Search Results (1,424)

Morocco’s recent legalization of industrial and medicinal cannabis has created a rapidly expanding seed-oil sector whose sustainability has yet to be fully assessed. This study applies an environmental life cycle assessment (LCA) in accordance with ISO 14040:2006 and ISO 14044:2006, complemented by a qualitative social responsibility assessment based on ISO 26000:2010, aiming to evaluate the life cycle sustainability of Moroccan cannabis seed oil. Three representative processing chains, traditional artisanal presses, producer cooperatives and regulated industrial plants are compared using a functional unit of 1 kg of cold-pressed oil packaged for local distribution. Inventory data were drawn from field measurements and interviews and were modeled in OpenLCA with background datasets from Ecoinvent 3.8 and Agribalyse v3.1. Impact assessment used the ReCiPe 2016 (H) method at the midpoint level across nine categories (climate change, fossil resource scarcity, water use, freshwater eutrophication, terrestrial acidification, land occupation, carcinogenic, non-carcinogenic human toxicity, and fine particulate matter formation). Sensitivity analyses varied seed yield, electricity mix and transport distances by ±20% to gauge uncertainty. Results show that the cooperative scenario achieves the lowest impacts across nearly all categories because of higher extraction yields (3 kg seed per kg oil), lower energy use (0.54 kWh kg⁻¹ oil) and more effective co-product recovery. In contrast, artisanal extraction requires approximately 1 kg of additional seed input per functional unit compared to optimized scenarios, significantly increasing upstream environmental burdens and causing upstream agricultural burdens to multiply. Industrial facilities perform comparably to cooperatives if powered by renewable electricity. Integrating a semi-quantitative social responsibility assessment reveals that legalization has markedly improved organizational governance, labor conditions, consumer protection and community involvement. Cooperatives display the most balanced social performance, whereas industrial plants excel in governance and quality control. A set of recommendations, including drip irrigation, cultivar improvement, co-product valorisation, renewable energy adoption, eco-designed packaging and cooperative governance, is proposed to enhance the environmental and socio-economic sustainability of Morocco’s emerging cannabis seed-oil industry. Full article

Globally, the combined pollution of fine particulate matter (PM_2.5) and ground-level ozone (O₃) poses severe challenges to public health and sustainable urban development. Recent data indicate that the annual average PM_2.5 concentration in the vast majority of cities worldwide fails to meet World Health Organization safety standards, with air pollution causing millions of premature deaths annually. As a nature-based solution, the purification efficacy of vegetation remains poorly quantified due to unclear coupling mechanisms with local meteorological conditions. This study systematically reviewed and synthesized 229 empirical studies published between 2000 and 2025 from Web of Science and China National Knowledge Infrastructure (CNKI), aiming to clarify the quantitative relationships and regulatory mechanisms of plant–meteorological synergistic purification of PM_2.5–O₃. Following double-blind independent screening (κ = 0.85) and data extraction, a quantitative minimal feasible synthesis approach was adopted due to high data heterogeneity. The results indicated the following. (1) The median canopy purification efficiency of urban vegetation for PM_2.5 was 18.2% (IQR: 12.5–30.1%, n = 17), with a median dry deposition velocity (Vd–PM) of 0.05 cm s⁻¹ (0.02–30 cm s⁻¹, n = 15). The median dry deposition velocity (Vd–O₃) for O₃ was 0.55 cm s⁻¹ (0.12–1.82 cm s⁻¹, n = 8), with non-stomatal deposition contributing approximately 35%. (2) Meteorological factors exhibit nonlinear regulation: relative humidity (RH) > 70% significantly enhances PM_2.5 adsorption, wind speeds of 1.5–3.0 m s⁻¹ are optimal for PM_2.5 deposition, and temperatures > 30 °C generally inhibit plant uptake of both pollutants (n = 7). (3) Functional traits strongly correlate with purification efficacy: species with high leaf roughness (R² = 0.8), high stomatal conductance, and low BVOC emissions (e.g., Ginkgo biloba, Platycladus orientalis) exhibit optimal synergistic purification potential. Species with high BVOC emissions (Populus przewalskii, Eucalyptus robusta) can increase daily net O₃ pollution equivalents by up to 86 g and must be strictly avoided. Based on quantitative evidence, a green space planning decision matrix indexed by climate zone and pollution type was developed, specifying vegetation configuration patterns, functional group selection, and key design parameters (canopy closure, green belt width, etc.) for different scenarios. This study provides an actionable scientific basis for precision planning and climate-adaptive management of urban green infrastructure. Full article

