Average Concentrations Of PM2 Research Articles

Spatiotemporal distribution of PM2.5 and health risk assessment in Kolkata, India: Evaluation of non-carcinogenic health hazards and premature mortality

The State of Global Air Report (2022) says that Kolkata is the second most polluted city worldwide, with continuously high PM2.5 concentrations. This study examines the spatial distribution of PM2.5 and its associated health risks in the region. The yearly average concentrations of PM2.5 were found to consistently surpass the National Ambient Air Quality Standard (NAAQS) levels in India, China, United States of America (USA), and Canada including the prescribed standards of World Health Organization (WHO) and European Union (EU). Additionally, PM2.5 levels exceeded the 24-h NAAQS for a substantial number of days in a year. The study reveals significant correlation between meteorological factors and PM2.5, with temperature playing major role in the distribution of PM2.5. The hazard quotient (HQ) values continuously exceeded the threshold level, indicating severity of air pollution. Long-term exposure to PM2.5 resulted in a significant number of premature deaths, with ischemic heart disease (IHD) being the leading cause in Kolkata. The study indicates that reducing PM2.5 levels has a positive impact on mortality rates and health outcomes. For every unit reduction (1 μg/m3) of PM2.5 concentration, the corresponding decrease in premature mortality by 2.40%. Furthermore, reductions of 5 μg/m3 and 10 μg/m3 were associated with health improvements of 13.85% and 33.40% respectively. Overall, this study provides valuable information to effectively regulate air quality, implement targeted interventions, and protect public health.

Characteristics of PM2.5 in Hachinohe, the priority pollution control city in Japan

In the urban area of Hachinohe, Japan, PM2.5 sampling was carried out from May 2015 to February 2021. The average concentration of PM2.5 during the entire sampling period was approximately 11.7 μg m−3, with 4.4 μg m−3 for water soluble ions, 3.3 μg m−3 for carbonaceous species, 0.5 μg m−3 for trace metals, and 3.5 μg m−3 for other species. Based on this comprehensive component information, eight sources were quantitatively explored, among which ship emissions (29%), traffic emissions (19%), and secondary organic aerosols (15%) had relatively high contributions for PM2.5 concentration level. The health risk assessment indicated that the children in Hachinohe City faced serious non-carcinogenic and carcinogenic risks, with corresponding values of 8.0 for HI and 1.2 × 10−4 for CR. The pollutants from ship emissions, secondary nitrates plus coal combustion, and industrial emissions should be of concern. High risk metals included Pb, As, Sb, V, and Cr(VI). Specifically, ship emissions exhibited the highest concentration (5.5 μg m−3) and health risks (HI = 2.2 and CR = 3.0 × 10−5) in summer; priority should be given to controlling pollution in the Port of Hachinohe. The other two sources had the highest concentration in winter (2.0 and 0.5 μg m−3) and were mainly influenced by the polluted air masses from Akita Prefecture, with HI values of 2.4 and 2.5 and CR values of 4.9 × 10−5 and 3.2 × 10−5, respectively. Overall, our study comprehensively revealed the characteristics of PM2.5 in Hachinohe City and conducted an in-depth investigated into its causes of pollution. This information could serve as a scientific basis for developing specific strategies to improve air quality.

Insights into the morphology, composition, and sources of atmospheric particulate matter on Mount Qomolangma (Everest)

Aerosol category and its physicochemical properties are key factors influencing its climate and environmental effects. To further enhance our understanding of aerosols in the Himalayas-Tibetan Plateau region, atmospheric particulate matter samples were collected at three different altitudes on Mount Qomolangma (Everest). Using an automated scanning electron microscope system, the composition, size, and morphology of 52,349 particles were analyzed. The average mass concentrations of PM1, PM2.5, and PM10 were 0.678, 5.054, and 16.698 µg/m3, respectively. Aluminosilicate particles dominated the samples, accounting for 71.5 % to 82.8 % of PM10 mass and 34.7 % to 62.4 % of the particle number. Quartz particles, carbonate particles, metal oxides, and sulfates were also observed at different periods. Carbonaceous particles made up a significant portion of aerosols, comprising 2.1 % to 9.7 % of PM10 mass and 10.4 % to 45.4 % of particle number, with their concentration showing an upward trend with altitude. Small amounts of tar ball particles and fly ash particles were also observed, providing direct evidence of anthropogenic influences on high-altitude regions, even at altitudes exceeding 6000 m. The size distribution and abundance of different particle categories were closely related to the transport trajectories of air masses. The morphology of different particle categories varied, with fly ash particles mainly spherical and carbonaceous particles exhibiting higher irregularity.

