Search Results (196)

Soil fugitive dust (SFD) emissions pose a significant threat to both human health and the environment, highlighting the need for accurate and reliable estimation and assessment in the desert regions of northwest China. This study used climate, soil, and vegetation data from Bayingolin Prefecture (2001–2022) and applied the WEQ model to analyze temporal and spatial variations in total suspended particulate (TSP), PM₁₀, and PM_2.5 emissions and their driving factors. The region exhibited high emission factors for TSP, PM₁₀, and PM_2.5, averaging 55.46 t km⁻² a⁻¹, 27.73 t km⁻² a⁻¹, and 4.14 t km⁻² a⁻¹, respectively, with pronounced spatial heterogeneity and the highest values observed in Yuli, Qiemo, and Ruoqiang. The annual average emissions of TSP, PM₁₀, and PM_2.5 were 3.23 × 10⁷ t, 1.61 × 10⁷ t, and 2.41 × 10⁶ t, respectively. Bare land was the dominant source, contributing 72.55% of TSP emissions. Both total emissions and emission factors showed an overall upward trend, reaching their lowest point around 2012, followed by significant increases in most counties during 2012–2022. Annual precipitation, wind speed, and temperature were identified as the primary climatic drivers of soil dust emissions across all counties, and their influences exhibited pronounced spatial heterogeneity in Bazhou. In Ruoqiang, Bohu, Korla, and Qiemo, dust emissions are mainly limited by precipitation, although dry conditions and sparse vegetation can amplify the role of wind. In Heshuo, Hejing, and Yanqi, stable vegetation helps to lessen wind’s impact. In Yuli, wind speed and temperature are the main drivers, whereas in Luntai, precipitation and temperature are both important constraints. These findings highlight the need to consider emission intensity, land use, or surface condition changes, and the potential benefits of increasing vegetation cover in severely desertified areas when formulating regional dust mitigation strategies. Full article

Clean and safe air quality is essential for public health, yet particulate matter (PM) significantly degrades air quality and poses serious health risks. The Gulf Cooperation Council (GCC) countries are particularly vulnerable to frequent and intense dust storms due to their vast desert landscapes. This study presents the first health risk assessment of carcinogenic and non-carcinogenic risks associated with exposure to PM2.5 and PM10 bound heavy metals and polycyclic aromatic hydrocarbons (PAHs) based on air quality data collected during the years of 2016–2018 near Dubai International Airport and Abu Dhabi International Airport. The results reveal no significant carcinogenic risks for lead (Pb), cobalt (Co), nickel (Ni), and chromium (Cr). Additionally, AI-based regression analysis was applied to time-series dust monitoring data to enhance predictive capabilities in environmental monitoring systems. The estimated incremental lifetime cancer risk (ILCR) from PAH exposure exceeded the acceptable threshold (10⁻⁶) in several samples at both locations. The relationship between visibility and key environmental variables—PM1, PM2.5, PM10, total suspended particles (TSPs), wind speed, air pressure, and air temperature—was modeled using three machine learning algorithms: linear regression, support vector machine (SVM) with a radial basis function (RBF) kernel, and artificial neural networks (ANNs). Among these, SVM with an RBF kernel showed the highest accuracy in predicting visibility, effectively integrating meteorological data and particulate matter variables. These findings highlight the potential of machine learning models for environmental monitoring and the need for continued assessments of air quality and its health implications in the region. Full article

