Particulate Matter ( PM 10 and PM 2 . 5 ) in Subway Systems : Health-Based Economic Assessment

Particulate matter (PM) is implicated in severely negative health effects, and subway-system PM is potentially more genotoxic than several other particle types. However, there are insufficient studies on subway-system PM-pollution reduction and control and the potential economic benefits thereof. Thus, the present study undertakes to assess the potential economic benefits resulting from a 10 μg/m3 reduction in PM10 and PM2.5 concentrations in a subway system, and to evaluate the importance of prevention and management of PM generally and subway-system PM specifically. Socioeconomic benefits such as medical expense curtailment, the precautionary effect on premature death, and the precautionary effect on productivity loss among subway passengers and workers were estimated by the cost-of-illness (COI) method. The health endpoints included two categories of disease: all lung cancer and cardiovascular diseases. The results showed that the total annual economic value in cost savings was 328.2 million KRW: 124.2 million KRW in direct costs, 186.4 million KRW in premature mortality costs, and 17.6 million KRW in productivity loss costs, respectively. These findings suggest that the control of PM10 and PM2.5 levels in subway systems should be promoted, as such effort certainly can produce significant economic benefits.


Introduction
In today's society, the subway, with its high speed, comfort, environmental friendliness, and large transport capacity, is a lifeline of urban development [1].It is a highly promoted alternative means of public transportation that relieves traffic congestion in city centers and reduces environmental pollution.The confined and underground spaces of underground subway systems, however, can accumulate pollutants from various sources.In particular, high levels of particulate matter (PM) have been detected in the underground subway systems of London, Helsinki, Berlin, Stockholm, Rome, Cairo, Beijing, and Seoul [2][3][4][5][6][7][8].
PM is composed of several compounds, including carbon-centered combustion particles, secondary inorganics, and crustal-derived particles [9].In addition to particles entering from the outside environment, subway aerosol particles are generated by the mechanical abrasion of rail tracks, wheels, catenary chains and brake pads, while passenger movement mixes and suspends them [10].
Inhalation of PM can affect the heart and lungs, resulting in serious health problems.The most dangerous particles in this regard are PM 10 and PM 2.5 , which are smaller than or equal to 10 µm and 2.5 µm in aerodynamic diameter, respectively [11].These particles have been reported to increase the risk of respiratory-related diseases such as lung cancer, chronic obstructive pulmonary disease, asthma exacerbation, and cardiovascular-related diseases including irregular heartbeat, vascular dysfunction, and arrhythmia.Global studies also have significantly associated them with acute and chronic premature death [12,13].Karlsson et al. (2005) reported that subway particles are more genotoxic than street particles, tire-road-wear particles, and particles from diesel and wood combustion [14].Therefore, a great concern of governments and health organisations worldwide is the adverse health effects of such PM on subway passengers and employees, especially considering the potentially high concentrations of PM combined with prolonged exposure times [15][16][17].
In a study of pollutants monitored in subway lines in Seoul (Korea), the concentrations of PM 10 and PM 2.5 inside trains were notably higher than those measured on platforms and in ambient air.PM 10 concentrations inside subway lines 1, 2 and 4 exceeded the Korea indoor air quality standard of 150 µg/m 3 .The average percentage exceeding the PM 10 standard was 83.3% on line 1, 37.9% on line 2 and 63.1% on line 4, respectively.Besides, PM 2.5 accounted for most of PM 10 and polluted subway air, and PM 2.5 level ranged from 77.7 µg/m 3 to 158.2 µg/m 3 , which were much higher than the ambient air PM 2.5 standard published by United States Environmental Protection Agency (24 h arithmetic mean: 65 µg/m 3 ) [5].Sohn et al. (2008) reported that in case of 24 h measurement, the mean levels of PM 10 of platform and waiting room were 156.18 µg/m 3 and 111.00 µg/m 3 [7].A study showed that the average PM 10 and PM 2.5 concentrations in subway cabins of line 2 were 132.8 µg/m 3 and 81.4 µg/m 3 , meanwhile those figures of line 5 were 154.4 µg/m 3 and 73.1 µg/m 3 , respectively [18].In Korea, efforts to improve air quality in subway cabins and tunnels along with the effects of PM-level reduction by different technologies have been reported [18][19][20][21].The goals of such studies are to protect public health and to curtail the inevitable economic losses associated with the health problems caused by PM exposure.
In order to determine the amount of benefit associated with PM pollution control, a cost-of-illness (COI) study can be used.The COI model, one of the earliest economic evaluation methods utilized in the healthcare field, estimates the economic burden of illness in a society in terms of healthcare-resources consumption and the productivity loss [22].
The economic valuation of the health effects of environmental pollution using the COI approach has been reported worldwide [23][24][25].COI model was applied to assess the economic benefits of reducing particulate air pollution in Lebanese urban areas [23].A monetary valuation of PM 10 -related health risks in 16 districts and 4 functional zones in Beijing (China) was performed, in which COI method was used in integration with an epidemiological exposure-response model and another economic valuation method [24].Not many studies have evaluated the economic benefit specifically of decreased subway-PM concentrations.Thus, the present study has been undertaken with the aims of assessing the potential economic benefits associated with 10 µg/m 3 reduction in PM 10 and PM 2.5 concentrations in subway systems and of providing basic data for improved public awareness of the importance of prevention and management of PM, especially subway-system PM.In addition, estimation framework, methods, and basic data in this study can provide basic information and reference for the estimation of economic benefit in correspondence with particulate pollution control.In this paper, social benefits resulting from the reduction of PM 10 and PM 2.5 levels in a subway system, specifically medical expense curtailment, the precautionary effect on premature death, and the precautionary effect on productivity loss of subway passengers and workers, are considered.Population-based data were collected and computed to obtain the theoretical annual economic values due to decreased levels of PM 10 and PM 2.5 in nine subway lines in Seoul, Korea.The results propose that the economic benefits of PM 10 and PM 2.5 concentration reduction in subway systems can be significant.
The remaining of the paper is organized as follows.In section "Materials and Methods", estimation framework and economic benefit estimation methods are presented.Estimation framework includes selection of health endpoints, cost-of-illness (COI) model, and exposed population.Subsection "Economic benefit estimation methods" describes methods of mortality and morbidity characterization and economic benefit estimation.Next, estimation results are mentioned in section "Results".This is followed by section "Discussion", in which discussion about results together with strengths and limitations of the study is presented.Finally, several conclusions are given in section "Conclusions".

