Distribution Of Particulate Matter Research Articles

Physical mechanisms, dynamics and interconnections of multiple estuarine turbidity maximum in the Pearl River estuary

The formation and dynamics of individual estuarine turbidity maximum (ETM) in the Pearl River estuary (PRE) have been investigated but the temporal variability of the ETMs and interconnections among them remain poorly understood. To address these open questions, the distribution and transport of suspended particulate matter (SPM) in the PRE for the period of 2017–2020 are investigated by numerical modeling. The simulated sediment transport flux is decomposed into several major components associated with specific physical processes. Then, the relative contribution of each component to the formation of the ETMs is evaluated. Results suggest the coexistence of three prominent ETMs in the Lingding Bay of the PRE. They are formed by different physical mechanisms and characterized by remarkable seasonality in the spatial extension. In the two ETMs located at the west shoal and middle shoal, advection dominates the sediment transport flux, whilst tidal pumping plays a crucial role in maintaining the ETMs. A sharp bathymetric gradient leads to an entrapment of sediment flux within the bottom layer in the west channel ETM, a phenomenon referred to as topographical trapping. The interconnection analysis shows that the sediment transport between the ETMs varies with seasons, which is attributed to the variation of stratification driven by the monsoon-mediated river runoff. Our results provide new insights into the physical dynamics and interconnections of the ETMs in the PRE, which can serve as scientific base for estuarine sediment management and engineering.

Comprehensive evaluation of natural ventilation potential of buildings in urban areas under the influence of multiple environment-related factors

Realistic natural ventilation potential estimation is critical for energy-efficient building design. For buildings in urban areas, evaluations using design expectations tend to overestimate potential. This study presents a comprehensive framework and corresponding method to predict the natural ventilation potential of buildings, integrating multiple key environmental factors of climate features, local wind status, and air pollution level. It addresses their complex interplay. Climate impacts are evaluated through simulating using a pre-set natural ventilation rate. Computational fluid dynamics models typical neighbourhood wind fields, giving empirical wind pressure coefficient distributions by layout and wind direction to enable real-time ventilation rate calculation. Substituting real-time for pre-set rates, the natural ventilation potential considering the community layout can be obtained. Based on the data-driven rapid pollutant prediction model established in the previous study, the predictions of the temporal and spatial distributions of particulate matter can be obtained. Filtering excessive pollution periods yields air quality-integrated potentials. Natural ventilation potentials are compared under different analysis scenarios for a case-study building. Hotter climates and more frequent heatwaves lower the potential. Morphology-based potentials are significantly lower than design expectations using constant ventilation rates. Deteriorating urban air quality also directly limits ventilation use, substantially decreasing potentials when filtering for poor conditions. By capturing complex environmental interactions, this research provides a robust platform to reveal localization potentials and limitations.

Characteristics of number concentration, size distribution and components of particulate matter emitted from a typical large civil airport

Civil airports are recognized as significant contributors to fine particulate matter, especially ultra-fine particulate matter (UFP). The pollutants from airport activities have a notable adverse impact on global climate, urban air quality, and public health. However, there is a lack of practical observational studies on the characterization of integrated pollutant emissions from large civil airports. This study aims to focus on the combined emission characteristics of particulate number concentration (PNC), size distribution, and components at a large civil airport, especially UFP. The findings reveal that airport activities significantly contribute to elevated PNC levels during aircraft activity in downwind conditions (four times higher than background levels) and upwind conditions (7.5 times higher). UFP dominates the PNC around the airport. The particle size distribution shows two peaks occurring around 10-30 nm and 60-80 nm. Notably, particles within the ranges of 17-29 nm and 57-101 nm account for 65.9 % and 12.0 % of the total PNC respectively. Aircraft landing has the greatest impact on particles sized between 6 and 17 nm while takeoff affects particles sized between 29 and 57 nm resulting in a respective increase in PNC by factors of approximately 3.27 and 35.4-fold increase compared to background levels. Different aircraft types exhibit varying effects on PNC with A320 and A321 showing more pronounced effects during takeoff and landing.The presence of airports leads to roughly five-fold rise in elemental component concentrations with Si being highest followed by OC, Ca, Al, Fe, Ca2+, EC, and Mg2+. The OC/EC ratio under high aircraft activity in downwind conditions falls within range of approximately 2.5–3.5. These characteristic components and ratio can be considered as identifying species for civil airports. PMF model show about 75 % of the particulate emissions at the airport boundary were related to airport activities.

