Data on Indoor Air Quality (IAQ) in kindergartens with different surrounding activities

Assessment of natural ventilation strategy to decrease the risk of COVID 19 infection at a rural elementary school

Natural ventilation in low-budget elementary schools is the main focus to ensure the health and comfort of its occupants, specifically when looking at the global pandemic related to SARS-COV-2. This paper presents an experimental and novel study of natural ventilation in a public elementary school (Los Zumacales), with a particularly low economic budget. The study was carried out during the winter months of the Covid 19 pandemic. The school is located in the rural area of Castilla y León (North-Western Spain) far from high traffic roads. In this study, a methodology of measuring CO2 concentration was applied in nine classrooms in a school. The experimental study shows the level of natural ventilation in each classroom, expressed in Air Changes per Hour (ACH), using the Decay CO2 concentration method. The method is proven by comparing the experimental values of the obtained ACH with those determined by the most powerful methods to achieve appropriate ventilation levels. Thus, ensuring health protection protocol in rural schools, against the COVID 19 pandemic. Harvard guide and Spanish regulations (RITE), two widely recognized methods have been used together with the experimentally obtained standard by Rey et al. Only one classroom showed a value lower than 3 indicating poor ventilation. In this study, the degree of thermal comfort in the nine classrooms were also analyzed according to the EN15251 standard. An average indoor temperature of approximately 19 °C was obtained, and the relative humidity was stable and correct according to Spanish regulations. In addition, the risk of infection in each classroom was estimated following the international method recommended by the federation of European Heating, Ventilation, and Air Conditioning Associations (REHVA). The probability of infection in all the cases studied was less than 14%. Therefore, this study provides a strong response against infections illnesses, such as Covid 19, in educational buildings where economic budgets of their facilities are low in both, maintenance and investment.

Definition of an Indoor Air Sampling Strategy for SARS-CoV-2 Detection and Risk Management: Case Study in Kindergartens

In the last two years, the world has been overwhelmed by SARS-CoV-2. One of the most important ways to prevent the spread of the virus is the control of indoor conditions: from surface hygiene to ventilation. Regarding the indoor environments, monitoring the presence of the virus in the indoor air seems to be promising, since there is strong evidence that airborne transmission through infected droplets and aerosols is its dominant transmission route. So far, few studies report the successful detection of SARS-CoV-2 in the air; moreover, the lack of a standard guideline for air monitoring reduces the uniformity of the results and their usefulness in the management of the risk of virus transmission. In this work, starting from a critical analysis of the existing standards and guidelines for indoor air quality, we define a strategy to set-up indoor air sampling plans for the detection of SARS-CoV-2. The strategy is then tested through a case study conducted in two kindergartens in the metropolitan city of Milan, in Italy, involving a total of 290 children and 47 teachers from 19 classrooms. The results proved its completeness, effectiveness, and suitability as a key tool in the airborne SARS-CoV-2 infection risk management process. Future research directions are then identified and discussed.

Environmental effects of prohibiting urban fireworks and firecrackers in Jinan, China

Eight national air quality monitoring stations were selected to examine the environmental effects of prohibiting fireworks and firecrackers since January 1, 2018, in Jinan, China, by using an air quality index (AQI) on three time scales. In 2014-2018, the average annual AQI decreased year on year, but a downward trend in 2018 was only found by applying a Daniel trend test. The change in monthly data for 2016-2018 followed a "W" pattern. The overall AQI value was lower on New Year's Eve than during Spring Festival, and the 2-day AQI in 2018 was lower than that in 2017. The GIS analysis method was used for spatial visualization. The AQI in the built-up part of Jinan was high in the west and low in the east on New Year's Eve and Spring Festival of 2017, being lowest in the Development Zone. The AQI spatial distribution was high in the city core but low in its periphery; in 2018, the high-AQI center appeared near the Provincial Seed Warehouse on New Year's Eve and Spring Festival. Multiple linear regression was used to analyze the relationship between AQI and pollutants. Six pollutants were found to be positively correlated with the AQI. PM_2.5 and PM₁₀ had the strongest correlations on New Year's Eve and Spring Festival, for which the correlations of SO₂, CO, and NO₂ were significantly weaker in 2018 than in 2017.

Data on combustion, performance and emissions of a 6.8 L, 6-cylinder, Tier II diesel engine

A diesel engine has been a desirable machine due to its better fuel efficiency, reliability, and higher power output. It is widely used in transportations, locomotives, power generation, and industrial applications. The combustion of diesel fuel emits harmful emissions such as unburned hydrocarbons (HC), particulate matter (PM), nitrogen oxides (NO_x), and carbon monoxides (CO). This article presents data on the efficiency, combustion, and emission of a 4-stroke diesel engine. The engine is a 6.8 L turbocharged 6-cylinder Tier II diesel engine fitted with a common rail injection system. The test was carried out at the Powerhouse Energy Campus, Colorado State University Engines and Energy Conversion facility. The ISO Standard 8178:4 Cycle D2 cycle was adopted for this study consists of five test runs at 1800 rpm. During the testing, CO, carbon dioxide (CO₂), oxygen (O₂), NO_x, PM, unburned HC as a total HC (THC), methane (CH₄), formaldehyde (CH₂O), and volatile organic compound (VOC) emissions were measured. At the same time, the data acquisition system recorded the combustion data. The engine's performance is characterized by the brake specific fuel combustion (BSFC) and thermal efficiency. A dataset of correlations among the parameters was also presented in this article.

Data on greenhouse gases emission of fuels in power plants in Malaysia during the year of 1990-2017

Energy has a significant influence on Malaysia's industry. It is used in electricity generation, refineries, gas processing plants and end-user applications such as transportation, residential, agriculture and fishing. These burning fossil fuel activities produce greenhouse gases (GHG) emissions. This article presents the emissions data of fuel used in power plants in Malaysia during the year of 1990 until 2017. The fuel used in power plants is coal and coke, natural gas, diesel oil and residual fuel oil. The energy data used in power plants were gathered from the Malaysia Energy Information Hub, published by the Malaysian Energy Commission. The GHG emissions data were calculated using the emission factors method. The climate impact of different GHGs in terms of CO₂-equivalent (CO₂-e) was also calculated using global warming potentials. The article also presents population data in Malaysia during the year. A correlation between the fuels, GHG emission and the population is also investigated using statistical analysis. The data presented here may facilitate the Malaysian government to identify the source of the pollutants and undertake a climate change mitigation plan.

Public Environment Emotion Prediction Model Using LSTM Network

Public environmental sentiment has always played an important role in public social sentiment and has a certain degree of influence. Adopting a reasonable and effective public environmental sentiment prediction method for the government’s public attention in environmental management, promulgation of local policies, and hosting characteristics activities has important guiding significance. By using VAR (vector autoregressive), the public environmental sentiment level prediction is regarded as a time series prediction problem. This paper studies the development of a mobile “impression ecology” platform to collect time spans in five cities in Lanzhou for one year. In addition, a parameter optimization algorithm, WOA (Whale Optimization Algorithm), is introduced on the basis of the prediction method. It is expected to predict the public environmental sentiment more accurately while predicting the atmospheric environment. This paper compares the decision performance of LSTM (Long Short-Term Memory) and RNN (Recurrent Neural Network) models on the public environment emotional level through experiments, and uses a variety of error assessment methods to quantitatively analyze the prediction results, verifying the LSTM’s performance in prediction performance and level decision-making effectiveness and robustness.