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50 results for “indoor air”

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dryad40/100

Data from: Do the health benefits of boiling drinking water outweigh the negative impacts of increased indoor air pollution exposure?

Background: Billions of the world's poorest households are faced with the lack of access to both safe drinking water and clean cooking. One solution to microbiologically contaminated water is boiling, often promoted without acknowledging the additional risks incurred from indoor air degradation from using solid fuels. Objectives: This modeling study explores the tradeoff of increased air pollution from boiling drinking water under multiple contamination and fuel use scenarios typical of low-income settings. Methods: We calculated the total change in disability-adjusted life years (DALYs) from indoor air pollution (IAP) and diarrhea from fecal contamination of drinking water for scenarios of different source water quality, boiling effectiveness, and stove type. We used Uganda and Vietnam, two countries with a high prevalence of water boiling and solid fuel use, as case studies. Results: Boiling drinking water reduced the diarrhea disease burden by a mean of 1110 DALYs and 368 DALYs per 10,000 people for adults and children <5 years in Uganda, respectively, for high-risk water quality and the most efficient (lab-level) boiling scenario, with smaller reductions for less contaminated water and ineffective boiling. Similar results were found in Vietnam, apart from fewer avoided DALYs in children due to different demographics. In both countries, for households with high baseline IAP from existing solid fuel use, adding water boiling to cooking on a given stove was associated with a limited increase in IAP DALYs due to the log-linear dose-response curves. Boiling, even at low effectiveness, was associated with net DALY reductions for medium- and high-risk water, even if using unclean stoves/fuels. Replacing traditional stoves with improved stoves coupled with effective boiling practices significantly reduced total DALYs. Discussion: Boiling water generally resulted in a net decrease in DALYs. Future efforts should empirically measure health outcomes from IAP vs. diarrhea associated with boiling drinking water using field studies with different boiling methods and stove types.

opencc-zeroMar 2024View details →

zenodo40/100

assessment of aldehydes to PTR-MS m/z 69 in indoor air measurements - data set

<ul> <li>contact: Lisa Ernle (lisa.ernle@mpic.de), Nijing Wang (nijing.wang@mpic.de), Jonathan Williams (jonathan.williams@mpic.de)</li> <li>instruments: fast GC-MS SOFIA (MPIC), PTR-ToF-MS 8000 (Ionicon)</li> <li>merged dataset</li> <li>calibrated with VOC standard gas mix (Apel-Riemer Environmental Inc., Colorado, USA)</li> <li>units (filename): <ul> <li>normalized counts per second [ncps] (20210426_p_ncps.txt, bar_mean.txt, bar_std.txt)</li> <li>parts per billion [ppb] (all_sub_20210426_ppb.txt)</li> </ul> </li> <li>for information concerning updated versions, please see ReadMe.txt</li> </ul>

opencc-by-4.0Jul 2023View details →

zenodo36/100

Application of artificial neural network to forecast indoor air temperature in a building with artificial ventilation: impact of early stopping.

Indoor air temperature prediction can facilitate energy-saving actions without compromising the indoor thermal comfort of occupants. The aim of this study was to analyse the performance of various artificial neural networks with a view to proposing an optimal approach for predicting the indoor temperature of a tertiary building with artificial ventilation. The MLP, CNN, LSTM models and the CNN-LSTM combination (long short-term memory network) were used and coupled with the optimisation algorithms (Adam, SGD) and the independent hyper-parameters early stopping and dropout. The parameters used are outdoor ambient temperature, outdoor relative humidity, indoor relative humidity, wet bulb temperature, black globe temperature and mean radiant temperature. The data is collected in an artificially ventilated building in Yaoundé, Cameroon. A numerical code was developed in Python to run the simulations. In order to study the impact of the parameters on the prediction, two scenarios were distinguished in this work: (1) all the parameters are input to the network, (2) only the parameters whose absolute value of the correlation coefficient was greater than or equal to 0.5 were used. The impact of early stopping is assessed by distinguishing two case studies: the first without early stopping, the second with early stopping. The results showed that without early stopping, the MLP, CNN, LSTM and CNN-LSTM networks are adequate for predicting the temperature with the second scenario, mainly with both the SGD and Adam algorithms, and CNN-LSTM is the most appropriate model because the MSE and MAE values obtained in this case were closer to 0. With early stopping, the learning time is reduced and the learning curves are improved; the models optimised better with the SGD algorithm in general, but the best neural network model was obtained with the Adam algorithm and the LSTM network for the performances MSE=0.0005, MAE=0.0130 with the second scenario.Keywords: prediction, indoor temperature, artificial neural network, early stopping, artificially ventilated building.

opencc-by-4.0Nov 2023View details →

zenodo36/100

Indoor Air Temperature and Occupant Behavior in Classroom of higher education building in Mediterranean climate

Data collection Include the measurement of indoor and outdoor environmental parameters (air temperature and relative humidity) and occupant interactions with building systems (window and door status: open/closed, blind state, and thermostat/air-conditioning adjustment).The outdoor air temperature, relative humidity, and wind speed were collected as potential control variables to indicate different outdoor conditions.The indoor air temperature and relative humidity in the classroom were monitored using wireless sensors. Six RHT sensors were placed at different locations: one in the center (F98), two on the ceiling next to grilles (FA1 et F9B), one on the carpentry of one of the windows (F9D), one near the writing board (F94), and one in the corridor outside the classroom (F96). Indoor parameters were recorded at ten minutes intervals.The number of occupants was determined hourly (morning and afternoon) by counting and surveying (attendance sheets). The usage schedules of the classroom were 8:30–18:00. The number of occupants varied from 0 to 31.The states of doors and windows (open or close) were monitored using magnetic sensor that detects the opening of doors and windows. The states of the door and windows were recorded at ten minute intervals.The window-blind closing rate was determined by visual observation. The closing rates were 0%, 25%, 50%, 75%, and 100%. Observations were conducted throughout the day in the morning and afternoon at 1 h intervals.The state of the heating/air-conditioning system was determined to be off or on every hour (in the morning and afternoon) using the HVAC control panel (HMI)

opencc-by-4.0Oct 2023View details →

ClinicalTrials.gov36/100

Health Effects of Indoor Air Filtration in Healthy Chinese Adults

ClinicalTrials.gov study NCT02712333. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

dryad36/100