Seasonal analysis of submicron aerosol in Old Delhi using high-resolution aerosol mass spectrometry: chemical characterisation, source apportionment and new marker identification

We present the first real-time composition of submicron particulate matter (PM1) in Old Delhi using high-resolution aerosol mass spectrometry (HR-AMS). Old Delhi is one of the most polluted locations in the world, and PM1 concentrations reached ∼ 750 µg m−3 during the most polluted period, the post-monsoon period, where PM1 increased by 188 % over the pre-monsoon period. Sulfate contributes the largest inorganic PM1 mass fraction during the pre-monsoon (24 %) and monsoon (24 %) periods, with nitrate contributing most during the post-monsoon period (8 %). The organics dominate the mass fraction (54 %–68 %) throughout the three periods, and, using positive matrix factorisation (PMF) to perform source apportionment analysis of organic mass, two burning-related factors were found to contribute the most (35 %) to the post-monsoon increase. The first PMF factor, semi-volatility biomass burning organic aerosol (SVBBOA), shows a high correlation with Earth observation fire counts in surrounding states, which links its origin to crop residue burning. The second is a solid fuel OA (SFOA) factor with links to local open burning due to its high composition of polyaromatic hydrocarbons (PAHs) and novel AMS-measured marker species for polychlorinated dibenzodioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs). Two traffic factors were resolved: one hydrocarbon-like OA (HOA) factor and another nitrogen-rich HOA (NHOA) factor. The N compounds within NHOA were mainly nitrile species which have not previously been identified within AMS measurements. Their PAH composition suggests that NHOA is linked to diesel and HOA to compressed natural gas and petrol. These factors combined make the largest relative contribution to primary PM1 mass during the pre-monsoon and monsoon periods while contributing the second highest in the post-monsoon period. A cooking OA (COA) factor shows strong links to the secondary factor, semi-volatility oxygenated OA (SVOOA). Correlations with co-located volatile organic compound (VOC) measurements and AMS-measured organic nitrogen oxides (OrgNO) suggest SVOOA is formed from aged COA. It is also found that a significant increase in chloride concentrations (522 %) from pre-monsoon to post-monsoon correlates well with SVBBOA and SFOA, suggesting that crop residue burning and open waste burning are responsible. A reduction in traffic emissions would effectively reduce concentrations across most of the year. In order to reduce the post-monsoon peak, sources such as funeral pyres, solid waste burning and crop residue burning should be considered when developing new air quality policy

Relationships between neuronal oscillatory amplitude and dynamic functional connectivity

Event related fluctuations of neural oscillatory amplitude are reported widely in the context of cognitive processing and are typically interpreted as a marker of brain ‘activity’. However, the precise nature of these effects remains unclear; in particular, whether such fluctuations reflect local dynamics, integration between regions, or both, is unknown. Here, using magnetoencephalography (MEG), we show that movement induced oscillatory modulation is associated with transient connectivity between sensorimotor regions. Further, in resting state data, we demonstrate a significant association between oscillatory modulation and dynamic connectivity. A confound with such empirical measurements is that increased amplitude necessarily means increased signal to noise ratio (SNR): this means that the question of whether amplitude and connectivity are genuinely coupled, or whether increased connectivity is observed purely due to increased SNR is unanswered. Here we counter this problem by analogy with computational models which show that, in the presence of global network coupling and local multistability, the link between oscillatory modulation and long range connectivity is a natural consequence of neural networks. Our results provide evidence for the notion that connectivity is mediated by neural oscillations, and suggest that time-frequency spectrograms are not merely a description of local synchrony but also reflect fluctuations in long range connectivity

Moving magnetoencephalography towards real-world applications with a wearable system

Imaging human brain function with techniques such as magnetoencephalography1 (MEG) typically requires a subject to perform tasks whilst their head remains still within a restrictive scanner. This artificial environment makes the technique inaccessible to many people, and limits the experimental questions that can be addressed. For example, it has been difficult to apply neuroimaging to investigation of the neural substrates of cognitive development in babies and children, or in adult studies that require unconstrained head movement (e.g. spatial navigation). Here, we develop a new type of MEG system that can be worn like a helmet, allowing free and natural movement during scanning. This is possible due to the integration of new quantum sensors2,3 that do not rely on superconducting technology, with a novel system for nulling background magnetic fields. We demonstrate human electrophysiological measurement at millisecond resolution whilst subjects make natural movements, including head nodding, stretching, drinking and playing a ball game. Results compare well to the current state-of-the-art, even when subjects make large head movements. The system opens up new possibilities for scanning any subject or patient group, with myriad applications such as characterisation of the neurodevelopmental connectome, imaging subjects moving naturally in a virtual environment, and understanding the pathophysiology of movement disorders