Pollution characteristics, source apportionment, and health risk assessment of PM10 and PM2.5 in rooftop and kerbside environment of Lanzhou, NW China.

To investigate air pollution in the kerbside environment and its associated human health risks, a study was conducted in Lanzhou during December 2018, as well as in April, June, and September 2019. The research aimed to characterize the composition of PM10 and PM2.5, including elements, ions, and carbonaceous components, at both rooftop and kerbside locations. Additionally, source apportionment and health risk assessment were conducted. The results showed that the average mass concentrations of PM10 on the rooftop were 176.01 ± 83.23μg/m3, and for PM2.5, it was 94.07 ± 64.89μg/m3. The PM10 and PM2.5 levels at the kerbside are 2.21 times and 1.79 times, respectively, greater than those on the rooftop. Moreover, the concentrations of elements, ions, and carbonaceous components in kerbside PM were higher than those at the rooftop location. Chemical mass closure analysis identified various sources, including organic matter, mineral dust, secondary ions, other ions, elements, and other components. In comparison to rooftop particulate matter (PM), mineral dust makes a more substantial contribution to kerbside PM. Secondary ions show an opposite trend, making a greater contribution to rooftop PM. The contribution of organic components within PM of the same particle size remains relatively consistent. The outcome of the health risk assessment indicates that Co, Cd, and As in PM within the kerbside and rooftop environments do not pose a notable carcinogenic risk. However, Al and Mn do present specific non-carcinogenic risks, particularly in the kerbside environment. Furthermore, children experience elevated non-carcinogenic risk compared to adults. These findings can serve as a scientific foundation for formulating policies within the local health department.

Long-term PM2.5 exposure associated with severity of angina pectoris and related health status in patients admitted with acute coronary syndrome: Modification effect of genetic susceptibility and disease history

Long-term particulate matter with aerodynamic diameters ≤2.5 μm (PM2.5) exposure has been associated with the occurrence of acute coronary syndrome (ACS). However, the impact of PM2.5 exposure and its components on the severity of angina pectoris and disease-related health status in patients hospitalized for ACS is understudied. To assess the association between long-term exposure to PM2.5 components and the angina pectoris severity in ACS patients, as well as the modification effects of genetic factors and disease history in north China. During 2017–2019, 6729 ACS patients were collected in Shandong Province and Beijing, with their angina pectoris severity evaluated using Seattle Angina Questionnaire (SAQ). The 0–3 years' average concentrations of PM2.5 and its five major components were assigned to each patient's residential address. Linear mixed-effects model, weighted quantile regression, and quantile g-computation were used to estimate the effects of both single and joint associations between PM2.5 components and SAQ scores. The interactive effect was estimated by polygenic risk scores and disease history. For each interquartile range increase in PM2.5, the overall SAQ score changed by −3.71% (95%CI: −4.54% to −2.88%), with score of angina stability more affected than angina frequency and other dimensions of angina pectoris severity. Sulfate and ammonium were major contributors to the effect of PM2.5 exposure. Significant modification effect was only observed for disease history, especially for the dimension of physical limitation. Among a series of pre-existing diseases, patients with a family history of coronary artery disease, previous percutaneous coronary intervention or coronary artery bypass grafting, and stroke were more susceptible to PM2.5 exposure than others. Greater exposure to PM2.5 is associated with more serious angina pectoris and worse disease-related health status in ACS patients. Public health and clinical priority should be given to cutting down key effective components and protecting highly vulnerable individuals.

