Sensors and systems for air quality assessment monitoring and management: A review

APTES functionalization in SBA-15: the effect on SO2 capture and detection applications

The development of adsorbents for air pollutant remediation and effective monitoring is of interest. Then, the effect of the APTES functionalization ratio on the impact of the adsorption and detection of SO2 molecules was evaluated. The higher APTES functionalization material (SBA-15_6.1APTES) shows a high uptake of 1.15 mmol g-1 at 0.001 bar and 298 K. Fluorescence, time-resolved photoluminescence, and quantum yield experiments revealed a turn-on effect specifically for SO2 molecules, indicating high selectivity, suggesting host-to-guest energy transfer. Attractively, XPS measurement provided an understanding of the mechanism, suggesting hydrogen bonding and dipole-dipole interactions as the main interactions between SO2 molecules and SBA-15_6.1APTES. DFT calculations were performed to confirm these interactions. Furthermore, this study highlights the application of SBA-15 materials with different amino modifications for SO2 treatment and provides insight into the interaction mechanism using experimental techniques.

Sulfur dioxide may predominate in the adverse effects of ambient air pollutants on semen quality among the general population in Hefei, China

Previous studies have reported potential adverse effects of exposure to ambient air pollutants on semen quality in infertile men, but studies on the general population have been limited and inconsistent, and the pollutants that play a major role remain unclear. This study aimed to explore the potential association between exposure to six air pollutants (PM_2.5, PM₁₀, NO₂, SO₂, O₃ and CO) during different sperm development periods and semen quality among the general population, and to explore the interaction between different air pollutant exposures. We included 1515 semen samples collected from the Human Sperm Bank. We improved individuals' exposure level estimation by combining inverse distance weighting (IDW) interpolation with satellite remote sensing data. Multivariate linear regression models, restricted cubic spline functions and double-pollutant models were used to assess the relationship between exposure to six air pollutants and sperm volume, concentration, total sperm number and sperm motility. A negative association was found between SO₂ exposure and progressive motility and total motility during 0-90 lag days and 70-90 lag days, and SO₂ exposure during 10-14 lag days adversely affected sperm concentration and total sperm number. Sensitive analyses for qualified sperm donors and the double-pollutant models obtained similar results. Additionally, there were nonlinear relationships between exposure to PM, NO₂, O₃, CO and a few semen parameters, with NO₂ and O₃ exposure above the threshold showing negative correlations with total motility and progressive motility, respectively. Our study suggested that SO₂ may play a dominant role in the adverse effects of ambient air pollutants on semen quality in the general population by decreasing sperm motility, sperm concentration and total sperm number. Also, even SO₂ exposure lower than the recommended standards of the World Health Organization (WHO) could still cause male reproductive toxicity, which deserves attention.

Low-Cost Sensor Node for Air Quality Monitoring: Field Tests and Validation of Particulate Matter Measurements

Air pollution is still a major public health issue, which makes monitoring air quality a necessity. Mobile, low-cost air quality measurement devices can potentially deliver more coherent data for a region or municipality than stationary measurement stations are capable of due to their improved spatial coverage. In this study, air quality measurements obtained during field tests of our low-cost air quality sensor node (sensor-box) are presented and compared to measurements from the regional air quality monitoring network. The sensor-box can acquire geo-tagged measurements of several important pollutants, as well as other environmental quantities such as light and sound. The field test consists of sensor-boxes mounted on utility vehicles operated by municipalities located in Central Switzerland. Validation is performed against a measurement station that is part of the air quality monitoring network of Central Switzerland. Often not discussed in similar studies, this study tests and discusses several data filtering methods for the removal of outliers and unfeasible values prior to further analysis. The results show a coherent measurement pattern during the field tests and good agreement to the reference station during the side-by-side validation test.

