PM10 Levels Research Articles

Long-term exposure to reduced specific-size ambient particulate matter and progression of arterial stiffness among Chinese adults

To assess the associations of ambient specific-size PM with brachial-ankle pulse wave velocity (baPWV) and the progression of arterial stiffness. Participants were included from the Kailuan study, the cross-sectional study involved 36,486 participants, while the longitudinal study enrolled 16,871 participants. PM exposures was assessed through satellite-based random forest approaches at a 1 km resolution. Initial observations indicated a link between baseline baPWV and heightened levels of PM1, PM2.5, and PM10 exposure, and greater effects were observed for PM1 (β: 22.52, 95% CI: 18.14–26.89), followed by PM2.5 (β: 9.76, 95% CI: 7.52–12.00), and PM10 (β: 8.88, 95% CI: 7.32–10.45). Furthermore, the growth rate of baPWV was higher in participants exposed to high levels of PM1 exposure (β: 2.77, 95% CI: 1.19–4.35), succeeded by PM2.5 and PM10. Throughout a median follow-up period of 4.04 years, arterial stiffness was diagnosed in 1709 subjects. Long-term exposure to PM was linked with an increased risk of incident arterial stiffness, estimated HR for fixed 10 µg/m3 increments in annual average PM1 was 2.20 (95% CI: 2.01–2.42), PM2.5 was 1.48 (95% CI: 1.41–1.55), and PM10 1.32 (95% CI: 1.27–1.36). PM had a greater impact on men and older individuals (P for interaction <0.001). Long-term exposures to ambient PM1, PM2.5, and PM10 were positively associated with baPWV and an increased risk of arterial stiffness. Higher estimated effects were observed for PM1 than PM2.5 and PM10.

Assessing the effects of air pollution and residential greenness on frailty in older adults: a prospective cohort study from China.

Many studies have established a correlation between air pollution and green space with age-related diseases, yet the relationship between air pollution, green space, and frailty among older adults is not fully understood. The primary objective of this investigation is to examine the longitudinal association among air pollution, green space, and frailty in older adults, as well as the potential interaction and mediating effect. Analyzed data were obtained from the multi-wave CLHLS investigation (2008-2018). The participants' environmental exposure was evaluated using six air pollutants (PM1, PM2.5, PM10, PM10-2.5, O3, and NO2), and normalized difference vegetation index (NDVI). Annual ambient air pollutants were estimated using satellite-based spatiotemporal models. Time-varying Cox proportional risk models were employed to investigate the longitudinal relationships between air pollutants, greenness, and the onset of frailty in the elderly population. We conducted a variety of subgroup analyses, sensitivity analyses, and assessed potential interaction and causal mediating effects. A total of 6953 eligible elderly individuals were enrolled in our study. In the fully adjusted model, per IQR uptick in levels of PM1, PM2.5, PM10, PM10-2.5, O3, and NO2 corresponded to a 17% (95% CI 1.10-1.24), 25% (95% CI 1.17-1.34), 29% (95% CI 1.20-1.39), 35% (95% CI 1.24-1.47), 12% (95% CI 1.04-1.20), and 11% (95% CI 1.05-1.18) increase in frailty risk, respectively. For NDVI, increased IQR was significantly negatively associated with the risk of frailty (aHR 0.82, 95% CI 0.77-0.87). Our results revealed a significant interaction effect among O3, NO2, and residential greenness. PM1, PM2.5, PM10, and PM10-2.5 play a mediating role in the estimated relationship between residential greenness and frailty. In summary, our study reveals that PM1, PM2.5, PM10, PM10-2.5, O3, and NO2 correspond to elevated risks of frailty in the elderly. Residential greenness is associated with a lower risk of frailty in the elderly. Residential greenness can exert a positive impact on frailty by reducing particulate matter concentrations.

Performance of machine learning models to forecast PM10 levels

Machine learning techniques have garnered considerable attention in modern technologies due to their promising outcomes across various domains. This paper presents the comprehensive methodology of an optimized and efficient forecasting approach for Particulate Matter 10, specifically tailored to predefined locations. The execution of a comparative analysis involving eight models enables the identification of the most suitable model that aligns with the primary research objective. Notably, the test results underscore the superior performance of an ensemble model, which integrates state-of-the-art methodologies, surpassing the performance of the other seven state-of-the-art models. Adopting a case-specific methodology with machine learning techniques contributes to achieving a notably high regression coefficient (R²≈1) across all models. Furthermore, the study underscores the potential for future endeavors in predicting location-specific environmental factors.•This study focused on forecasting PM10 with machine learning models with the consideration of air quality factors and meteorological factors•Ensemble model was developed for the forecasting purposes with higher performance.

