BTEX levels in rural households: Heating system, building characteristic impacts and lifetime excess cancer risk assessment

BTEX (benzene, toluene, ethylbenzene, and xylene) are a group of toxic organic compounds that exposure to them can cause adverse short and long terms health effects. We measured the levels of BTEX in the indoor and outdoor air of rural areas in Ardebil, Iran. We further assessed their health risks and determinants parameters. BTEX were sampled by drawing air through activated charcoal tubes, using low flow SKC pumps. Samples were extracted by adding carbon disulfide and analyzed by subjecting the aromatic fraction to GC-FID. The results indicated that the concentrations of BTEX in the indoor air were significantly higher than those of outdoor (p-value<0.05). The mean indoor concentrations of benzene, toluene, ethylbenzene, and xylene were 41.69 ± 30.70, 96.73 ± 60.75, 38.73 ± 33.59, and 59.42 ± 35.99 μg m^-3, while the mean outdoor concentrations of them were 8.94 ± 7.32, 36.93 ± 21.82, 7.66 ± 5.63, and 18.14 ± 10.25 μg m^-3, respectively. The concentrations of BTEX in indoor and outdoor of the rural areas that used kerosene fuel for heating systems were significantly higher than those used natural gas. The results indicated that the tobacco smoke is a notable temporary source of indoor BTEX. The mean inhalation lifetime cancer risk (LTCR) value of benzene for residents of rural houses with the natural gas and kerosene heating systems were 28.6 × 10^-6 and 97.2 × 10^-6, while for ethylbenzene these figures stood out at 29.1 × 10^-6 and 95.8 × 10^-6, respectively. LTCR value for residents who used kerosene fuel for heating was higher than the World Health Organization (WHO) recommended limit.

Assessing temporal-spatial land use simulation effects with CLUE-S and Markov-CA models in Beijing

In order to solve the problem of extensive land use in rural residential areas and promote the construction of a new socialist countryside and farmland protection, it is important to graspe the temporal and spatial evolution of rural settlements. In this study, with Beijing as an example, the efficiencies of CLUE-S and Markov-CA models in simulating spatial temporal evolution of rural residential areas are analyzed. With 14 driving factors chosen, the land uses in Beijing in 2000 and 2005 are simulated and predicted using the two models. Grid size of 220 m × 220 m is used. The accuracies and Kappa coefficients of the simulation and prediction results are analyzed and the following conclusions are made. The core part of CLUE-S model is logistic regression, which grants it obvious advantage in capturing the trends of land use changes with more discrete distributions. Meanwhile, Markov-CA model takes neighborhood into consideration, making it relatively more advantageous in simulating and predicting changes of land uses with the character of nearest-neighbor diffusion. However, among various land uses, only urban land use has this character, the correct prediction rates of the CLUE-S model in 2000 and 2005 were 60.99% and 81.35%, respectively, while the accuracy of the Markov-CA model prediction was 51.33% and 73.68%, and it is the main reason that CLUE-S model returns better simulation and prediction results of rural residential areas for both 2000 and 2005 than Markov-CA model.