A smart indoor air quality sensor network

With the proposal of the construction goal of the new power system, it is expected that China's power grid will usher in the future large-scale new energy grid connection by 2030. As a result, the randomness and volatility of the power supply side will gradually increase. It is urgent to tap the potential of flexible regulation of load side resources and promote the transformation of power grid from source to load to source network interaction. Due to the thermal inertia of the building itself and the good regulation ability of the building automatic control system, the central air conditioning system of large public buildings is the largest load side adjustable resource pool of the urban power grid. This paper first makes a refined modeling of the central air-conditioning refrigeration system, quantifies the influence of different adjustment parameters on the adjustable potential of the central air-conditioning system, and then analyzes the influence of different comfort requirements on the maximum power regulation and steady-state power regulation of the central air-conditioning refrigeration system under the control mode of “constant flow and variable temperature difference” selected by the air-conditioning. Finally, taking actual air-condition system as an example, the adjustable potential of virtual power plants formed by the aggregation of urban buildings is evaluated, and the adjustable potential of various types of buildings is formed, which provides a reference basis for virtual power plants to screen adjustable resources.

The cold arc plasma jet was introduced to decontaminate chemical and biological warfare (CBW) agents for the application of a portable CBW decontamination system. The cold arc plasma jet is a low temperature, high density plasma that produces highly reactive species such as oxygen atoms and ozone. Moreover, it is possible to maintain stable plasma without He or Ar. The discharge operated on N2-02 mixture of 30 L/min, and the high voltage pulse (23 kHz, 30% duty ratio) power was applied to the inner electrode. In the decontamination experiments, Bacillus subtilis and Escherichia Coli were chosen as simulants for biological agents and DMMP (Dimethylmethylphosphonate) was chosen as a simulant for chemical agents. The experimental results showed that B. subtilis and E. coli were efficiently decontaminated. Also, DMMP absorbed on the metal substrate and the glass substrate was removed after plasma treatment. Finally, we identified that DMMP was decomposed very well in the plasma effluent.

PurposeAt the beginning of the Corona Virus Disease 2019 (COVID-19) pandemic, a digitalized construction environments surfaced in the heating, ventilation and air conditioning (HVAC) systems in the form of a modern delivery system called demand controlled ventilation (DCV). Demand controlled ventilation has the potential to solve the building ventilation's biggest problem of managing indoor air quality (IAQ) for controlling COVID-19 transmission in indoor environments. However, the improper evaluation and information management of infection prevention on dense crowd activities such as measurement errors and volatile organic compound (VOC) generation failure rates, is fragmented so the aim of this research is to integrate this and explore potentials with machine learning algorithms (MLAs).Design/methodology/approachThe method used is a thorough systematic literature review (SLR) approach. The results of this research consist of a detailed description of the DCV system and digitalized construction process of its IAQ elements.FindingsThe discussion revealed that DCV has a potential for being further integrated by perceiving it as a MLAs and hereby enabling the management of IAQ level from the perspective of health risk function mechanism (i.e. VOC and CO2) for maintaining a comfortable thermal environment and save energy of public and private buildings (PPBs). The appropriate MLA can also be selected in different occupancy patterns for seasonal variations, ventilation behavior, building type and locations, as well as current indoor air pollution control strategies. Furthermore, the conceptual framework showed that MLA application such as algorithm design/Model Predictive Control (MPC) integration can alleviate the high spread limitation of COVID-19 in the indoor environment.Originality/valueFinally, the research concludes that a large unexploited potential within integration and innovation is recognized in the DCV system and MLAs which can be improved to optimize level of IAQ from the perspective of health throughout the building sector DCV process systems. The requirements of CO2 based DCV along with VOC concentrations monitoring practice should be taken into consideration through further research and experience with adaption and implementation from the ventilation control initial stage of the DCV process.

