Vertical variation of PM2.5 mass and chemical composition, particle size distribution, NO2, and BTEX at a high rise building

Climatic conditions and concentrations of BTEX compounds in atmospheric media

Climatic and meteorological conditions are among the factors affecting the ambient concentrations of BTEX compounds. This systematic review and meta-analysis aimed to interrogate the seasonal effect of climatic conditions on the concentrations of BTEX compounds. Three electronic bibliographic databases including Scopus, PubMed, and Web of Science were systematically searched up to November 14, 2023. The search algorithm followed PRISMA guidance and consisted of three groupings of keywords and their possible combinations. For various climatic conditions, the overall mean and 95% confidence interval (CI) of effect size related to BTEX concentrations were calculated using a random-effect model. In total, 104 articles were included for evaluation in this review. BTEX ambient concentration was higher in winter (ranging from 36 out of 79 relevant studies for xylene to 52 out of 97 relevant studies for benzene) followed by summer and autumn. For humidity conditions, the highest exposure values for BTEX were detected for rainy weather (ranging from 3 out of 5 relevant studies for toluene and xylene to 4 out of 5 relevant studies for benzene and ethyl benzene) compared to dry conditions. The pooled concentration (μg/m3) of benzene, toluene, ethyl benzene, and xylene were computed as 2.61, 7.12, 2.21, and 3.61 in spring, 2.13, 7.53, 1.61, and 2.75 in summer, 3.04, 9.59, 3.14, and 5.50 in autumn, and 3.56, 8.71, 2.35, and 3.91 in winter, respectively. Moreover, the pooled concentrations (μg/m3) of BTEX were measured as 2.98, 7.22, 1.90, and 3.03 in dry weather and 3.15, 6.30, 2.14, and 3.86 in rainy or wet weather, respectively. In most seasons, the ambient concentrations of BTEX were higher in countries with low and middle incomes and in Middle Eastern countries and East/Southeast Asia compared to those in other regions (P < 0.001). The increasing concentrations of BTEX in winter and autumn followed by the summer season and during rainy/wet weather appear to be reasonably consistent despite variations in study methods, quality, or geography. Therefore, it is recommended that more serious control measures are considered for decreasing exposure to BTEX in these climatic conditions.

Vertical variation of source-apportioned PM2.5 and selected volatile organic compounds near an elevated expressway in an urban area

Fine particulate matter (PM2.5) and volatile organic compounds (VOCs) are associated with adverse health effects and show spatial variation in three dimensions. The present study attempted to evaluate source contributions of PM2.5 and toxic VOCs in a metropolitan area focusing on the associated vertical variations. A special emphasis is put on the effects of the elevated expressway on the vertical variability of contribution estimates of the identified sources. Nine source factors, i.e., soil dust, sea salt/oil combustion, secondary nitrate, industrial emission, aged VOCs/secondary aerosol, traffic-related I, solvent use/industrial process, secondary sulfate, and traffic-related II, were identified using positive matrix factorization (PMF). The main contributors to PM2.5 were secondary sulfate (19.1%) and traffic-related emissions (traffic-related I and II, 16.1%), whereas the largest contributors to VOCs were traffic-related emissions (37.6%). The influence of the elevated expressway is suggested to be particularly critical on vertical variations of traffic-related emissions, including aging and secondary formation of locally accumulated air pollutants near roads. Increasing the building porosity under the viaduct could reduce the accumulation of air pollutants caused by the shelter effect. Additionally, in-street barriers would be beneficial in reducing population exposure to traffic-related emissions by altering the airflows near roads.