The short-term effect of ambient particulate matter on suicide death

BackgroundAir pollution can cause various respiratory and neurological diseases and continuous exposure can lead to death. Previous studies have reported that particulate matter (PM) exposure increases the risk of depression, suicidal thoughts, and suicidal death; however, the results are inconsistent and limited. This study aimed to examine the relationship between short-term PM exposure and suicide deaths, as well as investigate the short-term effects of PM on suicide death within vulnerable groups based on factors such as sex, age group, suicide-related information (note, method, and cause), psychiatric disorders, and physical diseases.MethodsData on a total of 28,670 suicide deaths from 2013 to 2017, provided by the Korea Foundation for Suicide Prevention, were analyzed. The study design employed a time-series analysis with a two-stage approach. In the first step, a generalized additive model combined with a distributed lag nonlinear model was used to estimate the short-term effect of PM exposure on suicide risk specific to each city. In the second step, the estimated results from each city were pooled through a meta-analysis to derive the overall effect. We determined the effects of single lag, cumulative lag, and moving average PM concentrations from days 0–7 before suicide.ResultsWe confirmed an association between exposure to PM10 (≤ 10 μm in diameter) and deaths due to suicide. In particular, among individuals with psychiatric disorders and those who employed non-violent suicide methods, increased exposure to PM10 was associated with a higher risk of death by suicide, with percentage changes of 5.92 (95% confidence interval [CI]: 3.95–7.92) and 11.47 (95% CI: 7.95–15.11), respectively. Furthermore, in the group with psychiatric disorders, there was an observed tendency of increasing suicide risk as PM10 levels increased up to 120 µg/m3, whereas in the group with non-violent suicide deaths, there was a pronounced trend of rapid increase in suicide risk with an increase in PM10 up to 100 µg/m3.ConclusionsThese results show an association between short-term exposure to PM and suicide. Our study adds evidence for the benefits of reducing PM in preventing diseases and improving mental health.

The effects of ambient particulate matter air pollution on platelets and hemostasis.

Elevated ambient pollution exposure is potentially linked to thromboembolism. However, the mechanisms by which particulate matter (PM) interferes with the balance of hemostatic system remain unclear. This study investigates PM-mediated hemostatic changes in individuals across unique seasonal variations of ambient pollution. This prospective study was conducted between February and July 2020 during alterations in ambient pollution in Chiang Mai, Thailand. Blood tests from 30 healthy subjects were assessed at four-week intervals, four times in total. Various coagulation tests, including prothrombin time (PT), activated partial thromboplastin time (aPTT), von Willebrand factor (vWF), platelet count, and platelet functions, were evaluated. A mixed-effects model was used to analyze the impact of high PM2.5 and PM10 on hemostatic parameters. Thirty male subjects with mean age of 38.9 ± 8.2 years, were included. High levels of PM2.5 and PM10 were significantly associated with PT shortening, with no such effect observed in aPTT. PM2.5 and PM10 values also positively correlated with vWF function, while vWF antigen levels remained unchanged. Soluble P-selectin showed a strong positive association with PM2.5 and PM10 levels. Platelet function analysis revealed no correlation with PM values. Short-term exposure to elevated PM2.5 and PM10 concentrations was linked to shortened PT and enhanced vWF function in healthy individuals. Exploring the impact of these changes on clinically relevant thrombosis is crucial. Additional studies on the pathogenesis of pollution-related thrombosis are warranted for maintaining good health.

