Evaluation Of Air Quality Research Articles

Indoor air quality of urban public transportation stations in China: Based on air quality evaluation indexes

Indoor air quality (IAQ) of urban public transportation stations (UPTS) has adverse health impacts on the station employees and commuters. However, there is a lack of comprehensively evaluations of IAQ in waiting rooms of UPTS. Therefore, it is crucial to select appropriate air quality indexes (including fuzzy synthetic index (B), comprehensive index (P), I1 index, and indoor air quality index (IAQI)) to assess air quality and potential health risks. Our study is a subsample of the CPPEHS 2019 study, which included 224 UPTS in 126 cities of China. We found that P index showed an excellent air quality rate of 95.96% in the railway stations and 83.19% in the inter-city bus stations. The P index was correlated with UPTS usage years, useable area, and per passenger useable area. Furthermore, waiting rooms in UPTS with good (OR = 1.9187, 95% CI: 1.1204, 3.2859) and bad (OR = 2.0854, 95% CI: 1.2182, 3.5698) air quality evaluated by P index had a higher risk of rhinitis compared to those with excellent air quality. Similarly, UPTS with good (OR = 2.2202, 95% CI: 1.3427, 3.6711), bad (OR = 1.7897, 95% CI: 1.0807, 2.9637), and serious (OR = 1.7478, 95% CI: 1.0098, 3.0250) air quality evaluated by P index were associated with a higher risk of pharyngitis. These findings suggested that P index is the optimal index for assessing air quality in UPTS, while IAQI may exaggerate indoor air pollution and the B index may underestimate it. Overall, this study aims to identify and evaluate the more suitable air quality index (P) in nationwide UPTS, providing valuable insights for control of IAQ and guiding the air quality management and standards.

Temporal Variation Characteristics of Negative Air ion Concentration and Air Quality Evaluation in Songshan National Nature Reserve, Beijing

Temporal Variation Characteristics of Negative Air ion Concentration and Air Quality Evaluation in Songshan National Nature Reserve, Beijing

Air Pollution Governance and Residents’ Happiness: Evidence of Blue Sky Defense in China

Enhancing people’s happiness should be the standard of public policies. With the growing prominence of air pollution issues, governments and scholars have started to pay attention to happiness as it relates to air pollution. However, the relevant research has been limited in China, and the results are not clear, with little attention given to subjective perception related to air pollution. In recent years, China has strengthened its efforts in containing air pollution, striving to fulfill the people’s longing for a blue sky. In this study, we aimed to investigate the impact of pollution governance on residents’ happiness, considering both objective and subjective aspects. Using the Chinese General Social Survey and data on PM10, our study was diachronic in nature, analyzing residents’ happiness and the improvement in air quality as well as people’s evaluation of governance concerning pollution. The statistical methods used primarily included t-tests and multiple linear regression. The results showed the following: (1) Residents’ happiness showed a significant improvement from 2013 to 2021, accompanied by enhancements in both an objective improvement in air quality and subjective evaluation of pollution governance. (2) Both an objective improvement in air quality and the subjective evaluation of pollution governance showed positive effects on residents’ happiness. This study not only enriches the theoretical understanding of the relationship between air pollution and happiness but also provides valuable insights for formulating policies that are more conducive to pollution governance and public happiness. To enhance happiness, the government needs to continue improving the air quality and guide residents in appropriately evaluating pollution governance.

Unmasking air quality: A novel image-based approach to align public perception with pollution levels

