A subclass of evening cells promotes the switch from arousal to sleep at dusk.

Animals exhibit rhythmic patterns of behavior that are shaped by an internal circadian clock and the external environment. Although light intensity varies across the day, there are particularly robust differences at twilight (dawn/dusk). These periods are also associated with major changes in behavioral states, such as the transition from arousal to sleep. However, the neural mechanisms by which time and environmental conditions promote these behavioral transitions are poorly defined. Here, we show that the E1 subclass of Drosophila evening clock neurons promotes the transition from arousal to sleep at dusk. We first demonstrate that the cell-autonomous clocks of E2 neurons primarily drive and adjust the phase of evening anticipation, the canonical behavior associated with "evening" clock neurons. We next show that conditionally silencing E1 neurons causes a significant delay in sleep onset after dusk. However, rather than simply promoting sleep, activating E1 neurons produces time- and light-dependent effects on behavior. Activation of E1 neurons has no effect early in the day but then triggers arousal before dusk and induces sleep after dusk. Strikingly, these activation-induced phenotypes depend on the presence of light during the day. Despite their influence on behavior around dusk, in vivo voltage imaging of E1 neurons reveals that their spiking rate and pattern do not significantly change throughout the day. Moreover, E1-specific clock ablation has no effect on arousal or sleep. Thus, we suggest that, rather than specifying "evening" time, E1 neurons act, in concert with other rhythmic neurons, to promote behavioral transitions at dusk.

Hotspot driven air pollution during crop residue burning season in the Indo-Gangetic Plain, India.

Intensive crop residue burning (CRB) in northern India triggers severe air pollution episodes over the Indo-Gangetic Plain (IGP) each year during October and November. We have quantified the contribution of hotspot districts (HSDs) and total CRB to poor air quality over the IGP. Initially, we investigated the spatiotemporal distribution of CRB fire within the domain and pinpointed five HSD in each Punjab and Haryana. Furthermore, we have simulated air quality and quantified the impact of CRB using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), incorporating recent anthropogenic emissions (EDGAR v5) and biomass burning emissions (FINN v2.4) inventories, along with MOZART-MOSAIC chemistry. The key finding is that HSDs contributed ∼80% and ∼50% of the total fire counts in Haryana and Punjab, respectively. The model effectively captured observed PM2.5 concentrations, with a normalized mean bias (NMB) below 0.2 and R-squared (R2) exceeding 0.65 at the majority of validation sites. However, some discrepancies were observed at a few sites in Delhi, Punjab, Haryana, and West Bengal. The National Capital Region experienced the highest PM2.5 concentrations, followed by Punjab, Haryana, Uttar Pradesh, Bihar, and West Bengal. Moreover, HSDs were responsible for about 70% of the total increase in CRB-induced PM2.5 in the western, central, and eastern cities, and around 50% in the northern cities. By eliminating CRB emissions across the domain, we could potentially save approximately 18,000 lives annually. Policymakers, scientists, and institutions can leverage the framework to address air pollution at national and global scales by targeting source-specific hotspots. This approach, coupled with appropriate technological and financial solutions, can contribute to achieving climate change and sustainable development goals.

Source apportionment of fine aerosol particles of water-soluble and carbonaceous species measured in semi-urban (Kharagpur) and megacity (Kolkata) atmospheres over the eastern Indo-Gangetic plain: Chemical characterisation, relative abundance and anthropogenic contributions.

