PM10 Levels Research Articles

A novel method for establishing typical daily profile of PM concentrations in underground railway stations

The air quality in underground railway stations (URS) poses a significant public health concern due to extremely high concentrations of particulate matter: PM10 and PM2.5. Indeed, PM sources are strong and numerous, such as train braking and tunnel effect and URS are often confined spaces with low air change rates. Despite multiple PM measurements within URS, the variability of those concentrations from stations to stations is still poorly understood. We present here a methodology for establishing a daily profile of particle mass concentrations, based on a 5-year long measurement series in a Parisian URS. This approach incorporates an extensive data cleaning process based on the identification of URS operation periods and physically inconsistent or mathematically aberrant data, together with a linear regression model. This methodology delivers three usable outcomes: a typical profile for weekdays, a typical profile for weekends, and a PM concentration Daily Amplitude Coefficient (DAC) for the considered period. The DAC is a daily metric of the pollution levels, that enables the analysis of temporal trends and facilitates the comparison with other data with other acquisition frequency. The methodology developed here in a specific URS for PM10 measurements can be easily applied to different particle size fractions or to other measured parameters exhibiting a daily profile. Weekdays PM10 concentrations exhibit two distinct peaks corresponding to morning and evening rush hours, with an average daytime concentration of 193 µg/m³. These peaks are delayed by ∼1 hour compared to the train traffic. Weekends show consistently lower PM levels with no observable peaks, averaging 157 µg/m³ during the day. Our analysis reveals the long-term temporal evolution of PM concentration within the URS, highlighting seasonal patterns with higher PM10 concentrations observed in summer (up to 400 µg/m3) and lower values in winter (down to 250 µg/m3). This indoor seasonal evolution is not correlated with the outdoor temporal evolution, showing higher concentrations during the winter. Furthermore, our results show that the optimal period (DAC∼1) for conducting experiments to obtain reliable profiles is during the spring months (April, May, June).

Impact of Exposure to Environmental Particulate Matter on the Onset of Giant Cell Arteritis.

The aim of this study was to investigate the association between exposure to particulate matter with an aerodynamic diameter ≤10 μm (PM10) and the development of giant cell arteritis (GCA) and its ischemic complications. This was case-crossover study on consecutive patients who received a diagnosis of GCA in three hospitals in northern Italy between 2013 and 2021. The PM10 hourly and daily average concentrations, collected in the Italian monitoring network and archived by Istituto Superiore per la Protezione e la Ricerca Ambientale, were determined using European reference. We used a Bayesian hierarchical model to determine patients' daily exposures to them. We employed conditional logistic regression to estimate the effect of exposure on GCA symptoms onset or ischemic complications. We included 232 patients. A positive association was observed between exposure to PM10 and GCA risk, with an incremental odd of 27.1% (95% confidence interval 5.8-52.6) for every 10-μg/m3 increase in PM10 concentration within a 60-day period. We did not find any significant association for shorter periods or with ischemic complications. Subgroup analysis found a significantly higher incremental risk at a 60-day lag for patients ≥70 years old. Comparing patients who were chronically exposed to high PM10 levels (26.9 ± 13.8 μg/m3) to those who were not (11.9 ± 7.9 μg/m3) revealed that only in the former group was there an association between GCA onset and increased PM10 levels in the preceding 60 days. Exposure to environmental PM10 in the preceding 60 days seems to be associated with an increased risk of developing GCA, especially in older individuals with prolonged exposure to high levels of air pollution.

How international conflicts and global crises can intertwine and affect the sources and levels of air pollution in urban areas.

