Air Quality Levels Research Articles

Data-driven model for predicting indoor air quality and thermal comfort levels in naturally ventilated educational buildings using easily accessible data for schools

Modeling indoor air quality and thermal conditions in educational buildings is significant for protecting students' health, well-being, and productivity. The predictive models in existing studies were mainly built and applied in controlled environments with HVAC systems. These models did not involve occupant-related factors, had limited scope in a single building or space, and required indoor environmental monitoring data for the model input. This limits the applicability and generalization ability of the model in a large number of schools relying on natural ventilation, where indoor air quality and thermal conditions are significantly affected by occupants’ activities and ventilation practices. Hence, this paper proposes a methodology to develop a data-driven model for predicting the level of indoor air quality and thermal comfort in naturally ventilated educational buildings, and identifies the key influential factors. The model was developed using the class-weighted random forest algorithm with data collected from a large measurement campaign. The developed model demonstrated good accuracy, generalization ability, and robustness. The analysis concluded that occupancy, windows and doors operation, and outdoor environmental parameters are key factors must be involved, whereas building characteristics have no practical contribution to the prediction. The model inputs are easily accessible data for schools. Once the model is developed with data collected from an initial measurement campaign in representative educational buildings, it can be used in all local schools without requiring monitoring sensor networks, thereby rendering a “cost-effective” way of assessing indoor air quality and thermal comfort to help relevant stakeholders improve building management practices in schools.

Study on peak hours, ventilation, and resident activities towards indoor air quality on PM2.5 in Surabaya

Several parameters contribute to the standard level of indoor air quality (IAQ), such as PM2.5 concentration, which is the primary parameter in this study. Several variables influence IAQ, such as infiltration and resident activities of outside air pollution through ventilation. Peak-hour traffic congestion can significantly raise outdoor air pollution. This study was aimed at finding out more about the influence of peak hours, the relationship between ventilation, resident activities, and indoor air quality (IAQ) in houses near the main road, as well as suggestions for improving IAQ. AirVisual Pro was the tool used to record PM2.5 concentration, temperature, and humidity; Kestrel 5500 was used to assess wind direction; and questionnaires were completed by residents. SPSS software is utilized for data analysis including the Pearson correlation coefficient, multicollinearity, autocorrelation, and multiple linear regression (MLR). The average indoor and outdoor PM2.5 concentrations in all units during peak and non-peak hours meet the quality standards on weekday and weekend. According to the correlation analysis, indoor and outdoor PM2.5 concentrations had no significant correlation regardless of whether the window is open or closed. Temperature (-1.473), humidity (-0.033), the number of furniture (3.660), fan usage (-8.005), cooking activities (3.755), cleaning activities (14.940), smoking behavior (4.545), and peak hours (6.896) associated with PM2.5 concentration, according to the MLR analysis. Increase fan use, no smoking inside the house, and less furniture are advised to improve IAQ.

CO2 and thermal comfort analysis of schools in Lugano – a wide-scale monitoring

Energy consumption and indoor air quality are closely linked, especially in crowded buildings such as schools. This project aims to provide a complete overview of CO2 and temperature levels in the classrooms of primary and pre-schools in the municipality of Lugano. More than 100 classrooms are live-monitored thanks to the sensors installed. The monitoring is still ongoing, and some initial analyses show air quality levels most of the time within admissible ranges, with episodes of overheating. The next phase of the project will be to better assess thermal comfort and to use CO2 and temperature to indirectly assess occupancy, which will be linked to energy consumption.

The expected environmental impact of the King Abdulaziz Public Transport Project in Riyadh on the level of air quality

The expected environmental impact of the King Abdulaziz Public Transport Project in Riyadh on the level of air quality

Effects of environmental health literacy-based interventions on indoor air quality and urinary concentrations of polycyclic aromatic hydrocarbons, volatile organic compounds, and cotinine: A randomized controlled trial