Air pollution caused by pesticide residues is an emerging concern in urban environments influenced by nearby agricultural activities. In this study, weekly air samples were collected between May 2018 and March 2020 in Strasbourg, France, to quantify 104 pesticides in both gas and particle phases using GC-MS/MS and LC-MS/MS. Herbicides and fungicides were the most frequently detected classes, appearing in 98% of both phases followed by insecticides. Key compounds such as metalaxyl-M, diphenylamine, and bifenthrin were present in over 90% of samples. Concentrations ranged from 2.5 to 63 ng m⁻³ weekly, with cumulative annual loads exceeding 1200 ng m⁻³. Gas–particle partitioning revealed that highly volatile compounds like azinphos-ethyl favored the gas phase, while less volatile ones like bifenthrin and tebuconazole partitioned >95% into particles. A third-degree polynomial regression (R² of 0.74) revealed a nonlinear relationship between Kₚ and particle-phase concentrations, highlighting a threshold above Kₚ of 0.025 beyond which compounds accumulate disproportionately in the particulate phase. Seasonal variability showed that 36% of the annual pesticide load occurred in autumn, with total airborne levels peaking near 400 ng m⁻³, while the lowest load occurred during summer. Principal component analysis identified rainfall and total suspended particles as major drivers of pesticide phase distribution. The inhalation health risk assessed yielded hazard index values < 1 × 10⁻⁷ for all population groups, suggesting negligible non-cancer risk. This study highlights the prevalence, seasonal dynamics, and partition behavior of airborne pesticides in urban air and underscores the need for regulatory attention to this overlooked exposure route. Full article

Antimicrobial use in food animals selects for antimicrobial-resistant (AMR) bacteria, which most commonly reach humans via the food chain. However, AMR bacteria can also escape the feedyard via agricultural runoff, manure used as crop fertilizer, and even dust. A study published in 2015 reported AMR genes in dust from cattle feedyards; however, one of the study’s major limitations was the failure to investigate gene presence in viable bacteria, or more importantly, viable bacteria of importance to human health. Our main objective was to investigate the presence and quantity of viable bacteria and antimicrobial-resistant (AMR) determinants in fugitive bioaerosols from cattle feedyards in the downwind environment. Six bioaerosol sampling campaigns were conducted at three commercial beef cattle feedyards to assess variability in viable bacteria and AMR determinants associated with geographic location, meteorological conditions, and season. Dust samples were collected using four different sampling methods, and spiral plated in triplicate on both non-selective and antibiotic-selective media. Colonies of total aerobic bacteria, Enterococcus spp., Salmonella enterica, and Escherichia coli were enumerated. Viable bacteria, including AMR bacteria, were identified in dust from cattle feedyards. Bacteria and antimicrobial resistance genes (ARGs via qPCR) were mainly found in downwind samples. Total suspended particles (TSPs) and impinger samples yielded the highest bacterial counts. Genes encoding beta-lactam resistance (bla_CMY-2 and bla_CTX-M) were detected while the most common ARG was tet(M). The predominant Salmonella serovar identified was Lubbock. Further research is needed to assess how far viable AMR bacteria can travel in the ambient environment downwind from cattle feedyards, to model potential public health risks. Full article

The reduction of environmental pollutant emissions—including greenhouse gases, particulate matter, and other harmful substances—represents one of the foremost challenges in climate policy, economics, and industrial management today. Excessive emissions of CO₂, NO_X, and suspended particulates exert significant impacts on climate change as well as human health and welfare. Consequently, numerous studies and regulatory and technological initiatives are underway to mitigate these emissions. One critical area is intra-plant transport within manufacturing facilities, which, despite its localized scope, can substantially contribute to a company’s total emissions. This paper aims to assess the potential of computer simulation using FlexSim software as a decision-support tool for planning inter-operational transport, with a particular focus on environmental aspects. The study analyzes real operational data from a selected production plant (case study), concentrating on the optimization of the number of transport units, their routing, and the layout of workstations. It is hypothesized that reducing the number of trips, shortening transport routes, and efficiently utilizing transport resources can lead to lower emissions of carbon dioxide (CO₂) and nitrogen oxides (NO_X). The findings provide a basis for a broader adoption of digital tools in sustainable production planning, emphasizing the integration of environmental criteria into decision-making processes. Furthermore, the results offer a foundation for future analyses that consider the development of green transport technologies—such as electric and hydrogen-powered vehicles—in the context of their implementation in the internal logistics of manufacturing enterprises. Full article

