Atmospheric pollution can be defined as a set of changes that occur in the composition of the air, making it unsuitable and/or harmful and thereby generating adverse effects on human health. The regular practice of physical exercise (PE) is associated with the preservation and/or improvement of health; however, it can be influenced by neuroimmunoendocrine mechanisms and external factors such as air pollution, highlighting the need for studies involving the practice of PE in polluted environments. Herein, 24 male C57BL/6 mice were evaluated, distributed into four groups (exposed to a high concentration of pollutants/sedentary, exposed to a high concentration of pollutants/exercised, exposed to ambient air/sedentary, and exposed to ambient air/exercised). The exposure to pollutants occurred in the environmental particle concentrator (CPA) and the physical training was performed on a treadmill specially designed for use within the CPA. Pro- and anti-inflammatory markers in blood and bronchoalveolar lavage (BALF), BALF cellularity, and lung tissue were evaluated. Although the active group exposed to a high concentration of pollution showed a greater inflammatory response, both the correlation analysis and the ratio between pro- and anti-inflammatory cytokines demonstrated that the exercised group presented greater anti-inflammatory activity, suggesting a protective/adaptative effect of exercise when carried out in a polluted environment.

BACKGROUND: Plants can retain atmospheric particulate matter (PM) through their unique foliar microstructures, which has a profound impact on the phyllosphere microbial communities. Yet, the underlying mechanisms linking atmospheric particulate matter (PM) retention by foliar microstructures to variations in the phyllosphere microbial communities remain a mystery. In this study, we conducted a field experiment with ten Ulmus lines. A series of analytical techniques, including scanning electron microscopy, atomic force microscopy, and high-throughput amplicon sequencing, were applied to examine the relationship between foliar surface microstructures, PM retention, and phyllosphere microbial diversity of Ulmus L.
RESULTS: We characterized the leaf microstructures across the ten Ulmus lines. Chun exhibited a highly undulated abaxial surface and dense stomatal distribution. Langya and Xingshan possessed dense abaxial trichomes, while Lieye, Zuiweng, and Daguo had sparsely distributed, short abaxial trichomes. Duomai, Qingyun, and Lang were characterized by sparse stomata and flat abaxial surfaces, whereas Jinye had sparsely distributed but extensive stomata. The mean leaf retention values for total suspended particulate (TSP), PM2.5, PM2.5-10, PM10-100, and PM> 100 were 135.76, 6.60, 20.10, 90.98, and 13.08 µg·cm- 2, respectively. Trichomes substantially contributed to PM2.5 retention, while larger undulations enhanced PM2.5-10 retention, as evidenced by positive correlations between PM2.5 and abaxial trichome density and between PM2.5-10 and the adaxial raw microroughness values. Phyllosphere microbial diversity patterns varied among lines, with bacteria dominated by Sediminibacterium and fungi by Mycosphaerella, Alternaria, and Cladosporium. Redundancy analysis confirmed that dense leaf trichomes facilitated the capture of PM2.5-associated fungi, while bacteria were less impacted by PM and struggled to adhere to leaf microstructures. Long and dense trichomes provided ideal microhabitats for retaining PM-borne microbes, as evidenced by positive feedback loops between PM2.5, trichome characteristics, and the relative abundances of microorganisms like Trichoderma and Aspergillus.
CONCLUSIONS: Based on our findings, a three-factor network profile was constructed, which provides a foundation for further exploration into how different plants retain PM through foliar microstructures, thereby impacting phyllosphere microbial communities.