Самоочищение атмосферного воздуха от пылевых частиц в условиях прибрежного морского климата

The intensity of air pollution depends on the characteristics of pollution sources, distance from the sea, terrain, density of residential buildings and the presence of a green zone. The influence of climatic conditions on the distribution of suspended particulate matter in the atmospheric surface layer of Vladivostok was studied. The aim of the study was to determine the particle size distribution of suspended particulate matter in the atmospheric surface layer and to assess the conditions for self-purification of urban air under the influence of the coastal maritime climate. The assessment of the urban air pollution with suspended particulate matter was carried out in two areas with different levels of pollution. The human breathing zone was examined. The temperature, humidity, air pressure, wind direction and speed at sampling points were determined. We used monitoring of meteorological data (dew point, wind gusts, clouds, fog) from the nearest regional meteorological stations. Statistical processing included the “Canonical Analysis” module. In the polluted area of the city the atmospheric circulation (direction, speed and gusts of wind) greatly affect the distribution of fine-dispersed particles (2.5–10.0 µm) that are hazardous to health. The impact of temperature and humidity conditions is reduced in power. The temperature and humidity regime, atmospheric pressure, cloudiness with frequent fog affect the ultrafine dust spectrum in the slightly polluted island area of the city. The above factors contribute to the enhancement of the nanoparticle nucleation process. Self-purification of the air surface layer from dust particles in both polluted and slightly polluted areas of Vladivostok is associated with the southerly winds. The monsoon maritime climate defines the air purifying in the region. The temperature and humidity regime actively affects the dust disperse composition in both sampling sites. In the island area of the city it determines the content of particles up to 10 µm; in the continental area – fractions of 0.1–50.0 µm.

Seasonal characteristics of emission, distribution, and radiative effect of marine organic aerosols over the western Pacific Ocean: an investigation with a coupled regional climate aerosol model

Abstract. Organic aerosols from marine sources over the western Pacific Ocean of East Asia were investigated using an online coupled regional chemistry–climate model RIEMS-Chem for the entire year 2014. Model evaluation against a wide variety of observations from research cruises and in situ measurements demonstrated a good skill of the model in simulating temporal variation and spatial distribution of particulate matter with aerodynamic diameter less than 2.5 and 10 µm (PM2.5 and PM10), black carbon (BC), organic carbon (OC), sodium, and aerosol optical depth (AOD) in the marine atmosphere. The inclusion of marine organic aerosols improved model performance on OC concentration by reducing model biases of up to 20 %. The regional and annual mean near-surface marine organic aerosol (MOA) concentration was estimated to be 0.27 µg m−3, with the maximum in spring and the minimum in winter, and contributed 26 % of the total organic aerosol concentration on average over the western Pacific. Marine primary organic aerosol (MPOA) accounted for the majority of marine organic aerosol (MOA) mass, and the MPOA concentration exhibited the maximum in autumn and the minimum in summer, whereas marine secondary organic aerosol (MSOA) was approximately 1–2 orders of magnitude lower than MPOA, having a distinct summer maximum and a winter minimum. MOA induced a direct radiative effect (DREMOA) of −0.27 W m−2 and an indirect radiative effect (IREMOA) of −0.66 W m−2 at the top of the atmosphere (TOA) in terms of annual and oceanic average over the western Pacific, with the highest seasonal mean IREMOA up to −0.94 W m−2 in spring. IREMOA was stronger than, but in a similar magnitude to, the IRE due to sea salt aerosol on average, and it was approximately 9 % of the IRE due to anthropogenic aerosols in terms of annual mean over the western Pacific. This ratio increased to 19 % in the northern parts of the western Pacific in autumn. This study reveals an important role of MOA in perturbing cloud properties and shortwave radiation fluxes in the western Pacific of East Asia.