A Q-learning-based routing scheme for smart air quality monitoring system using flying ad hoc networks

Air pollution has changed ecosystem and atmosphere. It is dangerous for environment, human health, and other living creatures. This contamination is due to various industrial and chemical pollutants, which reduce air, water, and soil quality. Therefore, air quality monitoring is essential. Flying ad hoc networks (FANETs) are an effective solution for intelligent air quality monitoring and evaluation. A FANET-based air quality monitoring system uses unmanned aerial vehicles (UAVs) to measure air pollutants. Therefore, these systems have particular features, such as the movement of UAVs in three-dimensional area, high dynamism, quick topological changes, constrained resources, and low density of UAVs in the network. Therefore, the routing issue is a fundamental challenge in these systems. In this paper, we introduce a Q-learning-based routing method called QFAN for intelligent air quality monitoring systems. The proposed method consists of two parts: route discovery and route maintenance. In the part one, a Q-learning-based route discovery mechanism is designed. Also, we propose a filtering parameter to filter some UAVs in the network and restrict the search space. In the route maintenance phase, QFAN seeks to detect and correct the paths near to breakdown. Moreover, QFAN can quickly identify and replace the failed paths. Finally, QFAN is simulated using NS2 to assess its performance. The simulation results show that QFAN surpasses other routing approaches with regard to end-to-end delay, packet delivery ratio, energy consumption, and network lifetime. However, communication overhead has been increased slightly in QFAN.

Temporal and spatial impact of lockdown during COVID-19 on air quality index in Haryana, India

This paper presents the evaluation of air quality in different districts of Haryana. Geo-spatial techniques were used to estimate gaseous and particulate pollutant's spatial and temporal variation during complete nationwide lockdown period and same month of previous year 2019 (March to May). Data of six fixed pollutants were collected from Central Pollution Control Board (CPCB). In this context, the data of air pollutants (PM₁₀, PM_2.5, O₃, NOx, SO₂, and CO) were analyzed for 2019 and 2020. The Spatio-temporal distribution of the Air Quality Index (AQI) clearly depicts difference in lockdown and unlock period. The result was showed that the air quality was very poor to satisfactory in 2019 and an improvement was observed from satisfactory to good in 2020 due to COVID-19 lockdown. On the basis of result, it will be concluded that automobile and industry are the major contributor in increase the pollutant concentration.

Applications of artificial intelligence in the field of air pollution: A bibliometric analysis

Background

Artificial intelligence (AI) has become widely used in a variety of fields, including disease prediction, environmental monitoring, and pollutant prediction. In recent years, there has also been an increase in the volume of research into the application of AI to air pollution. This study aims to explore the latest trends in the application of AI in the field of air pollution.

Methods

All literature on the application of AI to air pollution was searched from the Web of Science database. CiteSpace 5.8.R1 was used to analyze countries/regions, institutions, authors, keywords and references cited, and to reveal hot spots and frontiers of AI in atmospheric pollution.

Results

Beginning in 1994, publications on AI in air pollution have increased in number, with a surge in research since 2017. The leading country and institution were China (N = 524) and the Chinese Academy of Sciences (N = 58), followed by the United States (N = 455) and Tsinghua University (N = 33), respectively. In addition, the United States (0.24) and the England (0.27) showed a high degree of centrality. Most of the identified articles were published in journals related to environmental science; the most cited journal was Atmospheric Environment, which reached nearly 1,000 citations. There were few collaborations among authors, institutions and countries. The hot topics were machine learning, air pollution and deep learning. The majority of the researchers concentrated on air pollutant concentration prediction, particularly the combined use of AI and environmental science methods, low-cost air quality sensors, indoor air quality, and thermal comfort.

Conclusion

Researches in the field of AI and air pollution are expanding rapidly in recent years. The majority of scholars are from China and the United States, and the Chinese Academy of Sciences is the dominant research institution. The United States and the England contribute greatly to the development of the cooperation network. Cooperation among research institutions appears to be suboptimal, and strengthening cooperation could greatly benefit this field of research. The prediction of air pollutant concentrations, particularly PM_2.5, low-cost air quality sensors, and thermal comfort are the current research hotspot.