Impact of air pollution on cardiorespiratory morbidities in Southern Thailand

Background/Objectives: Air pollution seriously threatens human health; even low-level exposure can have negative consequences. The study aimed to explore the influence of air pollution on cardiorespiratory diseases, adjusting for climatic conditions. MethodsPoisson regression using a generalized additive model (GAMs) and a distributed lag non-linear model (DLNM) were used to explore the relationships between air pollution and cardiorespiratory illnesses. ResultsAsthma, chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD), and lung cancer were reported at 88 per 10,000 population. Annual PM10 and PM2.5 levels were higher than WHO guidelines. PM10 and PM2.5 appeared to be on an upward trend, while NO2 and SO2 appeared to be on a downward trend. The 10 % increase in PM10 was significantly associated with an increase in inpatient department (IPD) admissions for asthma at a lag of 12–13 days and lung cancer at a lag of 14–15 days, whereas PM2.5 was associated with an increase in IPD admissions for asthma at a lag of 10–14 days and 13–15 days, respectively. A 10 % rise in PM10 was associated with an increase in COPD inpatient admissions at lag 2–6 days, while PM2.5 was associated with an increase in cardiovascular inpatient admissions at lag 4–5 days. A 10 % increase in NO2 increased admissions to COPD at all 15 lags, whereas a 10 % increase in CO increased admissions to lung cancer at lags 9–15 days. ConclusionsThe rise in PM2.5 and PM10 levels in this area leads to increased exposure to PM pollutants, hence elevating the likelihood of developing cardiorespiratory diseases.

Impact of Lockdown on Air Quality in the Most Polluted Cities of India.

COVID-19 has become a global pandemic, prompting lockdowns in practically every country. To prevent the spread of the disease, India has enforced a rigorous nationwide lockdown that commenced in March 2020. The lockdown imposed amid the pandemic ensured that most commercial activities and vehicle transportation ceased, resulting in a significant reduction in air pollution levels. The value of air pollutants PM10, PM2.5, NO2, and SO2 from January to May 2020 was obtained from the Indian Central Pollution Control Board. Before lockdown and during lockdown, relative fluctuations in ambient concentrations of four air contaminants were investigated. The Box-Jenkins approach was used to estimate future air pollution data points using time series data analysis. The PM10 level reduced by 61%, 30%, 68%, 37%, and 43% in the selected cities, respectively. Comparison of other pollutant concentrations before and after the lockdown also found a reduction in ambient pollutant concentrations, resulting in improved air quality. Inference of predicted model values to observed values revealed a significant increase in the concentrations of all pollutants. The percentage increases in AQImean from predicted to observed values were 206% in Ghaziabad, 148% in Delhi, 59% in Hyderabad, and 160% in Cochin. The strict lockdown has resulted in a significant drop in air pollutant levels. Upgrading present technologies could help keep pollution to a minimum of 37% under control. The findings would prompt the government to consider how to strictly reduce vehicle and industrial pollution to improve air quality and maintain improved public health.

Particulate matter air pollution in the Republic of Slovenia and its national spatial emissions release

In this work, an impact of particulate matter (PM) on air pollution and its emissions released is investigated. Concentration levels of PM10 and PM2.5 in the Republic of Slovenia (RS) were analyzed on daily, weekly, monthly, quartile, and annual data and then compared. The study was conducted from January 1, 2020 to December 31, 2020 at two monitoring stations, so-called urban traffic and urban industrial. Obtained results showed that the highest concentrations of PM in air were during the cold months of the year when the temperatures are lower (&lt; 6%). Results from heating seasons (from October 1, 2019 and March 31, 2020 and from October 1, 2020 to March 31, 2021) showed the highest concentration levels of PM10 between 9:00 and 14:00 and between 18:00 and 22:00, with highest concentrations recorded during the winter months (January and February). Additionally, emission building inventory was built based on PM10 and PM2.5 emissions released. The study includes yearly spatial building emissions release model considering currently installed high PM emitted stoves. The building model for the Republic of Slovenia has been developed by using 500 x 500 m model grid. Obtained results showed that changing currently low efficient appliance with advanced appliance would result in significant lower PM emissions released from building sector.

Spatio-temporal air quality assessment in Tehran, Iran, during the COVID-19 lockdown periods

Based on ground-based and satellite-based data, spatio-temporal analyses of air quality in Tehran were carried out during the lockdown periods (February-April) in 2020 and 2021. We evaluated the differences in temporal emissions of six air pollutants (NO2, CO, SO2, O3, PM2.5, and PM10) at various time scales, including diurnal, monthly, and relative changes. The results of ground-based measurements indicated that for all pollutants except O3, the magnitude decreased in 2020 (11-42%) compared to the baseline period (2015-2021). As a result of eased restrictions and unfavorable meteorological conditions, the reduction in air pollutants was lower in 2021 (5-32%), and PM2.5 and PM10 levels increased (3.75 and 11.22%). Satellite-based concentrations (NO2, CO, SO2, and AOD) varied from −8 to 54% in 2020 and from −41 to 60% in 2021 compared to 2019 as the pre-lockdown year. Concerning AOD, the trend is consistent with dust events during March and April in our region.