This paper analyses the intertwined impacts of the 2018 US sanctions on Iran and the COVID-19 pandemic (as examples of unplanned international conflicts and global crises) on the source and extent of air pollution in Tehran, the capital of Iran. The impacts are parametrized using the levels of criteria air pollutants (CAP) for 5years (2015-2020), which were previously deweathered using the promising machine learning technique of Random Forest (RF). The absolute principal component scores-multiple linear regression (APCS-MLR) method and the bivariate polar plot (BPP) technique are used here to analyze the source apportionment profile of the city for the business as usual (BAU; 2015 to 2018), sanctions (2019), and COVID-19 and sanctions (2020) intervals. The results show the severe impact of the 2018 US sanctions on Tehran's air quality (AQ); O3, NO2, CO, PM2.5, and PM10 levels increased by 117%, 55%, 20%, 35%, and 10%, respectively, while SO2 levels decreased by 30%. The sanctions also triggered a number of events, such as the disruption of the high-grade fuel supply chain and the Mazut crisis, which directly or indirectly accelerated the photochemical production of local tropospheric ozone to some extent. Sanctions also disrupted Tehran's AQ response to the pandemic, with CAP levels decreasing by only 2-7% during the pandemic. The ozone and PM10 BPPs show that the source apportionment profile of the city is dominated by local anthropogenic emission sources, especially urban transport, after the sanctions and the pandemic. Results also show that the impact of soft wars, such as the US sanctions against Iran, on urban air quality degradation is much stronger than that of hard wars, such as the Russia-Ukraine war.

Evaluating Indoor Air Quality in Schools: Is the Indoor Environment a Haven during High Pollution Episodes?

Pollution data were collected at five schools in Hong Kong using low-cost, sensor-based monitors both indoors and outdoors during two consecutive high pollution episodes. The pollutants monitored included NO2, O3, PM2.5, and PM10, which were also used as input to a health risk communication protocol known as Air Quality Health Index (AQHI). CO2 was also measured simultaneously. The study aimed to assess the relationship between indoor pollutant concentrations and AQHI levels with those outdoors and to evaluate the efficacy of building operating practices in protecting students from pollution exposure. The results indicate that the regular air quality monitoring stations and outdoor pollutant levels at schools exhibit similar patterns. School AQHI levels indoors were generally lower than those outdoors, with PM10 levels showing a larger proportional contribution to the calculated values indoors. NO2 levels in one school were in excess of outdoor values. CO2 monitored in classrooms commonly exceeded indoor guidelines, suggesting poor ventilation. One school that employed air filtration had lower indoor PM concentrations compared to other schools; however, they were still similar to those outdoors. O3 levels indoors were consistently lower than those outdoors. This study underscores the utility of on-site, sensor-based monitoring for assessing the health impacts of indoor and community exposure to urban air pollutants. The findings suggest a need for improved ventilation and more strategic air intake placement to enhance indoor air quality.

Risk of cardiovascular and respiratory diseases attributed to satellite-based PM2.5 over 2017-2022 in Sanandaj, an area of Iran.

The risk of cardiovascular and respiratory diseases attributed to satellite-based PM2.5 has been less investigated. In this study, the attributable risk was estimated in an area of Iran. The predicted air PM2.5 using satellite data and a two-stage regression model was used as the predictor of the diseases. The dose-response linkage between the bias-corrected predictor employing a strong statistical approach and the outcomes was evaluated using the distributed lag nonlinear model. We considered two distinct scenarios of PM2.5 for the risk estimation. Alongside the risk, the attributable risk and number were estimated for different levels of PM2.5 by age and gender categories. The cumulative influence of PM2.5 particles on respiratory illnesses was statistically significant at 13-16µg/m3 relative to the reference value (median), mostly apparent in the middle delays. The cumulative relative risk of 90th and 95th percentiles were 2.03 (CI 95%: 1.28, 3.19) and 2.25 (CI 95%: 1.28, 3.96), respectively. Nearly 600 cases of the diseases were attributable to the non-optimum values of the pollutant during 2017-2022, of which more than 400 cases were attributed to high values range. The predictor's influence on cardiovascular illnesses was along with uncertainty, indicating that additional research into their relationship is needed. The bias-corrected PM2.5 played an essential role in the prediction of respiratory illnesses, and it may likely be employed as a trigger for a preventative strategy.

COVID-19 lockdown impact on air quality and associated health benefit in two contrasting urban cities in Eastern Indo Gangetic Plain

