Total Particulate Matter Research Articles

Particle composition-based water classification method for estimating chlorophyll-a in coastal waters from OLCI images

The complex composition of seawater presents significant challenges for accurately estimating biogeochemical data through optical measurements, both in situ and via satellite data. To address the regional applicability of single bio-optical or remote sensing algorithms caused by these challenges, we explored a water optical classification method based on inherent optical properties and particle composition. The ratio of organic particulate matter to total suspended particulate matter concentration (POM/SPM) serves as an optical discriminator of water bodies based on the proportions of organic and mineral particles. The boundary value is determined by the relationship between the particulate backscattering coefficient bbp(λ) and POM/SPM. By analyzing in situ data collected from the coastal waters of Qinhuangdao in the Bohai Sea, China, we developed empirical algorithms to estimate both the POM/SPM ratio and chlorophyll-a (Chl-a) concentration, the latter being a key parameter derived from current ocean remote sensing that indicates phytoplankton abundance. The evaluation of our algorithms demonstrates that accounting for POM/SPM variations significantly improves Chl-a estimate accuracy across the optically-complex coastal waters near Qinhuangdao compared to algorithms that do not consider changes in particle composition, such as the well-known OC4Me algorithm. Furthermore, we determined the distribution of monthly averaged Chl-a concentration and POM/SPM ratio on the coast of Qinhuangdao, Bohai Sea, in 2023. Our results show, for the first time, that the monthly average variations of the POM/SPM ratio in the Bohai Sea and Chl-a concentrations exhibit pronounced seasonal fluctuations.

Particulate matter and potentially toxic element content in urban ornamental plant species to assess pollutants trapping capacity.

Urban environments are usually polluted by anthropogenic activities like traffic, a major source of potentially toxic elements (PTEs), and ornamental plant species may reduce contamination by trapping traffic-related air pollutants in their leaves. The purpose of this study was tested the trapping pollutant capacity of four species commonly used in green areas of Seville city (SW Spain) to better choose species in urban green planning. Composition of particulate matter (PM) obtained from foliar surfaces (sPM) and wax-included (wPM) was determined by EDX-SEM analysis in samples from different city locations. Concentration of different PTEs (Ba, Cd, Cr, Cu, Fe, Mn, Ni, Pb, V and Zn), by microwave induced-plasma optical emission spectroscopy (MP-AES) were also analyzed in unwashed leaves of one of the selected species (Citrus aurantium) since it is the most cultivated species in Seville. Results showed that Nerium oleander was the plant species which trapped best superficial total and coarse PM. This capacity was enhanced by the presence of a waxy-cuticle and by cuticle thickness but not by leaf hairs. The only species unable to trap fine particles was Bougainvillea glabra. The most representative sPM on leaf surfaces from all species was the largest fraction (59-75%), followed by coarse (25-37%) and fine fractions (2.2-4.4%). In the wax PM, 48% of coarse particles were found in Citrus aurantium. Particulate matter deposited on surface foliage in general did not vary seasonally, while the large fraction of wPM in summer was significantly higher than in winter. The seasonal differences also existed in the level of PTE (Cd, Fe, Ni and V) in leaves. This work indicates that the leaf traits should be taken into account to evaluate the pollutants caption capacity, especially when planning of recreational green urban areas. Particulate matter composition was different according to the pollution sources and mostly contained Al, C, Ca, Fe, K, and Mg, but potentially toxic elements such as Si, As, Cr, Cu and Zn just accounted for 0.11-1.95% of the total elemental content.

Buff-tailed bumblebee, an underrated indicator of air pollution: a comparison of particulate matter accumulation by Bombus terrestris L. and Apis mellifera L.

