Concentrations Of Airborne Particulate Matter Research Articles

Microbial dynamics and climatic interactions in pig sheds: Insights into airborne microbes and particulate matter concentrations.

Emissions of airborne pollutants from livestock buildings affect indoor air quality, the health and well-being of farmers, animals and the environment. This study aimed to evaluate the microbial count within pig sheds and its relationship with meteorological variables (temperature, relative humidity and air velocity) and particulate matter (PM10 and PM2.5) and microbial diversity. Sampling was conducted both inside and outside of two pig sheds over three seasons (summer, rainy and winter), with regular monitoring at fortnightly intervals. Results showed that the bacterial and fungal counts ranged from 0.07 to 3.98 x 103 cfu/m3 inside the sheds and 0.01 to 1.82 x 103 cfu/m3 outside. Seasonal variations were observed, with higher concentrations of particulate matter detected during the winter season, followed by summer. Climatic variables such as temperature, air velocity and relative humidity demonstrated significant impacts on the abundance of Enterobacteriaceae and fungi, while air velocity specifically influenced the presence of mesophilic bacteria and staphylococci. Importantly, no significant disparities were found between microbial counts and particulate matter levels. Staphylococcaceae emerged as the predominant bacterial family, while Aspergillus and Cladosporium spp. were the dominant fungal species within the pig sheds. The average levels of airborne bacteria and fungi in pig sheds were found to be within the recommended range, which can be attributed to the loose housing design and lower animal population on the farms.

Matrix-valued isotropic covariance functions with local extrema

Multivariate random fields are commonly used in spatial statistics and natural science to model coregionalized variables. In this context, the matrix-valued covariance function plays a central role in capturing their spatial continuity and interdependence. This study aims to contribute to the literature on covariance modeling by proposing new parametric families of isotropic matrix-valued functions exhibiting non-monotonic behaviors, namely hole effects and cross-dimples. The benefit of the proposed models is shown on a bivariate data set consisting of concentrations of airborne particulate matter.

Metal Element Traces Sampled from Peri-Urban Road Verge Particulate Matter

The objective of this research is to analyze metal elements, such as Na (sodium), Mg (magnesium), Al (aluminum), Si (silicon), Pb (lead), K (potassium), Ca (calcium), and Fe (iron), found in dust particles within two distinct areas from which the samplings were taken. The first sampling was taken from the road verge of a highly trafficked road section, while the second sampling was taken from a residential garden area 90 m away from the road. Several metal elements were detected with a high difference in Si, which presented higher concentrations in the dust samples from the road verge area. Pb has only been detected in the samples taken from the road verge, which could be explained by residual remnants from old lead gasoline and wheel weights. Additionally, during the same investigation, airborne particulate matter (PM) concentrations were measured in comparison between the road verge and the garden area; this presented a substantial difference in the concentration levels, suggesting that dense vegetation is protecting and blocking a majority of airborne PM. A literature highlight of the health effects of different metal elements and PM concentrations is presented.

Airborne Particulate Matter Size and Chronic Obstructive Pulmonary Disease Exacerbations: A Prospective, Risk-Factor Analysis Comparing Global Initiative for Obstructive Lung Disease 3 and 4 Categories.