Indoor air pollution (IAP) caused by particulate matter (PM), aromatic and volatile chemicals is one of the leading causes of adverse reproductive health effects, such as infertility, decreased gestational age, and low birth weight. It is well documented that environmental health literacy (EHL) can promote reductions in IAP and the level of environmental pollutants in the body. We conducted randomized control trial to evaluate the effect of EHL-based intervention on the indoor air quality and urinary levels of polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs), and cotinine in reproductive aged women in Korea. The concentration of indoor PM2.5, PM10, CO2, and total VOCs (measured with a personal air quality monitoring device) as well as the urinary concentrations of PAHs, VOCs, and cotinine were measured in an experimental (n = 32) and control group (n = 31). The geometric mean concentrations of 2-NAP, tt-MA, and cotinine were 4.74, 82.82, and 0.60 μg/L respectively. The EHL intervention resulted in significant differences between experimental and control groups in the level of tt-MA (z = −1.44, p = .031), cotinine (z = −1.07, p = .015) in urine and PM2.5 (z = 56.39, p < .01), PM10 (z = 57.85, p < .01), CO2 (z = 8.92, p < .01), and total VOCs (z = 10.11, p < .01) of indoor air, and the mean score of EHL (z = 6.30, p < .01). Our results show the value in developing EHL interventions to reduce exposure to air pollutants originating from indoor activity and daily-life materials. Further studies are required to find ways to optimize general EHL for use in populations around the world.

School exposure to air pollution in the UK and Ireland: Indoor-Outdoor air quality assessment

Abstract Background Globally, 93% of children under the age of 15 (1.8 billion) are exposed to fine particulate matter (PM2.5) levels that might cause respiratory/cardiovascular disorders, impaired neuro/cognitive development, and cancer. Air pollution causes 1/10 deaths in children under the age of 5. Methods The purpose of this observational study was to collect data on indoor and outdoor air quality in 24 schools over 10 school days and compare it with city-wide average air quality levels reported in rural/urban areas in the UK and Northern Ireland. Nitrogen dioxide, volatile organic compounds (VOCs) and PM10,2.5,1 were monitored inside the classrooms and school surroundings with personal pollution monitors. City-wide averages were taken from local reference air quality and ground-source monitoring stations. Data was expressed using the Plume Labs Air Quality Index (PAQI) thresholds [low (0-20), moderate (21-50), high (51-100), very high (101+) pollution level] aligned with WHO's exposure recommendations. Results Despite both AQIs being in the moderate threshold, the school's average indoor AQI was ∼44% higher than the city-wide average. Only 27% of schools’ indoor air quality indexes matched the city-wide average, 59% were higher and 14% were lower. Rural schools had a marginally higher average indoor AQI than urban schools. The primary classroom pollutants observed were VOCs, followed by PM10. The average walk-exposure AQI of 33% of total schools was higher than the city's average. The school reporting the highest average walk AQI was seven times higher than the lowest. Conclusions Poor indoor air quality and higher levels of pollutants were found inside the majority of the schools across the regions. There is a need for mitigation strategies to identify responsible factors for indoor air pollution. A regulatory framework for school ventilation and behavior interventions like nasal washes may reduce pollution exposure and its impact on children's health outcomes and mortality. Key messages • Poor indoor air quality (∼44% higher than the city-wide average) and higher levels of pollutants (volatile organic compounds and PM10) were found inside the majority of the schools across the regions. • A regulatory framework for school ventilation and behavior interventions like nasal washes may reduce air pollution exposure and its impact on children’s health outcomes and mortality.

3.E. Workshop: Air pollution health impacts and costs in EU accessing countries

Abstract Air pollution is detrimental for health, climate, national economies and well-being in the European Union (EU) and in EU candidate countries. In many locations air quality levels are above the EU or national legal standards and/or the guidelines recommended by the World Health Organization. Very often EU neighbours and countries in the process of accessing the EU are disproportionately more affected by the impact of air pollution than others. The newest global evidence is suggesting that human health is affected even at low pollution levels below the current European standards and WHO guideline values. In 2021, the WHO published new air quality guidelines structured in Intermediate targets and final targets which are lower than the previous recommendations published in 2005. Estimating the costs of air pollution requires analysis of extensive data sets by means of econometric and statistical methodologies. In this session is discussed the assessment at country level for Europe and worldwide which is particularly valuable for estimating statistical value of life (VSL) and the value of a life year (VOLY) for counties that have not been the focus of global estimations so far. Moreover, this workshop adds valuable new information on the Western Balkans and Ukraine that could be used in systematic efforts to develop evidence-based air pollution reduction plans and to track progress toward reducing air pollution and consequently the health impacts. The studies included in this session represent valuable efforts to acquire evidence in support of further alignment of national air quality policies with those of the EU. Key messages • The newest evidence supports efforts to further aligning the Western Balkans and Ukrainian air quality policies with those of the EU and contribute to national and local implementation. • The meta-analysis of existing literature leads to the development of econometric models for estimating VSL and VOLY at country level worldwide.