Correlations between Urban Morphological Indicators and PM2.5 Pollution at Street-Level: Implications on Urban Spatial Optimization

During rapid urbanization, microclimate environment deterioration through events such as haze pollution and heat waves has continuously occurred in cities, which greatly affects the living environment, production activities, and health of urban residents. Therefore, it is particularly necessary to explore methods for controlling and optimizing the urban microclimate environment. In this paper, based on the mechanism of the effect of urban spatial structure at street-level on the distribution of atmospheric particulate matter, an indicator system that can be employed to comprehensively describe and quantify urban morphological structure at street-level was constructed from eight aspects: the spatial morphology of street-valleys, intensity of land use and development, geometric structure of buildings, inhomogeneity of buildings, roughness of the underlying surface, distribution of ecological landscapes, 3D architectural landscape morphology, and ventilation potential. Furthermore, using satellite remote sensing images and vector thematic maps of Shanghai, indicator factors were quantified by applying GIS technique. The intrinsic mechanism of the influence of the urban morphology on the diffusion and transport of atmospheric particulate matter was comprehensively analyzed by combining statistical methods and data mining algorithm, and eight key dominant factors were identified that can be considered to improve the urban ventilation conditions and help control urban air pollution, namely, the land use intensity, urban canopy resistance, vegetation cover, spatial congestion rate, comprehensive porosity, height-to-gross floor area ratio, building density, and average building volume ratio. As such, according to the quantitative analysis results for various combinations of the dominant factors, a spatial optimization strategy at street-level that can help improve the urban air quality was proposed in terms of identifying the pathways through which urban spatial elements affect the distribution of particulate matter, i.e., controlling the source–flow diversion–flow convergence process.

Investigating the influence of platform design on the distribution of traffic particulate matter at the bus stop

Passengers waiting at bus stops are regularly exposed to vehicle emissions from nearby roads. Thus, comprehending the pollution levels within the stop is crucial for devising measures to improve the waiting environment. Here, we measured PM2.5 and black carbon (BC) concentrations at bus stops and simulated the effects of different bus platforms on particle distribution under varying winds. The main findings are: (1) Particle concentrations were higher in the front of the bus stop than in the rear. The exit area of the bus stop at downwind of the road showed the highest concentration, especially for BC, with a maximum increase of 58% compared to other front locations, likely attributed to increased emissions from road vehicles interfered by departing buses. PM2.5 concentrations at the stop increased by 21–38% in the evening compared to the morning, while BC concentrations decreased by about 27% due to a reduction in diesel vehicles and increased wind speeds. (2) As wind-road angle increased, turbulence caused particles from the road to migrate towards the stop and its rear. A 45° wind-road angle facilitated particle diffusion, while higher wind speeds brought an overall reduction in particulate matter. These results varied among the three simulated bus platforms, with differences more pronounced for PM2.5 than for BC. (3) Enhancements in the front enclosures of the bus stop promoted a significant decrease in particles behind the stop, particularly in BC compared to PM2.5. Therefore, installing closed or semi-enclosed front barriers, considering prevailing wind conditions, has the potential to improve air quality within the bus stop.

Ambient particulate matter monitoring using bright field imaging-based sensor

The bright-field imaging of the particulate matter is demonstrated with the customized bright-field (BF) microscopic unit. Polystyrene spheres of size 4.5 µm as reference objects have been imaged to validate the claim. An image-processing-based algorithm has been used which uses pixel size for particle sizing and distribution. The lateral resolution of the device is found to be approximately 0.5 µm and the field of view is 56 µm × 42 µm which has been utilized for the imaging of the ambient PM. The device gives the results in the form of particulate matter concentration, size distribution, and shape of particulate matter.

Estimation of the percentile of Birnbaum-Saunders distribution and its application to PM2.5 in Northern Thailand.

The Birnbaum-Saunders distribution plays a crucial role in statistical analysis, serving as a model for failure time distribution in engineering and the distribution of particulate matter 2.5 (PM2.5) in environmental sciences. When assessing the health risks linked to PM2.5, it is crucial to give significant weight to percentile values, particularly focusing on lower percentiles, as they offer a more precise depiction of exposure levels and potential health hazards for the population. Mean and variance metrics may not fully encapsulate the comprehensive spectrum of risks connected to PM2.5 exposure. Various approaches, including the generalized confidence interval (GCI) approach, the bootstrap approach, the Bayesian approach, and the highest posterior density (HPD) approach, were employed to establish confidence intervals for the percentile of the Birnbaum-Saunders distribution. To assess the performance of these intervals, Monte Carlo simulations were conducted, evaluating them based on coverage probability and average length. The results demonstrate that the GCI approach is a favorable choice for estimating percentile confidence intervals. In conclusion, this article presents the results of the simulation study and showcases the practical application of these findings in the field of environmental sciences.