Current research primarily emphasizes the generalized correlations between airborne pollution and respiratory diseases, seldom considering the differential impacts of particular particulate matter sizes on chronic obstructive pulmonary disease (COPD) exacerbations in distinct Global Initiative for Obstructive Lung Disease (GOLD) categories. This study hypothesizes a critical association between particulate matter sizes (PM 1.0, PM 2.5, and PM 10) and exacerbation frequency in COPD patients categorized under GOLD 3 and GOLD 4, with a potential augmenting role played by proximity to main roads and industrial areas. This research aspires to offer a nuanced perspective on the exacerbation patterns in these groups, setting the stage for targeted intervention strategies. Utilizing a prospective design, this study followed 79 patients divided into GOLD 3 (n = 47) and GOLD 4 (n = 32) categories. The participants were monitored for ten days for daily activity levels, symptoms, living conditions, and airborne particulate matter concentrations, with spirometric evaluations employed to measure lung function. Statistical analyses were used to identify potential risk factors and significant associations. The analysis revealed substantial disparities in airborne particulate matter sizes between the two groups. The mean PM 1.0 concentration was notably higher in GOLD 4 patients (26 µg/m3) compared to GOLD 3 patients (18 µg/m3). Similarly, elevated PM 2.5 levels were observed in the GOLD 4 category (35 µg/m3) in contrast to the GOLD 3 category (24 µg/m3). A vital finding was the increased frequency of exacerbations in individuals residing within 200 m of main roads compared to those living further away (OR = 2.5, 95% CI: 1.5-4.1). Additionally, patients residing in homes smaller than 50 square meters demonstrated a greater frequency of exacerbations. Spirometry results corroborated the exacerbated condition in GOLD 4 patients, indicating a significant decline in lung function parameters compared to the GOLD 3 group. This study substantiates a significant association between airborne particulate matter sizes and exacerbation frequencies in COPD patients, particularly accentuating the increased risk in GOLD 4 patients. Our findings underscore the pivotal role of environmental factors, including the size of living areas and proximity to main roads, in influencing COPD exacerbations. These results suggest the need for personalized healthcare strategies and interventions, which account for environmental risk factors and the distinctions between GOLD 3 and GOLD 4 categories of COPD patients.

Size-resolved inhalation intake fractions for particles released from human activities in residential indoor environments

Inhalation exposure to elevated concentrations of airborne particulate matter is a public health concern. Assessment of exposure can be enhanced through better knowledge of source-receptor relationships, which can be characterized through the inhalation intake fraction metric. This case study provides new insights on variations in particle inhalation intake fractions for indoor sources associated with common human activities in residential buildings. In a controlled climate chamber (air temperature: 24 ± 1 °C, relative humidity: 50 ± 5%), we investigated size-resolved intake fractions for particles in relation to four scripted activities performed by a human volunteer: sitting, walking, cooking, and vacuuming. We measured size- and time-resolved particle number concentrations at the volunteer's breathing zone to characterize intake fractions. In addition, we measured particles at four different stationary locations across the climate chamber to assess the degree of spatial heterogeneity in particulate matter concentrations. The results show that particles released from human skin and clothing during sitting were associated with the highest total inhalation intake fraction (13‰), followed by cooking (9‰), vacuuming (5.7‰), and walking (3.9‰). These results highlight how breathing zone proximity to localized emission sources and low indoor air mixing can enhance inhalation exposure to particles. Sitting and cooking caused a maximum inhalation intake fraction in the size range of 1–3 μm. Findings also show that the assumption of a perfectly mixed environment could lead to an underestimation of the inhalation intake of particles by up to 3.2-fold. The results of this case study provide a basis for achieving more accurate personal inhalation exposure assessment and improved indoor air quality management.

Association between airborne particulate matter（PM 2.5） concentration and the incidence of allergic rhinitis in Shanghai

Objective:To explore the impact of PM 2.5 concentration in Shanghai on the incidence of allergic rhinitis（AR） in the population, and provide strategies for early warning and prevention of AR. Methods:Collect daily average concentrations of atmospheric pollutants monitored in Shanghai from January 1, 2017 to December 31, 2019, and clinical data of AR patients from five hospitals in Shanghai during the same period. We used a time-series analysis additive Poisson regression model to analyze the correlation between PM 2.5 levels and outpatient attendance for AR patients. Results:During the study period, a total of 56 500 AR patients were included, and the daily average concentration of PM 2.5 was（35.28±23.07）μg/m³. There is a correlation between the concentration of PM 2.5 and the number of outpatient attendance for AR cases. There is a positive correlation between the daily average number of outpatient for AR and levels of PM 2.5 air pollution（（P<0.05）） . We found that every 10 μg/m³ increase in PM 2.5, the impact of on the number of AR visits was statistically significant on the same day, the first day behind, and the second day behind, with the strongest impact being the exposure on the same day. Every 10 μg/m³ increases in PM 2.5, the number of outpatient visits increased by 0.526% on the same day（95%CI 1.000 50-1.010 04）. Conclusion:The atmospheric PM 2.5 concentration in Shanghai is positively correlated with the number of outpatient for AR, and PM 2.5 exposure is an independent factor in the onset of AR. This provides an important theoretical basis for AR.

