Airborne Pollutant Concentrations Research Articles

Variations in the leaf economics spectrum, anatomical, ultrastructural, and stomatal traits of five tree species in the urban-rural air pollution environment

Rapid urbanization has contributed to global increases in air pollution derived from urban areas. Unlike natural forests, urban forests are exposed to higher concentrations of airborne pollutants due to the strong urban-suburban-rural pollutant emission gradients. However, there remains a pressing lack of available information pertaining to the urban air pollution-related effects on the leaf economics spectrum, anatomical, ultrastructural, and stomatal traits of tree species along an urban-rural gradient. Here, the degree to which urban air pollution impacts the adaption of greening tree species and associated service functions was assessed by sampling five common tree species (Acer pictum, Fraxinus chinensis, Koelreuteria paniculata, Salix babylonica, Sophora japonica) along urban-rural-natural forests in the Beijing metropolitan region of China. These analyses revealed a significant reduction in leaf mass per unit area (–13.4 %), leaf thickness (–16.7 %), and stomatal area (–27.5 %) with increasing proximity to areas of greater air pollution that coincide with significant increases in leaf tissue density (+12.6 %), leaf nitrogen content (+10.1 %), relative chlorophyll content (+2.7 %), and stomatal density (+11.9 %). Higher air pollution levels were associated with organelle changes including gradual disintegration of chloroplasts, larger intercellular spaces and apparent starch and plastoglobuli deposition. Air pollution was conducive to the strengthening of the trade-off potential and adaptation strategies of trees in urban ecosystems, which are associated with trees with a rapid investment return strategy associated with thick leaves and strong photosynthetic capacity. These results provide strong empirical evidence of the profound air pollution-induced changes in leaf functional traits and adaption ability of urban forest tree species.

The Impact of Air Pollution from Industrial Fires in Urban Settings: Monitoring, Modelling, Health, and Environmental Justice Perspectives

Industrial fires at facilities including waste management sites, warehouses, factories, chemical works, and fuel storage depots are relatively frequent occurrences. Often, these fires occur adjacent to urban communities and result in ground-level airborne pollutant concentrations that are well above guideline values. Land, water, livestock, and crops may also be contaminated by the emissions and by firefighting activities. Moreover, impacted communities tend to have a higher proportion of minority ethnic populations as well as individuals with underlying health vulnerabilities and those of lower socio-economic status. Nevertheless, this is an aspect of air quality that is under-researched, and so this review aims to highlight the public health hazards associated with industrial fires and the need for an effective, coordinated, public health response. We also review the range of monitoring techniques that have been utilised in such fires and highlight the role of dispersion modelling in predicting plume trajectories and in estimating population exposure. We recommend establishing 1 h guideline values for particulate matter to facilitate timely public health interventions, and we highlight the need to review regulatory and technical controls for sites prone to fires, particularly in the waste sector.

A review of the status of air quality monitoring in the Permian Basin, USA, and its implications for effective long-term monitoring of industrial operations

The Permian Basin is the largest petroleum producing region in the United States. Few studies have investigated impact the Petroleum Industry had on the air quality of the Permian Basin. Industry operations in the Basin has two primary impacts on air quality, (a) it significantly contributes carbon dioxide (CO2) and methane (CH4) to the atmosphere during extraction, transport and use of fossil fuels, (b) industry operations influence public health especially when populations are in proximity to all categories of oil and gas facilities. The advent of Covid-19 in late 2019 and a severe drought that began prior to 2019 in West Texas have created unusual circumstances regarding air quality and public health in the Permian Basin. This study has monitored airborne pollen, spores, fungal spores, and other organically derived particles, and inorganic particulate matter (1, 2.5, 10-micron particulate matter (PM) and total suspended particles (TSP)), volatile organic compounds (VOCs), and sulfur dioxide (SO2). The pandemic caused a historic reduction in oil and gas production slowing operations in the Permian Basin to near zero during the study period, reducing environmental effects on air quality and concomitantly an increase in flaring of natural gas. Although there were increases in the release of VOCs fixed base monitors detected no notable increase. However, an increase in the concentration of airborne pollutants (VOCs and SO2) shows a close correlation with proximity to oil and gas facilities and the intensity of industry operations. This phenomenon is attributed to the highly local nature of flaring and CH4 plume emissions from leaking oil and gas facilities. Air quality improved in the Permian Basin due to reduction in production activity because of the Covid-19 pandemic, and the reduction of particulate matter due to the ongoing drought during the study period.

