Good Air Quality Research Articles

Numerical simulations of the effects of green infrastructure on PM2.5 dispersion in an urban park in Bangkok, Thailand

Traffic emission has been identified as one of the dominant sources of fine particulate matter (PM2.5) in Thailand, and urban green spaces have the capacity to mitigate air pollution. Taking Bangkok as the study area, one of the most polluted cities in Thailand, this study investigated the effect of vegetation on PM2.5 concentration at three different sites with different vegetation characteristics in Chatuchak Park, an urban park located in Bangkok. Sensors were installed at the park to measure PM2.5 and metrological parameters at the roadside and different distances inside the park away from the road, and the ENVI-met model was run to simulate PM2.5 concentration in the three study sites. The result shows that PM2.5 concentration behind the vegetation barrier was reduced 34% on average compared to the concentration next to the road at the three sites. The effect of vegetation on meteorological factors was clearly seen near the park border with a hedgerow grown along the border. The order of influence of meteorological factors on PM2.5 concentration was relative humidity > potential temperature > wind speed > wind direction. Two scenarios including changes in weather patterns and types of vegetation that affect PM2.5 concentration were studied. Changing in the wind direction from oblique to perpendicular to the park had no significant effect on PM2.5 concentration as long as there is a dense hedgerow along the park border. Comparing to the current vegetation, sparse vegetation with less leaf area density and higher crown base heights had lower impact to mitigate PM2.5 concentration in the park. Our study provides information on vegetation and landscape strategies which can provide good air quality in the urban parks for better park design in the future.

Short-sea shipping contributions to particle concentration in coastal areas: Impact and mitigation

• Short-sea shipping visibly contributes to particles concentrations in coastal areas. • The bottom-up model predicts consistent particle number concentration, PN, values. • Differently, contribution of ships on PM2.5 cannot be inferred by bottom-up model. • Existing regional fuel quality restrictions mostly reduce PM2.5 emissions. • Cleaner fuels and available after-treatment systems highly reduce PN/PM2.5 levels. Several studies indicate that short-sea shipping is an important source of air pollution for coastal areas and port cities. This paper reports results of a non-reactive particles dispersion model and a new set of experiments implemented for the Channel of Procida (Italy), an area with a high signal-to-noise ratio, due to intense marine traffic and low background pollution. The model successfully predicts particle number concentrations but underestimates PM 2.5 data. Model and experiments show that thanks to the EU policies on marine fuels, the Channel of Procida already has good air quality levels. Besides, the paper demonstrates that fostering the use of LNG or methanol or the application of an exhaust-gas-cleaning-system may allow reducing particles emissions well above 90%. The reliability of control strategies and the benefits for the population suggest that the introduction of regulations on particles emissions for ships can be a realistic option for the future environmental policy agenda.

Device for Measurement Concentration of Toxic Gas in an Enclosure

AbstractThis article presents the design and practical realization of a device for measuring the concentration of gases in an enclosure (rooms, production halls, and laboratory). In addition to measuring the concentrations of gases in the room (carbon dioxide, carbon monoxide, ozone, etc.), the main objective is to simulate the integration of the prototype itself in a ventilation system, resulting in a quality index of the air as suitable as possible depending on the occupancy level of the room. A ventilation control system according to the needs of the occupants of the enclosure is a system, which monitors a sequence of parameters such as: ambient temperature, toxic gas concentrations, and air humidity, and thus, according to the measured values, will introduce a quantity corresponding air quality ensuring an optimal air quality factor. This system offers a good compromise between minimizing energy consumption and ensuring good air quality, especially given that the occupancy rate of the monitored rooms is variable and unpredictable. The built‐in system is designed to constantly monitor air quality by adjusting the amount of clean air introduced, so that it will keep the level of CO, CO2, and O3 below a maximum allowable limit, thus adjusting the ventilation as needed.