Wearable Resonator-Based Respirable Dust Monitor for Underground Coal Mines

Exposure to respirable coal dust and diesel exhaust in underground coal mines can cause detrimental airway diseases such as coal worker’s pneumoconiosis (CWP), silicosis, and lung cancer. In this article, we present the design, fabrication, and experimental evaluation of a low-cost wearable respirable dust monitor (WEARDM) that uses a dual-resonator gravimetric sensing approach for real-time measurement of respirable airborne particulate matter (PM) concentrations. The sensor selects for the ISO-respirable dust fraction using a miniature virtual impactor (VI) and removes moisture from the collected dust to ensure accurate mass measurement. WEARDM uses a novel dual-resonator mass sensor (DRMS) that is composed of a quartz crystal microbalance (QCM) and a film bulk acoustic resonator (FBAR). The QCM measures the mass concentration of particles generated from coal mining operations [typically >2.5- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> aerodynamic diameter (AD)], separated using inertial impaction. Thermophoretic precipitation is used to deposit the fine and ultrafine particles, such as those emitted from diesel sources [typically <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ &lt; 0.1~\mu \text{m}$ </tex-math></inline-formula> AD) on FBAR. This allows the WEARDM system to maintain a large dynamic range and uniform collection efficiency (CE) across the entire respirable fraction. The WEARDM system is optimized for a low flow rate of 250 mL/min which results in low power usage and a small form factor and is an order of magnitude smaller and less expensive than currently available devices.

Overlooked Impacts of Urban Environments on the Air Quality in Naturally Ventilated Schools Amid the COVID-19 Pandemic

The density, form, and dimensions of urban morphology are important for healthy living conditions in cities, especially if they are related to the climate and air pollution. Morphology and environmental conditions determine the relationship between open and built space, the width of street spaces, the aerodynamic characteristics of wind currents, albedo, and the retention of pollutants, as well as determining the radiative exchange with the atmosphere. Studies on the COVID-19 pandemic have focused on the assumption of a possible relationship between the spread of the SARS-CoV-2 virus and the presence and concentration of airborne particulate matter (PM10 and PM2.5). This paper focuses on the research of indoor air quality (IAQ) in two schools with naturally ventilated classrooms in Ljubljana, Slovenia. The presence of particulate matter (PM2.5 and PM10) and the concentration of CO2 were studied, along with other microclimatic conditions, e.g., ambient temperature, relative humidity, air pressure, and wind conditions. These were compared and assessed via analysis of variance (ANOVA) and Duncan’s post hoc test. The main concern was to see how effective different ventilation strategies are, as well as how the openings in the classroom impact the concentrations of CO2 relative to the concentrations of PM2.5 and PM10 particles as a side effect of these ventilation strategies. The inconsistent application of recommended COVID-19 ventilation strategies suggests that IAQ in naturally ventilated classrooms is highly determined by individual perceptions of indoor air quality. The results also suggest that the IAQ is significantly affected by the schools’ urban environment; however, this is not considered within the national COVID-19 ventilation recommendations. Future ventilation guidelines for pandemics should also include the urban environment as a risk factor for inadequate IAQ, instead of focusing solely on pathogen characteristics.

Effects of Green Network Management of Urban Street Trees on Airborne Particulate Matter (PM2.5) Concentration.