Analyses and Simulations of PM2.5 Pollution Characteristics under the Influence of the New Year’s Day Effects in China

Regional haze often occurs after the New Year holiday. To explore the characteristics of PM2.5 pollutions under the influence of the New Year’s Day effect, this study analyzed the spatiotemporal changes relating to PM2.5 during and around the New Year’s Day holiday in China from 2015 to 2022, and used the Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) model to study the effects of human activities and meteorological factors on PM2.5 pollutions, as well as the differences in the contributions of different industries to PM2.5 pollutions. The results show that for the entire study period (i.e., before, during, and after the New Year’s Day holiday) from 2015 to 2022, the average concentrations of PM2.5 in China decreased by 41.9% overall. In 2019~2022, the New Year’s Day effect was significant, meaning that the average concentrations of PM2.5 increased by 18.9~46.8 μg/m3 from before to after the New Year’s Day holiday, with its peak occurring (64.3~74.9 μg/m3) after the holiday. In terms of spatial differences, the average concentrations of PM2.5 were higher in the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and central China. Moreover, the Beijing–Tianjin–Hebei region and its surrounding areas, the Chengdu–Chongqing region, the Fenwei Plain, and the middle reaches of the Yangtze River region were greatly affected by the New Year’s Day effect. Human activities led to higher increases in PM2.5 in Henan, Hubei, Hebei, and Anhui on 3 and 4 January 2022. If the haze was accompanied by cloudy days or weak precipitation, the accumulation of surface water vapor and atmospheric aerosols further increased the possibility of heavy pollution. It was found that, for the entire study period, PM2.5 generated by residential sources contributed the vast majority (60~100 μg/m3) of PM2.5 concentrations, and that the main industry sources that caused changes in time distributions were industrial and transportation sources.

Decoding temporal patterns and trends of PM10 pollution over Delhi: a multi-year analysis (2015-2022).

The current study delved into an extensive analysis of multi-year observations on PM10 to have trends at various time scales in Delhi, India. High-resolution ground observations from all 37 monitoring stations from 2015 to 2022 were used. This study used non-parametric generalized additive model (GAM) based smooth-trend and Theil-Sen slope estimator techniques to analyze temporal trends and variations. The long-term PM10 concentration, both in its ambient and de-seasonalized forms, exhibited a statistically significant decreasing trend. An average decrease of - 7.57 [95% confidence interval (CI) - 16.51, 0.18] µg m-3year-1 for ambient PM10 and - 8.45 [95% CI - 11.96, - 5.58] µg m-3year-1 for de-seasonalized PM10 mass concentration was observed. Breaking it down into seasons, we observed significant declines in PM10 concentrations during monsoon (- 10.71µgm-3year-1, p < 0.1) and post-monsoon (- 7.49µgm-3year-1, p < 0.001). On the other hand, summer and winter displayed statistically insignificant declining trends of - 5.32µgm-3year-1 and - 6.06µgm-3year-1, respectively. Remarkably, all months except March displayed declining PM10 concentrations, suggesting a gradual reduction in particle pollution across the city. Further analysis of PM10 across various wind sectors revealed a consistent decreasing trend in all wind directions. The most substantial decrease was observed from the northwest (- 10.24µgm-3year-1), while the minimum reduction occurred from the east (- 5.67µgm-3year-1). Throughout the 8-year study period, the daily average PM10 concentration remained at 228 ± 124µgm-3, ranging from 33 to 819µgm-3. Seasonal variations were apparent, with concentrations during winter, summer, monsoon, and post-monsoon seasons averaging 279 ± 133, 224 ± 117, 135 ± 95, and 323 ± 142µgm-3, respectively. November had the highest and August had the lowest concentration. Weekend PM10 concentration is slightly lower than weekdays. These findings emphasize the need for more stringent government action plans.

Outdoor air pollution as a risk factor for testing positive for SARS-CoV-2: A nationwide test-negative case-control study in the Netherlands

