Evaluating the PM damage cost due to urban air pollution and vehicle emissions in Seoul, Korea

Estimating the economic value of ultrafine particle information: a contingent valuation method

Global concern regarding ultrafine particles (UFPs), which are particulate matter (PM) with a diameter of less than 100 nm, is increasing. These particles are difficult to measure using the current methods because their characteristics are different from those of other air pollutants. Therefore, a new monitoring system is required to obtain accurate UFP information, which will raise the financial burden of the government and people. In this study, we estimated the economic value of UFP information by evaluating the willingness-to-pay (WTP) for the UFP monitoring and reporting system. We used the contingent valuation method (CVM) and the one-and-one-half-bounded dichotomous choice (OOHBDC) spike model. We analyzed how the respondents' socio-economic variables, as well as their cognition level of PM, affected their WTP. Therefore, we collected WTP data of 1040 Korean respondents through an online survey. The estimated mean WTP for building a UFP monitoring and reporting system is KRW 6958.55-7222.55 (USD 6.22-6.45) per household per year. We found that people satisfied with the current air pollutant information, and generally possessing relatively greater knowledge of UFPs, have higher WTP for a UFP monitoring and reporting system. We found that people are willing to pay more than the actual installation and operating costs of current air pollution monitoring systems. If the collected UFP data is disclosed in an easily accessible manner, as is current air pollutant data, it will be possible to secure more public acceptance for expanding the UFP monitoring and reporting system nationwide.

Is there a stronger willingness to pay for air quality improvement with high education: new evidence from a survey in China

As a developing country with the largest population and serious environmental pollution in the world, China has made great efforts in air pollution. Air quality improvement depends not only on government administrative regulations but also on public support, especially how much the public is willing to pay for air quality improvement. Higher education will encourage the public to take action to improve air quality. However, the confirmation of the causality relationship between WTP and education has been missing. This study uses the Chinese General Social Survey (CGSS) to find the relationship between the two, and the conclusions are drawn: OLS regression model and instrumental variable both determine the positive influence of education level on air quality improvement WTP, and Heckman model further verifies the robustness of the conclusion. The positive influence of education level is greater in the groups of men, higher income, higher awareness of acid rain, and more air purifiers, and it has a greater impact on married people in rural areas than in urban areas. The function mechanism of education can improve residents' WTP by increasing regional GDP, promoting urbanization level, expanding afforestation areas, decreasing private car ownership and the number of newly registered civil cars, and reducing sulfur dioxide emissions, nitrogen oxides, and smoke (powder) dust. The total social and economic value of air quality improvement in China is 34.572 billion CNY to 672.42 trillion CNY.

The association between short-term exposure to ambient carbon monoxide and hospitalization costs for bronchitis patients: A hospital-based study

Ambient carbon monoxide (CO) is associated with bronchitis morbidity, but there is no evidence concerning its correlation with hospitalization costs for bronchitis patients. This study aimed to investigate the relationship between short-term ambient CO exposure and hospitalization costs for bronchitis patients in Chongqing, China. Baseline data for 3162 hospitalized bronchitis patients from November 2013 to December 2019 were collected. Multiple linear regression analysis was used to determine the association, delayed and cumulative, between short-term CO exposure and hospitalization costs. Additionally, subgroup analyses were performed by gender, age, season, and comorbidity. Positive association between CO and hospitalization costs for bronchitis patients was observed. The strongest association was observed at lag 015 days, with per 1 mg/m³ increase of CO concentrations corresponded to 5834.40 Chinese Yuan (CNY) (95% CI: 2318.71, 9350.08; P < 0.001) (845.97 US dollars) increment in hospitalization costs. Stratified analysis results showed that the association was more obvious among those males, elderly, with comorbidities, and in warm seasons. More importantly, there was strongest correlation between CO and bronchitis patients with coronary heart disease. In summary, short-term exposure to ambient CO, even lower than Chinese and WHO standards, can be associated with increased hospitalization costs for bronchitis. Controlling CO exposure can be helpful to reduce medical burden associated with bronchitis patients. The results also suggest that when setting air quality standards and formulating preventive measures, susceptible subpopulations ought to be considered.