Nonlinear GARCH-type models for ordinal time series

AbstractDespite their relevance in various areas of application, only few stochastic models for ordinal time series are discussed in the literature. To allow for a flexible serial dependence structure, different ordinal GARCH-type models are proposed, which can handle nonlinear dependence as well as kinds of an intensified memory. The (logistic) ordinal GARCH model accounts for the natural order among the categories by relying on the conditional cumulative distributions. As an alternative, a conditionally multinomial model is developed which uses the softmax response function. The resulting softmax GARCH model incorporates the ordinal information by considering the past (expected) categories. It is shown that this latter model is easily combined with an artificial neural network response function. This introduces great flexibility into the resulting neural softmax GARCH model, which turns out to be beneficial in three real-world time series applications (air quality levels, fear states, cloud coverage).

The performance of artificial neural networks for modeling daily concentrations of particulate matter from meteorological data.

The use of techniques based on artificial intelligence and machine learning for the simulation of many processes is becoming increasingly important in environmental sciences, with applications in the study of time series of atmospheric properties, such as pollution levels. The present work aimed to evaluate the efficiency of a model based on Artificial Neural Networks (ANN) in the simulation PM10 from meteorological data observed between 2018 and 2019 in Guaíba, southern Brazil, thus also having an estimate of the influence of atmospheric conditions on local air pollution. For this purpose, meteorological and PM10 data obtained from the stations Parque 35, sustained by Celulose Riograndense (CMPC), and A-801, sustained by the National Institute of Meteorology (INMET), were used. The ANN used for the simulation was of the Multilayer Perceptron type, trained by the backpropagation algorithm with cross-validation. The results obtained indicate that the simulation was satisfactory with a Nash-Sutcliffe index (NSE) of 0.64, a linear correlation coefficient (R) of 0.81, a relative error (Er) of 26% and a root mean square error (RMSE) of 7.40µg/m3. Thus, even with some difficulty in estimating extreme concentrations, the model was suitable for the largest range observed, of 10µg/m3 to 50µg/m3. For this dataset, the model proved to be an useful assessment tool and has the potential to be applied operationally to contribute to the monitoring and control of air quality levels both in the study area and in other regions of Brazil and the world.

Impact of air pollutants on climate change and prediction of air quality index using machine learning models

The impact of air pollution in Chennai metropolitan city, a southern Indian coastal city was examined to predict the Air Quality Index (AQI). Regular monitoring and prediction of the Air Quality Index (AQI) are critical for combating air pollution. The current study created machine learning models such as XGBoost, Random Forest, BaggingRegressor, and LGBMRegressor for the prediction of the AQI using the historical data available from 2017 to 2022. According to historical data, the AQI is highest in January, with a mean value of 104.6 g/gm, and the lowest in August, with a mean AQI value of 63.87 g/gm. Particulate matter, gaseous pollutants, and meteorological parameters were used to predict AQI, and the heat map generated showed that of all the parameters, PM2.5 has the greatest impact on AQI, with a value of 0.91. The log transformation method is used to normalize datasets and determine skewness and kurtosis. The XGBoost model demonstrated strong performance, achieving an R2 (correlation coefficient) of 0.9935, a mean absolute error (MAE) of 0.02, a mean square error (MSE) of 0.001, and a root mean square error (RMSE) of 0.04. In comparison, the LightGBM model's prediction was less effective, as it attained an R2 of 0.9748. According to the study, the AQI in Chennai has been increasing over the last two years, and if the same conditions persist, the city's air pollution will worsen in the future. Furthermore, accurate future air quality level predictions can be made using historical data and advanced machine learning algorithms.