3D AQI Mapping Data Assessment of Low-Altitude Drone Real-Time Air Pollution Monitoring

Air pollution primarily originates from substances that are directly emitted from natural or anthropogenic processes, such as carbon monoxide (CO) gas emitted in vehicle exhaust or sulfur dioxide (SO2) released from factories. However, a major air pollution problem is particulate matter (PM), which is an adverse effect of wildfires and open burning. Application tools for air pollution monitoring in risk areas using real-time monitoring with drones have emerged. A new air quality index (AQI) for monitoring and display, such as three-dimensional (3D) mapping based on data assessment, is essential for timely environmental surveying. The objective of this paper is to present a 3D AQI mapping data assessment using a hybrid model based on a machine-learning method for drone real-time air pollution monitoring (Dr-TAPM). Dr-TAPM was designed by equipping drones with multi-environmental sensors for carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), particulate matter (PM2.5,10), and sulfur dioxide (SO2), with data pre- and post-processing with the hybrid model. The hybrid model for data assessment was proposed using backpropagation neural network (BPNN) and convolutional neural network (CNN) algorithms. Experimentally, we considered a case study detecting smoke emissions from an open burning scenario. As a result, PM2.5,10 and CO were detected as air pollutants from open burning. 3D AQI map locations were shown and the validation learning rates were apparent, as the accuracy of predicted AQI data assessment was 98%.

Air Quality Sensors Systems as Tools to Support Guidance in Athletics Stadia for Elite and Recreational Athletes

While athletes have high exposures to air pollutants due to their increased breathing rates, sport governing bodies have little guidance to support events scheduling or protect stadium users. A key limitation for this is the lack of hyper-local, high time-resolved air quality data representative of exposures in stadia. This work aimed to evaluate whether air quality sensors can describe ambient air quality in Athletics stadia. Sensing nodes were deployed in 6 stadia in major cities around the globe, monitoring NO2, O3, NO, PM10, PM2.5, PM1, CO, ambient temperature, and relative humidity. Results demonstrated that the interpretation of hourly pollutant patterns, in combination with self-organising maps (SOMs), enabled the interpretation of probable emission sources (e.g., vehicular traffic) and of atmospheric processes (e.g., local vs. regional O formation). The ratios between PM size fractions provided insights into potential emission sources (e.g., local dust re-suspension) which may help design mitigation strategies. The high resolution of the data facilitated identifying optimal periods of the day and year for scheduling athletic trainings and/or competitions. Provided that the necessary data quality checks are applied, sensors can support stadium operators in providing athlete communities with recommendations to minimise exposure and provide guidance for event scheduling.

A Novel Bike-Mounted Sensing Device with Cloud Connectivity for Dynamic Air-Quality Monitoring by Urban Cyclists

We present a device based on low-cost electrochemical and optical sensors, designed to be attached to bicycle handlebars, with the aim of monitoring the air quality in urban environments. The system has three electrochemical sensors for measuring NO₂ and O₃ and an optical particle-matter (PM) sensor for PM₂.₅ and PM₁₀ concentrations. The electronic instrumentation was home-developed for this application. To ensure a constant air flow, the input fan of the particle sensor is used as an air supply pump to the rest of the sensors. Eight identical devices were built; two were collocated in parallel with a reference urban-air-quality-monitoring station and calibrated using a neural network (R² > 0.83). Several bicycle routes were carried out throughout the city of Badajoz (Spain) to allow the device to be tested in real field conditions. An air-quality index was calculated to facilitate the user's understanding. The results show that this index provides data on the spatiotemporal variability of pollutants between the central and peripheral areas, including changes between weekdays and weekends and between different times of the day, thus providing valuable information for citizens through a dedicated cloud-based data platform.