The concentration of gases in the atmosphere is a topic of growing concern due to its effects on health, ecosystems etc. Its monitoring is commonly carried out through ground stations which offer high precision and temporal resolution. However, in countries with few stations, such as Ecuador, these data fail to adequately describe the spatial variability of pollutant concentrations. Remote sensing data have great potential to solve this complication. This study evaluates the spatiotemporal distribution of nitrogen dioxide (NO2) and ozone (O3) concentrations in Quito and Cuenca, using data obtained from ground-based and Sentinel-5 Precursor mission sources during the years 2019 and 2020. Moreover, a Linear Regression Model (LRM) was employed to analyze the correlation between ground-based and satellite datasets, revealing positive associations for O3 (R2 = 0.83, RMSE = 0.18) and NO2 (R2 = 0.83, RMSE = 0.25) in Quito; and O3 (R2 = 0.74, RMSE = 0.23) and NO2, (R2 = 0.73, RMSE = 0.23) for Cuenca. The agreement between ground-based and satellite datasets was analyzed by employing the intra-class correlation coefficient (ICC), reflecting good agreement between them (ICC ≥0.57); and using Bland and Altman coefficients, which showed low bias and that more than 95% of the differences are within the limits of agreement. Furthermore, the study investigated the impact of COVID-19 pandemic-related restrictions, such as social distancing and isolation, on atmospheric conditions. This was categorized into three periods for 2019 and 2020: before (from January 1st to March 15th), during (from March 16th to May 17th), and after (from March 18th to December 31st). A 51% decrease in NO2 concentrations was recorded for Cuenca, while Quito experienced a 14.7% decrease. The tropospheric column decreased by 27.3% in Cuenca and 15.1% in Quito. O3 showed an increasing trend, with tropospheric concentrations rising by 0.42% and 0.11% for Cuenca and Quito respectively, while the concentration in Cuenca decreased by 14.4%. Quito experienced an increase of 10.5%. Finally, the reduction of chemical species in the atmosphere as a consequence of mobility restrictions is highlighted. This study compared satellite and ground station data for NO2 and O3 concentrations. Despite differing units preventing data validation, it verified the Sentinel-5P satellite\'s effectiveness in anomaly detection. Our research\'s value lies in its applicability to developing countries, which may lack extensive monitoring networks, demonstrating the potential use of satellite technology in urban planning.

[This corrects the article DOI: 10.3389/fpubh.2022.913169.].

This study analyzes for the first time uniformly and causally the level of pollution and air quality for the NE-Romania Region, one of the poorest region in the European Union. Knowing the level of pollution and air quality in this region, which can be taken as a benchmark due to its positional and economic-geographical attributes, responds to current scientific and practical needs. The study uses an hourly database (for five pollutants and five climate elements), from 2009 to 2020, from 19 air quality monitoring stations in northeastern Romania. Pollutant levels were statistically and graphically/cartographically modeled for the entire 2009-2020 interval on the distributive-spatial and regime, temporal component. Inter-station differences and similarities were analyzed causally. Taking advantage of the emergency measures between March 16 and May 14, 2020, we observed the impact of the event on the regional air quality in northeastern Romania. During the emergency period, the metropolitan area of Suceava (with over 100,000 inhabitants) was quarantined, which allowed us to analyze the impact of the quarantine period on the local air quality. We found that, in this region, air quality falls into class I (for NO2, SO2 and CO), II for O3 and III for PM10. During the lockdown periods NO2 and SO2 decreased for the entire region by 8.6 and 14.3%, respectively, and in Suceava by 13.9 and 40.1%, respectively. The causes of the reduction were anthropogenic in nature.

The dominant chemical form of nitrogen pollution in the atmosphere in the U.S. is shifting from oxidized nitrogen, primarily from combustion of fossil fuels, to reduced nitrogen from agricultural animal waste and fertilizer applications. Does it matter to lichens? In this synthesis, we characterize U.S. air concentrations of the most ubiquitous gaseous forms of reduced and oxidized nitrogen, NO2 and NH3, respectively, and their direct effects on lichens. In the U.S., the 3-year average (2017-2019) of the annual mean for each monitoring site ranges up to 56.4 μg NO2 m-3 (~30 ppb) and 6 μg NH3 m-3 (~9 ppb). The spatial coverage of current routine monitoring of NO2 and NH3 likely does not accurately represent exposures of NO2 to ecosystems in rural areas or capture spikes of NH3 concentrations proximal to intensive agriculture, which are documented to exceed 700 μg NH3 m-3 (~1000 ppb) for short durations. Both NO2 and NH3 can act as nutrients to lichens, but as exposures rise, both can cause physiological stress and mortality that then change community composition and diversity. There is a growing body of evidence that lichen community composition is altered at current levels of exposure in the U.S. with estimated no effect or lowest effect concentrations from <1-3 μg m-3 NO2 and <1 μg m-3 NH3. Better spatial characterization of both NO2 and NH3 concentrations, especially near intensive agriculture, would help to characterize the extent of the impacts across the U.S. These findings are discussed in the context of U.S. air pollution policy.