An Evaluation of the Air Quality in the Neighborhoods Surrounding the KMML Industrial Area in Chavara using Machine Learning Techniques

<p>The nuclear power plants are another kind of power sources. Throughout the summer as well as winter of 2010 through 2022, researchers analyzed data from residential neighborhoods bordering the Chavara manufacturing industry in the Kollam region of southern India. The research set out to compare the quality of the air in the industrial area with that of the surrounding residential areas, along with the concentrations of several airborne pollutants (SO2, NO2, RPM, Free Cl2, and TSPM). To forecast a value close to the true value, a combination of artificial intelligence techniques is utilized. The optimum method for making accurate predictions of the relevant Air Quality indicators is then determined by comparing various metrics of error across the different approaches. The next statement presents the analysis's ultimate.</p>

Urban green spaces and their impact on environmental health: A Global Review

Urbanization has become an irreversible global phenomenon, with a majority of the world's population residing in cities. As urban areas expand, the significance of incorporating green spaces into these landscapes is paramount for environmental health. This study provides a concise overview of a global review that explores the multifaceted impact of urban green spaces on sustainability, well-being, and ecological balance. Urban green spaces play a crucial role in biodiversity preservation, acting as vital habitats for diverse plant and animal species. These spaces contribute to the maintenance of urban ecosystems, fostering ecological balance in densely populated areas. The presence of varied flora and fauna supports environmental health by enhancing biodiversity and promoting a more sustainable urban environment. One of the significant contributions of urban green spaces is their role in improving air quality. Through the natural process of photosynthesis, plants absorb pollutants and release oxygen, serving as an effective filtration system. Trees, in particular, act as natural air purifiers, reducing the concentration of airborne particles and pollutants. This contributes to enhanced air quality, mitigating the adverse effects of urban pollution on environmental health. Urban green spaces also play a pivotal role in temperature regulation, combating the phenomenon of urban heat islands. By providing shade and reducing surface temperatures, greenery helps create a cooler and more comfortable urban living environment. This temperature regulation not only contributes to the well-being of residents but also addresses the challenges posed by rising temperatures in urban areas. Beyond environmental considerations, access to urban green spaces has profound effects on the mental and physical well-being of urban dwellers. These spaces serve as outlets for recreation, exercise, and relaxation, contributing to stress reduction and improved mental health. Additionally, urban green spaces act as hubs for social interaction and community engagement, fostering a sense of belonging and strengthening social cohesion. In conclusion, this global review emphasizes the pivotal role of urban green spaces in promoting environmental health through biodiversity preservation, air quality improvement, temperature regulation, and enhancing overall well-being. Recognizing the multifaceted benefits of these spaces is crucial for guiding sustainable urban development practices worldwide.

Spatiotemporal distribution of pollutants and impact of local meteorology on source influence on pollutants' level in a traffic air-shed in Lagos megacity, Nigeria.

Pollution from vehicular emissions is a major cause of poor air quality observed in many urban and semi-urban towns and cities. As such, this study was conducted to assess air quality and the spatiotemporal distribution of vehicular and traffic-related pollutants in several air sheds of Lagos megacity, the economic nerve centre of Nigeria. A setup of low-cost air quality sensors comprising five (5) units was deployed between November 2018 and February 2019 within traffic corridors in the heart of the city. Diurnal variation of pollutants indicated that carbon dioxide (CO2) peaked during the early hours of the day, total oxide (Ox = NO2+O3) peaked at mid-day while carbon monoxide (CO) had two distinct peaks which correspond to morning and evening rush hours. Nitrogen dioxide (NO2) concentration peaked during evening hours. Average concentrations are NO2 (97.1 ± 9.7) ppb, Ox (78.6 ± 27.2) ppb, CO2 (450.1 ± 31.2) ppm, and CO (2285.63 ± 743.7) ppb. Average concentrations of pollutants were above thresholds set by the World Health Organization (WHO) except for NO2 which was within the range permissible limits. The implication of this is that the atmosphere is polluted due to elevated concentrations of airborne pollutants, an indication which is of both health and environmental concern. The air quality index (AQI) indicates that the quality of ambient air varies from good to very unhealthy for Ox, and unhealthy to very unhealthy for CO, while AQI for PM2.5 and PM10 showed hazardous at all the sampling locations except at UNILAG where it is unhealthy for the sensitive group. For all of the sampling sites, conditional bivariate probability function (CBPF) plots show a significant agreement with the location of known pollution sources.