The influence of meteorological parameters on PM2.5 and PM10 values in Ümraniye and Silivri districts of İstanbul

In this study, spatiotemporal relationship between PM2.5 and PM10 concentrations and meteorological parameters were investigated for Silivri and Umraniye districts in Istanbul for 2014-2020. For this purpose, hourly PM2.5 and PM10 concentrations values of two air quality monitoring stations and meteorological data (wind speed, wind direction, relative humidity, total precipitation, minimum and maximum temperature) were examined. In all seasons, while PM concentrations were lowest at 06:00 local time (LT), PM2.5 and PM10 have peak values around 09:00 and 19:00 LT both in Silivri and Umraniye mainly due to anthropogenic activities such as vehicle exhaust emissions. In daily perspective, highest PM values were observed on Sundays in winter at Silivri. On the other side, peak PM values are shown on Fridays at Umraniye. It was found that local emission sources during low wind speeds cause the highest PM2.5 concentrations during winter months and southerly winds exceeding 8 m/s increase the PM10 levels at Silivri and Umraniye. The statistical analysis showed that PM2.5 and PM10 concentrations at Silivri were negatively correlated with wind speed with correlation coefficients of -0.56 (winter), -0.47 (autumn), respectively. Wind speed is negatively associated with PM2.5 (r=-0.48) and PM10 (r=-0.38) in winter season at Umraniye. In addition to this, relative humidity showed negative relationship with PM10 (r=-0.43) in spring at Silivri, while a positive correlation was found between PM10 (r=0.40) and PM2.5 (r=0.38) measured in the summer season and the maximum temperature. In addition to the anthropogenic factors (e.g. urbanization, transportation, and industrialization) that decrease air quality of Istanbul, local meteorological variables and atmospheric transport of pollution are observed to be the other factors that contribute to air pollution.

Assessment of seasonal variability of PM, BC and UFP levels at a highway toll stations and their associated health risks

As a part of their occupation, workers at toll stations are exposed to traffic emissions during the working shift, which sometimes stretches to 12 h. To assess the exposure and subsequent health risk of these workers, a study was performed on a highway toll station in India. PM1, PM2.5, PM10, BC and UFP concentration were determined inside a toll collectors’ cabin and outside in a free-flowing traffic section (125 m from the toll cabin). The concentrations varied in the following range: PM1 (40.69–226.13 μg m−3), PM2.5 (49.71–247.36 μg m−3), PM10 (83.15–458.14 μg m−3) and BC (2.1–87.5 μg m−3) and UFP: 101–53705 pt cm−3. The mean concentration inside the cabin was 1.34 (PM1), 1.35 (PM2.5), 1.16 (PM10) and 2.91 (BC) times the concentration outside for the summer season. The corresponding levels in the winter season were 1.14 (PM1), 1.11 (PM2.5), 1.11 (PM10), 2.50 (BC) and 1.82 (UFP). In addition to the exhaust emission, the non-exhaust emissions such as resuspension of crustal particles, fly ash and bioaerosols were identified. Using the Multiple Path Particle Dosimetry model for two groups - adults (18–21 years) and adults (21+ years), it was estimated that the pulmonary deposition of in-cabin workers were 50% (PM2.5) −75% (PM1) higher than the workers outside the cabin. Particle mass deposition was found to be higher for adults (21+ years) than adults (18–21 years) for both the seasons. The study quantitatively assessed the health risk faced by the workers in terms of exposure concentration and deposition in respiratory tract. More such studies at different traffic mix and climate can provide better estimates of health risk of toll workers that can be used to devise appropriate strategies for control of it.