Abstract Anthropogenic and natural particulate matter (PM) affects urban and agricultural areas and contaminates the bodies of Apis mellifera (honeybee) and Bombus terrestris (buff-tailed bumblebee). Although both species accumulate PM, scientific interest has primarily focused on A. mellifera as a pollution indicator. This study directly compared the efficacy of honeybees and bumblebees as indicators of PM and its associated trace elements (TEs). Insects were collected from ecological and conventional apple orchards and underwent quantitative analysis of total PM, PM size fractions, and TEs. To establish an environmental context, plant samples and bee products were obtained from both plantations, and screened for PM and/or TEs. Bombus terrestris accumulated 191.3 μg total PM per individual, whereas A. mellifera accumulated 64 μg. Particular PM size fractions were also significantly more abundant on bumblebees. Accumulation patterns of total and large PM on bumblebees differed between the ecological and conventional orchards. Total PM accumulated by both species combined correlated strongly with the total PM covering apple tree foliage in the ecological orchard (r = 0.836) and with grass in the conventional orchard (r = 0.851). The amount of total PM accumulated by B. terrestris strongly correlated with the concentrations of Fe (r = 0.927) and Mn (r = 0.91) in this species. Accumulation of Fe by A. mellifera correlated with the content of this metal in pollen (r = 0.912) and bee bread (r = 0.91), whereas the reverse trend was found for Mo in bee bread (r = –0.912). The results indicate that B. terrestris is a more accurate pollution indicator, with potentially greater efficacy in more polluted areas. The observed differences are most likely attributed to the distinct morphology and behavior of the species.

Emission Reduction Characteristics of Heavy-Fuel Aircraft Piston Engine Fueled with 100% HEFA Sustainable Aviation Fuel.

With the projected expansion of the general aviation sector and recent breakthroughs in sustainable aviation fuels (SAF), accurately measuring emissions from novel aircraft engines powered by SAF is paramount for evaluating the role of aviation industry in emission reduction trends and environmental consequences. Current SAF research primarily centers on low blend ratios, neglecting data on 100% SAF. This study bridges this gap by experimentally determining emissions indices for gaseous pollutants (CO, CO2, HC, NOx), total particulate matter (PM) counts and sizes, and non-volatile particulate matter (nvPM) number and mass concentrations from a heavy-fuel aircraft piston engines (HF-APE) using hydroprocessed esters and fatty acids-derived SAF (HEFA-SAF), adhering to airworthiness-standard sampling and measurement protocols. Additionally, nvPM morphology and structure are analyzed to auxiliarily assess the emission reduction. The results demonstrate that HEFA-SAF stands out for its marked reduction in both CO and HC gaseous pollutant compared to RP-3 aviation kerosene (RP3), as well as effectively reduces PM emissions compared to Diesel and RP3 across all load conditions. Notably, HEFA-SAF significantly curbs the generation of both nucleation-mode and accumulation-mode PM. Specifically, the use of HEFA-SAF leads to a 43% and 24% decrease in average nvPM number concentration, and a 65% and 53% reduction in average nvPM mass concentration respectively, compared to Diesel and RP3. At 50% load, nvPM produced by HEFA-SAF exhibits distinct nanostructural properties, characterized by fewer exposed pores and active sites within agglomerated particles. Rigorous emission testing has conclusively validated the substantial benefits of 100% HEFA-SAF in reducing emissions, offering a compelling rationale for the development of airworthiness regulations and environmental oversight frameworks designed to foster sustainable aviation practices.

Longitudinal associations between air pollution and incident dementia as mediated by MRI-measured brain volumes in the UK Biobank.

