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With the improvement of people's requirements for living environment, the effect of indoor radon radioactivity on health has been widely concerned. In this paper, taking a university in Shanghai as an example, indoor radon concentration and its related environmental parameters were measured in some of its rooms to understand the level of indoor radon concentration on its campus. The results showed that the average concentrations of student dormitory, office buildings, and academic buildings were 14.79±9.15 Bq/m3,22.67±5.72 Bq/m3, and 12.55±6.71 Bq/m3 respectively. Indoor radon was positively correlated with indoor temperature and humidity, and ventilation was also an effective method to reduce indoor radon.

Few studies pay attention to the possible associations between indoor VOCs and childhood asthma in Shanghai considering the effect of household ventilation. From March 2013 to December 2014, we collected air samples from 358 children's bedrooms in Shanghai, China. Information on childhood asthma, environmental exposures, and lifestyle behaviors was collected. Two-sample of Mann-Whitney U test, bivariate logistic regressions, and multiple logistic regression were used to obtain the association between VOCs and childhood asthma. Association analysis stratified by frequency of window opening during night was conducted to explore its possible effect on the target association. High concentrations of toluene and dodecane were significantly associated with childhood wheeze, and a high concentration of toluene was significantly associated with diagnosed asthma. In the bedrooms with low frequency of window opening during night, high concentrations of toluene, dodecane, and TVOC were significantly associated with childhood wheeze, and a high concentration of toluene was significantly associated with diagnosed asthma. Our results indicate that exposure to VOCs may be a risk factor for childhood asthma, especially in the bedrooms with poor ventilation during night. Family avoidance behaviors of household ventilation should be considered in the study of associations of household VOCs with childhood asthmatic symptoms.

Household dust mites (HDMs) are the important allergens causing allergic diseases in children. A predictive model can help us understand the concentration of HDMs in different areas of China to better prevent and control this kind of allergen. This study used 454 household inspection samples in childrens’ room obtained from China, Children, Homes, Health (CCHH) phase 2 study, conducted during 2013-2014. Spearman correlation and multiple logistic regression were used to explore the influencing factors of HDMs concentrations, by comprehensively considering residents’ lifestyle, building characteristics, environmental exposure, especially dampness-related exposures. This study used the Gradient Boosting Decision Tree(GBDT) algorithm to build the prediction model. The data from CCHH were used to established the prediction model. It was found that there were some differences in the influencing factors between two types of HDMs. The concentration of HDMs were found a significant correlation (p0. 9). This paper provides a reference for predicting the HDMs concentrations in children's bedrooms and the influence of the influencing factors.

In this paper, the CO2 (carbon dioxide) exhaled by indoor occupants was used as the tracer gas, and the variation of indoor CO2 concentration in the whole day was observed. Among the 454 samples, 114 and 345 valid samples were screened out which can be used to calculate the daytime and night ventilation rate by the tracer gas method through the sectioning method. The results indicated that seasonal differences in daytime average ventilation are greater than night average ventilation. The highest daytime- average ventilation was found in autumn, next was summer, there was the lowest ventilation in winter. According to the necessary fresh air rate when sleeping at night, night average ventilation rate of 30.1% of samples met the standard. The ventilation rates of different building characteristics were analyzed, residential location, construction age, living floor, room size, window structure, indoor dampness, and window opening habits were all included. The results revealed that the window opening habits was the most significant factor for natural ventilation. Both daytime and night average ventilation rate are basically in the ratio of 2:1 when the windows are opened and closed. moreover, there was higher impact of outdoor climate on daytime average ventilation when window was closed. Significant impact of window structure and indoor dampness on night average ventilation was observed when window was opened. Present study proposed the measurement and determination methods of ventilation when daytime activities of occupants were happened

Exposure of phthalate has adverse effects on child health. Currently, the field measurement on PAEs concentration in children’s bedrooms were limited, and the test of PAEs is laborious. Based on the data of home detection in 454 residences from March 2013 to December 2014 in Shanghai, the association of PAEs in children's bedroom and building characteristics, residents’ lifestyle and indoor environment characterization were built by Spearman correlation. According to the Spearman correlation coefficient method, the concentration of PAEs, such as residential area was significantly correlated with DMP, BBP and DiBP in children’s bedroom (sig 0), and the use of chemicals was significantly associated with DEP and DiBP in children’s bedroom (sig 0). Then a gradient-boosting decision tree model with higher prediction accuracy is established. The influencing factors of the studied PAEs were determined by comprehensive consideration of the current study and literature review. 11 influencing factors of PAEs concentrations from three aspects were finally established in this study. The training model of GBDT has a reasonable accuracy( R2>0.9). This paper provides a reference for the prediction of PAEs concentration in the residential bedroom and the influence degree of influencing factors.

This report presents the results of a performance assessment of the applicability of bus rapid transit on conventional highways in the setting of a site-specific case study along the Lincoln Boulevard corrider in Santa Monica, California. When bus rapid transit systems are implemented on conventional highways, especially on arterials, there are numerous bus priority treatments that can be applied and each has associated with it issues that need to be investigated. In this study, we are investigating concurrent flow curb bus lanes based on the removal of peak period parking along the Lincoln Boulevard corridor. We have focused on traffic and ridership impacts associated with this type of bus rapid transit system implementation.

Red-Light-Running (RLR) is the leading cause of urban crashes. This report presents the findings from an investigation of signal control-related countermeasures to reduce the occurrences of RLR and related collisions. The objectives of this study were to identify and evaluate potential modifications to signal timing schemes in reducing the frequency of RLR and to develop online adaptive RLR collision avoidance algorithms to reduce the possibility of RLR collisions. High quality, second-by-second vehicle count and signal timing and phasing data were collected at arterial intersections. A signal-cycle-based data analysis was performed to determine RLR contributing factors that are statistic significant, having substantial impact on RLR and controllable via signal timing modifications. These findings were then applied to develop signal timing schemes that aim to increase the probability of stopping and online signal timing adaptation strategies to avoid potential collision when RLR occurs. We demonstrate the effectiveness of signal timing modifications in reducing RLR while maintaining intersections’ level of service, under both microscopic simulation and macroscopic simulation environments. In addition, an investigation of drivers’ decision making at signalized intersections using real world data and a probabilistic RLR prediction model are presented.