Potential Impacts of Green Infrastructure on NOx and PM10 in Different Local Climate Zones of Brindisi, Italy

This study delves into Green Infrastructure (GI) planning in Brindisi, Italy, evaluating its influence on urban air quality and thermal comfort. Employing an LCZ-centered Geographic Information System (GIS)-based classification protocol, the prevalence of LCZ 6 (Open low-rise) and LCZ 2 (Compact mid-rise) is highlighted. Despite generally low PM10 levels in Brindisi, intermittent NOx spikes surpassing WHO and EU standards pose health risks. Within LCZ 2, diverse GI interventions (green walls, hedges, trees) were tested, with green walls emerging as the most effective, albeit falling short of expectations, while trees exhibited adverse air quality impacts. LCZ 6 demonstrated enhanced air quality attributed to wind patterns, GI, and urban canyon improvements. Thermal comfort analysis consistently revealed positive outcomes across various GI types, reducing discomfort by a minimum of 10%. The study emphasized GI’s favorable comfort impact on sidewalks but cautioned against trees in street canyons with aspect ratios exceeding 0.7, heightening pollutant levels and implying increased exposure risks. Conversely, street canyons with lower aspect ratios displayed variable conditions influenced by prevailing regional wind patterns. In conclusion, the integrated assessment of LCZ and GI holds promise for informed urban planning, guiding decisions that prioritize healthier, more sustainable cities. This underscores the crucial need to balance GI strategies for optimal urban development, aligning with the overarching goal of promoting urban well-being and sustainability.

Past and recent changes in the pollution characteristics of PM10 and SO2 in the largest industrial city in South Korea

PM10 and SO2 are major air pollutants in Ulsan, the largest industrial city in South Korea. The purpose of this study was to evaluate the effectiveness of pollution reduction policies by comparing the pollution characteristics of PM10 and SO2 between 2012 and 2020. The concentrations of PM10 and SO2 have substantially decreased over time. Local industry was the primary driver of SO2 levels, whereas various sources were associated with PM10 levels. In 2012 and 2020, the spatial distributions of SO2 were similar, with hot spots around petrochemical and nonferrous industrial complexes, whereas the sources of PM10 differed between the two years. Although industrial emissions of PM10 decreased from 2012 to 2020, the concentrations derived from an air dispersion model were slightly higher in 2020 due to changes in both wind patterns and the strength of the emission sources. In contrast, modeled SO2 levels were higher in 2012 than in 2020 due to greater emissions in 2012. Eastern and northeastern China were identified as potential source areas for the long-range atmospheric transport of both PM10 and SO2, though its influence was significantly lower in 2020. A comparison of the PM10 and SO2 concentrations predicted by a random forest model and the measured concentrations indicates that government policies have been effective in reducing the emissions and ambient air concentrations of both pollutants. In conclusion, this study demonstrates that local industry remains a significant contributor to air pollution in Ulsan despite the effects of governmental policy. Furthermore, as the influence of external sources weakens, there is a pressing need to strengthen the management of local sources to effectively mitigate air pollution in Ulsan.

The Relationships between Air Pollution, Warming, and Health in Tehran Metropolis, Iran, during 2015–2019

Introduction: Since global warming and air pollution was caused by human activities have increased dramatically in recent decades, studies have been conducted to determine how environmental parameters and air pollutants interact and, subsequently, how these pollutants affect ecosystems and human health. Accordingly, this study aimed to investigate the relationship between temperature and air pollutants in Tehran, Iran, in order to determine whether warming is associated with an increase in air pollutants.&#x0D; Materials and Methods: During 2015-2019, Iran Air Quality Monitoring Station (AQMS) and the Landsat 8 satellite were employed to retrieve data on pollutants such as PM, CO, O3, NO2, SO2, and air quality index (AQI), as well as climate-related metrics including ambient temperature and land surface temperature (LST). Pearson correlation coefficient and linear regression model were used to analyze the data.&#x0D; Results: A positive correlation was found between temperature variables and PM10 (ρ = 0.29, p = 0.001) and O3 (ρ= 0.55, p = 0.001) pollutants. PM10 and O3 levels were evaluated by 0.46 (95% Confidence Interval (CI) = (0.25, 0.67), p = 0.001) and 1.13 (95% CI = 0.89, 1.37), p = 0.001) units for each one-unit rise in temperature.&#x0D; Conclusion: Given the fact that temperature predominantly evidenced a significant synergistic association with PM10 and O3, it was determined that there was no substantial positive association between all pollutants and warming.