Although there is increasing evidence that environmental exposures are associated with the risk of neurodegenerative conditions, there is still limited mechanistic evidence evaluating potential mediators in human populations. UK Biobank is a large long-term study of 500,000 adults enrolled from 2006 to 2010 age 40-69years. ICD-10 classified reports of dementia cases up to 2022 (Alzheimer's disease, vascular dementia, dementia in other classified diseases, and unspecified dementia) were identified from health record linkage. Estimates of residential air pollution, traffic noise, and greenspace exposure have been modelled. Structural brain MRI was conducted from 2014 to 2022, with brain volumes relevant to dementia identified a priori. Associations between environmental exposures, brain volumes, and dementia cases (diagnosed post-MRI) were tested using linear and logistic regression and adjusted for age, sex, household income, ethnicity, education, smoking, and area-level deprivation. Mediation of exposure-outcome associations by plausible brain volumes (those associated with both environmental exposure and dementia outcomes) were modelled using the quasi-Bayesian Monte Carlo method (N=34,817-39,772). Small but significant mediating effects (2%-8% of relationships mediated) were observed between PM2.5abs exposure and dementia risk by reduced total brain volume, NOx and Alzheimer's disease risk by reduced peripheral cortical grey matter, PM2.5abs and vascular dementia risk by reduced peripheral cortical grey matter, PM2.5abs and other dementia risk by reduced total grey matter, and PM10 and other dementia risk by reduced total grey matter. Greenspace and noise were not associated with dementia outcomes in the subset of the cohort providing brain imaging data. This study adds to existing evidence of associations between environmental exposures and dementia outcomes. Our findings provide novel evidence that differences in brain volume may mediate these relationships. Future research is required to prove this mechanism and establish the other mechanisms through which exposure to air pollution might increase dementia risk.

An evaluation approach to PM2.5 policy effectiveness over South Korea based on a newly proposed scalable spatial decomposition method

Abstract Air quality management policies often exhibit spatial inconsistencies in effectiveness due to the diverse spatial scales of air pollution variability, which result from source characteristics as well as geographical and meteorological factors. To address this, the present study proposes a scalable spatial decomposition method to separate spatiotemporal air pollution data into background (nationwide), intercity-scale (tens of kilometers), and neighborhood-scale (several kilometers) components. This decomposition was achieved by introducing spatially varying effective ranges for intercity-scale variability at each station, based on the correlation coefficient distance of the background-removed component. Applying this approach to hourly fine particulate matter (PM2.5) concentrations from 535 monitoring stations across South Korea for 2021–2022, we evaluated the effectiveness of PM2.5 management policies. During the polluted cold season (December to March), the intercity-scale component contributed an average of approximately 18% of the total PM2.5 concentration in the Seoul Metropolitan Area (SMA) and Central Area (CA), which are densely populated and industrialized regions. In contrast, this component helped reduce PM2.5 levels in southeastern coastal areas, where high winds facilitate dispersion. The neighborhood-scale component contributed positively to PM2.5 levels near industrial complexes and ports but negatively in residential and commercial areas. The results demonstrate the effectiveness of central government-led intercity-scale regulations on total emissions allowances in the SMA and CA and highlight the need for additional local management targeting individual point sources near industrial complexes and ports. This study provides intuitive spatial decomposition tools for understanding PM2.5 pollution across spatial scales and offers policymakers a foundation for developing multi-scale mitigation strategies.

An Interpretable CatBoost Model Guided by Spectral Morphological Features for the Inversion of Coastal Water Quality Parameters

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.

Quality range of ENplus A1 certified wood pellets and their combustion behavior in residential pellet stoves and pellet boilers

AbstractUse of wood pellets is continuously increasing in Europe, and most of them carry the ENplus A1 label. In this research, 28 assortments of such A1-labeled wood pellets were collected from the fuel market and were comprehensively analyzed and combusted in two residential pellet stoves; up to 8 pellet assortments were also combusted in two residential pellet boilers. Correlation analyses have been carried out, including the results from 31 additional pellet assortments from an own pelleting process where specifically tailored pellet characteristics were produced using different wood species. In particular, the effects of pellet length, moisture content, fines content, raw material preparation, additives, and wood species were investigated to identify the dependencies between the investigated fuel parameters and pollutant emissions. The most important parameters influencing pollutant emissions especially from domestic pellet stoves were identified as the potassium content of the fuel, the hardwood content, the carbonate content of the fuel ash, the Si/K molar ratio, and the energy density. Total particulate matter (TPM) emissions ranged from 45 to 246 mg/m3 for pellet stove 1, whereas for pellet stove 2, TPM emissions from 23 to 58 mg/m3 have been observed. Not the same pellets performed best or worst in both pellet stoves. Overall, the current state of the art stoves showed a greater response to variations in pellet quality compared with boilers, and improvements in pellet stove technology and control are recommended.

