Checking complex networks indicators in search of singular episodes of the photochemical smog

A Breath of Trouble: Unraveling the Impact of Air Pollution on Atrial Fibrillation

Air pollution is a pervasive global challenge with profound implications for public health. This review explores the intricate relationship between air pollution and atrial fibrillation (AF), a prevalent cardiac arrhythmia associated with significant morbidity and mortality. Drawing on a comprehensive analysis of the existing literature, this review synthesizes current evidence linking various air pollutants, including particulate matter, nitrogen dioxide, ozone, and carbon monoxide, to the development and exacerbation of AF. The review delves into the role of air pollution as a global health issue alongside its specific sources, such as traffic-related emissions and industrial pollutants. It also examines the underlying mechanisms through which air pollution may contribute to the pathogenesis of AF, encompassing oxidative stress, inflammation, and autonomic nervous system dysregulation. In addition, it explores the impact of individual pollutants and the results of meta-analyses. It considers the results of vulnerable populations, including sex differences between the individuals and those with pre-existing cardiovascular conditions, who may be disproportionately affected. We also address critical research gaps in this area. Overall, air pollution has been increasingly recognized as a significant trigger for AF, with evidence linking exposure to particulate matter and gaseous pollutants to an increased incidence in short- as well as long-term exposure, highlighting the need for targeted public health interventions and further research to mitigate its cardiovascular impact.

Acceptable risk assessment and management of environmental pollution emergency events base on distance model

Existing environmental management regulations and assessment methods can help understand and relieve pollution problems greatly affecting the natural world. However, what is lacking is awareness and targeted recommendations for environmental pollution emergency events (EPEEs). Here we analyzed a total of 2164 EPEEs in China from 2000 to 2021 in terms of annual variations, spatial distributions, phases of regional development, and pollution sources. The findings showed that regional economies can have significant impacts on the occurrence of EPEEs. Regarding the four causes of pollution, the contribution from industrial sources was above 80 %, especially water pollution events, for which it accounted for 84 %. The probability of pollution events specifically relates to regional GDP and it was highest in those provinces with the highest GDP rankings, albeit there were marked differences in the industrial structure. In order to better manage EPEEs, an Environment-Emergency Distance Model (EEDM) is proposed. This model uses the Multidimensional psychological distance to quantitatively evaluate the acceptable public risk level after the occurrence of EPEEs. This method provides a basis for pollution prevention and remediation by visualizing the risk acceptable for the public and provides guidance for sustainable environmental development.

Environmental pollution in North-Eastern Italy and its influence on chronic obstructive pulmonary disease: time series modelling and analysis using visibility graphs

The impact on human health from environmental pollution is receiving increasing attention. In the case of respiratory diseases such as chronic obstructive pulmonary disease (COPD), the relationship is now well documented. However, few studies have been carried out in areas with low population density and low industrial production, such as the province of Belluno (North-Eastern Italy). The aim of the study was to analyze the effect of exposure to certain pollutants on the temporal dynamics of hospital admissions for COPD in the province of Belluno. Daily air pollution concentration, humidity, precipitations, and temperature were collected from the air monitoring stations in Belluno. Generalized additive mixed models (GAMM) and visibility graphs were used to determine the effects of the short-term exposure to environmental agents on hospital admissions associated to COPD. In the case of the city of Belluno, the GAMM showed that hospital admissions were associated with NO₂, PM₁₀, date, and temperature, while for the city of Feltre, GAMM produced no associated variables. Several visibility graph indices (average edge overlap and interlayer mutual information) showed a significant overlap between environmental agents and hospital admission for both cities. Our study has shown that visibility graphs can be useful in establishing associations between environmental agents and COPD hospitalization in sparsely populated areas.

The Potential Impact of Smog Spell on Humans' Health Amid COVID-19 Rages

Rapid and unchecked industrialization and the combustion of fossil fuels have engendered a state of fear in urban settlements. Smog is a visible form of air pollution that arises due to the over-emissions of some primary pollutants like volatile organic compounds (VOCs), hydrocarbons, SO₂, NO, and NO₂ which further react in the atmosphere and give rise to toxic and carcinogenic secondary smog components. Smog reduces the visibility on roads and results in road accidents and cancellation of flights. Uptake of primary and secondary pollutants of smog is responsible for several deleterious diseases of which respiratory disorders, cardiovascular dysfunction, neurological disorders, and cancer are discussed here. Children and pregnant women are more prone to the hazards of smog. The worsening menace of smog on one hand and occurrence of pandemic i.e., COVID-19 on the other may increase the mortality rate. But the implementation of lockdown during pandemics has favored the atmosphere in some ways, which will be highlighted in the article. On the whole, the focus of this article will be on the dubious relationship between smog and coronavirus.

