Environmental Health: Indoor Exposures, Assessments and Interventions
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The ranges are also listed in Table 3 , and the influence on the rankings of intervention strategies is illustrated in Figure 5. The top five parameters have great influence on the final rankings of the intervention strategies for rural households, while only outdoor PAH concentration represented by BaP; notes of Table 3 include a full list of the contributing PAH congeners has significant influence on this ranking for urban households.
The sixth and seventh factors penetration coefficient and smoking quantity of all family members are already shown to have negligible effect on the general ranking, and thus smaller factors are not included in this analysis. The overlapping of these ranges would suggest a change in ranking if taking the uncertainty in parameters into account, while non-overlapping ranges would suggest a reasonably robust ranking when the input changes in a reasonable range.
In other words, apart from the top five parameters included here, the ranking of different intervention strategies is rather robust to different parameter values. Better robustness is observed for intervention rankings among urban households. These results are also a demonstration of the uncertainty associated with the model inputs, stating that the five inputs or groups of inputs above are the main source of uncertainty in the simulated ranking of different interventions.
Its value has been shown to have large influence on the modeled lung cancer risk as expressed by PAF values . We used the value of 4. But the risk estimates can change dramatically when the alternative URR 1.
Nonetheless, this fact draws the attention to the important role that URR plays in determining risk estimate. The value 4.
It is therefore considered suitable for the application in the population in this study. However, it can never be too cautious when choosing a value for URR. This method can quantify the effectiveness of different interventions, and is thus very useful in rational policy making when deciding what intervention to support in reality. The results here support that the most effective way to reduce PAH exposure and related risk is to clean the atmosphere.
But atmospheric cleaning requires great and integrated effort of the whole society, including reducing industrial and fire plant emissions, reducing automobile emissions and transforming the structure of energy use. Therefore, it is highly infeasible in reality despite of its effectiveness. This gap between the great benefit and the equally great difficulty of enforcement calls for designs of interventions in presence of the current atmospheric pollution level.
Indoor cleaner is only one of these choices with demonstrated effects in our study, and further research would benefit the intervention design. A major limitation comes with the model adopted for indoor PAH concentration estimation. This model has several important assumptions that need to be addressed. First, it is assumed that indoor air is well mixed; thus, one concentration can represent an entire residence.
If the proportion of time spent at different rooms within the house is of interest, models accounting for spatial heterogeneity should be used instead. Further, it is assumed that airborne PAHs follow the linear instantaneous equilibrium assumption, and that indoor emissions are evenly distributed over a 24 hour period. These two assumptions do not significantly bias the calculation of total airborne concentrations, and are therefore suitable for assessing inhalation exposure .
However, the proposed model in this study cannot be used to calculate phase-specific PAH concentrations and thus cannot be used to estimate dermal or ingestion exposure  , and it is why we only studied the inhalation exposure. And since calculated indoor concentrations are averaged values throughout the season, it is only valid for chronic disease; acute disease risk, like acute lower respiratory infections, cannot be assessed using this model framework.
There might be joint changes in other input parameters in the model when simulating a certain intervention by changing one or several input parameters. Atm-WHO-r , people can choose to spend more time outdoors to avoid the heavy indoor pollution. This behavioral phenomenon is hard to predict by model but should be acknowledged when using the modeling technique to predict changes in population exposure and risk. It should also be noted that, the result that smoking prohibition brings little impact on indoor PAH concentration and related exposure and lung cancer risk is restricted to PAHs.
It is well-known that cigarette smoke contains a large family of toxics, including nicotine. Comprehensive evaluation of cigarette control effects should involve all these harmful pollutants, although it is beyond the scope of our current study.
In this study, we used 1-stage MC simulation rather than a 2-stage simulation. We did not incorporate 2-stage uncertainty analysis into this study for clarity considerations: as a pilot study using modeling method for intervention assessment, the addition of uncertainty makes the presentation of the results extremely difficult.
In order to keep the paper readable but still convey the core idea, we decided not to involve 2-stage uncertainty in this study.
HUMAN EXPOSURE ASSESSMENT
Instead, we conducted the sensitivity analysis based on the 1-stage MC simulation. This sensitivity analysis incorporated the ranges of the model input values and thus could reveal the uncertainty associated with the model and its inputs to some extent. In this manner, the results are scientifically complete and also clear to read. But, of course, a 2-stage MC simulation will provide a more comprehensive understanding of the effectiveness of different interventions, and give more insights of the robustness of the rankings of these interventions.