A key challenge in controlling deteriorating urban air quality is a lack of clear understanding of the regional emissions characteristics and their impact on human health. COVID-19 lockdown provided an opportunity to enhance understanding of background air quality. Towards this, we studied the effect of the lockdown on air pollutants level and associated health benefits in two contrasting urban cities of Eastern IGP, Asansol (industrial) and Kolkata (metropolitan), by analyzing data from 2019 to 2021. The outcomes revealed that the level of exceedance of air pollutants is usually higher in Asansol but significantly decreased in both cities during the lockdown period. Particle concentrations were reduced by 50–70 % compared to Pre-Lockdown and by 20–35 % against the same period in 2019. Kolkata witnessed a higher reduction in PM levels than Asansol. Diurnal variation comparison showed a higher reduction of particle levels during lockdown in the morning at Asansol while in the evening at Kolkata. The health benefits associated with the reduction in PM2.5 concentration were quantified using the BenMAP-CE model, which revealed that improving air quality, like during the lockdown period, would save annually 0.46 and 2.91 deaths per 100,000 persons in Asansol and Kolkata, respectively. Altogether, this study's outcomes provide essential insights to policymakers for regional factors associated to varying air quality and health benefits associated to improvement in air quality.

A comparative study of indoor and outdoor air quality and associated potential health effects in Gurugram, India.

Abstract Deteriorating air quality remains a leading reason for global human illness and death. This study aims to investigate and compare inside and outside ambient air quality in 15 locations of Gurugram, India. Research technique adopted comprised of extracting outdoor air quality parameter values via Air Quality Index reporting stations for the entire year 2022. Parallel to that, indoor air quality parameter values of houses close to those monitoring stations were recorded through low-cost IoT based AirCubic sensors (T1595) as PM2.5, PM10, relative humidity (RH) and carbon monoxide (CO). After statistical evaluation, it was found that there were significant variations in outdoor and indoor air quality with maximum variations seen in colder season. The study revealed that PM levels were generally lower inside homes than outsides, while CO and RH concentrations were higher indoors. However, these levels often exceeded the acceptable thresholds set by both Indian national and global air quality standards, that could potentially result in harmful health effects, as well as increased risk of short and long-term illnesses, and even death. It may be concluded that more research, extensive air quality monitoring, data sharing, citizen awareness, and better technical and policy inputs are required to maintain healthy air quality for a sustainable lifestyle.

Impact of Outdoor Air Pollutants Exposure on the Severity and Outcomes of Community-Acquired Pneumonia in Gabes Region, Tunisia.

Background Acute community-acquired pneumonia (CAP) is considered the leading cause of infectious death worldwide. Air pollution and prolonged exposure to airborne contaminants have been implicated in various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). However, the specific impact of air pollution on pneumonia, particularly CAP, remains underexplored. Given the rising levels of urban air pollution and its potential health ramifications, our study aimed to examine the association between exposure to outdoor air pollution and severity as well as the outcomes of pneumonia cases requiring hospitalization. Methodology A cohort analytical study with retrospective data collection was carried out in the pulmonology department of the Gabès University Hospital between January and October 2022. We compared levels of particulate matter less than or equal to 10µm in aerodynamic diameter (PM10),sulfur dioxide (SO2),ozone (O3), moisture and ambient temperature with severity and outcomes ofpneumonia requiring hospitalization. The choice of these specific pollutants and environmental factors was based on their established impact on respiratory health and their prevalence in the study region. Results Increased sulfur dioxide (SO2) levels were associated with increased use of non-invasive ventilation (NIV) (r = 0.400). Higher levels of particulate matter (PM10) were significantly associated with the development of lung abscesses. Similarly, increased humidity and ambient temperature were strongly correlated with the development of lung abscesses. Increased air SO2 levels were correlated with a higher CURB65 score (r = 0.299). High outdoor SO2 levels and increasing moisture content were associated with increased Pneumonia Severity Index (PSI) score (r = 0.303 and = 0.310, respectively). Higher levels of PM10 were associated with an increased risk of pleural effusion, a serious complication of pneumonia. Finally, higher ambient temperatures were correlated with more extensive opacities on chest X-rays (r = 0.706), suggesting the severity of pneumonia. Conclusion This study highlights the significant associations between environmental factors and various clinical parameters in pneumonia patients. The findings underscore the importance of considering environmental exposures, such as air quality and weather conditions, in understanding and managing the severity of pneumonia.

