Search

Accurate and quantitative profiling of air particulate matter (PM) compositions is essential for assessing local pollution information. The method combining mobile aerial sampling using unmanned aerial vehicles (UAVs) and prompt analysis excels in this regard as it allows spatiotemporal mapping of air pollution, especially in the vertical direction. However, applications of the method are still scarce as it is limited by a lack of sampling reliability due to insufficient aerial sampling time and a lack of accurate, portable quantification techniques. In this work, by integrating mobile aerial sampling with in-flight tethered charging and smartphone-based colorimetric analysis in a cost-effective paper microfluidic device, we present a method for quantitative, reliable profiling of spatiotemporal variation in air PM compositions. The method extends aerial sampling time to 12–15 flight hours per deployment, thereby significantly improving sampling reliability while maintaining the maneuverability of the UAVs. Also, smartphone-based colorimetric analysis combined with paper-based microfluidics enables portable, economically efficient analysis and is well-suited for using in low-resource settings. We demonstrated the utility of the method by carrying out a spatiotemporal variation study of air PM trace metal components (Fe, Ni, and Mn) at 4 geographical locations in Fuzhou, China, for a period of 21 days, and the results were in good agreement with results obtained from using a commercial instrument. Beside air PM composition study, this method is universally applicable and holds great potential to be extended to multipollutant analysis, such as prompt detection of airborne viruses, bacteria, and others. [ABSTRACT FROM AUTHOR]

Urban air pollution poses a significant challenge, negatively affecting visibility, agriculture, health, and transportation. This research focused on exploring the variability of aerosols using the autoregressive distributed lag (ARDL) approach. To achieve this, monthly aerosol data were obtained from the Aura satellite's Ozone Monitoring Instrument (OMI) at a distance of 500 nautical miles. In addition, meteorological factors such as Cloud Fraction (CF), Relative Humidity (RH), Tropopause Height (TH), Total Column Water Vapor (TCWV), Water Vapor Mass Mixing Ratio (WVMMR), Surface Skin Temperature (SST), Surface Air Temperature (SAT), and Geopotential Height (GH) were gathered from the atmospheric infrared sounder (AIRS) onboard the AQUA satellite. The MERRA-2 model provided the Total Surface Precipitation (TSP) and Surface Wind Speed (SWS). To assess the short- and long-term relationship between aerosols and meteorological parameters, the ARDL bounds testing technique was applied. The study found evidence of a long-term relationship and co-integration between the variables of interest and aerosols when aerosols were the dependent variable. Particularly, GH, SST, and SWS exhibited both long-term and short-term impacts on aerosol variability. SWS, in particular, was found to have a significant influence on aerosol variability. Conversely, CF, TSP, and WVMMR were found to have no significant impact on aerosol variability. To ensure the stability of the model, the CUSUM test was employed, confirming its stability. Furthermore, the prediction model demonstrated a good fit, bolstering the reliability of our findings. [ABSTRACT FROM AUTHOR]

Solid and liquid particles in the atmosphere, referred to as airborne particulate matter (PM), have been rising significantly over the past two decades. Exposure to PM carries significant health risks such as lungs damage, heart disease, cancer, and death. PM2.5 is a subgroup of PM particles that are smaller than 2.5 µm and is a major concern as it is more harmful to health and more difficult to detect. One problematic component of PM2.5 is magnetite nanoparticles (<200 nm), which are readily absorbed into the bloodstream through the respiratory system. Eventually, magnetite nanoparticles deposit inside the brain causing neurodegenerative diseases such as Alzheimer's or cancerous tumors by inducing oxidative stress. Additionally, Magnetite nanoparticles are often surrounded by heavy metal nanoparticles such as Cadmium and lead which are a great concern to the environment and health. Traditional PM detection methods such as laser scattering are bulky, expensive, and incapable of detecting particles smaller than 200 nm such as magnetite nanoparticles. Therefore, developing a low-cost highly sensitive sensor for monitoring magnetite nanoparticles is vital. Tunneling Magneto-Resistance (TMR) sensors are an attractive option due to their low-cost and high sensitivity toward magnetic nanoparticle detection. Moreover, developing a cheap, portable, and precise remote monitoring technique will allow for the creation of high spatial resolution highly sensitive monitoring networks for magnetic PM2.5. This work focuses on developing, modeling, and simulation of low-cost highly sensitive TMR sensor based on Magnetic Tunnel Junction (MTJ) that can detect and count magnetite nanoparticles. [ABSTRACT FROM AUTHOR]

Pollution introduces harmful substances into the natural environment, resulting in environmental alterations. Industries are a major source of air pollution because of many power plants, chamber processes, cleaning operations, and material burning processes. Volatile organic compounds and other contaminants are discharged into the atmosphere. Sensor fusion technology-based air pollution has been proposed in this study to meet the national air quality criteria, and several nations have implemented emission monitoring systems. It is common knowledge that PM2.5 sensors can be found in air purifiers, in the immediate surroundings, and in everyday lives. PM2.5 particles can be more accurately detected by increasing the sensitivity to smaller particles and optimizing the flow of particles into the detecting region. The sensors' responsiveness and sensitivity are excellent based on the testing results. A low-cost monitoring system can benefit from this technology since it may be used to give data on the levels of nitrogen, particulate matter, and inorganic nitrates. [ABSTRACT FROM AUTHOR]

During the Covid-19 pandemic and resulting lockdowns, road traffic volumes reduced significantly leading to reduced pollutant concentrations and noise levels. Noise and the air pollution data during the lockdown period and loosening of restrictions through five phases in 2021 are examined for a school site in the United Kingdom. Hourly and daily average noise level as well as the average over each phase, correlations between noise and air pollutants, variations between pollutants, and underlying reasons explaining the temporal variations are explored. Some strong linear correlations were identified between a number of traffic-sourced air pollutants, especially between the differently sized particulates PM1, PM2.5, and PM10 (0.70 < r <0.98) in all phases and an expected inverse correlation between nitrogen dioxide (NO2) and ground-level ozone (O3) (–0.68 < r < –0.78) as NO2 is a precursor of O3. Noise levels exhibit a weak correlation with the measured air pollutants and moderate correlation with meteorological factors, including wind direction, temperature, and relative humidity. There was a consistent and significant increase in noise levels (p < 0.01) of up to 3 dB with initial easing, and this was maintained through the remaining phases. [ABSTRACT FROM AUTHOR]

In the current era of busy and eventful daily routines, the need for self-driven, robust, and low maintenance healthcare systems emerges significantly more than in earlier times. The nanogenerator (NG) technology provides a new pathway by utilizing nanostructured and eco-friendly materials toward biomedical systems by harvesting biomechanical energy. Triboelectric NGs (TENGs) have been well-developed to cater all these matters, giving self-powered, sustainable, environment-friendly, and low footprint devices. TENG comes up with great potential, therefore, we have summarized various dimensions of its applications in healthcare management, including prevention, detection, diagnosis, and treatment. We have reviewed different aspects of TENG healthcare systems that provide wearable, minimally invasive, and simple solutions while harvesting human motion as the power source. Here, recent advancements of triboelectric devices are compiled while discussing their significance, structure, capabilities, performance, and future potential. Meanwhile, the impact of TENG on protecting and treating various internal and external human organs, such as the heart, neural tissues, skin, and hair, has been described in detail. Moreover, TENG-based solutions have also included minimizing the effects of contemporary and lingering challenges such as air pollution and viral infectious diseases on human health. In the very end, we have concluded with the opportunities and possible solutions for anticipated challenges. [ABSTRACT FROM AUTHOR]