Remote Sensing Observations of a Coastal Water Environment Based on Neural Network and Spatiotemporal Fusion Technology: A Case Study of Hangzhou Bay

The coastal environment is characterized by high, multi-scale dynamics and the corresponding observations from a single remote sensing sensor are still facing challenges in achieving both high temporal and spatial resolution. This study proposed a spatiotemporal fusion model for coastal environments, which could fully enhance the efficiency of remote sensing data use and overcome the shortcomings of traditional spatiotemporal models that are insensitive to small-scale disturbances. The Enhanced Deep Super-Resolution Network (EDSR) was used to reconstruct spatial features in the lower spatial resolution GOCI-II data. The spatial features obtained instead of GOCI-II data were fed into the spatiotemporal fusion model, which enabled the fusion data to achieve an hour-by-hour observation of the water color and morphology information changes at 30 m resolution, including the changes in the spatial and temporal distributions of suspended particulate matter (SPM), the characterization of the vortex street caused by the bridge piers, the inundation process of the tidal flats, and coastline changes. In addition, this study analyzed the various factors affecting fusion accuracy, including spectral difference, errors in both temporal difference and location distance, and the structure of the EDSR model on the fusion accuracy. It is demonstrated that the location distance error and the spectral difference have the most significant impact on the fusion data, which may lead to the introduction of some ambiguous or erroneous spatial features.

Source-specific risks assessment of size-resolved PM bound multiple toxicants: Variation of source-specific risks in respiratory tracts

Inhalation risks depend on the size distribution of ambient particulate matter (PM) bound toxicants, which are influenced by various sources. This study explored variations in source-specific risks across different respiratory tract regions and their association with size-resolved toxicants. Potentially toxic elements contribute significantly, accounting for over 93% of the inhalation risk, followed by polycyclic aromatic hydrocarbons at 0.3%–7%, and organophosphate esters at less than 0.01%. For adults, the contribution of non-cancer and cancer risks within respiratory tracts was as follows: upper respiratory tract (∼61%) > pulmonary (∼30%) region > tracheobronchial (∼8%) region. Differently, for children, the order was pulmonary (∼43%) > upper respiratory tract (∼41%) > tracheobronchial (∼17%). In the adult population, resuspended dust (RD) presented elevated non-cancer risks in the upper respiratory tract compared to the tracheobronchial and pulmonary regions, while coal combustion (CC), traffic emission (TE), and waste recycling or incineration (WI) contribute most to pulmonary risks. In contrast, for children, non-cancer risk associated with CC surpassed RD in the upper respiratory tract, whereas RD exceeded CC in the tracheobronchial region. This discrepancy arises because RD primarily consists of coarse PM with higher deposition fractions in the adults' upper respiratory tract and children's tracheobronchial region, while toxicants from CC, TE and WI, with PM < 0.43 μm, are inclined to have a greater impact on the pulmonary region. Regarding source cancer risks, RD exhibited a consistent decrease across all three respiratory tract regions, with primary dominance observed in industrial emissions (IE), CC, and TE sources.

LSTM‐based post‐processing for noise reduction in SVD‐based particulate matter digital twinning on lightweight embedded devices

AbstractAmong various applications of digital twins, measuring particulate matter in the context of air pollution has become increasingly important due to growing interest in atmospheric environments. By obtaining particle count values from analogue‐to‐digital converter raw data through photodiode and calibrating them, particulate matter values can be acquired. The singular‐value–decomposition is efficient transfer function learning method. However, due to the linear nature of the transfer function of singular‐value–decomposition, it also conveys noise information, necessitating post‐processing to suppress noise. This study proposes a method that utilizes long short‐term memory neural networks to effectively stabilize noise based on the output of continuous dust sensor particulate matter distributions. Using the singular value decomposition based particulate matter shows relative root‐mean‐square error (4.4761, 100%) as a reference, the proposed long short‐term memory post‐processing demonstrates an improved result (2.9328, 65.52%) compared to other post‐processing method: the mean filter (3.6704, 82.00%), low‐pass filter (3.7719, 84.27%) and Kalman filter (3.5550, 79.42%). Furthermore, to address the limitation of initial delay in long short‐term memory to achieve stable data output, a method of iteratively training the initial input data sample is proposed. For the initial input data, the non‐initialization approach exhibited a relative error of 87.57% and it took around 5 samples to predict stable output. In contrast, the proposed iterative training method, applied five times to the initial data sample, achieved a relative error of 9.51% and immediately obtained stable data.