A post-occupancy study of ventilation effectiveness from high-resolution CO2 monitoring at live theatre events to mitigate airborne transmission of SARS-CoV-2

Mass-gathering events were closed around the world in 2020 to minimise the spread of the SARS-CoV-2 virus. Emerging research on the transmission of SARS-CoV-2 emphasised the importance of sufficient ventilation. This paper presents the results of an indoor air quality (IAQ) monitoring study over 82 events in seven mechanically ventilated auditoria to support the UK government Events Research Programme. Indoor carbon dioxide concentration was measured at high resolution before, during, and after occupancy to allow for assessment of the ventilation systems. Generally, good indoor air quality was measured in all auditoria, with average IAQ found to be excellent or very good for 70% of spaces. In some auditoria, spatial variation in IAQ was identified, indicating poor mixing of the air. In addition, surface and air samples were taken and analysed for the presence of bacteria by culture and SARS-CoV-2 using RT-qPCR in one venue. SARS-CoV-2 RNA was detected on a small number of surfaces at very low copy numbers, which are unlikely to pose an infection risk. Under the ventilation strategies and occupancy levels investigated, it is likely that most theatres pose a low risk of long-range transmission of COVID-19.

Good air quality and stock market returns

This paper examines whether an extreme good air quality index (GAQI) is the superior predictor of stock market returns in China based on ordinary least squares method. This GAQI index is constructed based on data series from China Stock Market & Accounting Research Database. The results demonstrate that good air quality can increase stock market returns’ forecasting accuracy more than most popular variables, thereby confirming the prediction validity of the GAQI. The GAQI further exhibits superior portfolio performance when considering different risk appetites and transaction costs, thereby revealing that risk-seeking investors use GAQI information to obtain better portfolio performance over risk-averse investors. The findings offer new insights for stock market returns’ prediction based on air quality on the condition that air quality is undeniably a sharp focus in society.

Relation between Velocity and Pressure in Circular and Non-Circular Shape Duct for Local Exhaust Ventilation (LEV) System

Local exhaust ventilation (LEV) is used to isolate contaminants from the source. It is vital to ensure good air quality, particularly for employees exposed to hazardous gases such as high-temperature factories and kitchens where food is cooked. This simulation explores how a particular cross-sectional duct structure influences the ventilation device’s velocity distribution and pressure decrease. The system’s design consists of a collecting hood, 90° bends, 45° bends, and a straight pipe. Three models use the same volume around the unit. There are circular (Model A), square (Model B), and rectangular (Model C). This study is carried out using Computational Fluid Dynamic (CFD). The simulation shows the behaviour of the airflow from the inlet to the exit in all three models. Model A is the most preferable of the results produced because the velocity distribution in this circular line is evener and more balanced. The average velocity of the model A device is a mean of 2.80m/s, and the lowest mild pressure is -76.74 Pa. Changesin the path of the system’s flow creates eddy and disturbance to the system’s flow. Higher pressure to sustain the optimal flow speed will increase energy consumption and help more robust preliminary designs for local exhaust ventilation.

The Development of an IoT-based Indoor Air Monitoring System Towards Smart Energy Efficient Classroom

Indoor air quality has become a crucial issue, specifically during COVID 19 pandemic. The good indoor air quality will lead to occupants’ comfort condition, thus affecting their productivity. Indoor air temperature and relative humidity are two essential components of thermal comfort. This paper presents the development of a temperature and relative humidity monitoring system for the classroom using the Internet of Things (IoT). This system consists of three main components: logger nodes, a gateway logger, and an interconnected cloud server. The logger node (ESP8266 / ESP32 microcontroller and DHT22 sensor) is a device at the edge of the IoT system and is placed at the monitoring location. The logger gateway is built on a Raspberry Pi 4, which serves as an intermediate server. It receives periodic data (temperature and humidity) from the logger nodes through the publish-subscribe MQTT protocol and sends it to the MongoDB Atlas cloud database. The logger gateway saves all received logs into the SQLite database as temporary local storage and then uploads the data to the MongoDB Atlas cloud at a predefined interval. The MongoDB data is then displayed on a monitoring dashboard using MongoDB charts. The logger node with the DHT22 sensor has been adjusted using a linear model and successfully tested to monitor indoor and outdoor air conditions with satisfactory results. The recorded data has also been successfully modeled using the Gaussian Mixture Model and a simple Fuzzy model. These models can capture the dynamic of air condition in the room and predict the state of the cooling system.