Mapping air quality variations in industrial and agricultural zones: Understanding spatial and temporal patterns of selected air pollutants

With the escalating threat of air pollution on the biota, the need for research on pollutants has become imperative in industrial, urbanized and heavily populated cities. This study aimed to determine the level of major outdoor atmospheric pollutants (PM10, PM2.5, TVOCs, and HCHO) in specific locations including industrial and agricultural zones. Field measurements for this research, spanning 52 locations, were collected in the spring season using a portable instrument, and spatial variation maps were developed using ArcGIS software. The study measured PM10 concentrations ranging from 31 μg/m3 to 267 μg/m3 and PM2.5 concentrations ranging from 27 μg/m3 to 253 μg/m3. PM2.5 levels at most sites exceeded USEPA, WHO, and PEQS standards, while PM10 levels generally met these standards. The highest values of coarse and fine particulate matter were observed in areas dominated by emissions from the textile industry. Furthermore, particulate matter concentrations showed spatial variations, increasing towards the North and East directions. The Total Volatile Organic Compounds (TVOCs) concentrations, ranging from 0 mg/m3 to 4.019 mg/m3, exceeded Hong Kong SAR standards at five locations, thereby elevating health risks for residents in these areas. Similarly, formaldehyde levels, ranging from 0 mg/m3 to 0.754 mg/m3, surpassed the Hong Kong SAR standard at seven locations, indicating a substantial threat of exposure for the local population. In general, air quality at most field sampling sites was registered as UNQUALIFIED but was also observed to be FRESH at a few locations. This research is significant for understanding the levels of major pollutants so that the concerned authorities can take appropriate measures to tackle air pollution by introducing pollution control technologies in industries, switching to renewable energy options, and managing road dust.

Indoor Air Quality and Sick Building Syndrome among Caretakers in Childcare Centers

Sick Building Syndrome (SBS) refers to the health-related symptoms of exposure to an unhealthy microenvironment, especially poor indoor air quality This study investigated the associations of indoor air quality including particulate matter (PM10 and PM2 5), Carbon Dioxide (CO2), Carbon Monoxide (CO), Total Volatile Organic Compounds (TVOCs), and childcare center environment with SBS symptoms among caretakers. SBS was assessed using a self-reported questionnaire The prevalence of SBS was 77 78% among caretakers PM10 negatively correlated with CO2 (r=0 -558; p<0 01) PM10 levels were associated with any reported symptoms of SBS (OR=7 38; 95%CI 1 98 - 27 53), and upper respiratory symptoms (OR= 12 00; 95%CI 1 46 – 98 08) PM2 5 showed no association with SBS Using air refresher was associated with upper respiratory symptoms (OR=5 10; 95%CI 1 46 – 13 59), lower respiratory symptoms (OR=3 64; ; 95%CI 1.22 - 10.82).

Media Sentiment on Air Pollution: Seasonal Trends in Relation to PM10 Levels

Air pollution remains a major public health concern globally, especially in the Western Balkan countries facing severe air quality problems. This study investigates the relationship between air quality, news media sentiment, and public discourse in Macedonia over a ten-year period (2014–2023). We employed sentiment analysis to examine the emotional tone of news coverage related to air pollution, and topic modeling to uncover recurring themes within news articles. Our analysis revealed a distinct seasonal pattern, with negative media sentiments peaking during winter months when PM10 levels were the highest. This finding aligns with the increased reliance on polluting fuels for winter heating. Interestingly, despite a stable number of neutral articles, a rise in positive-sentiment articles suggests a potential decrease in pollution levels or the effectiveness of new government policies. We identified recurring topics like air quality concerns in specific cities, public unease regarding factories, and ongoing scrutiny of government policies. Emerging topics included the impact of the COVID-19 pandemic on air quality, public discourse surrounding heating practices, and growing concerns about waste management. This study contributes to a deeper understanding of the complex interplay between air pollution data, public discourse, and media framing, offering valuable insights for policymakers and media outlets in Macedonia.

Patterns and disparities in indoor particulate matter levels in selected primary schools in Kigali, Rwanda