Effect of Heating Emissions on the Fractal Size Distribution of Atmospheric Particle Concentrations

Excessive particle concentrations during heating periods, which greatly affect people’s physical and mental health and their normal lives, continue to be a concern. It is more practical to understand and analyze the relationship between the fractal dimension and particle size concentration distribution of atmospheric particulate matter before and after adjusting heating energy consumption types. The data discussed and analyzed in this paper were collected by monitoring stations and measured from 2016 to 2018 in Xi’an. The data include fractal dimension and particle size concentration changes in the atmospheric particulate matter before and after adjusting the heating energy consumption types. The results indicate that adjusting the heating energy consumption types has a significant impact on particulate matter. The average concentration of PM2.5 decreased by 26.4 μg/m3. The average concentration of PM10 decreased by 31.8 μg/m3. At the same time, the different particle sizes showed a downward trend. The particles ranging from 0.265 to 0.475 μm demonstrated the maximum decrease, which was 8.80%. The heating period in Xi’an mainly involves particles ranging from 0 to 0.475 μm. The fractal dimensions of the atmospheric particulate matter before and after adjusting the heating energy consumption types were 4.809 and 3.397, respectively. After adjusting the heating energy consumption types, the fractal dimension decreased by 1.412. At that time, the proportions of particle sizes that were less than 1.0 μm, 2.0 μm, and 2.5 μm decreased by 1.467%, 0.604%, and 0.424%, respectively. This paper provides new methods and a reference value for the distribution and effective control of atmospheric particulate matter by adjusting heating energy consumption types.

Effect of FIPs strategy and nanoparticles additives into the renewable fuel blends on NOX emissions, PM size distribution and soot oxidation in CRDI diesel engine

‏ The search of next prospective source of fuel is highlighted by the research community to compensate the aspects of shortcomings of diesel fuel and to treat the harmful emissions in the transportation sector. Among numerous economic sectors, the use of petro-diesel still the main fuel using for driving heavy machinery despite the high advance in electric motors for transportation systems. The characteristics of engine performance, nitrogen oxide (NOX) emissions, size distribution of particulate matter (PM), number and oxidation reactivity of soot particles from the effects of low and high fuel injection pressures (FIPs) and adding two types of nanoparticles (Al2O3 and TiO2) to the M20B10 blend (20 % of microalgae biodiesel, 10 % n-butanol and 70 % of diesel) were studied in common-rail diesel engine at different loads. The results revealed that the adding Al2O3 and TiO2 to the M20B10 blend decreased the BSFC by 21.28 % and 22.84 %, respectively, and also improved BTE by 4.46 % and 2.35 %, respectively, for different engine loads. It is observed that the applied HFIP and M20B10+Al2O3 blend also enhance the fuel consumption by 21.28 % and increase BTE by 9.63 % in comparison with M20B10+ TiO2 blend. In contrast, the presence HFIP with nano blends increased the NOX emissions by 24.73 % with respect to the LFIP, meanwhile the NOX emissions decreased from nano blends combustion more than to the neat M20B10 blend. The combustion of M20B10+Al2O3 blend decreased the PM concentration (by 52.82 %) and number (by 33.62 %) and when compared with blend of M20B10+ TiO2 and neat M20B10 blend combustion at HFIP. Furthermore, the reactivity of soot oxidation significantly increased when adding Al2O3 into the M20B10 blend and applying HFIP compared to the M20B10+ TiO2 blend.