The Influence of Air Pollution on Happiness and Willingness to Pay for Clean Air in the Bohai Rim Area of China

Air pollution imposes detrimental impacts on residents’ health and the general quality of life. Quantifying the influential mechanism of air pollution on residents’ happiness and the economic value brought by environmental quality improvement could provide a scientific basis for the construction of livable cities. This study estimated urban residents’ willingness to pay for air pollution abatement by modeling the spatial relationship between air quality and self-rated happiness with a Bayesian multi-level ordinal categorical response model. Using large-scale geo-referenced survey data, collected in the Bohai Rim area of China (including 43 cities), we found that a standard deviation decrease in the number of polluted days over a year was associated with about a 15 percent increase in the odds of reporting a higher degree of happiness, after controlling for a wide range of individual- and city-scale covariate effects. On average, urban residents in the Bohai Rim region were willing to pay roughly 1.42 percent of their average monthly household income for mitigating marginal reductions in air pollution, although great spatial variability was also presented. Together, we hoped that these results could provide solid empirical evidence for China’s regional environmental policies aiming to promote individuals’ well-being.

Monetary valuation of air quality improvement with the stated preference technique: A multi-pollutant perspective

Air pollution is an important global issue, and governments are making efforts to reduce air pollutant emissions. The elaborate calculation of the social costs of air pollution is essential for justifying the substantial public expenditure on air pollution control policies. This study aims to derive the monetary value of reducing different kinds of air pollutants through the public's willingness to pay (WTP). A choice experiment survey is conducted to examine the public perception of air quality improvement, and the collected stated preference data are analysed with the hierarchical Bayesian logit model. The analysis results show that the public valuation of the emission reduction differs for individual pollutants. On average, the monetary value of one ton of emission reduction follows the order of PM_2.5, PM₁₀, SO_x, TSP, NO_x, and VOC. Based on the elicited WTP, the economic feasibility of the two air pollutant reduction plans of the South Korean Government is assessed. The benefit-to-cost ratio of the plans is 0.61 and 0.66, respectively, based on the mean WTP, indicating that they are not economically feasible at the moment. Implications for the efficient budget allocation of air pollution control policies are provided based on the results.

Valuation of Local Demand for Improved Air Quality: The Case of the Mae Moh Coal Mine Site in Thailand

While the district of Mae Moh, Thailand has been well known for its atmospheric pollution associated with coal power production, economic assessment of demand for improved air quality has not been conducted to date. This study estimated local residents’ individual and aggregate willingness to pay (WTP) for mitigation of atmospheric pollution in Mae Moh using the contingent valuation method (CVM), and analyzed the factors associated with the individual WTP using the bivariate tobit and double-hurdle regression techniques. Primary data were collected through face-to-face interviews with a stratified sample of 200 residents. The hypothetical scenarios used in the CVM module were 50% and 80% mitigation of atmospheric concentrations of major pollutants. The weighted average WTP was found to be THB 251.3 and 307.9 per annum (USD 8.4 and 10.3) for the 50% and 80% reduction scenarios, respectively. The aggregate WTP for the entire population of Mae Moh was THB 10,008,733 and 12,264,761 per annum (USD 336,294 and 412,096), respectively. Education, occupation type, income, expenses, satisfaction with ambient quality, and perceived sources of pollution had significant associations with the individual WTP. The paper concludes by discussing policy implications for atmospheric pollution management and avenues for future research.