Air pollution is a global environmental and public health challenge. There is limited evidence about the air quality in Rwanda, and the concentrations of particulate matter (PM), namely PM2.5 and PM10 in schools have not been well documented. This study evaluated patterns and disparities in indoor PM levels in selected primary schools in Kigali, Rwanda.The study collected PM2.5 and PM10 concentrations from six classrooms in six selected primary schools during the regular school study period in the dry season. Data were collected using mobile air sensors (purple air/PA–II–SD air quality) and an observation checklist. A Kruskal-Wallis test was performed to assess the difference in PM2.5 and PM10 concentrations between the six schools. The post-hoc Mann-Whitney test was used to compare all group pairs.The results indicated a significant difference in both the indoor PM2.5 concentration (H (5) = 41.01, p < 0.001) and the indoor PM10 concentration (H (5) = 38.5, p < 0.001). The maximum concentration observed was 133.6 μg/m3 for PM2.5 and 158.5 μg/m3 for PM10. Schools in highly exposed areas tended to have higher concentrations of PM than schools in moderately exposed areas. Specifically, the daily average concentration of PM2.5 in schools located in highly exposed areas ranged from 39 μg/m³ to 118 μg/m³, while PM10 levels ranged from 44.0 μg/m³ to 126 μg/m³. In contrast, schools in moderately exposed areas had daily PM2.5 average concentrations ranging from 32.0 μg/m³ to 111.0 μg/m³ and daily PM10 average concentrations ranging from 38.0 μg/m³ to 119 μg/m³.Overall, the recorded values for both PM2.5 and PM10 in all sampled schools were higher than the World Health Organization air quality guidelines. Indoor air quality is poorer in schools situated in highly exposed areas. This study suggests interventions to improve school air quality for the benefit of school communities.

Predicting hospital admissions for upper respiratory tract complaints: An artificial neural network approach integrating air pollution and meteorological factors.

This study uses artificial neural networks (ANNs) to examine the intricate relationship between air pollutants, meteorological factors, and respiratory disorders. The study investigates the correlation between hospital admissions for respiratory diseases and the levels of PM10 and SO2 pollutants, as well as local meteorological conditions, using data from 2017 to 2019. The objective of this study is to clarify the impact of air pollution on the well-being of the general population, specifically focusing on respiratory ailments. An ANN called a multilayer perceptron (MLP) was used. The network was trained using the Levenberg-Marquardt (LM) backpropagation algorithm. The data revealed a substantial increase in hospital admissions for upper respiratory tract diseases, amounting to a total of 11,746 cases. There were clear seasonal fluctuations, with fall having the highest number of cases of bronchitis (N = 181), sinusitis (N = 83), and upper respiratory infections (N = 194). The study also found demographic differences, with females and people aged 18 to 65years having greater admission rates. The performance of the ANN model, measured using R2 values, demonstrated a high level of predictive accuracy. Specifically, the R2 value was 0.91675 during training, 0.99182 during testing, and 0.95287 for validating the prediction of asthma. The comparative analysis revealed that the ANN-MLP model provided the most optimal result. The results emphasize the effectiveness of ANNs in representing the complex relationships between air quality, climatic conditions, and respiratory health. The results offer crucial insights for formulating focused healthcare policies and treatments to alleviate the detrimental impact of air pollution and meteorological factors.

Estimation of Particulate Matter (PM10) Over Middle Indo-Gangetic Plain (Patna) of India: Seasonal Variation and Source Apportionment

Despite extensive research on particulate matter (PM) pollution in India’s Indo-Gangetic Plain (IGP), source apportionment remains challenging. This study investigates the effect of particulate matter (PM10)-associated water soluble inorganic ions (WSIIs) on ambient air concentration across the middle IGP from January to December 2018. Moreover, the seasonal fluctuation and chemical characterization of PM10 were assessed for the year 2018. The results revealed a high concentration of PM10 (156 µg/m3), exceeding the WHO and National Ambient Air Quality Standard (NAAQS) limits. The highest PM10 levels were observed during autumn, winter, summer, and the rainy season. The study identified SO42− and NH4+ as the most common WSIIs, constituting 46% and 23% of the total WSIIs. Source apportionment analysis indicated that street dust, biomass burning, and vehicle and industrial emissions together with secondary formation significantly contributed to IGP’s PM pollution. Additionally, the investigation of air mass back trajectory suggests that air quality in IGP is largely influenced by eastern and western Maritime air masses originated from the Arabian Sea, the Bay of Bengal, Gujarat, Afghanistan, Pakistan, and Bangladesh.

