health endpoints, we ask how health conditions improve and disease-related costs are reduced if the PM 10 concentration improves by 1 μg/m 3 . We start with a comparison with the average concentration in 2012-2016. As an example, we choose cases of chronic bronchitis, which represent the highest cost item (Table A .3 in the Appendix). From the results shown in Figure 6 , we can see that the decrease in concentration has a significant effect on the reduction of cases of chronic bronchitis. From the results, we can deduce that a reduction of PM 10 in the air by 1 μg/m 3 will result in a reduction of cases or days of all health endpoints and monetary costs by 4%. Even though we observed long-term improvements in air pollution, the resulting condition still carries high monetary values. 

This study has shown that high concentrations of PM 10 in the vicinity of the D1 motorway in the Czech Republic have a significant impact on the morbidity of the affected population, which implies a considerable financial burden. Some of our policy recommendations are aimed at long-term reduction of air pollution exposure through several tools, such as the integration of health information into the impact assessment of infrastructure projects as suggested by Seethaler et al. (2003) , technical improvements in vehicles and fuels, a consistent application of the polluter-pays principle by internalizing the external costs of traffic-related air pollution into transport pricing and taxation schemes, and further travel demand management measures.

Similar studies have been conducted to assess the health impacts of air pollution. They are summarized in Table 7 . The research results cannot be compared because they employ different assumptions and approaches. However, all studies, including ours, have in common the fact that they show that air pollution (mostly from transport) affects a large part of the population with consequences for the total number of cases of mortality and morbidity, with a significant economic burden. A comparable approach was used by Künzli et al. (2000) , who estimated the public-health impacts of current patterns of air pollution in three European countries. They also used an increase in PM 10 to quantify the effects of air pollution using exposure-response functions. They modelled population exposure for each km 2 with traffic-related fraction separation. They similarly concluded that the public-health consequences are considerable. Our conclusion confirms the findings of Ostro and Chestnut (1998) There is a strong emphasis in the literature on greenhouse gas emissions with regard to climate change. According to the European Commission Handbook on external costs of transport (2019) air pollution burden achieves a very similar level with respect to all means of road transport (see Figure 8 ). Studies usually show macroeconomic impacts as a total (e.g. , or focus on very local contexts, usually with regard to vegetation (Bignal et al., 2007 (Bignal et al., , 2008 . This study provides a comparison of health effects with monetary evaluation for more population risk groups in a long-term view. The commercial importance of technologies in road transport is usually looked at by comparing greenhouse gas emissions, but as is clear from the graph below, a similar emphasis should be placed on comparing pollutant emissions when generally formulating environmental regulatory requirements in road transport. This study, using the example of the key D1 motorway in the Czech Republic, where cargo linehauls account for more than 23% of traffic, outlines other options for assessing technologies in road transport. The emphasis lies not only on climate change, but also on differentiating the significance of other negative impacts in urbanized space and taking into account different risk groups in the population.

This quantification also testifies to the potential effects of tightening environmental regulations in the automotive industry. This case demonstrates the long-run positive impact of coordinated EU policies on the environment and transport. For this reason, it is necessary to emphasize the coordination of sectoral policies. In transport policy, it is possible to propose suitable bypasses near urban areas. However, the territorial development policy, with other tools at the regional and municipal levels, should respect these findings and try to limit residential construction in the vicinity of express or heavy traffic roads, since long-term neglect of this process can have severe health and economic consequences.

In addition to these generally accepted and targeted tools, it is necessary to monitor the concentrations of pollutants in the vicinity of motorways in the Czech Republic, in particular to obtain more accurate results that would enter into the Environmental Impact Assessment, including the health impacts of each new transport project. Regarding the planning of broader long-term emission reduction -not only in the vicinity of motorways -strategic planning of traffic infrastructure development should also be undertaken. Tolmasquim et al. (2001) , for example, considered the strategic planning of sectoral development involving the quantification of health risks to be crucial, as the cost of environmental pollution may otherwise increase further.