PM2.5 Pollution: Health and Economic Effect Assessment Based on a Recursive Dynamic Computable General Equilibrium Model

At present particulate matter (PM_2.5) pollution represents a serious threat to the public health and the national economic system in China. This paper optimizes the whitening coefficient in a grey Markov model by a genetic algorithm, predicts the concentration of fine particulate matter (PM_2.5), and then quantifies the health effects of PM_2.5 pollution by utilizing the predicted concentration, computable general equilibrium (CGE), and a carefully designed exposure-response model. Further, the authors establish a social accounting matrix (SAM), calibrate the parameter values in the CGE model, and construct a recursive dynamic CGE model under closed economy conditions to assess the long-term economic losses incurred by PM_2.5 pollution. Subsequently, an empirical analysis was conducted for the Beijing area: Despite the reduced concentration trend, PM_2.5 pollution continued to cause serious damage to human health and the economic system from 2013 to 2020, as illustrated by various facts, including: (1) the estimated premature deaths and individuals suffering haze pollution-related diseases are 156,588 (95% confidence intervals (CI): 43,335-248,914)) and six million, respectively; and (2) the accumulated labor loss and the medical expenditure negatively impact the regional gross domestic product, with an estimated loss of 3062.63 (95% CI: 1,168.77-4671.13) million RMB. These findings can provide useful information for governmental agencies to formulate relevant environmental policies and for communities to promote prevention and rescue strategies.

Airborne particles in the city center of Kuala Lumpur: Origin, potential driving factors, and deposition flux in human respiratory airways

Equatorial warming conditions in urban areas can influence the particle number concentrations (PNCs), but studies assessing such factors are limited. The aim of this study was to evaluate the level of size-resolved PNCs, their potential deposition rate in the human respiratory system, and probable local and transboundary inputs of PNCs in Kuala Lumpur. Particle size distributions of a 0.34 to 9.02 μm optical-equivalent size range were monitored at a frequency of 60 s between December 2016 and January 2017 using an optical-based compact scanning mobility particle sizer (SMPS). Diurnal and correlation analysis showed that traffic emissions and meteorological confounding factors were potential driving factors for changes in the PNCs (D_p ≤1 μm) at the modeling site. Trajectory modeling showed that a PNC <100/cm³ was influenced mainly by Indo-China region air masses. On the other hand, a PNC >100/cm³ was influenced by air masses originating from the Indian Ocean and Indochina regions. Receptor models extracted five potential sources of PNCs: industrial emissions, transportation, aged traffic emissions, miscellaneous sources, and a source of secondary origin coupled with meteorological factors. A respiratory deposition model for male and female receptors predicted that the deposition flux of PM₁ (particle mass ≤1 μm) into the alveolar (AL) region was higher (0.30 and 0.25 μg/h, respectively) than the upper airway (UA) (0.29 and 0.24 μg/h, respectively) and tracheobronchial (TB) regions (0.02 μg/h for each). However, the PM_2.5 deposition flux was higher in the UA (2.02 and 1.68 μg/h, respectively) than in the TB (0.18 and 0.15 μg/h, respectively) and the AL regions (1.09 and 0.91 μg/h, respectively); a similar pattern was also observed for PM₁₀.

A systematic review of financial implications of air pollution on health in Asia

Economic losses due to health-related implications of air pollution were huge and incurred significant burdens towards healthcare providers. The objective of this study is to systematically review published literature on the financial implications of air pollution on health in Asia. Four databases: PubMed, Scopus, NHS Economic Evaluation Database (NHS EED), and Web of Science (WoS) were used to identify all the relevant articles. It was limited to all articles that had been published in the respected databases from January 2007 until March 2017. Twenty-four articles were included in this review. Five of the 24 studies (20.8%) reported financial implications of air pollution-related disease through value of statistical life (VOSL) which ranged from USD180 million to USD2.2 billion, six (25%) studies used cost of illness (COI) to evaluate air pollution-related morbidity and found that the cost ranged from USD5.4 million to USD9.1 billion. Another six studies (25%) used a combination of VOSL and COI for both mortality and morbidity valuation and found that the financial implications ranging from USD253 million to USD2.9 billion. Thirteen (54.2%) studies reported healthcare cost associated with both hospital admission and outpatient visit, five (20.1%) on hospital admission only, and one (4.2%) on outpatient visit only. Economic impacts of air pollution can be huge with significant deterioration of health among the Asians.

