Fine Particle Concentrations Research Articles

Analysis on the current situation and influencing factors of residents' satisfaction with the built environment of China's Hygienic City Initiative

Objective: To understand the current situation and the main influencing factors of residents' satisfaction with the built environment of China's Hygienic City Initiative. Methods: From the list of China's hygienic cities (excluding county-level cities), 61 cities were randomly selected in equal proportion and the eligible respondents were randomly selected by using the "Questionnaire Star" network platform to carry out the online questionnaire survey. A self-made satisfaction evaluation scale was used to investigate the satisfaction of the included respondents with the urban built environment and search for relevant data on the city level. The two-level multi-factor mixed effect model was constructed to analyze the influencing factors of residents' satisfaction with the built environment of China's Hygienic City Initiative. Results: The age range of 2 465 respondents was mainly between 18 and 40 years old (79.9%), with males being the main group (45.8%). The total score of residents' satisfaction with the built environment of China's hygienic cities was (69.14±13.24) points. Based on four standardized dimensions of sense of gain, the result showed that the satisfaction of urban governance had the highest score (65.08 points), followed by urban environmental sanitation (63.68 points), urban lifestyle (59.97 points) and urban basic function (59.02 points). The analysis results of the two-level multi-factor mixed effect model showed that compared with residents with an annual average concentration of inhalable fine particles in the environment>48 micrograms/cubic meter, residents with an average concentration between 38 and 48 micrograms/cubic meter [β (95%CI): 1.65 (0.08, 3.21)] and≤37 micrograms/cubic meter or less [β (95%CI): 1.98 (0.53, 3.43)] had higher satisfaction. Compared with residents whose proportion of the secondary industry to GDP was≤40.9%, residents in cities with a larger proportion had a lower satisfaction level [residents with a proportion of 40.9%-48.03%, β (95%CI):-2.21 (-3.93, -0.49); residents with a proportion greater than 48.03%, β (95%CI):-2.58 (-4.58, -0.59)]. Compared with residents with a junior high school or lower education level, residents with a higher education level had a lower satisfaction level [β (95%CI):-2.37 (-4.57, -0.17)]. Residents of universities and above [β (95%CI):-3.82 (-6.05, -1.60)], regularly participate in physical exercise [β (95%CI): 5.78 (4.71, 6.84)] and self-rated good health status [β (95%CI): 6.39 (5.33, 7.45)] had a higher satisfaction level. Conclusion: The satisfaction of residents with the built environment of China's hygienic cities is still acceptable. Satisfaction is related to individual characteristics such as residents' cultural level, type of residence, frequent participation in physical exercise, and self-rated good health status, as well as urban-level factors such as green coverage rate in built-up areas, annual average concentration of inhalable fine particles, and the proportion of GDP in the secondary industry.

Spatial variability of fine particle number concentration in an urban area: The effect of aspect ratio and vehicular traffic

The condensation particle number counting (CPC) technique was used to measure the number concentration of fine particles (FPs) from 20 to 1000 nm in several monitoring campaigns inside an urban area, with the aim of studying the spatial variability inside the urban area at pedestrian level and its correlation with aspect ratio H/W (height of buildings/street width) and local traffic. The effect of aspect ratio on the FP concentrations was studied in the range of 0 ≤ H/W ≤ 4.04. To study the effect of local traffic on FP concentrations, measurements were carried out in adjacent and with similar geometrics (building height, street width, and aspect ratio) pedestrian and traffic-streets. Vertical profiles were also studied measuring FP concentration in a deep street canyon at heights from 1 to 12 m. The study shows a significant impact of local traffic on FP concentrations (traffic streets 2.7 times higher than pedestrian streets). With the increase of the value of the aspect ratio, FP concentrations exceed European averages. Additionally, It was observed a clear decrease in FP concentrations with height in deep street canyons. These results emphasize the importance of aspect ratio and local traffic when assessing the impact of FPs pollution.

Characteristics of fine particles from incense burning at temple premises of Kathmandu Valley, Nepal.