To discuss the limits of the research, it is necessary to consider the quality of secondary data used for both air pollution and traffic intensity. We used the most detailed territorial units statistically monitored in the Czech Republic (basic settlement units). Population data are based on the 2011

Census (CSO, 2011) . A further limitation comes from the real daily mobility and temporality, because inhabitants may commute to work in another settlement unit and not be exposed to the highest emissions in their place of residence. However, such precise data are not available. A related limitation is the accuracy of the extrapolation model of air pollution, as the measuring station is not always located near the road. Nevertheless, in its model, the Czech

Hydrometeorological Institute provides raster data with accuracy much higher than that of population records. A related limitation of our study concerns the dataset provided by the Czech Hydrometeorological Institute, which covers long-term average concentrations and not detailed daily concentrations in the relevant units. The availability and compatibility of corresponding data from different original sources are important in this research. This is because a different authority is responsible for every agenda used, e.g. the Transport Census is provided only once a

year and population information is also provided annually but demographic structure is collected only once every decade, in the population census. These regulatory related data limitations are very relevant for further research where smart monitoring systems will hopefully provide better conditions in the future.

As far as air pollution assessment regarding human health is concerned, the results of our study are limited by several factors. We took into account the linear exposure-response function in all studies, resulting in equally significant gains in the number of cases of health endpoints, even though a non-linear relationship is observed in reality (e.g., Liu et al., 2014) , which may also vary due to air pollution mix, climate, and the health of the population (Samoli et al., 2006) . Exposureresponse functions were then taken from particular epidemiological studies (recommended projects HEATCO, EEA, and HEIMTSA), which do not reflect the detailed specific socio-demographic characteristics of the population in the area, but only relevant target age groups. The same studies also included estimates of the monetary values of the health endpoints, which were transferred to Czech Republic prices on the EU HICP and the PPP exchange rate. Unfortunately, this may be a very inaccurate estimate that would be more appropriately replaced by results from a survey in the Czech Republic. The disadvantage of this environmental impact assessment is that it is impossible to add the outcomes to health endpoints due to different types of outcomes (cases, days). Furthermore, these were selected, classically monitored endpoints that do not cover all health effects, so it is not possible to quantify the overall health effects of PM 10 pollution around the D1 motorway.

Our results can be a starting point for further research. We see an opportunity especially in the comparison of more relevant similar motorway examples with completely different geographical conditions across more countries. Another point of further research would be to compare air pollution caused by motorways with lower hierarchical road networks; in the case of municipal roads located in sparsely populated areas, it is possible that traffic does not play a key role in air pollution.

This paper estimates the health cost of morbidity-related health endpoints from the concentration of PM 10 in the surroundings of the D1 motorway in the Czech Republic. We succeeded in identifying the areas where it is possible to closely monitor the impact of traffic and the transport infrastructure on the population or to monitor their health impacts. The article uses a precise methodological framework of assessing and analyzing the long-term impacts of road transport on the population with the application example of the D1 motorway in the Czech Republic and increased air pollution with PM 10 . From the first part of the analysis, the importance of the route of high-speed roads in the proximity of towns or urban agglomerations can be seen. These sections can have a very significant impact on the population, which is subsequently exposed to an increased environmental burden and may experience negative health consequences with corresponding economic impacts. Moreover, these urban areas are usually located at lower altitudes, which means worse conditions to handle the pollutant concentration naturally through meteorological circumstances, as we show in a regression analysis.

The existence of highways with traffic intensity from 30,000 to almost 100,000 cars a day near residential areas decreases air quality with regard to PM 10 in the range of 0.421 to 1.072 μg/m 3 compared with the outer zone of 2,500 m (see Table 4 ). The impact mentioned above is relevant for at least 30,868 inhabitants living less than 500 m from the D1 highway. This population represents an average Czech district town. A long-term comparison of the air pollution burden in the vicinity of the highway in the 2007-2011 and 2012-2016 periods shows a decrease between 7.41% and 8.11% (see Table 5 ). As no safe concentration of PM 10 that would not adversely affect human health has been established, any reduction in concentration may also reduce the number of individual health endpoints and the total costs per year.