Air pollution is a known risk factor for several diseases, but the extent to which it influences COVID-19 compared to other respiratory diseases remains unclear. We performed a test-negative case-control study among people with COVID-19-compatible symptoms who were tested for SARS-CoV-2 infection, to assess whether their long- and short-term exposure to ambient air pollution (AAP) was associated with testing positive (vs. negative) for SARS-CoV-2. We used individual-level data for all adult residents in the Netherlands who were tested for SARS-CoV-2 between June and November 2020, when only symptomatic people were tested, and modeled ambient concentrations of PM10, PM2.5, NO2 and O3 at geocoded residential addresses. In long-term exposure analysis, we selected individuals who did not change residential address in 2017–2019 (1.7 million tests) and considered the average concentrations of PM10, PM2.5 and NO2 in that period, and different sources of PM (industry, livestock, other agricultural activities, road traffic, other Dutch sources, foreign sources). In short-term exposure analysis, individuals not changing residential address in the two weeks before testing day (2.7 million tests) were included in the analyses, thus considering 1- and 2-week average concentrations of PM10, PM2.5, NO2 and O3 before testing day as exposure. Mixed-effects logistic regression analysis with adjustment for several confounders, including municipality and testing week to account for spatiotemporal variation in viral circulation, was used. Overall, there was no statistically significant effect of long-term exposure to the studied pollutants on the odds of testing positive vs. negative for SARS-CoV-2. However, significant positive associations of long-term exposure to PM10 and PM2.5 from specifically foreign and livestock sources, and to PM10 from other agricultural sources, were observed. Short-term exposure to PM10 (adjusting for NO2) and PM2.5 were also positively associated with increased odds of testing positive for SARS-CoV-2. While these exposures seemed to increase COVID-19 risk relative to other respiratory diseases, the underlying biological mechanisms remain unclear. This study reinforces the need to continue to strive for better air quality to support public health.

Perinatal exposure to traffic related air pollutants and the risk of infection in the first six months of life: a cohort study from a low-middle income country

ObjectiveThere is limited study from low-and-middle income countries on the effect of perinatal exposure to air pollution and the risk of infection in infant. We assessed the association between perinatal exposure to traffic related air pollution and the risk of infection in infant during their first six months of life.MethodsA prospective cohort study was performed in Jakarta, March 2016–September 2020 among 298 mother-infant pairs. PM2.5, soot, NOx, and NO2 concentrations were assessed using land use regression models (LUR) at individual level. Repeated interviewer-administered questionnaires were used to obtain data on infection at 1, 2, 4 and 6 months of age. The infections were categorized as upper respiratory tract (runny nose, cough, wheezing or shortness of breath), lower respiratory tract (pneumonia, bronchiolitis) or gastrointestinal tract infection. Logistic regression models adjusted for covariates were used to assess the association between perinatal exposure to air pollution and the risk of infection in the first six months of life.ResultsThe average concentrations of PM2.5 and NO2 were much higher than the WHO recommended levels. Upper respiratory tract infections (URTI) were much more common in the first six months of life than diagnosed lower respiratory tract or gastro-intestinal infections (35.6%, 3.5% and 5.8% respectively). Perinatal exposure to PM2.5 and soot suggested increase cumulative risk of upper respiratory tract infection (URTI) in the first 6 months of life per IQR increase with adjusted OR of 1.50 (95% CI 0.91; 2.47) and 1.14 (95% CI 0.79; 1.64), respectively. Soot was significantly associated with the risk of URTI at 4–6 months age interval (aOR of 1.45, 95%CI 1.02; 2.09). All air pollutants were also positively associated with lower respiratory tract infection, but all CIs include unity because of relatively small samples. Adjusted odds ratios for gastrointestinal infections were close to unity.ConclusionOur study adds to the evidence that perinatal exposure to fine particles is associated with respiratory tract infection in infants in a low-middle income country.

Pollution characteristics and transformation mechanisms of secondary inorganic components during winter heavy pollution in Handan city in 2018–2020

The mechanism of secondary transformation of nitrate and sulfate under different relative humidity (RH), and different atmospheric oxidizing conditions is still controversial. This study focuses on the winter heavy pollution process in Handan city from 2018 to 2020, and the following results were obtained: The average concentration of PM2.5 showed a decreasing trend from year to year, but the SNA (SO42−, NO3− and NH4+) showed an increasing trend from year to year, and the concentration of nitrate exceeded that of sulfate. During the haze formation period from 2018 to 2020, a significant disparity was observed in NO2 and SO2 emissions, with NO2 emissions surpassing those of SO2. Mobile sources were found to contribute more emissions than fixed sources, particularly during the haze persistence period (HP). However, sulfate conversion outweighed nitrate conversion. HighRH and photochemical reactions induced by ozone favored the formation of nitrate and sulfate, which formated heavy haze. When 40% ≤ RH ≤ 70%, atmospheric oxidants (Ox = O3 + NO2) concentration was the highest, nitrogen oxidation ratio (NOR) and sulfur oxidation ratio (SOR) increased rapidly, and nitrate was mainly generated by photochemical reactions during daytime. When RH > 70% and the concentration of Ox was low, RH will weaken the photochemical intensity. Therefore, the liquid-phase oxidation reaction of N2O5 contributed significantly to atmospheric nitrate at night, and the liquid-phase oxidation reaction of sulfate was faster than the gas-phase one. Local emissions contributed significantly to particulate matter pollution during heavy pollution in 2018–2020, and the main sources were secondary conversion, coal combustion, biomass burning, and road dust.