The impact of incense burning on ambient air quality was investigated by measuring the concentrations of fine particles (PM2.5), elemental carbon (EC), organic carbon (OC), and their oxidative potential (OP) at three temple premises in Kathmandu Valley, Nepal. These temples, namely, Bajrabarahi, Bagalamukhi, and Bhadrakali, are located in three distinct environments: forest, residential, and roadside, respectively. During the incense burning event days, the PM2.5 concentration at Bhadrakali (431.4 μgm-3) was significantly higher than that measured at the Bagalamukhi (135.2 μgm-3) and Bajrabarahi (84.7 μgm-3) temple premises. This observation is consistent with the fact that Bhadrakali Temple had the highest intensity of incense burning. Additionally, the temple premises were also influenced by vehicular emissions from transportation facilities. Carbonaceous aerosols significantly increased during incense burning events, indicating that incense burning contributes significantly to the formation of primary and secondary OC. Moreover, the OP of PM2.5 during the incense burning event days was higher compared to non-event days (p < 0.05), suggesting an elevated health risk due to the increased concentration and toxicity of fine particles. These findings highlight the substantial impact of incense burning on air quality in temple premises, emphasizing the need to implement effective strategies to mitigate the associated health risks.

Global spatial and temporal patterns of fine particulate concentrations and exposure risk assessment in the context of SDG indicator 11.6.2

Assessing the spatiotemporal patterns, exposure risks and sustainability of PM2.5 pollution from multi–scale spatial and temporal characteristics are relevant for achieving Goal 11.6.2 of the 2030 Agenda for Sustainable Development (assessing the effects of urban fine particulate matter pollution on public health). According to this study, methodological models combining trend analysis, spatial statistics, and population exposure risk assess the spatiotemporal patterns, exposure risks, and sustainability characteristics of PM2.5 pollution at global, continental, and national scales, and ultimately achieve SDG 11.6.2 Assessment framework at different spatial scales. The results indicate that: (1) The annual decline in global PM2.5 concentrations exhibited a rate of –0.023 µg/m3/yr, with more than 30 % of regions experiencing a decrease in PM2.5 concentrations of more than 0.1 µg/m3/yr, and showed significant spatial distribution differences. (2) The global population exposed to <10 µg/m3 continues to grow from 4.8 % in 2000 to 6.2 % in 2010 and further to 10.3 % in 2020 during the study period. By comparison, the population exposed to >70 µg/m3 shows a U–shaped change. Spatially, more than 60 % of the world’s regions are exposed to low risk (Low risk and Lower risk) and less than 30 % are exposed to high risk (Higher, High and Extremely high risk). Asia has the highest exposure (2.95), while Oceania has the lowest (0.11). (3) Inequalities in sustainable PM2.5 exposure risks between developed and developing countries will continually increase. The results of the study not only shed light on the present state of sustainable global air quality progress but also offer valuable insights and a blueprint for conducting similar multi–scale assessments in the coming times.

Impact of spatial scales of control measures on the effectiveness of ozone pollution mitigation in eastern China

Ozone (O3) pollution is becoming the primary air pollution issue with the large decrease in fine particulate concentrations in eastern China. The development of widely recognized policies for controlling O3 pollution episodes is urgent. This study aims to provide actionable and comprehensive suggestions for O3 control policy development, with an emphasis on the precursor emission reductions.Here, we compared the impacts of different spatial scale reductions on a widespread O3 pollution episode in eastern China by a state-of-the-art regional air quality model. We find that region-scale joint control (in >30 cities) is much more effective than city-scale sporadic reduction in reducing O3 concentration. Sporadic controls only reduce the maximum daily 8-h average (MDA8) O3 by ∼1 μg/m3 in the controlled city, whereas regional controls lead to a MDA8 O3 decrease of ∼8 μg/m3 in the controlled region. In addition, the emission reduction effectiveness increased by 2.6 times from <5 cities to >30 cities. Continuous reductions have a cumulative effect on the decrease of MDA8 O3, showing the strongest effects within 24 h and diminishing after 48 h, which underscores the importance of reducing emissions 24 h prior to an episode. Moreover, the effect of control measures on MDA8 O3 varies spatially depending on the ratio of volatile organic compounds (VOCs) to nitrogen oxides (NOx) (VOCs/NOx). Both the reductions of VOC and NOx emissions have a positive effect on the decrease of MDA8 O3 in summer, but the effects of VOC reductions are 1.2 to 1.7 times higher than those of NOx reductions. The residential sector, due to its high VOCs/NOx emission ratio, exhibits the highest efficiency in the reduction of O3 concentrations. Our results highlight the importance of regional joint control and synergistic reduction of VOCs and NOx in eastern China.

