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This study introduces a neural network-based approach to predict dust emissions, specifically PM2.5 particles, during almond harvesting in California. Using a feedforward neural network (FNN), this research predicted PM2.5 emissions by analyzing key operational parameters of an advanced almond harvester. Preprocessing steps like outlier removal and normalization were employed to refine the dataset for training. The networks architecture was designed with two hidden layers and optimized using tanh activation and MSE loss functions through the Adam algorithm, striking a balance between model complexity and predictive accuracy. The model was trained on extensive field data from an almond pickup system, including variables like brush speed, angular velocity, and harvester forward speed. The results demonstrate a notable predictive accuracy of the FNN model, with a mean squared error (MSE) of 0.02 and a mean absolute error (MAE) of 0.01, indicating high precision in forecasting PM2.5 levels. By integrating machine learning with agricultural practices, this research provides a significant tool for environmental management in almond production, offering a method to reduce harmful emissions while maintaining operational efficiency. This model presents a solution for the almond industry and sets a precedent for applying predictive analytics in sustainable agriculture.

Land use changes in the wake of urbanization have arranged the urban residential sites to be encircled by anthropogenic activities, resulting in greater exposure to air pollution. There are limited studies carried out on source apportionment of urban landscape-induced air pollution and their spatial representativeness for urban residential sites, and the present study addressed this issue. Characterization, dissemination analysis, and source apportionment of particulate matter (PM)2.5 in a residential area, Lucknow have been carried out. Samples of PM2.5 were collected during mid-winter 2021–2022 at near-road-locations (viz. L1, L2, L3, and L4) of the study area. Samples of PM2.5 chemical speciation were done by inductively coupled plasma mass spectrometry and ion chromatography for 26-elements and 5-ions, respectively. Mean PM2.5 was recorded highest at L3 in the daytime and lowest at L4 in the nighttime with the values 166 μg/m3 and 76 μg/m3 respectively. ArcGIS-inverse distance weighing (IDW) simulations identified the representativeness of PM2.5 for the entire residential site. IDW mapping identified the PM2.5 propagation up to 200 m–500 m over the study area. The diversity in the concentration of PM2.5 observed within the residential area due to the influence of the respective day and night times, multiple sources mix and their spatial distribution. Chemical speciation data of PM2.5 applied to Positive Matrix Factorization version 5.0—as receptor model (PMF v5.0) run for individual sites separately and pool all four sites' data together as a single dataset, which ultimately confirmed seven factors. The PMF model outputs ascertained the mean apportionment of PM2.5 by the determined seven local sources that is, vehicular exhaust—21%, resuspended road dust—15%, cooking fuel emission—12%, waste burning—12%, diesel generator sets exhaust—10%, secondary aerosols—10%, and Construction and Demolition—7%. The remaining 13% mean contribution to PM2.5 was evidenced by unaccounted sources. The present study outcomes qualify the significance to undertake source apportionment studies in connection with urban landscape patterns for air pollution abatement specifically for rapidly changing cities. [ABSTRACT FROM AUTHOR]

The outcome of the first tests of a measuring system designed for the monitoring of atmospheric aerosol and meteorological parameters is considered. Based on the measurement results from August 2022, the data of the system prototypes, a calibrated optical aerosol sensor, and the on-site meteorological station are compared. The revealed drawbacks of the system design are described. The plans to improve and extend the system capabilities are discussed. A blueprint of a future multipoint aerosol monitoring network in Yekaterinburg and its neighborhood is presented. [ABSTRACT FROM AUTHOR]