In the quest to reconcile public perception of air pollution with scientific measurements, our study introduced a pioneering method involving a gradient boost-regression tree model integrating PM2.5 concentration, visibility, and image-based data. Traditional stationary monitoring often falls short of accurately capturing public air quality perceptions, prompting the need for alternative strategies. Leveraging an extensive dataset of over 20,000 public visibility perception evaluations and over 8,000 stationary images, our models effectively quantify diverse air quality perceptions. The predictive prowess of our models was validated by strong performance metrics for perceived visibility (R = 0.98, RMSE = 0.19), all-day PM2.5 concentrations (R: 0.77–0.78, RMSE: 8.31–9.40), and Central Weather Bureau visibility records (R = 0.82, RMSE = 9.00). Interestingly, image contrast and light intensity hold greater importance than scenery clarity in the visibility perception model. However, clarity is prioritized in PM2.5 and Central Weather Bureau models. Our research also unveiled spatial limitations in stationary monitoring and outlined the variations in predictive image features between near and far stations. Crucially, all models benefit from the characterization of atmospheric light sources through defogging techniques. The image-based insights highlight the disparity between public perception of air pollution and current policy implementation. In other words, policymakers should shift from solely emphasizing the reduction of PM2.5 levels to also incorporating the public's perception of visibility into their strategies. Our findings have broad implications for air quality evaluation, image mining in specific areas, and formulating air quality management strategies that account for public perception.

2D/2D Dy2O3 Nanosheet/MoO3 Nanoflake Heterostructures for Humidity-Independent and Sensitive Ammonia Detection.

Chemiresistive ammonia gas (NH3) sensors have been playing a significant role in the fields of environmental protection, food safety monitoring, and air quality evaluation. Nevertheless, balancing the high sensitivity and humidity tolerance remains challenging. Herein, the two-dimensional (2D) heterostructures of molybdenum trioxide (MoO3) nanoflakes decorated with dysprosium oxide (Dy2O3) nanosheets (termed Dy2O3/MoO3) were synthesized via a facile probe-sonication method. With respect to pristine MoO3 counterparts, the optimal Dy2O3/MoO3 sensors possessed a 4.49-fold larger response at a lower temperature (30.52@328.2 °C vs 6.8@369.7 °C toward 10 ppm of NH3), shorter response/recovery times (11.6/2.9 s vs 26.9/43.4 s), 52.6-fold higher sensitivity (17.35/ppm vs 0.33/ppm), and a lower theoretical detection limit (1.02 vs 32.82 ppb). Besides the nice reversibility, wide detection range (0.45-100 ppm) and robust long-term stability, inspiringly, the Dy2O3/MoO3 sensors showed a nearly humidity-independent response. These impressive improvements in the NH3-sensing performance were attributed to numerous heterojunctions to strengthen the carrier concentration modulation and the compensation/protection effect of Dy2O3 to mitigate the humidity effect. Moreover, the Dy2O3/MoO3 sensors showed preliminary application potential in monitoring pork freshness. This work provides a universal methodology for constructing NH3 gas sensors with high sensitivity and good humidity resistance and probably extends the application scenarios of MoO3-based sensors in the Internet of Things in the future.

Estimation and Inference for Spatio-Temporal Single-Index Models

To better fit the actual data, this paper will consider both spatio-temporal correlation and heterogeneity to build the model. In order to overcome the “curse of dimensionality” problem in the nonparametric method, we improve the estimation method of the single-index model and combine it with the correlation and heterogeneity of the spatio-temporal model to obtain a good estimation method. In this paper, assuming that the spatio-temporal process obeys the α mixing condition, a nonparametric procedure is developed for estimating the variance function based on a fully nonparametric function or dimensional reduction structure, and the resulting estimator is consistent. Then, a reweighting estimation of the parametric component can be obtained via taking the estimated variance function into account. The rate of convergence and the asymptotic normality of the new estimators are established under mild conditions. Simulation studies are conducted to evaluate the efficacy of the proposed methodologies, and a case study about the estimation of the air quality evaluation index in Nanjing is provided for illustration.