Street trees are crucial for air pollutant reduction in urban areas. Herein, we used computational fluid dynamics (CFD) simulation to identify changes in airborne particulate matter (PM2.5) concentration based on wind characteristics (direction and velocity) and the green network of street trees. The green network was assessed based on composition of the green area of street trees in the central reserve area and between the motor and pedestrian roads. The PM2.5 concentration varied according to the presence or absence of major reserve planting and the planting structure of the street trees, but not according to the wind direction or velocity. The concentration was lower when the wind direction was 45° (than when the wind direction was 0°), whereas it showed a more significant decrease as the wind velocity increased. Despite variation at each measurement site, the PM2.5 reduction was generally higher when the central reserve and street trees had a multi-planting structure. Hence, to ensure an effective reduction in the PM2.5 concentration on motor roads and reduce its negative impact on pedestrians, both arbors and shrubs should be planted in the central reserve area. The study results will serve as reference for managing the green area network and linear green infrastructure in terms of improving the atmospheric environment.

7Be, 210Pb, airborne particulate matter and PM10 concentrations in relation to meteorological conditions in southern Poland in 1998–2016

An analysis of the concentration of 7Be in aerosol samples collected in one of the most polluted areas in Europe (Katowice and Krakow in southern Poland) indicated seasonal variability, with a maximum in the summer months. The average concentrations of 7Be were 4616.1 μBq m−3 in Katowice and 3259.4 μBq m−3 in Krakow, respectively, and they are among the highest values recorded in Poland in the studied period (1998–2016). These cities are also characterised by Poland's highest concentrations of 210Pb (547.8 μBq m−3 and 513.2 μBq m−3). The highest radioactive concentrations of 210Pb were observed in the winter and autumn, since in the case of these industrial areas, the combustion processes related to heating in the cold season of the year are an additional source of this isotope, next to its natural origin. The airborne particulate matter concentrations at both locations correlate with the concentrations of 210Pb. The average values of PM10 concentrations (71.1 μg m−3 in Krakow to 45.0 μg m−3 in Katowice), were 2–3 times higher than the average ones recorded in northern Poland. It has been proven that air temperature is the key parameter affecting the transport of isotopes, especially in the warm season of the year, when its increase causes increased thermal convection, leading to intense vertical mixing and exchange in the troposphere. Analyses using the machine learning method allowed for an indication of the correlation between relative humidity and atmospheric precipitation, as well as higher wind speed and concentrations of 7Be which is inversely proportional. Geographical factors (the latitude of the station and the land elevation) have no impact on near-surface concentrations of 7Be in Poland.

Effect of Airborne Particulate Matter on Cardiovascular Diseases

Context: Airborne particulate matter (PM) attracts heightened attention due to its implication in various diseases, especially cardiovascular diseases. Although numerous epidemiological studies have been published worldwide in developing countries on risks associated with exposure to PM, such studies are still scarce in developing countries such as Saudi Arabia. Objective: To examine the association between the concentration of airborne particulate matter (PM) and hospital admissions resulting from cardiovascular diseases (CVD) in the Eastern Region of Saudi Arabia, specifically in the cities of Dammam and Khobar. Methodology: The daily concentrations of PM10 and PM2.5 were obtained from 10 monitoring stations distributed around the two hospitals. There was an examination of the discharge data of patients diagnosed with cardiac arrhythmias, acute myocardial infarction, and heart failure as their primary diagnoses. The data were obtained from two big governmental hospitals in the Eastern Region. The primary cause of hospital admission of 259 patients was identified as acute cardiac condition. Results: For PM10 and PM2.5, the 24 h mean was calculated as 101.2 and 37.1 µg/m3, respectively; such means are considered higher than the Air Quality Guidelines (AQGs). We found evidence of an increased risk of cardiovascular events for long-term exposure to PM2.5–10 concentrations, and a correlation with the IHD hospital admission within 6 days of the peak PM10 or PM2.5 concentration. In addition, the increased PM2.5 concentration also had a correlation with hospital admissions; however, analysis shows an increase in mortality at lag1, lag2, and lag3 prior to hospital admission. Conclusions: Hospital admissions for several cardiovascular diseases acutely increase in response to higher ambient PM concentrations. It is recommended that residents need to use personal protection, especially those residents with cardiovascular disease, while the government needs to strengthen the governance of air pollution in areas with lighter air pollution.