Selection of Health Endpoints
In this study, the background data on the social costs of PM 10 and PM 2.5 were obtained using the conditional valuation model, which calculates the probability that these particles can cause human health problems, especially respiratory and cardiovascular diseases.For selection of health endpoints of particulate pollution, the following principles were taken into account: • health endpoints reported with a robust characterization of exposure-response relationships; • health endpoints provided by reliable statistics, such as those based on the International Classification of Diseases (ICD).
According to these principles, two disease categories, namely malignant neoplasm of trachea, bronchus and lung cancer (hereafter: "all lung cancer") and cardiovascular diseases were selected as representative PM-induced diseases (Table 1), and the total socioeconomic benefits associated with 10 µg/m 3 PM 10 and PM 2.5 decrements in subway tunnels and train cabins were estimated.[26][27][28].Generally, to determine the economic costs of any illness, three types of costs should be considered: direct costs, indirect costs, and intangible costs.Intangible costs reflect the patient's level of pain and suffering as well as the limitations imposed by the pain and suffering on his quality of life [29].As intangible costs have seldom been estimated in COI studies (due to measurement difficulties and related controversies [30]), we quantified the socioeconomic benefits in terms of direct and indirect costs only.
We calculated the cost savings by identifying several institutions' information sources that contain population-based data on resource utilization by patients with all lung cancer and cardiovascular diseases.The major indexes for calculation of each cost item and the sources of those indexes are summarized in Table 2.