Improving the sustainability of remote arctic communities with high-energy-efficiency houses

Qaanaaq, a remote community in Greenland, faces significant challenges in achieving sustainable development goals (SDGs) due to its extremely high heating demands, reliance on diesel fuel, limited transportation options, declining population, and shortage of skilled labor in the construction industry. Addressing these issues is particularly complex due to the harsh climate and limited resources. In this study, we propose a strategy to achieve SDGs in Qaanaaq by replacing 263 single-family homes with 66 highly insulated multi-family nearly zero-energy apartment houses (n-ZEAHs) and installing rooftop solar photovoltaic (PV) systems. Simulation results indicate that replacing homes with n-ZEAHs could reduce annual heating energy consumption by 9575 MWh and that the rooftop PV systems could offset approximately 57% of annual electricity sales, significantly improving Qaanaaq's energy security. However, field measurements revealed that indoor humidity in n-ZEAHs was low and that CO2 and PM10 levels occasionally exceeded standards. Additionally, the rooftop PV systems may not fully meet monthly electricity demands during certain seasons. To address these issues, it will be necessary to introduce appropriate ventilation systems and consider the implementation of combined systems that integrate other renewable energy sources and batteries. Given the limited availability of skilled technicians in Qaanaaq, simple and user-friendly systems are preferred. The findings of this study provide valuable insights on how to achieve SDGs in cold and remote climate communities like Qaanaaq.

Forecasting PM10 Concentrations Using Artificial Neural Network in Imphal City

In this study, a forecasting system is developed for predicting PM10 levels in Imphal City over the next three days (+1, +2, and +3 days) using artificial neural networks (ANN). The experimental findings indicate that the ANN model can achieve reasonably accurate predictions of air pollutant levels. Moreover, optimizations in model performance are explored through variations in input parameters and experimental setups. Initially, predictions for each of the +1, +2, and +3 days are made independently using the same training dataset. Subsequently, cumulative predictions for +2 and +3 days are generated using previously predicted values from preceding days, yielding improved prediction accuracy. Additionally, the study identifies the optimal size of the training dataset, determining that using data spanning 3 to 15 past days yields the minimum error rates in predicting pollutant concentrations. Finally, the investigation includes the consideration of days-of-week as an input parameter, which enhances forecast accuracy noticeably.

Trends of air pollution variations during pre-Diwali, Diwali and post-Diwali periods and health risk assessment using HAQI in India

During 2019 to 2022, a comprehensive study was conducted across 71 stations spanning over 15 states in India to investigate air quality variations during the pre-Diwali, Diwali, and post-Diwali periods. The investigation, centred on the extensive use of firecrackers during festive times, revealed a short-term deterioration in air quality with long-lasting impacts. PM2.5 and PM10 levels were found to significantly increase during Diwali and the post-Diwali period compared to the pre-Diwali period. The levels of PM2.5 and PM10 experience respective increases of 44.66% and 11.03%, 29.12% and 05.41%, 52.63% and 23.38%, and 39.65% and 17.61% during Diwali period from 2019 to 2022, where 68.84% and 34.32%, 44.42% and 29.42%, and 48.70% and 25.22% in 2019, 2021 and 2022 in post-Diwali period compared to the pre-Diwali. The Mann–Kendall and Sen's slope analyses reveal statistically significant positive trends (increasing) in both PM10 and PM2.5 levels during the Diwali festival period. Notably, the study introduced the Health Hazard-based Air Quality Index (HAQI), which demonstrated higher values than the National Ambient Air Quality Index (NAAQI). Unlike NAAQI, which considers individual pollutant sub-index values, and considered highest one of sub-index values in AQI analysis. HAQI provides a more comprehensive assessment of health risks associated with exposure to multiple pollutants effects. In 2022, AQI and HAQI values during pre-Diwali were 114 and 146.18, during Diwali were 173 and 332.18, and post-Diwali were 191 and 414.14, respectively. These findings underscore the elevated risk during Diwali and the post-Diwali period due to heightened pollution levels, emphasizing the need for public awareness and potential mitigation strategies. The study calls attention to the importance of considering cumulative health impacts and suggests the adoption of HAQI for more informative air quality assessments.

SEM-EDS-based rapid measurement and size-fractionated speciation of airborne particulate matter and associated metals utilizing plant leaves.