K-NN Based Air Classification as Indicator of the Index of Air Quality in Palembang

Good air quality is something that is wanted by every human who lives in big cities. Clean air and no pollution is one of the proper environmental requirements. One of the most severe causes of air pollution is due to large-scale forest fires due to the long dry season or is carried out by irresponsible persons which they commonly refer to as land clearing in an easy and inexpensive way by utilizing the reason of the dry season. The purpose of this study is to classify air quality in Palembang using a data mining approach. Then use the results of the classification as an indicator of the level of air quality in the city of Palembang. The data mining approach that researchers use is the K-Nearest Neighbor algorithm. Based on the test results of K-NN calculations and measured using a confusion matrix produce an accuracy of 80 percent, 82.3 percent for precision, and 93.3 percent for recall. The measurement results show that the calculation using the K-NN algorithm can be used as an indicator in measuring air quality, of the 20 that have been trained and tested only 4 inaccurate data, this inaccuracy occurs because the source data has unbalanced classes such as unhealthy and very unhealthy healthy have 1 sample each. So it proves that the performance of classifiers using the K-NN algorithm relevant as an indicator of air quality levels in the city of Palembang.

Indoor air quality and health in schools: A critical review for developing the roadmap for the future school environment

Several research studies have ranked indoor pollution among the top environmental risks to public health in recent years. Good indoor air quality is an essential component of a healthy indoor environment and significantly affects human health and well-being. Poor air quality in such environments may cause respiratory disease for millions of pupils around the globe and, in the current pandemic-dominated era, require ever more urgent actions to tackle the burden of its impacts.The poor indoor quality in such environments could result from poor management, operation, maintenance, and cleaning. Pupils are a different segment of the population from adults in many ways, and they are more exposed to the poor indoor environment: They breathe in more air per unit weight and are more sensitive to heat/cold and moisture. Thus, their vulnerability is higher than adults, and poor conditions may affect proper development.However, a healthy learning environment can reduce the absence rate, improves test scores, and enhances pupil/teacher learning/teaching productivity. In this article, we analyzed recent literature on indoor air quality and health in schools, with the primary focus on ventilation, thermal comfort, productivity, and exposure risk. This study conducts a comprehensive review to summarizes the existing knowledge to highlight the latest research and solutions and proposes a roadmap for the future school environment. In conclusion, we summarize the critical limitations of the existing studies, reveal insights for future research directions, and propose a roadmap for further improvements in school air quality. More parameters and specific data should be obtained from in-site measurements to get a more in-depth understanding at contaminant characteristics. Meanwhile, site-specific strategies for different school locations, such as proximity to transportation routes and industrial areas, should be developed to suit the characteristics of schools in different regions. The socio-economic consequences of health and performance effects on children in classrooms should be considered. There is a great need for more comprehensive studies with larger sample sizes to study on environmental health exposure, student performance, and indoor satisfaction. More complex mitigation measures should be evaluated by considering energy efficiency, IAQ and health effects.