Background: Ambient air pollution, particularly particulate matter (PM), has been linked to metabolic disorders, including diabetes. We evaluated associations between long-term exposure to coarse particulate matter (PM₁₀) and fine particulate matter (PM_2.5) and glycated hemoglobin (HbA1c) levels in a Korean population and assessed whether specific subgroups exhibited heightened susceptibility. Methods: We analyzed 6940 participants without diabetes from the Korean Genome and Epidemiology Study (KoGES) Ansan-Ansung cohort. Participants contributed 35,395 observations across a mean follow-up of 5.1 visits (2005–2017). Linear mixed models estimated associations between PM exposure and HbA1c while adjusting for covariates, including body mass index (BMI), time, and region. Subgroup analyses stratified by sex, age, BMI, region, education, smoking status, drinking status, and exercise. Results: Higher long-term PM₁₀ exposure was associated with elevated HbA1c (β = 0.0347 per interquartile range [IQR] increase of 9.48 μg/m³; 95% CI: 0.0220, 0.0473; p < 0.001). PM_2.5 showed a comparable positive association (β = 0.0166 per IQR of 8.67 μg/m³; 95% CI: 0.0010, 0.0321; p = 0.037). Associations were stronger among older adults (≥60 years: β = 0.0789 vs. <60 years: β = 0.0210; p-interaction < 0.001), rural Ansung residents (β = 0.0963 vs. Ansan: β = 0.0398; p-interaction < 0.001), participants with lower educational attainment (≤middle school: β = 0.0637; p-interaction < 0.001), and never smokers (β = 0.0455; p-interaction = 0.035). Conclusions: Among nondiabetic Korean adults, long-term PM₁₀ exposure was associated with higher HbA1c; PM_2.5 demonstrated a similar positive association. Associations were more pronounced among older adults, rural residents, individuals with lower educational attainment, and never smokers. These findings support ambient air pollution as an environmental contributor to subclinical glycemic changes and underscore the need for targeted public health strategies for vulnerable populations. Full article

Ambient fine particulate matter (PM2.5) is associated with increased mortality at concentrations below current regulatory standards. Studies of low-level exposure often rely on large administrative cohorts whose geographic and demographic composition may influence observed associations. In a prior analysis, we observed an association between long-term PM2.5 and all-cause mortality among Native American Medicare beneficiaries living in zip codes within the lowest decile of PM2.5 exposure. The present study, a case–control analysis of 1,713,399 low-PM2.5-exposed beneficiaries enrolled in traditional Medicare during 2015–2016, evaluated whether this association could be explained by geographic context, socioeconomic position (SEP), or baseline health status. We used principal components analysis to summarize area-level SEP indicators and beneficiary-level chronic disease diagnoses. In fully adjusted pooled models, PM2.5 was more strongly associated with mortality among Native American beneficiaries (odds ratio, OR = 1.12 per ug/m³; 95% CI 1.06–1.18) than among non-Native American beneficiaries (OR = 1.01 per ug/m³; 95% CI 1.001–1.02). Sequential adjustment among Native Americans showed that state-level geographic clustering accounted for most attenuation of the PM2.5 coefficient, with additional modest attenuation after adjustment for SEP and chronic disease patterns. These findings suggest that PM2.5–mortality associations observed in low-exposure populations may partly reflect geographic composition and underlying health differences within these large cohorts. Full article