Characterizing nighttime vertical profiles of atmospheric particulate matter and ozone in a megacity of south China using unmanned aerial vehicle measurements

Daytime atmospheric pollution has received wide attention, while the vertical structures of atmospheric pollutants at night play a crucial role in the photochemical process on the following day, which is still less reported. Focusing on Guangzhou, a megacity of South China, we established an unmanned aerial vehicle (UAV) equipped with micro detectors to collect consecutive high-resolution samples of fine particle (PM2.5), submicron particle (PM1.0), black carbon (BC) and ozone (O3) concentrations in the atmosphere, as well as the air temperature (AT) and relative humidity (RH) within a 500 m altitude during nighttime from Oct. 24th to Nov. 6th, 2018. The measurements showed that PM2.5, PM1.0, and BC decreased with altitude and were influenced by the nighttime shallow planetary boundary layer (PBL) where BC was more accumulated and fluctuated. In contrast, O3 was positively correlated with altitude. Backward trajectory clustering and Pasquill stability classification showed that advection and convection significantly influenced the vertical distribution of all pollutants, particularly particulate matter. External air masses carrying high concentrations of pollutants increased PM1.0 and PM2.5 levels by 145% and 455%, respectively, compared to unaffected periods. The ratio of BC to PM2.5 indicated that local emissions had a minor role in nighttime particulate matter. Vertical transport caused by atmospheric instability reduced the differences in pollutant concentrations at various heights. Geodetector and generalized additive model showed that RH and BC accumulation in the PBL were significant factors influencing vertical changes of the secondary aerosol intensity as indicated by the ratio of PM1.0 to PM2.5. The joint explanation of RH and atmospheric stability with other variables such as BC is essential to understand the generation of secondary aerosols. These findings provide insights into regional and local measures to prevent and control night-time particulate matter pollution.

Reactive Oxygen Species-Triggered Curcumin Release from Hollow Mesoporous Silica Nanoparticles for PM2.5-Induced Acute Lung Injury Treatment

Exposure to fine particulate matter with a diameter ≤2.5 μm (PM_2.5) can result in serious inflammation and oxidative stress in lung tissue. However, there is presently very few effective treatments for PM_2.5-induced many pulmonary diseases, such as acute lung injury (ALI). Herein, curcumin-loaded reactive oxygen species (ROS)-responsive hollow mesoporous silica nanoparticles (Cur@HMSN-BSA) are proposed for scavenging the intracellular ROS and suppressing inflammatory responses against PM_2.5-induced ALI. The prepared nanoparticles were coated with bovine serum albumin (BSA) via an ROS-sensitive thioketal (TK)-containing linker, in which the TK-containing linker would be cleaved by the excessive amounts of ROS in inflammatory sites to induce the detachment of BSA from the nanoparticles surface and thus triggering release of loaded curcumin. The Cur@HMSN-BSA nanoparticles could be used as ROS scavengers because of their excellent ROS-responsiveness, which were able to efficiently consume high concentrations of intracellular ROS. Furthermore, it was also found that Cur@HMSN-BSA downregulated the secretion of several important pro-inflammatory cytokines and promoted the polarization from M1 phenotypic macrophages to M2 phenotypic macrophages for eliminating PM_2.5-induced inflammatory activation. Therefore, this work provided a promising strategy to synergistically scavenge intracellular ROS and suppress the inflammation responses, which may serve as an ideal therapeutic platform for pneumonia treatment.

Short and medium-chain chlorinated paraffins in indoor dust from a multistory residential building in Beijing, China: Vertical distribution and potential health risks

In this study, we conducted a preliminary investigation of the vertical distribution and potential health risks of short and medium-chain chlorinated paraffins (SCCPs and MCCPs) in indoor dust from a multistory residential building in Beijing, China. Forty-eight SCCP and MCCP congener groups in dust from different floors of the multistory residential building were determined by two-dimensional gas chromatography coupled with electron capture negative ionization mass spectrometry. The concentration ranges for SCCPs and MCCPs in the dust samples were 0.0239-207 μg/g and 0.135-2903 μg/g, respectively. MCCPs were the dominant group, on average accounting for 76.8 % of ∑CPs. Generally, the concentrations of both SCCPs and MCCPs greatly decreased as the floor level increased, which indicated that the CP contamination was attributed to exogenous atmospheric transport and deposition. C₁₃Cl_7-8 and C₁₄Cl_7-8 were the dominant SCCP and MCCP congener groups, possibly indicating the use of industrial CP-52 products was the main source of CPs. In the worst-case scenario using the maximum concentrations of CPs, the daily intake of SCCPs for toddlers was of the same order of magnitude as the reference dose. It should be noted that CPs exposure may be more serious when indoor decorations, furniture, and various plastic products are taken into consideration. Overall, more attention should be paid to CPs exposure and control measures in high-rise buildings.