Response of South Asia PM2.5 pollution to ammonia emission changes and associated impacts on human health.

Countries in South Asia are suffering severe PM2.5 pollution with rapid economic development, impacting human health and the environment. Whilst much attention has been given to understanding the contribution of primary emissions, the contribution of agriculture to PM2.5 concentrations, especially from agricultural ammonia (NH3) emissions, remains less explored. Using an advanced regional atmospheric chemistry and transport modelling system (WRF-EMEP) with a new estimate of anthropogenic NH3 emissions inputs, we estimate the influence of agricultural NH3 emissions on surface PM2.5 in South Asia and evaluate the health impacts and the economic losses attributable to PM2.5 in 2018. Results show that WRF-EMEP can reproduce magnitudes and variations of PM2.5 well, with a high annual mean PM2.5 concentration that exceeds 120µg/m2 and mainly appeared in the Indo-Gangetic Plain. We estimate 2,228,000 (95% Confidence Interval: 2,052,000-2,400,000) premature deaths and US$ 596,000 (95% CI: 549,000-642,000) million in economic losses are attributable to total ambient PM2.5 under the current emissions. We calculate that NH3 emissions are associated with 11% of the annual average PM2.5 concentrations across South Asia. Changes in PM2.5 concentrations follow a non-linear response to NH3 emissions reductions, highlighting increased efficiency with 70%-100% reductions in NH3 emissions reductions. We estimate that 247,000 (227,000-265,000) premature deaths and US$ 66,000 (61,000-71,000) million economic losses through this pathway can be attributed to NH3 emissions. These findings confirm that in the current NH3-rich chemical environment of South Asia, the efficiency of PM2.5 reduction is only moderately sensitive to the reduction in intensity of NH3 emissions until emissions are cut very severely. Thus, SO2, NOx and NH3 emissions controls need to be considered jointly for greater mitigation of ambient secondary PM2.5 in South Asia.

Analysis of the Generation and Spatiotemporal Distributions of Dust During Tunnel Construction

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.

Enhancing indoor air quality and cardiopulmonary health in patients with asthma by photocatalytic oxidation and filters air cleaner

BackgroundAir purifiers can enhance indoor air quality and health outcomes, and studies have primarily focused on filters and particulate matter (PM) in households. Photocatalytic oxidation (PCO) is a promising technique for eliminating gaseous pollutants and bioaerosols. However, no field study was conducted in household. Therefore, this study evaluated the effects of the PCO and PCO + filters intervention on indoor air pollutants and cardiopulmonary endpoints in households. MethodsA randomized, double-blind crossover clinical trial was conducted. Indoor air pollutants, including PM, bioaerosols, and gaseous pollutants and cardiopulmonary endpoints including lung function, fractional exhaled nitric oxide (FeNO), respiratory symptoms, and blood pressure were assessed before and after intervention. FindingsThis was the first study to evaluate the effects of PCO and PCO + filters interventions on indoor air pollutants and cardiopulmonary health in households. Indoor total volatile organic compounds (TVOC) and sulfur dioxides (SO2) significantly reduced after PCO intervention, however, we also observed the significant reduction in percentage of predicted values of forced vital capacity (FVC%) and forced expiratory volume in 3 s (FEV3%) and increased in FeNO after 13 days of PCO intervention. The PCO + filters intervention significantly reduced the levels of indoor PM1, PM2.5, PM4, PM10, total suspended particulate matter, ultrafine particles, airborne bacteria, fungi, endotoxin, mites, TVOC, nitrogen dioxide, and SO2, and marginal reduction in carbon monoxide. However, indoor carbon dioxide significantly increased after PCO/PCO + filters intervention. As for cardiopulmonary health, FVC%, and FEV1 % marginally increased 7 days after the PCO + filters intervention.