: In this present world climate, chemical and biological warfare (CBW) is a realistic threat to U.S. Air Force personnel. Medical care for conventional and chemical casualties in the CBW environment requires individual protection, group protection, and decontamination as well as supply and patient transfer through contaminated areas. CBW stirs terror in individuals both because of the particular psychological fears it arouses and the tremendous difficulties presented by the need to continue to operate after an attack. Recommendations derived from CBW research cover the issues of command (e.g., maintenance of communications and morale, and command policy in the face of mass casualties), medical care (e.g., alcohol use as a risk factor in CBW environment, low dose exposured, internal SCPS-M management, and unique stressors of the CBW environment), performance (e.g., group responses to contamination and isolation effects on performance), and training (e.g., unit reconstitution following heavy losses, grief leadership, buddy care, development of first aid capability within squadrons, crews, and work units, maintenance of cohesion in flying and ground crews, and training for commanders in command posts). These recommendations should serve as the basis for the development of command policy, training scenarios, medical command and medical care procedures and the direction of future research in this area.

In recent years, the quest has been focused on energy efficient building design. To achieve this in terms of high efficiency air conditioning schemes for hot climate cooling, the combination of variable refrigerant volume (VRV) with variable air volume (VAV) systems have become popular. In this paper, attention is focused on achieving good thermal comfort and indoor air quality (IAQ) combined with energy savings by using multi-zone VAV air conditioning (A/C) that incorporates a genetic based fuzzy logic controller (FLC). Experimental analysis based on a combined demand controlled ventilation (DCV) with economizer cycle (EC) was performed on an inverter driven multi-zone direct expansion (DX) VAV A/C system integrated with a fuzzy logic controller and optimized by a genetic algorithm (GA). The opening of the VAV box damper was controlled using the fuzzy logic controller. Based on the test results, the proposed fuzzy logic operated system maintained supply air temperature close to 13°C and an occupant zone at a consistent temperature of around 24 °C. In DCV mode, the concentration of CO2 was maintained between 950 ppm and 1040 ppm while, when the system was operated under a combined DCV-EC ventilation scheme, the CO2 concentration was maintained at between 350 ppm and 970 ppm. The experimental results suggested that CO2 concentration obtained experimentally was well within the permissible limit for the varying load conditions. Under the combined DCV-EC mode of ventilation, using a fuzzy-genetic algorithm, the maximum power obtained for the supply air fan and variable speed compressor was 415 W and 3.4 kW respectively. The compressor was totally turned OFF during the economizer cycle thus contributing to total power savings. The energy savings potential of the proposed fuzzy controlled multi-zone DX VAV A/C system yielded 70% and 89% under DCV and combined DCV economizer cycle ventilation modes respectively when compared with a constant air volume (CAV) A/C system operated under the DCV technique. The test results suggested that it was feasible for this fuzzy control methodology, integrated with the developed genetic algorithm, to provide a proper control of IAQ, thermal comfort and energy conservation.

Host susceptibility to indoor air pollution

Why should the world be more averse to chemical and biological warfare (CBW) than to other forms of war, repeatedly attempting to ban such warfare by means of international law? Is there some objective difference between poison gas or plague bacilli on one hand, and steel bullets, or B-52 high explosive bombs, or nuclear weapons on the other? Or does CBW instead illustrate the faddishness and superficiality of disarmament campaigns in general? The Allied Powers after 1919 may indeed have condemned chemical warfare simply to reinforce the impression that Germany was morally culpable in World War I. The Germans supposedly had initiated the of gas warfare (although the first use here might really have to be pinned on France instead); if Germans had used such weapons first, such must have been evil. Perhaps instead the aversion to gas reflected the horrors of such warfare for the soldier exposed to it, although casualty statistics suggest that fewer soldiers died or were permanently maimed after being incapacitated by gas, as compared with those stricken by other means. Perhaps instead the techniques of chemistry and biology have seemed unclean and unmanly, the products of odd-ball scientists ensconced in laboratories, reflecting perverted scientific ingenuity rather than the bravery of cold steel. The