Relationship between Leaf Traits and PM-Capturing Capacity of Major Urban-Greening Species

High concentrations of airborne particulate matter (PM) in urban areas are of great concern to human health. Urban greening has been shown to be an effective and eco-friendly way to alleviate particle pollution, and attention to its role in mitigating particle pollution has increased worldwide. The species-specific PM-capturing capacity of ten urban-greening species in Seoul was evaluated by leaf functional traits (average leaf area (ALA), specific leaf area (SLA), and leaf width-to-length ratio (W/L)), microstructures (roughness, stomata, and trichomes), and physicochemical traits (contact angle (θw), surface free energy (rs), the work of adhesion for water (Wa), and epicuticular wax loads (EWL)). The relationships between leaf traits and PM adsorption by leaves were revealed by Pearson’s correlations and principal component analysis (PCA). A gravimetric method was used to quantify, by particle size, the PM adsorbed on leaf surfaces or embedded in leaf epicuticular wax layers. The key factors for PM adsorption on leaf surfaces were the SLA, the mean roughness value (Ra), and stomatal size. The SLA and Ra of adaxial leaf surfaces were negatively correlated with PM accumulation on leaf surfaces, while stomatal length and width were positively correlated with surface PM load. The rs and EWL positively affected the in-wax PM load. Species-specific PM deposition was the result of complicated mechanisms of various leaf traits. Three evergreen shrub species, Buxus sinica (Rehder &amp; E.H. Wilson) M.Cheng var. insularis (Nakai) M.Cheng, Taxus cuspidata Siebold &amp; Zucc., and Euonymus japonicus Thunb., were efficient in capturing both surface PM and in-wax PM. The PCA revealed that the high PM accumulation efficiency of these three species might be attributable to the interaction between stomatal size and EWL. Aesculus turbinata Blume, Chionanthus retusus Lindl. &amp; Paxton, and Rhododendron schlippenbachii Maxim. had intermediate PM adsorption ability, which might be a result of interactions among stomatal density, the Wa of adaxial surfaces, and ALA. Magnolia denudata Desr., Styphnolobium japonicum (L.) Schott, Liriodendron tulipifera L., and Ginkgo biloba L. had low PM accumulation efficiency. These four species exhibited correlations among SLA, the Ra of adaxial leaf surfaces, and W/L, which had negative effects on PM adsorption.

Evidence of seasonal changes in airborne particulate matter concentration and occupation-specific variations in pulmonary function and haematological parameters among some workers in Enugu Southeast Nigeria: a randomized cross-sectional observational study