To elucidate the compositional characteristics and sources of heavy metals in aerosols at China’s Great Wall Station in Antarctica, high-volume aerosol sampling was conducted from 4 January to 26 December 2022, on Fildes Peninsula, King George Island. Ten heavy metals (V, Cr, Mn, Fe, Ni, Cu, Zn, As, Cd, and Pb) in total suspended particulates (TSPs) were quantified via inductively coupled plasma mass spectrometry (ICP-MS). Enrichment factor (EF) analysis, correlation metrics, and backward trajectory clustering were integrated to identify potential sources. The results revealed pronounced enrichment (EF > 10) for Cr, As, Zn, Cd, and Pb, indicating dominant non-crustal contributions. Source apportionment identified three pathways: (1) long-range transported anthropogenic emissions, including Southern Hemisphere marine traffic (e.g., V and Ni from ship fuel combustion) and industrial pollutants from South America (Pb and Cd); (2) local anthropogenic sources, primarily diesel generators and tourism-related gasoline combustion (Cu and Zn); and (3) crustal inputs via glacial melt and weathering (Fe and Mn). This study pioneers the quantification of direct anthropogenic impacts (e.g., power generation and tourism) on aerosol heavy metals in Antarctic research zones, offering critical insights into transboundary pollutant dynamics and regional mitigation strategies. Full article

Wastewater treatment plants (WWTPs) have been confirmed as reservoirs of antibiotic resistance genes (ARGs). This study systematically investigated the distribution patterns of ARGs across different treatment units in municipal WWTPs, along with the environmental drivers, dissemination characteristics, and exposure risks of aerosol-borne ARGs in aerated tank environments. The results revealed a high compositional similarity in aerosol-borne ARGs across the sampling sites, with multidrug ARGs predominating at an average relative abundance of 52%, followed sequentially by tetracycline (11%), MLS (10%), and glycopeptide resistance genes (7%). The diffusion of aerosol-borne ARGs is significantly influenced by environmental factors including temperature, relative humidity, wind speed, and total suspended particulate (TSP) concentration, with temperature being the most dominant factor affecting the dispersion of ARGs. The atmospheric dispersion model demonstrates that aerosol-borne ARGs decay with increasing downwind distance, showing potential for transport from aeration tanks to locations exceeding 1500 m along the prevailing wind direction. Both within wastewater treatment units and downwind areas, adult males had higher respiratory exposure doses but lower skin contact doses compared to females, with respiratory doses exceeding skin contact by 3–4 orders of magnitude. This study highlights the potential health risks posed by aerosol-borne ARG transmission from WWTP operations. Full article

Harmful Algal Blooms (HABs), predominantly driven by cyanobacteria, pose significant risks to water quality, public health, and aquatic ecosystems. Lake Erie, particularly its western basin, has been severely impacted by HABs, largely due to nutrient pollution and climatic changes. This study aims to identify key physical, chemical, and biological drivers influencing HABs using a multivariate regression analysis. Water quality data, collected from multiple monitoring stations in Lake Erie from 2013 to 2020, were analyzed to develop predictive models for chlorophyll-a (Chl-a) and total suspended solids (TSS). The correlation analysis revealed that particulate organic nitrogen, turbidity, and particulate organic carbon were the most influential variables for predicting Chl-a and TSS concentrations. Two regression models were developed, achieving high accuracy with R² values of 0.973 for Chl-a and 0.958 for TSS. This study demonstrates the robustness of multivariate regression techniques in identifying significant HAB drivers, providing a framework applicable to other aquatic systems. These findings will contribute to better HAB prediction and management strategies, ultimately helping to protect water resources and public health. Full article

The emissions of particulates from burning agricultural fields threaten the environment and human health, contributing to air pollution and increasing the risk of respiratory and cardiovascular diseases. An analysis of total suspended particulate (TSP), PM2.5, and PM10 emissions from crop residue burning is presented in this study. A primary goal is to improve emission estimation accuracy by integrating satellite imagery from modes of Moderate Resolution Imaging Spectroradiometers (MODIS) and Visible Infrared Imaging Radiometers (VIIRS) with traditional data. Particulate emissions were estimated using Tier 1 and Tier 2 methodologies outlined in the EEA/EMEP Emission Inventory Guidebook based on thermal anomaly data from satellite observations. According to the findings, burning wheat, maize, barley, and rice residue accounts for most emissions, with significant variations identified in India, China, and the United States. The variations highlight the need for a location-specific approach to emission management. Particulate emissions cause adverse environmental and health impacts, which can be minimized by targeting mitigation strategies at key emission hotspots. The research provides important insights to inform policymakers and support developing strategies to reduce fine particulate agricultural emissions. Full article