Association between ambient particulate matters and anhedonia among patients with depression

Recent studies have linked ambient air pollution to depression. Anhedonia is a core symptom of depression which severely impacts on prognosis. The present study aims to investigate the association of PM2.5 and PM10 exposure with anhedonia in depressed patients. A total of 538 patients with depression who were hospitalized at the Fourth People’s Hospital of Hefei between June 2017 and December 2021 were included. We estimated ambient particulate matters exposure, including PM2.5 and PM10, using a satellite-based spatiotemporal model at a resolution of 1 km2. The revised Physical Anhedonia Scale (RPAS) and the revised Social Anhedonia Scale (RSAS) were evaluated. The association of ambient particulate matters and anhedonia was examined using multiple linear regression models, adjusted for potential confounders. We observed that exposure to PM2.5 were significantly associated with increased RSAS score and RPAS score, with the major effect in the 12-month exposure window (β = 1.238; 95%CI, 0.353, 2.123) and 18-month exposure window (β = 1.888; 95%CI, 0.699, 3.078), respectively. Meanwhile, PM10 levels were also significantly associated with increased RSAS score and RPAS score, with the major effect in the 18-month exposure window (β = 1.220; 95%CI, 0.439, 2) and 3-month exposure window (β = 1.602; 95%CI, 0.062, 3.143), respectively. Subgroup analysis showed that both PM2.5 and PM10 were significantly associated with anhedonia in females, patients < 40 years old, low family income group, and those who had a higher educational level. Our study suggests that long-term PM2.5 and PM10 exposure are associated with more severe anhedonia in patients with depression. These associations were different in subgroup by age, gender, family income, and educational level.

Impacts of Building Environment and Urban Green Space Features on Urban Air Quality: Focusing on Interaction Effects and Nonlinearity

Air pollution is a rising environmental concern that has detrimental effects on human health and the environment. Building environment and urban green space features play a crucial role in the dispersion and accumulation of air pollutants. This study examines the impacts of building environment and urban green space on air pollution levels in the highly urbanized city of Hong Kong, focusing on their interaction effects and potential nonlinearity. For the analysis, this paper investigates how building density, building height, building types, urban green space size, and number of urban green space clusters, as well as their interplays, impact PM2.5 concentrations using high-resolution, satellite-based PM2.5 grids coupled with spatial analysis techniques. The findings reveal that a unit increase in the size of urban green space and the standard deviation of building height contribute to a 0.0004 and a 0.0154 reduction in PM levels, respectively. In contrast, air pollution levels are found to be positively associated with building density (0.1117), scatteredness of urban green space (0.0003), and share of commercial buildings (1.0158). Moreover, it has been found that building height presents a U-shape relationship with PM2.5 concentrations. Finally, the negative association between the size of urban green space and air pollution levels tends to be enlarged in districts with more low-rise buildings. This study conveys important building environment and urban green space planning implications.

Mediation of metabolic syndrome in the association between long-term exposure to particulate matter and incident cardiovascular disease: Evidence from a population-based cohort in Chengdu

BackgroundParticulate matter (PM) exposure has been linked with cardiovascular disease (CVD) and metabolic syndrome (MetS), the latter characterized by concurrent multiple metabolic disorders. As a result, the mechanisms assumption from PM to CVD through MetS have emerged, thus requiring further epidemiological evidence. This cohort study aimed to assess whether MetS mediates the associations of PM with CVD risk. MethodsThis study included 14,195 participants from the Chengdu cohort of the China Multi-Ethnic Cohort (CMEC) study in 2018. The primary outcome of incident CVD diagnoses was identified using matched hospital records from the Health Information Center of Sichuan Province. Residence-specific levels of PM with aerodynamic diameters of ≤ 1 µm (PM1), ≤ 2.5 µm (PM2.5), and ≤ 10 µm (PM10) were estimated by spatiotemporal models. Causal mediation analyses were applied to evaluate the indirect effect of MetS. ResultsIncreased exposure levels to PM were significantly associated with MetS and CVD. Mediation analyses indicated that the associations between PM exposure and CVD were mediated by MetS, with the proportion of multiple mediations being 19.3%, 12.1%, and 13.5% for PM1, PM2.5, and PM10, respectively. Further moderated mediation analyses suggested that male, overweight individuals, alcohol drinkers, and those suffering from indoor air pollution may experience more significant adverse effects from PM exposure on CVD via MetS than others. ConclusionsOur findings suggest that MetS partially mediates the association between long-term exposure to PM and CVD. These mediation effects appear to be amplified by demographic characteristics and unhealthy lifestyles.

