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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]