We conducted the source apportionment of fine aerosol particles (aerodynamic diameter ≤1.6µm) collected with the indigenously designed-fabricated submicron aerosol sampler (SAS) in the eastern Indo-Gangetic plain (IGP) semi-urban (Kharagpur, KGP) and megacity (Kolkata, KOL) atmospheres, examining the chemical characteristics at KGP (January 2015-February 2016), and accentuating their abundance and the sources of anthropogenic pollution relative to KOL. The fine water-soluble inorganic ions (WSII) at KGP predominantly constituted Ca2+ (52 %) and equivalent amounts (12 % each) of Cl-, Mg2+ and secondary inorganic aerosols (sum of SO42-, NO3- and NH4+). The annual mean of SO42- at KGP was twice (thrice) larger than NO3- (NH4+); this of organic carbon (OC) was thrice elemental carbon (EC), with secondary OC being 37 % of the total OC. The concordance in peaks of OC with K+ concentrations was identified during the seasonal open biomass burning at KGP (November and May). While the annual mean of OC (EC) concentration at KGP was slightly lower than (nearly equivalent to) KOL; K+, NO3-, NH4+ and F- concentrations at KOL were twice larger than KGP. Source quantification using Positive Matrix Factorization (PMF) revealed the regional dust with crustal elements marked as clean (polluted) at KGP (KOL) constituted the largest fractional contribution among the six identified factors at both KGP and KOL. The combustion-derived anthropogenic pollution comprising about 60 % (50 %) of fine particles at KOL (KGP) was predominantly from the transportation sector (in vehicular emissions and regional dust), coal combustion (industries) and open biomass burning at KOL; it was from brick kilns, residential biofuel combustion, and open biomass burning at KGP. The source-wide distribution of measured aerosol species showed their emergence from largely different sources at KGP and KOL; thereby suggesting a prioritised strategy for sustainable emissions mitigation considering the prominent sources of combustion-derived anthropogenic pollution and aerosol species for megacity and semi-urban atmospheres.

A subclass of evening cells promotes the switch from arousal to sleep at dusk.

Animals exhibit rhythmic patterns of behavior that are shaped by an internal circadian clock and the external environment. While light intensity varies across the day, there are particularly robust differences at twilight (dawn/dusk). These periods are also associated with major changes in behavioral states, such as the transition from arousal to sleep. However, the neural mechanisms by which time and environmental conditions promote these behavioral transitions are poorly defined. Here, we show that the E1 subclass of Drosophila evening clock neurons promotes the transition from arousal to sleep at dusk. We first demonstrate that the cell-autonomous clocks of E2 neurons alone are required to drive and adjust the phase of evening anticipation, the canonical behavior associated with "evening" clock neurons. We next show that conditionally silencing E1 neurons causes a significant delay in sleep onset after dusk. However, rather than simply promoting sleep, activating E1 neurons produces time- and light- dependent effects on behavior. Activation of E1 neurons has no effect early in the day, but then triggers arousal before dusk and induces sleep after dusk. Strikingly, these phenotypes critically depend on the presence of light during the day. Despite their influence on behavior around dusk, in vivo voltage imaging of E1 neurons reveals that their spiking rate does not vary between dawn and dusk. Moreover, E1-specific clock ablation has no effect on arousal or sleep. Thus, we suggest that, rather than specifying "evening" time, E1 neurons act, in concert with other rhythmic neurons, to promote behavioral transitions at dusk.

Black carbon health impacts in the Indo-Gangetic plain: Exposures, risks, and mitigation.

A large discrepancy between simulated and observed black carbon (BC) surface concentrations over the densely populated Indo-Gangetic plain (IGP) has so far limited our ability to assess the magnitude of BC health impacts in terms of population exposure, morbidity, and mortality. We evaluate these impacts using an integrated modeling framework, including successfully predicted BC concentrations. Population exposure to BC is notable, with more than 60 million people identified as living in hotspots of BC concentration (wintertime mean, >20 µg m-3). The attributable fraction of the total cardiovascular disease mortality (CVM) burden to BC exposures is 62% for the megacity. The semiurban area comprised about 49% of the total BC-attributable CVM burden over the IGP. More than 400,000 lives can potentially be saved from CVM annually by implementing prioritized emission reduction from the combustion of domestic biofuel in the semiurban area, diesel oil in transportation, and coal in thermal power plant and brick kiln industries in megacities.