BackgroundUpsurge in cardiopulmonary dysfunctions in Enugu, Nigeria, involved mainly cement workers, automobile spray painters, woodworkers, and Cleaners and was worsened in the dry season, suggesting the need for an occupation-specific characterization of the disease features and seasonal evaluation of air quality for prevention and management.MethodsWe conducted a randomized cross-sectional study of eighty consenting participants (in Achara Layout, Enugu), comprising 20 cement workers (39.50 ± 14.95 years), 20 automobile spray painters (40.75 ± 9.85 years), 20 woodworkers (52.20 ± 9.77 years), and 20 cleaners (42.30 ± 9.06 years). The air quality, some haematological (fibrinogen-Fc, and C-reactive protein-CRP), and cardiopulmonary parameters were measured and analyzed using ANCOVA, at p < 0.05.ResultsThe dry season particulate matter (PM) in ambient air exceeded the WHO standards in the New layout [PM10 = 541.17 ± 258.72 µg/m3; PM2.5 = 72.92 ± 25.81 µg/m3] and the University campus [PM10 = 244 ± 74.79 µg/m3; PM2.5 = 30.33 ± 16.10 µg/m3], but the former was twice higher. The PM differed significantly (p < 0.05) across the sites. Forced expiratory volume at the first second (FEV1) (F = 6.128; p = 0.001), and Peak expiratory flow rate (PEFR) (F = 5.523; p = 0.002), differed significantly across the groups. FEV1/FVC% was < 70% in cement workers (55.33%) and woodworkers (61.79%), unlike, automobile spray painters (72.22%) and cleaners (70.66%). FEV1 and work duration were significantly and negatively related in cement workers (r = -0.46; r2 = 0.2116; p = 0.041 one-tailed). CRP (normal range ≤ 3.0 mg/L) and Fc (normal range—1.5–3.0 g/L) varied in cement workers (3.32 ± 0.93 mg/L versus 3.01 ± 0.85 g/L), automobile spray painters (2.90 ± 1.19 mg/L versus 2.54 ± 0.99 mg/L), woodworkers (2.79 ± 1.10 mg/L versus 2.37 ± 0.92 g/L) and cleaners (3.06 ± 0.82 mg/L versus 2.54 ± 0.70 g/L).Conclusion(s)Poor air quality was evident at the study sites, especially in the dry season. Cement workers and automobile spray painters showed significant risks of obstructive pulmonary diseases while woodworkers had restrictive lung diseases. Cement workers and cleaners recorded the highest risk of coronary heart disease (CRP ≥ 3.0 mg/L). The similarity in Fc and CRP trends suggests a role for the inflammation-sensitive proteins in the determination of cardiovascular risk in cement workers and cleaners. Therefore, there are occupation-specific disease endpoints of public health concern that likewise warrant specific preventive and management approaches among the workers.

Spatial heterogeneity of river effects on PM2.5 pollutants in waterfront neighborhoods based on mobile monitoring

Concentrations of airborne particulate matter (PM) are influenced by land cover types. Water bodies in cities can influence the spatial distribution of air pollution by altering the microclimate. However, the influence of water bodies on PM2.5 concentrations is complex and requires further exploration, especially at the microscale. In this study, a geographically weighted regression (GWR) model was constructed to explore the spatially heterogeneous effect of a river on the PM2.5 concentrations in nearby riverside neighborhoods using mobile monitoring in Wuhan. The results showed that the GWR model was applicable at the neighborhood scale and had a good explanatory performance for PM2.5 concentrations, with a higher R2 (0.71). The river had multiple effects on the PM2.5 concentrations in the riverfront neighborhoods and strongly affected the air quality in neighborhoods within an 800 m distance due to wind infiltration, with the strongest effect at a 500–700 m distance. Moreover, considering spatial nonstationarity, the effect of the large river on street-level air quality largely depended on its effect on wind, and good ventilation conditions could amplify that effect. Commercial, road intersections, second-level roads and parks were identified as sensitive environmental factors affecting the river's influence on PM2.5 concentrations. In addition, urban parks had a greater mitigating effect on PM2.5 pollution than did water bodies in this study. These results help to clarify the impact of rivers on air quality and provide a theoretical basis for urban design to mitigate PM2.5 pollution.

Protecting playgrounds: local-scale reduction of airborne particulate matter concentrations through particulate deposition on roadside ‘tredges’ (green infrastructure)

Exposure to traffic-related particulate air pollution has been linked with excess risks for a range of cardiovascular, respiratory and neurological health outcomes; risks likely to be exacerbated in young children attending schools adjacent to highly-trafficked roads. One immediate way of reducing airborne PM concentrations at the local (i.e., near-road community) scale is installation of roadside vegetation as a means of passive pollution abatement. Roadside vegetation can decrease airborne PM concentrations, through PM deposition on leaves, but can also increase them, by impeding airflow and PM dispersion. Critical to optimizing PM removal is selection of species with high particle deposition velocity (Vd) values, currently under-parameterised in most modelling studies. Here, the measured amounts of leaf-deposited magnetic PM after roadside greening (‘tredge’) installation, and measured reductions in playground PM, particle number and black carbon concentrations demonstrate that air quality improvements by deposition can be achieved at the local, near-road, community/playground scale. PM deposition on the western red cedar tredge removed ~ 49% of BC, and ~ 46% and 26% of the traffic-sourced PM2.5 and PM1, respectively. These findings demonstrate that roadside vegetation can be designed, installed and maintained to achieve rapid, significant, cost-effective improvement of air quality by optimising PM deposition on plant leaves.