• Mathematical equations clarified the association between PM 2.5 and meteorology. • Suggested model to predict PM 2.5 levels was tested in UK and Madrid City. • Compiling current legislation's requirements, the model performed well. • Historical PM 2.5 concentrations were estimated in the UK from 2000 to 2021. • Lowest PM 2.5 levels occurred in Northlands, particularly in summer and autumn. • Highest seasonally PM 2.5 levels were in spring and winter. Scientific evidence sustains PM 2.5 particles' inhalation may generate harmful impacts on human beings' health; therefore, their monitoring in ambient air is of paramount relevance in terms of public health. Due to the limited number of fixed stations within the air quality monitoring networks, development of methodological frameworks to model ambient air PM 2.5 particles is primordial to providing additional information on PM 2.5 exposure and its trends. In this sense, this work aims to offer a global easily-applicable tool to estimate ambient air PM 2.5 as a function of meteorological conditions using a multivariate analysis. Daily PM 2.5 data measured by 84 fixed monitoring stations and meteorological data from ERA5 (ECMWF Reanalysis v5) reanalysis daily based data between 2000 and 2021 across the United Kingdom were attended to develop the suggested approach. Data from January 2017 to December 2020 were employed to build a mathematical expression that related the dependent variable (PM 2.5) to predictor ones (sea-level pressure, planetary boundary layer height, temperature, precipitation, wind direction and speed), while 2021 data tested the model. Evaluation indicators evidenced a good performance of model (maximum values of RMSE, MAE and MAPE : 1.80 µg/m3, 3.24 µg/m3, and 20.63%, respectively), compiling the current legislation's requirements for modelling ambient air PM 2.5 concentrations. A retrospective analysis of meteorological features allowed estimating ambient air PM 2.5 concentrations from 2000 to 2021. The highest PM 2.5 concentrations relapsed in the Mid- and Southlands, while Northlands sustained the lowest concentrations. [ABSTRACT FROM AUTHOR]

This study introduces a neural network-based approach to predict dust emissions, specifically PM2.5 particles, during almond harvesting in California. Using a feedforward neural network (FNN), this research predicted PM2.5 emissions by analyzing key operational parameters of an advanced almond harvester. Preprocessing steps like outlier removal and normalization were employed to refine the dataset for training. The network’s architecture was designed with two hidden layers and optimized using tanh activation and MSE loss functions through the Adam algorithm, striking a balance between model complexity and predictive accuracy. The model was trained on extensive field data from an almond pickup system, including variables like brush speed, angular velocity, and harvester forward speed. The results demonstrate a notable predictive accuracy of the FNN model, with a mean squared error (MSE) of 0.02 and a mean absolute error (MAE) of 0.01, indicating high precision in forecasting PM2.5 levels. By integrating machine learning with agricultural practices, this research provides a significant tool for environmental management in almond production, offering a method to reduce harmful emissions while maintaining operational efficiency. This model presents a solution for the almond industry and sets a precedent for applying predictive analytics in sustainable agriculture.

Over the past decades, technological and economic growth has significantly contributed to the improvement of the health care system through increased investment in technological research, training and the application of new technologies in health care. There is evidence that rapid technological and economic growth, contributing to the improvement of the health care system, also leads to a reduction in mortality rates, as well as to a longer life expectancy, which indirectly affects human activity in scientific and political spheres. At the same time, there is evidence that 2.5-micron particles (PM2.5 particles), as a result of industrial development and increased car use, have a negative impact on health outcomes around the world. This paper analyses the impact of increased concentration of PM2.5 particles in the air on economic and technological development, in order to explore whether there is an interdependent relationship between them. The paper aims to clarify the long-term effect of PM2.5 on health outcomes, control of technological and economic growth, as well as other important factors, based on the analysis of given panel data and the application of modern econometric methods. Accordingly, the purpose of this paper is to provide information on the negative impact of PM2.5 particles on human health, as an indirect impact on the development of new materials and technologies, taking into account socio-economic factors at the global level.

This study employs an activity-based model to estimate the PM2.5 particle emissions from ships, cargo-handling equipment, and heavy-duty vehicles in the Port of Kaohsiung, Taiwan. External health costs, the index of health impact (IHI), and external environmental costs are assessed to quantify the impact of PM2.5 particle emissions. The mitigation regulations applied in this study are the Intended Nationally Determined Contribution Act (INDC) and the Greenhouse Gas Reduction and Management Act (GGRMA). The provisions in these acts are incorporated into Scenario-INDC and Scenario-GGRMA. The results are as follows: from 2005 to 2017, PM2.5 particle emissions caused an external health cost of 3238.30 DALY (disability-adjusted life year), an IHI value of 8.53%, and environmental cost of USD 2176.04 million annually. For Scenario-INDC and Scenario-GGRMA, it is predicted that PM2.5-related external health costs, IHI value, and external environmental cost will decrease by 927.64 DALY, 2.45%, and USD 608.86 million and by 1736.28 DALY, 4.58%, and USD 1139.84 million, respectively, as compared to BAU-2030 and BAU-2050. The results indicate that compliance with INDC and GGRMA regulations will lead to a significant mitigation of PM2.5 particle emissions, resulting in a significant improvements in air quality and human health in addition to a reduction in environmental costs. [ABSTRACT FROM AUTHOR]