Building on prior evidence that biomass cooking drives personal air pollution in rural and peri-urban Bangladesh, we measured kitchen pollution alongside personal exposure and examined the influence of outdoor industrial and traffic emissions on personal and indoor air quality. In an mHealth based-behavior change communication (BCC) intervention study (NCT05570552), 400 women were enrolled from rural Matlab and peri-urban Araihazar in Bangladesh. We measured 24 h personal exposure to fine particulate matter 2.5 (PM_2.5) and black carbon (BC) using personal monitors (UPAS V2), and 72–120 h PM_2.5 in 200 kitchens and outdoors of households using air quality sensors (PurpleAir Flex). Compared to clean fuel users, biomass users showed greater personal and kitchen exposure to PM_2.5, showing good correlation between personal and indoor PM_2.5 measurements (R² = 0.722). Daily average personal PM_2.5 and kitchen PM_2.5 during both cooking and non-cooking periods were higher in rural than peri-urban areas. Geographic information system mapping revealed that personal PM_2.5 was inversely related to the distance of factories from households when below <300 m in both rural and urban areas. Only in Araihazar, personal BC was higher in households located near factories or roads (<200–300 m) compared to those situated further away. Higher personal BC exposure was found in peri-urban women than rural women (p < 0.001). Higher levels of PM_2.5 and increased BC were found in rural and peri-urban households, respectively, which were located in close proximities to formal/informal factories and main roads. These findings highlight the need for sustainable household energy transitions and improved air quality management to reduce air pollution exposure in Bangladesh. Full article

We evaluated the indoor environmental quality of the administrative office at University Medical Center Ho Chi Minh City branch 2 and implemented a multi-stage engineering control strategy to optimize occupational health conditions. A cross-sectional assessment monitored important air quality parameters, including carbon dioxide (CO₂), fine particulate matter (PM2.5 and PM10), humidity, and illumination. Following baseline measurements, an integrated system was deployed to address pollutant mass balance, consisting of High-Efficiency Particulate Air (HEPA) filtration units for mechanical particle scrubbing, ceiling-mounted axial fans to induce forced convection, and ultraviolet-C germicidal lamps for photochemical disinfection. Post-intervention results demonstrated significant gains in system removal efficiency. CO₂ concentrations decreased by over 60% due to enhanced volumetric air exchange, while PM2.5 levels decreased by more than 40% through interception and diffusion mechanisms within the HEPA media. Furthermore, UVC irradiation achieved a 90% reduction in viable airborne microbial colonies. The results of this study show that low-cost, scalable environmental engineering controls and fluid dynamic optimizations effectively mitigate indoor air pollution and enhance workplace stability in healthcare administrative settings. Full article

Traffic-related emissions significantly contribute to fine particulate matter (PM_2.5) in urban roadside environments, where limited dispersion elevates human exposure and health risks. This study provides an integrated assessment of PM_2.5 exposure in a traffic-dominated roadside microenvironment in Jaipur, India, and evaluates seasonal variability, respiratory deposition dose (RDD), elemental composition, source characteristics, and inhalation health risk. Ambient PM_2.5 sampling was performed from October to February, and gravimetric and elemental analyses were conducted. RDD was quantified, and non-carcinogenic and carcinogenic risks were estimated using USEPA guidelines. PM_2.5 concentrations showed strong seasonal variability, peaking at 97 ± 5.85 µg/m³ during low-temperature winter weekdays, exceeding national and World Health Organization guidelines by 1.6 and 6.5 times, respectively. Winter conditions also led to higher RDD (~80% deposition in the head region) and the enrichment of traffic-related metals, particularly chromium, cadmium, and lead. Backward trajectory analysis indicated dominant local traffic influence with episodic regional transport. Non-carcinogenic risk surpassed unity for children during winters, while carcinogenic risk, primarily driven by chromium, exceeded acceptable thresholds (1 × 10⁻⁶), reaching 610 times higher during low-temperature winter weekdays. This first integrated PM_2.5 health risk assessment for Jaipur underscores the need of dose- and composition-based assessment in traffic-influenced urban environments. Full article