In this study, we adopted a Monte Carlo population exposure assessment model to quantify and compare different intervention strategies for inhalation exposure to PAHs and associated lung cancer risk for Beijing population in the year Our results gave the first application of MC method for intervention analyses, and showed that MC method can be well utilized in this situation.
The quantitative results showed that, atmospheric cleaning has the greatest potentials to remove PAH inhalation exposure and related lung cancer risk, while indoor particle cleaner and clean fuel also have appreciable impact on the exposure and risk. The exposure pattern analysis provided rationale behind the relative performance of these interventions, and showed how each intervention affected the different components of the total inhalation exposure.
The rankings of these strategies are relatively robust with most of the input parameters expect the infiltration air exchange rate, outdoor PAH concentration, residential area, the amount of solid fuels used indoors and the time spent at different microenvironments. This robustness should still be further checked with a more comprehensive 2-stage MC simulation. Conceived and designed the experiments: B.
Zhou B. Performed the experiments: B. Analyzed the data: B. Wrote the paper: B. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract It is difficult to evaluate and compare interventions for reducing exposure to air pollutants, including polycyclic aromatic hydrocarbons PAHs , a widely found air pollutant in both indoor and outdoor air.
Introduction Polycyclic aromatic hydrocarbons PAHs are a widespread class of semi-volatile organic compounds SVOCs from incomplete combustion of organic matter. Intervention Cases We analyzed and compared the effectiveness of the intervention strategies listed in Table 1. Download: PPT. Table 2.
Unit | Environmental Exposures and Health
Median and standard deviation of the seasonal averaged atmospheric concentrations of PAHs used in this study. Figure 1. The population attributable fractions PAFs dark column in the Baseline scenarios, the remaining PAFs dark column and their percentage reductions from the Baseline line , and the potential impact fractions PIFs light column for different interventions, sorted by PIF values. Figure 2. Figure 3.
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Figure 4. Discussion Comparison with Existing Studies No studies on the quantitative performance of PAH pollution intervention were found at the time of this study. Sensitivity and Uncertainty The five most sensitive parameters for either urban or rural sub-population as identified in  , were included in sensitivity analysis; this would thus include seven parameters in total in this analysis.
Figure 5. Table 3. Ranges of percentage change in annual inhalation dose with a standard deviation change in selected parameters with their baseline values and standard deviations: top five most sensitive parameters for either urban or rural sub-population. Table 4. Implication This method can quantify the effectiveness of different interventions, and is thus very useful in rational policy making when deciding what intervention to support in reality.
Limitations A major limitation comes with the model adopted for indoor PAH concentration estimation. Conclusions In this study, we adopted a Monte Carlo population exposure assessment model to quantify and compare different intervention strategies for inhalation exposure to PAHs and associated lung cancer risk for Beijing population in the year Supporting Information. Table S1. Author Contributions Conceived and designed the experiments: B. References 1.
International Agency of Research on Cancer Some non-heterocyclic polycyclic aromatic hydrocarbons and some related exposures. View Article Google Scholar 2. Environmental Health Perspectives — View Article Google Scholar 3. View Article Google Scholar 4. Environmental Science and Technology — View Article Google Scholar 5. Environment International — View Article Google Scholar 6.
View Article Google Scholar 7. Journal of Occupational Health — View Article Google Scholar 8. Science of the Total Environment — View Article Google Scholar 9. Environmentrics — View Article Google Scholar Journal of Exposure Analysis and Environmental Epidemiology 87— Journal of Exposure Analysis and Environmental Epidemiology — Zhou B, Zhao B Population inhalation exposure to polycyclic aromatic hydrocarbons and associated lung cancer risk in Beijing region: Contributions of indoor and outdoor sources and exposures.
Atmospheric Environment — Beijing Municipal Bureau of Statistics Beijing statistical yearbook. Beijing: China Statistics Press. Geneva: World Health Organization. Regulatory Toxicology and Pharmacology — As discussed in prior chapters, the presence of dampness, water damage, and visible mold in buildings is associated with negative respiratory health effects, and as a result, remediation aimed at drying and removing building materials affected by these conditions from indoor environments is often considered.
Significant questions remain about what constitutes normal microbial ecology in different building types and under different conditions.