Assessing Population Health Risks Posed by Air Pollution Related to Coal Mine Reclamation

Introduction: There is an urgent need for reclamation of abandoned coal mine lands in Kuzbass to reduce their adverse effects on the environment. Objective: To assess risks to public health posed by air pollution associated with coal mine liquidation and land reclamation. Materials and methods: We used data on the volume of maximum permissible emissions of the abandoned coal mine area for 2019 to assess the spread and exposure to pollutants at 40 points selected on the map of the city of Prokopyevsk, Kemerovo Region, and to estimate the maximum and mean concentrations of the contaminants. The values of carcinogenic risk and risks of non-carcinogenic effects were calculated in compliance with the Russian Guidelines R 2.1.10.1920–04, Human Health Risk Assessment from Environmental Chemicals, while risks of chronic toxicity were assessed using methods by Shcherbo et al. The calculated risk values were then compared with their acceptable levels. Risks were also assessed with account for the contribution of background concentrations of chemicals. Results: The list of priority pollutants included inorganic dust containing 20–70 % SiO2, nitrogen dioxide, nitrogen oxide, sulfur dioxide, carbon (soot), carbon monoxide, and inorganic dust containing < 20 % SiO2. The estimated maximum and mean concentrations of airborne pollutants during mine reclamation activities were not exceeded at any exposure point. Hazard indices for acute exposures to pollutants exceeded the acceptable level at three points located closer to the sources. Carcinogenic risks did not exceed the level of acceptable risk at any point. Total risks of chronic toxicity were 1.2 and 1.75 times higher than acceptable at two points. Hazard indices estimated with account for the background levels of pollutants were 1.448–2.603 times higher than the acceptable level at all exposure points. The carcinogenic risk assessed taking into account the background levels was found to be acceptable. The most affected (target) critical organs and systems were respiratory organs and blood. Total risks of chronic poisoning assessed with account for background concentrations of ambient air pollutants exceeded the acceptable level by 3.15–5.40 times at all points. Conclusions: Reclamation activities have no significant effect on ambient air quality and health of the population of Prokopyevsk. Total risks of chronic toxicity and hazard indices exceed acceptable levels in residential areas located close to the reclamation site. High hazard indices and total risks of chronic toxicity established at all exposure points taking into account background concentrations of contaminants, are determined by generally poor environmental conditions in the city with the intensively developing coal industry.

High spatial resolution assessment of air quality in urban centres using lichen carbon, nitrogen and sulfur contents and stable-isotope-ratio signatures

Air pollution and poor air quality is impacting human health globally and is a major cause of respiratory and cardiovascular disease and damage to human organ systems. Automated air quality monitoring stations continuously record airborne pollutant concentrations, but are restricted in number, costly to maintain and cannot document all spatial variability of airborne pollutants. Biomonitors, such as lichens, are commonly used as an inexpensive alternative to assess the degree of pollution and monitor air quality. However, only a few studies combined lichen carbon, nitrogen and sulfur contents, with their stable-isotope-ratio signatures (δ13C, δ15N and δ34S values) to assess spatial variability of air quality and to ‘fingerprint’ potential pollution sources. In this study, a high-spatial resolution lichen biomonitoring approach (using Xanthoria parietina and Physcia spp.) was applied to the City of Manchester (UK), the centre of the urban conurbation Greater Manchester, including considerations of its urban characteristics (e.g., building heights and traffic statistics), to investigate finer spatial detail urban air quality. Lichen wt% N and δ15N signatures, combined with lichen nitrate (NO3−) and ammonium (NH4+) concentrations, suggest a complex mixture of airborne NOx and NHx compounds across Manchester. In contrast, lichen S wt%, combined with δ34S strongly suggest anthropogenic sulfur sources, whereas C wt% and δ13C signatures were not considered reliable indicators of atmospheric carbon emissions. Manchester’s urban attributes were found to influence lichen pollutant loadings, suggesting deteriorated air quality in proximity to highly trafficked roads and densely built-up areas. Lichen elemental contents and stable-isotope-ratio signatures can be used to identify areas of poor air quality, particularly at locations not covered by automated air quality measurement stations. Therefore, lichen biomonitoring approaches provide a beneficial method to supplement automated monitoring stations and also to assess finer spatial variability of urban air quality.Graphical abstract