An agent‐based model to evaluate agricultural vulnerability and risk facing climate change in strawberry production

AbstractHuman activity has created an environmental imbalance, which is reflected in climate change and resulting temperature variations, harmful levels of air quality, and more frequent and more intense meteorological phenomena, thus having significant consequences on the agricultural sector, for example, changes in soil, climate, and water characteristics as well as in available resources for agricultural production. This paper presents an agent‐based model (ABM) to evaluate the behavior of the strawberry's (Fragaria × ananassa) agricultural system in Veracruz, Mexico, to determine the degree of existing vulnerability through a dynamic assessment of agricultural risk. Development of the ABM allows the evaluation of variations in the active entities (critical variables of the model) to determine the factors that present a better crop yield. The research showed that temperature and precipitation are determining factors in strawberry quality, as they have a direct relationship with the growth and different phenological stages of the crop from an early age to a stage of greater maturity from the budding, flowering, and fruiting stages of the strawberry.

Effect of Heating, Ventilation, and Air Conditioning (HVAC) System on Indoor Air Quality in a Medical Facility

Indoor Air Quality (IAQ) refers to the stationary air within an inhabited or occupied structure. Previously, there were fewer studies on indoor air quality in medical facilities in Malaysia especially in Terengganu. Most indoor air quality issues are caused by insufficient Heating, Ventilation, and Air Conditioning (HVAC) systems, which regulate three parameters. The purpose of this study is to assess the indoor air quality of a medical facility and determine if it complies with the Industry Code of Practice 2010 (ICOP 2010) and ASHRAE 170-2017. In this investigation, a total of 3 locations namely Administration Office, Surgical Outpatient Department (SOPD) waiting area and Ophthalmology Consultation Room in Hospital Sultanah Nur Zahirah (HSNZ) were evaluated. Walkthrough inspections were done at the locations before data collection to determine the IAQ. Two IAQ meters, notably VelociCalc and Testo, were used to collect data to assess the temperature, relative humidity, and air flow of the selected locations. Samples were taken every 2 hours for each location from 8 a.m. to 5 p.m. The data then were analysed. All three locations' temperatures were lower than ICOP 2010's acceptable limit (23-26°C), but still within ASHRAE 170-207's 21-24°C range, except for the SOPD waiting room. All three locations met ICOP 2010 and ASHRAE 170-207 relative humidity standards. Meanwhile, only the SOPD waiting room had an appropriate air flow of 0.16-0.17m/s per ICOP 2010. The study also revealed that there was a correlation between the number of occupancies and the performance of HVAC system with the indoor air quality level.

Air Quality Monitoring and Analysis for Sustainable Development of Solid Waste Dump Yards Using Smart Drones and Geospatial Technology

Air pollution has become a global health issue, affecting millions of people annually. It is essential to obtain accurate and up-to-date information on air quality levels to reduce the impact of air pollution on human health. Traditional air quality monitoring methods are limited by spatial coverage and the time required for sample collection and analysis. However, the Internet of Things (IoT), drone technology, and Geographic Information System (GIS) technology have enabled the development of real-time air quality monitoring systems that provide accurate and up-to-date information on air quality levels across large areas. The study found that gas concentration varied significantly at different heights, with the highest concentration at 3 m from the ground and the lowest at 9 m. The concentration of gases also varied by month, with the highest concentration of CO recorded in March at 45 ppm at 3 m, and the highest concentration of NH4 recorded in September at 66.7 ppm at 3 m. Kodungaiyur dump yard needs environmental monitoring due to the high levels of environmental pollution and health risks caused by unsegregated waste. In conclusion, developing real-time air quality monitoring systems using GIS technology is necessary to obtain accurate and up-to-date information on air quality levels. It is essential to monitor the Kodungaiyur dump yard and other, similar sites regularly to prevent the health risks associated with environmental pollution.

Monitoring Sensors for Urban Air Quality: The Case of the Municipality of Lisbon.