Condensable Particulate Matter Removal and Its Mechanism by Phase Change Technology During Wet Desulfurization Process

Limestone-gypsum wet flue gas desulfurization (WFGD) played a key role in SOx removal and clean emissions. However, it would also affect the condensable particulate matter (CPM) removal and compositions. The effects of the WFGD system on the removal of CPM and the contents of soluble ions in CPM were investigated in a spray desulfurization tower at varied conditions. The results indicate that the emission concentration of CPM decreased from 7.5 mg/Nm3 to 3.7 mg/Nm3 following the introduction of cold water spray and hot alkali droplet spray systems. This resulted in a CPM reduction rate of approximately 51%, reducing the percentage of CPM in total particulate matter and solving the problem of substandard particulate matter emission concentrations in some coal-fired power plants. The concentrations of NO3−, SO42−, and Cl− among the soluble ions decreased by 41–66.6%. As the liquid-to-gas ratio of the cold water spray and hot alkali droplet spray increased, CPM came into contact with more spray, which accelerated dissolution and chemical reactions. Consequently, the CPM emission concentration decreased by 17.4–19%. The liquid-to-gas ratio has a great effect on the ion concentrations of NO3−, SO42−, Cl− and NH4+, with a decrease of 28–66%. The temperatures of the cold water spray and the hot alkali droplet spray primarily affect the ionic concentrations of SO42− and Ca2+, leading to a decrease of 32.3–51%. When the SO2 concentration increased from 0 mg/Nm3 to 1500 mg/Nm3, large amounts of SO2 reacted with the desulfurization slurry to form new CPM and its precursors, the CPM emission concentration increased by 57–68.4%. This study addresses the issue of high Concentration of CPM emissions from coal-fired power plants in a straightforward and efficient manner, which is significant for enhancing the air quality and reducing hazy weather conditions. Also, it provides a theoretical basis and technical foundation for the efficient removal of CPM from actual coal-fired flue gas.