Risk analysis of air pollutants and types of anemia: a UK Biobank prospective cohort study.

Previous studies have suggested that exposure to air pollutants may be associated with specific blood indicators or anemia in certain populations. However, there is insufficient epidemiological data and prospective evidence to evaluate the relationship between environmental air pollution and specific types of anemia. We conducted a large-scale prospective cohort study based on the UK Biobank. Annual average concentrations of NO2, PM2.5, PM2.5-10, and PM10 were obtained from the ESCAPE study using the Land Use Regression (LUR) model. The association between atmospheric pollutants and different types of anemia was investigated using the Cox proportional hazards model. Furthermore, restricted cubic splines were used to explore exposure-response relationships for positive associations, followed by stratification and effect modification analyses by gender and age. After adjusting for demographic characteristics, 3-4 of the four types of air pollution were significantly associated with an increased risk of iron deficiency, vitamin B12 deficiency and folate deficiency anemia, while there was no significant association with other defined types of anemia. After full adjustment, we estimated that the hazard ratios (HRs) of iron deficiency anemia associated with each 10 μg/m3 increase in NO2, PM2.5, and PM10 were 1.04 (95%CI: 1.02, 1.07), 2.00 (95%CI: 1.71, 2.33), and 1.10 (95%CI: 1.02, 1.20) respectively. The HRs of folate deficiency anemia with each 10 μg/m3 increase in NO2, PM2.5, PM2.5-10, and PM10 were 1.25 (95%CI: 1.12, 1.40), 4.61 (95%CI: 2.03, 10.47), 2.81 (95%CI: 1.11, 7.08), and 1.99 (95%CI: 1.25, 3.15) respectively. For vitamin B12 deficiency anemia, no significant association with atmospheric pollution was found. Additionally, we estimated almost linear exposure-response curves between air pollution and anemia, and interaction analyses suggested that gender and age did not modify the association between air pollution and anemia. Our research provided reliable evidence for the association between long-term exposure to PM10, PM2.5, PM2.5-10, NO2, and several types of anemia. NO2, PM2.5, and PM10 significantly increased the risk of iron deficiency anemia and folate deficiency anemia. Additionally, we found that the smaller the PM diameter, the higher the risk, and folate deficiency anemia was more susceptible to air pollution than iron deficiency anemia. No association was observed between the four types of air pollution and hemolytic anemia, aplastic anemia, and other types of anemia. Although the mechanisms are not well understood, we emphasize the need to limit the levels of PM and NO2 in the environment to reduce the potential impact of air pollution on folate and iron deficiency anemia.

Insights into civil aviation emissions in China: Analysis of an emission inventory of air pollutants and the ChinaHighAirPollutants (CHAP) dataset

The emissions from the civil aviation sector are a significant source of CO2 and air pollutants, which represent a serious threat to ambient air quality and public health. To gain a deeper understanding of civil aviation airport emissions, it is imperative to develop a precise and comprehensive emission inventory of China's civil aviation airports. However, there are limited studies dedicated to analyzing and verifying the accuracy and completeness of China's civil aviation emission inventory. Here, this study explored pollution characteristics from temporal trends and spatial distribution perspectives based on a previously developed 2019–2020 high-resolution air pollution and CO2 emission inventory of the landing and take-off (LTO) cycle of civil aviation airports in China and the ChinaHighAirPollutants (CHAP) dataset. Besides, this study established an empirical model to evaluate the relationship between the air pollutant emissions of China's civil aviation sector in 2019–2020 and the pollutant concentration from the CHAP dataset. Compared to those in 2019, the total NOx, CO, PM, and SO2 emissions during the LTO phase in China's civil aviation sector in 2020 decreased by 14.29%–24.32%, and the average concentrations of NO2, CO, PM10, and SO2 in 2020 decreased by 6.33%–9.45%. The eastern, central, and southern regions of China are characterized by high emissions of pollutants, a phenomenon closely related to the economic prosperity and tourism development in these areas. They tend to boast higher route densities, increasing air transport activity and consequently resulting in elevated emissions. In addition, NOx had the highest correlation coefficient in the empirical model, with a correlation coefficient of 0.603 in 2019. Our findings provide new insights into civil aviation emissions in China from the analysis of the emission inventory of air pollutants and the CHAP dataset and provide a new method for verifying the accuracy and completeness of China's civil aviation emission inventory.