Impact of air pollution on cause-specific mortality in Korea: Results from Bayesian Model Averaging and Principle Component Regression approaches

Health effects related to air pollution are a major global concern. Related studies based on reliable exposure assessment methods would potentially enable policy makers to propose appropriate environmental management policies. In this study, integrated Bayesian Model Averaging (BMA) and Principle Component Regression (PCR) were adopted to assess the severity of air pollution impacts on mortality related to circulatory, respiratory and skin diseases in 25 districts of Seoul, South Korea for the years 2005-2015. These methods were consistent in determining the best regression models and most important pollutants related to mortality in those highly susceptible to poor air quality. Specifically, the results demonstrated that pneumonia was highly associated with air pollution, with a large determination coefficient (BMA: 0.46, PCR: 0.51) and high model's posterior probability (0.47). The most reliable prediction model for pneumonia was indicated by the lowest Bayesian Information Criterion. Among the pollutants, particulate matter with an aerodynamic diameter of 10 μm or less (PM₁₀) was associated with serious health risks on evaluation, with the highest posterior inclusion probabilities (range, 80.20 to 100.00%) and significantly positive correlation coefficients (range, 0.14 to 0.34, p < 0.05). In addition, excessive PM₁₀ concentration (approximately 2.54 times the threshold) and a continuous increase in mortality due to respiratory diseases (approximately 1.50-fold in 10 years) were also exhibited. Overall, the results of this study suggest that currently, socio-environmental policies and international collaboration to mitigate health effects of air pollution is necessary in Seoul, Korea. Moreover, consideration of uncertainty of the regression model, which was verified in this research, will facilitate further application of this approach and enable optimal prediction of interactions between human and environmental factors.

Contingent valuation of health and mood impacts of PM2.5 in Beijing, China

Air pollution from PM₂.₅ affects many cities worldwide, causing both health impacts and mood depression. One of the obstacles to implementing environmental regulations for PM₂.₅ reduction is that there are limited studies of PM₂.₅ welfare loss and few investigations of mood depression caused by PM₂.₅. This article describes a survey study conducted in Beijing, China to estimate the welfare loss due to PM₂.₅. In total, 1709 participants completed either a face-to-face or online survey. A contingent valuation method was applied to elicit people's willingness to pay to avoid PM₂.₅ pollution and willingness to accept a compensation for such pollution. The payment/compensation was evaluated for two outcome variables: perceived health impacts and mood depression caused by PM₂.₅ pollution. This is one of few papers that explicitly studies the effects of PM₂.₅ on subjective well-being, and to the authors' knowledge, the first to estimate welfare loss from PM₂.₅ using a random forest model. Compared to the standard Turnbull, probit, and two-part models, the random forest model gave the best fit to the data, suggesting that this may be a useful tool for future studies too. The welfare loss due to health impacts and mood depression is CNY 1388.4/person/year and CNY 897.7/person/year respectively, indicating that the public attaches great importance to mood, feelings and happiness. The study provides scientific support to the development of economic policy instruments for PM₂.₅ control in China.