Particulate Matter Concentrations over South Korea: Impact of Meteorology and Other Pollutants

Air pollution is a serious challenge in South Korea and worldwide, and negatively impacts human health and mortality rates. To assess air quality and the spatiotemporal characteristics of atmospheric particulate matter (PM), PM concentrations were compared with meteorological conditions and the concentrations of other airborne pollutants over South Korea from 2015 to 2020, using different linear and non-linear models such as linear regression, generalized additive, and multivariable linear regression models. The results showed that meteorological conditions played a significant role in the formation, transportation, and deposition of air pollutants. PM2.5 levels peaked in January, while PM10 levels peaked in April. Both were at their lowest levels in July. Further, PM2.5 was the highest during winter, followed by spring, autumn, and summer, whereas PM10 was the highest in spring followed by winter, autumn, and summer. PM concentrations were negatively correlated with temperature, relative humidity, and precipitation. Wind speed had an inverse relationship with air quality; zonal and vertical wind components were positively and negatively correlated with PM, respectively. Furthermore, CO, black carbon, SO2, and SO4 had a positive relationship with PM. The impact of transboundary air pollution on PM concentration in South Korea was also elucidated using air mass trajectories.

Impact of the 2020 COVID-19 lockdown on NO2 and PM10 concentrations in Berlin, Germany

In March 2020, the World Health Organization declared a pandemic due to the rapid and worldwide spread of the SARS-CoV-2 virus. To prevent spread of the infection social contact restrictions were enacted worldwide, which suggest a significant effect on the anthropogenic emission of gaseous and particulate pollutants in urban areas. To account for the influence of meteorological conditions on airborne pollutant concentrations, we used a Random Forest machine learning technique for predicting business as usual (BAU) pollutant concentrations of NO2 and PM10 at five observation sites in the city of Berlin, Germany, during the 2020 COVID-19 lockdown periods. The predictor variables were based on meteorological and traffic data from the period of 2017–2019. The differences between BAU and observed concentrations were used to quantify lockdown-related effects on average pollutant concentrations as well as spatial variation between individual observation sites. The comparison between predicted and observed concentrations documented good overall model performance for different evaluation periods, but better performance for NO2 (R2 = 0.72) than PM10 concentrations (R2 = 0.35). The average decrease of NO2 was 21.9% in the spring lockdown and 22.3% in the winter lockdown in 2020. PM10 concentrations showed a smaller decrease, with an average of 12.8% in the spring as well as the winter lockdown. The model results were found sensitive to depict local variation of pollutant reductions at the different sites that were mainly related to locally varying modifications in traffic intensity.

RNA-Seq Analysis of UPM-Exposed Epithelium Co-Cultivated with Macrophages and Dendritic Cells in Obstructive Lung Diseases.

Background. Elevated concentrations of airborne pollutants are correlated with an enlarged rate of obstructive lung disease morbidity as well as acute disease exacerbations. This study aimed to analyze the epithelium mRNA profile in response to airborne particulate matter in the control, asthma, and COPD groups. Results. A triple co-culture of nasal epithelium, monocyte-derived macrophages, and monocyte-derived dendritic cells obtained from the controls, asthma, and COPD were exposed to urban particulate matter (UPM) for 24 h. RNA-Seq analysis found differences in seven (CYP1B1, CYP1B1-AS1, NCF1, ME1, LINC02029, BPIFA2, EEF1A2), five (CYP1B1, ARC, ENPEP, RASD1, CYP1B1-AS1), and six (CYP1B1, CYP1B1-AS1, IRF4, ATP1B2, TIPARP, CCL22) differentially expressed genes between UPM exposed and unexposed triple co-cultured epithelium in the control, asthma, and COPD groups, respectively. PCR analysis showed that mRNA expression of BPIFA2 and ENPEP was upregulated in both asthma and COPD, while the expression of CYP1B1-AS1 and TIPARP was increased in the epithelium from COPD patients only. Biological processes changed in UPM exposed triple co-cultured epithelium were associated with epidermis development and epidermal cell differentiation in asthma and with response to toxic substances in COPD. Conclusions. The biochemical processes associated with pathophysiology of asthma and COPD impairs the airway epithelial response to UPM.