3D CFD-DEM study on fine particle migration in packed proppant layers

Fine particle migration is a crucial issue in hydraulic fracturing-based production from unconventional reservoirs and can significantly affect the effective conductivity of the packed proppant layers. In this study, to investigate the conveying behavior of fine particles, a Eulerian-Lagrangian method, namely the computational fluid dynamics–discrete element method (CFD-DEM), is adopted for 3D simulations. Two types of particles are involved in this process: larger proppant particles and fine particles. Proppant particles form a background skeleton, and a two-phase flow of fine particles and fluid is constrained in the connected pore networks. We adopt a unified CFD-DEM framework to resolve the non-slip boundary condition on the proppant particle surface and track fine particles using a four-way coupling strategy. In particular, a forcing term based on the fictitious domain method is embedded in a regular volume-averaged Navier-Stokes equation to solve the fluid motion. A perfectly packed skeleton of a face-centered cube is designed for subsequent numerical studies. The results show that the fluid and particle flow velocities are higher near the fracture surfaces than inside the interlayers. In addition, it is found that as the fine particle concentration increases, the average horizontal velocity increases, whereas the settling velocity tends to decrease.

Forecast of Fine Particles in Chengdu under Autumn-Winter Synoptic Conditions.

We conducted an evaluation of the impact of meteorological factor forecasts on the prediction of fine particles in Chengdu, China, during autumn and winter, utilizing the European Cooperation in Science and Technology (COST)733 objective weather classification software and the Community Multiscale Air Quality model. This analysis was performed under four prevailing weather patterns. Fine particle pollution tended to occur under high-pressure rear, homogeneous-pressure, and low-pressure conditions; by contrast, fine particle concentrations were lower under high-pressure bottom conditions. The forecasts of fine particle concentrations were more accurate under high-pressure bottom conditions than under high-pressure rear and homogeneous-pressure conditions. Moreover, under all conditions, the 24 h forecast of fine particle concentrations were more accurate than the 48 and 72 h forecasts. Regarding meteorological factors, forecasts of 2 m relative humidity and 10 m wind speed were more accurate under high-pressure bottom conditions than high-pressure rear and homogeneous-pressure conditions. Moreover, 2 m relative humidity and 10 m wind speed were important factors for forecasting fine particles, whereas 2 m air temperature was not. Finally, the 24 h forecasts of meteorological factors were more accurate than the 48 and 72 h forecasts, consistent with the forecasting of fine particles.

Smoke emissions from biomass burning in Central Mexico and their impact on air quality in Mexico City: May 2019 case study

Smoke emissions from biomass burning considerably influence regional and local air quality. Many natural wildfires and agricultural burns occur annually in Central Mexico during the hot, dry season (March to May), potentially leading to air quality problems. Nevertheless, the impact of these biomass burning emissions on Mexico City's air quality has not been investigated in depth. This study examines a severely deteriorated air quality case from 11 to 16 May 2019, during which fine particle concentrations (PM2.5) exceeded the 99th percentile of the available official dataset (2005–2019). Specifically, this work aims to highlight the role of fires and regional pollution in the severe episode observed in Mexico City, identifying the fires that were the sources of regional pollution, the type of fuel burned in those fires, and the dominant atmospheric transport pattern. Biomass burning emissions were calculated for different land cover types using satellite data from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate-Resolution Imaging Spectroradiometer (MODIS). PM2.5 increased by a factor of 2 at some monitoring sites, and ozone concentration increased to 40 % in Mexico City during the poor air quality episode. Our results indicate that over 50 % of the fire activity observed during the 2019 fire season was concentrated in May in Central Mexico. The burning activity was mainly seen over shrubland and forest between 10 and 15 May. Moreover, the fire radiative power analysis indicates that most energy was associated with burning shrubland and forests. Organic carbon emissions were estimated highest on 14 and 15 May, coinciding with the largest number of fires. Back trajectory analysis indicates that enhanced concentration of air pollutants in Mexico City originated from biomass burning detected in neighboring states: Guerrero, Michoacán, and the State of Mexico. Smoke from fires on the specific vegetation cover was advected into Mexico City and contributed to the bad air quality episode. Further meteorological analysis evidenced that the fire intensity and emissions were worsened by low humidity and the late onset of the rainy season in Central Mexico.