GREEN WALLS CAN IMPROVE INDOOR AIR QUALITY IN THE CLASSROOM

Buildings are places where people live and perform daily activities. Good indoor air quality is essential for them to live healthily inside. In order to provide satisfactory indoor air quality, a green wall should be installed to reduce indoor carbon dioxide (CO2). Several research studies have been conducted examining the effects of indoor CO2 levels. However, this research emphasizes studying the impact of light sensitivity on the ability of plants to reduce CO2. Four types of ornamental plants constituted our sample, Nephrolepis exaltata, Chlorophytum comosum, Epipremnum aureum (Marble Queen), and Epipremnum aureum (Spotted Betel). The plants were placed in an experimental box to allow us to investigate their ability to reduce CO2 over a 24-hour period. The most effective plant was then tested on a green wall in a classroom enabling the study of CO2 reduction in three different locations with varying light levels. It was found that green walls with Marble Queen placed perpendicular to windows allowing optimum light exposure were able to reduce CO2 at the most favorable rate of 1.74 ppm/min.

Indoor air CO2 concentrations and ventilation rates in two residences in İzmir, Turkey

Houses are the places where people spend most of their time. That is why indoor air quality at home is essential for public health. Sufficient ventilation is the factor to avoid accumulation of pollutants in indoor air, which include microorganisms, such as SARS-CoV-2. Therefore, adequate ventilation is needed to provide good indoor air quality for human health and reduce infection risk at home. There are no reports of residential ventilation rates in Turkey. In this study, CO2 concentrations were measured in two residences in Izmir, Turkey. Three experiments were conducted to determine background concentrations and the rate of natural ventilation with infiltration and opening windows. Results show that air exchange provided by infiltration is low for both case rooms, while adequate ventilation could be achieved with natural ventilation under the studied conditions. Infiltration provided air exchange and ventilation rates of 0.18 h-1 and 5.9 m3/h for Case 1 and 0.29 h-1 and 8.23 m3/h for Case 2, respectively. Air exchange and ventilation rates were increased to 2.36 h-1 and 76.9 m3/h for Case 1 and 1.2 h-1 and 34 m3/h for Case 2, respectively, by opening the windows. Although ventilation can be provided by opening the windows, the other factors that determine its rate, e.g., meteorological variables, cannot be controlled by the occupants. Consequently, people cannot ensure the good indoor air quality in bedrooms and sufficient reduction in transmission of pathogenic microorganisms; therefore, risk of spreading diseases such as COVID-19 at home.

Optimal Residence: Does Air Quality Affect Settlement Decisions of Urban Floating Migrants?

There is a strong association between urban prosperity and human resources. Rapid urbanization and industrialization have caused severe air pollution to the urban cities over the past decades, and permanently moving to locations with clean air has been considered to be one of the most effective strategies to avoid air pollution. However, whether this is in fact still unclear. In order to fill the research gaps, this study combines individual-level data derived from a national survey and city-level data obtained from various sources to empirically exam the effect that air quality has on settlement decisions of urban floating migrants in China. We focus on the target floating migrants who have urban hukou in the origin city and housing in the destination city, and investigate the relationship between air quality and settlement decisions from the move-in and move-out city perspective. In addition, we exam not only the willingness of urban floating migrants to settling in the move-in city, but also their actual migration decision in the reality. The bivariate probit analysis and panel data regression are applied. Results show that poor air quality in the move-out cities has a significant positive impact on urban floating migrants’ willingness to settling in the move-in cities, while bad air quality in the move-in cities has a significant negative impact, indicating that poor air quality does act as a push force to drive floating migrants away but good air quality can be a pulling force for floating migrants to settling down. In addition, the robust tests show good agreement with the main regression results, with almost all relationships reported having the same direction as those indicated by the regression analysis. Our findings can help policy makers and urban planners assess and design interventions to improve the urban air quality aimed at attracting talent, with broader implications for urban sustainable development.

