Distribution Of Particulate Matter Research Articles

Size-resolved particulate matter inside selected fire stations and preliminary evaluation of the effectiveness of washing machines in reducing its concentrations

The study aimed to determine and compare the mass concentration and size distribution of particulate matter (PM) at two Polish fire stations, one equipped with a washing machine intended for the decontamination of uniforms (FSN) and the other not equipped with this type of device (FSC), to assess the effectiveness of washing machines in reducing PM concentrations inside fire stations and estimate PM doses inhaled by firefighters while performing activities in truck bays and changing rooms during one work shift. The average PM concentrations at the FSN were 18.2–28.9 µg/m3 and 27.5–37.3 µg/m3, while at FSC they were 27.4–37.9 µg/m3 and 24.6–32.8 µg/m3 in the truck bays and changing rooms, respectively. At each measurement point, most of the PM mass (65–75%) was accumulated as fine particles. The dominance of fine particles in the total mass of PM results in high values of PM deposition coefficients (0.59–0.61) in three sections of the respiratory tract at each monitoring site. This study initially indicates the effectiveness of washing machines in reducing the concentration of fine particles and demonstrates the necessity, as well as directions for further research in this area.

Characterizing the distribution pattern of traffic-related air pollutants in near-road neighborhoods.

In near-road neighborhoods, residents are more frequently exposed to traffic-related air pollution (TRAP), and they are increasingly aware of pollution levels. Given this consideration, this study adopted portable air pollutant sensors to conduct a mobile monitoring campaign in two near-road neighborhoods, one in an urban area and one in a suburban area of Shanghai, China. The campaign characterized spatiotemporal distributions of fine particulate matter (PM2.5) and black carbon (BC) to help identify appropriate mitigation measures in these near-road micro-environments. The study identified higher mean TRAP concentrations (up to 4.7-fold and 1.7-fold higher for PM2.5 and BC, respectively), lower spatial variability, and a stronger inter-pollutant correlation in winter compared to summer. The temporal variations of TRAP between peak hour and off-peak hour were also investigated. It was identified that district-level PM2.5 increments occurred from off-peak to peak hours, with BC concentrations attributed more to traffic emissions. In addition, the spatiotemporal distribution of TRAP inside neighborhoods revealed that PM2.5 concentrations presented great temporal variability but almost remained invariant in space, while the BC concentrations showed notable spatiotemporal variability. These findings provide valuable insights into the unique spatiotemporal distributions of TRAP in different near-road neighborhoods, highlighting the important role of hyperlocal monitoring in urban micro-environments to support tailored designing and implementing appropriate mitigation measures.

Study on Mapping and Identifying Risk Areas for Multiple Particulate Matter Pollution at the Block Scale Based on Local Climate Zones

As China’s urbanization process accelerates, the issue of air pollution becomes increasingly prominent and urgently requires improvement, based on the fact that environmental conditions such as meteorology and topography are difficult to change. Therefore, relevant optimization studies from the perspective of architectural patterns are operable to mitigate pollution. This paper takes the Wenhua Road block in Shenyang, China, as the research object; obtains the concentration data of three kinds of particulate matter through fixed and mobile monitoring; and analyzes the spatial distribution characteristics of Local Climate Zones ( LCZ) and particulate matter in the block based on the ArcGIS platform, identifies high-risk areas, and excavates the influence of LCZ on the concentrations of three kinds of particulate matter. The results show that the spatial distribution characteristics of PM1, PM2.5, and PM10 under the same pollution level are relatively similar, while the spatial heterogeneity of the distribution of the same particulate matter under different pollution levels is higher. The time-weighted results show that the PM1 pollution level in the block ranges from 44 to 51 μg/m³, PM2.5 ranges from 75 to 86 μg/m³, and PM10 ranges from 87 to 99 μg/m³. The pollution hot spots throughout the year are located in the central, eastern and western parts of the study area. In terms of the relationship between the LCZ and particulate matter, with the increase in the particulate matter diameter, the correlation between the three kinds of particulate matter and LCZ are all enhanced. The built-up LCZ always has a larger average concentration of particulate matter than that of the natural LCZ, and building height and building density are the main factors causing the difference. In the optimal design of the risk area, the proportion of natural vegetation or water surface should be increased and the building height should be properly controlled and the building density should be reduced in the renewal of the urban building form. This study will largely improve the spatial refinement of the optimization of urban architectural patterns oriented to mitigate particulate matter pollution.

Spatiotemporal variations in the grain size distribution in the water column and sediments from the Yellow River Delta to distal areas under coastal currents

The differentiation of sediment grain size from large river deltas to distal areas in a coastal flow system and its evolution are vital because they greatly contribute to matter transport, pollution accumulation, and carbon cycling on the inner shelf. Here, the Yellow River sedimentary system in the adjacent seas is studied, including the proximal delta of the Yellow River and the distal mud patch. The grain size distributions of the suspended particulate matter (SPM), surface sediments, and core sediments in the Shandong Peninsula Coastal Current (SPCC) system were integrated and analyzed. The results show that apparent variations in the grain size distribution exist in the SPM and sediments in the SPCC system. The grain size distribution of the SPM near the proximal delta of the Yellow River is multimodal and variable with water depth, whereas that in the distal mud area is typically unimodal. The coarse-grained endmember of suspended sediments is restricted in the proximal area by ocean fronts under fair weather conditions in both summer and winter and is only transported to the distal mud area under strengthened coastal currents in winter. In contrast, fine-grained endmembers can be transported far away under tidal currents and coastal currents year-round. The temporal grain size variation near the proximal delta is also significantly affected by historical shifts in the Yellow River mouth, while the strength of coastal currents associated with the East Asian Winter Monsoon (EAWM) controls the grain size distribution in the distal mud area. The roles of river behaviors, ocean fronts, tides, and winds are all highlighted in the control of grain size differentiation. These results potentially have significance for understanding sediment dynamics and mass transport processes in similar coastal current systems involving large rivers worldwide.

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.