Early life PM2.5 exposure, childhood cognitive ability and mortality between age 11 and 86: A record-linkage life-course study from Scotland

BackgroundLiving in areas with high air pollution concentrations is associated with all-cause and cause-specific mortality. Exposure in sensitive developmental periods might be long-lasting but studies with very long follow-up are rare, and mediating pathways between early life exposure and life-course mortality are not fully understood. MethodsData were drawn from the Scottish Longitudinal Study Birth Cohort of 1936, a representative record-linkage study comprising 5% of the Scottish population born in 1936. Participants had valid age 11 cognitive ability test scores along with linked mortality data until age 86. Fine particle (PM2.5) concentrations estimated with the EMEP4UK atmospheric chemistry transport model were linked to participants’ residential address derived from the National Identity Register in 1939 (age 3). Confounder-adjusted Cox regression estimated associations between PM2.5 and mortality; regression-based causal mediation analysis explored mediation through childhood cognitive ability. ResultsThe final sample consisted of 2734 individuals with 1608 deaths registered during the 1,833,517 person-months at risk follow-up time. Higher early life PM2.5 exposure increased the risk of all-cause mortality (HR = 1.03, 95% CI: 1.01–1.04 per 10 μg m−3 increment), associations were stronger for mortality between age 65 and 86. PM2.5 increased the risk of cancer-related mortality (HR = 1.05, 95% CI: 1.02–1.08), especially for lung cancer among females (HR = 1.11, 95% CI: 1.02–1.21), but not for cardiovascular and respiratory diseases. Higher PM2.5 in early life (≥50 μg m−3) was associated with lower childhood cognitive ability, which, in turn, increased the risk of all-cause mortality and mediated 25% of the total associations. ConclusionsIn our life-course study with 75-year of continuous mortality records, we found that exposure to air pollution in early life was associated with higher mortality in late adulthood, and that childhood cognitive ability partly mediated this relationship. Findings suggest that past air pollution concentrations will likely impact health and longevity for decades to come.

Effectiveness of equipping bus stop shelters with cooling and filtering systems in a city with tropical climate

PurposeWaiting for a bus may represent a period of intense exposure to traffic particles in hot and noisy conditions in the street. To lessen the particle load and tackle heat in bus stops a shelter was equipped with an electrostatic precipitator and a three-step adiabatic cooling system capable of dynamically adjust its operation according to actual conditions. This study evaluates the effectiveness of the Airbitat Oasis Smart Bus Stop, as the shelter was called, to provide clean and cool air.Design/methodology/approachThe particle exposure experienced in this innovative shelter was contrasted with that in a conventional shelter located right next to it. Mass concentrations of fine particles and black carbon, and particle number concentration (as a proxy of ultrafine particles) were simultaneously measured in both shelters. Air temperature, relative humidity and noise level were also measured.FindingsThe new shelter did not perform as expected. It only slightly reduced the abundance of fine particles (−6.5%), but not of ultrafine particles and black carbon. Similarly, it reduced air temperature (−1 °C), but increased relative humidity (3%). Its operation did not generate additional noise.Practical implicationsThe shelter's poor performance was presumably due to design flaws induced by a lack of knowledge on traffic particles and fluid dynamics in urban environments. This is an example where harnessing technology without understanding the problem to solve does not work.Originality/valueIt is uncommon to come across case studies like this one in which the performance and effectiveness of urban infrastructure can be assessed under real-life service settings.

