Effects of PM10 and Weather on Respiratory and Cardiovascular Diseases in the Ciuc Basin (Romanian Carpathians)

The association between ambient air pollution and migraine: a systematic review

Some studies have shown the effect of air pollution on migraine. However, it needs to be confirmed in larger-scale studies, as scientific evidence is scarce regarding the association between air pollution and migraine. Therefore, this systematic review aims to determine whether there are associations between outdoor air pollution and migraine. A literature search was performed in Scopus, Medline (via PubMed), EMBASE, and Web of Science. A manual search for resources and related references was also conducted to complete the search. All observational studies investigating the association between ambient air pollution and migraine, with inclusion criteria, were entered into the review. Fourteen out of 1417 identified articles met the inclusion criteria and entered the study. Among the gaseous air pollutants, there was a correlation between exposure to nitrogen dioxide (NO2) (78.3% of detrimental relationships) and carbon monoxide (CO) (68.0% of detrimental relationships) and migraine, but no apparent correlation has been found for sulfur dioxide (SO2) (21.2% of detrimental relationships) and ozone (O3) (55.2% of detrimental relationships). In the case of particulate air pollutants, particulate matter with a diameter of 10 μm or less (PM10) (76.0% of detrimental relationships) and particulate matter with a diameter of 2.5 μm or less (PM2.5) (61.3% of detrimental relationships) had relationships with migraine. In conclusion, exposure to NO2, CO, PM10, and PM2.5 is associated with migraine headaches, while no conclusive evidence was found to confirm the correlation between O3 and SO2 with migraine. Further studies with precise methodology are recommended in different cities around the world for all pollutants with an emphasis on O3 and SO2.

The role of large-scale atmospheric circulations on long-term variations of PM10 concentrations over Turkey

PM10 is widely identified as an important atmospheric pollutant posing a serious threat to human health and environment as well as it influences the climate system. To unearth the mechanism involved in its sources and circulation behavior in environment, this study focuses on the role of large-scale atmospheric circulation on the long-term variability of PM10 over Turkey by applying rotated empirical orthogonal functions (REOF) analysis. As a result of the implementation of REOF to the daily PM10 data for 80 air quality stations throughout the period 2010-2020, first REOF mode (REOF1 44.9% in winter, 43.2% in spring, 39.5% in summer and 31.6% in fall) for all the four seasons indicated the role of local emission sources on the variations of PM10, which show high PM10 values in different geographical regions. The results of the second mode (REOF2, 17.9% in winter, 14.0% in spring, 14.0% in summer and 16.3% in fall) indicate the role of large-scale atmospheric circulations on the values of PM10. From the REOF2 analysis and extracted synoptic composite maps, the strength of southerly winds and the presence of southwesterly winds at low levels are very important in transporting of dust pollutants from the Arabian Peninsula and Northern Africa, respectively, to the eastern (EAR) and southeastern (SEAR) regions of Turkey during winter. In spring, sand particles in the interior terrestrial part of the country are carried to the northern regions by the effect of large-scale southerly winds, which cause above-normal PM10 concentrations in the Black Sea region of Turkey. In summer, dust particles together with warm dry air intrusion to the eastern region of Turkey by strong easterly winds are sourced by Caspian Sea and result in high PM10 values. Our findings emphasize that the long-term variations in air quality over Turkey are affected secondary by the variations in the large-scale atmospheric circulations with primary contributions from the changes in local emission sources.

Examination of air pollutants and their risk for human health in urban and suburban environments for two Romanian cities: Brasov and Iasi

To detect the spatial differences of atmospheric pollutants in urban and suburban areas is important for observing their aspects on regional air quality, climate, and human health. This study is focused on the evolution of PM2.5, PM10, NOx and SO2, concentrations, and meteorological parameters from 2010 to 2022, at urban and suburban area in the two Romanian city: Brasov and Iasi. The daily patterns of most pollutants in urban and suburban areas, are strongly linked to land-traffic emissions. The seasonal differences were observation of the studied air pollutants displays visible decreasing in warm period and increased concentrations in cold period. Significant higher (25%- Brasov, 28%- Iasi) PM10 were found in urban area concentration probably caused by enhanced vehicular emissions over these areas induced by urban planning and mobility policies. The average relative risk caused by PM10 for all-cause mortality in the urban region was 1.021 (±0.004) in Brasov, and significantly higher in Iasi 1.030 (±0.005). In suburban regions this risk was lower with 33 % 1.014 (±0.006) in Brasov and 30 % 1.021 (±0.003) in Iasi. The main objective of this research was to identify the difference of air pollutants and meteorological parameters in the urban and suburban region of the studied city.