At the same time, it is clear from the results of the analysis that during the ten years under review, an average reduction in PM 10 pollution of 2 μg/m 3 (see Figure 4) Source: Andersen, 2013; Friedrich, 2011; Bickel et al., 2006 

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Shape of concentrationresponse curves between long-term particulate matter exposure and morbidities of chronic bronchitis: a review of epidemiological evidence

Handbook on estimation of external costs in the transport sector

Analýza sociálněvědních dat (nejen) v SPSS, Masarykova univerzita

Travel time savings, accessibility gains and equity effects in cost-benefit analysis

Health impacts and economic costs of air pollution in the metropolitan area of Skopje

Air pollution and respiratory health of school children in industrial, commercial and residential areas of Delhi

Ambient air pollution and cardiovascular emergency department visits

Adverse cardiovascular effects of air pollution

Decree on the method of assessing and evaluating the level of pollution, on the extent of informing the public about the level of pollution and in smog situations

Transport projects database for European Structural Funding in the framework of Operational Program on Transport

Estimating health impacts and economic costs of air pollution in the Republic of Macedonia

Ministry of the Environment, 2020. Environmental Quality Standards in Japan -Air Quality

A dynamic approach to measure the impact of freight transport on air quality in cities

Atmospheric composition change-global and regional air quality

Health impacts related to urban and transport planning: a burden of disease assessment

Urban air pollution and lung cancer in Stockholm

Purchasing power parities (PPP) (indicator)

Fine particulate air pollution and mortality in nine California counties: results from CALFINE

Assessing the health benefits of reducing particulate matter air pollution in the United States

Ambient air pollution and respiratory emergency department visits

Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution

Air pollution and lung cancer incidence in 17 European cohorts: prospective analyses from the European Study of Cohorts for Air Pollution Effects (ESCAPE)

Development of a methodology to assess population exposed to high levels of noise and air pollution close to major transport infrastructure

The urban road pricing scheme to curb pollution in Milan, Italy: Description, impacts and preliminary cost-benefit analysis assessment

Headquarters of roads and motorways of the Czech Republic. Publication for project of Motorway D1

Estimating the Exposure-Response Relationships between Particulate Matter and Mortality within the APHEA Multicity Project

Association between air pollution and sleep disordered breathing in children

Improving assessment of transport policies by dynamic cost-benefit analysis

Economic Costs of Air Pollution Related Health Impacts-an Impact Assessment Project of

Pricing and Internalizing Noise Externalities in Road Freight Transportation

Long-term exposure to ambient air pollution and incidence of cerebrovascular events: results from 11 European cohorts within the ESCAPE project

TIA, 2020. Territorial Impact Assessment

Health impact assessment of transport policies in Rotterdam: Decrease of total traffic and increase of electric car use

Environmental valuation for long-term strategic planning-the case of the Brazilian power sector

Occupational exposure to particulate air pollution and mortality due to ischemic heart disease and cerebrovascular disease

Altitude as a factor in air pollution

Policy Assessment for the Review of the National Ambient Air Quality Standards for Particulate Matter

Impacts of air pollution on building materials. Pollution Atmosphérique

Cardiovascular and cerebrovascular emergency department visits associated with wildfire smoke exposure in California in 2015

Air Quality Guidelines, Regional Office for Europe

Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide: global update 2005: summary of risk assessment

Health risks of air pollution in Europe-HRAPIE project recommendations for concentration-response functions for cost-benefit analysis of particulate matter, ozone and nitrogen dioxide

Evaluating near highway air pollutant levels and estimating emission factors: Case study of Tehran

Comparative study of PM10/PM2. 5-bound PAHs in downtown Beijing, China: concentrations, sources, and health risks

The effect of fine and coarse particulate air pollution on mortality: a national analysis

Children's respiratory morbidity prevalence in relation to air pollution in four Chinese cities

Long-term exposure to ambient air pollution and mortality due to cardiovascular disease and cerebrovascular disease in Shenyang