Search Results (7)

This article presents a new way to determine odor nuisance based on the proposed odor air quality index ($OAQ{I}_I$), using an instrumental method. This indicator relates the most important odor features, such as intensity, hedonic tone and odor concentration. The research was conducted at the compost screening yard of the municipal treatment plant in Central Poland, on which a self-constructed gas sensor array was placed. It consisted of five commercially available gas sensors: three metal oxide semiconductor (MOS) chemical sensors and two electrochemical ones. To calibrate and validate the matrix, odor concentrations were determined within the composting yard using the field olfactometry technique. Five mathematical models (e.g., multiple linear regression and principal component regression) were used as calibration methods. Two methods were used to extract signals from the matrix: maximum signal values from individual sensors and the logarithm of the ratio of the maximum signal to the sensor baseline. The developed models were used to determine the predicted odor concentrations. The selection of the optimal model was based on the compatibility with olfactometric measurements, taking the mean square error as a criterion and their accordance with the proposed $OAQ{I}_I$. For the first method of extracting signals from the matrix, the best model was characterized by RMSE equal to 8.092 and consistency in indices at the level of 0.85. In the case of the logarithmic approach, these values were 4.220 and 0.98, respectively. The obtained results allow to conclude that gas sensor arrays can be successfully used for air quality monitoring; however, the key issues are data processing and the selection of an appropriate mathematical model. Full article

Accurate and fine-grained individual air quality index (IAQI) prediction is the basis of air quality index (AQI), which is of great significance for air quality control and human health. Traditional approaches, such as time series, recurrent neural network or graph convolutional network, cannot effectively integrate spatial-temporal and meteorological factors and manage the dynamic edge relationship among scattered monitoring stations. In this paper, a ST-CCN-IAQI model is proposed based on spatial-temporal causal convolution networks. Both the spatial effects of multi-source air pollutants and meteorological factors were considered via spatial attention mechanism. Time-dependent features in the causal convolution network were extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-IAQI were tuned by Bayesian optimization. Shanghai air monitoring station data were employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results showed that: (1) For a single station, the RMSE and MAE values of ST-CCN-IAQI were 9.873 and 7.469, decreasing by 24.95% and 16.87% on average, respectively. R² was 0.917, with an average 5.69% improvement; (2) For all nine stations, the mean RMSE and MAE of ST-CCN-IAQI were 9.849 and 7.527, respectively, and the R² value was 0.906. (3) Shapley analysis showed PM₁₀, humidity and NO₂ were the most influencing factors in ST-CCN-IAQI. The Friedman test, under different resampling, further confirmed the advantage of ST-CCN-IAQI. The ST-CCN-IAQI provides a promising direction for fine-grained IAQI prediction. Full article

Ensuring the comfort and health of occupants is the main objective of properly functioning building systems. Regardless of the season and building types, it is the priority of the designers and building managers. The indoor air parameters affect both the well-being and health of users. Furthermore, it could impact the effectiveness of their work and concentration abilities. In hotel facilities, the guests’ comfort is related directly to positive opinions or customer complaints, which is related to financial benefits or losses. The main goal of this study is the analysis of the indoor environmental quality in guests’ rooms, based on the example of a hotel in Poland. The article assesses the variability of air parameters, including temperature, humidity, and carbon dioxide concentrations, and the acceptability of indoor conditions. The research was carried out in November 2020. Based on the collected data, the dynamics of changes of selected air parameters were analyzed. The article analyzes the comfort indicators inside guest rooms, including the Predicted Mean Vote (PMV) and Predicted Percentage of the Dissatisfied (PPD) index. The obtained results were compared with the optimal conditions of use to ensure the guests’ comfort. As the analysis showed, the temperature and humidity conditions are maintained at a satisfactory level for most of the time. It was noticed that the CO₂ concentrations temporarily exceeded the value of 2000 ppm in two of the analyzed guests’ rooms, which could cause discomfort to hotel guests. In these rooms, the increase in the volume of ventilation airflow should be considered. The measured parameters dynamically varied over time, and there was no repeatability or clear patterns of variation. This is due to the individual preferences and behavior of users. A detailed analysis is extremely difficult due to the possibility of opening windows by users, the irregular presence of hotel guests in the rooms, and the inability to verify the exact number of users in the room during the measurements. Full article