Performance evaluation of a recurrent deep neural network optimized by swarm intelligent techniques to model particulate matter

Atmospheric pollution refers to the presence of substances in the air such as particulate matter (PM) which has a negative impact in population ́s health exposed to it. This makes it a topic of current interest. Since the Metropolitan Zone of the Valley of Mexico's geographic characteristics do not allow proper ventilation and due to its population's density a significant quantity of poor air quality events are registered. This paper proposes a methodology to improve the forecasting of PM10 and PM2.5, in largely populated areas, using a recurrent long-term/short-term memory (LSTM) network optimized by the Ant Colony Optimization (ACO) algorithm. The experimental results show an improved performance in reducing the error by around 13.00% in RMSE and 14.82% in MAE using as reference the averaged results obtained by the LSTM deep neural network. Overall, the current study proposes a methodology to be studied in the future to improve different forecasting techniques in real-life applications where there is no need to respond in real time.Implications: This contribution presents a methodology to deal with the highly non-linear modeling of airborne particulate matter (both PM10 and PM2.5). Most linear approaches to this modeling problem are often not accurate enough when dealing with this type of data. In addition, most machine learning methods require extensive training or have problems when dealing with noise embedded in the time-series data. The proposed methodology deals with this data in three stages: preprocessing, modeling, and optimization. In the preprocessing stage, data is acquired and imputed any missing data. This ensures that the modeling process is robust even when there are errors in the acquired data and is invalid, or the data is missing. In the modeling stage, a recurrent deep neural network called LSTM (Long-Short Term Memory) is used, which shows that regardless of the monitoring station and the geographical characteristics of the site, the resulting model shows accurate and robust results. Furthermore, the optimization stage deals with enhancing the capability of the data modeling by using swarm intelligence algorithms (Ant Colony Optimization, in this case). The results presented in this study were compared with other works that presented traditional algorithms, such as multi-layer perceptron, traditional deep neural networks, and common spatiotemporal models, which show the feasibility of the methodology presented in this contribution. Lastly, the advantages of using this methodology are highlighted.

Outdoor particulate matter exposure and upper respiratory tract infections in children and adolescents: A systematic review and meta-analysis

BACKGROUND

While the relationship between outdoor particulate matter (PM) and lower respiratory tract infections in children and adolescents is accepted, we know little about the impacts of outdoor PM on the risk of developing or aggravating upper respiratory tract infections (URTIs).

METHODS

We aimed to review the literature examining the relationship between outdoor PM exposure and URTIs in children and adolescents. A systematic search of EMBASE, MEDLINE, PubMed, Scopus, CINAHL and Web of Science databases was undertaken on April 3, 2020 and October 27, 2021. Comparable short-term studies of time-series or case-crossover designs were pooled in meta-analyses using random-effects models, while the remainder of studies were combined in a narrative analysis. Quality, risk of bias and level of evidence for health effects were appraised using a combination of emerging frameworks in environmental health.

RESULTS

Out of 1366 articles identified, 34 were included in the systematic review and 16 of these were included in meta-analyses. Both PM_2.5 and PM₁₀ levels were associated with hospital presentations for URTIs (PM_2.5: RR = 1.010, 95%CI = 1.007-1.014; PM₁₀: RR = 1.016, 95%CI = 1.011-1.021) in the meta-analyses. Narrative analysis found unequivocally that total suspended particulates were associated with URTIs, but mixed results were found for PM_2.5 and PM₁₀ in both younger and older children.

CONCLUSION

This study found some evidence of associations between PM and URTIs in children and adolescents, the relationship strength increased with PM₁₀. However, the number of studies was limited and heterogeneity was considerable, thus there is a need for further studies, especially studies assessing long-term exposure and comparing sources.

Long-term exposure to PM10 above WHO guidelines exacerbates COVID-19 severity and mortality

BACKGROUND

Age, sex, race and comorbidities are insufficient to explain why some individuals remain asymptomatic after SARS-CoV-2 infection, while others die. In this sense, the increased risk caused by the long-term exposure to air pollution is being investigated to understand the high heterogeneity of the COVID-19 infection course.

OBJECTIVES

We aimed to assess the underlying effect of long-term exposure to NO2 and PM10 on the severity and mortality of COVID-19.

METHODS

A retrospective observational study was conducted with 2112 patients suffering COVID-19 infection. We built two sets of multivariate predictive models to assess the relationship between the long-term exposure to NO2 and PM10 and COVID-19 outcome. First, the probability of either death or severe COVID-19 outcome was predicted as a function of all the clinical variables together with the pollutants exposure by means of two regularized logistic regressions. Subsequently, two regularized linear regressions were constructed to predict the percentage of dead or severe patients. Finally, odds ratios and effects estimates were calculated.