A. Direct Costs
In this study, the direct costs included direct medical costs (medical expenditures) and direct non-medical costs (transportation costs, caregiver costs).The direct medical costs indicated the sum of medical expenses paid by outpatients and inpatients at medical institutions for treatment of diseases.The direct non-medical costs included transportation costs for visits to medical institutions and caregiver costs for inpatients.The round-trip transportation costs included those for outpatient visits and admission.Caregiver costs were estimated as the opportunity costs for guardians or the personnel expenses for paid caregivers to care inpatients.
The direct cost was calculated by Equation (1): where a = 1, 2, . . ., n diseases; i = 1, 2 genders; j = 0, 1, . . ., n age; E a ij is the total treatment amount of inpatients for a, i and j in the health insurance data; OE a ij is the total treatment amount of outpatients for a, i and j in the health insurance data; α is the percentage of hospitalization expenses borne by the patient; β is the percentage of outpatient expenses borne by the patient; O a ij is the number of outpatient-visit days for a, i and j; OM is the average round-trip transportation costs per outpatient visit; N a ij is the number of hospitalization days for a, i and j; M is the average round-trip transportation costs of admission, and I is the daily average costs of caregivers for inpatients.

B. Indirect Costs
Indirect costs included the loss of income related to premature death and the cost associated with productivity loss.For estimation of indirect costs, the human capital approach, by which productivity loss due to disease is calculated, is almost unanimously used [37].
Future income loss due to premature death was estimated according to the number of deaths related to the targeted diseases by gender and age (in Appendix A) [34], the annual expected income of a person if he does not die early, the labor force participation rate by gender and age (in Appendix A) [35], the employment rate by gender and age (in Appendix A) [35], and the discount rate.
The future income loss due to premature death was calculated by Equation ( 2): where e ij is the employment rate, and r is the discount rate.
The cost of productivity loss is defined as the cost of workday loss due to hospitalization and work-time loss due to outpatient care [38].The productivity loss cost was calculated by Equation (3): where a = 1, 2, . . ., n diseases; i = 1, 2 genders; j = 0, 1, . . ., n age; N a ij is the number of hospitalization days for a, i and j; δ is the non-production rate for an outpatient vs. a hospitalized patient; O a ij is the number of outpatient-visit days for a, i and j; p ij is the labor force participation rate; e ij is the employment rate, and y ij is patients' daily average income.

Exposed Population
In the subway, exposure targets of hazardous substances are passengers and workers in subway tunnels.In the present study, the average number of passengers per cabin was set as 262, the average boarding time per person as 1 h, the average number of people exposed to hazardous substances as 13,000 people per day, the average number of workers in a tunnel as 10, and the average total number of workers in the Seoul metropolitan area, considering the entire history of lines 1-9, as 5060 people.The daily exposure time of the workers in subway tunnels was set to 3 h [21].

Economic Benefit Estimation Methods
In consideration of the data that have been acquired thus far in studies worldwide, the present assessment process entailed the following assumptions: • there is no exposure threshold below which PM 10 and PM 2.5 are not a cause of morbidity or mortality; • there are no differences in exposure or susceptibility among different populations; • differences in methodology or sample size among epidemiological studies are ignored; • the data for all age groups will be applied to the estimations in cases where the data for a particular age is unavailable [23].
The social benefits of decreased PM 10 and PM 2.5 levels were set up, and the economic values were calculated using the COI analytical model.Generally, the average value for each cost category was estimated, and the economic value was calculated by multiplying that average cost by the number of cases theoretically decreased by each 10 µg/m 3 reduction in PM 10 and PM 2.5 levels.

Mortality and Morbidity Characterization
The theoretical decreases in morbidity cases were obtained by multiplying the prevention rate by the total number of patients and calculating the ratio of the exposed population size to the nationwide population size.The decreases in mortality cases, meanwhile, were estimated by multiplying the prevention rate by the total number of deaths and calculating the ratio of the exposed population size to the nationwide population size.