Health status, mental health and air quality: evidence from pensioners in Europe

Environmental quality is an important determinant of individuals' well-being and one of the main concerns of the governments is the improvement on air quality and the protection of public health. This is especially the case of sensitive demographic groups, such as the old aged people. However, the question this study attempts to answer is how do individuals value the effects on the environment. The study explores the effects of old and early public pension schemes, as well as the impact of air pollution on health status of retired citizens. The empirical analysis relies on detailed micro-level data derived from the Survey of Health, Ageing and Retirement in Europe (SHARE). As proxies for health, we use the general health status and the Eurod mental health indicator. We examine two air pollutants: the sulphur dioxide (SO₂) and ground-level ozone (O₃). Next, we calculate the marginal willingness-to-pay (MWTP) which shows how much the people are willing to pay for improvement in air quality. We apply various quantitative techniques and approaches, including the fixed effects ordinary least squares (OLS) and the fixed effects instrumental variables (IV) approach. The last approach is applied to reduce the endogeneity problem coming from possible reverse causality between the air pollution, pensions and the health outcomes. For robustness check, we apply also a structural equation modelling (SEM) which is proper when the outcomes are latent variables. Based on our favoured IV estimates and the health status, we find that the MWTP values for one unit decrease in SO₂ and O₃ are respectively €221 and €88 per year. The respective MWTP values using the Eurod measure are €155 and €68. Overall, improvement of health status implies reduction in health expenditures, and in previous literature, ageing has been traditionally considered the most important determinant. However, this study shows that health lifestyle and socio-economic status, such as education and marital status, are more important, and furthermore, air pollution cannot be ignored in the agenda of policy makers.

Public Value of Enforcing the PM2.5 Concentration Reduction Policy in South Korean Urban Areas

As the number of cars and the electricity produced from coal-fired generation has been increasing, PM2.5, particles smaller than 2.5 μm in diameter, has become a serious problem in South Korean urban areas. This is especially notable, given that the PM2.5 warning was issued 89 times during 2016. Because of this, the South Korean government is seeking to enforce a policy of reducing the number of PM2.5 warnings by half using various policy instruments from now until 2022. This article tries to obtain information about the public value of the enforcement. For this purpose, household willingness to pay (WTP) for the enforcement is investigated, applying the contingent valuation (CV) approach. A survey of 1000 households was carried out in South Korean urban areas. The data on the WTP were gathered using a dichotomous choice question and analyzed employing the spike model. The mean WTP estimate is obtained as KRW 5591 (USD 4.97) per household per year, which is statistically significant. The total public value expanded to the population amounts to KRW 98.9 billion (USD 87.8 million) per year. The information can be utilized in policy-making and decision-making about the reduction of the PM2.5 concentration.

The study to estimate the floating population in Seoul, Korea

Traffic-related pollutants have been reported to increase the morbidity of respiratory diseases. In order to apply management policies related to motor vehicles, studies of the floating population living in cities are important. The rate of metro rail transit system use by passengers residing in Seoul is about 54% of total public transportation use. Through the rate of metro use, the people-flow ratios in each administrative area were calculated. By applying a people-flow ratio based on the official census count, the floating population in 25 regions was calculated. The reduced level of deaths among the floating population in 14 regions having the roadside monitoring station was calculated as assuming a 20% reduction of mobile emission based on the policy. The hourly floating population size was calculated by applying the hourly population ratio to the regional population size as specified in the official census count. The number of people moving from 5 a.m. to next day 1 a.m. could not be precisely calculated when the population size was applied, but no issue was observed that would trigger a sizable shift in the rate of population change. The three patterns of increase, decrease, and no change of population in work hours were analyzed. When the concentration of particulate matter less than 10 μm in aerodynamic diameter was reduced by 20%, the number of excess deaths varied according to the difference of the floating population. The effective establishment of directions to manage the pollutants in cities should be carried out by considering the floating population. Although the number of people using the metro system is only an estimate, this disadvantage was supplemented by calculating inflow and outflow ratio of metro users per time in the total floating population in each region. Especially, 54% of metro usage in public transport causes high reliability in application.