To meet the increased need for food and energy because of the economic shift brought about by the Industrial Revolution in the 19th century, there has been an increase in persistent organic pollutants (POPs), atmospheric emissions and metals in the environment. Several studies have reported a relationship between these pollutants and obesity, and diabetes (type 1, type 2 and gestational). All of the major pollutants are considered to be endocrine disruptors because of their interactions with various transcription factors, receptors and tissues that result in alterations of metabolic function. POPs impact adipogenesis, thereby increasing the prevalence of obesity in exposed individuals. Metals impact glucose regulation by disrupting pancreatic β-cells, causing hyperglycemia and impaired insulin signaling. Additionally, a positive association has been observed between the concentration of endocrine disrupting chemicals (EDCs) in the 12 weeks prior to conception and fasting glucose levels. Here, we evaluate what is currently known regarding the link between environmental pollutants and metabolic disorders. In addition, we indicate where further research is required to improve our understanding of the specific effects of pollutants on these metabolic disorders which would enable implementation of changes to enable their prevention.

Mercury (Hg) fate and transport research requires more effort to obtain a deep knowledge of its biogeochemical cycle, particularly in the Southern Hemisphere and Tropics that are still missing of distributed monitoring sites. Continuous monitoring of atmospheric Hg concentrations and trend worldwide is relevant for the effectiveness evaluation of the Minamata Convention on Mercury (MCM) actions. In this context, Gaseous Elemental Mercury (GEM) and total mercury (THg) in precipitations were monitored from 2013 to 2019 at the Amsterdam Island Observatory (AMS - 37°48\'S, 77°34\'E) to provide insights into the Hg pathway in the remote southern Indian Ocean, also considering ancillary dataset of Rn-222, CO2, CO, and CH4. GEM average concentration was 1.06 ± 0.07 ng m-3, with a slight increase during the austral winter due to both higher wind speed over the surface ocean and contributions from southern Africa. In wet depositions, THg average concentration was 2.39 ± 1.17 ng L-1, whereas the annual flux averaged 2.04 ± 0.80 μg m-2 year-1. In general, both GEM and Volume-Weighted Mean Concentration (VWMC) of THg did not show an increasing/decreasing trend over the seven-year period, suggesting a substantial lack of evolution about emission of Hg reaching AMS. Air masses Cluster Analysis and Potential Source Contribution Function showed that oceanic evasion was the main Hg contributor at AMS, while further contributions were attributable to long-range transport events from southern Africa, particularly when the occurrence of El Niño increased the frequency of wildfires.

Air pollution causes millions of premature deaths every year. Thus, air quality assessment is essential to preserve human health and support authorities to identify proper policies. In this study, concentration levels of 6 air contaminants (benzene, carbon monoxide, nitrogen dioxide, ground level ozone, particulate matters) as monitored in 2019, 2020 and 2021 by 37 stations, located in Campania (Italy) were analysed. Particular attention has been paid to March-April 2020 period to get clues on the possible effects of the lockdown regulations, imposed in Italy from March 9th to May 4th to limit COVID-19 spread, on atmospheric pollution. Air Quality Index (AQI), an algorithm developed by the United States Environmental Protection Agency (US-EPA), allowed us to classify the air quality from moderately unhealthy to good for sensitive groups. The evaluation of air pollution impact on human health by using the AirQ+ software evidenced a significant decrement of adult mortality in 2020 respect to 2019 and 2021. Among the six pollutants considered, PM10 and PM2.5 resulted the less affected by the lockdown restrictions. Finally, a comparison between NO2 ground level concentration and the reprocessed Level 2 NO2 tropospheric column concentration obtained from satellite surveys highlighted as concentration measured at the ground level stations can be strongly influenced by the station position and its surroundings.

This study focuses on the analysis of the distribution, both spatial and temporal, of the PM10 (particulate matter with a diameter of 10 µm or less) concentrations recorded in nine EMEP (European Monitoring and Evaluation Programme) background stations distributed throughout mainland Spain between 2001 and 2019. A study of hierarchical clusters was used to classify the stations into three main groups with similarities in yearly concentrations: GC (coastal location), GNC (north-central location), and GSE (southeastern location). The highest PM10 concentrations were registered in summer. Annual evolution showed statistically significant decreasing trends in PM10 concentration in all the stations covering a range from -0.21 to -0.50 µg m-3/year for Barcarrota and Víznar, respectively. Through the Lamb classification, the weather types were defined during the study period, and those associated with high levels of pollution were identified. Finally, the values exceeding the limits established by the legislation were analyzed for every station assessed in the study.