Comprehensive quantitative evaluation of the water resource carrying capacity in Wuhan City based on the “human–water–city” framework: Past, present and future

Given China's rapid urbanization process, the study of water resource carrying capacity (WRCC) has important theoretical and practical significance for urban sustainable development. In this paper, a “human–water–city” (HWC) framework was proposed that offers a comprehensive consideration of the development of human society, water quality and quantity, and the progress of urban construction. Based on this framework, we established an HWC evaluation indicator system and constructed the WRCC-HWC approach, to assess, compare, and forecast the WRCC. Taking Wuhan, China, as an example, principal component analysis (PCA) was adopted to evaluate the current (2009–2019) and future (2020–2030) WRCC-HWC. Specifically, global climate models (GCMs) and a grey model, GM (1,1), were used to forecast the value of the future index system. The results show that the WRCC-HWC in Wuhan gradually increased from −3.04 to 3.23 during 2009–2019, being mainly affected by the income of urban residents, per capita GDP, reuse ratio of water used by industry, built-up area, and the output value of new and high-tech industry. With a rapid growth rate from 2011 to 2016, the WRCC-HWC reached the Grade I (excellent) state in 2016. In addition, the forecast results show that, with the exceptions of precipitation and good air quality rate, all HWC indicators show upward trends from 2020 to 2030 in Wuhan. In particular, the per capita disposable income of urban residents (N1), per capita GDP (N2), and the output value of new and high-tech industry (N11) have faster growth rates, with relative growth rates from 2020 to 2030 exceeding 100%. Correspondingly, the WRCC-HWC from 2020 to 2030 were predicted to grow steadily each year, with a higher absolute growth rate starting in 2025. This study supports a comprehensive understanding of the current and future situation for WRCC-HWC in Wuhan and can provide a reference for the formulation of scientific and sustainable development strategies.

Improving the current air quality index with new particulate indicators using a robust statistical approach

To convey the severity of ambient air pollution level to the public, air quality index (AQI) is used as a communication tool to reflect the concentrations of individual pollutants on a common scale. However, due to the enhanced air pollution control in recent years, air quality has improved, and the roles of some air pollutant species included in the existing AQI as urban air pollutants have diminished. In this study, we suggest the current AQI should be revised in a way that new air pollution indicators would be considered so that it would better represent the health effects caused by local combustion processes from traffic and residential burning. Based on the air quality data of 2017–2019 in three different sites in Helsinki metropolitan area, we assumed the statistical distributions of the current indicators (NO2 and PM2.5) and the proposed particulate indicators (BC, LDSA and PNC) were related as they have similar sources in urban regions despite the varying correlations between the current and proposed indicators (NO2: r = 0.5–0.85, PM2.5: r = 0.28–0.72). By fitting the data to an optimal distribution function, together with expert opinions, we improved the current Finnish AQI and determined the AQI breakpoints for the proposed indicators where this robust statistical approach is transferrable to other cities. The addition of the three proposed indicators to the current AQI would decrease the number of good air quality hours in all three environments (largest decrease in urban traffic site, ~22 %). The deterioration of air quality class appeared more severe during peak hours in the urban traffic site due to vehicular emission and evenings in the detached housing site where domestic wood combustion often takes place. The introduction of the AQI breakpoints of the three new indicators serve as a first step of improving the current AQI before further air quality guideline levels are updated.

Analisis Perbandingan Metode K-Nn Dan Decision Tree Dalam Klasifikasi Kenyamanan Thermal Bangunan

Thermal comfort and good air quality can have a positive influence on a person's health and activities. Human knowledge will determine the right quality for a building or room that can only be used at a minimum first if it is based on the value of temperature and humidity standard measuring instruments that display raw data so that a system is needed that makes it easy for humans to determine thermal building comfort. Most systems that discuss thermal comfort revolve around temperature and humidity monitoring still do not use a machine learning model to maximize data analysis. In this study, a comparison of machine learning models is designed that is able to provide a classification to predict the category or label given to a data set consisting of the thermal comfort variable. The output produced in this comparison is the result of labeling predictions from 3 temperature comfort levels by testing 30% praise data and 70% training data. From the results of the segmentation accuracy level using K-Nearest Neighbor, the accuracy reaches 100% with the highest accuracy at a value of K = 1, while the Decision tree comparison method also gets an accuracy value that reaches 100%, this shows that thermal comfort can be applied in the classification machine learning method