Efficacy of Low-Cost Sensor Networks at Detecting Fine-Scale Variations in Particulate Matter in Urban Environments

The negative health impacts of air pollution are well documented. Not as well-documented, however, is how particulate matter varies at the hyper-local scale, and the role that proximal sources play in influencing neighborhood-scale patterns. We examined PM_2.5 variations in one airshed within Indianapolis (Indianapolis, IN, USA) by utilizing data from 25 active PurpleAir (PA) sensors involving citizen scientists who hosted all but one unit (the control), as well as one EPA monitor. PA sensors report live measurements of PM_2.5 on a crowd sourced map. After calibrating the data utilizing relative humidity and testing it against a mobile air-quality unit and an EPA monitor, we analyzed PM_2.5 with meteorological data, tree canopy coverage, land use, and various census variables. Greater proximal tree canopy coverage was related to lower PM_2.5 concentrations, which translates to greater health benefits. A 1% increase in tree canopy at the census tract level, a boundary delineated by the US Census Bureau, results in a ~0.12 µg/m³ decrease in PM_2.5, and a 1% increase in "heavy industry" results in a 0.07 µg/m³ increase in PM_2.5 concentrations. Although the overall results from these 25 sites are within the annual ranges established by the EPA, they reveal substantial variations that reinforce the value of hyper-local sensing technologies as a powerful surveillance tool.

Urban buildings configuration and pollutant dispersion of PM 2.5 particulate to enhance air quality

A pivotal element for metropolitan planning and an essential component describing the urban design is block typology, affecting the pollution concentration. Consequently, this research examines the influence of various urban block typologies on urban pollutant distribution. Four typologies are simulated by ENVI-MET software. These typologies are cubic-shaped, L-shaped, C-shaped, and linear-shaped models. Urban air quality was assessed using relative humidity, temperature, and pollution PM2.5 concentration. The performance of typologies in terms of temperature, relative humidity, and reduction of air permeability is strongly dependent on the blocks' orientation, the block shape's rotation concerning the horizontal and vertical extensions, the height of the blocks, and the type of typology. According to these parameters, the performance is different in each of these studied typologies. Regression models propose a more reliable prediction of PM2.5 when the independent variables are temperature, relative humidity, and height of buildings, among various block typologies. Hence, this article suggests a machine learning approach, and the model evaluation shows that the Polynomial Linear Regression (PLR) model is excellent for measuring air pollution and temperature.

A three-dimensional LUR framework for PM2.5 exposure assessment based on mobile unmanned aerial vehicle monitoring

Land use regression (LUR) models have been widely used in epidemiological studies and risk assessments related to air pollution. Although efforts have been made to improve the performance of LUR models so that they capture the spatial heterogeneity of fine particulate matter (PM_2.5) in high-density cities, few studies have revealed the vertical differences in PM_2.5 exposure. This study proposes a three-dimensional LUR (3-D LUR) assessment framework for PM_2.5 exposure that combines a high-resolution LUR model with a vertical PM_2.5 variation model to investigate the results of horizontal and vertical mobile PM_2.5 monitoring campaigns. High-resolution LUR models that were developed independently for daytime and nighttime were found to explain 51% and 60% of the PM_2.5 variation, respectively. Vertical measurements of PM_2.5 from three regions were first parameterized to produce a coefficient of variation for the concentration (CVC) to define the rate at which PM_2.5 changes at a certain height relative to the ground. The vertical variation model for PM_2.5 was developed based on a spline smoothing function in a generalized additive model (GAM) framework with an adjusted R² of 0.91 and explained 92.8% of the variance. PM_2.5 exposure levels for the population in the study area were estimated based on both the LUR models and the 3-D LUR framework. The 3-D LUR framework was found to improve the accuracy of exposure estimation in the vertical direction by avoiding exposure estimation errors of up to 5%. Although the 3-D LUR-based assessment did not indicate significant variation in estimates of premature mortality that could be attributed to PM_2.5, exposure to this pollutant was found to differ in the vertical direction. The 3-D LUR framework has the potential to provide accurate exposure estimates for use in future epidemiological studies and health risk assessments.