•
Table 3 summarizes the data used in the calculations of the per-year reductions in morbidity and mortality cases due to 10 µg/m 3 decrement of PM 10 and PM 2.5 levels.Generally, country-specific or local epidemiological studies are the most proper indicators for the assessment of associations between environmental pollution and health outcome in a given region.Nevertheless, given that such studies require time and cost investments as well as there are inadequate domestic data, epidemiological studies established in other countries can be adopted assuming that human reaction is similar in different regions [21,23].In this study, analytical epidemiological studies used as data sources of the prevention rates in correspondence with 10 µg/m 3 PM 10 and PM 2.5 decrements were considered based on some essential criteria, including: studies assess the association between the change in concentration of PM 10 or PM 2.5 and one of health endpoints; • the range of change in concentration of PM 10 or PM 2.5 in health assessment is 10 µg/m 3 ; • some approaches are applied to handle potential confoundings or uncertainties.
Extended inclusion criteria were used for further consideration, including sample size, number of studies examined in meta-analyses, and the age groups of participants or subjects in studies.For identification of eligible articles, an initial screen of titles or abstracts was performed, followed by a full-text review and finally a recordation of the reasons of exclusion.Six analytical epidemiological studies in the period 2000-2015 were selected (Table A1).  a Data are calculated for people between the ages of 15 and 74.

A. Economic Benefit from Direct Cost Curtailment
Given the lack of sufficient data on patients who are subway passengers and employees, this study estimated the benefit from direct cost savings by multiplying the average direct cost for a specific targeted disease by the total number of patients that could be saved by each 10 µg/m 3 decrement of PM 10 and PM 2.5 levels in the subway system.The total scale of the direct costs of the two categories of health endpoint was calculated based on Equation (1).The average direct costs per capita were then calculated by dividing the total direct cost by the nationwide number of patients.
Data from the National Health Insurance Statistical Yearbook of 2015 [31] were used in the estimations of outpatient and inpatient medical expenditures (Tables A2 and A3).The average ratio of non-benefit cost to treatment cost in terms of disease (Table A4) was computed from the total medical expense and the insurance benefit for inpatients and outpatients, again using the National Health Insurance Statistical Yearbook of 2015 [31].The average round-trip transportation cost per visit was 8607 Korean Won (KRW, the currency of South Korea) for outpatients and 21,334 KRW for admission in 2005 [32].These estimations were adjusted, according to the 2017 traffic price index [46], to an average round-trip transportation cost of 10,411 KRW for outpatients and 25,805 KRW for admission.The average daily expense for paid caregivers suggested by The Korea Patient Helper Society (75,000 KRW) [33] was input as the opportunity costs for guardians or personnel expenses for caregivers.

B. Economic Benefit from Indirect Cost Curtailment
The future income loss due to premature death resulting from diseases related to PM 10 and PM 2.5 was estimated by applying the human capital approach following Equation (2).It was assumed that productive activity is not performed from age 0 to 14 years and after age 74 in the life cycle.The annual expected income was computed by multiplying the average monthly wage of "all-workers" group [36] by 12 (months) and the expected number of years of productive activity.The discount rate (r) applied to convert future income to present value was 0% for convenience in calculation [30].Because all passengers cannot be considered to participate in economic activities, the labor force participation rate and employment rate were determined for gender and age (Table A5) [35].These rates for the workers were assumed to be 100%.The average human capital loss per capita was then determined by dividing the total human loss by the nationwide number of premature death cases of people between the ages of 15 and 74 (Table A6) [34].By multiplying the average premature death costs by the number of mortality cases avoided by each 10 µg/m 3 reduction of PM 10 and PM 2.5 levels, the economic benefit of preventing unexpected death could be obtained.
The costs of productivity loss were calculated based on Equation (3).According to the assumption that the productivity loss for one day of hospitalization is similar to that for three outpatient visits, the number of non-productive days was calculated by adding the number of hospitalization days and one-third of the outpatient-visit days [47].The average labor force participation rate and employment rate (Table A5) [35] were used as inputs to estimate the productivity loss.The daily average income was calculated by dividing the average monthly wage by 21, which is the average number of working days in a month for all workers [36].The average productivity loss cost per capita was then estimated by dividing the total productivity loss cost by the nationwide number of patients.The annual economic value of the precautionary effect on productivity loss was computed from the average productivity loss costs and the theoretical number of morbidity cases reduced due to each 10 µg/m 3 decrement in the PM 10 and PM 2.5 concentrations, respectively.