Silver linings in the dark clouds of COVID-19: Improvement of air quality over India and Delhi metropolitan area from measurements and WRF-CHIMERE model simulations.

The current study examines the impact of the COVID-19 lockdown (25th March until May 17, 2020) period in particulate matter (PM) concentrations and air pollutants (NOx, SO2, CO, NH3, and O3) at 63 stations located at Delhi, Uttar Pradesh and Haryana states within the Delhi-NCR, India. Large average reductions are recorded between the stations in each state such as PM10 (-46 to -58%), PM2.5 (-49 to -55%), NO2 (-27 to -58%), NO (-54% to -59%), CO (-4 to -44%), NH3 (-2 to -38%), while a slight increase is observed for O3 (+4 to +6%) during the lockdown period compared to same periods in previous years. Furthermore, PM and air pollutants are significantly reduced during lockdown compared to the respective period in previous years, while a significant increase in pollution levels is observed after the re-opening of economy. The meteorological changes were rather marginal between the examined periods in order to justify such large reductions in pollution levels, which are mostly attributed to traffic-related pollutants (NOx, CO and road-dust PM). The WRF-CHIMERE model simulations reveal a remarkable reduction in PM2.5, NO2 and SO2 levels over whole Indian subcontinent and mostly over urban areas, due to limitation in emissions from the traffic and industrial sectors. A PM2.5 reduction of -48% was simulated in Delhi in great consistency with measurements, rendering the model as a powerful tool for simulations of lower pollution levels during lockdown period.

Discerning the pre-monsoon urban atmosphere aerosol characteristic and its potential source type remotely sensed by AERONET over the Bengal Gangetic plain.

In the present study, we evaluated the pre-monsoon urban atmosphere (UA) aerosol characteristics remotely sensed by Aerosol Robotic Network (AERONET) over the Bengal Gangetic plain (BGP) at Kolkata (KOL) and their implication in potential source types and spatiotemporal features. About 70% of the AERONET-sensed aerosol optical depth at 0.50 µ m, AOD0.5 (Angstrom exponent, α at 0.44-0.87 µ m) during the pre-monsoon period (February to June) was greater than 0.50 (≤ 1); the pre-monsoon mean of AOD0.5 (α) was 0.73 (0.83) which was found being slightly higher (lower) than nearby AERONET stations (Dhaka/Bhola) located over the eastern Ganges basin. The volume geometric mean radius for the fine mode (FM) (coarse mode, CM) UA aerosol from AERONET retrievals was estimated to be 0.14-0.17 (2.24-2.75) µ m. The spectral distribution of the monthly mean of UA aerosol single-scattering albedo (SSA) exhibited an increasing trend with an increase in wavelength throughout all wavelengths during April, unlike the rest of the pre-monsoon months. Investigation of aerosol types indicated the pre-dominance of dust during April and a mixture of urban/open burning with mixed desert dust during the rest of the pre-monsoon months. Potential aerosol source fields were identified over the Indo-Gangetic Plain (IGP), east coast, northwestern India, and oceanic regions; these were estimated at elevated layers of atmosphere during April and May but that at surface layers during February and June. Comparison of aerosol characteristics over the BGP (at Kolkata, KOL) with that at six other coincident AERONET sites over India revealed mean AOD at KOL being 11 to 91% higher than the rest of the AERONET stations, with the relative increase at KOL being the highest during March; this was attributed to persistent high values of both FM and CM AOD unlike the rest of the stations. The monthly mean of SSA was the lowest at KOL among AERONET stations, during February and March. Comparison of the AOD from the AERONET aerosol retrievals over the BGP UA with the coincident Moderate Resolution Imaging Spectroradiometer (MODIS) latest retrievals (C005 and C006) indicated a moderate correlation between the two retrievals; discrepancy in MODIS-retrieved relative distribution of FM and CM AOD was inferred compared to AERONET in the UA.