Factors Influencing Classroom Exposures to Fine Particles, Black Carbon, and Nitrogen Dioxide in Inner-City Schools and Their Implications for Indoor Air Quality

BACKGROUND

School classrooms, where students spend the majority of their time during the day, are the second most important indoor microenvironment for children.

OBJECTIVE

We investigated factors influencing classroom exposures to fine particulate matter (PM2.5), black carbon (BC), and nitrogen dioxide (NO2) in urban schools in the northeast United States.

METHODS

Over the period of 10 y (2008-2013; 2015-2019) measurements were conducted in 309 classrooms of 74 inner-city schools during fall, winter, and spring of the academic period. The data were analyzed using adaptive mixed-effects least absolute shrinkage and selection operator (LASSO) regression models. The LASSO variables included meteorological-, school-, and classroom-based covariates.

RESULTS

LASSO identified 10, 10, and 11 significant factors (p<0.05) that were associated with indoor PM2.5, BC, and NO2 exposures, respectively. The overall variability explained by these models was R2=0.679, 0.687, and 0.621 for PM2.5, BC, and NO2, respectively. Of the model's explained variability, outdoor air pollution was the most important predictor, accounting for 53.9%, 63.4%, and 34.1% of the indoor PM2.5, BC, and NO2 concentrations. School-based predictors included furnace servicing, presence of a basement, annual income, building type, building year of construction, number of classrooms, number of students, and type of ventilation that, in combination, explained 18.6%, 26.1%, and 34.2% of PM2.5, BC, and NO2 levels, whereas classroom-based predictors included classroom floor level, classroom proximity to cafeteria, number of windows, frequency of cleaning, and windows facing the bus area and jointly explained 24.0%, 4.2%, and 29.3% of PM2.5, BC, and NO2 concentrations, respectively.

DISCUSSION

The adaptive LASSO technique identified significant regional-, school-, and classroom-based factors influencing classroom air pollutant levels and provided robust estimates that could potentially inform targeted interventions aiming at improving children's health and well-being during their early years of development. https://doi.org/10.1289/EHP10007.

Using AI-MCDM Model to Boost Sustainable Energy System Development: A Case Study on Solar Energy and Rainwater Collection in Guangdong Province

Rural areas in southern China receive ample rainfall annually as well as over 1600 h of annual sunshine. Despite a generally severe urban–rural development imbalance, these rural areas feature well-developed basic infrastructure and diverse economic activities. Rural revitalization policies in these areas have emphasized the development of cultural and ecological tourism, which has spurred economic development and given rise to a trend of villa construction. Residential buildings sit on large areas where natural resources are abundant. These advantages are conducive to the development and use of sustainable resources. This study proposes an incentive policy encouraging rural residents to renovate their buildings to include rainwater conservation and solar power generation. The Delphi method, an analytic hierarchy process, and fuzzy logic theory were combined to establish an AI-MCDM model, with applications of artificial intelligence and multiple-criteria decision making. Using Conghua District, Guangdong Province as an example, the study suggested that the model is beneficial to increasing the willingness of rural residents to reconstruct and renovate their residences, promoting the development of a low-carbon ecological region, Wenquan Township. We conducted the Delphi process twice to assess and validate incentives for installing natural resource conservation structures in agricultural areas. Nine criteria were identified, which can be divided into three main dimensions of participation situation, generating capacity, and storage facilities. The proposed AI-MCDM model developed using the Delphi–Fuzzy Analytic Hierarchy Process Model has high objectivity and can support rural areas in developing low-carbon, sustainable characteristics. The findings can serve as a reference for governments formulating incentives to encourage the installation of rainwater conservation and solar energy generation structures by rural households.