Characteristics of bioaerosols under high-ozone periods, haze episodes, dust storms, and normal days in Xi’an, China

The characteristics of bioaerosols and their effects on human beings’ health have become a major public concern in the recent years. This study compared the characteristics of bioaerosols under different types of pollution (high-ozone [HO] episodes, haze episodes, and dust storms [DSs]), with those of bioaerosols on normal days (NDs) in Xi'an, China. The concentrations, diurnal variations, and size distribution of bacteria, fungi, and total airborne microbes (TAMs) were investigated. The results showed that the bacterial and fungal concentrations were the highest during DSs and on NDs, reaching 425.2 and 956.9 colony-forming units/m3, respectively. The concentration of TAMs was the highest during HO episodes, reaching 10.7 × 106 cells/m3, which was significantly higher than that during the other events. The maximum concentrations of bacteria and fungi during HO episodes were observed in the morning, while they appeared separately in the morning and evening during haze episodes. Bacteria and TAMs during DSs reached peak concentrations in the morning and evening, which is opposite to the particulate matter (PM) distribution pattern. On NDs, temperature and relative humidity had significant effects on bioaerosols. The size distributions of airborne bacteria exhibited bimodal patterns, whereas unimodal distributions were observed for fungi. Fungi mainly attached in fine particles (<2.1 μm) with proportions of 67.4% and 60.5% during haze episodes and DSs, respectively, which means that fungi were easily penetrated into the human respiratory system. The evaluation of influence factors revealed that PM2.5 and PM10 had negative effects on fungal concentration during DSs, and ozone concentration and temperature were inversely correlated with fungal concentration during HO episodes. These results provide valuable reference data for elucidating the formation and evolution of bioaerosols under different types of pollution.

Spatial distribution of heavy metals, source identification, risk assessment and particulate matter in the M4 motorway.

Metal-containing dust is a potential severe environmental and human health threat. Metals present in dust may originate from car exhausts, tear and wear of tires, and vehicular emissions, which are less manageable. Metal-containing dust from roads can contaminate the soils, and crops alongside motorway. This study aimed to investigate the Pb Cd, Cu, Ni, and Zn concentrations in dust, soil, and vegetation collected from the M4 motorway Faisalabad. The results indicated that average metal concentrations in dust from all sites varies (Pb) 44.01mgkg-1, (Cd) 1.22mgkg-1, (Cu) 49.5mgkg-1, (Ni) 28.3mgkg-1, and (Zn) 113.7mgkg-1. The pollution assessment indices CF and PLI of Industrial city and Painsra had comparatively maximum levels of environmental pollution. Moreover, the geo-accumulation index (Igeo) of metals was high at Chak 115 and Painsra, while Igeo at ten sites was in the following descending order: Cd > Pb > Cu > Ni > Zn. Furthermore, it was identified that the maximum ecological risk index (Eir) was in declining order, i.e., Cd > Pb > Cu > Ni > Zn, at all sites. The potential ecological risk was categorized as high risk in all respective sites. The particulate matter fractions PM2.5 and PM10 represented the maximum risk at the Industrial city site, which was unhealthy, although the Painsra site had poor air quality. The total suspended particulate was classified as hazardous at FDA city and Painsra. In contrast, food crops (maize, sugar cane, and sesame) and soil along the M4 motorway have similar Pb, Cd, Cu, Ni, and Zn contamination patterns like dust. However, two crops, maize and sugarcane, along the M4 motorway were found to be more polluted. The level of metals contamination through dust disposition was consistently higher adjacent to roads, possibly indicating depraved impacts on food crops.

Selection of a Particulate Filter for a Gasoline-Powered Vehicle Engine in Static and Dynamic Conditions

Current challenges in terms of exhaust emission limits are related to the reduction of the particle numbers in spark ignition direct injection engines. The article concerns the analysis of the thermodynamic parameters of engine operation, allowing the selection of the particulate filter configuration and its technical parameters. The designed system consisting of an internal combustion engine and an exhaust system with an exhaust gas treatment system should be sufficient to meet ecological requirements in the form of reducing particulate matter emissions. The analysis of particulate matter emissions for the system without a filter and with a filter installed in the engine exhaust system was carried out for the mass, number and dimensional distribution of particulate matter. The result was an assessment of filtration efficiency for the entire spectrum of particulate diameters in the identified engine operating ranges. As a result, it was found that the particulate filter used in the engine exhaust system effectively reduces the particle number due to the greater filtration efficiency of large particles. The summary of the work related to the analysis of the ecological parameters of a spark ignition engine with direct fuel injection was a simulation of road tests of a vehicle with the proposed modified vehicle exhaust system equipped with a particulate filter. For this configuration, the analysis of particulate number emissions in the parameterized engine operating areas showed that it is possible to meet the particulate number emission limits, and the obtained road emission results are fully acceptable in terms of the obtained absolute values.