Pollution remains a major global public health concern increasingly associated with cancer incidence. This systematic review and meta-analyses examined the association between cancer risk and pollution across air, water, and land following the PRISMA guidelines. From 26,367 records initially identified in PubMed, Web of Science, and Scopus (January 2014–June 2025), 168 studies met the eligibility criteria. Meta-analyses conducted on 11 groups of studies revealed significant associations of lung cancer with fine particulate matter (HR_pooled = 1.347; 95% CI: 1.158–1.536), black carbon (HR_pooled = 1.096; 95% CI: 1.014–1.179) and ozone (HR_pooled = 0.941; 95% CI: 0.908–0.975), and breast cancer with nitrogen dioxide (HR_pooled = 1.064; 95% CI: 1.011–1.117). The association of ozone with cancer risks was inconsistent. While 155 studies reported on cancer risks from air pollution, only 10 studies focused on water pollutants and two on land pollutants, primarily heavy metals. Also, 79% of reviewed studies originated from only six high-income countries. The findings suggest that while particulate matter is a consistent risk factor, the global evidence base remains imbalanced based on pollution type and economic status of countries. Addressing these data gaps through targeted research in underrepresented regions and prioritizing the reduction of exposure to identified carcinogenic pollutants could reduce the global cancer burden. Full article

Fine particulate matter (PM_2.5) poses a significant global environmental health threat and is closely associated with diseases across multiple organ systems. This review systematically summarizes the toxic effects and underlying mechanisms of PM_2.5 in the respiratory, cardiovascular, nervous, immune, endocrine, digestive, and genitourinary systems. Key pathogenic processes involve shared pathways such as oxidative stress, inflammatory responses, endoplasmic reticulum stress, autophagy, and apoptosis, along with the activation of system-specific signaling networks. The complex composition and notable spatiotemporal variability of PM_2.5 present challenges for assessing its health risks and clarifying its mechanisms. Moving forward, integrating multi-omics and molecular epidemiology approaches will be essential to unravel its multi-system pathogenic networks and support the development of effective intervention strategies. Full article

To prevent fouling in blast furnace gas top pressure recovery turbine units, the dust content of the gas must be reduced, necessitating its deep purification. A critical challenge to be addressed is the low collection efficiency of fine particulate matter. To improve the collection efficiency of the fine particulate dust in BFG by wet electrostatic precipitators (WESPs), this study implemented measures such as optimizing nozzle atomization performance and the spatial distribution of droplets, along with adding chemical agglomeration agents and surfactants. These approaches promoted the chemical agglomeration of fine dust and enhanced dust collection efficiency. In this study, five nozzle types, six chemical agglomerating agents, and three surfactants were tested. The results show that, under overlapping spray conditions, the 1/8 solid cone nozzle produced the smallest droplet size with the most uniform spatial distribution, exhibiting a d50 of 141.17 μm. When this nozzle was used in combination with guar gum (GG) as a chemical agglomerant, the d50 of BFG dust increased from 8.46 μm to 14.75 μm. The synergistic application of 5 mg/m³ sesbania gum (SBG) and 5 mg/m³ octylphenol ethoxylate (OP-10) further increased the dust d50 to 19.08 μm. Using the 1/8 solid cone nozzle and an XTG concentration of 5 mg/m³ resulted in the highest dust collection efficiency of 96.76%, while the synergistic use of SBG/OP-10 achieved an efficiency of 97.69%. This study elucidates the influence of nozzle atomization characteristics and spray liquid type on dust agglomeration and collection efficiency, achieving an improvement in dust-removal efficiency and the capture of fine-particulate dust, and providing both theoretical and practical foundations for the deep purification of blast furnace gas. Full article