Phytosampling-a supplementary tool for particulate matter (PM) speciation characterization

Ambient air particulate matter (PM) and PM-associated environmentally persistent free radicals (EPFRs) have been documented to contribute to pollution-related health effects. Studies of ambient air PM potentially bear artifacts stemming from the collection methods. We have investigated the applicability of PM phytosampling (PHS) as a supplementary tool to a classic PM sampler in respect of achieving better PM chemical composition assessment (primarily organic fraction). Phytosampling is a static PM collection method relying on the particle entrapment by the plant's leaf through electrostatic forces and surface trichomes. We have investigated the differences in the EPFR and polycyclic aromatic hydrocarbon (PAH) speciation and concentration on ambient air PM for PHS and high-volume PM sampler (HVS). The advantages of PHS are easy particle recovery from the matrix, collection under natural environmental conditions, and the ability to apply a dense collection network to accurately represent spatial pollutant distribution. The experimental results show that the PHS can provide valuable speciation information, sometimes different from that observed for HVS. For PM collected by PHS, we detected the larger contribution of oxygen-centered EPFRs, different decay behavior, and more consistent PAH distribution between different PM sizes compared to the PM from HVS. These results indicate that the isolation of samples from the ambient during HVS sampling and exposure to high-volume airflow may alter the chemical composition of the samples, while the PHS method could provide details on the original speciation and concentration and be more representative of the PM surface. However, PHS cannot evaluate an absolute air concentration of PM, so it serves as an excellent supplementary tool to work in conjunction with the standard PM collection method.

BTEX indoor air characteristic values in rural areas of Jordan: Heaters and health risk assessment consequences in winter season

Benzene, toluene, ethylbenzene, and xylene isomers (BTEX) are known to affect environmental air and health quality. In this study, the levels of BTEX compounds were determined in indoor air environments during the winter generated by several different heaters: diesel pot-bellied heater with chimney (DH); electric heater (EH); unfluted gas heater (GH); kerosene heater (KH); and wood pot-bellied heater with chimney (WH). The samples were collected using a diffusion passive adsorbent (activated charcoal) and then analyzed by gas chromatography-mass spectrometry (GC-MS). The results showed that the heaters differ in the quantity of BTEX released during operation. The KH was the most polluted heater based on BTEX measurement, followed by DH. The ∑BTEX for heaters were observed as follows: KH (290 μg m^-3); DH (120 μg m^-3); GH (84 μg m^-3); WH (31 μg m^-3); EH (16 μg m^-3). Toluene was the predominant compound in all air samples. In KH and DH, the toluene to benzene ratios (T/B) were higher than 4 due to fuel evaporation, while GH had a T/B ratio of 3.9, indicating that the combustion of liquefied petroleum gas (LPG) was the main source. Moreover, a risk assessment was performed to evaluate where the cancer risks (CR) for benzene and ethylbenzene exceeded the critical values (10^-6). KH was found to be the most harmful heater for residents, followed by DH and GH. For non-carcinogenic compounds, hazard quotients (HQ) were found to be less than one and thus unlikely to cause health problems.

Land use regression modelling of PM2.5 spatial variations in different seasons in urban areas

As one of the principal components of haze, fine particulate matter (PM_2.5) has potential negative health effects, causing widespread concern. Identification of the pollutant spatial variation is a prerequisite of understanding ambient air pollution exposure and further improving air quality. Seven urban built-up areas in Liaoning central urban agglomeration (LCUA) were used for land use regression (LUR) modelling of PM_2.5 concentrations using small amounts of spatially aggregated data and to assess the model's seasonal consistency. LUR models explained 52-61% of the variation in the PM_2.5 concentrations at urban scales. The average building floor area was the key predictor in each model, and the percent water area was predictor with a negative coefficient. Good seasonal consistency was observed between the heating-seasonal model and annual average model, showing that the annual average PM_2.5 pollution in the LCUA was mainly influenced by pollution during the heating season. Extending the linear LUR model with regression kriging improved the model's explanatory ability and predictive performance. The predicted PM_2.5 concentrations in Shenyang and Anshan were the highest and that in Yingkou was the lowest. The building three-dimensional variables played important roles in the urban spatial modelling of air pollution.