Fine Particulate Matter, Its Constituents, and Spontaneous Preterm Birth

The associations of exposure to fine particulate matter (PM2.5) and its constituents with spontaneous preterm birth (sPTB) remain understudied. Identifying subpopulations at increased risk characterized by socioeconomic status and other environmental factors is critical for targeted interventions. To examine associations of PM2.5 and its constituents with sPTB. This population-based retrospective cohort study was conducted from 2008 to 2018 within a large integrated health care system, Kaiser Permanente Southern California. Singleton live births with recorded residential information of pregnant individuals during pregnancy were included. Data were analyzed from December 2023 to March 2024. Daily total PM2.5 concentrations and monthly data on 5 PM2.5 constituents (sulfate, nitrate, ammonium, organic matter, and black carbon) in California were assessed, and mean exposures to these pollutants during pregnancy and by trimester were calculated. Exposures to total green space, trees, low-lying vegetation, and grass were estimated using street view images. Wildfire-related exposure was measured by the mean concentration of wildfire-specific PM2.5 during pregnancy. Additionally, the mean exposure to daily maximum temperature during pregnancy was calculated. The primary outcome was sPTB identified through a natural language processing algorithm. Discrete-time survival models were used to estimate associations of total PM2.5 concentration and its 5 constituents with sPTB. Interaction terms were used to examine the effect modification by race and ethnicity, educational attainment, household income, and exposures to green space, wildfire smoke, and temperature. Among 409 037 births (mean [SD] age of mothers at delivery, 30.3 [5.8] years), there were positive associations of PM2.5, black carbon, nitrate, and sulfate with sPTB. Adjusted odds ratios (aORs) per IQR increase were 1.15 (95% CI, 1.12-1.18; P < .001) for PM2.5 (IQR, 2.76 μg/m3), 1.15 (95% CI, 1.11-1.20; P < .001) for black carbon (IQR, 1.05 μg/m3), 1.09 (95% CI, 1.06-1.13; P < .001) for nitrate (IQR, 0.93 μg/m3), and 1.06 (95% CI, 1.03-1.09; P < .001) for sulfate (IQR, 0.40 μg/m3) over the entire pregnancy. The second trimester was the most susceptible window; for example, aORs for total PM2.5 concentration were 1.07 (95% CI, 1.05-1.09; P < .001) in the first, 1.10 (95% CI, 1.08-1.12; P < .001) in the second, and 1.09 (95% CI, 1.07-1.11; P < .001) in the third trimester. Significantly higher aORs were observed among individuals with lower educational attainment (eg, less than college: aOR, 1.16; 95% CI, 1.12-1.21 vs college [≥4 years]: aOR, 1.10; 95% CI, 1.06-1.14; P = .03) or income (<50th percentile: aOR, 1.17; 95% CI, 1.14-1.21 vs ≥50th percentile: aOR, 1.12; 95% CI, 1.09-1.16; P = .02) or who were exposed to limited green space (<50th percentile: aOR, 1.19; 95% CI, 1.15-1.23 vs ≥50th percentile: aOR, 1.12; 95% CI, 1.09-1.15; P = .003), more wildfire smoke (≥50th percentile: aOR, 1.19; 95% CI, 1.16-1.23 vs <50th percentile: aOR, 1.13; 95% CI, 1.09-1.16; P = .009), or extreme heat (aOR, 1.51; 95% CI, 1.42-1.59 vs mild temperature: aOR, 1.11; 95% CI, 1.09-1.14; P < .001). In this study, exposures to PM2.5 and specific PM2.5 constituents during pregnancy were associated with increased odds of sPTB. Socioeconomic status and other environmental exposures modified this association.

Effect of sludge-based additive on particulate matter emission during the combustion of agricultural biomass pellet

In this study, the effects of a sludge-based additive on particulate matter emission during the cornstalk pellet combustion were investigated using a vertical fixed bed reactor combined with a Dekati low pressure impactor (DLPI). It was found that the PM1 and total PM10 yields from the cornstalk pellet combustion were much higher than those from the individual combustion of sludge-based additive. With the addition of sludge-based additive, the alkali and alkaline earth metals (AAEMs) in biomass would react with the Al/Si-containing and P-containing inorganic minerals in sludge-based additive to form various aluminosilicates, silicates, and K-Ca-P compounds, which could enhance the potassium fixation and retain it in the coarse particles, such as fly ash and residual ash. The PM1 and total PM10 yields from the composite pellet combustion could be effectively reduced by 62 %, with the mixing ratio of sludge-based additive increasing to 75 %. The mixing ratio of sludge-based additive showed a positive synergistic effect in reducing the emission of PM and a mixing ratio of <30 % was recommended based on comprehensive considerations to produce relatively high-quality pellet fuel with respect to good heating value and low ash content and PM emission in later combustion application.

Life cycle environmental impact assessment of the “Sindhuja-I” wave energy converter

The life cycle assessment (LCA) is an inevitable part of ocean wave energy systems. This paper presents an LCA of the Sindhuja-I ocean wave energy converter (WEC). The WEC is a point absorber type WEC tested at the Tuticorin port in India. This study utilized the ISO 14044 standard and SimaPro LCA software to assess the environmental impact of the WEC in comparison to a coal power plant, offshore wind, and tidal energy devices. The functional unit considered is the generation of 1 kWh of electricity. The WEC has a global warming potential of 174 gCO2-eq/kWh, showcasing an impressive 81.9% reduction in emissions compared to coal power plants. Stainless steel contributes significantly to emissions, accounting for 93.7%, 94.6%, 99.4%, and 94.1% of total CO2, NOx, SO2, and PM2.5 emissions, respectively. The paper emphasizes the potential environmental benefits of recycling WEC materials at the end of their lifespan. Furthermore, the study suggests that the longevity of a point absorber device can positively impact its overall environmental footprint, given its minimal moving parts and low maintenance requirements. The WEC has a promising environmental footprint of ocean wave energy conversion, particularly compared to traditional fossil fuel-based power generation.