Criteria air pollutants and diabetes mortality classified by different subtypes and complications: A nationwide, case-crossover study

The associations between air pollution and diabetes mortality of different subtypes and complications were largely unclear. We performed an individual-level, time-stratified case-crossover study among over 0.9 million diabetes deaths from all administrative regions of Chinese mainland during 2013–2019. Daily concentrations of fine particles (PM2.5), coarse particles (PM2.5–10), nitrogen dioxide (NO2) and ozone (O3) were obtained for each decedent using high-resolution prediction models. Conditional logistic regression models were utilized to analyze the data. Each interquartile range increment in PM2.5, PM2.5–10, NO2 and O3 concentrations on lag 0–2 d increased the risks of overall diabetes mortality by 2.81 %, 1.92 %, 3.96 % and 2.15 %, respectively. Type 2 diabetes had stronger associations with air pollution than type 1 diabetes. Air pollutants were associated with diabetic ketoacidosis and diabetic nephropathy, but not other complications. The exposure-response curves were approximately linear with a plateau at higher concentrations of PM2.5, PM2.5–10, and NO2, while the associations for O3 appear to be statistically significant beyond 60 μg/m3. This nationwide study reinforces the evidence of higher risks of acute diabetic events following short-term air pollution exposure. We identified differential effects of air pollutants on various subtypes and complications of diabetes, which require further mechanistic investigations.

A field measurement study of the effects of outdoor pollutants and room volumes on indoor fine particle and ozone concentrations

Air pollution, represented by ozone (O3) and fine particulate matter (PM2.5), threatens the health of urban residents. This study examined 24 residential buildings in Nanjing to investigate the factors that affect indoor ozone and PM2.5 concentrations. Windows closed/open state, room geometry, ventilation rate, and pollutant removal and emission rates are taken as influencing factors. The correlation between influence factors and indoor air quality was analyzed to help residents in Nanjing understand which factors significantly affect indoor air quality. Fitted methods were employed to assess the removal and emission rates of pollutants. It was found that for the bedroom of 10–20 m2 (i.e., 30–60 m3), the air change rate (ACR, h−1) of infiltration is relatively more correlated with the room volume than other room volumes. The increase in indoor ozone concentration due to windows opening is below the indoor air quality standard in China (160 μg/m3) and the USA (70 μg/m3) under the test period in these measured cases. Therefore, when natural ventilation is adopted for residential buildings in Nanjing, outdoor fine particle concentrations should be a priority concern.

Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition

Estimating health benefits of reducing fossil fuel use from improved air quality provides important rationales for carbon emissions abatement. Simulating pollution concentration is a crucial step of the estimation, but traditional approaches often rely on complicated chemical transport models that require extensive expertise and computational resources. In this study, we develop a machine learning framework that is able to provide precise and robust annual average fine particle (PM2.5) concentration estimations directly from a high-resolution fossil energy use dataset. Applications of the framework with Chinese data reveal highly heterogeneous health benefits of avoiding premature mortality by reducing fossil fuel use in different sectors and regions in China with a mean of $19/tCO2 and a standard deviation of $38/tCO2. Reducing rural and residential coal use offers the highest co-benefits with a mean of $151/tCO2. Our findings prompt careful policy designs to maximize cost-effectiveness in the transition toward a carbon-neutral energy system.

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.

Development of point sampling technology for identifying high-emitting vehicles in narrow and deep street canyons

Side road measurements of fine particle (FP) concentrations were performed in two narrow and deep street canyons in the historic center of Naples using point sampling technology (PS). The FP concentration in the range of 20–103 nm was measured using a condensation particle number counter (CPC) with a 1 Hz frequency. Video recording was used to detect passing vehicles classified into three categories: motorcycles, cars, and light-duty vehicles. The two monitored streets were adjacent, with similar geometrics and orientation but different high- and low-traffic levels. FP concentration time series data were processed to identify concentration peaks generated by the exhausts of passing vehicles. A sensitivity analysis study was carried out to determine how data processing parameters could affect high-emitting vehicle (high emitter) identification. Once the optimum parameters were defined, the contribution of high emitters exhaust emissions to local air quality was assessed. The results showed that PS technology offered viable opportunities for application in narrow street canyons where deploying more common crossroad and top-down monitoring can be troublesome. However, some issues remain to be solved to accurately identify high emitterss in high-traffic cases. PS technology can be useful for developing effective air quality management policies in historical centres.