Nighttime light (NTL), a crucial indicator of human activity intensity, has not been systematically analyzed for its interactive mechanisms with air pollution and climate change. This study first investigates the spatiotemporal evolution of China’s total nighttime light (TNTL) and average nighttime light (ANTL), alongside key indicators of meteorological parameters and air pollution, at the grid scale from 2000 to 2023. We then employ prefecture-level city data and a geographically and temporally weighted regression (GTWR) model to quantify the spatiotemporally heterogeneous associations of temperature (TMP), precipitation (PRE), fine particulate matter (PM_2.5), ozone (O₃), land use (LUL), topography, and socioeconomic factors with NTL. The results indicate that (1) China’s NTL exhibits a significant overall upward trend, with areas of increase or significant increase comprising 92.04% of the total study area. TNTL growth demonstrates regional heterogeneity, expanding by a factor of 4.91 in East China and 2.65 in Northeast China; (2) meteorological and air pollution indicators display spatiotemporal non-stationarity, with the synergistic effect between O₃ and PRE being the strongest; (3) among NTL drivers, LUL contributes most significantly (0.44), followed by TMP (0.14) > PM_2.5 (−0.33 × 10⁻¹) > O₃ (0.17 × 10⁻¹) > PRE (−0.33 × 10⁻⁶); (4) TMP and PRE may primarily influence NTL by altering ecological conditions and nighttime activity patterns. TMP shows a strong positive correlation with NTL in the junction zone of South, East, and Central China, whereas PRE predominantly exerts a negative influence; (5) air pollution exhibits distinct spatiotemporal effects: high PM_2.5 and O₃ generally correspond to lower NTL, though positive correlations persist in some areas due to industrial structures, highlighting the need for integrated policies that balance air quality management with sustainable urban planning; (6) the 2013 “Air Pollution Prevention and Control Action Plan” significantly strengthened the negative correlation between PM_2.5 and NTL in North China. However, O₃ concentrations increased by 28.9% after 2017, underscoring the challenge of coordinating VOC and NOx controls for long-term atmospheric sustainability. Full article

This study evaluates the ability of three classification procedures to distinguish areas with different levels of atmospheric pollution, based on biomonitoring carried out by analyzing the color and spectral characteristics of mulberry (Morus L.) and linden (Tilia L.) leaves. Sampling was carried out in areas that were grouped into four classes according to the concentrations of fine particulate matter (PM2.5, PM10) and gaseous pollutants (TVOC, NOx, SOx, CO, and eCO₂), measured using a specialized multisensor device. A total of 57 informative features were analyzed, representing indices obtained from two color models (RGB and Lab), as well as from VIS and NIR spectral characteristics measured for the adaxial and abaxial leaf surfaces. The data processing methodology includes feature selection using the ReliefF method and a comparative analysis between two approaches to dimensionality reduction—principal components (PC) and latent variables (LV). The results indicate that data reduction using PC provides significantly higher accuracy and better class separability, regardless of the classifier used, compared to LV, where errors exceed 40%. The comparison between classifiers shows a clear superiority of nonlinear models. While linear discriminant analysis demonstrates low efficiency, quadratic discriminant analysis (Q and DQ) and SVM with radial basis function (RBF) achieve high accuracy of class separability, reaching 100% in the SVM-RBF model for both tree species. The study also reveals functional asymmetry: the adaxial side of the leaves is more informative for spectral indices, while the abaxial side is more sensitive to color changes. The results confirm that the combined optical characteristics obtained from the leaf surface of bioindicators form a reliable method for ecological monitoring of air quality in urban areas. Full article