Assessing 3-D Spatial Extent of Near-Road Air Pollution around a Signalized Intersection Using Drone Monitoring and WRF-CFD Modeling

In this study, we have assessed the three-dimensional (3-D) spatial extent of near-road air pollution around a signalized intersection in a densely populated area using collaborating methodologies of stationary measurements, drone monitoring, and atmospheric dispersion modeling. Stationary measurement data collected in the roadside apartment building showed a substantial effect of emitted pollutants, such as nitrogen oxides (NO_x), black carbon (BC), and ultrafine particles (UFPs), especially during the morning rush hours. Vertical drone monitoring near the road intersection exhibited a steeper decreasing trend with increasing altitude for BC concentration rather than for fine particulate matter (PM_2.5) concentration below the apartment building height. Atmospheric NO_x dispersion was simulated using the weather research and forecasting (WRF) and computational fluid dynamics (CFD) models for the drone measurement periods. Based on the agreement between the measured BC and simulated NO_x concentrations, we concluded that the air pollution around the road intersection has adverse effects on the health of residents living within the 3-D spatial extent within at least 120 m horizontally and a half of building height vertically during the morning rush hours. The comparability between drone monitoring and WRF-CFD modeling can further guarantee the identification of air pollution hotspots using the methods.

Validation of PM2.5 model particle through physicochemical evaluation and atherosclerotic plaque formation in ApoE-/- mice

PM_2.5 particles are regarded as prominent risk factors that contribute to the development of atherosclerosis. However, the composition of PM_2.5 is rather complicated. This study aimed to provide a model particle that simulates the behavior of actual PM_2.5, for subsequent use in exploring mechanisms and major complications arising from PM_2.5. To establish model particles of PM_2.5, a series of monodisperse SiO₂ microspheres with different average grain diameters were mixed according to the size distribution of actual PM_2.5. The organic carbon (OC) was removed from PM_2.5 and coated onto the SiO₂ model particle, to formulate simulant PM_2.5. Results showed that the size distribution of the model particle was highly approximate to that of the PM_2.5 core. The polycyclic aromatic hydrocarbon (PAHs) composition profile of the simulated PM_2.5 were approximate to PM_2.5, and loading efficiency was approximately 80%-120%. Furthermore, compared to the control, SiO₂-only model particle had negligible cytotoxicity on cell viability and oxidative stress of HUVECs, and marginal effect on the lipid metabolism and atherosclerotic plaque formation in ApoE^-/- mice. In contrast, simulated PM_2.5 exhibited similar cytotoxic and detrimental effects on lipid metabolism and atherosclerotic plaque formation with actual PM_2.5. Traffic-related PM_2.5 had negative effects on endothelial function and led to the formation of atherosclerosis via oxidative stress. The simulated PM_2.5 simulated the outcomes of actual PM_2.5 exposure. Here, we show that SiO₂ particle model cores coated with OC could significantly assist in the evaluation of the effects of specific organic compositions bound on PM_2.5, specifically in the context of environmental health and safety.

Shotgun Metagenomics of Gut Microbiota in Humans with up to Extreme Longevity and the Increasing Role of Xenobiotic Degradation

The gut microbiome of long-lived people display an increasing abundance of subdominant species, as well as a rearrangement in health-associated bacteria, but less is known about microbiome functions. In order to disentangle the contribution of the gut microbiome to the complex trait of human longevity, we here describe the metagenomic change of the human gut microbiome along with aging in subjects with up to extreme longevity, including centenarians (aged 99 to 104 years) and semisupercentenarians (aged 105 to 109 years), i.e., demographically very uncommon subjects who reach the extreme limit of the human life span. According to our findings, the gut microbiome of centenarians and semisupercentenarians is more suited for xenobiotic degradation and shows a rearrangement in metabolic pathways related to carbohydrate, amino acid, and lipid metabolism. Collectively, our data go beyond the relationship between intestinal bacteria and physiological changes that occur with aging by detailing the shifts in the potential metagenomic functions of the gut microbiome of centenarians and semisupercentenarians as a response to progressive dietary and lifestyle modifications.IMPORTANCE The study of longevity may help us understand how human beings can delay or survive the most frequent age-related diseases and morbidities. In this scenario, the gut microbiome has been proposed as one of the variables to monitor and possibly support healthy aging. Indeed, the disruption of host-gut microbiome homeostasis has been associated with inflammation and intestinal permeability as well as a general decline in bone and cognitive health. Here, we performed a metagenomic assessment of fecal samples from semisupercentenarians, i.e., 105 to 109 years old, in comparison to young adults, the elderly, and centenarians, shedding light on the longest compositional and functional trajectory of the human gut microbiome with aging. In addition to providing a fine taxonomic resolution down to the species level, our study emphasizes the progressive age-related increase in degradation pathways of pervasive xenobiotics in Western societies, possibly as a result of a supportive process within the molecular continuum characterizing aging.