Results
According to the predicted values of mortality and morbidity effects (Table 4), the economic benefits associated with the reduction of PM 10 and PM 2.5 levels in subway systems were estimated by the COI method.It was determined that medical cost savings of 124.2 million KRW per year would be obtained for each 10-µg/m 3 decrement in PM 10 and PM 2.5 concentrations (Table 5).As for the indirect costs, the economic value of the precautionary effect on premature mortality was estimated to be 186.4 million KRW per year (Table 6), while that of the precautionary effect on productivity loss was 17.6 million KRW per year (Table 7).Given the assumed 13,000 passengers and 5060 workers in tunnels, the total annual economic benefit due to each 10 µg/m 3 decrement in PM 10 and PM 2.5 concentrations was calculated to be 328.2 million KRW.

Discussion
In health economics, it is rather common to use the COI framework to quantify the costs of different health risk factors in monetary terms [29].Besides, figures for COI analysis may be obtained from official statistics, and may be easier and less expensive to obtain than some other methods [25].As a result, this model was applied in the present study.
The estimation outcomes demonstrate that an enormous socioeconomic burden could result from PM pollution, but equally, that a vast economic value could be associated with the control of PM 10 and PM 2.5 levels.In correspondence with every 10 µg/m 3 decrement in PM 10 and PM 2.5 levels, the direct cost and cost of premature death related to PM 2.5 and PM 10 decreased substantially, compared with productivity loss.According to the cost items, the benefit of saving future income loss accounted for the largest proportion of the total benefit, or 56.8%, and the benefits of direct cost saving (37.8%) and saving productivity loss (5.4%) followed in order.The subway is a highly promoted mode of public transport, and a large number of people could be exposed to PM pollution in subway systems every day.In different sampling locations, PM 10 levels were higher than 150 µg/m 3 , and even a high concentration of 480.1 µg/m 3 was reported [5, 7,8].Studies also indicated high levels of PM 2.5 more than 65 µg/m 3 in ground and underground sampling sites among subway systems [8].Considering that subway PM 10 and PM 2.5 levels notably exceed air quality standards in numerous locations, efforts to decrease PM 10 and PM 2.5 concentrations in subway cabins and tunnels promise high economic returns.
In this study, the total scale of healthcare utilization (excepting uninsured items) can be grasped when patients with targeted diseases utilize medical institutions, since all citizens in Korea have been mandatorily insured within a single insurance claim system [48].In addition, as this study utilized health insurance data along with other nationwide-scale data to estimate direct medical costs, direct non-medical costs (transportation costs, caregiver costs), future income loss due to premature mortality, and the loss of productivity following absence from work, it is expected that the reliability and validity and of its estimation outcomes will be strengthened.
Nonetheless, this study has some limitations that are inevitable or controllable.First, the health insurance data on targeted diseases as additional diagnoses were excluded, so the directs costs and productivity loss could have been underestimated.Second, uninsured medical costs were determined only by estimating the ratio of non-benefit cost to treatment cost based on the data in the National Health Insurance Statistical Yearbook [31], since data on actual uninsured medical costs were not available.Third, given the unavailability of data on the rate of in-home care-giving and the correlation between the rate of care-giving service demand and disease severity, caregiver costs were calculated only for inpatients.However, to limit the underestimation when excluding in-home caregiver costs, we assumed that inpatients needed full-time care-giving.Fourth and finally, due to the insufficiency of data on the sick leave of patients who have neither inpatient nor outpatient visits, the calculation of productivity loss related to absence from work could have been underestimated.A variety of uncertainties arise, since economic benefits were predicted on the basis of epidemiological studies reviewed in the literature, including uncertainty in selection of studies and statistical uncertainty from referenced studies.
By applying COI model, it is difficult to compare findings across COI studies due to the use of different data and methods.Moreover, a number of limitations and uncertainties in economic benefit estimation are recorded in the present study.These shortcomings, however, should not delay the development of PM control measures in subway systems.Furthermore, the present study is valuable, since it can identify some gaps in the data which would be required for a full accounting of costs [29].This leads to a suggestion that data collections and further analyses should be conducted aimed at improving estimations.Additionally, estimation framework, methods, and basic data of the present study are expected to provide basic information and reference for the estimation of economic benefit in correspondence with particulate pollution control.

