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α-Synuclein: An All-Inclusive Trip About its Structure, Impacting Factors and also Used Tactics.
Highly mass-resolved OA mass spectra at one urban and downwind site were factorized into three primary organic aerosol (POA) factors including one traffic-related and two solid-fuel combustion (SFC), and three oxidized OA (OOA) factors. Whereas unit mass resolution OA at the other urban site was factorized into two POA factors related to traffic and SFC, and one OOA factor. OOA constituted a majority of the total OA mass (45-55)% with maximum contribution during afternoon hours ~(70-80)%. Significant differences in the absolute OOA concentration between the two urban sites indicated the influence of local emissions on the oxidized OA formation. Similar PM chemical composition, diurnal and temporal variations at the three sites suggest similar type of sources affecting the particulate pollution in Delhi and adjoining cities, but variability in mass concentration suggest more local influence than regional.Identifying the nature and extent of atmospheric PM2.5-bound toxic organic pollutants is beneficial to evaluate human health risks of air pollution. Seasonal observations of PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs (NPAHs) in the Yangtze River Delta (YRD) were investigated, along with criteria air pollutants and meteorological parameters. With the elevated PM2.5 level, the percentage of 4-ring PAHs and typical NPAH including 3-Nitrobiphenyl (3-NBP) and 2-Nitrofluoranthene (2-NFLT) increased by 19-40%. PM2.5-bound 2-NFLT was positively correlated with O3 and NO2, suggesting the contribution of atmospheric oxidation capacity to enhance the secondary formation of NPAHs in the atmosphere. Positive matrix factorization (PMF) analysis indicated that traffic emissions (44.9-48.7%), coal and biomass combustion (27.6-36.0%) and natural gas and volatilization (15.3-27.5%) were major sources of PAHs, and secondary formation (39.8-53.8%) was a predominant contributor to total NPAH concentrations. Backward trajectory analysis showed that air masses from North China transported to the YRD region increased PAH and NPAH concentrations. Compare to clean days, the BaP equivalent concentrations of total PAHs and NPAHs during haze pollution days were enhanced by 10-25 and 2-6 times, respectively. The Incremental Lifetime Cancer Risks (ILCRs) of PAHs by inhalation exposure also indicated high potential health risks in the YRD region. The results implied that the health risks of PM2.5-bound PAHs and NPAHs could be sharply enhanced with the increase of PM2.5 concentrations.In this study, to clarify the interaction between dissolved heavy metals and the coexisting chemical factors in karst wetland waters, surface water samples were collected from the Caohai Wetland during a water year, and the hydrochemistry and heavy metal pollution characteristics of the samples were analyzed. The main influencing factors of heavy metals in different water periods were identified through a cooccurrence network analysis. To further analyze the influence mechanism of these main influencing factors, the forms of heavy metals in the water were simulated with PHREEQC software, and the effects of these main influencing factors on the forms were analyzed by redundancy analysis. The results show that Ca2+ was the main cation in the wetland water, while the main anion was HCO3-. The hydrochemical facies of the Caohai Wetland in the wet and dry seasons were Ca-Mg-SO4-HCO3 and Ca-HCO3, respectively. Cd was the main pollutant in the Caohai Wetland, with Cd levels seriously exceeding the standards. The characteristics of the karst water in the Caohai Wetland are apparent. find more The cooccurrence network analysis shows that pH, dissolved oxygen (DO), electrical conductivity (EC), SO42- and HCO3- are the main factors regulating heavy metals. The results of morphological simulation and analysis were used to explore the mechanism of action of these factors. These data provide geochemical information useful for water quality assessment and management plans on heavy metal pollution.Tree-based ecosystems are critical to climate change mitigation. The study analysed carbon (C) stock patterns and examined the importance of environmental variables in predicting carbon stock in biomass and soils of the Indian Himalayan Region (IHR). We conducted a synthesis of 100 studies reporting biomass carbon stock and 67 studies on soil organic carbon (SOC) stock from four land-uses forests, plantation, agroforest, and herbaceous ecosystem from the IHR. Machine learning techniques were used to examine the importance of various environmental variables in predicting carbon stock in biomass and soils. Despite large variations in biomass C and SOC stock (mean ± SD) within the land-uses, natural forests have the highest biomass C stock (138.5 ± 87.3 Mg C ha-1), and plantation forests exhibited the highest SOC stock (168.8 ± 74.4 Mg C ha-1) in the top 1-m of soils. The relationship between the environmental variables (altitude, latitude, precipitation, and temperature) and carbon stock was not significantly correlated. The prediction of biomass carbon and SOC stock using different machine learning techniques (Adaboost, Bagging, Random Forest, and XGBoost) shows that the XGBoost model can predict the carbon stock for the IHR closely. Our study confirms that the carbon stock in the IHR vary on a large scale due to a diverse range of land-use and ecosystems within the region. Therefore, predicting the driver of carbon stock on a single environmental variable is impossible for the entire IHR. The IHR possesses a prominent carbon sink and biodiversity pool. Therefore, its protection is essential in fulfilling India's commitment to nationally determined contributions (NDC). Our data synthesis may also provide a baseline for the precise estimation of carbon stock, which will be vital for India's National Mission for Sustaining the Himalayan Ecosystem (NMSHE).
Few studies have comprehensively assessed multiple environmental exposures affecting children's health. This study applied machine-learning methods to evaluate how indoor environmental conditions at home and school contribute to asthma and allergy-related symptoms.

We randomly selected 10 public schools representing different socioeconomic statuses in New York State (2017-2019) and distributed questionnaires to students to collect health status and home-and school-environmental exposures. Indoor air quality was measured at school, and ambient particle exposures (PM
and components) were measured using real-time personal monitors for 48h. We used random forest model to identify the most important risk factors for asthma and allergy-related symptoms, and decision tree for visualizing the inter-relationships among the multiple risk factors with the health outcomes.

The top contributing factors identified for asthma were family rhinitis history (relative importance 10.40%), plant pollen trigger (5.48%); bedroom carpet (3.
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