Evaluation of Nitrogen Dioxide, Benzene, Toluene, Ethylbenzene and Xylene Concentrations in the Urban Environment of Meknes City, Morocco

The present study focuses on the monitoring and the evaluation of air quality in the city of Meknes, based on the tracers of pollution of proximity cars (NO2 (nitrogen dioxide) and BTEX (Benzene, Toluene, Ethylbenzene, and Xylene). In the absence of a telemetric station and a mobile laboratory within the city, passive diffusion tubes, analyzed offline in the laboratory after exposure, were the most plausible solution. The low cost, ease of implementation of the technique, and the possibility of covering a large geographic area with many samplers all argue for adopting this approach.
 The coupling of the results of the measurement campaigns and counting sessions under a geographical information system allowed us to determine the most affected areas by automobile pollution and to carry out cartography at a large scale of spatial resolution of the prospected pollutants.
 The deployment of passive sensors at 14 measurement sites revealed the existence of spatio-temporal variability of the pollutants studied. This variability is due to the climatic conditions that prevailed during the measurement campaigns, notably wind and precipitation, the photochemical nature of the said markers, the proximity to the emission sources, the type of measurement site, and the topography and road infrastructure.
 The approach developed could be used as a decision-making tool for the competent authorities in this field and adapted for monitoring other types of pollutants (pesticides, tracers generated by industrial units, furan dioxins, etc.).

Experimental Investigation on PM10 and PM2.5 Concentrations in North Bandung

North Bandung is an area where there are several tourist attractions, including the Dago Dream Park. The main road to several locations in North Bandung is via the road in front of Terminal Dago. For this reason, the purpose of this study was to collect data on PM2.5 and PM10 concentrations at both locations, namely in front of Terminal Dago and Dago Dream Park. Data collection was carried out for seven days, from Monday to Sunday, from 08.00 to 16.00 local time. To evaluate air quality in these two places, the national standard, namely PPRI No. 22 of 2021 and international standards from WHO were applied. The measurement results show that the concentrations of PM2.5 and PM10 in these two locations are still within the national standard. Meanwhile, when evaluated with WHO standards, PM10 concentrations in both locations are still within standard, but for PM2.5, there are several days where the air quality is out of the standard. The average concentrations of PM2.5 and PM10 during the 7 days of measurement at Terminal Dago are 19.9 μg/m3 and 21.6 μg/m3, respectively. While the average concentration of PM2.5 and PM10 during 1 week of data collection were 18.9 μg/m3 and 19.9 μg/m3, respectively. This means that the concentration of PM2.5 and PM10 at Terminal Dago is slightly higher than that of at Dago Dream Park. In addition, based on an evaluation using national standards, Dago Dream Park tourist attractions still have good air quality and are safe for local residents and tourists. From the data recorded at the Health Centre (Puskesmas), the number of ARI cases in the two locations in 2020 and 2021 is relatively not much different.

A pilot numerical study of odorant transport to the olfactory region during sensory evaluations following ISO 16000-28

We numerically analyzed odorant transportation from a sniffing device (funnel) to the olfactory region of the nasal cavity during breathing. We followed the procedure for the perceived air quality evaluations described in the ISO 16000-28 standard, and used acetone defined as the standard test substance for pi-scale evaluations in this standard; we also used ammonia and acetic acid, as acetone also emitted by humans. We modelled two breathing conditions: normal breathing (through nose) and sniffing. We evaluated olfactory receptor access under these breathing conditions. The acetone absorption flux to the olfactory epithelial tissues was analyzed using a computer-simulated person with a numerical respiratory tract model and a physiologically based pharmacokinetic model that was used to validate the prediction accuracy. The absorption flux and sensible/latent heat flux to the olfactory epithelial tissue were analyzed quantitatively. We also analyzed the impact of flow through the ortho- and retro-nasal pathways on the absorption flux to the olfactory region. The transient inhalation/exhalation airflow profile, breathing, and sniffing conditions had a significant impact on the absorption flux to the olfactory region of the nasal cavity. We observed two peaks of odorant absorption flux in one breath one during inhalation and one during exhalation. For example, 0.5μg/(m2s) of peak acetone absorption flux in the olfactory region during inhalation and 0.1μg/(m2s) during exhalation was observed.

