Assessing the co-benefits of greenhouse gas reduction: health benefits of particulate matter related inspection and maintenance programs in Bangkok, Thailand.

Since the 1990s, the capital city of Thailand, Bangkok has been suffering from severe ambient particulate matter (PM) pollution mainly attributable to its wide use of diesel-fueled vehicles and motorcycles with poor emission performance. While the Thai government strives to reduce emissions from transportation through enforcing policy measures, the link between specific control policies and associated health impacts is inadequately studied. This link is especially important in exploring the co-benefits of greenhouse gas emissions reductions, which often brings reduction in other pollutants such as PM. This paper quantifies the health benefits potentially achieved by the new PM-related I/M programs targeting all diesel vehicles and motorcycles in the Bangkok Metropolitan Area (BMA). The benefits are estimated by using a framework that integrates policy scenario development, exposure assessment, exposure-response assessment and economic valuation. The results indicate that the total health damage due to the year 2000 PM emissions from vehicles in the BMA was equivalent to 2.4% of Thailand's GDP. Under the business-as-usual (BAU) scenario, total vehicular PM emissions in the BMA will increase considerably over time due to the rapid growth in vehicle population, even if the fleet average emission rates are projected to decrease over time as the result of participation of Thailand in post-Copenhagen climate change strategies. By 2015, the total health damage is estimated to increase by 2.5 times relative to the year 2000. However, control policies targeting PM emissions from automobiles, such as the PM-oriented I/M programs, could yield substantial health benefits relative to the BAU scenario, and serve as co-benefits of greenhouse gas control strategies. Despite uncertainty associated with the key assumptions used to estimate benefits, we find that with a high level confidence, the I/M programs will produce health benefits whose economic impacts considerably outweigh the expenditures on policy implementation.

Inorganic arsenic in drinking water and bladder cancer: a meta-analysis for dose-response assessment.

Most arsenic cancer risk assessments have been based solely on epidemiological studies to characterize the dose-response relationship for arsenic-associated cancer and to perform risk calculations. However, current epidemiological evidence is too inconsistent and fraught with uncertainty regarding arsenic exposure to provide reliable estimates. This makes it hard to draw a firm conclusion about the shape and slope of the dose-response relationship from individual studies. Meta-analysis is a statistical approach to combining results across studies and offers expanded opportunities for obtaining an improved dose-response relationship. In this study, a meta-analysis of arsenic studies was conducted by combining seven epidemiological studies from different regions to get an overall dose-response relationship between the amount of arsenic intake and the excess probability of bladder cancer. Both the fixed-effect and random-effect models were used to calculate the averaged coefficient of the linear-logistic regression model. A homogeneity test was also conducted. The final product of this research is an aggregated dose-response model in the range of empirical observation of arsenic. Considering the most recent arsenic MCL (maximum contaminant level, i.e. 10microg/L), the associated bladder cancer risk (lifetime excess probability) at this MCL is 2.29 x 10-5.

Nonlinear dose-response relationships and inducible cellular defence mechanisms.

With the inclusion of inducible radioprotective mechanisms in a radiobiological state-vector model it was possible to explain plateaus in dose-response relationships for neoplastic transformation produced by in vitro irradiation of different cell lines with low-LET irradiation at high dose rates. The current study repeated the simulation of one data set that contains a plateau at mid doses. In contrast to earlier studies, the new one did not model the repair of double-strand breaks (DSBs) located in bulk DNA (likely via non-homologous end joining) as being inducible. Repair of specific DSBs located in actively transcribed genes was assumed to occur via homologous recombination and was considered to be inducible. This reduced the number of parameters that have to be determined by fitting the model to data. In addition, all types of radical scavengers were formerly considered to be inducible by radiation. This was redefined in the current work and the effectiveness of scavengers was implemented in a refined way. The current work investigated whether these and other model adjustments lead to an improved fit of the data set.

Analysis of radon-induced lung cancer risk by a stochastic state-vector model of radiation carcinogenesis.

A biologically based state-vector model (SVM) of radiation carcinogenesis has been extended to incorporate stochasticity of cellular transitions and specific in vivo irradiation conditions in the lungs. Dose-rate-dependent cellular transitions related to the formation of double-stranded DNA breaks, repair of breaks, interactions (translocations) between breaks, fixation of breaks, cellular inactivation, stimulated mitosis and promotion through loss of intercellular communication are simulated by Monte Carlo methods. The stochastic SVM has been applied to the analysis of lung cancer incidence in uranium miners exposed to alpha-emitting radon progeny. When incorporating in vivo features of cell differentiation, stimulated cell division and heterogeneity of cellular doses into the model, excellent agreement between epidemiological data and modelling results could be obtained. At low doses, the model predicts a nonlinear dose-response relationship; e.g., computed lung cancerrisk at 20WLM is about half of current lung cancer estimates based on the linear hypothesis. The model also predicts a slight dose rate effect; e.g., at a cumulative exposure of 20 WLM, calculated lung cancer incidence for an exposure rate 0.27 WLM/year (assuming an exposure time of 73 years) is smaller by a factor of 1.2 than that for an exposure rate of 10 WLM/year.