Conclusions
In this study, the COI method was utilized to assess the economic value of a 10 µg/m 3 decrement in PM 10 and PM 2.5 concentrations.The economic benefit analysis was conducted by setting the benefits of medical expense curtailment, of the precautionary effect on premature death and of the precautionary effect on productivity loss in terms of subway passengers and workers and their respective numbers.As for the results, the total annual economic benefit was calculated to be 328.2 million KRW for each 10 µg/m 3 decrement in PM 10 and PM 2.5 concentrations.For each 10-µg/m 3 decrement in PM 10 and PM 2.5 concentrations, it was determined that the annual savings of medical cost, premature mortality and productivity loss would be 124.2 million KRW, 186.4 million KRW and 17.6 million KRW per year, respectively.In light of these significant economic dividends, it is considered that PM 10 -and PM 2.5 -level reductions in subway systems should be promoted.Also, the results of this study are considered to be useful in providing basic information and reference for the estimation of economic benefit in correspondence with particulate pollution control.
Meta-analyses of studies examining the relationship of exposure to PM 2.5 and PM 10 with lung cancer incidence and mortality were conducted.In total, 18 studies met authors' inclusion criteria and provided the information necessary to estimate the change in lung cancer risk per 10 µg/m 3 increase in exposure to PM. Random-effects analyses were used to allow between-study variability to contribute to meta-estimates.Estimates were robust to restriction to studies that considered potential confounders, as well as subanalyses by exposure assessment method.
Prevention rate for all lung cancer morbidity associated with each 10 µg/m 3 decrement in PM 10 concentration; Prevention rate for all lung cancer morbidity associated with each 10 µg/m 3 decrement in PM 2.5 concentration  Table A6.Deaths related to all lung cancer and cardiovascular diseases (units: persons) a .

Table 1 .
Categories of targeted diseases a .

Table 2 .
Categories related to all lung cancer and cardiovascular diseases, with data sources.

Table 3 .
Data used to calculate per-year reductions in morbidity and mortality cases due to 10 µg/m 3 decrement of PM 10 and PM 2.5 levels.

Table 4 .
Morbidity and mortality cases avoided per year due to 10 µg/m 3 decrement of PM 10 and PM 2.5 concentrations.

Table 6 .
Annual economic value of precautionary effect on unexpected death (unit: 1,000,000 KRW).

Table 7 .
Annual economic value of precautionary effect on productivity loss (unit: 1,000,000 KRW).

Table A2 .
Numbers of in-and outpatients, and corresponding costs of treatment related to lung cancer and cardiovascular diseases (unit: person; 1000 KRW) a .

Table A4 .
Ratio of non-benefit cost to treatment cost (unit: %) a . ICD-

10 Code(s) Ratio of Non-Benefit Cost to Treatment Cost
[31]e data was calculated based on data collected from the National Health Insurance Statistical Yearbook of 2015[31].

Table A5 .
[35]r force participation rate and employment rate by gender and age a .Source: Statistics Korea, Korean Statistical Information Service.Economically Active Population Survey[35]. a