Long-term airborne measurements of pollutants over the United Kingdom to support air quality model development and evaluation

Abstract. The ability of regional air quality models to skilfully represent pollutant distributions throughout the atmospheric column is important to enabling their skilful prediction at the surface. This provides a requirement for model evaluation at elevated altitudes, though observation datasets available for this purpose are limited. This is particularly true of those offering sampling over extended time periods. To address this requirement and support evaluation of regional air quality models such as the UK Met Offices Air Quality in the Unified Model (AQUM), a long-term, quality-assured dataset of the three-dimensional distribution of key pollutants was collected over the southern United Kingdom from July 2019 to April 2022. Measurements were collected using the Met Office Atmospheric Survey Aircraft (MOASA), a Cessna 421 instrumented for this project to measure gaseous nitrogen dioxide, ozone, sulfur dioxide and fine-mode (PM2.5) aerosol. This paper introduces the MOASA measurement platform, flight strategies and instrumentation and is not intended to be an in-depth diagnostic analysis but rather a comprehensive technical reference for future users of these data. The MOASA air quality dataset includes 63 flight sorties (totalling over 150 h of sampling), the data from which are openly available for use. To illustrate potential uses of these upper-air observations for regional-scale model evaluation, example case studies are presented, which include analyses of the spatial scales of measured pollutant variability, a comparison of airborne to ground-based observations over Greater London and initial work to evaluate performance of the AQUM regional air quality model. These case studies show that, for observations of relative humidity, nitrogen dioxide and particle counts, natural pollutant variability is well observed by the aircraft, whereas SO2 variability is limited by instrument precision. Good agreement is seen between observations aloft and those on the ground, particularly for PM2.5. Analysis of odd oxygen suggests titration of ozone is a dominant chemical process throughout the column for the data analysed, although a slight enhancement of ozone aloft is seen. Finally, a preliminary evaluation of AQUM performance for two case studies suggests a large positive model bias for ozone aloft, coincident with a negative model bias for NO2 aloft. In one case, there is evidence that an underprediction in the modelled boundary layer height contributes to the observed biases at elevated altitudes.

Maximizing ozone control by spatial sensitivity-oriented mitigation strategy in the Pearl River Delta Region, China

The Pearl River Delta (PRD) has long been plagued by severe O3 pollution, particularly during the autumn. A regional O3 pollution episode influenced by the Western Pacific Subtropical High in September 2021 was characterized by near-surface O3 escalation due to strong photochemical reactions within the planetary boundary layer. This event was targeted to develop effective control strategies through investigation of precursor control type and scope based on the high-order decoupled direct method (HDDM) and integrated source apportionment method (ISAM) of CMAQ. Generally, the majority of areas (67.0 %) were under NOx-limited regime, which should strengthen afternoon NOx control inferred by positive convex O3 responses. However, high emission and heavily polluted areas located in central PRD were under VOC-limited regime (11.6 %) or mixed regime (15.0 %). The remaining areas (6.4 %) were under NOx-titration or insensitive conditions. Regarding source apportionment, Guangdong province contributed 32.3 %–58.4 % to MDA8 O3 of PRD, especially higher proportion (>50 %) to central areas. Overall, local-focused NOx/VOC emission reductions had limited effects on O3 mitigation for receptor cities compared to regional-cooperative regulation. When region-wide VOC emission reduction was implemented, MDA8 O3 in VOC-limited grids exhibited the largest declines (2.3 %–4.1 %, 3.9– 7.0 μg·m−3). However, unified NOx control contributed to increasing MDA8 O3 in VOC-limited grids (most stations located for air quality evaluation) whereas decreased MDA8 O3 by 2.1 %– 5.7 %, 3.0– 8.2 μg·m−3 in large-scale NOx-limited grids. The sensitivity-oriented regional control avoided O3 rebound and achieved the greatest decline of 3.4 %– 5.0 %, 5.7– 8.4 μg·m−3 in VOC-limited grids; additionally, time-refined dynamic aggressive NOx control decreased peak O3 by an extra 1.2– 6 μg·m−3, both of which facilitate the regulation for the forecasting O3 episodes. These findings suggest that in heavily polluted environments, the enhancement of O3 regulation benefits requires meticulous, coordinated, and dynamic NOx and VOC controls spanning the entire region based on high-resolution analysis of heterogeneous O3-NOx-VOC sensitivity. Furthermore, emission reduction gains should be more reasonably reflected through increasing in-situ observations covering multi-sensitivity regions.