This study investigated the impact of optically active substances on light attenuation in a tropical eutrophic urban reservoir under different seasonal conditions. Diffuse attenuation coefficients for photosynthetically active radiation (Kd_PAR) and ultraviolet radiation (Kd_UVA and Kd_UVB) were measured at three representative sites and correlated with water quality parameters (chlorophyll-a, total suspended solids [TSS], dissolved organic carbon, and colored dissolved organic matter [CDOM]). The results revealed significant spatial and seasonal differences, with the highest attenuation observed during the rainy season. The Ilha site exhibited the greatest coefficients (Kd_PAR = 6.0 m⁻¹, Kd_UVA = 17.9 m⁻¹, Kd_UVB = 19.0 m⁻¹), while lower values were recorded at Barragem (Kd_PAR = 2.4 m⁻¹, Kd_UVA = 9.1 m⁻¹, Kd_UVB = 12.0 m⁻¹) and Igrejinha (Kd_PAR = 3.1 m⁻¹, Kd_UVA = 10.8 m⁻¹, Kd_UVB = 11.9 m⁻¹). Statistical analyses showed strong correlations between TSS and Kd_PAR (r = 0.66) and between CDOM and both Kd_UVA (r = 0.66) and Kd_UVB (r = 0.59), with regression models confirming TSS and CDOM as key predictors of light attenuation. These findings underscore the pivotal role of particulate and dissolved organic matter in underwater light dynamics, emphasizing the need to reduce their input during periods of heavy rainfall. Full article

Stone mill operations contribute significantly to air pollution and increase health risks not only for workers but also for nearby communities. This study aimed to assess the health impacts of stone mill industries on nearby residents. The research was conducted in two areas: a primary region with a high number of stone mills and an area without stone mills. A questionnaire-based survey was employed, and potential health risks were evaluated using the hazard quotient (HQ) method. Total suspended particulates (TSPs) and particulate matter-10 micron (PM10) were analyzed as hazard factors based on monitoring data from seven stone mills collected between 2008 and 2021. The study found that residents in major stone mill areas reported higher hazard quotients (HQs) than those living farther from the mills, with a statistically significant association (p < 0.01). Seasonal variations also influenced dust distribution, with the highest TSP and PM10 levels recorded during winter, exacerbating health risks for local populations. This study highlights the need for improved community settlement planning, consideration of meteorological conditions, regulatory interventions by relevant agencies, and enhancements in environmental monitoring systems to mitigate the adverse health effects of stone mill operations. Full article

As an important component of urban ecosystems, changes in microbial communities in urban wetland ecosystems have a profound impact on human beings. In this paper, we studied the changes in microbial communities in urban wetland ecosystems (three major interfaces: atmosphere, foliage and water) under the background of atmospheric pollution by high-throughput techniques. The α-diversity of microorganisms at each interface showed that the species richness of the sample communities did not differ significantly at different levels of contamination and it was all at a high level. And the β-diversity showed a significantly larger between-group gap than within-group gap between the samples. The functions predicted a higher metabolic function in water samples and atmospheric samples, and a higher function of microorganisms harmful to humans in the microbial community on the leaf surface. Further analysis of the correlation between atmospheric particulate matter and environmental microorganisms revealed that the atmospheric microbial communities that were strongly negatively correlated with TSP, PM10, PM2.5, and PM1 were Actinobacteriota, Cyanobacteria, and Verrucomicrobiota. Among the microbial communities on the leaf surface, only Bacteroidota was strongly positively correlated with total suspended particle (TSP), particles with a diameter of 10 micrometers or less (PM10), particles with a diameter of 2.5 micrometers or less (PM2.5) and particles with a diameter of 1 micrometers or less (PM1). As for the microbial communities in the water column, Firmicutes, Bacteroidota, Campilobacterota, and Deferribacteres were strongly and positively correlated with the different particle sizes. There was no significant correlation between the functions of the three interfacial microorganisms and the particle size of the atmospheric particles. This paper studies the structure and function of microbial communities within three interfaces at three pollution levels and explores the resulting changes with the aim of providing directions for monitoring urban wetland ecosystems and for species diversity conservation. Full article