North American Fine Particulate Matter Chemical Composition for 2000-2022 from Satellites, Models, and Monitors: The Changing Contribution of Wildfires.

Air quality management benefits from an in-depth understanding of the emissions associated with, and composition of, local PM2.5 concentrations. Here, we investigate the changing role of biomass burning emissions to North American PM2.5 exposure by combining multiple satellite-, ground-, and simulation-based data sets biweekly at a 0.01° × 0.01° resolution from 2000 to 2022. We also developed a Buffered Leave Cluster Out (BLeCO) method to address autocorrelation and computational cost in cross-validation. Biomass burning emissions contribute an increasingly large fraction to PM2.5 exposure in the United States and Canada, with national annual population-weighted mean contributions increasing from 0.4 μg/m3 (3-5%) in 2000-2004 to 0.8-0.9 μg/m3 (9-14%) by 2019-2022, led by western North American 2019-2022 annual contributions of 1.4-1.9 μg/m3 (15-27%) and maximum seasonal contributions of 3.3-5.5 μg/m3 (29-49%). Other components such as nonbiomass burning Organic Matter (OM) and nitrate can be regionally as (or more) important, albeit with distinct seasonal variability. The contribution of total OM to PM2.5 exposure in the United States in 2016-2022 is 42.2%, comparable to all other anthropogenically sourced components combined. Comparison of BLeCO and random 10-fold cross-validation suggests that random 10-fold cross-validation may significantly underrepresent true uncertainty for total PM2.5 concentrations due to the clustered nature of PM2.5 ground-based monitoring.

Long-term emission demonstration using pretreated urban non-woody biomass residues as fuel for small scale boilers

There is currently a lack of non-woody biogenic solid biofuels to replace fossil fuels to expand renewable heat energy due to their combustion-critical behavior (slagging and emissions). For this reason, this study demonstrates a best practice under which non-woody solid biofuels can be put into practice. For this demonstration, a homogeneous non-woody solid biofuel was produced from urban park foliage in accordance with ISO 17225–6:2021 on industrial scale (>50 t). Long-term combustion tests were performed in a small-scale combustion system (80 kW) under field test conditions. Compliance with all emission thresholds of the legal framework was demonstrated (Carbon monoxide (CO): 44.9 mg/m³, Total particulate matter (TPM): 11.9 mg/m³, Nitrogen oxides (NOx): 279.6 mg/m³, Polychlorinated dibenzodioxins and dibenzofurans + Dioxin-like polychlorinated biphenyls (PCDD/F + dl-PCB): 0.01 ng/m³ and Benzo[a]pyrene (B(a)P): 7.3*10−6 mg/m³). Additionally, nutrient cycles can be closed by applying the bottom ash as soil fertilizer additive. Based on the study, greenhouse gas-neutral fuels from non-woody biomass residues for comparable applications can be produced based on the demonstration.