Evaluation of air quality and health risks associated with trace elements in respirable particulates (PM2.5) from Islamabad, Pakistan.

Fine atmospheric particulates are associated with numerous environmental and health issues as they can penetrate deeply in the respiratory tract thereby adversely affecting the human health. This study aimed to investigate the concentrations of trace elements in the respirable (PM2.5) fraction of the atmospheric particulates and to understand their pollution status and health risks. The samples were collected from Islamabad, and the metals were extracted using HNO3 and HCl based extraction method. Atomic absorption spectroscopy was employed to quantify the concentrations of selected trace elements. PM2.5 exhibited considerable variations in their minimum (4.737µg/m3) and maximum (108.1µg/m3) levels. The significant contributors among the selected elements bound to PM2.5 were Ca (1016ng/m3), K (759.8ng/m3), Mg (483.0ng/m3), Fe (469.7ng/m3), and Zn (341.1ng/m3), while Ag (0.578ng/m3) was found at the lowest levels with an overall descending order: Ca > K > Mg > Fe > Zn > Cu > Pb > Ni > Cd > Mn > Sr > Cr > Co > Li > Ag. Multivariate PCA and CA identified industrial activities, combustion processes and automobile emissions as the main anthropogenic contributors to particulate pollution. Enrichment factors and geoaccumulation indices were computed to assess the pollution status. The results also revealed that among the trace elements, Cd showed extremely high contamination, followed by Ag, Zn, and Pb, which showed moderate to high contamination in the atmospheric particulates. Carcinogenic health risks from Pb and Ni were found to be within the safe limit (1.0 × 10-6); however, Cr, Co, and Cd exposure was linked to significant cancer risks. The present elemental levels in PM2.5 were also compared with the reported levels from other regions around the world.

Evaluation of Air Quality inside Self-Contained Breathing Apparatus Used by Firefighters

(1) Background: When a fire breaks out, combustibles are burned and toxic substances such as carbon monoxide (CO), polycyclic aromatic hydrocarbons (PAH), benzene, and hydrogen cyanide are produced. The purpose of this study is to evaluate the air quality inside self-contained breathing apparatus (SCBA) by comparing it to that in the environment where the SCBA charger is installed. (2) Methods: The design of this study was a simulation-based case-control experiment study, and the experiment was conducted at two fire stations located on land and on water. When charging the SCBA, it was differentiated according to the presence or absence of exposure to harmful substances and the degree of exposure. The air quality inside the SCBA in the charging room installed in the fire station garages located on land and in the water, which were not completely isolated from harmful substances, was evaluated. CO, carbon dioxide (CO2), water, and oil mist were measured and analyzed to determine the air quality inside the SCBA. (3) Results: In the case of land firefighting stations, the mean CO among the SCBA internal air quality items was 20 times higher than the outside the SCBA, and higher than the safe range in the group with the highest exposure at the sites of firefighting buildings completely isolated from hazardous substances. The CO levels of all items of water were analyzed to be higher than the safe range in the floating fire station. (4) Conclusions: It was confirmed that the installation environment of an SCBA charging room can affect the safety of the charged internal air quality components. The results of this study can be actively used for the operation and management of SCBA charging room environments when building firefighting buildings in the future for the hygiene, safety, and health of firefighters.