Vertical profiling of fine particulate matter and black carbon by using unmanned aerial vehicle in Macau, China

An unmanned aerial vehicle (UAV) equipped with miniature monitors was used to study the vertical profiles of PM_2.5 (particulate matter with a ≤2.5-μm diameter) and black carbon (BC) in Macau, China, from the surface to 500 m above ground level (AGL). Twelve- and 11-day measurements were conducted during February and March 2018, respectively. In total, 46 flights were conducted between 05:00 and 06:00 AM Local Time (LT). The average concentrations of PM_2.5 and BC were significantly lower in March (40.1 ± 17.9 and 2.3 ± 2.0 μg m^-3, respectively) when easterly winds prevailed, compared with those in February (69.8 ± 35.7 and 3.6 ± 2.0 μg m^-3, respectively) when northerly winds dominated. In general, PM_2.5 concentrations decreased with height, with a vertical decrement of 0.2 μg m^-3 per 10 m. BC concentrations exhibited diverse vertical profiles with an overall vertical decrement of 0.1 μg m^-3 per 10 m. Meteorological analyses including back-trajectory analysis and atmospheric stability categorization revealed that both advection and convection transports may have notable influences on the vertical profiles of PM pollutants. The concentration of PM pollutants above the boundary layer was lower than that within the layer, thus exhibiting a sigmoid profile in some cases. In addition, the lighting of firecrackers and fireworks on February 16 (first day of the Chinese New Year) resulted in the elevated concentrations of PM_2.5 and BC within 150 m AGL. The takeoff of a civil flight on February 10 may have resulted in a substantial increase in the PM_2.5 concentrations from 80.8 (±2.1) μg m^-3 at the ground level to 119.2 (±9.3) μg m^-3 at a height of 330 m. Although the results are confined to a height of 500 m AGL, the current study provides a useful dataset for PM vertical distributions, complementing the spatiotemporal variations by ground-based measurements.

Seasonal exposure to PM2.5-bound polycyclic aromatic hydrocarbons and estimated lifetime risk of cancer: A pilot study

Limited researches are available on seasonal variation of inhalation exposure of polycyclic aromatic hydrocarbons (PAHs) and its cancer risk assessment in China. We recruited 20 fresh postgraduates and measured outdoor and indoor (dormitories, offices and laboratories) daily PM_2.5 concentrations in four seasons (seven consecutive days in every season) during 2014 -2015, calculated daily potential doses of personal exposure to total Benzo[a]pyrene equivalent concentration (BaPeq) in the microenvironments based on the total BaPeq and the time-activity patterns, and estimated incremental lifetime cancer risk (ILCR) using Monte Carlo method. Daily average concentrations of PM_2.5-bound ∑PAHs on the campus ranked from high to low were winter, autumn, spring, summer in the dormitories and offices. Daily average concentration of PM_2.5-bound ∑PAHs were higher in indoor environments than outdoor in the same season, except for that of PM_2.5-bound ∑PAHs in laboratories in the winter. Median values of ILCR in both sexes from high to low were winter (men vs. women: 5.35e^-9 vs. 4.96e^-9), spring (3.71e^-9 vs. 4.00e^-9), autumn (2.92e^-9 vs. 3.02e^-9), summer (1.71e^-9 vs. 1.87e^-9). Indoor and outdoor PM_2.5-bound PAHs concentrations showed seasonal and spatial variations. The ILCR value for PM_2.5-bound PAHs was higher in women than in men.