Innovative experimental approach for spatial mapping of source-specific risk contributions of potentially toxic trace elements in PM10

Exposure to potentially toxic trace elements (PTTEs) in inhalable particulate matter (PM10) is associated with an increased risk of developing cardiorespiratory diseases. Therefore, in multi-source polluted urban contexts, a spatially-resolved evaluation of health risks associated with exposure to PTTEs in PM is essential to identify critical risk areas. In this study, a very-low volume device for high spatial resolution sampling and analysis of PM10 was employed in Terni (Central Italy) in a wide and dense network (23 sampling sites, about 1 km between each other) during a 15-month monitoring campaign. The soluble and insoluble fraction of 33 elements in PM10 was analysed through a chemical fractionation procedure that increased the selectivity of the elements as source tracers. Total carcinogenic risk (CR) and non-carcinogenic risk (NCR) for adults and children due to concentrations of PTTEs in PM10 were calculated and quantitative source-specific risk apportionment was carried out by applying Positive Matrix Factorization (PMF) to the spatially-resolved concentrations of the chemically fractionated elements. PMF analysis identified 5 factors: steel plant, biomass burning, brake dust, soil dust and road dust. Steel plant showed the greatest risk contribution. Total CR and NCR, and source-specific risk contributions at the 23 sites were interpolated using the ordinary kriging (OK) method and mapped to geo-reference the health risks of the identified sources in the whole study area. This also allowed risk estimation in areas not directly measured and the assessment of the risk contribution of individual sources at each point of the study area. This innovative experimental approach is an effective tool to localize the health risks of spatially disaggregated sources of PTTEs and it may allow for better planning of control strategies and mitigation measures to reduce airborne pollutant concentrations in urban settings polluted by multiple sources.

A Bayesian Non-Linear State Space Copula Model for Air Pollution in Beijing

AbstractAir pollution is a serious issue that currently affects many industrial cities in the world and can cause severe illness to the population. In particular, it has been proven that extreme high levels of airborne contaminants have dangerous short-term effects on human health, in terms of increased hospital admissions for cardiovascular and respiratory diseases and increased mortality risk. For these reasons, an accurate estimation of airborne pollutant concentrations is crucial. In this paper, we propose a flexible novel approach to model hourly measurements of fine particulate matter and meteorological data collected in Beijing in 2014. We show that the standard state space model, based on Gaussian assumptions, does not correctly capture the time dynamics of the observations. Therefore, we propose a non-linear non-Gaussian state space model where both the observation and the state equations are defined by copula specifications, and we perform Bayesian inference using the Hamiltonian Monte Carlo method. The proposed copula state space approach is very flexible, since it allows us to separately model the marginal distributions and to accommodate a wide variety of dependence structures in the data dynamics. We show that the proposed approach allows us not only to accurately estimate particulate matter measurements, but also to capture unusual high levels of air pollution, which were not detected by measured effects.

Methodological Approaches to Processing Laboratory Results of Ambient Air Quality Monitoring for the Purposes of Human Health Risk Assessment

Introduction: The problem of practical application of recommended methods of processing ambient air pollutant concentra­ tions below the limit of detection in chemical analysis raises uncertainties in human health risk assessment. The Federal Clean Air Project has increased legal significance of the latter and the relevance of interdepartmental cooperation in air monitoring activities. Objective: To review methodological approaches to handling ambient air quality test results and to develop proposals for uncertainty reduction in human health risk assessment. Materials and methods: We analyzed ways of dealing with values of ambient air pollutant concentrations lying below the limit of quantification of various analytical chemistry methods proposed by the European Bureau for Integrated Pollution Preven­ tion and Control and Russian researchers. A human (population) health risk assessment was conducted based on air quality parameters obtained in 2020 within environmental and public health monitoring in the city of Omsk using average concentrations of airborne pollutants measured by different techniques in accordance with national guidelines R 2.1.10.1920–04. Results: Different approaches to processing air quality test results produced different average pollutant concentrations accounting for health risk values ranging from the acceptable level to that of concern. Our estimates showed that certain tech­ niques of quantitative chemical analysis of priority air contaminants are inappropriate for the purposes of human health risk assessment. Photometry, for instance, was found unacceptable for measuring ambient concentrations of formaldehyde to assess carcinogenic risks due to unreasonable overestimation of the latter. Conclusions: We have proposed criteria for selecting appropriate methods of air quality testing based on estimated values of the lower limit of quantification of chemicals and for optimizing procedures of processing test results for correct assessment of health risks and harmonization of interdepartmental approaches.