Comparative Study of Air Quality Assessment in Bonny, Bille And Degema Communities in The Niger Delta Region, Nigeria

Purpose: This study was undertaken to ascertain the air quality in the oil-producing towns for the purpose of making recommendations that address the health challenges of the inhabitants Gas flaring has a negative impact on air quality in oil-producing towns. 
 Methodology: This paper adopted Robert King's Exposure Risk Theory from the late 1970s, this research employs an experimental design to analyze air quality in Bonny, Degema, and Bille towns located in Rivers State. Data was collected from these towns, followed by rigorous laboratory analysis to evaluate air quality. A comparison of these findings with the air quality guidelines established by the World Health Organization (WHO) was made to facilitate an understanding of potential health implications associated with air quality issues in the study area.
 Findings: The study reveals that nitrogen (IV) oxide (NO2) concentrations spanned from 0.006 to 0.085 ppm in Station 3 and 0.018 to 0.006 ppm in Station 1. Notably, statistically significant differences (P < 0.05) in NO2 levels were observed across the diverse towns of the Niger Delta investigated in this study. The high recorded levels of nitrogen (IV) oxide (NO2) can potentially be attributed to heightened vehicular emissions in Bonny relative to Degema and Bille. Furthermore, the study identifies hydrogen sulphide (H2S) concentrations ranging from 0.001 ppm to 0.123 ppm, with the highest levels in Bonny. Carbon (II) oxide (CO) levels varied between 0.032 and 3.355 ppm in Degema and Bonny, respectivelyy. No statistically significant difference (P > 0.05) was found. The study also identifies ammonia (NH3) levels ranging from 0.001 to 0.008 ppm in Degema, Bille, and Bonny communities. Notably, no significant variance in ammonia concentration was observed among the study's locations. In conclusion, the study underscores the detrimental impact of oil-related activities, including gas flaring, on air quality within the examined areas, consequently endangering residents' well-being.
 Unique Contribution to Theory, Practice and Policy (Recommendations): This study offers a forward stride in the Exposure Risk Theory's application by contextualizing it within the study area. Through this lens, we uncover the ramifications of prevalent practices such as gas flaring on air quality. Practical implications emerge as valuable guidance for stakeholders including industries and regulatory bodies to sculpt targeted pollution control strategies addressing specific pollutants. Moreover, local communities and health agencies stand to harness these insights to champion cleaner air and enhanced living conditions.

Predicting Air Quality Index using Ensemble Machine Learning

This study focuses on utilizing Ensemble Machine Learning to predict and forecast the Air Quality Index (AQI). The research is motivated by the adverse effects of industrialization and population growth on air quality, leading to detrimental impacts on human health. While numerous methodologies exist for air quality prediction, it is crucial to anticipate future air conditions to minimize their larger consequences. Hence, this study proposes an air quality evaluation system to facilitate future predictions. The study comprises three primary modules: Data Preparation, AQI Forecasting, and Evaluating Air Quality. The Data Preparation Module involves real-time data collection and formatting to ensure compatibility with subsequent modules. In this research, the Sparse Spectrum GPR (SSGPR) method is employed for AQI forecasting, while the cloud model is adopted for air quality evaluation. The study's findings demonstrate the capability of the proposed model to account for the uncertainty and randomness inherent in air quality prediction. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) are employed to evaluate the models' effectiveness. Based on the evaluation results, it can be concluded that the Ensemble Machine Learning method utilized in this study effectively predicts and forecasts the Air Quality Index. These predictions play a crucial role in minimizing the adverse impact of air pollution on human health by providing insights into future air conditions. Overall, this research contributes significantly to comprehending and addressing the increasingly urgent challenges associated with air quality.

Spatial evaluation of air quality by biomonitoring of toxic element accumulation in lichens in urban green areas and nature parks on the Anatolian side of Istanbul.

The city of Istanbul is constantly exposed to air pollution due to its high population, heavy traffic - sea and air transport - and urban industry. This study basically aims to determine the recent level of airborne heavy metals, using lichen biomonitoring method. The cosmopolitan foliose lichen Xanthoria parietina growing abundantly on trees was sampled from 16 urban green spaces in 8 districts on the Anatolian side of Istanbul. Multi-element analysis by ICP-MS was applied to measure the accumulation of 10 potentially toxic trace elements in lichen samples. Spatial distributions of element levels in the air in the sampling areas are shown by mapping. According to the analysis data, the sequence of element deposition levels in lichen samples was as follows; Al > Fe > Mn > Zn > Cr > Cu > Pb > V > Ni > As. Most of the measured atmospheric element amounts yielded results much higher than the reference material in all areas. It was detected that the highest pollution in terms of Al, Cu, Fe, Mn, and Ni elements was in Elmasburnu Nature Park area in Beykoz district, which is a touristic place by the sea. Changes in the city's air quality over the years have been evaluated by comparing element levels in these locations in a previous biomonitoring study and some differences were found. The resulting data is valuable for periodic monitoring of toxic elements in the air, for determining causes of air pollution, and for taking precautions.