The increasing incidence of extreme wildfire is becoming a concern for public health. Although long-term exposure to wildfire smoke is associated with respiratory illnesses, reports on the association between short-term occupational exposure to wildfire smoke and lung function remain scarce. In this cross-sectional study, we analyzed data from 218 Royal Canadian Mounted Police officers (mean age: 38 ± 9 years) deployed at the Fort McMurray wildfires in 2016. Individual exposure to air pollutants was calculated by integrating the duration of exposure with the air quality parameters obtained from the nearest air quality monitoring station during the phase of deployment. Lung function was measured using spirometry and body plethysmography. Association between exposure and lung function was examined using principal component linear regression analysis, adjusting for potential confounders. In our findings, the participants were predominantly male (71%). Mean forced expiratory volume in 1 s (FEV₁), and residual volume (RV) were 76.5 ± 5.9 and 80.1 ± 19.5 (% predicted). A marginal association was observed between air pollution and higher RV [β: 1.55; 95% CI: −0.28 to 3.37 per interquartile change of air pollution index], but not with other lung function indices. The association between air pollution index and RV was significantly higher in participants who were screened within the first three months of deployment (2.80; 0.91 to 4.70) than those screened later (−0.28; −2.58 to 2.03), indicating a stronger effect of air pollution on peripheral airways. Acute short-term exposure to wildfire-associated air pollutants may impose subtle but clinically important deleterious respiratory effects, particularly in the peripheral airways. Full article

The aim of the study was to determine daily changes in some egg quality parameters, indirectly reflecting egg freshness, and to assess the possibility of predicting time from laying using mathematical methods. The study material consisted of 365 table eggs of medium (M, ≥53 g and <63 g) and large (L, ≥63 g and <73 g) weight classes (commercial stock, cage system, brown-shelled eggs) collected on the same day. Eggs were numbered individually and placed on transport trays and stored (14 °C, 70% RH). Every day, for 35 days, egg quality characteristics were analyzed (10 eggs per group). The change of traits in time was analyzed on the basis of linear and polynomial regression equations, depending on the trait. Based on model fitting, eight traits were selected as those most affected by storage time: egg weight and specific weight, Haugh units, albumen weight, air cell depth, yolk index, albumen and yolk pH. These traits, excluding those related to the weight, were then used in a multiple linear regression model to predict egg age. All regression models presented in this study were characterized by high predictive efficiency, which was confirmed by comparison of the observed and estimated values. Full article

Background: A growing body of scientific literature indicates that risk factors for COVID-19 contribute to a high level of psychological distress. However, there is no consensus on which factors contribute more to predicting psychological health. Objectives: The present study quantifies the importance of related risk factors on the level of psychological distress and further explores the threshold effect of each rick factor on the level of psychological distress. Both subjective and objective measures of risk factors are considered in the model. Methods: We sampled 937 individual items of data obtained from an online questionnaire between 20 January and 13 February 2020 in China. Objective risk factors were measured in terms of direct distance from respondents’ housing to the nearest COVID-19 hospital, direct distance from respondents’ housing to the nearest park, and the air quality index (AQI). Perceived risk factors were measured in regard to perceived distance to the nearest COVID-19 hospital, perceived air quality, and perceived environmental quality. Psychological distress was measured with the Kessler psychological distress scale K6 score. The following health risk factors and sociodemographic factors were considered: self-rated health level, physical health status, physical activity, current smoker or drinker, age, gender, marital status, educational attainment level, residence location, and household income level. A gradient boosting decision tree (GBDT) was used to analyse the data. Results: Health risk factors were the greatest contributors to predicting the level of psychological distress, with a relative importance of 42.32% among all influential factors. Objective risk factors had a stronger predictive power than perceived risk factors (23.49% vs. 16.26%). Furthermore, it was found that there was a dramatic rise in the moderate level of psychological distress regarding the threshold of AQI between 40 and 50, and 110 and 130, respectively. Gender-sensitive analysis revealed that women and men responded differently to psychological distress based on different risk factors. Conclusion: We found evidence that perceived indoor air quality played a more important role in predicting psychological distress compared to ambient air pollution during the COVID-19 pandemic. Full article

An accurate, reliable and stable air quality prediction system is conducive to the public health and management of atmospheric ecological environment; therefore, many models, individual or hybrid, have been implemented widely to deal with the prediction problem. However, many of these models do not take into consideration or extract improperly the period information in air quality index (AQI) time series, which impacts the models’ learning efficiency greatly. In this paper, a period extraction algorithm is proposed by using a Luenberger observer, and then a novel period-aware hybrid model combined the period extraction algorithm and tradition time series models is build to exploit the comprehensive forecasting capacity to the AQI time series with nonlinear and non-stationary noise. The hybrid model requires a multi-phase implementation. In the first step, the Luenberger observer is used to estimate the implied period function in the one-dimensional AQI series, and then the analyzed time series is mapped to the period space through the function to obtain the period information sub-series of the original series. In the second step, the period sub-series is combined with the original input vector as input vector components according to the time points to establish a new data set. Finally, the new data set containing period information is applied to train the traditional time series prediction models. Both theoretical proof and experimental results obtained on the AQI hour values of Beijing, Tianjin, Taiyuan and Shijiazhuang in North China prove that the hybrid model with period information presents stronger robustness and better forecasting accuracy than the traditional benchmark models. Full article