Predicting airborne pollutant concentrations and events in a commercial building using low-cost pollutant sensors and machine learning: A case study

Prediction of indoor airborne pollutant concentrations can enable a smart indoor air quality control strategy that potentially reduces building energy use and improves occupant comfort. In service of this overarching goal, this work pursues four objectives: 1) Determine which low-cost airborne pollutant sensors are useful for prediction of indoor air quality variables of interest, investigating whether a few commercially available sensors held value for making such predictions. 2) Investigate which algorithms are most useful for making these predictions. 3) Develop an understanding of how far into the future we can conceivably predict indoor concentrations based on low-cost airborne pollutant signals. 4) Investigate methods for predicting elevated concentration events from historical data. Four different methods (Rolling Average, Random Forest, Gradient Boosting, and Long-Short Term Memory) for predicting eight indoor pollutant concentrations (carbon dioxide, nitrogen dioxide, ozone, PM 1, PM 2.5, PM 10, formaldehyde, total volatile organic compounds) are compared for their ability to predict future sensor signals in a single commercial building in California. Long-Short Term Memory was consistently the best method for predicting indoor pollutants, though the best combinations of input variables differed depending on pollutant of interest. To predict elevated concentration events, results show that indirect classification through a regression prediction that was then compared to a threshold performed marginally better than a direct classification prediction for all pollutants except PM1.

Use of Unmanned Aerial Vehicles for 3D topographic Mapping and Monitoring the Air Quality of Open-pit Mines

Recently remarkable advancement development of unmannedaerial vehicles (UAVs) has been observed and their applications have beenshown in many fields such as agriculture, industry, and environmentalmanagement. However, in the mining industry, the application of UAVtechnology remains potential. This paper presents a low-cost unmannedaerial vehicle technology-based system for 3D mapping and air qualitymonitoring at open-pit mine sites in Vietnam. The system includes severaldust sensors that are mounted on a low-cost rotary-wing type UAV. Thesystem collects a variety of data, mainly images and airborne pollutantconcentrations. To evaluate the performance of the proposed system, fieldtests were carried out at the Coc Sau coal mine. Based on the imagestransmitted to the ground monitoring station, large scale 3D topographicmaps were successfully modeled. In addition, sensors mounted on theUAV system were able to monitor the levels of environmental variablesassociated with the air quality within the pit such as temperature, dust, CO,CO2, and NOx. The field test results in this study illustrate the applicabilityof the low-cost UAV for the 3D mapping and the air quality monitoring atlarge and deep coal pits with relatively high accuracy.

Hygienic Assessment of Ambient Air Pollution, Health Status and Laboratory Parameters of Workers and Residents of the Oil Production Area

Introduction: Petroleum industry is a fundamental part of the economy of the Russian Federation. One of the approaches to up-to-date assessment of human health effects of subthreshold concentrations of pollutants is to determine changes in the immune and hormonal status in people with non-occupational or occupational exposure to industrial contaminants. Objective: To establish body mass indices, hormonal and immune status in workers of an oil producing enterprise and the population environmentally exposed to low concentrations of airborne pollutants emitted by oil production facilities. Materials and methods: The survey covered the population living in the area affected by oil production facilities and 131 oil extraction workers. We estimated the body mass index (BMI) of the subjects and tested their blood serum for the four main classes of antibodies (immunoglobulins IgA, IgM, IgG, and IgE), circulating immune complexes (CICs), and the levels of thyroid-stimulating hormone (TSH), thyroxine (free T4), and cortisol. Results: We established that all the subjects (in both observation groups (workers (group I) and residents (group II), and the comparison group) were overweight but BMI of the industrial workers was within the normal range. We also found that the hormonal parameters did not differ significantly between the groups but the level of the stress hormone cortisol was lower among the workers. IgA and IgE levels were significantly higher in the exposed residents. A high level of circulating immune complexes in both population groups indicates high prevalence of chronic disorders. Conclusions: The study proves the need for an in-depth study of indices of the endocrine and immune systems and a clinical examination of both the exposed population and industrial workers. It is especially important to study the markers of exposure to oil production emissions in the biological media of the general population.