Grey relational analysis model with cross-sequences and its application in evaluating air quality index

It is important to detect the internal operating regularity in system developing with poor information. To identify the real relationship among multi factors, we propose a grey relational analysis (GRA) method inspired by the characteristics of sequences variation. The proposed model considers the changes of fluctuating sequences like cross-sequences both in domain time and between time intervals. To obtain the quantitative change about sequences, relative angle change is employed to determine the variation in each interval, and the relative angle oscillation change is utilized for measuring variations between intervals. To find the optimal time lag or time intervals, the corresponding cycles are extracted by time-delay models. The reliability of the proposed models will be verified through cases in identifying crucial factors for air quality, and the final detection will then be made. To compare with existing representative GRA models clearly, the relation between two fluctuating sequences shaped in cross-sequences is examined by the proposed model. The empirical results show that the relation degree between pollutants and air quality is reasonable. The compared experiment shows that the GRA for cross-sequences can effectively identify the relationship among fluctuating sequences and the impact of time-delay is small for the proposed model with similar shapes.

Research on the Evaluation of Air Quality in Underground Coal Mines Based on a Generalized Contrastive Weighted Comprehensive Scale Index Method

In this study, an optimization model was established based on the generalized contrastive weighted comprehensive scale index method. This model gives the evaluation indicators of SO2, NOx, CO, and TSP. It also innovatively introduces gas, the most harmful substance in underground coal mines, into the evaluation indicators. Moreover, the obvious hazardous concentration limit is used as the third standard concentration of the model. The scale sub-indices and the weights of SO2, NOx, CO, TSP, and gas are calculated, leading to the comprehensive scale index. Finally, the classification standard of the underground air quality is determined. An underground excavation face in Shaanxi Province is used as an example for air quality assessment. The air quality is generally poor at the points close to the working face, while that at the points far away from the working face is generally better. Furthermore, air quality optimization measures are given for areas with poor air quality.

Energy saving, indoor thermal comfort and indoor air quality evaluation of an office environment using corner impinging jet ventilation

The performance of a corner based impinging jet ventilation system (CIJV) in an office environment was evaluated numerically. The evaluation was done both in terms of the local thermal comfort and the local indoor air quality. Three different inlet configurations were tested for a range of outdoor temperatures that included both winter and summer conditions. In terms of indoor air quality, the results showed that CIJV performed better than a traditional mixing system. The study also revealed that CIJV creates a stronger temperature stratification in summertime compared to wintertime. When evaluating the energy saving potential the results showed a possible reduction of 7% for the ventilation flowrate when the outdoor temperatures were between −15 °C and −5 °C, 8% when the outdoor temperatures were between 0 °C and 10 °C and 9% when the outdoor temperatures were between 15 °C and 25 °C.

Analysis of Indoor Air Quality in a Kindergarten

Abstract Today in addition to the design of structures, layout solutions, and the design of suitable materials, the modern construction industry also addresses meeting the requirements for the energy performance of a building, with which the topic of the quality of the indoor environment is fully intertwined. Comfort in the use of buildings, and especially the provision of thermal comfort, is a fundamental aspect in the design of technical equipment systems, where a properly selected system regulating the indoor environment can affect b oth t he e nergy p erformance o f t he building and the quality of the indoor environment. One of the important factors is the air quality, where the main factor that af-fects the indoor environment is the concentration of CO2, whose value affects the biological functions of the human organism. The subject of this research is an evaluation of the indoor air quality in a kindergarten because children are more sensitive to environmental influences.