Characteristic of PM2.5 concentration and source apportionment during winter in Seosan, Korea

Seosan is a concentrated industrial complex in the midwestern region of Korea. A study was conducted from December 2020 to January 2021, measuring PM2.5 and chemical components in Seosan using online instruments every hour. The concentration of PM2.5 during the winter season was 31.4±17.8 μg/m3, exceeding the national ambient air quality standard of Korea. The mass fraction of organic matter, elemental carbon, three major ions, five minor ions, crustal elements, and trace elements in PM2.5 accounted for 24.5%, 4.36%, 32.0%, 2.82%, 4.11%, and 5.17% of the total PM2.5 mass concentration, respectively. Source identification was conducted using positive matrix factorization modeling, revealing eight sources of PM2.5: Secondary inorganic aerosol (SIA), vehicle exhaust, industry, coal combustion, biomass burning/incinerator, oil combustion, soil, and aged sea salt. Source contributions varied during high pollution episodes (HPE), with SIA dominating in HPE1 and soil and aged sea salt in HPE2. The potential source contribution function and conditional probability function were utilized to estimate the potential local and regional emission areas for the identified sources. In Seosan, vehicle exhaust and biomass burning/incinerator were primarily influenced by local sources. SIA, industry, and oil combustion sources were significantly affected by short-range transport from eastern China. Soil and aged sea salt, which exhibited high contributions during HPE2, were associated with long-range transport from Inner Mongolia. Coal combustion was attributed to both local sources, particularly large industrial complexes near Seosan, and long-range transport from Northeast China and Inner Mongolia.Graphical

Aged and Obscured Wildfire Smoke Associated with Downwind Health Risks.

Fine-mode particulate matter (PM2.5) is a highly detrimental air pollutant, regulated without regard for chemical composition and a chief component of wildfire smoke. As wildfire activity increases with climate change, its growing continental influence necessitates multidisciplinary research to examine smoke's evolving chemical composition far downwind and connect chemical composition-based source apportionment to potential health effects. Leveraging advanced real-time speciated PM2.5 measurements, including an aerosol chemical speciation monitor in conjunction with source apportionment and health risk assessments, we quantified the stark pollution enhancements during peak Canadian wildfire smoke transport to New York City over June 6-9, 2023. Interestingly, we also observed lower-intensity, but frequent, multiday wildfire smoke episodes during May-June 2023, which risk exposure misclassification as generic aged organic PM2.5 via aerosol mass spectrometry given its extensive chemical transformations during 1 to 6+ days of transport. Total smoke-related organic PM2.5 showed significant associations with asthma exacerbations, and estimates of in-lung oxidative stress were enhanced with chemical aging, collectively demonstrating elevated health risks with increasingly frequent smoke episodes. These results show that avoiding underestimated aged biomass burning PM2.5 contributions, especially outside of peak episodes, necessitates real-time chemically resolved PM2.5 monitoring to enable next-generation health studies, models, and policy under far-reaching wildfire impacts in the 21st century.

Examining the structural properties of hydrophilic and hydrophobic organic aerosols using 1H NMR: diurnal variations and source apportionment.

The behaviour of the carbonaceous aerosols during the rainy season and the diurnal variations in their structural groups have not been thoroughly examined. The present study aims to understand the structural composition of hydrophilic and hydrophobic organic aerosols (OA) at an urban background location in Mumbai, India. The carbonaceous fractions, i.e., Elemental (EC) and Organic (OC) Carbon, accounted for 14-34% of the total PM10 (Particulate matter with aerodynamic diameter ≤ 10 μm). The PM10 and EC were maximum in the morning, while OC was the highest in the evening. The aliphatic structural groups were more concentrated in the total fraction, contributing 53-62% of the total resonances. The total concentrations of the structural groups in both hydrophilic (29.2±9.8 μmol/m3) and total (197.8±154.3 μmol/m3) fractions were highest in the morning. Traffic emissions impacted the morning and evening aerosols, as suggested by the broad aliphatic and sharp aromatic resonances observed in the total fraction. This is further corroborated by the variability in EC and OC, their significant correlations with Volatile OC and Nitrogen oxides, and their contribution to regression models and principal components. The afternoon aerosols demonstrated characteristics of Secondary OA. Our work extends the present understanding of the diurnal variability and the heterogeneity of the hydrophilic and hydrophobic structural groups in organic aerosols.