Air pollution is a global issue that impacts environmental inequalities, and air quality sensors can have a decisive role in city policymaking for future cities. Science and society are already aware that during the most challenging times of COVID-19, the levels of air pollution in cities decreased, especially during lockdowns, when road traffic was reduced. Several pollution parameters can be used to analyse cities' environmental challenges, and it is more pressing than ever to have city climate decisions supported by sensor data. We have applied a data science approach to understand the evolution of the levels of carbon monoxide, nitrogen dioxide, particulate matter 2.5, and particulate matter 10 between August 2021 and July 2022. The analysis of the air quality levels, captured for the first time via 80 monitoring stations distributed throughout the municipality of Lisbon, has allowed us to realize that nitrogen dioxide and particulate matter 10 exceed the levels that are recommended by the World Health Organization, thereby increasing the health risk for those who live and work in Lisbon. Supported by these findings, we propose a central role for air quality sensors for policymaking in future cities, taking as a case study the municipality of Lisbon, Portugal, which is among the European cities that recently proposed be climate-neutral and smart city by 2030.

Strategi Mitigasi Kebakaran Lahan Terhadap Kualitas Udara dan Risiko Kesehatan Masyarakat (Studi Kasus: Kecamatan Pangkalan Kerinci, Kabupaten Pelalawan, Provinsi Riau)

Land fires are occurred almost every year during the dry season in Pangkalan Kerinci District. These condition resulted in damage and loss in terms of economic, social and environment that will inhibit the rate of development in Pangkalan Kerinci District so that necessary control measures against land and forest fires. The research method used was a case study, the samples for this study were selected through purposive sampling method were the data for this study were obtained through questioner, interview and observation. The results of this study that there are influence between the incidence of land fires with the level of air quality and the level of public health that occurs in Pangkalan Kerinci District, the formulation of a mitigation strategy for preventing land fires in Pangkalan Kerinci District in quadrant 1, which means that the land fire mitigation strategy must carry out a progressive strategy by utilizing internal; strengths to take advantage of external opportunities to achieve the optimal strategy.

Quality Evaluation of Dried Red Chili (Capsicum annuum L) with Convection and Radiation Drying Methods

Red chili is one of the most important horticultural products. Red chili is a perishable commodity that easily damages, caused by bacteria or fungal. Changes in the enzyme activity in chili that cause shrinkage or wrinkles during storage, transport, and fresh chili packing can reduce the market price. The objective of this study was to determine the changes that occur in the drying process. The material used in this study was red chili (Capsicum annuum). The equipment used Far Infrared Ray (FIR) dryer, electrical oven, and equipment for analysis. This study employed a randomized block design consisting of 5 drying treatments with 3 replications for each treatment. This study showed that the use of FIR dryer at 50°C for drying red chilies gave the best drying time (11 hours), compares with sun drying (38 hours), and oven 50°C (46 hours). The rehydration property of the water content produced is also not much different from an oven dryer at 60°C (39 hours) and sun drying. Drying with FIR caused loss of volatile more than the oven method, however drying time for FIR dryer at 50oC and 60oC were shorter. Air quality levels, ash levels, VRS, and the yield obtained was not significantly different from the oven dryer which took longer drying time. Far Infrared dryer (FIR) and sun drying are radiation drying method, and electric oven as a convection method can provide the best results.

Sistem Monitoring dan Deteksi Dini Pencemaran Udara Berbasis Internet Of Things (IOT)

The problem of environmental pollution has been a concern for a long time and continues to be an important issue today. One form of pollution that is very detrimental is air pollution, which causes a decrease in air quality and disrupts its normal function. . The ISPU Scale usually consists of several categories that describe the level of air quality, such as the range 1-50 is said to be "Good", the range 51-100 is said to be "Moderate", the range 101-200 is said to be "Unhealthy", the range 201-300 is said to be "Very Unhealthy", and a range of 301+ is said to be "Dangerous". To monitor the level of air pollution, it is necessary to use a tool that can measure air quality accurately. Indoor pollution, as is often the case in office environments, is one of the most serious threats to the health of modern society. A worker can spend up to eight hours a day in the room. The air we breathe everyday comes from the building's air duct system. However, these duct systems not only circulate air, they can also spread contaminants from one room to another. Therefore, it is important to maintain indoor air quality for the comfort and health of workers, because this has a direct impact on their productivity. Based on this, an idea emerged to develop a prototype detector of carbon monoxide gas that is sensitive to indoor air pollution using a microcontroller and web-based. This research begins with analyzing and designing the system, both in terms of hardware and software. After that, the next step involves coding and programming configuration and setup on the Thingspeak web. The last stage is testing tools and systems to ensure that they operate according to the plan that has been set. This system is designed to be able to detect and monitor the level of air pollution, as well as provide a graphical display on Thingspeak. This tool works by using a microcontroller to read the MQ-135 sensor attached to the Ardino Wemos ESP8266. This sensor is used to measure gas levels in a predetermined room. The tool will connect to the Wi-Fi hotspot on the Android device to provide notifications via Thigspeak based on a graph