Agroforestry, where trees and shrubs are planted in row-alley systems, can utilize the natural ability of plants to interact with pollutants and serve as a passive biotechnological method for improving air quality. A method for programming air phytoremediation processes is presented, using appropriately shaped plant structures, considering species characteristics and the spatial configuration of plants in row-alley plantings. The main objectives of this study were: to determine the relationship between pollution reduction and the characteristics of plant communities, considering the parameters of individual plants and group characteristics, to determine strategic parameters for the interaction between plants and pollutant flows, and to identify optimization paths for each stage. The optimization of the air phytoremediation process is presented using the example of changes in the fine particulate matter (PM_2.5) concentration pattern, analyzed through numerical experiments using micrometeorological computational fluid dynamics models (ENVI-met software). Ex-ante analysis of hypothetical scenarios showed that introducing appropriate configurations of variable vegetation structure could lead to pollution reductions of up to 19%. The effectiveness of the presented plant systems qualifies this method as a type of bioengineering technology, supporting the multifunctionality of agroforestry systems. Full article

This study investigates the vertical variation and temporal characteristics and indicates the sources of black carbon (BC) and brown carbon (BrC) within particulate matter fraction PM₁ during light (November–December 2024) and heavy (January–February 2025) haze episodes in Bangkok, Thailand, a topic where data are still limited data regarding Southeast Asian megacities. Continuous measurements were conducted at 30 and 110 m above ground level, together with particle size distribution measurement, micrometeorological observations, and backward air mass trajectory analysis. During the haze periods, the highest particle number concentrations occurred in the 0.3–0.4 µm size range, indicating dominant contributions from combustion-related emissions and secondary aerosol formation. Mean PM₁ mass concentrations during the heavy haze episodes were more than 2.5 times higher than those during light haze. BC concentrations increased substantially during heavy haze, while the BC fraction of PM₁ remained relatively constant (~10%). In contrast, the BrC fraction reached nearly 20%, reflecting an increasing influence of biomass burning emissions associated with regional transport. Combined analyses of BC/BrC relationships, wind-direction dependence, and air mass trajectories demonstrate mixed contributions from local fossil fuel combustion and long-range transport of biomass burning aerosols during severe haze events. Full article

Crop residue open burning is a major source of atmospheric pollutants that degrade regional air quality, enhance climate forcing, and threaten public health through emissions of particulate matter, greenhouse gases, and toxic species. In southern China, satellite-based emission estimates are often underestimated because frequent cloud cover and limited spatiotemporal resolution hinder the detection of agricultural fires. In this study, crop residue open burning emissions in Guangxi province from 2017 to 2023 were quantified using a statistical approach. The open burning proportion (OBP) was updated on an annual basis using the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG), and recently reported emission factors (EF_S) were adopted to enhance estimation accuracy. Annual emissions of pollutants were then spatially distributed to 0.05° × 0.05° grid cells based on satellite-detected fire counts and land cover information. The results indicated the total emissions of black carbon (BC), organic carbon (OC), sulfur dioxide (SO₂), nitric oxide (NO_X), carbon monoxide (CO), carbon dioxide (CO₂), fine particles (PM_2.5), coarse particles (PM₁₀), ammonia (NH₃), methane (CH₄) and non-methane volatile organic compound (NMVOC) in Guangxi province during 2017–2023 were 58.90, 230.48, 37.90, 213.95, 4234.41, 108,775.48, 583.09, 667.70, 46.36, 322.74 and 710.20 Gg, respectively. Sugarcane residue burning was identified as the dominant contributor, accounting for 41.26–64.38% of total emissions, followed by rice (20.66–43.06%), corn (5.11–17.25%), and cassava (4.33–6.45%). Emissions exhibited clear interannual variability, declining from 2017 to 2020 under strict control measures and increasing again from 2021 to 2023 as enforcement weakened. Incorporating annually updated VIIRS-derived OBP_S into the statistical inventory improves the temporal representation and reliability of multi-year emission estimates for agricultural burning. Full article