Ten questions concerning indoor ultrafine particles

Ultrafine particles (≤100 nm in diameter) are the smallest condensed-phase materials in air. By number, they are the most abundant particles both outdoors and indoors. Because of their small size, they contribute little to mass concentrations of fine particles, a regulated air pollutant with large associated health risks. Inhaling ultrafine particles raises health concerns, in part owing to their ability to migrate from the site of deposition in the respiratory tract. Buildings provide partial protection against ultrafine particles of outdoor origin. The degree of that protection is broadly variable, influenced by particle size and by building design and operational factors, such as ventilation rates and the efficiency of particle filters. Because of the large proportion of time spent by people indoors, much exposure to ultrafine particles occurs in buildings, even for particles of outdoor origin. Diverse indoor sources, including cooking and the use of candles, contribute materially to indoor ultrafine particle concentrations. Indoor sourced ultrafine particle concentrations are also influenced by building design and operational factors. Whether of indoor or outdoor origin, dynamic processes influence the concentrations, sizes, and chemical composition of ultrafine particles. Such processes include deposition to indoor surfaces, coagulation, and sorptive partitioning of semivolatile organic compounds. Advancing scientific and technical knowledge about building factors and dynamic processes that influence indoor ultrafine particle concentrations can contribute (a) to better understanding of associated health risks and (b) to the more effective design and implementation of mitigation strategies and technologies.

Deep-Learning-Enhanced CT Image Analysis for Predicting Hydraulic Conductivity of Coarse-Grained Soils

Permeability characteristics in coarse-grained soil is pivotal for enhancing the understanding of its seepage behavior and effectively managing it, directly impacting the design, construction, and operational safety of embankment dams. Furthermore, these insights bridge diverse disciplines, including hydrogeology, civil engineering, and environmental science, broadening their application and relevance. In this novel research, we leverage a Convolutional Neural Network (CNN) model to achieve the accurate segmentation of coarse-grained soil CT images, surpassing traditional methods in precision and opening new avenues in soil granulometric analysis. The three-dimensional (3D) models reconstructed from the segmented images attest to the effectiveness of our CNN model, highlighting its potential for automation and precision in soil-particle analysis. Our study uncovers and validates new empirical formulae for the ideal particle size and the discount factor in coarse-grained soils. The robust linear correlation underlying these formulae deepens our understanding of soil granulometric characteristics and predicts their hydraulic behavior under varying gradients. This advancement holds immense value for soil-related engineering and hydraulic applications. Furthermore, our findings underscore the significant influence of granular composition, particularly the concentration of fine particles, on the tortuosity of water-flow paths and the discount factor. The practical implications extend to multiple fields, including water conservancy and geotechnical engineering. Altogether, our research represents a significant step in soil hydrodynamics research, where the CNN model’s application unveils key insights into soil granulometry and hydraulic conductivity, laying a strong foundation for future research and applications.

Measurement of fine particle concentrations and estimation of air quality index (AQI) over northeast Douala, Cameroon.

Due to absence of data on air quality monitoring and pollutant emissions in Douala, a measurement campaign along the principal street passage to the college grounds was started. Using the OC 300 Laser Dust Particle, fine particle concentrations are monitored during 1week from Monday to Sunday. The instrument used detects four different sizes of particles: PM10, PM5, PM2.5, and PM1. The daily average concentrations measured ranged from 9.47 ± 0.26 to 50.14 ± 2.42µg·m-3 for PM1.0; 13.13 ± 0.38 to 86.65 ± 3.96µg·m-3 for PM2.5; 13.60 ± 0.40 to 100.56 ± 4.20µg·m-3 for PM5; and 14.52 ± 0.42 to 114.59 ± 4.60µg·m-3 for PM10. Exceptions made from PM5 and PM1.0 which were not in relation to the WHO (World Health Organization) guideline values, the level of PM10 and PM2.5 is higher than the WHO standards. The air quality index (AQI) is between very poor and poor during this measurement campaign, indicating that residents of the study region are highly exposed. Through the use of correlation studies, it has been demonstrated that the predominant source of fine particles in the studied region is vehicular activity. As a result, traffic density is the most significant factor causing the different air pollution levels seen in the tested areas.