Chemical explosion, COVID-19, and environmental justice: Insights from low-cost air quality sensors

ObjectivesTo examine the impact of the Intercontinental Terminals Company (ITC) fire and COVID-19 on airborne particulate matter (PM) concentrations and the PM disproportionally affecting communities in Houston using low-cost sensors. MethodsWe compared measurements from a network of low-cost sensors with a separate network of monitors from the Environmental Protection Agency (EPA) in the Houston metropolitan area from Mar 18, 2019, to Dec 31, 2020. Further, we examined the associations between neighborhood-level sociodemographic status and air pollution patterns by linking the low-cost sensor data to EPA environmental justice screening and mapping systems. FindingsWe found increased PM levels during ITC fire and pre-COVID-19, and lower PM levels after the COVID-19 lockdown, comparable to observations from the regulatory monitors, with higher variations and a greater number of locations with high PM levels detected. In addition, the environmental justice analysis showed positive associations between higher PM levels and the percentage of minority, low-income population, and demographic index. ImplicationOur study indicates that low-cost sensors provide pollutant measures with higher spatial variations and a better ability to identify hot spots and high peak concentrations. These advantages provide critical information for disaster response and environmental justice studies. SynopsisWe used measurements from a low-cost sensor network for air pollution monitoring and environmental justice analysis to examine the impact of anthropogenic and natural disasters.

High-resolution mapping of premature mortality induced by atmospheric particulate matter in China

Air pollution, measured by the concentration of airborne particulate matter with diameter below 2.5 μm (PM2.5), is known to cause globally adverse health effects leading to premature mortality, yet high-resolution spatial distribution of premature deaths is lacking due to the absence of detailed data for the calculation of premature mortality. Here, we mapped the impact of PM2.5 on premature mortality at high resolution in 2018 using the global exposure mortality model, then we carried out statistical analysis in provincial-level administrative divisions. Results show that the PM2.5 threshold of 35 μg/m3 is exceeded in the Xinjiang Uygur Autonomous Region, and in mideastern, highly urbanized and industrial regions of Tianjin, Hebei, Shandong, Henan, Jiangsu, Hubei, Anhui, and Shanghai. Premature deaths due to PM2.5 exposure in China amounted to 1.44 million, including 510,847 from ischemic heart disease, 471,487 from cerebrovascular disease, 220,837 from chronic obstructive pulmonary disease, 131,321 from lung cancer, and 103,658 from lower respiratory infection. Premature mortality was higher in men, of 879,521 persons accounting for 61%, than women, of 558,629 persons. In provincial-level administrative divisions, high premature deaths were mainly concentrated in the North China Plain, with the highest per capita premature mortality in the Xinjiang Uygur Autonomous Region, Henan, Hebei, and Shandong. Our finer resolution data, together with the age- and sex-specific population data, have improved the estimation of health impacts of PM2.5 exposure.

Measurement report: Interpretation of wide-range particulate matter size distributions in Delhi

