American Journal of Sustainable Science and Technology

Aerosols exert diverse influences on human health, agriculture, air quality, and climate systems. Despite continuous progress in aerosol measurement technologies and methodologies, significant uncertainties persist in quantifying their physical properties across many applications. Accurate characterization of airborne particles particularly in terms of number and mass concentrations, size distributions, and light adsorption properties is essential for understanding their atmospheric behavior and environmental fate. This study evaluates the physical properties of aerosols with a focus on measurement protocols and documentary standards that ensure traceability and enable interoperability of data obtained using different instruments across spatial and temporal scales. To enhance awareness and highlight inconsistencies in measurement practices across aerosol science subfields, existing standards and regulatory frameworks related to air quality and particle emissions from transportation (automobiles, aviation, shipping) and stationary sources are critically reviewed. Emphasis is placed on the urgent need for harmonization of measurement methodologies, supported by specific examples and practical recommendations. Owing to its comprehensive treatment of aerosol measurement practices and documentary standards across multiple disciplines, this review serves as a valuable reference for researchers and policymakers seeking to improve measurement accuracy and consistency.

Keywords: Atmospheric aerosols: Nanoparticles; Health impacts; Climate change; Radioactive aerosols, Exposure assessment.

Atmospheric radionuclides are transported through the environment as gases or particle-associated contaminants, making their behavior closely dependent on aerosol properties and atmospheric conditions. Understanding their movement, deposition, persistence, and potential exposure pathways therefore requires integration of aerosol physics with reliable environmental surveillance. This review discusses the major physical principles governing atmospheric radionuclide dynamics, with emphasis on particle size, transport, deposition, resuspension, and interactions with radiation and light. Current exposure-assessment approaches based on environmental sampling and laboratory measurements are examined alongside emerging spectroscopic and remote sensing (SRS) technologies for atmospheric and terrestrial monitoring. Particular attention is given to the growing role of artificial intelligence and machine learning in interpreting complex environmental datasets and supporting short-term forecasting. Recent PM₁₀ research demonstrates that XGBoost, CatBoost, artificial neural networks, LSTM, Bi-LSTM, and ensemble models can integrate air-quality and meteorological data to provide location-specific predictions. Although these approaches were developed for particulate air pollution rather than radionuclides, their data-driven framework provides a useful direction for future radionuclide surveillance. Integrating conventional measurements with SRS, sensor networks, and AI-assisted prediction could improve spatial coverage, early detection, exposure assessment, and environmental decision-making.

Keywords: Atmospheric radionuclides; Aerosols; Exposure assessment; Spectroscopic remote sensing; Machine learning; Environmental surveillance

Accurate prediction of the transport and deposition of atmospheric radioactive aerosols necessitates the integration of multiple tightly coupled multiphysical processes. This study systematically evaluates the governing principles underlying transport behaviour, deposition fluxes, charge development, radioactive particles with particular emphasis on hot, arid environments and dust storm conditions. In addition, it presents a critical synthesis of recent progress in the modelling radioactive aerosol transport in the open atmosphere, highlighting the coupled interaction between electrical charging and transport phenomena. This work is intended both as a structured introduction for newcomers and as an updated critical resource for experts aiming to identify decent technological progress, key knowledge gaps, and future research priorities in the field.

Keywords: Radioactive aerosols, Transport dynamics, Charge evolution, Deposition mechanisms, Dispersion Modelling, Atmospheric simulation

Atmospheric aerosols influence climate processes, air quality, ecosystem health, agricultural productivity, and human exposure, making reliable particle characterization essential for environmental research and regulatory decision-making. Despite substantial progress in aerosol science, uncertainties remain in the measurement of particle number and mass concentrations, size distributions, optical properties, and chemical composition. This review examines recent developments in aerosol characterization with particular emphasis on analytical instrumentation, measurement traceability, standardized procedures, and cross-platform comparability. Advanced gas-phase techniques, including proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS) and gas chromatography–mass spectrometry (GC-MS), provide complementary capabilities for real-time monitoring and detailed identification of volatile organic compounds and their oxidation products. Particle-phase properties can be evaluated using high-resolution time-of-flight aerosol mass spectrometry (HR-ToF-AMS), aerosol chemical speciation monitoring (ACSM), condensation particle counting (CPC), and scanning mobility particle sizing (SMPS). Emerging approaches such as chemical ionization mass spectrometry coupled with filter inlet for gases and aerosols (FIGAERO-CIMS) and extractive electrospray ionization time-of-flight mass spectrometry (EESI-ToF-MS) further enable molecular-level investigation of complex gas–particle systems and secondary organic aerosol formation. Alongside instrumental advances, robust calibration, metrological traceability, and harmonized measurement protocols are necessary to ensure that aerosol datasets obtained across laboratories, monitoring networks, and application sectors remain comparable. The review also considers documentary standards and regulatory measurement requirements relevant to ambient air and particle emissions from transportation and stationary sources. Remaining challenges associated with methodological differences, instrument-specific uncertainties, and data harmonization are critically discussed, together with opportunities for improving measurement consistency and supporting reliable aerosol monitoring across environmental and regulatory applications.

Keywords: Atmospheric aerosols; Aerosol characterization; Analytical instrumentation; Measurement traceability; Secondary organic aerosols; Measurement standardization

Mycotoxin contamination remains a persistent challenge across agricultural and food systems because toxin formation is governed by interconnected environmental, biological, storage, and processing conditions. Temperature, moisture, relative humidity, water activity, pH, substrate composition, fungal species, and storage duration can collectively influence fungal growth and toxin production, making conventional endpoint monitoring insufficient for proactive risk management. At the same time, advances in functional materials and analytical technologies are creating new opportunities for sensitive detection and targeted removal of mycotoxins. This review connects three increasingly important dimensions of mycotoxin management: environmental conditions, data-driven assessment, and advanced materials. Particular attention is given to carbon-based materials, including graphene, graphene oxide, carbon nanotubes, carbon dots, and related nanocomposites, together with quantum dots and magnetic nanomaterials used for sensing, adsorption, and detoxification. Their physicochemical, optical, electrochemical, and surface properties enable sensitive recognition of toxins such as aflatoxin B1 while also providing opportunities for contaminant capture and removal. Emerging control strategies involving cold atmospheric plasma, polyphenols, natural essential oils, and functional nanomaterials are further evaluated from efficacy and sustainability perspectives. Building on these developments, the review discusses how environmental monitoring data, material-performance information, and sensing outputs could be integrated through data-driven models to support contamination prediction, material selection, and timely intervention. Rather than treating detection and remediation as independent processes, an integrated environment–data–materials framework is proposed for moving toward predictive and adaptive mycotoxin management. Remaining challenges include matrix effects, material safety, scalability, data quality, sensor reproducibility, and validation under real food and agricultural conditions.

Keywords: Mycotoxins; Environmental Monitoring; Data-Driven Prediction; Graphene-Based Materials; Sustainable Remediation