Spatiotemporal neural characterization of prediction error valence and surprise during reward learning in humans

Reward learning depends on accurate reward associations with potential choices. These associations can be attained with reinforcement learning mechanisms using a reward prediction error (RPE) signal (the difference between actual and expected rewards) for updating future reward expectations. Despite an extensive body of literature on the influence of RPE on learning, little has been done to investigate the potentially separate contributions of RPE valence (positive or negative) and surprise (absolute degree of deviation from expectations). Here, we coupled single-trial electroencephalography with simultaneously acquired fMRI, during a probabilistic reversal-learning task, to offer evidence of temporally overlapping but largely distinct spatial representations of RPE valence and surprise. Electrophysiological variability in RPE valence correlated with activity in regions of the human reward network promoting approach or avoidance learning. Electrophysiological variability in RPE surprise correlated primarily with activity in regions of the human attentional network controlling the speed of learning. Crucially, despite the largely separate spatial extend of these representations our EEG-informed fMRI approach uniquely revealed a linear superposition of the two RPE components in a smaller network encompassing visuo mnemonic and reward areas. Activity in this network was further predictive of stimulus value updating indicating a comparable contribution of both signals to reward learning

Emissions of intermediate-volatility and semi-volatile organic compounds from domestic fuels used in Delhi, India

Biomass burning emits significant quantities of intermediate-volatility and semi-volatile organic compounds (I/SVOCs) in a complex mixture, probably containing many thousands of chemical species. These components are significantly more toxic and have poorly understood chemistry compared to volatile organic compounds routinely quantified in ambient air; however, analysis of I/SVOCs presents a difficult analytical challenge. The gases and particles emitted during the test combustion of a range of domestic solid fuels collected from across Delhi were sampled and analysed. Organic aerosol was collected onto Teflon (PTFE) filters, and residual low-volatility gases were adsorbed to the surface of solid-phase extraction (SPE) discs. A new method relying on accelerated solvent extraction (ASE) coupled to comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC×GC-ToF-MS) was developed. This highly sensitive and powerful analytical technique enabled over 3000 peaks from I/SVOC species with unique mass spectra to be detected. A total of 15 %-100% of gas-phase emissions and 7 %-100% of particle-phase emissions were characterised. The method was analysed for suitability to make quantitative measurements of I/SVOCs using SPE discs. Analysis of SPE discs indicated phenolic and furanic compounds were important for gas-phase I/SVOC emissions and levoglucosan to the aerosol phase. Gas- and particle-phase emission factors for 21 polycyclic aromatic hydrocarbons (PAHs) were derived, including 16 compounds listed by the US EPA as priority pollutants. Gas-phase emissions were dominated by smaller PAHs. The new emission factors were measured (mg kg-1) for PAHs from combustion of cow dung cake (615), municipal solid waste (1022), crop residue (747), sawdust (1236), fuelwood (247), charcoal (151) and liquefied petroleum gas (56). The results of this study indicate that cow dung cake and municipal solid waste burning are likely to be significant PAH sources, and further study is required to quantify their impact alongside emissions from fuelwood burning

Emissions of non-methane volatile organic compounds from combustion of domestic fuels in Delhi, India

Twenty-nine different fuel types used in residential dwellings in northern India were collected from across Delhi (76 samples in total). Emission factors of a wide range of non-methane volatile organic compounds (NMVOCs) (192 compounds in total) were measured during controlled burning experiments using dualchannel gas chromatography with flame ionisation detection (DC-GC-FID), two-dimensional gas chromatography (GC×GC-FID), proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS) and solid-phase extraction two-dimensional gas chromatography with time-offlight mass spectrometry (SPE-GC×GC-ToF-MS). On average, 94% speciation of total measured NMVOC emissions was achieved across all fuel types. The largest contributors to emissions from most fuel types were small non-aromatic oxygenated species, phenolics and furanics. The emission factors (in g kg-1) for total gas-phase NMVOCs were fuelwood (18.7, 4.3-96.7), cow dung cake (62.0, 35.3-83.0), crop residue (37.9, 8.9-73.8), charcoal (5.4, 2.4-7.9), sawdust (72.4, 28.6-115.5), municipal solid waste (87.3, 56.6- 119.1) and liquefied petroleum gas (5.7, 1.9-9.8). The emission factors measured in this study allow for better characterisation, evaluation and understanding of the air quality impacts of residential solid-fuel combustion in India