Air pollution and willingness to pay for health risk reductions in Egypt: A contingent valuation survey of Greater Cairo and Alexandria households

Egypt ranks first worldwide for the number of deaths attributable to PM2.5 air pollution, yet the economic value of improved air quality and the value of reducing mortality risk due to industrial air pollution has never been evaluated using primary data for Egypt. In this paper, we fill this gap focusing on the Greater Cairo and Alexandria metropolitan areas, where more than 80 percent of the country’s industrial activities take place. We find that 73% of the 1051 surveyed households are supportive of improved air quality strategy and a reduction by 50 percent from current level of pollution is valued between 212 and 302 LE per month (13.5–19.3US$). The health risk reduction is measured with Value of Statistical Life (VSL) measures that range between 3.81 and 7.0 million LE (242,675–446,000US$). The results confirm that residents are sensitive to environmental air quality levels. Furthermore, when provided with information about the health consequences of pollution, survey respondents increased both their level of support for the policy, and their Willingness to Pay for it.

PERBANDINGAN KNN DAN SVM UNTUK KLASIFIKASI KUALITAS UDARA DI JAKARTA

The growth and economic development of a city is one of the factors causing air pollution because air quality has been mixed with various components of chemical compounds such as motor vehicle exhaust gases and factory smoke waste. Data mining is a method to find out information about air pollution in the city of Jakarta. The data mining method used is classification because this method can process air pollution standard index (AQI) parameter data into information that can show the level of air quality per day using the K-Nearest Neighbor algorithm and Support Vector Machine. &#x0D; The result of the application of data mining for air quality classification in Jakarta is that the Support Vector Machine algorithm has better accuracy performance compared to the K-Nearest Neighbor algorithm. The Support Vector Machine algorithm uses the RBF kernel and 100 kernel parameter gets an accuracy value of 98%, precission of 97%, recall of 97%, and F1-Score of 97% while the K-Nearest Neighbor algorithm uses the number of K as much as 6 gets an accuracy value of 96%, precission of 96%, recall of 93%, and F1-Score of 94%.

Image-based Air Quality Prediction using Convolutional Neural Networks and Machine Learning

Air quality has become a major public concern due to the significant threat posed by air pollution to human health, and rapid and efficient monitoring of air quality is crucial for pollution control and human health. In this paper, deep learning and image-based models are proposed to estimate air quality. To evaluate the level of air quality, the model collects feature information from landscape photos taken by mobile cameras. To analyze public perception of air quality, researchers collected questionnaire data from 257 people. The Smartpls method allows for structural analysis to determine the influence of each variable on other variables and the extent of their contribution to the final variable of overall perception of air quality. This study aims to develop a novel approach for air quality prediction using image-based data and machine learning techniques. The research used convolutional neural networks to extract features from images and predict the air quality index. The study was conducted using a dataset obtained from a network of air quality sensors across the city. The results of the study showed that the proposed approach can provide accurate air quality predictions compared to the traditional methods. The developed model was able to capture the complex relationships between air quality and environmental factors, such as temperature and humidity. The implications of the study suggest that image-based air quality prediction can be a powerful tool for improving public health and reducing the impact of air pollution. The study's findings hold promise for a healthier future by facilitating more effective pollution management and improved air quality regulation. The study's primary novelty lies in its approach to air quality prediction by deploying convolutional neural networks to extract image features for predicting air quality indices. This application of advanced machine learning techniques to image-based data for air quality estimation marks a significant advancement.