Kevin P. Wyche

Non-methane volatile organic compounds (NMVOCs) in the atmosphere have garnered considerable attention worldwide because of their significance in atmospheric chemistry and their usefulness in identifying emission sources and chemical sinks. Despite the importance of VOCs, their routine measurement in the UK and across Europe is somewhat limited, and as such, there is limited information available regarding their sources, particularly in sensitive locations such as densely populated urban areas. In this study, hourly NMVOC concentration data measured between January 2015 and December 2023 were analyzed across four monitoring sites spanning an urban-to-rural gradient in England. The Marylebone and Eltham sites showed significantly higher concentrations of alkanes, alkenes, and aromatic hydrocarbons compared to the rural sites of Chilbolton and Auchencorth, with highest recorded VOC values of 29.9 ± 9.1 and 15.3 ± 4.3 µg m⁻³, respectively. Ethane (C₂H₆) and propane (C₃H₈) showed their highest ozone formation potential (OFP) levels at Marylebone Road, with the contribution of ethane to ozone formation ranging from 4% in Eltham to 8% in Chilbolton. This indicates that the emissions from vehicles and industries have a greater impact on the reactivity of these compounds. The potential source contribution function (PSCF) showed that the Marylebone and Eltham sites are mainly impacted by local traffic sources, while Chilbolton and Auchencorth receive VOC emissions from wider geographical areas. The COVID-19 lockdowns caused significant reductions in VOC concentrations at urban sites, which resulted in total VOCs declining by 26% at Marylebone Road and 11% in Eltham, while rural sites remained unaffected. The study results demonstrate new insights into NMVOC long-term temporal patterns and source identification across different UK environments, with important implications for air quality management and emission control policies.

Urban air pollution poses significant risks to human health, ecosystems, and the environment, highlighting the need for accurate monitoring of atmospheric pollutants. This study investigated the spatial and temporal variability of key tropospheric pollutants, including nitrogen dioxide (NO₂), sulfur dioxide (SO₂), nitrous acid (HONO), formaldehyde (HCHO), and ozone (O₃), in Brighton, UK, using an integrated approach that combined ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS), Long-Path Differential Optical Absorption Spectroscopy (LP-DOAS), and Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) observations. Ground-based measurements comprised four MAX-DOAS campaigns conducted between 2021 and 2024 and a long-term LP-DOAS dataset spanning 2017–2023, complemented by coincident TROPOMI observations. The datasets were spatially co-located, temporally aligned, quality-controlled, and analysed using statistical methods, time-series analysis, and polar plot techniques to assess pollutant variability, identify emission sources, and evaluate the agreement between satellite and ground-based observations. The results revealed clear seasonal and diurnal variations in pollutant levels, with elevated NO₂ during winter and enhanced O₃ during summer, reflecting the influence of anthropogenic emissions and photochemical processes. Polar plot analysis further identified distinct wind-dependent pollutant patterns, indicating the importance of local emission sources. Comparisons between ground-based and satellite observations showed that TROPOMI successfully captured the temporal variability of NO₂ measured by means of LP-DOAS, with a moderate positive correlation (r_s = 0.55), but underestimated NO₂ relative to MAX-DOAS observations (r_s = 0.38), reflecting differences in measurement geometry, spatial resolution, and retrieval sensitivity. The overall findings demonstrate that integrating ground-based and satellite observations provides a more comprehensive understanding of urban air quality than either approach alone. This combined monitoring framework improves confidence in satellite-derived atmospheric products and supports more effective air quality assessment and management in Brighton and similar urban environments.