Measuring and modeling the effects of green barriers on the spatial distribution of fine particulate matter at roadside

Green barriers are regarded as a viable strategy for improving road air quality, and yet there lacks consensus regarding how they can actively regulate air pollution, particularly under dynamic traffic, meteorology and built environments. To address this, field measurements were conducted to assess the levels of fine particulate matter (PM2.5) before and after green barriers along an urban expressway. The measurements revealed distinct spatial attenuation patterns in vegetation-rich and vegetation-sparse areas. Specifically, the first-layer green barriers were found to significantly reduce PM2.5 levels by 8.4–9.3% in the vegetation-rich area. The magnitude of PM2.5 reduction at all locations distant from the motorway varied depending on the wind direction. On average, there are reductions of 7.8% (winter) and 13.0% (summer) in PM2.5 levels under cross-road winds. A validated hybrid model further demonstrated the significant impact of green barriers on pollutant diffusion, showing an unstable spatial variation trend under changing wind conditions. A combination of trees and hedgerows with a tree spacing of 3 m and obovate or spherical tree crowns proved effective in reducing roadside pollution. Simulations also suggested significant pollution reduction when considering appropriate spatial distribution of traffic flow and solid barrier alongside green barriers. These findings underscore the potential of combining green barriers with diverse measures to maximize the reduction of traffic-related pollution at roadside.

Mobile measurements for distribution and attribution of particulate matter in urban environments

Particulate matter (PM) sources differ in urban environments and may show spatiotemporal distinct patterns for varying particle aerodynamic diameters (DP). We here assess such patterns using high-resolution PM ≤10 μm data recorded with a cargo bike along a 14 km route through urban, suburban, and rural areas in Mainz (Germany). The measurements conducted twice a day between May and August 2021 reveal decreasing particle number concentration (PNC) with increasing DP including ∼6000 times higher particle numbers at DP 0.22–0.25 μm compared to DP 4–5 μm. Total mass concentration is bi-modally distributed and dominated by particles <0.3 μm and from 3 to 5 μm representing 36 and 22% of the entire air load, respectively. PM concentrations in Mainz are significantly higher in the morning than in the afternoon, and PM1 and PM10 are 13 and 31% higher in urban compared to surrounding suburban and rural areas. The high-resolution measurements also revealed 30% higher PM concentrations at DP 3–5 μm in the urban compared to the rural sectors, which is indicative for road dust, brake and tyre abrasion as the main source. DP distribution in rural hotspots is generally shifted toward larger particles >3 μm, most likely related to natural dust from agricultural fields. These findings show that high-resolution PM profiles can skillfully be recorded using bikes as mobile platform to identify spatial pollution patterns and attribute DP spectra to particular emission sources.

Combustion aerosols and suspended dust with controlled processes in Lhasa: Elemental analysis and size distribution characteristics

To explore air contamination resulting from special biomass combustion and suspended dust in Lhasa, the present study focused on the size distribution and chemical characteristics of particulate matter (PM) emission resulting from 7 types of non-fossil pollution sources. We investigated the concentration and size distribution of trace elements from 7 pollution sources collected in Lhasa. Combining Lhasa's atmospheric particulate matter data, enrichment factors (EFs) have been calculated to examine the potential impact of those pollution sources on the atmosphere quality of Lhasa. The highest mass concentration of total elements of biomass combustion appeared at PM0.4, and the second highest concentration existed in the size fraction 0.4–1 µm; the higher proportion (12 %) of toxic metals was produced by biomass combustion. The elemental composition of suspended dust and atmospheric particulate matter was close (except for As and Cd); the highest concentration of elements was all noted in PM2.5-10 (PM3-10). Potassium was found to be one of the main biomass markers. The proportion of Cu in suspended dust is significantly lower than that of atmospheric particulate matter (0.53 % and 3.75 %), which indicates that there are other anthropogenic sources. The EFs analysis showed that the Cr, Cu, Zn, and Pb produced by biomass combustion were highly enriched (EFs > 100) in all particle sizes. The EFs of most trace elements increased with decreasing particle size, indicating the greater influence of humanfactors on smaller particles.