Monitoring of Indoor Ultrafine Particulate Matter at the Sites of the Czech Armed Forces

Ultrafine particles and nanoparticles in the air are evaluated as a risk factor for the development of respiratory and other healthsymptoms due to their inhalation from the ambient air. The Czech Army professionals are expected to have frequent presence in apolluted environment and regular exposure to air with increased concentration of airborne pollutants. The report evaluates the presenceof ultra-fine particles (in the range of about 7.6–299.6 nm) in rooms often used by soldiers during their working hours whenthey are not deployed. The purpose is to assess whether the presence of troops in these workplaces is safe and does not pose a riskof adverse health effects in itself. Testing took place in three military rooms (classroom 1, classroom 2 and exercise flight simulatorroom). Seven samples of air were analysed in time by the scanning mobility particle sizer in succession. Mean particle concentrationswere found at 1.79×104, 7.53×103 and 8.39×103 N·cm-3 for the classroom 1, classroom 2 and exercise flight simulator room.Conclusions of the research have shown that particle concentrations in the places of the Czech Army can reach values that borderthe immission limits stated by the World Health Organisation.

Dynamic simulation of airborne pollutant concentrations associated with the effect of climate change in Batu Muda region, Malaysia

Air pollution has been a rising concern of the 21st due to its effects to public health. Air Monitoring Stations are state-of-the-art equipment used to measure airborne pollutants concentration i.e. carbon monoxide, nitrogen oxide, sulphur dioxide, particulate matter (PM10) and ozone (O3), as well as the meteorological parameters (i.e. ambient air temperature, relative humidity, wind speed and wind direction). Effects of climate change will affect the ambient temperature and humidity, which may induce a direct effect on air quality. In light of this, feed forward artificial neural network was employed to simulate the dynamic variations of PM10 and O3 with relative humidity, temperature, and windspeed data being the inputs under 12 different training algorithms. Based on the results obtained, Bayesian regularization with 12 hidden neurons is the optimized network structure, with mean absolute percentage error in testing dataset of O3 and PM10 at 51.31% and 36.49%, respectively. The models performed better in O3 prediction as it is a photochemical reaction where ozone concentration varies according to temperature, the effect of meteorological parameters is significant. On the other hand, PM10 is not heavily dependent on meteorological parameters as the diversity of particulate matter components where most of its sources are dormant to changes in climate.

Effects of short-term exposure to ambient airborne pollutants on COPD-related mortality among the elderly residents of Chengdu city in Southwest China

BackgroundChronic obstructive pulmonary disease (COPD) has become a severe global burden in terms of both health and the economy. Few studies, however, have thoroughly assessed the influence of air pollution on COPD-related mortality among elderly people in developing areas in the hinterland of southwestern China. This study is the first to examine the association between short-term exposure to ambient airborne pollutants and COPD-related mortality among elderly people in the central Sichuan Basin of southwestern China.MethodsData on COPD-related mortality among elderly people aged 60 and older were obtained from the Population Death Information Registration and Management System (PDIRMS). Data on airborne pollutants comprised of particulate matter < 2.5 μm in aerodynamic diameter (PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) were derived from 23 municipal environmental monitoring sites. Data on weather conditions, including daily mean temperature and relative humidity, were obtained from the Chengdu Meteorological Bureau. All data were collected from January 1, 2015, to December 31, 2018. A quasi-Poisson general additive model (GAM) was utilized to assess the effects of short-term exposure to airborne pollutants on COPD-related mortality among elderly people.ResultsA total of 61,058 COPD-related deaths of people aged 60 and older were obtained. Controlling the influences of daily temperature and relative humidity, interquartile range (IQR) concentration increases of PM2.5 (43 μg/m3), SO2 (8 μg/m3), NO2 (18 μg/m3), CO (0.4 mg/m3), and O3 (78 μg/m3) were associated with 2.7% (95% CI 1.0–4.4%), 4.3% (95% CI 2.1–6.4%), 3.6% (95% CI 1.7–5.6%), 2.7% (95% CI 0.6–4.8%), and 7.4% (95% CI 3.6–11.3%) increases in COPD-related mortality in people aged 60 and older, respectively. The exposure-response curves between each pollutant and the log-relative risk of COPD-related mortality exhibited linear relationships. Statistically significant differences in the associations between pollutants and COPD-related mortality were not observed among sociodemographic factors including age, gender, and marital status. The effects of O3 remained steady after adjusting for PM2.5, SO2, NO2, and CO each time in the two-pollutant models.ConclusionsIncreased concentrations of ambient airborne pollutants composed of PM2.5, SO2, NO2, O3, and CO were significantly and positively associated with COPD-related mortality in the central Sichuan Basin, which is located in the hinterland of southwestern China. The adverse effects of O3 were stable, a finding that should receive more attention.