Mortality and morbidity due to exposure to outdoor air pollution in Mashhad metropolis, Iran. The AirQ model approach

In the past two decades, epidemiological studies have shown that air pollution is one of the causes of morbidity and mortality. In this study the effect of PM10, PM2.5, NO2, SO2 and O3 pollutants on human health among the inhabitants of Mashhad has been evaluated. To evaluate the health effects due to air pollution, the AirQ model software 3.3.2, developed by WHO European Centre for Environment and Health, was used. The daily data related to the pollutants listed above has been used for the short term health effects (total mortality, cardiovascular and respiratory mortality, hospitalization due to cardiovascular and respiratory diseases, chronic obstructive pulmonary disease and acute myocardial infarction). PM2.5 had the most health effects on Mashhad inhabitants. With increasing in each 10μg/m3, relative risk rate of pollutant concentration for total mortality due to PM10, PM2.5, SO₂, NO₂ and O₃ was increased of 0.6%, 1.5%, 0.4%, 0.3% and 0.46% respectively and, the attributable proportion of total mortality attributed to these pollutants was respectively equal to 4.24%, 4.57%, 0.99%, 2.21%, 2.08%, and 1.61% (CI 95%) of the total mortality (correct for the non-accident) occurred in the year of study. The results of this study have a good compatibly with other studies conducted on the effects of air pollution on humans. The AirQ software model can be used in decision-makings as a useful and easy tool.

Physicochemical factors and sources of particulate matter at residential urban environment in Kuala Lumpur

Long-term measurements (2004-2011) of PM10 (particulate matter with an aerodynamic diameter <10 μm) and trace gases (carbon monoxide [CO], ozone [O₃], nitrogen oxide [NO], oxides of nitrogen [NO(x)], nitrogen dioxide [NO₂], sulfur dioxide [SO₂], methane [CH₄], nonmethane hydrocarbon [NMHC]) have been conducted to study the effect of physicochemical factors on the PM10 concentration. In addition, this study includes source apportionment of PM10 in Kuala Lumpur urban environment. An advanced principal component analysis (PCA) technique coupled with absolute principal component scores (APCS) and multiple linear regression (MLR) has been applied. The average annual concentration of PM10 for 8 yr is 51.3 ± 25.8 μg m⁻³, which exceeds the Recommended Malaysian Air Quality Guideline (RMAQG) and international guideline values. Detail analysis shows the dependency of PM10 on the linear changes of the motor vehicles in use and the amount of biomass burning, particularly from Sumatra, Indonesia, during southwesterly monsoon. The main sources of PM10 identified by PCA-APCS-MLR are traffic combustion (28%), ozone coupled with meteorological factors (20%), and wind-blown particles (1%). However, the apportionment procedure left 28.0 μg m⁻³, that is, 51% of PM10 undetermined.

Value of a statistical life estimation of carcinogenic chemicals for socioeconomic analysis in Korea

OBJECTIVES

To protect public health from risk, the Minister of Environment in Korea legislated an act concerning the registration and evaluation of chemical substances. In this study, we estimated the value of a statistical life (VSL) of carcinogenic chemicals to evaluate the socioeconomic analysis in Korea.

METHODS

The estimation of the health benefit can be calculated through an individual's VSL and willingness to pay (WTP). To estimate the VSL and WTP, we used a contingent valuation method through a web-based survey.

RESULTS

The survey is conducted with 1434 people living in Seoul and six large cities. An analysis of the survey is essential to review the distribution of the characteristics of the target population. The statistically significant variables affecting the WTP are location, age, household income, quality of life. Through the review of data, we secured statistical validity. The WTP was estimated as 41205 Korean won (KRW)/person, and the estimated VSL appeared as 796 million KRW/person.

CONCLUSIONS

There is a case in which the amount of statistical life value is estimated in connection with domestic environmental policy, fine dust, etc. However, there are no cases of evaluation for chemical. The utilization of this result is possible for conducting other study with chemicals.