The Indian capital Delhi experienced an environmental emergency in early November 2017 when levels of toxic PM 2.5 particles surpassed WHO guidelines by 25 times (11 times by Indian Standards) for a prolonged period of a week (peak 24 h average ~650 μg/m3). We hereby demonstrate the role that monsoon dynamics played in linking and mixing dust emitted from a large natural dust storm, 3000 km away in the Middle East, with smoke from agriculture fires in northwest India. This dust and smoke rich air was then transported to Delhi where, under stagnant conditions, it mixed with local emissions resulting in very high pollution levels. The heavy aerosol-laden air altered the land-skin surface air temperature difference resulting in increased surface wind speeds, favouring faster dispersion and an unusual sharp decline in PM 2.5 (PM 2.5 –110 μg/m3). Understanding the multi-scale nature of such events is important in improving our abilities to forecast these events and in developing effective air quality management strategies for the mega cities. Unlabelled Image • A thick haze triggered emergency condition in Delhi during winter 2017. • Monsoon dynamics and dust storm played a pivotal role in deterioration of air quality. • Heavy aerosol load altered land skin surface air temperature and it helps to fast dispersion. [ABSTRACT FROM AUTHOR]

Abstract The performance of aerosol classification schemes based on intensive optical parameters and applied to mixed particle populations monitored at the surface has been investigated to test the ability of optical parameters to identify different types of particles/particle mixtures and explore their complex features. The results show that the combination of two intensive optical parameters does not allow for the unique identification of different particle types. The classification scheme based on the Absorption Ångström Exponent (AAE) as a function of the Scattering Ångström Exponent (SAE) and color-coded by the Single Scattering Albedo difference (dSSA) is a good graphical framework to discriminate between different types of particle/particle mixtures. This aerosol classification scheme has been applied to study the optical properties of heterogeneous PM2.5 particles that were monitored at a coastal site of the Central Mediterranean and were significantly affected by both natural and anthropogenic sources also because of long-range transport from surrounding countries. The calculated AAE, SAE, and dSSA hourly means smoothly and continuously vary within their respective range (0.6–3.4, −0.7–3.0, and −0.33–0.52, respectively) because of the different mixing degree of different types of particles. Consequently, the main features of the particle populations depend on the range of the AAE, SAE, and dSSA values. Eight different clusters have been selected within the used graphical framework to identify four key particle populations (dust, marine, OC-dominated, and BC-dominated particles) and four particle mixtures dominated by key aerosol populations. In addition, their main features have been characterized. Particle mixtures consisting of large and low-absorbing particles (LLAP), small and high-absorbing particles (SHAP), dominated by dust, and large organic particles have been characterized. Marine, LLAP, and mixed dust clusters with a SAE value below 1 are responsible for aerosol scattering coefficients (σ s ; at 470 nm) below 100 Mm−1. Conversely, SHAP, BC-dominated, and OC-based mixtures are responsible for the highest σ s values and represent dominant particles species. Highlights • Characterization of extensive and intensive optical properties of PM2.5 particles. • Aerosol classification schemes based on intensive parameters were tested. • Selection of the best intensive optical parameters for particle identification. • PM2.5 key and mixed particle populations were classified by 8 different clusters. • The key particle types include desert dust, marine, OC- and BC-dominated. [ABSTRACT FROM AUTHOR]

Electrostatic precipitator is the most efficient device used for the removal of fly ash particles from the flue gases produced by coal-fired boilers in power plants. However, the fractional collection efficiency of electrostatic precipitators steeply decreases for particles smaller than 1 μm, and particles in the size range of 200–500 nm are removed with lower collection efficiency than those outside this range. These particles are dangerous to humans and have detrimental environmental effects, so there is a need for novel more efficient technologies for their control. One of the answers to this challenge is the two-stage electrostatic precipitator, in which the electrostatic charging and precipitation processes have been separated. The PM2.5 particles (of a size < 2.5 μm) are electrically charged in a separate device (precharger) to a maximal possible electric charge, and then precipitated in a parallel plate collector, free of corona discharge. The electric field in the collection stage can be higher than in an electrostatic precipitator due to the lack of sharp discharge points. A higher electric field allows an increase of the collection efficiency for PM2.5 particles. Another solution is the agglomeration of submicron particles to form larger particles before their precipitation by a parallel-plate collector, conventional electrostatic precipitator or any other gas cleaning device. In some of the reviewed devices, both of these processes were combined in a single device that allowed further increases in the collection efficiency for submicron particles. Devices of this type have been tested in a lab- or semi-industrial scale for the removal of PM2.5 particles from flue gases or diesel engine exhausts. In this paper, various constructions of two-stage electrostatic precipitators, comprising a precharger and/or agglomerator in the first stage, and an electrostatic collector in the second stage, have been reviewed. Some of these devices were able to increase the mass collection efficiency above 95% for PM2.5 particles. [ABSTRACT FROM AUTHOR]