Monitoring Particulate Matters and Total Suspended Particles Along the Roadside and Public Area in Phnom Penh

This study aims to investigate the Total Suspended Particles (TSP) and Particulate Matter (PM) of the exposed area of vehicles emission along a boulevard (Russian Federation Boulevard/RFB) and public park (Wat Phnom Historical Park/WP) in Phnom Penh as a beneficial fact for passengers and tourists of air quality that they breathed in. This present study was carried out in 8 hours per sampling, twice a month, and 2 months long (March and April) of 2022 by using a high-volume sampler (HV, Shibata 500V) to determine TSP and Modified Nano-sampler (NS) to define PM (TSP/PM10/PM2.5/PM1/PM0.1). Conforming to the results, the highest percentage of relative humidity found during the sampling was when it rained (97.30%) and in the morning. Meanwhile, the lowest relative humidity was only about 48.70% which was during the hot afternoon. Additionally, TSP of RFB peak’s data was accounted as 234.04 μg/m3 which is beneath the standard of Royal Government of Cambodia (RGC), 330 μg/m3. Nevertheless, the climax concentration of fine and ultrafine particles (PM2.5 and PM0.1) that wandered in the same area had surprisingly stood in an immensely high number as 95.56 and 38.04 μg/m3. To summarize, low level of TSP in the ambient atmosphere is not correlating to less pollution since the acquired results of PM2.5 and PM0.1 had detected in a dominant level that disturb the public health respiratory system. Same thing happened to WP which is a tree-based site as well.

Impact of ultrafine particles and total particle number concentration on five cause-specific hospital admission endpoints in three German cities

IntroductionNumerous studies have shown associations between daily concentrations of fine particles (e.g., particulate matter with an aerodynamic diameter ≤2.5 µm; PM2.5) and morbidity. However, evidence for ultrafine particles (UFP; particles with an aerodynamic diameter of 10–100 nm) remains conflicting. Therefore, we aimed to examine the short-term associations of UFP with five cause-specific hospital admission endpoints for Leipzig, Dresden, and Augsburg, Germany. Material and methodsWe obtained daily counts of (cause-specific) cardiorespiratory hospital admissions between 2010 and 2017. Daily average concentrations of UFP, total particle number (PNC; 10–800 nm), and black carbon (BC) were measured at six sites; PM2.5 and nitrogen dioxide (NO2) were obtained from monitoring networks. We assessed immediate (lag 0–1), delayed (lag 2–4, lag 5–7), and cumulative (lag 0–7) effects by applying station-specific confounder-adjusted Poisson regression models. We then used a novel multi-level meta-analytical method to obtain pooled risk estimates. Finally, we performed two-pollutant models to investigate interdependencies between pollutants and examined possible effect modification by age, sex, and season. ResultsUFP showed a delayed (lag 2–4) increase in respiratory hospital admissions of 0.69% [95% confidence interval (CI): −0.28%; 1.67%]. For other hospital admission endpoints, we found only suggestive results. Larger particle size fractions, such as accumulation mode particles (particles with an aerodynamic diameter of 100–800 nm), generally showed stronger effects (respiratory hospital admissions & lag 2–4: 1.55% [95% CI: 0.86%; 2.25%]). PM2.5 showed the most consistent associations for (cardio-)respiratory hospital admissions, whereas NO2 did not show any associations. Two-pollutant models showed independent effects of PM2.5 and BC. Moreover, higher risks have been observed for children. ConclusionsWe observed clear associations with PM2.5 but UFP or PNC did not show a clear association across different exposure windows and cause-specific hospital admissions. Further multi-center studies are needed using harmonized UFP measurements to draw definite conclusions on the health effects of UFP.