Impact of the COVID-19 lockdown to a port-city area: A two-year comparative PMF analysis of PM10 of polluting sources

The objective of this study was to evaluate the effects that the COVID-19 lockdown had in the PM10 levels and sources in a city adjacent port where the transport of bulk materials is one of the main activities. For this, the 2020 PM10 chemistry and sources will be compared to an annual baseline period of 2017-18 in the same area.A total of 337 samples were chemically analyzed (30 chemical species) and a positive matrix factorization model was used to identify major PM10 sources. A Mann-Whitney test was used to find statistically relevant differences between periods.The model identified 8 factors two of them directly related to port activities (Shipping emissions and bulk materials), one associated to road traffic, three linked secondary sources (aged sea salt, secondary nitrate, ammonium sulfate) and two natural sources including fresh sea salt and long-range transport from Saharan intrusions.Road traffic and Shipping sources show a significant reduction in the lockdown period. However, after this period, the road traffic source quickly recovered showing no differences on a yearly basis with the reference year. Only the Shipping emissions source were greatly reduced in 2020 compared to 2017-18. On the other hand, the influence of natural sources kept PM10 from reducing drastically during the lockdown year (2020).

Forecasting PM10 levels in Sri Lanka: A comparative analysis of machine learning models PM10

Forecasting of particulate matter (PM10) which adversely impacts air quality is highly important in ever-urbanizing cities. The relationship between particulate matter and other air quality parameters and climatic parameters is frequently investigated due to their nonlinearity. Machine learning models have been extensively used in these nonlinear predictions and showcased their ability and robustness. However, among the tested machine learning models, a comparative analysis is essential in the localized context to understand the best model that can be used to forecast future scenarios. Therefore, this research investigates the applicability of eight state-of-the-art machine learning models (ANN, Bi-LSTM, Ensemble, XGBoost, CatBoost, LightGBM, LSTM, and GRU) in the prediction of particulate matter in two urbanized areas (Battaramulla and Kandy) Sri Lanka. Regression coefficient, Root Mean Squared Error, Mean Squared Error, Mean Absolute Error, Mean Absolute Relative Error, and Nash-Sutcliffe Efficiency were incorporated to assess the best-suited model for both cities. Results revealed that the Ensemble model has the capability of accurate and precise prediction of PM10 for both cities outperforming all other models (R2≈1). Therefore, the Ensemble model is recommended for future investigation of PM10 for Sri Lanka which has a growing concern due to high air pollution levels.

Air quality and characterization of synoptic circulation weather patterns in a South American city from Argentina

The potential cause-effect relationship between synoptic meteorological conditions and levels of criteria air pollutants, including CO, NO2, O3, PM10, PM2.5 and SO2, in Bahia Blanca, Argentina, was assessed for the period of 2018–2019. Daily back-trajectories and global meteorological data fields were employed to characterize the primary transport paths of air masses reaching the study site, and to identify the synoptic meteorological patterns responsible for these atmospheric circulations. Time series of surface-level meteorological parameters and midday mixing layer height were collected to examine the impact of the synoptic meteorological patterns on local meteorology. Furthermore, the NAAPS global aerosol model was utilized to identify days when contributions from long-range transport processes, such as dust and/or biomass burning smoke, impacted air quality. By applying this methodology, it was determined that the air masses coming from the N, NW and W regions significantly contributed to increased mean concentrations of coarse particles in this area through long-range transport events involving dust and smoke. Indeed, the high average levels of PM10 recorded in 2018–2019 (annual mean values of 47 and 52 μg/m3, respectively) represent the main air quality concern in Bahía Blanca. Moreover, PM10, PM2.5 and NO2 emissions should be reduced in order to meet recommended air quality guidelines. On the other hand, the results from this study suggest that the sources and meteorological processes leading to the increase in the concentrations of CO and SO2 have a local-regional origin, although these air pollutants did not reach high values probably as a consequence of the strong wind speed registered in this region during any synoptic meteorological pattern.