A biomonitoring field study was conducted to test the deposition of airborne metals on chicken eggshell. The goal was to correlate PM2.5 particle concentration in air, absolute (metals in air) and relative (metals in PM2.5 particles) metals, and metal accumulation on eggshell. The PM2.5 sample was collected for 8 h at different air pollution sites as well as an unpolluted site with glass fiber filters via a fine particulate sampler (with and without addition of eggshell). The PM2.5 particle concentration was high at a cement factory site and low at a sugar factory site. The highest absolute total mass of metal (total mass of all metals in air) was found in the traffic site, and the highest relative total mass of metal (total mass of all metals in PM2.5 particles) was found at the tannery factory. The accumulation of Zn and Pb in eggshell was high at the tannery and sugar factory sites, and Fe was high in the other areas. Three common metals including Cd, As, and Pb were found with significant positive correlation between absolute and relative metals with the metals accumulated in eggshell. The results concluded that the eggshells might be useful tools for monitoring the airborne metals. [ABSTRACT FROM AUTHOR]

Objetivo: Evaluar la contaminación por humo ambiental de tabaco en una muestra de locales de hostelería antes y después de la nueva legislación sobre tabaquismo. Métodos: Estudio evaluativo antes-después realizado en 2008 y 2011, tras la prohibición total. Medición de partículas respirables (PM2,5) mediante SidePack Aerosol Monitor en el interior de los locales y en el exterior. Se incluyeron 43 locales con mediciones antes y después de la legislación. Resultados: La concentración mediana de PM2,5 en el interior de los locales en 2008 fue 204,2μg/m³, y en 2011 fue 18,62μg/m³; en el exterior, en 2008 fue 47,04μg/m³ y en 2011 fue 18,82μg/m³. Las concentraciones más altas se encontraron en bares y cafeterías, seguidos de los locales de ocio nocturno. La contaminación en 2008 era 4,34 veces más alta en el interior de los locales que en el exterior; en 2011, la concentración de PM2,5 disminuyó un 90,88% en el interior. Conclusiones: La prohibición total de fumar en los locales de hostelería protege a trabajadores y clientes de los riesgos para la salud de la exposición al humo ambiental de tabaco.Objective: To evaluate pollution by second-hand smoke in a sample of hospitality venues before and after the implementation of smoke-free legislation. Methods: A cross sectional, before-after study was conducted in 2008 and 2011 after the total ban. A SidePack Aerosol monitor was used both inside and outside the hospitality venues to measure fine breathable particles (PM2.5). A total of 43 places with pre- and post-legislation measurements were included. Results: The median indoor pollution in hospitality venues was 204.2μg/m³ in 2008 and 18.82μg/m³ in 2011; the average outdoor PM2.5 concentration was 47.04μg/m³ in 2008 and 18.82μg/m³ in 2011. Pollution was higher in bars and cafeterias, followed by pubs and discos. Before the law was implemented, pollution was 4.34 times higher indoors than outdoors; in 2011 the average indoor PM2.5 concentration decreased by 90.88%. Conclusions: Only a complete ban is able to protect workers and customers against the health risks of second-hand smoke exposure

In this paper we make an attempt to estimate the dry deposition velocity of ammonium sulfate particles using the results of sulfur and nitrogen balance determined between the atmosphere and a forest ecosystem for the years of 1996–1998. Results of these measurements and of a campaign conducted during summer of 2001 demonstrate that ammonium and sulfate ions exist in nearly equivalent ratio in particle phase. According to the size distribution measurements majority (92%) of ammonium sulfate can be found in the fraction of PM2.5. For the balance calculation, results of throughfall, stemflow and wet-only deposition measurements have been used, together with dry deposition measurements of gaseous sulfur dioxide and ammonia. Dry deposition velocity of ammonium sulfate particles determined from sulfur and nitrogen balance were v&ges;0.82±0.25 and =0.84±0.25 cm s−1, respectively. The two figures determined by different ways are in good agreement and they are in accordance with other experimental results found in the literature. The results suggest the necessity of the revision of the models applied during the theoretical calculation of dry deposition velocity of PM2.5 particles. [Copyright &y& Elsevier]