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Air Quality Monitoring and Improvement: Latest Advances and Prospects

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Dear Colleagues,

Among the many environmental challenges we are currently facing, air quality is particularly difficult to manage. Air quality monitoring involves the long-term assessment of pollutant levels, which helps to assess the extent of pollution and provide information on air quality trends. Emerging technologies have a lot to offer to that end, both helping to reduce pollution and contaminants and to clean up conventional operations.

The purpose of this Special Issue is to provide the latest advances and prospects on air quality monitoring as well as emerging technologies for air quality improvement. Potential topics of the journal include, but are not limited to, the following:

Technical developments of air quality monitoring systems (including advanced sensor technologies);
Methodological approaches to assess the impact of air pollution on human health and studies to assess long-term individual and population exposure;
Monitoring and health data fusion techniques and evaluation of air quality models using data from advanced air quality management systems;
Innovative technologies to improve both indoor and outdoor environments (photocatalysis, innovative materials, smart air conditioning, smart air purification systems, green agriculture, clean industry and vehicle technologies, etc.);
Novel methods to quantify and monitor air pollution (sensor network, analytics, and communication tools, etc.).

Dr. Thomas Maggos
Guest Editor

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Research

20 pages, 5100 KiB

Open AccessArticle

Improvement of Buildings’ Air Quality and Energy Consumption Using Air Purifying Paints

by Thomas Maggos, Vassiliοs Binas, Panagiotis Panagopoulos, Evangelia Skliri, Konstantinos Theodorou, Aristotelis Nikolakopoulos, George Kiriakidis, Effrosyni Giama, Georgios Chantzis and Agis Papadopoulos

Appl. Sci. 2024, 14(14), 5997; https://doi.org/10.3390/app14145997 - 9 Jul 2024

Viewed by 610

Abstract

Among the existing techniques to mitigate the problem of contamination in the indoor environment, photocatalytic technology is considered to be the most promising solution in terms of effectiveness and cost. To that end, in the frame of the LIFEVISIONS project, a novel photocatalytic powder (photo-powder) was mixed in paints’ matrix, producing a photocatalytic building material (photo-paint) able to improve indoor air quality (IAQ), upon its application, without downgrading paint physical properties. As a result, of IAQ improvement, less energy will be needed from ventilation systems, addressing not only health issues related to air quality but also energy reduction targets. Many powder formulae were synthesized using different synthetic pathways, concentration of dopants, and TiO₂ particles’ size. They were tested in a photocatalytic reactor (lab-scale tests), according to EN 16980-1:2021, under visible light and the results showed that the most promising photocatalytic performance degrades 85.4% and 32.4% of nitrogen oxide (NO) and toluene, respectively. This one was used for the production of two different kinds of paints, organic (with organic binder) and inorganic (with potassium silicate binder), in an industrial scale. Both were tested in the Demo Houses’ prototype demonstrator (real-scale tests) with an ultimate scope to estimate their effectiveness to degrade air pollutants under real-world conditions. In addition, the reduced energy consumption as a result of less ventilation needs was calculated in Demo Houses. More specifically, the energy reduction based on simulation results on Demo Houses was more than 7%. Although lab-scale tests showed better photocatalytic performance than the real scale, the efficiency of the paints under a more complicated environment was very promising. Full article

(This article belongs to the Special Issue Air Quality Monitoring and Improvement: Latest Advances and Prospects)

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20 pages, 6706 KiB

Open AccessArticle

IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data

by Monika Maciejewska, Andi Azizah and Andrzej Szczurek

Appl. Sci. 2024, 14(10), 4249; https://doi.org/10.3390/app14104249 - 17 May 2024

Viewed by 625

Abstract

This study explores the capability of machine learning techniques (MLTs) in predicting IAQ in apartments. Sensor data from kitchen air monitoring were used to determine the conditions in the living room. The analysis was based on several air parameters—temperature, relative humidity, CO₂ concentration, and TVOC—recorded in five apartments. Multiple input–multiple output prediction models were built. Linear (multiple linear regression and multilayer perceptron (MLP)) and nonlinear (decision trees, random forest, k-nearest neighbors, and MLP) methods were investigated. Five-fold cross-validation was applied, where four apartments provided data for model training and the remaining one was the source of the test data. The models were compared using performance metrics (R², MAPE, and RMSE). The naive approach was used as the benchmark. This study showed that linear MLTs performed best. In this case, the coefficients of determination were highest: R² = 0.94 (T), R² = 0.94 (RH), R² = 0.63 (CO₂), R² = 0.84 (TVOC, based on the SGP30 sensor), and R² = 0.92 (TVOC, based on the SGP30 sensor). The prediction of distinct indoor air parameters was not equally effective. Based on the lowest percentage error, best predictions were attained for indoor air temperature (MAPE = 1.57%), relative humidity (MAPE = 2.97%RH), and TVOC content (MAPE = 0.41%). Unfortunately, CO₂ prediction was loaded with high error (MAPE = 20.83%). The approach was particularly effective in open-kitchen apartments, and they could be the target for its application. This research offers a method that could contribute to attaining effective IAQ control in apartments. Full article

(This article belongs to the Special Issue Air Quality Monitoring and Improvement: Latest Advances and Prospects)

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