RESULTS

We found that the long-term exposure to PM10 is a more important variable than some already stated comorbidities (i.e.: COPD/Asthma, diabetes, obesity) in the prediction of COVID-19 severity and mortality. PM10 showed the highest effects estimates (1.65, 95% CI 1.32-2.06) on COVID-19 severity. For mortality, the highest effect estimates corresponded to age (3.59, 95% CI 2.94-4.40), followed by PM10 (2.37, 95% CI 1.71-3.32). Finally, an increase of 1 µg/m3 in PM10 concentration causes an increase of 3.06% (95% CI 1.11%-4.25%) of patients suffering COVID-19 as a severe disease and an increase of 2.68% (95% CI 0.53%-5.58%) of deaths.

DISCUSSION

These results demonstrate that long-term PM10 burdens above WHO guidelines exacerbate COVID-19 health outcomes. Hence, WHO guidelines, the air quality standard established by the Directive 2008/50/EU, and that of the US-EPA should be updated accordingly to protect human health.

Long-term exposure to PM10 above WHO guidelines exacerbates COVID-19 severity and mortality

BACKGROUND

Age, sex, race and comorbidities are insufficient to explain why some individuals remain asymptomatic after SARS-CoV-2 infection, while others die. In this sense, the increased risk caused by the long-term exposure to air pollution is being investigated to understand the high heterogeneity of the COVID-19 infection course.

OBJECTIVES

We aimed to assess the underlying effect of long-term exposure to NO₂ and PM₁₀ on the severity and mortality of COVID-19.

METHODS

A retrospective observational study was conducted with 2112 patients suffering COVID-19 infection. We built two sets of multivariate predictive models to assess the relationship between the long-term exposure to NO₂ and PM₁₀ and COVID-19 outcome. First, the probability of either death or severe COVID-19 outcome was predicted as a function of all the clinical variables together with the pollutants exposure by means of two regularized logistic regressions. Subsequently, two regularized linear regressions were constructed to predict the percentage of dead or severe patients. Finally, odds ratios and effects estimates were calculated.

RESULTS

We found that the long-term exposure to PM₁₀ is a more important variable than some already stated comorbidities (i.e.: COPD/Asthma, diabetes, obesity) in the prediction of COVID-19 severity and mortality. PM₁₀ showed the highest effects estimates (1.65, 95% CI 1.32-2.06) on COVID-19 severity. For mortality, the highest effect estimates corresponded to age (3.59, 95% CI 2.94-4.40), followed by PM₁₀ (2.37, 95% CI 1.71-3.32). Finally, an increase of 1 µg/m³ in PM₁₀ concentration causes an increase of 3.06% (95% CI 1.11%-4.25%) of patients suffering COVID-19 as a severe disease and an increase of 2.68% (95% CI 0.53%-5.58%) of deaths.

DISCUSSION

These results demonstrate that long-term PM₁₀ burdens above WHO guidelines exacerbate COVID-19 health outcomes. Hence, WHO guidelines, the air quality standard established by the Directive 2008/50/EU, and that of the US-EPA should be updated accordingly to protect human health.

Ambient air pollutants, diabetes and risk of newly diagnosed drug-resistant tuberculosis

BACKGROUND

Drug-resistant tuberculosis (DR-TB), diabetes and exposure to air pollution are thought to be important threat to human health, but no studies have explored the effects of ambient air pollutants on DR-TB when adjusting diabetes status so far.

METHODS

We performed a study among 3759 newly diagnosed TB cases with drug-susceptibility testing results, diabetes status, and individual air pollution data in Shandong from 2015 to 2019. Generalized linear mixed models (GLMM) including three models (Model 1: without covariates, Model 2: adjusted by diabetes status only, Model 3: with all covariates) were applied.

RESULTS

Of 3759 TB patients enrolled, 716 (19.05%) were DR-TB, and 333 (8.86%) had diabetes. High exposure to O₃ was associated with an increased risk of RFP-resistance (Model 2 or 3: odds ratio (OR) = 1.008, 95% confidence intervals (CI): 1.002-1.014), ethambutol-resistance (Model 3: OR = 1.015, 95%CI: 1.004-1.027) and any rifampicin+streptomycin resistance (Model 1,2,3: OR = 1.01, 95%CI: 1.002-1.018) at 90 days. In contrast, NO₂ was associated with a reduced risk of DR-TB (Model 3: OR = 0.99, 95%CI: 0.981-0.999) and multidrug-resistant TB (MDR-TB) (Model 3: OR = 0.977, 95%CI: 0.96-0.994) at 360 days. Additionally, SO₂ (Model 1, 2, 3: OR = 0.987, 95%CI: 0.977-0.998) showed a protective effect on MDR-TB at 90 days. PM_2.5 (90 days, Model 2: OR = 0.991, 95%CI: 0.983-0.999), PM₁₀ (360 days, Model 2: OR = 0.992, 95%CI: 0.985-0.999) had protective effects on any RFP+SM resistance.

CONCLUSIONS

O₃ contributed to an elevated risk of TB resistance but PM_2.5, PM₁₀, SO₂, NO₂ showed an inverse effect. Air pollutants may affect the development of drug resistance among TB cases by adjusting the status of diabetes.