Investigation of the chemical nature of precipitation and source apportionment of its constituents in Tehran metropolis, Iran.

Precipitation is a key process for purifying the atmosphere of pollutants. However, precipitation chemistry is also a significant environmental catastrophe on a global scale. Tehran Metropolitan Area, Iran's capital, is one of the world's most polluted cities. Nonetheless, little effort has been paid to determining the chemical composition of precipitation in this polluted metropolis. The chemical components and likely sources of trace metals and water-soluble ions in precipitation samples collected from 2021 to 2022 at an urban location in Tehran, Iran, were investigated in this study. The pH of the rainwater samples varied from 6.330 to 7.940 (mean 7.313, volume weighted mean (VWM) 7.523). The following is the order of the VWM concentration of main ions: Ca2+ > HCO3- > Na+ >SO42- > NH4+ > Cl- > NO3- > Mg2+> K+> F-. Furthermore, we discovered that VWM concentrations for trace elements are modest, with the exception of Sr (39.104 eq L-1). The primary neutralizing species for precipitation acidity were Ca2+ and NH4+. Vertical feature mask (VFM) diagrams derived from cloud-aerosol lidar and infrared pathfinder satellite observation (CALIPSO) track data indicated that polluted dust was the most common pollutant in the Tehran sky that might contribute significantly to the neutralization of precipitation. A study of species concentration ratios in seawater and the earth's crust indicated that virtually all Se, Sr, Zn, Mg2+, NO3-, and SO42- were anthropogenic. While Cl- was largely obtained from sea salt, K+ was obtained from both the earth's crust and the sea, with the earth's crust playing a larger role in K+. The earth's crust, aged sea salt, industry, and combustion processes were all verified as sources of trace metals and water-soluble ions by positive matrix factorization analysis.

Time-Lagged Multidimensional Pattern Connectivity (TL-MDPC): An EEG/MEG pattern transformation based functional connectivity metric.

Functional and effective connectivity methods are essential to study the complex information flow in brain networks underlying human cognition. Only recently have connectivity methods begun to emerge that make use of the full multidimensional information contained in patterns of brain activation, rather than unidimensional summary measures of these patterns. To date, these methods have mostly been applied to fMRI data, and no method allows vertex-to-vertex transformations with the temporal specificity of EEG/MEG data. Here, we introduce time-lagged multidimensional pattern connectivity (TL-MDPC) as a novel bivariate functional connectivity metric for EEG/MEG research. TL-MDPC estimates the vertex-to-vertex transformations among multiple brain regions and across different latency ranges. It determines how well patterns in ROI X at time point tx can linearly predict patterns of ROI Y at time point ty. In the present study, we use simulations to demonstrate TL-MDPC's increased sensitivity to multidimensional effects compared to a unidimensional approach across realistic choices of number of trials and signal-to-noise ratios. We applied TL-MDPC, as well as its unidimensional counterpart, to an existing dataset varying the depth of semantic processing of visually presented words by contrasting a semantic decision and a lexical decision task. TL-MDPC detected significant effects beginning very early on, and showed stronger task modulations than the unidimensional approach, suggesting that it is capable of capturing more information. With TL-MDPC only, we observed rich connectivity between core semantic representation (left and right anterior temporal lobes) and semantic control (inferior frontal gyrus and posterior temporal cortex) areas with greater semantic demands. TL-MDPC is a promising approach to identify multidimensional connectivity patterns, typically missed by unidimensional approaches.

Task modulation of spatiotemporal dynamics in semantic brain networks: An EEG/MEG study.

How does brain activity in distributed semantic brain networks evolve over time, and how do these regions interact to retrieve the meaning of words? We compared spatiotemporal brain dynamics between visual lexical and semantic decision tasks (LD and SD), analysing whole-cortex evoked responses and spectral functional connectivity (coherence) in source-estimated electroencephalography and magnetoencephalography (EEG and MEG) recordings. Our evoked analysis revealed generally larger activation for SD compared to LD, starting in primary visual area (PVA) and angular gyrus (AG), followed by left posterior temporal cortex (PTC) and left anterior temporal lobe (ATL). The earliest activation effects in ATL were significantly left-lateralised. Our functional connectivity results showed significant connectivity between left and right ATL, PTC and right ATL in an early time window, as well as between left ATL and IFG in a later time window. The connectivity of AG was comparatively sparse. We quantified the limited spatial resolution of our source estimates via a leakage index for careful interpretation of our results. Our findings suggest that the different demands on semantic information retrieval in lexical and semantic decision tasks first modulate visual and attentional processes, then multimodal semantic information retrieval in the ATLs and finally control regions (PTC and IFG) in order to extract task-relevant semantic features for response selection. Whilst our evoked analysis suggests a dominance of left ATL for semantic processing, our functional connectivity analysis also revealed significant involvement of right ATL in the more demanding semantic task. Our findings demonstrate the complementarity of evoked and functional connectivity analysis, as well as the importance of dynamic information for both types of analyses.

Mapping the spatial sources of atmospheric dust using GLUE and Monte Carlo simulation.

Atmospheric dust has many negative impacts within different ecosystems and it is therefore beneficial to assemble reliable evidence on the key sources of the dust problem. In this study, for first time, two different source modelling approaches comprising generalized likelihood uncertainty estimation (GLUE) and Monte Carlo simulation were applied to map spatial source contributions to atmospheric dust samples collected in Ahvaz, Khuzestan province, Iran. A total of 264 surficial soil samples were collected from five potential spatial dust sources. Additionally, nine dust samples were collected in February 2015. The performance of both GLUE and Monte Carlo simulation for quantifying uncertainty associated with the source contributions predicted using an un-mixing model were assessed and compared using mean absolute fit (MAF) and goodness-of-fit (GOF) estimators as well as 14 virtual sediment mixtures (VSM). Finally, the erodible fraction (EF) of topsoils and HYSPLIT model were used as further tests for validating the results of the GLUE and Monte Caro simulation. Based on both uncertainty modelling approaches, the loamy sand soil texture was recognized as the main spatial source of the target dust samples. Silty clay soils were estimated to be the least important spatial source of the target dust samples using the two modelling approaches. Both GLUE and Monte Carlo simulation returned MAF and GOF estimates >80%, with Monte Carlo performing slightly better. Based on the virtual mixture tests, the RMSE and MAE of the Monte Carlo simulation (<13.5% and <11%, respectively) was better than for GLUE (<20% and <16.3%, respectively). Spatial source maps generated using both GLUE and Monte Carlo simulation were consistent with the EF map generated using multiple regression (MR) and with routes dust transportation detected by HYSPLIT. Therefore, we recommend that future research into to the sources of atmospheric dust pollution integrates modelling approaches, VSM, EF and HYSPLIT model to quantify and map dust provenance reliably.