Fireworks are often used in celebrations and are a known transient source of extreme particulate air pollution, and the particles produced by fireworks are known to contain potentially harmful heavy metals. This study investigated ambient particulate metal concentrations associated with heavy firework use during the United States Independence Day holiday in July 2020 and July 2021 in Fullerton, California, located within the greater Los Angeles metropolitan area. For this study, barium (Ba), chromium (Cr), copper (Cu), lead (Pb), and strontium (Sr) were quantified, with Ba, Cu, and Sr being known tracers for fireworks and Cr and Pb being potentially harmful. Total suspended particulates (TSP) were collected with filters and then extracted and analyzed by graphite furnace atomic absorption spectroscopy. Hourly ambient particulate concentrations at a nearby monitoring station exceeded 500 μg m⁻³ and 300 μg m⁻³ in 2020 and 2021, respectively. Greater concentrations of overall particulate matter and ambient metal concentrations were observed during 2020 when compared to 2021, consistent with studies in the literature that have shown increased firework use in the area, likely due to the COVID-19 restrictions in place in 2020. In 2021, the Ba, Cu, and Sr concentrations peaked overnight on 4–5 July as expected, but the Cr and Pb concentrations peaked in the afternoon on July 5. In 2020, the peak concentrations of Cr and Pb were 510 ± 40 ng m⁻³ and 710 ± 30 ng m⁻³, respectively, while 4900 ± 200 ng m⁻³, 3860 ± 40 ng m⁻³, and 1810 ± 30 ng m⁻³ were observed for Ba, Cu, and Sr, respectively, among the highest ever observed to our knowledge. Full article

Chlorophyll-a (Chla) and total suspended solid (TSS) concentrations are important parameters for water quality assessment, and in recent years, machine learning has been shown to have great potential in this field. However, current water quality parameter inversion models lack interpretability and rarely consider the morphological characteristics of the spectrum. To address this limitation, we used Sentinel-3 OLCI data to construct an interpretable CatBoost model guided by spectral morphological characteristics for remote sensing monitoring of Chla and TSS along the coast of Fujian. The results show that the coastal waters of Fujian Province can be divided into five clusters, and the areas of different clusters will change with the alternation of seasons. Clusters 2 and 4 are the main types of coastal waters. The CatBoost model combined with spectral feature engineering has a high accuracy in predicting Chla and TSS, among which Chla is slightly better than TSS (R² = 0.88, MSE = 8.21, MAPE = 1.10 for Chla predictions; R² = 0.77, MSE = 380.49, MAPE = 2.48 for TSS predictions). We further conducted an interpretability analysis on the model output and found that the combination of BRI and TBI indexes composed of bands such as b8, b9, and b10 and the fluctuation of spectral curves will have a significant impact on the prediction of model output. The interpretable CatBoost model based on spectral morphological features proposed in this study can provide an effective technical means of estimating the chlorophyll-a and total suspended particulate matter concentrations in the coastal areas of Fujian. Full article

The dust generated during tunnel construction poses serious health risks to workers, as it not only causes respiratory obstruction but also leads to pneumoconiosis and respiratory failure after prolonged exposure. However, most existing studies focus on specific construction stages or particular particle sizes and often assume an ideal airflow, neglecting the complex flow fields, vortex effects, and dust composition variations at different stages in tunnel and mine construction. This study systematically analyzes the spatiotemporal distribution characteristics of dust at various stages of tunnel construction and proposes targeted prevention and control strategies. On the basis of measured data from three construction stages—the working face, initial support, and secondary lining stages—and SPSS 27 statistical analysis, a dynamic analysis was conducted on the concentration and distribution patterns of total suspended particulates (TSPs) and particulate matter of different sizes (PM10, PM4, PM2.5, and PM1). The results show that coarse particles dominate during the working face stage, whereas fine particles gradually accumulate during the initial support and secondary lining stages. Finally, this work establishes a dust concentration–excavation time/tunnel depth equation and proposes targeted dust control measures. These findings offer important practical value for enhancing construction safety and air quality. Full article