BACKGROUND: children spend several hours at school daily. Previous large epidemiological surveys in European schools (e.g. HESE and SINPHONIE) have pointed out a poor indoor air quality (IAQ) in several countries, including Italy, with possible respiratory health effects. AIM: to assess IAQ at schools in a large multicenter survey in Italy and possible associations with respiratory health. METHODS: measurements of IAQ and pollutants were performed in 130 classrooms (44 schools either elementary or junior high schools) of eight Italian cities (Udine, Milan, Sondrio, Pisa, Rome, Bari, Palermo, Cagliari) in 2011-13. Information on exposure at school were provided by the teachers, on home exposure and children's health by the parents. Symptoms in the last week and related risk factors were assessed through questionnaire in 1718 children. Descriptive statistics and logistic regression models, adjusted for sex, age, passive smoking at home, asthma/rhinitis familial history, asthma/rhinitis diagnosis, allergy diagnosis, study centre, were performed. Significance level was set at p≤0.05. RESULTS: analyses regard 1718 Italian schoolchildren (mean age 10±1.8yr, 50.9% males) attending either elementary or junior high schools. Almost 65% of schools were built before the year 1974, 61% were painted in the last ten years, only 13% had a central system of Heating, Ventilation, and Air Conditioning (HVAC), 27% were close to heavy traffic roads, and 36% to petrol filling stations. In the whole sample, mean levels were: 1196 ± 539 ppm, CO2; 29.1 ± 15.8 µg/m3, PM2.5. Prevalence rates (%) of symptoms in the last week were: respiratory symptoms (dry cough/breathlessness/wheeze) 32.8 (16.8 at school); dry cough 25.7 (11.9 at school); nasal problems (runny/dry nose) 73.3 (48.1 at school). PM2.5 was associated with respiratory symptoms at school in the monitored week – OR 1.88 (95% CI 1.03-3.43) for 1-log increment (p=0.04) – and with dry cough at school in the monitored week, OR 1.94 (95% CI 0.95-3.98) for 1-log increment (p=0.07). Further, an association was found between CO2 and nasal problems at school in the monitored week – OR 1.02 (95% CI 1.00-1.04) for a 100 ppm increment (p=0.03). DISCUSSION: we found a poor IAQ in Italian elementary schools, associated with respiratory symptoms. Remedial measurements in the existing schools and preventative actions, especially for improving ventilation, should be carried out by the public authorities.

This study applies indoor air quality modelling to investigate the potential advantages and drawbacks of demand-controlled ventilation as an indoor air quality control strategy. The study uses a multiple zone pollutant transport model to evaluate the performance of several indoor air quality control strate gies under a variety of conditions. The pollutant transport model is incorpo rated into the TRNSYS simulation program to allow simultaneous calculation of both building pollutant concentrations and building energy use. A specific office building situation consisting of a main zone and a conference room was studied. The simulations showed that a carbon dioxide-based demand con trolled ventilation system can provide better control of indoor air quality than a constant outdoor airflow at the ASHRAE Standard 62-1989 prescribed level under a wide range of building conditions. The constant outdoor air approach lacks the capability of providing additional outdoor air when required by poor building conditions such as a low pollutant removal effectiveness and a high occupant density. The demand-controlled ventilation system saves energy compared to the constant outdoor air approach under all conditions consid ered. It was also found that a demand-controlled ventilation strategy consist ing of a step controller combined with scheduled 100% outdoor air purges can protect building occupants from exposure to high concentrations of non-occu pant-generated pollutants while still providing energy savings.

A novel quantitative assessment framework of the IAQ resilience performance of buildings: The resilience score metric

Simulation of Occupancy and CO2-based Demand-controlled Mechanical Ventilation Strategies in an Office Room Using EnergyPlus

The control of outdoor air intake rates in mechanically ventilated buildings based on indoor carbon dioxide (CO 2 ) levels, often referred to as CO 2 demand controlled ventilation (DCV), has the potential for reducing the energy consumption associated with building ventilation in some commercial and institutional buildings. Carbon dioxide DCV has been discussed, promoted, studied and demonstrated for about twenty years, but questions still remain regarding the actual energy savings potential as a function of climate, ventilation system features, and building occupancy. In addition, questions exist as to the indoor air quality (IAQ) impacts of the approach and the best way to implement CO 2 DCV in general and in a given building. This report presents a state-of-the-art review of CO 2 DCV technology and application including discussion of the concept and its application, and a literature review. In addition the regulatory and standard requirements impacting CO 2 DCV are also examined.