State of air pollutants and related health risk over Haryana India as viewed from satellite platform in COVID-19 lockdown scenario

COVID-19 driven lockdown has affected air quality worldwide. Changes in air pollutants concentration, Air Quality Index (AQI), and associated Excess Health Risk (ER%) were assessed using satellite data of before (2019), and during (2020) COVID-19 periods in the industrially, agriculturally developed and highly populated area of Haryana in the northern region of Indo-Gangetic Plains. Parameters such as Aerosol Optical Depth (AOD), Particulate matters (PM), Sulphur Di-Oxide (SO2), Nitrogen Di-Oxide (NO2), Carbon Mono-oxide (CO), and Methane (CH4) were derived using satellite data and validated using ground-based observations (n = 23). The coefficient of correlation (r) 0.91, 0.90, 0.95, 0.73, 0.81 and 0.80 were established with AOD, PM2.5, PM10, SO2, NO2 and CO, respectively. Significant reduction (p  < 0.005) in the concentration of air pollutants, viz. 38% in AOD, 55% in PM2.5, 61% in PM10, 31% in SO2, 10% in NO2, 5% in CO and 1% in CH4 were observed during lockdown. Significant (p  < 0.00) improvement in air quality was observed due to a 44% reduction in pollution level, which led to the reduction in ER% by 71%, which is quite significant. AQI and ER% from satellite and ground showed a high r2 i.e. 0.88 and 0.99 respectively, suggesting the potential application of satellite data for periodic AQI and ER% assessment.

Low-Cost Air Quality Measurement System Based on Electrochemical and PM Sensors with Cloud Connection

This paper presents a portable device for outdoor air quality measurement that provides concentration values for the main pollutants: NO₂, NO, CO, O₃, PM_2.5 and PM₁₀, and other values such as temperature, humidity, location, and date. The device is based on the use of commercial electrochemical gas and optical particle matter sensors with a careful design of the electronics for reducing the electrical noise and increasing the accuracy of the measurements. The result is a low-cost system with IoT technology that connects to the Internet through a GSM module and sends all real-time data to a cloud platform with storage and computational potential. Two identical devices were fabricated and installed on a mobile reference measurement unit and deployed in Badajoz, Spain. The results of a two-month field campaign are presented and published. Data obtained from these measurements were calibrated using linear regression and neural network techniques. Good performance has been achieved for both gaseous pollutants (with a Pearson correlation coefficient of up to 0.97) and PM sensors.

The Unmanned Systems Research Laboratory (USRL): A New Facility for UAV-Based Atmospheric Observations

The Unmanned Systems Research Laboratory (USRL) of the Cyprus Institute is a new mobile exploratory platform of the EU Research Infrastructure Aerosol, Clouds and Trace Gases Research InfraStructure (ACTRIS). USRL offers exclusive Unmanned Aerial Vehicle (UAV)-sensor solutions that can be deployed anywhere in Europe and beyond, e.g., during intensive field campaigns through a transnational access scheme in compliance with the drone regulation set by the European Union Aviation Safety Agency (EASA) for the research, innovation, and training. UAV sensor systems play a growing role in the portfolio of Earth observation systems. They can provide cost-effective, spatial in-situ atmospheric observations which are complementary to stationary observation networks. They also have strong potential for calibrating and validating remote-sensing sensors and retrieval algorithms, mapping close-to-the-ground emission point sources and dispersion plumes, and evaluating the performance of atmospheric models. They can provide unique information relevant to the short- and long-range transport of gas and aerosol pollutants, radiative forcing, cloud properties, emission factors and a variety of atmospheric parameters. Since its establishment in 2015, USRL is participating in major international research projects dedicated to (1) the better understanding of aerosol-cloud interactions, (2) the profiling of aerosol optical properties in different atmospheric environments, (3) the vertical distribution of air pollutants in and above the planetary boundary layer, (4) the validation of Aeolus satellite dust products by utilizing novel UAV-balloon-sensor systems, and (5) the chemical characterization of ship and stack emissions. A comprehensive overview of the new UAV-sensor systems developed by USRL and their field deployments is presented here. This paper aims to illustrate the strong scientific potential of UAV-borne measurements in the atmospheric sciences and the need for their integration in Earth observation networks.