Ambient fine particulate matter is associated with daily outpatient visits for ankylosing spondylitis: A time-series analysis in Beijing, China

Exposure to ambient fine particulate matter (PM2.5) has a great impact on human body's immune system, but the correlation between PM2.5 and ankylosing spondylitis has not yet been clarified. We extracted 58,600 outpatient visits for ankylosing spondylitis from the Beijing Medical Claim Data for Employees database from 2010 to 2017. The percentage of outpatient visits following PM2.5 concentrations was estimated using generalized additive models with Poisson connections. Increase by 10 μ g/m3, PM2.5 is associated with daily outpatient visits for ankylosing spondylitis. In this test, the average concentration of PM2.5 was 86.8 ± 74.3 μ g/m3. For every 10 μg/m3 increase in PM2.5 concentration, there was a 0.34% (95% CI, 0.26–0.42%) increase in the risk of patients who visited the doctor on the same day. Females and younger patients were most susceptible to the impact of PM2.5 exposure (P＜0.05). This study revealed the relationship between exposure to PM2.5 and ankylosing spondylitis, and future research can further confirm this finding and explore the potential mechanisms.

Effects of long-term exposure to outdoor air pollution on COVID-19 incidence: A population-based cohort study accounting for SARS-CoV-2 exposure levels in the Netherlands

Several studies have linked air pollution to COVID-19 morbidity and severity. However, these studies do not account for exposure levels to SARS-CoV-2, nor for different sources of air pollution. We analyzed individual-level data for 8.3 million adults in the Netherlands to assess associations between long-term exposure to ambient air pollution and SARS-CoV-2 infection (i.e., positive test) and COVID-19 hospitalisation risks, accounting for spatiotemporal variation in SARS-CoV-2 exposure levels during the first two major epidemic waves (February 2020–February 2021). We estimated average annual concentrations of PM10, PM2.5 and NO2 at residential addresses, overall and by PM source (road traffic, industry, livestock, other agricultural sources, foreign sources, other Dutch sources), at 1 × 1 km resolution, and weekly SARS-CoV-2 exposure at municipal level. Using generalized additive models, we performed interval-censored survival analyses to assess associations between individuals’ average exposure to PM10, PM2.5 and NO2 in the three years before the pandemic (2017–2019) and COVID-19-outcomes, adjusting for SARS-CoV-2 exposure, individual and area-specific confounders. In single-pollutant models, per interquartile (IQR) increase in exposure, PM10 was associated with 7% increased infection risk and 16% increased hospitalisation risk, PM2.5 with 8% increased infection risk and 18% increased hospitalisation risk, and NO2 with 3% increased infection risk and 11% increased hospitalisation risk. Bi-pollutant models suggested that effects were mainly driven by PM. Associations for PM were confirmed when stratifying by urbanization degree, epidemic wave and testing policy. All emission sources of PM, except industry, showed adverse effects on both outcomes. Livestock showed the most detrimental effects per unit exposure, whereas road traffic affected severity (hospitalisation) more than infection risk. This study shows that long-term exposure to air pollution increases both SARS-CoV-2 infection and COVID-19 hospitalisation risks, even after controlling for SARS-CoV-2 exposure levels, and that PM may have differential effects on these COVID-19 outcomes depending on the emission source.