Air pollution is already a global public health crisis, as it kills seven million people each year, according to the World Health Organization. Presently, a correlation between health and air pollution levels recorded in the past exists. The paper presents series of instruments concerning IAC (Indoor Air Quality) measurements and focuses on indoor air quality measurements taken in different closed spaces, as example. Based on the concentration values detected, the conclusion of the paper indicates that, depending on the technological process or activity in enclosed spaces, it is mandatory to check the IAQ. This action must be taken as prevention, in order to reduce potential health risks upon humans, acting in their homes, or schools and offices or industrial halls, in general in closed spaces, even when artificially ventilated.

It is important to know whether SARS-CoV-2 is spread through the air conditioning systems. Taking the central air conditioning system as an example, we analyze the mechanism and potential health risk of respiratory virus transmission in air-conditioned rooms and propose a method to study the risk of virus transmission in central air conditioning systems by investigating the data from medical experiments. The virus carrying capacity and the decay characteristics of indoor pathogen droplets are studied in this research. Additionally, the effects of air temperature and relative humidity on the virus survival in the air or on surfaces are investigated. The removal efficiency of infectious droplet nuclei by using an air conditioning filter was then determined. Thus, the transmission risk during the operation of the centralized air conditioning system is evaluated. The results show that the indoor temperature and humidity are controlled in the range of 20–25 °C and 40–70% by central air conditioning during the epidemic period, which not only benefits the health and comfort of residents, but also weakens the vitality of the virus. The larger the droplet size, the longer the viruses survive. Since the filter efficiency of the air conditioning filter increases with the increase in particle size, increasing the number of air changes of the circulating air volume can accelerate the removal of potential pathogen particles. Therefore, scientific operation of centralized air conditioning systems during the epidemic period has more advantages than disadvantages.Graphical abstract

Indoor comfort parameters and outdoor environmental parameters are adjusted by controlling the compressor speed to adjust the refrigerant circulation and flow rate entering the indoor heat exchanger, timely meeting the indoor cooling and heating load requirements, and achieving summer cooling and winter heating. The problem of energy waste caused by the operation of CAC (Central Air Conditioning) systems also comes with it. In this regard, this article integrates DL (Deep Learning) to design energy consumption prediction and optimization algorithms for CAC systems. The results indicate that the energy consumption varies with the equipment speed at different optimal settings, and the overall energy consumption optimization effect is significant. Without optimization control, the average hourly power consumption was 264.7kw, and after optimization, the average hourly power consumption was 252.4kw. However, in the testing of system efficiency, it can be found that the data comparison before and after improving system efficiency optimization shows that the system efficiency improved by about 5% after using the algorithm in this paper. This article aims to help managers control energy consumption, save costs, and ultimately achieve the goal of reducing operational management costs through the study of optimizing energy consumption control in CAC systems using DL. Indoor comfort parameters and outdoor environmental conditions are regulated by adjusting the compressor speed to control the refrigerant circulation and flow rate entering the indoor heat exchanger. This ensures the timely fulfillment of indoor cooling and heating load requirements, enabling effective summer cooling and winter heating. Additionally, it addresses the issue of energy wastage resulting from the operation of CAC systems. In response to this challenge, this paper integrates DL techniques to develop energy consumption prediction and optimization algorithms tailored for CAC systems. The findings reveal that energy consumption fluctuates with equipment speed under various optimal settings, demonstrating a substantial overall improvement in energy consumption optimization. In the absence of optimization control, the average hourly power consumption stood at 264.7 kW, which decreased to 252.4 kW after implementing optimization measures. However, during the system efficiency testing, it was observed that the comparison of data before and after implementing system efficiency optimization revealed an approximate 5% enhancement in system efficiency through the utilization of the algorithm presented in this paper. This article's objective is to assist managers in effectively managing energy consumption, reducing costs, and ultimately achieving the goal of minimizing operational management expenses by investigating energy consumption optimization in CAC systems through the application of DL.