This review explores advanced sensing technologies and deep learning (DL) methodologies for monitoring airborne particulate matter (PM), which is critical for environmental health assessments. It begins with discussing the significance of PM monitoring and introduces surface plasmon resonance (SPR) as a promising technique in environmental applications, alongside the role of DL neural networks in enhancing these technologies. This review analyzes advancements in airborne PM sensing technologies and the integration of DL methodologies for environmental monitoring. This review emphasizes the importance of PM monitoring for public health, environmental policy, and scientific research. Traditional PM sensing methods, including their principles, advantages, and limitations, are discussed, covering gravimetric techniques, continuous monitoring, optical and electrical methods, and microscopy. The integration of DL with PM sensing offers potential for enhancing monitoring accuracy, efficiency, and data interpretation. DL techniques, such as convolutional neural networks (CNNs), autoencoders, recurrent neural networks (RNNs), and their variants, are examined for applications like PM estimation from satellite data, air quality prediction, and sensor calibration. This review highlights the data acquisition and quality challenges in developing effective DL models for air quality monitoring. Techniques for handling large and noisy datasets are explored, emphasizing the importance of data quality for model performance, generalizability, and interpretability. The emergence of low-cost sensor technologies and hybrid systems for PM monitoring is discussed, acknowledging their promise while recognizing the need for addressing data quality, standardization, and integration issues. This review identifies areas for future research, including the development of robust DL models, advanced data fusion techniques, applications of deep reinforcement learning, and considerations of ethical implications.

This review explores advanced sensing technologies and Deep Learning (DL) methodologies for monitoring airborne particulate matter (PM), critical for environmental health assessment. It begins with discussing the significance of PM monitoring and introduces surface plasmon resonance (SPR) as a promising technique in environmental applications, alongside the role of DL neural networks in enhancing these technologies. This review analyzes advancements in airborne PM sensing technologies and the integration of DL methodologies for environmental monitoring. The review emphasizes the importance of PM monitoring for public health, environmental policy, and scientific research. Traditional PM sensing methods, including their principles, advantages, and limitations, are discussed, covering gravimetric techniques, continuous monitoring, optical and electrical methods, and microscopy. The integration of DL with PM sensing offers potential for enhancing monitoring accuracy, efficiency, and data interpretation. DL techniques such as convolutional neural networks (CNNs), autoencoders, recurrent neural networks (RNNs), and their variants, are examined for applications like PM estimation from satellite data, air quality prediction, and sensor calibration. The review highlights data acquisition and quality challenges in developing effective DL models for air quality monitoring. Techniques for handling large and noisy datasets are explored, emphasizing the importance of data quality for model performance, generalizability, and interpretability. The emergence of low-cost sensor technologies and hybrid systems for PM monitoring is discussed, acknowledging their promise while recognizing the need for addressing data quality, standardization, and integration issues. The review identifies areas for future research, including the development of robust DL models, advanced data fusion techniques, applications of deep reinforcement learning, and considerations of ethical implications.

Airborne particulate matter (PM) is of great concern in the modern-day atmosphere owing to its association with a variety of health impacts, such as respiratory and cardiovascular diseases. Of the various size fractions of PM, it is the finer fractions that are most harmful to health, in particular ultrafine particles (PM_0.1; UFPs), with an aerodynamic diameter ≤ 100 nm. The smaller size fractions, of ≤2.5 µm (PM_2.5; fine particles) and ≤0.1 µm (PM_0.1; ultrafine particles), have been shown to have numerous linkages to negative health effects; however, their collection/sampling remains challenging. This review paper employed a comprehensive literature review methodology; 200 studies were evaluated based on the rigor of their methodologies, including the validity of experimental designs, data collection methods, and statistical analyses. Studies with robust methodologies were prioritised for inclusion. This review paper critically assesses the health risks associated with fine and ultrafine particles, highlighting vehicular emissions as the most significant source of particulate-related health effects. While coal combustion, diesel exhaust, household wood combustors’ emissions, and Earth’s crust dust also pose health risks, evidence suggests that exposure to particulates from vehicular emissions has the greatest impact on human health due to their widespread distribution and contribution to air pollution-related diseases. This article comprehensively examines current sampling technologies, specifically focusing on the collection and sampling of ultrafine particles (UFP) from ambient air to facilitate toxicological and physiochemical characterisation efforts. This article discusses diverse approaches to collect fine and ultrafine particulates, along with experimental endeavours to assess ultrafine particle concentrations across various microenvironments. Following meticulous evaluation of sampling techniques, high-volume air samplers such as the Chem Vol Model 2400 High Volume Cascade Impactor and low-volume samplers like the Personal Cascade Impactor Sampler (PCIS) emerge as effective methods. These techniques offer advantages in particle size fractionation, collection efficiency, and adaptability to different sampling environments, positioning them as valuable tools for precise characterisation of particulate matter in air quality research and environmental monitoring.

The Volume 12 published for IJEPR Journal.