This study was conducted to assess particulate matter pollution and the accumulation of airborne toxic metals by studying the foliar deposition pattern in an urban environment. To this end, two commonly growing plants (Senna siamea (Lam.) H.S.Irwin & Barneby and Alstonia scholaris (L.) R.Br.) from the busiest traffic squares of the city (Nehru Chowk) in Bilaspur, India, were selected for detailed study. For this purpose, plant leaf samples of both plant species were collected from pollution-affected areas and a reference site (unpolluted) in the city and examined by scanning electron microscopy and energy dispersive X-ray spectroscopy (SEM-EDS) to estimate the accumulation of PM-bound toxic metals at the leaf surfaces. The results of this study showed that the leaves of both plants accumulate PM in different size ranges. Although both plant leaves showed accumulation of PM from respirable suspended particulate matter (RSPM) to ultra-fine particles (UFPs: < 0.1: less than 100nm) range along with toxic metals, S. siamea retained a higher level of PM than A. scholaris due to better micro-morphological properties on both leaf surfaces. The size of some PM was found to be smaller than the stoma openings. The EDS study proved the presence of harmful airborne toxic metals (Pb, Cd, Cu, Zr, Al, Co, etc.) in these PMs of ambient air. This indicates that toxic metals can enter the leaves through stomatal openings. The results of this study recommended that both plants can be used as a tool to minimise PM pollution.

An investigation of inequalities in exposure to PM2.5 air pollution across small areas in Ireland

The link between exposure to air pollution and adverse effects on human health is well documented. Yet, in a European context, research on the spatial distribution of air pollution and the characteristics of areas is relatively scarce, and there is a need for research using different spatial scales, a wider variety of socioeconomic indicators (such as ethnicity) and new methodologies to assess these relationships. This study uses comprehensive data on a wide range of demographic and socioeconomic indicators, matched to data on PM2.5 concentrations for small areas in Ireland, to assess the relationship between social vulnerability and PM2.5 air pollution. Examining a wide range of socioeconomic indicators revealed some differentials in PM2.5 concentration levels by measure and by rural and urban classification. However, statistical modelling using concentration curves and concentration indices did not present substantial evidence of inequalities in PM2.5 concentrations across small areas. In common with other western European countries, an overall decline in the levels of PM2.5 between 2011 and 2016 was observed in Ireland, though the data indicates that almost all small areas in Ireland were found to have exceeded the World Health Organization (WHO)’s PM2.5 annual guideline (of 5 µg/m3), calling for greater policy efforts to reduce air pollution in Ireland. The recent Clean Air Strategy contains a commitment to achieve the WHO guideline limits for PM2.5 by 2040, with interim targets at various points over the next two decades. Achieving these targets will require policy measures to decarbonise home heating, promote active travel and the transition to electric vehicles, and further regulations on burning fossil fuels and enforcing environmental regulations more tightly. From a research and information-gathering perspective, installing more monitoring stations at key points could improve the quality and spatial dimension of the data collected and facilitate the assessment of the implementation of the measures in the Clean Air Strategy.

Particulate matter and black carbon exposure in Seoul subway: Implications for human health risk

Air pollution and the associated health risks to humans caused by exposure to particle matter in enclosed subway systems are currently concerns. This research first time investigated the substantial health hazards (cancerous, non-cancerous, relative risk, and excess mortality rates associated with all-cause, cardiovascular, and respiratory mortality) posed by metals and black carbon (BC) bound to particulate matter (PM10, PM2.5) within subway systems. The Seoul subway system has been found to show notably higher levels of PM (2–1.5 times) and BC (2.3–1.3 times) compared to outdoor environments. Inhalation of PM-containing trace metals such as Cr, Ni, Cd, Pb, and BC significantly contributes to cancer risk, irrespective of age or PM type. The risk of developing inhalation-related cancer due to PM10 and PM2.5-bound metals is notably higher (1.2–2 times) within the subway environment compared to outdoors. Inhalation cancer risk levels (ICRBC) associated with PM-bound BC are 1.8 times greater at subway stations compared to outdoor stations, with a strong association (1.93–1.98 times) with PM2.5-bound BC compared to PM10. Notably, PM levels in subway stations have a considerable impact on relative mortality risk, with approximately 1.98 times the observed increase in mortality attributed to PM2.5-bound BC. The trace metals and BC bound to PM that contribute to human health risks might originate from vehicle emissions. This includes both surface and underground transportation systems. These findings underscore the significance of addressing air quality within subway systems to mitigate health risks.