Susceptibility and exposure risk to airborne aerosols in intra-urban microclimate: Evidence from subway system of mega-cities.

The health of intra-urban population in modern megacities relies largely on the biosafety within the microclimate of subway system, which can be vulnerable to epidemical challenges brought by virus-laden bioaerosols under varying factors. The literature has yet to address the association between the exposure risks to infectious pathogens and the dynamic changes of boundary conditions in this densely populated microclimate. This study aims at characterizing the bioaerosol dispersion, evaluating the exposure risks under various train arrival scenarios and hazard releasing positions in a real-world double-decker subway station. The results provide the evidence for the dominating airflow pattern, bioaerosols dispersion behaviors, exposure risk, and evacuation guidance in a representative microclimate of mega-cities. The tunnel effects of nearby pedestrian passageways are found to be dominating the airflow pattern, leading to the discharging of airborne bioaerosols. At least 60 % increasing of discharging rate of bioaerosol is attributed to the arrival of one or two trains at the subway platform compared with the scenario with no train arriving. Results from risk assessment with improved Wells-Riley model estimate 57.62 % of maximum infectivity probability with no train arriving. Large areas near the source at the platform floor still cannot be considered safe within 20 min. For the other two scenarios where trains arrive at the platform, the maximum probability of infection is below 5 %. Moreover, the majority of train carriages can be regarded as safe zones, as the ventilation across the screen door are mostly directed towards the platform. Additionally, releasing the bioaerosols at the platform floor poses the most severe threats to human health, and the corresponding evacuation strategies are suggested. These findings offer practical guidance for the design of the intra-urban microclimate, reinforcing the need for exposure reduction device or contingency plans, and providing potential evacuation strategy towards improved health outcomes.

Pyrolytic synthesis and luminescence of porous lanthanide Eu-MOF.

A lanthanide metal coordination polymer [Eu2(BDC)3(DMSO)(H2O)] was synthesized by the reaction of europium oxide with benzene-1,3-dicarboxylic acid (H2BDC) in a mixed solution of dimethyl sulfoxide (DMSO) and water under hydrothermal conditions. The crystal structure of Eu2(BDC)3(DMSO)(H2O) was characterized by X-ray diffraction (XRD). Thermo-gravimetric analysis of Eu2(BDC)3(DMSO)(H2O) indicated that coordinated DMSO and H2O molecules could be removed to create Eu2(BDC)3(DMSO)(H2O)-py with permanent microporosity, which was also verified by powder XRD (PXRD) and elemental analysis. Both Eu2(BDC)3(DMSO)(H2O) and Eu2(BDC)3(DMSO)(H2O)-py showed mainly Eu-based luminescence and had characteristic emissions in the range 550-700 nm.

Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters.

Measurements of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915 measured samples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rate and heat loss coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat loss coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08.