Spatiotemporal Variation in Air Pollution Characteristics and Influencing Factors in Ulaanbaatar from 2016 to 2019

Ambient air pollution is a global environmental issue that affects human health. Ulaanbaatar (UB), the capital of Mongolia, is one of the most polluted cities in the world, and it is of great importance to study the temporal and spatial changes in air pollution in this city, along with their influencing factors. To understand the characteristics of atmospheric pollutants in UB, the contents of PM10, PM2.5, SO2, NO2, CO, and O3, as well as their influencing factors, were analyzed from data obtained from automatic air quality monitoring stations. These analyses yielded six major findings: (1) From 2016 to 2019, there was a total of 883 pollution days, and PM2.5 and PM10 were the primary pollutants on 553 and 351 of these days, respectively. The air pollution was dominated by PM10 in spring and summer, affected by both PM2.5 and PM10 in autumn, and dominated by PM2.5 in winter. (2) Compared with 2016, the number of days with good air quality in UB in 2019 increased by 45%, and the number of days with unhealthy or worse levels of pollution decreased by 56%, indicating that the air quality improved year by year. (3) From 2016 to 2019, the annual average PM2.5/PM10 ratio dropped from 0.55 to 0.45, and the proportion of PM2.5 in particulate matter decreased year by year. The PM concentration and PM2.5/PM10 ratio were highest in winter and lowest in summer. When comparing the four-season averages, the average PM2.5 concentration decreased by 89% from its highest level, and the PM10 concentration decreased by 67%, indicating stronger seasonal differences in PM2.5 than in PM10. (4) The hourly changes in PM concentration showed a bimodal pattern, exhibiting a decrease during the day and a slight increase in the afternoon due to temperature inversion, so the PM2.5/PM10 ratio increased at night in all four seasons. The PM concentration during the heating season was significantly higher than that in the non-heating season, indicating that coal-fired heating was the main cause of air pollution in UB. (5) Sand dust and soot were the two main types of pollution in UB. (6) Correlation analysis and linear fitting analysis showed that PM2.5 and PM10 caused by coal-firing had an important impact on air quality in UB. Coal combustion and vehicle emissions with SO2, NO2, and CO as factors made large contributions to PM2.5.

Actual energy performance and indoor climate in Finnish NZEB daycare and school buildings

The study addressed the improvement of building energy performance along with a good thermal comfort and indoor air quality (IAQ) in newly built Finnish daycare and school buildings. Indoor Environmental Quality (IEQ), calculated and actual energy performance, investment and life cycle cost (LCC) were monitored. Ventilation airflow rates, temperature, and CO2 were measured onsite in five daycare and school buildings during summer and winter, and an occupant questionnaire survey was conducted in parallel with onsite measurements. Energy performance was evaluated based on an energy performance certificate (EPC) and actual metered consumption to figure out possible performance gaps. Simulations were conducted to calibrate models and analyse improvements in ventilation system operation, which showed energy-saving potential while ensuring good thermal comfort and IAQ. The thermal comfort and IEQ of all buildings were excellent and were in line with the results of the questionaries survey except in ‘School 3’, which had specific odour and noise problems. The measured energy use of all buildings except ‘School 3’ was increased by a factor of 2.1–2.6 compared to the EPC value, caused by poor control of a ventilation system and the presence of some non-regulated uses such as hot kitchens, washing machines, and dryers. LCC considerably depended on actual energy use stressing the importance of accurate energy prediction. It was shown that the reduction of energy use by a factor of 1.9–3.1 is possible without compromising indoor climate by adjustment of correct operation hours, improving ventilation system control, and limiting excessive outdoor airflows in some cases.