Source apportionment and health risk assessment of PM10 bound carbonaceous and elemental species in the central Himalayas

In this extensive investigation, the sources and health risk assessment of carbonaceous aerosols, as well as the elemental compositions (Ca, Al, Fe, S, K, Mg, Pd, B, Mo, Zn, Ag, Nb, Ga, Cl, Ti, Zr, Na, Cr, Cu, Mn, Cs, P, Y, Sb, Ni, Sn, F, Sr, Br, Pm, U, Pb, Th, Br, Rb, and V) of PM10, were conducted throughout January-December 2021 in Nainital, a central Himalayan region of India. The average annual mass concentration of PM10 was determined to be 64±6 µg m-3. Carbonaceous aerosols (CAs), comprising OC, EC, WSOC, and SOC, exhibited annual averages of 9.3±0.8 µg m-3, 4.9±0.5 µg m-3, 1.5±0.2 µg m-3, 2.7±0.2 µg m-3, and 2.97±0.45 µg m-3, respectively. The elemental composition featured major contributors such as Ca, Al, Fe, S, and K, alongside trace levels of various elements (Mg, Pd, B, Mo, Zn, Ag, Nb, Ga, Cl, Ti, Zr, Na, and Cr). Positive Matrix Factorization (PMF) identified six primary contributors to PM10, each with varying percentage contributions: crustal/soil/road dust, soil re-suspension, combustion, vehicular emissions, industrial emissions, and biomass burning. The health risk assessment revealed elevated Hazard Quotient (HQ) values for Cr and Mn in children, indicating a non-carcinogenic health risk. Adults exposed to high Cr levels may face potential carcinogenic risks, while elements like Al, Pb, Cu, Zn, and Ni pose no health hazards, aligning with USEPA guidelines.

Mapping nationwide concentrations of sulfate and nitrate in ambient PM2.5 in South Korea using machine learning with ground observation data

Particulate matter (PM) is a major air pollutant in Northeast Asia, with frequent high PM episodes. To investigate the nationwide spatial distribution maps of PM2.5 and secondary inorganic aerosols in South Korea, prediction models for mapping SO42− and NO3− concentrations in PM2.5 were developed using machine learning with ground-based observation data. Specifically, the random forest algorithm was used in this study to predict the SO42− and NO3− concentrations at 548 air quality monitoring stations located within the representative radii of eight intensive air quality monitoring stations. The average concentrations of PM2.5, SO42−, and NO3− across the entire nation were 17.2 ± 2.8, 3.0 ± 0.6, and 3.4 ± 1.2 μg/m3, respectively. The spatial distributions of SO42− and NO3− concentrations in 2021 revealed elevated concentrations in both the western and central regions of South Korea. This result suggests that SO42− concentrations were primarily influenced by industrial activities rather than vehicle emissions, whereas NO3− concentrations were more associated with vehicle emissions. During a high PM2.5 event (November 19–21, 2021), the concentration of SO42− was primarily influenced by SOX emissions from China, while the concentration of NO3− was affected by NOX emissions from both China and Korea. The methodology developed in this study can be used to explore the chemical characteristics of PM2.5 with high spatiotemporal resolution. It can also provide valuable insights for the nationwide mitigation of secondary PM2.5 pollution.

Decadal trends (2009–2018) in Saharan dust transport at Mt. Martano EMEP station, Italy

Saharan dust advections in the Mediterranean Basin are widely studied and subjected to specific regulations due to their impact on the air quality of European cities. As a natural phenomenon, their contribution to PM should be accurately quantified in order to correctly evaluate their impact in anthropized areas. The identification and quantification of Saharan dust advections should be carried out in rural regional background sites. However, there is a lack of suitable sites in Central and Southern Italy, despite its strategic positioning in the Central Mediterranean area. The rural regional background site of Monte Martano site can fill this gap. This study aims at characterising the 10 years climatology of dust at the Monte Martano (MM) site and integrate it in the wider Mediterranean Basin monitoring network. Monte Martano is barely affected by local emissions, with average PM10 concentration of 11.0 ± 7.4 μg m−3, and a marked seasonality compatible with the evolution of the planetary boundary layer (PBL) during the year. Saharan dust advections contribute for 45.9 days per year on average, with a dust load that accounts for 48% of the background PM10 value. The Saharan dust advections' frequency, duration and intensity were evaluated, and a clear seasonality was detected, with minimum probability in summer and long-lasting events mainly occurring in spring and autumn. The dust load calculation was performed with 3 different methods, the EU guideline method, the modified DIAPASON methodology specifically designed for Central Italy and the DREAM8b model. The comparison between the three methods showed a substantial overestimation of the dust load by the model, while the two experimental methods are in accordance (R = 0.97). In conclusion, the MM site has proven to be suitable for Saharan dust monitoring in Central Italy, providing a new insight in the Central Mediterranean Saharan dust observation network.