Value of a statistical life in road safety: a benefit-transfer function with risk-analysis guidance based on developing country data

We model a value of statistical life (VSL) transfer function for application to road-safety engineering in developing countries through an income-disaggregated meta-analysis of scope-sensitive stated preference VSL data. The income-disaggregated meta-analysis treats developing country and high-income country data separately. Previous transfer functions are based on aggregated datasets that are composed largely of data from high-income countries. Recent evidence, particularly with respect to the income elasticity of VSL, suggests that the aggregate approach is deficient because it does not account for a possible change in income elasticity across income levels. Our dataset (a minor update of the OECD database published in 2012) includes 123 scope-sensitive VSL estimates from developing countries and 185 scope-sensitive estimates from high-income countries. The transfer function for developing countries gives VSL=1.3732E-4×(GDP per capita)(∧)2.478, with VSL and GDP per capita expressed in 2005 international dollars (an international dollar being a notional currency with the same purchasing power as the U.S. dollar). The function can be applied for low- and middle-income countries with GDPs per capita above $1268 (with a data gap for very low-income countries), whereas it is not useful above a GDP per capita of about $20,000. The corresponding function built using high-income country data is VSL=8.2474E+3×(GDP per capita)(∧).6932; it is valid for high-income countries but over-estimates VSL for low- and middle-income countries. The research finds two principal significant differences between the transfer functions modeled using developing-country and high-income-country data, supporting the disaggregated approach. The first of these differences relates to between-country VSL income elasticity, which is 2.478 for the developing country function and .693 for the high-income function; the difference is significant at p<0.001. This difference was recently postulated but not analyzed by other researchers. The second difference is that the traffic-risk context affects VSL negatively in developing countries and positively in high-income countries. The research quantifies uncertainty in the transfer function using parameters of the non-absolute distribution of relative transfer errors. The low- and middle-income function is unbiased, with a median relative transfer error of -.05 (95% CI: -.15 to .03), a 25th percentile error of -.22 (95% CI: -.29 to -.19), and a 75th percentile error of .20 (95% CI: .14 to .30). The quantified uncertainty characteristics support evidence-based approaches to sensitivity analysis and probabilistic risk analysis of economic performance measures for road-safety investments.

A real-time, dynamic early-warning model based on uncertainty analysis and risk assessment for sudden water pollution accidents

A real-time, dynamic, early-warning model (EP-risk model) is proposed to cope with sudden water quality pollution accidents affecting downstream areas with raw-water intakes (denoted as EPs). The EP-risk model outputs the risk level of water pollution at the EP by calculating the likelihood of pollution and evaluating the impact of pollution. A generalized form of the EP-risk model for river pollution accidents based on Monte Carlo simulation, the analytic hierarchy process (AHP) method, and the risk matrix method is proposed. The likelihood of water pollution at the EP is calculated by the Monte Carlo method, which is used for uncertainty analysis of pollutants' transport in rivers. The impact of water pollution at the EP is evaluated by expert knowledge and the results of Monte Carlo simulation based on the analytic hierarchy process. The final risk level of water pollution at the EP is determined by the risk matrix method. A case study of the proposed method is illustrated with a phenol spill accident in China.

Risk assessment of water quality using Monte Carlo simulation and artificial neural network method

There is always uncertainty in any water quality risk assessment. A Monte Carlo simulation (MCS) is regarded as a flexible, efficient method for characterizing such uncertainties. However, the required computational effort for MCS-based risk assessment is great, particularly when the number of random variables is large and the complicated water quality models have to be calculated by a computationally expensive numerical method, such as the finite element method (FEM). To address this issue, this paper presents an improved method that incorporates an artificial neural network (ANN) into the MCS to enhance the computational efficiency of conventional risk assessment. The conventional risk assessment uses the FEM to create multiple water quality models, which can be time consuming or cumbersome. In this paper, an ANN model was used as a substitute for the iterative FEM runs, and thus, the number of water quality models that must be calculated can be dramatically reduced. A case study on the chemical oxygen demand (COD) pollution risks in the Lanzhou section of the Yellow River in China was taken as a reference. Compared with the conventional risk assessment method, the ANN-MCS-based method can save much computational effort without a loss of accuracy. The results show that the proposed method in this paper is more applicable to assess water quality risks. Because the characteristics of this ANN-MCS-based technique are quite general, it is hoped that the technique can also be applied to other MCS-based uncertainty analysis in the environmental field.