Tracer-based characterization of fine carbonaceous aerosol in Beijing during a strict emission control period

Strict emission controls were implemented in Beijing and the surrounding regions in the North China Plain to guarantee good air quality during the 2014 Asia-Pacific Economic Cooperation (APEC) summit. Thus, the APEC period provides a good opportunity to study the sources and formation processes of atmospheric organic aerosol. Here, fine particles (PM2.5, particulate matter with a diameter of 2.5 μm or less) collected in urban Beijing before and during the APEC period were analyzed for molecular tracers of primary and secondary organic aerosol (SOA). The primary organic carbon (POC) and secondary organic carbon (SOC) were also reconstructed using a tracer-based method. The concentrations of biogenic SOA tracers ranged from 1.09 to 34.5 ng m−3 (mean 10.3 ± 8.51 ng m−3). Monoterpene oxidation products were the largest contributor to biogenic SOA, followed by isoprene- and sesquiterpene-derived SOA. The concentrations of biogenic SOA tracers decreased by 50 % during the APEC, which was largely attributed to the implementation of emission controls by the Chinese government. The increasing mass fractions of biogenic SOA tracers from isoprene and sesquiterpene during the pollution episodes implied that their photooxidation processes contributed to the poor air quality in urban Beijing. The reconstructed biogenic and anthropogenic SOC and POC concentrations were 89.6 ± 96.8 ng m−3, 570 ± 611 ng m−3, and 2.49 ± 2.08 μg m−3, respectively, accounting for 21.9 ± 11.4 % of OC in total. Biomass-burning derived OC was the largest contributor to carbonaceous aerosol over the North China Plain. By comparing the results before and during the APEC, the emission controls effectively mitigated about 34 % of the estimated OC and were more effective at reducing SOC than POC. This suggests that the reduction of the primary organic aerosol loading is harder than SOA over the North China Plain.

Indoor air quality level influence sick building syndrome among occupants in educational buildings

In educational facilities, good and healthy indoor air quality is critical to ensure students’ focus and academic activities can be carried out efficiently. The goal of this study was to evaluate the link between indoor air quality level with sick building syndrome and health complaints among occupants. A cross-sectional study was conducted which involved 513 occupants. A modified MM040Na questionnaire was used in this study. Occupants’ workspaces were monitored for indoor air quality. The score obtained from the questionnaire and environmental factors were tested using statistical analyses incorporating test of differences namely Chi–Square, t-test, and non-parametric analysis. Logistic regression was conducted to evaluate relationship between exposure and occupants’ complaint. Results shows that some indoor air pollutants (carbon dioxide, formaldehyde, and particulate matter) are related to overall sick building syndrome (Odd Ratio, OR=1.348, 2.493, 1.958 respectively). General score of sick building syndrome (SBS) shows significant relationship with air motion (OR=2.220) and air flow (OR=2.515). Mucosal score of SBS suggest exposure risk towards indoor factors namely formaldehyde (OR=2.799), while dermal effect was associated from the exposure of Particulate (OR=2.88), carbon dioxide (OR=4.000), and air flow (OR=2.679). In educational environments, indoor contaminants have an impact on reported symptoms.<br /><br />

GREEN WALLS CAN IMPROVE INDOOR AIR QUALITY IN THE CLASSROOM

Buildings are places where people live and perform daily activities. Good indoor air quality is essential for them to live healthily inside. In order to provide satisfactory indoor air quality, a green wall should be installed to reduce indoor carbon dioxide (CO2). Several research studies have been conducted examining the effects of indoor CO2 levels. However, this research emphasizes studying the impact of light sensitivity on the ability of plants to reduce CO2. Four types of ornamental plants constituted our sample, Nephrolepis exaltata, Chlorophytum comosum, Epipremnum aureum (Marble Queen), and Epipremnum aureum (Spotted Betel). The plants were placed in an experimental box to allow us to investigate their ability to reduce CO2 over a 24-hour period. The most effective plant was then tested on a green wall in a classroom enabling the study of CO2 reduction in three different locations with varying light levels. It was found that green walls with Marble Queen placed perpendicular to windows allowing optimum light exposure were able to reduce CO2 at the most favorable rate of 1.74 ppm/min.