Indoor Air Quality and Bioaerosols in Spanish University Classrooms.

A comprehensive study assessed indoor air quality parameters, focusing on relevant air pollutants such as particulate matter (PM10 and PM2.5), gaseous compounds (CO, CO2, formaldehyde, NO2) and volatile/semi-volatile organic chemicals, as well as respiratory viruses (including SARS-CoV-2), fungi and bacteria in Spanish university classrooms. Non-target screening strategies evaluated the presence of organic pollutants inside and outside the classrooms. Saliva samples from teachers and students were collected to explore correlations between respiratory viruses in the air and biological samples. Indoor results revealed the punctual exceedance of recommended guidelines for CO2, formaldehyde (HCHO), volatile organic compounds (TVOCs) and PM in the least naturally ventilated classrooms. Significant differences occurred between the classes, with the least ventilated one showing higher average concentrations of CO2, HCHO, NO2, PM10 and PM2.5. A respiratory virus (rhinovirus/enterovirus) was detected in the medium naturally ventilated classroom, although saliva samples tested negative. Suspect screening tentatively identified 65 substances indoors and over 200 outdoors, with approximately half reporting a high toxicological risk based on the Cramer rules. The study provides a comprehensive overview of indoor air quality, respiratory viruses and organic pollutants in university classrooms, highlighting the variations and potential health risks associated with ventilation differences.

Simulation of Submicron Particulate Matter (PM1) Dispersion Due to Traffic Rerouting to Establish a Walkable Cultural Tourism Route in Ratchaburi’s Old Town, Thailand

Cultural tourism helps preserve cultural heritage and provides economic opportunities for local communities. A walkable cultural tourism route has been developed for the old town of Ratchaburi, Thailand. Here, we assessed changes in PM1 after cars were banned from the walkable tourist route. A near-roadway dispersion model, R-LINE, was evaluated and used to explore the base case (BC) and two scenarios, S1 and S2. In the BC, road traffic activities reflected the current situation; in S1, all vehicles were banned from the walkable route; and in S2, all drivers were encouraged to park their vehicles outside the study area. The road traffic activities in the study area were observed and used to calculate the PM1 emission rates for the model inputs. The model was capable of simulating PM1 concentration, especially the average PM1 concentration over the monitoring period. An increase in PM1 concentration was seen at the main road in S1 due to the increased traffic volume that had been redirected from the walkable route, with an increase in daily PM1 of 4.5% compared to BC. S2 showed a decrease in the PM1 concentration of 8.9%. These findings suggest the need for traffic mitigation measures prior to initiating a walkable route for cultural tourism, to meet environmental sustainability requirements.

Long-term PM1 exposure and hypertension hospitalization: A causal inference study on a large community-based cohort in South China

Limited evidence exists on the effect of submicronic particulate matter (PM1) on hypertension hospitalization. Evidence based on causal inference and large cohorts is even more scarce. In 2015, 36,271 participants were enrolled in South China and followed up through 2020. Each participant was assigned single-year, lag0–1, and lag0–2 moving average concentration of PM1 and fine inhalable particulate matter (PM2.5) simulated based on satellite data at a 1-km resolution. We used an inverse probability weighting approach to balance confounders and utilized a marginal structural Cox model to evaluate the underlying causal links between PM1 exposure and hypertension hospitalization, with PM2.5-hypertension association for comparison. Several sensitivity studies and the analyses of effect modification were also conducted. We found that a higher hospitalization risk from both overall (HR: 1.13, 95% CI: 1.05–1.22) and essential hypertension (HR: 1.15, 95% CI: 1.06–1.25) was linked to each 1 µg/m3 increase in the yearly average PM1 concentration. At lag0–1 and lag0–2, we observed a 17%–21% higher risk of hypertension associated with PM1. The effect of PM1 was 6%–11% higher compared with PM2.5. Linear concentration-exposure associations between PM1 exposure and hypertension were identified, without safety thresholds. Women and participants that engaged in physical exercise exhibited higher susceptibility, with 4%–22% greater risk than their counterparts. This large cohort study identified a detrimental relationship between chronic PM1 exposure and hypertension hospitalization, which was more pronounced compared with PM2.5 and among certain groups.