Impact of weather and emission changes on NO2 concentrations in China during 2014-2019

Nitrogen dioxide (NO₂) is one of the most important air pollutants that highly affect the formation of secondary fine particles and tropospheric ozone. In this study based on hourly NO₂ observations from June 2014 to May 2019 and a regional air quality model (WRF-CMAQ), we comprehensively analyzed the spatiotemporal variations of NO₂ concentrations throughout China and in 12 urban agglomerations (UAs) and quantitatively showed the anthropogenic and meteorological factors controlling the interannual variations (IAVs). The ground observations and tropospheric columns show that high NO₂ concentrations are predominantly concentrated in UAs such as Beijing-Tianjin-Hebei (BTH), the Shandong Peninsula (SP), the Central Plain (CP), Central Shaanxi (CS), and the Yangtze River Delta (YRD). For different UAs, the NO₂ IAVs are different. The NO₂ increased first and then decreased in 2016 or 2017 in BTH, YRD, CS, and Cheng-Yu, and decreased from 2014 to 2019 in Harbin-Changchun, CP, SP, Northern Slope of Tianshan Mountain, and Beibu-Gulf, while increased slightly in the Pearl River Delta (PRD) and Hohhot-Baotou-Erdos-Yulin (HBEY). The NO₂ IAVs were primarily dominated by emission changes. The net wintertime decreases of NO₂ in BTH, Yangtze River Middle-Reach, and PRD were mostly contributed by emission reductions from 2014 to 2018, and the significant increase in the wintertime in HBEY was also dominated by emission changes (93%). Weather conditions also have an important effect on the NO₂ IAVS. In BTH and HBEY, the increases of NO₂ in winter of 2016 are mainly attributed to the unfavorable weather conditions and for the significant decreases in the winter of 2017, the favorable weather conditions also play a very important role. This study provides a basic understanding on the current situation of NO₂ pollution and are helpful for policymakers as well as those interested in the study of tropospheric ozone changes in China and downwind areas.

The city-level precision industrial emission reduction management based on enterprise performance evaluation and path design: A case of Changzhi, China

Realizing precision management, which is of great importance in city-level emission reduction management, requires scientific identification of key enterprises and differentiated emission reduction measures. However, current studies have not considered the enterprises units, or have not proposed the emission reduction paths of them. To solve this problem, this study chooses Changzhi, an industrialized city in China as a case, and considers 54 enterprises from thermal-power, cement, coking, and iron and steel sectors. The pollution performances, including the indicators of energy intensity and emission intensities of SO₂, NO_x, and PM of the 54 enterprises are evaluated. After identifying the key enterprises, this study designs their emission reduction paths including three types of measures, and quantifies the emission reduction potential. The results show that: (1) The 54 enterprises have imbalanced pollution performances, as the values have difference of 2-4 orders of magnitude. 13, 10, and 19 enterprises are classified into level A, B, and C respectively. (2) The emission reduction paths of 24 key enterprises are designed, which can reduce 3441.21, 4507.85, and 1683.12 tons of SO₂, NO_x, and PM. This accounts for 29.4%, 21.2%, and 14.9% of the total emissions. Based on these results, this study puts forward some policy suggestions of precision management measures in Changzhi. In sum, this study provides a methodology into quantitative analysis of precision air pollutant emission reduction management at city level, and put forward some critical insights of cleaner and sustainable production of the enterprises.

Improving graph-based detection of singular events for photochemical smog agents

Recently, a set of graph-based tools have been introduced for the identification of singular events of O₃, NO₂ and temperature time series, as well as description of their dynamics. These are based on the use of the Visibility Graphs (VG). In this work, an improvement of the original approach is proposed, being called Upside-Down Visibility Graph (UDVG). It adds the possibility of investigating the singular lowest episodes, instead of the highest. Results confirm the applicability of the new method for describing the multifractal nature of the underlying O₃, NO₂, and temperature. Asymmetries in the NO₂ degree distribution are observed, possibly due to the interaction with different chemicals. Furthermore, a comparison of VG and UDVG has been performed and the outcomes show that they describe opposite subsets of the time series (low and high values) as expected. The combination of the results from the two networks is proposed and evaluated, with the aim of obtaining all the information at once. It turns out to be a more complete tool for singularity detection in photochemical time series, which could be a valuable asset for future research.