Abstract. Delhi is one of the world's most polluted cities, with very high concentrations of airborne particulate matter. However, little is known about the factors controlling the characteristics of wide-range particle number size distributions. Here, new measurements are reported from three field campaigns conducted in winter and pre-monsoon and post-monsoon seasons at the Indian Institute of Technology campus in the south of the city. Particle number size distributions were measured simultaneously, using a scanning mobility particle sizer and a GRIMM optical particle monitor, covering 15 nm to &gt;10 µm diameter. The merged, wide-range size distributions were categorized into the following five size ranges: nucleation (15–20 nm), Aitken (20–100 nm), accumulation (100 nm–1 µm), large fine (1–2.5 µm), and coarse (2.5–10 µm) particles. The ultrafine fraction (15–100 nm) accounts for about 52 % of all particles by number (PN10 is the total particle number from 15 nm to 10 µm) but just 1 % by PM10 volume (PV10 is the total particle volume from 15 nm to 10 µm). The measured size distributions are markedly coarser than most from other parts of the world but are consistent with earlier cascade impactor data from Delhi. Our results suggest substantial aerosol processing by coagulation, condensation, and water uptake in the heavily polluted atmosphere, which takes place mostly at nighttime and in the morning hours. Total number concentrations are highest in winter, but the mode of the distribution is largest in the post-monsoon (autumn) season. The accumulation mode particles dominate the particle volume in autumn and winter, while the coarse mode dominates in summer. Polar plots show a huge variation between both size fractions in the same season and between seasons for the same size fraction. The diurnal pattern of particle numbers is strongly reflective of a road traffic influence upon concentrations, especially in autumn and winter, although other sources, such as cooking and domestic heating, may influence the evening peak. There is a clear influence of diesel traffic at nighttime, when it is permitted to enter the city, and also indications in the size distribution data of a mode &lt; 15 nm, which is probably attributable to CNG/LPG vehicles. New particle formation appears to be infrequent and is, in this dataset, limited to 1 d in the summer campaign. Our results reveal that the very high emissions of airborne particles in Delhi, particularly from traffic, determine the variation in particle number size distributions.

Modeling particulate matter concentration in indoor environment with cellular automata framework

This study attempts to develop a new 3D approach for modeling concentration of airborne particulate matter (PM) in indoor environment with cellular automata (CA) framework. So far numerical simulations of transient 3D indoor PM transport and distribution are mostly using the Lagrangian and Eulerian approaches. Each approach has its own advantages and drawbacks. For the purpose of increasing numerical efficiency without losing necessary accuracy, a new 3D CA-based approach for transient PM concentration estimation is proposed by considering four major PM transport mechanisms (flow advection, turbulent diffusion, gravitational settling, and boundary deposition). This CA approach enables us to use simple explicit algebraic equations to simulate the above four mechanisms without adopting numerical iteration and/or matrix solving, so that the numerical efficiency can be enhanced. Two test cases having reliable measured PM concentration profiles in indoor chambers are used to verify the accuracy and efficiency of the proposed approach with two Eulerian drift flux models and two Lagrangian particle tracking models for comparison. The simulated results indicate that the proposed approach can simulate as accurate as the two Eulerian drift flux models with significant reduction on its execution time. For the present two test cases, it can be at least 483.4%–564.6% faster than the other Eulerian drift flux models. Thus, it has considerable potentials as a useful tool for 3D numerical simulations of indoor PM transport and distribution.

Evaluation of low-cost particulate matter sensors OPC N2 and PM Nova for aerosol monitoring

Low-cost sensors have the potential to revolutionize air pollution research by providing high spatial resolution data. However, data accuracy from the low-cost sensors remains a major concern. Therefore, it is necessary to evaluate the performance of the low-cost sensors. The current study evaluated the performance of two such low cost PM (particulate matter) sensors viz. OPC N2 (∼400 USD) and PM Nova sensor (∼20 USD) for four different uncontrolled test environments i.e. (a) Indoor (b) Indoor with incense stick (c) Ambient air (outdoor) and (d) Traffic, using an aerosol spectrometer (GRIMM 1.108) as the reference instrument. The results showed that both OPC N2 and PM Nova can record the airborne PM concentration with good accuracy as demonstrated through linear regression with Grimm (OPC N2, R2 = 0.954–0.987 and PM Nova, R2 = 0.872–0.981). The performance of the sensors was better for the finer particles (PM1 and PM2.5) than coarser particles (PM10). Meteorological parameters such as wind speed, temperature and RH did not have any significant impact on the sensor performance. Using a sampling tube did not have an impact on sensor performance. A modified python script for data retrieval from the sensors in a user-friendly format has been developed. More evaluations of used sensors and in diverse environmental settings can further increase the understanding about the sensor performance.