An overview of the development of vertical sampling technologies for ambient volatile organic compounds (VOCs)

Atmospheric volatile organic compounds (VOCs) are harmful to human health and the environment, and are precursors of other toxic air pollutants, e.g. ozone (O₃) and secondary organic aerosols (SOAs). In recent years, due to scientific and technological advancements, vertical VOC profile in the atmosphere has been increasingly studied since it plays an essential role in the atmospheric research by providing multilevel three-dimensional data. Such information will improve the predictive ability of existing air quality models. This review summarizes the latest development of vertical VOC sampling technologies, highlighting the technical and non-technical challenges with possible solutions and future applications of vertical VOC sampling technologies. Further, other important issues concerning ambient VOCs have also been discussed, e.g. emission sources, VOC air samplers, VOC monitoring strategies, factors influencing airborne VOC measurement, the use of VOC data in air quality models and future smart city air quality management. Since ambient VOC levels can fluctuate significantly with altitude, technologies for vertical VOC profiling have been developed from building/tower-based measurements and tethered balloons to aircrafts, unmanned aerial vehicles (UAVs) and satellites in order to improve the temporal-spatial capacity and accuracy. Between the existing sampling methods, so far, UAVs are capable of providing more reliable VOC measurements and better temporal-spatial capacities. Heretofore, their disadvantages and challenges, e.g. sampling height, sampling time, sensitivity of the sensors and interferences from other chemical species, have limited the application of UAV for vertical VOC profiling.

Evaluation of floor-wise pollution status and deposition behavior of potentially toxic elements and nanoparticles in air conditioner dust during urbanistic development

The study was undertaken to investigate deposition behaviors of various size-segregated particles and indoor air quality using dust accumulated on the air conditioner filter acting as a sink for PTEs and nanoparticles that can pose a significant health risk. However, the particulate matter size and chemical composition in AC dust and its relationship with PTEs remains uncertain. Current study aims to investigate the PTEs and nanoparticles composition of AC dust using different analytical approaches including ICP-MS, XRD, XPS, SEM/TEM along with EDS and Laser Diffraction particle size analyzer. The mean concentration of PTEs like Al, As, Cd, Cu, Li, Pb, Sb, Se, Sn, Ti, V and Zn exceeded the corresponding background value. Pb, As, Sn, Sb, Cd were categorizing under geo-accumulation index class IV. Most of the particles were found to be > 100 μm and it decreased significantly with increase in floor altitude. A significantly negative correlation was found between particles size and PTEs concentration showing a significant increase in PTEs content with decrease in particles size. The XPS results showed dominant peaks for TiO₂, Ti-O-N, As₂O₃, Fe⁺³, Fe⁺², Al-OH and Al₂0₃. Additionally, As, Pb, Si and Fe were dominant metallic nanoparticles identified using SEM/TEM along with EDS.

Modelling the vertical gradient of nitrogen dioxide in an urban area

INTRODUCTION

Land use regression models environmental predictors to estimate ground-floor air pollution concentration surfaces of a study area. While many cities are expanding vertically, such models typically ignore the vertical dimension.

METHODS

We took integrated measurements of NO₂ at up to three different floors on the facades of 25 buildings in the mid-sized European city of Basel, Switzerland. We quantified the decrease in NO₂ concentration with increasing height at each facade over two 14-day periods in different seasons. Using predictors of traffic load, population density and street configuration, we built conventional land use regression (LUR) models which predicted ground floor concentrations. We further evaluated which predictors best explained the vertical decay rate. Ultimately, we combined ground floor and decay models to explain the measured concentrations at all heights.

RESULTS

We found a clear decrease in mean nitrogen dioxide concentrations between measurements at ground level and those at higher floors for both seasons. The median concentration decrease was 8.1% at 10 m above street level in winter and 10.4% in summer. The decrease with height was sharper at buildings where high concentrations were measured on the ground and in canyon-like street configurations. While the conventional ground floor model was able to explain ground floor concentrations with a model R² of 0.84 (RMSE 4.1 μg/m³), it predicted measured concentrations at all heights with an R² of 0.79 (RMSE 4.5 μg/m³), systematically overpredicting concentrations at higher floors. The LUR model considering vertical decay was able to predict ground floor and higher floor concentrations with a model R² of 0.84 (RMSE 3.8 μg/m³) and without systematic bias.

DISCUSSION

Height above the ground is a relevant determinant of outdoor residential exposure, even in medium-sized European cities without much high-rise. It is likely that conventional LUR models overestimate exposure for residences at higher floors near major roads. This overestimation can be minimized by considering decay with height.